The predicted value for venous thromboembolism based on a modified model of caprini combined with fasting blood glucose and D-dimer: a retrospective study by Ping Wang, Yusi Yang, Lin Zheng, Xiaotong Qi, Sheng Yan, Honglin Dong

“…Logistic regression screened for: age (OR 1.04, 95% CI 1.02—1.06), D-dimer (OR 1.06, 95% CI 1.03—1.08), Caprini (OR 1.17, 95% CI 1.08—1.27) and fasting blood glucose (FBG) (OR 1.18, 95% CI 1.03—1.35), which also appeared in the LASSO seven-factor model. …”
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Development and evaluation of functional biscuits infused with functional spices and white pea flour: A comprehensive study on optimizing glycemic control and incorporating anti-di... by Minhaj Uddin, Nabil Mashahid, Juairia Absar Anta, Mahima Ranjan Acharjee

“…Sample B exhibited the highest moisture (10.34 ± 0.13 %), protein (16.22 ± 0.12 %), and fiber (3.79 ± 0.01 %) content. …”
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Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients by Aref Andishgar, Sina Bazmi, Kamran B. Lankarani, Seyed Alireza Taghavi, Mohammad Hadi Imanieh, Gholamreza Sivandzadeh, Samira Saeian, Nazanin Dadashpour, Alireza Shamsaeefar, Mahdi Ravankhah, Hamed Nikoupour Deylami, Reza Tabrizi, Mohammad Hossein Imanieh

“…RSF with Ridge achieved the highest performance (C-index: 0.699) for BC prediction, while RSF with RSF had the highest performance (C-index: 0.784) for mortality prediction. Top BC predictors were LT graft types, IBD in recipients, recipient’s BMI, recipient’s history of PVT, and previous LT history. …”
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Biometrías fetales y variables maternas como predictores de desviaciones de la condición trófica al nacer by Disney Borrego Gutierrez, Elizabeth Alvarez Guerra González, Nélida Liduvina Sarasa Muñoz, Danay Vázquez Rivero, Celidanay Ramírez Mesa, Calixto Orozco Muñoz

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Functional Dependency as a Marker for Positive SARC-F Screen among Older Persons at the Emergency Department by Edward Chong, Eileen Fabia Goh, Wee Shiong Lim

“…MBI (AUC=0.82, 95% CI 0.77–0.84) and IADL (AUC=0.78, 95% CI 0.72–0.84) showed superior discrimination for SARC-F+ compared to other measures (AUC=0.58–0.70). …”
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Correlation analysis between mild cognitive impairment and the traditional Chinese medicine body constitution types among the elderly in a community in Changning District, Shanghai by XU Lei, ZHANG Min, WANG Wenwen, YU Li, ZHU Xuting, LYU Xiaorui

“…The highest detection rate of MCI in the elderly are those with a Qi stagnation constitution (10.8%), followed by those with a dampness-heat constitution (9.1%) and a Qi deficiency constitution (8.4%). …”
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The Validation of a Novel, Sex-Specific LDL-Cholesterol Equation and the Friedewald, Sampson-NIH, and Extended-Martin–Hopkins Equations Against Direct Measurement in Korean Adults... by Hyun Suk Yang, Soo-Nyung Kim, Seungho Lee, Mina Hur

“…<b>Results:</b> The derived SSLE equation was as follows: for TG < 200 mg/dL, LDLc = 0.963 × TC − 0.881 × HDLc − 0.111 × TG + 0.982 × Sex − 6.958; for TG ≥ 200 mg/dL, LDLc = 0.884 × TC − 0.646 × HDLc − 0.126 × TG + 3.742 × Sex − 3.214 (male = 1, female = 0). …”
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Spatial distribution and determinants of improved shared sanitation facilities among households in Ethiopia: Using 2019 mini-Ethiopian Demographic and Health Survey. by Baye Tsegaye Amlak, Daniel Gashaneh Belay

“…Individual-level variables significantly associated with the use of improved shared toilet facilities included being a household head aged 55 years or older [AOR = 0.48; 95% CI: 0.33, 0.71], having secondary education or higher [AOR = 2.43; 95% CI: 1.80, 3.28], and belonging to middle or rich wealth status [middle: AOR = 2.32; 95% CI: 1.35, 3.96; rich: AOR = 6.23; 95% CI: 3.84, 10.11]. …”
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Catlet scoring system as a new predictor for in-stent restenosis in patients with chronic coronary artery disease undergoing percutaneous coronary intervention with drug-eluting st... by Juan Wang, Mingchao Zhang, Weipeng Gan, Mingxing Xu, JiaYan Zhou, Lingfei Yang, Yongsheng Ke

“…Results A total of 165 patients (46 women and 119 men) with a mean ages of 66.19 ± 10.54 years were finally enrolled in this study. …”
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Improvement of a MALDI-TOF database for the reliable identification of Candidozyma auris (formally Candida auris) and related species by Hui-Hui Zhu, Miao-Miao Liu, Teun Boekhout, Qi-Ming Wang

“…The validation of this database was performed with 34 other strains of the genus Candidozyma, and the result was compared with the identification results when using DBRs v1.0.0.4 (Tianrui, China). Our library allowed a 100% identification of the evaluated strains with all strains showing log scores of >2.0. …”
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Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, Ethiopia by Wudneh Ketema Moges, Awoke Seyoum Tegegne, Aweke A. Mitku, Esubalew Tesfahun, Solomon Hailemeskel

“…The estimated ATE for the S-learner was 0.284, which is lower than the true ATE of 0.216. …”
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DeepGenMon: A Novel Framework for Monkeypox Classification Integrating Lightweight Attention-Based Deep Learning and a Genetic Algorithm by Abdulqader M. Almars

“…On dataset 1, it achieves a precision of 0.985, recall of 0.984, F-score of 0.985, and accuracy of 0.985. Similarly, on dataset 2, the model attains a precision of 0.981, recall of 0.982, F-score of 0.982, and accuracy of 0.982. …”
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Quality improvement of plant-based white chocolate enriched with encapsulated blueberry juice by Rossi Indiarto, Natallina Wiranto Putri, Fetriyuna Fetriyuna, Edy Subroto, Arif Nanda Irawan, Dimas Rahadian Aji Muhammad, Mohd Nizam Lani

“…VWC exhibited desirable V(β) and VI(β) crystal forms, with median particle sizes (78.37–104.8 µm) comparable to those of commercial products. …”
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The association between serum albumin levels and metabolic syndrome based on the NHANES and two sample Mendelian randomization study by Yile Tang, Yong Fan, Jin Su, Zisen Yang, Zaoling Liu

“…When the serum albumin levels were used as categorical variables, the adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for metabolic syndrome across higher serum albumin levels quartiles were 0.981 (0.842–1.143), 1.290 (1.115–1.492), and 1.244 (1.064–1.454) compared to the lowest quartile, respectively. …”
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Development of High-performance Proton Transfer Reaction Time-of-Flight Mass Spectrometer by Chun-guang XIE, Ji-yang ZHANG, Tong YANG, Yu-liang HUANG, Shu-guang XIE, Hui ZHU, Jing-xuan SUN, Ping CHENG

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Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study by Stanford Martinez, Carolina Ramirez-Tamayo, Syed Hasib Akhter Faruqui, Kal Clark, Adel Alaeddini, Nicholas Czarnek, Aarushi Aggarwal, Sahra Emamzadeh, Jeffrey R Mock, Edward J Golob

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Using a digital health intervention “INTERCEPT” to improve secondary prevention in coronary heart disease (CHD) patients: protocol for a mixed methods non-randomised feasibility st... by Catriona Jennings, Irene Gibson, Marissa Corcoran, John William McEvoy, Lis Neubeck, Lisa Hynes, Faisal Sharif, David Wood, Andrew W Murphy, Molly Byrne

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Prospective Study about the Utility of Endoscopic Ultrasound for Predicting the Safety of Endoscopic Submucosal Dissection in Early Gastric Cancer (T-HOPE 0801) by Daisuke Kikuchi, Toshiro Iizuka, Shu Hoteya, Akihiro Yamada, Tsukasa Furuhata, Satoshi Yamashita, Kaoru Domon, Masanori Nakamura, Akira Matsui, Toshifumi Mitani, Osamu Ogawa, Mitsuru Kaise

“…Mean procedure time was significantly longer in group R (105.4 min) than in group P (65.5 min) (P<0.001). …”
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The risks of adverse events with mirtazapine for adults with major depressive disorder: a systematic review with meta-analysis and trial sequential analysis by Caroline Barkholt Kamp, Johanne Juul Petersen, Pascal Faltermeier, Sophie Juul, Christina Dam Bjerregaard Sillassen, Faiza Siddiqui, Rebecca Kjaer Andersen, Joanna Moncrieff, Mark Abie Horowitz, Michael Pascal Hengartner, Irving Kirsch, Christian Gluud, Janus Christian Jakobsen

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Digital transformation of relocated higher education institutions in Ukraine under martial law by Hanna Alieksieieva, Nataliia Kravchenko, Larysa Horbatiuk, Tetyana Nestorenko, Viktoriia Zhyhir, Antonina Kalinichenko, Yana Glazova

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