Integrative Analyses of Bulk and Single-Cell RNA Seq Identified the Shared Genes in Acute Respiratory Distress Syndrome and Rheumatoid Arthritis.

Acute respiratory distress syndrome (ARDS), a progressive status of acute lung injury (ALI), is primarily caused by an immune-mediated inflammatory disorder, which can be an acute pulmonary complication of rheumatoid arthritis (RA). As a chronic inflammatory disease regulated by the immune system, RA is closely associated with the occurrence and progression of respiratory diseases. However, it remains elusive whether there are shared genes between the molecular mechanisms underlying RA and ARDS. The objective of this study is to identify potential shared genes for further clinical drug discovery through integrated analysis of bulk RNA sequencing datasets obtained from the Gene Expression Omnibus database, employing differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). The hub genes were identified through the intersection of common DEGs and WGCNA-derived genes. The Random Forest (RF) and least absolute shrinkage and selection operator (LASSO) algorithms were subsequently employed to identify key shared target genes associated with two diseases. Additionally, RA immune infiltration analysis and COVID-19 single-cell transcriptome analysis revealed the correlation between these key genes and immune cells. A total of 59 shared genes were identified from the intersection of DEGs and gene clusters obtained through WGCNA, which analyzed the integrated gene matrix of ALI/ARDS and RA. The RF and LASSO algorithms were employed to screen for target genes specific to ALI/ARDS and RA, respectively. The final set of overlapping genes (FCMR, ADAM28, HK3, GRB10, UBE2J1, HPSE, DDX24, BATF, and CST7) all exhibited a strong predictive effect with an area under the curve (AUC) value greater than 0.8. Then, the immune infiltration analysis revealed a strong correlation between UBE2J1 and plasma cells in RA. Furthermore, scRNA-seq analysis demonstrated differential expression of these nine target genes primarily in T cells and NK cells, with CST7 showing a significant positive correlation specifically with NK cells. Beyond that, transcriptome sequencing was conducted on lung tissue collected from ALI mice, confirming the substantial differential expression of FCMR, HK3, UBE2J1, and BATF. This study provides unprecedented evidence linking the pathophysiological mechanisms of ALI/ARDS and RA to immune regulation, which offers novel understanding for future clinical treatment and experimental research.

Case report of pregnancy complicated by severe pulmonary hypertension from mitral stenosis and placenta accreta spectrum disorder: management of two life-threatening conditions.

Background: Antenatal cardiovascular disease is a major cause of maternal morbidity and mortality. Severe rheumatic mitral stenosis is especially poorly tolerated during pregnancy. Case Summary: We present a young woman with severe pulmonary hypertension secondary to rheumatic mitral stenosis. She presented at 25 weeks 4 days gestation for evaluation of a pregnancy complicated by placenta accreta spectrum disorder. Invasive hemodynamic testing was carried out to delineate her hemodynamics, and a multidisciplinary cardio-obstetrics team collaborated closely with the patient and her partner to create a management plan. Ultimately, the patient was initiated on veno-arterial extracorporeal membrane oxygenation and underwent caesarean section delivery followed by hysterectomy and subsequent valve replacement surgery. Discussion: This case describes the treatment options considered to balance the risk of decompensation in the setting of severe pulmonary hypertension with hemorrhage associated with placenta accreta spectrum disorder. It highlights the importance of a multidisciplinary, team-based approach to the management of high-risk cardiac conditions throughout pregnancy.

Developing a novel image marker to predict the clinical outcome of neoadjuvant chemotherapy (NACT) for ovarian cancer patients.

OBJECTIVE: Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the clinical outcomes to NACT vary significantly among different subgroups. Partial responses to NACT may lead to suboptimal debulking surgery, which will result in adverse prognosis. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy prognosis prediction of NACT at an early stage. METHODS: For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. This cluster was used as input for developing and optimizing support vector machine (SVM) based classifiers, which indicated the likelihood of receiving suboptimal cytoreduction after the NACT treatment. Two different kernels for SVM algorithm were explored and compared. A total of 42 ovarian cancer cases were retrospectively collected to validate the scheme. A nested leave-one-out cross-validation framework was adopted for model performance assessment. RESULTS: The results demonstrated that the model with a Gaussian radial basis function kernel SVM yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.806 ± 0.078. Meanwhile, this model achieved overall accuracy (ACC) of 83.3%, positive predictive value (PPV) of 81.8%, and negative predictive value (NPV) of 83.9%. CONCLUSION: This study provides meaningful information for the development of radiomics based image markers in NACT treatment outcome prediction.

Modern cancer therapy: cryoablation meets immune checkpoint blockade.

Cryoablation, as a minimally invasive technology for the treatment of tumors, destroys target tumors with lethal low temperatures. It simultaneously releases a large number of tumor-specific antigens, pro-inflammatory cytokines, and nucleoproteins, known as "danger signals", activating the body's innate and adaptive immune responses. However, tumor cells can promote the inactivation of immune effector cells by reprogramming immune checkpoints, leading to the insufficiency of these antigens to induce an immune response capable of eradicating the tumor. Immune checkpoint blockers rejuvenate exhausted T cells by blocking immune checkpoints that induce programmed death of T cells, and are therefore considered a promising therapeutic strategy to enhance the immune effects of cryoablation. In this review, we provide a detailed explanation of the immunological mechanisms of cryoablation and articulate the theoretical basis and research progress of the treatment of cancer with cryoablation combined with immune checkpoint blockers. Preliminary data indicates that this combined treatment strategy exhibits good synergy and has been proven to be safe and effective.

Impact of Congenital Heart Disease on the Outcomes of Very Low Birth Weight Infants.

OBJECTIVE: To investigate the association of congenital heart disease (CHD) with morbidity and mortality of very low birth weight (VLBW) infants. STUDY DESIGN: This matched case-control study included VLBW infants born at a single institution between 2001 and 2015. The primary outcome was mortality. Secondary outcomes included necrotizing enterocolitis, bronchopulmonary dysplasia (BPD), sepsis, retinopathy of prematurity, and intraventricular hemorrhage. These outcomes were assessed by comparing VLBW-CHDs with control VLBW infants matched by gestational age within a week, birth weight within 500 g, sex, and birth date within a year using conditional logistic regression. Multivariable logistic regression analyzed differences in outcomes in the VLBW-CHD group between two birth periods (2001-2008 and 2009-2015) to account for changes in practice. RESULTS: In a cohort of 44 CHD infants matched with 88 controls, the mortality rate was 27% in infants with CHD and 1% in controls (p < 0.0001). The VLBW-CHDs had increased BPD; (odds ratio [OR]: 7.70, 95% confidence interval [CI]: 1.96-30.29) and sepsis (OR: 10.59, 95% CI: 2.99-37.57) compared with the control VLBWs. When adjusted for preoperative ventilator use, the VLBW-CHDs still had significantly higher odds of BPD (OR: 6.97, 95% CI: 1.73-28.04). VLBW-CHDs also had significantly higher odds of both presumed and culture-positive sepsis as well as late-onset sepsis than their matched controls. There were no significant differences in outcomes between the two birth periods. CONCLUSION: VLBW-CHDs showed higher odds of BPD, sepsis, and mortality than VLBW infants without CHD. Future research should focus on the increased mortality and specific complications encountered by VLBW infants with CHD and implement targeted strategies to address these risks. KEY POINTS: · Incidence of CHD is higher in preterm infants than in term infants but the incidence of their morbidities is not well described.. · VLBW infants with CHD have higher odds of mortality, bronchopulmonary dysplasia, and sepsis.. · Future research is needed to implement targeted preventive responses..

Paralemmin-3 augments lipopolysaccharide-induced acute lung injury with M1 macrophage polarization via the notch signaling pathway.

BACKGROUND: Acute lung injury (ALI) involves severe lung damage and respiratory failure, which are accompanied by alveolar macrophage (AM) activation. The aim of this article is to verify the influence of paralemmin-3 (PALM3) on alveolar macrophage (AM) polarization in ALI and the underlying mechanism of action. METHODS: An ALI rat model was established by successive lipopolysaccharide (LPS) inhalations. The influence of PALM3 on the survival rate, severity of lung injury, and macrophage polarization was analyzed. Furthermore, we explored the underlying mechanism of PALM3 in regulating macrophage polarization. RESULTS: PALM3 overexpression increased mortality of ALI rats, augmented lung pathological damage, and promoted AM polarization toward M1 cells. Conversely, PALM3 knockdown had the opposite effects. Mechanistically, PALM3 might promote M1 polarization by acting as an adaptor to facilitate transduction of Notch signaling. CONCLUSION: PALM3 aggravates lung injury and induces macrophage polarization toward M1 cells by activating the Notch signaling pathway in LPS-induced ALI, which may shed light on ALI/ARDS treatments.

Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy.

Background and Objective: 2D and 3D tumor features are widely used in a variety of medical image analysis tasks. However, for chemotherapy response prediction, the effectiveness between different kinds of 2D and 3D features are not comprehensively assessed, especially in ovarian-cancer-related applications. This investigation aims to accomplish such a comprehensive evaluation. Methods: For this purpose, CT images were collected retrospectively from 188 advanced-stage ovarian cancer patients. All the metastatic tumors that occurred in each patient were segmented and then processed by a set of six filters. Next, three categories of features, namely geometric, density, and texture features, were calculated from both the filtered results and the original segmented tumors, generating a total of 1403 and 1595 features for the 2D and 3D tumors, respectively. In addition to the conventional single-slice 2D and full-volume 3D tumor features, we also computed the incomplete-3D tumor features, which were achieved by sequentially adding one individual CT slice and calculating the corresponding features. Support vector machine (SVM)-based prediction models were developed and optimized for each feature set. Five-fold cross-validation was used to assess the performance of each individual model. Results: The results show that the 2D feature-based model achieved an AUC (area under the ROC curve (receiver operating characteristic)) of 0.84 ± 0.02. When adding more slices, the AUC first increased to reach the maximum and then gradually decreased to 0.86 ± 0.02. The maximum AUC was yielded when adding two adjacent slices, with a value of 0.91 ± 0.01. Conclusions: This initial result provides meaningful information for optimizing machine learning-based decision-making support tools in the future.

Developing a Novel Image Marker to Predict the Responses of Neoadjuvant Chemotherapy (NACT) for Ovarian Cancer Patients.

OBJECTIVE: Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the patients' responses to NACT varies significantly among different subgroups. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy response prediction of the NACT at an early stage. METHODS: For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. Using this cluster as the input, an SVM based classifier was developed and optimized to create a final marker, indicating the likelihood of the patient being responsive to the NACT treatment. To validate this scheme, a total of 42 ovarian cancer patients were retrospectively collected. A nested leave-one-out cross-validation was adopted for model performance assessment. RESULTS: The results demonstrate that the new method yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.745. Meanwhile, the model achieved overall accuracy of 76.2%, positive predictive value of 70%, and negative predictive value of 78.1%. CONCLUSION: This study provides meaningful information for the development of radiomics based image markers in NACT response prediction.

Protocol of a multicenter, single-blind, randomized, parallel controlled trial evaluating the effect of microbiological rapid on-site evaluation (M-ROSE) guiding anti-infection treatment in patients with severe hospital-acquired pneumonia.

INTRODUCTION: The mortality rate of hospitalized patients with severe hospital-acquired pneumonia (SHAP) remains high. Empirical broad-spectrum antibiotic coverage and the misuse of high-grade antibiotics could lead to the emergence of multi-drug and even pandrug-resistant bacteria. In addition to metagenomic next-generation sequencing (mNGS), microbiological rapid on-site evaluation (M-ROSE) might be a useful technique to identify the pathogens in the early stage; however, the effect of M-ROSE guiding anti-infection treatment on prognostic outcomes of SHAP patients is still unclear. METHODS/DESIGN: This is a multicenter, single-blind, prospective, randomized controlled trial to evaluate the effect of M-ROSE guiding anti-infection treatment in SHAP patients, which will provide new strategies for the prevention and control of clinical multi-drug resistance bacteria. A total of 166 patients with SHAP, aged 18 years and over, will be recruited from seven centers in Beijing and randomly assigned to the intervention group (M-ROSE combined with mNGS) or the control group (mNGS only) in a 1:1 ratio using the central randomization system. Patients in the intervention group will accept M-ROSE and mNGS analysis, and the control group will accept mNGS analysis. Individualized anti-infective treatment and routine treatment will be selected according to the analysis results. The primary outcome is the ICU outcome (mortality). The safety of the intervention measures will be evaluated during the entire trial period. This trial will be the first randomized controlled trial to evaluate the effect of M-ROSE guiding treatment on mortality in patients with SHAP and may change the prevalence of multi-drug resistant bacteria. ETHICS AND DISSEMINATION: This trial adheres to the Declaration of Helsinki and guidelines of Good Clinical Practice. Signed informed consent will be obtained from all participants. The trial has been approved by the Chinese PLA General Hospital (Approval Number: 20220322001). TRIAL REGISTRATION: ClinicalTrials.gov NCT05300776. Registered on 25 March 2022.

A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms.

BACKGROUND: Developing computer aided diagnosis (CAD) schemes of mammograms to classify between malignant and benign breast lesions has attracted a lot of research attention over the last several decades. However, unlike radiologists who make diagnostic decisions based on the fusion of image features extracted from multi-view mammograms, most CAD schemes are single-view-based schemes, which limit CAD performance and clinical utility. PURPOSE: This study aims to develop and test a novel CAD framework that optimally fuses information extracted from ipsilateral views of bilateral mammograms using both deep transfer learning (DTL) and radiomics feature extraction methods. METHODS: An image dataset containing 353 benign and 611 malignant cases is assembled. Each case contains four images: the craniocaudal (CC) and mediolateral oblique (MLO) view of the left and right breast. First, we extract four matching regions of interest (ROIs) from images that surround centers of two suspicious lesion regions seen in CC and MLO views, as well as matching ROIs in the contralateral breasts. Next, the handcrafted radiomics (HCRs) features and VGG16 model-generated automated features are extracted from each ROI resulting in eight feature vectors. Then, after reducing feature dimensionality and quantifying the bilateral and ipsilateral asymmetry of four ROIs to yield four new feature vectors, we test four fusion methods to build three support vector machine (SVM) classifiers by an optimal fusion of asymmetrical image features extracted from four view images. RESULTS: Using a 10-fold cross-validation method, results show that a SVM classifier trained using an optimal fusion of four view images yields the highest classification performance (AUC = 0.876 ± 0.031), which significantly outperforms SVM classifiers trained using one projection view alone, AUC = 0.817 ± 0.026 and 0.792 ± 0.026 for the CC and MLO view of bilateral mammograms, respectively (p < 0.001). CONCLUSIONS: The study demonstrates that the shift from single-view CAD to four-view CAD and the inclusion of both DTL and radiomics features significantly increases CAD performance in distinguishing between malignant and benign breast lesions.

Quantifying Intracellular Nanoparticle Distributions with Three-Dimensional Super-Resolution Microscopy.

Super-resolution microscopy can transform our understanding of nanoparticle-cell interactions. Here, we established a super-resolution imaging technology to visualize nanoparticle distributions inside mammalian cells. The cells were exposed to metallic nanoparticles and then embedded within different swellable hydrogels to enable quantitative three-dimensional (3D) imaging approaching electron-microscopy-like resolution using a standard light microscope. By exploiting the nanoparticles' light scattering properties, we demonstrated quantitative label-free imaging of intracellular nanoparticles with ultrastructural context. We confirmed the compatibility of two expansion microscopy protocols, protein retention and pan-expansion microscopy, with nanoparticle uptake studies. We validated relative differences between nanoparticle cellular accumulation for various surface modifications using mass spectrometry and determined the intracellular nanoparticle spatial distribution in 3D for entire single cells. This super-resolution imaging platform technology may be broadly used to understand the nanoparticle intracellular fate in fundamental and applied studies to potentially inform the engineering of safer and more effective nanomedicines.

Using symmetric illumination and color camera to achieve high throughput Fourier ptychographic microscopy.

This study aims to develop a high throughput Fourier ptychographic microscopy (FPM) technique based on symmetric illumination and a color detector, which is able to accelerate image acquisition by up to 12 times. As an emerging technology, the efficiency of FPM is limited by its data acquisition process, especially for color microscope image reconstruction. To overcome this, we built an FPM prototype equipped with a color camera and a 4×/0.13 NA objective lens. During the image acquisition, two symmetric LEDs illuminate the sample simultaneously using white light, which doubles the light intensity and reduces the total captured raw patterns by half. A standard USAF 1951 resolution target was used to measure the system's modulation transfer function (MTF) curve, and the H&E-stained ovarian cancer samples were then imaged to assess the feature qualities depicted on the reconstructed images. The results showed that the measured MTF curves of red, green, and blue channels are generally comparable to the corresponding curves generated by conventional FPM, while symmetric illumination FPM preserves more tissue details, which is superior to the results captured by conventional 20×/0.4 NA objective lens. This investigation initially verified the feasibility of symmetric illumination based color FPM.

Facile Entry to Pharmaceutically Important 3-Difluoromethyl-quinoxalin-2-ones Enabled by Visible-Light-Driven Difluoromethylation of Quinoxalin-2-ones.

CF2H moiety has a significant potential utility in drug design and discovery, and the incorporation of CF2H into biologically active molecules represents an important and efficient strategy for seeking lead compounds and drug candidates. On the other hand, quinoxalin-2-one is of great interest to pharmaceutical chemists as a common skeleton frequently occurring in plenty of natural products and bioactive compounds. Herein, we reported a practical and efficient protocol for the synthesis of 3-CF2H-quinoxalin-2-ones. Thus, in the presence of 3 mol% of photocatalyst and S-(difluoromethyl)sulfonium salt as difluoromethyl radical sources, a wide range of quinoxalin-2-ones readily underwent a visible-light redox-catalyzed difluoromethylation reaction, to deliver structurally diverse 3-difluoromethyl-quinoxalin-2-ones. We believe that this would facilitate increasing chances and possibilities for seeking potential lead compounds and drug candidates and further boost the development of fluorine-containing pharmaceuticals.

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction.

Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.

Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms.

Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operations, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying or mapping corresponding breast lesion features computed from unregistered multiple mammograms. This motivated us to leverage the architecture of Multi-view Vision Transformers to capture long-range relationships of multiple mammograms from the same patient in one examination. For this purpose, we employed local transformer blocks to separately learn patch relationships within four mammograms acquired from two-view (CC/MLO) of two-side (right/left) breasts. The outputs from different views and sides were concatenated and fed into global transformer blocks, to jointly learn patch relationships between four images representing two different views of the left and right breasts. To evaluate the proposed model, we retrospectively assembled a dataset involving 949 sets of mammograms, which included 470 malignant cases and 479 normal or benign cases. We trained and evaluated the model using a five-fold cross-validation method. Without any arduous preprocessing steps (e.g., optimal window cropping, chest wall or pectoral muscle removal, two-view image registration, etc.), our four-image (two-view-two-side) transformer-based model achieves case classification performance with an area under ROC curve (AUC = 0.818 ± 0.039), which significantly outperforms AUC = 0.784 ± 0.016 achieved by the state-of-the-art multi-view CNNs (p = 0.009). It also outperforms two one-view-two-side models that achieve AUC of 0.724 ± 0.013 (CC view) and 0.769 ± 0.036 (MLO view), respectively. The study demonstrates the potential of using transformers to develop high-performing computer-aided diagnosis schemes that combine four mammograms.

Active Treatment of Infants Born at 22-25 Weeks of Gestation in California, 2011-2018.

OBJECTIVE: To determine the rate and trend of active treatment in a population-based cohort of infants born at 22-25 weeks of gestation and to examine factors associated with active treatment. STUDY DESIGN: This observational study evaluated 8247 infants born at 22-25 weeks of gestation at hospitals in the California Perinatal Quality Care Collaborative between 2011 and 2018. Multivariable logistic regression was used to relate maternal demographic and prenatal factors, fetal characteristics, and hospital level of care to the primary outcome of active treatment. RESULTS: Active treatment was provided to 6657 infants. The rate at 22 weeks was 19.4% and increased with each advancing week, and was significantly higher for infants born between days 4 and 6 at 22 or 23 weeks of gestation compared with those born between days 0 and 3 (26.2% and 78.3%, respectively, vs 14.1% and 65.9%, respectively; P < .001). The rate of active treatment at 23 weeks increased from 2011 to 2018 (from 64.9% to 83.4%; P < .0001) but did not change significantly at 22 weeks. Factors associated with increased odds of active treatment included maternal Hispanic ethnicity and Black race, preterm premature rupture of membranes, obstetrical bleeding, antenatal steroids, and cesarean delivery. Factors associated with decreased odds included lower gestational age and small for gestational age birth weight. CONCLUSIONS: In California, active treatment rates at 23 weeks of gestation increased between 2011 and 2018, but rates at 22 weeks did not. At 22 and 23 weeks, rates increased during the latter part of the week. Several maternal and infant factors were associated with the likelihood of active treatment.

Recent advances and clinical applications of deep learning in medical image analysis.

Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis. Despite the success, the further improvement of deep learning models in medical image analysis is majorly bottlenecked by the lack of large-sized and well-annotated datasets. In the past five years, many studies have focused on addressing this challenge. In this paper, we reviewed and summarized these recent studies to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical image analysis, which are summarized based on different application scenarios, including classification, segmentation, detection, and image registration. We also discuss major technical challenges and suggest possible solutions in the future research efforts.

Using Fourier ptychography microscopy to achieve high-resolution chromosome imaging: an initial evaluation.

SIGNIFICANCE: Searching analyzable metaphase chromosomes is a critical step for the diagnosis and treatment of leukemia patients, and the searching efficiency is limited by the difficulty that the conventional microscopic systems have in simultaneously achieving high resolution and a large field of view (FOV). However, this challenge can be addressed by Fourier ptychography microscopy (FPM) technology. AIM: The purpose of this study is to investigate the feasibility of utilizing FPM to reconstruct high-resolution chromosome images. APPROACH: An experimental FPM prototype, which was equipped with 4 × / 0.1 NA or 10 × / 0.25 NA objective lenses to achieve a theoretical equivalent NA of 0.48 and 0.63, respectively, was developed. Under these configurations, we first generated the system modulation transfer function (MTF) curves to assess the resolving power. Next, a group of analyzable metaphase chromosomes were imaged by the FPM system, which were acquired from the peripheral blood samples of the leukemia patients. The chromosome feature qualities were evaluated and compared with the results accomplished by the corresponding conventional microscopes. RESULTS: The MTF curve results indicate that the resolving power of the 4 × / 0.1 NA FPM system is equivalent and comparable to the 20 × / 0.4 NA conventional microscope, whereas the performance of the 10 × / 0.25 NA FPM system is close to the 60 × / 0.95 NA conventional microscope. When imaging the chromosomes, the feature qualities of the 4 × / 0.1 NA FPM system are comparable to the results under the conventional 20 × / 0.4 NA lens, whereas the feature qualities of the 10 × / 0.25 NA FPM system are better than the conventional 60 × / 0.95 NA lens and comparable to the conventional 100 × / 1.25 NA lens. CONCLUSIONS: This study initially verified that it is feasible to utilize FPM to develop a high-resolution and wide-field chromosome sample scanner.

Clinical Characteristics and CT Findings in 148 Non-COVID-19 Influenza-Like Illness Cases: A Retrospective Control Study.

Background: This study was to collect clinical features and computed tomography (CT) findings of Influenza-Like Illness (ILI) cases, and to evaluate the correlation between clinical data and the abnormal chest CT in patients with the Influenza-Like Illness symptoms. Methods: Patients with the Influenza-Like Illness symptoms who attended the emergency department of The Six Medical Center of The PLA General Hospital from February 10 to April 1, 2020 were enrolled. Clinical and imaging data of the enrolled patients were collected and analyzed. The association between clinical characteristics and abnormal chest CT was also analyzed. Results: A total of 148 cases were enrolled in this study. Abnormalities on chest CT were detected in 61/148 (41.2%) patients. The most common abnormal CT features were as follows: patchy consolidation 22/61(36.1%), ground-glass opacities 21/61(34.4%), multifocal consolidations 17/61(27.9%). The advanced age and underlying diseases were significantly associated with abnormal chest CT. Conclusions: Abnormal chest CT is a common condition in Influenza-Like Illness cases. The presence of advanced age and concurrent underlying diseases is significantly associated with abnormal chest CT findings in patients with ILI symptoms. The chest CT characteristic of ILI is different from the manifestation of COVID-19 infection, which is helpful for differential diagnosis.

PFKFB4 promotes lung adenocarcinoma progression via phosphorylating and activating transcriptional coactivator SRC-2.

BACKGROUND: To investigate the role and its potential mechanism of 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 4 (PFKFB4) in lung adenocarcinoma. METHODS: Co-immunoprecipitation was performed to analyze the interaction between PFKFB4 and SRC-2. Western blot was used to investigate the phosphorylation of steroid receptor coactivator-2 (SRC-2) on the condition that PFKFB4 was knockdown. Transcriptome sequencing was performed to find the downstream target of SRC-2. Cell Counting Kit-8 (CCK-8) assay, transwell assay and transwell-matrigel assay were used to examine the proliferation, migration and invasion abilities in A549 and NCI-H1975 cells with different treatment. RESULTS: In our study we found that PFKFB4 was overexpressed in lung adenocarcinoma associated with SRC family protein and had an interaction with SRC-2. PFKFB4 could phosphorylate SRC-2 at Ser487, which altered SRC-2 transcriptional activity. Functionally, PFKFB4 promoted lung adenocarcinoma cells proliferation, migration and invasion by phosphorylating SRC-2. Furthermore, we identified that CARM1 was transcriptionally regulated by SRC-2 and involved in PFKFB4-SRC-2 axis on lung adenocarcinoma progression. CONCLUSIONS: Our research reveal that PFKFB4 promotes lung adenocarcinoma cells proliferation, migration and invasion via enhancing phosphorylated SRC-2-mediated CARM1 expression.