Acute viral hepatitis (AVH) represents an important global health problem; however, the progress in understanding AVH is limited because of the priority of combating persistent HBV and HCV infections. Therefore, an improved understanding of the burden of AVH is required to help design strategies for global intervention. Data on 4 major AVH types, including acute hepatitis A, B, C, and E, excluding D, were collected by the Global Burden of Disease (GBD) 2019 database. Age-standardized incidence rates and disability-adjusted life year (DALY) rates for AVH were extracted from GBD 2019 and stratified by sex, level of socio-demographic index (SDI), country, and territory. The association between the burden of AVH and socioeconomic development status, as represented by the SDI, was described. In 2019, there was an age-standardized incidence rate of 3,615.9 (95% CI 3,360.5-3,888.3) and an age-standardized DALY rate of 58.0 (47.3-70.0) per 100,000 person-years for the 4 major types of AVH. Among the major AVH types, acute hepatitis A caused the heaviest burden. There was a significant downward trend in age-standardized DALY rates caused by major incidences of AVH between 1990 and 2019. In 2019, regions or countries located in West and East Africa exhibited the highest age-standardized incidence rates of the 4 major AVH types. These rates were stratified by SDI: high SDI and high-middle SDI locations recorded the lowest incidence and DALY rates of AVH, whereas the low-middle SDI and low SDI locations showed the highest burden of AVH. The socioeconomic development status and burden of AVH are associated. Therefore, the GBD 2019 data should be used by policymakers to guide cost-effective interventions for AVH. We identified a negative association between socioeconomic development status and the burden of acute viral hepatitis. The lowest burden of acute viral hepatitis was noted for rich countries, whereas the highest burden of acute viral hepatitis was noted for poor countries.

Purpose: COVID-19 has become global threaten. CT acts as an important method of diagnosis. However, human–based interpretation of CT imaging is time consuming. More than that, substantial inter-observer-variation cannot be ignored. We aim at developing a diagnostic tool for artificial intelligence (AI)–based classification of CT images for recognizing COVID-19 and other common infectious diseases of the lung. Experimental Design: In this study, images were retrospectively collected and prospectively analyzed using machine learning. CT scan images of the lung that show or do not show COVID-19 were used to train and validate a classification framework based on convolutional neural network. Five conditions including COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, pulmonary tuberculosis, and normal lung were evaluated. Training and validation set of images were collected from Wuhan Jin Yin-Tan Hospital whereas test set of images were collected from Zhongshan Hospital Xiamen University and the fifth Hospital of Wuhan. Results: Accuracy, sensitivity, and specificity of the AI framework were reported. For test dataset, accuracies for recognizing normal lung, COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, and pulmonary tuberculosis were 99.4%, 98.8%, 98.5%, 98.3%, and 98.6%, respectively. For the test dataset, accuracy, sensitivity, specificity, PPV, and NPV of recognizing COVID-19 were 98.8%, 98.2%, 98.9%, 94.5%, and 99.7%, respectively. Conclusions: The performance of the proposed AI framework has excellent performance of recognizing COVID-19 and other common infectious diseases of the lung, which also has balanced sensitivity and specificity.

Long non-coding RNAs (lncRNAs) play important roles in gastric cancer (GC), but the mechanism is not fully clear. ERICH3-AS1 (ERICH3 antisense RNA1) is affiliated with the non-coding RNA class which has proven to be involved in the prognostic of GC, but the function of ERICH3-AS1 is still unclear. In this study, we aim to explore the potential function of ERICH3-AS1 in the development of GC and analyze the prognostic role of ERICH3-AS1 in GC. We found that the lncRNA ERICH3-AS1 was significantly up-regulated in GC tissues in the analysis of The Cancer Genome Atlas (TCGA) data; the Kaplan-Meier analysis showed that the higher the expression of ERICH3-AS1 was, the earlier the recurrence and the poorer the prognosis would be in patients. Cox univariate and multivariate analyses revealed that ERICH3-AS1 was a risk factor of disease-free survival (DFS) (p < 0.05) and overall survival (OS) (p < 0.05) of patients. Through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, it demonstrated that the ERBB pathways, the mitogen-activated protein kinase (MAPK) pathways, the MTOR pathways, p53 pathways and Wnt pathways were differentially enriched in ERICH3-AS1 high expression phenotype. Furthermore, the correlation analysis showed that ERICH3-AS1 had significant correlations with apoptosis-related proteins such as BCL2L10 and CASP14; cell cycle-associated proteins CDK14 and invasion and migration-associated proteins such as MMP20, MMP26 and MMP27. In summary, we identified that increased ERICH3-AS1 might be a potential biomarker for diagnosis and independent prognostic factor of GC. Moreover, ERICH3-AS1 might participate in the oncogenesis and development of tumors via cell cycle and apoptosis pathway mediated by ERBB, MAPK, MTOR, p53 and Wnt pathways.

Although abnormal liver chemistries are linked to a higher risk of coronavirus disease 2019 (COVID-19)–related death, liver manifestations may be diverse and even confusing. Thus, we performed a meta-analysis of published liver manifestations and described the liver damage in patients with COVID-19 who died or discharged alive. We searched PubMed, Google Scholar, medRxiv, bioRxiv, the Cochrane Library, Embase, and three Chinese electronic databases through April 22, 2020. We analyzed pooled data on liver chemistries stratified by the main clinical outcome of COVID-19, using a fixed or random-effects model. In our meta-analysis of 19 studies, which included a total of 4,103 patients, the pooled mean alanine aminotransferase and aspartate aminotransferase levels were, respectively, 31.7 IU/L and 51.0 IU/L in the patients with COVID-19 who died and 27.7 IU/L and 32.9 IU/L in those discharged alive (both P < 0.0001). Compared with the patients discharged alive, those who died tended to have lower albumin levels but longer prothrombin time and higher international normalized ratio. Conclusion: In this meta-analysis, according to the main clinical outcome of COVID-19, we comprehensively describe three patterns of liver impairment related to COVID-19: hepatocellular injury, cholestasis, and hepatocellular disfunction. The patients who died from COVID-19 tended to have different liver chemistries from those discharged alive. Special caution should be given to the patients with a relatively higher index of liver chemistries.

Background: This study compared clinical features of the Delta variant of coronavirus disease 2019 (COVID-19) in children and adults. Methods: Clinical data included 80 children and 132 adults with the Delta variant of COVID-19, hospitalized in the Affiliated Hospital of Putian College between September and October 2021. The data was analyzed retrospectively. Results: The proportion of mild patients in the children group (50%) was higher than that in the adults group (17.9%). Cough (25%, 20/80) and diarrhea (1.3%, 1/80) symptoms in children group were significantly less frequent. Compared with adults, there was no significant difference in the viral load of SARS-CoV-2 in samples collected by nasopharyngeal swabs. In children, lymphocyte count was higher [1.98 (0.25–4.25) vs 1.20 (0.29–4.27) ×10⁹/L], whereas the interleukin-6 level was lower [5.87 (1.50–61.40) vs 15.15 (1.79–166.30) pg/mL] than that in adults group. Additionally, the incidence of liver injury in children group was lower than that in adults group. There was no significant difference in the incidence of proteinuria (22/75 vs 45/112) between the two groups, but the serum creatinine level in children was lower [42.0 (28.0–73.0) vs 57.0 (32.0–94.0) µmol/L]. Conclusion: Compared with adults, children with the Delta variant of COVID-19 have differences in symptoms, clinical classification, inflammatory indices, and liver/kidney function injury. Children’s illness is relatively mild. Clinicians should pay attention to their differences and use drugs accurately.

Purpose: Human-based medical-image interpretation always falls into the predicament between specialized practitioners and expanding medical imaging. We aim at developing a diagnostic tool for pathological-image classification by using transfer learning that can be applied to diverse tumor types. Experimental Design: In this study, images were retrospectively collected and prospectively analyzed using machine learning. Microscopic images of liver tissue that show or do not show hepatocellular carcinoma were used to train and validate a classification framework based on convolutional neural network. To evaluate the universal classification performance of the artificial-intelligence (AI) framework, histological images from colorectal tissue and breast were also collected. Training and validation set of images were collected from Xiamen Hospital of Traditional Chinese Medicine whereas test set of images were collected from Zhongshan Hospital Xiamen University. Results: Accuracy, sensitivity, and specificity were reported and compared to human image interpretation and other AI image classification systems such as AlexNet and GoogLeNet. For the test dataset, sensitivity, specificity, and area under the curve of the AI framework were 99.1%, 98.0%, and 0.960, respectively. In human-machine comparisons, the accuracy of the AI framework was 98.5%, while the accuracy of human experts fluctuated between 93.0% and 95.0%. Based on transfer learning, the AI framework accuracy for colorectal carcinoma, breast invasive ductal carcinoma, were 96.8%, and 96.0%, respectively. Conclusions: The performance of the proposed AI framework in classifying histological images with hepatocellular carcinoma is comparable to the classification by human experts. With limited training, the proposed AI framework has potential universality in histological image classification. Study Highlights: WHAT IS KNOWN: ✓ Accurate recognition of medical images is the basis for clinical decision-making. ✓ Unresolved challenge exists between specialized practitioners and expanding medical imaging output. WHAT IS NEW HERE: ✓ Proposed AI framework has excellent performance in classifying hepatocellular carcinoma. ✓ The AI framework has universal feature in classifying other types of histological images. ✓ The AI framework may help to interpret emerging imaging technology.