Autism Spectrum Disorders (ASDs) are reported as a group of neurodevelopmental disorders. The structural changes of brain regions including the hippocampus were widely reported in autistic patients and mouse models with dysfunction of ASD risk genes, but the underlying mechanisms are not fully understood. Here, we report that deletion of Trio, a high-susceptibility gene of ASDs, causes a postnatal dentate gyrus (DG) hypoplasia with a zigzagged suprapyramidal blade, and the Trio-deficient mice display autism-like behaviors. The impaired morphogenesis of DG is mainly caused by disturbing the postnatal distribution of postmitotic granule cells (GCs), which further results in a migration deficit of neural progenitors. Furthermore, we reveal that Trio plays different roles in various excitatory neural cells by spatial transcriptomic sequencing, especially the role of regulating the migration of postmitotic GCs. In summary, our findings provide evidence of cellular mechanisms that Trio is involved in postnatal DG morphogenesis.
Animals ; Dentate Gyrus/metabolism* ; Mice ; Morphogenesis/physiology* ; Neurons/pathology* ; Cell Movement ; Mice, Inbred C57BL ; Autism Spectrum Disorder/pathology* ; Mice, Knockout ; Neural Stem Cells ; Male ; Neurogenesis ; Autistic Disorder/genetics*

Objective:To investigate the therapeutic effect of naringenin on diabetes retinopathy (DR) rats by regulating Janus kinase 2 (JAK2) /signal transducer and activator of transcription 3 (STAT3) /suppressor of cytokine signaling 1 (SOCS1) signaling pathway.Methods:A DR rat model was constructed and randomly separated into DR group, naringenin group, activator group, and naringenin+activator group, with normal rats as the control group. After intervention according to corresponding groups, blood glucose and glycosylated hemoglobin of rats were detected. Retinal tissue was separated, and interleukin-6 (IL-6), IL-1β, glutathione (GSH), and catalase (CAT) were detected. The pathological changes in rats and blood retinal vascular barrier permeability were detected, and the retinal tissue adhesion factor 1 (VCAM-1), anti vascular endothelial growth factor (VEGF) mRNA, and JAK2/STAT3/SOCS1 pathway related proteins were detected.Results:In the control group, blood glucose levels were (5.16±0.53) mmoL, glycosylated hemoglobin (4.26±0.45) %, IL-6 (63.11±6.35) pg/mL, IL-1β (23.11±2.38) pg/mL, Evans blue (EB) content (4.72±0.49) ng/mg, VCAM-1 (1.02±0.11), VEGF mRNA expression (0.93±0.10), p-JAK2/JAK2 (0.24±0.03), p-STAT3/STAT3 (0.19±0.02), GSH (17.62±1.81) nmoL/mg, CAT (11.68±1.19) IU/mg, and SOCS1 expression 1.44±0.16; while in DR group, blood glucose were (18.85±1.89) mmoL, HBA1c (11.62±1.18) %, IL-6 (89.17±8.99) pg/mL, IL-1β (52.11±5.28) pg/mL, EB (10.24±1.08) ng/mg, VCAM-1 1.56±0.16, VEGF 1.61±0.18, P-JAK2/JAK2 0.55±0.06 and P-STAT3/STAT3 0.47±0.05, all decreased compared with that of the control group ( P<0.05). The expressions of GSH were (8.27±0.88) nmoL/mg, CAT (6.85±0.71) IU/mg and SOCS1 0.86±0.09 in group DR, all increased compared with those of the control group ( P<0.05). In naringin group, blood glucose was (13.11±1.34) mmoL, HBA1c (7.36±0.76) %, IL-6 (67.08±6.75) pg/mL, IL-1β (31.61±3.22) pg/mL, EB content was (6.15±0.63) ng/mg, VCAM-1 1.15±0.12, VEGF mRNA expression 1.17±0.12, P-JAK2 /JAK2 0.29±0.03, and P-STAT3 /STAT3 0.21±0.03, all lower than those in DR Group ( P<0.05). However, the expressions of GSH were (15.22±1.59) nmoL/mg, CAT (10.95±1.11) IU/mg, and SOCS1 (1.37±0.15) ,all higher than those of DR group ( P<0.05). The activator reversed the protective effect of naringenin on DR Rats. Conclusion:Naringenin improves DR rat injury by regulating the JAK2/STAT3/SOCS1 signaling pathway.

Objective To predict the constitutive parameters of a superelastic model of plantar soft tissues based on random forest(RF)and backpropagation(BP)neural network algorithms to improve the efficiency and accuracy of the method for obtaining constitutive parameters.Methods First,a finite element model for a spherical indentation experiment of plantar soft tissues was established,and the spherical indentation experiment process was simulated to obtain a dataset of nonlinear displacement and indentation force,divided into training and testing sets.The established RF and BP neural network(BPNN)models were trained separately.The constitutive parameters of plantar soft tissues were predicted using experimental data.Finally,the mean square error(MSE)and coefficient of determination(R2)were introduced to evaluate the accuracy of the model prediction,and the effectiveness of the model was verified by comparison with the experimental curves.Results Combining the RF and BPNN models with finite element simulation was an effective and accurate method for determining the superelastic constitutive parameters of plantar soft tissues.After training,the MSE of the RF model reached 1.370 2×10-3,and R2 was 0.982 9,whereas the MSE of the BPNN model reached 4.858 1×10-5,and R2 was 0.999 3.The inverse-determined constitutive parameters of the plantar soft tissues suitable for simulation were obtained.The calculated response curves for the two predicted sets of constitutive parameters were in good agreement with the experimental curves.Conclusions The prediction accuracy for the superelastic constitutive parameters of plantar soft tissues based on an artificial intelligence algorithm model is high,and the relevant research results can be applied to study other mechanical properties of plantar soft tissues.This study provides a new method for obtaining the constitutive parameters of plantar soft tissues and helps to quickly diagnose clinical problems,such as plantar soft tissue lesions.

Objective To investigate the application of machine learning to predict the hemodynamic parameters of combined stenotic left coronary artery(LCA)aneurysms.Methods Parameterized modeling and simulation based on the geometric parameter range of combined stenosis LCA aneurysms in clinical statistics were conducted.The obtained simulation data was used as the dataset,and two common machine learning models were constructed and trained for optimization to predict two key hemodynamic parameters:wall shear stress(WSS)and pressure.By comparing and analyzing the performances of these models on the training and testing sets,the accuracy of each model was evaluated,and the effectiveness of the data-driven prediction of hemodynamic parameters for LCA aneurysms with concomitant stenosis was verified.Results The effectiveness of machine learning method in inverting the hemodynamic parameters of aneurysms was determined.For WSS prediction,the trained deep learning model and random forest model achieved mean squared error(MSE),mean absolute error(MAE),and determination coefficient R2 of 0.052 8,0.032 2,0.988 3,and 0.078 2,0.046 3,and 0.976 6,respectively.For pressure prediction,the accuracies of the deep learning models and random forest models were comparable,with MSE,MAE,and R2 of 4.67×10-6,3×10-4,0.999 7,and 1.07×10-5,5×10-4,and 0.999 3,respectively.Conclusions Machine learning methods show high accuracy in predicting the hemodynamic parameters of combined stenotic coronary artery aneurysm models.The predictive accuracy of the model,computational efficiency,and needs of the application scenarios need to be considered in machine learning prediction so that the appropriate model can be selected according to the specific situation.This study has clinical significance,helping doctors to more accurately evaluate a patient's condition and provide new ideas and method for the diagnosis and treatment of cardiovascular diseases.

Objective To compare the anti-inflammatory,antitumor and anti-bacterial effects of the single extract(in granules)and the prepared drug in pieces of Forsythia Suspense(Lianqiao,a traditional Chinese herbal medicine).Methods In zebrafish embryo models of CuSO4 exposure,tail transection and LPS microinjection-induced inflammation,the anti-inflammatory effects of 10 μg/mL DEX,single extract of Forsythia Suspense,and the water extract of the prepared drug(400,600,and 800 μg/mL)were evaluated by observing neutrophil counts,RT-qPCR,HE staining and survival analysis.Zebrafish embryo models bearing different human tumor cell xenografts were used to assess the anti-tumor effect of the drugs in different dosage forms by fluorescence staining and HE staining.The microbroth dilution method was used to evaluate the antibacterial efficacy of the drugs.Results In the zebrafish embryo models of inflammation,both of the two dosage forms of Forsythia Suspense significantly inhibited neutrophil aggregation,reduced the mRNA expressions of TNF-α,IL-6,P38,Jnk,Erk and P65,and increased the survival rate of zebrafish.They both showed obvious inhibitory effects against xenografts of different human cancer cells including colon cancer cells(HCT116),pancreas adenocarcinoma cells(PANC-1),lung cancer cells(A549),liver cancer cells(Hep3B)and cervical carcinoma cells(Hela)in zebrafish embryos,and exhibited strong anti-bacterial effects at the concentration of 15.63 mg/mL.Conclusion The two dosage forms of Forsythia Suspense have similar anti-inflammatory,antitumor and antibacterial effects,but their effects for inhibiting IL-6,P65,and Jnk mRNA expressions and HCT116 cell proliferation differ significantly at low doses in zebrafish.

Schistosomiasis was once hyper-endemic in Yunnan Province. Following concerted efforts for over 70 years, remarkable achievements have been made for schistosomiasis control in the province. In 2004, the Mid- and Long-term Plan for Schistosomiasis Prevention and Control in Yunnan Province was initiated in Yunnan Province, and the target for transmission control of schistosomiasis was achieved in the province in 2009. Following the subsequent implementation of the Outline for Key Projects in Integrated Schistosomiasis Control Program (2009—2015) and the 13th Five - year Plan for Schistosomiasis Control in Yunnan Province, no acute schistosomiasis had been identified in Yunnan Province for successive 12 years, and no local Schistosoma japonicum infections had been detected in humans, animals or Oncomelania hupensis snails for successive 6 years in the province by the end of 2020. The transmission of schistosomiasis was interrupted in Yunnan Province in 2020. This review summarizes the history of schistosomiasis, changes in schistosomiasis prevalence and progress of schistosomiasis control in Yunnan Province, and proposes the future priorities for schistosomiasis control in the province.

Objective To predict the potential geographic distribution of Oncomelania hupensis in Yunnan Province using random forest (RF) and maximum entropy (MaxEnt) models, so as to provide insights into O. hupensis surveillance and control in Yunnan Province. Methods The O. hupensis snail survey data in Yunnan Province from 2015 to 2016 were collected and converted into O. hupensis snail distribution site data. Data of 22 environmental variables in Yunnan Province were collected, including twelve climate variables (annual potential evapotranspiration, annual mean ground surface temperature, annual precipitation, annual mean air pressure, annual mean relative humidity, annual sunshine duration, annual mean air temperature, annual mean wind speed, ≥ 0 ℃ annual accumulated temperature, ≥ 10 ℃ annual accumulated temperature, aridity and index of moisture), eight geographical variables (normalized difference vegetation index, landform type, land use type, altitude, soil type, soil textureclay content, soil texture-sand content and soil texture-silt content) and two population and economic variables (gross domestic product and population). Variables were screened with Pearson correlation test and variance inflation factor (VIF) test. The RF and MaxEnt models and the ensemble model were created using the biomod2 package of the software R 4.2.1, and the potential distribution of O. hupensis snails after 2016 was predicted in Yunnan Province. The predictive effects of models were evaluated through cross-validation and independent tests, and the area under the receiver operating characteristic curve (AUC), true skill statistics (TSS) and Kappa statistics were used for model evaluation. In addition, the importance of environmental variables was analyzed, the contribution of environmental variables output by the models with AUC values of > 0.950 and TSS values of > 0.850 were selected for normalization processing, and the importance percentage of environmental variables was obtained to analyze the importance of environmental variables. Results Data of 148 O. hupensis snail distribution sites and 15 environmental variables were included in training sets of RF and MaxEnt models, and both RF and MaxEnt models had high predictive performance, with both mean AUC values of > 0.900 and all mean TSS values and Kappa values of > 0.800, and significant differences in the AUC (t = 19.862, P < 0.05), TSS (t = 10.140, P < 0.05) and Kappa values (t = 10.237, P < 0.05) between two models. The AUC, TSS and Kappa values of the ensemble model were 0.996, 0.954 and 0.920, respectively. Independent data verification showed that the AUC, TSS and Kappa values of the RF model and the ensemble model were all 1, which still showed high performance in unknown data modeling, and the MaxEnt model showed poor performance, with TSS and Kappa values of 0 for 24%(24/100) of the modeling results. The modeling results of 79 RF models, 38 MaxEnt models and their ensemble models with AUC values of > 0.950 and TSS values of > 0.850 were included in the evaluation of importance of environmental variables. The importance of annual sunshine duration (SSD) was 32.989%, 37.847% and 46.315% in the RF model, the MaxEnt model and their ensemble model, while the importance of annual mean relative humidity (RHU) was 30.947%, 15.921% and 28.121%, respectively. Important environment variables were concentrated in modeling results of the RF model, dispersed in modeling results of the MaxEnt model, and most concentrated in modeling results of the ensemble model. The potential distribution of O. hupensis snails after 2016 was predicted to be relatively concentrated in Yunnan Province by the RF model and relatively large by the MaxEnt model, and the distribution of O. hupensis snails predicted by the ensemble model was mostly the joint distribution of O. hupensis snails predicted by RF and MaxEnt models. Conclusions Both RF and MaxEnt models are effective to predict the potential distribution of O. hupensis snails in Yunnan Province, which facilitates targeted O. hupensis snail control.

Objective This study aims to systematically evaluate the experiences of patients with hematologic neoplasm undergoing chimeric antigen receptor T-cell(CAR-T)therapy and nursing care,and to provide a reference basis for healthcare providers to develop personalized intervention strategies.Methods PubMed,Web of Science,Embase,CINAHL,Cochrane Library,PsycINFO,Scopus,Australia Joanna Briggs Institute(JBI)EBP Database,China National Knowledge Infrastructure(CNKI),Wanfang Data,VIP Database,and China Biology Medicine(CBM)Database were systematically searched for qualitative studies on the experiences of patients with hematologic neoplasm undergoing CAR-T therapy and nursing care.The search period extended up to September 2023.The quality of the literature was reviewed according to the JBI Qualitative Assessment and Review Instrument,and a pooled analysis was applied to integrate the results.Results A total of 15 studies were included,generating 76 findings,which were then organized into 8 categories,resulting in 3 integrated findings.(1)Patients experienced conflicting emotions,with expectations often at odds with reality,leading to emotional fluctuations:expectations about treatment were entangled with concerns about risks;emotions fluctuated with expectations and actual experiences.(2)Patients experienced physical and social functional impairments,but as the disease eased,normal functioning recovered:rapid decline and improvement in physical functions;loss and recovery of social functions.(3)Patients desired access to professional guidance and psychosocial support.Conclusion Patients undergoing CAR-T therapy often contend with emotional conflicts.Changes in physical and social functions after the completion of treatment result in needs for seeking comprehensive medical and nursing support.It is recommended that healthcare providers conduct dynamic assessments and interventions regarding patients'emotional states before treatment,focus on early identification and management of adverse reactions after treatment,and offer continuous nursing services after discharge to enhance patients'confidence in participating in CAR-T therapy and improve their overall quality of life.

Syncytial giant cells (SGCs) are epithelial giant cells with multiple uniform and similar nuclei. Clear cell renal cell carcinoma (ccRCC) with SGCs is a rare microscopic morphology. A 70-year-old man was admitted to hospital with multiple nodules in both lungs found by chest CT examination 2 weeks before. A left renal carcinoma with lung metastasis was considered by PET-CT. Cytoreductive nephrectomy was performed, and postoperative pathological diagnosis confirmed ccRCC with SGCs of the left renal. Combined immunotherapy and targeted therapies were administered. There was no disease progression during follow-up period of 28 months.

Objective:To explore the value of machine-learning models based on magnetic resonance imaging(MRI)radiomics features for the preoperative prediction of the blood supply in pituitary neuroendocrine tumors.Methods:A retrospective analysis was performed on the clinical and imaging data of 136 patients with pathologically confirmed pituitary neuroendocrine tumors(diameter>10 mm)from April 2013 to April 2023 at Yi Jishan Hospital of Wannan Medical College.Based on the intraoperative findings,the patients were assigned into richly vascularized(n=50)and normally vascularized(n=86)groups.All patients were allocated randomly in a 7:3 ratio into a training(n=96)or a validation group(n=40).Three machine-learning algorithms,multivariate Logistic regression(LR),random forest(RF),and support vec-tor machine(SVM),were used to establish radiomics prediction models.Receiver operating characteristic(ROC)curves were plotted to eval-uate the diagnostic performance of the models;decision curve analysis(DCA)was used to assess the net clinical benefit of the models.Res-ults:The clinical model achieved areas under the ROC curve(AUC)of 0.74 and 0.82 in the training and validation groups,respectively.The radiomics models using T1-weighted imaging(WI),T2WI,T1WI-enhanced,and combined sequences achieved AUCs of 0.80,0.84,0.82,and 0.84 in the training group and 0.82,0.80,0.85,and 0.83 in the validation group,respectively.The LR,RF,and SVM models had AUCs of 0.85,0.87,and 0.84 in the training group and 0.85,0.85,and 0.83 in the validation group,respectively.All radiomics models demonstrated great-er diagnostic efficacy than the clinical model.DCA indicated that the LR,SVM,and combined-sequence models achieved good net clinical be-nefits;the LR model showed the best results.Conclusions:Machine-learning models based on MRI radiomics exhibit high predictive value,surpassing the clinical judgment of radiologists based on MRI images alone,and offer a favorable net clinical benefit.