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Objective To develop and test computer software to detect, quantify, and monitor progression of pneumonia associated with COVID-19 using chest CT scans. Methods One hundred twenty chest CT scans from subjects with lung infiltrates were used for training deep learning algorithms to segment lung regions and vessels. Seventy-two serial scans from 24 COVID-19 subjects were used to develop and test algorithms to detect and quantify the presence and progression of infiltrates associated with COVID-19. The algorithm included (1) automated lung boundary and vessel segmentation, (2) registration of the lung boundary between serial scans, (3) computerized identification of the pneumonitis regions, and (4) assessment of disease progression. Agreement between radiologist manually delineated regions and computer-detected regions was assessed using the Dice coefficient. Serial scans were registered and used to generate a heatmap visualizing the change between scans. Two radiologists, using a five-point Likert scale, subjectively rated heatmap accuracy in representing progression. Results There was strong agreement between computer detection and the manual delineation of pneumonic regions with a Dice coefficient of 81% (CI 76–86%). In detecting large pneumonia regions (> 200 mm3), the algorithm had a sensitivity of 95% (CI 94–97%) and specificity of 84% (CI 81–86%). Radiologists rated 95% (CI 72 to 99) of heatmaps at least “acceptable” for representing disease progression. Conclusion The preliminary results suggested the feasibility of using computer software to detect and quantify pneumonic regions associated with COVID-19 and to generate heatmaps that can be used to visualize and assess progression. Key Points • Both computer vision and deep learning technology were used to develop computer software to quantify the presence and progression of pneumonia associated with COVID-19 depicted on CT images. • The computer software was tested using both quantitative experiments and subjective assessment. • The computer software has the potential to assist in the detection of the pneumonic regions, monitor disease progression, and assess treatment efficacy related to COVID-19. Electronic supplementary material The online version of this article (10.1007/s00330-020-07156-2) contains supplementary material, which is available to authorized users.

To define the uniqueness of chest CT infiltrative features associated with COVID-19 image characteristics as potential diagnostic biomarkers. We retrospectively collected chest CT exams including n = 498 on 151 unique patients RT-PCR positive for COVID-19 and n = 497 unique patients with community-acquired pneumonia (CAP). Both COVID-19 and CAP image sets were partitioned into three groups for training, validation, and testing respectively. In an attempt to discriminate COVID-19 from CAP, we developed several classifiers based on three-dimensional (3D) convolutional neural networks (CNNs). We also asked two experienced radiologists to visually interpret the testing set and discriminate COVID-19 from CAP. The classification performance of the computer algorithms and the radiologists was assessed using the receiver operating characteristic (ROC) analysis, and the nonparametric approaches with multiplicity adjustments when necessary. One of the considered models showed non-trivial, but moderate diagnostic ability overall (AUC of 0.70 with 99% CI 0.56–0.85). This model allowed for the identification of 8–50% of CAP patients with only 2% of COVID-19 patients. Professional or automated interpretation of CT exams has a moderately low ability to distinguish between COVID-19 and CAP cases. However, the automated image analysis is promising for targeted decision-making due to being able to accurately identify a sizable subsect of non-COVID-19 cases. • Both human experts and artificial intelligent models were used to classify the CT scans. • ROC analysis and the nonparametric approaches were used to analyze the performance of the radiologists and computer algorithms. • Unique image features or patterns may not exist for reliably distinguishing all COVID-19 from CAP; however, there may be imaging markers that can identify a sizable subset of non-COVID-19 cases.

Fungal diversity notes is one of the important journal series of fungal taxonomy that provide detailed descriptions and illustrations of new fungal taxa, as well as providing new information of fungal taxa worldwide. This article is the 11th contribution to the fungal diversity notes series, in which 126 taxa distributed in two phyla, six classes, 24 orders and 55 families are described and illustrated. Taxa in this study were mainly collected from Italy by Erio Camporesi and also collected from China, India and Thailand, as well as in some other European, North American and South American countries. Taxa described in the present study include two new families, 12 new genera, 82 new species, five new combinations and 25 new records on new hosts and new geographical distributions as well as sexual-asexual reports. The two new families areEriomycetaceae(Dothideomycetes, familyincertae sedis) andFasciatisporaceae(Xylariales, Sordariomycetes). The twelve new genera compriseBhagirathimyces(Phaeosphaeriaceae),Camporesiomyces(Tubeufiaceae),Eriocamporesia(Cryphonectriaceae),Eriomyces(Eriomycetaceae),Neomonodictys(Pleurotheciaceae),Paraloratospora(Phaeosphaeriaceae),Paramonodictys(Parabambusicolaceae),Pseudoconlarium(Diaporthomycetidae, genusincertae sedis),Pseudomurilentithecium(Lentitheciaceae),Setoapiospora(Muyocopronaceae),Srinivasanomyces(Vibrisseaceae) andXenoanthostomella(Xylariales, generaincertae sedis). The 82 new species compriseAcremonium chiangraiense,Adustochaete nivea,Angustimassarina camporesii,Bhagirathimyces himalayensis,Brunneoclavispora camporesii,Camarosporidiella camporesii,Camporesiomyces mali,Camposporium appendiculatum,Camposporium multiseptatum,Camposporium septatum,Canalisporium aquaticium,Clonostachys eriocamporesiana,Clonostachys eriocamporesii,Colletotrichum hederiicola,Coniochaeta vineae,Conioscypha verrucosa,Cortinarius ainsworthii,Cortinarius aurae,Cortinarius britannicus,Cortinarius heatherae,Cortinarius scoticus,Cortinarius subsaniosus,Cytospora fusispora,Cytospora rosigena,Diaporthe camporesii,Diaporthe nigra,Diatrypella yunnanensis,Dictyosporium muriformis,Didymella camporesii,Diutina bernali,Diutina sipiczkii,Eriocamporesia aurantia,Eriomyces heveae,Ernakulamia tanakae,Falciformispora uttaraditensis,Fasciatispora cocoes,Foliophoma camporesii,Fuscostagonospora camporesii,Helvella subtinta,Kalmusia erioi,Keissleriella camporesiana,Keissleriella camporesii,Lanspora cylindrospora,Loratospora arezzoensis,Mariannaea atlantica,Melanographium phoenicis,Montagnula camporesii,Neodidymelliopsis camporesii,Neokalmusia kunmingensis,Neoleptosporella camporesiana,Neomonodictys muriformis,Neomyrmecridium guizhouense,Neosetophoma camporesii,Paraloratospora camporesii,Paramonodictys solitarius,Periconia palmicola,Plenodomus triseptatus,Pseudocamarosporium camporesii,Pseudocercospora maetaengensis,Pseudochaetosphaeronema kunmingense,Pseudoconlarium punctiforme,Pseudodactylaria camporesiana,Pseudomurilentithecium camporesii,Pseudotetraploa rajmachiensis,Pseudotruncatella camporesii,Rhexocercosporidium senecionis,Rhytidhysteron camporesii,Rhytidhysteron erioi,Septoriella camporesii,Setoapiospora thailandica,Srinivasanomyces kangrensis,Tetraploa dwibahubeeja,Tetraploa pseudoaristata,Tetraploa thrayabahubeeja,Torula camporesii,Tremateia camporesii,Tremateia lamiacearum,Uzbekistanica pruni,Verruconis mangrovei,Wilcoxina verruculosa,Xenoanthostomella chromolaenaeandXenodidymella camporesii. The five new combinations areCamporesiomyces patagoniensis,Camporesiomyces vaccinia,Camposporium lycopodiellae,Paraloratospora gahniaeandRhexocercosporidium microsporum. The 22 new records on host and geographical distribution compriseArthrinium marii,Ascochyta medicaginicola,Ascochyta pisi,Astrocystis bambusicola,Camposporium pellucidum,Dendryphiella phitsanulokensis,Diaporthe foeniculina,Didymella macrostoma,Diplodia mutila,Diplodia seriata,Heterosphaeria patella,Hysterobrevium constrictum,Neodidymelliopsis ranunculi,Neovaginatispora fuckelii,Nothophoma quercina,Occultibambusa bambusae,Phaeosphaeria chinensis,Pseudopestalotiopsis theae,Pyxine berteriana,Tetraploa sasicola,Torula gaodangensisandWojnowiciella dactylidis. In addition, the sexual morphs ofDissoconium eucalyptiandPhaeosphaeriopsis pseudoagavacearumare reported fromLaurus nobilisandYucca gloriosain Italy, respectively. The holomorph ofDiaporthe cynaroidisis also reported for the first time.

Background The effect of comorbid hypertension and type 2 diabetes mellitus (T2DM) on coronary artery plaques examined by coronary computed tomography angiography (CCTA) is not fully understood. We aimed to comprehensively assess whether comorbid hypertension and T2DM influence coronary artery plaques using CCTA. Materials and methods A total of 1100 T2DM patients, namely, 277 normotensive [T2DM(HTN−)] and 823 hypertensive [T2DM(HTN +)] individuals, and 1048 normotensive patients without T2DM (control group) who had coronary plaques detected on CCTA were retrospectively enrolled. Plaque type, coronary stenosis, diseased vessels, the segment involvement score (SIS) and the segment stenosis score (SSS) based on CCTA data were evaluated and compared among the groups. Results Compared with patients in the control group, the patients in the T2DM(HTN−) and T2DM(HTN +) groups had more partially calcified plaques, noncalcified plaques, segments with obstructive stenosis, and diseased vessels, and a higher SIS and SSS (all P values 3 (OR = 2.233 and 3.769; both P values 5 (OR = 2.057 and 3.580; both P values 3 (OR = 1.647; P = 0.001) and an SSS > 5 (OR = 1.625; P = 0.001). Conclusion T2DM is related to the presence of partially calcified plaques, obstructive CAD, and more extensive coronary artery plaques. Comorbid hypertension and diabetes further increase the risk of partially calcified plaques, and more extensive coronary artery plaques.

Background Patients with severe acute pancreatitis (SAP) have a high mortality, thus early diagnosis and interventions are critical for improving survival. However, conventional tests are limited in acute pancreatitis (AP) stratification. We aimed to assess AP severity by integrating the informative clinical measurements with cell free DNA (cfDNA) methylation markers. Methods One hundred and seventy-five blood samples were collected from 61 AP patients at multiple time points, plus 24 samples from healthy individuals. Genome-wide cfDNA methylation profiles of all samples were characterized with reduced representative bisulfite sequencing. Clinical blood tests covering 93 biomarkers were performed on AP patients within 24 h. SAP predication models were built based on cfDNA methylation and conventional blood biomarkers separately and in combination. Results We identified 565 and 59 cfDNA methylation markers informative for acute pancreatitis and its severity. These markers were used to develop prediction models for AP and SAP with area under the receiver operating characteristic of 0.92 and 0.81, respectively. Twelve blood biomarkers were systematically screened for a predictor of SAP with a sensitivity of 87.5% for SAP, and a specificity of 100% in mild acute pancreatitis, significantly higher than existing blood tests. An expanded model integrating 12 conventional blood biomarkers with 59 cfDNA methylation markers further improved the SAP prediction sensitivity to 92.2%. Conclusions These findings have demonstrated that accurate prediction of SAP by the integration of conventional and novel blood molecular markers, paving the way for early and effective SAP intervention through a non-invasive rapid diagnostic test.