Tag: pubmed

Probl Endokrinol (Mosk). 2022 Jan 27;68(1):94-100. doi: 10.14341/probl12849.

ABSTRACT

BACKGROUND: Melatonin is the main hormone of the pineal gland. By regulating circadian rhythms and being an immune regulator and antioxidant, this hormone takes part in the work of the ovaries: its high concentrations block apoptosis and neutralize reactive oxygen species involved in folliculogenesis, ovulation, egg maturation and corpus luteum formation.

AIM: To study melatonin status and its relationship with menstrual dysfunction and sleep disorders in obese women of reproductive age.

MATERIALS AND METHODS: In a one-stage comparative study, women 18-35 years old took part: 30 patients with obesity and menstrual disorders of an inorganic nature and 30 healthy women in the comparison group with normal weight and regular menstrual cycle. All participants underwent a questionnaire to identify somnological disorders, and the level of melatonin in saliva and 6-sulfatoxymelatonin in urine was also investigated.

RESULTS: In the group of patients with obesity (n=30), various sleep disorders were encountered in 47% of cases (p=0.003), including more often obstructive sleep apnea syndrome was recorded (30% of cases), and a correlation was found between the indicators of the questionnaire survey of subjective sleep characteristics and body mass index of patients (r=0.450, p=0.030) compared with a group of healthy women with normal weight (n=30). In the main group, the level of melatonin in saliva was statistically significantly lower than in the control: median 12.6 pg / ml and 25.5 pg / ml, respectively (p=0.008), the same pattern was recorded for 6-sulfatoxymelatonin: 14, 72 pg / ml and 31.12 pg / ml, respectively.

CONCLUSION: Patients with obesity and menstrual dysfunction are more likely to suffer from various sleep disorders and have lower levels of melatonin in saliva and 6-sulfatoxymelatonin in urine.

PMID:35262300 | DOI:10.14341/probl12849

J Sep Sci. 2022 Mar 9. doi: 10.1002/jssc.202100864. Online ahead of print.

ABSTRACT

Pre-packed columns have been increasingly used in process development and biomanufacturing thanks to their ease of use and consistency. Traditionally, packing quality is predicted through rate models, which require extensive calibration efforts through independent experiments to determine relevant mass transfer and kinetic rate constants. Here we propose machine learning as a complementary predictive tool for column performance. A machine learning algorithm, extreme gradient boosting, was applied to a large data set of packing quality (plate height and asymmetry) for pre-packed columns as a function of quantitative parameters (column length, column diameter, particle size) and qualitative attributes (backbone and functional mode). The machine learning model offered excellent predictive capabilities for the plate height and the asymmetry (90% and 93%, respectively), with packing quality strongly influenced by backbone (∼70% relative importance) and functional mode (∼15% relative importance), well above all other quantitative column parameters. The results highlight the ability of machine learning to provide reliable predictions of column performance from simple, generic parameters, including strategic qualitative parameters such as backbone and functionality, usually excluded from quantitative considerations. Our results will guide further efforts in column optimization, e.g. by focusing on improvements of backbone and functional mode to obtain optimised packings. This article is protected by copyright. All rights reserved.

PMID:35262290 | DOI:10.1002/jssc.202100864

J Appl Clin Med Phys. 2022 Mar 9:e13583. doi: 10.1002/acm2.13583. Online ahead of print.

ABSTRACT

PURPOSE: To develop a 3D-Unet dose prediction model to predict the three-dimensional dose distribution of volumetric modulated arc therapy (VMAT) for cervical cancer and test the dose prediction performance of the model in endometrial cancer to explore the feasibility of model generalization.

METHODS: One hundred and seventeen cases of cervical cancer and 20 cases of endometrial cancer treated with VMAT were used for the model training, validation, and test. The prescribed dose was 50.4 Gy in 28 fractions. Eight independent channels of contoured structures were input to the model, and the dose distribution was used as the output of the model. The 3D-Unet prediction model was trained and validated on the training set (n = 86) and validation set (n = 11), respectively. Then the model was tested on the test set (n = 20) of cervical cancer and endometrial cancer, respectively. The results between clinical dose distribution and predicted dose distribution were compared in the following aspects: (a) the mean absolute error (MAE) within the body, (b) the Dice similarity coefficients (DSCs) under different isodose volumes, (c) the dosimetric indexes including the mean dose (D_mean ), the received dose of 2 cm³ (D_2cc) , the percentage volume of receiving 40 Gy dose of organs-at-risk (V₄₀ ), planning target volume (PTV) D_98% , and homogeneity index (HI), (d) dose-volume histograms (DVHs).

RESULTS: The model can accurately predict the dose distribution of the VMAT plan for cervical cancer and endometrial cancer. The overall average MAE and maximum MAE for cervical cancer were 2.43 ± 3.17% and 3.16 ± 4.01% of the prescribed dose, respectively, and for endometrial cancer were 2.70 ± 3.54% and 3.85 ± 3.11%. The average DSCs under different isodose volumes is above 0.9. The predicted dosimetric indexes and DVHs are equivalent to the clinical dose for both cervical cancer and endometrial cancer, and there is no statistically significant difference.

CONCLUSION: A 3D-Unet dose prediction model was developed for VMAT of cervical cancer, which can predict the dose distribution accurately for cervical cancer. The model can also be generalized for endometrial cancer with good performance.

PMID:35262273 | DOI:10.1002/acm2.13583

Plant Genome. 2022 Mar 8:e20197. doi: 10.1002/tpg2.20197. Online ahead of print.

ABSTRACT

Sweet corn (Zea mays L.) is consistently one of the most highly consumed vegetables in the United States, providing a valuable opportunity to increase nutrient intake through biofortification. Significant variation for carotenoid (provitamin A, lutein, zeaxanthin) and tocochromanol (vitamin E, antioxidants) levels is present in temperate sweet corn germplasm, yet previous genome-wide association studies (GWAS) of these traits have been limited by low statistical power and mapping resolution. Here, we employed a high-quality transcriptomic dataset collected from fresh sweet corn kernels to conduct transcriptome-wide association studies (TWAS) and transcriptome prediction studies for 39 carotenoid and tocochromanol traits. In agreement with previous GWAS findings, TWAS detected significant associations for four causal genes, β-carotene hydroxylase (crtRB1), lycopene epsilon cyclase (lcyE), γ-tocopherol methyltransferase (vte4), and homogentisate geranylgeranyltransferase (hggt1) on a transcriptome-wide level. Pathway-level analysis revealed additional associations for deoxy-xylulose synthase2 (dxs2), diphosphocytidyl methyl erythritol synthase2 (dmes2), cytidine methyl kinase1 (cmk1), and geranylgeranyl hydrogenase1 (ggh1), of which, dmes2, cmk1, and ggh1 have not previously been identified through maize association studies. Evaluation of prediction models incorporating genome-wide markers and transcriptome-wide abundances revealed a trait-dependent benefit to the inclusion of both genomic and transcriptomic data over solely genomic data, but both transcriptome- and genome-wide datasets outperformed a priori candidate gene-targeted prediction models for most traits. Altogether, this study represents an important step toward understanding the role of regulatory variation in the accumulation of vitamins in fresh sweet corn kernels.

PMID:35262278 | DOI:10.1002/tpg2.20197

J Appl Clin Med Phys. 2022 Mar 9:e13582. doi: 10.1002/acm2.13582. Online ahead of print.

ABSTRACT

This study presents a methodology to develop an X-ray technique chart for portable chest and abdomen imaging which utilizes patient data available in the modality worklist (MWL) to reliably achieve a predetermined exposure index (EI) at the detector for any patient size. The method assumes a correlation between the patients’ tissue equivalent thickness and the square root of the ratio of the patient’s weight to height. To assess variability in detector exposures, the EI statistics for 75 chest examinations and 99 abdominal portable X-ray images acquired with the new technique chart were compared to those from a single portable unit (chest: 3877 images; abdomen: 200 images) using a conventional technique chart with three patient sizes, and to a stationary radiography room utilizing automatic exposure control (AEC) (chest: 360 images; abdomen: 112 images). The results showed that when using the new technique chart on a group of portable units, the variability in EI was significantly reduced (p < 0.01) for both AP chest and AP abdomen images compared to the single portable using a standard technique chart with three patient sizes. The variability in EI for the images acquired with the new chart was comparable to the stationary X-ray room with an AEC system (p > 0.05). This method could be used to streamline the entire imaging chain by automatically selecting an X-ray technique based on patient demographic information contained in the MWL to provide higher quality examinations to clinicians by eliminating outliers. In addition, patient height and weight can be used to estimate the patients’ tissue equivalent thickness.

PMID:35262265 | DOI:10.1002/acm2.13582

J Neuroimaging. 2022 Mar 9. doi: 10.1111/jon.12987. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: To compare quantitative susceptibility mapping (QSM) and high-pass-filtered (HPF) phase imaging for (1) identifying chronic active rim lesions with more myelin damage and (2) distinguishing patients with increased clinical disability in multiple sclerosis.

METHODS: Eighty patients were scanned with QSM for paramagnetic rim detection and Fast Acquisition with Spiral Trajectory and T2prep for myelin water fraction (MWF). Chronic lesions were classified based on the presence/absence of rim on HPF and QSM images. A lesion-level linear mixed-effects model with MWF as the outcome was used to compare myelin damage among the lesion groups. A multiple patient-level linear regression model was fit to establish the association between Expanded Disease Status Scale (EDSS) and the log of the number of rim lesions.

RESULTS: Of 2062 lesions, 188 (9.1%) were HPF rim+/QSM rim+, 203 (9.8%) were HPF rim+/QSM rim-, and the remainder had no rim. In the linear mixed-effects model, HPF rim+/QSM rim+ lesions had significantly lower MWF than both HPF rim+/QSM rim- (p < .001) and HPF rim-/QSM rim- (p < .001) lesions, while the MWF difference between HPF rim+/QSM rim- and HPF rim-/QSM rim- lesions was not statistically significant (p = .130). Holding all other factors constant, the log number of QSM rim+ lesion was associated with EDSS increase (p = .044). The association between the log number of HPF rim+ lesions and EDSS was not statistically significant (p = .206).

CONCLUSIONS: QSM identifies paramagnetic rim lesions that on average have more myelin damage and stronger association with clinical disability than those detected by phase imaging.

PMID:35262241 | DOI:10.1111/jon.12987

Pharm Stat. 2022 Mar 9. doi: 10.1002/pst.2202. Online ahead of print.

ABSTRACT

Databases derived from electronic health records (EHRs) are commonly subject to left truncation, a type of selection bias that occurs when patients need to survive long enough to satisfy certain entry criteria. Standard methods to adjust for left truncation bias rely on an assumption of marginal independence between entry and survival times, which may not always be satisfied in practice. In this work, we examine how a weaker assumption of conditional independence can result in unbiased estimation of common statistical parameters. In particular, we show the estimability of conditional parameters in a truncated dataset, and of marginal parameters that leverage reference data containing non-truncated data on confounders. The latter is complementary to observational causal inference methodology applied to real-world external comparators, which is a common use case for real-world databases. We implement our proposed methods in simulation studies, demonstrating unbiased estimation and valid statistical inference. We also illustrate estimation of a survival distribution under conditionally independent left truncation in a real-world clinico-genomic database.

PMID:35262259 | DOI:10.1002/pst.2202

Mov Disord. 2022 Mar 9. doi: 10.1002/mds.28987. Online ahead of print.

ABSTRACT

BACKGROUND: To date, variants in the GBA gene represent the most frequent large-effect genetic factor associated with Parkinson’s disease (PD). However, the reason why individuals with the same GBA variant may or may not develop neurodegeneration and PD is still unclear.

OBJECTIVES: Therefore, we evaluated the contribution of rare variants in genes responsible for lysosomal storage disorders (LSDs) to GBA-PD risk, comparing the burden of deleterious variants in LSD genes in PD patients versus asymptomatic subjects, all carriers of deleterious variants in GBA.

METHODS: We used a custom next-generation sequencing panel, including 50 LSD genes, to screen 305 patients and 207 controls (discovery cohort). Replication and meta-analysis were performed in two replication cohorts of GBA-variant carriers, of 250 patients and 287 controls, for whom exome or genome data were available.

RESULTS: Statistical analysis in the discovery cohort revealed a significantly increased burden of deleterious variants in LSD genes in patients (P = 0.0029). Moreover, our analyses evidenced that the two strongest modifiers of GBA penetrance are a second variation in GBA (5.6% vs. 1.4%, P = 0.023) and variants in genes causing mucopolysaccharidoses (6.9% vs. 1%, P = 0.0020). These results were confirmed in the meta-analysis, where we observed pooled odds ratios of 1.42 (95% confidence interval [CI] = 1.10-1.83, P = 0.0063), 4.36 (95% CI = 2.02-9.45, P = 0.00019), and 1.83 (95% CI = 1.04-3.22, P = 0.038) for variants in LSD genes, GBA, and mucopolysaccharidosis genes, respectively.

CONCLUSION: The identification of genetic lesions in lysosomal genes increasing PD risk may have important implications in terms of patient stratification for future therapeutic trials. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson Movement Disorder Society.

PMID:35262230 | DOI:10.1002/mds.28987

Stat Med. 2022 Mar 9. doi: 10.1002/sim.9370. Online ahead of print.

ABSTRACT

We propose a new approach to test associations between binary trees and covariates. In this approach, binary-tree structured data are treated as sample paths of binary fission Markov branching processes (bMBP). We propose a generalized linear regression model and developed inference procedures for association testing, including variable selection and estimation of covariate effects. Simulation studies show that these procedures are able to accurately identify covariates that are associated with the binary tree structure by impacting the rate parameter of the bMBP. The problem of association testing on binary trees is motivated by modeling hierarchical clustering dendrograms of pixel intensities in biomedical images. By using semi-synthetic data generated from a real brain-tumor image, our simulation studies show that the bMBP model is able to capture the characteristics of dendrogram trees in brain-tumor images. Our final analysis of the glioblastoma multiforme brain-tumor data from The Cancer Imaging Archive identified multiple clinical and genetic variables that are potentially associated with brain-tumor heterogeneity.

PMID:35262202 | DOI:10.1002/sim.9370

Pacing Clin Electrophysiol. 2022 Mar 9. doi: 10.1111/pace.14478. Online ahead of print.

ABSTRACT

BACKGROUND: Cardiac contractility modulation (CCM), being reserved for patients with symptomatic chronic heart failure (HF) and narrow QRS complex under guideline directed medical therapy, can recover initially reduced left ventricular ejection fraction (LVEF); however, the influence of pre-implantation LVEF on long-term outcomes is not fully understood. This study aimed to compare the effects of lower and higher pre-implantation LVEF on long-term outcomes in CCM-therapy.

METHODS: One-hundred seventy-two patients from our single-centre registry were retrospectively included (2002 – 2019). Follow-up data were collected up to five years after implantation. Patients were divided into Group 1 (baseline LVEF≤ 30%) and Group 2 (≥ 31%). Both groups were compared based on differences in survival, echocardiographic- and clinical parameters including LVEF, tricuspid annular plane systolic excursion (TAPSE), NYHA class or Minnesota living with heart failure questionnaire-score (MLWHFQ).

RESULTS: 11 % of the patients did have a LVEF ≥ 31%. Mean LVEF±SD for both groups were 21.98±5.4 vs. 35.2±3.7%, respectively. MLWHFQ (47±21.2 vs. 42±21.4) and mean peak oxygen consumption (VO2, 13.6±4.1 vs. 12.7±2.8 ml/kg/min) were comparable between both groups. LVEF-grouping did not influence survival. Lower baseline LVEF resulted in significantly better recovery of echocardiographic parameters such as LVEF and TAPSE. Irrespective from baseline LVEF, both groups showed nearly comparable improvements for clinical parameters like NYHA-class and MLWHFQ.

CONCLUSION: Long-term biventricular systolic recovery potential in CCM-therapy might be better for pre-implantation LVEF values ≤ 30%, whereas clinical parameters such as NYHA-class can improve irrespective from baseline LVEF. This article is protected by copyright. All rights reserved.

PMID:35262210 | DOI:10.1111/pace.14478