Analysis Platform Research Articles

Development of Aptamer-Functionalized Gold Nanoparticles as Probes in Point-of-Care Diagnostic Device for Rapid Detection of Multidrug-Resistant Bacteria in Bombyx mori L. .

The sericulture industry suffers severe crop losses due to various silkworm diseases, necessitating the development of further technologies for rapid pathogen detection. Here, we report an all-in-one portable biosensor that combines conjugated gold nanoparticles (Au NPs) with an aptamer-based lateral flow assay (LFA) platform for the real-time analysis of Mammaliicoccus sp. and Pseudomonas sp. Our platform enables sample-to-answer naked eye detection within 5 min without any cross-reactivity with other representatives of the silkworm pathogenic bacterial group. This assay was based on the sandwich-type format using a bacteria-specific primary aptamer (Apt1) conjugated with 23 nm ± 1.27 nm Au NPs as a signal probe and another bacteria-specific secondary aptamer (Apt2)-coated nitrocellulose membrane as a capture probe. The hybridization between the signal probe and the capture probe in the presence of bacteria develops a red band in the test line, whose intensity is directly proportional to the bacterial concentration. Under the optimal experimental conditions, the visual limit of detection of the strip for Mammaliicoccus sp. and Pseudomonas sp. was 1.5 × 104 CFU/mL and 1.5 × 103 CFU/mL, respectively. Additionally, the performance of the LFA device was validated by using a colorimetric assay, and the results from the colorimetric assay are consistent with those obtained from the LFA. Our findings indicate that the developed point-of-care diagnostic device has significant potential for providing a cost-effective, scalable alternative for the rapid detection of silkworm pathogens.

High-Performance Suspension Bead Sensor Based on Optical Tweezers and Immuno-Rolling Circle Amplification.

In recent years, optical tweezers have become an effective bioassay tool due to their unique advantages, especially in combination with suspension beads, which can be applied to develop a high-performance analysis platform capable of high-quality imaging and stable signal output. However, the optical tweezer-assisted bead analysis is still at the early stage, and further development of different favorable methods is in need. Herein, we have first developed the optical tweezer-assisted immuno-rolling circle amplification (immuno-RCA) on beads for protein detection. Prostate-specific antigen was selected as the model analyte, and the immunosandwich structure on beads was built by the high affinity of "antibody-antigen". The "protein-nucleic acid" signals were effectively converted through the covalent coupling procedure of antibodies and oligonucleotides, further initiating the RCA reaction to achieve signal amplification. The individual beads with the strong irregular Brownian motion in a fluid environment were eventually trapped by the optical tweezers to acquire the accurate and high-quality signal. Compared with the conventional immunoassay on beads, the sensitivity of the developed strategy was increased by 587 times with a limit of detection of 4.29 pg/mL (0.13 pM), as well as excellent specificity, stability, and reproducibility. This study developed the new optical tweezer-assisted beads imaging strategy for protein targets, which has great potential for being applied to clinical serology research and expands the application of optical tweezers in the bioassays.

Direct operational data-driven workflow for dynamic voltage prediction of commercial alkaline water electrolyzers based on artificial neural network (ANN)

The thermal inertia of alkaline water electrolysis (AWE) system under dynamic operation reduces efficiency and poses security risks. To mitigate this, predictive model control (MPC) emerges as a potential solution, aiming to precisely regulate temperature through temperature trend prediction, which relies on accurate voltage predictions. However, the current MPC implementation for AWE system utilizes empirical model for voltage prediction, requiring extensive experimental data for calibration, making it time-consuming and costly for large-scale water electrolysis projects. Data-driven models are a promising alternative for voltage prediction, but research is limited by the lack of long-term commercial AWE operational data and the ambiguity in developing effective data-driven models. In this study, a 10-kW level AWE test system underwent 300 h of simulated solar-driven dynamic operation to assess the resilience of AWE system to dynamic conditions. Subsequently, a robust data-driven workflow using artificial neural networks (ANNs) was developed for precise dynamic voltage prediction based on obtained operational data. The data-driven workflow incorporates automated processes for voltage prediction, including data importing, data cleaning, ANN architecture optimization, model training, and ultimately voltage prediction, avoiding the need for manual parameters fitting. The model’s performance was evaluated and compared with empirical model across four dynamic operation types (cold-start, ramp, stepwise, and random response), achieving an impressive accuracy over 98 %. Furthermore, the ANN model also exhibits application potential in U-I curves prediction (accuracy > 98 %), influential features assessment, operational optimization and MPCs. This ANN-based workflow serves as a versatile framework for precise voltage prediction under dynamic operations and provides an analytical platform for operational data analysis, system optimization, and advanced control system development.

Development of an advanced separation and characterization platform for mRNA and lipid nanoparticles using multi-detector asymmetrical flow field-flow fractionation.

In recent years, the use of lipid nanoparticles (LNPs) for delivery of messenger RNA (mRNA)-based therapies has gained substantial attention in the field of drug development. In such an application, multiple LNP attributes have to be carefully characterized to ensure product safety and quality, whereas accurate and efficient characterization of these complex mRNA-LNP formulations remains a challenging endeavor. Here, we present the development and application of an online separation and characterization platform designed for the isolation and in-depth analysis of mRNAs and mRNA-loaded LNPs. Our asymmetrical flow field-flow fractionation with a multi-detector (MD-AF4) method has demonstrated exceptional resolution between mRNA-LNPs and mRNAs, delivering excellent recoveries (over 70%) for both analytes and exceptional repeatability. Notably, this platform allows for comprehensive and multi-attribute LNP characterization, including online particle sizing, morphology characterization, and determination of encapsulation efficiency, all within a single injection. Furthermore, real-time online sizing by synchronizing multi-angle light scattering (MALS) and dynamic light scattering (DLS) presented higher resolution over traditional batch-mode DLS, particularly in differentiating heterogeneous samples with a low abundance of large-sized particles. Additionally, our method proves to be a valuable tool for monitoring LNP stability under varying stress conditions. Our work introduces a robust and versatile analytical platform using MD-AF4 that not only efficiently provides multi-attribute characterizations of mRNA-LNPs but also holds promise in advancing studies related to formulation screening, quality control, and stability assessment in the evolving field of nanoparticle delivery systems for mRNAs.

Deep learning assisted quantitative analysis of Aβ and microglia in patients with idiopathic normal pressure hydrocephalus in relation to cognitive outcome.

Neuropathologic changes of Alzheimer disease (AD) including Aβ accumulation and neuroinflammation are frequently observed in the cerebral cortex of patients with idiopathic normal pressure hydrocephalus (iNPH). We created an automated analysis platform to quantify Aβ load and reactive microglia in the vicinity of Aβ plaques and to evaluate their association with cognitive outcome in cortical biopsies of patients with iNPH obtained at the time of shunting. Aiforia Create deep learning software was used on whole slide images of Iba1/4G8 double immunostained frontal cortical biopsies of 120 shunted iNPH patients to identify Iba1-positive microglia somas and Aβ areas, respectively. Dementia, AD clinical syndrome (ACS), and Clinical Dementia Rating Global score (CDR-GS) were evaluated retrospectively after a median follow-up of 4.4 years. Deep learning artificial intelligence yielded excellent (>95%) precision for tissue, Aβ, and microglia somas. Using an age-adjusted model, higher Aβ coverage predicted the development of dementia, the diagnosis of ACS, and more severe memory impairment by CDR-GS whereas measured microglial densities and Aβ-related microglia did not correlate with cognitive outcome in these patients. Therefore, cognitive outcome seems to be hampered by higher Aβ coverage in cortical biopsies in shunted iNPH patients but is not correlated with densities of surrounding microglia.

Accelerated discovery of perovskite solid solutions through automated materials synthesis and characterization

Accelerating perovskite solid solution discovery and sustainable synthesis is crucial for addressing challenges in wireless communication and biosensors. However, the vast array of chemical compositions and their dependence on factors such as crystal structure, and sintering temperature require time-consuming manual processes. To overcome these constraints, we introduce an automated materials discovery approach encompassing machine learning (ML) assisted material screening, robotic synthesis, and high-throughput characterization. Our proposed platform for rapid sintering and dielectric analysis streamlines the characterization of perovskites and the discovery of disordered materials. The setup has been successfully validated, demonstrating processing materials within minutes, in stark contrast to conventional procedures that can take hours or days. Following setup validation with established samples, we showcase synthesizing single-phase solid solutions within the barium family, such as (BaxSr1-x)CeO3, identified through ML-guided chemistry.

Preliminary investigation on the mechanism of anti-periodontitis effect of Scutellariae Radix based on bioinformatics analysis and in vitro verification

ObjectiveTo investigate the material basis, targets and molecular mechanism of Scutellariae Radix against periodontitis to provide theoretical basis for clinical applications. Materials and methodsThe active compounds and targets of Scutellariae Radix were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, and the periodontitis-related targets were collected by integrating Online Mendelian Inheritance in Man (OMIM), Therapeutic Target Database (TTD), Genecards and Gene Expression Omnibus (GEO) database together. The potential targets of Scutellariae Radix against periodontitis were obtained from the intersection of two target sets. Metascape database was used for Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Discovery Studio software was used for molecular docking between key targets and compounds to evaluate their binding affinity. Western blot was used to check the expression of PTGS2 and MMP9 to verify the regulatory effects of baicalein, the main active compound of Scutellariae Radix, on human periodontal ligament stem cells (hPDLSCs) cultured under inflammatory environment which induced by lipopolysaccharide (LPS). Results15 active compounds of Scutellariae Radix and 53 common targets for periodontitis treatment were identified. Among these targets, the 10 core targets were AKT1, IL-6, TNF, VEGFA, TP53, PTGS2, CASP3, JUN, MMP9 and HIF1A. GO and KEGG analysis mainly focused on response to LPS and pathways in cancer. Molecular docking showed that the main active compounds had good binding affinity with key targets. Cell experiments confirmed that baicalein can interfere the expression of pro-inflammatory factors PTGS2 and MMP9 proteins and exert anti-inflammatory effects. ConclusionCurrent study preliminarily analyzed the mechanism of Scutellariae Radix against periodontitis, which provide a new idea for the utilization of Scutellariae Radix and the development of novel medicine for the clinical treatment of periodontitis.

Clinical drug interactions between linezolid and other antibiotics: For adverse drug event monitoring.

Detailed data on safety associated with drug-drug interactions (DDIs) between Linezolid (LZD) and other antibiotics are limited. The aim of this study was to investigate the safety signals related to these DDIs and to provide a reference for clinically related adverse drug event monitoring. Adverse event (AE) information from 1 January 2004 to 16 June 2022 of the target antibiotics including LZD using alone or in combination with LZD was extracted from the OpenVigil FDA data platform for safety signal analysis. The combined risk ratio model, reporting ratio method, Ω shrinkage measure model, and chi-square statistics model were used to analyze the safety signals related to DDIs. Meanwhile, we evaluated the correlation and the influence of sex and age between the drug(s) and the target AE detected. There were 18991 AEs related to LZD. There were 2293, 1726, 4449, 821, 2431, 1053, and 463 AE reports when LZD was combined with amikacin, voriconazole, meropenem, clarithromycin, levofloxacin, piperacillin-tazobactam, and azithromycin, respectively. Except for azithromycin, there were positive safety signals related to DDIs between LZD and these antibiotics. These DDIs might influence the incidence of 13, 16, 7, 7, 6, and 15 types of AEs, respectively, and is associated with higher reporting rates of AEs compared with use alone. Moreover, sex and age might influence the occurrence of AEs. We found that the combinations of LZD and other antibiotics are related to multiple AEs, such as hepatotoxicity, drug resistance and electrocardiogram QT prolonged, but further research is still required to investigate their underlying mechanisms. This study can provide a new reference for the safety monitoring of LZD combined with other antibiotics in clinical practice.

Single-molecule epiallelic profiling of DNA derived from routinely collected Pap specimens for noninvasive detection of ovarian cancer.

Recent advances in molecular analyses of ovarian cancer have revealed a wealth of promising tumour-specific biomarkers, including protein, DNA mutations and methylation; however, reliably detecting such alterations at satisfactorily high sensitivity and specificity through low-cost methods remains challenging, especially in early-stage diseases. Here we present PapDREAM, a new approach that enables detection of rare, ovarian-cancer-specific aberrations of DNA methylation from routinely-collected cervical Pap specimens. The PapDREAM approach employs a microfluidic platform that performs highly parallelized digital high-resolution melt to analyze locus-specific DNA methylation patterns on a molecule-by-molecule basis at or near single CpG-site resolution at a fraction (<1/10th) of the cost of next-generation sequencing techniques. We demonstrate the feasibility of the platform by assessing intermolecular heterogeneity of DNA methylation in a panel of methylation biomarker loci using DNA derived from Pap specimens obtained from a cohort of 43 women, including 18 cases with ovarian cancer and 25 cancer-free controls. PapDREAM leverages systematic multidimensional bioinformatic analyses of locus-specific methylation heterogeneity to improve upon Pap-specimen-based detection of ovarian cancer, demonstrating a clinical sensitivity of 50% at 99% specificity in detecting ovarian cancer cases with an area under the receiver operator curve of 0.90. We then establish a logistic regression model that could be used to identify high-risk patients for subsequent clinical follow-up and monitoring. The results of this study support the utility of PapDREAM as a simple, low-cost screening method with the potential to integrate with existing clinical workflows for early detection of ovarian cancer. KEY POINTS: We present a microfluidic platform for detection and analysis of rare, heterogeneously methylated DNA within Pap specimens towards detection of ovarian cancer. The platform achieves high sensitivity (fractions <0.00005%) at a suitably low cost (∼$25) for routine screening applications. Furthermore, it provides molecule-by-molecule quantitative analysis to facilitate further study on the effect of heterogeneous methylation on cancer development.

Advances on multiclass pesticide residue determination in citrus fruits and citrus-derived products - A critical review.

The application of agrochemicals in citrus fruits is widely used to improve the quality of crops, increase production yields, and prolong post-harvest life. However, these substances are potentially toxic for humans and the ecosystem due to their widespread use, high stability, and bioaccumulation. Conventional techniques for determining pesticide residues in citrus fruits are chromatographic methods coupled with different detectors. However, in recent years, the need for analytical strategies that are less polluting for the environment has encouraged the appearance of new alternatives, such as sensors and biosensors, which allow selective and sensitive detection of pesticide residues in real time. A comprehensive overview of the analytical platforms used to determine pesticide residues in citrus fruits and citrus-derived products is presented herein. The review focuses on the evolution of these methods since 2015, their limitations, and possible future perspectives for improving pesticide residue determination and reducing environmental contamination.

Disease trend analysis platform accurately predicts the occurrence of cervical cancer under mixed diseases

Cervical cancer (CC) is one of the most common gynecological malignancies. Cytological screening, while being the most common and accurate method for detecting cervical cancer, is both time-consuming and costly. Predicting CC based on bioinformatics can assist in the rapid early screening of CC in clinical practice. Most recent CC prediction methods require a large amount of detection data or sequencing data and are not ideal for CC detection in complex disease samples. We developed the Disease trend analysis platform (Dtap), which can quickly predict the occurrence of diseases using only blood routine data. Blood routine data was collected from 1,292 cervical cancer patients, 4,860 patients with complex diseases, and 4,980 healthy individuals from various sources. The results show that the Dtap-based trend model maintained good and stable performance in the prediction task of multiple datasets as well as complex disease samples. Finally, we built DTAPCC (http://bioinfor.imu.edu.cn/dtapcc), a Dtap-based CC disease prediction platform, to help users quickly predict CC and visualize trend features.

A novel aptamer-antibody sandwich electrochemical sensor for detecting ADAR1 in complex biological samples

Human adenosine deaminase acting on RNA1 (ADAR1) is an adenosine-to-inosine (A-to-I) RNA-editing enzyme involved in various types of cancer progression. ADAR1 has emerged as a novel prognostic biomarker for cancer. This study describes the application of a newly identified 70-nt DNA aptamer (Apt38483) against ADAR1 to develop a portable and simple electrochemical biosensor platform for the rapid and sensitive detection of ADAR1 in cell lysates. We selected an ADAR1-specific DNA aptamer from a randomized 70-nt single-stranded DNA library using a competitive in vitro selection method. ADAR1 in the cell lysate was sandwiched onto a bare carbon working electrode of an electro-chemically printed chip between the ADAR1 antibody and gold nanoparticles (40 nm) conjugated with Apt38483, followed by electrochemical analysis using differential pulse voltammetry (DPV) for sensor demonstration. A highly sensitive change in current was observed for as little as 0.53 nM ADAR1 in human embryonic kidney cell lysate. Thus, the merging of a novel DNA aptamer probe for ADAR1 with an electrochemical transduction method enabled the development of a simple, low-cost, and rapid method for the direct measurement of ADAR1 in cell lysates and indicated great potential for the development of an ADAR1 analysis platform, which would be useful in cancer prognosis.

Improving cyber-physical-power system stability through hardware-in-loop co-simulation platform for real-time cyber attack analysis

With advancements in communication systems and measurement technologies, smart grids have become more observable and controllable, evolving into cyber-physical-power systems (CPPS). The impact of network security and secondary equipment on power system stability has become more evident. To support the existing grid toward a smart grid scenario, smart metering plays a vital role at the customer end side. Cyber-Physical systems are vulnerable to cyber-attacks and various techniques have been evolved to detect a cyber attack in the smart grid. Weighted trust-based models are suggested as one of the most effective security mechanisms. A hardware-in-loop CPPS co-simulation platform is established to facilitate the theoretical study of CPPS and the formulation of grid operation strategies. This paper examines current co-simulation platform schemes and highlights the necessity for a real-time hard-ware-in-the-loop platform to accurately simulate cyber-attack processes. This consideration takes into account the fundamental differences in modeling between power and communication systems. The architecture of the co-simulation platform based on RT-LAB and OPNET is described, including detailed modeling of the power system, communication system, and security and stability control devices. Additionally, an analysis of the latency of the co-simulation is provided. The paper focuses on modeling and implementing methods for addressing DDOS attacks and man-in-the-middle at-tacks in the communication network. The results from simulating a 7-bus system show the effectiveness and rationality of the co-simulation platform that has been designed.

Research Progress of Biosensor Based on Organic Photoelectrochemical Transistor.

In order to solve the food safety problem better, it is very important to develop a rapid and sensitive technology for detecting food contamination residues. Organic photoelectrochemical transistor (OPECT) biosensor rely on the photovoltage generated by a semiconductor upon excitation by light to regulate the conductivity of the polymer channels and realize biosensor analysis under zero gate bias. This technology integrates the excellent characteristics of photoelectrochemical (PEC) bioanalysis and the high sensitivity and inherent amplification ability of organic electrochemical transistor (OECT). Based on this, OPECT biosensor detection has been proven to be superior to traditional biosensor detection methods. In this review, we summarize the research status of OPECT biosensor in disease markers and food residue analysis, the basic principle, classification, and biosensing mechanism of OPECT biosensor analysis are briefly introduced, and the recent applications of biosensor analysis are discussed according to the signal strategy. We mainly introduced the OPECT biosensor analysis methods applied in different fields, including the detection of disease markers and food hazard residues such as prostate-specific antigen, heart-type fatty acid binding protein, T-2 toxin detection in milk samples, fat mass and objectivity related protein, ciprofloxacin in milk. The OPECT biosensor provides considerable development potential for the construction of safety analysis and detection platforms in many fields, such as agriculture and food, and hopes to provide some reference for the future development of biosensing analysis methods with higher selectivity, faster analysis speed and higher sensitivity.

Case Study: Leveraging Databricks to Process Health Care Claims Data and Detect Risks

The insurance industry heavily relies on efficient data processing and risk detection to maintain operational effectiveness and customer satisfaction. This case study defines the use of Databricks, a unified analytics platform, combined with PySpark, for processing claims data and detecting potential risks. We detail the setup of the Databricks environment, the import and cleaning of claims data, feature engineering, building a risk detection model, and evaluating its performance. Visual aids such as diagrams and flowcharts are included to illustrate the process. Key Words: Claims Data, Risk Detection, Databricks, PySpark, Data Processing, Machine Learning

Predictions of Aboveground Herbaceous Production from Satellite-Derived APAR Are More Sensitive to Ecosite than Grazing Management Strategy in Shortgrass Steppe

The accurate estimation of aboveground net herbaceous production (ANHP) is crucial in rangeland management and monitoring. Remote and rural rangelands typically lack direct observation infrastructure, making satellite-derived methods essential. When ground data are available, a simple and effective way to estimate ANHP from satellites is to derive the empirical relationship between ANHP and plant-absorbed photosynthetically active radiation (APAR), which can be estimated from the normalized difference vegetation index (NDVI). While there is some evidence that this relationship will differ across rangeland vegetation types, it is unclear whether this relationship will change across grazing management regimes. This study aimed to assess the impact of grazing management on the relationship between ground-observed ANHP and satellite-derived APAR, considering variations in plant communities across ecological sites in the shortgrass steppe of northeastern Colorado. Additionally, we compared satellite-predicted biomass production from the process-based Rangeland Analysis Platform (RAP) model to our empirical APAR-based model. We found that APAR could be used to predict ANHP in the shortgrass steppe, with the relationship being influenced by ecosite characteristics rather than grazing management practices. For each unit of added APAR (MJ m−2 day−1), ANHP increased by 9.39 kg ha−1, and ecosites with taller structured herbaceous vegetation produced, on average, 3.92–5.71 kg ha−1 more ANHP per unit APAR than an ecosite dominated by shorter vegetation. This was likely due to the increased allocation of plant resources aboveground for C3 mid-grasses in taller structured ecosites compared to the C4 short-grasses that dominate the shorter structured ecosites. Moreover, we found that our locally calibrated empirical model generally performed better than the continentally calibrated process-based RAP model, though RAP performed reasonably well for the dominant ecosite. For our empirical models, R2 values varied by ecosite ranging from 0.49 to 0.67, while RAP R2 values ranged from 0.07 to 0.4. Managers in the shortgrass steppe can use satellites to estimate herbaceous production even without detailed information on short-term grazing management practices. The results from our study underscore the importance of understanding plant community composition for enhancing the accuracy of remotely sensed predictions of ANHP.

Integrating Digital Health Solutions with Immunization Strategies: Improving Immunization Coverage and Monitoring in the Post-COVID-19 Era

The COVID-19 pandemic underscored the critical importance of vaccination to global health security and highlighted the potential of digital health solutions to improve immunization strategies. This article explores integrating digital health technologies with immunization programs to improve coverage, monitoring, and public health outcomes. It examines the current landscape of digital tools used in immunization initiatives, such as mobile health apps, electronic health records, and data analytics platforms. Case studies from different regions demonstrate the effectiveness of these technologies in addressing challenges such as vaccine hesitancy, logistics, and real-time monitoring of vaccine distribution and adverse events. The paper also examines ethical considerations, data privacy issues, and the need for a robust digital infrastructure to support these innovations. By analyzing the successes and limitations of digital health interventions in immunization campaigns during and after the COVID-19 pandemic, we provide recommendations for future integration strategies to ensure resilient and responsive immunization systems. This research aims to guide policymakers, health professionals, and technologists in leveraging digital health to strengthen immunization efforts and prepare for future public health emergencies.

Network pharmacology-based exploration of molecular mechanisms underlying therapeutic effects of Jianpi Huatan Quyu recipe on chronic heart failure with spleen Qi deficiency syndrome.

Chronic heart failure is a complex clinical syndrome. The Chinese herbal compound preparation Jianpi Huatan Quyu recipe has been used to treat chronic heart failure; however, the underlying molecular mechanism is still not clear. To identify the effective active ingredients of Jianpi Huatan Quyu recipe and explore its molecular mechanism in the treatment of chronic heart failure. The effective active ingredients of eight herbs composing Jianpi Huatan Quyu recipe were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The target genes of chronic heart failure were searched in the Genecards database. The target proteins of active ingredients were mapped to chronic heart failure target genes to obtain the common drug-disease targets, which were then used to construct a key chemical component-target network using Cytoscape 3.7.2 software. The protein-protein interaction network was constructed using the String database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed through the Metascape database. Finally, our previously published relevant articles were searched to verify the results obtained via network pharmacology. A total of 227 effective active ingredients for Jianpi Huatan Quyu recipe were identified, of which quercetin, kaempferol, 7-methoxy-2-methyl isoflavone, formononetin, and isorhamnetin may be key active ingredients and involved in the therapeutic effects of TCM by acting on STAT3, MAPK3, AKT1, JUN, MAPK1, TP53, TNF, HSP90AA1, p65, MAPK8, MAPK14, IL6, EGFR, EDN1, FOS, and other proteins. The pathways identified by KEGG enrichment analysis include pathways in cancer, IL-17 signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, calcium signaling pathway, cAMP signaling pathway, NF-kappaB signaling pathway, AMPK signaling pathway, etc. Previous studies on Jianpi Huatan Quyu recipe suggested that this Chinese compound preparation can regulate the TNF-α, IL-6, MAPK, cAMP, and AMPK pathways to affect the mitochondrial structure of myocardial cells, oxidative stress, and energy metabolism, thus achieving the therapeutic effects on chronic heart failure. The Chinese medicine compound preparation Jianpi Huatan Quyu recipe exerts therapeutic effects on chronic heart failure possibly by influencing the mitochondrial structure of cardiomyocytes, oxidative stress, energy metabolism, and other processes. Future studies are warranted to investigate the role of the IL-17 signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, and other pathways in mediating the therapeutic effects of Jianpi Huatan Quyu recipe on chronic heart failure.

A MEPS-UHPLC-MS/MS analytical platform to detect isoprostanoids and specialized pro-resolving mediators in the urinary extracellular vesicles of mountain ultramarathon runners

Oxylipins are powerful signalling compounds derived from polyunsaturated fatty acids (PUFAs) and involved in regulating the immune system response. A mass spectrometry-based method was developed and validated for the targeted profiling of 52 oxylipins (e.g., isoprostanoids, prostaglandins, epoxy- and hydroxy-fatty acids, specialized pro-resolving mediators) and 4 PUFAs in small urinary extracellular vesicles (uEVs). Ultrasound-assisted extraction using a 50:50 v/v MeOH:H2O mixture ensured optimal analytical performances. Limits of detection ranged between 10-400 pg/mL for oxylipins and 0.10-3 ng/mL for PUFAs. Satisfactory recoveries (85-116%) and good intra- and inter-day precisions (RSD ≤15%) were obtained for all the analytes. The reliability of the procedure was tested in a real case scenario by monitoring ultramarathon runners during the world Tor des Géants® (TDG) race. Both F2- and E2-isoprostanes were detected in small uEVs of the ultramarathon runners, suggesting the onset of an oxidant insult. 5-F2t-IsoP exhibited significant pre- to post-race variations, thus potentially representing a non-invasive marker of in-vivo lipid peroxidation. The presence of specialized pro-resolving mediators suggests the activation of pro-resolution signalling cascade resolving inflammation. These outcomes may help manage post-exercise recovery and improve training.

Impact of racial disparities in follow-up and quality of colonoscopy on colorectal cancer outcomes.

The benefits of colorectal cancer (CRC) screening programs rely on completing follow-up colonoscopy when a noncolonoscopy test is abnormal and on quality of colonoscopy screening as measured by the endoscopists' adenoma detection rate. Existing data demonstrate substantially lower follow-up colonoscopy rates and adenoma detection rate for Black Americans than White Americans. However, the contributions of racial differences in follow-up colonoscopy and adenoma detection rate on CRC outcomes have not been rigorously evaluated. We used established and validated CRC-Adenoma Incidence and Mortality (CRC-AIM) model as our analysis platform, with inputs from published literature that report lower follow-up colonoscopy rates and adenoma detection rate in Black adults compared with White adults (15% and 10% lower, respectively). We simulated screening with annual fecal immunochemical test, triennial multitarget stool DNA, and colonoscopy every 10 years between ages 45 and 75 years using real-world utilization of the screening modalities vs no screening. We reported lifetime outcomes per 1000 Black adults. Elimination of Black-White disparities in follow-up colonoscopy rates would reduce CRC incidence and mortality by 5.2% and 9.3%, respectively, and improve life-years gained with screening by 3.4%. Elimination of Black-White disparities in endoscopists' adenoma detection rate would reduce CRC incidence and mortality by 9.4% and improve life-years gained by 3.7%. Elimination of both disparities would reduce CRC incidence and mortality by 14.6% and 18.7%, respectively, and improve life-years gained by 7.1%. This modeling study predicts eliminating racial differences in follow-up colonoscopy rates, and quality of screening colonoscopy would substantially reduce Black-White disparities in CRC incidence and mortality.