PENCIL imaging: A novel approach for neuromelanin sensitive MRI in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is associated with the loss of neuromelanin (NM) and increased iron in the substantia nigra (SN). Magnetization transfer contrast (MTC) is widely used for NM visualization but has limitations in brain coverage and scan time. This study aimed to develop a new approach called Proton-density Enhanced Neuromelanin Contrast in Low flip angle gradient echo (PENCIL) imaging to visualize NM in the SN. METHODS: This study included 30 PD subjects and 50 healthy controls (HCs) scanned at 3T. PENCIL and MTC images were acquired. NM volume in the SN pars compacta (SNpc), normalized image contrast (Cnorm), and contrast-to-noise ratio (CNR) were calculated. The change of NM volume in the SNpc with age was analyzed using the HC data. A group analysis compared differences between PD subjects and HCs. Receiver operating characteristic (ROC) analysis and area under the curve (AUC) calculations were used to evaluate the diagnostic performance of NM volume and CNR in the SNpc. RESULTS: PENCIL provided similar visualization and structural information of NM compared to MTC. In HCs, PENCIL showed higher NM volume in the SNpc than MTC, but this difference was not observed in PD subjects. PENCIL had higher CNR, while MTC had higher Cnorm. Both methods revealed a similar pattern of NM volume in SNpc changes with age. There were no significant differences in AUCs between NM volume in SNpc measured by PENCIL and MTC. Both methods exhibited comparable diagnostic performance in this regard. CONCLUSIONS: PENCIL imaging provided improved CNR compared to MTC and showed similar diagnostic performance for differentiating PD subjects from HCs. The major advantage is PENCIL has rapid whole-brain coverage and, when using STAGE imaging, offers a one-stop quantitative assessment of tissue properties.

Enhancing Nigrosome-1 Sign Identification via Interpretable AI using True Susceptibility Weighted Imaging.

BACKGROUND: Nigrosome 1 (N1), the largest nigrosome region in the ventrolateral area of the substantia nigra pars compacta, is identifiable by the "N1 sign" in long echo time gradient echo MRI. The N1 sign's absence is a vital Parkinson's disease (PD) diagnostic marker. However, it is challenging to visualize and assess the N1 sign in clinical practice. PURPOSE: To automatically detect the presence or absence of the N1 sign from true susceptibility weighted imaging by using deep-learning method. STUDY TYPE: Prospective. POPULATION/SUBJECTS: 453 subjects, including 225 PD patients, 120 healthy controls (HCs), and 108 patients with other movement disorders, were prospectively recruited including 227 males and 226 females. They were divided into training, validation, and test cohorts of 289, 73, and 91 cases, respectively. FIELD STRENGTH/SEQUENCE: 3D gradient echo SWI sequence at 3T; 3D multiecho strategically acquired gradient echo imaging at 3T; NM-sensitive 3D gradient echo sequence with MTC pulse at 3T. ASSESSMENT: A neuroradiologist with 5 years of experience manually delineated substantia nigra regions. Two raters with 2 and 36 years of experience assessed the N1 sign on true susceptibility weighted imaging (tSWI), QSM with high-pass filter, and magnitude data combined with MTC data. We proposed NINet, a neural model, for automatic N1 sign identification in tSWI images. STATISTICAL TESTS: We compared the performance of NINet to the subjective reference standard using Receiver Operating Characteristic analyses, and a decision curve analysis assessed identification accuracy. RESULTS: NINet achieved an area under the curve (AUC) of 0.87 (CI: 0.76-0.89) in N1 sign identification, surpassing other models and neuroradiologists. NINet localized the putative N1 sign within tSWI images with 67.3% accuracy. DATA CONCLUSION: Our proposed NINet model's capability to determine the presence or absence of the N1 sign, along with its localization, holds promise for enhancing diagnostic accuracy when evaluating PD using MR images. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Diagnosing Parkinson's disease by combining neuromelanin and iron imaging features using an automated midbrain template approach.

BACKGROUND AND PURPOSE: Early diagnosis of Parkinson's disease (PD) is still a clinical challenge. Most previous studies using manual or semi-automated methods for segmenting the substantia nigra (SN) are time-consuming and, despite raters being well-trained, individual variation can be significant. In this study, we used a template-based, automatic, SN subregion segmentation pipeline to detect the neuromelanin (NM) and iron features in the SN and SN pars compacta (SNpc) derived from a single 3D magnetization transfer contrast (MTC) gradient echo (GRE) sequence in an attempt to develop a comprehensive imaging biomarker that could be used to diagnose PD. MATERIALS AND METHODS: A total of 100 PD patients and 100 age- and sex-matched healthy controls (HCs) were imaged on a 3T scanner. NM-based SN (SNNM) boundaries and iron-based SN (SNQSM) boundaries and their overlap region (representing the SNpc) were delineated automatically using a template-based SN subregion segmentation approach based on quantitative susceptibility mapping (QSM) and NM images derived from the same MTC-GRE sequence. All PD and HC subjects were evaluated for the nigrosome-1 (N1) sign by two raters independently. Receiver Operating Characteristic (ROC) analyses were performed to evaluate the utility of SNNM volume, SNQSM volume, SNpc volume and iron content with a variety of thresholds as well as the N1 sign in diagnosing PD. Correlation analyses were performed to study the relationship between these imaging measures and the clinical scales in PD. RESULTS: In this study, we verified the value of the fully automatic template based midbrain deep gray matter mapping approach in differentiating PD patients from HCs. The automatic segmentation of the SN in PD patients led to satisfactory DICE similarity coefficients and volume ratio (VR) values of 0.81 and 1.17 for the SNNM, and 0.87 and 1.05 for the SNQSM, respectively. For the HC group, the average DICE similarity coefficients and VR values were 0.85 and 0.94 for the SNNM, and 0.87 and 0.96 for the SNQSM, respectively. The SNQSM volume tended to decrease with age for both the PD and HC groups but was more severe for the PD group. For diagnosing PD, the N1 sign performed reasonably well by itself (Area Under the Curve (AUC) = 0.783). However, combining the N1 sign with the other quantitative measures (SNNM volume, SNQSM volume, SNpc volume and iron content) resulted in an improved diagnosis of PD with an AUC as high as 0.947 (using an SN threshold of 50ppb and an NM threshold of 0.15). Finally, the SNQSM volume showed a negative correlation with the MDS-UPDRS III (R2 = 0.1, p = 0.036) and the Hoehn and Yahr scale (R2 = 0.04, p = 0.013) in PD patients. CONCLUSION: In summary, this fully automatic template based deep gray matter mapping approach performs well in the segmentation of the SN and its subregions for not only HCs but also PD patients with SN degeneration. The combination of the N1 sign with other quantitative measures (SNNM volume, SNQSM volume, SNpc volume and iron content) resulted in an AUC of 0.947 and provided a comprehensive set of imaging biomarkers that, potentially, could be used to diagnose PD clinically.

Visualizing the deep cerebellar nuclei using quantitative susceptibility mapping: An application in healthy controls, Parkinson's disease patients and essential tremor patients.

The visualization and identification of the deep cerebellar nuclei (DCN) (dentate [DN], interposed [IN] and fastigial nuclei [FN]) are particularly challenging. We aimed to visualize the DCN using quantitative susceptibility mapping (QSM), predict the contrast differences between QSM and T2* weighted imaging, and compare the DCN volume and susceptibility in movement disorder populations and healthy controls (HCs). Seventy-one Parkinson's disease (PD) patients, 39 essential tremor patients, and 80 HCs were enrolled. The PD patients were subdivided into tremor dominant (TD) and postural instability/gait difficulty (PIGD) groups. A 3D strategically acquired gradient echo MR imaging protocol was used for each subject to obtain the QSM data. Regions of interest were drawn manually on the QSM data to calculate the volume and susceptibility. Correlation analysis between the susceptibility and either age or volume was performed and the intergroup differences of the volume and magnetic susceptibility in all the DCN structures were evaluated. For the most part, all the DCN structures were clearly visualized on the QSM data. The susceptibility increased as a function of volume for both the HC group and disease groups in the DN and IN (p < .001) but not the FN (p = .74). Only the volume of the FN in the TD-PD group was higher than that in the HCs (p = .012), otherwise, the volume and susceptibility among these four groups did not differ significantly. In conclusion, QSM provides clear visualization of the DCN structures. The results for the volume and susceptibility of the DCN can be used as baseline references in future studies of movement disorders.

An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images.

Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution in deep gray matter (DGM) nuclei. We hypothesized that deep learning (DL) could be used to automatically segment all DGM nuclei and use relevant features for a better differentiation between PD and healthy controls (HC). In this study, we proposed a DL-based pipeline for automatic PD diagnosis based on QSM and T1-weighted (T1W) images. This consists of (1) a convolutional neural network model integrated with multiple attention mechanisms which simultaneously segments caudate nucleus, globus pallidus, putamen, red nucleus, and substantia nigra from QSM and T1W images, and (2) an SE-ResNeXt50 model with an anatomical attention mechanism, which uses QSM data and the segmented nuclei to distinguish PD from HC. The mean dice values for segmentation of the five DGM nuclei are all >0.83 in the internal testing cohort, suggesting that the model could segment brain nuclei accurately. The proposed PD diagnosis model achieved area under the the receiver operating characteristic curve (AUCs) of 0.901 and 0.845 on independent internal and external testing cohorts, respectively. Gradient-weighted class activation mapping (Grad-CAM) heatmaps were used to identify contributing nuclei for PD diagnosis on patient level. In conclusion, the proposed approach can potentially be used as an automatic, explainable pipeline for PD diagnosis in a clinical setting.

Automatic detection of neuromelanin and iron in the midbrain nuclei using a magnetic resonance imaging-based brain template.

Parkinson disease (PD) is a chronic progressive neurodegenerative disorder characterized pathologically by early loss of neuromelanin (NM) in the substantia nigra pars compacta (SNpc) and increased iron deposition in the substantia nigra (SN). Degeneration of the SN presents as a 50 to 70% loss of pigmented neurons in the ventral lateral tier of the SNpc at the onset of symptoms. Also, using magnetic resonance imaging (MRI), iron deposition and volume changes of the red nucleus (RN), and subthalamic nucleus (STN) have been reported to be associated with disease status and rate of progression. Further, the STN serves as an important target for deep brain stimulation treatment in advanced PD patients. Therefore, an accurate in-vivo delineation of the SN, its subregions and other midbrain structures such as the RN and STN could be useful to better study iron and NM changes in PD. Our goal was to use an MRI template to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and NM contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC-GRE) imaging sequence. The short echo TE = 7.5 ms data from a 3D MTC-GRE sequence was used to find the NM-rich region, while the second echo TE = 15 ms was used to calculate the quantitative susceptibility map for 87 healthy subjects (mean age ± SD: 63.4 ± 6.2 years old, range: 45-81 years). From these data, we created both NM and iron templates and calculated the boundaries of each midbrain nucleus in template space, mapped these boundaries back to the original space and then fine-tuned the boundaries in the original space using a dynamic programming algorithm to match the details of each individual's NM and iron features. A dual mapping approach was used to improve the performance of the morphological mapping of the midbrain of any given individual to the template space. A threshold approach was used in the NM-rich region and susceptibility maps to optimize the DICE similarity coefficients and the volume ratios. The results for the NM of the SN as well as the iron containing SN, STN, and RN all indicate a strong agreement with manually drawn structures. The DICE similarity coefficients and volume ratios for these structures were 0.85, 0.87, 0.75, and 0.92 and 0.93, 0.95, 0.89, 1.05, respectively, before applying any threshold on the data. Using this fully automatic template-based deep gray matter mapping approach, it is possible to accurately measure the tissue properties such as volumes, iron content, and NM content of the midbrain nuclei.

Dynamic pulmonary MRI using motion-state weighted motion-compensation (MostMoCo) reconstruction with ultrashort TE: A structural and functional study.

PURPOSE: To improve the quality of structural images and the quantification of ventilation in free-breathing dynamic pulmonary MRI. METHODS: A 3D radial ultrashort TE (UTE) sequence with superior-inferior navigators was used to acquire pulmonary data during free breathing. All acquired data were binned into different motion states according to the respiratory signal extracted from superior-inferior navigators. Motion-resolved images were reconstructed using eXtra-Dimensional (XD) UTE reconstruction. The initial motion fields were generated by registering images at each motion state to other motion states in motion-resolved images. A motion-state weighted motion-compensation (MostMoCo) reconstruction algorithm was proposed to reconstruct the dynamic UTE images. This technique, termed as MostMoCo-UTE, was compared with XD-UTE and iterative motion-compensation (iMoCo) on a porcine lung and 10 subjects. RESULTS: MostMoCo reconstruction provides higher peak SNR (37.0 vs. 35.4 and 34.2) and structural similarity (0.964 vs. 0.931 and 0.947) compared to XD-UTE and iMoCo in the porcine lung experiment. Higher apparent SNR and contrast-to-noise ratio are achieved using MostMoCo in the human experiment. MostMoCo reconstruction better preserves the temporal variations of signal intensity of parenchyma compared to iMoCo, shows reduced random noise and improved sharpness of anatomical structures compared to XD-UTE. In the porcine lung experiment, the quantification of ventilation using MostMoCo images is more accurate than that using XD-UTE and iMoCo images. CONCLUSION: The proposed MostMoCo-UTE provides improved quality of structural images and quantification of ventilation for free-breathing pulmonary MRI. It has the potential for the detection of structural and functional disorders of the lung in clinical settings.

Imaging iron and neuromelanin simultaneously using a single 3D gradient echo magnetization transfer sequence: Combining neuromelanin, iron and the nigrosome-1 sign as complementary imaging biomarkers in early stage Parkinson's disease.

Diagnosing early stage Parkinson's disease (PD) is still a clinical challenge. Previous studies using iron, neuromelanin (NM) or the Nigrosome-1 (N1) sign in the substantia nigra (SN) by themselves have been unable to provide sufficiently high diagnostic performance for these methods to be adopted clinically. Our goal in this study was to extract the NM complex volume, iron content and volume representing the entire SN, and the N1 sign as potential complementary imaging biomarkers using a single 3D magnetization transfer contrast (MTC) gradient echo sequence and to evaluate their diagnostic performance and clinical correlations in early stage PD. A total of 40 early stage idiopathic PD subjects and 40 age- and sex-matched healthy controls (HCs) were imaged at 3T. NM boundaries (representing the SN pars compacta (SNpc) and parabrachial pigmented nucleus) and iron boundaries representing the total SN (SNpc and SN pars reticulata) were determined semi-automatically using a dynamic programming (DP) boundary detection algorithm. Receiver operating characteristic analyses were performed to evaluate the utility of these imaging biomarkers in diagnosing early stage PD. A correlation analysis was used to study the relationship between these imaging measures and the clinical scales. We also introduced the concept of NM and total iron overlap volumes to demonstrate the loss of NM relative to the iron containing SN. Furthermore, all 80 cases were evaluated for the N1 sign independently. The NM and SN volumes were lower while the iron content was higher in the SN for PD subjects compared to HCs. Interestingly, the PD subjects with bilateral loss of the N1 sign had the highest iron content. The area under the curve (AUC) values for the average of both hemispheres for single measures were: .960 for NM complex volume; .788 for total SN volume; .740 for SN iron content and .891 for the N1 sign. Combining NM complex volume with each of the following measures through binary logistic regression led to AUC values for the averaged right and left sides of: .976 for total iron content; .969 for total SN volume, .965 for overlap volume and .983 for the N1 sign. We found a negative correlation between SN volume and UPDRS-III (R2 = .22, p = .002). While the N1 sign performed well, it does not contain any information about iron content or NM quantitatively, therefore, marrying this sign with the NM and iron measures provides a better physiological explanation of what is happening when the N1 sign disappears in PD subjects. In summary, the combination of NM complex volume, SN volume, iron content and the N1 sign as derived from a single MTC sequence provides complementary information for understanding and diagnosing early stage PD.

A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19.

OBJECTIVES: To develop a predictive model and scoring system to enhance the diagnostic efficiency for coronavirus disease 2019 (COVID-19). METHODS: From January 19 to February 6, 2020, 88 confirmed COVID-19 patients presenting with pneumonia and 80 non-COVID-19 patients suffering from pneumonia of other origins were retrospectively enrolled. Clinical data and laboratory results were collected. CT features and scores were evaluated at the segmental level according to the lesions' position, attenuation, and form. Scores were calculated based on the size of the pneumonia lesion, which graded at the range of 1 to 4. Air bronchogram, tree-in-bud sign, crazy-paving pattern, subpleural curvilinear line, bronchiectasis, air space, pleural effusion, and mediastinal and/or hilar lymphadenopathy were also evaluated. RESULTS: Multivariate logistic regression analysis showed that history of exposure (ß = 3.095, odds ratio (OR) = 22.088), leukocyte count (ß = - 1.495, OR = 0.224), number of segments with peripheral lesions (ß = 1.604, OR = 1.604), and crazy-paving pattern (ß = 2.836, OR = 2.836) were used for establishing the predictive model to identify COVID-19-positive patients (p < 0.05). In this model, values of area under curve (AUC) in the training and testing groups were 0.910 and 0.914, respectively (p < 0.001). A predicted score for COVID-19 (PSC-19) was calculated based on the predictive model by the following formula: PSC-19 = 2 × history of exposure (0-1 point) - 1 × leukocyte count (0-2 points) + 1 × peripheral lesions (0-1 point) + 2 × crazy-paving pattern (0-1 point), with an optimal cutoff point of 1 (sensitivity, 88.5%; specificity, 91.7%). CONCLUSIONS: Our predictive model and PSC-19 can be applied for identification of COVID-19-positive cases, assisting physicians and radiologists until receiving the results of reverse transcription-polymerase chain reaction (RT-PCR) tests. KEY POINTS: • Prediction of RT-PCR positivity is crucial for fast diagnosis of patients suspected of having coronavirus disease 2019 (COVID-19). • Typical CT manifestations are advantageous for diagnosing COVID-19 and differentiation of COVID-19 from other types of pneumonia. • A predictive model and scoring system combining both clinical and CT features were herein developed to enable high diagnostic efficiency for COVID-19.

Clinical Features and Chest CT Manifestations of Coronavirus Disease 2019 (COVID-19) in a Single-Center Study in Shanghai, China.

OBJECTIVE. Confronting the new coronavirus infection known as coronavirus disease 2019 (COVID-19) is challenging and requires excluding patients with suspected COVID-19 who actually have other diseases. The purpose of this study was to assess the clinical features and CT manifestations of COVID-19 by comparing patients with COVID-19 pneumonia with patients with non-COVID-19 pneumonia who presented at a fever observation department in Shanghai, China. MATERIALS AND METHODS. Patients were retrospectively enrolled in the study from January 19 through February 6, 2020. All patients underwent real-time reverse transcription-polymerase chain reaction (RT-PCR) testing. RESULTS. Eleven patients had RT-PCR test results that were positive for severe acute respiratory syndrome coronavirus 2, whereas 22 patients had negative results. No statistical difference in clinical features was observed (p > 0.05), with the exception of leukocyte and platelet counts (p < 0.05). The mean (± SD) interval between onset of symptoms and admission to the fever observation department was 4.40 ± 2.00 and 5.52 ± 4.00 days for patients with positive and negative RT-PCR test results, respectively. The frequency of opacifications in patients with positive results and patients with negative results, respectively, was as follows: ground-glass opacities (GGOs), 100.0% versus 90.9%; mixed GGO, 63.6% versus 72.7%; and consolidation, 54.5% versus 77.3%. In patients with positive RT-PCR results, GGOs were the most commonly observed opacification (seen in 100.0% of patients) and were predominantly located in the peripheral zone (100.0% of patients), compared with patients with negative results (31.8%) (p = 0.05). The median number of affected lung lobes and segments was higher in patients with positive RT-PCR results than in those with negative RT-PCR results (five vs 3.5 affected lobes and 15 vs nine affected segments; p < 0.05). Although the air bronchogram reticular pattern was more frequently seen in patients with positive results, centrilobular nodules were less frequently seen in patients with positive results. CONCLUSION. At the point during the COVID-19 outbreak when this study was performed, imaging patterns of multifocal, peripheral, pure GGO, mixed GGO, or consolidation with slight predominance in the lower lung and findings of more extensive GGO than consolidation on chest CT scans obtained during the first week of illness were considered findings highly suspicious of COVID-19.

Application of ordinal logistic regression analysis to identify the determinants of illness severity of COVID-19 in China.

Corona Virus Disease 2019 (COVID-19) has presented an unprecedented challenge to the health-care system across the world. The current study aims to identify the determinants of illness severity of COVID-19 based on ordinal responses. A retrospective cohort of COVID-19 patients from four hospitals in three provinces in China was established, and 598 patients were included from 1 January to 8 March 2020, and divided into moderate, severe and critical illness group. Relative variables were retrieved from electronic medical records. The univariate and multivariate ordinal logistic regression models were fitted to identify the independent predictors of illness severity. The cohort included 400 (66.89%) moderate cases, 85 (14.21%) severe and 113 (18.90%) critical cases, of whom 79 died during hospitalisation as of 28 April. Patients in the age group of 70+ years (OR = 3.419, 95% CI: 1.596-7.323), age of 40-69 years (OR = 1.586, 95% CI: 0.824-3.053), hypertension (OR = 3.372, 95% CI: 2.185-5.202), ALT >50 µ/l (OR = 3.304, 95% CI: 2.107-5.180), cTnI >0.04 ng/ml (OR = 7.464, 95% CI: 4.292-12.980), myohaemoglobin>48.8 ng/ml (OR = 2.214, 95% CI: 1.42-3.453) had greater risk of developing worse severity of illness. The interval between illness onset and diagnosis (OR = 1.056, 95% CI: 1.012-1.101) and interval between illness onset and admission (OR = 1.048, 95% CI: 1.009-1.087) were independent significant predictors of illness severity. Patients of critical illness suffered from inferior survival, as compared with patients in the severe group (HR = 14.309, 95% CI: 5.585-36.659) and in the moderate group (HR = 41.021, 95% CI: 17.588-95.678). Our findings highlight that the identified determinants may help to predict the risk of developing more severe illness among COVID-19 patients and contribute to optimising arrangement of health resources.

CT characteristics of non-small cell lung cancer with epidermal growth factor receptor mutation: a systematic review and meta-analysis.

BACKGROUND: To systematically investigate the relationship between CT morphological features and the presence of epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC). METHODS: All studies about the CT morphological features of NSCLC with EGFR mutations published between January 1, 2000 and March 15, 2015 were searched in the PubMed and EMBASE databases. Qualified studies were selected according to inclusion criteria. The frequency of EGFR mutations and CT features of ground-glass opacity (GGO) content, tumor size, cavitation, air-bronchogram, lobulation, and spiculation were extracted. The relationship between EGFR mutations and each of these CT features was tested based upon the weighted mean difference or inverse variance in the form of an odds ratio at a 95% confidence interval using Forest Plots. The publication bias was examined using Egger's test. RESULTS: A total of 13 studies, consisting of 2146 NSCLC patients, were included, and 51.12% (1097/2146) of patients had EGFR mutations. The EGFR mutations were present in NSCLC with part-solid GGO in contrast to nonsolid GGO (OR = 0.49, 95% CI = 0.25-0.96, P = 0.04). Other CT features such as tumor size, cavitation, air-bronchogram, lobulation and spiculation did not demonstrate statistically significant correlation with EGFR mutations individually (P = 0.91; 0.67; 0.12; 0.45; and 0.36, respectively). No publication bias among the selected studies was noted in this meta-analysis (Egger's tests, P > 0.05 for all). CONCLUSION: This meta-analysis demonstrated that NSCLC with CT morphological features of part-solid GGO tended to be EGFR mutated, which might provide an important clue for the correct selection of patients treated with molecular targeted therapies.

A multiple cross-linking strategy to develop an environment-friendly and water resistance wheat gluten protein wood adhesive.

Wheat gluten (WG) shows great promise to synthesize environment-friendly wood adhesives. However, their weak bonding strength and poor water resistance have limited its application in the commercial wood-based panel industry. In this study, a novel WG-based adhesive was developed by constructing a multiple cross-linking network generated by covalent and non-covalent bonds. The potential mechanism was revealed by FT-IR analysis. Furthermore, their surface morphology, thermal stability, viscosity, and residual rate of adhesives with different compositions were systematically characterized and compared. The results showed that the hydrogen bonding, reactions between amine groups and tannin, and ring opening reaction of epoxy, synergistically contributed to generate a highly crosslinked network. The wet/boil water strength of the plywood prepared from WG/tannin/ethylene imine polymer (PEI)-glycerol triglycidyl ether (GTE) adhesive with the addition of 15 % GTE could reach 1.21 MPa and 1.20 MPa, respectively, and a mildew resistance ability was observed. This study provides a facile strategy to fabricate high-performance plant protein-based adhesives with desirable water resistance for practical application.

MRI index of glymphatic system mediates the influence of locus coeruleus on cognition in Parkinson's disease.

INTRODUCTION: Although locus coeruleus (LC) has been demonstrated to play a critical role in the cognitive function of Parkinson's disease (PD), the underlying mechanism has not been elucidated. The objective was to investigate the relationship among LC degeneration, cognitive performance, and the glymphatic function in PD. METHODS: In this retrospective study, 71 PD subjects (21 with normal cognition; 29 with cognitive impairment (PD-MCI); 21 with dementia (PDD)) and 26 healthy controls were included. All participants underwent neuromelanin-sensitive magnetic resonance imaging (NM-MRI) and diffusion tensor image scanning on a 3.0 T scanner. The brain glymphatic function was measured using diffusion along the perivascular space (ALPS) index, while LC degeneration was estimated using the NM contrast-to-noise ratio of LC (CNRLC). RESULTS: The ALPS index was significantly lower in both the whole PD group (P = 0.04) and the PDD subgroup (P = 0.02) when compared to the controls. Similarly, the CNRLC was lower in the whole PD group (P < 0.001) compared to the controls. In the PD group, a positive correlation was found between the ALPS index and both the Montreal Cognitive Assessment (MoCA) score (r = 0.36; P = 0.002) and CNRLC (r = 0.26; P = 0.03). Mediation analysis demonstrated that the ALPS index acted as a significant mediator between CNRLC and the MoCA score in PD subjects. CONCLUSION: The ALPS index, a neuroimaging marker of glymphatic function, serves as a mediator between LC degeneration and cognitive function in PD.

Derivation and external validation of a nomogram predicting the occurrence of severe illness among hospitalized coronavirus disease 2019 patients: a 2020 Chinese multicenter retrospective study.

Background: The worldwide pandemic of coronavirus disease 2019 (COVID-19) has still been an overwhelming public health challenge, and it is vital to identify determinants early to forecast the risk of severity using indicators easily available at admission. The current multicenter retrospective study aimed to derive and validate a user-friendly and effective nomogram to address this issue. Methods: A training cohort consisting of 437 confirmed COVID-19 cases from three hospitals in Hubei province (Tongji Hospital affiliated with Huazhong University of Science and Technology, Wuhan Third Hospital of Wuhan University and Wuhan Jinyintan Hospital in Hubei province) was retrospectively analyzed to construct a predicting model, and another cohort of 161 hospitalized patients from Public Health Clinical Center of Shanghai was selected as an external validation cohort from January 1, 2020 to March 8, 2020. Determinants of developing into severe COVID-19 were probed using univariate regression together with a multivariate stepwise regression model. The risk of progression to severe COVID-19 was forecasted using the derived nomogram. The performances of the nomogram regarding the discrimination and calibration were assessed in the cohort of training as well as the cohort of external validation, respectively. Results: A total of 144 (32.95%) and 54 (33.54%) patients, respectively, in cohorts of training and validation progressed to severe COVID-19 during hospitalization. Multivariable analyses showed determinants of severity consisted of hypertension, shortness of breath, platelet count, alanine aminotransferase (ALT), potassium, cardiac troponin I (cTnI), myohemoglobin, procalcitonin (PCT) and intervals from onset to diagnosis. The nomogram had good discrimination with concordance indices being 0.887 (95% CI: 0.854-0.919) and 0.850 (95% CI: 0.815-0.885) in internal and external validation, respectively. Calibration curves exhibited excellent concordance between the predictions by nomogram and actual observations in two cohorts. Conclusions: We have established and validated an early predicting nomogram model, which can contribute to determine COVID-19 cases at risk of progression to severe illness.

CT perfusion imaging can detect residual lung tumor early after radiofrequency ablation: a preliminary animal study on both tumoral and peri-tumoral region assessment.

BACKGROUND: Radiofrequency ablation (RFA) is a minimally invasive procedure to treat lung cancer. Timely evaluation on residual lung tumor after RFA is crucial to the prognosis, hence, our objective is to assess CT perfusion (CTP) on detection of residual lung tumor early after RFA. METHODS: CTP imaging was performed in 24 lung VX2 tumor models 1 day before and within 1 hour after RFA. CTP maps with dual-input (n=24) and single-input [n=13, with predominant ground glass opacity (GGO) after RFA] models were generated using the maximal slope method. Regions of interest were independently placed on the maximal cross-sectional tumor before and after RFA and on GGO after RFA by two thoracic radiologists. The bronchial flow (BF), pulmonary flow (PF) and perfusion index (PI) were compared between pre-RFA and post-RFA images. The parameters (BF, PF and PI of tumor; PF of GGO) of the complete and incomplete RFA groups were compared based on nicotinamide adenine dinucleotide hydrogen (NADH) and TdT-mediated dUTP nick-end labeling (TUNEL) staining and were correlated with the microvascular density (MVD). RESULTS: The BF and PF decreased after RFA (all P values <0.03). The decrease in BF and PF (ΔBF and ΔPF) in the complete RFA group was higher (P=0.01; 0.02). The areas under the curve (AUC) of ΔBF and ΔPF at 14.85 and 17.25 mL/min/100 mL in determination of tumor with complete ablation were 0.80 and 0.78, respectively. ΔBF was positively correlated with MVD (P=0.046, r=0.468). PF of GGO with incomplete RFA was higher (P=0.001). The AUC of PF ≤29.4 mL/min/100 mL in determination of tumor with complete ablation was 0.99. CONCLUSIONS: CTP could detect residual lung tumor early after RFA in a rabbit model, which might provide a clinical solution to early treatment assessment after RFA.

Stability of AI-Enabled Diagnosis of Parkinson's Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging.

Purpose: Parkinson's disease (PD) diagnosis algorithms based on quantitative susceptibility mapping (QSM) and image algorithms rely on substantia nigra (SN) labeling. However, the difference between SN labels from different experts (or segmentation algorithms) will have a negative impact on downstream diagnostic tasks, such as the decrease of the accuracy of the algorithm or different diagnostic results for the same sample. In this article, we quantify the accuracy of the algorithm on different label sets and then improve the convolutional neural network (CNN) model to obtain a high-precision and highly robust diagnosis algorithm. Methods: The logistic regression model and CNN model were first compared for classification between PD patients and healthy controls (HC), given different sets of SN labeling. Then, based on the CNN model with better performance, we further proposed a novel "gated pooling" operation and integrated it with deep learning to attain a joint framework for image segmentation and classification. Results: The experimental results show that, with different sets of SN labeling that mimic different experts, the CNN model can maintain a stable classification accuracy at around 86.4%, while the conventional logistic regression model yields a large fluctuation ranging from 78.9 to 67.9%. Furthermore, the "gated pooling" operation, after being integrated for joint image segmentation and classification, can improve the diagnosis accuracy to 86.9% consistently, which is statistically better than the baseline. Conclusion: The CNN model, compared with the conventional logistic regression model using radiomics features, has better stability in PD diagnosis. Furthermore, the joint end-to-end CNN model is shown to be suitable for PD diagnosis from the perspectives of accuracy, stability, and convenience in actual use.

Preoperative pembrolizumab combined with chemoradiotherapy for oesophageal squamous cell carcinoma (PALACE-1).

BACKGROUND: To investigate the safety and activity of preoperative pembrolizumab combined with chemoradiotherapy for resectable oesophageal squamous cell carcinoma (ESCC) (ClinicalTrials.gov number, NCT03792347). METHODS: Twenty resectable ESCC patients, regardless of programmed death ligand-1 status, received preoperative pembrolizumab with concurrent chemoradiotherapy (PPCT). Preoperative therapy includes carboplatin (area under the curve of 2 mg per milliliter per minute, once a week for 5 weeks), paclitaxel (50 mg/m2, once a week for 5 weeks), radiotherapy (23 fractions of 1.8 Gy, 5 fraction a week) and pembrolizumab (2 mg/kg) on days 1 and 22. Within 4-6 weeks after preoperative therapy, patients underwent surgery. The primary end-point was safety and secondary outcome measures were feasibility, pathologic complete response (pCR) rate and radiographic response. Immune signature of CD8+ T cells was evaluated in surgical specimens using immunohistochemistry and immunofluorescence. RESULTS: All patients have received PPCT successfully, except one patient who missed the last dose of chemotherapy due to leukopenia. Grade III and higher adverse events (AEs) were observed in 13 patients (13/20, 65%), and one patient had a grade V AE. The most frequent grade III AE was lymphopenia (12/13, 92%). Eighteen patients underwent surgery within 4-9 weeks after PPCT and the pCR rate was 55.6% (10/18). The percentage of transcription factor 1 positive cells was significantly higher in specimens of pCR group than those of non-pCR group (p value = 0.010). CONCLUSIONS: PPCT was safe, did not delay surgery, and induced a pCR in 55.6% of resected tumours. A phase II multicentre study is undergoing for further confirmation of efficacy (NCT04435197).

A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients.

BACKGROUND: The assessment of the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far. We aimed to establish a deep learning (DL) model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of COVID-19. METHODS: One hundred ninety-six hospitalized patients with confirmed COVID-19 were enrolled from January 20 to February 10, 2020 in our centre, and were divided into severe and non-severe groups. The clinico-radiological data on admission were retrospectively collected and compared between the two groups. The optimal clinico-radiological features were determined based on least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and a predictive nomogram model was established by five-fold cross-validation. Receiver operating characteristic (ROC) analyses were conducted, and the areas under the receiver operating characteristic curve (AUCs) of the nomogram model, quantitative CT parameters that were significant in univariate analysis, and pneumonia severity index (PSI) were compared. RESULTS: In comparison with the non-severe group (151 patients), the severe group (45 patients) had a higher PSI (P<0.001). DL-based quantitative CT indicated that the mass of infection (MOICT) and the percentage of infection (POICT) in the whole lung were higher in the severe group (both P<0.001). The nomogram model was based on MOICT and clinical features, including age, cluster of differentiation 4 (CD4)+ T cell count, serum lactate dehydrogenase (LDH), and C-reactive protein (CRP). The AUC values of the model, MOICT, POICT, and PSI scores were 0.900, 0.813, 0.805, and 0.751, respectively. The nomogram model performed significantly better than the other three parameters in predicting severity (P=0.003, P=0.001, and P<0.001, respectively). CONCLUSIONS: Although quantitative CT parameters and the PSI can well predict the severity of COVID-19, the DL-based quantitative CT model is more efficient.