Comparison of triamcinolone with pentoxifylline and vitamin E efficacy in the treatment of stage 2 and 3 oral submucous fibrosis: A randomized clinical trial.

Objective: The objective of this study was to assess the effectiveness of triamcinolone in combination with pentoxifylline and vitamin E in the treatment of patients with level two and three oral submucous fibrosis. Methods: The study aimed to compare the efficacy of corticosteroid injections (triamcinolone) versus pentoxifylline and vitamin E in the therapy of patients with stage two and three oral submucous fibrosis. A total of 42 participants with indications and features of oral submucous fibrosis were enrolled between January 2020 and September 2021. The patients' age and mouth opening were evaluated, and descriptive statistics and paired t-test were used for analytical investigation. Results: The study showed a statistically significant improvement in both treatment groups (p = 0.001) concerning pre- and post-treatment deviations. However, when comparing the standard differences in treatment outcomes between the two study groups, only mouth opening exhibited a statistically significant difference (p = 0.001). Conclusions: The findings indicate that a treatment regimen combining pentoxifylline and triamcinolone can significantly alleviate oral submucous fibrosis symptoms, including mouth opening, pain, and flaring agitation, thereby enhancing the affected individual's quality of life.

Association of leukocyte telomere attrition in coronary artery disease in Pakistani population: A case-control study with meta-analysis.

BACKGROUND: Coronary arteries disease (CAD) is one of the primary causes of mortality worldwide. Genetic, epigenetic and environmental factors have been hypothesized in the pathogenesis of CAD. Leukocyte telomere length (LTL) has been proposed as a potential biomarker for early detection of atherosclerosis. Telomere is the DNA-Protein structure that maintains stability and integrity of chromosomes and is associated with the aging-related cellular mechanisms. This study is designed to investigate the association of LTL with CAD pathogenesis. MATERIAL AND METHOD: This prospective case-control study included 100 patients and 100 control individuals. DNA was extracted from the peripheral blood samples, and LTL was measured using real-time PCR. Data were normalized with single copy gene and presented as relative telomere length T/S ratio. Comprehensive meta-analysis was conducted to ascertain the pivotal role of telomere length in CAD pathology across multiple populations. RESULTS: Our results showed shorter telomere length in CAD patients as compared to control. The correlation analysis revealed a significant (P-value <0.01) negative correlation between telomere length with basal metabolic index (BMI), total cholesterol, and low-density lipoprotein cholesterol (LDL-C) and a positive correlation with high-density lipoprotein cholesterol (HDL-C). Meta-analysis results indicated a significantly shorter telomere length in the Asian population and a non-significant shorter telomere length in other populations. Receiver operator curve (ROC) analysis demonstrated an area under the curve (AUC) of 0.814 with cut-off value of 0.691 exhibited sensitivity of 72.2%, and specificity of 79.1%, for the diagnosis of CAD. CONCLUSION: In conclusion, LTL is associated with the onset of CAD and could be used as a diagnostic predictor to screen individuals with CAD.

Contrast enhanced sonothrombolysis using streptokinase loaded phase change nano-droplets for potential treatment of deep venous thrombosis.

Current thrombolytic therapies for deep venous thrombosis are limited due to the wide side effect profile. Contrast mediated sonothrombolysis is a promising approach for thrombus treatment. The current study examines the effectiveness of in vitro streptokinase (SK) loaded phase-change nanodroplet (PCND) mediated sonothrombolysis at 7 MHz for the diagnosis of deep venous thrombosis. Lecithin shell and perfluorohexane core nanodroplets were prepared via the thin-film hydration method and morphologically characterized. Sonothrombolysis was performed at 7 MHz at different mechanical indexes of samples i.e., only sonothrombolysis, PCND mediated sonothrombolysis, sonothrombolysis with SK and SK loaded PCND mediated sonothrombolysis. Thrombolysis efficacy was assessed by measuring clot weight changes during 30 min US exposure, recording the mean gray intensity from the US images of the clot by computer software ImageJ, and spectrophotometric quantification of the hemoglobin in the clot lysate. In 15 minutes of sonothrombolysis performed at high mechanical index (0.9 and 1.2), SK loaded PCNDs showed a 48.61% and 74.29% reduction of mean gray intensity. At 0.9 and 1.2 MI, 86% and 92% weight loss was noted for SK-loaded PCNDs in confidence with spectrophotometric results. A significant difference (P < 0.05) was noted for SK-loaded PCND mediated sonothrombolysis compared to other groups. Loading of SK inside the PCNDs enhanced the efficacy of sonothrombolysis. An increase in MI and time also increased the efficacy of sonothrombolysis. This in vitro study showed the potential use of SK-loaded perfluorohexane core PCNDs as sonothrombolytic agents for deep venous thrombosis.

Determinants of Response at 2 Months of Treatment in a Cohort of Pakistani Patients with Pulmonary Tuberculosis.

Mycobacterium tuberculosis infection continues to be a major global challenge. All patients with pulmonary tuberculosis are treated with a standard 6-month treatment regimen. Historical data suggest that even with shortened treatment, most patients achieve long-term remission. Risk stratification is a goal for reducing potentially toxic prolonged treatment. This study aimed to determine the factors associated with the early clearance of sputum acid-fast bacilli (AFB). A total of 297 freshly diagnosed patients with pulmonary tuberculosis were included and enrolled in this study. Information related to their ethno-demographic and anthropometric characteristics was collected. We also assessed their complete blood counts, and blood iron, folate, and vitamin B12 levels. We found that the presence of higher levels of acid-fast bacilli (AFB) in diagnostic sputum microscopy was the single most significant prognostic factor associated with early clearance of sputum AFB after 2 months of treatment. All of our patients achieved treatment success after 6 months of treatment and were disease free. Our results support the data obtained from previous studies indicating that AFB clearance at 2 months is unlikely to be a clinically useful biomarker or indicator for therapeutic stratification. Furthermore, demographic, anthropometric, and nutritional factors are not clinically useful biomarkers.

Evaluating the use of coronary artery calcium scoring as a tool for coronary artery disease (CAD) risk stratification and its association with coronary stenosis and CAD risk factors: a single-centre, retrospective, cross-sectional study at a tertiary centre in Pakistan.

OBJECTIVE: Coronary artery disease (CAD) risk stratification plays a fundamental role in the early detection and optimal management of CAD. The aim of our study is to investigate the use of coronary artery calcium scoring (CACS) as a tool for CAD risk stratification through evaluation of its correlation with the degree of coronary stenosis and its association with conventional cardiovascular risk factors in asymptomatic patients. DESIGN: Single-centre, retrospective, cross-sectional study. SETTING: The study was conducted at a tertiary centre (Shifa International Hospital) in Islamabad, Pakistan, through review of medical records of patients who underwent coronary CT between the years 2016 and 2020. PARTICIPANTS: A total of 1014 patients were included in the study. The study population was analysed for presence of conventional risk factors (gender, age, diabetes, hypertension, body mass index, dyslipidaemia) and association with CACS (zero: n=534; minimal: 0 to ≤10, n=70; mild: >10 to ≤100, n=130; moderate: >100 to ≤400, n=118; and severe: >400, n=49). The association of CACS with the degree of coronary artery stenosis seen on CT scan (significant: ≥50% stenosis, n=216; non-significant: <50% stenosis, n=685) was also analysed. OUTCOME MEASURES: The main outcome was the association of coronary artery stenosis with CACS. The secondary outcome was the association of CACS with conventional CAD risk factors. RESULTS: A significant positive association was shown between CACS and coronary artery stenosis (zero vs minimal: OR 0.39, 95% CI 0.20 to 0.79, p=0.01; zero vs mild: OR 0.16, 95% CI 0.10 to 0.27, p<0.0001; zero vs moderate: OR 0.05, 95% CI 0.03 to 0.08, p<0.0001; zero vs severe: OR 0.02, 95% CI 0.01 to 0.050, p<0.0001). Age >45 (OR 1.03, 95% CI 1.01 to 1.05, p<0.0001), hypertension (OR 1.16, 95% CI 0.79 to 1.71, p=0.001) and diabetes (OR 1.33, 95% CI 0.88 to 1.99, p<0.0001) were associated with an increased risk of coronary artery stenosis. Moreover, plaques with higher calcium burden were found in the left anterior descending artery (mean CACS: 386.15±203.89), followed by right coronary (239.77±219.83) and left circumflex (175.56±153.54) arteries. CONCLUSION: The results indicate a strong positive association of CACS with coronary artery stenosis. CACS was also significantly associated with conventional CAD risk factors in this population.

Antiplatelet response to clopidogrel is associated with a haplotype in CYP2C19 gene in Pakistani patients.

Clopidogrel, an antiplatelet drug, is frequently prescribed to patients diagnosed with ischemic diseases such as those suffering from acute coronary syndromes or ischemic stroke. Despite the drug being effective in majority of the patients, some still experience ischemic events early in the treatment which might be due to poor platelet inhibition. This study aims to investigate the association of cytochrome P450 2C19 (CYP2C19) loss-of-function polymorphisms, haplotypes as well as a wide range of clinical and demographic variables with platelet aggregation phenotypes to clopidogrel in a Pakistani cohort. The study comprised of a total of 120 patients diagnosed with cardiovascular diseases and were treated with clopidogrel. Antiplatelet response to clopidogrel was monitored by Helena AggRAM (HL-2-1785P) and patients with maximal platelet aggregation more than 50% were categorized as low responders and those with less than 50% as high responders. Our results show that 56.6% of patients were homozygous for the CYP2C19 wild-type allele, 38.3% of patients possessed one copy of the CYP2C19*2 allele and 5% of patients possessed both CYP2C19*2 alleles. No CYP2C19*3 allele was found in our patient cohort. There was no statistically significant difference between the high and low responder groups to clopidogrel in terms of extensive, intermediate and poor metabolizer genotypes. However, haplotype (H1), leukocyte count, random blood glucose, and history of diabetes mellitus was associated with the antiplatelet response to clopidogrel. The prevalence of clopidogrel resistance in our population was in line with that reported for other regional and global populations.

Nanoparticles as a Novel Tool to Inhibit Inflammatory Cytokines in Human Lymphocytes and Macrophages of Coronary Artery Disease.

TNFα and NF-kB contribute in activation of pro-inflammatory signaling pathways and complications of coronary artery diseases (CAD). Current study highlights novel properties of Au (15 ± 2 nm), ZnO (77 ± 45 nm) and MgO (11 ± 4 nm) nanoparticles (NPs) as possible anti-inflammatory agents with greater efficacy and lower toxicity. Decrease in TNFα and NF-kB levels in Single Vessel Disease (SVD), Double Vessel Disease (DVD) and Triple-Vessel coronary artery disease (TVD) macrophage and lymphocyte cultures at varying concentrations of NPs has been studied to find an effective therapeutic concentration (ETC). Au and MgO NPs exhibits 5 µg/ml ETC compared to 1 µg/ml ZnO in all three CAD categories with negligible toxicity. ZnO remains most statistically significant (p < 0.001) in SVD and TVD cultures whereas MgO shows efficacy in DVD and TVD cultures with more than 50% reduction in TNFα and NF-kB levels at their respective ETCs. Au NPs exhibit prominent effect in DVD cultures. The mRNA expression results support the down-regulation of TNFα and NF-kB after NPs exposure in respective cultures. Findings of this prospective observational cohort study suggest use of NPs as an alternate anti-inflammatory agent in coronary artery and other diseases.

Development of a cost-effective CVD prediction model using lifestyle factors. A cohort study in Pakistan.

BACKGROUND: Cardiovascular diseases (CVD) such as hypertension and ischemic heart diseases cause 35 to 40% of deaths every year in Pakistan. Several lifestyle factors such as dietary habits, lack of exercise, mental stress, body habitus (i.e., body mass index, waist), personal habits (smoking, sleep, fitness) and clinical conditions (i.e., diabetes, dyslipidemia and hypertension) have been shown to be strongly associated with the etiology of CVD. Epidemiological studies in Pakistan have shown poor adherence of people to healthy lifestyle and lack of knowledge in adopting healthy alternatives. There are well validated cardiovascular risk estimation tools (QRISK model) that cn predict the probability of future cardiac events. The existing tools are based on laboratory investigations of biochemical test but there is no widely accepted tool available that predicts the CVD risk probability based on lifestyle factors. AIMS: Aim of the current study was to develop alternative CVD risk estimation model based on lifestyle factors and physical attributes (without using laboratory investigation) using QRISK model as the gold standard. STUDY DESIGN: Clinical and lifestyle data of one hundred and sixty subjects were collected to formulate a regression model for predicting CVD risk probability. METHODS: Lifestyle factors as independent variables (IV) include BMI, waist circumference, physical activities (stamina, strength, flexibility, posture), smoking, general illnesses, dietary intake, stress and physical characteristics. CVD risk probability of QRISK Intervention computed through clinical variables was used as a dependent variable (DV) in present research. Chronological age was also included in analysis in addition to selected lifestyle factors. Regression analysis, principal component analysis and bivariate correlations were applied to assess the relationship among predictor variables and cardiovascular risk score. RESULTS: Chronological age, waist circumference, BMI and strength showed significant effect on CVD risk probability. The proposed model can be used to calculate CVD risk probability with 72.9% accuracy for the targeted population. CONCLUSION: The model involves only those features which can be measured without any clinical test. The proposed model is rapid and less costly hence appropriate for use in developing countries like Pakistan.

Association of postexercise heart rate recovery with body composition in healthy male adults: Findings from Pakistan.

BACKGROUND: This study investigated the effect of body mass index (BMI) and body fat ratio with postexercise heart rate recovery (HRR) after 2 minutes of rest. METHODS: Sixty-four healthy males aged between 25 and 55 years participated in the study. BMI, body fat ratio, waist circumference, and physical activity were recorded. Peak heart rate after exercise and HRR after 2 min of rest were obtained. RESULTS: Mean age of participants was 35.53 ± 6.57. Mean BMI and HRR were 25.06 ± 4.62 and 26.07 ± 7.43, respectively. BMI and body fat ratio had significant negative correlation with HRR with r values of -.833 and -.877, respectively (p < .001*). Linear regression showed BMI and body fat ratio with significant coefficient of -0.426 (p = .04*) and -0.627 (p < .001*) with HRR, respectively. Participants with BMI Ë‚ 25 had higher HRR in comparison to participants with BMI ≥ 25 (p < .001*). Participants with body fat ratio of ˂25 had significantly higher HRR of 35.9 ± 3.98 in comparison to participants with body fat ratio ≥ 25 (p = <.001*). CONCLUSION: Body mass index and body fat ratio are strong predictors of HRR in Pakistani healthy male adults, suggesting a strong link between metabolic risk factors and impaired autonomic nervous system.

Regression analysis for detecting epileptic seizure with different feature extracting strategies.

Due to the excitability of neurons in the brain, a neurological disorder is produced known as epilepsy. The brain activity of patients suffering from epilepsy is monitored through electroencephalography (EEG). The multivariate nature of features from time domain, frequency domain, complexity and wavelet entropy based, and the statistical features were extracted from healthy and epileptic subjects using the Bonn University database and seizure and non-seizure intervals using the CHB MIT database. The robust machine learning regression methods based on regression, support vector regression (SVR), regression tree (RT), ensemble regression, Gaussian process regression (GPR) were employed for detecting and predicting epileptic seizures. Performance was measured in terms of root mean square error (RMSE), squared error, mean square error (MSE) and mean absolute error (MAE). Moreover, detailed optimization was performed using a RT to predict the selected features from each feature category. A deeper analysis was conducted on features and tree regression methods where optimal RMSE and MSE results were obtained. The best optimal performance was obtained using the ensemble boosted regression tree (BRT) and exponential GPR with an RMSE of 0.47, an MSE (0.22), an R Square (RS) (0.25) and an MAE (0.30) using the Bonn University database and support vector machine (SVM) fine Gaussian with RMSE (0.63634), RS (0.03), MSE (0.40493) and MAE (0.31744); squared exponential GPR and rational quadratic GPR with an RMSE of 0.63841, an RS (0.03), an MSE (0.40757) and an MAE (0.3472) was obtained using the CHB MIT database. A further deeper analysis for the prediction of selected features was performed on an RT to compute the optimal feasible point, observed and estimated function values, function evaluation time, objective function evaluation time and overall elapsed time.

Quantifying the dynamics of electroencephalographic (EEG) signals to distinguish alcoholic and non-alcoholic subjects using an MSE based K-d tree algorithm.

In this paper, we have employed K-d tree algorithmic based multiscale entropy analysis (MSE) to distinguish alcoholic subjects from non-alcoholic ones. Traditional MSE techniques have been used in many applications to quantify the dynamics of physiological time series at multiple temporal scales. However, this algorithm requires O(N2), i.e. exponential time and space complexity which is inefficient for long-term correlations and online application purposes. In the current study, we have employed a recently developed K-d tree approach to compute the entropy at multiple temporal scales. The probability function in the entropy term was converted into an orthogonal range. This study aims to quantify the dynamics of the electroencephalogram (EEG) signals to distinguish the alcoholic subjects from control subjects, by inspecting various coarse grained sequences formed at different time scales, using traditional MSE and comparing the results with fast MSE (fMSE). The performance was also measured in terms of specificity, sensitivity, total accuracy and receiver operating characteristics (ROC). Our findings show that fMSE, with a K-d tree algorithmic approach, improves the reliability of the entropy estimation in comparison with the traditional MSE. Moreover, this new technique is more promising to characterize the physiological changes having an affect at multiple time scales.

Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies.

Prostate is a second leading causes of cancer deaths among men. Early detection of cancer can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and multiresolution of MRIs from prostate cancer require a proper diagnostic systems and tools. In the past researchers developed Computer aided diagnosis (CAD) systems that help the radiologist to detect the abnormalities. In this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machine (SVM) kernels: polynomial, radial base function (RBF) and Gaussian and Decision Tree for detecting prostate cancer. Moreover, different features extracting strategies are proposed to improve the detection performance. The features extracting strategies are based on texture, morphological, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) features. The performance was evaluated based on single as well as combination of features using Machine Learning Classification techniques. The Cross validation (Jack-knife k-fold) was performed and performance was evaluated in term of receiver operating curve (ROC) and specificity, sensitivity, Positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR). Based on single features extracting strategies, SVM Gaussian Kernel gives the highest accuracy of 98.34% with AUC of 0.999. While, using combination of features extracting strategies, SVM Gaussian kernel with texture + morphological, and EFDs + morphological features give the highest accuracy of 99.71% and AUC of 1.00.

Diagonal Earlobe Crease as a Significant Marker for Coronary Artery Disease: A Case-control Study.

OBJECTIVES: To investigate the association between diagonal earlobe crease (DELC) and coronary artery disease (CAD). Limited data exists in South Asia and no prior studies have been performed in Pakistan to assess this relationship. METHODS: In this case-control study, 200 participants from December 2015 to March 2016 at Shifa International Hospital, Islamabad, Pakistan were enrolled. Consecutive non-probability sampling was used to recruit patients. Cases were enrolled from cardiac care unit (CCU) of the hospital with angiography-proven CAD. Controls were selected from surgical, medical and neurology units of the hospital if they had no previously established evidence or symptoms of CAD. Patients were evaluated in terms of age and any history of hypertension, diabetes and/or smoking. Cases and controls were examined separately by two investigators for the unilateral or bilateral presence of DELC of the lobular portion of either auricle. Patients with ear piercings were excluded from the study. The data was analyzed in statistical product and service solutions (SPSS) (IBM, Delaware, Chicago), and an online statistical software. RESULTS: Out of the 200 patients, 126 (63%) were males and 74 (37%) were females. In the 100 cases, 76 had DELC and 24 had no crease whereas, among the 100 controls, 36 had DELC and 64 had no DELC (p <0.001, OR = 5.63, CI = 2.91-10.93). The prevalence of diseases such as hypertension, diabetes, smoking among the cases and controls were 66%, 53%, 27% and 27%, 18%, 25% respectively. The effect of hypertension and diabetes on the presence of DELC was statistically significant (p <0.05) but the impact of smoking on DELC presence was insignificant (p >0.05). CONCLUSION: There is a significant association between DELC and CAD. This is the first case-control study from South Asia disclosing this important correlation. Our study also reports a high frequency of DELC in patients suffering from hypertension and diabetes mellitus. No association between smoking and DELC was found.

Inverse Correlation between Heart Rate Variability and Heart Rate Demonstrated by Linear and Nonlinear Analysis.

The dynamical fluctuations in the rhythms of biological systems provide valuable information about the underlying functioning of these systems. During the past few decades analysis of cardiac function based on the heart rate variability (HRV; variation in R wave to R wave intervals) has attracted great attention, resulting in more than 17000-publications (PubMed list). However, it is still controversial about the underling mechanisms of HRV. In this study, we performed both linear (time domain and frequency domain) and nonlinear analysis of HRV data acquired from humans and animals to identify the relationship between HRV and heart rate (HR). The HRV data consists of the following groups: (a) human normal sinus rhythm (n = 72); (b) human congestive heart failure (n = 44); (c) rabbit sinoatrial node cells (SANC; n = 67); (d) conscious rat (n = 11). In both human and animal data at variant pathological conditions, both linear and nonlinear analysis techniques showed an inverse correlation between HRV and HR, supporting the concept that HRV is dependent on HR, and therefore, HRV cannot be used in an ordinary manner to analyse autonomic nerve activity of a heart.