Identification of veterinary and medically important blood parasites using contrastive loss-based self-supervised learning by Supasuta Busayakanon, Morakot Kaewthamasorn, Natchapon Pinetsuksai, Teerawat Tongloy, Santhad Chuwongin, Siridech Boonsang, Veerayuth Kittichai

“…As a result, multi-class classification prediction demonstrated that model performance exceeded 91% for the F1 score, except for the early stage of Trypanosoma evansi, which showed an F1 score of 87%. …”
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Age and sex specific thresholds for risk stratification of cardiovascular disease and clinical decision making: prospective open cohort study by Matthew Sperrin, Angela M Wood, Lois Kim, Zhe Xu, Emanuele Di Angelantonio, Stephen Kaptoge, Juliet Usher-Smith, Matthew Arnold, Lisa Pennells, Ryan Chung

“…Objective To quantify the potential advantages of using 10 year risk prediction models for cardiovascular disease, in combination with risk thresholds specific to both age and sex, to identify individuals at high risk of cardiovascular disease for allocation of statin treatment.Design Prospective open cohort study.Setting Primary care data from the UK Clinical Practice Research Datalink GOLD, linked with hospital admissions from Hospital Episode Statistics and national mortality records from the Office for National Statistics in England, 1 January 2006 to 31 May 2019.Participants 1 046 736 individuals (aged 40-85 years) with no cardiovascular disease, diabetes, or a history of statin treatment at baseline using data from electronic health records.Main outcome measures 10 year risk of cardiovascular disease, calculated with version 2 of the QRISK cardiovascular disease risk algorithm (QRISK2), with two main strategies to identify individuals at high risk: in strategy A, estimated risk was a fixed cut-off value of ≥10% (ie, as per the UK National Institute for Health and Care Excellence guidelines); in strategy B, estimated risk was ≥10% or ≥90th centile of age and sex specific risk distributions.Results Compared with strategy A, strategy B stratified 20 241 (149.8%) more women aged ≤53 years and 9832 (150.2%) more men aged ≤47 years as having a high risk of cardiovascular disease; for all other ages the strategies were the same. …”
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4D-CTA image and geometry dataset for kinematic analysis of abdominal aortic aneurysmsZenodo by Mostafa Jamshidian, Adam Wittek, Saeideh Sekhavat, Farah Alkhatib, Jens Carsten Ritter, Paul M. Parizel, Donatien Le Liepvre, Florian Bernard, Ludovic Minvielle, Antoine Fondanèche, Jane Polce, Christopher Wood, Karol Miller

“…The images were acquired at Fiona Stanley Hospital in Western Australia and provided to the researchers at the Intelligent Systems for Medicine Laboratory at The University of Western Australia (ISML-UWA), where image-based AAA kinematic analysis was performed using a newly created algorithm, as described in [1]. The AAA geometries were extracted using an automated image processing pipeline comprising AI-based segmentation with PRAEVAorta software by NUREA (https://www.nurea-soft.com/), automated post-processing with the ISML-UWA in-house code (https://arxiv.org/abs/2403.07238), and surface model extraction using the freely available BioPARR (Biomechanics-based Prediction of Aneurysm Rupture Risk) (https://bioparr.mech.uwa.edu.au/) and 3D Slicer (https://www.slicer.org/) software packages [2,3]. …”
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An upgraded high-precision gridded precipitation dataset for the Chinese mainland considering spatial autocorrelation and covariates by J. Hu, C. Miao, J. Su, Q. Zhang, J. Gou, J. Gou, Q. Sun, Q. Sun

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Systematic pan-cancer analysis identified NCOA4 as an immunological and prognostic biomarker and validated in lung adenocarcinoma by An Wang, Yun-ye Mao, Tao Li, Xin Zhou, Yi-bing Bai, Jia-pei Qin, Yi Dong, Ting Wang, Tong Zhang, Zhi-qiang Ma, Yi Hu

“…LUAD samples were stained using immunohistochemistry (IHC). TIDE algorithm was used to predict immune checkpoint blockade (ICB) response within the TCGA-LUAD cohort. …”
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Characterization of the salivary microbiome in healthy individuals under fatigue status by Xianhui Peng, Na Han, Yanan Gong, Lihua He, Yanli Xu, Di Xiao, Tingting Zhang, Yujun Qiang, Xiuwen Li, Wen Zhang, Jianzhong Zhang

“…Taxonomic profiling revealed a significant enrichment in the relative abundance of potential periodontopathogenic genera, including Streptococcus and Filifactor, within the FTG group, concomitantly with a significant depletion of health-associated genera such as Rothia and Neisseria. A predictive model constructed using the Boruta-SHAP algorithm, based on 15 key genera, effectively discriminated between fatigue and non-fatigue states, achieving an area under the receiver operating characteristic curve (AUC) of 0.948. …”
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Aβ status assessment in a hypothetical scenario prior to treatment with disease‐modifying therapies: Evidence from 10‐year real‐world experience at university memory clinics by Matthias Brendel, Tandis Parvizi, Johannes Gnörich, Christof Elias Topfstedt, Katharina Buerger, Daniel Janowitz, Boris‐Stephan Rauchmann, Robert Perneczky, Carolin Kurz, Dirk Mehrens, Wolfgang G. Kunz, Julia Kusche‐Palenga, Agnes Bernadette Kling, Antonia Buchal, Elizabet Nestorova, Sara Silvaieh, Raphael Wurm, Tatjana Traub‐Weidinger, Sigrid Klotz, Günther Regelsberger, Axel Rominger, Alexander Drzezga, Johannes Levin, Elisabeth Stögmann, Nicolai Franzmeier, Günter U. Höglinger

“…RESULTS In both cohorts, a CSF Aβ42/40 ratio ≥ 7.1% was associated with a low risk of a positive Aβ PET scan (negative predictive value: 94.3%). Implementing two cutoffs revealed 14% to 16% of patients with intermediate results (CSF Aβ42/40 ratio: 5.5%–7.1%), which had a strong benefit from Aβ PET imaging (44%–52% Aβ PET positivity). …”
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Mapping clay fraction oxides in Brazil using Earth observation strategy by Jorge Tadeu Fim Rosas, José A.M. Demattê, Nícolas Augusto Rosin, Raul Roberto Poppiel, Nélida E.Q. Silvero, Merilyn Taynara Accorsi Amorim, Heidy S. Rodríguez-Albarracín, Letícia Guadagnin Vogel, Bruno dos Anjos Bartsch, José João Lelis Leal de Souza, Lucas de Carvalho Gomes, Danilo César de Mello

“…A dataset, comprising 5,330 sites (0–20 cm and 80–100 cm) across all 27 states was employed in prediction. Six spectral variables obtained from the historical Landsat series (bare soil) and seven terrain attributes derived from a digital elevation model were employed to generate the Fe2O3, Al2O3, and SiO2 maps, using the Random Forest algorithm. …”
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Classifying schizophrenia using functional MRI and investigating underlying functional phenomena by Yangyang Liu, Bi Wan, Zixuan Liu, Shuaiqi Zhang, Pei Liu, Ningning Ding, Yuxin Wang, Jun Dong, Moiz Kabeer Ahmad, Haisan Zhang

“…Results: The average prediction accuracy of various ML classifiers reached 0.9241 by fALFF, ReHo, and DC values. …”
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Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer by Luqian Liu, Fengyu Zhang, Jiaye Yu, Pingping Dong, Jing Deng, Weiwei Liu, Xinlei Cao, Juanjuan Huang, Xiaoming Lin, Xiangyang Xue

“…Meanwhile, the ESTIMATE algorithm was used for immune infiltration analysis and prediction of immunotherapy response, and the Genomics of Drug Sensitivity in Cancer (GDSC) database was utilized for the drug sensitivity analysis of NUBPL. …”
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Epidemiological, molecular, and evolutionary characteristics of G1P[8] rotavirus in China on the eve of RotaTeq application by Rui Peng, Rui Peng, Mengxuan Wang, Saleha Shahar, Guangping Xiong, Qing Zhang, Lili Pang, Hong Wang, Xiangyu Kong, Dandi Li, Zhaojun Duan

“…Identity and phylogeny analysis showed that Chinese G1P[8] from before 2018 was generally stable with possible genetic recombination among local strains.DiscussionThese findings not only are of great significance for predicting the prevalence of G1P [8] in China, but also provide data reference for evaluating rotavirus vaccine efficacy.…”
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Transcriptome analysis provides new insights into the berry size in ‘Summer Black’ grape under a two-crop-a-year cultivation system by Peiyi Ni, Shengdi Yang, Yunzhang Yuan, Chunyang Zhang, Hengliang Zhu, Jing Ma, Shuangjiang Li, Guoshun Yang, Miao Bai

“…Moreover, based on the results of interactive analysis of TO-GCN and transcriptional regulation prediction of L1–L3 genes, we constructed a unique hierarchical regulatory network for the heat stress regulation of berry size. …”
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Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+ by HU Chengxi, TAN Lixin, WANG Wenyin, SONG Min

“…Ultimately, through a sequence of convolutional operations and upsampling procedures, a prediction map congruent in resolution with the original image was generated, enabling the precise demarcation of tea shoot harvesting points.…”
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Multi-Target Mechanism of Compound Qingdai Capsule for Treatment of Psoriasis: Multi-Omics Analysis and Experimental Verification by Qiao Y, Li C, Chen C, Wu P, Yang Y, Xie M, Liu N, Gu J

“…The ingredients of CQC were detected by UPLC-MS/MS, and target prediction was performed by systems pharmacology. Machine learning, including Lasso regression, Random Forest, and Support Vector Machine (SVM), were utilized to screen core targets of psoriasis. …”
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Detection of platelet-associated immunoglobulins and complement system components in patients with aplastic anemia and hemoblastosis by A. F. Rakhmani, E. A. Mikhaylova, I. V. Galtseva, Yu. O. Davidova, N. M. Kapranov, I. V. Dubinkin, S. M. Kulikov, T. V. Gaponova, Z. T. Fidarova, V. V. Troitskaya, E. N. Parovichnikova, V. G. Savchenko

“…In addition to application of a certain transfusion therapy algorithm it is also necessary to detect PAIg (G, M, A) and PAC3 / C4 for prediction of severe refractoriness to donor's platelet transfusions.…”
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Multi-institutional TSA-amplified Multiplexed Immunofluorescence Reproducibility Evaluation (MITRE) Study by Bernard A Fox, Chichung Wang, Kristin Roman, Carla Coltharp, Carmen Ballesteros-Merino, Shawn M Jensen, Janis M Taube, David L Rimm, Cliff Hoyt, Mei Jiang, Edwin R Parra, Ignacio Wistuba, Elizabeth L Engle, John McGuire, Bethany Remeniuk, Darren Locke

“…Background Emerging data suggest predictive biomarkers based on the spatial arrangement of cells or coexpression patterns in tissue sections will play an important role in precision immuno-oncology. …”
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To reveal biomarkers related to macrophage and lactic acid metabolism in renal fibrosis and explore their mechanisms by Chen Yong, Chen Yong, Yongfei Yu, Yongfei Yu, Yuan Wei, Yuan Wei, Guoshun Huang, Guoshun Huang, Lianghui Shu, Lianghui Shu, Kun Gao, Kun Gao, Kun Gao, Enchao Zhou, Enchao Zhou, Enchao Zhou, Enchao Zhou

“…Subsequently, candidate genes were incorporated into machine learning algorithms to identify key feature genes associated with RF. …”
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Improving accessibility to radiotherapy services in Cali, Colombia: cross-sectional equity analyses using open data and big data travel times from 2020 by Luis Gabriel Cuervo, Carmen Juliana Villamizar, Daniel Cuervo, Pablo Zapata, Maria B. Ospina, Sara Marcela Valencia, Alfredo Polo, Ángela Suárez, Maria O. Bula, J. Jaime Miranda, Gynna Millan, Diana Elizabeth Cuervo, Nancy J. Owens, Felipe Piquero, Janet Hatcher-Roberts, Gabriel Dario Paredes, María Fernanda Navarro, Ingrid Liliana Minotta, Carmen Palta, Eliana Martínez-Herrera, Ciro Jaramillo, on behalf of the AMORE Project Collaboration

“…We predicted accessibility cumulative opportunities (ACO) for traffic ranging from peak congestion to free-flow conditions with hourly assessments for 6–12 July 2020 and 23–29 November 2020. …”
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Functional Complete Revascularization as Determined by an Optimized Scoring System After Revascularization: A Post Hoc Analysis from Multi‐Center PANDA III Trial by Rui Zhang, Shaoyu Wu, Qianqian Liu, Changdong Guan, Hao‐Yu Wang, Sheng Yuan, Lihua Xie, Yunfei Huang, Zheng Qiao, Weida Liu, Rui Fu, Lei Feng, Chenggang Zhu, Lei Song, Dong Yin, Kefei Dou

“…The m‐rFSS is more predictive of 2‐year MACE than c‐rFSS (difference in C‐index 0.07, p < 0.001). …”
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LcProt: Proteomics‐based identification of plasma biomarkers for lung cancer multievent, a multicentre study by Hengrui Liang, Runchen Wang, Ran Cheng, Zhiming Ye, Na Zhao, Xiaohong Zhao, Ying Huang, Zhanpeng Jiang, Wangzhong Li, Jianqi Zheng, Hongsheng Deng, Yu Jiang, Yuechun Lin, Yun Yan, Lei Song, Jie Li, Xin Xu, Wenhua Liang, Jun Liu, Jianxing He

“…ABSTRACT Background Plasma protein has gained prominence in the non‐invasive predicting of lung cancer. We utilised Zeolite Zotero NaY‐based plasma proteomics to investigate its potential for multiple event predicting, including lung cancer diagnosis (task #1), lymph node metastasis detection (task #2) and tumour‒node‒metastasis (TNM) staging (task #3). …”
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