Anatomical consideration of Dhamani marma in Ayurveda

Ayurveda is an ancient health science devoted to the cure on human suffering and for the care of the health of the people. Injuries involving various types of the structures in the body like dhamanis (arteries), muscles, nerves, bones and the visceral organs in general and when in combination specifying marma. Among the hidden sciences of India, marma science is the most important. Marmas are not superficial landmarks on the body surface but these are deep-seated important physio-anatomical structures. Marma in Ayurvedic classics is illustrated as the vital point in human body, the injury of which leads to termination of life. Descriptions of 107 marmas given by all acharyas being classified into five varieties on the basis of structure involved; five on the basis of effect of injury and five on the basis of location on the body. According to anatomical consideration marmas can be divided into mansa-marma, sira-marma, snayu-marma, sandhi-marma, and asthi-marma (respectively, marma of muscle, blood vessel, ligament, joint and bone). According to Vagabhatta there are six types of marma. He has enumerated a sixth group of marma known as dhamani marma. Dhamani marma is one such vital region in human anatomy which falls under the classification on the basis of structure involved. This study is aimed to emphasize on why Ashtanga Hridaya has considered a separate group called dhamani marmas of which other acharyas have considered under different groups & finally to conclude with clearing out the controversy & thereby to fulfill the lacuna in the subject

Machine learning using institution-specific multi-modal electronic health records improves mortality risk prediction for cardiac surgery patientsCentral MessagePerspective

Background: The Society of Thoracic Surgeons risk scores are widely used to assess risk of morbidity and mortality in specific cardiac surgeries but may not perform optimally in all patients. In a cohort of patients undergoing cardiac surgery, we developed a data-driven, institution-specific machine learning–based model inferred from multi-modal electronic health records and compared the performance with the Society of Thoracic Surgeons models. Methods: All adult patients undergoing cardiac surgery between 2011 and 2016 were included. Routine electronic health record administrative, demographic, clinical, hemodynamic, laboratory, pharmacological, and procedural data features were extracted. The outcome was postoperative mortality. The database was randomly split into training (development) and test (evaluation) cohorts. Models developed using 4 classification algorithms were compared using 6 evaluation metrics. The performance of the final model was compared with the Society of Thoracic Surgeons models for 7 index surgical procedures. Results: A total of 6392 patients were included and described by 4016 features. Overall mortality was 3.0% (n = 193). The XGBoost algorithm using only features with no missing data (336 features) yielded the best-performing predictor. When applied to the test set, the predictor performed well (F-measure = 0.775; precision = 0.756; recall = 0.795; accuracy = 0.986; area under the receiver operating characteristic curve = 0.978; area under the precision-recall curve = 0.804). eXtreme Gradient Boosting consistently demonstrated improved performance over the Society of Thoracic Surgeons models when evaluated on index procedures within the test set. Conclusions: Machine learning models using institution-specific multi-modal electronic health records may improve performance in predicting mortality for individual patients undergoing cardiac surgery compared with the standard-of-care, population-derived Society of Thoracic Surgeons models. Institution-specific models may provide insights complementary to population-derived risk predictions to aid patient-level decision making

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Application of Chiral Transfer Reagents to Improve Stereoselectivity and Yields in the Synthesis of the Antituberculosis Drug Bedaquiline

Bedaquiline (BDQ) is an important drug for treating multidrug-resistant tuberculosis (MDR-TB), a worldwide disease that causes more than 1.6 million deaths yearly. The current synthetic strategy adopted by the manufacturers to assemble this molecule relies on a nucleophilic addition reaction of a quinoline fragment to a ketone, but it suffers from low conversion and no stereoselectivity, which subsequently increases the cost of manufacturing BDQ. The Medicines for All Institute (M4ALL) has developed a new reaction methodology to this process that not only allows high conversion of starting materials but also results in good diastereo- and enantioselectivity toward the desired BDQ stereoisomer. A variety of chiral lithium amides derived from amino acids were studied, and it was found that lithium (R)-2-(methoxymethyl)pyrrolidide, obtained from d-proline, results in high assay yield of the desired syn-diastereomer pair (82%) and with considerable stereocontrol (d.r. = 13.6:1, e.r. = 3.6:1, 56% ee), providing BDQ in up to a 64% assay yield before purification steps toward the final API. This represents a considerable improvement in the BDQ yield compared to previously reported conditions and could be critical to further lowering the cost of this life-saving drug