Stroke-associated sarcopenia is a serious post-stroke complication that can have a significant impact on patient’s functional recovery. However， currently available assessment tools for sarcopenia require specialized equipment and personnel， which are difficult to access in resource-limited settings. This article presents the role of biomarkers as an objective method in the pathogenesis， prevention， diagnosis， and prognostic assessment of stroke-associated sarcopenia， with the focus on neuromuscular， inflammatory， metabolic， and nutritional indicators.
Stroke ; Sarcopenia ; Biomarkers

Cancer is characterized by abnormal cell proliferation. Cyclins and cyclin-dependent kinases (CDKs) have been recognized as essential regulators of the intricate cell cycle, orchestrating DNA replication and transcription, RNA splicing, and protein synthesis. Dysregulation of the CDK pathway is prevalent in the development and progression of human cancers, rendering cyclins and CDKs attractive therapeutic targets. Several CDK4/6 inhibitors have demonstrated promising anti-cancer efficacy and have been successfully translated into clinical use, fueling the development of CDK-targeted therapies. With this enthusiasm for finding novel CDK-targeting anti-cancer agents, there have also been exciting advances in the field of targeted protein degradation through innovative strategies, such as using proteolysis-targeting chimera, heat shock protein 90 (HSP90)‍-mediated targeting chimera, hydrophobic tag-based protein degradation, and molecular glue. With a focus on the translational potential of cyclin- and CDK-targeting strategies in cancer, this review presents the fundamental roles of cyclins and CDKs in cancer. Furthermore, it summarizes current strategies for the proteasome-dependent targeted degradation of cyclins and CDKs, detailing the underlying mechanisms of action for each approach. A comprehensive overview of the structure and activity of existing CDK degraders is also provided. By examining the structure‍‒‍activity relationships, target profiles, and biological effects of reported cyclin/CDK degraders, this review provides a valuable reference for both CDK pathway-targeted biomedical research and cancer therapeutics.
Humans ; Neoplasms/metabolism* ; Cyclin-Dependent Kinases/antagonists & inhibitors* ; Cyclins/metabolism* ; Proteolysis ; Antineoplastic Agents/pharmacology* ; Molecular Targeted Therapy ; Proteasome Endopeptidase Complex/metabolism* ; Animals

Immune suppression in the tumor microenvironment (TME) is closely related to abnormal glycolysis. Tumor cells gain metabolic advantages and suppress immune responses through the "Warburg effect". Traditional Chinese medicine (TCM) has been shown to regulate key glycolysis enzymes (such as HK2 and PKM2), metabolic signaling pathways (such as PI3K/AKT/mTOR, HIF-1α) and non-coding RNAs at multiple targets, thus synergistically inhibiting lactate accumulation, improving vascular abnormalities, and relieving metabolic inhibition of immune cells. Studies have shown that TCM monomers and formulas can promote immune cell infiltration and functions, improve metabolic microenvironment, and with the assistance by the nano-delivery system, enhance the precision of treatment. However, the dynamic mechanism of the interaction between TCM-regulated glycolysis and TME has not been fully elucidated, for which single-cell sequencing and other technologies provide important technical support to facilitate in-depth analysis and clinical translational research. Future studies should be focused on the synergistic strategy of "metabolic reprogramming-immune activation" to provide new insights into the mechanisms of tumor immunotherapy.
Humans ; Tumor Microenvironment/immunology* ; Glycolysis/drug effects* ; Neoplasms/drug therapy* ; Medicine, Chinese Traditional ; Signal Transduction ; Drugs, Chinese Herbal/pharmacology*

Objective: To investigate the significant factors influencing the need for perioperative blood transfusion in patients undergoing hepatectomy. Methods: Medical records of patients who underwent elective hepatectomy in our hospital from January 2020 to December 2021 were retrospectively collected. Variables associated with transfusion were analyzed using traditional logistic regression (LR) and machine learning algorithm, the Classification and Regression Tree (CRT). The predictive values of the two methods were compared by the area under the curve (AUC) of the ROC curve. Results: Among the 402 patients, 82(20.4%) received blood transfusions. Multivariable logistic regression analysis identified several risk factors for perioperative blood transfusion, including vascular invasion, preoperative hemoglobin level, intraoperative blood loss, duration of surgery, postoperative hemoglobin level, and postoperative complications (P<0.05). In the CRT model for predicting blood transfusion, intraoperative blood loss (cutoff: 450 mL) was the parent node, with preoperative Hb, postoperative complications, and hospital stay as child nodes. The LR model demonstrated superior predictive performance compared to the CRT model, with an AUC of 0.971 (95% CI: 0.956-0.985) vs 0.937 (95% CI: 0.909-0.965). The difference in AUC between the two methods was statistically significant (P<0.05). Conclusion: Although the CRT model did not outperform logistic regression in overall predictive accuracy, it still provides a valuable tool for assisting clinicians in making decisions about blood transfusion in the perioperative period of hepatectomy, thereby facilitating more individualized guidance for preoperative blood preparation in clinical practice.

AIM:The objective of this study is to examine the expression of profilin 1(PFN1)in mice with di-abetic nephropathy and determine its association with immune cell infiltration.METHODS:This study presents an analy-sis of PFN1 expression and immune cell infiltration in patients with diabetic nephropathy,utilizing transcriptome expres-sion data from kidney tissue microarray.Additionally,the findings were validated in a diabetic nephropathy mouse model.Sixteen C57BL/6 mice were randomly assigned into two groups,namely the normal group and the model group,in an equal manner.The model group underwent the establishment of the diabetic nephropathy model through intraperitoneal injection of streptozotocin.Subsequently,the expression levels of CD11b,F4/80,CC chemokine receptor 4(CCR4),interleukin-1 receptor type I(IL-1R1),B-cell lymphoma-2(Bcl-2),Bcl-2-associated X protein(Bax)and caspase-3 in kidney tissue were assessed upon successful establishment of the diabetic nephropathy model.Furthermore,the overexpression of PFN1 was observed in a cellular model of diabetic nephropathy,and the protein expression levels of monocyte chemotactic pro-tein-1(MCP-1)and caspase-3 were assessed.RESULTS:The expression of PFN1 was found to be significantly in-creased in the GSE30122 dataset of transcriptome expression in kidney tissues affected by diabetic nephropathy(P＜0.01).This increase in PFN1 expression was found to be correlated with the presence of macrophages and T cells.Fur-thermore,the renal tissue of the diabetic nephropathy model group exhibited significant pathological changes.In this mod-el group,the expression levels of PFN1,CD11b,F4/80,CCR4,IL-1R1,Bax,Bcl-2,and caspase-3 were all significant-ly increased(P＜0.01).Overexpression of PFN1 could enhance the expression of MCP-1 and caspase-3 proteins.CON-CLUSION:Macrophages and Th17 cells were identified within the renal tissue of mice with diabetic nephropathy,con-comitant with an up-regulation in the expression of PFN1.This up-regulation was observed to facilitate the induction of apoptosis in the context of diabetic nephropathy.

Diarrhea-irritable bowel syndrome （IBS-D） is one of the common functional bowel diseases in clinical practice. Since it pathogenesis is complex and has not been fully elucidated， effective treatment methods remains to be developed for this disease. Establishing the animal models of IBS-D in accordance with the clinical characteristics of traditional Chinese medicine （TCM） and Western medicine helps to reveal the pathogenesis of this disease and improve the treatment plan. The fitting degree of an animal model with clinical characteristics is an indicator to evaluate the effectiveness of the animal model in simulating the disease characteristics of Western medicine and the syndromes of TCM based on the latest diagnostic standards. By reviewing the relevant articles about the animal models of IBS-D， we discovered that rats were the preferred animals for modeling， and the models were mainly induced by single factors， double factors， or the combination of multiple factors. The established animal models mainly present symptoms or signs associated with visceral hypersensitivity or/and gastrointestinal motility abnormalities. The single factor-induced rat models of IBS-D had high fitting degrees with the clinical characteristics of Western medicine but low fitting degrees with the TCM syndromes. The animal models induced by two or more factors had high but varied fitting degrees with the clinical characteristics of Western medicine. In addition， the animal models of IBS-D considering TCM syndromes mainly focuses on the syndrome of liver depression and spleen deficiency， and few models were established for the syndromes of spleen-kidney Yang deficiency， spleen-stomach dampness-heat， spleen deficiency and dampness excess， and cold and heat in complexity. Therefore， it is essential to improve the existing or develop new animal models of IBS-D in the future， so as to provide more tools for deciphering the mechanisms of TCM and Western medicine and developing treatment methods for this disease.

Background::Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors.Methods::The Breast Cancer Cohort Study in Chinese Women, a large ongoing prospective dynamic cohort study, includes 122,058 women aged 25-70 years old from the eastern part of China. We developed multiple machine-learning risk prediction models using parametric models (penalized logistic regression, bootstrap, and ensemble learning), which were the short-term ensemble penalized logistic regression (EPLR) risk prediction model and the ensemble penalized long-term (EPLT) risk prediction model to estimate BC risk. The models were assessed based on calibration and discrimination, and following this assessment, they were externally validated in new study participants from 2017 to 2020.Results::The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set. For the long-term EPLT risk prediction model, the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations, respectively. The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model (HCBCP) models for external validation was 0.193 and 0.233, respectively, indicating that the EPLT model has higher classification accuracy.Conclusions::We developed the EPLR and EPLT models to screen populations with a high risk of developing BC. These can serve as useful tools to aid in risk-stratified screening and BC prevention.

Background and objective Both of lung cancer incidence and mortality rank first among all cancers in China.Previous lung cancer screening trials were mostly selective screening for high-risk groups such as smokers.Non-smoking women accounted for a considerable proportion of lung cancer cases in Asia.This study aimed to evaluate the outcome of community-based mass screening in Guangzhou and identify the high-risk factors for lung cancer.Methods Residents aged 40-74 years in Guangzhou were screened with low-dose computed tomography(LDCT)for lung cancer and the pulmonary nodules were classified and managed according to China National Lung Cancer Screening Guideline with Low-dose Computed Tomography(2018 version).The detection rate of positive nodules was calculated.Before the LDCT examination,residents were required to complete a"lung cancer risk factors questionnaire".The risk factors of the questionnaire were analyzed by least absolute shrinkage and selection operator(LASSO)penalized Logistic regression analysis.Results A total of 6256 residents were included in this study.1228 positive nodules(19.63％)and 117 lung cancers were confirmed,including 6 cases of Tis,103 cases of stage Ⅰ(accounting for 88.03％of lung cancer).The results of LASSO penalized Logistic regression analysis indicated that age ≥50 yr(OR=1.07,95％CI:1.06-1.07),history of cancer(OR=3.29,95％CI:3.22-3.37),textile industry(OR=1.10,95％CI:1.08-1.13),use coal for cooking in childhood(OR=1.14,95％CI:1.13-1.16)and food al-lergy(OR=1.10,95％CI:1.07-1.13)were risk factors of lung cancer for female in this district.Conclusion This study highlighted that numerous early stages of lung cancer cases were detected by LDCT,which could be applied to screen-ing of lung cancer in women.Besides,age ≥50 yr,personal history of cancer,textile industry and use coal for cooking in childhood are risk factors for women in this district,which suggested that it's high time to raise the awareness of early lung cancer screening in this group.

Objective To investigate the causal association of liver function and lipid metabolism levels with sleep disorders based on the Mendelian randomization analysis.Methods The analysis was conducted using the data from genome-wide association studies,with the exposure factors of liver function and lipid metabolism levels(alanine aminotransferase[ALT],aspartate aminotransferase[AST],gamma-glutamyl transpeptidase[GGT],albumin[Alb],serum total protein[TP],total bilirubin[TBil],alkaline phosphatase[ALP],triglyceride[TG],triglyceride-to-glycerol-3-phosphate[TG/G3P]ratio,total cholesterol[TC],high-density lipoprotein cholesterol[HDL-C],low-density lipoprotein cholesterol[LDL-C],poly-unsaturated fatty acids[PUFA],total fatty acids[TFA],PUFA/TFA ratio)and the outcome factor of sleep disorders(nonorganic).The regression models including inverse variance weighted,MR-Egger,Simple mode,weighted median,and Weighted mode were used to perform the Mendelian randomization analysis.Results Serum Alb(odds ratio[OR]=0.728,95%confidence interval[CI]:0.535-0.989,P＜0.05),HDL-C(OR=0.879,95%CI:0.784-0.986,P＜0.05),and PUFA/TFA ratio(OR=0.800,95%CI:0.642-0.998,P＜0.05)were negatively associated with sleep disorders,while TG/G3P ratio(OR=1.222,95%CI:1.044-1.431,P＜0.05)was positively associated with sleep disorders.The results of Mendelian randomization did not show a causal association of ALT,AST,GGT,TP,TBil,ALP,TG,TC,LDL-C,PUFA,and TFA with sleep disorders(all P＞0.05).The results of the MR-Egger intercept test showed no pleiotropy(P＞0.05),and Mendelian randomization was a valid method for causal inference in this study.Conclusion According to the results of the Mendelian randomization analysis,liver function and lipid metabolism show significant association with sleep disorders.Liver function and lipid metabolism can be used as indicators for predicting the risk of sleep disorders and performing intervention.