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简介：AbstractMucormycosis is a lethal human disease caused by fungi of the order Mucorales. Mucormycosis is caused by fungi mainly belonging to the genera Mucor, Rhizopus, and Lichtheimia, all of which belong to the order Mucorales. The number of individuals with mucormycosis-causing disorders has increased in recent years, hence, leading to the spread of mucormycosis. Throughout the coronavirus disease 2019 (COVID-19) pandemic, numerous cases of mucormycosis in COVID-19-infected patients have been reported worldwide, and the illness is now recognized as COVID-19-associated mucormycosis, with most of the cases being reported from India. Immunocompromised patients such as those with bone marrow sickness and uncontrolled diabetes are at a greater risk of developing mucormycosis. Genes, pathways, and other mechanisms have been studied in Mucorales, demonstrating a direct link between virulence and prospective therapeutic and diagnostic targets. This review discusses several proteins such as high-affinity iron permease (FTR1), calcineurin, spore coat protein (CotH), and ADP-ribosylation factors involved in the pathogenesis of mucormycosis that might prove to be viable target(s) for the development of novel diagnostic and therapeutic methods.

简介：AbstractIn order to effectively implement the Tianjin Biosecurity Guidelines for Codes of Conduct for Scientists, biosecurity awareness-raising and education are essential because if these are not in place scientists will not understand the need for biosecurity codes of conduct. In an effort to assist in the implementation of the guidelines, a small-scale survey was carried out in early 2022 of biosecurity awareness-raising and education projects that have been developed over the last two decades to discover what resources and experience have been accumulated. It is argued that the survey demonstrates that much of what is needed to implement the guidelines effectively has been developed, but that there are specific deficiencies that need to be remedied quickly. In particular, an updated teaching resource covering the core issues related to the Biological and Toxin Weapons Convention (BTWC) and the problem of dual use in scientific research needs to be made widely available and translated into at least the six official United Nations (UN) languages. Additionally, more specialists from the Humanities with expertise in ethics need to become involved in biosecurity awareness-raising and education activities. While advantage should be taken now of the available national, regional and international networks of people involved in related activities, it is suggested that in the longer term cooperation in biosecurity awareness-raising and education will benefit from the development of an equivalent organisation to the International Nuclear Security Education Network (INSEN) organised through the International Atomic Energy Agency (IAEA).

简介：AbstractBackground:Zoonoses are public health threats that cause severe damage worldwide. Zoonoses constitute a key indicator of One Health (OH) and the OH approach is being applied for zoonosis control programmes of zoonotic diseases. In a very recent study, we developed an evaluation system for OH performance through the global OH index (GOHI). This study applied the GOHI to evaluate OH performance for zoonoses in sub-Saharan Africa.Methods:The framework for the OH index on zoonoses (OHIZ) was constructed including five indicators, 15 subindicators and 28 datasets. Publicly available data were referenced to generate the OHIZ database which included both qualitative and quantitative indicators for all sub-Sahara African countries (n = 48). The GOHI algorithm was used to estimate scores for OHIZ. Indicator weights were calculated by adopting the fuzzy analytical hierarchy process.Results:Overall, five indicators associated with weights were generated as follows: source of infection (23.70%), route of transmission (25.31%), targeted population (19.09%), capacity building (16.77%), and outcomes/case studies (15.13%). Following the indicators, a total of 37 sub-Sahara African countries aligned with OHIZ validation, while 11 territories were excluded for unfit or missing data. The OHIZ average score of sub-Saharan Africa was estimated at 53.67/100. The highest score was 71.99 from South Africa, while the lowest score was 40.51 from Benin. It is also worth mentioning that Sub-Sahara African countries had high performance in many subindicators associated with zoonoses, e.g., surveillance and response, vector and reservoir interventions, and natural protected areas, which suggests that this region had a certain capacity in control and prevention or responses to zoonotic events.Conclusions:This study reveals that it is possible to perform OH evaluation for zoonoses in sub-Saharan Africa by OHIZ. Findings from this study provide preliminary research information in advancing knowledge of the evidenced risks to strengthen strategies for effective control of zoonoses and to support the prevention of zoonotic events.

简介：AbstractBackground:Being able to predict with confidence the early onset of type 2 diabetes from a suite of signs and symptoms (features) displayed by potential sufferers is desirable to commence treatment promptly. Late or inconclusive diagnosis can result in more serious health consequences for sufferers and higher costs for health care services in the long run.Methods:A novel integrated methodology is proposed involving correlation, statistical analysis, machine learning, multi-K-fold cross-validation, and confusion matrices to provide a reliable classification of diabetes-positive and -negative individuals from a substantial suite of features. The method also identifies the relative influence of each feature on the diabetes diagnosis and highlights the most important ones. Ten statistical and machine learning methods are utilized to conduct the analysis.Results:A published data set involving 520 individuals (Sylthet Diabetes Hospital, Bangladesh) is modeled revealing that a support vector classifier generates the most accurate early-onset type 2 diabetes status predictions with just 11 misclassifications (2.1% error). Polydipsia and polyuria are among the most influential features, whereas obesity and age are assigned low weights by the prediction models.Conclusion:The proposed methodology can rapidly predict early-onset type 2 diabetes with high confidence while providing valuable insight into the key influential features involved in such predictions.