Technology and Health Care最新文献

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.

Background: Gliomas are malignant brain tumors with poor prognosis, and aging is believed to play a role in their malignant transformation. However, the relationship between aging and glioma prognosis remains unclear.

Objective: This study aims to construct and validate a prognostic risk model based on aging-related differential expression genes (ARDEGs) to understand their role in glioma prognosis and tumorigenesis, with a particular focus on immune responses.

Methods: ARDEGs were identified between LGG and HGG through LASSO regression and Cox regression. A prognostic risk model was developed and validated. GSEA and KEGG pathway analyses were performed to explore tumorigenic mechanisms in high- and low-risk groups. The correlation between the model genes and immune cell infiltration, as well as immune checkpoint molecules, was also analyzed. The protein expression of NOG was evaluated in glioma cells using WB and IHC.

Results: Three aging-related genes-IGFBP2, AGTR1, and NOG-were identified, and a prognostic model was established. KEGG and GSEA analysis revealed that the high-risk group enriched pathways related to inflammation and immune responses, while the low-risk group showed enrichment in oxidative phosphorylation and metabolism pathways. IGFBP2 and AGTR1 expression correlated positively with immunosuppressive cells and immune checkpoint molecules, whereas NOG showed an opposite trend. NOG protein expression was reduced in glioma cells and lower in high-grade gliomas compared to low-grade gliomas.

Conclusions: The prognostic risk model based on aging-related genes shows strong predictive power for glioma prognosis, highlighting the potential role of immune-related pathways and NOG in tumor progression.

BackgroundCoronary artery disease (CAD) is a leading cause of ischemic heart disease, and accurate identification of coronary lesion-specific ischemia (CLSI) is crucial for treatment. Coronary computed tomography angiography (CCTA) provides detailed visualization of coronary lesions, but its multiparameter analysis for predicting ischemia remains underexplored.ObjectiveTo develop a nomogram prediction model for CLSI based on multiparameters derived from CCTA.MethodsA total of 160 patients with CAD were divided into non-ischemic and ischemic groups according to the target-vessel CT-fractional flow reserve (CT-FFR). The baseline data of the two groups were collected, and the quantitative parameters of CCTA were compared. The predictive value of these parameters for CLSI was analyzed by the receiver operator characteristic (ROC) curve, and independent risk factors were analyzed by logistic regression.ResultsThe ischemic group showed significant differences in maximum diameter stenosis (MDS), maximum area stenosis (MAS), minimum lumen area (MLA), plaque burden (PB), pericoronary fat attenuation index (FAI), and low-attenuation plaque compared to the non-ischemic group (P < 0.05). Logistic regression revealed that MAS, MLA, FAI, and PB were independent risk factors for CLSI. The area under the curve (AUC) for MAS, MLA, FAI, and PB were 0.783, 0.947, 0.804, and 0.935, respectively. The calibration curve of the nomogram showed a good fit to the actual values [0.995 (95%CI: 0.988-1.000)].ConclusionsThis study constructed a nomogram risk prediction model for CLSI based on MAS, MLA, FAI, and PB, which holds significant clinical value.

Background: Post-stroke cognitive impairment (PSCI) poses significant challenges to patient independence, yet technological interventions like high-frequency repetitive transcranial magnetic stimulation (rTMS) remain underexplored in clinical neurorehabilitation.

Objective: This study evaluates the integration of high-frequency rTMS into standard care, focusing on its technological efficacy in modulating neuroplasticity and serum biomarkers to enhance cognitive and functional recovery.

Methods: A retrospective analysis of 80 PSCI patients (2021-2023) compared outcomes between a conventional care group (n = 30) and an rTMS group (n = 50) receiving 20 Hz stimulation (YRD-CCY-I device) targeting the dorsolateral prefrontal cortex. Key metrics included Montreal Cognitive Assessment (MoCA), Barthel Index (BI), cortical silent period (CL), central motor conduction time (CMCT), and serum neurotrophic factors (BDNF, VEGF, IGF-1).

Results: Post-intervention, the rTMS group demonstrated superior MoCA scores (19.25 vs. 15.24, p = 0.001), BI (76.36 vs. 70.13, p = 0.001), and IADL (20.38 vs. 18.13, p = 0.001) compared to controls. Neurophysiological markers revealed prolonged CL (25.30 vs. 24.02 ms, p = 0.001) and shortened CMCT (12.05 vs. 12.98 ms, p = 0.001), alongside elevated BDNF (9.56 vs. 7.34 ng/mL), VEGF (156.48 vs. 110.54 pg/mL), and IGF-1 (153.74 vs. 112.90 ng/mL, p = 0.001). Overall efficacy was 94% for rTMS versus 73.33% for conventional care (p = 0.047).

Conclusion: High-frequency rTMS, as a targeted neurostimulation technology, enhances cognitive recovery and cortical adaptability in PSCI by modulating neuroplasticity and upregulating neurotrophic biomarkers. These findings underscore its potential as a scalable adjunct in technology-driven neurorehabilitation programs.