Study and management of type-2 diabetes mellitus in patients with hypertension at tertiary care hospital

Background: This study was aimed to study and management of hypertension in diabetic patients.Methods: A prospective, observational study was conducted in 160 diabetic hypertensive patients admitted in general medicine wards at Andhra Pradesh Vaidya Vidhana Parishad Hospital, (APVVP), Proddatur. Patients who signed informed consent form were only included in the study. All the data were recorded from patients’ case files and analyzed.Results: Of enrolled 160 patients, 86 (53.75%) were female and 74 (46.25%) were male and maximum number of the patients 32.5% were found in the age group of 60-69 years. Out of 160 admitted patients, (51) patients treated with metformin, glibenclamide and atenolol, (18) patients treated with metformin, glimiperide, and amlodipine, (6) patients treated with metformin and amlodipine, (28) patients treated with metformin, glimiperide and atenolol, (19) patients treated with metformin and atenolol, (17) patients treated with metformin, glibenclamide and amlodipine,(9) patients treated with metformin, glibenclamide and losartan, (5) patients treated with metformin and losartan, (7) patients treated with metformin, glimiperide, and losartan.Conclusions: There was less awareness among the patients regarding the control of type-2 diabetes mellitus with hypertension. Majority of diabetic patients noticed with hypertension and β adrenergic blockers remained first choice of drug for hypertension in diabetes. Calcium channel blockers were also prescribed to many patients and were successful to achieve target blood pressure. Among anti-diabetic drugs, biguanides were most frequently prescribed class of drugs. Metformin was the most prescribed drug and Sulphonyl urea were the next most prescribed class of drug

Temporal characterization of biogas slurry: a pre-requisite for sustainable nutrigation in crop production

Biogas slurry serves as a useful organic fertilizer due to its substantial nutrient content, and its characterization enables the evaluation of nutrient content and its efficient utilization. This study focuses on the variations in the nutrient content of biogas slurry from different dairy farm systems located near the ICAR-Indian Agricultural Research Institute (IARI) (New Delhi), Daryapur Kalan (New Delhi), and Madanpur (Uttar Pradesh) during the pre-monsoon, monsoon, and post-monsoon seasons. The study reveals significant variations in macronutrient levels, particularly nitrogen (N), which showed variations exceeding 3% and a wider range of almost 6% during the pre-monsoon and post-monsoon periods. Spatial differences between dairy farms also contributed to the variance, with more than 10% differences observed between IARI and Daryapur Kalan and between IARI and Madanpur. Phosphorus (P) remained stable across seasons with spatial variation, while potassium (K) exhibited a reverse trend. Correlation analysis highlighted strong positive associations between N content and phosphorus (0.959), organic carbon (0.954), pH (0.813), and electrical conductivity (0.806). The findings suggest the use of biogas slurry has a potential to reduce the synthetic fertilizer consumption of N, P, and K by approximately 8.78%, 11.01%, and 14.33%, respectively and using them for further for nutrigation

Whole Genome Sequencing of Mycobacterium tuberculosis Clinical Isolates From India Reveals Genetic Heterogeneity and Region-Specific Variations That Might Affect Drug Susceptibility

Whole genome sequencing (WGS) of Mycobacterium tuberculosis has been constructive in understanding its evolution, genetic diversity and the mechanisms involved in drug resistance. A large number of sequencing efforts from across the globe have revealed genetic diversity among clinical isolates and the genetic determinants for their resistance to anti-tubercular drugs. Considering the high TB burden in India, the availability of WGS studies is limited. Here we present, WGS results of 200 clinical isolates of M. tuberculosis from North India which are categorized as sensitive to first-line drugs, mono-resistant, multi-drug resistant and pre-extensively drug resistant isolates. WGS revealed that 20% of the isolates were co-infected with M. tuberculosis and non-tuberculous mycobacteria species. We identified 12,802 novel genetic variations in M. tuberculosis isolates including 343 novel SNVs in 38 genes which are known to be associated with drug resistance and are not currently used in the diagnostic kits for detection of drug resistant TB. We also identified M. tuberculosis lineage 3 to be predominant in the northern region of India. Additionally, several novel SNVs, which may potentially confer drug resistance were found to be enriched in the drug resistant isolates sampled. This study highlights the significance of employing WGS in diagnosis and for monitoring further development of MDR-TB strains

Nanosizing techniques for improving bioavailability of drugs

The poor solubility of significant number of Active Pharmaceutical Ingredients (APIs) has become a major challenge in the drug development process. Drugs with poor solubility are difficult to formulate by conventional methods and often show poor bioavailability. In the last decade, attention has been focused on developing nanocrystals for poorly water soluble drugs using nanosizing techniques. Nanosizing is a pharmaceutical process that changes the size of a drug to the sub-micron range in an attempt to increase its surface area and consequently its dissolution rate and bioavailability. The effectiveness of nanocrystal drugs is evidenced by the fact that six FDA approved nanocrystal drugs are already on the market. The bioavailabilities of these preparations have been significantly improved compared to their conventional dosage forms. There are two main approaches for preparation of drug nanocrystals; these are the top-down and bottom-up techniques. Top-down techniques have been successfully used in both lab scale and commercial scale manufacture. Bottom-up approaches have not yet been used at a commercial level, however, these techniques have been found to produce narrow sized distribution nanocrystals using simple methods. Bottom-up techniques have been also used in combination with top-down processes to produce drug nanoparticles. The main aim of this review article is to discuss the various methods for nanosizing drugs to improve their bioavailabilities

Normal Serum Levels of Otolin-1 in Patients with Meniere Disease in Remission

Introduction Degenerative changes in the otolithic organs have been theorized to be caused by the mechanical obstruction to endolymphatic flow, possibly resulting in endolymphatic hydrops (ELH). Otolin-1 is an otoconial matrix protein that crosses the blood labyrinth barrier and has been found in the serum of healthy and diseased patients

Factors affecting the ability of the spectral domain optical coherence tomograph to detect photographic retinal nerve fiber layer defects.

To evaluate the ability of normative database classification (color-coded maps) of spectral domain optical coherence tomograph (SDOCT) in detecting wedge shaped retinal nerve fiber layer (RNFL) defects identified on photographs and the factors affecting the ability of SDOCT in detecting these RNFL defects.In a cross-sectional study, 238 eyes (476 RNFL quadrants) of 172 normal subjects and 85 eyes (103 RNFL quadrants with wedge shaped RNFL defects) of 66 glaucoma patients underwent RNFL imaging with SDOCT. Logistic regression models were used to evaluate the factors associated with false positive and false negative RNFL classifications of the color-coded maps of SDOCT.False positive classification at a p value of <5% was seen in 108 of 476 quadrants (22.8%). False negative classification at a p value of <5% was seen in 16 of 103 quadrants (15.5%). Of the 103 quadrants with RNFL defects, 64 showed a corresponding VF defect in the opposite hemisphere and 39 were preperimetric. Higher signal strength index (SSI) of the scan was less likely to have a false positive classification (odds ratio: 0.97, p = 0.01). Presence of an associated visual field defect (odds ratio: 0.17, p = 0.01) and inferior quadrant RNFL defects as compared to superior (odds ratio: 0.24, p = 0.04) were less likely to show false negative classifications.Scans with lower signal strengths were more likely to show false positive RNFL classifications, and preperimetric and superior quadrant RNFL defects were more likely to show false negative classifications on color-coded maps of SDOCT

Enhancing the Efficiency of Diabetes Prediction through Training and Classification using PCA and LR Model

In this paper, we introduce a new approach for predicting the risk of diabetes using a combination of Principal Component Analysis (PCA) and Logistic Regression (LR). Our method offers a unique solution that could lead to more accurate and efficient predictions of diabetes risk. To develop an effective model for predicting diabetes, it is important to consider various clinical and demographic factors contributing to the disease's development. This approach typically involves training the model on a large dataset that includes these factors. By doing so, we can better understand how different characteristics can impact the development of diabetes and create more accurate predictions for individuals at risk. The PCA method is employed to reduce the dataset's dimensions and augment the model's computational efficacy. The LR model then classifies patients into diabetic or non-diabetic groups. Accuracy, precision, recall, the F1-score, and the area under the ROC curve (AUC) are only a few of the indicators used to evaluate the performance of the proposed model. Pima Indian Diabetes Data (PIDD) is used to evaluate the model, and the results demonstrate a significant improvement over the state-of-the-art methods. The proposed model presents an efficient and effective method for predicting diabetes risk that may have significant implications for improving healthcare outcomes and reducing healthcare costs. The proposed PCA-LR model outperforms other algorithms, such as SVM and RF, especially in terms of accuracy, while optimizing computational complexity. This approach can potentially provide a practical and efficient solution for large-scale diabetes screening programs

Peripapillary retinal nerve fiber layer assessment of spectral domain optical coherence tomography and scanning laser polarimetry to diagnose preperimetric glaucoma.

To compare the abilities of peripapillary retinal nerve fiber layer (RNFL) parameters of spectral domain optical coherence tomograph (SDOCT) and scanning laser polarimeter (GDx enhanced corneal compensation; ECC) in detecting preperimetric glaucoma.In a cross-sectional study, 35 preperimetric glaucoma eyes (32 subjects) and 94 control eyes (74 subjects) underwent digital optic disc photography and RNFL imaging with SDOCT and GDx ECC. Ability of RNFL parameters of SDOCT and GDx ECC to discriminate preperimetric glaucoma eyes from control eyes was compared using area under receiver operating characteristic curves (AUC), sensitivities at fixed specificities and likelihood ratios (LR).AUC of the global average RNFL thickness of SDOCT (0.786) was significantly greater (p<0.001) than that of GDx ECC (0.627). Sensitivities at 95% specificity of the corresponding parameters were 20% and 8.6% respectively. AUCs of the inferior, superior and temporal quadrant RNFL thickness parameters of SDOCT were also significantly (p<0.05) greater than the respective RNFL parameters of GDx ECC. LRs of outside normal limits category of SDOCT parameters ranged between 3.3 and 4.0 while the same of GDx ECC parameters ranged between 1.2 and 2.1. LRs of within normal limits category of SDOCT parameters ranged between 0.4 and 0.7 while the same of GDx ECC parameters ranged between 0.7 and 1.0.Abilities of the RNFL parameters of SDOCT and GDx ECC to diagnose preperimetric glaucoma were only moderate. Diagnostic abilities of the RNFL parameters of SDOCT were significantly better than that of GDx ECC in preperimetric glaucoma