A Systematic Review of the Application of Machine Learning in CpG island (CGI) Detection and Methylation Prediction

Background: CpG island (CGI) detection and methylation prediction play important roles in studying the complex mechanisms of CGIs involved in genome regulation. In recent years, machine learning (ML) has been gradually applied to CGI detection and CGI methylation prediction algorithms in order to improve the accuracy of traditional methods. However, there are a few systematic reviews on the application of ML in CGI detection and CGI methylation prediction. Therefore, this systematic review aims to provide an overview of the application of ML in CGI detection and methylation prediction. Method: The review was carried out using the PRISMA guideline. The search strategy was applied to articles published on PubMed from 2000 to July 10, 2022. Two independent researchers screened the articles based on the retrieval strategies and identified a total of 54 articles. After that, we developed quality assessment questions to assess study quality and obtained 46 articles that met the eligibility criteria. Based on these articles, we first summarized the applications of ML methods in CGI detection and methylation prediction, and then identified the strengths and limitations of these studies. Result and Discussion: Finally, we have discussed the challenges and future research directions. Conclusion: This systematic review will contribute to the selection of algorithms and the future development of more efficient algorithms for CGI detection and methylation prediction

Socioeconomic inequality in health care use among cancer patients in China : evidence from the China Health and Retirement Longitudinal Study

Background: Cancer is a major public health problem worldwide and the leading cause of death in China, with increasing incidence and mortality rates. This study sought to assess socioeconomic-related inequalities in health care use among cancer patients in China and to analyze factors associated with this disparity. Methods: This study used data collected for the China Health and Retirement Longitudinal Study in 2018. Patients who reported having cancer were included. The annual per capita household expenditure was classified into five groups by the quintile method. We calculated the distribution of actual, need-predicted, and need-standardized health care use across different socioeconomic groups among patients with cancer. The concentration index (CI) was used to evaluate inequalities in health care use. Influencing factors of inequalities were measured with the decomposition method. Results: A total of 392 people diagnosed with cancer were included in this study. The proportion of cancer patients who utilized outpatient and inpatient services was 23.47% and 40.82%, respectively, and the CIs for actual outpatient and inpatient service use were 0.1419 and 0.1960. The standardized CIs (CI for outpatient visits = 0.1549; CI for inpatient services = 0.1802) were also both positive, indicating that affluent cancer patients used more health services. The annual per capita household expenditure was the greatest factor favoring the better-off, which contributed as much as 78.99% and 83.92% to the inequality in outpatient and inpatient services use, followed by high school education (26.49% for outpatient services) and living in a rural village (34.53% for inpatient services). Urban Employee Basic Medical Insurance exacerbated the inequality in inpatient services (21.97%) while having a negative impact on outpatient visits (−22.19%). Conclusions: There is a pro-rich inequality in outpatient and inpatient services use among cancer patients in China. A lower socioeconomic status is negatively associated with cancer care use. Hence, more targeted financial protection for poor people would relieve cancer patients of the burden caused by the high cost of cancer care

The circadian rhythms regulated by Cx43-signaling in the pathogenesis of Neuromyelitis Optica

IntroductionNeuromyelitis Optica (NMO) is an inflammatory demyelinating disease of the central nervous system (CNS). NMO manifests as selective and severe attacks on axons and myelin of the optic nerve and spinal cord, resulting in necrotic cavities. The circadian rhythms are well demonstrated to profoundly impact cellular function, behavior, and disease. This study is aimed to explore the role and molecular basis of circadian rhythms in NMO.MethodsWe used an Aquaporin 4(AQP4) IgG-induced NMO cell model in isolated astrocytes. The expression of Cx43 and Bmal1 were detected by real-time PCR and Western Blot. TAT-Gap19 and DQP-1105 were used to inhibit Cx43 and glutamate receptor respectively. The knockdown of Bmal1 were performed with the shRNA containing adenovirus. The levels of glutamate, anterior visual pathway (AVP), and vasoactive intestinal peptide (VIP) were quantified by ELISA kits.ResultsWe found that Bmal1 and Clock, two essential components of the circadian clock, were significantly decreased in NMO astrocytes, which were reversed by Cx43 activation (linoleic acid) or glutamate. Moreover, the expression levels of Bmal1 and Clock were also decreased by Cx43 blockade (TAT-Gap19) or glutamate receptor inhibition (DQP-1105). Furthermore, adenovirus-mediated Bmal1 knockdown by shRNA (Ad-sh-Bmal1) dramatically decreased the levels of glutamate, AVP, and VIP from neurons, and significantly down-regulated the protein level of Cx43 in NMO astrocytes with Cx43 activation (linoleic acid) or glutamate treatment. However, Bmal1 knockdown did not alter these levels in normal astrocytes with Cx43 blockade (TAT-Gap19) or glutamate receptor inhibition (DQP-1105).DiscussionCollectively, these results suggest that Cx43-glutamate signaling would be a critical upstream regulator that contributes to the NMO-induced rhythmic damage in SCN astrocytes

Recruitment of cyanobacteria from the sediments in the eutrophic Shanzi Reservoir

This study investigated the impact of four environmental factors on the recruitment of cyanobacteria from bottom sediments in the eutrophic Shanzi Reservoir. Temperature and light were identified as the key determinants for the recruitment of Microcystis and Oscillatoria. Cyanobacteria became dominant at higher temperature (20°C) and light intensity (2000 lx) and Microcystis and Oscillatoria were the major species. Detailed recruitment simulation undertaken with the respective gradients of temperature and light suggested that both Microcystis and Oscillatoria are temperature sensitive and that their critical temperature point was 10°C. However, distinct light impacts were observed only on Microcystis. The recruitment of Oscillatoria was light independent, whereas Microcystis had a positive relationship with light intensity. Physical disturbance promoted Microcystis recruitment and also affected the structure of the recruited cyanobacterial community at the water–sediment interface, based on quantitative polymerase chain reaction (qPCR) and phylogenetic analysis

A comprehensive review of artificial intelligence for pharmacology research

With the innovation and advancement of artificial intelligence, more and moreartificial intelligence techniques are employed in drug research, biomedicalfrontier research, and clinical medicine practice, especially, in the field ofpharmacology research. Thus, this review focuses on the applications ofartificial intelligence in drug discovery, compound pharmacokinetic prediction,and clinical pharmacology. We briefly introduced the basic knowledge anddevelopment of artificial intelligence, presented a comprehensive review, and then summarized the latest studies and discussed the strengths and limitations of artificial intelligence models. Additionally, we highlighted several important studies and pointed out possible research directions

A Factor Graph Based Indoor Localization Approach for Healthcare

In healthcare facilities, indoor localization technology has a broad range of applications. Traditional Pedestrian Dead Reckoning (PDR) and WiFi fingerprint-based methods each have their limitations. To address these challenges, this study introduces a multi-source fusion indoor localization system that uses a Factor Graph to integrate inertial positioning algorithms with WiFi fingerprint-based localization. The system processes accelerometer and gyroscope data using a data-driven PDR algorithm. For WiFi localization, considering that the extensive data collection required is a significant barrier to the deployment of WiFi-based localization methods, the proposed approach applies Gaussian process regression techniques to limited WiFi fingerprint data, significantly reducing initial deployment costs and enhancing accuracy. Finally, the entire system employs a Factor Graph for the integration of the data-driven PDR and WiFi fingerprint localization results. Experimental results show that, compared to using only inertial or WiFi data for localization, this method significantly improves localization accuracy. The findings suggest that this approach could prompt the utilization of indoor localization technology in healthcare facilities.<br/