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PreTKcat: A pre-trained representation learning and machine learning framework for predicting enzyme turnover number.
Comput Biol Chem
January 2025
College of Artificial Intelligence, Tianjin University of Science and Technology, No. 9, 13th Street, Tianjin Economic-Technological Development Area, Tianjin, 300457, China. Electronic address:
The enzyme turnover number (k) is crucial for understanding enzyme kinetics and optimizing biotechnological processes. However, experimentally measured k values are limited due to the high cost and labor intensity of wet-lab measurements, necessitating robust computational methods. To address this issue, we propose PreTKcat, a framework that integrates pre-trained representation learning and machine learning to predict k values.
View Article and Find Full Text PDFStyle mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation.
Med Image Anal
December 2024
Department of Electrical Engineering, Yale University, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA. Electronic address:
Unsupervised domain adaptation (UDA) has shown impressive performance by improving the generalizability of the model to tackle the domain shift problem for cross-modality medical segmentation. However, most of the existing UDA approaches depend on high-quality image translation with diversity constraints to explicitly augment the potential data diversity, which is hard to ensure semantic consistency and capture domain-invariant representation. In this paper, free of image translation and diversity constraints, we propose a novel Style Mixup Enhanced Disentanglement Learning (SMEDL) for UDA medical image segmentation to further improve domain generalization and enhance domain-invariant learning ability.
View Article and Find Full Text PDFStereo-EEG around the world: State of the art in Italy.
Neurophysiol Clin
January 2025
Neuroscience and Human Genetics Department, Meyer Children's Hospital IRCCS, Full Member of European Reference Network on Rare and Complex Epilepsies, EpiCARE, viale Pieraccini 24, 50139, Florence, Italy; Neurofarba Department, University of Florence, viale Pieraccini 6, 50139, Florence, Italy.
Stereo-EEG is not just a diagnostic examination but a complex methodology, requiring an accurate synthesis of many data (anatomical, clinical, neurophysiological, cognitive, metabolic, and genetic). The implantation scheme is decided based on a hypothesis (or hypotheses) of epileptogenic zone localization. Subsequently, intracerebral electrical stimulation is used to define the extent of highly functional cortical regions and to reproduce the clinical symptoms and signs associated with seizures.
View Article and Find Full Text PDFHigh prevalence of reverse transcriptase inhibitors associated resistance mutations among people living with HIV on dolutegravir-based antiretroviral therapy in Francistown, Botswana.
J Antimicrob Chemother
January 2025
Research Laboratory, Botswana Harvard Health Partnership, Gaborone, Botswana.
Objectives: We assessed HIV-1 drug resistance profiles among people living with HIV (PLWH) with detectable viral load (VL) and on dolutegravir-based antiretroviral therapy (ART) in Botswana.
Methods: The study utilised available 100 residual HIV-1 VL samples from unique PLWH in Francistown who had viraemia at-least 6 months after initiating ART in Botswana's national ART program from November 2023 to January 2024. Viraemia was categorized as low-level viraemia (LLV) (VL: 200-999 copies/mL) or virologic failure (VF) (VL ≥1000 copies/mL).
Technological advances in clinical individualized medication for cancer therapy: from genes to whole organism.
Per Med
January 2025
Department of Clinical Pharmacy, Zhejiang Provincial Key Laboratory for Drug Evaluation and Clinical Research, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Efforts have been made to leverage technology to accurately identify tumor characteristics and predict how each cancer patient may respond to medications. This involves collecting data from various sources such as genomic data, histological information, functional drug profiling, and drug metabolism using techniques like polymerase chain reaction, sanger sequencing, next-generation sequencing, fluorescence in situ hybridization, immunohistochemistry staining, patient-derived tumor xenograft models, patient-derived organoid models, and therapeutic drug monitoring. The utilization of diverse detection technologies in clinical practice has made "individualized treatment" possible, but the desired level of accuracy has not been fully attained yet.
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