Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective: To analyze the characteristics and key risk factors of abnormal blood disposal throughout the 2024-2025 blood collection and supply process based on nationwide multi-site monitoring data, develop a comprehensive prevention and control strategy across the entire chain, reduce preventable blood waste, and ensure clinical blood safety. Methods: According to the Guidelines for Haemovigilance (T/CSBT 001-2026), we extracted 74 cases of abnormal blood disposal events reported by the China Blood Transfusion Association′s Blood Safety Monitoring System from January 1 2024, to December 31 2025, and conducted descriptive statistical analysis on the distribution of events, types of deviations, root causes, and classifications of disposal manifestations. Results: A total of 664 adverse events related to blood collection and supply were reported over two years, with 74 cases involving discarded abnormal blood units, accounting for 11.1% of the total. Among these, blood component preparation (40.5%) and blood collection (37.8%) remained traditional high-risk stages. The proportion of blood waste occurring during storage, distribution, and transportation increased from 7.7% in 2024 to 34.3% in 2025. Deviations from standard operating procedures (SOP) by personnel were the primary cause, accounting for 74.3% of cases. Discarded blood units were categorized into four types: compromised closed blood bag systems (40 cases), mainly due to inadequate heat sealing, physical damage, or dropped bags; blood out of the cold chain (12 cases), primarily resulting from blood being left at work sites; imbalanced proportion of preservation solution (10 cases), mostly caused by blood collection volumes exceeding the standard capacity of blood bags; and other causes (12 cases). Conclusion: Human operational errors are the core cause of abnormal blood waste. The collection and preparation stages have long been at high-risk, and the risks in the storage and transportation stages have been increasing year by year. We should focus on standardizing personnel management, improving specialized operation norms such as heat sealing, transportation, and storage, establishing a full-process intelligent cold chain monitoring and multi-node visual verification mechanism, and systematically reducing the occurrence rate of abnormal blood waste through a closed-loop quality intervention system, thereby improving the utilization efficiency of voluntary blood donation resources.

[摘 要] 目的：探讨环指蛋白8（RNF8）对子宫内膜癌（EC）细胞顺铂（CDDP）敏感性的影响及可能机制。方法：基于在线数据库分析RNF8在EC组织中的表达及拷贝数变异；采用慢病毒介导shRNA稳定敲低Ishikawa细胞中RNF8表达并进行CDDP处理，按sh-NC+溶剂对照、sh-RNF8+溶剂对照、sh-NC + CDDP、sh-RNF8 + CDDP分组处理，并设置sh-RNF8 + CDDP + KU60019抑制实验。采用CCK-8、克隆形成、Annexin V-FITC/PI流式细胞术、Hoechst 33258/PI染色、WB及γ-H2AX免疫荧光检测细胞活力、克隆形成、凋亡、DNA损伤累积及ATM/CHK2通路相关蛋白表达。结果：RNF8在EC组织和细胞中表达升高。RNF8敲低降低细胞存活率和IC50，抑制克隆形成，并进一步增强CDDP诱导的凋亡；同时BAX、Cleaved-Caspase-3和γ-H2AX表达升高，BCL-2、p-ATM、p-CHK2和RAD21表达降低。KU60019处理进一步促进DNA损伤累积和细胞凋亡，并降低细胞活力。结论：RNF8可能通过ATM/CHK2轴促进DNA损伤应答并降低EC细胞对CDDP的敏感性；敲低RNF8可增加DNA损伤累积并增强CDDP敏感性。

Lung cancer constitutes the most prevalent and lethal malignant tumor in China. Approximately 85% of lung cancer diagnoses correspond to the non-small cell histological subtype [non-small cell lung cancer (NSCLC)]. Despite surgery being the mainstay for early-stage disease, postoperative recurrence remains high and adjuvant chemotherapy offers limited benefit. In recent years, targeted therapy has demonstrated substantial advantages in driver mutation-positive NSCLC. To this end, the Lung Cancer Medical Education Committee of the Chinese Medical Education Association developed guidelines based on a systematic review of evidence through November 2025, using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach and a modified Delphi method. Focusing on epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK), and addressing ROS proto-oncogene 1 (ROS1), B-Raf proto-oncogene serine/threonine kinase (BRAF) V600E mutation, and mesenchymal-epithelial transition factor (MET) exon 14 (METex14) skipping, the guideline covers molecular testing, neoadjuvant/adjuvant therapy, perioperative strategies, minimal residual disease monitoring, and postoperative surveillance. It defines testing requirements, specifies stage-directed and subtype-specific treatments, and standardizes minimal residual disease monitoring. These recommendations emphasize precision and feasibility to improve survival and quality of life.

Aim To investigate the effects of Congrong San(CRS)on learning and memory ability,hippocam-pal neuronal injury,and ferroptosis in rats with Alzhei-mer's disease(AD)and to explore the related mecha-nisms.Methods AD rat models were established and divided into Sham,Model,CRS low-dose,CRS medium-dose,CRS high-dose,and memantine groups.After treatment,Morris water maze,HE and Nissl staining,transmission electron microscopy,immunofluorescence staining,Western blot,and kit assays were performed to assess learning and memory ability,hippocampal neuro-nal injury,ferroptosis-related indicatorsand glucose reg-ulated protein 78 ku(GRP78)-(proteinkinaseR-li-keERkinase)PERK-(activating transcription factor 4)ATF4 pathway protein expression.Results Com-pared with the model group,rats in the CRS medium-and high-dose groups and the memantine group showed significant improvement in learning and memory abili-ty,reduced hippocampal neuronal injury,increased number of Nissl bodies,and ameliorated endoplasmic reticulum swelling and mitochondrial damage.In addi-tion,the expressions of GRP78,p-PERK/PERK,and ATF4 were downregulated,while GPX4 expression was upregulated in the CRS medium-and high-dose groups and the memantine group.Moreover,MDA content de-creased,and SOD and GSH-PX levels increased in these groups.Conclusions CRS can improve the learning and memory ability in AD rats,reduce hipp-ocampal neuronal injury and ferroptosis,and its mecha-nism may be related to the inhibition of the GRP78-PERK-ATF4 pathway,enhancement of GPX4 expres-sion,and reduction of oxidative stress levels,providing a new approach for the clinical treatment of AD.

Objective To systematically evaluate the effect of inspiratory muscle training(IMT)on weaning outcomes in patients with weaning failure.Methods Literatures in Chinese and English were retrieved from databases such as PubMed,Cochrane Library,Web of Science,Embase,CNKI,VIP,Wanfang data and CBM for researches on the effect of IMT in mechanical ventila-tion weaning failure,from the inception of the databases to October 22,2024.The methodological quality of the researches was evaluated with PEDro scale,and data were extracted for a systematic review.Results Nine randomized controlled trials were included,published between 2011 and 2023,from Brazil,China,the United States,Iran and Australia,with a total of 499 patients.The scores of the PEDro scale ranged five to eight.The population included patients with prolonged weaning,difficult weaning and tracheostomy.The IMT methods included threshold load training and tapered flow resistance training.The training intensity was 30%to 80%of maximal inspiratory pressure(MIP),and some researches did not set the training intensity based on MIP.The pro-gression of intensity varied widely across researches.The intervention frequency ranged from five to 30 breaths per set,with at least one minute rest between sets,two to six sets per session,one to two sessions per day,and five to seven days per week.The duration of the intervention ranged from successful weaning,one week after weaning,extubation,or four days to eight weeks.Regarding the efficacy of the intervention,IMT was not benefi-cial for the duration of mechanical ventilation and intensive care unit(ICU)length of stay on weaning failure pa-tients.However,the effect of IMT on weaning successful rates,duration of weaning,MIP and mortality was in-consistent.Conclusion IMT can not improve the duration of mechanical ventilation and ICU length of stay for weaning failure pa-tients,and there is still debate regarding its effect on successful rate of weaning,duration of weaning,MIP and mortality.

Pulpitis is an inflammatory response of dental pulp tissue triggered by external stimuli such as bacterial infection, mechanical trauma, and iatrogenic injury. Accurate diagnosis of pulpitis is essential for the success of minimally invasive endodontic treatments. However, the current diagnostic framework lacks objective indicators capable of precisely assessing the severity of pulp inflammation. Recent studies have revealed that biomarkers such as matrix metalloproteinases, cytokines, chemokines, non-coding RNAs, growth factors, and signaling molecules are closely associated with the degree of pulp inflammation. These findings suggested that biomarker-based molecular diagnostics held significant promise for advancing precision and minimally invasive approaches in endodontics. This review systematically discusses the current research progress, challenges, and future directions of biomarkers in the diagnosis and treatment of pulpitis.

Objective:To screen high risk children for lysosomal storage diseases (LSD) in southern China and analyze the spectrum characteristics of LSD in this region.Methods:A cross-sectional study was conducted. A total of 7 435 children at high risk of LSD were screened at Guangzhou Women and Children′s Medical Center, Guangzhou Medical University from January 2009 to December 2024. The activities of 22 lysosomal enzymes from peripheral blood leukocytes or plasma were measured by fluorescence or colorimetric assays with synthetic substrates to screen for 24 LSD subtypes.Results:Among the 7 435 high risk children, 759 children were diagnosed with LSD (10.2%). The diagnosed cases included 506 males and 253 females, with an age at diagnosis of 3.0 (2.5, 5.5) years. The common disease types were mucopolysaccharidosis (MPS) (390 cases (51.4%)), sphingolipidoses (269 cases (35.4%)), glycogen storage disease (62 cases (8.2%)), and mucolipidosis types Ⅱ and Ⅲ (29 cases (3.8%)). Among the positive cases, 21 disease subtypes were identified. The 5 frequent subtypes, in descending order, were MPS type Ⅱ (197 cases (26.0%)), Gaucher disease (111 cases (14.6%)), MPS type ⅣA (87 cases (11.5%)), glycogen storage disease type Ⅱ (62 cases (8.2%)), and metachromatic leukodystrophy (MLD) (49 cases (6.5%)). The rarest subtypes were mannosidosis, multiple sulfatase deficiency and Wolman disease, each with 1 case (0.1%).Conclusions:Enzyme activity screening is essential for diagnosing high risk children with LSD. In Southern China, the most common LSD subtypes are MPS Ⅱ, Gaucher disease, MPS ⅣA, glycogen storage disease type Ⅱ, and MLD, while mannosidosis, multiple sulfatase deficiency and Wolman disease are the rarest.