Search Results (1,180)

The life sciences are currently undergoing a serious transition from the reductive biochemical analysis of dissociated tissues to non-destructive “spatial forensics”. In addition to discovering new molecules, we are moving towards finding out their precise tissue localization and performing in situ interrogation to uncover a biological logic within preserved cellular “neighborhoods”. Our perspective is focused on exploring the spatial imperative, including the structural logic and “neighborhood effects” of the tissue microenvironment, which is a prerequisite to understanding cellular function in normal and in pathological conditions. Beginning with a historical foundation of the origins of histochemistry, dating back to the 19th century with pioneer botanist François-Vincent Raspail, we emphasize the technological metamorphosis, transitioning from classical immunohistochemistry to modern multi- and high-plex spatial multi-omics. A critical evaluation of the current operational landscape has been made, addressing the engineering strategies behind multiplexed immunofluorescence (mIF), the challenges of experimental design in spatial transcriptomics, and the functional symbiosis between targeted and unbiased spatial proteomics. There are many layers of genomic and proteomic information we have to consider in order to unravel the mechanisms underlying body function. If we learn how to combine all this information together, we will be able to better understand how cells communicate with each other and what disrupts their communication, leading to cancer and many other pathologies. It is obvious that by implementing spatial biology tools, it becomes possible to develop new medicines and treat diseases in the most efficient ways. At the same time, we realize that there is an urgent need to learn how to put data pieces together so that they blend seamlessly into a meaningful output, further transitioning spatial biology over time into a routine tool to cure for both common and rare diseases and improve our lives and health. Full article

Accurate estimation of lithium-ion battery State of Health (SoH) is critical for emerging applications such as reconfigurable battery systems. Although data-driven machine learning methods are promising, they often rely on costly, time-intensive aging experiments and extensive feature engineering. This work proposes a lightweight SoH-prediction framework validated on both physics-informed synthetic aging data and the NASA battery aging dataset. We evaluated Random Forest (RF) and Feedforward Neural Network (FNN) models that use only a limited number of samples from an early segment of the raw discharge voltage curve as input. Results show that RF consistently outperforms FNN across input sizes in deterministic or noise-free environments, achieving an RMSE of 0.07% SoH using just 5 voltage samples. In inherently stochastic experimental data, however, FNN can achieve an RMSE 50% lower than RF (1.28 vs. 2.87), but requires 37× more mathematical operations per inference. These findings emphasize the predictive value of the early-discharge-voltage region and demonstrate that compact, low-feature-complexity models can deliver accurate SoH estimates. Overall, the approach supports a goal of combining informed synthetic data with limited real measurements to build robust, scalable SoH predictors, reducing dependence on labor-intensive degradation testing and feature-heavy pipelines. Full article

Background: Accurate mortality prediction remains a major challenge in public health due to the complex interactions among demographic, socioeconomic, behavioral, and medical factors. This problem is particularly relevant for identifying high-risk groups and improving preventive healthcare strategies. While existing studies demonstrate strong predictive performance, they mainly rely on clinically structured data and focus on model performance. Challenges such as misclassification and atypical cases remain less explored. Methods: Using the Integrated Public Use Microdata Series National Health Interview Survey (IPUMS-NHIS) 2010 and 2015 datasets (193,765 records, 104 features), this study investigates mortality prediction through comparative Machine Learning. Data preprocessing included feature engineering, categorical encoding, and removal of missing entries. Class imbalance was addressed using SMOTE and SMOTE-ENN resampling, followed by hyperparameter tuning. Three models—Logistic Regression, Random Forest, and XGBoost—were trained to classify mortality, with recall prioritized to ensure accurate identification of deceased cases. Results: Results showed that XGBoost achieved the best performance (Recall = 69%, F1 = 0.39, AUC = 0.92), outperforming other models in balancing sensitivity and specificity. Feature importance and permutation analyses highlighted age, employment status, self-reported health, and lifestyle indicators as key predictors. Misclassification analysis combined with Isolation Forest revealed atypical profiles not captured by standard models. Conclusions: The findings underscore XGBoost’s effectiveness and demonstrate the value of integrating anomaly detection with classification to improve mortality prediction and inform public health planning. Full article

Major depressive disorder (MDD) affects over 330 million people globally, yet up to 30% of patients fail initial pharmacotherapy due to genetic variability in drug metabolism. This narrative review synthesizes evidence on pharmacogenomic (PGx) guided approaches for MDD, emphasizing their integration with POC diagnostics and engineering solutions. Approximately 40–50% of patients carry actionable variants in CYP2C19 or CYP2D6, which govern the metabolism of selective serotonin reuptake inhibitors. Landmark trials (GUIDED, PRIME Care, GAPP-MDD) and meta-analyses demonstrate that PGx-informed prescribing modestly but significantly improves remission and response rates, particularly in treatment-resistant depression. Established guidelines from CPIC and the Dutch Pharmacogenetics Working Group provide actionable recommendations for CYP2D6 and CYP2C19 phenotypes. Emerging POC platforms, including Genomadix Cube and Genedrive, now deliver CYP2C19 results within one hour, supporting rapid clinical decisions. However, psychiatric-specific implementation data remain limited compared to cardiology; current POC devices lack multi-gene capabilities, and most studies underrepresent diverse populations. Persistent barriers include variable reimbursement, limited clinician education, and fragmented electronic health record integration. Future directions include pre-emptive genotyping, expanded multi-gene panels, and embedded clinical decision support. With continued engineering innovation and rigorous validation, PGx-guided care holds promise for reducing the trial-and-error burden and advancing precision psychiatry. Full article

Air pollution caused by fine particulate matter (PM_2.5) poses a serious public health threat in many South Asian megacities where monitoring networks remain limited. Lahore, Pakistan—frequently ranked among the world’s most polluted cities—still lacks reliable short-term PM_2.5 forecasting systems. This study develops a performance-weighted ensemble machine learning framework that integrates satellite observations, meteorological reanalysis data, and ground monitoring measurements to improve daily PM_2.5 prediction. Eleven predictor variables were processed using a unified Google Earth Engine pipeline, including MODIS aerosol optical depth, Sentinel-5P trace gases (CO, NO₂, SO₂), and ERA5 meteorological parameters. Four tree-based machine learning algorithms—Random Forest, XGBoost, LightGBM, and CatBoost—were trained using daily observations from 2019 to 2023. Model evaluation using an independent 2024 dataset showed strong predictive capability, with Random Forest achieving R² = 0.77 (RMSE = 24.75 µg m⁻³), XGBoost R² = 0.76 (RMSE = 26.32 µg m⁻³), CatBoost R² = 0.73 (RMSE = 30.39 µg m⁻³), and LightGBM R² = 0.70 (RMSE = 32.75 µg m⁻³). To further enhance performance, the best models were combined into a weighted ensemble (RF 0.5, XGBoost 0.3, and CatBoost 0.2), which produced the highest validation accuracy (R² = 0.77; RMSE = 23.37 µg m⁻³). Statistical testing using paired t-tests and Diebold–Mariano tests confirmed that the ensemble significantly reduced forecast errors compared with individual models. Feature importance analysis revealed that surface pressure, temperature, CO, and NO₂ were the most influential predictors of PM_2.5 variability. The proposed framework demonstrates that combining satellite data, reanalysis meteorology, and ground observations through ensemble learning can provide accurate and scalable air quality forecasting for data-limited urban environments. Full article

In the context of the rapid growth of the electric and hybrid vehicle fleet, ensuring the reliability, safety, and durability of traction lithium-ion battery packs has become a key scientific and engineering challenge. The technical condition of battery packs, characterized by such parameters as state of charge (SOC), state of health (SOH), and remaining useful life (RUL), directly affects vehicle performance and the total cost of ownership of electric vehicles. This review article systematizes and analyzes current approaches to assessing the technical condition of battery packs. Fundamental degradation mechanisms and factors are considered, including operational, thermal, and mechanical effects. A detailed analysis is presented for the three main classes of diagnostic methods: model-based approaches, data-driven approaches (machine learning and deep learning), and hybrid methods combining the advantages of the former two. Particular attention is paid to methods for early fault detection, thermal runaway prediction, and condition assessment based on real-world operational data. The article presents quantitative results demonstrating the accuracy and effectiveness of various algorithms and also discusses key challenges and promising research directions, such as the use of cloud platforms, digital twins, and explainable artificial intelligence methods. Full article

Personalized transdermal drug delivery systems (TDDS) represent a transformative approach in precision medicine by enabling patient-specific, non-invasive, and controlled therapeutic administration. Conventional transdermal patches are limited by fixed dosing, passive diffusion, and interindividual variability in skin permeability and metabolism, often leading to suboptimal therapeutic outcomes. Recent advances in materials science, nanotechnology, microneedle engineering, and digital health have enabled the development of next-generation personalized TDDS capable of programmable, adaptive, and feedback-controlled drug release. Smart wearable patches integrating biosensors, microfluidics, microneedles, and wireless connectivity allow real-time monitoring of physiological and biochemical parameters, enabling closed-loop drug delivery tailored to individual metabolic profiles. Nanocarriers such as lipid nanoparticles, polymeric nanoparticles, and stimuli-responsive hydrogels further enhance drug stability, penetration, and controlled release, while 3D-printing technologies facilitate patient-specific customization of patch geometry, drug loading, and release kinetics. Artificial intelligence (AI) and machine learning tools are increasingly being employed to predict drug permeation behavior, optimize enhancer combinations, and personalize dosing regimens based on pharmacogenomic and pharmacokinetic data. Despite these advances, regulatory complexity, manufacturing standardization, long-term biocompatibility, and cybersecurity considerations remain critical challenges for clinical translation. This review highlights recent innovations in personalized TDDS, discusses their clinical potential, and examines regulatory and technological barriers. Collectively, these emerging smart transdermal platforms offer a promising pathway toward adaptive, patient-centered therapeutics that can significantly improve treatment efficacy, safety, and compliance. Future research should focus on integrating multimodal biosensing, advanced biomaterials, scalable manufacturing strategies, and robust regulatory frameworks to enable clinically validated, fully autonomous transdermal systems that can dynamically adapt to real-time patient needs in diverse therapeutic settings. Full article

Municipal solid waste disposed of in open dumpsites and unlined landfills contaminates groundwater, soils, and air across urban areas of low- and middle-income countries. Nevertheless, impacts across all three environmental media have not been systematically assessed together. We conducted a PRISMA 2020-compliant systematic review of 286 peer-reviewed studies from PubMed, Dimensions, and OpenAlex, applying structured eligibility screening and quality appraisal using an adapted JBI checklist. Heavy metals—lead, cadmium, chromium, and zinc—were the most frequently detected contaminants in leachate and groundwater, commonly exceeding WHO drinking water guidelines by one to three orders of magnitude. Soil contamination by potentially toxic elements was documented at virtually all open dumpsites studied, persisting for decades after site closure. Particulate matter at South Asian MSW sites reached up to 41 times the WHO 2021 annual guideline. Microplastics acting as heavy metal carriers and dumpsite leachate as a source of antimicrobial resistance genes were identified as emerging risks outside standard monitoring frameworks. Non-carcinogenic hazard indices exceeded acceptable thresholds in the majority of health risk studies reviewed. Engineered containment was the strongest predictor of contamination severity across all sites. Phytoremediation, constructed wetlands, and biofiltration showed promise as mitigation approaches. Critical evidence gaps remain for Central Asia, harmonized reporting standards, and longitudinal monitoring data. Full article

Offshore wind energy has developed rapidly in recent years as a crucial component of renewable energy. However, offshore wind turbines (OWTs) face significant challenges in operations under complex marine environmental conditions, such as multimodal nonlinear vibrations, reliable structural monitoring, efficient maintenance, and sustainable long-term operations. The model-updating-based parameter identification takes advantages of structural vibration measurements, assisting in structural health monitoring. However, the traditional methods have not fully accounted for the parameter uncertainties and the need for real-time state updating, making them insufficient to meet the long-term online monitoring requirements for OWTs. This study introduces an innovative structural parameter identification framework that integrates modal parameter identification with Bayesian recursive updating. The proposed framework enables more efficient updates and uncertainty quantification of critical physical parameters for OWTs. It combines the covariance-driven stochastic subspace identification (COV-SSI) method for automatic modal parameter identification with the unscented Kalman filter (UKF) for parameter estimation. A 10 MW jacket-type offshore wind turbine was used as a case study. First, the numerical simulations were conducted to generate synthetic measurements for method validation and demonstration, enabling stepwise updating of the tower material’s elastic modulus across different sea conditions. A comparison of update speed and the convergence rate with the traditional time-step-based UKF method demonstrated the superiority of the proposed sea-condition-based approach in terms of computational efficiency and stability. Finally, the proposed framework was systematically validated using scaled model experimental data of a jacket-type OWT with a 4.2% identification error, confirming its engineering applicability. This research provides reliable technical support for the safety assessment of offshore wind turbine structures. Full article

Brucellosis poses a persistent threat to livestock health in high-altitude pastoral regions of China, where harsh environments and semi-nomadic grazing increase transmission risk. Existing surveillance systems rely mainly on periodic serological testing and lack effective early warning capability. This study proposes an ensemble learning-based early warning framework integrating veterinary epidemiological indicators with environmental and herd-movement data. A total of 4826 herd-level records collected over five years (2019–2024) were analyzed, with an overall positivity rate of 11.4%. Multi-source data, including serological, clinical, reproductive, vaccination, meteorological, pasture-management, and herd-movement information (from GPS tracking and structured surveys), were integrated through epidemiology-guided feature engineering. To address class imbalance and temporal dynamics, Synthetic Minority Over-sampling Technique (SMOTE) resampling and sliding time-window features were applied. The proposed ensemble model combines Random Forest, XGBoost, and LightGBM using a soft-voting strategy, with logistic regression as a baseline. Results show that the ensemble model outperforms single models, achieving an AUC of 0.86 and a PR-AUC of 0.65. After threshold optimization, sensitivity increased from 0.78 to 0.87. Under field conditions, the system provided herd-level early warnings with an average lead time of approximately 12 days before confirmed outbreaks, demonstrating its feasibility and practical value for proactive brucellosis surveillance in high-altitude pastoral systems. Full article

Industrialization has led to the substantial release of heavy metals and organic pollutants into soil and groundwater, resulting in severe contaminated site issues that pose significant threats to ecosystems and human health. This review aims to systematically review the current development status and challenges of contaminated site remediation technologies, and explore the potential of artificial intelligence (AI) applications in site remediation, to provide a theoretical reference for advancing intelligent remediation. Conventional remediation technologies mainly include physical methods (e.g., solidification/stabilization (S/S), soil vapor extraction (SVE), thermal desorption, pump and treat (P&T), groundwater circulation wells (GCWs)), chemical methods (e.g., chemical oxidation/reduction, electrokinetic remediation (EKR), soil washing), and biological methods (phytoremediation, microbial remediation), along with combined strategies that integrate multiple approaches. Although these technologies have achieved certain successes in engineering practice, they still face common challenges such as risks of secondary pollution, long remediation periods, high costs, poor adaptability to complex hydrogeological conditions, and insufficient long-term stability, making it difficult to fully meet the remediation demands of complex contaminated sites. Subsequently, the potential of emerging technologies—including nanomaterial-based remediation, bioelectrochemical systems, and molecular biology-assisted remediation—is introduced. On this basis, the forefront applications of AI in contaminated site remediation are discussed, covering site monitoring and characterization, risk assessment, remedial strategy selection, process prediction and parameter optimization, material design, and post-remediation intelligent stewardship. Machine learning (ML), explainable AI (XAI), and hybrid modeling approaches have markedly improved remediation efficiency and decision-making. Looking forward, with advancements in XAI, mechanism-data fusion models, and environmental foundation models, AI is poised to drive a paradigm shift toward intelligent and precision remediation. However, challenges related to data quality, model interpretability, and interdisciplinary expertise remain key barriers to overcome. Full article

As the core power unit of complex electromechanical systems, accurate health assessment of diesel engines is essential for safe operation. The Interval Belief Rule Base (IBRB) method integrates observed data with expert knowledge to support system assessment. However, engine operating parameters change over time because of wear and aging. Additionally, traditional optimization methods struggle to balance global search speed with local convergence efficiency. To address these issues, this paper proposes an Interval Belief Rule Base method based on Hybrid Optimization and Adaptive Intervals (IBRB-HOAI). First, an adaptive reference interval is introduced by combining K-means clustering and quantile interval estimation, dynamically generated based on the actual operating state of the engine. The health assessment baseline is optimized. The applicability of the model is enhanced. Second, the global exploration ability of particle swarm optimization is combined with the local refinement ability of the projected covariance matrix adaptation evolution strategy. The model parameters are collaboratively optimized. Finally, experimental verification is conducted on a diesel engine dataset containing 2700 sample points. Compared with the traditional IBRB method, the proposed method achieves a significant reduction in MSE of 97.5%. It outperforms other machine learning methods. The effectiveness of the proposed method is verified. Full article

Background: Diabetic foot ulcers recur frequently after healing. The first three months carry the highest risk. Remission is a vulnerable phase that demands precise self-care and timely feedback. Evidence supports thermometry and protective footwear with gradual return to activity, yet adherence at home is inconsistent. Objective: To describe the design and planned evaluation of a conversational agent (chatbot) that guides patients through the remission phase following diabetic limb reconstruction. Methods: This protocol describes a conversational agent (chatbot) that turns remission guidance into daily actions, grounded in clinical expertise and established care guidelines. Walking is dosed like a drug, with careful titration based on tissue response. The agent integrates automatic data capture (smartphone step counts, skin temperature, shoe step streams, smartwatch step streams, Bluetooth thermometry when available, and app session timestamps) with manual patient entries (shoe wear time, skin redness persistence, and symptom checks). It doses walking activity, guides footwear break-in, prompts photo-confirmed concerns, following clinician-informed rules and escalation pathways. We define data quality checks for missingness and physiologic plausibility, and the agent reinforces reducing weight-bearing activity when risk signals appear. We outline device drift. The study is designed as a single-arm feasibility pilot (n = 30) to assess engagement, safety, and implementation fidelity. Results: No clinical outcome results are reported because this is a protocol study and enrollment has not yet begun. This study presents the prespecified sensing-to-decision workflow, escalation logic, and pilot endpoints, along with internal technical verification procedures (e.g., message delivery reliability, data completeness checks, and rule-engine consistency testing). Conclusions: A remission chatbot is a plausible method to extend specialist support into the home, reflecting integration of clinical expertise with digital health tools. This protocol defines how feasibility, safety, and usability will be evaluated. Clinical efficacy should be confirmed in future studies. Full article

Psychological stress is a major public health problem associated with adverse outcomes in physical and mental health. This study proposes an approach to predicting clinical stress levels using metabolic and endocrine biomarkers combined with machine learning models based on genetic algorithms. Data were obtained from 87 university students, including measurements of glucose, insulin, and cortisol, as well as perceived stress scores assessed using the Perceived Stress Scale (PSS). Stress levels were categorized into low ($n=5$), moderate ($n=22$), and high ($n=60$) classes, reflecting an imbalanced dataset. Feature engineering and genetic algorithm–based selection identified glucose concentration, the insulin–glucose ratio, and the insulin–cortisol ratio as the most relevant features. These were used to train XGBoost and Elastic Net models, which were evaluated using leave-one-out cross-validation. The XGBoost model achieved the best performance, with an accuracy of 0.77 and strong predictive capability for high stress levels. The results demonstrate the usefulness of machine learning based on metabolic biomarkers as an objective tool for stress assessment in psychological and clinical research. Full article

Office workers are exposed to a range of occupational health risks, including prolonged sedentary behaviour, postural load, elevated heart rate, and noise, yet objective and continuous monitoring of these risk factors in workplace settings remains uncommon. This study aimed to co-design occupational health visualisations based on smartphone and smartwatch data, through a multi-stakeholder group of office workers and occupational health professionals. A generative co-design framework was applied, comprising a pre-design phase with a field study and questionnaire, a structured multi-stakeholder workshop, and a follow-up evaluation session. Thematic analysis of the workshop transcript yielded 17 occupational health themes, which were subsequently assessed for technical feasibility relative to the available sensing platform. Of the 27 discrete visualisation elements proposed across both groups, the majority were classified as directly addressable using smartphone and smartwatch sensor data. Visualisations covering physical activity, heart rate, environmental noise exposure, and postural load were implemented in Python using real-world data collected from office workers. The follow-up session provided qualitative confirmation that the developed visualisations were interpretable and aligned with the stakeholder expectations. The generative co-design framework proved well-suited to the occupational health visualisation context, enabling structured translation of stakeholder requirements into technically feasible and interpretable visualisation outputs. Full article