Navigating electronic health record accuracy by examination of sex incongruent conditions.

OBJECTIVE: The increasing reliance on electronic health records (EHRs) for research and clinical care necessitates robust methods for assessing data quality and identifying inconsistencies. To address this need, we develop and apply the incongruence rate (IR) using sex-specific medical conditions. We also characterized participants with incongruent records to better understand the scope and nature of data discrepancies. MATERIALS AND METHODS: In this cross-sectional study, we used the All of Us Research Program's latest version 7 (v7) EHR data to identify prevalent sex-specific conditions and evaluated the occurrence of incongruent cases, quantified as IR. RESULTS: Among the 92 597 males and 152 551 females with condition occurrence data available from All of Us and sex-conformed gender, we identified 167 prevalent sex-specific conditions. Among the 37 537 biological males and 95 499 biological females with these sex-specific conditions, we detected an overall IR of 0.86%. Attempt to include non-cisgender participants result in inflated overall IR. Additionally, a significant proportion of participants with incongruent conditions also presented with conditions congruent to their biological sex, indicating a mix of accurate and erroneous records. These incongruences were not geographically or temporally isolated, suggesting systematic issues in EHR data integrity. DISCUSSION: Our findings call attention to the existence of systemic data incongruences in sex-specific conditions and the need for robust validation checks. Extending IR evaluation to non-cisgender participants or non-sex-based conditions remain a challenge. CONCLUSION: The sex condition-specific IR, when applied to adult populations, provides a valuable metric for data quality assessment in EHRs.

Mapping cellular interactions from spatially resolved transcriptomics data.

Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications.

Insight into the Structural and Magnetic Properties of PrZnSi and NdZnSi Compounds Featuring Large Low-Temperature Magnetocaloric Effects.

The magnetocaloric (MC) and magnetic phase transition (MPT) properties in various types of rare earth (RE)-based magnetic materials have been intensively investigated recently, which are aimed at developing suitable MC materials for low-temperature cooling applications and better elucidating their inherent physical properties. We herein provide a combined experimental and theoretical investigation into two new light RE-based magnetic materials, namely, PrZnSi and NdZnSi compounds, regarding their structural, magnetic, MPT, and low-temperature MC properties. Both of these compounds crystallize in an AlB2-type hexagonal structure with a symmetry of the crystallographic space group P6/mmm and reveal a typical second-order-type MPT with ordering temperatures (TC) at approximately 13.5 and 18.5 K for PrZnSi and NdZnSi compounds, respectively. Moreover, they all exhibit large reversible low-temperature MC effects and remarkable performances, which are identified by the parameters of maximum magnetic entropy changes, relative cooling power, and temperature-averaged entropy change (temperature lift 5 K). The deduced values of these MC parameters under a magnetic field change of 0-7 T reach 16.3 J/kgK, 294.46 J/kg, and 15.79 J/kgK for PrZnSi and 15.4 J/kgK, 284.84 J/kg, and 14.95 J/kgK for NdZnSi, respectively, which are evidently better than those of most updated light RE-based magnetic materials with remarkable low-temperature MC performances, indicating that PrZnSi and NdZnSi compounds hold potentials for practical cooling applications.

Bayesian Landmark-based Shape Analysis of Tumor Pathology Images.

Medical imaging is a form of technology that has revolutionized the medical field over the past decades. Digital pathology imaging, which captures histological details at the cellular level, is rapidly becoming a routine clinical procedure for cancer diagnosis support and treatment planning. Recent developments in deep-learning methods have facilitated tumor region segmentation from pathology images. The traditional shape descriptors that characterize tumor boundary roughness at the anatomical level are no longer suitable. New statistical approaches to model tumor shapes are in urgent need. In this paper, we consider the problem of modeling a tumor boundary as a closed polygonal chain. A Bayesian landmark-based shape analysis model is proposed. The model partitions the polygonal chain into mutually exclusive segments, accounting for boundary roughness. Our Bayesian inference framework provides uncertainty estimations on both the number and locations of landmarks, while outputting metrics that can be used to quantify boundary roughness. The performance of our model is comparable with that of a recently developed landmark detection model for planar elastic curves. In a case study of 143 consecutive patients with stage I to IV lung cancer, we demonstrated the heterogeneity of tumor boundary roughness derived from our model effectively predicted patient prognosis (p-value < 0.001).

The incidence of acute exacerbation of idiopathic pulmonary fibrosis: a systematic review and meta-analysis.

Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease with a high incidence of acute exacerbation and an increasing mortality rate. Currently, treatment methods and effects are limited. Therefore, we conducted a meta-analysis of the incidence of acute exacerbation in patients with IPF, hoping to provide reference for the prevention and management of IPF. We systematically searched the PubMed, Embase, Cochrane Library and Web of Science databases. From the creation of the database to the cohort study on April 3, 2023, we collected studies on the incidence of acute exacerbation of IPF patients, and used Stata software (version 16.0) for meta analysis. We used the Newcastle Ottawa Quality Assessment Scale (NOS) to assess the risk of bias for each study. We calculated the incidence of acute exacerbation in IPF patients and analyzed the risk factors for acute exacerbation in IPF patients and prognostic factors for overall survival from the initial IPF diagnosis. A total of ten cohort studies on the incidence of AE-IPF were included, including 11,855 IPF patients. The results showed that the incidence of acute exacerbation within one year was 9%; the incidence of acute exacerbation within 2 years is 13%; the incidence of acute exacerbation within 3 years is 19%; the incidence of acute exacerbation within 4 years is 11%. In addition, one study reported an acute exacerbation rate of 1.9% within 30 days. The incidence of acute exacerbation within ten years reported in one study was 9.8%. Mura et al.'s article included a retrospective cohort study and a prospective cohort study. The prospective cohort study showed that the incidence of acute exacerbation within 3 years was 18.6%, similar to the results of the retrospective cohort study meta-analysis. Our system evaluation and meta-analysis results show that the incidence of AE-IPF is relatively high. Therefore, sufficient attention should be paid to the research results, including the management and prevention of the disease, in order to reduce the risk of AE.Trial registration: PROSPERO, identifier CRD42022341323.

Discussion of spinal cord neurons apoptosis and neuroprotection mechanism of NGF gene transfection mediated by recombinant adenovirus in EAE mice.

To investigate the spinal cord neuron apoptosis and neuroprotective mechanism of nerve growth factorganismsor (NGF) gene mediated by recombinant adenovirus (Ad-NGF) via peripheral transfection in mice with experimental autoimmune encephalomyelitis (EAE). Forty healthy female C57BL/6 mice were randomly divided into a control group, adenovirus (AdV) group, EAE group, and Ad-NGF transfection group; the control group received no treatment; the AdV group received adenovirus injection via the tail vein; the EAE and Ad-NGF transfection groups were induced with experimental autoimmune encephalomyelitis (EAE) using myelin oligodendrocyte glycoprotein 35-55 (MOG35-55), Ad-NGF transfection group received Ad-NGF injection via the tail vein, and daily neurological impairment scores were obtained. AQThe TUNEL method was employed to observe spinal neuron apoptosis in each group of mice; protein immunoblotting (western blot) and RT-PCR were used to measure NGF levels in the spinal cord tissues of each group, and western blotting was used to assess levels of cleaved caspase-3, Bax, and Bcl-2. ELISA and RT-PCR were employed to detect protein and mRNA levels of neuron-specific enolase (NSE) in spinal cord tissues, respectively. The control group and AdV mice did not develop symptoms. Compared to the EAE group, in the Ad-NGF transfection group, neurological function scores, TUNEL-positive cell counts, the ratio of NeuN + TUNEL to NeuN, levels of Bax and cleaved caspase-3 apoptotic proteins were significantly reduced, while Bcl-2 protein expression was increased. Expression levels of NGF, NGF-mRNA, NSE, and NSE-mRNA in spinal cord tissues were significantly elevated (P < 0.01). Immunofluorescence labeling revealed a significant punctate aggregation of apoptotic cells in spinal neurons of the EAE group, while the aggregation phenomenon was less pronounced in the Ad-NGF transfection group. Ad-NGF transfected by the periphery has a protective effect on spinal cord neurons in EAE mice by up-regulation NGF level, down-regulating apoptotic protein Caspase-3 in spinal cord neurons, inhibiting spinal cord neuron apoptosis and promoting NSE expression.

Psychometric properties of computerized adaptive testing for chronic obstructive pulmonary disease patient-reported outcome measurement.

BACKGROUND: Computerized adaptive testing (CAT) is an effective way to reduce time, repetitious redundancy, and respond burden, and has been used to measure outcomes in many diseases. This study aimed to develop and validate a comprehensive disease-specific CAT for chronic obstructive pulmonary disease (COPD) patient-reported outcome measurement. METHODS: The discrimination and difficulty of the items from the modified patient-reported outcome scale for COPD (mCOPD-PRO) were analyzed using item response theory. Then the initial item, item selection method, ability estimation method, and stopping criteria were further set based on Concerto platform to form the CAT. Finally, the reliability and validity were validated. RESULTS: The item discrimination ranged from 1.05 to 2.71, and the item difficulty ranged from - 3.08 to 3.65. The measurement reliability of the CAT ranged from 0.910 to 0.922 using random method, while that ranged from 0.910 to 0.924 using maximum Fisher information (MFI) method. The content validity was good. The correlation coefficient between theta of the CAT and COPD assessment test and modified Medical Research Council dyspnea scale scores using random method was 0.628 and 0.540 (P < 0.001; P < 0.001) respectively, while that using MFI method was 0.347 and 0.328 (P = 0.007; P = 0.010) respectively. About 11 items (reducing by 59.3%) on average were tested using random method, while about seven items (reducing by 74.1%) on average using MFI method. The correlation coefficient between theta of the CAT and mCOPD-PRO total scores using random method was 0.919 (P < 0.001), while that using MFI method was 0.760 (P < 0.001). CONCLUSIONS: The comprehensive disease-specific CAT for COPD patient-reported outcome measurement is well developed with good psychometric properties, which can provide an efficient, accurate, and user-friendly measurement for patient-reported outcome of COPD.

A citrullinated antigenic vaccine in treatment of autoimmune arthritis.

Rheumatoid arthritis (RA) is an inflammatory autoimmune disease triggered by antigenic peptides with environmental and genetic risk factors. It has been shown that antigen-specific targeting could be a promising therapeutical strategy for RA by restoring immune tolerance to self-antigens without compromising normal immunity. Citrullination of antigens enhances antigenic properties and induces autoimmune responses. Here, we showed that citrullinated antigenic (citAg) vaccine ameliorated collagen-induced arthritis (CIA) with decreased T-helper 1 (Th1) and Th17 cells, downregulated proinflammatory cytokines including interlukin-6 and tumor necrosis factor-α, and inhibited antigen recall responses. B cell receptor (BCR) sequencing further revealed that citAg vaccine could dampen the dysregulated V(D)J recombination, restoring the immune repertoire. Taken together, the results demonstrated that citAg vaccine might have a therapeutic effect on RA.

Efficacy and Safety Evaluation of 177Lu-FAP-2286 in the Treatment of Advanced Lung Cancer.

PURPOSE: The aim of this study was to evaluate the efficacy and safety of peptide-targeted radionuclide therapy (PTRT) with 177Lu-FAP-2286 in advanced lung cancer. PATIENTS AND METHODS: This single-center prospective study included 9 patients diagnosed with advanced lung cancer. These patients met the inclusion criteria and received PTRT with 177Lu-FAP-2286. Short-term efficacy was assessed using RECIST 1.1 and PERCIST 1.0 criteria. Long-term efficacy was evaluated through overall survival, progression-free survival (PFS), overall response rate, EORTC QLQ-C30 v3.0, Eastern Cooperative Oncology Group, and Karnofsky Performance Status. Toxicity response was assessed using CTCAE v5.0. RESULTS: The results based on RECIST 1.1 and PERCIST 1.0 criteria were comparable, with 44% of patients showing a partial metabolic response, 33.3% with stable metabolic disease, and 22.22% with progressive metabolic disease. The highest metabolic response after treatment reached 66.89%, and the overall response rate could reach 77.78%. In the long-term efficacy assessment, the median overall survival and PFS were 10 months and 6 months, respectively. The 2 patients with the lowest PFS (3 months) started PTRT relatively late. EORTC QLQ-C30 v3.0, Eastern Cooperative Oncology Group, and Karnofsky Performance Status scores showed that the overall health status, symptom response, and quality of life of patients improved after 177Lu-FAP-2286 treatment. The most noticeable improvements in clinical symptoms were dyspnea and cancer-related pain. No grade III/IV toxicity events were observed during follow-up period, and fibrinogen decreased significantly after treatment. CONCLUSIONS: 177Lu-FAP-2286 has the potential to be a viable PTRT option for patients with advanced lung cancer.

Automatic driving image matching via Random Sample Consensus (RANSAC) and Spectral Clustering (SC) with monocular camera.

In today's big data era, with the development of the Internet of Things (IoT) technology and the trend of autonomous driving prevailing, visual information has shown a blowout increase, but most image matching algorithms have problems such as low accuracy and low inlier rates, resulting in insufficient information. In order to solve the problem of low image matching accuracy and low inlier rate in the field of autonomous driving, this research innovatively applies spectral clustering (SC) in the field of data analysis to image matching in the field of autonomous driving, and a new image matching algorithm "SC-RANSAC" based on SC and Random Sample Consensus (RANSAC) is proposed. The datasets in this research are collected based on the monocular cameras of autonomous driving cars. We use RANSAC to obtain the initial inlier set and the SC algorithm to filter RANSAC's outliers and then use the filtered inliers as the final inlier set. In order to verify the effectiveness of the algorithm, it shows the matching effect from three angles: camera translation, rotation, and rotation and translation. SC-RANSAC is also compared with RANSAC, graph-cut RANSAC, and marginalizing sample consensus by using two different types of datasets. Finally, we select three representative pictures to test the robustness of the SC-RANSAC algorithm. The experimental results show that SC-RANSAC can effectively and reliably eliminate mismatches in the initial matching results; has a high inlier rate, real-time performance, and robustness; and can be effectively applied in the environment of autonomous driving.

Radiomic and deep learning analysis of dermoscopic images for skin lesion pattern decoding.

This study aims to explore the efficacy of a hybrid deep learning and radiomics approach, supplemented with patient metadata, in the noninvasive dermoscopic imaging-based diagnosis of skin lesions. We analyzed dermoscopic images from the International Skin Imaging Collaboration (ISIC) dataset, spanning 2016-2020, encompassing a variety of skin lesions. Our approach integrates deep learning with a comprehensive radiomics analysis, utilizing a vast array of quantitative image features to precisely quantify skin lesion patterns. The dataset includes cases of three, four, and eight different skin lesion types. Our methodology was benchmarked against seven classification methods from the ISIC 2020 challenge and prior research using a binary decision framework. The proposed hybrid model demonstrated superior performance in distinguishing benign from malignant lesions, achieving area under the receiver operating characteristic curve (AUROC) scores of 99%, 95%, and 96%, and multiclass decoding AUROCs of 98.5%, 94.9%, and 96.4%, with sensitivities of 97.6%, 93.9%, and 96.0% and specificities of 98.4%, 96.7%, and 96.9% in the internal ISIC 2018 challenge, as well as in the external Jinan and Longhua datasets, respectively. Our findings suggest that the integration of radiomics and deep learning, utilizing dermoscopic images, effectively captures the heterogeneity and pattern expression of skin lesions.

Ubiquitin-modifying enzymes in thyroid cancer：Mechanisms and functions.

Thyroid cancer is the most common malignant tumor of the endocrine system, and evidence suggests that post-translational modifications (PTMs) and epigenetic alterations play an important role in its development. Recently, there has been increasing evidence linking dysregulation of ubiquitinating enzymes and deubiquitinases with thyroid cancer. This review aims to summarize our current understanding of the role of ubiquitination-modifying enzymes in thyroid cancer, including their regulation of oncogenic pathways and oncogenic proteins. The role of ubiquitination-modifying enzymes in thyroid cancer development and progression requires further study, which will provide new insights into thyroid cancer prevention, treatment and the development of novel agents.

Optimizing air quality and health Co-benefits of mitigation technologies in China: An integrated assessment.

Carbon mitigation technologies lead to air quality improvement and health co-benefits, while the practical effects of the technologies are dependent on the energy composition, technological advancements, and economic development. In China, mitigation technologies such as end-of-pipe treatment, renewable energy adoption, carbon capture and storage (CCS), and sector electrification demonstrate significant promise in meeting carbon reduction targets. However, the optimization of these technologies for maximum co-benefits remains unclear. Here, we employ an integrated assessment model (AIM/enduse, CAM-chem, IMED|HEL) to analyze air quality shifts and their corresponding health and economic impacts at the provincial level in China within the two-degree target. Our findings reveal that a combination of end-of-pipe technology, renewable energy utilization, and electrification yields the most promising results in air quality improvement, with a reduction of fine particulate matter (PM2.5) by -34.6 µg m-3 and ozone by -18.3 ppb in 2050 compared to the reference scenario. In contrast, CCS technology demonstrates comparatively modest improvements in air quality (-9.4 µg m-3 for PM2.5 and -2.4 ppb for ozone) and cumulative premature deaths reduction (-3.4 million from 2010 to 2050) compared to the end-of-pipe scenario. Notably, densely populated regions such as Henan, Hebei, Shandong, and Sichuan experience the most health and economic benefits. This study aims to project effective future mitigation technologies and climate policies on air quality improvement and carbon mitigation. Furthermore, it seeks to delineate detailed provincial-level air pollution control strategies, offering valuable guidance for policymakers and stakeholders in pursuing sustainable and health-conscious environmental management.

Single nucleotide polymorphisms in SEPALLATA 2 underlie fruit length variation in cucurbits.

Complete disruption of critical genes is generally accompanied by severe growth and developmental defects, which dramatically hinder its utilization in crop breeding. Identifying subtle changes, such as single nucleotide polymorphisms (SNPs), in critical genes that specifically modulate a favorable trait is a prerequisite to fulfill breeding potential. Here, we found two SNPs in the E-class floral organ identity gene cucumber (Cucumis sativus) SEPALLATA2 (CsSEP2) that specifically regulate fruit length. Haplotype (HAP) 1 (8G2667A) and HAP2 (8G2667T) exist in natural populations, whereas HAP3 (8A2667T) is induced by ethyl methanesulfonate mutagenesis. Phenotypic characterization of four near-isogenic lines and a mutant line showed that HAP2 fruits are significantly longer than those of HAP1, and those of HAP3 are 37.8% longer than HAP2 fruit. The increasing fruit length in HAP1-3 was caused by a decreasing inhibitory effect on CRABS CLAW (CsCRC) transcription (a reported positive regulator of fruit length), resultinged in enhanced cell expansion. Moreover, a 7638G/A-SNP in melon (Cucumis melo) CmSEP2 modulates fruit length in a natural melon population via the conserved SEP2-CRC module. Our findings provide a strategy for utilizing essential regulators with pleiotropic effects during crop breeding.

ZBTB7A regulates LncRNA HOTAIR-mediated ELAVL1/SOX17 axis to inhibit malignancy and angiogenesis in endometrial carcinoma.

BACKGROUND: Endometrial cancer (EC) is the sixth most frequent cancer in women worldwide and has higher fatality rates. The pathophysiology of EC is complex, and there are currently no reliable methods for diagnosing and treating the condition. Long non-coding RNA (lncRNA), according to mounting evidence, is vital to the pathophysiology of EC. HOTAIR is regarded as a significant prognostic indicator of EC. ZBTB7A decreased EC proliferation and migration, according to recent studies, however the underlying mechanism still needs to be clarified. METHODS: The research utilized RT-qPCR to measure HOTAIR expression in clinical EC tissues and various EC cell lines. Kaplan-Meier survival analysis was employed to correlate HOTAIR levels with patient prognosis. Additionally, the study examined the interaction between ZBTB7A and HOTAIR using bioinformatics tools and ChIP assays. The experimental approach also involved manipulating the expression levels of HOTAIR and ZBTB7A in EC cell lines and assessing the impact on various cellular processes and gene expression. RESULTS: The study found significantly higher levels of HOTAIR in EC tissues compared to adjacent normal tissues, with high HOTAIR expression correlating with poorer survival rates and advanced cancer characteristics. EC cell lines like HEC-1 A and KLE showed higher HOTAIR levels compared to normal cells. Knockdown of HOTAIR in these cell lines reduced proliferation, angiogenesis, and migration. ZBTB7A was found to be inversely correlated with HOTAIR, and its overexpression led to a decrease in HOTAIR levels and a reduction in malignant cell behaviors. The study also uncovered that HOTAIR interacts with ELAVL1 to regulate SOX17, which in turn activates the Wnt/ß-catenin pathway, promoting malignant behaviors in EC cells. CONCLUSION: HOTAIR is a critical regulator in EC, contributing to tumor growth and poor prognosis. Its interaction with ZBTB7A and regulation of SOX17 via the Wnt/ß-catenin pathway underlines its potential as a therapeutic target.

Effectiveness of telerehabilitation in patients with post-COVID-19: a systematic review and meta-analysis of randomised controlled trials.

OBJECTIVE: To assess the effects of telerehabilitation on clinical symptoms, physical function, psychological function and quality of life (QoL) in patients with post-COVID-19. DESIGN: Systematic review and meta-analysis of randomised controlled trials (RCTs). DATA SOURCES: PubMed, Web of Science, Embase and Cochrane Library were searched for publications from 1 January 2020 to 17 April 2024. ELIGIBILITY CRITERIA: RCTs investigating the effects of telerehabilitation in patients with post-COVID-19 were included. The outcomes of interest encompassed clinical symptoms, physical function, psychological function and QoL. Only studies reported in English were included. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently extracted data and evaluated the risk of bias. Statistical analysis was conducted using Review Manager V.5.3, employing mean difference (MD) with a 95% CI, and the corresponding P value was used to ascertain the treatment effect between groups. Heterogeneity was quantified using the I2 statistic. The quality of evidence was assessed by GRADE. RESULTS: 16 RCTs (n=1129) were included in this systematic review, 15 of which (n=1095, 16 comparisons) were included in the meta-analysis. The primary pooled analysis demonstrated that, compared with no rehabilitation or usual care, telerehabilitation can improve physical function (measured by 30 s sit-to-stand test [6 RCTs, n=310, MD=1.58 stands, 95% CI 0.50 to 2.66; p=0.004]; 6 min walking distance [6 RCTs, n=324, MD=76.90 m, 95% CI 49.47 to 104.33; p<0.00001]; and physical function from the 36-item short-form health survey [5 RCTs, n=380, MD=6.12 units, 95% CI 2.85 to 9.38; p=0.0002]). However, the pooled results did not indicate significant improvements in clinical symptoms, pulmonary function, psychological function or QoL. The quality of the evidence was graded as low for physical function and Hospital Anxiety and Depression Scale-anxiety and very low for other assessed outcomes. The overall treatment completion rate was 78.26%, with no reports of severe adverse events in any included trials. CONCLUSIONS: Despite the lack of significant improvements in certain variables, telerehabilitation could be an effective and safe option for enhancing physical function in patients with post-COVID-19. It is advisable to conduct further well-designed trials to continue in-depth exploration of this topic. STUDY REGISTRATION: PROSPERO, CRD42023404647.

Rheumatoid arthritis patients harbour aberrant enteric bacteriophages with autoimmunity-provoking potential: a paired sibling study.

OBJECTIVES: Viruses have been considered as important participants in the development of rheumatoid arthritis (RA). However, the profile of enteric virome and its role in RA remains elusive. This study aimed to investigate the atlas and involvement of virome in RA pathogenesis. METHODS: Faecal samples from 30 pairs of RA and healthy siblings that minimise genetic interferences were collected for metagenomic sequencing. The α and ß diversity of the virome and the virome-bacteriome interaction were analysed. The differential bacteriophages were identified, and their correlations with clinical and immunological features of RA were analysed. The potential involvement of these differential bacteriophages in RA pathogenesis was further investigated by auxiliary metabolic gene annotation and molecular mimicry study. The responses of CD4+ T cells and B cells to the mimotopes derived from the differential bacteriophages were systemically studied. RESULTS: The composition of the enteric bacteriophageome was distorted in RA. The differentially presented bacteriophages correlated with the immunological features of RA, including anti-CCP autoantibody and HLA-DR shared epitope. Intriguingly, the glycerolipid and purine metabolic genes were highly active in the bacteriophages from RA. Moreover, peptides of RA-enriched phages, in particular Prevotella phage and Oscillibacter phage could provoke the autoimmune responses in CD4+ T cells and plasma cells via molecular mimicry of the disease-associated autoantigen epitopes, especially those of Bip. CONCLUSIONS: This study provides new insights into enteric bacteriophageome in RA development. In particular, the aberrant bacteriophages demonstrated autoimmunity-provoking potential that would promote the occurrence of the disease.

A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis.

Patient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histologic imaging via hematoxylin and eosin (H&E) staining is routinely performed on PDX samples, which could be harnessed for computational analysis. Prior studies of large clinical H&E image repositories have shown that deep learning analysis can identify intercellular and morphologic signals correlated with disease phenotype and therapeutic response. In this study, we developed an extensive, pan-cancer repository of >1,000 PDX and paired parental tumor H&E images. These images, curated from the PDX Development and Trial Centers Research Network Consortium, had a range of associated genomic and transcriptomic data, clinical metadata, pathologic assessments of cell composition, and, in several cases, detailed pathologic annotations of neoplastic, stromal, and necrotic regions. The amenability of these images to deep learning was highlighted through three applications: (i) development of a classifier for neoplastic, stromal, and necrotic regions; (ii) development of a predictor of xenograft-transplant lymphoproliferative disorder; and (iii) application of a published predictor of microsatellite instability. Together, this PDX Development and Trial Centers Research Network image repository provides a valuable resource for controlled digital pathology analysis, both for the evaluation of technical issues and for the development of computational image-based methods that make clinical predictions based on PDX treatment studies. Significance: A pan-cancer repository of >1,000 patient-derived xenograft hematoxylin and eosin-stained images will facilitate cancer biology investigations through histopathologic analysis and contributes important model system data that expand existing human histology repositories.

Rapid and sensitive detection of heart-type fatty acid binding protein using aggregation-induced emission nanoparticles on digital microfluidics workstation.

Early and rapid diagnostic of acute myocardial infarction (AMI) during its developing stage is crucial due to its high fatality rate. Heart-type fatty acid binding protein (h-FABP) is an ideal biomarker for the quantitative diagnosis of AMI, surpassing traditional markers such as myoglobin, creatine phosphokinase-MB, and troponin in terms of sensitivity, specificity, and prognostic value. To obtain diagnostic and prognostic information, a precise and fully quantitative measurement of h-FABP is essential, typically achieved through an immunosorbent assay like the enzyme-linked immunosorbent assay. Nevertheless, this method has several limitations, including extended detection time, complex assay procedures, the necessity for skilled technicians, and challenges in implementing automated detection. This research introduces a novel biosensor, utilizing aggregation-induced emission nanoparticles (AIENPs) and integrated with a digital microfluidic (DMF) workstation, designed for the sensitive, rapid, and automated detection of h-FABP in low-volume serum samples. AIENPs and magnetic beads in nanoscale were served as the capture particles and the fluorescent probe, which were linked covalently to anti-h-FABP antibodies respectively. The approach was based on a sandwich immunoassay and performed on a fully automated DMF workstation with assay time by 15 min. We demonstrated the determination of h-FABP in serum samples with detection limit of 0.14 ng/mL using this biosensor under optimal condition. Furthermore, excellent correlations (R2 = 0.9536, n = 50) were obtained between utilizing this biosensor and commercialized ELISA kits in clinical serum detecting. These results demonstrate that our flexible and reliable biosensor is suitable for direct integration into clinical diagnostics, and it is expected to be promising diagnostic tool for early detection and screening tests as well as prognosis evaluation for AMI patients.