Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

Obstructive sleep apnea(OSA)is a common sleep disorder characterized by repeated episodes of upper airway collapse and obstruction during sleep.Polysomnography is the gold standard for diagnosing OSA,but it is expensive,time-consuming,and can cause discomfort for patients.In recent years,acoustic-based approaches for detecting OSA have emerged as a research focus.This review summarizes recent advances in OSA automatic detection techniques based on snoring and speech signals,and systematically examines their applications in diagnosis,severity assessment,and localization of obstruction sites.Findings indicate that the acoustic features of snoring and speech signals hold significant value for OSA screening,and when combined with machine learning and deep learning models,it can achieve high diagnostic accuracy.Future research should focus on elucidating the relationship between acoustic features and the pathophysiological mechanisms of OSA,integrating multimodal information,and advancing the clinical application of wearable devices,with the aim of promoting intelligent,non-invasive,and cost-effective screening technologies for OSA.

Aim To investigate the effect of oroxylin A(OA)on apoptosis in Ishikawa cell line of endometrial cancer and the underlying mechanism through the phosphatidylinositol-3 kinase/protein kinase B(PI3K/AKT)signaling pathway.Methods Ishikawa cells were treated with different concentrations of OA(0,4,8,10,12,and 20 μmol·L-1)for 24 h-72 h,the cell viability was detected by CCK-8 assay,apoptosis was detected by flow cytometry,and the protein ex-pression levels of B-cell lymphoma-2(Bcl-2),Bcl-2-associated X protein(Bax),PI3K/AKT,recombinant cytochrome P450 1B1(CYP1B1),and catechol-O-methyltransferase(COMT)were detected by Western blot technique.Results OA inhibited the prolifera-tion of Ishikawa cells in a concentration-and time-de-pendent manner.Compared with the blank control group,the expression of Bax protein increased signifi-cantly,while the expression of Bcl-2 protein decreased significantly with the increase of OA concentration.The expression of COMT protein increased significant-ly,while the expression of CYP1B1 protein decreased significantly.PI3K/AKT:IGF-1(PI3 K agonist)sup-plementation reversed the effect,the expression of COMT protein significantly decreased,and the expres-sion of CYP1B1 protein significantly increased.Con-clusions OA exerts anti-tumor effects in Ishikawa cells of endometrial cancer,which may be related to cell apoptosis mediated by the inhibition of the PI3K/AKT signaling pathway.

Objective:This study aims to provide insights into the clinical features of anti-melanoma differentiation-associated protein-5(MDA5)-positive dermatomyositis (MDA5-DM) patients with fatal outcomes, leveraging pathogenic microbiota metagenomic analysis, to guide the clinical assessment and treatment choices.Methods:From January 2020 to August 2023, deceased patients diagnosed with MDA5-DM were identified at the Department of Rheumatology and Immunology, the Second Affiliated Hospital of Xi ′an Jiaotong University. Clinical data were retrospectively collected and analyzed using Mann Whitney U test and Fisher ′s exact test to summarize risk factors and treatment assessment for MDA5-DM patients with fatal outcomes. Results:①The proportion of male patients was higher than females among MDA5-DM patients with fatal outcomes, which differed from the incidence pattern, possibly associated with smoking and gender proportions (6/11 vs. 0/7, P=0.037). ②94%(17/18) patients presented initially with elevated ferritin levels [(1 350±942)ng/ml] and CRP [(47±36)mg/L]. ③All patients (18/18) exhibited early involvement of the upper lung lobes, including multiple nodules in 9/18, ground-glass opacities in 5/18, and solitary nodules in 4/18. ④Metagenomic next-generation sequencing (mNGS) of bronchoalveolar lavage fluid was negative in 4/16 cases, with cytomegalovirus and pneumocystis jirovecii being the most commonly detected pathogens in 5/16 cases each. ⑤89%(16/18) of patients continued to have lymphocyte counts persistently <0.5×10 9/L irrespective of treatment. Conclusion:Smoking may have adverse effects on male MDA5 patients. Early involvement of the upper lobe of the lungs is more common in MDA5 antibody positive deaths, and persistent lymphocyte depletion is an important factor in poor response. Enhancing mNGS analysis of bronchoalveolar lavage fluid and vigilance towards cytomegalovirusand Pneumocystis jirovecii could provide valuable clinical guidance.

Objective To investigate the effects and mechanisms of Huatan Quyu Decoction on learning and memory abilities in rats with vascular dementia(VD).Methods Totally 112 male SD rats were randomly selected with 16 rats as the sham-operation group,the remaining rats were used to prepare VD models by segmental ligation of the common carotid artery.The successfully modeled rats were randomly divided into model group,Huatan Quyu Decoction low-,medium-and high-dosage groups(6.1,12.1,24.2 g/kg),donepezil hydrochloride group(0.5 mg/kg)and combination group(Huatan Quyu Decoction 12.1 g/kg+donepezil hydrochloride 0.5 mg/kg),with 16 rats in each group.Each group was given the corresponding treatment measures for 4 weeks.The Morris water maze test was used to assess learning and memory abilities,neurological function was evaluated using Garcia score,HE staining was used to observe the morphology of the hippocampal tissue,ELISA was employed to detect the serum content of Aβ,immunohistochemistry was utilized to observe the β-catenin,LRP6 and GSK-3β protein expression in brain tissue.Results Compared with the sham-operation group,the escape latency of the model group rats was prolonged(P<0.01),the number of crossing platforms was reduced(P<0.01),and the neurological deficit score was decreased(P<0.01),the arrangement of hippocampal tissue cells was disorderly,and the tissue was severely damaged,the serum Aβ content increased(P<0.01),the expressions of β-catenin and LRP6 protein in brain tissue decreased,and the expression of GSK-3β protein increased(P<0.01).Compared with the model group,the escape latency of rats in each administration group was shortened,the number of crossing platforms increased,the neurological deficit score increased,the number of hippocampal cells was relatively more,the arrangement was more orderly,and the structure was relatively complete,the serum Aβ content decreased,the expressions of β-catenin and LRP6 proteins increased,and the expression of GSK-3β protein decreased.Among them,Huatan Quyu Decoction high-dosage group had a significantly better effect than Huatan Quyu Decoction low-and medium-dosage groups(P<0.01),and there was no statistical significance in various indicators compared with the donepezil hydrochloride group(P>0.05).Compared with the donepezil hydrochloride group,the combination group showed significant improvements in learning and memory abilities(P<0.01),the neurological deficit score significantly increased(P<0.01),the number of hippocampal cells significantly increased,arranged neatly,and structurally intact,the serum Aβ content significantly decreased(P<0.01),the expression of β-catenin and LRP6 proteins significantly increased,and the expression of GSK-3β protein significantly decreased(P<0.01).Conclusion Huatan Quyu Decoction can repair cognitive function in VD rats,improve learning and memory abilities,and alleviate VD symptoms by activating the Wnt/β-catenin signaling pathway to reduce serum Aβ content,decrease the apoptosis of nerve cells and alleviate the degree of pathological damage in hippocampal tissue.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Objective To evaluate the efficacy of combining surgical treatment with biological agents for perianal fistulizing Crohn's disease(pfCD).Methods Sixty patients with perianal fistulizing Crohn's disease(pfCD)admitted to our hospital from May 2021 to December 2023 were randomly allocated into two groups:Treatment Group A(n=30)and Treatment Group B(n=30).Treatment Group A received pfCD surgery combined with inflix-imab(IFX)and azathioprine(AZA),while Treatment Group B underwent pfCD surgery along with ustekinumab(UST)and AZA.The CDAI score,PDAI score,and Assche score were monitored for both groups,and postoperative MRI examinations were conducted to evaluate the healing of pfCD.Results There were statistically significant differences in CDAI,PDAI,and Assche scores between pre-treatment and post-treatment comparisons within treat-ment groups A and B(P<0.05),as well as in the magnitude of change at each time point.Comparisons of CDAI,PDAI,and Assche scores at 8,16,24,and 32 weeks,and PDAI scores at 40 weeks between groups A and B using independent samples t-tests did not yield statistically significant results(P>0.05).However,significant differences were observed for CDAI and Assche scores at 40 weeks(P<0.05).Significant changes in CDAI,PDAI,and Assche scores were noted at 8,16,24,32,and 40 weeks post-treatment within both groups A and B(P<0.05).Multiple comparisons using the LSD method revealed that the changes in these scores at each time point were statistically significant(P<0.05).The data indicate a temporal trend in the changes of CDAI,PDAI,and Assche scores,with group B showing a more rapid decline compared to group A.In terms of fistula response rates,both groups A and B achieved 100%(30/30).However,the clinical healing rate of fistulas was higher in group B at 86.7%(26/30)compared to 76.7%(23/30)in group A.Conclusion The combination of surgical treatment with IFX/UST plus AZA is safe and effective for treating pfCD.However,the long-term efficacy of combining surgical treatment with UST appears to be superior.

Postoperative infection of internal fixation of closed fractures the lower limbs in adults represents a devastating complication, characterized by diagnostic challenges, prolonged treatment duration and high disability rates. Current management of these infections faces multiple challenges, such as difficulties in early accurate diagnosis, and various controversies about the treatment plan, leading to poor overall diagnosis and treatment results. To address these issues, based on evidence-based medicine and principles with emphasis on scientific rigor, clinical applicability and innovation, the Trauma Branch of the Chinese Medical Association, Orthopedic Branch of the Chinese Medical Doctor Association, Orthopedics Branch of the Chinese Medical Association, and Trauma Orthopedics and Polytrauma Group of the Resuscitation and Emergency Committee of the Chinese Medical Doctor Association have collaboratively organized a panel of relevant experts to develop the Guideline for diagnosis and treatment of infection after internal fixation of closed lower limb fractures in adults ( version 2025). The guideline proposed 10 recommendations, aiming to provide a foundation for standardized diagnosis and treatment of postoperative infection in adults with closed lower limb fractures.

Acute symptomatic osteoporotic thoracolumbar compression fracture (ASOTLF) can lead to chronic low back pain, kyphosis deformity, pulmonary dysfunction, loss of mobility, and even life-threatening complications. Vertebral augmentation is currently the mainstream treatment method for this condition. In 2019, the Editorial Board of Chinese Journal of Trauma and the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association collaboratively led the development of Clinical guideline for vertebral augmentation for acute symptomatic osteoporotic thoracolumbar compression fractures. Six years later, with advances in clinical diagnosis and treatment techniques as well as accumulating evidence in related fields, the 2019 guideline requires updating. To this end, the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association, the Spinal Health Professional Committee of China Human Health Science and Technology Promotion Association, and the Minimally Invasive Orthopedics Professional Committee of Shaanxi Medical Doctor Association have organized experts in the field to develop the Clinical guideline for vertebral augmentation of acute symptomatic osteoporotic thoracolumbar compression fractures ( version 2025) , based on the latest evidence-based medical researches. This guideline incorporates 3 recommendations retained from the 2019 version with updated strength of evidence, along with 12 new recommendations. It provides recommendations from six aspects of diagnosis, pain management, treatment option selection, prevention of postoperative complications, anti-osteoporosis therapy, and postoperative rehabilitation, aiming to provide a reference for standard treatment of vertebral augmentation for ASOTLF in hospitals at all levels.

Intraoperative neural monitoring (IONM) combines electrophysiology with anatomy to monitor nerve function during thyroid surgery,and has become an important auxiliary technology for neuroprotection. After more than ten years of development,the technology has been widely applied and popularized in China,promoting the development of a number of new technologies in the industry. Combined with the questionnaire survey data of the Chinese Neural Monitoring Study Group,this paper aims to summarize the clinical effect of IONM technology, the application status and existing problems at home and abroad, and propose the possible future development direction.