The effect of salinity on some endocommensalic ciliates from shipworms

The ciliates, Nucleocorbula adherens, Boveria teredinidi, Trichodina balakrishnia, Thingmozoon fencheli and Nyctothereus marina, live inside the mantle cavity of the shipworms in the estuaries and backwaters of the south-west coast of India. Seasonal incidence and relative abundance of these ciliates showed that they were more abundant during the low saline than the high saline periods. Even though these ciliates can endure higher salinities through gradual acclimatization of their habitat it was found that they prefer low salinity for active growth and healthy existence

Zooplankton standing stock and community structure along Karnataka Coast, west coast of India

Distribution of zooplankton along two transects at Karwar and Ratnagiri, west coast of India, was studied. The standing stock of zooplankton was relatively high in the neritic zone with the highest value [358 ml/100 m super(3)] in the area off Ratnagiri due to the aggregation of fish larvae and hydromedusae. Maximum zooplankton production in these areas was noticed with the low temperature and low dissolved oxygen during postmonsoon season. At Karwar the highest biomass [188 ml/100 m super(3)] was observed from the nearshore station due to swarms of the cladoceran Penilia avirostris and the pteropod Cresis acicula when the salinity was low. The fluctuations in numerical abundance and percentage composition of all the major planktonic groups are discussed. The fishery of these areas is compared with the zooplankton standing stock

Nephroprotective effect of silymarin in hyperglycemia-induced oxidative stress in rats

Background: Diabetes mellitus is a group of metabolic disorders characterized by hyperglycemia. Hyperglycemia is the etiological factor for oxidative stress-induced microvascular and macrovascular complications. Many animal experimental models and clinical trials have proved the antioxidant defense mechanism of flavonoids in ameliorating the progression of chronic diabetic complications. Hence, the objective of this study was to evaluate the nephroprotective effects of silymarin in alloxan induced Type I diabetes.Methods: Male Wistar rats were divided into five groups of six each. Group I served as control. Group II, III, IV and V were diabetic rats. Group II diabetic rats received the vehicle. Groups III and IV were treated with 200 mg/kg and 400 mg/kg of silymarin, respectively. Group V was treated with glibenclamide (0.5 mg/kg). After 3 weeks, blood samples were collected from all the groups of animals to measure serum glucose, urea and creatinine. Lipid peroxidation study and histopathological study were conducted in the renal tissue to confirm the oxidative damage.Results: The serum glucose, urea and creatinine significantly increased in untreated diabetic rats. In addition, there was a significant rise in lipid peroxidation with a glomerular atrophy and necrotic tubular epithelium in the renal tissue. The rise in serum glucose, urea and creatinine was ameliorated by silymarin. The renal tissue showed increased antioxidant levels, decreased lipid peroxides and only mild changes in glomeruli and tubules.Conclusion: The results of this study indicate silymarin is an effective nutritional supplement to prevent complications of diabetes

Oral Health Status and Treatment Needs of Dairy Workers in Salem, Tamilnadu: A Cross Sectional study

AIM OF THE STUDY: The present study was conducted to assess the oral health status and treatment needs of dairy plant workers of Salem District Co-operative Milk Producers Union Limited, Salem city, Tamilnadu. OBJECTIVES: 1. To assess the oral health status of dairy plant workers in Salem city, Tamil Nadu using modified WHO Oral Health Assessment Form- 1997. 2. To assess the treatment needs of dairy plant workers in Salem city, Tamil Nadu using modified WHO Oral Health Assessment Form - 1997. 3. To gather baseline data regarding their demographic profile and oral hygiene practices. METHODOLOGY: A cross - sectional descriptive survey was conducted to assess the oral health status and treatment needs of 750 dairy plant workers in dairy plant, Salem, Tamilnadu. Convenient sampling technique was used to recruit the study subjects. Data was collected using World Health Organization (WHO) Oral Health Surveys – Basic Methods Proforma (1997). The collected data was subjected to statistical analysis using, Statistical Package for the Social Sciences (SPSS) software version 20. RESULTS: Majority of the dairy plant workers are males 513(68.4%) and 237 (31.6%) were females. About 29.7% workers had dental fluorosis. About 25.06% workers had periodontal diseases based on CPI score 4 - 6 mm or more of pocket depth and 10% had loss of attachment. The prevalence of dental trauma was found to be 5.6%. The prevalence of dental caries among the study population was 75.2% and with the mean Decayed/ Missing / Filled Teeth (DMFT) was 5.19± 4.478. Only 25 (3.3%) workers were using upper/lower partial dentures. CONCLUSION: The oral health status of dairy plant workers was poor with high prevalence of dental caries and periodontal disease. It was observed that there was a lack of awareness towards oral health which could be improved through health education and preventive measures by dental health professionals and primary health care workers for prompt and preventive measures

Machine Learning-based Early Detection and Prognosis of the Covid-19 Pandemic

The outbreak of Covid-19 has caused a global health crisis, presenting numerous challenges to the healthcare system with its severe respiratory symptoms and variable presentation. Early and accurate diagnosis of the virus is critical in controlling its spread and reducing the burden on healthcare facilities. To address this issue and relieve the strain on the healthcare system, this paper proposes a machine learning-based approach for Covid-19 diagnosis. Four algorithms were used for analyzing early Covid-19 detection, i.e., logistic regression, random forest, decision tree, and naive Bayes, using a data set of basic symptoms such as fever, shortness of breath, etc. for predicting positive and negative Covid-19 cases. Furthermore, development of a web portal that provides information on global vaccine distribution and the most widely used vaccines by country along with Covid-19 predictions. Our evaluation results demonstrate that the decision tree model outperformed the other models, achieving an accuracy of 97.69%. This study provides a practical solution to the ongoing Covid-19 crisis through an improved diagnosis method and access to vaccination information

Machine Learning-based Early Detection and Prognosis of the Covid-19 Pandemic

The outbreak of Covid-19 has caused a global health crisis, presenting numerous challenges to the healthcare system with its severe respiratory symptoms and variable presentation. Early and accurate diagnosis of the virus is critical in controlling its spread and reducing the burden on healthcare facilities. To address this issue and relieve the strain on the healthcare system, this paper proposes a machine learning-based approach for Covid-19 diagnosis. Four algorithms were used for analyzing early Covid-19 detection, i.e., logistic regression, random forest, decision tree, and naive Bayes, using a data set of basic symptoms such as fever, shortness of breath, etc. for predicting positive and negative Covid-19 cases. Furthermore, development of a web portal that provides information on global vaccine distribution and the most widely used vaccines by country along with Covid-19 predictions. Our evaluation results demonstrate that the decision tree model outperformed the other models, achieving an accuracy of 97.69%. This study provides a practical solution to the ongoing Covid-19 crisis through an improved diagnosis method and access to vaccination information