Search Results (194,935)

Background: High-grade serous ovarian carcinoma (HGSOC) can be subdivided into four prognostic molecular subtypes based on gene expression: C1/Mesenchymal (C1.MES), C2/Immunoreactive (C2.IMM), C4/Differentiated (C4.DIF) and C5/Proliferative (C5.PRO), each representing distinct biological characteristics with immune and stromal microenvironments. PrOTYPE enables prognosis and treatment guidance from biopsy material. Metastatic biopsies are often more accessible than primary adnexal sampling; their utility assumes stable tumor-intrinsic properties relative to the primary. Metastases may diverge due to microenvironmental pressure as well as the site-specific subtype dynamics. Methods: Treatment-naïve HGSOC specimens from 138 patients were profiled using the 55-gene nanostring PrOTYPE assay at adnexal, contralateral adnexal, and/or metastatic sites. Results: Adnexal PrOTYPE yielded expected distributions (21% C1.MES, 31% C2.IMM, 23% C4.DIF, 25% C5.PRO) with moderate reproducibility (κ = 0.49). Same-site replicate analysis showed substantial reproducibility (κ = 0.7). Non-adnexal sites were enriched for immune/mesenchymal subtypes (C1.MES/C2.IMM, 36/63 cases), most prominently at the omentum (24/32 C1.MES). C5.PRO was distinctly underrepresented at non-adnexal sites. Subtype shifts from adnexal to extra-adnexal sites were enriched for the second-place adnexal type prediction (p < 0.001). Detailed 55-gene analysis showed POSTN/CTSK were most commonly upregulated across metastatic sites. EMT pathway enrichment increased with metastatic distance (from adnexa to omentum, adj p < 0.05), paralleling—but independent of—C1.MES predominance. Conclusions: Adnexal PrOTYPE showed good stability. However, non-random subtype shifts and EMT enrichment at metastatic sites suggest dissemination selects pre-existing transcriptional plasticity rather than acquiring states de novo as HGSOC adapts to new microenvironments. Microenvironment changes may help predict metastatic potential and should be considered for precision medicine targeting. Full article

Immune exclusion refers to the phenomenon in which immune cells are restricted to the peritumoral stroma. This phenomenon arises from complex interactions within the tumor microenvironment (TME) that limit immune cell infiltration. Consequently, immune exclusion represents a major barrier to effective antitumor immunity and a key obstacle to the success of immunotherapy. The principal components of the TME that orchestrate immune exclusion are (i) the tumor vasculature, (ii) the extracellular matrix (ECM), and (iii) stromal cells and their chemokine-mediated signaling. Understanding immune exclusion is critical for designing therapies that enhance the efficacy of immunotherapy and improve clinical outcomes. This review synthesizes current knowledge of the molecular and cellular mechanisms underlying immune exclusion and discusses emerging therapeutic strategies aimed at overcoming this phenomenon. Full article

Many bioactive constituents of medicinal plants depend on microbial biotransformation for their pharmacological activity, positioning postbiotics from plant substrates as microbially matured phytomedicines. An emerging framework integrates prebiotic phytochemicals with probiotic strains to modulate gut microbiota and host health. In this study, we explored the functional properties of heat-inactivated Lactobacillus strains following the fermentation of oligosaccharides obtained from sesame hulls (Sesamum indicum L.), underutilised agro-industrial residues. Cell-wall oligosaccharides were obtained by alkaline or enzymatic (Celluclast^® 1.5 L (Novonesis, Copenhagen, Denmark)) extraction with Ultraflo^® L (Novonesis, Copenhagen, Denmark) hydrolysis and fermented with Lactobacillus acidophilus, L. casei, or L. paracasei. Heat-inactivated pre-postbiotic preparations were profiled for antioxidant capacity, inhibition of metabolic enzymes implicated in obesity and type 2 diabetes, and anti-Helicobacter pylori urease activity. Moreover, these preparations were incorporated into a barley malt (Hordeum vulgare L.) beverage. Bioactivity was strain- and substrate-dependent: L. casei-derived postbiotics most strongly inhibited pancreatic lipase (47.82%) and α-glucosidase (52.14%); L. acidophilus most strongly inhibited α-amylase (43.67%); and L. paracasei exhibited the strongest urease inhibition (20.66%). All strains displayed enhanced antioxidant activity, with ABTS scavenging reaching 87.02%. The supplemented beverages improved antioxidant activity by ~20%. The fermentation of these oligosaccharides thus yields a microbially matured phytomedicine with multi-target activity, supporting postbiotics as active mediators of plant-based therapeutics. Full article

This work aims to study and improve the long-term stability of conductimetric biosensors for urea detection in clinical and environmental samples, which are fundamentally limited by complex thermal and temporal drifts due to temperature-sensitive enzyme kinetics, variations in ionic mobility, and the progressive degradation of the sensing layer. The biosensor targets the urea concentration range 0.01–30 mM, validated against experimental data and covering the clinically relevant range for blood urea detection (2.5–7.5 mM), urine (20–40 mM), and environmental monitoring applications. Conventional calibration techniques, such as the conventional calibration method (based on reference measurements), and purely deterministic correction methods, such as deterministic methods (based on known fixed equations), often prove insufficient because they struggle to capture the non-stationary and inherently stochastic nature of these drifts. In this work, we propose an original hybrid probabilistic framework that synergistically combines machine learning and Bayesian inference for robust adaptive drift compensation. A Random Forest model is first implemented to model the deterministic nonlinear relationships between environmental parameters (temperature, pH, CO₂ concentration) and the sensor response. The residual temporal drift is then explicitly modeled as a non-stationary latent stochastic process using Bayesian inference based on a Gaussian process. This approach allows continuous online model updating, real-time uncertainty quantification, and automatic detection of anomalies. The models were trained and validated on a large dataset obtained from multiphysics simulations carried out in COMSOL Multiphysics 5.6. These simulations incorporated enzymatic reactions, thermal effects, and chemical dynamics taking place inside the sensor. Experimental results show that the hybrid approach substantially enhances sensor performance, lowering the root mean square error (RMSE) to below 0.8 μS/cm (corresponding to less than 0.5% of the full-scale response) over a wide temperature range (15–45 °C) and across extended operating periods. This represents a clear improvement over conventional compensation method. By merging the predictive power of ensemble learning with a probabilistic Bayesian model of dynamic drift, this study introduces a fresh perspective on the design of intelligent, self-adaptive, and drift-resistant conductimetric biosensors. The proposed framework holds strong potential for reliable, long-term autonomous operation in urea reliable, long-term autonomous operation in urea monitoring across biomedical diagnostics (kidney/liver function assessment) and environmental surveillance (water eutrophication prevention). Full article

Reliable and rapid forecasting of wind turbine output power is vital for operators, particularly day-ahead and intraday market scheduling and reserve allocation. However, the inherent unpredictability, intermittency, and volatility of wind turbine output make forecasting processes difficult. To address this challenge, this study proposes a Sinusoidal Representation Network (SIREN)-based forecasting model for high-accuracy, rapid direct multi-horizon forecasting of wind turbine output power. SIREN is selected due to the periodic and symmetrical mathematical structure of its sinusoidal activation function, which allows the model to represent both low-frequency trends and high-frequency sudden changes in wind energy data. To improve data quality, compensate for asymmetric fluctuations in wind data, and provide more suitable inputs for SIREN training. Several preprocessing steps are utilized before feeding the data into the model. The proposed preprocessing step includes a moving median filter, robust scaling based on median and interquartile range, Winsorizing clipping, and a Hampel filter to reduce the effects of instantaneous noise, outliers, and local peaks without disrupting temporal continuity. Subsequently, a Savitzky–Golay smoothing is applied to attenuate high-frequency measurement noise while preserving curvature, local peaks, and physically meaningful short-term dynamics in the data. The sliding-window approach is used to formulate the multi-horizon forecasting problem directly, and a direct h-step-ahead forecasting architecture is designed, preserving structural symmetry in the time series. The SIREN is trained and tested using MATLAB with the help of two different datasets: Dataset-1 has a 10 min resolution for 1 year, and Dataset-2 has a 1 h resolution for 15 years. The forecast horizon parameter h is considered separately for each step, and the proposed SIREN is independently trained, validated, and tested for each target horizon while maintaining chronological order. The results demonstrate that the proposed model is able to yield high forecast performance for a wide spectrum of horizons ranging from 10 min to 15 days. The accuracy of the proposed model for Dataset-1 is R² of 99.6%, MSE of 0.085%, MAE of 1.7%, and MAPE of 12%, while for Dataset-2, the accuracy is R² of 98.8%, MSE of 0.3%, MAE of 3.6%, and MAPE of 23%. Ablation and sensitivity analyses are conducted to evaluate the impact of the basic components used in the proposed model on forecasting performance. In addition, combative experiments are performed using traditional time series, ML, and DL forecasting techniques to better assess the contribution of the model. The obtained results show that the SIREN-based direct forecasting approach provides strong learning capability, as well as high forecasting accuracy, for both high-resolution and low-resolution wind power data. Overall, its ability to capture the symmetric and periodic characteristics inherent in wind turbine power data makes it a promising alternative for multi-horizon wind power forecasting applications. Full article

Fascia is increasingly recognized as a dynamic functional system. It can actively sense, transmit, and regulate mechanical, sensory, and metabolic signals. Why does fascia play such a critical role in chronic pain and movement disorders? Researchers are now rethinking the pathophysiological mechanisms underlying this role. Previous systematic reviews have typically focused primarily on specific mechanisms or interventions. In contrast, this study takes a holistic view of fascial function. It integrates multiple physiological functions of the fascia: mechanical integration, sensory modulation, cellular and matrix remodeling, as well as metabolic and immune regulation. From the perspective of functional imbalance, we further explore the pathological mechanisms associated with the fascia. Building on this, we then focus on how to assess fascial function from multiple dimensions and on specific targeted interventions. For assessment, we have systematically compiled a set of multi-stage quantitative techniques. These include clinical palpation, ultrasound, and elastography, tissue mechanics testing, microdialysis, omics approaches, electrophysiological testing, and digital modeling. For interventions, we have listed a range of modulating approaches, such as manual therapy, exercise rehabilitation, dry needling and acupuncture, fascial injections, targeted drugs, and biotechnological materials derived from tissue engineering. This review summarizes a clinical decision-making framework guided by the assessment of fascial functional status. It emphasizes a systematic approach and links quantitative diagnosis with precise interventions. Additionally, it provides a literature synthesis for understanding fascial mechanisms and related disorders and offers a reference foundation for the field’s transition from empirical treatment to measurable, reproducible, and individualized practice. Full article

Background/Objectives: Rumex obtusifolius L. (Polygonaceae) has been traditionally used to treat various disorders, including hepatic and gastrointestinal diseases. However, the phytochemical constituents of its roots and their potential protective effects against skeletal muscle atrophy remain poorly understood. This study aimed to isolate and characterize bioactive constituents from R. obtusifolius roots and evaluate their protective effects against dexamethasone (DEX)-induced muscle atrophy in C2C12 myotubes. Methods: LC–MS-guided phytochemical investigation of the ethanol extract of R. obtusifolius roots, followed by successive column chromatography and HPLC purification, resulted in the isolation of four naphthalene-type glycosides. Their structures were elucidated using 1D and 2D NMR spectroscopy, HR-ESIMS, and chemical transformation. The protective effects of compounds 1 and 4 against dexamethasone (DEX)-induced muscle atrophy were evaluated by assessing myotube morphology, myogenic and atrophy-related protein expression, and PI3K/Akt/mTOR signaling. Results: A new naphthalene malonylglucoside, nepodin-8-O-β-D-(6′-O-malonyl)-glucopyranoside (1), together with three known glycosides (2–4), was identified. Among the isolated compounds, compound 1 significantly attenuated DEX-induced muscle atrophy in a concentration-dependent manner by increasing myotube diameter and improving myotube morphology. It restored the expression of the myogenic markers MyoD and myogenin while suppressing the atrophy-related proteins MuRF1 and MAFBX. Furthermore, compound 1 reversed DEX-induced suppression of the PI3K/Akt/mTOR signaling pathway, indicating recovery of anabolic signaling. Conclusions: This study reports a new naphthalene malonylglucoside (1) from R. obtusifolius roots and demonstrates that compound 1 protects against DEX-induced skeletal muscle atrophy through restoration of myogenic differentiation and activation of the PI3K/Akt/mTOR pathway. These findings suggest that compound 1 is a promising natural lead compound for the development of therapeutics targeting muscle wasting disorders. Full article

Traumatic spinal cord injury (SCI) is a severe medical condition, often resulting in permanent disability, with significant impacts on patients’ quality of life and burden on healthcare systems. Current therapeutic approaches for SCI are insufficient, advocating for the development of more effective treatments. As changes in transcriptome post-SCI can provide clues for novel treatment strategies and targets, substantial efforts have been made recently to characterize such transcriptional changes and their spatiotemporal features. This narrative review focuses on how transcriptomics, alone or in combination with other omics data, can contribute to understanding SCI pathobiology and the mechanisms of post-SCI regeneration and guide the development of novel SCI therapies. It covers an arsenal of tools for transcriptomics studies and provides a concise summary of findings from the latest relevant studies (predominantly from 2020 to 2025), representing the major directions in the field. Full article

The COVID-19 pandemic accelerated telehealth adoption, but disparities in digital access hinder its potential, especially for older adults in nonmetropolitan areas. This study examined associations between sociodemographic factors and access to select digital resources among nonmetropolitan Medicare beneficiaries. This cross-sectional study used the 2022 Medicare Current Beneficiary Survey Public Use File, including 1732 Medicare beneficiaries aged ≥65 in nonmetropolitan areas. The dependent variable of digital access was categorized as (1) access to both a computer/tablet and the internet, (2) access to either, and (3) access to neither. A survey-weighted multinomial logit model was conducted to examine associations between sociodemographic factors and digital access, with no access to either a computer/tablet or the internet as the reference category. Approximately 71.7% of nonmetropolitan beneficiaries had both computer/tablet and internet access, 14.4% had one or the other, and 13.9% had neither. About one-third of study beneficiaries lacked full digital access. Older age, male, minority race/ethnicity, lower education, and lower income were associated with reduced digital access among nonmetropolitan beneficiaries. Targeted interventions to expand digital access for these at-risk populations are needed. Full article

Myocardial ischemia–reperfusion injury (MIRI) represents a major contributor to morbidity and mortality in patients undergoing reperfusion therapy after acute myocardial infarction. Although timely restoration of coronary blood flow is essential for myocardial salvage, reperfusion paradoxically initiates a complex cascade of molecular and cellular events that may aggravate myocardial injury. Oxidative stress is considered one of the central mechanisms underlying MIRI, primarily through excessive production of reactive oxygen species (ROS) and reactive nitrogen species (RNS), leading to mitochondrial dysfunction, calcium overload, endothelial injury, inflammatory activation, and cardiomyocyte death. This review summarizes the current understanding of the pathophysiological mechanisms involved in oxidative stress-mediated reperfusion injury, with emphasis on mitochondrial permeability transition pore opening, inflammasome activation, cytokine release, neutrophil extracellular trap formation, macrophage polarization, and interconnected cell death pathways including PANoptosis. Emerging evidence regarding immunometabolic regulation and epigenetic modulation in MIRI is also discussed. In addition, current pharmacological and non-pharmacological cardioprotective strategies targeting oxidative stress, mitochondrial dysfunction, and inflammatory signaling are reviewed, highlighting both promising experimental findings and the persistent challenges in clinical translation. A deeper understanding of the molecular interplay between oxidative stress and inflammatory pathways may facilitate the development of integrated therapeutic approaches aimed at improving myocardial recovery and long-term cardiovascular outcomes following reperfusion therapy. Full article

Ferroptosis is a regulated form of cell death driven by iron-dependent lipid peroxidation and is mechanistically distinct from apoptosis, necrosis and pyroptosis. Increasing evidence indicates that ferroptosis plays a critical role in cancer biology, including lymphoproliferative disorders, where chronic redox imbalance, dysregulated iron metabolism, and metabolic rewiring create a permissive environment for ferroptotic vulnerability. In these malignancies, altered iron handling, elevated reactive oxygen species, and a strong reliance on antioxidant systems such as glutathione and glutathione peroxidase 4 tightly control ferroptotic sensitivity. Dysregulation of key components, including SLC7A11, lipid metabolism pathways, and intracellular iron homeostasis, further shapes the susceptibility of malignant lymphoid cells to ferroptosis. Importantly, emerging preclinical studies suggest that therapeutic targeting of ferroptosis may overcome resistance to conventional chemotherapy, targeted agents, and immunotherapy, offering novel opportunities particularly in relapsed or refractory disease. This review provides a comprehensive overview of the molecular mechanisms governing ferroptosis in lymphoproliferative disorders, highlights the interplay between ferroptosis and major cellular and metabolic pathways, and discusses current and emerging strategies to pharmacologically induce ferroptosis, with an emphasis on biomarker-driven clinical translation. Full article

Pseudomonas aeruginosa is an opportunistic pathogen causing healthcare-associated infections. Colistin is a last-resort antibiotic for multidrug-resistant Gram-negative bacteria. Resistance arises through mutations in two-component systems (TCS) regulating the arn operon. Data on colistin resistance in P. aeruginosa from Pakistan remain limited. A total of 3189 clinical samples (urine, blood, sputum, pus, wound swabs) were cultured. P. aeruginosa was identified by Gram staining, biochemical tests (catalase, oxidase, API 20E), and oprL gene amplification. Antibiotic susceptibility was determined by disk diffusion and MIC strips. Resistance genes (PhoP, PhoQ, PmrA, PmrB, mcr-1, oprD) were detected by PCR and Sanger sequencing. Wild-type protein structures were retrieved from PDB; mutant structures were predicted using AlphaFold3. ANP (phosphoaminophosphonic acid-adenylate ester) was docked using MOE 2019.0102. Of 3189 samples, 384 (12.0%) yielded P. aeruginosa. Wound/pus (38.0%) and surgical wards (30.0%) were the predominant sources. Colistin and polymyxin B showed 99.0% susceptibility (MIC₅₀/MIC₉₀ = 1 µg/mL). High resistance was observed for Piperacillin–Tazobactam (96.4%), Aztreonam (70.6%), and Gentamicin (64.2%). oprD was the most prevalent gene (87.5%), followed by PmrB (54.0%), PhoQ (44.0%), PhoP (36.0%), PmrA (18.0%), and mcr-1 (8.0%). Docking revealed the strongest binding in wild-type PhoQ (1ID0; −12.0 kcal/mol, LYS392), wild-type PmrB (2JSO; −9.8 kcal/mol, ASP37), and wild-type PhoP (2PKX; −9.1 kcal/mol, LYS87/ARG111). Mutant proteins showed reduced binding affinities and dispersed interaction networks. Mutant PhoP formed 16 contacts (strongest −4.3 kcal/mol) versus wild-type PhoP with 13 contacts (−9.1 kcal/mol). Colistin remains highly effective against P. aeruginosa in this setting (99.0% susceptibility). The presence of mcr-1 (8.0%) and high oprD prevalence (87.5%) require continued surveillance. Mutations in TCS proteins reduce ANP binding affinity and alter interaction specificity, suggesting that ATP-competitive inhibitors targeting these kinases merit further investigation and experimental validation. Full article

Geant4 (version 11.3.2) simulations were used to study particle-dependent radiation interaction in MAPbI₃ under electron, photon, and neutron irradiation. The analysis focused on spatial distributions of interaction events, released energy, secondary-particle generation, and process-specific contributions. A 1 mm single-layer MAPbI₃ target was used to identify the intrinsic material response, while multilayer MAPbI₃ containing detector geometries were considered to assess device-like effects. Electrons produced extended charged particle tracks governed by direct energy loss and secondary-electron cascades. Photons showed weak direct energy deposition, with the response mainly controlled by secondary electrons generated in discrete electromagnetic interactions. Neutrons produced sparse but locally intense energy-release patterns dominated by recoil particles and nuclear-reaction products. The results show that total released energy alone is insufficient to describe radiation response in MAPbI₃; spatial morphology and the balance between primary and secondary contributions are essential for interpreting both detector operation and possible radiation-induced degradation. Full article

This paper presents a five-coil array integrated wireless power transfer (WPT) system designed for implantable medical devices. The proposed structure features a collaborative design of driving and radiating coils, where each driving coil excites its corresponding radiating coil to emit power. All coil units are precisely tuned to operate at the 13.56 MHz ISM band. The unique array configuration generates a highly uniform magnetic field distribution within the target area, enabling excellent tolerance to lateral misalignment. System analysis based on scattering parameters (S-parameters) confirms the design’s outstanding power transfer efficiency at the operating frequency. By integrating the five-coil array onto a double-layer printed circuit board, the system achieves the miniaturization and high integration level required for implantable applications. The experimental results demonstrate that the system reaches a maximum transfer efficiency of 55% under ideal alignment conditions (with a transfer distance of 10–20 mm). Notably, even with a lateral displacement of 5–10 mm at a 10 mm transfer distance, the system maintains stable performance, with efficiency consistently exceeding 50%. These results validate the system’s capability for reliable and efficient wireless power delivery in clinical settings. Full article

Background/Objectives: Poly(lactic-co-glycolic acid) (PLGA) microparticles are widely used for sustained drug delivery, yet the release behavior reported in the literature remains difficult to predict across studies. It was hypothesized that this limitation reflects insufficient information content in commonly reported formulation variables rather than model inadequacy. Methods: A curated dataset of 321 PLGA microparticle formulations from 113 publications comprising 89 drugs and 4913 release observations was analyzed. Early time release was parameterized using Korsmeyer–Peppas descriptors (n, K), and burst release was quantified as the 24 h cumulative release. Machine learning models were evaluated using formulation-grouped cross-validation, applicability-domain analysis, and leave-one-study-out validation to assess cross-laboratory transportability. Results: Under formulation-grouped validation, predictability was limited (stacked ensemble: $R^2=0.156$ for n, $R^2=0.169$ for K, burst $R^2=0.100$). Leave-one-study-out validation yielded negative pooled $R^2$ values for all targets ($-0.061$, $-0.040$, and $-0.180$, respectively), indicating failure to generalize across laboratories. Applicability-domain filtering did not materially improve performance, supporting the interpretation that prediction is limited by missing or inconsistently reported variables rather than covariate extrapolation alone. Conclusions: These results reveal an information-limited regime in PLGA release prediction in which the literature covariates enable only weak formulation-level prediction under grouped validation and cannot support transferable models. Minimum reporting priorities are therefore proposed, including standardized characterization of polymer molecular weight, end-group chemistry, quantitative emulsification and solvent-removal parameters, and microstructural or porosity measurements, to enable reproducible formulation screening. Full article