Browse Articles

Suppression of Aedes aegypti may not affect sympatric Aedes albopictus populations: findings from two years of entomological surveillance in Singapore

Non-thermal plasma as promising anti-cancer therapy against bladder cancer by inducing DNA damage and cell cycle arrest

Short-term effects of brief stair climbing interruptions on postprandial hyperglycemia during prolonged sitting: a randomized cross-over trial

Abstract Prolonged sitting can negatively impact postprandial glucose levels and cognitive function. While short bouts of stair climbing are thought to mitigate these risks, the findings remain inconclusive. The present study aimed to explore the effects of stair climbing bouts on postprandial glucose and cognitive functions during prolonged sitting. Twenty-eight sedentary young adults (aged 20–30 years) underwent two intervention visits after standardised lunch for two hours: (1) STAIR: the participants climbed two flight of stairs for two minutes every 30 min; (2) SIT: the participants continued to sit. Blood glucose was measured using capillary finger prick method while attention function was measured using computer-based cognitive tests at baseline, end of 1st hour and 2nd hour. Significant interaction (F2, 54 = 15.96, p < 0.001) was observed for conditions and time. During STAIR visit, significant changes in postprandial glucose at 1st hour (β = − 2.6 mmol/dl, p < 0.001) and 2nd hour (β = 3.0 mmol/dl, p < 0.001). No significant difference in the attention functions with time and conditions was observed. Stair climbing interruptions may serve as a feasible and effective countermeasure to high glycaemic variability or excursions that occur during prolonged sitting after postprandial hyperglycaemia.

Complete mitochondrial genome sequence analysis revealed double matrilineal components in Indian Ghoongroo pigs

Spatial analysis and risk mapping of Crimean-Congo hemorrhagic fever (CCHF) in Sub-Saharan Africa

Abstract Crimean Congo hemorrhagic fever (CCHF) is a re-emerging tick-borne zoonosis that is caused by CCHF virus (CCHFV). The geographical distribution of the disease and factors that influence its occurrence are poorly known. We analysed historical records on its outbreaks in various countries across the Sub-Saharan Africa (SSA) to identify hotspots and determine socioecological and demographic factors associated with these outbreaks. We used data from historical outbreaks that were reported between 1981 and 2022 in various countries in SSA. To develop a common framework for merging the outbreak data and potential explanatory variables, we generated a common shapefile that combined Level 2 administrative units in all the countries. Several climatic, environmental, socioecological data were obtained from on-line GIS databases and extracted using the shapefile. The data were analysed using an approximate Bayesian hierarchical model using the R-INLA package. The outcome was a Boolean variable which indicated whether an administrative unit in the shapefile was affected in a given year or not. A neighborhood structure was also generated and used to account for spatial autocorrelation in the analysis. The final model that was obtained from the analysis was used to build a CCHF risk map. A total of 54 CCHF outbreaks were compiled across 414 districts in nine SSA countries. Factors that were positively associated with CCHF outbreaks included human population density, land area under grassland, bare soil cover and shrub cover. Conversely, high precipitation during wet months, elevated mean temperature and slope had negative effects. The risk map generated shows that CCHF occurrence risk is higher in arid and semi-arid land (ASAL) of West Africa, the Sahelian region, Central Africa, and the Eastern and Southern Africa region. The analysis identified ecological and demographic factors that are associated with CCHF outbreaks in SSA. This finding suggests the need to improve surveillance for the disease especially in the grasslands where the human population is increasing.

A sustainable and green method for controlling acidic corrosion on mild steel using leaves of Araucaria heterophylla

Evolutionary analysis of the DHHCs in Saccharinae

Equity analysis of bus network balance from the perspective of spatial morphology and service capability

The microbial mechanism of maize residue decomposition under different temperature and moisture regimes in a Solonchak

Practical Implementation of Federated Learning for Detecting Backdoor Attacks in a Next-word Prediction Model

Exploring technology acceptance of flight simulation training devices and augmented reality in general aviation pilot training

An In Vitro Study of the Effects of Methotrexate Loaded Biocomposite Beads on MG63 Osteoblast Cells

Abstract This study aims to synthesize a new localized drug delivery system of bioglass, polyvinyl alcohol (PVA), cellulose (CNC), and sodium alginate (SA) beads as a carrier for methotrexate (MTX) drugs for the treatment of osteosarcoma. Methotrexate /Bioglass-loaded Polyvinyl/Cellulose/Sodium alginate biocomposite beads were prepared via the dropwise method with different concentrations of (65%SiO2-30%CaO- 5%P2O5) bioglass. Samples were named B0, S0, S1, S2, and S3, respectively. Calcium chloride (CaCl2) was used as a cross-linking agent. The obtained biocomposite beads were investigated by different techniques FTIR, XRD, SEM, etc. The bioactivity of MTX/BG-loaded PVA-CNC-SA biocomposite beads was tested by immersion in simulated body fluid (SBF). The profile release of methotrexate was investigated with UV–vis spectroscopy for 30 days. A cytotoxicity study of the methotrexate was performed by a human osteosarcoma (MG-63) cell line. Results indicated that the formation of a hydroxyapatite layer on the bead’s surface confirmed its biological activity. Bioactivity was directly proportional to the BG content. All samples of B1, S0, S1, S2, and S3 exhibited significant maximum release up to 6 days and were controlled gradually. Cytotoxicity results of biocomposite beads showed that high cell death was detected on the MG-63 cells, with (IC-50 ± SD) of S3 (116.16 ± 1.57) compared with B1 (306.99 ± 2.72) and S1 (204.74 ± 4.55) due to the high release of MTX, which was confirmed by the results of the drug release profile. Results prove that the prepared biocomposite beads can be used as bioactive, drug delivery systems, and anticancer materials.

Changes in the potato rhizosphere microbiota richness and diversity occur in a growth stage-dependent manner

Impact of protein intake from a caloric-restricted diet on liver lipid metabolism in overweight and obese rats of different sexes

Explainable analysis of infrared and visible light image fusion based on deep learning

Coupling between topological edge state and defect mode-based biosensor using phononic crystal

Database energy saving strategy using blockchain and Internet of Things

Abstract This paper aims to construct a green environmental protection system by advancing database energy-saving techniques and optimizing the energy-saving mechanism against the backdrop of blockchain integration. The protocol classification of wireless sensor networks is examined within the context of the rapid growth of information technology. The analysis draws upon the database storage and sharing model and recent research examples that connect blockchain and database technology. Additionally, the paper investigates the blockchain data structure and storage path by analyzing the spatial–temporal correlation properties of blockchain data. The findings demonstrate that increasing the number of network nodes in the database system within a reasonable range can reduce the overall execution time. Additionally, as time progresses, the query traffic of the system database continues to increase. The research has practical implications in the domains of databases and the Internet of Things.

Investigation of the physicochemical factors affecting the in vitro digestion and glycemic indices of indigenous indica rice cultivars

Abstract Rice (Oryza sativa) is a vital food crop and staple diet for most of the world’s population. Poor dietary choices have had a significant role in the development of type-2 diabetes in the population that relies on rice and rice-starch-based foods. Hence, our study investigated the in vitro digestion and glycemic indices of certain indigenous rice cultivars and the factors influencing these indices. Cooking properties of rice cultivars were estimated. Further, biochemical investgations such as amylose content, resistant starch content were estimated using iodine-blue complex method and megazyme kit respectively. The in vitro glycemic index was estimated using GOPOD method. The rice cultivars considered in our study were classified into low-, intermediate-, and high-amylose rice varieties. The rice cultivars were subjected to physicochemical characterization by using Fourier transform infrared (FTIR) spectroscopy and differential scanning calorimetry (DSC) techniques. FTIR spectral analysis revealed prominent bands at 3550-3200, 2927-2935, 1628-1650, 1420-1330, and 1300-1000 cm−1, which correspond to –OH groups, C=O, C=C, and C–OH stretches, and H–O–H and –CH bending vibrations, confirming the presence of starch, proteins, and lipids. Additionally, the FTIR ratio R(1047/1022) confirmed the ordered structure of the amylopectin. DSC analysis revealed variations in the gelatinization parameters, which signifies variations in the fine amylopectin structures and the degree of branching inside the starch granules. The percentage of resistant starch (RS) ranged from 0.50–2.6%. The swelling power (SP) of the rice flour ranged between 4.1 and 24.85 g/g. Furthermore, most of the rice cultivars are classified as having a high glycemic index (GI) based on the estimated in vitro GI (eGI), which varies from 73.74–90.88. The cooking properties of these materials were also investigated. Because the amylose content is one of the key factors for determining the cooking, eating, and digestibility properties of rice, we investigated the relationships between the amylose content and other biochemical characteristics of rice cultivars. The SP and GI were negatively correlated with the amylose content, whereas the RS had a positive relationship. The findings of our study can be beneficial in illustrating the nutritional profile and factors affecting the digestibility of traditional rice cultivars which will promote their consumption, cultivation, and contributes to future food security.

Pre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything model

Abstract Complex structures can be understood as compositions of smaller, more basic elements. The characterization of these structures requires an analysis of their constituents and their spatial configuration. Examples can be found in systems as diverse as galaxies, alloys, living tissues, cells, and even nanoparticles. In the latter field, the most challenging examples are those of subdivided particles and particle-based materials, due to the close proximity of their constituents. The characterization of such nanostructured materials is typically conducted through the utilization of micrographs. Despite the importance of micrograph analysis, the extraction of quantitative data is often constrained. The presented effort demonstrates the morphological characterization of subdivided particles utilizing a pre-trained artificial intelligence model. The results are validated using three types of nanoparticles: nanospheres, dumbbells, and trimers. The automated segmentation of whole particles, as well as their individual subdivisions, is investigated using the Segment Anything Model, which is based on a pre-trained neural network. The subdivisions of the particles are organized into sets, which presents a novel approach in this field. These sets collate data derived from a large ensemble of specific particle domains indicating to which particle each subdomain belongs. The arrangement of subdivisions into sets to characterize complex nanoparticles expands the information gathered from microscopy analysis. The presented method, which employs a pre-trained deep learning model, outperforms traditional techniques by circumventing systemic errors and human bias. It can effectively automate the analysis of particles, thereby providing more accurate and efficient results.

Proteome-wide mendelian randomization identifies causal plasma proteins in interstitial lung disease

Abstract Interstitial lung disease (ILD) has shown limited treatment advancements, with minimal exploration of circulating protein biomarkers causally linked to ILD and its subtypes beyond idiopathic pulmonary fibrosis (IPF). In this study, we aimed to identify potential drug targets and circulating protein biomarkers for ILD and its subtypes. We utilized the most recent large-scale plasma protein quantitative trait loci (pQTL) data detected from the antibody-based method and ILD and its subtypes’ GWAS data from the updated FinnGen database for Mendelian randomization analysis. To enhance the reliability of causal associations, we conducted external validation and sensitivity analyses, including Bayesian colocalization and bidirectional Mendelian randomization analysis. Our study identified eight plasma proteins genetically associated with ILD or its subtypes. Among these, three proteins—CDH15 (Cadherin-15), LTBR (Lymphotoxin-beta receptor), and ADAM15 (A disintegrin and metalloproteinase 15)—emerged as priority biomarkers and potential therapeutic targets, demonstrating more reliable associations by passing a series of sensitivity analyses compared to the others. Based on these findings, we propose for the first time that CDH15, ADAM15, and LTBR hold promise as novel potential circulating protein biomarkers and therapeutic targets for the diagnosis and treatment of ILD, IPF, and sarcoidosis, respectively, especially ADAM15, and these findings have the potential to provide new perspectives for advancing the research on the heterogeneity of ILD.