Treatment of tendinopathies with multipotent mesenchymal stromal cells (MSCs) is a promising option in equine and human medicine. However, conclusive clinical evidence is lacking. The purpose of this study was to gain insight into clinical treatment efficacy and to identify suitable outcome measures for larger clinical studies. Fifteen horses with early naturally occurring tendon disease were assigned to intralesional treatment with allogeneic adipose-derived MSCs suspended in serum or with serum alone by block randomization (dosage adapted to lesion size). Clinicians and horse owners remained blinded to the treatment during 12 months (7 horses per group) and 18 months (7 MSC- and 5 control-group horses) of follow-up including clinical examinations and diagnostic imaging. Clinical inflammation-, lameness- and ultrasonography scores improved more over time in the MSC-group. The lameness score difference significantly improved in the MSC- compared to the control-group after 6 months. In the MSC-group, 5 out of 7 horses were free of re-injuries and back to training until 12 and 18 months. In the control-group, 3 out of 7 horses were free of re-injuries until 12 months. These results suggest that MSCs are effective for treatment of early phase tendon disease and provide a basis for a larger controlled study.

Major depressive disorder (MDD) and substance use disorder (SUD) are of immense medical and social concerns. Although significant progress on neuronal involvement in mood and reward circuitries has been achieved, only relatively recently the role of glia in these disorders have attracted attention. Detail understanding of the glial function in these devastating diseases could offer novel interventions. Here, following a brief review of circuitries involved in mood regulation and reward perception, specific contribution of the neurotrophic factors, neuroinflammation and gut microbiota to these diseases are highlighted. In this context, the role of specific glial cells (e.g., microglia, astroglia, oligodendrocytes and synantocytes) on phenotypic manifestation of MDD or SUD are emphasized. In addition, potential use of this knowledge in developing novel therapeutics is touched upon.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of ATP- binding cassette (ABC) transporters-X (X, a particular gene/protein) at 2nd order level after drug administration. These rankings reveal which ABC-X combinations might be working synergistically in CRC. If found true, oncologists can further test the com- bination of interest in wet lab and determine the mechanism of functioning between the ABC and X. In this research work, we cover combinations of ABC with ubiquitin- conjugating enzymes (UBE2), Interleukin (IL), BCL and Caspase (CASP).

White adipose tissue (WAT) is distributed in several depots that have distinct metabolic and inflammatory functions. In our body there are subcutaneous (sWAT), visceral (vWAT) and bone marrow fat depots (BFAT). Obesity affects size, function and inflammatory state of WATs. This process can alter the stem cell niches present in these tissues and affect the functions of stem cells residing within. In particular, obesity may affect the activity of mesenchymal stromal cells (MSCs) present in WAT. MSCs are an heterogenous population containing stromal cells, progenitor cells, fibroblasts and stem cells that are able to differentiate in adipocytes, chondrocytes, osteocytes and other mesodermal derivatives. We performed a comparison of the effects of obesity on MSCs obtained from sWAT, vWAT and BFAT. Our study evidenced that obesity affected mainly the biological functions of MSCs obtained from bone marrow and vWAT with a decrease in proliferation rate, reduced percentage of cells in S phase and trigger of senescence. The onset of senescence was confirmed by expression of genes belonging to RB and P53 pathways. Our study evidenced that negative consequences of obesity on body physiology may be related also to impairment in the functions of stromal compartment present in the several adipose tissues. This finding provides new insights on the targets that should be considered for an effective treatment of obesity-related diseases.

Mesenchymal Stromal Cells (MSCs) are multipotent, non-hematopoietic progenitor cells with a wide range of immune conditioning and regenerative potential which qualify them as potential component of cell based therapy for various autoimmune / chronic inflammatory ailments. Their immunomodulatory properties include the secretion of immunosuppressive cytokines, the ability to suppress T-cell activation and differentiation, and the induction of regulatory T-cells. In view of this and our interest, we here discuss the significance of MSC for the management of Graft-versus-Host Disease (aGVHD), one of the autoimmune manifestation in human.. In pre-clinical models, MSCs have been shown to reduce the severity of aGVHD symptoms, including skin and gut damage, which are the most common and debilitating manifestations of this disease. While initial clinical studies of MSCs in aGVHD cases were promising, the results were variable in randomized studies. So, further studies are warranted to fully understand their potential benefits, safety profile, and optimal dosing regimens. In view of these inevitable issues, here we discuss various mechanisms, how MSCs can be employed in managing aGVHD, as a therapeutic option for this disease.

The Epithelial-Mesenchymal-Transition (EMT) phenotype, identified as a significant clinical indicator in cancer, manifests as a biological process where cells transition from epithelial to mesenchymal characteristics. Physiologically, EMT plays a crucial role in tissue remodeling, promoting healing, repair, and responding to various tissue damages. This study investigates the impact of BNE-RRC on oral cancer cells (KB) and reveals its significant effects on cancer cell growth, migration, invasion, and EMT. BNE-RRC induces the epithelial-like morphology in KB cells, effectively reversing EMT to Mesenchymal-Epithelial Transition (MET). Extraordinarily, sustained culture of cancer cells with BNE-RRC for 14 days maintains an epithelial status even after treatment withdrawal, suggesting BNE-RRC as a potential therapeutic agent for cancer. These findings highligh the promise of BNE-RRC as a comprehensive therapeutic agent in cancer treatment by inhibiting cancer cell growth, migration, and invasion, while also orchestrating a reversal of the EMT process. The study proposes that BNE-RRC could be effective agent for cancer treatment.

Purpose of Review Multipotent Mesenchymal stromal cells (MSCs) have recently risen to prominence in regenerative medicine for their powerful intrinsic properties of self-regeneration, immunomodulation, and multi-potency. They exhibited an excellent safety profile in early-phase clinical trials. Nevertheless, MSCs-based therapy suffers reduced efficacy in randomized clinical trials due to a poor understanding of their proper delivery modalities. Here, we intend to explore the current application of MSCs after tissue injuries. We specifically focus on tissue source, delivery routes, metabolic fitness, and cell dose. We further discuss potential approaches to optimize in vivo engraftment. Recent findings MSCs therapeutic effect mediates by paracrine and contact factors, which are cells' intrinsic physiological processes. The cell culture expansion process does not destroy MSCs' intrinsic properties. In vivo persistence of administered MSCs determines their therapeutic potency. Cell viability, fitness, immune match, and delivery route meticulously regulate their in vivo persistency. Different genetic or chemical modification strategies are currently applied to extend their in vivo lifespan and boost their pharmaceutical effect. Summary Improving the in vivo persistence of implanted MSCs could facilitate its clinical translation. This review highlights the pros and cons of different MSCs’ delivery strategies used in clinical or preclinical studies, emphasizing various modification approaches. These can promote prolonged graft cell survival.

Mesenchymal stem cells (MSCs) showed in vitro mesoderm-lineage differentiation and self-renew capacity. However, no comparative study was reported on the biological characteristics of stem cells derived from skeletal muscle (SM-MSCs), dermal skin (DS-MSCs), and adipose tissues (A-MSCs) from a single donor in camels. The present study aimed to evaluate the influence of MSCs source on stem cell characteristics. We evaluated proliferation capacity and mesoderm-lineage differentiation potential from SM-MSCs, DS-MSCs, and A-MSCs. They showed spindle-like morphology after homogenization. The proliferation ability was no significant difference in all groups. Furthermore, the portion of the cell cycle and expression of pluripotent markers (Oct4, Sox2, and Nanog) were similar in all cell lines at passage 3. The differentiation capacity of A-MSCs into adipocytes was significantly higher than that of SM-MSCs and DS-MSCs. However, the osteoblast differentiation capacity of A-MSCs was significantly lower than that of SM-MSCs and DS-MSCs. Additionally, after osteoblast differentiation, the ALP activity and calcium content was significantly decreased in A-MSCs as compared to SM-MSCs and DS-MSCs. To the best of our knowledge, we primally established MSCs from the single camel and demonstrated their comparative characteristics including expression of pluripotent factors and proliferation, and in vitro differentiation capacity into adipocytes and osteoblasts.

Mutations in p53 and KRAS are seen in most cases of colon cancer. The impact of these mutations on signaling pathways related to cancer growth has been studied in depth, but relatively less is known on their effects on amino acid transporters in cancer cells. This represents a significant knowledge gap because amino acid nutrition in cancer cells profoundly influences macropinocytosis and ferroptosis, two processes with opposing effects on tumor growth. Here we used isogenic colon cancer cell lines to investigate the effects of p53 loss and KRAS activation on two amino acid transporters relevant to macropinocytosis (SLC38A5) and ferroptosis (SLC7A11). Our studies show that the predominant effect of p53 loss is to induce SLC7A11 with resultant potentiation of antioxidant machinery and protection of cancer cells from ferroptosis whereas KRAS activation induces not only SLC7A11 but also SLC38A5, thus offering protection from ferroptosis as well as improving amino acid nutrition in cancer cells via accelerated macropinocytosis. Niclosamide, an FDA-approved anti-helminthic, blocks the functions of SLC7A11 and SLC38A5, thus inducing ferroptosis and suppressing macropinocytosis with resultant effective reversal of tumor-promoting actions of oncogenic mutations in p53 and KRAS. These findings underscore the potential of this drug in colon cancer treatment.

Mesenchymal stromal cells (MSCs) have shown a high potential for cartilage repair. Collagen-based scaffolds are used to deliver and retain cells at the site of cartilage damage. The aim of the work was a comparative analysis of the capacity of the MSCs from human adipose tissue to differentiate into chondrocytes in vitro and to stimulate the regeneration of articular cartilage in an experimental model of rabbit knee osteoarthrosis when cultured on microheterogenic collagen-based hydrogel (МCH) and the microparticles of decellularized porcine articular cartilage (DPC). The morphology of samples was evaluated using scanning electron microscopy and histological staining methods. On the surface of the DPC, the cells were distributed more uniformly than on the MCH surface. On day 28, the cells cultured on the DPC produced glycosaminoglycans more intensely compared to the MCH with the synthesis of collagen type II. However, in the experimental model of osteoarthrosis, the stimulation of the cartilage regeneration was more effective when the MSCs were administered to the MCH carrier. The present study demonstrates the way to regulate the action of the MSCs in the area of cartilage regeneration: the MCH is more conducive to stimulating cartilage repair by the MSCs, while the DPC is an inducer for a formation of a cartilage-like tissue by the MSCs in vitro.

The most common type of head and neck cancer (HNC) is head and neck squamous cell carcinoma (HNSCC). It describes a heterogenous neoplastic disease that arises from the mucosal epithelium of the of the upper respiratory system and the gastrointestinal tract. It is characterized by high morbidity and mortality, being is the eighth most common cancer worldwide, with 931,931 newly diagnosed cases and 467,125 deaths according to the latest Global Cancer Statistics (GLOBOCAN) data. Despite the use of modern low-invasive surgical techniques and application of promising new chemoradiotherapies or combination treatment, the frequency of recurrences and the five-year survival of these patients remains unsatisfactory. The microenvironment of head and neck cancer comprises various cells that regulate neoplastic expansion. It is believed that the mesenchymal stromal cells (MSCs) present in the tumour milieu play a key role in the modulation of tumour initiation, development and progression, in the regulation of cancer treatment response, survival rates, patient outcomes and long-term prognosis; they also influence the resistance to cisplatin-based chemotherapy, the gold standard for advanced HNSCC. MSCs, are multipotent, heterogeneous, mobile cells, which meet specific stemness criteria defined by the International Society for Cellular Therapy (ISCT). Although no MSC-specific markers exist, they can be recognized based on several others, such as CD73, CD90 and CD105, while lacking the presence of CD45, CD34, CD14 or CD11b, CD79α, or CD19 and HLA-DR antigens; they share phenotypic similarity with stromal cells and their capacity to differentiate into other cell types. In the tumour niche, MSC populations are characterized by cell quiescence, self-renewal capacity, low reactive oxygen species production and the acquisition of epithelial-to-mesenchymal transition properties, they may play a key role in the process of acquiring drug resistance, and thus treatment failure. MSCs must also demonstrate plastic adherence in vitro and be able to differentiate into specific mesodermal cell types after stimulation i.e., adipocytes, chondrocytes, and osteoblasts when cultured under well-defined conditions. The present narrative review examines the links between mesenchymal stromal/stem cells and HNC, as well as the different mechanisms involved in the development of resistance to current chemo- and radiotherapies in HNSCC. It provides an overview of the current literature, including key opinion-forming systematic reviews, as well as cross-sectional, longitudinal, prospective and interventional studies based on cell culture models in vitro, animal or in vivo models of HNSCC; it also examines the possibilities of pharmacological targeting of stemness-related chemoresistance in HNSCC. It describes promising new strategies to optimize chemoradiotherapy with the potential to personalize patient treatment approaches, and highlights future therapeutic perspectives in HNC. The data concerning tumour-associated MSCs is arranged according to increasing clinical credibility.

To elucidate mesenchymal stromal cells (MSC)-tumor intricate and ambiguous interactions, the reliable cell labeling and tracking techniques are imperative. Traditional methods of cell labelling have limitations mostly relevant to need for genetic modification what demands the exploration for alternative strategies. Here, we propose using fluorescent photoconvertible microcapsules containing Rhodamine B (RhB) to study mMSC migration in brain tumors. These 3 μm sized microcapsules are readily internalised by cells and undergo photoconversion under 561 nm laser exposure with fluorescence blue shift on demand. Optimal photoconversion was achieved with a laser irradiation duration of 0.4 ms, which provide a maximal brightness of photoconverted label without an excessive laser exposure on cells. Capsules modified with polyallylamine hydrochloride demonstrated the best potential for intracellular uptake without significantly affecting cell viability, motility, or proliferation. The optimal ratio of 20 capsules per mMSC was determined for labeling. Moreover, migration of individual mMSCs within 2D and 3D glioblastoma cell (EPNT-5) colonies over 2 days and in vivo tumor settings over 7 days was tracked. Our study unveils a robust platform for investigating MSC-tumor dynamics, offering insights into therapeutic strategies. Photoconvertible microcapsules could also become an indispensable tool for studying complex fundamental processes of cell-cell interactions for a wide range of problems in biology and medicine.

2-(4-Benzyloxy-3-methoxyphenyl)-5-(carbethoxyethylene)-7-methoxy-benzofuran (BMBF), a benzofuran derivative, is an intermediate found in the process of total synthesis of ailanthoidol. Benzofuran derivatives are a class of compounds that possess various biological and pharmacological activities. The present study explored the anti-metastasis effects in hepatocellular carcinoma (HCC). Our preliminary findings indicate that BMBF suppresses the proliferation and changes the morphology of Huh7, an HCC cell line with a mutated p53 gene (Y220C). According to scratching motility assay, noncytotoxic concentrations of BMBF significantly inhibited the motility and migration in Huh7 cells. BMBF upregulated the expression of E-cadherin and downregulated the expression of vimentin, Slug, and MMP9, which are associated with epithelial-mesenchymal-transition (EMT) and metastasis in Huh7 cells. BMBF decreased the expression of integrin α7 and deactivated its downstream signal FAK/AKT and inhibited p53 protein levels. Cell transfection with p53 siRNA resulted in prevention of cell invasion because of the reduced expression of integrin α7, Slug, and MMP-9 in Huh7 cells. BMBF had anti-metastatic effects in PLC/PRF/5, an HCC cell line with R249S, a mutated p53 gene. Our findings indicate that BMBF has anti-metastatic effects in that it downregulates p53 and mediates the suppression of integrin α7, EMT, and MMP-9 in HCC cells with mutated p53 gene.

The objective of the this study was to reduce the benzo[a]pyrene-induced hepatotoxicity method in herbal medicine products and to give proof that neferine, daidzein, genistein possess antihepa-totoxic effects. B[a]P which classified as a group 1 carcinogen and metabolized to B[a]P-7,8-dihydrodiol-9,10-epoxide (BPDE) causes mutagenic DNA addition products. The reduc-tion of BPDE-DNA adduct formation by B[a]P-7,8-dihydrodiol-9,10-epoxide (BPDE) was derived by benzo[a]pyrene (B[a]P). In HepG2 cells, B[a]P exhibited toxicity and substance treatment of the cells with B[a]P with neferine in lotus and daidzein, genistein in soybean reduced the BPDE-DNA ad-ducts level. The level of B[a]P-metabolites in the substance treatment of the cells was presented that BPDE levels were reduced by neferine in lotus and daidzein, genisten in soybean. These results suggest that neferine in lotus and daidzein, genistein in soybean prevent B[a]P-induced hepato-toxicity for BPDE-DNA adduct formation.

Mesenchymal stem cells (MSCs) have emerged as a promising treatment for inflammatory diseases. It is described that the immunomodulatory effect of MSCs takes place both by direct cell-to-cell contact and by means of soluble factors that leads to an increased accumulation of regulatory immune cells at the sites of inflammation. Similar efficacy of MSCs has been described regardless the route of administration used, the inflammation conditions and the MHC context. These observations arise the question as to whether the migration of the MSCs to the inflamed tissues is a pre-requisite to achieve their beneficial effect. To address this, we examined the biodistribution and the efficacy of intraperitoneal luciferase-expressing human expanded adipose derived stem cells (Luci-eASCs) in a mouse model of colitis. Luci-eASC-infused mice were stratified according to their response to the Luci-eASC treatment. According to the stratification criteria, there was a tendency to increase the bioluminescence signal in the intestine at the expense of a decrease in the bioluminescence signal in the liver in the `responder´ mice. These data thus suggest that the accumulation of the eASCs to the inflamed tissues is beneficial to achieve an optimal modulation of inflammation.

Triple negative breast cancer (TNBC) is associated with a generally poor prognosis due to its highly aggressive and metastatic nature, lack of targetable receptors, as well as the frequent development of resistance to chemotherapy. We previously reported that AU1, a small molecule developed as an inhibitor of BPTF (bromodomain PHD finger containing transcription factor), was capable of sensitizing preclinical models of triple negative breast cancer to chemotherapy, in part via the promotion of autophagy. In studies reported here, we identify an additional property of this compound, specifically that sensitization is associated with inhibition of the P-glycoprotein efflux pump. In silico molecular docking studies indicate that AU1 binds to active regions of the efflux pump, in a manner consistent with inhibition of pump function. This work identifies a novel chemical structure that can influence multidrug efflux, an established mechanism of drug resistance in triple negative breast cancer, that has not yet been successfully addressed by clinical efforts.

Background: Since there is no cure for amyotrophic lateral sclerosis (ALS) currently, it is very important for searching diagnostic biomarkers and novel treatments to reduce the severity of this disease; one of these treatment approaches is stem cell transplantation. This study aims to evaluate the safety and efficacy of repeated transplantation of autologous bone marrow-derived mesenchymal stem cells (BM-MSCs) in patients with ALS by analyzing molecular data. Methods: In this single-centre, open-label, phase II clinical trial, retrospective study, fifteen confirmed ALS patients entered the study based on defined inclusion and exclusion criteria and underwent repeated stem cell transplantation (3 times BM-MSCs transplantation (1×10^6) , MSC/Kg BW per injection) concurrently intrathecally (IT) and intravenously (IV), with one-month interval). Clinical assessment of molecular investigation by evaluating specific microRNAs expression (Mir206, 133a-3p, 338-3p) in the patient's serum and Cerebra spinal fluid (CSF) samples were done three times during the 3-month follow-up. Statistical analysis includes paired T-test or Wilcoxon signed ranks. Results: No serious adverse effects were reported following stem cell transplantation. Besides, the mentioned microRNA expression slightly increased but still non-significant (P>0.05). Conclusions: Although Our study demonstrated non-signifying results. However, the still increasing of these MircRNAs in serum and CSF overall, can indicate that repeated transplantation of BM-MSCs was a safe procedure in ALS patients, leading to delay in disease progression and improvement in clinical symptoms. Future studies are needed to confirm these results.

Breast cancer is currently the second leading cause of death worldwide, accounting for 15% of all deaths (8.2 million deaths). Approximately 20% of breast cancer diagnoses are triple-negative breast cancer (TNBC), a type of cancer that lacks receptors for estrogen (ER negative), progesterone (PR negative), and human epidermal growth factor receptor 2 (HER2/neu negative). Consequently, TNBC doesn’t respond to hormonal or targeted therapies, making it challenging to treat due to its rapid growth, metastasis, and high recurrence rate within the first three years of therapy. Alternative chemotherapies are needed to address this problem. Pharmacological dose of vitamin C (high-dose VC) has been identified as a potential treatment for some cancer cells. The present study aimed to evaluate whether VC has a therapeutic effect on TNBC using MDA-MB-231 cells as the model. MTT assay, Hoechst 33342 staining, Nuclear ID Red/Green staining, Rhodamine 123 staining, and Western blot analysis were employed to test the hypothesis that pharmacological dose of VC can kill TNBC cells. Upregulation of Apaf-1, caspases -3, -6, -7, -8, and -9, inhibition of matrix metalloproteinases (MMP-2 and MMP-9), reduction in cell cycle protein expression, and enhancement of tumor suppressor proteins such as p53 and p21 indicated that pharmacological dose of VC has promising anti-cancer properties in the treatment of TNBC.

We have collected theoretical arguments supporting the functional role of nano-metallic coatings of solar cells, which enhance their efficiency via by plasmon strengthening of absorption of sun-light photons and reduction of binding energy of photo-excitons. The quantum character of plasmonic effect related with the absorption of photons (called as optical plasmonic effect) is described in terms of Fermi golden rule for quantum transitions of semiconductor band electrons induced by plasmons from a nano-metallic coating. The plasmonic effect related with the lowering of exciton binding energy (called as electrical plasmonic effect) is also quantumly characterized with particular significance for metalized perovskite solar cells. Quantum coupling between plasmons in nanoparticles from a coating with band electrons in a semiconductor substrate significantly modifies material properties (dielectric functions) both of particles and of a semiconductor, beyond the ability of a classical electrodynamics description.

Fragile X Syndrome (FX) is the most common form of inherited cognitive impairment and falls under the broader category of Autism Spectrum Disorders (ASD). FX is caused by a CGG trinucleotide repeat expansion in the non-coding region of the X-linked Fragile X Messenger Ribonucleoprotein 1 (FMR1) gene, leading to its hypermethylation and epigenetic silencing. Animal models of FX rely on the deletion of the Fmr1 gene which fail to replicate the epigenetic silencing mechanism of the FMR1 gene seen in human patients. Human stem cells carrying FX repeat expansions have provided a better understanding of the basis of the epigenetic silencing of FMR1. Previous studies have found that 5-Azacytidine (5Azac) can reverse this methylation; however, 5Azac can be toxic and may limit its therapeutic potential. Here, we show that the dietary factor Ascorbic Acid (AsA) can reduce DNA methylation in the FMR1 locus and lead to an increase in FMR1 gene expression in FX iPSCs and cerebral organoids. In addition, AsA treatment rescued neuronal gene expression and the morphological defects observed in FX iPSC-derived cerebral organoids. Hence, we demonstrate that the dietary co-factor AsA can partially revert molecular and morphological defects seen in human FX models in vitro. Our findings have implications for the development of novel therapies for FX in the future.

Introduction: Cartilage injuries and osteoarthritis are very prevalent and considered a public health problem, as they are highly disabling and represent an economic burden. Mesenchymal Stromal Cells (MSCs) can be isolated from different tissues and have the immunomodulatory capacity to regulate local articular joint environment. This translational study aims to compare the cartilage restoration from MSCs from the synovial membrane (SM) and dental pulp (DP), by tissue engineered treatment in a Good Manufacturing Practices. Materials and Methods: A controlled experimental study in fourteen miniature pigs was performed, using scaffold-free Tissue Engineering Construct (TEC) from DP and SM MSCs, with 6 months follow-up. Total thickness cartilage defects were performed in both posterior knees. The defect was left empty on one side, and the other received TEC from DP (n=7) or from SM (n=7). MRI assessed morphology with the MOCART scoring system. T2 mapping assessed water and collagen fiber composition. Histology evaluated cartilage repair using the ICRS-2 score. Results: The mean MOCART value in the untreated group was 46.2 ± 13.4, while the group treated with TEC from SM was 65.7 ± 15.5 (p

One of the most used markers of cancer stem cells in several cancers, including colorectal cancer and breast cancer, is CD44. CD44 is a glycoprotein that traverses the cell membrane and binds to many ligands including hyaluronan resulting in activation of signaling cascades. Several reports have shown conflicting data on the expression of CD44 and that the expression depends on modes of investigations and subtypes of cancers. In addition, the correlation between CD44 expression and drug resistance, immune infiltration, EMT, metastasis and patients prognosis in several cancer types remains unclear. This study investigated CD44 expression in several cancers and explored its relationship with tumorigenesis using various publicly available databases, including The Cancer Genome Atlas, GEPIA, Oncomine, Genomics of Drug Sensitivity in Cancer and Tumor Immune Estimation Resource. Our analysis reveals that CD44 is differentially expressed in different cancers. CD44 expression is significantly associated with cancer patients’ survival in gastric, pancreatic and colorectal cancers. In addition, CD44 expression is closely linked with immune infiltration and immune suppressive features in pancreatic, colon adenocarcinoma and stomach cancer. High CD44 expression was significantly correlated with the expression of drug resistance-, EMT- and metastasis- linked genes. Tumors expressing high CD44 have higher mutation burden and afflict older patients than tumors expressing low CD44. Cell lines expressing high CD44 are more resistant to anti-cancer drugs compared to those expressing low CD44. Protein-protein interaction investigations and functional enrichment analysis showed that CD44 interacts with gene products related to cell-substrate adhesion, migration, platelet activation, and cellular response to stress. KEGG pathway analysis revealed that these genes play key roles in biological adhesion, cell component organization, locomotion, G-α-signaling and the response to stimulus. Overall, this investigation reveals that CD44 play significant roles in tumorigenesis, can be used as a prognostic biomarker in several cancers and can be therapeutically targeted in cancer therapy.

HSF1 is a well-known Heat Shock Protein expression regulator in response to stress. It also regulates processes important for growth, development, or tumorigenesis. Here, we studied the HSF1 influence on the phenotype of non-tumorigenic human mammary epithelial (MCF10A and MCF12A) and several triple-negative breast cancer cell lines. MCF10A and MCF12A differ by HSF1 levels, morphology, growth in the matrigel, expression of epithelial (CDH1) and mesenchymal (VIM) markers (MCF10A are epithelial cells, MCF12A resemble mesenchymal cells). HSF1 down-regulation led to reduced proliferation rate and spheroid formation in matrigel by MCF10A cells, while it did not affect the MCF12A proliferation but led to CDH1 up-regulation and the formation of better-organized spheroids. HSF1 overexpression in MCF10A resulted in reduced CDH1 and increased VIM expression, and the acquisition of elongated fibroblast-like morphology. The above results suggest that elevated levels of HSF1 may direct mammary epithelial cells toward a mesenchymal phenotype while lowering HSF1 could reverse the mesenchymal phenotype to an epithelial one. Therefore, HSF1 may be involved in the remodeling of mammary gland architecture over the female lifetime. Moreover, HSF1 levels positively correlated with the invasive phenotype of triple-negative breast cancer cells, and their growth was inhibited by the HSF1 inhibitor, DTHIB.

Epithelial ovarian cancer (EOC) remains the most lethal gynecologic malignancy, largely due to metastasis and drug resistant recurrences. Fifteen percent of ovarian tumors carry mutations in BRCA1 or BRCA2, rendering them vulnerable to treatment with PARP inhibitors such as olaparib. Recent studies have shown that TGFβ can induce “BRCAness” in BRCA wild-type cancer cells. Given that TGFβ is a known driver of epithelial to mesenchymal transition (EMT), and the con-nection between EMT and metastatic spread in EOC and other cancers, we asked if TGFβ and EMT alter susceptibility of EOC to PARP inhibition. Epithelial EOC cells were transiently treated with soluble TGFβ and their clonogenic potential, expression and function of EMT and DNA repair genes, and response to PARP inhibitors compared with untreated controls. A second epithelial cell line was compared to its mesenchymal derivative for EMT and DNA repair gene expression and drug responses. We found that TGFβ and EMT resulted in downregulation of genes responsible for homologous recombination (HR) and sensitized cells to olaparib. HR efficiency was reduced in a dose-dependent manner. Furthermore, mesenchymal cells displayed sensitivity to olaparib, cis-platin, and the DNA-PK inhibitor Nu-7441. Therefore, treatment of disseminated, mesenchymal tumors may represent an opportunity to expand clinical utility of PARP inhibitors and similar agents.

Pancreatic ductal adenocarcinoma (PDAC) is a fatal disease due to early metastatic spread, late diagnosis and the lack of efficient therapies. A major driver of cancer progression and hurdle to successful treatment is transforming growth factor (TGF)-β. Recent data from pancreatic cancer mouse models have shown that transcriptionally active p73 (TAp73), a p53 family member, inhibits tumor progression through promoting tumor suppressive canonical TGF-β/Smad signaling, while preventing non-canonical TGF-β signaling through extracellular signal-regulated kinases (ERK)1/2. Here, we have studied whether this mechanism also operates in human PDAC. Using the PDAC-derived tumor cell lines PANC-1 and HPAFII, we show that TAp73 induces the expression of the epithelial marker and invasion suppressor E-cadherin and the common-mediator Smad, SMAD4, while at the same time suppressing expression of the EMT master regulator SNAIL and basal and TGF-β1-induced activation of ERK1 and ERK2. Using dominant-negative and RNA interference-based inhibition of SMAD4 function we went on to show that inhibition of ERK activation by TAp73 is mediated through SMAD4. Intriguingly, both SMAD4 and the α isoform of TAp73 - but not the isoform - interfered with cell migration as shown by xCELLigence technology. Our findings highlight the role of TAp73-SMAD4 signaling in tumor suppression of human PDAC and identify direct inhibition of basal and TGF-β-stimulated pro-invasive ERK activation as an underlying mechanism.

High rates of phosphorus (P) currently being applied to soils for the production of vegetables in the Mekong Delta, Vietnam has led to the concern of its negative effect on the economics and the environment. This research presents a comprehensive study on the determination of P supplying capacity in this region of Vietnam to examine the possibility of reducing P fertilizer input. One hundred twenty (120) soil samples were collected to evaluate total P and Bray 1 available P in the soils. Phosphorus maximum sorption, degree of P saturation, P release, and the effect of P fertilizer on corn (Zea mays L.) yield in greenhouse and fields were also determined. Total P concentrations of 56.7% soil samples evaluated yielded high P concentrations (>560 mg P/kg), while 74.2% of the samples had high Bray 1 available P concentrations (>20 mg P/kg soil). Maximum P sorption ranged from 149 to 555 mg P/kg soil, respectively and has negative correlation to available P (r = - 0.63*), degrees of P saturation ranged from 0.63 to 5.46% correlated to available P (r = 0.98**) and maximum P release ranged from 1.2 to 61.9 mg P/kg soil, respectively correlated to available P (r = 0.96**). Corn grown in soils with available P concentrations >15 mg P/kg did not respond to P fertilizer in greenhouse or field experiments. We conclude that many farmers in this region can reduce P fertilizer input, thus increasing their profits and reducing negative environmental impacts associated with excess soil P for sustainable agriculture.

The brain being arguably the most complex organ in the human body, its detailed functioning is yet to be fully deciphered, let alone accurately modeled. Nonetheless, the medical community has gathered quite a remarkable amount of studies about the consequences of illnesses and injuries on its development and functioning. This bibliographic review aims to cover mostly the findings related to the changes in the physical distribution of neurons and their connections - the connectome -, both structural and functional, as well as their modelling. It does not intend to offer an extensive medical description of all injuries and diseases affecting the brain, rather presenting the most common ones succinctly so the need for accurate brain modelling can be fully understood and pondered, offering propositions to this aim.

The onset and progression of human inflammatory joint diseases are strongly controlled by the activation of resident synovium/infrapatellar fat pad (IFP) pro-inflammatory and pain-transmitting signaling. We recently reported that intra-articularly injected IFP-derived Mesenchymal Stem/Stromal Cells (IFP-MSC) acquire a potent immunomodulatory phenotype and actively degrade Substance P (SP) via neutral endopeptidase CD10 (neprilysin). Our hypothesis is that IFP-MSC robust immunomodulatory therapeutic ef-fects are largely exerted via their CD10-bound exosomal secretome (IFP-MSC EXOs) by attenuating synoviocyte pro-inflammatory activation and articular cartilage degradation. Herein, IFP-MSC EXOs were isolated from CD10High- and CD10Low-expressing IFP-MSC cultures and their exosomal miRNA cargo was assessed using multiplex methods. Functionally, we interrogated the effect of CD10High and CD10Low EXOs on stimulated by inflammatory/fibrotic cues synoviocytes monocultures and cocultures with IFP-MSC de-rived chondropellets. Finally, CD10High EXOs were tested in vivo for their therapeutic capacity in an animal model of acute synovitis/fat pad fibrosis. Our results showed that CD10High and CD10Low EXOs possess distinct miRNA profiles. Reactome analysis of miRNAs highly present in exosomes showed their involvement in the regulation of six gene groups, particularly the immune system. Stimulated synoviocytes exposed to IFP-MSC EXOs demonstrated significantly reduced proliferation and altered inflammation-related molecular profiles compared to control stimulated synoviocytes. Importantly, CD10High EXOs treatment of stimulated chondropellets/synoviocytes cocultures indicated a significant chondroprotective effects. Therapeutically, CD10High EXOs treatment resulted in robust chondroprotective effects by retaining articular cartilage structure/composition and PRG4 (lubricin)-expressing cartilage cells in the animal model of acute synovitis/IFP fibrosis. Our study suggests that CD10High EXOs possess immuno-modulatory miRNA attributes with strong chondroprotective/anabolic effects for articular cartilage in vivo. The results could serve as a foundation for EXOs-based therapeutics for the resolution of detrimental aspects of immune-mediated inflammatory joint changes associated with conditions such as osteoarthritis (OA).

Abstract This study focuses on the toxicity assessment of various flavonoids, with particular emphasis on Hesperidin. Among the investigated compounds, Hesperidin exhibits notable potential and low toxicity in both short and long-term evaluations. The toxicity parameters for Hesperidin include a low Max. Tolerated Dose (Human) (0.525 log mg/kg/day), indicative of its minimal potential harm to humans. Additionally, its Oral Rat Acute Toxicity (LD50) (2.506 mol/kg) suggests a relatively low acute toxicity level. The Oral Rat Chronic Toxicity (LOAEL) (3.167 log mg/kg_bw/day) signifies a higher dose threshold for observing adverse effects over the long term. Furthermore, Hesperidin demonstrates low Minnow Toxicity (7.131 log mM), suggesting minimal impact on aquatic organisms. Overall, this comprehensive assessment positions Hesperidin as a promising flavonoid with favorable properties and minimal toxicity.

This paper presents MADS (Multi-Agents for Data Science), an innovative and simple open-source multi-agent framework designed for systemic pipeline execution in applied supervised machine learning. By leveraging the capabilities of multi-agent systems (MAS), we introduce a universal approach to optimize and streamline machine learning pipelines. Our framework highlights the differences between various types of agents, such as reinforcement learning (RL) agents and large language model (LLM) agents, and their distinct contributions to the process. While we currently employ LLM agents to automate and enhance machine learning tasks, we acknowledge the potential of incorporating RL agents in future iterations to further improve performance and adaptability. The primary objective is to enhance the efficiency, scalability, and adaptability of supervised learning applications across various domains. This integration addresses the complexity and manual effort typically associated with machine learning workflows, paving the way for more automated, robust, and scalable solutions. Our approach demonstrates significant improvements in task automation, reduced human intervention, and enhanced model performance. The MADS framework, which will soon be available as an open-source implementation, represents a pivotal contribution to the field of machine learning, promising to facilitate broader adoption and collaborative advancement.

Covariant scalar field quantization, nicknamed $(φ^r)_n$, where $r$ denotes the power of the interaction term and $n=s+1$ where $s$ is the spatial dimension and $1$ adds time. Models such that $r<2n/(n-2)$ can be treated by canonical quantization, while models such that $r>2n/(n-2)$ are trivial, or, if treated as a unit, emerge as `free theories'. Models such as $r=2n/(n-2)$, e.g., $r=n=4$, again using canonical quantization also become `free theories', which must be considered quantum failures. However, there exists a different approach called affine quantization that promotes a different set of classical variables to become the basic quantum operators and it offers different results. It is well-known that the canonical quantization of $\varphi^4_4$ fails. Here is how to fix it along with many other problems.

Electromobility has gained significant attention in the auto- motive industry, with car manufacturers striving to improve the charging experience for their users. In this study, we compare multiple realistic charging strategies in order to quantify the benefits arising from better planning of charging sessions. We base our evaluation on electricity price data from three regions: Sweden (SE3 region), Germany (DE region), and Denmark (DK2 region). This data is obtained from the ENTSO-E Transparency Platform, covering the two full years 2021 and 2022. We enhance this dataset with commercial price forecasts from the company StormGeo and benchmark it against a naive forecasting method. The primary aim of our work is to compare how the strategies of varying levels of complexity perform across the three different countries in terms of minimizing energy costs for electric car users.

In this work, several random Boolean networks (RBN) are generated and analyzed from two characteristics: their time evolution diagram and their transition diagram. To do this, its randomness is estimated using three metrics, of which algorithmic complexity is the only one capable of revealing both transitions towards the chaotic regime and the algorithmic contribution of certain states to the transition diagram and their relationship with the order they occupy in the temporal evolution of the respective RBN.

Artificial Intelligence (AI) is poised to revolutionize the architectural design and energy management of green buildings, offering significant advancements in sustainability and efficiency. This paper explores the transformative impact of AI on improving energy efficiency and reducing carbon emissions in commercial buildings. By leveraging AI algorithms, architects can optimize building performance through advanced environmental analysis, automation of repetitive tasks, and real-time data-driven decision-making. AI facilitates precise energy consumption forecasting and integration of renewable energy sources, enhancing the overall sustainability of buildings. Our study demonstrates that AI can reduce energy consumption and CO2 emissions by approximately 8% and 19%, respectively, in typical mid-size office buildings by 2050 compared to conventional methods. Further, the combination of AI with energy efficiency policies and low-emission energy production is projected to yield reductions of up to 40% in energy consumption and 90% in CO2 emissions. This paper provides a systematic approach for quantifying AI's benefits across various building types and climate zones, offering valuable insights for decision-makers in the construction industry.

Orthostatic hypotension (OH) is a condition marked by a significant drop in blood pressure upon standing, resulting from impaired autonomic regulation of vascular tone and blood volume. This study investigates the prevalence, physiological mechanisms, and occupational risk factors for OH among high-risk professions in Nepal, such as traffic police, security guards, and shopkeepers. Using a cross-sectional design, participants were evaluated for blood pressure variability, autonomic function, and fluid balance. The findings reveal a high incidence of OH, linked to reduced baroreceptor sensitivity and impaired sympathetic activation due to prolonged standing and inadequate hydration. This study highlights the need for tailored clinical interventions and workplace modifications to mitigate OH in vulnerable occupational groups.

Cardiofaciocutaneous (CFC) syndrome, linked to mutations in genes including KRAS, BRAF, MEK1, and MEK2, leads to dysregulated RAS-MAPK signaling with limited therapeutic options. This study utilized molecular docking simulations to screen natural compounds against mutated KRAS, BRAF, MEK1, and MEK2 structures. Promising candidates such as Amentoflavone and Hypericin displayed strong binding affinities and favorable interactions with mutated sites, suggesting their potential as lead molecules for targeted therapies in CFC syndrome. This research offers novel insights into therapeutic interventions for this rare genetic disorder.

We prove through path integral Monte Carlo computer experiments that the affine quantization of the $\varphi_4^4$ scaled Euclidean covariant relativistic scalar field theory is a valid quantum field theory with a well defined continuum limit of the one- and two-point-function. Affine quantization leads to a completely satisfactory quantization of field theories using situations that involve scaled behavior leading to an unexpected, $\hbar^2/\varphi^2$ which arises only in the quantum aspects.

Previously, we demonstrated that IR triggers the invasion/migration of A549 cells via activation of an EGFR–p38/ERK–STAT3/CREB-1–EMT pathway. Here, we have demonstrated the involvement of a novel intracellular signaling mechanism in γ-ionizing radiation (IR)-induced migration/invasion. Expression of receptor-interacting protein (RIP) 1 was initially increased upon exposure of A549, a non-small cell lung cancer (NSCLC) cell line, to IR. IR-induced RIP1 is located downstream of EGFR and involved in the expression/activity of matrix metalloproteases (MMP-2 and MMP-9) and vimentin, suggesting a role in epithelial-mesenchymal transition (EMT). Our experiments showed that IR-induced RIP1 sequentially induces Src-STAT3-EMT to promote invasion/migration. Inhibition of RIP1 kinase activity and expression blocked induction of EMT by IR and suppressed the levels and activities of MMP-2, MMP-9, and vimentin. IR-induced RIP1 activation was additionally associated with stimulation of the transcriptional factor NF-κB. Specifically, exposure to IR triggered NF-κB activation and inhibition of NF-κB suppressed IR-induced RIP1 expression followed by a decrease in invasion/migration as well as EMT. Based on the collective results, we propose that IR concomitantly activates EGFR and NF-κB and subsequently triggers the RIP1–Src/STAT3–EMT pathway, ultimately promoting metastasis.

The Semantic-Enhanced Tree-LSTM (SeT-LSTM) network, a novel advancement in the realm of linguistic modeling, marks a significant step forward from traditional Tree-based Long Short Term Memory (LSTM) networks. By intricately weaving in the nuances of typed grammatical dependencies, SeT-LSTMs offer a more nuanced understanding of language semantics. Traditional models often overlook the semantic shift caused by variations in word or phrase roles, a gap this paper aims to fill by focusing on the types of grammatical connections, or typed dependencies, within sentences. Our proposed architecture, dubbed the Semantic Relationship-Guided LSTM (SRG-LSTM), leverages a control mechanism to model the interplay between sequence elements. Additionally, we present a novel Tree-LSTM variant, the Semantic Dependency Tree-LSTM (SDT-LSTM), which integrates dependency parse structures with dependency types for more robust sentence embedding. The SDT-LSTM demonstrates superior performance in Semantic Relatedness Scoring and Sentiment Analysis compared to its predecessors. Qualitatively, it shows resilience to changes in sentence voice and heightened sensitivity to nominal alterations, aligning well with human intuition. This research underlines the pivotal role of grammatical relationships in sentence understanding and paves the way for further exploration in this domain.

Historically, the military and non-religious, sub-continent’s intellectual elite Muslim men crafted state ideologies before partition, by actively contributing to sociocultural constructs prevalent to this day, in service of colonial patriarchal concepts. The purpose of this ideological collaboration has been to mould a Pakistani cis-gendered female child primarily into the ideal “Pakistan-Muslim” bride for marriage, carried out through her family and society. This has shaped the lives of cis-gendered female children in Pakistan through coerced, gendered control, implemented as prescriptive societal norms. These norms are rooted in colonial, patriarchal ideologies and are functions of capital and coercion intentionally perpetuated to erase her pluralistic identity, reducing her to a central resource within a family unit. To the military concept of nationalism, her mind and her body symbolize the unification and centralization of a statist ideology. Thus, the military state plays an essential role in shaping her purpose around the concept of nationalism. The intellectual elite, via the state, uphold colonial, patriarchal ideologies, where the primary identity of a cis-gendered female child is actualized through her marriage. The cis-gendered female child’s life is a vehicle to unify the state’s two-fold “Pakistani-Muslim” agenda, by implementing a ‘singular statist narrative’. A narrative that attempts to resolve two major tensions: First, it seeks to wipe out the possibility of a nation connecting with and exploring its own immense, internal ethnocultural diversity. It does this by imposing an imagined “homogenous Muslim” identity — which doesn’t exist — and is instead imported from multiple countries to reinforce a ‘singular statist narrative’, Second, it aims to control the mixing of local, sociocultural narratives by bridging any disjuncture between them by labelling colonial institutional patriarchy concepts as inherently “Pakistani”; in service of its preferred ‘singular statist narrative’.

Sewage sludge is an organic waste generated in waste-water treatment plants with certain content of nutrients, what may potentially be used as a source of slow release fertilizer, especially as phosphorus (P) source, but it demands some pre-treatment and the content of P is much lower compared to soluble mineral fertilizers. For these reasons, composted sewage sludge was used to manufacture pelletized organomineral fertilizers, mixing it with the inorganic sources monoammonium phosphate (MAP) and AshDec® (ASD) (thermochemically incinerated sewage sludge). The fertilizers were physiochemically characterized and evaluated as to the dynamics of soil P solubilization, and the forms of P remaining according to its lability. The sources evaluated were: organic compost of sewage sludge powder (SSC), pelletized SSC (SCP), pelletized organominerals SSC+MAP (S+MAP) and SSC+ASD (S+ASD), ASD alone, all compared to a soluble mineral fertilizer (MAP), and a control without P fertilizer. A test was conducted in leaching columns with 50 g of soil (Oxisol), where the fertilizers were applied at a dose of 100 mg P/column, and 30 mL of water or 2% citric acid were added daily for 30 consecutive days. The collected leachates had pH measured and P content determined. Pelletizing process resulted in denser products and promoted more gradual P release. The organomineral S+MAP was the most water-soluble recycled source, solubilizing about 70% of the total P, while the others presented much lower solubilization (&lt;20%). In contrast, all fertilizers showed high solubility in 2% citric acid (except for S+ASD). After leaching, the greatest amount of P remaining in the soil as labile and moderately labile. Composting and the AshDec process produced materials with slow P solubilization, which would be favoured to solubilize in acidic soils and acidic rhizosphere conditions. In turn, the organomineral mixture of SSC with a highly soluble mineral fertilizer (MAP) resulted in a promising product with intermediate nutrient solubility, better synchronized with crop demand, thus increasing efficiency in P use.

This paper examines the transformative impact of data science, machine learning (ML), and artificial intelligence (AI) on network management in telecommunications, focusing on techniques such as network monitoring, predictive maintenance, anomaly detection, automated network configuration, and self-healing mechanisms. We analyze specific methodologies, including deep learning for anomaly detection and federated learning for predictive maintenance, and address current challenges such as data quality, system integration, and model interpretability. Emerging technologies like edge computing, federated learning, and quantum computing are explored for their potential to enhance predictive maintenance and network management. The paper provides an overview of how AI-driven solutions are revolutionizing telecom networks, offering unprecedented efficiency, reliability, and performance while highlighting the need for ongoing research to tackle complex issues of explainability and privacy.

The novel coronavirus disease (COVID-19) pandemic has resulted in over 720 million confirmed cases and 7 million deaths worldwide, with insufficient treatment options. Innumerable efforts are being made around the world for faster identification of therapeutic agents to treat the deadly disease. Postacute sequelae of SARS-CoV-2 infection or COVID-19 (PASC), also called Long COVID, is still being understood and lacks treatment options as well. A growing list of drugs are being suggested by various in silico, in vitro and ex vivo models, however currently only two treatment options are widely used: the RNA-dependent RNA polymerase (RdRp) inhibitor remdesivir, and the main protease inhibitor nirmatrelvir in combination with ritonavir. Computational drug development tools and in silico studies involving molecular docking, molecular dynamics, entropy calculations and pharmacokinetics can be useful to identify new targets to treat COVID-19 and PASC, as shown in this work and our recent paper that identified alendronate as a promising candidate. We have now investigated all bisphosphonates which can bind competitively to nidovirus RdRp-associated nucleotidyl (NiRAN) transferase domain, and systematically down selected seven candidates (CHEMBL608526, CHEMBL196676, CHEMBL164344, CHEMBL4291724, CHEMBL4569308, CHEMBL387132, CHEMBL98211), two of which closely resemble the approved drugs minodronate and zoledronate. This work and our recent paper together provide an in silico mechanistic explanation for alendronate and zoledronate users having dramatically reduced odds of SARS-CoV-2 testing, COVID-19 diagnosis, and COVID-19-related hospitalizations, and indicate that similar observational studies with minodronate could be valuable.

Presence detection is essential in many applications, including smart homes, office automation, and public space management. Traditional technologies like infrared sensors and cameras frequently encounter obstacles due to privacy concerns, high costs, and complexity. The study provides a novel way to detect presence based on Wi-Fi signals and machine learning algorithms, utilizing the ESP8266 microcontroller. The suggested system attempts to reliably detect the presence of people in a given place by evaluating fluctuations in Wi-Fi signal strength and patterns. The theoretical basis of this strategy is investigated, including the ESP8266’s capabilities for Wi-Fi data gathering and the use of machine learning methods for data analysis. The methodology includes data collecting with the ESP8266, preprocessing, feature extraction, and training and evaluating machine learning models. This system’s potential uses include smart home automation, office occupancy tracking, and crowd management in public settings. The paper also discusses potential issues such as signal interference, data privacy concerns, and the limitations of Wi-Fi detection. Future work is also mentioned, including the suggested system’s installation and testing and enhancement proposals to improve accuracy and scalability. This research aims to provide a cost-effective, non-intrusive, and scalable presence detection system by harnessing the power of Wi-Fi signals and machine learning, enhancing intelligent environments

The space exploration landscape has transformed dramatically over the past several decades, with a significant rise in the number of annual launches. Key factors driving this increase include technological advancements, the miniaturization of satellites, and the surge in commercial satellite deployments for communication, Earth observation, and remote sensing. This paper examines the evolution of space activities from the launch of Sputnik 1 in 1957 to recent developments in commercial spaceflight and international collaborations. The paper discusses notable milestones in space exploration history, including early space missions, Apollo Moon landings, and the development of space stations like the International Space Station. It highlights the shift from state-led space programs to a more diverse landscape, characterized by private sector involvement and international partnerships. The "Space 4.0" phase, beginning around 2006, has seen the entry of commercial players like SpaceX, leading to a significant upsurge in orbital launches. Statistical data reveals an upward trend in launch activity, with over 200 launches in 2023 alone, driven by large satellite constellations like SpaceX's Starlink and the steady growth of China's space program. The increasing launch rate poses challenges such as space debris, emphasizing the need for mitigation strategies. Looking ahead, the paper projects a continued increase in launch activity, with over 150 launches expected annually by 2026 and 200-300 launches by 2040. Commercial space ventures, deep space exploration, lunar activities, and space tourism drive this growth. The paper concludes by emphasizing the importance of addressing space debris and promoting the long-term sustainability of space activities, which is crucial as humanity's presence in space continues to expand.

In small animal models of severe acute respiratory syndrome coronaviruses (SARS-CoV and SARS-CoV-2) infection, ferrets (Mustela putorius furo) have been used to investigate the pathogenesis. Podoplanin (PDPN) is an essential marker in lung type I alveolar epithelial cells, kidney podocytes, and lymphatic endothelial cells. Monoclonal antibodies (mAbs) against ferret PDPN (ferPDPN) are useful for the pathological analyses of those tissues. We previously established an anti-ferPDPN mAb, PMab-292 using the Cell-Based Immunization and Screening (CBIS) method. In this study, we determined the critical epitope of PMab-292 using flow cytometry. The N-terminal ferPDPN deletion mutants analysis revealed that the Val34 is located at the N-terminus of the PMab-292 epitope. Furthermore, the PA tag-substituted analysis (PA scanning) showed that Asp39 is located at the C-terminus of PMab-292 epitope. The epitope sequence (₃₄-VRPEDD-₃₉) also exists between Val26 and Asp31 of ferPDPN, indicating that PMab-292recognizes the tandem repeat of the VRPEDD sequence of ferPDPN.

Different materials prepared by appropriate synthesis methods and applying different post syn-thesis processes could be used as hole transport layer in perovskite solar cells. In the present work annealing was employed to enhance physical properties of copper oxide (CuO) thin films fabricated via spin coating technique and the effects of annealing on their optical, morphological, electrical and structural properties were investigated. The XRD findings revealed the polycrystalline structure with large crystallite size. Optical spectra showed larger transmission beyond the visible region, reduced absorption, and a decrease in bandgap energy from 1.93 eV to 1.81 eV. The Ag phonon modes at 299.22 and 304 cm-1 shown by the Raman shift confirmed the development of the CuO pure phase. FTIR spectra showed the Cu-O stretching band at 759, 764 and 446 cm−1. SEM revealed the agglomeration of nano crystallites with large grains on the surface. The carrier type conductivity of annealed CuO thin films was determined via two-point hot probe technique. Low resistivity values were determined via four-point probe method. These findings indicate that CuO thin films may be successfully used as a companion with an n-type layer in p-n junction solar cells, and a hole transport layer in perovskite solar cells.

A seroepidemiological study conducted from 2020 to 2022 in Guangzhou, China, aimed to evaluate varicella antibody levels. We measured varicella antibody concentrations using an enzyme-linked immunosorbent assay (ELISA) in a cohort of 3,300 subjects. The mean varicella IgG-specific antibody level was 171.2 mIU/mL (95% CI: 158.9, 184.4), with an overall positivity rate of 67.00% (95% CI: 65.37, 68.60). Varicella antibody levels were found to be influenced by age, vaccination dosage, and history of varicella infection. Notably, antibody concentrations increased with age and the number of vaccinations. Subjects with a vaccination history maintained positive antibody levels six years post-vaccination. We recommend two doses of the varicella vaccine for both children and adults. Before incorporating the varicella vaccine into Guangzhou's routine immunization programs, a comprehensive assessment of disease burden and the economic viability of vaccination strategies is essential.

This paper aims to explore the potential application of Extreme Learning Machines (ELM) in stock price prediction and optimise the ELM model by combining the Crown Porcupine Optimisation Algorithm and the Adaptive Bandwidth Kernel Function Density Estimation Algorithm. Specifically, the model's root mean square error (RMSE) on the test set is 0.018, which shows its good prediction accuracy. Meanwhile, the correlation coefficient R reaches 0.968, proving the model's effectiveness in predicting stock prices. In addition, the coefficient of determination R² of the training set and test set are 0.987 and 0.954, respectively, which are not much different, indicating that the model has good generalisation ability and is not prone to overfitting. This is crucial for practical application, as it implies that the model fits the existing data well and makes accurate predictions for future data. In terms of other evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), the performance is excellent in both the training and test sets. Together, these results indicate that after optimising the ELM by introducing the crown porcupine optimisation algorithm and the adaptive bandwidth kernel function density estimation algorithm, the model can effectively capture the changing stock price pattern, thus achieving efficient and accurate stock price prediction. In summary, this study not only demonstrates the great potential of ELM in stock price prediction but also highlights the importance of improving the model performance through advanced algorithm optimisation, which provides a new way of thinking and methodology for financial market analysis. This strongly supports investors' decision-making and lays the foundation for related research.

This study explores the use of Generative Artificial Intelligence (GAI) in assessing pilot fatigue risk by integrating facial recognition and physiological signals with Inertial Measurement Units (IMUs). By leveraging IMU technology's precise, real-time data on movement and combining it with GAI's advanced data analysis capabilities, the study aims to enhance the accuracy of fatigue prediction models. The analysis reveals that while traditional classifiers like Extreme Random Trees and Random Forests offer modest performance, advanced models such as Support Vector Machines and Naive Bayes demonstrate superior recall rates, highlighting their potential to identify true positives. This integration of AI and IMUs offers a promising approach to developing comprehensive, real-time fatigue monitoring systems, improving safety and efficiency in aviation by providing actionable insights and facilitating more effective fatigue management.

The study addresses the challenge of efficiently peeling pineapples, which have a distinct elliptical form, thick skin, and small eyes that are difficult to detect with conventional automated methods. This results in significant flesh waste. To improve the process, the researchers developed an integrated system combining an enhanced BlendMask method, termed SAAF-BlendMask, and a PCP method. SAAF-BlendMask improves the detection of small pineapple eyes, while PCP ensures accurate posture adjustment for precise path planning. The system uses 3D vision and deep learning technologies, achieving an average precision (AP) of 73.04% and a small object precision (APs) of 62.54% in eye detection, with a path planning success rate reaching 99%. The fully automated electromechanical system was tested on 110 real pineapples, demonstrating a reduction in flesh waste by 11.7% compared to traditional methods. This study highlights the potential of advanced machine vision and robotics in enhancing the efficiency and precision of food processing.

In this paper, we investigate the proftability of investing in movies for retail investors, focusing on the return on investment (ROI) metric, which is calculated as revenue divided by budget. Our analysis encompasses 5000 movies from the movie database TMDB, examining factors such as production scale, genres, key actors, directors, and more. Additionally, we employ textual analysis techniques like Latent Semantic Analysis (LSA) on movie overviews and tags to incorporate movie themes. We then evaluate various supervised classifcation models including Logistic Regression, Random Forest, and Light Gradient Boosting (LightGBM), comparing their performance. Our fndings highlight the signifcance of production scale, team structure, and movie themes in identifying potential high-return opportunities for investors.

This article seeks to strengthen efforts in technological developments based on the classification of PET plastic that positively impacts sustainable development and contributes to suitable solutions in collection centers in Mexico. Three experiment designs and machine learning tools for data processing were developed. For the experimentation, three factors were considered: the bottle's size, the amount of liquids inside the bottle, and the label on the bottle. The first experiment was to identify the distance of the sensor from post-consumer PET bottles. The second experiment was to determine the detection ability of the sensor with different levels of liquids inside the bottles, and the third was to determine the detection ability on the bottle labels. A digital lux meter on a microcontroller was developed to monitor illuminance on post-consumer PET plastic when it contains liquid as it passes through a conveyor belt for processing at an average rate of three bottles per second. With the implemented methodology, liquids were satisfactorily detected inside the transparent PET bottles when they had beverages between 25% and 100% of their capacity. Finally, this paper highlights that it is possible to implement an affordable design to identify bottles with liquids for collection centers.

The ordinary 3D wave equation for nondissipative, homogeneous, isotropic media admits solutions where the point sources are permitted to move, but it does not admit solutions where the receiver is allowed to move. To overcome this limitation, the new wave equation is derived, permitting both the receiver and the source to move. This new wave equation is a generalization of the standard wave equation, and it reduces to the standard wave equation when the receiver is at rest. To derive this new wave equation, we first mathematically define a diverging spherical wave caused by the stationary point source. From this purely mathematical definition, the wave equation for the stationary source and the moving receiver is derived together with the corresponding free-space Green's function. Utilizing the derived Green's function, it was shown that this new wave equation also permits the solutions where both the receiver and the source are permitted to move. In conclusion, this paper demonstrates that, instead of an ordinary wave equation, the wave equation for the moving source and the moving receiver governs the waves emitted by the moving point sources and received by the moving receivers.

The future index prices are viewed as a critical issue for any trader and investor. For this craving, various models have shown up in the literature. The Geometric Brownian motion (GBM) is one of the popular models. This work examines four types of GBM as per the presence of memory or kind of volatility. These models include classical GBM with memoryless and constant volatility assumptions, SVGBM with memoryless and stochastic volatility assumption, GFBM with memory and constant volatility assumption, and SVGFBM with memory and stochastic volatility assumption. These models are utilized in an empirical study to forecast the future index price of Energy Sector in the Saudi Stock Exchange Market. The assessment was led by utilizing two error standards, mean square error (MSE) and mean absolute percentage error (MAPE). The outcomes showed that the SVGFBM occupies the highest level of accuracy according to smallest values of MSE and MAPE. While the accuracy of GBM come in the tail of the list models under study. These results have affirmed the positive affection of combining memory and stochastic volatility assumption into the GBM model, which agreed findings of numerous earlier works. Furthermore, the findings showed the GFBM models are more accurate than GBM models regardless of the type of volatility. While, under the same type of memory, the models with stochastic volatility assumption are more accurate than the corresponding models of constant volatility assumption. In general, all models considered in this work uncovered high accuracy through the value of MAPE ≤ 10%. This indicates of the ability of applying these models in a real financial environment. Through this empirical study, we can say that the Energy Sector in Saudi Arabia is predictable and stable and afterward we urge financial investors and stockholder to trade and invest in this sector.

New advances in creating effective and affordable copper-based catalysts for water splitting represent a promising strategy for driving sustainable energy technologies. However, ma- terials containing copper are susceptible to corrosion and agglomeration. In this study, we describe a synthesis route to produce olamine-functionalized CuO nanoparticles using the Hot Injection method. Nanoparticles were obtained and subsequently deposited on FTO substrates, undergoing thermal treatment at 500°C and 600°C in atmospheres containing O2 and N2, aiming to enhance the adhesion of the active material to the substrate and modify its oxidation state. Microstructural properties were analyzed using XRD, SEM, and TEM, while electrochemical properties were studied using CV, LSV, and EIS. Electrodes treated thermally in an N2 atmosphere at 600°C exhibited higher ESCA, indicating a greater area of active sites exposed to surface reactions and, therefore, superior catalytic activity in Hy- drogen Evolution Reaction (HER). These findings show an interesting strategy for avoiding rapid electron-hole recombination and, consequently, CuO corrosion. For Oxygen Evolution Reaction (OER), samples treated thermally in O2 atmosphere at 500°C and 600°C appear to be more effective.

Over the last few decades, advancements in astrophysics have been closely linked to the development of powerful machine-learning models that can accurately classify celestial bodies. At the same time, however, many astronomical datasets are filled with new features collected by increasingly powerful telescopes. These features can cause overfitting, clouding predictive abilities, and dampening the ability of many models to classify images. Therefore, motivation to design more efficient models has skyrocketed—aiming to optimize for lower run times and high accuracies, even with fewer provided features. In our project, we seek to optimize a convolutional neural network model using a technique known as wavelet analysis. This technique allows us to pick the key features of an astronomical image and accentuate niche details, saving time and boosting accuracy. We applied it to the MiraBest Dataset, a dataset compiled from the FIRST sky survey using a virtual telescope. In the end, after training our neural network on the original images and the five filters (approximation, horizontal, vertical, diagonal, and combined), we found that with fewer features and less overfitting, the vertical Daubechies-family wavelet filter outperformed the original runs with the unaltered images by over 10%. Our findings suggest that wavelet analysis can help harvest the most valuable features in images of celestial bodies–leading to enhanced predictions in astronomical applications and perhaps bolstering modern astrophysical theory.

To evaluate whether autologous mesenchymal cells, adipose derived stem cells and platelet-rich plasma, grafted into the supracoroideal space by surgical treatment according to Limoli retinal restoration technique (LRRT), can produce growth factors in order to exert a beneficial effect in retinitis pigmentosa (RP) patients. Twenty-one eyes underwent surgery and divided based on retinal foveal thickness ≤ 190 or >190 µm into group A and group B, respectively. The specific LRRT triad was grafted in a deep scleral pocket above the choroid of each eye. At 6-month follow-up, group B showed an improvement in residual close-up visus and sensitivity at microperimetry compared to group A. After an in-depth review of molecular biology studies concerning degenerative phenomena underlying the etiopathogenesis of RP, it can be confirmed that further research is needed on tapeto-retinal degenerations both from a clinical and molecular point of view to obtain better functional results. In particular, it is necessary to increase the number of patients, extend observation times, and treat subjects in the presence of still trophic retinal tissue to allow adequate biochemical and functional catering.

Cancer researchers are fascinated by the chemistry of diverse natural products which show exciting potential as anticancer agents. In this study, we aimed to investigate the anticancer properties of watermelon rind extract (WRE) by examining its effects on cell proliferation, apoptosis, senescence, and global gene expression in human renal cell adenocarcinoma cells (HRAC-769-P) in vitro. Our metabolome data analysis of WRE exhibited untargeted phyto-constituents and targeted citrulline (22.29 µg/mg). HRAC-769-P cells were cultured in RPMI-1640 media and treated with 22.4, 44.8, 67.2, 88.6, 112, 134.4, and 156.8 mg mL-1 for 24, 48, and 72 hrs. At 24 hrs after treatment, (88.6 mg ml-1 of WRE) cell proliferation significantly reduced, more than 34% compared to the control. Cell viability decreased 48 and 72 hours after treatment to 45% and 37%, respectively. We also examined poly caspase, SA-beta-galactosidase (SA-beta-gal), and wound healing activities using WRE. All treatments induced an early poly caspase response and a significant reduction in cell migration. Further, we analyzed the transcript profile of the cells grown at 44.8 mg ml-1 of WRE after 6 hours using RNA sequencing (RNAseq) analysis. We identified 186 differentially expressed genes (DEGs), including 149 upregulated genes and 37 downregulated genes, in cells treated with WRE compared to the control. The differentially expressed genes were associated with NF-Kappa B signaling and TNF pathways. Crucial apoptosis-related genes such as BMF, NPTX1, NFKBIA, NFKBIE, and NFKBID might induce intrinsic and extrinsic apoptosis. Another possible mechanism is a high quantity of citrulline may lead to induction of apoptosis by the production of increased nitric oxide. Hence, our study suggests the potential anticancer properties of WRE and provides insights into its effects on cellular processes and gene expression in HRAC-769-P cells.

OVOL proteins (OVOL1 and OVOL2), vertebrate homologs of Drosophila OVO, are critical regulators of epithelial lineage determination and differentiation during embryonic development in tissues such as kidney, skin, mammary epithelia, testis. OVOL inhibits EMT and can promote MET; moreover, they can regulate the stemness of cancer cells, thus playing an important role during cancer cell metastasis. Due to their central role in differentiation and maintenance of epithelial lineage, OVOL overexpression has been shown to be capable of reprogramming fibroblasts to epithelial cells. Here, we review the roles of OVOL mediated epithelial differentiation across multiple contexts – embryonic development, cancer progression, and cellular reprogramming.

This paper explores the application of machine learning in financial time series analysis, focusing on predicting trends in financial enterprise stocks and economic data. It begins by distinguishing stocks from stocks and elucidates risk management strategies in the stock market. Traditional statistical methods such as ARIMA and exponential smoothing are discussed in terms of their advantages and limitations in economic forecasting. Subsequently, the effectiveness of machine learning techniques, particularly LSTM and CNN-BiLSTM hybrid models, in financial market prediction is detailed, highlighting their capability to capture nonlinear patterns in dynamic markets. The study demonstrates the advancements in predictive accuracy and robustness achieved by deep learning methods through empirical analysis and model validation. The findings contribute significantly to academic discourse and offer practical insights for investors, financial analysts, and policymakers navigating market volatility and optimizing investment strategies. Finally, the paper outlines prospects for machine learning in financial forecasting, laying a theoretical foundation and methodological framework for achieving more precise and reliable economic predictions.

It is difficult to automatically recognize defects using digital image processing method in X-ray radiograph that tested from lap joint of unequal thickness plates. The continuous change of the wall thickness for the lap joint workpiece brings about the very different gray levels in X-ray background image. Besides, due to the shape and fixturing of the workpiece, the distribution of the weld seam in the radiograph is not vertical and results in an existence of a certain angle with the vertical direction. This makes it difficult for automatic defect detection and localization. In this paper, a method of X-ray image correction based on invariant moments is presented to solve the problem. In addition, an automatic defect detection method combined of image noise suppression, background removal, image segmentation and mathematical morphology is introduced. The results show that the proposed method can effectively recognize the gas pores in lap joint of unequal thickness and it is suitable for automatic detection.

This study employs molecular docking techniques to investigate a spectrum of targets within Escherichia coli (E. coli), with a specific focus on four key proteins: Beta-lactamase, MccE protein, Penicillin G acylase, and DNA polymerase II. The research explores the binding interactions of various natural compounds, emphasizing Hypericin. Through computational analyses, the study aims to unveil the potential interactions between these compounds and essential proteins in E. coli. Notably, Hypericin demonstrates remarkable binding energy scores of approximately -10.5 kcal/mol within the Ligand Binding Site of the specified proteins. This observation suggests Hypericin's potential as a potent modulator of E. coli targets, prompting further investigation into its antimicrobial properties and therapeutic applications.

The assessment of Histone deacetylases in conjunction with Curcumin, conducted through the Mcule Database employing the Autodock Vina algorithm, involves an examination of molecular interactions. This analysis includes the calculation of binding energies, revealing the strength and stability of the interactions between Curcumin and various Histone deacetylase targets. The comparison encompasses diverse aspects such as binding energies, specific binding sites, interactions across different targets, structural details, and potential implications for biological activity. The results imply that Curcumin shows heightened affinity for Histone deacetylase 4 in comparison to other investigated histone deacetylases.

Wireless network technologies, including 3G, 4G, and 5G, are transforming telecommunications infrastructure globally. However, the adoption and effectiveness of these technologies vary significantly across regions and industries, posing unique challenges and opportunities for Small and Medium Enterprises (SMEs). Understanding the critical factors influencing network deployment and optimization in different contexts is essential for telecom companies and business leaders. This systematic review aims to evaluate the infrastructure, performance, and strategic implications of wireless network technologies (3G, 4G, and 5G) across multiple industries and geographic regions, providing insights for SMEs and telecom companies on adopting these technologies to enhance operational efficiency and competitiveness. A comprehensive search of academic databases, including Google Scholar, Web of Science, and SCOPUS, was conducted using keywords such as “wireless network,” “3G,” “4G,” “5G,” “evaluation,” and “infrastructure.” Studies were selected based on pre-established eligibility criteria, and a risk of bias assessment was performed using the Newcastle-Ottawa Scale. Statistical synthesis and sensitivity analyses were conducted to identify key trends and challenges. A total of 121 studies were included, with the majority focusing on 5G technology (42%) and its infrastructure. Key findings highlight the importance of network densification, high-speed connectivity, and low-latency applications, particularly in urban regions. The analysis also revealed significant regional disparities in infrastructure deployment, with developing countries facing challenges in expanding coverage and integrating advanced technologies. Industry-specific customization of wireless networks is essential for sectors such as manufacturing, healthcare, and retail. Wireless network technologies present vast opportunities for SMEs, but their successful implementation requires addressing regional infrastructure gaps and tailoring solutions to industry-specific needs. Telecom companies must prioritize strategic investments in network densification, scalability, and security to fully leverage the benefits of 5G. The findings of this review provide actionable insights for business leaders and policymakers aiming to optimize wireless technology deployments for enhanced performance and competitiveness.

This paper comprehensively explores the integration of cloud computing and advanced recommendation systems, emphasizing their pivotal roles in enhancing user experiences and operational efficiencies across digital platforms. It reviews the evolution of recommendation algorithms, highlighting their application in diverse domains such as e-commerce and media. The study evaluates the performance of advanced models like UniLLMRec against traditional counterparts using datasets from news and e-commerce domains. Additionally, the paper discusses the infrastructure architecture of cloud computing, demonstrating its capability to support scalable and efficient data processing. Through experimental insights and methodology, the research underscores the transformative impact of cloud technologies on optimizing recommendation system performance, thereby advancing digital engagement and competitiveness.

The cyclic regeneration of human endometrium is guaranteed by the proliferative capacity of Endometrial Mesenchymal Stromal Cells (E-MSCs). Due to this, the autologous infusion of E-MSCs has been proposed to support endometrial growth in a wide range of gynecological diseases. We aimed to compare two different endometrial sampling methods, the surgical curettage and the Vacuum Aspiration Biopsy Random Assay, and to validate a novel xeno-free method to culture human E-MSCs. Six E-MSCs cell lines were isolated after a mechanical tissue homogenization and cultured using human platelet lysate. E-MSCs were characterized for the colony formation capacity, proliferative potential and multilineage differentiation. The expression of mesenchymal and stemness markers was tested by FACS analysis and Real-Time PCR, respectively. Chromosomal alterations were evaluated by karyotype analysis, whereas tumorigenic capacity and invasiveness were tested by soft agar assay. Both endometrial sampling techniques allowed to efficiently isolate and expand E-MSCs using a xeno-free method preserving their mesenchymal and stemness phenotype, proliferative potential and multi-lineage differentiation ability during the culture. No chromosomal alterations and invasive/tumorigenic capacity were observed. Herein we report the first evidence of efficient E-MSCs isolation and culture in Good Manufacturing Practice compliance conditions, suggesting Vabra endometrial sampling as alternative to surgical curettage.

The escalation of mining activities in the karst regions of Guizhou Province has heightened the occurrence of water-inrush incidents in deep coal mines. This study focused on water-inrush phenomena within the Xinhua mining area of Jinsha County, Guizhou Province, aiming to investigate the sources of these incidents. The findings indicated that the overlying limestone of the Changxing Formation in the coal seam served as a vulnerable aquifer under certain conditions, leading to water inrushes. The analysis of the spatiotemporal distribution patterns of water-inrush incidents at the working face indicated that previous mining operations damaged the shallow Changxing Formation limestone, resulting in the accumulation of goaf water and the formation of numerous mining-induced fractures. These fractures served as rapid conduits for water inrushes from both atmospheric precipitation and underground sources at the deep working face. The examination of surface water and mine water quality demonstrated that both exhibited similar characteristics, predominantly featuring bicarbonate, sulfate, and sodium compositions. Investigation into the relationship between mine water inflow and atmospheric precipitation established that atmospheric precipitation influenced the mine water supply cycle, with a replenishment period of ~10 months during the operational phase of the Jinyuan Coal Mine and about one month post-closure. The fractures induced by mining activities within the Changxing Formation limestone facilitated water flow, with atmospheric precipitation serving as the primary water source for the mine. This study offered a valuable scientific foundation for addressing water-related damage resulting from atmospheric precipitation in mines susceptible to water inrushes under analogous hydrogeological conditions.

first_pagesettingsOrder Article ReprintsOpen AccessArticleThe Carbon Footprint of Spanish University Websitesby Sonia Sanchez-Cuadrado ¹ andJorge Morato ^2,*1Department of Library and Information Science, Complutense University of Madrid, 28010 Madrid, Spain²Department of Computer Science, Carlos III University of Madrid, Leganes, 28911 Madrid, Spain^*Author to whom correspondence should be addressed.Sustainability 2024, 16(13), 5670; https://doi.org/10.3390/su16135670 (registering DOI)Submission received: 31 May 2024 / Revised: 22 June 2024 / Accepted: 23 June 2024 / Published: 3 July 2024(This article belongs to the Special Issue Sustainable Education in the Digital Society: Applying Communication Technologies and Digital Tools to Education) Downloadkeyboard_arrow_down Browse Figures Versions NotesAbstractThe consumption of digital content is essential for economic and scientific development, leading to a high dependence on information and communication technologies, which significantly impacts the environment. Quantifying this ecological impact is challenging due to its intangible nature and the diversity of factors. This research focuses on assessing the energy consumption and CO₂ production of Spanish university websites to evaluate their levels of digital pollution and environmental impact. The calculation of the carbon footprint considers factors related to energy consumption and the use of green or renewable energies. The findings indicate that only 17% of university websites can be considered environmentally friendly. There are significant differences in CO₂ consumption and emissions depending on whether the websites implement environmentally sound practices. This study highlights the lack of initiatives aimed at reducing energy consumption and greenhouse gas emissions associated with web information management. The study shows that Spanish universities have the potential to contribute to the reduction in CO₂ emissions by implementing sustainable development practices on their websites. Furthermore, it fosters awareness about technology usage and its environmental impact, promoting the adoption of green and sustainable approaches.

Cities are complex systems requiring urban design models that balance order and disorder. Collective creativity initiatives engage citizens in these processes, empowering bottom-up approaches that prioritize people and social well-being within urban development. This paper investigates an 'Urban Laboratory' as a case study, examining the potential of collective creativity to address urban complexity. The successful and ongoing project 'El Campo de Cebada' in Madrid, Spain, demonstrates how a community transformed a vacant lot into a vibrant social hub. The phases of this study include case selection, data collection, data analysis, and presentation of the results. This study identifies key enabling factors, including agents, management, social dynamics, infrastructure, and actions. These insights offer a methodological framework for designing future collaborative, resilient, and inclusive urban spaces, addressing the complex needs of communities within our cities.

Cybersecurity ecosystem is an important facet in protecting sensitive information and securing critical infrastructure for countering modern cyber threats. With the increasing complexity and frequency of security incidents, there is an escalating demand for development of innovative solutions beyond current human capabilities pertaining to cybersecurity measures. Artificial Intelligence or AI can be utilized in a myriad of areas of cybersecurity. It emerged as a technological innovation to enhance cyber protection by facilitating faster and real-time threat detection for known and unknown threats, automating processes to minimize human error, and optimal decision-making. Harnessing the power of AI in cybersecurity creates formidable defense capabilities against the constantly changing cyber threats of future while empowering the cybersecurity personnel with threat intelligence and proactive foresight to safeguard critical assets and confidential information with unparalleled precision and effectiveness. This research paper aims to investigate the potential of AI-enabled cybersecurity systems and focuses on deducing the benefits of using AI in enhancing cybersecurity processes for organizations seeking to manage their risk profile. Through a comprehensive literature review, the wide-ranging applications of AI in cybersecurity have been analyzed such as intrusion detection, predictive simulation, and automated emergency response management. The study examines the benefits of implementing AI-based cyber defenses such as improved promptness and accuracy in vulnerability assessment and threat management, reduced false positives, and recognize patterns. The future potential of AI in cybersecurity will take a leap forward in expanding protection mechanisms to evaluate the strengths and weaknesses of attack vectors to prevent an adversarial attack.

This research introduces a hybrid feature extraction approach that combines Linear Discriminant Analysis (LDA) and Multilayer Perceptron (MLP) methods to address the challenges of reducing feature vector dimensionality and accurately classifying smartphone-based human activities. Moreover, to refine activity classification accuracy, Support Vector Machine (SVM) optimization with Stochastic Gradient Descent (SGD) is employed. LDA, a statistical tool, is leveraged to derive a new feature space for data projection, enhancing class separation and test feature label prediction. The proposed approach, named LMSS, was evaluated using the UCI-HAR dataset and compared with state-of-the-art models. The results demonstrate that the proposed approach outperformed the best-performing method over this dataset. It achieved an accuracy rate of 99.52%, precision of 99.55%, recall of 99.53%, and an F1-score of 99.54%, highlighting the effectiveness of the proposed method in accurately classifying the data.

Sublingual vaccines offer the benefits of inducing mucosal immunity to protect against respiratory viruses, including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and influenza, while also enabling needle-free self-administration. Previously, we developed a sublingual vaccine formulated with the SARS-CoV-2 receptor binding domain antigen and Poly(I:C) adjuvant in nonhuman primates, cynomolgus macaques. This study examined the immune and inflammatory responses elicited by the sublingual influenza vaccine containing hemagglutinin (HA) antigen and Poly(I:C) adjuvant and assessed the safety of this vaccine in nonhuman primates. The Poly(I:C)-adjuvanted sublingual vaccine induced both mucosal and systemic immunities. Specifically, the sublingual vaccineproduced HA-specific secretory IgA antibodies in saliva and nasal washings, and HA-specific IgA and IgG were detected in the blood. This vaccine appeared to be safe, as judged from the results of blood tests and plasma C-reactive protein levels. Notably, sublingual vaccination neither increased the production of inflammation-associated cytokines—IFN-alpha, IFN-gamma, and IL-17—in the blood, nor upregulated the gene expression of proinflammatory cytokines—IL12A, IL12B, IFNA1, IFNB1, CD69, and granzyme B—in white blood cells. Moreover, DNA microarray analyses revealed that sublingual vaccination evoked both enhancing and suppressing expression changes in genes associated with immune-related responses in cynomolgus monkeys. Therefore, the sublingual vaccine with Poly(I:C) adjuvant is safe and creates a balanced state of enhancing and suppressing the immune-related response.

The work contains a detailed analysis of the electromagnetic field originating from a point source placed above the layered structure.The surface impedance of the multilayer dielectric structure was determined. Based on the boundary conditions, a method for determining the surface impedance of a layered structure with a perfectly conductive screen and or a magnetic screen is presented. The resonance conditions and the conditions for the excitation of surface waves are derived. The integral expression defining the generalized impedance was analyzed with a view to adapting it to numerical implementation. This allowed the development of a computer program based on which calculations were made of the effect of excitation of waves on multilayer dielectric substrates.

Conventional text style transfer (TST) methodologies primarily utilize style classifiers to segregate the content and stylistic elements of text for effective style transformation. Despite the pivotal role of these classifiers, their influence on TST techniques remains largely unexplored. This study embarks on a detailed exploration of the limitations inherent in style classifiers within current TST frameworks. We reveal that these classifiers often inadequately comprehend sentence syntax, leading to diminished performance in TST models. In response, we introduce the Syntax-Enhanced Style Transfer (SEST) model, a groundbreaking approach incorporating a syntax-sensitive style classifier. This classifier ensures that the extracted style representations robustly encapsulate syntax nuances, enhancing TST effectiveness. Rigorous evaluations across diverse TST benchmarks demonstrate that SEST significantly surpasses contemporary models in performance. Additionally, our case studies highlight SEST's proficiency in producing syntactically coherent sentences that aptly retain original content.

Based on the detailed exploration of AI techniques for the early identification of learning difficulties in education, this study underscores the pivotal role of educational data analysis in enhancing student outcomes. Educators can proactively identify and address learning challenges through systematic data collection and analysis, thereby minimizing long-term academic obstacles and emotional stress. The integration of AI, particularly through natural language processing and computer vision technologies, facilitates personalized learning paths and real-time interventions, offering tailored support that maximizes each student's learning potential. These advancements optimize educational resource allocation and foster a more inclusive and effective learning environment, paving the way for future advancements in personalized education and intelligent teaching practices.

In this paper, we introduce the (ρ,q)-analogue of the p-adic factorial function. By utilizing some properties of (ρ,q)-numbers, we obtain several new and interesting identities and formulas. We then construct the p-adic (ρ,q)-gamma function by means of the mentioned factorial function. We investigate several properties and relationships belonging to the foregoing gamma function, some of which are given for the case p = 2. We also derive more representations of the p-adic (ρ,q)-gamma function in general case. Moreover, we consider the p-adic (ρ,q)-Euler constant derived from the derivation of p-adic (ρ,q)-gamma function at x = 1. Furthermore, we provide a limit representation of aforementioned Euler constant based on (ρ,q)-numbers. Finally, we consider (ρ,q)-extension of the p-adic beta function via the p-adic (ρ,q)-gamma function and we then investigate various formulas and identities.

We propose a novel framework for understanding quantum phenomena through the lens of a modified electromagnetic spacetime governed by Einstein field equations. By assuming the existence of an electromagnetic spacetime that adheres to these modified geodesic and Einstein field equations, provided the field equations satisfy the Coulomb force law in weak-field approximations, we explore the derivation of Maxwell’s equations and highlight the necessity of incorporating spin to validate these equations. We derive the wave equation for the normalized transverse metric and establish its equivalence with the de Broglie wave proposition, suggesting a relationship between this metric and the particle's wavefunction. This approach offers a classical explanation for several quantum phenomena, including the double-slit experiment, entanglement, tunneling, non-locality, and wavefunction collapse. Additionally, it posits a method to visualize atomic orbital structures in line with quantum mechanical predictions. These findings suggest that the foundational principles of quantum mechanics can be interpreted as linearized approximations of general relativity applied to electromagnetic spacetime. While this theory does not yet extend to the correlations between general relativity, quantum electrodynamics (QED), and quantum chromodynamics (QCD), it provides a valuable visualization tool for physicists and advances our understanding of the unresolved challenges linking quantum mechanics with general relativity.

Inflammation and insulin resistance are recognized as central features in the pathology of psoriasis, profoundly affecting the lives of those affected by this condition. The relationship between lifestyle choices and psoriasis is well-documented, highlighting the potential benefits of dietary modifications and physical activity in managing the condition during exacerbations. Furthermore, growing evidence emphasizes the significant impact of nutritional interventions on various skin disorders, particularly those characterized by inflammation and insulin dysregulation. Notably, the role of dietary fats is of particular interest, with their influence on inflammation and insulin resistance in psoriasis garnering increasing attention. Moreover, the association between excessive adiposity and heightened psoriatic severity underscores the importance of weight management strategies in disease management. This review explores the intricate relationship between specific dietary fats and inflammation in psoriasis, offering a comprehensive analysis of their implications for disease progression and management.

Recommender Systems (RecSys) are crucial in managing information overload and enhancing user satisfaction across various digital platforms, including e-commerce and entertainment. Evolving from traditional models to Deep Neural Networks (DNNs) and, more recently, Large Language Models (LLMs), these systems leverage sophisticated algorithms to analyze user behaviors and preferences. LLMs, such as GPT-4, are trained on extensive datasets to comprehend and generate natural language, significantly advancing their ability to deliver personalized recommendations. This tutorial explores the transformative impact of LLMs on RecSys, discussing their development, application in handling complex datasets, and the integration of contextual insights. Real-world examples illustrate how LLMs enhance recommendation accuracy and user experience, highlighting challenges and future directions in the field.

The paper explores using generative artificial intelligence (AI) in financial market data management and forecasting. By integrating multiple data sources and feature extraction techniques, such as fundamental analysis, technical indicators, global economic data, and sentiment analysis, generative AI constructs a comprehensive deep learning framework that significantly enhances financial data management efficiency and market forecasts' accuracy. Specifically, technologies like generative adversarial networks (Gans) and variational autoencoders (VAE) demonstrate substantial data augmentation and model optimisation potential. The application value of the model in real-time market prediction and trading strategy optimization is further amplified through reinforcement learning methods.

We introduce a novel algebraic structure, termed algebra B, which generalizes Lie algebras through the definition of a non-associative and unital algebra. We demonstrate that algebra B retains essential properties such as bilinearity and antisymmetry, and satisfies both the Jacobi and Malcev identities; this occurs when the product operations of Lie and Malcev algebras are derived from the product operation defined in our algebra. By examining the connections between algebra B, Malcev algebras, and Lie algebras, we establish that algebra B effectively generalizes these structures. Furthermore, we show that algebra B can generate complex entities with a novel structure distinct from those currently known. Our findings lay the groundwork for future investigations into the practical applications and further theoretical development of this new algebraic framework.

Postpartum haemorrhage remains a significant contributor to maternal mortality and morbidity worldwide, particularly in low- and middle-income countries. PPH continues to pose challenges in both developed and developing nations despite being preventable. This review examines the current state of knowledge regarding PPH within the context of Vietnam, a country with its unique healthcare landscape. This review identifies critical areas for improvement and future research directions based on a comprehensive literature analysis, including epidemiological insights, risk factors, prevention strategies, and management approaches. Discrepancies in PPH definitions and prevalence rates across regions highlight the need for tailored interventions and policies. Understanding the specific challenges and opportunities related to PPH in Vietnam is crucial for informing evidence-based interventions and policies to improve maternal health outcomes. This review contributes to the broader global effort to reduce maternal mortality by addressing gaps in knowledge and proposing strategies to enhance PPH prevention and management in Vietnam.

Neisseria gonorrhoeae is a bacterium that causes the sexually transmitted infection gonorrhea. This study employed molecular docking simulations to investigate potential interactions between natural molecules and the Fe(3+) ions import ATP-binding protein fbpC in Neisseria gonorrhoeae FA 1090. Among the screened molecules, Hypericin exhibited a remarkable binding energy of approximately -13.2 kcal/mol, indicating a robust and favorable binding affinity with the target protein. This promising outcome suggests that Hypericin could serve as a potential candidate for further exploration as a therapeutic agent against Neisseria gonorrhoeae infections. However, it is imperative to emphasize that these in silico findings necessitate experimental validation through in vitro and in vivo studies to ascertain the actual efficacy and safety of Hypericin as a treatment option.

The study focused on Molecular Docking simulations using Autodock Vina in conjunction with the Pyrx program to investigate potential drugs and natural compounds against the Bcr-Abl oncoprotein. From initial virtual screening of 200 compounds, 11 potential candidates were selected for further evaluation. Additional investigations included toxicity prediction using the pKCSM server. Among the compounds, Diosmin and Hesperidin showed high maximum tolerated doses in humans and low toxicity. Conversely, only Eltrombopag among the selected drugs exhibited excellent parameters, albeit with a potential risk of liver damage at high dosages. These findings underscore the need for comprehensive evaluation of efficacy and safety in the development of potential treatments targeting the Bcr-Abl oncoprotein.

For the first time, this study identified seven potential ligands with high binding energies through the docking approach against the Crystal Structure of human Melanoma-associated antigen B1 (MAGEB1). The ligands—Imatinib, Bafatinib, Diosmin, Ginkgetin, Bilobetin, Amentoflavone, and Hypericin—showing strong binding energies around -10 kcal/mol, indicate a potentially robust interaction with the target protein. Moreover, the additional step of predicting toxicity properties using the pKCSM server provides valuable insights into the safety profiles of these ligands. From these results, the parameters for Diosmin: —Max. tolerated dose (human),- Oral Rat Acute Toxicity (LD50), and - Oral Rat Chronic Toxicity (LOAEL)—suggest a favorable safety profile, making it a potential candidate for further consideration against Melanoma. Moving forward, it's advisable to conduct further experimental validations, including in vitro and in vivo studies, to confirm the efficacy and safety of Diosmin and other potential ligands. Consideration of pharmacokinetics, metabolism, and specific mechanisms of action will contribute to a comprehensive understanding of these compounds' potential as therapeutic agents.

Thoracic aortic aneurysms (TAAs) pose critical health risks, often asymptomatic until a rupture or dissection occurs. Guidelines recommend surgical repair based on specific aortic diameters and risk factors, emphasizing the importance of early detection and intervention. Despite established clinical risk factors for early detection in TAAs, the influence of racial and ethnic disparities on TAAs remains underexplored. This study aims to provide a comprehensive summary of existing research on racial and ethnic disparities in the presentation and outcomes of TAAs. Methods: This literature review was conducted using a systematic search strategy, in order to explore racial and ethnic differences in the presentation and surgical outcomes of patients with TAAs. Results: The findings demonstrated that black patients were younger at presentation and had a higher incidence of ruptured TAAs than non-black patients. Furthermore, compared to non-black patients, black patients had higher rates of cardiac arrhythmia and COPD, as well as comorbidities such as diabetes, hypertension, and renal insufficiency. For black patients undergoing open surgery, the surgical results showed improved 5-year survival rates after repair but higher perioperative mortality rates. All-cause or in-hospital mortality did not significantly differ between the racial groups, according to four studies. Discussion: This review highlights significant racial and ethnic disparities in TAA presentation and outcomes, underscoring the need for personalized risk stratification models. Standardized racial and ethnic definitions are essential for consistent and reliable research. Future studies should focus on refining risk assessment models to enhance diagnostic and therapeutic strategies, ultimately improving patient outcomes across diverse populations.

Jujube is the homology of medicine and food, and polyphenols are key compounds that determined the functional effects of jujubes. In this study, characteristic polyphenols in 15 varieties of Chinese jujubes were investigated based on untargeted metabolomics. Result showed a total of 79 characteristic polyphenols were identified in 15 varieties of Chinese jujube, and 55 characteristic polyphenols such as syringetin, spinosin and kaempferol were reported for the first time. Scopoletin (63.94% in LZYZ), pectolinarin (22.63% in HZ) and taxifolin (19.69% in HZ) contributed great and presented significant (p<0.05) differences in 15 varieties of Chinese jujubes. HZ can be characterized by pectolinarin, erianin and wogonoside. While, XSHZ, NYDZ and RQHZ with similar polyphenol profile were characterized by (+)-catechin, combretastatin A4 and tectorigenin. JSBZ, HMDZ, TZ, JCJZ and HPZ had similar polyphenol profile of galangin, isoferulic acid and hydroxysafflor yellow A. In conclusion, metabolomics is critical to grasp the fully nutritional components of jujubes, and the differences of polyphenol profiles and characteristic individual polyphenol of 15 varieties of Chinese jujubes can be well analyzed by principal component analysis (PCA).Jujube is the homology of medicine and food, and polyphenols are key compounds that determined the functional effects of jujubes. In this study, characteristic polyphenols in 15 varieties of Chinese jujubes were investigated based on untargeted metabolomics. Result showed a total of 79 characteristic polyphenols were identified in 15 varieties of Chinese jujube, and 55 characteristic polyphenols such as syringetin, spinosin and kaempferol were reported for the first time. Scopoletin (63.94% in LZYZ), pectolinarin (22.63% in HZ) and taxifolin (19.69% in HZ) contributed great and presented significant (p<0.05) differences in 15 varieties of Chinese jujubes. HZ can be characterized by pectolinarin, erianin and wogonoside. While, XSHZ, NYDZ and RQHZ with similar polyphenol profile were characterized by (+)-catechin, combretastatin A4 and tectorigenin. JSBZ, HMDZ, TZ, JCJZ and HPZ had similar polyphenol profile of galangin, isoferulic acid and hydroxysafflor yellow A. In conclusion, metabolomics is critical to grasp the fully nutritional components of jujubes, and the differences of polyphenol profiles and characteristic individual polyphenol of 15 varieties of Chinese jujubes can be well analyzed by principal component analysis (PCA).

On-chip integration of 2D materials with exceptional optical properties provides an attractive solution for next-generation photonic integrated circuits to address the limitations of conventional bulk integrated platforms. Over the past two decades, significant advancements have been made in the interdisciplinary field of 2D material integrated photonics, greatly narrowing the gap between laboratory research and industrial applications. In this paper, we provide a perspective on the developments of this field towards industrial manufacturing and commercialization. First, we review recent progress towards commercialization. Next, we provide an overview of cutting-edge fabrication techniques, which are categorized into large-scale integration, precise patterning, dynamic tuning, and device packaging. Both the advantages and limitations of these techniques are discussed in relation to industrial manufacturing. Finally, we highlight some important issues related to commercialization, including fabrication standards, recycling, service life, and environmental implications.

Introduction: Early childhood caries is a significant public health problem among preschool children. The COVID-19 pandemic, which restricted dental services, altered family dynamics, affected child development, and created new demands on caregivers, may have exacerbated this issue. Objective: Analyze the occurrence and severity of caries and identify associated factors in preschool children attending public municipal daycare centers in Salvador, Bahia, Brazil, Methodology: A cross-sectional epidemiological study was conducted to investigate caries in children aged 2 to 5 years in 2022 and 2023. Caries was assessed using the decayed, extracted, filled, and missing teeth (DEFT) index, and prevalence was calculated. Sociodemographic, family dynamic, dietary, and healthcare utilization data were obtained through a questionnaire. After descriptive and exploratory analyses, robust Poisson multivariable regression was conducted at a 5% significance level, considering socioeconomic, psychosocial, and behavioral aspects. Results: A total of 440 preschoolers participated, with a mean age of 55 months. The majority were Black or mixed race (88.86%) and female (52.73%). The prevalence of caries was 30.91% and the mean DEFT score was 1.1. In the multivariable analysis, low family income (up to one minimum wage) and not playing outdoors during the pandemic were associated with a higher prevalence of caries. Conclusion: The prevalence and severity of caries remain high, and the pandemic has contributed to the maintenance of this epidemiological scenario. Public health measures and policies promoting social justice should be planned to improve oral health conditions in childhood.

In this study, we propose a conservative and compact finite difference scheme designed to preserve both the mass change rate and energy for solving the sixth-order Boussinesq equation with the surface tension. Theoretical analysis confirms that the proposed scheme achieves second-order accuracy in temporal discretization and fourth-order accuracy in spatial discretization. The solvability, convergence, and stability of the difference scheme are rigorously established through the application of the discrete energy method. Additionally, a series of numerical experiments are conducted to illustrate the effectiveness and reliability of the conservative scheme for long-time simulations.

This study employs molecular docking simulations using Autodock Vina with the Pyrx program to investigate the binding interactions between Human prostate-specific antigen (PSA) and natural compounds, namely Amentoflavone, Ginketin, Rutin, and Hypericin. The evaluation reveals excellent binding energy values, averaging around -10 kcal/mol, suggesting robust and favorable interactions between the selected compounds and PSA. The results provide valuable insights into the potential therapeutic applications of these natural compounds in the context of prostate cancer treatment or management. Further experimental validation is warranted to confirm these findings and explore the viability of these compounds as promising candidates for innovative therapeutic strategies. This research contributes to the growing body of knowledge in the field of molecular docking and its applications in drug discovery and bioinformatics.

Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), coronavirus disease 2019 (COVID-19)-induced infection is strongly associated with various coagulopathies that may result in either bleeding and thrombocytopenia or hypercoagulation and thrombosis. Thrombotic and bleeding or thrombotic pathologies are significant accompaniments to acute respiratory syndrome and lung complications in COVID-19. Thrombotic events and bleeding, often occurs in subjects with weak multiple risk factors and co-morbidities. Of particular interest are the various circulating inflammatory coagulation biomarkers involved directly in clotting, with specific focus on fibrin(ogen), D-dimer, P-selectin and von Willebrand Factor (vWF). Central to activity of these biomarkers are their receptors and signaling pathways on endothelial cells, platelets and erythrocytes. In this review, we discuss vascular implications of COVID-19, and relate this to circulating biomarker, endothelial, erythrocyte and platelet dysfunction. During the progression of the disease, these markers may either be within healthy levels, upregulated or eventually depleted. Most significant is that patients need to be treated early in the disease progression, when high levels of vWF, P-selectin and fibrinogen are present with still low levels of D-dimer. Progression to vWF and fibrinogen depletion with high D-dimer levels and even higher P-selectin levels, followed by the cytokine storm, will be indicative of a poor prognosis. We conclude by looking at point-of-care devises and methodologies in COVID-19 management and suggest that a personalized medicine approach should be considered in the treatment of patients.

In this paper, we introduce a new class of $(p,q)$-analogue type of Fubini numbers and polynomials and investigate some properties of these polynomials. We establish summation formulas of these polynomials by summation techniques series. Furthermore, we consider some relationships for $(p,q)$-Fubini polynomials associated with $(p,q)$-Bernoulli polynomials, $(p,q)$-Euler polynomials and $(p,q)$-Genocchi polynomials and $(p,q)$-Stirling numbers of the second kind.

My innovative entropy measure, Ismail entropy, namely, (IE) is an ultimate generalization to numerous generalized entropies within the literature. The fractal dimension of IE is identified in this paper. On another applicative note, this paper offers some potential applications of fractal dimension to smart cities. The paper ends with closing remarks combined with some challenging open problems and the next phase of research.

This perspective describes the current status and future prospects of developing long- to ultralong-acting anti-obesity peptides. First, we review the current status of lipidation, PEGylation, and Fc fusion technologies to obtain long-acting peptides administered once weekly. Next, we describe the approach and current results of using macromolecular peptide prodrugs with pre-programmed releasable linkers to achieve longer-acting peptides that can be administered weekly or monthly. Finally, we posit novel modifications of the latter technology that could provide ultralong half-lives of over one month by advantageously exploiting the clearance rates of released peptides. As examples, we posit that prodrugs of low-clearance GLP-1 agonists derived from peptides already modified by lipidation, PEGylation, and Fc fusion could produce ultralong-acting agonists with dosing intervals reaching 3 to even 6 months.

Background/Objective: The Dark Web has played a pivotal role in the progress and sophistication of cybercrime. It provides an incubation network beyond the reach of traditional search engines where cybercriminals create and display exploit kits, offers illicit goods and services, and exchange confidential insider intelligence. Cybercriminals are extremely adept at selecting targets, applying tools to achieve their objectives, and minimizing red tape. The increasing sophistication of cybercriminals and the exponential rise of cybercrime against critical infrastructure underlines the necessity of identifying emerging threats. The objective of this research is to investigate the evolving threats within the Dark Web including crimeware-as-a-service and the integration of AI/ML into cyberattacks to inform risk management strategies and strengthen security measures.Research Problem: The exponential rise in cybercrime against critical infrastructure reflects growing sophisticationpresenting a significant challenge to organizations and society. The motivation behind cybercrime is fundamentally driven by self-greed which has contributed drastically to the magnitude and changes in methods used by cybercriminals to enhance profitability. The impact of cybercrime on business organizations presents an adverse impact on society and carries significant risks for the progress of individuals and the world at large. As cybercriminals adopt new technologies and services such as crimeware-as-a-service, identifying emerging trends becomes crucial to developing proactive strategies for the detection and prevention of cyber threats.Methodology: This research employs a systematic literature review approach to analyze emerging trends in cybercrime originating from the Dark Web. The review includes scholarly articles, news sources, and blog posts sourced from platforms like Google Scholar, IEEE Xplore, and various libraries. The key focus is to answer questions regarding the relationship between the Dark Web and cybercrime, accelerating cybercrime activities, and the benefits and implications of these new trends.Results: Key findings of this paper range from the rise of crimeware-as-a-service attacks and the increasing use of artificial intelligence and/or machine learning capabilities by cybercriminals to automate attacks across various businesses and organizations are also propounded along with information related to entry points and cybercrime attack pathways. The emergence of sophisticated cybercrime techniques, including ransomware-as-a-service, targeted attacks using AI, and exploitation of IoT vulnerabilities, are identified as critical trends. Social engineering, malware, and the rise of remote work have expanded the attack surface for cybercriminals.Discussion: As the use of cybercrime continues to metamorphose, the identification of new threats and extrapolation of emerging trends is critical to investigate the challenges associated with the monitoring and detection of illegitimate activities on the Dark Web as well as for the establishment of proactive risk management strategies and implementation of robust security measures. The research highlights the transformation of cybercrime into a structured and scalable ecosystem driven by technological advancements and service-based attack models. Cybercriminals now leverage AI/ML increasing the sophistication and success of their attacks. The commoditization of cybercrime has enabled less skilled individuals to participate amplifying the volume and diversity of threats faced by organizations.Conclusion: As cybercrime continues to evolve and adopt emerging technologies, organizations must remain vigilant and adaptable. The findings emphasize the need for proactive risk management, continuous monitoring of cybercrime trends, and robust security measures to mitigate the increasing threats originating from the Dark Web. Future research should focus on deeper exploration of AI-driven attacks and the development of more advanced countermeasures to safeguard critical infrastructure.

ABSTRACTCaffeine is known to impair both growth and development in the model social amoeba Dictyostelium discoideum. However, little is known about the transcriptional regulation in response to caffeine treatment in Dictyostelium. In yeast cells, caffeine mimics the effect of rapamycin in inhibiting mTORC1 activity. Here, we investigated whether changes in growth and development of Dictyostelium in the presence of caffeine or rapamycin are mediated by mTORC1 and/or mTORC2. Results: Treatment of wild type cells with caffeine or rapamycin resulted in severely impaired growth and in the formation of small aggregates in comparison to untreated cells. rip3‾ cells (lacking a mTORC2 component) and lst8‾cells (lacking a component of both mTOR complexes) showed higher and slower growth rates, respectively, in comparison to parental AX3 cells. Parental AX3 and rip3‾ cells displayed similar sensitivity to rapamycin and caffeine, while lst8‾ cells were more sensitive to these compounds. When developed in the presence of caffeine or rapamycin, AX3 cells formed aggregates after a long delay. Caffeine did not alter the morphology of small rip3‾ mounds and lst8‾ cells continued to remain solitary. However, in the presence of 1µM rapamycin rip3‾cells failed to form mounds while AX3 cells streamed and formed aggregates. Complementation of lst8‾ cells with the wild type lst8 gene rescued the aggregation phenotype. cDNA microarray analysis to uncover the spectrum of differentially regulated genes in response to caffeine treatment revealed 104 and 193 differentially expressed genes at 3-and 6-hours of development, respectively, in comparison to control cells. After 6 hours of starvation in the presence of caffeine, 25 genes encoding ribosomal proteins and 4 encoding elongation factors were up regulated. Furthermore, we identified that upon caffeine treatment at the 3- and 6-hours’ time points, 12 (10 up and 2 down) and 21 (13 up and 8 down) genes, respectively, encoding proteins involved in early development. Conclusion: We conclude that caffeine and rapamycin have similar effects in decreasing the growth rate and in reducing the aggregate size of D. discoideum. Studies with lst8‾ and rip3‾ cells suggest that these compounds affect cell growth and aggregation by acting on both mTOR complexes, mTORC1 and mTORC2. Analysis of the differentially expressed genes after caffeine treatment suggests a key role of these genes in growth and aggregation.

Optimizing warehouse operations and leveraging data insights are driving forces behind the rise of automation. This research explores the development of the SWIF warehouse management system (WMS) using the Agile methodology, a departure from traditional software development approaches. Agile’s iterative and step-by-step approach facilitated a streamlined development process. It allowed for quick adjustments based on evolving needs and feedback from stakeholders. This resulted in a substantial decrease in development time and a noticeable improvement in the final product’s quality. The study emphasizes the positive effect of Agile on teamwork and customer satisfaction. Developers reported enhanced communication and collaboration, leading to a stronger sense of ownership and engagement. Customers expressed greater satisfaction with the features and functionalities delivered in stages. The successful implementation of Agile in SWIF’s development showcases its effectiveness in managing complex and ever-changing software development projects. The study encourages the adoption of Agile methodology for similar projects to boost responsiveness, quality, and overall project success.

The adoption of Data Networks and Application Programming Interfaces (APIs) has become crucial for small and medium enterprises (SMEs) to streamline operations, improve efficiency, and reduce costs. However, SMEs often face challenges such as resource limitations and security vulnerabilities, which hinder their ability to fully leverage these technologies. This systematic review examines the role of Data Networks and APIs in enhancing operational efficiency within SMEs, focusing on key metrics such as speed, cost reduction, scalability, and security challenges. Following PRISMA 2020 guidelines, we conducted a systematic search across multiple databases including Web of Science, Scopus, IEEE Xplore, and Google Scholar. Studies published between 2014 and 2024, focused on SMEs and addressing the role of Data Networks and APIs in operational efficiency, were included. A total of 49 studies met the inclusion criteria and were analyzed for key outcomes related to operational efficiency, cost-effectiveness, and security risks. The review found that Data Networks and APIs significantly improve operational efficiency by increasing process speed (12% increase), reducing operational costs (8% reduction), and enhancing overall productivity. However, security challenges, particularly related to API vulnerabilities, were a major concern, with cyberattacks on APIs increasing by 400% in Q1 2023 alone. Despite these risks, the benefits of implementing Data Networks and APIs in SMEs, particularly in terms of scalability and real-time data processing, were evident across industries. Data Networks and APIs offer substantial improvements in operational efficiency for SMEs, though security remains a significant challenge. Future efforts should focus on developing security frameworks tailored to SMEs while maintaining the operational benefits of these technologies. Further research is needed to explore scalable and secure API models for SMEs.