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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.
Tilia species (linden) exert anti-cancer effects on MIA PaCa-2 cells through the modulation of oxidative stress and inflammation
Fabrication of composite ceramic polymeric membranes for agricultural wastewater treatment
Abstract Humans have contaminated water supplies with harmful compounds, including different heavy metals. Heavy metals can interfere with human and animal vital organs and metabolic processes. They are also persistent and bioaccumulative. So, this study aimed to fabricate composite ceramic membranes (CCM) from Egyptian raw substances to eliminate heavy metals from agricultural wastewater. A ceramic supporting (CS) filter constructed from ball clay, kaolin, feldspar, and quartz using corn starch flour as a pore-developing agent. CS fired at two different temperatures and soaking times. Then, a thin polyamide 6 (PA6) coating was dip-coated over the upper layer of the support membranes. The raw materials and prepared CCM were subjected to characterization and applied to treat agricultural wastewater from the Kitchener drain in Kafr El-Sheikh Governorate, Egypt. The results showed that the CCM (M2) (membrane sintered at 1000 °C/30 min soaking time and modified with PA6) had a higher pure water permeability of 558.5 L h−1 m−2 than the membrane (M4) (membrane sintered at 1100 °C/180 min soaking time and modified with PA6). The study examined how effectively the membranes removed toxic substances from wastewater. The findings exhibited an excellent removal of > 80% and up to 97.02%, > 80% and up to 99.97% of the heavy metals, and optimum fluxes of 341.07 and 276.35 L h−1 m−2 were achieved in the cases of M2 and M4, respectively. Furthermore, with a low flux decline ratio and a high permeate recovery of 92.3% for wastewater, the modified M4 membrane demonstrated remarkable antifouling capabilities.
A lower atherogenic index of plasma was associated with a higher incidence of sarcopenia
Hybrid multi-objective optimization of µ-synthesis robust controller for frequency regulation in isolated microgrids
Abstract Frequency regulation in isolated microgrids is challenging due to system uncertainties and varying load demands. This study presents an optimal µ-synthesis robust control strategy that regulates microgrid frequency while enhancing system performance and stability—a proposed fixed-structure approach for selecting performance and robustness weights, informed by subsystem frequency analysis. The controller is optimized using multi-objective particle swarm optimization (MOPSO) and multi-objective genetic algorithm (MOGA) under inequality constraints, employing a Pareto front to identify optimal solutions. Comparative analyses demonstrate that the MOPSO-optimized controller achieves superior robustness and performance, tolerating up to 236% uncertainty compared to 171% for conventional µ-synthesis controllers. Additionally, it significantly reduces frequency deviation and enhances transient response. Nyquist stability analysis confirms robustness across renewable energy uncertainties. The results highlight the proposed controller’s effectiveness in isolated microgrid frequency regulation, with future work focused on discrete-time implementation for practical digital signal processing (DSP) applications.
In vitro antitumor effects of methanolic extracts of three Ganoderema mushrooms
Abstract Ganoderma mushrooms have a variety of pharmacological activities and may have antitumor effects. Therefore, the antitumor activity of the methanolic fruiting body extracts of three Ganoderma spp. will be evaluated by estimating cell viability, cell cycle parameters and the mode of cellular death. In this regard, Sulfo-rhodamine B staining and flow cytometry were used. Hepatocellular carcinoma (HepG2) and breast ductal carcinoma (T-47D) cell lines were used as cancer models, while mouse normal liver (BNL) and oral epithelial cell (OEC) lines were used as respective controls. The results revealed that Ganoderma resinaceum extract decreased the viability of BNL at an IC50 > 100 µg/mL but not that of HepG2 at an IC50 of 72.32 µg/mL. Additionally, Ganoderma australe and Ganoderma mbrekobenum decreased the viability of OEC cell line at an IC50 of 328.29 and 271.56 µg/ mL, respectively. On the other hand, the IC50 of T-47D were 221.95 and 236.45 µg/mL, respectively. The three extracts arrested the cell life cycle at the G1 phase in each case. G. resinaceum extract stimulated total apoptosis (Q2 + Q4) of 19.99% with low necrosis (Q1). However, the percentages of total cell necrosis in the T-47D cell line treated with the other two extracts were 31.10% and 18.28%, respectively while the percentages of total cell apoptosis were 6.83% and 1.78%, respectively. Thus, G. resinaceum significantly inhibited the viability of the HepG2 cell line, while both the G. australe and G. mbrekobenum extracts significantly decreased the viability of the T-47D cell line. These results may encourage speculation about their possible use for the therapeutic management of hepatocellular carcinoma and breast ductal carcinoma after further investigation.
The role of host plants, land cover and bioclimate in predicting the invasiveness of Aromia bungii on a global scale
Performance improvements and increased ruminal microbial interactions in Angus heifers via supplementation with native rumen bacteria during high-grain challenge
Efficacy and safety of neoadjuvant therapy with tislelizumab plus axitinib for nonmetastatic renal cell carcinoma with inferior vena cava tumor thrombus: a retrospective study
Sociodemographic factors associated with dental students knowledge and attitudes regarding disinfection as a control measure to reduce the spread of COVID-19
Abstract Despite maintaining a lower mortality rate and greater control of victims infected by COVID-19, the world’s population and science are still confronted with this coronavirus. Therefore, the aim was to assess the association between sociodemographic factors and the level of knowledge and attitudes of dental students regarding disinfection as a control measure to reduce the spread of COVID-19. This cross-sectional study evaluated 503 dental students from the capital city and one Peruvian province between February and June 2022. A validated 13-item questionnaire was used. A Poisson regression model with robust variance was used to evaluate the influence of the variables sex, age, year of study, marital status, place of origin, death of a family member due to COVID-19, and history of COVID-19, with the level of knowledge and attitudes of the dental students, considering a significance level of p < 0.05. Of the total participants, 14.3% showed sufficient knowledge, and 89.3% showed positive attitudes regarding disinfection as a control measure to reduce the spread of COVID-19. Furthermore, those from the capital city were 52% less likely to have sufficient knowledge regarding disinfection as a control measure to reduce the spread of COVID-19, compared to those from the province (APR = 0.48; 95% CI: 0.31–0.75). Moreover, none of the variables considered in this study were significantly associated with attitudes toward this topic (p > 0.05). A minority of dental students presented sufficient knowledge, while the majority presented positive attitudes regarding disinfection as a control measure to reduce the spread of COVID-19. In addition, being from the capital city was a limiting factor for sufficient knowledge. The variables sex, age, year of study, marital status, place of origin, death of a family member due to COVID-19, and history of COVID-19 were not influential factors for positive attitudes on this topic.
Prognostic factors for survival in patients with advanced cholangiocarcinoma treated with percutaneous transhepatic drainage
Abstract Biliary drainage is then one of the necessary procedures to help patients suffering from icterus to reduce serum bilirubin levels and relieve symptoms. The aim of this study was identifying risk factors for survival in patients with cholangiocarcinoma (CCA) treated with percutaneous transhepatic biliary drainage (PTBD) and to develop a simple scoring system predicting survival from PTBD insertion. This single-centre retrospective study included 175 consecutive patients undergoing PTBD for extrahepatic CCA (perihilar and distal). Prognostic factors affecting survival of patients with CCA treated with PTBD were analysed. A multivariate analysis showed that mass forming tumor with mass larger than 5 cm and presence of metastasis at the time of PTBD served as a negative prognostic factor (p = 0.002), better survival was associated with lower preprocedural bilirubin and lower CRP (p = 0.003). Multivariate analysis identified two significant risk factors for 3-month mortality: mass-forming tumors and bilirubin levels exceeding 185 µmol/L. A simple scoring system was developed to predict 3-month mortality after PTBD in patients with advanced CCA, demonstrating 86.3% negative predictive value and 43.2% positive predictive value.
Socioeconomic disparities in mortality from indoor air pollution: A multi-country study
Background Indoor air pollution is a major public health concern, contributing to approximately 2.9 million deaths and 81.1 million disability-adjusted life years lost annually. This issue disproportionately affects underprivileged communities that depend on solid fuels for cooking. As a result, these communities suffer from heightened exposure to indoor air pollutants, which increases the risk of morbidity, mortality, and worsening health disparities. Objective This study investigates the association between socioeconomic status and mortality related to indoor air pollution across multiple countries. Methods Data from the 2019 Demographic and Health Survey, WHO, and World Bank were utilized to examine the impact of socioeconomic status on indoor air pollution-related mortality. The primary outcome was mortality associated with solid fuel use, with income quintiles as the independent variable. Linear and logistic regression analyses were applied to assess these relationships. Results Logistic regression analysis revealed a strong negative association where household income increases and indoor air pollution-related mortality significantly decreases. Specifically, Households in the highest income quartile showed a 22% reduction progressively in the odds of mortality risk compared to the lowest income quintile. Additionally, access to clean fuel correlated with a 0.59 times lower odds of mortality, highlighting the clean energy sources’ protecting effect. Conclusion The findings highlight the critical need to prioritize clean fuel access, particularly in low-income communities, to reduce indoor air pollution mortality. Policies should focus on increasing clean energy accessibility and supporting vulnerable populations through targeted subsidies and poverty alleviation programs to reduce indoor air pollution exposure disparities.