The interaction between m⁶A-methylated RNA and chromatin modification remains largely unknown. We found that targeted inhibition of bromodomain-containing protein 4 (BRD4) by siRNA or its inhibitor (JQ1) significantly decreases mRNA m⁶A levels and suppresses the malignancy of breast cancer (BC) cells via increased expression of demethylase AlkB homolog 5 (ALKBH5). Mechanistically, inhibition of BRD4 increases the mRNA stability of ALKBH5 via enhanced binding between its 3' untranslated regions (3'UTRs) with RNA-binding protein RALY. Further, BRD4 serves as a scaffold for ubiquitin enzymes tripartite motif containing-21 (TRIM21) and ALKBH5, resulting in the ubiquitination and degradation of ALKBH5 protein. JQ1-increased ALKBH5 then demethylates mRNA of extra spindle pole bodies like 1 (ESPL1) and reduces binding between ESPL1 mRNA and m⁶A reader insulin like growth factor 2 mRNA binding protein 3 (IGF2BP3), leading to decay of ESPL1 mRNA. Animal and clinical studies confirm a critical role of BRD4/ALKBH5/ESPL1 pathway in BC progression. Further, our study sheds light on the crosstalks between histone modification and RNA methylation.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

In recent years, the deep integration of artificial intelligence (AI) into medical education has created new opportunities for teaching Biochemistry and Molecular Biology, while also offering innovative solutions to the pedagogical challenges associated with protein structure and function. Focusing on the case of anaplastic lymphoma kinase (ALK) gene mutations in non-small-cell lung cancer (NSCLC), this study integrates AI into case-based learning (CBL) to develop an AI-CBL hybrid teaching model. This model features an intelligent case-generation system that dynamically constructs ALK mutation scenarios using real-world clinical data, closely linking molecular biology concepts with clinical applications. It incorporates AI-powered protein structure prediction tools to accurately visualize the three-dimensional structures of both wild-type and mutant ALK proteins, dynamically simulating functional abnormalities resulting from conformational changes. Additionally, a virtual simulation platform replicates the ALK gene detection workflow, bridging theoretical knowledge with practical skills. As a result, a multidimensional teaching system is established—driven by clinical cases and integrating molecular structural analysis with experimental validation. Teaching outcomes indicate that the three-dimensional visualization, dynamic interactivity, and intelligent analytical capabilities provided by AI significantly enhance students’ understanding of molecular mechanisms, classroom engagement, and capacity for innovative research. This model establishes a coherent training pathway linking “fundamental theory-scientific research thinking-clinical practice”, offering an effective approach to addressing teaching challenges and advancing the intelligent transformation of medical education.

[Objective] To evaluate the feasibility of a novel outpatient infusion model for blinatumomab in two acute lymphoblastic leukemia (ALL) patients, aiming to address challenges of poor treatment tolerance, high healthcare costs, and compromised quality of life, thereby providing clinical insights for broader adoption of this approach. [Methods] Two post-allogeneic hematopoietic stem cell transplantation (allo-HSCT) patients undergoing blinatumomab maintenance therapy were selected to evaluate the efficacy of the outpatient infusion model. Patient selection criteria, nursing protocols, standardized workflows, and advancements in infusion practices were systematically analyzed combined with a review of global developments in this field. [Results] Both patients completed outpatient blinatumomab infusion without severe adverse events, demonstrating preliminary feasibility and safety of this model. The novel approach enhanced treatment convenience, reduced hospitalization costs, and improved quality of life. [Conclusion] Despite the limited sample size, this pilot study highlights the potential of outpatient blinatumomab administration as a viable alternative to traditional inpatient regimens.

Chinese-foreign joint education program of clinical medicine is an important means to achieve the globalization of medical education. Chongqing Medical University and University of Leicester in the UK have jointly established a Chinese-foreign joint education program of clinical medicine to achieve the integration of Chinese and British cultivation concepts, management systems, teaching resources, teacher teams, evaluation systems, and multiculturalism. They have also constructed an internal teaching quality assurance system with the main contents of the improvement of management mechanisms, the formulation of training programs, the construction of teaching staff, the design of syllabuses, the curriculum assessment system, and teaching quality evaluation, as well as an external teaching quality assurance system with the core components of clinical medicine accreditation, Chinese-foreign joint education program evaluations, international quality audits, and professional quality monitoring. Both systems can help to comprehensively improve teaching quality.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective:To observe the effect of high-frequency repetitive transcranial magnetic stimulation (rTMS) at different frequencies on cognitive impairment due to high-altitude hypoxia.Methods:Sixty officers and soldiers displaying cognitive impairment in a hypoxic high-altitude environment were randomly divided into 15Hz, 20Hz and 25Hz groups, each of 20. They were given rTMS at those frequencies for 30 days. Before the stimulation and after 15 and 30 days, event-related potentials, latencies of mismatched negativity (MMN) and P300 signals were recorded. The participants′ cognition was also evaluated using the Montreal Cognitive Assessment Scale (MoCA). Correlation between the electrophysiological indexes and the MoCA scores was computed.Results:After 15 days, all had shorter MMN latencies, higher total MoCA scores and better memory scores. The only significant difference among the three groups was in the average memory scores. After 15 days, MMN latency was significantly negatively correlated with the memory scores in all three groups ( r=0.44 to -0.54). Conclusions:rTMS at frequencies above 15Hz can effectively relieve cognitive impairment, especially memory dysfunction, resulting from high-altitude hypoxia.

Objective:To identify the risk factors for acute graft-versus-host disease (aGVHD) in patients with acute myeloid leukemia (AML) undergoing haploidentical hematopoietic stem cell transplantation (HID-HSCT) .Methods:A total of 141 AML patients who underwent HID-HSCT at the Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences, from January 2020 to July 2021 were included. The cumulative incidence of aGVHD was analyzed using the Fine-Gray competing risk model, with relapse and death as competing events, to compare differences between groups. Potential risk factors were evaluated by univariable and multivariable Cox proportional hazards regression analyses to determine their independent effects on aGVHD.Results:Among the 141 patients, 86 (61.0%) were male and 55 (39.0%) were female, with a median age at transplantation of 34 years. Within 100 days post-transplant, 59 patients developed grade Ⅱ-Ⅳ aGVHD, whereas 86 patients experienced no or grade Ⅰ aGVHD (the grade 0-Ⅰ aGVHD group) . Survival analysis showed that the 3-year overall survival was 68.7% (95% CI: 57.7%-81.9%) in the grade Ⅱ-Ⅳ aGVHD group, compared with 78.8% (95% CI: 70.4%-88.3%) in the grade 0 - Ⅰ aGVHD group, with the difference not being statistically significant ( P=0.190) . Univariable analysis identified donor age ( P=0.020, HR=1.020, 95% CI: 1.000-1.040) and the female donor-male recipient sex combination ( P=0.033, HR=1.980, 95% CI: 1.160-3.380) as risk factors for grade Ⅱ-Ⅳ aGVHD. Multivariable analysis confirmed that donor age ( P=0.005, HR=1.026, 95% CI: 1.008-1.047) and the female donor-male recipient sex combination ( P=0.002, HR=2.339, 95% CI: 1.354-4.037) were independent risk factors for aGVHD. Patients receiving grafts from donors aged >45 years had a significantly higher 100-day cumulative incidence of grade Ⅱ-Ⅳ aGVHD compared with those receiving grafts from donors ≤45 years [54.7% (95% CI: 42.3%-67.0%) vs 31.6% (95% CI: 21.0%-42.1%) , P=0.006]. Similarly, patients with the female donor-male recipient sex combination had a higher 100-day cumulative incidence of grade Ⅱ-Ⅳ aGVHD than those with other sex combinations [56.8% (95% CI: 40.4%-73.1%) vs 36.9% (95% CI: 27.5%-46.3%) , P=0.015]. Conclusion:Older donor age and the female donor-male recipient sex combination remain independent risk factors for aGVHD in patients with AML undergoing HID-HSCT.

Objective:To analyze the epidemiological characteristics and influencing factors of single-cigarette use and dual cigarette-cigar use in China.Methods:This study was a cross-sectional study that selected 85 638 urban and rural residents who met the inclusion criteria from the 2018 China Health Literacy Survey as research subjects. An analysis was conducted on 21 849 users of cigarettes and cigars among them. Due to the small number of individuals who exclusively used cigars (247 cases), the research subjects were divided into two categories: exclusive cigarette users and dual users of cigarettes and cigars. The groups were categorized by age (18-34 years, 35-54 years, ≥55 years), gender (male, female), education level (primary school and below, junior high school and high school, university and above) and annual household income (<20 000 yuan, 20 000-<80 000 yuan, ≥80 000 yuan) to compare the tobacco usage rate and conduct subgroup analyses for each subgroup. Multivariate logistic regression analysis was employed, incorporating general demographic characteristic information to explore the influencing factors of exclusive cigarette use and dual use of cigarettes and cigars, respectively.Results:The rate of exclusive cigarette use in our country was 24.3%, while the dual use rate of cigarettes and cigars was 0.9%. The exclusive cigarette use rate and the dual use rate of cigarettes and cigars among males were significantly higher than those among females (48.25% vs 2.48%, and 1.84% vs 0.06%) (both P<0.001). For males, the high-risk factors for exclusive cigarette use included living in urban areas ( OR: 1.37, 95% CI: 1.23-1.54), being Han ethnicity ( OR: 1.73, 95% CI: 1.51-1.98), and having an annual household income ≥20 000 yuan ( OR: 1.54, 95% CI: 1.38-1.82) while having a junior high school education or higher was a protective factor ( OR: 0.68, 95% CI: 0.52-0.90). Age≥35 years ( OR: 3.36, 95% CI: 2.62-4.32) and having a junior high school education or higher ( OR: 1.30, 95% CI: 1.02-1.67) were risk factors for dual use of cigarettes and cigars in males. Among females, living in urban areas ( OR: 1.53, 95% CI: 1.19-1.97) and being Han ethnicity ( OR: 5.96, 95% CI: 4.47-7.96) were risk factors for exclusive cigarette use, while having a university education or higher was a protective factor ( OR: 0.28, 95% CI: 0.18-0.42). However, for female dual use of cigarettes and cigars, no significant effects were observed for any demographic characteristics. Conclusions:The use rate of cigarettes alone in China is significantly higher than that of cigarette-cigar dual use, and the rates of cigarette use alone and cigarette-cigar dual use in men are significantly higher than those in women. Tobacco use is being affected by sociodemographic factors, among which place of residence, ethnicity and education level are the main influencing factors of cigarette use alone, and gender, age and education level are the main influencing factors of cigarette-cigar dual use.