Study of UV-Vis molar absorptivity variation and quantitation of anthocyanins using molar relative response factor.

The effects of anthocyanin's substitution groups on the UV-Vis molar absorptivity were examined by analyzing a group of 31 anthocyanidin/anthocyanin reference standards with ultra-high performance liquid chromatography-diode array detector (UHPLC-DAD). The substitution groups on aglycones were found to associate with molar absorptivity variations, often neglected in anthocyanin quantitation, resulting in significant analytical errors. A simple yet comprehensive strategy based on the molar relative response factors (MRRFs) and a single master reference calibration (i.e., cyanidin-3-glucoside) was proposed to quantify anthocyanins in red cabbage, blueberry, and strawberry samples with improved analytical accuracy. The results indicate this approach provides an effective, inexpensive, and accurate analytical method for anthocyanins in food materials without using individual reference standards. MRRFs of 617 anthocyanins/anthocyanidins were calculated, and the information is freely available at https://BotanicalDC.online/anthocyanin/. This study could be critical to developing new reference methods for anthocyanin analysis and harmonizing results and data from various sources.

A Fast and Simple Solid Phase Extraction-Based Method for Glucosinolate Determination: An Alternative to the ISO-9167 Method.

Glucosinolates (GLSs) are a well-studied sulfur-containing compound found in Brassicaceae plants that play critical roles in plant resistance and human health. Correctly identifying and reliably quantifying the total and individual GLS content is of great importance. An improved method as an alternative to the ISO 9167-1 (ISO) method is developed in the present study. An efficient extraction and purification procedure is proposed with a commercially available dimethylaminopropyl (DEA)-based weak anion exchange solid-phase extraction (SPE) cartridge instead of using the self-prepared ion-exchange columns in the ISO method. The GLSs are identified and quantified by ultra high-performance liquid chromatography (UHPLC) high-resolution mass spectrometry (HRMS). The method demonstrates a comparable quantification of total and individual GLSs on certified rapeseeds and other Brassicaceae vegetables when compared to the ISO method. The developed SPE method is simpler and more efficient, thus allowing for applications to a large sample size with reduced analysis time, improved repeatability and accuracy, and possible automation.

Interpretable medical image Visual Question Answering via multi-modal relationship graph learning.

Medical Visual Question Answering (VQA) is an important task in medical multi-modal Large Language Models (LLMs), aiming to answer clinically relevant questions regarding input medical images. This technique has the potential to improve the efficiency of medical professionals while relieving the burden on the public health system, particularly in resource-poor countries. However, existing medical VQA datasets are small and only contain simple questions (equivalent to classification tasks), which lack semantic reasoning and clinical knowledge. Our previous work proposed a clinical knowledge-driven image difference VQA benchmark using a rule-based approach (Hu et al., 2023). However, given the same breadth of information coverage, the rule-based approach shows an 85% error rate on extracted labels. We trained an LLM method to extract labels with 62% increased accuracy. We also comprehensively evaluated our labels with 2 clinical experts on 100 samples to help us fine-tune the LLM. Based on the trained LLM model, we proposed a large-scale medical VQA dataset, Medical-CXR-VQA, using LLMs focused on chest X-ray images. The questions involved detailed information, such as abnormalities, locations, levels, and types. Based on this dataset, we proposed a novel VQA method by constructing three different relationship graphs: spatial relationships, semantic relationships, and implicit relationship graphs on the image regions, questions, and semantic labels. We leveraged graph attention to learn the logical reasoning paths for different questions. These learned graph VQA reasoning paths can be further used for LLM prompt engineering and chain-of-thought, which are crucial for further fine-tuning and training multi-modal large language models. Moreover, we demonstrate that our approach has the qualities of evidence and faithfulness, which are crucial in the clinical field. The code and the dataset is available at https://github.com/Holipori/Medical-CXR-VQA.

High temperature requirement A1 and macrophage migration inhibitory factor in the cerebrospinal fluid; a potential marker of conversion from relapsing-remitting to secondary progressive multiple sclerosis.

BACKGROUND: Predictive and prognostic biomarkers for multiple sclerosis (MS) remain a significant gap in MS diagnosis and treatment monitoring. Currently, there are no timely markers to diagnose the transition to secondary progressive MS (SPMS). OBJECTIVE: This study aims to evaluate the discriminatory potential of the High temperature requirement serine protease (HTRA1)/Macrophage migration inhibitory factor (MIF) cerebrospinal fluid (CSF) ratio in distinguishing relapsing-remitting (RRMS) patients from SPMS patients. METHODS: The MIF and HTRA1 CSF levels were determined using ELISA in healthy controls (n = 23), RRMS patients before (n = 22) and after 1 year of dimethyl fumarate treatment (n = 11), as well as in SPMS patients before (n = 11) and after 2 years of mitoxantrone treatment (n = 7). The ability of the HTRA1/MIF ratio to discriminate the different groups was determined using receiver operating curve (ROC) analyses. RESULTS: The ratio was significantly increased in treatment naïve RRMS patients while decreased again in SPMS patients at baseline. Systemic administrated disease modifying treatment (DMT) only significantly affected the ratio in RRMS patients. ROC analysis demonstrated that the ratio could discriminate treatment naïve RRMS patients from SPMS patients with 91% sensitivity and 100% specificity. CONCLUSION: The HTRA1/MIF ratio is a strong candidate as a MS biomarker for SPMS conversion.

Focal Traumatic Brain Injury Impairs the Integrity of the Basement Membrane of Hindlimb Muscle Fibers Revealed by Extracellular Matrix Immunoreactivity.

Traumatic brain injury (TBI) stands as a prominent global cause of disability, with motor deficits being a common consequence. Despite its widespread impact, the precise pathological mechanisms underlying motor deficits after TBI remain elusive. In this study, hindlimb postural asymmetry (HL-PA) development in rats subjected to focal TBI was investigated to explore the potential roles of collagen IV and laminin within the extracellular matrix (ECM) of selected hindlimb muscles in the emergence of motor deficits following TBI. A focal TBI was induced by ablating the left sensorimotor cortex in rats and motor deficits were assessed by measuring HL-PA. The expression of laminin and collagen IV in eight selected muscles on each side of the hindlimbs from both TBI- and sham-operated rats were studied using immunohistochemistry and semi-quantitatively analyzed. The results indicated that the TBI rats exhibited HL-PA, characterized by flexion of the contralateral (right) hindlimb. In the sham-operated rats, the immunoreactive components of laminin and collagen IV were evenly and smoothly distributed along the border of the muscle fibers in all the investigated muscles. In contrast, in the TBI rats, the pattern was broken into aggregated, granule-like, immunoreactive components. Such a labeling pattern was detected in all the investigated muscles both from the contra- and ipsilateral sides of the TBI rats. However, in TBI rats, most of the muscles from the contralateral hindlimb showed a significantly increased expression of these two proteins in comparison with those from the ipsilateral hindlimb. In comparison to sham-operated rats, there was a significant increase in laminin and collagen IV expression in various contralateral hindlimb muscles in the TBI rats. These findings suggest potential implications of laminin and collagen IV in the development of motor deficits following a focal TBI.

Metabolic pathway and network analysis integration for discovering the biomarkers in pig feces after a controlled fruit-vegetable dietary intervention.

This study aimed to establish a strategy for identifying dietary intake biomarkers using a non-targeted metabolomic approach, including metabolic pathway and network analysis. The strategy was successfully applied to identify dietary intake biomarkers in fecal samples from pigs fed two doses of a polyphenol-rich fruit and vegetable (FV) diet following the Dietary Guidelines for Americans (DGA) recommendations. Potential biomarkers were identified among dietary treatment groups using liquid chromatography-high resolution mass spectrometry (LC-HRMS) based on a non-targeted metabolomic approach with metabolic pathway and network analysis. Principal component analysis (PCA) results showed significant differences in fecal metabolite profiles between the control and two FV intervention groups, indicating a diet-induced differential fecal metabolite profile after FV intervention. Metabolites from common flavonoids, e.g., (epi)catechin and protocatechuic acid, or unique flavonoids, e.g., 5,3',4'-trihydroxy-3-methoxy-6,7-methylenedioxyflavone and 3,5,3',4'-tetrahydroxy-6,7-methylenedioxyflavone, were identified as highly discriminating factors, confirming their potential as fecal markers for the FV dietary intervention. Microbiota pathway prediction using targeted flavonoids provided valuable and reliable biomarker exploration with high confidence. A correlation network analysis between these discriminatory ion features was applied to find connections to possible dietary biomarkers, further validating these biomarkers with biochemical insights. This study demonstrates that integrating metabolic pathways and network analysis with a non-targeted metabolomic approach is highly effective for rapid and accurate identification and prediction of fecal biomarkers under controlled dietary conditions in animal studies. This approach can also be utilized to study microbial metabolisms in human clinical research.

A New Benchmark: Clinical Uncertainty and Severity Aware Labeled Chest X-Ray Images with Multi-Relationship Graph Learning.

Chest radiography, commonly known as CXR, is frequently utilized in clinical settings to detect cardiopulmonary conditions. However, even seasoned radiologists might offer different evaluations regarding the seriousness and uncertainty associated with observed abnormalities. Previous research has attempted to utilize clinical notes to extract abnormal labels for training deep-learning models in CXR image diagnosis. However, these methods often neglected the varying degrees of severity and uncertainty linked to different labels. In our study, we initially assembled a comprehensive new dataset of CXR images based on clinical textual data, which incorporated radiologists' assessments of uncertainty and severity. Using this dataset, we introduced a multi-relationship graph learning framework that leverages spatial and semantic relationships while addressing expert uncertainty through a dedicated loss function. Our research showcases a notable enhancement in CXR image diagnosis and the interpretability of the diagnostic model, surpassing existing state-of-the-art methodologies. The dataset address of disease severity and uncertainty we extracted is: https://physionet.org/content/cad-chest/1.0/.

The Left-Right Side-Specific Neuroendocrine Signaling from Injured Brain: An Organizational Principle.

A neurological dogma is that the contralateral effects of brain injury are set through crossed descending neural tracts. We have recently identified a novel topographic neuroendocrine system (T-NES) that operates via a humoral pathway and mediates the left-right side-specific effects of unilateral brain lesions. In rats with completely transected thoracic spinal cords, unilateral injury to the sensorimotor cortex produced contralateral hindlimb flexion, a proxy for neurological deficit. Here, we investigated in acute experiments whether T-NES consists of left and right counterparts and whether they differ in neural and molecular mechanisms. We demonstrated that left- and right-sided hormonal signaling is differentially blocked by the δ-, κ- and µ-opioid antagonists. Left and right neurohormonal signaling differed in targeting the afferent spinal mechanisms. Bilateral deafferentation of the lumbar spinal cord abolished the hormone-mediated effects of the left-brain injury but not the right-sided lesion. The sympathetic nervous system was ruled out as a brain-to-spinal cord-signaling pathway since hindlimb responses were induced in rats with cervical spinal cord transections that were rostral to the preganglionic sympathetic neurons. Analysis of gene-gene co-expression patterns identified the left- and right-side-specific gene co-expression networks that were coordinated via the humoral pathway across the hypothalamus and lumbar spinal cord. The coordination was ipsilateral and disrupted by brain injury. These findings suggest that T-NES is bipartite and that its left and right counterparts contribute to contralateral neurological deficits through distinct neural mechanisms, and may enable ipsilateral regulation of molecular and neural processes across distant neural areas along the neuraxis.