Seroprevalence, Risk Factors and Maternal-Fetal Outcomes of Toxoplasma gondii in Pregnant Women from WHO Eastern Mediterranean Region: Systematic Review and Meta-Analysis.

BACKGROUND: The protozoan parasite Toxoplasma gondii may cause serious illness in the immunocompromised. The Toxoplasma gondii seropositive prevalence in pregnant women in WHO Eastern Mediterranean Region countries is inconsistent in the literature and it is associated with outcomes that have not be fully elucidated, hence the need for a better understanding of the pooled seroprevalence and associated maternal and fetal outcomes. OBJECTIVE: The objective was to conduct a systematic literature review and determine the pooled prevalence of WHO Eastern Mediterranean Regional countries' pregnant women's seroprevalence of Toxoplasma gondii and the maternal-fetal outcomes. METHODS: This quantitative study examined WHO Eastern Mediterranean countries' maternal-fetal outcomes and Toxoplasma gondii prevalence in pregnant women. The targeted population was pregnant women, while the primary outcome was seropositivity of Toxoplasma gondii, while other outcomes such as maternal and fetal associations and risk factors were determined PubMed, SCOPUS, MEDLINE, and Index Medicus for the Eastern Mediterranean Region (IMEMR) databases were searched up until 30 January 2023. The search terms used were "Toxoplasma gondii" OR "Toxoplasma infection" AND "Pregnant woman" or pregnan* OR Antenatal OR Prenatal OR Gravidity OR Parturition OR Maternal AND WHO Eastern Mediterranean Region). OpenMeta-Analyst and Jamovi were used to analyze the generated data. RESULTS: In total, 95 of 2947 articles meeting the inclusion criteria examined Toxoplasma gondii prevalence in pregnant women from WHO Eastern Mediterranean countries. The pooled prevalence of Toxoplasma gondii in pregnant women was 36.5% (95%CI: 32.6-40.4) with a median value of 35.64%, range values of 1.38-75.30%, with 99.61% heterogeneity. The pooled seroprevalence of IgG of Toxoplasma gondii was 33.5% (95%CI: 29.8-37.2) with a median value of 33.51%, and a range values of 1.38-69.92%; the pooled seroprevalence of IgM was 3.6% (95%CI: 3.1-4.1)) with a median value of 3.62 and range values of 0.20-17.47%, while cases of pooled seroprevalence of both IgG and IgM positivity was 3.0% (95%CI: 1.9-4.4) with a median value of 2.05 and a range values of 0.05-16.62%. Of the Toxoplasma gondii seropositive women, 1281/3389 (34.8%) 174/1765 (32.9%), 1311/3101 (43.7%), and 715/1683 (40.8%) of them had contact with cats, drank unprocessed milk, ate raw or undercooked meat and ate unwashed raw vegetables, respectively. The maternal-fetal outcomes associated with Toxoplasma gondii seropositivity were a history of abortions, miscarriage, stillbirth, intrauterine fetal death, and premature birth, which were found in 868/2990 (32.5%), 112/300 (36.1%), 111/375 (25.7%), 3/157 (1.9%) and 96/362 (20.1%) of women who tested positive for Toxoplasma gondii antibodies. CONCLUSION: The study found a high proportion of Toxoplasma gondii seroprevalence in pregnant women in the WHO Eastern Mediterranean Region, which may be linked to poor outcomes for mothers and their babies. Thus, pregnant women require monitoring and comprehensive prevention strategies for Toxoplasma gondii infection.

Unleashing the power of artificial intelligence for diagnosing and treating infectious diseases: A comprehensive review.

Infectious diseases present a global challenge, requiring accurate diagnosis, effective treatments, and preventive measures. Artificial intelligence (AI) has emerged as a promising tool for analysing complex molecular data and improving the diagnosis, treatment, and prevention of infectious diseases. Computer-aided detection (CAD) using convolutional neural networks (CNN) has gained prominence for diagnosing tuberculosis (TB) and other infectious diseases such as COVID-19, HIV, and viral pneumonia. The review discusses the challenges and limitations associated with AI in this field and explores various machine-learning models and AI-based approaches. Artificial neural networks (ANN), recurrent neural networks (RNN), support vector machines (SVM), multilayer neural networks (MLNN), CNN, long short-term memory (LSTM), and random forests (RF) are among the models discussed. The review emphasizes the potential of AI to enhance the accuracy and efficiency of diagnosis, treatment, and prevention of infectious diseases, highlighting the need for further research and development in this area.

Clinical features and outcome of human Mpox (Monkeypox) in Saudi Arabia: An observational study of travel-related cases.

BACKGROUND: The 2022 Monkeypox virus (Mpox) outbreak had involved multiple countries around the globe. Here, we report clinical features and outcome of human Mpox of the first cases in Saudi Arabia. METHODS: We obtained records of confirmed Mpox cases in Saudi Arabia from the public electronic health information system, Health Electronic Surveillance Network (HESN) and the healthcare providers completed a de-identified structured clinical data collection form. RESULTS: The reported seven cases were travel-related and all were males between 24 and 41 years of age (mean age + SD) was 30.14 (+ 6.69) years. Of the cases, three (43 %) had heterosexual contact and the others had other intimate encounters while traveling abroad. They presented with skin lesions (100 %), fever (86 %), and lymphadenopathy (71 %). The illness was mild to moderate, did not require antiviral medications, and lasted 7-15 days. The mean duration of skin rash (+ SD) was 10 (+ 2.68) days. Routine laboratory tests (CBC, BUN, serum electrolytes, and liver enzymes) were within normal limits, and initial screening for HIV was negative. Expanded contact tracing did not reveal secondary cases of Mpox in the community or the healthcare setting. CONCLUSION: The current study showed heterosexual transmission of Mpox and the clinical course was mild and non-complicated. Therefore, clinicians and public health professionals should consider Mpox among individuals presenting with skin rash especially in the context of the investigation of HIV and other sexually transmitted diseases.

Prevalence of Mutated Colistin-Resistant Klebsiella pneumoniae: A Systematic Review and Meta-Analysis.

The emergence of genetic mutations in chromosomal genes and the transmissible plasmid-mediated colistin resistance gene may have helped in the spread of colistin resistance among various Klebsiella pneumoniae (K. pneumoniae) isolates and other different bacteria. In this study, the prevalence of mutated colistin-resistant K. pneumoniae isolates was studied globally using a systematic review and meta-analysis approach. A systematic search was conducted in databases including PubMed, ScienceDirect, Scopus and Google Scholar. The pooled prevalence of mutated colistin resistance in K. pneumoniae isolates was analyzed using Comprehensive Meta-Analysis Software (CMA). A total of 50 articles were included in this study. The pooled prevalence of mutated colistin resistance in K. pneumoniae was estimated at 75.4% (95% CI = 67.2−82.1) at high heterogeneity (I2 = 81.742%, p-value < 0.001). Meanwhile, the results of the subgroup analysis demonstrated the highest prevalence in Saudi Arabia with 97.9% (95% CI = 74.1−99.9%) and Egypt, with 4.5% (95% CI = 0.6−26.1%), had the lowest. The majority of mutations could be observed in the mgrB gene (88%), pmrB gene (54%) and phoQ gene (44%). The current study showed a high prevalence of the mutation of colistin resistance genes in K. pneumoniae. Therefore, it is recommended that regular monitoring be performed to control the spread of colistin resistance.

Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, in late 2019 and created a global pandemic that overwhelmed healthcare systems. COVID-19, as of July 3, 2021, yielded 182 million confirmed cases and 3.9 million deaths globally according to the World Health Organization. Several patients who were initially diagnosed with mild or moderate COVID-19 later deteriorated and were reclassified to severe disease type. OBJECTIVE: The aim is to create a predictive model for COVID-19 ventilatory support and mortality early on from baseline (at the time of diagnosis) and routinely collected data of each patient (CXR, CBC, demographics, and patient history). METHODS: Four common machine learning algorithms, three data balancing techniques, and feature selection are used to build and validate predictive models for COVID-19 mechanical requirement and mortality. Baseline CXR, CBC, demographic, and clinical data were retrospectively collected from April 2, 2020, till June 18, 2020, for 5739 patients with confirmed PCR COVID-19 at King Abdulaziz Medical City in Riyadh. However, of those patients, only 1508 and 1513 have met the inclusion criteria for ventilatory support and mortalilty endpoints, respectively. RESULTS: In an independent test set, ventilation requirement predictive model with top 20 features selected with reliefF algorithm from baseline radiological, laboratory, and clinical data using support vector machines and random undersampling technique attained an AUC of 0.87 and a balanced accuracy of 0.81. For mortality endpoint, the top model yielded an AUC of 0.83 and a balanced accuracy of 0.80 using all features with balanced random forest. This indicates that with only routinely collected data our models can predict the outcome with good performance. The predictive ability of combined data consistently outperformed each data set individually for intubation and mortality. For the ventilator support, chest X-ray severity annotations alone performed better than comorbidity, complete blood count, age, or gender with an AUC of 0.85 and balanced accuracy of 0.79. For mortality, comorbidity alone achieved an AUC of 0.80 and a balanced accuracy of 0.72, which is higher than models that use either chest radiograph, laboratory, or demographic features only. CONCLUSION: The experimental results demonstrate the practicality of the proposed COVID-19 predictive tool for hospital resource planning and patients' prioritization in the current COVID-19 pandemic crisis.