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5. Predicting Semen Analysis Parameters from Testicular Ultrasonography Images Using Deep Learning Algorithms: An Innovative Approach to Male Infertility Diagnosis.

Author

Sagir, Lutfullah, Kaba, Esat, Huner Yigit, Merve, Tasci, Filiz, and Uzun, Hakki

Subjects
MACHINE learning, SEMEN analysis, DEEP learning, ARTIFICIAL intelligence, MALE infertility
Abstract

Objectives: Semen analysis is universally regarded as the gold standard for diagnosing male infertility, while ultrasonography plays a vital role as a complementary diagnostic tool. This study aims to assess the effectiveness of artificial intelligence (AI)-driven deep learning algorithms in predicting semen analysis parameters based on testicular ultrasonography images. Materials and Methods: This study included male patients aged 18–54 who sought evaluation for infertility at the Urology Outpatient Clinic of our hospital between February 2022 and April 2023. All patients underwent comprehensive assessments, including blood hormone profiling, semen analysis, and scrotal ultrasonography, with each procedure being performed by the same operator. Longitudinal-axis images of both testes were obtained and subsequently segmented. Based on the semen analysis results, the patients were categorized into groups according to sperm concentration, progressive motility, and morphology. Following the initial classification, each semen parameter was further subdivided into "low" and "normal" categories. The testicular images from both the right and left sides of all patients were organized into corresponding folders based on their associated laboratory parameters. Three distinct datasets were created from the segmented images, which were then augmented. The datasets were randomly partitioned into an 80% training set and a 20% test set. Finally, the images were classified using the VGG-16 deep learning architecture. Results: The area under the curve (AUC) values for the classification of sperm concentration (oligospermia versus normal), progressive motility (asthenozoospermia versus normal), and morphology (teratozoospermia versus normal) were 0.76, 0.89, and 0.86, respectively. Conclusions: In our study, we successfully predicted semen analysis parameters using data derived from testicular ultrasonography images through deep learning algorithms, representing an innovative application of artificial intelligence. Given the limited published research in this area, our study makes a significant contribution to the field and provides a foundation for future validation studies. [ABSTRACT FROM AUTHOR]

Published
2025
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6. White Tea Reduces Dyslipidemia, Inflammation, and Oxidative Stress in the Aortic Arch in a Model of Atherosclerosis Induced by Atherogenic Diet in ApoE Knockout Mice.

Author

Huner Yigit, Merve, Atak, Mehtap, Yigit, Ertugrul, Topal Suzan, Zehra, Kivrak, Mehmet, and Uydu, Huseyin Avni

Subjects
*THORACIC aorta, *LOW density lipoprotein receptors, *PHOSPHOLIPASE A2, *APOLIPOPROTEIN E, *ARCH model (Econometrics)
Abstract

Objective: In this study, we aimed to evaluate the potential effects of white tea (WT) in the atherosclerosis process characterized by oxidative stress, inflammation, and dyslipidemia. Methods: In our study, apolipoprotein E knockout (ApoE−/−) mice (RRID: IMSR_JAX:002052) and C57BL/6J mice (RRID: IMSR_JAX:000664) were used. In the atherosclerosis model induced by an atherogenic diet (AD), WT was administered via oral gavage at two different concentrations. The animals were sacrificed by decapitation under anesthesia, and their serum and aortic tissues were collected. Total cholesterol (TC), triglyceride (TG), interleukin (IL)-1β, IL-6, IL-10, IL-12, tumor necrosis factor-α (TNF-α), interferon-γ, myeloperoxidase, paraoxonase-1, lipoprotein-associated phospholipase A2, oxidized low-density lipoprotein (Ox-LDL), lectin-like oxidized LDL receptor (LOX-1), a disintegrin, and metalloprotease (ADAM) 10 and 17 activities were determined via colorimetric, enzyme-linked immunoassay, and fluorometric methods. Results: WT supplementation decreased serum Ox-LDL, LOX-1, TC, and TG levels by approximately 50%. TNF- and IL-6 levels were reduced by approximately 30% in the aortic arch. In addition, ADAM10/17 enzyme activities were found to be reduced by approximately 25%. However, no change in the AD-induced fibrotic cap structure was observed in the aortic root. Conclusions: The findings indicate that white tea effectively reduced oxidative stress, inflammation, and dyslipidemia in atherosclerosis but does not affect atheroma plaque morphology. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Propolis Reduces Inflammation and Dyslipidemia Caused by High-Cholesterol Diet in Mice by Lowering ADAM10/17 Activities.

Author

Yigit, Ertugrul, Deger, Orhan, Korkmaz, Katip, Huner Yigit, Merve, Uydu, Huseyin Avni, Mercantepe, Tolga, and Demir, Selim

Abstract

Atherosclerosis is one of the most important causes of cardiovascular diseases. A disintegrin and metalloprotease (ADAM)10 and ADAM17 have been identified as important regulators of inflammation in recent years. Our study investigated the effect of inhibiting these enzymes with selective inhibitor and propolis on atherosclerosis. In our study, C57BL/6J mice (n = 16) were used in the control and sham groups. In contrast, ApoE-/- mice (n = 48) were used in the case, water extract of propolis (WEP), ethanolic extract of propolis (EEP), GW280264X (GW-synthetic inhibitor), and solvent (DMSO and ethanol) groups. The control group was fed a control diet, and all other groups were fed a high-cholesterol diet for 16 weeks. WEP (400 mg/kg/day), EEP (200 mg/kg/day), and GW (100 µg/kg/day) were administered intraperitoneally for the last four weeks. Animals were sacrificed, and blood, liver, aortic arch, and aortic root tissues were collected. In serum, total cholesterol (TC), triglycerides (TGs), and glucose (Glu) were measured by enzymatic colorimetric method, while interleukin-1β (IL-1β), paraoxonase-1 (PON-1), and lipoprotein-associated phospholipase-A2 (Lp-PLA2) were measured by ELISA. Tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), myeloperoxidase (MPO), interleukin-6 (IL-6), interleukin-10 (IL-10), and interleukin-12 (IL-12) levels were measured in aortic arch by ELISA and ADAM10/17 activities were measured fluorometrically. In addition, aortic root and liver tissues were examined histopathologically and immunohistochemically (ADAM10 and sortilin primary antibody). In the WEP, EEP, and GW groups compared to the case group, TC, TG, TNF-α, IL-1β, IL-6, IL-12, PLA2, MPO, ADAM10/17 activities, plaque burden, lipid accumulation, ADAM10, and sortilin levels decreased, while IL-10 and PON-1 levels increased (p < 0.003). Our study results show that propolis can effectively reduce atherosclerosis-related inflammation and dyslipidemia through ADAM10/17 inhibition. [ABSTRACT FROM AUTHOR]

Published
2024
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10. A different way to diagnosis acute appendicitis: machine learning.

Author

Harmantepe, Ahmet Tarik, Dikicier, Enis, Gönüllü, Emre, Ozdemir, Kayhan, Kamburoglu, Burak, and Yigit, Merve

Subjects
MACHINE learning, APPENDICITIS, ARTIFICIAL neural networks, ARTIFICIAL intelligence, SUPPORT vector machines, K-nearest neighbor classification
Abstract

Indroduction: Machine learning is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. Aim: Our aim is to predict acute appendicitis, which is the most common indication for emergency surgery, using machine learning algorithms with an easy and inexpensive method. Materials and methods: Patients who were treated surgically with a prediagnosis of acute appendicitis in a single center between 2011 and 2021 were analyzed. Patients with right lower quadrant pain were selected. A total of 189 positive and 156 negative appendectomies were found. Gender and hemogram were used as features. Machine learning algorithms and data analysis were made in Python (3.7) programming language. Results: Negative appendectomies were found in 62% (n = 97) of the women and in 38% (n = 59) of the men. Positive appendectomies were present in 38% (n = 72) of the women and 62% (n = 117) of the men. The accuracy in the test data was 82.7% in logistic regression, 68.9% in support vector machines, 78.1% in k-nearest neighbors, and 83.9% in neural networks. The accuracy in the voting classifier created with logistic regression, k-nearest neighbor, support vector machines, and artificial neural networks was 86.2%. In the voting classifier, the sensitivity was 83.7% and the specificity was 88.6%. Conclusions: The results of our study show that machine learning is an effective method for diagnosing acute appendicitis. This study presents a practical, easy, fast, and inexpensive method to predict the diagnosis of acute appendicitis. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Molecular and Morphological Characterization of Turkish Local Eggplant (Solanum melongena L.) Populations

Author

UYSAL, Emrah, YİGİT, Merve, PAKASHTİCA, Vese, and PİNAR, Hasan

Subjects
Ziraat, Agriculture, General Medicine, SRAP, Description, genetic diversity, population structure, ISSR, Tanımlama, genetik çeşitlilik, popülasyon yapısı
Abstract

This study was conducted for molecular and morphological characterization of 28 local eggplant genotypes (so-called “Yamula” eggplant) collected from Kayseri province, 3 Kemer eggplant, and 1 Manisa eggplant genotype, commonly cultivated in Turkey. Molecular analyses were carried out with the use of 10 ISSR and SRAP primers and 30 morphological characteristics. Morphological analyses revealed the nearest genotypes as ERU 3006 - ERU 3007. The polymorphism ratio was identified as 77.36% and 73.72% with ISSR and SRAP markers, respectively. The 25 differenet characters selected from the UPOV description list were used for the morphological characterization of the accessions. While no variation was observed in “intensity of anthocyanin coloration in calyx”, the highest variation coefficient was calculated for “leaf color” and “leaf blade margin shape” (89.60% and 86.15%, respectively). When all plant parts were divided into groups, the highest variation belonged to leaf characteristics (Variation coefficient: 59.39%), followed by the plant stem and fruit characteristics (48.14% and 43.20%, respectively). Results showed that variations exist within Yamula and between Yamula and control genotypes. Present variations could be used in eggplant breeding programs. It was also concluded that regional genetic populations inhabit a wide eggplant genetic diversity which can be a good source for further breeding programs., Bu çalışma, Kayseri ilinden toplanan, “Yamula Patlıcanı” olarak adlandırılan 28 yerel patlıcan genotipi ile Türkiye'de yaygın olarak yetiştirilen 3 Kemer patlıcanı ve 1 Manisa patlıcanı genotipinin moleküler ve morfolojik karakterizasyonu için yapılmıştır. 10 ISSR ve SRAP primeri ile 30 morfolojik karakter kullanılarak analizler gerçekleştirilmiştir. Morfolojik olarak birbirine en çok benzeyen genotipler ERU 3006 ve ERU 3007'dir. Moleküler çalışmalarda polimorfizm oranı ISSR tekniğinde %77,36, SRAP tekniğinde ise %73,72 olarak tespit edilmiştir. Genotiplerin morfolojik karakterizasyonu için UPOV kriterleri listesinden seçilen toplam 25 karakter kullanılmıştır. “Kaliksteki antosiyanin renklenme yoğunluğu” karakterinde herhangi bir varyasyon görülmezken, en yüksek varyasyon katsayısı “yaprak rengi” ve “yaprak kenarı şekli” karakterleri için sırasıyla %89.60 ve %86.15 olarak hesaplanmıştır. Tüm bitki kısımları gruplara ayrıldığında en yüksek varyasyonun yaprak özelliklerinde meydana geldiği anlaşılmıştır (Varyasyon katsayısı: %59.39). Bunu bitki gövde ve meyve özellikleri sırasıyla %48,14 ve %43,20 olarak izlemiştir. Mevcut bulgular, Yamula patlıcan genotipleri ile kontrol genotipleri arasında önemli farklılıklar olduğunu ve Yamula patlıcan genotiplerinde büyük bir varyasyon olduğunu ortaya koymuştur. Mevcut varyasyonlar patlıcan yetiştirme programlarında kullanılabilir. Ayrıca bölgesel genetik popülasyonların geniş bir patlıcan genetik çeşitliliğini içerdiği ve ileriki ıslah programları için iyi bir kaynak olabileceği sonucuna varılmıştır.

Published
2023
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13. Monitoring the 5′UTR landscape reveals isoform switches to drive translational efficiencies in cancer

Author

Weber, Ramona, primary, Ghoshdastider, Umesh, additional, Spies, Daniel, additional, Duré, Clara, additional, Valdivia-Francia, Fabiola, additional, Forny, Merima, additional, Ormiston, Mark, additional, Renz, Peter F., additional, Taborsky, David, additional, Yigit, Merve, additional, Bernasconi, Martino, additional, Yamahachi, Homare, additional, and Sendoel, Ataman, additional

Published
2022
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14. The Efficiency of the BTR-Pen System in Removing Different Types of Broken Instruments from Root Canals and Its Effect on the Fracture Resistance of Roots

Author

Helvacioglu-Yigit, Merve Dulundu and Dilek

Subjects
broken instrument removal, BTR-Pen, K-file, NiTi rotary instrument, ultrasonic
Abstract

The study aimed to evaluate the efficiency of the BTR-Pen system in removing different types of instrument fragments from root canals and to assess its effect on fracture resistance of the roots after the removal of the instruments. One hundred thirty human teeth were divided into 10 groups (2 control groups and 8 study groups) according to the localization and type of the fractured fragment as well as the retrieval techniques. Broken instruments were extracted either with BTR-Pen system loops or removed using solely ultrasonic tips. The success rate of instrument removal and consumed time were recorded. All the teeth were subjected to a load at a 1 mm/min rate in a universal testing machine for mechanical testing. The success of removing broken instruments using the BTR-Pen and ultrasonic was 86.7% and 83.3%, respectively (p > 0.05). When the time is compared, the BTR-Pen system (23.97 ± 8.35 min) showed similar results to that of the ultrasonic technique (24.1 ± 8.28 min) (p > 0.05). The BTR-Pen group required less force to fracture than the ultrasonic group (p = 0.024). In conclusion, the BTR-Pen and ultrasonic groups showed no significant difference in terms of the success rate and removal time. The roots that underwent instrument removal using the BTR-Pen system had less fracture resistance.

Published
2022
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15. IDENTIFICATION OF GENETIC DIVERSITY IN WILD PEAR (Pyrus Elaeagrifolia Pall.) GENOTYPES COLLECTED FROM DIFFERENT REGIONS OF TURKEY WITH SSR MARKER SYSTEM IDENTIFIKACIJA GENETSKE RAZNOLIKOSTI POMOĆU SSR MARKERA KOD GENOTIPOVA DIVLJE KRUŠKE (Pirus Elaeagrifolia Pall.) PRIKUPLJENIH IZ RAZLIČITIH REGIJA TURSKE

Author

Ercisli, Sezai, GÜRCAN, KAHRAMAN, Yilmaz, Kadir Uğurtan, Ozturk, Gökhan, Uysal, Mehmet, Karakaya, Arif, Cakiroglu, Yaşar, Yigit, Merve A., YAMAN, MEHMET, PINAR, HASAN, and UZUN, AYDIN

Abstract

© 2022. Genetika.All Rights ReservedTurkey with diverse ecologies is among the unique countries in terms of plant species and diversity. Among these plant species, naturally growing wild pears (Pyrus elaeagrifolia Pall.) are resistant to chlorosis and drought and could be used in rootstock development programs. In present study, genetic diversity in 96 wild pear genotypes collected from 11 different provinces (Kayseri, Ankara, Kahramanmaraş, Adana, Nevşehir, Konya, Isparta, Denizli, Uşak, Afyonkarahisar, Eskişehir) and regions of Turkey through selection was investigated with the use of SSR (Simple Sequence Repeat) molecular marker system. Present analyses carried out in ABI (Applied Biosystem) 3500 capillary electrophoresis system revealed 93 scorable and all polymorphic bands, thus polymorphism rate was 100%. In UPGMA (Unweighted pair group method with arithmetic mean) dendrogram of wild pear genotypes, similarity index values varied between 0.20-0.83 and a large variation was observed among the genotypes. Present finding may have significant contributions to further studies to be conducted for preservation of gene sources and breeding of wild pear genotype

Published
2022

17. Identification of genetic diversity in wild pear (Pyrus elaeagrifolia Pall.) genotypes collected from different regions of turkey with SSR marker system

Author

Uzun, Aydın, primary, Pinar, Hasan, additional, Yaman, Mehmet, additional, Yigit, Merve, additional, Cakiroglu, Yaşar, additional, Karakaya, Arif, additional, Uysal, Mehmet, additional, Ozturk, Gökhan, additional, Yilmaz, Kadir, additional, Gurcan, Kahraman, additional, and Ercisli, Sezai, additional

Published
2022
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18. Age and frailty are independently associated with increased covid-19 mortality and increased care needs in survivors: results of an international multi-centre study

Author

Alsahab, Mustafa, Beishon, Lucy, Brown, Bryony, Burn, Elinor, Burton, Jenni K, Cox, Natalie, Dani, Melanie, Elhadi, Muhammed, Freshwater, Sarah, Gaunt, Victoria, Gordon, Adam, Goujon, Marie, Hale, Matthew, Hughes, Terry, Jackson, Thomas A, Jelley, Benjamin, Khan, Asma, Khiroya, Heena, Lal, Rajni, Madden, Katy, Magill, Laura, Masoli, Jane, Masud, Tahir, McCluskey, Lauren, McNeela, Natalie, Mohammedseid-Nurhussien, Awolkhier, Moorey, Hannah, Lochlainn, Mary Ni, Nirantharakumar, Krishnarajah, Okoth, Kelvin, Osuafor, Christopher N, Patterson, Katherine, Pearson, Grace M E, Perry, Rita, Pettitt, Michala, Pigott, Jennifer, Pinkney, Thomas, Quinn, Terence, Reynolds, Abigail, Richardson, Sarah, Sanyal, Nik, Seed, Adam, Sleeman, Isobel, Soo, Chee, Steves, Claire, Strain, W David, Taylor, Joanne, Torsney, Kelli, Welch, Carly, Wilson, Daisy, Witham, Miles, Elazeem, Hossam Aldein S Abd, Abdelhafez, Mohammed H, Abdelmalak, Amir, Abdelwahab, Omar A, Abdulhadi, Osama M A S, Adewole, Olubayode, Ahmad, Mohammed, Ahmed, Eltayeb A, Ahmed, Hazem, Ahmed, Islam A, Akcay, Mertcan, Akdeniz, Yeşim, Akın, Emrah, Akladious, Carolyn, Alessandri, Francesco, Ali, Ali, Aljafari, Abdulmalek, Aljafari, Abdulmoiz, Al-Sadawi, Mohammed, Al-Sodani, Lobna, Altintoprak, Fatih, Amaratungaz, Gitanjali, Amer, Jocelyn, Amini, Sylvia, Amir, Taha, Anandarajah, Cheran, Anders, Rachael, Ansari, Muhammed H, Appiah, Kingsley, Atia, Jolene, Atkin, Catherine, Aujayeb, Avinash, Awad, Elsayed M, Azab, Mohammed A, Azam, Mohammad T, Aziz, Sally, Azzam, Ahmed Y, Babar, Laxmi, Babb, Laura, Badh, Manpreet, Baguneid, Clare, Bailey, Emily, Baili, Efstratia, Baldwin, Sarah, Baloyiannis, Ioannis, Bannerjee, Moulinath, Barnard, Anna, Barra, Fabio, Bashir, Hannah, Bawor, Monica, Bayhan, Zülfü, Belcher, James, Belgamwar, Ravindra, Bentley, Corrina, Birchenough, Amy, Bo, Yen Nee J, Boden, Hayley R, Bouhuwaish, Ahmad, Brachini, Gioia, Bremner, Laura, Bridgwater, Hannah, Bryant, Catherine, Budd, Gabrielle, Budd, Sharon, Budzikoski, Adam, Bulla, Reem, Buondonno, Antonio, Burden, Nicole, Butt, Hejab, Capoglu, Recayi, Caracostea, Andra, Cardoso, Rifa, Carr, Alexis, Carrasco-Prats, Milagros, Cattel, Caterina, Ceccarelli, Giancarlo, Cecere, Giuseppe, Charalabopoulos, Alexandros, Charsley, Evelyn, Cheney-Lowe, Hannah, Chevallier, Theodore, Choudhry, Asad J, Ciccarone, Flavia, Cicerchia, Pierfranco M, Cirillo, Bruno, Collins, Fatma D, Comerford, Victoria, Cordie, Ahmed, Coulter, Siobhan, Coulthard, Nick, Cox, Catrin, Cox, Victoria, Crowe, Andrew, Cullen, Jack, Cummings, Jean, Cunningham, Niamh, Curley, Daniel, Currie, Hannah, Daly, Madeleine, Darley, Jay, Dattani, Nikhita, Davakis, Spyridon, Davies, Rowan, De Paola, Gilda, De Toma, Giorgio, Del Valle-Ruiz, Sergio, Deldar, Benyamin, Demir, Hakan, Desai, Arjun, Desai, Nirali, Devaney, Alice, Dew, Lindsey, Dhesi, Jugdeep, Dias, Maria, Dick, Gordon, Doddamani, Parveen, Dogra, Gurinder, Doll, Tina, Dooley, Hannah C, Dost, Samiullah, Dotchin, Catherine, Dowell, Hannah, Draghita, Ioan M, Dundas, James M, Duranti, Giulia, Dusara, Hiren, Dwivedi, Rajesh, Dyer, Adam H, Eastaugh, Alison, Edwards, Elinor, Elghazaly, Shrouk M, Elmehrath, Ahmed O, Elrick, Hope, El-Shazly, Mostafa, Emery, Alexander, Etchill, Eric W, Evans, Sarah, Evison, Felicity, Fairhead, Cassandra, Faulkner, Margherita, Felska, Agnieszka, Fernandez, Antia, Fernández-Fernández, Pedro V, Ferraiolo, Antonella, Ferrero, Simone, Fiori, Enrico, Firat, Necattin, Fisk, Gracie, Fleck, Anna, Fonsi, Giovanni B, Gabre-Kidan, Alodia, Gallo, Gaetano, Gandhi, Ratnam, Garner, Madeleine, Georgiou, Nikolaos, Gerretsen, Hannah, Ghannam, Nourhan A A, Ghobrial, Andrew, Ghobrial, Hedra, Ghufoor, Zaynub, Gibbon, Jake, Gilbert, Georgia F, Giles, Marie, Giménez-Francés, Clara, Gonullu, Emre, Gray, Amy, Gray, Joshua H, Green, Deirdre, Greene, Charlotte, Griffin, Ellanna, Griffith, Karla, Grubb, Anthony, Guan, Yue, Guerero, Daniel N, Gupta, Ayushi, Gustavino, Claudio, Guzman, Laurenny, Hadreiez, Ahmed K M, Hajiioannou, Jiannis, Hanji, Deevia, Madhavan, Deepthy Hari, Harmantepe, Tarık, Harrison, Patrick, Hart, Barbara, Haslam, Aidan, Haunton, Victoria, Haut, Elliott R, Heinsohn, Torben, Hennah, Lindsay, Hetta, Helal F, Hickman, Alexander, Hobill, Abigail, Hogan, Patrick C P, Hogan, Vesna, Holmes, Elizabeth, Honney, Katie, Hood, Katharine, Hopkinson, Katherine, Howells, Lara, Hrouda, Nicole, Hunsley, Danielle, Hurst, William, Hussein, Rand A, Ibrahim, Mohamed Eltaher A A, Ibtida, Ishmam, Ibukunoluwakitan, Aina, Ishlek, Irem, Iyer, Rishi, Jackson, Karl, Jackson, Rosie, James, Ellen, Jarvis, Hayley, Jeffs, Sophie, Jenko, Nathan, Jeyakumar, Sasha, Kabir, Shahriar, Kainth, Harjinder, Kalloo, Jason, Kanzaria, Akhil, Karapanou, Amalia, Kardaman, Nuha, Karthikeyan, Sandeep, Karunatilleke, Anne, Kelly, Mairead, Kelly, Nicola I, Khalid, Hesham, Khan, Haris, Khan, Muhammad S, King, Matthew, Kneen, Thomas, Kok, Li, Kratochwila, Chiara, Kuzeva, Aneliya, Lapolla, Pierfrancesco, Lau, Rebecca, Law, Kar Yee, Leadbetter, Aimee, Lee, Gabriel, Lee, Helena, Levinson, Gavriella, Lewis, Grace, Liakakos, Theodore, Lim, Stephen, Lis, Danielle, Livesey, Emma, López-Morales, Pedro, Lowes, Lily, Lunt, Eleanor, Lyon, Emily, Madan, Suvira, Majid, Zeinab, Malapati, Harsha, Man, Jade, Mandane, Baguiasri, Manning, Sarah H, Mantoglu, Baris, Martínez-Sanz, Nuria, Marx, William, Masood, Almontacer E B, Maughan, Tom, Mawhinney, Jamie, Maxfield, Dominic, Mayer, Jordan, Maynard, Henry, McDonald, Claire, McGovern, Aine, Mclachlan, Sophie, Medina-Manuel, Esther, Meneghini, Simona, Metcalf, Michelle, Millwood-Hargrave, John, Mingoli, Andrea, Miu, Kelvin, Mohamed, Fawsiya, Mohamed, Soha M, Hussein, Aliae A R Mohamed, Mohammad, Abdulkader, Mohammed, Aaliya, Momen, Ahmed A, Moomo, Farhana, Mora-Guzmán, Ismael, Moriarty, Lizzie, Morrin, Hamilton, Morris, Claire, Moss, Nicholas, Moustafa, Mohamed M, Mpoura, Maria, Mubin, Mohammed, Muhtaroglu, Ali, Muir, Georgina, Mulhern, Stephanie, Muller, Daniel, Murphy, Declan C, Muzammil, Bushra, Nadkarni, Varun, Nageh, Mariam Albatoul, NasrEldin, Yasmin K, Nawaz, Wasim, Nguyen, Hanna, Cheallaigh, Cliona Ni, Noar, Alexander, North, Samuel, Nwolu, Favour, O’Docherty, Alice, Odutola, Omoteniola, O’Dwyer, Sinead, Ogochukwu, Olebu, O’Mahony, Catherine, Orlando, Lia, Osterdahl, Marc, Page, Christina, Panayotidis, Ismini, Pancholi, Shivam, Parkin, Jessica, Passby, Lauren C, Pastor-Pérez, Patricia, Patel, Harnish, Patel, Shefali, Penfold, Rose, Perinpanathan, Rupini, Perivoliotis, Konstantinos, Perra, Teresa, Pinkney, Martha, Pinotti, Enrico, Porcu, Alberto, Price, Angeline, Pugliese, Francesco, Puri, Prabhleen, Pytraczyk, Sylvia, Qaiser, Yusra, Qurashi, Maria, Radenkovic, Dina, Rajeswaran, Thurkka, Rapaport, Sarah F, Razzak, Tahmina, Reilly, Lara, Reynolds, Paul, Richardson, Alexandra, Roberts, Amelia, Roberts-Rhodes, Charlotte, Robinson, Tanya, Rocca, Aldo, Ross-Skinner, Emily, Ruiz-Marín, Miguel, Ryall, Rebecca, Saad, Alshaimaa M, Saad, Mahmoud M, Sadiq, Ambreen, Sammarco, Giuseppe, Sampanis, Michail A, Sanghvi, Hazel, Sapienza, Paolo, Sayers, Ross, Scott, Luca, Sen, Michael, Shaban, Mosab A A, Shakespeare, Kathleen T, Shaw, Ellie, Shaw, Hannah, Sheldrake, Jonathan, Sim, Sing Yang, Simonelli, Luigi, Sipsas, Nikolaos V, Sivam, Jarita, Sivarajan, Sri, Smith, Jennifer, Speranza, Fabio, Spice, Claire, Stafford, Amanda, Stambollouian, Katharine, Stevens, Kent A, Stewart, Jack, Stratton, Emma, Street, Hannah, Surtees, Michael, Swinnerton, Emma, Taher, Ahmed S A, Tait, Caroline, Taylor, Amybel, Thake, Miriam, Thin, Katie, Thould, Hannah, Thyn, Thyn, To, Benjaman, Tobiss, Hannah, Toppley, Kathryn, Townsend, Liam, Tullo, Ellen, Tzovaras, George, Umeadi, Anthony, Vaidya, Hrisheekesh, Valero-Soriano, María, Varden, Rosanna, Vergani, Vittoria, Vervoort, Dominique, Vescio, Giuseppina, Vettasseri, Mark, Virk, Madiha, Vyas, Vaishali, Wagland, Joanne, Wallis, Stephanie, Warner, Chloe, Watkins, Eleanor, Watson, Hannah, Webb, Rachael, Welsh, Sarah H, West, Ruth, Whelan, Elisha, Whitney, Julie, Whitsey, Mark, Wilcock, Catherine, Wilkinson, Iain, Williams, David, Williamson, Megan, Willott, Ruth H, Wimalasundera, Mettha, Win, Yu Lelt, Winter, Laura, Worrall, Stephanie, Wright, Rebecca, Yeo, Natalie, Yeung, Eirene, Yigit, Merve, Yildiz, Yasin A, Yusuf, Humza, Zambon, Martina, Zaw, Hein, and Elabedeen, Omar Zein

Subjects
Male, Aging, medicine.medical_specialty, Frail Elderly, COVID-19, delirium, frailty, mortality, transitions of care, Cohort Studies, AcademicSubjects/MED00280, Interquartile range, Internal medicine, medicine, Dementia, Humans, Survivors, Aged, Proportional hazards model, business.industry, SARS-CoV-2, Hazard ratio, Odds ratio, General Medicine, medicine.disease, Confidence interval, frailty,COVID-19, Ageing, Delirium, Female, medicine.symptom, Geriatrics and Gerontology, business, Cohort study, Research Paper
Abstract

Introduction Increased mortality has been demonstrated in older adults with coronavirus disease 2019 (COVID-19), but the effect of frailty has been unclear. Methods This multi-centre cohort study involved patients aged 18 years and older hospitalised with COVID-19, using routinely collected data. We used Cox regression analysis to assess the impact of age, frailty and delirium on the risk of inpatient mortality, adjusting for sex, illness severity, inflammation and co-morbidities. We used ordinal logistic regression analysis to assess the impact of age, Clinical Frailty Scale (CFS) and delirium on risk of increased care requirements on discharge, adjusting for the same variables. Results Data from 5,711 patients from 55 hospitals in 12 countries were included (median age 74, interquartile range [IQR] 54–83; 55.2% male). The risk of death increased independently with increasing age (>80 versus 18–49: hazard ratio [HR] 3.57, confidence interval [CI] 2.54–5.02), frailty (CFS 8 versus 1–3: HR 3.03, CI 2.29–4.00) inflammation, renal disease, cardiovascular disease and cancer, but not delirium. Age, frailty (CFS 7 versus 1–3: odds ratio 7.00, CI 5.27–9.32), delirium, dementia and mental health diagnoses were all associated with increased risk of higher care needs on discharge. The likelihood of adverse outcomes increased across all grades of CFS from 4 to 9. Conclusion Age and frailty are independently associated with adverse outcomes in COVID-19. Risk of increased care needs was also increased in survivors of COVID-19 with frailty or older age.

Published
2021

19. Monitoring the 5’UTR landscape reveals 5’terminal oligopyrimidine (TOP) motif switches to drive translational efficiencies

Author

Weber, Ramona, primary, Ghoshdastider, Umesh, additional, Spies, Daniel, additional, Duré, Clara, additional, Valdivia-Francia, Fabiola, additional, Forny, Merima, additional, Ormiston, Mark, additional, Renz, Peter F., additional, Taborsky, David, additional, Yigit, Merve, additional, Yamahachi, Homare, additional, and Sendoel, Ataman, additional

Published
2021
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23. NEW SOURCE FOR HIGH ANTHER CULTURE RESPONSE TURKISH LOCAL EGGPLANT (Solanum melongena L.) GENOTYPE.

Author

Pakashtica, Vese, Yigit, Merve A., Uysal, Emrah, Bulbul, Cansu, Simsek, Duran, and Pinar, Hasan

Abstract

In this study, 28 local (Yamula) eggplant genotypes, 1 Manisa eggplant genotype and 3 Kemer eggplant genotypes commonly grown in Turkey were subjected to anther culture. While the genotypes ERU-3008, ERU-3011 and ERU-3016 did not have anther development, the others had anther developments at different ratios varied between 3.33% (ERU-3009 and ERU-3012) and 50% (ERU-961). Of the genotypes with anther development, 10 had embryo formation at different ratios varied between 6.25% (ERU-3015) 37.5% (ERU-952). Embryo formation was not observed in control group genotypes (Kemer and Manisa). It was observed in the present study that Yamula eggplant prominent especially with fruit flesh firmness yielded positive responses to anther culture. [ABSTRACT FROM AUTHOR]

Published
2022

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