Browse Articles

Positive prognostic implications of SPHK1 expression in patients with papillary thyroid carcinoma undergoing radioactive iodine therapy

Aryl and Alkyl Radiotrifluoromethylation via Metallaphotoredox‐Mediated Radical Cross‐Coupling

ABSTRACT Given the short half‐life of radionuclides used for positron emission tomography (PET), such as fluorine‐18 ( t 1/2 = 109.8 min), efficient and mild methods for the radiolabeling of PET tracers are highly sought after. Previous studies have shown how photoredox catalysis can be used to unlock unique reactivity by enabling radiofluorinations via radical formation of either non‐radioactive substrates or carbon‐centered 18 F‐labeled reagents. However, the cross‐coupling of two different radical species, one of which is radiolabeled, remains thus far elusive. Herein, we report the metallaphotoredox‐mediated radiotrifluoromethylation of arenes and alkanes via radical cross‐coupling reactions (RCCR). All protocols proceed under mild conditions and use a commercially available Ru‐based photoredox catalyst and copper(I) iodide to mediate the radical coupling. The reactions are operationally simple and can be set up without the use of a glovebox or Schlenk line. The good functional group tolerance and applicability to PET tracer synthesis is demonstrated by the radiotrifluoromethylation of a range of substrates, incl. bioactive boscalid and PET tracer [ 18 F]SL25.1188. In a broader context, this work sets the stage for the further exploration of RCCR for radiolabeling reactions beyond radiotrifluoromethylation.

Structure-guided optimization of N-sulfonylpiperidines toward potent multi-target anticancer agents

Abstract Cancer poses a significant therapeutic challenge due to its multifactorial origin and resistance to conventional remedies. Our previous efforts identified N -sulfonylpiperidine derivatives as potent VEGFR-2 inhibitors (Compound A). Herein, we report a rational design, synthesis, and biological assessment of a new series of analogs to enhance anticancer efficacy through scaffold optimization and multi-target engagement. Strategic modifications on A included group repositioning, diversifying the terminal aromatic substituents, and linker replacement. Among the synthesized derivatives, compound 16 exhibited promising cytotoxic activities across three cell lines, in comparison with the lead compound A and vinblastine. Preliminary mechanistic studies confirmed that 16 induced G0/G1 cell cycle arrest and promoted early apoptosis in MCF-7 cells. Enzyme inhibition assays further revealed that our derivative acts as a dual inhibitor of VEGFR-2 and EGFR versus moderate activity against topoisomerase II. Molecular docking studies supported these findings by showing favorable binding orientations and interactions within the VEGFR-2 active site. The binding pattern of 16 showed key hydrogen bonding and hydrophobic contacts that enhance ligand affinity. Altogether, this work highlights the impact of these modifications and introduces 16 as a promising lead to develop multitargeted anticancer therapeutics that inhibit tyrosine kinases and DNA-processing enzymes.

Optimization of greywater treatment using UiO-66 nanomaterial: artificial neural network modeling

Abstract Metal–organic frameworks (MOFs) have attracted considerable attention in wastewater treatment due to their high porosity and tunable structures. In this study, UiO-66 materials were synthesized using different molar ratios of zirconium tetrachloride to terephthalic acid (metal-to-ligand ratios) to evaluate their performance in greywater treatment. Structural and physicochemical properties of the synthesized materials were characterized using XRD, BET, FTIR, SEM, and EDS analyses. Among the prepared samples, UiO-66 with a metal-to-ligand ratio of 1:0.75 exhibited improved crystallinity, a higher surface area (1387 m²/g), and a larger total pore volume (1.66 cm³/g), indicating favorable structural properties for adsorption applications. Batch adsorption experiments were conducted to investigate the effects of initial COD concentration, adsorbent dosage, pH, and contact time on greywater treatment efficiency. To model the adsorption process and analyze the interactions between operational parameters, an artificial neural network (ANN) model was developed and combined with a genetic algorithm (GA) for process optimization. The model demonstrated high predictive capability and enabled the identification of optimal operating conditions, namely an initial COD concentration of 236 ppm, adsorbent dosage of 500 ppm, pH 2, and contact time of 3 h, yielding a maximum COD removal efficiency of 88.4%. Adsorption equilibrium analysis indicated that the Langmuir model provided the best fit to the experimental data, with a maximum adsorption capacity of 722 mg/g. Kinetic analysis revealed that the adsorption process followed a pseudo-second-order model. The results demonstrate that the optimized UiO-66 adsorbent exhibits promising potential for greywater treatment and that the combined experimental–modeling approach provides valuable insights for optimizing MOF-based adsorption processes.

Protective effects of Astragalus membranaceus polysaccharide against aluminum oxide nanoparticle-induced growth retardation and oxidative-immunological disruption in Oreochromis niloticus

Abstract Although the toxicity of aluminum-based nanoparticles (Al 2 O 3 -NPs) in fish has been widely studied, strategies to mitigate their harmful effects remain limited. Moreover, the modulatory role of Astragalus membranaceus polysaccharides (APS) under this toxic stress is still poorly understood, despite the widespread presence of Al 2 O 3 -NPs in aquatic ecosystems and the need for effective mitigation strategies for sustainable aquaculture. In this study , Oreochromis niloticus was exposed to Al 2 O 3 -NPs (10 mg/L) with or without dietary APS (1.5 or 3 mg/kg) for four weeks. A total of 270 fish (initial weight = 25.28 ± 0.086 g) were randomly distributed into six triplicate groups ( n  = 15 per group) as control, APS (1.5 mg/kg–3 mg/kg), Al 2 O 3 -NPs, and two co-treated groups (Al 2 O 3 -NPs + APS). Al 2 O 3 -NPs exposure showed significant effects on growth performance compared to the control group ( P  < 0.05), as shown by the decrease in final weight (FW: -9.4%), weight gain (WG: − 21.4%), specific growth rate (SGR: -17.1%), along with an increase in feed conversion ratio (FCR: + 16.9%). Liver function was severely affected, as evidenced by an increase in ALT levels (+ 39%) and decrease in total protein (− 26.5%) and IgM levels (− 19.3%) ( P  < 0.05). In addition, the expression of antioxidant-related genes, such as CAT and SOD , was downregulated, while the expression of pro-inflammatory cytokines, such as TNF-β and IL-1β , as well as stress-related gene, such as MT , was upregulated in the liver and gills ( P  < 0.05). Histopathological examination showed noticeable degenerative and necrotic changes in the liver, kidney, muscle, gill, and spleen tissues. Co-treatment with APS, particularly at 3 mg/kg, significantly improved growth parameters (FW, 20.3% increase; WG, 53.1% increase; and SGR, 38.8% increase) ( P  < 0.05), ameliorated hepatic and renal histopathological changes, restored antioxidant gene expression and normalized inflammatory mediators compared with the Al 2 O 3 -NPs-exposed group. Compared with the Al 2 O 3 -NPs–exposed group, the survival rate increased by 5.3%, ALT levels decreased by 60.1%, while total protein and IgM levels increased by 29.6% and 8.9%, respectively. Overall, APS supplementation effectively mitigated Al 2 O 3 -NPs-induced toxicity in O. niloticus by regulating antioxidant capacity, suppressing inflammatory responses, and improving tissue structure, highlighting its potential as an eco-friendly functional food additive to protect fish health under nano-toxic stress .

Association of surgery with survival in small cell carcinoma of the digestive system: a population-based observational study using machine learning

Chiral Heptagon‐Embedded Double [6]Helicenes via Scholl Reaction

ABSTRACT A series of heptagon‐embedded multiple helicenes was synthesized in which the heptagon subunit was fused to the π‐framework through Knoevenagel condensation reactions. By controlling the conditions of the cyclodehydrogenation (Scholl) reactions, different fused chiral and twisted PAHs were accessible. The optical and electronic properties of all products were investigated by UV–vis, fluorescence spectroscopy, and cyclic voltammetry. One member of the series displays an unusual anti‐Kasha‐like fluorescence emission. In addition, all the enantiopure [6]helicenes were separated by chiral HPLC and characterized by circular dichroism spectroscopy.

Dried blood spot sample extraction for metabolomics and proteomics profiling for clinical trials: a descriptive exploratory study

Abstract Biomarker research relies on venous blood sampling, which demands trained professionals and proper sample handling, presenting challenges for both rural health care providers and large-scale studies. This study aims to investigate the use of dried blood spots (DBS) as a minimally invasive, patient-centric alternative for biomarker discovery in preparation of a decentralized type 2 diabetes (T2D) trial. To optimize and validate the method, DBS were collected from 2 and 14 healthy participants, respectively. Protein extraction was optimized, evaluated, and samples were analyzed by multi-spot antibody assays, two-dimensional gel electrophoresis, liquid chromatography coupled to tandem mass spectrometry, and nuclear magnetic resonance spectroscopy. The main findings revealed adequate extraction from filter paper, with detection of low abundant inflammatory cytokines. Additionally, proteins and metabolites previously implicated in T2D pathophysiology were detected. In conclusion, the findings in this study support DBS-based proteomics and metabolomics for the identification of novel biomarkers associated with human disease, which offers a scalable and accessible alternative to conventional blood sampling methods for clinical trial studies.

Impact of defects and asymmetry on the acoustic transmission of serial resonators

Tetranectin is significantly elevated in the synovial fluid of patients with full-thickness rotator cuff tears

Antimicrobial-resistance in clinically harmful bacteria contaminating household drinking water in Eastern Uganda highlights urgent need for enhanced antibiotic resistance stewardship

Fast and accurate extraction of microwave filter coupling matrix via physics-informed deep learning

Spectroscopy of heat transport and violation of the Wiedemann-Franz law in GaAs hydrodynamic mesoscopic channel

Integrative cross-study analysis of maize microarray datasets identifies key abiotic stress-responsive genes, highlighting ZmOSM34 and ZmANAH

Explainable machine learning for predicting mechanical ventilation in Alzheimer’s patients with pneumonia: a SHAP-guided XGBoost nomogram based on MIMIC-IV

Hybrid ML and metaheuristic optimization of slag-fly ash-gypsum modified solidified sludge for construction

Abstract Conventional sludge disposal, including incineration and landfilling, is unsustainable and can cause secondary pollution; thus, sludge solidification is emerging as a sustainable alternative. This study aims to combine machine learning (ML) and metaheuristic optimization to maximize the unconfined compressive strength (UCS) of municipal sludge modified with slag, desulfurized gypsum, and fly ash. A total of 190 specimens were tested, and predictive models based on Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Regression (SVR), LightGBM, XGBoost, CatBoost, K-Nearest Neighbors (KNN), and Histogram Gradient Boosting (HistGBoost) were coupled with the Whale Optimization Algorithm (WOA). In addition, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Gazelle optimization algorithm (GOA), Octopus Optimization Algorithm (OOA), Hiking Optimization Algorithm (HOA), and Young’s double-slit experiment optimizer (YDSE) were applied for comparison. Sensitivity analysis identified optimal WOA–ML parameter settings. The results demonstrated that the WOA–RF model outperformed all metaheuristic and other WOA–ML approaches by achieving the highest predicted UCS (8.29851 MPa). The WOA-ML models yielded an average optimal mix comprising sludge (44.2%), gypsum (19%), slag (18.7%), fly ash (16%), and NaOH (2.1%). Among the metaheuristic algorithms, PSO, GOA, OOA, TJO, DOA, GA, and YDSE demonstrated competitive performance. GWO achieved the highest UCS (8.226109 MPa), while HOA yielded the lowest (5.15366 MPa). The optimal mix averaged 38.9% sludge, 23.7% gypsum, 21.6% fly ash, 13.4% slag, and 2.5% NaOH. Partial dependence analysis confirmed the nonlinear effects of these parameters, while SHAP sensitivity analysis validated the optimization results. RSM validation further confirmed that both WOA–ML and metaheuristic approaches reliably predict the optimal UCS of modified sludge.

Sol–gel synthesis and comprehensive characterization of MgO nanostructures: structural, optical, and dielectric insights

Abstract MgO nanostructures synthesized via the sol–gel method were examined for their structural, microstructural, optical, and dielectric properties. XRD indicated a crystalline cubic phase with an average crystallite size of approximately 30–50 nm, corroborated by TEM findings of quasi-spherical to polyhedral morphologies. Mapping of electron density distribution validated the face-centered cubic structure, while the modified Scherrer, Williamson-Hall, size-strain plot, and Halder-Wagner methods indicated peak broadening attributable to lattice strain and stacking faults. Optical measurements indicated a reduced band gap of approximately 4.48 eV compared to bulk MgO, attributed to surface states, oxygen vacancies, and quantum confinement effects. The Urbach energy (approximately 168 meV) enhanced the identification of defect-related localized states within the band gap. AC conductivity increased with frequency, supporting Jonscher’s universal power law, whereas impedance spectroscopy revealed grain-dominated dielectric relaxation exhibiting non-Debye behavior in the Cole–Cole plot, characterized by a single semicircular arc. Frequency-dependent dielectric constant and loss demonstrated interfacial and dipolar polarization. This study is the inaugural investigation to directly associate microstructural defects and strain from the synthesis process with the optical and electronic transport properties of the material, demonstrating the sol–gel method’s capability for fabricating functional ceramics with customized attributes for advanced electronic and optoelectronic applications.

Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models

ABSTRACT The integration of artificial intelligence (AI) into catalysis is fundamentally reshaping the research paradigm of catalyst discovery. Unlike traditional trial‐and‐error approaches, AI‐empowered data‐driven technologies, particularly large AI models such as universal machine learning interatomic potentials (MLIPs) and large language models (LLMs), offer unprecedented capabilities in exploring complex spaces, predicting catalytic performance, and accelerating rational design. Standing at the forefront of data‐driven science, we underscore how databases, universal MLIPs, and LLMs are revolutionizing the traditional catalysis paradigm and bridging the ontology‐concept‐computation‐experiment continuum. We then demonstrate significant recent progress, and discuss their potential and challenges in the catalytic field. By leveraging cutting‐edge universal MLIPs and LLMs, researchers can conduct large‐scale simulations, highly efficient data acquisition, training, and prediction, and even self‐directed research in the field of catalysis. Looking ahead, these advantages enable the rapid development of target catalysts, which will be propelled by integrated universal MLIPs, multimodal LLMs, and automation systems. Developments in these domains will pave the way toward AI‐empowered closed‐loop platforms and cross‐disciplinary Digital Materials Ecosystems that broaden the discovery landscape and foster cross‐materials innovation, marking the dawn of a new era in which catalyst materials discovery is perpetually accelerating.

Predictive value of preoperative T1 slope minus cervical lordosis for clinical outcomes after standalone laminectomy in elderly degenerative cervical myelopathy

Abstract Standalone cervical laminectomy is frequently performed in elderly patients with degenerative cervical myelopathy (DCM), yet practical prognostic determinants remain limited. We examined whether the preoperative T1 slope minus cervical lordosis (T1S-CL) predicts 2-year clinical outcomes and yields clinically useful decision thresholds. In this retrospective cohort of elderly DCM patients who underwent multilevel standalone laminectomy with a minimum 2-year follow-up, outcomes were assessed using the modified Japanese Orthopaedic Association (mJOA) score and Visual Analog Scale (VAS) for neck and arm pain. The primary endpoint was achievement of the minimal clinically important difference (MCID), prespecified as a ≥ 2-point increase in mJOA. Cervical alignment parameters including T1 slope (T1S), C2–C7 lordosis (CL), and T1S-CL were measured pre- and postoperatively. Predictive performance was evaluated using receiver operating characteristic (ROC) analysis and multivariable logistic regression with internal bootstrap validation. Among 68 patients (mean age 68.38 ± 3.15 years), 82.4% achieved MCID at 2 years. Laminectomy was associated with a modest decline in lordosis, and a corresponding increase in T1S-CL. Preoperative T1S-CL independently predicted MCID achievement (adjusted odds ratio per 1° = 0.556; p  < 0.001) and demonstrated superior discrimination compared with T1S or CL alone. A two-threshold strategy emerged: T1S-CL ≤ 16.5° showed high specificity (91.7%) and positive predictive value (97.2%) for favourable outcome, whereas T1S-CL > 20° provided strong sensitivity (94.6%) for identifying poor outcome. These findings support T1S-CL as a promising preoperative predictor for exploratory risk stratification. However, the proposed thresholds require external validation before routine clinical use.

Evaluation of enamel surface after interproximal reduction using different methods, with and without polishing: an in vitro study

Abstract To evaluate the changes in enamel surface roughness and elemental concentrations following interproximal reduction (IPR), with and without polishing, in comparison with enamel without stripping. Selected premolars were randomly divided into a control group and four experimental groups; Group A (control): enamel without stripping, Group B: IPR with diamond bur, Group C: IPR with diamond disc, Group D: IPR with manual strip, Group E: IPR with mechanical oscillating strip. Each experimental group was divided into two subgroups where one subgroup was also polished with fine Sof-Lex discs. A total of 108 enamel samples (12 samples/subgroup and 12 samples for the control group) were used. Enamel was evaluated with atomic force microscope, energy-dispersive x-ray spectroscopy, and scanning electron microscope. The IPR produced higher [average roughness (nm)] in all groups compared with the enamel without stripping which measured 79.5 ± 17.4 nm. The only significant difference among the experimental groups was the higher roughness in the disc group compared to the oscillating strip group, p  = 0.035. Polishing produced smoother enamel surfaces in all groups.The Ca/P weight (%) was 1.68 ± 0.45% when the enamel was not stripped and the ratio ranged from 2.03 ± 0.69% to 2.58 ± 0.47% in the IPR/IPR+polishing groups. Scanning electron microscope confirmed an increase in surface roughness following IPR with subsequent improvement after polishing. The IPR increased enamel surface roughness and affected the elements weight (%) and atomic (%). Polishing improved the surface quality. The IPR with oscillating strip followed by polishing with Sof-Lex strip produced the most favorable results.