Background/Objectives: Artificial intelligence has made significant strides in healthcare, contributing to diagnosing, treating, monitoring, preventing, and testing various diseases. Despite its broad adoption, clinical consensus on AI’s role in infection control remains uncertain. This scoping review aims to understand the characteristics of AI applications in bacterial infection control. Results: This review examines the characteristics of AI applications in bacterial infection control, analyzing 54 eligible studies across 5 thematic scopes. The search from 3 databases yielded a total of 1165 articles, only 54 articles met the eligibility criteria and were extracted and analyzed. Five thematic scopes were synthesized from the extracted data; countries, aim, type of AI, advantages, and limitations of AI applications in bacterial infection prevention and control. The majority of articles were reported from high-income countries, mainly by the USA. The most common aims are pathogen identification and infection risk assessment. The most common AI used in infection control is machine learning. The commonest reported advantage is predictive modeling and risk assessment, and the commonest disadvantage is generalizability of the models. Methods: This scoping review was developed according to Arksey and O’Malley frameworks. A comprehensive search across PubMed, Embase, and Web of Science was conducted using broad search terms, with no restrictions. Publications focusing on AI in infection control and prevention were included. Citations were managed via EndNote, with initial title and abstract screening by two authors. Data underwent comprehensive narrative mapping and categorization, followed by the construction of thematic scopes. Conclusions: Artificial intelligence applications in infection control need to be strengthened for low-income countries. More efforts should be dedicated to investing in models that have proven their effectiveness in infection control, to maximize their utilization and tackle challenges.

Antimicrobial resistance (AMR) is the current silent pandemic responsible for approximately five million deaths annually. According to the WHO, antimicrobial resistance is one of the top global public health threats and the third leading cause of death worldwide. This systematic review and meta-analyses aims to provide Qatar's first comprehensive epidemiological assessment of AMR. We conducted extensive search of three databases (PubMed, Embase and Web of Science) using broad search terms with no restrictions. The meta-analysis of prevalence was done using the Freeman-Turkey transformation and random effects models. Subgroup analysis was performed for three categories; the composite isolates, otherwise responsive isolates, and otherwise resistant isolates. The search yielded a total of 1258 publications, of which 55 publications were included. The overall prevalence of all isolates was 13.64 % (95 %CI: 6.80 - 22.11, I²=99 %), all the studies were cross-sectional of convenience sampling, conducted in healthcare settings. The subgroup prevalence for the composite isolates was 8.87 % (95 %CI: 2.72 - 17.77, I²=98.7), otherwise responsive isolates was 11.37 % (95 %CI: 4.31 - 20.72, I²=98.3 %), otherwise resistant isolates was 23.55 % (95 %CI: 10.12 - 40.14, I²=99.1). The otherwise resistant isolates stratified analyses revealed that ESBL prevalence was 38.94 % (95 %CI: 21.63 - 57.79, I²=99.2 %), MDR was 15.99 % (95 %CI: 2.46 - 37.00, I²=99 %), MRSA was 52.37 % (95 %CI: 13.91 - 89.50, I2=88 %), Nosocomial infections prevalence was 23.55 % (95 %CI: 10.12 - 40.14, I²=98.2 %). The ESKAPE bacterial strains accounted for the majority of resistance. Qatar's AMR overall prevalence is close to the global estimates, however the resistant isolates prevalence is higher than average according to the global estimates for high-income countries. The AMR public health response including national action plan to combat AMR and antimicrobial stewardship programs need to be orchestrated. AMR epidemiological research needs improvement in expanding coverage across diverse population groups to ensure greater clarity and precision in identifying bacterial infections and antibiotic classifications.

Screening for the metabolomic signature of coronary heart disease (CHD) before disease onset could help in early diagnosis and potentially disease prevention. In this study, the levels of 17 CHD metabolic biomarkers in apparently healthy overweight females were compared to lean counterparts, and their associations with conventional clinical risk factors were determined. Clinical and metabolic data from 200 apparently healthy non-obese Qatari females were collected from Qatar Biobank (discovery cohort). Logistic regression was used to assess the association between body mass index (BMI) groups and 17 CHD metabolic biomarkers, and receiver operating characteristic (ROC) analysis was used to evaluate the prognostic value of CHD metabolic biomarkers in overweight. Stepwise linear regression was performed to identify the classical risk factors associated with CHD metabolites differentiating the two BMI groups. Validation of the association of CHD metabolic biomarkers with BMI groups was performed in 107 subjects (replication cohort). Out of the tested CHD metabolic biomarkers, five were significantly different between lean and overweight females in the discovery cohort (AUC = 0.73). Among these, the association of mannose, asparagine, and linoleate with BMI groups was confirmed in the replication cohort (AUC = 0.97). Significant correlations between predictors of CHD in overweight healthy women and classical risk factors were observed, including serum levels of cholesterol, testosterone, triiodothyronine, thyroxine, creatinine, albumin, bilirubin, glucose, c-peptide, uric acid, calcium and chloride. Apparently, healthy overweight females exhibit significantly different levels of specific CHD metabolites compared to their lean counterparts, offering a prognostic potential with preventative value.

Gut microbiota has emerged as a critical mediator of immune homeostasis and cancer biology. Increasing evidence suggests that gut dysbiosis may play a significant role in the pathogenesis of diffuse large B-cell lymphoma (DLBCL), the most common and aggressive subtype of non-Hodgkin lymphoma (NHL). However, a comprehensive synthesis of the microbial alterations associated with DLBCL remains poorly defined. We systematically reviewed and meta-analyzed thirteen studies (n = 4087 cases) to assess gut microbiota alterations in DLBCL. Both observational and Mendelian randomization designs were included. Pooled odds ratios (OR) were calculated for mendelian randomization studies using random-effects models, and microbial shifts were analyzed at the species level to contextualize biological relevance. Observational studies were used for qualitative assessment. Meta analysis was done for the mendelian randomization studies (n = 3737 cases). The overall pooled OR was 0.96 (95 % CI: 0.93-1.00), suggested a slight shift towards depletion, with considerable heterogeneity (I² = 78.7 %). Species-level analysis revealed significant enrichment of pro-inflammatory or potentially oncogenic taxa in DLBCL, including Bilophila (OR = 1.78), Desulfovibrionaceae (OR = 1.58), and Coprobacter (OR = 1.37). Conversely, beneficial commensals with anti-inflammatory and metabolic regulatory roles such as Eubacterium coprostanoligenes group (OR = 0.19), Alistipes (OR = 0.57), Ruminococcaceae UCG011 (OR = 0.75) were significantly depleted. This first comprehensive synthesis demonstrates a reproducible species-level microbial signature in DLBCL, characterized by depletion of protective commensals and enrichment of pro-tumorigenic taxa. Mechanistically, these shifts may promote lymphomagenesis via inflammatory, metabolic, and immune-modulatory pathways. Our findings highlight the gut microbiota as a latent biomarker source and therapeutic target, supporting microbiota-modulating strategies in precision lymphoma care.

Background: The decrease in the number of adolescents showing genuine interest in the fields of healthcare has been one of the recent concerns worldwide. A plethora of studies have discussed the factors that influence career choices of high school students, including science educational pedagogies, gender, environment, the student’s cognitive capabilities, and social perceptions of occupations being gender-based. As reported in 2012, a majority of the Qatari high-school students have shown a greater interest in business, technological, and administrative careers and a lower interest in healthcare. Comprehensive national and institutional strategies have since been utilized to direct the interest of Qatari generation toward healthcare careers.Objective: The primary objective of this case-control study is to assess the effect of schooling type on the enrollment in the Empower Generations (EG) career training in healthcare at the Qatar University. The secondary objectives are: (1) to describe the effect of initial career interest on the EG and healthcare majors composite’s enrollments and (2) assess the association between the history of enrollment in EG and university GPAs.Method: This is a case-control study that utilized the Qatar University’s enrollment databases for the health professions majors, that is, Health Sciences, Medicine, Pharmacy, and Dentistry. The datasets were collected from the registration records between 2013 and 2020. The statistical analysis was performed on the Statistical Package for the Social Sciences (SPSS) software version 26; the study used Chi-Square Test and Independence and logistic regression to assess the effect of schooling type and initial career interest on the enrollment in the EG training at the Qatar University. All statistics were tested for p = 0.05 and 95% CI.Results: Total QU-Health records of admissions from 2013 to 2020 involve 562 eligible students. A total of 180 students (32%) attended EG training before they were admitted to QU-Health, whereas a total of 382 (68%) were enrolled to QU-Health without attending EG training. The study revealed significant findings regarding the association between EG training and international schools (p < 0.001). Among the group who attended EG training, there were 63 students (75%) who reported that they did not have an initial career interest before they joined the EG training compared to 21 students (25%) reported that they did not have an initial career interest but enrolled immediately to healthcare majors. The findings indicate insignificant association between the history of EG training and the high school percentage p = 0.397. However, the association between a history of EG training and the university’s GPA is significant, with a p < 0.001, OR 5.016 (2.954–8.518).Conclusion: The study has shown significant association between the EG training enrollment and the type of school and the initial career interest of high school students. The EG training is perceived to direct the interest of high school students toward the careers of healthcare and is thought to enhance the performance of college students through their university’s GPAs.