Pectoralis major tendon rupture is a relatively rare injury, resulting from violent, eccentric contraction of the muscle. Over 50% of these injuries occur in athletes, classically in weight-lifters during the 'bench press' manoeuvre. We present 13 cases of distal rupture of the pectoralis major muscle in athletes. All patients underwent open surgical repair. Magnetic resonance imaging was used to confirm the diagnosis in all patients. The results were analysed using (1) the visual analogue pain score, (2) functional shoulder evaluation and (3) isokinetic strength measurements. At the final follow-up of 23.6 months (14-34 months), the results were excellent in six patients, good in six and one had a poor result. Eleven patients were able to return to their pre-injury level of sports. The mean time for a return to sports was 8.5 months. The intraoperative findings correlated perfectly with the reported MRI scans in 11 patients and with minor differences in 2 patients. We wish to emphasise the importance of accurate clinical diagnosis, appropriate investigations, early surgical repair and an accelerated rehabilitation protocol for the distal rupture of the pectoralis major muscle as this allows complete functional recovery and restoration of full strength of the muscle, which is essential for the active athlete.
We investigate the origin of rare star-formation in an otherwise red-and-dead population of S0 galaxies using spatially resolved spectroscopy. Our sample consists of 120 low redshift (z < 0.1) star-forming S0 (SF-S0) galaxies from the SDSS-IV MaNGA DR15. We have selected this sample after a visual inspection of deep images from the DESI Legacy Imaging Surveys DR9 and the Subaru/HSC-SSP survey PDR3, to remove contamination from spiral galaxies. We also construct two control samples of star-forming spirals (SF-Sps) and quenched S0s (Q-S0s) to explore their evolutionary link with the star-forming S0s. To study star-formation at resolved scales, we use dust-corrected Hα luminosity and stellar density (Σ⋆) maps to construct radial profiles of star-formation rate (SFR) surface density (ΣSFR) and specific SFR (sSFR). Examining these radial profiles, we find that star-formation in SF-S0s is centrally dominated as opposed to disc dominated star-formation in spirals. We also compared various global (size-mass relation, bulge-to-total luminosity ratio) and local (central stellar velocity dispersion) properties of SF-S0s to those of the control sample galaxies. We find that SF-S0s are structurally similar to the quenched S0s and are different from star-forming spirals. We infer that SF-S0s are unlikely to be fading spirals. Inspecting stellar and gas velocity maps, we find that more than $50{{\ \rm per\ cent}}$ of the SF-S0 sample shows signs of recent galaxy interactions such as kinematic misalignment, counter-rotation, and unsettled kinematics. Based on these results, we conclude that in our sample of SF-S0s, star-formation has been rejuvenated, with minor mergers likely to be a major driver.
Researchers have been investigating various approaches to accurately forecast stock market prices. Trading professionals can gain better insights regarding data, such as potential trends, by using useful prediction tools. Additionally, since the study predicts future market conditions, investors stand to gain significantly. Using machine learning algorithms for predicting is one such approach. The goal of this study is to increase the accuracy of stock market predictions made using stock valuation. Many academics have developed various approaches to address this issue, primarily using conventional approaches up to this point, like artificial neural networks, which are ways to identify hidden patterns in data and classify it for use in stock market prediction. This initiative suggests a fresh approach to stock forecasting.
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