Predicted vs. measured paraspinal muscle activity in adolescent idiopathic scoliosis patients: EMG validation of optimization-based musculoskeletal simulations.

Musculoskeletal (MSK) models offer great potential for predicting the muscle forces required to inform more detailed simulations of vertebral endplate loading in adolescent idiopathic scoliosis (AIS). In this work, simulations based on static optimization were compared with in vivo measurements in two AIS patients to determine whether computational approaches alone are sufficient for accurate prediction of paraspinal muscle activity during functional activities. We used biplanar radiographs and marker-based motion capture, ground reaction force, and electromyography (EMG) data from two patients with mild and moderate thoracolumbar AIS (Cobb angles: 21° and 45°, respectively) during standing while holding two weights in front (reference position), walking, running, and object lifting. Using a fully automated approach, 3D spinal shape was extracted from the radiographs. Geometrically personalized OpenSim-based MSK models were created by deforming the spine of pre-scaled full-body models of children/adolescents. Simulations were performed using an experimentally controlled backward approach. Differences between model predictions and EMG measurements of paraspinal muscle activity (both expressed as a percentage of the reference position values) at three different locations around the scoliotic main curve were quantified by root mean square error (RMSE) and cross-correlation (XCorr). Predicted and measured muscle activity correlated best for mild AIS during object lifting (XCorr's ≥ 0.97), with relatively low RMSE values. For moderate AIS as well as the walking and running activities, agreement was lower, with XCorr reaching values of 0.51 and comparably high RMSE values. This study demonstrates that static optimization alone seems not appropriate for predicting muscle activity in AIS patients, particularly in those with more than mild deformations as well as when performing upright activities such as walking and running

Fake anabolic androgenic steroids on the black market – a systematic review and meta-analysis on qualitative and quantitative analytical results found within the literature

Abstract Objective Supraphysiologic doses of anabolic androgenic steroids (AAS) are widely used to improve body image and sport performance goals. These substances can easily be acquired over the internet, leading to a substantial black market. We reviewed literature that assessed the quality and quantity of AAS found on the black market. Methods We searched PubMed/Medline, Embase and Google Scholar for articles published before March 2022. Additional hand searches were conducted to obtain studies not found in the primary literature search. Studies were included if they report on qualitative and/or quantitative analytical findings of AAS from the black market. Primary outcomes were proportions of counterfeit or substandard AAS. Eligible articles were extracted; quality appraisal was done using the ToxRTool for in-vitro studies. We used random-effects models to calculate the overall mean estimates for outcomes. The review protocol has been published and registered in INPLASY. Results Overall, 19 studies, which in total comprised 5,413 anabolic samples, met the inclusion criteria, and passed the quality appraisal from two WHO world regions that reported findings, the Americas and Europe. Most studies were nonclinical laboratory studies (95%) and provided samples seized by authorities (74%). In 18 articles, proportions of counterfeit substances and in eight articles, proportions of substandard substances were presented. The overall mean estimate for counterfeit anabolic steroids found on the black market was 36% (95% CI = 29, 43). An additional 37% (95% CI = 17, 63) were of substandard quality. We also demonstrate that these drugs could contain no active ingredient, or in another amount than that labeled, a wrong active ingredient, as well as not all or more active ingredients than were labeled. High heterogeneity among all analyses and significant differences between geographical subgroups were found. Conclusion With this systematic review and meta-analysis, we demonstrate that substantial mean proportions of black-market AAS are counterfeit and of substandard quality. These products pose a considerable individual and public health threat, and the very wide range in proportions of fake black-market AAS puts the user in a situation of unpredictable uncertainty. There is a great need for future prevention and harm-reduction programs to protect users from these substances

Cannabis adulterated with the synthetic cannabinoid receptor agonist MDMB-4en-PINACA and the role of European drug checking services.

BackgroundEuropean drug checking services exchange information on drug trends within the Trans European Drug Information (TEDI) network, allowing monitoring and coordination of responses. Starting in Spring 2020, several services detected the synthetic cannabinoid receptor agonist MDMB-4en-PINACA in adulterated low-THC cannabis products.MethodsCannabis products suspected of adulteration were analyzed for the presence of MDMB-4en-PINACA by 9 services in 8 countries within the TEDI network. If available, phytocannabinoid analysis was also performed.Results1142 samples sold as cannabis in herbal, resin and e-liquid form were analyzed, of which 270 were found to contain MDMB-4en-PINACA. All cannabis samples contained low THC (ConclusionAdulteration of cannabis with synthetic cannabinoid receptor agonists is a new phenomenon that carries risk for people who use it. Given that cannabis consumers are not a usual target group for drug checking services, services and associated harm reduction interventions could be reconfigured to include them

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.