Search Results (1,047)

Background/Objective: Predicting pharmacological response in cancer remains a key challenge in precision oncology due to intertumoral heterogeneity and the complexity of drug–gene interactions. While machine learning models using multi-omics data have shown promise in predicting pharmacological response, selecting the features with the highest predictive power critically affects model performance and biological interpretability. This study aims to compare computational and biologically informed gene selection strategies for predicting drug response in cancer cell lines and to propose a feature selection strategy that optimizes performance. Methods: Using gene expression and drug response data, we trained models on both data-driven and biologically informed gene sets based on the drug target pathways to predict IC₅₀ values for seven anticancer drugs. Several feature selection methods were tested on gene expression profiles of cancer cell lines, including Recursive Feature Elimination (RFE) with Support Vector Regression (SVR) against gene sets derived from drug-specific pathways in KEGG and CTD databases. The predictability was comparatively analyzed using both AUC and IC₅₀ values and further assessed on proteomics data. Results: RFE with SVR outperformed other computational methods, while pathway-based gene sets showed lower performance compared to data-driven methods. The integration of computational and biologically informed gene sets consistently improved prediction accuracy across several anticancer drugs, while the predictive value of the corresponding proteomic features was significantly lower compared with the mRNA profiles. Conclusions: Integrating biological knowledge into feature selection enhances both the accuracy and interpretability of drug response prediction models. Integrative approaches offer a more robust and generalizable framework with potential applications in biomarker discovery, drug repurposing, and personalized treatment strategies. Full article

This study examines how objective (OSES) and subjective (SSES) socioeconomic status influence adolescent moral reasoning through distinct psychological mechanisms. Analyzing 4122 Chinese adolescents (Mage = 14.38), we found SSES enhanced moral internalization via strengthened social identity, while OSES reduced moral stereotyping through cognitive flexibility. Contrary to expectations, parental emotional warmth failed to buffer against SSES-related declines in internalization, with higher SSES predicting reduced internalization across parenting contexts. Results reveal socioeconomic status operates through dual pathways—social identity processes for SSES and cognitive flexibility for OSES—while challenging assumptions about parenting’s protective role. The findings suggest tailored interventions: identity-building programs for SSES-related moral development and cognitive training for OSES-linked reasoning biases, advancing theoretical understanding of moral development in diverse socioeconomic contexts. Full article

Organismal biosensing leverages the olfactory acuity of living systems to detect volatile organic compounds (VOCs) associated with cancer, offering a low-cost and non-invasive complement to conventional diagnostics. Early studies demonstrate its feasibility across diverse platforms. In C. elegans, chemotaxis assays on urine samples achieved sensitivities of 87–96% and specificities of 90–95% in case–control cohorts (n up to 242), while calcium imaging of AWC neurons distinguished breast cancer urine with ~97% accuracy in a small pilot cohort (n ≈ 40). Trained canines have identified prostate cancer from urine with sensitivities of ~71% and specificities of 70–76% (n ≈ 50), and AI-augmented canine breath platforms have reported accuracies of ~94–95% across ~1400 participants. Insects such as locusts and honeybees enable ultrafast neural decoding of VOCs, achieving 82–100% classification accuracy within 250 ms in pilot studies (n ≈ 20–30). Collectively, these platforms validate the principle that organismal behavior and neural activity encode cancer-related VOC signatures. However, limitations remain, including small cohorts, methodological heterogeneity, and reliance on binary outputs. This review proposes a Dual-Pathway Framework, where Pathway 1 leverages validated indices (e.g., the Chemotaxis Index) for high-throughput screening, and Pathway 2 applies machine learning to high-dimensional behavioral vectors for cancer subtyping, staging, and monitoring. By integrating these approaches, organismal biosensing could evolve from proof-of-concept assays into clinically scalable precision diagnostics. Full article

In response to water scarcity and low agricultural water-use efficiency in arid regions in Northwest China, this study conducted field experiments in Wuwei, Gansu Province, from 2023 to 2024. It aimed to develop a water stress diagnosis model for spring maize to provide a scientific basis for precision irrigation and water management. In this work, two irrigation methods—plastic film-mulched drip irrigation (FD, where drip lines are laid on the soil surface and covered with film) and plastic film-mulched shallow-buried drip irrigation (MD, where drip lines are buried 3–7 cm below the surface under film)—were tested under five irrigation gradients. Multispectral UAV remote sensing data were collected from key growth stages (i.e., the jointing stage, the tasseling stage, and the grain filling stage). Then, vegetation indices were extracted, and the leaf water content (LWC) was retrieved. LWC inversion models were established using Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Regression (SVR). Different irrigation treatments significantly affected LWC in spring maize, with higher LWC under sufficient water supply. In the correlation analysis, plant height (hc) showed the strongest correlation with LWC under both MD and FD treatments, with R² values of −0.87 and −0.82, respectively. Among the models tested, the RF model under the MD treatment achieved the highest prediction accuracy (training set: R² = 0.98, RMSE = 0.01; test set: R² = 0.88, RMSE = 0.02), which can be attributed to its ability to capture complex nonlinear relationships and reduce multicollinearity. This study can provide theoretical support and practical pathways for precision irrigation and integrated water–fertilizer regulation in smart agriculture, boasting significant potential for broader application of such models. Full article

Spaceflight imposes unique physiological stressors that profoundly disrupt immune regulation, including impaired lymphocyte activation, latent viral reactivation, and chronic low-grade inflammation. While structured exercise is the cornerstone countermeasure for musculoskeletal and cardiovascular health, current protocols rarely integrate immune endpoints into their design. This review aims to synthesize current evidence on the immunological effects of exercise in spaceflight and propose a novel framework for immune-focused physical activity guidelines tailored to long-duration missions. Evidence indicates that exercise intensity and modality critically determine immune outcomes. Acute strenuous exercise may transiently suppress immunity via cortisol and reactive oxygen species pathways, whereas chronic moderate-to-vigorous training enhances immune surveillance, reduces systemic inflammation, and supports T-cell and NK-cell function. Exerkines such as IL-15, IL-7, and irisin emerge as central mediators of exercise-induced immunomodulation, with potential applications for spaceflight countermeasures. Incorporating immune health into exercise guidelines represents a necessary paradigm shift for astronaut care. A structured framework—emphasizing aerobic, resistance, and HIIT modalities; moderate-to-vigorous intensity; daily training; immune biomarker monitoring; and integration with nutrition and sleep—can enhance resilience against infection, viral reactivation, and cancer risk. Immune-focused countermeasures will be essential to safeguard astronaut health and ensure mission success on future deep-space expeditions. Full article

Civil engineering today faces the challenge of responding to climate change, rapid urbanization, and the need to reduce environmental impacts. These factors drive the search for more sustainable approaches and the adoption of digital technologies. This article addresses three principal dimensions: advanced low-impact materials, resilient structural designs, and digital tools applied throughout the infrastructure life cycle. To this end, a systematic search was conducted considering studies published between 2020 and 2025, including both experimental and review works. The results show that materials such as geopolymers, biopolymers, natural fibers, and nanocomposites can significantly reduce the carbon footprint; however, they still face regulatory, cost, and adoption barriers. Likewise, modular, adaptable, and performance-based design proposals enhance infrastructure resilience against extreme climate events. Finally, digital tools such as Building Information Modeling, digital twins, artificial intelligence, the Internet of Things, and 3D printing provide improvements in planning, construction, and maintenance, though with limitations related to interoperability, investment, and training. In conclusion, the integration of materials, design, and digitalization presents a promising pathway toward safer, more resilient, and sustainable infrastructure, aligning with the Sustainable Development Goals and the concept of smart cities. Full article

The daily behavioral patterns of giraffes reflect their health status and well-being. Behaviors such as licking, walking, standing, and eating are not only essential components of giraffes’ routine activities but also serve as potential indicators of their mental and physiological conditions. This is particularly relevant in captive environments such as zoos, where certain repetitive behaviors may signal underlying well-being concerns. Therefore, developing an efficient and accurate automated behavior detection system is of great importance for scientific management and welfare improvement. This study proposes a multi-behavior automatic detection method for giraffes based on YOLO11-Pose and the spatial-adaptive two-stream network (SATSN). Firstly, YOLO11-Pose is employed to detect giraffes and estimate the keypoints of their mouths. Observation-Centric SORT (OC-SORT) is then used to track individual giraffes across frames, ensuring temporal identity consistency based on the keypoint positions estimated by YOLO11-Pose. In the SATSN, we propose a region-of-interest extraction strategy for licking behavior to extract local motion features and perform daily behavior classification. In this network, the original 3D ResNet backbone in the slow pathway is replaced with a video transformer encoder to enhance global spatiotemporal modeling, while a Temporal Attention (TA) module is embedded in the fast pathway to improve the representation of fast motion features. To validate the effectiveness of the proposed method, a giraffe behavior dataset consisting of 420 video clips (10 s per clip) was constructed, with 336 clips used for training and 84 for validation. Experimental results show that for the detection tasks of licking, walking, standing, and eating behaviors, the proposed method achieves a mean average precision (mAP) of 93.99%. This demonstrates the strong detection performance and generalization capability of the approach, providing robust support for automated multi-behavior detection and well-being assessment of giraffes. It also lays a technical foundation for building intelligent behavioral monitoring systems in zoos. Full article

Background: Congenital talipes equinovarus (clubfoot) affects 1–2 per 1000 newborns worldwide. The Ponseti method, based on staged manipulations and casting, is the gold standard for correction. However, the biomechanical processes underlying these corrections remain poorly understood, as infants rarely undergo imaging. Computational modeling may offer a non-invasive approach to studying correction pathways and exploring novel applications, such as customized casts. Methods: We developed a proof-of-concept framework using iterative finite element analysis (iFEA) to approximate the surface-level geometric corrections targeted in Ponseti treatment. A 3D surface model of a training clubfoot foot was scanned, meshed, and deformed stepwise under applied computational loads. The model was assumed to be homogeneous and hyperelastic, and correction was quantified using Cavus, Adductus, Varus, Equinus, and Derotation angles. We also introduced a secondary adult leg 3D surface model to assess whether model simplification influences correction outcomes, by comparing a homogeneous soft tissue model with a non-homogeneous model incorporating bone structure. Results: In the training model, iFEA generated progressive deformations consistent with Ponseti correction, with mean angular deviations of ±3.2°. In the adult leg model, homogeneous and non-homogeneous versions produced comparable correction geometries, differing by <2° in outcomes. The homogeneous model required less computation, supporting its use for feasibility testing. Applied loads were computational drivers, not physiological forces. Conclusions: This feasibility study shows that iFEA can reproduce surface-level geometric changes consistent with Ponseti correction, independent of model homogeneity. While not replicating clinical biomechanics, this framework lays the groundwork for future work that incorporates clinician-applied forces, pediatric tissue properties, and patient-specific geometries, with potential applications in customized 3D-printed casts. Full article

(1) Background: IgA nephropathy (IgAN) is a leading cause of chronic kidney disease worldwide. Despite its prevalence, the molecular mechanisms of IgAN remain poorly understood, partly due to limited research scale. Identifying key genes involved in IgAN’s pathogenesis is critical for novel diagnostic and therapeutic strategies. (2) Methods: We identified differentially expressed genes (DEGs) by analyzing public datasets from the Gene Expression Omnibus. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to elucidate the biological roles of DEGs. Hub genes were screened using weighted gene co-expression network analysis combined with machine learning algorithms. Immune infiltration analysis was conducted to explore associations between hub genes and immune cell profiles. The hub genes were validated using receiver operating characteristic curves and area under the curve. (3) Results: We identified 165 DEGs associated with IgAN and revealed pathways such as IL-17 signaling and complement and coagulation cascades, and biological processes including response to xenobiotic stimuli. Four hub genes were screened: three downregulated (FOSB, SLC19A2, PER1) and one upregulated (SOX17). The AUC values for identifying IgAN in the training and testing set ranged from 0.956 to 0.995. Immune infiltration analysis indicated that hub gene expression correlated with immune cell abundance, suggesting their involvement in IgAN’s immune pathogenesis. (4) Conclusion: This study identifies FOSB, SLC19A2, PER1, and SOX17 as novel hub genes with high diagnostic accuracy for IgAN. These genes, linked to immune-related pathways such as IL-17 signaling and complement activation, offer promising targets for diagnostic development and therapeutic intervention, enhancing our understanding of IgAN’s molecular and immune mechanisms. Full article

Objectives: The aim of this study was to synthesize current evidence on artificial intelligence (AI) adoption in cardiovascular imaging across low- and middle-income countries (LMICs), highlighting diagnostic performance, implementation barriers, and potential solutions. Methods: We conducted a systematic review of PubMed, Embase, Cochrane Library, Web of Science, and Scopus for studies evaluating AI-based echocardiography, cardiac CT, or cardiac MRI in LMICs. Articles were screened according to PRISMA guidelines, and data on diagnostic outcomes, challenges, and enabling factors were extracted and narratively synthesized. Results: Twelve studies met the inclusion criteria. AI-driven methods frequently surpassed 90% accuracy in detecting coronary artery disease, rheumatic heart disease, and left ventricular hypertrophy, often enabling task shifting to non-expert operators. Challenges included limited dataset diversity, operator dependence, infrastructure constraints, and ethical considerations. Insights from high-income countries, such as automated segmentation and accelerated imaging, suggest potential for broader AI integration in cardiac MRI and CT. Conclusions: AI holds promise for enhancing cardiovascular care in LMICs by improving diagnostic accuracy and workforce efficiency. However, multi-center data sharing, targeted training, reliable infrastructure, and robust governance are essential for sustainable adoption. This review underscores AI’s capacity to bridge resource gaps in LMICs, offering practical pathways for future research, clinical practice, and policy development in global cardiovascular imaging. Full article

Metabolic reprogramming has recently been recognized as related to immune disorders in ulcerative colitis (UC), but the specific metabolic pathways and genes involved remain unclear. Here, Mendelian randomization confirmed that mannose and mannonate exhibited a negative causal relationship with UC, and that the immune cell phenotype HLA DR on CD33dim HLA DR+ CD11b− mediated the effect of mannonate on UC. Bulk RNA sequencing data revealed that mannose metabolism abnormity is critical for driving the innate and acquired immune response. A well-performing diagnostic model related to mannose metabolism was constructed using SVM analysis, achieving an AUC-ROC value of 0.987 in the training set and an AUC-ROC value of 0.899 in the validation set. Single-cell analysis revealed that epithelial cells in which the mannose metabolism pathway was inactivated demonstrated increased intercell communication with myeloid cells, T cells, and B cells. In vitro experiments confirmed that KHK and AKR1B10 were suppressed under inflammatory stimulation, which may hinder mannose-related metabolism. This study elucidates the protective role of mannose metabolism in UC and provides a novel gene signature for diagnosis and treatment. Full article

The digital economy, as an advanced economic form, exerts a profound yet unclear influence on water environmental quality within large-scale watersheds. Focusing on the Yellow River Basin (YRB), the second-largest river in China, this study investigates this complex relationship. We developed a novel dual-engine coupling model integrating Support Vector Machines (SVM) and Light Gradient Boosting Machines (LightGBM) to establish comprehensive multi-input, multi-output linkages between digital economy indicators and water quality parameters. Results show that (1) There are notable spatial disparities and synergies in the basin, regions with more developed digital economy generally have better water environmental quality. (2) The SVM model effectively captures the complex spatial relationship between digital economy inputs and water quality outputs, with an average training accuracy above 0.80 and average validation accuracy above 0.70, indicating that digital economy variables are sensitive to water quality changes. (3) The LightGBM model identifies key driving factors and contributions, revealing that digital industrialization has a more significant impact on water quality improvement than industrial digitization. Thus, digital industrialization is a crucial pathway for green transformation in large—scale catchments. Full article

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder in which early diagnosis improves quality of life and reduces disability. However, diagnostic delays remain common, particularly in low- and middle-income countries. This study investigated clinical and system-level factors contributing to diagnostic delay in Thailand. Methods: A retrospective chart review was conducted on patients newly diagnosed with PD at Thammasat University Hospital between June 2020 and June 2024. Demographic, clinical, and healthcare access data were analyzed. Diagnostic intervals were defined as onset-to-visit (OTV), visit-to-diagnosis (VTD), and onset-to-diagnosis (OTD). Age-at-onset groups included early-onset Parkinson’s disease (EOPD, <50 years), regular-onset PD, and very-late-onset PD (≥80 years). Results: Of 1093 patients screened, 109 newly diagnosed PD cases met the inclusion criteria. The median OTV was 360 days, and the median VTD was 10 days. Tremor was the most frequent initial symptom (75%). Patients with higher education and extended family support sought care earlier, whereas those under the Universal Coverage Scheme (UCS) experienced longer OTD durations (median, 541 vs. 181 days in privately insured patients). More than half of patients were initially misdiagnosed, especially when first evaluated by non-neurologists. Conclusions: Diagnostic delay in Thai PD patients stems mainly from late help-seeking and inequities in healthcare access. Addressing these gaps requires public awareness, physician training, streamlined UCS referral pathways, and adoption of biomarker-supported digital tools to ensure earlier and more equitable diagnosis. Full article

Bedside echocardiography stands as a cornerstone diagnostic tool in internal medicine, offering rapid, real-time evaluation of cardiac structure and function across a wide spectrum of acute and chronic conditions. Its application, particularly when combined with lung and inferior vena cava (IVC) ultrasound, significantly enhances diagnostic accuracy for fluid balance assessment, dyspnea, and hypotensive states, guiding timely therapeutic decisions. Focused cardiac ultrasound (FoCUS) enables internists to assess left ventricular function, right atrial pressure, valvular abnormalities, and pericardial effusion, facilitating differentiation between cardiac and non-cardiac causes of symptoms such as dyspnea, chest pain, and hemodynamic instability. While operator-dependent, echocardiography can be effectively integrated into internal medicine practice through structured training programs that combine theoretical knowledge with supervised hands-on experience. This integration enhances clinical decision-making, optimizes patient management, and reduces the need for immediate specialist consultation. Widespread adoption of focused ultrasound techniques in internal medicine wards promises not only improved patient outcomes but also more efficient utilization of healthcare resources. Continued education and institutional support are fundamental to embedding echocardiography into routine care, ensuring internists are equipped to leverage this powerful bedside modality. This narrative review aims to underscore the transformative impact of bedside echocardiography in internal medicine, demonstrating its capacity, when combined with lung and IVC ultrasound, to optimize diagnostic pathways and treatment decisions across diverse acute and chronic settings. Full article

Background: Cardiovascular diseases (CVD) are major contributors to global morbidity and mortality, and their association with cognitive impairment has gained increasing attention. Recent studies indicate that the prevalence of post-myocardial infarction (MI) cognitive impairment ranges from 22% to 37%, with attention being one of the most frequently affected domains. Moreover, novel approaches, such as normobaric hypoxic training in cardiac rehabilitation, show potential in improving both cardiovascular and cognitive outcomes. Aim: This narrative review aims to synthesize current evidence on the role of hypoxia in the development of cognitive dysfunction among patients with cardiac diseases, emphasizing shared mechanisms along the heart–brain axis. Methods: We performed a narrative search of PubMed, Scopus, and Web of Science databases using the keywords “hypoxia”, “cognitive impairment”, “myocardial infarction”, “heart failure”, and “CABG surgery”. We included original studies, reviews, and meta-analyses published between 2000 and up to the present in English. Priority was given to peer-reviewed human studies; animal models were included when providing mechanistic insights. Exclusion criteria included case reports, conference abstracts, and non-peer-reviewed sources. Narrative reviews, while useful for providing a broad synthesis, carry an inherent risk of selective bias. To minimize this limitation, independent screening of sources and discussions among multiple authors were conducted to ensure balanced inclusion of the most relevant and high-quality evidence. Results: Hypoxia contributes to cognitive decline through multiple pathophysiological pathways, including blood–brain barrier disruption, white matter degeneration, oxidative stress, and chronic neuroinflammation. The concept of “cardiogenic dementia”, although not yet formally classified, highlights cardiac-related contributions to cognitive impairment beyond classical vascular dementia. Clinical assessment tools such as the Stroop test, Trail Making Test (TMT), and Montreal Cognitive Assessment (MoCA) are useful in detecting subtle executive dysfunctions. Both pharmacological treatments (ACE inhibitors, ARBs) and innovative rehabilitation methods (including normobaric hypoxic training) may improve outcomes. Conclusions: Cognitive impairment in cardiac patients is common, clinically relevant, and often underdiagnosed. Routine cognitive screening after cardiac events and integration of cognitive rehabilitation into standard cardiology care are recommended. Future studies should incorporate cognitive endpoints into cardiovascular trials. Full article