Browse Articles

Network toxicology and bioinformatics analysis reveal the molecular mechanisms of polyethylene terephthalate microplastics in exacerbating diabetic nephropathy

Cryptic absence and genetic variation of Plasmodium falciparum PfHRP2 and PfHRP3 from isolates in Papua, Indonesia

Suppressing Phase Transitions and High-Pressure Amorphization through Tethered Organic Cations in Organochalcogenide-Halide Perovskites

Association between endogenous lactate accumulation and dysregulated activation of the NLRP3 inflammasome pathway in schizophrenia

Methodological analysis of synthesizing graphene from carbon source solvents

Drosophila melanogaster as a model organism to investigate sex specific differences

Abstract Sex differences in physiology, anatomy, behavior, and genetics are well-documented throughout the animal kingdom. These differences are often neglected in research. This imbalance can have detrimental effects, as seen in cases where certain drugs have stronger side effects in females than in males. The fruit fly, Drosophila melanogaster, presents a promising model for studying these sex-specific differences because it shares many disease-related genes and is easy to use. RNA of 10-day-old and 30-day-old D. melanogaster (w1118) was isolated and sequenced. In 10-day-old flies 3969 genes are significantly higher expressed in males than in females, and 7176 genes are significantly lower expressed in males. In 30-day-old males 3735 genes are significantly higher expressed than in females, and 7101 genes are significantly lower expressed. In detail, the present study shows that male flies exhibit higher expression levels of genes involved in toll signaling, Imd signaling, insulin signaling, and lipid metabolism. These findings highlight D. melanogaster as a valuable model organism for studying sex differences in these highly conserved signaling pathways. This model could help analyzing the sex-specific effects of dietary interventions or drugs, ultimately leading to a better understanding of sex-specific interconnections and improving the development of more effective, sex-specific medical treatments.

NECKCHECK PROJECT: enhancing diagnostic accuracy in oropharyngeal squamous cell carcinoma through computer-based radiological tools

Abstract The interpretation of radiological images for head and neck tumors often lacks standardized protocols, increasing the risk of diagnostic inconsistencies. This study introduces a computerized radiological checklist designed to enhance diagnostic accuracy and standardize the evaluation of oropharyngeal squamous cell carcinoma (OPSCC) imaging among otolaryngologists (ENTs). A radiological checklist was developed based on a comprehensive literature review and digitized into an intuitive interface. A concordance study involving 15 ENTs was conducted, assessing 90 OPSCC cases in two phases: before and after using the checklist. Diagnostic agreement with radiologists was measured using Cohen’s kappa coefficient, and a mixed-effects linear model evaluated accuracy improvements, accounting for patient sex, age, stage, and HPV status. The checklist significantly improved diagnostic concordance, increasing Cohen’s kappa from 0.28 (95% CI: 0.09–0.46) without it to 0.66 (95% CI: 0.55–0.77) with it (p < 0.01). The mixed-effects model showed a mean improvement of 2.66 correct responses in the checklist group (SE 0.31, p < 0.001). This study supports the effectiveness of this checklist in improving the diagnostic consistency and accuracy of OPSCC imaging. This method shows promise as a practical tool to reduce errors and enhance clinical practice among ENT specialists.

3D-printed temperature and shear stress-controlled rocker platform for enhanced biofilm incubation

Abstract Growing biofilms of thermophilic (heat-loving) and psychrotrophic (cold-tolerant) bacteria pose several challenges due to specific environmental requirements. Thermophilic bacteria typically grow between 45 and 80  $$^{\circ }$$ C, while psychrotrophic bacteria thrive between 0 and 15  $$^{\circ }$$ C. Maintaining the precise temperature and fluid conditions required for biofilm growth can be technically challenging. To overcome these challenges, we designed the Bio-Rocker, a temperature- and shear stress-controlled rocker platform for biofilm incubation. The platform supports temperatures between − 9 and 99  $$^{\circ }$$ C, while its digital controller can adjust the rocking speed from 1 to 99 $$^{\circ }$$ /s and set rocking angles up to ±19 $$^{\circ }$$ . This ability, together with data from analytical models and multi-physics simulations, provides control over the shear stress distribution at the growth surfaces, peaking at 2.4 N/m $$^2$$ . Finally, we evaluated the system’s ability to grow bacteria at different temperatures, shear stress, and materials by looking at the coverage and thickness of the biofilm, as well as the total biomass. A step-by-step guide, 3D CAD files, and controller software is provided for easy replication of the Bio-Rocker, using mostly 3D-printed and off-the-shelf components. We conclude that the Bio-Rocker’s performance is comparable to high-end commercial systems like the Enviro-Genie (Scientific Industries) yet costs less than $350 dollars to produce.

Effect of dolutegravir-based antiretroviral therapy on glycemic control in female mice

Powdery mildew resistance prediction in Barley (Hordeum Vulgare L) with emphasis on machine learning approaches

Abstract By employing machine-learning models, this study utilizes agronomical and molecular features to predict powdery mildew disease resistance in Barley (Hordeum Vulgare L). A 130-line F8-F9 barley population caused Badia and Kavir to grow at the Gonbad Kavous University Research Farm on three planting dates (19 November, 19 January, and 19 March), with three replicates in 2018/2019 and 2019/2020. The study employed RReliefF, MRMR, and F-Test feature selection algorithms to identify essential phenotype traits and molecular markers. Subsequently, Decision Tree, Random Forest, Neural Network, and Gaussian Process Regression models were compared using MAE, RMSE, and R2 metrics. The Bayesian algorithm was utilized to optimize the parameters of the machine-learning models. The results indicated that the Neural Network model accurately predicted powdery mildew disease resistance in barley lines. The evaluation based on high R2 values, as well as low MAE and RMSE, highlighted the efficacy of these models in identifying significant phenotype traits and molecular markers associated with disease resistance. The findings demonstrate machine learning models’ potential in accurately predicting powdery mildew disease resistance in Barley. The neural network model specifically showed excellent results in this area because it managed to identify critical phenotypic traits and molecular markers very well. This research highlights the importance of combining AI with molecular markers for improved disease resistance and other desirable crop traits during plant breeding.

Impact of incubation and gestation periods on the dynamics of a spatially heterogeneous eco-epidemiological model

Analysis of the influencing factors for adverse reproductive outcomes in patients with positive TPOAb and the establishment of a nomogram prediction model

Reliability assessment and small signal analysis of the enhanced switched impedance inverter with low input current ripple

Comprehensive assessment of the acute lethal, risk level, and sub-lethal effects of four insecticides on Trichogramma ostriniae

Trichogramma ostriniae is one of the most successfully used natural enemies in the integrated management of agroforestry pests. However, the extensive use of insecticides poses a significant threat to the survival and efficacy of T. ostriniae. To assess the compatibility of chemical pesticides with T. ostriniae, we investigated the acute toxicity, risk level, and sub-lethal effects of four insecticides (chlorfenapyr, emamectin benzoate, phoxim, and lambda-cyhalothrin) on reproduction, parasitism, detoxification enzymes, protective enzyme activities, and active substances under laboratory conditions. The results revealed that phoxim had the highest acute toxicity, with a median lethal concentration value of 2.8 × 10−7 mg/mL, whereas chlorfenapyr had the lowest at 5.06 × 10−3 mg/mL. Emamectin benzoate was classified as high risk, whereas the others were classified as extremely high risk. Insecticide exposure during the larval and pupal stages significantly reduced the emergence of T. ostriniae (P < 0.05). Lambda-cyhalothrin, emamectin benzoate, and chlorfenapyr extended the time required for prey-handling and reduced parasitism efficiency by 0.70%, 2.45%, and 4.50%. In contrast, phoxim increased the time required for prey-handling and improved parasitism efficiency by 25.37%. All insecticides affected protective enzyme activities, induced detoxification enzyme activity, reactive oxygen species, malondialdehyde and mitochondrial respiratory chain complex I levels, and decreased the adenosine triphosphate level. These findings underscore the differential impacts of insecticides on T. ostriniae and emphasize the need for cautious pesticide selection to balance pest control and natural enemy conservation, providing essential scientific guidance for sustainable agroforestry pest management.

Effect of HER2-low expression on the efficacy of neoadjuvant chemotherapy in triple-negative breast cancer

SEIQRS model analysis and optimal control with two delays

This paper addresses the limitations of assuming a bilinear infection rate in computer virus propagation models by proposing a more realistic nonlinear dual-delay SEIQRS model. The key focus of the research includes analyzing the local stability of the disease-free equilibrium point and the existence of positive equilibrium points with different delays. To further enhance control effectiveness, time-varying control terms are introduced, and a corresponding Hamiltonian function is constructed. The optimal control strategy is derived using the Pontryagin’s maximum principle. Numerical simulation experiments are conducted to validate the dynamic characteristics of the model, and the model’s properties are verified on real-time propagation networks. The experimental results demonstrate that appropriate control strategies can effectively suppress the spread of computer viruses.

Anisotropic persistent random walk model simulates T-cells migration over curved landscapes

Abstract Cell migration is an important cellular process to study, as it plays a fundamental role in tissue structuring and development, while abnormal cell migration may be the cause of certain diseases. Among the known factors influencing cell migration, substrate curvature is one, with cells naturally moving towards concave areas while avoiding convex ones. The underlying causes of migration guidance by curvature remain unclear, and in particular, the way in which cell persistence is affected is still not well understood. We introduce an anisotropic persistent random walk model which includes cell heterogeneity to simulate T-cell migration across various corrugate landscapes. We compared the trajectories generated by the model with in vitro T-cells trajectories over the same topographies. The model accurately captures key features of cell trajectories on flat surfaces as well as on curved surfaces, such as a directional bias toward concave regions. The model also reveals a superdiffusive behavior on curvature, demonstrating more efficient movement compared to flat surfaces. The anisotropic randomness incorporated in the model appears as a critical feature which shapes T-cells persistence mechanisms by increasing cellular activity in the axis of concave valleys and promoting migration towards concave areas.

Changes in working and health conditions in different sectors between 2010 and 2019 in a representative sample of French workers

Background Studies on evolution of working conditions and workers’ health in developed countries show conflicting results, and often fail to identify evolutions by occupational sector. This study aimed to describe recent evolutions of working conditions and health status of French workers over a decade across different activity sectors. Methods and findings Data from a large national French observatory of working conditions and health were used. This observatory collects, for a random sample of employees, data on their working conditions and health. Data on working conditions and health status of employees in the 13 main French activity sectors in 2010–2011 and 2018–2019 were used. A 2 steps weighting method was used to allow results to be extrapolated to the French employees. Overall, 23,951 and 26,538 questionnaires were collected for the 2010–2011 and 2018–2019 periods, respectively. Job demand significantly decreased and job resources significantly increased between 2010–2011 and 2018–2019 while physical workload decreased. Neck, wrist, elbow and back pain disorders significantly decreased as well as anxiety, whereas shoulder disorders, fatigue and sleep disorders increased. However, there were large and significant disparities in evolutions between sectors, for exposures and health status. The most worrisome sector was the Human health and social work sector with no improvement in the working conditions and increasing health problems. Conclusion Overall, evolutions and prevalence of physical exposure or health problems do not bring the same information. It is important to understand the extent to which psychosocial, physical working conditions and health status differ among various industry categories, and to study the determinants of such disparities such that policy and practice interventions to reduce disparities can be appropriately directed. It is also important to continue monitoring health of workers, as the impact of changes in working conditions may not have an immediate effect, particularly for conditions with a long latency period.

Huangqi fuling decoction inhibits the invasion and metastasis of gastric cancer via the TNF signaling pathway

Probing the binding hypothesis of Smad3 modulators by molecular dynamic simulations for Atherosclerosis Cardiovascular Disease (ASCVD)

Transforming Growth Factor β (TGFβ) pathway has been recognized as one of the major processes involved in fibrotic diseases including the Atherosclerosis Cardiovascular Disease (ASCVD). Many drugs have been proposed and are under clinical trials for modulation of the TGFβ pathway by targeting TGFβ receptor. Recently, various proof of the concept studies have advocated that the inhibition of TGFβ-mediated Smad pathway could produce more focused effect with less off target toxicities in ASCVD. As these studies lack the mechanism and binding profile of Smad3 modulators, characterization of binding pattern for Smad3 inhibitors can provide a platform for the lead optimization against ASCVD. We utilized dimeric inhibitors from the PubChem dataset (PubChem ID: 630) of Smad3-FoxH1 binding inhibitors to generate binding hypothesis of Smad3 inhibitors. The selected compounds from the dataset were docked and ligand-protein complexes were simulated for 250 ns for further sampling of conformational space and to obtain stable binding hypothesis. Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF) and hydrogen bond analysis of ligand-protein complexes after simulations revealed that Asn320 in Smad3 provides an efficient inhibition site for the two most potent small inhibitors (hereby named SM1 and SM2) of Smad3-FoxH1 binding. Although the diverse nature of compounds produce variable interaction patterns with FoxH1 binding site in Smad3, Gln315, Gln364 and Arg367 were observed to be the most common hydrogen bond interaction points in this binding site. Additionally, two compounds (hereby named SM8 and SM19) detached from the FoxH1 binding site and formed a highly stable complex around Tyr323 via hydrophobic complementarity, suggesting a new binding site for modulation of Smad3 activity.