OBJECTIVE: To identify prognostic genes associated with lysosome-dependent cell death (LDCD) in patients with gastric cancer (GC). METHODS: Differentially expressed genes (DEGs) were identified using The Cancer Genome Atlas - Stomach Adenocarcinoma. Weighted gene co-expression network analysis was performed to identify the key module genes associated with LDCD score. Candidate genes were identified by DEGs and key module genes. Univariate Cox regression analysis, and least absolute shrinkage and selection operator regression and multivariate Cox regression analyses were performed for the selection of prognostic genes, and risk module was established. Subsequently, key cells were identified in the single-cell dataset (GSE183904), and prognostic gene expression was analyzed. Cell proliferation and migration were assessed using the Cell Counting Kit-8 assay and the wound healing assay. RESULTS: A total of 4,465 DEGs, 95 candidate genes, and 4 prognostic genes, including C19orf59, BATF2, TNFAIP2, and TNFSF18, were identified in the analysis. Receiver operating characteristic curves indicated the excellent predictive power of the risk model. Three key cell types (B cells, chief cells, and endothelial/pericyte cells) were identified in the GSE183904 dataset. C19orf59 and TNFAIP2 exhibited predominant expression in macrophage species, whereas TNFAIP2 evolved over time in endothelial/pericyte cells and chief cells. Functional experiments confirmed that interfering with C19orf59 inhibited proliferation and migration in GC cells. CONCLUSION C19orf59, BATF2, TNFAIP2, and TNFSF18 are prognostic genes associated with LDCD in GC. Furthermore, the risk model established in this study showed robust predictive power.
Stomach Neoplasms/pathology* ; Humans ; Prognosis ; Lysosomes/physiology* ; RNA-Seq ; Cell Death ; Single-Cell Analysis ; Gene Expression Regulation, Neoplastic ; Cell Proliferation ; Single-Cell Gene Expression Analysis

Soil cadmium pollution can adversely affect the cultivation of the ornamental plant, Coleus scutellarioides. Upon cadmium contamination of the soil, the growth of C. scutellarioides is impeded, and it may even succumb to the toxic accumulation of cadmium. In this study, we investigated the effects of arbuscular mycorrhizal fungi (AMF) on the adaptation of C. scutellarioides to cadmium stress, by measuring the physiological metabolism and the degree of root damage of C. scutellarioides, with Aspergillus oryzae as the test fungi. The results indicated that cadmium stress increased the activity of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), and the content of malondialdehyde (MDA) and proline (Pro) within the cells of C. scutellarioides, but inhibited mycorrhizal infestation rate, root vigour and growth rate to a great degree. With the same cadmium concentration, the inoculation of AMF significantly improved the physiological indexes of C. scutellarioides. The maximum decrease of MDA content was 42.16%, and the content of secondary metabolites rosemarinic acid and anthocyanosides could be increased by up to 27.43% and 25.72%, respectively. Meanwhile, the increase of root vigour was as high as 35.35%, and the DNA damage of the root system was obviously repaired. In conclusion, the inoculation of AMF can promote the accumulation of secondary metabolites, alleviate root damage, and enhance the tolerance to cadmium stress in C. scutellarioides.
Cadmium/toxicity* ; Mycorrhizae/physiology* ; Plant Roots/drug effects* ; Soil Pollutants/toxicity* ; Stress, Physiological ; Superoxide Dismutase/metabolism*

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective:To explore the potential of brain age gap (BAG) as a biomarker of brain health and analyze its causal relationship with common brain diseases.Methods:Brain structural magnetic resonance imaging (sMRI) data from public databases (UK Biobank, ADNI, PPMI) were selected and input into a simple fully convolutional network (SFCN) to estimate BAG. The disease group (with corresponding codes or labels, n=6 796) and healthy control group (without corresponding codes or labels, n=9 660) were defined according to the presence or absence of ICD-10 codes and corresponding brain disease labels. The two-sample t-test was used to compare the BAG differences between the disease and healthy control group; genome-wide association study (GWAS) was used to find genomic regions significantly associated with BAG in 31 520 people in the UK Biobank. The causal effects between BAG and 14 brain diseases were analyzed by Mendelian randomization (MR). Results:The mean absolute error (MAE) between the subject′s chronological age and estimated brain age for the 1 932 subjects in the healthy control group used for model testing was 2.364 years. Compared with the healthy control group, Alzheimer′s disease ( t=33.42), anxiety disorders ( t=2.38), bipolar disorder ( t=3.76), stroke ( t=2.75), demyelinating disease ( t=7.45), major depressive disorder ( t=3.49), Parkinson′s disease ( t=17.69), and post-traumatic stress disorder ( t=2.34) BAG was significantly increased ( PFDR<0.05). There were 8 independent genome-wide risk regions associated with BAG in the GWAS ( P<5×10 -8), 4 of which were novel(related genes: PICK1, TBC1D9, SIAH3, and TMEM98). In MR analysis, a strong causal association between Alzheimer′s disease and BAG was observed (β=0.23,95% CI=0.08-0.38, PFDR=0.030). Conclusion:BAG can be used as a biomarker that reflects brain health information. The occurrence of Alzheimer′s disease will lead to an increase in BAG.

Objective To study the anti-reflux effect of conical gastric stump embedding in radical resection of esophageal cancer.Methods A total of 60 patients who planned to undergo radical resection of esophageal cancer in our hospital from June 2020 to June 2022 were selected as the study objects,and the patients were divided into the observation group and the control group by random number table method,with 30 cases in each group.Patients in both groups underwent laparoscopic radical resection of esophageal cancer and esophagogastric end-to-side mechanical anastomosis.The observation group adopted the conical gastric stump embedding technique after esophagogastric end-to-side mechanical anastomosis.The perioperative related indexes,postoperative complications and gastroesophageal reflux of patients in the two groups were compared.The postoperative anti-reflux effect was evaluated by reflux disease questionnaire(RDQ)score and 24-hour intraesophageal pH monitoring.Results The operation time and digestive tract reconstruction time of patients in the observation group were longer than those in the control group(P<0.05),while there was no statistically significant difference in the amount of intraoperative bleeding,the number of lymph node dissection,the first exhaust time,or the postoperative hospital stay of patients between the two groups(P>0.05).There was no statistically significant difference in the overall incidence of postoperative complications between the two groups(P>0.05).The severity of postoperative gastroesophageal reflux of patients in the observation group was lighter than that in the control group,and the difference was statistically significant(P<0.05).The RDQ score,24-hour reflux frequency,>5 minutes reflux frequency,pH<4 time,and longest reflux time of patients in the observation group was significantly lower/less/shorter than those in the control group(P<0.05).Conclusion The conical gastric stump embedding technique is safe and feasible in the radical resection of esophageal cancer.Although the operation time and digestive tract reconstruction time are slightly prolonged,it does not increase the perioperative risks,which can significantly reduce the occurrence and severity of postoperative gastroesophageal reflux of patients,and achieve a good anti-reflux effect.

Sturgeon cartilage has a wide range of applications as it is rich in biologically active substances such as chondroitin sulphate and protein. In this study, the safety evaluation of sturgeon cartilage peptide in NIH/3T3 and C2C12 cells was conducted, and the results showed that sturgeon cartilage peptide did not induce apoptosis and necrosis in NIH/3T3 and C2C12 cells compared to the blank control, which provides an in vitro experimental basis for the transdermal drug delivery of sturgeon cartilage peptide. Further evaluation of the scavenging activity of sturgeon cartilage peptides against the 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radicals showed that sturgeon cartilage peptides have strong antioxidant activity. A stable sturgeon cartilage peptide ointment containing 7% sturgeon cartilage peptide was prepared and analysed for its transdermal absorption in mouse skin. This experiment was approved by the Animal Ethics Committee of the Institute of Materia Medica, Chinese Academy of Medical Sciences (approval number: 00007684). It was found that the 12-hour cumulative release of sturgeon cartilage peptide ointment reached 92%. In this study, we further analyzed the ability of sturgeon cartilage peptide ointment to inhibit ear swelling in mice after xylene-induced inflammation. The sturgeon cartilage peptide ointment showed significant anti-inflammatory activity compared to the matrix group and the control group. In conclusion, the present study clearly demonstrated that sturgeon cartilage peptide has good safety and antioxidant activity, and the prepared sturgeon cartilage peptide ointment provides an experimental basis for further application of sturgeon cartilage peptide as well as the study of transdermal drug delivery.

Influenza imposes a significant disease burden on society and individuals annually, and influenza vaccination is considered a significant public health measure to prevent influenza and reduce influenza-related severe disease and death. The low influenza vaccination rate in China is partly due to certain factors affecting the willingness and behavior of individuals to receive them. Scientific research and targeted interventions on these factors can effectively improve the vaccination situation. Commonly used individual-level theoretical models for influenza vaccination behavior include the health belief model, protection motivation theory, and theory of planned behavior. This study reviews theoretical models commonly employed in researching influenza vaccination willingness and behavior. An overview of these practical applications and challenges models is presented to provide references for relevant research and intervention programs in China.

Objective:To explore the potential of brain age gap (BAG) as a biomarker of brain health and analyze its causal relationship with common brain diseases.Methods:Brain structural magnetic resonance imaging (sMRI) data from public databases (UK Biobank, ADNI, PPMI) were selected and input into a simple fully convolutional network (SFCN) to estimate BAG. The disease group (with corresponding codes or labels, n=6 796) and healthy control group (without corresponding codes or labels, n=9 660) were defined according to the presence or absence of ICD-10 codes and corresponding brain disease labels. The two-sample t-test was used to compare the BAG differences between the disease and healthy control group; genome-wide association study (GWAS) was used to find genomic regions significantly associated with BAG in 31 520 people in the UK Biobank. The causal effects between BAG and 14 brain diseases were analyzed by Mendelian randomization (MR). Results:The mean absolute error (MAE) between the subject′s chronological age and estimated brain age for the 1 932 subjects in the healthy control group used for model testing was 2.364 years. Compared with the healthy control group, Alzheimer′s disease ( t=33.42), anxiety disorders ( t=2.38), bipolar disorder ( t=3.76), stroke ( t=2.75), demyelinating disease ( t=7.45), major depressive disorder ( t=3.49), Parkinson′s disease ( t=17.69), and post-traumatic stress disorder ( t=2.34) BAG was significantly increased ( PFDR<0.05). There were 8 independent genome-wide risk regions associated with BAG in the GWAS ( P<5×10 -8), 4 of which were novel(related genes: PICK1, TBC1D9, SIAH3, and TMEM98). In MR analysis, a strong causal association between Alzheimer′s disease and BAG was observed (β=0.23,95% CI=0.08-0.38, PFDR=0.030). Conclusion:BAG can be used as a biomarker that reflects brain health information. The occurrence of Alzheimer′s disease will lead to an increase in BAG.