Browse Articles
Discover research articles across all indexed journals
Cone snail venom-inspired somatostatin receptor 4 (SSTR4) agonists as new drug leads for peripheral pain
Mathematical models predict the physiological dimensions of selected canine carpal joint structures across imaging modalities in healthy dogs
Three-dimensional dynamic simulation of motion characteristics of metal particle in DC GIL
Development and validation of combined in vitro and in vivo assays for evaluating the efficacy of strontium-chelating compounds
Abstract The aim of this study is to develop a combined in vitro and in vivo assay for the comprehensive efficacy evaluation of strontium-chelating compounds. The complexation and removal of harmful radioactive strontium ( 90 Sr) from the living organism may be necessary in the event of an accidental nuclear disaster or nearby nuclear war event, to prevent its incorporation into bones and thus avoid its long-term harmful health effects. To validate the model, we used a strontium-selective chelator macrocyclic substance (Decorporol) in our measurements, which potentially could be approved as a drug without significant toxic side effects. It has been proven to effectively remove strontium before it is incorporated into bones without significantly affecting calcium homeostasis in the body, and without relevant toxicity or side effects. In this study, we established an extremely sensitive test system that allows for the examination of chelator molecules in preventing the incorporation of non-radioactive SrCl 2 into mineralized extracellular matrix in cell culture. We also optimized an in vivo mouse model suitable to investigate strontium incorporation and the effect of chelators. These assays also provide an opportunity for the safe evaluation of similar compounds.
Demand forecasting of smart tourism integrating spatial metrology and deep learning
Determination of nutritional and mineral constituents and physical characteristics of Yubka kidney bean seeds from Talas region of Kyrgyzstan
A mechanical model based on watermelons for the study of dynamic cranial remolding orthoses
Abstract Deformational plagiocephaly is a head deformity in newborns that can be treated in some cases with cranial remodeling orthoses that constrain head growth to reshape it. To mitigate complications arising from the treatment with these orthoses, and for the exploration of novel functionally graded lattice structures, a biological model that mimics some aspects of human head growth could provide a development and testing platform for these lattices. In this work, we propose a novel biological model of infant heads in which watermelons are used for the study of cranial remodeling orthoses during head growth and the correction of deformities. First, we reshaped ten watermelons with infant head shapes with deformities via custom molds, which were generated from MRI scans of infants with head deformities. The shaped watermelons were subsequently compared with the original head scans to assess the accuracy of the process via standard clinical measurements. Finally, the growth of four of these watermelon shapes was monitored after the molds were left for several days. The watermelon head shapes registered an average shape difference from the original models of 1.6 millimeters, with a standard deviation of 1.88 millimeters. After leaving the molds, the shapes continued growing, maintaining the ability to be reshaped by external physical constraints. By mimicking two key mechanical aspects of head growth in newborns — growth and deformability — this preliminary approach to a biological phantom offers a promising platform for studying the mechanical behavior of novel lattice structures in the development of cranial remodeling orthoses.
Epigallocatechin-3-gallate ameliorates lipopolysaccharide-induced inflammation via the 67LR/JAK2/STAT3 signaling pathway
Characterisation of human in vitro tumour-associated macrophage models to define translational relevance
Abstract Tumour-associated macrophages (TAMs) are key components of the tumour microenvironment with a demonstrated ability to modulate anti-tumour T-cell responses and immunotherapy outcomes. With increasing realisation that the M1/M2 paradigm does not reflect the complexity of macrophage phenotypes in cancer patients, an urgent need has arisen to develop improved, translatable in vitro models for human TAMs. To address this gap, we have screened conditioned media from a panel of tumour cell lines for their ability to induce suppressive marker upregulation on human monocyte-derived macrophages, as well as active T-cell immunosuppression. We performed secretome characterization of these tumour-conditioned media (TCM) to shed light on cancer cell-derived soluble factors that may contribute to TAM polarisation. Furthermore, we characterized the proteomic and transcriptomic signatures of macrophages exposed to either TCM or primary ascites fluid from ovarian cancer patients and performed bioinformatics analysis to determine the most translationally relevant models of TAMs. In summary, our work provides mechanistic insights on tumour-macrophage crosstalk in the context of establishing suppressive TAM phenotypes and addresses the long-standing gap of defining translationally relevant human in vitro TAM models.
An image encryption scheme using PRESENT-RC4, chaos and secure key generation
Endophytic Bacillus velezensis BDKishJoy 6B promotes rice growth and enhances resistance to Magnaporthe oryzae
Evaluation of probiotic properties of Bacillus aryabhattai HY1 isolated from Vietnamese pickled mustard greens
Sustainable EV routing using spectral clustering and fuzzy reinforcement learning with energy constrained A* under mobility index and waiting constraints
Abstract This research presents a comprehensive electric vehicle (EV) routing framework designed to address the complex interplay of real-world constraints in EV navigation. The proposed system integrates spectral clustering, fuzzy reinforcement learning, and enhanced pathfinding algorithms to compute optimal routes while considering battery limitations, traffic dynamics, terrain elevation, and charging station delays. Unlike conventional multi-objective EV routing solutions, which typically optimize metrics such as energy, time, and charging delays independently-this work addresses four major gaps in the field: (1) fragmented and isolated optimization lacking dynamic interdependency modeling, (2) limited real-time adaptability to traffic and charging dynamics, (3) inadequate topological modeling with respect to network clustering and geographic scalability, and (4) evaluation restricted to constrained environments. The system introduces four core innovations: (1) a topologically adaptive clustering mechanism using spectral clustering with geodesic distance metrics and elliptical regional modeling;(2) a time-dependent arrival simulation model that predicts charging station occupancy with high accuracy by incorporating temporal demand and station-specific dynamics; (3) a fuzzy reinforcement learning-based charging station evaluator that incorporates spatial density, occupancy trends, and temporal availability; and (4) an enhanced A* algorithm with integrated elevation-aware energy profiling, real-time traffic sensitivity, and adaptive SOC constraint modeling.Experimental evaluations conducted across diverse topographies demonstrate superior performance over baseline and established algorithms including Dijkstra, A*, Hybrid A*, and EVRP + Charging Aware techniques. The proposed method achieves a 22.8% reduction in total journey time (from 877 to 677 minutes), 19.6% improvement in energy efficiency (from 224.5 to 180.5 kWh), and a 63.3% decrease in waiting time (from 34.2 to 12.5 minutes) when compared to the traditional distance-based routing. Additionally, the system achieves a 90.0% reduction in battery violations (from 18.0% to 1.8%), addressing range anxiety through improved SOC-aware planning.The findings confirm that the framework advances beyond both established algorithms and recent multi-objective solutions, offering a more unified and effective approach to EV routing. Performance gains remain consistent across urban, rural, and elevation-intensive routes, with measured improvements of up to 27.2% over conventional routing algorithms. This research directly contributes to SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities) by enabling energy-efficient, reliable EV navigation, thereby supporting the broader vision of clean transportation and smart city integration.
Zinc doped BiOBr impregnated into PVDF sponge as a dip-Photocatalyst for RhB removal from wastewater
Abstract Organic contamination of water has sparked concerns since it has an adverse impact on both human health and the ecosystem as a whole. In this study, Bismuth Oxybromide (BiOBr) was prepared via a Solvothermal approach. Subsequently, BiOBr was doped with Zinc metal to improve the photocatalytic activity through introduce Oxygen vacancies (O Vs ). The as-prepared materials were characterized using various techniques; Field-Emission scanning electron microscopy (FE-SEM) & Energy dispersive X-ray (EDAX) and elemental composition analysis, X-Ray diffraction (XRD), FTIR spectroscopy and X-ray Photoelectron spectroscopy (XPS). Additionally, optical features (Optical Absorption, band gab, and PL Spectroscopy) and electrochemical impedance spectroscopy (EIS) were also evaluated. The FE-SEM confirmed that, the formation of BiOBr and Zn-BiOBr in a hierarchical microspheres structure constructed from nano-leaves. The physico-chemical characterizations confirm the generation of O Vs upon doping with zinc, the optical features results showed a slight increase in optical band gab of BiOBr (2.811 eV) upon doping with Zinc (2.831 eV) while, the PL of BiOBr is higher than that of Zn-BiBOr and EIS results confirms the lower resistance of charge transfer of Zn-BiBOr indicating electron–hole separation leading to improvement and enhancement RhB’s degradation efficiency, with achieving 100% removal after 35 min of reaction. However, the challenge of photocatalyst (as a suspension) separation after the degradation reaction remained. To resolve this issue, we developed a simple technique to impregnate Zn-BiOBr into a highly porous sponge based on Polyvinylidene Fluoride polymer (PVDF) as a dip-photocatalyst, offering potential as a re-usable photocatalyst matrix. Furthermore, the 3D Zn-BiOBr photocatalyst sponge was tested and Found to sustain up to five cycles in consecutive cycles with almost the same photocatalytic effectiveness. In conclusion, the PVDF − Zn-BiOBr sponge is a promising material for energy conversion applications and environmental purposes and enables the reuse of the photocatalyst several times easily.
Cisplatin-encapsulated zeolitic imidazolate framework-8 nanosystem enhanced radiosensitivity of non-small cell lung cancer
Blockchain-enhanced incentive-compatible mechanisms for multi-agent reinforcement learning systems
Gaussian bare‑bone JAYA algorithm for multi-threshold medical image segmentation
The brain activation on upper extremity motor control tasks in different forces levels
Predicting suicide death among veterans after psychiatric hospitalization using transformer based models with social determinants and NLP
Abstract Predictions of suicide death of patients discharged from psychiatric hospitals (PDPH) can guide intervention efforts including intensive post-discharge case management programs, designed to reduce suicide risk among high-risk patients. This study aims to determine if additions of social and behavioral determinants of health (SBDH) as predictors could improve the prediction of suicide death of PDPH. We analyzed a cohort of 197,581 US Veterans discharged from 129 VHA psychiatric hospitals across the US between January 1, 2017, and July 1, 2019 with a total of 414,043 discharges. Predictive variables included administrative data and SBDH, the latter derived from unstructured clinical notes via a natural language processing (NLP) system and ICD codes, observed within a 365-day window prior to discharge. We evaluated the impact of SBDH on the predictive performance of two advanced models: an ensemble of traditional machine learning models and a transformer-based deep learning foundation model for electronic health records (TransformEHR). We measured sensitivity, positive predictive value (PPV), and area under the receiver operating characteristic curve (AUROC) overall and by gender. Calibration analysis was also conducted to measure model reliability. TransformEHR with SBDH achieved AUROC of 64.0 Specifically, ICD-based SBDH improved AUROC by 3.1% (95% CI, 1.6% – 4.5%) for the ensemble model and by 2.9% (95% CI, 0.5% – 5.4%) for TransformEHR, compared to models without SBDH. NLP-extracted SBDH further improved the AUROC: 1.7% (95% CI, 0.1%– 3.3%) for ensemble model and 1.8% (95% CI, 0.6%– 2.9%) for TransformEHR. TransformEHR achieved 0.2%, 0.4%, 0.8%, 1.6% PPV per 100 PDPH 7, 30, 90, 180 respectively. Moreover, TransformEHR showed superior calibration and fairness compared to ensemble model, with SBDH further improving fairness across both predictive models. In conclusion, both ICD-based SBDH and NLP-extracted SBDH improved the performance, calibration, and model fairness of prediction of suicide death for Veterans after their psychiatric discharge.