Prenatal diagnosis of fetuses with Emanuel syndrome: Results of ultrasound examination and invasive genetic testing.

OBJECTIVE: To investigate prenatal manifestations of Emanuel syndrome (ES) by retrospectively analyzing the results of prenatal diagnosis. METHODS: Thirteen fetuses were collected from five hospitals, of which six were confirmed with 47,der(22)t(11;22; ES) by karyotype and chromosomal microarray analysis (CMA). Seven were diagnosed with 46,t(11;22) balanced translocations by karyotype, including one de novo mosaic 46,XX,t(11;22). In 3/7, CMA was performed but did not identify chromosomal imbalances. The results of prenatal diagnoses were reviewed, including ultrasound examinations and genetic testing. RESULTS: In ES fetuses, the derivative 22 was consistently inherited from the mother, while in the balanced translocation group, the t(11;22) chromosome was of paternal origin in 3/6 cases, All ES fetuses presented with multiple abnormalities by ultrasound examinations. Diaphragm hernia (3/6), Dandy-Walker complex (3/6), and kidney aplasia (3/6), were the most common ultrasound findings. Sonographic soft markers such as increased nuchal translucency, increased nuchal fold thickness appeared in 3 cases and all of these were associated with other anomalies. However, none of the ultrasound findings differentiated ES from other genetic syndromes during fetal period. CONCLUSIONS: In this series, in fetuses with a der(22), the derivative chromosome was consistently of maternal origin. In contrast, 46,t(11;22) balanced translocations were of maternal or paternal origin. The results contribute to the literature regarding the fetal phenotype of ES. Due to the absence of specific features distinguishing ES from other genetic syndromes, confirming the diagnosis through invasive genetic testing is necessary.

Attitudes of medical workers in China toward artificial intelligence in ophthalmology: a comparative survey.

BACKGROUND: In the development of artificial intelligence in ophthalmology, the ophthalmic AI-related recognition issues are prominent, but there is a lack of research into people's familiarity with and their attitudes toward ophthalmic AI. This survey aims to assess medical workers' and other professional technicians' familiarity with, attitudes toward, and concerns about AI in ophthalmology. METHODS: This is a cross-sectional study design study. An electronic questionnaire was designed through the app Questionnaire Star, and was sent to respondents through WeChat, China's version of Facebook or WhatsApp. The participation was voluntary and anonymous. The questionnaire consisted of four parts, namely the respondents' background, their basic understanding of AI, their attitudes toward AI, and their concerns about AI. A total of 562 respondents were counted, with 562 valid questionnaires returned. The results of the questionnaires are displayed in an Excel 2003 form. RESULTS: There were 291 medical workers and 271 other professional technicians completed the questionnaire. About 1/3 of the respondents understood AI and ophthalmic AI. The percentages of people who understood ophthalmic AI among medical workers and other professional technicians were about 42.6 % and 15.6 %, respectively. About 66.0 % of the respondents thought that AI in ophthalmology would partly replace doctors, about 59.07 % having a relatively high acceptance level of ophthalmic AI. Meanwhile, among those with AI in ophthalmology application experiences (30.6 %), above 70 % of respondents held a full acceptance attitude toward AI in ophthalmology. The respondents expressed medical ethics concerns about AI in ophthalmology. And among the respondents who understood AI in ophthalmology, almost all the people said that there was a need to increase the study of medical ethics issues in the ophthalmic AI field. CONCLUSIONS: The survey results revealed that the medical workers had a higher understanding level of AI in ophthalmology than other professional technicians, making it necessary to popularize ophthalmic AI education among other professional technicians. Most of the respondents did not have any experience in ophthalmic AI but generally had a relatively high acceptance level of AI in ophthalmology, and there was a need to strengthen research into medical ethics issues.

Management of a pregnant patient with chylomicronemia from a novel mutation in GPIHBP1: a case report.

BACKGROUND: Familial chylomicronemia syndrome (FCS) is a rare autosomal recessive lipid disorder often associated with recurrent episodes of pancreatitis. It is documented in most cases with FCS due to the mutations of key proteins in lipolysis, including LPL, APOC2, APOA5, LMF1 and GPIHBP1. CASE PRESENTATION: We report the successful management of a 35-year-old pregnant woman carrying a novel homozygous frameshift mutation c.48_49insGCGG (p.P17A fs*22) in the GPIHBP1 gene with previous severe episodes of acute pancreatitis triggered by pregnancy, resulting in adverse obstetrical outcomes. With careful monitoring, the patient underwent an uneventful pregnancy and delivered a baby with no anomalies. CONCLUSIONS: The case report contributes to the understanding of GPIHBP1-deficient familial chylomicronemia syndrome (FCS) and highlights gestational management of FCS patient.

Association between long-chain non-coding RNA SRA1 gene single-nucleotide polymorphism and polycystic ovary syndrome susceptibility.

OBJECTIVE: To study the association between single-nucleotide polymorphism (SNP) of long-chain non-coding RNA steroid receptor RNA activator (lncRNA SRA1) gene and polycystic ovary syndrome (PCOS) susceptibility. METHODS: Sanger sequencing was used to analyze the genotypes of the lncRNA SRA1 gene rs801460, rs10463297, and rs250426 in 315 PCOS patients and 315 control groups. RESULTS: There was no correlation between lncRNA SRA1 gene rs801460, rs250426 SNP, and PCOS susceptibility (p > 0.05). The T allele at the rs10463297 locus of the SRA1 gene has a lower risk of PCOS than the C allele (OR = 0.63, 95%CI: 0.50-0.79, p < 0.01). Among people with a BMI ≥ 26.5 kg/m2, when carrying the TC genotype and CC genotype at rs801460, the risk of PCOS susceptibility was lower than the TT genotype (OR = 0.54, 95%CI: 0.33-0.89, p = 0.02). At different ages and BMI stratifications, there was a significant association between rs10463297 SNP and PCOS susceptibility (p < 0.05). Multi-factor dimensionality reduction (MDR) analysis results showed that age, BMI, rs801460, rs10463297, and rs250426 interactions constitute a "high-risk combination." PCOS susceptibility risk was 5.96 times that of a "low-risk combination" (95%CI: 4.14-8.56, p < 0.01). SRA1 gene rs801460, rs10463297, rs250426 constructed TCT haplotype was associated with increased risk of PCOS susceptibility (OR = 1.66, 95%CI: 1.20-2.30, p < 0.01); the CTT haplotype was associated with a decreased risk of PCOS susceptibility (OR = 0.56, 95%CI: 0.36-0.87, p = 0.01). LncRNA SRA1 gene rs10463297 SNP was correlated with the level of lncRNA SRA1 in the peripheral blood leukocytes (p < 0.01). CONCLUSION: From this study, we found that the lncRNA SRA1 gene rs10463297 SNP is associated with PCOS susceptibility.

A novel lncRNA-mRNA-miRNA signature predicts recurrence and disease-free survival in cervical cancer.

Cervical cancer (CC) patients have a poor prognosis due to the high recurrence rate. However, there are still no effective molecular signatures to predict the recurrence and survival rates for CC patients. Here, we aimed to identify a novel signature based on three types of RNAs [messenger RNA (mRNAs), microRNA (miRNAs), and long non-coding RNAs (lncRNAs)]. A total of 763 differentially expressed mRNAs (DEMs), 46 lncRNAs (DELs), and 22 miRNAs (DEMis) were identified between recurrent and non-recurrent CC patients using the datasets collected from the Gene Expression Omnibus (GSE44001; training) and The Cancer Genome Atlas (RNA- and miRNA-sequencing; testing) databases. A competing endogenous RNA network was constructed based on 23 DELs, 15 DEMis, and 426 DEMs, in which 15 DELs, 13 DEMis, and 390 DEMs were significantly associated with disease-free survival (DFS). A prognostic signature, containing two DELs (CD27-AS1, LINC00683), three DEMis (hsa-miR-146b, hsa-miR-1238, hsa-miR-4648), and seven DEMs (ARMC7, ATRX, FBLN5, GHR, MYLIP, OXCT1, RAB39A), was developed after LASSO analysis. The built risk score could effectively separate the recurrence rate and DFS of patients in the high- and low-risk groups. The accuracy of this risk score model for DFS prediction was better than that of the FIGO (International Federation of Gynecology and Obstetrics) staging (the area under receiver operating characteristic curve: training, 0.954 vs 0.501; testing, 0.882 vs 0.656; and C-index: training, 0.855 vs 0.539; testing, 0.711 vs 0.508). In conclusion, the high predictive accuracy of our signature for DFS indicated its potential clinical application value for CC patients.

Research on an Intelligent Lightweight-Assisted Pterygium Diagnosis Model Based on Anterior Segment Images.

AIMS: The lack of primary ophthalmologists in China results in the inability of basic-level hospitals to diagnose pterygium patients. To solve this problem, an intelligent-assisted lightweight pterygium diagnosis model based on anterior segment images is proposed in this study. METHODS: Pterygium is a common and frequently occurring disease in ophthalmology, and fibrous tissue hyperplasia is both a diagnostic biomarker and a surgical biomarker. The model diagnosed pterygium based on biomarkers of pterygium. First, a total of 436 anterior segment images were collected; then, two intelligent-assisted lightweight pterygium diagnosis models (MobileNet 1 and MobileNet 2) based on raw data and augmented data were trained via transfer learning. The results of the lightweight models were compared with the clinical results. The classic models (AlexNet, VGG16 and ResNet18) were also used for training and testing, and their results were compared with the lightweight models. A total of 188 anterior segment images were used for testing. Sensitivity, specificity, F1-score, accuracy, kappa, area under the concentration-time curve (AUC), 95% CI, size, and parameters are the evaluation indicators in this study. RESULTS: There are 188 anterior segment images that were used for testing the five intelligent-assisted pterygium diagnosis models. The overall evaluation index for the MobileNet2 model was the best. The sensitivity, specificity, F1-score, and AUC of the MobileNet2 model for the normal anterior segment image diagnosis were 96.72%, 98.43%, 96.72%, and 0976, respectively; for the pterygium observation period anterior segment image diagnosis, the sensitivity, specificity, F1-score, and AUC were 83.7%, 90.48%, 82.54%, and 0.872, respectively; for the surgery period anterior segment image diagnosis, the sensitivity, specificity, F1-score, and AUC were 84.62%, 93.50%, 85.94%, and 0.891, respectively. The kappa value of the MobileNet2 model was 77.64%, the accuracy was 85.11%, the model size was 13.5 M, and the parameter size was 4.2 M. CONCLUSION: This study used deep learning methods to propose a three-category intelligent lightweight-assisted pterygium diagnosis model. The developed model can be used to screen patients for pterygium problems initially, provide reasonable suggestions, and provide timely referrals. It can help primary doctors improve pterygium diagnoses, confer social benefits, and lay the foundation for future models to be embedded in mobile devices.

Five-Category Intelligent Auxiliary Diagnosis Model of Common Fundus Diseases Based on Fundus Images.

Purpose: The discrepancy of the number between ophthalmologists and patients in China is large. Retinal vein occlusion (RVO), high myopia, glaucoma, and diabetic retinopathy (DR) are common fundus diseases. Therefore, in this study, a five-category intelligent auxiliary diagnosis model for common fundus diseases is proposed; the model's area of focus is marked. Methods: A total of 2000 fundus images were collected; 3 different 5-category intelligent auxiliary diagnosis models for common fundus diseases were trained via different transfer learning and image preprocessing techniques. A total of 1134 fundus images were used for testing. The clinical diagnostic results were compared with the diagnostic results. The main evaluation indicators included sensitivity, specificity, F1-score, area under the concentration-time curve (AUC), 95% confidence interval (CI), kappa, and accuracy. The interpretation methods were used to obtain the model's area of focus in the fundus image. Results: The accuracy rates of the 3 intelligent auxiliary diagnosis models on the 1134 fundus images were all above 90%, the kappa values were all above 88%, the diagnosis consistency was good, and the AUC approached 0.90. For the 4 common fundus diseases, the best results of sensitivity, specificity, and F1-scores of the 3 models were 88.27%, 97.12%, and 84.02%; 89.94%, 99.52%, and 93.90%; 95.24%, 96.43%, and 85.11%; and 88.24%, 98.21%, and 89.55%, respectively. Conclusions: This study designed a five-category intelligent auxiliary diagnosis model for common fundus diseases. It can be used to obtain the diagnostic category of fundus images and the model's area of focus. Translational Relevance: This study will help the primary doctors to provide effective services to all ophthalmologic patients.

A novel lncRNA-mRNA-miRNA signature predicts recurrence and disease-free survival in cervical cancer

Cervical cancer (CC) patients have a poor prognosis due to the high recurrence rate. However, there are still no effective molecular signatures to predict the recurrence and survival rates for CC patients. Here, we aimed to identify a novel signature based on three types of RNAs [messenger RNA (mRNAs), microRNA (miRNAs), and long non-coding RNAs (lncRNAs)]. A total of 763 differentially expressed mRNAs (DEMs), 46 lncRNAs (DELs), and 22 miRNAs (DEMis) were identified between recurrent and non-recurrent CC patients using the datasets collected from the Gene Expression Omnibus (GSE44001; training) and The Cancer Genome Atlas (RNA- and miRNA-sequencing; testing) databases. A competing endogenous RNA network was constructed based on 23 DELs, 15 DEMis, and 426 DEMs, in which 15 DELs, 13 DEMis, and 390 DEMs were significantly associated with disease-free survival (DFS). A prognostic signature, containing two DELs (CD27-AS1, LINC00683), three DEMis (hsa-miR-146b, hsa-miR-1238, hsa-miR-4648), and seven DEMs (ARMC7, ATRX, FBLN5, GHR, MYLIP, OXCT1, RAB39A), was developed after LASSO analysis. The built risk score could effectively separate the recurrence rate and DFS of patients in the high- and low-risk groups. The accuracy of this risk score model for DFS prediction was better than that of the FIGO (International Federation of Gynecology and Obstetrics) staging (the area under receiver operating characteristic curve: training, 0.954 vs 0.501; testing, 0.882 vs 0.656; and C-index: training, 0.855 vs 0.539; testing, 0.711 vs 0.508). In conclusion, the high predictive accuracy of our signature for DFS indicated its potential clinical application value for CC patients.