Tumor-associated macrophages correlate with better outcome in SHH medulloblastoma by Jin Zhang, Shuting Li, Yuan Wang, Jingjing Liu, Yan Liu, Xiaojun Gong, Yanling Sun, Liming Sun, Zhigang Li, Tianyou Wang, Shuxu Du, Wanshui Wu

“…Using multiple immunofluorescence staining on paraffin-embedded sections, we detected the activated phenotype (M1/M2) by monoclonal antibodies for CD68, HLA-DR and CD163. …”
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Multi-level phenotypic models of cardiovascular disease and obstructive sleep apnea comorbidities: A longitudinal Wisconsin sleep cohort study. by Duy Nguyen, Ca Hoang, Tien Truong, Dang Nguyen, Hillary Gia Lam, Abhay Sharma, Trung Quoc Le, Phat Kim Huynh

“…Current methodologies predominantly lack the dynamic and longitudinal perspective necessary to accurately predict CVD progression in the presence of OSA. This study addresses these limitations by proposing a novel multi-level phenotypic model that analyzes the progression and interaction of these comorbidities over time. …”
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A Four‐Gene Autophagy‐Related Prognostic Model Signature and Its Association With Immune Phenotype in Lung Squamous Cell Carcinoma by Lumeng Luo, Jiaying Deng, Qiu Tang

“…Conclusion This study provided an effective autophagy‐related prognostic signature, which could also predict the immune phenotype.…”
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SmartSense-D: A safety, feasibility, and acceptability pilot study of digital phenotyping in young people with major depressive disorder by Andres Camargo, Scott D Tagliaferri, Simon D’Alfonso, Tianyi Zhang, Zamantha Munoz, Pemma Davies, Mario Alvarez-Jimenez, Niels van Berkel, Vassilis Kostakos, Lianne Schmaal

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Alveolar cell composition in interstitial lung disease and the development of a pulmonary progressive fibrosing phenotype: a retrospective cohort study by Iris A. Simons, Bart G. Boerrigter, Maud C.M. Hovestadt, Kirsten A. Mooij-Kalverda, Shiqi Zhang, Leonoor S. Boers, Anke H. Maitland– van der Zee, Esther J. Nossent, Jan Willem Duitman

“…We evaluated whether the alveolar cellular composition at initial diagnosis is predictive of the development of progressive pulmonary fibrosis (PPF) in patients with ILD. …”
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Integrative analysis of multi-omics data identified PLG as key gene related to Anoikis resistance and immune phenotypes in hepatocellular carcinoma by Xueyan Wang, Lei Gao, Haiyuan Li, Yanling Ma, Bofang Wang, Baohong Gu, Xuemei Li, Lin Xiang, Yuping Bai, Chenhui Ma, Hao Chen

“…Therefore, there is an urgent need for a multi-omics-based classification system for HCC that clarifies the molecular mechanisms underlying the establishment of immune phenotypes and Anoikis resistance in HCC cells. In this study, we employed advanced clustering algorithms to analyze and integrate multi-omics data from HCC patients, with the objective of identifying key genes that possess prognostic potential associated with the Anoikis resistance phenotype. …”
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Apple phenotyping using deep learning and 3D depth analysis: An experimental study on fruitlet sizing during early development by Giorgio Checola, Damiano Moser, Paolo Sonego, Cristian Iob, Franco Micheli, Pietro Franceschi

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High sensitivity C-reactive protein implicates heterogeneous metabolic phenotypes and severity in metabolic dysfunction associated-steatotic liver disease by Hao Wang, Junzhao Ye, Youpeng Chen, Yanhong Sun, Xiaorong Gong, Hong Deng, Zhiyong Dong, Lishu Xu, Xin Li, Bihui Zhong

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Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation by Zongqi Xia, Prerna Chikersal, Shruthi Venkatesh, Elizabeth Walker, Anind K Dey, Mayank Goel

“…Further, sensor data were largely sufficient for predicting symptom severity, while the prediction of depressive symptoms benefited from minimal active patient input in the form of responses to 2 brief questions on the day before the prediction point. …”
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Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine‐based approach in diverse healthcare systems by Anna O. Basile, Anurag Verma, Leigh Anne Tang, Marina Serper, Andrew Scanga, Ava Farrell, Brittney Destin, Rotonya M. Carr, Anuli Anyanwu‐Ofili, Gunaretnam Rajagopal, Abraham Krikhely, Marc Bessler, Muredach P. Reilly, Marylyn D. Ritchie, Nicholas P. Tatonetti, Julia Wattacheril

“…Therapeutic interventions are rapidly advancing for its inflammatory phenotype, nonalcoholic steatohepatitis (NASH) at all stages of disease. …”
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SEC61 translocon gamma subunit is correlated with glycolytic activity, epithelial mesenchymal transition and the immune suppressive phenotyp... by Zhou Changshuai, Cui Huanhuan, Yang Yuechao, Chen Lei, Feng Mingtao, Gao Yang, Li Deheng, Li Liangdong, Chen Xin, Li Xiaoqiu, Cao Yiqun

“…In conclusion, our comprehensive analyses position SEC61G as a potential prognostic biomarker intricately linked to glycolytic metabolism, the EMT pathway, and the establishment of an immune-suppressive phenotype in LUAD. These findings underscore the potential of SEC61G as a therapeutic target and predictive marker for immunotherapeutic responses in LUAD patients.…”
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Genetic prediction of male pattern baldness. by Saskia P Hagenaars, W David Hill, Sarah E Harris, Stuart J Ritchie, Gail Davies, David C Liewald, Catharine R Gale, David J Porteous, Ian J Deary, Riccardo E Marioni

“…Male pattern baldness can have substantial psychosocial effects, and it has been phenotypically linked to adverse health outcomes such as prostate cancer and cardiovascular disease. …”
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Predicting synthetic rescues in metabolic networks by Adilson E Motter, Natali Gulbahce, Eivind Almaas, Albert‐László Barabási

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Evaluation of high-moisture oat silage inoculated with synthetic lactic acid bacteria consortia in mini-silos: fermentation, microbial, metabolic and safety profiles by Cheng Zong, Longxin Wang, Jie Zhao, Zhihao Dong, Junfeng Li, Xianjun Yuan, Chengti Xu, Tao Shao

“…In microbial phenotype analysis, CM2 and CM4 reduced pathogenic risks associated with the microbial load in oat silage, as evidenced by the lower proportions of undesirable phenotypes compared to fresh oat and control. …”
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Significant Serpents: Predictive Modelling of Bioclimatic Venom Variation in Russell's Viper. by Navaneel Sarangi, R R Senji Laxme, Kartik Sunagar

“…Multiple regression models were developed to evaluate the relationship between venom variability and the historical climate data, specifically temperature and precipitation. Furthermore, predictive models were employed to map venom phenotypes across the distribution range of D. russelii.…”
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Pork-YOLO: Automated collection of pork quality traits by Jiacheng Wei, Xi Tang, Jinxiu Liu, Ting Luo, Yan Wu, Junhui Duan, Shijun Xiao, Zhiyan Zhang

“…For marbling scoring, an image classification task yielded an average accuracy of 98.9 %, with a strong correlation (R2 = 0.999) between predicted and actual values. This study presents an innovative method for rapid automated assessment of pork quality traits, offering valuable insights for future phenotypic measurement automation.…”
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Trans-ancestral rare variant association study with machine learning-based phenotyping for metabolic dysfunction-associated steatotic liver disease by Robert Chen, Ben Omega Petrazzini, Áine Duffy, Ghislain Rocheleau, Daniel Jordan, Meena Bansal, Ron Do

“…We then developed models to accurately predict PDFF and MASLD status in the UK Biobank and tested associations with these predicted phenotypes to increase statistical power. …”
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Risk management of progression of chronic obstructive pulmonary disease at the outpatient stage, taking into account the clinical phenotype and peculiarities of the course of the d... by T. V. Tayutina

“…The purpose of the study is to evaluate the effectiveness of using the "Program for predicting an unfavorable outcome, the development of cardiovascular complications and the effectiveness of rehabilitation measures in patients with chronic obstructive pulmonary disease (CardioRisk)" (Certificate number of state registration RU2023666935, registration date 08.08.2023) to manage the risks of COPD progression at the outpatient stage, taking into account the clinical phenotype and features of the course of the disease.Materials and methods. …”
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Mapping variants in thyroid hormone transporter MCT8 to disease severity by genomic, phenotypic, functional, structural and deep learning integration by Stefan Groeneweg, Ferdy S. van Geest, Mariano Martín, Mafalda Dias, Jonathan Frazer, Carolina Medina-Gomez, Rosalie B. T. M. Sterenborg, Hao Wang, Anna Dolcetta-Capuzzo, Linda J. de Rooij, Alexander Teumer, Ayhan Abaci, Erica L. T. van den Akker, Gautam P. Ambegaonkar, Christine M. Armour, Iiuliu Bacos, Priyanka Bakhtiani, Diana Barca, Andrew J. Bauer, Sjoerd A. A. van den Berg, Amanda van den Berge, Enrico Bertini, Ingrid M. van Beynum, Nicola Brunetti-Pierri, Doris Brunner, Marco Cappa, Gerarda Cappuccio, Barbara Castellotti, Claudia Castiglioni, Krishna Chatterjee, Alexander Chesover, Peter Christian, Jet Coenen-van der Spek, Irenaeus F. M. de Coo, Regis Coutant, Dana Craiu, Patricia Crock, Christian DeGoede, Korcan Demir, Cheyenne Dewey, Alice Dica, Paul Dimitri, Marjolein H. G. Dremmen, Rachana Dubey, Anina Enderli, Jan Fairchild, Jonathan Gallichan, Luigi Garibaldi, Belinda George, Evelien F. Gevers, Erin Greenup, Annette Hackenberg, Zita Halász, Bianka Heinrich, Anna C. Hurst, Tony Huynh, Amber R. Isaza, Anna Klosowska, Marieke M. van der Knoop, Daniel Konrad, David A. Koolen, Heiko Krude, Abhishek Kulkarni, Alexander Laemmle, Stephen H. LaFranchi, Amy Lawson-Yuen, Jan Lebl, Selmar Leeuwenburgh, Michaela Linder-Lucht, Anna López Martí, Cláudia F. Lorea, Charles M. Lourenço, Roelineke J. Lunsing, Greta Lyons, Jana Krenek Malikova, Edna E. Mancilla, Kenneth L. McCormick, Anne McGowan, Veronica Mericq, Felipe Monti Lora, Carla Moran, Katalin E. Muller, Lindsey E. Nicol, Isabelle Oliver-Petit, Laura Paone, Praveen G. Paul, Michel Polak, Francesco Porta, Fabiano O. Poswar, Christina Reinauer, Klara Rozenkova, Rowen Seckold, Tuba Seven Menevse, Peter Simm, Anna Simon, Yogen Singh, Marco Spada, Milou A. M. Stals, Merel T. Stegenga, Athanasia Stoupa, Gopinath M. Subramanian, Lilla Szeifert, Davide Tonduti, Serap Turan, Joel Vanderniet, Adri van der Walt, Jean-Louis Wémeau, Anne-Marie van Wermeskerken, Jolanta Wierzba, Marie-Claire Y. de Wit, Nicole I. Wolf, Michael Wurm, Federica Zibordi, Amnon Zung, Nitash Zwaveling-Soonawala, Fernando Rivadeneira, Marcel E. Meima, Debora S. Marks, Juan P. Nicola, Chi-Hua Chen, Marco Medici, W. Edward Visser

“…Abstract Predicting and quantifying phenotypic consequences of genetic variants in rare disorders is a major challenge, particularly pertinent for ‘actionable’ genes such as thyroid hormone transporter MCT8 (encoded by the X-linked SLC16A2 gene), where loss-of-function (LoF) variants cause a rare neurodevelopmental and (treatable) metabolic disorder in males. …”
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The Implication of NT-proBNP in the Assessment of the Clinical Phenotype of Patients with Type 2 Diabetes Mellitus, Without Established Cardiovascular Disease by Ioannis Gastouniotis, Christos Fragoulis, Alexis Antonopoulos, Alexandrina Kouroutzoglou, Marina Noutsou, Anastasia Thanopoulou, Christina Chrysohoou, Konstantinos P. Tsioufis

“…Finally, there was a negative correlation between right ventricle end diastolic volume in CMR and predicted maximum oxygen consumption (b = −0.225 ± 0.11, <i>p</i> = 0.046). …”
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