Frontiers in Healthcare Technology is a peer-reviewed journal that focuses on cutting-edge research and advancements in the field of healthcare technology.
Research Progress on Shenling Baizhu Powder in the Treatment of Diabetic Nephropathy
Diabetic nephropathy (DKD) is one of the most common microvascular complications of diabetes mellitus and a major cause of end-stage renal disease (ESRD). It has the characteristics of insidious onset, progressive progression and poor prognosis, which brings a heavy burden to society and families. At present, the clinical treatment of DKD is mainly based on controlling blood glucose, blood pressure and reducing urinary protein, but it is difficult to effectively delay the progression of renal function. Shenling Baizhu Powder, a classic prescription in traditional Chinese medicine, has the effects of invigorating the spleen and replenishing qi, eliminating dampness and relieving diarrhea. It is highly consistent with the core pathogenesis of DKD such as spleen-kidney deficiency and dampness turbidity internal accumulation. In recent years, a large number of basic and clinical studies have confirmed that Shenling Baizhu Powder can effectively improve the clinical symptoms of DKD patients, reduce urinary protein, protect renal function, and has high safety. This article systematically reviews the research progress of Shenling Baizhu Powder in the treatment of DKD from the aspects of TCM pathogenesis, prescription composition and compatibility, modern pharmacology, basic research, clinical application, mechanism of action, existing problems and prospects, in order to provide reference for the clinical application and in-depth research of Shenling Baizhu Powder in the treatment of DKD.
Copyright (c) 2026 Zheyi Xu, Huiying Teng, Xiaona Li, Jiaxin Tang, Yanping Xu, Hongxuan Li, Xin Liu (Author) https://creativecommons.org/licenses/by/4.0
Research Progress and Clinical Translation Prospects of Polysaccharide-Based Drugs in Anti-Hepatocellular Carcinoma Therapy
Hepatocellular carcinoma (HCC), a highly lethal malignancy worldwide, faces significant clinical treatment challenges due to tumor heterogeneity, multidrug resistance, and therapeutic limitations. There is an urgent need for the development of novel, highly effective, and low-toxicity therapeutic agents [1]. Polysaccharides, natural macromolecular compounds widely found in plants, fungi, and marine organisms, possess advantages such as good biocompatibility, multi-target characteristics, and low toxicity, making them a significant focus in anti-HCC drug development [2]. This article systematically reviews the anti-HCC mechanisms of polysaccharide-based drugs, including direct inhibition of tumor cell proliferation, induction of apoptosis, cell cycle arrest, suppression of invasion and metastasis, as well as indirect pathways such as regulating the tumor immune microenvironment and reversing chemotherapy resistance. It elaborates on the anti-HCC activity and structural characteristics of representative polysaccharides from sources like Astragalus, Bupleurum, and Phyllanthus emblica, analyzing the regulatory role of structure-activity relationships on their bioactivity. Furthermore, it summarizes the research progress in applying polysaccharides in nano-targeted delivery systems for HCC. Key challenges hindering the clinical translation of polysaccharide-based drugs, including low bioavailability, insufficiently elucidated mechanisms, and lack of unified quality control standards, are analyzed. Finally, future research directions and clinical application prospects are discussed, aiming to provide a theoretical reference and practical basis for fundamental research and industrial development of polysaccharide-based anti-HCC drugs.
Copyright (c) 2026 Jiaxin Tang, Huiying Teng, Xiaona Li, Yanping Xu, Hongxuan Li, Zheyi Xu, Xin Liu (Author) https://creativecommons.org/licenses/by/4.0
A Proportion-Based Anatomical Landmark Model for Standardized Acupoint Localization Using Digital Body Surface Mapping
Standardized acupoint localization is a longstanding challenge in acupuncture education and clinical practice, as traditional positioning methods rely heavily on textual descriptions, proportional measurements, and practitioner experience, which often results in variability and limited reproducibility. With the growing demand for objective, teachable, and clinically applicable standards, digital body surface mapping provides a promising pathway to support the standardization of acupuncture point localization while preserving the theoretical foundations of traditional Chinese medicine. In this study, we propose a Proportion-Based Anatomical Landmark Model (PB-ALM) for standardized acupoint localization using digital body surface images. The proposed model digitizes classical acupuncture principles—such as proportional measurement (cun) and anatomical landmark referencing—by defining acupoints as relative positions within a proportion-based coordinate system rather than fixed absolute coordinates. Stable body surface anatomical landmarks are used as references, while lightweight computer-assisted image analysis is employed only to support landmark identification and geometric mapping, without reliance on deep learning or complex artificial intelligence techniques. A digital acupoint localization assistance prototype was developed to demonstrate the practical applicability of the proposed framework in acupuncture teaching and clinical guidance. Experimental validation was performed on multiple commonly used limb acupoints by comparing PB-ALM–based localization results with expert manual annotations. The proposed method achieved a mean absolute localization error of 4.2 mm and an RMSE of 5.1 mm. Compared with manual proportional measurement, PB-ALM reduced inter-operator standard deviation from 4.5 mm to 3.0 mm and improved the intraclass correlation coefficient from 0.72 to 0.91, demonstrating enhanced consistency and reproducibility.
Copyright (c) 2026 Lixian Li, Xinyu Shi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
DentaScope-AI: A Real-Time k-mer–Driven CNN-Attention Framework for Rapid Pathogen Identification and Virulence Prediction in Endodontic Infections Using Nanopore Sequencing
Acute endodontic infections, including pulp abscess and periapical abscess, are rapidly progressing polymicrobial conditions in which delayed pathogen identification often leads to empirical antibiotic overuse and suboptimal clinical outcomes. Although real-time long-read sequencing offers new opportunities for point-of-care molecular diagnostics, translating high-error Nanopore data into actionable clinical information remains challenging. To address this gap, we propose DentaScope-AI, a real-time k-mer–driven CNN-Attention framework for rapid pathogen identification and virulence prediction in endodontic infections using Nanopore sequencing. The system integrates multi-scale k-mer encoding (k = 5, 7, 9) with a multi-branch one-dimensional convolutional neural network and an attention-based feature fusion module to capture both taxonomic and functional genomic signatures directly from streaming reads. A multi-task learning strategy simultaneously performs species-level classification and multi-label prediction of antibiotic resistance genes and virulence factors, enabling precision antimicrobial guidance. In a curated dataset comprising 2.3 million simulated and clinical Nanopore reads from 25 common oral pathogens, DentaScope-AI achieved 96.4% species-level accuracy, a 93.1% F1-score for antibiotic resistance prediction, and a 91.8% F1-score for virulence factor identification, while generating stable diagnostic outputs within 25 minutes of sequencing initiation. These results highlight the robustness and real-time clinical applicability of the proposed k-mer–driven AI diagnostic framework.
Copyright (c) 2026 Haojun Xu, Junyan Ge, Yihao Ou (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Dual-Memory Temporal-Spatial Encoder for Acute Stroke Evolution Segmentation
Acute stroke lesions evolve rapidly, making it essential to model both temporal progression signals and anatomical constraints. DME-Net incorporates complementary temporal and spatial memory banks that collaboratively stabilize predictions across different lesion stages. The temporal memory captures evolving intensity and shape patterns from diffusion-weighted and perfusion-weighted imaging, while the spatial memory preserves stable anatomical structures that prevent spurious expansion. A gating mechanism adaptively balances the influence of both memory sources based on lesion characteristics. Evaluated on ISLES2018 (3,263 slices; 228 subjects), DME-Net achieves a Dice of 0.893, outperforming ConvLSTM-UNet (0.813, +9.8%) and 3D-UNet (0.846, +4.7%). HD95 declines from 14.7 mm to 8.9 mm (−39.5%), and false positives decrease by 13.6%.
Copyright (c) 2026 Giulia Bianchi, Marco Conti, Lorenzo Esposito (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Structure Aware Metal Artifact Reduction for Computed Tomography
Metal implants can cause severe artifacts in CT images, including streaking and photon starvation effects that degrade diagnostic visibility. Building on hybrid convolution–attention feature modeling exemplified by CTLformer, this paper introduces a residual learning approach for metal artifact reduction that combines image-domain denoising with sinogram-guided attention cues. The method uses a guidance branch derived from corrected sinograms to steer attention toward artifact-affected regions while preserving bone and soft-tissue edges. Evaluations on a clinical dataset of 4,800 scans (about 120,000 slices) show improvements of 0.9–1.5 dB in PSNR and 0.012–0.020 in SSIM compared with U-Net, CNN-based MAR, and transformer-only baselines. Artifact index metrics also decrease by 10%–18% in implant-adjacent regions.
Copyright (c) 2026 Luca Bernasconi, Martina Keller, Tobias Schmid (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Automatic Breast Cancer WSI Grading Based on TransMIL Framework under Weak Supervision
The grading of breast cancer via Whole Slide Images is a pivotal component of histopathological diagnosis and prognosis, directly influencing therapeutic strategies. Traditional manual grading, primarily based on the Nottingham Grading System, is labor-intensive, subjective, and prone to inter-observer variability. While deep learning has shown promise in automating this process, the gigapixel resolution of whole slide images and the scarcity of pixel-level annotations present significant computational and logistical challenges. This paper investigates the application of the TransMIL framework, a Transformer-based Multiple Instance Learning approach, for the automatic grading of breast cancer under weak supervision. Unlike conventional Multiple Instance Learning methods that rely on independent instance assumptions, TransMIL leverages self-attention mechanisms to model morphological and spatial dependencies between patches across the entire slide. By utilizing only slide-level labels, the proposed method eliminates the need for expensive region-of-interest annotations. We present a comprehensive analysis of the framework's architecture, including the incorporation of pyramid position encoding and conditional convolution to capture multi-scale context. Experimental validation on public datasets demonstrates that the TransMIL-based approach achieves superior classification performance compared to standard multiple instance learning baselines, offering a robust and interpretable solution for computational pathology.
Copyright (c) 2026 Arthur Miller, Jun-Ho Lee (Author) https://creativecommons.org/licenses/by-sa/4.0
Monocular Endoscopic Depth Estimation and 3D Reconstruction Fusing Anatomical Priors and NeRF
Minimally invasive surgery has fundamentally altered the landscape of modern medicine, yet the reliance on monocular endoscopic feeds presents a persistent challenge regarding the loss of depth perception. This limitation forces surgeons to infer three-dimensional geometric structures from two-dimensional projections, increasing cognitive load and the risk of procedural error. While recent advancements in computer vision have introduced deep learning techniques for depth estimation, the specific domain of endoscopy suffers from unique difficulties, including texture scarcity, specular reflections, and complex deformable topology. This paper introduces a novel framework that integrates Neural Radiance Fields (NeRF) with domain-specific anatomical priors to achieve robust dense depth estimation and high-fidelity 3D reconstruction from monocular endoscopic video sequences. By leveraging the implicit continuous representation capabilities of NeRF, we overcome the discretization errors inherent in traditional voxel-based methods. Furthermore, we constrain the optimization process using geometric priors derived from the tubular and cavity-like structures typical of the gastrointestinal tract, thereby regularizing the solution space in ill-posed regions. We present a comprehensive evaluation of our method against state-of-the-art self-supervised learning approaches. Our results demonstrate that fusing anatomical priors with neural implicit representations significantly improves depth consistency and reconstruction accuracy, offering a promising pathway toward real-time intraoperative surgical navigation.
Copyright (c) 2026 Thomas J. Reynolds, Christopher D. Evans, Martha L. Hughes (Author) https://creativecommons.org/licenses/by-sa/4.0
EEG Biomarker Role of Spindle-Slow Wave Coupling in Early Alzheimer's Memory Impairment
Alzheimer's disease represents a growing neurodegenerative crisis characterized predominantly by progressive memory failure and cognitive decline. While the accumulation of amyloid-beta plaques and tau neurofibrillary tangles remains the hallmark of the pathology, recent investigations have identified sleep disturbance as both a precursor to and a consequence of the disease process. Specifically, the precise temporal synchronization between neocortical slow oscillations and thalamic sleep spindles during non-rapid eye movement sleep is critical for the consolidation of declarative memory. This paper explores the hypothesis that the uncoupling of these specific neurophysiological events serves as a robust, non-invasive biomarker for early Alzheimer's disease and amnestic mild cognitive impairment. By reviewing current electroencephalographic methodologies and pathophysiological evidence, we demonstrate that the disruption of phase-amplitude coupling correlates strongly with medial temporal lobe atrophy and amyloid burden in the prefrontal cortex. Furthermore, this disruption predicts the severity of overnight memory retention failure. We argue that quantifying the integrity of spindle-slow wave coupling offers a superior diagnostic sensitivity compared to analyzing sleep macro-architecture alone, providing a window into the functional synaptic integrity of thalamocortical networks before the onset of frank dementia.
Copyright (c) 2026 Hiroshi Tanaka, Elizabeth Roberts (Author) https://creativecommons.org/licenses/by-sa/4.0
Overcoming Data Scarcity in Pediatric Medicine: Federated Learning for Multi-institutional Diagnostic Models Without Data Migration
Pediatric disease diagnosis is often constrained by limited sample sizes, fragmented data distribution, and strict privacy requirements across medical institutions. To address these challenges, this study proposes a dynamic weighted federated learning framework (DW-FL) for multi-institutional pediatric disease diagnosis, enabling collaborative model training without sharing raw patient data. The proposed framework introduces a contribution-aware aggregation strategy that dynamically adjusts client weights based on model performance and data characteristics, and incorporates a weighted loss function to mitigate class imbalance commonly observed in pediatric datasets. Experiments conducted under both independent and non-independent data distribution settings demonstrate that the proposed approach achieves improved diagnostic performance and communication efficiency compared with conventional federated averaging methods. These results indicate that dynamic weighting mechanisms can enhance the robustness of federated learning in heterogeneous pediatric scenarios, providing a feasible solution for privacy-preserving multi-center clinical collaboration.
Copyright (c) 2026 Yan Zeng, Caifeng Li (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Forecasting Pediatric Ward Census:A Machine Learning Model to Optimize Bed Allocation and Reduce Operational Waste
Efficient bed allocation in pediatric wards is a persistent challenge for hospital operations due to strong seasonality, demand uncertainty, and limited resource flexibility. Inappropriate bed allocation may lead to congestion during peak periods and idle capacity during low-demand periods, resulting in operational waste and reduced service quality. This study proposes a machine learning–assisted forecasting framework for pediatric ward census prediction using historical hospital data. A classical time-series model is employed as the baseline to capture long-term trends and seasonal patterns, while a machine learning–based residual learning strategy is introduced to adapt to short-term demand fluctuations. The forecasting results are further translated into actionable indicators for bed allocation and staff scheduling. A case study based on real pediatric ward census data demonstrates that the proposed framework provides stable short-term forecasts and supports proactive hospital resource management. Rather than pursuing extreme prediction accuracy, the study emphasizes operational usability and decision relevance, offering a practical solution to reduce congestion risk and idle bed capacity in pediatric wards.
Longitudinal Study of Public Health Interventions for Aging Populations using Causal Inference Methods
The global demographic shift towards an aging population presents unprecedented challenges for public health systems, necessitating evidence-based interventions that effectively mitigate age-related decline and chronic disease burden. However, evaluating the true efficacy of such interventions is often complicated by time-varying confounding and feedback loops inherent in longitudinal observational data. This paper presents a comprehensive longitudinal study utilizing advanced causal inference methods, specifically Marginal Structural Models and Inverse Probability of Treatment Weighting, to assess the impact of community-based health programs and telemedicine integration on functional independence in adults over the age of sixty-five. By analyzing a ten-year dataset comprising 15,000 participants, we move beyond traditional correlational analyses which often yield biased estimates due to the healthy-user effect or reverse causality. Our findings indicate that when properly adjusted for time-dependent confounders, sustained engagement in community health initiatives significantly reduces the rate of decline in activities of daily living. Furthermore, the causal efficacy of telemedicine interventions varies significantly by baseline mobility status, a nuance often obscured in standard regression models. The results underscore the critical importance of employing robust causal methodologies in epidemiologic research to inform policy decisions that maximize healthspan and resource allocation.
Copyright (c) 2026 Eleanor Vance (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Predicting Efficacy of Immune Checkpoint Inhibitors in Targeted Oncology Therapy using Multi-Modal Deep Learning
The advent of immune checkpoint inhibitors has revolutionized the landscape of oncological treatment, particularly for solid tumors such as melanoma and non-small cell lung cancer. However, the efficacy of these therapies remains heterogeneous, with a significant fraction of patients failing to exhibit a durable objective response. Traditional biomarkers, including PD-L1 expression levels and tumor mutational burden, lack the requisite sensitivity and specificity to accurately stratify patients in a clinical setting. This paper proposes a comprehensive multi-modal deep learning framework designed to predict the therapeutic efficacy of immune checkpoint inhibitors by integrating whole slide histopathology images, genomic sequencing data, and baseline clinical demographics. By employing a late-fusion architecture that utilizes attention mechanisms to weigh the relative importance of distinct modalities, the proposed model captures the complex non-linear interactions between the tumor microenvironment and the host immune system. Experimental results on a large-scale retrospective cohort demonstrate that this multi-modal approach significantly outperforms unimodal baselines and current standard-of-care biomarkers. The study provides a pathway toward precision immuno-oncology, highlighting the critical role of artificial intelligence in deciphering the biological heterogeneity of cancer response mechanisms.
Copyright (c) 2026 James Smith, Robert Anderson, William Miller (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Exploring the Link Between Gut Microbiota and Insomnia Diagnosis Based on the Concept of "If the Stomach is Unharmonious, the Sleep will be Restless"
This template explains and demonstrates how to prepare your camera-ready paper In the dual context of global financial digital transformation and intensified competition Objective: To investigate the association between gut microbiota and the diagnosis and treatment of insomnia. Methods: First, a two-sample Mendelian randomization analysis was conducted between gut microbiota data and insomnia, using the inverse variance weighted method as the primary outcome measure. Subsequently, genetic variants associated with insomnia were annotated to identify corresponding genes. Results: Our analysis identified six pathogenic bacterial taxa associated with insomnia, and found that SUCLG2 and TM9SF2 may serve as key regulatory genes. Conclusion: Our study provides insights for the development of personalized treatment strategies for insomnia and offers preliminary candidate targets for future drug development.
Copyright (c) 2026 Zhe Wang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Research Progress on Small Intestinal Bacterial Overgrowth in Liver Cirrhosis: From Diagnosis via Methane and Hydrogen Breath Tests to Clinical Management
In this paper, the author has tried to present a systematic review of the current research developments on the topic of small intestinal bacterial overgrowth (SIBO) in liver cirrhosis, focusing on its pathophysiology, non - invasive diagnosis, and its impact on patient outcomes. It discusses the mechanisms by which the changes in the intestinal environment consequent to cirrhosis promote abnormal bacteria growth in the small intestine by a synthesis of recent basic and clinical evidence. In addition, it discusses the mechanisms where the increased intestinal permeability, inflammation, and bacterial translocation worsen the progression of diseases and complications. It also evaluates the applicability, limitations, and optimization plans of the hydrogen breath test of this category of patients. The existing evidence indicates that SIBO is strongly common among the patients with cirrhosis and is closely associated with the level of disease and its complications manifested as hepatic encephalopathy, infections, and nutritional disorders. Meanwhile, hydrogen breath test is one of the main screening tools that are suitable to be utilized on cirrhotic patients. To sum up, SIBO has an important role in the course of cirrhosis and emergence of related issues, and it has probable potential in the early identification of high - risk patients and treatment recommendations.
Copyright (c) 2026 Nong Ziqin, Li Hongxuan, Huang Hongna, Huang Jingjing (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Regulatory Mechanisms of Inflammatory Signaling Pathways in Atherosclerosis by Traditional Chinese Medicine
Atherosclerosis is the main pathological basis of cardiovascular disease. According to the Report on Cardiovascular Health and Diseases in China 2023, there are about 290 million patients in China, and related deaths account for more than 40% of total mortality. Inflammation plays a central role in the onset and progression of atherosclerosis by impairing endothelial function, promoting foam cell formation, and destabilizing plaques, thus forming a persistent “inflammation–injury” cycle. Current Western medical treatments, such as lipid-lowering drugs, antiplatelet therapy, and vascular interventions, remain limited by adverse drug reactions and restenosis. Guided by the holistic perspective and pattern-based treatment of Traditional Chinese Medicine (TCM), therapeutic strategies emphasize the regulation of yin–yang balance and pathological factors such as phlegm, stasis, and toxins, showing multi-component and multi-target advantages. Recent studies have demonstrated that single compounds, herb pairs, and formulae can modulate inflammatory pathways including NF-κB, TLRs, MAPK, and JAK/STAT, thereby suppressing pro-inflammatory cytokines, improving endothelial function, regulating lipid metabolism, and slowing plaque progression. This article reviews research advances over the past five years on TCM interventions in atherosclerosis, aiming to provide theoretical support for mechanistic studies and clinical applications.
Copyright (c) 2026 Zhonghao Yuan, Mengqian Liu, Hanwen Zheng, Binghao Pan, Ting Guo, Yan Chen (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Mechanistic Study of Erchen Decoction Combined with Taohong Siwu Decoction in Ameliorating Atherosclerosis in ApoE⁻/⁻ Mice by Inhibiting Ferroptosis via the p53/SLC7A11 Pathway
Objective： This study aimed to evaluate whether Erchen Decoction combined with Taohong Siwu Decoction exerts anti-atherosclerotic effects by modulating the p53/SLC7A11 pathway and ferroptosis. Methods： An atherosclerosis model was established by feeding ApoE⁻/⁻ mice a high-fat diet. The mice were randomly divided into five groups: model group, low-, medium-, and high-dose TCM groups, and a simvastatin group, with a normal-diet group serving as control. Following 4 weeks of treatment, aortic plaque formation was examined, serum levels of SOD, MDA, and GSH were assessed, and protein and mRNA expression in aortic tissues were detected via immunohistochemistry and RT-PCR. Results： Compared with the model group, the combined administration of Erchen Decoction and Taohong Siwu Decoction markedly reduced aortic plaque area, elevated serum SOD and GSH activities, and lowered MDA levels (P < 0.05 or P < 0.01). Additionally, it downregulated mRNA expression of p53, PTGS2, and NOX1 and COX2 protein expression in aortic tissue, while upregulating mRNA expression of SLC7A11, GPX4, and FTH1, as well as FTH1 protein expression. Conclusion: Erchen Decoction combined with Taohong Siwu Decoction may exert anti-atherosclerotic effects by inhibiting p53 expression and activating the SLC7A11/GPX4 axis, thereby alleviating oxidative damage and suppressing ferroptosis.
Copyright (c) 2026 Zhonghao Yuan, Shiyu Lin, Binghao Pan, Mengqian Liu, Ting Guo, Hanwen Zheng, Yan Chen (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Explainable ICU Mortality Prediction with Temporal Attention and Clinically Constrained Feature Attributions
The rapid digitization of healthcare infrastructure has resulted in the proliferation of Electronic Health Records (EHRs), providing a fertile ground for data-driven clinical decision support systems. Among the most critical applications is the prediction of mortality in Intensive Care Units (ICUs), where early identification of deteriorating patients can significantly influence survival outcomes. While Deep Learning (DL) models, particularly Recurrent Neural Networks (RNNs) and Transformers, have demonstrated superior predictive performance compared to traditional scoring systems, their deployment is frequently hindered by a lack of interpretability. This paper introduces a novel architecture that integrates Temporal Attention mechanisms with Clinically Constrained Feature Attributions to predict ICU mortality. Unlike standard interpretability methods that provide post-hoc explanations, our approach incorporates domain knowledge directly into the training process via a regularization term that penalizes physiologically implausible feature associations. We evaluate our model on the MIMIC-III dataset, demonstrating that it achieves state-of-the-art predictive performance while generating explanations that align with clinical consensus. The results indicate that enforcing clinical constraints does not degrade accuracy; rather, it improves the model's robustness and trustworthiness, facilitating safer integration into clinical workflows.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Pathology Whole-Slide Classification with Hierarchical Tokenization and Multi-Instance Learning
The digitization of histopathology has ushered in a new era of computational diagnostics, wherein Whole-Slide Images (WSIs) serve as the primary data modality for automated disease classification and grading. However, the gigapixel resolution of WSIs presents a significant computational bottleneck, necessitating the division of slides into tens of thousands of patches. This granularity introduces a "bag-of-instances" problem typically addressed via Multiple Instance Learning (MIL). While conventional MIL approaches aggregate patch-level features, they often fail to capture long-range spatial dependencies and tissue macro-architecture due to the prohibitive sequence lengths when applied to standard Transformer models. This paper introduces a novel framework: Hierarchical Tokenization with Multi-Instance Learning (HT-MIL). Our approach employs a dynamic, multi-scale tokenization strategy that groups spatially coherent and semantically similar patches into super-tokens before processing them through a hierarchical attention mechanism. This reduces the effective sequence length while preserving local cellular details and global tissue context. We evaluate HT-MIL on two large-scale public benchmark datasets. The results demonstrate that our method achieves state-of-the-art classification performance while significantly reducing computational overhead compared to non-hierarchical vision transformers.
Multi-Instance Learning, Vision Transformers
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Metabolomics-Based Discovery of Biomarkers for Oral Squamous Cell Carcinoma and Their Clinical Utility
The most common pathological type of the oral malignancies is oral squamous cell carcinoma (OSCC). Most patients visit the doctor when they notice signs like pain, ulceration, or dysphagia, and as such, patients have not been identified as soon as possible and recurrence and lymph node metastasis have formed the basis of survival. Metabolomics, which concentrates on small-molecule metabolites, has the potential to generate comprehensive >single cell-type phenotypic readout of tumor cell metabolic reorganization, host immune reactions, nutrition, and microecological disturbances on biofluids and tissues, thereby providing translatable leads in noninvasive screening, risk stratification, and longitudinal follow-up herein OSCC. As a strategy, this article consists of synthizing available evidence as well as systematically re-presenting resources of published findings, providing the discovery pathway of OSCC metabolic biomarkers based on sample types (plasma/serum/saliva/tissue), analytical platforms (NMR and mass spectrometry) and statistical modeling (multivariate discrimination and targeted validation). Quantitative findings directly presented in three published papers are compiled into re-usable graphical evidence: subgroup effects on ROC performance (AUC, sensitivity, specificity) of a plasma multivariate model based on NMR; monotonic fluctuations of key serum metabolites between stages I and IV (Lu et al., 2025); AUC ranking of various biomarkers of selected metabolites in tumor tissue (Zhou et al., 2020). Taken together, the evidence indicates that evidence of OSCC metabolic biomarkers can be extrapolated beyond “case control discrimination to staging aggressiveness stratification in spite of more stringent cohort matching coupled with confounding controls, cross-platform reproducibility, more rigorous targeted quantification workflows and multicenter prospective validation models.
Copyright (c) 2025 Zhou Tianyi, Xu Jiayi, Zhou Wenjing, Wang Mengge, Li Ying (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Progress in Early Diagnosis of Oral Squamous Cell Carcinoma: From Cytology to AI-Based Imaging Analysis
Oral squamous cell carcinoma (OSCC) is the most common type of oral malignancy, and its five-year survival rate is highly dependent on the timing of diagnosis. In recent years, advances in cytology, optical imaging technologies, and artificial intelligence (AI) based image analysis have enriched the available strategies for early detection of OSCC. This review summarizes traditional diagnostic approaches, including oral brush cytology, liquid-based cytology, tissue biopsy, and immunohistochemistry. It further highlights recent developments in optical techniques such as narrow-band imaging and autofluorescence-based methods, and provides an overview of emerging deep learning driven automated recognition technologies. Current evidence suggests that future OSCC early-diagnosis systems will evolve toward multimodal, intelligent, and non-invasive frameworks, offering greater potential for improving clinical outcomes.
Evaluation of Depth-Sensing Accuracy for AR Surgical Guidance in Dynamic Operating Room Environments
Operating rooms pose challenges for depth sensing due to variable illumination and instrument occlusion. We benchmarked Kinect v2, RealSense D455, and HoloLens 2 across 100 trials simulating surgical conditions. Metrics included mean depth error, temporal stability, and robustness to glare. Results showed Kinect v2 error at 2.7 mm, HoloLens 2 at 1.9 mm, and RealSense at 1. 1 mm. Stability under surgeon motion was highest with RealSense (94% valid frames). Recommendations include hybrid calibration workflows and adaptive denoising for clinical adoption.
Research on Influencing Factors of Information Addiction in the Elderly Based on the COM-B Model
[Purpose/Significance] Studying the influencing factors of information addiction in the elderly is conducive to promoting the elderly to use various types of information more scientifically and healthily, and helping them better adapt to the digital transformation of society. [Method/Process] Based on the COM-B model, this study collected 350 valid questionnaires, focusing on three dimensions: Capability (information literacy, cognitive function), Opportunity (technological accessibility, social support level), and Motivation (positive psychological capital, fear of missing out). Empirical research was conducted using structural equation modeling. [Result/Conclusion] The results show that information literacy, cognitive function, social support level, and positive psychological capital have a significant negative impact on the elderly's tendency toward information addiction, while technological accessibility and fear of missing out have a significant positive impact. The mediating effect test reveals that positive psychological capital and fear of missing out play significant mediating roles in multiple pathways.
Copyright (c) 2025 Tingting Li (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Efficient Knowledge-Synthesized Feature Learning for Industrial Food Safety Inspection
The rapid advancement of machine learning and computer vision technologies has revolutionized industrial food safety inspection systems. However, traditional approaches often rely on isolated feature extraction methods that fail to capture the complex interdependencies between visual, spectroscopic, and contextual information critical for comprehensive food safety assessment. This paper proposes a novel Knowledge-Synthesized Feature Learning (KSFL) framework that integrates multi-modal sensory data through a hierarchical knowledge synthesis architecture. The proposed methodology combines Convolutional Neural Networks (CNNs) with domain-specific knowledge graphs to enhance feature representation and improve detection accuracy for various food safety hazards including contamination, spoilage, and foreign object detection. Our experimental evaluation on a comprehensive dataset of 15,000 food samples across five industrial processing facilities demonstrates significant improvements in detection accuracy (94.7%) compared to traditional computer vision approaches (87.2%) and conventional machine learning methods (82.1%). The system achieves real-time processing capabilities with an average inference time of 23.4 milliseconds per image, making it suitable for high-throughput industrial applications. Furthermore, the knowledge synthesis approach demonstrates superior generalization across different food categories and environmental conditions, reducing false positive rates by 31% and improving overall system reliability. This research contributes to the advancement of intelligent food safety inspection systems by providing a robust framework for knowledge-guided feature learning that bridges the gap between automated inspection technology and domain expertise.
Research Progress and Treatment Strategy of Asthma
Asthma, as a common respiratory disease, is primarily caused by airway inflammation and airway wall remodeling, which are also its key pathological features. Its development is closely related to genetic, environmental, and immune system abnormalities. Asthma presents in a variety of phenotypes, including allergic, non-allergic, adult-onset, and persistent airflow limitation types, and these phenotypes may vary with age, gender, and environmental factors. In terms of treatment, inhaled corticosteroids, long-acting β2 receptor agonists, and biologics have become standard therapeutic approaches. In recent years, biologics have provided new treatment hope for T2-high asthma, but further research is still needed for T2-low asthma treatment. Therefore, this review summarizes the epidemiological characteristics, pathophysiological mechanisms, disease classification criteria, and current treatment and prevention strategies of asthma, aiming to provide insights for future exploration of the disease's underlying mechanisms and innovative treatment methods, to alleviate the disease burden and improve patients' health outcomes.
Copyright (c) 2025 Danni Hui (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Progress on Adipose Tissue Dysfunction in the Pathogenesis of Obesity-Related Hypertension
Obesity has become one of the most prominent public health challenges of the new century, and its close association with hypertension constitutes a major global disease burden. Once considered a passive energy reservoir, adipose tissue is now redefined as a metabolically active endocrine and paracrine organ. In obesity, abnormal expansion and dysfunction of adipose tissue drive the elevation of blood pressure through multiple complex and intertwined mechanisms. This review aims to explore in depth the central role of adipose tissue dysfunction in the pathogenesis of obesity-related hypertension, with a particular focus on the latest advances regarding key pathways such as physical compression, imbalance in adipokine secretion, local activation of the renin-angiotensin-aldosterone system (RAAS), chronic low-grade inflammation, and insulin resistance. Understanding these mechanisms is crucial for developing novel strategies for the prevention and treatment of obesity-related hypertension.
Copyright (c) 2025 Shiyu Lin, Ke Liu, Zhonghao Yuan, Hanwen Zheng, Ziyi Gu, Yiwen Fan, Bingbing Ning, Hui Zheng, Zimiao Lin (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Therapeutic Factors in Nature-Based Therapies: Uncovering the Healing Benefits of Integrating Nature into Psychotherapy
While the integration of nature into therapeutic practice, often termed Nature-Based Therapies (NBTs) or ecotherapy, is gaining empirical validation for improving mental health outcomes, the specific mechanisms underlying these benefits remain insufficiently consolidated. Traditional therapeutic factors, such as the therapeutic alliance and catharsis, are well-understood in clinical settings, yet it is unclear how these factors are expressed, altered, or augmented by the introduction of the natural environment as an active component of the treatment. This research investigates the unique and synergistic therapeutic factors inherent in NBTs. The primary objective is to develop a synthesized framework that identifies and analyzes the specific mechanisms through which the natural environment contributes to therapeutic change, differentiating passive environmental exposure from the active integration of nature into psychotherapeutic processes. This paper utilizes a simulated qualitative descriptive methodology, synthesizing data derived from foundational literature and a simulated cohort of NBT practitioners and clients to explore these underlying factors. The analysis culminates in the identification of three primary meta-factors: (1) The Environment as a Restorative Container, facilitating regulation and cognitive restoration; (2) Nature as an Active Co-Therapist, providing metaphorical resonance and processing pathways; and (3) The Ecological Reframing of the Therapeutic Dyad, which de-pathologizes distress and enhances the therapeutic alliance through embodied experience. The findings suggest that the efficacy of NBTs relies not merely on the restorative setting, but on the environment's active role as a catalyst for psychodynamic, cognitive, and relational processes. This framework offers significant implications for clinical training, therapeutic standardization, and the optimization of NBT interventions.
Copyright (c) 2025 Rui Chen (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Wearable Health Devices in Chronic Disease Management: Opportunities, Challenges, and Future Directions
Wearable health devices have emerged as powerful tools in chronic disease management, offering real-time health monitoring, early detection of complications, and improved patient engagement. These devices, including smartwatches, fitness trackers, and specialized medical sensors, are increasingly integrated into healthcare systems to manage conditions such as diabetes, cardiovascular diseases, and respiratory disorders. Despite their potential, challenges remain, including data privacy concerns, device accuracy, interoperability issues, and user adherence. This paper explores the current opportunities, challenges, and future directions for wearable health devices in chronic disease management, highlighting the importance of integrating these technologies with robust data security frameworks and personalized healthcare approaches.
Copyright (c) 2025 Dr. Emily Thompson (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Blockchain Applications in Healthcare: Ensuring Data Security and Patient Privacy
Blockchain technology has emerged as a transformative solution for the healthcare sector, addressing critical challenges in data security, patient privacy, and interoperability. By leveraging decentralized and immutable ledgers, blockchain ensures that medical records remain secure, tamper-proof, and accessible only to authorized parties. This paper explores the applications of blockchain in healthcare, highlighting its role in safeguarding patient data, preventing fraud, reducing operational costs, and enabling efficient data sharing. Furthermore, it discusses current limitations, regulatory challenges, and future prospects for large-scale adoption.
Copyright (c) 2025 Dr. Sophia Martinez (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
"Internet+" Mobile Application for Blood Pressure Management in Hypertensive Patients: A Meta-Analysis
Objective: To investigate the effectiveness of mobile applications in blood pressure control. Methods: Randomized controlled trials (RCTs) examining the effects of "Internet+" mobile application-based nursing interventions on blood pressure management in hypertensive patients were retrieved from five databases: China National Knowledge Infrastructure (CNKI), Wanfang, VIP, PubMed, and Embase. The search spanned up to April 7, 2024. Following literature screening, abstract review, and risk-of-bias assessment, a meta-analysis was conducted using RevMan 5.4.1 software. Results: Seven studies were included, with the following outcome measures analyzed: ① Systolic blood pressure (SBP): MD = 2.60, 98% CI (1.47, 3.73), P < 0.05; ② Diastolic blood pressure (DBP): MD = 1.63, 98% CI (0.52, 2.75), P < 0.05; ③ Medication adherence: SMD = 0.36, 98% CI (0.21, 0.51), P < 0.05. Conclusion: "Internet+" mobile healthcare nursing interventions significantly improve blood pressure management outcomes in hypertensive patients, enhance medication adherence, and promote better blood pressure control.