Category: Nevin Manimala

Nutrition. 2022 Apr 10;99-100:111681. doi: 10.1016/j.nut.2022.111681. Online ahead of print.

ABSTRACT

OBJECTIVE: School-aged children in São Miguel Island, Azores, Portugal, are a population with a long history of iodine deficiency, and a recent governmental program for iodized salt (IS) consumption was implemented. This study investigated urinary iodine concentration (UIC), household and school IS consumption, and iodine-rich food intake in school-aged children.

METHODS: In this cross-sectional study, spot urine samples and dietary iodine intake were collected. Urinary iodine concentration was evaluated using the fast colorimetric method. Dietary iodine intake was calculated by determining the iodine content of reported food intake using a Food Frequency Questionnaire (FFQ).

RESULTS: The median UIC was 106.7 µg/L, and 55.5% of children had UIC >100 µg/L. Iodized salt was used by 100% of schools and 48.3% of school-aged children’s households. Excluding iodine in IS, the median dietary iodine intake was 105.5 µg/d. No significant correlation was found between UIC and dietary iodine intake. Milk and dairy products, with a median intake of 311.1 g/d, provided 81.5 µg iodine/d. Seafood, with a median intake of 30.5 g/d, provided 16.8 µg iodine/d. Dairy product intake was not statistically correlated with UIC (P = 0.567).

CONCLUSIONS: School-aged children in São Miguel Island did not have iodine deficiency after the governmental program for IS consumption. Adequate iodine status of school-aged children probably reflects not only an increase in iodine intake, through IS, but also an improvement of food intake patterns. Future studies are needed to ensure the sufficient iodine status of school-aged children in the Azores, and political commitment and efforts are required to prevent the possible reemergence of iodine deficiency.

PMID:35605337 | DOI:10.1016/j.nut.2022.111681

Ecotoxicol Environ Saf. 2022 May 20;239:113650. doi: 10.1016/j.ecoenv.2022.113650. Online ahead of print.

ABSTRACT

This paper focuses on vegetation health conditions (VHC) assessment and mapping using high resolution airborne hyperspectral AVIRIS-NG imagery and validated with field spectroscopy-based vegetation spectral data. It also quantified the effect of mining on vegetation health for geo-environmental impact assessment at a fine level scale. In this study, we have developed and modified vegetation indices (VIs) based model for VHC assessment and mapping in coal mining sites. We have used thirty narrow banded VIs based on the statistical measurement for suitable VIs identification. The highest Pearson’s r, R², lowest RMSE, and P values indices have been used for VIs combined pixels analysis. The highest different (Healthy vs. unhealthy) vegetation combination index (VCI) has been selected for VHC assessment and mapping. We have also compared VIs model-based VHC results to ENVI (software) forest health tool and Spectral-based SAM classification results. The 1st VCI result showed the highest difference (72.07%) from other VCI. The AUC values of the ROC curve have shown a better fit for the VIs model (0.79) than Spectral classification (0.74), and ENVI FHT (0.68) based on VHC results. The VHC results showed that unhealthy vegetation classes are located at low distances from mine sites, and healthy vegetation classes are situated at high distances. It is also seen that there is a highly significant positive relationship (R² =0.70) between VHC classes and distance from mines. These results will provide a guideline for geo-environmental impact assessment in coal mining sites.

PMID:35605326 | DOI:10.1016/j.ecoenv.2022.113650

Ecotoxicol Environ Saf. 2022 May 20;239:113661. doi: 10.1016/j.ecoenv.2022.113661. Online ahead of print.

NO ABSTRACT

PMID:35605330 | DOI:10.1016/j.ecoenv.2022.113661

Ecotoxicol Environ Saf. 2022 May 20;239:113669. doi: 10.1016/j.ecoenv.2022.113669. Online ahead of print.

ABSTRACT

In the mining area affected by coal mining activities for a long time, heavy metal Zn pollution poses a serious threat to soil quality and human health, and direct evidence showing the relationship between Zn accumulation mechanism in soils and mining activities is lacking. In this study, the Zn content and isotopes composition (δ⁶⁶Zn) from soil and environmental samples around mining area were determined and analyzed to clarify the Zn characteristics in soil. Moreover, the distribution and source of Zn content in soil of mining area were analyzed by mathematical statistics, correlation analysis and isotope mass mixing model. The results showed that: (1) the Zn content in soil ranged from 95 to 327 mg·kg^-1 (mean: 233 mg·kg^-1), exceeding the control point and the soil background value of Anhui Province; (2) the results of Zn isotope analysis showed that Zn in soil mainly derived from the wind dispersion input of fine particles in gangue and fly ash, followed by the natural weathering of parent material; (3) isotopic mass mixing model can be used to distinguish the contribution of anthropogenic and natural Zn sources. Mining input was the main contribution source of Zn in soil (mean: 67%), followed by natural background (mean: 33%). The employment of Zn isotopes can effectively evaluate the impact of anthropogenic and natural long-term processes on Zn in the soil of the mining area, and provide important information for the formulation of soil metal pollution control measures.

PMID:35605319 | DOI:10.1016/j.ecoenv.2022.113669

Comput Biol Chem. 2022 Apr 30;98:107691. doi: 10.1016/j.compbiolchem.2022.107691. Online ahead of print.

ABSTRACT

Methanogenic archaea carry homologs of dissimilatory sulfite reductase (Dsr), called Dsr Like proteins (DsrLP). Dsr reduces sulfite to sulfide, a key step in an Earth’s ancient metabolic process called dissimilatory sulfate reduction. The DsrLPs do not function as Dsr, and a computational approach is needed to develop hypotheses for guiding wet bench investigations on DsrLP’s function. To make the computational analysis process efficient, the DsrLP amino acid sequences were transformed using only eight alphabets functionally representing twenty amino acids. The resultant reduced amino acid sequences were analyzed to identify conserved signature patterns in DsrLPs. Many of these patterns mapped on critical structural elements of Dsr and some were associated tightly with particular DsrLP groups. A search into the UniProtKB database identified several proteins carrying DsrLP’s signature patterns; cysteine desulfurase, nucleosidase, and uroporphyrinogen III methylase were such matches. These outcomes provided clues to the functions of DsrLPs and highlighted the utility of the computational approach used.

PMID:35605307 | DOI:10.1016/j.compbiolchem.2022.107691

Comput Biol Chem. 2022 May 10;98:107693. doi: 10.1016/j.compbiolchem.2022.107693. Online ahead of print.

ABSTRACT

Accurately identifying protein-metal ion ligand binding residues is the key to study protein functions. Because the number of binding residues and non-binding residues is significantly imbalanced, false positives is hard to be eliminated from the binding residues prediction result. Therefore, identification of protein-metal ion ligand binding residues remains challenging. In this paper, the binding site of 7 metal ions (Ca²⁺, Mg²⁺, Zn²⁺, Fe³⁺, Mn²⁺, Cu²⁺ and Co²⁺) were used as the objects of the study. Besides generally adopted parameters: amino acids and predicted secondary structure information, we creatively introduced ten orthogonal properties as a parameter. These orthogonal properties are clustering of 188 physical and chemical characteristics that can be used to describe three-dimension structural information. With the optimized parameters, we used the Random Forest algorithm to predict ion ligand binding residues. The proposed method obtained good prediction results with the MCC values of Mg²⁺, Ca²⁺ and Zn²⁺ reaching 0.255, 0.254, 0.540, respectively. Comparing to the IonSeq method, the method developed in this paper has advantages on the binding residues prediction of some ions.

PMID:35605305 | DOI:10.1016/j.compbiolchem.2022.107693

Neural Netw. 2022 May 6;152:380-393. doi: 10.1016/j.neunet.2022.04.028. Online ahead of print.

ABSTRACT

Traditionally, Convolutional Neural Networks make use of the maximum or arithmetic mean in order to reduce the features extracted by convolutional layers in a downsampling process known as pooling. However, there is no strong argument to settle upon one of the two functions and, in practice, this selection turns to be problem dependent. Further, both of these options ignore possible dependencies among the data. We believe that a combination of both of these functions, as well as of additional ones which may retain different information, can benefit the feature extraction process. In this work, we replace traditional pooling by several alternative functions. In particular, we consider linear combinations of order statistics and generalizations of the Sugeno integral, extending the latter’s domain to the whole real line and setting the theoretical base for their application. We present an alternative pooling layer based on this strategy which we name “CombPool” layer. We replace the pooling layers of three different architectures of increasing complexity by CombPool layers, and empirically prove over multiple datasets that linear combinations outperform traditional pooling functions in most cases. Further, combinations with either the Sugeno integral or one of its generalizations usually yield the best results, proving a strong candidate to apply in most architectures.

PMID:35605303 | DOI:10.1016/j.neunet.2022.04.028

Brief Bioinform. 2022 May 24:bbac185. doi: 10.1093/bib/bbac185. Online ahead of print.

ABSTRACT

Molecular signatures have been excessively reported for diagnosis of many cancers during the last 20 years. However, false-positive signatures are always found using statistical methods or machine learning approaches, and that makes subsequent biological experiments fail. Therefore, signature discovery has gradually become a non-mainstream work in bioinformatics. Actually, there are three critical weaknesses that make the identified signature unreliable. First of all, a signature is wrongly thought to be a gene set, each component of which keeps differential expressions between or among sample groups. Second, there may be many false-positive genes expressed differentially found, even if samples derived from cancer or normal group can be separated in one-dimensional space. Third, cross-platform validation results of a discovered signature are always poor. In order to solve these problems, we propose a new feature selection framework based on ensemble classification to discover signatures for cancer diagnosis. Meanwhile, a procedure for data transform among different expression profiles across different platforms is also designed. Signatures are found on simulation and real data representing different carcinomas across different platforms. Besides, false positives are suppressed. The experimental results demonstrate the effectiveness of our method.

PMID:35605226 | DOI:10.1093/bib/bbac185

Braz J Cardiovasc Surg. 2022 May 23;37(3):356-369. doi: 10.21470/1678-9741-2020-0731.

ABSTRACT

INTRODUCTION: Oral anticoagulants are the treatment of choice for diverse types of coagulation disorders. Warfarin is widely used by the Brazilian population, possibly due to its lower cost than other oral anticoagulants. However, it has a high risk of serious adverse effects if used incorrectly. The Anticoagulation Knowledge Tool (AKT) can assess a patient’s knowledge about her/his oral anticoagulant therapy and can assist health professionals in identifying patients with difficulties in adherence. This study aimed to translate, culturally adapt, and validate the AKT into Brazilian Portuguese.

METHODS: After a standard forward-backward procedure to translate the AKT into Brazilian Portuguese (AKT-Br), a version of the instrument was applied in three groups (patients, pharmacists, and the general population). The reliability of the AKT-Br was tested using an internal consistency measure and test-retest. The validity of the instrument was confirmed with data from the contrasted groups. All statistical analyses were performed with RStudio.

RESULTS: The median scores obtained with the AKT-Br were 29.0, 17.0, and 7.5 for pharmacists, patients, and the general population, respectively (maximum score of 35 points). There was moderate internal consistency for the instrument and test-retest reliability was satisfactory. Analysis of variance for validity of the groups revealed a significant relationship between the total score and the evaluated groups.

CONCLUSION: The ATK-Br is a reliable and valid tool to assess knowledge about oral anticoagulants. AKT-Br can be used in clinical practice as an auxiliary tool to improve patient care through personalised educational interventions.

PMID:35605217 | DOI:10.21470/1678-9741-2020-0731

Braz J Cardiovasc Surg. 2022 May 23;37(1):281-291. doi: 10.21470/1678-9741-2021-0483.

ABSTRACT

INTRODUCTION: Pediatric heart transplantation is the definitive therapy for children with end-stage heart failure. This paper describes our initial experience in pediatric heart transplantation in a tertiary center in Brazil.

METHODS: This is a historical prospective descriptive cohort study based on a review of the medical records of children undergoing heart transplantation at Hospital de Base and Hospital da Criança e Maternidade de São José do Rio Preto. Variables were displayed as frequency, mean, or median. Statistical analysis and Kaplan-Meier actuarial curve were obtained with the aid of Microsoft® Excel® 2019 and STATSDirect version 3.3.5.

RESULTS: Between January 2010 and December 2020, ten children underwent bicaval orthotopic heart transplantation, 30% of which were under one year of age. Nine patients had end-stage heart failure (International Society for Heart and Lung Transplantation-Heart Failure D) and 50% of the recipients were transplanted under conditions of progressive clinical deterioration (Interagency Registry for Mechanically Assisted Circulatory Support ≤ 2). Forty percent of the recipients had a panel-reactive antibody > 20% on virtual crossmatch. In the postoperative period, 80% of patients required high dose of inotropic support (vasoactive-inotropic score > 10) for > 48 hours. The death-free survival rate at 131 months was 77.1±14.4%. Most patients (88.9%) in late follow-up had an episode of active cytomegalovirus infection. Cellular rejection, with or without clinical repercussion, was present in 44.4% of the patients.

CONCLUSION: Pediatric heart transplantation produces acceptable and feasible outcomes as definitive therapy for children with end-stage heart failure.

PMID:35605214 | DOI:10.21470/1678-9741-2021-0483