Comprehensive analysis of basement membrane and immune checkpoint related lncRNA and its prognostic value in hepatocellular carcinoma via machine learning.

Background: Hepatocellular carcinoma (HCC), which is characterized by its high malignancy, generally exhibits poor response to immunotherapy. As part of the tumor microenvironment, basement membranes (BMs) are involved in tumor development and immune activities. Presently, there is no integrated analysis linking the basement membrane with immune checkpoints, especially from the perspective of lncRNA. Methods: Based on transcriptome data from The Cancer Genome Atlas, BMs-related and immune checkpoint-related lncRNAs were identified. By applying univariable Cox regression and Machine learning (LASSO and SVM-RFE algorithm), a 10-lncRNA prognosis signature was constructed. The prognostic significance of this signature was assessed by survival analysis. GSEA, ssGSEA, and drug sensitivity analysis were conducted to investigate potential functional pathways, immune status, and clinical implications of guiding individual treatments in HCC. Finally, the promoting migration effect of LINC01224 was validated via in vitro experiments. Results: The multiple Cox regression, receiver operating characteristic curves, and stratified survival analysis of clinical subgroups exhibited the robust prognostic ability of the lncRNA signature. Results of the GSEA and drug sensitivity analysis revealed significant differences in potential functional pathways and response to drugs between the two risk groups. In addition, the risk level of HCC patients was distinctly correlated with immune cell infiltration status. More importantly, LINC01224 was independently associated with the OS of HCC patients (P < 0.05), suppressing the expression of LINC01224 inhibited the migration of HCC cells. Conclusion: This study developed a reliable signature for the prognosis of HCC based on BM and immune checkpoint related lncRNA, revealing that LINC01224 might be a prognostic biomarker for HCC associated with the progression of HCC.

Definition of a Novel Immunogenic Cell Death-Relevant Gene Signature Associated with Immune Landscape in Gastric Cancer.

Immunogenic cell death (ICD) induces anti-tumor immunity and aids in dismantling the immunosuppressive immune microenvironment (TME), which belongs to a type of regulated cell death. The differentiation of gastric cancer (GC) subtypes and the discovery of prognostic biomarkers are crucial for its treatment because GC is a disease that is both highly heterogeneous and aggressive. However, although the induction of ICD in tumor cells is associated with a favorable prognosis, the exact mechanism of its role in GC remains unclear. Transcriptome profiling data and clinical data of GC patients were retrieved from The Cancer Genome Atlas (TCGA) database. Herein, patients were classified with the consensus clustering algorithm, and the associated biological functions and immune microenvironment infiltration were explored based on the expression of ICD-associated genes. A risk score signature consisting of 11 ICD-related genes was established via the least absolute shrinkage and selection operator regression (LASSO) method. We have retrieved similar studies in recent years and compared them with our study using the time-dependent receiver operating characteristic (ROC) curves. Gene set variation analysis (GSVA) and single sample gene set enrichment analysis (ssGSEA) were performed to explore the association between the signature and tumor microenvironment (TME). Two distinct subtypes associated with ICD in GC were identified, each with a different prognosis. The ICD-high expression subtype was associated with higher immune cell infiltration and a better prognosis. The ICD-related gene signature containing 11 genes (CGB5, Z84468.1, APOA5, EPHA8, CLEC18C, TLR7, MUC7, MUC15, CTLA4, CALB2, and UGT2B28), could independently and accurately predict the prognosis of GC. In this study, an ICD-based classification was conducted to assist in the diagnosis and personalized therapy for GC. The ICD-related genes risk score model was established to predict prognosis.

EyeScreen: Development and Potential of a Novel Machine Learning Application to Detect Leukocoria.

Purpose: Early diagnosis and treatment of retinoblastoma are of paramount importance for a positive clinical outcome. The most common sign of retinoblastoma is leukocoria, or white pupil. Effective, easy-to-perform, community-based screening is needed to improve outcomes in lower-income regions. The EyeScreen (developed by Joshua Meyer from the University of Michigan) Android (Google LLC) smartphone application is an important step toward addressing this need. The purpose of this study was to examine the potential of the novel use of low-cost technologies-a cell phone application and machine learning-to identify leukocoria. Design: A cell phone application was developed and refined with the feedback from on-site, single-population use in Ethiopia. Application performance was evaluated in this technology validation study. Participants: One thousand four hundred fifty-seven participants were recruited from ophthalmology and pediatric clinics in Addis Ababa, Ethiopia. Methods: Photographs obtained with inexpensive Android smartphones running the EyeScreen Application were used to train an ImageNet (ResNet) machine learning model and to measure the performance of the app. Eighty percent of the images were used in training the model, and 20% were reserved for testing. Main Outcome Measures: Performance of the model was measured in terms of sensitivity, specificity, receiver operating characteristic (ROC) curve, and precision-recall curve. Results: Analyses of the participant images resulted in the following at the participant level: sensitivity, 87%; specificity, 73%; area under the ROC curve, 0.93; and area under the precision-recall curve, 0.77. Conclusions: EyeScreen has the potential to serve as an effective screening tool in the areas of the world most affected by delayed retinoblastoma diagnosis. The relatively high initial performance of the machine learning model with small training datasets in this early-phase study can serve as a proof of concept for future use of machine learning and artificial intelligence in ophthalmic applications.

Annexin A2 (ANXA2) regulates the transcription and alternative splicing of inflammatory genes in renal tubular epithelial cells.

BACKGROUND: Renal inflammation plays a crucial role during the progression of Chronic kidney disease (CKD), but there is limited research on hub genes involved in renal inflammation. Here, we aimed to explore the effects of Annexin A2 (ANXA2), a potential inflammatory regulator, on gene expression in human proximal tubular epithelial (HK2) cells. RNA-sequencing and bioinformatics analysis were performed on ANXA2-knockdown versus control HK2 cells to reveal the differentially expressed genes (DEGs) and regulated alternative splicing events (RASEs). Then the DEGs and RASEs were validated by qRT-PCR. RESULTS: A total of 220 upregulated and 171 downregulated genes related to ANXA2 knockdown were identified. Genes enriched in inflammatory response pathways, such as interferon-mediated signaling, cytokine-mediated signaling, and nuclear factor κB signaling, were under global transcriptional and alternative splicing regulation by ANXA2 knockdown. qRT-PCR confirmed ANXA2-regulated transcription of chemokine gene CCL5, as well as interferon-regulating genes ISG15, IFI6, IFI44, IFITM1, and IRF7, in addition to alternative splicing of inflammatory genes UBA52, RBCK1, and LITAF. CONCLUSIONS: The present study indicated that ANXA2 plays a role in inflammatory response in HK2 cells that may be mediated via the regulation of transcription and alternative splicing of inflammation-related genes.

Epidemiological big data and analytical tools applied in the control programmes on parasitic diseases in China: NIPD's sustained contributions in 70 years.

The analysis of epidemiological data has played an important role for the academic research carried out by the National Institute of Parasitic Diseases, China CDC, since its foundation in 1950s. Those researches, e.g., the temporal-spatial patterns of disease transmission and the identification of risk factors, have contributed significantly to the national parasitic disease control and elimination programmes in China. With the development and application of epidemiological data analysis in the last decade, all research results improve our understanding of parasitic diseases epidemiology and related health issues through the application platform of epidemiological big data and analytical tools. In particular, implementation research on analytical predictions on disease outbreak or epidemic risks have provided references to the scientific guidance on effective preventions and interventions in the parasitic disease elimination in China, such as fliariasis, malaria and schistosomiasis. This review has reflected the function of data accumulation and application of temporospatial tools in parasitic diseases control, and the ways of the NIPD's sustained contributions to the disease control programmes in China.

A field-based modeling study on ecological characterization of hourly host-seeking behavior and its associated climatic variables in Aedes albopictus.

BACKGROUND: The global spread of mosquito-borne diseases (MBD) has presented increasing challenges to public health. The transmission of MBD is mainly attributable to the biting behaviors of female mosquitoes. However, the ecological pattern of hourly host-seeking behavior in Aedes albopictus and its association with climatic variables are still not well understood, especially for a precise requirement for establishing an effective risk prediction system of MBD transmission. METHODS: Mosquito samples and data on mosquito hourly density and site-specific climatic variables, including temperature, relative humidity, illuminance and wind speed, were collected simultaneously in urban outdoor environments in Guangzhou during 2016-2018. Kernel regression models were used to assess the temporal patterns of hourly host-seeking behavior in mosquito populations, and negative binomial regression models in the Bayesian framework were used to investigate the associations of host-seeking behavior with climatic variables. RESULTS: Aedes albopictus was abundant, constituting 82% (5569/6790) of the total collected mosquitoes. Host-seeking behavior in Ae. albopictus varied across time and was significantly influenced by climatic variables. The predicted hourly mosquito densities showed non-linear relationships with temperature and illuminance, whereas density increased with relative humidity but generally decreased with wind speed. The range of temperature estimates for female biting was 16.4-37.1 °C, peaking at 26.5 °C (95% credible interval: 25.3-28.1). During the favorable periods, biting behavior of female Ae. albopictus was estimated to occur frequently all day long, presenting a bimodal distribution with peaks within 2-3 h around both dawn and dusk (05:00-08:00 h and 16:00-19:00 h). Moreover, a short-term association in hourly density between the females and males was found. CONCLUSIONS: Our field-based modeling study reveals that hourly host-seeking behavior of Ae. albopictus exhibits a complex pattern, with hourly variation constrained significantly by climatic variables. These findings lay a foundation for improving MBD risk assessments as well as practical strategies for vector control. For instances of all-day-long frequent female biting during the favorable periods in Guangzhou, effective integrated mosquito control measures must be taken throughout the day and night.

Flow-dependent regulation of vascular function and gene expression in rat superior mesenteric artery.

BACKGROUND: Mesenteric artery thrombosis is prone to occur at specific arterial regions with different fluid flow patterns, yet mechanistic links between blood flow and vascular function remain unclear. This study aimd to investigate the role of blood flow in regulation of vascular function and gene expression in rats. METHODS: Isometric tension was recorded in wire myograph to examine vascular function of specific regions (trunk parts and proximal parts from the origin) with different blood flow in superior mesenteric artery (SMA). Endothelial nitric oxide syntheses (eNOS), phosphorylated-eNOS (p-eNOS), serine-threonine kinase Akt and phosphorylated-Akt (p-Akt) protein expressions in SMA were examined by Western blotting. Significance was analyzed using a Student's t test or analysis of variance (ANOVA) followed by a Dunnett's multiple-comparison post hoc test. RESULTS: Compared with trunks, proximal parts exhibited severely impaired relaxant responses to acetylcholine (Ach) (1 nmol/L to 10 micromol/L) (P < 0.01). p-eNOS and p-Akt protein levels were significantly reduced in proximal parts of SMA (0.37 +/- 0.03, 0.42 +/- 0.03 respectively) versus trunk parts (0.82 +/- 0.03, 0.72 +/- 0.03 respectively, both P < 0.05) while total eNOS and Akt expressions remain comparable in both regions by Western blotting analysis (0.70 +/- 0.03 vs 0.82 +/- 0.03; 0.70 +/- 0.03 vs 0.77 +/- 0.03 respectively, both P > 0.05). CONCLUSION: Critical components that drive the vascular function and influence the localization of mesenteric artery thrombosis are flow-responsive elements within the vascular endothelium.