Several strategies have been employed in the breeding of passion fruit with a view to the generation of superior progeny. In an effort to develop more precise methods in breeding, we compared the efficiency of the Post-Hoc Blocking Row-Col technique, which is an a posteriori technique that consists of the overlapping of a block structure on the original-field design, with a randomized-block design and compared different selection strategies within and among half-sib families, using the REML/BLUP mixed-model methodology. Twenty-three half-sib families from the third cycle of recurrent selection of the breeding program of Universidade Estadual do Norte Fluminense Darcy Ribeiro -UENF were evaluated. The trial took place in the experimental unit of UENF, in Itaocara -RJ, Brazil. Plants were trained on vertical stakes, with four replicates and three plants per plot. They were assessed individually for the traits number of fruits per plant, fruit mass per plant, fruit length, fruit diameter, peel thickness, total soluble solids, pH, pulp percentage, and production per plant. No significant difference was found in the test of efficiency of the designs for any of the evaluated traits. Withinfamily heritability (h 2 ad ) had a similar magnitude to individual heritability (h 2 a ), indicating that even in the 4th cycle of recurrent ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 18 (2): gmr18305 N.R. Cavalcante et al. 2selection, genetic variability still exists within the evaluated progeny. Selection within half-sib families provided superior gains when compared with selection among families for the traits number of fruits; production; fruit mass, length, and diameter; total soluble solids; pH; and pulp percentage. The best selection strategy was within families, as it generated higher selection-gain estimates than those obtained with selection between families and the directselection and index-selection approach.
Passion fruit is a native fruit of tropical America, with Brazil being the world's leading producer and consumer of this fruit, with an estimated annual production of 554,598 Mg, and yield of 13,500 kg.ha-1 Phenotyping through digital images has been used to evaluate morphological characteristics of seeds. Knowledge of the degree of genetic divergence plays an important role, as it assists in the adoption of appropriate strategies for improvement in passion fruit populations.. The objective of this work was to estimate the genetic divergence among full-sibling families of passion fruit through morphophysiological characteristics of seeds using the Ward-Modified Location Model (Ward-MLM) method. Seeds of 98 full-sibling families (FSF) of passion fruit from the breeding program of the Mato Grosso State University were evaluated considering physiological descriptors of seeds, using germination and vigor tests; and morphological descriptors of seeds, using a digital imaging seed analysis device and software. We found that the Ward-MLM method was efficient in detecting genetic divergence using seed morphological and physiological descriptors, simultaneously. The descriptors that contributed the most to the genetic divergence among ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 18 (3): gmr18331 L.R. Fachi et al. 2 FSF were those related to seed geometry. This method formed three heterotic groups. Group II had the largest mean emergence speed index (1.106) and seedling emergence percentage (65.8%), and Group III had the largest means of seedling dry weight (4.140 g), radicle length (6.30 mm), germination speed index (2.503), and seed germination percentage (90 %). We conclude that crosses between FSF groups II and III are a good option to improve seed characteristics.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak has threatened the human population globally as the numbers of reinfection cases even after large-scale vaccination. Trials have been carried out to find drugs effective in fighting the disease, as COVID-19 is being considered a treatable disease only after we have antivirals. A clinical candidate originally developed for HIV treatment, AZVUDINE (FNC), is a promising drug in the treatment of COVID-19, being able to reduce the patient's viral load leading to cure. To predict the clinical outcome of COVID-19, we examined the course of viral load, every 48hs, by RT-PCR, and disease severity using a promising antiviral drug, AZVUDINE (FNC) with 281 participants. A randomized clinical trial was performed to evaluate the efficacy of FNC added to standard treatment, compared with placebo group added to standard treatment, for patients with mild COVID-19. RT-qPCR and ddPCR were applied to estimate the viral load in samples from patients, which was performed every 48 hours throughout the treatment. Also, the clinical improvement was evaluated as well as the liver and kidney function. Notably, the FNC treatment in the mild COVID-19 patients may shorten the time of the nucleic acid negative conversion (NANC) versus placebo group. In addition, the FNC was effective in reducing the viral load of these participants, in the first days (D3, D5, D7, D9). Therefore, the present clinical trial results showed that the FNC accelerate the elimination of the virus in and could reduce treatment time of mild patients and save a lot of medical resources, making it a strong candidate for the outpatient and home treatment of COVID-19. Trial registration number:NCT05033145 https://clinicaltrials.gov/ct2/show/NCT05033145
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.