Eosinophilic cationic protein as a non-invasive marker of the nature of inflammatory response in patients with chronic obstructive pulmonary disease by M. A. Karnaushkina, S. V. Fedosenko, R. S. Danilov, I. S. Komarova, V. A. Petrov

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Genomic Landscape and Prediction of Udder Traits in Saanen Dairy Goats by Xiaoting Yao, Jiaxin Li, Jiaqi Fu, Xingquan Wang, Longgang Ma, Hojjat Asadollahpour Nanaei, Ali Mujtaba Shah, Zhuangbiao Zhang, Peipei Bian, Shishuo Zhou, Ao Wang, Xihong Wang, Yu Jiang

“…This study leveraged genotyping imputation to explore the genetic parameters and architecture of udder traits and assess the efficiency of genomic prediction methods. Using data from 635 Saanen dairy goats, genotyped for over 14,717,075 SNP markers and phenotyped for three udder traits, heritability was 0.16 for udder width, 0.32 for udder depth, and 0.13 for teat spacing, with genetic correlations of 0.79, 0.70, and 0.45 observed among the traits. …”
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Prediction of food allergy reaction severity: biomarkers and host factors by David J. Fitzhugh

“…Specifically, BAT demonstrates superior discriminatory power for severe peanut and baked egg reactions, whereas Arah2 component level above 1.4 kU/L suggest a more severe peanut allergy phenotype. Host factors, including comorbid conditions, age, and behavioral variables, further complicate severity prediction. …”
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Exploring the Inter-Individual Variability in Response to Food in Seniors Living at Home: The MetabotypAGE Project by Claudine Manach, Cécile Gladine, Christine Morand, Laurent Mosoni, Estelle Pujos-Guillot, Didier Rémond, Sergio Polakof

“…Previous studies demonstrated the possibility to predict the postprandial glycemic response to food in healthy adults based on deep phenotyping. …”
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Impact of Selection Signature on Genomic Prediction and Heritability Estimation in Livestock by Hongzhi Zhang, Zhixu Pang, Wannian Wang, Liying Qiao, Wenzhong Liu

“…Cross-validation with real phenotypic data from Holsteins and pigs demonstrated that implementing selection-adjust methods improved prediction accuracy by 0.015 for FP in Holsteins and 0.01 for T1 in pigs, while enhancing the unbiasedness of heritability estimates across all traits. …”
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Radiogenomics and machine learning predict oncogenic signaling pathways in glioblastoma by Abdul Basit Ahanger, Syed Wajid Aalam, Tariq Ahmad Masoodi, Asma Shah, Meraj Alam Khan, Ajaz A. Bhat, Assif Assad, Muzafar Ahmad Macha, Muzafar Rasool Bhat

“…This study explores the utility of radiogenomics and machine learning (ML) in predicting these oncogenic signaling pathways in GBM patients. …”
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HDAC6 inhibition by ITF3756 modulates PD-L1 expression and monocyte phenotype: insights for a promising immune checkpoint blockade co-treatment therapy by Valeria Spadotto, Chiara Ripamonti, Andrea Ghiroldi, Elisabetta Galbiati, Pietro Pozzi, Roberta Noberini, Tiziana Bonaldi, Tiziana Bonaldi, Christian Steinkühler, Gianluca Fossati

“…While PD-L1 expression on tumor cells is an established predictive biomarker for therapeutic response, emerging evidence highlights the importance of PD-L1 expression on myeloid cells, both in the periphery and within the tumor microenvironment (TME). …”
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Patient-specific gene co-expression networks reveal novel subtypes and predictive biomarkers in lung adenocarcinoma by Patricio López-Sánchez, Federico Ávila-Moreno, Enrique Hernández-Lemus, Marieke L. Kuijjer, Jesús Espinal-Enríquez

“…Recently, studies have shown that multiple cancerous phenotypes share a distinct GCN architecture, suggesting that network topology holds promise for understanding disease pathology. …”
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Predictive Value of Direct Disk Diffusion Testing from Positive Blood Cultures for Detection of Antimicrobial Nonsusceptibility by Tammy Ting-Yan Wong, Chung-Ho Lee, Hester Wing-Sum Luk, Cindy Wing-Sze Tse, Pak-Leung Ho

“…A total of 9754 organism–antibiotic pairs were analyzed. The positive predictive values were more than 98% for clinically significant resistant phenotypes, including ceftriaxone, ceftazidime, cefepime, and meropenem nonsusceptibility in Enterobacterales, ceftazidime and meropenem nonsusceptibility in <i>P. aeruginosa</i>, and cefoxitin resistance in <i>S. aureus</i>. …”
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Predictive capabilities of polygenic scores in an East-Asian population-based cohort: the Singapore Chinese health study by Xuling Chang, Chih Chuan Shih, Jieqi Chen, Ai Shan Lee, Patrick Tan, Ling Wang, Jianjun Liu, Jingmei Li, Jian-Min Yuan, Chiea Chuen Khor, Woon-Puay Koh, Rajkumar Dorajoo

“…It is unclear how these perform in risk predictions among East-Asians. We generated 2173 PGSs from 519 traits and assessed their associations with 58 baseline phenotypes in the Singapore Chinese Health Study, a prospective cohort of 23,622 Chinese adults residing in Singapore. …”
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Psychosocial behavioral phenotypes of racially/ethnically minoritized older adults enrolled in HABS‐HD differ on neuroimaging measures of brain age gap, hippocampal volume, and cor... by Alexandra L. Clark, Makenna B. McGill, Alexandra J. Weigand, Julie K. Wisch, Kalen Petersen, Beau Ances, Meredith N. Braskie, Sid O'Bryant, Kelsey R. Thomas, HABS‐HD Study Team

“…METHODS Latent profile analysis (LPA) employed in a sample of 1820 community‐dwelling older adults (1118 Hispanic and 702 Black) replicated previous Low Resource/Low Distress, High Resource/Low Distress, and Low Resource/High Distress phenotype classifications. Analyses of covariance (ANCOVAs) adjusting for relevant factors examined phenotype differences on neuroimaging outcomes of predicted brain age gap (BAG) (DeepBrainNet Predicted Age – Chronological Age), hippocampal volume, and cortical thickness of a meta‐temporal region of interest. …”
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Genomic and machine learning approaches to predict antimicrobial resistance in Stenotrophomonas maltophilia by Xin Liu, Shanshan Long, Fangyuan Chen, Chang Liu, Peng Han, Hua Yu, Xiaobo Huang, Chun Pan, Ruiming Yue, Wentao Feng, Guanhua Rao, Han Shen, Lingai Pan

“…This study collected 441 S. maltophilia strains, performed whole-genome sequencing, and used machine learning to identify key resistance determinants for LEV and SXT, constructing predictive models for resistance phenotypes. The 441 S. maltophilia strains we collected show significant genomic diversity and representative lineage distribution. …”
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Development and indirect validation of a model predicting frailty in the French healthcare claims database by Hana Lahbib, Laurence Mandereau-Bruno, Sarah Goria, Vérène Wagner, Marion J Torres, Catherine Féart, Catherine Helmer, Karine Pérès, Laure Carcaillon-Bentata

“…We indirectly validated the model by comparing (1) the predicted frailty prevalence in the overall French population in the SNDS with the expected prevalence and (2) the predictive ability of the model for 6-year mortality with that of Fried’s frailty phenotype. …”
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Coronal alignment does not enable to predict the degree of femoral and tibial torsion by Leonard Grünwald, Sophie Schmidt, Marc‐Daniel Ahrend, Tina Histing, Stefan Döbele

“…The mechanical angles were for knee phenotyping according to the coronal plane alignment of the knee classification. …”
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Comprehensive genetic analysis and predictive evaluation of milk electrical conductivity for subclinical mastitis in Chinese Holstein cows by Xubin Lu, Mingxue Long, Zhijian Zhu, Haoran Zhang, Fuzhen Zhou, Zongping Liu, Yongjiang Mao, Zhangping Yang

“…Results The results revealed significant phenotypic and strong genetic correlations (-0.286 to 0.457) between milk electrical conductivity, somatic cell score, milk yield, activity quantity, and milking speed. …”
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Flow Cytometry Analysis of Mesenchymal Stem Cells: A Predictive Biomarker for Leukemia Transformation in Myelodysplastic Syndrome by Mireia Atance, Cristina Serrano, Carlos Soto, Juan Manuel Alonso‐Domínguez, Carlos Blas, Raquel Mata, Tamara Castaño, Sara Perlado, Teresa Arquero, Jose Luis López‐Lorenzo, M. Ángeles Pérez, Belen Rosado, Rafael Martos, Ana Rio‐Machin, Pilar Llamas‐Sillero, Rocio N. Salgado, Juana Serrano‐López

“…Conclusion MSC‐like cell content at MDS diagnosis may serve as a novel biomarker of predicting malignant transformation to AML. Further validation in larger cohorts and better phenotypic characterization of this cell population are needed. …”
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Feature Engineering for the Prediction of Scoliosis in 5q‐Spinal Muscular Atrophy by Tu‐Lan Vu‐Han, Vikram Sunkara, Rodrigo Bermudez‐Schettino, Jakob Schwechten, Robin Runge, Carsten Perka, Tobias Winkler, Sebastian Pokutta, Claudia Weiß, Matthias Pumberger

“…As disease‐modifying therapies alter disease progression and patient phenotypes, paediatricians and consulting disciplines face new unknowns in their treatment decisions. …”
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Considerations for informing precision psychiatry in eating disorders: Foundations for future practice by Nicole Obeid, Niana Lavallée, Abigail H. M. Bradley, Mark L. Norris

“…Large-scale, multiaxial datasets are necessary to elucidate ED etiology and enable phenotyping. This is a critical step towards implementing future precision psychiatry and personalized treatment advances. …”
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Molecular epidemiological characteristics, variant spectrum and genotype-phenotype correlation of glucose-6-phosphate dehydrogenase deficiency in China: A population-based multicen... by Minyi Tan, Xiulian Liu, Yinhong Zhang, Yifan Yin, Ting Chen, Yulin Li, Lulu Feng, Bo Zhu, Chunjing Xu, Chengfang Tang, Meng Sun, Liyun Jia, Weiwei Jin, Chunna Fan, Hui Huang, Xiaohua Wang, Jizhen Feng, Hui Zou, Lianshu Han, Jingkun Miao, Baosheng Zhu, Cidan Huang, Yonglan Huang

“…The overall positive predictive values (PPVs) of biochemical and genetic NBS were 79.95% and 47.57%, respectively. …”
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Feasibility of milk mid-infrared spectroscopy for large-scale screening of hematic traits in dairy buffaloes by S. Magro, R. Matera, G. Neglia, V. Longobardi, A. Costa, G. Pedota, M. De Marchi

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