Polyimide biocomposites coated with tantalum pentoxide for stimulation of cell compatibility and enhancement of biointegration for orthopedic implant.

Orthopedic implants are an important tool in the treatment of musculoskeletal conditions and helped many patients to improve their quality of life. Various inorganic-organic biocomposites have been broadly investigated particularly in the area of load-bearing orthopedic/dental applications. Polyimide (PI) is a promising organic material and shows excellent mechanical properties, biocompatibility, bio-stability, and its elastic modulus is similar to human bone but it lacks bioactivity, which is very important for cell adhesion and ultimately for bone regeneration. In this research, tantalum pentoxide (Ta2O5) coating was prepared on the surface of PI by polydopamine (PDA) bonding. The results showed that Ta2O5 was evenly coated on the surface of PI, and with the concentration of Ta2O5 in the PDA suspension increased, the content of Ta2O5 particles on the surface of PI increased significantly. In addition, the Ta2O5 coating significantly increased the roughness and hydrophilicity of the PI matrix. Cell experiments showed that PI surface coating Ta2O5 could promote the proliferation, adhesion, and osteogenic differentiation of bone marrow-derived stromal cells (BMSCs). The results demonstrated that fabricating Ta2O5 coating on the surface of PI through PDA bonding could improve the biocompatibility as well as bioactivity of PI, and increase the application potential of PI in the field of bone repair materials.

Friedewald's equation for calculating LDL-cholesterol: Is it the time to say "Goodbye" and adopt direct LDL cholesterol methods?

OBJECTIVES: To measure correlation and concordance between measured LDL cholesterol (mLDLc) and Friedewald's calculated LDL cholesterol (cLDLc). To compare the mLDLc and cLDLc values for various anthropometric measures and biochemical indices including insulin resistance, nephropathy, glycated hemoglobin and triglycerides. METHODS: Two hundred thirty two subjects were included in this cross-sectional analysis from Jan-2016 to July-2017 from a target population visiting PNS HAFEEZ hospital. Mean age of the subjects was 46.56(±11.95) years (n=232). These subjects underwent clinical evaluation including measurement of anthropometric measurements, biochemical testing for fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), lipid profile, urine albumin creatinine ratio (UACR), and insulin. Correlation and concordance between mLDLc and Friedewald's cLDLc were measured. Finally, Comparison of risk evaluation for mLDLc and cLDLc between groups formulated based upon UACR (Based upon a cut off of 2.5 mg/g) and fasting triglycerides (Group-1 :< 1.0 mmol/L, Group-2: 1.0-1.99 mmol/L and Group-3 :> 1.99 mmol/) was carried out. RESULTS: There was significant positive linear correlation between mLDLc and cLDLc [r=0.468, <0.001]. Kendall's Coefficient of concordance between mLDLc and cLDLc was 0.055 (p<0.001). Differences evaluated by one way ANOVA analysis for mLDLc between various triglycerides groups were only significant between group-1 and group-2 [{Group-1:Mean=2.40, (2.19-2.61), n=43}, {Group-2:Mean=2.81, (2.69-2.92), n=136}, [{Group-3:Mean=2.59,(2.37-2.81), n=53}],(p=0.004) in comparison to cLDLc [{Group-1:Mean=2.63, (2.43-2.84), n=43}, {Group-2:Mean=2.85, (2.76-2.93), n=136}, [{Group-3:Mean=2.75, (2.60-2.90), n=53}]. Calculated method for LDLc showed higher UACR than mLDLc. (p=0.021). CONCLUSION: cLDLc over estimates LDL-cholesterol in comparison to mLDLc. The correlation between cLDLc and mLDLc was only moderate. However, cLDLc provided better degree of risk prediction for nephropathy and glycated hemoglobin than mLDLc.

Glucose Tolerance versus HbA1c Results as Depictive of Gestational Diabetes Mellitus.

OBJECTIVE: To evaluate glucose tolerance patterns in pregnant ladies undergoing 2-hour oral glucose tolerance test (OGTT) for comparing fasting, 1-hour, 2-hour post-glucose load results, HbA1c, sum of all glucose readings with and without gestational diabetes mellitus (GDM) using International Association of the Diabetes and Pregnancy Study Group (IADPSG) diagnostic criteria. STUDY DESIGN: Cross-sectional analysis. PLACE AND DURATION OF STUDY: PNS Hafeez, Naval Hospital, Islamabad, from January 2016 to July 2017. METHODOLOGY: For 280 evaluated subjects reporting in mid-pregnancy for OGTT, results were segregated into four groups based upon comparison of 2-hour glucose result with 1-hour glucose. Group-1 2-hour results drop being >2.0 mmol/L than1-hour results, group-2 with 2-hour result between <2.0 to >0.5 mmol/L than peak at 1-hour, and group-3 with either 2-hour glucose drop being <0.5mmol/L or >1-hour results. Further, the ROC curve analysis was performed to compare the AUC for fasting plasma glucose, 1-hour post OGTT result, 2-hour post-OGTT result, factor additive of all OGTT readings and HbA1c. RESULTS: There was a progressive rise in HbA1c from group-1 to group-3 (p<0.001). Area under curve (AUC) for various diagnostic parameters for diagnosing GDM for additive value of all glucose results was 0.962 (95% CI: 0.935-0.988), 0.881 (95% CI: 0.818-0944) for plasma glucose at 2-hour, for plasma glucose at 1-hour 0.898 (95% CI: 0.0.842-0.954), 0.831 (95% CI: 0.0.762-0.901) for fasting plasma glucose and 0.668 (95% CI: 0.0.578-0.759) for HbA1c (p<0.001). CONCLUSION: Pregnant ladies demonstrating poor tolerance to glucose at 2-hour were observed to have higher HbA1c levels.

Metabolic clustering of risk factors: evaluation of Triglyceride-glucose index (TyG index) for evaluation of insulin resistance.

BACKGROUND: Metabolic syndrome over the years have structured definitions to classify an individual with the disease. Literature review suggests insulin résistance is hallmark of these metabolic clustering. While measuring insulin resistance directly or indirectly remains technically difficult in general practice, along with multiple stability issues for insulin, various indirect measures have been suggested by authorities. Fasting triglycerides-glucose (TyG) index is one such marker, which is recently been suggested as a useful diagnostic marker to predict metabolic syndrome. However, limited data is available on the subject with almost no literature from our region on the subject. OBJECTIVE: 1. To correlate TyG index with insulin resistance, anthropometric indices, small dense LDLc, HbA1c and nephropathy. 2. To evaluate TyG index as a marker to diagnose metabolic syndrome in comparison to other available markers. DESIGN-CROSS-SECTIONAL ANALYSIS: Place and duration of study-From Jun-2016 to July-2017 at PSS HAFEEZ hospital Islamabad. SUBJECTS AND METHODS: From a finally selected sample size of 227 male and female subjects we evaluated their anthropometric data, HbA1c, lipid profile including calculated sdLDLc, urine albumin creatinine raito(UACR) and insulin resistance (HOMAIR). TyG index was calculated using formula of Simental-Mendía LE et al. Aforementioned parameters were correlated with TyG index, differences between subjects with and without metabolic syndrome were calculated using Independent sample t-test. Finally ROC curve analysis was carried out to measure AUC for candidate parameters including TyG Index for comparison. RESULTS: TyG index in comparison to other markers like fasting triglycerides, HOMAIR, HDLc and non-HDLc demonstrated higher positive linear correlation with BMI, atherogenic dyslipidemia (sdLDLc), nephropathy (UACR), HbA1c and insulin resistance. TyG index showed significant differences between various markers among subjects with and without metabolic syndrome as per IDF criteria. AUC (Area Under Curve) demonstrated highest AUC for TyG as [(0.764, 95% CI 0.700-0.828, p-value ≤ 0.001)] followed by fasting triglycerides [(0.724, 95% CI 0.656-0.791, p-value ≤ 0.001)], sdLDLc [(0.695, 95% CI 0.626-0.763, p-value ≤ 0.001)], fasting plasma glucose [(0.686, 95% CI 0.616-0.756, p-value ≤ 0.001)], Non-HDLc [(0.640, 95% CI 0.626-0.763, p-value ≤ 0.001)] and HOMAIR [(0.619, 95% CI 0.545-0.694, p-value ≤ 0.001)]. CONCLUSION: TyG index, having the highest AUC in comparison to fasting glucose, triglycerides, sdLDLc, non-HDLc and HOMAIR can act as better marker for diagnosing metabolic syndrome.