A cross-sectional study on knowledge, attitude and practice regarding hypertension among the population age 18 years old and above in the area of sungai Kerubong, Sarikei from 6th July to 29th August 2008

Hypertension is becoming an increasingly common health problem globally. In 2002, WHO estimated that there are a staggering 1 billion individuals with hypertension and 7.1 million deaths yearly contributed to hypertension. Currently, it is estimated that the number of hypertensive patients in Malaysia is 4.8 million, according to the Ministry of Health. It is even more disquieting that approximately two-thirds of individuals with hypertension in Malaysia were unaware that they were hypertensive

The level of knowledge, attitude and practice on complementary feeding among caregivers in Kampung Jeriah, Sibu, Sarawak from 23rd September 2013 to 8th December 2013

Background: The period for complementary feeding is crucial for young infants. Lack of awareness in knowledge, attitude and practice towards complementary feeding among caregivers will lead to improper practice of complementary feeding which may affect the health of children under their care. Objective: The purpose of this study is to determine the level of knowledge, attitude, and practice (KAP) on complementary feeding among caregivers in Kampung Jeriah, Sibu, Sarawak. Methods: This is a cross-sectional study done among 60 caregivers in Kampung Jeriah, Sibu, Sarawak. Non-probability sampling method was adopted to select at least one caregiver from each household in the village. A self-administered style of data collection was used. The data was analysed for descriptive data of mean, median, frequencies and standard deviation using SPSS version 20.0. Results: Among the respondents, the levels of KAP are 61.7%, 50% and 60% respectively. In this study, the respondents with lower income have significantly better knowledge regarding complementary feeding (p=0.01). The results also showed that respondents aged 25-44 years old had good practice towards complementary feeding as compared to younger or older group, which is reflected in the p value 0.032. Almost half of the respondents with good level of knowledge (48.6%) started to give complementary food to their child at age 6 months old. Conclusion: Generally, the respondents had a satisfactory level of knowledge and practices towards complementary feeding. However. the level of attitude on complementary feeding was relatively low compared to the level of knowledge and practice. Statistically, there was no significant correlation between the three components (knowledge, attitude and practice on complementary feeding). It was recommended that more health education should be held by the health authority on complementary feeding so that the knowledge, attitude and practice of caregivers on the topic could be improved. II

An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients

Background: Whilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings. Methods: Here, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries. Results: Our analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61-0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population. Conclusions: Although clinical studies with matching viral genomic information should remain a priority, our approach combining publicly available data on variant frequency and a multi-country clinical characterisation dataset with more than 100,000 records allowed analysis of data from a wide range of settings and novel insights on real-world heterogeneity of COVID-19 presentation and clinical outcome

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients