Search

Head-mounted loupes can increase the user's visual acuity to observe the details of an object. On the other hand, optical see-through head-mounted displays (OST-HMD) are able to provide virtual augmentations registered with real objects. In this article, we propose AR-Loupe, combining the advantages of loupes and OST-HMDs, to offer augmented reality in the user's magnified field-of-vision. Specifically, AR-Loupe integrates a commercial OST-HMD, Magic Leap One, and binocular Galilean magnifying loupes, with customized 3D-printed attachments. We model the combination of user's eye, screen of OST-HMD, and the optical loupe as a pinhole camera. The calibration of AR-Loupe involves interactive view segmentation and an adapted version of stereo single point active alignment method (Stereo-SPAAM). We conducted a two-phase multi-user study to evaluate AR-Loupe. The users were able to achieve sub-millimeter accuracy ( 0.82 mm) on average, which is significantly ( ) smaller compared to normal AR guidance ( 1.49 mm). The mean calibration time was 268.46 s. With the increased size of real objects through optical magnification and the registered augmentation, AR-Loupe can aid users in high-precision tasks with better visual acuity and higher accuracy.

BackgroundTo investigate the renal dysfunction in patients with hyperuricemia by employing a multiparametric MRI protocol, consisting of quantitative water molecule diffusion, microstructure, microscopic perfusion, and oxygenation measurements in kidneys.Materials and methodsA total of 48 patients with hyperuricemia (HU) and 22 age-matched healthy control subjects (HC) were enrolled in the study. For each participant, three different functional magnetic resonance imaging (fMRI) sequences were acquired and analyzed, including intravoxel incoherent motion imaging (IVIM), diffusion tensor imaging (DTI), and blood-oxygen-level-dependent MRI (BOLD). Thereafter, an independent two-samplet-test was applied to discover the significant differences of MRI indices between the hyperuricemia (HU) and HC groups, and the specific potential biomarkers between two subgroups of HU group (asymptomatic hyperuricemia group (AH) and gouty arthritis group (GA)). Further, multivariate logistic regression analyses were performed to classify the AH from the GA group using the MRI indices with significant between-group differences. The receiver operating characteristic (ROC) curve was plotted, and the area under the ROC curve (AUC) was calculated to assess the performance of each MR index for differentiation between the AH and GA groups.ResultsTen parametric values of the HU group were significantly lower than those of the HC group among the 14 fMRI parameters (P *, andfvalues and medullary D and R2*values had significant differences between the AH and GA groups (P fvalues and medullary R2* value gave the best diagnostic efficacy, yielding an AUC, sensitivity, and specificity of 0.967 ± 0.022, 91.67%, and 95.83%, respectively.ConclusionsA multiparametric MR analysis plays an important role in the evaluation of renal dysfunction in hyperuricemia from multiple perspectives. It could be a promising method for noninvasive detection and identification of the early-stage renal damage induced by hyperuricemia.

Background The histological grade of bladder cancer (BCa) is an important factor associated with the treatment and prognosis. However, accurate determination of the preoperative histological grade of BCa remains a challenge. Purpose To investigate the diagnostic potential of synthetic MRI (SyMRI) in evaluating the histological grade of BCa. Study type Prospective. Subjects Sixty patients (48 men and 12 women; mean age, 65 ± 11 years) with pathologically confirmed BCa (33 with high-grade BCa and 27 with low-grade BCa) were enrolled. Field strength/sequence Diffusion-weighted imaging (DWI) acquired by a single-shot echo-planar sequence and SyMRI acquired by a multidynamic multiecho (MDME) sequence at 3.0 T. Assessment Preoperative quantitative longitudinal relaxation time (T1 ), transverse relaxation time (T2 ), proton density (PD), and apparent diffusion coefficient (ADC) values of BCa were independently measured by two radiologists. Statistical tests Interclass correlation coefficient (ICC), independent sample t-test, Mann-Whitney U test, Delong test, and receiver operating characteristic curve (ROC) analysis were used. Results Significant differences were found in the mean of all the T1 , T2 , PD, and ADC values between high- and low-grade BCa. The best diagnostic performance was found for the mean ADC value with an area under the ROC curve (AUC) of 0.869, while the AUC values of the mean PD, T1 , and T2 values were 0.755, 0.740, and 0.723, respectively. Data conclusion SyMRI may be a potential noninvasive technique for evaluating the histological grade of BCa. However, the overall diagnostic performance of SyMRI-derived parameters was inferior to the ADC value. Levels of evidence 2. Technical efficacy stage 2.

Irritable bowel syndrome with diarrhea (IBS-D) is chronic intestinal dysfunction with diarrhea and other complicated clinical symptoms, and it has a great impact on the daily life and mental state of patients. Some studies have reported that ingestion of probiotics can significantly alleviate a variety of intestinal diseases. The purpose of this study was to investigate the IBS-D-alleviating effects of a probiotic strain, Lactobacillus plantarum (L. plantarum) CCFM8610, with multiple health-promoting effects. The study was a 12-week, randomized, double-blind, placebo-controlled, pilot clinical trial. Seventy-five patients were randomly assigned to receive the placebo, oligosaccharides, or L. plantarum CCFM8610 (1 × 1010 colony-forming units (CFU) per day), with a 2-week run-in period, an 8-week intervention period, and a 2-week follow-up observation period. The patients’ clinical symptoms and quality of life were examined by the IBS symptom severity scale (IBS-SSS) and the IBS quality of life scale (IBS-QOL). Changes in gut microbiota composition and diversity were measured at the end of the intervention period. The oral administration of L. plantarum CCFM8610 significantly decreased the IBS-SSS and IBS-QOL scores, reduced IBS-D symptom severity, recovered gut microbiota diversity, decreased the relative abundance of bloating-related genus Methanobrevibacter, and increased the relative abundance of butyric acid-producing genera, including Anaerostipes, Anaerotruncus, Bifidobacterium, Butyricimonas, and Odoribacter. These findings suggest that ingestion of L. plantarum CCFM8610 can significantly alleviate clinical symptoms and gut microbiota dysbiosis in IBS-D patients. The IBS-D-alleviating effect of L. plantarum CCFM8610 may be related to the increase in the relative abundance of butyric acid-producing genera in the intestine.

BACKGROUND: Myocardial injury has been found using magnetic resonance imaging in recovered coronavirus disease 2019 (COVID-19) patients unselected or with ongoing cardiac symptoms. PURPOSE: To evaluate for the presence of myocardial involvement in recovered COVID-19 patients without cardiovascular symptoms and abnormal serologic markers during hospitalization. STUDY TYPE: Prospective. POPULATION: Twenty-one recovered COVID-19 patients and 20 healthy controls (HC). FIELD STRENGTH/SEQUENCE: 3.0 T, cine, T2-weighted imaging, T1 mapping, and T2 mapping. ASSESSMENT: Cardiac ventricular function includes end-diastolic volume, end-systolic volume, stroke volume, cardiac output, left ventricle (LV) mass, and ejection fraction (EF) of LV and right ventricle (RV), and segmental myocardial T1 and T2 values were measured. STATISTICAL TESTS: Student's t-test, univariate general linear model test, and chi-square test were used for analyses between two groups. Ordinary one-way analyses of variance or Kruskal-Wallis H test were used for analyses between three groups, followed by post-hoc analyses. RESULTS: Fifteen (71.43%) COVID-19 patients had abnormal magnetic resonance findings, including raised myocardial native T1 (5, 23.81%) and T2 values (10, 47.62%), decreased LVEF (1, 4.76%), and RVEF (2, 9.52%). The segmental myocardial T2 value of COVID-19 patients (49.20 [46.1, 54.6] msec) was significantly higher than HC (48.3 [45.2, 51.7] msec) (P < 0.001), while the myocardial native T1 value showed no significant difference between COVID-19 patients and HC. The myocardial T2 value of serious COVID-19 patients (52.5 [48.1, 57.1] msec) was significantly higher than unserious COVID-19 patients (48.8 [45.9, 53.8] msec) and HC (48.3 [45.2, 51.7]) (P < 0.001). COVID-19 patients with abnormally elevated D-dimer, C-reactive protein, or lymphopenia showed higher myocardial T2 values than without (all P < 0.05). DATA CONCLUSION: Cardiac involvement was observed in recovered COVID-19 patients with no preexisting cardiovascular disease, no cardiovascular symptoms, and elevated serologic markers of myocardial injury during the whole course of COVID-19. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 5.

Existing indoor positioning systems based on Wi-Fi or Bluetooth confront difficulties and problems, such as low positioning accuracy, unstable, and small-signal coverage. A system using an acoustic ranging signal is presented in this article, and it is available for almost all commercial off-the-shelf (COTS) smartphones. A solution was developed to detect the modulated signals transmitted by specially designed anchor stations at each consisting of a speaker. The detector is a two-stage algorithm based on a generalized cross correlation (GCC) method and a time–frequency-based feature extraction. The detected timestamps are converted to time-difference-of-arrivals (TDOAs) observables, which can be applied with a least-squares (LS) estimator to obtain the smartphone positions in static cases. Furthermore, an extended Kalman filter (EKF) is introduced to integrate data from inertial measurement units (IMUs) and acoustic TDOA ranging observables for estimating a better trajectory in dynamic cases. Experimental results show that the signal detection algorithm is accurate with a mean error of about 0.3–1 m in terms of ranging estimation. The proposed method offers a static positioning accuracy of better than 0.5 m. For dynamic tests, positioning accuracy of 0.5 m at 50% and 1.6 m at 95% is achieved with four anchors in the first scene. In the second scene with a large test site, it achieves a dynamic positioning accuracy of 0.5 m at 50% and 1.82 m at 95% with eight anchors, which is comparable with the positioning accuracy of the ultrawideband (UWB) technology.

To explore the value of combined diffusion tensor imaging (DTI) and blood oxygenation level-dependent magnetic resonance imaging (BOLD-MRI) in detecting early renal alterations in patients with hyperuricemia. Seventy-one individuals were enrolled in this study and divided into three groups according to their serum uric acid (SUA) level and clinical symptoms: healthy controls (HC, n = 23), asymptomatic hyperuricemia (AH, n = 22) and gouty arthritis (GA, n = 26). All patients underwent both DTI and BOLD-MRI examination. Renal cortical and medullary ADC, FA and R2* values were calculated, respectively, and compared among the three groups. Correlations between ADC, FA and R2* with estimated glomerular filtration rate (eGFR) and SUA in hyperuricemia were evaluated, respectively. In the renal cortex, the ADC, FA and R2* values of the AH and GA groups were significantly lower than those of the HC groups (p

Introduction There is limited data on the association of kidney dysfunction with prognosis in COVID-19, and the extent to which acute kidney injury (AKI) predisposes patients to severe illness and inferior outcomes is unclear. We aim to assess the incidence of AKI among patients with COVID-19 and examine their associations with patient outcomes as reported in the available literature thus far. Methods We systematically searched MEDLINE, EMBASE, SCOPUS, and MedRxiv databases for full-text articles available in English published from December 1, 2019 to May 24, 2020. Clinical information was extracted and examined from 20 cohorts that met inclusion criteria, covering 13137 mostly hospitalized patients confirmed to have COVID-19. Two authors independently extracted study characteristics, results, outcomes, study-level risk of bias, and strength of evidence across studies. Neither reviewer was blind to journal titles, study authors, or institutions. Results Median age was 56 years, with 55% male patients. Approximately 43% of patients had severe COVID-19 infection, and approximately 11% died. Prevalence of AKI was 17%; 77% of patients with AKI experienced severe COVID-19 infection, and 52% died. AKI was associated with increased odds of death among COVID-19 patients (pooled odds ratio 15.27, 95% CI 4.82-48.36), although there was considerable heterogeneity across studies and among different regions in the world. Approximately 5% of all patients required use of renal replacement therapy (RRT) Conclusions Kidney dysfunction is common among patients with COVID-19, and patients who develop AKI have inferior outcomes. Additional research into management and potential mechanisms of this association is needed.

The application of resting state functional MRI (RS-fMRI) in Parkinson’s disease (PD) was widely performed using standard statistical tests, however, the machine learning (ML) approach has not yet been investigated in PD using RS-fMRI. In current study, we utilized the mean regional amplitude values as the features in patients with PD (n = 72) and in healthy controls (HC, n = 89). The t-test and linear support vector machine were employed to select the features and make prediction, respectively. Three frequency bins (Slow-5: 0.0107–0.0286 Hz; Slow-4: 0.0286–0.0821 Hz; conventional: 0.01–0.08 Hz) were analyzed. Our results showed that the Slow-4 may provide important information than Slow-5 in PD, and it had almost identical classification performance compared with the Combined (Slow-5 and Slow-4) and conventional frequency bands. Similar with previous neuroimaging studies in PD, the discriminative regions were mainly included the disrupted motor system, aberrant visual cortex, dysfunction of paralimbic/limbic and basal ganglia networks. The lateral parietal lobe, such as right inferior parietal lobe (IPL) and supramarginal gyrus (SMG), was detected as the discriminative features exclusively in Slow-4. Our findings, at the first time, indicated that the ML approach is a promising choice for detecting abnormal regions in PD, and a multi-frequency scheme would provide us more specific information.

Objective: To investigate the profiles of brain volumetric measurements in children with attention deficit hyperactivity disorder (ADHD), and the consistency of these brain volumetric measurements derived from the synthetic and conventional T1 weighted MRI (SyMRI and cT1w MRI).Methods: Brain SyMRI and cT1w images were prospectively collected for 38 pediatric patients with ADHD and 38 healthy children (HC) with an age range of 6–14 years. The gray matter volume (GMV), white matter volume (WMV), cerebrospinal fluid (CSF), non-WM/GM/CSF (NoN), myelin, myelin fraction (MYF), brain parenchyma volume (BPV), and intracranial volume (ICV) were automatically estimated from SyMRI data, and the four matching measurements (GMV, WMV, BPV, ICV) were extracted from cT1w images. The group differences of brain volumetric measurements were performed, respectively, using analysis of covariance. Pearson correlation analysis and interclass correlation coefficient (ICC) were applied to evaluate the association between synthetic and cT1w MRI-derived measurements.Results: As for the brain volumetric measurements extracted from SyMRI, significantly decreased GMV, WMV, BPV, and increased NON volume (p < 0.05) were found in the ADHD group compared with HC; No group differences were found in ICV, CSF, myelin volume and MYF (p > 0.05). With regard to GMV, WMV, BPV, and ICV estimated from cT1w images, the group differences between ADHD and HC were consistent with the results estimated from SyMRI. And these four measurements showed noticeable correlation between the two approaches (r = 0.692, 0.643, 0.898, 0.789, respectively, p < 0.001; ICC values are 0.809, 0.782, 0.946, 0.873, respectively).Conclusion: Our study demonstrated a global brain development disability, but normal whole-brain myelination in children with ADHD. Moreover, our results demonstrated the high consistency of brain volumetric indices between synthetic and cT1w MRI in children, which indicates the high reliability of SyMRI in the child-brain volumetric analysis.

Macroscopic structural abnormalities in the thalamus and thalamic circuits have been shown to contribute to the neuropathology of major depressive disorder (MDD). However, cytoarchitectonic properties underlying these macroscopic abnormalities remain unknown. The purpose of this study was to identify systematic deficits of brain architecture in depression, from structural brain network organization to microstructural properties. A multi-modal neuroimaging approach including diffusion, anatomical and quantitative magnetic resonance imaging (MRI) was used to examine structural-related alternations in 56 MDD patients compared with 35 age- and sex-matched controls. Structural networks were constructed and analyzed using seed-based probabilistic tractography. Morphometric measurements, including cortical thickness and voxel-based morphometry (VBM), were evaluated across the whole brain. A conjunction analysis was then conducted to identify key regions showing common structural alternations across modalities. The microstructural properties, macromolecular tissue volume (MTV) and T1 relaxation times of identified key regions were then calculated. Results showed multiple alterations of structural connectivity within a set of subcortical areas and their connections to cortical regions in MDD patients. These subcortical regions included the putamen, thalamus and caudate, which are predominately involved in the limbic-cortical-striatal-pallidal-thalamic network (LCSPT). Structural connectivity was disrupted within and between large-scale networks, mainly including subcortical networks, default mode networks and salience/ventral attention networks. Consistently, these regions also exhibited widespread volume reductions in MDD patients, specifically the bilateral thalamus, left putamen and right caudate. Importantly, the microstructural properties, T1 relaxation time of left thalamus were increased and negatively correlated with its gray matter volume in MDD patients. The present work to date sheds light on the neuropathological disruptions of LCSPT circuit in MDD, providing the first multi-modal neuroimaging evidence for the macro-micro structural abnormalities of the thalamus in patients with MDD. These findings have implications in understanding the abnormal changes of brain structures across development of MDD.

Background The current study investigated the performance of intravoxel incoherent motion diffusion (IVIM) technology in monitoring early renal injury in streptozotocin rats. Methods Forty-eight Sprague-Dawley (SD) rats were divided into a control group and a diabetic mellitus (DM) group. Six rats in each group were randomly selected for MR scans at four different time points (0, 4, 8, and 12 weeks). The IVIM-derived parameters (D, D*, f and ADC values) of the renal cortex (CO), outer and inner stripe of the outer medulla (OS, IS), and internal medulla (IM) were acquired. Changes in each IVIM-derived parameter over time were analyzed, and differences between the two groups at each point were assessed. The associations between the IVIM parameters and IV collagen expression, urine volume (UV), blood urea nitrogen (BUN), and serum creatinine (Scr) were investigated. Results The D and D* values of CO and the ADC values of CO, OS, IS and IM displayed significantly different trends between the two groups over time (Pr=0.527, P=0.033; r=0.617, P=0.005), between the ADC value of IM and BUN (r=0.557, P=0.019) and between the f value of IM and BUN (r=0.527, P=0.033). No correlation was found between IVIM parameters and IV collagen expression and Scr. Conclusions IVIM is a potential sensitive and noninvasive technology for the simultaneous assessment of early renal cortical and medullary injuries induced by diabetes.

The present study aimed to investigate the association between the single-nucleotide polymorphisms (SNPs) rs4612666 and rs10754558 in the NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3) gene and the susceptibility to rheumatoid arthritis (RA) in a Han Chinese population. mRNA expression of NLRP3, apoptosis-associated speck-like protein (ASC), and caspase-1 were determined in peripheral blood mononuclear cells (PBMCs) and neutrophils using reverse-transcription quantitative PCR. The results demonstrated that the C allele at rs4612666 locus and the G allele at rs10754558 locus were associated with significantly increased risk of RA. A statistical significance was also revealed in the dominant model (CC+CT vs. TT: OR=1.549; 95% CI=1.120-2.144; and GG + GC vs. CC: OR=2.000; 95% CI=1.529-2.616; P

To investigate the quantitative profiles of brain grey matter (GM) in pediatric drug-naive ADHD patients using synthetic magnetic resonance imaging (SyMRI). A total of 37 drug-naive pediatric ADHD and 27 age- and gender-matched healthy controls (HC) were enrolled in this study. Each subject underwent both SyMRI and conventional 3D T1-FSPGR scans. Quantitative parameters, T1 and T2 maps, were extracted from the SyMRI data. Between-group quantitative maps were compared using a general linear model analysis. Pearson correlation analysis was conducted to assess the association between significantly altered MR indices and clinical measurements in ADHD. Compared with the HC group, altered T1 and T2 relaxometry times in the ADHD group were mainly distributed in GM regions of the cerebellum, attention and execution control network, default mode network, and limbic areas. Moreover, the T1 value of the right cerebellum 8 was negatively correlated with the attention concentration level in ADHD (R = 0.140, P = 0.0225). With regards to T2 map, the associations were observed between the attention level of ADHD patients and left fusiform gyrus (R = 0.251, P = 0.0016), and right cerebellum crus2 (R = 0.142, P = 0.0214). Altered T1, T2 values found in specific regions of GM, including cerebellum, attention and execution control network, default mode network, and limbic areas, may reveal widespread micromorphology changes, i.e., brain iron deficiency, low myelin content, and enlarged vascular interstitial space in ADHD patients. Thus, T1, T2 values might be promising imaging markers for future ADHD studies.

The immunosuppressive and hypoxic tumor microenvironment (TME) remains a major obstacle to impede cancer immunotherapy. Here, we showed that elevated levels of Delta-like 1 (DLL1) in the breast and lung TME induced long-term tumor vascular normalization to alleviate tumor hypoxia and promoted the accumulation of interferon γ (IFN-γ)-expressing CD8+ T cells and the polarization of M1-like macrophages. Moreover, increased DLL1 levels in the TME sensitized anti-cytotoxic T lymphocyte-associated protein 4 (anti-CTLA4) treatment in its resistant tumors, resulting in tumor regression and prolonged survival. Mechanically, in vivo depletion of CD8+ T cells or host IFN-γ deficiency reversed tumor growth inhibition and abrogated DLL1-induced tumor vascular normalization without affecting DLL1-mediated macrophage polarization. Together, these results demonstrate that elevated DLL1 levels in the TME promote durable tumor vascular normalization in a CD8+ T cell- and IFN-γ-dependent manner and potentiate anti-CTLA4 therapy. Our findings unveil DLL1 as a potential target to persistently normalize the TME to facilitate cancer immunotherapy.

The inter-tract/region dependencies of white-matter in Parkinson’s disease are usually ignored by standard statistical tests. Moreover, it remains unclear whether the disruption of white-matter tracts/regions suffices to identify Parkinson’s disease patients from healthy controls. A machine learning approach was applied to capture the interdependencies between white-matter tracts/regions and to differentiate PD patients from healthy controls. First, the mean regional white-matter profiles, including white-matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, were extracted as features in Parkinson’s disease patients (N = 78) and in healthy controls (N = 91). Then, the feature selection and classification were performed using t-test and linear support vector machine, respectively. Last, the relationships between clinical variables and regional magnetic resonance indices were estimated. Our results showed the combined features (white-matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) had the best performance with an accuracy of 75.15% and area under curve of 0.8171, respectively. The most discriminative white-matter features were centered on the association fibers, commissural fibers, projection fibers, and striatal fibers. The discriminative regions of right anterior limb of internal capsule had positive association trends with the Unified Parkinson Disease Rating Scale III score; while the genu of corpus callosum and right retrolenticular part of internal capsule had positively association trends with the Hamilton Depression Rating Scale score. Our finding showed the multivariate machine learning approach is a promising tool to detect abnormal white-matter tracts/regions in Parkinson’s disease, and provides us a multidimensional means for neuroimaging classification.

Purpose To describe trends and correlation between antibacterial exposure and bacterial resistance from hospitalized patients in a hospital in southern China. Patients and methods This study used hospital-wide data regarding antimicrobial resistance and consumption between January 1, 2014 and December 31, 2018. Antibacterial consumption was expressed as antimicrobial use density (AUD). The changes in trends and associations between antibacterial utilization and resistance were analyzed using linear regression and time series analysis. Results The total AUD of all antimicrobials decreased year by year (50.66 in 2014 vs 44.28 in 2018, P=0.03). The annual use of antimicrobials, such as penicillins, monobactams, aminoglycosides, macrolides, and lincosamides, significantly decreased (P

Functional constipation (FC) is a common functional gastrointestinal disorder (FGID). Neuroimaging studies on patients with FC showed brain functional abnormalities in regions involved in emotional process modulation, somatic and sensory processing and motor control. Brain structural imaging studies in patients with FGID have also shown disease-related alterations in cortical morphometry, but whether and how FC affects brain structure remains unclear. Structural Magnetic Resonance Imaging and surface-based morphometry analysis were used to investigate the impact of FC on cortical morphometry in 29 patients with FC and 29 healthy controls (HC). Results showed that patients with FC compared to HC had significantly decreased cortical thickness in the left middle frontal gyrus (MFG), dorsomedial (DMPFC) and ventromedial prefrontal gyrus (VMPFC), right dorsal anterior cingulate cortex (dACC), left orbitofrontal cortex (OFC), posterior cingulate cortex (PCC)/precuneus, middle temporal gyrus (MTG), and supplementary motor area (SMA) (P

Background The methylated septin 9 (mSEPT9) assay was the first blood-based test approved by the United States Food and Drug Administration as a colorectal screening test. However, the diagnostic and prognostic role of preoperative mSEPT9 for colorectal cancer (CRC) in Chinese patients is still unknown. Aim To improve the understanding of diagnostic and prognostic factors, serum mSEPT9 was detected in Chinese CRC patients. Methods A retrospective analysis of 354 cases, of which 300 had CRC and 54 were normal, was performed in China. Patients' characteristics, treatments, and laboratory data, including age, the date of surgery, Union for International Cancer Control (UICC) stages, distant metastasis (M), and so on, were collected. Methylation levels of SEPT9 were quantified by quantitative, methylation-specific polymerase chain reaction before surgery. In addition, the effects of mSEPT9 on the occurrence and prognosis of 330 CRC cases from The Cancer Genome Atlas (TCGA) database were evaluated using bioinformatics analyses. Potential prognostic factors for overall survival (OS) and progression-free survival (PFS) were evaluated by Kaplan-Meier univariate analysis. Results In Chinese CRC patients, positive mSEPT9 was strongly associated with advanced UICC stages, deeper invasion by the primary tumor, and more distant metastasis. Methylation levels of SEPT9 were stage-dependent and showed a stepwise increase in UICC stages (I-IV), primary tumor categories (T1-T4), regional node categories (N0-N2), and distant metastasis categories (M0-M1). The patients with positive mSEPT9 showed a tendency toward lower PFS. After analyzing TCGA clinical data, the high mSEPT9 group was found to be obviously correlated only with more distant metastasis. The patients with high mSEPT9 levels showed a tendency toward lower OS. Besides, nine meaningful mSEPT9 sites were found to provide guidance for the follow-up studies. Conclusion MSEPT9 analysis may add valuable information to current tumor staging. Serum mSEPT9 in Chinese CRC patients appears to offer promising novel prognostic markers and might be considered for monitoring CRC recurrence.

OBJECTIVES The aim of this study is to investigate whether heat shock protein 70 (Hsp70) gene polymorphisms are implicated in systemic lupus erythematous (SLE) susceptibility, the efficacy of glucocorticoids (GCs) treatment, and improvement of health-related quality of life. METHODS A total of 499 SLE patients and 499 controls were included in a case-control study, and 468 SLE patients treated with GCs for 12 weeks were involved in a follow-up study. Patients who completed the 12-week follow-up were divided into GCs-sensitive and GCs-insensitive group by using the SLE disease activity index. The SF-36 was used to evaluate the health-related quality of life of SLE patients, and genotyping was performed by improved multiplex ligation detection reaction. RESULTS rs2075800 was associated with SLE susceptibility (adjusted odds ratio [ORadj], 1.437; 95% confidence interval [CI], 1.113-1.855; Padj = 0.005; PBH = 0.020 by dominant model; ORadj, 1.602; 95% CI, 1.072-2.395; Padj = 0.022; PBH = 0.029 by TT vs CC model; ORadj = 1.396; 95% CI = 1.067-1.826; Padj = 0.015; PBH = 0.029 by TC vs CC model). In the follow-up study, rs2075799 was associated with the improvement in mental health (p = 0.004, PBH = 0.044), but we failed to find any association between the efficacy of GCs and Hsp70 gene polymorphisms. CONCLUSIONS Hsp70 gene polymorphisms may be associated with susceptibility to SLE and improvement of mental health in Chinese Han population.

This paper presents a dual-band eight-element multiple-input multiple-output (MIMO) array using a multi-slot decoupling technique for the fifth generation (5G) mobile communication. By employing a compact dual-loop antenna element, the proposed array obtains two broad bandwidths of 12.2% and 15.4% for sub-6GHz operation. To reduce the mutual coupling between antenna elements, a novel dual-band decoupling method is proposed by employing a multi-slot structure. The proposed MIMO array achieves 15.5-dB and 19.0-dB isolations across the two operating bands. Furthermore, three decoupling modes generated by different bent slots can be independently tuned. Zero ground clearance is also realized by the coplanar arrangement of the antenna elements and decoupling structures. The proposed MIMO array was simulated, fabricated, and measured. Experimental results agree well with the simulations, showing that the dual-band MIMO array has good impedance matching, high isolation, and high efficiency. In addition, the envelope correlation coefficient and channel capacity are calculated and analyzed to validate the MIMO performance of the 5G terminal array. Such a dual-band high-isolation eight-element MIMO array with zero ground clearance is a promising candidate for 5G or future mobile applications.

Recent technical advancements support the application of Optical See-Through Head-Mounted Displays (OST-HMDs) in critical situations like navigation and manufacturing. However, while the form-factor of an OST-HMD occupies less of the user's visual field than in the past, it can still result in critical oversights, e.g., missing a pedestrian while driving a car. In this paper, we design and compare two methods to compensate for the loss of awareness due to the occlusion caused by OST-HMDs. Instead of presenting the occluded content to the user, we detect motion that is not visible to the user and highlight its direction either on the edge of the HMD screen, or by activating LEDs placed in the user's peripheral vision. The methods involve an offline stage, where the occluded visual field and location of each indicator and its associated occluded region of interest (OROI) are determined, and an online stage, where an enhanced optical flow algorithm tracks the motion in the occluded visual field. We have implemented both methods on a Microsoft HoloLens and an ODG R-9. Our prototype systems achieved success rates of 100% in an objective evaluation, and 98.90% in a pilot user study. Our methods are able to compensate for the loss of safety-critical information in the occluded visual field for state-of-the-art OST-HMDs and can be extended for their future generations.

Background Anterior cervical discectomy and fusion (ACDF) has been widely used in cervical spondylosis, but adjacent segment degeneration/disease (ASD) was inevitable. Cervical total disc replacement (TDR) could reduce the stress of adjacent segments and retard ASD in theory, but the superiority has not been determined yet. This analysis aimed that whether TDR was superior to ACDF for decreasing adjacent segment degeneration (ASDeg) and adjacent segment disease (ASDis). Methods A meta-analysis was performed according to the guidelines of the Cochrane Collaboration with PubMed, EMBASE, Cochrane Library and CBM (China Biological Medicine) databases. It included randomized controlled trials (RCTs) that reported ASDeg, ASDis, and reoperation on adjacent segments after TDR and ACDF. Two investigators independently selected trials, assessed methodological quality, and evaluated the quality of this meta-analysis using the grades of recommendation, assessment, development, and evaluation (GRADE) approach. Results Eleven studies with 2632 patients were included in the meta-analysis. The overall rate of ASD in TDR group was lower than ACDF group (OR = 0.6; 95% CI [0.38, 0.73]; P

Introduction Next-generation sequencing (NGS)-based circulating tumor DNA (ctDNA) detection is a promising monitoring tool for lymphoid malignancies. Studies for T cell lymphoma are limited. Methods We explored whether this technology is applicable to T cell lymphoma with different subtypes and assessed its performance in clinical settings. Results Thirty tumor and 74 blood samples were analyzed in our study. Malignant clone was identified in 23 of the 30 (76.7%) tumor samples through high-throughput sequencing (HTS) combined with PCR. We detected the same tumor clone in plasma in 18out of the 23 (78.3%) patients. Circulating tumor DNA fraction correlated with lactate dehydrogenase (LDH) (r = .52, P = .017), high level of ctDNA predicted treatment failure (P = .0003) and there was a trend patients with high ctDNA burden would have poorer PFS Furthermore, ctDNA changed in concordance with clinical outcome and was more sensitive than PET/CT. Also, recurrence of ctDNA was an important clue for relapse. Conclusion In conclusion, our study indicated that ctDNA monitoring was suitable for T cell lymphoma. High level of pretreatment ctDNA was a poor prognosis factor and changes of ctDNA correlated well with clinical courses and was sensitive to find early relapse.

Background COVID-19 has overwhelmed health systems worldwide. It is important to identify severe cases as early as possible, such that resources can be mobilized and treatment can be escalated. Objective This study aims to develop a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data. Methods Clinical data—including demographics, signs, symptoms, comorbidities, and blood test results—and chest computed tomography scans of 346 patients from 2 hospitals in the Hubei Province, China, were used to develop machine learning models for automated severity assessment in diagnosed COVID-19 cases. We compared the predictive power of the clinical and imaging data from multiple machine learning models and further explored the use of four oversampling methods to address the imbalanced classification issue. Features with the highest predictive power were identified using the Shapley Additive Explanations framework. Results Imaging features had the strongest impact on the model output, while a combination of clinical and imaging features yielded the best performance overall. The identified predictive features were consistent with those reported previously. Although oversampling yielded mixed results, it achieved the best model performance in our study. Logistic regression models differentiating between mild and severe cases achieved the best performance for clinical features (area under the curve [AUC] 0.848; sensitivity 0.455; specificity 0.906), imaging features (AUC 0.926; sensitivity 0.818; specificity 0.901), and a combination of clinical and imaging features (AUC 0.950; sensitivity 0.764; specificity 0.919). The synthetic minority oversampling method further improved the performance of the model using combined features (AUC 0.960; sensitivity 0.845; specificity 0.929). Conclusions Clinical and imaging features can be used for automated severity assessment of COVID-19 and can potentially help triage patients with COVID-19 and prioritize care delivery to those at a higher risk of severe disease.

We aimed to explore the effect of N-retinylidene-N-retinylethanolamine (A2E) on the uptake and release of calcium in lysosomes and mitochondria by establishing a model of human retinal pigment epithelial (RPE) cell injury induced by exposure to blue light. Primary human RPE cells were cultured from passages 4 to 6 and exposed to blue light at an intensity of 2000 ± 500 lux for 6 hours. After blue light exposure, the culture was maintained for 24 hours. A2E at a final concentration of 25 μM was added to the culture 2 hours before light exposure, and nifedipine at a final concentration of 10−4 M was added 1 hour before light exposure. The levels of Ca2+ in the cytosol (CaTM/2AM), mitochondria (Rhod/2AM), and lysosomes (LysoTracker Red and Fluo-3/AM) were determined. In order to measure the calcium levels in the different organelles, RPE were imaged using a laser scanning confocal microscope. Moreover, changes in the mitochondrial membrane potential were detected by flow cytometry analysis of JC-1-stained cells. The obtained results revealed that blue light illumination increased the calcium fluorescence intensity in the cytoplasm, mitochondria, and lysosomes of human RPE cells when compared with the control cells ( P < 0.05 ). After A2E treatment, the fluorescence intensity of the calcium in the cytoplasm was further increased ( P < 0.05 ), while that in the mitochondria and lysosomes decreased ( P < 0.05 ). In addition, we observed that nifedipine reduced the fluorescence intensity of calcium in the RPE cells. Our results also showed that the mitochondrial membrane potential in the RPE treated with blue light and A2E was lower than that in the control, blue light, and A2E-treated cells ( P < 0.05 ). Blue light increased calcium levels in the cytoplasm, lysosomes, and mitochondria of RPE cells. A2E damages the lysosomal and mitochondrial membranes, resulting in calcium release into the cytoplasm. Finally, our results demonstrated that both blue light and A2E treatments reduced mitochondrial membrane potential, increasing cytosolic Ca2+ levels, which can contribute to the activation of RPE death.

Introduction A large proportion of patients with COVID-19 develop acute kidney injury (AKI). While the most severe of these cases require renal replacement therapy (RRT), little is known about their clinical course. Methods We describe the clinical characteristics of COVID-19 patients in the ICU with AKI requiring RRT at an academic medical center in New York City and followed patients for outcomes of death and renal recovery using time-to-event analyses. Results Our cohort of 115 patients represented 23% of all ICU admissions at our center, with a peak prevalence of 29%. Patients were followed for a median of 29 days (2542 total patient-RRT-days; median 54 days for survivors). Mechanical ventilation and vasopressor use were common (99% and 84%, respectively), and the median Sequential Organ Function Assessment (SOFA) score was 14. By the end of follow-up 51% died, 41% recovered kidney function (84% of survivors), and 8% still needed RRT (survival probability at 60 days: 0.46 [95% CI: 0.36–0.56])). In an adjusted Cox model, coronary artery disease and chronic obstructive pulmonary disease were associated with increased mortality (HRs: 3.99 [95% CI 1.46–10.90] and 3.10 [95% CI 1.25–7.66]) as were angiotensin-converting-enzyme inhibitors (HR 2.33 [95% CI 1.21–4.47]) and a SOFA score >15 (HR 3.46 [95% CI 1.65–7.25). Conclusions and relevance Our analysis demonstrates the high prevalence of AKI requiring RRT among critically ill patients with COVID-19 and is associated with a high mortality, however, the rate of renal recovery is high among survivors and should inform shared-decision making.

Background Previous studies have indicated that quantitative MRI (qMR) is beneficial for diagnosis of breast cancer. As a novel qMR technology, synthetic MRI (syMRI) may be advantageous by offering simultaneous generation of T1 and T2 mapping in one scan within a few minutes and without concern to the deposition of the gadolinium contrast agent in cell nucleus. In this study, the potential of quantitative mapping derived from Synthetic MRI (SyMRI) to diagnose breast cancer was investigated. Methods From April 2018 to May 2019, a total of 87 patients with suspicious breast lesions underwent both conventional and SyMRI before treatment. The quantitative metrics derived from SyMRI, including T1 and T2 values, were measured in breast lesions. The diagnostic performance of SyMRI was evaluated with unpaired Student’s t-tests, receiver operating characteristic curve analysis and multivariate logistic regression analysis. The AUCs of quantitative values were compared using Delong test. Results Among 77 patients who met the inclusion criteria, 48 were diagnosed with histopathological confirmed breast cancers, and the rest had benign lesions. The breast cancers showed significantly higher T1 (1611.61 ± 215.88 ms) values and lower T2 (80.93 ± 7.51 ms) values than benign lesions. The area under the ROC curve (AUC) values were 0.931 (95% CI: 0.874–0.989) and 0.883 (95% CI: 0.810–0.956) for T1 and T2 maps, respectively, in diagnostic discrimination between breast cancers and benign lesions. A slightly increased AUC of 0.978 (95% CI: 0.915–0.993) was achieved by combining those two relaxation-based quantitative metrics. Conclusion In conclusion, our preliminary study showed that the quantitative T1 and T2 values obtained by SyMRI could distinguish effectively between benign and malignant breast lesions, and T1 relaxation time showed the highest diagnostic efficiency. Furthermore, combining the two quantitative relaxation metrics further improved their diagnostic performance.

The recently developed myelin imaging method, inhomogeneous magnetization transfer (ihMT), is a surrogate measure of myelin content. The goal of the current study was to investigate alterations in myelin integrity in patients with recurrent major depressive disorder (rMDD).Fifty-two patients with rMDD (36 female and 16 male) and 42 healthy controls (HC, 29 female and 13 male) were included. Two ihMT indices, quantitative ihMT (qihMT) and quantitative MT (qMT), were estimated from the ihMT data. A 50 white matter atlas was used to extract the regional quantitative values for each subject. The differences in qihMT and qMT values between the rMDD and HC groups were compared by a general linear model. Pearson correlation analyses were conducted to investigate associations between the significantly altered ihMT indices and clinical measures (Hamilton Depression Rating Scale scores and disease duration) in rMDD group.The rMDD group showed significantly lower qihMT values in the fornix, left anterior limb of internal capsule, and left sagittal stratum, and lower qMT values in the fornix and left anterior limb of internal capsule than those of the HC group (p0.05, false discovery rate corrected). Both the qihMT and qMT values in the fornix of patients with rMDD were negatively correlated with disease duration (qihMT: r = -0.478, p0.001, Bonferroni corrected; qMT: r = -0.433, p = 0.001, Bonferroni corrected).Our findings suggested that rMDD is associated with myelin impairment in the fornix, left anterior limb of internal capsule, and left sagittal stratum. In addition, this disruption of myelin integrity in the fornix could be cumulative as the disease progresses.

A flexible endoscope introduces more dexterity to the image capturing in endoscopic surgery. However, manual control or automatic control based on instrument tracking does not handle the misorientation between the endoscopic video and the surgeon. We propose an automatic flexible endoscope control method that tracks the surgeon’s head with respect to the object in the surgical scene. The robotic flexible endoscope is actuated so that it captures the surgical scene from the same perspective as the surgeon. The surgeon wears a head-mounted display to observe the endoscopic video. The frustum of the flexible endoscope is rendered as an augmented reality overlay to provide surgical guidance. We developed the prototype, FlexiVision, integrating a 6-DOF robotic flexible endoscope based on the da Vinci Research Kit and Microsoft HoloLens. We evaluated the proposed automatic control method via a lesion observation task, and evaluated the AR surgical guidance in a lesion targeting task. The multi-user study results demonstrated that, for both tasks, FlexiVision significantly reduced the completion time (by 59% and 58%), number of errors (by 75% and 95%) and subjective task load level. With FlexiVision, the flexible endoscope could act as the surgeon’s eyes teleported into the abdominal cavity of the patient.

Class malocclusion is the most common type of orthodontic malocclusion in clinical orthodontics, and the prevalence of Chinese...

Background: High myopia (HM) is a risk factor for several pathological structural changes in retinal and choroidal thickness or vessel. To date, changes in retinal and choroidal microvasculature circulations in HM have yielded inconsistent results. Objectives: The objective of this article was to evaluate alterations in retinal and choroidal thickness, and capillary microvasculature using optical coherence tomography angiography (OCTA) in HM. Methods: PubMed, Embase, and the Cochrane Library databases were searched for relevant published studies. Primary outcomes were foveal avascular zone, vessel density, retinal nerve fiber layer (RNFL) thickness, foveal thickness, sub-foveal choroidal thickness, and chorio-capillary density. Alterations in outcomes were evaluated by standardized mean difference with a 95% confidence interval. Results: Eleven eligible articles were included for the meta-analysis. The whole superficial and deep vessel densities of macular and parapapillary superficial vessel density were lower in HM than in control eyes. The thickness of parafoveal RNFL, parapapillary RNFL, and sub-foveal choroid was significantly lower in HM eyes. Also, chorio-capillary density was shown to be lower in HM eyes. Conclusions: The retinal and choroidal vessel network change may be related to the axial elongation in the progression of myopia. Furthermore, OCTA is an effective and noninvasive technology for monitoring the progress of myopia eyes.

Highlights • CEST MRI under non-equilibrium conditions is prone to measurement variabilities. • The QUASS algorithm reconstructs the equilibrium CEST effect from measurements. • The QUASS CEST algorithm enables fast and equilibrium tumor imaging at 3 T., Chemical exchange saturation transfer (CEST) MRI, versatile for detecting endogenous mobile proteins and tissue pH, has proved valuable in tumor imaging. However, CEST MRI scans are often performed under non-equilibrium conditions, which confound tissue characterization. This study proposed a quasi-steady-state (QUASS) CEST MRI algorithm to standardize fast and accurate tumor imaging at 3 T. The CEST signal evolution was modeled by longitudinal relaxation rate during relaxation delay (Td) and spinlock relaxation during RF saturation time (Ts), from which the QUASS CEST effect is derived. Numerical simulation and human MR imaging experiments (7 healthy volunteers and 19 tumor patients) were conducted at 3 T to compare the CEST measurements obtained under two representative experimental conditions. In addition, amide proton transfer (APT), combined magnetization transfer (MT) and nuclear overhauser enhancement (NOE) effects, and direct water saturation were isolated using a 3-pool Lorentzian fitting in white matter and gray matter of healthy volunteers and for patients in the contralateral normal-appearing white matter and tumor regions. Finally, the student's t-test was performed between conventional and QUASS CEST measurements. The routine APT and combined MT & NOE measures significantly varied with Ts and Td (P .05), indicating the accuracy and robustness of QUASS CEST MRI for tumor imaging. To summarize, the QUASS CEST reconstruction algorithm enables fast and accurate tumor CEST imaging at 3 T, promising to expedite and standardize clinical CEST MRI.

ObjectivesThis study aims to develop a machine learning approach for automated severity assessment of COVID-19 patients based on clinical and imaging data.Materials and MethodsClinical data—demographics, signs, symptoms, comorbidities and blood test results—and chest CT scans of 346 patients from two hospitals in the Hubei province, China, were used to develop machine learning models for automated severity assessment of diagnosed COVID-19 cases. We compared the predictive power of clinical and imaging data by testing multiple machine learning models, and further explored the use of four oversampling methods to address the imbalance distribution issue. Features with the highest predictive power were identified using the SHAP framework.ResultsTargeting differentiation between mild and severe cases, logistic regression models achieved the best performance on clinical features (AUC:0.848, sensitivity:0.455, specificity:0.906), imaging features (AUC:0.926, sensitivity:0.818, specificity:0.901) and the combined features (AUC:0.950, sensitivity:0.764, specificity:0.919). The SMOTE oversampling method further improved the performance of the combined features to AUC of 0.960 (sensitivity:0.845, specificity:0.929).DiscussionImaging features had the strongest impact on the model output, while a combination of clinical and imaging features yielded the best performance overall. The identified predictive features were consistent with findings from previous studies. Oversampling yielded mixed results, although it achieved the best performance in our study.ConclusionsThis study indicates that clinical and imaging features can be used for automated severity assessment of COVID-19 patients and have the potential to assist with triaging COVID-19 patients and prioritizing care for patients at higher risk of severe cases.

Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression.A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors.Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses.

Because of excellent power density, good reversibility, and superior stability, supercapacitors have attracted more attention in microelectronic system developments. However, the large-scale preparation of high-performance supercapacitors with low-cost and high mechanical flexibility is still challenging. Herein we report the convenient fabrication of an all-in-one pseudocapacitor by magnetron sputtering and electro-deposition of a MnO2@Ni cathode and an activated carbon@Ni anode on the two sides of a glass fiber separator. Specifically, the MnO2 cathode deposited on Ni Nanocone arrays (NNAs) showed good cycle performance (92.2% after 5000 cycles) and excellent capacitance (644 F/g) and. The NNAs@AC (activated carbon) anode deposited on the other side showed a 224 F/g high capacitance and favorable cycle performance (105.9% after 5000 cycles). Benefiting from the special integration method, the porous architecture of the electrodes, and the use of zinc ions as the charge carrier, our all-in-one device achieved favorable mechanical flexibility, high energy density (103.4 Wh/kg at 1.7 kW/kg) with a long cycle(76% after 5000 cycles). Besides, our fabrication approach can be easily extended to the mass production of low-cost devices and provides a new alternative strategy for flexible high-performance energy storage devices.

Image noise plays a vital role in digital image processing. However, in some specific application scenarios, random noise has an uncontrollable effect on digital image processing. Besides, a large number of hyper parameters which need to be fine-tuned can lead to inefficient projects. Therefore, we propose a Image Noise Level Classification(INLC) technique for specific application scenarios by comparing image quality assessment(IQA) methods, fitting curves and designing two neural networks. For low-accuracy, we come up with a soft way by setting a tolerance rate to achieve a higher acceptable accuracy. Experiments show that our INLC is more accurate and efficient.

Objective: To evaluate the feasibility and efficacy of the modified reverse puncture device (mRPD) technique for transanal anastomosis in total laparoscopic colorectal resection with natural orific...

Pilots often face and need to overcome a diverse range of unfavorable conditions, of which hypoxic exposure is the most common. Studies have reported that hypoxia can induce a decrease in cerebral blood flow (CBF) in the brains of both humans and animals. Hypoxia and the associated cerebral hemodynamic changes can contribute to cognitive performance deficits that may endanger flight safety and increase the risk of accidents. In this study, we aimed to identify region-specific alterations in CBF in male pilots after exposure to hypoxia. We used 3D pseudo-continuous arterial spin labeling sequences in 35 healthy male pilots (mean age: 30.6 ± 4.82 years) under simulated hypoxic conditions with a 3.0-T magnetic resonance imaging scanner. The generated CBF maps were measured and averaged in several regions of interest. Hypoxia decreased CBF in various brain regions, including the right temporal and bilateral occipital lobes, the anterior and posterior lobes of the cerebellum, the culmen and declive, and the inferior semilunar lobule of the cerebellum. These changes may impact the functional activity of the brains of pilots experiencing hypoxia in flight, but the related mechanisms require further investigation.

Depression is the most common non-motor symptom accompanying Parkinson’s disease (PD) with high prevalence but unclear pathophysiological mechanism. Relatively little is known about the topological patterns of white matter structural networks in depressed patients with PD. In this study, we used diffusion-tensor imaging (DTI) and graph theory approaches to explore the brain structural connectome in non-depressed patients with PD (n = 47), depressed patients with PD (n = 20) and healthy controls (n = 46). All three groups exhibited small-world topology. Compared with healthy controls, non-depressed patients with PD and depressed patients with PD showed a significant reduction of network efficiency in the cortico-subcortical circuits. Moreover, depressed patients with PD exhibited higher network efficiency in fronto-limbic system, compared to non-depressed patients with PD. To sum up, our data indicated a disrupted integrity in the large-scale brain systems in depressed patients with PD patients. The structural connectome provided a basis for functional alterations in depressed patients with PD that may advance our current understanding of pathophysiological mechanism underlying Parkinson’s disease.

Resistance to radiotherapy is one of the main obstacles to improving cancer prognoses. To effectively destroy cancer cells, novel radiation sensitizers are needed. Recently, several natural products have been shown to exhibit promising tumor-killing properties. However, little is known about the specific mechanisms of these natural compounds on cancer treatment. In this study, after screening a high-throughput natural product library, we identified tanshinone I (Tan I) as a potential radiation sensitizer in lung cancer cells.Lung cancer radioresistant cell lines, H358-IR and H157-IR, were first established to confirm the radioresistant phenotypes. After that, a natural product library was used to screen the potential radiation sensitizer. We further examined the inhibition functions of Tan I on radioresistant cancer cells via a series of experiments.Tan I significantly inhibited cell proliferation and clone formation, consequently enhancing radiosensitivity in radioresistant lung cancer cells, H358-IR and H157-IR. Stable isotope labeling of amino acids in cell culture (SILAC)-based quantitative proteomics indicated that Tan I downregulates expression of pro-oncogenic protein phosphoribosyl pyrophosphate aminotransferase (PPAT) in both H358-IR and H157-IR cells. Further analysis of molecular docking showed that Tan I is well-docked into the active pocket of the structure of PPAT, serving as a potential PPAT inhibitor.Taken together, these findings suggest that inhibition of the tumor promoter PPAT by Tan I exerts marked inhibitory effects on radioresistant lung cancer cells, improving radiation efficacy.

18F-FDG PET/CT is recommended for staging and response assessment in FDG-avid lymphomas, such as HL and DLBCL. But evidence of PET/CT regarding T-cell lymphomas (TCLs) was limited. In this retrospective study, we conducted survival analysis using interim PET/CT in 51 TCLs patients. PET/CT were reported using the Deauville 5-point score (DS) and the ΔSUV

The global demand for data storage is currently outpacing the world's storage capabilities. DNA, the carrier of natural genetic information, offers a stable, resource- and energy-efficient and sustainable data storage solution. In this review, we summarize the fundamental theory, research history, and technical challenges of DNA storage. From a quantitative perspective, we evaluate the prospect of DNA, and organic polymers in general, as a novel class of data storage medium.

Objective: Patients with diabetes mellitus, diabetic nephropathy (DN) and healthy donor were analyzed to test whether the early DN patients can be detected using both blood oxygenation level dependent (BOLD) and diffusion tensor imaging. Methods: This study was approved by the Ethics Committee of our hospital. MR images were acquired on a 3.0-Tesla MR system (Discovery MR750, General Electric, Milwaukee, WI). 30 diabetic patients were divided into NAU (normal to mildly increased albuminuria, N = 15) and MAU (moderately increased albuminuria, N = 15) group based on the absence or presence of microalbuminuria. 15 controls with sex- and age-matched were enrolled in the study. Prior to MRI scan, all participants were instructed to collect their fresh morning urine samples for quantitative measurement of urinary microalbumin and urinary creatinine. Then, the estimations of serum creatinine, serum uric acid, HbAlc and fasting plasma glucose as well as fundus examinations were performed in all subjects. Then, the values of albumin–creatinine ratio (ACR) and estimated glomerular filtration rate were also calculated. All subjects underwent renal diffusion tensor imaging (DTI) and BOLD acquisition after fasting for 4 h. Regions of interest were placed in renal medulla and cortex for evaluating apparent diffusion coefficient (ADC), fractional anisotropy (FA) and R2* values by two experienced radiologists. The consistency between the two observations was estimated using intragroup correlation coefficients. To test differences in ADC, FA and R2* values across the three groups, the data were analyzed using separate one-way ANOVAs. Post-hoc pair wise comparisons were then performed using t-test. To investigate the clinical relevance of imaging parameters in both regions across the three groups, the correlations of values of the ACR/estimated glomerular filtration rate and of the ADC/FA/R2* were calculated. Results: There was a high level of consistency of those ADC, FA and R2* values across the three groups on both renal cortex and medulla measured by the two doctors. The FA value of medulla in MAU group was lower than that in control (p < 0.01). The R2* value of medulla in the NAU group was higher than that in the control (p < 0.01), and the R2* value of medulla in the MAU group was lower than that in the control (p = 0.009) . Moreover, the current study revealed a decreasing trend in FA values of the renal medulla from the control group to NAU and MAU groups. Finally, a weak negatively correlation between medullary R2* and ACR was found in current study. Conclusion: Medullary R2* value might be a new more sensitive predictor of early DN. Meanwhile, BOLD imaging detected the medullary hypoxia at the simply diabetic stage, while DTI didn’t identify the medullary directional diffusion changes at this stage. Based on our assumption mentioned above, it’s presumable that BOLD imaging may be more sensitive for assessment of the early renal function changes than DTI. These imaging techniques are more accurate and practical than conventional tests. Advances in knowledge: Non-invasive MRI was used to detect renal function changes at early DN stage.

Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses.

A large amount of continuous input data is used to estimate groundwater level (GWL) by using machine learning models. However, data collection is very difficult and costly in undeveloped countries. Therefore, obtaining a general model and using less input data is the key to popularizing the application of machine learning models for estimating groundwater levels. This study evaluated the potential of the kernel-based nonlinear extension of the Arps decline model (KNEA), long short-term memory network (LSTM) and gated recurrent unit (GRU) for accurately estimating GWL in the Hetao Irrigation District in China. All models were developed using monthly records from 143 monitoring wells between 1990 and 2015. Eight input combinations (including the one-month prior GWL, air temperature, global solar radiation, precipitation and amount of irrigation) were applied to explore the possibility of improving model accuracy using less input data. In addition, the general performance of the models was evaluated by cross validation. The results showed that the KNEA model was superior to the LSTM and GRU models for all input combinations using the local application. For cross-district application, the average statistical results indicated that the LSTM (RMSE = 0.45 m and R2 = 0.78) and GRU (RMSE = 0.48 m and R2 = 0.76) models performed better than the KNEA model (RMSE = 0.70 m and R2 = 0.62), and the LSTM model achieved the highest accuracy and stability. For input data, these three models had difficulty obtaining satisfactory monthly GWLs using meteorological and irrigation data without pervious GWL data. Adding meteorological data on the basis of the historical GWL greatly improved the accuracy of the models. Compared with PREC and GSR, adding temperature input had the best improvement. However, adding large-scale average irrigation data did not significantly improve the accuracy of the models. In addition, the LSTM model and input data of the historical GWLs and temperature were recommended in arid and semiarid agricultural areas with limited data.

Purpose The estimation of brain volumetric measurements based on Synthetic MRI (SyMRI) is easy and fast, however, the consistency of brain volumetric and morphologic measurements based on SyMRI and 3D T1WI should be further addressed. The current study evaluated the impact of spatial resolution on brain volumetric and morphologic measurements using SyMRI, and test whether the brain measurements derived from SyMRI were consistent with those resulted from 3D T1WI. Method Brain volumetric and fractal analysis were applied to thirty healthy subjects, each underwent four SyMRI acquisitions with different spatial resolutions (1 × 1 × 2 mm, 1 × 1x3mm, 1 × 1 × 4 mm, 2 × 2 × 2 mm) and a 3D T1WI (1 × 1 × 1 mm isotropic). The consistency of the SyMRI measurements was tested using one-way non-parametric Kruskal-Wallis test and post hoc Dwass-Steel-Critchlow-Fligner test. The association between SyMRI and 3D T1WI derived measurements was evaluated using linear regression models. Results Our results demonstrated that both in- and through-plane resolutions show an impact on brain volumetric measurements, while brain parenchymal volume showed high consistency across the SyMRI acquisitions, and high association with the measurements from 3D T1WI. In addition, SyMRI with 1 × 1 × 4 mm resolution showed the strongest association with 3D T1WI compared to other SyMRI acquisitions in both volumetric and fractal analyses. Moreover, substantial differences were found in fractal dimension of both gray and white matter between the SyMRI and 3D T1WI tissue segmentations. Conclusions Our results suggested that the measurements from SyMRI with relatively higher in-plane and lower through-plane resolution (1 × 1 × 4 mm) are much closer to 3D T1WI.

Based on the questionnaires of 9192 students, we found 74% of them have generated plate waste in university canteens and food waste amounted to 61.03 g per student per meal, of which the largest percentage comprised of wheat (25.78%), rice (20.36%), and vegetables (18.61%). Also, this paper found that the factors that affected university students’ food waste generation are multidimensional, including individual-level and family-level characteristics, catering features, and regional locations. In this study, eight factors impacted both indicators of plate waste. Specifically, females wasted more food than males. Education helps in reducing food waste. Bad daily waste habits could exacerbate food waste. The improvement of family economic conditions encourages food waste. Relative to eating alone, dining together with peers caused more serious food waste. The greater the time pressure, the more serious is the food waste. The higher the satisfaction of food taste, the less is the food waste. Compared with students from the north of China, food waste among students in the south is more serious. To reduce plate waste in university canteens, it is recommended that university decision-makers and canteen managers attach great importance to food waste of Chinese university students, by introducing targeted measures to reduce food waste, such as finding effective ways to reduce staple food and vegetable waste, introducing differentiated policies for university students with different individual-level and family-level characteristics, and reducing plate waste caused by unfavorable catering characteristics.