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Voice Quality as Digital Biomarker in Bipolar Disorder: A Systematic Review.
J Voice
January 2025
Department of Surgery, UMONS Research Institute for Health Sciences and Technology, University of Mons (UMons), Mons, Belgium; Division of Laryngology and Bronchoesophagology, Department of Otolaryngology Head Neck Surgery, EpiCURA Hospital, Baudour, Belgium; Department of Otolaryngology-Head and Neck Surgery, Foch Hospital, School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Paris Saclay University), Paris, France; Department of Otolaryngology, Elsan Hospital, Paris, France. Electronic address:
Background: Voice analysis has emerged as a potential biomarker for mood state detection and monitoring in bipolar disorder (BD). The systematic review aimed to summarize the evidence for voice analysis applications in BD, examining (1) the predictive validity of voice quality outcomes for mood state detection, and (2) the correlation between voice parameters and clinical symptom scales.
Methods: A PubMed, Scopus, and Cochrane Library search was carried out by two investigators for publications investigating voice quality in BD according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements.
Evaluation of a next generation sequencing assay for Hepatitis B antiviral drug resistance on the oxford nanopore system.
J Clin Virol
January 2025
Division of Medical Microbiology and Virology, St. Paul's Hospital, Providence Health Care, Vancouver, British Columbia, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada. Electronic address:
Background: Next-generation sequencing (NGS) for Hepatitis B virus (HBV) antiviral resistance (AVR) testing is a highly sensitive diagnostic method, able to detect low-level mutant subpopulations. Our clinical virology laboratory previously transitioned from DNA hybridization (INNO-LiPA) to NGS, initially with the GS Junior System and subsequently the MiSeq. The Oxford Nanopore Technology (ONT) sequencing system was evaluated for HBV resistance testing, with regards to sequencing accuracy and turn-around time.
View Article and Find Full Text PDFIntegrating machine learning, suspect and nontarget screening reveal the interpretable fates of micropollutants and their transformation products in sludge.
J Hazard Mater
January 2025
School of Environmental Studies, China University of Geosciences, Wuhan, Hubei 430074, China; National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China. Electronic address:
Activated sludge enriches vast amounts of micropollutants (MPs) when wastewater is treated, posing potential environmental risks. While standard methods typically focus on target analysis of known compounds, the identity, structure, and concentration of transformation products (TPs) of MPs remain less understood. Here, we employed a novel approach that integrates machine learning for the quantification of nontarget TPs with advanced target, suspect, and nontarget screening strategies.
View Article and Find Full Text PDFOrganization of the chromosomal passenger complex clusters at inner centromeres in mitosis.
Curr Opin Cell Biol
January 2025
Division of Experimental Pathology, Cancer Institute of the Japanese Foundation for Cancer Research (JFCR), Tokyo, Japan; Department of JFCR Cancer Biology, Institute of Science Tokyo, Tokyo, Japan. Electronic address:
Stable transmission of the genome during cell division is crucial for all life forms and is universally achieved by Aurora B-mediated error correction of the kinetochore-microtubule attachments. Aurora B is the enzymatic subunit of the tetrameric protein complex called the chromosomal passenger complex (CPC), and its centromeric enrichment is required for Aurora B to ensure accurate chromosome segregation. How cells enrich the CPC at centromeres is therefore an outstanding question to be elucidated.
View Article and Find Full Text PDFMachine learning outperforms humans in microplastic characterization and reveals human labelling errors in FTIR data.
J Hazard Mater
December 2024
Discipline of Chemistry, The University of Newcastle, University Drive, Newcastle, New South Whales 2308, Australia; School of Chemistry, Monash University, Wellington Road, Melbourne, Victoria 3800, Australia. Electronic address:
Microplastics are ubiquitous and appear to be harmful, however, the full extent to which these inflict harm has not been fully elucidated. Analysing environmental sample data is challenging, as the complexity in real data makes both automated and manual analysis either unreliable or time-consuming. To address challenges, we explored a dense feed-forward neural network (DNN) for classifying Fourier transform infrared (FTIR) spectroscopic data.
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