Background: We aimed to predict Montreal Cognitive Assessment (MoCA) scores in Parkinson’s disease patients at year 4 using handcrafted radiomics (RF), deep (DF), and clinical (CF) features at year 0 (baseline) applied to hybrid machine learning systems (HMLSs). Methods: 297 patients were selected from the Parkinson’s Progressive Marker Initiative (PPMI) database. The standardized SERA radiomics software and a 3D encoder were employed to extract RFs and DFs from single-photon emission computed tomography (DAT-SPECT) images, respectively. The patients with MoCA scores over 26 were indicated as normal; otherwise, scores under 26 were indicated as abnormal. Moreover, we applied different combinations of feature sets to HMLSs, including the Analysis of Variance (ANOVA) feature selection, which was linked with eight classifiers, including Multi-Layer Perceptron (MLP), K-Neighbors Classifier (KNN), Extra Trees Classifier (ETC), and others. We employed 80% of the patients to select the best model in a 5-fold cross-validation process, and the remaining 20% were employed for hold-out testing. Results: For the sole usage of RFs and DFs, ANOVA and MLP resulted in averaged accuracies of 59 ± 3% and 65 ± 4% for 5-fold cross-validation, respectively, with hold-out testing accuracies of 59 ± 1% and 56 ± 2%, respectively. For sole CFs, a higher performance of 77 ± 8% for 5-fold cross-validation and a hold-out testing performance of 82 + 2% were obtained from ANOVA and ETC. RF+DF obtained a performance of 64 ± 7%, with a hold-out testing performance of 59 ± 2% through ANOVA and XGBC. Usage of CF+RF, CF+DF, and RF+DF+CF enabled the highest averaged accuracies of 78 ± 7%, 78 ± 9%, and 76 ± 8% for 5-fold cross-validation, and hold-out testing accuracies of 81 ± 2%, 82 ± 2%, and 83 ± 4%, respectively. Conclusions: We demonstrated that CFs vitally contribute to predictive performance, and combining them with appropriate imaging features and HMLSs can result in the best prediction performance.

This systematic review and meta-analysis synthesized evidence of glymphatic dysfunction in Parkinson’s disease (PD) and related Parkinsonian syndromes using the diffusion tensor imaging along the perivascular space (DTI-ALPS) index. Following the PRISMA 2020 guidelines, 28 studies published up to May 12, 2025, were included. The primary meta-analysis of 21 studies (1678 patients with PD and 1088 HCs) demonstrated a significant reduction in the DTI-ALPS index in patients with PD (Cohen’s d = −0.57; 95% CI: −0.67 to −0.47; p < 0.001), indicating moderate glymphatic dysfunction. This impairment was significantly associated with clinical progression, including longer disease duration (β = −0.08, p = 0.005) and higher Hoehn and Yahr (H-Y) stages (β = −0.18, p = 0.057). This association was particularly strong in patients with early-stage PD (H-Y < 2.5). Phenotypic analyses revealed a gradient of dysfunction, with the most severe impairment found in patients with PD with dementia (Glass’s Δ = −1.04) and atypical Parkinsonian syndromes (Δ = −1.01). Technical subgroup analyses showed consistent findings, with region-of-interest size significantly moderating effect size (p = 0.04). The methodological quality of the included studies was high, with no evidence of publication bias. The methodological quality was high, with no evidence of publication bias (p ≥ 0.88). These findings underscore the progressive glymphatic decline with PD severity and duration, independent of age or cognition, and highlight distinct impairment patterns across different clinical phenotypes.

Coronavirus disease 2019 (COVID‐19) is a viral disease spread by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Because the recent pandemic has resulted in significant morbidity and mortality, understanding various aspects of this disease has become critical. SARS‐CoV‐2 can affect a variety of organs and systems in the body. The autonomic nervous system plays an important role in regulating body functions, and its dysfunction can cause a great deal of discomfort for patients. In this study, we focused on the effect of COVID‐19 on the autonomic system and syndromes associated with it, such as postural orthostatic syndrome (POTS).

Objective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets such as head and neck cancer (HNCa) to enhance lung cancer (LCa) survival outcome predictions, analyzing handcrafted and deep radiomic features (HRF/DRF) from PET/CT scans with hybrid machine learning systems (HMLSs). Methods: We collected 199 LCa patients with both PET and CT images, obtained from TCIA and our local database, alongside 408 HNCa PET/CT images from TCIA. We extracted 215 HRFs and 1024 DRFs by PySERA and a 3D autoencoder, respectively, within the ViSERA 1.0.0 software, from segmented primary tumors. The supervised strategy (SL) employed an HMLS–PCA connected with six classifiers on both HRFs and DRFs. The SSL strategy expanded the datasets by adding 408 pseudo-labeled HNCa cases (labeled by the Random Forest algorithm) to 199 LCa cases, using the same HMLS techniques. Furthermore, principal component analysis (PCA) linked with four survival prediction algorithms were utilized in the survival hazard ratio analysis. Results: The SSL strategy outperformed the SL method (p << 0.001), achieving an average accuracy of 0.85 ± 0.05 with DRFs from PET and PCA + Multi-Layer Perceptron (MLP), compared to 0.69 ± 0.06 for the SL strategy using DRFs from CT and PCA + Light Gradient Boosting (LGB). Additionally, PCA linked with Component-wise Gradient Boosting Survival Analysis on both HRFs and DRFs, as extracted from CT, had an average C-index of 0.80, with a log rank p-value << 0.001, confirmed by external testing. Conclusions: Shifting from HRFs and SL to DRFs and SSL strategies, particularly in contexts with limited data points, enabling CT or PET alone, can significantly achieve high predictive performance.

Background. The present study aimed to evaluate the effect of nanocurcumin and curcumin on liver transaminases, lipid profile, oxidant and antioxidant system, and pathophysiological changes in aluminium phosphide (ALP) induced hepatoxicity. Material and Methods. In this experimental study, thirty‐six male Wistar rats were randomly divided into six groups curcumin (Cur), nanocurcumin (Nanocur), ALP, ALP+Cur, and ALP+Nanocur. All treatments were performed by oral gavage for seven days. After treatment, animals were sacrificed, and liver and blood samples were taken. Serum levels of aspartate aminotransferase (AST), alanine transaminase (ALT), alkaline phosphatase (AP), total bilirubin, cholesterol, triglyceride, high‐density lipoprotein (HDL), low‐density lipoprotein (LDL), and very‐low‐density lipoprotein (VLDL) were measured by photometric methods. Total antioxidant capacity (TAC) and malondialdehyde (MDA) as parameters of oxidative stress and mRNA expression of the nonenzyme protein including Sirtuin 1 (STR1), Forkhead box protein O1 (FOXO1) and protein O3 (FOXO3), catalase (CAT), and glutathione peroxidase (GPX) as the enzyme protein in homogenized tissues have been investigated. A histologist analyzed liver tissue sections after staining with hematoxylin‐eosin. Results. In the aluminium phosphide group, there was a significant increase in MDA, ALT, AST, and AP and total bilirubin, cholesterol, triglyceride, LDL, and VLDL; AST, ALT, total bilirubin, LDL, VLDL, cholesterol, and MDA were significantly decreased; and HDL and TAC were significantly increased compared to ALP (P < 0.05). In the ALP+Nanocur group, ALT, AST, ALP, total bilirubin, cholesterol, LDL, VLDL, triglyceride, and MDA were significantly decreased and HDL and TAC were increased significantly (P < 0.05). The effect of nanocurcumin on controlling serum levels of LDL, VLDL, triglyceride, and MDA in ALP‐poisoned rats was significantly more than curcumin (P < 0.05). The ALP group had significant changes in genes SIRT1, FOXO1a, FOXO3a, CAT, and GPX compared to healthy controls (P < 0.05). Nanocurcumin mice expressed more SIRT1, FOXO1a, CAT, and GPX genes than controls, and curcumin‐treated mice expressed more SIRT1 and FOXO1a genes (P < 0.05). Histopathological findings also indicated a more significant protective effect of nanocurcumin relative to curcumin against ALP‐induced hepatotoxicity. Conclusion. Nanocurcumin significantly protects the liver against aluminum phosphide toxicity. It is suggested that nanocurcumin‐based drugs be developed to reduce the toxic effects of ALP in poisoned patients.

Objective: Parkinson’s disease (PD) is an age-related neurodegenerative condition characterized mostly by motor symptoms. Although a wide range of non-motor symptoms (NMS) are frequently experienced by PD patients. One of the important and common NMS is cognitive impairment, which is measured using different cognitive scales. Monitoring cognitive impairment and its decline in PD is essential for patient care and management. In this study, our goal is to identify the most effective cognitive scale in predicting cognitive decline over a 5-year timeframe initializing clinical biomarkers and DAT SPECT. Methods: Machine Learning has previously shown superior performance in image and clinical data classification and detection. In this study, we propose to use machine learning with different types of data, such as DAT SPECT and clinical biomarkers, to predict PD-CD based on various cognitive scales. We collected 330 DAT SPECT images and their clinical data in baseline, years 2,3,4, and 5 from Parkinson’s Progression Markers Initiative (PPMI). We then designed a 3D Autoencoder to extract deep radiomic features (DF) from DAT SPECT images, and we then concatenated it with 17 clinical features (CF) to predict cognitive decline based on Montreal Cognitive Assessment (MoCA) and The Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS-I). Results: The utilization of MoCA as a cognitive decline scale yielded better performance in various years compared to MDS-UPDRS-I. In year 4, the application of the deep radiomic feature resulted in the highest achievement, with a cross-validation AUC of 89.28, utilizing the gradient boosting classifier. For the MDS-UPDRS-I scale, the highest achievement was obtained by utilizing the deep radiomic feature, resulting in a cross-validation AUC of 81.34 with the random forest classifier. Conclusions: The study findings indicate that the MoCA scale may be a more effective predictor of cognitive decline within 5 years compared to MDS-UPDRS-I. Furthermore, deep radiomic features had better performance compared to sole clinical biomarkers or clinical and deep radiomic combined. These results suggest that using the MoCA score and deep radiomic features extracted from DAT SPECT could be a promising approach for identifying individuals at risk for cognitive decline in four years. Future research is needed to validate these findings and explore their utility in clinical practice.