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Background Plants have been pivotal in traditional and modern medicine globally, with historical evidence supporting their therapeutic applications. Nigella (Nigella sativa L.) is an annual herbaceous plant of the Ranunculaceae family and is cultivated in the Middle East, Eastern Europe, and Western and Central Asia. The medicinal use of plants dates back thousands of years, documented in ancient writings from various civilizations. Alkaloids, phenolics, saponins, flavonoids, terpenoids, anthraquinones, and tannins found in plants exhibit antioxidant, immunomodulatory, anti-inflammatory, anticancer, antibacterial, and antidiabetic activities. Methodology This study specifically examines the pharmacological potential of Nigella sativa L., emphasizing thymoquinone—a compound with diverse nutraceutical benefits. The extraction, characterization, and quantification of thymoquinone, alongside other physicochemical parameters, were carried out using ethanol through Soxhlet extraction procedures on five nigella varieties. HPLC analysis was performed to determine the maximum accumulation of thymoquinone in the released variety of the plant and the chemical composition of the seed oil isolated from Nigella sativa L., varieties utilized in the study was determined through GC-MS analysis. Results The research revealed that the Ajmer nigella-20 variety stands out, exhibiting elevated levels of thymoquinone (0.20 ± 0.07%), antioxidants (76.18 ± 1.78%), and substantial quantities of total phenols (31.85 ± 0.97 mg GAEg−1 seed) and flavonoids (8.150 ± 0.360 mg QE 100 g−1 seed) compared to other varieties. The GC-MS profiling showed the presence of 11 major compounds in the studied varieties, with p-cymene, longifolene, and myristic acid identified as the major chemical compounds present in the oil. Conclusion The observed variations among Nigella varieties indicate the Ajmer nigella-20 variety as particularly promising for thymoquinone and bioactive compound extraction. This study underscores Nigella’s potential as a source of pharmacologically active compounds, highlighting the need for further exploration in therapeutic applications.

Introduction: Iodine deficiency disorders (IDD) have remained an unresolved public health problem in India. In this survey, we have estimated the prevalence of IDD among 6–12 years of school children in rural areas of north Karnataka, India and estimated the prevalence of low iodine content (

The clinical management of pregnancy and spontaneous preterm birth (sPTB) relies on estimates of gestational age (GA). Our objective was to evaluate the effect of GA dating uncertainty on the observed performance of a validated proteomic biomarker risk predictor, and then to test the generalizability of that effect in a broader range of GA at blood draw. In a secondary analysis of a prospective clinical trial (PAPR; NCT01371019), we compared two GA dating categories: both ultrasound and dating by last menstrual period (LMP) (all subjects) and excluding dating by LMP (excluding LMP). The risk predictor's performance was observed at the validated risk predictor threshold both in weeks 191/7-206/7 and extended to weeks 180/7-206/7. Strict blinding and independent statistical analyses were employed. The validated biomarker risk predictor showed greater observed sensitivity of 88% at 75% specificity (increases of 17% and 1%) in more reliably dated (excluding-LMP) subjects, relative to all subjects. Excluding dating by LMP significantly improved the sensitivity in weeks 191/7-206/7. In the broader blood draw window, the previously validated risk predictor threshold significantly stratified higher and lower risk of sPTB, and the risk predictor again showed significantly greater observed sensitivity in excluding-LMP subjects. These findings have implications for testing the performance of models aimed at predicting PTB.

The emergence of antibiotic resistance (AR) poses a significant threat to both public health and aquatic ecosystems. Wastewater treatment plants (WWTPs) have been identified as potential hotspots for disseminating AR in the environment. However, only a limited number of studies have been conducted on AR dissemination through WWTPs in Sri Lanka. To address this knowledge gap in AR dissemination through WWTP operations in Sri Lanka, we critically examined the global situation of WWTPs as hotspots for transmitting antibiotic-resistant bacteria (ARB) and antibiotic-resistant genes (ARGs) by evaluating more than a hundred peer-reviewed international publications and available national publications. Our findings discuss the current state of operating WWTPs in the country and highlight the research needed in controlling AR dissemination. The results revealed that the impact of different wastewater types, such as clinical, veterinary, domestic, and industrial, on the dissemination of AR has not been extensively studied in Sri Lanka; furthermore, the effectiveness of various wastewater treatment techniques in removing ARGs requires further investigation to improve the technologies. Furthermore, existing studies have not explored deeply enough the potential public health and ecological risks posed by AR dissemination through WWTPs.

High-spatial resolution MRI produces abundant structural information, enabling highly accurate clinical diagnosis and image-guided therapeutics. However, the acquisition of high-spatial resolution MRI data typically can come at the expense of less spatial coverage, lower signal-to-noise ratio (SNR), and longer scan time due to physical, physiological and hardware limitations. In order to overcome these limitations, super-resolution MRI deep-learning-based techniques can be utilised. In this work, different state-of-the-art 3D convolution neural network models for super resolution (RRDB, SPSR, UNet, UNet-MSS and ShuffleUNet) were compared for the super-resolution task with the goal of finding the best model in terms of performance and robustness. The public IXI dataset (only structural images) was used. Data were artificially downsampled to obtain lower-resolution spatial MRIs (downsampling factor varying from 8 to 64). When assessing performance using the SSIM metric in the test set, all models performed well. In particular, regardless of the downsampling factor, the UNet consistently obtained the top results. On the other hand, the SPSR model consistently performed worse. In conclusion, UNet and UNet-MSS achieved overall top performances while RRDB performed relatively poorly compared to the other models.

AbstractObjectivesPreterm birth occurs in more than 10% of U.S. births and is the leading cause of U.S. neonatal deaths, with estimated annual costs exceeding $25 billion USD. Using real-world data, we modeled the potential clinical and economic utility of a prematurity-reduction program comprising screening in a racially and ethnically diverse population with a validated proteomic biomarker risk predictor, followed by case management with or without pharmacological treatment.MethodsThe ACCORDANT microsimulation model used individual patient data from a prespecified, randomly selected sub-cohort (N=847) of a multicenter, observational study of U.S. subjects receiving standard obstetric care with masked risk predictor assessment (TREETOP; NCT02787213). All subjects were included in three arms across 500 simulated trials: standard of care (SoC, control); risk predictor/case management comprising increased outreach, education and specialist care (RP-CM, active); and risk predictor/case management with pharmacological treatment (RP-MM, active). In the active arms, only subjects stratified as higher-risk by the predictor were modeled as receiving the intervention, whereas lower-risk subjects received standard care. Higher-risk subjects’ gestational ages at birth were shifted based on published efficacies, and dependent outcomes, calibrated using national datasets, were changed accordingly. Subjects otherwise retained their original TREETOP outcomes. Arms were compared using survival analysis for neonatal and maternal hospital length of stay, bootstrap intervals for neonatal cost, and Fisher’s exact test for neonatal morbidity/mortality (significance, p

Abstract Background Studies of the molecular mechanisms of nerve regeneration have led to the discovery of several proteins that are induced during successful nerve regeneration. RICH proteins were identified as proteins induced during the regeneration of the optic nerve of teleost fish. These proteins are 2’,3’-cyclic nucleotide, 3’-phosphodiesterases that can bind to cellular membranes through a carboxy-terminal membrane localization domain. They interact with the tubulin cytoskeleton and are able to enhance neuronal structural plasticity by promoting the formation of neurite branches. Results PC12 stable transfectant cells expressing a fusion protein combining a red fluorescent protein with a catalytically inactive mutant version of zebrafish RICH protein were generated. These cells were used as a model to analyze effects of the protein on neuritogenesis. Differentiation experiments showed a 2.9 fold increase in formation of secondary neurites and a 2.4 fold increase in branching points. A 2.2 fold increase in formation of secondary neurites was observed in neurite regeneration assays. Conclusions The use of a fluorescent fusion protein facilitated detection of expression levels. Two computer-assisted morphometric analysis methods indicated that the catalytically inactive RICH protein induced the formation of branching points and secondary neurites both during differentiation and neurite regeneration. A procedure based on analysis of random field images provided comparable results to classic neurite tracing methods. Graphical Abstract

The energy E(G) of a graph G is the sum of the absolute values of eigenvalues of G and the Seidel energy ES(G) is the sum of the absolute values of eigenvalues of the Seidel matrix S of G. In this paper, some relations between the energy and Seidel energy of a graph in terms of different graph parameters are presented. Also, the inertia relations between the graph eigenvalue and Seidel eigenvalue of a graph are given. The results in this paper generalize some of the existing results.

Most literature on hashing functions speaks in terms of hashing functions being either ‘good’ or ‘bad’. In this paper, we demonstrate how a hashing function that gives good results for one key set, performs badly for another. We also demonstrate that, for a single key set, we can find hashing functions that hash the keys with varying performances ranging from perfect to worst distributions. We present a study on the effect of changing the prime number ‘$p$’ on the performance of a hashing function from $H_1$ Class of Universal Hashing Functions. This paper then explores a way to characterize hashing functions by studying their performance over all subsets of a chosen Universe. We compare the performance of some popular hashing functions based on the average search performance and the number of perfect and worst-case distributions over different key sets chosen from a Universe. The experimental results show that the division-remainder method provides the best distribution for most key sets of the Universe when compared to other hashing functions including functions from $H_1$ Class of Universal Hashing Functions.

The design and development of composite electrodes towards the trace-level detection of Hg(II) ions has triggered paramount importance in the area of electrochemistry. In this context, pencil graphite electrode was modified with NiFe2O4/G-C3N4, wherein bimetallic oxides were heterogeneously grown over the g-C3N4 sheets through a wet chemical approach. The results derived from transmission electron microscopy and X-ray diffraction analysis indeed reflected that NiFe2O4 were uniformly distributed over the sheet surface and complemented with effective interactions between them. The composites were coated over a pencil graphite electrode (PGE) and were used for the electrochemical detection of Hg(II) ions under various conditions. The cyclic voltammetry (CV) and differential pulse anodic stripping voltammetry (DPASV) reveal better electrochemical performance of modified electrodes compared to bare PGE, G-C3N4/PGE, and NiFe2O4/PGE. The improved charge transfer kinetics at the electrode-electrolyte interface were the dominant factor to exhibit higher performance. The proposed sensor showed acceptable reproducibility and stability under the optimal reaction conditions. Additionally, the sensor showed a broad linear range from 10 to 800 nM with a limit of detection (LOD) of 2.49 nM. Further, the fabricated sensor has been successfully employed to determine Hg(II) contents in real water sample matrices.