Objective:To study the characteristics of laryngopharyngeal reflux (LPR) events in patients with obstructive sleep apnea (OSA).Methods:This cross-sectional study analyzed OSA patients who were admitted in the Department of Otolaryngology-Head and Neck Surgery, the Sixth Medical Center of the Chinese PLA General Hospital between November 2020 to July 2023[OSA group, 52 males, 6 females, aged 23-69 (41.22±11.42) years], and non-OSA patients admitted during the same period serve as the control group[non-OSA group, 40 males, 1 female, aged 21-68 (45.12±11.30) years]. All participants completed the Reflux Symptom Index (RSI), Reflux Finding Score (RFS) scale, and 24-hour Hypopharyngeal Esophageal Multichannel Intraluminal Impedance-pH (HEMII-pH) monitoring. LPR events were categorized based on their physical composition-liquid, gas, or gas-liquid mixed, according to the change of impedance values; and further classified by pH levels as acidic, weakly acidic, or alkaline. Differences in LPR events physical properties and the time trends of LPR events between the two groups were compared. Group comparisons were performed using t-test or Mann-Whitney U-test. Analyses were conducted using Pearson, Spearman, or Kendall′s tau-b correlation analysis. Categorical data were analyzed using chi-square test. Results:A total of 99 patients were enrolled, including 58 with OSA and 41 without OSA. Of these, 88.89% (88/99) met the diagnostic criteria for laryngopharyngeal reflux disease (LPRD). In LPRD patients, the median proportion of non gas reflux events and the number of alkaline reflux were significantly higher in the OSA with LPRD group than in the non OSA with LPRD group (70.00% vs 36.36%, 0 vs 0, Z-values respectively -3.373, -3.134, P<0.01). Liquid reflux proportion and the number of both liquid and mixed reflux events showed a positive correlation with the apnea-hypopnea index (AHI) ( r-values respectively 0.304, 0.326, 0.268, P<0.05), while the gas reflux constituent ratio was inversely correlated with AHI ( r=-0.358, P<0.01). The frequency and proportion of nocturnal reflux events showed a positive correlation with AHI ( r-values respectively 0.250, 0.211, P<0.05). A significantly higher proportion of OSA with LPRD group experienced both daytime and nighttime reflux compared to non OSA with LPRD group (66.67% vs 38.71%, P<0.05). In LPRD patients, over 50% of all LPR events occurred within 3 hours after each of the three main meals. Conclusions:In OSA with LPRD patients, LPR events are predominantly non gas in nature. OSA with LPRD patients exhibits a higher proportion and frequency of nocturnal reflux events and a greater number of alkaline reflux episodes compared to non OSA with LPRD patients.

Objective:To investigate the impact of perceived barriers to cardiac rehabilitation on adherence to cardiac rehabilitation and the mediating effect of cardiac impact denial in elderly patients after percutaneous coronary intervention (PCI) .Methods:This study was a cross-sectional survey. From August 2023 to July 2024, 336 elderly patients with PCI who were discharged from the First Affiliated Hospital of Wenzhou Medical University and needed phaseⅡ cardiac rehabilitation were selected for the study. General Information Questionnaire, Adherence of Cardiac Rehabilitation Assessment Scale in Patients with Coronary Heart Disease (ACRAS-CHD) , Cardiac Rehabilitation Barriers Scale (CRBS) , and Cardiac Denial of Impact Scale (CDIS) were used to survey the patients. Bootstrap method was used to sample 5 000 for mediating effect tests.Results:A total of 336 questionnaires were distributed and 324 valid questionnaires were recovered, with a valid recovery rate of 96.43% (324/336) . Among 324 elderly patients after PCI, ACRAS-CHD score, CRBS score, and CDIS score were (73.19±5.46) , (79.35±6.60) , and (30.57±3.66) , respectively. The direct effect of perceived barriers to cardiac rehabilitation on adherence to cardiac rehabilitation in elderly patients after PCI was -0.368, which accounted for 74.49% (-0.368/-0.494) of the total effect. The mediating effect of cardiac impact denial between perceived barriers to cardiac rehabilitation and adherence to cardiac rehabilitation in elderly patients after PCI was -0.126, which accounted for 25.51% of the total effect (-0.126/-0.494) .Conclusions:Elderly patients after PCI have poor adherence to cardiac rehabilitation. Perceived cardiac rehabilitation barriers reduce cardiac rehabilitation adherence in elderly patients after PCI, and cardiac impact denial exerts a partial mediating effect between perceived cardiac rehabilitation barriers and cardiac rehabilitation adherence.

Guided by the theory of latent pathogens， it is believed that the basic pathogenesis of adult-onset Still's disease is the latent pathogens in the deep yin level. The onset of the disease is fundamentally characterized by the deficiency of both qi and yin as the root， with dampness， heat， phlegm， and blood stasis as the branch， which triggered by intruding pathogens activate the latent pathogens in yin level. The treatment focuses on nourishing yin and dispersing heat as the key therapeutic method. It is proposed that clearing and resolving dampness-heat， expelling pathogens outward， dispersing the latent pathogens， reinforcing healthy qi and consolidating the root， boosting qi and nourishing yin as treatment idea. In clinic， Qinghao Biejia Decoction （青蒿鳖甲汤） could be used as the basic formula， and modified with characteristic herb pairs such as Qinghao （Artemisia annua） - Digupi （Lycium chinense） to enrich yin and clear heat， and enforce the power of clearing deficient heat； Biejia （Lawsonia inermis） - Xuchangqing （Vincetoxicum mukdenense） to enrich yin and activate blood， unblock the collaterals and dissipate masses； Duhuo （Angelica biserrata） - Mudanpi （Paeonia × suffruticosa） to dispel wind and activate blood， resolve dampness and unblock the collaterals， so as to clear and warm simultaneously， and regulate qi and blood at the same time； and Chuanshanlong （Dioscorea nipponica） - Difuzi （Bassia scoparia） to dissolve stasis and dispel phlegm， explore and dispel latent pathogens.

Identifying drug-drug interactions (DDIs) is essential to prevent adverse effects from polypharmacy. Although deep learning has advanced DDI identification, the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits. Here, we developed a Multi-Dimensional Feature Fusion model named MDFF, which integrates one-dimensional simplified molecular input line entry system sequence features, two-dimensional molecular graph features, and three-dimensional geometric features to enhance drug representations for predicting DDIs. MDFF was trained and validated on two DDI datasets, evaluated across three distinct scenarios, and compared with advanced DDI prediction models using accuracy, precision, recall, area under the curve, and F1 score metrics. MDFF achieved state-of-the-art performance across all metrics. Ablation experiments showed that integrating multi-dimensional drug features yielded the best results. More importantly, we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs. Among 12 real-world adverse drug reaction reports, the predictions of 9 reports were supported by relevant evidence. Additionally, MDFF demonstrated the ability to explain adverse DDI mechanisms, providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.

OBJECTIVE: To investigate the current status of kinesiophobia after lumbar fusion surgery in patients with lumbar disc herniation (LDH) and to analyze its influencing factors. METHODS: A total of 489 LDH patients who underwent lumbar fusion surgery in our hospital from January 2021 to December 2022 and effectively filled out the tampa scale for kinesiophobia (TSK) and other questionnaires on the first day after surgery were collected as the study subjects, the current status of kinesiophobia in LDH patients after lumbar fusion surgery were investigated using the TSK. The LDH patients were grouped into a kinesiophobia group (n=221) and a non kinesiophobia group (n=268) based on whether there was kinesiophobia after lumbar fusion surgery. The self-designed general data questionnaires were used to collect data, and Logistic regression was applied to analyze independent risk factors for kinesiophobia after lumbar fusion surgery in the LDH patients. RESULTS: In the study, 221 out of the 489 LDH patients (45.19%) had kinesiophobia after lumbar fusion surgery. Univariate analysis showed that there were statistically significant differences between the phobic group and the non phobic group in terms of gender, education level, course of disease, whether there was hypoproteinemia, pain level, self-efficacy, social support, whether there was anxiety, and whether there was depression (P < 0.05). There were no statistically significant differences in terms of age, body mass index, monthly family income, marital status, residence, medical expense payment form, whether there was hypertension, whether there was diabetes, whether there was cardiovascular and cerebrovascular disease, whether there was respiratory disease, whether there was surgery experience, whether there was anemia, work conditions, and responsibility segments (P>0.05). Male (95%CI: 3.289-10.586, P < 0.001), education level below undergraduate level (95%CI: 6.533-45.162, P < 0.001), severe pain (95%CI: 10.348-72.025, P < 0.001), moderate pain (95%CI: 6.247-37.787, P < 0.001), low self-efficacy (95%CI: 4.238-15.095, P < 0.001), and medium self-efficacy (95%CI: 2.804-8.643, P < 0.001) were influencing factors for kinesiophobia after lumbar fusion surgery in the patients with LDH (P < 0.05). CONCLUSION The incidence of kinesiophobia after lumbar fusion surgery in patients with LDH is high, and its influencing factors include gender, education level, pain level, and self-efficacy.
Humans ; Spinal Fusion/psychology* ; Intervertebral Disc Displacement/psychology* ; Lumbar Vertebrae/surgery* ; Male ; Female ; Middle Aged ; Phobic Disorders/etiology* ; Surveys and Questionnaires ; Adult ; Risk Factors ; Aged ; Fear ; Kinesiophobia

Objective To explore the causal association between immune cells and allergic rhi-nitis using Mendelian randomization(MR)approach.Methods GWAS data for 731 types of im-mune cells and allergic rhinitis were obtained from genome-wide association study(GWAS)databas-es.A two-sample bidirectional MR analysis was conducted,with the inverse-variance weighted(IVW)method as the primary analytical approach,and the weighted median method,MR-Egger re-gression,simple mode method,and weighted mode method as supplementary approaches.Sensitivity analyses,including heterogeneity tests,pleiotropy tests,and the leave-one-out method,were per-formed.Bonferroni correction was applied to the preliminary results to enhance their reliability and rigor.Results The two-sample forward MR analysis revealed correlations between 67 immune cell phenotypes and allergic rhinitis.After Bonferroni correction,four immune cell phenotypes were finally identified.Among them,the expression of CD3 on CD39-positive activated CD4 regulatory T cells(OR=0.953,95％CI,0.931 to 0.978,P＜0.001,Padj=0.007),the expression of herpesvirus entry mediator(HVEM)on CD45RA-negative CD4+T cells(OR=0.965,95％CI,0.948 to 0.983,P＜0.001,Padj=0.008),and the percentage of human leukocyte antigen class DR(HLA-DR)-high-expressing monocytes among leukocytes(OR=0.929,95％CI,0.885 to 0.974,P=0.002,Padj=0.157)were protective factors for allergic rhinitis.In contrast,the percentage of transitional B cells among B cells(OR=1.094,95％CI,1.032 to 1.161,P=0.003,Padj=0.183)was a risk factor for allergic rhinitis.The reverse MR analysis showed no causal relationship between allergic rhinitis and the four immune cell phenotypes.Conclusion The two-sample forward MR analysis confirms a caus-al link between immune cells and allergic rhinitis.MR analysis has the advantages of reducing con-founding factor interference and avoiding reverse causation,providing a theoretical basis for in-depth research on immune mechanisms,sensitive biomarkers,and drug treatment targets of allergic rhinitis.

Identifying drug-drug interactions(DDIs)is essential to prevent adverse effects from polypharmacy.Although deep learning has advanced DDI identification,the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits.Here,we developed a Multi-Dimensional Feature Fusion model named MDFF,which integrates one-dimensional simplified molec-ular input line entry system sequence features,two-dimensional molecular graph features,and three-dimensional geometric features to enhance drug representations for predicting DDIs.MDFF was trained and validated on two DDI datasets,evaluated across three distinct scenarios,and compared with advanced DDI prediction models using accuracy,precision,recall,area under the curve,and F1 score metrics.MDFF achieved state-of-the-art performance across all metrics.Ablation experiments showed that integrating multi-dimensional drug features yielded the best results.More importantly,we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs.Among 12 real-world adverse drug reaction reports,the predictions of 9 reports were supported by relevant evidence.Additionally,MDFF demon-strated the ability to explain adverse DDI mechanisms,providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.

Objective:To develop an intelligent surveillance system for identifying upper gastrointestinal high-risk patients and assigning surveillance intervals, and to verify its efficacy.Methods:The endoscopic and pathological reports of 23 035 patients undergoing endoscopy at Renmin Hospital of Wuhan University from January to October 2021 were collected retrospectively. A training set of 17 934 patients (January to August) and a test set of 5 101 patients (September to October) were established. Keywords in the endoscopic and pathological reports were extracted by the intelligent surveillance system, and high-risk patients were automatically identified and classified into 7 risk levels. Then the standardized surveillance intervals were assigned based on the guideline. Guideline-based surveillance intervals assigned by expert endoscopists based on endoscopic and pathological reports were used as the golden standard. The accuracy of the intelligent surveillance system was calculated. Of the patients within the test set, 189 were hospitalized and the surveillance intervals given by physicians could be obtained from the electronic health records. The accuracy of the intelligent surveillance system with that of physicians from different departments was compared. Then 67 patients were randomly selected from 189 patients by simple random sampling to evaluate the adjunctive effect of the system in assigning surveillance intervals among 3 endoscopists.Results:The overall accuracy of the intelligent surveillance system in identifying upper gastrointestinal high-risk patients was 99.94% (5 098/5 101), and that of assigning surveillance intervals to correctly included patients was 100.00% (534/534). The intelligent surveillance system achieved significantly higher accuracy compared with all physicians from different departments [98.94% (187/189) VS 35.45% (67/189), χ2=118.01, P<0.001] as well as physicians from department of gastroenterology [100.00% (117/117) VS 24.79% (29/117), χ2=86.01, P<0.001]. With the assistance of the intelligent surveillance system, the endoscopists' accuracy of assigning surveillance intervals to 67 patients was significantly improved [55.22% (111/201) VS 22.39% (45/201), χ2=58.68, P<0.001]. Conclusion:The intelligent surveillance system can accurately identify upper gastrointestinal high-risk patients and assign surveillance intervals according to risk levels, which can alleviate the workload of doctors and improve the follow-up rate of patients.

Objective:To develop an artificial intelligence (AI) calculation system for the effective withdrawal time of colonoscopy and to evaluate its clinical application value.Methods:First, 17 118 colonoscopy pictures from Renmin Hospital of Wuhan University were used for training and testing to establish a deep convolutional neural network model to recognize various colonoscopy fields. Then this model was integrated with the internal and external recognition model and cecum recognition model developed by the research group to create an AI system for automatic calculation of the effective withdrawal time. Finally, 944 colonoscopy videos from the Endoscopy Center of Renmin Hospital of Wuhan University from July 1, 2020 to October 10, 2020 were included in a retrospective analysis. AI automatic computing system was used to calculate the effective withdrawal time, and 89 of them were manually calculated to evaluate the accuracy of the AI automatic computing system. The remaining 855 cases were divided into two groups according to AI calculations, namely, the effective withdrawal time <6 min group ( n=615) and the effective withdrawal time ≥6 min group ( n=240), and the differences in the overall detection rate of adenoma and polyp were compared and analyzed. Results:The accuracy of AI automatic calculation system for effective withdrawal time reached 92.1% (82/89). The overall adenoma detection rate in the group with effective withdrawal time ≥6 min was 37.5% (90/240), that in the group with effective withdrawal time <6 min was 19.0% (117/615), and the difference was statistically significant ( χ2=32.11, P<0.001). The overall polyp detection rate in the group with effective withdrawal time ≥6 min was 75.0% (180/240), and that in the group with effective withdrawal time <6 min was 45.2% (278/615), with statistical significance ( χ2=61.62, P<0.001). Conclusion:AI automatic computing system can accurately calculate the effective withdrawal time of colonoscopy, and can be used to monitor the effective withdrawal time of clinical colonoscopy. In addition, effective withdrawal time ≥6 min can effectively improve the detection rate of adenoma and polyps.

Objective:To explore status and influencing factors of surveillance in colorectal post-polypectomy patients.Methods:Patients who underwent colorectal polypectomy in Renmin Hospital of Wuhan University between April 1, 2019 and June 30, 2019 were retrospectively studied. The surveillance information was obtained through electronic health record and telephone call. Status and influencing factors of surveillance in colorectal post-polypectomy patients were evaluated. Logistic regression model was used for multivariate analysis to determine independent risk factors influencing surveillance.Results:A total of 268 colorectal post-polypectomy patients and their surveillance information were reviewed, of whom 153 (57.09%) patients received surveillance colonoscopy, and 115 (42.91%) patients did not. Univariate analysis showed that the source of patients (outpatients VS inpatients, χ 2=5.68, P=0.017), department (others VS department of gastroenterology, χ 2=6.64, P=0.010), and the number of polyps (1/(2~4)/≥5, χ2=7.32, P=0.026) influenced the outcome of surveillance. Logistic regression model indicated that department of gastroenterology ( P=0.039, OR=2.12, 95% CI:1.04-4.34), risk level 3 ( P=0.040, OR=1.92, 95% CI:1.03-3.58) and the number of polyps ≥5 ( P=0.016, OR=2.89, 95% CI:1.22-6.83) were independent risk factors influencing surveillance. Conclusion:Patients visit the department of gastroenterology or had a risk level 3 or ≥5 polyps are more likely to opt for surveillance following the procedure.