Browse Articles

Edge-assisted post-quantum authentication protocol for IoMT: a privacy-preserving and lightweight approach

Marker‐Agnostic Tumor Anchoring Chimeras Enable pH‐Gated Immune Engagement

ABSTRACT Targeted cancer immunotherapies typically depend on cell surface antigens to direct immune responses. However, many tumors either lack tumor‐specific markers or downregulate their expression, limiting the applicability of antigen‐directed strategies. In contrast, tumor acidosis represents a nearly universal hallmark of solid cancers, arising from dysregulated metabolism that acidifies the extracellular tumor microenvironment (TME). Here, we leverage this feature to develop Marker‐Agnostic Tumor Anchoring Chimeras (MATRACs), pH‐sensitive fusion proteins that mediate ternary complex formation between cancer cells and immune effector cells in an antigen‐independent manner. MATRACs harness the diphtheria toxin translocation domain (tdDT), whose pH‐dependent conformational switch drives membrane insertion, allowing tdDT and its Fc‐fused form (tdDT‐Fc) to anchor selectively to cells in TME‐like acidity while remaining inactive at physiological pH. To demonstrate pH‐dependent immune activation, we designed a fluorescein‐tagged tdDT‐Fc variant that anchors to tumor cells in acidic conditions and presents a synthetic epitope enabling recruitment of antifluorescein CAR T cells. These findings establish tdDT‐based MATRACs as a modular, TME‐responsive platform for antigen‐agnostic immune engagement and support their potential for next‐generation pH‐targeted immunotherapies.

SLC25A43 in hepatocellular carcinoma: bioinformatics insights into progression and immune microenvironment

Correction: Study of the effects and mechanism of allogeneic platelet-rich plasma gel on layered skin flaps

AI-assisted versus manual sustainability assessment of a high-throughput LC–MS/MS method for psychotropic and OTC drugs of abuse in human plasma

Abstract A rapid, sensitive, and selective LC–MSMS method was developed and validated for the quantification of dextromethorphan (DXM), pseudoephedrine (PSE), olanzapine (OLA), and fluoxetine (FLU) in human plasma. Mixture 1 (DXM/PSE) and mixture 2 (OLA/FLU) are fixed-dose combinations commonly misused at high doses for their euphoric effects. The proposed method employed a simple protein precipitation technique for sample preparation, using a cost-effective cross-over internal standard strategy; OLA for mixture 1 and DXM for mixture 2. Chromatographic separation was achieved on a Hypersil GOLD column (100 × 3 mm, 1.9 µm) using an isocratic mobile phase consisting of acetonitrile and 0.1% formic acid (70:30, v/v) at a flow rate of 0.3 mL/min. The short runtime of 2.5 min enables high-throughput analysis. Detection was performed in positive ionization mode using multiple reaction monitoring (MRM). The method exhibited linearity over concentration ranges of 0.05–25.0 ng/mL for DXM, 2.0–1000.0 ng/mL for PSE, 0.2–20.0 ng/mL for OLA, and 0.5–50.0 ng/mL for FLU with lower limits of quantification (LLOQs) of 0.05, 2.0, 0.2, and 0.5 ng/mL, respectively. The method was successfully validated in accordance with FDA and ICH bioanalytical method validation guidelines, demonstrating satisfactory selectivity, accuracy, and precision. The validated method demonstrated high extraction recovery (> 90%), limited, reproducible matrix effects (IS-normalized matrix factor CV ≤ 15%). This study represents a novel application of artificial intelligence (AI)-assisted evaluation, utilizing a universally accessible model to assess the greenness and whiteness of the proposed LC–MS/MS method through the Auto-AGREE and Auto-RGB 12 frameworks. The AI-generated assessments demonstrated high agreement with traditional metrics, highlighting the potential of AI tools to provide rapid and objective holistic sustainability evaluations for the global analytical community.

Quantum enhanced colony predation algorithm with episodic memory and energy fatigue for multilevel color image thresholding

Abstract Multilevel thresholding is a commonly adopted technique in image segmentation, where an image is partitioned into several meaningful regions. The selection of optimal threshold values is regarded as a difficult problem, and the difficulty is more pronounced for complex and multimodal colour images. A large number of optimization algorithms have been reported for this purpose, although their effectiveness is often constrained by premature convergence, limited population diversity, and the absence of adaptive exploration. An Improved Colony Predation Algorithm (ICPA) is proposed in this paper to mitigate these concerns. Three enhancements are introduced into the behavioural phases of the baseline algorithm. The encircling phase is first supplemented with a quantum tunneling so that stagnation at a local optimum can be avoided. Inter-agent communication is then enriched through an episodic memory pool that retains historically successful positions. A biologically motivated energy fatigue mechanism is finally employed to regulate the search behaviour in accordance with dynamic metabolic constraints. The Minimum Cross Entropy Measure (MCEM) is taken as the objective function for the assessment of segmentation quality. The proposed method is evaluated on images drawn from the Berkeley Segmentation Dataset (BSDS500) and is compared with the Whale Optimization Algorithm (WOA), Aquila Optimization (AO), Parrot Optimization (PO), Particle Swarm Optimization (PSO), and Equilibrium Optimizer (EO). The experimental results indicate that better PSNR, SSIM, and FSIM values are obtained across the tested threshold levels, and a greater degree of statistical stability is observed in comparison with the competing algorithms.

Modeling ozone generation via non-thermal plasma using response surface methodology (RSM) and artificial neural network (ANN) for textile wastewater application

Identifying critical period of type 2 diabetes in Chinese population with leading molecules based on dynamic network biomarkers

Enhanced structural anomaly detection through improved image inpainting and feature-level discrimination

Fabrication and evaluation of a bi-layered electrospun polyacrylonitrile/polycaprolactone-chitosan/gelatin/polyethylene oxide nanofibers containing antibiotics and nanoparticles: antibacterial and antibiofilm activity

Effect of 3D‑printed customized base designs on the mechanical retention and bonding strength of metal brackets

In vitro evaluation of Curcuma longa extract for antifungal, antibiofilm, and synergistic activity against Candida spp.

Abstract The rising incidence of non -albicans Candida (NAC) species and their developing resistance to traditional antifungal medications make candidiasis a significant problem in clinical microbiology. Another factor that leads to persistence and treatment failure is biofilm development. The purpose of this study was to assess the antifungal, antibiofilm, and synergistic properties of an ethanolic extract of Curcuma longa (C. longa) , which was dried and then reconstituted in distilled water, against clinical isolates of Candida , with chemical characterization confirmed by Fourier Transform Infrared (FT-IR) spectroscopy. Using the Vitek 2 technique, chromogenic agar, and the germ tube test (GTT), 40 Candida isolates were identified from a variety of clinical cases. Using the broth microdilution to detect Minimal Inhibitory Concentration (MIC) and disc diffusion techniques, antifungal susceptibility was assessed in compliance with CLSI (2022) recommendations. Combination disc testing was used to evaluate the synergy between C. longa extract and fluconazole or amphotericin B, and the crystal violet microtiter plate assay was used to examine the antibiofilm activity. The chemical composition of the extract was analyzed by FT-IR (Bruker TENSOR 37, Kalkar, Germany) in the 450–3500 cm⁻¹ range. Candida species that are non- albicans accounted for the majority (67.5%), with C. glabrata being the most common isolate (50%). The ethanolic extract of C. longa showed modest antifungal activity, with MIC values ranging from 6.4 to 815 µg/mL and mean inhibition zones of 12.33 ± 7.17 mm. The extract exhibited significant synergistic effects with fluconazole ( p  < 0.001) and amphotericin B ( p  = 0.05), resulting in enhanced inhibition zones when compared to the medicines alone. Significant antibiofilm properties were also shown by the extract, which, on average, prevented biofilm formation by 31.7%. FT-IR analysis revealed prominent absorption bands at 3424.55 cm −1 (O–H stretching, phenols), 1650.44 cm −1 (C = O stretching, amides), and 1161.08 cm −1 (C–O stretching, ethers), confirming the presence of bioactive functional groups such as polyphenols, curcuminoids, and terpenoids, which are associated with antifungal and antioxidant activity. Curcuma longa ’s ethanolic extract, when reconstituted in distilled water, showed encouraging antifungal and antibiofilm properties and enhanced the effectiveness of common antifungal medications. These results point to C. longa extract as a viable natural adjuvant treatment option for treating Candida infections linked to biofilms and overcoming antifungal resistance. FT-IR characterization confirmed the presence of key functional groups related to curcumin derivatives, supporting its potential as a natural adjunctive therapeutic candidate for managing biofilm-associated Candida infections and combating antifungal resistance.

Serum sialic acid binding immunoglobulin-like lectin-1 (sSIGLEC-1) in Egyptian patients with lupus nephritis: correlation with renal activity in non-European ancestry

Abstract Serum sialic acid binding immunoglobulin-like lectin-1 (sSIGLEC-1) is a type I interferon-associated biomarker previously linked to lupus nephritis (LN) in European ancestry populations, but its utility in non-European cohorts remains poorly defined. This study aimed to validate the association of sSIGLEC-1 with LN in Egyptian SLE patients (non-European ancestry) and to test its correlation with world health organization (WHO) pathological classes, chronic kidney disease (CKD) stages, systemic lupus international collaborating clinics-renal activity score (SLICC-RAS), systemic lupus erythematosus disease activity index (SLEDAI), 24-hour urinary protein and proinflammatory cytokines. This cross-sectional study included 80 SLE patients (47 with LN, 33 without LN) and 20 healthy controls. Renal biopsy was classified according to WHO criteria. Estimated glomerular filtration rate (eGFR) and CKD stages were calculated. Median levels of sSIGLEC-1 were significantly higher in SLE patients than controls (113.5 vs. 11.2 pg/mL) and in LN patients than non-LN patients (117.7 vs. 110.0 pg/mL). Multivariable logistic regression confirmed sSIGLEC-1 as an independent predictor of LN (OR = 1.02, p  = 0.04). sSIGLEC-1 correlated positively with SLEDAI ( r  = 0.26), SLICC-RAS ( r  = 0.31), and proinflammatory cytokines (IL-1β, IL-6, TNF-α), but did not correlate with 24-hour urinary protein ( r = − 0.175). However, no significant differences in sSIGLEC-1 were observed across WHO pathological classes or CKD stages. ROC analysis showed poor discriminative ability for LN (AUC = 0.6928, sensitivity 93.6%, specificity 39.4%). In Egyptian SLE patients, sSIGLEC-1 is elevated in LN and correlates with disease activity but does not reflect histological severity or chronic kidney damage. Its low specificity limits diagnostic utility; however, high sensitivity suggests potential as a rule-out screening test for LN in non-European populations.

Copper‐Catalyzed Enantioselective C–H Phosphorylation Enabled by a 3,3’‐Difluorinated BINOL Ligand

ABSTRACT The asymmetric construction of C–P bonds via transition‐metal‐catalyzed enantioselective C–H activation remains a significant and unresolved challenge. Herein, we report an unprecedented copper‐catalyzed enantioselective C–H phosphorylation through oxidative C–H/P–H coupling. This method employs earth‐abundant copper catalyst and readily accessible 3,3′‐difluorinated BINOL (1,1'‐bi‐2‐naphthol) ligand, which proved essential for achieving high reactivity and enantioselectivity. A broad range of unmasked H‐phosphonates and secondary phosphine oxides (SPOs) serve as practical coupling partners, delivering planar‐chiral metallocenes in good yields with high enantioselectivity (up to 99:1 er). The robustness of this approach is demonstrated through gram‐scale synthesis, facile directing‐group removal, and versatile downstream transformations. Furthermore, the resulting chiral phosphines were applied as a new class of Lewis base catalysts in the enantioselective allylation of aldehydes, underscoring their potential utility in asymmetric synthesis.

ForestSAR-YOLO: real-time detection of small human targets in UAV imagery

Integrated characterization of gamma-induced pomegranate mutants using morphological, pathological and SSR-based analysis

Physical activity and fruit and vegetable intake among Chinese college students through psychological pathways

Effects of acoustic enrichment on settlement of a critically endangered broadcast spawning coral

NAT10 drives head and neck squamous cell carcinoma proliferation via ac4C modification of DDIT4 mRNA

A novel scoring system based on post-radiotherapy changes in pain, performance status, and adverse events to stratify survival in bone metastases

Abstract Although pain response has been associated with overall survival (OS) in palliative radiotherapy for bone metastases, the prognostic impact of integrating pain response with changes in performance status (PS) and treatment-related adverse events (AEs) remains unclear. This study aimed to investigate their combined effects on OS in this context. This study included 361 patients treated with palliative radiotherapy for bone metastases between January 2013 and October 2024, with 1-year OS as the primary endpoint. A composite score was developed by assigning weights to pain response, PS change, and AEs based on their respective 1-year OS rates (point = OS%/10). Internal validation used bootstrap resampling (1,000 iterations). Incremental prognostic value was assessed using Net Reclassification Improvement (NRI). The score (range, 4.8–15.8) significantly stratified OS (p < 0.001) with a C-index of 0.73. The composite model demonstrated superior discriminative performance compared with individual factor based on AUC comparisons (all p < 0.001). NRI analysis showed incremental prognostic value of the composite model over pain response (p = 0.017), PS change (p < 0.001), and AEs (p < 0.001). In conclusion, the scoring model incorporating pain response, PS changes, and AEs stratified OS-risk after palliative radiotherapy for bone metastases, providing incremental predictive value beyond pain response alone, but requires external validation before clinical application.