Background: Healthcare-associated infections are infections that patients acquire during hospitalization or while receiving healthcare in other facilities. They represent the most frequent negative outcome in healthcare, can be entirely prevented, and pose a burden in terms of financial and human costs. With the development of new AI and ML algorithms, hospitals could develop new and automated surveillance and prevention models for HAIs, leading to improved patient safety. The aim of this review is to systematically retrieve, collect, and summarize all available information on the application and impact of AI in HAI surveillance and/or prevention. Methods: We conducted a systematic review of the literature using PubMed and Scopus to find articles related to the implementation of artificial intelligence in the surveillance and/or prevention of HAIs. Results: We identified a total of 218 articles, of which only 35 were included in the review. Most studies were conducted in the US (n = 10, 28.6%) and China (n = 5; 14.3%) and were published between 2021 and 2023 (26 articles, 74.3%) with an increasing trend over time. Most focused on the development of ML algorithms for the identification/prevention of surgical site infections (n = 18; 51%), followed by HAIs in general (n = 9; 26%), hospital-acquired urinary tract infections (n = 5; 9%), and healthcare-associated pneumonia (n = 3; 9%). Only one study focused on the proper use of personal protective equipment (PPE) and included healthcare workers as the study population. Overall, the trend indicates that several AI/ML models can effectively assist clinicians in everyday decisions, by identifying HAIs early or preventing them through personalized risk factors with good performance. However, only a few studies have reported an actual implementation of these models, which proved highly successful. In one case, manual workload was reduced by nearly 85%, while another study observed a decrease in the local hospital’s HAI incidence from 1.31% to 0.58%. Conclusions: AI has significant potential to improve the prevention, diagnosis, and management of healthcare-associated infections, offering benefits such as increased accuracy, reduced workloads, and cost savings. Although some AI applications have already been tested and validated, adoption in healthcare is hindered by barriers such as high implementation costs, technological limitations, and resistance from healthcare workers. Overcoming these challenges could allow AI to be more widely and cost-effectively integrated, ultimately improving patient care and infection management.

Opioids have been utilized for both medical and recreational purposes since their discovery. Primarily recognized for their analgesic properties, they are also associated with the development of tolerance and dependence, contributing to a significant public health concern worldwide. Sex differences in opioid use disorder reveal that while men historically exhibit higher rates of abuse, women may develop dependence more quickly and are more susceptible to the addictive nature of opioids. This narrative review explores sex differences in opioid response in both clinical and experimental models, focusing on opioid receptor mechanisms, pain modulation, and hormonal influences. Additionally, it discusses the complexities of opioid addiction and withdrawal, highlighting sex-specific responses and the role of opioid replacement therapies. Diverse experimental outcomes, together with observational data, underscore the need for further research into sex-specific opioid biological mechanisms in a wider context, including demographic, cultural, and health-related factors. A comprehensive understanding of these complexities holds the potential to enhance personalized opioid therapies.

Background: Cardiac echinococcosis is a rare but significant cause of mortality and morbidity, particularly in regions endemic to the Echinococcus parasite. Its high mortality rate, reported to be 15–20%, is mainly attributed to its associated severe complications. However, mostly the condition is asymptomatic until complicated or presented with sudden death. Main body. This article reports three different cases and studies fatal cardiac hydatid cysts in 38 articles which were eligible according to the inclusion criteria and involving 46 cases; their demographic distribution, the features of the cysts, other organ involvement, their presentation, and the cause of death. Males under 40 years were the mostly affected by this disease, predominantly in regions such as Turkey, Tunisia, and Iran. Although the cysts were found in all cardiac parts, the left ventricle was the most commonly affected. Isolated cardiac cysts were found in over half of the cases, while the remainder had additional cysts in other organs, mainly in the liver, kidneys, and lung. At the end, hydatid cysts of the heart were found to cause death by arrhythmias, heart failure, anaphylactic shock, pulmonary hydatid embolism, or cerebral hydatid embolism. Conclusion: This article highlights the importance of early detection of hydatid cysts of the heart, as many cases are diagnosed postmortem after sudden collapse and death. Early detection is crucial, and cardiac echinococcosis should be considered in the differential diagnosis of cysts that are found in any body part, especially in endemic areas. To our knowledge, this is the first review that focuses exclusively on autopsy-confirmed fatal cases of cardiac hydatid cysts, including a novel case series from Jordan. Furthermore, this review emphasizes the need for continued research to understand the mechanism of fatality, hoping that the current high mortality rate would be reduced significantly.

Background: Data sharing is now a mandatory prerequisite for several major funders and journals, where researchers are obligated to deposit the data resulting from their studies in an openly accessible repository. Biomedical open data are now widely available in almost all disciplines, where researchers can freely access and reuse these data in new studies. We aim to assess the impact of open data in terms of publications generated using open data and citations received by these publications, where we will analyze publications that used the Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) as an example. Methods: As of July 2019, there was a total of 194 datasets stored in BioLINCC repository and accessable through their portal. We requested the full list of publications that used these datasets from BioLINCC, and we also performed a supplementary PubMed search for other publications. We used Web of Science (WoS) to analyze the characteristics of publications and the citations they received. Results: 1,086 published articles used data from BioLINCC repository, but only 987 (90.88%) articles were WoS indexed. The number of publications has steadily increased since 2002 and peaked in 2018 with a total number of 138 publications on that year. The 987 open data publications received a total of 34,181 citations up to 1st October 2019. The average citation per item for the open data publications was 34.63. The total number of citations received by open data publications per year has increased from only 2 citations in 2002, peaking in 2018 with 2361 citations. Conclusion: The vast majority of studies that used BioLINCC open data were published in WoS indexed journals and are receiving an increasing number of citations.