Tumors are dynamic metabolic systems which highly augmented metabolic fluxes and nutrient needs to support cellular proliferation and physiological function. For many years, a central hallmark of tumor metabolism has emphasized a uniformly elevated aerobic glycolysis as a critical feature of tumorigenecity. This led to extensive efforts of targeting glycolysis in human cancers. However, clinical attempts to target glycolysis and glucose metabolism have proven to be challenging. Recent advancements revealing a high degree of metabolic heterogeneity and plasticity embedded among various human cancers may paint a new picture of metabolic targeting for cancer therapies with a renewed interest in glucose metabolism. In this review, we will discuss diverse oncogenic and molecular alterations that drive distinct and heterogeneous glucose metabolism in cancers. We will also discuss a new perspective on how aberrantly altered glycolysis in response to oncogenic signaling is further influenced and remodeled by dynamic metabolic interaction with surrounding tumor-associated stromal cells.
Cell Proliferation ; Glucose ; Glycolysis ; Humans ; Metabolism ; Paint ; Plastics ; Population Characteristics ; Stromal Cells ; Tumor Microenvironment

There has been a conscious effort to address osteoporosis in the aging population. As bisphosphonate and intermittent parathyroid hormone (PTH) therapy become more widely prescribed to treat osteoporosis, it is important to understand their effects on other physiologic processes, particularly the impact on spinal fusion. Despite early animal model studies and more recent clinical studies, the impact of these medications on spinal fusion is not fully understood. Previous animal studies suggest that bisphosphonate therapy resulted in inhibition of fusion mass with impeded maturity and an unknown effect on biomechanical strength. Prior animal studies demonstrate an improved fusion rate and fusion mass microstructure with the use of intermittent PTH. The purpose of this study was to determine if bisphosphonates and intermittent PTH treatment have impact on human spinal fusion. A systematic review of the literature published between 1980 and 2015 was conducted using major electronic databases. Studies reporting outcomes of human subjects undergoing 1, 2, or 3-level spinal fusion while receiving bisphosphonates and/or intermittent PTH treatment were included. The results of relevant human studies were analyzed for consensus on the effects of these medications in regards to spinal fusion. There were nine human studies evaluating the impact of these medications on spinal fusion. Improved fusion rates were noted in patients receiving bisphosphonates compared to control groups, and greater fusion rates in patients receiving PTH compared to control groups. Prior studies involving animal models found an improved fusion rate and fusion mass microstructure with the use of intermittent PTH. No significant complications were demonstrated in any study included in the analysis. Bisphosphonate use in humans may not be a deterrent to spinal fusion. Intermittent parathyroid use has shown early promise to increase fusion mass in both animal and human studies but further studies are needed to support routine use.
Aging ; Animals ; Consensus ; Diphosphonates ; Humans* ; Lumbar Vertebrae ; Models, Animal ; Osteoporosis ; Parathyroid Hormone ; Spinal Fusion*

Purpose: Compared to the microbiome of other body sites, the urinary microbiome remains poorly understood. Although noninvasive voided urine specimens are convenient, contamination by urethral microbiota may confound understanding of the bladder microbiome. Herein we compared the voiding- versus catheterization-associated urine microbiome of healthy men and women. Methods: An asymptomatic, healthy cohort of 6 women and 14 men underwent midstream urine collection, followed by sterile catheterization of the bladder after bladder refilling. Urine samples underwent urine dipstick testing and conventional microscopy and urine cultures. Samples also underwent Illumina MiSeq-based 16S ribosomal RNA gene amplification and sequencing. Results: All organisms identified by urine culture were also identified by 16S amplification; however, next-generation sequencing (NGS) also detected bacteria not identified by cultivation. Lactobacillus and Streptococcus were the most abundant species. Abundances of the 9 predominant bacterial genera differed between the urethra and bladder. Voided and catheterized microbiomes share all dominant (>1%) genera and Operational Taxonomic Units but in similar or different proportions. Hence, urethra and bladder microbiomes do not differ in taxonomic composition, but rather in taxonomic structure. Women had higher abundance of Lactobacillus and Prevotella than men. Conclusions Our findings lend credence to the hypothesis that Lactobacilli are important members of the healthy urine microbiome. Our data also suggest that the microbiomes of the urethra and bladder differ from one another. In conclusion, urine collection method results in different 16S-based NGS data, likely due to the sensitivity of NGS methods enabling detection of urethral bacteria present in voided but not catheterized urine specimens.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.