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The construction of stereochemically enriched molecules from readily accessible meso and prochiral precursors via catalytic enantioselective desymmetrization represents a powerful synthetic strategy. Herein, we report an efficient enantioselective Michael/oxa-Michael cascade reaction between C4-functionalized meso-2,5-cyclohexadienones and β-ketoesters, catalyzed by a Takemoto chiral squaramide. This protocol enables the efficient construction of densely functionalized oxabicyclo[3.3.1]nonane frameworks bearing two or three stereogenic centers, with good to excellent diastereo- and enantioselectivities. Furthermore, the method is amenable to scale-up and offers opportunities for late-stage functionalization.
Non-steroidal anti-inflammatory drugs (NSAIDs) are associated with cardiovascular adverse events that are not fully explained by cyclooxygenase (COX) inhibition. Here, we investigated the contribution of membrane-mediated mechanisms to NSAID-induced toxicity using integrated in vivo, developmental, and biophysical approaches. Ketoprofen, indomethacin, and celecoxib were evaluated in rats (serum biomarkers and histopathology) and zebrafish embryos (cardiac function), alongside mechanistic studies in cardiac-mimicking lipid membranes using differential scanning calorimetry, NMR spectroscopy, and molecular dynamics simulations. Ketoprofen significantly increased serum creatine kinase and lactate dehydrogenase levels and induced moderate myocardial damage. Indomethacin produced minimal changes in circulating biomarkers but caused mild-to-moderate histological alterations. Celecoxib showed limited toxicity in adult tissues but significantly reduced zebrafish heart rate, indicating pronounced developmental cardiotoxicity. In membrane systems, ketoprofen increased transition temperature and enthalpy, consistent with bilayer condensation and enhanced lipid order. Celecoxib decreased transition enthalpy and increased vesicle heterogeneity, indicating membrane destabilization. Indomethacin reduced transition cooperativity with modest effects on bilayer structure. NMR and simulation data supported differential drug-insertion and lipid-interaction profiles. These findings demonstrate that NSAIDs exhibit compound-specific toxicity profiles that depend on biological context and are linked to distinct drug-membrane interactions. Membrane perturbation represents a COX-independent mechanism contributing to NSAID-associated cardiotoxicity and provides a framework for integrating molecular and organism-level toxicological responses.
Modern day healthcare has seen an increase in polypharmacy, which is the prescription of multiple drugs as medication to treat illnesses simultaneously. Therefore there is an increased risk of adverse drug reactions resulting from drug-drug interactions. Existing techniques in the field of pharmacovigilance suffer from many drawbacks. Many machine learning approaches using single models face difficulties in identifying complex interaction patterns. Some methods also overlook the fact that a single adverse event may contain a large number of drugs. In terms of interpretability in a clinical context, mere predicting a combination to be risky or not may not provide a clear enough picture. In an effort to address these challenges, in this study, we propose an ensemble learning approach to effectively predict adverse drug combinations using data obtained from the FDA Adverse Event Reporting System (FAERS). Our proposed framework also makes use of DrugBank for mapping drugs and incorporates binary feature vector representations to handle the complexities of the pharmacovigilance data. The ensemble model developed in this study composed of logistic regression, random forest, and CatBoost algorithms proved to be effective compared to several existing techniques in detecting drug interactions with an accuracy of 93.6%, recall of 97.9%, ROC-AUC of 97.5% and PR-AUC of 96%. In addition to achieving strong predictive performance, the model also calculates a confidence score representing the risk associated with specific drug combinations. These results show how ensemble learning can help to enhance the detection of adverse drug reactions and serve as a clinical decision support tool.
The demand for high-performance photodetectors has driven extensive research into materials with superior optoelectronic properties. This study presents the fabrication and ultraviolet (UV) photodetection capabilities of antimony (Sb)-doped cadmium sulfide (CdS) thin films. The thin films were deposited via the nebulizer spray pyrolysis (NSP) method onto glass substrates. X-ray diffraction (XRD) analysis confirms the presence of a hexagonal crystal structure in all films, with the 2 wt% Sb-doped CdS film exhibiting the highest crystallinity. Morphological examination using FESEM revealed that Sb-doping led to the formation of larger, densely packed grains for 2 wt% doped film, enhancing its charge transport properties. Optical studies showed enhanced light absorption with Sb incorporation, making the material more effective for UV detection. The photodetector performance was assessed through current vs. voltage (I-V) measurements. The photodetector performance was evaluated under 365 nm UV illumination with an intensity of 1 to 5 mW cm⁻² at an applied bias of ± 5 V. Among the prepared thin films, the 2 wt% Sb-doped CdS film demonstrated remarkable photoresponse, achieving a responsivity (R) of 1.84 A W-1, detectivity (D*) of 4.60 × 109 Jones and an external quantum efficiency (EQE) of 626%. The device exhibited rapid response and recovery times of 1.02 s and 0.56 s, respectively. Furthermore, the photodetector maintained stable performance over extended cycles, highlighting its reliability for long-term UV sensing applications. These results highlight the potential of Sb-doped CdS thin films as efficient and durable materials for advanced UV photodetectors.
Mechanical properties of liposomes, particularly bending rigidity (kc), membrane area compressibility modulus (ka), and effective Young's modulus (E), are increasingly recognized as potentially important factors influencing clinical translation and regulatory assessment. This review establishes liposome mechanics as an underexplored determinant in predictive performance, moving beyond conventional descriptors such as size and surface charge. We provide an in-depth and systematic analysis of how bilayer mechanics governs key biological processes, including circulation stability, deformability, trans-barrier transport, and cellular uptake. Special emphasis is placed on elastic liposomes, where reduction in kc (often to <10 kBT) and modulation of ka enable extreme deformability, facilitating pore-squeezing and enhanced tissue penetration. We integrate fundamental frameworks such as Helfrich elasticity with experimental and computational approaches (atomic force microscopy, micropipette aspiration, scattering, and molecular dynamics) to establish quantitative structure-mechanics-function relationships. The review further examines how lipid composition, sterols, and edge activators tune mechanical constants and how these parameters translate into drug-delivery performance. Emerging concepts such as mechanomorphic liposomes and mechanically triggered drug release are also critically discussed. Importantly, we discuss the potential for parameters such as kc and ka to be considered as future CQAs, pending further standardization and validation of measurement methodologies. This review, therefore, provides a roadmap for engineering mechanics-driven liposomal systems.
Intravascular thrombus poses a significant risk for ischemic events, yet precise characterization and selective mechanical intervention remain challenging. Here, we present a laser-generated focused ultrasound (LGFU) platform incorporating a long-focused photoacoustic lens (focal length: 28 mm), which achieved a 1.79-fold increase in penetration depth compared with previously reported LGFU systems while maintaining a tight focal spot (60 μm lateral, 150 μm axial) and broadband acoustic output (- 6 dB bandwidth: ~ 15 MHz). Unlike previously reported LGFU systems developed primarily for therapeutic applications, the present platform combines LGFU-based ultrasound generation with co-aligned hydrophone reception for spectral characterization of thrombi under vessel-mimicking phantom conditions. Empirical mode decomposition (EMD) applied to complex backscattered signals enabled discrimination of thrombus thicknesses down to ~ 100 μm, highlighting the utility of broadband photoacoustic signals for sub-millimeter spectral characterization. Finite-element simulations supported the thickness-dependent spectral shift underlying this discrimination. Then, we confirmed that thrombus fragmentation linewidths were dependent on an input laser energy, which was evaluated by using a photoacoustic lens with a 15 mm focal length. Finally, fragmentation of the vessel-mimicking phantom was demonstrated with a longer focal length (28 mm). The observed fragmentation behavior was consistent with cavitation-mediated mechanical effects under the tested conditions. These results support the feasibility of an LGFU platform for combined thrombus characterization and localized fragmentation under proof-of-concept phantom and ex vivo conditions.
Titanium alloy Grade 5 (Ti-6Al-4V) is widely used for biomedical applications due to its high strength and high corrosion resistance. In this work, the reverse electric discharge machining (R-EDM) for the fabrication of macro-pillared array structures on Ti-6Al-4V work piece is carried out using tungsten, copper and copper-tungsten electrodes. The effect of pulse-on time (Ton), flushing pressure (Fp), peak current (I), voltage (U), duty factor (τ) and electrode material on surface roughness, microhardness, recast layer thickness and surface crack density is investigated. A Box Behnken design of response surface methodology (RSM) is used to assess the significance of the parameters and their interaction effects. A meticulous scanning electron microscopy (SEM) analysis is carried out to evaluate the machined surface quality. Multi-response optimization is carried out using the additive ratio assessment (ARAS) method and is coupled with the Firefly Algorithm (FA) to achieve the optimum results. The optimum machining condition is validated by conducting a confirmative test showing an improvement of 6.86 percentage. The proposed work is useful for selecting ideal process conditions while machining Titanium (Ti-6Al-4V) work piece for biomedical application.
Mucormycosis is a rapidly progressive, angioinvasive fungal infection with particularly high mortality in thoracic/pulmonary involvement. This article reviews the epidemiology, pathogenesis, diagnostic challenges, and evolving surgical strategies for pulmonary and thoracic mucormycosis. Survival depends on early diagnosis (recognizing clinical/radiological clues and securing histopathological proof). The twin pillars of treatment are prompt, high-dose liposomal amphotericin B and aggressive surgical resection to debride necrotic tissue and achieve clear margins. Advances in intra-operative navigation, peri-operative care, and adjunctive antifungal options are discussed. Outcomes, prognostic factors, complications, and long-term sequelae are highlighted, underscoring that a proactive, combined-modality strategy offers the best chance for survival.
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Urban pipeline infrastructure plays a vital role in ensuring the operational efficiency and service reliability of modern utility systems, especially in industrial regions. While previous studies have focused primarily on pipe failure prediction, limited research has addressed the forecasting of key pipeline performance indicators such as velocity, pressure, and head loss within the context of infrastructure asset management. This study investigates the performance of advanced machine learning (ML) models, PSO-ANN, Genetic CNN, Quantum SVR, Fuzzy Logic Tree, and Bayesian GPR, in predicting three critical output variables: velocity, head loss, and pressure. A dataset comprising 91 instances with geometric and hydraulic descriptors was employed, and descriptive statistics revealed significant variability in flow-dependent parameters. SHAP-based sensitivity analysis highlighted elevation (0.9287) as the dominant factor for pressure prediction, while flow rate (0.4574) and diameter (0.2273) strongly influenced head loss. For velocity, flow rate (0.1139) emerged as the most influential, though other parameters also contributed, justifying their inclusion in the modeling framework. The models were trained using data from the Gadhra Water Distribution Network (District Metered Area-03) in East Singhbhum, Jamshedpur, India. Model evaluation was conducted using R², RMSE, MAE, and MAPE. Results demonstrated a clear performance hierarchy, with Bayesian GPR and Fuzzy Logic Tree exhibiting superior accuracy and stability (R² ≥ 0.98, RMSE ≤ 0.06, MAPE ≤ 0.13), whereas PSO-ANN and Genetic CNN showed relatively weaker performance. The near-perfect R² observed for Fuzzy Logic Tree reflects the small dataset size and its high capacity, highlighting that generalization may be limited in larger or unseen datasets. The analysis of regressor plots, residual distributions, and normalized accuracy matrices further validated these findings. Overall, the study establishes Bayesian GPR and Fuzzy Logic Tree as robust predictive tools for hydraulic modeling while acknowledging dataset constraints that may affect generalization.
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A persistent divergence exists in prosthodontics between research-supported biopsychosocial frameworks and traditional occlusal perspectives on temporomandibular disorder (TMD) etiology, shaping clinical approaches and satisfaction. This study aimed to analyze the latent cognitive structures of prosthodontists by examining relationships between clinical experience, etiological beliefs (occlusal and psychosocial), and satisfaction in managing TMDs. A cross-sectional survey of 124 prosthodontists was analyzed using latent variable structural equation modeling (SEM). Confirmatory factor analysis validated reflective constructs for occlusal and psychosocial etiology beliefs and use of technical modalities. Clinical experience was a formative composite. The structural model tested pathways from experience to beliefs and then to management satisfaction. A strong negative covariance confirmed a dichotomy between occlusal and psychosocial beliefs. Greater clinical experience predicted stronger occlusal beliefs (β=.226, P<.001) and weaker Psychosocial beliefs (β=-.257, P<.001). For early-stage TMDs, occlusal beliefs (β=.91, P=.028) and psychosocial beliefs (β=.59, P=.014) both predicted higher satisfaction. For late-stage TMDs, psychosocial beliefs strongly negatively impacted satisfaction (β=-1.00, P=.004). Occlusal and psychosocial beliefs function as competing paradigms. Clinician satisfaction was profoundly mediated by these beliefs, with an occlusal orientation linked to higher early-stage satisfaction. A psychosocial orientation predicts significantly lower satisfaction in managing chronic TMDs. Bridging this belief gap through integrated education is crucial for effective TMD management and clinician well-being.
Event-based cameras provide high-temporal-resolution motion information and wide dynamic range, making them suitable for depth estimation in challenging dynamic environments. However, existing depth learning approaches often rely on ground-truth supervision, handcrafted event representations, or computationally expensive fusion strategies, limiting scalability and real-time deployment. Recent self-supervised methods partially address annotation dependency but struggle with cross-frame-rate alignment, temporal instability, and inefficient fusion of event and frame modalities. To overcome these limitations, this study proposes a novel Self-Supervised Event-Frame Self-Attention Transformer (EF-SAT) framework for depth estimation without ground-truth depth supervision. The proposed method employs a Vision Transformer-based encoder to jointly model event streams and intensity frames, while a cross-frame-rate self-attention mechanism captures spatiotemporal dependencies across heterogeneous data rates. The framework further carries revolutionary self-sampling based primarily on bilinear interpolation, timescale-consistent intensity decoding, and ego-motion optimization that uses absolute trajectory error (ATE) and relative pose error (RPE) constraints to improve geometric balances in successive frames. Depth and pose are learned using photometric reconstruction, event-frame alignment, temporal consistency, and smoothness constraints in a fully self-supervised manner. Experiments conducted on the DAVIS-240 C event-frame dataset demonstrate that the proposed approach achieves superior performance, attaining an Absolute Relative Error of 0.114 and RMSE of 0.163, outperforming state-of-the-art deep learning baselines. Overall, the proposed EF-SAT model demonstrates robust, temporally stable, and accurate depth estimation in dynamic scenes, highlighting its potential for real-world applications such as robotics, autonomous navigation, and event-driven perception systems.
Automated electrocardiogram (ECG) arrhythmia classification remains challenging due to morphological complexity, severe class imbalance, and poor model generalization across heterogeneous datasets acquired under varying clinical conditions. To address these challenges, this paper proposes CaReS-BiNet, a Convolutional and Residual Squeeze-and-Excitation Bidirectional-LSTM Network that integrates parallel multi-scale one-dimensional convolutional branches, residual connections, lightweight Squeeze-and-Excitation channel attention, and bidirectional LSTM temporal modelling into a unified end-to-end framework. This design enables joint learning of local morphological features and long-range temporal dependencies directly from ECG heartbeat segments, with Gaussian noise injection employed to improve robustness to class imbalance and signal variability. Evaluated on the MIT-BIH Arrhythmia Database under the AAMI EC57 five-class protocol, CaReS-BiNet achieves an accuracy of 98.74%, precision of 98.70%, recall of 98.73%, and F1-score of 98.71%, outperforming the majority of compared state-of-the-art methods on recall and F1-score. Independent evaluation on the PTB Diagnostic ECG Database yields 99.31% accuracy, 99.27% precision, 99.16% F1-score, and an AUC of 0.999, demonstrating superior performance across all reported metrics against a recently proposed hybrid architecture evaluated on the same dataset. This robustness is further supported by consistent performance on an additional heterogeneous ECG dataset, achieving 98.08% accuracy. Binary ventricular ectopic beat (VEB) and supraventricular ectopic beat (SVEB) detection further confirms clinical reliability, with SVEB precision of 97.99% substantially exceeding existing methods. A systematic ablation study validates the individual contribution of each architectural component across both datasets. These results establish CaReS-BiNet as an effective framework for automated arrhythmia classification across diverse ECG datasets.
The standard melt quenching technique was used to prepare samples with nominal composition (50-x)B2O3-15TeO2-20Li2O-15SrO-xCr2O3, where 0 < x < 0.5. XRD validates the non-crystalline nature of BTLSC glass. Density and molar volume exhibited an inverse trend, and other physical parameters were calculated. SEM and EDS determined the morphological and elemental composition of the BTLSC samples. The FTIR and Raman spectra explain reduced pentaborate units and caused the production of ortho and pyroborate units, including tetrahedral [CrO4] and octahedral [CrO6] units in the glasses with Cr2O3 incorporation. The UV-Vis absorption band showed redshift from 351 nm to 461 nm, with three major bands at 626 nm, 663 nm, and 701 nm of Cr3+ (octahedral sites) in the BTLSC glasses. The direct bandgap and indirect bandgap were reduced from 3.44 eV to 2.58 eV and from 3.01 eV to 2.08 eV, respectively. The observed Urbach energy increases from 0.216 eV to 0.266 eV, confirming the distortion in the BTLSC glasses. Furthermore, optical properties were calculated from refractive index and bandgap values. The photoluminescence spectra showed a small narrow band emission at ~ 690 nm and a broad band ~ 750 nm with two excitation wavelengths, ~ 420 nm and ~ 580 nm. The CIE coordinates and CCT values indicate that BTLSC-3 glass is ideal for deep red-light solid-state lighting and NIR luminescence material applications.
Large deformation in soft rock tunnels remains a critical challenge in mining and underground engineering, with conventional rigid supports prone to overstressing and failure due to limited deformation compatibility. Although yielding supports with resistance limiters such as inflatable airbags and circular steel bellows (CSBs) provide improved adaptability, current design approaches remain largely empirical or overly simplified. As a result, they lack a unified constitutive framework that can quantitatively describe their multi-stage mechanical behavior and effectively link it to performance-based design. To address this gap, this study develops and experimentally validates a novel constitutive model that characterizes yielding supports through three explicit phases: elastic yielding (initial stiffness ko = 0.18-0.25 kN/mm), post-yield hardening (hardening stiffness kh = 0.42-0.68 kN/mm), and locking (ultimate resistance FL = 2.0-2.4 kN). To enable quantitative performance assessment, eight dimensionless indices are introduced, including the yielding ductility ratio (μ), deformation capacity envelope (Ω), plasticity index (ψ), strain localization index (LƐ), strain uniformity index (UƐ), support adaptability index (SAI), and energy-reaction coupling coefficient (η). Large-scale model tests demonstrate that airbag limiters reduce vertical deformation by approximately 35% relative to rigid supports and exhibit superior early-stage adaptability (Ω ≈ 0.46), whereas CSBs provide higher energy dissipation (Ed = 50-55 J), greater strain uniformity (Uε ≈ 0.79), and enhanced stability through post-yield hardening. The proposed framework further integrates these indices into practical design tools, including stability maps based on stiffness ratios (α = ko/kr, β = kh/kr) and the Yield-Hardening Design Surface (YHDS), enabling performance-based selection and optimization of yielding supports under squeezing conditions. This work provides a validated, mechanics-based, and metrics-driven approach to mining and tunnel support design, effectively bridging the gap between constitutive modeling and practical engineering application.
The activation of stimulator of interferon genes (STING) for the treatment of cancer is a long-standing therapeutic goal. However, the clinical use of orthosteric STING agonists has shown limited utility in patients with advanced/metastatic cancer. C92 is a potent representative of a first-in-class chemical series of allosteric small-molecule human STING agonists that are distinguished by their binding site which lies within STING's proton channel and their consequently unique functional properties. This study characterizes the pharmacology and the anticancer efficacy of C92. Allosteric binding was established using radioligand binding assays. Type I interferon (IFN)-mediated immune signaling due to the activation of STING by C92 was studied in human and murine primary cells. IFN independent actions of C92 were assessed by monitoring inflammasome and autophagy markers in the human monocytic THP-1 cell line. Anticancer activity of CRD3874-SI by the intravenous route as a single agent or in combination with checkpoint inhibitors (CPIs) was evaluated in human STING knock-in C57BL/6 mice. The comparative effects of activating STING in either tumor fibroblasts or non-tumor host tissue were studied in human STING-expressing murine cells that were implanted in wt C57BL/6 and BALB/c mice. C92 is an allosteric agonist that activates STING in the absence of cyclic guanosine monophosphate-adenosine monophosphate (cGAMP). In addition, C92 can also potentiate the binding of cGAMP to STING. The allosteric binding site lies within a hydrophobic transmembrane proton channel formed by intertwined helices of two STING monomers. C92 generates strong Type I IFN responses, but without IFN-independent actions such as autophagy and inflammasome formation. Intravenous, oral or intratumoral administration of C92 as a single agent led to robust antitumor immune responses and synergized with CPI treatment providing a path for the clinical translation of C92. The unique species selectivity profile of C92 enabled a demonstration that anticancer effects can be mediated by STING either in tumor cells or in non-tumor host cells. The allosteric small-molecule human STING agonist C92 that blocks STING's proton channel is significantly differentiated from orthosteric STING agonists by its unique pharmacology making it a promising immune therapeutic for the treatment of cancer.
Pearl millet is an important crop in arid regions, but its yield is reduced by foliar diseases like Downy Mildew and Rust. Traditional and deep learning methods struggle with accurate lesion detection, severity estimation, and robustness under complex field conditions, and often lack interpretability for practical agricultural deployment. To address these challenges, this study proposes the Adaptive Severity-Aware Swin Attention Network (ASA-SAN), an integrated framework designed for disease segmentation, classification, and severity estimation in pearl millet leaves. The proposed architecture combines a Swin Transformer encoder for hierarchical feature extraction with a ResUNet++ decoder for accurate lesion segmentation. This is further enhanced using Adaptive Channel Attention to improve feature discrimination and a dual-stream classification network to jointly capture local lesion characteristics and global contextual information. Additionally, an Adaptive Disease Severity Index (ADSI) is introduced to quantitatively assess disease progression based on lesion area ratio, color degradation, edge irregularity, and texture variations. Experimental evaluations conducted on a pearl millet leaf dataset demonstrate that the proposed method achieves a Dice score of 97.8%, IoU of 95.6%, classification accuracy of 98.3%, and F1-score of 98.2%, outperforming several state-of-the-art methods. Furthermore, Grad-CAM visualizations enhance model interpretability by highlighting disease-relevant regions. Overall, the ASA-SAN framework provides a robust, interpretable, and severity-aware solution for automated pearl millet disease analysis, enabling early detection and supporting precision agriculture practices for improved crop protection and yield optimization.
Gamma-Poly Glutamic Acid (γ-PGA) is an eco-friendly biopolymer that is biodegradable and water soluble and has been used in different industries such as foods, pharmaceuticals, agriculture, and environment. Unlike other artificial polymers, γ-PGA is a natural polymer that can be obtained through microbial fermentation. Various strains have been employed to obtain the desired polymer but Bacillus subtilis has been the most common one studied so far. In the current study, the possibility of using soybean extracts to produce γ-PGA in both solid state fermentation and submerged state fermentation was examined. Of all the strains, white and black soybeans yielded the highest amount in SSF and SBF respectively at 347.67 mg (1.390%) and 935 mg (3.116%). To eliminate concerns regarding external glutamate addition, only a trace amount of monosodium glutamate (MSG) was used in the seed culture, and no glutamate was added during fermentation, indicating that γ-PGA was produced predominantly through production without external glutamate supplementation during fermentation. The initial screening showed that black soybean waste could be used as an inexpensive substrate for optimization via SBF. A Central Composite Design (CCD) design with four factors and five levels, with response surface methodology consisting of 30 different experiments, was conducted to determine the influence of the substrate concentration, pH, temperature, and inoculum amount. The optimized parameters for the maximum γ-PGA production, namely 6% substrate, pH of 6.0, 35 °C temperature, and 3% inoculum, resulted in γ-PGA production equaling 7.5 g/L, which is close to the predicted value of 7.48 g/L. The study highlights that substrate selection and statistical optimization are equally critical for enhancing γ-PGA production, positioning black soybean waste as a promising low-cost substrate with potential for future scale-up and sustainable γ-PGA bioprocess development following further pilot-scale and techno-economic validation.