Technology and Health Care最新文献

ObjectiveEvaluate the usability and user satisfaction of an oral medicine application among public health professionals.MethodsA cross-sectional observational study was conducted with 101 dentists registered in the application, determined through sample size calculation. Data were collected using an online questionnaire. The System Usability Scale (SyUS) was used to assess usability, and an adapted questionnaire evaluated user satisfaction. Variables influencing satisfaction and usability were also analyzed.ResultsMost participants were female (73.3%), aged between 20 and 59 years (98%), with up to 10 years of professional experience (73%). The majority had a specialization (81%), including 24.8% in Collective and Family Health, and 80.2% worked in Primary Health Care. The mean SyUS usability score was 91.25 (scale: 0-100), exceeding the threshold of 70 for a viable product. Participants expressed high satisfaction with the app's theoretical and clinical support. Suggested improvements included a lesion database, chat functionality, interactive notifications, expanded attachment capacity, training initiatives, and broader specialty coverage.ConclusionThe application achieved high usability and satisfaction scores, proving essential, intuitive, and effective. It complements public health systems by supporting diagnosis and treatment, enhancing professional collaboration, and improving care quality while addressing continuity and problem-solving needs.

BackgroundCardiac Arrest (CA) is a major cause of mortality globally, often occurring suddenly without prior warning, making early detection and timely intervention crucial to saving lives. Traditional methods of predicting CA have proven inadequate due to the lack of clear warning signs. With the integration of Machine Learning (ML) techniques, the potential for more accurate early detection and intervention can improve survival rates.ObjectiveThis study proposes a machine learning-based approach for the early prediction of Cardiac Vascular Disease (CVD), which is a primary contributor to CA. The model incorporates various patient data, including lab results, vital signs, and Electrocardiogram (ECG) signal readings, to enhance prediction accuracy.MethodsThe study employs a range of advanced machine learning techniques, including Gradient-Boosting Algorithm (GBA), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN). To process the data, Wavelet Transform (WT) is used to decompose the ECG signals, isolating important features while minimizing noise. Feature selection is performed through an innovative Modified Recursive Feature Elimination (MRFE) technique.ResultsThe machine learning models were validated using the MATLAB simulator, with evaluation metrics including accuracy, precision, recall, and F-score. Among the models, ANN demonstrated the highest performance, achieving 96.3% accuracy, 96.1% precision, 95% recall, and 94.65% F-score.ConclusionThis work demonstrates the effectiveness of machine learning in the early prediction of CA, enabling timely medical intervention and potentially saving lives. The results suggest that the proposed model could become a valuable tool for healthcare professionals in managing and preventing cardiac arrest.

Background: The main treatment plan for bladder cancer is surgery combined with postoperative chemotherapy. Patients often suffer from various adverse reactions after chemotherapy, which reduces the quality of life. Moreover, after chemotherapy, the resistance to chemotherapy drugs of tumor is often increased, and the tumor resistance to chemotherapy drugs is often accompanied by the deterioration of pathological classification, distant metastasis, and the decline of patients' survival period. Recent studies have found that cancer stem cells play a crucial role in tumor proliferation, invasion, metastasis and drug resistance.

Objective: This study would prove oncolytic adenovirus Ad5-E1A-UPII-PSCAE emerges as a potent agent against bladder cancer stem-like cells (CSCs), and triggers reactive oxygen species (ROS) accumulation, culminating in pyroptosis.

Methods: This study is based on transcriptome and proteomic analysis, supplemented by in vivo and in vitro experiments for validation.

Result: In vitro studies confirmed dose-dependent CSC killing (IC50: 3.6 × 10⁹ PFU), while transcriptomic and proteomic analyses highlighted mitochondrial dysfunction and ROS-driven pathways as central mechanisms. In vivo, OV-treated xenografts exhibited significant tumor regression and histopathological necrosis. By exploiting the NO/ROS-pyroptosis axis, Ad5-E1A-UPII-PSCAE overcomes CSC-mediated chemoresistance, offering a dual strategy to eradicate aggressive tumor subpopulations and suppress recurrence.

Conclusion: This study results demonstrated that OVs could kill cancer stem-like cells by promoting ROS levels, which induce cell pyroptosis.

BackgroundAbdominal Henoch - Schönlein purpura (AHSP), being the most prevalent form of Henoch - Schönlein purpura, has a significant impact on the short - term prognosis of the disease and often involves the kidneys, leading to renal complications that affect children's long - term prognosis. However, the existing early assessment criteria for AHSP and its renal complications are inadequate. The urinary proteome may offer valuable insights.ObjectiveTo confirm the significance of urinary proteomics in the early detection of AHSP and its renal complications in children.MethodsThe urinary proteome of AHSP patients (with and without renal involvement) was compared with that of healthy controls using liquid chromatography - tandem mass spectrometry (LC - MS/MS) in data - independent acquisition (DIA) mode. Differentially expressed proteins were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Mfuzz was employed to analyze the expression levels of proteins related to disease onset and progression. The STRING database was used for protein - protein interaction analysis of relevant biological pathways. Selected differential proteins were verified using parallel reaction monitoring (PRM).ResultsA total of 441 dysregulated differentially expressed proteins (DEPs) were associated with the pathogenesis of AHSP, mainly related to cell adhesion, signal transduction or regulation, and reactions or pathways mediated by inflammatory cells or factors, and predominantly enriched in the lysosomal pathway. A total of 275 DEPs related to renal complications of AHSP were mainly associated with immune processes mediated by immunoglobulins, predominantly enriched in the regulatory pathways of the actin cytoskeleton. Time series clustering analysis identified 10 discrete clusters; three upregulated and two downregulated clusters were chosen to form respective panels. These panels involved various biological processes such as immune and inflammatory processes, lipid metabolism, glycosylation, coagulation, oxidative detoxification processes, and the Wnt signaling pathway, with several important biological pathways being enriched. Protein - protein interaction analysis of key pathways revealed three distinct MCODE networks, mainly involving proteins related to immunity, coagulation, collagen, and integrins. In the validation phase, at least eight urinary proteins useful for diagnosing AHSP or its renal complications were identified, demonstrating good diagnostic performance.ConclusionThis study offers novel perspectives on the pathogenesis of AHSP and its renal complications in children, and the related proteins may serve as potential biomarkers for diagnosing AHSP and identifying the onset of renal damage. The findings of this study emphasize the importance of urinary proteomics in understanding the disease mechanisms and provide a basis for further research on early diagnosis and treatment.

BackgroundContinuous Renal Replacement Therapy (CRRT), is essential for managing acute kidney injury (AKI) Dynamic monitoring of transmembrane pressure (TMP) during CRRT is crucial for predicting filter clotting and optimizing filter lifespan, which indirectly supports anticoagulation management.ObjectiveTo prolong the lifespan of CRRT circuits and enhance the precision of anticoagulation therapy by developing a predictive early warning model for CRRT circuit life, based on dynamic TMP monitoring.MethodsWe conducted a retrospective analysis in the ICU of the First Affiliated Hospital of Army Medical University. Leveraging the TMP data recorded by CRRT machines, we established an adaptive real-time predictive modeling framework, termed DTP (Dynamic Transmembrane Pressure Prediction), utilizing Long Short-Term Memory (LSTM) networks. This framework predicts TMP trends as an early indicator of filter clotting. Our models were validated using over 20,000 min of clinical data from 405 CRRT cases, predicting TMP trajectories within 50 min.ResuitsIn simulated treatment evaluations, our LSTM models accurately identified impending TMP increases, achieving recall rates exceeding 0.97 and F2 scores above 0.93. Notably, an average warning time of 23 min was provided prior to the TMP reaching the critical 260 mmHg threshold, indicating substantial filter clotting. An analysis of false alarms revealed patterns consistent with emerging instability and transient artifacts.ConclusionThe personalized early warning model developed within the DTP framework effectively predicts TMP changes, enhancing the accuracy and timeliness of medical interventions. This improvement reduces the incidence of adverse events, maximizes the lifespan of CRRT circuits, and ultimately decreases treatment and personnel costs.

Background: Septic patients with heart failure (HF) have higher mortality and poorer prognosis than patients with either disease alone. Currently, no tool exists for predicting survival rate in such patients.

Objective: This study aimed to develop an interpretable prediction model to predict survival rate for septic patients with HF.

Methods: Severe septic patients with HF were recruited from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database (as external validation cohorts). Four models including Deep Learning Survival (DeepSurv) were constructed and evaluated. Furthermore, Shapley Additive Explanations (SHAP) method was employed to explain the DeepSurv model.

Results: A total of 11,778 patients were included and 22 features were identified to construct the models. Among the 4 models, the DeepSurv model had the highest area under the curve (AUC) values with an AUC of 0.851 (internal) and 0.801 (external) and C-index of 0.8329 (internal) and 0.7816 (external). The mean cumulative/dynamic AUC values exceeded 0.85 in both internal and external validations. The Integrated Brier Score values were well below 0.25, at 0.068 and 0.093, respectively. Furthermore, the Decision Curve Analysis showed that the DeepSurv model achieved favorable net benefit. The SHAP method further confirmed the reliability of the DeepSurv model.

Conclusion: Our DeepSurv model was the most comprehensive interpretable prediction model specifically developed and validated for septic critically ill patients with HF. It demonstrated good model performance in predicting the 28-day survival rate of such patients and will provide valuable decision support for clinicians.

Background: Heart disease is the leading cause of death worldwide and predicting it is a complex task requiring extensive expertise. Recent advancements in IoT-based illness prediction have enabled accurate classification using sensor data.

Objective: This research introduces a methodology for heart disease classification, integrating advanced data preprocessing, feature selection, and deep learning (DL) techniques tailored for IoT sensor data.

Methods: The work employs Clustering-based Data Imputation and Normalization (CDIN) and Robust Mahalanobis Distance-based Outlier Detection (RMDBOD) for preprocessing, ensuring data quality. Feature selection is achieved using the Improved Binary Quantum-based Avian Navigation Optimization (IBQANO) algorithm, and classification is performed with the Deep Long-Term Recurrent Convolutional Network (DLRCN), fine-tuned using the Adaptive Botox Optimization Algorithm (ABOA).

Results: The proposed models tested on the Hungarian, UCI, and Cleveland heart disease datasets demonstrate significant improvements over existing methods. Specifically, the Cleveland dataset model achieves an accuracy of 99.72%, while the UCI dataset model achieves an accuracy of 99.41%.

Conclusion: This methodology represents a significant advancement in remote healthcare monitoring, crucial for managing conditions like high blood pressure, especially in older adults, offering a reliable and accurate solution for heart disease prediction.

Background: Chronic kidney disease (CKD) in the elderly necessitates innovative therapeutic technologies to address systemic complications. Advanced hemodialysis systems, integrating real-time biochemical monitoring and optimized filtration, offer potential enhancements in clinical outcomes, yet their impact on inflammatory pathways and metabolic equilibrium remains underexplored.

Objective: This study evaluated the efficacy of a next-generation hemodialysis system in modulating inflammatory biomarkers, renal function parameters, and calcium-phosphorus homeostasis among elderly CKD patients.

Methods: Eighty-four elderly CKD patients were randomized into a control group (standard therapy) and an intervention group (standard therapy + advanced hemodialysis). The intervention utilized a fully automated dialysis machine with bicarbonate dialysate, precision-calibrated blood flow (180-200 mL/min), and real-time metabolic tracking. Serum levels of TNF-α, IL-6, IL-1, hs-CRP, BUN, Scr, β2-MG, calcium, phosphorus, and Ca × P were analyzed pre- and post-intervention using ELISA and biochemical assays.

Results: The intervention group demonstrated a higher total efficacy rate (85.71% vs. 64.29%, P < 0.05). Post-treatment, significant reductions in inflammatory markers (TNF-α: 1.35 ± 0.24 vs. 4.06 ± 0.42 ng/mL; IL-6: 13.05 ± 1.52 vs. 17.62 ± 2.24 ng/L), renal toxins (BUN: 7.82 ± 1.75 vs. 10.12 ± 2.02 mmol/L; Scr: 401.32 ± 15.76 vs. 489.95 ± 16.14 μmol/L), and phosphorus (1.62 ± 0.34 vs. 2.16 ± 0.46 mmol/L) were observed (P < 0.05). Calcium levels improved (3.19 ± 0.56 vs. 2.26 ± 0.53 mmol/L), alongside stabilized Ca × P products (52.92 ± 5.05 vs. 60.34 ± 7.06 mg²/dL).

Conclusion: Advanced hemodialysis systems significantly enhance therapeutic outcomes in elderly CKD patients by attenuating inflammation, restoring renal function, and optimizing calcium-phosphorus metabolism. These findings underscore the clinical value of integrating technology-driven dialysis protocols for precision care.

Background: Postoperative hypoxemia is a severe complication in patients undergoing surgery for acute Type A aortic dissection (AAD), with significant impacts on recovery and clinical outcomes. Technological advancements in risk assessment models offer opportunities for early intervention and optimized care.

Objective: To develop and validate a technology-driven predictive model for hypoxemia based on clinical and intraoperative risk factors, enhancing postoperative management strategies.

Methods: A retrospective cohort of 242 patients was analyzed, including 77 with hypoxemia (PaO₂/FiO₂ ≤ 200 mmHg) and 165 without. Key clinical variables, intraoperative factors, and postoperative outcomes were examined. Spearman correlation analysis and receiver operating characteristic (ROC) curve analysis were conducted to identify and validate predictive markers.

Results: Prolonged time from symptom onset to surgery (>48 h), aortic cross-clamp time, and deep hypothermic circulatory arrest time (DHCA) emerged as the most significant predictors (all p < 0.001). DHCA time demonstrated the highest sensitivity (0.961) and area under the curve (AUC = 0.891). Additional significant predictors included intraoperative blood product use and prolonged mechanical ventilation, with cumulative predictive value for hypoxemia risk.

Conclusion: The integration of clinical variables into a technology-enhanced prediction model provides robust early warnings of postoperative hypoxemia risk. Implementing timely surgical interventions and refined intraoperative management can minimize adverse respiratory outcomes, improving recovery in AAD patients.

BackgroundRobot-assisted feeding systems aim to promote independence for individuals with motor impairments. Despite significant technological progress, widespread adoption remains limited due to challenges related to adaptability, safety, and cost.ObjectiveThis review investigates recent advancements in robot-assisted feeding, highlights key technical and usability challenges, and outlines future directions to improve system adaptability, autonomy, and cost-effectiveness.MethodsA systematic literature search was conducted for peer-reviewed articles published in the past decade. The analysis focuses on critical domains including hardware architecture, human-robot interaction (HRI) modalities, and control strategies.ResultsAdvances in artificial intelligence (AI) and HRI have enhanced system autonomy and user adaptability. Nevertheless, unresolved issues persist in handling diverse food types, achieving real-time responsiveness, and minimizing system costs. Emerging solutions-such as adaptive learning, Artificial Intelligence of Things (AIoT) integration, and modular design-offer promising pathways to overcome these barriers and support scalable deployment in real-world care settings.