Search Results (1,100)

The prevalence of diabetes and heart failure rises sharply with age, and their coexistence amplifies cardiovascular and renal risk. Elderly patients display unique clinical and biological profiles characterised by frailty, multimorbidity, and pharmacodynamic variability that challenge conventional treatment strategies. Sodium–glucose co-transporter-2 inhibitors (SGLT2i) have emerged as a cornerstone of cardio–renal–metabolic protection, with the most consistent cardiovascular benefit being the reduction in heart failure hospitalisation, whereas effects on cardiovascular death and major adverse cardiovascular events vary according to baseline cardiovascular risk, heart failure phenotype, diabetic status, and trial design. However, real-world use among the elderly remains limited due to concerns about tolerability, polypharmacy, and cost. This review analyses the pharmacological rationale and evidence base for SGLT2i therapy in older adults, highlighting mechanisms beyond glucose control, quantitative data from pivotal trials, and practical issues for geriatric implementation. Full article

Background/Objectives: The prognostic significance of mucinous colorectal adenocarcinoma (MAC) is controversial. Some studies report good outcomes relative to conventional colorectal adenocarcinoma (CRC) as is similarly described for MACs in, e.g., the breast, lung, pancreas and prostate. However, other studies refute this, proclaiming either no difference or worse outcomes. Herein, we proffer additional insights into the biology of MAC to explain these conflicting findings. Methods: A literature search was undertaken using keywords pertaining to MAC. Archival cases from our database were analyzed to provide context for our findings. Main Findings: The unifying histologic feature of MACs is their >50% content of extracellular mucin, but they should not be viewed as a monolithic entity, as is commonly portrayed in databases. Instead, MAC is a heterogenous disease as defined by histologic and molecular phenotypes. For example, MACs arising from adenoma-like CRC have relatively good outcomes unlike those from traditional serrated adenomas. Likewise, other factors such as histologic grade (grade 1–3), genomics (e.g., BRAF, KRAS, TP53), microsatellite instability (MSI-H, MSI-L), consensus molecular subtypes (CMS1–CMS4), and mucin types (MUC2, MUC5AC) significantly influence prognosis. These pathophysiologic features, demographics (age and sex) and specific anatomic regions/topography (right/left colon/rectum) can be captured and used to improve prognostic stratification. Conclusions: In contrast to previous studies that largely demarcated MAC as a discrete entity, this paper shows the limitations of this approach by highlighting the various sub-entities comprising MAC. Recognition of this heterogeneity may help to inform future treatment algorithms. Full article

Background/Objectives: The Duffy (Fy) blood group system, encoded by the ACKR1 gene, plays a key role in transfusion medicine and host susceptibility to malarial infections such as Plasmodium vivax. The Duffy-null phenotype [Fy(a−b−)] is associated with lower baseline white blood cell (WBC) and absolute neutrophil count (ANC) values despite a benign clinical course. This study aimed to establish specific reference intervals for WBC and ANC in Duffy-null individuals to improve diagnostic accuracy and reduce unnecessary clinical interventions. Methods: We conducted a retrospective analysis of 1695 adult patients who underwent complete blood count testing and red blood cell phenotyping between 1 January 2020 and 1 January 2026. Among these, 122 healthy individuals with confirmed Fy(a−b−) phenotype met inclusion criteria. Reference intervals were established using 95% reference interval nonparametric methods in accordance with Clinical Laboratory Standards Institute (CLSI) guidelines. Results: The median WBC and ANC values in the Duffy-null cohort were significantly lower than institutional reference medians (3.24 vs. 6.65 × 10³/µL and 1.45 vs. 4.45 × 10³/µL, respectively; p < 0.01). The derived 95% reference intervals (WBC: 2.43–7.05 × 10³/µL; ANC: 1.01–4.34 × 10³/µL) fell below conventional thresholds, with the lower ANC bound below the standard neutropenia cutoff. Conclusions: These findings support the need for Duffy-null-specific hematologic reference ranges. Adoption of such intervals may reduce misclassification, avoid unnecessary diagnostic procedures, and promote personalized clinical care. Full article

Alzheimer’s disease (AD) is a heterogeneous neurodegenerative disorder driven by complex interactions between genetic susceptibility, molecular pathways, and progressive brain alterations. Key genetic factors, including APOE, TREM2, and MAPT, contribute to pathological processes such as amyloid-β accumulation, tau aggregation, neuroinflammation, and synaptic dysfunction. Despite substantial advances in understanding these mechanisms, translating molecular insights into clinically accessible biomarkers remains a major challenge. Radiomics and machine learning (ML) have emerged as promising approaches for extracting high-dimensional quantitative features from medical imaging data and identifying complex patterns associated with disease processes. Radiomic features capture spatial heterogeneity and subtle characteristics of neurodegeneration that are not discernible using conventional imaging analysis. When integrated with ML, these features may serve as noninvasive surrogates of molecular activity, enabling the identification of imaging signatures associated with specific genetic backgrounds and biological pathways. This review aims to explore how radiomics and ML can bridge the gap between genetic and molecular mechanisms and in vivo imaging phenotypes in AD. We summarize current knowledge on genetic determinants and molecular pathways and discuss advances in molecular imaging, particularly tracers targeting amyloid and tau pathology. Furthermore, we analyze the emerging role of radiomics and ML in linking imaging phenotypes with underlying biological processes. This integrative framework may support improved disease stratification, early diagnosis, and prediction of therapeutic response, contributing to the development of precision medicine strategies and future theranostic approaches in Alzheimer’s disease. Full article

Relapsed and refractory multiple myeloma remains the principal cause of myeloma-related mortality despite major advances in therapeutic options, including proteasome inhibitors, immunomodulatory drugs, monoclonal antibodies, and T-cell-based immunotherapies. Conventional resistance models based on linear genetic clonal evolution inadequately explain reversible drug resistance, heterogeneous responses, and relapse after deep remissions. Emerging data from longitudinal genomics, single-cell analyses, and functional studies support a paradigm in which relapsed and refractory multiple myeloma is driven by adaptive plasticity rather than irreversible genetic change alone. Myeloma cells undergo reversible cell state transitions through transcriptional, epigenetic, metabolic, and proteostatic reprogramming, shaped by bone marrow microenvironmental cues and immune pressure. This narrative review integrates current evidence supporting adaptive plasticity as a central driver of therapeutic failure in relapsed and refractory multiple myeloma and discusses clinical and translational implications, highlighting adaptive treatment strategies and approaches targeting phenotypic flexibility to improve durability of response. Full article

Atypical Salmonella enterica strains that evade conventional detection pose significant challenges to food safety surveillance. A hydrogen sulfide (H₂S)-negative and serologically untypable S. enterica strain (SF1060) was detected by qPCR from cooked farmed mussels in Galicia, Spain, and characterized using phenotypic and genomic approaches. Despite typical biochemical profiles, SF1060 failed to produce black colonies on Xylose Lysine Deoxycholate (XLD) agar and lacked detectable somatic antigens by conventional serotyping. Hybrid genome assembly using nanopore and illumina sequencing yielded a closed chromosome and five plasmids. In silico analyses identified the strain as S. Senftenberg ST14. Comparative genomics revealed a chromosomal inversion at the rfb operon (encoding enzymes needed to synthesize deoxysugars and O antigens) mediated by IS5-family transposase ISEc68, which truncated the rfbD gene and separated the remaining rfb genes at rfbD, disrupting O-antigen biosynthesis, explaining the inconclusive phenotypic serotyping results. The phs operon responsible for H₂S production lacked premature stop codons, suggesting the H₂S-negative phenotype may result from an alternative mechanism. This study demonstrates how whole-genome sequencing resolves identification of atypical strains that fail culture-based detection and emphasizes the critical need for molecular surveillance methods in seafood safety programs, particularly in regions where atypical S. enterica variants may be endemic. Full article

Facklamia species are Gram-positive, catalase-negative cocci within the family Aerococcaceae. Historically misidentified as streptococci or enterococci due to phenotypic similarity and conventional biochemical testing limitations, their clinical significance remains incompletely defined. This narrative review synthesizes the current literature on the microbiology, epidemiology, clinical manifestations, diagnostic challenges, and antimicrobial susceptibilities of Facklamia infections, with emphasis on urogynecologic relevance. A structured literature search identified peer-reviewed reports of microbiologically confirmed human infection published through December 2025. These reports indicate global distribution and a broad clinical spectrum. Invasive infections were reported most often in older adults and individuals with structural abnormalities, chronic comorbidities, or recent surgical interventions; however, cases in otherwise healthy patients are described. Emerging microbiome data suggest that F. hominis may be enriched in adult females with lower urinary tract symptoms, supporting the possibility that the female urogenital tract functions as a potential reservoir and site of pathogenic activity. Antimicrobial susceptibility patterns vary, with documented resistance to penicillin, macrolides, clindamycin, and tetracyclines, and susceptibility to cephalosporins, vancomycin, and linezolid. Overall, Facklamia species should be recognized as underdiagnosed opportunistic pathogens with both invasive and urogynecologic relevance. Increased awareness, improved diagnostic identification, and systematic susceptibility profiling are needed to clarify their pathogenic role and guide appropriate antimicrobial therapy. Full article

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BrCa), owing to its lack of targetable receptors and resistance to chemical and molecularly targeted therapeutic approaches. While chemotherapy and surgical resection remain the standard of care, these interventions have significant side effects and varying patient outcomes. Thermally ablative focused ultrasound (T-FUS)—a non-invasive and non-ionizing therapy that utilizes targeted acoustic energy to debulk tumors—has displayed immunomodulatory effects in BrCa. However, T-FUS as a monotherapy has had limited clinical efficacy in TNBC due to the presence of anti-inflammatory immunosuppressive myeloid cells (isMCs). We hypothesized that the elimination of isMCs or initiating tumoricidal activity from them would lead to augmented activity of T-FUS. Thus, we interrogated the ability of myeloablative chemotherapies and antibodies; myeloid recruiting chemokine receptor blockade; and TLR agonists to remodel the tumor myeloid populations. Consistent with our previous studies, we found that while myeloablative chemotherapies decreased circulating isMCs, they had little impact on intratumoral isMCs. In contrast, antibodies targeting Ly6C and Ly6G ablated intratumoral isMCs and systemic isMCs, yet their effect was transient and was accompanied by a surprising depletion of T cells. While targeting CCR2, the dominant chemokine receptor for intratumoral isMC diminished a large subset of immunosuppressive cells within the TME; it also depleted T cells and dendritic cells. Contrary to previous studies, TLR stimulation failed to repolarize myeloid cells into a pro-inflammatory, tumoricidal phenotype but did lead to their depletion from the tumor microenvironment (TME) and mobilization of conventional dendritic cells to the draining lymph nodes. We therefore hypothesized that combining isMC depletion and TLR-driven immune activation would enhance FUS efficacy; however, this combinatorial regimen did not enhance overall survival or control tumor volume after T-FUS treatment. Thus, the BrCa TME is highly resistant to approaches intended to remodel the myeloid cell component which fail to synergize with T-FUS-mediated tumor ablation. Full article

Saline ecosystems, including saline lakes, are indeed major hotbeds of microbial novelty, harboring diverse and largely unexplored microbes. The sebkha of Lake Naïla (Morocco), an ecologically protected area registered under the Ramsar Convention in 1998, remains largely unexplored. Isolation using three different selective media enabled seven phenotypically distinct actinobacterial isolates to be obtained. Molecular characterization, based on 16S RNA gene sequencing, was used to identify strains as members of the genera Streptomyces, Nocardiopsis, and Prauserella. Three strains showed antimicrobial potential against pathogenic microorganisms, with Streptomyces sp. strain 43 exhibiting the most potent effects. Additionally, all isolates displayed plant-growth-promoting (PGP) traits, including phosphate solubilization, auxin (IAA) synthesis, siderophore secretion, and ammonia production. Notably, Nocardiopsis sp. strain 42 produced the highest IAA levels (282 μg/mL), while Streptomyces sp. strain 39, Streptomyces sp. strain 43, and Streptomyces sp. strain 48 excelled in phosphate solubilization. GC-MS profiling of Streptomyces sp. strain 43 revealed a complex metabolite repertoire, including 1,2-propanediol and nonanal, highlighting the strain’s versatile secondary metabolism. These findings highlight that the sebkha of Lake Naïla represents a rich source of halophilic actinobacteria with promising dual potential for antimicrobial and biofertilizer applications. The findings provide a solid basis for new perspectives on biotechnology applications and sustainable agriculture. Full article

Cancer research has undergone a fundamental transformation in recent decades due to the integration of artificial intelligence (AI) models into the study of tumor biology. However, tumor evolution, driven by genetic and phenotypic alterations leading to heterogeneity, resistance and metastasis, remains a major challenge in oncology. To understand these processes is crucial for developing effective therapeutic strategies and improving patient outcomes. Conventional methods often fail to capture the complexity and dynamics of these processes. In contrast, AI tools have the ability to integrate and analyze large-scale multi-omics, imaging and clinical data, offering the capability to decode tumor complexity. AI-driven methods facilitate multi-modal data integration, enabling the recognition of patterns that connect molecular alterations with phenotypic outcomes. In functional genomics, AI tools predict the effects of genetic variants, identify regulatory elements and map dysregulated pathways, thus clarifying mechanisms underlying tumor development and resistance. In the imaging field, deep learning techniques improve tumor segmentation, characterization and longitudinal monitoring, providing more accurate insights into tumor progression and treatment response. Predictive modeling could allow the anticipation of tumor evolution and drug response, supporting adaptive therapeutic plans and real-time treatment adjustments. Moreover, AI supports biomarker discovery, patient stratification and decision support systems that can improve clinical trial design and accelerate the development of personalized therapies. However, these advances raise important ethical challenges, including data privacy, algorithmic bias and the preservation of patient autonomy. Addressing these concerns is essential to ensure the responsible deployment of AI in oncology. Full article

Osteoarthritis (OA) is a highly prevalent musculoskeletal disorder and a major cause of disability, posing growing challenges for healthcare systems worldwide. Conventional supervised clinical assessments provide valuable insights but are largely limited to cross-sectional snapshots and often fail to reflect the variability of real-world functioning, physical activity patterns, and symptom fluctuations experienced by individuals with OA, especially those with knee OA. This perspective introduces a multisensor digital phenotyping framework for smart knee OA assessment, integrating supervised laboratory evaluations with unsupervised continuous monitoring in daily living environments using wearable sensors, smart insoles, activity trackers, and mobile devices. Feasibility was tested in 40 participants (20 knee OA patients, 20 controls). Raw data from questionnaires, electronic goniometry, dynamometry, force plate, connected insoles, and seven-day home monitoring were harmonized via a standardized pipeline aligned with the ICF framework. The pipeline employed anomaly detection, missing data imputation, z-score normalization, and cloud-based storage. This framework is envisioned to facilitate advanced data integration and machine-learning-ready analytics, enabling longitudinal monitoring, pattern recognition, and individualized health profiling. By conceptually bridging cross-sectional and continuous sensing modalities, this approach has the potential to enhance ecological validity, support earlier identification of functional decline, and inform data-driven clinical decision-making. Key methodological, technological, and ethical challenges—including data quality, interpretability, privacy, digital literacy, and clinical adoption—are also highlighted. Overall, this paper underscores the promise of AI-enabled multisensor digital phenotyping to advance smart, personalized, and precision healthcare for individuals with knee OA. Full article

In this study, Lactiplantibacillus plantarum XHQ-007, sourced from fermented pineapple pomace, was investigated using whole-genome sequencing (WGS) and systematic phenotypic assays to evaluate its biosafety and functional potential in food applications. The strain showed a 79.94% reduction in detectable tyramine under complex fermentation conditions. In the absence of pH-matched abiotic controls, this reduction cannot be exclusively attributed to enzymatic degradation and may also involve chemical or acid-mediated matrix effects. Further studies are required to distinguish biological degradation from physicochemical contributions. Genomic analysis suggested that this capability may be associated with a putative candidate tyramine-associated metabolic pathway involving the multicopper oxidase cueO and the putative hpa gene cluster, rather than conventional amine oxidases. Furthermore, the isolate displayed strong resilience against gastrointestinal stressors, such as acidic conditions and bile salts. Safety assessments confirmed the absence of hemolytic activity and mobile antibiotic resistance genes. Notably, WGS also identified a plantaricin (pln) operon containing regulatory elements (plnABCD) and structural genes (plnMNOP), suggesting genomic potential for bacteriocin-related functions. The identification of poxL and nox2 may further indicate energy metabolism and redox homeostasis. Overall, this study provides a genome-informed and phenotype-supported characterization of L. plantarum XHQ-007 in a fermentation context. However, all mechanistic interpretations remain putative and require further experimental validation. Full article

Accurate and reliable prediction of lettuce fresh weight is essential for optimising protected cultivation management and improving the yield and quality. Multimodal data combined with machine learning models have been widely used for monitoring crop growth. However, existing approaches often fail to capture dynamic physiological changes during crop growth, whereas conventional machine learning models are frequently limited by their black-box nature and thus cannot reveal the intrinsic relationships between features and targets. To address the above issues, this study developed a stationary, multi-sensor integrated data acquisition platform under controlled greenhouse conditions. By fusing multi-view morphological structure, color and texture, and multispectral features, the study constructed interpretable machine learning models for predicting the fresh weight of lettuce. Based on the data collected by the platform, 66 initial features covering morphology, color texture, and vegetation indices were extracted from the data. A two-stage feature-selection strategy combining Pearson correlation screening and variance inflation factor (VIF)-based multicollinearity elimination was used to select nine optimal input variables for the model. To achieve an accurate estimation of the fresh weight of lettuce, the system compared six models: Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosted Decision Tree Regression (GBDT), K-nearest neighbour regression (KNN), XGBoost, and Backpropagation Neural Network (BPNN). The results indicate that the SVR model based on multimodal data fusion performed best, with an R² of 0.93, an RMSE of 3.23 g, an RMSEn of 5.60%, and an MAE of 2.31 g, demonstrating a significantly higher prediction accuracy than the other models. Furthermore, the SHAP interpretation method was used to reveal the contributions of key features to fresh weight estimation and their interaction mechanisms. This study provides a feasible approach and technical guidance for non-destructive estimation of fresh weight in lettuce under controlled conditions, and offers a preliminary basis for the development of phenotypic monitoring models for protected cultivation. Full article

Background/Objectives: Cigarette smoking is a major risk factor for periodontitis, but the salivary host-response profile associated with smoking-related periodontal inflammation remains incompletely characterized. This study compared salivary NETosis-related and oxidative-inflammatory biomarkers among current smokers, former smokers, and never-smokers with periodontitis. Methods: This cross-sectional study included 159 systemically healthy adults with periodontitis (53 per group: current smokers, former smokers, never-smokers). Individuals with systemic diseases or concomitant medications that could interfere were excluded. Unstimulated whole saliva was analyzed for NETosis-related biomarkers (MPO-DNA complexes, citrullinated histone H3, neutrophil elastase, cell-free DNA) and oxidative-inflammatory markers (MMP-8, IL-1β, IL-6, TNF-α, 8-OHdG, total antioxidant capacity). Results: Salivary MPO-DNA complexes differed significantly among groups (current smokers 33.52 ± 9.96, former smokers 26.90 ± 8.38, never-smokers 19.20 ± 7.50 ng/mL; p < 0.001, η² = 0.317). The composite NETosis score (η² = 0.702) and oxidative-inflammatory score (η² = 0.718) showed the same graded pattern. Biochemical verification confirmed clear group separation (salivary cotinine: current smokers 312.3 ± 77.0, former smokers 9.7 ± 5.1, never-smokers 3.2 ± 1.4 ng/mL). Smoking exposure was positively correlated with biomarker levels and the severity of periodontal disease. Smoking status remained independently associated with MPO-DNA complexes and the NETosis score after covariate adjustment. Conclusions: Current smoking was associated with an enhanced salivary NETosis-related and oxidative-inflammatory phenotype. Former smokers displayed an intermediate profile. Salivary MPO-DNA complexes and composite biomarker scores warrant further investigation as candidate non-invasive indicators of smoking-associated periodontal inflammatory burden, pending diagnostic performance analyses and prospective validation. Full article

Background/Objectives: Psoriasis is a chronic immune-mediated inflammatory disease increasingly recognized as a systemic disorder associated with significant metabolic and cardiovascular comorbidities. Among these, obesity (defined as BMI > 30 kg/m²) plays a pivotal role, acting both as a risk factor for psoriasis development and as a modifier of disease severity, clinical phenotype, and therapeutic response. The relationship between psoriasis and obesity is bidirectional and sustained by shared inflammatory and metabolic pathways. This review aims to provide a comprehensive and updated synthesis of the epidemiological association between psoriasis and obesity, to elucidate the underlying pathophysiological mechanisms, and to discuss the clinical and therapeutic implications of excess body weight in psoriasis management. Methods: A narrative review of the literature was conducted, including epidemiological studies, mechanistic research, clinical trials, and real-world evidence addressing the interplay between psoriasis and obesity. Relevant data were identified from peer-reviewed publications focusing on inflammatory pathways, metabolic dysfunction, cardiovascular risk, and treatment outcomes in obese patients with psoriasis. The graphical figures included in this manuscript were created with the assistance of a large language model–based image-generation tool, ChatGPT-5 by OpenAI, using author-defined prompts. The prompts requested schematic medical illustrations summarizing the pathophysiological links between obesity and psoriasis, including adipose tissue dysfunction, adipokine imbalance, systemic inflammation, and activation of the IL-23/Th17 axis. For the therapeutic algorithm, the prompt requested a stepwise clinical flowchart for obese patients with psoriasis, including BMI assessment, comorbidity screening, universal weight-management measures, psoriasis severity stratification, obesity-adapted biologic selection, and management of suboptimal response. The generated images were subsequently reviewed, edited, and approved by the authors to ensure scientific accuracy, clarity, and consistency with the manuscript content. Results: Epidemiological evidence consistently demonstrates a higher prevalence of obesity among patients with psoriasis, with obesity independently associated with increased disease severity. Shared mechanisms include adipose tissue–driven cytokine production, dysregulated adipokine secretion, insulin resistance, endothelial dysfunction, and activation of the IL-23/Th17 axis, collectively contributing to systemic inflammation and accelerated atherogenesis. Obesity negatively impacts the efficacy, pharmacokinetics, and long-term drug survival of conventional systemic agents and biologic therapies, leading to suboptimal clinical outcomes. Conclusions: Obesity is a key determinant of psoriasis burden, influencing disease expression, comorbidities, and therapeutic response. Integrating weight reduction strategies into personalized psoriasis management may improve both dermatological outcomes and overall cardiometabolic health, supporting a holistic approach to patient care. Full article