Effect of losartan and amlodipine on insulin sensitivity in non-diabetic hypertensive patients

Background: Hypertensive patients show higher insulin levels than normotensive controls. Hypertension is linked to impaired glucose tolerance and resistance to the action of insulin. Untreated hypertensive patients are at risk of developing new onset diabetes mellitus. Different antihypertensive drugs affect the insulin sensitivity distinctly. Few studies have demonstrated the beneficial effects of losartan, an angiotensin receptor blocker on the glucose insulin metabolism, but other studies have failed to demonstrate the insulin resistance lowering effect of losartan. Amlodipine, a long acting calcium channel blocker is considered to have neutral effects on the glucose-insulin metabolism.Methods: In a prospective, open-label, parallel group study, non-diabetic patients with mild to moderate hypertension were randomized to either losartan (titrated from 50 to 100 mg /day, n=20) or amlodipine (titrated from 5 to 10 mg/ day, n=20) for period of 24 weeks. At baseline, 12 weeks and 24 weeks fasting plasma glucose, fasting plasma insulin, homeostasis model assessment for insulin resistance (HOMA-IR) apart from lipid parameters, mean systolic and diastolic blood pressures levels were determined.Results: Intragroup comparison shows that both losartan and amlodipine significantly reduced the HOMA-IR index (P < 0.05, 24 weeks vs. baseline). Losartan reduced HOMA-IR more than amlodipine but this reduction was not statistically significant.Conclusions: Losartan and amlodipine lowered insulin resistance in patients of mild to moderate hypertension

COMBINATION OF MOMORDICA CHARANTIA AND PHYLLANTHUS AMARUS FOR HEPATOPROTECTIVE ACTIVITY IN ETHANOL AND ANTI-TUBERCULAR DRUGS INDUCED HEPATOTOXICITY IN RATS

Objective: To evaluate the synergistic protective effect of Momordica charantia and Phyllanthus amarus combination (MC+PA) of doses 200 and 400 mg/kg on the liver in different experimental models of hepatotoxicity. Methods: The hepatoprotective activity was evaluated in ethanol and anti-tubercular drugs (isoniazid-INH, rifampicin-RIF) induced hepatotoxicity models. Hepatotoxicity in both models was induced to all groups except the normal control. Intoxicated rats were treated with silymarin and various doses of MC+PA for 8 d in ethanol-induced and 21 d in INH+RIF induced hepatotoxicity models. At the completion of study, the biochemical markers and the anti-oxidant status (SOD and MDA) were measured and also the histopathological evaluation of the liver tissue was carried out. Results: Combination therapy remarkably reduced the elevated profile of the biochemical markers and thereby improved the anti-oxidant status, thus exhibiting the synergistic hepatoprotective effect when compared with the positive control group (p&lt;0.001). Histopathological evaluation demonstrated that MC+PA decreased the liver damage significantly in comparison with the positive group. Conclusion: The current work suggests that the combined extract showed synergistic effects on ethanol and anti-tubercular drugs induced hepatotoxicity models by significantly decreasing the liver damage

Changing the Trends in Surgery during the COVID-19 Times: An Experience from the Eastern Uttar Pradesh State, India

Background: The COVID-19 pandemic has an immense effect not only on the social and economic lives of people but also on the surgical lives of surgeons, residents, nursing staff, and patients as well as ground level staff. Amidst this COVID pandemic, emergency surgeries were being done but at a decreased rate, whereas elective cases depended on the will of hospitals, surgeons, and patients. Study aims to promulgate a "Neo–Surgical Check Box" by amalgamating the WHO surgical checklist and the results obtained from the questionnaires. Subjects and Methods: After receiving ethical clearance from the Institute Ethical Committee, an online questionnaire with 50 questions divided into three sections was distributed to 235 surgeons in Uttar Pradesh. Results:Two hundred and eight surgeons had responded, out of which 154 were male and 54 were female. Only 41.4% (86) were actually conducting the surgery. The emergency surgery rate was 60.3%, whereas 18.6% of elective surgeries were done, 11.8% of surgeries were avoided, and the rate of minimal access surgery was 9.3%. Conclusion: The "Neo-Surgical Check Box" will not only increase the number of surgeries per day but also provide protection to the healthcare workers while handling not only COVID-positive patients but also any airborne communicable diseases

A rare extra scrotal spermatocele: a rare case presentation

Right Inguinal region pain is a common complaint evaluated in surgical department. The number of differential diagnoses is lowered when the pain in a male patient is associated with a palpable tender mass. These diagnoses include inguinal hernia, inflamed inguinal lymph node, rectus sheath hematoma, cryptorchidism, mass derived from the spermatic cord, and polyorchidism. Right Inguinal region mass and pain caused by a spermatocele are unusual. Here we report a case of extra scrotal spermatocele causing right Inguinal region swelling and pain. To our knowledge this is a second reported case

Genomic Selection for Wheat Blast in a Diversity Panel, Breeding Panel and Full-Sibs Panel

Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 +/- 0.13) and the fixed effects model (0.62 +/- 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 +/- 0.11), GBLUP (0.55 +/- 0.1), and ABLUP (0.48 +/- 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical

Genomic selection for wheat blast in a diversity panel, breeding panel and full-sibs panel

Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 ± 0.13) and the fixed effects model (0.62 ± 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 ± 0.11), GBLUP (0.55 ± 0.1), and ABLUP (0.48 ± 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical

Genome-wide association mapping indicates quantitative genetic control of spot blotch resistance in bread wheat and the favorable effects of some spot blotch loci on grain yield

Spot blotch caused by the fungus Bipolaris sorokiniana poses a serious threat to bread wheat production in warm and humid wheat-growing regions of the world. Hence, the major objective of this study was to identify consistent genotyping-by-sequencing (GBS) markers associated with spot blotch resistance using genome-wide association mapping on a large set of 6,736 advanced bread wheat breeding lines from the International Maize and Wheat Improvement Center. These lines were phenotyped as seven panels at Agua Fria, Mexico between the 2013–2014 and 2019–2020 crop cycles. We identified 214 significant spot blotch associated GBS markers in all the panels, among which only 96 were significant in more than one panel, indicating a strong environmental effect on the trait and highlights the need for multiple phenotypic evaluations to identify lines with stable spot blotch resistance. The 96 consistent GBS markers were on chromosomes 1A, 1B, 1D, 2A, 3B, 4A, 5B, 5D, 6B, 7A, 7B, and 7D, including markers possibly linked to the Lr46, Sb1, Sb2 and Sb3 genes. We also report the association of the 2NS translocation from Aegilops ventricosa with spot blotch resistance in some environments. Moreover, the spot blotch favorable alleles at the 2NS translocation and two markers on chromosome 3BS (3B_2280114 and 3B_5601689) were associated with increased grain yield evaluated at several environments in Mexico and India, implying that selection for favorable alleles at these loci could enable simultaneous improvement for high grain yield and spot blotch resistance. Furthermore, a significant relationship between the percentage of favorable alleles in the lines and their spot blotch response was observed, which taken together with the multiple minor effect loci identified to be associated with spot blotch in this study, indicate quantitative genetic control of resistance. Overall, the results presented here have extended our knowledge on the genetic basis of spot blotch resistance in bread wheat and further efforts to improve genetic resistance to the disease are needed for reducing current and future losses under climate change

Genomic selection for spot blotch in bread wheat breeding panels, full-sibs and half-sibs and index-based selection for spot blotch, heading and plant height

A major biotic stress challenging bread wheat production in regions characterized by humid and warm weather is spot blotch caused by the fungus Bipolaris sorokiniana. Since genomic selection (GS) is a promising selection tool, we evaluated its potential for spot blotch in seven breeding panels comprising 6736 advanced lines from the International Maize and Wheat Improvement Center. Our results indicated moderately high mean genomic prediction accuracies of 0.53 and 0.40 within and across breeding panels, respectively which were on average 177.6% and 60.4% higher than the mean accuracies from fixed effects models using selected spot blotch loci. Genomic prediction was also evaluated in full-sibs and half-sibs panels and sibs were predicted with the highest mean accuracy (0.63) from a composite training population with random full-sibs and half-sibs. The mean accuracies when full-sibs were predicted from other full-sibs within families and when full-sibs panels were predicted from other half-sibs panels were 0.47 and 0.44, respectively. Comparison of GS with phenotypic selection (PS) of the top 10% of resistant lines suggested that GS could be an ideal tool to discard susceptible lines, as greater than 90% of the susceptible lines discarded by PS were also discarded by GS. We have also reported the evaluation of selection indices to simultaneously select non-late and non-tall genotypes with low spot blotch phenotypic values and genomic-estimated breeding values. Overall, this study demonstrates the potential of integrating GS and index-based selection for improving spot blotch resistance in bread wheat

Dissecting the genetic architecture of phenology affecting adaptation of spring bread wheat genotypes to the major wheat-producing zones in India

Spring bread wheat adaptation to diverse environments is supported by various traits such as phenology and plant architecture. A large-scale genome-wide association study (GWAS) was designed to investigate and dissect the genetic architecture of phenology affecting adaptation. It used 48 datasets from 4,680 spring wheat lines. For 8 years (2014–2021), these lines were evaluated for days to heading (DH) and maturity (DM) at three sites: Jabalpur, Ludhiana, and Samastipur (Pusa), which represent the three major Indian wheat-producing zones: the Central Zone (CZ), North-Western Plain Zone (NWPZ), and North-Eastern Plain Zone (NEPZ), respectively. Ludhiana had the highest mean DH of 103.8 days and DM of 148.6 days, whereas Jabalpur had the lowest mean DH of 77.7 days and DM of 121.6 days. We identified 119 markers significantly associated with DH and DM on chromosomes 5B (76), 2B (18), 7D (10), 4D (8), 5A (1), 6B (4), 7B (1), and 3D (1). Our results clearly indicated the importance of the photoperiod-associated gene (Ppd-B1) for adaptation to the NWPZ and the Vrn-B1 gene for adaptation to the NEPZ and CZ. A maximum variation of 21.1 and 14% was explained by markers 2B_56134146 and 5B_574145576 linked to the Ppd-B1 and Vrn-B1 genes, respectively, indicating their significant role in regulating DH and DM. The results provide important insights into the genomic regions associated with the two phenological traits that influence adaptation to the major wheat-producing zones in India

Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data

The success of genomic selection (GS) in breeding schemes relies on its ability to provide accurate predictions of unobserved lines at early stages. Multigeneration data provides opportunities to increase the training data size and thus, the likelihood of extracting useful information from ancestors to improve prediction accuracy. The genomic best linear unbiased predictions (GBLUPs) are performed by borrowing information through kinship relationships between individuals. Multigeneration data usually becomes heterogeneous with complex family relationship patterns that are increasingly entangled with each generation. Under these conditions, historical data may not be optimal for model training as the accuracy could be compromised. The sparse selection index (SSI) is a method for training set (TRN) optimization, in which training individuals provide predictions to some but not all predicted subjects. We added an additional trimming process to the original SSI (trimmed SSI) to remove less important training individuals for prediction. Using a large multigeneration (8 yr) wheat (Triticum aestivum L.) grain yield dataset (n = 68,836), we found increases in accuracy as more years are included in the TRN, with improvements of ∼0.05 in the GBLUP accuracy when using 5 yr of historical data relative to when using only 1 yr. The SSI method showed a small gain over the GBLUP accuracy but with an important reduction on the TRN size. These reduced TRNs were formed with a similar number of subjects from each training generation. Our results suggest that the SSI provides a more stable ranking of genotypes than the GBLUP as the TRN becomes larger