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Electrostatic-repulsion-based transfer of van der Waals materials

Commensal yeast promotes Salmonella Typhimurium virulence

Abstract Enteric pathogens engage in complex interactions with the host and the resident microbiota to establish gut colonization 1–3 . Although mechanistic interactions between enteric pathogens and bacterial commensals have been extensively studied, whether and how commensal fungi affect enteric infections remain largely unknown 1 . Here we show that colonization with the common human gut commensal fungus Candida albicans worsened infections with the enteric pathogen Salmonella enterica subsp. enterica serovar Typhimurium. The presence of C. albicans in the mouse gut increased Salmonella caecal colonization and systemic dissemination. We investigated the underlying mechanism and found that Salmonella binds to C. albicans via type 1 fimbriae and uses its type 3 secretion system to deliver effector proteins into C. albicans . A specific effector, SopB, was sufficient to manipulate C. albicans metabolism and trigger the release of millimolar amounts of arginine into the extracellular environment. The released arginine, in turn, induced expression of the type 3 secretion system in Salmonella , increasing its invasion of epithelial cells. C. albicans deficient in arginine production was unable to increase Salmonella virulence. Arginine-producing C. albicans also dampened the inflammatory response during Salmonella infection. Arginine supplementation in the absence of C. albicans increased the systemic spread of Salmonella and decreased the inflammatory response, phenocopying the presence of C. albicans . In summary, we identified C. albicans colonization as a susceptibility factor for disseminated Salmonella infection and arginine as a central metabolite in the cross-kingdom interaction between fungi, bacteria and host.

Inhibition of XPO1 by selinexor enhances terminal erythroid maturation through modulation of HSP70 trafficking in severe β0-thalassemia/HbE

Ineffective erythropoiesis is a hallmark of β-thalassemia, characterized by impaired erythroid maturation and increased apoptosis of erythroid precursors in the bone marrow, resulting in chronic anemia. Heat shock protein 70 (HSP70) trafficking has emerged as a critical regulator of erythroid maturation. Inhibition of nuclear export protein exportin-1 (XPO1) retains HSP70 in the nucleus, thereby promoting terminal erythroid maturation (TEM) through stabilization of the transcription factor GATA1. In this study, we screened nine XPO1 inhibitors, including the natural compounds curcumin, piperlongumine, plumbagin, and oridonin, as well as the synthetic agents KPT-185, KPT-276, selinexor, verdinexor, and eltanexor, in erythroid progenitors from patients with severe β0-thalassemia/HbE to identify the most effective inducer of TEM and to investigate the downstream molecular mechanisms involved. Selinexor, an FDA-approved drug for multiple myeloma, showed the greatest efficacy in enhancing TEM across nine independent patient samples without altering hemoglobin composition. Combination treatments with hydroxyurea (a γ-globin inducer) and SIS3 (a SMAD3 inhibitor) confirmed selinexor’s dominant effect. Mechanistically, selinexor-induced TEM was associated not only with stabilization of nuclear HSP70 and GATA1 but also with a dose-dependent increase in cytoplasmic HSP70. These findings suggest that cytoplasmic HSP70 trafficking may contribute to erythroid maturation in severe β0-thalassemia/HbE, implicating regulatory pathways beyond nuclear GATA1 stabilization. Collectively, our findings highlight the therapeutic potential of repurposing selinexor to enhance erythroid maturation in β-thalassemia and suggest that cytoplasmic HSP70 trafficking warrants further investigation as a contributor to terminal erythroid maturation in β-thalassemia.

Interventions for treating posterior cruciate ligament injuries of the knee in adults: A systematic review and meta-analysis protocol

Introduction There are many different surgical and conservative interventions for treating posterior cruciate ligament (PCL) injuries of the knee in adults. However, in the literature, there is no consensus regarding the best intervention for treating these patients. The objective of this systematic review and meta-analysis is to analyse the effectiveness of interventions (surgical and conservative) for treating PCL injuries of the knee in adults. Methods and analysis Starting in November 2025, the authors will accomplish a detailed search using the MEDLINE/PubMed, EMBASE, Cochrane Central Register of Controlled Trials and LILACS databases. Relevant gray literature (academic papers, reference lists theses, technical reports and conference abstracts) will also be included. Two authors will independently screen and extract the information found from randomized controlled trials in the literature. The bias and quality of the included studies will be evaluated using the Risk of Bias 2 tool provided by the Cochrane Collaboration. Statistical analyses will be performed using Review Manager V.5.4/Review Manager Web software. Discussion This systematic review is aimed at providing practical information for Orthopaedic surgeons on the effectiveness of the interventions (surgical and conservative) for treating PCL injuries of the knee in adults. PROSPERO registration number CRD42023430493.

Role of weight-adjusted waist circumference index and non-high-density lipoprotein cholesterol/high-density lipoprotein cholesterol ratio in prediabetes risk: A mediation analysis

Objective This study aims to investigate the prevalence of prediabetes among U.S. adults using NHANES 2017–2023 data, explore the relationship between the weight-adjusted waist circumference index (WWI) and the non-high-density lipoprotein cholesterol/high-density lipoprotein cholesterol ratio (NHHR) and the risk of prediabetes, and provide a theoretical foundation for the early identification and intervention of prediabetes. Methods Logistic regression, linear regression model, subgroup analysis, restricted cubic spline (RCS) analysis, and mediation analysis were employed to examine the effect of WWI on prediabetes and the mediating role of NHHR. Results A total of 9,713 adults were included in the analysis, with an age range of 20–80 and a mean age of 51.26 ± 17.25. Of these, 3,208 cases (33.03%) were prediabetic. The adjusted model showed that WWI (OR = 1.55, 95% CI: 1.46, 1.64) may increase the probability of prediabetes. The results of subgroup analysis, stratified by factors such as gender and age, largely supported these findings. The adjusted model also indicated that NHHR (OR = 1.20, 95% CI: 1.16, 1.25) may increase the probability of prediabetes. The RCS analysis revealed a nonlinear relationship between both WWI and NHHR with the risk of prediabetes. Further mediation analysis indicated that NHHR mediated 10% of the effect of WWI on prediabetes. Conclusion WWI and NHHR were significantly associated with the risk of prediabetes, and NHHR partially mediated the effect of WWI on prediabetes. This study provides a new theoretical basis for early prediabetes screening, prevention, and control. In the future, interventions targeting WWI and NHHR through lifestyle changes may be effective in preventing prediabetes.

Electrostatic-based transfer keeps 2D materials ultra-clean

Multi-task learning by using contextualized word representations for syntactic parsing of a morphologically rich language

We address the challenge of syntactic parsing for Urdu, a morphologically rich language, and present state-of-the-art results for both constituency and dependency parsing. This paper offers four major contributions: 1) the conversion of the CLE-UTB phrase structure treebank into a dependency treebank by developing language-specific head-word and phrase-to-dependency label mapping rules; 2) a novel sequence labeling scheme that transforms the parsing task into a unified representation; 3) the training of contextualized word representations on a large 220 million tokens Urdu corpus collected from the web; and 4) development of parsing framework using two learning paradigms, single-task and multi-task learning. Several post-processing rules are applied to improve the quality of the automatically converted dependency structure treebank. The proposed sequence labeling scheme enables the use of a shared architecture that learns the syntactic structures from both grammatical structures simultaneously and hence improves generalization. Experiments show that the multi-task learning setup significantly enhances parsing performance, achieving an F1 score of 91.39 for constituency parsing (an improvement of 3.29 points) and a labeled attachment score of 85.69 for dependency parsing (an improvement of 1.49 points). These results demonstrate that learning cross-task representations provides measurable benefits and advances the state of syntactic parsing for Urdu.

Expanding the cytokine receptor alphabet reprograms T cells into diverse states

Abstract T cells respond to cytokines through receptor dimers that have been selected over the course of evolution to activate canonical JAK–STAT signalling and gene expression programs1. However, the potential combinatorial diversity of JAK–STAT receptor pairings can be expanded by exploring the untapped biology of alternative non-natural pairings. Here we exploited the common γ chain (γc) receptor as a shared signalling hub on T cells and enforced the expression of both natural and non-natural heterodimeric JAK–STAT receptor pairings using an orthogonal cytokine receptor platform2–4 to expand the γc signalling code. We tested receptors from γc cytokines as well as interferon, IL-10 and homodimeric receptor families that do not normally pair with γc or are not naturally expressed on T cells. These receptors simulated their natural counterparts but also induced contextually unique transcriptional programs. This led to distinct T cell fates in tumours, including myeloid-like T cells with phagocytic capacity driven by orthogonal GSCFR (oGCSFR), and type 2 cytotoxic T (TC2) and helper T (TH2) cell differentiation driven by orthogonal IL-4R (o4R). T cells with orthogonal IL-22R (o22R) and oGCSFR, neither of which are natively expressed on T cells, exhibited stem-like and exhaustion-resistant transcriptional and chromatin landscapes, enhancing anti-tumour properties. Non-native receptor pairings and their resultant JAK–STAT signals open a path to diversifying T cell states beyond those induced by natural cytokines.

Exploring the molecular mechanisms and immune cell responses in brucellosis: Insights from gene expression profiles and immune cell scores

Background Brucellosis is a typical zoonotic disease. The study aimed to identify key molecular markers and immune cell imbalances in brucellosis by integrating transcriptomic profiling, co-expression network analysis, and immune cell scoring. Methods Gene expression profile of 103 patients with brucellosis and 46 healthy controls were obtained from the GSE69597 dataset. Differential analysis was conducted on gene expression and immune cell score, followed by co-expression network construction. Diagnostic value of module genes was assessed using Gradient Boosting Machine (GBM) and Random Forest (RF) models to screen marker genes. Peripheral blood samples from 88 brucellosis patients and 70 healthy individuals were collected for validation using flow cytometry, RT-qPCR and Western blot. Results A total of 4924 DEGs were identified, and 11 co-expression modules were constructed. The Brown module showed the highest positive correlation with normal controls, while the Greenyellow module had the highest negative correlation. Enrichment analysis revealed that genes in the Brown module were mainly involved in the cell cycle and virus infection, genes in the Greenyellow module were primarily associated with the PI3K-Akt and Wnt signaling pathway. CDK1, MAPK11, and PDIA3 were identified as marker genes with high importance in both models. The marker genes were significantly higher expression in brucellosis. CD8 + T cells and NK cells were higher in brucellosis than in the control group, whereas B cells and CD4 + T cells were lower, which was confirmed by flow cytometry. Conclusion The abnormal levels of CDK1, MAPK11, PDIA3, and immune cells in brucellosis may be involved in the disease’s pathogenic mechanisms.

Universities must move with the times: how six scholars tackle AI, mental health and more

Phytochemical analysis and green synthesis of silver nanoparticles using Centella asiatica leaf and stem extracts: An investigation of antibacterial activity

Centella asiatica (L.) Urban is a traditionally revered plant possessing several therapeutic applications. This research evaluated the phytochemical and antibacterial properties of Centella asiatica. In addition, silver nanoparticles were synthesized using the stem and leaf aqueous extracts. The presence of alkaloids, flavonoids, glycosides, terpenoids, and phenolics in plant extracts indicates their high medicinal value. Methanolic leaf extract showed a higher phenolic and flavonoid content of 43.73 ± 0.33 mg GAE/g and 19.76 ± 1.12 mg QE/g, respectively. It also contained the most antioxidant activity with the lowest DPPH inhibitory concentration (IC50) of 49.31 ± 1.48 µg/mL. Plant extracts and synthesized nanoparticles were active against gram-positive and gram-negative bacteria. Methanolic leaf extract displayed an MIC of 27.5 and MBC of 55 mg/mL against S. aureus. Synthesized nanoparticles were characterized using different spectroscopic techniques. UV-visible spectra of nanoparticles contained distinct absorption peaks resulting from surface plasmon resonance at 405 and 408 nm. The nanoparticles showed face-centered cubic crystallinity in powder X-ray Diffraction analysis. Fourier Transfer Infrared spectra suggested the possible involvement of organic functional groups in nanoparticle synthesis. Field Emission Scanning Electron Microscopy and Transmission Electron Microscopy analysis revealed the spherical shape with non-uniform size distribution. Mean particle sizes were 20 nm and 19 nm for leaf and stem extract synthesized nanoparticles, respectively. In conclusion, Centella asiatica is rich in important plant secondary metabolites and biological activities, and its aqueous extract synthesizes silver nanoparticles that show potential antibacterial activity.

A disproportionality analysis of insulin glargine in the overall population and in pregnant women using the FDA adverse event reporting system (FAERS) database

Background Insulin glargine (IG) is a commonly prescribed medication for diabetes management in clinical practice, however, there has yet to be a comprehensive systematic study examining its associated adverse events (AEs). In particular, due to the inherent limitations of clinical trials conducted during pregnancy, the safety profile of medications utilized in this period cannot be determined with absolute certainty. This study aims to evaluate the signals of AEs related to IG with in the overall population and among pregnant women, utilizing data from the FDA Adverse Event Reporting System (FAERS) database. Methods We employed standardized MedDRA queries to identify adverse event (AE) reports related to pregnancy. Through disproportionate analysis, we identified and analyzed AE reports from the FAERS database spanning January 2004 to June 2024. We used Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM) for signal detection. Further identification of signal strength based on the BCPNN method was conducted by categorizing signals into four levels based on the Information Component (IC) value and its 95% confidence interval: weak signals (0 < IC025 ≤ 1.5), moderate signals (1.5 < IC025 ≤ 3), and strong signals (IC025 > 3). Additionally, an analysis of the temporal distribution characteristics of AEs was performed. Results We obtained 70 strong or medium signals of AEs for IG in the overall population and 28 positive signals of AEs in pregnant women. In the overall population, the most significant signals included blood glucose abnormal (IC025 = 4.86), blood glucose fluctuation (IC025 = 4.69), blood glucose decreased (IC025 = 4.44), hypoglycaemic seizure (IC025 = 4.44) and hypoglycaemic unconsciousness (IC025 = 4.31). In pregnant women, hypoglycaemia (IC025 = 4.25) was detected as a strong signal, hypoglycaemia neonatal (IC025 = 2.96) as a medium signal, while ketoacidosis (IC025 = 0.76), decreased insulin requirement (IC025 = 0.24), and underweight (IC025 = 0.09) were identified as weak signals. The median time-to-onset of AEs was significantly longer in pregnant women compared to the overall population (186 days vs. 61 days). Conclusion This study has identified unexpected AE signals associated with IG in pregnant women. Our research provides valuable evidence for the clinical application of IG, offers real-world data to support safe medication practices during pregnancy, and establishes a foundation for further clinical investigations.

Correction: Emergence of monopoly–Copper exchange networks during the Late Bronze Age in the western and central Balkans

Predicting stock returns using machine learning combined with data envelopment analysis and automatic feature engineering: A case study on the Vietnamese stock market

In financial markets, predicting stock returns is an essential task for investors. This paper is one of the first studies using business efficiency scores calculated from data envelopment analysis to predict stock returns. In the meantime, this is also one of the first studies to comprehensively investigate the performance of machine learning models and automatic feature engineering techniques in the context of predicting returns in the Vietnamese stock market. Specifically, the data from 2019 to 2024 of 26 real-estate enterprises on Ho Chi Minh Stock Exchange are collected. Based on relevant technical indicators, fundamental indicators, and business efficiency scores, a comparison of various machine learning models’ performance is provided. The results indicate that incorporating business efficiency scores significantly enhances the models’ accuracy. For example, the deep neural network model shows a decrease in RMSE from 0.926 to 0.375, MAE from 0.337 to 0.196, and MAPE from 134.63 to 114.71. Furthermore, the gradient boosted tree model, when combined with business efficiency scores and automatic feature engineering, achieves the best results, yielding an MAE of 0.122 and an MAPE of 103.19. The obtained results reveal a significant improvement in terms of accuracy when using the business efficiency score with the automated feature engineering technique.

NASP modulates histone turnover to drive PARP inhibitor resistance

Isolation and molecular identification of pathogens causing sea turtle egg fusariosis in key nesting beaches in Costa Rica

The global rise of fungal pathogens presents an emerging threat to biodiversity, with significant risks to species such as endangered sea turtles. The fungal disease known as sea turtle egg fusariosis (STEF) is associated with high embryo mortality rates and represents a substantial conservation challenge. This disease is caused by two fungal species, namely Fusarium falciforme (Ff) and Fusarium keratoplasticum (Fk), and their identification is essential for guiding future efforts to address potential fungal infections, particularly on important nesting beaches such as those in Costa Rica. In this study, we conducted fungal isolations from sea turtle eggshells and nest sand at four key nesting beaches along the Pacific and Caribbean coasts of Costa Rica to evaluate the presence of STEF-causing species. For accurate identification, we employed a multilocus sequence typing (MLST) approach, analyzing three genetic loci. We obtained 147 axenic cultures, of which 32% belonged to the STEF-causing species Ff (n = 32) and Fk (n = 15). Fusarium falciforme was found across all study locations on both coasts of Costa Rica, whereas Fk was only detected at one beach on the Caribbean coast. This study represents the first survey to accurately identify STEF-causing species in Costa Rica, revealing a widespread presence on the main nesting beaches. Currently, STEF is not severely affecting sea turtles in Costa Rica; however, various factors, such as changes in the nesting beach environment and sand composition, could increase the incidence and severity of the disease, posing a threatening risk to embryonic development. Therefore, a better understanding of the presence and distribution of these pathogens is critical for preventing the development of this emerging disease.

Learn!Bio—A time-limited cross-sectional study on biosciences students’ pathway to resilience during and post the Covid-19 pandemic at a UK university from 2020–2023 and insights into future teaching approaches

Higher education in biosciences is substantiallyinformed by hands-on field trips and practical laboratory skills-training. With the first Covid-19 national lock-down in England in March 2020, on-campus education at higher education institutions was swiftly moved to alternative provisions, including online only options, a mix of synchronous or asynchronous blended, or hybrid adaptions. Students enrolled on an undergraduate bioscience programme have been faced with unprecedented changes and interruptions to their education. This study aimed to evaluate bioscience students’ ability to adjust to a fast-evolving learning environment and to capture students’ journey building up resilience and graduate attributes. A total of 317 Bioscience undergraduate students in years 1–3 at the biology department at a northwest English university participated in this anonymous, cross-sectional, mixed-method study with open and closed questions evaluating their perception and feedback to remote and blended learning provisions during the Covid-19 pandemic and post pandemic learning capturing academic years 2019/20–2022/23. The Covid-19 pandemic and the consequent restriction of personal social interaction resulted in an significant decrease in the mental wellbeing of undergraduate bioscience students in this study, cumulating in poor or very poor self-rating of wellbeing in spring 2021; while at the same time students showed evidence of advanced adaption to the new learning and social environment by acquisition of additional technical, social and professional graduate-level skills. Post pandemic, bioscience students worry about the increased living costs and are strongly in favour of a mixture of face-to-face and blended learning approaches. Our results show that bioscience students can self-report poor mental health while developing resilience, indicating tailored support can aid in developing students’ resilience. Students have adjusted with ease to digital teaching provisions and now expect higher education institutions continue to offer both, face-to-face, and blended teaching, reducing the burden on students’ notably risen living costs.

A new external jugular venipuncture technique for efficient vascular access that exploits a murine anatomical variation

Vascular access in mice is a cornerstone of biomedical research, with peripheral venous approaches like the lateral tail vein, retrobulbar venous sinus, facial vein, and saphenous vein being common. However, central venous approaches are challenging due to animal size and required expertise. To address this, we developed the Sternoclavicular joint-Targeted External jugular venipuncture Method (STEM). This technique provides reliable, longitudinal vascular access for frequent blood sampling using palpable surface anatomy landmarks. Moreover, STEM eliminates the need for fur shaving, specialized restraints, or deep sedation, allowing a single operator to perform the procedure safely and efficiently. Our protocol, based on a comprehensive anatomical analysis, revealed that the external jugular vein in mice traverses anteriorly to the clavicle before draining into the subclavian vein – a key anatomical difference from humans. This finding enabled a refined technique using the sternoclavicular joint as a landmark, improving the success and reproducibility of central venous access. Finally, STEM facilitates efficient blood collection and accurate intravenous administration with minimal setup time. It is straightforward and easily replicable, allowing researchers of all expertise levels to achieve high precision and reproducibility. The simplified learning process and consistent results make STEM valuable for various mouse-based experiments in biomedical research.

A Histopathological Study of Pulmonary Changes in Pediatric and Adult Medico-legal Autopsies at a Tertiary Care Centre: A 5-year Audit

Background: Lungs are incidentally involved in nearly all terminal deathevents and are frequently studied during postmortem examination. Histopathological lung examination helps us to understand disease processes and detect incidental 𿿿ndings and study their association with different variables. Aims & objectives: a. To evaluate the pulmonary histopathological features in adult and pediatric medico-legal autopsies. b. To determine the frequencies of the various causes of death and study their association with age and gender. Methods: Lung specimens of all medico-legal autopsies received betweenJanuary2018 to December 2023 at a tertiary care centre were retrospectively studied for gross morphological𿿿ndings. Further,the specimens were𿿿xed and representative ctions were submitted, processed and stained with H & E routinely to study histomorphological𿿿ndings. Final diagnosis was given after correlating gross and htomorphological𿿿ndings. Results: Among 270 autopsy cases, the most common cause of death in adults was cardiac pathology (28.9%), followed by primary pulmonary pathology (20.4%). Common pulmonary 𿿿ndings in cardiac deaths included edema and chric venous congestion, while interstitial pneumonitis (10.2%), edema, and bronchopneumonia were predominant in primary pulmonary causes. In children, deaths were mainly due to accidental emergencies (32%), respiratory failure

Mapping spatiotemporal distribution of forest carbon density in Xizang, China

Climate warming is a major global challenge, and forests, essential carbon sinks, are critical in mitigating its effects. Forest carbon density is a key parameter in assessing the carbon sinks. Traditional estimating methods of forest carbon density are time-consuming, labor-intensive, and difficult to apply on a large scale. Combining multispectral data with machine learning offers a promising solution, but accurately estimating forest carbon density remains challenging due to the band limitations of multi-spectral data. This study proposes a novel approach to address this limitation gap. We utilized Landsat 8 data and 919 samples from Xizang, China, simultaneously constructed geographic (GEO) and environmental factors (GEF) for estimating forest carbon density for the first time, and adopted three models to evaluate the effectiveness. The results indicate that the extreme gradient boosting (XGB) model is significantly better, the average R2 exceeds 0.77, especially in Rikaze exceeds 0.96. The total relative importance of GEF in the modelling exceeded 60%, Geo was the most critical variable, followed by CI. This study successfully used multi-spectral data to quantify the spatiotemporal distribution of forest carbon density and demonstrated that GEO and GEF are indispensable, which is expected to provide new perspectives and technical support for global carbon sink monitoring.