Search Results (3,861)

The demand for computing power has increased at a rate never seen before due to the quick development of artificial intelligence (AI) technologies and applications. Consequently, AI data centers, referring to computing facilities specifically designed for large-scale artificial intelligence workloads, have become one of the fastest-growing electricity consumers globally. Therefore, it is essential to understand the load characteristics of AI data centers and their impact on the grid. This paper provides a comprehensive review of the evolving energy landscape of AI data centers. Specifically, this paper (i) presents the energy consumption structure in AI data centers and analyzes the key workload features and patterns in four stages, emphasizing how high power density, temporal variability, and cooling requirements shape total energy use, (ii) examines the impacts of AI data centers for power systems, including impacts on grid stability, reliability and power quality, electricity markets and pricing, economic dispatch and reserve scheduling, and infrastructure planning and coordination, (iii) presents key technological, operational and sustainability challenges for AI data centers, including renewable energy integration, waste heat utilization, carbon-neutral operation, and water–energy nexus constraints, (iv) evaluates emerging solutions and opportunities, spanning grid-side measures, data-center-side strategies, and user-side demand-flexibility mechanisms, (v) identifies future research priorities and policy directions to enable the sustainable co-evolution of AI infrastructure and electric power systems. The review aims to support utilities, system operators, and researchers in maintaining reliable, resilient, and sustainable grid operation in the context of the rapid development of AI data centers. Full article

Melatonin, an indolic neuromodulator with putative oncostatic and proposed anti-inflammatory properties, primarily demonstrated in preclinical models, is produced at extrapineal sites—most notably in the gut. Its canonical actions are mediated by high-affinity GPCRs (MT1/MT2) and by NQO2, a cytosolic enzyme with a melatonin-binding site (historically termed “MT3”). A growing body of work highlights a bidirectional interaction between the gut microbiota and host melatonin. We integrated two lines of work: (i) three clinical cohorts—cardiac arrhythmias (n = 111; 46–75 y), epilepsy (n = 77; 20–59 y), and stage III–IV solid cancers (25–79 y)—profiled with stool 16S rRNA sequencing, SCFA measurements, and circulating melatonin/urinary 6-sulfatoxymelatonin and (ii) an age-spanning cognitive cohort with melatonin phenotyping, microbiome analyses, and exploratory immune/metabolite readouts, including a novel observation of melatonin binding on bacterial membranes. Across all three disease cohorts, we observed moderate-to-severe dysbiosis, with reduced alpha-diversity and shifted beta-structure. The core dysbiosis implicated tryptophan-active taxa (Bacteroides/Clostridiales proteolysis and indolic conversions) and depletion of SCFA-forward commensals (e.g., Faecalibacterium, Blautia, Akkermansia, and several Lactobacillus/Bifidobacterium spp.). Synthesised literature indicates that typical human gut commensals rarely secrete measurable melatonin in vitro; rather, their metabolites (SCFAs, lactate, and tryptophan derivatives) regulate host enterochromaffin serotonin/melatonin production. In arrhythmia models, dysbiosis, bile-acid remodelling, and autonomic/inflammatory tone align with melatonin-sensitive antiarrhythmic effects. Epilepsy exhibits circadian seizure patterns and tryptophan–metabolite signatures, with modest and heterogeneous responses to add-on melatonin. Cancer cohorts show broader dysbiosis consistent with melatonin’s oncostatic actions. In the cognitive cohort, the absence of dysbiosis tracked with preserved learning across ages, and exploratory immunohistochemistry suggested melatonin-binding sites on bacterial membranes in ~15–17% of samples. A unifying microbiota–tryptophan–melatonin axis plausibly integrates circadian, electrophysiologic, and immune–oncologic phenotypes. Practical levers include fiber-rich diets (to drive SCFAs), light hygiene, and time-aware therapy, with indication-specific use of melatonin. Our conclusions regarding microbiota–melatonin crosstalk rely primarily on local paracrine effects within the gut mucosa (where melatonin concentrations are 10–400× plasma levels), whereas systemic chronotherapy conclusions depend on circulating melatonin amplitude and phase. This original research article presents primary data from four prospectively enrolled clinical cohorts (total n = 577). Full article

This review examines and hypothesizes cytoprotection as a conceptual therapeutic criterion for antiarrhythmic drugs, referring to the possibility of suppressing arrhythmias while avoiding adverse electrophysiological or systemic effects. Toward a theoretically complete cytoprotective profile—preserving benefits and eliminating toxicity—the criterion was the degree of counteraction of arrhythmias (i.e., bradycardia, tachycardia, atrioventricular (AV) block, ventricular tachycardia (VT), ST-segment changes, prolonged P, PR, QRS, and QT/QTc intervals, and repolarization). Conventional and new antiarrhythmics share class I–IV ≈ partial cytoprotection/narrow range; late INa inhibitors, IKs enhancers, RyR2 stabilizers, gap junction modulators, and atrial-selective antiarrhythmics ≈ partial cytoprotection/more extended range. Still predominantly in preclinical models, stable gastric pentadecapeptide BPC 157, in the clinic, has not demonstrated adverse effects in available human trials (non-cardiac) to date. As a prominent cytoprotection mediator (LD1 not achieved in toxicology studies), it demonstrates well-matched cytoprotective–antiarrhythmic effects, BPC 157 ≈ full cytoprotection/wide-range homeostasis. In vivo, this was across models of hypo-/hyperkalemia, hypermagnesemia, ischemia–reperfusion, myocardial infarction, drug-induced arrhythmias (including local anesthetics), and vascular occlusion. BPC 157 restores sinus rhythm, normalizes P/QRS/QT intervals, prevents AV block, suppresses VT, attenuates ST-segment changes, and stabilizes heart rate, even when insults are advanced. In vitro, HEK293 studies confirm direct membrane-stabilizing actions: BPC 157 prevents hypokalemia-induced hyperpolarization, reduces hyperkalemia- and hypermagnesemia-induced depolarization, and mitigates local anesthetic-induced Na⁺/Ca²⁺ dysregulation, reflecting bidirectional homeostatic modulation of membrane potential. Thus, to confirm the hypothesis, these BPC 157 conditional, not constitutive effects, in rodent models or in vitro systems (HEK293 cells), mandate expansion of now limited clinical data and mechanisms in human investigated as a translational cytoprotective strategy for complex arrhythmias. Full article

Background: Attention Deficit Hyperactivity Disorder (ADHD) is associated with executive function deficits—such as planning, organization, and prospective memory—that impair autonomy and daily functioning, increase family stress, and create challenges in educational contexts. These consequences underscore the need for accessible and ecologically valid interventions addressing the cognitive, familial, and educational dimensions. Traditional approaches often lack ecological validity, and pharmacological treatment shows a limited impact on functional cognition. Objectives: This protocol outlines a feasibility study of DiverAcción, a web-based telerehabilitation system designed to enhance functional cognition through interactive and gamified tasks integrated into a comprehensive healthcare programme. Methods: A quasi-experimental feasibility study before and after the study will recruit 30 participants aged 9 to 17 years with ADHD. The study comprises an initial face-to-face session for instructions and baseline assessment (T0), followed by twelve supervised online sessions over six weeks. Therapeutic support is provided via integrated chat, email, and two scheduled videoconference check-ins. Feasibility Outcomes: include recruitment, adherence, retention, usability (SUS), acceptability (TAM), satisfaction, user-friendly design, therapeutic alliance (WAI-I), and professionals’ attitudes toward technology (e-TAP-T). Exploratory Measures: include parental self-efficacy (BPSES), parenting stress (PSI-4-SF), ADHD symptomatology (SNAP-IV), executive functioning (BRIEF-2), time management (Time-S), emotional regulation (ERQ-CA), prospective memory (PRMQ-C), and health-related quality of life (KIDSCREEN-52). Analyses emphasize descriptive statistics for feasibility metrics (recruitment, adherence, retention, dropout and fidelity). Assessments are conducted post-intervention (T1) and at three-month follow-up (T2) and analyzed relative to baseline using repeated-measures ANOVA or Friedman tests, depending on data distribution. Conclusions: This feasibility protocol will provide preliminary evidence on the usability, acceptability, and implementation of DiverAcción. Findings will guide refinements and inform the design of a subsequent randomized controlled trial. Full article

Background: Emergency radiology often demands rapid integration of clinical cues, biochemical markers, and imaging findings to support time-critical diagnostic reasoning. However, educational resources that explicitly structure this interdisciplinary integration particularly between radiology and laboratory medicine remain limited. Objective: Our objective was to develop an Integrated Radiology–Biochemistry Diagnostic Flow Framework as a simulation-based methodological proof-of-concept and to document its structure, logic pathways, and internal consistency across common emergency presentations. Methods: We designed an algorithmic framework combining (i) clinical triggers, (ii) targeted biochemical markers with predefined threshold and trajectory rules, (iii) imaging indication and modality selection (US/CTA/MRI/NCCT), and (iv) key radiologic patterns linked to escalation pathways. No patient data or human participants were included. Instead, forty fully synthetic emergency scenarios were generated to populate the framework and to examine logical completeness, branching coherence, and red-flag escalation routes. Results: The framework yielded scenario-specific diagnostic flowcharts that systematically connect biochemical escalation cues with imaging selection and expected imaging findings. The synthetic scenario library demonstrated consistent branching logic across conditions and enabled transparent visualization of imaging-centered decision pathways suitable for simulation-based teaching and structured case discussion. Conclusions: This study reports a reproducible methodological proof-of-concept framework and a synthetic emergency scenario library. Further learner-based studies are required to evaluate usability, perceived realism, and educational effectiveness in authentic training settings. Full article

Background: Cutaneous squamous cell carcinoma (CSCC) is the second most common neoplasm in humans and the most frequent in Brazil (80% in the head and neck region, 20% mortality). Brazil is a world leader in organ transplants (more than 30,000 transplants in 2019). The risk of transplant patients (Tx) developing CSCC is 65–250 times higher, with deeper infiltration, advanced stage, higher local recurrence, occult metastases, and worse survival. Objective: To investigate the prognostic factors of locally advanced cutaneous squamous cell carcinoma (LACSCC) of the head and neck region in transplant patients. Methods: 16-year retrospective, single-center series of patients with LACSCC in the head and neck region who underwent surgical treatment. Clinical and Tx data, clinical/pathological stage, surgical treatment, parotid/regional and distant metastases, recurrence, and survival were analyzed. Results: 156 patients were included: 69.2% women, 65.3 years; mean primary size: 4.24 cm, 66% T3/T4 tumors, 71% grade 2/3 differentiation, 20.5% transplant recipients, follow-up: 33.6 months. The most affected regions were malar/nasal (28.8%) and auricular (19.2%). Surgeries included wide resection with reconstruction (58.9%), exenteration (14.1%), and temporalectomy (11.5%). Univariate analysis: Recurrence: immunosuppressor drugs (p = 0.009), transplanted (p = 0.006), compromised margin (p = 0.049); Mortality: immunosuppression (p = 0.028), total resection and reconstruction (p = 0.013), stage (8ed) III-IV (p < 0.001), compromised margin (p < 0.001), neck metastasis with extranodal extension (p = 0.018). Multivariate analysis: Recurrence: transplanted HR: 3.69 (p < 0.001), neck metastasis extranodal extension HR: 5.41 (p < 0.001), evolution to distant metastasis HR: 5.27 (p < 0.001); Mortality: neck metastasis extranodal extension HR: 1.94, (p = 0.032), compromised margins HR: 1.87 (p = 0.001). Main surgical procedures: temporalectomy HR: 2.83 (p = 0.007), major rhinectomy HR: 2.47 (p = 0.005); Worst overall survival: Tx compared to NonTx (p = 0.069); Worst survival with recurrence: Tx compared to NonTx (p = 0.005). Conclusions: The LACSCC and transplanted (immunosuppressed) group present low survival, worse prognosis; The formulation of specific guidelines to standardize treatment and predict outcomes on this population are strictly necessary. Full article

Modern healthcare systems increasingly depend on wearable Internet of Medical Things (IoMT) devices for the continuous monitoring of patients’ physiological parameters. It remains challenging to differentiate between genuine physiological anomalies, sensor faults, and malicious cyber interference. In this work, we propose a hybrid fusion framework designed to attribute the most plausible source of an anomaly, thereby supporting more reliable clinical decisions. The proposed framework is developed and evaluated using two complementary datasets: CICIoMT2024 for modelling security threats and a large-scale intensive care cohort from MIMIC-IV for analysing key vital signs and bedside interventions. The core of the system combines a supervised XGBoost classifier for attack detection with an unsupervised LSTM autoencoder for identifying physiological and technical deviations. To improve clinical realism and avoid artefacts introduced by quantised or placeholder measurements, the physiological module incorporates quality-aware preprocessing and missingness indicators. The fusion decision policy is calibrated under prudent, safety-oriented constraints to limit false escalation. Rather than relying on fixed fusion weights, we train a lightweight fusion classifier that combines complementary evidence from the security and clinical modules, and we select class-specific probability thresholds on a dedicated calibration split. The security module achieves high cross-validated performance, while the clinical model captures abnormal physiological patterns at scale, including deviations consistent with both acute deterioration and data-quality faults. Explainability is provided through SHAP analysis for the security module and reconstruction-error attribution for physiological anomalies. The integrated fusion framework achieves a final accuracy of 99.76% under prudent calibration and a Matthews Correlation Coefficient (MCC) of 0.995, with an average end-to-end inference latency of 84.69 ms (p95 upper bound of 107.30 ms), supporting near real-time execution in edge-oriented settings. While performance is strong, clinical severity labels are operationalised through rule-based proxies, and cross-domain fusion relies on harmonised alignment assumptions. These aspects should be further evaluated using realistic fault traces and prospective IoMT data. Despite these limitations, the proposed framework offers a practical and explainable approach for IoMT-based patient monitoring. Full article

Background/Objectives: Colorectal cancer (CRC) is a major public health challenge in Brazil, characterized by marked regional disparities. Although national legislation mandates that treatment begin within 60 days after diagnosis, compliance remains inconsistent, particularly within the Unified Health System (SUS). This study aimed to analyze the time to treatment initiation for colon (C18) and rectal (C20) cancer in Brazil from 2013 to 2024, assessing regional inequalities, temporal trends, and factors associated with treatment delays. Methods: We conducted an ecological study using secondary data from the Ministry of Health’s PAINEL-Oncologia platform, which integrates information from SIA/SUS, SIH/SUS, and SISCAN. Records of patients diagnosed with colon and rectal cancer (ICD-10 C18–C20) were evaluated. Temporal trends were analyzed using Joinpoint regression, and factors associated with delayed treatment initiation (>60 days) were identified through multiple logistic regression models. Results: Persistent discrepancies were observed between diagnostic and treatment trends from 2013 to 2024, with the Annual Percent Change (APC) for diagnosis exceeding that for treatment, particularly among adults aged 55–69 years. The Southeast and South regions accounted for over 70% of all diagnosed cases, starkly contrasting with the less than 25% in the North and Northeast. More than 50% of patients across all clinical stages initiated treatment after the legally mandated 60-day period. Women with rectal cancer had a 28% higher risk (RR = 1.28) of being diagnosed at stage IV. Chemotherapy was the predominant initial therapeutic modality, while the need for combined chemo-radiotherapy was associated with markedly elevated risk ratios for delay (e.g., RR = 26.53 for stage IV rectal cancer). Treatment initiation delays (>60 days) were significantly associated with residence in the North/Northeast regions, female sex (for rectal cancer), advanced-stage disease, and complex therapeutic regimens. Conclusions: The study demonstrates persistent regional inequalities and highlights a substantial mismatch between diagnostic capacity and therapeutic availability in Brazil. These gaps contribute to treatment delays and reinforce the need to strengthen and expand oncological care networks to ensure equitable access and improve outcomes, particularly in underserved regions. Full article

The triad association among type 2 diabetes mellitus (T2DM), metabolic associated fatty liver disease (MAFLD), and incretin secretion dysfunction, including GLP-1 (glucagon-like peptide-1) secretion dysfunction, maintains a critical cardiovascular risk and liver-related mortality. The aim of this study is to establish interactions between the GLP-1 plasma levels and metabolic syndrome clusters and adipokines profile (leptin, adiponectin, resistin) and proinflammatory cytokines (TNFα, IL-6, IL1β, IL-17) in diabetic subjects with or without MAFLD. The data revealed that insulin resistance (HOMA-IR) is present in all groups. MAFLD is more common in men than in women. The average FLI score in group IV was ≥70, confirming the diagnosis of MAFLD. The disorder of GLP-1 secretion is more pronounced in women than in men. HOMA-IR is negatively associated with plasma GLP-1 depletion in the MAFLD, T2DM, and MAFLD + T2DM groups. Adiponectin levels are decreased in all groups, as for GLP-1. In contrast, leptin, resistin, TNFα, IL-6, IL-1β, and IL-17 levels show an inverse correlation with GLP-1. GLP-1 accurately reflects metabolic and inflammatory status in subjects with MAFLD, T2DM, and diabetes—steatosis. The applied multivariate linear regression model confirms a highly significant association between MAFLD and GLP-1. It appears that plasma GLP-1 can be considered as biomarker in MAFLD and T2DM related to sex-gender disparities. Longitudinal studies are required to confirm these data. Full article

Background/Objectives: Lithium (Li), silicon (Si), and boron (B) are proposed nutritional trace elements with potential roles in metabolic, neurobiological, endocrine, inflammatory, and bone-related processes. This review provides a critical synthesis of data on Li–Si–B, emphasizing (i) physiological and mechanistic pathways, (ii) human clinical relevance, (iii) shared biological domains, and (iv) safety considerations. Methods: A narrative review was conducted across PubMed, Scopus, and Web of Science from inception to January 2025. Predefined search strings targeted dietary, environmental, and supplemental exposures of lithium, silicon, or boron in relation to metabolism, endocrine function, neurobiology, inflammation, bone health, and the gut microbiome. Inclusion criteria required peer-reviewed studies in English. Data extraction followed a structured template, and evidence was stratified into human, animal, cellular, and ecological tiers. Methodological limitations were critically appraised. Results: Li, Si, and B influence overlapping molecular pathways including oxidative stress modulation, mitochondrial stability, inflammatory signaling, endocrine regulation, and epithelial/gut barrier function. Human evidence remains limited: Li is supported primarily by small trials; Si by bone-related observational studies and biomarker-oriented interventions; and B by metabolic, inflammatory, and cognitive studies of modest sample size. Convergence across elements appears in redox control, barrier function, and neuroimmune interactions, but mechanistic synergism remains hypothetical. Conclusions: Although Li–Si–B display compelling mechanistic potential, current human data are insufficient to justify dietary recommendations or supplementation. Considerable research gaps—including exposure assessment, dose–response characterization, toxicity thresholds, and controlled human trials—must be addressed before translation into public health policy. Full article

Background: Heart failure (HF) affects not only the cardiovascular system but also mental health. The majority of patients with HF experience symptoms of mental disorders, such as depression, which are proportionally related to the severity of HF. This results in a significant comorbidity of HF, which might be associated with poor clinical outcomes, including decreased health-related quality of life (HRQOL). In Türkiye, data concerning the extent of this complication among outpatients with HF are limited. Therefore, the aim of this study was to assess the prevalence of depression in outpatients with HF and consequently the HRQOL; the secondary aim was to identify the related factors contributing to the incidence of depression and HRQOL in patients with HF in Bursa, Türkiye. Methods: An outpatient, descriptive, observational, cross-sectional study was conducted in a cardiology outpatient clinic in Bursa Province, Türkiye, between September and December 2022. The study was conducted via a validated questionnaire consisting of four sections. Depression was measured using the Beck Depression Inventory (BDI) scale, and the HRQOL of HF patients was evaluated using the Turkish version of the Minnesota Living with HF Questionnaire (MLHFQ). Simple linear regression and multiple linear regression analyses were used to determine the effects of variables. Limitations of the study include its design as a descriptive, observational, cross-sectional study from a single center that relies on self-reported data. Results: A total of 166 patients were enrolled, with a mean age of 64.96 ± 11.33 years. Nearly half of the participants had moderate or severe depression (33.1% and 15.7%, respectively). The mean MLHFQ score of the study participants was 54.15 ± 18.20. Patients suffering from severe depression had the lowest HRQOL (71.46 ± 12.4). There was a significant increase in depression level, and a decrease in HRQOL in patients with a duration of HF diagnosis of more than 3 years (p = 0.001), a number of HF hospitalizations (p = 0.001), and those diagnosed with NYHA class IV (p = 0.001). Multiple linear regression analysis revealed a significant relationship between the duration of HF disease, number of comorbidities, number of medications used, and BDI [(0.30 < r: 0.31/0.43/0.43 ≤ 0.70), respectively]. The simple linear regression analysis revealed that the BDI has positive and significant explanatory power for the MLHFQ (F: 168.29; R²: 0.51; t: 12.97; p < 0.001), and 51% of the change in the MLHFQ score is recorded by the BDI (R²: 0.51). Conclusions: The results of this study revealed that comorbid depression and HRQOL are closely related. This was observed in nearly half of the patients with HF, who had comorbid moderate and severe depression, which is associated with poor HRQOL. The factors associated with high depression and poor HRQOL were the duration of HF diagnosis of more than 3 years, an increased number of HF hospitalizations, polypharmacy, and NYHA class IV diagnoses. Full article

This paper constructs a two-way fixed effects model using data from 4623 Chinese A-share listed enterprises from 2011 to 2022, confirming that firm digital transformation can enhance access to sustainable trade credit. Specifically, for every 1% increase in the standard deviation of digital transformation, the trade credit obtained by enterprises increases by 2.14% in relation to their average value. We employed instrumental variable (IV) and propensity score matching (PSM) methods, utilizing the Broadband China pilot policy as a quasi-natural experiment to conduct a multi-period propensity score matching-difference in differences (PSM-DID) analysis to address potential issues of reverse causality and sample selection bias. Mechanism analysis indicates that the diversification of supplier structures, R&D innovation, and market share facilitated by digitalization are three main channels. This effect is particularly significant in state-owned enterprises, mature enterprises, and those with higher social trust. Finally, the study also found that the spillover effects of digital transformation encourage client enterprises to allocate credit resources to downstream firms, thereby promoting the sustainable development of supply chain finance. Furthermore, the digital transformation primarily alleviates short-term credit challenges for enterprises and reduces their reliance on bank credit. Full article

Wetlands in dryland regions are rapidly degrading under the combined effects of climate change and human regulation, yet long-term, seasonally resolved assessments of surface water extent (SWE) and its dynamics remain scarce. Here, we map and analyze seasonal surface water extent (SWE) over the period 2000–2024 in the Ile River Delta (IRD), south-eastern Kazakhstan, using Landsat TM/ETM+/OLI data within the Google Earth Engine (GEE) framework. We integrate multiple indices using the modified Normalized Difference Water Index (mNDWI), Automated Water Extraction Index (AWEI) variants, Water Index 2015 (WI2015), and Multi-Band Water Index (MBWI) with dynamic Otsu thresholding. The resulting index-wise binary water maps are merged via ensemble agreement (intersection, majority, union) to delineate three SWE regimes: stable (persists most of the time), periodic (appears regularly but not in every season), and ephemeral (appears only occasionally). Validation against Sentinel-2 imagery showed high accuracy F1-Score/Overall accuracy (F1/OA ≈ 0.85/85%), confirming our workflow to be robust. Hydroclimatic drivers were evaluated through modified Mann–Kendall (MMK) and Spearman’s (r) correlations between SWE, discharge (D), water level (WL), precipitation (P), and air temperature (AT), while a hybrid ensemble–occurrence framework was applied to identify degradation and transition patterns. Trend analysis revealed significant long–term declines, most pronounced during summer and fall. Discharge is predominantly controlled by stable spring SWE, while discharge and temperature jointly influence periodic SWE in summer–fall, with warming reducing the delta surface water. Ephemeral SWE responds episodically to flow pulses, whereas precipitation played a limited role in this semi–arid region. Spatially, area(s) of interest (AOI)-II/III (the main distributary system) support the most extensive yet dynamic wetlands. In contrast, AOI-I and AOI-IV host smaller, more constrained wetland mosaics. AOI-I shows persistence under steady low flows, while AOI-IV reflects a stressed system with sporadic high-water levels. Overall, the results highlight the dominant influence of flow regulation and distributary allocation on IRD hydrology and the need for ecologically timed releases, targeted restoration, and transboundary cooperation to sustain delta resilience. Full article

Snakebite envenomation (SBE) is a major public health issue in sub-Saharan Africa (SSA), particularly in Cameroon. This Phase IV, multicenter, open-label study was conducted from June 2024 to December 2024 to evaluate the safety and efficacy of PANAF-Premium™, a World Health Organization (WHO)-approved polyvalent antivenom that was introduced in Cameroon in 2023, given that prospectively gathered data and studies on this antivenom’s safety in SSA are limited. In total, 130 victims admitted to four district hospitals in North Cameroon with confirmed SBE were included. Data on envenomation syndromes, clinical outcomes, adverse events, and treatment response were recorded. Echis species were responsible for most bites, while clinical syndromes included hemotoxic (68.5%), cytotoxic (30.8%), and neurotoxic (0.8%) presentations. On average, victims required 3.34 vials and 5.55 days for clinical recovery. Early antivenom administration significantly reduced the number of antivenom vials required to manage the symptoms (p = 0.003) and hospital stay (p = 0.049). Seventeen patients experienced mild to moderate adverse events. Two deaths and a case of kidney injury were noted, all unrelated to antivenom use. These study findings indicate the safety and effectiveness of PANAF-Premium™ antivenom, meeting WHO performance targets. The results highlight the importance of timely antivenom administration in treating SBEs. Full article

Despite widespread adoption, Electronic Medical Record (EMR) systems remain limited in providing intelligent clinical decision support, particularly for early detection of patient deterioration. We present MedROAD V2 (Medical Records Organization, Analysis, and Display), an open-source EMR that integrates AI-driven physiological analysis with comprehensive patient management. The system combines continuous vital sign monitoring and laboratory data using an ensemble of the following four complementary machine learning models: gradient boosting for supervised prediction, isolation forests for anomaly detection, autoencoders for pattern recognition, and Long Short-Term Memory networks for temporal modeling. A novel framework couples these predictions with a large language model (Claude AI) to generate explainable differential diagnoses grounded in medical literature. Validation on the MIMIC-IV database demonstrated excellent 12 h deterioration prediction. MedROAD demonstrates that combining quantitative prediction with natural language explanation can enhance clinical decision support while extending quality care to populations that would otherwise lack access. Full article