A Multidisciplinary Diagnostic Approach Reveals a Higher Prevalence of Indolent Systemic Mastocytosis: 15-Years’ Experience of the GISM Network

Systemic mastocytosis (SM) and other adult clonal mast cell disorders (CMD) are often underestimated, and their epidemiology data are scarce. We aimed at evaluating the impact of the activity of the Interdisciplinary Group for Study of Mastocytosis (GISM) of Verona on the prevalence and incidence of CMD. We examined the data of 502 adult patients diagnosed with CMD and residing in the Veneto Region, consecutively referred to GISM between 2006 and 2020. SM was diagnosed in 431 cases, while 71 patients had cutaneous mastocytosis or other CMD. Indolent SM represented the most frequent SM variant (91.0%), mainly with the characteristics of bone marrow mastocytosis (54.8%). The prevalence of SM in the adult population of the Veneto region and of the Verona province was 10.2 and 17.2/100,000 inhabitants, respectively. The mean incidence of new SM cases in Verona was 1.09/100,000 inhabitants/year. Hymenoptera venom allergy was the main reason (50%) leading to the CMD diagnosis. Osteoporosis, often complicated by fragility fractures, was present in 35% of cases, even in young patients, especially males. Our data show a higher prevalence and incidence of SM than previously reported, confirming that reference centers with multidisciplinary approach are essential for the recognition and early diagnosis of CMD.

Is There an Increasing Incidence of Gastroesophageal Junctional Adenocarcinoma and Barrett Esophagus in Asia? A Review of Diagnostic Conundrums

<b><i>Background:</i></b> Epidemiology data of gastroesophageal junction (GEJ) cancers in Asia are extremely scarce. It is hardly registered by any cancer registry in the region, and only a few reports are available. Based on existing literature works, the overall trend indicates similar or gradually increasing GEJ cancers in Asia but comparably less than the West. The increasing trend in Asia is likely a result of rising risk factors, especially of gastroesophageal reflux disease and obesity. <b><i>Summary:</i></b> However, epidemiology data may be misleading due to several contentious diagnostic issues. The diagnostic conundrums are due to inherent complexity of the GEJ as a functional and pathological unit. Challenging diagnostic issues in Asia include the following: nonstandardized landmark of the GEJ, misclassification of Barrett esophagus, targeted versus nontargeted tissue sampling, histopathology disagreement and challenges in screening or surveillance of dysplastic BE and early GEJ cancer. The recent Asian-Pacific survey led by the Asian Barrett Consortium (ABC) has provided useful insights into these contentious issues. A key learning point from these diagnostic limitations is that the awareness of the disease and adherence to existing recommendations or guidelines are poor in the region. <b><i>Key Messages:</i></b> Standardization in diagnostic methodology is vital for accurate epidemiology data, and this can only come from better awareness and adherence through educational and international efforts. Last, surveillance strategy may need a paradigm shift from a purely diagnostic approach to a combined targeted surveillance and treatment approach using novel endoscopic techniques.

An accurate mathematical model predicting number of dengue cases in tropics

Dengue fever is a systemic viral infection of epidemic proportions in tropical countries. The incidence of dengue fever is ever increasing and has doubled over the last few decades. Estimated 50million new cases are detected each year and close to 10000 deaths occur each year. Epidemics are unpredictable and unprecedented. When epidemics occur, health services are over whelmed leading to overcrowding of hospitals. At present there is no evidence that dengue epidemics can be predicted. Since the breeding of the dengue mosquito is directly influenced by environmental factors, it is plausible that epidemics could be predicted using weather data. We hypothesized that there is a mathematical relationship between incidence of dengue fever and environmental factors and if such relationship exists, new cases of dengue fever in the succeeding months can be predicted using weather data of the current month. We developed a mathematical model using machine learning technique. We used Island wide dengue epidemiology data, weather data and population density in developing the model. We used incidence of dengue fever, average rain fall, humidity, wind speed, temperature and population density of each district in the model. We found that the model is able to predict the incidence of dengue fever of a given month in a given district with precision (RMSE between 18- 35.3). Further, using weather data of a given month, the number of cases of dengue in succeeding months too can be predicted with precision (RMSE 10.4—30). Health authorities can use existing weather data in predicting epidemics in the immediate future and therefore measures to prevent new cases can be taken and more importantly the authorities can prepare local authorities for outbreaks.

1508Comparison of two waves of the COVID-19 pandemic in Rural Bikaner: A record based analysis

Background In comparison to the slow progression of first wave of the COVID-19 pandemic, by March 2021, the second wave appeared to be much more aggressive with many more cases. We aimed to evaluate reported COVID-19 epidemiology data to better understand the pandemic's progression in Rural Bikaner. Methods A record-based descriptive analysis study between first and second covid-19 waves, on the variables of interest including positivity rates, case fatality rate, demographic profile of positive patients. Results the daily reported cases at the peak of the first wave in rural area in 2020 was 20-25 cases per day and in second wave number of cases was increased double to first wave. Death rate and severity was also increased in second wave. In second wave the mean age of positive patients was decreased. Conclusions In our analysis rural area had a more severe second wave of the COVID-19 pandemic than the first and highlights the importance of examining multiple epidemiological variables down to the regional and country levels over time. These country-specific and regional results informed the implementation of continent-wide initiatives and supported equitable distribution of supplies and technical assistance. Monitoring and analysis of these data over time are essential for continued situational awareness, especially as Member States attempt to balance controlling COVID-19 transmission with ensuring stable economies and livelihoods. Key messages Demographic profile of rural area plays a key role in spread of Covid-19 cases.

Repurposing of Linagliptin Similar FDA Approved Drugs as Antidiabetic Agents

Diabetes mellitus is considered a global epidemic disease and is one of the metabolic diseases affecting individuals irrespective of age, sex, and race. According to WHO epidemiology data, the DM prevalence globally has risen from 4.7% to 8.5 % from 1980 to 2014. The discovery of new drugs has become more challenging for the pharmaceutical companies even though major investment has made in the conventional drug discovery approach. To overcome this obstacle, drug repurposing is an emerging field of development where an existing drug is tested for treatment. Successful repurposing of zidovudine, minoxidil, sildenafil, celecoxib, aspirin, and topiramate are reported for respective diseases. The present study focused on the computational approach to fetch the favorable drugs from the pool of FDA approved drugs against diabetes. Initially, structure similarity studies were carried out by using the template structure of standard DPP-IV inhibitor, Linagliptin. About 26 drugs have shown similarity, and the other 14 drugs filtered by Pass Online binding energies are determined by molecular docking at the binding site of DPP-IV (PDB ID 2i78). Among these, pranlukast and mirabegron have shown good binding interactions with dock scores of -13.81 and -13.06.

The diagnostic problems if right-sided heart failure patient with cardiomyopathy cirrhosis

Heart failure may cause liver damage, known as cardiomyopathy cirrhosis. Conversely, liver disease could also lead to heart failure and cardiovascular abnormalities. Cirrhotic cardiomyopathy is a relatively rare disease. Diagnosis should be found out as early as possible to prevent complications. Unfortunately, epidemiology data of cirrhotic cardiomyopathy is still limited, and the prevalence is still not precisely known. Hence, we reported a case of a young man with the main problem of right-sided heart failure and suspected cardiac cirrhosis with unclear manifestation. This case has a good outcome so that we hope this report could give a great reference on treating cardiomyopathy cirrhosis.

The effect of the health poverty alleviation project on the burden of congenital heart disease for Kizilsu Kirghiz Autonomous Prefecture, China

Background: The implement of the health poverty alleviation project is a very important initiative for reducing poverty. Although previous studies have analyzed the effect of impoverishment, few papers compared the differences of the economy and the morbidity after the implement of the health poverty alleviation project in China. Methods: To analyze the effect of the health poverty alleviation project on the burden of congenital heart disease, we collected the hospitalization expenses and epidemiology data of congenital heart disease in Kizilsu Kirghiz Autonomous Prefecture from 2016 to 2018, in which the health poverty alleviation project was implemented. SPSS 23.0 and Disability Adjusted Life Years were applied to analyze the burden of the disease. Results: Our study found that the prevalence rate of congenital heart disease was increasing year by year. In total, 4719 poor people enjoyed the welfare of health poverty alleviation in the People's Hospital of Kezhou. The out-of-pocket ratio was only 2.25%. After the health poverty alleviation project, the loss of Disability Adjusted Life Years was smaller and smaller. 24 sessions have been carried out to operate 208 patients with congenital heart disease, all of which have been successful. Conclusions: This study found that the health poverty alleviation project not only bringed healthy life expectancy to poor families, but also greatly reduces their financial burden.

Statistical Modeling for the Prediction of Infectious Disease Dissemination With Special Reference to COVID-19 Spread

In this review, we have discussed the different statistical modeling and prediction techniques for various infectious diseases including the recent pandemic of COVID-19. The distribution fitting, time series modeling along with predictive monitoring approaches, and epidemiological modeling are illustrated. When the epidemiology data is sufficient to fit with the required sample size, the normal distribution in general or other theoretical distributions are fitted and the best-fitted distribution is chosen for the prediction of the spread of the disease. The infectious diseases develop over time and we have data on the single variable that is the number of infections that happened, therefore, time series models are fitted and the prediction is done based on the best-fitted model. Monitoring approaches may also be applied to time series models which could estimate the parameters more precisely. In epidemiological modeling, more biological parameters are incorporated in the models and the forecasting of the disease spread is carried out. We came up with, how to improve the existing modeling methods, the use of fuzzy variables, and detection of fraud in the available data. Ultimately, we have reviewed the results of recent statistical modeling efforts to predict the course of COVID-19 spread.