Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

Prospective Cohort Study

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

AKT/PKB Signaling: Navigating the Network

OBJECTIVE In metazoans, target of rapamycin complex 1 (TORC1) plays the key role in nutrient- and hormone-dependent control of metabolism. However, the role of TORC1 in regulation of triglyceride storage and metabolism remains largely unknown. RESEARCH DESIGN AND METHODS In this study, we analyzed the effect of activation and inhibition of the mammalian TORC1 (mTORC1) signaling pathway on the expression of adipose triglyceride lipase (ATGL), hormone-sensitive lipase (HSL), lipolysis, lipogenesis, and lipid storage in different mammalian cells. RESULTS Activation of mTORC1 signaling in 3T3-L1 adipocytes by ectopic expression of Rheb inhibits expression of ATGL and HSL at the level of transcription, suppresses lipolysis, increases de novo lipogenesis, and promotes intracellular accumulation of triglycerides. Inhibition of mTORC1 signaling by rapamycin or by knockdown of raptor stimulates lipolysis primarily via activation of ATGL expression. Analogous results have been obtained in C2C12 myoblasts and mouse embryonic fibroblasts with genetic ablation of tuberous sclerosis 2 ( TSC2 ) gene. Overexpression of ATGL in these cells antagonized the lipogenic effect of TSC2 knockout. CONCLUSIONS Our findings demonstrate that mTORC1 promotes fat storage in mammalian cells by suppression of lipolysis and stimulation of de novo lipogenesis.

https://diabetes.diabetesjournals.org/content/diabetes/59/4/775.full.pdf

Mammalian target of rapamycin complex 1 suppresses lipolysis, stimulates lipogenesis, and promotes fat storage.

FoxO1 controls insulin-dependent adipose triglyceride lipase (ATGL) expression and lipolysis in adipocytes.

SIRT1 controls lipolysis in adipocytes via FOXO1-mediated expression of ATGL

One of the basic functions of insulin in the body is to inhibit lipolysis in adipocytes. Recently, we have found that insulin inhibits lipolysis and promotes triglyceride storage by decreasing transcription of adipose triglyceride lipase via the mTORC1-mediated pathway (P. Chakrabarti et al., Diabetes 59:775-781, 2010), although the mechanism of this effect remained unknown. Here, we used a genetic screen in Saccharomyces cerevisiae in order to identify a transcription factor that mediates the effect of Tor1 on the expression of the ATGL ortholog in yeast. This factor, Msn4p, has homologues in mammalian cells that form a family of early growth response transcription factors. One member of the family, Egr1, is induced by insulin and nutrients and directly inhibits activity of the ATGL promoter in vitro and expression of ATGL in cultured adipocytes. Feeding animals a high-fat diet increases the activity of mTORC1 and the expression of Egr1 while decreasing ATGL levels in epididymal fat. We suggest that the evolutionarily conserved mTORC1-Egr1-ATGL regulatory pathway represents an important component of the antilipolytic effect of insulin in the mammalian organism.

Partha Chakrabarti

Papers

Mammalian target of rapamycin complex 1 suppresses lipolysis, stimulates lipogenesis, and promotes fat storage.

FoxO1 controls insulin-dependent adipose triglyceride lipase (ATGL) expression and lipolysis in adipocytes.

SIRT1 controls lipolysis in adipocytes via FOXO1-mediated expression of ATGL

Insulin Inhibits Lipolysis in Adipocytes via the Evolutionarily Conserved mTORC1-Egr1-ATGL-Mediated Pathway

Distance functions in digital geometry