The rapid growth in global population necessitates efficient energy management solutions for sustainable living. Smart Building Energy Management Systems (SBEMS) play a crucial role in achieving this goal by leveraging automation and advanced analytics. This study proposes a novel Deep Learning and IoT-based SBEMS approach to predict energy consumption, classify buildings into energy-demand clusters, and optimize the monitoring and operation of electrical equipment. While traditional statistical methods have been widely used for load forecasting, recent advancements in deep learning provide robust alternatives to address the inherent complexity of nonlinear energy consumption patterns. This research employs regression analysis and state-of-the-art neural network architectures, including Single-Step and Multi-Step Dense Models, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks, to enhance prediction accuracy. Additionally, K-means clustering is introduced to segment buildings into distinct energy-demand categories, ensuring optimal energy utilization. Unlike prior studies that often lack a comprehensive approach, this work integrates all critical features under a unified framework. By applying these advanced methodologies to a unique dataset, the proposed system demonstrates improved accuracy in energy load forecasting and clustering, providing a significant contribution to the field of smart building energy management. The experimental results demonstrate that CNN and LSTM models significantly outperform conventional statistical approaches in capturing nonlinear energy consumption patterns. These outcomes support proactive energy scheduling, peak-demand mitigation, and scalable smart building energy management, offering practical value for facility managers, utility operators, and policymakers.

The COVID-19 pandemic exposed critical vulnerabilities in healthcare systems worldwide, placing healthcare workers (HCWs) at severe infection risk through direct patient contact. Epidemiological data confirm that HCWs were approximately seven times more likely to develop severe COVID-19 than other occupations, with over 7000 HCW deaths recorded globally by mid-2020. This paper presents the design and laboratory proof-of-concept validation of an IoT-based remote patient-monitoring system prototype—the IoT-Based Isolation Ward Monitoring System Prototype—designed to eliminate unnecessary patient-to-HCW physical contact while maintaining continuous, real-time physiological surveillance. The system integrates multi-sensor hardware comprising an AD8232 ECG module, a MAX30100 pulse oximeter, an NTC thermistor, and an MQ-135 CO2 sensor. These sensors interface with an Arduino UNO for data acquisition, while localized edge computing is executed on a Raspberry Pi 3B. A convolutional neural network (CNN) trained on the MIT-BIH Arrhythmia Database classifies heartbeats into five distinct categories. By utilizing SMOTE resampling on 109,446 samples, the network achieves an on-device inference latency of under 200 ms. The sensor data are transmitted to a Firebase Realtime Database via an authenticated REST API, which synchronizes data across dual front-end interfaces: a LabVIEW desktop dashboard for clinical oversight and a cross-platform Flutter mobile application for mobile monitoring. End-to-end technical validation under controlled laboratory conditions confirmed round-trip cloud latencies between 300 and 800 ms, error-free threshold alert generation, and sub-second latency for the integrated chat utility. The proposed system uniquely combines hardware sensing, ML-based ECG classification, cloud storage, a LabVIEW physician dashboard, and bidirectional doctor–patient mobile communication into a single unified, low-cost platform.

The rapid advancement of AI generation technologies has led to the widespread use of AI-generated multimedia content, including images, videos, and 3D contents, across various applications. While significant progress has been made in quality evaluation for 2D content, evaluating the quality of 3D content synthesized from single image remains an underexplored problem. To bridge this gap, we introduce the first comprehensive subjective evaluation database tailored for assessing the quality of 3D content generated from single image. Our database, named AIGC-SI23DCQA, includes three distinct categories of input images, i.e., realistic images, AI-generated images, and computer graphic (CG) images, with 100 images in each category. Using five representative single-image-to-3D algorithms, we produce 1,500 3D contents and collect 94,500 annotations across three quality dimensions, including texture fidelity, shape accuracy, and overall quality. Based on the constructed database, we first benchmark and evaluate the performance of existing quality assessment methods revealing their limitations in addressing this novel task. Thus, we further propose a novel objective quality assessment method, termed I3DQA, for effective single-image-to-3D content quality assessment. Specifically, I3DQA first extracts the reference features from the source image, and the multi-modal features from the generated 3D content, including the projected video, patches, and large-multimodal model (LMM) features. These features are integrated through symmetric transformer blocks, enabling effective quality-related feature fusion and score prediction. Extensive experiments demonstrate the superior performance of our method and validate the effectiveness of its components. This work provides a foundational resource and a robust framework for advancing research in this emerging field, and our database and model will be released.

MicroRNAs in sepsis diagnosis: A systematic review and meta-analysis toward evidence-based biomarker development.

Centrality measures quantify node importance, but they typically evaluate it globally across an entire network. However, nodes often belong to distinct categories, and a user analyzing influence may prioritize certain categories over others. It is therefore natural to evaluate node importance according to a specific preference ranking of these categories. To address this, we extend standard core decomposition and the well-known peeling algorithm to a lexicographic setting, introducing lexicographic core decomposition and lexipeeling, which we show is a special case of weighted node core decomposition. We evaluate lexipeeling against 79 baselines on 7 real-world networks. Lexipeeling ranks the true top spreader higher than any other baseline on 5 of 7 datasets, with MRR ranging from 0.478 on iPhone-Samsung to 1.0 on PolBlogs, PolBooks, and LastFM-Asia. However, like standard coreness, it assigns the same rank to many nodes, so weak spreaders may appear among the highest ranked ones. To address this, we propose composite methods that break lexipeeling's ties using a secondary measure. Among these, lexipeeling-degree most reliably places truly influential nodes among its highest ranked ones across all datasets. Our code is publicly available: https://github.com/rdenni/Lexicoreness.

Abstract Suicide among young people is a major yet preventable public health issue. Existing national and international policies and guidelines advocate to implement suicide prevention efforts within educational institutions, emphasizing on mental health promotion, early identification, and structured referral pathways through institutional mechanisms such as student wellness teams (SWTs). However, implementation of these efforts may remain challenging due to multiple systemic and operational barriers. One key limitation is the reliance on traditional categorization of determinants of suicide into “risk” and “protective” factors, which is inadequate to capture and address the dynamic, contextual, and time-sensitive nature of student distress within academic environments. In this article, we propose reorganizing these determinants into distinct categories across three operational dimensions – stability, predictability, and modifiability to better reflect the temporal and contextual characteristics of stressors within educational settings. Furthermore, these determinant categories can be grouped into three strategic clusters – Structural Baseline, Stress Wave, and Acute Escalation – to guide SWT-based interventions. The new framework conceptualizes suicide among students as a dynamic and evolving trajectory shaped by continuous interactions among multiple factors across structural, cyclical, and acute domains. By moving beyond traditional categorization, this conceptual reframing will enable the development of more actionable, context-sensitive, and time-responsive prevention strategies based on the realities of educational settings. In addition, the new framework offers a multidimensional, governance-informed, and operationally actionable model for suicide prevention in educational institutions. Furthermore, it provides a practical roadmap for strengthening institutional mental health systems and improving the effectiveness of SWT-led interventions.

Resistance-centered pharmacology of DNA damage response-targeted therapy: Mechanisms, predictive biomarkers, and biomarker-guided adaptive treatment strategies in solid tumors.

Cognitive mechanisms of creative meaning decoding for non-literal language-An eye-tracking study on Chinese logogriphs.

The proteomics analysis of mitochondria-enriched fractions reveals alteration of key mitochondrial pathways in breast cancer cell lines.

Background:Thermogenic fat plays a crucial role in regulating energy metabolism and improving obesity-related metabolic diseases. However, a systematic analysis of the research trends and hotspots in the field of thermogenic fat is lacking. This study aimed to fill this gap by employing bibliometric methods to analyze the global research landscape of thermogenic fat from 2000 to 2023.Methods:A systematic search was conducted in the Web of Science Core Collection to retrieve publications related to thermogenic fat. Various bibliometric tools (HistCite, CiteSpace, and VOSviewer) were used to analyze and visualize the data, including annual publication trends, geographical distribution, institutional and author contributions, citation analysis, and keyword analysis.Results:A comprehensive search yielded a total of 5246 English-language articles, revealing a significant upward trend in the number of publications focused on thermogenic fat over the past 24 years. The United States stands out as the most productive country, with Harvard University emerging as the leading research institution in this field. Bruce M. Spiegelman was identified as a pivotal figure in advancing thermogenic fat research. The journals that have published the highest number of articles in this field include Molecular Metabolism, Journal of Biological Chemistry, and Scientific Reports, with Cell Metabolism receiving the highest number of citations. The primary research keywords cluster into 9 distinct categories, with the most frequently occurring terms being “obesity,” “brown adipose tissue,” “thermogenesis,” “brown adipocytes,” “energy expenditure,” “browning,” “UCP1,” “beige fat,” and “type 2 diabetes.” Notably, white fat browning and beige fat have emerged as cutting-edge research trends.Conclusion:This investigation provides a comprehensive overview of the global research trends in thermogenic fat through bibliometric analysis. Enhanced insights into the historical development and evolutionary trajectory of this field offer a novel perspective on potential future research avenues. The findings of this study are poised to serve as an invaluable resource for researchers, thereby fostering continued advancement in the study of thermogenic fat.

This critical narrative review examines the nonmedical or aesthetic repurposing of pharmacological agents to accelerate weight loss, suppress appetite, manipulate fluid balance, and alter body composition in glamour-, fitness-, and appearance-oriented settings. The review uses the term "chemical diet" as an interpretive framework for this pattern of use and proposes "pharmacological malnutrition" as a conceptual description of drug-associated nutritional vulnerability, rather than as an established clinical diagnosis. Incretin-based therapies have substantial clinical value when prescribed for medically indicated obesity or metabolic disease under appropriate supervision. However, their aesthetic repurposing outside clinical governance may create nutritional and safety concerns, particularly when appetite suppression, gastrointestinal intolerance, restrictive eating, discontinuation-related weight regain, or compounded and counterfeit products are involved. Metformin is discussed mainly because of mechanistic plausibility, vitamin B12 relevance, and potential interaction with training adaptation, rather than because of strong evidence for widespread aesthetic misuse. Stimulants, β2-agonists, diuretics, thyroid hormones, anabolic-androgenic steroids, and selective androgen receptor modulators represent distinct risk categories, with hazards differing by evidence strength, severity, reversibility, and likelihood of interaction. The chemical diet is best understood as an interface between pharmacology, nutrition, and public health rather than simply as off-label drug use. Its highest risk forms involve nonmedical use, pharmacological stacking, restrictive intake, dehydration practices, and uncertain supply chains. Potential consequences include gastrointestinal intolerance, impaired dietary adequacy, lean mass loss, electrolyte instability, renal stress, arrhythmogenic risk, and exposure to falsified, counterfeit, or compounded products. Greater regulatory oversight, nutrition-focused monitoring, and careful distinction between supervised therapy and unsafe aesthetic repurposing are needed.

Intracholecystic neoplasms (ICNs) of gallbladder are a challenging and relatively poorly understood family of tumors. Recently, a distinct category of ICNs, characterized by pedunculated polyps composed of lobules of back-to-back small tubular/acinar units lined by minimally mucinous or nonmucinous cells, has been found to be biologically, behaviorally, and molecularly different from other ICNs, and termed intracholecystic tubular nonmucinous neoplasm (ICTN). To review the clinicopathologic characteristics of ICTN of the gallbladder. This review is based on the literature and the author's own research. ICTNs, initially recognized by their distinctive morphology and growth pattern, have distinct clinicopathologic characteristics. They are unlikely to be diffuse and multifocal like ordinary intracholecystic papillary neoplasms (ICPNs), and they do not appear to confer the field risk that ICPNs do. Instead, they form solitary pedunculated polyps (and often appear as detached debris in the lumen) and occur in the background of uninvolved gallbladder. In fact, they appear to be arising in cholesterol polyps. By morphology and MUC6 expression they resemble pancreatic/biliary intraductal tubulopapillary neoplasms, and they have been also called "complex nonmucinous pyloric gland adenomas" in the past. Although their complexity warrants the diagnosis of high-grade dysplasia/carcinoma in situ (and they have been typically diagnosed as "tubular adenocarcinoma" in Asia and South America), ICTNs are invasion resistant, with no documented invasive carcinoma developing within this lesion to date. The presence of morules, which display nuclear β-catenin expression, places this lesion in the so-called BROCN family of tumors, which are believed to be hormonally driven. Further molecular studies are needed to explore the roots of their behavior.

Accurate prognostic stratification of endometrial carcinoma (EC) remains challenging in resource-limited settings lacking molecular sequencing. We investigated whether immunohistochemical (IHC) assessment of combined L1CAM/β-catenin expression, integrated with mismatch repair (MMR) status and p53 expression, could refine prognostic risk stratification in EC in absence of POLE sequencing. A retrospective cohort study evaluated 140 surgically staged EC cases for L1CAM, β-catenin MMR, and p53 IHC expression with clinicopathological correlation. Survival analysis was performed on 111 cases (median follow up:38 months). L1CAM and β-catenin demonstrated mutually exclusive expression patterns (27.1% and 6.4% respectively; 66.5% double negative). L1CAM + tumors demonstrated significantly worse disease specific survival (DSS) (HR:4, 95%CI: 1.6-9.9) and disease-free survival (DFS) (HR:4.9, 95%CI: 2.2-11.4), compared to double-negative (64.5% vs 90.3% DSS, 41.9% vs 12.5% relapse rate). β-catenin alone didn't predict outcome but contributed to prognostic refinement when combined with L1CAM status. This prognostic gradient persisted in the pMMR/p53wt subgroup (L1CAM + mean DFS: 21.7 months vs double-negative: 69.6 months; p ≤ 0.001). The combined L1CAM/β-catenin IHC profile categorized patients into prognostically distinct categories and offers pragmatic prognostic refinement, particularly for pMMR/p53wt tumors in centers lacking POLE sequencing. This approach doesn't replace comprehensive molecular testing and requires prospective validation before clinical implementation.

ABSTRACT Orthodox Judaism has highly structured beliefs and rituals regarding death, bereavement, and the afterlife and as such, with implications for the impact on the bereavement experience. A qualitative content analysis study was conducted to investigate the experience of 21 Orthodox Jewish participants who had suffered a loss of a close relative within the last five years. Participants were interviewed using a semi-structured interview that inquired about their experiences of death, the funeral, and the year of mourning. Analysis revealed several distinct categories that included rational and mystical continuing bonds, how Torah and rituals may give comfort and meaning, and how family and community helped or hurt the process of bereavement.

Background: The rapid expansion of functional ready-to-drink (RTD) beverages-formulated with prebiotic fibers, botanical extracts, and reduced sugar-has outpaced systematic characterization of their small-molecule composition. Methods: We applied dual-mode untargeted high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS), integrating hydrophilic interaction (HILIC) and reversed-phase C18 separations, to profile five commercial RTD beverages spanning distinct formulation categories: Coca-Cola®, Poppi® Orange, OLIPOP® Cream Soda, Pure Leaf® Unsweetened Black Tea, and BeePop™ Peach + Orange Blossom Honey. Results: Across all products, 478 compounds were structurally annotated at Metabolomics Standards Initiative (MSI) Levels 1 and 2, of which 42 matched compounds with reported bioactivity in a curated literature-based reference database. Seventeen compounds-including the NAD+ precursor trigonelline and multiple B vitamins-were detected across all five products. The number and diversity of compounds with reported bioactivity varied substantially by product and correlated with botanical ingredient complexity. Conclusions: This work presents a qualitative molecular survey of the RTD beverage category using standardized, dual-mode untargeted metabolomics, providing a reference dataset for future targeted quantitation studies.

Peer review is often portrayed as a consensus-driven process in which reviewer recommendations largely determine editorial decisions. However, manuscript-level evidence describing how reviewer disagreement unfolds across review rounds-and how it is resolved in routine editorial practice-remains limited. We conducted a meta-research analysis of anonymized editorial data fromHealthcare(MDPI, Basel, Switzerland; ISSN 2227-9032) (2021-2025), including manuscripts that were ultimately published and manuscripts rejected after external peer review. Reviewer recommendations (accept, minor revision, major revision, reject) were examined at the manuscript level across review rounds. Alignment between reviewer recommendations and editorial outcomes was assessed primarily using the majority recommendation in the final review round, applying a conservative tie-breaking rule (reject > major > minor > accept). Reviewer discordance was defined as the presence of two or more distinct recommendation categories within a review round. Longitudinal trajectories from first to final round were analyzed, with sensitivity analyses using alternative definitions of unfavorable recommendations. The analysis was conducted using anonymized editorial records. The author did not access identifiable reviewer or author information, and the dataset was analyzed in aggregated form to minimize the risk of re-identification. The analysis included 12,187 published manuscripts and 3,819 rejected manuscripts, corresponding to 21,497 and 5,189 review-round records, respectively. Reviewer discordance was common in the first round for both published and rejected manuscripts (74.8% and 85.8%). Across rounds, discordance declined markedly among published manuscripts (to 29.3% in the final round) but remained high among rejected manuscripts (71.4%). Overall alignment between the majority recommendation in the final round and the editorial outcome was high (≈88%) but not absolute: 6.3% of published manuscripts had a majority recommendation of rejection in the final round, and 8.0% retained at least one rejection recommendation. Longitudinally, discordance was frequently resolved among published manuscripts (53.1% transitioned from discordant to concordant), whereas it often persisted among rejected manuscripts (61.4% remained discordant). In routine practice, peer review functions less as a voting mechanism and more as an adjudicative process in which editorial judgment integrates heterogeneous reviewer input. The asymmetric resolution of disagreement across outcomes underscores the central role of editorial decision-making when reviewer recommendations diverge.

The role of forensic evidence in Indonesia's criminal justice

This study examines how inpatients with catatonia responded when treated with or without antipsychotic medications. It employs two metrics to account for both the catatonic symptoms and the more traditionally psychotic symptoms. Length of stay is a secondary metric. The primary investigator retrospectively collected data on 164 patients diagnosed with catatonia on an academic inpatient service from July 2018 through September 2023. The treatments of these patients were separated into two distinct categories: Group A (n = 81) received antipsychotic medication with benzodiazepines and/or electroconvulsive therapy, and Group B (n = 83) received benzodiazepines and/or electroconvulsive therapy without antipsychotics. Scores on admission from the Bush Francis Catatonia Rating Scale and positive symptom subscale from the Positive and Negative Syndrome Scale were collected and compared with scores at discharge. ANOVA analysis was used to compare outcomes. The antipsychotic-treated group demonstrated significantly higher Bush Francis Catatonia Rating Scale scores (p < 0.0001) and positive scale of the Positive and Negative Syndrome Scale scores (p < 0.0001) at discharge than the group receiving only benzodiazepines and/or electroconvulsive therapy. Patients hospitalized multiple times and treated under both conditions were used as their own controls. Bush Francis Catatonia Rating Scale scores (p < 0.0001) and positive scale of the Positive and Negative Syndrome Scale scores (p < 0.0001) were higher at discharge when antipsychotics were added to treatment. Patients treated without antipsychotics showed greater improvement in both their traditional catatonic symptoms and in their psychotic symptoms.

Nontraditional lipid parameters are increasingly recognized for clinical evaluation, yet their comparative ability to predict mortality risk in the general population remains ambiguous. This study aims to systematically explore the associations between 7 nontraditional lipid parameters and all-cause as well as cardiovascular mortality. This study analyzed data from the National Health and Nutrition Examination Survey database spanning the period from 1999 to 2018, which included 19,710 participants. The participants in this study were classified into 2 distinct categories: survivors (n = 16,364) and non-survivors (n = 3346). The study investigated the relationships between nontraditional lipid parameters and both all-cause and cardiovascular mortality. Cox proportional hazards regression models were utilized to evaluate the associations, supplemented by restricted cubic splines, receiver operating characteristic curves, as well as subgroup, interaction, and sensitivity analyses. Elevated levels of the atherogenic index of plasma and remnant cholesterol were associated with all-cause and cardiovascular mortality in unadjusted analyses, with risk rising progressively as levels increased. These associations were most pronounced in individuals under 65 years but were attenuated after adjustment for potential risk factors. Receiver operating characteristic curve analysis indicated that both parameters exhibited modest predictive capabilities, though their clinical significance requires further investigation. This study suggests that both the atherogenic index of plasma and remnant cholesterol are associated with all-cause and cardiovascular mortality in individuals under 65 years, exhibiting a dose–response relationship. Integrating these readily accessible parameters into age-specific risk stratification may be considered for exploratory purposes, but further validation is required before any clinical application. Future prospective studies are warranted to establish causality and evaluate targeted interventions based on these findings.

The Lancet Commission on the Future of Care and Clinical Research in Autism proposed the construct of “profound autism” as a recognizable subtype of autism. Supporters argue that this classification is necessary to ensure that autistic persons with severe impairment receive appropriate research attention and policy support, whereas critics contend that the construct lacks scientific validity and may reflect social or political considerations more than biological distinction. To inform this debate, we evaluate whether the proposed “profound autism” category represents a distinct genetic phenotype using multiple molecular data types collected in a large cohort. Across genomic, transcriptomic, and regulatory analyses, we find no evidence supporting “profound autism” as a biologically distinct phenotypic group. Instead, differences emerge primarily in inferred gene regulatory networks distinguishing nonspeaking from speaking autistic children, suggesting potential regulatory mechanisms contributing to speech ability. These findings suggest that future research into severe impairment may be more productive if focused on specific traits—such as speech impairment—rather than attempting to define a distinct biological subtype within the multidimensional phenomenon of autism.