Mean Percentage Research Articles

Assessment of Genetic Variability and Trait Associations in Chickpea (Cicer arietinum L.) Germplasms for Targeted Breeding Strategies

Chickpea (Cicer arietinum L.) is a pivotal legume crop with substantial contributions to global food security and agricultural sustainability due to its high protein content, adaptability and nitrogen-fixing ability. The efficiency of breeding programs in enhancing chickpea productivity and resilience relies heavily on understanding and utilizing genetic variability within the germplasm. This study investigates the extent of genetic variability among forty-three chickpea genotypes, focusing on key traits such as number of pods (NPP), biological yield (BY), harvest index (HI), days to flowering (DTF), days to maturity (DTM), that influence yield and other agronomic characteristics. Analysis of variance showed significant variability among genotypes for various traits. Higher phenotypic coefficients of variation (PCV) compared to genotypic coefficients (GCV) indicated the presence of environmental influence, particularly for traits like number of pods per plant (NPP) and biological yield (BY). High heritability was observed for days to flowering (DTF) and maturity (DTM), while genetic advance as percent of mean (GAM) varied, indicating additive gene action for some traits. Correlation and path analysis revealed positive relationships between plot yield and traits such as Days to Flowering (DTF), plant height (PH), and harvest index (HI). Principal component analysis (PCA) and cluster analysis indicated significant genetic diversity, grouping genotypes into clusters and identifying top performers like GJG1801 and NBEG 924. This study emphasizes the importance of genetic variability in breeding programs, aiding in the selection of elite genotypes for targeted traits improvement.

Standardization of Honey Application on Acute Partial Thickness Wound

Backgrounds: : Inthe past few years, clinicians worldwide have been using honey for wound treatment. But until now, there was no such standard on method of honey application on wound. In our center, honey was applied on wound by once a day application and the result was observationally satisfactory. What if application of honey were done once every two days? Would the result become more satisfactory? This study aims to search honey application method, which gives the best result on wound treatment. Methods: This is single-blinded non-randomize clinical trial, which was conduscted in Cipto Mangunkusumo Hospital Jakarta from July until September 2012. 14 Patients with acute partial thickness wound resulted from STSG harvesting were involved in this study. Patients were devided into 2 groups: control (once every two days application of honey) and the rate of wound healing were evaluated. Rate of wound healing will be assessed as number of percentage of reduced non-epithelialized areas on the seventh day of application. Results: The mean percentage of non-epithelialized area reduction on treatment gruop was 86,76% and 97,97% on control group. There was significant difference on percentage of reduced area between control and treatment group (p<0,00). Conclusion: There was statistically signi!cant difference between once a day and once every two days application of honey. However, changing of honey dressing once a day is still a preferable method in wound treatment.

Long-term effects of esaxerenone in patients with type 2 diabetes, diabetic kidney disease, and hypertension (JDDM77).

This clinical study assessed the three-year, long-term effects of esaxerenone, a non-steroidal aldosterone receptor blocker, on Japanese patients with type 2 diabetes, diabetic kidney disease, and hypertension who were receiving renin-angiotensin system inhibitors. Data from a computerized diabetic care database were used to retrospectively compare esaxerenone users (Group A) with non-esaxerenone users (Group B). Propensity score weighting was applied to Group B. The study primarily focused on percent changes in the Urine Albumin-Creatinine Ratio (UACR) from baseline and also examined the estimated Glomerular Filtration Rate (eGFR), blood pressure, serum potassium levels, and HbA1c. There were 199 patients in Group A and 199 in Group B, matched 1:1 using propensity scores. UACR and blood pressure were significantly lower in Group A than in Group B. Geometric mean percent changes in UACR from baseline between the two groups were as follows: - 62.7% at 1year (95% Confidence Interval (CI): - 91.0 to - 34.1%), - 48.9% at 2years (95% CI: - 79.4 to - 19.3%), and - 63.8% at 3years (95% CI: - 107.4 to - 20.2%). Additionally, the present study examined the impact of combining esaxerenone with SGLT2 inhibitors and GLP-1 receptor agonists and showed consistent effects on UACR irrespective of these medications. Esaxerenone slightly lowered eGFR with a low risk of hyperkalemia but did not adversely impact glucose metabolism. Esaxerenone exerted antihypertensive and antialbuminuric effects in patients with type 2 diabetes, diabetic kidney disease, and hypertension.

The Role of AI and ChatGPT in Enhancing Learning Achievement Among University Students: A Quantitative Analysis

This study aimed to explore the students’ Role of AI and ChatGPT in Enhancing Learning Achievement Among University students in public sector universities. A quantitative research method was used to conduct the research investigation. The study's population consisted of 9854 students from the three public universities in district Multan. To complete this research, a sample of one hundred and ninety-seven students (n = 197), including female (120) and seventy-six male (76) students, was selected through a convenient sampling approach from the faculty of education. The data was analyzed using descriptive statistics (frequency, percentage mean, and standard deviation) and inferential statistics (t-test). The results indicated that the students were positive toward the artificial intelligence ChatGPT. The study recommended that training workshops be used to demonstrate to students how to leverage these technologies responsibly and efficiently in educational institutions.

The menopausal transition and multiple physiologic measures of early brain health in the Coronary Artery Risk Development in Young Adults study.

This study proposed to investigate the cross-sectional and longitudinal associations of menopausal status with physiologic brain magnetic resonance imaging measures. The sample included women from the Coronary Artery Risk Development in Young Adults study who self-reported their reproductive histories and participated in the brain magnetic resonance imaging substudies at the year 25 (n = 292) and year 30 (n = 258) follow-up examinations. Menopausal status was classified based on natural menstrual cycle regularity/cessation at both time points. Gray matter cerebrovascular reactivity (CVR) was calculated as mean percent change in blood oxygen level-dependent signals in activated voxels following a breath-hold challenge. Gray matter cerebral blood flow (CBF) was assessed using pseudo-continuous arterial spin labeling. Linear regression models were used to examine cross-sectional and longitudinal associations of menopausal status with gray matter CVR and CBF after adjustment for potential age-related covariates. Women were mean age 50 years at year 25; 37% were Black; and 46% were postmenopausal. Relative to premenopause or perimenopause, postmenopause was associated with lower gray matter CVR at year 30 cross-sectionally (1.86% vs 1.69%, P = 0.03, respectively) and longitudinally for women who were postmenopausal at both time points (-0.32% [95% CI, -0.63% to -0.02%]) after covariate adjustment. Mean CVR values were also lower for these women when compared with women who remained premenopausal or perimenopausal (1.71% compared with 2.04%, respectively). Menopausal status was unrelated to either concurrent or longitudinal gray matter CBF. These findings suggest that the ability of vessels to adapt in response to hypercapnia may be impaired during menopause, even within a relatively short time window.

Comparative study of generalized-sampling-theorem-based digital super-resolution for 2D data

The size of pixels in a digital recording device, such as a CCD array, limits the spatial resolution in images obtained by an optical imaging system, thereby degrading the image quality. Digital super-resolution (DSR) techniques are used to reconstruct a high-resolution (HR) image from multiple sub-pixel-shifted low-resolution images in order to improve the image quality. In this article, we formulate a mathematical framework for DSR using the generalized sampling theorem (GST). The GST-based DSR method’s performance is evaluated by comparing it to existing resolution enhancement methods on the basis of evaluation metrics like percentage mean square error (%MSE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). The GST DSR method exhibits an overall superior quality image reconstruction based on quantitative analysis (with a near zero %MSE, SSIM around one, and improved PSNR in dB) and qualitative comparison. The robustness of the GST DSR method is further demonstrated in the presence of frame-to-frame shift estimation error using %MSE and SSIM by comparing it with multi-frame interpolation approaches.

Genetic Variability and Characters Association for Lodging, Yield and Related Traits of Field Pea (Pisum sativum L.) Genotypes in Contrasting Plant Types

A total of 49 field pea genotypes, belonging to two different plant types were evaluated for 13 traits at Bekoji and Kofele in 2020 to assess the extent of genetic variability and association among morpho-agronomic traits. Analysis of variance revealed a highly significant difference between plant types and among the tested genotypes within each plant type for most of traits. Relatively, genotypes within prostrate type are more variable in thousand seed weight and genotypes within semi-leaf less type are more diverse in plant height, ascochyta blight and lodging severity score. Moderate to high genotypic coefficient of variation (GCV), heritability in broad sense (H2b) and genetic advance as a percent of mean (GAM) combination were recorded from seed yield, thousand seed weight, plant height and number of seeds per plant in both plant types. The path and correlation analysis showed that lodging score had significant negative association and negative direct effect on seed yield in prostrate type. Number of seeds per plant and plant height had both positive direct effects and significant positive association with seed yield consistently in both plant types. Thousand seed weight also showed both positive direct effects and significant positive association with seed yield in semi-leaf less type. Therefore, great emphasis should be given for those traits while making selection of field pea genotypes for high seed yield. However, for the improvement of yield and lodging simultaneously there should be balance between yield and lodging related traits especially in prostrate type. The principal component analysis (PCA) indicated that the first five principal component axes accounted for 71.67% of the total variability. Generally, the results suggested the possibility of further improvement of seed yield and associated desired traits through selection in both plant types.

A comprehensive evaluation of machine learning and deep learning algorithms for wind speed and power prediction

Accurate wind speed and power predictions are crucial for renewable wind energy applications. This study compares and evaluates twelve machine learning (ML) and deep learning (DL) algorithms, including single and ensemble models across various time scales from 10 min to a day and a half ahead with a particular focus on ensemble prediction algorithms. Moreover, the study proposes a wind speed and power prediction system where the outcome of the wind speed prediction (WSP) model serves as input for the wind power prediction (WPP) model. Several evaluation metrics, such as mean absolute percentage error (MAPE) and mean square error (MSE) were calculated to benchmark different model accuracies. For WSP, the extremely randomized trees, decision tree, and bagging ensemble algorithms demonstrated high accuracy across different time scales where the MAPE ranged from 3.4% to 9.2%, the MSE ranged from 0.17 to 1.15, and the adjusted coefficient of determination ranged from 94% to 99%. For WPP, bagging ensemble algorithms and extremely randomized trees were also effective for predicting different time scales where the MAPE ranged from 4.12% to 11.7% and the MSE ranged from 10945 to 2.4. Ensemble ML algorithms provide better and more accurate results than single ML algorithms. The extreme gradient boosting model showed relatively small computational time and memory according to computational cost. Moreover, this study conducted a sensitivity analysis where air pressure, wind vane, and humidity were the key predictors for WSP and WPP.

Impact of astaxanthin supplementation on markers of cardiometabolic health and tactical performance among firefighters.

Firefighters are at risk for cardiovascular disease due to occupational-related inflammation, oxidative stress, and lifestyle practices. Astaxanthin (AX) possesses anti-inflammatory/antioxidant and purported ergogenic properties. This study examined the impact of supplementing the diet with 12 mg/d AX for four weeks on markers of inflammation, oxidative stress, cardiometabolic health, exercise capacity, and occupation-related performance in career firefighters. In a randomized, double-blinded, placebo-controlled, crossover fashion, 15 male career firefighters (34.5 ± 7.4 years; 177.7 ± 7.0 cm; 95.6 ± 12.0 kg; 30.1 ± 2.9 kg/m2; 11.03 ± 6.85 years of service) ingested 12 mg/d of AX (AstaReal®, AstaReal AB, Nacka, SWE) or placebo (PLA) for four weeks while following a standardized resistance training program. After each treatment, testing sessions were completed to assess inflammatory markers, oxidative stress markers, cardiopulmonary exercise capacity, and performance to a fire ground test (FGT) consisting of nine fire suppressive activities. Data were analyzed using general linear model (GLM) analysis with repeated measures. Clinical significance was assessed via mean changes from baseline with 95% confidence intervals. Analysis of mean percent changes from baseline revealed that AX supplementation lessened the inflammatory response to to performing an incremental maximal exercise test and attenuated increases in interleukin-1β, cortisol, and uric acid in response to performing fire suppressive activities compared to when they ingested PLA. However, most of these effects were within groups rather than between groups. Additionally, there was evidence that AX ingestion increased the ventilatory anaerobic threshold. Four weeks of AX supplementation did not significantly affect fasting markers of oxidative stress, blood lipids, performance during the FGT, general clinical chemistry panels, or self-reported side effects. Results provide some evidence that AX supplementation may help mediate occupation-related inflammation in response to high-intensity, short-duration exercise in firefighters. More research is warranted to determine if long-term supplementation can improve cardiometabolic risk in this population. ISRCTN10901752.

Pediatric to Adult Care Transition in the Hospital Context (PATCH) Tool: A Novel Tool to Assess Pediatric Institutional Guidelines for Inpatient Care of Adults.

The growing number of adults with childhood onset chronic conditions (COCC) is reflected in the increase of adult-aged admissions to pediatric institutions. Despite national bodies advising pediatric institutions to have a pediatric to adult healthcare transition (HCT) policy, little guidance is available on if or how to include inpatient care. We sought to create a framework-based Pediatric to Adult Transitional Care in the Hospital Context (PATCH) tool to assess how inpatient care of adults is addressed in pediatric institutional guidelines or policies (hereafter guidelines) as a first step towards informing future PATCH guideline development. We used convenience and snowball sampling to obtain 11 pediatric institutional guidelines. Combining the GotTransition® core elements with Coller et al's inpatient transition conceptual model through iterative consensus building, we developed the PATCH tool. Interrater reliability was assessed by using mean percent agreement amongst raters. A three-phase content validity process utilizing existing guidelines refined the finalized tool. The PATCH tool included 42 items within nine domains. There was a high degree of agreeability amongst reviewers, and qualitative analysis revealed no missing items. Twenty-five (59%) of our 42 PATCH tool items were present in at least one of the reviewed guidelines, with age being present in all. We developed the PATCH tool as a guide for pediatric institutions regarding the care of adolescent and adult patients. The PATCH tool, embedded in multidisciplinary stakeholder discussion and patient- and system-specific knowledge, may help institutions incorporate HCT into processes for adolescent and adult patients with COCCs.

Genetic Variability Studies in a Biparental RIL Population for Blast Disease in Pearl Millet

Aim: This study aimed to estimate genetic parameters within a pearl millet population comprising 288 recombinant inbred lines (RILs) of the F7 generation along with two parental lines. The investigation focused on exploring the relationships among 13 quantitative traits and Blast disease score across two environments (ENV-I, ENV-II) as well as in pooled environments. Study Design: Alpha Lattice Design. Place and Duration of Study: Patancheru, Telangana and Vizianagaram, Andhra Pradesh during Kharif, 2023. Methodology: The RILs used for this current study developed through the single seed descent method at ICRISAT, Patancheru. The genotypes were assessed at ICRISAT, Patancheru, Hyderabad (ENV-I), and Agricultural Research Station, Vizianagaram, Andhra Pradesh (ENV-II). Combined Analysis of Variance, Heritability, Phenotypic Coefficient of Variation (PCV), Genotypic Coefficient of Variation (GCV), Genetic Advance as percent of Mean (GAM), and Correlation studies were performed in R 4.4.0. Results: The analysis identified significant variation in both the quantitative traits and Blast disease scores among the RILs, highlighting the potential for genetic improvement and selection. High heritability coupled with substantial genetic advance was observed for traits such as Plant Height, Number of Tillers, Panicle Length, Thousand Seed Weight, Seed Yield, Harvest Index, and Blast Score across ENV-I, ENV-II and Pooled environments. PCV and GCV were notably high for the Number of Tillers, Number of Productive Tillers, Thousand Seed Weight, Seed Yield, and Blast Score across ENV-I, ENV-II, and Pooled Environments. Seed Yield showed positive and significant correlations with the Number of Productive Tillers, Harvest Index, Plant Height, and Number of Tillers in all environments. Conversely, Blast Score exhibited negative associations with all studied traits across the environments. Conclusion: High heritability coupled with high GAM, along with medium to high PCV and GCV for traits such as plant height, panicle length, seed yield, harvest index, and thousand seed weight, indicates that these traits are predominantly genetically controlled and can be improved significantly through selection, making them valuable for breeding. Blast disease was negatively correlated with seed yield, thousand seed weight, days to maturity, harvest index, number of leaves, number of productive tillers, plant height, and number of tillers, suggesting that increased susceptibility to blast disease negatively affects key agronomic traits. This implies that genotypes with higher blast disease scores tend to exhibit lower productivity, delayed maturity, and reduced overall plant vigor, making resistance to blast disease a critical factor for achieving optimal crop performance and yield stability.

Risk Factors for Failure of Non-operative Management in Isolated Unilateral Non-displaced Facet Fractures of the Subaxial Cervical Spine: Systematic Review and Meta-Analysis.

systematic review. To evaluate risk factors associated with failure of non-operative management of isolated unilateral facet fractures of the subaxial cervical spine in neurologically intact patients. A systematic review of the PubMed, Embase, LILACS, and Cochrane Library databases was conducted in order to determine risk factors associated with failure of non-operative management in isolated unilateral facet fractures of the subaxial cervical spine without facet and/or vertebral displacement, in neurologically intact patients. Our research was in line with the PRISMA Statement and registered on PROSPERO (CRD42023405699). A total of 1639 studies were identified through a database search on May 5, 2023. In total, 7 studies from the databases were included, along with 1 study found through a manual citation search. The evidence showed high clinical heterogeneity, a serious risk of bias according to the ROBINS-I tool, and a predominance of retrospective cohort studies. In comparison to less complex facet fractures, lateral floating mass fractures were found to have 5.41 times higher odds of failure of non-operative management (OR = 5.41; 95% CI = 1.32, 22.19). We calculated the potential association between lower absolute fracture height and non-operative treatment success [Fracture height (percentage) Mean Difference = -17.51 (-28.22, -6.79 95% CI); Absolute height Mean Difference: -0.46 (-0.60, -0.31 95% CI)]. Other risk factors were not included in the meta-analysis due to lack of data. The level of certainty was rated as "very low". Lateral floating mass cervical facet fractures and larger fracture fragment size (measured either in absolute terms or as a percentage) are significant risk factors for failure of non-operative treatment.

Machine Learning Approaches for Battery Durability Prediction: Leveraging Diverse Datasets and Advanced Feature Engineering Techniques

Lithium-ion batteries play a pivotal role in various applications due to their high energy density and longevity. However, accurately predicting their lifespan remains a significant challenge due to the complex and varied degradation patterns inherent in battery chemistry. This study employs advanced machine learning (ML) techniques to forecast the State of Health (SOH) of lithium-ion batteries, focusing on a diverse range of chemistries.The study encompasses a comprehensive analysis of battery datasets, combining in-house data and publicly available datasets featuring varied chemistries. By incorporating data from multiple sources, including chemistries such as NMC 811, NMC 532, LFP, etc the model's robustness is enhanced, enabling it to better handle the variability in battery chemistry.Feature engineering is a critical component of the model development process. Various battery characteristics, including anode-cathode balance, resistance metrics, and charge/discharge behavior, are extracted and integrated. Advanced techniques such as Minimum Redundancy Maximum Relevance (MRMR) feature selection and Box-Cox transformation are employed to optimize feature relevance, reduce data dimensionality, and enhance model interpretability.Model performance is rigorously evaluated using established metrics such as Mean Absolute Percentage Error (MAPE) and Mean Squared Percentage Error (MSPE). Initial predictions using the first 10 cycles reveal a trend of increasing prediction errors as SOH values rise. However, when focusing on SOH values above 0.75, prediction accuracy significantly improves, highlighting the effectiveness of this approach for batteries in good condition.The study also explores the evolving importance of different feature sets across battery lifetimes. Capacity-based features prove crucial for early-cycle SOH prediction, reflecting the initial health status and degradation patterns of the battery. As the battery undergoes more cycles, the predictive power of CC/CV curve-based features becomes increasingly significant, capturing nuanced charging and discharging behaviors.MRMR feature selection further refines the model, identifying the top 40 influential features that are most relevant to SOH prediction. This process enhances model interpretability and focuses on features that capture the complex relationships between battery characteristics and degradation dynamics.Optimized Random Forest regression models demonstrate robust performance across various prediction scenarios. For SOH at 250 cycle predictions, the model achieves an MSPE of 0.13% and MAPE of 2.54%, underscoring its accuracy and reliability for batteries in good condition.In conclusion, this research demonstrates the potential of ML techniques in enhancing lithium-ion battery durability prediction. By leveraging diverse datasets, advanced feature engineering techniques, and rigorous model evaluation, we can significantly improve prediction accuracy. This advancement holds promise for proactive battery management, optimization of battery performance, and acceleration of battery technology development. Figure 1

A Generative Super‐Resolution Model for Enhancing Tropical Cyclone Wind Field Intensity and Resolution

AbstractExtreme winds associated with tropical cyclones (TCs) can cause significant loss of life and economic damage globally, highlighting the need for accurate, high‐resolution modeling and forecasting for wind. However, due to their coarse horizontal resolution, most global climate and weather models suffer from chronic underprediction of TC wind speeds, limiting their use for impact analysis and energy modeling. In this study, we introduce a cascading deep learning framework designed to downscale high‐resolution TC wind fields given low‐resolution data. Our approach maps 85 TC events from ERA5 data (0.25° resolution) to high‐resolution (0.05° resolution) observations at 6‐hr intervals. The initial component is a debiasing neural network designed to model accurate wind speed observations using ERA5 data. The second component employs a generative super‐resolution strategy based on a conditional denoising diffusion probabilistic model (DDPM) to enhance the spatial resolution and to produce ensemble estimates. The model is able to accurately model intensity and produce realistic radial profiles and fine‐scale spatial structures of wind fields, with a percentage mean bias of −3.74% compared to the high‐resolution observations. Our downscaling framework enables the prediction of high‐resolution wind fields using widely available low‐resolution and intensity wind data, allowing for the modeling of past events and the assessment of future TC risks.

Incidence of Banana Pseudostem Weevil, Odoiporous longicollis Olivier (Coleoptera: Curculionidae) in Major Banana Growing Districts of Northern Karnataka, India

A roving survey during 2020-21 was undertaken in different banana growing districts of Northern Karnataka to study the prevalence and the magnitude of the infestation of the banana pseudostem weevil, O.longicollis and to know the varietal reaction and preference to the banana pseudostem weevil. Observations of the survey revealed that the districts - wise mean percent infestation varied from 3.73 in Belagavi to 15.35 in Vijayanagar district. The maximum mean percent infestation was recorded in Vijayanagar (15.35) and Kalaburgi (14.67) districts. This was followed by the lowest infestation in Belagavi (3.73) district. The mean per cent infestation varied from 2.50 in the Rajapuri variety to 22.53 in the Sugandhi cultivar across the varieties in different districts. In respect to varieties, Sugandhi banana recorded the maximum level of infestation (22.53) followed by Red banana (22.00), whereas, Elakki recorded 12.53 and 11.33 mean percent infestation in Vijayanagar and Uttara Kannada districts. Grand-Naine was cultivated in all the districts, while redbanana, Sugandhi and Sakkare bale were cultivated only in Kalaburgi and Vijayanagar districts. The mean damage grade index of banana pseudostem weevil across seven varieties in seven districts of North Karnataka revealed highest damage grade index in redbanana (0.98) at Kalaburgi followed by Sugandhi (0.75) in Vijayanagar district. There were statistically significant differences in percent infestation among seven varieties and also among seven districts. Hence, the study highlighted the infestation of banana pseudostem weevil varied in North Karnataka based on the variety, cultivation practices and geographical location.

Genetic Variability Studies in Dolichos Bean (Lablab purpureus L.) for Yield and Yield Traits

Aims: To study the genetic variability by phenotypic coefficient of variation, genotypic coefficient of variation, heritability and genetic advance as percent of mean in advanced breeding lines of Dolichos bean. Study Design: Augmented RCBD with six blocks. Place and Duration of Study: Department of Genetics and Plant Breeding, College of Agriculture, Shivamogga, during Late Kharif 2023. Methodology: The experimental material consisted of 64 advanced breeding lines of Dolichos bean including four checks viz., Hebbal avere-1, Hebbal avere-3, Hebbal avere-4 and Arka Jaya. Results: Analysis of variance revealed that significant variation was observed among the genotypes for all the characters studied. A high genotypic coefficient of variation (GCV) was observed for pods per plant (48.10%) followed by pod yield per plant, clusters per plant, pods per cluster, plant height and pod width, which indicates better scope for improvement of characters. A high phenotypic coefficient of variation (PCV) was observed for pod yield per plant (50%) followed by pods per plant, clusters per plant, pods per cluster, plant height and pod width. High heritability (>60%) was observed for all the traits studied. High heritability combined with high genetic advance as percent of mean was found for pods per plant (95.49%) followed by pod yield per plant, clusters per plant, pods per cluster, pod width and plant height which indicates that improvement of these charateristics through selection can be possible due to additive gene action. Conclusion: Yield can be increased by selecting the number of pods per plant, pod yield per plant, seed yield per plant number of cluster per pod, number of pods per plant and plant height.

Assessment of podocyte detachment as a pivotal step in the development of focal segmental glomerulosclerosis

BackgroundPodocytopenia refers to a decrease in the number of podocytes. When podocytes are injured, they may detach leading to podocytopenia, which represents a critical step in the development of podocytopathy and subsequently deterioration of renal functions. Pathological assessment of podocytopenia plays a crucial role in diagnosing underlying kidney diseases.AimTo assess detached podocytes and evaluate their diagnostic role in the development of focal segmental glomerulosclerosis.Materials and methodsThis is a retrospective study, conducted on 67 archival renal biopsies with the clinical diagnosis of steroid-resistant or steroid-dependent nephrotic syndrome (SRNS) and diagnosed as focal segmental glomerulosclerosis (FSGS) and podocytopathy with detached podocytes by electron microscopy (EM). Colloidal iron stain and Desmin immunohistochemical stain were performed. Assessment of the mean percent of stained pixels in relation to the surface tuft area of the glomerulus, i.e., mean percent of stained area (PSA) was done using image analysis system (ImageJ 1.52a) software.ResultsPodocytopathy with detached podocytes was diagnosed in 35 (52.24%) cases, while FSGS was diagnosed in 32 (47.76%) cases. Regarding detached podocytes, 27 (49.3%) cases showed no detached podocytes by light microscopy (LM), while only 4 (6%) showed severe podocyte detachment. There was a statistically significant difference between control cases and both podocytopathy with detached podocytes and FSGS regarding mean PSA (p ≤ 0.001).ConclusionStandardized reporting of detached podocyte cells is becoming mandatory as they have a high positive predictive value for the expected EM picture.

Efficacy and Safety of Fixed-Dose Clindamycin Phosphate 1.2%/Adapalene 0.15%/Benzoyl Peroxide 3.1% Gel in Black Participants with Moderate-to-Severe Acne

Introduction: Topical clindamycin phosphate 1.2%/adapalene 0.15%/benzoyl peroxide 3.1% (CAB) gel is the first fixed-dose, triple-combination formulation approved for the treatment of acne, and is indicated in patients 12 years of age and older. In 3 clinical studies of participants with moderate-to-severe acne, CAB gel demonstrated superior efficacy to vehicle and component dyads, with good safety/tolerability. To better understand the efficacy and safety of CAB gel in patients with darker skin phototypes, this post hoc analysis included participants who self-identified as ‘Black or African American’ (hereafter referred to as Black). Methods: Data were pooled from a phase 2 (N=741) and two phase 3 (N=183; N=180), double-blind, randomized, 12-week studies. Participants aged ≥9 years with moderate to severe acne were randomized to receive once-daily CAB or vehicle gel. Endpoints included ≥2-grade reduction from baseline in Evaluator’s Global Severity Score and clear/almost clear skin (treatment success) and least-squares mean percent change from baseline in inflammatory/ noninflammatory lesion counts. Treatment-emergent adverse events (TEAEs) were also assessed. Results: Of 657 participants randomized to CAB or vehicle gel, 104 (15.8%) self-identified as Black (n=64 CAB, n=40 vehicle). At week 12, 31.5% of Black participants treated with CAB achieved treatment success, compared to 17.3% with vehicle. Inflammatory lesion reductions with CAB were significantly greater than with vehicle (69.0% vs 53.6%, P<0.01), and noninflammatory lesion reductions were numerically greater with CAB than with vehicle (58.3% vs 51.9%). The rate of TEAEs with CAB treatment in Black participants was generally lower than in the overall study population (23.4% vs 24.6-36.2%), and most TEAEs were of mild-to-moderate severity. Conclusions: Fixed-dose, triple-combination CAB gel was efficacious and well tolerated in Black participants with moderate-to-severe acne. Despite the limited number of self-identified Black participants across the clinical studies of CAB gel, these post hoc analyses add valuable information to the limited literature describing treatment effects of fixed-dose combination acne treatments in Black individuals. Funding: Ortho Dermatologics

Efficacy and Safety of Clindamycin Phosphate 1.2%/Adapalene 0.15%/Benzoyl Peroxide 3.1% Gel: Post Hoc Analysis by Baseline Disease Severity

Introduction: Clindamycin phosphate (CLIN) 1.2%/adapalene (ADAP) 0.15%/benzoyl peroxide (BPO) 3.1% gel (CAB) is the only triple-combination topical treatment approved for acne. These analyses assessed efficacy and safety of CAB gel by baseline acne severity vs component dyads and branded ADAP/BPO gel. Methods: These post hoc analyses used data pooled from two phase 2 and two phase 3, double-blind, 12-week studies (NCT03170388, NCT04892706, NCT04214639, NCT04214652). Eligible participants aged ≥9 years (≥12 years in NCT04892706) were randomized to once-daily CAB or vehicle gel. One phase 2 study included dyad combinations of CAB’s active ingredients: ADAP/BPO, CLIN/BPO, and CLIN/ADAP. The other phase 2 study included a head-to-head comparison of CAB gel and branded ADAP 0.3%/BPO 2.5% gel. Efficacy assessments included least-squares mean percent change from baseline in inflammatory/noninflammatory lesions and treatment success (≥2-grade reduction from baseline in Evaluator’s Global Severity Score [EGSS] and clear/almost clear skin). Safety assessments included treatment-emergent adverse events (TEAEs) and cutaneous safety/tolerability. Participants were categorized by baseline disease severity: moderate (EGSS = 3; n=1557) or severe (EGSS = 4; n=230). Results: After 12 weeks, inflammatory lesion reductions in participants with moderate acne were significantly greater with CAB (77.1%) than vehicle (54.1%; P<0.001), the 3 dyads (range, 64.4-69.4%; P≤0.001, all), and branded ADAP/BPO (72.8%; P<0.05). In the severe group, inflammatory lesion reductions with CAB (74.5%) were significantly greater than vehicle (44.4%; P<0.001) and 2 dyads (ADAP/BPO 63.9%, CLIN/BPO 61.3%; P<0.05), and similar to branded ADAP/BPO and CLIN/ADAP (75.4%/73.7%). Reductions in inflammatory lesions were greater than noninflammatory. Over half of CAB-treated participants with moderate disease achieved treatment success at week 12 (53.9% vs 19.5% vehicle; P<0.001), significantly greater than approximately one-third treated with dyads (range, 31.5-35.3%) or branded ADAP/BPO (38.1%; P≤0.001, all). Only CAB and CLIN/ADAP demonstrated significantly greater treatment success rates than vehicle in participants with severe acne at week 12 (30.9% and 34.0% vs 9.0%, respectively, P<0.05). Across all groups, most TEAEs were of mild to moderate severity. Mean cutaneous safety/tolerability scores were ≤1 (1 = mild) across severity groups. Conclusions: CAB gel demonstrated efficacy and safety in participants with both moderate and severe acne. Acne improvements with CAB were superior to the 3 dyads tested and branded ADAP/BPO in participants with moderate acne and generally numerically greater in participants with severe acne. To our knowledge, these analyses of combination topical acne treatments include data from the only double-blind, vehicle-controlled, head-to-head study to date. Funding: Ortho Dermatologics.

Limited Sampling Strategy for Predicting the Area under Plasma Concentration–Time Curve of Nadolol in Healthy Subjects

AbstractNadolol is a hydrophilic β‐adrenoceptor blocker with a relatively long half‐life and negligible metabolism. It is a substrate of P‐glycoprotein and organic anion transporting polypeptide 1A2, and may serve as an in vivo probe drug for the assessment of drug–drug and food–drug interactions mediated by these transporters. In the present study, we aimed to develop limited sampling strategy (LSS) models for predicting the area under the plasma concentration–time curve (AUC0‐∞) of nadolol. Plasma concentration data (Ct) in healthy volunteers reported in four previous studies were randomly divided into a training dataset for model development (n = 15) and a test dataset for model validation (n = 16). By multiple linear regression analysis, we confirmed that four out of the eight models using two time points and all models using three time points met the acceptable criteria. In particular, the three time point models using (C3, C6, and C24) and (C4, C8, and C24) showed better predictive performances with r2 values of 0.983 and 0.980, respectively. In drug interaction studies of nadolol with itraconazole, rifampicin, grapefruit juice, and green tea extract, both LSS models accurately predicted the AUC0‐∞ with percent mean absolute error ≤11% and percent root mean square error ≤12%. In addition, using digitized pharmacokinetic data of nadolol, both LSS models were further validated by predicting the AUC0‐∞ in different doses. The results suggest that the LSS models using three time points allow a reliable prediction of AUC0‐∞ of nadolol in healthy individuals.