Hypertensive Disorders during Pregnancy and Risk of Bronchopulmonary Dysplasia in Very Preterm Infants.

 It is not yet fully known whether hypertensive disorders (HTD) during pregnancy impose an increased risk of development of bronchopulmonary dysplasia (BPD) in preterm newborn infants. OBJECTIVE:  To test the hypothesis that preeclampsia and other HTD are associated with the development of BPD in preterm infants. MATERIALS AND METHODS:  Data on mothers and preterm infants with gestational age 24 to 30 weeks were prospectively analyzed in 11 Portuguese level III centers. Statistical analysis was performed using IBM SPSS statistics 23. RESULTS:  A total of 494 preterm infants from 410 mothers were enrolled, and 119 (28%) of the 425 babies, still alive at 36 weeks, developed BPD. The association between chronic arterial hypertension, chronic arterial hypertension with superimposed preeclampsia, and gestational hypertension in mothers and BPD in preterm infants was not significant (p = 0.115; p = 0.248; p = 0.060, respectively). The association between preeclampsia-eclampsia and BPD was significant (p = 0.007). The multivariate analysis revealed an association between preeclampsia-eclampsia and BPD (odds ratio [OR] = 4.6; 95% confidence interval [CI] 1.529-13.819; p = 0.007) and a protective effect for BPD when preeclampsia occurred superimposed on chronic arterial hypertension in mothers (OR = 0.077; 95%CI 0.009-0.632; p = 0.017). CONCLUSION:  The results of this study support the association of preeclampsia in mothers with BPD in preterm babies and suggest that chronic hypertension may be protective for preterm babies.info:eu-repo/semantics/publishedVersio

Ultrastructural and molecular characterization of Glugea serranus n. sp., a microsporidian infecting the blacktail comber, Serranus atricauda (Teleostei: Serranidae), in the Madeira Archipelago (Portugal)

A new microsporidian infecting the connective tis sue of the coelomic cavity of the blacktail comber Serranus atricauda, in the Madeira Archipelago (Portugal), is described on the basis of morphological, ultrastructural, and molecular features. The microsporidian formed large whitish xenomas adhering to the peritoneal visceral organs of the host. Each xenoma consisted of a single hypertrophic cell, in the cyto plasm of which mature spores proliferated within parasitophorous vacuoles surrounded by numerous collagen fibers. Mature spores were ellipsoidal and uninucleated, mea suring an average of 6.5 ± 0.5 μm in length and 3.4 ± 0.6 μm in width. The anchoring disk of the polar filament was subter minal, laterally shifted from the anterior pole of the spore. The isofilar polar filament coiled in 18–19 turns, forming two rows that surrounded the posterior vacuole. The latter occupied about one third of the spore length. The polaroplast surround ing the apical and uncoiled portion of the polar filament displayed two distinct regions: a lamellar region and an electron-dense globule. Molecular analysis of the rRNA genes, including the internal transcribed spacer region, and phylogenetic analysis using maximum likelihood and neigh bor joining demonstrated that this microsporidian parasite clustered with some Glugea species. Based on the differences found both at the morphological and molecular levels, to other members of the genus Glugea, the microsporidian infecting the blacktail comber is considered a new species, thus named Glugea serranus n. sp.info:eu-repo/semantics/publishedVersio

(CDRGI)-Cancer detection through relevant genes identification

Cancer is a genetic disease that is categorized among the most lethal and belligerent diseases. An early staging of the disease can reduce the high mortality rate associated with cancer. The advancement in high throughput sequencing technology and the implementation of several Machine Learning algorithms have led to significant progress in Oncogenomics over the past few decades. Oncogenomics uses RNA sequencing and gene expression profiling for the identification of cancer-related genes. The high dimensionality of RNA sequencing data makes it a complex and large-scale optimization problem. CDRGI presents a Discrete Filtering technique based on a Binary Artificial Bee Colony coupling Support Vector Machine and a two-stage cascading classifier to identify relevant genes and detect cancer using RNA seq data. The proposed approach has been tested for seven different cancers, including Breast Cancer, Stomach Cancer (STAD), Colon Cancer (COAD), Liver Cancer, Lung Cancer (LUSC), Kidney Cancer (KIRC), and Skin Cancer. The results revealed that the CDRGI performs better for feature reduction while achieving better classification accuracy for STAD, COAD, LUSC and KIRC cancer types

Ultrastructural and molecular characterization of a microsporidian infectingSerranus atricauda(Teleostei, Serranidae) in the Madeira Archipelago

info:eu-repo/semantics/publishedVersio

Hematopoietic Stem Cell Transplantation in Children with Leukemia: A Single Institution Experience with Respect to Donors

Aim of this study was to compare the outcomes of transplantation by donor source and to help select the best alternative donor in children with leukemia. Donor sources included matched related donor (MRD, n = 35), allele-matched unrelated donor (M-UD, n = 10) or -mismatched (MM)-UD (n = 13) or unrelated umbilical cord blood (UCB, n = 11). UCB group had a significantly higher incidence of grade II-IV acute graft versus host disease (MRD, 11.8%; M-UD, 30.0%; MM-UD, 15.4%, UCB, 54.4%, P = 0.004) but there was no difference in incidence of chronic graft versus host disease between 4 groups. The 5-yr leukemia-free survival (LFS) was 76.7%, 60.0%, 69.2%, and 45.5%, respectively (P = 0.128). MRD group showed higher LFS rate than UCB group (P = 0.022). However, LFS of M-UD and MM-UD together (65.2%) was not different from that of MRD group (76.7%, P = 0.325), or from that of UCB (45.5%, P = 0.190). The relapse incidence at 5 yr was 17.1%, 20.0%, 15.4%, and 0%, respectively (P = 0.460). The 100-day treatment-related mortality was 2.9%, 20.0%, 7.7%, and 36.4%, respectively (P = 0.011). Despite the limitations of small number of patients, unrelated donor transplants including even allele-mismatched ones, seem to be as effective in children with leukemia lacking suitable relative donors. Also, UCB transplant may serve as another possible option in urgent transplants

A case report of a 4-year-old child with glucose-6-phosphate dehydrogenase deficiency: An evidence based approach to nutritional management

Pinto A, MacDonald A, Cleto E, Almeida MF, Ramos PC, Rocha JC. A case report of a 4-year-old child with glucose-6-phosphate dehydrogenase deficiency: An evidence based approach to nutritional management. Turk J Pediatr 2017; 59: 189-192. The objective was to describe the nutritional management of a 4-year-old child with glucose-6-phosphate dehydrogenase (G6PD) deficiency. A 4-year-old male child, African descent, born from non-consanguineous parents presented with a clinical history of frequent respiratory infections, usually treated with antibiotics. At 30 months of age, G6PD diagnosis was made after eating one portion (40 - 60 g) of fava beans, resulting in severe hemolytic anemia hospitalization for 5 days. Diagnosis was confirmed by G6PD activity measurement. Nutritional counseling was given to avoid dietary oxidative stressors particularly the exclusion of fava beans and accidental ingestion of other similar beans. Dietary intake of high vitamin C containing foods was discouraged and adequate hydration advised. Nutritional management is crucial in preventing acute stress events in patients with G6PD deficiency.info:eu-repo/semantics/publishedVersio

Parallel tensor factorization for relational learning

Link prediction is a statistical relational learning problem that has a variety of applications in recommender systems, expert systems, and knowledge bases. Numerous approaches have already been devised to solve the problem. Tensor factorization is one of the ways to solve the link prediction problem. Many tensor factorization techniques have been devised in the last few decades, including Tucker, CANDECOMP/PARAFAC, and DEDICOM. RESCAL is one of the famous tensor factorization technique that can solve large scale problems with relatively less time and space complexity. The time complexity of RESCAL can further be reduced by making it parallel. This variant can also be applied to large scale datasets. This article focuses on devising a parallel version for RESCAL. A decent decrease in execution time has been observed in the execution of parallel RESCAL

Glutathione for food and health applications with emphasis on extraction, identification, and quantification methods: a review

Glutathione is a naturally occurring compound that plays a crucial role in the cellular response to oxidative stress through its ability to quench free radicals, thus mitigating the risk of potential damage, including cell death. While glutathione is endogenously present in different plants and animal cells, their concentration varies considerably. The alteration in glutathione homeostasis can be used as a potential marker for human diseases. In the case of the depletion of endogenous glutathione, exogenous sources can be used to replenish the pool. To this end, both natural and synthetic glutathione can be used. However, the health benefit of glutathione from natural sources derived from fruits and vegetables is still debated. There is increasingly growing evidence of the potential health benefits of glutathione in different diseases; however, the determination and in situ quantification of endogenously produced glutathione remains a major challenge. For this reason, it has been difficult to understand the bioprocessing of exogenously delivered glutathione in vivo. The development of an in situ technique will also aid in the routine monitoring of glutathione as a biomarker for different oxidative stress-mediated diseases. Furthermore, an understanding of the in vivo bioprocessing of exogenously delivered glutathione will also aid the food industry both towards improving the longevity and profile of food products and the development of glutathione delivery products for long-term societal health benefits. In this review, we surveyed the natural plant-derived sources of glutathione, the identification and quantification of extracted glutathione from these sources, and the role of glutathione in the food industry and its effect on human health.info:eu-repo/semantics/publishedVersio

Glutathione for Food and Health Applications with Emphasis on Extraction, Identification, and Quantification Methods: A Review

Glutathione is a naturally occurring compound that plays a crucial role in the cellular response to oxidative stress through its ability to quench free radicals, thus mitigating the risk of potential damage, including cell death. While glutathione is endogenously present in different plants and animal cells, their concentration varies considerably. The alteration in glutathione homeostasis can be used as a potential marker for human diseases. In the case of the depletion of endogenous glutathione, exogenous sources can be used to replenish the pool. To this end, both natural and synthetic glutathione can be used. However, the health benefit of glutathione from natural sources derived from fruits and vegetables is still debated. There is increasingly growing evidence of the potential health benefits of glutathione in different diseases; however, the determination and in situ quantification of endogenously produced glutathione remains a major challenge. For this reason, it has been difficult to understand the bioprocessing of exogenously delivered glutathione in vivo. The development of an in situ technique will also aid in the routine monitoring of glutathione as a biomarker for different oxidative stress-mediated diseases. Furthermore, an understanding of the in vivo bioprocessing of exogenously delivered glutathione will also aid the food industry both towards improving the longevity and profile of food products and the development of glutathione delivery products for long-term societal health benefits. In this review, we surveyed the natural plant-derived sources of glutathione, the identification and quantification of extracted glutathione from these sources, and the role of glutathione in the food industry and its effect on human health

Counterfactual explanation of Bayesian model uncertainty

Artificial intelligence systems are becoming ubiquitous in everyday life as well as in high-risk environments, such as autonomous driving, medical treatment, and medicine. The opaque nature of the deep neural network raises concerns about its adoption in high-risk environments. It is important for researchers to explain how these models reach their decisions. Most of the existing methods rely on softmax to explain model decisions. However, softmax is shown to be often misleading, particularly giving unjustified high confidence even for samples far from the training data. To overcome this shortcoming, we propose Bayesian model uncertainty for producing counterfactual explanations. In this paper, we compare the counterfactual explanation of models based on Bayesian uncertainty and softmax score. This work predictively produces minimal important features, which maximally change classifier output to explain the decision-making process of the Bayesian model. We used MNIST and Caltech Bird 2011 datasets for experiments. The results show that the Bayesian model outperforms the softmax model and produces more concise and human-understandable counterfactuals