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Parkinson’s disease (PD) is one of the most prevalent neurological diseases, described by complex clinical phenotypes. The manifestations of PD include both motor and non-motor symptoms. We constituted an experimental protocol for the assessment of PD motor signs of lower extremities. Using a pair of sensor insoles, data were recorded from PD patients, Elderly and Adult groups. Assessment of PD patients has been performed by neurologists specialized in movement disorders using the Movement Disorder Society—Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)-Part III: Motor Examination, on both ON and OFF medication states. Using as a reference point the quantified metrics of MDS-UPDRS-Part III, severity levels were explored by classifying normal, mild, moderate, and severe levels of PD. Elaborating the recorded gait data, 18 temporal and spatial characteristics have been extracted. Subsequently, feature selection techniques were applied to reveal the dominant features to be used for four classification tasks. Specifically, for identifying relations between the spatial and temporal gait features on: PD and non-PD groups; PD, Elderly and Adults groups; PD and ON/OFF medication states; MDS-UPDRS: Part III and PD severity levels. AdaBoost, Extra Trees, and Random Forest classifiers, were trained and tested. Results showed a recognition accuracy of 88%, 73% and 81% for, the PD and non-PD groups, PD-related medication states, and PD severity levels relevant to MDS-UPDRS: Part III ratings, respectively.

Objectives: The present study aimed to determine the reference intervals for complete blood count and total protein parameters in Greek indigenous Capra prisca goats and to evaluate their associations with parasitic burden, age and reproductive stage. Methods: Two-hundred clinically health goats were grouped by parasite status (gastrointestinal nematodes, Eimeria spp., and lungworm infection), age (3–6-month-old growing kids; lactating non-pregnant goats ≤ 3 or >3 years old) and reproductive stage (non-lactating pregnant goats; lactating non-pregnant goats). Blood samples were analyzed for erythrogram, leukogram and megakaryocytic parameters using an automated analyzer and manual blood smears. Total plasma proteins were measured using refractometry. Results: Gastrointestinal nematode-infected animals (>300 eggs per gram of feces) were associated with a significant reduction in red blood cell counts and hematocrit estimation, and an increase in mean corpuscular hemoglobin and mean corpuscular hemoglobin concentrations, while lungworm-infected animals were associated with decreased red blood cells, red cell distribution width and neutrophils, and increased lymphocytes compared to non-infected animals. Eimeria spp. affected only basophils in growing kids. Age influenced all erythrocytic and leukocytic parameters (apart from neutrophils and monocytes), as well as all megakaryocytic parameters and total proteins, with younger animals showing higher red and white blood cell counts and platelets compared to adults. Pregnant does had elevated hemoglobin, hematocrit, neutrophils and monocytes compared with lactating non-pregnant does. Conclusions: The calculated 95% reference intervals for our demographic groups of animals provide a useful diagnostic framework for assessing Capra prisca health in Greek goat farming.

The aim of the present study was to assess, for the first time to our knowledge, CFU changes, as well as to determine the transcription of key virulence genes, namely, and after in vitro exposure to human gastric and duodenal aspirates. Furthermore, investigations of the potential correlation between CFU changes and gene regulation with factors influencing gastric (proton pump inhibitor intake and presence of gastric atrophy) and duodenal pH were the secondary study aims. Gastric and duodenal fluids that were collected from 25 individuals undergoing upper gastrointestinal endoscopy were inoculated with serotype 4b strain LQC 15257 at 9 log CFU·mL and incubated at 37°C for 100 min and 2 h, respectively, with the time corresponding to the actual exposure time to gastric and duodenal fluids in the human gastrointestinal tract. Sampling was performed upon gastric fluid inoculation, after incubation of the inoculated gastric fluids, upon pathogen resuspension in duodenal fluids and after incubation of the inoculated duodenal fluids. CFU changes were assessed by colony counting, as well as reverse transcription quantitative PCR by using as a target. Gene transcription was assessed by reverse transcription quantitative PCR. In 56% of the cases, reduction of the pathogen CFU occurred immediately after exposure to gastric aspirate. Upregulation of and was observed in 52 and 58% of the cases, respectively. On the contrary, no upregulation or downregulation was noticed regarding and . In addition, and transcription was positively and negatively associated, respectively, with an increase of the pH value, and transcription was negatively associated with the presence of gastric atrophy. Finally, a positive correlation between the transcriptomic responses of and was detected. This study revealed that the CFU of the pathogen was negatively affected after exposure to human gastroduodenal aspirates, as well as significant correlations between the characteristics of the aspirates with the virulence potential of the pathogen.

The rapid developments in artificial intelligence (AI) research in radiology have produced numerous models that are scattered across various platforms and sources, limiting discoverability, reproducibility and clinical translation. Herein, OpenRad was created, a curated, standardized, open-access repository that aggregates radiology AI models and providing details such as the availability of pretrained weights and interactive applications. Retrospective analysis of peer reviewed literature and preprints indexed in PubMed, arXiv and Scopus was performed until Dec 2025 (n = 5239 records). Model records were generated using a locally hosted LLM (gpt-oss:120b), based on the RSNA AI Roadmap JSON schema, and manually verified by ten expert reviewers. Stability of LLM outputs was assessed on 225 randomly selected papers using text similarity metrics. A total of 1694 articles were included after review. Included models span all imaging modalities (CT, MRI, X-ray, US) and radiology subspecialties. Automated extraction demonstrated high stability for structured fields (Levenshtein ratio > 90%), with 78.5% of record edits being characterized as minor during expert review. Statistical analysis of the repository revealed CNN and transformer architectures as dominant, while MRI was the most commonly used modality (in 621 neuroradiology AI models). Research output was mostly concentrated in China and the United States. The OpenRad web interface enables model discovery via keyword search and filters for modality, subspecialty, intended use, verification status and demo availability, alongside live statistics. The community can contribute new models through a dedicated portal. OpenRad contains approx. 1700 open access, curated radiology AI models with standardized metadata, supplemented with analysis of code repositories, thereby creating a comprehensive, searchable resource for the radiology community.