Eczacılık Meslek Bilimleri Bölümü Diğer Yayınlar Koleksiyonu
Bu koleksiyon için kalıcı URI
Güncel Gönderiler
Öğe Deep learning algorithms for the detection of suspicious pigmented skin lesions in primary care settings: a systematic review and meta-analysis(Springer nature, 2024) Abdalla, Ahmed R.; Hageen, Ahmed W.; Saleh, Haneen H.; Al-Azzawi, Omar; Ghalab, Mahmoud; Harraz, Amani; Eldoqsh, Bola S.; Elawady, Fatma E.; Alhammadi, Ayman H.; Elmorsy, Hesham Hassan; Jano, Majd; Elmasry, Mohamed; Bahbah, Eshak I.; Elgebaly, AhmedEarly detection of suspicious pigmented skin lesions is crucial for improving the outcomes and survival rates of skin cancers. However, the accuracy of clinical diagnosis by primary care physicians (PCPs) is suboptimal, leading to unnecessary referrals and biopsies. In recent years, deep learning (DL) algorithms have shown promising results in the automated detection and classification of skin lesions. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of DL algorithms for the detection of suspicious pigmented skin lesions in primary care settings. A comprehensive literature search was conducted using electronic databases, including PubMed, Scopus, IEEE Xplore, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science. Data from eligible studies were extracted, including study characteristics, sample size, algorithm type, sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and receiver operating characteristic curve analysis. Three studies were included. The results showed that DL algorithms had a high sensitivity (90%, 95% CI: 90-91%) and specificity (85%, 95% CI: 84-86%) for detecting suspicious pigmented skin lesions in primary care settings. Significant heterogeneity was observed in both sensitivity (p = 0.0062, I-2 = 80.3%) and specificity (p < 0.001, I-2 = 98.8%). The analysis of DOR and PLR further demonstrated the strong diagnostic performance of DL algorithms. The DOR was 26.39, indicating a strong overall diagnostic performance of DL algorithms. The PLR was 4.30, highlighting the ability of these algorithms to influence diagnostic outcomes positively. The NLR was 0.16, indicating that a negative test result decreased the odds of misdiagnosis. The area under the curve of DL algorithms was 0.95, indicating excellent discriminative ability in distinguishing between benign and malignant pigmented skin lesions. DL algorithms have the potential to significantly improve the detection of suspicious pigmented skin lesions in primary care settings. Our analysis showed that DL exhibited promising performance in the early detection of suspicious pigmented skin lesions. However, further studies are needed.Öğe Urease inhibitors for the treatment of H. pylori(Taylor and francis ltd., 2024) Güzel-Akdemir, Özlen; Akdemir, AtillaIntroductionHelicobacter pylori infects almost half of the World population. Although many infected people are symptom free, the microorganism can still cause a variety of gastrointestinal disorders and gastric adenocarcinoma. It is considered a priority pathogen for the development of new antibiotics by the World Health Organisation (WHO). Many virulence factors of H. pylori have been described. This paper will on H. pylori Urease (HPU).Area coveredThis paper will discuss the (patho)physiology and structure of HPU. In addition, urease inhibitors with known activity against the HPU or inhibitors that show H. pylori growth inhibition will be discussed.Expert opinionIncrease in selectivity, affinity and potency of HPU inhibitors can be achieved by the design of compounds that interact with distinct regions within the enzyme active site. Especially, covalent interactions seem promising as they clearly effect the dose requirement of the drug candidate.Öğe Considerations into pharmacogenomics of COVID-19 pharmacotherapy: hope, hype and reality(Academic Press, 2022) Al Taie, Anmar; Büyük, Ayşe Şeyma; Şardaş, SemraCOVID-19 medicines, such as molnupiravir are beginning to emerge for public health and clinical practice. On the other hand, drugs display marked variability in their efficacy and safety. Hence, COVID-19 medicines, as with all drugs, will be subject to the age-old maxim “one size prescription does not fit all”. In this context, pharmacogenomics is the study of genome-by-drug interactions and offers insights on mechanisms of patient-to-patient and between-population variations in drug efficacy and safety. Pharmacogenomics information is crucial to tailoring the patients' prescriptions to achieve COVID-19 preventive and therapeutic interventions that take into account the host biology, patients’ genome, and variable environmental exposures that collectively influence drug efficacy and safety. This expert review critically evaluates and summarizes the pharmacogenomics and personalized medicine aspects of the emerging COVID-19 drugs, and other selected drug interventions deployed to date. Here, we aim to sort out the hope, hype, and reality and suggest that there are veritable prospects to advance COVID-19 medicines for public health benefits, provided that pharmacogenomics is considered and implemented adequately. Pharmacogenomics is an integral part of rational and evidence-based medical practice. Scientists, health care professionals, pharmacists, pharmacovigilance practitioners, and importantly, patients stand to benefit by expanding the current pandemic response toolbox by the science of pharmacogenomics, and its applications in COVID-19 medicines and clinical trials.
