The Asia-Pacific region has disparate hepatitis C virus (HCV) epidemiology, with prevalence ranging from 0·1% to 4·7%, and a unique genotype distribution. Genotype 1b dominates in east Asia, whereas in south Asia and southeast Asia genotype 3 dominates, and in Indochina (Vietnam, Cambodia, and Laos), genotype 6 is most common. Often, availability of all-oral direct-acting antivirals (DAAs) is delayed because of differing regulatory requirements. Ideally, for genotype 1 infections, sofosbuvir plus ledipasvir, sofosbuvir plus daclatasvir, or ombitasvir, paritaprevir, and ritonavir plus dasabuvir are suitable. Asunaprevir plus daclatasvir is appropriate for compensated genotype 1b HCV if baseline NS5A mutations are absent. For genotype 3 infections, sofosbuvir plus daclatasvir for 24 weeks or sofosbuvir, daclatasvir, and ribavirin for 12 weeks are the optimal oral therapies, particularly for patients with cirrhosis and those who are treatment experienced, whereas sofosbuvir, pegylated interferon, and ribavirin for 12 weeks is an alternative regimen. For genotype 6, sofosbuvir plus pegylated interferon and ribavirin, sofosbuvir plus ledipasvir, or sofosbuvir plus ribavirin for 12 weeks are all suitable. Pegylated interferon plus ribavirin has been replaced by sofosbuvir plus pegylated interferon and ribavirin, and all-oral therapies where available, but cost and affordability remain a major issue because of the absence of universal health coverage. Few patients have been treated because of multiple barriers to accessing care. HCV in the Asia-Pacific region is challenging because of the disparate epidemiology, poor access to all-oral therapy because of availability, cost, or regulatory licensing. Until these problems are addressed, the burden of disease is likely to remain high.
Background and study aims Artificial intelligence (AI) is set to impact several fields within gastroenterology. In gastrointestinal endoscopy, AI-based tools have translated into clinical practice faster than expected. We aimed to evaluate the status of research for AI in gastroenterology while predicting its future applications. Methods All studies registered on Clinicaltrials.gov up to November 2021 were analyzed. The studies included used AI in gastrointestinal endoscopy, inflammatory bowel disease (IBD), hepatology, and pancreatobiliary diseases. Data regarding the study field, methodology, endpoints, and publication status were retrieved, pooled, and analyzed to observe underlying temporal and geographical trends. Results Of the 103 study entries retrieved according to our inclusion/exclusion criteria, 76 (74 %) were based on AI application to gastrointestinal endoscopy, mainly for detection and characterization of colorectal neoplasia (52/103, 50 %). Image analysis was also more frequently reported than data analysis for pancreaticobiliary (six of 10 [60 %]), liver diseases (eight of nine [89 %]), and IBD (six of eight [75 %]). Overall, 48 of 103 study entries (47 %) were interventional and 55 (53 %) observational. In 2018, one of eight studies (12.5 %) were interventional, while in 2021, 21 of 34 (61.8 %) were interventional, with an inverse ratio between observational and interventional studies during the study period. The majority of the studies were planned as single-center (74 of 103 [72 %]) and more were in Asia (45 of 103 [44 %]) and Europe (44 of 103 [43 %]). Conclusions AI implementation in gastroenterology is dominated by computer-aided detection and characterization of colorectal neoplasia. The timeframe for translational research is characterized by a swift conversion of observational into interventional studies.
The COVID-19 pandemic, caused by the coronavirus, SARS-CoV-2, has claimed millions of lives worldwide in the past two years. Fatalities among the elderly with underlying cardiovascular disease, lung disease, and diabetes have particularly been high. A bibliometrics analysis on author's keywords was carried out, and searched for possible links between various coronavirus studies over the past 50 years, and integrated them. We found keywords like immune system, immunity, nutrition, malnutrition, micronutrients, exercise, inflammation, and hyperinflammation were highly related to each other. Based on these findings, we hypothesized that the human immune system is a multilevel super complex system, which employs multiple strategies to contain microorganism infections and restore homeostasis. It was also found that the behavior of the immune system is not able to be described by a single immunological theory. However, one main strategy is "self-destroy and rebuild", which consists of a series of inflammatory responses: 1) active self-destruction of damaged/dysfunctional somatic cells; 2) removal of debris and cells; 3) rebuilding tissues. Thus, invading microorganisms' clearance could be only a passive bystander response to this destroy-rebuild process. Microbial infections could be self-limiting and promoted as an indispensable essential nutrition for the vast number of genes existing in the microorganisms. The transient nutrition surge resulting from the degradation of the self-destroyed cell debris coupled with the existing nutrition state in the patient may play an important role in the pathogenesis of COVID-19. Finally, a few possible coping strategies to mitigate COVID-19, including vaccination, are discussed.