Serum GFAP levels correlate with astrocyte reactivity, post-mortem brain atrophy and neurofibrillary tangles.

Glial fibrillary acidic protein (GFAP), a proxy of astrocyte reactivity, has been proposed as biomarker of Alzheimer's disease. However, there is limited information about the correlation between blood biomarkers and post-mortem neuropathology. In a single-centre prospective clinicopathological cohort of 139 dementia patients, for which the time-frame between GFAP level determination and neuropathological assessment was exceptionally short (on average 139 days), we analysed this biomarker, measured at three time points, in relation to proxies of disease progression such as cognitive decline and brain weight. Most importantly, we investigated the use of blood GFAP to detect the neuropathological hallmarks of Alzheimer's disease, while accounting for potential influences of the most frequent brain co-pathologies. The main findings demonstrated an association between serum GFAP level and post-mortem tau pathology (ß = 12.85; P < 0.001) that was independent of amyloid deposits (ß = 13.23; P = 0.02). A mediation analysis provided additional support for the role of astrocytic activation as a link between amyloid and tau pathology in Alzheimer's disease. Furthermore, a negative correlation was observed between pre-mortem serum GFAP and brain weight at post-mortem (r = -0.35; P < 0.001). This finding, together with evidence of a negative correlation with cognitive assessments (r = -0.27; P = 0.005), supports the role of GFAP as a biomarker for disease monitoring, even in the late phases of Alzheimer's disease. Moreover, the diagnostic performance of GFAP in advanced dementia patients was explored, and its discriminative power (area under the receiver operator characteristic curve at baseline = 0.91) in differentiating neuropathologically-confirmed Alzheimer's disease dementias from non-Alzheimer's disease dementias was determined, despite the challenging scenario of advanced age and frequent co-pathologies in these patients. Independently of Alzheimer's disease, serum GFAP levels were shown to be associated with two other pathologies targeting the temporal lobes-hippocampal sclerosis (ß = 3.64; P = 0.03) and argyrophilic grain disease (ß = -6.11; P = 0.02). Finally, serum GFAP levels were revealed to be correlated with astrocyte reactivity, using the brain GFAP-immunostained area as a proxy (ρ = 0.21; P = 0.02). Our results contribute to increasing evidence suggesting a role for blood GFAP as an Alzheimer's disease biomarker, and the findings offer mechanistic insights into the relationship between blood GFAP and Alzheimer's disease neuropathology, highlighting its ties with tau burden. Moreover, the data highlighting an independent association between serum GFAP levels and other neuropathological lesions provide information for clinicians to consider when interpreting test results. The longitudinal design and correlation with post-mortem data reinforce the robustness of our findings. However, studies correlating blood biomarkers and neuropathological assessments are still scant, and further research is needed to replicate and validate these results in diverse populations.

Development and validation of an image biomarker to identify metabolic dysfunction associated steatohepatitis: MR-MASH score.

BACKGROUND AND AIMS: Diagnosis of metabolic dysfunction-associated steatohepatitis (MASH) requires histology. In this study, a magnetic resonance imaging (MRI) score was developed and validated to identify MASH in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). Secondarily, a screening strategy for MASH diagnosis was investigated. METHODS: This prospective multicentre study included 317 patients with biopsy-proven MASLD and contemporaneous MRI. The discovery cohort (Spain, Portugal) included 194 patients. NAFLD activity score (NAS) and fibrosis were assessed with the NASH-CRN histologic system. MASH was defined by the presence of steatosis, lobular inflammation, and ballooning, with NAS ≥4 with or without fibrosis. An MRI-based composite biomarker of Proton Density Fat Fraction and waist circumference (MR-MASH score) was developed. Findings were afterwards validated in an independent cohort (United States, Spain) with different MRI protocols. RESULTS: In the derivation cohort, 51% (n = 99) had MASH. The MR-MASH score identified MASH with an AUC = .88 (95% CI .83-.93) and strongly correlated with NAS (r = .69). The MRI score lower cut-off corresponded to 88% sensitivity with 86% NPV, while the upper cut-off corresponded to 92% specificity with 87% PPV. MR-MASH was validated with an AUC = .86 (95% CI .77-.92), 91% sensitivity (lower cut-off) and 87% specificity (upper cut-off). A two-step screening strategy with sequential MR-MASH examination performed in patients with indeterminate-high FIB-4 or transient elastography showed an 83-84% PPV to identify MASH. The AUC of MR-MASH was significantly higher than that of the FAST score (p < .001). CONCLUSIONS: The MR-MASH score has clinical utility in the identification and management of patients with MASH at risk of progression.

Imaging biomarkers and radiomics in pediatric oncology: a view from the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project.

This review paper presents the practical development of imaging biomarkers in the scope of the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project, as a noninvasive and reliable way to improve the diagnosis and prognosis in pediatric oncology. The PRIMAGE project is a European multi-center research initiative that focuses on developing medical imaging-derived artificial intelligence (AI) solutions designed to enhance overall management and decision-making for two types of pediatric cancer: neuroblastoma and diffuse intrinsic pontine glioma. To allow this, the PRIMAGE project has created an open-cloud platform that combines imaging, clinical, and molecular data together with AI models developed from this data, creating a comprehensive decision support environment for clinicians managing patients with these two cancers. In order to achieve this, a standardized data processing and analysis workflow was implemented to generate robust and reliable predictions for different clinical endpoints. Magnetic resonance (MR) image harmonization and registration was performed as part of the workflow. Subsequently, an automated tool for the detection and segmentation of tumors was trained and internally validated. The Dice similarity coefficient obtained for the independent validation dataset was 0.997, indicating compatibility with the manual segmentation variability. Following this, radiomics and deep features were extracted and correlated with clinical endpoints. Finally, reproducible and relevant imaging quantitative features were integrated with clinical and molecular data to enrich both the predictive models and a set of visual analytics tools, making the PRIMAGE platform a complete clinical decision aid system. In order to ensure the advancement of research in this field and to foster engagement with the wider research community, the PRIMAGE data repository and platform are currently being integrated into the European Federation for Cancer Images (EUCAIM), which is the largest European cancer imaging research infrastructure created to date.

Clinical and serological characteristics of patients allergic to LTP.

BACKGROUND: Allergy to lipid transfer proteins (LPT) is common in Mediterranean Europe, and it causes severe reactions in patients and affects multiple foods, impairing the quality of life. OBJECTIVE: This study aimed to describe the clinical and sensitization profile of patients with LTP syndrome and to determine a clinical pattern of severity. Molecular diagnosis is shown in a broad population through microarrays. MATERIAL AND METHODS: This study was performed at the LTP Allergy Consultation of the Reina Sofia Hospital in Murcia, Spain. We analyzed the patients' characteristics, reactions, cofactors, food implicated, quality of life, skin prick test to food and aeroallergens, and serologic parameters, such as total immunoglobulin E, peach LTP (Pru p 3 IgE) and immunoglobulin G4, and microarray Immuno Solid-phase Allergen Chip (ISAC). We related the severity of the reactions with other variables. RESULTS: We presented a series of 236 patients diagnosed with LTP allergy, 54.66% suffering from anaphylaxis, 36.02% from urticaria angioedema, and 9.32% from oral allergy syndrome. The most frequently implicated food was peach, producing symptoms in 70% of patients, followed by walnut in 55%, peanut in 45%, hazelnut in 44%, and apple in 38% patients. Regarding the food that provoked anaphylaxis, walnut was the most frequent instigator, along with peach, peanut, hazelnut, almond, sunflower seed, and apple. According to the severity of LPT reaction, we did not discover significant differences in gender, age, food group involved, and serologic parameters. We found differences in the presence of cofactors, with 48.84% of cofactors in patients with anaphylaxis, compared to 27.1% in patients without anaphylaxis and in family allergy background (P < 0.0001). CONCLUSION: In our series of patients, 54% presented anaphylaxis, and the foods that most frequently produced symptoms were peaches, apples, and nuts. Cofactors and family allergy backgrounds were associated with the severity of LPT reaction.

Non-invasive biomarkers for mild cognitive impairment and Alzheimer's disease.

Alzheimer's disease is the most common type of dementia in the elderly. It is a progressive degenerative disorder that may begin to develop up to 15 years before clinical symptoms appear. The identification of early biomarkers is crucial to enable a prompt diagnosis and to start effective interventions. In this work, we conducted a metabolomic study using proton Nuclear Magnetic Resonance (1H NMR) spectroscopy in serum samples from patients with neuropathologically confirmed Alzheimer's disease (AD, n = 51), mild cognitive impairment (MCI, n = 27), and cognitively healthy controls (HC, n = 50) to search for metabolites that could be used as biomarkers. Patients and controls underwent yearly clinical follow-ups for up to six years. MCI group included samples from three subgroups of subjects with different disease progression rates. The first subgroup included subjects that remained clinically stable at the MCI stage during the period of study (stable MCI, S-MCI, n = 9). The second subgroup accounted for subjects which were diagnosed with MCI at the moment of blood extraction, but progressed to clinical dementia in subsequent years (MCI-to-dementia, MCI-D, n = 14). The last subgroup was composed of subjects that had been diagnosed as dementia for the first time at the moment of sample collection (incipient dementia, Incp-D, n = 4). Partial Least Square Discriminant Analysis (PLS-DA) models were developed. Three models were obtained, one to discriminate between AD and HC samples with high sensitivity (93.75%) and specificity (94.75%), another model to discriminate between AD and MCI samples (100% sensitivity and 82.35% specificity), and a last model to discriminate HC and MCI with lower sensitivity and specificity (67% and 50%). Differences within the MCI group were further studied in an attempt to determine those MCI subjects that could develop AD-type dementia in the future. The relative concentration of metabolites, and metabolic pathways were studied. Alterations in the pathways of alanine, aspartate and glutamate metabolism, pantothenate and CoA biosynthesis, and beta-alanine metabolism, were found when HC and MCI- D patients were compared. In contrast, no pathway was found disturbed in the comparison of S-MCI with HC groups. These results highlight the potential of 1H NMR metabolomics to support the diagnosis of dementia in a less invasive way, and set a starting point for the study of potential biomarkers to identify MCI or HC subjects at risk of developing AD in the future.

Automated prostate multi-regional segmentation in magnetic resonance using fully convolutional neural networks.

OBJECTIVE: Automatic MR imaging segmentation of the prostate provides relevant clinical benefits for prostate cancer evaluation such as calculation of automated PSA density and other critical imaging biomarkers. Further, automated T2-weighted image segmentation of central-transition zone (CZ-TZ), peripheral zone (PZ), and seminal vesicle (SV) can help to evaluate clinically significant cancer following the PI-RADS v2.1 guidelines. Therefore, the main objective of this work was to develop a robust and reproducible CNN-based automatic prostate multi-regional segmentation model using an intercontinental cohort of prostate MRI. METHODS: A heterogeneous database of 243 T2-weighted prostate studies from 7 countries and 10 machines of 3 different vendors, with the CZ-TZ, PZ, and SV regions manually delineated by two experienced radiologists (ground truth), was used to train (n = 123) and test (n = 120) a U-Net-based model with deep supervision using a cyclical learning rate. The performance of the model was evaluated by means of dice similarity coefficient (DSC), among others. Segmentation results with a DSC above 0.7 were considered accurate. RESULTS: The proposed method obtained a DSC of 0.88 ± 0.01, 0.85 ± 0.02, 0.72 ± 0.02, and 0.72 ± 0.02 for the prostate gland, CZ-TZ, PZ, and SV respectively in the 120 studies of the test set when comparing the predicted segmentations with the ground truth. No statistically significant differences were found in the results obtained between manufacturers or continents. CONCLUSION: Prostate multi-regional T2-weighted MR images automatic segmentation can be accurately achieved by U-Net like CNN, generalizable in a highly variable clinical environment with different equipment, acquisition configurations, and population. KEY POINTS: • Deep learning techniques allows the accurate segmentation of the prostate in three different regions on MR T2w images. • Multi-centric database proved the generalization of the CNN model on different institutions across different continents. • CNN models can be used to aid on the diagnosis and follow-up of patients with prostate cancer.

Association between Clinical Frailty Scale (CFS) and clinical presentation and outcomes in older inpatients with COVID-19.

BACKGROUND: Frailty is a physiological condition characterized by a decreased reserve to stressors. In patients with COVID-19, frailty is a risk factor for in-hospital mortality. The aim of this study was to assess the relationship between clinical presentation, analytical and radiological parameters at admission, and clinical outcomes according to frailty, as defined by the Clinical Frailty Scale (CFS), in old people hospitalized with COVID-19. MATERIALS AND METHODS: This retrospective cohort study included people aged 65 years and older and admitted with community-acquired COVID-19 from 3 March 2020 to 31 April 2021. Patients were categorized using the CFS. Primary outcomes were symptoms of COVID-19 prior to admission, mortality, readmission, admission in intensive care unit (ICU), and need for invasive mechanical ventilation. Analysis of clinical symptoms, clinical outcomes, and CFS was performed using multivariable logistic regression, and results were expressed as odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Of the 785 included patients, 326 (41.5%, 95% CI 38.1%-45.0%) were defined as frail (CFS ≥ 5 points): 208 (26.5%, 95% CI 23.5%-29.7%) presented mild-moderate frailty (CFS 5-6 points) and 118 (15.0%, 95% CI 12.7%-17.7%), severe frailty (7-9 points). After adjusting for epidemiological variables (age, gender, residence in a nursing home, and Charlson comorbidity index), frail patients were significantly less likely to present dry cough (OR 0.58, 95% CI 0.40-0.83), myalgia-arthralgia (OR 0.46, 95% CI 0.29-0.75), and anosmia-dysgeusia (OR 0.46, 95% CI 0.23-0.94). Confusion was more common in severely frail patients (OR 3.14; 95% CI 1.64-5.97). After adjusting for epidemiological variables, the risk of in-hospital mortality was higher in frail patients (OR 2.79, 95% CI 1.79-4.25), including both those with mild-moderate frailty (OR 1.98, 95% CI 1.23-3.19) and severe frailty (OR 5.44, 95% CI 3.14-9.42). Readmission was higher in frail patients (OR 2.11, 95% CI 1.07-4.16), but only in mild-moderate frailty (OR 2.35, 95% CI 1.17-4.75).. CONCLUSION: Frail patients presented atypical symptoms (less dry cough, myalgia-arthralgia, and anosmia-dysgeusia, and more confusion). Frailty was an independent predictor for death, regardless of severity, and mild-moderate frailty was associated with readmission.

Evolution of laboratory parameters in patients with lipid transfer protein syndrome after 3 years of immunotherapy.

Lipid transfer protein (LTP) syndrome is an increasingly prevailing disease, especially in the young population, with severely affected quality of life. Since 2013, a specific treatment, called sublingual immunotherapy (SLIT), with peach extract (SLIT-peach®) has been used, but with no long-term effectiveness studies. The main objective of the present study was to assess the long-term effectiveness of SLIT-peach® and to relate the clinical evolution of patients. This was an ambispective study conducted for 3 years. A total of 25 patients with LTP syndrome were selected and treated with SLIT-peach®. They underwent a provocation test in the first year with reintroduced foods that had produced symptoms in the past. Analytical determination of specific immunoglobulin E (IgE) and immunoglobulin G4 (IgG4) to peach (Pru p 3) was performed at the beginning of treatment, at the first year of initiation, and at the end of treatment. These data were compared with the control group comprising 14 patients with LTP syndrome without treatment. A statistically significant decrease in specific IgE to Pru p 3 at the end of the treatment and an increase in specific IgG4 to Pru p 3 1 year after treatment initiation were observed in the active group in relation to tolerance to foods with LTPs. These results indicate that food tolerance begins after the first year and is maintained after the end of 3 years of treatment. In conclusion, treatment with SLIT-peach® for 3 years is effective for patients with LTP syndrome, preventing the evolution of the disease, allowing patients to restart a diet with plant foods, and improving their quality of life.

Digital tomosynthesis improves chest radiograph accuracy and reduces microbiological false negatives in COVID-19 diagnosis.

PURPOSE: Diagnosing pneumonia by radiograph is improvable. We aimed (a) to compare radiograph and digital thoracic tomosynthesis (DTT) performances and agreement for COVID-19 pneumonia diagnosis, and (b) to assess the DTT ability for COVID-19 diagnosis when polymerase chain reaction (PCR) and radiograph are negative. METHODS: Two emergency radiologists with 11 (ER1) and 14 experience-years (ER2) retrospectively evaluated radiograph and DTT images acquired simultaneously in consecutively clinically suspected COVID-19 pneumonia patients in March 2020-January 2021. Considering PCR and/or serology as reference standard, DTT and radiograph diagnostic performance and interobserver agreement, and DTT contributions in unequivocal, equivocal, and absent radiograph opacities were analysed by the area under the curve (AUC), Cohen's Kappa, Mc-Nemar's and Wilcoxon tests. RESULTS: We recruited 480 patients (49 ± 15 years, 277 female). DTT increased ER1 (from 0.76, CI95% 0.7-0.8 to 0.79, CI95% 0.7-0.8; P=.04) and ER2 (from 0.77 CI95% 0.7-0.8 to 0.80 CI95% 0.8-0.8, P=.02) radiograph-AUCs, sensitivity, specificity, predictive values, and positive likelihood ratio. In false negative microbiological cases, DTT suggested COVID-19 pneumonia in 13% (4/30; P=.052, ER1) and 20% (6/30; P=.020, ER2) more than radiograph. DTT showed new or larger opacities in 33-47% of cases with unequivocal opacities in radiograph, new opacities in 2-6% of normal radiographs and reduced equivocal opacities by 13-16%. Kappa increased from 0.64 (CI95% 0.6-0.8) to 0.7 (CI95% 0.7-0.8) for COVID-19 pneumonia probability, and from 0.69 (CI95% 0.6-0.7) to 0.76 (CI95% 0.7-0.8) for pneumonic extension. CONCLUSION: DTT improves radiograph performance and agreement for COVID-19 pneumonia diagnosis and reduces PCR false negatives.

Automated Whole-Liver MRI Segmentation to Assess Steatosis and Iron Quantification in Chronic Liver Disease.

Background Standardized manual region of interest (ROI) sampling strategies for hepatic MRI steatosis and iron quantification are time consuming, with variable results. Purpose To evaluate the performance of automatic MRI whole-liver segmentation (WLS) for proton density fat fraction (PDFF) and iron estimation (transverse relaxometry [R2*]) versus manual ROI, with liver biopsy as the reference standard. Materials and Methods This prospective, cross-sectional, multicenter study recruited participants with chronic liver disease who underwent liver biopsy and chemical shift-encoded 3.0-T MRI between January 2017 and January 2021. Biopsy evaluation included histologic grading and digital pathology. MRI liver sampling strategies included manual ROI (two observers) and automatic whole-liver (deep learning algorithm) segmentation for PDFF- and R2*-derived measurements. Agreements between segmentation methods were measured using intraclass correlation coefficients (ICCs), and biases were evaluated using Bland-Altman analyses. Linear regression analyses were performed to determine the correlation between measurements and digital pathology. Results A total of 165 participants were included (mean age ± standard deviation, 55 years ± 12; 96 women; 101 of 165 participants [61%] with nonalcoholic fatty liver disease). Agreements between mean measurements were excellent, with ICCs of 0.98 for both PDFF and R2*. The median bias was 0.5% (interquartile range, -0.4% to 1.2%) for PDFF and 2.7 sec-1 (interquartile range, 0.2-5.3 sec-1) for R2* (P < .001 for both). Margins of error were lower for WLS than ROI-derived parameters (-0.03% for PDFF and -0.3 sec-1 for R2*). ROI and WLS showed similar performance for steatosis (ROI AUC, 0.96; WLS AUC, 0.97; P = .53) and iron overload (ROI AUC, 0.85; WLS AUC, 0.83; P = .09). Correlations with digital pathology were high (P < .001) between the fat ratio and PDFF (ROI r = 0.89; WLS r = 0.90) and moderate (P < .001) between the iron ratio and R2* (ROI r = 0.65; WLS r = 0.64). Conclusion Proton density fat fraction and transverse relaxometry measurements derived from MRI automatic whole-liver segmentation (WLS) were accurate for steatosis and iron grading in chronic liver disease and correlated with digital pathology. Automated WLS estimations were higher, with a lower margin of error than manual region of interest estimations. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Moura Cunha and Fowler in this issue.

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.

Precise whole liver automatic segmentation and quantification of PDFF and R2* on MR images.

OBJECTIVE: To automate the segmentation of whole liver parenchyma on multi-echo chemical shift encoded (MECSE) MR examinations using convolutional neural networks (CNNs) to seamlessly quantify precise organ-related imaging biomarkers such as the fat fraction and iron load. METHODS: A retrospective multicenter collection of 183 MECSE liver MR examinations was conducted. An encoder-decoder CNN was trained (107 studies) following a 5-fold cross-validation strategy to improve the model performance and ensure lack of overfitting. Proton density fat fraction (PDFF) and R2* were quantified on both manual and CNN segmentation masks. Different metrics were used to evaluate the CNN performance over both unseen internal (46 studies) and external (29 studies) validation datasets to analyze reproducibility. RESULTS: The internal test showed excellent results for the automatic segmentation with a dice coefficient (DC) of 0.93 ± 0.03 and high correlation between the quantification done with the predicted mask and the manual segmentation (rPDFF = 1 and rR2* = 1; p values < 0.001). The external validation was also excellent with a different vendor but the same magnetic field strength, proving the generalization of the model to other manufacturers with DC of 0.94 ± 0.02. Results were lower for the 1.5-T MR same vendor scanner with DC of 0.87 ± 0.06. Both external validations showed high correlation in the quantification (rPDFF = 1 and rR2* = 1; p values < 0.001). In both internal and external validation datasets, the relative error for the PDFF and R2* quantification was below 4% and 1% respectively. CONCLUSION: Liver parenchyma can be accurately segmented with CNN in a vendor-neutral virtual approach, allowing to obtain reproducible automatic whole organ virtual biopsies. KEY POINTS: • Whole liver parenchyma can be automatically segmented using convolutional neural networks. • Deep learning allows the creation of automatic pipelines for the precise quantification of liver-related imaging biomarkers such as PDFF and R2*. • MR "virtual biopsy" can become a fast and automatic procedure for the assessment of chronic diffuse liver diseases in clinical practice.

Clinical Frailty Score vs Hospital Frailty Risk Score for predicting mortality and other adverse outcome in hospitalised patients with COVID-19: Spanish case series.

OBJECTIVES: Frailty can be used as a predictor of adverse outcomes in people with coronavirus disease 2019 (COVID-19). The aim of the study was to analyse the prognostic value of two different frailty scores in patients hospitalised for COVID-19. MATERIAL AND METHODS: This retrospective cohort study included adult (≥18 years) inpatients with COVID-19 and took place from 3 March to 2 May 2020. Patients were categorised by Clinical Frailty Score (CFS) and Hospital Frailty Risk Score (HFRS). The primary outcome was in-hospital mortality, and secondary outcomes were tocilizumab treatment, length of hospital stay, admission in intensive care unit (ICU) and need for invasive mechanical ventilation. Results were analysed by multivariable logistic regression and expressed as odds ratios (ORs), adjusting for age, sex, kidney function and comorbidity. RESULTS: Of the 290 included patients, 54 were frail according to the CFS (≥5 points; prevalence 18.6%, 95% confidence interval [CI]: 14.4-23.7) vs 65 by HFRS (≥5 points; prevalence: 22.4%, 95% CI 17.8-27.7). Prevalence of frailty increased with age according to both measures: 50-64 years, CFS 1.9% vs HFRS 12.3%; 65-79 years, CFS 31.5% vs HFRS 40.0%; and ≥80 years, CFS 66.7% vs HFRS 40.0% (P < .001). CFS-defined frailty was independently associated with risk of death (OR 3.67, 95% CI 1.49-9.04) and less treatment with tocilizumab (OR 0.28, 95% CI 0.08-0.93). HFRS-defined frailty was independently associated with length of hospital stay over 10 days (OR 2.89, 95% CI 1.53-5.44), ICU admission (OR 4.18, 95% CI 1.84-9.52) and invasive mechanical ventilation (OR 5.93, 95% CI 2.33-15.10). CONCLUSION: In the spring 2020 wave of the COVID-19 pandemic in Spain, CFS-defined frailty was an independent predictor for death, while frailty as measured by the HFRS was associated with length of hospital stay over 10 days, ICU admission and use of invasive mechanical ventilation.

Cerebral Venous Thrombosis after BNT162b2 mRNA SARS-CoV-2 vaccine.

The development of SARS-CoV-2 vaccines has raised several concerns regarding venous thromboembolism, namely cerebral venous thrombosis. Although cerebral venous thrombosis has been reported after administration of a viral vector vaccine, due to a possible auto-immune mechanism inducing thrombocytopenia, the same has not happened in mRNA vaccines. We report two cases of cerebral venous thrombosis, shortly after administration of mRNA vaccine. In both patients, there was no evidence of thrombocytopenia or antiplatelet antibodies, and alternative causes for cerebral venous thrombosis were found. As such, despite the temporal relation of both cases to vaccine administration, these types of cerebral venous thrombosis do not seem to be pathophysiological different from cerebral venous thrombosis not associated to SARS-CoV-2 vaccination. Continuous pharmacovigilance is necessary to monitor possible new events and clarify this association.

Palliative Sedation in COVID-19 End-of-Life Care. Retrospective Cohort Study.

Background and Objectives: Descriptions of end-of-life in COVID-19 are limited to small cross-sectional studies. We aimed to assess end-of-life care in inpatients with COVID-19 at Alicante General University Hospital (ALC) and compare differences according to palliative and non-palliative sedation. Material and Methods: This was a retrospective cohort study in inpatients included in the ALC COVID-19 Registry (PCR-RT or antigen-confirmed cases) who died during conventional admission from 1 March to 15 December 2020. We evaluated differences among deceased cases according to administration of palliative sedation. Results: Of 747 patients evaluated, 101 died (13.5%). Sixty-eight (67.3%) died in acute medical wards, and 30 (44.1%) received palliative sedation. The median age of patients with palliative sedation was 85 years; 44% were women, and 30% of cases were nosocomial. Patients with nosocomial acquisition received more palliative sedation than those infected in the community (81.8% [9/11] vs 36.8% [21/57], p = 0.006), and patients admitted with an altered mental state received it less (20% [6/23] vs. 53.3% [24/45], p = 0.032). The median time from admission to starting palliative sedation was 8.5 days (interquartile range [IQR] 3.0-14.5). The main symptoms leading to palliative sedation were dyspnea at rest (90%), pain (60%), and delirium/agitation (36.7%). The median time from palliative sedation to death was 21.8 h (IQR 10.4-41.1). Morphine was used in all palliative sedation perfusions: the main regimen was morphine + hyoscine butyl bromide + midazolam (43.3%). Conclusions: End-of-life palliative sedation in patients with COVID-19 was initiated quite late. Clinicians should anticipate the need for palliative sedation in these patients and recognize the breathlessness, pain, and agitation/delirium that foreshadow death.

Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data.

PURPOSE: Development of a fully automatic algorithm for the automatic localization and identification of vertebral bodies in computed tomography (CT). MATERIALS AND METHODS: This algorithm was developed using a dataset based on real-world data of 232 thoraco-abdominopelvic CT scans retrospectively collected. In order to achieve an accurate solution, a two-stage automated method was developed: decision forests for a rough prediction of vertebral bodies position, and morphological image processing techniques to refine the previous detection by locating the position of the spinal canal. RESULTS: The mean distance error between the predicted vertebrae centroid position and truth was 13.7 mm. The identification rate was 79.6% on the thoracic region and of 74.8% on the lumbar segment. CONCLUSION: The algorithm provides a new method to detect and identify vertebral bodies from arbitrary field-of-view body CT scans.

Diagnosis of Mesenteric Ectopic Pancreas by Secretin-Enhanced Magnetic Resonance Cholangiopancreatography.

Ectopic pancreas (EP) is defined as pancreatic tissue found outside its usual anatomical position, with no ductal or vascular communication with the native pancreas.We report the case of a 59-year-old woman with a mesenteric jejunal EP initially suspected on computed tomography and confirmed by secretin-enhanced magnetic resonance cholangiopancreatography.Mesenteric EP is a very rare finding with nonspecific clinical presentation, so that, classic radiological findings have not been well described previously.

Shelf-life of fresh blueberries coated with quinoa protein/chitosan/sunflower oil edible film.

BACKGROUND: The aim of this study was to evaluate quinoa protein (Q), chitosan (CH) and sunflower oil (SO) as edible film material as well as the influence of this coating in extending the shelf-life of fresh blueberries stored at 4 °C and 75% relative humidity. These conditions were used to simulate the storage conditions in supermarkets and represent adverse conditions for testing the effects of the coating. The mechanical, barrier, and structural properties of the film were measured. The effectiveness of the coating in fresh blueberries (CB) was evaluated by changes in weight loss, firmness, color, molds and yeast count, pH, titratable acidity, and soluble solids content. RESULTS: The tensile strength and elongation at break of the edible film were 0.45 ± 0.29 MPa and 117.2% ± 7%, respectively. The water vapor permeability was 3.3 × 10(-12) ± 4.0 × 10(-13) g s(-1) m(-1) Pa(-1). In all of the color parameters CB presented significant differences. CB had slight delayed fruit ripening as evidenced by higher titratable acidity (0.3-0.5 g citric acid 100 g(-1)) and lower pH (3.4-3.6) than control during storage; however, it showed reduced firmness (up to 38%). CONCLUSION: The use of Q/CH/SO as a coating in fresh blueberries was able to control the growth of molds and yeasts during 32 days of storage, whereas the control showed an increasing of molds and yeast, between 1.8 and 3.1 log cycles (between 20 and 35 days).

Protection against live rotavirus challenge in mice induced by parenteral and mucosal delivery of VP6 subunit rotavirus vaccine.

Live oral rotavirus (RV) vaccines are part of routine childhood immunization but are associated with adverse effects, particularly intussusception. We have developed a non-live combined RV - norovirus (NoV) vaccine candidate consisting of human RV inner-capsid rVP6 protein and NoV virus-like particles. To determine the effect of delivery route on induction of VP6-specific protective immunity, BALB/c mice were administered a vaccine containing RV rVP6 intramuscularly, intranasally or a combination of both, and challenged with murine RV. At least 65 % protection against RV shedding was observed regardless of delivery route. The levels of post-challenge serum VP6-specific IgA titers correlated with protection.