Nevin Manimala

J Neurosurg. 2022 Sep 23:1-9. doi: 10.3171/2022.8.JNS221516. Online ahead of print.

ABSTRACT

OBJECTIVE: Preservation of neurological function is a priority when performing a resection of a vestibular schwannoma (VS). Few studies have examined the radiographic value of a fundal fluid cap-i.e., cerebrospinal fluid in the lateral end of a VS within the internal auditory canal-for prediction of postoperative neurological function. The aim of this study was to clarify whether the presence of a fundal fluid cap on preoperative magnetic resonance images has a clinical impact on facial nerve function after resection of VSs.

METHODS: The presence of a fundal fluid cap and its prognostic impact on long-term postoperative facial nerve function were analyzed.

RESULTS: A fundal fluid cap was present in 102 of 143 patients who underwent resection of sporadic VSs via the retrosigmoid approach. Facial nerve function was acceptable (House-Brackmann grade I-II) immediately after surgery in 82 (80.4%) patients with a fundal fluid cap and in 26 (63.4%) of those without this sign. The preservation rate of facial nerve function increased in a time-dependent manner after surgery in patients with a fundal fluid cap but plateaued by 3 months postoperatively in those without a fundal fluid cap; the difference was statistically significant at 12 months (96.1% vs 82.9%, p = 0.013) and 24 months (97.1% vs 82.9%, p = 0.006) after surgery. The presence of a fundal fluid cap had a significantly positive effect on long-term facial nerve function at 24 months after surgery when tumor size and intraoperative neuromonitoring response were taken into account (OR 5.55, 95% CI 1.12-27.5, p = 0.034).

CONCLUSIONS: Neuromonitoring-guided microsurgery for total resection of VSs is more likely to be successful in terms of preservation of facial nerve function if a fundal fluid cap is present. This preoperative radiographic sign could be helpful when counseling patients and deciding the treatment strategy.

PMID:36152320 | DOI:10.3171/2022.8.JNS221516

Genome Biol Evol. 2022 Sep 24:evac143. doi: 10.1093/gbe/evac143. Online ahead of print.

ABSTRACT

Local adaptation can lead to elevated genetic differentiation at the targeted genetic variant and nearby sites. Selective sweeps come in different forms, and depending on the initial and final frequencies of a favored variant, very different patterns of genetic variation may be produced. If local selection favors an existing variant that had already recombined onto multiple genetic backgrounds, then the width of elevated genetic differentiation (high FST) may be too narrow to detect using a typical windowed genome scan, even if the targeted variant becomes highly differentiated. We therefore used a simulation approach to investigate the power of SNP-level FST (specifically, the maximum SNP FST value within a window, or FST_MaxSNP) to detect diverse scenarios of local adaptation, and compared it against whole-window FST and the Comparative Haplotype Identity statistic. We found that FST_MaxSNP had superior power to detect complete or mostly complete soft sweeps, but lesser power than full-window statistics to detect partial hard sweeps. Nonetheless, the power of FST_MaxSNP depended highly on sample size, and confident outliers depend on robust precautions and quality control. To investigate the relative enrichment of FST_MaxSNP outliers from real data, we applied the two FST statistics to a panel of Drosophila melanogaster populations. We found that FST_MaxSNP had a genome-wide enrichment of outliers compared to demographic expectations, and though it yielded a lesser enrichment than window FST, it detected mostly unique outlier genes and functional categories. Our results suggest that FST_MaxSNP is highly complementary to typical window-based approaches for detecting local adaptation, and merits inclusion in future genome scans and methodologies.

PMID:36152314 | DOI:10.1093/gbe/evac143

Methods Mol Biol. 2023;2578:219-236. doi: 10.1007/978-1-0716-2732-7_16.

ABSTRACT

Peptide microarrays are a powerful tool to identify linear epitopes of food allergens in a high-throughput manner. The main advantages of the microarray-based immunoassay are as follows: the possibility to assay thousands of targets simultaneously, the requirement of a low volume of serum, the more robust statistical analysis, and the possibility to test simultaneously several immunoglobulin subclasses. Among them, the last one has a special interest in the field of food allergy, because the development of tolerance to food allergens has been associated with a decrease in IgE and an increase in IgG4 levels against linear epitopes. However, the main limitation to the clinical use of microarray is the automated analysis of the data. Recent studies mapping the linear epitopes of food allergens with peptide microarray immunoassays have identified peptide biomarkers that can be used for early diagnosis of food allergies and to predict their severity or the self-development of tolerance. Using this approach, we have worked on epitope mapping of the two most important food allergens in the Spanish population, cow’s milk, and chicken eggs. The final aim of these studies is to define subsets of peptides that could be used as biomarkers to improve the diagnosis and prognosis of food allergies. This chapter describes the protocol to produce microarrays using a library of overlapping peptides corresponding to the primary sequences of food allergens and data acquisition and analysis of IgE and IgG4 binding epitopes.

PMID:36152291 | DOI:10.1007/978-1-0716-2732-7_16

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