Ticks are important disease vectors that pose serious threats to human health and animal husbandry. Understanding seasonal fluctuation in tick populations and their influencing factors provides a scientific basis for population management and the prevention and control of tick-borne diseases. Tick life stages are influenced by factors including population density, activity levels, survival rates, and other biological features that exhibit periodic changes. Seasonal fluctuations in tick populations directly impact population dynamics and the risk of disease transmission. This review primarily examines the factors that influence seasonal tick population fluctuations.

Hepatic encephalopathy （HE） is a common complication of severe liver disease in the end stage， and it is urgently needed to improve the rate of effective treatment and clarify the pathogenesis of HE. The liver is a crucial hub for immune regulation， and disruption of immune homeostasis is a key factor in the pathological mechanisms of HE. As the main metabolites of intestinal flora， short-chain fatty acids （SCFAs） play a vital role in the biological processes of both innate and adaptive immunity and can regulate the proliferation and differentiation of immune cells maintain the homeostasis of intestinal microenvironment and the integrity of barrier function. Studies have shown that SCFAs participate in bidirectional and dynamic interactions with the liver-gut-brain axis through immunomodulatory pathways， thereby playing an important role in the diagnosis， treatment， and prognostic evaluation of HE. Starting from the immunoregulatory effect of SCFAs， this article summarizes and analyzes the crosstalk relationship between SCFAs and the liver-gut-brain axis and the significance of SCFAs in the diagnosis and treatment of HE， in order to provide new ideas for optimizing clinical prevention and treatment strategies.

Objective To investigate the effect of loganin(Log)on glutamine metabolism in cervical cancer through the regula-tion of nuclear factor red blood cell 2 related factor 2(NFE2L2)-ferritin heavy chain 1(FTH1)-glutathione peroxidase 4(GPX4).Methods Bioinformatics analysis was conducted to identify common targets of Log,glutamine metabolism,and cervical cancer.Hela cells were divided into Blank,control(Ctrl),Log,cisplatin(DDP),NFE2L2 activator(TBHQ),NFE2L2 inhibitor(ML385),and TBHQ+Log groups to detect cell proliferation,invasion,apoptosis,glutamine metabolism,and the protein expression of NFE2L2,FTH1,and GPX4.A cervical cancer xenograft mouse model was established to investigate the in vivo effects of Log on the progression of cervical cancer.Results Bioinformatics analysis confirmed that NFE2L2 might be a target of Log in the treatment of cervical cancer.Both Log and DDP reduced the proliferation and invasion abilities of Hela cells,increased apoptosis,and decreased the levels of glutamine and glutamic acid,as well as the protein expression of glutaminase(GLS1)and glutamic dehydrogenase(GLUD1,P<0.05).The NFE2L2 activator TBHQ had opposite effects,whereas ML385 had a similar impact on the Log.Additionally,Log treatment inhibited the protein expression of NFE2L2,FTH1,and GPX4(P<0.05).Animal experiments showed that Log significantly inhibited cervical cancer progression(P<0.05).Conclusion Log affects cervical cancer progression via glutamine metabolism by inhibiting the NFE2L2-FTH1-GPX4 signaling pathway.

Objective To investigate the role of Sneathia sanguinegens(S.sanguinegens)in the development of unexplained recurrent spontaneous abortion(URSA).Methods A case-control study was conducted to analyze the vaginal flora characteristics of 65 patients with URSA and 18 healthy controls through 16S rRNA gene sequencing.Toxicity profile of S.sanguinegens on human cervical cancer cells(ME-180),human umbilical vein endothelial cells(HUVEC)and human placental choriocarcinoma cells(JEG-3)was analyzed at the cellular level to assess the mechanism of it in adverse pregnancy outcomes.And S.sanguinegens was used to infect C57BL/6J mice to explore the toxic effect on living organisms.Results The relative abundance of Sneathia was increased in patients with URSA compared with healthy controls.It was positively correlated with the number of miscarriages,and was attributed to S.sanguinegens.We also found that S.sanguinegens damaged ME-180,JEG-3 and HUVEC cells.The degree of cellular damage was related to the level of S.sanguinegens added.Intravenous infection with S.sanguinegens caused inflammatory damage in several organs and extramedullary hematopoiesis in the spleen.Conclusion S.sanguinegens is closely related to URSA and should be emphasized in patients with high vaginal bacterial load.

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.

Objective To propose a novel identification algorithm based on ensemble learning for assessing the severity of acute respiratory distress syndrome(ARDS)to achieve continuous monitoring of the disease severity.Methods Firstly,leve-raging the open-source MIMIC-Ⅳ database,a variety of non-invasive physiological parameters of patients were extracted and subjected to preliminary preprocessing.A multivariate feature selection algorithm was employed to rank these parameters and calculate feature importance scores through weighted computation.Secondly,based on the feature importance scores,a subset search algorithm was utilized to identify the subset of features that could yield optimal performance across four machine learning algorithms:neural networks,logistic regression,AdaBoost and XGBoost.Finally,a soft voting ensemble method was designed using a generalized linear regression model to integrate the results of each single machine learning algorithm,and a multivariate ensemble learning algorithm was proposed by combining the optimal feature subsets.The algorithm proposed when used to identify the severity of ADRS was evaluated with MIMIC-Ⅳ database,and compared with the traditional algorithms.Results The sensitivity,specificity,accuracy and AUC of the algorithm were 87.15％,89.23％,88.34％and 0.923 4,respectively,all of which outperformed those of the traditional algorithms.Conclusion The ARDS severity identification algorithm based on ensemble learning is capable of achieving continuous and real-time monitoring of the severity of ARDS,thereby offering robust support for the early identification and warning of ARDS in patients.[Chinese Medical Equipment Journal,2025,46(2):1-9]

ObjectiveNon-invasive magnetic stimulation technology has been widely used in the treatment of Alzheimer’s disease (AD), but there is a lack of convenient and timely methods for evaluating and providing feedback on the effectiveness of the stimulation, which can be used to guide the adjustment of the stimulation protocol. This study aims to explore the possibility of heart rate variability (HRV) in diagnosing AD and guiding AD magnetic stimulation intervention techniques. MethodsIn this study, we used a 40 Hz, 10 mT pulsed magnetic field to expose AD mouse models to whole-body exposure for 18 d, and detected the behavioral and electroencephalographic signals before and after exposure, as well as the instant electrocardiographic signals after exposure every day. ResultsUsing one-way ANOVA and Pearson correlation coefficient analysis, we found that some HRV indicators could identify AD mouse models as accurately as behavioral and electroencephalogram(EEG) changes (P<0.05) and significantly distinguish the severity of the disease (P<0.05), including rMSSD, pNN6, LF/HF, SD1/SD2, and entropy arrangement. These HRV indicators showed good correlation and statistical significance with behavioral and EEG changes (r>0.3, P<0.05); HRV indicators were significantly modulated by the magnetic field exposure before and after the exposure, both of which were observed in the continuous changes of electrocardiogram (ECG) (P<0.05), and the trend of the stimulation effect was more accurately observed in the continuous changes of ECG. ConclusionHRV can accurately reflect the pathophysiological changes and disease degree, quickly evaluate the effect of magnetic stimulation, and has the potential to guide the pattern of magnetic exposure, providing a new idea for the study of personalized electromagnetic neuroregulation technology for brain diseases.

OBJECTIVE: Blood culture remains the gold standard for diagnosing bloodstream infections. Clinical laboratories must ensure the quality of blood culture processes from receipt to obtaining definitive results. We examined laboratory analytical indicators associated with positive blood culture results. METHODS: Blood cultures collected from Peking Union Medical College Hospital between January 1, 2020, and December 31, 2022, were retrospectively analyzed. The mode of transportation (piping logistics delivery vs. staff), source of blood cultures (outpatient/emergency department vs. inpatient department), rotation of personnel, and time of reception (8:00-19:59 vs. 20:00-07:59) were compared between blood culture-positive and -negative results. RESULTS: Between 2020 and 2022, the total positive rate of blood culture was 8.07%. The positive rate of blood cultures in the outpatient/emergency department was significantly higher than that in the inpatient department (12.46% vs. 5.83%; P < 0.0001). The time-to-detection of blood cultures was significantly affected by the delivery mode and personnel rotation. The blood culture positive rate of the total pre-analytical time within 1 h was significantly higher than that within 1-2 h or > 2 h ( P < 0.0170). CONCLUSION Laboratory analytical indicators such as patient source, transportation mode, and personnel rotation significantly impacted the positive detection rate or time of blood culture.
Blood Culture/statistics & numerical data* ; Humans ; Retrospective Studies ; Emergency Service, Hospital/statistics & numerical data*

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*