Objective: To explore the association between CDKAL1 rs35261542, FAIM2 rs 3205718, and VGLL4 rs 2574704 polymorphisms with childhood obesity and related metabolic phenotypes to provide evidence for personalized prevention and management strategies. Methods: Based on the 2023 Long term Nutritional Health Effects of Early Childhood Nutrition Package Intervention project, the study enrolled 1 078 children aged 5-7 years from four counties in Henan (Songxian and Ruyang countries) and Guizhou (Guiding and Fuquan countries) provinces. Using BMI Z scores, 87 overweight and obese(OVOB) children were selected and matched by sex, age, and BMI Z score with 117 normal weight controls. Participants were further stratified into four metabolic phenotype groups: metabolically healthy normal weight (MHNW, n =51), metabolically unhealthy normal weight (MUNW, n =66), metabolically healthy obesity (MHO, n =31) and metabolically unhealthy obesity (MUO, n =56) based on four conventional cardiometabolic risk factor (CR) criteria. Data were collected through questionnaires, anthropometric measurements, serum biochemical tests, and KASP genotyping. The distribution of three genetic polymorphisms ( CDKAL1 rs35261542, FAIM2 rs3205718, VGLL4 rs 2574704) across metabolic subgroups was analyzed. Multivariate Logistic regression models assessed associations between these polymorphisms and obesity/metabolic phenotypes. Results: Multivariate Logistic regression analysis showed that Homozygous mutant AA genotype of CDKAL1 rs 35261542 was positively associated with OVOB( OR =3.63), MHO ( OR =11.04), MUO ( OR = 4.88 ) ( P <0.05). Homozygous TT genotype of FAIM2 rs 3205718 increased OVOB risk ( OR =4.44, P <0.05) but showed no association with metabolic phenotypes ( P >0.05). Homozygous mutant TT of VGLL4 rs 2574704 reduced the risks of MHO and MUO ( OR = 0.30, 0.24， P <0.05). Cumulative genetic effects analysis demonstrated carriers of 1 or 2 risk genotypes of rs 35261542 and rs 3205718 had progressively higher OVOB risk ( OR =2.53, 20.79), and the combination of rs 35261542 and rs 2574704 increased risks for both MHO ( OR =8.50) and MUO ( OR =5.00) ( P <0.05). Conclusions The AA genotype of rs 35261542 ( CDKAL1 ) positively correlates with childhood obesity and metabolic abnormalities. The TT genotype of rs 3205718 ( FAIM 2) increases obesity risk but not metabolic phenotypes. The TT genotype of rs 2574704 ( VGLL 4) shows protective effects against metabolic dysfunction. Risk genotypes exhibit dosedependent cumulative effects on obesity and metabolic outcomes.

ObjectiveThe scale of microalgae farming industry is huge. During farming, it is easy for microalgae to be affected by miscellaneous bacteria and other contaminants. Because of that, periodic test is necessary to ensure the growth of microalgae. Present microscopy imaging and spectral analysis methods have higher requirements for experiment personnel, equipment and sites, for which it is unable to achieve real-time portable detection. For the purpose of real-time portable microalgae detection, a real-time microalgae detection system of low detection requirement and fast detection speed is needed. MethodsThis study has developed a microalgae detection system based on deep learning. A microscopy imaging device based on bright field was constructed. With imaged captured from the device, a neural network based on YOLOv3 was trained and deployed on microcomputer, thus realizing real-time portable microalgae detection. This study has also improved the feature extraction network by introducing cross-region residual connection and attention mechanism and replacing optimizer with Adam optimizer using multistage and multimethod strategy. ResultsWith cross-region residual connection, the mAP value reached 0.92. Compared with manual result, the detection error was 2.47%. ConclusionThe system could achieve real-time portable microalgae detection and provide relatively accurate detection result, so it can be applied to periodic test in microalgae farming.

Ankylosing spondylitis (AS) combined with lower cervical fracture is often categorized into unstable fracture, with a high incidence of neurological injury and a high rate of disability and morbidity. As factors such as shoulder occlusion may affect the accuracy of X-ray imaging diagnosis, it is often easily misdiagnosed at the primary diagnosis. Non-operative treatment has complications such as bone nonunion and the possibility of secondary neurological damage, while the timing, access and choice of surgical treatment are still controversial. Currently, there are no clinical practice guidelines for the treatment of AS combined with lower cervical fracture with or without dislocation. To this end, the Spinal Trauma Group of Orthopedics Branch of Chinese Medical Doctor Association organized experts to formulate Clinical guidelines for the treatment of ankylosing spondylitis combined with lower cervical fracture in adults ( version 2024) in accordance with the principles of evidence-based medicine, scientificity and practicality, in which 11 recommendations were put forward in terms of the diagnosis, imaging evaluation, typing and treatment, etc, to provide guidance for the diagnosis and treatment of AS combined with lower cervical fracture.

Objective:To establish and validate a gallstones classification method based on deep learning.Methods:A total of 618 gallstones samples were collected from East Hospital Affiliated to Tongji University, and 1 023 high-definition cross-sectional gallstones profile images were captured to construct a cross-sectional gallstones profile image dataset. Based on the traditional eight-category gallstones classification method, a lightweight network model, MobileNet V3, was trained using deep learning and transfer learning methods. The classification performance of MobileNet was evaluated using a confusion matrix with metrics such as accuracy rate, precision rate, F1 score, and recall rate. The MobileNet V3 was improved and further validated using accuracy and loss values.Results:The accuracy rate (94.17%), precision rate (94.03%), F1 score (92.96%) and recall rate (92.99%) of the improved MobileNet V3 model were better than other networks. The improved MobileNet V3 model achieved the highest accuracy rate (94.17%) in gallstones profile classification and was validated by the test set. The confusion matrix showed a weighted average of accuracy rate (92.0%), precision rate (92.6%), and F1 score (92.2%) for each category of gallstones.Conclusions:Based on deep learning, a high-accuracy gallstones classification method is proposed, which provides a new idea for the intelligent identification of gallstones.

A boy,aged 7 months,presented with severe global developmental delay(GDD),refractory epilepsy,hypotonia,nystagmus,ocular hypertelorism,a broad nasal bridge,everted upper lip,a high palatal arch,and cryptorchidism.Genetic testing revealed a de novo heterozygous missense mutation of c.364G>A(p.E122K)in the EEF1A2 gene,and finally the boy was diagnosed with autosomal dominant developmental and epileptic encephalopathy 33 caused by the EEF1A2 gene mutation.This case report suggests that for children with unexplained infancy-onset severe to profound GDD/intellectual disability and refractory epilepsy,genetic testing for EEF1A2 gene mutations should be considered.This is particularly important for those exhibiting hypotonia,nonverbal communication,and craniofacial deformities,to facilitate a confirmed diagnosis.

Objective To explore the molecular epidemiological characteristics of carbapenem-resistant Klebsiella pneumoniae(CRKP),and reveal its mechanism of resistance to ceftazidime/avibactam(CZA).Methods CZA-re-sistant CRKP strains initially isolated from the Affiliated Hospital of Xuzhou Medical University from January 2021 to September 2023 were collected.The carriage of 5 carbapenemase genes(blaKPC,blaNDM,blaOXA,blaVIM,blaIMp)were detected with gene amplification method and colloidal gold method.The relative copy number and expression level of Klebsiella pneumoniae(KP)carbapenemase-producing KP(KPC-KP)was detected with real-time quantita-tive polymerase chain reaction(RT-qPCR),mutation sites of KPC mutation strains were analyzed with whole-ge-nome sequencing,and epidemic characteristics of CRKP and resistance mechanism to CZA were analyzed.Results A total of 73 CZA-resistant CRKP strains were isolated,with 37(50.68％)being KPC and NDM co-producing strains,33(45.21％)NDM-producing alone(23 strains producing NDM-5 and 10 strains producing NDM-1),and 3 KPC-producing alone.KP-2842 strain was identified as ST11-type KPC-33 variant,KP-2127 and KP-2189 strains produced KPC-2.Compared with KP ATCC BAA-1705,the copy number of blaKPC in these strains up-regulated by 1.04-3.86 fold,and the expression increased by 6.66-12.93 fold,respectively.Colloidal gold and PCR methods demonstrated good consistency and the ability to detect the enzyme co-producing and KPC-33 variant.Conclusion In this hospital,the resistance of CRKP to CZA is primarily mediated by the metalloenzyme NDM,with co-produc-tion of NDM and KPC being a characteristic of CRKP.High copy number and expression level of blaKPC-2 also con-tribute to CZA resistance.This study identified the KPC-33 variant for the first time in ST11-type CRKP in Jiangsu Province.

Objective To explore the potential value of three-dimensional fast spin echo(3D-SPACE)combined with multilayer spiral CT(MSCT)in the diagnosis of knee cruciate ligament injury,to provide a new direction for the optimization of subsequent clinical diagnosis.Methods A total of 120 patients with knee cruciate ligament injury were treated from April 2020 to April 2021,aged from 21 to 68 with an average of(41.52±4.13)years old.For all patients,separate MSCT scanner scans,3D-SPACE sequence scans alone and 3D-SPACE sequence combined with MSCT scans were used.The injury and classifica-tion of the anterior and posterior cruciate ligament of the knee were compared,the length of the anterior-medial bundle and posterolateral bundle and its angle of the knee with the horizontal plane were observed,the diagnostic value of 3 diagnostic methods in knee cruciate ligament injury were determined.Results There was no significant difference between the 3D-SPACE sequence scan alone and the MSCT test alone on the total diagnostic rate and grading total diagnostic rate(P＞0.05).The total diagnostic rate and grading total diagnostic rate of 3D-SPACE scan combined with MSCT were significantly higher than those of 3D-SPACE scan or MSCT alone(P＜0.05).The 3D-SPACE sequence scan alone and the MSCT detection alone had no signifi-cant difference in the measurement values related to the anterior and posterior cruciate ligaments of the knee joint(P＞0.05).3D-SPACE sequence scanning combined with MSCT detection on the knee joint anterior and posterior cruciate ligament related mea-surements were significantly higher than the 3D-SPACE sequence scan or MSCT detection alone(P＜0.05).The area under the ROC curve estimated by 3D-SPACE sequence scanning combined with MSCT was 0.960,which was significantly higher than that of 3D-SPACE sequence scanning and MSCT alone evaluating the area under the ROC curve line of 0.756 and 0.795.The com-bined 3D-SPACE sequence scanning and 3D-SPACE sequence scanning MSCT analysis and prediction models were statistically different(Z=2.236,P＜0.05),and MSCT alone and 3D-SPACE sequence scanning combined with MSCT analysis and prediction models were statistically different(Z=2.653,P＜0.05).Conclusion The application of 3D-SPACE sequence combined with MSCT scanning for knee cruciate ligament injury can improve the diagnosis rate of patients with knee cruciate ligament injury.It can be used as a diagnostic tool for patients with knee cruciate ligament injury and is worthy of clinical application.

Objective To investigate clinical effect of tendons pulling,poking and kneading for the treatment of external humeral epicondylitis.Metods From January 2018 to December 2021,a multicenter randomized controlled study was per-formed to collect 192 patients with external humeral epicondylitis in Wangjing Hospital,Beijing Dianli Hospital,and Beijing Fengsheng Osteotraumatology Hospital,respectively,and they were divided into treatment group and control group by random number table method.There were 96 patients in treatment group,including 36 males and 60 females,aged from 28 to 60 years old with an average of(41.20±5.50)years old;the course of disease ranged from 1 to 14 days with an average of(5.24±1.35)days;they were treated once every other day for 2 weeks.There were 96 patients in control group,including 33 males and 63 females,aged from 26 to 60 years old with an average of(43.35±7.75)years old;the course of disease ranged from 1 to 14 days with an average of(5.86±1.48)days;they were treated with topical voltaalin combined with elbow joint fixation for 2 weeks.Visual analogue scale(VAS)and Hospital for Surgery Scoring System(HSS)elbow pronation and supination angles,wrist metacarpal flexion and dorsal extension angles,elbow tenderness between two groups were compared before treatment and at 1,3,5,7,11 and 13 days after treatment;Hospital for Surgery Scoring System 2(HSS2)was compared before treatment and the final treatment.Results All patients were followed up for 10 to 14 days with an average of(12±1.6)days.VAS between treatment group and control group before treatment were 6.83±1.36 and 6.79±1.58,respectively,and decreased to 1.49±1.09 and 2.11±1.81 after the final treatment.VAS of treatment group were significantly lower than those of control group at 1,3,5,7,9,11 and 13 days after treatment(P＜0.05).HSS between two groups were 61.73±11.00 and 36.47±12.45 before treatment,respectively,and increased to 94.42±5.9 and 91.44±9.11 at the final treatment.HSS of treatment group were signifi-cantly higher than those of control group at 1,3,5,7,9,11 and 13 days after treatment(P＜0.05).On the 5th day after treat-ment,the external and internal rotation angles of elbow in treatment group were(66.41±12.69)° and(66.35±13.54)°,while those in control group were(62.08±16.03)° and(61.77±16.35)°.On the 7th day after treatment,the external and internal ro-tation angles of elbow were(69.79±12.64)° and(70.02±13.55)° in treatment group,and(65.28±15.86)° and(65.09± 16.67)° in control group.Elbow joint motion in treatment group was higher than that in control group(P＜0.05).On the 5th day after treatment,angles of wrist dorsiflexion and palm flexion were(39.43±15.94)°and(46.68±11.10)° in treatment group,and(38.51±18.49)° and(44.27±13.58)° in control group.On the 7th day after treatment,angles of wrist dorsiflexion and palm flexion were(42.52±16.50)° and(49.23±10.96)° in treatment group,and(41.18±20.09)° and(46.64±14.63)° in control group.The motion of wrist joint in treatment group was higher than that in control group(P＜0.05).On the 13th day after treatment,HSS2 in treatment group 93.61±6.32 were higher than those in control group 92.06±7.94(P＜0.05).There was no significant difference in elbow tenderness between two groups at each time point(P＞0.05).Conclusion Voltaren external treatment combined with elbow fixation and tendons pulling,poking and kneading could effectively improve symptoms of exter-nal humeral epicondylitis.Compared with voltaren external treatment,tendons pulling,poking and kneading has advantages of longer analgesic time and better elbow function recovery.

Objective To explore the efficiency of artificial intelligence and radiomics-assisted X-ray in diagnosis of lumbar osteoporotic vertebral compression fractures(OVCF).Methods The clinical data of 455 patients diagnosed as lumbar OVCF by MRI in our hospital were selected.The patients were divided into the training group(n=364)and the validation group(n=91),X-ray films were extracted,the image delineation,feature extraction and data analysis were carried out,and the artificial intelligence radiomics deep learning was applied to establish a diagnostic model for OVCF.After verifying the effectiveness of the model by receiver operating characteristic(ROC)curve,area under the curve(AUC),calibration curve,and decision curve analysis(DCA),the efficiencies of manual reading,model reading,and model-assisted manual reading of X-ray in the early diagnosis of OVCF were compared.Results The ROC curve,AUC and calibration curve proved that the model had good discrimination and calibration,and excellent diagnostic performance.DCA demonstrated that the model had a higher clinical net benefit.The diagnostic efficiency of the manual reading group:the accuracy rate was 0.89,the recall rate was 0.62.The diagnostic efficiency of the model reading group:the accuracy rate was 0.93,the recall rate was 0.86,the model diagnosis showed good predictive performance,which was significantly better than the manual reading group.The diagnostic efficiency of the model-assisted manual reading group:the accuracy rate was 0.92,the recall rate was 0.72,and the recall rate of the model-assisted manual reading group was higher than that of the manual reading group,but lower than that of the model reading group,indicating the superiority of the model diagnosis.Conclusion The diagnostic model established based on artificial intelligence and radiomics in this study has reached an ideal level of efficacy,with better diagnostic efficacy compared with manual reading,and can be used to assist X-ray in the early diagnosis of OVCF.

Objective To develop a clinical prediction model for predicting risk factors for prolonged hospital stay after anterior cervical discectomy and fusion(ACDF).Methods The clinical data of 914 patients underwent ACDF treatment for cervical spondylotic myelopathy(CSM)were retrospectively analyzed.According to the screening criteria,800 eligible patients were eventually included,and the patients were divided into the development cohort(n=560)and the validation cohort(n=240).LASSO regression was used to screen variables,and multivariate Logistic regression analysis was used to establish a prediction model.The prediction model was evaluated from three aspects:differentiation,calibration and clinical effectiveness.The performance of the model was evaluated by area under the curve(AUC)and Hosmer-Lemeshow test.Decision curve analysis(DCA)was used to evaluate the clinical effectiveness of the model.Results In this study,the five factors that were significantly associated with prolonged hospital stay were male,abnormal BMI,mild-to-moderate anemia,stage of surgery(morning,afternoon,evening),and alcohol consumption history.The AUC of the development cohort was 0.778(95%CI:0.740 to 0.816),with a cutoff value of 0.337,and that of the validation cohort was 0.748(95%CI:0.687 to 0.809),with a cutoff value of 0.169,indicating that the prediction model had good differentiation.At the same time,the Hosmer-Lemeshow test showed that the model had a good calibration degree,and the DCA proved that it was effective in clinical application.Conclusion The prediction model established in this study has excellent comprehensive performance,which can better predict the risk of prolonged hospital stay,and can guide clinical intervention as soon as possible,so as to minimize the postoperative hospital stay and reduce the cost of hospitalization.