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《Journal of pharmaceutical sciences》2019,108(11):3515-3520
Particle size analysis in the pharmaceutical industry has long been a source of debate regarding how best to define measurement accuracy; the degree to which the result of a measurement or calculation conforms to the true value. Defining a “true” value for the size of a particle can be challenging as the output of its measurement will differ because of variations in measurement approaches, instrumental differences and calculation methods. Consequently, for “real” particles, a universal “true” value does not exist and accuracy is therefore not a definable characteristic. Accordingly, precision is then a measure of the ability to reproducibly achieve a measurement of unknown relevance.This article proposes, in place of accuracy, a means to define the “appropriateness” of a measurement in line with the critical quality attributes (CQA) of the material being characterized. The decision as to whether the measurement is correct should involve a link to the CQA; that is, correlation should be demonstrated, without which the measured particle size cannot be defined as a critical material attribute.Correspondingly, methods should also be able to provide sufficient precision to demonstrate discrimination relating to variation in the CQA. The benefits and challenges of this approach are discussed. 相似文献
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IntroductionPredicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.Materials and methodsWe retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.ResultsThe WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).ConclusionOur study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction. 相似文献
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Filomena Napolitano Valentina Di Iorio Giuseppe Di Iorio Mariarosa Anna Beatrice Melone Fernando Gianfrancesco Francesca Simonelli 《Ophthalmic genetics》2019,40(1):39-42
Background: Extracellular matrix molecular components, previously linked to multisystem syndromes include collagens, fibrillins and laminins. Recently, we described a novel multisystem syndrome caused by the c.9418G>A p.(V3140M) mutation in the laminin alpha-5 (LAMA5) gene, which affects connective tissues of all organs and apparatus in a three generation family. In the same family, we have also reported a myopic trait, which, however, was linked to the Prolyl 4-hydroxylase subunit alpha-2 (P4HA2) gene. Results of investigation on vitreous changes and their pathogenesis are reported in the present study.Materials and Methods: Nineteen family individuals underwent complete ophthalmic examination including best-corrected visual acuity (BCVA), fundus examination, fundus photography, intraocular pressure measurement, axial length measurement using ocular biometry, Goldmann visual field examination, standard electroretinogram, SD-OCT. Segregation analysis of LAMA5 and P4HA2 mutations was performed in enrolled members.Results: The vitreous alterations fully segregated with LAMA5 mutation in both young and adult family members. Slight reduction of retinal thickness and peripheral retinal degeneration in only two patients were reported.Conclusions: In this work we showed that PVD is a common trait of LAMA5 multisystem syndrome, therefore occurring as an age-unrelated trait. We hypothesize that the p.(V3140M) mutation results in a reduction of retinal inner limiting membrane (ILM) stability, leading to a derangement in the macromolecular structure of the vitreous gel, and PVD. Further investigations will be necessary to elucidate the role of wild type and mutated LAMA5 in the pathogenesis of PVD. 相似文献
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《Diagnostic and interventional imaging》2020,101(9):555-564
PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%. 相似文献
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A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross‐covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity. 相似文献