A rare case of cutaneous granular cell tumor with unusual Melan-A expression in a child.

Granular cell tumors (GCTs) are rare, indolent neoplasms classically characterized by eosinophilic granular cytoplasm, infiltrations of polygonal cells in the collagenous stroma, and pustulo-ovoid bodies of Milian. We describe a case of a 10-year-old female presenting with a GCT of the upper arm, remarkable for positive Melan-A expression without additional melanocytic features. The differentiation between granular cells versus melanocytic neoplasms carries significant implications for clinical management, and such diagnoses should be considered carefully in the setting of unusual immunophenotypes.

Metastatic pulmonary calcification on bone scintigraphy of a patient with lupus nephritis and end-stage renal disease.

A 65-year-old male complaining of low back pain was noted to have diffuse, homogenous bilateral lung uptake on Tc-99m methylene diphosphate (Tc99m-MDP) bone scintigraphy. The patient had no prior history of pulmonary disease with no apparent respiratory symptoms at time of imaging, but did endorse a long history of lupus nephritis and end-stage renal disease on hemodialysis. Review of prior chest CT and chest X-ray imaging over the last 5 years revealed diffuse ground-glass opacities and extensive parenchymal calcifications, consistent with metastatic pulmonary calcification. These radiological findings were further corroborated by laboratory studies, which demonstrated longstanding secondary hyperparathyroidism with a most recent work-up including an iPTH level of 1251 pg/mL. The differential diagnosis of bilateral, diffuse Tc99m-MDP uptake on bone scintigraphy includes tracer contamination, pulmonary etiologies such as pleural effusion or mesothelioma, metabolic diseases such as metastatic pulmonary calcification, and genetic diseases including pulmonary alveolar microlithiasis. In the setting of longstanding renal dysfunction and chronic hypercalcemia as in this patient, such radiological findings are a classic presentation of metastatic pulmonary calcification.

Second window ICG predicts postoperative MRI gadolinium enhancement in high grade gliomas and brain metastases.

A prospective trial evaluating the utility of second window indocyanine green (SWIG) in predicting postoperative MRI gadolinium enhancement was performed on high-grade gliomas (HGGs) and brain metastases. Compared to white light alone, SWIG demonstrated a higher sensitivity, negative predictive value, and accuracy in predicting residual neoplasm on MRI. The specificity of SWIG for predicting MRI enhancement was higher in HGGs than brain metastases. Clinically, near-infrared (NIR) imaging was better able to predict tumor recurrence than postoperative MRI. These results illustrate how SWIG is able to take advantage of gadolinium-like distribution properties to extravasate into the tumor microenvironment, enabling guidance in surgical resection. The video can be found here: https://stream.cadmore.media/r10.3171/2021.10.FOCVID21204.

Update on the limited sensitivity of computed tomography relative to RT-PCR for COVID-19: a systematic review.

Purpose: The global and ongoing COVID-19 outbreak has compelled the need for timely and reliable methods of detection for SARS-CoV-2 infection. Although reverse transcription-polymerase chain reaction (RT-PCR) has been widely accepted as a reference standard for COVID-19 diagnosis, several early studies have suggested the superior sensitivity of computed tomography (CT) in identifying SARS-CoV-2 infection. In a previous systematic review, we stratified studies based on risk for bias to evaluate the true sensitivity of CT for detecting SARS-CoV-2 infection. This study revisits our prior analysis, incorporating more current data to assess the sensitivity of CT for COVID-19. Material and methods: The PubMed and Google Scholar databases were searched for relevant articles published between 1 January 2020, and 25 April 2021. Exclusion criteria included lack of specification regarding whether the study cohort was adult or paediatric, whether patients were symptomatic or asymptomatic, and not identifying the source of RT-PCR specimens. Ultimately, 62 studies were included for systematic review and were subsequently stratified by risk for bias using the QUADAS-2 quality assessment tool. Sensitivity data were extracted for random effects meta-analyses. Results: The average sensitivity for COVID-19 reported by the high-risk-of-bias studies was 68% [CI: 58, 80; range: 38-96%] for RT-PCR and 91% [CI: 87, 96; range: 47-100%] for CT. The average sensitivity reported by the low-risk-of-bias studies was 84% [CI: 0.75, 0.94; range: 70-97%] for RT-PCR and 78% [CI: 71, 0.86; range: 44-92%] for CT. Conclusions: On average, the high-risk-of bias studies underestimated the sensitivity of RT-PCR and overestimated the sensitivity of CT for COVID-19. Given the incorporation of recently published low-risk-of-bias articles, the sensitivities according to low-risk-of-bias studies for both RT-PCR and CT were higher than previously reported.

Auto-weighted centralised multi-task learning via integrating functional and structural connectivity for subjective cognitive decline diagnosis.

Early diagnosis and intervention of mild cognitive impairment (MCI) and its early stage (i.e., subjective cognitive decline (SCD)) is able to delay or reverse the disease progression. However, discrimination between SCD, MCI and healthy subjects accurately remains challenging. This paper proposes an auto-weighted centralised multi-task (AWCMT) learning framework for differential diagnosis of SCD and MCI. AWCMT is based on structural and functional connectivity information inferred from magnetic resonance imaging (MRI). To be specific, we devise a novel multi-task learning algorithm to combine neuroimaging functional and structural connective information. We construct a functional brain network through a sparse and low-rank machine learning method, and also a structural brain network via fibre bundle tracking. Those two networks are constructed separately and independently. Multi-task learning is then used to identify features integration of functional and structural connectivity. Hence, we can learn each task's significance automatically in a balanced way. By combining the functional and structural information, the most informative features of SCD and MCI are obtained for diagnosis. The extensive experiments on the public and self-collected datasets demonstrate that the proposed algorithm obtains better performance in classifying SCD, MCI and healthy people than traditional algorithms. The newly proposed method has good interpretability as it is able to discover the most disease-related brain regions and their connectivity. The results agree well with current clinical findings and provide new insights into early AD detection based on the multi-modal neuroimaging technique.

Immune phenotyping of diverse syngeneic murine brain tumors identifies immunologically distinct types.

Immunotherapy has emerged as a promising approach to treat cancer, however, its efficacy in highly malignant brain-tumors, glioblastomas (GBM), is limited. Here, we generate distinct imageable syngeneic mouse GBM-tumor models and utilize RNA-sequencing, CyTOF and correlative immunohistochemistry to assess immune-profiles in these models. We identify immunologically-inert and -active syngeneic-tumor types and show that inert tumors have an immune-suppressive phenotype with numerous exhausted CD8 T cells and resident macrophages; fewer eosinophils and SiglecF+ macrophages. To mimic the clinical-settings of first line of GBM-treatment, we show that tumor-resection invigorates an anti-tumor response via increasing T cells, activated microglia and SiglecF+ macrophages and decreasing resident macrophages. A comparative CyTOF analysis of resected-tumor samples from GBM-patients and mouse GBM-tumors show stark similarities in one of the mouse GBM-tumors tested. These findings guide informed choices for use of GBM models for immunotherapeutic interventions and offer a potential to facilitate immune-therapies in GBM patients.

Assessing the effects of body weight on subchondral bone formation with quantitative 18F-sodium fluoride PET.

OBJECTIVES: The aim of this study was to quantify subchondral bone remodeling in the elbows, hands, knees, and feet using volumetric and metabolic parameters derived from 18F-sodium fluoride positron emission tomography (NaF-PET) and to assess the convergent validity of these parameters as an index of joint degeneration and preclinical osteoarthritis. METHODS: A retrospective analysis was conducted in 34 subjects (32 males, 2 females) with metastatic bone disease who underwent full-body NaF-PET/CT scans. An adaptive contrast-oriented thresholding algorithm was applied to segment NaF-avid regions in the bilateral elbows, hands, knees, and feet of each subject, and metabolically active volume (MAV), maximum standardized uptake value (SUVmax), mean metabolic volumetric product (MVPmean), and partial volume-corrected MVPmean (cMVPmean) of the segmented regions were calculated. Global parameters for MAV, SUVmax, MVPmean, and cMVPmean were defined as the sum of the corresponding values in all the joints of a subject. Inter-rater reliability was determined with Lin's concordance correlation, and associations of global values with subject body weight and age were assessed with Pearson correlation and Spearman correlation analyses. RESULTS: Inter-rater reliability was observed to be the highest in SUVmax (ρc = 0.99), followed by MVPmean (ρc = 0.96), cMVPmean (ρc = 0.93), and MAV (ρc = 0.93). MAV, MVPmean, and cMVPmean were observed to significantly increase with weight (all p < 0.0001) determined by Pearson correlation. In addition, Spearman rank-order analysis demonstrated a significant correlation between SUVmax and weight in addition to MAV, MVPmean, and cMVPmean and weight (all p < 0.01). No significant association between age and any PET parameter was observed. CONCLUSIONS: These preliminary data demonstrate the feasibility and reliability of assessing bone turnover at the joints using quantitative NaF-PET. Our findings corroborate the fact that biomechanical factors including mechanical loading and weight-bearing are contributors to osteoarthritis disease progression.

Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease.

Detection of early stages of Alzheimer's disease (AD) (i.e., mild cognitive impairment (MCI)) is important to maximize the chances to delay or prevent progression to AD. Brain connectivity networks inferred from medical imaging data have been commonly used to distinguish MCI patients from normal controls (NC). However, existing methods still suffer from limited performance, and classification remains mainly based on single modality data. This paper proposes a new model to automatically diagnosing MCI (early MCI (EMCI) and late MCI (LMCI)) and its earlier stages (i.e., significant memory concern (SMC)) by combining low-rank self-calibrated functional brain networks and structural brain networks for joint multi-task learning. Specifically, we first develop a new functional brain network estimation method. We introduce data quality indicators for self-calibration, which can improve data quality while completing brain network estimation, and perform correlation analysis combined with low-rank structure. Second, functional and structural connected neuroimaging patterns are integrated into our multi-task learning model to select discriminative and informative features for fine MCI analysis. Different modalities are best suited to undertake distinct classification tasks, and similarities and differences among multiple tasks are best determined through joint learning to determine most discriminative features. The learning process is completed by non-convex regularizer, which effectively reduces the penalty bias of trace norm and approximates the original rank minimization problem. Finally, the most relevant disease features classified using a support vector machine (SVM) for MCI identification. Experimental results show that our method achieves promising performance with high classification accuracy and can effectively discriminate between different sub-stages of MCI.

MicroRNA-26b/PTEN Signaling Pathway Mediates Glycine-Induced Neuroprotection in SAH Injury.

Subarachnoid hemorrhage (SAH) is a form of stroke associated with high mortality and morbidity. Despite advances in treatment for SAH, the prognosis remains poor. We have previously demonstrated that glycine, a non-essential amino acid is involved in neuroprotection following intracerebral hemorrhage via the Phosphatase and tensin homolog (PTEN)/protein kinase B (AKT) signaling pathway. However, whether it has a role in inducing neuroprotection in SAH is not known. The present study was designed to investigate the role of glycine in SAH. In this study, we show that glycine can reduce brain edema and protect neurons in SAH via a novel pathway. Following a hemorrhagic episode, there is evidence of downregulation of S473 phosphorylation of AKT (p-AKT), and this can be reversed with glycine treatment. We also found that administration of glycine can reduce neuronal cell death in SAH by activating the AKT pathway. Glycine was shown to upregulate miRNA-26b, which led to PTEN downregulation followed by AKT activation, resulting in inhibition of neuronal death. Inhibition of miRNA-26b, PTEN or AKT activation suppressed the neuroprotective effects of glycine. Glycine treatment also suppressed SAH-induced M1 microglial polarization and thereby inflammation. Taken together, we conclude that glycine has neuroprotective effects in SAH and is mediated by the miRNA-26b/PTEN/AKT signaling pathway, which may be a therapeutic target for treatment of SAH injury.

Low Rank Self-calibrated Brain Network Estimation and Autoweighted Centralized Multi-Task Learning for Early Mild Cognitive Impairment Diagnosis.

Detection of mild cognitive impairment (MCI) is important, and appropriate interventions can be taken to delay or prevent its progression to Alzheimer's disease (AD). The construction of brain networks based on brain image data to depict the interaction of brain functions or structures at the level of brain connections has been widely used to identify individuals with MCI/AD from the normal control (NC). Exploring the structural and functional connections and interactions between brain regions is beneficial to detect MCI. For this reason, we propose a new model for automatic MCI diagnosis based on this information. Firstly, a new functional brain network estimation method is proposed. Self-calibration is introduced using quality indicators, and functional brain network estimation is performed at the same time. Then we integrate the functional and structural connected neuroimaging patterns into our multitask learning model to select informative feature. By identifying synergies and differences between different tasks, the most discriminative features are determined. Finally, the most relevant features are sent to the support vector machine classifier for diagnosis and identification of MCI. The experimental results based on the public Alzheimer's disease neuroimaging (ADNI) show that our method can effectively diagnose different stages of MCI and assist the physician to improve the MCI diagnostic accuracy. At the same time, compared with the existing classification methods, the proposed method achieves relatively high classification accuracy. In addition, it can identify the most discriminative brain regions. These findings suggest that our approach not only improves classification performance, but also successfully identifies important biomarkers associated with disease.

MelanomaNet: An Effective Network for Melanoma Detection.

Melanoma is one of the most deadly skin lesion, which often uses the skin dermoscopy to detect it. However, the low interclass variations between melanoma images make manual dermoscopic detection time-consuming and laborious. Therefore, an automatic recognition algorithm of skin image is highly desirable. However, the traditional methods still have the limitations (e.g., weak robustness and generalization ability). To meet the challenge, we propose an effective architecture based on residual - squeeze - and - excitation -Inception-v4 network (MelanomaNet) to detect melanoma. Specifically, Inception-v4 structure is utilized to get the rich spatial features and increase feature diversity. We also consider the relationship between feature channels by adding residual-squeeze-and-excitation (RSE) blocks in Inception- v4 network using the feature recalibration strategies. Finally, we use the support vector machine (SVM) as the classifier for the skin lesion classification. We evaluate our proposed method on the public available ISIC skin lesion challenge datasets in 2018 for training and evaluation. The experimental results show that the proposed method has achieved better performance over the state-of-the-arts methods.

Efficacy of 18F-FDG and 18F-NaF PET/CT imaging: A novel semi-quantitative assessment of the effects of age and obesity on hip joint inflammation and bone degeneration.

OBJECTIVE: Osteoarthritis (OA) is characterized by synovial tissue inflammation and underlying bone degeneration in the joints. Aging and obesity are among the major risk factors. This study evaluated the effects of aging and body mass index (BMI) on hip joint inflammation and bone degeneration using fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) and fluorine-18 sodium fluoride (18F-NaF) PET/CT imaging, respectively. SUBJECTS AND METHODS: In this retrospective study, a total of 116 subjects (58 males and 58 females) who had undergone both 18F-FDG and 18F-NaF PET/CT imaging were analyzed. The mean age of these subjects was 48.6±14.5 with an age range of 21-75 years. Fluorine-18-FDG and 18F-NaF PET/CT imaging was conducted 180min and 90min (respectively) after intravenous administration of the appropriate tracer. The hip joint was segmented on fused PET/CT images using OsiriX MD v.9.5 (DICOM viewer and image-analysis program, Pixmeo SARL; Bernex, Switzerland). The region of interest (ROI) for the hip joint was indicated by using a 3D-growing region algorithm with upper/lower Hounsfield Units (HU) followed by a morphological closing algorithm. The metabolic activity for the left and right side of the joint was measured and correlated with age and BMI. RESULTS: Fluorine-18-FDG uptake in the hip was 0.83±0.22 (right side: 0.83±0.23, left side: 0.83±0.22, P=0.82). Fluorine-18-NaF uptake in the hip was 3.20±1.07 (right side: 3.25±1.14, left side: 3.15±1.04, P=0.02). Body mass index positively correlated with both 18F-FDG (r=0.29, P=0.001) and NaF (r=0.26, P=0.005) uptake. No significant correlation was seen between age and either 18F-FDG (r=0.12, P=0.19) or 18F-NaF (r=0.03, P=0.78) uptake. CONCLUSION: Body mass index had a significant impact on 18F-FDG and 18F-NaF uptake, whereas age had no correlation with either tracer uptake. Obesity increases the mechanical forces applied on weight-bearing joints such as the hip. Body mass index was related to increased joint inflammation and bone degeneration. These findings further support the studies explaining the role of adipose tissue in promoting OA.