Diagnostics and treatment of gastroesophageal reflux disease: clinical guidelines of the Russian gastroenterological association by V. T. Ivashkin, I. V. Mayev, I. . A.s. Trukhmanov, Ye. K. Baranskaya, O. B. Dronova, O. V. Zayratyants, R. G. Sayfutdinov, A. A. Sheptulin, T. L. Lapina, S. S. Pirogov, Yu. A. Kucheryavy, O. A. Storonova, D. N. Andreyev

“…The treatment of GERD has to be individualized according to the pattern and severity of clinical symptoms. The goal of treatment is symptom relief, at erosive esophagitis - healing of erosions and complication prevention, at Barret's esophagus - prophylaxis of disease progression and dysplasia and adenocarcinoma development. …”
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Identify suitable artificial groundwater recharge zones using hybrid deep learning models by Navaz Khalillollahi, Mohsen Isari, Hamed Faroqi, Kaywan Othman Ahmed, Kamran Nobakht Vakili, Miklas Scholz, Saad Sh. Sammeng

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Anteromedial facet coronoid fractures: an algorithmic approach for assessment and management based on 3 dimensional computed tomography humeral subtraction by Armin Badre, MD, MSc, FRCSC, Moayd Abdullah H. Awad, MBBS, FRCSC, Robert Chan, MD, MSc, FRCSC, Michael Lapner, MD, FRCSC

“…Patients with an AMF fracture meeting radiographic features consistent with a VPMRI mechanism were managed operatively, and those with an AMF fracture that did not meet one of the radiographic features of a VPMRI mechanism were considered for nonoperative management. …”
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GradCAM-PestDetNet: A deep learning-based hybrid model with explainable AI for pest detection and classification by Ramitha Vimala, Saharsh Mehrotra, Satish Kumar, Pooja Kamat, Arunkumar Bongale, Ketan Kotecha

“…The GradCAM-PestDetNet methodology utilizes object detection models like YOLOv8m, YOLOv8s and YOLOv8n, alongside transfer learning techniques such as VGG16, ResNet50, EfficientNetB0, MobileNetV2, InceptionV3 and DenseNet121 for feature extraction. Additionally, Vision Transformers (ViT) and Swim Transformers were explored for their ability to process complex data patterns. …”
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MobilitApp: A Deep Learning-Based Tool for Transport Mode Detection to Support Sustainable Urban Mobility by Gerard Caravaca Ibanez, Luis J. de la Cruz Llopis, Adrian Catalin Diaconeasa, Alberto Bazan Guillen, Monica Aguilar Igartua

“…By analyzing travel patterns, transport modes, and mode-switching behaviors, it delivers actionable insights to city planners, aiding in the enhancement of urban mobility, promotion of sustainable development, and transition to greener cities.…”
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Integration of Machine Learning and Wavelet Algorithms for Processing Probing Signals: An Example of Oil Wells by Zukhra Abdiakhmetova, Zhanerke Temirbekova

“…By integrating wavelet-based feature extraction with machine learning-driven analysis, this approach enhances the ability to detect complex wave propagation patterns, leading to more precise subsurface modeling. …”
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Automated Pipeline for Robust Cat Activity Detection Based on Deep Learning and Wearable Sensor Data by Md Ariful Islam Mozumder, Tagne Poupi Theodore Armand, Rashadul Islam Sumon, Shah Muhammad Imtiyaj Uddin, Hee-Cheol Kim

“…Our study culminates in the development of an automated system for robust pet (cat) activity analysis using artificial intelligence techniques, featuring a 1D-CNN-based approach. …”
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Navigating cognitive boundaries: the impact of CognifyNet AI-powered educational analytics on student improvement by Mrim M. Alnfiai, Faiz Abdullah Alotaibi, Mona Mohammed Alnahari, Nouf Abdullah Alsudairy, Asma Ibrahim Alharbi, Saad Alzahrani

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Identification of hub genes in myocardial infarction by bioinformatics and machine learning: insights into inflammation and immune regulation by Juan Yang, Xiang Li, Li Ma, Jun Zhang

“…Notably, S100A9, FCAR, and MMP9 emerged as druggable targets.ConclusionThe five hub genes identified in this study (ANPEP, S100A9, MMP9, DAPK2, and FCAR) significantly contribute to MI development by modulating inflammatory responses and immune regulation. …”
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Wind load impact on tall building facades: damage observations during severe wind events and wind tunnel testing by Omar Metwally, Haitham A. Ibrahim, Amal Elawady, Amal Elawady, Ioannis Zisis, Ioannis Zisis, Arindam Gan Chowdhury, Arindam Gan Chowdhury

“…As global urbanization accelerates, the construction of tall buildings has surged, becoming a defining feature of modern cityscapes. Tall buildings, while contributing to economic growth and urban development, face substantial risks from extreme wind events, such as hurricanes and downbursts. …”
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Hemimegalencephaly and Epileptic Encephalopathy Associated with a Variant of Uncertain Significance of the TRIO Gene by Eugenia ROZA, Ramona ANDREI, Raluca Ioana TELEANU

“…Pathogenic variants in the TRIO gene are associated with autosomal dominant mental retardation type 44 (MRD44), which is characterized by mildly delayed global development resulting in variable intellectual deficits, learning difficulties, and variable dysmorphic features mostly represented by facial asymmetry, microcephaly, abnormalities of the fingers, and dental anomalies. …”
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Multiomics integration analysis identifies tumor cell-derived MIF as a therapeutic target and potentiates anti-PD-1 therapy in osteosarcoma by Yan Liao, Hao Yao, Weidong Chen, Yutong Zou, Ji Fang, Jiongfeng Zhang, Zehao Guo, Jian Tu, Junkai Chen, Zijun Huo, Lili Wen, Xianbiao Xie

“…This study aimed to leverage multiomics integration to develop a new risk model, characterizing the immune features of osteosarcoma to uncover novel therapeutic targets.Methods Metabolomics profiling was conducted to identify key metabolites in osteosarcoma. …”
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iMESc – an interactive machine learning app for environmental sciences by Danilo Cândido Vieira, Danilo Cândido Vieira, Fabiana S. Paula, Luciana Erika Yaginuma, Gustavo Fonseca

“…To address these issues, we developed iMESc, an interactive ML app designed to streamline and simplify ML workflows for environmental data. …”
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Deep Learning Based Breast Cancer Detection Using Decision Fusion by Doğu Manalı, Hasan Demirel, Alaa Eleyan

“…First, SVM distinguishes between different tumor types using local binary pattern (LBP) features. Second, a pre-trained CNN extracts features, and SVM identifies potential tumors. …”
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Novel post-translational modification learning signature reveals B4GALT2 as an immune exclusion regulator in lung adenocarcinoma by Ge Zhang, Zhenfa Zhang, Lianmin Zhang, Guangyao Zhou, Pengpeng Zhang, Dingli Wang, Shuai Jiang

“…The spatial distribution patterns between B4GALT2 and immune cell populations, specifically CD8+ T lymphocytes and CD20+ B lymphocytes, were elucidated through multiplexed immunofluorescence analysis. …”
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Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention Framework by Md. Shadman Abid, Razzaqul Ahshan, Mohammed Al-Abri, Rashid Al Abri

“…In this context, a novel robust deep learning model, termed the Convolutional Neural Network-Bidirectional Long Short-Term Memory model with spatiotemporal attention mechanism (CNN-BiLSTM-STA), is developed in this study. The suggested model integrates the feature extraction expertise of CNNs with the sequence modeling proficiency of BiLSTM networks to capture spatial linkages and temporal interdependence adeptly. …”
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Assessment and Application of Multi-Source Precipitation Products in Cold Regions Based on the Improved SWAT Model by Zhaoqi Tang, Yi Wang, Wen Chen

“…This is specifically reflected in the rationality of the spatial and temporal distribution patterns of the inverted precipitation, the accuracy observed in capturing precipitation events and actual precipitation characteristics, the goodness of fit in driving hydrological models, and the observed precision in reflecting the composition of watershed runoff, all of which are superior to those pertaining to other precipitation products. (3) The glacier melt calculated using the improved SWAT model informed by CMA V2.0 shows that during the study period, the basin formed a pattern with a positive–negative glacier balance demarcation at 36.5° N, featuring melting at higher latitudes and accumulation at lower latitudes. …”
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Detection and mapping of Antarctic lichen using drones, multispectral cameras, and supervised deep learning by Narmilan Amarasingam, Juan Sandino, Ashray Doshi, Diana King, Elka Blackman, Johan Barthelemy, Barbara Bollard, Sharon A. Robinson, Felipe Gonzalez

“…The difficulty of accurately detecting lichens in Antarctic landscapes, due to their fine-scale spatial patterns and low spectral contrast, drives the need for high-resolution drone-based remote sensing imagery to develop and validate robust mapping methods. …”
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Selenium exposure on breast cancer risk and progression: Comprehensive analysis identifies MSRB1 as a novel therapeutic target by Jingsong Cheng, Diyuan Zhang, Ningxi Wang, Ziheng Zheng, Cong Zhang, Nanting Chen, Yuanyuan Wan, Xue Chen, Qingyao Yin, Xinyi Zhu, Yu Zhao, Liming Yi, Na Sun

“…Transcriptome profiling of 25 selenoproteins, combined with the TCGA-BRCA and METABRIC cohorts, identified Se-related patterns, including two Se clusters, and a Se-related risk score (SeRS) was developed via machine learning. …”
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Characteristics and formation mechanism of carbon emission efficiency spatial correlation network: Perspective from Shandong Province by Li Zhang, Hongrui Wang, Beinan Guo, Xuan Liu, Caiyun Deng, Ziyang Zhao, Xin Jiang, Yiyang Li

“…Accurately characterizing the spatial correlation network features and mechanisms of carbon emission efficiency (CEE) is critical for promoting regionally coordinated development. …”
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