Frontiers in Healthcare Technology
type: channel
description: Frontiers in Healthcare Technology is a peer-reviewed journal that focuses on cutting-edge research and advancements in the field of healthcare technology.
publisher: INTERNATIONAL STUDY COUNSELOR
language: en
publication name: Frontiers in Healthcare Technology
issn: 3079-6601
items: Research Progress on Shenling Baizhu Powder in the Treatment of Diabetic Nephropathy
items: Research Progress and Clinical Translation Prospects of Polysaccharide-Based Drugs in Anti-Hepatocellular Carcinoma Therapy
items: A Proportion-Based Anatomical Landmark Model for Standardized Acupoint Localization Using Digital Body Surface Mapping
items: DentaScope-AI: A Real-Time k-mer–Driven CNN-Attention Framework for Rapid Pathogen Identification and Virulence Prediction in Endodontic Infections Using Nanopore Sequencing
items: Dual-Memory Temporal-Spatial Encoder for Acute Stroke Evolution Segmentation
items: Structure Aware Metal Artifact Reduction for Computed Tomography
items: Automatic Breast Cancer WSI Grading Based on TransMIL Framework under Weak Supervision
items: Monocular Endoscopic Depth Estimation and 3D Reconstruction Fusing Anatomical Priors and NeRF
items: EEG Biomarker Role of Spindle-Slow Wave Coupling in Early Alzheimer's Memory Impairment
items: Overcoming Data Scarcity in Pediatric Medicine: Federated Learning for Multi-institutional Diagnostic Models Without Data Migration
items: Forecasting Pediatric Ward Census:A Machine Learning Model to Optimize Bed Allocation and Reduce Operational Waste
items: Longitudinal Study of Public Health Interventions for Aging Populations using Causal Inference Methods
items: Predicting Efficacy of Immune Checkpoint Inhibitors in Targeted Oncology Therapy using Multi-Modal Deep Learning
items: Exploring the Link Between Gut Microbiota and Insomnia Diagnosis Based on the Concept of "If the Stomach is Unharmonious, the Sleep will be Restless"
items: Research Progress on Small Intestinal Bacterial Overgrowth in Liver Cirrhosis: From Diagnosis via Methane and Hydrogen Breath Tests to Clinical Management
items: Regulatory Mechanisms of Inflammatory Signaling Pathways in Atherosclerosis by Traditional Chinese Medicine
items: Mechanistic Study of Erchen Decoction Combined with Taohong Siwu Decoction in Ameliorating Atherosclerosis in ApoE⁻/⁻ Mice by Inhibiting Ferroptosis via the p53/SLC7A11 Pathway
items: Explainable ICU Mortality Prediction with Temporal Attention and Clinically Constrained Feature Attributions
items: Pathology Whole-Slide Classification with Hierarchical Tokenization and Multi-Instance Learning
items: Metabolomics-Based Discovery of Biomarkers for Oral Squamous Cell Carcinoma and Their Clinical Utility
items: Progress in Early Diagnosis of Oral Squamous Cell Carcinoma: From Cytology to AI-Based Imaging Analysis
items: Evaluation of Depth-Sensing Accuracy for AR Surgical Guidance in Dynamic Operating Room Environments
items: Research on Influencing Factors of Information Addiction in the Elderly Based on the COM-B Model
items: Efficient Knowledge-Synthesized Feature Learning for Industrial Food Safety Inspection
items: Research Progress and Treatment Strategy of Asthma
items: Progress on Adipose Tissue Dysfunction in the Pathogenesis of Obesity-Related Hypertension
items: Therapeutic Factors in Nature-Based Therapies: Uncovering the Healing Benefits of Integrating Nature into Psychotherapy
items: Wearable Health Devices in Chronic Disease Management: Opportunities, Challenges, and Future Directions
items: Blockchain Applications in Healthcare: Ensuring Data Security and Patient Privacy
items: "Internet+" Mobile Application for Blood Pressure Management in Hypertensive Patients: A Meta-Analysis

Research Progress on Shenling Baizhu Powder in the Treatment of Diabetic Nephropathy
type: item
description: 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.
subject: Articles
  topic: section
subject: Shenling Baizhu Powder
  topic: keywords
subject: Diabetic Nephropathy
  topic: keywords
subject: Spleen-Kidney Deficiency
  topic: keywords
subject: Renal Fibrosis
  topic: keywords
subject: Clinical Application
  topic: keywords
subject: Mechanism of Action
  topic: keywords
creator: Zheyi Xu
creator: Huiying Teng
creator: Xiaona Li
creator: Jiaxin Tang
creator: Yanping Xu
creator: Hongxuan Li
creator: Xin Liu
rights: 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
date: 2026-03-25
publication date: 2026-03-25
starting page: 149
ending page: 170
doi: 10.71465/fht754

Research Progress and Clinical Translation Prospects of Polysaccharide-Based Drugs in Anti-Hepatocellular Carcinoma Therapy
type: item
description: 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.
subject: Articles
  topic: section
subject: Polysaccharides
  topic: keywords
subject: Hepatocellular carcinoma
  topic: keywords
subject: Mechanism of action
  topic: keywords
subject: Immune regulation
  topic: keywords
subject: Structure-activity relationship
  topic: keywords
subject: Nano-delivery system
  topic: keywords
subject: Clinical translation
  topic: keywords
creator: Jiaxin Tang
creator: Huiying Teng
creator: Xiaona Li
creator: Yanping Xu
creator: Hongxuan Li
creator: Zheyi Xu
creator: Xin Liu
rights: 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
date: 2026-03-25
publication date: 2026-03-25
starting page: 134
ending page: 148
doi: 10.71465/fht753

A Proportion-Based Anatomical Landmark Model for Standardized Acupoint Localization Using Digital Body Surface Mapping
type: item
description: 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.
subject: Articles
  topic: section
subject: Acupoint localization
  topic: keywords
subject: Acupuncture standardization
  topic: keywords
subject: Digital body surface model;
  topic: keywords
subject: Proportion-based model
  topic: keywords
creator: Lixian Li
creator: Xinyu Shi
rights: Copyright (c) 2026 Lixian Li, Xinyu Shi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-29
publication date: 2026-03-29
starting page: 123
ending page: 133
doi: 10.71465/fht748

DentaScope-AI: A Real-Time k-mer–Driven CNN-Attention Framework for Rapid Pathogen Identification and Virulence Prediction in Endodontic Infections Using Nanopore Sequencing
type: item
description: 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.
subject: Articles
  topic: section
subject: Nanopore sequencing
  topic: keywords
subject: k-mer analysis
  topic: keywords
subject: deep learning
  topic: keywords
subject: CNN-Attention
  topic: keywords
subject: pathogen identification
  topic: keywords
subject: virulence prediction
  topic: keywords
subject: antibiotic resistance
  topic: keywords
subject: endodontic infection
  topic: keywords
subject: real-time diagnostics
  topic: keywords
creator: Haojun Xu
creator: Junyan Ge
creator: Yihao Ou
rights: Copyright (c) 2026 Haojun Xu, Junyan Ge, Yihao Ou (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-17
publication date: 2026-03-17
starting page: 112
ending page: 122
doi: 10.71465/fht723

Dual-Memory Temporal-Spatial Encoder for Acute Stroke Evolution Segmentation
type: item
description: 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%.
subject: Articles
  topic: section
subject: Acute stroke segmentation
  topic: keywords
subject: temporal-spatial networks
  topic: keywords
subject: lesion evolution modeling
  topic: keywords
subject: ISLES2018
  topic: keywords
subject: DWI/PWI imaging
  topic: keywords
subject: neurovascular analysis
  topic: keywords
creator: Giulia Bianchi
creator: Marco Conti
creator: Lorenzo Esposito
rights: Copyright (c) 2026 Giulia Bianchi, Marco Conti, Lorenzo Esposito (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-05
publication date: 2026-03-05
starting page: 104
ending page: 111
doi: 10.71465/fht710

Structure Aware Metal Artifact Reduction for Computed Tomography
type: item
description: 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.
subject: Articles
  topic: section
subject: Metal artifact reduction
  topic: keywords
subject: CT reconstruction
  topic: keywords
subject: sinogram guidance
  topic: keywords
subject: convolution–attention networks
  topic: keywords
subject: clinical imaging
  topic: keywords
creator: Luca Bernasconi
creator: Martina Keller
creator: Tobias Schmid
rights: Copyright (c) 2026 Luca Bernasconi, Martina Keller, Tobias Schmid (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 95
ending page: 103
doi: 10.71465/fht709

Automatic Breast Cancer WSI Grading Based on TransMIL Framework under Weak Supervision
type: item
description: 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.
subject: Articles
  topic: section
subject: Breast cancer grading
  topic: keywords
subject: Whole Slide Images (WSI)
  topic: keywords
subject: TransMIL framework
  topic: keywords
subject: Weak supervision
  topic: keywords
subject: Multiple Instance Learning (MIL)
  topic: keywords
subject: Transformer-based self-attention
  topic: keywords
creator: Arthur Miller
creator: Jun-Ho Lee
rights: Copyright (c) 2026 Arthur Miller, Jun-Ho Lee (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-01
publication date: 2026-02-01
starting page: 86
ending page: 94
doi: 10.71465/fht688

Monocular Endoscopic Depth Estimation and 3D Reconstruction Fusing Anatomical Priors and NeRF
type: item
description: 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.
subject: Articles
  topic: section
subject: Neural Radiance Fields
  topic: keywords
subject: Monocular Depth Estimation
  topic: keywords
subject: Endoscopic Reconstruction
  topic: keywords
subject: Anatomical Priors
  topic: keywords
creator: Thomas J. Reynolds
creator: Christopher D. Evans
creator: Martha L. Hughes
rights: Copyright (c) 2026 Thomas J. Reynolds, Christopher D. Evans, Martha L. Hughes (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-01
publication date: 2026-02-01
starting page: 76
ending page: 85
doi: 10.71465/fht687

EEG Biomarker Role of Spindle-Slow Wave Coupling in Early Alzheimer's Memory Impairment
type: item
description: 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.
subject: Articles
  topic: section
subject: Alzheimer's Disease
  topic: keywords
subject: Sleep Spindles
  topic: keywords
subject: Slow Oscillations
  topic: keywords
subject: EEG
  topic: keywords
subject: Memory Consolidation
  topic: keywords
creator: Hiroshi Tanaka
creator: Elizabeth Roberts
rights: Copyright (c) 2026 Hiroshi Tanaka, Elizabeth Roberts (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-15
publication date: 2026-02-15
starting page: 69
ending page: 75
doi: 10.71465/fht686

Overcoming Data Scarcity in Pediatric Medicine: Federated Learning for Multi-institutional Diagnostic Models Without Data Migration
type: item
description: 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.
subject: Articles
  topic: section
subject: Federated learning
  topic: keywords
subject: Pediatric diagnosis
  topic: keywords
subject: Data privacy
  topic: keywords
subject: Multi-center collaboration
  topic: keywords
subject: Dynamic aggregation
  topic: keywords
creator: Yan Zeng
creator: Caifeng Li
rights: Copyright (c) 2026 Yan Zeng, Caifeng Li (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-09
publication date: 2026-02-09
starting page: 57
ending page: 68
doi: 10.71465/fht641

Forecasting Pediatric Ward Census:A Machine Learning Model to Optimize Bed Allocation and Reduce Operational Waste
type: item
description: 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.
subject: Articles
  topic: section
subject: pediatric ward census
  topic: keywords
subject: hospital bed allocation
  topic: keywords
subject: machine learning
  topic: keywords
subject: time-series forecasting
  topic: keywords
subject: hospital operations management
  topic: keywords
creator: Yan Zeng
creator: Caifeng Li
rights: Copyright (c) 2026 Yan Zeng, Caifeng Li (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-09
publication date: 2026-02-09
starting page: 48
ending page: 56
doi: 10.71465/fht640

Longitudinal Study of Public Health Interventions for Aging Populations using Causal Inference Methods
type: item
description: 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.
subject: Articles
  topic: section
subject: Causal Inference
  topic: keywords
subject: Aging Epidemiology
  topic: keywords
subject: Marginal Structural Models
  topic: keywords
subject: Public Health Policy
  topic: keywords
creator: Eleanor Vance
rights: Copyright (c) 2026 Eleanor Vance (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-18
publication date: 2026-01-18
starting page: 40
ending page: 47
doi: 10.71465/fht619

Predicting Efficacy of Immune Checkpoint Inhibitors in Targeted Oncology Therapy using Multi-Modal Deep Learning
type: item
description: 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.
subject: Articles
  topic: section
subject: Immuno-oncology
  topic: keywords
subject: Multi-modal Deep Learning
  topic: keywords
subject: Predictive Modeling
  topic: keywords
subject: Computational Pathology.
  topic: keywords
creator: James Smith
creator: Robert Anderson
creator: William Miller
rights: Copyright (c) 2026 James Smith, Robert Anderson, William Miller (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-18
publication date: 2026-01-18
starting page: 31
ending page: 39
doi: 10.71465/fht613

Exploring the Link Between Gut Microbiota and Insomnia Diagnosis Based on the Concept of "If the Stomach is Unharmonious, the Sleep will be Restless"
type: item
description: 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.
subject: Articles
  topic: section
subject: Gut Microbiota
  topic: keywords
subject: Insomnia
  topic: keywords
subject: Traditional Chinese Medicine
  topic: keywords
creator: Zhe Wang
rights: Copyright (c) 2026 Zhe Wang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-28
publication date: 2026-01-28
starting page: 26
ending page: 30
doi: 10.71465/fht597

Research Progress on Small Intestinal Bacterial Overgrowth in Liver Cirrhosis: From Diagnosis via Methane and Hydrogen Breath Tests to Clinical Management
type: item
description: 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.
subject: Articles
  topic: section
subject: Breath hydrogen test
  topic: keywords
subject: Cirrhosis
  topic: keywords
subject: Small intestinal bacterial overgrowth
  topic: keywords
subject: Diagnosis
  topic: keywords
creator: Nong Ziqin
creator: Li Hongxuan
creator: Huang Hongna
creator: Huang Jingjing
rights: Copyright (c) 2026 Nong Ziqin, Li Hongxuan, Huang Hongna, Huang Jingjing (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-18
publication date: 2026-01-18
starting page: 15
ending page: 25
doi: 10.71465/fht587

Regulatory Mechanisms of Inflammatory Signaling Pathways in Atherosclerosis by Traditional Chinese Medicine
type: item
description: 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.
subject: Articles
  topic: section
subject: Atherosclerosis
  topic: keywords
subject: Inflammation; Signaling Pathways
  topic: keywords
subject: Traditional Chinese Medicine
  topic: keywords
subject: NF-κB
  topic: keywords
creator: Zhonghao Yuan
creator: Mengqian Liu
creator: Hanwen Zheng
creator: Binghao Pan
creator: Ting Guo
creator: Yan Chen
rights: 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
date: 2026-01-18
publication date: 2026-01-18
starting page: 7
ending page: 14
doi: 10.71465/fht584

Mechanistic Study of Erchen Decoction Combined with Taohong Siwu Decoction in Ameliorating Atherosclerosis in ApoE⁻/⁻ Mice by Inhibiting Ferroptosis via the p53/SLC7A11 Pathway
type: item
description: 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.
subject: Articles
  topic: section
subject: Erchen Decoction
  topic: keywords
subject: Taohong Siwu Decoction
  topic: keywords
subject: Atherosclerosis
  topic: keywords
subject: Ferroptosis
  topic: keywords
subject: , p53/SLC7A11 Pathway
  topic: keywords
creator: Zhonghao Yuan
creator: Shiyu Lin
creator: Binghao Pan
creator: Mengqian Liu
creator: Ting Guo
creator: Hanwen Zheng
creator: Yan Chen
rights: 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
date: 2026-01-18
publication date: 2026-01-18
starting page: 1
ending page: 6
doi: 10.71465/fht581

Explainable ICU Mortality Prediction with Temporal Attention and Clinically Constrained Feature Attributions
type: item
description: 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.
subject: Articles
  topic: section
subject: ICU Mortality Prediction
  topic: keywords
subject: Deep Learning,
  topic: keywords
subject: Temporal Attention
  topic: keywords
subject: Explainable AI,
  topic: keywords
subject: Clinical Decision Support.
  topic: keywords
creator: Bo Zhu
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 133
ending page: 140
doi: 10.71465/fht554

Pathology Whole-Slide Classification with Hierarchical Tokenization and Multi-Instance Learning
type: item
description: 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.
subject: Articles
  topic: section
subject: Computational Pathology
  topic: keywords
subject: Whole-Slide Imaging
  topic: keywords
subject: Multi-Instance Learning, Vision Transformers
  topic: keywords
subject: Hierarchical Tokenization.
  topic: keywords
creator: Minglei Xie
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 141
ending page: 148
doi: 10.71465/fht555

Metabolomics-Based Discovery of Biomarkers for Oral Squamous Cell Carcinoma and Their Clinical Utility
type: item
description: 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.
subject: Articles
  topic: section
subject: Oral squamous cell carcinoma
  topic: keywords
subject: Metabolomics
  topic: keywords
subject: Liquid biopsy
  topic: keywords
subject: Staging
  topic: keywords
subject: ROC
  topic: keywords
subject: Lipid metabolism
  topic: keywords
creator: Zhou Tianyi
creator: Xu Jiayi
creator: Zhou Wenjing
creator: Wang Mengge
creator: Li Ying
rights: Copyright (c) 2025 Zhou Tianyi, Xu Jiayi, Zhou Wenjing, Wang Mengge, Li Ying (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-23
publication date: 2025-12-23
starting page: 124
ending page: 132
doi: 10.71465/fht491

Progress in Early Diagnosis of Oral Squamous Cell Carcinoma: From Cytology to AI-Based Imaging Analysis
type: item
description: 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.
subject: Articles
  topic: section
subject: Oral squamous cell carcinoma (OSCC)
  topic: keywords
subject: Early diagnosis
  topic: keywords
subject: Cytology
  topic: keywords
subject: Optical imaging
  topic: keywords
subject: Narrow-band imaging
  topic: keywords
subject: Autofluorescence
  topic: keywords
subject: Artificial intelligence (AI)
  topic: keywords
subject: Deep learning
  topic: keywords
creator: Jiayi Xu
creator: Wenjing Zhou
creator: Liping Gui
creator: Hengrui Deng
creator: Tianyi Zhou
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-07
publication date: 2025-12-07
starting page: 117
ending page: 124
doi: 10.71465/fht462

Evaluation of Depth-Sensing Accuracy for AR Surgical Guidance in Dynamic Operating Room Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: depth-sensing evaluation
  topic: keywords
subject: AR surgical navigation
  topic: keywords
subject: device benchmarking
  topic: keywords
subject: operating room robustness
  topic: keywords
creator: Adrian K. Wong
creator: Victor T. Ho
creator: Samuel C. Lee
creator: Mei L. Chan
creator: Carmen Y. Lau
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-30
publication date: 2025-11-30
starting page: 111
ending page: 116
doi: 10.71465/fht453

Research on Influencing Factors of Information Addiction in the Elderly Based on the COM-B Model
type: item
description: [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.
subject: Articles
  topic: section
subject: COM-B Model
  topic: keywords
subject: Information Addiction
  topic: keywords
subject: Elderly
  topic: keywords
subject: Influencing Factors
  topic: keywords
subject: Structural Equation Modeling
  topic: keywords
creator: Tingting Li
rights: Copyright (c) 2025 Tingting Li (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-18
publication date: 2025-10-18
starting page: 91
ending page: 110
doi: 10.71465/fht386

Efficient Knowledge-Synthesized Feature Learning for Industrial Food Safety Inspection
type: item
description: 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.
subject: Articles
  topic: section
subject: Knowledge synthesis
  topic: keywords
subject: feature learning
  topic: keywords
subject: food safety inspection
  topic: keywords
subject: machine learning
  topic: keywords
subject: computer vision
  topic: keywords
subject: industrial automation
  topic: keywords
creator: Eva Smit
creator: Tom de Boer
creator: Lisa Meijer
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 77
ending page: 90
doi: 10.71465/fht373

Research Progress and Treatment Strategy of Asthma
type: item
description: 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.
subject: Articles
  topic: section
subject: Asthma
  topic: keywords
subject: Biopreparations
  topic: keywords
subject: Targeted Therapy
  topic: keywords
subject: Mechanisms of pathology
  topic: keywords
creator: Danni Hui
rights: Copyright (c) 2025 Danni Hui (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-27
publication date: 2025-09-27
starting page: 69
ending page: 76
doi: 10.71465/fht363

Progress on Adipose Tissue Dysfunction in the Pathogenesis of Obesity-Related Hypertension
type: item
description: 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.
subject: Articles
  topic: section
subject: Obesity-related hypertension
  topic: keywords
subject: Adipose tissue dysfunction
  topic: keywords
subject: Pathogenesis
  topic: keywords
creator: Shiyu Lin
creator: Ke Liu
creator: Zhonghao Yuan
creator: Hanwen Zheng
creator: Ziyi Gu
creator: Yiwen Fan
creator: Bingbing Ning
creator: Hui Zheng
creator: Zimiao Lin
rights: 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
date: 2025-09-13
publication date: 2025-09-13
starting page: 64
ending page: 68
doi: 10.71465/fht335

Therapeutic Factors in Nature-Based Therapies: Uncovering the Healing Benefits of Integrating Nature into Psychotherapy
type: item
description: 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.
subject: Articles
  topic: section
subject: Nature-Based Therapies
  topic: keywords
subject: Ecotherapy
  topic: keywords
subject: Therapeutic Factors
  topic: keywords
subject: Psychotherapy Integration
  topic: keywords
subject: Attention Restoration Theory (ART)
  topic: keywords
subject: Biophilia Hypothesis
  topic: keywords
subject: Therapeutic Mechanisms
  topic: keywords
subject: Environmental Psychology
  topic: keywords
creator: Rui Chen
rights: Copyright (c) 2025 Rui Chen (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-12
publication date: 2025-09-12
starting page: 51
ending page: 63
doi: 10.71465/fht332

Wearable Health Devices in Chronic Disease Management: Opportunities, Challenges, and Future Directions
type: item
description: 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.
subject: Articles
  topic: section
subject: wearable health devices
  topic: keywords
subject: chronic disease management
  topic: keywords
subject: telehealth
  topic: keywords
subject: patient engagement
  topic: keywords
subject: data privacy
  topic: keywords
subject: remote monitoring
  topic: keywords
creator: Dr. Emily Thompson
rights: Copyright (c) 2025 Dr. Emily Thompson (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 27
ending page: 33
doi: 10.71465/fht298

Blockchain Applications in Healthcare: Ensuring Data Security and Patient Privacy
type: item
description: 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.
subject: Articles
  topic: section
subject: blockchain
  topic: keywords
subject: healthcare technology
  topic: keywords
subject: data security
  topic: keywords
subject: patient privacy
  topic: keywords
subject: interoperability
  topic: keywords
subject: digital health
  topic: keywords
creator: Dr. Sophia Martinez
rights: Copyright (c) 2025 Dr. Sophia Martinez (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 18
ending page: 26
doi: 10.71465/fht295

"Internet+" Mobile Application for Blood Pressure Management in Hypertensive Patients: A Meta-Analysis
type: item
description: 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.
subject: Articles
  topic: section
subject: Hypertension
  topic: keywords
subject: Internet
  topic: keywords
subject: Meta-analysis.
  topic: keywords
creator: Xuan Fan1,
creator: Jinzhen Jin1
rights: Copyright (c) 2025 Dr. Sophia Martinez (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 8
doi: 10.71465/fht293