Objective To investigate the risk factors of postoperative delirium in elderly patients undergoing spine surgery.Methods The basic case data of 566 patients who underwent spine surgery under general anesthesia from January 2021 to Jan-uary 2023 were retrospectively analyzed.There were 296 males and 270 females with an average age of(71.58±4.21)years old.There were 195 cases of cervical spine surgery,26 cases of thoracic spine surgery and 345 cases of lumbar spine surgery.According to the occurrence of postoperative delirium,the patients were divided into postoperative delirium group(41 patients)and non-delirium group(525 patients).Univariate analysis was used to analyze the possible influencing factors such as gen-der,age,weight,smoking history,drinking history,surgical site,preoperative anxiety,intraoperative hypotension times,blood loss and so on,and binary Logistic regression was used to analyze the univariate factors with P＜0.05.Results A total of 41 pa-tients developed postoperative delirium.Univariate analysis showed that age(P=0.000),duration of surgery(P=0.039),preop-erative anxiety(P=0.001),blood loss(P=0.000),history of opioid use(P=0.003),history of stroke(P=0.005),C-reactive protein(P=0.000),sodium ion(P=0.000)were significantly different between delirium group and non-delirium group.These factors were included in the binary Logistic regression analysis,and the results showed that age[OR=0.729,95％CI(0.569,0.932),P=0.012],opioid use[OR=21.500,95％CI(1.334,346.508),P=0.031],blood loss[OR=0.932,95％CI(0.875,0.993),P=0.029],C-reactive protein[OR=0.657,95％CI(0.485,0.890),P=0.007],preoperative anxiety[OR=23.143,95％CI(1.859,288.090),P=0.015],and sodium[OR=1.228,95％CI(1.032,1.461),P=0.020]were independent risk factors for the development of delirium after spinal surgery in elderly patients.Conclusion Age,opioid use,blood loss,preoper-ative anxiety,elevated c-reactive protein,and hyponatremia are independent risk factors for the development of postoperative delirium in elderly patients undergoing spinal surgery.

A prokaryotic expression vector for the mutant diphtheria toxin CRM197 was constructed and expressed in Esch-erichia coli cells.Anti-CRM197 antibody concentrations were detected in serum samples of healthy volunteers.The crm 197 gene was codon-optimized in E.coli and cloned into the plasmid pET28a(+)under optimized expression conditions.CRM197 was purified using Ni-NTA spin columns and ion exchange chromatography,and confirmed by western blot analysis.The puri-fied CRM197 was used to detect specific anti-CRM197 antibody levels in serum samples of different age groups.The results showed that soluble codon-optimized CRM197 was successfully expressed under optimized expression conditions.The purity of CRM197 was more than 95％,as determined with Ni-NTA spin columns and ion exchange chromatography,consistent with the single specific bands obtained by western blot analysis and detection of serum levels of the anti-CRM197 antibody.Collec-tively,these results confirmed that the proposed expression strategy achieved high-yield production of soluble CRM197,al-though high levels in human serum may affect evaluation of immune interactions with glycan-CRM197 conjugates for applica-tion as a diagnostic antigen.The diphtheria mutant toxin CRM197 is used in many conjugate vaccines.The synthetic crm 197 gene with codon optimization in pET28a was transformed into E.coli Origami B(DE3)cells.CRM197 was induced by isopro-pyl β-d-1-thiogalactopyranoside and high level accumulation of soluble CRM197 was purified using Ni-NTA spin columns and ion exchange chromatography.The purity of the final prepara-tion reached 95％.CRM197 was used to detect the concentra-tions of the anti-CRM197 antibody in serum samples of healthy volunteers of different ages.The proposed expression strategy yielded high production of CRM197,which could interfere with evaluations of induced immune interactions by glycan-CRM197 conjugates and prohibit application as a diagnostic antigen.

This study was aimed at investigating the pathogenic and molecular characteristics of Klebsiella pneumoniae(KP)in fecal samples of diarrhea cases in a district of Beijing.Fecal samples from diarrhea cases in an outpatient department in a district of Beijing from 2018 to 2021 were collected,and used for isolation and culture of KP.The KP strains isolated strains were subjected to drug resistance phenotype testing and whole-genome sequencing.Multilocus sequence typing and whole-genome phyletic evolution analysis were performed on the sequencing results.The cases'epidemiological and clinical characteristics were analyzed.From 2018 to 2021,1 103 fecal samples were collected and detected.The total detection rate of KP was 10.43％(115/1 103),and the infection rate of KP mixed with other diarrhea-causing pathogens was 42.61％(49/115).The positivity rate was slightly high(12.47％,61/489)a-mong females and was highest in young adults 16-45 years of age.Small peaks were observed in January,April to May,and August to September.The gastrointestinal symptoms in cases were mainly nausea and watery stool,and the suspicious food was unknown.Ampicillin,tetracycline,and sulfafurazole were the top three antibiotics to which these 115 KP strains showed resistance,and 29 strains were resistant to multiple antibiotics.The strains were divided into 72 sequence types,among which ST23 was dominant.According to the phylogenetic tree,the strains were divided into four main branches,among which 14 ST23 strains had a very close genetic relationship with the highly virulent NTUH-K2044 reference strain.KP infection persisted in fecal samples from diarrhea cases in the district of Beijing.Women and young adults were particularly susceptible.The drug resistance of KP strains in this region was very serious,and the ST types were diverse.Moreover,the ST23 pathogenic strains were closely related to high virulence strains.

Objective:To demonstrate the type of CEBPA gene mutations among patients with acute myeloid leukemia (AML), clinical characteristics, and prognostic effect on patient outcomes.Methods:Demographic data, clinical features, laboratory characteristics, and data about treatment and follow-up of 57 patients with CEBPA mutated AML diagnosed at Peking Union Medical College Hospital between April 2016 and November 2022 were collected and analyzed.Results:In total, 57 patients with CEBPA mutation accounted for 16.1% of all the 353 patients with AML, among which 28 patients had CEBPA-bZIPinf and 29 had CEBPA-other. Compared with the CEBPA-other group, the CEBPA-bZIPinf group was younger (54 vs 64 years, P=0.010), de novo AML was more common ( P=0.001), and the level of bone marrow blast was higher (68.0% vs 36.3%, P=0.001). Moreover, 24 patients from the CEBPA-bZIPinf group and 19 from the CEBPA-other group received chemotherapy. The one-course complete remission (CR) rate of the CEBPA-bZIPinf group was significantly higher than that of the CEBPA-other (87.5% vs 47.4%, P=0.010) and CEBPA-wt (87.5% vs 50.3%, P=0.002) groups. After a median follow-up of 11 months, the median OS of the CEBPA-bZIPinf group was significantly longer than that of the CEBPA-wt group (not reached vs 22.1 months, P=0.012) . Conclusion:CEBPA-bZIPinf mutated AML is a unique clinical entity, with a younger age of diagnosis, better response to chemotherapy, and better prognosis.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective To observe the efficacy and safety of Morinda officinalis oligosaccharides in the continuation treatment of mild and moderate depression.Methods An open,single-arm,multi-center design was adopted in our study.Adult patients with mild and moderate depression who had received acute treatment of Morinda officinalis oligosaccharides were enrolled and continue to receive Morinda officinalis oligosaccharides capsules for 24 weeks,the dose remained unchanged during continuation treatment.The remission rate,recurrence rate,recurrence time,and the change from baseline to endpoint of Hamilton Depression Scale(HAMD),Hamilton Anxiety Scale(HAMA),Clinical Global Impression-Severity(CGI-S)and Arizona Sexual Experience Scale(ASEX)were evaluated.The incidence of treatment-related adverse events was reported.Results The scores of HAMD-17 at baseline and after treatment were 6.60±1.87 and 5.85±4.18,scores of HAMA were 6.36±3.02 and 4.93±3.09,scores of CGI-S were 1.49±0.56 and 1.29±0.81,scores of ASEX were 15.92±4.72 and 15.57±5.26,with significant difference(P＜0.05).After continuation treatment,the remission rate was 54.59％(202 cases/370 cases),and the recurrence rate was 6.49％(24 cases/370 cases),the recurrence time was(64.67±42.47)days.The incidence of treatment-related adverse events was 15.35％(64 cases/417 cases).Conclusion Morinda officinalis oligosaccharides capsules can be effectively used for the continuation treatment of mild and moderate depression,and are well tolerated and safe.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.