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Introduction: Chronic Low Back Pain (CLBP), a well-known public health burden which progressively increased with time and development of trigger points in Quadratus Lumborum (QL) is frequently the cause of CLBP. Extracorporeal Shockwave Therapy (ESWT) is an entirely novel and effective non invasive therapy for treating myofascial trigger points. Aim: There is no comprehensive analysis which assess the effect of ESWT on QL trigger points associated with CLBP. As a result, aim of this review is to outlined the existing evidence available. Materials and Methods: Electronic databases which include PubMed/Medline, Scopus, Physiotherapy Evidence Database (PEDro), and Cochrane were searched. Searches were limited to full-text english language and publication between Jan 2000 to Jan 2024. In addition, reference list of the retrieved articles was also explored. A total of 678 relevant articles which includes 142 participants were identified in which participants were included. After duplicates were removed, articles were assessed for eligibility criteria. Five (n=5) articles met all criteria and were included in the final data synthesis. Results: The results shows that patients treated with ESWT shows statistically significant improvement in pain (p<0.05) for Visual Analogue Scale (VAS). Also, some studies show statistically significant improvement (p<0.05) in ODI (Oswestry Disability Index) and SF-36 (Short Form Health survey). [ABSTRACT FROM AUTHOR]

A letter published in the Journal of Multidisciplinary Healthcare critiques a study titled "Effectiveness of Asthma Home Management Manual and Low-Cost Air Filter on Quality of Life Among Asthma Adults: A 3-Arm Randomized Controlled Trial." The letter raises several concerns about the study, including the use of keywords not aligned with Medical Subject Headings, the need to consider minimum values of outcome measures before enrolling participants, the importance of considering the duration of asthma, the lack of clarity in data analysis methods, and the absence of descriptions for within-group and between-group analyses. The author encourages the study authors to address these concerns to improve the clarity and quality of the findings. [Extracted from the article]

A recent study titled "Multi-Sensory Stimuli Improve Relaxation and Sleep Quality in Rotating Shift Workers: A Randomized Controlled Trial" suggests that multisensory stimuli can improve the quality of sleep and relaxation for rotating shift workers. However, the authors of a letter to the editor raise several concerns about the study. They note that the trial registration number was not provided, which goes against journal guidelines, and that the article's keywords do not match the journal's requirements. Additionally, they point out that there is insufficient information about sample size estimation, the age range of participants is not adequately justified, and the results section lacks information about the normality of the data. The authors respectfully request that these concerns be addressed to enhance the integrity and clarity of the published results. [Extracted from the article]

Intraday trading is popular among traders due to its ability to leverage price fluctuations in a short timeframe. For traders, real-time price predictions for the next few minutes can be beneficial for making strategies. Real-time prediction is challenging due to the stock market's non-stationary, complex, noisy, chaotic, dynamic, volatile, and non-parametric nature. Machine learning models are considered effective for stock forecasting, yet, their hyperparameters need tuning with the latest market data to incorporate the market's complexities. Usually, models are trained and tested in batches, which smooths the correction process and speeds up the learning. When making intraday stock predictions, the models should forecast for each instance in contrast to the whole batch and learn simultaneously to ensure high accuracy. In this paper, we propose a strategy based on two different learning approaches: incremental learning and Offline-Online learning, to forecast the stock price using the real-time stream of the live market. In incremental learning, the model is updated continuously upon receiving the stock's next instance from the live-stream, while in Offline-Online learning, the model is retrained after each trading session to make sure it incorporates the latest data complexities. These methods were applied to univariate time-series (established from historical stock price) and multivariate time-series (considering historical stock price as well as technical indicators). Extensive experiments were performed on the eight most liquid stocks listed on the American NASDAQ and Indian NSE stock exchanges, respectively. The Offline-Online models outperformed incremental models in terms of low forecasting error., Competing Interests: Conflict of interestThe authors have no relevant financial or non-financial interests to disclose., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)