Synthesized 7T MPRAGE From 3T MPRAGE Using Generative Adversarial Network and Validation in Clinical Brain Imaging: A Feasibility Study.

BACKGROUND: Ultra-high field 7T MRI can provide excellent tissue contrast and anatomical details, but is often cost prohibitive, and is not widely accessible in clinical practice. PURPOSE: To generate synthetic 7T images from widely acquired 3T images with deep learning and to evaluate the feasibility of this approach for brain imaging. STUDY TYPE: Prospective. POPULATION: 33 healthy volunteers and 89 patients with brain diseases, divided into training, and evaluation datasets in the ratio 4:1. SEQUENCE AND FIELD STRENGTH: T1-weighted nonenhanced or contrast-enhanced magnetization-prepared rapid acquisition gradient-echo sequence at both 3T and 7T. ASSESSMENT: A generative adversarial network (SynGAN) was developed to produce synthetic 7T images from 3T images as input. SynGAN training and evaluation were performed separately for nonenhanced and contrast-enhanced paired acquisitions. Qualitative image quality of acquired 3T and 7T images and of synthesized 7T images was evaluated by three radiologists in terms of overall image quality, artifacts, sharpness, contrast, and visualization of vessel using 5-point Likert scales. STATISTICAL TESTS: Wilcoxon signed rank tests to compare synthetic 7T images with acquired 7T and 3T images and intraclass correlation coefficients to evaluate interobserver variability. P < 0.05 was considered significant. RESULTS: Of the 122 paired 3T and 7T MRI scans, 66 were acquired without contrast agent and 56 with contrast agent. The average time to generate synthetic images was ~11.4 msec per slice (2.95 sec per participant). The synthetic 7T images achieved significantly improved tissue contrast and sharpness in comparison to 3T images in both nonenhanced and contrast-enhanced subgroups. Meanwhile, there was no significant difference between acquired 7T and synthetic 7T images in terms of all the evaluation criteria for both nonenhanced and contrast-enhanced subgroups (P ≥ 0.180). DATA CONCLUSION: The deep learning model has potential to generate synthetic 7T images with similar image quality to acquired 7T images. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Generative Adversarial Network-based Noncontrast CT Angiography for Aorta and Carotid Arteries.

Background Iodinated contrast agents (ICAs), which are widely used in CT angiography (CTA), may cause adverse effects in humans, and their use is time-consuming and costly. Purpose To develop an ICA-free deep learning imaging model for synthesizing CTA-like images and to assess quantitative and qualitative image quality as well as the diagnostic accuracy of synthetic CTA (Syn-CTA) images. Materials and Methods A generative adversarial network (GAN)-based CTA imaging model was trained, validated, and tested on retrospectively collected pairs of noncontrast CT and CTA images of the neck and abdomen from January 2017 to June 2022, and further validated on an external data set. Syn-CTA image quality was evaluated using quantitative metrics. In addition, two senior radiologists scored the visual quality on a three-point scale (3 = good) and determined the vascular diagnosis. The validity of Syn-CTA images was evaluated by comparing the visual quality scores and diagnostic accuracy of aortic and carotid artery disease between Syn-CTA and real CTA scans. Results CT scans from 1749 patients (median age, 60 years [IQR, 50-68 years]; 1057 male patients) were included in the internal data set: 1137 for training, 400 for validation, and 212 for testing. The external validation set comprised CT scans from 42 patients (median age, 67 years [IQR, 59-74 years]; 37 male patients). Syn-CTA images had high similarity to real CTA images (normalized mean absolute error, 0.011 and 0.013 for internal and external test set, respectively; peak signal-to-noise ratio, 32.07 dB and 31.58 dB; structural similarity, 0.919 and 0.906). The visual quality of Syn-CTA and real CTA images was comparable (internal test set, P = .35; external validation set, P > .99). Syn-CTA showed reasonable to good diagnostic accuracy for vascular diseases (internal test set: accuracy = 94%, macro F1 score = 91%; external validation set: accuracy = 86%, macro F1 score = 83%). Conclusion A GAN-based model that synthesizes neck and abdominal CTA-like images without the use of ICAs shows promise in vascular diagnosis compared with real CTA images. Clinical trial registration no. NCT05471869 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Zhang and Turkbey in this issue.

DeepScaffold: A Comprehensive Tool for Scaffold-Based De Novo Drug Discovery Using Deep Learning.

The ultimate goal of drug design is to find novel compounds with desirable pharmacological properties. Designing molecules retaining particular scaffolds as their core structures is an efficient way to obtain potential drug candidates. We propose a scaffold-based molecular generative model for drug discovery, which performs molecule generation based on a wide spectrum of scaffold definitions, including Bemis-Murcko scaffolds, cyclic skeletons, and scaffolds with specifications on side-chain properties. The model can generalize the learned chemical rules of adding atoms and bonds to a given scaffold. The generated compounds were evaluated by molecular docking in DRD2 targets, and the results demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold and de novo drug design of potential drug candidates with specific docking scores.

TF3P: Three-Dimensional Force Fields Fingerprint Learned by Deep Capsular Network.

Molecular fingerprints are the workhorse in ligand-based drug discovery. In recent years, an increasing number of research papers reported fascinating results on using deep neural networks to learn 2D molecular representations as fingerprints. It is anticipated that the integration of deep learning would also contribute to the prosperity of 3D fingerprints. Here, we unprecedentedly introduce deep learning into 3D small molecule fingerprints, presenting a new one we termed as the three-dimensional force fields fingerprint (TF3P). TF3P is learned by a deep capsular network whose training is in no need of labeled data sets for specific predictive tasks. TF3P can encode the 3D force fields information of molecules and demonstrates the stronger ability to capture 3D structural changes, to recognize molecules alike in 3D but not in 2D, and to identify similar targets inaccessible by other 2D or 3D fingerprints based on only ligands similarity. Furthermore, TF3P is compatible with both statistical models (e.g., similarity ensemble approach) and machine learning models. Altogether, we report TF3P as a new 3D small molecule fingerprint with a promising future in ligand-based drug discovery. All codes are written in Python and available at https://github.com/canisw/tf3p.

Quantitative radiomic biomarkers for discrimination between neuromyelitis optica spectrum disorder and multiple sclerosis.

BACKGROUND: Precise diagnosis and early appropriate treatment are of importance to reduce neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS) morbidity. Distinguishing NMOSD from MS based on clinical manifestations and neuroimaging remains challenging. PURPOSE: To investigate radiomic signatures as potential imaging biomarkers for distinguishing NMOSD from MS, and to develop and validate a diagnostic radiomic-signature-based nomogram for individualized disease discrimination. STUDY TYPE: Retrospective, cross-sectional study. SUBJECTS: Seventy-seven NMOSD patients and 73 MS patients. FIELD STRENGTH/SEQUENCE: 3T/T2 -weighted imaging. ASSESSMENT: Eighty-eight patients and 62 patients were respectively enrolled in the primary and validation cohorts. Quantitative radiomic features were automatically extracted from lesioned regions on T2 -weighted imaging. A least absolute shrinkage and selection operator analysis was used to reduce the dimensionality of features. Finally, we constructed a radiomic nomogram for disease discrimination. STATISTICAL TESTS: Features were compared using the Mann-Whitney U-test with a nonnormal distribution. We depicted the nomogram on the basis of the results of the logistic regression using the rms package in R. The Hmisc package was used to investigate the performance of the nomogram via Harrell's C-index. RESULTS: A total of 273 quantitative radiomic features were extracted from lesions. A multivariable analysis selected 11 radiomic features and five clinical features to be included in the model. The radiomic signature (P < 0.001 for both the primary and validation cohorts) showed good potential for building a classification model for disease discrimination. The area under the receiver operating characteristic curve was 0.9880 for the training cohort and 0.9363 for the validation cohort. The nomogram exhibited good discrimination, a concordance index of 0.9363, and good calibration in the primary cohort. The nomogram showed similar discrimination, concordance (0.9940), and calibration in the validation cohort. DATA CONCLUSION: The diagnostic radiomic-signature-based nomogram has potential utility for individualized disease discrimination of NMOSD from MS in clinical practice. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1113-1121.

Graph-based generative models for de Novo drug design.

The discovery of new chemical entities is a crucial part of drug discovery, which requires the lead compounds to have desired properties to be pharmaceutically active. De novo drug design aims to generate and optimize novel ligands for macromolecular targets from scratch. The development of graph-based deep generative neural networks has provided a new method. In this review, we gave a brief introduction to graph representation and graph-based generative models for de novo drug design, summarized them as four architectures, and concluded each's characteristics. We also discussed generative models for scaffold- and fragment-based design and graph-based generative models' future directions.

Calcium-Mobilizing Behaviors of Neutral Cyclic ADP-Ribose Mimics that Integrate Modifications to the Nucleobase, Northern Ribose and Pyrophosphate.

Cyclic adenosine diphosphate ribose (cADPR) is an endogenous Ca2+ mobilizer involved in diverse cellular processes. Mimics of cADPR play a crucial role in investigating the molecular mechanism(s) of cADPR-mediated signaling. Here, compound 3, a mimic of cADPR in which a neutral triazole moiety and an ether linkage were introduced to substitute the pyrophosphate and "northern" ribose components, respectively, was synthesized for the first time. The pharmacological activities in Jurkat cells indicated that this mimic is capable of penetrating plasma membrane and inciting Ca2+ release from the endoplasmic reticulum (ER) through the action of ryanodine receptors (RyRs) and triggering Ca2+ influx. Furthermore, a uridine moiety was introduced in place of adenine and the new cADPR mimics 4 and 5 were synthesized. The results of biological investigation showed that these mimics also targeted RyRs and retained moderate Ca2+ agonistic activities. The results indicated that the neutral cADPR mimics had the same targets for inducing Ca2+ signaling.

[Analysis of traditional Chinese medicine prescriptions based on support vector machine and analytic hierarchy process].

Combined use of drugs is a hot spot in the research of new drugs nowadays, and traditional Chinese medicine (TCM) is a classic practice in the combined use of drugs. In this paper, the compatibility of TCM prescriptions and the related properties of composed herbs were calculated and studied to verify and discuss the feasibility of the results in guiding compatibility. Research Group on New Drug Design, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences had established a structured database of TCM prescriptions by using traditional Chinese medicine inheritance support system (TCMISS V2.0), including 4 012 prescription compatibilities, 2 072 drug components, 381 kinds of TCM diseases, 316 kinds of TCM syndromes and 26 kinds of drug properties. On the basis of the created database above, Support Vector Machine (SVM) was used to analyze the prescription compatibility data and establish a model for predicting feasibility of drug compatibilities. Analytic Hierarchy Process (AHP) and cluster analysis were used to study the influence of drug properties in the rationality of prescription compatibility. The computational results showed that the accuracy in efficacy prediction of two data sets, i.e. prescription-disease and prescription-syndrome, was up to 90% in the linear SVM model. The macro₋averaging and micro₋averaging of the two models were around 0.92, 0.46, respectively. After AHP mapping, most of the incompatible combinations showed significant difference with other drug combinations during the clustering process in the vertical icicle, indicating that the proper machine learning algorithm can be used to lay the foundation for further exploring the combination rules in TCM and establishing more detailed drug-disease and syndrome predicting models, and provide theoretical guidance for the study of the combined use of drugs to a certain degree.

Effect of alpha1-blockers on stentless ureteroscopic lithotripsy.

OBJECTIVE: To evaluate the clinical efficiency of alpha1-adrenergic antagonists on stentless ureteroscopic lithotripsy treating uncomplicated lower ureteral stones. MATERIALS AND METHODS: From January 2007 to January 2013, 84 patients who have uncomplicated lower ureteral stones treated by ureteroscopic intracorporeal lithotripsy with the holmium laser were analyzed. The patients were divided into two groups, group A (44 patients received indwelled double-J stents) and group B (40 patients were treated by alpha1-adrenergic antagonists without stents). All cases of group B were treated with alpha1 blocker for 1 week. RESULTS: The mean operative time of group A was significantly longer than group B. The incidences of hematuria, flank/abdominal pain, frequency/urgency after surgery were statistically different between both groups. The stone-free rate of each group was 100%. CONCLUSIONS: The effect of alpha1-adrenergic antagonists is more significant than indwelling stent after ureteroscopic lithotripsy in treating uncomplicated lower ureteral stones.

Profiling functional networks identify activation of corticostriatal connectivity in ET patients after MRgFUS thalamotomy.

BACKGROUND: MR-guided focused ultrasound (MRgFUS) thalamotomy is a novel and effective treatment for medication-refractory tremor in essential tremor (ET), but how the brain responds to this deliberate lesion is not clear. OBJECTIVE: The current study aimed to evaluate the immediate and longitudinal alterations of functional networks after MRgFUS thalamotomy. METHODS: We retrospectively obtained preoperative and postoperative 30-day, 90-day, and 180-day data of 31 ET patients subjected with MRgFUS thalamotomy from 2018 to 2020. Their archived resting-state functional MRI data were used for functional network comparison as well as graph-theory metrics analysis. Both partial least squares (PLS) regression and linear regression were conducted to associate functional features to tremor symptoms. RESULTS: MRgFUS thalamotomy dramatically abolished tremors, while global functional network only sustained immediate fluctuation within one week after the surgery. Network-based statistics have identified a long-term enhanced corticostriatal subnetwork by comparison between 180-day and preoperative data (P = 0.019). Within this subnetwork, network degree, global efficiency and transitivity were significantly recovered in ET patients right after MRgFUS thalamotomy compared to the pre-operative timepoint (P < 0.05), as well as hemisphere lateralization (P < 0.001). The PLS main component significantly accounted for 33.68 % and 34.16 % of the total variances of hand tremor score and clinical rating scale for tremor (CRST)-total score (P = 0.037 and 0.027). Network transitivity of this subnetwork could serve as a reliable biomarker for hand tremor score control prediction at 180-day after the surgery (ß = 2.94, P = 0.03). CONCLUSION: MRgFUS thalamotomy promoted corticostriatal connectivity activation correlated with tremor improvement in ET patient after MRgFUS thalamotomy.

Tumor characteristics, brain functional activity, and connectivity of tinnitus in patients with vestibular schwannoma: a pilot study.

Background: The mechanism underlying tinnitus remains unclear, and when it coexists with vestibular schwannoma (VS), it can significantly diminish the quality of life for affected patients. This study aimed to determine the correlation between preoperative clinical characteristics of VS, postoperative changes in brain function, and tinnitus in patients with VS through a cohort study. Methods: We collected data from 80 patients with VS preoperatively and 28 patients with VS preoperatively and postoperatively, and recruited 28 healthy controls. We used Chi-squared tests and unpaired t-tests to identify clinical characteristics with a significant preoperative effect. We used paired t-tests to identify brain regions where patients demonstrated significant changes in amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) postoperatively. Tinnitus severity was evaluated using the Tinnitus Handicap Inventory (THI) and Visual Analogue Scale (VAS). Pearson correlation coefficients were applied to assess the relationship between the changes in ALFF and ReHo and the changes in THI and VAS scores postoperatively. We also conducted seed- and region of interest (ROI)-based functional connectivity (FC) analyses. Results: Before surgery, patients with VS with tinnitus (n=49) had smaller tumors (t=3.293; P<0.001), more solid tumor (χ2=4.559; P=0.033), and less extrusion into the cerebellum brain stem (χ2=10.345; P=0.001) than those without tinnitus (n=31). After surgery, the 28 patients with VS showed a significant reduction in ALFF in the left Cerebellum_Crus2 (a lobule in the cerebellum anatomy) (ROI 1) and a significant reduction in ReHo in the left Cerebellum_Crus1 (a lobule in the cerebellum anatomy) (ROI 2) and the right precuneus (ROI 3). Conversely, ReHo was significantly increased in the right precentral gyrus (ROI 4) [cluster-level P value family-wise error (PFWE) <0.05]. The changes in ALFF values were negatively correlated with changes in the VAS score (r=-0.32; P<0.05). The FC strengths of patients between ROI 2 and the left and right posterior cingulate gyrus were significantly decreased postoperatively [false discovery rate (FDR) correction; P<0.05]. Conclusions: Preoperative tinnitus in patients with VS may be influenced by tumor characteristics. The functional activities of brain regions are possibly altered postoperatively, which may be involved in the maintenance of postoperative tinnitus. Notably, the changes in ALFF are correlated with tinnitus.

Corrigendum: Atypical functional hierarchy contributed to the tinnitus symptoms in patients with vestibular schwannoma.

[This corrects the article DOI: 10.3389/fnins.2023.1084270.].

Atypical functional hierarchy contributed to the tinnitus symptoms in patients with vestibular schwannoma.

Objective: Tinnitus is frequently found in patients with vestibular schwannoma (VS), but its underlying mechanisms are currently unclear. Methods: Both preoperative (VS pre ) and postoperative (VS post ) functional MR images were collected from 32 patients with unilateral VS and matched healthy controls (HCs). Connectome gradients were generated for the identification of altered regions and perturbed gradient distances. Tinnitus measurements were conducted for predictive analysis with neuroimaging-genetic integration analysis. Results: There were 56.25% of preoperative patients and 65.63% of postoperative patients suffering from ipsilateral tinnitus, respectively. No relevant factors were identified including basic demographics info, hearing performances, tumor features, and surgical approaches. Functional gradient analysis confirmed atypical functional features of visual areas in VS pre were rescued after tumor resection, while the gradient performance in the postcentral gyrus continues to maintain (VS post vs. HC : P = 0.016). The gradient features of the postcentral gyrus were not only significantly decreased in patients with tinnitus (P FDR = 0.022), but also significantly correlated with tinnitus handicap inventory (THI) score (r = -0.30, P = 0.013), THI level (r = -0.31, P = 0.010), and visual analog scale (VAS) rating (r = -0.31, P = 0.0093), which could be used to predict VAS rating in the linear model. Neuropathophysiological features linked to the tinnitus gradient framework were linked to Ribosome dysfunction and oxidative phosphorylation. Conclusion: Altered functional plasticity in the central nervous system is involved in the maintenance of VS tinnitus.

Magnetic Resonance-Guided Focused Ultrasound Thalamotomy Rebalances Atypical Functional Hierarchy in Patients with Essential Tremor.

Magnetic resonance-guided focused ultrasound (MRgFUS) has brought thalamotomy back to the frontline for essential tremor (ET). As functional organization of human brain strictly follows hierarchical principles which are frequently deficient in neurological diseases, whether additional damage from MRgFUS thalamotomy induces further disruptions of ET functional scaffolds are still controversial. This study was to examine the alteration features of brain functional frameworks following MRgFUS thalamotomy in patients with ET. We retrospectively obtained preoperative (ETpre) and postoperative 6-month (ET6m) data of 30 ET patients underwent MRgFUS thalamotomy from 2018 to 2020. Their archived functional MR images were used to functional gradient comparison. Both supervised pattern learning and stepwise linear regression were conducted to associate gradient features to tremor symptoms with additional neuropathophysiological analysis. MRgFUS thalamotomy relieved 78.19% of hand tremor symptoms and induced vast global framework alteration (ET6m vs. ETpre: Cohen d = - 0.80, P < 0.001). Multiple robust alterations were identified especially in posterior cingulate cortex ([Formula: see text] ET6m vs. [Formula: see text] ETpre: Cohen d = 0.87, P = 0.048). Compared with matched health controls (HCs), its gradient distances to primary communities were significantly increased in [Formula: see text] ETpre patients with anomalous stepwise connectivity (P < 0.05 in ETpre vs. HCs), which were restored after MRgFUS thalamotomy. Both global and regional gradient features could be used for tremor symptom prediction and were linked to neuropathophysiological features of Parkinson disease and oxidative phosphorylation. MRgFUS thalamotomy not only suppress tremor symptoms but also rebalances atypical functional hierarchical architecture of ET patients.

Evaluation of postcontrast images of intracranial tumors at 7T and 3T MRI: An intra-individual comparison study.

AIM: This study aimed to evaluate the diagnostic value of ultrahigh-field magnetic resonance imaging (MRI) for brain tumors in clinical practice. METHODS: Thirty patients with brain tumors underwent 7- and 3-T MRI. The performance and diagnostic confidence of 7- and 3-T MRI in the visualization of tumor details such as internal structure and feeding artery were evaluated by radiologists. Contrast-enhanced region performance and tumor detail diagnostic confidence score (DCS) were calculated and compared between 7 and 3T using Wilcoxon rank sum test. RESULTS: In 19 with obvious enhancement and 11 cases without obvious enhancement, 7- and 3-T MRI showed similar performance. The tumors' internal structure and feeding artery were more clearly depicted by 7-T MRI (62.2% and 54.4%, respectively) than by 3-T MRI (2.2% and 6.7%, respectively). Furthermore, the mean DCSs of both internal structure and feeding artery were higher at 7T than at 3T (internal structure: 16.29 ± 9.67 vs. -5.79 ± 4.12, p = 0.028; feeding artery: 21.96 ± 6.93 vs. 4.46 ± 7.07, p = 0.028). The DCS was more significantly improved in the senior radiologist group. CONCLUSION: Better visualization of brain tumor details and higher tumor detail diagnostic confidence can be obtained with 7-T MRI.

Association of fluid-attenuated inversion recovery vascular hyperintensity with ischaemic events in internal carotid artery or middle cerebral artery occlusion.

BACKGROUND AND PURPOSE: Individuals with intracranial artery occlusion have high rates of ischaemic events and recurrence. It has been challenging to identify patients who had high-risk stroke using a simple, valid and non-invasive screening approach. This study aimed to investigate whether fluid-attenuated inversion recovery (FLAIR) vascular hyperintensity (FVH), a specific imaging sign on the FLAIR sequence, could be a predictor of ischaemic events in a population with internal carotid artery (ICA) or middle cerebral artery (MCA) occlusion. METHODS: We retrospectively analysed 147 patients (mean 60.43±12.83 years) with 149 lesions, including 37 asymptomatic and 112 symptomatic cases of ICA or MCA occlusion. Symptomatic occlusion was considered if ischaemic events were present in the relevant territory within 90 days. FVH Alberta Stroke Program Early Computed Tomography Score (FVH-ASPECTS: 0-7, with 0 indicating absence of FVH and 7 suggesting prominent FVH) and collateral circulation grade were assessed for each participant. Multivariable logistic regression analysis was performed to detect independent markers associated with symptomatic status. RESULTS: A lower FVH-ASPECTS was associated with a more favourable collateral circulation grade (rho=-0.464, p<0.0001). The FVH-ASPECTS was significantly lower in the asymptomatic occlusion group than in the symptomatic occlusion group (p<0.0001). FVH-ASPECTS (Odd ratio, 2.973; 95% confidence interval, 1.849 to 4.781; p<0.0001) was independently associated with symptomatic status after adjustment for age, sex, lesion location and collateral circulation grade in the multivariate logistic regression. The area under the curve was 0.861 for the use of FVH-ASPECTS to identify symptomatic occlusion. CONCLUSIONS: The ability to discriminate symptomatic from asymptomatic occlusion suggests that FVH may be a predictor of stroke. As a simple imaging sign, FVH may serve as a surrogate for haemodynamic impairments and can be used to identify high-risk stroke cases early in ICA or MCA occlusion.

Translational large animal model of coronary microvascular embolism: characterization by serial cardiac magnetic resonance and histopathology.

This study aimed to construct a large animal model of coronary microvascular embolism, and investigate whether it could mimic the clinical imaging phenotypes of myocardial hypoperfusion in patients with ST-segment elevation myocardial infarction (STEMI). Nine minipigs underwent percutaneous coronary embolization with microspheres, followed by cardiac magnetic resonance (CMR) on week 1, 2 and 4 post operation. Microvascular obstruction (MVO) was defined as the isolated hypointense core within the enhanced area on late gadolinium enhancement images, which evolved during a 4-week follow-up. Fibrotic fraction of the segments was measured by Masson trichrome staining using a panoramic analysis software. Iron deposit and macrophage infiltration were quantified based on Perl's blue and anti-CD163 staining, respectively. Seven out of 9 (77.8%) minipigs survived and completed all of the imaging follow-ups. Four out of 7 (57.1%) minipigs were identified as transmural infarct with MVO. The systolic wall thickening (SWT) of MVO zone was similar to that of infarct zone (P = 0.762). Histopathology revealed transmural deposition of collagen, with microvessels obstructed by microspheres. The fibrotic fraction of infarct with MVO segments was similar to that of infarct without MVO segments (P = 0.954). The fraction of iron deposit in infarct with MVO segments was higher than that of infarct without MVO segments (P < 0.05), but the fraction of macrophage infiltration between these two segments did not show statistical difference (P = 0.723). Large animal model of coronary microvascular embolism could mimic most clinical imaging phenotypes of myocardial hypoperfusion in patients with STEMI, demonstrated by serial CMR and histopathology.

Challenging Reverse Screening: A Benchmark Study for Comprehensive Evaluation.

As an efficient way of computational target prediction, reverse docking can find not only potential targets but also binding modes for a query ligand. Though the number of available docking tools keeps expanding, there is still not a comprehensive evaluation study which can uncover the advantages and limitations of these strategies in the research field of computational target-fishing. In this study, we propose a brand-new evaluation dataset tailor-made for reverse docking, which is composed of a true positive set (the core set) and two negative sets (the similar decoy set and the dissimilar decoy set). The proposed evaluation dataset can assess the prediction performance of docking tools as various values affected by varying degrees of inter-target ranking bias. The performance of four classical docking programs (AutoDock, AutoDock Vina, Glide and GOLD) was evaluated utilizing our dataset, and a biased prediction performance was observed regarding binding site properties. The results demonstrated that Glide (SP) and Glide(XP) had the best capacity to find true targets whether there was inter-target ranking bias or not.

Deep learning with weak annotation from diagnosis reports for detection of multiple head disorders: a prospective, multicentre study.

BACKGROUND: A large training dataset with high-quality annotations is necessary for building an accurate and generalisable deep learning system, which can be difficult and expensive to prepare in medical applications. We present a novel deep-learning-based system, requiring no annotator but weak annotation from a diagnosis report, for accurate and generalisable performance in detecting multiple head disorders from CT scans, including ischaemia, haemorrhage, tumours, and skull fractures. METHODS: Our system was developed on 104 597 head CT scans from the Chinese PLA General Hospital, with associated textual diagnosis reports. Without expert annotation, we used keyword matching on the reports to automatically generate disorder labels for each scan. The labels were inaccurate because of the unreliable annotator-free strategy and inexact because of scan-level annotation. We proposed RoLo, a novel weakly supervised learning algorithm, with a noise-tolerant mechanism and a multi-instance learning strategy to address these issues. RoLo was tested on retrospective (2357 scans from the Chinese PLA General Hospital), prospective (650 scans from the Chinese PLA General Hospital), cross-centre (1525 scans from the Brain Hospital of Hunan Province), cross-equipment (1484 scans from the Chinese PLA General Hospital), and cross-nation (CQ500 public dataset from India) test datasets. Four radiologists were tested on the prospective test dataset before and after viewing system recommendations to assess whether the system could improve diagnostic performance. FINDINGS: The area under the receiver operating characteristic curve for detecting the four disorder types was 0·976 (95% CI 0·976-0·976) for retrospective, 0·975 (0·974-0·976) for prospective, 0·965 (0·964-0·966) for cross-centre, and 0·971 (0·971-0·972) for cross-equipment test datasets, and 0·964 (0·964-0·966) for CQ500 (with only haemorrhage and fracture). The system achieved similar performance to four radiologists and helped to improve sensitivity and specificity by 0·109 (95% CI 0·086-0·131) and 0·022 (0·017-0·026), respectively. INTERPRETATION: Without expert annotated data, our system achieved accurate and generalisable performance for head disorder detection. The system improved the diagnostic performance of radiologists. Because of its accuracy and generalisability, our computer-aided diganostic system could be used in clinical practice to improve the accuracy and efficiency of radiologists in different hospitals. FUNDING: National Key R&D Program of China, National Natural Science Foundation of China, and Beijing Natural Science Foundation.

Uncovering Brain Differences in Preschoolers and Young Adolescents with Autism Spectrum Disorder Using Deep Learning.

Identifying brain abnormalities in autism spectrum disorder (ASD) is critical for early diagnosis and intervention. To explore brain differences in ASD and typical development (TD) individuals by detecting structural features using T1-weighted magnetic resonance imaging (MRI), we developed a deep learning-based approach, three-dimensional (3D)-ResNet with inception (I-ResNet), to identify participants with ASD and TD and propose a gradient-based backtracking method to pinpoint image areas that I-ResNet uses more heavily for classification. The proposed method was implemented in a preschool dataset with 110 participants and a public autism brain imaging data exchange (ABIDE) dataset with 1099 participants. An extra epilepsy dataset with 200 participants with clear degeneration in the parahippocampal area was applied as a verification and an extension. Among the datasets, we detected nine brain areas that differed significantly between ASD and TD. From the ROC in PASD and ABIDE, the sensitivity was 0.88 and 0.86, specificity was 0.75 and 0.62, and area under the curve was 0.787 and 0.856. In a word, I-ResNet with gradient-based backtracking could identify brain differences between ASD and TD. This study provides an alternative computer-aided technique for helping physicians to diagnose and screen children with an potential risk of ASD with deep learning model.