Thomas George

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Parkinson’s disease (PD) is a brain disorder, that affects a person’s body movement causing stiffness, shaking and imbalance. Earlier detection of PD is a challenging task for researchers. In this paper, earlier detection of PD is performed using the Cross-Non-Decimated Wavelet Transform (CNDWT) and Bayesian Optimized Multiple Linear Regression (BOMLR) algorithm. The PD voice data is amplitude-sliced, augmented and processed with CNDWT. The CNDWT decomposes amplitude-sliced augmented PD voice data with the Haar transform and reconstruction is performed using Daubechies wavelet of order 3 (DB3) transformations. Preprocessed data is correlated for identification of highly influential data attributes of PD. The Bayesian Optimized Multiple Linear Regression (BOMLR) method is applied to the highly correlated data attributes for earlier PD prediction. A voice data set is created in this study, which consists of 31 voice recordings of which 23 are from individuals affected by PD. The proposed CNDWT method is compared with existing methods. The results show that the proposed CNDWT method outperforms other traditional algorithms with an accuracy of 99% in predicting Parkinson’s disease.

At re:Invent 2025, AWS detailed its move to an intelligence-first cloud, introducing silicon advances, agentic AI, and sovereign AI factories designed to help enterprises, events

Comparative metabolomics may shed light on host immunity and biology in oral candidiasis (oral thrush). Untargeted metabolomic analyses were performed on oral rinses collected from 26 primary oral candidiasis patients (OT), 12 patients after antifungal treatment (AT), and 12 unaffected individuals (C). Host immune modulation metabolites against oral candidiasis, Candida virulence and antifungal properties were identified. The upregulation of C17 sphinganine, L-leucine, monoacylglycerol, phosphatidylethanolamine, and spermine, in OT and AT groups, highlights the role of host immunity in Candida clearance. The altered sphingolipid levels suggest disrupted membrane integrity and immune function, while dysregulated amino acid, purine, and glutathione metabolism reflect oxidative stress and inflammation. Antifungal metabolites, specifically dichloroacetate, 1-monopalmitin, and undecane-2-one, were significantly upregulated in the OT group; conversely, fatty acids (palmitic amide, linoleamide, stearamide, and pentadecanal) were downregulated. Metabolomic similarities between oral candidiasis and xerostomia were evident, with shared markers such as L-valine, L-leucine, D-proline and 4-hydroxyphenylpyruvic acid. Increases in lipid metabolites, carboxylic acids, and amino acids, particularly L-leucine and hypoxanthine in patients upon resolution of oral candidiasis following antifungal treatment suggests fungal clearance, immune activation and recovery from oxidative stress. Some metabolites identified in oral candidiasis patients have reported roles in oral carcinogenesis, however, the findings remain observational and warrant further validation. Our results demonstrate that oral candidiasis is associated with distinct metabolomic alterations compared with healthy controls, and that antifungal therapy reshapes the oral metabolic profiles via complex host–microbiome–fungal metabolic pathways. The identification of oral candidiasis-associated metabolites also highlights their potential as non-invasive biomarkers and therapeutic targets for oral healthcare.

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Simple Summary Pancreatic cancer is one of the deadliest cancers, largely because it is often diagnosed late and treatment options are limited.

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