Publications by authors named "M Suhail Khan"

Background: Catheter-directed interventions (CDIs) for pulmonary embolism (PE) continue to evolve. However, due to the paucity of data, their use has been limited in patients with underlying kidney disease.

Methods: The National Readmission Database (2016-2020) was utilized to identify intermediate to high-risk PE (IHR-PE) patients requiring CDI (thrombectomy, thrombolysis, and ultrasound-assisted thrombolysis).

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This experiment aimed to compare the efficacy of an antimicrobial peptide (AMP) with a conventional antibiotic growth promoter (AGP) during necrotic enteritis (NE) challenge in broilers. In total, 720 1-day-old exclusively male broiler chicks (Ross-308) were allocated to five treatments, each with six replicates of 24 birds (n = 144/treatment), for 35 days. The treatments were as follows: (1) uninfected control (UC) with basal diet, (2) infected control (IC) with C.

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Coronary artery disease (CAD) is a multigenic condition influenced by both nature and nurture (60% to 40%). Prognosis of CAD is based on familial patterns. This study examined and analyzed the susceptibility of CAD to genetic variants in various Pakistani families.

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Aims: We aimed to perform a retrospective cohort study using the Centers for Disease Control and Prevention's (CDC's) Wide-Ranging Online Data for Epidemiologic Research (WONDER) database to analyse the trends in cardiovascular disease (CVD)-related mortality in patients with myeloproliferative neoplasms (MPNs) from 1999 to 2020.

Methods And Results: We analysed the death certificate data from the CDC WONDER database from 1999 to 2020 for CVD with co-morbid myeloproliferative disorders in the US population. Age-adjusted mortality rates (AAMRs) and 95% confidence intervals (CIs) were computed per 1 million population by standardizing crude mortality rates to the 2000 US census population.

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Background Artificial intelligence (AI) is revolutionizing healthcare globally by enhancing diagnostic accuracy, predicting patient outcomes, and enabling personalized treatment plans. However, in low- and middle-income countries (LMICs) like Pakistan, the integration of AI into healthcare is limited due to challenges such as lack of funding, provider resistance, and inadequate training. Despite these barriers, there is growing interest among healthcare providers in understanding and adopting AI technologies to improve professional efficiency.

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