Hepatic encephalopathy is a clinical syndrome of central nervous system dysfunction caused by liver insufficiency.It severely affects the quality of life of patients and may lead to death.Accurate prediction of the risk of developing hepatic encephalopathy is crucial for early intervention and treatment.In order to identify the risk of hepatic encephalopathy in patients in advance,many studies have been devoted to efforts to develop tools and methods to identify the risk of hepatic encephalopathy as early as possible,so as to develop preventive and early management strategies.Most conventional hepatic encephalopathy risk prediction models currently assess the prob-ability of a patient developing hepatic encephalopathy by analysing factors such as clinical data and biochemical indicators,however,their accuracy,sensitivity and positive predictive value are not high.The application of artificial intelligence to clinical predictive modelling is a very hot and promising area,which can use large amounts of data and complex algorithms to improve the accuracy and efficiency of diagnosis and prognosis.To date,there have been few studies using AI techniques to predict hepatic encephalopathy.Therefore,this paper reviews the research progress of hepatic encephalopathy risk prediction models,and also discusses the prospect of AI application in hepatic encephalopathy risk prediction models.It also points out the challenges and future research directions of AI in HE risk prediction model research in order to promote the development and clinical application of hepatic encephalopathy risk prediction models.

Objective: To investigate the clinical presentation and genetic characteristics of malignant infantile osteopetrosis. Methods: This was a retrospective case study. Thirty-seven children with malignant infantile osteopetrosis admitted into Beijing Children's Hospital from January 2013 to September 2022 were enrolled in this study. According to the gene mutations, the patients were divided into the CLCN7 group and the TCIRG1 group. Clinical characteristics, laboratory tests, and prognosis were compared between two groups. Wilcoxon test or Fisher exact test were used in inter-group comparison. The survival rate was estimated with the Kaplan-Meier method and the Log-Rank test was used to compare the difference in survival between groups. Results: Among the 37 cases, there were 22 males and 15 females. The age of diagnosis was 0.5 (0.2, 1.0) year. There were 13 patients (35%) and 24 patients (65%) with mutations in CLCN7 and TCIRGI gene respectively. Patients in the CLCN7 group had an older age of diagnosis than those in the TCIRGI group (1.2 (0.4, 3.6) vs. 0.4 (0.2, 0.6) years, Z=-2.60, P=0.008). The levels of serum phosphorus (1.7 (1.3, 1.8) vs. 1.1 (0.8, 1.6) mmol/L, Z=-2.59, P=0.010), creatine kinase isoenzyme (CK-MB) (457 (143, 610) vs. 56 (37, 82) U/L, Z=-3.38, P=0.001) and the level of neutrophils (14.0 (9.9, 18.1) vs. 9.2 (6.7, 11.1) ×10⁹/L, Z=-2.07, P=0.039) at diagnosis were higher in the CLCN7 group than that in the TCIRG1 group. However, the level of D-dimer in the CLCN7 group was lower than that in the TCIRGI group (2.7 (1.0, 3.1) vs. 6.3 (2.5, 9.7) μg/L, Z=2.83, P=0.005). After hematopoietic stem cell transplantation, there was no significant difference in 5-year overall survival rate between the two groups (92.3%±7.4% vs. 83.3%±7.6%, χ²=0.56, P=0.456). Conclusions: TCIRGI gene mutations are more common in children with osteopetrosis. Children with TCIRGI gene mutations have younger age, lower levels of phosphorus, CK-MB, and neutrophils and higher level of D-dimer at the onset. After hematopoietic stem cell transplantation, patients with CLCN7 or TCIRGI gene mutations have similar prognosis.
Child ; Male ; Female ; Humans ; Osteopetrosis/therapy* ; Retrospective Studies ; Prognosis ; Genes, Recessive ; Phosphorus ; Chloride Channels/genetics* ; Vacuolar Proton-Translocating ATPases/genetics*

Objective:To assess the early physical growth and development of human immunodeficiency virus-exposed uninfected (HEU) children by longitudinally comparing the differences of growth and development between HEU group and the healthy human immunodeficiency virus-unexposed uninfected (HUU) control group of children aged 0 to 18 months.Methods:A retrospective cohort study was designed.Maternal information of the human immunodeficiency virus (HIV) infected mothers and follow-up information at 0, 1, 3, 6, 9, 12, and 18 months postpartum of their children (born between January 2013 and December 2019 in Chengdu City) were collected from the Information System of Prevention of Mother-to-Child Transmission of Human Immunodeficiency Virus Management. The HUU control group was matched with HEU group by maternal age, gestational age at birth, and infant gender at a ratio of 1∶1. There were 385 children each included in the HEU and HUU groups. Matched samples t-test and the multilevel models were used to compared the physical developmental differences between the two groups. Results:Weight for age Z scores (WAZ) at 0, 3, 6 months of HEU group were -0.72±1.03, -0.09±1.18 and 0.05±1.09, respectively, which were all lower than WAZ of HUU group (-0.21±1.04, 0.42±1.19 and 0.41±1.16, respectively), which were all significantly different ( t=8.41, 7.47 and 5.18, respectively, all P<0.001). Length for age Z scores (LAZ) at 3, 6, 12, 18 months of HEU group were -0.23±1.36, -0.01±1.48, -0.18±1.20 and -0.32±1.13, respectively, which were all lower than LAZ of HUU group (0.24±1.26, 0.30±1.26, 0.07±1.11 and 0.04±1.05, respectively), which were all significantly different ( t=6.14, 4.04, 2.72 and 4.30, respectively, all P<0.01). Weight for length Z scores (WLZ) at 0, 3, 6 months of HEU group were -1.05±1.18, 0.23±1.03 and 0.22±0.95, respectively, which were all lower than WLZ of HUU group (-0.20±0.98, 0.44±1.03 and 0.45±1.00, respectively), which were all significantly different ( t=10.90, 2.95 and 2.96, respectively, all P<0.01). After possible confounding factors were corrected, the WAZ of HEU children at 0, 3, 6 months were still lower than those of HUU children, the LAZ of 3, 6, 12, 18 months were still lower than those of HUU children, and the WLZ of 0, 3, 6 months were still lower than those of HUU children. Conclusions:The differences between HEU and HUU children in Chengdu City mainly occur within six months of age, but the differences of body length persist until 18 months of age.Prenatal exposure to HIV infection affects both fetal and postnatal body growth and development.

OBJECTIVES: To reduce the dimension of characteristic information extracted from pelvic CT images by using principal component analysis (PCA) and partial least squares (PLS) methods. To establish a support vector machine (SVM) classification and identification model to identify if there is pelvic injury by the reduced dimension data and evaluate the feasibility of its application. METHODS: Eighty percent of 146 normal and injured pelvic CT images were randomly selected as training set for model fitting, and the remaining 20% was used as testing set to verify the accuracy of the test, respectively. Through CT image input, preprocessing, feature extraction, feature information dimension reduction, feature selection, parameter selection, model establishment and model comparison, a discriminative model of pelvic injury was established. RESULTS: The PLS dimension reduction method was better than the PCA method and the SVM model was better than the naive Bayesian classifier (NBC) model. The accuracy of the modeling set, leave-one-out cross validation and testing set of the SVM classification model based on 12 PLS factors was 100%, 100% and 93.33%, respectively. CONCLUSIONS In the evaluation of pelvic injury, the pelvic injury data mining model based on CT images reaches high accuracy, which lays a foundation for automatic and rapid identification of pelvic injuries.
Algorithms ; Bayes Theorem ; Data Mining ; Least-Squares Analysis ; Support Vector Machine

A 54-year-old asymptomatic man underwent a video-assisted thoracoscopic left pneumonectomy for squamous-cell carcinoma. During the surgery, a complete left pericardial defect was unexpectedly discovered, but no special intervention was made. The preoperative chest CT was reciewed, which showed the heart extended unusually to the left, but the left pericardial defect was not evident. The operation time was 204 min and the patient was discharged from hospital upon recovery 9 days after the surgery. The pathological result indicated moderately differentiated squamous-cell carcinoma (T2N1M0, stage ⅡB), and metastasis was found in the parabronchial lymph nodes (3/5). The patient did not receive chemotherapy after the surgery, and there was no signs of recurrence 6 months after the surgery. Complete pericardial defects usually do not endanger the lives of patients, and if the patient is asymptomatic, pneumonectomy is feasible.

Child ; Chronic Disease ; Epstein-Barr Virus Infections/complications* ; Familial Primary Pulmonary Hypertension/complications* ; Herpesvirus 4, Human ; Humans ; Persistent Infection ; Pulmonary Arterial Hypertension

Objective To compare the performance of three deep-learning models （VGG19, Inception-V3 and Inception-ResNet-V2） in automatic bone age assessment based on pelvic X-ray radiographs. Methods A total of 962 pelvic X ray radiographs taken from adolescents （481 males, 481 females） aged from 11.0 to 21.0 years in five provinces and cities of China were collected, preprocessed and used as objects of study. Eighty percent of these X ray radiographs were divided into training set and validation set with random sampling method and used for model fitting and hyper-parameters adjustment. Twenty percent were used as test sets, to evaluate the ability of model generalization. The performances of the three models were assessed by comparing the root mean square error （RMSE）, mean absolute error （MAE） and Bland-Altman plots between the model estimates and the chronological ages. Results The mean RMSE and MAE between bone age estimates of the VGG19 model and the chronological ages were 1.29 and 1.02 years, respectively. The mean RMSE and MAE between bone age estimates of the Inception-V3 model and the chronological ages were 1.17 and 0.82 years, respectively. The mean RMSE and MAE between bone age estimates of the Inception-ResNet-V2 model and the chronological ages were 1.11 and 0.84 years, respectively. The Bland-Altman plots showed that the mean value of differences between bone age estimates of Inception-ResNet-V2 model and the chronological ages was the lowest. Conclusion In the automatic bone age assessment of adolescent pelvis, the Inception-ResNet-V2 model performs the best while the Inception-V3 model achieves a similar accuracy as VGG19 model.
Adolescent ; Adult ; Age Determination by Skeleton ; Child ; China ; Female ; Humans ; Male ; Pelvis ; Radiography ; Young Adult

OBJECTIVETo study the influence of cow's milk protein allergy (CMPA) on the diagnosis of functional gastrointestinal diseases (FGID) based on the Rome IV standard in infants and young children.

METHODSA total of 84 children aged 1 month to 3 years who were diagnosed with CMPA were enrolled as the case group, and 84 infants and young children who underwent physical examination and had no CMPA were enrolled as the control group. The pediatricians specializing in gastroenterology asked parents using a questionnaire for the diagnosis of FGID based on the Rome IV standard to assess clinical symptoms and to diagnose FGID.

RESULTSThe case group had a significantly higher incidence rate of a family history of allergies than the control group (P<0.05). In the case group, 38 (45%) met the Rome IV standard for the diagnosis of FGID, while in the control group, 13 (15%) met this standard (P<0.05). According to the Rome IV standard for FGID, the case group had significantly higher diagnostic rates of reflex, functional diarrhea, difficult defecation, and functional constipation than the control group (P<0.05). The children who were diagnosed with FIGD in the control group were given conventional treatment, and those in the case group were asked to avoid the intake of cow's milk protein in addition to the conventional treatment. After 3 months of treatment, the case group had a significantly higher response rate to the treatment than the control group (P<0.05).

CONCLUSIONSIn infants and young children, CMPA has great influence on the diagnosis of FGID based on the Rome IV standard. The possibility of CMPA should be considered during the diagnosis of FGID.

Objective To realize the automated bone age assessment by applying deep learning to digital radiography(DR)image recognition of left wrist joint in Uyghur teenagers, and explore its practical ap-plication value in forensic medicine bone age assessment. Methods The X-ray films of left wrist joint after pretreatment, which were taken from 245 male and 227 female Uyghur nationality teenagers in Uygur Autonomous Region aged from 13.0 to 19.0 years old, were chosen as subjects. And AlexNet was as a regression model of image recognition. From the total samples above, 60% of male and fe-male DR images of left wrist joint were selected as net train set, and 10% of samples were selected as validation set. As test set, the rest 30%were used to obtain the image recognition accuracy with an error range in ±1.0 and ±0.7 age respectively, compared to the real age. Results The modelling results of deep learning algorithm showed that when the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the net train set was 81.4% and 75.6% in male, and 80.5% and 74.8% in female, respectively. When the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the test set was 79.5% and 71.2% in male, and 79.4% and 66.2% in female, respectively. Conclusion The combination of bone age research on teenagers' left wrist joint and deep learning, which has high accuracy and good feasi-bility, can be the research basis of bone age automatic assessment system for the rest joints of body.

Deep learning and neural network models have been new research directions and hot issues in the fields of machine learning and artificial intelligence in recent years. Deep learning has made a breakthrough in the applications of image and speech recognitions, and also has been extensively used in the fields of face recognition and information retrieval because of its special superiority. Bone X-ray images express different variations in black-white-gray gradations, which have image features of black and white contrasts and level differences. Based on these advantages of deep learning in image recognition, we combine it with the research of bone age assessment to provide basic datum for constructing a forensic automatic system of bone age assessment. This paper reviews the basic concept and network architectures of deep learning, and describes its recent research progress on image recognition in different research fields at home and abroad, and explores its advantages and application prospects in bone age assessment.