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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Validity of the Simplified Computerized Comprehensive Learning Ability Screening Test for the Early Detection of Learning Disabilities by Eun Kyoung Lee, Hannah Huh, Woo Young Kim, Hyunju Lee, Hanik Yoo

“…In the SCLTS-RD, the sensitivity and specificity values were 81.1% and 85.6%, and the positive and negative predictive values were 84.9% and 81.9%, respectively. In the SCLST-MD, the sensitivity and specificity values were 97.4% and 76.9%, and the positive and negative predictive values were 80.9% and 96.8%, respectively. …”
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Comparison of RPR and ELISA with TPHA for the Diagnosis of Syphilis: Implication for Updating Syphilis Point-of-Care Tests in Ethiopia by Markos Negash, Tadelo Wondmagegn, Demeke Geremew

“…The sensitivity, specificity, and positive and negative predictive values of ECOTEST-RPR were 100%, 80.8%, 76.2%, and 100%, respectively. …”
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The Least Limiting Water Range to Estimate Soil Water Content Using Random Forest Integrated with GIS and Geostatistical Approaches by Orhan Dengiz, Pelin Alaboz

“…The distribution of obtained and predicted values in surface soils was similar. However, variations were found in the distribution of areas with low LLWR below the surface. …”
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Association between clinical and pulmonary function features and diagnosis of cough variant asthma: a case–control study by Shan Liu, Yan Li, Ying Zhang, Kaiwen Ni, Yiting Wang, Junchao Yang, Sujie Wang, Xinlei Zhang

“…Differences between CVA and CC in forced vital capacity (FVC) in percent predicted values (FVC% pred)(94.4 (57.3) vs 91.60 (94.10), p value=0.006), forced expiratory volume in 1 s/FVC (FEV1/FVC) (%) (84.65±6.82 vs 86.91±6.71, p value<0.001), peak expiratory flow in per cent predicted values (PEF% pred) (93.00 (81.10) vs 98.00 (108.00), p value=0.005), maximal mid-expiratory flow in percent predicted values (MMEF% pred) (74.50 (100.60) vs 90.85 (170.30), p value<0.001), forced expiratory flow (FEF) at 50% of FVC in per cent predicted values (FEF50% pred) (78.9(113.50) vs 93.10(169.80), p value<0.001) and FEF at 75% of FVC in per cent predicted values (FEF75% pred) (69.70 (137.60) vs 85.60 (225.80), p value<0.001) were significant. …”
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Diagnostic accuracy of intraoral mobile photography for oral health screening in children: a pilot study by Lia Mania, Ketevan Nanobashvili, Tinatin Manjavidze, Mamuka Benashvili, Ia Astamadze

“…Sensitivity, specificity, and positive and negative predictive values of dental photography were evaluated, and Cohen’s kappa was calculated. …”
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Forecasting Crude Oil Price and Stock Price by Jump Stochastic Time Effective Neural Network Model by Jun Wang, Huopo Pan, Fajiang Liu

“…The experiment analysis shows that when the price fluctuation is small, the predictive values are close to the actual values, and when the price fluctuation is large, the predictive values deviate from the actual values to some degree. …”
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Endometrial Volume Measured by VOCAL Compared to Office Hysteroscopy for Diagnosis of Endometrial Polyps in Premenopausal Women with Abnormal Uterine Bleeding by Mohamed Laban, Sherif H. Hussain, Alaa S. Hassanin, Waleed M. Khalaf, Mohamed K. Etman, Mohammed S. E. Elsafty, Ahmed M. Bahaa Eldin, Ahmad S. Hasanien, Noha A. Sakna, Mohammed Taema, Mohammed H. Mostafa, Marwa M. Eisa

“…Endometrial thickness of >7.5 mm has sensitivity, specificity, positive and negative predictive values, and overall accuracy of 82%, 37.3%, 52.6%, 71%, and 57.8%, respectively. …”
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Ratios of neutrophil,lymphocyte and monocyte to high-density lipoprotein cholesterol in acute pancreatitis complicated with acute kidney injury by Wei Mao-bi, Zhang Zhi-qin, Ma Zhou, Wu Xiao-yan

“…Objective To explore the clinical predictive values of admission neutrophil,lymphocyte and monocyte to high-density lipoprotein cholesterol ratio（NHR/LHR/MHR）in acute pancreatitis（AP）related with acute kidney injury（AKI）.Methods For this retrospective cohort study,a total of 302 AP patients were divided into AKI and non-AKI groups according to the KDIGO-AKI criteria.The inter-group differences of clinical profiles were compared for NHR,LHR and MHR.Results The incidence of AKI was 21.5%（65/302）.And the clinical stage was 1（n=32,10.6%）,2（n=16,5.3%）and 3（n=17,5.6%）.NHR,LHR and MHR were markedly higher in AKI group than those in NAKI group and the difference were statistically significant（Z=7.356,5.062 & 6.446,P<0.01）.After adjusting basic renal function,gender,concomitant chronic diseases,etiology of AP,basic vital signs and blood biochemical parameters on admission,multivariate logistic forward stepwise regression analysis revealed that admission NHR（OR=1.081,95%CI 1.043～1.121,P<0.01）,MHR（OR=2.445,95%CI 1.514～3.947,P<0.01）and LHR（OR=1.713,95%CI 1.306～2.246,P<0.01）were the independent risks factors for AP-AKI.ROC curve indicated that the above parameters had excellent predictive values for AP-AKI and AUC were 0.798,0.761 and 0.705 respectively（all P<0.01）.For understanding the impact of blood lipid levels on the predictive values of NHR,MHR,and LHR for AP-AKI,subgroup analysis showed that AUC of the above parameters were 0.709,0.667 and 0.615 in hyperlipidemic group and 0.830,0.790 and 0.707 in non-hyperlipidemic group respectively.No statistically significant inter-group differences existed in NHR,MHR or LHR（all P>0.05）.Furthermore,vasopressors,mechanical ventilation and renal replacement therapy during hospitalization were defined as special hospital interventions.Fulfilling one of the above required special treatments.And the values of AUC were 0.782,0.702 and 0.679 respectively（all P<0.05）.At the same time,it had an excellent correlation with APACHE II and SIRS scores for assessing the severity of AP.Conclusions Admission NHR,MHR,LHR as the comprehensive inflammatory parameters along with complete blood count and HDL-C are positively correlated with the severity of AP and serve as independent risk factors for AP-AKI.…”
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Role of multi-parametric MRI of the prostate for screening and staging: Experience with over 1500 cases by Geoffrey Gaunay, Vinay Patel, Paras Shah, Daniel Moreira, Simon J. Hall, Manish A. Vira, Michael Schwartz, Jessica Kreshover, Eran Ben-Levi, Robert Villani, Ardeshir Rastinehad, Lee Richstone

“…Overall sensitivity, specificity, positive and negative predictive values were 94%, 37%, 58%, and 87%, respectively and 95%, 31%, 42%, and 93%, respectively for overall cancer detection and Gleason score ≥7 disease. …”
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Development and validation of a machine learning approach for screening new leprosy cases based on the leprosy suspicion questionnaire by Mateus Mendonça Ramos Simões, Filipe Rocha Lima, Helena Barbosa Lugão, Natália Aparecida de Paula, Cláudia Maria Lincoln Silva, Alexandre Ferreira Ramos, Marco Andrey Cipriani Frade

“…We compared them based on sensitivity, specificity, positive predicted value, negative predicted value, and area under the ROC curve. …”
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Prediction of activity of low-molecular inhibitors of the classic complement pathway using computational screening approach by D. M. Karlinsky, A. P. Kaplun, M. E. Popov

“…The theoretically predicted values of IC50 allow selecting ligands with the highest inhibitory potential for further in vitro experiments…”
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Research on Accelerated Degradation Test Design and Life Prediction for Aluminum Electrolytic Capacitor by YANG Tao, WANG Xu, XIAO Jianglin

“…By comparing the degradated capacitance predicted by BP neural network with the measured data of the degradation test and the predicted value of the least squares linear fitting of the experimental data, the results show that the prediction error of capacitance based on the BP neural network is within 3%, while the predicted value of the least squares linear fitting is around 6%, which verifies the superiority of the BP neural network life prediction algorithm, and provides strong support for development and application of the subsequent on-line monitoring technology of board level capacitors.…”
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Evaluation of energy and protein intake and prediction of growth performance of growing-finishing pigs based on meta-analysis by DI Hanqiu, WANG Shanshan, ZHONG Yifan, CHEN Bin, WANG Haifeng

“…The results showed that all the three functional models provided a reasonable fit for the DEI and CPI, and the polynomial function had a greater goodness of fit than did the power function and Bridges function. The predicted values of the DEI of the polynomial function and Bridges function were slightly higher than those of the Nutrient Requirements of Swine published by the National Research Council (NRC) in 2012 [referred to as NRC (2012)], while the predicted values of the CPI of the three functions were slightly lower than or close to those of NRC (2012). …”
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Compositions Optimization of Antang Corundum for Developing Advanced Ceramic by Olawale Monsur Sanusi, M Dauda, Malachy Sumaila, Abdulkarim S Ahmed, M T Isa, O A Oyelaran, O O Martins

“…The optimized density, compressive strength and flexural strength of the sintered Antang corundum are 3.45 g/cm3 g, 1982 MPa and 295 MPa respectively; while the respective RSM prediction values are 3.45 g/cm3 g, 1982 MPa and 295 MPa. …”
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The association between muscle strength and z scores of pulmonary function by Laura Petraglia, Klara Komici, Leonardo Bencivenga, Giuseppe Rengo, Raffaella Pagliaro, Angela Sciattarella, Nicola Ferrara, Andrea Bianco, Germano Guerra

“…Abstract Z scores and percent predicted values of spirometry parameters are widely used for the evaluation of pulmonary function and detection of respiratory diseases. …”
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