Search Results (43,920)

Background: Chronic kidney disease (CKD) requires strict dietary management tailored to disease stage and individual needs. Recent advances in artificial intelligence (AI) have introduced chatbot-based tools capable of generating dietary recommendations. However, their accuracy, personalization, and practical applicability in clinical nutrition remain largely unvalidated, particularly in non-Western settings. Methods: Simulated patient profiles representing each CKD stage were developed and used to prompt GPT-4 (OpenAI), Gemini (Google), and Copilot (Microsoft) with the same request for meal planning. AI-generated diets were evaluated by three physicians using a 5-point Likert scale across three criteria: personalization, consistency with guidelines, practicality, and availability. Descriptive statistics, Kruskal–Wallis tests, and Dunn’s post hoc tests were performed to compare model performance. Nutritional analysis of four meal plans (Initial, GPT-4, Gemini, and Copilot) was conducted using both GPT-4 estimates and manual calculations validated against clinical dietary sources. Results: Scores for personalization and consistency were significantly higher for Gemini and GPT-4 compared with Copilot, with no significant differences between Gemini and GPT-4 (p = 0.0001 and p = 0.0002, respectively). Practicality showed marginal significance, with GPT-4 slightly outperforming Gemini (p = 0.0476). Nutritional component analysis revealed discrepancies between GPT-4’s internal estimations and manual values, with occasional deviations from clinical guidelines, most notably for sodium and potassium, and moderate overestimation for phosphorus. Conclusions: While AI chatbots show promise in delivering dietary guidance for CKD patients, with Gemini demonstrating the strongest performance, further development, clinical validation, and testing with real patient data are needed before AI-driven tools can be fully integrated into patient-centered CKD nutritional care. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder whose primary manifestation is motor dysfunction. Previous research showed that (1R,2R,6S)-3-methyl-6-(prop-1-en-2-yl)cyclohex-3-ene-1,2-diol (Prottremine) exhibits potent antiparkinsonian activity in animal models of PD, with an efficacy comparable to levodopa. Herein, we report the synthesis of a new Prottremine derivative, (1R,2R,6S)-3-methyl-6-(3-(4-phenylpiperidin-1-yl)prop-1-en-2-yl)cyclohex-3-ene-1,2-diol. The compound was fully characterized and its structure was confirmed through single-crystal X-ray diffraction analysis. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a sexually dimorphic condition, with higher prevalence in men than in women. Sex differences in hepatic lipid metabolism and the modulatory role of sex hormones have been described but are still insufficiently understood. The aim of this study was to introduce the variables sex and sex hormones into a human in vitro model of hepatic steatosis. Methods: Primary human hepatocytes (PHHs) were isolated from male and female donors, treated with free fatty acids (FFA) to induce steatosis, and further exposed to physiological concentrations of estrogen, progesterone, or testosterone. Intracellular triacylglyceride (TAG) content, lipid droplet (LD) formation, FFA uptake, and very-low-density lipoprotein (VLDL) excretion were assessed. In parallel, the expression of lipid metabolism-related genes was quantified by qPCR. Results: FFA treatment induced comparable uptake and intracellular TAG storage in both sexes. However, female PHHs secreted approximately twice as many VLDL particles as male cells. Steatosis significantly increased expression of LDLR, CPT2, and PLA1A only in male PHHs. Sex hormones exerted distinct, sex-specific effects: estrogen reduced TAG accumulation in female PHHs; whereas testosterone reduced TAG in male but increased it in female PHHs after prolonged treatment. LD characterization confirmed sex- and hormone-dependent differences in lipid storage patterns. In male PHHs, progesterone promoted lipid storage and increased apoB-100 secretion, accompanied by reduced LDLR and APOA5 expression, and testosterone increased the FFA-mediated CPT2 even further. Conclusions: Sex and sex hormones distinctly shape hepatocellular lipid handling under steatotic conditions. While female PHHs demonstrated greater lipid excretion capacity, male PHHs exhibited stronger transcriptional responses. Sex-specific responses to estrogen and testosterone resembled clinical observations on sex hormone effects. These findings highlight the need to account for sex-specific differences in MASLD pathophysiology and therapeutic strategies. Full article

The increasing complexity of care home residents’ needs, driven by demographic shifts, multimorbidity and late-stage admissions, poses significant challenges for food service provision in Swiss care homes. Current systems struggle to meet individual nutritional and psychosocial needs due to staff shortages, financial constraints and rigid infrastructures. This perspective article explores the necessity for a more individualised and systematic approach to food services in long-term care settings, with a particular focus on developing resident personas as a guiding tool. Such personas, based on typical end-of-life disease trajectories, can support tailored food service planning and staffing. The authors highlight the inadequacy of current food service models, which often fail to accommodate residents’ diverse health conditions and personal preferences. They emphasise the importance of interdisciplinary collaboration and the integration of resident-centred strategies. Achieving this calls for a shift from fragmented, chef-driven decisions to a holistic, evidence-based system. By aligning food services with specific resident profiles, care homes can optimise resources, improve quality of life and enhance overall care. Future research should focus on applied, interdisciplinary solutions that address the interdependencies between nutrition, health and operational feasibility in care homes. Full article

Background: High-throughput metabolomics studies have promoted the discovery of candidate biomarkers linked to atherosclerosis (AS). This narrative systematic review summarises metabolomics studies conducted in (1) individuals with subclinical AS (assessed by imaging techniques such as carotid intimal media thickness, IMT, and coronary artery calcium, CAC), (2) patients with established atherosclerotic plaques, and (3) individuals with AS risk factors. Methods: The systematic search was conducted in the PubMed database according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. The inclusion criteria were as follows: (i) publication date between 2009 and 2024; (ii) identification of potential biomarkers for AS in subjects with a diagnosis of AS or with one or more traits characteristic of the disease (i.e., CAC or IMT); (iii) identification of potential AS biomarkers in subjects with atherogenic clinical conditions (i.e., Down’s syndrome, DS, polycystic ovarian syndrome, PCOS, and systemic lupus erythematosus, SLE); (iv) metabolomic studies; and (iv) studies in human samples. Exclusion criteria comprised the following: (i) studies on lipid metabolic diseases unrelated to AS, (ii) “omics” results not derived from metabolomics, (iii) reviews and studies in animal models or cell cultures, and (iv) systematic reviews and meta-analyses. Of 90 eligible studies screened, 24 met the inclusion criteria. Results: Across subclinical and overt AS, consistent disturbances were observed in amino acid, lipid, and carbohydrate metabolism. Altered profiles included branched-chain amino acids (BCAAs), aromatic amino acids (AACs) and derivatives (e.g., kynurenine–tryptophan pathway), bile acids (BAs), androgenic steroids, short-chain fatty acids (FAs)/ketone intermediates (e.g., acetate, 3-hydroxybutyrate, 3-HB), and Krebs cycle intermediates (e.g., citrate). Several metabolites (e.g., glutamine, lactate, 3-HB, phosphatidylcholines, PCs/lysophosphatidylcholines, lyso-PCs) showed reproducible associations with vascular phenotypes (IMT/CAC) and/or clinical AS. Conclusions: The identification of low-weight metabolites altered in both subclinical and overt AS suggests their potential as candidate biomarkers for early AS diagnosis. Given the steady increase in deaths from cardiovascular disease, a manifestation of advanced AS, this finding could have significant clinical relevance. Full article

Background/Objectives: In the present study, the Metabolic dysfunction-associated fatty liver disease (MAFLD) and Metabolic dysfunction-associated steatotic liver disease (MASLD) diagnostic criteria were applied to evaluate the relative performance in predicting short-term advanced fibrosis (AF) progression (AFpr) and hepatocellular carcinoma (HCC), as well as an ancillary outcome, i.e., the occurrence of acute cardiovascular events (ACEs) in steatotic liver disease (SLD) patients. Methods: We retrospectively analyzed the data stored in the University Hospital (UH)’s Official Health Documents Digitization Archive of 931 SLD patients, with a follow-up of 3 years. Based on the Body Mass Index (BMI), patients were subdivided into lean “L” (BMI < 25 kg/m²) (n = 134) and not-lean “NL” (n = 797), and, subsequently, into NL-MASLD (n = 206), NL-MASLD/MAFLD (n = 481), NL-MAFLD (n = 110), L-MASLD (n = 39), L-MASLD/MAFLD (n = 68), and L-MAFLD (n = 27). All study outcomes (AFpr, HCC, and ACE) were primarily evaluated in NL-SLD and by conducting a sub-analysis of L-SLD individuals. Results: MASLD and MAFLD criteria similarly estimated [p = 0.076] the overall 3-year risk of AF progression in NL-SLD. In the L-SLD sub-analysis, MAFLD criteria better estimated the overall 3-year risk of AF progression [p = 0.006]. Multivariate competing risk analysis (adjusted for sex, age, diabetes, steatosis, and fibrosis severity) revealed diabetes [adjusted Hazard Ratio (aHR) = 2.113, p = 0.001], high-sensitivity C-reactive protein (aHR = 1.441; p = 0.02), and Homeostatic Model Assessment for Insulin Resistance (aHR = 1.228; p = 0.03) as being associated with AF progression in L-MAFLD. Compared to MAFLD, MASLD diagnostic criteria similarly estimated the 3-year risk of HCC occurrence both in NL [HR = 1.104, C.I. 95%: 0.824–1.593, p = 0.741] and L [HR = 1.260, C.I. 95%: 0.768–2.104, p = 0.701] patients. Finally, no significant differences were reported between the MAFLD or MASLD criteria for ACE risk occurrence in all study groups. Conclusions: The MAFLD criteria better estimate the AF progression risk, limited to L-SLD patients. Full article

Background/Objectives: Heart disease (HD) is recently becoming the foremost cause of death worldwide, underlining the importance of early and correct diagnosis to improve patient outcomes. Although Internet of Things (IoT)-enabled machine learning approaches have demonstrated encouraging outcomes in screening, existing approaches often face challenges such as imbalanced dataset handling, influential feature selection identification, and the ability to adapt to evolving HD data forms. To tackle the aforementioned challenges, we present a sequential hybrid approach, RAVE-HD (ResNet And Vanilla RNN Ensemble for HD), that combines a number of cutting-edge techniques to enhance screening. Methods: Preprocessing phase includes duplicates removal and feature scaling for data consistency. Recursive Feature Elimination is employed to extract the most informative features, while a proximity-weighted random synthetic sampling technique addresses class imbalance to reduce class biases. The proposed RAVE model in RAVE-HD approach sequentially integrates a Residual Network (ResNet) for high-level feature extraction and Vanilla Recurrent Neural Network to capture the non-linearity of the feature relationships present in the HDHI medical dataset. Results: Compared to ResNet and Vanilla RNN baselines, the proposed RAVE model attained superior results: 92.06% accuracy and 97.12% ROC-AUC. Stratified 10-fold cross-validation validated the robustness of RAVE, while Sensitivity-to-Prevalence analysis demonstrated stable recall and predictable precision across varying disease prevalence levels. Additional evaluations, including bootstrap and DeLong analyses, showed statistical significance $(p<0.001)$ of the discriminative gains of RAVE. Minimum Clinically Important Difference (MCID) evaluation confirmed clinically meaningful improvements $(\ge$$3\%)$ over strong baselines. Cross-dataset validation using the CVD dataset verified robust generalization (92.4% accuracy). SHAP analysis provided interpretability to build clinical trust. Conclusions: RAVE-HD shows promise as a reliable, explainable, and scalable solution for large-scale HD screening, consistently performing well across diverse evaluations and datasets. Through statistical validation, the RAVE-HD approach emerges as a practical decision-support tool in HD predictive screening results. Full article

Proper functioning of the endoplasmic reticulum (ER) plays a key role in maintaining the internal homeostasis of the cell. A common feature of many common diseases (such as diabetes and inflammatory bowel diseases) is the induction of ER stress in cells. While some ER stress is beneficial for cellular survival, high levels of stress can lead to cell death. For this reason, many studies are focused on understanding the exact mechanism of the ER stress response. There are a variety of well-established stressors on the market that can be used to induce ER stress under laboratory conditions (i.e., thapsigargin and tunicamycin). However, new scientific results suggest that these ER stressors act very differently on the stress response mechanism and, therefore, cannot always be used reliably. By using various mathematical methods, our systems biology approach presented here seeks to answer how the well-known ER stressors affect the dynamic characteristic of the control network, specifically highlighting how we can delay the negative impact of ER stress. Furthermore, using mathematical models, we make suggestions on which ER stressors may be useful in which therapeutic treatment. Full article

The dental pulp is a dynamic connective tissue essential for tooth vitality, sensory function, immune defense, and reparative dentinogenesis. Conventional endodontic procedures, while effective in eradicating infection, often result in a non-functional, devitalized tooth, highlighting the need for biologically based regenerative approaches. The emergence of three-dimensional (3D) culture systems has transformed pulp biology and endodontic research by providing physiologically relevant microenvironments that better reproduce the dentino-pulp interface, vascular and neural networks, and immune interactions. This review synthesizes current advances in 3D dental pulp modeling, from scaffold-based and hydrogel systems to spheroids, organoids, bioprinted constructs, and microfluidic “tooth-on-a-chip” platforms. Each system’s composition, biological relevance, and translational potential are critically examined with respect to odontogenic differentiation, angiogenesis, neurogenesis, and inflammatory response. Applications in disease modeling, biomaterial screening, and regenerative endodontics are highlighted, showing how these models bridge fundamental biology and therapeutic innovation. Finally, we discuss key challenges including vascularization, innervation, standardization, and clinical translation, and propose integrative strategies combining bioprinting, stem-cell engineering, and organ-on-chip technologies to achieve functional pulp regeneration. Overall, 3D pulp models represent a paradigm shift from reductionist cultures to bioinstructive, patient-relevant platforms that accelerate the development of next-generation endodontic therapies. Full article

 Modern biomedicine frequently contextualizes disease around isolated molecular or organ-specific mechanisms, but numerous chronic diseases, including Alzheimer’s disease, multiple sclerosis, depression, diabetes, and sepsis, share common trajectories of systemic destabilization. An increasing body of evidence indicates that health is not a property of single organs but the emergent property of interdependent feedback networks linking the microbiome, endocannabinoidome, neuroimmune system, and metabolic regulators. We propose the Endocannabinoid–Microbiota–Neuroimmune Super-System (EMN-S) as an evolutionarily conserved conceptual model that describes how these fields of influence reciprocally interact through feedback control. The microbial communities constituting the EMN-S encode environmental and dietary inputs, endocannabinoid signaling serves as an integrative regulator that synchronizes neural and immune activity, and neuroimmune circuits effectuate adaptive behaviors that alter microbiotal and lipid ecosystems. This review formalizes the EMN-S, contending that it is a unitary and cohesive model of physiological resilience, as well as offering a framework for precision feedback therapeutics. We describe how three mechanisms—encoder drift, integrator detuning, and executor overutilization—convert stabilizing negative feedback into runaway feedback cascades that underlie chronic, recurrent, and multisystemic disease. We then specify the EMN-S signature—integrated microbiome, lipidomic, and immune readouts—as an early indicator of resilience collapse and prospective preclinical state. Finally, we recapitulate the potential of AI-driven digital twins to illuminate feedback collapse, predict tipping points, and direct closed-loop intervention and treatments to restore dynamic equilibrium. By anchoring complexity in concrete and measurable feedback principles, the EMN-S shifts focus to investigate pathophysiology as opposed to reductionist lesion models of systemic derangements and embraces a systemic, empirically testable theory of stability.  Full article

Background/Objectives: Pre-analytical variation is a major challenge in metabolomics, yet most stability studies have focused on healthy volunteers and have overlooked the impact of disease and medication. To address this gap, we conducted a pilot study in systemic lupus erythematosus (SLE) to assess serum metabolite stability under delayed centrifugation. Methods: Peripheral blood from 10 SLE patients and 5 healthy controls (HC) was stored at room temperature for 1–24 h before processing and analyzed by untargeted LC-MS-based metabolomics. This design enabled direct evaluation of the effect of pre-analytical delay within the context of clinical heterogeneity. Results: Principal component trajectories showed reproducible temporal shifts in HC but dispersed patterns in SLE, indicating disease- and treatment-related influences. Linear mixed-effects models identified metabolites with condition-specific kinetics, including glucose, choline, glycerophosphocholine, and pyroglutamic acid. Mycophenolate intake was further associated with distinct AMP dynamics. Conclusions: These findings demonstrate that both disease state and medication reshape apparent metabolite stability, highlighting the need for strictly controlled sample handling and well-characterized clinical cohorts in metabolomics studies. Full article

Multiple sclerosis is an inflammatory and neurodegenerative disease that leads to motor, cognitive, and sensory impairments, significantly affecting walking and quality of life. This study aimed to analyze the relationship between quality of life and kinematic walking parameters in individuals with multiple sclerosis, as well as to evaluate the influence of fatigue, balance, and cognitive performance on different aspects of quality of life. A cross-sectional observational study was conducted with 32 patients diagnosed with multiple sclerosis with Expanded Disability Status Scale scores of ≤5.5. Quality of life was assessed using the MusiQoL questionnaire, and clinical variables included fatigue (Fatigue Scale for Motor and Cognitive Functions, Borg scale), balance (Berg Balance Scale), and cognitive performance (Trail Making Test). Walking kinematics were analyzed using the Vicon motion capture system to obtain walking speed, step frequency, and joint asymmetry indices. Spearman correlations and linear regression models were applied. Results showed significant correlations between quality of life and walking speed (rho = 0.506), step frequency (rho = 0.508), and knee asymmetry (rho = −0.525), as well as strong associations with cognitive fatigue (rho = −0.796) and balance (rho = 0.635). Regression models explained up to 58.4% of the variance in the Activities of Daily Living dimension. These findings indicate that quality of life in multiple sclerosis is influenced by both clinical and biomechanical factors, highlighting the importance of comprehensive assessments to guide physiotherapeutic interventions. Full article

Neurological damage, a debilitating condition closely associated with chronic neuroinflammation, currently lacks disease-modifying treatments, with management limited to symptomatic relief. Vitamins B6 (VB6), B12 (VB12), and proteolipid protein 1 (PLP-1) exhibit multimodal neuroprotective and anti-inflammatory effects; however, their therapeutic potential is limited by low bioavailability and inadequate ability to cross the blood–brain barrier (BBB). To address these limitations, we developed an ursolic acid-based nanoparticle system for the intranasal co-delivery of VB6, VB12, and recombinant PLP-1. The PLP-1 model predicted by AlphaFold3 was used for molecular docking. The docking results confirmed high-affinity binding interactions with VB6 and VB12, elucidating the mechanistic basis of their synergy. In vitro studies using a glucose-deprived PC12 cell injury model identified an optimal synergistic molar ratio of 10:1:2 (VB6: VB12: PLP-1). This combination significantly upregulated neuroprotective markers (PLP-1 and PGC-1α) and downregulated the pro-inflammatory cytokine TNF-α. In a mouse model of neural damage, the nano-encapsulated combination therapy demonstrated improved pharmacokinetics and significantly attenuated neuroinflammation and oxidative stress in brain tissue. This was evidenced by lower TNF-α and IL-1β levels and elevated GSH and SOD concentrations compared to free drug controls. The treatment regimen showed no detectable hepatorenal toxicity. Our findings demonstrate that this nanoformulation represents a safe, effective, and promising disease-modifying strategy to treat vestibular dysfunction by synergistically targeting its underlying neuroimmunological mechanisms. Full article

Background: Endocrine autoimmune diseases (AIDs) exhibit special polygenic characteristics in human leucocyte antigen (HLA) region. Current understanding of their association with lipid metabolism remains constrained by imprecise polygenic risk score (PRS) modeling. Advanced analytical approaches are needed to elucidate the association between genetic susceptibility and lipid metabolic dysregulation. Methods: We proposed a genetic distance-based clumping gPRS to account for linkage disequilibrium in the HLA region. gPRS and pathway gPRS were constructed for individuals diagnosed with type I diabetes (T1D), Graves’ disease (GD), Hashimoto thyroiditis (HT) and Addison’s disease (AD) in the UK Biobank, with sex considered as a stratification factor. Latent correlations between gPRS and phenotypes were explored using Kendall’s tau test, two-trait LD score regression (LDSC) and gene annotation. Results: Lipid metabolism served an important function through immune and inflammatory biomarkers across multiple traits. Males with low genetic risk tended to have lower high-density lipoprotein cholesterol level, while the correlation presented the opposite pattern in females. Increased genetic susceptibility to AIDs was associated with elevated levels of low-density lipoprotein cholesterol, triglycerides in low-density lipoprotein (LDL) and very-low-density lipoprotein (VLDL) across all traits. Moreover, levels of polyunsaturated fatty acids, including omega-3 and omega-6, decreased with higher PRS in males and females, while those of monounsaturated fatty acids exhibited an increasing trend. Conclusion: Our study constructed more precise polygenic risk scores of AIDs, highlighting inflammation-mediated lipid metabolism as a potential pathogenic mechanism in endocrine AIDs, offering valuable insights into shared etiology for future comprehensive investigations. Full article

Macrolides are crucial for the management and treatment of bovine respiratory disease (BRD). However, antimicrobial resistance (AMR) threatens the efficacy of these and other antimicrobials. We developed real-time recombinase polymerase amplification (RPA) assays targeting three clinically relevant macrolide antimicrobial resistance genes (ARGs)—msrE-mphE and erm42—in ≤30 min using extracted DNA. A set of 199 deep nasopharyngeal swabs (DNPS) collected from feedlot calves near the time of arrival were selected based on bacterial culture (BC) results for Mannheimia haemolytica, Pasteurella multocida, and Histophilus somni and antimicrobial susceptibility testing (AST) for tulathromycin, tilmicosin, tildipirosin, or gamithromycin. Samples were also tested for the same targets using RPA and polymerase chain reaction (PCR). In samples that were culture-positive for one or more macrolide-resistant BRD-associated bacteria (n = 101), msrE-mphE and/or erm42 were detected in 95% of cases using RPA. The remaining 98 samples were either culture-negative, or the recovered bacteria were macrolide-susceptible: 43% of these were RPA-positive for at least one macrolide ARG. Together with BC-AST and PCR, Bayesian latent class modelling estimated the clinical sensitivity of RPA for macrolide ARGs to be 95% and specificity to be 58%, with moderate agreement between RPA and BC-AST (κ = 0.52) or PCR (κ = 0.55). The estimated sensitivity of the RPA multiplex assay for the targeted macrolide ARGs was very good, although estimated specificity was limited. However, Sanger sequencing confirmed RPA detection of msrE-mphE in BC-AST/PCR-negative samples (n = 23), reflecting the presence of this locus in non-target bacteria, as well as potential ARG variants among BRD bacteria. These findings support the potential of RPA for rapid ARG detection from extracted DNA. Continued assay optimization and evaluation for detection of respiratory bacteria and ARGs will further enhance its diagnostic utility. Full article