With the rapid advancement of hemodynamic indices and monitoring technologies, their classification methods and application processes have become increasingly complex. Currently, no unified standard hasbeen established, making it difficult to fully meet the clinical requirements for hemodynamic management. To assist in hemodynamic monitoring assessment and therapeutic decision-making in critically ill patients, the Critical Hemodynamic Therapy Collaborative Group, in conjunction with the Critical Ultrasound Study Group, has jointly developed the Standard for the Application of Hemodynamic Monitoring Techniques in Critical Care. The first part of this standard systematically categorizes hemodynamic indicators into flow indicators, pressure and its derivative indicators, and tissue perfusion indicators, while elaborating on the clinical application of each. The second part establishes a standardized clinical implementation pathway for hemodynamic monitoring. It proposes a tiered monitoring strategy-comprising basic, advanced, indication-specific, and special scenario monitoring-tailored to different clinical settings. It emphasizes the central role of critical care ultrasound across all levels of monitoring and establishes hemodynamic assessment standards for organs such as the brain, kidneys, and gastrointestinal tract. This standard aims to provide a unified framework for clinical practice, teaching, training, and research in critical care medicine, thereby promoting standardized development within the discipline.

ObjectiveTo explore the microbiological characteristics of Staphylococcus epidermidis with hemolytic phenotype （SEHP）. MethodsHemolytic phenotype was detected using the three-point inoculation method， involving a total of 5 strains of SEHP and 5 strains of Staphylococcus epidermidis with non-hemolytic phenotype （SENHP） . Bacterial species were identified using the Microflex LT MALDI-TOF mass spectrometer， and a phylogenetic tree was constructed through 16S rRNA sequence alignment. Growth curves were monitored through the microcultivation assay. Biofilm formation ability was assessed by microplate crystal violet staining. Red blood cell toxicity was detected using the microplate method. Antimicrobial susceptibility testing of SEHP and SENHP against commonly used antibiotics was performed using a VITEK 2 GP639 test kit. Antagonistic effects of SEHP and SENHP against Staphylococcus aureus and Corynebacterium striatum were evaluated by the Oxford cup inhibition assay. ResultsCompared with SENHP， SEHP exhibited a marked decrease in growth rate during the late logarithmic phase， accompanied by significant hemolytic toxicity. Additionally， it showed lower resistance rates to levofloxacin and moxifloxacin， and could antagonize Staphylococcus aureus and Corynebacterium striatum. ConclusionThe microbiological characteristics of SEHP differ from those of SENHP in that SEHP demonstrates antagonistic effects against S. aureus and C. striatum.

Objective To systematically review the studies on predictive models for delayed graft function (DGF) after kidney transplantation. Methods Databases including China Biology Medicine Database, China National Knowledge Infrastructure, Wanfang Database, VIP Database, PubMed, Web of Science and CINAHL were searched to collect studies on predictive models for DGF after kidney transplantation published from the establishment of each database to June 29, 2025. Two researchers screened the literatures according to the inclusion and exclusion criteria, evaluated the quality of the literatures using the prediction model risk of bias assessment tool (PROBAST), and conducted a meta-analysis of the common predictors of the models using R software. Results A total of 12 literatures were included, involving 14 predictive models with sample sizes ranging from 103 to 24 653 cases. Donor serum creatinine level, cold ischemia time, donor age and donor body mass index were the top four common predictors. All the predictive models were at high risk of bias and low in applicability. The results of meta-analysis showed that abnormal donor body mass index, advanced donor age, prolonged cold ischemia time and elevated donor serum creatinine level were all associated with an increased risk of DGF after transplantation (all P<0.01), but there was high heterogeneity among the studies. Fixed-effect model and random-effect model were used to re-pool the effect sizes separately. The results indicated that the fixed-effect model and random-effect model had good consistency in terms of donor body mass index, donor age and cold ischemia time, while there was a significant difference in the effect sizes of the two models for donor serum creatinine level. Conclusions The predictive models for DGF risk after kidney transplantation have good predictive performance, but the overall risk of bias is high. In the future, large-sample, multicenter and high-quality prospective clinical studies should be carried out to optimize the predictive models, so as to improve their predictive ability and clinical application value.

Objective To screen metabolic core genes in colon cancer based on machine learning algorithms and analyze their functional mechanisms.Methods Data were obtained from The Cancer Genome Atlas(TCGA)database and the Gene Expression Omnibus(GEO)database.The TCGA co-hort included 375 tumor samples and 32 adjacent normal tissue samples,while the GSE39582 cohort comprised 419 tumor samples.Univariate Cox regression analysis combined with random forest,sup-port vector machine recursive feature elimination(SVM-RFE),and least absolute shrinkage and selec-tion operator(LASSO)regression algorithms were employed to screen for metabolic core genes.Re-ceiver operating characteristic(ROC)curves were plotted,and the area under the curve(AUC)was used to evaluate the predictive efficacy of the core genes.Real-time fluorescent quantitative polymerase chain reaction(qRT-PCR)and immunohistochemistry(IHC)methods were adopted to detect the ex-pression of the core genes.The core genes were knocked down to explore their roles in colon cancer.Results Three core genes,namely CPT2,SCP2 and NR3C2,were screened based on machine learning algorithms.According to the comparison results of the AUCs of the ROC curves,NR3C2 exhibited the best predictive efficacy.qRT-PCR detection results showed that NR3C2 mRNA was lowly ex-pressed in colon cancer cell lines;IHC detection results revealed that NR3C2 was lowly expressed in colon cancer tissues.Knocking down NR3C2 significantly promoted the proliferation and migration of colon cancer cells.Conclusion NR3C2 is identified as a core metabolic inhibitory gene in colon cancer by cross-applying three machine learning algorithms,which may provide a new strategy for metabolic targeted therapy.

Objective To explore the epidemiological characteristics and treatment outcomes of venomous snake bites in Yunnan Province from 2023 to 2024.Methods A retrospective analysis was performed on 1,727 snakebite cases collected from 16 administrative regions in Yunnan Province between January 2023 and December 2024,focusing on the following indicators:(1)demographic characteristics(age and gender distribution);(2)characteristics of injury events(location,time distribution,and affected body parts);(3)composition of venomous snake species;(4)clinical features and treatment protocols;(5)efficacy evaluation indicators(time for local symptom relief,duration of hospital stay,and follow-up results).Results High-incidence areas for snake bites in Yunnan Province included Wenshan(308/1727),Kunming(285/1727),Qujing(270/1727),Lincang(139/1727),Baoshan(120/1727),and Dali(105/1727),with incidents primarily occurring from June to September.The most frequently encountered venomous snake species were Ovophis and Trimeresurus,known for their hemotoxic venom.Bite victims were mainly young adults.Clinical manifestations primarily included pain and swelling,with bites mainly occurring on limbs and in mountainous or forested areas.Anti-venom treatment concentrated on anti-ancistrodon acutus serum.Swelling generally subsided within 2(2,3)days,with an average hospital stay of 4(3,5)days.96.46%of patients receiving standardized treatment fully recovered,with a follow-up disability rate of 0.35%.Conclusion The incidents of venomous snake bites in Yunnan Province have significant regional and seasonal concentration,with Wenshan,Kunming,Qujing and other areas being high-risk areas.The peak period of incidents is from June to September in summer,and the main types of snakes causing injuries are those containing blood toxins;The victim group is mainly young people,and bites often occur in the limbs,with typical symptoms of pain and swelling;The main treatment is anti snake serum;Snake bite patients who receive formal diagnosis and treatment have a higher recovery rate and a lower follow-up disability rate,but there is still a need to raise public awareness of standardized diagnosis and treatment for snake bites.

Pregnancy complicated with acute myeloid leukemia is uncommon,requiring the collaborative management by specialists from departments of hematology,obstetrics,anesthesiology,and neonatology for both the parturient and the neonate.This article reports an anesthesic management process of a parturient woman with acute myeloid leukemia and reviews relevant literature published in recent years to systematically summarize the approach for anesthesia-related perinatal management of such patients.
Humans ; Female ; Pregnancy ; Leukemia, Myeloid, Acute/complications* ; Pregnancy Complications, Neoplastic ; Adult ; Anesthesia, Obstetrical/methods*

Objective To investigate the incidence and types of patient-ventilator asynchrony(PVA)in mechanically ventilated patients within the intensive care unit(ICU),and to identify associated high-risk factors,thereby providing a basis for reducing PVA,enhancing mechanical ventilation efficiency,and refining ventilation strategies.Methods A prospective observational study was conducted among patients admitted to the general ICU of the Affiliated Hospital of Guizhou Medical University from October to December 2024 who were receiving mechanical ventilation.Inclusion criteria were as follows:age ≥18 years and mechanical ventilation duration ≥12 hours.Exclusion criteria included complete controlled mechanical ventilation,palliative care or do-not-resuscitate status,and lack of informed consent.Senior respiratory therapists performed daily bedside observations of ventilator waveforms for 10～15 minutes between 08:00 and 12:00.PVA was diagnosed based on pressure-time and flow-time waveforms,with the types of PVA being recorded.Demographic and clinical data,including age,sex,body mass index(BMI),primary diagnosis,comorbidities,APACHEⅡ score at ICU admission,blood gas analysis,ventila-tion mode and parameters,analgesia and sedation status,duration of mechanical ventilation,and length of ICU stay,were collected.The incidence and types of PVA during the observation period were analyzed.Univariate and multivariate logistic regression analyses were performed to identify high-risk factors for PVA.Clinical outcomes were compared between patients with and without PVA.Results A total of 105 patients and 453 episodes of assisted mechanical ventilation waveforms were analyzed.Among these,60.95％(64/105)experienced at least one episode of PVA.Of the 453 ventilation waveforms assessed,35.76％(162/453)demonstrated PVA.The types of PVA,ranked by incidence,were as follows:cycling mismatch(12.58％,57/453),double triggering(11.92％,54/453),ineffective triggering(9.49％,43/453),flow starvation(5.30％,24/453),and exhalation flow limitation(1.77％,8/453).The incidence of PVA varied significantly across different ventilation modes:45.7％in volume-assist/control ventilation(V-A/C),38.1％in pressure-assist/control ventilation(P-A/C),42.9％in synchronized intermittent mandatory ventilation(SIMV),and 16.7％in pressure support ventilation(PSV)(P<0.001).Multi-variate logistic regression analysis revealed that the mechanical ventilation mode[reference:PSV;V-A/C:OR=4.687,95％CI:2.140～10.263,P<0.001;P-A/C:OR=2.922,95％CI:1.489～5.734,P=0.002;SIMV:OR=4.682,95％CI:1.758～12.466,P=0.002]and actual respiratory rate(OR=1.07,95％CI:1.016～1.127,P=0.011)were significant high-risk factors for PVA.Patients with PVA had a significantly longer duration of mechanical ventilation[8.21(5.35,13.91)days vs.3.00(1.96,5.71)days,P<0.001]compared to those without PVA.Conclusions PVA is commonly observed in ICU patients receiving assisted invasive mechanical ventilation,with cycling mismatch,double triggering,and ineffective triggering being the most prevalent types.The incidence of PVA tends to be lower when using the PSV mode.Clinically,real-time monitoring of patient-ventilator synchrony via ventilator waveforms,along with the optimization of ventilator modes and parameters,should be employed to minimize the occurrence of PVA and enhance the efficiency of mechanical ventilation.

Hypothermia is a clinical syndrome characterized by core body temperature<35℃,caused by significant heat loss from body surface in cold environment.As a systemic cold injury,it can be lethal if treatment is delayed.Emergent diagnosis and treatment of hypothermia are expected to improve the prognosis of patients.In 2005,the U.S.Army Research Institute of Environmental Medicine(USARIEM)issued guidelines for the prevention and management of cold injuries,but there has been no corresponding standard in China.Therefore,Emergency Branch of Chinese Medical Rescue Association,Emergency Medical Equipment Society of China Association of Medical Equipment,Integrated Rehabilitation Medical Branch of Chinese Medical Rescue Association,and Pre-Hospital Emergency Care Working Committee of Chinese Aging Well Association jointly developed the Chinese Expert Consensus on Emergent Treatment of Hypothermia(2025 edition).The consensus covers the pathophysiology,etiology and epidemiology,diagnosis and severity grading,prehospital treatment,and in-hospital treatment of hypothermia,including 15 recommendations in total,aiming to provide guidance for the relevant clinical rescue work.

Objective Metabolomics combined with machine learning algorithms was used to systematically study the preoperative serum metabolites of patients with early postoperative cognitive dysfunction(POCD)after heart valve replacement,so as to screen biomarkers that may predict early POCD after heart valve replacement and explore the corresponding metabolic regulatory mechanisms.Methods A total of 60 patients underwent heart valve replacement under extracorporeal circulation were selected and divided into early-POCD group(group P)and non-POCD group(group N)according to whether POCD occurred or not.Metabolomic analysis was performed on preoperative serum samples of patients in group P and group N to screen the differential metabolites and metabolic pathways.The biomarkers related to early POCD were identified by random forest algorithm.Results A total of 532 differential metabolites were detected by metabonomics analysis,and 5 biomarkers were screened by random forest algorithm,namely quinoline,3'-sialyllactose,sphingomyelin(d18∶1/20∶0),lysophosphatidylcholine[P-18∶1(9Z)]and 25-hydroxycholesterol.Among them,the main metabolic pathways were phenylalanine metabolism,primary bile acid biosynthesis,ascorbic acid and aldonate metabolism,pentose and glucuronate interconversion,tryptophan metabolism,drug metabolism-cytochrome P450,porphyrin and chlorophyll metabolism.Conclusion Many metabolic pathways in patients with early POCD after heart valve replacement under extracorporeal circulation have changed before operation,which may lead to the occurrence of early POCD.Quinoline,3'-sialyllactose,sphingomyelin(d18∶1/20∶0),lysophosphatidylcholine[P-18∶1(9Z)]and 25-hydroxycholesterol may be biomarkers for predicting early POCD.

Objective:To use machine learning to predict the efficacy of accelerated corneal collagen cross-linking (A-CXL) surgery, identify prognostic factors, and construct models to predict postoperative disease progression.Methods:A single-center retrospective study was conducted.A total of 82 keratoconus patients (112 eyes) who underwent A-CXL surgery at the West China Hospital of Sichuan University between March and December 2021 were enrolled.Preoperative and follow-up examinations included anterior segment evaluation by slit-lamp microscopy, corneal topography using Pentacam, and corneal biomechanical indices using Corvis ST.Disease progression was defined as an increase in maximum keratometry (Kmax) of ≥1 D from the preoperative level at the last follow-up.Various machine learning algorithms were employed to analyze corneal topography, biomechanical parameters and corneal densitometry values to identify prognostic factors and construct models for predicting postoperative disease progression.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of West China Hospital, Sichuan University (No.2023496).Written informed consent was obtained from each subject.Results:During follow-up, 15.1% (17/112) of the eyes showed progression after A-CXL.The preoperative astigmatism and stress-strain index (SSI) in the progression group were (-5.41±2.72)D and 1.41±0.78, respectively, which were significantly higher than (-3.30±2.54)D and 0.95±0.98 in the non-progression group ( t=2.80, 2.03; both P<0.05).Cox regression analysis identified preoperative astigmatism (hazard ratio [HR]=1.20), SSI (HR=1.10), and anterior corneal densitometry of 2-6 mm (CDA6) (HR=2.10) as significant risk factors for post-A-CXL progression.Among various machine learning models developed and validated, the area under the curve (AUC) values for logistic regression, multilayer perceptron (MLP) model, and random forest (RF) exceeded 0.700.For F1-score, the AUC values for logistic regression, MLP, and RF were 0.870, 0.880, and 0.880, respectively.The network structure of the visualized MLP was a single-layer, 24-neurons neural network with 80% accuracy in predicting whether progression occurred after A-CXL.The clinical nomogram developed in conjunction with astigmatism, SSI, and CDA6 predicted the cumulative probability of progression at 0.5, 1, and 2 years postoperatively based on the sum of the specified values for each variable, and based on the optimal cutoff value, keratoconus corneas could be classified into high-, intermediate-, and low-risk groups, respectively.The time-dependent subject operating characteristic curves of the nomogram showed AUCs of 0.734, 0.685, and 0.935 at 0.5, 1, and 2 years postoperatively, respectively, all of which performed well in predicting progression. Conclusions:Preoperative astigmatism, SSI, and CDA6 are significant risk factors for post-A-CXL progression in keratoconus.The MLP model can accurately predict postoperative disease progression, and the clinical nomogram combining preoperative astigmatism, SSI, and CDA6 can effectively differentiate between low-, medium-, and high-risk postoperative progression outcomes.