A comprehensive comparison on cell-type composition inference for spatial transcriptomics data.

Spatial transcriptomics (ST) technologies allow researchers to examine transcriptional profiles along with maintained positional information. Such spatially resolved transcriptional characterization of intact tissue samples provides an integrated view of gene expression in its natural spatial and functional context. However, high-throughput sequencing-based ST technologies cannot yet reach single cell resolution. Thus, similar to bulk RNA-seq data, gene expression data at ST spot-level reflect transcriptional profiles of multiple cells and entail the inference of cell-type composition within each ST spot for valid and powerful subsequent analyses. Realizing the critical importance of cell-type decomposition, multiple groups have developed ST deconvolution methods. The aim of this work is to review state-of-the-art methods for ST deconvolution, comparing their strengths and weaknesses. In particular, we construct ST spots from single-cell level ST data to assess the performance of 10 methods, with either ideal reference or non-ideal reference. Furthermore, we examine the performance of these methods on spot- and bead-level ST data by comparing estimated cell-type proportions to carefully matched single-cell ST data. In comparing the performance on various tissues and technological platforms, we concluded that RCTD and stereoscope achieve more robust and accurate inferences.

Comparison of diffusion tensor imaging (DTI) tissue characterization parameters in white matter tracts of patients with multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD).

OBJECTIVE: To investigate the microstructural properties of T2 lesion and normal-appearing white matter (NAWM) in 20 white matter tracts between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) and correlations between the tissue damage and clinical variables. METHODS: The white matter (WM) compartment of the brain was segmented for 56 healthy controls (HC), 48 patients with MS, and 38 patients with NMOSD, and for the patients further subdivided into T2 lesion and NAWM. Subsequently, the diffusion tensor imaging (DTI) tissue characterization parameters of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were compared for 20 principal white matter tracts. The correlation between tissue damage and clinical variables was also investigated. RESULTS: The higher T2 lesion volumes of 14 fibers were shown in MS compared to NMOSD. MS showed more microstructure damage in 13 fibers of T2 lesion, but similar microstructure in seven fibers compared to NMOSD. MS and NMOSD had microstructure damage of NAWM in 20 fibers compared to WM in HC, with more damage in 20 fibers in MS compared to NMOSD. MS patients showed higher correlation between the microstructure of T2 lesion areas and NAWM. The T2 lesion microstructure damage was correlated with duration and impaired cognition in MS. CONCLUSIONS: Patients with MS and NMOSD show different patterns of microstructural damage in T2 lesion and NAWM areas. The prolonged disease course of MS may aggravate the microstructural damage, and the degree of microstructural damage is further related to cognitive impairment. CLINICAL RELEVANCE STATEMENT: Microstructure differences between T2 lesion areas and normal-appearing white matter help distinguish multiple sclerosis and neuromyelitis optica spectrum disorder. In multiple sclerosis, lesions rather than normal-appearing white matter should be a concern, because the degree of lesion severity correlated both with normal-appearing white matter damage and cognitive impairment. KEY POINTS: • Multiple sclerosis and neuromyelitis optica spectrum disorder have different damage patterns in T2 lesion and normal-appearing white matter areas. • The microstructure damage of normal-appearing white matter is correlated with the microstructure of T2 lesion in multiple sclerosis and neuromyelitis optica spectrum disorder. • The microstructure damage of T2 lesion in multiple sclerosis is correlated with duration and cognitive impairment.

Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease.

BACKGROUND: Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear. OBJECTIVE: The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD). METHODS: Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs. RESULTS: A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics. CONCLUSIONS: These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.

Computed tomography-based 3D convolutional neural network deep learning model for predicting micropapillary or solid growth pattern of invasive lung adenocarcinoma.

PURPOSE: To investigate the value of a computed tomography (CT)-based deep learning (DL) model to predict the presence of micropapillary or solid (M/S) growth pattern in invasive lung adenocarcinoma (ILADC). MATERIALS AND METHODS: From June 2019 to October 2022, 617 patients with ILADC who underwent preoperative chest CT scans in our institution were randomly placed into training and internal validation sets in a 4:1 ratio, and 353 patients with ILADC from another institution were included as an external validation set. Then, a self-paced learning (SPL) 3D Net was used to establish two DL models: model 1 was used to predict the M/S growth pattern in ILADC, and model 2 was used to predict that pattern in ≤ 2-cm-diameter ILADC. RESULTS: For model 1, the training cohort's area under the curve (AUC), accuracy, recall, precision, and F1-score were 0.924, 0.845, 0.851, 0.842, and 0.843; the internal validation cohort's were 0.807, 0.744, 0.756, 0.750, and 0.743; and the external validation cohort's were 0.857, 0.805, 0.804, 0.806, and 0.804, respectively. For model 2, the training cohort's AUC, accuracy, recall, precision, and F1-score were 0.946, 0.858, 0.881,0.844, and 0.851; the internal validation cohort's were 0.869, 0.809, 0.786, 0.794, and 0.790; and the external validation cohort's were 0.831, 0.792, 0.789, 0.790, and 0.790, respectively. The SPL 3D Net model performed better than the ResNet34, ResNet50, ResNeXt50, and DenseNet121 models. CONCLUSION: The CT-based DL model performed well as a noninvasive screening tool capable of reliably detecting and distinguishing the subtypes of ILADC, even in small-sized tumors.

Enlarged choroid plexus related to cortical atrophy in multiple sclerosis.

OBJECTIVES: To investigate the correlation between choroid plexus volume and whole brain morphology in patients with multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD). METHODS: Fifty-one patients with MS, 42 patients with NMOSD, and 56 healthy controls (HC) were recruited. The morphological changes in choroid plexus and whole brain tissue were compared between three groups and the correlations between choroid plexus volume and brain atrophy were further investigated. The longitudinal alterations of brain morphology in 25 MS and 20 NMOSD patients were compared. RESULTS: Compared to the HC group, the choroid plexus volumes were increased in the MS group (p < 0.001) but not in the NMOSD group (p > 0.05). Compared to the HC group, the MS group showed reduced cortex thickness, deep gray matter volume, and increased ventricle system volume, and the NMOSD group showed increased third ventricle volume (all p < 0.05, false discovery rate corrected). In the MS group, there were widespread correlations between enlarged choroid plexus volume and reduced cerebral cortex thickness (p < 0.05, r = -0.292~-0.538, false discovery rate corrected). The interval time was not significantly different between the MS (median: 1.37 years) and NMOSD group (median: 1.25 years) (p > 0.05). In MS, compared with the baseline, the right hippocampus and nucleus accumbens volumes were decreased in long follow-up, and bilateral lateral ventricle volumes were increased both in short and long follow-up (all p < 0.05, false discovery rate corrected). CONCLUSIONS: The enlarged choroid plexus related to reduced cortical thickness and progressive local brain atrophy are shown in MS patients, but not obvious in NMOSD patients. KEY POINTS: • MS and NMOSD have different altered patterns in choroid plexus volume and brain atrophy. • The enlarged choroid plexus related to brain atrophy is shown in MS patients, but not obvious in NMOSD patients. • Progressive local brain atrophy is shown in MS patients, but not obvious in NMOSD patients.

Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706).

Individual variations of white matter (WM) tracts are known to be associated with various cognitive and neuropsychiatric traits. Diffusion tensor imaging (DTI) and genome-wide single-nucleotide polymorphism (SNP) data from 17,706 UK Biobank participants offer the opportunity to identify novel genetic variants of WM tracts and explore the genetic overlap with other brain-related complex traits. We analyzed the genetic architecture of 110 tract-based DTI parameters, carried out genome-wide association studies (GWAS), and performed post-GWAS analyses, including association lookups, gene-based association analysis, functional gene mapping, and genetic correlation estimation. We found that DTI parameters are substantially heritable for all WM tracts (mean heritability 48.7%). We observed a highly polygenic architecture of genetic influence across the genome (p value = 1.67 × 10-05) as well as the enrichment of genetic effects for active SNPs annotated by central nervous system cells (p value = 8.95 × 10-12). GWAS identified 213 independent significant SNPs associated with 90 DTI parameters (696 SNP-level and 205 locus-level associations; p value < 4.5 × 10-10, adjusted for testing multiple phenotypes). Gene-based association study prioritized 112 significant genes, most of which are novel. More importantly, association lookups found that many of the novel SNPs and genes of DTI parameters have previously been implicated with cognitive and mental health traits. In conclusion, the present study identifies many new genetic variants at SNP, locus and gene levels for integrity of brain WM tracts and provides the overview of pleiotropy with cognitive and mental health traits.

Radiological classification, gene-mutation status, and surgical prognosis of synchronous multiple primary lung cancer.

OBJECTIVE: To investigate the radiological classification, gene-mutation status, and surgical prognosis of synchronous multiple primary lung cancer (sMPLC). METHODS: From January 2013 to October 2019, 192 consecutive patients with sMPLC were investigated. The clinical, CT, molecular, and pathological features of all patients were analyzed. Furthermore, the prognosis of 89 patients who only underwent surgical resection was evaluated. RESULTS: Among 192 patients, all lesions pathologically confirmed or highly suspected as tumors based on radiological findings were retrospectively analyzed, and the CT findings of sMPLC were classified into three types: (I) all lesions manifested as solid nodules/masses (14.06%, 27/192), (II) all lesions manifested as subsolid nodules/masses (43.23%, 83/192), and (III) tumor lesions manifested as a combination of ≥ 2 of the following patterns: solid nodules/masses, subsolid nodules/masses, cystic airspace, and focal consolidation (42.71%, 82/192). For 252 tumors undergoing epidermal growth factor receptor (EGFR)-mutation testing, the EGFR-mutation rate was higher in subsolid tumors than that in solid tumors (p < 0.05). Among 19 patients with all tumors undergoing surgery and driver-gene testing, genetic heterogeneity was prevalent among the multiple tumors (63.16%,12/19). The highest clinical stage of non-I, ipsilateral distribution of tumors, and CT classification of I indicated a poor prognosis for patients with sMPLC (all p < 0.05). CONCLUSION: Subsolid lesions are the most common presentation of sMPLC. Genetic heterogeneity in driver mutations among sMPLC may be present. Prognosis in patients with sMPLC is determined by the highest clinical TNM stage, distribution, and radiological classification among the multiple tumors. KEY POINTS: • Synchronous multiple primary lung cancer (sMPLC) has three types of CT findings. • Genetic heterogeneity may be prevalent among the multiple tumors. • Prognosis in patients with sMPLC is associated with the highest clinical TNM stage, distribution, and radiological classification among the multiple tumors.

Abnormal static and dynamic functional connectivity of networks related to cognition in patients with subcortical ischemic vascular disease.

PURPOSE: To investigate the specific features of static functional connectivity (SFC) and dynamic functional connectivity (DFC) of networks related to cognition in patients with subcortical ischemic vascular disease (SIVD). METHODS: In this retrospective study, resting-state functional MRI data and a series of cognitive scores were obtained from 38 patients with SIVD and 23 normal controls. Independent component analysis, sliding window method, k-means clustering analysis and graph theory method were used to examine FC between the default mode network (DMN), dorsal attention network (DAN), frontoparietal network (FPN), salience network (SN) and executive control network (ECN) in patients with SIVD. Then, correlations between abnormal FC features and cognition were assessed. RESULTS: Compared with normal controls, SFC within the DMN significantly increased and SFC between the DMN and DAN significantly decreased in patients with SIVD. The decreased DFC mainly occurred in weakly connected states, especially the DFC of the SN; but the increased DFC, global network efficiency and local network efficiency and the decreased mean dwell time (MDT) and frequency mainly occurred in strongly connected states in SIVD patients. Moreover, aberrant SFC, DFC and MDT were significantly correlated with patients' cognitive scores. CONCLUSION: The overall results are suggestive of abnormal functional segregation and integration of SFC and DFC among networks related to cognition, especially in the SN. This may advance our comprehensive understanding of the abnormal changes in brain network connectivity in patients with SIVD. Our findings also highlight DFC may be an effective neuroimaging marker for the clinical diagnosis of SIVD.

Combined model of radiomics, clinical, and imaging features for differentiating focal pneumonia-like lung cancer from pulmonary inflammatory lesions: an exploratory study.

BACKGROUND: Only few studies have focused on differentiating focal pneumonia-like lung cancer (F-PLC) from focal pulmonary inflammatory lesion (F-PIL). This exploratory study aimed to evaluate the clinical value of a combined model incorporating computed tomography (CT)-based radiomics signatures, clinical factors, and CT morphological features for distinguishing F-PLC and F-PIL. METHODS: In total, 396 patients pathologically diagnosed with F-PLC and F-PIL from two medical institutions between January 2015 and May 2021 were retrospectively analyzed. Patients from center 1 were included in the training (n = 242) and internal validation (n = 104) cohorts. Moreover, patients from center 2 were classified under the external validation cohort (n = 50). The clinical and CT morphological characteristics of both groups were compared first. And then, a clinical model incorporating clinical and CT morphological features, a radiomics model reflecting the radiomics signature of lung lesions, and a combined model were developed and validated, respectively. RESULTS: Age, gender, smoking history, respiratory symptoms, air bronchogram, necrosis, and pleural attachment differed significantly between the F-PLC and F-PIL groups (all P < 0.05). For the clinical model, age, necrosis, and pleural attachment were the most effective factors to differentiate F-PIL from F-PLC, with the area under the curves (AUCs) of 0.838, 0.819, and 0.717 in the training and internal and external validation cohorts, respectively. For the radiomics model, five radiomics features were found to be significantly related to the identification of F-PLC and F-PIL (all P < 0.001), with the AUCs of 0.804, 0.877, and 0.734 in the training and internal and external validation cohorts, respectively. For the combined model, five radiomics features, age, necrosis, and pleural attachment were independent predictors for distinguishing between F-PLC and F-PIL, with the AUCs of 0.915, 0.899, and 0.805 in the training and internal and external validation cohorts, respectively. The combined model exhibited a better performance than had the clinical and radiomics models. CONCLUSIONS: The combined model, which incorporates CT-based radiomics signatures, clinical factors, and CT morphological characteristics, is effective in differentiating F-PLC from F-PIL.

Increased Internal Carotid Artery Tortuosity is a Risk Factor for Spontaneous Cervicocerebral Artery Dissection.

OBJECTIVE: Spontaneous cervicocerebral artery dissection (sCCD) is an important cause of ischaemic stroke that often occurs in young and middle aged patients. The purpose of this study was to investigate the correlation between tortuosity of the carotid artery and sCCD. METHODS: Patients with confirmed sCCD who underwent computed tomography angiography (CTA) were reviewed retrospectively. Age and sex matched patients having CTA were used as controls. The tortuosity indices of the cervical arteries were measured from the CTA images. The carotid siphon and the extracranial internal carotid artery (ICA) were evaluated according to morphological classification. The carotid siphons were classified into five types. The extracranial ICA was categorised as simple tortuosity, coiling or kinking. Independent risk factors for sCCD were investigated using multivariable analysis. RESULTS: The study included sixty-six patients with sCCD and 66 controls. There were no differences in vascular risk factors between the two groups. The internal carotid tortuosity index (ICTI) (25.24 ± 12.37 vs. 15.90 ± 8.55, respectively; p < .001) and vertebral tortuosity index (VTI) (median 11.28; interquartile range [IQR] 6.88, 18.80 vs. median 8.38; IQR 6.02, 12.20, respectively; p = .008) were higher in the patients with sCCD than in the controls. Type III and Type IV carotid siphons were more common in the patients with sCCD (p = .001 and p < .001, respectively). The prevalence of any vessel tortuosity, coiling and kinking of the extracranial ICA was higher in the patients with sCCD (p < .001, p = .018 and p = .006, respectively). ICTI (odds ratio [OR] 2.964; p = .026), VTI (OR 5.141; p = .009), and Type III carotid siphons (OR 4.654; p = .003) were independently associated with the risk of sCCD. CONCLUSION: Arterial tortuosity is associated with sCCD, and greater tortuosity of the cervical artery may indicate an increased risk of arterial dissection.

Altered local and matrix functional connectivity in depressed essential tremor patients.

BACKGROUND: Depression in essential tremor (ET) has been constantly studied and reported, while the associated brain activity changes remain unclear. Recently, regional homogeneity (ReHo), a voxel-wise local functional connectivity (FC) analysis of resting-state functional magnetic resonance imaging, has provided a promising way to observe spontaneous brain activity. METHODS: Local FC analyses were performed in forty-one depressed ET patients, 49 non-depressed ET patients and 43 healthy controls (HCs), and then matrix FC and clinical depression severity correlation analyses were further performed to reveal spontaneous neural activity changes in depressed ET patients. RESULTS: Compared with the non-depressed ET patients, the depressed ET patients showed decreased ReHo in the bilateral cerebellum lobules IX, and increased ReHo in the bilateral anterior cingulate cortices and middle prefrontal cortices. Twenty-five significant changes of ReHo clusters were observed in the depressed ET patients compared with the HCs, and matrix FC analysis further revealed that inter-ROI FC differences were also observed in the frontal-cerebellar-anterior cingulate cortex pathway. Correlation analyses showed that clinical depression severity was positively correlated with the inter-ROI FC values between the anterior cingulate cortex and bilateral middle prefrontal cortices and was negatively correlated with the inter-ROI FC values of the anterior cingulate cortex and bilateral cerebellum lobules IX. CONCLUSION: Our findings revealed local and inter-ROI FC differences in frontal-cerebellar-anterior cingulate cortex circuits in depressed ET patients, and among these regions, the cerebellum lobules IX, middle prefrontal cortices and anterior cingulate cortices could function as pathogenic structures underlying depression in ET patients.

Differentiating malignant and benign necrotic lung lesions using kVp-switching dual-energy spectral computed tomography.

BACKGROUND: Necrotic pulmonary lesions manifest as relatively low-density internally on contrast-enhanced computed tomography (CT). However, using CT to differentiate malignant and benign necrotic pulmonary lesions is challenging, as these lesions have similar peripheral enhancement. With the introduction of dual-energy spectral CT (DESCT), more quantitative parameters can be obtained and the ability to differentiate material compositions has been highly promoted. This study investigated the use of kVp-switching DESCT in differentiating malignant from benign necrotic lung lesions. METHODS: From October 2016 to February 2019, 40 patients with necrotic lung cancer (NLC) and 31 with necrotic pulmonary mass-like inflammatory lesion (NPMIL) were enrolled and underwent DESCT. The clinical characteristics of patients, CT morphological features, and DESCT quantitative parameters of lesions were compared between the two groups. Binary logistic regression analysis was performed to identify the independent prognostic factors differentiating NPMIL from NLC. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of single-parameter and multiparametric analyses. RESULTS: Significant differences in age, C-reactive protein concentration, the slope of the spectral curve from 40 to 65 keV (K40-65 keV) of necrosis in non-contrast-enhanced scanning (NCS), arterial phase (AP) and venous phase (VP), effective atomic number of necrosis in NCS, and iodine concentration (IC) of the solid component in VP were observed between groups (all p < 0.05). The aforementioned parameters had area under the ROC curve (AUC) values of 0.747, 0.691, 0.841, 0.641, 0.660, 0.828, and 0.754, respectively, for distinguishing between NLC and NPMIL. Multiparametric analysis showed that age, K40-65 keV of necrosis in NCS, and IC of the solid component in VP were the most effective factors for differentiating NLC from NPMIL, with an AUC of 0.966 and percentage of correct class of 88.7%. CONCLUSIONS: DESCT can differentiate malignant from benign necrotic lung lesions with a relatively high accuracy.

Spectral CT in Lung Cancer: Usefulness of Iodine Concentration for Evaluation of Tumor Angiogenesis and Prognosis.

OBJECTIVE. The purpose of this study was to investigate the correlation between iodine concentration (IC) derived from spectral CT and angiogenesis and the relationships between IC and clinical-pathologic features associated with lung cancer prognosis. SUBJECTS AND METHODS. Sixty patients with lung cancer were enrolled and underwent spectral CT. The IC, IC difference (ICD), and normalized IC (NIC) of tumors were measured in the arterial phase, venous phase (VP), and delayed phase. The microvessel densities (MVDs) of CD34-stained specimens were evaluated. Correlation analysis was performed for IC and MVD. The relationships between the IC index showing the best correlations with MVD and clinical-pathologic findings of pathologic types, histologic differentiation, tumor size, lymph node status, pathologic TNM stage, and intratumoral necrosis were investigated. RESULTS. The mean (± IQR) MVD of all tumors was 42.00 ± 27.50 vessels per field at ×400 magnification, with two MVD distribution types. The MVD of lung cancer correlated positively with the IC, ICD, and NIC on three-phase contrast-enhanced scanning (r range, 0.581-0.800; all p < 0.001), and the IC in the VP showed the strongest correlation with MVD (r = 0.800; p < 0.001). The correlations between IC and MVD, ICD and MVD, and NIC and MVD varied depending on whether the same scanning phase or same IC index was used. The IC in the VP showed statistically significant differences in the pathologic types of adenocarcinoma and squamous cell carcinoma, histologic differentiation, tumor size, and status of intratumoral necrosis of lung cancer (p < 0.05), but was not associated with nodal metastasis and pathologic TNM stages (p > 0.05). CONCLUSION. IC indexes derived from spectral CT, especially the IC in the VP, were useful indicators for evaluating tumor angiogenesis and prognosis.

Deep Convolutional Radiomic Features on Diffusion Tensor Images for Classification of Glioma Grades.

The grading of glioma has clinical significance in determining a treatment strategy and evaluating prognosis to investigate a novel set of radiomic features extracted from the fractional anisotropy (FA) and mean diffusivity (MD) maps of brain diffusion tensor imaging (DTI) sequences for computer-aided grading of gliomas. This retrospective study included 108 patients who had pathologically confirmed brain gliomas and DTI scanned during 2012-2018. This cohort included 43 low-grade gliomas (LGGs; all grade II) and 65 high-grade gliomas (HGGs; grade III or IV). We extracted a set of radiomic features, including traditional texture, morphological, and novel deep features derived from pre-trained convolutional neural network models, in the manually-delineated tumor regions. We employed support vector machine and these radiomic features for two classification tasks: LGGs vs HGGs, and grade III vs IV. The area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity was reported as the performance metrics using the leave-one-out cross-validation method. When combining FA+MD, AUC = 0.93, accuracy = 0.94, sensitivity = 0.98, and specificity = 0.86 in classifying LGGs from HGGs, while AUC = 0.99, accuracy = 0.98, sensitivity = 0.98, and specificity = 1.00 in classifying grade III from IV. The AUC and accuracy remain close when features were extracted from only the solid tumor or additionally including necrosis, cyst, and peritumoral edema. Still, the effects in terms of sensitivity and specificity are mixed. Deep radiomic features derived from pre-trained convolutional neural networks showed higher prediction ability than the traditional texture and shape features in both classification experiments. Radiomic features extracted on the FA and MD maps of brain DTI images are useful for noninvasively classification/grading of LGGs vs HGGs, and grade III vs IV.

Effects of thalamic infarction on the structural and functional connectivity of the ipsilesional primary somatosensory cortex.

OBJECTIVES: To identify regions causally influenced by thalamic stroke by measuring white matter integrity, cortical volume, and functional connectivity (FC) among patients with thalamic infarction (TI) and to determine the association between structural/functional alteration and somatosensory dysfunction. METHODS: Thirty-one cases with TI-induced somatosensory dysfunction and 32 healthy controls underwent magnetic resonance imaging scanning. We reconstructed the ipsilesional central thalamic radiation (CTR) and assessed its integrity using fractional anisotropy (FA), assessed S1 ipsilesional changes with cortical volume, and identified brain regions functionally connected to TI locations and regions without TI to examine the potential effects on somatosensory symptoms. RESULTS: Compared with controls, TI patients showed decreased FA (F = 17.626, p < 0.001) in the ipsilesional CTR. TI patients exhibited significantly decreased cortical volume in the ipsilesional top S1. Both affected CTR (r = 0.460, p = 0.012) and S1 volume (r = 0.375, p = 0.049) were positively correlated with somatosensory impairment in TI patients. In controls, the TI region was highly functionally connected to atrophic top S1 and less connected to the adjacent middle S1 region in FC mapping. However, T1 patients demonstrated significantly increased FC between the ipsilesional thalamus and middle S1 area, which was adjacent to the atrophic S1 region. CONCLUSIONS: TI induces remote changes in the S1, and this network of abnormality underlies the cause of the sensory deficits. However, our other finding that there is stronger connectivity in pathways adjacent to the damaged ones is likely responsible for at least some of the recovery of function. KEY POINTS: • TI led to secondary impairment in the CTR and cortical atrophy in the ipsilesional top of S1. • TI patients exhibited significantly higher functional connectivity with the ipsilateral middle S1 which was mainly located within the non-atrophic area of S1. • Our results provide neuroimaging markers for non-invasive treatment and predict somatosensory recovery.

A Pilot Study of Third-Generation Dual-Source Computed Tomography for the Assessment of Global Dynamic Changes in Left Ventricular Structure and Function in a Porcine Model of Acute Myocardial Infarction.

BACKGROUND First-generation and second-generation dual-source computed tomography (DSCT) are useful for analyzing left ventricle (LV) structure and function. This pilot study aimed to investigate the feasibility and role of third-generation DSCT for the evaluation of dynamic changes in LV structural and functional characteristics in a Diannan small-ear pig model of acute myocardial infarction (AMI). MATERIAL AND METHODS The model of AMI was established by balloon occlusion of the distal third of the left anterior descending (LAD) coronary artery in 14 Diannan small-eared pigs. Third-generation DSCT was performed to observe dynamic changes in LV structure and function before and after AMI was induced, with a follow-up period of 30 days. RESULTS The mean structural measurements at baseline included interventricular septum thickness (8.50±0.90 mm), LV anterior wall thickness (8.40±1.30 mm), LV posterior wall thickness (7.80±1.20 mm), LV end-diastolic dimension (LVEDD) (45.00±4.90 mm), and LV end-systolic dimension (LVESD) (25.90±4.10 mm). The mean functional measurements at baseline included the LV end-diastolic volume (LVEDV) (74.62±13.54 ml), LV end-systolic volume (LVESV) (23.06±7.46 ml), LV ejection fraction (LVEF) (69.29±6.83%), LV mass (86.35±14.02 g), stroke volume (SV) (51.56±9.77 ml), and cardiac output (CO) (4.22±2.14 l/min). Trends of time-dependent changes were observed for LVESV, LVEF, SV, and CO, but not for LVEDV or LV mass. CONCLUSIONS Third-generation DSCT was validated as a tool for assessing dynamic changes in LV global function in a porcine model of AMI.

Assessment the reliability of ultrasonography in the imaging of the plantar fascia: a comparative study.

BACKGROUND: Imaging methods for the plantar fascia have included radiography, ultrasound and magnetic resonance imaging (MRI), all of which have provided valuable information. This study assessed the reliability of ultrasonography examinations of the plantar fascia using a comparative study. METHODS: Fifty healthy adult volunteers (25 males and 25 females, mean age 31.6 ± 3.5 years) were included in this study. Images of the plantar fascia from 100 ft were acquired with ultrasonography, CT and MRI. Ultrasound was used to measure the thickness of the plantar fascia. Imaging data from CT and MRI in a DICOM format were transformed into the Materialise Mimics Innovation Suite 16.0 software for digital analysis. SPSS software (SPSS, USA) was used for statistical analysis. The reliability was established by a t-test. Moreover, 42 patients with unilateral plantar fasciitis were examined by ultrasonography. RESULTS: There were no significant differences between the three imaging modalities for patients of the same sex (P > 0.05). There were no statistically significant differences between the left and right sides for patients of the same sex (P > 0.05), but the difference between males and females was statistically significant (P < 0.01). There were no significant differences between US, CT and MRI in the normal group, but there were significant differences in the plantar fasciitis group evaluated with ultrasound. The plantar fascii of normal male subjects are significantly thicker than those of the normal female. CONCLUSION: Ultrasonography can be a relatively simple and reliable method for the measurement of plantar fascia thickness.

Application of MRI and CT Energy Spectrum Imaging in Hand and Foot Tendon Lesions.

To deeply analyze the tendon lesions of hands and feet, the application of Computed Tomography (CT) energy spectrum imaging and magnetic resonance imaging (MRI) in anatomy and lesions is mainly studied. Firstly, the related information of the subjects is introduced in turn. Secondly, Gemstone Spectral Imaging (GSI) and MRI examinations are performed respectively. Through energy spectrum analysis software, suitable single energy value (KeV) is selected, the mixed energy image is converted into the single energy image, and a variety of image recombination methods are used to observe the energy spectrum CT image and compare the results with MRI. The results of the study show that GSI could display the morphology, continuous walking and dead point of the tendon, especially the three-dimensional spatial relationship of the tendon, bone and muscle, which is superior to MRI. There is no statistically significant difference between GSI and MRI in the display of tendon rupture, thickening, deletion and compression. And GSI is not as clear as MRI in the display of tendon adhesion, degeneration and tendon sheath lesions, and the difference is statistically significant. Therefore, MRI is still the first choice in hand and foot tendon lesions, especially in the display of early pathological changes of the tendon and tendon sheath diseases, as well as the evaluation of postoperative functional rehabilitation of the tendon. And CT energy spectrum imaging, as a new imaging mode, can clearly show the anatomy of normal tendon of hand and foot and most tendon lesions, especially in the observation of tendon morphology, which has a high diagnostic value.

Intrathecal circHIPK3 shRNA alleviates neuropathic pain in diabetic rats.

Neuropathic pain is one of the most common diabetic complications and significantly decrease the quality of life. The aetiology of the painful diabetic neuropathic pain is not fully clear. Circular RNAs (circRNAs) have been identified as miRNA sponges and involved in various biological processes, including pain. CircHIPK3 is a circRNA that have been shown to be an oncogene or tumor suppressor to regulate cancer cells growth by sponging multiple miRNAs. However, the role of circHIPK3 in diabetic neuropathic pain remains unknown. The aim of the present study was to elucidate the possible role of circHIPK3 in the control of diabetic neuropathic pain. We found that circHIPK3 are highly abundant in serum from diabetes patients who suffered from neuropathic pain and in dorsal root ganglion from STZ-induced diabetes rats. Upregulation of circHIPK3 was positively associated with grade neuropathic pain in patients with type 2 diabetes. Silencing circHIPK3 alleviated neuropathic pain in diabetic rats, which was involved in neuroinflammation. Further mechanistic investigation demonstrated that circHIPK3 interacted with miR-124 and negatively regulated its expression. MiR-124 inhibitor can reverse circHIPK3 knockdown-mediated alleviation of neuropathic pain and inhibition of neuroinflammation in diabetic rats. We present the first evidence that intrathecal circHIPK3 shRNA treatment can be used to treat neuropathic pain of diabetic rats.

LINC00673 rs11655237 Polymorphism Is Associated With Increased Risk of Cervical Cancer in a Chinese Population.

Cervical cancer is the fourth most commonly diagnosed cancer and the fourth leading cause of cancer deaths in women worldwide. Few single-nucleotide polymorphisms associated with risk of cervical cancer have been identified, yet genetic predisposition contributes significantly to this malignancy. Long noncoding RNA LINC00673 has been widely explored for its role in the development and prognosis of many tumors, and 2 genome-wide association studies identified that LINC00673 rs11655237 was associated with susceptibility to pancreatic cancer. In the current study, using a case-control study design, we found rs11655237 significantly increased susceptibility of cervical cancer in a Chinese population (odds ratio = 1.27; 95% confidence interval = 1.08-1.50; P = .005). Expression of LINC00673 was significantly higher in adjacent normal tissues than in paired cancer tissues ( P < .01) and significantly lower in the cancer or paired adjacent normal tissues of patients with cervical cancer having rs11655237 allele A than in those having rs11655237 allele G ( P < .001). Our results indicate that LINC00673 rs11655237 is associated with increased risk of cervical cancer, possibly by downregulating LINC00673 expression in cervical tissues.