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Recent studies have illustrated the functional significance of DNA recognition in the activation of innate immune responses among a variety of diseases. The cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway has been found to be modulated by post-translational modifications and can regulate the immune response via type I IFNs. Accumulating evidence indicates a pivotal role of cGAS-STING signaling, being protective or pathogenic, in the development of diseases. Thus, a comprehensive understanding of the post-translational modifications of cGAS-STING pathway and their role in disease development will provide insights in predicting individual disease outcomes and developing appropriate therapies. In this review, we will discuss the regulation of the cGAS-STING pathway and its implications in disease pathologies, as well as pharmacologic strategies to target the cGAS-STING pathway for therapeutic intervention.

Magnetic particle imaging (MPI) is a radiation-free vessel- and target-imaging modality that can sensitively detect nanoparticles. A static magnetic gradient field, referred to as a selection field, is required in MPI to provide a field-free region (FFR) for spatial encoding. The image resolution of MPI is closely related to the size of the FFR, which is determined by the selection field gradient amplitude. Because of the limitations of existing gradient coil hardware, the image resolution of MPI cannot satisfy the clinical requirements of human in vivo imaging. Pulsed excitation has been confirmed to improve the image resolution of MPI by breaking down the 'relaxation wall.' This work proposes the use of a pulsed waveform magnetic gradient from magnetic resonance imaging to further improve the image resolution of MPI. Through alignment of the gradient direction along the field-free line (FFL), each location on the FFL is able to have a unique excitation field strength that generates a specific relaxation-induced decay signal. Through excitation of nanoparticles on the FFL with many gradient profiles, a high-resolution, one-dimensional (1D) image can be reconstructed on the FFL. For larger magnetic nanoparticles, simulation results revealed that a pulsed excitation field with a greater flat portion generates a 1D bar pattern phantom image with a higher correlation and spatial resolution. With parallel FFL and gradient coil movements, high-resolution, two-dimensional (2D) Shepp-Logan phantom and brain vessel maps were reconstructed through repetition of the spatially resolved measurement of magnetic nanoparticles on the FFL.

Myeloid-derived suppressor cells (MDSCs) are heterogeneous myeloid cell populations with immunosuppressive capacity, which contribute to immune regulation and tolerance induction. We previously reported impaired MDSC function in patients with primary Sjögren’s syndrome (pSS) and mice with experimental SS (ESS). However, the molecular mechanisms underlying MDSCs dysfunction remain largely unclear. In this study, we first found that aryl hydrocarbon receptor (AhR) was highly expressed by human and murine polymorphonuclear MDSCs (PMN-MDSCs). Indole-3-propionic acid (IPA), a natural AhR ligand produced from dietary tryptophan, significantly promoted PMN-MDSC differentiation and their suppressive function on CD4+ T cells. In contrast, feeding tryptophan-free diet resulted in decreased PMN-MDSC response, a phenotype that could be reversed by IPA supplementation. The functional importance of PMN-MDSCs was demonstrated in ESS mice by cell-depletion approach. Notably, AhR expression was reduced in PMN-MDSCs during ESS development, while AhR antagonism resulted in exacerbated ESS pathology and dysregulated T effector cells, which could be phenocopied by tryptophan-free diet. Interferon regulatory factor 4 (IRF4), a repressive transcription factor, was upregulated in PMN-MDSCs during ESS progression. Chromatin immunoprecipitation analysis revealed the binding capacity of IRF4 to the promoter region of AhR, while IRF4 deficiency markedly enhanced AhR-mediated PMN-MDSC responses. Furthermore, dietary supplementation of IPA markedly ameliorated salivary glandular pathology in ESS mice with restored MDSC immunosuppressive function. Together, our results identify a novel function of AhR in modulating PMN-MDSC response and demonstrate the therapeutic potential of targeting AhR for the treatment of pSS.

The augmented reality-assisted navigation system (AR-ANS) has been initially applied to the management of hepatolithiasis. The current study examines the safety and efficacy of the AR-ANS for hepatectomy in the treatment of hepatolithiasis. It is the first study to assess the preoperative and long-term outcomes of hepatectomy guided by the AR-ANS for hepatolithiasis.From January 2018 to December 2021, 77 patients with hepatolithiasis who underwent hepatectomy at Zhujiang Hospital of Southern Medical University were included. The subjects were divided into the AR group (n = 31) and the non-AR group (n = 46) according to whether the surgery was guided by the AR-ANS. Clinical baseline features, operation time, intraoperative blood loss, immediate postoperative residual stone rate, postoperative stone recurrence rate at 6 months, and postoperative complications were analyzed.There was no significant difference between preoperative baseline data from the 2 groups (p0.05). The AR group had a longer surgical time than the non-AR group (p0.001). The intraoperative blood loss in the AR group was lower than in the non-AR group (p0.001). Alanine transaminase, aminotransferase, immediate residual stone rate, and stone recurrence rate in the AR group were lower than in the non-AR group (p0.05). There was no significant difference in postoperative complications between the 2 groups.The application of the AR-ANS in hepatectomy for hepatolithiasis has not only achieved satisfactory therapeutic efficacy, but has also shown significant advantages in reducing intraoperative blood loss, immediate stone residual rate, and stone recurrence rate, which has clinical promotion value.

To build radiomics based OS prediction tools for local advanced cervical cancer (LACC) patients treated by concurrent chemoradiotherapy (CCRT) alone or followed by adjuvant chemotherapy (ACT). And, to construct adjuvant chemotherapy decision aid.83 patients treated by ACT following CCRT and 47 patients treated by CCRT were included in the ACT cohort and non-ACT cohort. Radiomics features extracted from primary tumor area of T2-weighted MRI. Two radiomics models were built for ACT and non-ACT cohort in prediction of 3 years overall survival (OS). Elastic Net Regression was applied to the the ACT cohort, meanwhile least absolute shrinkage and selection operator plus support vector machine was applied to the non-ACT cohort. Cox regression models was used in clinical features selection and OS predicting nomograms building.The two radiomics models predicted the 3 years OS of two cohorts. The receiver operator characteristics analysis was used to evaluate the 3 years OS prediction performance of the two radiomics models. The area under the curve of ACT and non-ACT cohort model were 0.832 and 0.879, respectively. Patients were stratified into low-risk group and high-risk group determined by radiomics models and nomograms, respectively. And, the low-risk group patients present significantly increased OS, progression-free survival, local regional control, and metastasis free survival compare with high-risk group (P 0.05). Meanwhile the prognosis prediction performance of radiomics model and nomogram is superior to the prognosis prediction performance of Figo stage.The two radiomics model and the two nomograms is a prognosis predictor of LACC patients treated by CCRT alone or followed by ACT.

The near-infrared window II (NIR-II, 1000-1700 nm) imaging, including NIR-IIa (1300-1400 mm) and NIR-IIb (1500-1700 mm), outperforms the near-infrared window I (NIR-I, 700-900 nm) imaging in biological researches. However, the advantages of NIR-IIa/IIb imaging in human study are ambiguous. This study aims to apply the NIR-IIa/IIb imaging to glioma resection and evaluate their performance by using the developed imaging instrument and intraoperative image fusion method.A multispectral fluorescence imaging instrument that integrated NIR-I/II/IIa/IIb fluorescence imaging and an intraoperative image fusion method have been developed. Seven patients with grade III/IV glioma have been enrolled. NIR-I/II images of the tumor and NIR-I/II/IIa/IIb images of cerebral vessels were acquired with the administration of indocyanine green. Images were fused using the specialized fusion method to synchronously provide the distribution of the vessels and the surgical boundaries.The NIR-IIa/IIb imaging was successfully applied to the clinic. High imaging resolution and contrast have been attained in the NIR-IIa/IIb spectra. Besides, capillaries with an apparent diameter as small as 182 μm were acquired using NIR-IIb imaging. Tumor-feeding arteries were precisely blocked and tumors were excised to the maximum extent for all patients. The blood loss volume during surgery was significantly reduced compared with the control group.The multispectral fluorescence imaging showed high performance, which led to a significant reduction in blood loss volume.The novel multispectral fluorescence imaging technology can assist surgeons in other vascular surgeries in the future.

The surgery of glioblastoma (GBM) requires a maximal resection of the tumor when it is safe and feasible. The infiltrating growth property of the GBM makes it a challenge for neurosurgeons to identify the tumor tissue even with the assistance of the surgical microscope. This highlights the urgent requirement for imaging techniques that can differentiate tumor tissues during surgery in real time. Fluorescence image-guided surgery of GBM has been investigated using several non-specific fluorescent probes that emit light in the visible and the first near-infrared window (NIR-I, 700-900 nm), which limit the detection accuracy because of the non-specific targeting mechanism and spectral characteristics. Targeted NIR-II (1000-1700 nm) fluorescent probes for GBM are thus highly desired. The folate receptor (FR) has been reported to be upregulated in GBM, which renders it to be a promising target for specific tumor imaging.In this study, the folic acid (FA) that can target the FR was conjugated with the clinically approved indocyanine green (ICG) dye and DOTA chelator for radiolabeling withsup64/supCu to achieve targeted positron emission tomography (PET) and fluorescence imaging of GBM.Surprisingly it was found that the resulted bioconjugate, DOTA-FA-ICG and non-radioactivesupnat/supCu-DOTA-FA-ICG, were both self-assembled into nanoparticles with NIR-II emission signal. The radiolabeled DOTA-FA-ICG,sup64/supCu-DOTA-FA-ICG, was found to specifically accumulate in the orthotopic GBM models using in vivo PET, NIR-II, and NIR-I fluorescence imaging. The best time window of fluorescence imaging was demonstrated to be 24 h after DOTA-FA-ICG injection. NIR-II fluorescence image-guided surgery was successfully conducted in the orthotopic GBM models using DOTA-FA-ICG. All the fluorescent tissue was removed and proved to be GBM by the Hamp;E examination.Overall, our study demonstrates that the probes,sup64/supCu-DOTA-FA-ICG and DOTA-FA-ICG, hold promise for preoperative PET examination and intraoperative NIR-II fluorescence image-guided surgery of GBM, respectively.

Lymph node (LN) metastasis is significantly associated with worse prognosis for patients with intrahepatic cholangiocarcinoma (ICC). Improvement in preoperative assessment on LN metastasis helps in treatment decision-making. We aimed to investigate the role of radiomics-based method in predicting LN metastasis for patients with ICC.A total of 296 patients with ICC who underwent curative-intent hepatectomy and lymphadenectomy at two centers in China were analyzed. Radiomic features, including histogram- and wavelet-based features, shape and size features, and texture features were extracted from four-phase computerized tomography (CT) images. The clinical and conventional radiological variables which were independently associated with LN metastasis were also identified. A combined nomogram predicting LN metastasis was developed, and its performance was determined by discrimination, calibration, and stratification of long-term prognosis. The results were validated by the internal and external validation cohorts.Twenty-four radiomic features were selected into the nomogram. The established nomogram demonstrated good discrimination and calibration, with areas under the curve (AUCs) of 0.98 [95% confidence interval (CI) 0.96-0.99], 0.93 (0.88-0.98), and 0.89 (0.81-0.96) in the training and two validation cohorts, respectively. The 5-year overall survival (OS) and recurrence-free survival (RFS) rates of patients with high risk of LN metastasis as grouped by nomogram were poorer than those of patients with low risk in the training cohort (OS 28.8% versus 53.9%, p0.001; RFS 26.3% versus 44.2%, p = 0.001). Similar results were observed in the two validation cohorts.Radiomics-based method provided accurate prediction of LN metastasis and prognostic assessment for ICC patients, and might aid the preoperative surgical decision.

Introduction Surgical resection of medulloblastoma (MB) remains a challenge. At present, a variety of tracers have been used for intraoperative tumor visualization. However, there are few reports on the intraoperative visualization of MB. Hence, we reported our experience of applying fluorescein sodium (FS) in MB surgery. Methods We retrospectively analyzed the clinical information of patients with MB confirmed by surgery and pathology from January 2016 to December 2020 from Sun Yat-sen University Cancer Center. A total of 62 patients were enrolled, of which 27 received intraoperative FS and 35 did not. The intraoperative dose of FS was 3 mg/kg. Results Among the 62 patients, 42 were males, and twenty were females. The age of onset in the FS group was 9.588 ± 7.322, which in the non-fluorescein sodium group was 13.469 ± 10.968, p = 0.198. We did not find significant differences in tumor location, tumor size, tumor resection, tumor histology, and preoperative symptoms (hydrocephalus, headache, vomit, balance disorder) between the groups. There was no significant difference in the postoperative symptoms (hydrocephalus, headache, vomiting, balance disorder, and cerebellar mutism). However, patients in the FS group had a relatively low incidence of balance disorder and cerebellar mutism. There was definite fluorescence of tumor in all cases of the FS group, and even the tiny metastatic lesion was visible. No case had side effects related to the use of FS. Conclusions FS is safe and effective in MB surgery. Whether the application of FS for surgery can reduce complications remains to be studied in the future.

This translational study aims to investigate the clinical benefits of indocyanine green (ICG) based near-infrared window II (NIR-II) fluorescence image-guided surgery (FGS) on high-grade glioma (HGG) patients.Patients were randomly assigned to receive FGS or traditional white light image-guided surgery (WLS). The detection rate of NIR-II fluorescence was observed. Complete resection rate, progression-free survival (PFS), overall survival (OS), and neurological status were compared. Tissue samples were obtained from the FGS group, with the diagnosis based on the surgeons and the fluorescence recorded for comparison of diagnostic capability. Patients with WHO grade III gliomas or glioblastomas (GBM) were analyzed separately.15 GBM and 4 WHO grade III glioma patients in the FGS group and 18 GBM and 4 WHO grade III glioma patients in the WLS group were enrolled. The detection rate of NIR-II fluorescence was 100% for GBM. The complete resection rate was significantly increased by the FGS for GBM (FGS, 100% [95% CI 73.41-100] vs. WLS, 50% [95% CI 29.03-70.97], P = 0.0036). The PFS and OS of the FGS group were also significantly prolonged (Median PFS: FGS, 9.0 months vs. WLS, 7.0 months, P0.0001; Median OS: FGS, 19.0 months vs. WLS, 15.5 months, P = 0.0002). No recurrence was observed in WHO grade III glioma patients.NIR-II FGS achieves a much better complete resection rate of GBM than conventional WLS, leading to greatly improved survival of GBM patients.NIR-II FGS is a highly promising technique worthy of exploring more clinical applications.

Automatic segmentation of lung nodules on computed tomography (CT) images is challenging owing to the variability of morphology, location, and intensity. In addition, few segmentation methods can capture intra-nodular heterogeneity to assist lung nodule diagnosis. In this study, we propose an end-to-end architecture to perform fully automated segmentation of multiple types of lung nodules and generate intra-nodular heterogeneity images for clinical use. To this end, a hybrid loss is considered by introducing a Faster R-CNN model based on generalized intersection over union loss in generative adversarial network. The Lung Image Database Consortium image collection dataset, comprising 2,635 lung nodules, was combined with 3,200 lung nodules from five hospitals for this study. Compared with manual segmentation by radiologists, the proposed model obtained an average dice coefficient (DC) of 82.05% on the test dataset. Compared with U-net, NoduleNet, nnU-net, and other three models, the proposed method achieved comparable performance on lung nodule segmentation and generated more vivid and valid intra-nodular heterogeneity images, which are beneficial in radiological diagnosis. In an external test of 91 patients from another hospital, the proposed model achieved an average DC of 81.61%. The proposed method effectively addresses the challenges of inevitable human interaction and additional pre-processing procedures in the existing solutions for lung nodule segmentation. In addition, the results show that the intra-nodular heterogeneity images generated by the proposed model are suitable to facilitate lung nodule diagnosis in radiology.

Clinical symptoms and inflammatory markers cannot reliably distinguish the etiology of CAP, and chest radiographs have abundant information related with CAP. Hence, we developed a context-fusion convolution neural network (CNN) to explore the application of chest radiographs to distinguish the etiology of CAP in children. This retrospective study included 1769 cases of pediatric pneumonia (viral pneumonia, n = 487; bacterial pneumonia, n = 496; and mycoplasma pneumonia, n = 786). The chest radiographs of the first examination, C-reactive protein (CRP), and white blood cell (WBC) were collected for analysis. All patients were stochastically divided into training, validation, and test cohorts in a 7:1:2 ratio. Automatic lung segmentation and hand-crafted pneumonia lesion segmentation were performed, from which three image-based models including a full-lung model, a local-lesion model, and a context-fusion model were built; two clinical characteristics were used to build a clinical model, while a logistic regression model combined the best CNN model and two clinical characteristics. Our experiments showed that the context-fusion model which integrated the features of the full-lung and local-lesion had better performance than the full-lung model and local-lesion model. The context-fusion model had area under curves of 0.86, 0.88, and 0.93 in identifying viral, bacterial, and mycoplasma pneumonia on the test cohort respectively. The addition of clinical characteristics to the context-fusion model obtained slight improvement. Mycoplasma pneumonia was more easily identified compared with the other two types. Using chest radiographs, we developed a context-fusion CNN model with good performance for noninvasively diagnosing the etiology of community-acquired pneumonia in children, which would help improve early diagnosis and treatment.

Early detection and complete resection of oral squamous cell carcinoma (OSCC) are crucial to improving patient survival and prognosis. However, specifically targeted imaging probes for OSCC detection are limited. Our study aimed to synthesize a novel near-infrared fluorescence (NIRF) probe for precision detection and fluorescence image-guided surgery in OSCC. Bioinformatics data indicated that glucose transporter 1 (GLUT1) is highly expressed in patients with OSCC. We demonstrated high and specific GLUT1 expression upon immunohistochemical staining of samples from 20 patients with OSCC. The specific expression of GLUT1 was further validated in both human OSCC cell lines and OSCC tumor xenografts. Based on these findings, the GLUT1 inhibitor WZB117 was utilized to synthesize a novel NIRF imaging probe, WZB117-IR820. The fluorescence molecular imaging data revealed that WZB117-IR820 could specifically bind to the tumor areas in an orthotopic OSCC mouse model after intravenous injection and could be further applied for precision fluorescence image-guided surgery with no residual tumor in the orthotopic CAL27-fLUC mouse tumor model. For further clinical translational application in patients with OSCC, precise delineation of OSCC tumor areas was achieved after topical application of the WZB117-IR820 imaging probe and was validated by histopathological and immunohistochemical analyses. In conclusion, we synthesized a novel fluorescent imaging probe, WZB117-IR820, which has potential clinical applications for early detection and fluorescence image-guided surgery in OSCC with no observable toxicity.

The high quality of MRI reporting of the prostate is the most critical component of the service provided by a radiologist. Prostate MRI structured reporting with PI-RADS v. 2.1 has been proven to improve consistency, quality, guideline-based care in the management of prostate cancer. There is room for improved accuracy of prostate mpMRI reporting, particularly as PI-RADS core criteria are subjective for radiologists. The application of artificial intelligence may support radiologists in interpreting MRI scans. This review addresses the quality of prostate multiparametric MRI (mpMRI) structured reporting (include improvements in acquisition using artificial intelligence) in terms of size of prostate gland, imaging quality, lesion location, lesion size, TNM staging, sector map, and discusses the future prospects of quality in MR reporting.

Extracellular vesicles (EVs) contain thousands of proteins and nucleic acids, playing an important role in cell-cell communications. Sertoli cells have been essential in the testis as a "nurse cell". However, EVs derived from human Sertoli cells (HSerCs) have not been well investigated.EVs were isolated from HSerCs via ultracentrifugation and characterized by transmission electron microscopy, tunable resistive pulse sensing, and Western blotting. The cargo carried by HSerCs-EVs was measured via liquid chromatography-mass spectrometry and GeneChip miRNA Arrays. Bioinformatic analysis was performed to reveal potential functions of HSerCs-EVs.A total of 860 proteins with no less than 2 unique peptides and 88 microRNAs with high signal values were identified in HSerCs-EVs. Biological processes related to molecular binding, enzyme activity, and regulation of cell cycle were significantly enriched. Specifically, many proteins in HSerCs-EVs were associated with spermatogenesis and regulation of immune system, including Septins, Large proline-rich protein BAG6, Clusterin, and Galectin-1. Moreover, abundant microRNAs within HSerCs-EVs (miR-638, miR-149-3p, miR-1246, etc.) had a possible impact on male reproductive disorders such as asthenozoospermia and oligozoospermia.Our study has shown that HSerCs-EVs contain diverse components such as proteins and microRNAs. Further research is required to evaluate HSerCs-EVs in spermatogenesis, which are underutilized but highly potent resources with particular promise for male infertility.

Microvascular invasion (MVI) is an important risk factor in hepatocellular carcinoma (HCC), but its diagnosis mandates postoperative histopathologic analysis. We aimed to develop and externally validate a predictive scoring system for MVI.From July 2015 to November 2020, consecutive patients underwent surgery for HCC with preoperative gadoxetate disodium (EOB)-enhanced MRI was retrospectively enrolled. All MR images were reviewed independently by two radiologists who were blinded to the outcomes. In the training centre, a radio-clinical MVI score was developed via logistic regression analysis against pathology. In the testing centre, areas under the receiver operating curve (AUCs) of the MVI score and other previous MVI schemes were compared. Overall survival (OS) and recurrence-free survival (RFS) were analysed by the Kaplan-Meier method with the log-rank test.A total of 417 patients were included, 195 (47%) with pathologically-confirmed MVI. The MVI score included: non-smooth tumour margin (odds ratio [OR] = 4.4), marked diffusion restriction (OR = 3.0), internal artery (OR = 3.0), hepatobiliary phase peritumoral hypointensity (OR = 2.5), tumour multifocality (OR = 1.6), and serum alpha-fetoprotein400 ng/mL (OR = 2.5). AUCs for the MVI score were 0.879 (training) and 0.800 (testing), significantly higher than those for other MVI schemes (testing AUCs: 0.648-0.684). Patients with model-predicted MVI had significantly shorter OS (median 61.0 months vs not reached, P .001) and RFS (median 13.0 months vs. 42.0 months, P .001) than those without.A preoperative MVI score integrating five EOB-MRI features and serum alpha-fetoprotein level could accurately predict MVI and postoperative survival in HCC. Therefore, this score may aid in individualized treatment decision making.

The present study aimed to explore the difference in the brain function and structure between patients with major depressive disorder (MDD) and healthy controls (HCs) using two-center and multi-modal MRI data, which would be helpful to investigate the pathogenesis of MDD.The subjects were collected from two hospitals. One including 140 patients with MDD and 138 HCs was used as primary cohort. Another one including 29 patients with MDD and 52 HCs was used as validation cohort. Functional and structural magnetic resonance images (MRI) were acquired to extract four types of features: functional connectivity (FC), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and gray matter volume (GMV). Then classifiers using different combinations among the four types of selected features were respectively built to discriminate patients from HCs. Different templates were applied and the results under different templates were compared.The classifier built with the combination of FC, ALFF, and GMV under the AAL template discriminated patients from HCs with the best performance (AUC=0.916, ACC=84.8%). The regions selected in all the different templates were mainly located in the default mode network, affective network, prefrontal cortex.First, the sample size of the validation cohort was limited. Second, diffusion tensor imaging data were not collected.The performance of classifier was improved by using multi-modal MRI imaging. Different templates would be suitable for different types of analysis. The regions selected in all the different templates are possibly the core regions to investigate the pathophysiology of MDD.

The traditional finite element method-based fluorescence molecular tomography (FMT)/ X-ray computed tomography (XCT) imaging reconstruction suffers from complicated mesh generation and dual-modality image data fusion, which limits the application of in vivo imaging. To solve this problem, a novel standardized imaging space reconstruction (SISR) method for the quantitative determination of fluorescent probe distributions inside small animals was developed. In conjunction with a standardized dual-modality image data fusion technology, and novel reconstruction strategy based on Laplace regularization and L1-fused Lasso method, the in vivo distribution can be calculated rapidly and accurately, which enables standardized and algorithm-driven data process. We demonstrated the method's feasibility through numerical simulations and quantitatively monitored in vivo programmed death ligand 1 (PD-L1) expression in mouse tumor xenografts, and the results demonstrate that our proposed SISR can increase data throughput and reproducibility, which helps to realize the dynamically and accurately in vivo imaging.

Purpose Radical hysterectomy combined with pelvic lymphadenectomy is the standard treatment for early-stage cervical cancer, but unrecognized pelvic nerves are vulnerable to irreversible damage during surgery. This early clinical trial investigated the feasibility and safety of intraoperative near-infrared (NIR) fluorescence imaging (NIR-FI) with indocyanine green (ICG) for identifying pelvic nerves during radical hysterectomy for cervical cancer. Methods Sixty-six adults with cervical cancer were enrolled in this prospective, open-label, single-arm, single-center clinical trial. NIR-FI was performed in vivo to identify genitofemoral (GN), obturator (ON), and hypogastric (HN) nerves intraoperatively. The primary endpoint was the presence of fluorescence in pelvic nerves. Secondary endpoints were the ICG distribution in a nerve specimen and potential underlying causes of fluorescence emission in pelvic nerves. Results In total, 63 patients were analyzed. The ON was visualized bilaterally in 100% (63/63) of patients, with a mean fluorescence signal-to-background ratio (SBR) of 5.3±2.1. The GN was identified bilaterally in 93.7% (59/63) of patients and unilaterally in the remaining 4 patients, with a mean SBR of 4.1±1.9. The HN was identified bilaterally in 81.0% (51/63) of patients and unilaterally in 7.9% (5/63) of patients, with a mean SBR of 3.5±1.3. ICG fluorescence was detected in frozen sections of a nerve specimen, and was mainly distributed in axons. No ICG-related complications were observed. Conclusion This early clinical trial demonstrated the feasibility and safety of NIR-FI to visualize pelvic nerves intraoperatively. Thus, NIR-FI may help surgeons adjust surgical decision-making, avoid nerve damage, and improve surgical outcomes. Trial registration ClinicalTrials.gov NCT04224467

The purpose of this study was to explore a new algorithm model capable of leverage deep learning to screen and diagnose specific types of left-to-right shunt congenital heart disease (CHD) in children.Using deep learning, screening models were constructed to identify 884 heart sound recordings from children with left-to-right shunt CHD. The most suitable model for each type was summarized and compared with expert auscultation. An exploratory analysis was conducted to assess whether there were correlations between heart sounds and left ventricular ejection fraction (LVEF), pulmonary artery pressure, and malformation size.The residual convolution recurrent neural network (RCRnet) classification model had higher accuracy than other models with respect to atrial septal defect (ASD), ventricular septum defect (VSD), patent ductus arteriosus (PDA) and combined CHD, and the best auscultation sites were determined to be the 4th, 5th, 2nd and 3rd auscultation areas, respectively. The diagnostic results of this model were better than those derived from expert auscultation, with sensitivity values of 0.932-1.000, specificity values of 0.944-0.997, precision values of 0.888-0.997 and accuracy values of 0.940-0.994. Absolute Pearson correlation coefficient values between heart sounds of the four types of CHD and LVEF, right ventricular systolic pressure (RVSP) and malformation size were all less than 0.3.The RCRnet model can preliminarily determine types of left-to-right shunt CHD and improve diagnostic efficiency, which may provide a new choice algorithmic CHD screening in children.

Numerous papers have reported variants in microRNAs (miRNAs) associated with cancer risk; these results, however, are controversial. We seek to offer an updated, comprehensive synopsis of genetic associations between single nucleotide polymorphisms (SNPs) in miRNAs and cancer risk. We did a systematic field synopsis and meta-analysis to investigate 29 SNPs in 24 miRNAs associated with risk of 18 different kinds of cancer based on data from 247 eligible articles. We graded levels of cumulative epidemiological evidence of significant association using Venice criteria and a false-positive report probability (FPRP) test. We constructed functional annotations for these variants using data from the Encyclopedia of DNA Elements Project. We used FPRP to find additional noteworthy associations between 278 SNPs in 117 miRNAs and risk of 26 cancers based on each SNP with one data source. Sixteen SNPs were statistically associated with risk of 17 cancers. We graded the cumulative epidemiological evidence as strong for statistical associations between 10 SNPs in 8 miRNAs and risk of 11 cancers, moderate for 9 SNPs with 12 cancers and weak for 11 SNPs with 11 cancers. Bioinformatics analysis suggested that the SNPs with strong evidence might fall in putative functional regions. In addition, 38 significant associations were observed in 38 SNPs and risk of 13 cancers. This study offered a comprehensive research on miRNA gene variants and cancer risk and provided referenced information for the genetic predisposition to cancer risk in future research.

The application of indocyanine green fluorescence-guided hepatectomy for liver metastases from colorectal cancer is in the preliminary stage of clinical practice; thus, its efficacy needs to be determined. This study compared the number of intrahepatic colorectal liver metastases detected intraoperatively and postoperative recovery data between patients who underwent traditional hepatectomy (nonindocyanine green group) and traditional hepatectomy plus intraoperative indocyanine green fluorescence imaging (indocyanine green group).Between January 2018 and March 2020, patients with potentially resectable colorectal liver metastases were randomly assigned to the nonindocyanine green or indocyanine green group. The number of intrahepatic colorectal liver metastases identified intraoperatively and based on postoperative recovery data were compared between both groups.Overall, we recruited 80 patients, among whom 72 eligible patients were randomly assigned. After allocation, 64 patients, comprising 32 in each group, underwent the allocated intervention and follow-up. Compared with the nonindocyanine green group, the mean number of intrahepatic colorectal liver metastases identified intraoperatively in the indocyanine green group was significantly greater (mean [standard deviation], 3.03 [1.58] vs 2.28 [1.35]; p = 0.045), the postoperative hospital stay was shorter (p = 0.012) and the 1-year recurrence rate was also lower (p = 0.017). Postoperative complications and 90-day mortality were comparable, with no statistical differences.Indocyanine green fluorescence imaging significantly increases the number of intrahepatic colorectal liver metastases identified and reduces postoperative hospital stay and 1-year recurrence rate without increasing hepatectomy-related complications and mortality rates.

Background Early identification of the malignant propensity of pulmonary ground‐glass nodules (GGNs) can relieve the pressure from tracking lesions and personalized treatment adaptation. The purpose of this study was to develop a deep learning‐based method using sequential computed tomography (CT) imaging for diagnosing pulmonary GGNs. Methods This diagnostic study retrospectively enrolled 762 patients with GGNs from West China Hospital of Sichuan University between July 2009 and March 2019. All patients underwent surgical resection and at least two consecutive time‐point CT scans. We developed a deep learning‐based method to identify GGNs using sequential CT imaging on a training set consisting of 1524 CT sections from 508 patients and then evaluated 256 patients in the testing set. Afterwards, an observer study was conducted to compare the diagnostic performance between the deep learning model and two trained radiologists in the testing set. We further performed stratified analysis to further relieve the impact of histological types, nodule size, time interval between two CTs, and the component of GGNs. Receiver operating characteristic (ROC) analysis was used to assess the performance of all models. Results The deep learning model that used integrated DL‐features from initial and follow‐up CT images yielded the best diagnostic performance, with an area under the curve of 0.841. The observer study showed that the accuracies for the deep learning model, junior radiologist, and senior radiologist were 77.17%, 66.89%, and 77.03%, respectively. Stratified analyses showed that the deep learning model and radiologists exhibited higher performance in the subgroup of nodule sizes larger than 10 mm. With a longer time interval between two CTs, the deep learning model yielded higher diagnostic accuracy, but no general rules were yielded for radiologists. Different densities of components did not affect the performance of the deep learning model. In contrast, the radiologists were affected by the nodule component. Conclusions Deep learning can achieve diagnostic performance on par with or better than radiologists in identifying pulmonary GGNs.

Pro-tumoral and immunosuppressive M2-like tumor-associated macrophages (TAMs) contribute to tumor progression and distal metastasis. However, current TAMs-modulating therapeutic strategies often encounter challenges including insufficient immune activation, weak antigen presentation ability and unsatisfactory antitumor immune performance. Herein, cyclic RGD peptide functionalized and manganese doped eumelanin-like nanocomposites (RMnMels) were designed for combined hyperthermia-immunotherapy against PC3 prostate cancer. The RMnMels could promote M2-to-M1 macrophage repolarization via scavenging reactive oxygen species and remodel the tumor immunosuppressive microenvironment (TIME). Following near-infrared light irradiation, RMnMels-mediated local hyperthermia not only could destroy tumor cells directly through thermal ablation, but also elicit the release of damage associated molecular patterns and tumor-associated antigens, which could act as effective immunological stimulators to provoke robust tumor immunogenicity and strong antitumor immune responses. The results showed that RMnMels could effectively scavenge reactive oxygen species and promote M2-to-M1 macrophage repolarization both in vitro and in vivo. Synergistically enhanced anti-tumor therapeutic efficacy was achieved following single administration of RMnMels plus single round of laser irradiation, evidenced by decreased primary tumor sizes and decreased number of distant liver metastatic nodules. The as-developed RMnMels may represent a simple and high-performance therapeutic nanoplatform for immunomodulation and enhanced antitumor immune responses.

Aims The aim of this study was to identify brain regions with local, structural, and functional abnormalities in dementia with Lewy bodies (DLB) and uncover the differences between DLB and Alzheimer's disease (AD). The neural networks involved in the identified abnormal brain regions were further described. Methods PubMed, Web of Science, OVID, Science Direct, and Cochrane Library databases were used to identify neuroimaging studies that included DLB versus healthy controls (HCs) or DLB versus AD. The coordinate‐based meta‐analysis and functional meta‐analytic connectivity modeling were performed using the activation likelihood estimation algorithm. Results Eleven structural studies and fourteen functional studies were included in this quantitative meta‐analysis. DLB patients showed a dysfunction in the bilateral inferior parietal lobule and right lingual gyrus compared with HC patients. DLB patients showed a relative preservation of the medial temporal lobe and a tendency of lower metabolism in the right lingual gyrus compared with AD. The frontal‐parietal, salience, and visual networks were all abnormally co‐activated in DLB, but the default mode network remained normally co‐activated compared with AD. Conclusions The convergence of local brain regions and co‐activation neural networks might be potential specific imaging markers in the diagnosis of DLB. This might provide a pathway for the neural regulation in DLB patients, and it might contribute to the development of specific interventions for DLB and AD., This study shows that the brain structure and function damage of dementia with Lewy body is selective and specific, which is significantly different from that of AD. The frontal‐parietal, salience, and visual networks were all abnormally co‐activated in DLB, but the default mode network remained normally co‐activated compared with AD.

Pancreatic cancer is one of the common malignant tumors of the digestive system, and its clinical treatment is still very challenging. Most of the pancreatic cancer chemotherapeutic drugs have poor plasma stability, low cell uptake efficiency, and are prone to developing drug resistance and toxic side effects. Besides, pancreatic cancer often has a dense extracellular matrix, which consists of collagens, hyaluronic acid, and other proteoglycans. Among them, hyaluronic acid is a key component of the dense matrix, which results in vascular compression and insufficient perfusion, and hinders the delivery of chemotherapeutic drugs. In this study, we explore using hyaluronidase in tumor-bearing mice to eliminate the hyaluronic acid barrier, to reduce blood vessel compression and reshape the tumor microenvironment. In addition, we evaluate using doxorubicin-loaded nanoprobes to improve the stability and local tumor-killing effect of the drug. The nanoprobes have the characteristics of near-infrared optical imaging, which are used to monitor the tumor size in real-time during the treatment process, and dynamically observe the tumor inhibitory effect. The results show that elimination of the hyaluronic acid barrier combined with the doxorubicin-loaded nanoprobes can greatly increase drug penetration into tumor tissue and improve the effectiveness of chemotherapy drugs. This study provides a novel strategy for the treatment of pancreatic cancer.

The grade groups (GGs) of Gleason scores (Gs) is the most critical indicator in the clinical diagnosis and treatment system of prostate cancer. End-to-end method for stratifying the patient-level pathological appearance of prostate cancer (PCa) in magnetic resonance (MRI) are of high demand for clinical decision. Existing methods typically employ a statistical method for integrating slice-level results to a patient-level result, which ignores the asymmetric use of ground truth (GT) and overall optimization. Therefore, more domain knowledge (e.g., diagnostic logic of radiologists) needs to be incorporated into the design of the framework. The patient-level GT is necessary to be logically assigned to each slice of a MRI to achieve joint optimization between slice-level analysis and patient-level decision-making. In this paper, we propose a framework (PCa-GGNet-v2) that learns from radiologists to capture signs in a separate two-dimensional (2-D) space of MRI and further associate them for the overall decision, where all steps are optimized jointly in an end-to-end trainable way. In the training phase, patient-level prediction is transferred from weak supervision to supervision with GT. An association route records the attentional slice for reweighting loss of MRI slices and interpretability. We evaluate our method in an in-house multi-center dataset ( N = 570) and PROSTATEx ( N = 204), which yields five-classification accuracy over 80% and AUC of 0.804 at patient-level respectively. Our method reveals the state-of-the-art performance for patient-level multi-classification task to personalized medicine.

Purpose To develop and validate a pretreatment computed tomography (CT)-based deep-learning (DL) model for predicting the treatment response to concurrent chemoradiation therapy (CCRT) among patients with locally advanced thoracic esophageal squamous cell carcinoma (TESCC). Methods and Materials We conducted a prospective, multicenter study on the therapeutic efficacy of CCRT among TESCC patients across 9 hospitals in China (ChiCTR2000039279). A total of 306 patients with locally advanced TESCC diagnosed by histopathology from August 2015 to May 2020 were included in this study. A 3-dimensional DL radiomics model (3D-DLRM) was developed and validated based on pretreatment CT images to predict the response to CCRT. Furthermore, the prediction performance of the newly developed 3D-DLRM was analyzed according to 3 categories: radiation therapy plan, radiation field, and prescription dose used. Results The 3D-DLRM achieved good prediction performance, with areas under the receiver operating characteristic curve of 0.897 (95% confidence interval, 0.840-0.959) for the training cohort and 0.833 (95% confidence interval, 0.654-1.000) for the validation cohort. Specifically, the 3D-DLRM accurately predicted patients who would not respond to CCRT, with a positive predictive value (PPV) of 100% for the validation cohort. Moreover, the 3D-DLRM performed well in all 3 categories, each with areas under the receiver operating characteristic curve of >0.8 and positive predictive values of approximately 100%. Conclusion The proposed pretreatment CT-based 3D-DLRM provides a potential tool for predicting the response to CCRT among patients with locally advanced TESCC. With the help of precise pretreatment prediction, we may guide the individualized treatment of patients and improve survival.

VEXAS syndrome, an autoinflammatory syndrome due to a Ubiquitin Like Modifier Activating Enzyme 1 (UBA1) somatic mutation, has a high thrombotic burden. We report a case of a 69-year-old male that was diagnosed with VEXAS syndrome who developed venous thromboembolism (VTE). Review of literature of existing VEXAS syndrome cases showed a high thrombotic burden, with the reported incidence of VTE (36.4%) being markedly higher than arterial thrombosis (1.6%), with deep vein thrombosis being more common than pulmonary embolism. Somatic mutation in the UBA1 gene results in decreased ubiquitylation which is a key driver in the development of thrombosis in VEXAS syndrome, due to chronic inflammation and cytokine release from abnormal crosstalk between the intrinsic effector mechanism of innate immune cells, platelets and endothelium resulting in dysregulated haemostasis and endothelial dysfunction. Targeting endothelial dysfunction and reducing inflammatory milieu causing hypercoagulability with immunosuppressants and immunomodulatory agents, together with anticoagulation may be the strategy to prevent recurrent thrombotic events. Supplementary Information The online version contains supplementary material available at 10.1007/s11239-021-02608-y.

Objective In observational studies, testosterone has been reported to be associated with some types of cancers. However, the direction and magnitude of the causal association between testosterone and different types of cancer remain unclear. This Mendelian randomization study assessed the causal associations of total testosterone (TT) and bioavailable testosterone (BT) with cancer risk in men. Methods We performed two-sample Mendelian randomization using publicly available GWAS summary statistics to investigate the genetically causal association between testosterone and the risk of 22 kinds of cancers in men. Causal estimates were calculated by the inverse variance weighted method. We also performed additional sensitivity tests to evaluate the validity of the casualty. Results Genetically predicted BT level were significantly associated with an increased risk of prostate cancer [odds ratio (OR) = 1.17 95% confidence interval (CI): 1.09–1.26, P = 2.51E−05] in the MR analysis with the IVW method. TT was found to be the suggestive protective factor against stomach cancer (OR = 0.66, 95% CI: 0.48–0.93, P = 0.0116) as well as pancreatic cancer (OR = 0.59, 95% CI: 0.36–0.96, P = 0.0346). A suggestive association was found between TT and the occurrence of small intestine cancer (OR = 1.0004, 95% CI: 1.0001–1.0007, P = 0.0116). However, testosterone had no significant association with other cancers. Conclusion This study investigated the role of testosterone in the development of prostate cancer, stomach cancer, pancreatic cancer, and small intestine cancer but found no strong association with the other cancers in men.

Background: Lacunar stroke accounts for a quarter of all strokes, but little is known about the underlying pathological mechanisms. Analysis of serum metabolites may allow better understanding of the underlying biological processes. Mendelian randomization (MR) can provide information on the causality of associations. Aims: To identify causal relationships between serum metabolites and lacunar stroke. Methods: We applied a two-sample MR analysis to evaluate relationships between 486 serum metabolites and lacunar stroke. The inverse-variance weighted (IVW) method was used to estimate the causal relationship of the exposure on the outcome, while sensitivity analyses were performed using MR-Egger, weighted median, and MR-PRESSO to eliminate the pleiotropy. We also performed a metabolic pathway analysis to identify potential metabolic pathways. Results: We identified 15 known (8 risk and 7 protective) and 14 unknown serum metabolites associated with lacunar stroke. Among the known risk metabolites, two were lipids (1-linoleoylglycerophosphoethanolamine and dihomo-linolenate (20:3n3 or n6)), five amino acids (kynurenine, isobutyrylcarnitine, aspartate, trans-4-hydroxyproline, and 3-methyl-2-oxovalerate), and one peptide (ADSGEGDFXAEGGGVR). The known protective metabolites included four lipids (4-androsten-3beta,17beta-diol disulfate 1, 1-palmitoleoylglycerophosphocholine, adrenate (22:4n6), and glycodeoxycholate), one amino acid (methionine), and two exogenous metabolites (homostachydrine and 2-methoxyacetaminophen sulfate). Metabolic pathway analysis identified several pathways that might be involved in the disease. Conclusion: We identified eight risk and seven protective human serum metabolites associated with lacunar stroke. Isobutyrylcarnitine was positively associated with an increased risk of lacunar stroke. In addition, 3-methyl-2-oxovalerate and aspartate may be involved in the disease pathogenesis through metabolic pathways.

The goal of this study was to identify genes that were differentially methylated and differentially expressed and their related signaling pathways in ovarian endometriosis tissue. First, the DNA methylation and gene expression profiles in the endometrial tissue of patients with ovarian endometriosis were studied using Illumina 450K methylation microarray analysis and the GSE141549 gene expression dataset. Second, differentially methylated and differentially expressed genes, herein referred to as differentially methylated/expressed genes, were identified and protein-protein interaction networks and functional analysis of these genes were determined. Third, qPCR and immunohistochemistry of patient samples was used to confirm the differential expression of a subset of differentially methylated/expressed genes. Finally, the GSE7305 dataset was used confirm the expression profile of differentially methylated/expressed genes and to determine the potential usefulness of these genes for diagnosis of endometriosis. A total of 37 hypermethylated low-expression genes and 66 hypomethylated high-expression genes were identified in ovarian endometriosis patients. Protein-protein interaction and functional analysis highlighted 8 hypermethylated low-expression genes (KRT19, KRT8, ESR1, PRL, SFN, IL20RA, IL2RB, and PAX8) and 4 hypomethylated high-expression genes (CYP11A1, NR5A1, ME1, and GSTM1). Significantly, both of these gene sets had a diagnostic value for patients with ovarian endometriosis. Signaling pathways that were identified included JAK-STAT (involving IL20RA and IL2RB), prolactin (involving PRL and ESR1), Staphylococcus aureus infection (involving KRT19), viral protein interaction with cytokine and cytokine receptor (involving IL20RA and IL2RB), cytokine-cytokine receptor interaction (involving IL20RA and IL2RB), and drug metabolism-cytochrome P450 (involving GSTM1). The differentially methylated/expressed genes and enriched signaling pathways identified in this study are likely to be associated with the process of ovarian endometriosis.

Gastric cancer (GC) is the third leading cause of cancer-associated deaths globally. Accurate risk prediction of the overall survival (OS) for GC patients shows significant prognostic value, which helps identify and classify patients into different risk groups to benefit from personalized treatment. Many methods based on machine learning algorithms have been widely explored to predict the risk of OS. However, the accuracy of risk prediction has been limited and remains a challenge with existing methods. Few studies have proposed a framework and pay attention to the low-level and high-level features separately for the risk prediction of OS based on computed tomography images of GC patients. To achieve high accuracy, we propose a multi-focus fusion convolutional neural network. The network focuses on low-level and high-level features, where a subnet to focus on lower-level features and the other enhanced subnet with lateral connection to focus on higher-level semantic features. Three independent datasets of 640 GC patients are used to assess our method. Our proposed network is evaluated by metrics of the concordance index and hazard ratio. Our network outperforms state-of-the-art methods with the highest concordance index and hazard ratio in independent validation and test sets. Our results prove that our architecture can unify the separate low-level and high-level features into a single framework, and can be a powerful method for accurate risk prediction of OS.

Background This study evaluated the predictive value of gene signatures for biochemical recurrence (BCR) in primary prostate cancer (PCa) patients. Methods Clinical features and gene expression profiles of PCa patients were attained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets, which were further classified into a training set (n = 419), a validation set (n = 403). The least absolute shrinkage and selection operator Cox (LASSO‐Cox) method was used to select discriminative gene signatures in training set for biochemical recurrence‐free survival (BCRFS). Selected gene signatures established a risk score system. Univariate and multivariate analyses of prognostic factors about BCRFS were performed using the Cox proportional hazards regression models. A nomogram based on multivariate analysis was plotted to facilitate clinical application. Kyoto Encyclopedia of Gene and Genomes (KEGG) and Gene Ontology (GO) analyses were then executed for differentially expressed genes (DEGs). Results Notably, the risk score could significantly identify BCRFS by time‐dependent receiver operating characteristic (t‐ROC) curves in the training set (3‐year area under the curve (AUC) = 0.820, 5‐year AUC = 0.809) and the validation set (3‐year AUC = 0.723, 5‐year AUC = 0.733). Conclusions Clinically, the nomogram model, which incorporates Gleason score and the risk score, could effectively predict BCRFS and potentially be utilized as a useful tool for the screening of BCRFS in PCa., In this work, we established the risk score consisting of six gene signatures, which can predict biochemical recurrence in primary prostate cancer. Clinically, the nomogram model, which incorporates the Gleason score and the risk score, could effectively predict BCRFS and potentially be utilized as a useful tool for the screening of BCRFS in PCa.

Accurate liver necroinflammatory activity diagnosis could guide the clinical decision-making in chronic hepatitis B (CHB) patients.This study aimed to build a non-invasive diagnostic model for liver necroinflammatory activity by incorporating deep learning features and clinico-biochemical characteristics in CHB patients. A total of 239 CHB patients who underwent liver biopsy were recruited and randomly divided into a training cohort (n = 179) and an independent validation cohort (n = 60). Bidirectional stepwise selection identified independent clinico-biochemical characteristics. Multivariate logistic regression analysis was used to establish the final combined model by incorporating clinico-biochemical and deep learning features. Predictive performance was evaluated by discrimination and clinical usefulness. Immunoglobulin M, platelets, laminin, type IV collagen, gamma-glutamyl transferase, alanine aminotransferase, aspartate transaminase, alkaline phosphatase, direct bilirubin, and total bilirubin were identified as independent factors. The combined model exhibited better performance than models based on clinico-biochemical characteristics alone, with an AUC of 0.942 (95% confidence interval [CI], 0.912-0.969) for necroinflammatory activity ≥G2 and 0.885 (95% CI, 0.829-0.934) for ≥G3 in the training cohort, and 0.938 (95% CI, 0.867-0.993) and 0.854 (95% CI, 0.764-0.934) in the validation cohort, respectively. The decision curve confirmed its clinical usefulness. The combined model provided an accurate non-invasive prediction of liver necroinflammatory activity, which might contribute to clinical decision-making in CHB patients.

Objective: Radiomics, an emerging tool for medical image analysis, is potential towards precisely characterizing gastric cancer (GC). Whether using one-slice 2D annotation or whole-volume 3D annotation remains a long-time debate, especially for heterogeneous GC. We comprehensively compared 2D and 3D radiomic features' representation and discrimination capacity regarding GC, via three tasks. Methods: Four-center 539 GC patients were retrospectively enrolled and divided into the training and validation cohorts. From 2D or 3D regions of interest (ROIs) annotated by radiologists, radiomic features were extracted respectively. Feature selection and model construction procedures were customed for each combination of two modalities (2D or 3D) and three tasks. Subsequently, six machine learning models (Model_2D^LNM, Model_3D^LNM; Model_2D^LVI, Model_3D^LVI; Model_2D^pT, Model_3D^pT) were derived and evaluated to reflect modalities' performances in characterizing GC. Furthermore, we performed an auxiliary experiment to assess modalities' performances when resampling spacing is different. Results: Regarding three tasks, the yielded areas under the curve (AUCs) were: Model_2D^LNM's 0.712 (95% confidence interval, 0.613-0.811), Model_3D^LNM's 0.680 (0.584-0.775); Model_2D^LVI's 0.677 (0.595-0.761), Model_3D^LVI's 0.615 (0.528-0.703); Model_2D^pT's 0.840 (0.779-0.901), Model_3D^pT's 0.813 (0.747-0.879). Moreover, the auxiliary experiment indicated that Models_2D are statistically more advantageous than Models3D with different resampling spacings. Conclusion: Models constructed with 2D radiomic features revealed comparable performances with those constructed with 3D features in characterizing GC. Significance: Our work indicated that time-saving 2D annotation would be the better choice in GC, and provided a related reference to further radiomics-based researches., Published in IEEE Journal of Biomedical and Health Informatics

Metabolism plays an important role in the pathogenesis of prostate cancer (PCa). Hence, we explored candidate metabolic-related genes attributed to biochemical relapse (BCR) of PCa. Gene expression profile and clinical parameters were downloaded from GSE70769 as a "training set". Using univariate Cox and LASSO-COX regression models, risk scores (RSs) were constructed. Kaplan-Meier (K-M) survival and time-dependent receiver operating characteristic (t-ROC) curves were employed. Univariate and multivariate Cox models were utilized to validate prognostic factors for biochemical relapse-free survival (BCRFS). Nomogram was plotted to facilitate clinical application. The dataset obtained from GSE70768 served as "validation set". RSs were constructed by using 7 metabolic-related genes. RSs could significantly predict 1, 3, 5-year BCRFS (AUCs for training set: 0.810-0.836; AUC for validation set: 0.673-0.827). Nomograms could effectively predicted BCRFS (training set: C-index=0.831; validation set: C-index=0.737). RSs model is an independent prognostic factor for BCR, holding greater predictive value than traditional clinicopathological parameters. Clinical Relevance- We built the prognostic nomogram based on metabolic-related gene signatures and clinicopathological features. The nomogram might further optimize biochemical relapse risk stratification for prostate cancer patients with crucial accuracy.

To compare two laparoscopic vaginoplasties using a single peritoneal flap (SPF), namely the Hebei I technique and the Hebei II technique, for creation of a neovagina in patients with Mayer-Rokitansky-Kuster-Hauser (MRKH) syndrome. A comparative retrospective study was conducted at a university-based tertiary care hospital. From September 2008 to September 2019, 72 patients with MRKH syndrome underwent either the Hebei I technique (n = 49) or the Hebei II technique (n = 23). The perioperative results, complications and anatomical outcomes of two groups were recorded and compared. The functional results of patients who became sexually active were assessed through the Female Sexual Function Index (FSFI) questionnaire. Two techniques achieved anatomical and functional success without intraoperative complications. There was no significant difference in perioperative results, anatomical findings and the FSFI scores between the two groups. Patients in the Hebei II group had a relatively shorter operative time than those in the Hebei I group (P = 0.064). What is more, compared with the Hebei I group, the Hebei II group had significantly fewer granulomatous polyps at the top of the neovagina (P = 0.029) and less mucous production of the neovagina (P = 0.025) during the first 3 months after surgery. Both the Hebei I and Hebei II techniques are feasible approaches for creating a neovagina which can bring satisfactory anatomical and sexual outcomes in patients with MRKH syndrome. However, the Hebei II technique may be a good alternative to the Hebei I technique because of its relatively shorter operative time, fewer neovaginal secretions and fewer granulomatous polyps.

Background Although sedation during gastrointestinal endoscopy is widely used in China, the characteristics of sedation use, including regional distribution, personnel composition, equipment used, and drug selection, remain unclear. The present study aimed to provide insights into the current practice and regional distribution of sedation for gastrointestinal endoscopy in China. Methods A questionnaire consisting of 19 items was distributed to directors of anaesthesiology departments and anaesthesiologists in charge of endoscopic sedation units in mainland China through WeChat. Results The results from 2758 participating hospitals (36.7% of the total) showed that 9 808 182 gastroscopies (69.3%) and 4 353 950 colonoscopies (30.7%), with a gastroscopy-to-colonoscopy ratio of 2.3, were conducted from January to December 2016. Sedation was used with 4 696 648 gastroscopies (47.9%) and 2 148 316 colonoscopies (49.3%), for a ratio of 2.2. The most commonly used sedative was propofol (61.0% for gastroscopies and 60.4% for colonoscopies). Haemoglobin oxygen saturation (SpO2) was monitored in most patients (96.1%). Supplemental oxygen was routinely administered, but the availability of other equipment was variable (anaesthesia machine in 64.9%, physiological monitor in 84.4%, suction device in 72.3%, airway equipment in 75.5%, defibrillator in 32.7%, emergency kit in 57.0%, and difficult airway kit in 20.8% of centres responding). Conclusions The sedation rate for gastrointestinal endoscopy is much lower in China than in the USA and in Europe. The most commonly used combination of sedatives was propofol plus an opioid (either fentanyl or sufentanil). Emergency support devices, such as difficult airway devices and defibrillators, were not usually available.

Objectives To develop and validate a nomogram based on fetal nuchal translucency thickness (NT) and ultrasonographic facial markers for screening for trisomy 21 in the first trimester of pregnancy. Methods This was a retrospective case–control study using stored two‐dimensional midsagittal fetal profile images captured at 11 + 0 to 13 + 6 weeks' gestation in singleton pregnancies. We included images from 302 trisomy‐21 pregnancies and 322 euploid pregnancies. Cases were divided into a training set (200 euploid + 200 with trisomy 21) and a validation set (122 euploid + 102 with trisomy 21) at a ratio of approximately 2:1. For each, the maternal age, gestational age, fetal NT and karyotype were noted, and 12 ultrasonographic fetal facial markers were measured. The least absolute shrinkage and selection operator (LASSO) method and multivariable analysis were used to select automatically the discriminative markers. Logistic regression was used to develop a LASSO model, based on the selected markers, to screen for trisomy 21 in the first trimester of pregnancy. Furthermore, 60 of the 624 images were selected randomly as a retest set to evaluate the model's robustness. The predictive performance of screening for trisomy 21 of a model based on fetal NT and maternal age and of the LASSO model was assessed using the area under the receiver‐operating‐characteristics curve (AUC). A nomogram was developed as an individualized tool to predict patient‐specific probability for trisomy 21, which is a more visual presentation of the LASSO model. The performance of the nomogram was assessed using the C‐index and calibration curve. Results Into the LASSO model were incorporated eight markers, including fetal NT, prenasal‐thickness‐to‐nasal‐bone‐length ratio, facial profile line, frontomaxillary facial angle, frontonasal facial angle, mandibulomaxillary facial angle, maxilla‐nasion‐mandible angle and d2 (distance between the anterior edge of the prefrontal skin and the mandibulomaxillary line) (all P

Lung cancer is the leading cause of cancer‐related deaths. LIM domain kinase (LIMK) 1 is a member of serine/threonine kinase family and highly expressed in various cancers. Luteolin, a polyphenolic plant flavonoid, has been reported to suppress tumour proliferation through inducing apoptosis and autophagy via MAPK activation in glioma. However, the mechanism of luteolin on suppressing lung cancer growth is still unclear. We found that luteolin targeted LIMK1 from the in silico screening and significantly inhibited the LIMK1 kinase activity, which was confirmed with pull‐down binding assay and computational docking models. Treatment with luteolin inhibited lung cancer cells anchorage‐independent colony growth and induced apoptosis and cell cycle arrest at G1 phase. Luteolin also decreased the expression of cyclin D1 and increased the levels of cleaved caspase‐3 by down‐regulating LIMK1 signalling related targets, including p‐LIMK and p‐cofilin. Furthermore, luteolin suppressed the lung cancer patient‐derived xenograft tumour growth by decreasing Ki‐67, p‐LIMK and p‐cofilin expression in vivo. Taken together, these results provide insight into the mechanism that underlies the anticancer effects of luteolin on lung cancer, which involved in down‐regulation of LIMK1 and its interaction with cofilin. It also provides valuable evidence for translation towards lung cancer clinical trials with luteolin.

Background: Many children worldwide do not eat recommended amounts of vegetables. Disliking vegetables is a key factor associated with low intake. Objective: This umbrella review synthesized systematic reviews to determine the effectiveness of sensory and behavioral strategies to facilitate liking of vegetables (primary outcome) in young children up to 5 y of age, as key predictors of vegetable intake (secondary outcome). Methods: Nine databases were searched up to May 2019 (updated in September 2020). Two reviewers independently conducted study screening and selection, data extraction, and assessment of methodological quality using AMSTAR 2 (A MeaSurement Tool to Assess systematic Reviews). Eleven reviews (n = 1 rated strong quality, n = 4 moderate quality, n = 6 low/critically low quality)examining 85 primary studies met the review criteria: systematic reviews and meta-analyses of primary studies (any quantitative design) that examined sensory or behavioral strategies on vegetable liking or intake (outcomes reported separately for children ≤5y). Strategy effectiveness was synthesized into 3 categories based on evidence strength: 1) promising (large and consistent body of moderate quality evidence), 2) emerging (small to moderate body of mixed consistency and quality evidence), and 3) limited (small body of limited consistency and quality evidence). Results: Promising evidence was identified for repeated exposure to a single or a variety of vegetables. Emerging evidence was identified for several strategies that increase familiarity with vegetable flavors(e.g., via exposure in utero and through breast milk, and a “vegetable first” approach to complementary feeding) and/or willingness to try vegetables (e.g., via parental role modeling, nonfood rewards, and vegetable-based story books). Conclusions: Current evidence supports incorporation of tailored advice into guideline documents for parents and carers to repeatedly expose their children to a variety of vegetables to increase vegetable intake. Ongoing robust research on strategies to facilitate children’s liking of vegetables is warranted to strengthen the evidence base underpinning advice for parents and health professionals Refereed/Peer-reviewed

Accurate screening of trisomy 21 in the first trimester can provide an early opportunity for decision-making regarding reproductive choices.To develop and validate a deep learning model for screening fetuses with trisomy 21 based on ultrasonographic images.This diagnostic study used data from all available cases and controls enrolled at 2 hospitals in China between January 2009 and September 2020. Two-dimensional images of the midsagittal plane of the fetal face in singleton pregnancies with gestational age more than 11 weeks and less than 14 weeks were examined. Observers were blinded to subjective fetus nuchal translucency (NT) marker measurements. A convolutional neural network was developed to construct a deep learning model. Data augmentation was applied to generate more data. Different groups were randomly selected as training and validation sets to assess the robustness of the deep learning model. The fetal NT was shown and measured. Each detection of trisomy 21 was confirmed by chorionic villus sampling or amniocentesis. Data were analyzed from March 1, 2021, to January 3, 2022.The primary outcome was detection of fetuses with trisomy 21. The receiver operating characteristic curve, metrics of accuracy, area under the curve (AUC), sensitivity, and specificity were used for model performance evaluation.A total of 822 case and control participants (mean [SD] age, 31.9 [4.6] years) were enrolled in the study, including 550 participants (mean [SD] age, 31.7 [4.7] years) in the training set and 272 participants (mean [SD] age, 32.3 [4.7] years) in the validation set. The deep learning model showed good performance for trisomy 21 screening in the training (AUC, 0.98; 95% CI, 0.97-0.99) and validation (AUC, 0.95; 95% CI, 0.93-0.98) sets. The deep learning model had better detective performance for fetuses with trisomy 21 than the model with NT marker and maternal age (training: AUC, 0.82; 95% CI, 0.77-0.86; validation: AUC, 0.73; 95% CI, 0.66-0.80).These findings suggest that this deep learning model accurately screened fetuses with trisomy 21, which indicates that the model is a potential tool to facilitate universal primary screening for trisomy 21.

Background This study aimed to investigate the association between dietary fiber intake and long-term cardiovascular disease (CVD) risk based on the National Health and Nutrition Examination Survey (NHANES) database. Methods A total of 14,947 participants aged 20–79 from the NHANES database were included in this study between 2009 and 2018. The atherosclerotic cardiovascular disease (ASCVD) score was utilized to predict the 10-year risk of CVD in individuals (low, borderline, intermediate, and high risk). Weighted univariate and multinomial multivariate logistic regression analyses were used to analyze the association between dietary fiber intake and long-term CVD risk. Results Higher dietary fiber density may be associated with a reduced ASCVD risk in participants with intermediate risk [odds ratio (OR) = 0.76; 95% confidence interval (CI), 0.61–0.94] and high risk (OR = 0.60; 95%CI, 0.45–0.81) compared with those in the group with low risk. Higher total dietary fiber intake may also reduce ASCVD risk in participants with high risk (OR = 0.84; 95%CI, 0.75–0.95). Subgroup analyses showed that higher dietary fiber density may be related to reduced ASCVD risk in intermediate-risk participants aged 20–39 (OR = 0.62; 95%CI, 0.43–0.89) and 40–59 (OR = 0.67; 95%CI, 0.49–0.94). In high-risk participants, higher dietary fiber density may reduce ASCVD risk in 20–39-year-old (OR = 0.38; 95%CI, 0.19–0.77), 40–59-year-old (OR = 0.37; 95%CI, 0.20–0.70), male (OR = 0.47; 95%CI, 0.23–0.97) and female (OR = 0.57; 95%CI, 0.38–0.86) participants. Conclusion Higher dietary fiber density and total dietary fiber intake were associated with a lower long-term CVD risk, especially in the 20–39 and 40–59 age groups, where the reduction was most significant.

The majority of adenovirus (Ad) vectors are based on human Ad type 5, which is a member of Ad species C. Species C also includes the closely-related types 1, 2, 6, 57 and 89. It is known that coagulation factors bind to Ad5 hexon and play a key role in the liver tropism of Ad5 vectors, but it is unclear how coagulation factors affect vectors derived from other species C Ads. We evaluated species C Ad vectors both in vitro and following intravenous injection in mice. To assess the impact of hexon differences, we constructed chimeric Ad5 vectors that contain the hexon hypervariable regions from other species C types, including vectors with hexon mutations that decreased coagulation factor binding. After intravenous injection into mice, vectors with Ad5 or Ad6 hexon had strong liver tropism, while vectors with chimeric hexon from other Ad types had weaker liver tropism due to inhibition by natural antibodies and complement. In addition, we discovered a novel ability of hexon to bind prothrombin, which is the most abundant coagulation factor in blood, and we found striking differences in the affinity of Ads for human, mouse and bovine coagulation factors. When compared to Ad5, vectors with non-Ad5 species C hexons had considerably higher affinity for both human and mouse prothrombin. Most of the vectors tested were strongly dependent on coagulation factors for liver transduction, but vectors with chimeric Ad6 hexon showed much less dependence on coagulation factors than other vectors. We found that in vitro neutralization experiments with mouse serum predicted in vivo behavior of Ad5 vectors, but in vitro experiments did not predict the in vivo behavior of vectors based on other Ad types. In sum, hexons from different human Ad species C viruses confer diverse properties on vectors, including differing abilities to target the liver.

Bio-inspired transverse brachiation robots mimic the movement of human climbers as they traverse along ledges on a vertical wall. The constraints on the locomotion of these robots differ considerably from those of conventional brachiation robots due primarily to the need for robust hand-eye coordination. This paper describes the development of a motion control strategy for a brachiation robot navigating between wall ledges positioned on a level plane or at different elevations. We based our robot on a four-link arm-body-tail system performing a four-phase movement, including a release phase, body reversal phase, swing-up phase, and grasping phase. We designed a gripper that uses passive wrist joint motion to grasp the ledge during the tail swing. We also developed a dynamic model by which to coordinate the swing-up movement, define the phase switching conditions, and time the grasping action of the grippers. In experiments, the robot proved highly effective in traversing between wall ledges of the same or different elevations.

Purpose Surgery is the predominant treatment modality of human glioma but suffers difficulty on clearly identifying tumor boundaries in clinic. Conventional practice involves neurosurgeon’s visual evaluation and intraoperative histological examination of dissected tissues using frozen section, which is time-consuming and complex. The aim of this study was to develop fluorescent imaging coupled with artificial intelligence technique to quickly and accurately determine glioma in real-time during surgery. Methods Glioma patients (N = 23) were enrolled and injected with indocyanine green for fluorescence image–guided surgery. Tissue samples (N = 1874) were harvested from surgery of these patients, and the second near-infrared window (NIR-II, 1000–1700 nm) fluorescence images were obtained. Deep convolutional neural networks (CNNs) combined with NIR-II fluorescence imaging (named as FL-CNN) were explored to automatically provide pathological diagnosis of glioma in situ in real-time during patient surgery. The pathological examination results were used as the gold standard. Results The developed FL-CNN achieved the area under the curve (AUC) of 0.945. Comparing to neurosurgeons’ judgment, with the same level of specificity >80%, FL-CNN achieved a much higher sensitivity (93.8% versus 82.0%, P 70% of the errors made by neurosurgeons. FL-CNN was also able to rapidly predict grade and Ki-67 level (AUC 0.810 and 0.625) of tumor specimens intraoperatively. Conclusion Our study demonstrates that deep CNNs are better at capturing important information from fluorescence images than surgeons’ evaluation during patient surgery. FL-CNN is highly promising to provide pathological diagnosis intraoperatively and assist neurosurgeons to obtain maximum resection safely. Trial registration ChiCTR ChiCTR2000029402. Registered 29 January 2020, retrospectively registered

BACKGROUND Focal cortical dysplasia IIIa (FCD IIIa) is a common histopathological finding in temporal lobe epilepsy. However, subtle alterations in the temporal neocortex of FCD IIIa renders presurgical diagnosis and definition of the resective range challenging. PURPOSE To explore neuroimaging phenotyping and structural-metabolic-electrophysiological alterations in FCD IIIa. STUDY TYPE Retrospective. SUBJECTS One hundred and sixty-seven subjects aged 4-39 years, including 64 FCD IIIa patients, 89 healthy controls and 14 FCD I patients as disease controls. FIELD STRENGTH/SEQUENCE 3 T, fast-spin-echo T2 -weighted fluid-attenuated inversion recovery (FLAIR), synthetic T1 -weighted magnetization prepared rapid acquisition gradient echo (MPRAGE). ASSESSMENT Surface-based linear model was applied to reveal neuroimaging phenotyping in FCD IIIa and assess its relationship with clinical variables. Logistic regression was implemented to identify FCD IIIa patients. Epileptogenicity mapping (EM) was conducted to explore the structural-metabolic-electrophysiological alterations in temporal neocortex of FCD IIIa. STATISTICAL TESTS Student's t-test was applied to determine the significance of paired differences. Calibration curves were plotted to assess the goodness-of-fit (GOF) of the models, combined with the Hosmer-Lemeshow test. RESULTS FCD IIIa exhibited widespread hyperintensities in temporal neocortex, and these alterations correlated with disease duration (Puncorrected