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Background: Both clinicians and researchers have a growing interest in assessment of muscle mass utilizing diagnostic abdominal computed tomography (CT) scans. Different imaging analysis software tools exist for the assessment of muscle mass;however, minimal information is available to describe the agreement between tools. The objective of this project was to determine the agreement, reliability, and strength of the relationship between skeletal muscle cross-sectional area (CSA) and muscle quality at the third lumbar region (L3) between two different image analysis software tools (3D Slicer vs SliceOmatic) using a convenient sample of individuals who have undergone diagnostic abdominal CT scan imaging. Method(s): A retrospective sample of individuals who had undergone a diagnostic abdominal CT scan was utilized. For both image analysis software tools, L3 skeletal muscle CSA was identified using a Hounsfield Unit (HU) range of -30 to +150 and muscle quality was defined as the mean HU. For each patient, L3 skeletal muscle CSA (cm2) and mean HU was calculated using 3D Slicer (version 5.0.3) and SliceOmatic (version 4.3, TomoVision, Quebec, Canada). Lin's correlation coefficient (LCC), intraclass correlation coefficient (ICC), and Spearman correlation coefficient (SCC) were used to examine the agreement, reliability, and strength of the relationship with both L3 skeletal muscle CSA and muscle quality using3D Slicer versus SliceOmatic. Bland Altman plots were created to depict the agreement of L3 CSA and muscle quality between the two tools. Result(s): A total of 504 patients were included;the sample included 128 healthy adults and 376 patients who had the following diagnoses: breast cancer (n = 175), colorectal cancer (n = 127), sepsis (n = 37) and COVID-19 (n = 37). The mean L3 skeletal muscle CSA measured using SliceOmatic was 140.6 +/- 36.0 cm2 and using 3D Slicer was 137.6 +/- 36.1 cm2. When examining the agreement, reliability, and strength of the relationship of L3 skeletal muscle CSA between SliceOmatic and 3D Slicer, LCC was 0.934 (p < 0.001), ICC was 0.968 (p < 0.001), and SCC was 0.930 (p < 0.001). The mean muscle quality measured using SliceOmatic was 35.1 +/- 10.8 HU and using 3D Slicer was 34.6 +/- 11.0 HU;LCC was 0.928 (p < 0.001), ICC was 0.964 (p < 0.001), and SCC was 0.957 (p < 0.001). Both the Bland Altman plots for L3 skeletal muscle CSA and muscle quality using SliceOmatic and 3D Slicer displayed overall strong agreement (Figures 1 and 2). However, 27 outliers were identified when visualizing the agreement L3 skeletal muscle CSA;further investigation of these outliers revealed that most of these measurements were conducted in critically ill patients (sepsis and COVID-19). Examining L3 skeletal muscle CSA between SliceOmatic and 3D Slicer among a subgroup of patients with sepsis and COVID revealed lower overall agreement (LCC: 0.679, p < 0.0001), reliability (ICC: 0.811, p < 0.001), and strength (SCC: 0.642, p < 0.001). Similar findings were observed with muscle quality between SliceOmatic and 3D Slicer among a subgroup of patients with sepsis and COVID (LCC: 0.585, p < 0.0001;ICC: 0.741, p < 0.001;SCC: 0.592, p < 0.001). Conclusion(s): Overall, both the SliceOmatic and 3D Slicer imaging analysis software tools had strong agreement, reliability, and strength when examining muscle mass and muscle quality. However, the agreement, reliability, and strength between muscle mass and muscle quality was lower between the two tools among critically ill patients compared to healthy controls and patients with cancer. Further research is needed to describe the etiology of this lower agreement in critically ill patients. (Table Presented).
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Background: SARS-CoV-2 infection is accompanied by acute olfactory disturbance in as high as 70% of cases. This loss is associated with decreased olfactory bulb volume. As time passes, the anosmia tends to subside, but the OB volume decrease does not. Volume reductions in primary and secondary olfactory cortex are also seen following SARS-CoV-2 infection. Nevertheless, concurrent SARS-CoV-2 infection effects on olfactory discrimination, olfactory bulb volume, primary olfactory cortex and its targets have not been investigated. To explore this possibility, we measured olfactory discrimination, olfactory bulb volume, primary olfactory cortex and basal ganglia volume in patients who had SARS-CoV-2 infection more than 12 weeks previously, who were then divided into COVID and long-COVID groups on the basis of selfreported fatigue and concentration complaints. Method(s): This cross-sectional study included 25 post-infection and 19 demographically-matched, no-COVID control participants, we investigated effects on olfaction using NIH Toolbox Odor Identification Test and the Monell Smell Questionnaire. GM structure was assessed with voxel-based morphometry and manual delineation of high resolution (1mm3), T1- and T2-weighted MRI data. Linear regression was used to model group effects on GM structure, adjusting for age, sex, education and total intracranial volume. CAT12/SPM12 and R were used for image processing and statistical modeling. Result(s): Results. The NIH Toolbox Odor Identification Test failed to show differences among the groups. In contrast, the Monell Smell Questionnaire revealed persistently diminished and distorted smell in 50% of the long-COVID sample. Olfactory bulb volume was lower in the long-COVID group (p=0.02). Primary olfactory cortex volume was reduced in the long-COVID group (p=0.004). Caudate volume was also lower in the long-COVID group (p=0.04). Conclusion(s): Conclusions. In the absence of olfactory discrimination problems, long-COVID, but not COVID, patients experience persistent olfactory loss and distortion. These perceptual problems are associated with lower olfactory bulb, primary olfactory cortex, and caudate volume, suggesting that the effects of SARS-CoV-2 infection can extend beyond the olfactory periphery in some cases to affect central targets. (Figure Presented).
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In today's era, digital documents are used in official proceedings and various commercial online platforms. With the advent of Digital locker facilities, a digital document is admissible in various Government and private sectors. Due to this, the misuse of digital documents is increasing and criminal activity has surged during COVID times. Digital documents can be easily manipulated with the help of image-processing software applications. Manipulations include forgeries and duplication in the documents, counterfeiting in currency notes, and alteration and tampering in government documents or personal documents have increased manifolds. In the present study, a preliminary attempt has been made to analysed digitally manipulated documents by using different software which includes JPEG snoop, Adobe Photoshop CS5, and forensically beta software. Apart from that, in this paper new methodology to authenticate manipulations in scanned documents is devised. The authors were able to detect manipulations in altered documents like mark sheets, COVID certificates, Id cards, driving licenses, and passports. The morphological attributes like changes in background colour, variations in pixel size, and specifically appearance of dots and layers were observed. The results obtained were conclusive and were able to detect the manipulations, as well as the date and time of alterations, were detected with help of Forensic beta software.Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
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Introduction: NIRS reduces intubation rate in COVID-19 pneumonia. Outcome is related to age, comorbidities, and baseline illness severity. Thoracic CT has prognostic value in COVID-19 pneumonia. Forced Oscillatory Technique (FOT) allows non-invasive assessment of respiratory reactance (Xrs) that is related to lung compliance. A pilot study showed FOT feasibility in patients with COVID-19 pneumonia receiving NIRS (1). Aim(s): Measuring Xrs in COVID19 patients receiving NIRS and correlate with CT. Method(s): The local ERB approved the study. 32 consecutive patients with moderate-severe COVID-19 ARDS were enrolled. Patients underwent non-invasive ventilation (NIV) alternated to High Flow Nasal Cannula (HFNC). In the first 24 hours of hospitalization triplicate FOT measurements were performed (Resmon ProFULL) according to current guidelines during HFNC. Within 1 week 28 patients underwent computed tomographic pulmonary angiography (CTPA) and collapsed, infiltrated and normally inflated areas were quantified (3D Slicer software). Result(s): 12 patients had altered Xrs-z score. Collapsed areas correlated with Xrs z-score (rho=0.37;p=0.046) and almost with inspiratory Xrs (rho=-0.36;p=0.055). Inflated areas correlated with inspiratory Xrs (rho=0.42;p=0.024) while infiltrated areas didn't. In our cohort CTPA and FOT parameters didn't discriminate outcomes but inflated areas were inversely related to hospitalization (rho=-0.43;p=0.04). Conclusion(s): FOT showed abnormal Xrs in a subset of patients. Xrs z-score is a noninvasive index of collapsed areas in COVID-19 pneumonia and could be useful in patients assessment and follow up.
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Aim/Introduction: Persisting cerebral symptoms such as fatigue and cognitive dysfunction after Sars-CoV-2 infection are typical for post COVID-19. Positron emission tomography (PET) can contribute to the understanding of post COVID-19 related brain disorders. The aim of this study was to investigate cerebral blood flow (CBF) and neuroinflammation with PET in post COVID-19 patients. Material(s) and Method(s): Data from eight healthy controls (HC) and four subjects with post COVID-19 symptoms were included. At the time of the PET investigation, three subjects had remaining post COVID-19 symptoms, of which one had severe headache. All subjects underwent a 6 min dynamic 15O-water PET scan to measure CBF and a 60 min dynamic 11CPK11195 PET scan to measure neuroinflammation. In addition, all subjects received a T1weighted MRI that was co-registered to the PET images. Parametric images, showing 15O-water CBF and 11CPK11195 binding potential (BPND) at the voxel level, were calculated. Mean total grey matter CBF and BPND values were calculated for all subjects. The co-registered MRI images were normalized to MNI standard space using statistical parametric mapping (SPM12) and the transformation matrices were applied to the respective parametric images. A voxel-wise z-test was performed in SPM12 to compare each 15Owater CBF and 11CPK11195 BPND image from the post COVID-19 patients to the HC CBF and BPND images, respectively. A statistical threshold of p<0.05 was applied. Result(s): Two of the subjects with remaining post COVID-19 symptoms demonstrated a significantly increased CBF in the whole brain compared to the HC. Total grey matter CBF values were 1.27 and 1.41 mL/cm3/min in these two subjects, compared to a mean +/- SD of 0.65 +/- 0.19 mL/cm3/min in the HC group. The subject with persisting headache also showed large clusters of significant increased 11C-PK11195 BPND in the meninges. Mean total grey matter 11CPK11195 BPND values in post COVID-19 subjects were within the range of values in the HC group. The other two subjects did not show increased CBF and no significant increase of 11C-PK11195 BPND. Conclusion(s): Neurological symptoms from post COVID-19 may be due to increased CBF and inflammation. However, further investigations are needed with larger study cohort to better understand the relation between post COVID-19 symptoms and neurological dysfunctions investigated with PET.
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Aim/Introduction: Persisting cerebral symptoms such as fatigue and cognitive dysfunction after Sars-CoV-2 infection are typical for post COVID-19. Positron emission tomography (PET) can contribute to the understanding of post COVID-19 related brain disorders. The aim of this study was to investigate cerebral blood flow (CBF) and neuroinflammation with PET in post COVID-19 patients. Material(s) and Method(s): Data from eight healthy controls (HC) and four subjects with post COVID-19 symptoms were included. At the time of the PET investigation, three subjects had remaining post COVID-19 symptoms, of which one had severe headache. All subjects underwent a 6 min dynamic 15O-water PET scan to measure CBF and a 60 min dynamic 11CPK11195 PET scan to measure neuroinflammation. In addition, all subjects received a T1weighted MRI that was co-registered to the PET images. Parametric images, showing 15O-water CBF and 11CPK11195 binding potential (BPND) at the voxel level, were calculated. Mean total grey matter CBF and BPND values were calculated for all subjects. The co-registered MRI images were normalized to MNI standard space using statistical parametric mapping (SPM12) and the transformation matrices were applied to the respective parametric images. A voxel-wise z-test was performed in SPM12 to compare each 15Owater CBF and 11CPK11195 BPND image from the post COVID-19 patients to the HC CBF and BPND images, respectively. A statistical threshold of p<0.05 was applied. Result(s): Two of the subjects with remaining post COVID-19 symptoms demonstrated a significantly increased CBF in the whole brain compared to the HC. Total grey matter CBF values were 1.27 and 1.41 mL/cm3/min in these two subjects, compared to a mean +/- SD of 0.65 +/- 0.19 mL/cm3/min in the HC group. The subject with persisting headache also showed large clusters of significant increased 11C-PK11195 BPND in the meninges. Mean total grey matter 11CPK11195 BPND values in post COVID-19 subjects were within the range of values in the HC group. The other two subjects did not show increased CBF and no significant increase of 11C-PK11195 BPND. Conclusion(s): Neurological symptoms from post COVID-19 may be due to increased CBF and inflammation. However, further investigations are needed with larger study cohort to better understand the relation between post COVID-19 symptoms and neurological dysfunctions investigated with PET.
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SESSION TITLE: Technological Innovations in Imaging SESSION TYPE: Original Investigations PRESENTED ON: 10/17/22 1:30 PM - 2:30 PM PURPOSE: Central airway stenosis (CAS) is an important cause of pulmonary morbidity and mortality. Current grading and classification systems include subjective qualitative components, with limited data on reproducibility. We propose a novel radiographic segmentation approach to more objectively quantify CAS. Inter-rater reliability of this novel outcome, which is used in an ongoing randomized controlled trial (NCT04996173), has not been previously assessed. METHOD(S): Computed tomography (CT) scans demonstrating tracheal stenoses were identified in the Vanderbilt University Medical Center Benign Tracheal Stenosis registry. CTs were analyzed in OsiriX (Geneva, Switzerland) after upload via a secured cloud transfer service. Four independent readers with variable experience in CT interpretation were chosen (one chest radiologist, one pulmonary fellow, two internal medicine residents). Readers identified the point of nadir airway lumen, measured 1.5 cm above and below that point, then manually segmented visible tracheal lumen area on the soft tissue window of each axial CT slice within that 3 cm length. Missing ROI's were then generated in-between manual segmented areas. The Repulsor function was used to manually adjust the boundaries of the ROI to achieve fit. Intraclass correlation (ICC) was used to calculate the inter-rater reliability of the tracheal lumen volume of between readers. Other data collection variables included the type of CT scan, axial slice interval, the suspected underlying cause of CAS, and average stenotic volume. RESULT(S): Fifty CT scans from 38 individual patients identified in the registry from 2011-2021 were randomly chosen for inclusion. Most (22 of 38, 57.9%) had iatrogenic BCAS (either post-intubation or post-tracheostomy) and 10 (26.3%) had idiopathic subglottic stenosis. Half of the scans (n=25, 50%) were contrasted neck CT and half were non contrasted chest CTs. Scan slice thickness ranged 1 to 5 mm, median 2 mm (1.25-2.875). The median stenotic volume across all readers was 3.375 cm3 (2.52-4.51). The average ICC across all four readers was 0.969 (95% CI 0.944 - 0.982). CONCLUSION(S): Our proposed volume rendering and segmentation approach to BCAS proves to have substantial precision and agreement amongst readers of different skill levels. CLINICAL IMPLICATIONS: A NOVEL METHOD TO ASSESS SEVERITY OF BENIGN CENTRAL AIRWAY STENOSIS DISCLOSURES: No relevant relationships by Leah Brown No relevant relationships by Alexander Gelbard no disclosure on file for Robert Lentz;PI ofan investigator-initiated study relationship with Medtronic Please note: >$100000 by Fabien Maldonado, value=Grant/Research Support PI on investigator-initiated relationship with Erbe Please note: $5001 - $20000 by Fabien Maldonado, value=Grant/Research Support Consulting relationship with Medtronic Please note: $5001 - $20000 by Fabien Maldonado, value=Honoraria co-I industry-sponsored trial relationship with Lung Therapeutics Please note: $5001 - $20000 by Fabien Maldonado, value=Grant/Research Support Board of director member relationship with AABIP Please note: $1-$1000 by Fabien Maldonado, value=Travel No relevant relationships by Khushbu Patel No relevant relationships by Ankush Ratwani Consultant relationship with Medtronic/Covidien Please note: $1001 - $5000 by Otis Rickman, value=Consulting fee No relevant relationships by Evan Schwartz Copyright © 2022 American College of Chest Physicians
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COVID-19 pandemic encourages the utilization of local food sources to ensure food availability. Busil (Xanthosoma sagittifolium) was readily available and affordable in Banjarnegara Regency in the Province of Central Java in Indonesia. However, the busil starch utilization was still rare due to the low functional properties of the native busil starch. The objective of this study was to explore busil starch physicochemical characterization enhancement after microwave irradiation treatment, especially on the stability of heat processing. This research was conducted in two steps. First, microwave treatment (with a variation of energy and irradiation time) of native busil starch (NBS), and the second was modified busil starch (MBS) physicochemical characterization. A rise in amylose was observed on MBS. SEM analysis was shown MBS granules are breakdown. Through viscosity, final viscosity, setback viscosity, peak time, and the pasting temperature of MBS generally were increased. Meanwhile, peak viscosity and breakdown viscosity of MBS was decreased. Thermal properties of MBS like onset (To), peak (Tp), and conclusion (Tc) temperatures were also increased. The degree of whiteness index (DW) of MBS was decreased. FTIR analysis has shown that microwave treatment did not cause functional group alteration. XRD analysis has also demonstrated no change in the diffraction pattern but a slight change in the crystallinity index. Generally, microwave treatment leads to MBS thermal stability and potentially broaden MBS utilization on food processing product.
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Background: Human SARS-CoV-2 infection can induce a wide spectrum of organ dysfunctions, including microvascular impairment [1]. S1 subunit of viral receptor-binding domain binds to the angiotensin-converting enzyme 2 receptor on endothelium and S2 subunit allows the virus to enter endothelial cells. The resulting breakdown of barrier integrity drives a cascade of infammatory and thrombotic events, that aggravate the course of COVID-19 together with other risk factors [2-4]. Up to date, a lower capillary density has been reported in several distinct body districts, using sublingual video microscopy, ocular optical coherence tomography angiography, skin functional laser Doppler perfusion imaging and nailfold videocapillaroscopy (NVC) [5-8]. NVC examination has been performed in adult COVID-19 patients, however, without a control group [8]. Objectives: To confrm the statistical signifcance of the reduction in capillary density per linear millimeter evaluated by NVC in comparison with primary Ray-naud's phenomenon (PRP) patients and control subjects (CNT) and to evaluate the impact of an aggressive therapy against COVID-19 on the sparing in the number of capillaries. Methods: Sixty-one COVID-19 survivors, thirty-one PRP patients and thirty CNT age and sex-matched underwent NVC analysis. Demographic and clinical data of COVID-19 survivors were collected with special regard to concomitant therapies, that included antivirals, antibiotics, anticoagulants and anti-infamma-tory/immunomodulant drugs (glucocorticoids, hydroxychloroquine, IL-6 receptor antagonist). COVID-19 survivors were divided in two subgroups according to the severity of the active infection: thirty-four survivors with past mild-moderate disease (either unneedy for oxygen supplementation or need for Venturi mask) and twenty-seven survivors with past severe disease (need for Continuous Positive Airways Pressure and/or mechanical ventilation). The same Rheumatologist performed NVC evaluations in all patients and controls, using an optical probe, equipped with a 200x magnifcation lens and connected to a picture analysis software (Videocap, DS Medica, Milan, Italy). Absolute capillary number per linear millimeter was counted. Results: COVID-19 survivors underwent NVC examination after a mean period of 126±53 days from the disease onset. Multivariate analysis showed differences in absolute capillary number per linear millimeter (p<0.001) after adjusting for age, sex, body mass index, comorbidities and concomitant drugs. The mean (± standard deviation) absolute nailfold capillary number per linear millimeter was signifcantly lower in severe (8.2±1.15) and mild-moderate (8.4±0.75) COVID-19 survivors than in both PRP (8.7±0.68) and CNT subjects (9.3±0.53) (p<0.001). The analysis of the impact of treatments on capillary density in the severe COVID-19 patients showed a positive trend (preservation of the capillary number) with antivirals (no: 7.8±1.53;yes: 8.5±0.64;p=0.35) and anti-IL-6 receptor antagonist administration (no: 7.8±1.36;yes: 8.6±0.74;p=0.16), while none of the other drugs was shown to be effective (glucocorticoids p = 0.46;antibiotics = 0.52;anticoagulants not evaluable as they were used in all COVID-19 patients). Conclusion: SARS-CoV-2 infection seems associated to a signifcant capillary loss as distinctive NVC feature and data concerning the comparison of capillary density pre COVID-19 and post COVID-19 are desirable to reinforce this observation. The positive trend in saving the number of capillaries induced by aggressive anti-infammatory therapies in COVID-19 survivors needs larger cohorts of patients.
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Objective: This study investigates the effects of COVID-19 on brain microstructure among those recently recovering from COVID-19 through self isolation. Background: Microstructural differences have previously been detected in comparisons of COVID-19 patients with controls, particularly in regions related to the olfactory system. The olfactory system is connected with the caudate, putamen, thalamus, precuneus, and cingulate regions. Design/Methods: Here we report diffusion magnetic resonance imaging (3 T Siemens MRI) findings from 40 patients (mean age: 43.7, 68% female) who self-isolated after testing positive for COVID (COV+), and 14 COVID negative (COV-) subjects (mean age: 43, 64% female) who had flu-like symptoms. This is part of the Canadian-based NeuroCOVID-19 study. Fractional anisotropy (FA), mean diffusivity (MD), mode of anisotropy (MO), free water fraction (F), tissue-specific FA (FAt) and tissue-specific MD (MDt) were obtained using data with b=700 and 1400 (DIPY free-water model). Regions of interest in the grey matter and white matter were delineated using FreeSurfer. Differences between groups were assessed using an analysis of variance (ANOVA), the Kruskal-Wallis Test and the Mann-Whitney Test, corrected for false-discovery rate of 0.05. Effect size (Cohen's d) was also computed (d>0.45 deemed large effect). Results: In the COV+ group, all three tests revealed decreased FA and FAt in the insula, and increased MD in the parstriangularis cortex. Increased FA and FAt in the cuneus (along with decreased MD) was also found. MD was reduced in COV+ in the temporal and supramarginal areas. MO was lower in COV+ in the insula and amygdala regions. Conclusions: In patients, higher MD with lower FA and MO suggest increased extracellular fluids, while lower MD with decreased FA and MO may suggest necrotic debris built up following inflammation. The cuneus and insula are involved in visual and taste processing, respectively. This study highlights the need to study neurological effects of COVID-19.
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Objective: This study investigates the chronic effects of COVID-19 on brain microstructure. Background: Microstructural differences have previously been detected in comparisons of COVID-19 patients with controls, particularly in the insula, cuneus, inferior temporal and anterior cingulate regions. Design/Methods: Here we report diffusion magnetic resonance imaging (3 T Siemens MRI) findings from 20 participants (mean age: 45.3, 55% female), both immediately after recovery and at a 3-month follow-up. Fractional anisotropy (FA), mean diffusivity (MD), mode of diffusivity (MO), free water fraction (F), tissue-specific FA (FAt) and tissue-specific MD (MDt) were obtained using DTI data with b=700 and 1400 (DIPY free-water model). Regions of interest in the grey matter and white matter were delineated using FreeSurfer. To assess differences between baseline and follow-up, a paired t-test, the Wilcoxon Test and Friedman Test were performed, corrected for false-discovery rate of 0.05. Effect size (Cohen's d) was also computed (d>0.45 deemed large effect). Results: All three tests revealed decreased F in the hippocampus and decreased MD in the parahippocampal region of the WM at follow-up. In the GM, F was increased in the medial orbitofrontal region. In the WM, MD was increased in the paracentral region and MDt was increased in the parahippocampal and lateral orbitofrontal regions. Conclusions: These results suggest that microstructural abnormalities persist following recovery. Increased extracellular fluid (i.e. F and MD) in the frontal lobe suggest spreading of COVID-19 impact, while decreased F and MD in the hippocampal region suggest debris accumulation as part of the inflammatory process. None of the regions affected in sub-acute COVID-19 appear to fully recover within three months.
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Background-Aim: While there's a wide literature on CT abnormalities in COVID-19 sequelae, the role of lung perfusion scintigraphy have been scarcely investigated. Recent findings reported lung microvascular and endothelial alterations in patients recovered from COVID-19 without pulmonary embolism, presenting persistent dyspnea (POST-COVID). We compared perfusion scintigraphy and CT findings of these patients with dyspneic subjects in whom lung scintigraphy excluded pulmonary embolism (NON-COVID). In POST-COVID patients, the correlation between lung perfusion scintigraphic findings and (1) CT abnormalities, and (2) clinical/ biochemical parameters were also assessed. Methods: 24 POST-COVID and 33 NON-COVID patients who underwent lung perfusion scintigraphy for dyspnea from March 2020 to December 2021 were retrospectively enrolled. High-resolution chest CT performed 15 days before/after lung perfusion scintigraphy were available in 15/24 POST-COVID and 15/33 NON-COVID patients. From scintigraphic images counting rates for upper, middle, and lower fields were calculated in order to compute their ratio with total lung counts (UTR, MTR, and LTR, respectively) for both right and left lungs (RL and LL, respectively). CT images were analyzed using a semi-automated segmentation algorithm of 3D Slicer ( http://www.slicer.org), obtaining total, infiltrated and blood vessels' volumes, in order to calculate the infiltration rate (IR) and vascular density (VD). White blood cells, platelets, PT, INR, PTT, fibrinogen, and D-dimer of 15/24 POST-COVID patients were also collected from blood tests performed before the lung perfusion scintigraphy. Results: POST-COVID patients with persistent dyspnea showed reduced LTR (RL 22.4% ± 6.6%;LL 24.7% ± 3.1%) and higher MTR (RL 55.2% ± 5.2%;LL 49.1% ± 3.3%) compared to non- COVID patients (RL-LTR 29.6% ± 6.0%, p<0.0001;LL-LTR 28.3% ± 4.6%, p = 0.001;RL-MTR 47.3% ± 4.2%, p<0.0001;LL-MTR 47.3% ± 3.0%, p = 0.036), while UTR resulted bilaterally superimposable between the two groups. Similar IR and VD values at CT imaging were documented bilaterally in both groups. In POSTCOVID patients, no significant correlations between lung perfusion scintigraphy and CT findings were observed. Correlation analysis indicated D-dimer levels as associated with UTR (Pearson's r = 0.664;p = 0.007) and MTR (Pearson's r = - 0.555;p = 0.032), while no parameter significantly associated with LTR was observed. Conclusions: Lung perfusion scintigraphy can reveal reduced perfusion rates of lower pulmonary fields in POST-COVID patients with persistent dyspnea in the absence of pulmonary embolism, independently from CT abnormalities, infection duration and coagulation biomarkers. Although mechanisms underlying these findings need to be supported by pathological lung tissue examination, lung nonthrombotic microvascular and endothelial dysfunction may be involved.
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Background-Aim: A potential link has been investigated between hyposmia after COVID-19 and an increased risk to develop neurological long-term sequelae also in patients who experienced mild or moderate disease. Hyposmia is a common feature PD and parkinsonism has been reported after COVID-19 suggesting a potential link between SARS-CoV2 infection and PD. [18F]FDG PET may represent a suitable tool to capture potential common metabolic signature of hyposmia after COVID-19 and in PD patients. We aimed to evaluate brain metabolic correlates of isolated persistent hyposmia after mild-to-moderate COVID-19 and to compare them with metabolic signature of hyposmia in drug-naive PD patients. Methods: Forty-four patients who experienced hyposmia after SARSCOV2 infection underwent brain [18F]FDG-PET in the first 6 months after recovery. Olfaction was assessed by means of the 16-item ''Sniffin-Sticks'' test and patients were classified as with or without persistent hyposmia (COVID-hyposmia and COVID-no-hyposmia respectively). Brain [18F]FDG-PET of post-COVID subgroups were compared in SPM12. COVID-hyposmia patients were also compared with eighty-two drug-naïve PD patients with hyposmia. Multiple-regression- analysis was used to identify correlations between olfactory test-scores and brain metabolism in patients' subgroups. Results: COVID-hyposmia patients (n = 21) exhibited significant hypometabolism in bilateral gyrus rectus and orbitofrontal cortex with respect to COVID-non-hyposmia (n = 23) (p<0.002) and in middle and superior temporal gyri, medial/middle frontal gyri and right insula with respect to PD-hyposmia (p<0.012). With respect to COVIDhyposmia, PD-hyposmia patients showed hypometabolism in inferior/ middle occipital gyri and cuneus bilaterally. Olfactory test-scores were directly correlated with metabolism in bilateral rectus and medial frontal gyri and in right middle temporal and anterior-cingulate gyri in COVID-hyposmia patients (p<0.006) and with bilateral cuneus/precuneus and left lateral occipital-cortex in PD-hyposmia patients (p<0.004). Conclusions: Metabolic signature of persistent hyposmia after COVID-19 encompasses cortical regions involved in olfactory perception and does not overlap metabolic correlates of hyposmia in PD. An impairment in olfactory judgement seem to underlie hyposmia in PD patients while a more restricted perception deficit seems to explain hyposmia in COVID-19. The potential long term neurological sequelae of COVID-19 are of interest from the clinical and economical point of view. Studies targeting symptoms common to COVID-19 and chronic neurological diseases and aiming to explore potential common pathways are of interest also to avoid unjustified claims about a future high incidence of neurodegenerative diseases secondary to the SARS-CoV-2 pandemic.
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Background: Angiotensin-converting enzyme 2, the target cellular receptor of SARS-CoV-2, is known to be present in adipose tissue. SARS-CoV-2 could enter the heart via the epicardium because the myocardium and the epicardium share the same microcirculation and are not separated from one another by a fascial layer. Previous studies demonstrated that macrophages play an important role in inflammation in adipose, including epicardial, tissue. In this study, we explore two hypotheses: a) there is no significant difference between the density of macrophages in the epicardium of patients who died with Covid-19 infection and those who died with non-Covid-19 acute lung injury, and b) the density of macrophages in the epicardium does not correlate with histological evidence of focal myocyte necrosis in patients who die of Covid-19 infection. Design: We compared the density of macrophages in the epicardium of 10 patients who died of complications of Covid-19 infection to that in a control group of 10 decedents with non-Covid related acute lung injury. Further, macrophage densities of those with and without histological evidence of focal myocardial damage were compared within the Covid-19 group. Three blocks were routinely sampled from each case (right ventricle, left ventricle and septum). All the sections were stained with CD68 as a macrophage marker. The density of CD68-positive cells in the epicardium was determined by counting the number of cells in five hot spot regions (each 3 mm2) at 100x. Quantification was performed using imageJ and is expressed as cells/mm2. The densities of CD68- positive macrophage were compared using T-test. The clinical characteristics between the groups were compared using Fischer exact test. P-value < 0.05 is significant. Results: The density of CD68-positive macrophages in the epicardium is significantly higher in Covid-19 patients compared to the control group. The CD68-macrophage count is also significantly higher in hearts of Covid-19 decedents with histological evidence of focal myocyte necrosis than those with no evidence of myocyte necrosis. There are no significant differences in other characteristics between the groups (Table, Figure). Conclusions: Contrary to our hypotheses, the density of CD68-positive macrophages is strongly correlated with Covid-19 infection and Covid-19 related myocyte necrosis. Further studies are needed to understand the pathophysiologic relationship between epicardial inflammation and myocyte necrosis.
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The purpose of the present study is to evaluate inter-operator and intra-operator variations in the manual segmentation of the hippocampus from high resolution T1-weighted magnetic resonance (MR) images. The hippocampus was segmented manually in MR images of 118 epileptic and 25 non-epileptic patients (65 males, 78 females;median age of 36 years, mean age of 39 years) by three operators (M1, M2, M3) and three automated methods (FreeSurfer, LocalInfo, ABSS). To determine how much the manual segmentations performed by one operator differ from those of another operator, inter-operator variability was evaluated. To determine how much the manual segmentations done by an operator vary over time, i.e., to assess intra-operator variability, manual segmentations from each operator were compared to the automated segmentations. To this end, rational absolute value degree (RAVD), volume asymmetry, Dice coefficient, precision, similarity, specificity, accuracy, negative predictive value (NPV), Hausdorff distance, root mean square (RMS), average symmetric surface distance (ASSD), and mean distance were calculated. The segmentation results of one of the operators were considered as the ground truth for the evaluation of the segmentation results of the other operators and the automatic segmentation methods. Hausdorff distance and precision were different when using automated techniques as the test segmentation and the M3 segmentation as the ground truth, rather than M1 and M2 segmentations. The standard deviation of performance measures tended to be higher when using operator M3 as the ground truth and either operator M1 or M2 as the test segmentation. Variation in performance measures when using M3 as the ground truth is indicative of inter-operator variation. When comparing performance measures generated by automated versus manual techniques, standard deviations were larger when using operator M2 as the ground truth than when using operator M1. This suggests that operator M2 exhibited a larger intra-operator variation than operator M1. Among the automatic segmentation methods, ABSS was the most effective method in many regards (RAVD Dice coefficient, similarity, specificity, accuracy, NPV, Hausdorff distance, RMS, ASSD) while FreeSurfer and LocalInfo were more effective for the precision, mean distance, and lateralization of epileptogenicity. Inter-operator error was likely due to the temporal separation of the segmentations and thus, it may be reduced by having all operators working in the laboratory simultaneously and undergoing the same training, although some inter-operator variability may be unavoidable. Intra-operator variation can likely be reduced with further training and supervision of the operators by a neuroradiologist with expertise in hippocampus anatomy. Future automated segmentation techniques may incorporate elements of both atlas-based (FreeSurfer and LocalInfo) and neural-network-based (ABSS) segmentation techniques for optimal performance.
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Background. Lymphovascular invasion (LVI) and breast tumor emboli within dermal and breast lymphatic vessels are prognostic for metastatic spread and poor outcomes, and are abundant in Inflammatory breast cancer (IBC). IBC is an aggressive breast cancer that presents suddenly with breast swelling and redness due to tumor emboli in lymphatics. Lack of breast-feeding and obesity are IBC risk factors. We sought to demonstrate the combinatorial effects of a high-fat diet and nursing on lymphatic function and compare these to IBC tumor induced changes in lymphatic function. We hypothesize that risk factors for aggressive breast cancer may alter lymphatic function in the normal gland prior to tumor initiation. Methods. Following two rounds of pregnancy in 20 multiparous SCID Beige immunocompromised mice, half of the mice were force weaned while half nursed pups. Prior to forced weaning, half of each of these groups were fed a high fat diet (HFD: 60 Kcal %, N = 10) while the other half received a low-fat diet (LFD: 10 Kcal %, N = 10). Consecutive dynamic near-infrared fluorescence (NIRF) lymphatic imaging was performed at 6-7 months (covid interruption) and 14 months after initiating the diet by injecting a near-IR fluorophore into the mammary fatpad and recording lymphatic pulsing over 8 minutes using V++. Matlab and ImageJ were used to quantify pulsing rates on the ventral lymphatics in each animal. Fatpads were Ssubsequently inoculated with SUM149 IBC cells and imaging was repeated 16 months post diet initiation. Lymphatic imaging over time by HFD vs LFD was further studied in nulliparous animals. Tissues were collected for further analyses. ResultsData analysis prior to tumor injection, demonstrated lymphatic pulsing (pulses/4 minutes) increased over time in HFD force weaned (HFFW) and HFD nursing (HFN) animals only (65.5 vs 72.6, P=0.059;60.1 vs 76.6, P=0.0099, respectively). Comparing HFFW and HFN to matched LFD groups (LFFW and LFN), at 14 weeks HFD was associated with increased pumping after forced weaning (62.3 vs. 72.6, P=0.074), and nursing (62.5 vs 76.6, P=0.0023). There was an increase in pulsing after tumor initiation (16 months after initiation of diet) in all groups (80.1, 84.1, 83.2, 82.4, P > 0.05 all comparisons to initial timepoint). In a separate experiment examining HFD (N=5) vs LFD (N=5) in nulliparous mice, lymphatic contractile activity increased in all animals over. time, average ventral lymphatic contractile frequency for LFD and HFD at week 8, 11 and 14 weeks after diet initiation were 5, 8.64, 15.9 pumps/4 mins vs 11.8, 18.5, 28.2 pumps/4 mins, (P = 0.01, 0.05, and 0.0005 respectively). ConclusionsHFD increased lymphatic pulsing rate over time to a significantly greater extent than LFD continuing over 14 months independent of reproductive and nursing status. Tumor initiation prompted further increased pulsing rates beyond that observed after HFD across all groups. The magnitude of the effect of HFD on lymphatic pulsing approached the rate after tumor initiation, while reproductive variables did not impact lymphatic pulsing. Further studies are warranted to demonstrate the relationship if any between lymphatic pumping pre-initiation and LVI after tumor initiation and examine the role of intervention on reducing LVI.
ABSTRACT
Purpose: Altered bone turnover is a factor in many diseases including osteoarthritis (OA), osteoporosis, inflammation, and viral infection. The absence of obvious symptoms and insufficiently sensitive biomarkers in the early stages of bone loss limits early diagnosis and treatment. Therefore, it is urgent to identify novel, more sensitive, and easy-to-detect biomarkers which can be used in the diagnosis and prognosis of bone health. Our previous data using standard micro-computed tomography (μCT) measurements showed that SARS-CoV-2 infection in mice significantly decreased trabecular bone volume at the lumbar spine, suggesting that decreased bone mass, increased fracture risk, and OA may be underappreciated long-haul comorbidities for COVID patients. In this study, we applied integrated state-of-the-art radiomics and machine learning models to identify more sensitive image-based biomarkers of SARS-CoV-2-induced bone loss from μCT images. These radiomic biomarkers can potentially provide a non-invasive way of quantifying and monitoring systemic bone loss and evaluating treatment efficacy in both research and clinical practices. Methods: All animal use was performed with approval of the Institutional Animal Care and Use Committee. To quantify SARS-CoV-2-induced bone loss, 6-week-old transgenic mice (16 male, 16 female) expressing humanized ACE2 receptors were inoculated with a 2020 strain of SARS-CoV-2 or phosphate-buffered saline (Control) [Fig. A]. Viral infection was confirmed by detection of infectious SARS-CoV-2 in throat swabs and histological identification of SARS-CoV-2 labeled cells. At 6-14 days post-infection, lumbar vertebral bodies (L5) were scanned with μCT (μCT 35, SCANCO Medical AG;6 μm nominal voxel size). The open-source research platform 3D Slicer v2020 with a built-in Python console v3.8 was used for medical image computing and fully automated segmentation of cortical and trabecular bone. Standard μCT assessment of bone microstructure was performed. Radiomic feature extraction and data processing were performed using python based PyRadiomics v3.0.1. A total of 120 radiographic features were extracted from the segmented images [Fig. B]. Principle component analysis (PCA) for feature selection, a support vector machine learning (SVML) predictive model for classification, holdback method for model validation, and all statistical analyses (significance at p<0.05) were performed using JMP Pro v15 (SAS). Results: Using standard μCT methods, SARS-CoV-2 infection significantly reduced the bone volume fraction (BV/TV) by 10 and 10.5% (p= 0.04) and trabecular thickness (Tb.Th) by 8 and 9% (p= 0.02) in male and female mice, respectively, compared to PBS control mice [Fig. C]. Radiomics detected a 20-fold greater magnitude in change over standard methods. SARS-CoV-2 infection significantly changed radiographic parameters with the largest change being a 300% increase in the second-order parameter: cluster shade [Fig. D]. The 45 radiomic features comprising the first 3 principal components were selected for inclusion in the SVML model. The SVML Model (radial basis function kernel;cost = 4.8;gamma = 0.46) produced an area under the receiver operating characteristic curve (AUC) of 1.0 which reflects a perfectly accurate test [Fig. E]. Conclusions: SARS-CoV-2 infection of humanized ACE2 expressing mice caused significant bone changes, suggesting that decreased bone mass, increased fracture risk, OA, and other musculoskeletal complications could be long-term comorbidities for people infected with COVID-19. We developed an open-source, fully automated segmentation and radiomics system to assess systemic bone loss using μCT images. When coupled with machine learning, this system was able to identify novel radiographic biomarkers of bone loss that better discriminate differences in bone microstructure between SARS-CoV-2 infected and control mice than standard bone morphometric indices. The high accuracy of the SVML model in classifying SARS-CoV-2 infected mice opens the possibility of translating these biom rkers to the clinical setting for early detection of skeletal changes associated with long-haul COVID. The methods presented here were demonstrated using SARS-CoV-2 as a model system and can also be adapted to other diseases associated with altered bone turnover. Development of machine-learning methods for radiomic applications is a crucial step toward clinically relevant radiomic biomarkers of bone health and provides a non-invasive way of quantifying and monitoring systemic bone loss and evaluating treatment efficacy. [Formula presented]
ABSTRACT
Introduction & Objectives: Imparting the required psychomotor skills for trainees to become proficient in Percutaneous Nephrolithotomy (PCNL) and Retrograde Intrarenal Surgery (RIRS) is tricky for surgical educators, due to the challenging nature of the procedures and the lack of realistic simulators. The current COVID-19 pandemic has compounded these issues by reducing learning opportunities for trainees through reduced case numbers and availability of surgical skills courses. To address these contemporaneous issues, we have developed 3D printed inexpensive combined RIRS and PCNL training models for both in-person and video conference skills training. Materials & Methods: Anonymised Computed Tomography data was used to develop the training model, using medical image processing software (3D Slicer, version 4.12, Harvard, USA). The model was 3D printed using flesh-coloured resin which best approximated the appearance of the collecting system during ureteroscopy. The face validity of the simulator was assessed by surgical educators for its suitability for both in-person and remote training. Results: The RIRS and PCNL training model was evaluated by expert Urologists involved in the national training of the procedures and found to be more realistic and affordable when compared to available alternatives. The 3D printed model was developed for under €3, allowing multiple identical copies to be 3D printed for both in-person courses and scheduled video conferencing workshops with the models distributed to each participating centre beforehand. This “hub and spoke” method of surgical skills training is greatly facilitated by the affordability of the 3D printed models. Conclusions: We have developed an inexpensive combined RIRS and PCNL training model for both in-person and remote training at USANZ and other international training courses. 3D printed simulators have great future potential in the training of endourological and other urological procedures, enhancing connectivity and facilitating the decentralisation of training courses for the acquisition of key surgical skills.
ABSTRACT
Introduction: Content Based Image Retrieval (CBIR) system is an innovative technology to retrieve images from various media types. One of the CBIR applications is Content Based Medical Image Retrieval (CBMIR). The image retrieval system retrieves the most similar images from the historical cases, and such systems can only support the physician's decision to diagnose a disease. To extract the useful features from the query image for linking similar types of images is the major challenge in the CBIR domain. The Convolution Neural Network (CNN) can overcome the drawbacks of traditional algorithms, dependent on the low-level feature extraction technique. Objective: The objective of the study is to develop a CNN model with a minimum number of convolution layers and to get the maximum possible accuracy for the CBMIR system. The minimum number of convolution layers reduces the number of mathematical operations and the time for the model's training. It also reduces the number of training parameters, like weights and bias. Thus, it reduces the memory requirement for the model storage. This work mainly focused on developing an optimized CNN model for the CBMIR system. Such systems can only support the physicians' decision to diagnose a disease from the images and retrieve the relevant cases to help the doctor decide the precise treatment. Methods: The deep learning-based model is proposed in this paper. The experiment is done with several convolution layers and various optimizers to get the maximum accuracy with a minimum number of convolution layers. Thus, the ten-layer CNN model is developed from scratch and used to derive the training and testing images' features and classify the test image. Once the image class is identified, the most relevant images are determined based on the Euclidean distance between the query features and database features of the identified class. Based on this distance, the most relevant images are displayed from the respective class of images. The general dataset CIFAR10, which has 60,000 images of 10 different classes, and the medical dataset IRMA, which has 2508 images of 9 various classes, have been used to analyze the proposed method. The proposed model is also applied for the medical x-ray image dataset of chest disease and compared with the other pre-trained models. Results: The accuracy and the average precision rate are the measurement parameters utilized to compare the proposed model with different machine learning techniques. The accuracy of the proposed model for the CIFAR10 dataset is 93.9%, which is better than the state-of-the-art methods. After the success for the general dataset, the model is also tested for the medical dataset. For the x-ray images of the IRMA dataset, it is 86.53%, which is better than the different pre-trained model results. The model is also tested for the other x-ray dataset, which is utilized to identify chest-related disease. The average precision rate for such a dataset is 97.25%. Also, the proposed model fulfills the major challenge of the semantic gap. The semantic gap of the proposed model for the chest disease dataset is 2.75%, and for the IRMA dataset, it is 13.47%. Also, only ten convolution layers are utilized in the proposed model, which is very small in number compared to the other pre-trained models. Conclusion: The proposed technique shows remarkable improvement in performance metrics over CNN-based state-of-the-art methods. It also offers a significant improvement in performance metrics over different pre-trained models for the two different medical x-ray image datasets.
ABSTRACT
Background: A high prevalence of depression, anxiety, insomnia and PTSD has been reported in COVID-19 survivors [1]. This is similar to what previously observed in other Coronavirus-related diseases such as SARS and MERS [2]. The pathophysiology of post-infection neuropsychiatric symptoms is likely to be multifactorial, with a role played by inflammatory and immunological factors [3], but it is still largely unknown;we thus investigated COVID-19 survivors via 3T MRI imaging to identify neural underpinnings of post-infection neuropsychiatric symptoms in order to further elucidate their complex pathophysiology. Methods: Covid-19 survivors were recruited during an ongoing prospective cohort study at IRCCS San Raffaele Hospital in Milan;psychopathology was initially measured via several self-report questionnaires (Impact of Events Scale-Revised (IES-R), Zung Self-Rating Depression Scale (ZSDS), 13-item Beck's Depression Inventory (BDI));subsequently patients (n=28) underwent 3T MRI scanning (Philips 3T Ingenia CX scanner with 32-channel sensitivity encoding SENSE head coil). T1 weighted images were processed using Computational Anatomy Toolbox (CAT12) for Statistical Parametric Mapping 12 (SPM12) in Matlab R2016b;segmentation into Gray Matter, White Matter and cerebrospinal fluid, bias regularization, non-linear modulation and normalization to MNI space were performed;measures of Total Intracranial Volume (TIV) were obtained and images were smoothed with an 8-mm full width at half maximum Gaussian filter. Multiple regressions were performed using SPM12 software package: with no a priori regions of interest selected, whole-brain gray matter volumes were used as dependent variables, psychometric scales scores as independent variables, and age, sex and TIV as nuisance covariates. Results: After VBM regression analysis covarying for age, sex and TIV, ZSDS Index scores were inversely correlated with gray matter volume in the Bilateral Anterior Cingulate Cortex (MNI 2, 24, 28, cluster level pFWE = 0.045, k=767);furthermore 3 cluster were identified comprising again the anterior cingulate cortex and the insular cortex bilaterally in which IES-R scores were inversely correlated with gray matter volumes (Cluster 1: MNI -30, 9, 3, cluster level pFWE = 0.005, k=1284;Cluster 2: MNI 36, -3, -3, cluster level pFWE = 0.037, k=773;Cluster 3: MNI 9, 30, 28, cluster level pFWE = 0.038, k=766). No other statistical significant result was found. Conclusions: Our study identified an inverse correlation between anterior cingulate cortex volumes and depressive symptomatology, measured via ZSDS, and between bilateral insulae and anterior cingulate cortex volumes and the degree of distress in response to the traumatic event, measured via the IES-R. Analogous findings have already been reported in patients with Major depression [4] and PTSD [5], and our study confirms the role of volumetric reductions of these brain regions in depressive and post-traumatic symptomatology. Given the nature of our study it is not possible to infer whether the reduction of gray matter volume is a consequence of the Covid-19 infection itself or, as it appears more likely, precede the infection acting as predisposing factor for the subsequent development of depressive and post-traumatic symptomatology. No conflict of interest