The aim of this study was to investigate the virulence determinants and genetic diversity of foodborne Yersinia enterocolitica from Wenzhou.A total of 71 strains of Yersinia enterocolitica were isolated from food and foodborne diarrhea ca-ses in Wenzhou,and their biotypes,serotypes,and drug resistance were analyzed.On the basis of whole genome sequencing,we assessed virulence gene profiles,and performed multilocus sequence typing(MLST)and core gene multilocus sequence typ-ing(cgMLST).A total of 94.4％(67/71)of isolates belonged to biotype 1A,and the highest proportion had serotype lA/O∶5(29.6％,21/71).The sensitivity rates of the isolates to 14 antibiotics exceeded 95.8％.A total of 16 categories and 126 viru-lence genes were identified,with two strains carrying the pYV plasmid and chromosome-related virulence genes.ST3(31.6％,12/38)was the most widespread MLST type,and cgMLST analysis revealed no dense clusters of genotypes except for strains sharing the same ST.In conclusion,pathogenic strains were identified from foodborne Yersinia enterocolitica in Wenzhou and were found to exhibit high genetic polymorphism.Enhanced regulatory supervision is essential to prevent the outbreak of food-borne diseases caused by Yersinia enterocolitica.

Cancer seriously threatens human life and health, it is urgent for the development of rapid detection, precise localization and effective treatment of tumors. Chemical fluorescent probes that are sensitive to tumor-specific microenvironments have important significance in tumor theranostics and a variety of such probes have been developed. In this review, we classified chemical fluorescent probes that are sensitive to tumor microenvironments according to biological characteristics and microenvironmental changes while combining spectroscopy or response mechanisms, and systematically introduced the research progress of chemical fluorescent probes with sensitivity to hypoxia, low polarity, high viscosity, abnormal pH values and abundant reactive oxygen species in tumor microenvironments, in order to provide references for the development and applications of these probes.

Objective:To explore the feasibility of the AI intelligent reconstruction model based on knee joint magnetic resonance data developed by Nuctech Company Limited for evaluating knee cartilage injury.Methods:Thirty-three patients (a total of forty-one knees) who were hospitalized with severe knee osteoarthritis in Beijing Tsinghua Changgung Hospital from May 2021 to April 2022 were selected. All of them were planned to be performed total knee arthroplasty (TKA) for the treatment of knee osteoarthritis. Fifteen males with an average age of 71±5 years old and twenty six females with an average age of 71±9 years old were included in this study. There were 19 cases of left knee and 22 cases of right knee. Thin layer MRI examination on the patients' knee joints was performed before the surgery, and artificial intelligence model based on the thin layer MRI data of the knee joint was reconstructed. The cartilage part of the model was selected and corrected by Principal Component Analysis (PCA) in order to realize model straightening. The tibial plateau cartilage of knee joint which intercepted during operation was classified according to the International Cartilage Repair Society (ICRS). Finally the results were compared with the ICRS classification results of knee artificial intelligence reconstruction model and artificial recognition of knee joint MRI images.Results:Compared with the grade of cartilage injury intercepted during our operation which was according to the ICRS classification, the sensitivity of artificial intelligence reconstruction model for the diagnosis of cartilage injury with ICRS classification grade four was 93.1%. The specificity of artificial intelligence reconstruction model was 91.4%. The positive predictive value (PPV) of artificial intelligence reconstruction model was 92.2%. And the negative predictive value (NPV) of artificial intelligence reconstruction model was 80.3%. The area under ROC curve (AUC) was 0.92. The ICRS classification consistency between artificial intelligence model and physical inspection results was good with kappa value 0.81 ( P<0.001) . In the aspect of artificial recognition of cartilage injury grading in MRI images, the sensitivity of artificial recognition was 92.10% compared with the manual identification of cartilage injury classification in MRI images. The specificity of artificial recognition was 91.60%. The positive predictive value (PPV) of artificial recognition was 97.20% and the negative predictive value (NPV) of artificial recognition was 78.8%. The kappa value of the cartilage injury classification in MRI images consistency between artificial recognition and manual identification was 0.79 ( P<0.001). Conclusion:Based on the evaluation of cartilage injury by AI reconstruction model of knee joint, the sensitivity and specificity of the diagnosis of ICRS grade IV cartilage injury can be acceptable, but still needs to be improved.

Objective: To evaluate the risk of depressive disorders using memory task indicators, so as to provide insights into clinical assessment of depressive disorders. Methods: A total of 68 patients with depressive disorders undergoing treatments in the departments of psychiatrics and clinical psychology in a tertiary hospital during the period from January to September, 2021 were enrolled as the case group, while a total of 31 hospital employees, social volunteers and university students served as controls. The error rate and response time of classical memory task experiments were compared between the two groups, including implicit memory, short-term memory and working memory tasks. In addition, the predictive indicators of depressive disorders were identified using multivariable logistic regression analysis and receiver operative characteristics (ROC) curve. Results: The case group included 29 men and 39 women and had a mean age of (24.12±7.40) years, including 46 subjects with an educational level higher than diploma. The control group included 15 men and 16 women and had a mean age of (26.45±6.65) years, including 23 subjects with an educational level higher than diploma. Multivariable logistic regression analysis showed significant associations of age of >18 years (OR=3.431, 95%CI: 1.259-9.350), error rate of 2-back task (OR=1.056, 95%CI: 1.016-1.097) and error rate of short-term memory tasks (OR=1.078, 95%CI: 1.009-1.152) with the development of depressive disorders. ROC curve analysis showed that the error rate of 2-back tasks showed an area under the ROC curve (AUC) of 0.730 (95%CI: 0.630-0.831), cutoff of 22.5%, sensitivity of 42.6% and specificity of 93.5% for prediction of the risk of depressive disorders, and the error rate of short-term memory tasks showed an AUC of 0.717 (95%CI: 0.605-0.829), cutoff of 23.5%, sensitivity of 67.6% and specificity of 71.0% for prediction of the risk of depressive disorders. In addition, the combination of the error rate of 2-back tasks and the error rate of short-term memory tasks showed an AUC of 0.829 (95%CI: 0.734-0.923), sensitivity of 75.0% and specificity of 80.6% for prediction of the risk of depressive disorders. Conclusion Short-term and working memory task indicators are feasible for assessment of the risk of depressive disorders.

In order to improve the operation difficulties in the narrow space of the nasal maxillary sinus, the nasal continuum minimally invasive surgical robot system is designed. The ball-and-socket joints and NiTiNol tubes are used as the main body of the continuum structure to improve the degree of freedom. The hardware systems and software systems are designed. The security control policies are planned. Finally, the robot confirmed prototype experiments are conducted and the feasibility of continuum robot confirmed through master-slave control experiment and animal experiment.
Animals ; Biomechanical Phenomena ; Equipment Design ; Minimally Invasive Surgical Procedures ; Robotic Surgical Procedures ; Robotics ; Software

Objective: To analyze the working memory impairment in patients with different degrees of depressive disorder and its influencing factors, so as to provide the basis for the clinical evaluation of cognitive function in patients with depressive disorder. Methods: From January 2019 to November 2020, the patients with depressive disorder were selected in the psychiatry and clinical psychology departments of a tertiary hospital. Demographic information, disease course and medication were collected. Depression subscale ( DEP ) of Mental Health Inventory ( PHI ) was used to measure the degree of depressive disorder. N-back tasks were used to test working memory, with error rate and reaction time as experimental variables to compare the working memory impairment of patients with different degrees of depressive disorder. The multivariate linear regression model was used to analyze the influencing factors for working memory impairment in patients with depressive disorder. Results: A total of 75 patients with depressive disorder were included, including 45 mild cases and 30 moderate and severe cases. The error rates of mild cases and moderate and severe cases in the 3-back task were ( 27.47±20.59 )% and ( 39.60±25.95 )%, the response time between the two groups in 0-back, 1-back, 2-back and 3-back task were ( 458.29±104.19 ) ms and ( 527.90±99.58 )ms,( 499.87±85.46 ) ms and ( 574.57±126.25 ) ms, ( 567.44±114.86 ) ms and ( 675.37±137.54 ) ms, ( 663.76±167.99 ) ms and ( 753.27±152.06 ) ms, the differences between two groups were significant ( P<0.05 ). The multivariate linear regression analysis showed that the severity of depressive disorder was correlated with the response time of 0-back to 3-back tasks and the error rate of 3-back task; whether patients took medicine or not was depressive disorder correlated with the 0-back task response time ( all P<0.05 ). Conclusion There are differences in working memory impairment among patients with different degrees of depressive disorder and the severity of impairment is correlated with the severity of depressive disorder and the use of antidepressant drugs.

Objective:To construct a three-dimensional imaging model of the pancreatic head based on the embryological fusion plane, and to provide morphological parameters of the pancreatic head for future developments of basic and clinical researches on the pancreas.Methods:Histologic cross-sections of the pancreatic head with its adjacent structures were made from healthy cadavers. Immunohistochemical analysis of pancreatic polypeptide antibody was then performed to verify the existence and location of the embryological fusion plane reported previously. The histologically positioning method of the embryological fusion plane was then applied to the corresponding sections on computed tomography (CT). Based on the results of the above work, volunteers from the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University were then used as research objects. A three-dimensional visualization reconstruction software was used to perform CT image-based structures to include the abdominal pancreas, dorsal pancreas head, and embryo fusion surface. Three-dimensional reconstruction of the pancreatic head, and morphological measurements of the relevant structures of the pancreatic head were then made.Results:Immunohistochemical analysis verified the existence and the position of the embryological fusion plane. The histologically positioning method was then successfully applied to the CT sections. The three-dimensional imaging model of the pancreatic head containing the embryological fusion plane, dorsal segment and ventral segment of head were built based on CT images. A total of 35 volunteers were included, including 19 males and 16 females, aged (48.26±8.26) years, and with a BMI of (22.29±1.78) kg/m 2. The morphological results showed that the volume of the pancreatic head, dorsal pancreas and abdominal pancreas were (32.80±8.15) cm 3, (22.21±6.94) cm 3, (10.59±3.87) cm 3, and the area of the embryo fusion surface was (12.46±3.20) cm 2. All volunteers were then grouped according to gender. Statistical analysis showed that there were no significant differences in the total pancreatic head volume, dorsal pancreatic head volume, abdominal pancreatic volume, and embryo fusion area among the groups ( P>0.05). Conclusions:It was feasible and practical to build a three-dimensional imaging model of the pancreatic head based on the embryological fusion plane by using a 3D computer system. This model and its morphological parameters could provide a new tool for research on pancreas.

OBJECTIVE: To choose the disease-causing gene in a Chinese pedigree with ankylosing spondylitis (AS) by whole-exome sequencing (WES), and provide theory basis for mechanism of disease. METHODS: Clinical data of AS pedigree were collected, including 2 males, the age were 48 and 18 years old, the course of disease were 23 and 4 years. Whole blood genomic DNA of AS was extracted to perform whole exome sequencing, the results were compared with human databases, common variations which had been reported were wiped out, then non synonymous single nucleotide variants(SNVs) from the family members were combined, and candidate genes was selected initially. RESULTS: Totally 80 G data was obtained from AS family with high quality.By comparing results between patient and normal subject, and filtering with number of biological database, the result showed heterozygous mutation of JAK2 gene 12 exon c.1709 A>G (p.Tyr570Cys) may be the potential disease-causing gene. The variant c.1151T>C of MUC3A gene may be one of the causes of intestinal symptoms in the family members. CONCLUSION It is feasible to find t candidate gene mutations of AS by Exon sequencing. The mutation c.1709 A>G in gene JAK2 identified by whole exome sequencing might be the pathogenic mutation in this AS pedigree.
Exome ; Humans ; Male ; Mucin-3 ; Mutation ; Pedigree ; Spondylitis, Ankylosing ; Whole Exome Sequencing

Worldwide, colorectal cancer (CRC) is one of the most common malignant tumors, leading to immense social and economic burdens. Currently, the main treatments for CRC include surgery, chemotherapy, radiotherapy and immunotherapy. Despite advances in the diagnosis and treatment of CRC, the prognosis for CRC patients remains poor. Furthermore, the occurrence of side effects and toxicities severely limits the clinical use of these therapies. Therefore, alternative medications with high efficacy but few side effects are needed. An increasing number of modern pharmacological studies and clinical trials have supported the effectiveness of Chinese herbal medicines (CHMs) for the prevention and treatment of CRC. CHMs may be able to effectively reduce the risk of CRC, alleviate the adverse reactions caused by chemotherapy, and prolong the survival time of patients with advanced CRC. Studies of molecular mechanisms have provided deeper insight into the roles of molecules from CHMs in treating CRC. This paper summarizes the current understanding of the use of CHMs for the prevention and treatment of CRC, the main molecular mechanisms involved in these processes, the role of CHMs in modulating chemotherapy-induced adverse reactions, and CHM's potential role in epigenetic regulation of CRC. The current study provides beneficial information on the use of CHMs for the prevention and treatment of CRC in the clinic, and suggests novel directions for new drug discovery against CRC.

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipe-line and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to gen-erate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.