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1. Knee acoustic emissions as a noninvasive biomarker of articular health in patients with juvenile idiopathic arthritis: a clinical validation in an extended study population

   https://doi.org/10.1186/s12969-023-00842-7  

   Goossens, Quentin; Locsin, Miguel; Gharehbaghi, Sevda; Brito, Priya; Moise, Emily; Ponder, Lori A; Inan, Omer T; Prahalad, Sampath (December 2023, Pediatric Rheumatology)

   Abstract BackgroundJoint acoustic emissions from knees have been evaluated as a convenient, non-invasive digital biomarker of inflammatory knee involvement in a small cohort of children with Juvenile Idiopathic Arthritis (JIA). The objective of the present study was to validate this in a larger cohort. FindingsA total of 116 subjects (86 JIA and 30 healthy controls) participated in this study. Of the 86 subjects with JIA, 43 subjects had active knee involvement at the time of study. Joint acoustic emissions were bilaterally recorded, and corresponding signal features were used to train a machine learning algorithm (XGBoost) to classify JIA and healthy knees. All active JIA knees and 80% of the controls were used as training data set, while the remaining knees were used as testing data set. Leave-one-leg-out cross-validation was used for validation on the training data set. Validation on the training and testing set of the classifier resulted in an accuracy of 81.1% and 87.7% respectively. Sensitivity / specificity for the training and testing validation was 88.6% / 72.3% and 88.1% / 83.3%, respectively. The area under the curve of the receiver operating characteristic curve was 0.81 for the developed classifier. The distributions of the joint scores of the active and inactive knees were significantly different. ConclusionJoint acoustic emissions can serve as an inexpensive and easy-to-use digital biomarker to distinguish JIA from healthy controls. Utilizing serial joint acoustic emission recordings can potentially help monitor disease activity in JIA affected joints to enable timely changes in therapy. 

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2. Deep learning predicts prevalent and incident Parkinson’s disease from UK Biobank fundus imaging

   https://doi.org/10.1038/s41598-024-54251-1  

   Tran, Charlie; Shen, Kai; Liu, Kang; Ashok, Akshay; Ramirez-Zamora, Adolfo; Chen, Jinghua; Li, Yulin; Fang, Ruogu (December 2024, Scientific Reports)

   Parkinson’s disease is the world’s fastest-growing neurological disorder. Research to elucidate the mechanisms of Parkinson’s disease and automate diagnostics would greatly improve the treatment of patients with Parkinson’s disease. Current diagnostic methods are expensive and have limited availability. Considering the insidious and preclinical onset and progression of the disease, a desirable screening should be diagnostically accurate even before the onset of symptoms to allow medical interventions. We highlight retinal fundus imaging, often termed a window to the brain, as a diagnostic screening modality for Parkinson’s disease. We conducted a systematic evaluation of conventional machine learning and deep learning techniques to classify Parkinson’s disease from UK Biobank fundus imaging. Our results suggest Parkinson’s disease individuals can be differentiated from age and gender-matched healthy subjects with 68% accuracy. This accuracy is maintained when predicting either prevalent or incident Parkinson’s disease. Explainability and trustworthiness are enhanced by visual attribution maps of localized biomarkers and quantified metrics of model robustness to data perturbations. 

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3. Fusion of Low-Level Descriptors of Digital Voice Recordings for Dementia Assessment

   https://doi.org/10.3233/JAD-230560  

   Karjadi, Cody; Xue, Chonghua; Cordella, Claire; Kiran, Swathi; Paschalidis, Ioannis Ch; Au, Rhoda; Kolachalama, Vijaya B (November 2023, Journal of Alzheimer's Disease)

   Babulal, Ganesh (Ed.)

   Digital voice recordings can offer affordable, accessible ways to evaluate behavior and function. We assessed how combining different low-level voice descriptors can evaluate cognitive status. Using voice recordings from neuropsychological exams at the Framingham Heart Study, we developed a machine learning framework fusing spectral, prosodic, and sound quality measures early in the training cycle. The model’s area under the receiver operating characteristic curve was 0.832 (±0.034) in differentiating persons with dementia from those who had normal cognition. This offers a data-driven framework for analyzing minimally processed voice recordings for cognitive assessment, highlighting the value of digital technologies in disease detection and intervention. 

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4. The Effects of Nonresponse and Sampling Omissions on Estimates on Various Topics in Federal Surveys: Telephone and IVR Surveys of Address-Based Samples

   https://doi.org/10.2478/jos-2020-0032  

   Fowler, Floyd J.; Brenner, Philip; Roman, Anthony M.; Hargraves, J. Lee (July 2020, Journal of Official Statistics)

   null (Ed.)

   Abstract With declining response rates and challenges of using RDD sampling for telephone surveys, collecting data from address-based samples has become more attractive. Two approaches are doing telephone interviews at telephone numbers matched to addresses and asking those at sampled addresses to call into an Interactive Voice Response (IVR) system to answer questions. This study used in-person interviewing to evaluate the effects of nonresponse and problems matching telephone numbers when telephone and IVR were used as the initial modes of data collection. The survey questions were selected from major US federal surveys covering a variety of topics. Both nonresponse and, for telephone, inability to find matches result in important nonresponse error for nearly half the measures across all topics, even after adjustments to fit the known demographic characteristics of the residents. Producing credible estimates requires using supplemental data collection strategies to reduce error from nonresponse. 

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5. A hybrid framework for disease biomarker discovery in microbiome research combining Bayesian networks, machine learning, and network-based methods

   https://doi.org/10.1093/biomethods/bpaf089  

   Aghdam, Rosa; Shan, Shan; Lankau, Richard; Solís-Lemus, Claudia (January 2026, Biology Methods and Protocols)

   Abstract Microbiome research faces two central challenges, namely constructing reliable networks, where nodes represent microbial taxa and edges represent their associations, and identifying significant disease-associated taxa. To address the first challenge, we developed CMIMN, a novel R package that applies a Bayesian network framework based on conditional mutual information to infer microbial interaction networks. To further enhance reliability, we construct a consensus microbiome network by integrating results from CMIMN and three widely used methods, including Sparse Inverse Covariance Estimation for Ecological Association Inference (SPIEC-EASI), Semi-Parametric Rank-based correlation and partial correlation Estimation (SPRING), and Sparse Correlations for Compositional Data (SPARCC). This consensus approach, which overlays and weights edges shared across methods, reduces inconsistencies and provides a more biologically meaningful view of microbial relationships. To address the second challenge, we designed a multi-method feature selection framework that combines machine learning with network-based strategies. Our machine learning pipeline applies distinct algorithms and identifies key taxa based on their consistent importance across models. Complementing this, we employ two network-based strategies that prioritize taxa based on centrality differences between networks constructed from healthy samples and disease-affected samples, as well as a composite scoring system that ranks nodes using integrated network metrics. We applied CMIMN on soil microbiome data from potato fields affected by common scab disease. Bootstrap analysis confirmed the robustness of CMIMN, and the consensus network further improved stability and interpretability. The multi-method framework enhances confidence in identifying soil microbial taxa associated with potato disease. Notably, we identified Bacteroidota, WPS-2, and Proteobacteria at the Phylum level; Actinobacteria, AD3, Bacilli, Anaerolineae, and Ktedonobacteria at the Class level; and C0119, Defluviicoccales, Bacteroidales, and Ktedonobacterales at the Order level as key taxa associated with disease status. 

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