Browse Articles

Hmox1 expression indicates distinct injury phenotypes of proximal tubule cells in sepsis-associated acute kidney injury

Intrinsically accelerated cellular degradation is amplified by TDP-43 loss in ALS-vulnerable motor neurons in a zebrafish model

Improving phenolic antioxidants in Illicium griffithii fruit through optimizing subcritical water extraction method

Single-cell mRNA-regulation analysis reveals cell type-specific mechanisms of type 2 diabetes

Abstract Perturbed secretion of insulin and other pancreatic islet hormones is the main cause of type 2 diabetes (T2D). The islets harbor five cell types that are potentially altered differently by T2D. Whole-islet transcriptomics and single-cell RNA-sequencing (scRNAseq) studies have revealed differentially expressed genes without reaching consensus. Here, we demonstrate that further insights into T2D disease mechanisms can be obtained by network-based analysis of scRNAseq data from individual cell types. We developed differential gene coordination network analysis (dGCNA) and analyzed islet SmartSeq2 scRNAseq data from 16 T2D and 16 non-T2D individuals. dGCNA reveals T2D-induced cell type-specific networks of dysregulated genes with remarkable ontological specificity, thus allowing for a comprehensive and unbiased functional classification of genes involved in T2D. In beta cells eleven networks of genes are detected, revealing that mitochondrial electron transport chain, glycolysis, cytoskeleton organization, cell proliferation, unfolded protein response and three networks of beta cell transcription factors are perturbed, whereas exocytosis, lysosomal regulation and insulin translation programs are instead enhanced in T2D. Furthermore, we validated the ability of dGCNA to reveal disease mechanisms and predict the functional context of genes by showing that TMEM176A/B regulates beta cell microfilament organization and that CEPBG is an important regulator of the unfolded protein response. In addition, when comparing beta- and alpha cells, we found substantial differences, reproduced across independent datasets, confirming cell type-specific alterations in T2D. We conclude that analysis of networks of differentially coordinated genes provides detailed insight into cell type-specific gene function and T2D pathophysiology.

Experimental evidence for social learning in semi-natural, wild-type Norway rats

Abstract Animals may acquire information about their environment by social learning. Social transmission can affect the rate of trait acquisition and performance. It is often unclear, how behaviours are acquired when social information is available. In particular, the role of social learning in the acquisition of non-intuitive tasks is currently obscure. We asked whether wild-type Norway rats, Rattus norvegicus , in six semi-natural outside colonies benefit from each other in the acquisition and performance of a non-intuitive task by social learning. The task involved the opening of a seesaw mechanism to obtain a food reward. We induced innovations in four of six colonies and controlled the number of trained individuals and the relatedness composition. The latency to the first successful seesaw manipulation was shorter for naïve rats living with four experienced rats than for those living with zero or two experienced rats. The intervals between successful seesaw manipulations were not affected by the number of experienced rats and the colonies’ relatedness composition. Rats in four colonies innovated seesaw manipulations by trial-and-error learning. Our data show that information about the solution of a non-intuitive food acquiring problem can be socially transmitted among wild-type Norway rats, irrespective of the colonies’ relatedness composition.

Author Correction: Barcoded HIV-1 reveals viral persistence driven by clonal proliferation and distinct epigenetic patterns

Assessing quality of life in papillary thyroid cancer through a cross-sectional comparative study of SF-26 versus ThyPRO questionnaire

Accelerated growth increases the somatic epimutation rate in trees

Abstract Trees are integral to ecosystems and hold considerable economic importance. Their exceptional longevity and modular structure also make them valuable models for studying the long-term accumulation of somatic mutations and epimutations in plants. Empirical evidence indicates that the annual rate of these stochastic events correlates negatively with generation time, suggesting that species with long lifespans have evolved mechanisms to mitigate the build-up of deleterious somatic variants. It has been hypothesized that this reduction is achieved by slowing growth and minimizing the number of cell divisions per unit time, thereby reducing errors associated with DNA replication. However, a direct test of this “mitotic-rate hypothesis” remains technically challenging. Here we take advantage of a 150 year-old experiment in European beech to show that a thinning-induced growth acceleration increases the annual rate of somatic epimutations in main stems and lateral branches of trees. We demonstrate that this effect is accompanied by a proportional increase in the rate of cell divisions per unit time. These findings support the notion that life-history constraints on growth rates in trees are not merely a trade-off between resource allocation and structural stability but also a strategy to preserve genetic and epigenetic fidelity over extended lifespans.

LAMP using distance based paper device for quantitative detection of ESBL producing Escherichia coli in UTI patients

Cucurbitacin B suppresses glioblastoma via the STAT3/ROS/endoplasmic reticulum stress pathway

Abstract Cucurbitacin B (CuB), the tetracyclic triterpenoid compound belonging to cucurbitaceae family, demonstrates significant anticancer properties across various tumor types. However, its efficacy against glioblastoma (GBM), particularly in overcoming blood-brain barrier (BBB) and unique tumor microenvironment, remains a critical area of investigation. Our study reveals that CuB causes GBM cell apoptosis via STAT3/ROS/endoplasmic reticulum (ER) stress pathway. To enhance targeted delivery, we developed M@CuB-Lips, a novel formulation of CuB encapsulated in cancer cell membrane-coated liposomes. In vitro and in vivo experimental results verified targeted M@CuB-Lips delivery to brain tumor sites, demonstrating significant tumor inhibition and enhanced effector T-cell infiltration. These findings underscore CuB’s potential in GBM therapy, particularly through STAT3 suppression and ROS-ER Stress pathway activation, while M@CuB-Lips offers a promising strategy for targeted drug delivery.

Comparison of inflammatory cells, C-reactive protein, and lipid profile in atherosclerotic cardiovascular disease patients and healthy controls in Northwest Ethiopia

Abstract Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of global mortality and disability. Inflammation is increasingly recognized as a key driver in the pathogenesis of ASCVD. However, data on the specific distribution of inflammatory cells, high-sensitivity C-reactive protein (hsCRP), and lipid profiles among ASCVD patients in this region remain limited. Therefore, we aimed to assess the distribution of inflammatory cells, hsCRP, and lipid profile among ASCVD patients and healthy controls. A comparative cross-sectional study was conducted from June to November 2024 at the University of Gondar Comprehensive Specialized Hospital, recruiting 101 ASCVD patients and 101 healthy controls using simple random sampling. Sociodemographic, behavioral, and clinical data were collected using a structured questionnaire, measurement, and chart review. Six milliliters of fasting venous blood were drawn for complete blood count (CBC), hsCRP, and lipid profile analyses. Data were analyzed using STATA version 14. Means were compared using independent t-tests and ANOVA at a significance level of p  ≤ 0.05 with a 95% confidence interval. Of the 202 study participants, 101 were ASCVD patients and 101 were healthy controls. The mean ages were 57.42 ± 12.88 years for ASCVD patients and 56.30 ± 10.93 years for healthy controls. ASCVD patients had higher mean WBC and neutrophil counts, and higher median monocytes, NLR, MLR, hsCRP, TC, and LDL ( p  ≤ 0.05) than controls, while median eosinophil levels were significantly lower ( p  ≤ 0.05). Our study showed that ASCVD patients had higher inflammatory cell counts, ratios (NLR, MLR), hsCRP, total cholesterol, and LDL than controls, highlighting the potential roles of inflammation and dyslipidemia in ASCVD development.

Design of cold asphalt concrete with experimental emulsion by magnetic induction

Sonochemically enhanced activated charcoal with improved oral palatability for efficient in vitro detoxification of acetaminophen

Characterization and evaluation of the fruit mucilage of Cordia africana as tablet binder in paracetamol tablet formulation: optimization study

Development and validation of predictive model for emesis in cervical cancer patients receiving concurrent chemoradiotherapy based on multi-institutional retrospective study

Abstract Chemotherapy-induced nausea and vomiting (CINV) remains a significant barrier to treatment adherence and quality of life in cervical cancer patients receiving chemoradiotherapy. No validated predictive models exist to assess CINV risk in this population. We aimed to develop and temporal validate a predictive model for CINV incidence in cervical cancer patients receiving concurrent chemoradiotherapy (CCRT). This multi-institutional, retrospective cohort study analyzed 921 patients who received CCRT with weekly cisplatin (40 mg/m 2 ) between January 2016 and March 2024. Candidate predictors were selected through expert consultations and literature reviews. A multivariate logistic regression model was developed using training data, and the model with the highest receiver operating characteristic-area under the curve (ROC-AUC) was tested using validation data. The model (age, smoking history, total radiation dose, chemotherapy history, 5-hydroxytryptamine 3 receptor antagonist use, cancer stage) showed good discrimination. In the training dataset, the model achieved an ROC-AUC of 0.772 (95% confidence interval [CI], 0.717–0.827). In the validation dataset, the model showed high discriminative ability (ROC-AUC, 0.808; 95%CI, 0.763–0.853) and good calibration (intraclass correlation coefficient, 0.826; p  < 0.001). We developed and validated a clinically useful CINV prediction model for cervical cancer patients receiving CCRT. This tool may individualize antiemetic strategies and improve care.

Bioinformatics approach for discovery of potential lead compound of NSP6 of SARS-CoV-2 using structure based virtual screening and molecular dynamics simulations

Abstract Non-Structural Protein 6 (NSP6) is a crucial protein for SARS-CoV-2 as it performs a vital role in the replication and transcription of the virus. NSP6 plays a role in the stress response of the endoplasmic reticulum through binding to the sigma receptor 1 (SR1). Therefore, NSP6 is an interesting target for fighting SARS-CoV-2. The best model of the tertiary structure of NSP6 was predicted using the AlphaFold server, then this model was refined using the DeepRefiner server to construct a good-quality model. The current study utilized the virtual screening of the ZINC20 database for the identification of possible inhibitors using the computational docking Autodock Vina program and the post-docking analysis of energy calculations and interactions followed by ADMET studies which were widely used in potential hit identification and lead optimization. From the final hits, our study revealed the best eight compounds that have more potential to be considered as lead compounds for inhibition of a vital role of NSP6 in the replication and transcription of the virus with docking score and binding energy (E_score) with values ranging from − 49.64 to − 43.13 and all these compounds displayed no cellular cytotoxicity. These complexes in addition to the apoprotein were validated and analyzed using molecular dynamic simulations (MDS) for 100 ns. The NSP6-ZINC-141,457,420, NSP6-ZINC-075486396 complexes, revealed the highest complexes in H-bonds formation, NSP6-ZINC-018529632 complex was found to have the lowest binding energy score and NSP6-ZINC-089639800, NSP6-ZINC-157,610,683, NSP6-ZINC-075484852, NSP6-ZINC-016611776, and NSP6-ZINC-141,457,420 complexes presented no a significant deviation from the NSP6-apo protein in the Rg and SASA analysis and revealed promising performance in the RMSD and RMSF. This study exhibited that ZINC-141,457,420, ZINC-075486396, and ZINC-018529632 ligands have a promising potential for development as potential vaccines inhibiting SARS-CoV-2 NSP6 protein.

The size of the functional base of support decreases with age

Abstract Falls occur more often as we age. To identify people at risk of falling, balance analysis requires an accurate base-of-support model. We previously developed a functional base-of-support (fBOS) model for standing young adults and showed that its area is smaller than the footprint area. Our fBOS model is a polygon that contains centre-of-pressure (COP) trajectories recorded as standing participants move their COP in the largest possible loop while keeping their feet flat on the ground. Here we assess how the size of the fBOS changes with age by comparing 38 younger (YA), 14 middle-aged (MA), and 34 older adults (OA). The fBOS area is smaller in older adults: OA area is 58% of the YA area ( $$p<0.001$$ ), and 59% of the MA area ( $$p=0.001$$ ), with no difference between YA and MA. The reduction in fBOS area among the OA is primarily caused by a reduction in the length of the fBOS. In addition, among older adults smaller fBOS areas correlated with a lower score on the Short Physical Performance Battery ( $$\tau$$ =0.28, $$p=0.04$$ ), a reduced walking speed ( $$\tau$$ =0.25, $$p=0.04$$ ), and a higher frailty level ( $$p=0.09$$ ). So that others can extend our work, we have made our fBOS models available online.

Stochastic geometry analysis of UAV-assisted networks with probabilistic UAV activation

Abstract Unmanned aerial vehicles (UAVs) are prominent to modern wireless networks but are severely limited by onboard energy, making continuous operation of a large swarm infeasible. To address this, this paper proposes a probabilistic activation scheme in which UAVs from a larger candidate pool can enter sleep states to conserve energy. Leveraging a tractable probabilistic activation strategy, each UAV is independently switched on with an optimized probability that can be easily derived in closed form. This differentiates our work from earlier heuristic sleep-mode techniques which rely on traffic-threshold rules without analytical guarantees. We thus develop a tractable analytical framework using stochastic geometry to evaluate this scheme, modeling the active UAVs as a 3D Poisson Point Process (PPP) under a probabilistic Line-of-Sight (LoS)/Non Line-of-Sight (NLoS) propagation conditions. Novel analytical expressions are derived for the coverage probability, average achievable rate, and network energy efficiency, with their accuracy rigorously validated by Monte Carlo simulations. Our analysis reveals a fundamental trade-off between network performance and power consumption, demonstrating the existence of an optimal activation probability that maximizes the system performance. This analytical framework enables network operators to optimize activation and transmit power profiles across the entire network. The goal of this optimization is to improve outage probability, ergodic rate, and energy efficiency.

Inter-machine harmonization of multicenter echocardiographic images for improvement of left ventricular ejection fraction prediction model

A chaos-based augmented image encryption scheme for satellite images using Fredkin logic

Abstract This article presents a novel augmented image encryption algorithm tailored for securing satellite images, addressing the critical need for robust protection of sensitive geographic data. Implementing Shannon’s principles of confusion and diffusion, the method begins by augmenting multiple plain images into a single large image, followed by a three-stage encryption process. Initially, the augmented image is separated into its three color channels, which are transformed into one-dimensional (1D) bit-streams, split and altered using the Gauss Circle map, and restructured via Fredkin Gates to enhance unpredictability. Subsequently, the bit-streams are converted into 1D bytes and $$2 \times 2$$ matrices, processed through three systems incorporating hyperchaos-induced keys and dynamic Hill Cipher matrices for additional confusion and diffusion. The final stage combines these encrypted streams into one image while preserving the integrity of color data. The proposed method achieves strong security metrics, including an average Number of Pixels Change Rate (NPCR) of $$99.6115\%$$ , a Unified Average Changing Intensity (UACI) of $$31.71\%$$ , and high entropy values (e.g., 7.9989) for encrypted images, ensuring robust resistance to differential and statistical attacks. The encryption demonstrates computational efficiency with an encryption time of 0.2817s for $$256\times 256$$ images and maintains a low Peak Signal-to-Noise Ratio (PSNR) of 8.1 dB, reflecting effective data obfuscation. This multistage chaos-based approach, leveraging Fredkin logic gates and hyperchaos-induced keys, significantly enhances security, scalability, and efficiency, making it ideal for high-stakes satellite imagery applications where data integrity and confidentiality are paramount.