Objective:To explore the clinical manifestations and genetic etiology of a child with Progressive familial intrahepatic cholestasis (PFIC2).Methods:From April 2024 to June 2024, a child with jaundice, hepatomegaly and abnormal liver function who was repeatedly admitted to the First Department of Pediatrics of Qinzhou Maternal and Child Health Care Hospital was selected as the study subject. Clinical data of the child were collected. Peripheral blood samples were collected from the child and her parents. Genomic DNA was extracted for trio-whole exome sequencing, the candidate variant was verified by Sanger sequencing and bioinformatic analysis using REVEL, BLAST/BLAT, Swiss-Model and Swiss-Pdb Viewer software. This study was approved by the Medical Ethics Committee of the Qinzhou Maternal and Child Health Care Hospital (Ethics No.: L20240116).Results:The child was a 1.5-month-old female. Her main clinical manifestations included jaundice, hepatomegaly, brownish urine and earth-like stool. Laboratory examination showed increased levels of bilirubin, mainly direct bilirubin, increased aminotransferase, especially glutamic oxalacetic aminotransferase, accompanied by increased bile acid. Genetic testing revealed that the she has harbored a homozygous c. 3410T>G (p.V1137G) variant of the ABCB11 gene, for which both of her parents were heterozygous carriers. The variant was unreported previously, and was predicted to be pathogenic based on REVEL. Prediction with BLAST/BLAT software showed that the amino acids were highly conserved among different species. Swiss-Pdb Viewer software predicted that the variant has resulted in changes in hydrogen bonds between amino acids. According to the guidelines from the American Collage for Medical Genetics and Genomics (ACMG), the variant was determined to be likely pathogenic (PM1+ PM2_Supporting+ PM3_Supporting+ PP3_Moderate). Conclusion:The homozygous variant of the ABCB11 gene may be the genetic cause of this child. Genetic testing is helpful for confirming the diagnosis and enrich the mutational spectrum of the ABCB11 gene.

OBJECTIVE: To explore the clinical manifestations and genetic etiology of a child with Progressive familial intrahepatic cholestasis (PFIC2). METHODS: From April 2024 to June 2024, a child with jaundice, hepatomegaly and abnormal liver function who was repeatedly admitted to the First Department of Pediatrics of Qinzhou Maternal and Child Health Care Hospital was selected as the study subject. Clinical data of the child were collected. Peripheral blood samples were collected from the child and her parents. Genomic DNA was extracted for trio-whole exome sequencing, the candidate variant was verified by Sanger sequencing and bioinformatic analysis using REVEL, BLAST/BLAT, Swiss-Model and Swiss-Pdb Viewer software. This study was approved by the Medical Ethics Committee of the Qinzhou Maternal and Child Health Care Hospital (Ethics No.: L20240116). RESULTS: The child was a 1.5-month-old female. Her main clinical manifestations included jaundice, hepatomegaly, brownish urine and earth-like stool. Laboratory examination showed increased levels of bilirubin, mainly direct bilirubin, increased aminotransferase, especially glutamic oxalacetic aminotransferase, accompanied by increased bile acid. Genetic testing revealed that the she has harbored a homozygous c.3410T>G (p.V1137G) variant of the ABCB11 gene, for which both of her parents were heterozygous carriers. The variant was unreported previously, and was predicted to be pathogenic based on REVEL. Prediction with BLAST/BLAT software showed that the amino acids were highly conserved among different species. Swiss-Pdb Viewer software predicted that the variant has resulted in changes in hydrogen bonds between amino acids. According to the guidelines from the American Collage for Medical Genetics and Genomics (ACMG), the variant was determined to be likely pathogenic (PM1+PM2_Supporting+PM3_Supporting+PP3_Moderate). CONCLUSION The homozygous variant of the ABCB11 gene may be the genetic cause of this child. Genetic testing is helpful for confirming the diagnosis and enrich the mutational spectrum of the ABCB11 gene.
Humans ; Cholestasis, Intrahepatic/genetics* ; Female ; Infant ; ATP Binding Cassette Transporter, Subfamily B, Member 11/genetics* ; Homozygote ; Mutation

Objective:To develop a deep learning-based automated analysis system for precise segmentation of the optic cup and disc in fundus images and automatic measurement of the vertical cup-to-disc ratio (CDR) for early risk assessment and screening of chronic glaucoma.Methods:The proposed automated system comprised three modules: a dual coding-attention U-net (DCoAtUNet) segmentation network for optic cup and disc segmentation, a conditional random field (CRF) post-processing module, and a CDR measurement and glaucoma screening module based on the segmentation results.The system was designed to enhance boundary detection accuracy and measurement stability and its performance was evaluated on the publicly available Drishti-GS dataset.The dataset was divided into a training set and a test set in a 1∶1 ratio.Dice coefficient and intersection over union (IoU) were used to quantify segmentation accuracy and regional consistency, while accaracy, precision, recall, and F1-score were employed to assess glaucoma screening performance.Results:The DCoAtUNet combined with CRF post-processing achieved Dice coefficients of 0.976 0 for the optic disc and 0.908 1 for the optic cup, with corresponding IoU values of 0.953 4 and 0.837 9, demonstrating high segmentation precision and stability in boundary identification and region overlap.In glaucoma screening, the system achieved an accuracy of 0.843 1, precision of 0.840 9, recall of 0.973 7, and F1-score of 0.902 4, indicating good diagnostic sensitivity and accuracy.Conclusions:By integrating high-precision segmentation and automated measurement strategies, the DCoAtUNet+ CRF model significantly improves the accuracy and stability of CDR evaluation.It effectively assists in identifying high-risk individuals during early glaucoma screening and shows strong potential for clinical application in computer-aided diagnosis workflows.

Objective:To develop a deep learning-based automated analysis system for precise segmentation of the optic cup and disc in fundus images and automatic measurement of the vertical cup-to-disc ratio (CDR) for early risk assessment and screening of chronic glaucoma.Methods:The proposed automated system comprised three modules: a dual coding-attention U-net (DCoAtUNet) segmentation network for optic cup and disc segmentation, a conditional random field (CRF) post-processing module, and a CDR measurement and glaucoma screening module based on the segmentation results.The system was designed to enhance boundary detection accuracy and measurement stability and its performance was evaluated on the publicly available Drishti-GS dataset.The dataset was divided into a training set and a test set in a 1∶1 ratio.Dice coefficient and intersection over union (IoU) were used to quantify segmentation accuracy and regional consistency, while accaracy, precision, recall, and F1-score were employed to assess glaucoma screening performance.Results:The DCoAtUNet combined with CRF post-processing achieved Dice coefficients of 0.976 0 for the optic disc and 0.908 1 for the optic cup, with corresponding IoU values of 0.953 4 and 0.837 9, demonstrating high segmentation precision and stability in boundary identification and region overlap.In glaucoma screening, the system achieved an accuracy of 0.843 1, precision of 0.840 9, recall of 0.973 7, and F1-score of 0.902 4, indicating good diagnostic sensitivity and accuracy.Conclusions:By integrating high-precision segmentation and automated measurement strategies, the DCoAtUNet+ CRF model significantly improves the accuracy and stability of CDR evaluation.It effectively assists in identifying high-risk individuals during early glaucoma screening and shows strong potential for clinical application in computer-aided diagnosis workflows.

Objective:To explore the clinical manifestations and genetic etiology of a child with Progressive familial intrahepatic cholestasis (PFIC2).Methods:From April 2024 to June 2024, a child with jaundice, hepatomegaly and abnormal liver function who was repeatedly admitted to the First Department of Pediatrics of Qinzhou Maternal and Child Health Care Hospital was selected as the study subject. Clinical data of the child were collected. Peripheral blood samples were collected from the child and her parents. Genomic DNA was extracted for trio-whole exome sequencing, the candidate variant was verified by Sanger sequencing and bioinformatic analysis using REVEL, BLAST/BLAT, Swiss-Model and Swiss-Pdb Viewer software. This study was approved by the Medical Ethics Committee of the Qinzhou Maternal and Child Health Care Hospital (Ethics No.: L20240116).Results:The child was a 1.5-month-old female. Her main clinical manifestations included jaundice, hepatomegaly, brownish urine and earth-like stool. Laboratory examination showed increased levels of bilirubin, mainly direct bilirubin, increased aminotransferase, especially glutamic oxalacetic aminotransferase, accompanied by increased bile acid. Genetic testing revealed that the she has harbored a homozygous c. 3410T>G (p.V1137G) variant of the ABCB11 gene, for which both of her parents were heterozygous carriers. The variant was unreported previously, and was predicted to be pathogenic based on REVEL. Prediction with BLAST/BLAT software showed that the amino acids were highly conserved among different species. Swiss-Pdb Viewer software predicted that the variant has resulted in changes in hydrogen bonds between amino acids. According to the guidelines from the American Collage for Medical Genetics and Genomics (ACMG), the variant was determined to be likely pathogenic (PM1+ PM2_Supporting+ PM3_Supporting+ PP3_Moderate). Conclusion:The homozygous variant of the ABCB11 gene may be the genetic cause of this child. Genetic testing is helpful for confirming the diagnosis and enrich the mutational spectrum of the ABCB11 gene.

Objective The aim of this study was to analyze the incidence and mortality of gastric cancer in tumor registration areas of Yunnan province in 2020,as well as the changing trends from 2012 to 2020,and provide suggestion for the prevention and treatment of gastric cancer in Yunnan province.Methods The incidence and death cases of gastric cancer in tumor registration areas of Yunnan province from 2012 to 2020 were collected and complied.After the quality control,the data was included in 89 monitoring points in 2020.Excel 2016 and SPSS 18.0 software were used to calculate the crude incidence,crude mortality,age-standardized inci-dence rate by World standard population(ASIRW),age-standardized mortality rate by World standard population(ASMRW),cumula-tive rate and other indicators of gastric cancer in Yunnan province in 2020.Joinpoint 4.8.0.1 software was used to calculate the annu-al percentage change(APC)and 95%CI of the ASIRW and ASMRW of gastric cancer from 2012 to 2020,and analyze the trend of change.Results In 2020,the crude incidence and ASIRW of gastric cancer in Yunnan province were 11.59/100,000 and 7.60/100,000,respectively.Males(14.90/100,000 and 10.25/100,000)were higher than those in females(8.10/100,000 and 5.04/100,000).In 2020,the crude mortality and ASMRW of gastric cancer in the Yunnan in 2020 were 9.06/100,000 and 5.82/100,000,respectively.Males(11.51/100,000 and 7.89/100,000)were higher than those in females(6.48/100,000 and 3.82/100,000).The crude incidence and mortality of gastric cancer in Yunnan province increased with age.They were at a low level before the age of 45 years old,and then increased rapidly.The 80-84 age group reached the peak(64.12/100,000 and 72.67/100,000),respectively.The APC for ASIRW and ASMRW of gastric cancer in Yunnan province from 2012 to 2020 were-0.35%and 0.22%,re-spectively,there were no significant difference in the trend of change(P>0.05).Conclusion ASIRW and ASMRW of gastric cancer of Yunnan province in 2020 are higher for men than women.The trend of ASIRW and ASMRW maintained stable from 2012 to 2020,and the males and middle-aged elderly people over 45 years old in Yunnan province are the key population for gastric cancer preven-tion and control.

Objective The aim of this study was to analyze the incidence and mortality of gastric cancer in tumor registration areas of Yunnan province in 2020,as well as the changing trends from 2012 to 2020,and provide suggestion for the prevention and treatment of gastric cancer in Yunnan province.Methods The incidence and death cases of gastric cancer in tumor registration areas of Yunnan province from 2012 to 2020 were collected and complied.After the quality control,the data was included in 89 monitoring points in 2020.Excel 2016 and SPSS 18.0 software were used to calculate the crude incidence,crude mortality,age-standardized inci-dence rate by World standard population(ASIRW),age-standardized mortality rate by World standard population(ASMRW),cumula-tive rate and other indicators of gastric cancer in Yunnan province in 2020.Joinpoint 4.8.0.1 software was used to calculate the annu-al percentage change(APC)and 95%CI of the ASIRW and ASMRW of gastric cancer from 2012 to 2020,and analyze the trend of change.Results In 2020,the crude incidence and ASIRW of gastric cancer in Yunnan province were 11.59/100,000 and 7.60/100,000,respectively.Males(14.90/100,000 and 10.25/100,000)were higher than those in females(8.10/100,000 and 5.04/100,000).In 2020,the crude mortality and ASMRW of gastric cancer in the Yunnan in 2020 were 9.06/100,000 and 5.82/100,000,respectively.Males(11.51/100,000 and 7.89/100,000)were higher than those in females(6.48/100,000 and 3.82/100,000).The crude incidence and mortality of gastric cancer in Yunnan province increased with age.They were at a low level before the age of 45 years old,and then increased rapidly.The 80-84 age group reached the peak(64.12/100,000 and 72.67/100,000),respectively.The APC for ASIRW and ASMRW of gastric cancer in Yunnan province from 2012 to 2020 were-0.35%and 0.22%,re-spectively,there were no significant difference in the trend of change(P>0.05).Conclusion ASIRW and ASMRW of gastric cancer of Yunnan province in 2020 are higher for men than women.The trend of ASIRW and ASMRW maintained stable from 2012 to 2020,and the males and middle-aged elderly people over 45 years old in Yunnan province are the key population for gastric cancer preven-tion and control.

Objective:To observe the protective effect of Qishen Yiqi Dripping Pills on myocardial ischemia-reperfusion injury in type 2 diabetic rats and its effects on mitochondrial autophagy phosphoglycerate mutase family member 5 (PGAM5)/Fun14 domain-containing protein 1 (FUNDC1) signaling pathway.Methods:48 male SD rats were divided into a blank control group, sham operation group, No.1 myocardial ischemia reperfusion injury (MIRI) group, No.2 MIRI group, inhibitor group, and Qishen Yiqi group. In addition to the blank control group and the No.1 MIRI group, the other 32 rats were fed with a high-fat diet combined with intraperitoneal injection of streptozotocin to establish animal models of diabetes. Then, the rats in the Qishen Yiqi group were ig Qishen Yiqi Gropping Pills 450 mg/kg, once daily. The rats in the inhibitor group were given Qishen Yiqi Gropping Pills and trimethylamine (3-MA) by intraperitoneal injection 100 mmol/L, once daily. And the rats in the other four groups were ig normal saline. One week after intragastric administration, except for the blank control group and the sham operation group, the rats in the other four groups were used to establish the animal model of myocardial ischemia-reperfusion injury by ligating the anterior descending branch of the left coronary artery for 30 min and reperfusion for 2 h. Then, the materials were taken after reperfusion for 2 h. Finally, the mortality of rats was calculated, the changes in creatine kinase (CK), lactate dehydrogenase (LDH), aspartate aminotransferase (AST), and the levels of superoxide dismutase (SOD) and malondialdehyde (MDA) in myocardial tissue were detected, and the expression level of PGAM5/FUNDC1 pathway node protein in myocardial tissue was measured by real-time fluorescence quantitative PCR.Results:Compared with the No.1 MIRI group, serum indicators of the AST, LDH, CK, and MDA levels in the No.2 MIRI model group increased (all P < 0.05), while the level of SOD decreased ( P < 0.05). Compared with the No.1 MIRI group, myocardial tissue indicators of FUNDC1, PGAM5, B cell lymphoma-xL (Bcl-xL), light chain 3 (LC3), autophagy associated protein 5 (ATG5), and Beclin-1 level decreased (all P < 0.05), the level of P62 increased ( P < 0.05), while the level of cysteinyl aspartate specific proteinase-9 (Caspase-9) increased, but he difference is not statistically significant ( P > 0.05). Compared with the No.2 MIRI group and the inhibitor group, serum indicators of the AST, LDH, CK, and MDA levels in the Qishen Yiqi group decreased (all P < 0.05), and the level of SOD increased ( P < 0.05). Compared with the No.2 MIRI group and the inhibitor group, myocardial tissue indicators of FUNDC1, PGAM5, Bcl-xL, LC3, ATG5, and Beclin-1 levels increased (all P < 0.05), while the levels of P62 and Caspase-9 decreased (all P < 0.05). Conclusions:High blood sugar levels can aggravate MIRI. Qishen Yiqi Dripping Pills can regulate mitochondrial autophagy through the PGAM5/FUNDC1 pathway and alleviate myocardial ischemia-reperfusion injury. MIRI plays a protective role in the myocardium of diabetic rats.

Objective:To evaluate the efficiency of ResNet50-OC model based on deep learning for multiple classification of color fundus photographs.Methods:The proprietary dataset (PD) collected in July 2018 in BenQ Hospital of Nanjing Medical University and EyePACS dataset were included.The included images were classified into five types of high quality, underexposure, overexposure, blurred edges and lens flare according to clinical ophthalmologists.There were 1 000 images (800 from EyePACS and 200 from PD) for each type in the training dataset and 500 images (400 from EyePACS and 100 from PD) for each type in the testing dataset.There were 5 000 images in the training dataset and 2 500 images in the testing dataset.All images were normalized and augmented.The transfer learning method was used to initialize the parameters of the network model, on the basis of which the current mainstream deep learning classification networks (VGG, Inception-resnet-v2, ResNet, DenseNet) were compared.The optimal network ResNet50 with best accuracy and Micro F1 value was selected as the main network of the classification model in this study.In the training process, the One-Cycle strategy was introduced to accelerate the model convergence speed to obtain the optimal model ResNet50-OC.ResNet50-OC was applied to multi-class classification of fundus image quality.The accuracy and Micro F1 value of multi-classification of color fundus photographs by ResNet50 and ResNet50-OC were evaluated.Results:The multi-classification accuracy and Micro F1 values of color fundus photographs of ResNet50 were significantly higher than those of VGG, Inception-resnet-v2, ResNet34 and DenseNet.The accuracy of multi-classification of fundus photographs in the ResNet50-OC model was 98.77% after 15 rounds of training, which was higher than 98.76% of the ResNet50 model after 50 rounds of training.The Micro F1 value of multi-classification of retinal images in ResNet50-OC model was 98.78% after 15 rounds of training, which was the same as that of ResNet50 model after 50 rounds of training.Conclusions:The proposed ResNet50-OC model can be accurate and effective in the multi-classification of color fundus photograph quality.One-Cycle strategy can reduce the frequency of training and improve the classification efficiency.

Objective:To observe and analyze the accuracy of the optic disc positioning and segmentation method of fundus images based on deep learning.Methods:The model training strategies were training and evaluating deep learning-based optic disc positioning and segmentation methods on the ORIGA dataset. A deep convolutional neural network (CNN) was built on the Caffe framework of deep learning. A sliding window was used to cut the original image of the ORIGA data set into many small pieces of pictures, and the deep CNN was used to determine whether each small piece of picture contained the complete disc structure, so as to find the area of the disc. In order to avoid the influence of blood vessels on the segmentation of the optic disc, the blood vessels in the optic disc area were removed before segmentation of the optic disc boundary. A deep network of optic disc segmentation based on image pixel classification was used to realize the segmentation of the optic disc of fundus images. The accuracy of the optic disc positioning and segmentation method was calculated based on deep learning of fundus images. Positioning accuracy=T/N, T represented the number of fundus images with correct optic disc positioning, and N represented the total number of fundus images used for positioning. The overlap error was used to compare the difference between the segmentation result of the optic disc and the actual boundary of the optic disc.Results:On the dataset from ORIGA, the accuracy of the optic disc localization can reach 99.6%, the average overlap error of optic disc segmentation was 7.1%. The calculation errors of the average cup-to-disk ratio for glaucoma images and normal images were 0.066 and 0.049, respectively. Disc segmentation of each image took an average of 10 ms.Conclusion:The algorithm can locate the disc area quickly and accurately, and can also segment the disc boundary more accurately.