Objective To propose a lead attention network-based ST-T change recognition algorithm to detect ECG ST-T changes accurately.Methods Firstly,heartbeat signals were extracted through R-wave localization,and a 12-lead heartbeat matrix was generated by correlation-based screening and merging to realize data augmentation.Secondly,a lead attention module was constructed by combining depthwise convolution(DWConv)with the channel attention squeeze-and-excitation block(SE-block)structure to perceive the differences in ST-T status among electrocardiogram leads.Thirdly,the mapping output by two independent attention modules was fused and splicing with the original signal residual was carried out,so that attention information extraction and original information transfer were enhanced effectively.Finally,SE-ResNet was used as the backbone network to extract signal features to complete the classification and identification of ST-T changes.To validate the recognition performance of the proposed algorithm for ST-T changes in ECG,the 12-lead ECG data of 97 472 patients containing different ECG rhythms were collected for ablation and comparison experiments at the First Affiliated Hospital of Army Medical University.Results The proposed algorithm achieved an AUC of 0.965 with a sensitivity of 90.51%,specificity of 90.23%,positive predictive value of 89.24%and overall accuracy of 90.36%on an independent test set.Comparative analysis demonstrated superior performance to four benchmark architectures,including VGG16,ResNet18,MobileNetV3-Small and ShuffleNet,in terms of both classification accuracy and computational efficiency.Conclusion The algorithm designed can accurately detect ST-T changes and can be used for wearable ECG automatic analysis to assist in the early warning of cardiovascular diseases in both acute and chronic patients and highland residents.[Chinese Medical Equipment Journal,2025,46(7):1-11]

Diaphragmatic dysfunction often occurs in ICU patients with prolonged mechanical ventilation, which seriously affects patients′prognosis. This article reviewed the progress of rehabilitation treatment of diaphragmatic dysfunction in ICU patients with mechanical ventilation, focused on the concept and assessment of diaphragm rehabilitation, the current status, evaluation indexes, emphasized the importance of nurses, analyzed the dilemmas and countermeasures in the application, which aimed to provide a reference for the promotion of diaphragm rehabilitation in clinical practice.

ObjectiveTo explore the effects of the Tai Chi training on bone density， bone turnover markers， and heart rate variability for people with high-risk osteoporosis， and to provide evidence for the prevention of osteoporosis at early stage. MethodsSixty-six cases of people with high risk of osteoporosis were included， and they were divided into 33 cases each in the intervention group and the control group using the random number table method. The control group received osteoporosis health education three times a week， and the intervention group received Tai Chi training under the guidance of a trainer three times a week for 40 mins each time on the basis of the control group， and both groups were intervened for 12 weeks. Dual-energy X-ray absorptiometry was used to measure the bone density of L₁~L₄ vertebrae， bilateral femoral necks and bilateral total hips in the two groups before and after the intervention； enzyme-linked immunosorbent assay was used to determine bone turnover markers before and after the intervention， including pro-collagen type Ⅰ pro-amino-terminal prepropyl peptide （P1NP） and β-collagen type Ⅰ cross-linking carboxy-terminal peptide （β-CTX）. Seven cases with good compliance in the intervention group were selected. After wearing the heart rate sensor， they successively performed Tai Chi training and walking activities recommended by the guideline for 20 mins each， and the heart rate variability （HRV） during exercise was collected， including time-domain indexes such as standard deviation of normal sinus intervals （SDNN）， root-mean-square of the difference between adjacent RR intervals （RMSSD）， frequency-domain metrics such as low-frequency power （LF）， high-frequency power （HF）， and low-frequency/high-frequency power ratio （LF/HF）， as well as nonlinear metrics such as approximate entropy （ApEn）， sample entropy （SampEn）. ResultsFinally， 63 cases were included in the outcome analysis， including 30 cases in the intervention group and 33 cases in the control group. After the intervention， the differences of L₁~L₄ vertebrae， bone density of bilateral femoral neck and bilateral total hip in the intervention group were not statistically significant when compared with those before intervention （P＞0.05）， while the bone density of all parts of the control group decreased significantly compared with that before intervention （P＜0.05）， and the difference in the bone density of the L₁~L₄ vertebrae， bilateral femoral neck， and the right total hip before and after the intervention of the intervention group was smaller than that of the control group （P＜0.05）. The differences in P1NP and β-CTX between groups before and after intervention was not statistically significant （P＞0.05）. Compared with walking exercise， LF decreased， HF increased and LF/HF decreased during Tai Chi exercise （P＜0.05）； the time domain indexes and non-linear indexes between groups had no statistically significant difference （P＞0.05）. ConclusionTai Chi exercise can maintain lumbar， hip， and femoral bone density and improve sympathetic/parasympathetic balance in people at high risk for osteoporosis， but cannot significantly improve bone turnover markers.

OBJECTIVE: This study aimed to evaluate the association between susceptibility genes and non-alcoholic fatty liver disease (NAFLD) in children with obesity. METHODS: We conducted a two-step case-control study. Ninety-three participants were subjected to whole-exome sequencing (exploratory set). Differential genes identified in the small sample were validated in 1,022 participants using multiplex polymerase chain reaction and high-throughput sequencing (validation set). RESULTS: In the exploratory set, 14 genes from the NAFLD-associated pathways were identified. In the validation set, after adjusting for sex, age, and body mass index, ECI2 rs2326408 (dominant model: OR = 1.33, 95% CI: 1.02-1.72; additive model: OR = 1.22, 95% CI: 1.01-1.47), C6orf201 rs659305 (dominant model: OR = 1.30, 95% CI: 1.01-1.69; additive model: OR = 1.21, 95% CI: 1.00-1.45), CALML5 rs10904516 (pre-ad dominant model: OR = 1.36, 95% CI: 1.01-1.83; adjusted dominant model: OR = 1.40, 95% CI: 1.03-1.91; and pre-ad additive model: OR = 1.26, 95% CI: 1.04-1.66) polymorphisms were significantly associated with NAFLD in children with obesity ( P < 0.05). Interaction analysis revealed that the gene-gene interaction model of CALML5 rs10904516, COX11 rs17209882, and SCD5 rs3733228 was optional ( P < 0.05), demonstrating a negative interaction between the three genes. CONCLUSION In the Chinese population, the CALML5 rs10904516, C6orf201 rs659305, and ECI2 rs2326408 variants could be genetic markers for NAFLD susceptibility.
Humans ; Non-alcoholic Fatty Liver Disease/genetics* ; Child ; Male ; Female ; Genetic Predisposition to Disease ; Case-Control Studies ; Exome Sequencing ; Adolescent ; Polymorphism, Single Nucleotide ; Obesity/complications* ; Pediatric Obesity/complications* ; China

Objective To summarize the best evidence for diaphragm rehabilitation in mechanically ven-tilated ICU patients with ventilator-associated diaphragm dysfunction based on evidence-based methods.Meth-ods A systematic search was conducted across guideline websites,professional associations,and Chinese/English databases for evidence regarding diaphragm rehabilitation in mechanically ventilated ICU patients.The search timeframe spanned from database inception to December 31,2024.Two researchers independently per-formed quality assessment and synthesized the evidence.Results Twenty articles were included:2 clinical de-cisions,1 guideline,2 evidence summaries,3 systematic reviews,7 randomized controlled trials(RCT),and 5 expert consensuses/opinions.Twenty-seven pieces of evidence were formed across 6 themes:rehabilitation team,rehabilitation assessment,rehabilitation interventions,outcome evaluation,precautions,and education/training.Conclusion This study summarizes the best evidence for diaphragm rehabilitation in ICU mechani-cally ventilated patients.Healthcare professionals should implement diaphragm rehabilitation by integrating this evidence with specific clinical contexts to improve patient outcomes and enhance nursing quality.

Diaphragmatic dysfunction often occurs in ICU patients with prolonged mechanical ventilation, which seriously affects patients′prognosis. This article reviewed the progress of rehabilitation treatment of diaphragmatic dysfunction in ICU patients with mechanical ventilation, focused on the concept and assessment of diaphragm rehabilitation, the current status, evaluation indexes, emphasized the importance of nurses, analyzed the dilemmas and countermeasures in the application, which aimed to provide a reference for the promotion of diaphragm rehabilitation in clinical practice.

Objective To propose a lead attention network-based ST-T change recognition algorithm to detect ECG ST-T changes accurately.Methods Firstly,heartbeat signals were extracted through R-wave localization,and a 12-lead heartbeat matrix was generated by correlation-based screening and merging to realize data augmentation.Secondly,a lead attention module was constructed by combining depthwise convolution(DWConv)with the channel attention squeeze-and-excitation block(SE-block)structure to perceive the differences in ST-T status among electrocardiogram leads.Thirdly,the mapping output by two independent attention modules was fused and splicing with the original signal residual was carried out,so that attention information extraction and original information transfer were enhanced effectively.Finally,SE-ResNet was used as the backbone network to extract signal features to complete the classification and identification of ST-T changes.To validate the recognition performance of the proposed algorithm for ST-T changes in ECG,the 12-lead ECG data of 97 472 patients containing different ECG rhythms were collected for ablation and comparison experiments at the First Affiliated Hospital of Army Medical University.Results The proposed algorithm achieved an AUC of 0.965 with a sensitivity of 90.51%,specificity of 90.23%,positive predictive value of 89.24%and overall accuracy of 90.36%on an independent test set.Comparative analysis demonstrated superior performance to four benchmark architectures,including VGG16,ResNet18,MobileNetV3-Small and ShuffleNet,in terms of both classification accuracy and computational efficiency.Conclusion The algorithm designed can accurately detect ST-T changes and can be used for wearable ECG automatic analysis to assist in the early warning of cardiovascular diseases in both acute and chronic patients and highland residents.[Chinese Medical Equipment Journal,2025,46(7):1-11]

Objective:To explore the facilitators and barriers in implementing the orthokeratology lens-wearing education guidance program from the perspectives of children, their families, and medical and nursing staff.Methods:Based on phenomenological research, purposive sampling was used to select five medical workers, 18 children wearing orthokeratology lenses and family members from the Optometry Center of Peking University Third Hospital as interviewees for semi-structured interviews. Colaizzi's 7-step method was used to analyze interview data.Results:Two themes (facilitators and barriers) were extracted, among which facilitators included two sub-themes (strong demand for educational guidance, trust in medical and nursing staff), and barriers consisted of two sub-themes (patient factors, external support factors) .Conclusions:In promoting the educational guidance intervention program for wearing orthokeratology lenses, medical and nursing staff need to fully play the role of facilitators, analyze and solve barriers, and ultimately promote the smooth implementation of the intervention program.

Objective:To explore the factors influencing lymph node metastasis and prognosis in patients with Siewert type Ⅱ/Ⅲ adenocarcinoma of the esophagogastric junction (AEG).Methods:A retrospective analysis was conducted on clinical data of 239 patients with Siewert Type Ⅱ/Ⅲ adenocarcinoma of the esophagogastric junction who underwent surgical treatment at Beijing Friendship Hospital, Capital Medical University, from July 2013 to December 2022. Among them, there were 204 males and 35 females. The patients′ ages ranged from 27 to 83 years, with a mean age of 63.1 years. Statistical analysis was performed using SPSS 26.0 software, with categorical data presented as n(%) and compared using χ2 tests, corrected χ2 tests, or Fisher′s exact tests. Ordinal data were expressed as frequencies and percentages and compared using rank-sum tests. Multivariate analysis was conducted using Logistic regression, and survival analysis was performed using the Cox regression model. Follow-up was conducted every 6 months, with the last follow-up conducted in November 2023. Results:Multivariate analysis identified infiltration depth ( OR=0.038, 95% CI: 0.011-0.139, P<0.001), tumor deposit ( OR=0.101, 95% CI: 0.011-0.904, P=0.040) and intravascular cancer embolus ( OR=0.234, 95% CI: 0.108-0.507, P<0.001) as independent predictors of LNM. Lymph nodes No. 1, 2, 3, 4, 7, 10, and 11 were more prone to metastasis in the abdominal cavity. Notably, Siewert Ⅲ AEG patients showed a higher metastatic rate in nodes No. 5 and No. 6 compared to Siewert Ⅱ. Mediastinal LNM was predominantly found in nodes No. 110 and No. 111 for Siewert Ⅱ AEG, with rates of 5.45% and 3.64%, respectively. A three-year survival analysis underscored LNM as a significant prognostic factor ( P=0.001). Conclusions:Siewert Ⅱ AEG patients should undergo removal of both celiac and mediastinal lymph nodes, specifically nodes No. 1, 2, 3, 4, 7, 10, 11, 110, and 111. Dissection of nodes No. 5 and No. 6 is not indicated for these patients. In contrast, Siewert Ⅲ AEG patients do not require mediastinal LND, but pyloric lymphadenectomy for nodes No.5 and No.6 is essential. The presence of LNM is associated with poorer long-term prognosis. Perioperative chemotherapy may offer a survival advantage for AEG patients.

Objective To develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia(AVNRT)and atrioventricular re-entrant tachycardia(AVRT)using 12-lead wearable electrocardiogram devices.Methods A total of 356 samples of 12-lead supraventricular tachycardia(SVT)electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model,and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October,2021 to March,2023 were selected as the testing set.The changes in electrocardiogram parameters before and during induced tachycardia were compared.Based on multiscale deep neural network,an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated.The 3-lead electrocardiogram signals from Ⅱ,Ⅲ,and V1 were extracted to build new classification models,whose diagnostic efficacy was compared with that of the 12-lead model.Results Of the 101 patients with SVT in the testing set,68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study.The pre-trained model achieved a high area under the precision-recall curve(0.9492)and F1 score(0.8195)for identifying AVNRT in the validation set.The total F1 scores of the lead Ⅱ,Ⅲ,V1,3-lead and 12-lead intelligent diagnostic models in the testing set were 0.5597,0.6061,0.3419,0.6003 and 0.6136,respectively.Compared with the 12-lead classification model,the lead-Ⅲ model had a net reclassification index improvement of-0.029(P=0.878)and an integrated discrimination index improvement of-0.005(P=0.965).Conclusion The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.