Model development of isopropanol dehydration from binary and ternary mixtures with pervaporation: Comparison and environmental evaluation of hybrid separation methods by Huyen Trang Do Thi, Eniko Haaz, Andras Jozsef Toth

“…Two pervaporation models (Model I and Model II) were used for parameter estimation, with Model II more accurately predicting flux behaviour, as it accounts for concentration-dependent diffusion coefficients. …”
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Interplay between fecundity, sexual and growth selection on the spring phenology of European beech (Fagus sylvatica L.). by Oddou-Muratorio, Sylvie, Bontemps, Aurore, Gauzere, Julie, Klein, Etienne K

“…This interplay between fecundity and sexual selection calls for an integrative approach to predict the evolution of spring phenology under a changing climate.…”
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Knowledge and Use of Caries Detection Methods among Dental Students and Dental Practitioners in Riyadh, Saudi Arabia by Waseem Radwan, Abeer A. AlNasser, Hesah Aloqab, Khalid Al-Saggaf, Nora A. Almuhtab, Bothinah Alnasyan

“…Regression models were used to predict associations. Results. 42.3% and 32.7% use sharp explorers in diagnosing caries always and most of the time, respectively. …”
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HTR1D regulates the PI3K/Akt signaling pathway to impact hepatocellular carcinoma development and resistance to sorafenib by Yingai Zhang, Yuting Zhang, Shuai Zhou, Mujeeb Ur Rehman, Fankai Lin, Jianquan Zhang, Hailong Zhou

“…Finally, the potential of HTR1D as a predictive marker for patient prognosis was evaluated by immunohistochemistry. …”
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Identification of lethality-related m7G methylation modification patterns and the regulatory features of immune microenvironment in sepsis by Dan Wang, Rujie Huo, Lu Ye

“…Conclusion: The constructed survival status diagnostic model based on the five lethality-related m7G signature genes may help predict the survival status of patients, and the 10 hub genes obtained may be potential therapeutic targets for sepsis.…”
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Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles by Khaled Tarmissi, Hanan Abdullah Mengash, Noha Negm, Yahia Said, Ali M. Al-Sharafi

“…Finally, to ensure the transparency of the model's predictions, XAIFTL-AWCDAV integrates explainable AI (XAI) techniques, utilizing SHAP to visualize and interpret each feature's impact on the model's decision-making process. …”
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Anthropometric and Body Composition Measurements Related to Osteoporosis in Geriatric Population by Sadiye Murat, Bilinç Doğruöz Karatekin, Filiz Demirdağ, Esma Nur Kolbaşı

“…In multiple regression analysis, SMI contributes most in predicting FN and LS T-scores. Conclusions: Anthropometric measurements should be carefully selected in the geriatric population. …”
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New inflammatory markers associated with disease activity in rheumatoid arthritis: pan-immune-inflammation value, systemic immune-inflammation index, and systemic inflammation resp... by İpek Okutan, Recai Aci, Âdem Keskin, Melek Bilgin, Halit Kızılet

“…Systemic immune-inflammation index had a predictive accuracy of 75.26%, PIV 71.13%, and SIRI 72.16%. …”
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Tuberculosis and COVID-19: Lessons from the Past Viral Outbreaks and Possible Future Outcomes by Radu Crisan-Dabija, Cristina Grigorescu, Cristina-Alice Pavel, Bogdan Artene, Iolanda Valentina Popa, Andrei Cernomaz, Alexandru Burlacu

“…Despite the rapidly growing number of cases, the data needed to predict the impact of the COVID-19 pandemic on patients with latent TB and TB sequelae still lies ahead. …”
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Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea by Ross Mandeville, Hooman Sedghamiz, Perry Mansfield, Geoffrey Sheean, Chris Studer, Derrick Cordice, Ghodsieh Ghanbari, Atul Malhotra, Shamim Nemati, Jejo Koola

“…Notably, by averaging the predicted probabilities of each segment for individual patients, the model correctly classified up to 82% of control and OSA patients. …”
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Hsa-miR-105-1 Regulates Cisplatin-Resistance in Ovarian Carcinoma Cells by Targeting ANXA9 by Xinxin Kou, Hui Ding, Lei Li, Hongtu Chao

“…The target gene of Hsa-miR-105-1 was predicted by using the TargetScan and Starbase databases and verified by the double luciferase reporter gene assay. …”
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Mutation Analysis of Synthetic DNA Barcodes in a Fission Yeast Gene Deletion Library by Sanger Sequencing by Minho Lee, Shin-Jung Choi, Sangjo Han, Miyoung Nam, Dongsup Kim, Dong-Uk Kim, Kwang-Lae Hoe

“…Peculiarly, the frequency of substitutions (67.6%) was unexpectedly higher than in budding yeast (~28%) and well above the predicted error of Sanger sequencing (~2%), which might have arisen during the solid-phase oligonucleotide synthesis and PCR amplification of the barcodes during strain construction. …”
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Effect of Preoperative Vitamin D Deficiency on Hypocalcemia in Patients with Acute Hypoparathyroidism after Thyroidectomy by Peeradon Vibhatavata, Pongthep Pisarnturakit, Mongkol Boonsripitayanon, Paveena Pithuksurachai, Nattachet Plengvidhya, Sirinart Sirinvaravong

“…Preoperative vitamin D level <19.6 ng/mL could predict significant and symptomatic hypocalcemia in postoperative hypoparathyroidism with sensitivity of 90% and 82% and specificity of 70% and 69%, respectively. …”
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Understanding Code Blue Activations: Insights From Early Warning and Palliative Scores in a Tertiary Hospital by Giwangkancana GW, Setiasih YG, Hasanah A, Persiyawati Y, Wawan

“…Additionally, the integration of palliative care considerations into hospital protocols is crucial for improving patient outcomes and resource allocation.Conclusion: Early warning system and palliative care scoring may predict code blue activation and if managed can reduce its number.Keywords: In-hospital cardiac arrest, code blue, early warning score, palliative care, emergency response, resuscitation…”
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Lifestyle scores and their potential to estimate the risk of multiple non-communicable disease-related endpoints: a systematic review by Jie Ding, Ruojin Fu, Tanwei Yuan, Hermann Brenner, Michael Hoffmeister

“…Given the common risk factors for some of the major NCDs, we conducted a systematic review to evaluate the potential of existing lifestyle scores in predicting the risk of multiple NCD-related endpoints. …”
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Patient “No-Show” Prior to Elective Primary Total Hip Arthroplasty Increases Risk of Postoperative Anemia by Jeffrey S. Mun, BA, Matthew W. Parry, MD, MS, Alex Tang, MD, Jesse J. Manikowski, MS, Cory Crinella, PA-C, John J. Mercuri, MD, MA

“…Regression analyses were run to identify relationships between NS status and postoperative outcomes, as well as factors that would predict NS status. Results: Compared to AA patients, NS patients had an increased odds of a postoperative complication (odds ratio:1.3, P = .0005), specifically postoperative anemia (odds ratio: 1.3, P = .0004). …”
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Joint trajectories of physical activity, health, and income before and after statutory retirement: A 22-year follow-up. by Tea Lallukka, Petteri Kolmonen, Ossi Rahkonen, Eero Lahelma, Jouni Lahti

“…People with obesity had a 22 percentage-point (21-24) higher predicted probability of belonging to Group 1 than people with normal weight. …”
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Insight on the Genetics of Atrial Fibrillation in Puerto Rican Hispanics by Ariel F. Gonzalez-Cordero, Jorge Duconge-Soler, Hilton Franqui-Rivera, Roberto Feliu-Maldonado, Abiel Roche-Lima, Israel Almodovar-Rivera

“…Furthermore, we performed a machine learning analysis and determined that Native American admixture and heart failure were strongly predictive of AF in PHR. For the first time, this study provides some genetic insight into AF’s mechanisms in a Puerto Rican Hispanic cohort.…”
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Disability of military personnel in Ukraine for 2014-2022 by I.Y. Khanyukova, N.O. Hondulenko, N.A. Sanina

“…The rate of primary disability of servicemen between 2014 and 2022 gradually increased, with a slight “dip” in 2020, and given the intensity of hostilities, further increases in the number of persons with war-related disabilities can be predicted. According to the structure of disability groups, most military personnel are persons with disabilities of the third group (58.8%). …”
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ALL-Net: integrating CNN and explainable-AI for enhanced diagnosis and interpretation of acute lymphoblastic leukemia by Abhiram Thiriveedhi, Swetha Ghanta, Sujit Biswas, Ashok K. Pradhan

“…Additionally, XAI techniques, specifically the Local Interpretable Model-Agnostic Explanations (LIME) algorithm is employed to interpret the model’s predictions, providing insights into the decision-making process of our ALL-Net CNN. …”
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