ObjectiveTo establish a risk recognition model for coronary artery stenosis by using a machine learning method and to identify the key causative factors. MethodsPatients aged ≥18 years，diagnosed with coronary heart disease through coronary angiography from January 2013 to May 2020 in two prominent hospitals in Shanxi Province， were continuously enrolled. Logistic regression，back propagation neural network （BPNN）， and random forest（RF）algorithms were used to construct models for detecting the causative factors of coronary artery stenosis. Sensitivity （TPR）， specificity （TNR）， accuracy （ACC）， positive predictive value （PV+）， negative predictive value （PV-）， area under subject operating characteristic curve （AUC）， and calibration curve were used to compare the discrimination and calibration performance of the models. The best model was then employed to predict the main risk variables associated with coronary stenosis. ResultsThe RF model exhibited superior comprehensive performance compared to logistic regression and BPNN models. The TPR values for logistic regression，BPNN，and RF models were 75.76%， 74.30%， and 93.70%， while ACC values were 74.05%， 72.30%， and 79.49%， respectively. The AUC values were：logistic regression 0.739 9； BPNN 0.723 1； RF 0.752 2. Manifestations such as chest pains，abnormal ST segments on ECG，ventricular premature beats with hypertension， atrial fibrillation， regional wall motion abnormalities（RWMA） by color echocardiography， aortic regurgitation（AR）， pulmonary insufficiency （PI）， family history of cardiovascular diseases，and body mass index（BMI）were identified as top ten important variables affecting coronary stenosis according to the RF model. ConclusionsRandom forest model shows the best comprehensive performance in identification and accurate assessment of coronary artery stenosis. The prediction of risk factors affecting coronary artery stenosis can provide a scientific basis for clinical intervention and help to formulate further diagnosis and treatment strategies so as to delay the disease progression.

[This corrects the article DOI: 10.1016/j.apsb.2019.01.013.].

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

[This corrects the article DOI: 10.1016/j.apsb.2019.01.013.].

Objective To investigate the spontaneous activity of brain neurons in patients with obsessive－compulsive disorder (OCD) under resting state. Methods Forty－eight OCD patients and 50 age－, gender－ and year of education－matched normal controls were enrolled. All the subjects underwent 3.0 T fMRI to acquire resting state brain image. The brain regions with significant differences in amplitude of low－frequency fluctuation (ALFF) between patients and controls were analyzed. Whole－brain functional connectivity (FC) were analyzed using the brain regions with significant differences as seed points, and the correlation between brain regions with significant differences in ALFF and FC analysis and obsessive－compulsive symptoms was analyzed. Results Compared to the control group, the ALFF of left dorsolateral prefrontal cortex increased in patients with OCD (t=4.305, P＜0.001). Compared to the controls, the analysis of whole－brain FC (based on MNI template) with the left dorsolateral prefrontal cortex as the regions of interest showed that the FC strength between the left dorsolateral prefrontal cortex and right orbital inferior frontal cortex (t=3.897, P＜0.001), left anterior cingulate cortex (t=3.370, P＜0.001), right anterior cingulate cortex (t=4.299, P＜0.001), left middle cingulate cortex (t=3.220, P＜0.001), right middle cingulate cortex (t=4.607, P＜0.001) enhanced; the FC strength between the left dorsolateral prefrontal cortex and the left opercular part of inferior frontal gyrus (t=－4.630, P＜0.001) weakened in patients with OCD. The FC between the left dorsolateral prefrontal cortex and the left opercular part of inferior frontal gyrus was negatively correlated with obsessions score(r=－0.369, P=0.014), compulsions score (r=－0.392, P=0.009) and total score (r=－0.393, P=0.008) of the Yale－Brown obsessive compulsive scale (Y－BOCS). Conclusion In patients with OCD, spontaneous neural activity of the left dorsolateral prefrontal cortex is enhanced in resting state, and the FC with multiple brain regions is abnormal. The FC strength between the left dorsolateral prefrontal cortex and the left opercular part of inferior frontal gyrus is associated with obsessive－compulsive symptoms.

Bicyclol is a synthetic drug for hepatoprotection in clinic since 2004. Preliminary clinical observations suggest that bicyclol might be active against hepatitis C virus (HCV) with unknown mechanism. Here, we showed that bicyclol significantly inhibited HCV replication and in hepatitis C patients. Using bicyclol as a probe, we identified glycolipid transfer protein (GLTP) to be a novel restrictive factor for HCV replication. The GLTP preferentially bound host vesicle-associated membrane protein-associated protein-A (VAP-A) in competition with the HCV NS5A, causing an interruption of the complex formation between VAP-A and HCV NS5A. As the formation of VAP-A/NS5A complex is essential for viral RNA replication, up-regulation of GLTP by bicyclol reduced the level of VAP-A/NS5A complex and thus inhibited HCV replication. Bicyclol also exhibited an inhibition on HCV variants resistant to direct-acting antiviral agents (DAAs) with an efficacy identical to that on wild type HCV. In combination with bicyclol, DAAs inhibited HCV replication in a synergistic fashion. GLTP appears to be a newly discovered host restrictive factor for HCV replication, Up-regulation of GLTP causes spontaneous restriction of HCV replication.

Objective To summarize the efficacy of extracorporeal membrane oxygenation (ECMO)utilization in Emergency Department (ED),as well as the establishment of emergency ECMO team.Methods A retrospective analysis was carried out in 16 patients treated with ECMO between April 2015 to December 2016 in ED.The clinical data including demographics,diagnosis,initiating ECMO timing,place of ECMO establishment,intubation approaches,duration of ECMO,complications and outcomes were collected and analyzed.Results Eight patients were successfully weaned from ECMO,and 7 of them survived to discharge from hospital.The duration of ECMO support was 4 to 384 hours.The emergency ECMO team was set up.Conclusions Emergency medical team can successfully operate the ECMO process.The emergency medical team-initiated ECMO can provide effectively adjuvant measures to support patients with respiratory failure,circulatory failure and cardiac arrest.

Objective To explore the effects of core stability training on gross motor function and walking ability of children with spasticcerebral palsy. Methods 60 children with spastic cerebral palsy were divided into 2 groups. The control group (n=30) received routine rehabilitation.The experimental group (n=30) received core stability training for 15~20 minutes during exercise therapy training in routine rehabilitation.Before and 3 months after training, they were assessed with D and E domains of Gross Motor Function Measure (GMFM) andfootprints analysis. Results The scores of D and E domains of GMFM, the step length, the step width and the velocity were better after training(P<0.05), especially in experimental group (P<0.05). Conclusion Core stability training combined with routine rehabilitation is effectiveon improving gross motor function and walking ability of children with spastic cerebral palsy.

Objective To investigate the potential association of Ghrelin(GHRL)gene polymorphisms susceptible to schizophrenia by case-control study.Methods Six hundred and thirty-four patients,six hundred and six healthy control subjects were recruited.Four SNPs rs696217,rs26802,rs27647 and rs26311 were detected by the polymerase chain reaction-based-restriction fragment length polymorphism analysis.Results No significant differences in genotype or allele frequencies of the four SNPs were observed between schizophrenic patients and healthy controls (Pvalues of genotype frequencies were 0.649,0.944,0.410,0.826;P values of allele frequencies were 0.773,0.992,0.301,0.723).However,seven haplotypes(GAAG,GAGC,GAGG,GCGC,GCGG,TAGC,TAGG)showed significant differences in frequency between schizophrenic and control groups(P values were 0.011,0.001,1.76×10-6,9.84×10-10,1.38×10-9,2.12×10-5,2.57×10-6).Conclusion These data suggest that the GHRL gene may not be associated with susceptibility to schizophrenia in the Chinese Han population.However,the haplotype of GA may be the susceptive factor of schizophrenia.

Predictive factors of neoadjuvant chemotherapy play an important role in selecting sensitive chemotherapy regimen and avoiding unsuitable regimen for breast cancer patients. At present, estrogen receptor (ER), progestin receptor (PR) and HER-2 status are the main predictive factors. Other factors such as Ki-67 also seem to be promising for optimizing neoadjuvant chemotherapy.