Promoting ‘testicular awareness’: Co‐design of an inclusive campaign using the World Café Methodology by Mohamad M. Saab, Varsha N. Shetty, Megan McCarthy, Martin P. Davoren, Angela Flynn, Ann Kirby, Steve Robertson, Gillian W. Shorter, David Murphy, Michael J. Rovito, Frances Shiely, Josephine Hegarty

“…Future research will be needed to evaluate the feasibility, acceptability, cost and effect of the campaign on promoting testicular awareness and early detection of testicular diseases. …”
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High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds by S. Bovo, M. Bolner, G. Schiavo, G. Galimberti, F. Bertolini, S. Dall’Olio, A. Ribani, P. Zambonelli, M. Gallo, L. Fontanesi

“…The molecular data were analysed using a bioinformatics pipeline specifically designed for identifying differentially abundant metabolites between the two breeds in a robust and statistically significant manner, including the Boruta algorithm, which is a Random Forest wrapper, and sparse Partial Least Squares Discriminant Analysis (sPLS-DA) for feature selection. After thoroughly evaluating the impact of random components on missing value imputation, 100 discriminant metabolites were selected by Boruta and 17 discriminant metabolites (all included within the previous list) were identified with sPLS-DA. …”
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CS-6-induced p62 accumulation exacerbates DNA damage in colorectal cancer by Yitong Gong, Yitong Gong, Menghan Dong, Xiaofei Feng, Xiaofei Feng, Naiyu Zhang, Xuehui Cui, Liushuyue Wang, Qi Qi, Chiu-fai Kuok, Qingling Jiang, Sixue Bi

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Construction of a transfer learning-based depression detection model for female breast cancer patients: text sentiment analysis by Jiaqi Fu, Shisi Deng, Wanting Zheng, Chunrao Zheng, Jianhong Liu, Wenji Li, Yinghua Zeng, Hongpo Xie, Yuchang Mai, Chaixiu Li, Jie Lai, Yujie Zhang, Zihan Guo, Jianyao Tang, Chuhan Zhong, Huihui Zhao, Yanni Wu

“…Abstract Background Social networks have become a vital space for breast cancer (BC) patients to share deeply personal emotions they might avoid expressing in real life. …”
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Comparative analysis of machine learning techniques in metabolomic-based preterm birth prediction by Ying-Chieh Han, Jane Shearer, Chunlong Mu, Donna M. Slater, Suzanne C. Tough, Gavin E. Duggan

“…Results: Model performance was evaluated based on confusion matrices, area under the receiver operating characteristic (AUROC) curve analysis, and feature importance rankings. …”
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An end-to-end interpretable machine-learning-based framework for early-stage diagnosis of gallbladder cancer using multi-modality medical data by Huiyu Zhao, Chuang Miao, Yidi Zhu, Yijun Shu, Xiangsong Wu, Ziming Yin, Xiao Deng, Wei Gong, Ziyi Yang, Weiwen Zou

“…A novel end-to-end interpretable diagnostic framework for GBC is proposed to handle multiple medical modalities, including CT imaging, demographics, tumor markers, coagulation function tests, and routine blood tests. To achieve better feature extraction and fusion of the imaging modality, a novel global-hybrid-local network, namely GHL-Net, has also been developed. …”
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Identification of Diagnostic Biomarkers for Colorectal Polyps Based on Noninvasive Urinary Metabolite Screening and Construction of a Nomogram by Yang Xie, Yiyi Jin, Zide Liu, Jun Li, Qing Tao, Yonghui Wu, Youxiang Chen, Chunyan Zeng

“…Metabolite screening was performed using weighted gene coexpression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine‐recursive feature elimination (SVM‐RFE). …”
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Orchard-Wide Visual Perception and Autonomous Operation of Fruit Picking Robots: A Review by CHEN Mingyou, LUO Lufeng, LIU Wei, WEI Huiling, WANG Jinhai, LU Qinghua, LUO Shaoming

“…For example, low-level feature fusion utilizes basic attributes such as color, shapes and texture to distinguish fruits from backgrounds, while high-level feature learning employs more complex models like convolutional neural networks to interpret the contextual relationships within the data. …”
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A Non-Vacuum Coating Process That Fully Achieves Technical Goals of Bipolar Plates via Synergistic Control of Multiple Layer-by-Layer Strategy by Qiaoling Liu, Xiaole Chen, Menghan Wu, Weihao Wang, Yinru Lin, Zilong Chen, Shuhan Yang, Yuhui Zheng, Qianming Wang

“…The assembly of layered graphite and a polystyrene sphere could maintain both the high corrosion resistance feature and excellent electrical conductivity. In particular, the intrinsic vacant space in the above physical barriers has been filled with fine powders of indium tin oxide (ITO) due to its small size, and the interconnected conductive network with vertical/horizontal directions would be formed. …”
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Classification of Individuals With COVID-19 and Post–COVID-19 Condition and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near–Real-Time Monitoring C... by Carlos Alberto Sanches, Andre Felipe Henriques Librantz, Luciana Maria Malosá Sampaio, Peterson Adriano Belan

“…Classification models were developed using supervised machine learning algorithms (decision tree, support vector machines, k-nearest neighbor, and neural networks) and evaluated through cross-validation. A contextual clinical variable indicating recent SARS-CoV-2 infection was incorporated into 1 model configuration to assess its impact on classification performance. …”
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Spatial transcriptomics reveals Inhba/Smad2/E2f4 axis in Lrp2high thecal cell proliferation in androgen-induced PCOS mice by Man Luo, Man Luo, Xiaona Tian, Li Li, Guomei Zhang, Wenzhi Liu, Linlin Mei, Haoran Li, Xiaoyan You, Dongmei Zhang, Mengsi Zhou, Mengsi Zhou, Cheng Xiao, Cheng Xiao, Jianping Ye, Jianping Ye, Xiaofeng Yang, Xiaofeng Yang

“…Differential expression analysis, pathway enrichment, and spatial co-localization were performed to identify regulatory networks. Functional assays were conducted in cultured TCs using siRNA-mediated knockdown of target genes, and cell proliferation and cell cycle progression were evaluated using EdU incorporation and flow cytometry.ResultsSpatial transcriptomic profiling revealed widespread transcriptional changes in the ovaries of PCOS mice, including a marked expansion of a TCs subpopulation with high Lrp2 expression. …”
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The dysbiosis of gut microbiota and dysregulation of metabolites in IgA nephropathy and membranous nephropathy by Lei Zhang, Lan Hu, Lan Hu, Lan Hu, Li Tan, Zhenjie Zhang, Mengying Chen, Wenbo Gan, Li Chen, Yan Zou, Shi Wang, Yu Pang, Zhenxin Fan, Junjie Liu

“…Multivariate statistical analyses and biomarker modeling were employed to identify discriminative features and evaluate diagnostic performance. Microbiota-metabolite correlation networks were constructed to explore potential mechanistic links.ResultsMetagenomic analysis showed that both the IgAN and MN groups had significantly reduced α-diversity. …”
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Harnessing deep learning to detect bronchiolitis obliterans syndrome from chest CT by Mateusz Koziński, Doruk Oner, Jakub Gwizdała, Catherine Beigelman-Aubry, Pascal Fua, Angela Koutsokera, Alessio Casutt, Argyro Vraka, Michele De Palma, John-David Aubert, Horst Bischof, Christophe von Garnier, Sahand Jamal Rahi, Martin Urschler, Nahal Mansouri

“…Abstract Background Bronchiolitis Obliterans Syndrome (BOS), a fibrotic airway disease that may develop after lung transplantation, conventionally relies on pulmonary function tests (PFTs) for diagnosis due to limitations of CT imaging. Deep neural networks (DNNs) have not previously been used for BOS detection. …”
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Deviations From Normative Functioning Underlying Emotional Episodic Memory Revealed Cross-Scale Neurodiverse Alterations Linked to Affective Symptoms in Distinct Psychiatric Disord... by Yang Xiao, Mingzhu Li, Xiao Zhang, Yuyanan Zhang, Yuqi Ge, Zhe Lu, Mengying Ma, Yuqing Song, Hao-Yang Tan, Dai Zhang, Weihua Yue, Hao Yan

“…Background: Affective symptoms are a prevalent psychopathological feature in various psychiatric disorders. However, the underlying neurobiological mechanisms are complex and not yet fully understood. …”
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Deep learning-driven modality imputation and subregion segmentation to enhance high-grade glioma grading by Jiabin Yu, Qi Liu, Chenjie Xu, Qinli Zhou, Jiajun Xu, Lingying Zhu, Chen Chen, Yahan Zhou, Binggang Xiao, Lin Zheng, Xiaofeng Zhou, Fengming Zhang, Yuhang Ye, Hongmei Mi, Dongping Zhang, Li Yang, Zhiwei Wu, Jiayi Wang, Ming Chen, Zhirui Zhou, Haoyang Wang, Vicky Y. Wang, Enyu Wang, Dong Xu

“…We propose a PatchGAN-based modality imputation network with an Aggregated Residual Transformer (ART) module combining Transformer self-attention and CNN feature extraction via residual links, paired with a U-Net variant for segmentation. …”
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Deep learning can predict cardiovascular events from liver imaging by Gregory Patrick Veldhuizen, Tim Lenz, Didem Cifci, Marko van Treeck, Jan Clusmann, Yazhou Chen, Carolin V. Schneider, Tom Luedde, Peter W. de Leeuw, Ali El-Armouche, Daniel Truhn, Jakob Nikolas Kather

“…This study explores the use of transformer neural networks on liver magnetic resonance imaging (MRI) data to enhance cardiovascular risk prediction. …”
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Automatic identification of clinically important Aspergillus species by artificial intelligence-based image recognition: proof-of-concept study by Chi-Ching Tsang, Chenyang Zhao, Yueh Liu, Ken P. K. Lin, James Y. M. Tang, Kar-On Cheng, Franklin W. N. Chow, Weiming Yao, Ka-Fai Chan, Sharon N. L. Poon, Kelly Y. C. Wong, Lianyi Zhou, Oscar T. N. Mak, Jeremy C. Y. Lee, Suhui Zhao, Antonio H. Y. Ngan, Alan K. L. Wu, Kitty S. C. Fung, Tak-Lun Que, Jade L. L. Teng, Dirk Schnieders, Siu-Ming Yiu, Susanna K. P. Lau, Patrick C. Y. Woo

“…In this proof-of-concept study, using 2813, 2814 and 1240 images from four clinically important Aspergillus species for training, validation and testing, respectively; the performances and accuracies of automatic Aspergillus identification using colonial images by three different convolutional neural networks were evaluated. Results demonstrated that ResNet-18 outperformed Inception-v3 and DenseNet-121 and is the best algorithm of choice because it made the fewest misidentifications (n = 8) and possessed the highest testing accuracy (99.35%). …”
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Hybrid closed-loop systems for managing blood glucose levels in type 1 diabetes: a systematic review and economic modelling by Asra Asgharzadeh, Mubarak Patel, Martin Connock, Sara Damery, Iman Ghosh, Mary Jordan, Karoline Freeman, Anna Brown, Rachel Court, Sharin Baldwin, Fatai Ogunlayi, Chris Stinton, Ewen Cummins, Lena Al-Khudairy

“…The EAG base case applies the EAG RCT network meta-analysis estimate of −0.29% HbA1c for HCL relative to CSII + CGM. …”
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Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction by Maria Vittoria Chiaruttini, Giulia Lorenzoni, Marco Daverio, Luca Marchetto, Francesca Izzo, Giovanna Chidini, Enzo Picconi, Claudio Nettuno, Elisa Zanonato, Raffaella Sagredini, Emanuele Rossetti, Maria Cristina Mondardini, Corrado Cecchetti, Pasquale Vitale, Nicola Alaimo, Denise Colosimo, Francesco Sacco, Giulia Genoni, Daniela Perrotta, Camilla Micalizzi, Silvia Moggia, Giosuè Chisari, Immacolata Rulli, Andrea Wolfler, Angela Amigoni, Dario Gregori

“…Moreover, the model calibration was evaluated. <b>Results</b>: Out of 43,794 records, 1861 admissions met the inclusion criteria, with 678 complete cases and 97 NIV failures. …”
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Design and Construction of an Automatic Discharge and Sediment Load Monitoring Equipment by Hasan rezaei moghadam, Vahedberdi Sheikh, Maryam Azarakhshi, Mohsen Hosseinalizadeh, Jahangir Mohamadi

“…The device boasts a remarkable advantage in the form of its SMS notification feature, which keeps the user informed about the monitoring site and equipment status, including runoff sampling, power outages, and battery status. …”
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