FeS2‐Imbedded Mixed Conducting Matrix as a Solid Battery Cathode

A new concept of pairing an active material and a mixed conductor is explored as a solid‐state battery electrode. By imbedding nano‐FeS₂ domains into an amorphous LiTiS₂ matrix, a hybrid power‐energy system is achieved while additionally improving upon many common solid electrode design flaws. High‐resolution transmission electron microscopy is used to probe the active material/mixed conductor interface over the course of cycling. Arguably the most beneficial development is enhancement of charge transfer, manifesting in a significantly increased exchange current as captured in a Tafel analysis. By developing a solution to active material isolation and creating a more homogenous electrode design, cycling at a high rate of C/2 for 500 cycles is obtained. Additionally, the electrode can recover full capacity simply by reducing system rate. Capacity recovery implicates a lack of active material isolation, a common problem in solid‐state batteries.     

The influence and biomechanical role of cartilage split line pattern on tibiofemoral cartilage stress distribution during the stance phase of gait

This study presents an evaluation of the role that cartilage fibre ‘split line’ orientation plays in informing femoral cartilage stress patterns. A two-stage model is presented consisting of a whole knee joint coupled to a tissue-level cartilage model for computational efficiency. The whole joint model may be easily customised to any MRI or CT geometry using free-form deformation. Three ‘split line’ patterns (medial–lateral, anterior–posterior and random) were implemented in a finite element model with constitutive properties referring to this ‘split line’ orientation as a finite element fibre field. The medial–lateral orientation was similar to anatomy and was derived from imaging studies. Model predictions showed that ‘split lines’ are formed along the line of maximum principal strains and may have a biomechanical role of protecting the cartilage by limiting the cartilage deformation to the area of higher cartilage thickness.     

Life history evolution and cellular mechanisms associated with increased size in high‐altitude Drosophila

Understanding the physiological and genetic basis of growth and body size variation has wide‐ranging implications, from cancer and metabolic disease to the genetics of complex traits. We examined the evolution of body and wing size in high‐altitude Drosophila melanogaster from Ethiopia, flies with larger size than any previously known population. Specifically, we sought to identify life history characteristics and cellular mechanisms that may have facilitated size evolution. We found that the large‐bodied Ethiopian flies laid significantly fewer but larger eggs relative to lowland, smaller‐bodied Zambian flies. The highland flies were found to achieve larger size in a similar developmental period, potentially aided by a reproductive strategy favoring greater provisioning of fewer offspring. At the cellular level, cell proliferation was a strong contributor to wing size evolution, but both thorax and wing size increases involved important changes in cell size. Nuclear size measurements were consistent with elevated somatic ploidy as an important mechanism of body size evolution. We discuss the significance of these results for the genetic basis of evolutionary changes in body and wing size in Ethiopian D. melanogaster.     

Is MTHFR 677 C>T Polymorphism Clinically Important in Polycystic Ovarian Syndrome (PCOS)? A Case-Control Study,Meta-Analysis and Trial Sequential Analysis

Background

Optimum efficiency of the folate pathway is considered essential for adequate ovarian function. 677 C>T substitution in the 5, 10-methylene tertrahydrofolatereductase (MTHFR) gene compromises activity of the MTHFR enzyme by about 50%. The significance of correlation between 677C>T substitution and PCOS remains dubious due to the low power of published studies.

Methods and Results

We analyzed MTHFR 677 C>T site in ethnically two different PCOS case-control groups (total 261 cases and 256 controls) from India. The data analysis revealed a lack of association between this polymorphism and PCOS [OR = 1.11 (95%CI = 0.71–1.72), P = 0.66]. Group-wise analysis on the basis of ethnicity also revealed no association in any of the ethnic groups [Indo-Europeans, P = 1; Dravidians, P = 0.70]. Homocysteine levels did not differ significantly between cases (15.51 μmol/L, SD = 2.89) and controls (15.89 μmol/L, SD = 2.23). We also undertook a meta-analysis on 960 cases and 1028 controls, which suggested a significant association of the substitution with PCOS in the dominant model of analysis (OR = 1.47 (95%CI = 1.04–2.09), P = 0.032]. Trial sequential analysis corroborated findings of the traditional meta-analysis. However, we found that the conclusions of meta-analysis were strongly influenced by studies that deviated from the Hardy Weinberg equilibrium. A careful investigation of each study and a trial sequential analysis suggested that 677 C>T substitution holds no clinical significance in PCOS in most of the populations.

Conclusion

In conclusion, MTHFR 677 C>T polymorphism does not affect PCOS risk in India. The association seen in the meta-analysis is due to an outlier study and studies showing deviation from the Hardy Weinberg equilibrium.     

Selection of Treatment Strategies among Patients with Type 2 Diabetes Mellitus in Malaysia: A Grounded Theory Approach

Background

Diabetes Mellitus is a multifaceted chronic illness and its life-long treatment process requires patients to continuously engage with the healthcare system. The understanding of how patients manoeuvre through the healthcare system for treatment is crucial in assisting them to optimise their disease management. This study aims to explore issues determining patients’ treatment strategies and the process of patients manoeuvring through the current healthcare system in selecting their choice of treatment for T2DM.

Methods

The Grounded Theory methodology was used. Twelve patients with Type 2 Diabetes Mellitus, nine family members and five healthcare providers from the primary care clinics were interviewed using a semi-structured interview guide. Three focus group discussions were conducted among thirteen healthcare providers from public primary care clinics. Both purposive and theoretical samplings were used for data collection. The interviews were audio-taped and transcribed verbatim, followed by line-by-line coding and constant comparison to identify the categories and core category.

Results

The concept of “experimentation” was observed in patients’ help-seeking behaviour. The “experimentation” process required triggers, followed by information seeking related to treatment characteristics from trusted family members, friends and healthcare providers to enable decisions to be made on the choice of treatment modalities. The whole process was dynamic and iterative through interaction with the healthcare system. The decision-making process in choosing the types of treatment was complex with an element of trial-and-error. The anchor of this process was the desire to fulfil the patient’s expected outcome.

Conclusion

Patients with Type 2 Diabetes Mellitus continuously used “experimentation” in their treatment strategies and help-seeking process. The “experimentation” process was experiential, with continuous evaluation, information seeking and decision-making tinged with the element of trial-and-error. The theoretical model generated from this study is abstract, is believed to have a broad applicability to other diseases, may be applied at varying stages of disease development and is non-context specific.     

Kinetic Characterization of 100 Glycoside Hydrolase Mutants Enables the Discovery of Structural Features Correlated with Kinetic Constants

The use of computational modeling algorithms to guide the design of novel enzyme catalysts is a rapidly growing field. Force-field based methods have now been used to engineer both enzyme specificity and activity. However, the proportion of designed mutants with the intended function is often less than ten percent. One potential reason for this is that current force-field based approaches are trained on indirect measures of function rather than direct correlation to experimentally-determined functional effects of mutations. We hypothesize that this is partially due to the lack of data sets for which a large panel of enzyme variants has been produced, purified, and kinetically characterized. Here we report the k_cat and K_M values of 100 purified mutants of a glycoside hydrolase enzyme. We demonstrate the utility of this data set by using machine learning to train a new algorithm that enables prediction of each kinetic parameter based on readily-modeled structural features. The generated dataset and analyses carried out in this study not only provide insight into how this enzyme functions, they also provide a clear path forward for the improvement of computational enzyme redesign algorithms.