Fingerprinting hyperglycemia using predictive modelling approach based on low-cost routine CBC and CRP diagnostics.

Hyperglycemia is an outcome of dysregulated glucose homeostasis in the human body and may induce chronic elevation of blood glucose levels. Lifestyle factors such as overnutrition, physical inactivity, and psychosocials coupled with systemic low-grade inflammation have a strong negative impact on glucose homeostasis, in particular, insulin sensitivity. Together, these factors contribute to the pathophysiology of diabetes (DM) and expanding landscape of its prevalence regionally and globally. The rapid rise in the prevalence of type 2 diabetes, therefore, underscores the need for its early diagnosis and treatment. In this work, we have evaluated the discriminatory capacity of different diagnostic markers including inflammatory biomolecules and RBC (Red Blood Cell) indices in predicting the risk of hyperglycemia and borderline hyperglycemia. For that, 208,137 clinical diagnostic entries obtained over five years from Chugtai Labs, Pakistan, were retrospectively evaluated. The dataset included HbA1c (n = 142,011), complete blood count (CBC, n = 84,263), fasting blood glucose (FBG, n = 35,363), and C-reactive protein (CRP, n = 9035) tests. Our results provide four glycemic predictive models for two cohorts HbA1c and FBG) each having an overall predictive accuracy of more than 80% (p-value < 0.0001). Next, multivariate analysis (MANOVA) followed by univariate analysis (ANOVA) was employed to identify predictors with significant discriminatory capacity for different levels of glycemia. We show that the interplay between inflammation, hyperglycemic-induced derangements in RBC indices, and altered glucose homeostasis could be employed for prognosticating hyperglycemic outcomes. Our results then conclude a glycemic predictor with high sensitivity and specificity, employing inflammatory markers coupled with RBC indices, to predict glycemic outcomes (ROC p-value < 0.0001). Taken together, this study outlines a predictor of glycemic outcomes which could assist as a prophylactic intervention in predicting the early onset of hyperglycemia and borderline hyperglycemia.

Curative potential of Populus ciliata Wall ex. Royle extract against adjuvant-induced arthritis and peripheral neuropathy in Wistar rats.

Populus ciliata (PCCR) is traditionally used to treat muscular swelling, inflammation, pain, and fever. The current study was designed to validate the potential of aqueous ethanolic extract of the plant against inflammation, peripheral neuropathy, and pain in arthritic rats. The PCCR was chemically characterized by gas chromatography-mass spectroscopy and high-performance liquid chromatography. In vitro antioxidant, and in vitro anti-inflammatory assays were carried out on PCCR. For anti-arthritic potential, Wistar rats' rear paws were injected with 0.1 ml Complete Freund's Adjuvant using methotrexate (3 mg/kg/week) as standard control. PCCR at 100, 200, and 400 mg/kg was given orally to arthritic rats for 21 days. The PCCR exhibited significant inhibition of bovine serum albumin denaturation (IC-50: 202.1 µg/ml), egg albumin denaturation (IC-50:553.5 mg/ml) and RBC membrane stabilization (IC-50: 122.5 µg/ml) and antioxidant (IC-50 = 49.43 µg/ml) activities. The PCCR notably decreased the paw diameter and increased body weight of treated arthritic animals as equated to diseased control. The treatment notably (p < 0.05-0.0001) decreased malondialdehyde, and increased superoxide dismutase, reduced glutathione, and catalase in the liver and sciatic nerve homogenate in compared to diseased rats. The PCCR treatment remarkably (p < 0.05-0.0001) regulated the levels of nor-adrenaline and serotonin in sciatic nerve in contrast to diseased rats. Treatment with PCCR improved the motor activity, pain, ligament degeneration, and synovial hyperplasia in arthritic rats. Moreover, PCCR significantly (p < 0.01-0.0001) decreased the IL-6 and TNF-α. It is evident from the current study that PCCR had ameliorated polyarthritis and peripheral neuropathy through reduction of inflammatory markers, and improvement of oxidative stress might be due to presence of phenolic acids, flavonoids, phytosterols, and other fatty acids.

NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches.

Cotton (Gossypium hirsutum) is an economically important crop and is widely cultivated around the globe. However, the major problem of cotton is its high vulnerability to biotic and abiotic stresses. It has been around three decades since the cotton plant was genetically engineered with genes encoding insecticidal proteins (mainly Cry proteins) with an aim to protect it against insect attack. Several studies have been reported on the impact of these genes on cotton production and fiber quality. However, the metabolites responsible for conferring resistance in genetically modified cotton need to be explored. The current work aims to unveil the key metabolites responsible for insect resistance in Bt cotton and also compare the conventional multivariate analysis methods with deep learning approaches to perform clustering analysis. We aim to unveil the marker compounds which are responsible for inducing insect resistance in cotton plants. For this purpose, we employed 1H-NMR spectroscopy to perform metabolite profiling of Bt and non-Bt cotton varieties, and a total of 42 different metabolites were identified in cotton plants. In cluster analysis, deep learning approaches (linear discriminant analysis (LDA) and neural networks) showed better separation among cotton varieties compared to conventional methods (principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLSDA)). The key metabolites responsible for inter-class separation were terpinolene, α-ketoglutaric acid, aspartic acid, stigmasterol, fructose, maltose, arabinose, xylulose, cinnamic acid, malic acid, valine, nonanoic acid, citrulline, and shikimic acid. The metabolites which regulated differently with the level of significance p < 0.001 amongst different cotton varieties belonged to the tricarboxylic acid cycle (TCA), Shikimic acid, and phenylpropanoid pathways. Our analyses underscore a biosignature of metabolites that might involve in inducing insect resistance in Bt cotton. Moreover, novel evidence from our study could be used in the metabolic engineering of these biological pathways to improve the resilience of Bt cotton against insect/pest attacks. Lastly, our findings are also in complete support of employing deep machine learning algorithms as a useful tool in metabolomics studies.

Study of variants associated with ventricular septal defects (VSDs) highlights the unique genetic structure of the Pakistani population.

BACKGROUND: Ventricular septal defects (VSDs) are one of the leading causes of death due to cardiac anomalies during the first months of life. The prevalence of VSD in neonates is reported up to 4%. Despite the remarkable progress in medication, treatment and surgical procedure for VSDs, the genetic etiology of VSDs is still in infancy because of the complex genetic and environmental interactions. METHODS: Three hundred fifty subjects (200 VSD children and 150 healthy controls) were recruited from different pediatric cardiac units. Pediatric clinical and demographic data were collected. A total of six variants, rs1017 (ISL1), rs7240256 (NFATc1), rs36208048 (VEGF), variant of HEY2, rs11067075 (TBX5) and rs1801133 (MTHFR) genes were genotyped by tetra-ARMS PCR and PCR-RFLP methods. RESULTS: The results showed that in cases, the rs1017 (g.16138A > T) variant in the ISL1 gene has an allele frequency of 0.42 and 0.58 respectively for the T and A alleles, and 0.75 and 0.25 respectively in the controls. The frequencies of the AA, TA and TT genotypes were, 52%, 11% and 37% in cases versus 21%, 8% and 71% respectively in the controls. For the NFATc1 variant rs7240256, minor allele frequency (MAF) was 0.43 in cases while 0.23 in controls. For the variant in the VEGF gene, genotype frequencies were 0% (A), 32% (CA) and 68% (CC) in cases and 0.0%, 33% and 67% respectively in controls. The allele frequency of C and A were 0.84 and 0.16 in cases and 0.83 and 0.17 respectively in controls. The TBX5 polymorphism rs11067075 (g.51682G > T) had an allelic frequency of 0.44 and 0.56 respectively for T and G alleles in cases, versus 0.26 and 0.74 in the controls. We did not detect the presence of the HEY2 gene variant (g.126117350A > C) in our pediatric cohort. For the rs1801133 (g.14783C > T) variant in the MTHFR gene, the genotype frequencies were 25% (CC), 62% (CT) and 13% (TT) in cases, versus 88%, 10% and 2% in controls. The ISL1, NFATc1, TBX5 and MTHFR variants were found to be in association with VSD in the Pakistani pediatric cohort whilst the VEGF and HEY2 variants were completely absent in our cohort. CONCLUSION: We propose that a wider programme of genetic screening of the Pakistani population for genetic markers in heart development genes would be helpful in reducing the risk of VSDs.

1H-NMR-Based Metabolomics: An Integrated Approach for the Detection of the Adulteration in Chicken, Chevon, Beef and Donkey Meat.

Meat is a rich source of energy that provides high-value animal protein, fats, vitamins, minerals and trace amounts of carbohydrates. Globally, different types of meats are consumed to fulfill nutritional requirements. However, the increasing burden on the livestock industry has triggered the mixing of high-price meat species with low-quality/-price meat. This work aimed to differentiate different meat samples on the basis of metabolites. The metabolic difference between various meat samples was investigated through Nuclear Magnetic Resonance spectroscopy coupled with multivariate data analysis approaches like principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). In total, 37 metabolites were identified in the gluteal muscle tissues of cow, goat, donkey and chicken using 1H-NMR spectroscopy. PCA was found unable to completely differentiate between meat types, whereas OPLS-DA showed an apparent separation and successfully differentiated samples from all four types of meat. Lactate, creatine, choline, acetate, leucine, isoleucine, valine, formate, carnitine, glutamate, 3-hydroxybutyrate and α-mannose were found as the major discriminating metabolites between white (chicken) and red meat (chevon, beef and donkey). However, inosine, lactate, uracil, carnosine, format, pyruvate, carnitine, creatine and acetate were found responsible for differentiating chevon, beef and donkey meat. The relative quantification of differentiating metabolites was performed using one-way ANOVA and Tukey test. Our results showed that NMR-based metabolomics is a powerful tool for the identification of novel signatures (potential biomarkers) to characterize meats from different sources and could potentially be used for quality control purposes in order to differentiate different meat types.

Host biomarkers for monitoring therapeutic response in extrapulmonary tuberculosis.

PURPOSE: The aim of this study was to explore the utility of inflammatory biomarkers in the peripheral blood to predict response to treatment in extrapulmonary tuberculosis (EPTB). METHODS: A Luminex xMAP-based multiplex immunoassay was used to measure 40 inflammatory biomarkers in un-stimulated plasma of 91 EPTB patients (48 lymphadenitis, and 43 pleuritis) before and at 2 and 6 months of treatment. RESULTS: Overall a significant change was observed in 28 inflammatory biomarkers with treatment in EPTB patients. However, MIG/CXCL9, IP-10/CXCL10, and CCL23 decreased in all patients' groups with successful treatment at both time points. At 2 months, 29/64 (45%) patients responded partially while 35/64 (55%) showed complete regress. Among good responders, a higher number of biomarkers (16/40) reduced significantly as compared to partial responders (1/40). Almost half (14/29) of partial responders required longer treatment than 6 months to achieve satisfactory response. The levels of MIG, IP-10, MIF, CCL22 and CCL23 reduced significantly among 80, 74, 60, 71, 51% good responders, as compared to 52, 52, 52, 59, 52% partial responders, respectively. A biosignature, defined by a significant decrease in any one of these five biomarkers, corresponded with satisfactory response to treatment in 97% patients at 2 month and 99% patients at 6 months of treatment. CONCLUSION: Change in inflammatory biomarkers correlates with treatment success. A five biomarker biosignature (MIG, IP-10, MIF, CCL22 and CCL23) could be used as an indicator of treatment success.

First report of polymorphisms in MTRR, GATA4, VEGF, and ISL1 genes in Pakistani children with isolated ventricular septal defects (VSD).

BACKGROUND: Ventricular septal defects (VSDs) are malformations in the septum separating the heart's ventricles. VSDs may present as a single anomaly (isolated/nonsyndromic VSD) or as part of a group of phenotypes (syndromic VSD). The exact location of the defect is crucial in linking the defect to the underlying genetic cause. The number of children visiting cardiac surgery units is constantly increasing. However, there are no representative data available on the genetics of VSDs in Pakistani children. METHODS: Two hundred forty-two subjects (121 VSD children and 121 healthy controls) were recruited from pediatric cardiac units of Lahore. The clinical and demographic data of the subjects were collected. A total of four SNPs, one each from MTRR, GATA4, VEGF, and ISL1 genes were genotyped by PCR-RFLP. RESULTS: The results showed that the minor allele (T) frequency (MAFs) for the MTRR gene variant rs1532268 (c.524C > T) was 0.20 and 0.41 in the controls and the cases, respectively, with the genotype frequencies 3, 35, 62% in the controls and 12, 59 and 29% in the cases for TT, CT, CC genotypes, respectively (allelic OR: 5.73, CI: 3.82-8.61, p-value: 5.11 × 10- 7). For the GATA4 variant rs104894073 (c.886G > A), the MAF for the controls and the cases was 0.16 and 0.37, respectively, the frequencies of AA, GA and GG genotypes were 2, 28, and 70% in the controls and 5, 64 and 31% of the cases (allelic OR: 3.08, CI: 2.00-4.74, p-value: 8.36 × 10- 8). The rs699947 (c.-2578C > A) of VEGF gene showed MAF 0.36 and 0.53 for the controls and cases, respectively, with the genotype frequencies 13, 42, and 45% in the controls and 22, 15, and 63% in the cases for the AA, CA, CC (allelic OR: 2.03, CI: 1.41-2.92, p-value: 0.0001). The ISL1 gene variant rs6867206 (g.51356860 T > C), the MAFs were 0.26 and 0.31 in the controls and cases, respectively. The genotype frequencies were 48, 52, 0% in the controls and 39, 61, 0% in the cases for TT, TC, CC genotypes (allelic OR: 0.27, CI: 0.85-1.89, p-value: 0.227). The MTRR, GATA4 and VEGF variants showed association while ISL1 variant did not appear to be associated with the VSD in the recruited cohort. CONCLUSION: This first report in Pakistani children demonstrates that single nucleotide polymorphisms in genes encoding transcription factors, signaling molecules and structural heart genes involved in fetal heart development are associated with developmental heart defects., however further work is needed to validate the results of the current investigation.

An evaluation of lipid profile and pro-inflammatory cytokines as determinants of cardiovascular disease in those with diabetes: a study on a Mexican American cohort.

Sedentary life styles coupled with high-calorie diets and unhealthy social habits such as smoking, have put an ever-increasing number of people at risk of cardiovascular disorders (CVD), worldwide. A concomitant increase in the prevalence of type 2-diabetes (hyperglycemia), a risk factor for CVD, has further contributed towards escalating CVD-related mortalities. The increase in number of cases of type 2-diabetes underscores the importance of early diagnosis of cardiovascular disease in those with diabetes. In this work, we have evaluated the sensitivity and specificity of dyslipidemia and proinflammatory cytokines to be used as biomarkers for predicting the risk of CVD in those with diabetes. We hypothesize that interplay between dyslipidemia and diabetes-induced low-grade inflammation in those with type 2-diabetes increases the risk of CVD. A total of 215 participants were randomly recruited from the Cameron County Hispanic Cohort (CCHC). Of these, 99% were Mexican Americans living on Texas-Mexico border. Levels of cytokines, adipokines and lipid profile were measured. Cardiovascular disease (CVD) for this study was defined as prior diagnosis of heart attack, angina and stroke, while diabetes was defined by fasting blood glucose (FBG) of > 100 mg/dL and HbA1c of > 6.5, in accordance with American Diabetes Association (ADA) guidelines. Depending on type and distribution of data, various statistical tests were performed. Our results demonstrated higher rates of heart attack (14% vs 11.8%) and stroke (19.8% vs 10%) in those with diabetes as compared to non-diabetes. The odds of having a heart attack were eight times higher in the presence of elevated triglycerides and pro-inflammatory markers (TNFα and IL6) as compared to presence of pro-inflammatory markers only. The odds for heart attack among those with diabetes, increased by 20 fold in presence of high levels of triglycerides, TNFα, and IL6 when coupled with low levels of high-density lipid cholesterol (HDL-C). Lastly, our analysis showed that poorly controlled diabetes, characterized by HbA1c values of > 6.5 increases the odds of stroke by more than three fold. The study quantifies the role of lipid profile and pro-inflammatory markers in combination with standard risk factors towards predicting the risk of CVD in those with type 2-diabetes. The findings from the study can be directly translated for use in early diagnosis of heart disease and guiding interventions leading to a reduction in CVD-associated mortality in those with type 2-diabetes.

Cost effectiveness evaluation of hepatitis C therapy in Lahore.

Approximately 10 million Pakistan's of population is a victim of Hepatitis C virus. A comparative study of two treatments for Hepatitis C being provided in private clinics and government hospitals was conducted to evaluate the cost effectiveness of these treatments. The quality adjusted life years (QALYs) for each treatment plan was determined with the help of health utilities, using EQ-5D scores. A comprehensive data collection form aided in scrutinizing the cause and effect of each treatment on the patient's quality of life. The total sample size for this study is 200 total from the public and private sectors. For both the treatment strategies, values for quality adjusted life years (QALYs), incremental cost effective ratio (ICER) and cost effective analysis (CEA) were calculated. The Hepatitis C virus 3a and 3b genotypic patients who were treated with pegylated interferon α-2a and ribavirin combination (strategy 2) showed an increased quality adjusted life years (QALYs) of two years, as compared to those who received interferon α-2a and ribavirin regimen (strategy 1). An incremental cost effectiveness ratio (ICER) of Rs 144673.5 per quality adjusted life year (QALYs) was gained by patients treated with strategy 2. The therapy followed by the government sector (strategy 1) is relatively inexpensive accounting for Rs 654.5/quality adjusted life years (QALY) and therapy provided at the clinic sector (strategy 2) is relatively expensive Rs 5620.6/ quality adjusted life years (QALY). However, the cost effectiveness analysis for the pegylated interferon therapy is quite comparable with the other standard treatments; hence it can be called cost effective according to the quality adjusted life years (QALYs) gained and efficacy of the said therapy.