Fluorescence and photoacoustic (FL/PA) dual-modal probe: Responsive to reactive oxygen species (ROS) for atherosclerotic plaque imaging.

Accurate and early detection of atherosclerosis (AS) is imperative for their effective treatment. However, fluorescence probes for efficient diagnosis of AS often encounter insufficient deep tissue penetration, which hinders the reliable assessment of plaque vulnerability. In this work, a reactive oxygen species (ROS) activated near-infrared (NIR) fluorescence and photoacoustic (FL/PA) dual model probe TPA-QO-B is developed by conjugating two chromophores (TPA-QI and O-OH) and ROS-specific group phenylboronic acid ester. The incorporation of ROS-specific group not only induces blue shift in absorbance, but also inhibits the ICT process of TPA-QO-OH, resulting an ignorable initial FL/PA signal. ROS triggers the convertion of TPA-QO-B to TPA-QO-OH, resulting in the concurrent amplification of FL/PA signal. The exceptional selectivity of TPA-QO-B towards ROS makes it effectively distinguish AS mice from the healthy. The NIR emission can achieve a tissue penetration imaging depth of 0.3 cm. Moreover, its PA775 signal possesses the capability to penetrate tissues up to a thickness of 0.8 cm, ensuring deep in vivo imaging of AS model mice in early stage. The ROS-triggered FL/PA dual signal amplification strategy improves the accuracy and addresses the deep tissue penetration problem simultaneously, providing a promising tool for in vivo tracking biomarkers in life science and preclinical applications.

Matrix stiffness regulates mitochondria-lysosome contacts to modulate the mitochondrial network, alleviate the senescence of MSCs.

The extracellular microenvironment encompasses the extracellular matrix, neighbouring cells, cytokines, and fluid components. Anomalies in the microenvironment can trigger aging and a decreased differentiation capacity in mesenchymal stem cells (MSCs). MSCs can perceive variations in the firmness of the extracellular matrix and respond by regulating mitochondrial function. Diminished mitochondrial function is intricately linked to cellular aging, and studies have shown that mitochondria-lysosome contacts (M-L contacts) can regulate mitochondrial function to sustain cellular equilibrium. Nonetheless, the influence of M-L contacts on MSC aging under varying matrix stiffness remains unclear. In this study, utilizing single-cell RNA sequencing and atomic force microscopy, we further demonstrate that reduced matrix stiffness in older individuals leads to MSC aging and subsequent decline in osteogenic ability. Mechanistically, augmented M-L contacts under low matrix stiffness exacerbate MSC aging by escalating mitochondrial oxidative stress and peripheral division. Moreover, under soft matrix stiffness, cytoskeleton reorganization facilitates rapid movement of lysosomes. The M-L contacts inhibitor ML282 ameliorates MSC aging by reinstating mitochondrial network and function. Overall, our findings confirm that MSC aging is instigated by disruption of the mitochondrial network and function induced by matrix stiffness, while also elucidating the potential mechanism by which M-L Contact regulates mitochondrial homeostasis. Crucially, this presents promise for cellular anti-aging strategies centred on mitochondria, particularly in the realm of stem cell therapy.

Improving Thermosensitive Bioink Scaffold Fabrication with a Temperature-Regulated Printhead in Robot-Assisted In Situ Bioprinting System.

In situ bioprinting enables precise 3D printing inside the human body using modified bioprinters with thermosensitive bioinks such as gelatin methacrylate (GelMA). However, these devices lack refined temperature-regulated mechanisms essential for ensuring bioink viscosity, as compared to traditional bio-3D printers. Addressing this challenge, this study presents a temperature-regulated printhead designed to improve the fabrication of thermosensitive bioink scaffolds in in situ bioprinting, integrated into a UR5 robotic arm. Featuring a closed-loop system, it achieves a temperature steady error of 1 °C and a response time of approximately 1 min. The effectiveness of the printer was validated by bioprinting multilayer lattice 3D bioscaffolds. Comparisons were made with or without temperature control using different concentrations of GelMA + LAP. The deformation of the bioscaffolds under both conditions was analyzed, and cell culture tests were conducted to verify viability. Additionally, the rheology and mechanical properties of GelMA were tested. A final preliminary in situ bioprinting experiment was conducted on a model of a damaged femur to demonstrate practical application. The fabrication of this printhead is entirely open source, facilitating easy modifications to accommodate various robotic arms. We encourage readers to advance this prototype for application in increasingly complex in situ bioprinting situations, especially those utilizing thermosensitive bioinks.

Multi-omics analysis reveals the key factors involved in the severity of the Alzheimer's disease.

Alzheimer's disease (AD) is a debilitating neurodegenerative disorder with a global impact, yet its pathogenesis remains poorly understood. While age, metabolic abnormalities, and accumulation of neurotoxic substances are potential risk factors for AD, their effects are confounded by other factors. To address this challenge, we first utilized multi-omics data from 87 well phenotyped AD patients and generated plasma proteomics and metabolomics data, as well as gut and saliva metagenomics data to investigate the molecular-level alterations accounting the host-microbiome interactions. Second, we analyzed individual omics data and identified the key parameters involved in the severity of the dementia in AD patients. Next, we employed Artificial Intelligence (AI) based models to predict AD severity based on the significantly altered features identified in each omics analysis. Based on our integrative analysis, we found the clinical relevance of plasma proteins, including SKAP1 and NEFL, plasma metabolites including homovanillate and glutamate, and Paraprevotella clara in gut microbiome in predicting the AD severity. Finally, we validated the predictive power of our AI based models by generating additional multi-omics data from the same group of AD patients by following up for 3 months. Hence, we observed that these results may have important implications for the development of potential diagnostic and therapeutic approaches for AD patients.

Predictive models and treatment efficacy for liver cancer patients with bone metastases: A comprehensive analysis of prognostic factors and nomogram development.

Background: Bone metastasis considerably undermines the prognosis of advanced primary liver cancer patients. Though its impact is well-recognized, the clinical field still lacks robust predictive models that can accurately forecast patient outcomes and aid in treatment effectiveness evaluation. Addressing this gap is paramount for improving patient management and survival. Materials and methods: We conducted an extensive analysis using data from the SEER database (2010-2020). COX regression analysis was applied to identify prognostic factors for primary liver cancer with bone metastasis (PLCBM). Nomograms were developed and validated to predict survival outcomes in PLCBM patients. Additionally, propensity score matching and Kaplan-Meier survival analyses lent additional insight by dissecting the survival advantage conferred by various treatment strategies. Results: A total of 470 patients with PLCBM were included in our study. The median overall survival (OS) and cancer-specific survival (CSS) for these patients were both 5 months. We unveiled several independent prognosticators for OS and CSS, spanning demographic to therapeutic parameters like marital status, cancer grade, histological type, and treatments received. This discovery enabled the formulation of two novel nomograms-now verified to eclipse the predictive prowess of the traditional TNM staging system regarding discrimination and clinical utility. Additionally, propensity score matching analysis showed the effectiveness of surgeries, radiotherapy, and chemotherapy in improving OS and CSS outcomes for PLCBM patients. Conclusions: Our investigation stands out by introducing pioneering nomograms for prognostic evaluation in PLCBM, a leap forward compared to existing tools. Far exceeding mere academic exercise, these nomograms hold immense clinical value, serving as a foundation for nuanced risk stratification systems and delivering dynamic, interactive guides, allowing healthcare professionals and patients to assess individual bone metastasis survival probabilities and personalize treatment selection.

Copper-induced oxidative stress inhibits asexual reproduction of Aurelia coerulea polyps.

OBJECTIVE: Our research aims to investigate the specific mechanisms by which copper inhibits the asexual proliferation of Aurelia coerulea polyps. METHODS: Aurelia coerulea polyps were exposed to various CuSO4 concentrations to study metamorphosis and budding proliferation. Oxidative stress markers (ROS, MDA, CAT, H2O2, T-AOC, SOD) were measured in polyps and early strobilae. Transcriptomic analysis were used to compare differences in gene expression and enrichment pathways between untreated and copper-exposed polyps. Additionally, RT-qPCR was used to analyze the expression of key molecules. Antioxidant L-Ascorbic acid was applied to determine the role of oxidative stress in asexual reproduction of Aurelia coerulea polyps when exposed to copper. RESULTS: Copper inhibited strobilization and budding of Aurelia coerulea polyps in a dose-dependent manner, in which oxidative stress was involved. Transcriptomic data suggested that the DNA replication pathway was significantly enriched in early strobilae compared to polyps. However, copper treatment repealed the difference of DNA replication pathway between early strobilae compared and polyps. Transcriptomic data suggested that alanine, aspartate, and glutamate metabolism pathways were enriched in untreated budding polyps compared to copper-exposed polyps. After applying the antioxidant L-Ascorbic acid to copper-exposed polyps, various oxidative indicators changed to different extents, with increases in ROS, MDA, CAT, H2O2, and SOD and a decrease in T-AOC. Further more, the time required for polyps to develop into early strobila was shortened, indicating that the delay in metamorphosis caused by copper exposure was effectively alleviated. And the budding rate increased, indicating that the inhibition of budding proliferation caused by copper exposure was effectively alleviated. The expression of key genes were consist with the transcriptomic sequencing results. CONCLUSION: Copper exposure causes oxidative stress resulting in the inhibition of asexual reproduction in Aurelia coerulea polyps, including metamorphosis and budding.

Reflections on Small-Class Teaching in Veterinary Medicine Undergraduate Programs in China.

As a cornerstone of higher education in China, the quality of undergraduate teaching is crucial for nurturing high-caliber innovative talents that meet the needs of national and social development. Small-class teaching has emerged as a focal point in the reform of undergraduate education and represents an important approach to cultivating top-notch innovative talents. Veterinary medicine is a scientific discipline that encompasses the prevention, control, diagnosis, and treatment of animal diseases. It also involves efforts to prevent the transmission of animal diseases to humans. The training of professionals in this field should emphasize the integration of theoretical knowledge with practical skills. Therefore, small-class teaching facilitates open communication between educators and students, which is instrumental in fostering a new generation of veterinarians equipped with robust professional knowledge, excellent practical abilities, and strong scientific literacy. This paper provides a preliminary exploration into the strategy and direction of small-class teaching within China's veterinary medicine undergraduate programs by summarizing its characteristics, advantages, and existing challenges. The unique traits of veterinary medicine are also taken into consideration during this analysis.

Prognostic prediction signature and molecular subtype for liver cancer: A CTC/CTM­related gene prediction model and independent external validation.

Liver cancer is the second leading cause of tumor-related death worldwide, and a serious threat to lives and health. Circulating tumor cells (CTCs) facilitate the progression of various cancers, including liver cancer. The relationship between CTC/circulating tumor microemboli-related genes (CRGs) and the prognosis of liver cancer is unclear. The aim of the present study was to identify CTC/circulating tumour microemboli-related genes (CRGs) in hepatocellular carcinoma and to investigate their clinical significance. Transcriptomic data from The Cancer Genome Atlas (International Cancer Genome Consortium (ICGC) and GSE117623 databases were combined, and differentially expressed CRGs were identified. These were subsequently analyzed via least absolute shrinkage and selection operator and multivariate Cox analyses, and a five-gene risk signature was constructed. The signature was validated in the ICGC and GSE14520 dataset with survival analysis and receiver operating characteristic curve analysis. Immunocyte infiltration, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE), and the somatic mutation rate were also compared between high- and low-risk groups, based on the median predictive index, to further evaluate the immunotherapeutic value of the model. Molecular subtypes of liver cancer were characterized by the non-negative matrix factorization method and potential therapeutic compounds were evaluated for different subtypes. Nomograms were utilized to predict the prognosis of patients, and the signature was compared with previous literature models. Additionally, the biological function of one of the CRGs, tumor protein p53 inducible protein 3 (TP53I3), in liver cancer was further explored through in vitro experiments. Analysis of the prognostic characteristics of the five CRGs led to the identification of two liver cancer subtypes. Patients in the low-risk group had a longer survival compared with those in the high-risk group, and patients in the latter group were associated with a higher TMB, immunocyte infiltration and somatic mutation rate, and lower TIDE scores. The prognostic profile was validated in the ICGC and GSE14520 datasets and exhibited a good predictive performance. In vitro analysis showed that the knockdown of TP53I3 suppressed liver cancer cell proliferation. In summary, CRGs were used to develop a new prognostic signature to predict the prognosis of patients with liver cancer. This signature may be used to assess the prognosis of patients and may provide new insights for clinical management strategies. In addition, TP53I3 is potentially a therapeutic target for liver cancer.

Pipette-tip kapok fiber-based solid-phase extraction/in-situ derivatization for the rapid and green analysis of furfural compounds.

Furfural compounds, including 5-hydroxymethylfurfural, furfural, and 5-methylfurfural, are common in foods and pose health risks. This study presents a pipette-tip solid-phase extraction with in-situ derivatization (PT-KF-SPE/ISD) method for rapid analysis of furfural compounds in various food matrices. Utilizing natural kapok fiber as an efficient adsorbent, this method integrates extraction and derivatization into a single step via a simple pull-push operation. Derivatization with 2,4-dinitrophenylhydrazine increases the hydrophobicity and ultraviolet absorption of furfural compounds, enabling sensitive liquid chromatography-ultraviolet detection. The method shows good linearity, sensitivity, and reproducibility, with limits of detection in ranges of 3.9-6.0 ng/mL. Real sample analysis confirms its applicability in detecting furfural compounds in beverages and herbal products, offering a reliable and eco-friendly solution for food safety and quality control. Five greenness assessment metrics demonstrate the method's excellent environmental friendliness. This approach highlights the advantages of combining natural adsorbents with in-situ derivatization for efficient food analysis.

The influence of { 10 1 ¯ 2 } twinning on the corrosion behavior of AZ31B magnesium alloy.

This study investigates the effect of twinning on the corrosion behavior of AZ31B magnesium alloy using solid solution heat treatment (SHT) and laser shock peening (LSP) techniques. The corrosion characteristics are assessed by scanning electron microscopy (SEM), scanning Kelvin probe force microscopy (SKPFM), zero resistance ammeter (ZRA), scanning vibrating electrode technique (SVET), and electrochemical tests. Results indicate that the twinning region in AZ31B magnesium alloy, enriched with { 10 1 ¯ 2 } tensile twins induced by laser shock, demonstrates increased corrosion susceptibility. This region exhibits higher electrochemical activity and an accelerated corrosion rate compared to the matrix region. Micro-galvanic coupling between the twinned and matrix regions promotes faster dissolution of the alloy. Additionally, the corrosion product film on the surface is extensively cracked and propagates to the matrix corrosion surface, confirming that { 10 1 ¯ 2 } tensile twins provide inadequate protection against corrosion in AZ31B alloy.

Lactate supports Treg function and immune balance via MGAT1 effects on N-glycosylation in the mitochondria.

Current research reports that lactate affects Treg metabolism, although the precise mechanism has only been partially elucidated. In this study, we presented evidence demonstrating that elevated lactate levels enhanced cell proliferation, suppressive capabilities, and oxidative phosphorylation (OXPHOS) in human Tregs. The expression levels of Monocarboxylate Transporters 1/2/4 (MCT1/2/4) regulate intracellular lactate concentration, thereby influencing the varying responses observed in naive Tregs and memory Tregs. Through mitochondrial isolation, sequencing, and analysis of human Tregs, we determined that Alpha-1,3-Mannosyl-Glycoprotein 2-Beta-N-Acetylglucosaminyltransferase (MGAT1) served as the pivotal driver initiating downstream N-glycosylation events involving progranulin (GRN) and hypoxia-upregulated 1 (HYOU1), consequently enhancing Treg OXPHOS. The mechanism by which MGAT1 was upregulated in mitochondria depended on elevated intracellular lactate that promoted the activation of XBP1s, which, in turn, supported MGAT1 transcription as well as the interaction of lactate with the translocase of the mitochondrial outer membrane 70 (TOM70) import receptor, facilitating MGAT1 translocation into mitochondria. Pre-treatment of Tregs with lactate reduced mortality in a xenogeneic graft-versus-host disease (GvHD) model. Together, these findings underscored the active regulatory role of lactate in human Treg metabolism through the upregulation of MGAT1 transcription and its facilitated translocation into the mitochondria.

MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction.

Drug therapy remains the primary approach to treating tumours. Variability among cancer patients, including variations in genomic profiles, often results in divergent therapeutic responses to analogous anti-cancer drug treatments within the same cohort of cancer patients. Hence, predicting the drug response by analysing the genomic profile characteristics of individual patients holds significant research importance. With the notable progress in machine learning and deep learning, many effective methods have emerged for predicting drug responses utilizing features from both drugs and cell lines. However, these methods are inadequate in capturing a sufficient number of features inherent to drugs. Consequently, we propose a representational approach for drugs that incorporates three distinct types of features: the molecular graph, the SMILE strings, and the molecular fingerprints. In this study, a novel deep learning model, named MCMVDRP, is introduced for the prediction of cancer drug responses. In our proposed model, an amalgamation of these extracted features is performed, followed by the utilization of fully connected layers to predict the drug response based on the IC50 values. Experimental results demonstrate that the presented model outperforms current state-of-the-art models in performance.

Robust Chiral Metal-Organic Framework Coatings for Self-Activating and Sustainable Biofouling Mitigation.

Surface coatings are designed to mitigate pervasive biofouling herald, a new era of surface protection in complex biological environments. However, existing strategies are plagued by persistent and recurrent biofilm attachment, despite the use of bactericidal agents. Herein, a chiral metal-organic framework (MOF)-based coating with conformal microstructures to enable a new anti-biofouling mode that involves spontaneous biofilm disassembly followed by bacterial eradication is developed. A facile and universal metal-polyphenol network (MPN) is designed to robustly anchor the MOF nanoarmor of biocidal Cu2+ ions and anti-biofilm d-amino acid ligands to a variety of substrates across different material categories and surface topologies. Incorporating a diverse array of chiral amino acids endows the resultant coatings with widespread signals for biofilm dispersal, facilitating copper-catalyzed chemodynamic reactions and inherent mechano-bactericidal activities. This synergistic mechanism yields unprecedented anti-biofouling efficacy elucidated by RNA-sequencing transcriptomics analysis, enhancing broad-spectrum antibacterial activities, preventing biofilm formation, and destroying mature biofilms. Additionally, the chelation-directed amorphous/crystalline coatings can activate photoluminescent properties to inhibit the settlement of microalgae biofilms. This study provides a distinctive perspective on chirality-enhanced antimicrobial behaviors and pioneers a rational pathway toward developing next-generation anti-biofouling coatings for diverse applications.

Association between long-term exposure to PM2.5 chemical components and metabolic syndrome in middle-aged and older adults.

Background: Previous studies indicated that exposure to ambient fine particulate matter (PM2.5) could increase the risk of metabolic syndrome (MetS). However, the specific impact of PM2.5 chemical components remains uncertain. Methods: A national cross-sectional study of 12,846 Chinese middle-aged and older adults was conducted. Satellite-based spatiotemporal models were employed to determine the 3-year average PM2.5 components exposure, including sulfates (SO4 2-), nitrates (NO3 -), ammonia (NH4 +), black carbon (BC), and organic matter (OM). Generalized linear models were used to investigate the associations of PM2.5 components with MetS and the components of MetS, and restricted cubic splines curves were used to establish the exposure-response relationships between PM2.5 components with MetS, as well as the components of MetS. Results: MetS risk increased by 35.1, 33.5, 33.6, 31.2, 32.4, and 31.4% for every inter-quartile range rise in PM2.5, SO4 2-, NO3 -, NH4 +, OM and BC, respectively. For MetS components, PM2.5 chemical components were associated with evaluated risks of central obesity, high blood pressure (high-BP), high fasting glucose (high-FBG), and low high-density lipoprotein cholesterol (low-HDL). Conclusion: This study indicated that exposure to PM2.5 components is related to increased risk of MetS and its components, including central obesity, high-BP, high-FBG, and low-HDL. Moreover, we found that the adverse effect of PM2.5 chemical components on MetS was more sensitive to people who were single, divorced, or widowed than married people.

MCC950 promotes diabetic wound healing through modulating macrophage polarization in an MDSC-dependent manner.

Diabetic foot ulcers (DFUs) are serious skin injuries whereby the wound healing process is frequently stalled in the inflammatory phase. Currently, there is a lack of effective therapeutic strategies. MCC950, a highly selective nod-like receptor family pyrin domain containing 3 (NLRP3) inhibitor, has been reported to show strong anti-inflammation effects in many diseases. In this study, we unveiled the role of MCC950 in DFU mice model and its underlying molecular mechanisms. MCC950 could significantly accelerate diabetic wound healing, as shown by shortened healing time and better healing quality. Moreover, increased M2 phenotype macrophages and decreased pro-inflammatory genes were observed in MCC950-treated DFU mice. Additionally, myeloid-derived suppressor cells (MDSCs) were significantly increased in blood, spleen and wound tissues at different time courses. Specifically, MCC950 could recruit more MDSCs in an early phase in DFU mice, exerting an anti-inflammation effect. We identified the cell crosstalk between macrophages and MDSCs with MCC950 treatment process. Depleting MDSCs in vivo could eliminate the therapeutic effect of MCC950 on diabetic wound healing through inhibiting M2 macrophage polarization. Besides, MDSCs isolated from the wounds of MCC950 or saline treated mice were cocultured with bone marrow derived macrophage (BMDM) in a transwell system. Results confirmed that MDSCs sorted from MCC950 treated mice caused a significant increased percentage of M2 macrophages. Collectively, our findings suggest that the administration of MCC950 has the potential to accelerate diabetic wound healing by promoting M2 macrophage polarization in an MDSC-dependent manner. This study provides valuable insights into the utilization of pharmacological agents for DFU treatment.

Accurate prediction of discontinuous crack paths in random porous media via a generative deep learning model.

Pore structures provide extra freedoms for the design of porous media, leading to desirable properties, such as high catalytic rate, energy storage efficiency, and specific strength. This unfortunately makes the porous media susceptible to failure. Deep understanding of the failure mechanism in microstructures is a key to customizing high-performance crack-resistant porous media. However, solving the fracture problem of the porous materials is computationally intractable due to the highly complicated configurations of microstructures. To bridge the structural configurations and fracture responses of random porous media, a unique generative deep learning model is developed. A two-step strategy is proposed to deconstruct the fracture process, which sequentially corresponds to elastic deformation and crack propagation. The geometry of microstructure is translated into a scalar of elastic field as an intermediate variable, and then, the crack path is predicted. The neural network precisely characterizes the strong interactions among pore structures, the multiscale behaviors of fracture, and the discontinuous essence of crack propagation. Crack paths in random porous media are accurately predicted by simply inputting the images of targets, without inputting any additional input physical information. The prediction model enjoys an outstanding performance with a prediction accuracy of 90.25% and possesses a robust generalization capability. The accuracy of the present model is a record so far, and the prediction is accomplished within a second. This study opens an avenue to high-throughput evaluation of the fracture behaviors of heterogeneous materials with complex geometries.

The association between salivary pepsin and gastroesophageal reflux disease: A meta-analysis.

BACKGROUND AND PURPOSE: The definitive diagnosis of gastroesophageal reflux disease (GERD) often requires invasive investigations like upper gastrointestinal endoscopy or reflux monitoring. We aimed to explore the relationship between salivary pepsin and GERD and its value as a non-invasive diagnostic tool. METHODS: Databases (PubMed, Web of Science, Cochran Library, and EMBASE) were searched from their inception to January 22, 2024 to explore the correlation of salivary pepsin with GERD. The meta-analysis data retrieved were summarized, including the salivary pepsin concentration, sensitivity of diagnosis (SEN), specificity of diagnosis (SPE), negative likelihood ratio, positive likelihood ratio, diagnostic odds ratio, and receiver operating characteristic (ROC) curve. RESULTS: The meta-analysis comparing salivary pepsin concentration in two groups (proven GERD and non-GERD) with 18 studies revealed that the proven GERD group had higher salivary pepsin concentration than the non-GERD group (SMD = 1.74 [95% CI 1.14-2.34]). The meta-analysis of salivary pepsin diagnostic value for proven GERD incorporated 23 studies. The results showed pooled SEN (0.73 [95% CI 0.66-0.80]), SPE (0.72 [95% CI 0.65-0.78]), positive likelihood ratio (2.61 [95% CI 2.02-3.39]), negative likelihood ratio (0.37 [95% CI 0.28-0.50]), diagnostic odds ratio (7.03 [95% CI 4.24-11.66]) and area under the SROC curve (0.79 [95% CI 0.75-0.82]). CONCLUSION: GERD patients presented a higher salivary pepsin concentration. Salivary pepsin is both sensitive and specific in identifying GERD, making it a promising non-invasive marker for diagnosis.

Utility of plasma nucleocapsid protein in predicting severity and prognosis in severe COVID-19 patients with comorbidities.

OBJECTIVES: The COVID-19 pandemic poses ongoing challenges to global public health systems, emphasizing the critical necessity for efficient diagnostic and prognostic markers. This study evaluates the MAGLUMI® SARS-CoV-2 Ag N protein chemiluminescent immunoassay (MAG-CLIA) for its analytical performance and its role in predicting disease severity and prognosis among severe COVID-19 patients with comorbidities. METHODS: Analytical validation of plasma MAG-CLIA SARS-CoV-2 Ag N protein encompassed precision, interference, LoQ and linearity. Plasma N protein concentrations and other biomarkers were measured within 48 h of admission, tracked until discharge or death. The Mann-Whitney U test explored the association between plasma N protein and COVID-19 severity or prognosis. Longitudinal monitoring of plasma N protein dynamics was conducted in representative patients. RESULTS: MAG-CLIA demonstrated precise quantification of plasma N protein with a CV below 10 % and minimal interference. The LoQ was 0.88 ng/L, with a broad linear range. Plasma N protein showed high diagnostic accuracy for COVID-19, achieving 95.42 % specificity and 78.32 % sensitivity at 2.388 ng/L. Plasma N protein emerged as a valuable prognostic indicator, correlating with mechanical ventilation need and patient survival. Plasma N protein concentrations ≥ 424.3 ng/L (AUC 0.8102, sensitivity 78.38 %, specificity 85.48 %) were associated with poor prognosis in severe COVID-19 patients with comorbidities. CONCLUSIONS: MAG-CLIA's SARS-CoV-2 N protein detection in plasma demonstrates both analytical reliability and clinical relevance in our inaugural evaluation. As a promising prognostic biomarker for severe COVID-19 patients, it offers crucial insights into disease severity and progression, emphasizing the significance of early monitoring and intervention, especially for patients with comorbidities.

Prenatal low-dose Bisphenol A exposure impacts cortical development via cAMP-PKA-CREB pathway in offspring.

Bisphenol A (BPA) is a widely used plasticizer known to cause various disorders. Despite a global reduction in the use of BPA-containing products, prenatal exposure to low-dose BPA, even those below established safety limits, has been linked to neurological and behavioral deficits in childhood. The precise mechanisms underlying these effects remain unclear. In the present study, we observed a significant increase in the number of cortical neurons in offspring born to dams exposed to low-dose BPA during pregnancy. We also found that this prenatal exposure to low-dose BPA led to increased proliferation but reduced migration of cortical neurons. Transcriptomic analysis via RNA sequencing revealed an aberrant activation of the cAMP-PKA-CREB pathway in offspring exposed to BPA. The use of H89, a selective PKA inhibitor, effectively rescued the deficits in both proliferation and migration of cortical neurons. Furthermore, offspring from dams exposed to low-dose BPA exhibited manic-like behaviors, including hyperactivity, anti-depressant-like responses, and reduced anxiety. While H89 normalized hyperactivity, it didn't affect the other behavioral changes. These results suggest that the overactivation of PKA plays a causative role in BPA-induced changes in neuronal development. Our data also indicate that manic-like behaviors induced by prenatal low-dose BPA exposure may be influenced by both altered neuronal development and abnormal PKA signaling in adulthood.