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  1. Article: The Relationship between Urinary Incontinence, Osteoarthritis, and Musculoskeletal System Disorders.

    Celik, Nursanem / Celik, Suleyman / Seyhan, Zuleyha / Dasdelen, Muhammed Furkan / Almas, Furkan / Albayrak, Selami / Horuz, Rahim / Laguna, Pilar / de la Rosette, Jean / Kocak, Mehmet

    Journal of clinical medicine

    2024  Volume 13, Issue 8

    Abstract: Background/ ... ...

    Abstract Background/Objectives
    Language English
    Publishing date 2024-04-14
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662592-1
    ISSN 2077-0383
    ISSN 2077-0383
    DOI 10.3390/jcm13082272
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: When Bladder and Brain Collide: Is There a Gender Difference in the Relationship between Urinary Incontinence, Chronic Depression, and Anxiety?

    Dasdelen, Muhammed Furkan / Almas, Furkan / Celik, Suleyman / Celik, Nursanem / Seyhan, Zuleyha / Laguna, Pilar / Albayrak, Selami / Horuz, Rahim / Kocak, Mehmet / de la Rosette, Jean

    Journal of clinical medicine

    2023  Volume 12, Issue 17

    Abstract: In longitudinal and cross-sectional studies, depression and anxiety have been associated with urinary incontinence (UI) in women. However, this association has not been studied in men. Utilizing data from the 2008 Turkish Health Studies Survey conducted ... ...

    Abstract In longitudinal and cross-sectional studies, depression and anxiety have been associated with urinary incontinence (UI) in women. However, this association has not been studied in men. Utilizing data from the 2008 Turkish Health Studies Survey conducted by the Turkish Statistical Institute, we analyzed 13,830 participants aged 15 years and above. We investigated the association of UI with psychological discomfort in both sexes using multivariable logistic regression. High psychological discomfort significantly correlated with UI in males (OR 2.30, 95% CI 1.43-3.71) and females (OR 2.78, 95% CI 1.80-4.29). Anxiety increased UI likelihood in females (OR 2.36, 95% CI 1.61-3.46) and males (OR 2.37, 95% CI 1.10-5.13). Depression related significantly to UI in females (OR 2.54, 95% CI 1.81-3.58) but not males (OR 1.63, 95% CI 0.71-3.76). Antidepressant and anxiolytic use was not significantly related to UI in either gender. Anxiety and psychological discomfort contribute to UI in both genders. While depression significantly correlates with UI in females, it does not show the same magnitude and significance in males. Antidepressant and anxiolytic use did not significantly influence the association. These findings underscore the psychological distress-UI link, advocating a holistic approach for managing UI in individuals with mental health conditions.
    Language English
    Publishing date 2023-08-25
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662592-1
    ISSN 2077-0383
    ISSN 2077-0383
    DOI 10.3390/jcm12175535
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Design and synthesis of novel caffeic acid phenethyl ester (CAPE) derivatives and their biological activity studies in glioblastoma multiforme (GBM) cancer cell lines.

    Sucu, Bilgesu Onur / Koc, Elif Beyza / Savlug Ipek, Ozgecan / Mirat, Afranur / Almas, Furkan / Guzel, Melike Aybala / Dogan, Berna / Uludag, Damla / Karakas, Nihal / Durdagi, Serdar / Guzel, Mustafa

    Journal of molecular graphics & modelling

    2022  Volume 113, Page(s) 108160

    Abstract: Glioblastoma Multiforme (GBM) is the most aggressive brain tumor and classified as one of the deadliest cancers. The current treatment plans for GBM remains to be ineffective because of its rapid progress and inability of the drugs used to cross the ... ...

    Abstract Glioblastoma Multiforme (GBM) is the most aggressive brain tumor and classified as one of the deadliest cancers. The current treatment plans for GBM remains to be ineffective because of its rapid progress and inability of the drugs used to cross the blood-brain barrier (BBB). Thus, developing more effective and potent medicines for GBM are needed. There have been several reports demonstrating that CAPE presents reasonably good anti-cancer activity in certain cancer cell lines and can penetrate the blood-brain barrier. Accordingly, in this study we synthesized several novel CAPE analogs with the addition of more druggable handles and solubilizing entities and subsequently evaluated their in vitro therapeutic efficacies in GBM cell lines (T98G and LN229). The most potent compound was then examined extensively and results showed that the 50 μM novel CAPE analog (compound 10) significantly decreases the viability of both T98G and LN229 GBM cells as compared to CAPE itself. Moreover, the compound 10 was not cytotoxic to healthy human cells (fibroblast-like mesenchymal stem cells) at the same concentration. Apoptotic (32.8%, and 44.6%) cell populations were detected in the compound 10 treated groups for LN229 and T98G, respectively. As an indication of apotosis, significantly increased PARP cleavage was detected in compound 10 versus CAPE treated LN229. In addition, we conducted molecular docking and molecular dynamics (MD) simulations studies on certain targets playing roles on GBM disease pathway such as NF-κB, EGFR, TNF-α, ERK2, PAPR1, hCA IX and hCA XII. Our findings demonstrated that designed CAPE analogs have anti-cancer activity on GBM cells and in silico studies also demonstrate the inhibitory ability of suggested compounds via interactions with critical residues in binding pockets of studied targets. Here, we suggest the novel CAPE analog to study further against GBM. Therefore, identification of the compound related molecular signature may provide more to understand the mechanism of action.
    MeSH term(s) Caffeic Acids ; Cell Line ; Cell Line, Tumor ; Cell Proliferation ; Glioblastoma/drug therapy ; Glioblastoma/metabolism ; Glioblastoma/pathology ; Humans ; Molecular Docking Simulation ; Phenylethyl Alcohol/analogs & derivatives
    Chemical Substances Caffeic Acids ; caffeic acid phenethyl ester (G960R9S5SK) ; Phenylethyl Alcohol (ML9LGA7468)
    Language English
    Publishing date 2022-02-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1396450-1
    ISSN 1873-4243 ; 1093-3263
    ISSN (online) 1873-4243
    ISSN 1093-3263
    DOI 10.1016/j.jmgm.2022.108160
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3491: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
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  4. Book ; Online: GenerateCT

    Hamamci, Ibrahim Ethem / Er, Sezgin / Simsar, Enis / Sekuboyina, Anjany / Prabhakar, Chinmay / Tezcan, Alperen / Simsek, Ayse Gulnihan / Esirgun, Sevval Nil / Almas, Furkan / Doğan, Irem / Dasdelen, Muhammed Furkan / Reynaud, Hadrien / Pati, Sarthak / Bluethgen, Christian / Ozdemir, Mehmet Kemal / Menze, Bjoern

    Text-Conditional Generation of 3D Chest CT Volumes

    2023  

    Abstract: In this paper, we introduce GenerateCT, a novel approach for generating CT volumes conditioned on free-form medical text prompts. GenerateCT includes a text encoder and three key components: a novel causal vision transformer for encoding CT volumes, a ... ...

    Abstract In this paper, we introduce GenerateCT, a novel approach for generating CT volumes conditioned on free-form medical text prompts. GenerateCT includes a text encoder and three key components: a novel causal vision transformer for encoding CT volumes, a text-image transformer for aligning CT and text tokens, and a text-conditional super-resolution diffusion model. GenerateCT can produce realistic, high-resolution, and high-fidelity 3D chest CT volumes, validated by low FID and FVD scores. To explore GenerateCT's clinical applications, we evaluated its utility in a multi-abnormality classification task. First, we established a baseline by training a multi-abnormality classifier on our real dataset. To further assess the model's generalization to external datasets and its performance with unseen prompts in a zero-shot scenario, we employed an external dataset to train the classifier, setting an additional benchmark. We conducted two experiments in which we doubled the training datasets by synthesizing an equal number of volumes for each set using GenerateCT. The first experiment demonstrated an 11% improvement in the AP score when training the classifier jointly on real and generated volumes. The second experiment showed a 7% improvement when training on both real and generated volumes based on unseen prompts. Moreover, GenerateCT enables the scaling of synthetic training datasets to arbitrary sizes. As an example, we generated 100,000 CT volumes, fivefold the number in our real dataset, and trained the classifier exclusively on these synthetic volumes. Impressively, this classifier surpassed the performance of the one trained on all available real data by a margin of 8%. Lastly, domain experts evaluated the generated volumes, confirming a high degree of alignment with the text prompt. Our code and pre-trained models are available at: https://github.com/ibrahimethemhamamci/GenerateCT
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2023-05-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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