The Electroanatomic Volume of the Left Atrium as a Determinant of Recurrences in Patients with Atrial Fibrillation After Pulmonary Vein Isolation: A Prospective Study by Amaia Martínez León, David Testa Alonso, María Salgado, Ruth Álvarez Velasco, Minel Soroa, Daniel Gracia Iglesias, David Calvo

“…Electroanatomic mapping (EAM) offers a more accurate evaluation of LA geometry and volume, which may enhance the prediction of ablation outcomes. <b>Methods</b>: This prospective study included 197 patients with AF who were referred for PVI to our center (Hospital Universitario Central de Asturias, Spain) between 2016 and 2020. …”
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Plasma Levels of Free Fatty Acids in Women with Gestational Diabetes and Its Intrinsic and Extrinsic Determinants: Systematic Review and Meta-Analysis by Jose Rafael Villafan-Bernal, Mariana Acevedo-Alba, Rodrigo Reyes-Pavon, Guillermo Andres Diaz-Parra, Diana Lucia Lip-Sosa, Hilda Imelda Vazquez-Delfin, Martha Hernandez-Muñoz, Daniel Ely Bravo-Aguirre, Francesc Figueras, Raigam Jafet Martinez-Portilla

“…More investigation is needed to assess the potential role of free fatty acids in the prediction of gestational diabetes earlier in pregnancy.…”
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Prognostic Value of Inflammatory and Nutritional Indicators in Non-Metastatic Soft Tissue Sarcomas by Yan Y, Zhang Y, Chen Y, Zhong G, Huang W, Zhang Y

“…The combination index of the SIRI+AGR+Enneking stage provides a more robust prediction of clinical prognosis in STS patients.Keywords: soft tissue sarcoma, prognostic index, inflammatory, survival analysis…”
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Explore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh by Ashikur Rahman, Md. Habibur Rahman

“…Model evaluation metrics like accuracy, specificity, sensitivity, negative predictive value, $$F_1$$ F 1 score, positive predictive value, k-fold cross-validation, and area under the curve (AUC) techniques were used to evaluate the performance of the models. …”
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Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0 by Z. Engberg, R. Teoh, T. Abbott, T. Dean, M. E. J. Stettler, M. L. Shapiro

“…This is achieved by extending the existing trajectory-based contrail cirrus prediction model (CoCiP), which simulates contrails formed along flight paths, to a grid-based approach that initializes an infinitesimal contrail segment at each point in a 4D spatiotemporal grid and tracks them until their end of life. …”
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Wildfire indicators modeling for reserved forest of Vellore district (Tamil Nadu, India) by Yara EzAl Deen Sultan, Kanni Raj Arumugam Pillai, Archana Sharma

“…This paper assists in applying the models to predict the future wildfire risk under climate change and land use conditions.…”
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Ultrasound-based radiomics and clinical factors-based nomogram for early intracranial hypertension detection in patients with decompressive craniotomy by Zunfeng Fu, Lin Peng, Laicai Guo, Chao Qin, Yanhong Yu, Jiajun Zhang, Yan Liu

“…The SHAP method was adopted to explain the prediction models.ResultsAmong the machine learning models, the LR model demonstrated superior predictive efficiency and robustness at threshold values of 15 mmHg and 20 mmHg. …”
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Comprehensive Evaluation and Error-Component Analysis of Four Satellite-Based Precipitation Estimates against Gauged Rainfall over Mainland China by Guanghua Wei, Haishen Lü, Wade T. Crow, Yonghua Zhu, Jianbin Su, Li Ren

“…Moreover, V06C and V06UC rainfall estimates are compared against the Precipitation Estimation from Remotely Sensed Imagery using Artificial Neural Networks (PERSIANN)-Climate Data Record (CDR) and the Climate Prediction Center morphing technique (CMORPH) gauge-satellite blended (BLD) products. …”
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Comparing clinical only and combined clinical laboratory models for ECPR outcomes in refractory cardiac arrest by Chun-Chieh Chiu, Yu-Jun Chang, Chun-Wen Chiu, Ying-Chen Chen, Yung-Kun Hsieh, Shun-Wen Hsiao, Hsu-Heng Yen, Fu-Yuan Siao

“…Model 1(F1) and Model 2(F2) revealed prediction power for good neurological outcomes, with AUROCs of 0.80 and 0.79, respectively. …”
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Peningkatan Performa Ensemble Learning pada Segmentasi Semantik Gambar dengan Teknik Oversampling untuk Class Imbalance by Arie Nugroho, M. Arief Soeleman, Ricardus Anggi Pramunendar, Affandy Affandy, Aris Nurhindarto

“…In reality, a lot of data has unbalanced classes or labels, of course, it will affect the accuracy of a prediction. This research discusses how to improve the accuracy of image semantic segmentation in the ensemble learning method to deal with the problem of unbalanced data in image segmentation. …”
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Correlation Between the Ratio of Uric Acid to High-Density Lipoprotein Cholesterol (UHR) and Diabetic Retinopathy in Patients with Type 2 Diabetes Mellitus:A Cross-Sectional Study by Wang L, Liu L, Luo H, Wu Y, Zhu L

“…Leran Wang,1 Lei Liu,2 Huilan Luo,3– 5 Yiling Wu,3– 5 Lingyan Zhu3– 5 1Queen Mary School, Jiangxi Medical College, Nanchang University, Nanchang City, People’s Republic of China; 2Department of Endocrinology, Lu’an Hospital of Anhui Medical University, Lu’an City, Anhui Province, People’s Republic of China; 3Department of Endocrinology and Metabolism, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang City, People’s Republic of China; 4Jiangxi Clinical Research Center for Endocrine and Metabolic Disease, the First Affiliated Hospital of Nanchang University, Nanchang City, People’s Republic of China; 5Jiangxi Branch of National Clinical Research Center for Metabolic Disease, Nanchang City, People’s Republic of ChinaCorrespondence: Lingyan Zhu, Jiangxi Branch of National Clinical Research Center for Metabolic Disease, No. 17, Yongwaizheng Street, Nanchang City, Jiangxi Province, People’s Republic of China, Tel +86-13870690788, Email zly982387@126.comBackground/Objective: Considering the uncertain relationship between high-density lipoprotein cholesterol (HDL-C) and uric acid (UA) with diabetic retinopathy (DR),this study investigates the link between Uric Acid to High-Density Lipoprotein Cholesterol (UHR) and DR in T2DM patients, evaluating its potential for DR diagnosis and early prediction.Study Design and Data Collection: This retrospective study analyzed 1450 type 2 diabetes patients, divided into NDR and DR groups by retinal exams. …”
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Estimation Model for Cotton Canopy Structure Parameters Based on Spectral Vegetation Index by Yaqin Qi, Xi Chen, Zhengchao Chen, Xin Zhang, Congju Shen, Yan Chen, Yuanying Peng, Bing Chen, Qiong Wang, Taijie Liu, Hao Zhang

“…These results confirm the strong predictive capacity of <i>NDVI</i> for <i>LAI</i>, with the power function model offering the best estimation accuracy. …”
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scPharm: Identifying Pharmacological Subpopulations of Single Cells for Precision Medicine in Cancers by Peng Tian, Jie Zheng, Keying Qiao, Yuxiao Fan, Yue Xu, Tao Wu, Shuting Chen, Yinuo Zhang, Bingyue Zhang, Chiara Ambrogio, Haiyun Wang

“…Its superior performance and computational efficiency are confirmed through comparative evaluations with other single‐cell prediction tools. Additionally, scPharm predicted combination drug strategies by gauging compensation or booster effects between drugs and evaluated drug toxicity in healthy cells in the tumour microenvironment. …”
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Which variables are associated with recruitment failure? A nationwide review on obstetrical and gynaecological multicentre RCTs (2003–2023) by Ben W Mol, Madelon van Wely, L Ramos, J J Duvekot, J B Derks, Fulco van der Veen, Ruben Duijnhoven, H J van Beekhuizen, M J E Mourits, J A M van der Post, E Pajkrt, M A Oudijk, H C J Scheepers, Mariette Goddijn, J Huirne, Marijke C van der Weide, A Kwee, C Willekes, M Y Bongers, Judith Rikken, Romee Casteleijn, S Middeldorp, I M Custers, M P Lambregtse – van den Berg, V Mijatovic, F J M Broekmans, A Hoek, J P de Bruin, M H Mochtar, S Mastenbroek, J P W R Roovers, F Mol, A Vollebregt, C H van der Vaart, K B Kluivers, M E Vierhout, R C Painter, P M A J Geomini

“…The most relevant variables for recruitment failure in multivariable risk prediction modelling were presence of a no-treatment arm (where treatment is standard clinical practice), a compensation fee of less than €200 per included patient, funding of less than €350 000, while a preceding pilot study lowered this risk.Conclusions We identified that the presence of a no-treatment arm, low funding and a low compensation fee per included patient were the most relevant risk factors for recruitment failure within the preplanned period, while a preceding pilot study lowered this risk. …”
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Prognostic Significance of Chronic Kidney Disease (CKD-EPI Equation) and Anemia in Patients with Chronic Heart Failure Secondary to Chagas Cardiomyopathy by Marcelo Arruda Nakazone, Maurício Nassau Machado, Ana Paula Otaviano, Ana Maria Silveira Rodrigues, Augusto Cardinalli-Neto, Reinaldo Bulgarelli Bestetti

“…We evaluated the risk prediction performance of the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation and anemia in CC patients. …”
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Impaired hippocampal neurogenesis associated with regulatory ceRNA network in a mouse model of postoperative cognitive dysfunction by Jingrun Lin, Xiaoqiu Zhu, Xuan Li, Yu Hong, Yaohui Liang, Siqi Chen, Chenzhuo Feng, Lin Cao

“…Additionally, drugs were predicted as potential therapeutic candidates for POCD treatment. …”
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Analysis of mutations in CDC27, CTBP2, HYDIN and KMT5A genes in carotid paragangliomas by E. N. Lukyanova, A. V. Snezhkina, D. V. Kalinin, A. V. Pokrovsky, A. L. Golovyuk, O. A. Stepanov, E. A. Pudova, G. S. Razmakhaev, M. V. Orlova, A. P. Polyakov, M. V. Kiseleva, A. D. Kaprin, A. V. Kudryavtseva

“…In this work, ten genes (ZNF717, CDC27, FRG2C, FAM104B, CTBP2, HLA-DRB1, HYDIN, KMT5A, MUC3A, and PRSS3) characterized by the highest level of mutational load were analyzed. Using several prediction algorithms (SIFT, PolyPhen-2, MutationTaster, and LRT), potentially pathogenic mutations were identified in four genes (CDC27, CTBP2, HYDIN, and KMT5A). …”
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Increased nerve density adversely affects outcome in colorectal cancer and denervation suppresses tumor growth by Hao Wang, Ruixue Huo, Kexin He, Weihan Li, Yuan Gao, Wei He, Minhao Yu, Shu-Heng Jiang, Junli Xue

“…Incorporating PNI and NND into ROC curve analysis improved the sensitivity and specificity of survival predictions. In the murine model, chemical denervation of sympathetic, parasympathetic, and sensory nerves significantly reduced rectal tumor volume. …”
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Machine learning-based plasma metabolomics for improved cirrhosis risk stratification by Jingru Song, Ziwei Gao, Liqun Lai, Jie Zhang, Binbin Liu, Yi Sang, Siqi Chen, Jiachen Qi, Yujun Zhang, Huang Kai, Wei Ye

“…Similarly, the combination of metabolomics with APRI also improved predictive performance compared to APRI alone (Harrell’s C: 0.747 vs. 0.718, ΔC = 0.029, 95% CI 0.022–0.035, NRI: 0.378 [0.366–0.389]). …”
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Analisis Dampak Stabilisasi Ekonomi terhadap Pembatalan Impor Beras dari India ke Indonesia pada Tahun 2023 by Subrantas Ifantri, Diva Zaidan Patria Kumara

“…At the end of 2023, it was canceled but this was beyond the prediction of the Indian food ministry and the government there was no ban on the export of non-basmati rice Using a descriptive qualitative research type method. …”
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