Blood transfusion is an important life-support measure in modern medicine. As clinical demand for transfusion continues to rise, blood supply remains under persistent strain, making the efficient utilization of this precious medical resource a critical component of blood management. Accurate prediction of transfusion needs is of great significance for optimizing blood resource allocation and conserving blood products. Traditional transfusion decision-making relies on clinical experience and simplified scoring systems, which are insufficient to meet the demands of precision medicine. Machine learning (ML) technology can integrate multidimensional data—including demographic characteristics, laboratory indicators, surgical information, and dynamic physiological waveforms—to construct high-performance predictive models, offering a new approach to transfusion prediction. This article systematically reviews the application progress of ML in transfusion prediction across clinical scenarios such as trauma, the perioperative period, and obstetrics. In trauma, ML has been applied to early massive transfusion prediction, prehospital transfusion decision-making, and precise prediction in pediatric trauma. In the perioperative field, applications span specialties including traumatic brain injury, orthopedic surgery, cardiovascular and major vascular surgery, and hepatic surgery. In obstetrics, models can effectively predict postpartum hemorrhage and transfusion requirements associated with cesarean section. Studies have shown that algorithms such as random forest, gradient boosting machines, and deep neural networks achieve area under the receiver operating characteristic curve (AUC) values of 0.83-0.98 in massive transfusion prediction, 0.75-0.97 in perioperative scenarios, and 0.80-0.89 in obstetric transfusion prediction—significantly outperforming traditional scoring tools. However, current studies are generally limited by single-center retrospective designs, insufficient external validation, and inadequate reporting standards; limited model interpretability and challenges in clinical integration further constrain practical translation. Future research should focus on constructing multicenter data cohorts, integrating multimodal data, conducting prospective implementation studies, and deeply embedding models within electronic health record systems, thereby advancing the application of ML to optimize clinical transfusion decision-making.

Objective To investigate the prognostic value of albumin-corrected anion gap (ACAG) in patients with advanced lung cancer receiving immune checkpoint inhibitors (ICIs). Methods A total of 637 patients with advanced lung cancer who received ICI treatment were enrolled. Baseline clinical data and laboratory parameters were collected, and ACAG was calculated. The optimal prognostic Cut-off value for ACAG (11.58 mmol/L) was determined using X-Tile software, and patients were divided into low (≤11.58 mmol/L) and high (>11.58 mmol/L) ACAG groups. Propensity score matching was performed to control for confounding factors. Survival curves were plotted using the Kaplan-Meier method. A Cox proportional hazards regression model was employed to analyze prognostic factors, and subgroup analyses were conducted. Results Among 637 patients, 440 patients remained in the matched cohort. Multivariate Cox analysis showed that after adjusting for clinical and laboratory parameters, ACAG >11.58 mmol/L was an independent risk factor for OS (HR=1.74, 95%CI: 1.02–2.98, P=0.044) but was not an independent predictor for PFS. After adjusting for different sets of covariates, stratified analysis further confirmed that ACAG >11.58 mmol/L was consistently associated with a significantly shortened OS. Survival curves demonstrated that the median OS in the high ACAG group (47.0 months) was significantly lower than that in the low ACAG group. Subgroup analysis indicated that the predictive value of ACAG for OS was more pronounced in males, patients aged <65 years, and patients with squamous cell carcinoma than in other patients. Conclusion In patients with advanced lung cancer treated with ICIs, a high baseline ACAG level (>11.58 mmol/L) is an independent risk factor for shortened OS but is not significantly associated with PFS. ACAG may serve as a simple and effective biological marker for assessing overall survival prognosis in patients with advanced lung cancer undergoing immunotherapy.

In this work,four salicylhydrazone compounds(L1?L4)were designed and synthesized by using vanillin derivatives as raw material.The structures were confirmed by nuclear magnetic resonance(NMR)and high-resolution mass spectrometry(HRMS).The optical experiments showed that probe L1 and probe L3 could be used as aluminium ion(Al3+)fluorescence probes.The fluorescence color of probe L1 solution changed from colorless to blue after adding Al3+,and the limit of detection was 25.1 nmol/L.Compared with probe L1,the fluorescence color of probe L3 solution changed from colorless to green after complexing with Al3+,and the limit of detection was 17.3 nmol/L.Probe L1 and probe L3 showed the advantages of fast response speed,high selectivity and good anti-interference.The mechanism of Al3+recognition was further demonstrated by HRMS and 1H NMR.Cell imaging experiments showed that probe L1 and L3 had low cytotoxicity and had great application potential in detection of Al3+in vivo.

Objective:To assess the efficacy and safety of Gongxuening Capsules in reducing post-abortion bleeding following artificial abortion.Methods:A multicenter, randomized, double-blind study was conducted. From May 31, 2022 to March 31, 2023, 484 women who underwent vacuum aspiration abortion for early intrauterine pregnancy were enrolled in 11 centers and randomly assigned to control group and the study group at a 1∶1 ratio using a center-block randomization method. Control group were administered a placebo of Gongxuening Capsules for 9 d, while the study group received the actual Gongxuening Capsules for the same duration. The outcomes measured included vaginal bleeding volume, duration of vaginal bleeding, endometrial thickness, time to menstrual recovery, and complications.Results:1) A total of 484 subjects were enrolled, and 472 completed the study. Totally 450 subjects were included in the efficacy analysis set, with 224 in control group and 226 in the study group; 468 subjects were included in the safety analysis set, with 236 in control group and 232 in the study group. The baseline characteristics of the two groups were comparable (all P>0.05). 2) The vaginal bleeding volume was lower in the study group [(13.30±12.14) mL] than in control group [(19.00±17.67) mL, P<0.001]. The proportion of subjects in the study group with bleeding days less than 4 d [29.65% (67/226)] was higher than that in control group [19.20% (43/224), P=0.010]. 3) No significant differences were observed between the two groups in terms of time to menstrual recovery and endometrial thickness (all P>0.05). 4) In the study group, 3 subjects experienced non-therapeutic-related complications, while 11 subjects in control group. The incidence of complications was lower in the study group [1.29% (3/232)] than in control group [4.66% (11/236), P=0.033]. Conclusion:The administration of Gongxuening Capsules to women following artificial abortion significantly reduced vaginal bleeding volume and was associated with good safety, with the treatment being well-tolerated by the subjects.

Objective To explore the effect and mechanism of Slc1a2 overexpression on cognitive dysfunction in sleep-deprived mice.Methods A total of 130 mice were divided into five groups:normal sleep(NS),NS+ov-Slc1a2,sleep deprivation(SD),SD+ov-NC,and SD+ov-Slc1a2,with 26 mice in each group.The SD mice model was established using an automatic system based on a rotating rod,and overexpress Slc1a2 adenovirus was injected into the prefrontal cortex(PFC).Immunofluorescence and Western blotting were used to detect the expression of Slc1a2 in the mouse PFC.Electrophysiological tests were used to evaluate non-rapid eye movement(NREM)sleep time,rapid eye movement(REM)sleep time,and wakefulness time in mice.Real-time quantitative PCR was used to detect the expression of glutamate(Glu)and gamma-aminobutyric acid(GABA)metabolic enzymes in the mouse PFC.Whole-cell patch-clamp recording was used to detect the frequency and amplitude of miniature excitatory postsynaptic currents(mEPSC)in mouse PFC.Immunofluorescence was used to detect the proportion of GABA-positive cells in the mouse PFC.The C11-BODIPY fluorescent probe was used to detect lipid reac-tive oxygen species(ROS)levels in mouse PFC.Commercial kits were used to detect Fe2+and malondialdehyde(MDA)levels in the mouse PFC.Cognitive function in mice was evaluated using the open field,novel object recognition,and Y-maze tests.Results Compared with the NS group,the NREM sleep time,REM sleep time,central area stay time,recognition index,and novel wall selection index increased significantly,while wakefulness time decreased significantly in the NS+ov-Slc1a2 group(all P＜0.05).The percentage of Slc1a2+GFAP+cells,expression of Slc1a2 protein,expression of Glul,Slc6a1,and Abat mRNA,frequency and amplitude of mEPSC,and proportion of GABA-positive cells in the PFC increased significantly,whereas lipid ROS,Fe2+,and MDA levels decreased significantly(all P＜0.05).Compared with the NS group,the NREM sleep time,REM sleep time,central area stay time,recognition index,and novel wall selection index of the SD group and the SD+ov-NC group were significantly decreased,whereas wakefulness time was significantly increased(all P＜0.05).The percentage of Slc1a2+GFAP+cells,expression of Slc1a2 protein,expression of Glul,Slc6a1,and Abat mRNA,frequency and amplitude of mEPSC,and proportion of GABA-positive cells in the mouse PFC decreased significantly,whereas lipid ROS,Fe2+,and MDA levels increased significantly(all P＜0.05).Compared with the SD and SD+ov-NC groups,the NREM sleep time,REM sleep time,central area stay time,recognition index,and novel wall selection index of the SD+ov-Slc1a2 group increased significantly,whereas the wakeful-ness time decreased significantly(all P＜0.05).The percentage of Slc1a2+GFAP+cells,the expression of Slc1a2 protein,the expression of Glul,Slc6a1,and Abat mRNA,the frequency and amplitude of mEPSC,and the proportion of GABA-positive cells in the mouse PFC increased significantly,whereas lipid ROS,Fe2+,and MDA levels decreased significantly(all P＜0.05).Conclusion Ectopic overexpres-sion of Slc1a2 in the PFC can improve sleep disorders in SD mice,reduce the damage caused by SD to excitatory synaptic transmission and GABAergic neuron function in the PFC,and alleviate cognitive impairment and anxiety-like behavior in these mice.Its mechanism may be related to the improvement of Glu/GABA metabolic imbalance in the PFC and inhibition of ferroptosis.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.

Objective To compare five time series models and predict the monthly incidence of bacillary dysentery in Qinghai Province in 2024,and provide reference for the prevention and control.Methods The epidemic charac-teristics of bacterial dysentery in Qinghai Province from 2014 to 2023 were analyzed.R4.3.1 software was used for establishing seasonal autoregressive integrated moving average(SARIMA)model,Holt-Winters triple exponential smoothing(Holt Winters)model,exponential smoothing(ETS)model,neural network autoregression(NNAR)model,and trigonometric seasonality,Box-Cox transformation,ARMA errors,trend and seasonal components(TBATS)model.Fitting effect of the models was analyzed and accuracy was compared.Results From 2014 to 2023,a total of 5 833 cases of bacterial dysentery were reported in Qinghai Province,without deaths,male to fe-male ratio being 1.23∶1.The highest incidence was reported in 2016(15.45 per 100 000 people),and the lowest in-cidence was reported in 2023(3.68 per 100 000 people).Incidence increased from 2014 to 2016,then decreased,showing an obvious overall downward trend.Case number in＜5 years age group was the highest,accounting for 29.76％of the total cases(n=1 736).Regarding population distribution,the top three were children in childcare institutions and scattered children(35.56％),farmers(24.65％),and students(12.62％).Except the additive Holt-Winters model,the predicted trends of the other four models were consistent with actuality.The ETS model had the best fitting effect,with a relatively balanced overall performance(training set:MAE=0.13,RMSE=0.21,MAPE=19.55％;testing set:MAE=0.11,RMSE=0.16,MAPE=28.66％).It is recommended to pre-dict the incidence of bacillary dysentery in Qinghai Province based on ETS model.Conclusion From 2014 to 2023,bacterial dysentery in Qinghai Province showed a downward trend,with the peak of the epidemic from June to Au-gust.Preschool and scattered children were high-risk groups.Among the five prediction models,ETS model has the best fitting effect,and can be used to predict the incidence of bacillary dysentery.

Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43％(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38％,9.73％,and 8.47％,respectively.The prevalence of CRE strains in Escherichia coli was 1.99％.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)％,followed by blood(20.88±3.40)％and urine(18.40±3.45)％.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)％.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.

Objective To characterize the changing species distribution and antibiotic resistance profiles of respiratory isolates in hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Commercial automated antimicrobial susceptibility testing systems and disk diffusion method were used to test the susceptibility of respiratory bacterial isolates to antimicrobial agents following the standardized technical protocol established by the CHINET program.Results A total of 589 746 respiratory isolates were collected from 2015 to 2021.Overall,82.6％of the isolates were Gram-negative bacteria and 17.4％were Gram-positive bacteria.The bacterial isolates from outpatients and inpatients accounted for(6.0±0.9)％and(94.0±0.1)％,respectively.The top microorganisms were Klebsiella spp.,Acinetobacter spp.,Pseudomonas aeruginosa,Staphylococcus aureus,Haemophilus spp.,Stenotrophomonas maltophilia,Escherichia coli,and Streptococcus pneumoniae.Each microorganism was isolated from significantly more males than from females(P＜0.05).The overall prevalence of methicillin-resistant S.aureus(MRSA)was 39.9％.The prevalence of penicillin-resistant S.pneumoniae was 1.4％.The prevalence of extended-spectrum β-lactamase(ESBL)-producing E.coli and K.pneumoniae was 67.8％and 41.3％,respectively.The overall prevalence of carbapenem-resistant E.coli,K.pneumoniae,Enterobacter cloacae,Pseudomonas aeruginosa,and Acinetobacter baumannii was 3.7％,20.8％,9.4％,29.8％,and 73.3％,respectively.The prevalence of β-lactamase was 96.1％in Moraxella catarrhalis and 60.0％in Haemophilus influenzae.The H.influenzae isolates from children(＜18 years)showed significantly higher resistance rates to β-lactam antibiotics than the isolates from adults(P＜0.05).Conclusions Gram-negative bacteria are still predominant in respiratory isolates associated with serious antibiotic resistance.Antimicrobial resistance surveillance should be strengthened in clinical practice to support accurate etiological diagnosis and appropriate antimicrobial therapy based on antimicrobial susceptibility testing results.