Search Results (230,520)

Leiomyosarcoma (LMS) is a rare, aggressive soft-tissue sarcoma arising from smooth muscle cells. It has a high metastatic potential and limited therapeutic options. Despite advances in oncology, the molecular landscape of LMS remains incompletely understood, particularly regarding the genetic and epigenetic alterations that affect key signaling pathways. This review summarizes the current knowledge of mechanisms driving LMS pathogenesis, including somatic mutations in genes such as TP53 and RB1, chromosomal instability, dysfunction of DNA damage response and repair, aberrant DNA methylation, histone modifications, and non-coding RNAs. Emerging treatment strategies include inhibitors of PI3K/AKT/mTOR and CDK4/6 signaling, epigenetic drugs, immunotherapy, and combinations of these approaches. However, the coexistence of multiple genetic abnormalities complicates diagnosis, prognosis, and therapeutic selection. Companion diagnostic tools that test candidate therapies ex vivo or in vitro may help exclude potentially ineffective targeted treatments. Full article

Evaluations of financial early-warning models can appear stronger when temporal separation is incomplete or when the event definition uses information unavailable at the forecast origin. This study examines one-day-ahead risk ranking of large absolute Bitcoin returns using 334 daily observations from 31 August 2024 to 30 July 2025, five expanding-window folds, lagged predictors, training-fold preprocessing, and train-only classification-threshold selection. The original q85 and q90 cutoffs, estimated from the full study-period return distribution, are retained as retrospective fixed-label benchmarks and are complemented by fold-specific train-only cutoffs. Under fixed q85 labels, class-weighted Logistic Regression with technical and market-activity variables achieved pooled ROC-AUC = 0.723 (moving-block 95% CI [0.556, 0.822]) and Average Precision = 0.338 ([0.103, 0.565]); Random Forest yielded 0.739 and 0.352. Pooled MCC was 0.207, but the primary model issued no positive warnings in three of the five fixed-q85 test folds. Under train-only q85 labels, ROC-AUC was 0.674 and Average Precision was 0.174, but the retained MCC threshold-selection rule produced no true positives. Conventional GARCH(1,1), EWMA, and 14-day historical-volatility scores did not exceed the primary model on both ranking metrics. Adding the complete six-variable sentiment and attention block lowered point estimates, although one-variable ablations and a matched-dimension control do not isolate sentiment content from dimensionality and small-sample overfitting. The results provide limited, sample-specific evidence for risk ranking, not evidence of deployment readiness, economic profitability, or cross-asset generalizability. Full article

This study presents a starch-modified calcined-ZnAl layered double-hydroxide (S-C-ZnAl-LDH) nanocomposite as a multifunctional nanofiller for poly(vinylidene fluoride) (PVDF) efficiently performing, simultaneously, ultrafiltration membrane filtration and efficient adsorbent for the recovery of Acid Blue dye-contaminated water. The synergistic effects of starch modification and thermal activation on nanofiller structure, interfacial compatibility, and membrane performance were systematically investigated through a comparison with pristine ZnAl-LDH, calcined ZnAl-LDH, starch-modified ZnAl-LDH, and calcined starch-modified ZnAl-LDH. SEM, TEM, and XRD analyses confirmed the formation of hierarchical layered nanosheet architectures with a uniform dispersion of crystalline ZnAl domains within a partially amorphous starch matrix, promoting enhanced polymer–nanofiller interfacial interactions Adsorption performance was influenced by solution pH, initial dye concentration, and temperature. Nonlinear kinetic analysis showed that the PFO model described the kinetic data better. However, the overall kinetic modeling findings suggest that Acid Blue 92 adsorption is governed by a combination of physicochemical interactions, suggesting a complex adsorption mechanism was involved. The starch-modified nanocomposite exhibited excellent regeneration stability, retaining approximately 88–90% of its adsorption capacity after five adsorption–desorption cycles. More importantly, the incorporation of S-C-ZnAl-LDH into PVDF membranes significantly enhanced membrane functionality, increasing water flux and permeance by 42.9% and 25%, respectively, while improving Acid Blue rejection by 35.7% to approximately 98%. These improvements are attributed to enhanced membrane hydrophilicity, optimized nanofiller dispersion, and favorable polymer–filler interfacial interactions that facilitate water transport while maintaining high separation efficiency. This work demonstrates an effective strategy for integrating renewable bio-based modifiers with layered nanomaterials to engineer advanced polymeric films exhibiting enhanced permeability, selectivity, durability, and reusability, providing a sustainable platform for multifunctional membrane technologies in water purification and environmental protection. Full article

Generative artificial intelligence has driven the cost of producing convincing artifacts of expertise toward zero. Signaling theory predicts that signals whose informational content rests on production cost lose that content when production becomes cheap. We formalize this for markets for expert services, a class of credence goods, modeling AI as a compression of the discernible headroom between what machines produce at negligible cost and what buyers can distinguish. Below a critical headroom no separating equilibrium in artifact-based production-side signals exists—for any single-crossing cost family, with the buyers’ discernment ceiling held fixed: the market pools, the competence premium vanishes, and able providers exit. We show that an outcome-contingent signal—a warranty backed by damages D with ex-post verifiability $\phi$—sustains a fully separating equilibrium at any level of AI capability whenever $\phi D\ge v$ (strictly for $\phi D>v$), where v is the value of a solved problem, under four institutional preconditions stated explicitly and priced in turn. The expected cost of liability turns on whether the problem is solved, not on how cheaply documents are produced, and is invariant to AI capability. Provenance certification priced as a type-independent stamp cannot restore full separation; a verified commitment to produce without generative assistance can, at the pre-AI signaling cost. Two further results endogenize the contract’s institutions: liability signaling has a minimum ticket size set by the fixed costs of a civil procedure and, under insurance, the signal-effective quantity is the retained, collectible exposure. Full article

Self-administered outpatient parenteral antimicrobial therapy (S-OPAT) is an increasingly recognised antimicrobial delivery model, yet comparative evidence on its safety and effectiveness remains limited. We compared clinical outcomes across OPAT delivery models and examined predictors of adverse outcomes in S-OPAT. Adult OPAT episodes at a large UK teaching hospital (January 2023–January 2026) were categorised as S-OPAT, healthcare professional home-administered (H-OPAT), or clinic-based (C-OPAT). Primary outcomes were treatment failure, complications, and 30-day unplanned hospitalisation. Outcome risks were compared using robust multivariable Poisson regression with generalised estimating equations and Bonferroni correction. Among 1064 OPAT episodes, 286 (26.9%) were S-OPAT, and cure/clinical improvement occurred in 912/1064 (85.7%). Treatment failure occurred in 26/286 (9.1%) S-OPAT episodes, with no significantly higher risk than H-OPAT (adjusted relative risk [aRR], 0.72; 98.3% CI, 0.43–1.19) or C-OPAT (aRR, 0.94; CI 0.48–1.84). Rates of unplanned hospitalisation and OPAT-related complications were broadly comparable, except potentially vascular access-related events. Within S-OPAT, older age, higher comorbidity burden, diabetes, peripheral vascular disease, and central venous access were associated with treatment failure. Overall, these findings suggest that S-OPAT can be delivered safely and effectively to appropriately selected patients, while enhanced monitoring may be warranted for older individuals and those with greater comorbidity burden or central venous access devices. Full article

This study presents an autoregressive (AR) modelling framework for characterising the waveform of recurring impulsive transients measured in single-wire earth return (SWER) distribution networks. The framework uses separate second-order high-frequency (HF) and low-frequency (LF) polynomials to reproduce the two dominant spectral components of the measured impulse at approximately 45 kHz and 18 kHz, respectively. The coefficients are derived from field measurements and are therefore specific to the network and impulse type used for identification. Model order is selected using an explicit pole-based criterion that balances the target resonance, exclusion of out-of-band modes and model complexity. All reported AR models are stable, with a maximum pole radius of 0.9945. Under the stated evaluation protocol, the dual-component response achieves a mean squared spectral error of 134 dB² over DC to 2.5 MHz, equivalent to a root mean square (RMS) spectral error of 11.6 dB. The corresponding errors are 387 dB² for an amplitude-modulated white noise model and 835 dB² and 880 dB² for two frequency-shaped rectangular pulse models. The framework reproduces the amplitude, width and dominant spectral components of the measured impulse, and its application to a second SWER network is demonstrated. The present model describes the waveform of an individual recurring impulse; modelling its arrival process, validating on held-out impulses and assessing communication-level performance are identified as future work. Full article

Spinal cord injury (SCI) leads to severe disability in patients, and one of the key obstacles to the recovery of lost functions is inefficient axonal remyelination. The high sensitivity of oligodendrocytes to secondary damage, the formation of a glial scar, and chronic neuroinflammation sustained by pro-inflammatory/neurotoxic microglia create a physical and chemical barrier to neuroplasticity processes. This review summarizes current knowledge on the ability of the serotonergic system to modulate the functional state of the main types of glial cells: oligodendrocytes, astrocytes, and microglia. It is shown that oligodendrocyte precursor cells (OPCs) and mature oligodendrocytes express 5-HT_1A, 5-HT_2A, and 5-HT₇ receptors, whose activation can either stimulate differentiation or suppress migration or induce apoptosis, depending on the developmental stage and microenvironment. In astrocytes, serotonin, predominantly via 5-HT_2B receptors, limits pro-inflammatory transformation, reduces NLRP3 inflammasome activity, and stimulates BDNF production, glycogenolysis, and lactate release. In microglia, serotonergic stimulation (via 5-HT_1A, 5-HT_2A, 5-HT_2B, and 5-HT₇ receptors) attenuates the pro-inflammatory phenotype by suppressing production of TNF-α and IL-1β. To date, the proposed therapeutic strategies include: the use of 5-HT receptor agonists/antagonists and transplantation of serotonergic cells (hNT2.19 and RN46A-B14 lines), which reduce neuropathic pain and promote motor function recovery in rodents after SCI. The key limitations for the effective translation of these findings into clinical practice are the heterogeneity of effects of different serotonin receptor subtypes, the dependence of outcomes on the time after injury, and pronounced species specificity. Thus, the serotonergic system represents a promising but complex target for inducing neuroplasticity processes; success requires the development of subtype-selective ligands and treatment protocols that take into account the stages of pathological processes in SCI. Full article

Hospital pathology laboratories represent challenging healthcare environments for Indoor Air Quality (IAQ) management due to the coexistence of hazardous chemical emissions, transient pollutant peaks, and stringent ventilation requirements. This study proposes an integrated data-driven framework for IAQ assessment and predictive ventilation management based on a high-resolution monitoring campaign conducted over 17 calendar days in a pathology grossing room (V = 113.90 m³) and an adjacent chemical storage room (V = 56.03 m³). The monitoring system generated 4356 synchronized 1-min observations, of which 4232 complete multivariate records were retained after data-quality screening. The proposed methodology combines three complementary analytical layers: (i) predictive modelling of pollution episodes using supervised machine learning architectures; (ii) multivariate anomaly detection to identify atypical environmental states; and (iii) temporal dependency analysis based on Granger causality and Bayesian networks to investigate predictive relationships between occupancy-related indicators, ventilation behaviour, and pollutant evolution. This integrated framework enables the transition from descriptive IAQ assessment toward predictive environmental management in healthcare facilities. Baseline statistical diagnostics demonstrated the limited capability of conventional linear approaches, with an Ordinary Least Squares (OLS) model explaining only 7.4% of TVOC variability (R² = 0.074). Ventilation assessment identified an approximately 38% deficit relative to the selected ASHRAE 170 ventilation requirement in the monitored grossing room. Among the evaluated predictive models, Random Forest achieved the highest test-set performance (R² = 0.78; MAE = 10.5 ppb), enabling short-term forecasting of TVOC evolution. Isolation Forest identified 212 atypical environmental states, corresponding to 5.01% of the valid analytical observations, with substantially higher TVOC concentrations than under normal operating conditions. The proposed framework establishes a transferable methodology for predictive IAQ assessment and ventilation management in chemically intensive healthcare facilities, providing decision-support information for risk-informed HVAC operation within existing regulatory and ventilation requirements. Full article

Selective Laser Melting (SLM) of Ti6Al4V produces implants with surface-adhered, weakly bonded powder particles that require post processing prior to biomedical use. Chemical etching with hydrofluoric acid (HF) is effective but often causes excessive material loss. In this study, SLM-fabricated Ti6Al4V samples were chemically etched using 1–3% HF solutions with and without the addition of propylene glycol. The influence of etching conditions on mass loss, surface morphology, roughness, wettability, chemical composition, internal porosity (using X-ray micro- computed tomography) and biological response was evaluated. The results indicted that increasing HF concentration increased material loss and powder removal efficiency. The addition of propylene glycol substantially reduced mass loss (up to 50%) while preserving effective powder removal and surface quality. Etched samples exhibited reduced roughness parameters and no cytotoxic effects toward Saos-2 cells. Moreover, selected etching conditions shifted the porous architecture toward pore-size distributions considered potentially more favorable for bone tissue ingrowth. These findings indicate that HF etching with propylene glycol is a promising post processing strategy for SLM Ti6Al4V implants, offering improved surface control with reduced material degradation. Full article

The generation of biomass residues and biowaste is both a challenge to the environment and to resource management worldwide but is also a significant renewable resource for energy and material recovery. Biomass residues are utilised along multiple pathways such as direct use for energy production, anaerobic digestion and biogas production, biofuel generation, composting, soil amendment, and conversion into value-added products at a global scale. On the other hand, many lignocellulosic and other biomass residues are characterized by low bulk density, heterogeneous characteristics, and seasonal availability, thus complicating collection, handling, storage, and transportation. Densification into solid fuel pellets is a viable solution for transforming these low-density residues into a compact, handleable, and transportable form with increased energy density. Although biomass residues have global potential, past pelletisation studies in Punjab, Pakistan, have generally considered only a few of two to three seasonal feedstocks that do not cover the uncertainty of potential feedstock availability in several seasons of the year, alongside the judgment of suitable biomass input. This study was, therefore, designed to explore nine agro-residues with different seasons in Punjab, e.g., tree residues, grass clippings, animal waste, crop residues, and herb biomass, for robust conversion into solid fuel pellets. Gate-to-gate Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) methods were used to evaluate the production system of the pellets from biomass residues. A functional unit of 1 tonne of biomass solid fuel pellets was defined, and the resulting environmental impacts were modelled using GaBi software and the ReCiPe 2016 methodology. Process performance and production hotspots were assessed by attributing the environmental impacts that occurred at individual pellet-production stages to the overall impacts, while LCC was used to evaluate economic costs, both internal and external, from pellet production. The results of hotspot analysis indicated that flash/pneumatic drying represented the most important contributor for all investigated impact categories, followed by the pellet-milling and packaging stages. The total estimated cost of the pellets was USD 72.6 t⁻¹, with an internal cost of USD 51.7 t⁻¹ and an environmental cost of USD 20.9 t⁻¹. Two other energy scenarios provided evidence through which to understand the potential for reductions in GHG emissions linked to the production of pellets. These findings suggest that differing and seasonally accessible biomass residues in Punjab can be converted into solid biofuels to concurrently aid sustainable waste management, energy security, climate-change amelioration, and rural economic growth. This study is novel because it compares nine heterogeneous biomass residues in a seasonal common-pellet production framework, using an integrated environmental and economic approach to provide stakeholders with practical evidence they can use to select the biomass feedstock that is most environmentally and economically preferable. These results help achieve the Sustainable Development Goals 7, 11, 12, and 13. Full article

Reliable estimation of the air change rate from CO₂ decay measurements is essential for ventilation assessment, but results may strongly depend on the assumed background concentration and the selected analysis window. This study compares the ISO 12569 two-point and multi-point methods with a study-specific nonlinear least-squares (NLSQ) exponential-fitting approach developed for systematic window-sweep analysis. A synthetic benchmark with known decay parameters was used to quantify the sensitivity of the estimated air change rate N to background-concentration error and time-window selection. The framework was then applied to CO₂ decay measurements from four rooms and to an OpenFOAM-generated CFD decay case using dense N(t_beg,t_end) stability maps and automatic plateau selection. The benchmark showed that admissible windows shrink as background uncertainty increases, while late, tail-dominated intervals are particularly prone to bias. The two ISO methods produced similar results, whereas the NLSQ approach generally preserved stable solutions over longer windows when fitting started sufficiently early. For three measured rooms, stable regions yielded effective N values of approximately 0.14, 0.75, and 1.24 1/h, while one room remained non-stationary. To assess energy implications, N was propagated into the ventilation heat-loss coefficient H_vent = 0.34 NV. Illustrative late-tail scenarios showed that background-sensitive window selection may translate into substantial over- or underestimation of ventilation-related heat-loss indicators. Full article

Multimodal studies of prostate cancer typically report aggregate fusion gains from histology and transcriptomics but rarely characterize when each modality is informative. We asked whether morphology and transcriptomics contribute differently to distinct clinical endpoints, using Gleason grading and nodal status prediction. On 401 TCGA-PRAD patients with matched whole-slide images, bulk RNA sequencing, and clinical data, we evaluated 45 morphological configurations, seven RNA configurations, and six fusion strategies across four grading formulations, T-stage, and N-stage. To avoid per-task model selection, morphology used a single fixed configuration (gated ABMIL on UNI features). Contribution estimates used repeated stratified five-fold cross-validation, with a locked conformal-prediction protocol and gene set enrichment analysis. The two endpoints showed opposite modality dependence. For Gleason grade, the fixed morphological model exceeded the best RNA configuration across all 45 configurations (five-class macro-F1: 0.445 versus 0.398). For nodal status, no morphological configuration exceeded AUROC 0.62, whereas transcriptomics reached 0.68 (permutation p = 0.037); a direct interaction test confirmed the reversal (bootstrap 95% CI excluding zero). Fusion gains were modest and task-dependent; enrichment analysis linked the nodal signal to loss of smooth muscle programs, consistent with established dedifferentiation biology. The informative modality is task-dependent: morphology predicts grade, transcriptomics predicts nodal status. Full article

Developing sustainable paradigms for nanomaterial synthesis is essential to reduce the environmental impact of conventional chemical routes. Herein, we report a green, phytochemically mediated synthesis of MnFe₂O₄NPs using aqueous grapefruit (Citrus paradisi) peel extracts. The bioflavonoid and polyphenolic constituents functioned as dual-functional biogenic reducing and stabilizing agents. FTIR analysis verified spinel lattice metal–oxygen bonds and surface-bound biogenic groups, while SEM imaging revealed the surface morphology and structural aggregation of the nanoparticles. Once integrated into an electroanalytical platform (MnFe₂O₄NPs/GCE), the modified interface exhibited robust electrocatalytic activity toward sulfite oxidation in an acetate buffer (pH = 4.0). Mixed-valence dynamics (Mn²⁺/Mn³⁺ and Fe²⁺/Fe³⁺) significantly amplified anodic currents and lowered the kinetic overpotential. Under optimized chronoamperometric conditions, the sensor demonstrated a wide linear range (1–100 μM, r = 0.999), a low detection limit (LOD = 0.045 μM), a limit of quantification (LOQ = 0.136 μM) and excellent selectivity against co-existing ionic and organic interferents. This study provides a simple, eco-friendly, and technically robust strategy for fabricating high-performance electrochemical platforms for food and environmental monitoring. Full article

Liposomal drug-delivery systems can improve the formulation performance of poorly soluble compounds by enhancing aqueous dispersion, protecting encapsulated agents, and modifying release behavior. Co-encapsulation of pharmacologically distinct compounds may provide a formulation strategy for comparing combined delivery with a single agent nanoliposomal system. This study aimed to develop and characterize atorvastatin–rutin co-loaded nanoliposomes and to compare their antioxidant, anti-inflammatory, and SRB-based cytotoxic activity with the corresponding free-drug and single-loaded nanoliposomal formulations. Nanoliposomes were prepared by thin-film hydration and characterized by particle size, polydispersity index, zeta potential, encapsulation efficiency, lyophilization-associated retention of encapsulation efficiency, morphology, and in vitro release. A reverse-phase HPLC method was validated for simultaneous atorvastatin and rutin quantification, and lyophilized formulations were evaluated for retention of encapsulation efficiency. In vitro assays included DPPH radical scavenging, nitrite inhibition in LPS-stimulated RAW 264.7 macrophages, and SRB-based cytotoxicity screening across human cancer cell lines and normal periodontal ligament fibroblasts. The co-loaded nanoliposomes achieved encapsulation efficiencies of 88.46% for atorvastatin and 81.74% for rutin; after lyophilization, encapsulation efficiency decreased to 72.31% for atorvastatin and 76.63% for rutin. The nanoliposomal formulations showed measurable DPPH radical-scavenging activity, nitrite-inhibition activity in LPS-stimulated macrophages, and SRB-based antiproliferative activity in several cancer cell lines, while showing no detectable cytotoxicity toward PDL fibroblasts within the tested concentration range. With exquisite similarity to apoptogenic Anti-VEGF antiangiogenesis chemotherapeutic efficacies ofcisplatin; nanoliposomal atorvastatin and co-loaded atorvastatin with rutin were remarkable comparable (in descending order of human VEGF mitigations) in mammary T47D> uterine cervix HeLa> lung A549 adherent monolayers post 72 h incubations. These findings support further investigation of atorvastatin–rutin co-loaded nanoliposomes as an in vitro formulation platform; however, formal synergy analysis, cellular uptake studies, mechanistic assays, pharmacokinetic evaluation, and in vivo safety testing remain necessary. Further in vivo studies are required to determine pharmacokinetic behavior, tissue distribution, therapeutic relevance, and systemic safety. Full article

A field-oriented, real-time implementation of the previously developed vision-based bridge weigh-in-motion (V-BWIM) framework is presented for vehicle-load monitoring under variable-speed and mixed-traffic conditions. Vehicle and wheel positions are obtained through YOLOv5-based object detection, binocular measurement, and coordinate transformation, whereas bridge responses are measured by strain gauges and synchronized with the vision-derived axle trajectories. A field-image dataset constructed from full-scale bridge experiments is used to compare object-detection performance, inference efficiency, and model complexity, and YOLOv5s is selected for field implementation. The calibrated bridge influence line is then combined with synchronized axle positions and structural responses for axle-load identification. Controlled field tests on a simply supported bridge quantitatively evaluate vehicle positioning and load identification under constant-speed, variable-speed, and two-vehicle car-following conditions. A further continuous-beam bridge experiment demonstrates vehicle-information estimation under random traffic. Because independent reference weights were unavailable for the randomly passing vehicles, the continuous-bridge results are interpreted as a field demonstration rather than an independent validation of load-identification accuracy. Full article