To develop a rapid differential detection method for porcine epidemic diarrhea virus(PEDV)and transmissible gastroenteritis virus(PEDV),M gene sequences of PEDV and TGEV were compared,the inner and outer primer pairs and probes were designed according to the highly conserved region.PEDV-Probe was labeled with FAM at5'end and BHQ1 at 3'end.TGEV-Probe was labeled with CY5.5 at the 5'end and BHQ2 at the 3'end.After optimizing the reaction condi-tions and system,a dual fluorescent RT-LAMP assay for PEDV and TGEV rapid identification was established.The amplification could be completed within 60 min in a 63 ℃ water bath and then stopped at 85 ℃ for 10 min.Then the tubes were placed in a multicolor imaging system,and the re-sults could be observed under 520 nm and 690 nm dual channels.There was no cross-reaction when other common swine viral pathogens were detected by this method.The sensitivity of the assay was evaluated with a 10-fold diluted recombinant plasmid as templates.The lowest detection limit was 102 copies/μL recombinant plasmid,which was 10 times more sensitive than the conventional RT-PCR method.Seventy-two PEDV-positive samples,49 TGEV-positive samples,and 40 PEDV and TGEV co-infected samples were detected from 175 anal swab samples of diarrheic piglets by the established method,which were all higher than the detection rates of the conventional RT-PCR method.The dual fluorescent RT-LAMP method established in this study can be used to amplify the target gene in an ordinary water bath without gel electrophoresis,which provides technical sup-port for rapid and convenient differential diagnosis of PED and TGE and simultaneous detection of PEDV and TGEV co-infection.

Objective:To establish a predictive model for post-induction hypotension (PIH) in the patients undergoing colorectal cancer resection using machine learning algorithms based on muscle CT parameters.Methods:This was a single-center, retrospective study. Electronic medical records from 318 patients who underwent colorectal cancer resection from September 1, 2018 to September 30, 2021 at our hospital were collected. Predictive variables included age, gender, body mass index, hemoglobin, American Society of Anesthesiologists Physical Status classification, TNM staging, age-adjusted Charlson comorbidity index, prognostic nutritional index, L 3 level skeletal muscle index, and muscle quality assessed by Hounsfield unit average calculation. The outcome variable was PIH. The training and testing sets were divided based on the timeline (patients before September 1, 2020 were included in the training set, and those after that date were included in the testing set). The filtering method was used to screen the feature variables. Eight models, including logistic regression, Bayesian models, K-nearest neighbors, support vector machines, neural networks, decision trees, extreme gradient boosting trees, and random forests, were established in the training set using over-sampling technique, repeated cross-validation and hyperparameter optimization. After selecting the best model, a sorting chart of the feature variables, a univariate partial dependency profile, and a breakdown profile were drawn. In the testing set, the confusion matrix and parameters were calculated, and the receiver operating characteristic curve, precision recall curve, calibration curve, and decision curve were drawn to evaluate the performance of the predictive model. Results:The screened feature variables were Hounsfield unit average calculation value, age, L 3 level skeletal muscle index, prognostic nutritional index, hemoglobin and body mass index. The random forest was the optimal model, with an accuracy of 0.985 9, a MCC of 0.970 8, the area under the receiver operating characteristic curve was 1.0, and the area under the precision recall curve was 1.0. The Brier score of the calibration curve was 0.076 6; the decision curve showed the highest clinical net benefit of 0.6. Conclusions:In this study, machine learning algorithm is used to identify the important characteristic variables and establish a high-performance PIH prediction model based on muscle CT parameters.

CAPOS syndrome is an autosomal dominant neurological disorder caused by mutations in the ATP1A3 gene. Initial symptoms, often fever-induced, include recurrent acute ataxic encephalopathy in childhood, featuring cerebellar ataxia, optic atrophy, areflflexia, sensorineural hearing loss, and in some cases, pes cavus. This report details a case of CAPOS syndrome resulting from a maternal ATP1A3 gene mutation. Both the child and her mother exhibited symptoms post-febrile induction,including severe sensorineural hearing loss in both ears, ataxia, areflexia, and decreased vision. Additionally, the patient's mother presented with pes cavus. Genetic testing revealed a c. 2452G>A（Glu818Lys） heterozygous mutation in theATP1A3 gene in the patient . This article aims to enhance clinicians' understanding of CAPOS syndrome, emphasizing the case's clinical characteristics, diagnostic process, treatment, and its correlation with genotypeic findings.
Humans ; Child ; Female ; Cerebellar Ataxia/diagnosis* ; Talipes Cavus ; Hearing Loss, Sensorineural/diagnosis* ; Optic Atrophy/diagnosis* ; Mutation ; Phenotype ; Sodium-Potassium-Exchanging ATPase/genetics* ; Foot Deformities, Congenital ; Reflex, Abnormal

Objective To evaluate the correlation and consistency between an artificial intelligence(AI)bone mineral density(BMD)measurement system based on 3D-Densenet neural network technology and quantitative computed tomography(QCT)in measuring BMD,as well as to assess its effectiveness in diagnosing osteoporosis(OP).Methods A total of 1 201 participants who underwent low dose computed tomography(LDCT)were retrospectively included.The AI BMD measurement system and QCT were utilized to measure the BMD of T12,L1,L2 vertebrae,and the average BMD.Consistency and correlation of BMD measurements between the two methods were assessed using Bland-Altman,Pearson,and Kappa analyses.With QCT results as the reference standard,the receiver operating characteristic(ROC)curve was drawn to evaluate the accuracy of AI BMD measurement system in diagnosing OP.Results The r and r2 for the average BMD measured by the two methods were 0.997 and 0.993,respectively.The Kappa value for the diagnosis of normal BMD,low bone mass,and OP using the AI BMD measurement system was 0.905.The area under the curve(AUC)for diagnosing OP using the AI BMD measurement system was 0.998,with a sensitivity of 0.888 and specificity of 0.997.Conclusion The AI BMD measurement system based on 3D-Densenet neural network technology has a high correlation and consistency with the QCT measurement result,which can accurately diagnose normal BMD,low bone mass,and OP.

Objective:To investigate the high-risk factors affecting the prognosis of patients with pT 1-2N 1M 0 after mastectomy, establish a nomogram prediction model, perform risk stratification, and screen the radiotherapy benefit populations. Methods:Clinical data of 936 patients with pT 1-2N 1M 0 breast cancer undergoing mastectomy in the Fourth Hospital of Hebei Medical University from January 2010 to December 2016 were retrospectively analyzed and 908 cases had complete follow-up data. They were divided into the radiotherapy (RT) group ( n=583) and non radiotherapy (NRT) group ( n=325) according to the radiotherapy. After propensity score matching (PSM) was performed 1 vs. 1, 298 cases were assigned into the RT group and 298 in the NRT group. Overall survival (OS) and disease-free survival (DFS) were compared between two groups using log-rank test. Nomogram prediction model was established, the survival differences were compared among different risk groups, and the radiotherapy benefit populations were screened. Results:Univariate analysis showed that the 5- and 8-year OS and DFS in the RT group were significantly better than those in the NRT group (both P<0.001). Multivariate analysis showed that age, tumor quadrant, number of lymph node metastases, T staging, and Ki-67 level were the independent prognostic factors for OS. Age, tumor quadrant, and T staging were the independent prognostic factors for DFS. The OS nomogram analysis showed that the OS of patients in the high-risk group was significantly improved by post-mastectomy radiotherapy (PMRT) ( P=0.001), while PMRT did not show an advantage in the low- and medium-risk groups ( P=0.057, P=0.099). The DFS nomogram analysis showed that DFS was significantly improved by PMRT in patients in the medium- and high-risk groups ( P=0.036, P=0.001), whereas the benefits from PMRT were not significant in the low-risk group ( P=0.475). Conclusions:For patients with pT 1-2N 1M 0 breast cancer after mastectomy, age ≤ 40 years, tumor located in the inner quadrant or central area, T 2 staging, 2-3 lymph node metastases, Ki-67>30% are the high-risk factors affecting clinical prognosis. The nomogram prediction model can screen the populations that can benefit from PMRT, providing reference for clinical decision-making.

Objective: To investigate adolescent haze weather health protection behavior, and to provide scientific basis for behavioral intervention and health guidance for adolescents in haze weather. Methods: From June 2015 to April 2016, 1 025 adolescents were selected from 22 classes in two middle schools of Baoding City, Hebei Province, by stratified cluster sampling method. General information questionnaire and the Brief Haze Weather Health Protection Behavior Assessment Scale Adolescent Version (BHWHPBAS AV) were used. Multiple linear regressions were conducted to explore factors affecting adolescent haze weather health protection behavior. Different models were used to confirm associations between influencing factors and BHWHPBAS AV scores. Results: Adolescents had a low overall score of BHWHPBASAV (45.81±13.16). The score rate of self adjustment after haze weather was the highest (64.54%). The score rate of obtaining relevant knowledge before haze weather was the lowest (50.28%). Compared with adolescents in urban area, rural adolescents had a lower BHWHPBAS AV score ( β=-3.20, P <0.01). Compared with students (living with parents), those living without parents had a lower BHWHPBAS AV score ( β=-4.16, P =0.01). Compared with students never receive physical examination,those had received physical examination during the past years had a higher BHWHPBAS AV score ( β=4.44，5.66，9.04, P <0.01). Compared with students with no knowledge of respiratory system diseases, those with moderate to sufficient knowledge had a higher BHWHPBAS AV score ( β=9.34，12.19，P <0.01). These associations were stable and consistent.Multiple linear regression analysis showed that residence, residence with parents, physical examination and knowledge of respiratory diseases were the relevant factors of BHWHPBAS AV score ( P <0.05). Conclusion Adolescent haze weather health protection behavior level is low and is affected by many factors. Cooperation should be strengthened to conduct behavioral interventions and health guidance on haze health protection for adolescents, so as to promote healthy growth of adolescents.

ObjectiveTo explore the mechanism of Xianglian Huazhuo prescription in the treatment of chronic atrophic gastritis （CAG） based on network pharmacology and animal experiments，so as to provide scientific basis for clinical application. MethodThe possible targets and pathways of Xianglian Huazhuo prescription in the treatment of CAG were obtained based on the prediction of network pharmacology. The CAG rat model was induced by sodium salicylate，N-methyl-N'-nitro-N-nitrosoguanidine （MNNG） and hunger and satiety disorder. Then the CAG rats were treated with Xianglian Huazhuo prescription and morodan for 60 days. After administration，the rats were sacrificed，and the content of interleukin-6 （IL-6），tumor necrosis factor-α （TNF-α），interleukin-1β （IL-1β） and vascular endothelial growth factor （VEGF） in serum was determined by enzyme linked immunosorbent assay（ELISA）. In addition， the protein expression of Bad and Bcl-2 in gastric mucosa was detected by immunohistochemistry （IHC）. ResultA total of 241 active components of Xianglian Huazhuo prescription and 53 core targets were obtained. Xianglian Huazhuo prescription affected multiple biological processes，such as cell proliferation and apoptosis，inflammatory reaction，regulation of DNA metabolism，and cell response to redox，as well as phosphatidylinositol 3-kinase/protein kinase B （PI3K/Akt），TNF，mitogen-activated protein kinase （MAPK），cancer and cancer-related signaling pathways. The animal model verification showed that Xianglian Huazhuo prescription lowered the levels of IL-6，TNF-α，IL-1β and VEGF in serum of CAG rats，and reduced the protein expression of Bad and Bcl-2 in gastric tissue. ConclusionXianglian Huazhuo prescription could regulate PI3K/Akt signal pathway and improve gastric mucosal injury in CAG by participating in biological processes such as cell proliferation，apoptosis and inflammation.

AIM: To examine the effects of three commonly used general anesthetics on the proliferation and activation of glial cells in neonatal rats. METHODS: Neonatal rats were exposed to either isoflurane, sevoflurane or desflurane for 2 h on postnatal day 2 (P2). The animals were euthanatihed and the brain were harvested on P7 and P14, respectively. The immunohistochemical localihation of glial markers (vimentin, GFAP, Iba1) were examined. RESULTS: Activation of astrocyte in granular layer and molecular layer of dentate gyrus of hippocampus was significantly enhanced on P7 and P14 after desflurane exposure, while that in isoflurane group the change was only significantly different on P14. The activation of microglia in the granular layer of dentate gyrus but not in the pyramidal cell layer of CA1 region was significantly enhanced in the desflurane group on P7 and P14, while the isoflurane group only showed significant difference on P14. CONCLUSION: Short time exposure of different inhalation anesthetics has different effects on the activation of glial cells in different subregions of hippocampus in neonatal rats on postnatal day 2, and sevoflurane may have the least effect on it.

Objective:To evaluate the correlation between the timed up and go(TUG)test and fall risks in elderly frail patients.Methods:From July to September 2019, elderly frail patients who were treated at the cardiovascular department of our hospital were enrolled.Basic clinical data and fall-related information of patients were collected.Patients were divided into the fall group and the non-fall group.Results on the body mass index(BMI), TUG, 4-meter maximum walking speed(4 m MWS)and Barthel index were compared between the two groups.The correlation between TUG and each indicator was examined.Multivariate Logistic regression analysis was used to analyze the correlation between the TUG and falls in elderly patients.Results:A total of 96 eligible patients were enrolled, including 35 in the fall group and 61 in the non-fall group.The average TUG time was longer in the fall group than in the non-fall group(16.45±6.44 s vs.10.17±2.91 s, t=-6.556, P<0.001). The correlation analysis results showed that the TUG was correlated with falls and 4 m MWS( r=0.582 and 0.875, both P<0.001). Multivariate Logistic regression analysis showed that the TUG( OR=1.201, 95% CI: 1.111-1.470, P=0.004)and 4 m MWS( OR=1.146, 95% CI: 1.063-1.244, P=0.015)were risk factors for falls. Conclusions:The TUG is correlated with fall risks in elderly frail patients and should be recommended as a routine test in clinical practice.

Primary lymphoma of bone (PLB) is a rare extra nodal lymphoma, which accounts for 3%-7% of malignant bone tumors, about 5% of extra nodal lymphomas, and 2% of lymphomas in adults. PLB can occur in any age group, but it is more commonly found in middle-aged and elderly people with the average age of onset of 45-60 years old, and it is slightly more common in males than in females. The patients with PLB usually present with pain or swelling of local soft tissue, and the radiological feature is atypical. It must be diagnosed by pathological biopsy. At present, PLB is mainly treated by systemic chemotherapy combined with radiotherapy of the affected bone. This article will review the current progress in the diagnosis and treatment of PLB.