Introduction: Liposomal bupivacaine (LB) is a long-acting analgesic that, due to its liposomal formulation, purportedly extends its analgesic effect up to 72 hours. However, the clinical efficacy of LB appears mixed. This systematic review seeks to evaluate the effectiveness of liposomal bupivacaine in improving postoperative outcomes compared to ropivacaine (ROPI), another commonly used long-acting analgesic. -/- Materials and Methods: Prospective and randomized controlled trials (RCTs) evaluating the efficacy of LB compared to ROPI were selected for review. Primary outcomes included hospital (...) length of stay (LOS) and postoperative opioid consumption measured in oral morphine equivalents (OME). Secondary outcomes included analgesic cost. -/- Results: 14 studies met the review criteria. We found that LB and ROPI are equivalent in managing postoperative pain. 8 of the 14 trials reported equal efficacy between LB and ROPI as determined by OME post-procedure and 10 of the 14 trials reported similar LOS after surgery. These findings remained consistent across multiple surgical procedures and multiple drug administrative modalities. -/- Conclusion: Our systematic review found that LB was not superior to ROPI in reducing postoperative OME use and hospital LOS. The only consistent finding was the significantly increased cost of LB compared to that of ROPI. Therefore, the use of LB over ROPI cannot be justified. (shrink)

The Generalized Empirical Method as outlined by Henman initially seems a cogent approach that should be adopted by cognitive neuroscientists. However, some weaknesses in the presumptions of this method in light of modern neuroscience research may challenge its validity. As I am currently working on mapping cerebral-cerebellar networks using fMRI, I am intrigued by the practical utility of the GEM in experimental work.

Influenza virus (H1N1) caused seasonal influenza, is an acute respiratory infection that spreads to whole worldwide. The first case of influenza A virus pandemics in India came to light in May 2009 in Hyderabad city of Telangana. Human-to-human transmission led to considerable morbidity and mortality within the country until December.The total number of seasonal influenza cases in India from the year 2010 to 31st July 2020 is 1,73,488. Decreasing ambient temperature is often associated with influenza weather. The viral particle's ability (...) to survive long term in cold temperatures also plays an important role. Besides, coagulation during winter serves as a co-contributing factor in facilitating the spread of the influenza virus. The environmental temperature leads to physical stress and energy loss due to thermal regulation, which weakens the immune system and thereby increases the host's likelihood of infection. The prevention of epidemics in India was possible only by quick and efficient measures taken by the Government.Soon after cases were registered at Pune, Maharashtra, and other places across the country, steps were taken to revise the guidelines for the control of the disease in India.Classification of cases in health facilities and home environments, classification of cases, providing treatment to appropriate persons, public education, and other appropriate measures proved to be very effective in due course of time. In this paper, we discussed the total cases and mortality due to seasonal influenza virus in the states and union territories in India from the year 2010 to the year 2020. (shrink)

Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally (...) accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies. (shrink)

Thank you to Mr. Rastogi for responding to my article on generalized empirical method. It is not possible to respond to all of Rastogi’s comments in a dialogue-form article so I limit myself to central features of his response.