Efficacy of resistance training during adjuvant chemotherapy and radiation therapy in cancer care: a systematic review and meta-analysis.

PURPOSE: To determine the effect of resistance training during adjuvant chemotherapy and radiation therapy in cancer patients on measures of lean mass and muscle strength. Secondary aims were to analyse the prescription and tolerability of supervised resistance training in this population. METHODS: EMBASE, Medline, CINAHL, Cochrane Library and Web of Science were searched from inception until 29 March 2021. Eligible randomised controlled trials (RCTs) examining supervised resistance training > 6 weeks duration during adjuvant chemotherapy and/or radiation therapy in cancer patients with objective measurement of muscle strength and/or lean mass were included. The meta-analysis was performed using Revman 5.4. RESULTS: A total of 1910 participants from 20 articles were included (mean age: 54 years, SD = 10) and the majority were female (76.5%). Resistance training was associated with a significant increase in upper body strength (standardised mean difference (SMD) = 0.57, 95% CI 0.36 to 0.79, I2 = 64%, P < 0.0001), lower body strength (SMD = 0.58, 95% CI 0.18 to 0.98, I2 = 91%, P = 0.005), grip strength (mean difference (MD) = 1.32, 95% CI 0.37 to 2.27, I2 = 0%, P < 0.01) and lean mass (SMD = 0.23, 95% CI 0.03 to 0.42, I2 = 0%, P = 0.02). A P value of < 0.05 was considered statistically significant. The quality of the studies included was moderate to high with low risk of bias as per the PEDro scale. CONCLUSION: Resistance training is an effective adjunct therapy to improve muscle strength and lean mass in cancer patients undergoing chemotherapy and/or radiation therapy. PROSPERO REGISTRATION NUMBER: CRD42020180643.

In situ localization of Escherichia coli O157:H7 in food by confocal scanning laser microscopy.

The aim of this study was to use confocal scanning laser microscopy to examine the in situ localization of Escherichia coli O157:H7 on beef (knuckle or brisket) and carrots and in semisoft cheese made from pasteurized milk. Using a combination of specific immunolabeling and dual-excitation confocal scanning laser microscopy, it was possible to clearly demonstrate the localization of E. coli O157:H7 within various food types. In carrots, bacteria were found mainly at cell junctions and in intracellular spaces up to 50 microm deep. In beef, bacteria were located primarily between muscle fibers and within connective tissue (at a depth of 25 microm), whereas in cheese the bacteria occurred singly or in small clumps of up to 10 cells and were observed within the protein matrix of the cheese. These results revealed how E. coli O157:H7 can penetrate beef and carrot surfaces where it is protected from decontamination processes.

Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: application to gibberellic acid production.

Two rapid vibrational spectroscopic approaches (diffuse reflectance-absorbance Fourier transform infrared [FT-IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie-point pyrolysis (PyMS), were investigated in this study. A diverse range of unprocessed, industrial fed-batch fermentation broths containing the fungus Gibberella fujikuroi producing the natural product gibberellic acid, were analyzed directly without a priori chromatographic separation. Partial least squares regression (PLSR) and artificial neural networks (ANNs) were applied to all of the information-rich spectra obtained by each of the methods to obtain quantitative information on the gibberellic acid titer. These estimates were of good precision, and the typical root-mean-square error for predictions of concentrations in an independent test set was <10% over a very wide titer range from 0 to 4925 ppm. However, although PLSR and ANNs are very powerful techniques they are often described as "black box" methods because the information they use to construct the calibration model is largely inaccessible. Therefore, a variety of novel evolutionary computation-based methods, including genetic algorithms and genetic programming, were used to produce models that allowed the determination of those input variables that contributed most to the models formed, and to observe that these models were predominantly based on the concentration of gibberellic acid itself. This is the first time that these three modern analytical spectroscopies, in combination with advanced chemometric data analysis, have been compared for their ability to analyze a real commercial bioprocess. The results demonstrate unequivocally that all methods provide very rapid and accurate estimates of the progress of industrial fermentations, and indicate that, of the three methods studied, Raman spectroscopy is the ideal bioprocess monitoring method because it can be adapted for on-line analysis.