Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning

Background: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need. Objective: To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity. Methods: The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients’ clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features. Results: The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity (p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one. Conclusions: CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients

Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules

ObjectiveTo develop and validate the model for predicting benign and malignant ground-glass nodules (GGNs) based on the whole-lung baseline CT features deriving from deep learning and radiomics.MethodsThis retrospective study included 385 GGNs from 3 hospitals, confirmed by pathology. We used 239 GGNs from Hospital 1 as the training and internal validation set; 115 and 31 GGNs from Hospital 2 and Hospital 3 as the external test sets 1 and 2, respectively. An additional 32 stable GGNs from Hospital 3 with more than five years of follow-up were used as the external test set 3. We evaluated clinical and morphological features of GGNs at baseline chest CT and extracted the whole-lung radiomics features simultaneously. Besides, baseline whole-lung CT image features are further assisted and extracted using the convolutional neural network. We used the back-propagation neural network to construct five prediction models based on different collocations of the features used for training. The area under the receiver operator characteristic curve (AUC) was used to compare the prediction performance among the five models. The Delong test was used to compare the differences in AUC between models pairwise.ResultsThe model integrated clinical-morphological features, whole-lung radiomic features, and whole-lung image features (CMRI) performed best among the five models, and achieved the highest AUC in the internal validation set, external test set 1, and external test set 2, which were 0.886 (95% CI: 0.841-0.921), 0.830 (95%CI: 0.749-0.893) and 0.879 (95%CI: 0.712-0.968), respectively. In the above three sets, the differences in AUC between the CMRI model and other models were significant (all P < 0.05). Moreover, the accuracy of the CMRI model in the external test set 3 was 96.88%.ConclusionThe baseline whole-lung CT features were feasible to predict the benign and malignant of GGNs, which is helpful for more refined management of GGNs

Encoding Praise and Criticism During Social Evaluation Alters Interactive Responses in the Mentalizing and Affective Learning Networks

Verbal communication with evaluative characters of different emotional valence has a considerable impact on the extent to which social relations are facilitated or undermined. Here using functional magnetic resonance imaging, we investigated how the brain acts in response to social praise and criticism, leading to differential affective judgments. We engaged thirty men and women in a task associating sex-balanced, neutral faces with praising or criticizing comments targeting others or objects. A whole-brain analysis revealed that criticism as compared to praise enhanced the activation in the medial prefrontal cortex (mPFC), particularly its dorsal portion, whereas the right amygdala displayed an opposite pattern of changes. Comments on others relative to objects increased the reactivity in the left posterior superior temporal sulcus and posterior cingulate cortex (PCC) such that both praise and criticism of others produced stronger activation in these two regions than their object-targeted counterparts. The interaction of valence and target was identified in the mPFC with greater reactivity in the contrasts of criticism vs. praise in the social context and others- vs. object-targeted criticism. Comments also modulated the functional connectivity of prior activated regions with the left temporoparietal junction, bilateral caudate and left PCC/precuneus showing reduced connectivity in response to social criticism but greatly strengthened connectivity for social praise as compared to non-social counterparts. These neural effects subsequently led to altered likeability ratings for the faces. Neither behavioral nor neural effects observed were influenced by the gender of participants. Taken together, our findings suggest a fundamental interactive role of the mentalizing and affective learning networks in differential encoding of individuals associated with praising or criticizing others, leading to learning of valenced traits and subsequent approach or avoidance responses in social interactions

Oxytocin increases liking for a country's people and national flag but not for other cultural symbols or consumer products

The neuropeptide oxytocin enhances in-group favoritism and ethnocentrism in males. However, whether such effects also occur in women and extend to national symbols and companies/consumer products is unclear. In a between-subject, double-blind placebo controlled experiment we have investigated the effect of intranasal oxytocin on likeability and arousal ratings given by 51 adult Chinese males and females for pictures depicting people or national symbols/consumer products from both strong and weak in-groups (China and Taiwan) and corresponding out-groups (Japan and South Korea). To assess duration of treatment effects subjects were also re-tested after one week. Results showed that although oxytocin selectively increased the bias for overall liking for Chinese social stimuli and the national flag, it had no effect on the similar bias towards other Chinese cultural symbols, companies and consumer products. This enhanced bias was maintained one week after treatment. No overall oxytocin effects were found for Taiwanese, Japanese or South Korean pictures. Our findings show for the first time that oxytocin increases liking for a nation’s society and flag in both men and women, but not that for other cultural symbols or companies/consumer products

Metal–Oleate Complex-Derived Bimetallic Oxides Nanoparticles Encapsulated in 3D Graphene Networks as Anodes for Efficient Lithium Storage with Pseudocapacitance

Abstract In this manuscript, we have demonstrated the delicate design and synthesis of bimetallic oxides nanoparticles derived from metal–oleate complex embedded in 3D graphene networks (MnO/CoMn2O4 ⊂ GN), as an anode material for lithium ion batteries. The novel synthesis of the MnO/CoMn2O4 ⊂ GN consists of thermal decomposition of metal–oleate complex containing cobalt and manganese metals and oleate ligand, forming bimetallic oxides nanoparticles, followed by a self-assembly route with reduced graphene oxides. The MnO/CoMn2O4 ⊂ GN composite, with a unique architecture of bimetallic oxides nanoparticles encapsulated in 3D graphene networks, rationally integrates several benefits including shortening the diffusion path of Li+ ions, improving electrical conductivity and mitigating volume variation during cycling. Studies show that the electrochemical reaction processes of MnO/CoMn2O4 ⊂ GN electrodes are dominated by the pseudocapacitive behavior, leading to fast Li+ charge/discharge reactions. As a result, the MnO/CoMn2O4 ⊂ GN manifests high initial specific capacity, stable cycling performance, and excellent rate capability

Oxytocin Increases the Perceived Value of Both Self- and Other-Owned Items and Alters Medial Prefrontal Cortex Activity in an Endowment Task

The neuropeptide oxytocin (OXT) can influence self-processing and may help motivate us to value the attributes of others in a more self-like manner by reducing medial prefrontal cortex (mPFC) responses. We do not know however whether this OXT effect extends to possessions. We tend to place a higher monetary value on specific objects that belong to us compared to others, known as the “endowment effect”. In two double-blind, between-subject placebo (PLC) controlled experiments in subjects from a collectivist culture, we investigated the influence of intranasal OXT on the endowment effect, with the second study incorporating functional magnetic resonance imaging (fMRI). In the task, subjects decided whether to buy or sell their own or others’ (mother/father/classmate/stranger) possessions at various prices. Both experiments demonstrated an endowment effect in the self-owned condition which extended to close others (mother/father) and OXT increased this for self and all other-owned items. This OXT effect was associated with reduced activity in the ventral mPFC (vmPFC) in the self-owned condition but increased in the mother-condition. For the classmate- and stranger-owned conditions OXT increased activity in the dorsal mPFC (dmPFC). Changes in vmPFC activation were associated with the size of the endowment effect for self- and mother-owned items. Functional connectivity between the dmPFC and ventral striatum (VStr) was reduced by OXT in self- and mother-owned conditions and between vmPFC and precuneus in the self-condition. Overall our results show that OXT enhances the endowment effect for both self- and other-owned items in Chinese subjects. This effect is associated with reduced mPFC activation in the self-condition but enhanced activation in all other-conditions and involves differential actions on both dorsal and ventral regions as well as functional connectivity with brain reward and other self-processing regions. Overall our findings suggest that OXT increases the perceived value of both self- and other-owned items by acting on neural circuitry involved in self-processing and reward

A distributed fMRI-based signature for the subjective experience of fear

The specific neural systems underlying the subjective feeling of fear are debated in affective neuroscience. Here, we combine functional MRI with machine learning to identify and evaluate a sensitive and generalizable neural signature predictive of the momentary self-reported subjective fear experience across discovery (n = 67), validation (n = 20) and generalization (n = 31) cohorts. We systematically demonstrate that accurate fear prediction crucially requires distributed brain systems, with important contributions from cortical (e.g., prefrontal, midcingulate and insular cortices) and subcortical (e.g., thalamus, periaqueductal gray, basal forebrain and amygdala) regions. We further demonstrate that the neural representation of subjective fear is distinguishable from the representation of conditioned threat and general negative affect. Overall, our findings suggest that subjective fear, which exhibits distinct neural representation with some other aversive states, is encoded in distributed systems rather than isolated ‘fear centers’

Oxytocin Enhancement of Emotional Empathy: Generalization Across Cultures and Effects on Amygdala Activity

Accumulating evidence suggests that the neuropeptide oxytocin (OXT) can enhance empathy although it is unclear which specific behavioral and neural aspects are influenced, and whether the effects are modulated by culture, sex, and trait autism. Based on previous findings in Caucasian men, we hypothesized that a single intranasal dose of OXT would specifically enhance emotional empathy (EE) via modulatory effects on the amygdala in an Asian (Chinese) population and explored the modulatory role of sex and trait autism on the effects. We first conducted a double-blind, randomized between-subject design experiment using a modified version of the multifaceted empathy task to determine whether OXT’s facilitation of EE can be replicated in Chinese men (n = 60). To further explore neural mechanisms behind and potential sex differences, functional MRI and skin conductance measures were acquired in an independent experiment incorporating men and women (n = 72). OXT enhanced EE across experiments and sex, an effect that was accompanied by reduced amygdala activity and increased skin conductance responses. On the network level OXT enhanced functional coupling of the right amygdala with the insula and posterior cingulate cortex for positive valence stimuli but attenuated coupling for negative valence stimuli. The effect of OXT on amygdala functional connectivity with the insula was modulated by trait autism. Overall, our findings provide further support for the role of OXT in facilitating EE and demonstrate that effects are independent of culture and sex and involve modulatory effects on the amygdala and its interactions with other key empathy regions

Automatic differentiation of ruptured and unruptured intracranial aneurysms on computed tomography angiography based on deep learning and radiomics

Abstract Objectives Rupture of intracranial aneurysm is very dangerous, often leading to death and disability. In this study, deep learning and radiomics techniques were used to automatically detect and differentiate ruptured and unruptured intracranial aneurysms. Materials and methods 363 ruptured aneurysms and 535 unruptured aneurysms from Hospital 1 were included in the training set. 63 ruptured aneurysms and 190 unruptured aneurysms from Hospital 2 were used for independent external testing. Aneurysm detection, segmentation and morphological features extraction were automatically performed with a 3-dimensional convolutional neural network (CNN). Radiomic features were additionally computed via pyradiomics package. After dimensionality reduction, three classification models including support vector machines (SVM), random forests (RF), and multi-layer perceptron (MLP) were established and evaluated via area under the curve (AUC) of receiver operating characteristics. Delong tests were used for the comparison of different models. Results The 3-dimensional CNN automatically detected, segmented aneurysms and calculated 21 morphological features for each aneurysm. The pyradiomics provided 14 radiomics features. After dimensionality reduction, 13 features were found associated with aneurysm rupture. The AUCs of SVM, RF and MLP on the training dataset and external testing dataset were 0.86, 0.85, 0.90 and 0.85, 0.88, 0.86, respectively, for the discrimination of ruptured and unruptured intracranial aneurysms. Delong tests showed that there was no significant difference among the three models. Conclusions In this study, three classification models were established to distinguish ruptured and unruptured aneurysms accurately. The aneurysms segmentation and morphological measurements were performed automatically, which greatly improved the clinical efficiency. Clinical relevance statement Our fully automatic models could rapidly process the CTA data and evaluate the status of aneurysms in one minute. Graphical Abstrac

Gait influence diagrams in Parkinson's Disease

Previous studies have shown that gait patterns differ between Parkinson's disease (PD) patients and controls. However, almost all these studies focused only on univariate time series of a single variable. This approach cannot reveal detailed information of foot loading dynamics and the cooperative relationships of different anatomical plantar foot areas when the subjects walk. By contrast, we propose a novel multivariate method for analyzing gait patterns of the PD patients: Gait Influence Diagrams (GIDs). These are constructed by analyzing the Wiener-Akaike-Granger- Schweder influences between vertical ground reaction force signals at different plantar areas of both feet. In this paper, we use the particular case of WAGS influence measures known as “extended Granger causality analysis”. GIDs are directed graphs, with arrows indicating those influences that are significantly different between PD patients and healthy subjects. We confirm prior clinical observations that Parkinsonian gait differs significantly from the healthy one in the anterior-posterior movement direction. A new finding is that there are also pathological changes in the lateral-medial direction. Importantly, gait asymmetry for the PD patients is clearly evident in GIDs, even in earlier stages of the disease. These results suggest that GID might be of use in future PD gait pattern studies