Measuring recovery capital for people recovering from alcohol and drug addiction:A systematic review

Background: Recovery capital (RC) theory provides a biopsychosocial framework for identifying and measuring strengths and barriers that can be targeted to support recovery from alcohol and drug addiction. This systematic review analyzed and synthesized all quantitative approaches that have been used to measured recovery capital RC in the recent literaturesince 2016.Method: Systematic database searches were conducted in three databases to identifyThe reviewed studies were published from 2016 to 2023, . Eligible studiesand explicitly stated they measured RC recovery capital in participants recovering from alcohol and/or drug addiction. Studies focusing on other forms of addiction were excluded.Results: Sixty-nine studies met the inclusion criteria. Forty-six studies (66.7%) used one of the ten identified RC recovery capital questionnaires, and twenty-five studies (36.2%) used a measurement approach other than one of the ten RC recovery capital questionnaires. The ten RC recovery capital questionnaires are primarily developed for adult populations across clinical and community recovery settings, and between them measuredwere identified to measure altogether 41 separate RC recovery capital constructs. They, and are generally considered valid and reliable measures of RCrecovery capital. Nevertheless, a strong evidence base on the psychometric properties across diverse populations and settings is still needs to be established for all RC these questionnaires. Conclusion: The development of RC recovery capital questionnaires has been a significant advance in the addiction recovery field, in alignment with the modern emerging recovery-oriented approach to addiction recovery care. Additionally, the non-RC recovery capital questionnaire-based approaches to RC recovery capital measurement have an important place in the field. They could be used alongside RC recovery capital questionnaires to test RC theory, and in contexts where the application of the RC questionnaires is not feasible, such as analyses of data from online recovery forums

Molecular networks of human muscle adaptation to exercise and age

Physical activity and molecular ageing presumably interact to precipitate musculoskeletal decline in humans with age. Herein, we have delineated molecular networks for these two major components of sarcopenic risk using multiple independent clinical cohorts. We generated genome-wide transcript profiles from individuals (n = 44) who then undertook 20 weeks of supervised resistance-exercise training (RET). Expectedly, our subjects exhibited a marked range of hypertrophic responses (3% to +28%), and when applying Ingenuity Pathway Analysis (IPA) up-stream analysis to ~580 genes that co-varied with gain in lean mass, we identified rapamycin (mTOR) signaling associating with growth (P = 1.4×10−30). Paradoxically, those displaying most hypertrophy exhibited an inhibited mTOR activation signature, including the striking down-regulation of 70 rRNAs. Differential analysis found networks mimicking developmental processes (activated all-trans-retinoic acid (ATRA, Z-score = 4.5; P = 6×10−13) and inhibited aryl-hydrocarbon receptor signaling (AhR, Z-score = −2.3; P = 3×10−7)) with RET. Intriguingly, as ATRA and AhR gene-sets were also a feature of endurance exercise training (EET), they appear to represent “generic” physical activity responsive gene-networks. For age, we found that differential gene-expression methods do not produce consistent molecular differences between young versus old individuals. Instead, utilizing two independent cohorts (n = 45 and n = 52), with a continuum of subject ages (18–78 y), the first reproducible set of age-related transcripts in human muscle was identified. This analysis identified ~500 genes highly enriched in post-transcriptional processes (P = 1×10−6) and with negligible links to the aforementioned generic exercise regulated gene-sets and some overlap with ribosomal genes. The RNA signatures from multiple compounds all targeting serotonin, DNA topoisomerase antagonism, and RXR activation were significantly related to the muscle age-related genes. Finally, a number of specific chromosomal loci, including 1q12 and 13q21, contributed by more than chance to the age-related gene list (P = 0.01–0.005), implying possible epigenetic events. We conclude that human muscle age-related molecular processes appear distinct from the processes regulated by those of physical activity

Tutorial: Multivariate Classification for Vibrational Spectroscopy in Biological Samples

Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental

Honey Discrimination Using Fourier Transform-Infrared Spectroscopy

Infrared spectroscopy is a widely used method of analysis to monitor various characteristics in the honey products analysis, to highlight these changes and to detect fraudulent modifications. In this way honey products could not be avoided. This article reviews some of the most important applications of these spectroscopic procedures in order to discriminate different types of honey and other products published between 2015–2022

Honey Discrimination Using Fourier Transform-Infrared Spectroscopy

Infrared spectroscopy is a widely used method of analysis to monitor various characteristics in the honey products analysis, to highlight these changes and to detect fraudulent modifications. In this way honey products could not be avoided. This article reviews some of the most important applications of these spectroscopic procedures in order to discriminate different types of honey and other products published between 2015–2022