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Background: Landscape anthropization and interaction between domestic and wild animals are the major contributing factors involved in the emergence of new pathogens in wild animals. Theileriosis is an emerging issue of wild ungulates, especially in the tropical and subtropical areas of the globe. Aims: The current study investigated the mouflon sheep for Theileria infection using molecular methods and hematological analysis. Methods: This study was conducted on a total of 103 captive wild mouflon sheep present in eight different recreational zoos, and wildlife parks in Punjab, Pakistan to investigate the genotypic prevalence of Theileria spp. by targeting 18S rRNA and molecular evidence for Theileria spillover between domestic and wild mouflon sheep by phylogenetic analysis. The association of assumed risk factors and the effect of Theileria spp. on various hematological parameters were also assessed. Results: The results depicted that Theileria spp. was prevalent in 8 (7.77%, CI 95%: 3.99-14.59%), and 11 (10.68%, CI 95%: 06.07-18.12%) animals based on microscopy, and PCR, respectively. The phylogenetic analysis of the 18S rRNA gene of Theileria spp. from mouflon revealed a close resemblance with T. annulata from domestic animals. The risk factor analysis revealed that tick infestation, enclosure hygiene, previous tick infestation history, and the presence of wooden logs in the enclosure were significantly (P<0.05) associated with the occurrence of Theileria spp. infection in the captive mouflon sheep of Pakistan. Furthermore, a significant reduction in blood parameters like PCV, RBCs count, Hb, and platelets was observed in Theileria-positive animals. Conclusion: This study is the first evidence at the molecular level to characterize the spillover of Theileria spp. between the captive wild mouflon sheep and domestic animals of Pakistan, and it will be useful in developing control strategies for emerging theileriosis in captive wild animals. [ABSTRACT FROM AUTHOR]

Background: Despite the emerging application of clinical decision support systems (CDSS) in pregnancy care and the proliferation of artificial intelligence (AI) over the last decade, it remains understudied regarding the role of AI in CDSS specialized for pregnancy care., Objective: To identify and synthesize AI-augmented CDSS in pregnancy care, CDSS functionality, AI methodologies, and clinical implementation, we reported a systematic review based on empirical studies that examined AI-augmented CDSS in pregnancy care., Methods: We retrieved studies that examined AI-augmented CDSS in pregnancy care using database queries involved with titles, abstracts, keywords, and MeSH (Medical Subject Headings) terms. Bibliographic records from their inception to 2022 were retrieved from PubMed/MEDLINE (n=206), Embase (n=101), and ACM Digital Library (n=377), followed by eligibility screening and literature review. The eligibility criteria include empirical studies that (1) developed or tested AI methods, (2) developed or tested CDSS or CDSS components, and (3) focused on pregnancy care. Data of studies used for review and appraisal include title, abstract, keywords, MeSH terms, full text, and supplements. Publications with ancillary information or overlapping outcomes were synthesized as one single study. Reviewers independently reviewed and assessed the quality of selected studies., Results: We identified 30 distinct studies of 684 studies from their inception to 2022. Topics of clinical applications covered AI-augmented CDSS from prenatal, early pregnancy, obstetric care, and postpartum care. Topics of CDSS functions include diagnostic support, clinical prediction, therapeutics recommendation, and knowledge base., Conclusions: Our review acknowledged recent advances in CDSS studies including early diagnosis of prenatal abnormalities, cost-effective surveillance, prenatal ultrasound support, and ontology development. To recommend future directions, we also noted key gaps from existing studies, including (1) decision support in current childbirth deliveries without using observational data from consequential fetal or maternal outcomes in future pregnancies; (2) scarcity of studies in identifying several high-profile biases from CDSS, including social determinants of health highlighted by the American College of Obstetricians and Gynecologists; and (3) chasm between internally validated CDSS models, external validity, and clinical implementation., (©Xinnian Lin, Chen Liang, Jihong Liu, Tianchu Lyu, Nadia Ghumman, Berry Campbell. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.09.2024.)

Background: Despite previous research findings on higher risks of stillbirth among pregnant individuals with SARS-CoV-2 infection, it is unclear whether the gestational timing of viral infection modulates this risk., Objective: This study aimed to examine the association between timing of SARS-CoV-2 infection during pregnancy and risk of stillbirth., Study Design: This retrospective cohort study used multilevel logistic regression analyses of nationwide electronic health records in the United States. Data were from 75 healthcare systems and institutes across 50 states. A total of 191,403 pregnancies of 190,738 individuals of reproductive age (15-49 years) who had childbirth between March 1, 2020 and May 31, 2021 were identified and included. The main outcome was stillbirth at ≥20 weeks of gestation. Exposures were the timing of SARS-CoV-2 infection: early pregnancy (<20 weeks), midpregnancy (21-27 weeks), the third trimester (28-43 weeks), any time before delivery, and never infected (reference)., Results: We identified 2342 (1.3%) pregnancies with COVID-19 in early pregnancy, 2075 (1.2%) in midpregnancy, and 12,697 (6.9%) in the third trimester. After adjusting for maternal and clinical characteristics, increased odds of stillbirth were observed among pregnant individuals with SARS-CoV-2 infection only in early pregnancy (odds ratio, 1.75, 95% confidence interval, 1.25-2.46) and midpregnancy (odds ratio, 2.09; 95% confidence interval, 1.49-2.93), as opposed to pregnant individuals who were never infected. Older age, Black race, hypertension, acute respiratory distress syndrome or acute respiratory failure, and placental abruption were found to be consistently associated with stillbirth across different trimesters., Conclusion: Increased risk of stillbirth was associated with COVID-19 only when pregnant individuals were infected during early and midpregnancy, and not at any time before the delivery or during the third trimester, suggesting the potential vulnerability of the fetus to SARS-CoV-2 infection in early pregnancy. Our findings underscore the importance of proactive COVID-19 prevention and timely medical intervention for individuals infected with SARS-CoV-2 during early and midpregnancy., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Importance: Liquid biopsy has emerged as a complement to tumor tissue profiling for advanced non-small cell lung cancer (NSCLC). The optimal way to integrate liquid biopsy into the diagnostic algorithm for patients with newly diagnosed advanced NSCLC remains unclear., Objective: To evaluate the use of circulating tumor DNA (ctDNA) genotyping before tissue diagnosis among patients with suspected advanced NSCLC and its association with time to treatment., Design, Setting, and Participants: This single-group nonrandomized clinical trial was conducted among 150 patients at the Princess Margaret Cancer Centre-University Health Network (Toronto, Ontario, Canada) between July 1, 2021, and November 30, 2022. Patients referred for investigation and diagnosis of lung cancer were eligible if they had radiologic evidence of advanced lung cancer prior to a tissue diagnosis., Interventions: Patients underwent plasma ctDNA testing with a next-generation sequencing (NGS) assay before lung cancer diagnosis. Diagnostic biopsy and tissue NGS were performed per standard of care., Main Outcome and Measures: The primary end point was time from referral to treatment initiation among patients with advanced nonsquamous NSCLC using ctDNA testing before diagnosis (ACCELERATE [Accelerating Lung Cancer Diagnosis Through Liquid Biopsy] cohort). This cohort was compared with a reference cohort using standard tissue genotyping after tissue diagnosis., Results: Of the 150 patients (median age at diagnosis, 68 years [range, 33-91 years]; 80 men [53%]) enrolled, 90 (60%) had advanced nonsquamous NSCLC. The median time to treatment was 39 days (IQR, 27-52 days) for the ACCELERATE cohort vs 62 days (IQR, 44-82 days) for the reference cohort (P < .001). Among the ACCELERATE cohort, the median turnaround time from sample collection to genotyping results was 7 days (IQR, 6-9 days) for plasma and 23 days (IQR, 18-28 days) for tissue NGS (P < .001). Of the 90 patients with advanced nonsquamous NSCLC, 21 (23%) started targeted therapy before tissue NGS results were available, and 11 (12%) had actionable alterations identified only through plasma testing., Conclusions and Relevance: This nonrandomized clinical trial found that the use of plasma ctDNA genotyping before tissue diagnosis among patients with suspected advanced NSCLC was associated with accelerated time to treatment compared with a reference cohort undergoing standard tissue testing., Trial Registration: ClinicalTrials.gov Identifier: NCT04863924.

Objective: Identifying the time of SARS-CoV-2 viral infection relative to specific gestational weeks is critical for delineating the role of viral infection timing in adverse pregnancy outcomes. However, this task is difficult when it comes to Electronic Health Records (EHR). In combating the COVID-19 pandemic for maternal health, we sought to develop and validate a clinical information extraction algorithm to detect the time of clinical events relative to gestational weeks., Materials and Methods: We used EHR from the National COVID Cohort Collaborative (N3C), in which the EHR are normalized by the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). We performed EHR phenotyping, resulting in 270,897 pregnant women (June 1st, 2018 to May 31st, 2021). We developed a rule-based algorithm and performed a multi-level evaluation to test content validity and clinical validity, and extreme length of gestation (<150 or >300)., Results: The algorithm identified 296,194 pregnancies (16,659 COVID-19, 174,744 without COVID-19) in 270,897 pregnant women. For inferring gestational age, 95% cases (n = 40) have moderate-high accuracy (Cohen's Kappa = 0.62); 100% cases (n = 40) have moderate-high granularity of temporal information (Cohen's Kappa = 1). For inferring delivery dates, the accuracy is 100% (Cohen's Kappa = 1). The accuracy of gestational age detection for the extreme length of gestation is 93.3% (Cohen's Kappa = 1). Mothers with COVID-19 showed higher prevalence in obesity or overweight (35.1% vs. 29.5%), diabetes (17.8% vs. 17.0%), chronic obstructive pulmonary disease (0.2% vs. 0.1%), respiratory distress syndrome or acute respiratory failure (1.8% vs. 0.2%)., Discussion: We explored the characteristics of pregnant women by different gestational weeks of SARS-CoV-2 infection with our algorithm. TED-PC is the first to infer the exact gestational week linked with every clinical event from EHR and detect the timing of SARS-CoV-2 infection in pregnant women., Conclusion: The algorithm shows excellent clinical validity in inferring gestational age and delivery dates, which supports multiple EHR cohorts on N3C studying the impact of COVID-19 on pregnancy., Competing Interests: The authors have declared that no competing interests exist.

Introduction: Molecular profiling of tumor tissue is the gold standard for treatment decision-making in advanced non-small cell lung cancer (NSCLC). Results may be delayed or unavailable due to insufficient tissue, prolonged wait times for biopsy, pathology assessment and testing. We piloted the use of plasma testing in the initial diagnostic workup for patients with suspected advanced lung cancer., Methods: Patients with ⩽15 pack-year smoking history and suspected advanced lung cancer referred to the lung cancer rapid diagnostic program underwent plasma circulating-tumor DNA testing using a DNA-based mutation panel. Tissue testing was performed per standard of care, including comprehensive next-generation sequencing (NGS). The primary endpoint was time from diagnostic program referral to cancer treatment in stage IV NSCLC patients (Cohort A) compared to a contemporary cohort not enrolled in the study (Cohort B) and an historical pre-COVID cohort referred to the program between 2018 and 2019 (Cohort C)., Results: From January to June 2021, 20 patients were enrolled in Cohort A; median age was 70.5 years (range 33-87), 70% were female, 55% Caucasian, 85% never smokers, and 75% were diagnosed with NSCLC. Seven had actionable alterations detected in plasma or tissue (4/7 concordant). Fusions, not tested in plasma, were identified by immunohistochemistry for three patients. Mean result turnaround time was 17.8 days for plasma NGS and 23.6 days for tissue ( p  = 0.10). Mean time from referral to treatment initiation was significantly shorter in cohort A at 32.6 days (SD 13.1) versus 62.2 days (SD 31.2) in cohort B and 61.5 days (SD 29.1) in cohort C, both p  < 0.0001., Conclusion: Liquid biopsy in the initial diagnostic workup of patients with suspected advanced NSCLC can lead to faster molecular results and shorten time to treatment even with smaller DNA panels. An expansion study using comprehensive NGS plasma testing with faster turnaround time is ongoing (NCT04862924)., Competing Interests: Competing interests: The authors declare that there is no conflict of interest., (© The Author(s), 2022.)