Serum LCAT as a Gender-Neutral Biomarker for Early Osteoporosis: A Multicenter Cohort Validation Study by Wen Y, Cheng S, Gou L, Zhou W, Li Y, Wang R, Wu J, Dai X, Gao M, Wang L, Xue B, Wang Y

“…Moreover, serum LCAT levels exhibited significantly higher predictive value for osteoporosis than that of conventional serum 25-hydroxyvitamin D (25OHD) levels.Conclusion: We demonstrate for the first time that reduced serum LCAT concentrations predict osteoporosis risk independently of confounding factors, qualifying it as a promising early-stage diagnostic biomarker. …”
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Preoperative inflammatory markers and tumor markers in predicting lymphatic metastasis and postoperative complications in colorectal cancer: a retrospective study by Huiming Wu, Yize Wang, Min Deng, Zhensheng Zhai, Dingwen Xue, Fei Luo, Huiyu Li

“…Abstract Objective To analyze the impact of preoperative inflammatory markers and tumor markers on lymphatic metastasis and postoperative complications in colorectal cancer patients, and explore their predictive value for these outcomes. Furthermore, based on the preoperative inflammatory and tumor marker indicators with significant effects, predictive models for the risk of lymphatic metastasis and the incidence of postoperative complications will be constructed. …”
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Skin rash in metastatic hormone sensitive prostate cancer patients treated with apalutamide: a retrospective multicenter study in Korea by Won Tae Kim, Hyun Ho Han, Seok Joong Yun, Seong Hyeon Yu, Taek Won Kang, Yun-Sok Ha, Jun Nyung Lee, Tae Gyun Kwon, Byung Hoon Kim, Won Ik Seo, Chan Ho Lee, Jae Il Chung, Jung Ki Jo, U-Syn Ha, Ji Youl Lee, Hwang Gyun Jeon, Seong Il Seo, Kyo Chul Koo, Byung Ha Chung, Jong Wook Kim, Joongwon Choi, Jong Wook Park, Hongzoo Park, Sungchan Park, Soo Dong Kim, Hak Min Lee, Sung Kyu Hong, Jae Young Joung

“…This study aims to investigate the incidence rate of skin rash and the predictive value of inflammation markers for skin rash in real-world Korean patients. …”
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Flower lose, a cell fitness marker, predicts COVID‐19 prognosis by Michail Yekelchyk, Esha Madan, Jochen Wilhelm, Kirsty R Short, António M Palma, Linbu Liao, Denise Camacho, Everlyne Nkadori, Michael T Winters, Emily S Rice, Inês Rolim, Raquel Cruz‐Duarte, Christopher J Pelham, Masaki Nagane, Kartik Gupta, Sahil Chaudhary, Thomas Braun, Raghavendra Pillappa, Mark S Parker, Thomas Menter, Matthias Matter, Jasmin Dionne Haslbauer, Markus Tolnay, Kornelia D Galior, Kristina A Matkwoskyj, Stephanie M McGregor, Laura K Muller, Emad A Rakha, Antonio Lopez‐Beltran, Ronny Drapkin, Maximilian Ackermann, Paul B Fisher, Steven R Grossman, Andrew K Godwin, Arutha Kulasinghe, Ivan Martinez, Clay B Marsh, Benjamin Tang, Max S Wicha, Kyoung Jae Won, Alexandar Tzankov, Eduardo Moreno, Rajan Gogna

“…In patients presenting in the early phase of COVID‐19 illness, hFwe‐Lose expression accurately predicts subsequent hospitalization or death with positive predictive values of 87.8–100% and a negative predictive value of 64.1–93.2%. hFwe‐Lose outperforms conventional inflammatory biomarkers and patient age and comorbidities, with an area under the receiver operating characteristic curve (AUROC) 0.93–0.97 in predicting hospitalization/death. …”
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Projection selection and rapid atrial pacing improves early outcomes after self-expanding transcatheter aortic valves by María Tamargo, Enrique Gutiérrez, Jorge García Carreño, María Eugenia Vázquez Álvarez, Ricardo Sanz-Ruiz, Mike Huanca, Erika Ludeña, Javier Soriano, Jaime Elí, Francisco Fernández-Avilés, Javier Bermejo

“…The absence of the Wenckebach phenomenon during RAP had a negative predictive value of 97% (95%CI, 91-99) for pacemaker implantation at the follow-up, which significantly decreased the need for 24-hour temporary pacemaker monitoring in the COP + RAP group (91.8% vs 28.1%; P < .0001) and the median [IQR] length of stay (5.0 [4-8] days vs 2.0 [1-4] days; P < .0001). …”
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Airway-microbiome-driven mechanisms of disease during optimised self-management: a lesson learned from mechanistic study of the Colour-COPD trial by Alice M Turner, Daniella Spittle, Karl Staples, David Cleary

“…Since only half of exacerbations of chronic obstructive pulmonary disease (acute exacerbation of chronic obstructive pulmonary disease) are bacterial, and sputum colour has a good positive predictive value for bacterial presence, it is likely that our intervention will reduce antibiotic consumption. …”
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An Exosomal mRNA Urine Test for Detection and Risk Stratification of Human Kidney Transplant Rejection by Rania El Fekih, Kurt Franzen, James Hurley, Brian C. Haynes, Tamara Merhej, Areej Alghamdi, Elliot Hallmark, Shuran Xing, Sonia Kumar, John Choi, Zhabiz Solhjou, Christa Deban, Anis Saad, Ahmad Halawi, Nour Younis, Katherine Cashman, Maribel Dagher, Siawosh K. Eskandari, Soltan Al Chaar, Helmut Rennke, Astrid Weins, Reza Abdi, Anil Chandraker, James F. Markmann, Kassem Safa, Leonardo V. Riella, Matt McFaul, Chris Ventura, Alexandre V. Vlassov, Richard Formica, Camila Macedo, Johan Skog, Jamil R. Azzi

“…Results: Four mRNAs (IL32, B2M, CXCL11, and PGK1) performed well in distinguishing biopsies with rejection or significant inflammation from those without inflammation, achieving 94% sensitivity, 62% positive predictive value, and 52% specificity. Patients who tested positive by the signature but negative by biopsy were nearly twice as likely to experience adverse outcomes in the 5-year follow-up period, including subsequent rejection, thereby showing the limitations of kidney biopsies and the prognostic potential of molecular signatures. …”
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Carotid intima-media thickness, cardiovascular disease, and risk factors in 29,000 UK Biobank adults by Sayan Mitra, Raaj Kishore Biswas, Petra Hooijenga, Sophie Cassidy, Andrea Nova, Isabella De Ciutiis, Tian Wang, Cynthia M Kroeger, Emmanuel Stamatakis, Andrius Masedunskas, Raffaele De Caterina, Maria L Cagigas, Luigi Fontana

“…The cumulative cardiometabolic-risk biomarker index offers additional predictive value for subclinical atherosclerosis and future cardiovascular events. …”
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An App model that utilizes a logistic regression algorithm for predicting choledocholithiasis: A prospective clinical trial by F. García-Villarreal, L.M. Torres-Treviño, C. Herrera-Figueroa, J.O. Jáquez-Quintana, A.A. Garza-Galindo, C.A. Cortez-Hernández, D. García-Compeán, R.A. Jiménez-Castillo, H.J. Maldonado-Garza, J.A. González-González

“…In patients with an intermediate risk for CL, the AUC value was 0.72 and the positive predictive value (PPV) was 70%. In patients with a high risk for CL, the AUC value was 0.78 and the PPV was 89%. …”
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TET2 gene mutation status associated with poor prognosis of transition zone prostate cancer: a retrospective cohort study based on whole exome sequencing and machine learning model... by Yutong Wang, Yutong Wang, Yutong Wang, Yutong Wang, Yutong Wang, Ailing Yu, Ailing Yu, Ailing Yu, Ailing Yu, Ailing Yu, Ziping Gao, Ziping Gao, Ziping Gao, Ziping Gao, Xiaoying Yuan, Xiaochen Du, Peng Shi, Haoyun Guan, Shuang Wen, Honglong Wang, Liang Wang, Liang Wang, Liang Wang, Liang Wang, Bo Fan, Bo Fan, Bo Fan, Bo Fan, Zhiyu Liu, Zhiyu Liu, Zhiyu Liu, Zhiyu Liu

“…The constructed predictive nomogram provided evidence that TET2 mutant status integration conferred statistically significant improvements in model accuracy and clinical predictive value.ConclusionOur study elucidated the distinct genetic basis of prostate cancer in the transition zone and identified TET2 mutation as an independent prognostic determinant in TZ PCa. …”
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STRONG II trial: stereotactic body radiation therapy following chemotherapy for unresectable perihilar cholangiocarcinoma – a single-arm multicentre phase II study by Derk Jan de Groot, Luisa Vonghia, Bas Groot Koerkamp, Lydi M J W van Driel, Alejandra Méndez Romero, Anne Demols, Erik van Werkhoven, Dirk De Ruysscher, Christelle Bouchart, Marjolein Y V Homs, Joris I Erdmann, Martijn Intven, Ferry Eskens, Harm Westdorp, Timon Vandamme, Suus Y van Loosbroek, Maaike T W Milder, Rianne D W Vaes, Wilhelm den Toom, François Willemssen, Yvette Seppenwoolde, Nadia Haj Mohammad, Joep de Bruijne, Eva Versteijne, Anna M Bruynzeel, Freek Brandts, Pètra M Braam, Eric T T L Tjwa, Akos Gulyban, Jeroen Buijsen, Judith de Vos-Geelen, Maxime Dewulf, Ines Joye, Margriet Dieters, Fred J F Ubbels, Frederik J H Hoogwater, Ben Heijmen

“…In addition, we will explore the predictive value of the peripheral immunological status (immune-related proteins and serum functional immunological status assay) and its dynamics in determining survival outcomes. …”
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The impact of kidney function on Alzheimer’s disease blood biomarkers: implications for predicting amyloid-β positivity by Burak Arslan, Wagner S. Brum, Ilaria Pola, Joseph Therriault, Nesrine Rahmouni, Jenna Stevenson, Stijn Servaes, Kübra Tan, Paolo Vitali, Maxime Montembeault, Jesse Klostranec, Arthur C. Macedo, Cecile Tissot, Serge Gauthier, Juan Lantero-Rodriguez, Eduardo R. Zimmer, Kaj Blennow, Henrik Zetterberg, Pedro Rosa-Neto, Andrea L. Benedet, Nicholas J. Ashton

“…We evaluated the association between eGFR and blood biomarker levels with linear models and assessed whether eGFR provided added predictive value to determine Aβ-positivity with logistic regression models. …”
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The Correlation of Non-alcoholic Fatty Liver Disease with Visceral Fat Area and Thyroid Nodules in Patients with Type 2 Diabetes Mellitus by ZENG Jialing, MENG Yan, DENG Tingting, LI Jinhua, ZHAO Ping

“…Receiver operating characteristic (ROC) curve analysis evaluated the predictive value of BMI, waist-hip circumference, and waist-hip ratio, VFA, homeostatic model assessment for insulin resistance (HOMA-IR) in predicting NAFLD in T2DM patients and their optimal cut-off values. …”
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ALBI Grade Enables Risk Stratification for Bleeding Events and Refines Prognostic Prediction in Advanced HCC Following Atezolizumab and Bevacizumab by Stefanini B, Fulgenzi CAM, Scheiner B, Korolewicz J, Cheon J, Nishida N, Ang C, Marron TU, Wu YL, Saeed A, Wietharn B, Rimassa L, Pirozzi A, Cammarota A, Pressiani T, Pinter M, Balcar L, Huang YH, Mehan A, Phen S, Vivaldi C, Salani F, Masi G, Bettinger D, Vogel A, Schönlein M, von Felden J, Schulze K, Wege H, Samson A, Galle PR, Kudo M, Manfredi GF, Celsa C, Awosika N, Cortellini A, Singal AG, Sharma R, Chon HJ, Tovoli F, Piscaglia F, Pinato DJ, D'Alessio A

“…Overall survival (OS) stratified by ALBI was estimated using the Kaplan-Meier method and the predictive value for the 6-months OS landmark with ROC curves.Results: Of the 368 patients included in the analysis, 163 (44.3%), 192 (52.2%) and 13 (3.5%) had ALBI 1, ALBI 2, and ALBI 3, respectively. …”
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A protein-based classifier for differentiating follicular thyroid adenoma and carcinoma by Yaoting Sun, He Wang, Lu Li, Jianbiao Wang, Wanyuan Chen, Li Peng, Pingping Hu, Jing Yu, Xue Cai, Nan Yao, Yan Zhou, Jiatong Wang, Yingrui Wang, Liqin Qian, Weigang Ge, Mengni Chen, Feng Yang, Zhiqiang Gui, Wei Sun, Zhihong Wang, Minghua Ge, Yi He, Guangzhi Wang, Yongfu Zhao, Huanjie Chen, Xiaohong Wu, Yuxin Du, Wenjun Wei, Fan Wu, Dingcun Luo, Xiangfeng Lin, Haitao Zheng, Xin Zhu, Bei Wei, Jiafei Shen, Jincao Yao, Zhennan Yuan, Tong Liu, Jun Pan, Yifeng Zhang, Yangfan Lv, Qiaonan Guo, Qijun Wu, Tingting Gong, Ting Chen, Shu Zheng, Jingqiang Zhu, Hanqing Liu, Chuang Chen, Hong Han, Sathiyamoorthy Selvarajan, Michael Mingzhao Xing, Kennichi Kakudo, Erik K Alexander, Yijun Wu, Yu Wang, Dong Xu, Hao Zhang, Xiu Nie, Oi Lian Kon, N Gopalakrishna Iyer, Zhiyan Liu, Yi Zhu, Haixia Guan, Tiannan Guo, We-TEC Investigators

“…It exhibited a 95.7% negative predictive value for ruling out malignancy. This study presents a promising protein-based approach for the differential diagnosis of FTA and FTC, potentially enhancing diagnostic accuracy and clinical decision-making.…”
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Predictors of uveitic macular edema and functional prognostic outcomes: real-life data from the international AIDA Network uveitis registry by Jurgen Sota, Germán Mejía-Salgado, Silvana Guerriero, Gaafar Ragab, Gaafar Ragab, Stefania Costi, Maria Pia Paroli, Andrea Hinojosa-Azaola, Giuseppe Lopalco, Luciana Breda, Henrique Ayres Mayrink Giardini, Alex Fonollosa, Maria Sole Chimenti, Antonio Vitale, Carla Gaggiano, Blanca Aguilar-Barrera, Laura Daniela Rodríguez-Camelo, Guillermo Arturo Guaracha-Basañez, Mohamed Tharwat Hegazy, Mohamed Tharwat Hegazy, Rosanna Dammacco, Valeria Albano, Eduardo Martín-Nares, Santiago Espinosa-Lugo, Mahmoud Ghanema, Maria Morrone, Saverio La Bella, Rafael Alves Cordeiro, Francesco Carubbi, Alessandro Conforti, Piero Ruscitti, Ibrahim AlMaglouth, Rosaria Talarico, Stefano Gentileschi, Petros P. Sfikakis, Valeria Caggiano, Matteo Piga, Adele Civino, Francesca Ricci, Maissa Thabet, Marcello Govoni, Abdurrahman Tufan, Francesca Crisafulli, Jessica Sbalchiero, Sulaiman M. Al-Mayouf, Angela Mauro, Angela Mauro, Soad Hashad, Soad Hashad, Francesca Minoia, Alma Nunzia Olivieri, Samar Tharwat, Samar Tharwat, Maria Cristina Maggio, Abdelhfeez Moshrif, Gian Domenico Sebastiani, Daniela Opris-Belinski, Gülen Hatemi, Gülen Hatemi, Haner Direskeneli, Fatma Alibaz-Öner, Lampros Fotis, José Hernández-Rodríguez, Giovanni Conti, Piercarlo Sarzi Puttini, Ombretta Viapiana, Annarita Giardina, Patrizia Barone, Kalpana Babu, Rana Hussein Amin, Perla Ayumi Kawakami-Campos, Vishali Gupta, Annamaria Iagnocco, Ali Şahin, Antonella Insalaco, Andrés González-García, Ezgi Deniz Batu, Ester Carreño, Emanuela Del Giudice, Cecilia Beatrice Chighizola, Cecilia Beatrice Chighizola, Fabrizio Conti, Alberto Balistreri, Bruno Frediani, Luca Cantarini, Alejandra de-la-Torre, Claudia Fabiani

“…The study also highlights the limited predictive value of demographic and HLA-related factors, helping refine clinical risk stratification and predictive modeling in NIU.…”
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Lipid profile in hospitalized patients with COVID-19 depending on the outcome of its acute phase: data from the international registry "Dynamics analysis of comorbidities in SARS-C... by G. P. Arutyunov, E. I. Tarlovskaya, A. G. Arutyunov, Yu. N. Belenkov, A. O. Konradi, Yu. M. Lopatin, A. P. Rebrov, S. N. Tereshchenko, A. I. Chesnikova, G. G. Airapetyan, A. P. Babin, I. G. Bakulin, N. V. Bakulina, L. A. Balykova, A. S. Blagonravova, M. V. Boldina, M. I. Butomo, A. R. Vaisberg, A. S. Galyavich, V. V. Gomonova, N. Yu. Grigorieva, I. V. Gubareva, I. V. Demko, A. V. Evzerikhina, A. V. Zharkov, A. A. Zateyshchikova, U. K. Kamilova, Z. F. Kim, T. Yu. Kuznetsova, A. N. Kulikov, N. V. Lareva, E. V. Makarova, S. V. Malchikova, S. V. Nedogoda, M. M. Petrova, I. G. Pochinka, K. V. Protasov, D. N. Protsenko, D. Yu. Ruzanov, S. A. Saiganov, A. Sh. Sarybaev, N. M. Selezneva, A. B. Sugraliev, I. V. Fomin, O. V. Khlynova, O. Yu. Chizhova, I. I. Shaposhnik, D. A. Schukarev, A. K. Abdrakhmanova, S. A. Avetisyan, O. G. Avoyan, K. K. Azaryan, G. T. Aimakhanova, D. A. Aiypova, A. Ch. Akunov, M. K. Alieva, A. R. Almukhambedova, A. V. Aparkina, O. R. Aruslanova, E. Yu. Ashina, O. Yu. Badina, O. Yu. Barysheva, T. I. Batluk, A. S. Batchaeva, R. A. Bashkinov, A. M. Bitieva, I. U. Bikhteev, N. A. Borodulina, M. V. Bragin, V. A. Brazhnik, A. M. Budu, G. A. Bykova, K. R. Vagapova, D. D. Varlamova, N. N. Vezikova, E. A. Verbitskaya, O. E. Vilkova, E. A. Vinnikova, V. V. Vustina, E. A. Galova, V. V. Genkel, D. B. Giller, E. I. Gorshenina, E. V. Grigoryeva, E. Yu. Gubareva, G. M. Dabylova, A. I. Demchenko, O. Yu. Dolgikh, M. Y. Duishobaev, D. S. Evdokimov, K. E. Egorova, A. N. Ermilova, A. E. Zheldybaeva, N. V. Zarechnova, Yu. D. Zimina, S. Yu. Ivanova, E. Yu. Ivanchenko, M. V. Ilyina, M. V. Kazakovtseva, E. V. Kazymova, Yu. S. Kalinina, N. A. Kamardina, A. M. Karachenova, I. A. Karetnikov, N. A. Karoli, M. Kh. Karsiev, D. S. Kaskaeva, K. F. Kasymova, J. B. Kerimbekova, E. S. Kim, N. V. Kiseleva, D. A. Klimenko, A. V. Klimova, O. V. Kovalishena, S. V. Kozlov, E. V. Kolmakova, T. P. Kolchinskaya, M. I. Kolyadich, O. V. Kondryakova, M. P. Konoval, D. Yu. Konstantinov, E. A. Konstantinova, V. A. Kordyukova, E. V. Koroleva, A. Yu. Kraposhina, T. V. Kryukova, A. P. Kuznetsova, T. Yu. Kuzmina, K. V. Kuzmichev, Ch. K. Kulchoroeva, T. V. Kuprina, I. M. Kuranova, L. V. Kurenkova, N. Yu. Kurchugina, N. A. Kushubakova, V. I. Levankova, A. A. Ledyaeva, T. V. Lisun, V. E. Lisyanskaya, N. A. Lyubavina, N. A. Magdeeva, K. V. Mazalov, V. I. Mayseenko, A. S. Makarova, A. M. Maripov, N. V. Markov, A. A. Marusina, E. S. Melnikov, A. I. Metlinskaya, N. B. Moiseenko, F. N. Muradova, R. G. Muradyan, Sh. N. Musaelyan, E. S. Nekaeva, N. M. Nikitina, S. E. Nifontov, E. Yu. Obolentseva, A. A. Obukhova, B. B. Ogurlieva, A. A. Odegova, Yu. V. Omarova, N. A. Omurzakova, Sh. O. Ospanova, V. A. Pavlova, E. V. Pakhomova, L. D. Petrov, S. S. Plastinina, D. A. Platonov, V. A. Pogrebetskaya, D. V. Polyakov, D. S. Polyakov, E. V. Ponomarenko, L. L. Popova, A. A. Potanin, N. A. Prokofieva, Yu. D. Rabik, N. A. Rakov, A. N. Rakhimov, N. A. Rozanova, S. Serikbolkyzy, Ya. A. Sidorkina, A. A. Simonov, V. V. Skachkova, R. D. Skvortsova, D. S. Skuridin, D. V. Solovieva, I. A. Solovieva, I. M. Sukhomlinova, A. G. Sushilova, D. R. Tagaeva, Yu. V. Titoikina, E. P. Tikhonova, D. S. Tokmin, A. A. Tolmacheva, M. S. Torgunakova, K. V. Trenogina, N. A. Trostyanetskaya, D. A. Trofimov, M. A. Trubnikova, A. A. Tulichev, A. T. Tursunova, N. D. Ulanova, O. V. Fatenkov, O. V. Fedorishina, T. S. Fil, I. Yu. Fomina, I. S. Fominova, I. A. Frolova, S. M. Tsvinger, V. V. Tsoma, M. B. Cholponbaeva, T. I. Chudinovskikh, I. V. Shavrin, O. A. Shevchenko, D. R. Shikhaliev, E. A. Shishkina, K. Yu Shishkov, S. Yu. Shcherbakov, G. V. Shcherbakova, E. A. Yausheva

“…Determination of LDL-C can be included in the examination program for patients with COVID-19. However, the predictive value of this parameter requires further study in prospective clinical studies.…”
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Machine Learning Modeling of Disease Treatment Default: A Comparative Analysis of Classification Models by Michael Owusu-Adjei, James Ben Hayfron-Acquah, Frimpong Twum, Gaddafi Abdul-Salaam

“…Additionally, performance indicators such as the positive predicted value score for the four models ranged between 98.72%–98.87%, and the negative predicted values of gradient boosting, logistic regression, random forest, and support vector machine were 50%, 75%, 22.22%, and 50%, respectively. …”
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Analysis of Λb→Λμ+μ- Decay in Scalar Leptoquark Model by Shuai-Wei Wang, Ya-Dong Yang

“…For some measured observables, like the differential decay width, the longitudinal polarization of the dilepton system, the lepton-side forward-backward asymmetry, and the baryon-side forward-backward asymmetry, we find that the prediction values of SM are consistent with the current data in most q2 ranges, where the prediction values of these two NP models can also keep consistent with the current data with 1σ. …”
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Investigation of Tilt-Proprotor Loads Correlation Between Wind Tunnel Test Data and Comprehensive Modeling by Yin Ruan, Weite Wang, Wei Zhang

“…It is shown that there is a better correlation of alternating flap bending moments between test data and linear inflow model predicted values for the helicopter mode and a good correlation between measured data and free-wake predicted values for transition modes. …”
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