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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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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A Novel, Patient-Specific Mathematical Pathology Approach for Assessment of Surgical Volume: Application to Ductal Carcinoma in situ of The Breast by Mary E. Edgerton, Yao-Li Chuang, Paul Macklin, Wei Yang, Elaine L. Bearer, Vittorio Cristini

“…Using the ensuing ratios, we applied the model to determine a predicted surgical volume or tumor size. We then corroborated our hypothesis by comparing the predicted size of each tumor based on our model with the actual size of the pathological specimen after tumor excision (R2 = 0.74—0.88). …”
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Development of the Dutch translational knowledge agenda for inherited metabolic diseases by I. J. Hieltjes, J. H. van derLee, M. C. Groenendijk, G. vanHaaften, P. M. vanHasselt, R. J. Lunsing, G. J. J. vanProoijen, E. M. deRuiter, F. J. vanSpronsen, N. M. Verhoeven‐Duif, A. deVreugd, M. Wagenmakers, H. Zweers, H. Dekker, H. R. Waterham, C. D. vanKarnebeek, R. J. A. Wanders, R. A. Wevers

“…The resulting top 10 research questions cover multiple themes, i.e. prediction of disease progression, development of novel tools, mechanistic insights, improved diagnostics, therapeutic integration of multi‐omics techniques, assessment of impact on daily life, expanding treatment avenues, optimal study designs, effect of lifestyle interventions, and data utilization using FAIR principles. …”
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Evaluating the Performance of Artificial Intelligence-Based Large Language Models in Orthodontics—A Systematic Review and Meta-Analysis by Farraj Albalawi, Sanjeev B. Khanagar, Kiran Iyer, Nora Alhazmi, Afnan Alayyash, Anwar S. Alhazmi, Mohammed Awawdeh, Oinam Gokulchandra Singh

“…The quality of the included studies was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST), and R Studio software (Version 4.4.0) was employed for meta-analysis and heterogeneity assessment. …”
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Mutual associations among responsiveness to differential diagnostic tests for Cushing’s disease, tumor size, and somatostatin receptor 5 expression in corticotroph tumors by Karolina Budzen, Kosuke Mukai, Yuto Mitsui, Michio Otsuki, Atsunori Fukuhara, Satoru Oshino, Youichi Saitoh, Masaharu Kohara, Eiichi Morii, Iichiro Shimomura

“…The areas under the receiver operating characteristic curve for the prediction of high SSTR5 expression via the CRH test, DDAVP test, HDDST, ACTH/cortisol index, and tumor diameter were 0.79, 0.87, 0.80, 0.71, and 0.71, respectively. …”
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Shengxue Busui Decoction activates the PI3K/Akt and VEGF pathways, enhancing vascular function and inhibiting osteocyte apoptosis to combat steroid-induced femoral head necrosis by Manting Liu, Jiexiang Ye, Runtian Wu, Dongqiang Luo, Tao Huang, Dandan Dai, Kexin Wang, Yanping Du, Junwen Ou

“…Cytoscape and machine learning (SVM) were used for target prediction and molecular docking validation. A dexamethasone (Dex)-induced SONFH rat model was established, and SBD was administered for 60 days. …”
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Meteorological Anomalies During Earthquake Preparation: A Case Study for the 1995 Kobe Earthquake (M = 7.3) Based on Statistical and Machine Learning-Based Analyses by Masashi Hayakawa, Shinji Hirooka, Koichiro Michimoto, Stelios M. Potirakis, Yasuhide Hobara

“…Finally, we suggest a joint examination of our two meteorological quantities for their potential use in real short-term EQ prediction, as well as in the future lithosphere–atmosphere–ionosphere coupling (LAIC) studies as the information from the bottom part of LAIC.…”
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Machine Learning-Based Alzheimer’s Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation by Manash Sarma, Subarna Chatterjee

“…Based on the performance results obtained, and other factors such as early prediction capabilities, this study compares the efficacies of the two types of biomarkers for multistage diagnosis. …”
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