Induction of trained immunity using ß-glucan and its protective responses in Nile tilapia, Oreochromis niloticus.

Emerging and re-emerging diseases in fish cause drastic economic losses in the aquaculture sector. To combat the impact of disease outbreaks and prevent the emergence of infections in culture systems, understanding the advanced strategies for protecting fish against infections is inevitable in fish health research. Therefore, the present study aimed to evaluate the induction of trained immunity and its protective efficacy against Streptococcus agalactiae in tilapia. For this, Nile tilapia and the Tilapia head kidney macrophage primary culture were primed using ß-glucan @200 µg/10 g body weight and 10 µg/mL respectively. Expression profiles of the markers of trained immunity and production of metabolites were monitored at different time points, post-priming and training, which depicted enhanced responsiveness. Higher lactate and lactate dehydrogenase (LDH) production in vitro suggests heightened glycolysis induced by priming of the cells using ß-glucan. A survival rate of 60% was observed in ß-glucan trained fish post challenge with virulent S. agalactiae at an LD50 of 2.6 × 107 cfu/ml, providing valuable insights into promising strategies of trained immunity for combating infections in fish.

Study on restless leg syndrome and its relationship with quality of sleep among the general population of Mangalore, India.

Background: Restless leg syndrome (RLS) is a common neurological morbidity. It is, however, a frequently underdiagnosed medical condition. This study was hence done to assess the occurrence and severity of RLS among participants and to study its determinants and its association with quality of sleep. This was a cross-sectional study conducted among the general population of Mangalore in July 2021. Data were collected using a Google Form. The International Restless Legs Syndrome Study Group Rating Scale was used to diagnose RLS and its severity. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. Results: The prevalence of RLS among the 202 participants was 24(11.9%). Among them, 5 were already diagnosed with RLS. Their mean age at onset was 40.4 ± 25.3 years. Among the rest 197 participants, 19(9.6%) were newly diagnosed with RLS. The severity of RLS was mild, moderate and severe among 7(36.8%), 9(47.4%) and 3(15.8%) participants, respectively. Five (26.3%) of the 19 newly diagnosed participants were identified as RLS sufferers. In multivariable analysis, the presence of diabetes mellitus and family history of RLS were  associated with the presence of RLS among the participants. The mean Global PSQI value was 5.0 ± 3.1. Sleep latency was prolonged (p = 0.001), and sleep disturbances (p = 0.01) were higher among participants newly diagnosed with RLS (n = 19) compared to those without RLS (n = 178). Subjective sleep quality was poor (p = 0.038), and sleep disturbances (p = 0.016) were more among participants with severe degree RLS. Conclusions: The prevalence of RLS in the present study was higher than that reported in previous Indian studies. Unpleasant sensations in RLS affected sleep initiation and maintenance among the affected. A multi-disciplinary approach is required to control its determinants and address other sleep-related problems among the RLS affected population.

A citizen science initiative for open data and visualization of COVID-19 outbreak in Kerala, India.

OBJECTIVE: India reported its first coronavirus disease 2019 (COVID-19) case in the state of Kerala and an outbreak initiated subsequently. The Department of Health Services, Government of Kerala, initially released daily updates through daily textual bulletins for public awareness to control the spread of the disease. However, these unstructured data limit upstream applications, such as visualization, and analysis, thus demanding refinement to generate open and reusable datasets. MATERIALS AND METHODS: Through a citizen science initiative, we leveraged publicly available and crowd-verified data on COVID-19 outbreak in Kerala from the government bulletins and media outlets to generate reusable datasets. This was further visualized as a dashboard through a front-end Web application and a JSON (JavaScript Object Notation) repository, which serves as an application programming interface for the front end. RESULTS: From the sourced data, we provided real-time analysis, and daily updates of COVID-19 cases in Kerala, through a user-friendly bilingual dashboard (https://covid19kerala.info/) for nonspecialists. To ensure longevity and reusability, the dataset was deposited in an open-access public repository for future analysis. Finally, we provide outbreak trends and demographic characteristics of the individuals affected with COVID-19 in Kerala during the first 138 days of the outbreak. DISCUSSION: We anticipate that our dataset can form the basis for future studies, supplemented with clinical and epidemiological data from the individuals affected with COVID-19 in Kerala. CONCLUSIONS: We reported a citizen science initiative on the COVID-19 outbreak in Kerala to collect and deposit data in a structured format, which was utilized for visualizing the outbreak trend and describing demographic characteristics of affected individuals.