Factors Associated with Insulin Reluctance in Individuals with Type 2 Diabetes

Factors Associated with Insulin Reluctance in Individuals with Type 2 DiabetesbySoohyun Nam, RN, MSN, ANPABSTRACTBackground: There are many barriers to effective diabetes management for people with type 2 diabetes (T2D) and clinicians. Patients' reluctance to start insulin therapy is one of the barriers to effective management that may be influenced by patients' sociodemographic and psychosocial factors. Significant delay in starting insulin may increase complications and impair patients' quality of life. Little is known about insulin reluctance (IR) and its relationship with associated factors. Purpose: 1) Summarize existing knowledge regarding various barriers to diabetes management from the perspectives of both patients and clinicians; 2) investigate the concept of IR, resistance to using insulin therapy, by describing patients' perceived barriers and their relationships with associated factors; and 3) examine the effectiveness of culturally competent diabetes education (CCDE) among ethnic minorities with T2D.Methods: The first paper was a literature review regarding various barriers to diabetes management. The second study was a cross-sectional descriptive study. Data were collected from 178 people with T2D, who were 18 years or older, being treated with diabetic oral agents and able to speak English. The participants from general medicine practice clinics completed validated measures: Diabetes Attitude Scale, Diabetes Knowledge Test, Diabetes Self-efficacy Scale, Interpersonal processes of Care and Barriers to Insulin Treatment. Biomedical data were obtained from medical record reviews. The third study was a meta-analysis to evaluate the effectiveness of CCDE for ethnic minorities with T2D.Findings: The first paper revealed that patients' adherence, attitude, knowledge about diabetes, culture, language capability, financial resources, comorbidity and social support may affect diabetes management. Clinician barriers to following treatment guidelines include beliefs, attitudes and knowledge, patient-clinician interaction and communication, and the health care system. The second study demonstrated that people with T2D had moderate IR. Fear of hypoglycemia was the strongest barrier to insulin treatment. Women were more reluctant to use insulin than men. Ethnic minorities had more psychological barriers to insulin treatment than whites. Greater diabetes self-efficacy scores predicted significantly less IR and better perceived interaction with the clinician may reduce IR. The third study showed that CCDE appears to be effective in improving glycemic control for ethnic minorities. Word Count: 34

Evaluation of Various Packaging Systems on the Activity of Antioxidant Enzyme, and Oxidation and Color Stabilities in Sliced Hanwoo (Korean Cattle) Beef Loin during Chill Storage

The effects of various packaging systems, vacuum packaging (VACP), medium oxygen-modified atmosphere packaging (50% O2/20% CO2/30% N2, MOMAP), MOMAP combined with vacuum skin packaging (VSP-MOMAP), high oxygen-MAP (80% O2/20% CO2/0% N2, HOMAP), and HOMAP combined with VSP (VSP-HOMAP), on the activity of antioxidant enzyme, and oxidation and color stabilities in sliced Hanwoo (Korean cattle) beef loin were investigated at 4°C for 14 d. Higher (p<0.05) superoxide dismutase activity and total reducing ability were maintained in VSP-MOMAP beef than in HOMAP beef. Lipid oxidation (2-thiobarbituric acid reactive substances, TBARS) was significantly (p<0.05) retarded in MOMAP, VSP-MOMAP, and VSP-HOMAP beef compared with HOMAP beef. Production of nonheme iron content was lower (p<0.05) in VSP-MOMAP beef than in HOMAP beef. Red color (a*) was kept higher (p<0.05) in VSP-MOMAP beef compared with MOMAP, HOMAP, and VSP-HOMAP beef. However, VACP beef was found to have the most positive effects on the antioxidant activity, oxidation and red color stabilities among the various packaged beef. These findings suggested that VSP-MOMAP was second to VACP in improving oxidation and color stabilities in sliced beef loin during chill storage

Using the Implementation Research Logic Model as a Lens to View Experiences of Implementing HIV Prevention and Care Interventions with Adolescent Sexual Minority Men-A Global Perspective.

Adolescents and sexual minority men (SMM) are high priority groups in the United Nations' 2021 - 2016 goals for HIV prevention and viral load suppression. Interventions aimed at optimizing HIV prevention, testing and viral load suppression for adolescents must also attend to the intersectional realities influencing key sub-populations of SMM. Consequently, there is not a robust evidence-base to guide researchers and program partners on optimal approaches to implementing interventions with adolescent SMM. Using a multiple case study design, we integrated the Implementation Research Logic Model with components of the Consolidated Framework for Implementation Research and applied it as a framework for a comparative description of ten HIV related interventions implemented across five countries (Ghana, Kenya, Nigeria, Tanzania and United States). Using self-reported qualitative survey data of project principal investigators, we identified 17 of the most influential implementation determinants as well as a range of 17 strategies that were used in 90 instances to support intervention implementation. We highlight lessons learned in the implementation research process and provide recommendations for researchers considering future HIV implementation science studies with adolescent SMM

Discovery of Q203, a potent clinical candidate for the treatment of tuberculosis

New therapeutic strategies are needed to combat the tuberculosis pandemic and the spread of multidrug-resistant (MDR) and extensively drug-resistant (XDR) forms of the disease, which remain a serious public health challenge worldwide1, 2. The most urgent clinical need is to discover potent agents capable of reducing the duration of MDR and XDR tuberculosis therapy with a success rate comparable to that of current therapies for drug-susceptible tuberculosis. The last decade has seen the discovery of new agent classes for the management of tuberculosis3, 4, 5, several of which are currently in clinical trials6, 7, 8. However, given the high attrition rate of drug candidates during clinical development and the emergence of drug resistance, the discovery of additional clinical candidates is clearly needed. Here, we report on a promising class of imidazopyridine amide (IPA) compounds that block Mycobacterium tuberculosis growth by targeting the respiratory cytochrome bc1 complex. The optimized IPA compound Q203 inhibited the growth of MDR and XDR M. tuberculosis clinical isolates in culture broth medium in the low nanomolar range and was efficacious in a mouse model of tuberculosis at a dose less than 1 mg per kg body weight, which highlights the potency of this compound. In addition, Q203 displays pharmacokinetic and safety profiles compatible with once-daily dosing. Together, our data indicate that Q203 is a promising new clinical candidate for the treatment of tuberculosis

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Early Identification of At-risk Students and Understanding Their Behaviors

As enrollments and class sizes in postsecondary institutions have increased, instructors have sought automated means to identify students who are at risk of failing a course. This identification must be performed early enough in the term to allow instructors to assist those students before they fall irreparably behind. In this sense, this dissertation proposes early identification methods of at-risk students and characterizes behaviors of such students. The first part of this work describes a modeling methodology that predicts student final exam scores in the third week of the term by using different sets of easy-to-collect data including student clicker responses, prerequisite course grades, online quiz scores, and assignment grades. The work uses different machine learning techniques, trained on one term of a course, to predict outcomes in subsequent terms. This allowed making predictions across terms in a natural setting (different final exams, minor changes to course content). We applied this modeling technique to five different courses across the computer science curriculum, taught by three different instructors at two different institutions. The results show consistent performance across multiple courses in a curriculum, and across multiple institutions. Also, prerequisite course grades and clicker responses are more predictive than online quiz and assignment grades. The second part of the work is to understand what factors contribute to those students' difficulties. If we were able to better understand the characteristics of such students, we may be better able to help those students. This work examines the characteristics of lower- and higher-performing students through interviews with students from an introductory computing class. We identify a number of relevant areas of student behavior including how they approach their exam studies, how they approach completing programming assignments, whether they sought help after identifying misunderstandings, how and from whom they sought help, and how they reflected on assignments after submitting them. Particular behaviors within each area are coded and differences between groups of students are identified.This dissertation ends with stating some future work of automating the identification process for general public and crafting effective student intervention frameworks

Early Identification of At-risk Students and Understanding Their Behaviors

As enrollments and class sizes in postsecondary institutions have increased, instructors have sought automated means to identify students who are at risk of failing a course. This identification must be performed early enough in the term to allow instructors to assist those students before they fall irreparably behind. In this sense, this dissertation proposes early identification methods of at-risk students and characterizes behaviors of such students. The first part of this work describes a modeling methodology that predicts student final exam scores in the third week of the term by using different sets of easy-to-collect data including student clicker responses, prerequisite course grades, online quiz scores, and assignment grades. The work uses different machine learning techniques, trained on one term of a course, to predict outcomes in subsequent terms. This allowed making predictions across terms in a natural setting (different final exams, minor changes to course content). We applied this modeling technique to five different courses across the computer science curriculum, taught by three different instructors at two different institutions. The results show consistent performance across multiple courses in a curriculum, and across multiple institutions. Also, prerequisite course grades and clicker responses are more predictive than online quiz and assignment grades. The second part of the work is to understand what factors contribute to those students' difficulties. If we were able to better understand the characteristics of such students, we may be better able to help those students. This work examines the characteristics of lower- and higher-performing students through interviews with students from an introductory computing class. We identify a number of relevant areas of student behavior including how they approach their exam studies, how they approach completing programming assignments, whether they sought help after identifying misunderstandings, how and from whom they sought help, and how they reflected on assignments after submitting them. Particular behaviors within each area are coded and differences between groups of students are identified.This dissertation ends with stating some future work of automating the identification process for general public and crafting effective student intervention frameworks

Lipid membrane interface viewpoint: from viral entry to antiviral and vaccine development

Membrane-enveloped viruses are responsible for most viral pandemics in history, and more effort is needed to advance broadly applicable countermeasures to mitigate the impact of future outbreaks. In this Perspective, we discuss how biosensing techniques associated with lipid model membrane platforms are contributing to improving our mechanistic knowledge of membrane fusion and destabilization that is closely linked to viral entry as well as vaccine and antiviral drug development. A key benefit of these platforms is the simplicity of interpreting the results which can be complemented by other techniques to decipher more complicated biological observations and evaluate the biophysical functionalities that can be correlated to biological activities. Then, we introduce exciting application examples of membrane-targeting antivirals that have been refined over time and will continue to improve based on biophysical insights. Two ways to abrogate the function of viral membranes are introduced here: (1) selective disruption of the viral membrane structure and (2) alteration of the membrane component. While both methods are suitable for broadly useful antivirals, the latter also has the potential to produce an inactivated vaccine. Collectively, we emphasize how biosensing tools based on membrane interfacial science can provide valuable information that could be translated into biomedicines and improve their selectivity and performance.Ministry of Education (MOE)This work was supported by the Ministry of Education (MOE) in Singapore under grant RG111/20 and by a sponsored research agreement from LUCA AICell Inc. (RCA-LUCA AICell REQ0239282)