Microbes mediated immunogenic cell death in cancer immunotherapy.

Immunogenic cell death (ICD) is one of the 12 distinct cell death forms, which can trigger immune system to fight against cancer cells. During ICD, a number of cellular changes occur that can stimulate an immune response, including the release of molecules called damage-associated molecular patterns (DAMPs), signaling to immune cells to recognize and attack cancer cells. By virtue of their pivotal role in immune surveillance, ICD-based drug development has been a new approach to explore novel therapeutic combinations and personalized strategies in cancer therapy. Several small molecules and microbes can induce ICD-relevant signals and cause cancer cell death. In this review, we highlighted the role of microbe-mediate ICD in cancer immunotherapy and described the mechanisms through which microbes might serve as ICD inducers in cancer treatment. We also discussed current attempts to combine microbes with chemotherapy regimens or immune checkpoint inhibitors (ICIs) in the treatment of cancer patients. We surmise that manipulation of microbes may guide personalized therapeutic interventions to facilitate anticancer immune response.

Holistic immunomodulation for small cell lung cancer.

Small cell lung cancer (SCLC) is characterized by a high mortality rate, rapid growth, and early metastasis, which lead to a poor prognosis. Moreover, limited clinical treatment options further lower the survival rate of patients. Therefore, novel technology and agents are urgently required to enhance clinical efficacy. In this review, from a holistic perspective, we summarized the therapeutic targets, agents and strategies with the most potential for treating SCLC, including chimeric antigen receptor (CAR) T therapy, immunomodulating antibodies, traditional Chinese medicines (TCMs), and the microbiota, which have been found recently to improve the clinical outcomes and prognosis of SCLC. Multiomics technologies can be integrated to develop effective diagnostic methods and identify new targets for new drug discovery in SCLC. We discussed in depth the feasibility, potential, and challenges of these new strategies, as well as their combinational treatments, which may provide promising alternatives for enhancing the clinical efficacy of SCLC in the future.

Harnessing natural product polysaccharides against lung cancer and revisit its novel mechanism.

The incidence and mortality of lung cancer are on the rise worldwide. However, the benefit of clinical treatment in lung cancer is limited. Owning to important sources of drug development, natural products have received constant attention around the world. Main ingredient polysaccharides in natural products have been found to have various activities in pharmacological research. In recent years, more and more scientists are looking for the effects and mechanisms of different natural product polysaccharides on lung cancer. In this review, we focus on the following aspects: First, natural product polysaccharides have been discovered to directly suppress the growth of lung cancer cells, which can be effective in limiting tumor progression. Additionally, polysaccharides have been considered to enhance immune function, which can play a pivotal role in fighting lung cancer. Lastly, polysaccharides can improve the efficacy of drugs in lung cancer treatment by regulating the gut microbiota. Overall, the research of natural product polysaccharides in the treatment of lung cancer is a promising area that has the potential to lead to new clinical treatments. With better understanding, natural product polysaccharides have the potential to become important components of future lung cancer treatments.

MMP14high macrophages orchestrate progressive pulmonary fibrosis in SR-Ag-induced hypersensitivity pneumonitis.

Fibrotic hypersensitivity pneumonitis (FHP) is a fatal interstitial pulmonary disease with limited treatment options. Lung macrophages are a heterogeneous cell population that exhibit distinct subsets with divergent functions, playing pivotal roles in the progression of pulmonary fibrosis. However, the specific macrophage subpopulations and underlying mechanisms involved in the disease remain largely unexplored. In this study, a decision tree model showed that matrix metalloproteinase-14 (MMP14) had higher scores for important features in the up-regulated genes in macrophages from mice exposed to the Saccharopolyspora rectivirgula antigen (SR-Ag). Using single-cell RNA sequencing (scRNA-seq) analysis of hypersensitivity pneumonitis (HP) mice profiles, we identified MMP14high macrophage subcluster with a predominant M2 phenotype that exhibited higher activity in promoting fibroblast-to myofibroblast transition (FMT). We demonstrated that suppressing toll-like receptor 2 (TLR2) and nuclear factor kappa-B (NF-κB) could attenuate MMP14 expression and exosome secretion in macrophages stimulation with SR-Ag. The exosomes derived from MMP14-overexpressing macrophages were found to be more effective in regulating the transition of fibroblasts through exosomal MMP14. Importantly, it was observed that the transfer of MMP14-overexpressing macrophages into mice promoted lung inflammation and fibrosis induced by SR-Ag. NSC-405020 binding to the hemopexin domain (PEX) of MMP-14 ameliorated lung inflammation and fibrosis induced by SR-Ag in mice. Thus, MMP14-overexpressing macrophages may be an important mechanism contributing to the exacerbation of allergic reactions. Our results indicated that MMP14 in macrophages has the potential to be a therapeutic target for HP.

Dual inhibitors of DNMT and HDAC remodels the immune microenvironment of colorectal cancer and enhances the efficacy of anti-PD-L1 therapy.

Colorectal cancer is the second most prevalent and deadly cancer worldwide. The emergence of immune checkpoint therapy has provided a revolutionary strategy for the treatment of solid tumors. However, less than 5% of colorectal cancer patients respond to immune checkpoint therapy. Thus, it is of great scientific significance to develop "potentiators" for immune checkpoint therapy. In this study, we found that knocking down different DNMT and HDAC isoforms could increase the expression of IFNs in colorectal cancer cells, which can enhance the effectiveness of immune checkpoint therapy. Therefore, the combined inhibition of DNMT and HDAC cloud synergistically enhance the effect of immunotherapy. We found that dual DNMT and HDAC inhibitors C02S could inhibit tumor growth in immunocompetent mice but not in immunocompromised nude mice, which indicates that C02S exerts its antitumor effects through the immune system. Mechanistically, C02S could increase the expression of ERVs, which generated the intracellular levels of dsRNA in tumor cells, and then promotes the expression of IFNs through the RIG-I/MDA5-MAVS signaling pathway. Moreover, C02S increased the immune infiltration of DCs and T cells in microenvironment, and enhanced the efficacy of anti-PD-L1 therapy in MC38 and CT26 mice model. These results confirmed that C02S can activate IFNs through the RIG-I/MDA5-MAVS signaling pathway, remodel the tumor immune microenvironment and enhance the efficacy of immune checkpoint therapy, which provides new evidence and solutions for the development of "potentiator" for colorectal cancer immunotherapy.

Neuroanatomical signatures associated with dispositional optimism predict COVID-19-related posttraumatic stress symptoms.

The COVID-19 pandemic has been increasingly documented to cause negative impacts on mental health outcomes, e.g. posttraumatic stress symptoms (PTSS). Dispositional optimism ("optimism" hereinafter), a crucial psychological characteristic defined by positive expectancies for future outcomes, is considered to provide remarkable protection against PTSS. Accordingly, this study was designed to identify neuroanatomical signatures of optimism and further examine the mechanism through which optimism protects against COVID-19-specific PTSS. Here, 115 volunteers from a general population of university students completed MRI scans and optimism tests before (October 2019-January 2020) and after (February-April 2020) the onset of the COVID-19 pandemic. Whole-brain voxel-based morphometry analysis showed that a region from the dorsal anterior cingulate cortex (dACC) to the dorsomedial prefrontal cortex (dmPFC) was associated with optimism. Further seed-based structural covariance network (SCN) analysis using partial least-squares correlation found an optimism-linked SCN covarying with the combined dACC and dmPFC (the dACC-dmPFC). Additionally, mediation analyses revealed that the dACC-dmPFC volume and its SCN impacted COVID-19-specific PTSS through optimism. Our findings deepen the understanding of optimism and have the potential to identify vulnerable individuals during the COVID-19 pandemic or similar future events, as well as to guide optimism-related neural interventions to prevent and alleviate PTSS.

Disrupted brain gray matter connectome in social anxiety disorder: a novel individualized structural covariance network analysis.

Phenotyping approaches grounded in structural network science can offer insights into the neurobiological substrates of psychiatric diseases, but this remains to be clarified at the individual level in social anxiety disorder (SAD). Using a recently developed approach combining probability density estimation and Kullback-Leibler divergence, we constructed single-subject structural covariance networks (SCNs) based on multivariate morphometry (cortical thickness, surface area, curvature, and volume) and quantified their global/nodal network properties using graph-theoretical analysis. We compared network metrics between SAD patients and healthy controls (HC) and analyzed the relationship to clinical characteristics. We also used support vector machine analysis to explore the ability of graph-theoretical metrics to discriminate SAD patients from HC. Globally, SAD patients showed higher global efficiency, shorter characteristic path length, and stronger small-worldness. Locally, SAD patients showed abnormal nodal centrality mainly involving left superior frontal gyrus, right superior parietal lobe, left amygdala, right paracentral gyrus, right lingual, and right pericalcarine cortex. Altered topological metrics were associated with the symptom severity and duration. Graph-based metrics allowed single-subject classification of SAD versus HC with total accuracy of 78.7%. This finding, that the topological organization of SCNs in SAD patients is altered toward more randomized configurations, adds to our understanding of network-level neuropathology in SAD.

Comparative Genomics Reveals Insights into the Divergent Evolution of Astigmatic Mites and Household Pest Adaptations.

Highly diversified astigmatic mites comprise many medically important human household pests such as house dust mites causing ∼1-2% of all allergic diseases globally; however, their evolutionary origin and diverse lifestyles including reversible parasitism have not been illustrated at the genomic level, which hampers allergy prevention and our exploration of these household pests. Using six high-quality assembled and annotated genomes, this study not only refuted the monophyly of mites and ticks, but also thoroughly explored the divergence of Acariformes and the diversification of astigmatic mites. In monophyletic Acariformes, Prostigmata known as notorious plant pests first evolved, and then rapidly evolving Astigmata diverged from soil oribatid mites. Within astigmatic mites, a wide range of gene families rapidly expanded via tandem gene duplications, including ionotropic glutamate receptors, triacylglycerol lipases, serine proteases and UDP glucuronosyltransferases. Gene diversification after tandem duplications provides many genetic resources for adaptation to sensing environmental signals, digestion, and detoxification in rapidly changing household environments. Many gene decay events only occurred in the skin-burrowing parasitic mite Sarcoptes scabiei. Throughout the evolution of Acariformes, massive horizontal gene transfer events occurred in gene families such as UDP glucuronosyltransferases and several important fungal cell wall lytic enzymes, which enable detoxification and digestive functions and provide perfect drug targets for pest control. This comparative study sheds light on the divergent evolution and quick adaptation to human household environments of astigmatic mites and provides insights into the genetic adaptations and even control of human household pests.

Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.

Although a wide variety of machine learning (ML) algorithms have been utilized to learn quantitative structure-activity relationships (QSARs), there is no agreed single best algorithm for QSAR learning. Therefore, a comprehensive understanding of the performance characteristics of popular ML algorithms used in QSAR learning is highly desirable. In this study, five linear algorithms [linear function Gaussian process regression (linear-GPR), linear function support vector machine (linear-SVM), partial least squares regression (PLSR), multiple linear regression (MLR) and principal component regression (PCR)], three analogizers [radial basis function support vector machine (rbf-SVM), K-nearest neighbor (KNN) and radial basis function Gaussian process regression (rbf-GPR)], six symbolists [extreme gradient boosting (XGBoost), Cubist, random forest (RF), multiple adaptive regression splines (MARS), gradient boosting machine (GBM), and classification and regression tree (CART)] and two connectionists [principal component analysis artificial neural network (pca-ANN) and deep neural network (DNN)] were employed to learn the regression-based QSAR models for 14 public data sets comprising nine physicochemical properties and five toxicity endpoints. The results show that rbf-SVM, rbf-GPR, XGBoost and DNN generally illustrate better performances than the other algorithms. The overall performances of different algorithms can be ranked from the best to the worst as follows: rbf-SVM > XGBoost > rbf-GPR > Cubist > GBM > DNN > RF > pca-ANN > MARS > linear-GPR ≈ KNN > linear-SVM ≈ PLSR > CART ≈ PCR ≈ MLR. In terms of prediction accuracy and computational efficiency, SVM and XGBoost are recommended to the regression learning for small data sets, and XGBoost is an excellent choice for large data sets. We then investigated the performances of the ensemble models by integrating the predictions of multiple ML algorithms. The results illustrate that the ensembles of two or three algorithms in different categories can indeed improve the predictions of the best individual ML algorithms.

Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?

Machine-learning (ML)-based scoring functions (MLSFs) have gradually emerged as a promising alternative for protein-ligand binding affinity prediction and structure-based virtual screening. However, clouds of doubts have still been raised against the benefits of this novel type of scoring functions (SFs). In this study, to benchmark the performance of target-specific MLSFs on a relatively unbiased dataset, the MLSFs trained from three representative protein-ligand interaction representations were assessed on the LIT-PCBA dataset, and the classical Glide SP SF and three types of ligand-based quantitative structure-activity relationship (QSAR) models were also utilized for comparison. Two major aspects in virtual screening campaigns, including prediction accuracy and hit novelty, were systematically explored. The calculation results illustrate that the tested target-specific MLSFs yielded generally superior performance over the classical Glide SP SF, but they could hardly outperform the 2D fingerprint-based QSAR models. Although substantial improvements could be achieved by integrating multiple types of protein-ligand interaction features, the MLSFs were still not sufficient to exceed MACCS-based QSAR models. In terms of the correlations between the hit ranks or the structures of the top-ranked hits, the MLSFs developed by different featurization strategies would have the ability to identify quite different hits. Nevertheless, it seems that target-specific MLSFs do not have the intrinsic attributes of a traditional SF and may not be a substitute for classical SFs. In contrast, MLSFs can be regarded as a new derivative of ligand-based QSAR models. It is expected that our study may provide valuable guidance for the assessment and further development of target-specific MLSFs.

Upgrade of chrysomycin A as a novel topoisomerase II inhibitor to curb KRAS-mutant lung adenocarcinoma progression.

A primary strategy employed in cancer therapy is the inhibition of topoisomerase II (Topo II), implicated in cell survival. However, side effects and adverse reactions restrict the utilization of Topo II inhibitors. Thus, investigations focus on the discovery of novel compounds that are capable of inhibiting the Topo II enzyme and feature safer toxicological profiles. Herein, we upgrade an old antibiotic chrysomycin A from Streptomyces sp. 891 as a compelling Topo II enzyme inhibitor. Our results show that chrysomycin A is a new chemical entity. Notably, chrysomycin A targets the DNA-unwinding enzyme Topo II with an efficient binding potency and a significant inhibition of intracellular enzyme levels. Intriguingly, chrysomycin A kills KRAS-mutant lung adenocarcinoma cells and is negligible cytotoxic to normal cells at the cellular level, thus indicating a capability of potential treatment. Furthermore, mechanism studies demonstrate that chrysomycin A inhibits the Topo II enzyme and stimulates the accumulation of reactive oxygen species, thereby inducing DNA damage-mediated cancer cell apoptosis. Importantly, chrysomycin A exhibits excellent control of cancer progression and excellent safety in tumor-bearing models. Our results provide a chemical scaffold for the synthesis of new types of Topo II inhibitors and reveal a novel target for chrysomycin A to meet its further application.

CERS4 predicts positive anti-PD-1 response and promotes immunomodulation through Rhob-mediated suppression of CD8+Tim3+ exhausted T cells in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is one of the main malignant tumors with high mortality and short survival time. Immunotherapy has become the standard treatment for advanced NSCLC, but it has the problems of drug resistance and low response rate. Therefore, obtaining effective biomarkers to predict and enhance immune checkpoint inhibitors (ICIs) efficacy in NSCLC is important. Sphingolipid metabolism is recently found to be closely involved in tumor immunotherapy. CERS4, an important sphingolipid metabolizing enzyme, is positively correlated with the efficacy of anti-PD-1 therapy for NSCLC. Upregulation of CERS4 expression could improve the efficacy of anti-PD-1 therapy for NSCLC. High expression of CERS4 could downregulate the expression of Rhob in tumor. Significantly, the ratio of CD4+/CD8+ T cell increased and the ratio of Tim-3+/CD8+ T cell decreased in spleen and peripheral blood cells. When Rhob was knocked out, the efficacy of PD-1 mAb treatment increased, and the frequency of Tim-3+ CD8+ T cell decreased. This finding further confirmed the role of sphingolipid metabolites in regulating the immunotherapeutic function of NSCLC. These metabolites may improve the efficacy of PD-1 mAb in NSCLC by regulating the CERS4/Rhob/Tim-3 axis. Overall, this study provided a potential and effective target for predicting and improving the efficacy of ICIs for NSCLC. It also provided a new perspective for the study on the mechanisms of ICIs resistance for NSCLC.

ß-Elemene enhances erlotinib sensitivity through induction of ferroptosis by upregulating lncRNA H19 in EGFR-mutant non-small cell lung cancer.

Nearly half of all Asian non-small cell lung cancer (NSCLC) patients harbour epidermal growth factor receptor (EGFR) mutations, and first-generation EGFR tyrosine kinase inhibitors (TKIs) are one of the first-line treatments that have improved the outcomes of these patients. Unfortunately, 20% of these patients can not benefit from the treatment. The basis of this primary resistance is poorly understood. Therefore, overcoming EGFR-TKI primary resistance and maintaining the efficacy of TKIs has become a key issue. ß-Elemene, a sesquiterpene compound extracted from Curcuma aromatica Salisb. (wenyujing), has shown potent antitumor effects. In this research, we found that ß-elemene combined with erlotinib enhanced the cytotoxicity of erlotinib to primary EGFR-TKI-resistant NSCLC cells with EGFR mutations and that ferroptosis was involved in the antitumor effect of the combination treatment. We found that lncRNA H19 was significantly downregulated in primary EGFR-TKI-resistant NSCLC cell lines and was upregulated by the combination treatment. Overexpression or knockdown of H19 conferred sensitivity or resistance to erlotinib, respectively, in both in vitro and in vivo studies. The high level of H19 enhanced the cytotoxicity of erlotinib by inducing ferroptosis. In conclusion, our data showed that ß-elemene combined with erlotinib could enhance sensitivity to EGFR-TKIs through induction of ferroptosis via H19 in primary EGFR-TKI-resistant lung cancer, providing a promising strategy to overcome EGFR-TKI resistance in NSCLC patients.

Legacy and Emerging Per- and Polyfluoroalkyl Substances in a Subtropical Marine Food Web: Suspect Screening, Isomer Profile, and Identification of Analytical Interference.

The ban/elimination of legacy per- and polyfluoroalkyl substances (PFASs) has led to a dramatic increase in the production and use of various emerging PFASs over the past decade. However, trophodynamics of many emerging PFASs in aquatic food webs remain poorly understood. In this study, samples of seawaters and marine organisms including 15 fish species, 21 crustacean species, and two cetacean species were collected from the northern South China Sea (SCS) to investigate the trophic biomagnification potential of legacy and emerging PFASs. Bis(trifluoromethylsulfonyl)imide was found in seawater via suspect screening (concentration up to 1.50 ng/L) but not in the biota, indicating its negligible bioaccumulation potential. A chlorinated perfluorooctane sulfonate (PFOS) analytical interfering compound was identified with a predicted formula of C14H23O5SCl6- (most abundant at m/z = 514.9373). Significant trophic magnification was observed for 22 PFASs, and the trophic magnification factors of cis- and trans-perfluoroethylcyclohexane sulfonate isomers (1.92 and 2.25, respectively) were reported for the first time. Perfluorohexanoic acid was trophic-magnified, possibly attributed to the PFAS precursor degradation. The hazard index of PFOS was close to 1, implying a potential human health risk via dietary exposure to PFASs in seafood on the premise of continuous PFAS discharge to the SCS.

Towards a global partnership model in interprofessional education for cross-sector problem-solving.

OBJECTIVES: A partnership model in interprofessional education (IPE) is important in promoting a sense of global citizenship while preparing students for cross-sector problem-solving. However, the literature remains scant in providing useful guidance for the development of an IPE programme co-implemented by external partners. In this pioneering study, we describe the processes of forging global partnerships in co-implementing IPE and evaluate the programme in light of the preliminary data available. METHODS: This study is generally quantitative. We collected data from a total of 747 health and social care students from four higher education institutions. We utilized a descriptive narrative format and a quantitative design to present our experiences of running IPE with external partners and performed independent t-tests and analysis of variance to examine pretest and posttest mean differences in students' data. RESULTS: We identified factors in establishing a cross-institutional IPE programme. These factors include complementarity of expertise, mutual benefits, internet connectivity, interactivity of design, and time difference. We found significant pretest-posttest differences in students' readiness for interprofessional learning (teamwork and collaboration, positive professional identity, roles, and responsibilities). We also found a significant decrease in students' social interaction anxiety after the IPE simulation. CONCLUSIONS: The narrative of our experiences described in this manuscript could be considered by higher education institutions seeking to forge meaningful external partnerships in their effort to establish interprofessional global health education.

Benefits of Short-term Premature Mortality Reduction Attributed to PM2.5 Pollution: A Case Study in Long an Province, Vietnam.

PM2.5 pollution exposure is the leading cause of disease burden globally, especially in low- and middle-income countries, including Vietnam. Therefore, economic damage in this context must be quantified. Long An province in the Southern Key Economic (SKE) region was selected as a research area. This study aimed to evaluate PM2.5-related human health effects causing early deaths attributable to respiratory, cardiovascular, and circulatory diseases in all ages and genders. Health end-points and health impact estimation, economic loss model, groups of PM2.5 concentration data, data of exposed population, data of baseline premature mortality rate, and data of health impact functions were used. Hourly PM2.5 concentration data sets were generated specifically using the coupled Weather Research and Forecasting Model (WRF)/Community Multiscale Air Quality Modelling System (CMAQ) models. Daily PM2.5 pollution levels considered mainly in the dry season (from January to April 2018) resulted in 12.9 (95% CI - 0.6; 18.7) all-cause premature deaths per 100,000 population, of which 7.8 (95% CI 1.1; 7.1), 1.5 (95% CI - 0.2; 3.1), and 3.6 (95% CI - 1.5; 8.5) were due to respiratory diseases (RDs; 60.54%), cardiovascular diseases (CVDs; 11.81%), and circulatory system diseases (CSDs; 27.65%) per 100,000 population, respectively. The total economic losses due to acute PM2.5 exposure-related premature mortality cases reached 62.0 (95% CI - 2.7; 89.6) billion VND, equivalent to 8.3 (95% CI - 0.4; 12.0) million USD. The study outcomes contributed remarkably to the generation and development of data sources for effectively managing ambient air quality in Long An.

Natural Anticancer Molecules and Their Therapeutic Potential.

Cancer poses a significant global public health challenge [...].

Ginseng polysaccharides alter the gut microbiota and kynurenine/tryptophan ratio, potentiating the antitumour effect of antiprogrammed cell death 1/programmed cell death ligand 1 (anti-PD-1/PD-L1) immunotherapy.

OBJECTIVE: Programmed death 1 and its ligand 1 (PD-1/PD-L1) immunotherapy is promising for late-stage lung cancer treatment, however, the response rate needs to be improved. Gut microbiota plays a crucial role in immunotherapy sensitisation and Panax ginseng has been shown to possess immunomodulatory potential. In this study, we aimed to investigate whether the combination treatment of ginseng polysaccharides (GPs) and αPD-1 monoclonal antibody (mAb) could sensitise the response by modulating gut microbiota. DESIGN: Syngeneic mouse models were administered GPs and αPD-1 mAb, the sensitising antitumour effects of the combination therapy on gut microbiota were assessed by faecal microbiota transplantation (FMT) and 16S PacBio single-molecule real-time (SMRT) sequencing. To assess the immune-related metabolites, metabolomics analysis of the plasma samples was performed. RESULTS: We found GPs increased the antitumour response to αPD-1 mAb by increasing the microbial metabolites valeric acid and decreasing L-kynurenine, as well as the ratio of Kyn/Trp, which contributed to the suppression of regulatory T cells and induction of Teff cells after combination treatment. Besides, the microbial analysis indicated that the abundance of Parabacteroides distasonis and Bacteroides vulgatus was higher in responders to anti-PD-1 blockade than non-responders in the clinic. Furthermore, the combination therapy sensitised the response to PD-1 inhibitor in the mice receiving microbes by FMT from six non-responders by reshaping the gut microbiota from non-responders towards that of responders. CONCLUSION: Our results demonstrate that GPs combined with αPD-1 mAb may be a new strategy to sensitise non-small cell lung cancer patients to anti-PD-1 immunotherapy. The gut microbiota can be used as a novel biomarker to predict the response to anti-PD-1 immunotherapy.

Patterns of a structural covariance network associated with dispositional optimism during late adolescence.

Dispositional optimism (hereinafter, optimism), as a vital character strength, reflects the tendency to hold generalized positive expectancies for future outcomes. A great number of studies have consistently shown the importance of optimism to a spectrum of physical and mental health outcomes. However, less attention has been given to the intrinsic neurodevelopmental patterns associated with interindividual differences in optimism. Here, we investigated this important question in a large sample comprising 231 healthy adolescents (16-20 years old) via structural magnetic resonance imaging and behavioral tests. We constructed individual structural covariance networks based on cortical gyrification using a recent novel approach combining probability density estimation and Kullback-Leibler divergence and estimated global (global efficiency, local efficiency and small-worldness) and regional (betweenness centrality) properties of these constructed networks using graph theoretical analysis. Partial correlations adjusted for age, sex and estimated total intracranial volume showed that optimism was positively related to global and local efficiency but not small-worldness. Partial least squares correlations indicated that optimism was positively linked to a pronounced betweenness centrality pattern, in which twelve cognition-, emotion-, and motivation-related regions made robust and reliable contributions. These findings remained basically consistent after additionally controlling for family socioeconomic status and showed significant correlations with optimism scores from 2.5 years before, which replicated the main findings. The current work, for the first time, delineated characteristics of the cortical gyrification covariance network associated with optimism, extending previous neurobiological understandings of optimism, which may navigate the development of interventions on a neural network level aimed at raising optimism.

Down-regulating Nrf2 by tangeretin reverses multiple drug resistance to both chemotherapy and EGFR tyrosine kinase inhibitors in lung cancer.

Multiple drug resistance (MDR) is the major obstacle for both chemotherapy and molecular-targeted therapy for cancer, which is mainly caused by overexpression of ABC transporters or genetic mutation of drug targets. Based on previous studies, we hypothesized that ROS/Nrf2 is the common target for overcoming acquired drug resistance to both targeted therapy and chemotherapy treatments. In this study, we firstly proved that the levels of ROS and Nrf2 were remarkably up-regulated in both H1975 (Gefitinib-resistant lung cancer cells with T790M) and A549/T (paclitaxel-resistant) cells, which is consistent with the clinical database analysis results of lung cancer patients that Nrf2 expression level is negatively related to survival rate. Nrf2 Knockdown with siRNA or tangeretin (TG, a flavonoid isolated from citrus peels) inhibited the MDR cell growth by suppressing the Nrf2 pathway, and efficiently enhanced the anti-tumor effects of paclitaxel and AZD9291 (the third generation of TKI) in A549/T or H1975, respectively. Moreover, TG sensitized A549/T cells-derived xenografts to paclitaxel via inhibiting Nrf2 and its downstream target P-gp, leading to an increased paclitaxel concentration in tumors. Collectively, targeting Nrf2 to enhance ROS may be a common target for overcoming the acquired drug resistance and enhancing the therapeutic effects of chemotherapy and molecular-targeted therapy.