Osteoarthritis (OA) stands as a prevalent and progressively debilitating clinical condition globally, impacting joint structures and leading to their gradual deterioration through inflammatory mechanisms. While both non-modifiable and modifiable factors contribute to its onset, numerous aspects of OA pathophysiology remain elusive despite considerable research strides. Presently, diagnosis heavily relies on clinician expertise and meticulous differential diagnosis to exclude other joint-affecting conditions. Therapeutic approaches for OA predominantly focus on patient education for self-management alongside tailored exercise regimens, often complemented by various pharmacological interventions primarily targeting pain alleviation. However, pharmacological treatments typically exhibit short-term efficacy and local and/or systemic side effects, with prosthetic surgery being the ultimate resolution in severe cases. Thus, exploring the potential integration or substitution of conventional drug therapies with natural compounds and extracts emerges as a promising frontier in enhancing OA management. These alternatives offer improved safety profiles and possess the potential to target specific dysregulated pathways implicated in OA pathogenesis, thereby presenting a holistic approach to address the condition’s complexities.

Ulcerative colitis (UC) and Crohn’s disease (CD) represent the two main forms of chronic inflammatory bowel diseases (IBD). The exact IBD etiology is not yet revealed but CD and UC are likely induced by an excessive immune response against normal constituents of the intestinal microbial flora. IBD diagnosis is based on clinical symptoms often combined with invasive and costly procedures. Thus, the need for more non-invasive markers is urgent. Several routine laboratory investigations have been explored as indicators of intestinal inflammation in IBD, including blood testing for C-reactive protein, erythrocyte sedimentation rate, and specific antibodies, in addition to stool testing for calprotectin and lactoferrin. However, none has been universally adopted, some have been well-characterized, and others hold great promise. In recent years, the technological developments within the field of mass spectrometry (MS) and bioinformatics have greatly enhanced the ability to retrieve, characterize, and analyze large amounts of data. High-throughput research allowed enhancing the understanding of the biology of IBD permitting a more accurate biomarker discovery than ever before. In this review, we summarize currently used IBD serological and stool biomarkers and how proteomics and lipidomics are contributing to the identification of IBD biomarkers.

Copper (Cu) dyshomeostasis plays a pivotal role in several neuropathologies, such as Parkinson’s disease (PD). Metal accumulation in the central nervous system (CNS) could result in loss-of-function of proteins involved in Cu metabolism and redox cycling, generating reactive oxygen species (ROS). Moreover, neurodegenerative disorders imply the presence of an excess of misfolded proteins known to lead to neuronal damage. In PD, Cu accumulates in the brain, binds α-synuclein, and initiates its aggregation. We assessed the correlation between neuronal differentiation, Cu homeostasis regulation, and α-synuclein phosphorylation. At this purpose, we used differentiated SHSY5Y neuroblastoma cells to reproduce some of the characteristics of the dopaminergic neurons. Here, we reported that differentiated cells expressed a significantly higher amount of a copper transporter protein 1 (CTR1), increasing the copper uptake. Cells also showed a significantly more phosphorylated form of α-synuclein, further increased by copper treatment, without modifications in α-synuclein levels. This effect depended on the upregulation of the polo-like kinase 2 (PLK2), whereas the levels of the relative protein phosphatase 2A (PP2A) remained unvaried. No changes in the oxidative state of the cells were identified. The Cu dependent alteration of α-synuclein phosphorylation pattern might potentially offer new opportunities for clinical intervention.

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder, primarily associated with dopaminergic neuron depletion in the Substantia Nigra. Current treatment focuses on compensating for dopamine (DA) deficiency, but the blood–brain barrier (BBB) poses challenges for effective drug delivery. Using differentiated SH-SY5Y cells, we investigated the co-administration of DA and the antioxidant Grape Seed Extract (GSE) to study the cytobiocompability, the cytoprotection against the neurotoxin Rotenone, and their antioxidant effects. For this purpose, two solid lipid nanoparticle (SLN) formulations, DA-co-GSE-SLNs and GSE-ads-DA-SLNs, were synthesized. Such SLNs showed mean particle sizes in the range of 187–297 nm, zeta potential values in the range of −4.1–−9.7 mV, and DA association efficiencies ranging from 35 to 82%, according to the formulation examined. The results showed that DA/GSE-SLNs did not alter cell viability and had a cytoprotective effect against Rotenone-induced toxicity and oxidative stress. In addition, this study also focused on the evaluation of Alpha-synuclein (aS) levels; SLNs showed the potential to modulate the Rotenone-mediated increase in aS levels. In conclusion, our study investigated the potential of SLNs as a delivery system for addressing PD, also representing a promising approach for enhanced delivery of pharmaceutical and antioxidant molecules across the BBB.

Background: Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease, affecting countless individuals worldwide. PD is characterized by the onset of a marked motor symptomatology in association with several non-motor manifestations. The clinical phase of the disease is usually preceded by a long prodromal phase, devoid of overt motor symptomatology but often showing some conditions such as sleep disturbance, constipation, anosmia, and phonatory changes. To date, speech analysis appears to be a promising digital biomarker to anticipate even 10 years before the onset of clinical PD, as well serving as a useful prognostic tool for patient follow-up. That is why, the voice can be nominated as the non-invasive method to detect PD from healthy subjects (HS). Methods: Our study was based on cross-sectional study to analysis voice impairment. A dataset comprising 81 voice samples (41 from healthy individuals and 40 from PD patients) was utilized to train and evaluate common machine learning (ML) models using various types of features, including long-term (jitter, shimmer, and cepstral peak prominence (CPP)), short-term features (Mel-frequency cepstral coefficient (MFCC)), and non-standard measurements (pitch period entropy (PPE) and recurrence period density entropy (RPDE)). The study adopted multiple machine learning (ML) algorithms, including random forest (RF), K-nearest neighbors (KNN), decision tree (DT), naïve Bayes (NB), support vector machines (SVM), and logistic regression (LR). Cross-validation technique was applied to ensure the reliability of performance metrics on train and test subsets. These metrics (accuracy, recall, and precision), help determine the most effective models for distinguishing PD from healthy subjects. Result: Among all the algorithms used in this research, random forest (RF) was the best-performing model, achieving an accuracy of 82.72% with a ROC-AUC score of 89.65%. Although other models, such as support vector machine (SVM), could be considered with an accuracy of 75.29% and a ROC-AUC score of 82.63%, RF was by far the best one when evaluated across all metrics. The K-nearest neighbor (KNN) and decision tree (DT) performed the worst. Notably, by combining a comprehensive set of long-term, short-term, and non-standard acoustic features, unlike previous studies that typically focused on only a subset, our study achieved higher predictive performance, offering a more robust model for early PD detection. Conclusions: This study highlights the potential of combining advanced acoustic analysis with ML algorithms to develop non-invasive and reliable tools for early PD detection, offering substantial benefits for the healthcare sector.

Serum samples from eight participants during the XV winter-over at Concordia base (Antarctic expedition) collected at defined time points, including predeparture, constituted the key substrates for a specific metabolomics study. To ascertain acute changes and chronic adaptation to hypoxia, the metabolic profiles of the serum samples were analyzed using NMR spectroscopy, with principal components analysis (PCA) followed by partial least squares and orthogonal partial least squares discriminant analyses (PLS-DA and OPLS-DA) used as supervised classification methods. Multivariate data analyses clearly highlighted an adaptation period characterized by an increase in the levels of circulating glutamine and lipids, mobilized to supply the body energy needs. At the same time, a reduction in the circulating levels of glutamate and N-acetyl glycoproteins, stress condition indicators, and proinflammatory markers were also found in the NMR data investigation. Subsequent pathway analysis showed possible perturbations in metabolic processes, potentially related to the physiological adaptation, predominantly found by comparing the baseline (at sea level, before mission onset), the base arrival, and the mission ending collected values.