Objective:To investigate the value of a cross-combination machine learning approach in constructing a PET radiomics score (RadScore) for predicting early treatment response and prognosis in patients with primary gastrointestinal diffuse large B-cell lymphoma (PGI-DLBCL).Methods:This retrospective cohort study was conducted on 108 patients (59 males and 49 females, age (55.6±12.1) years) diagnosed with PGI-DLBCL between November 2016 and December 2021 at Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University ( n=85) and West China Hospital, Sichuan University ( n=23). Patients were divided into a training set ( n=86) and a validation set ( n=22) with the ratio of 8∶2 using stratified random sampling method. Seven machine learning models were employed to generate 49 feature selection-classification candidates, and the optimal candidate was selected to construct the RadScore, with five-fold cross-validation applied to determine the best-performing model. Logistic regression analysis was performed to identify risk factors for early treatment response, and a radiomics nomogram was developed by integrating RadScore with clinical predictors. Survival results between different groups of RadScore was compared by log-rank test. Results:Nineteen predictive features were selected from 111 radiomic features to construct the RadScore. In the training set, lactate dehydrogenase (LDH) (odds ratio ( OR)=3.53, 95% CI: 1.21-10.31, P=0.021), intestinal involvement ( OR=3.04, 95% CI: 1.04-8.88, P=0.042), total lesion glycolysis (TLG; OR=6.73, 95% CI: 2.23-20.29, P<0.001) and RadScore ( OR=15.11, 95% CI: 3.95-57.80, P<0.001) were identified as independent risk factors for predicting early treatment response. The combined model integrating RadScore, LDH, intestinal involvement, and TLG demonstrated good discriminatory ability for early treatment response (AUC=0.860 in the training set; AUC=0.902 in the validation set). Significant differences were observed in progression-free survival (PFS) and overall survival (OS) between different RadScore groups ( χ2 values: 13.92 and 8.56, both P<0.01). Conclusions:The machine learning-based RadScore may effectively predict survival outcomes in patients with PGI-DLBCL. The combined model integrating RadScore, clinical factors, and metabolic indicators can predict early treatment response in PGI-DLBCL patients.

Objective To design a digital intelligence-driven critical treatment platform to implement integrated treatment procedure inside and outside the hospital and intelligent whole-course managment from pre-hospital emergency care to discharge for critically ill patients.Methods The platform was designed with 5G,IoT,big data and aritificial intelligence techniques and developed with Java language,which adopted Oracle database-based data management and front-end and back-end separation mode,with the front end realized by Vue.js framework and the back end by microservice architecture.There were five functional modules for pre-hospital emergency care,multidisciplinary joint diagnosis and treatment,critical care,quality control management and post-discharge follow-up involved in the platform.Results The platform developed simplified the treatment procedure,enhanced the timeliness of emergency care,decreased the workload of nursing staffs and improved medical service efficiency and working efficiency effectively.Conclusion The platform increases first aid quality and treatment efficiency and provides critically ill patients with high-quality medical experience.[Chinese Medical Equipment Journal,2025,46(1):38-43]

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

Objective:To report the nationwide surveillance results of pathogenic profiles and antimicrobial resistance patterns of Gram-positive bloodstream infections in China in 2023.Methods:The clinical isolates of Gram-posttive bacteria from blood cultures were collected in member hospitals of National Bloodstream Infection Bacterial Resistant Investigation Collaborative System（BRICS）during January to December 2023. Antimicrobial susceptibility testing was performed using the dilution method recommended by the Clinical and Laboratory Standards Institute（CLSI）. Statistical analyses were conducted using WHONET 5.6 and SPSS 25.0 software.Results:A total of 4 385 Gram-positive bacterial isolates were obtained from 60 participating center. The top five pathogens were Staphylococcus aureus（ n=1 544，35.2%），coagulase-negative Staphylococci（ n=1 441，32.9%）， Enterococcus faecium（ n=574，13.1%）， Enterococcus faecalis（ n=385，8.8%），and α-hemolytic Streptococci（ n=187，4.3%）. The prevalence of methicillin-resistant Staphylococcus aureus（MRSA）and methicillin-resistant coagulase-negative Staphylococci（MRCNS）was 26.2%（405/1 544）and 69.8%（1 006/1 441），respectively. Notably，all Staphylococci remained susceptible to glycopeptide or daptomycin. Staphylococcus aureus demonstrated excellent susceptibility（>97.0%）to cephalobiol，rifampicin，trimethoprim-sulfamethoxazole，linezolid，minocycline，tigecycline，and eravacycline. No Enterococcus exhibiting resistance to linezolid were detected. Glycopeptide resistance was uncommon but more frequent in Enterococcus faecium（resistance to vancomycin and teicoplanin：both 1.7%）compared to Enterococcus faecalis（both 0.3%）. The detection rates of MRSA and MRCNS exhibited significant regional variations across the country（ χ2=17.674 and 148.650，respectively，both P<0.001）. No vancomycin-resistant Enterococci were detected in central China. Institutional comparison demonstrated higher prevalence of MRSA（ χ2=14.111， P<0.001）and MRCNS（ χ2=4.828， P=0.028）in provincial hospitals than that in municipal hospitals. Socioeconomic analysis identified elevated detection rates of both MRSA（ χ2=18.986， P<0.001）and MRCNS（ χ2=4.477， P=0.034）in less developed regions（per capita GDP

Objective:To report the results of bacterial resistant investigation collaborative system（BRICS）on the distribution and antimicrobial resistance profile of clinical Gram-negative bacteria isolates from bloodstream infections in China in 2023，and provide reference for clinical tretment of bloodstream infections and prevention and control of bacterial resistance.Methods:The clinical isolates of Gram-negative bacteria from blood cultures in member hospitals of BRICS were collected during January 2023 to December 2023. Antibiotic susceptibility tests were conducted by agar dilution or broth dilution methods recommended by Clinical and Laboratory Standards Institute（CLSI）. WHONET 5.6 and SPSS 25.0 were used to analyze the data.Results:During the study period，11 492 strains of Gram-negative bacteria were collected from 60 hospitals，of which 10 098（87.9%）were Enterobacterales and 1 394（12.1%）were non-fermentative bacteria. The top 5 bacterial species were Escherichia coli（50.0%）， Klebsiella pneumoniae（26.1%）， Pseudomonas aeruginosa（5.1%）， Acinetobacter baumannii complex（5.0%）and Enterobacter cloacae complex（4.1%）. The ESBL-producing rates in Escherichia coli， Klebsiella pneumoniae and Proteus mirablilis were 46.8%（2 685/5 741），18.3%（549/2 999）and 44.0%（77/175），respectively. The prevalence of carbapenem-resistant Escherichia coli（CREC）and carbapenem-resistant Klebsiella pneumoniae（CRKP）were 1.3%（76/5 741）and 15.0%（450/2 999）；32.9%（25/76）and 78.0%（351/450）of CREC and CRKP were sensitive to ceftazidime/avibactam combination，respectively. 94.7%（72/76）and 90.2%（406/450）of CREC and CRKP were sensitive to aztreonam/avibactam combination. Furthermore，57.9%（44/76）and 79.1%（356/450）were sensitive to imipenem/relebactam combination. The prevalence of carbapenem-resistant Acinetobacter baumannii（CRAB）complex was 64.6%（370/573），while more than 80.0% of CRAB complex was sensitive to tigecycline，eravacycline and polymyxin B. The prevalence of carbapenem-resistant Pseudomonas aeruginosa（CRPA）was 17.0%（99/581）. There were differences in the composition ratio of Gram-negative bacteria in bloodstream infections and the prevalence of important Gram-negative bacteria resistance among different regions in China，with statistically significant differences in the prevalence of CREC，CRKP，CRPA and CRAB complex（ χ2=10.6，28.6，10.8 and 19.3， P<0.05）. The prevalence of ESBL-producing Escherichia coli， CREC，CRAB complex and CRKP were higher in provincial hospitals than those in municipal hospitals（ χ2=12.5，9.8，12.7 and 57.8，all P<0.01）. Conclusions:Gram-negative bacteria are the main pathogens causing bloodstream infections in China，and Escherichia coli is ranked in the top，while the trend of Klebsiella pneumoniae increases continuously with time. CRKP infection shows a slow upward trend，CREC infecton maintains a low prevalence level，and CRAB complex infection continues to exhibit a high prevalence rate. The composition and resistance patterns of pathogens causing bloodstream infections vary to some extent across different regions and levels of hospitals in China.

Objective To design a digital intelligence-driven critical treatment platform to implement integrated treatment procedure inside and outside the hospital and intelligent whole-course managment from pre-hospital emergency care to discharge for critically ill patients.Methods The platform was designed with 5G,IoT,big data and aritificial intelligence techniques and developed with Java language,which adopted Oracle database-based data management and front-end and back-end separation mode,with the front end realized by Vue.js framework and the back end by microservice architecture.There were five functional modules for pre-hospital emergency care,multidisciplinary joint diagnosis and treatment,critical care,quality control management and post-discharge follow-up involved in the platform.Results The platform developed simplified the treatment procedure,enhanced the timeliness of emergency care,decreased the workload of nursing staffs and improved medical service efficiency and working efficiency effectively.Conclusion The platform increases first aid quality and treatment efficiency and provides critically ill patients with high-quality medical experience.[Chinese Medical Equipment Journal,2025,46(1):38-43]

Objective:To investigate the value of a cross-combination machine learning approach in constructing a PET radiomics score (RadScore) for predicting early treatment response and prognosis in patients with primary gastrointestinal diffuse large B-cell lymphoma (PGI-DLBCL).Methods:This retrospective cohort study was conducted on 108 patients (59 males and 49 females, age (55.6±12.1) years) diagnosed with PGI-DLBCL between November 2016 and December 2021 at Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University ( n=85) and West China Hospital, Sichuan University ( n=23). Patients were divided into a training set ( n=86) and a validation set ( n=22) with the ratio of 8∶2 using stratified random sampling method. Seven machine learning models were employed to generate 49 feature selection-classification candidates, and the optimal candidate was selected to construct the RadScore, with five-fold cross-validation applied to determine the best-performing model. Logistic regression analysis was performed to identify risk factors for early treatment response, and a radiomics nomogram was developed by integrating RadScore with clinical predictors. Survival results between different groups of RadScore was compared by log-rank test. Results:Nineteen predictive features were selected from 111 radiomic features to construct the RadScore. In the training set, lactate dehydrogenase (LDH) (odds ratio ( OR)=3.53, 95% CI: 1.21-10.31, P=0.021), intestinal involvement ( OR=3.04, 95% CI: 1.04-8.88, P=0.042), total lesion glycolysis (TLG; OR=6.73, 95% CI: 2.23-20.29, P<0.001) and RadScore ( OR=15.11, 95% CI: 3.95-57.80, P<0.001) were identified as independent risk factors for predicting early treatment response. The combined model integrating RadScore, LDH, intestinal involvement, and TLG demonstrated good discriminatory ability for early treatment response (AUC=0.860 in the training set; AUC=0.902 in the validation set). Significant differences were observed in progression-free survival (PFS) and overall survival (OS) between different RadScore groups ( χ2 values: 13.92 and 8.56, both P<0.01). Conclusions:The machine learning-based RadScore may effectively predict survival outcomes in patients with PGI-DLBCL. The combined model integrating RadScore, clinical factors, and metabolic indicators can predict early treatment response in PGI-DLBCL patients.

Objective:To report the nationwide surveillance results of pathogenic profiles and antimicrobial resistance patterns of Gram-positive bloodstream infections in China in 2023.Methods:The clinical isolates of Gram-posttive bacteria from blood cultures were collected in member hospitals of National Bloodstream Infection Bacterial Resistant Investigation Collaborative System（BRICS）during January to December 2023. Antimicrobial susceptibility testing was performed using the dilution method recommended by the Clinical and Laboratory Standards Institute（CLSI）. Statistical analyses were conducted using WHONET 5.6 and SPSS 25.0 software.Results:A total of 4 385 Gram-positive bacterial isolates were obtained from 60 participating center. The top five pathogens were Staphylococcus aureus（ n=1 544，35.2%），coagulase-negative Staphylococci（ n=1 441，32.9%）， Enterococcus faecium（ n=574，13.1%）， Enterococcus faecalis（ n=385，8.8%），and α-hemolytic Streptococci（ n=187，4.3%）. The prevalence of methicillin-resistant Staphylococcus aureus（MRSA）and methicillin-resistant coagulase-negative Staphylococci（MRCNS）was 26.2%（405/1 544）and 69.8%（1 006/1 441），respectively. Notably，all Staphylococci remained susceptible to glycopeptide or daptomycin. Staphylococcus aureus demonstrated excellent susceptibility（>97.0%）to cephalobiol，rifampicin，trimethoprim-sulfamethoxazole，linezolid，minocycline，tigecycline，and eravacycline. No Enterococcus exhibiting resistance to linezolid were detected. Glycopeptide resistance was uncommon but more frequent in Enterococcus faecium（resistance to vancomycin and teicoplanin：both 1.7%）compared to Enterococcus faecalis（both 0.3%）. The detection rates of MRSA and MRCNS exhibited significant regional variations across the country（ χ2=17.674 and 148.650，respectively，both P<0.001）. No vancomycin-resistant Enterococci were detected in central China. Institutional comparison demonstrated higher prevalence of MRSA（ χ2=14.111， P<0.001）and MRCNS（ χ2=4.828， P=0.028）in provincial hospitals than that in municipal hospitals. Socioeconomic analysis identified elevated detection rates of both MRSA（ χ2=18.986， P<0.001）and MRCNS（ χ2=4.477， P=0.034）in less developed regions（per capita GDP

Objective:To report the results of bacterial resistant investigation collaborative system（BRICS）on the distribution and antimicrobial resistance profile of clinical Gram-negative bacteria isolates from bloodstream infections in China in 2023，and provide reference for clinical tretment of bloodstream infections and prevention and control of bacterial resistance.Methods:The clinical isolates of Gram-negative bacteria from blood cultures in member hospitals of BRICS were collected during January 2023 to December 2023. Antibiotic susceptibility tests were conducted by agar dilution or broth dilution methods recommended by Clinical and Laboratory Standards Institute（CLSI）. WHONET 5.6 and SPSS 25.0 were used to analyze the data.Results:During the study period，11 492 strains of Gram-negative bacteria were collected from 60 hospitals，of which 10 098（87.9%）were Enterobacterales and 1 394（12.1%）were non-fermentative bacteria. The top 5 bacterial species were Escherichia coli（50.0%）， Klebsiella pneumoniae（26.1%）， Pseudomonas aeruginosa（5.1%）， Acinetobacter baumannii complex（5.0%）and Enterobacter cloacae complex（4.1%）. The ESBL-producing rates in Escherichia coli， Klebsiella pneumoniae and Proteus mirablilis were 46.8%（2 685/5 741），18.3%（549/2 999）and 44.0%（77/175），respectively. The prevalence of carbapenem-resistant Escherichia coli（CREC）and carbapenem-resistant Klebsiella pneumoniae（CRKP）were 1.3%（76/5 741）and 15.0%（450/2 999）；32.9%（25/76）and 78.0%（351/450）of CREC and CRKP were sensitive to ceftazidime/avibactam combination，respectively. 94.7%（72/76）and 90.2%（406/450）of CREC and CRKP were sensitive to aztreonam/avibactam combination. Furthermore，57.9%（44/76）and 79.1%（356/450）were sensitive to imipenem/relebactam combination. The prevalence of carbapenem-resistant Acinetobacter baumannii（CRAB）complex was 64.6%（370/573），while more than 80.0% of CRAB complex was sensitive to tigecycline，eravacycline and polymyxin B. The prevalence of carbapenem-resistant Pseudomonas aeruginosa（CRPA）was 17.0%（99/581）. There were differences in the composition ratio of Gram-negative bacteria in bloodstream infections and the prevalence of important Gram-negative bacteria resistance among different regions in China，with statistically significant differences in the prevalence of CREC，CRKP，CRPA and CRAB complex（ χ2=10.6，28.6，10.8 and 19.3， P<0.05）. The prevalence of ESBL-producing Escherichia coli， CREC，CRAB complex and CRKP were higher in provincial hospitals than those in municipal hospitals（ χ2=12.5，9.8，12.7 and 57.8，all P<0.01）. Conclusions:Gram-negative bacteria are the main pathogens causing bloodstream infections in China，and Escherichia coli is ranked in the top，while the trend of Klebsiella pneumoniae increases continuously with time. CRKP infection shows a slow upward trend，CREC infecton maintains a low prevalence level，and CRAB complex infection continues to exhibit a high prevalence rate. The composition and resistance patterns of pathogens causing bloodstream infections vary to some extent across different regions and levels of hospitals in China.

Background: There remains controversy over the relationship between serum magnesium levels and obesity in type 2 diabetes mellitus (T2DM). Therefore, the aim of this study was to assess whether there is any association of serum magnesium levels with obesity and abdominal obesity in T2DM. Methods: This cross-sectional, real-world study was conducted in 8,010 patients with T2DM, which were stratified into quintiles according to serum magnesium levels. The clinical characteristics and the prevalence of obesity and abdominal obesity were compared across serum magnesium quintiles in T2DM. Regression analyses were used to evaluate the relationship of serum magnesium with obesity and abdominal obesity in T2DM (clinical trial registration number: ChiCTR1800015893). Results: After adjustment for age, sex, and duration of diabetes, the prevalence of obesity and abdominal obesity was significantly declined across magnesium quintiles (obesity: 51.3%, 50.8%, 48.9%, 45.3%, and 43.8%, respectively, P<0.001 for trend; abdominal obesity: 71.5%, 70.5%, 68.2%, 66.4%, and 64.5%, respectively, P=0.001 for trend). After controlling for confounders, there were clearly negative associations of serum magnesium levels and quintiles with obesity and abdominal obesity in T2DM. Moreover, C-reactive protein partly mediates the effect of serum magnesium on obesity and abdominal obesity (P=0.016 and P=0.004, respectively). Conclusion The significantly negative relationship between serum magnesium and the risk of obesity and abdominal obesity was observed in T2DM. Furthermore, the independently negative association of serum magnesium with obesity may be explained by its anti-inflammatory functions. Serum magnesium levels may be applied to assess the risk of obesity and abdominal obesity in T2DM.