The hepatitis C virus (HCV) continues to pose a significant public health challenge in Iran, mirroring a worldwide concern. This situation calls for a cohesive strategy that aligns with the World Health Organization\'s (WHO) goals for HCV elimination by 2030. Central to this strategy is targeting high-risk groups, notably people who inject drugs and prisoners, with prevention, screening and treatment. The deployment of point-of-care testing and treatments in prisons and harm reduction facilities is vital. The adoption of cost-effective generic direct-acting antivirals represents a major step forward. Furthermore, innovative educational initiatives for healthcare providers and awareness campaigns for the public are critical. Additionally, tackling stigma, ensuring treatment affordability and upholding strict surveillance and data management, coupled with ongoing policy reviews, are vital components. This comprehensive and integrated approach is designed to drive Iran towards eliminating HCV and can serve as a blueprint for other countries with similar challenges.

BACKGROUND: In Nigeria, 97% of the population is at risk of contracting malaria. It is transmitted by female Anopheles mosquitoes carrying the Plasmodium parasite and can be lethal. An estimated 55 million illnesses and 80,000 deaths per year result from it. Children under five are more likely to contract malaria. Efforts to control malaria in Nigeria include indoor residual spraying, insecticide-treated bed nets, and quick detection and treatment of confirmed cases with effective antimalarial medications. These attempts have been impeded by limited healthcare access, poor financing, and drug-resistant parasites. Thus, the study of the relationship between malaria complications and housing for children under five is essential.
METHODS: The Demographic and Health Survey (DHS) Malaria Indicator Survey (MIS) 2021, a nationally representative data set from developing countries on population and health, was used for this study. A sample size of 13,727 was employed (n=13,727). Logistic regression analyses were conducted to test the association between the type of place of residence and malaria complications (outcome).
RESULTS: Overall, 4.2% (n=570, weight HV005) of participants in the sample reported malaria complications. The results of the logistic regression revealed that children residing in urban settlements (aOR 0.37, 95% CI 0.37-0.37, p-value <0.001), children from the poorest class families (aOR 11.63, 95% CI 1.62-1.63, p-value 0.004), children from poorer class families (aOR 7.56, 95% CI 7.55-7.57, p-value <0.001), children from middle-class families (aOR 4.05, 95% CI 4.03-9.06, p-value <0.001), children from richer class families (aOR 1.22, 95% CI 2.21-2.23, p-value <0.001), children of mothers with primary education (aOR 0.42, 95% CI 2.32-4.112, p-value 0.001), children of mothers with secondary education (aOR 0.24, 95% CI 3.21-3.22, p-value <0.001), children of mothers with higher education (aOR 0.08, 95% CI 0.72-0.80, p-value <0.001), and children of the female gender (aOR 0.65, 95% CI 0.65-0.66, p-value <0.001) are all associated with severe malaria complications.
CONCLUSIONS: In conclusion, the study examined malaria complications in Nigerian children under five by residency. The findings imply that rural children are more likely to have serious malaria complications than urban children. This emphasizes the necessity for targeted malaria therapies in rural areas with limited healthcare access.

Three Asian longhorned ticks (Haemaphysalis longicornis) were collected on Staten Island, Richmond County, New York, in 2014-2015 as part of a tick-borne disease surveillance program conducted by the New York City Department of Health and Mental Hygiene and the Defense Centers of Public Health - Aberdeen Tick-Borne Disease Laboratory. These records mark the earliest known occurrence of H. longicornis in New York State outside of quarantine areas, predating previously reported detections by several years. Robust populations of H. longicornis were collected in subsequent years at the Staten Island site where these few ticks were found, demonstrating that small infestations have the potential to proliferate quickly. Haemaphysalis longicornis is a 3-host ixodid tick native to eastern Asia but now established in the United States, as well as Australasia and several Pacific islands. Although H. longicornis has not yet been associated with human disease transmission in the United States, it warrants attention as a potential vector, as it is demonstrated to harbor various pathogens of medical and veterinary interest across its native and introduced range.

OBJECTIVE: Ovarian cancer (OC) is a prevalent and aggressive malignancy that poses a significant public health challenge. The lack of preventive strategies for OC increases morbidity, mortality, and other negative consequences. Screening OC through risk prediction could be leveraged as a powerful strategy for preventive purposes that have not received much attention. So, this study aimed to leverage machine learning approaches as predictive assistance solutions to screen high-risk groups of OC and achieve practical preventive purposes.
METHODS: As this study is data-driven and retrospective in nature, we leveraged 1516 suspicious OC women data from one concentrated database belonging to six clinical settings in Sari City from 2015 to 2019. Six machine learning (ML) algorithms, including XG-Boost, Random Forest (RF), J-48, support vector machine (SVM), K-nearest neighbor (KNN), and artificial neural network (ANN) were leveraged to construct prediction models for OC. To choose the best model for predicting OC, we compared various prediction models built using the area under the receiver characteristic operator curve (AU-ROC).
RESULTS: Current experimental results revealed that the XG-Boost with AU-ROC = 0.93 (0.95 CI = [0.91-0.95]) was recognized as the best-performing model for predicting OC.
CONCLUSIONS: ML approaches possess significant predictive efficiency and interoperability to achieve powerful preventive strategies leveraging OC screening high-risk groups.