Efficacy of the motile sperm organelle morphology examination (MSOME) in predicting pregnancy after intrauterine insemination

Background: Although the motile sperm organelle morphology examination (MSOME) was developed merely as a selection criterion, its application as a method for classifying sperm morphology may represent an improvement in the evaluation of semen quality. The aim of this study was to determine the prognostic value of normal sperm morphology using MSOME with regard to clinical pregnancy (CP) after intrauterine insemination (IUI).Methods: A total of 156 IUI cycles that were performed in 111 couples were prospectively analysed. Each subject received 75 IU of recombinant FSH every second day from the third day of the cycle. Beginning on the 10th day of the cycle, follicular development was monitored by vaginal ultrasound. When one or two follicles measuring at least 17 mm were observed, recombinant hCG was administered, and IUI was performed 12-14 h and 36-40 h after hCG treatment. Prior to the IUI procedure, sperm samples were analysed by MSOME at 8400x magnification using an inverted microscope that was equipped with DIC/Nomarski differential interference contrast optics. A minimum of 200 motile spermatozoa per semen sample were evaluated, and the percentage of normal spermatozoa in each sample was determined.Results: Pregnancy occurred in 34 IUI cycles (CP rate per cycle: 21.8%, per patient: 30.6%). Based on the MSOME criteria, a significantly higher percentage of normal spermatozoa was found in the group of men in which the IUI cycles resulted in pregnancy (2.6+/-3.1%) compared to the group that did not achieve pregnancy (1.2+/-1.7%; P = 0.019). Logistic regression showed that the percentage of normal cells in the MSOME was a determining factor for the likelihood of clinical pregnancy (OR: 1.28; 95% CI: 1.08 to 1.51; P = 0.003). The ROC curve revealed an area under the curve of 0.63 and an optimum cut-off point of 2% of normal sperm morphology. At this cut-off threshold, using the percentage of normal sperm morphology by MSOME to predict pregnancy was 50% sensitive with a 40% positive predictive value and 79% specificity with an 85% negative predictive value. The efficacy of using the percentage of normal sperm morphology by MSOME in predicting pregnancy was 65%.Conclusions: The present findings support the use of high-magnification microscopy both for selecting spermatozoa and as a routine method for analysing semen before performing IUI

Early and Late Pathogenic Events of Newborn Mice Encephalitis Experimentally Induced by Itacaiunas and Curionópolis Bracorhabdoviruses Infection

In previous reports we proposed a new genus for Rhabdoviridae and described neurotropic preference and gross neuropathology in newborn albino Swiss mice after Curionopolis and Itacaiunas infections. In the present report a time-course study of experimental encephalitis induced by Itacaiunas and Curionopolis virus was conducted both in vivo and in vitro to investigate cellular targets and the sequence of neuroinvasion. We also investigate, after intranasal inoculation, clinical signs, histopathology and apoptosis in correlation with viral immunolabeling at different time points. Curionopolis and Itacaiunas viral antigens were first detected in the parenchyma of olfactory pathways at 2 and 3 days post-inoculation (dpi) and the first clinical signs were observed at 4 and 8 dpi, respectively. After Curionopolis infection, the mortality rate was 100% between 5 and 6 dpi, and 35% between 8 and 15 dpi after Itacaiunas infection. We identified CNS mice cell types both in vivo and in vitro and the temporal sequence of neuroanatomical olfactory areas infected by Itacaiunas and Curionopolis virus. Distinct virulences were reflected in the neuropathological changes including TUNEL immunolabeling and cytopathic effects, more intense and precocious after intracerebral or in vitro inoculations of Curionopolis than after Itacaiunas virus. In vitro studies revealed neuronal but not astrocyte or microglial cytopathic effects at 2 dpi, with monolayer destruction occurring at 5 and 7 dpi with Curionopolis and Itacaiunas virus, respectively. Ultrastructural changes included virus budding associated with interstitial and perivascular edema, endothelial hypertrophy, a reduced and/or collapsed small vessel luminal area, thickening of the capillary basement membrane, and presence of phagocytosed apoptotic bodies. Glial cells with viral budding similar to oligodendrocytes were infected with Itacaiunas virus but not with Curionopolis virus. Thus, Curionopolis and Itacaiunas viruses share many pathological and clinical features present in other rhabdoviruses but distinct virulence and glial targets in newborn albino Swiss mice brain

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost