The Korean Medication Algorithm for Major Depressive Disorder (KMA-MDD): Report of the Korean Society of Depressive and Bipolar Disorders

Objective. There are many differences in biological characteristics, clinical situations, and medical insurance systems with ethnic groups or countries. The Korean Society of Depressive and Bipolar Disorders decided to develop a Korean treatment algorithm for major depressive disorder. Methods. The Korean Medication Algorithm Project for Major Depressive Disorder (KMAP-MDD) was designed with the following principles: (1) to be an ideal algorithm, (2) to be a Korean algorithm, (3) to be a medication algorithm, (4) to be an evidence-based and formal consensus algorithm. After collecting and reviewing many literature citations and reports by evidence-based rule, we constructed a survey questionnaire for formal consensus of Korean experts. By employing panels of experts to review the evidence and survey results thoroughly, we used evidence-based algorithm development as a component of a formal consensus development process. Results. We developed two algorithms for the KMA-MDD: one for major depressive disorder without psychotic feature and the other for major depressive disorder with psychotic features. Clinical guidelines for the implementation of KMA-MDD were also developed. The KMA-MDD provides specific treatment strategies for each stage. Conclusions. The KMA-MDD is the first Korean algorithm for treatment of major depressive disorder. It is based on evidence which supports the efficacy of each treatment, and it has obtained the consensus of Korean experts. We hope that the KMA-MDD will be good practical tool for clinicians who treat major depressive disorder in Korea.     

Automatic detection of one-on-one tackles and ruck events using microtechnology in rugby union

ObjectivesTo automate the detection of ruck and tackle events in rugby union using a specifically-designed algorithm based on microsensor data.DesignCross-sectional study.MethodsElite rugby union players wore microtechnology devices (Catapult, S5) during match-play. Ruck (n = 125) and tackle (n = 125) event data was synchronised with video footage compiled from international rugby union match-play ruck and tackle events. A specifically-designed algorithm to detect ruck and tackle events was developed using a random forest classification model. This algorithm was then validated using 8 additional international match-play datasets and video footage, with each ruck and tackle manually coded and verified if the event was correctly identified by the algorithm.ResultsThe classification algorithm’s results indicated that all rucks and tackles were correctly identified during match-play when 79.4 ± 9.2% and 81.0 ± 9.3% of the random forest decision trees agreed with the video-based determination of these events. Sub-group analyses of backs and forwards yielded similar optimal confidence percentages of 79.7% and 79.1% respectively for rucks. Sub-analysis revealed backs (85.3 ± 7.2%) produced a higher algorithm cut-off for tackles than forwards (77.7 ± 12.2%).ConclusionsThe specifically-designed algorithm was able to detect rucks and tackles for all positions involved. For optimal results, it is recommended that practitioners use the recommended cut-off (80%) to limit false positives for match-play and training. Although this algorithm provides an improved insight into the number and type of collisions in which rugby players engage, this algorithm does not provide impact forces of these events.     

Increased Efficacy and Safety of Enteral Nutrition Support with a Protocol (ASNET) in Noncritical Patients: A Randomized Controlled Trial

Background

Unintentional underfeeding is common in patients receiving enteral nutrition (EN), and is associated with increased risk of malnutrition complications. Protocols for EN in critically ill patients have been shown to enhance adequacy, resulting in better clinical outcomes; however, outside of intensive care unit (ICU) settings, the influence of a protocol for EN is unknown.

Modern treatment of retinoblastoma: A 2020 review

Retinoblastoma management remains complex, requiring individualized treatment based on International Classification of Retinoblastoma (ICRB) staging, germline mutation status, family psychosocial factors and cultural beliefs, and available institutional resources. For this 2020 retinoblastoma review, PubMed was searched for articles dated as early as 1931, with an emphasis on articles from 1990 to the present day, using keywords of retinoblastoma, chemotherapy, intravenous chemotherapy, chemoreduction, intra-arterial chemotherapy, ophthalmic artery chemosurgery, intravitreal chemotherapy, intracameral chemotherapy, cryotherapy, transpupillary thermotherapy, laser, radiation, external beam radiotherapy, plaque radiotherapy, brachytherapy, and enucleation. We discuss current treatment modalities as used in the year 2020, including intravenous chemotherapy (IVC), intra-arterial chemotherapy (IAC), intravitreal chemotherapy (IvitC), intracameral chemotherapy (IcamC), consolidation therapies (cryotherapy and transpupillary thermotherapy [TTT]), radiation-based therapies (external beam radiotherapy [EBRT] and plaque radiotherapy), and enucleation. Additionally, we present a consensus treatment algorithm based on the agreement of three North American retinoblastoma treatment centers, and encourage further collaboration amongst the world''s most expert retinoblastoma treatment centers in order to develop consensus management plans and continue advancement in the identification and treatment of this childhood cancer.     

A review on current advances in machine learning based diabetes prediction

Diabetes is a metabolic disorder comprising of high glucose level in blood over a prolonged period in the body as it is not capable of using it properly. The severe complications associated with diabetes include diabetic ketoacidosis, nonketotic hypersmolar coma, cardiovascular disease, stroke, chronic renal failure, retinal damage and foot ulcers. There is a huge increase in the number of patients with diabetes globally and it is considered a major health problem worldwide. Early diagnosis of diabetes is helpful for treatment and reduces the chance of severe complications associated with it. Machine learning algorithms (such as ANN, SVM, Naive Bayes, PLS-DA and deep learning) and data mining techniques are used for detecting interesting patterns for diagnosing and treatment of disease. Current computational methods for diabetes diagnosis have some limitations and are not tested on different datasets or peoples from different countries which limits the practical use of prediction methods. This paper is an effort to summarize the majority of the literature concerned with machine learning and data mining techniques applied for the prediction of diabetes and associated challenges. This report would be helpful for better prediction of disease and improve in understanding the pattern of diabetes. Consequently, the report would be helpful for treatment and reduce risk of other complications of diabetes.