Aberrant brain entropy in posttraumatic stress disorder comorbid with major depressive disorder during the coronavirus disease 2019 pandemic.

Aim: Previously, neuroimaging studies on comorbid Posttraumatic-Major depression disorder (PTSD-MDD) comorbidity found abnormalities in multiple brain regions among patients. Recent neuroimaging studies have revealed dynamic nature on human brain activity during resting state, and entropy as an indicator of dynamic regularity may provide a new perspective for studying abnormalities of brain function among PTSD-MDD patients. During the COVID-19 pandemic, there has been a significant increase in the number of patients with PTSD-MDD. We have decided to conduct research on resting-state brain functional activity of patients who developed PTSD-MDD during this period using entropy. Methods: Thirty three patients with PTSD-MDD and 36 matched TCs were recruited. PTSD and depression symptoms were assessed using multiple clinical scales. All subjects underwent functional magnetic resonance imaging (fMRI) scans. And the brain entropy (BEN) maps were calculated using the BEN mapping toolbox. A two-sample t-test was used to compare the differences in the brain entropy between the PTSD-MDD comorbidity group and TC group. Furthermore, correlation analysis was conducted between the BEN changes in patients with PTSD-MDD and clinical scales. Results: Compared to the TCs, PTSD-MDD patients had a reduced BEN in the right middle frontal orbital gyrus (R_MFOG), left putamen, and right inferior frontal gyrus, opercular part (R_IFOG). Furthermore, a higher BEN in the R_MFOG was related to higher CAPS and HAMD-24 scores in the patients with PTSD-MDD. Conclusion: The results showed that the R_MFOG is a potential marker for showing the symptom severity of PTSD-MDD comorbidity. Consequently, PTSD-MDD may have reduced BEN in frontal and basal ganglia regions which are related to emotional dysregulation and cognitive deficits.

Correction: Wan et al. Correlation between Hypoperfusion Intensity Ratio and Functional Outcome in Large-Vessel Occlusion Acute Ischemic Stroke: Comparison with Multi-Phase CT Angiography. J. Clin. Med. 2022, 11, 5274.

In the published publication [...].

Diagnostic identification of chronic insomnia using ALFF and FC features of resting-state functional MRI and logistic regression approach.

This study investigated whether the amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) features could be used as potentially neurological markers to identify chronic insomnia (CI) using resting-state functional MRI and machine learning method logistic regression (LR). This study included 49 CI patients and 47 healthy controls (HC). Voxel-wise features, including the amplitude of low-frequency fluctuations (ALFF) and functional connectivity (FC), were extracted from resting-state functional magnetic resonance brain images. Then, we divided the data into two independent cohorts for training (44 CI patients and 42 HC patients), and independent validation (5 CI patients and 5 HC patients) by using logistic regression. The model was evaluated using 20 rounds of fivefold cross­validation for training. In particular, a two-sample t-test (GRF corrected, p-voxel < 0.001, p-cluster < 0.05) was used for feature selection during the model training. Finally, single­shot testing of the final model was performed on the independent validation cohort. A correlation analysis (Bonferroni correction, p < 0.05/4) was also conducted to determine whether the features contributing to the prediction were correlated with clinical characteristics, including the Insomnia Severity Index (ISI), Pittsburgh sleep quality index (PSQI), self-rating anxiety scale (SAS), and self-rating depression scale (SDS). Results showed that resting-state features had a discrimination accuracy of 86.40%, with a sensitivity of 93.00% and specificity of 79.80%. The area under the curve (AUC) was 0.89 (all [Formula: see text]< 0.001). The ALFF and FC features showed significant differences between the CI patients and HC. The regions contributing to the prediction mainly included the anterior cingulate, prefrontal cortex, orbital part of the frontal lobe, angular gyrus, cingulate gyrus, praecuneus, parietal lobe, temporal gyrus, superior temporal gyrus, and middle temporal gyrus. Furthermore, some specific functional connectivity among related regions was positively correlated with the ISI, and also negatively related to the SDS in correlation analysis. Our current study suggested that ALFF and FC in the regions contributing to diagnostic identification might serve as potential neuromarkers for CI.

In situ growing 3D-Cu coating to improve the reversibility and reaction kinetics of Zn metal anodes.

The zinc metal anode is the most promising metal anode material in aqueous battery systems due to its low cost and high theoretical capacity. However, it still undergoes irreversible reactions such as premature failure of the dendrites/dead Zn during Zn stripping/plating, resulting in the inferior cycling stability of the Zn-based full cell. Here, we demonstrate a facile 3D-Cu alloy coating to improve Zn reversibility by providing spatial voids to accommodate the plated Zn to form dendrite-free morphology. Combining the larger 3D surface and the alloying-dealloying process, the Zn anode reactions exhibit enhanced reaction kinetics to meet large operating current densities. The 3D-Cu-coated Zn anode can deliver improved cycling stability for 350 h under a large areal capacity of 3 mAh cm-2. It also enables MnO2-Zn at the full cell level to achieve a specific capacity of 205 mAh g-1 and longer cycling for 350 cycles with 87.4% retention of the initial capacity. This research provides a new pathway to achieve high reversible Zn metal chemistry.

Correlation between Hypoperfusion Intensity Ratio and Functional Outcome in Large-Vessel Occlusion Acute Ischemic Stroke: Comparison with Multi-Phase CT Angiography.

Background and purpose: Previous studies have shown that Hypoperfusion Intensity Ratio (HIR) derived from Perfusion Imaging (PWI) associated with collateral status in large-vessel occlusion (LVO) acute ischemic stroke (AIS) and could predict the rate of collateral flow, speed of infarct growth, and clinical outcome after endovascular treatment (EVT). We hypothesized that HIR derived from CT Perfusion (CTP) imaging could relatively accurately predict the functional outcome in LVO AIS patients receiving different types of treatment. Methods: Imaging and clinical data of consecutive patients with LVO AIS were retrospectively reviewed. Multi-phase CT angiography (mCTA) scoring was performed by 2 blinded neuroradiologists. CTP images were processed using an automatic post-processing analysis software. Correlation between the HIR and the functional outcome was calculated using the Spearman correlation. The efficacy of the HIR and the CTA collateral scores for predicting prognosis were compared. The optimal threshold of the HIR for predicting favorable functional outcome was determined using receiver operating characteristic (ROC) curve analysis. Results: 235 patients with LVO AIS were included. Patients with favorable functional outcome had lower HIR (0.1 [interquartile range (IQR), 0.1−0.2]) vs. 0.4 (IQR, 0.4−0.5)) and higher mCTA collateral scores (3 [IQR, 3−4] vs. 3 [IQR, 2−3]; p < 0.001) along with smaller infarct core volume (2.1 [IQR, 1.0−4.5]) vs. (15.2 [IQR, 5.5−39.3]; p < 0.001), larger mismatch ratio (22.9 [IQR, 11.6−45.6]) vs. (5.8 [IQR, 2.6−14]); p < 0.001), smaller ischemic volume (59.0 [IQR, 29.7−89.2]) vs. (97.5 [IQR, 68.7−142.2]; p < 0.001), and smaller final infarct volume (12.6 [IQR, 7.5−18.4]) vs. (78.9 [IQR, 44.5−165.0]; p < 0.001) than those with unfavorable functional outcome. The HIR was significantly positively correlated with the functional outcome [r = 0.852; 95% confidence interval (CI): 0.813−0.884; p < 0.0001]. The receiver operating characteristic (ROC) analysis showed that the optimal threshold for predicting a favorable functional outcome was HIR ≤ 0.3 [area under the curve (AUC) 0.968; sensitivity 88.89%; specificity 99.21%], which was higher than the mCTA collateral score [AUC 0.741; sensitivity 82.4%; specificity 48.8%]. Conclusions: HIR was associated with the functional outcome of LVO AIS patients, and the correlation coefficient was higher than mCTA collateral score. HIR outperformed mCTA collateral score in predicting functional outcome.

All-In-One Second Near-Infrared Light-Responsive Drug Delivery System for Synergistic Chemo-Photothermal Therapy.

Light-responsive nanocarrier-based drug delivery systems (NDDSs), due to their unique advantages such as safety, minimal cross-reaction, and spatiotemporal precision, have received wide attention. Notably, second near-infrared (NIR-II) light, which has a high penetration depth for manipulating NDDSs to release drugs, is in high demand. Herein, polyethylene glycol (PEG)-modified hollow CuxS nanoparticles (NPs) are developed as an all-in-one NIR-II light-responsive NDDS for synergistic chemo-photothermal therapy. First, CuxS-PEG NPs were prepared under mild conditions by using Cu2O NPs as sacrificial templates. The morphology, photothermal effect, drug loading/releasing abilities, and synergistic chemo-photothermal therapy of CuxS-PEG NPs have been investigated. The CuxS-PEG NPs with hollow structures showed a high drug loading capacity (∼255 µg Dox per mg of CuxS NPs) and stimuli-responsive drug release triggered by NIR-II laser irradiation. The synergistic chemo-photothermal therapy based on the Dox/CuxS-PEG NPs showed 98.5% tumor elimination. Our study emphasizes the great potential of CuxS-PEG NPs as an all-in-one NIR-II light-responsive NDDS for applications in biomedicine.

Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images.

We propose a classification method using the radiomics features of CT chest images to identify patients with coronavirus disease 2019 (COVID-19) and other pneumonias. The chest CT images of two groups of participants (90 COVID-19 patients who were confirmed as positive by nucleic acid test of RT-PCR and 90 other pneumonias patients) were collected, and the two groups of data were manually drawn to outline the region of interest (ROI) of pneumonias. The radiomics method was used to extract textural features and histogram features of the ROI and obtain a radiomics features vector from each sample. Then, we divided the data into two independent radiomic cohorts for training (70 COVID-19 patients and 70 other pneumonias patients), and validation (20 COVID-19 patients and 20 other pneumonias patients) by using support vector machine (SVM). This model used 20 rounds of tenfold cross-validation for training. Finally, single-shot testing of the final model was performed on the independent validation cohort. In the COVID-19 patients, correlation analysis (multiple comparison correction-Bonferroni correction, P < 0.05/7) was also conducted to determine whether the textural and histogram features were correlated with the laboratory test index of blood, i.e., blood oxygen, white blood cell, lymphocytes, neutrophils, C-reactive protein, hypersensitive C-reactive protein, and erythrocyte sedimentation rate. The final model showed good discrimination on the independent validation cohort, with an accuracy of 89.83%, sensitivity of 94.22%, specificity of 85.44%, and AUC of 0.940. This proved that the radiomics features were highly distinguishable, and this SVM model can effectively identify and diagnose patients with COVID-19 and other pneumonias. The correlation analysis results showed that some textural features were positively correlated with WBC, and NE, and also negatively related to SPO2H and NE. Our results showed that radiomic features can classify COVID-19 patients and other pneumonias patients. The SVM model can achieve an excellent diagnosis of COVID-19.

Cortical thickness, gyrification and sulcal depth in trigeminal neuralgia.

Neuroimaging studies have documented brain structural alterations induced by chronic pain, particularly in gray matter volume. However, the effects of trigeminal neuralgia (TN), a severe paroxysmal pain disorder, on cortical morphology are not yet known. In this study, we recruited 30 TN patients and 30 age-, and gender-matched healthy controls (HCs). Using Computational Anatomy Toolbox (CAT12), we calculated and compared group differences in cortical thickness, gyrification, and sulcal depth with two-sample t tests (p < 0.05, multiple comparison corrected). Relationships between altered cortical characteristics and pain intensity were investigated with correlation analysis. Compared to HCs, TN patients exhibited significantly decreased cortical thickness in the left inferior frontal, and left medial orbitofrontal cortex; decreased gyrification in the left superior frontal cortex; and decreased sulcal depth in the bilateral superior frontal (extending to anterior cingulate) cortex. In addition, we found significantly negative correlations between the mean cortical thickness in left medial orbitofrontal cortex and pain intensity, and between the mean gyrification in left superior frontal cortex and pain intensity. Chronic pain may be associated with abnormal cortical thickness, gyrification and sulcal depth in trigeminal neuralgia. These morphological changes might contribute to understand the underlying neurobiological mechanism of trigeminal neuralgia.