PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7645
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Article Meal Timing Trajectories in Older Adults and Their Associations With Morbidity, Genetic Profiles, and Mortality(Springernature, 2025) Dashti, Hassan S.; Liu, Chloe; Deng, Hao; Sharma, Anushka; Payton, Antony; Maharani, Asri; Didikoglu, AltugBackgroundOlder adults are vulnerable to mistimed food intake due to health and environmental changes; characterizing meal timing may inform strategies to promote healthy aging. We investigated longitudinal trajectories of self-reported meal timing in older adults and their associations with morbidity, genetic profiles, and all-cause mortality.MethodsWe analyzed data from 2945 community-dwelling older adults from the University of Manchester Longitudinal Study of Cognition in Normal Healthy Old Age, with up to five repeated assessments of meal timing and health behaviors conducted between 1983 and 2017. Linear mixed-effects models, latent class analysis, and Cox regression were used to examine relationships between meal timing with illness and behavioral factors, genetic scores for chronotype and obesity, and mortality.ResultsHere we show older age is associated with later breakfast and dinner times, a later eating midpoint, and a shorter daily eating window. Physical and psychological illnesses, including fatigue, oral health problems, depression, anxiety, and multimorbidity, are primarily associated with later breakfast. Genetic profiles related to an evening chronotype, but not obesity, are linked to later meals. Later breakfast timing is also associated with increased mortality. Latent class analysis of meal timing trajectories identify early and late eating groups, with 10-year survival rates of 86.7% in the late eating group compared to 89.5% in the early eating group.ConclusionsMeal timing, particularly later breakfast, shifts with age and may reflect broader health changes in older adults, with implications for morbidity and longevity.Article Citation - WoS: 1Tcgex: a Powerful Visual Interface for Exploring and Analyzing Cancer Gene Expression Data(Springernature, 2025) Kus, M. Emre; Sahin, Cagatay; Kilic, Emre; Askin, Arda; Ozgur, M. Mert; Karahanogullari, Gokhan; Ekiz, H. AtakanAnalyzing gene expression data from the Cancer Genome Atlas (TCGA) and similar repositories often requires advanced coding skills, creating a barrier for many researchers. To address this challenge, we developed The Cancer Genome Explorer (TCGEx), a user-friendly, web-based platform for conducting sophisticated analyses such as survival modeling, gene set enrichment analysis, unsupervised clustering, and linear regression-based machine learning. TCGEx provides access to preprocessed TCGA data and immune checkpoint inhibition studies while allowing integration of user-uploaded data sets. Using TCGEx, we explore molecular subsets of human melanoma and identify microRNAs associated with intratumoral immunity. These findings are validated with independent clinical trial data on immune checkpoint inhibitors for melanoma and other cancers. In addition, we identify cytokine genes that can be used to predict treatment responses to various immune checkpoint inhibitors prior to treatment. Built on the R/Shiny framework, TCGEx offers customizable features to adapt analyses for diverse research contexts and generate publication-ready visualizations. TCGEx is freely available at https://tcgex.iyte.edu.tr, providing an accessible tool to extract insights from cancer transcriptomics data.Article Citation - WoS: 37Citation - Scopus: 48Microfluidic-Based Virus Detection Methods for Respiratory Diseases(Springernature, 2021) Tarım, Ergün Alperay; Karakuzu, Betül; Öksüz, Cemre; Sarıgil, Öykü; Kızılkaya, Melike; Al-Ruweidi, Mahmoud Khatib A. A.; Yalçın, Hüseyin Çağatay; Özçivici, Engin; Tekin, Hüseyin CumhurWith the recent SARS-CoV-2 outbreak, the importance of rapid and direct detection of respiratory disease viruses has been well recognized. The detection of these viruses with novel technologies is vital in timely prevention and treatment strategies for epidemics and pandemics. Respiratory viruses can be detected from saliva, swab samples, nasal fluid, and blood, and collected samples can be analyzed by various techniques. Conventional methods for virus detection are based on techniques relying on cell culture, antigen-antibody interactions, and nucleic acids. However, these methods require trained personnel as well as expensive equipment. Microfluidic technologies, on the other hand, are one of the most accurate and specific methods to directly detect respiratory tract viruses. During viral infections, the production of detectable amounts of relevant antibodies takes a few days to weeks, hampering the aim of prevention. Alternatively, nucleic acid-based methods can directly detect the virus-specific RNA or DNA region, even before the immune response. There are numerous methods to detect respiratory viruses, but direct detection techniques have higher specificity and sensitivity than other techniques. This review aims to summarize the methods and technologies developed for microfluidic-based direct detection of viruses that cause respiratory infection using different detection techniques. Microfluidics enables the use of minimal sample volumes and thereby leading to a time, cost, and labor effective operation. Microfluidic-based detection technologies provide affordable, portable, rapid, and sensitive analysis of intact virus or virus genetic material, which is very important in pandemic and epidemic events to control outbreaks with an effective diagnosis.