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Metabolic reprogramming is a hallmark of cancer. However, systematic characterizations of metabolites in triple-negative breast cancer (TNBC) are still lacking. Our study profiled the polar metabolome and lipidome in 330 TNBC samples and 149 paired normal breast tissues to construct a large metabolomic atlas of TNBC. Combining with previously established transcriptomic and genomic data of the same cohort, we conducted a comprehensive analysis linking TNBC metabolome to genomics. Our study classified TNBCs into three distinct metabolomic subgroups: C1, characterized by the enrichment of ceramides and fatty acids; C2, featured with the upregulation of metabolites related to oxidation reaction and glycosyl transfer; and C3, having the lowest level of metabolic dysregulation. Based on this newly developed metabolomic dataset, we refined previous TNBC transcriptomic subtypes and identified some crucial subtype-specific metabolites as potential therapeutic targets. The transcriptomic luminal androgen receptor (LAR) subtype overlapped with metabolomic C1 subtype. Experiments on patient-derived organoid and xenograft models indicate that targeting sphingosine-1-phosphate (S1P), an intermediate of the ceramide pathway, is a promising therapy for LAR tumors. Moreover, the transcriptomic basal-like immune-suppressed (BLIS) subtype contained two prognostic metabolomic subgroups (C2 and C3), which could be distinguished through machine-learning methods. We show that N-acetyl-aspartyl-glutamate is a crucial tumor-promoting metabolite and potential therapeutic target for high-risk BLIS tumors. Together, our study reveals the clinical significance of TNBC metabolomics, which can not only optimize the transcriptomic subtyping system, but also suggest novel therapeutic targets. This metabolomic dataset can serve as a useful public resource to promote precision treatment of TNBC.

I demonstrate that skill and scale are mismatched among actively managed equity mutual funds. Many mutual fund investors confuse the effects of fund exposures to common systematic factors with managerial skill when allocating capital among funds. Active mutual funds with positive factor-related past returns thus accumulate assets to the point that they significantly underperform. I also show that the negative aggregate benchmark-adjusted performance of active equity mutual funds is driven mainly by these oversized funds.

Professor Chen-Ning Yang is best known for his achievements in Physics. He has also made significant contributions to the development of mathematics, as mathematics is extensively used in his research. In his long and fruitful academic career, he has witnessed many important events in the fields of Physics and Mathematics, and has collaborated or interacted with many great scientists in history. This book records eight interviews with Professor Chen-Ning Yang, which were conducted by the authors from 2016 to 2019. Through Professor Yang's unique perspective, major scientific events in the 20th century were revisited vividly, elaborating the development and mutual influences of mathematics and physics, as well as unveiling the academic work, the daily lives, and the personalities of scientists, as well as their collaboration and competition, some stories unknown to the public before are also revealed in this book.

The tumor microenvironment is highly complex, and immune escape is currently considered an important hallmark of cancer, largely contributing to tumor progression and metastasis. Named for their capability of killing target cells autonomously, natural killer (NK) cells serve as the main effector cells toward cancer in innate immunity and are highly heterogeneous in the microenvironment. Most current treatment options harnessing the tumor microenvironment focus on T cell-immunity, either by promoting activating signals or suppressing inhibitory ones. The limited success achieved by T cell immunotherapy highlights the importance of developing new-generation immunotherapeutics, for example utilizing previously ignored NK cells. Although tumors also evolve to resist NK cell-induced cytotoxicity, cytokine supplement, blockade of suppressive molecules and genetic engineering of NK cells may overcome such resistance with great promise in both solid and hematological malignancies. In this review, we summarized the fundamental characteristics and recent advances of NK cells within tumor immunometabolic microenvironment, and discussed potential application and limitations of emerging NK cell-based therapeutic strategies in the era of presicion medicine.

Triple-negative breast cancers display heterogeneity in molecular drivers and immune traits. We previously classified triple-negative breast cancers into four subtypes: luminal androgen receptor (LAR), immunomodulatory, basal-like immune-suppressed (BLIS), and mesenchymal-like (MES). Here, we aimed to evaluate the efficacy and safety of subtyping-based therapy in the first-line treatment of triple-negative breast cancer. FUTURE-SUPER is an ongoing, open-label, randomised, controlled phase 2 trial being conducted at Fudan University Shanghai Cancer Center (FUSCC), Shanghai, China. Eligible participants were females aged 18–70 years, with an Eastern Cooperative Oncology Group performance status of 0–1, and histologically confirmed, untreated metastatic or recurrent triple-negative breast cancer. After categorising participants into five cohorts according to molecular subtype and genomic biomarkers, participants were randomly assigned (1:1) with a block size of 4, stratified by subtype, to receive, in 28-day cycles, nab-paclitaxel (100 mg/m2, intravenously on days 1, 8, and 15) alone (control group) or with a subtyping-based regimen (subtyping-based group): pyrotinib (400 mg orally daily) for the LAR-HER2mut subtype, everolimus (10 mg orally daily) for the LAR-PI3K/AKTmut and MES-PI3K/AKTmut subtypes, camrelizumab (200 mg intravenously on days 1 and 15) and famitinib (20 mg orally daily) for the immunomodulatory subtype, and bevacizumab (10 mg/kg intravenously on days 1 and 15) for the BLIS/MES-PI3K/AKTWT subtype. The primary endpoint was investigator-assessed progression-free survival for the pooled subtyping-based group versus the control group in the intention-to-treat population (all randomly assigned participants). Safety was analysed in all patients with safety records who received at least one dose of study drug. This study is registered with ClinicalTrials.gov (NCT04395989). Between July 28, 2020, and Oct 16, 2022, 139 female participants were enrolled and randomly assigned to the subtyping-based group (n=69) or control group (n=70). At the data cutoff (May 31, 2023), the median follow-up was 22·5 months (IQR 15·2–29·0). Median progression-free survival was significantly longer in the pooled subtyping-based group (11·3 months [95% CI 8·6–15·2]) than in the control group (5·8 months [4·0–6·7]; hazard ratio 0·44 [95% CI 0·30–0·65]; p<0·0001). The most common grade 3–4 treatment-related adverse events were neutropenia (21 [30%] of 69 in the pooled subtyping-based group vs 16 [23%] of 70 in the control group), anaemia (five [7%] vs none), and increased alanine aminotransferase (four [6%] vs one [1%]). Treatment-related serious adverse events were reported for seven (10%) of 69 patients in the subtyping-based group and none in the control group. No treatment-related deaths were reported in either group. These findings highlight the potential clinical benefits of using molecular subtype-based treatment optimisation in patients with triple-negative breast cancer, suggesting a path for further clinical investigation. Phase 3 randomised clinical trials assessing the efficacy of subtyping-based regimens are now underway. National Natural Science Foundation of China, Natural Science Foundation of Shanghai, Shanghai Hospital Development Center, and Jiangsu Hengrui Pharmaceuticals. For the Chinese translation of the abstract see Supplementary Materials section.

Harmful algal blooms are becoming increasingly prevalent due to climate warming and eutrophication. Leveraging machine learning tools to forecast algal blooms is crucial and promising for bloom management in various water systems. Notably, previous findings are site-specific, especially regarding the impacts of forecasting periods and important input features. However, there is a significant research gap in the application of machine learning for predicting algal blooms in the Great Lakes, the world’s largest freshwater system. Thus, based on the measurements of 16 water quality parameters from 2012 to 2022, the author established the extreme gradient boosting (XGBoost) model to forecast chlorophyll a (Chl a, a proxy for algal biomass) concentration for 1–7 d in Lake Erie. The XGBoost model performance is quite satisfactory, with the lowest MSE of 10.94 and the highest R2 of 0.99 for the 1 d forecast and an MSE of 83.90 and an R2 of 0.90 for the 7 d forecast. Once trained, the model takes only a few seconds to run on an Intel Core i7 personal laptop. Based on Shapley additive explanations (SHAP) feature importance, water depth (Depth) and water temperature (Temp), are more important input features for the 7 d forecasting model than the well-recognized phosphorus and nitrogen nutrients, including particulate organic nitrogen (PON), soluble reactive phosphorous (SRP), nitrate + nitrite (NN), and total phosphorous (TP). Achieving relatively high accuracy for the 7 d forecast, with an R2 of 0.83 and an MSE of 144.40, is possible by using only the top six most important input features: initial Chl a, Depth, Temp, PON, SRP, and NN, based on SHAP feature importance results. These findings highlight the accuracy and efficiency of the developed XGBoost model to predict Chl a in the world’s largest freshwater system. The model can enhance algal bloom monitoring efficiency through early detection and key predictive features, supporting an early warning system for timely interventions, while also informing policy decisions and optimizing resource allocation.

Empathy promotes positive physician-patient communication and is associated with improved patient satisfaction, treatment adherence and clinical outcomes. It has been suggested that personality traits should be taken into consideration in programs designed to enhance empathy in medical education due to the association found between personality and empathy among medical students. However, the associations between empathy and big five personality traits in medical education are still underrepresented in the existing literature and relevant studies have not been conducted among medical students in China, where tensions in the physician-patient relationship have been reported as outstanding problems in the context of China's current medical reform. Thus, the main objective of this study was to examine the associations between empathy and big five personality traits among Chinese medical students. A cross-sectional study was conducted in a medical university in Northeast China in June 2016. Self-reported questionnaires including the Interpersonal Reactivity Index (IRI) and Big Five Inventory (BFI) and demographic characteristics were distributed. A total of 530 clinical medical students became our final subjects. Hierarchical regression analysis was performed to explore the effects of big five personality traits on empathy. Results of this study showed that big five personality traits accounted for 19.4%, 18.1%, 30.2% of the variance in three dimensions of empathy, namely, perspective taking, empathic concern and personal distress, respectively. Specifically, agreeableness had a strong positive association with empathic concern (β = 0.477, P<0.01), and a moderate association with perspective taking (β = 0.349, P<0.01). Neuroticism was strongly associated with personal distress (β = 0.526, P<0.01) and modestly associated with perspective taking (β = 0.149, P<0.01). Openness to experience had modest associations with perspective taking (β = 0.150, P<0.01) and personal distress (β = -0.160, P<0.01). Conscientiousness had a modest association with perspective taking (β = 0.173, P<0.01). This study revealed that big five personality traits were important predictors of self-reported measures of both cognitive and affective empathy among Chinese medical students. Therefore, individualized intervention strategies based on personality traits could be integrated into programs to enhance empathy in medical education.