Explore the vast range of titles available.

Background Ferroptosis is an iron-dependent, lipid peroxide-mediated cell death that may be exploited to selective elimination of damaged and malignant cells. Recent studies have identified that small-molecule erastin specifically inhibits transmembrane cystine–glutamate antiporter system x c − , prevents extracellular cystine import and ultimately causes ferroptosis in certain cancer cells. In this study, we aimed to investigate the molecular mechanism underlying erastin-induced ferroptosis resistance in ovarian cancer cells. Methods We treated ovarian cancer cells with erastin and examined cell viability, cellular ROS and metabolites of the transsulfuration pathway. We also depleted cystathionine β-synthase (CBS) and NRF2 to investigate the CBS and NRF2 dependency in erastin-resistant cells. Results We found that prolonged erastin treatment induced ferroptosis resistance. Upon exposure to erastin, cells gradually adapted to cystine deprivation via sustained activation of the reverse transsulfuration pathway, allowing the cells to bypass erastin insult. CBS, the biosynthetic enzyme for cysteine, was constantly upregulated and was critical for the resistance. Knockdown of CBS by RNAi in erastin-resistant cells caused ferroptotic cell death, while CBS overexpression conferred ferroptosis resistance. We determined that the antioxidant transcriptional factor, NRF2 was constitutively activated in erastin-resistant cells and NRF2 transcriptionally upregulated CBS. Genetically repression of NRF2 enhanced ferroptosis susceptibility. Conclusions Based on these results, we concluded that constitutive activation of NRF2/CBS signalling confers erastin-induced ferroptosis resistance. This study demonstrates a new mechanism underlying ferroptosis resistance, and has implications for the therapeutic response to erastin-induced ferroptosis.

Background Targeted drugs are medications designed to treat diseases by targeting specific sites on cancerous or diseased cells. Multi-target drugs can target multiple protein sites to treat diseases, improving therapeutic efficiency, but are more challenging to design. Computer-aided targeted drug design can reduce costs and shorten development time, with most drugs being single-target. Recent research on multi-target drug design has focused on optimizing single-target drugs into multi-target drugs, but this approach has limitations. This study proposes a multi-target drug design method based on protein feature fusion, which encodes and integrates features based on the target’s sequence characteristics, enabling the design of multi-target drugs without prior knowledge of the targeted drug. The target protein sequences are embedded to extract features. Each target’s features are independently encoded into latent vectors, while the features of multiple targets are encoded into similarity latent vectors. By leveraging both individual target features and the similarity features among targets, multi-target drugs can be efficiently designed. Results We validated the proposed multi-target drug design method on three groups of targets: the 3CLpro and PLpro targets for COVID-19, the TAAR1 and DRD2 targets for schizophrenia, and the MEK1 and mTOR targets for tumors. The designed multi-target drugs can be docked with target proteins possessing unique molecular structures, tailored to the specific requirements of different target pocket structures. The excellent fit between the molecular structures of the multi-target drugs and the protein structures of multiple targets validates the performance of the proposed method. Conclusions The proposed method can efficiently design multi-target drugs with stronger predicted binding affinities than those reported in previous studies. These drugs are capable of adapting to multiple targets based on the features of the target proteins. Additionally, the model demonstrates excellent generalization ability for untrained multiple targets.

This study explores how the use of information technology (IT) influences the development of cognitive and non-cognitive abilities among primary school students in rural areas of western China. As IT tools become more integrated into daily life, especially in underdeveloped regions, their influence on education has grown significantly. Relying on Heckman's human capital development theory, we apply Propensity Score Matching combined with a Difference-in-Differences (PSM-DID) approach to analyze two rounds of panel data collected from third and fourth grade students between 2019 and 2020. The analysis suggests that while IT use does not significantly boost cognitive skills, a slight positive trend is observed, likely reflecting the coexistence of both beneficial and adverse effects. In terms of non-cognitive skills, frequent IT use appears to enhance students' openness and increase their fondness for school and teachers, yet it is also linked to rising levels of self-blame and anxiety. Additional findings show that the effects of IT use are more pronounced among older children, Han Chinese students, and those from households with lower income or lower parental education. These results contribute to a deeper understanding of IT's role in shaping human capital in rural settings and offer practical implications for policymakers. In particular, they highlight the need to maximize the positive aspects of IT in promoting equity in education while addressing its potential downsides through more customized support for diverse student populations.

Background Computational prediction of the interaction between drugs and protein targets is very important for the new drug discovery, as the experimental determination of drug-target interaction (DTI) is expensive and time-consuming. However, different protein targets are with very different numbers of interactions. Specifically, most interactions focus on only a few targets. As a result, targets with larger numbers of interactions could own enough positive samples for predicting their interactions but the positive samples for targets with smaller numbers of interactions could be not enough. Only using a classification strategy may not be able to deal with the above two cases at the same time. To overcome the above problem, in this paper, a drug-target interaction prediction method based on multiple classification strategies (MCSDTI) is proposed. In MCSDTI, targets are firstly divided into two parts according to the number of interactions of the targets, where one part contains targets with smaller numbers of interactions (TWSNI) and another part contains targets with larger numbers of interactions (TWLNI). And then different classification strategies are respectively designed for TWSNI and TWLNI to predict the interaction. Furthermore, TWSNI and TWLNI are evaluated independently, which can overcome the problem that result could be mainly determined by targets with large numbers of interactions when all targets are evaluated together. Results We propose a new drug-target interaction (MCSDTI) prediction method, which uses multiple classification strategies. MCSDTI is tested on five DTI datasets, such as nuclear receptors (NR), ion channels (IC), G protein coupled receptors (GPCR), enzymes (E), and drug bank (DB). Experiments show that the AUCs of our method are respectively 3.31%, 1.27%, 2.02%, 2.02% and 1.04% higher than that of the second best methods on NR, IC, GPCR and E for TWLNI; And AUCs of our method are respectively 1.00%, 3.20% and 2.70% higher than the second best methods on NR, IC, and E for TWSNI. Conclusion MCSDTI is a competitive method compared to the previous methods for all target parts on most datasets, which administrates that different classification strategies for different target parts is an effective way to improve the effectiveness of DTI prediction.

Growing evidence suggests that the gut-kidney axis may contribute to the pathogenesis of IgA nephropathy (IgAN). However, the effects of immunosuppressants on the intestinal microbiome remain unclear. We investigated how different therapeutic strategies influence gut microbial composition in IgAN patients. We enrolled 46 patients with IgAN and 37 healthy controls (HC). Patients were stratified by treatment regimen into a supportive care group or an immunosuppressive therapy group, with subgroups defined by the use of leflunomide and systemic glucocorticoids. Fecal samples from patients in clinical remission were analyzed and 16S rRNA gene sequencing was performed. We examined microbial α- and β-diversity, taxonomic differences, and predicted functional pathways using Linear Discriminant Analysis Effect Size and Kyoto Encyclopedia of Genes and Genomes (KEGG)-based annotation. Compared with HCs, IgAN patients showed significantly reduced microbial α-diversity, depletion of beneficial taxa such as , and enrichment of potential pathogens, including . Neither supportive care and systemic glucocorticoid therapy were not associated with an apparent restoration of overall microbial diversity or community structure. Conversely, leflunomide treatment was associated with higher microbial diversity and a taxonomic profile that showed a trend toward similarity with healthy controls. Notably, anti-inflammatory bacteria, including , and were significantly enriched (all  < 0.01). KEGG-based predictions revealed downregulation of pro-inflammatory pathways, accompanied by reduced levels of inflammatory markers (  < 0.05). Overall, the therapeutic efficacy of leflunomide in IgA nephropathy is associated with specific characteristics of the patients' gut microbiota, which may be linked to its potential to reverse dysbiosis and enhance anti-inflammatory effects.

Background Protein–protein interactions (PPIs) are the core of protein function, which provide an effective means to understand the function at cell level. Identification of PPIs is the crucial foundation of predicting drug-target interactions. Although traditional biological experiments of identifying PPIs are becoming available, these experiments remain to be extremely time-consuming and expensive. Therefore, various computational models have been introduced to identify PPIs. In protein-protein interaction network (PPIN), Hub protein, as a highly connected node, can coordinate PPIs and play biological functions. Detecting hot regions on Hub protein interaction interfaces is an issue worthy of discussing. Methods Two clustering methods, LCSD and RCNOIK are used to detect the hot regions on Hub protein interaction interfaces in this paper. In order to improve the efficiency of K-means clustering algorithm, the best k value is selected by calculating the distance square sum and the average silhouette coefficients. Then, the optimization of residue coordination number strategy is used to calculate the average coordination number. In addition, the pair potentials and relative ASA (PPRA) strategy is also used to optimize the predicted results. Results DataHub dataset and PartyHub dataset were used to train two clustering models respectively. Experiments show that LCSD and RCNOIK have the same coverage with Hub protein datasets, and RCNOIK is slightly higher than LCSD in Precision. The predicted hot regions are closer to the standard hot regions. Conclusions This paper optimizes two clustering methods based on PPRA strategy. Compared our methods for hot regions prediction against the well-known approaches, our improved methods have the higher reliability and are effective for predicting hot regions on Hub protein interaction interfaces.

Taste bud cells regenerate throughout life. Taste bud maintenance depends on continuous replacement of senescent taste cells with new ones generated by adult taste stem cells. More than a century ago it was shown that taste buds degenerate after their innervating nerves are transected and that they are not restored until after reinnervation by distant gustatory ganglion neurons. Thus, neuronal input, likely via neuron-supplied factors, is required for generation of differentiated taste cells and taste bud maintenance. However, the identity of such a neuron-supplied niche factor(s) remains unclear. Here, by mining a published RNA-sequencing dataset of geniculate ganglion neurons and by in situ hybridization, we demonstrate that R-spondin-2, the ligand of Lgr5 and its homologs Lgr4/6 and stem-cell-expressed E3 ligases Rnf43/Znrf3, is expressed in nodose-petrosal and geniculate ganglion neurons. Using the glossopharyngeal nerve transection model, we show that systemic delivery of R-spondin via adenovirus can promote generation of differentiated taste cells despite denervation. Thus, exogenous R-spondin can substitute for neuronal input for taste bud cell replenishment and taste bud maintenance. Using taste organoid cultures, we show that R-spondin is required for generation of differentiated taste cells and that, in the absence of R-spondin in culture medium, taste bud cells are not generated ex vivo. Thus, we propose that R-spondin-2 may be the long-sought neuronal factor that acts on taste stem cells for maintaining taste tissue homeostasis.

In this study, a two-step method was used to realize the liquefaction of waste sawdust under atmospheric pressure, and to achieve a high liquefaction rate. Specifically, waste sawdust was pretreated with NaOH, followed by liquefaction using phenol. The relative optimum condition for alkali–heat pretreatment was a 1:1 mass ratio of NaOH to sawdust at 140 °C. The reaction parameters including the mass ratio of phenol to pretreated sawdust, liquefaction temperature, and liquefaction time were optimized by response surface methodology. The optimal conditions for phenol liquefaction of pretreated sawdust were a 4.21 mass ratio of phenol to sawdust, a liquefaction temperature of 173.58 °C, and a liquefaction time of 2.24 h, resulting in corresponding liquefied residues of 6.35%. The liquefaction rate reached 93.65%. Finally, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), and X-ray diffraction (XRD) were used to analyze untreated waste sawdust, pretreated sawdust, liquefied residues, and liquefied liquid. SEM results showed that the alkali–heat pretreatment and liquefaction reactions destroyed the intact, dense, and homogeneous sample structures. FT-IR results showed that liquefied residues contain aromatic compounds with different substituents, including mainly lignin and its derivatives, while the liquefied liquid contains a large number of aromatic phenolic compounds. XRD showed that alkali–heat pretreatment and phenol liquefaction destroyed most of the crystalline regions, greatly reduced the crystallinity and changed the crystal type of cellulose in the sawdust.

Background Due to the chronic nature of HIV, mental health has become a critical concern in people living with HIV (PLWHIV). However, little knowledge exists about the association between fear of progression (FoP) and medical coping modes (MCMs) in PLWHIV in China. Methods A cohort of 303 PLWHIV were consecutively enrolled and their demographic, clinical and psychological information was collected. The Fear of Progression Questionnaire-Short Form (FoP-Q-SF), Social Support Rating Scale (SSRS), Internalized HIV Stigma Scale (IHSS) and MCMs Questionnaire were utilized. Results Of the participants, 215 PLWHIV were classified into the low-level FoP group, and 88 were grouped into the high-level FoP group based on their FoP-Q-SF scores, according to the criteria for the classification of dysfunctional FoP in cancer patients. The high-level group had a higher proportion of acquired immunodeficiency syndrome (AIDS) stage ( P  = 0.005), lower education levels ( P  = 0.027) and lower income levels ( P  = 0.031). Additionally, the high-level group had lower scores in social support ( P  < 0.001) and its three dimensions, with total SSRS scores showing a negative correlation with two dimensions of FoP-Q-SF, namely physical health (r 2  = 0.0409, P  < 0.001) and social family (r 2  = 0.0422, P  < 0.001). Further, the high-level group had higher scores in four dimensions of internalized HIV stigma, and a positive relationship was found to exist between IHSS scores and FoP-Q-SF scores for physical health (r 2  = 0.0960, P  < 0.001) and social family (r 2  = 0.0719, P  < 0.001). Social support (OR = 0.929, P  = 0.001), being at the AIDS stage (OR = 3.795, P  = 0.001), and internalized HIV stigma (OR = 1.028, P  < 0.001) were independent factors for FoP. Furthermore, intended MCMs were evaluated. FoP were positively correlated with avoidance scores (r 2  = 0.0886, P  < 0.001) and was validated as the only factor for the mode of confrontation (OR = 0.944, P  = 0.001) and avoidance (OR = 1.059, P  = 0.001) in multivariate analysis. Conclusion The incidence of dysfunctional FoP in our study population was relatively high. High-level FoP was associated with poor social support, high-level internalized HIV stigma and a negative MCM among PLWHIV.

The key to drug discovery is the identification of a target and a corresponding drug compound. Effective identification of drug–target interactions facilitates the development of drug discovery. In this paper, drug similarity and target similarity are considered, and graphical representations are used to extract internal structural information and intermolecular interaction information about drugs and targets. First, drug similarity and target similarity are fused using the similarity network fusion (SNF) method. Then, the graph isomorphic network (GIN) is used to extract the features with information about the internal structure of drug molecules. For target proteins, feature extraction is carried out using TextCNN to efficiently capture the features of target protein sequences. Three different divisions (CVD, CVP, CVT) are used on the standard dataset, and experiments are carried out separately to validate the performance of the model for drug–target interaction prediction. The experimental results show that our method achieves better results on AUC and AUPR. The docking results also show the superiority of the proposed model in predicting drug–target interactions.