Polymyalgia rheumatica (PMR) and giant cell arteritis (GCA) are systemic inflammatory diseases deeply rooted in age-related immunosenescence and inflammaging. Conventional long-term glucocorticoid (GC) therapy poses significant metabolic and infectious risks for older adults, necessitating safer alternatives. This review critically evaluates the pathophysiological rationale and clinical efficacy of small-molecule drugs, including Janus kinase inhibitors (JAKi) and conventional synthetic disease-modifying antirheumatic drugs (csDMARDs), as steroid-sparing treatments for PMR and GCA. By selectively inhibiting intracellular networks like the JAK-STAT pathway and nucleotide biosynthesis, these agents aim to attenuate maladaptive inflammation. Clinical evidence highlights that JAK inhibitors, particularly upadacitinib for GCA and tofacitinib or baricitinib for PMR, demonstrate the potential to induce remission and significantly reduce the required GC burden in a subset of patients. Although methotrexate remains the primary csDMARD, its modest overall efficacy suggests it should be reserved for patients with definitive contraindications or restricted access to JAK inhibitors. Furthermore, novel therapies like clofutriben demonstrate potential in reversing GC-induced morbidities without compromising disease control. Ultimately, integrating targeted small-molecule immunomodulators establishes a crucial therapeutic paradigm that attempts to maximize clinical remission while safeguarding the physiological integrity of geriatric patients against severe GC toxicities.

Human milk composition changes across lactation, but paired within-subject proteomic analyses comparing longitudinal trajectories in term and preterm milk remain limited. We aimed to characterize stage-associated proteomic changes within each cohort and determine whether longitudinal remodeling is shared or divergent between term and preterm lactation.

In this single-center prospective study conducted at the Neonatal Intensive Care Unit, Jagiellonian University Medical College, Kraków, Poland (October 2020-November 2021), 40 lactating mothers (20 preterm, <32 weeks' gestation, mean age 29.4 ± 6.1 years; 20 term, 37-42 weeks, mean age 30.2 ± 5.5 years) provided paired milk samples at ≤10 days postpartum and week 5. Milk serum proteomes were analyzed by quantitative data-independent acquisition mass spectrometry; differential abundance was assessed using two-sample t-tests with Storey false discovery rate correction (q < 0.05) and fold-change >1.5, followed by ClueGO pathway enrichment.

Stage-associated differential abundance was identified for 108 proteins in term milk (58 increased, 50 decreased) and 103 in preterm milk (64 increased, 39 decreased). Of these, 87 were shared between cohorts (80.6% of term, 84.5% of preterm set) with concordant directionality. Shared upregulated pathways included oxidative stress response and glycolysis (e.g., PRDX5, fold change 2.49, q = 0.044); shared downregulated pathways related to mucosal immunity (e.g., tenascin, fold change 9.61-11.41, q < 0.0001). Cohort-specific pathway signals were limited relative to shared remodeling.

The human milk serum proteome undergoes substantial longitudinal remodeling in both term and preterm lactation, with most changes following a common temporal pattern; prematurity-related differences appear selective rather than global. These findings support lactation stage as a key determinant of milk proteomic composition and underscore the value of longitudinal, stage-aware study designs, although formal time-by-group interaction testing was not performed.

Background: Mental well-being is an important yet often overlooked component of cardiometabolic health. Dietary habits may influence psychological outcomes, but evidence among high-risk populations in Central and Eastern Europe remains limited. This study investigated the association between dietary behaviors and mental well-being among adults with cardiometabolic risk in Hungary. Methods: A cross-sectional analysis was conducted using data from the European Health Interview Survey (EHIS) 2019. The study included 2785 adults with cardiometabolic high risk (obesity, hypertension, or hypercholesterolemia). Mental well-being was assessed using the WHO-5 Well-Being Index and categorized as poor (≤50) or better (>50). Dietary habits, sociodemographic factors, and lifestyle factors were analyzed. Weighted multivariable logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Results: Overall, 25.9% of participants had poor mental health. In multivariable analyses, low intake of vegetables (OR = 1.15), fruits (OR = 1.55), fruit juice (OR = 1.26), and fish (OR = 1.17), as well as inadequate water intake (OR = 1.38), were each independently associated with higher odds of poor mental health after adjustment for sex, education, income levels, self-perceived health status, physical activity, and alcohol consumption. Conclusions: Healthier dietary behaviors, particularly higher consumption of vegetables, fish, and adequate hydration, are associated with better mental well-being among individuals with cardiometabolic risk. These results underscore the need for comprehensive intervention strategies that simultaneously address physical health and psychological well-being among vulnerable populations.

Kidney transplantation is the preferred treatment for end-stage kidney disease (ESKD), but long-term outcomes remain limited by chronic allograft injury, infections, metabolic complications, and cardiovascular risk. Gut microbiota alterations and microbiota-derived metabolites may influence immune regulation, inflammation, drug metabolism, and graft outcomes through the gut-kidney axis. This review summarizes evidence on the gut microbiota in kidney transplantation, emphasizing immune tolerance, complications, cardiovascular risk, graft function, and perspectives.

A structured search was conducted in PubMed, Scopus, and Web of Science to May 2026. Eligible publications included studies involving kidney transplant recipients (KTR), kidney disease or solid organ transplant populations, and mechanistic models. Evidence was synthesized narratively.

Gut microbiota alterations in KTR reflect pre-transplant dysbiosis and post-transplant exposures, including antibiotics, immunosuppression, infection, diet, hospitalization, and graft function. Dietary factors and nutrient-derived substrates may modulate microbial composition and production of relevant metabolites, including short-chain fatty acids (SCFAs), trimethylamine N-oxide (TMAO), tryptophan-derived compounds, bile acid derivatives, and uremic toxins. Microbiota-related pathways may involve barrier dysfunction, microbial translocation, innate immune activation, altered regulatory T cell/T helper 17 (Treg/Th17) balance, metabolite signaling, uremic toxin generation, and endothelial stress. Clinical studies associate dysbiosis and microbial metabolites with diarrhea, infections, delayed graft function (DGF), rejection-related shifts, tacrolimus variability, cardiovascular risk, graft dysfunction, graft failure, and mortality. Most findings need validation.

Gut microbiota signatures and microbial metabolites are promising markers of transplant-related risk, but not established causal determinants or therapeutic targets. Clinical translation requires standardized methods, multi-omics integration, and prospective patient- and graft-centered trials.

Stroke remains one of the leading causes of death and disability worldwide, yet its clinical management is constrained by substantial biological heterogeneity that single-biomarker and single-omics approaches fail to resolve. The integration of multiple molecular data layers, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and immunomics, offers a transformative framework for investigating the underlying neuroinflammatory mechanisms of different stroke subtypes and endotypes. In this review, we synthesize the current multi-omics evidence in stroke by examining how genetic variants propagate through regulatory and immune pathways and generate measurable molecular signatures and clinically relevant biomarkers. We investigate the roles of microglia, infiltrating monocyte-derived macrophages, astrocytes, neutrophils, T cells, and endothelial cells as interacting nodes in the neuroimmune network after stroke, and analyze how spatially resolved single-cell transcriptomics illuminate state-specific programs previously undetectable in bulk tissue analyses. We discuss how proteomics and metabolomics translate these cellular programs into actionable circulating biomarkers and examine emerging evidence on blood-brain barrier disruption and neurovascular unit remodeling as multi-omics-defined targets. We then explore AI and machine learning frameworks enabling the integration of heterogeneous, high-dimensional datasets for endotype classification, patient stratification, and therapeutic response prediction. Finally, we address translational barriers, including analytical standardization, multi-ancestry generalizability, and regulatory readiness, and propose a roadmap for precision stroke medicine based on systems immunology. The core conceptual point of this review is the shift from describing omics findings in stroke cases to redefining biologically meaningful neuroinflammatory endotypes and using multi-omics to enable precision cerebrovascular medicine.

Serum biomarkers associated with abdominal aortic aneurysm (AAA) have been studied individually; however, an algorithm that considers panel of proteins to inform AAA prognosis may improve predictive accuracy. We conducted a prognostic study using a prospectively recruited cohort of patients with and without AAA (n = 452). Serum concentrations of seven biomarkers were measured at baseline, and the cohort was followed for 2 years. The primary outcome was major adverse aortic event (MAAE; composite of rapid AAA expansion [>0.5 cm/6 months or >1 cm/12 months] or AAA intervention). Using 10-fold cross-validation, we trained a random forest model to predict 2-year MAAE using: (1) clinical characteristics, (2) biomarkers, and (3) clinical characteristics and biomarkers. Two-year MAAE occurred in 114 (25%) patients. Four proteins were significantly elevated in patients with AAA compared to those without AAA (matrix metalloproteinase 3 [MMP-3], human epididymal secretory protein 4 [HE4/WFDC2], Chitinase 3-like-1, and Kallikrein 6/Neurosin), composing the protein panel. For predicting 2-year MAAE, our random forest model achieved an area under the receiver operating characteristic curve (AUROC) of 0.64 using clinical features alone and the addition of the four-protein panel improved performance to an AUROC of 0.80. Using a combination of clinical and biomarker data, we developed a model that accurately predicts 2-year MAAE.

Acute heart failure (AHF) and cardiogenic shock (CS) remain major causes of cardiovascular morbidity, mortality, and healthcare utilization worldwide. Although inotropic agents are central to the management of low-output states, their clinical utility is fundamentally constrained by mechanisms that increase myocardial oxygen consumption, disrupt calcium homeostasis, and promote arrhythmogenesis, without improving long-term outcomes. These limitations reflect not only pharmacological shortcomings, but a broader conceptual reliance on amplification of intracellular calcium flux as the primary means of augmenting contractility. While effective in increasing cardiac output, this strategy imposes substantial energetic and electrophysiological costs and fails to address key abnormalities of the failing myocardium, including impaired calcium recirculation and diastolic dysfunction. Istaroxime is a first-in-class agent that combines Na⁺/K⁺-ATPase inhibition with enhancement of sarcoplasmic reticulum Ca²⁺-ATPase (sarcoplasmic reticulum Ca²⁺-ATPase isoform 2a (SERCA2a)) function, thereby modulating both calcium availability and reuptake. This dual mechanism promotes a more coordinated pattern of excitation-contraction coupling, integrating systolic augmentation with improved diastolic relaxation. Early clinical studies demonstrate a distinct hemodynamic profile characterized by increased stroke volume, preservation of heart rate, and stabilization or elevation of arterial pressure. These properties suggest a potential role for istaroxime in specific hemodynamic phenotypes, particularly hypotensive AHF and early cardiogenic shock, where conventional inotropes are limited by tachycardia or vasodilatory effects. However, current evidence is limited to phase II studies focused on hemodynamic endpoints, and the impact of istaroxime on survival, organ function, and disease progression remains unknown. Istaroxime represents a mechanistically distinct approach to inotropic therapy, shifting the paradigm from calcium amplification toward partial restoration of calcium cycling. Its clinical relevance will depend on whether this strategy can translate into improved patient outcomes-an objective that has thus far eluded the entire class of inotropic agents.

The efficacy of enzyme therapy is limited by their poor stability under physiological conditions. Thermostable enzymes, derived from extremophilic organisms or generated by advanced protein engineering, offer a revolutionary solution to this long-standing challenge. They are widely used in industrial biocatalysis. Their therapeutic applications are poorly investigated and spread across diverse disciplines. While most applications are in the preclinical stages, emerging evidence from animal models demonstrates proof-of-concept for thermostable antioxidant enzymes in cardiovascular, neurodegenerative, and inflammatory diseases. This review critically assesses the translational landscape, distinguishing between established therapeutic enzymes (e.g., asparaginase, PEGylated SOD) and emerging experimental candidates. This narrative review consolidates existing knowledge about thermostable enzyme engineering and their emerging functions as molecular therapies, particularly in oxidative stress-related diseases. This review synthesizes recent advances in structural biology, computational protein design, biomaterials engineering, and translational antioxidant strategies, highlighting how breaking down disciplinary barriers is accelerating the development of sustainable and self-regenerating antioxidant platforms. By integrating molecular precision with systems-level therapeutic design, engineered thermostable antioxidant enzymes exemplify the future of biological development, where multidisciplinary collaboration drives innovation against oxidative stress-driven pathologies. Engineered thermostable enzymes provide a versatile basis for next-generation therapeutics, with the potential to address medical needs through improved stability, targeted activity, and multifunctional design.

Premature coronary artery disease (PCAD) is a growing public health concern, especially in South Asia, where traditional risk factors fail to fully explain the increasing incidence of early-onset myocardial infarction. To explore its molecular underpinnings, we conducted a pilot study analyzing plasma proteins and lipids to identify potential biomarkers and dysregulated pathways associated with PCAD. Label-free quantitative proteomics revealed distinct molecular signatures separating PCAD patients from age- and sex-matched healthy controls. Key alterations included upregulation of GALE, immunoglobulin genes, and KIF20B, suggesting enhanced inflammatory responses and proliferative activity associated with post-myocardial infarction cellular repair. Similarly, down regulations of various proteins linked to multiple functions, such as myocardial infarction, hemoglobinopathy, complement and coagulation cascade, and fatty acid and lipoprotein transport in hepatocytes, were observed. Untargeted lipidomics further revealed significant elevations in several phosphatidylcholine species (PC 42:5, PC 40:3, and PC 42:7), highlighting disruption of highly unsaturated phospholipid metabolism. Overall, these findings indicate that PCAD is a multifactorial disorder involving metabolic, immune, and vascular dysfunction beyond conventional lipid abnormalities, underscoring the need for larger cohort studies to validate these biomarkers and uncover novel therapeutic targets.

Widespread antiretroviral therapy has greatly extended the life expectancy of people living with HIV (PLWH), making cardiovascular disease (CVD) one of their primary comorbidities. Nevertheless, significant cross-specialty knowledge gaps persist in routine clinical practice. Siloed disciplinary expertise results in low clinical adherence to guideline-recommended risk management interventions, highlighting an urgent demand for integrated, evidence-based tools that break down interdisciplinary barriers. Large language models (LLMs) have demonstrated robust medical knowledge retrieval and reasoning capacity in recent years, yet no systematic evaluation has determined whether these models can bridge such knowledge gaps and facilitate multidisciplinary collaborative CVD management for PLWH.

This study compared the performance of four mainstream AI models (Deepseek-V3, Deepseek-R1, ChatGPT-4o, ChatGPT-o4-mini) and 12 human clinicians (8 infectious disease specialists and 4 cardiologists) in addressing guideline-based CVD management tasks for PLWH.

Based on four authoritative domestic and international guidelines on HIV and CVD care, a structured 25-question assessment battery was developed via two rounds of Delphi expert consultation, with standard reference answers and an evaluation framework finalized through expert consensus. Responses of the four LLMs were generated with standardized prompts, while 12 clinicians answered identical questions in one-on-one structured interviews, with all verbal replies transcribed verbatim. Six multidisciplinary experts independently rated all responses across four dimensions: accuracy, completeness, readability and reliability, using a 4-point ordinal scale ranging from 1 (poor) to 4 (excellent). Cumulative link mixed models (CLMMs) were applied to analyze intergroup differences.

All AI models achieved statistically significantly higher scores than clinicians across all evaluation dimensions (p < 0.01). The AI group had mean scores of 3.44-3.68 (median = 4, CV: 0.145-0.178). Restricted by individual factors including specialty background, knowledge reserve, clinical experience, clinicians obtained lower mean scores of 1.78-2.05 (median = 2, CV: 0.428-0.473) with markedly greater score dispersion. Among all AI models, Deepseek-R1 delivered the optimal performance and showed statistically significant advantages over ChatGPT-4o, ChatGPT-o4-mini and Deepseek-V3 (all p < 0.01). Specialty-stratified CLMM analysis revealed no significant overall score difference between cardiologists and infectious disease specialists (OR = 0.92, 95% CI: 0.84-1.01, p = 0.094). Dimension-specific CLMMs combined with Wilcoxon rank-sum tests confirmed that cardiologists only earned significantly higher scores in the accuracy dimension (OR = 0.81, 95% CI: 0.67-0.97, p = 0.0261). Domain-specific performance divergence was observed: cardiologists outperformed infectious disease specialists in CVD risk assessment (2.26 vs 1.83), whereas infectious disease specialists achieved higher scores on drug adverse effect evaluation (2.23 vs 1.65).

This structured Q&A study on CVD management for PLWH found that LLMs outperformed human clinicians on all assessment metrics, with Deepseek-R1 attaining a distinctly superior composite score. The findings support the promising potential of Deepseek-R1 as a cross-disciplinary decision-support tool: it integrates multi-domain complex clinical knowledge, which may help address cross-specialty knowledge barriers, could improve the completeness and precision of clinical information output, and may enhance communication and decision-making efficiency for patients with complicated multimorbidity. To maximize clinical benefits, AI systems should be integrated into multidisciplinary care workflows alongside targeted clinical training to optimize the management of complex comorbidities among PLWH.