Biomedicines最新文献

Background/Objective: Warfarin is the standard anticoagulant for patients with mechanical heart valve replacement (HVR). However, its narrow therapeutic index and interpatient variability complicate early postoperative management. Evidence on how valve position influences warfarin sensitivity is limited. This study evaluated the impact of prosthetic valve position and clinical factors on early warfarin response and developed a prediction model to guide initial warfarin dosing in HVR patients. Methods: A retrospective study was conducted on 310 adults who underwent mechanical aortic, mitral, or double valve replacement at Hamad Medical Corporation (2015-2022). Warfarin was initiated within 24 h postoperatively, and patients were monitored for three days. Outcomes included daily warfarin dose, international normalized ratio (INR) levels, attainment of therapeutic INR, INR overshoot (≥4), and the warfarin dose index on day 3 (WDI3). Predictors of WDI3 were analyzed using multivariable regression, and a LASSO model was applied to a dose prediction algorithm for the day 1 dose. Results: Mitral valve recipients required lower doses than aortic or double valve groups (p = 0.008) but had higher INR overshoot rates (18.75% vs. 16.05% and 4.55%; p = 0.033). Female sex and a higher baseline INR were associated with greater sensitivity (p < 0.01), whereas mitral/double valve position predicted reduced sensitivity (p = 0.010). Only half of the cohort reached therapeutic INR by day 3. The prediction model explained ~28% of dose variance with moderate performance. Conclusions: Valve position, sex, and baseline INR significantly influence early postoperative warfarin response. Incorporating these clinical factors into dosing algorithms may optimize initial warfarin management in HVR patients.

Background/Objectives: The incidence of dementia and the concurrent burden on healthcare will increase with a population that continues to age. Pharmaceutical interventions for dementia carry negative side effects, ineffectively treat underlying causes, and fail to prevent disease onset. Therefore, non-pharmaceutical interventions such as music therapy should to be explored as a standalone or co-therapy for dementia. Music therapy improves cognitive symptoms of dementia; however, the neural mechanisms underpinning these improvements are not fully understood. Methods: To investigate potential neural mechanisms, six participants with dementia completed the Standardised Mini Mental State Examination, an n-back task, and magnetoencephalography (MEG) scanning before and after a music therapy program structured around improving executive functioning. Results: After music therapy, scores on an n-back task improved, and the MEG data revealed increased connectivity in neural networks and areas associated with compensation during executive functioning tasks. Connectivity results suggest there is preliminary evidence that music therapy improves cognitive symptoms of dementia by activating compensatory neural networks and areas; however, given the small sample size, these results should be interpreted with caution. Conclusions: The results of this hypotheses study present music therapy as a potentially viable short-term intervention which may operate by targeting compensatory neural networks and could be a long-term intervention that incorporates positive modifiable lifestyle factors, protecting the brain from dementia.

In recent years, neurological research has focused on understudied neurological diseases, uncovering pathophysiological mechanisms and new therapeutic avenues [...].

Background/Objectives: Obesity is characterized by chronic low-grade inflammation, which plays a central role in the development of its metabolic complications. The genetic factors influencing this inflammatory phenotype remain incompletely understood. This study aimed to analyze the associations of functional polymorphisms in genes involved in extracellular matrix remodeling (MMP2, MMP9, MMP12, COL1A1), metabolism (MTHFR, CYP3A5), and vascular regulation (NOS3, AGTR1) with plasma cytokine profiles and to identify inflammatory subphenotypes in patients with obesity. Methods: The study included 127 individuals, comprising 73 patients with excess body weight (body mass index, BMI ≥ 25 kg/m²) and 54 individuals with normal weight (BMI 18.5-24.9 kg/m²). Genotyping of selected polymorphisms was performed using real-time PCR. Plasma concentrations of 47 cytokines and chemokines were measured by multiplex immunoassay. Results: Nominally significant associations between genetic variants and cytokine levels were identified. Polymorphisms COL1A1 rs1107946 (CA genotype) and MMP9 rs17576 (AG genotype) were associated with a favorable inflammatory profile (decreased IL-6 and increased IL-10, respectively). In contrast, the AGTR1 rs5186 (AC genotipe) variant was associated with elevated TNF-α, IP-10/CXCL10, while the MTHFR rs1801131 (AC genotipe) variant was linked to increased MIP-1β/CCL4, both reflecting a pro-inflammatory shift. Complex, pleiotropic associations were observed for MMP2 rs243865 (elevated IL-7 and Fractalkine/CX3CL1) and NOS3 rs1799983 (elevated MCP-1/CCL2 and Eotaxin/CCL11). Cluster analysis revealed distinct patient subpopulations with differing inflammatory signatures. In one well-defined subgroup, an exploratory model (test R² = 0.537) identified IL-8, IL-15, and albumin as candidate biomarkers predictive of BMI. Conclusions: The study identifies candidate genetic polymorphisms and inflammatory biomarkers associated with distinct patterns of systemic inflammation in obesity. These hypothesis-generating findings underscore the phenotypic heterogeneity of obesity and provide a basis for further research into the stratification of patients by the risk of developing metabolic complications.

Background: Cancer-related health outcomes are shaped by the interplay of aging, complex treatment exposures, and individual psychological characteristics. Mitochondrial dysfunction has been implicated as an underlying biological process affecting cancer-related outcomes. This secondary, exploratory pilot analysis aimed to examine age- and treatment-related differences in fatigue, coping self-efficacy, resilience, and skeletal muscle mitochondrial oxidative capacity, measured via phosphorus-31 magnetic resonance spectroscopy (³¹P-MRS). Methods: Eleven cancer survivors (mean age 53.3 ± 12.7 years) were recruited from a larger symptom management trial. Participants underwent ³¹P-MRS to assess mitochondrial function via phosphocreatine recovery time constant (τPCr). Patient-reported outcome measures and physical function assessments were collected. Group comparisons and correlation analyses were conducted to evaluate differences and associations based on age (<65 vs. ≥65 years) and treatment. Because treatment categories were not mutually exclusive and the time since last treatment was not collected, treatment-related comparisons are descriptive only. Given the small available sample size, we conducted this study as exploratory and hypothesis-generating. Results: Older survivors (≥65) had longer τPCr (59.5 vs. 50.1 s), weaker grip strength, higher fatigue, and lower physical performance compared to younger participants, although differences were not statistically significant. Treatment-related patterns were descriptive; participants receiving multiple treatments had shorter τPCr but lower muscular strength, while immunotherapy recipients reported higher fatigue and lower physical activity. Among younger participants, a negative correlation was observed between τPCr and fatigue (ρ = -0.71), and positive correlations were observed with resilience (ρ = 0.61) and coping self-efficacy (ρ = 0.74), reflecting a pattern that warrants cautious interpretation in this small sample. Conclusions: These preliminary results suggest age- and treatment-related differences in fatigue, physical performance, psychological factors, and skeletal muscle mitochondrial bioenergetics. These signals warrant further testing in larger, adequately powered cohorts to clarify mechanisms and inform the development of personalized survivorship care strategies.

Background: Metabolic dysfunction-associated steatohepatitis (MASH) is defined by hepatocellular damage accompanied by inflammation and fibrotic changes. Bile acids (BAs) and gut microbiota play pivotal roles in disease progression. However, the contribution of dietary cholic acid (CA), a primary BA, remains unclear. Methods: We investigated the effect of dietary CA supplementation in Tsumura-Suzuki obese diabetic (TSOD) and Tsumura-Suzuki non-obese (TSNO) mouse strains with distinct metabolic phenotypes. The mice were fed normal diet (ND) or 0.5% CA-supplemented ND. Liver injury, fibrosis, and macrophage dynamics were assessed by biochemical assays, histology, flow cytometry, and RT-qPCR. Gut microbiota composition and fecal BA profiles were analyzed using 16S rRNA sequencing and mass spectrometry. Results: CA supplementation induced hepatomegaly, liver injury, and lipid metabolism abnormalities in both strains. In TSNO mice, CA markedly enhanced hepatic fibrosis, increased Col1a1 and Timp1 expressions, and promoted CD11c⁺ monocyte-derived macrophage infiltration. In contrast, TSOD mice showed minimal fibrotic responses to CA but exhibited pronounced alterations in gut microbiota composition, including enrichment of Akkermansia muciniphila, along with changes in fecal BA profiles. Flow cytometry further revealed Kupffer cell numbers and increased macrophage recruitment in both strains after CA supplementation. Conclusions: Dietary CA exerts strain-dependent effects on MASH pathogenesis. CA promoted macrophage-driven hepatic fibrosis in TSNO mice, whereas it primarily modulated gut microbiota and BA metabolism in TSOD mice. These findings highlight the dual roles of CA in linking hepatic immune responses with intestinal homeostasis and suggest a context-dependent contribution to MASH progression.

Background: Embryoid bodies (EBs) play a central role in organoid engineering, where their formation fidelity and size critically influence downstream differentiation outcomes. Current EB production workflows primarily rely on retrospective quality assessment, which limits reproducibility in high-throughput culture systems. Objective: This study aimed to develop a prospective, non-invasive framework that integrates early-phase bright-field time-lapse imaging with a three-dimensional convolutional neural network to predict EB formation outcomes and final EB diameter within the microwell platform. Methods: Time-lapse image sequences collected during the first hours after cell seeding on the microwell array were used to train 3D-CNN models for classification (formation vs. non-formation) and regression (final diameter). A balanced dataset was constructed through under-sampling, and five-fold cross-validation with data augmentation was applied to evaluate model performance. Results: The classification model achieved an accuracy of 96.5%, reliably distinguishing between successful and failed EB formation using short-duration image sequences. The regression model predicted the final EB diameter with a mean absolute error of ±7.1 µm, reflecting strong agreement with measured values and capturing seeding-density-dependent size variations. Conclusions: Early aggregation dynamics captured by bright-field time-lapse imaging contain sufficient spatiotemporal information to enable accurate, prospective EB quality prediction. The proposed framework provides a label-free and automation-compatible strategy for improving reproducibility in large-scale EB manufacturing and supports the future development of adaptive and closed-loop organoid culture systems for clinical applications.

Background: Methamphetamine (MA) use in people living with HIV (PLWH) has been linked to neurocognitive and behavioral dysregulation. We hypothesized that PLWH with active MA use (MAHIV) would show poorer cognitive performance, greater emotional and sleep burden, higher behavioral risk, and alterations in circulating biomarkers of immune activation and neuronal injury, relative to PLWH without MA use and HIV-negative Controls. Methods: Cross-sectional analytic study of 121 adults: PLWH with MA use (MAHIV, n = 40), PLWH without use (n = 42), and HIV-negative Controls (n = 39). Outcomes were ART discontinuation, physical activity, neurocognition (MoCA), depression (BDI), anxiety (GAD-7), sleep (PSQI), and substance use (ASSIST). Circulating biomarkers measured by ELISA: sCD14, neuron-specific enolase (NSE), S100B, and neurofilament light chain (NfL). Results: MAHIV participants had more frequent ART discontinuation than PLWH and the lowest physical activity. Chemsex with polysubstance use, condomless sex, and multiple partners were most prevalent in MAHIV. This group showed the highest anxiety and depressive burdens, and the greatest sleep disturbances. Global cognition (MoCA) was lowest in MAHIV, with significant deficits in executive function, memory, attention, and language; 82.5% had at least mild cognitive impairment. sCD14 was significantly higher in MAHIV than in PLWH and Controls, and NSE was elevated in both MAHIV and PLWH versus Controls. sCD14 correlated inversely with MoCA and positively with GAD-7 and BDI-II. Conclusions: Among PLWH, MA use is associated with greater ART nonadherence, syndemic mental-health and sleep disturbances, broader neurocognitive deficits, and elevations in circulating sCD14 and NSE. The sCD14-cognition and sCD14-mood relationships highlight chronic immune activation as a candidate pathway for neurocognitive and affective impairment and support sCD14 and NSE as potential stratification and monitoring biomarkers in MA-using PLWH.

Background/Objectives: Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), and frontotemporal dementia (FTD) share molecular features yet differ clinically, suggesting underlying systems-level commonalities. We aimed to characterize shared and disease-specific multimorbidity architectures across AD, ALS, and FTD using an artificial intelligence-driven literature-based semantic network. Methods: We applied SemNet 2.0, constructed from over 35 million PubMed abstracts, to analyze disease and syndrome (DSYN) and pharmacological substance (PHSU) nodes. Nodes were ranked using HeteSim and mapped to a harmonized 13-category mechanistic ontology. We quantified pairwise disease intersections, ontology-level enrichment, rank similarity, and intersection-disease alignment, and constructed an integrated multimorbidity priority landscape integrating disease-specific and intersection-level hierarchies. Results: Across AD, ALS, and FTD, a convergent multimorbidity architecture centered on a shared metabolic and immune core was identified, accompanied by prominent neurobehavioral processes and intermediate systems including gastrointestinal, endocrine, hematological, hepatic, and sensory pathways. Disease-specific signatures shaped distinct vulnerability profiles within this shared structure, including cardiovascular enrichment in AD, neuromuscular and toxin-related pathways in ALS, and coupled neurobehavioral-metabolic features in FTD. PHSU patterns reinforced these findings, with centrally positioned compounds predominantly targeting inflammatory, metabolic, or neuromodulatory processes. Conclusions: These findings position AD, ALS, and FTD within a unified, AI-derived multimorbidity framework. This ontology-guided approach provides a computational, hypothesis-generating foundation for multimorbidity-aware biomarker discovery, risk stratification, and cross-disease therapeutic exploration in neurodegenerative disease.

Background/Objective: Long COVID is an important cause of disability following SARS-CoV-2 infection; yet, its underlying mechanisms are not completely understood. One proposed mechanism is the long-lasting dysregulation of the immune complement system. This systematic review is the first to summarize the current evidence and evaluate the potential role of long-lasting complement activation in people with long COVID. Methods: A systematic electronic search on PubMed, MEDLINE, CINAHL, and Embase was conducted up to 15 October 2025, to identify studies investigating complement activation in people with the post-COVID-19 condition. The Newcastle-Ottawa Scale was used to evaluate the risk of bias and methodological quality. Results: Among the 247 studies initially identified, eleven met the inclusion criteria, comprising 1435 individuals (age: 48.5 years, 70% females) with long COVID and 1124 controls (age: 43.6 years, 60% females). All studies were of a high quality, with scores ranging from 7 to 8 stars (mean: 7.6 ± 0.5). The activation of the classical complement pathway was investigated in nine studies, whereas the lectin, alternative, and terminal complement pathways were each assessed in three studies. Multiple studies investigated several complement pathways. The results were heterogeneous since several markers of complement activation spanning the classical (C2, C4a, C4b, and C1s-C1INH), alternative (Ba, iC3b, and Factor D), and terminal (C5bC6, C5a, C9, and TCC) pathways were elevated, whereas other markers were not significantly different (C3, C4, and C4d) between patients with/without long COVID. In addition, markers spanning the lectin complement pathway (MBL, and MASP1-C1INH) were not significantly different between individuals with and without long COVID. Conclusions: The current evidence suggests potential long-lasting complement system dysregulation in individuals with long COVID, although the clinical significance remains controversial, due to heterogenous findings. Specific post-COVID symptom clusters, such as fatigue, dyspnea, or brain fog, have been linked to a distinct pattern of complement dysregulation. Substantial methodological heterogeneity, including differences in follow-up periods, complement markers, assessment methods, and control groups, along with the small number of available studies, underscores the need for further research to clarify the mechanisms linking complement dysregulation to long COVID.