Background Acquired resistance of 5-fluorouracil (5-FU) remains a clinical challenge in colorectal cancer (CRC), and efforts to develop targeted agents to reduce resistance have not yielded success. Metabolic reprogramming is a key cancer hallmark and confers several tumor phenotypes including chemoresistance. Glucose metabolic reprogramming events of 5-FU resistance in CRC has not been evaluated, and whether abnormal glucose metabolism could impart 5-FU resistance in CRC is also poorly defined. Methods Three separate acquired 5-FU resistance CRC cell line models were generated, and glucose metabolism was assessed by measuring glucose and lactate utilization, RNA and protein expressions of glucose metabolism-related enzymes and changes of intermediate metabolites of glucose metabolite pool. The protein levels of hypoxia inducible factor 1α (HIF-1α) in primary tumors and circulating tumor cells of CRC patients were detected by immunohistochemistry and immunofluorescence. Stable HIF1A knockdown in cell models was established with a lentiviral system. The influence of both HIF1A gene knockdown and pharmacological inhibition on 5-FU resistance in CRC was evaluated in cell models in vivo and in vitro. Results The abnormality of glucose metabolism in 5-FU-resistant CRC were described in detail. The enhanced glycolysis and pentose phosphate pathway in CRC were associated with increased HIF-1α expression. HIF-1α-induced glucose metabolic reprogramming imparted 5-FU resistance in CRC. HIF-1α showed enhanced expression in 5-FU-resistant CRC cell lines and clinical specimens, and increased HIF-1α levels were associated with failure of fluorouracil analog-based chemotherapy in CRC patients and poor survival. Upregulation of HIF-1α in 5-FU-resistant CRC occurred through non-oxygen-dependent mechanisms of reactive oxygen species-mediated activation of PI3K/Akt signaling and aberrant activation of β-catenin in the nucleus. Both HIF-1α gene knock-down and pharmacological inhibition restored the sensitivity of CRC to 5-FU. Conclusions HIF-1α is a potential biomarker for 5-FU-resistant CRC, and targeting HIF-1a in combination with 5-FU may represent an effective therapeutic strategy in 5-FU-resistant CRC.

The myocardium is capable of utilizing different energy substrates, which is referred to as “metabolic flexibility.” This process assures ATP production from fatty acids, glucose, lactate, amino acids, and ketones, in the face of varying metabolic contexts. In the normal physiological state, the oxidation of fatty acids contributes to approximately 60% of energy required, and the oxidation of other substrates provides the rest. The accumulation of lactate in ischemic and hypoxic tissues has traditionally be considered as a by-product, and of little utility. However, recent evidence suggests that lactate may represent an important fuel for the myocardium during exercise or myocadiac stress. This new paradigm drives increasing interest in understanding its role in cardiac metabolism under both physiological and pathological conditions. In recent years, blood lactate has been regarded as a signal of stress in cardiac disease, linking to prognosis in patients with myocardial ischemia or heart failure. In this review, we discuss the importance of lactate as an energy source and its relevance to the progression and management of heart diseases.

In the title compound, Bi(2)B(8)O(15), the Bi atom is coordinated to five or six O atoms. The B atoms exhibit two kinds of hybridization, sp(2) and sp(3), seen in the BO(3) triangles and BO(4) tetrahedra, respectively. Three BO(3) triangles are connected to form a B(3)O(6) planar ring. All atoms in the structure are connected together to form an infinite three-dimensional network.

Background The early mortality after surgery for infective endocarditis is high. Although risk models help identify patients at high risk, most current scoring systems are inaccurate or inconvenient. The objective of this study was to construct an accurate and easy‐to‐use prediction model to identify patients at high risk of early mortality after surgery for infective endocarditis. Methods and Results A total of 476 consecutive patients with infective endocarditis who underwent surgery at 2 centers were included. The development cohort consisted of 276 patients. Eight variables were selected from 89 potential predictors as input of the XGBoost model to train the prediction model, including platelet count, serum albumin, current heart failure, urine occult blood ≥(++), diastolic dysfunction, multiple valve involvement, tricuspid valve involvement, and vegetation >10 mm. The completed prediction model was tested in 2 separate cohorts for internal and external validation. The internal test cohort consisted of 125 patients independent of the development cohort, and the external test cohort consisted of 75 patients from another center. In the internal test cohort, the area under the curve was 0.813 (95% CI, 0.670–0.933) and in the external test cohort the area under the curve was 0.812 (95% CI, 0.606–0.956). The area under the curve was significantly higher than that of other ensemble learning models, logistic regression model, and European System for Cardiac Operative Risk Evaluation II (all, P <0.01). This model was used to develop an online, open‐access calculator ( http://42.240.140.58:1808/ ). Conclusions We constructed and validated an accurate and robust machine learning–based risk model to predict early mortality after surgery for infective endocarditis, which may help clinical decision‐making and improve outcomes.