Bacterial Microbiota Profiling in Gastritis without Helicobacter pylori Infection or Non-Steroidal Anti-Inflammatory Drug Use

Recent 16S ribosomal RNA gene (rRNA) molecular profiling of the stomach mucosa revealed a surprising complexity of microbiota. Helicobacter pylori infection and non-steroidal anti-inflammatory drug (NSAID) use are two main contributors to gastritis and peptic ulcer. However, little is known about the association between other members of the stomach microbiota and gastric diseases. In this study, cloning and sequencing of the 16S rRNA was used to profile the stomach microbiota from normal and gastritis patients. One hundred and thirty three phylotypes from eight bacterial phyla were identified. The stomach microbiota was found to be closely adhered to the mucosa. Eleven Streptococcus phylotypes were successfully cultivated from the biopsies. One to two genera represented a majority of clones within any of the identified phyla. We further developed two real-time quantitative PCR assays to quantify the relative abundance of the Firmicutes phylum and the Streptococcus genus. Significantly higher abundance of the Firmicutes phylum and the Streptococcus genus within the Firmicutes phylum was observed in patients with antral gastritis, compared with normal controls. This study suggests that the genus taxon level can largely represent much higher taxa such as the phylum. The clinical relevance and the mechanism underlying the altered microbiota composition in gastritis require further functional studies

Association between investigator-measured body-mass index and colorectal adenoma: a systematic review and meta-analysis of 168,201 subjects

The objective of this meta-analysis is to evaluate the odds of colorectal adenoma (CRA) in colorectal cancer screening participants with different body mass index (BMI) levels, and examine if this association was different according to gender and ethnicity. The EMBASE and MEDLINE were searched to enroll high quality observational studies that examined the association between investigator-measured BMI and colonoscopy-diagnosed CRA. Data were independently extracted by two reviewers. A random-effects meta-analysis was conducted to estimate the summary odds ratio (SOR) for the association between BMI and CRA. The Cochran’s Q statistic and I2 analyses were used to assess the heterogeneity. A total of 17 studies (168,201 subjects) were included. When compared with subjects having BMI < 25, individuals with BMI 25–30 had significantly higher risk of CRA (SOR 1.44, 95% CI 1.30–1.61; I2 = 43.0%). Subjects with BMI ≥ 30 had similarly higher risk of CRA (SOR 1.42, 95% CI 1.24–1.63; I2 = 18.5%). The heterogeneity was mild to moderate among studies. The associations were significantly higher than estimates by previous meta-analyses. There was no publication bias detected (Egger’s regression test, p = 0.584). Subgroup analysis showed that the magnitude of association was significantly higher in female than male subjects (SOR 1.43, 95% CI 1.30–1.58 vs. SOR 1.16, 95% CI 1.07–1.24; different among different ethnic groups (SOR 1.72, 1.44 and 0.88 in White, Asians and Africans, respectively) being insignificant in Africans; and no difference exists among different study designs. In summary, the risk conferred by BMI for CRA was significantly higher than that reported previously. These findings bear implications in CRA risk estimation

Carboxyl-terminal truncated HBx regulates a distinct microRNA transcription program in Hepatocellular carcinoma development

Background: The biological pathways and functional properties by which misexpressed microRNAs (miRNAs) contribute to liver carcinogenesis have been intensively investigated. However, little is known about the upstream mechanisms that deregulate miRNA expressions in this process. In hepatocellular carcinoma (HCC), hepatitis B virus (HBV) X protein (HBx), a transcriptional trans-activator, is frequently expressed in truncated form without carboxyl-terminus but its role in miRNA expression and HCC development is unclear. Methods: Human non-tumorigenic hepatocytes were infected with lentivirus-expressing full-length and carboxyl-terminal truncated HBx (Ct-HBx) for cell growth assay and miRNA profiling. Chromatin immunoprecipitation microarray was performed to identify the miRNA promoters directly associated with HBx. Direct transcriptional control was verified by luciferase reporter assay. The differential miRNA expressions were further validated in a cohort of HBV-associated HCC tissues using real-time PCR. Results: Hepatocytes expressing Ct-HBx grew significantly faster than the full-length HBx counterparts. Ct-HBx decreased while full-length HBx increased the expression of a set of miRNAs with growth-suppressive functions. Interestingly, Ct-HBx bound to and inhibited the transcriptional activity of some of these miRNA promoters. Notably, some of the examined repressed-miRNAs (miR-26a, -29c, -146a and -190) were also significantly down-regulated in a subset of HCC tissues with carboxyl-terminal HBx truncation compared to their matching non-tumor tissues, highlighting the clinical relevance of our data. Conclusion: Our results suggest that Ct-HBx directly regulates miRNA transcription and in turn promotes hepatocellular proliferation, thus revealing a viral contribution of miRNA deregulation during hepatocarcinogenesis. © 2011 Yip et al.published_or_final_versio

The G1613A Mutation in the HBV Genome Affects HBeAg Expression and Viral Replication through Altered Core Promoter Activity

Infection of hepatitis B virus (HBV) causes acute and chronic hepatitis and is closely associated with the development of cirrhosis and hepatocellular carcinoma (HCC). Previously, we demonstrated that the G1613A mutation in the HBV negative regulatory element (NRE) is a hotspot mutation in HCC patients. In this study, we further investigated the functional consequences of this mutation in the context of the full length HBV genome and its replication. We showed that the G1613A mutation significantly suppresses the secretion of e antigen (HBeAg) and enhances the synthesis of viral DNA, which is in consistence to our clinical result that the G1613A mutation associates with high viral load in chronic HBV carriers. To further investigate the molecular mechanism of the mutation, we performed the electrophoretic mobility shift assay with the recombinant RFX1 protein, a trans-activator that was shown to interact with the NRE of HBV. Intriguingly, RFX1 binds to the G1613A mutant with higher affinity than the wild-type sequence, indicating that the mutation possesses the trans-activating effect to the core promoter via NRE. The trans-activating effect was further validated by the enhancement of the core promoter activity after overexpression of RFX1 in liver cell line. In summary, our results suggest the functional consequences of the hotspot G1613A mutation found in HBV. We also provide a possible molecular mechanism of this hotspot mutation to the increased viral load of HBV carriers, which increases the risk to HCC

What is unknown in using microbiota as a therapeutic?

Fecal microbiota transplantation (FMT) has been used extensively in the treatment of various gastrointestinal and extraintestinal conditions, despite that there are still a lot of missing gaps in our knowledge in the gut microbiota and its behavior. This article describes the unknowns in microbiota biology (undetected microbes, uncertain colonization, unclear mechanisms of action, uncertain indications, unsure long-term efficacy, or side effects). We discuss how these unknowns may affect the therapeutic uses of FMT, and the potentials and caveats of other related microbiota-based therapies. When used as an experimental therapy or last resort in difficult conditions, caution should be taken against inadvertent complications. Clear documentations of post-treatment events should be made mandatory, classified, and graded as in clinical trials. Further robust scientific experiments and properly designed clinical studies are needed

Mapping ethico-legal principles for the use of artificial intelligence in gastroenterology

The rapid development of artificial intelligence (AI) and digital health raise concerns about equitable access to innovative interventions, appropriate use of health data and privacy, inclusiveness, bias and discrimination, and even changes to the clinician-patient relationship. This article outlines a number of ethical and legal issues when examining the use of AI in gastroenterology. Substantive ethico-legal principles including respect for persons, privacy and confidentiality, integrity, conflict of interest, beneficence, nonmaleficence, and justice, are discussed. Much of what we articulated is relevant to the use of AI in other medical fields. Going forward, consorted efforts should be use to address more particular and concrete problems, but for now, a principle-based approach is best used in problem-solving

Data pre-processing for analyzing microbiome data – A mini review

The human microbiome is an emerging research frontier due to its profound impacts on health. High-throughput microbiome sequencing enables studying microbial communities but suffers from analytical challenges. In particular, the lack of dedicated preprocessing methods to improve data quality impedes effective minimization of biases prior to downstream analysis. This review aims to address this gap by providing a comprehensive overview of preprocessing techniques relevant to microbiome research. We outline a typical workflow for microbiome data analysis. Preprocessing methods discussed include quality filtering, batch effect correction, imputation of missing values, normalization, and data transformation. We highlight strengths and limitations of each technique to serve as a practical guide for researchers and identify areas needing further methodological development. Establishing robust, standardized preprocessing will be essential for drawing valid biological conclusions from microbiome studies

Randomized clinical trials of machine learning interventions in health care: a systematic review

Importance: Despite the potential of machine learning to improve multiple aspects of patient care, barriers to clinical adoption remain. Randomized clinical trials (RCTs) are often a prerequisite to large-scale clinical adoption of an intervention, and important questions remain regarding how machine learning interventions are being incorporated into clinical trials in health care. Objective: To systematically examine the design, reporting standards, risk of bias, and inclusivity of RCTs for medical machine learning interventions. Evidence Review: In this systematic review, the Cochrane Library, Google Scholar, Ovid Embase, Ovid MEDLINE, PubMed, Scopus, and Web of Science Core Collection online databases were searched and citation chasing was done to find relevant articles published from the inception of each database to October 15, 2021. Search terms for machine learning, clinical decision-making, and RCTs were used. Exclusion criteria included implementation of a non-RCT design, absence of original data, and evaluation of nonclinical interventions. Data were extracted from published articles. Trial characteristics, including primary intervention, demographics, adherence to the CONSORT-AI reporting guideline, and Cochrane risk of bias were analyzed. Findings: Literature search yielded 19737 articles, of which 41 RCTs involved a median of 294 participants (range, 17-2488 participants). A total of 16 RCTS (39%) were published in 2021, 21 (51%) were conducted at single sites, and 15 (37%) involved endoscopy. No trials adhered to all CONSORT-AI standards. Common reasons for nonadherence were not assessing poor-quality or unavailable input data (38 trials [93%]), not analyzing performance errors (38 [93%]), and not including a statement regarding code or algorithm availability (37 [90%]). Overall risk of bias was high in 7 trials (17%). Of 11 trials (27%) that reported race and ethnicity data, the median proportion of participants from underrepresented minority groups was 21% (range, 0%-51%). Conclusions and Relevance: This systematic review found that despite the large number of medical machine learning-based algorithms in development, few RCTs for these technologies have been conducted. Among published RCTs, there was high variability in adherence to reporting standards and risk of bias and a lack of participants from underrepresented minority groups. These findings merit attention and should be considered in future RCT design and reporting.Published versionThis study was supported by grants K23-DK125718 (Dr Shung) and K08-DE030216 (Dr Kann) from the National Institutes of Health, grant T32GM007753 from the National Institute of General Medical Sciences (Ms Plana), and grant F30-CA260780 from the National Cancer Institute (Ms Plana)

The way forward after COVID-19 vaccination : vaccine passports with blockchain to protect personal privacy

The COVID-19 pandemic has been circulating in the world for over a year since 2019, resulting in over 80 million cases with almost 1.8 million deaths in 2020. The first vaccine that hit the global market is BNT162b2, given by Pfizer/BioNTech, which was approved in December 2020. Stepping into 2021, more COVID-19 vaccines are becoming accessible in the global market. Until February 2021, four vaccines have been approved for full use, while six more have been authorised for early or limited use in different countries around the world