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- Manry, Jérémy, et al.
(författare)
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The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies.
- 2022
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 1091-6490. ; 119:21
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Tidskriftsartikel (refereegranskat)abstract
- Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection fatality rate (IFR) doubles with every 5 y of age from childhood onward. Circulating autoantibodies neutralizing IFN-α, IFN-ω, and/or IFN-β are found in ∼20% of deceased patients across age groups, and in ∼1% of individuals aged <70 y and in >4% of those >70 y old in the general population. With a sample of 1,261 unvaccinated deceased patients and 34,159 individuals of the general population sampled before the pandemic, we estimated both IFR and relative risk of death (RRD) across age groups for individuals carrying autoantibodies neutralizing type I IFNs, relative to noncarriers. The RRD associated with any combination of autoantibodies was higher in subjects under 70 y old. For autoantibodies neutralizing IFN-α2 or IFN-ω, the RRDs were 17.0 (95% CI: 11.7 to 24.7) and 5.8 (4.5 to 7.4) for individuals <70 y and ≥70 y old, respectively, whereas, for autoantibodies neutralizing both molecules, the RRDs were 188.3 (44.8 to 774.4) and 7.2 (5.0 to 10.3), respectively. In contrast, IFRs increased with age, ranging from 0.17% (0.12 to 0.31) for individuals <40 y old to 26.7% (20.3 to 35.2) for those ≥80 y old for autoantibodies neutralizing IFN-α2 or IFN-ω, and from 0.84% (0.31 to 8.28) to 40.5% (27.82 to 61.20) for autoantibodies neutralizing both. Autoantibodies against type I IFNs increase IFRs, and are associated with high RRDs, especially when neutralizing both IFN-α2 and IFN-ω. Remarkably, IFRs increase with age, whereas RRDs decrease with age. Autoimmunity to type I IFNs is a strong and common predictor of COVID-19 death.
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- Bastard, P, et al.
(författare)
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Preexisting autoantibodies to type I IFNs underlie critical COVID-19 pneumonia in patients with APS-1
- 2021
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Ingår i: The Journal of experimental medicine. - : Rockefeller University Press. - 1540-9538 .- 0022-1007. ; 218:7
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Tidskriftsartikel (refereegranskat)abstract
- Patients with biallelic loss-of-function variants of AIRE suffer from autoimmune polyendocrine syndrome type-1 (APS-1) and produce a broad range of autoantibodies (auto-Abs), including circulating auto-Abs neutralizing most type I interferons (IFNs). These auto-Abs were recently reported to account for at least 10% of cases of life-threatening COVID-19 pneumonia in the general population. We report 22 APS-1 patients from 21 kindreds in seven countries, aged between 8 and 48 yr and infected with SARS-CoV-2 since February 2020. The 21 patients tested had auto-Abs neutralizing IFN-α subtypes and/or IFN-ω; one had anti–IFN-β and another anti–IFN-ε, but none had anti–IFN-κ. Strikingly, 19 patients (86%) were hospitalized for COVID-19 pneumonia, including 15 (68%) admitted to an intensive care unit, 11 (50%) who required mechanical ventilation, and four (18%) who died. Ambulatory disease in three patients (14%) was possibly accounted for by prior or early specific interventions. Preexisting auto-Abs neutralizing type I IFNs in APS-1 patients confer a very high risk of life-threatening COVID-19 pneumonia at any age.
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- Sahin, O, et al.
(författare)
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International Multi-Specialty Expert Physician Preoperative Identification of Extranodal Extension n Oropharyngeal Cancer Patients using Computed Tomography: Prospective Blinded Human Inter-Observer Performance Evaluation
- 2024
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Ingår i: medRxiv : the preprint server for health sciences. - : Cold Spring Harbor Laboratory.
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundExtranodal extension (ENE) is an important adverse prognostic factor in oropharyngeal cancer (OPC) and is often employed in therapeutic decision making. Clinician-based determination of ENE from radiological imaging is a difficult task with high inter-observer variability. However, the role of clinical specialty on the determination of ENE has been unexplored.MethodsPre-therapy computed tomography (CT) images for 24 human papillomavirus-positive (HPV+) OPC patients were selected for the analysis; 6 scans were randomly chosen to be duplicated, resulting in a total of 30 scans of which 21 had pathologically-confirmed ENE. 34 expert clinician annotators, comprised of 11 radiologists, 12 surgeons, and 11 radiation oncologists separately evaluated the 30 CT scans for ENE and noted the presence or absence of specific radiographic criteria and confidence in their prediction. Discriminative performance was measured using accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and Brier score for each physician. Statistical comparisons of discriminative performance were calculated using Mann Whitney U tests. Significant radiographic factors in correct discrimination of ENE status were determined through a logistic regression analysis. Interobserver agreement was measured using Fleiss’ kappa.ResultsThe median accuracy for ENE discrimination across all specialties was 0.57. There were significant differences between radiologists and surgeons for Brier score (0.33 vs. 0.26), radiation oncologists and surgeons for sensitivity (0.48 vs. 0.69), and radiation oncologists and radiologists/surgeons for specificity (0.89 vs. 0.56). There were no significant differences between specialties for accuracy or AUC. Indistinct capsular contour, nodal necrosis, and nodal matting were significant factors in regression analysis. Fleiss’ kappa was less than 0.6 for all the radiographic criteria, regardless of specialty.ConclusionsDetection of ENE in HPV+OPC patients on CT imaging remains a difficult task with high variability, regardless of clinician specialty. Although some differences do exist between the specialists, they are often minimal. Further research in automated analysis of ENE from radiographic images is likely needed.
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- Ruda, M, et al.
(författare)
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Solid-phase synthesis of a 6-phenylquinolin-2(1H)-one library directed toward nuclear hormone receptors
- 2005
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Ingår i: Journal of combinatorial chemistry. - : American Chemical Society (ACS). - 1520-4766 .- 1520-4774. ; 7:4, s. 567-573
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Tidskriftsartikel (refereegranskat)abstract
- A library of 6-phenylquinolin-2(1H)-ones with diversity at position I and the ortho, meta, and para positions of the pendant phenyl ring has been synthesized using solid-phase parallel synthetic techniques. A key step in the synthesis of the library is a tandem alkylation cleavage in which diversity can be introduced at position 1 simultaneously to the cleavage from the resin. The yields of this step were significantly improved over what has previously been reported by addition of cesium carbonate to scavenge the acid that is formed during the reaction. Furthermore, we have shown that the solid support linkage is tolerant to Suzuki coupling and etherification reaction conditions and that selective cleavage of the linkage can take place in the presence of esters. The resulting 6-phenylquinolin-2(1H)-one library was screened against a panel of nuclear hormone receptors (androgen, estrogen alpha and beta isoforms, glucocorticoid, mineralocorticoid, and progesterone). Certain members of this library display moderate affinity for several of these receptors, and consequently, the 6-phenylquinolin-2(1H)-one core of the library may be considered a privileged structure for nuclear hormone receptors. In contrast, other members of the library display high selectivity for a particular receptor. The highest affinity ligand (9{2,1,1}) possesses an affinity of 330 nM for the androgen receptor, whereas the most selective ligand (9{2,4,1}) displays an affinity of 900 nM for the androgen receptor and a selectivity of 140-fold over the next highest affinity receptor.
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| 10. |
- Ye, Z, et al.
(författare)
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Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline
- 2023
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Ingår i: medRxiv : the preprint server for health sciences. - : Cold Spring Harbor Laboratory.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- PurposeSarcopenia is an established prognostic factor in patients diagnosed with head and neck squamous cell carcinoma (HNSCC). The quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical neck skeletal muscle (SM) segmentation and cross-sectional area. However, manual SM segmentation is labor-intensive, prone to inter-observer variability, and impractical for large-scale clinical use. To overcome this challenge, we have developed and externally validated a fully-automated image-based deep learning (DL) platform for cervical vertebral SM segmentation and SMI calculation, and evaluated the relevance of this with survival and toxicity outcomes.Materials and Methods899 patients diagnosed as having HNSCC with CT scans from multiple institutes were included, with 335 cases utilized for training, 96 for validation, 48 for internal testing and 393 for external testing. Ground truth single-slice segmentations of SM at the C3 vertebra level were manually generated by experienced radiation oncologists. To develop an efficient method of segmenting the SM, a multi-stage DL pipeline was implemented, consisting of a 2D convolutional neural network (CNN) to select the middle slice of C3 section and a 2D U-Net to segment SM areas. The model performance was evaluated using the Dice Similarity Coefficient (DSC) as the primary metric for the internal test set, and for the external test set the quality of automated segmentation was assessed manually by two experienced radiation oncologists. The L3 skeletal muscle area (SMA) and SMI were then calculated from the C3 cross sectional area (CSA) of the auto-segmented SM. Finally, established SMI cut-offs were used to perform further analyses to assess the correlation with survival and toxicity endpoints in the external institution with univariable and multivariable Cox regression.ResultsDSCs for validation set (n = 96) and internal test set (n = 48) were 0.90 (95% CI: 0.90 – 0.91) and 0.90 (95% CI: 0.89 - 0.91), respectively. The predicted CSA is highly correlated with the ground-truth CSA in both validation (r = 0.99,p< 0.0001) and test sets (r = 0.96,p< 0.0001). In the external test set (n = 377), 96.2% of the SM segmentations were deemed acceptable by consensus expert review. Predicted SMA and SMI values were highly correlated with the ground-truth values, with Pearson r β 0.99 (p < 0.0001) for both the female and male patients in all datasets. Sarcopenia was associated with worse OS (HR 2.05 [95% CI 1.04 - 4.04], p = 0.04) and longer PEG tube duration (median 162 days vs. 134 days, HR 1.51 [95% CI 1.12 - 2.08], p = 0.006 in multivariate analysis.ConclusionWe developed and externally validated a fully-automated platform that strongly correlates with imaging-assessed sarcopenia in patients with H&N cancer that correlates with survival and toxicity outcomes. This study constitutes a significant stride towards the integration of sarcopenia assessment into decision-making for individuals diagnosed with HNSCC.SUMMARY STATEMENTIn this study, we developed and externally validated a deep learning model to investigate the impact of sarcopenia, defined as the loss of skeletal muscle mass, on patients with head and neck squamous cell carcinoma (HNSCC) undergoing radiotherapy. We demonstrated an efficient, fullyautomated deep learning pipeline that can accurately segment C3 skeletal muscle area, calculate cross-sectional area, and derive a skeletal muscle index to diagnose sarcopenia from a standard of care CT scan. In multi-institutional data, we found that pre-treatment sarcopenia was associated with significantly reduced overall survival and an increased risk of adverse events. Given the increased vulnerability of patients with HNSCC, the assessment of sarcopenia prior to radiotherapy may aid in informed treatment decision-making and serve as a predictive marker for the necessity of early supportive measures.
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