https://journal.chestnet.org/article/S0012-3692(16)38415-X/pdf

Definitions for Sepsis and Organ Failure and Guidelines for the Use of Innovative Therapies in Sepsis

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

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

tion’, implying that most patients ‘should’ receive a particular action. In contrast, level 2 guidelines are essentially ‘suggestions’ and are deemed to be ‘weak’ or discretionary, recognising that management decisions may vary in different clinical contexts. Each recommendation was further graded from A to D by the quality of evidence underpinning them, with grade A referring to a high quality of evidence whilst grade D recognised a ‘very low’ evidence base. The overall strength and quality of the supporting evidence is summarised in table 1 . The guidelines focused on 4 key domains: (1) AKI definition, (2) prevention and treatment of AKI, (3) contrastinduced AKI (CI-AKI) and (4) dialysis interventions for the treatment of AKI. The full summary of clinical practice statements is available at www.kdigo.org, but a few key recommendation statements will be highlighted here.

/pdf/kdigo-clinical-practice-guidelines-for-acute-kidney-injury-27tybolw7l.pdf

KDIGO clinical practice guidelines for acute kidney injury.

Author(s): Go, Alan S; Mozaffarian, Dariush; Roger, Veronique L; Benjamin, Emelia J; Berry, Jarett D; Borden, William B; Bravata, Dawn M; Dai, Shifan; Ford, Earl S; Fox, Caroline S; Franco, Sheila; Fullerton, Heather J; Gillespie, Cathleen; Hailpern, Susan M; Heit, John A; Howard, Virginia J; Huffman, Mark D; Kissela, Brett M; Kittner, Steven J; Lackland, Daniel T; Lichtman, Judith H; Lisabeth, Lynda D; Magid, David; Marcus, Gregory M; Marelli, Ariane; Matchar, David B; McGuire, Darren K; Mohler, Emile R; Moy, Claudia S; Mussolino, Michael E; Nichol, Graham; Paynter, Nina P; Schreiner, Pamela J; Sorlie, Paul D; Stein, Joel; Turan, Tanya N; Virani, Salim S; Wong, Nathan D; Woo, Daniel; Turner, Melanie B; American Heart Association Statistics Committee and Stroke Statistics Subcommittee

Heart Disease and Stroke Statistics—2013 Update A Report From the American Heart Association

/pdf/response-to-letter-to-the-editor-incidence-of-hypoglycemia-2773bxmxoy.pdf

Response to Letter to the Editor: “Incidence of Hypoglycemia After Gastric Bypass vs Sleeve Gastrectomy: A Randomized Trial”

Pharmacokinetic profile of dodecanedioic acid, a proposed alternative fuel substrate

The dynamics of pedestrian crowds during exceptional tragic events are very complex depending on a series of human behaviors resulting from combinations of basic interaction principles and self-organization. The Alert–Panic–Control (APC) model is one of the mathematical models in the literature for representing such complicated processes, mainly focusing on psychologists’ points of view (i.e., emotion contagion). This work proposes a Hybrid APC (HAPC) model including new processes, such as the effect of resonance, the victims caused by people in state of panic, new interactions between populations based on imitation and emotional contagion phenomena and the ability to simulate multiple disaster situations. Results from simulated scenarios showed that in the first 5 min 54.45% of population move towards a state of alert, 13.82% enter the control state and 31.73% pass to the state of panic, highlighting that individuals respond to a terrible incident very quickly, right away after it occurs.

/pdf/hapc-model-of-crowd-behavior-during-crises-23fwmpks.pdf

HAPC Model of Crowd Behavior during Crises

This study describes a dynamic non-linear mathematical approach for modeling the course of disease in acquired brain injury (ABI) patients. Data from a multicentric study were used to evaluate the reliability of the Michaelis-Menten (MM) model applied to well-known clinical variables that assess the outcome of ABI patients. The sample consisted of 156 ABI patients admitted to eight neurorehabilitation subacute units and evaluated at baseline (T0), 4 months after the event (T1) and at discharge (T2). The MM model was used to characterize the trend of the first Principal Component Analysis (PCA) dimension (represented by the variables: feeding modality, RLAS, ERBI-A, Tracheostomy, CRS-r and ERBI-B) in order to predict the most plausible outcome, in terms of positive or negative Glasgow outcome score (GOS) at discharge. Exploring the evolution of the PCA dimension 1 over time, after day 86 the MM model better differentiated between the time course for individuals with a positive and negative GOS (accuracy: 85%; sensitivity: 90.6%; specificity: 62.5%). The non-linear dynamic mathematical model can be used to provide more comprehensive trajectories of the clinical evolution of ABI patients during the rehabilitation period. Our model can be used to address patients for interventions designed for a specific outcome trajectory. 

/pdf/modeling-outcome-trajectories-in-patients-with-acquired-dniwidhx.pdf

Modeling outcome trajectories in patients with acquired brain injury using a non-linear dynamic evolution approach.

A PC-based minimisation software written in C-language is described, which solves numerically both simple non-linear regression problems and problems expressed as systems of (unsolved) initial-value ordinary or partial differential equations. The software uses second-order iterated Runge-Kutta algorithm to approximate numerically the solution curves. It uses a quasi-Newton algorithm to minimize either sums of squares (weighted or unweighed) or NONMEM loss functions. Inverse Hessian approximation to the parameter dispersion and Monte Carlo generation of artificial samples are offered to test the robustness of the parameter values obtained. A real test problem is described, involving the hydrolysation of plasma Medium Chain Triglycerides to Free Fatty Acids and the uptake of these from plasma. Two competing models were evaluated, one involving linear terms for each transfer and one involving carrier-mediated, rate-limited hydrolysis and tissue absorption steps. The simpler linear model was found to be more robust and eventually used to describe the experimental data.

Andrea De Gaetano

Papers

Response to Letter to the Editor: “Incidence of Hypoglycemia After Gastric Bypass vs Sleeve Gastrectomy: A Randomized Trial”

Pharmacokinetic profile of dodecanedioic acid, a proposed alternative fuel substrate

HAPC Model of Crowd Behavior during Crises

Modeling outcome trajectories in patients with acquired brain injury using a non-linear dynamic evolution approach.

PC-based differential model fitting as a support for clinical research