Asian Pacific Journal of Tropical Medicine

Impact Factor 2021: 3.041 (@Clarivate Analytics)

5-Year Impact Factor: 2.776 (@Clarivate Analytics)

Impact Factor Rank: 10/24, Q2 (Tropical Medicine)

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Objective: Foodborne parasitic diseases, although with a declining overall incidence rate, are still endangering local public safety. This review aims to describe the current situation and epidemiological trends of foodborne parasitic diseases in China in order to explore possible reasons contributors to its high prevalence in some areas, and propose strategies for prevention and control accordingly. Methods: A scoping review was conducted by searching PubMed, CNKI, Wanfang, CQVIP, Embase, and the Cochrane Library using search formula “foodborne parasitic diseases (or foodborne parasites)” AND “China”. Studies on foodborne parasitic diseases in China were considered, but only articles in English or Chinese published between January 1980 and June 2020 were retrieved. Included studies were screened according to the eligibility criteria: 1) diseases consistent with the WHO definition of foodborne parasitic diseases; 2) the food carriers were included in the WHO food classification; 3) data related to epidemiology, pathogenicity, and prevention and control; 4) Foodborne parasitic diseases cases or outbreaks in China. Results: A total of 111 out of 665 records were included and summarized. The prevalence of clonorchiasis, angiostrongyliasis, echinococcosis, trichinellosis and cysticercosis was still increasing although the infection rate of soil-transmitted nematodes has substantially decreased in recent years. Diverse eating habits, close contact with animals, and urbanization were contributing factors to the increase. Conclusions: Foodborne parasitic diseases remain an important public health issue in China with the progress of economic globalization and food diversification. We should manage to raise public awareness about the prevention and control of foodborne parasitic diseases, improve health and safety inspections, as well as public health practice.

In the current pandemic, COVID-19 patients with predisposing factors are at an increased risk of mucormycosis, an uncommon angioinvasive infection that is caused by fungi with Mucor genus which is mainly found in plants and soil. Mucormycosis development in COVID-19 patient is related to various factors, such as diabetes, immunocompromise and neutropenia. Excessive use of glucocorticoids for the treatment of critically ill COVID-19 patients also leads to opportunistic infections, such as pulmonary aspergillosis. COVID-19 patients with mucormycosis have a very high mortality rate. This review describes the pathogenesis and various treatment approaches for mucormycosis in COVID-19 patients, including medicinal plants, conventional therapies, adjunct and combination therapies.

Objective: To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes. Methods: In this retrospective cohort study, we surveyed patient statistics and performed feature analysis to identify the most influential data features associated with readmissions. Classification of all-cause, 30-day readmission outcomes were modeled using logistic regression, artificial neural network, and EasyEnsemble. F1 statistic, sensitivity, and positive predictive value were used to evaluate the model performance. Results: We identified 14 most influential data features (4 numeric features and 10 categorical features) and evaluated 3 machine learning models with numerous sampling methods (oversampling, undersampling, and hybrid techniques). The deep learning model offered no improvement over traditional models (logistic regression and EasyEnsemble) for predicting readmission, whereas the other two algorithms led to much smaller differences between the training and testing datasets. Conclusions: Machine learning approaches to record electronic health data offer a promising method for improving readmission prediction in patients with diabetes. But more work is needed to construct datasets with more clinical variables beyond the standard risk factors and to fine-tune and optimize machine learning models.

Objective: To determine the proportion of HBV surface antigen (anti-HBs) antibody positive children under five years of age born to HBsAg-negative mothers and to analyze the possible related factors following implementation of a hepatitis B vaccination program for infants in Indonesia 22 years ago. Methods: Blood samples were taken from children under five years of age born to HBsAg-negative mothers who have completed primary vaccination series. Anti-HBs antibodies were determined by using rapid test. Data of age, gender, nutritional status, vaccination timing or vaccination compliance, and booster vaccination were collected from vaccination card. Results: Ninety children were enrolled, consisting of 47 females and 43 males with a mean age of 2.3 years. Twenty two (24.4%) children received booster vaccine between 18 and 24 months and 55 (61.1%) were anti-HBs positive. Among factors of age, gender, nutritional status, compliance to vaccination and booster vaccine, only administration of booster vaccine was significantly associated with anti-HBs status (OR 5.45, 95% CI 1.45, 20.52). Children who received booster vaccine at age of 18-24 months were 5.45 times more likely to be anti-HBs positive than that of children who did not receive booster vaccine. Conclusions: Booster vaccine rate is low among children under 5 years of age but is associated with anti-HBs positivity. Booster vaccination may be required to improve anti-HBs seropositivity.

Online since 5^th January 2018