Construction and analysis of a prognostic risk scoring model for gastric cancer anoikis-related genes based on LASSO regression by Ai CHEN, Xiaowei CHEN, Yanan WANG, Xiaobing SHEN

“…Gene expression levels in gastric cancer clinical samples and cells were detected by real-time quantitative PCR (RT-qPCR); Kaplan-Meier (KM) survival curves, univariate and multivariate Cox regression analyses were used to verify the predictive efficiency of the prognostic risk scoring model for the prognosis of gastric cancer patients; CIBERSORT and ESTIMATE algorithms were used to analyze the immune cell infiltration levels in patients with different risk groups; the correlation between risk scores and immune checkpoint expression levels in gastric cancer patients was analyzed using the R package "ggplot2" and "ggExtra", and the correlation between tumor mutation burden (TMB) and risk scores was assessed; chemotherapy drug sensitivity analysis was used to evaluate the value of the constructed prognostic risk scoring model in gastric cancer chemotherapy. …”
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Repeatability and agreement of three biometers measuring corneal keratometry and astigmatism in eyes with cataract and high myopia by Haisheng Zhou, Tong Bao, Shuchan Li, Yunlei Pang, Qinglei Shao, Lifeng Wang, Lihua Yin, Jinli Wang, Na Hu, Yuanyuan Qi, Bozhou Zhang

“…Further research is needed to refine measurement algorithms and optimize clinical accuracy.…”
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Leveraging ultrasonic-derived phenotypes and estimated breeding value to improve abdominal fat weight prediction in chickens throughout the egg laying period by Penghao Li, Zhengda Li, Fan Ying, Dan Zhu, Dawei Liu, Xianyi Song, Jie Wen, Guiping Zhao, Bingxing An

“…These findings underscore the feasibility of accurately prediction of hens'AF through fitting appropriate algorithms for different laying periods, that supporting delicacy feeding management in farm.…”
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Thymic characteristics in patients with autoimmune diseases: a multicentre radiological observational studyResearch in context by Haoxiao Chang, Junqiang Yan, Anxin Wang, Ningning Wang, Peng Liu, Yemin Shi, Xin Zhao, Jinqiao Zhu, Yuan Cai, Xue Xia, Yunyi Hao, Shiyi Yin, Jing Wang, Xinli Wang, Lina Sun, Jia Ma, Xiaoyu Huang, Haonan Guan, De-Cai Tian, Kaibin Shi, Ganqin Du, Wenhao Huang, Guoyan Qi, Hao Li, Yongjun Wang, Fu-Dong Shi, Zhang Zhang, Wei-Na Jin

“…Thymic fatty replacement was evaluated using a four-point visual scoring scale (0–3). …”
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Mapping Nationwide Subfield Division Dynamics in Saudi Arabia Using Temporal Patterns of Sentinel-2 NDVI and Machine Learning by Ting Li, Oliver Miguel Lopez Valencia, Matthew F. McCabe

“…However, segmentation algorithms for center-pivot fields often treat fields as single units, neglecting that a field can be subdivided into different sections caused by varied management practices, such as differing planting and harvesting dates, crop types, and rotations. …”
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Enhancing maize LAI estimation accuracy using unmanned aerial vehicle remote sensing and deep learning techniques by Zhen Chen, Weiguang Zhai, Qian Cheng

“…Therefore, this study evaluates the potential of multi-source feature fusion and convolutional neural networks (CNN) in estimating maize LAI. …”
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Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer by Yun Zhu, Shuni Zhang, Wei Wei, Li Yang, Lingling Wang, Ying Wang, Ye Fan, Haitao Sun, Zongyu Xie

“…Finally, the CIPRM Rad-score combined with clinical-radiological factors was used to construct a NM. The performance of different models were evaluated by receiver operating characteristic curve (ROC) analysis, calibration curve analysis, and decision curve analysis (DCA).ResultsIn our study, the 6-mm peritumoral size was considered to be the optimal peritumoral region. …”
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Prediction of Rice Chlorophyll Index (CHI) Using Nighttime Multi-Source Spectral Data by Cong Liu, Lin Wang, Xuetong Fu, Junzhe Zhang, Ran Wang, Xiaofeng Wang, Nan Chai, Longfeng Guan, Qingshan Chen, Zhongchen Zhang

“…Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). …”
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The spatiotemporal distribution patterns and impact factors of bird species richness: A case study of urban built-up areas in Beijing, China by Zheran Zhai, Siyao Liu, Zimeng Li, Ruijie Ma, Xiaoyu Ge, Haidong Feng, Yang Shi, Chen Gu

“…It examined species distribution across different seasons and land cover types, evaluated population fluctuations based on migratory behaviors, and assessed the relative abundance of bird families and species in hotspot areas. …”
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Machine learning in CTEPH: predicting the efficacy of BPA based on clinical and echocardiographic features by Qiumeng Xi, Juanni Gong, Jianfeng Wang, Xiaojuan Guo, Yuanhua Yang, Xiuzhang lv, Suqiao Yang, Yidan Li

“…By comparing the predictive performance of different algorithms, we aimed to establish a robust tool to identify patients most likely to benefit from BPA. …”
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High-resolution surface soil moisture retrieval: A hybrid machine learning framework integrating change detection and downscaling for precision water management by Zihao Wang, Qi Gao, Michele Crosetto, Maria Jose Escorihuela

“…The ML model was trained using in-situ SSM data collected from 2017 to 2021 and validated against independent in-situ measurement datasets. Among the evaluated algorithms, XGBoost model performed best, achieving an R2 of 0.933 and RMSE of 0.023 cm3/cm3. …”
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DYNAMICS OF MMP-8, SRANKL, AND OSTEOCALCIN CONCENTRATIONS IN THE ORAL FLUID OF PATIENTS WITH SECONDARY EDENTULISM AND AFTER PROSTHETIC TREATMENT by R.V. Tsynkush, O.V. Voznyi

“…The aim of the study was to evaluate changes in these markers in the oral fluid under different clinical conditions: in healthy volunteers (control group), in patients with secondary edentulism without treatment, and after receiving prosthetic care. …”
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Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features by Jingna Tao, Zhongmian Zhang, Linghan Meng, Liju Zhang, Jiaqi Wang, Zhihong Li

“…BackgroundThis study aimed to construct and validate diagnostic models for the Operative Link on Gastritis Assessment (OLGA) and Operative Link on Gastric Intestinal Metaplasia Assessment (OLGIM) staging systems using three different methodologies based on magnifying endoscopy with narrow-band imaging (ME-NBI) features, to evaluate model performance, and to analyse risk factors for high-risk OLGA/OLGIM stages.MethodsWe enrolled 356 patients who underwent white-light endoscopy and ME-NBI at the Department of Gastroenterology, Dongzhimen Hospital, Beijing University of Chinese Medicine, between January 2022 and September 2023. …”
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Identifying mating events of group-housed broiler breeders via bio-inspired deep learning models by Venkat U.C. Bodempudi, Guoming Li, J. Hunter Mason, Jeanna L. Wilson, Tianming Liu, Khaled M. Rasheed

“…The DLM framework included a bird detection model, data filtering algorithms based on mating duration, and logic frameworks for mating identification based on bird count changes. …”
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Combination of ultrasound-based radiomics and deep learning with clinical data to predict response in breast cancer patients treated with neoadjuvant chemotherapy by Wu Tenghui, Liu Xinyi, Si Ziyi, Zhang Yanting, Ma Ziqian, Zhu Yiwen, Gan Ling

“…Multiple machine learning algorithms were employed to model and validate the diagnostic performance of different types of features. …”
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AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP by Dr. Bharti Khemani, Dr. Sachin Malave, Samyukta Shinde, Mandvi Shukla, Razzaq Shikalgar, Harshita Talwar

“…This research underscores the potential of predictive modeling to enhance pharmacovigilance efforts and ensure safer clinical trial outcomes. • The research methodology includes a comparison of supervised learning algorithms, such as Logistic Regression, Random Forest, Gradient Boost, CNN, and genetic algorithms, to identify patterns and anomalies in clinical trial data. …”
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Predicting suicidality in people living with HIV in Uganda: a machine learning approach by Anthony B. Mutema, Anthony B. Mutema, Anthony B. Mutema, Lillian Linda, Lillian Linda, Daudi Jjingo, Segun Fatumo, Segun Fatumo, Eugene Kinyanda, Allan Kalungi, Allan Kalungi, Allan Kalungi

“…The model’s performance was evaluated using the area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), sensitivity, specificity, and Mathew’s correlation coefficient (MCC).ResultsWe trained and evaluated eight different ML algorithms, including logistic regression, support vector machines, Naïve Bayes, k-nearest neighbors, decision trees, random forests, AdaBoost, and gradient-boosting classifiers. …”
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A review of altimetry waveform retracking for inland water levels by Xinyuan Deng, Linghong Ke, Liguang Jiang, Karina Nielsen, Xiaomei Fan, Jida Wang, Chunqiao Song

“…By synthesizing pioneering studies on “retracking algorithms”, this review demonstrates, from a user perspective, why optimizing conventional retracking is still important and how it can extend reliable historical water level retrieval over more ungauged sites. …”
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Advancements in Hybrid Energy Storage Systems for Rural Electrification: A Comprehensive Case Study on Siwa Oasis in Egypt on Increasing Battery Longevity in Standalone PV Systems by Khalid Alqunun, Nehad M. Khattab, Ismail Marouani, Kamel Tlijani, Mohamed Abdelhameed Zaky, Loay Mohamed Ali El-Sayed, Ahmed Ibrahim Omar, Badr M. Alshammari, Tawfik Guesmi, Ahmed Hussain Elmetwaly, Ramy Adel Younis

“…Currently, there are a variety of HESS and associated energy management tactics to choose from, each tailored to specific uses. These systems differ in topology, intricacy, and control algorithms. …”
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Depression and Anxiety Screening for Pregnant Women via Free Conversational Speech in Naturalistic Condition by Rafael T. Sousa, Gustavo D. M. Silva, Paula L. L. Pinto, Juliano Backes, Amanda S. Mota, Thiago M. Paixao, Maria G. S. Teixeira, Wilian H. Hisatugu, Anilton S. Garcia, Kelly S. Prado, Rodrigo S. Dias, Marco A. Galletta, Hermano Tavares

“…The research involved collecting conversational speech samples from pregnant women attending a high-risk pregnancy outpatient service, employing smartphones to capture naturalistic speech in everyday contexts. Machine learning algorithms were utilized combined with different audio feature sets to analyze these recordings in conjunction with PHQ-4 questionnaire scores. …”
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