ObjectiveTo construct a risk prediction model for frequent acute exacerbations of chronic obstructive pulmonary disease （COPD） under disease-syndrome combination， thus providing decision support for precise clinical intervention. MethodsA total of 2 029 patients with acute exacerbations of COPD admitted to the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2020 to August 2024 were retrospectively included. These patients were classified into groups of frequent acute exacerbations （≥2 times/year） and infrequent acute exacerbations （<2 times/year） according to the hospitalization times per year. Risk factors were screened by LASSO regression combined with logistic regression， and a nomogram model was constructed. The model performance was assessed based on the area under the curve （AUC）， calibration curves， and decision curve analysis （DCA）. ResultsThe differences in baseline characteristics between the frequent acute exacerbations group （1 196 cases） and infrequent acute exacerbations group （833 cases） were not statistically significant. LASSO regression combined with multivariate logistic regression screened the following independent risk factors： body mass index （BMI）， hospitalization days， number of smoking years， place of residence， use of noninvasive ventilators， oxygen-demanding therapy， liver cirrhosis， use of systemic glucocorticosteroids， and traditional Chinese medicine syndrome （phlegm and stasis obstructing the lung）. The nomogram model showed good discrimination and calibration in both the training set （AUC=0.748） and validation set （AUC=0.774）. ConclusionThe risk prediction model for frequent acute exacerbations of COPD， integrating traditional Chinese medicine syndrome， constructed in this study has high accuracy. It can provide a scientific basis for early clinical identification of high-risk patients and individualized intervention.

ObjectiveThe most prevalent mRNA modification, N6-methyladenosine (m⁶A) plays an important role in various RNA metabolism, including gene expression and translation. By recruiting different “reader” proteins and their cofactors, m⁶A modification can affect messenger RNA (mRNA) degradation, splicing, nuclear export and translation. However, the selective mechanism by which m⁶A sites regulate mRNA translation through m⁶A reader YTHDF1 binding remains poorly understood, due to a lack of computational methods for identifying context-specific m⁶A sites that regulate translation. To address this, we developed a novel computational framework named m⁶ATEpre, the first tool designed to predict cell-specific m⁶A sites that regulate translation efficiency. Methodsm⁶ATEpre integrates multi-omics data, introduces a novel feature representation strategy for m⁶A site sequences, and employs an autoencoder to effectively capture embedded feature representations. Specifically, m⁶ATEpre first integrated MeRIP-seq data and PAR-CLIP data through overlapping m⁶A sites with YTHDF1 binding sites and identified YTHDF1-mediated m⁶A sites. Then, m⁶ATEpre detected the translation gene by analyzing the Ribo-seq data under YTHDF1 knockdown vs control condition. Genes whose translation is mediated by YTHDF1 in an m⁶A-dependent manner were identified by a significant decrease in translation efficiency upon YTHDF1 knockdown. Next, we proposed a binary vector indicating the presence or absence of YTHDF1 binding motifs to characterize each m⁶A site sequence. This represents a novel feature representation strategy for m⁶A sites. m⁶ATEpre utilized the autoencoder to extract the potentially important feature representations and constructed a multilayer perceptron neural networks model to predict potential m⁶A sites that regulating translation efficiency. ResultsA comprehensive evaluation of m⁶ATEpre was conducted through a series of experiments. We compared its performance against that of a similar prediction task model, as well as other classifiers. The results indicate that m⁶ATEpre achieved the best prediction performance. In addition, we analyzed different feature representation strategies and performed ablation experiments to validate the rationality of the model design. The results demonstrate that our proposed feature representation strategy has a greater advantage in improving prediction performance. In the HeLa cell line, bioinformatic analysis of the metagene distribution and sequence minimum free energy of m⁶A sites regulating translation efficiency (m⁶A-reg-TE sites) revealed their specific properties in translation regulation. Functional enrichment analysis indicated that m⁶A-reg-TE genes are associated with specific biological processes and KEGG pathways. By integrating the binding sites of YTHDF1 co-factors with m⁶A-reg-TE sites, we revealed that YTHDF1-mediated and m⁶A-dependent translation efficiency regulation requires the cooperation of multiple translation-regulatory RNA-binding proteins among its co-factors in the HeLa cell line. Furthermore, we extended our predictions to the dataset of the HEK293T cell line. Similarly, bioinformatic analysis of the metagene distribution and functional enrichment revealed the cell-specific characteristic of these predicted m⁶A-reg-TE sites in HEK293T cells. Likewise, integrated analysis of multiple YTHDF1 co-factors and m⁶A-reg-TE sites predicted in the HEK293T cell line reveals their m⁶A-dependent cooperation in regulating translation efficiency. Conclusionm⁶ATEpre is a timely tool that will advance our understanding of the mechanisms of m⁶A regulation in translation efficiency. The source code and datasets used in this work can be downloaded from https://www.scidb.cn/s/bAZZFr.

BackgroundLow medication compliance among patients with severe mental disorders increases the disease burden on both the patients' families and the society. Medication adherence is influenced by numerous factors. Traditional methods such as Logistic regression struggle to quantify the importance of these factors. By introducing Extreme Gradient Boosting （XGBoost） combined with Shapley Additive Explanations （SHAP）， enables the quantification of the relative contribution weights of each factor， providing support for identifying the core influencing factors. ObjectiveTo explore the influencing factors of medication adherence among patients with severe mental disorders in Zhuhai， aiming to provide references for optimizing patient management strategies. MethodsExtract the data of patients with severe mental disorders who were registered on the mental health system platform in Zhuhai City from January 1， 2023 to March 31， 2025. A total of 9 329 patients were finally included for analysis. Influencing factors were screened using univariate analysis and multivariate logistic regression analysis， and an XGBoost model combined with the SHAP algorithm was constructed to quantify the importance of each influencing factor. ResultsAmong 9 329 patients， 8 446 demonstrated medication adherence， yielding an adherence rate of 90.53%. Multivariable analysis identified several risk factors significantly associated with medication non-adherence， being unmarried （OR=1.237， 95% CI： 1.019–1.502） or divorced （OR=1.389， 95% CI： 1.038–1.832）， a diagnosis of mental retardation with psychiatric disorders （OR=3.025， 95% CI： 2.402–3.796） or paranoid psychosis （OR=5.117， 95% CI： 3.086–8.299）， a disease duration of 2–4 years （OR=1.355， 95% CI： 1.085–1.696）， 4–6 years （OR=2.143， 95% CI： 1.671–2.747）， or >6 years （OR=1.681， 95% CI： 1.365–2.079）， lack of guardian subsidies （OR=1.412， 95% CI： 1.099–1.801）， absence of a disability certificate （OR=1.900， 95% CI： 1.588–2.282）， not being enrolled in care and support groups （OR=1.384， 95% CI： 1.183–1.617） or community services （OR=1.313， 95% CI： 1.042–1.645）， and not cohabiting with a guardian （OR=1.257， 95% CI： 1.048–1.501）. Conversely， the enrollment in special outpatient disease programs （OR=0.716， 95% CI： 0.609–0.842） and a family history of mental illness （OR=0.713， 95% CI： 0.503–0.982） were identified as protective factors. The XGBoost model exhibited robust predictive performance， with a sensitivity of 0.433， specificity of 0.944， accuracy of 0.891， Area Under the Curve （AUC） of 0.837， and F1 value of 0.449. Feature importance ranking indicated that the top three factors were disease duration， diagnosis， and the acquisition of disability certificates. ConclusionPolicy-based support （acquisition of disability certificates， special outpatient disease enrollment） and clinical disease characteristics （disease duration， diagnosis type） are key factors affecting medication adherence among patients with severe mental disorders in Zhuhai City. ［Funded by Zhuhai Medical Research Project （number， 2220009000281）］

Background Work-related musculoskeletal disorders (WMSDs) are a major occupational health concern, particularly among workers exposed to adverse ergonomic conditions. Manganese production involves heavy physical demands, yet research on WMSDs among manganese workers remains limited. Objective To investigate the prevalence and influencing factors of WMSDs among manganese workers in a manganese enterprise in Guangxi. Methods A cross-sectional survey was conducted from May to June 2024 on workers at a manganese factory in Guangxi. The Chinese Musculoskeletal Disorders Questionnaire was used to collect information on demographic characteristics, distribution of musculoskeletal symptoms, and work-related exposures. χ² test was applied to compare differences in positive WMSDs rates across groups, and logistic regression analysis was performed to identify associated factors. Results A total of 1476 workers were enrolled in the study after pre-determined inclusion and exclusion criteria. The overall prevalence of WMSDs was 34.15%. The most commonly affected body regions were the lower back (17.28%), neck (16.67%), and shoulders (13.82%). The results of logistic regression analysis indicated that female, older age, and education level of college or above were associated with a higher risk of WMSDs (P<0.05). Awkward working postures were significantly associated with WMSDs in corresponding body regions; in particular, awkward postures of the neck, upper limbs, trunk, and lower limbs were related to an increased risk of WMSDs in multiple body sites (P<0.05). In addition, poor lighting conditions, high workplace temperature, frequent or sustained arm support during work, and high job demands were associated with an increased risk of overall or site-specific WMSDs (P<0.05). Conclusion The high prevalence of WMSDs among manganese workers is closely associated with demographic characteristics, working postures, and work environment and organizational factors. Targeted ergonomic interventions focusing on high-risk body regions and key ergonomic exposures are warranted to reduce the risk of WMSDs among manganese workers.

AIM: To investigate the effects of Conbercept on various optical coherence tomography(OCT)biomarkers in patients with retinal vein occlusion-related macular edema(RVO-ME), and to analyze the correlation of these biomarker changes with visual prognosis.METHODS: Retrospective study. A total of 57 patients(57 eyes)with RVO-ME, including 25 patients(25 eyes)with central retinal vein occlusion(CRVO)and 32 patients(32 eyes)with branch retinal vein occlusion(BRVO), were enrolled in this study. All the patients received intravitreal injection of conbercept once a month, three times in total. The preoperative and postoperative best-corrected visual acuity(BCVA), and changes in OCT biomarkers, including central macular thickness(CMT), the length of disorganization of the retinal inner layers(DRIL), the number of hyperreflective dots(HRD), the area of intraretinal fluid(IRF), the area of subretinal fluid(SRF), and the length of ellipsoid zone(EZ)disruption were compared. Furthermore, the relationship of these changes with BCVA was analyzed.RESULTS:Compared with the baseline, at 3 mo post-treatment, BCVA(LogMAR)was improved, CMT was decreased, the length of DRIL was shortened, the number of HRD was reduced, the area of IRF was decreased, the area of SRF was reduced, and the length of EZ disruption was shortened(all P<0.05). Spearman correlation analysis showed that there was no correlation between the changes in CMT, the length of DRIL, the number of HRD, the area of IRF, the area of SRF and the change in BCVA before and after treatment(P>0.05). However, the change in the length of EZ disruption was positively correlated with the change in BCVA(rs=0.34, P=0.011), and the R2 value of the fitting curve between the change in the length of EZ disruption and the change in BCVA was 0.113(P=0.011). When comparing the pre- and post-treatment changes in BCVA, the length of DRIL, the number of HRD, the area of IRF, the area of SRF, and the length of EZ disruption between patients in the CRVO group and BRVO group, no significant differences were observed(all P>0.05). In contrast, a significant difference was found in the change in CMT between the two groups(P=0.002).CONCLUSION:Conbercept effectively improves multiple OCT biomarkers in patients with RVO-ME. Repair of EZ disruption is a key driver of visual recovery, and its stability may serve as a novel indicator for personalized decision-making in anti-vascular endothelial growth factor therapy.

Objective To compare the setup errors between abdominal deep inspiration breath hold (ADIBH) guided by real-time position management (RPM) and free breathing (FB) for breast cancer patients who were treated with intensity modulated radiation therapy (IMRT) after breast-conserving surgery. Methods The data of 60 patients who underwent breast-conserving surgery for left-sided breast cancer and completed IMRT were analyzed retrospectively. Of these patients, 30 received ADIBH technique guided by RPM and 30 received FB technique. Setup errors in translational (X, Y, Z) and rotational (Rx, Ry, Rz) directions were assessed by comparing planning CT and cone-beam CT (CBCT) images for both patient groups. Results Compared with FB group (232 sets of CBCT images), ADIBH (261 sets of CBCT images) significantly reduced setup errors in the translational directions (X, Z) and rotational directions (Rx, Ry, and Rz) (Z values were 3.14, 2.42, 1.45, 1.93, 1.37, respectively; all P<0.05). In the ADIBH group, the difference in setup errors between the patients with BMI <24 kg/m² and those with BMI ≥24 kg/m² was not statistically significantly different (P≥0.05); no significant change in setup errors was detected when comparing the first treatment week with subsequent radiotherapy fractions (P≥0.05). The rotation error in the Ry direction was greater in the first treatment week than subsequent radiotherapy fractions in the FB group (Z=8.02, P=0.02). Conclusion In left-sided breast cancer patients receiving postoperative IMRT, the ADIBH technique demonstrates significantly smaller setup errors compared to FB technique, independent of BMI, thereby improving radiotherapy precision.

Objective: To understand the smoking-related behaviors and influencing factors of current smoking among medical staff in Fengtai District, Beijing Municipality, so as to provide the reference for reducing the current smoking rate of medical staff. Methods: Medical staff in 28 medical and health institutions in Fengtai District were selected as the survey subjects from February to March and July to August 2023. Basic information, smoking and smoking cessation behaviors, and the provision of brief smoking cessation intervention services were collected through electronic questionnaires. Factors affecting current smoking among medical staff were analyzed using a multivariable logistic regression model. Results: Totally 6 716 questionnaires were allocated, and 6 714 valid questionnaires were recovered, with an effective recovery rate of 99.97%. There were 1 590 males (23.68%) and 5 124 females (76.32%). There were 3 315 medical staff in clinical department, accounting for 49.37%. There were 457 current smokers and the current smoking rate among medical staff was 6.81%. The proportion of medical staff in clinical departments who were current smokers and provided brief smoking cessation intervention services was 72.99%, which was lower than that of non-current smokers at 85.18% (P<0.05). Multivariate logistic regression analysis showed that medical staff in secondary and above hospitals (OR=1.454, 95%CI: 1.136-1.862), male (OR=51.158, 95%CI: 34.810-75.183), work experience of 10~<20 years (OR=1.492, 95%CI: 1.065~2.092) or ≥30 years (OR=1.574, 95%CI: 1.011~2.449), those with positions (OR=1.644, 95%CI: 1.159-2.332), and those in logistics departments (OR=2.124, 95%CI: 1.278-3.528) or other departments (OR=2.011, 95%CI: 1.297-3.118) had a higher likelihood of being current smokers. On the contrary, medical staff with a bachelor's or junior college education level (OR=0.487, 95%CI: 0.346-0.685) or a master's degree or above (OR=0.268, 95%CI: 0.159-0.454), and those with an intermediate professional title (OR=0.430, 95%CI: 0.291-0.636) or a senior professional title (OR=0.452, 95%CI: 0.283-0.723) had a lower likelihood of being current smokers. A total of 214 medical staff successfully quit smoking, and the smoking cessation rate was 31.89%. Among them, 20, 18, and 17 medical staff had used the smoking cessation service hotline, visited smoking cessation clinics, and taken smoking cessation medications, respectively. In the past year, 199 medical staff who were current smokers (43.54%) had attempted to quit smoking, and 280 medical staff who were current smokers (61.27%) had the willingness to quit smoking. Conclusions The current smoking rate among medical staff in Fengtai District is relatively high. Hospital level, gender, educational level, work experience, position, professional title, and department are influencing factors for current smoking among medical staff. It is necessary to enhance the willingness of medical staff to quit smoking and their understanding of smoking cessation intervention services, so as to reduce the current smoking rate.

BACKGROUND: In the interim analysis of MONARCH plus, adding abemaciclib to endocrine therapy (ET) improved progression-free survival (PFS) and objective response rate (ORR) in predominantly Chinese postmenopausal women with HR+/HER2- advanced breast cancer (ABC). This study presents the final pre-planned PFS analysis. METHODS: In the phase III MONARCH plus study, postmenopausal women in China, India, Brazil, and South Africa with HR+/HER2- ABC without prior systemic therapy in an advanced setting (cohort A) or progression on prior ET (cohort B) were randomized (2:1) to abemaciclib (150 mg twice daily [BID]) or placebo plus: anastrozole (1.0 mg/day) or letrozole (2.5 mg/day) (cohort A) or fulvestrant (500 mg on days 1 and 15 of cycle 1 and then on day 1 of each subsequent cycle) (cohort B). The primary endpoint was PFS of cohort A. Secondary endpoints included cohort B PFS (key secondary endpoint), ORR, overall survival (OS), safety, and health-related quality of life (HRQoL). RESULTS: In cohort A (abemaciclib: n  = 207; placebo: n  = 99), abemaciclib plus a non-steroidal aromatase inhibitor improved median PFS vs . placebo (28.27 months vs . 14.73 months, hazard ratio [HR]: 0.476; 95% confidence interval [95% CI]: 0.348-0.649). In cohort B (abemaciclib: n  = 104; placebo: n  = 53), abemaciclib plus fulvestrant improved median PFS vs . placebo (11.41 months vs . 5.59 months, HR: 0.480; 95% CI: 0.322-0.715). Abemaciclib numerically improved ORR. Although immature, a trend toward OS benefit with abemaciclib was observed (cohort A: HR: 0.893, 95% CI: 0.553-1.443; cohort B: HR: 0.512, 95% CI: 0.281-0.931). The most frequent grade ≥3 adverse events in the abemaciclib arms were neutropenia, leukopenia, anemia (both cohorts), and lymphocytopenia (cohort B). Abemaciclib did not cause clinically meaningful changes in patient-reported global health, functioning, or most symptoms vs . placebo. CONCLUSIONS: Abemaciclib plus ET led to improvements in PFS and ORR, a manageable safety profile, and sustained HRQoL, providing clinical benefit without a high toxicity burden or reduced quality of life. TRIAL REGISTRATION ClinicalTrials.gov (NCT02763566).
Humans ; Female ; Fulvestrant/therapeutic use* ; Breast Neoplasms/metabolism* ; Aminopyridines/therapeutic use* ; Benzimidazoles/therapeutic use* ; Middle Aged ; Aromatase Inhibitors/therapeutic use* ; Aged ; Receptor, ErbB-2/metabolism* ; Adult ; Letrozole/therapeutic use* ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Anastrozole/therapeutic use*