Abstract: This paper outlines the methodology and results for a two species finite volume scalar computational drug transport model developed for simulating the mass transport of Poly(lactic-co-glycolic acid (PLGA) ) from a half-embedded single strut implanted in a coronary arterial vessel wall. The mathematical drug transport model incorporates the convection-diffusion equation in scalar form (dimensionless) with a two species (free-drug and bound-drug) mass transport setup including reversible equilibrium reaction source terms for the free and bound-drug states to account for the pharmaco-kinetic reactions in the arterial wall. The relative reaction rates of the added source terms control the interconversion of drug between the free and bound-drug states. The model is solved by a 2-D finite-volume method for discretizing and solving the free and bound drug transport equations with anisotropic vascular drug diffusivities. This model is an improvement over our previously developed model using the finite-difference method. A dimensionless characteristic scaling pre-analysis was conducted a priori to evaluate the significance of implementing the reaction source terms in the transport equations. This paper reports the findings of an investigation of the interstitial flow profile into the arterial wall and the free and bound drug diffusion profiles with a parametric study of varying polymer drug concentration (low and high), tortuosity, porosity, and Peclet and DamKöhler numbers over the course of 400 hours (16.67 days). The results also reveal how a single species drug delivery model that neglects both a reversible binding reaction source term and the porosity and tortuosity of the arterial wall cannot accurately predict the distribution of both the free and bound drug.

In this article, we introduce some multi-messenger studies of blazars. Main contents contain the following parts, 1. the subclasses of blazars and the observational characteristics, 2. emission variabilities from the multi-messenger observations, 3. the physical origin for the multi-messenger studies. Based on mid-IR observations collected from the $WISE$ database, we collect two sources (BZQ J1700+6830 and BZQ J1842+6809) to analyze the mid-IR variabilities. \\ (1) For BZQ J1700+6830, the period is $P\approx$ 42 hr at the four bands ($W1$, $W2$, $W3$, $W4$); for BZQ J1842+6809, the period is $P\approx$ 123 hr at the two bands ($W1$, $W2$). \\ (2) Using the periodicities, we calculate that the central black hole mass ($BH$) should be $M = (5.44\sim33.0)\times10^8M_{\odot}$ for BZQ J1700+6830 and $M = (1.13\sim6.88)\times10^9M_{\odot}$ for BZQ J1842+6809.\\ (3) For the two sources, during the flaring stage, we find anti-clockwise variation trends based on the time-based relations built by the spectral indices ($\alpha$) and flux densities ($F$).\\ (4) We calculate the Doppler factor ($\delta$) when the sources are in the dimming or brightening stages, and find that with the frequency increasing, the Doppler factor become stronger.

The paper considers new effects of the nanoscale state of matter, which open up prospects for the creation of electronic devices using new physical principles. The contact of chemically homogeneous different sizes hydrated nanoparticles of yttrium-stabilized zirconium oxide (ZrO2 – x %mol Y2O3, x=0, 3, 4, 8; YSZ) with particle sizes of 7.5 nm and 7,5 nm; 7.5 nm and 9 nm; 7.5 nm and 11 nm; 7.5 nm and 14 nm in the form of compacts obtained using high hydrostatic pressure (HP-compacts of 300MPa) was studied at direct and alternating current. A unique size effect of the nonlinear (semiconductor) dependence of the electrical properties (in the region U <2.5 V, I ≤ 2.7 mA) of the contact of different-sized YSZ nanoparticles of the same chemical composition is revealed, which indicates the possibility of creating semiconductor structures of a new type based on chemically homogeneous nanostructured systems. The electronic structure of the near-surface regions of nanoparticles of a special type of oxide materials and the possibility, on this basis, to obtain specifically rectifying properties of the contacts were studied theoretically. Models of surface states of the Tamm type are constructed, but considering the Coulomb long-range action. The discovered variance and its dependence on the curvature of the surface of nanoparticles made it possible to study the conditions for the formation of a contact potential difference in cases of nanoparticles of the same radius (synergistic effect), different radii (doped and undoped variants), as well as to discover the possibility of describing a group of powder particles from material within the Anderson model. The established effect makes it possible to solve the problem of diffusion instability of semiconductor heterojunctions and opens up prospects for creating electronics devices with a fundamentally new level of properties for use in various fields of the national economy and breakthrough critical technologies.

Fluorescence microscopy provides an unparalleled tool for imaging biological samples. However, producing high-quality volumetric images quickly and without excessive complexity remains a challenge. Here, we demonstrate a simple multi-camera structured illumination microscope (SIM) capable of simultaneously imaging multiple focal planes, allowing for the capture of 3D fluorescent images without any axial movement of the sample. This simple setup allows for the acquisition of many different 3D imaging modes, including 3D time lapses, high-axial-resolution 3D images, and large 3D mosaics.

Our work uses Iterative Boltzmann Inversion (IBI) to study the coarse-grained interaction between 20 amino acids and the representative carbon nanotube CNT55L3. IBI is a multi-scale simulation method that has attracted the attention of many researchers in recent years. It can effectively modify the coarse-grained model derived from the Potential of Mean Force (PMF). IBI is based on the distribution result obtained by All-Atom molecular dynamics simulation, that is, the target distribution function, the PMF potential energy is extracted, and then the initial potential energy extracted by the PMF is used to perform simulation iterations using IBI. Our research results have gone through more than 100 iterations, and finally, the distribution obtained by coarse-grained molecular simulation (CGMD) can effectively overlap with the results of all-atom molecular dynamics simulation (AAMD). In addition, our work lays the foundation for the study of force fields for the simulation of the coarse-graining of super-large proteins and other important nanoparticles.

Multi-morbidity and polypharmacy are common in older people and pose a challenge for health and social care systems especially in context of global population ageing. They are complex and interrelated concepts in the care of older people that require early detection and patient centred decision making that are underpinned by the principles of multidisciplinary led comprehensive geriatric assessment (CGA). Personalised care plans need to remain responsive and adaptable to the needs of a patient, enabling an individual to maintain their independence.

This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE) that would objectively blend real-time satellite quantitative precipitation estimates (SQPE) with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and a two-way blending routine that statistically fuses adjusted SQPE with radar estimates. The preprocessor not only corrects systematic bias in SQPE, but also improves the spatial distribution of precipitation based on SQPE and makes it closely resemble that of radar-based observations. It uses a more sophisticated radar-satellite merging technique to blend preprocessed datasets, and provides a better overall QPE product. The performance of the new satellite-radar-gauge blending module is assessed using independent rain gauge data over a 5-year period between 2003-2007, and the assessment evaluates the accuracy of newly developed satellite-radar-gauge (SRG) blended products versus that of radar-gauge products (which represents MPE algorithm currently used in the NWS operations) over two regions: I) inside radar effective coverage and II) immediately outside radar coverage. The outcomes of the evaluation indicate a) ingest of SQPE over areas within effective radar coverage improve the quality of QPE by mitigating the errors in radar estimates in region I; and b) blending of radar, gauge, and satellite estimates over region II leads to reduction of errors relative to bias-corrected SQPE. In addition, the new module alleviates the discontinuities along the boundaries of radar effective coverage otherwise seen when SQPE is used directly to fill the areas outside of effective radar coverage.

Bioelectrocatalysis provides the intrinsic catalytic-functions of redox enzymes to non-specific electrode reactions and is the most important and basic concept for biosensors. This review starts by describing fundamental characteristics of bioelectrocatalytic reactions in mediated and direct electron transfer types from a theoretical viewpoint and summarizes amperometric biosensors based on multi-enzymatic cascades and for multi-analyte detection. The review also introduces prospective aspects of two new concepts of biosensors: mass-transfer-controlled (pseudo)steady-state amperometry at microelectrodes with enhanced enzymatic activity without calibration curves and potentiometric coulometry at enzyme/mediator-immobilized biosensors for absolute determination.

This article proposes the use of a multi-scale and multi-sensor approach to collect and modelling 3D data concerning wide and complex areas in order to obtain a variety of metric information in the same 3D archive, based on a single coordinate system. The employment of these 3D georeferenced products is multifaceted and the fusion or integration among different sensors data, scales and resolutions is promising and could be useful for the generation of a model that could be defined as hybrid. The correct geometry, accuracy, radiometry and weight of the data models are hereby evaluated comparing integrated processes and results from Terrestrial Laser Scanner (TLS), Mobile Mapping System (MMS), Unmanned Aerial Vehicle (UAV), terrestrial photogrammetry, using Total Station (TS) and Global Navigation Satellite System (GNSS) as topographic survey. The entire analysis underlines the potentiality of the integration and fusion of different solutions and is a crucial part of the “Torino 1911” project whose main purpose is mapping and virtually reconstructing the 1911 Great Exhibition settled in the Valentino Park in Turin (Italy).

The central nervous system (CNS) is the final frontier in drug delivery because of the blood-brain barrier (BBB), which poses significant barriers to the access of most drugs to their targets. Kynurenic acid (KYNA), a tryptophan (Trp) metabolite, plays an important role in behavioral functions and abnormal KYNA levels have been observed in neuropsychiatric conditions. The current challenge lies in delivering KYNA to the CNS owing to its polar side chain. Recently, C-3 side chain modified KYNA analogs have been shown to cross the BBB; however, it is unclear whether they retain the biological functions of the parent molecule. This study examined the impact of KYNA analogs, specifically SZR-72, SZR-104, and the newly developed SZRG-21, on behavior. The analogs were administered intracerebroventricularly (i.c.v.), and their effects on the motor domain were compared with those of KYNA. Specifically, open field (OF) and rotarod (RR) tests were employed to assess motor activity and skills. SZR-104 increased horizontal exploratory activity in the OF test at a dose of 0.04 μmol/4 μL, while SZR-72 decreased vertical activity at doses of 0.04 and 0.1 μmol/4 μL. In the RR test, however, neither KYNA nor its analogs showed any significant differences in motor skills at either dose. Side chain modification affects affective motor performance and exploratory behavior, as the results show for the first time. In this study, we showed that KYNA analogs alter emotional components such as motor-associated curiosity and emotions. Consequently, drug design necessitates the development of precise strategies to traverse the BBB while paying close attention to modifications in their effects on behavior.

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by swelling in at least one joint. Owing to an overactive immune response, extra-articular manifestations are observed in certain cases, with interstitial lung disease (ILD) being the most common. Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is characterized by chronic inflammation of the interstitial space, which causes fibrosis and the scarring of lung tissue. Controlling inflammation and pulmonary fibrosis in RA-ILD is important because they are associated with high morbidity and mortality. Pirfenidone and nintedanib are specific drugs against idiopathic pulmonary fibrosis and have shown efficacy against RA-ILD in several clinical trials. Immunosuppressants and disease-modifying antirheumatic drugs (DMARDs) with antifibrotic effects have also been used to treat RA-ILD. Immunosuppressants moderate the overexpression of cytokines and immune cells to reduce pulmonary damage and slow the progression of fibrosis. DMARDs with mild antifibrotic effects target specific fibrotic pathways to regulate fibrogenic cellular activity, extracellular matrix homeostasis, and oxidative stress levels. Therefore, specific medications are required to effectively treat RA-ILD. In this review, the commonly used RA-ILD treatments are discussed based on their molecular mechanisms and clinical trial results. In addition, a computational approach is proposed to develop specific drugs for RA-ILD.

Wound infection is a common complication of chronic wounds. It can impair healing, which may not occur without external help. Antimicrobial dressings (AMDs) are a type of external help to infected chronic wounds. In this study, highly porous membranes made of only chitosan and containing the antiseptic polyhexanide (poly(hexamethylene biguanide); PHMB) were prepared by cryogelation, aiming to be used in AMDs. These membranes exhibited high water swelling capacity, fast water absorption and high water vapor transmission. The best drug loading method involved simultaneous loading by soaking in a PHMB solution and sterilization by autoclaving, resulting in sterilized, drug-loaded membranes. The drug release kinetics was comparable to that of a commercial PHMB-releasing AMD assayed under the same conditions. These membranes exhibited high antibacterial activity against Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa, which are bacterial species commonly found in infected wounds, and blood clotting activity. The obtained results suggest that these membranes may be suitable for use in the development of AMDs.

The COVID-19 was described as a respiratory illness, however further studies recognize it as a complex heterogeneous multisystemic disorder. Global efforts have been proposed to combat COVID-19, emerging diverse therapeutic options, in which discovering new drug therapies, development of vaccines and drug repurposing have been considered the most promising approaches to fight the virus. This study aimed to repurpose known drugs for use against the COVID-19, finding better therapeutic options. Seventeen biological databases were used in this study. The genetic algorithm (GA) was performed for a set of drug target classes and COVID-19 proteins as input, whose drug candidates are obtained according to the target similarities found in the target-target similarity predictive network, resulting in a drug-target interaction network. Thus, recommended drugs correspond to the union of the drug subsets found during each GA execution. Twenty-eight drugs were indicated to be the best therapeutic targets for the virus, in special, the Cyclosporine drug was administered as adjuvant to steroid treatment for COVID-19 patients which showed positive outcomes, reducing mortality in moderate and severe cases. The drugs found have used to treat other diseases, evidencing that the COVID-19 is a multisystemic disorder and suggests that the viruses’ mechanism of action presents some comorbidity with other human diseases. Evidence shows that the drugs found in this research might act together to fight the virus in a broader fashion, however further studies including in vitro and in vivo experiments are needed to find the best combination of these drugs.

Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multidisease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines via large scale modelling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is then compared to all other signatures that are then sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions in the platform used to create the drug-proteome signatures may be determined by any screening or docking method but the primary approach used thus far has been an in house similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and cheminformatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the corresponding two docking-based pipelines it was synthesized from, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking based signature generation methods can capture unique and useful signal for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.

Nanotechnology has enabled the development of novel therapeutic strategies such as targeted nanodrug delivery systems, control and stimulus-responsive release mechanisms, and the production of theranostic agents. As a prerequisite for the use of nanoparticles as drug delivery systems, the amount of loaded drug must be precisely quantified, a task for which two approaches are currently used. However, both approaches suffer from the inefficiencies of drug extraction and of the solid-liquid separation process, as well as from dilution errors. This work describes a new, reliable, and simple method for direct drug quantification in polymeric nanoparticles using attenuated total reflection Fourier transform infrared spectroscopy, which can be adapted for a wide variety of drug delivery systems. Silk fibroin nanoparticles and naringenin were used as model polymeric nanoparticle carrier and drug, respectively. The specificity, linearity, detection limit, precision and accuracy of the spectroscopic approach were determined in order to validate the method. A good linear relation was observed within 0.00 to 7.89 % of naringenin relative mass with an R2 of 0.973. The accuracy was determined by the spike and recovery method. Results showed an average 104% recovery. The limit of detection and limit of quantification of the drug loading content were determined to be 0.3 and 1.0 %, respectively. The method's robustness is demonstrated by the notable similarities between the calibrations carried out in two different equipment and institutions.

A 70-year-old female with a significant medical history of hypertension, hypothyroidism, chronic back pain, and a left hip prosthetic joint infection experienced complications following a total hip replacement. After multiple revisions and eventual discharge to a rehabilitation facility, she developed surgical site bleeding and a femoral head dislocation extending into the pelvis. At our hospital, imaging revealed a failed hip arthroplasty with extensive damage and infection, necessitating complex revision surgery and an antibiotic regimen. During her treatment, she developed a diffuse rash diagnosed as vancomycin-induced hypersensitivity reaction (DRESS), prompting a switch to oral linezolid and systemic corticosteroids. Despite transient eosinophilia and elevated inflammatory markers, her condition stabilized, and she was discharged with instructions for follow-up. This case underscores the importance of vigilant monitoring and timely intervention in managing complex postoperative infections and drug reactions.

Blood stream infections (BSI) due to carbapenem-resistant gram-negative bacilli (CR-GNB), especially Klebsiella pneumoniae carbapenemase (KPC) and New-Delhi metallo-β-lactamase (NDM) producers, are a major concern of public health, as they are associated with high morbidity and mortality. We conducted a single-center, retrospective, observational study at a tertiary-care, teaching Greek hospital, to determine the incidence, 30-day and overall, in-hospital, crude mortality, risk factors and efficacy of best available treatment options in patients with BSIs from KPC- or NDM-producing GR-GNB. During the one-year study period, from 1st January to 31st December 2023, we recorded 133 episodes of BSIs in 118 unique patients; 79 episodes were due to KPC-producing and 54 to NDM-producing CR-GNB. Our study showed relatively high incidence of BSIs by both KPC- and NDM-producing CR-GNB (0.49 and 0.31 episodes per 1000 patient-days respectively). We found high 30-day crude mortality rates for patients with BSIs due to KPC- producing CR-GNB (26.4 %), and NDM-producing CR-GNB (37%), while BSIs due to NDM-producing P. aeruginosa had the highest 30-day (47.1%) and overall (70.6%) in-hospital, crude mortality. Beta-lactam/beta-lactamase inhibitors as monotherapy or in combination with other active agents used to treat KPC-producing CR-GNB were associated with improved survival rates (p=0.009).

Musculoskeletal infections (MIs) are among the most difficult-to-treat staphylococcal diseases. The limited availability of effective antibiotics has encouraged the development of innovative strategies such as combinatory therapy to combat MI. The aim of this study was to investigate the in vitro antistaphylococcal activity of anti-inflammatory and the antimicrobial combinatory effect of celecoxib with oxacillin. The minimum inhibitory concentrations (MICs) of 17 anti-inflammatory drugs were determined for standard strains and clinical isolates of S. aureus strains, counting methicillin-resistant strains (MRSA), by the broth microdilution method. The fractional inhibitory concentration indices (FICIs) were evaluated by the checkerboard assays. Celecoxib produced the most potent antistaphylococcal effect against all the tested strains (MIC ranging from 32 to 64 mg/mL), followed by diacerein against MRSA3 and MRSA ATCC 33592 (MIC 64 mg/mL). Several synergistic effects were observed for tested S. aureus strains, including MRSA (FICI ranging from 0.087 to 0.471). The strongest synergistic interaction (FICI 0.087) was obtained against MRSA ATCC 33592 at a celecoxib concentration of 2 mg/mL with a 19-fold oxacillin MIC reduction (from 512 to 26.888 mg/mL). This is the first report on the antistaphylococcal combinatory effect of celecoxib with oxacillin. Our findings suggest celecoxib and its combination with oxacillin as perspective agents for research focused on the development of novel therapies for MI caused by S. aureus. Thus, this study presents novel findings that celecoxib could, in some cases, resensitise MRSA strains to become susceptible to certain β-lactams (e.g., oxacillin) not previously tested.

Background: Number of joint infections caused by Vancomycin-resistant pathogens is emerging. Currently no PMMA cement is commercially available to cover VRE. Daptomycin shows promising results in infection treatment, offers a good safety profile and reduced risk for resistance development. Purpose of this in vitro study is to investigate the mechanical stability, handling properties, elution behavior and antimicrobial effectiveness of PMMA cement loaded with three different Daptomycin concentrations in comparison to commercially available ALBC; Methods: Mechanical properties and handling characteristics (ISO 5833, DIN 53435), HPLC elution, antimicrobial effectiveness with proliferation assay (DIN 17025) and inhibition zone testing were investigated; Results: All tested Daptomycin concentrations fulfilled ISO and DIN mechanical strength. Dosage of 0.5. g added Daptomycin did not show any antimicrobial effectiveness in contrast to 1.0 g and1.5 g. Cement with 1.5 g Daptomycin was best in elution and effectiveness, showed good mechanical ISO strength, ISO doughing was a little longer sticky and setting was faster as Vancomycin containing reference cement; Conclusion: PMMA cement containing 0.5 g Gentamicin and 1.5 g Daptomycin could be a good alternative to the already established COPAL® G+V for PJI treatment of VRE.

Urban air pollution is a critical global environmental issue, necessitating an understanding of the spatiotemporal characteristics and driving factors for effective environmental protection and urban planning. This study introduced an advanced air quality assessment system that integrates environmental, socio-economic, and urban layout factors, addressing gaps in traditional models that often overlook the impact of urban spatial structures. Analyzing air quality data from Beijing's main urban area (2016-2020) alongside multi-source geographic data, the research develops a comprehensive evaluation system, incorporating 14 key driving factors. Employing Geographically Weighted Regression (GWR) and Multi-scale Geographically Weighted Regression (MGWR), the study quantitatively assessed the influence and spatial heterogeneity of these factors. Findings revealed an annual improvement in air quality, with a U-shaped seasonal pattern and significant spatial clustering (Global Moran’s I = 0.922). The MGWR model, in particular, provided a superior fit over GWR, effectively capturing the spatial variability of factors. Variables such as NDVI), economic output (GDP), and humidity space adjustment capability (HSAC) showed significant positive spatial impacts on air quality, while population density (POP), temperature (TEMP), and road density (RD) exhibited negative effects. These insights enhance the understanding of air pollution dynamics and aid in refining urban planning strategies.

In this paper, we consider a new class of polynomials which is called the multi-poly-Euler polynomials. Then, we investigate their some properties and relations. We provide that the type 2 degenerate multi-poly-Euler polynomials equals a linear combination of the degenerate Euler polynomials of higher order and the degenerate Stirling numbers of the first kind. Moreover, we provide an addition formula and a derivative formula. Furthermore, in a special case, we acquire a correlation between the type 2 degenerate multi-poly-Euler polynomials and degenerate Whitney numbers.

Inspired by the definition of degenerate multi-poly-Genocchi polynomials given by using the degenerate multi-polyexponential functions. In this paper, we consider a class of new generating function for the degenerate multi-poly-Bernoulli polynomials, called the type 2 degenerate multi-poly-Bernoulli polynomials by means of the degenerate multiple polyexponential functions. Then, we investigate their some properties and relations. We show that the type 2 degenerate multi-poly-Bernoulli polynomials equals a linear combination of the weighted degenerate Bernoulli polynomials and Stirling numbers of the first kind. Moreover, we provide an addition formula and a derivative formula. Furthermore, in a special case, we acquire a correlation between the type 2 degenerate multi-poly-Bernoulli numbers and degenerate Whitney numbers.

A multi-decadal change analysis of the irrigation ponds in Taoyuan, Taiwan was conducted by using multi-source data including digitized ancient maps, declassified single-band CORONA satellite images, and multispectral SPOT images. Supervised LULC classifications were conducted using four textural features derived from the single-band CORONA images and spectral features derived from SPOT images. Post-classification analysis revealed that the number of irrigation ponds in the study area decreased during the post-World War II farmland consolidation period (1945 – 1965) and the subsequent industrialization period (1970 – 2000). However, efforts on restoration of irrigation ponds in recent years have resulted in gradual increases in the number (9%) and total area (12%) of irrigation ponds in the study area.

Diabetes is a major health concern that the next-generation of on-demand insulin releasing implants may overcome for personalized therapy. Therein, 3D-printed phenylboronic acid-containing subcutaneous implants with on-demand glucose-triggered drug release abilities are produced using high resolution stereolithography technology. To that end, the methacrylation of phenylboronic acid is targeted following a two-step reaction. The resulting photocurable phenylboronic acid derivative is accordingly incorporated within bioinert polyhydroxyethyl methacrylate-based hydrogels at varying loadings. The end result are sub-centimeter scaled 3D-printed bionert implants that can be remotely activated with 1,2-diols and 1,3-diols such as glucose for on-demand drug administration such as insulin. As a proof of concept, varying glucose concentration from hypoglycemia to hyperglycemia levels readily allow the release of pinacol, i.e. a model molecule with similar release properties than insulin, at respectively low and high rates. In addition, the results demonstrated that adjusting the geometry and size of the 3D-printed part is a simple and suitable method for tailoring the release behavior and dosage.

Herein, we investigated hydrogels composed of boronic Acid functionalized alginate and blended with polyvinyl alcohol of different molecular weights to control the release of metoclopramide hydrochloride (MTC) as a function of pH and shear stress. The functionalization of alginate introduced dynamic covalent bonding and pH-responsive properties that can modulate network connectivity. The study investigated the viscoelastic properties of the hydrogels, their drug release profiles, and their responsiveness to changes in pH and shear forces. The results showed higher PVA molecular weight and alkaline pH conditions increased hydrogel viscosity and stiffness due to a more stable and interconnected network structure than acidic pH. Metoclopramide release revealed that the hydrogels exhibited pH-responsive drug release behavior. The drug was more readily released under acidic conditions due to the instability of sp2-hybridized boronate ester bonds. The influence of shear forces on the release of metoclopramide was also investigated at shear rates of 1, 10, and 100 s-1, revealing their effect on matrix stiffening. The research shows that AlgBA/PVA hydrogels have unique properties, such as dynamic covalent bonding, that make them sensitive to external mechanical forces. This sensitivity makes them ideal for applications where physiological conditions trigger drug release

Today, polymeric Drug Delivery Systems (DDS) appear as an interesting solution against bacterial resistance, having great advantages such as low toxicity, biocompatibility, and biodegradability. In this work, two polyketones (PK) have been post-functionalized with taurinate (PKT) or sulfanilate (PKSK) and employed as biocompatible carriers for Vancomycin against bacterial infections. Mod-ified PKs were easily prepared by Paal-Knorr reaction and loaded with Vancomycin at variable pH. All polymers were characterized by FT-IR, DSC, TGA, SEM and elemental analysis. Antimicrobial activity was tested against Gram positive Staphylococcus aureus ATCC 25923 and correlated to the different pH used for its loading (between 2.3 and 8.8). Distinctively, minimum inhibitory con-centrations achieved with PKT and PKSK loaded with Vancomycin are similar, 0.23μg/mL and 0.24μg/mL respectively, i.e. six times lower that Vancomycin alone. The use of post-functionalized aliphatic polyketones has thus been demonstrated to be a promising way to obtain very efficient polymeric DDS.

Salmonella sp. is a globally prevalent organism responsible for causing salmonellosis, a foodborne illness. Salmonella Typhimurium represents a type of Salmonella sp. not associated with typhoid fever but capable of causing stomach and intestinal inflammation or severe infection. In the present study, 150 uncharacterized proteins of S. Typhimurium were randomly selected from UniProtKB and analyzed using TMHMM, PROSITE, STRING, DEG, and BLASTp. Results indicated that 32 uncharacterized proteins (21%) were predicted to be transmembrane proteins involved in various biological pathways. Among them, 30 transmembrane proteins were predicted to be essential and non-host homologous. This study's findings suggest their potential as drug targets against salmonellosis.

Cancer, characterized by abnormal cell proliferation driven by genetic mutations, remains a significant health challenge. Tailoring effective therapies depends on factors like tumor location, grade, and stage, alongside patient condition. Leveraging Nano-carrier encapsulated drugs offers a promising avenue, as they accumulate preferentially at tumor sites, enhancing therapeutic efficacy while mitigating side effects. Polymeric Nano-carriers, with their customizable properties, further optimize drug delivery efficiency and specificity. Incorporating active targeting ligands such as antibodies, carbohydrates, peptides, folates, and aptamers-onto polymeric nanoparticle surfaces through techniques like functionalization, adsorption, and conjugation plays a pivotal role in directing therapeutic agents to tumor cells. Consequently, polymeric nanoparticles emerge as the carrier of choice for delivering drugs to various tumor types, including breast, colon, lung, prostate, liver, and spleen tumors.

Chemical combinations are believed to reduce drug toxicity, delay cancer cell growth, and achieve enhanced efficacy compared with single active drugs. This research investigated the synergistic effect of DOX and Zm-093, a new sulfonamide derivative, on improving the potential for inducing apoptosis in breast cancer (BC) cell lines. First, the Zm-093 compound was synthesized and its structure was confirmed by FT-IR and NMR. Then, the IC50 values for DOX and Zm-093 were calculated to be 1.21 and 51.24 μM, respectively. To investigate the synergistic effect, Compusyn software was utilized to calculate the combination index (CI). Different methods, including western blotting, flow cytometry, and TUNEL, were used to evaluate the potential synergistic effects of these compounds on inducing apoptosis in MCF-7 cells. Combining the indexes obtained at all concentrations revealed synergistic results. The proapoptotic proteins Bax, tBid, and caspase-3, however, increased 2.4, 3.3, and 5.7 times more than those in the control group. Flow cytometry analysis and the TUNEL method in cells treated with DOX and Zm-093 showed that apoptosis was significantly higher than in other groups. The results confirmed the synergistic effect of DOX and ZM-093 on apoptotic factors expression in MCF-7 cells.

Abstract: A. baumannii exhibits resilience in harsh environments and progressively develops resistance to diverse antibacterial agents over time. This adaptability poses a significant challenge in treating an infection caused by this bacterium within healthcare settings. The presence of peptidoglycan, a crucial precursor for cell wall synthesis, contributes to this challenge. Peptidoglycan serves not only as a structural support in bacterial cells but also provides protection against adverse environmental conditions. The synthesis of the peptidoglycan layer involves a group of substrates such as N-acetylglucosamine (GlcNAc) and N-acetylmuramic acid (MurNAc), within a three-partitioned process occurring in the membrane, periplasmic space, and cytoplasm of each bacterium. Initiated in the cytoplasm and extending to the inner cell membrane, this synthesis starts with the production of nucleotide precursors. The first step involves the synthesis of UDP-GlcNAc, orchestrated by Glm (GlmS, GlmM, and GlmU) enzymes using fructose-6-phosphate. Subsequently, enzymes from the Mur family, namely MurB, MurA, MurD, MurC, MurI, MurF, and MurE, synthesize UDP-N-acetylmuramyl-pentapeptide (UDP-Mpp) from UDP-GlcNAc. Concurrently, on the cytosolic side, the production of membrane-surrounded undecaprenyl phosphate occurs, involving membrane-side proteins MraY and MurG in the biosynthesis of peptidoglycan. This review emphasizes updated information on Mur family proteins, encompassing physicochemical properties, nature of binding site, number and type of binding site residues, functional domain details, three-dimensional structural aspects, and their functions within bacterial cells. Special attention is directed towards their enzymatic role in peptidoglycan biosynthesis due to the limited availability of precursors in the market. Additionally, the review provides a comprehensive summary of existing inhibitors and ongoing efforts in identifying new targeted inhibitors, recognizing these enzymes as potential candidates for antibacterial drug design and discovery.

Glioblastoma (GB), even though not frequent, is an extremely aggressive brain tumor, with a strong impact on patient outcomes, in terms of both length and quality of life. The protocol estab-lished by Stupp and coworkers in 2005, based on radiotherapy and temozolomide chemotherapy, after maximum safe surgical resection, remains the gold standard for GB treatment. However, it is evident nowadays that the extreme GB intratumoral and intertumoral heterogeneity, invasive-ness and recurrence are not compatible with a routinary and unfortunately uneffective treatment. In this review article, we summarize the principal challenging issues in finding new valuable therapies for GB and focus on the impact that extracellular vesicle (EV) research and exploitation can have in the field. EVs are natural particles delimited by a lipidic bilayer and full of functional cellular content, released and uptaken by cells as key means of cell communications. Moreover, being stable in body fluids, well-immunotolerated, able to cross physiological, inter-species, and inter-kingdom barriers, and to target specific cells, releasing inherent or externally loaded functionally active molecules, EVs can be potentially ideal allies in fighting GB and assuring to GB patients a better prognosis. Here we report the main results obtained until now, focusing on both EV sources and molecular cargo used in the different functional studies, mainly in vivo. Finally, we perform a SWOT analysis reporting both the main advantages and the pitfalls of developing EV-based GB therapeutic strategies to highlight the main directions to explore for realizing the promise of EV exploitation for GB treatment.

The field of biobanking human biospecimens has evolved significantly, transitioning from the basic, often poorly documented collection of clinical leftovers kept privately to well-organized and extensively documented collections overseen by both commercial and non-profit platforms. The increasing demand for high-quality and clinically annotated biospecimens is propelled by unprecedented levels of health research activities. Meeting this growing demand presents both new opportunities and challenges, particularly in developing strategies to establish international biospecimen sourcing (IBS) platforms. These platforms aim to facilitate collaboration among biobanks to address future biospecimen needs. In this manuscript, we delve into the advantages and challenges of establishing IBS platforms to provide high-quality and cost-effective biospecimens to drive drug discovery research, ultimately leading to improved health and quality of life for everyone.

Cardiovascular disease (CVD) accounts for nearly one-third of all global fatalities, establishing itself as a leading cause of death. To address this pressing issue, there is a growing demand for improved diagnostic methods and secure, non-invasive imaging techniques that can deliver precise insights into the progression of CVD. The success of personalized therapy for CVD depends on a couple of critical factors: accurate diagnosis and early detection. Therapeutic options for conditions like CVD, which develop over several decades, are highly specific to the individual's condition and the stage of the disease. In the field of biomedicine, nanotechnology has become increasingly prominent as a tool with diverse applications, including diagnostics, biosensing, and drug delivery. This review article offers an in-depth look at the most recent developments in the utilization of nanotechnology for detecting and managing prevalent cardiovascular diseases, providing valuable insights into this crucial area of healthcare research.

The contamination of aquatic environments by pharmaceutical products consistently takes the attention of researchers due to the compounds' toxicity even at low concentrations. In response, we have developed an ecologically sustainable biosurfactant derived from a microorganism and incorporated into the bacterial cellulose. This bioproduct, along with the bacterial cellulose itself, was employed as a sorbent for pharmaceuticals (hormones and paracetamol) present in water. Bacterial cellulose membranes were generated through the cultivation of Gluconacetobacter xylinus ATCC 53582. The biosurfactant was produced by pre-inoculating Bacillus subtilis in a synthetic medium, followed by immersing the bacterial cellulose membranes in the biosurfactant solution, for incorporation. Tests were conducted using water experimentally contaminated with parace-tamol and 17α-ethinylestradiol (EE2), evaluating the biosurfactant's effect on bacterial cellulose sorption. Paracetamol levels were analyzed using spectrophotometry, and EE2 levels were as-sessed using high-performance liquid chromatography. In summary, bacterial cellulose exhibited superior adsorption for EE2 compared to paracetamol. The incorporation of biosurfactant onto bacterial cellulose reduced hormone adsorption but enhanced paracetamol sorption. Our findings indicated that adsorption is more effective with bacterial cellulose in its original and freeze-dried forms, without the incorporation of biosurfactant. Notably, we achieved more promising results in remediating the hormone EE2 compared to the paracetamol.

Chitosan nanoparticle delivery systems have potential for enhancing bone healing and addressing osteomyelitis. The objective was to deliver antimicrobial agents capable of preventing or treating osteomyelitis. Each formulation was optimized to achieve desired characteristics in terms of size (ranging from 100 to 400 nm), PDI (less than 0.5), zeta potential (typically negative), and in vitro release profiles for gentamicin. Entrapment percentages varied with gentamicin ranging from 10% to 65%. The chitosan nanoparticles exhibited substantial antimicrobial efficacy, particularly against both P. aeruginosa and MRSA, with zones of inhibition ranging from 13 to 24 mm and complete reduction in colony forming units observed between 3 to 24 hours. These chitosan nanoparticle formulations loaded with antimicrobials hold promise for addressing orthopedic complications.

Drug delivery through Liposomes has shown tremendous potential in terms of the therapeutic application of nanoparticles. There are several drug-loaded liposomal formulations approved for clinical use that help mitigate harmful effects of life-threatening diseases. Developments in the field of liposomal formulations and drug delivery have made it possible for clinicians and researchers to find therapeutic solutions for complicated medical conditions. A key aspect in the development of drug-loaded liposomes is a careful review of optimization techniques to improve the overall formulation stability and efficacy. Optimization studies help in improving/modulating the various properties of drug-loaded liposomes and are vital for the development of this class of delivery systems. A comprehensive overview of the various process variables and factors involved in the optimization of drug-loaded liposomes is presented in this review. The influence of different independent variables on drug release and loading properties with the application of a statistical experimental design is also explained in this article.

The COVID-19 pandemic has unveiled the critical role that supercomputers play in addressing global health crises. This perspective article explores the transformative impact of supercomputing in pandemic response, highlighting how these powerful machines have transitioned from crisis management tools to essential components of preparedness strategies. In this article, we examine the key contributions of supercomputers in combating the pandemic, including their ability to accelerate scientific research, facilitate data analysis, model complex scenarios, and optimize resource allocation. Furthermore, we delve into the role of supercomputing in strengthening public health infrastructure and preparedness. We analyze the lessons learned from the pandemic and emphasize the need for proactive investment in supercomputing capabilities, data infrastructure, and computational expertise as a crucial element of pandemic preparedness efforts. By harnessing the potential of supercomputers, countries can establish robust computational frameworks that enable real-time monitoring, early detection, and effective response to future outbreaks. Moreover, we address the challenges faced by supercomputing initiatives, including the need for enhanced international collaboration, equitable access to computing resources, and the ethical considerations associated with data privacy and security. We discuss potential strategies to overcome these challenges and foster a global network of interconnected supercomputing centers dedicated to pandemic preparedness and response. Finally, we highlight ongoing research and development efforts aimed at advancing supercomputing technologies, such as exascale computing and quantum computing, and their potential to revolutionize pandemic response. We emphasize the importance of sustained investment in research and development to ensure that supercomputing remains at the forefront of future pandemic preparedness strategies.

Encapsulated nanofibers have emerged as a promising approach for the treatment of acne, owing to their ability to provide controlled release, targeted delivery, increased efficacy, and improved stability. Electrospinning is a well-established method for producing encapsulated nanofibers and has been shown to be effective for encapsulating various active ingredients. However, there are still several challenges that need to be addressed in the development of encapsulated nanofibers for acne treatment. One major challenge is the need for comprehensive in vitro and in vivo studies to evaluate the safety and efficacy of these treatments. The cost and scalability of production also need to be considered to make these treatments accessible and affordable for patients. In addition, the long-term stability of encapsulated active ingredients is another challenge in the development of encapsulated nanofibers for acne treatment. Regulatory frameworks need to be developed to ensure the safety and efficacy of these treatments. Future research may focus on developing multifunctional nanofibers that combine active ingredients with other properties, such as antimicrobial, anti-inflammatory, and wound-healing properties, to provide a comprehensive approach to acne treatment. Moreover, the development of nanofiber-based skincare products may have a significant impact on the cosmetic industry. Overall, while there are still challenges to overcome, the potential benefits of encapsulated nanofibers for acne treatment make them an exciting and promising area of research for the future. In particular, the integration of smart drug delivery systems and responsive materials may enable the development of more personalized and effective treatments for acne. The development of new materials and encapsulation techniques, as well as the exploration of combination therapies that target multiple aspects of acne pathogenesis, are also future perspectives for encapsulated nanofibers in acne treatment.

Background – Mali was one of the first countries in sub-Saharan Africa to initiate a National Schistosomiasis Control Programme (NSCP) in 1982. The WHO's 2021-30 roadmap sets out criteria for eliminating and controlling schistosomiasis as a public health problem. Our study aimed to assess the impact of annual Mass Drug Distribution (MDD) with praziquantel (PZQ) among school-age children in the sentinel sites (SS) of the NSCP. Methods –The study took place at twelve SS in Kayes and Koulikoro regions. Two-round observational cross-sectional studies were carried out in December 2014-2015 and in April 2018 after four to five years of annually MDD. Overall, 2442 schoolchildren aged 7 to 14 were successfully examined. The urine filtration and Kato-Katz method were used for determining Schistosoma haematobium and S. mansoni eggs, respectively. Results –Of the twelve SS treated from 2014-2015, one has achieved the criterion of elimination of S. haematobium as a public health problem (prevalence of heavy intensity infection PHI < 1%) (ie, ≥50 S. haematobium eggs per 10 mL of urine or ≥400 S. mansoni eggs per g of stool), four met the morbidity control criterion (PHI< 5%) while two sites remained confined below the morbidity control criterion (PHI>5%). Five SS had no heavy intensity infection. The prevalence of S. mansoni was less than 1%. Conclusion –The impact evaluation of MDD with praziquantel in the SS of NSCP highlights that MDD has significantly reduced the PHI of schistosomiasis. However, the high prevalence of schistosomiasis or its increase in some sites requires in-depth studies.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that has significant impact on quality of life and work capacity. Treatment of RA aims to control inflammation and alleviate pain, however achieving remission with minimal toxicity is frequently not possible with the current suite of drugs. This review aims to summarise current treatment practices and highlight the urgent need for alternative pharmacogenomic approaches to novel drug discovery. These approaches can elucidate new relationships between drugs, genes, and diseases to identify additional effective and safe therapeutic options. This review discusses how computational approaches such as connectivity mapping offers the ability to repurpose FDA approved drugs beyond their original treatment indication. This review also explores the concept of drug sensitisation, to predict co-prescribed drugs with synergistic effects that produce enhanced anti-disease efficacy by involving multiple disease pathways. Challenges of this computational approach are discussed including the availability of suitable high-quality datasets for comprehensive analysis and other data curation issues. The potential benefits include accelerated identification of novel drug combinations, and ability to trial and implement established treatments in a new index disease. This review underlines the huge opportunity to incorporate disease-related data and drug-related data to develop methods and algorithms which have strong potential to determine novel and effective treatment regimens.

Peptide mapping is an important tool to confirm the correct sequence expressed for a protein and to evaluate protein post-translational modifications (PTMs) that may arise during production, processing, or storage of protein drugs. Our new orally administered drug (Ab-1), a single-domain antibody, is highly stable and resistant to proteolysis. Analysis by the commonly used tryptic map method did not generate sufficient sequence coverage. Alternative methods were needed to study the Ab-1 drug substance (75 mg/mL) and drug product (3 mg/mL). To meet analytical needs, we developed two new peptide map methods with lysyl endopeptidase (Lys-C) digestion. These newly developed protein digestion protocols do not require desalting/buffer-exchange steps, thereby reducing sample preparation time and improving method robustness. Additionally, the protein digestion was performed under neutral pH with methionine acting as a scavenger to minimize artifacts, such as deamidation and oxidation, which are induced during sample preparation. Further, the method for low concentration samples performs comparably to the method for high concentration samples. Both methods provide 100% sequence coverage for Ab-1, and therefore, enable comprehensive characterization for its product quality attributes (PQA) assessment. Both methods can be used to study other antibody modalities.

This work deals with the synthesis of bare and curcumin (CUR)-loaded chitosan (CS)-based macroparticles by ionic gelation using sodium hydroxide (NaOH) or sodium tripolyphosphate (TPP) as cross-linking agents. The resulting spherical-shaped macroparticles were studied using various characterization techniques, Scanning Electron Microscopy (SEM), Fourier Transformed Infrared Spectroscopy (FTIR), Thermogravimetric Analysis (TGA), and Differential Scanning Calorimetry (DSC). The release of CUR from the CS-based particles with respect to time was analyzed, and the encapsulation efficiency and degree of swelling were studied. All formulations showed excellent CUR trapping efficiency, exceeding 90%. In particular, the TPP-crosslinked macrobeads released 34 wt% of the charged CUR within minutes, while the remaining 66 wt% was released slowly due to the biodegradation of CS. The results indicate that the correct choice of gelling agent and its concentration leads to spherical particles capable of encapsulating CUR and releasing it in a wide range of kinetics depending on the specific application.

Lippia javanica (Burm. f.) is a medicinal plant with multiple native uses in the treatment of colds, influenza, pneumonia, coughs, dermatitis, tuberculosis, asthma, and bronchial diseases. However, it remains underutilized in drug development. In this review, existing studies on the pharmacological activities of L. javanica, more specifically the antitumor, antiviral, and antibacterial properties as well as recent metabolites identified from the plant were appraised. Background information on the plant's ecological distribution, ethnobotanical importance, toxicity, and cytotoxicity activity was also provided. The information in the review was gathered from major scientific databases (Google scholar, BioMed Central, Scopus, PubMed, Springer, Web of Science, and Science Direct) using journals, conference proceedings, dissertations, books, and/or chapters. The review compiled 66 new metabolites from the plant while establishing the anticancer, antiviral, antibacterial, and other important pharmacological activities of the organic (most especially methanol and acetone) and aqueous extracts of various parts of the plant attributed to several secondary metabolites present in the volatile oil of the plant, which includes carvone, ipsdienone, limonene, linalool, myrcene, ocimenone, caryophyllene, piperitenone, sabinene, p-cymene, sabinene, myrcene, ipsenone, and tagetenone. While some of the metabolites of L. javanica have been linked through pharmacological studies to the various traditional uses of the plant, some others remain unexplored, emphasizing how underutilized the plant is in drug research and development. It is envisaged that the reports from this review will help to further encourage future research and guide further investigations into the pharmaceutical and medicinal uses of L. javanica.

Curcumin (CUR) is a phytochemical with potent anticancer activities as demonstrated by both preclinical and clinical studies. CUR bioformulations, including loading in biodegradable polymers, have been explored to increase CUR’s solubility and chemical stability, control its release, and improve its delivery to cancer cells. In this study, copolymers comprising poly (L-lactide)-poly (ethylene glycol)-poly (L-lactide) (PLA-PEG-PLA) and poly (ethylene glycol)-poly (L-lactide)-poly (ethylene glycol) (PEG-PLA-PEG) were designed and synthesized to assess and compare their curcumin delivery capacity and inhibitory potency on MCF-7 breast cancer cells. Molecular dynamics simulations indicated that PLA-PEG-PLA has a higher propensity to interact with the cell membrane and more negative free energy, suggesting it is the better carrier for cell membrane penetration. Our characterization indicated that the microsphere copolymers were synthesized successfully. Of the two formulations, PLA-PEG-PLA experimentally exhibited better results, with an initial burst release of 17.5%, followed by a slow, constant release of the encapsulated drug up to 80%. PLA-PEG-PLA-curcumin showed a significant increase in cell death in MCF-7 cancer cells (IC50 = 23.01 ± 0.85 µM) based on the MTT assay. These data were consistent with gene expression studies of Bax, Bcl2, and hTERT which showed that PLA-PEG-PLA-CUR induced apoptosis more efficiently in these cells. Our study integrates in vitro and in silico approaches to identify an optimal co-polymer for the delivery of CUR to cancer cells.

Kinases play an important role in regulating various intracellular signaling pathways that control cell proliferation, differentiation, survival and other cellular processes and their deregulation causes more than 400 diseases. Consequently, macrocyclization can be considered a noteworthy approach to develop new therapeutic agents for human diseases. Macrocyclization has emerged as an effective drug discovery strategy over the past decade to improve target selectivity and potency of small molecules. Small compounds with linear structures upon macrocyclization can lead to changes in their physicochemical and biological properties by firmly reducing conformational flexibility. A number of distinct protein kinases exhibit similar binding sites. Comparison of protein binding site provides crucial insights for the drug discovery and development. Binding site similarities are helpful to understand polypharmacology, identifying potential off-targets and repurposing of known drugs. In this review, we focused on comparing binding sites of those kinases for which macrocyclic inhibitors are available/studied so far. Furthermore, we calculated the volume of binding site pocket for each targeted kinase and then compared it with the binding site pocket of the kinase for which only acyclic inhibitors were designed till date. Our review and analysis of several explored kinases might be useful to target new protein kinases for macrocyclic drug discovery.

Sepsis is a life-threatening condition caused by a dysregulated host response to an invading pathogen such as multidrug-resistant bacteria. Despite recent advancements, sepsis is a leading cause of morbidity and mortality, resulting in a significant global impact and burden. This condition affects all age groups, with clinical outcomes depending mainly on timely diagnosis and appropriate early therapeutic intervention. Because of the unique features of nanosized systems, there is a growing interest in developing and designing novel solutions. Nanoscale-engineered materials allow a targeted and controlled release of bioactive agents, resulting in improved efficacy with minimal side effects. Additionally, nanoparticle-based sensors provide a quicker and more reliable alternative to conventional diagnostic methods for identifying infection and organ dysfunction. Despite recent advancements, fundamental nanotechnology principles are often presented in technical formats that presuppose advanced chemistry, physics, and engineering knowledge. Consequently, clinicians may not grasp the underlying science, hindering interdisciplinary collaborations and successful translation from bench to bedside. In this review, we abridge some of the most recent and most promising nanotechnology-based solutions for sepsis diagnosis and management using an intelligible format to stimulate a seamless collaboration between engineers, scientists, and clinicians.

Despite the recent approval of novel immunotherapies, as immunomodulatory drug, proteasome inhibitors and anti-CD38 monoclonal antibodies, Multiple Myeloma (MM) remains incurable and the acquisition of triple-refractoriness leads to really dismal outcomes, in even earlier lines of therapy. More recently, innovative therapeutic strategies targeting B cell maturation antigen (BCMA), highly expressed on the plasma cell surface, are drawing different future landscapes in terms of effectiveness and outcomes. Belantamab Mafodotin, a first-in-class anti-BCMA antibody drug conjugates, demonstrated good efficacy and safety profile in triple-refractory patients in the phase 2 DREAMM-2 trial and it was approved for the treatment of MM triple-exposed patients with >4 prior lines of therapy. Here, starting from Belantamab Mafodotin clinical trials also exploring combination studies and different schedules in order to improve its efficacy and toxicity, we focused on real life experiences all over the world, which have confirmed clinical trial data and encourage further Belantamab Mafodotin investigations

Abstract: Enfortumab vedotin (EV), an antibody-drug conjugate directed against Nectin-4, signif-icantly prolonged survival when compared with standard chemotherapy in patients with locally advanced or metastatic urothelial carcinoma who previously received platinum-based chemo-therapy and a PD-1 or PD-L1 inhibitor. The confirmed overall response rate in the phase 3 EV301 trial leading to approval was 40.6%. However, no data have been published about the activity in brain metastases. Here, we present three patients from different centers with brain metastases receiving EV. A 58-year-old male Caucasian patient, who was heavily pretreated for urothelial carcinoma with visceral metastases and a solitary clinically active brain metastasis, started on EV 1.25 mg/kg on days 1, 8, and 15 of a 28-day cycle. The first evaluation after three cycles of EV showed a partial remission by RECIST v1.1 with a near complete response in the brain metastasis and disappear-ance of the neurological complaints. The patient is currently still receiving EV. A second, 74-year-old male patient started on the same regimen, after previous progression on platinum-based chemotherapy and maintenance avelumab. The patient achieved complete response and remained on therapy for five months. However, therapy was discontinued at the patient’s re-quest. Shortly after, he developed new leptomeningeal metastases. Upon rechallenge with EV, there was a significant reduction in the diffuse meningeal infiltration. A third, 50-year-old male Caucasian patient also received EV, after previous progression on cisplatin-gemcitabine and ate-zolizumab maintenance followed by palliative whole brain radiotherapy and two cycles of vin-flunine. The first evaluation after three cycles of EV showed a significant reduction of the brain metastases. The patient is currently still receiving EV. These are the first reports on efficacy of EV in patients with urothelial carcinoma and active brain metastases.

Liposome-mediated delivery is a possible means to overcome several shortcomings with C. elegans as a model for identifying and testing drugs that retard aging. These include interactions between drugs and the nematodes’ bacterial food source, and failure of drugs to be taken up into nematode tissues. To explore this, we have tested liposome-mediated delivery of a range of fluorescent dyes and drugs in C. elegans. Liposome encapsulation led to enhanced effects on lifespan, using smaller quantities of compound, and enhanced uptake of three dyes into the gut lumen. However, one dye (Texas red) did not cross into nematode tissues, indicating that liposomes cannot ensure uptake of any compound. Of six compounds previously reported to extend lifespan (vitamin C, N-acetylcysteine, glutathione (GSH), trimethadione, thioflavin T (ThT) and rapamycin), this effect was reproduced for the latter four in a condition-dependent manner. For GSH and ThT, antibiotics abrogated life extension, implying a bacterially-mediated effect. With GSH, this was attributable to reduced early death from pharyngeal infection, and associated with alterations of mitochondrial morphology in a manner suggesting a possible innate immune training effect. By contrast, ThT exhibited antibiotic effects. For rapamycin, significant increases in lifespan were only seen when bacterial proliferation was prevented. These results document the utility and limitations of liposome-mediated drug delivery for C. elegans. They also show how nematode-bacteria interactions can determine the effects of compounds on C. elegans lifespan in a variety of ways.

The structure of wound dressing materials presents one of the most relevant characteristics for effective skin tissue repair. Electrospinning is a common technique used to produce polymeric fibres that can mimic fibrillar disposition of skin extracellular matrix, favouring cell migration, and thus regeneration of the damaged tissue. Moreover, beads, also known as by-products of electrospinning, have potential as reservoirs for sustained drug release. Processing parameters, such as molecular weight and viscosity of the polymer solution, can affect the desirable morphologies of electrospun films. Thereby, this work had the purpose of producing and characterized electrospun polycaprolactone (PCL) mats loaded with propolis, a popular extract in traditional medicine with potential for skin repair aid. Films with different morphologies were obtained depending on the storage period of the solution prior to the lectrospinning, probably due to the PCL hydrolysis. FTIR analyses of the extract confirmed propolis composition. GPC and viscosity analyses demonstrated that the decrease in molar mass over the storage period was responsible for nanostructure diversity. Propolis acts as a lubricant agent, affecting the spun solutions' viscosity and the thermal properties and hydrophilicity of the films. All films are within the value range of the water vapour transpiration rate of the commercial products. The presence of beads did not affect the propolis release pattern. However, "in vitro" wound healing assay showed that propolis-loaded films composed by beaded fibres increased the cell migration process. Thus, it can be inferred that these films presented the potential for wound dressing application.

Drug repurposing is an attractive strategy for developing new antibacterial molecules. Herein, we evaluated the in vitro antibacterial, antibiofilm, and antivirulence activities of eight FDA-approved “non-antibiotic” drugs, comparatively to tobramycin, against selected Pseudomonas aeruginosa strains from cystic fibrosis patients. MIC and MBC values were measured by broth microdilution methods. Time-kill kinetics was studied by the macro dilution method, and synergy studies were performed by checkerboard microdilution assay. The activity against preformed biofilm was measured by crystal violet and viable cell count assays. The effects on gene expression were studied by real-time quantitative PCR, while the cytotoxic potential was evaluated against IB3-1 bronchial CF cells. Ciclopirox, 5-fluorouracil, and actinomycin D showed the best activity against P. aeruginosa planktonic cells and, therefore, underwent further evaluation. Time-kill assays indicated actinomycin D and ciclopirox, contrarily to 5-fluorouracil and tobramycin, have the potential for bacterial eradication, although with strain-dependent efficacy. Ciclopirox was the most effective against the viability of the preformed biofilm. A similar activity was observed for other drugs, although they stimulate EPS production. Ribavirin showed a specific antibiofilm effect, not dependent on bacterial killing. Exposure to drugs and tobramycin generally caused hyperexpression of the virulence traits tested, except for actinomycin D, which downregulated the expression of alkaline protease and alginate polymerization. Ciclopirox and actinomycin D revealed high cytotoxic potential. Ciclopirox and ribavirin might provide chemical scaffolds for anti-P. aeruginosa drugs. Further studies are warranted to decrease ciclopirox cytotoxicity and evaluate the in vivo protective effects.

Multimodal therapy including surgery, radiation treatment and temozolomide (TMZ) is performed on glioblastoma (GBM). However, the prognosis is still poor and there is an urgent need to develop effective treatments to improve survival. Molecular biological analysis was conducted to exam-ine the signal activation patterns at GBM specimens and remains an open problem. Advanced macrolides, such as azithromycin, reduce the phosphorylation of p70 ribosomal protein S6 kinase (p70S6K), a downstream mammalian target of rapamycin (mTOR) effector, and suppress the proliferation of T-cells. We focused on its unique profile and screened for the antitumor activity of approved macrolide antibiotics. Clindamycin (CLD) reduced the viability of GBM cells in vitro. We assessed the effects of the candidate macrolide on the mTOR pathway through Western blotting. CLD attenuated p70S6K phosphorylation in a dose dependent manner. These effects of on GBM cells were enhanced by co-treatment with TMZ. Furthermore, CLD inhibited the expression of O6-methylguanine-DNA methyltransferase (MGMT) protein in cultured cells. In the mouse xenograft model, CLD and TMZ co-administration significantly suppressed the tumor growth and markedly decreased the number of Ki-67 (clone MIB-1) positive cells within the tumor. These results suggest that CLD suppresses GBM cell growth by the inhibiting mTOR signaling. Moreover, CLD and TMZ showed promising synergistic antitumor activity.

Dermatophytes are the most common cause of fungal infections worldwide, affecting millions of people annually. The emergence of resistance among dermatophytes along with the availability of antifungal susceptibility procedures suitable for testing antifungal agents against this group of fungi make the combinatorial approach particularly interesting to be investigated. Therefore, we reviewed the scientific literature concerning the antifungal combinations in dermatophytes. A literature search on the subject performed in PubMed yielded 68 publications: 37 articles referring to in vitro studies, and 31 articles referring to case reports/clinical studies. In vitro studies involved over 400 clinical isolates of dermatophytes (69% Trichophyton spp., 29% Microsporum spp., and 2% Epidermophyton floccosum). Combinations included two antifungal agents or an antifungal agent plus another chemical compound including plant extracts/essential oils, calcineurin inhibitors, peptides, disinfectant agents and others. In general, drug combinations yielded variable results spanning from synergism to indifference. Antagonism was rarely seen. In over 700 patients with documented dermatophyte infections an antifungal combination approach could be evaluated. The most frequent combination included a systemic antifungal agent administered orally (i.e.: azole [mainly itraconazole], terbinafine or griseofulvin) plus a topical medication (i.e.: azole, terbinafine, ciclopirox, amorolfine) for several weeks. Clinical results indicate that association of antifungal agents is effective, and it might be useful in accelerate the clinical and microbiological healing of a superficial infection. Antifungal combinations in dermatophytes have gained considerable scientific interest over the years and, in consideration of the interesting results available as far, it is desirable to continue the research in this field.

Choline kinase inhibitors are an important class of cytotoxic compounds useful for the treatment of different forms of cancer since aberrant choline metabolism is a feature of neoplastic cells. Here we present the characterization and the structure activity relationship of a series of non-symmetrical choline kinase inhibitors characterized by a 3-aminophenol moiety, bound to 4-(dimethylamino)- or 4-(pyrrolidin-1-yl)pyridinium cationic heads through several linkers. These derivatives were evaluated both for their inhibitory activity on the enzyme and for their antiproliferative activity in a panel of six human tumor cell lines. The compounds with the best inhibitory results were those connected to the linker by the N-atom (4a-h) and these results are supported by docking studies. The compounds with the best antiproliferative results were those connected to the linker by the O-atom (3a-h). On the other hand, as was predictable in both families, the inhibitory effect on the enzyme is greater the shorter the length of the linker, while in tumor cells, lipophilicity and choline uptake inhibition could play a decisive role. Interestingly compounds 3c and 4f, selected for both their ability to inhibit the enzyme and good antiproliferative activity, are endowed with a low toxicity in non-tumoral cells (e.g human peripheral lymphocytes) respect to cancer cells. These compounds were also able to induce to induce apoptosis in Jurkat leukemic cells without causing significative variations of cell cycle. It is worth to mention that these derivatives, beside their inhibitory effect on choline kinase, displayed a modest ability to inhibit choline uptake thus suggesting that this mechanism may also contribute to the observed cytotoxicity.

Nonsteroidal anti-inflammatory drugs (NSAIDs), which block the activity of cyclooxygenase (COX) isoenzymes and inhibit the synthesis of prostaglandin, have been used for pain relief. We have developed a method to separate a mixture of three NSAIDs, such as aspirin, paracetamol, and naproxen, using reverse-phase high-performance liquid chromatography (RP-HPLC). An isocratic mobile phase consisting of acidic water and acetonitrile was selected to run at a low flow rate, such as 0.8 mL/min. The mixture of three NSAIDs was injected at a low volume into a C18 column that was 150 mm in length and characterized using a UV detector at 230 nm. We identified three peaks in the chromatogram indicating the three compounds. The elution time of the peaks was less than 10 minutes. To identify multiple peaks on the isocratic flow using a short column, further studies are required regarding the proposed method to generate microfluidic devices for nanoLC.

Artificial Intellignece (AI) is a platform lending immense assistance in discovering and developing drugs and thus, various such approaches have been developed with the intent of simplifying and improving biomedical operations such as drug repurposing and drug discovery. In the past decade, AI-based investigation of nanomedicines, as well as non-nanomedicines has reached the clinical level. In semblance with the traditional methods of therapy, nanomedicine therapy is employed at limited doses. The study of a variety of drugs resulted in the conclusion that the effect of each drug is variable for every patient and, evaluating that perfect drug combination manually is a time-consuming as well as an inefficient treatment method. Therefore, the use of AI simplifies and reduces the time consumption in determining the perfect customized drug combination for nano-therapy. The area with the most potential for meeting this reality is to optimize the drug and dosage parameters. It is a universally known fact that cancer is dangerous and unique because of the exacting challenges it poses during treatment and, to achieve a better treatment, the therapeutic effect on each patient must be delineated even if the volume of data generated is massive. The article aims at analyzing the AI technologies that help yield results much quicker, make the analyses simple, and efficient.

Background: Convectional methods for drug delivery often faces setbacks due to systemic distribution, short half-life and degradation of therapeutics and therefore reduce concentrations of drug available to target tissue. Nanotheranostic provide a novel method for treating and diagnosing diseases Methodology: collection and review of relevant literatureResult: while nanotheranostic offer advantage of personalized medicine and often combines diagnosis and therapy using single molecular approach, nuclear medicine relies on radioactive isotopes to diagnosed and destroys cancer cells. In both cases, nanocarriers such as lipid-based, polymer-based, drug-conjugate, inorganic nanoparticles are used to deliver drugs/probes/isotopes to target site, generating images and thereafter chemotherapy/radiotherapy begins.Conclusion: Nanotheranostic plays important role in diseases diagnostic, therapy, imaging, monitoring of disease progression / response through the use of nanocarriers. This is made possible through nanoparticles/nanocarriers that delivers drug to the target tissues/cells.

Tailoring extracellular vesicles (EVs) as targeted drug delivery systems to enhance the therapeutic efficacy showed superior advantage over liposomal therapies. Herein, we developed a novel nanotool for targeting B16.F10 murine melanoma, based on EVs stabilized with Polyethylene glycol (PEG) and loaded with doxorubicin (DOX). Nanosized EVs were efficiently enriched from melanoma cells cultured under metabolic stress by ultrafiltration coupled with size exclusion chromatography (UF-SEC) and characterized by size, morphology, and proteome. To reduce their clearance in vivo, EVs were PEGylated and passively loaded with DOX (PEG-EV-DOX). Our data suggested that the low PEG coverage of EVs might still favor EV surface protein interactions with target proteins from intratumor cells, ensuring their use as “Trojan horses” to deliver DOX to the tumor tissue. Moreover, our results showed a superior antitumor activity of PEG-EV-DOX in B16.F10 murine melanoma models in vitro and in vivo compared to that exerted by clinically applied liposomal DOX in the same tumor model. The PEG-EV-DOX administration in vivo reduced NF-κB activation and increased BAX expression, suggesting better prognosis of EV-based therapy than liposomal DOX treatment. Collectively, our results highlight the promising potential of EVs as optimal tools for systemic delivery of DOX to solid tumors.

We report the treatment of a 21-year-old female Covid-19 patient by a novel combination of minocycline and a guanosine-restricted diet. Minocycline is an antibiotic with well documented broad spectrum anti-viral effects, including evidence of activity against SARS-CoV-2. Deprivation of guanosine has been documented as an effective anti-viral modality in vitro and in animal models, and specific in vitro activity against CoV-SARS-2 has been reported. The patient's symptoms resolved rapidly.

Anthelmintic drugs are the major line of defense against parasitic nematode infections, but the arsenal is limited and resistance threatens sustained efficacy of the available drugs. Discoveries of the modes of action of these drugs and mechanisms of resistance have predominantly come from studies of a related non-parasitic nematode species, Caenorhabditis elegans, and the parasitic nematode Haemonchus contortus. Here, we discuss how our understanding of anthelmintic resistance and modes of action came from the interplay of results from each of these species. We argue that this “cycle of discovery”, where results from one species inform the design of experiments in the other, can use the complementary strengths of both to understand anthelmintic modes of action and mechanisms of resistance.

The COVID-19 global pandemic has created dire consequences with an alarming rate of morbidity and mortality. There are not yet vaccine or efficacious treatment options to combat the causative SARS-CoV-2 infection. This paper describes the identification of potentially repurposable drugs for COVID-19 treatment by conducting pathway enrichment analysis on publicly available Gene Expression Omnibus datasets. We first determined SARS-CoV-2 infection-induced alterations of host gene expressions and pathways. We then identified drugs or compounds that target and counter virus-triggered cellular perturbations, suggesting their potential repurposing for COVID-19 treatment. The key findings are that SARS-CoV-2 infection in host cells induces mitochondrial dysfunction, inhibits oxidative phosphorylation, and activates several immune response and pro-inflammatory pathways. Triptolide, the major bioactive component of a traditional Chinese medicine herb, may rescue mitochondrial dysfunction by activating oxidative phosphorylation. Further in vitro and in vivo studies are necessary to verify these results prior to clinical application.

Background: This study discerns surgeons’ attitudes and practices in the determination of heart valve replacement for patients with endocarditis due to intravenous drug use (IVDU-IE). We aimed to identify factors contributing to surgeons’ decision-making process for initial and recurrent surgical heart valves, and the availability of institutional guidance. Methods: An IRB approved, anonymous mixed-methods survey instrument was designed and validated with 24 questions. Cardiothoracic surgeons in the U.S. and globally were recruited with a total of 220 enrolling in the study with 176 completing every question on the survey. Results: A cluster analysis revealed that although surgeons can be divided into sub-groups based on their previous experience with valve replacements, these groups are not perfectly homogenous, and the number of identified clusters is dependent on technique used. ANOVA analysis revealed that the variables that most clearly divided the surgeons into subgroups were, in order of importance, years of practice, number of valve replacements, and geography. Conclusions: Our analysis showed heterogeneity among cardiothoracic surgeons regarding how they make clinical decisions regarding re-operative valve replacement related to IVDU-IE Therefore, an opportunity exists for an interprofessional team to develop guidelines to decrease variability in surgical decision-making regarding valve replacement associated with IVDU-IE

Mycobacterium abscessus is a non-tuberculous mycobacteria notoriously known for causing severe, chronic infections. Treatment of these infections is challenging due to either intrinsic or acquired resistance of M. abscessus to multiple antibiotics. Despite prolonged poly-antimicrobial therapy, treatment of M. abscessus infections often fails, leading to progressive morbidity and eventual mortality. Great research efforts are invested in finding new therapeutic options for M. abscessus. Clofazimine and rifabutin are known anti-mycobacterial antibiotics, repurposed for use against M. abscessus. Novel antimicrobials active against M. abscessus include delamanid, pretomanid and PIPD1 and the recently approved beta-lactamase inhibitors avibactam, relebactam and vaborbactam. Previously unused antimicrobial combinations e.g. vancomycin-clarithromycin and dual beta-lactam therapy have been shown to have synergistic effect against M. abscessus in experimental models, suggesting their possible use in multiple-drug regimens. Finally, engineered phage therapy has been reported to be clinically successful in a severe case of disseminated M. abscessus infection. While many of these experimental therapeutics have shown activity against M. abscessus in vitro, as well as intracellular and/or animal models, most have little if any evidence of effect in humans infections. Clinical studies of M. abscesssus treatments are needed in order to reliably determine the value of their incorporation in therapeutic regimens.

As many if not most ligands at G protein-coupled receptor antagonists are inverse agonists, we have systematically reviewed inverse agonism at the nine adrenoceptor subtypes. Except for β3-adrenoceptors, inverse agonism has been reported for each of the adrenoceptor subtypes, most often for β2-adrenoceptors, including endogenously expressed receptors in human tissues. As with other receptors, detection and degree of inverse agonism depends on the cells and tissues under investigation, i.e. is greatest when the model has a high intrinsic tone/constitutive activity for the response being studied. Accordingly, it may differ between parts of a tissue, for instance atria vs. ventricles of the heart, and within a cell type between cellular responses. The basal tone of endogenously expressed receptors often is low, leading to less consistent detection and smaller extent of observed inverse agonism. The extent inverse agonism depends on specific molecular properties of a compound but clusters by chemical class. While inverse agonism is a fascinating facet in attempts to mechanistically understand observed drug effects, we are skeptical whether an a priori definition of extent of inverse agonism in the target product profile of a developmental candidate is a meaningful option in drug discovery and development.

Background and Objectives: In Libya, prescription medicines can easily be dispensed without a prescription, as self-medication with the subsequent of potential misuse and unnecessary risk for patients. This systematic review and meta-analysis explored the prevalence of self-medication among citizens in Libya. Methods: We systematically searched PubMed, ISI Web of Knowledge, EMBASE, Medline, Scopus, google scholar and ResearchGate databases for studies published in Libya reporting the prevalence of non-prescription use of medicines in community pharmacies, targeting studies published from Jan 2008 until Dec 2019. A random meta-analysis was performed to analyze pooled estimates of non-prescription usage of medicines. Results: Out of 63 articles identified, a total of 13 papers from 7 cities were met the inclusion criteria and involved a total of 4741 subjects. The overall pooled proportion of self-medication of drugs was 53.6% (95% CI: 0.93% - 1.08%), with low heterogeneity and the P-value of the whole population tasted was found to be = 1.000 (df = 12). Seven out of the 13 studies reported data on self-medication for antibiotics without prescription. The records ranged from 15.3% (95% CI 0.61–1.65) in Misurata to 76.6% (95% CI 0.80–1.25) in Tripoli. Conclusion: Self-medication use of medicines among Libyan population is a common phenomenon involving a high proportion use of antibiotics. This misuse of medications could enhance the development and spread of antibiotic resistance.

The vastness of chemical-space constrains traditional drug-discovery methods to the organic laws that are guiding the chemistry involved in filtering through candidates. Leveraging computing with machine-learning to intelligently generate compounds that meet a wide range of objectives can bring significant gains in time and effort needed to filter through a broad range of candidates. This paper details how the use of Generative-Adversarial-Networks, novel machine learning techniques to format the training dataset and the use of quantum computing offer new ways to expedite drug-discovery.

Background: Although no specific treatment for COVID 19 has been proven effective yet, some drugs with in vitro potential against SARS-CoV-2 virus have been proposed for clinical use. Hydroxychloroquine has in vitro anti-viral and immunomodulatory activity, but there is no current clinical evidence of its effectiveness on the outcome of the disease. Methods: We enrolled all 18-85 years old inpatients from Central Defense Hospital, Madrid, Spain, who were hospitalised due to COVID-19 and had a definitive outcome (either dead or discharged). We used a statistical survival analysis. Results: We analysed 220 medical records. 166 patients met the inclusion criteria. 48,8 % of patients not treated with HCQ died, versus 22% in the group of hydroxychloroquine (p=0,002). According to clinical picture at admission, hydroxychloroquine increased the mean cumulative survival in all groups from 1,4 to 1,8 times. This difference was statistically significant in the mild group. Conclusions: in a cohort of 166 patients between 18 to 85 years hospitalised with COVID-19, hydroxychloroquine treatment with an initial loading dose of 800mg improved patient survival when admitted in early stages of the disease. There was a non-statistically significant trend towards survival in all groups, which will need to be clarified in subsequent studies.

The study evaluates the toxic effects of acute and sub-acute oral administration of methanol extracts of Geophila obvallata in rats. During acute study, a dose of 1600, 2900 and 5000 mg/kg bw of extract was orally administered to rats. Rats were observed for signs of toxicity for two weeks. During sub-acute study (28 days), the extract, at doses of 100, 500 and 1000 mg/kg bw were administered orally to rats while control rats were given only tap water. At the end of the study, samples were collected for analyses. In acute toxicity studies, the extract did not induce death after single dose administration. Hence, the LD50 was estimated above 5000mg/kg. The results of sub-acute toxicity study show that no significant changes were observed in the body weights, organ weights, kidney function and organ histology. There were significant changes in hematology and biochemical indices investigated at elevated doses of 500 and 1000 mg/kg bw compared to the control. GOE may be considered non-toxic at a dose of 100 mg/kg with promising applications in drug therapy.

Background: Poisoning constitutes an important part of morbidity and mortality among environmental emergencies that can be prevented, frequent in childhood, requiring rapid diagnosis and treatment. This study aimed was to examine the epidemiological, clinical and therapeutic characteristics of drug and corrosive poisonings in terms of children. Materials and Methods: Between January 2010 and December 2018, 1678 patients between the ages of 1-16 who applied to the emergency department with drug and corrosive poisoning were included in the study. The patients were divided into three groups as 1-5, 6-11 and over 12 years of age, and the substances with known pharmacological agents were divided into 10 groups. The Zargar Classification (7) was used to determine the degree of damage caused by corrosive substances in the esophagus. Results: The mean age of the patients was 8.32 ± 3.23 years (1-15 years). The girl/boy ratio of the cases was 1.18 / 1 and 54.3% were girls. 18.9% of the cases were under five years of age, 61% were between 6-11 years and 20% were older than 12 years. 94.6% of the patients were poisoned by drugs and 5.4% of them were poisoned due to non-drug reasons. Accidental poisoning was most commonly seen in children aged 6-11 and suicides were seen in children older than 12 years. It was found that 62.2% of the poisonings were accidental and 37.8% were suicidal. The most common causes of poisoning were analgesics and paracetamol exposure as a pharmacological agent. Mortality was found to be most common in 1-5 years age group and accidental poisonings. Accidental poisoning was common in girls and suicidal poisoning was common in boys. Grade 3 damage was found endoscopically in all of the mortalities due to corrosive substances. Conclusion: It was found that accidental poisoning was observed in children under five years of age, suicides were observed in children older than 12 years and the most important cause of mortality was due to corrosive substances. Cause-specific rapid diagnosis and treatment will contribute to the reduction of morbidity and mortality.

Polymeric particles made up of biodegradable and biocompatible polymers such as poly(lactic-co-glycolic acid) (PLGA) are promising tools for several biomedical applications including drug delivery. Particular emphasis is placed on the size and surface functionality of these systems as they are regarded as the main protagonists in dictating the particle behavior in vitro and in vivo. Current methods of manufacturing polymeric drug carriers offer a wide range of achievable particle sizes, however, they are unlikely to accurately control the size while maintaining the same production method and particle uniformity, as well as final production yield. Microfluidics technology has emerged as an efficient tool to manufacture particles in a highly controllable manner. Here, we report on tuning the size of PLGA particles at diameters ranging from sub-micron to microns using a single microfluidics device, and demonstrate how particle size influences the release characteristics, cellular uptake and in vivo clearance of these particles. Highly controlled production of PLGA particles with ~100 nm, ~200 nm and >1000 nm diameter is achieved through modification of flow and formulation parameters. Efficiency of particle uptake by dendritic cells and myeloid-derived suppressor cells isolated from mice is strongly correlated with particle size and is most efficient for ~100 nm particles. Particles systemically administered to mice mainly accumulate in liver and ~100 nm particles are cleared slower. Our study shows the direct relation between the particle size varied through microfluidics and the pharmacokinetics behavior of particles, which provides a further step towards the establishment of a customizable production process to generate tailor-made nanomedicines.

(1) Background: The purpose of this study was therefore to analyze the predictive capacity of the three variables (impulsivity, sensation-seeking and drug use) in aggressors and victims of violence. (2) Methods: The design is cross-sectional quasi-experimental. A sample of 822 students aged 13 to 18 who had filled in an ad hoc questionnaire, the State Impulsivity Scale and the Sensation-Seeking Scale, was used for this. (3) Results: The results show that aggressors had high levels of gratification, automatism, attentional factor, disinhibition and susceptibility to boredom, and use alcohol and/or tobacco. The variables that could predict involvement as an aggressor in peer conflict are use of alcohol, smoking, high levels of gratification, automatism and attentional factors and a high degree of disinhibition and susceptibility to boredom. The disinhibition is the best predictor of aggressor. (4) Conclusions: Thus having available empirical evidence that facilitates detection of predictive variables for participation in violence is going to favor the design of effective education intervention for reducing risk behavior.

Diatom microalgae are the most outstanding natural source of porous silica. Diatom cell is enclosed in 3-D ordered nanopatterned silica cell wall, called frustule. The unique properties of diatoms frustule, including high specific surface area, thermal stability, biocompatibility, tailorable surface chemistry, make them really promising for biomedical applications. Moreover, diatoms are easy to cultivate in artificial environment and there is a huge availability of diatom frustules as fossil material (diatomite) in several areas of the world. For all these reasons, diatoms are an intriguing alternative to synthetic materials for the development of low-cost drug delivery systems. This review article focuses on the possible use of diatoms derived silica as drug carrier systems. The functionalization strategies of diatom micro-/nanoparticles for improving their biophysical properties, such as cellular internalization and drug loading/release kinetics, are described. In addition, the realization of hybrid diatom-based devices with advanced properties for theranostics and targeted or augmented drug delivery applications, are also discussed.

Paclitaxel is a natural, highly lipophilic anti proliferative drug widely used in medicine. We have studied the release of tritium-labeled paclitaxel (³H-PTX) from matrices destined for the coating of vascular stents and produced by the electrospinning method from the solutions of polycaprolactone (PCL) with paclitaxel (PTX) in hexafluoisoropropanol (HFIP) and/or solutions of PCL with PTX and human serum albumin (HSA) in HFIP or HIFP-dimethyl sulphoxide (DMSO) blend. The release of PTX has been shown to depend on the solvent and the composition of electrospinning solution, as well as the composition of the surrounding medium, particularly the concentration of free PTX and PTX-binding biomolecules present in human serum. It was shown that 3D matrices can completely release PTX without weight loss. Two-phase PTX release from optimized 3D matrices was obtained: ~27% of PTX was released in the first day, another 8% were released over the next 26 days. Wherein ~2.8%, ~2.3%, and ~0.25% of PTX was released on day 3, 9, and 27, respectively. Considering PTX toxicity, the rate of its diffusion through the arterial wall, and the data obtained the minimum cytostatic dose of the drug in the arterial wall will be maintained for at least three months.

Amyloids result from the aggregation of several unrelated proteins, due to either specific mutations or promoting intra- or extra-cellular conditions. Structurally, they are rich in intermolecular β-sheets and are the causative agents of several diseases, both neurodegenerative and systemic. It is believed that the most toxic species are small aggregates, referred to as oligomers, rather than the final fibrillar assemblies. Their mechanisms of toxicity are mostly mediated by aberrant interactions with the cell membranes, with resulting derangement of membrane-related functions. Much effort is being put in the search for natural antiamyloid agents, and/or in the development of synthetic molecules. Actually, it is well documented that the prevention of amyloid aggregation results in several cytoprotective effects. Here, we portray the state of the art in the field. Several natural compounds are effective antiamyloid agents, notably tetracyclines and polyphenols. They are generally non-specific, as documented by their partially overlapping mechanisms, and the capability to interfere with the aggregation of several unrelated proteins. Among rationally designed molecules, we mention the prominent examples of β-breakers peptides, whole antibodies and fragments thereof, and the special case of drugs contrasting transthyretin aggregation. In this framework, we stress the pivotal role of the computational approaches. When combined with biophysical methods, in several cases they have helped clarify in detail the protein/drug modes of interaction, which make it plausible that more effective drugs will be developed in the future.

In this study, we repositioned thiopurine drugs used for the treatment of acute leukaemia as new tyrosinase inhibitors. Tyrosinase catalyses two distinct and successive oxidations in melanin biosynthesis: the conversions of tyrosine to dihydroxyphenylalanine (DOPA) and DOPA to dopaquinone. Continuous efforts are underway to discover small molecule inhibitors of tyrosinase for therapeutic, cosmetic, and agricultural purposes. Structure-based virtual screening has predicted inhibitor candidates for mushroom tyrosinase from drugs approved by the US Food and Drug Administration (FDA). Enzyme assays have confirmed the thiopurine leukaemia drug, thioguanine, as a tyrosinase inhibitor. Two other thiopurine drugs, mercaptopurine and azathioprine, were also evaluated for their tyrosinase inhibitory activity; mercaptopurine caused stronger inhibition than thioguanine did, whereas azathioprine was a poor inhibitor. The inhibitory constants of thioguanine and mercaptopurine were calculated as 52 and 16 µM, respectively, and the value of mercaptopurine was comparable to that of the well-known inhibitor kojic acid (13 µM). The cell lysate and melanin content assay in B16F10 melanoma cells confirmed that the compounds inhibited mammalian tyrosinase. In particular, 50 µM thioguanine reduced the melanin content by 57% without cytotoxicity. Furthermore, the thiopurine drugs shared little chemical similarity with the known tyrosinase inhibitors.

Treatment of bladder cancer remains a critical unmet need and requires advanced approaches, particularly the development of local drug delivery systems. The physiology of the urinary bladder causes the main difficulties in the local treatment of bladder cancer: regular voiding prevents the maintenance of optimal concentration of the instilled drugs, while poor permeability of the urothelium limits the penetration of the drugs into the bladder wall. Therefore, much research effort is spent to overcome these hurdles, thereby improving the efficacy of available therapies. The explosive development of nanotechnology, polymer science and related fields has contributed to the emergence of a number of nanostructured vehicles (nano- and micro scale) applicable for intravesical drug delivery. Moreover, the engineering approach has facilitated the design of several macro-sized depot systems (centimeter scale) capable of remaining in the bladder for weeks and months. In this article, the main rationales and strategies for improved intravesical delivery are reviewed. Here, we focused on analysis of colloidal nano- and micro-sized drug carriers, and indwelling macro-scale devices which were evaluated for applicability in local therapy of bladder cancer in vivo.

The main regulatory factors during the adaptation of cancer cells to hypoxic stress are the hypoxia-inducible factors (HIFs), which are being increasingly recognized as an interesting and challenging target for the design of new chemotherapeutic molecules. HIF2A was found to have an large internal hydrophilic cavity within its PAS-B domain, unique to this sub-unit and is suggested to be a possible ligand-binding site. Regulation of HIF2A by cellular molecules is still greatly unknown. In This paper we have employed in-silico techniques, such as molecular docking and dynamic simulation, to design new direct inhibitors against HIF-2A subunit via targeting one of its critical domains and the final top screened molecules have been tested on hypoxic cancer cells for further validation of their inhibitory potential. we targeted the hydrophilic cavity inside the PAS-B domain of the HIF2A to identify novel molecules with a high binding capacity. Virtual Screening methodology was used for molecular docking of NSC library against the target domain inside the HIF2A PAS-B domain with the top 5% compounds with significant MolDock and Re-rank scores were selected for further analysis. The NSC 106416, NSC 217021, NSC 217026, and NSC 215639 compounds were selected based on their docking scores. NSC 215639 had the minimum polar solvation energy and also had a relative strong binding energy. NSC 217026 had the strongest binding energy among other compounds.

There are numerous approaches to model an influence of manufacturing parameters on the dissolution and disintegration of solid dosage forms. A modern approach for modelling and simulating complex and heterogeneous systems such as pharmaceutical tablets focuses on computationally-intensive finite-element or discreet element methods. Industrial challenges such as Process Analytical Technology (PAT) and Quality by Design are stimulating computer-based technologies to design, control and improve the quality of pharmaceutical compacts and their performance. In this study the disintegration and dissolution model based on the numerical solutions of the Noyes-Whitney equation and cellular automata supported disintegration model are proposed and evaluated. The results from in vitro release studies of the mefenamic acid formulations were compared to the calculated release patterns from the tablet structures obtained from microtomography experiments and the purely algorithmically created virtual tablets.

In this paper, we present a multi-task model that predicts disparities and confidence levels in deep stereo matching simultaneously. We do this by combining its successful model for each separate task and obtaining a multitask model that can be trained with a proposed loss function. We show the advantages of this model compared to training and predicting disparity and confidence sequentially. This method enables an improvement of 15% to 30% in the area under the curve (AUC) metric when trained parallel rather than sequential. In addition, the effect of weighting the components in the loss function on the stereo and confidence performance is investigated. By improving the confidence estimate, the practicality of stereo estimators for creating distance images is increased.

Device to Device (D2D) is a pivotal technology in the next generation of communication, allowing for direct task offloading between mobile devices (MDs) to improve the efficient utilization of idle resources. This paper proposes a novel algorithm for dynamic task offloading between active MDs and idle MDs in the D2D-MEC (Mobile Edge Computing) system by deploying multi-agent deep reinforcement learning (DRL) to minimize the long-term average delay of delay-sensitive tasks under deadline constraints. Our core innovation is a dynamic partitioning scheme for idle and active devices in the D2D-MEC system, accounting for stochastic task arrivals and multi-time-slot task execution, which has been insufficiently addressed in existing literature. We adopt a queue-based system to formulate a dynamic task offloading optimization problem. To address the challenges of large action space and coupling of actions across time slots, we model the problem as a Markov Decision Process (MDP) and perform the multi-agent DRL through Multi-Agent Proximal Policy Optimization (MAPPO). We employ a centralized training with decentralized execution (CTDE) framework to enable each MD to make offloading decisions solely based on its local system state. Extensive simulations demonstrate the efficiency and fast convergence of our algorithm. In comparison with the existing sub-optimal results deploying single-agent DRL, our algorithm reduces the average task completion delay by 11.0% and the ratio of dropped tasks by 17.0%.

Background: The pathophysiology of functional dyspepsia (FD) remains insufficiently understood, and the pain and economic constraints associated with endoscopic examinations hinder the precise definition and treatment of this disorder. Exploring the potential of multi-omics biomarkers for FD could yield promising targets for effectively diagnosing and treating this condition. Methods: We recruited 54 FD patients and 29 healthy volunteers and characterized their gut microbiota by shotgun metagenomic sequencing and their serum and urine metabolites by liquid chromatography-mass spectrometry (LC-MS). Results: We identified the changes in circulating metabolites associated with FD and linked to the gut microbial alterations. Among the differential circulating metabolites, we particularly highlighted the strong association of L-glutamate deficiency with the severity of FD symptoms and its strong predictive value for FD. Although no significant changes were noted in the gut microbial community structure of the FD samples, this might be attributed to symptom overlap between epigastric pain syndrome (EPS) and postprandial distress syndrome (PDS). However, significant alterations in the abundance of certain species were observed, including a substantial increase in the abundance of Streptococcus in the digestive tract of patients with FD. Finally, a cross-omics correlation network integrating metabolomic data with microbial community abundance was established, uncovering potential host-microbe-metabolite interactions. Conclusions: This work provides the first comprehensive description of the disrupted state of blood and urine metabolite composition and gut microbiota in patients with FD. These findings potentially offer promising targets for the diagnosis and treatment of this condition through a multi-omics approach, with particular emphasis on serum metabolomics.

Reverse engineering is applied to identify optimum polymerization conditions for the synthesis of polymer with pre-defined properties. The proposed approach uses multi-objective optimization (MOO) and provides multiple candidate polymerization procedures to reach the targeted polymer property. The objectives for optimization include maximal similarity of molar mass distributions (MMD) compared to the target MMD, minimal reaction time, and maximal monomer conversion. The method is tested for vinyl acetate radical polymerizations and can be adopted to other monomers. The data for the optimization procedure is generated by an in-house developed kinetic Monte-Carlo (kMC) simulator for a selected recipe search space. The proposed reverse engineering algorithm comprises several steps: kMC simulations for the selected recipe search space to derive initial data, performing MOO for a targeted MMD, and identification of the Pareto optimal space. The last step uses a weighted sum optimization function to calculate the weighted score of each candidate polymerization conditions. To decrease the execution time clustering of the search space based on MMDs is applied. The performance of the proposed approach was tested for various target MMDs. The suggested MOO-based reverse engineering provides multiple recipe candidates depending on competing objectives.

Jujube (Ziziphus jujuba Mill.) stands as a pivotal fruit tree with significant economic, ecological, and social value. Recent years have witnessed remarkable strides in multi-omics-based biological re-search on jujube. This review began by summarizing advancements in jujube genomics. Subse-quently, we provided a comprehensive overview of the integrated application of genomics, tran-scriptomics, and metabolomics to explore pivotal genes governing jujube domestication traits, quality attributes (including sugar synthesis, terpenoids, and flavonoids), and responses to abiotic stress, and discussed the transcriptional regulatory mechanisms underlying these traits. Further-more, challenges in multi-omics research on jujube biological traits were outlined, and we pro-posed the integration of resources such as pan-genomics and sRNAome to unearth key molecules and regulatory networks influencing diverse biological traits. Incorporating these molecules into practical breeding strategies, including gene editing, transgenic approaches, and progressive breeding, holds the potential for achieving molecular-design breeding and efficient genetic en-hancement of jujube.

IP Multimedia Subsystem (IMS) supports high-speed transmission of multi-media like data, audio, and video between mobiles and other wireless devices. IMS is the most suitable for multimedia communication across multiple networks using the Internet of Things (IoT) and cloud computing. IoT-IMS can efficiently manage the huge amount of data generated by smart devices for audio-video during live streaming applications. During these services, the main problem is that an intruder can launch a Session Initialization Protocol (SIP) messages flooding attack on IMS that does not have the functionality to prevent itself from these types of attacks. In this paper, an intrusion detecting and preventing system (IDPS) is proposed that effectively detects the register flooding and spoofing attacks using SIP messages. We proposed a watermark embedding algorithm on the user side and an extraction algorithm on receiving side. A test-bed is set up for IMS where we have considered four different scenarios to evaluate the IDPS approach as compared to counterparts. Results illustrate the dominance of our scheme in terms of response time, register flooding attack detection ratios, CPU load utilization, fault detection ratio, and memory utilization. The fault detection ratio averagely 98.18

Background: Medulloblastoma (MB) comprises four broad molecular subgroups - wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 respectively with subgroup-specific developmental origins, unique genetic profiles, distinct clinico-demographic characteristics, and diverse clinical outcomes. WNT-MB has the best outcomes (5-year survival >90%) with contemporary multi-modality treatment prompting attempts at treatment de-intensification to reduce late toxicity. This is a retrospective audit of clinical outcomes in WNT-MB patients treated at a tertiary-care comprehensive cancer centre. Methods: Patients with molecularly confirmed WNT-MB treated with maximal safe resection followed by post-operative standard-of-care risk-stratified adjuvant radio(chemo)therapy were identified retrospectively via electronic search of the neuro-oncology database. Data regarding clinico-demographic characteristics, histo-molecular features, treatment details, patterns of failure, and survival outcomes was retrieved from electronic medical records and/or hospital case files. Time-to-event outcomes were analyzed using Kaplan-Meier methods and compared with the log-rank test. Results: Between 2004 to 2020, 74 patients with WNT-MB were registered in the neuro-oncology unit of the institute. Seven patients treated on a prospective clinical trial of therapy de-intensification were excluded leaving 67 patients that constitute the present study cohort. Median age at presentation was 12 years (inter-quartile range 9-18 years) with a male preponderance (2:1). Six patients (1 post-operative mortality and 5 without adequate details of treatment or outcomes) were excluded from the survival analysis which was restricted to 61 patients. At a median follow-up of 72 months, Kaplan-Meier estimates of 5-year progression-free survival and overall survival were 87.7% and 91.2% respectively. Traditional high-risk features such as large residual tumor (≥1.5cm2) and leptomeningeal metastases (M+) did not significantly impact upon survival in this molecularly-characterized WNT-MB cohort. Conclusions: This retrospective clinical audit confirms excellent survival in WNT-MB patients treated with contemporary multi-modality therapy. Lack of prognostic impact of conventional high-risk features suggests the need for refined risk-stratification and potential de-intensification of therapy.

In this paper, we unite concepts from Husserlian phenomenology, the active inference framework in theoretical biology, and category theory in mathematics to develop a comprehensive framework for understanding social action premised on shared goals. We begin with an overview of Husserlian phenomenology, focusing on aspects of inner time consciousness, namely, retention, primal impression, and protention. We then review active inference as a formal approach to modeling agent behavior, based on variational (approximate Bayesian) inference. Expanding upon Husserl’s model of time consciousness, we consider collective goal-directed behavior, emphasizing shared protentions among agents and their connection to the shared generative models of active inference. This integrated framework aims to formalize shared goals in terms of shared protentions, and to thereby shed light on the emergence of group intentionality. Building on this foundation, we incorporate mathematical tools from category theory; particularly, sheaf and topos theory, to furnish a mathematical image of individual and group interactions within a stochastic environment. Specifically, we employ morphisms between polynomial representations of individual agent models, allowing predictions not only of their own behaviors but also those of other agents and environmental responses. Sheaf and topos theory facilitate the construction of coherent agent worldviews and provides a way of representing consensus or shared understanding. We explore the emergence of shared protentions, bridging the phenomenology of temporal structure, multi-agent active inference systems, and category theory. Shared protentions are highlighted as pivotal for coordination and achieving common objectives. We conclude by acknowledging the intricacies stemming from stochastic systems and uncertainties in realizing shared goals.

The fidelity of the decadal experiment in Coupled Model Intercomparison Project Phase-5 (CMIP5) has been examined, over different climate variables for different temporal and spatial scales, in many previous studies. However, most of the studies were for the temperature and temperature-based climate indices. A quite limited study was conducted on precipitation of decadal experiment and no attention was paid to a catchment level. This study evaluates the performances of eight GCMs (MIROC4h, EC-EARTH, MRI-CGCM3, MPI-ESM-MR, MPI-ESM-LR, MIROC5, CMCC-CM, and CanCM4) for the monthly hindcast precipitation of decadal experiment over the Brisbane River catchment in Queensland, Australia. First, the GCMs datasets were spatially interpolated onto a spatial resolution of 0.050×0.050 (5 km× 5 km) matching with the grids of observed data and then were cut for the catchment. Next, model outputs are evaluated for temporal skills, dry and wet periods, and total precipitation (over time and space) based on the observed values. Skill test results reveal that model performances varied over the initialization years and showed comparatively higher scores from the initialization year 1990 and onward. Models with finer spatial resolutions show comparatively better performances as opposed to the models of coarse spatial resolutions where MIROC4h outperformed followed by EC-EARTH and MRI-CGCM3. Comparing the skills, models are divided into three categories (Category-I: MIROC4h, EC-EARTH, and MRI-CGCM3; Category-II: MPI-ESM-LR and MPI-ESM-MR; and Category-III: MIROC5, CanCM4, and CMCC-CM). Three multimodel ensembles’ mean (MMEMs) are formed using the arithmetic mean of Category-I (MMEM1), Category-I and II (MMEM2), and all eight models (MMEM3). The performances of MMEMs are also assessed using the same skill tests and MMEM2 performed best which suggests evaluating the models before the formation of MMEM.

In this review, we will cover a general overview of the current panorama on lncRNAs with an actual or potential role as biological markers in cancer. We will discuss examples of multi-omics approaches that integrates information on somatic aberrations, gene expression and epigenomics, with the scope of providing a more comprehensive view of the functional impact of lncRNA profiles and how these paradigm can be exploited for the discovery and selection of lncRNAs with a functional role and their use as variables informing on progression and prognostic and help guiding the selection of therapeutic strategies for cancers. Finally, we propose a perspective for future evolution of the study of lncRNAs and the discovery of their functional and harnessing their potential to assist in clinical management of malignancies.

Yttrium oxide (Y2O3) resistive random-access memory (RRAM) devices were fabricated using the sol–gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics, without requiring a high voltage forming process. The effect of current compliance on the Y2O3 RRAM devices was investigated and revealed that as the set current compliance values increased, the resistance values gradually decreased. Consequently, intermediate resistance values were obtained, which are suitable for multi-level cell (MLC) switching. The fabricated Y2O3 RRAM devices were capable of functioning as an MLC with a capacity of 2 bits in one cell, utilizing three low resistance states and one common high resistance state. The potential of the Y2O3 RRAM for neural networks was further explored through numerical simulations. Hardware neural networks based on the Y2O3 RRAM demonstrated effective digit image classification with a high accuracy rate of approximately 88%, comparable to the ideal software-based classification (~92%). This suggests that the proposed RRAM has the potential to be utilized as a memory component in practical neuromorphic systems.

In this work, we propose a self-supervised multi-agent system that meets the online learning of clustering tasks for video behavior recognition spatio-temporal tasks. Encoding visual behavioral actions as discrete temporal sequence(DTS). Real-time clustering recognition task in a multi-agent system for continuous model building, training, and correction. Finally, we implemented a fully decentralized multi-agent system and completed its feasibility verification in a surveillance video application scenario on vehicle path clustering.

Along with the popularity of the Internet, people are exposed to more and more ways of micro-videos, and a huge amount of micro-video data has emerged. micro-videos have gradually become the Internet content preferred by the public, and a large number of micro-video apps have also emerged, such as Tiktok and Kwai. Intelligent classification and mining of micro-videos can greatly enhance user experience, improve business operation efficiency and enhance user experience. Through deep intelligent analysis and mining of micro-videos, important information in micro-videos can be extracted to provide an important basis for beautifying videos, content appreciation, video recommendation, content search, etc. In the past, content understanding for short videos often used human work annotation, but in recent years, with the great success of deep convolutional neural networks in image recognition, short video content understanding based on this method has gradually developed. Nowadays, most recognition algorithms extract the feature representation of each frame independently and then fuse them. However, while extracting the feature representation, some low-level semantic features are lost, which makes the algorithm unable to accurately distinguish the category of the video. At present, the algorithm of micro-video recognition based on deep learning has surpassed the iDT algorithm, making these traditional methods fade out of people’s view. In this paper according to the micro-video classification task, a new network model is proposed to concatenate features of each modality into the overall features of various modalities through the network, and then fuse the various modal features with the attention mechanism to obtain the whole micro-video features, which will be used for classification. In order to verify the effectiveness of the algorithm proposed in this paper, experiments are conducted in the public dataset, and it is shown the effectiveness of our model.

The giant challenge breeding flood-irrigated rice is to identify superior genotypes that present high-yielding with specific grain qualities, resistance to abiotic and biotic stresses, excellent adaptation to the target environment. Thus, the objectives of this study were to propose a bayesian multi-trait model, estimate genetic parameters, and select flood-irrigated rice genotypes with better genetic potentials in different evaluation environments. For this, twenty-five rice genotypes belonging to the flood-irrigated rice improvement program were evaluated. The grain yields, grain length, width and thickness, grain length, and grain width and weight of 100 grains in the agricultural year 2016/2017. The experimental design used in all experiments was a randomized block design with three replications. The Monte Carlo Markov Chain algorithm estimated genetic parameters and genetic values. The grain thickness trait was considered highly heritable, with a credibility interval ranging from: h^2: 0.9480; 0.9440; 0.8610, in environments 1, 2, and 3, respectively. The grain yields showed a low correlation estimate between grain thickness and 100-grain weight, in all environments, with a credibility interval ranging from (ρ= 0.5477; 0.5762; 0.5618 and 0.5973; 0.5247; 0.5632, grain thickness and 100-grain weight, in environments 1, 2, and 3, respectively). The Bayesian multi-trait model proved to be an adequate strategy for the genetic improvement of flood-irrigated. Genotypes 2 and 15 had similar potential in the three environments, they should be selected as high-performance multi-trait genotypes for the genetic breeding of flood-irrigated rice in the program.

Multi-objective optimization (MOO) is an optimization involving minimization or maximization of several objective functions more than the conventional one objective optimization, which is useful in many fields. Many of the current methodologies addresses challenges and solutions that attempt to solve simultaneously several Objectives with multiple constraints subjoined to each. Often MOO are generally subjected to linear inequality, equality and or bounded constraint that prevent all objectives from being optimized at once. This paper reviews some recent articles in area of MOO and presents deep analysis of Random and Uniform Entry-Exit time of objectives. It further break down process into sub-process and then provide some new concepts for solving problems in MOO, which comes due to periodical objectives that do not stay for the entire duration of process lifetime, unlike permanent objectives which are optimized once for the entire process duration. A methodology based on partial optimization that optimizes each objective iteratively and weight convergence method that optimizes sub-group of objectives are given. Furthermore, another method is introduced which involve objective classification, ranking, estimation and prediction where objectives are classified based on their properties, and ranked using a given criteria and in addition estimated for an optimal weight point (pareto optimal point) if it certifies a coveted optimal weight point. Then finally predicted to find how far it deviates from the estimated optimal weight point. A Sample Mathematical Tri-Objectives and Real world Optimization was analyzed using partial method, ranking and classification method, the result showed that an objective can be added or removed without affecting previous or existing optimal solutions. Therefore suitable for handling time governed MOO. Although this paper presents concepts work only, it’s practical application are beyond the scope of this paper, however base on analysis and examples presented, the concept is worthy of igniting further research and application.

This paper addresses a wildlife monitoring problem using a team of UAVs for efficient monitoring of wildlife. The state-of-the-art technology using UAVs has been an increasingly popular tool to monitor wildlife compared to the traditional methods such as satellite imagery-based sensing or GPS trackers. However, there still exist unsolved problems as to how the UAVs need to cover a spacious domain to detect animals as many as possible. In this paper, we propose the optimal transport-based wildlife monitoring strategy for a multi-UAV system, to prioritize monitoring areas while incorporating complementary information such as GPS trackers and satellite-based sensing. Through the proposed scheme, the UAVs can explore the large-size domain effectively and collaboratively with a given priority. The time-varying nature of wildlife due to their movements is modeled as a stochastic process, which is included in the proposed work to reflect the spatio-temporal evolution of their position estimation. In this way, the proposed monitoring plan can lead to efficient wildlife monitoring with a high detection rate. Various simulation results including statistical data are provided to validate the proposed work.

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria – hypothesis, data types, strategies, study design and study focus – to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw a particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

The increasing number of robots around us will soon create a demand for connecting these robots in order to achieve goal-driven teamwork in heterogeneous multi-robot systems. In this paper, we focus on robot teamwork specifically in dynamic environments. While the conceptual modeling of multi-agent teamwork has been studied extensively during the last two decades, related engineering concerns have not received the same degree of attention. Therefore, this paper makes two contributions. The analysis part discusses general design challenges that apply to robot teamwork in dynamic application domains. The constructive part presents a review of existing engineering approaches for challenges that arise with dynamically changing runtime conditions. An exhaustive survey of robot teamwork aspects would be beyond the scope of this paper. Instead, we aim at creating awareness for the manifold dimensions of the design space and highlight state-of-the-art technical solutions for dynamically adaptive teamwork, thus pointing at open research questions that need to be tackled in future work.

From ancient time, electric grid system developed as one way direction in which users get the electricity from generators to far end. However, it is not the consumer centric as its one way process and consumer have no way to communicate to the server. Thus, with the development of digital revolution, the grid converted to smart grid and meter converted to smart meter. In smart grid, the protocol follows the bidirectional way of communication with support of consumers in the system. Recently in 2016, Jo et al. proposed the scheme for smart grid system using privacy preserving model and claimed to be efficient and secure. However, in this paper we have analyzed the scheme of Jo et al. and proved that the scheme is vulnerable to Replay attack and afterwards shows the change in protocol to withstand against this attack.

Equality and incomparability multi-label ranking have not been introduced to learning before. This paper proposes new native ranker neural network to address the problem of multi-label ranking including incomparable preference orders using a new activation and error functions and new architecture. Preference Neural Network PNN solves the multi-label ranking problem, where labels may have indifference preference orders or subgroups which are equally ranked. PNN is a nondeep, multiple-value neuron, single middle layer and one or more output layers network. PNN uses a novel positive smooth staircase (PSS) or smooth staircase (SS) activation function and represents preference orders and Spearman ranking correlation as objective functions. It is introduced in two types, Type A is traditional NN architecture and Type B uses expanding architecture by introducing new type of hidden neuron has multiple activation function in middle layer and duplicated output layers to reinforce the ranking by increasing the number of weights. PNN accepts single data instance as inputs and output neurons represent the number of labels and output value represents the preference value. PNN is evaluated using a new preference mining data set that contains repeated label values which have not experimented on before. SS and PS speed-up the learning and PNN outperforms five previously proposed methods for strict label ranking in terms of accurate results with high computational efficiency.

The concept of transit-oriented development (TOD) has been widely recognized in recent years for its role in reducing car traffic, improving public transportation, and enhancing traffic sustainability. This paper conducts empirical research on a developed rail transit network, using Shanghai as a case study. In addition to traditional TOD features, other factors based on urban rail transit are introduced, including multi-level modeling (MLM), which is used to analyze the possible factors influencing rail patronage. To avoid the bias of research results led by the correlation between independent variables, factors are divided into two levels. The first level includes three groups of variables: the built environment, station characteristics, and socioeconomic and demographic characteristics. The second level includes a set of variables which are regional characteristics. Results show that the most significant impact on train patronage is station location in the business district area. Other factors that have a positive effect on promoting rail transit travel include the number of service facilities around the station, degree of employment around the station, economic level, intensity of residential development, if the station is a transfer station, the operating period of the station, and the size of the large transportation hub around the station.

We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments. The model, called a fully connected conditional random field (CRF), is demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous seafloors in Patricia Bay, British Columbia, and Bedford Basin, Nova Scotia. Unlike previously proposed discriminative machine learning algorithms, the CRF model considers both the relative backscatter magnitudes of different substrates and their relative proximities. The model therefore combines the statistical flexibility of a machine learning algorithm with an inherently spatial treatment of the substrate. The CRF model predicts substrates such that nearby locations with similar backscattering characteristics are likely to be in the same substrate class. The degree of proximity and allowable backscatter similarity are controlled by parameters that are learned from the data. CRF model results were evaluated against a popular generative model known as a Gaussian Mixture model that doesn't include spatial dependencies, only covariance between substrate backscattering response over different frequencies. Both models are used in conjunction with sparse bed observations/samples in a supervised classification. A detailed accuracy assessment, including a leave-one-out cross-validation analysis, was performed using both models. Using multispectral backscatter, the GMM model trained on 50% of the bed observations resulted in a 75% and 89% average accuracies in Patricia Bay and Bedford Basin, respectively. The same metrics for the CRF model were 78% and 95%. Further, the CRF model resulted in a 91% mean cross-validation accuracy across four substrate classes at Patricia Bay, and a 99.5% mean accuracy across three substrate classes at Bedford Basin, which suggest that the CRF model generalizes extremely well to new data. This analysis also showed that the CRF model was much less sensitive to the specific number and locations of bed observations than the generative model, owing to its ability to incorporate spatial autocorrelation in substrates. The CRF approach therefore may prove to be a powerful `spatially aware' alternative to other discriminative classifiers.