Abstract: The production of biosolids in Brazil has increased due to the expansion of Sewage Treatment Plants, making these materials a sustainable alternative for agricultural use. Composed of high organic matter and nutrients such as nitrogen, phosphorus, calcium, and magnesium, biosolids have the potential to improve the physical, chemical, and biological properties of tropical soils, contributing to greater fertility, water retention, and microbial activity. National literature demonstrates that these materials can par-tially replace mineral fertilizers and assist in the recovery of degraded areas. On the other hand, the presence of contaminants still represents a challenge. Heavy metals such as Cd, Pb, Ni, and Hg generally appear in low concentrations, while Cu and Zn tend to approach the maximum limits established by CONAMA Resolution No. 498/2020. Regarding pathogens, the efficiency of sanitization depends on the treatment method employed. Emerging organic pollutants, including pharmaceuticals and hor-mones, have been detected, but still lack specific regulations in Brazil. Thus, although biosolids present high agronomic potential, their safe use requires adequate monitor-ing, improvement in controlling the origin of sewage, and advances in legislation, es-pecially regarding emerging organic pollutants.

Abstract: The fabrication of complex architectures remains a central challenge in 3D bioprinting, where low mechanical properties of hydrogels restrict the range of feasible geometries. Four-dimensional (4D) bioprinting can mitigate these limitations by introducing programmed structure shape-morphing in response to external stimuli. However, in most existing approaches, shape-morphing behavior is introduced after hydrogel formation, limiting the complexity of the resulting deformation. Here, a proof-of-concept strategy is presented, in which shape-morphing is directly encoded during fabrication. By modulating light exposure time layer-by-layer in vat photopolymerization, spatial variations in crosslinking density are introduced in situ within GelMA hydrogel constructs. Upon immersion in aqueous media, these variations generate differential swelling, leading to controlled bending of the printed structures. This approach enables the programming of deformation pathways at the printing stage, without requiring additional materials or post-processing steps. The morphing behavior was further supported by finite element simulations, which reproduced the experimentally observed deformation and enabled prediction of the shape change. Overall, this study demonstrates that swelling-driven actuation can be encoded during fabrication. Although demonstrated on simplified geometries, this approach provides a versatile framework for process-driven shape morphing programming and represents a step toward more spatially resolved and potentially volumetric 4D bioprinting strategies.

Abstract: Globally, the burden of breast cancer remains high as it is the most prevalent malignancy among women and a major contributor to cancer mortality, with therapeutic success often limited by drug resistance, treatment toxicity, and tumor heterogeneity. Cold atmospheric plasma (CAP), a partially ionized gas enriched in reactive oxygen and nitrogen species (RONS) electromagnetic waves and ultraviolet radiation, has emerged as a selective antitumor therapy, inducing cancer-specific cytotoxicity while sparing normal tissue. Here, we review the mechanisms of CAP action against breast cancer, including RONS-mediated oxidative stress, mitochondrial disruption, apoptosis, immunogenic cell death, and suppression of metastatic and angiogenic pathways. Notably, This approach selectively targets therapy-resistant breast cancer stem cells and sensitizes the highly aggressive forms, particularly triple-negative breast cancer (TNBC). Its synergy with drug therapy, radiotherapy, immunotherapy and surgery further broadens therapeutic potential. Advances in delivery platforms, such as plasma-activated media, nanoparticles, and hydrogels, address CAPs instability and enhance tumor penetration. Despite promising preclinical results, clinical translation faces barriers such as the short half-life of RONS, device standardization, and unresolved immunomodulatory effects. Overcoming these challenges through interdisciplinary collaboration and optimized protocols may unlock the potential of CAP for precision oncology.

Abstract: In bipedal stance the central nervous system implements a pre-programmed ankle strategy to maintain upright balance and respond to internal perturbations. This strategy comprises a synchronized common neural drive delivered to synergistically grouped muscles. This study evaluated the normalized mutual information (MI) between surface electromyographic (EMG) signals of unilateral and bilateral homologous muscle pairs of the lower legs during various quiet standing tasks in normal healthy adults. The leg muscles examined included the right and left tibialis anterior (TA), medial gastrocnemius (MG), and soleus (S). MI, an information-theoretic measure that quantifies the reduction in uncertainty in predicting a signal from another known signal,, was estimated using MATLAB toolbox Mutual Information Distance and Entropy Reduction (MIDER). This method for inferring network structures from shared information between two signals was applied to pairs of filtered EMG signals in the alpha (8 – 13 Hz), beta (13 – 30 Hz), and gamma (30 – 100 Hz) neural frequency bands for feet together and feet tandem stances, under eyes open and eyes closed conditions. Results showed that normalized MI was greater in the medial gastrocnemius and soleus muscle pairs across the beta, lower gamma, and upper gamma frequency bands in the tandem standing posture under both eyes open and eyes closed conditions, and generally increased in antagonistic muscle pairs in less stable standing positions. It appears that functional muscle synergies are more important than limb dominance in tandem standing. Significant inter-trial and inter-participant variability is consistent with biological differences and control of a complex system. Our results suggest that the use of MI analyses in the clinical testing of tandem standing tasks might be a useful adjunct for persons with standing balance impairments.

Abstract: Robotic assistance in minimally invasive surgery has significantly improved precision and dexterity; however, many supportive tasks, such as blood aspiration, still rely on manual operation. This work presents the design and implementation of an autonomous robotic aspirator capable of detecting and removing intraoperative bleeding without continuous human intervention. The proposed system integrates a perception module based on a convolutional neural network for real-time blood segmentation, a task planner for high-level actions execution, and a control strategy based on artificial potential fields for autonomous navigation. Additionally, a mixed-reality human–robot interaction interface is incorporated to enable system supervision and seamless transition to teleoperation when required. The system was experimentally validated with a set of in-vitro experiments under three representative bleeding scenarios, evaluating four suction strategies based on the computation method for the target selection. Results demonstrate fast reaction times (below 0.04 s) and high blood removal rates (above 80% in all cases). The comparative analysis reveals that the performance of the suction strategies is scenario-dependent and highlights a trade-off between suction efficiency and removed area. These findings support the feasibility of autonomous robotic aspiration and provide insights into the design of adaptive strategies for surgical assistance, contributing toward increased autonomy and improved workflow efficiency in minimally invasive procedures.

Abstract: Introduction: Portable non-enzymatic electrochemical glucose sensors offer potential for decentralized healthcare and medical education; however, their integration into clinically meaningful teleconsultation workflows remains limited. This study presents the functional integration of a portable copper-modified electrochemical glucose sensor into a rural web- and Android-based telemedicine platform within a simulation-based medical education framework. Materials and Methods: Screen-printed carbon electrodes were electrochemically activated and modified via copper electrodeposition. Electrochemical characterization was performed using cyclic voltammetry to identify the glucose oxidation region and chronoamperometry for quantitative detection. Glucose solutions in PBS (pH 10) were measured using 70 µL samples, and the resulting signals were converted into glucose values (mg/dL) through a calibration model and incorporated into simulated gynecological teleconsultation workflows. Results: The sensor exhibited a stable amperometric response at +0.60 V, with a linear range of 3.125–50 mM (R2 = 0.9822), an area-normalized sensitivity of 0.061 µA·mM−1·cm−2, and a limit of detection of 1.39 mM. Implementation within the simulation scenario (n = 26) demonstrated 69% high/very high perceived usability and 88% high/very high educational value. Conclusion: These results support the feasibility of integrating portable electrochemical sensing into teleconsultation-based training environments and establishing a practical framework for future validation and deployment in rural telemedicine applications.

Abstract:

Methicillin-resistant Staphylococcus aureus (MRSA) is a major global health threat responsible for significant morbidity and mortality, accounting for approximately 19,000 deaths annually in the United States. MRSA resistance is primarily mediated by the mecA and mecC genes, which are carried on the staphylococcal cassette chromosome mec (SCCmec) integrated at the OrfX locus of Staphylococcus aureus, resulting in reduced susceptibility to β-lactam antibiotics. Rapid and accurate diagnostic methods are therefore essential to improve clinical outcomes and limit disease transmission. This mini-review evaluates current MRSA diagnostic approaches, including polymerase chain reaction (PCR) and its variants, isothermal amplification techniques (LAMP and RPA), CRISPR-based diagnostics, and electrochemical biosensors. These methods are compared in terms of diagnostic accuracy, clinical utility, cost-effectiveness, and practical limitations. Overall, isothermal amplification demonstrated a more favorable balance in cost-effectiveness and practical limitations compared to other methods. However, when considering clinical utility and diagnostic accuracy, the results were context dependent. No single method was universally optimal, and the choice of diagnostic approach depends on the clinical context and resource availability.

Abstract: Real-time ultrasound imaging through sonolucent cranial implants is an emerging modality for post-neurosurgical monitoring of the adult brain, but quantitative interpretation remains challenging due to speckle, attenuation, shadowing, and the difficulty of consistently delineating thin anatomical landmarks. We present a deep learning system developed at Longeviti Neuro Solutions for segmenting key intracranial structures–the ipsilateral and contralateral lateral ventricles and the cranial midline–in coronal-plane adult cranial ultrasound images from patients with Longeviti ClearFit^® Acoustic Brain Interface (ABI)TM implants. Our dataset comprises 456 proprietary, de-identified ultrasound frames (JPEG with known pixel spacing) annotated in CVAT with ventricle and midline labels. We benchmark multiple encoder–decoder segmentation architectures and address severe class imbalance via class-weighted optimization, test-time augmentation (horizontal flip with left–right label swapping), and class-specific post-processing to reduce spurious components and improve mask coherence. The best-performing configuration achieves a foreground macro Dice of 0.869 on a held-out test set, with ventricle Dice values above 0.92 and midline Dice of approximately 0.75. Finally, we transform predicted masks into geometry-based metrology by estimating maximal perpendicular ventricle spans and ventricle-to-midline distances, producing standardized measurement overlays suitable for downstream review.

Abstract: Quadriplegic (QP) patient’s emotion detection using Electroencephalogram (EEG) signal is challenging due to daily medication. During medications of existing QP patients, EEG signal bands such as: alpha, theta, and beta bands overlaps. Accurate detection of emotion from overlapped EEG band signal is the first major problem here. EEG signal acquisition during medication is termed as Pharmaco-EEG (pEEG) Emotion recognition methods used in drug-free EEG signals are not suitable for pEEG based emotion recognition. Quadriplegic patient’s pEEG signal has second major problem is the artefacts such as involuntary spasms, respiratory artefacts, or caregiver interactions. To solve the above two major problems RBMT frame work is proposed, which consists of SiO2, Nano Coated electrode and optimized algorithms to detect emotion from pEEG signals. Sio2 Nano coating-based graphene EEG electrode is developed in this paper which prevents overlapping of bands. pEEG signals are pre-processed with Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT) and removes the artefacts in pEEG signal. In this paper, proposed Rehabilitation BCI system with music therapy (RBMT) framework consists of Secretary Bird Optimization Algorithm (SBOA-LSTM) for spatial and temporal feature extraction. The RBMT framework consists of a Mutual-Cross-Attention mechanism tuned by SBOA and integrated with a SoftMax layer to classify the emotional states as anxiety or depression more effectively based on measurement of valence and arousal levels. Based on predicted emotion levels, a music therapy module in RBMT proposed framework is triggered through BCI audio interface to play songs. Hearing the play, QP patients reduces their anxiety / depression levels. The framework continuously evaluates emotion level through measuring valence and arousal levels, and dynamically changes the songs from the play list using the Fisher-Yates Shuffle algorithm to reduce emotions levels. The proposed RBMT performs as personalized therapeutic devices for QP patients. The emotion prediction accuracy of proposed RBMT framework is 98%, when compared to other traditional methods and EEG sensors.

Abstract: Martian missions call for renewable supply chains of the 3D-printable bioplastic polyhydroxybutyrate (PHB). Because food waste can be anaerobically digested into methane and then bacterially converted into PHB, we analyzed the possible coupling between food waste produced from exploration life support (ELS) crop cultivation and the space biomanufacturing process of PHB generation. We designed 45,116 nutritionally complete menus from 23 ELS crops, calculated how much PHB demand each menu attains after its crop waste is converted into PHB (loop closure), evaluated menu crop cultivation and PHB generation costs using the equivalent system mass (ESM) metric, and contrasted ESM cost with that of existing Mars menus, which include shipped foods. We demonstrate that our menus meet astronaut macronutrient and energy requirements, yield up to 10 times the daily PHB required for a 600-day crewed Mars mission, and have 19-32% lower ESM cost per unit loop closure than previous Mars menus.

Abstract: Radiotherapy response assessment commonly relies on scalar imaging metrics that may fail to capture spatially structured tumor dynamics. When tumor regions interact, such measures can obscure underlying coordination. We introduce a predictive framework to detect non-independent dynamics from longitudinal imaging data. The approach quantifies predictive improvement from spatial information using the Tumor Coupling Index (TCI), defined as the normalized reduction in prediction error between independent and spatial models. Simulations show that TCI remains near zero under independence, increases with spatial coupling, and collapses under randomization, demonstrating sensitivity and falsifiability. In contrast, conventional scalar metrics are insensitive to such structure. TCI provides a model-agnostic observable of non-independent tumor behavior, offering a principled approach for analyzing longitudinal imaging and potential applications in adaptive radiotherapy.

Abstract: Paper-based electrochemical diagnostics have emerged as promising point-of-care (POC) platforms for rapid, low-cost analysis of biofluids such as blood, urine, and saliva. Compared to colorimetric assays, electrochemical detection enables more objective and quantitative readouts while preserving portability and capillary-driven operation. This review summarizes recent advances in integrating electrochemical sensing with paper-based platforms, including microfluidic paper-based analytical devices and lateral flow systems, and their use in techniques such as voltammetry and impedance spectroscopy. Key challenges to reliable performance in real samples are identified, particularly biological matrix effects and miniaturization constraints. Variability in biofluid composition, including hematocrit, protein fouling, ionic strength, and viscosity, can disrupt mass transport, alter conductivity, and reduce signal stability. These effects are amplified in miniaturized systems, where small volumes and reduced electrode areas increase susceptibility to noise and drift. Engineering strategies to address these challenges include antifouling surface modifications, integrated sample conditioning, electrode optimization, redox-mediated signal enhancement, and matrix-aware calibration. Overall, future progress depends on integrated device designs that ensure reproducibility and reliability in real-world samples.

Abstract: Consolidated bioprocessing (CBP) has been widely studied as an integrated route for converting biomass into biofuels and bioproducts, yet most quantitative modeling work has focused on ethanol as a single response. Because CBP systems can generate multiple products and co-products, this study develops a literature-derived benchmark for multi-product CBP modeling using a standardized dataset assembled from published endpoint experiments. Product prediction is formulated as both an observed-only product-wise problem and a joint multi-output problem, allowing direct comparison under study-aware grouped validation. The modeling space integrates biomass composition, pretreatment descriptors, microbial and consortium characteristics, reactor information, operating conditions, and engineered categorical descriptors of feedstock, pretreatment family, and process configuration. Predictive performance was strongly product-dependent and was shaped by target support and missing-label structure. The observed-only product-wise formulation consistently outperformed the joint missing-as-zero multi-output strategy, indicating that naive zero-filling of unreported products is not well suited to sparse literature-derived CBP data. Among the evaluated products, butanol showed the clearest predictive signal, ethanol was only moderately learnable, and the sparsest co-products remained too weakly supported for strong quantitative inference. Overall, the study provides a benchmark for multi-product CBP modeling and clarifies both the potential and the current limitations of literature-derived data for broader data-driven biorefinery analysis.

Abstract: The integration of electronics with biological systems reached a critical inflection point in 2026. The traditional model of rigid, flat, silicon devices has mostly been replaced by a new class of "tissue-equivalent" electronics—systems that match the mechanical modu-lus, ionic conductivity, and dynamic geometry of living matter. This review provides an extensive analysis of the materials (conductive hydrogels, MXenes, bioresorbable metals) and architectures (filamentary meshes, kirigami structures, fractal coatings) that define this era. This review evaluates the transition from surface electronic sensing to volumetric ionic transduction, highlighting recent discoveries in the field of injectables and transient implants.

Abstract: Background: Multiple sclerosis (MS) affects mainly young and middle aged adults and cognitive impairment significantly impacts their quality of life. This study explores the effects of a cognitive rehabilitation therapy using a P300-based brain-computer interface (BCI) in people with MS (pwMS). Methods: The therapy consisted of 16 cognitive rehabilitation sessions using a P300-based BCI. In each session, three different tasks were rehearsed, designed to stimulate different cognitive functions. As outcomes of the study, the cognitive functions were evaluated via the brief international cognitive assessment for MS (BICAMS) component scales before and after the intervention. The quality of life was evaluated using the MS international quality of life (MUSIQoL) questionnaire. The amplitude and latency of the P300 were obtained from the EEG signal recorded during the first and last sessions. Results: Three pwMS completed the cognitive rehabilitation therapy. A general improvement was seen in the BICAMS and MUSIQoL. In particular, all the subjects achieved an increase higher than 10% in the component scale symbol digit modalities test (SDMT) and this difference is clinically meaningful. Concerning the characteristics of the P300, the mean latency of the group decreased 50.89 ms and the mean amplitude showed no change between the two moments. These results may reflect changes at cortical level after the intervention. Conclusions: These findings suggest that the therapy described could positively affect cognitive function and that the P300 may be considered as an objective indicator of cognitive performance which complements the evaluation of the effects of P300-based BCI therapies.

Abstract: Petrochemical-based plastics are widely used due to their convenience and low cost, with polyethylene (PE) being the most produced globally. However, the lack of efficient and sustainable treatment methods for conventional plastic wastes has led to severe environmental pollution. A new fungus strain capable of degrading PE was isolated from soil samples collected at a waste disposal site in Henan province and identified as Aspergillus sydowii W144. After 30 days of incubation under solid-state culture conditions, the strain demonstrated significant depolymerization and degradation of low-density polyethylene (LDPE). FTIR results revealed a substantial increase in the carbonyl index of the LDPE film, while differential scanning calorimetry (DSC) analysis detected an enhanced crystallinity in the LDPE film. Notably, distinct pitting and erosion marks was observed on the surface of LDPE film by scanning electron microscopy (SEM). Quantitative analysis showed a weight loss rate of 6.39% and a reduction in Weight-Average Molecular Weight (Mw) by 50.93%. Among currently identified PE-degrading strains polyethylene, A. Sydowii W144 exhibits particularly outstanding degradation efficiency, especially on untreated PE. Based on the whole-genome data of A. sydowii W144, a preliminary model of the putative polyethylene degradation pathway in A. sydowii W144 was constructed through homology-based sequence analysis and by referencing previously reported polyethylene degradation pathways. Laccase/multicopper oxidases plays a key role in the initial oxidation of PE. Heterologous expression of the candidate gene laccase4 in Pichia pastoris yielded an active enzyme (~56 kDa) with a laccase activity of 460 U/L, confirming its functionality. This study provides a novel microbial resource and potential enzymatic tools for PE biodegradation. The strain exhibits a promising application in complex ecosystems for PE pollution. IMPORTANCE: The polyethylene-degrading strain A. sydowii W144 isolated in this study exhibits highly efficient degradation capabilities, particularly under solid-state culture conditions. Genomic sequencing analysis enabled the construction of a potential polyethylene (PE) degradation pathway and facilitated the identification of key laccase and polyphenol oxidase genes involved in this process. he isolation of this novel strain enriches the microbial resources available for PE waste treatment and offers new insights into the mechanisms of plastic biodegradation.

Abstract: The squat is one of the most widely studied multi-joint movements in strength training and biomechanics. Although numerous experimental and computational studies have examined squat kinematics and joint loading, the mechanical mechanisms governing how squat technique adapts to increasing external load remain insufficiently under-stood. Most inverse dynamics approaches assume that the observed motion is mechanically feasible and do not explicitly account for limitations of joint moment capacity. This study proposes a computational framework for analyzing load-dependent adaptations of squat posture under increasing barbell load. The human body is represented as a multi-segment rigid-body system consisting of feet, shanks, thighs, pelvis, and torso. Joint behavior is modeled using nonlinear rotational elements with bounded moment capacity, allowing representation of elastic response followed by progressive softening when critical moments are approached. A reference squat trajectory is first generated kinematically, after which a constrained optimization procedure is applied at each motion frame to determine a mechanically admissible posture under the applied load. Numerical simulations demonstrate that increasing external load leads to characteristic modifications of squat posture, including posterior displacement of the pelvis, increased torso inclination, and redistribution of rotational demand from the knee toward the hip joint. The framework highlights joint moment capacity as a key mechanical constraint governing squat technique and provides a computational tool for studying load-dependent adaptations in human movement.

Abstract: Human squat motion is commonly analyzed using inverse dynamics, where joint moments are computed from experimentally measured kinematics. Such analyses typically assume that the observed motion is mechanically feasible and do not explicitly account for limitations of joint moment capacity. In this study, a computational framework is proposed for the load-constrained reconstruction of squat motion that integrates kinematic motion generation with a mechanical model of moment-limited joints. The human body is represented as a multi-segment system consisting of feet, shanks, thighs, pelvis, and torso. Joint behavior is modeled using nonlinear rotational springs with bounded moment capacity, allowing elastic response followed by progressive softening when critical moments are approached. A reference squat trajectory is first generated kinematically, after which a constrained optimization problem is solved at each motion frame to obtain a mechanically admissible posture under external loading. The objective function combines trajectory tracking with joint energy contributions, while gravitational loading from a barbell applied at the shoulders introduces external work. The formulation enables automatic correction of the reference motion when joint moment limits are exceeded, resulting in mechanically admissible squat postures. Numerical examples illustrate the evolution of pelvis trajectory, torso inclination, lower-limb segment angles, and reconstructed body configurations throughout the squat cycle.

Abstract: Accurate blood pressure (BP) monitoring traditionally relies on acoustic auscultation, but the mechanical microvibrations underlying Korotkoff sound (K-sound) generation remain insufficiently characterized. This study proposes a microvibrometric sensing strategy using a high-sensitivity semiconductor piezoresistive sensor (K-sensor) to capture pulse-resolved mechanical signatures during cuff deflation, circumventing the limitations of conventional air-conducted acoustic detection. Through expert-consensus annotation with simultaneous acoustic references, a coupling relationship between microvibration morphology and K-sound occurrence was established. A convolutional neural network (CNN) was implemented to automate the identification of K-sound–coupled (cK) pulses from microvibration signals. Across seven independent train–test splits in 49 healthy participants, the model achieved a recall of 93.1% ± 4.3% and a balanced accuracy of 95.0% ± 2.0%, with mean biases of 0.30 ± 2.25 mmHg for systolic BP (SBP) and −0.14 ± 2.14 mmHg for diastolic BP (DBP). Beyond classification performance, two distinct morphological archetypes—characterized as notch-type and shoulder-type waveforms—were consistently observed among cK pulses, reflecting differentiated patterns of arterial wall dynamics associated with K-sounds. These findings support microvibration sensing as a physiologically grounded and sensor-centric framework for automated noninvasive cardiovascular assessment beyond conventional acoustic paradigms.

Abstract: The selection of an optimal biomaterial is a critical determinant of the long-term clinical success of dental implants, requiring a careful balance among competing mechanical, biological, and clinical performance criteria. This study develops a comprehensive evaluation framework employing the Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy-PROMETHEE II) to conduct a systematic comparative analysis of 22 contemporary biomaterials across eight key criteria: elastic modulus, yield strength, ultimate tensile strength, density, osseointegration potential, corrosion resistance, biostability, and potential side effects. To address the inherent uncertainty in material property data, triangular fuzzy numbers (TFNs) were utilized to model both quantitative property intervals and qualitative linguistic variables—an approach justified by the fact that biomaterial properties are routinely reported as ranges rather than crisp scalar values. The Fuzzy-PROMETHEE method was selected over alternative MCDM approaches because of its capacity for pairwise outranking without the rank-reversal instability characteristic of TOPSIS, and its lower parametric burden compared to AHP when evaluating large alternative sets. The analysis identified the titanium alloy Ti-6Al-4V as the top-performing material, achieving the highest net outranking flow (Φnet = 0.3152), attributable to its uniquely balanced profile of fracture toughness, yield strength, and osseointegration potential. Zirconia ranked sixth (Φnet = 0.1659), reflecting a quantifiable mechanical trade-off relative to metallic alternatives despite its superior aesthetic properties. The robustness of the framework was corroborated by comparative analysis using TOPSIS (relative closeness = 0.839, identical top ranking) and confirmed stable by sensitivity analysis across Osseointegration criterion weight variations from 0% to 50%. This study presents a transparent, evidence-based decision-support tool to assist clinicians in navigating the complex trade-offs inherent in modern implantology.