Tag: pubmed

Environ Monit Assess. 2022 Sep 24;194(11):826. doi: 10.1007/s10661-022-10425-w.

ABSTRACT

In precision agriculture (PA), the usage of image processing, artificial intelligence, data analysis, and internet of things provides an increase in efficiency, energy, and time saving. In image processing-based applications, vegetation detection, in other words, segmentation that allows monitoring of plant growth and health as well as identification of weeds has a great importance. Vegetation indices (VIs) are widely used algorithms for segmentation. Their advantages include low computational cost and easy implementation and handling compared to the other algorithms. Nevertheless, they require a manual threshold detection that customizes the process and prevents generalization. In this study, a novel automatic segmentation method, which does not require a manual threshold detection by combining VIs with a classification algorithm, is proposed. It deals with the segmentation process as a two class classification problem (vegetation and background). As the classification algorithm, Discriminative Common Vector Approach (DCVA) that has a high discrimination power is used. Each image pixel is represented with a 3 × 1 dimensional vector whose elements correspond to Excess Green (ExG), Green minus Blue (GB), and Color Index of Vegetation (CIVE); VI values are obtained. Then, on the sample space accepting this pixel vector as a sample, DCVA is applied and a discriminative common vector for each class which is unique and describes that class in the best way possible is obtained and it is used for classification. Proposed segmentation method’s performance is compared with Convolutional Neural Networks (CNN) and Random Forest (RF) algorithm. The proposed segmentation algorithm outperformed both CNN’s and RF’s performance.

PMID:36152226 | DOI:10.1007/s10661-022-10425-w

Acta Neurochir (Wien). 2022 Sep 24. doi: 10.1007/s00701-022-05356-x. Online ahead of print.

ABSTRACT

BACKGROUND: Radiooncological scores are used to stratify patients for radiation therapy. We assessed their ability to predict overall survival (OS) in patients undergoing surgery for metastatic brain disease.

METHODS: We performed a post-hoc single-center analysis of 175 patients, prospectively enrolled in the MetastaSys study data. Score index of radiosurgery (SIR), graded prognostic assessment (GPA), and recursive partitioning analysis (RPA) were assessed. All scores consider age, systemic disease, and performance status prior to surgery. Furthermore, GPA and SIR include the number of intracranial lesions while SIR additionally requires metastatic lesion volume. Predictive values for case fatality at 1 year after surgery were compared among scoring systems.

RESULTS: All scores produced accurate reflections on OS after surgery (p ≤ 0.003). Median survival was 21-24 weeks in patients scored in the unfavorable cohorts, respectively. In cohorts with favorable scores, median survival ranged from 42 to 60 weeks. Favorable SIR was associated with a hazard ratio (HR) of 0.44 [0.29, 0.66] for death within 1 year. For GPA, the HR amounted to 0.44 [0.25, 0.75], while RPA had a HR of 0.30 [0.14, 0.63]. Overall test performance was highest for the SIR.

CONCLUSIONS: All scores proved useful in predicting OS. Considering our data, we recommend using the SIR for preoperative prognostic evaluation and counseling.

PMID:36152217 | DOI:10.1007/s00701-022-05356-x

Int Ophthalmol. 2022 Sep 24. doi: 10.1007/s10792-022-02535-8. Online ahead of print.

ABSTRACT

OBJECTIVES: To assess the relationship between orbital wall fractures connecting to paranasal sinuses (OWF-PNS) and SARS-CoV-2 ocular surface contamination (SARS-CoV-2-OSC) in asymptomatic COVID-19 patients.

METHODS: This was a prospective case-control study enrolling two asymptomatic COVID-19 patient cohorts with vs. without OWF-PNS in the case-control ratio of 1:4. All subjects were treated in a German level 1 trauma center during a one-year interval. The main predictor variable was the presence of OWF-PNS (case/control); cases with preoperative conjunctival positivity of SARS-CoV-2 were excluded to rule out the possibility of viral dissemination via the lacrimal gland and/or the nasolacrimal system. The main outcome variable was laboratory-confirmed SARS-CoV-2-OSC (yes/no). Descriptive and bivariate statistics were computed with a statistically significant P ≤ 0.05.

RESULTS: The samples comprised 11 cases and 44 controls (overall: 27.3% females; mean age, 52.7 ± 20.3 years [range, 19-85]). There was a significant association between OWF-PNS and SARS-CoV-2-OSC (P = 0.0001; odds ratio = 20.8; 95% confidence interval = 4.11-105.2; R-squared = 0.38; accuracy = 85.5%), regardless of orbital fracture location (orbital floor vs. medial wall versus both; P = 1.0).

CONCLUSIONS: Asymptomatic COVID-19 patients with OWF-PNS are associated with a considerable and almost 21-fold increase in the risk of SARS-CoV-2-OSC, in comparison with those without facial fracture. This could suggest that OWF-PNS is the viral source, requiring particular attention during manipulation of ocular/orbital tissue to prevent viral transmission.

PMID:36152172 | DOI:10.1007/s10792-022-02535-8

Biol Trace Elem Res. 2022 Sep 24. doi: 10.1007/s12011-022-03409-x. Online ahead of print.

ABSTRACT

A simple procedure for determination of Al, Cr, K, Mg, Mn, and Zn using diluted organic acid in the preparation of shrimp (Macrobrachium amazonicum) and crab samples (Ucides cordatus) was proposed in this study. Determinations were performed using microwave-induced plasma optical emission spectrometer (MIP OES). The contents of elements were evaluated after solubilization of samples in 50% formic acid (v v^-1) and subsequent heating in bath with stirring and heating at 90 °C. The accuracy of the proposed procedure was assessed using certified fish protein reference material (DORM-4) and the recovery percentages ranged from 91 to 117%. Microwave-assisted acid decomposition was used for a comparison of results with the procedure proposed using diluted formic acid, and the values obtained for all analytes were statistically equal at 95% confidence level. Cr levels were below the limit of detection. Potassium (7917-19,644 mg kg^-1), Mg (1319-5376 mg kg^-1), and Zn (43-307 mg kg^-1) were the most abundant elements in the crustacean species studied can be considered good sources of these constituents for human diet. The proposed procedure using diluted formic acid was considered simple and suitable to determine Al, Cr, K, Mg, Mn, and Zn concentrations in crustaceans using MIP OES.

PMID:36152170 | DOI:10.1007/s12011-022-03409-x

Methods Mol Biol. 2023;2571:241-255. doi: 10.1007/978-1-0716-2699-3_21.

ABSTRACT

Mass spectrometry (MS)-based metabolomics provides high-dimensional datasets; that is, the data include various metabolite features. Data analysis begins by converting the raw data obtained from the MS to produce a data matrix (metabolite × concentrations). This is followed by several steps, such as peak integration, alignment of multiple data, metabolite identification, and calculation of metabolite concentrations. Each step yields the analytical results and the accompanying information used for the quality assessment of the anterior steps. Thus, the measurement quality can be analyzed through data processing. Here, we introduce a typical data processing procedure and describe a method to utilize the intermediate data as quality control. Subsequently, commonly used data analysis methods for metabolomics data, such as statistical analyses, are also introduced.

PMID:36152165 | DOI:10.1007/978-1-0716-2699-3_21

Methods Mol Biol. 2023;2571:33-43. doi: 10.1007/978-1-0716-2699-3_4.

ABSTRACT

Due to the high impact of diet exposure on health, it is crucial the generation of robust data of regular dietary intake, hence improving the accuracy of dietary assessment. The metabolites derived from individual food or group of food have great potential to become biomarkers of food intake (BFIs) and provide more objective food consumption measurements.Herein, it is presented an untargeted metabolomic workflow for the discovery BFIs in blood and urine samples, from the study design to the biomarker identification. Samples are analyzed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). A wide variety of compounds are covered by separate analyses of medium to nonpolar molecules and polar metabolites based on two LC separations as well as both positive and negative electrospray ionization. The main steps of data treatment of the comprehensive data sets and statistical analysis are described, as well as the principal considerations for the BFI identification.

PMID:36152148 | DOI:10.1007/978-1-0716-2699-3_4

J Gambl Stud. 2022 Sep 24. doi: 10.1007/s10899-022-10159-x. Online ahead of print.

ABSTRACT

As real time soccer gambling is becoming a game of choice for many Nigerian youths, there is need to examine some predictive factors that could account for risky decision making in the population. We combined some cognitive tasks (memory, concentration, executive function and problem solving) and non-cognitive measures (time taken to complete a bet, years of gambling and addiction tendency measures) to derive a more parsimonious model of predicting risky decision making in this population. Twenty-eight undergraduate students that endorsed regular involvement (at least once a week) in soccer betting and were willing to come to the psychology lab for testing were recruited. Four neuropsychological measures (Craft Story 21: Immediate and delayed, Number Span Test: Forward and backward, Trail Making Test: A&B, Tower of Hannoi and a gambling questionnaire (Gamblers Anonymous Questionnaire) were used for the study. Study design was correlational and linear regression (step wise method) was used for data analysis. Step wise regression statistics yielded nine possible model combinations with high predictive strengths. Overall, model 9 (with adjusted R² = 0.57) that has 6 measures including one from non-cognitive and 5 from cognitive measures was adjudged to be most parsimonious putting into consideration its predictive strength and number of tasks required. The tasks in our most parsimonious model were: time taken to complete a bet (non-cognitive), Craft Story 21: immediate (cognitive: memory), Number Span Forward: Total correct and longest correct (cognitive: concentration), Trail Making Test: B (cognitive: executive function) and Tower of Hannoi: Time taken to complete (cognitive: problem solving). Pearson product moment correlation between the predictor variables and the dependent variable (number of odds selected) showed inverse correlation of Craft Story Immediate, Number Span total correct and Number span longest correct suggesting strong divergence of these variables to odd selection. Time taken to complete bet, Trail Making Test: B and time taken to complete Tower of Hannoi respectively had positive correlations with number of odds selected. Our results suggest that multiple domains of cognitive abilities and time taken to complete a bet are important for predicting gamblers at risk for poor decision making. It further suggests that use of single task for a particular cognitive domain could be sufficient in predicting persons at risk for decision making. Overall, our study suggests that risky decision making in real time sports betting could be predicted using fewer neuropsychological tasks measuring wider domains of brain behaviour and a non-cognitive measure.

PMID:36152112 | DOI:10.1007/s10899-022-10159-x

Appl Biochem Biotechnol. 2022 Sep 24. doi: 10.1007/s12010-022-04161-7. Online ahead of print.

ABSTRACT

In this pandemic situation, radiological images are the biggest source of information in healthcare and, at the same time, one of the foremost troublesome sources to analyze. Clinicians now-a-days must depend to a great extent on therapeutic image investigation performed by exhausted radiologists and some of the time analyzed and filtered themselves. Due to an overflow of patients, transmission of these medical data becomes frequent and maintaining confidentiality turns out to be one of the most important aspects of security along with integrity and availability. Chaos-based cryptography has proven a useful technique in the process of medical image encryption. The specialty of using chaotic maps in image security is its capability to increase the unpredictability and this causes the encryption robust. There are large number of literature available with chaotic map; however, most of these are not useful in low-precision devices due to their time-consuming nature. Taking into consideration of all these facts, a modified encryption technique is proposed for 2D COVID-19 images without compromising security. The novelty of the encryption procedure lies in the proposed design which is split into mainly three parts. In the first part, a variable length gray level code is used to generate the secret key to confuse the intruder and subsequently it is used as the initial parameter of both the chaotic maps. In the second part, one-stage image pixels are shuffled using the address code obtained from the sorting transformation of the first logistic map. In the final stage, a complete diffusion is applied for the whole image using the second chaotic map to counter differential and statistical attack. Algorithm validation is done by experimentation with visual image and COVID-19 X-ray images. In addition, a quantitative analysis is carried out to ensure a negligible data loss between the original and the decrypted image. The strength of the proposed method is tested by calculating the various security parameters like correlation coefficient, NPCR, UACI, and key sensitivity. Comparison analysis shows the effectiveness for the proposed method. Implementation statistics shows time efficiency and proves more security with better unpredictability.

PMID:36152105 | DOI:10.1007/s12010-022-04161-7

Arch Immunol Ther Exp (Warsz). 2022 Sep 24;70(1):23. doi: 10.1007/s00005-022-00660-x.

ABSTRACT

Angiotensin II type 1 receptor (AT1R) antibodies are considered non-HLA (human leukocyte antigen) antibodies connected with humoral rejection after kidney transplantation. The role of AT1R antibodies in the pathogenesis of glomerular diseases and systemic vasculitis is unknown. We assessed the level of AT1R antibodies in 136 patients with different types of glomerulonephritis and systemic vasculitis and we observed kidney function and proteinuria, serum albumin and total protein levels for 2 years. The mean levels of AT1R antibodies were the following: 6.00 ± 1.31 U/ml in patients with membranous nephropathy (n = 18), 5.67 ± 1.31 U/ml with focal and segmental glomerulosclerosis (n = 25), 6.26 ± 2.25 U/ml with lupus nephropathy (n = 17), 10.60 ± 6.72 U/ml with IgA nephropathy (n = 14), 6.69 ± 2.52 U/ml with mesangial proliferative (non IgA) glomerulonephritis (n = 6), 6.63 ± 1.38 U/ml with systemic vasculitis (n = 56), including c-ANCA (anti-neutrophil cytoplasmic antibodies) vasculitis: 11.22 ± 10.78 U/ml (n = 40) and p-ANCA vasculitis: 12.65 ± 14.59 U/ml (n = 16). The mean AT1R antibodies level was higher in patients with lupus nephropathy and systemic vasculitis compared to glomerulonephritis groups. An inverse statistically significant correlation between AT1R antibodies and serum albumin (r = – 0.51) in membranous nephropathy group was also found. Prospective analysis of creatinine levels indicated an increase of creatinine levels during time among patients with higher AT1R antibodies levels in p-ANCA vasculitis. Lupus nephropathy and systemic vasculitis patients may have high levels of AT1R antibodies. AT1R antibodies may be associated with the severity of membranous nephropathy and the course of p-ANCA vasculitis, although influence of concomitant factors is difficult to exclude.

PMID:36152104 | DOI:10.1007/s00005-022-00660-x

Environ Sci Pollut Res Int. 2022 Sep 24. doi: 10.1007/s11356-022-23154-x. Online ahead of print.

ABSTRACT

Americas have a mix of developing and developed economies. Thus, the pollution haven hypothesis (PHH) is expected in the developing countries of Latin America. Using the spatial Durbin model, the present study investigates the effects of foreign direct investment (FDI), exports, and imports on emissions in 18 Latin American countries from 1970 to 2019, including economic growth and the financial market development (FMD) in the model. The environmental Kuznets curve (EKC) is validated, and the region is found in the first stage of the EKC. Hence, Latin American economic growth has environmental consequences. Exports have a positive impact on home and neighboring countries’ CO₂ emissions and pollute the whole region, which validates the PHH. Imports could not affect the home economies but have positive environmental effects on neighboring economies and the entire Latin American region. The negative coefficient of imports is larger than the positive coefficient of exports. Therefore, the net effect of trade is environmentally pleasant in Latin America. Moreover, FDI has a statistically insignificant effect and the impact of FMD is positive on CO₂ emissions. The study recommends caring the exporting, financial, and economic activities for a sustainable environment in Latin America.

PMID:36152100 | DOI:10.1007/s11356-022-23154-x