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

Global estimates of the prevalence of diabetes for 2010 and 2030.

IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040.

The global burden of disease.

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

Microgrids can operate both in grid-connected and islanded modes. It is essential to protect microgrids against all types of faults in both of the operation modes. Conventional overcurrent protection schemes are not adequate for microgrids due to bidirectional flow of fault current and limited flow of fault current through semiconductor devices. In addition, the setting values of all the concerned relays need to be updated after any change in system operating conditions. Centralized and decentralized adaptive protection schemes are generally used for protection of microgrids. In this paper, a hybrid adaptive protection scheme has been proposed for protection of microgrids. Computational burden and data storage is distributed among the local controllers and the central controller. A gateway is proposed for communicating between the serial interfaced devices and the IEC 61850 process bus. Finally, a framework has been introduced for implementing the proposed hybrid protection scheme for adaptive protection of microgrids by using IEC 61850-based intelligent electronic devices (IEDs).

/pdf/a-hybrid-framework-for-adaptive-protection-of-microgrids-1453mhe1md.pdf

A Hybrid Framework for Adaptive Protection of Microgrids Based on IEC 61850

Background: The aim of the present study was to estimate the incidence of diabetic retinopathy (DR) among type 2 diabetic (T2D) subjects in Bangladesh.



Methods:  A random sample of 977 patients with T2D was recruited retrospectively in 2008 from newly diagnosed T2D patients who had attended the Bangladesh Institute of Research and Rehabilitation in Diabetes, Endocrine and Metabolic Disorder (BIRDEM) in 1993. Baseline information for the cohort was collected for 1993 from hospital records. The mean time until development of DR in newly diagnosed T2D patients was calculated using survival analysis. Cox’s proportional hazards model was used to assess factors affecting the time until development of DR.



Results:  The cumulative incidence of DR over the 15-year period was 50.6% (95% confidence interval [CI] 47.5%–53.8%). The incidence density (per 100 person-years) of DR was similar in the overall cohort (4.1; 95% CI 3.7–4.5) and in men (4.2; 95% CI 3.7–4.7) and women (4.1; 95% CI 3.6–4.6) separately. The mean time (in years) until development of DR in the cohort was 9.72 (95% CI 9.38–10.06), with similar times in men (9.8; 95% CI 9.3–10.3) and women (9.6; 95% CI 9.5–10.1) analyzed separately. Age, sex, hypertension, lipid profile, HbA1c, and serum creatinine were entered into the hazards model simultaneously. However, only age (hazard ratio [HR] 0.75; 95% CI 0.61–0.92) and HbA1c (HR 0.52; 95% CI 0.33–0.82) had a significant effect on the time until development of DR.



Conclusions:  Glucose deregulation is the most important factor in the development of DR.

Incidence of diabetic retinopathy in Bangladesh: a 15-year follow-up study.

The growth in number of electric vehicles (EVs) has resulted in increased dependence of transportation on the power sector. During power outages, especially for elongated time spans, the locally available energy may not be sufficient to fulfill the energy needs of all the EVs. Therefore, a multicriteria EV prioritization scheme is proposed in this study to fairly allocate available energy among EVs during power outages. The five major factors considered for EV prioritization are trip purpose, EV occupants, energy gap, departure time, and customer behavior. With different combinations of the five prioritization factors, three indices are formulated, one each for social welfare, community wellbeing, and individual satisfaction of the EV owners. To this end, a multiobjective optimization problem is formulated, based on the three indices, to allocate the available power among EVs with higher index values. The formulated multiobjective optimization problem is solved using the lexicographic method, which has superior performance over the conventionally used weighted-sum method and $\varepsilon $ -constraint (EC) method. The proposed method is not sensitive to the weights of individual functions and has the ability to handle multiple priority levels. In order to quantify the results, percent served and unserved indices are formulated for each of the three parameters (social welfare, community wellbeing, and individual satisfaction) and the results of the proposed method are compared with those of the weighted-sum method and the EC method. Sensitivity analysis of different uncertain factors, such as number of EVs, uncertainty in EV demand, uncertainty in renewable power, and error in battery state-of-charge estimation, is also carried out. Simulation results have shown the superiority of the proposed method in allocating power to EVs during outages.

EV Prioritization and Power Allocation During Outages: A Lexicographic Method-Based Multiobjective Optimization Approach

The purpose of the study is to investigate the use and awareness of Web 2.0 tools such as Facebook, Twitter, YouTube, RSS feed, Flickr, LinkedIn, Tumblr and Google+ within Saudi universities. The study reveals that the Kingdom's private universities have more consistency, with seven of the eight having Facebook profiles and all Facebook profiles having standard user names. 22 of 24 Saudi Government Universities have Facebook profiles and 12 use standard user names. Facebook and Twitter are the two most familiar Web 2.0 tools. The present study only focuses on usage of Web 2.0 tools by Saudi's Private and Public Saudi Universities. The study only considers those Web 2.0 tools which are linked on websites of universities. This is the first kind of study which revealed use and awareness of Web 2.0 tools such as Facebook, Twitter, YouTube, RSS feed, Flickr, LinkedIn, Tumblr and Google+ within Saudi Universities. Key Terms: Web 2.0, Facebook, Twitter,SGU, SPU. ABSTRACT ISSN 2229-5984 (P) 2249-5576 (e)

Use of web 2.0 in Saudi Arabia universities

The optimal operation of microgrids is challenging due to the presence of various uncertain factors, i.e., renewable energy sources, loads, market price signals, and arrival and departure times of electric vehicles (EVs). In order to incorporate these uncertainties into the operation model of microgrids, an adaptive robust optimization-based operation method is proposed in this paper. In particular, the focus is on the uncertainties in arrival and departure times of EVs. The optimization problem is divided into inner and outer problems and is solved iteratively by introducing column and constraint cuts. The unit commitment status of dispatchable generators is determined in the outer problem. Then, the worst-case realizations of all the uncertain factors are determined in the inner problem. Based on the values of uncertain factors, the generation amount of dispatchable generators, the amount of power trading with the utility grid, and the charging/discharging amount of storage elements are determined. The performance of the proposed method is evaluated using three different cases, and sensitivity analysis is carried out by varying the number of EVs and the budget of uncertainty. The impact of the budget of uncertainty and number of EVs on the operation cost of the microgrid is also evaluated considering uncertainties in arrival and departure times of EVs.

https://www.mdpi.com/1996-1073/11/10/2646/pdf

Akhtar Hussain

Papers

A Hybrid Framework for Adaptive Protection of Microgrids Based on IEC 61850

Incidence of diabetic retinopathy in Bangladesh: a 15-year follow-up study.

EV Prioritization and Power Allocation During Outages: A Lexicographic Method-Based Multiobjective Optimization Approach

Use of web 2.0 in Saudi Arabia universities

Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles