The circadian clock plays a critical role in regulating various physiological processes, including gene expression, metabolic regulation, immune response, and the sleep-wake cycle in living organisms. Post-translational modifications (PTMs) are crucial regulatory mechanisms to maintain the precise oscillation of the circadian clock. By modulating the stability, activity, cell localization and protein-protein interactions of core clock proteins, PTMs enable these proteins to respond dynamically to environmental and intracellular changes, thereby sustaining the periodic oscillations of the circadian clock. Different types of PTMs exert their effects through distincting molecular mechanisms, collectively ensuring the proper function of the circadian system. This review systematically summarized several major types of PTMs, including phosphorylation, acetylation, ubiquitination, SUMOylation and oxidative modification, and overviewed their roles in regulating the core clock proteins and the associated pathways, with the goals of providing a theoretical foundation for the deeper understanding of clock mechanisms and the treatment of diseases associated with circadian disruption.
Protein Processing, Post-Translational/physiology* ; Circadian Rhythm/physiology* ; Humans ; Animals ; CLOCK Proteins/physiology* ; Circadian Clocks/physiology* ; Phosphorylation ; Acetylation ; Ubiquitination ; Sumoylation

Paraplegia caused by spinal cord injury is a serious neurological complication, for which surgery is currently the main treatment method. Due to different surgical approaches, patients are usually expected to maintain a passive prone position for a long time or switch between the supine and prone positions. Affected by multiple factors such as neurogenic sensory disorders, pathological changes in muscle tone and operative duration, the risk of intraoperative acquired pressure injury (IAPI) is significantly increased. Current clinical prevention strategies for IAPI in these patients predominantly focus on localized pressure relief during positioning, lacking systematic, standardized comprehensive prevention protocols or evidence-based guidelines. To address it, Department of Nursing, Orthopedics Branch, China International Exchange and Promotive Association for Medical and Health Care, Spinal Trauma Professional Committee, Orthopedics Branch, Chinese Medical Doctor Association, Nursing Group of Spine and Spinal Cord Professional Committee of Chinese Association of Rehabilitation Medicine organized experts in relevant fields to formulate Guideline for the prevention of intraoperative acquired pressure injury in paraplegic patients with spinal cord injury ( version 2025), based on evidence-based medical evidence and latest research results and clinical practice at home and abroad. Eleven recommendations were put forward from the aspects of preoperative risk assessment, intraoperative prevention strategies, postoperative handover and monitoring, and supportive mechanisms for IAPI prevention, aiming to standardize the prevention measures and management strategies of IAPI in paraplegic patients with spinal cord injury and accelerate the recovery of patients and improve the therapeutic effect.

Objective:To explore the correlation among SMAD4 gene polymorphisms, early life traumatic experience and their interactions with clinical feature of obsessive-compulsive disorder (OCD). Methods:Totally 484 OCD patients who met the DSM-Ⅳ diagnostic criteria and 368 health controls who met the enrollment criteria were recruited from September 2013 to September 2018. The Yale-Brown obsessive-compulsive scale (Y-BOCS) was used to assess the severity of obsessive-compulsive symptoms, the Beck depression inventory Ⅱ (BDI-Ⅱ) was used to assess the severity of depressive symptoms, the Beck anxiety inventory (BAI) was used to assess the severity of anxiety symptoms, and early trauma inventory-short form (ETI-SF) was used to assess early traumatic experience. SMAD4: rs12452684, rs2276163, rs17663887 and rs3819122 were genotyped using the Taqman genotyping technique. Data were analyzed using SPSS 20.0 software, and comparisons among groups were performed using chi-square test, t-test, Mann-Whitney U non-parametric test and analysis of covariance. Correlation was analyzed using Spearman correlation analysis, and interactions were analyzed using general linear model. Results:All sites except rs17663887 met the Hardy-Weinberg equilibrium (rs12452684: χ2=0.29, P=0.59; rs2276163: χ2=2.58, P=0.11; rs3819122: χ2=0.22, P=0.64).Allele, genotype frequencies of SMAD4: rs12452684, rs2276163 and rs3819122 were not statistically significant between the OCD and the health control groups ( χ2=0.02, 1.20, 0.04, all P>0.05; χ2=1.85, 3.98, 1.45, all P>0.05). The results of covariance analysis (corrected for age and gender) showed that there were significantly differences in compulsion (CC: 12.47±4.23, CT: 12.53±4.15, TT: 13.97±3.11; AA: 12.63±4.08, AC: 12.49±4.19, CC: 13.87±2.93) and total Y-BOCS scores(CC: 25.31±6.42, CT: 25.68±5.90, TT: 27.75±6.01; AA: 25.54±6.52, AC: 25.56±5.98, CC: 27.63±5.75) among the three genotypes of the SMAD4: rs2276163 and rs3819122 between the two groups ( F=3.58, 3.87, 3.48, 3.73, all P<0.05). Emotional abuse in the ETI-SF was positively correlated with obsession and total Y-BOCS scores( r=0.14, 0.14, both P<0.05). The interactions of rs2276163, rs3819122 and emotional abuse were associated with obsession scores ( F=4.65, 3.63, 2.93, all P<0.01). Conclusions:The more emotional abuse experienced in early life, the more severe obsessive-compulsive symptoms, and the interaction between the SMAD4 gene and early traumatic experience is involved in the development of OCD.

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.

Objective To retrospectively analyze the association between metabolic factors and high-risk colorectal adenoma(CRA).Methods The medical records of patients aged 18-75 years who underwent their initial colonoscopy at Karamay Central Hospital of Xinjiang Uygur Autonomous Region from Jul 2000 to Mar 2017 were collected.The comparison between normal colonoscopy(NC)and high-risk CRA patients was conducted using an unpaired t-test,while chi-square test was used for categorical variables.Least absolute shrinkage and selection operator(LASSO)regression and Logistic regression were utilized to analyze the association between metabolic factors and high-risk CRA.Results A total of 1 798 patients meeting the inclusion and exclusion criteria were enrolled and divided into normal colonoscopy(NC)findings group(n=972)and high-risk CRA group(n=826).The high-risk CRA group exhibited significantly lower levels of high-density lipoprotein cholesterol(HDL-C)in comparison to the NC group,while uric acid and fibrosis 4(FIB-4)index levels were significantly higher than those observed in the NC group(all P<0.05).Based on LASSO regression analysis,we identified 12 variables that potentially influence the occurrence of high-risk CRA,including age,gender,smoking history,alcohol consumption history,non-alcoholic fatty liver disease(NAFLD),hypertension,coronary artery disease,hyperglycemia,hypercholesterolemia,low levels of HDL-C,elevated alanine aminotransferase,and elevated gamma-glutamyl transferase.Multivariate analysis revealed that individuals aged over 50 years,male gender,cigarette and alcohol consumption,low HDL-C levels,history of NAFLD and hypertension were identified as independent risk factors associated with high-risk CRA(P<0.05).In addition,without or with adjusting for age,sex,smoking,and drinking history,patients with a high TG/HDL-C ratio(the ratio≥2.68)had a significantly higher risk of high-risk CRA than those with a low TG/HDL-C ratio(the ratio<2.68)[odds ratios(ORs)were1.430 and 1.235 respectively,all P<0.05)].Without or with adjusting variables,the ORs for NAFLD patients with FIB-4 index>2.67 were 1.849(P=0.466)and 1.435(P=0.707),respectively.Conclusion A significant association exists between metabolic factors and high-risk CRA.Independent risk factors for high-risk CRA include older age(≥50 years),male,smoking history,alcohol consumption history,low levels of HDL-C,and a history of NAFLD and hypertension.Individuals exhibiting a TG/HDL-C ratio exceeding 2.68 manifest a significantly heightened susceptibility to the development of high-risk CRA.Therefore,elderly males with one or more aforementioned metabolic abnormalities should be considered a priority population for colorectal screening.

Objective:To identify the factors influencing the diagnostic accuracy of endoscopic ultrasonography (EUS) for colorectal submucosal tumors (SMT).Methods:A retrospective analysis was conducted on 330 colorectal SMT lesions (from 323 patients) diagnosed by EUS at Shenzhen Hospital of Southern Medical University from December 2015 to October 2023. Pathological diagnosis were confirmed through endoscopic resection, EUS-guided fine needle aspiration (EUS-FNA) or surgical resection. Diagnostic accuracy was calculated for each type of colorectal SMT. Univariate and multivariate logistic regression analysis were performed to identify factors affecting EUS diagnostic accuracy.Results:The overall diagnostic accuracy of EUS for colorectal SMT was 73.6% (243/330). Among 19 SMT subtypes enrolled, neuroendocrine neoplasms (51.2%, 169/330) and lipomas (15.5%, 51/330) were most prevalent, while 17 rare subtypes each accounted for <6%. Seven rare SMT (mucosal chronic inflammation, colorectal schwannoma, xanthogranulomatous inflammation, capillary hemangioma, colonic xanthoma, lymphadenoid complex, and angiomyolipoma) showed 0% diagnostic accuracy. Seven other subtypes (granular cell tumor, leiomyoma, rectal tonsil, intestinal schistosomiasis, fibrous tissue hyperplasia, gastrointestinal stromal tumor, and lymphangioma) showed accuracy <30%, whereas five subtypes (cyst, bowel endometriosis, neuroendocrine neoplasm, lipoma, and pneumatosis cystoides intestinalis) achieved >60% accuracy. Multivariate logistic regression analysis confirmed that the lesion location (left colon VS rectum: OR=0.06, 95% CI: 0.02-0.17, P<0.001; right colon VS rectum: OR=0.04, 95% CI: 0.01-0.13, P<0.001; ileocecal valve VS rectum: OR=0.09, 95% CI: 0.02-0.42, P=0.002); echogenicity (anechoic VS hypoechoic: OR=6.26, 95% CI: 1.31-29.97, P=0.022; hyperechoic VS hypoechoic: OR=13.39, 95% CI: 4.16-43.09, P<0.001) and ultrasonic layer (layer 4 VS layer 3: OR=0.22, 95% CI: 0.06-0.81, P=0.023) were independent influencing factors of EUS diagnostic accuracy for colorectal SMT. Conclusion:Neuroendocrine neoplasms and lipomas represent the most common colorectal SMT, whereas rare and uncommon SMT exhibit low EUS diagnostic accuracy. Lesion location, echogenicity, and ultrasonic layer significantly influence EUS diagnostic accuracy for colorectal SMT.

Glutamine provides carbon and nitrogen to support the proliferation of cancer cells. However, the precise reason why cancer cells are particularly dependent on glutamine remains unclear. In this study, we report that glutamine modulates the tumor suppressor F-box and WD repeat domain-containing 7 (FBW7) to promote cancer cell proliferation and survival. Specifically, lysine 604 (K604) in the sixth of the 7 substrate-recruiting WD repeats of FBW7 undergoes glutaminylation (Gln-K604) by glutaminyl tRNA synthetase. Gln-K604 inhibits SCFFBW7-mediated degradation of c-Myc and Mcl-1, enhances glutamine utilization, and stimulates nucleotide and DNA biosynthesis through the activation of c-Myc. Additionally, Gln-K604 promotes resistance to apoptosis by activating Mcl-1. In contrast, SIRT1 deglutaminylates Gln-K604, thereby reversing its effects. Cancer cells lacking Gln-K604 exhibit overexpression of c-Myc and Mcl-1 and display resistance to chemotherapy-induced apoptosis. Silencing both c-MYC and MCL-1 in these cells sensitizes them to chemotherapy. These findings indicate that the glutamine-mediated signal via Gln-K604 is a key driver of cancer progression and suggest potential strategies for targeted cancer therapies based on varying Gln-K604 status.
Glutamine/metabolism* ; Myeloid Cell Leukemia Sequence 1 Protein/genetics* ; Humans ; Proto-Oncogene Proteins c-myc/genetics* ; Cell Proliferation ; Signal Transduction ; Neoplasms/pathology* ; F-Box-WD Repeat-Containing Protein 7/genetics* ; Cell Survival ; Cell Line, Tumor ; Apoptosis

Due to its high sensitivity and non-destructive nature, Raman spectroscopy has become an essential analytical tool in biopharmaceutical analysis and drug development. Despite of the computational demands, data requirements, or ethical considerations, artificial intelligence (AI) and particularly deep learning algorithms has further advanced Raman spectroscopy by enhancing data processing, feature extraction, and model optimization, which not only improves the accuracy and efficiency of Raman spectroscopy detection, but also greatly expands its range of application. AI-guided Raman spectroscopy has numerous applications in biomedicine, including characterizing drug structures, analyzing drug forms, controlling drug quality, identifying components, and studying drug-biomolecule interactions. AI-guided Raman spectroscopy has also revolutionized biomedical research and clinical diagnostics, particularly in disease early diagnosis and treatment optimization. Therefore, AI methods are crucial to advancing Raman spectroscopy in biopharmaceutical research and clinical diagnostics, offering new perspectives and tools for disease treatment and pharmaceutical process control. In summary, integrating AI and Raman spectroscopy in biomedicine has significantly improved analytical capabilities, offering innovative approaches for research and clinical applications.