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Background: Post-kala-azar dermal leishmaniasis (PKDL) appears as a rash in some individuals who have recovered from visceral leishmaniasis caused by Leishmania donovani. Today, basic knowledge of this neglected disease and how to predict its progression remain largely unknown.
Methods And Findings: This study addresses the use of several biochemical, haematological and immunological variables, independently or through unsupervised machine learning (ML), to predict PKDL progression risk. In 110 patients from Sudan, 31 such factors were assessed in relation to PKDL disease state at the time of diagnosis: progressive (worsening) versus stable. To identify key factors associated with PKDL worsening, we used both a conventional statistical approach and multivariate analysis through unsupervised ML. The independent use of these variables had limited power to predict skin lesion severity in a baseline examination. In contrast, the unsupervised ML approach identified a set of 10 non-redundant variables that was linked to a 3.1 times higher risk of developing progressive PKDL. Three of these clustering factors (low albumin level, low haematocrit and low IFN-γ production in PBMCs after Leishmania antigen stimulation) were remarkable in patients with progressive disease. Dimensionality re-establishment identified 11 further significantly modified factors that are also important to understand the worsening phenotype. Our results indicate that the combination of anaemia and a weak Th1 immunological response is likely the main physiological mechanism that leads to progressive PKDL.
Conclusions: A combination of 14 biochemical variables identified by unsupervised ML was able to detect a worsening PKDL state in Sudanese patients. This approach could prove instrumental to train future supervised algorithms based on larger patient cohorts both for a more precise diagnosis and to gain insight into fundamental aspects of this complication of visceral leishmaniasis.
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http://dx.doi.org/10.1371/journal.pntd.0012924 | DOI Listing |
Anal Chim Acta
November 2025
Laser Spectroscopy Lab, Department of Physics, University of Agriculture Faisalabad, 38090, Pakistan. Electronic address:
Background: Classification of rose species and verities is a challenging task. Rose is used worldwide for various applications, including but not restricted to skincare, medicine, cosmetics, and fragrance. This study explores the potential of Laser-Induced Breakdown Spectroscopy (LIBS) for species and variety classification of rose flowers, leveraging its advantages such as minimal sample preparation, real-time analysis, and remote sensing.
View Article and Find Full Text PDFMol Pharmacol
August 2025
Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland. Electronic address:
Although multiparameter cellular morphological profiling methods and three-dimensional (3D) biological model systems can potentially provide complex insights for pharmaceutical discovery campaigns, there have been relatively few reports combining these experimental approaches. In this study, we used the U87 glioblastoma cell line grown in a 3D spheroid format to validate a multiparameter cellular morphological profiling screening method. The steps of this approach include 3D spheroid treatment, cell staining, fully automated digital image acquisition, image segmentation, numerical feature extraction, and multiple machine learning approaches for cellular profiling.
View Article and Find Full Text PDFSci Rep
September 2025
Fukushima Renewable Energy Institute, National Institute of Advanced Industrial Science and Technology (AIST), Fukushima, 9630298, Koriyama, Japan.
The increasing adoption of the Internet of Things (IoT) in energy systems has brought significant advancements but also heightened cyber security risks. Virtual Power Plants (VPPs), which aggregate distributed renewable energy resources into a single entity for participation in energy markets, are particularly vulnerable to cyber-attacks due to their reliance on modern information and communication technologies. Cyber-attacks targeting devices, networks, or specific goals can compromise system integrity.
View Article and Find Full Text PDFFront Neurol
August 2025
Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.
Background: Motor symptoms of Parkinson's disease (PD) patients affect their ability of daily activities. Identifying distinct trajectories of motor symptom progression in PD patients can facilitate long-term management.
Methods: A total of 155 PD patients were acquired from the Parkinson's Disease Progression Marker Initiative (PPMI).
Environ Sci Technol
September 2025
Oregon State University, Department of Biological & Ecological Engineering, Corvallis, Oregon 97331-4501, United States.
Chemical forensics aims to identify major contamination sources, but existing workflows often rely on predefined targets and known sources, introducing bias. Here, we present a data-driven workflow that reduces this bias by applying an unsupervised machine learning technique. We applied both nonmetric multidimensional scaling (NMDS) and non-negative matrix factorization (NMF) on the same nontargeted chemical data set to compare their different interpretations of environmental sources.
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