Search Results (991)

Pandemics place extraordinary pressure on healthcare systems, particularly in identifying and prioritizing high-risk groups such as the elderly, pregnant women, and individuals with chronic diseases. Existing Artificial Intelligence models often fall short, focusing on single diseases, lacking interpretability, and overlooking patient-specific vulnerabilities. To address these gaps, we propose an Explainable Deep Learning approach for identifying at-risk patients during pandemics (AI-RiskX). AI-RiskX performs risk classification of patients diagnosed with COVID-19 or related infections to support timely intervention and resource allocation. Unlike previous models, AI-RiskX integrates five public datasets (asthma, diabetes, heart, kidney, and thyroid), employs the Synthetic Minority Over-sampling Technique (SMOTE) for class balancing, and uses a hybrid convolutional neural network–long short-term memory model (CNN–LSTM) for robust disease classification. SHAP (SHapley Additive exPlanations) enables both individual and population-level interpretability, while a post-prediction rule-based module stratifies patients by age and pregnancy status. Achieving 98.78% accuracy, AI-RiskX offers a scalable, interpretable solution for equitable classification and decision support in public health emergencies. Full article

Objectives: Urinary tract infections (UTIs) are the most common healthcare-associated infections in Skilled Nursing Facilities (SNFs); they are associated with longer lengths of stay, higher levels of care, increased treatment costs, and higher mortality rates. This study aimed to develop a machine learning classification model to predict the risk of high catheter-associated urinary tract infection rates based on SNF characteristics. Methods: We analyzed 94,877 total SNF-year observations from 2019 to 2024, not unique facilities; thus, individual SNFs may appear in multiple years. The factor variables were average length of stay in days, number of staffed beds, total nurse and total physical therapy staffing hours per resident per day, facility ownership, geographic classification, facility accreditation, Accountable Care Organization affiliations, Centers for Medicare and Medicaid Services SNF Overall Star Rating, and the SNF-year of the observations. We utilized three machine learning models for this analysis: Random Forest, XGBoost, and LightGBM. We used Shapley Additive exPlanations to interpret the best-performing machine learning model by visualizing feature importance and examining the relationship between key predictors and the outcome. Results: We found that machine learning models outperformed traditional logistic regression in predicting UTIs in skilled nursing facilities. Using the best-performing model, Random Forest, we identified rural SNFs, and the number of staffed beds as the most influential predictors of high UTI rates, followed by average length of stay, and geographic location. Conclusions: This study demonstrates the value of using facility-level characteristics to predict the risk of UTIs in SNFs with machine learning models. Results from this study can inform infection prevention efforts in post-acute care settings. Full article

Inter-saccadic times or eye fixation durations (EFDs) are relatively stable at around 250 ms, equivalent to four saccades per second. However, the mean and standard deviation are not sufficient to describe the frequency histogram distribution of EFD. The exGaussian has been proposed for fitting the EFD histograms. The present report tries to adjust a competition model (C model) between the saccadic and the fixation network to the EFD histograms. This model is at a rather conceptual level (computational level in Marr’s classification). Both models were adjusted to EFD from an open database with data of 179,473 eye fixations. The C model showed to be able, along with exGaussian model, to be compatible with explaining the EFD distributions. The two parameters of the C model can be ascribed to (i) a refractory period for new saccades modeled by a sigmoid equation (A parameter), while (ii) the ps parameter would be related to the continuous competition between the saccadic network related to the saliency map and the eye fixation network, and would be modeled through a geometric probability density function. The model suggests that competition between neural networks would be an organizational property of brain neural networks to facilitate the decision process for action and perception. In the visual scene scanning, the C model dynamic justifies the early post-saccadic stability of the foveated image, and the subsequent exploration of a broad space in the observed image. The code to extract the data and to run the model is added in the ^{Supplementary Materials}. Additionally, entropy of EFD is reported. Full article

We (Public Interest Group on Cancer Research) started a podcast and guest blog series on cancer in 2024. Our objective in this Commentary is to describe our experience with this series, insights gained, adjustments made to our approach, and our recommendations for future series. Our group identified and invited guests to contribute a blog or podcast episode on cancer, lived experience of cancer, cancer care and research, or advocacy. The podcast episodes were recorded using the WebEx platform (version 45.9.0.33069) and edited using the Kdenlive software (version 23.08.4). The blogs and podcasts were edited, finalized, and posted online for public access. In this manuscript, we utilized descriptive statistics to define and summarize information about the podcast episodes, guest blogs, and categorical responses to guest feedback survey questions, while we presented the responses to open-ended survey questions as quotes and summaries. As a result, during the period of January 2024–July 2025, we aired 28 podcast episodes and 13 guest blogs involving 36 guests. Guests included people from various backgrounds (such as people with lived experience, advocates, scientists, and healthcare providers) and members of equity-deserving communities (such as women, Indigenous and 2SLGBTQIA+ communities). We contemplated and learned as we proceeded with this project and implemented changes to address the issues that arose. In most cases the guests had positive experiences; however, in rare cases, university practices or federal policies prevented guest compensation, creating an unusual barrier. In conclusion, podcasting and blogging are practical public engagement instruments that provide space for sharing messages and knowledge to communicate with members of the public. Systematic barriers, such as policies that hamper guest compensation, need to be fixed for equitable participation, compensation, and engagement. As there is an increased interest in public engagement and knowledge mobilization activities, our learnings shared in this commentary may help other groups initiate or improve their public engagement practices. Full article

Background/Objectives: This exploratory study aimed to evaluate the impact of an 80 km ultra-marathon trail running event on changes in faecal bacterial composition, and to investigate whether any correlations exist between exercise-associated gastrointestinal symptoms (Ex-GIS) with faecal bacterial profiles. Such events represent a unique physiological stressor and may impact the composition of the gut microbiota. Studying this impact may provide insights into acute (i.e., <24 h) gut microbiota changes under extreme conditions. Methods: Thirteen endurance athletes (n = 7 males, n = 6 females) aged 41 ± 8 years completed the 80 km Margaret River (Australia) ultra-marathon race in 2022. Faecal samples were collected pre- and post-race. Faecal bacterial profile, as per relative abundance (RA) of operational taxonomic units and the determination of α-diversity (Shannon Equitability Index (SEI)), was achieved by 16S rRNA amplicon gene sequencing. Changes in RA% and SEI pre- to post-race were assessed by the Wilcoxon signed-rank test. Correlations between Ex-GIS with bacterial profile and changes pre-, during, and post-ultra-marathon race were determined by Spearman’s rank correlation coefficients. Results: Bacterial calculations of phyla (n = 5), family (n = 23), and genus (n = 41) were detected for RA (≥0.5%). A significant decrease pre- to post-race of Actinobacteriota (p = 0.035) phyla, Bifidobacteriaceae (p = 0.007), and Clostridiaceae (p = 0.010) family, and Blautia (p = 0.039) and Subdoligranulum (p = 0.023) genus was determined; meanwhile, Oscillospiraceae (p = 0.016) and Monoglobaceae (p = 0.039) family significantly increased pre- to post-race. No other bacterial group changes were observed. No correlations were observed between pre- to post-ultra-marathon RA change and Ex-GIS. Conclusions: The completion of an 80 km ultra-marathon did not invoke substantial changes in the gut microbiota as determined by faecal bacterial profiling. Very strong and strong correlations were observed between certain bacterial groups and Ex-GIS; however, no significant correlations were observed between pre- to post-ultra-marathon changes in RA ≥ 0.5% and Ex-GIS. Full article

Global amphibian populations are declining rapidly and the development of effective cryopreservation protocols for germ cells has become a critical tool in ex situ conservation programs. Post-thaw membrane-integrity viability in four endangered Korean amphibians (Dryophytes suweonensis, Pelophylax chosenicus, Kaloula borealis, and Hynobius yangi) were evaluated. Sperm were cryopreserved using dimethyl sulfoxide (DMSO) or N,N-dimethylformamide (DMF) at 10–30% (v/v) in combination with 0.6 M sucrose, and were frozen at two suspension heights (5 cm vs. 10 cm) above liquid nitrogen. Post-thaw membrane-integrity viability was assessed using a SYBR-14/propidium iodide membrane-integrity assay (LIVE/DEAD kit). Low concentrations of permeating cryoprotectants (CPs) improved membrane-integrity viability, whereas high concentrations led to high toxicity, particularly with DMSO. Across species, DMF produced the highest membrane-integrity viability and the most consistent performance. The cooling rate influenced membrane-integrity viability, with samples frozen at 10 cm exhibiting greater viability, reflecting the balance between intracellular ice formation during rapid cooling and solution effects during slow cooling. Optimal conditions for D. suweonensis were 15% DMSO at 10 cm (86.5% membrane-integrity viability); for P. chosenicus, 10% DMF at 10 cm (75.5%); and for K. borealis, 10% DMSO at 5 cm (81.6% membrane-integrity viability). Hynobius yangi showed modest improvement under 15% DMF at 5 cm (19.7%), although overall membrane-integrity viability was low. ED₅₀ modeling indicated species-specific thresholds requiring low CP concentrations. Sperm cryopreservation outcomes in amphibians are strongly influenced by CP type, concentration, cooling regime, and species physiology. GLM and ED₅₀ modeling provide a methodological framework for refining cryopreservation strategies for non-model, endangered species. Full article

Background: Post-operative infections are a significant cause of morbidity in patients undergoing major elective surgery. Peri-operative antibiotics are used to reduce the risk of infection. Several antibiotics modulate the host immune response. Objectives: Our objective was to determine the ex vivo immunomodulatory properties of commonly used antibiotics (amoxicillin, cefuroxime, metronidazole, or combined cefuroxime–metronidazole) on monocyte and lymphocyte phenotypes in patients undergoing major elective surgery. Methods: We performed a prospective cohort study of patients aged ≥18 years admitted to the post-anaesthetic care unit following major elective non-cardiac surgery. Peripheral blood mononuclear cells isolated immediately after surgery were incubated with antibiotics with or without a monocyte (heat-killed E. coli) or lymphocyte (CD3/CD28 beads) stimulus ex vivo. Immune cell phenotype was characterised using flow cytometry. Results: Twenty-eight patients were included. All antibiotics tested were associated with a reduction in T-cell viability, and changes to monocytes were minimal. Among CD4⁺ and CD8⁺ lymphocytes, cefuroxime increased IFN-γ (at low and high doses) and increased CD4⁺ lymphocyte IL-2 and IL-2R at higher doses. Among CD4⁺ lymphocytes, at both doses, cefuroxime increased %T_h1 population, with a parallel decrease in %T_h2, %T_h17, IL-17A, FOX-P3, and T-bet. Among the T_h1 sub-population, changes were seen at higher cefuroxime doses, including increased viability and PD-1, and a decrease in FAS, IFN-γ and CD28, and IL-7R expression. Conclusions: The choice of antibiotics directly impacts immune function following major surgery, with cefuroxime associated with ex vivo immunomodulatory effects on CD4⁺ lymphocytes. The functional implications on the development of subsequent post-operative infectious complications and long-term cancer-free survival require further investigation. Full article

The strong heterogeneity in and complex engineering conditions of deep shale gas reservoirs make productivity prediction challenging, especially in nascent blocks where data is scarce. This scarcity constitutes a critical research gap for the application of data-driven methods. To bridge this gap, we develop an interpretable framework by combining grey relational analysis (GRA) with three machine learning algorithms: Random Forest (RF), Support Vector Machine (SVR), and eXtreme Gradient Boosting (XGBoost). Utilizing small-sample data from 87 shale gas wells in the study area, eight key controlling factors were identified, namely, total fracturing fluid volume, proppant intensity, average tubing head pressure, pipeline transfer pressure, casing head pressure, ceramic proppant fraction, fluid placement intensity, and flowback recovery ratio. These factors were used to train, optimize, and validate a productivity prediction model tailored for deep shale gas horizontal wells. The results demonstrate that XGBoost delivers the highest predictive accuracy and generalization capability, achieving an R² of 0.907 for productivity prediction—surpassing RF and SVR by 12.11% and 131.38%, respectively. Integrating SHapley Additive exPlanations (SHAP) interpretability analysis further enabled immediate post-fracturing productivity assessment and engineering parameter optimization. This research provides a reliable, data-driven strategy for predicting productivity and optimizing operations within the studied block, offering a valuable template for development in geologically similar areas. Full article

Background: Common variable immunodeficiency (CVID), the most prevalent symptomatic inborn error of immunity, involves impaired B-cell differentiation and antibody production, causing recurrent infections and the need for life-long immunoglobulin replacement therapy. Methods: This study evaluated the in vitro differentiation of memory B-cells (MBCs) into antibody-secreting cells (ASCs) in CVID patients. Peripheral blood mononuclear cells from 13 CVID patients and 10 healthy controls were stimulated using two protocols: (I) Staphylococcus aureus Cowan Strain I, Pokeweed mitogen, and CpG, or (II) a T-cell-dependent approach using CD40 ligand, interleukin-21, and CpG. B-cell subpopulations were analyzed by flow cytometry, ASC differentiation using ELISpot, and antibody levels in supernatants by ELISA. Results: Despite severely restricted in vivo antibody production, MBCs from all 13 CVID patients differentiated into IgG and IgM ASCs under adequate in vitro stimulation. Protocol II, mimicking T-cell help, was more effective than protocol I. As expected, the patients exhibited reduced class-switched MBCs ex vivo, but the MBCs differentiated and proliferated to an extent similar to those in healthy controls. IgA secretion remained significantly impaired post-stimulation. Specific IgG antibodies against tetanus toxoid and Streptococcus pneumoniae were detected in the patient supernatants, while no double-stranded DNA autoantibodies emerged after in vitro stimulation. Conclusions: These findings indicate that the MBCs of most patients retain functional B-cell differentiation and antigen-specific IgG secretion under T-cell dependent stimulation, though IgA secretion remains impaired. Tailored stimulation protocols could deepen our understanding of how to restore MBC formation in CVID patients in vivo. This methodology provides a platform to investigate antigen-specific functional memory responses like post-vaccination. Full article

Concerns over ground-mounted photovoltaics (PVs) on cropland have encouraged a shift toward rooftop PV systems on livestock and poultry farms. Using ex-post observational data and a doubly robust estimation approach, this study examines the determinants and economic effects of PV adoption among chicken farmers in Taiwan. Based on a population-wide agricultural census, we assess how socio-demographic factors, production practices, household composition, and electricity infrastructure influence adoption decisions. The results show that education level, household structure, and access to electricity are key drivers of adoption. PV adopters exhibit a 5.8% higher sales value of chicken products, mainly due to increased production volume rather than quality improvements. These findings highlight the potential dual benefits of integrating solar energy with poultry farming and provide policy-relevant insights for sustainable agricultural development. Full article

The 2020 Europe Strategy was designed as a comprehensive framework to promote smart, sustainable and inclusive growth in the European Union (EU), particularly emphasising the ‘20/20/20’ targets related to climate protection and energy policy. This study provides an ex post evaluation of the extent to which the strategy’s objectives were achieved in the member states of the EU in the period 2010–2020. The analysis is based on Eurostat data and uses Hellwig’s multidimensional comparative analysis to construct a synthetic indicator of progress. The results show that EU countries have made significant advances in reducing greenhouse gas emissions and increasing the share of renewable energy in gross final energy consumption, with Sweden and Finland identified as leaders, while Malta and Hungary lagged behind. Primary energy consumption overall decreased, although only a minority of the member states reached the planned thresholds. Progress was less evident in research and development (R&D) expenditure, where the average value of the EU remained below the 3% GDP target, and strong disparities persisted between innovation leaders and weaker performers. Improvements in higher education attainment were observed, contributing to the long-term goal of a knowledge-based economy, although labour market difficulties, especially among young people, remained unresolved. The findings suggest that, although the Strategy contributed to tangible progress in several areas, uneven achievements among member states limited its overall effectiveness. The study is limited by the reliance on aggregate statistical data and a single methodological approach. Future research should extend the analysis to longer time horizons, include qualitative assessments of national policies, and address implications for the implementation of the European Green Deal and subsequent EU development strategies. Full article

Scholars have long shown that post-separation abuse continues through legal channels and that legal institutions often reinforce existing social relations. Nevertheless, little is known about how abused mothers’ legal experiences shape their understanding of legality and how this dynamic may function to perpetuate coercive control. Drawing on in-depth interviews with 32 Israeli mothers co-parenting with abusive ex-partners, this study offers a phenomenological account of how post-separation abused mothers experience family law proceedings, based on a feminist imperative to bring their voices to center stage. The analysis reveals a dialectical legal consciousness comprising three interconnected orientations—characterized by internal contradictions and tensions that paradoxically serve to maintain rather than disrupt existing power relations: Institutional Trust and Disillusionment in the law’s protective promise, Institutional Asymmetry as experienced from the abused mothers’ perspective, and Recognizing Entrapment—the realization that legal processes reproduce the very dynamics they sought to escape. Abused mothers thus describe a paradoxical relationship with the law of both needing and distrusting a system that mandates continued contact with their abusers. Caught in a second-order abusive relationship, they feel compelled to comply with processes they perceive as harmful. We term this Entrapped Legal Consciousness—a form of legal subjectivity shaped by institutional norms that reconfigure resistance and reinscribe coercive control. This study offers empirical and theoretical insight into how legality may become a mechanism for cultivating continued control. Full article

Understanding household vehicle ownership dynamics in the post-COVID-19 era is critical for designing equitable, resilient, and sustainable transportation policies. This study employs an interpretable machine learning framework to model household vehicle ownership using data from the 2022 National Household Travel Survey (NHTS)—the first nationally representative U.S. dataset collected after the onset of the pandemic. A binary classification task distinguishes between single- and multi-vehicle households, applying an ensemble of algorithms, including Random Forest, XGBoost, Support Vector Machines (SVM), and Naïve Bayes. The Random Forest model achieved the highest predictive accuracy (86.9%). To address the interpretability limitations of conventional machine learning approaches, SHapley Additive exPlanations (SHAP) were applied to extract global feature importance and directionality. Results indicate that the number of drivers, household income, and vehicle age are the most influential predictors of multi-vehicle ownership, while contextual factors such as housing tenure, urbanicity, and household lifecycle stage also exert substantial influence highlighting the spatial and demographic heterogeneity in ownership behavior. Policy implications include the design of equity-sensitive strategies such as targeted mobility subsidies, vehicle scrappage incentives, and rural transit innovations. By integrating explainable artificial intelligence into national-scale transportation modeling, this research bridges the gap between predictive accuracy and interpretability, contributing to adaptive mobility strategies aligned with the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities), SDG 10 (Reduced Inequalities), and SDG 13 (Climate Action). Full article

Background: Hip fractures are common in the elderly and often require ICU admission post-surgery due to high ASA scores and comorbidities. Length of hospital stay after ICU is a crucial indicator affecting patient recovery, complication rates, and healthcare costs. This study aimed to develop and validate a machine learning-based model to predict the factors associated with extended hospital stay (>7 days from surgery to discharge) in hip fracture patients requiring postoperative ICU care. The findings could help clinicians optimize ICU bed utilization and improve patient management strategies. Methods: In this retrospective single-centre cohort study conducted in a tertiary ICU in Turkey (2017–2024), 366 ICU-admitted hip fracture patients were analysed. Conventional statistical analyses were performed using SPSS 29, including Mann–Whitney U and chi-squared tests. To identify independent predictors associated with extended hospital stay, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied for variable selection, followed by multivariate binary logistic regression analysis. In addition, machine learning models (binary logistic regression, random forest (RF), extreme gradient boosting (XGBoost) and decision tree (DT)) were trained to predict the likelihood of extended hospital stay, defined as the total number of days from the date of surgery until hospital discharge, including both ICU and subsequent ward stay. Model performance was evaluated using AUROC, F1 score, accuracy, precision, recall, and Brier score. SHAP (SHapley Additive exPlanations) values were used to interpret feature contributions in the XGBoost model. Results: The XGBoost model showed the best performance, except for precision. The XGBoost model gave an AUROC of 0.80, precision of 0.67, recall of 0.92, F1 score of 0.78, accuracy of 0.71 and Brier score of 0.18. According to SHAP analysis, time from fracture to surgery, hypoalbuminaemia and ASA score were the variables that most affected the length of stay of hospitalisation. Conclusions: The developed machine learning model successfully classified hip fracture patients into short and extended hospital stay groups following postoperative intensive care. This classification model has the potential to aid in patient flow management, resource allocation, and clinical decision support. External validation will further strengthen its applicability across different settings. Full article

High-energy (710 MeV) Bi ion track morphology in polycrystalline silicon nitride was investigated during post-irradiation annealing. Using both in-situ and ex-situ transmission electron microscopy, we monitored the recovery of crystallinity within initially amorphous ion track regions. In-situ annealing involved heating samples from room temperature to 1000 °C in 50 °C increments, each held for 10 s. We observed a steady decrease in both the size and number of tracks, with only a small number of residual crystalline defects remaining at 1000 °C. Ex-situ annealing experiments were conducted at 400 °C, 700 °C, and 1000 °C for durations of 10, 20, and 30 min. Complete restoration of the crystalline lattice occurred after 30 min at 700 °C and 20 min at 1000 °C. Due to inherent differences in geometry, heat flow, and stress conditions between thin lamella and bulk specimens, in-situ and ex-situ results cannot be compared. Molecular dynamics simulations further revealed that track shrinkage begins in cells within picoseconds, supporting the notion that recrystallization can start on very short timescales. Overall, these findings demonstrate that thermal recrystallization of damage induced by swift heavy ion irradiation in polycrystalline Si₃N₄ is possible. This study provides a foundation for future research aimed at better understanding radiation damage recovery in this material. Full article