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Introduction: Combination immunotherapy holds promise for improving survival in responsive glioblastoma (GBM) patients. Programmed death-ligand 1 (PD-L1) expression in immune microenvironment (IME) is the most important predictive biomarker for immunotherapy. Due to the heterogeneous distribution of PD-L1, post-operative histopathology fails to accurately capture its expression in residual tumors, making intra-operative diagnosis crucial for GBM treatment strategies. However, the current methods for evaluating the expression of PD-L1 are still time-consuming. Objective: To overcome the PD-L1 heterogeneity and enable rapid, accurate, and label-free imaging of PD-L1 expression level in GBM IME at the tissue level. Methods: We proposed a novel intra-operative diagnostic method, Machine Learning Cascade (MLC)-based Raman histopathology, which uses a coordinate localization system (CLS), hierarchical clustering analysis (HCA), support vector machine (SVM), and similarity analysis (SA). This method enables visualization of PD-L1 expression in glioma cells, CD8+ T cells, macrophages, and normal cells in addition to the tumor/normal boundary. The study quantified PD-L1 expression levels using the tumor proportion, combined positive, and cellular composition scores (TPS, CPS, and CCS, respectively) based on Raman data. Furthermore, the association between Raman spectral features and biomolecules was examined biochemically. Results: The entire process from signal collection to visualization could be completed within 30 min. In an orthotopic glioma mouse model, the MLC-based Raman histopathology demonstrated a high average accuracy (0.990) for identifying different cells and exhibited strong concordance with multiplex immunofluorescence (84.31 %) and traditional pathologists' scoring (R2 ≥ 0.9). Moreover, the peak intensities at 837 and 874 cm−1 showed a positive linear correlation with PD-L1 expression level. Conclusions: This study introduced a new and extendable diagnostic method to achieve rapid and accurate visualization of PD-L1 expression in GBM IMB at the tissular level, leading to great potential in GBM intraoperative diagnosis for guiding surgery and post-operative immunotherapy.

Abstract Genetic generalized epilepsies (GGE) exhibit widespread morphometric alterations in the subcortical structures. Subcortical structures are essential for understanding GGE pathophysiology, but their fine-grained morphological diversity has yet to be comprehensively investigated. Furthermore, the relationships between macroscale morphological disturbances and microscale molecular chemoarchitectures are unclear. High-resolution structural images were acquired from patients with GGE (n = 97) and sex- and age-matched healthy controls (HCs, n = 184). Individual measurements of surface shape features (thickness and surface area) of seven bilateral subcortical structures were quantified. The patients and HCs were then compared vertex-wise, and shape anomalies were co-located with brain neurotransmitter profiles. We found widespread morphological alterations in GGE and prominent disruptions in the thalamus, putamen, and hippocampus. Shape area dilations were observed in the bilateral ventral, medial, and right dorsal thalamus, as well as the bilateral lateral putamen. We found that the shape area deviation pattern was spatially correlated with the norepinephrine transporter and nicotinic acetylcholine (Ach) receptor (α 4 β 2 ) profiles, but a distinct association was seen in the muscarinic Ach receptor (M 1 ). The findings provided a comprehensive picture of subcortical morphological disruptions in GGE, and further characterized the associated molecular mechanisms. This information may increase our understanding of the pathophysiology of GGE.

Abstract Precise identification of glioblastoma (GBM) microinfiltration, which is essential for achieving complete resection, remains an enormous challenge in clinical practice. Here, the study demonstrates that Raman spectroscopy effectively identifies GBM microinfiltration with cellular resolution in clinical specimens. The spectral differences between infiltrative lesions and normal brain tissues are attributed to phospholipids, nucleic acids, amino acids, and unsaturated fatty acids. These biochemical metabolites identified by Raman spectroscopy are further confirmed by spatial metabolomics. Based on differential spectra, Raman imaging resolves important morphological information relevant to GBM lesions in a label‐free manner. The area under the receiver operating characteristic curve (AUC) for Raman spectroscopy combined with machine learning in detecting infiltrative lesions exceeds 95%. Most importantly, the cancer cell threshold identified by Raman spectroscopy is as low as 3 human GBM cells per 0.01 mm2. Raman spectroscopy enables the detection of previously undetectable diffusely infiltrative cancer cells, which holds potential value in guiding complete tumor resection in GBM patients.

Abstract The ultrahigh flexibility and elasticity achieved in freestanding single-crystalline ferroelectric oxide membranes have attracted much attention recently. However, for antiferroelectric oxides, the flexibility limit and fundamental mechanism in their freestanding membranes are still not explored clearly. Here, we successfully fabricate freestanding single-crystalline PbZrO3 membranes by a water-soluble sacrificial layer technique. They exhibit good antiferroelectricity and have a commensurate/incommensurate modulated microstructure. Moreover, they also have good shape recoverability when bending with a small radius of curvature (about 2.4 μm for the thickness of 120 nm), corresponding to a bending strain of 2.5%. They could tolerate a maximum bending strain as large as 3.5%, far beyond their bulk counterpart. Our atomistic simulations reveal that this remarkable flexibility originates from the antiferroelectric-ferroelectric phase transition with the aid of polarization rotation. This study not only suggests the mechanism of antiferroelectric oxides to achieve high flexibility but also paves the way for potential applications in flexible electronics.

Background: To explore the value of arterial spin labeled (ASL) and blood oxygen level dependent (BOLD) imaging in evaluating allogeneic kidney function after renal transplantation. Methods: One hundred and thirty-five renal transplant patients were included. Demographic and imaging data were collected. Transplanted renal function, pathology, ASL and BOLD parameters were obtained. The patients were divided into normal, mild and severe injury group. The correlation between BOLD/ASL parameters and clinical data were evaluated. The prediction models were based on ASL and BOLD parameters using multivariate logistic analysis. Cox proportional hazards regression model was used to analyze the effects of gender, age, ASL and BOLD on the survival of renal transplant patients. Results: ASL and BOLD parameters were independently associated with renal function injury and renal allograft positive pathology. The AUC of prediction model for renal allograft function based on ASL and BOLD parameters was 0.85, while the AUC based on BOLD parameters was 0.70. Renal transplantation time showed a positive correlation with age, BOLD parameters and SCr，while a negative correlation with ASL parameters and eGFR. ASL parameter was positively correlated with eGFR and negatively correlated with Scr. BOLD parameter was negatively correlated with eGFR, ASL and positively correlated with Scr. Cox proportional hazards regression model showed that the increase of age could reduce the risk of renal function injury and positive pathology. Conclusions: ASL and BOLD were associated with renal function injury and renal allograft positive pathology. ASL and BOLD had some value in predicting renal allograft function.

Purpose: The novel coronavirus pneumonia (COVID-19) has continually spread and mutated, requiring a patient risk stratification system to optimize medical resources and improve pandemic response. We aimed to develop a conformal prediction-based tri-light warning system for stratifying COVID-19 patients, applicable to both original and emerging variants. Methods: We retrospectively collected data from 3646 patients across multiple centers in China. The dataset was divided into a training set (n = 1451), a validation set (n = 662), an external test set from Huoshenshan Field Hospital (n = 1263), and a specific test set for Delta and Omicron variants (n = 544). The tri-light warning system extracts radiomic features from CT (computed tomography) and integrates clinical records to classify patients into high-risk (red), uncertain-risk (yellow), and low-risk (green) categories. Models were built to predict ICU (intensive care unit) admissions (adverse cases in training/validation/Huoshenshan/variant test sets: n = 39/21/262/11) and were evaluated using AUROC ((area under the receiver operating characteristic curve)) and AUPRC ((area under the precision-recall curve)) metrics. Results: The dataset included 1830 men (50.2 %) and 1816 women (50.8 %), with a median age of 53.7 years (IQR [interquartile range]: 42–65 years). The system demonstrated strong performance under data distribution shifts, with AUROC of 0.89 and AUPRC of 0.42 for original strains, and AUROC of 0.77–0.85 and AUPRC of 0.51–0.60 for variants. Conclusion: The tri-light warning system can enhance pandemic responses by effectively stratifying COVID-19 patients under varying conditions and data shifts.

The possibility to use ferroelastic materials as components of neuromorphic devices is discussed. They can be used as local memristors with the advantage that ionic transport is constraint to twin boundaries where ionic diffusion is much faster than in the bulk and does not leak into adjacent domains. It is shown that nano-scale ferroelastic memristors can contain a multitude of domain walls. These domain walls interact by strain fields where the interactions near surfaces are fundamentally different from bulk materials. We show that surface relaxations (∼image forces) are curtailed to short range dipolar interactions which decay as 1/d2 where d is the distance between domain walls. In bigger samples such interactions are long ranging with 1/d. The cross-over regime is typically in the range of some 200–1500 nm using a simple spring interaction model.

Neuromorphic computation is based on memristors, which function equivalently to neurons in brain structures. These memristors can be made more efficient and tailored to neuromorphic devices by using ferroelastic domain boundaries as fast diffusion paths for ionic conduction, such as of oxygen, sodium, or lithium. In this paper, we show that the local memristor generates a second, unexpected feature, namely, weak magnetic fields that emerge from moving ferroelastic needle domains and vortices. The vortices appear near ferroelastic “junctions” that are common when the external stimulus is a combination of electric fields and structural phase transitions. Many ferroelastic materials show such phase transitions near room temperatures so that device applications display a “multiferroic” scenario where the memristor is driven electrically and read magnetically. Our computer simulation study of an elastic spring model suggests magnetic fields in the order of 10−7 T, which opens the way for a fundamentally new way of running neuromorphic devices. The magnetism in such devices emerges entirely from intrinsic displacement currents and not from any intrinsic magnetism of the material.

Abstract Background Norepinephrine transporter (NET) is encoded by the SLC6A2 gene and is a potential target for studying the pathogenesis of PTSD. To the best of our knowledge, no prior investigations have examined SLC6A2 polymorphism-related neuroimaging abnormalities in PTSD patients. Methods In 218 Han Chinese adults who had lost their sole child, we investigated the association between the T-182 C SLC6A2 genotype and gray matter volume (GMV). Participants included 57 PTSD sufferers and 161 non-PTSD sufferers, and each group was further separated into three subgroups based on each participant’s SLC6A2 genotype (TT, CT, and CC). All participants received magnetic resonance imaging (MRI) and clinical evaluation. To assess the effects of PTSD diagnosis, genotype, and genotype × diagnosis interaction on GMV, 2 × 3 full factorial designs were used. Pearson’s correlations were used to examine the association between GMV and CAPS, HAMD, and HAMA. Results The SLC6A2 genotype showed significant main effects on GMV of the left superior parietal gyrus (SPG) and the bilateral middle cingulate gyrus (MCG). Additionally, impacts of the SLC6A2 genotype-diagnosis interaction were discovered in the left superior frontal gyrus (SFG). The CAPS, HAMA, and HAMD scores, as well as the genotype main effect and diagnostic SLC6A2 interaction, did not significantly correlate with each other. Conclusion These findings indicate a modulatory effect that the SLC6A2 polymorphism exerts on the SPG and MCG, irrespective of PTSD diagnosis. We found evidence to suggest that the SLC6A2 genotype-diagnosis interaction on SFG may potentially contribute to PTSD pathogenesis in adults who lost their sole child.

Abstract Glioma is the most common malignant tumor of the brain and enhancing the efficacy of chemotherapy in glioma is critical for improving patients’ prognosis. In this study, a glioma-targeting drug delivery system is constructed using biodegradable periodic mesoporous organosilica nanoparticles (PMO) that are modified with lactoferrin (Lf) ligands. The obtained PMO is doped with thioether groups and can be degraded in the high concentration of glutathione in tumor cells. The surface area and pore volume of PMO are 772 cm2/g and 0.98 cm3/g, respectively and the loading capacity of doxorubicin (Dox) is as high as 20%. The results of the confocal laser scanning microscope show that the uptake of PMO-Lf@Dox by C6 cells is higher than PMO@Dox. The quantitative analysis of the flow cytometer further demonstrates that more PMO-Lf@Dox enter C6 cells, indicating that the modification of lactoferrin can significantly increase the uptake of C6 cells. Finally, the therapeutic efficacy results show that Lf-modified PMO enhances the inhibitory effect of Dox on C6 cells when incubated for 24 h and 72 h. In summary, this lactoferrin receptor-mediated PMO drug carrier with biodegradability in glutathione in tumor cells can be used to enhance drug delivery into glioma without long-term accumulation in vivo. Graphical abstract In this study, a glioma-targeting drug delivery system is constructed using periodic mesoporous organosilica nanoparticles (PMO) that modified with lactoferrin (Lf) ligands. This lactoferrin receptor-mediated PMO drug carrier can be used to enhance drug delivery into brain glioma.

Abstract Background Brain entropy reveals complexity and irregularity of brain, and it has been proven to reflect brain complexity alteration in disease states. Previous studies found that bipolar disorder adolescents showed cognitive impairment. The relationship between complexity of brain neural activity and cognition of bipolar II disorder (BD-II) adolescents remains unclear. Methods Nineteen BD-II patients (14.63 ±1.57 years old) and seventeen age-gender matched healthy controls (HCs) (14.18 ± 1.51 years old) were enlisted. Entropy values of all voxels of the brain in resting-state functional MRI data were calculated and differences of them between BD-II and HC groups were evaluated. After that, correlation analyses were performed between entropy values of brain regions showing significant entropy differences and clinical indices in BD-II adolescents. Results Significant differences were found in scores of immediate visual reproduction subtest (VR-I, p = 0.003) and Stroop color-word test (SCWT-1, p = 0.015; SCWT-2, p = 0.004; SCWT-3, p = 0.003) between the two groups. Compared with HCs, BD-II adolescents showed significant increased brain entropy in right parahippocampal gyrus and right inferior occipital gyrus. Besides, significant negative correlations between brain entropy values of right parahippocampal gyrus, right inferior occipital gyrus and immediate visual reproduction subtest scores were observed in BD-II adolescents. Conclusions The findings of the present study suggested that the disrupted function of corticolimbic system is related with cognitive abnormality of BD-II adolescents. And from the perspective temporal dynamics of brain system, the current study, brain entropy may provide available evidences for understanding the underlying neural mechanism in BD-II adolescents.

Abstract Functional connectivity hierarchy is an important principle in the process of brain functional organization and an important feature reflecting brain development. However, atypical brain network hierarchy organization in Rolandic epilepsy have not been systematically investigated. We examined connectivity alteration with age and its relation to epileptic incidence, cognition, or underlying genetic factors in 162 cases of Rolandic epilepsy and 117 typically developing children, by measuring fMRI multi-axis functional connectivity gradients. Rolandic epilepsy is characterized by contracting and slowing expansion of the functional connectivity gradients, highlighting the atypical age-related change of the connectivity hierarchy in segregation properties. The gradient alterations are relevant to seizure incidence, cognition, and connectivity deficit, and development-associated genetic basis. Collectively, our approach provides converging evidence for atypical connectivity hierarchy as a system-level substrate of Rolandic epilepsy, suggesting this is a disorder of information processing across multiple functional domains, and established a framework for large-scale brain hierarchical research.

In recent years, the demand for advanced biomedical nanomaterials has seen a noticeable surge. Among the essential trace elements in the human body, selenium has gained recognition for its anti-cancer, antioxidant, and immune regulatory properties. However, traditional selenium-based semiconductor quantum dots (QDs) are often comprised of heavy metal elements that tend to be toxic, thereby limiting their usage in biomedical applications. Fortunately, the advent of elemental selenium quantum dots (SeQDs), a new kind of fluorescent nanomaterial with unique physicochemical properties, has provided a solution to this problem. These SeQDs are known for their low toxicity and good biocompatibility, making them a promising candidate for biomedical applications. In this mini-review, we delve into the synthesis methods of fluorescent SeQDs and the latest progress in their applications in bioimaging, biosensing, and diagnosis treatment. Finally, we identify the major challenges and future prospects in the field of SeQDs.

Abstract Objectives Besides plaque enhancement grade, the incremental value of enhancement-related high-resolution MRI features in defining culprit plaques needs further evaluation. This study was focused on assessing whether plaque enhancement features contribute to culprit plaque identification and further risk stratification. Methods We retrospectively studied patients who experienced an acute ischaemic stroke and transient ischaemic attack due to intracranial atherosclerosis from 2016 to 2022. The enhancement features included enhancement grade, enhanced length, and enhancement quadrant. Associations between plaque enhancement features and culprit plaques, as well as diagnostic value, were investigated using logistic regression and receiver operating characteristic analyses. Results Overall, 287 plaques were identified, of which 231 (80.5%) and 56 (19.5%) were classified as culprit and non-culprit plaques, respectively. Comparison of the pre- and post-enhancement images revealed enhanced length longer than the plaque length in 46.32% of the culprit plaques. Multivariate logistic regression showed that enhanced length longer than plaque length (OR 6.77; 95% CI 2.47–18.51) and grade II enhancement (OR 7.00; 95% CI 1.69–28.93) were independently associated with culprit plaques. The area under the curve value for the combination of stenosis and plaque enhancement grade for the diagnosis of culprit plaques was 0.787, which increased significantly to 0.825 on the addition of enhanced length longer than the plaque length (p = 0.026 for DeLong’s test). Conclusions Enhanced length longer than the plaque length and grade II enhancement were independently associated with culprit plaques. The combination of the enhanced plaque features resulted in better culprit plaque identification.

Precursor elastic effects are investigated in a displacive anharmonic spring model and shown to extend greatly into the paraelastic phase. Weak precursor effects can be detected near 2T_{tr}, where T_{tr} is the ferroelastic transition temperature. The precursor effects become strong at T

Background: Losing an only child (Shidu) is a grievous traumatic event that may affect brain structure, even if it does not lead to psychiatric disorders. However, longitudinal changes in brain structure and their relationship to subclinical psychiatric symptoms (SPS) have not been well investigated in Shidu parents without any psychiatric disorders (SDNP). Objectives: This study aimed to investigate cross-sectional and longitudinal changes in cortical thickness and surface area in SDNP, and to explore their relationship with SPS. Methods: A total of 50 SDNP and 40 matched healthy controls (HC) were enrolled. All participants underwent structural MRI scans and clinical assessment at baseline and at the 5-year follow-up. Differences in brain structural phenotypes (cortical thickness, surface area, and their annual rate of change) between the SDNP and HC groups were compared using FreeSurfer. Correlations between significant brain structural phenotypes and SPS in the SDNP group were evaluated using multiple linear regressions. Results: The SDNP group showed a smaller surface area in the left inferior parietal cortex than the HC group at baseline and follow-up. The SDNP group showed slower rates of cortical thinning and surface area loss in several brain regions than the HC group from baseline to follow-up. Moreover, slower rates of cortical thinning in the left insula, superior frontal cortex, and superior temporal cortex were associated with greater reductions in avoidance, depression, and trauma re-experiencing symptoms scores over time in the SDNP group, respectively. Conclusions: Shidu trauma-induced structural abnormalities in the inferior parietal cortex may persist over time and be independent of the severity of psychiatric symptoms. The expansion of prefrontal, temporal, and insular cortex implicated in emotional regulation may contribute to improvements in psychiatric symptoms in Shidu parents.

In recent years, with the development of artificial intelligence, especially deep learning technology, researches on automatic detection of cerebrovascular diseases on medical images have made tremendous progress and these models are gradually entering into clinical practice. However, because of the complexity and flexibility of the deep learning algorithms, these researches have great variability on model building, validation process, performance description and results interpretation. The lack of a reliable, consistent, standardized design protocol has, to a certain extent, affected the progress of clinical translation and technology development of computer aided detection systems. After reviewing a large number of literatures and extensive discussion with domestic experts, this position paper put forward recommendations of standardized design on the key steps of deep learning-based automatic image detection models for cerebrovascular diseases. With further research and application expansion, this position paper would continue to be updated and gradually extended to evaluate the generalizability and clinical application efficacy of such tools.

For the purpose of assessing the transient voltage stability of renewable energy grid more effectively and to measure the epistemic uncertainty of the evaluation results, a power system transient voltage stability assessment method based on residual stochastic differential equation network (SDE-Net) is proposed in this paper. Considering the transient stability characteristics is chronological, the proposed network takes the time series of the basic physical quantity measurement data of the power system as the input. The internal features of time series data are extracted by residual convolution block based stochastic differential equation network in this paper. Meanwhile, the proposed method introduces the focal loss function to eliminate the imbalance of the samples and improve the accuracy of transient stability assessment. In particular, the drift network for learning prediction of stability and the diffusion network for learning uncertainty measurement in residual SDE-Net are trained respectively. As a result, case study on modified IEEE 30- bus system demonstrate the residual SDE-Net’s superior performances on both transient voltage stability assessment and stability evaluation result confidence estimation.

The initial thickness ratio and number of layers of dissimilar metal components greatly influence the impact performance of laminated metal composites. In this paper, positive and lateral impact tests of 5-layer composite sheets with thickness ratios of 3:1, 1.35:1, and 1:2 and 80-layer composite sheets prepared by ARB (accumulative roll bonding) were conducted to study the influences of the thickness ratio and layer number on the impact fracture behavior of composite sheets. The results showed that the higher the proportion of AA7075, the higher the bending strength of the AA6061/AA7075 laminated composite sheet; compared with the 5-layer composite sheet, the side impact performance of the 80-layer composite sheet is obviously improved, and its side impact strength, energy absorbed in the crack initiation stage, and crack propagation stage are better than those of the 5-layer composite sheet. In addition, the toughening mechanism of the 80-layer composite sheet is mainly that the increase in the number of layers makes the cracks deflect more frequently. Under the rapid impact load, the impact energy absorbed by the sample increases with the increase in the number of layers.

Abstract Highly efficient and eco‐friendly antimicrobial agents are crucial to the development of future marine antifouling coatings. A high efficient and low bio‐toxicity bio‐based anti‐biofoulant, 2,5‐diformylfuran dioxime chelating with copper ions (E‐DFFD‐Cu), is evaluated. Its minimal bactericidal concentration and the concentration for 50% of maximal effect (EC50) are 4.95 and 7.03 µmol L−1, respectively. Excellent anti‐biofouling performance in marine environment in situ can be kept at least 90 days and with maximum release rate of copper ion of 0.01 µmol cm−2 d−1. Alkyl radical (R·) and hydrogen radical (H·) derived from E‐DFFD are enhanced via chelating copper(II) catalysis, which attracts and disrupts microbe cell membrane and pili between intimate cells, resulting in excellent anti‐biofouling effect. This provides a new perspective for developing anti‐biofoulant from anti‐biofouling mechanism in the future.

Abstract Background Tumor phototherapy especially photodynamic therapy (PDT) or photothermal therapy (PTT), has been considered as an attractive strategy to elicit significant immunogenic cell death (ICD) at an optimal tumor retention of PDT/PTT agents. Heptamethine cyanine dye (IR-780), a promising PDT/PTT agent, which can be used for near-infrared (NIR) fluorescence/photoacoustic (PA) imaging guided tumor phototherapy, however, the strong hydrophobicity, short circulation time, and potential toxicity in vivo hinder its biomedical applications. To address this challenge, we developed mesoporous polydopamine nanoparticles (MPDA) with excellent biocompatibility, PTT efficacy, and PA imaging ability, facilitating an efficient loading and protection of hydrophobic IR-780. Results The IR-780 loaded MPDA (IR-780@MPDA) exhibited high loading capacity of IR-780 (49.7 wt%), good physiological solubility and stability, and reduced toxicity. In vivo NIR fluorescence and PA imaging revealed high tumor accumulation of IR-780@MPDA. Furthermore, the combined PDT/PTT of IR-780@MPDA could induce ICD, triggered immunotherapeutic response to breast tumor by the activation of cytotoxic T cells, resulting in significant suppression of tumor growth in vivo. Conclusion This study demonstrated that the as-developed compact and biocompatible platform could induce combined PDT/PTT and accelerate immune activation via excellent tumor accumulation ability, offering multimodal tumor theranostics with negligible systemic toxicity. Graphical Abstract

Abstract Objective Psychotic symptoms are quite common in patients with pediatric bipolar disorder (PBD) and may affect the symptom severity and prognosis of PBD. However, the potential mechanisms are less well elucidated until now. Thus, the purpose of this study was to investigate the brain functional differences between PBD patients with and without psychotic symptoms. Method A total of 71 individuals including: 27 psychotic PBD (P-PBD), 25 nonpsychotic PBD (NP-PBD), and 19 healthy controls were recruited in the present study. Each subject underwent 3.0 Tesla functional magnetic resonance imaging scan. Four-dimensional (spatiotemporal) Consistency of local neural Activities (FOCA) was employed to detect the local brain activity changes. Analyses of variance (ANOVA) were used to reveal brain regions with significant differences among three groups groups of individuals, and inter-group comparisons were assessed using post hoc tests. Results The ANOVA obtained significant among-group FOCA differences in the left triangular inferior frontal gyrus, left supplementary motor area, left precentral gyrus, right postcentral gyrus, right superior occipital gyrus, and right superior frontal gyrus. Compared with the control group, the P-PBD group showed decreased FOCA in the left supplementary motor area and bilateral superior frontal gyrus and showed increased FOCA in the left triangular inferior frontal gyrus. In contrast, the NP-PBD group exhibited decreased FOCA in the right superior occipital gyrus and right postcentral gyrus and showed increased FOCA in the left orbital inferior frontal gyrus. Compared to the NP-PBD group, the P-PBD group showed decreased FOCA in the right superior frontal gyrus. Conclusion The present findings demonstrated that the two groups of PBD patients exhibited segregated brain functional patterns, providing empirical evidence for the biological basis of different clinical outcomes between PBD patients with and without psychotic symptoms.

Abstract Background In this study, we tested whether a combination of radiomic features extracted from baseline pre-immunotherapy computed tomography (CT) images and clinicopathological characteristics could be used as novel noninvasive biomarkers for predicting the clinical benefits of non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs). Methods The data from 92 consecutive patients with lung cancer who had been treated with ICIs were retrospectively analyzed. In total, 88 radiomic features were selected from the pretreatment CT images for the construction of a random forest model. Radiomics model 1 was constructed based on the Rad-score. Using multivariate logistic regression analysis, the Rad-score and significant predictors were integrated into a single predictive model (radiomics nomogram model 1) to predict the durable clinical benefit (DCB) of ICIs. Radiomics model 2 was developed based on the same Rad-score as radiomics model 1.Using multivariate Cox proportional hazards regression analysis, the Rad-score, and independent risk factors, radiomics nomogram model 2 was constructed to predict the progression-free survival (PFS). Results The models successfully predicted the patients who would benefit from ICIs. For radiomics model 1, the area under the receiver operating characteristic curve values for the training and validation cohorts were 0.848 and 0.795, respectively, whereas for radiomics nomogram model 1, the values were 0.902 and 0.877, respectively. For the PFS prediction, the Harrell’s concordance indexes for the training and validation cohorts were 0.717 and 0.760, respectively, using radiomics model 2, whereas they were 0.749 and 0.791, respectively, using radiomics nomogram model 2. Conclusions CT-based radiomic features and clinicopathological factors can be used prior to the initiation of immunotherapy for identifying NSCLC patients who are the most likely to benefit from the therapy. This could guide the individualized treatment strategy for advanced NSCLC.

The diagnosis based on clinical assessment of pediatric bipolar disorder (PBD) may sometimes lead to misdiagnosis in clinical practice. For the past several years, machine learning (ML) methods were introduced for the classification of bipolar disorder (BD), which were helpful in the diagnosis of BD. In this study, brain cortical thickness and subcortical volume of 33 PBD-I patients and 19 age-sex matched healthy controls (HCs) were extracted from the magnetic resonance imaging (MRI) data and set as features for classification. The dimensionality reduced feature subset, which was filtered by Lasso or f_classif, was sent to the six classifiers (logistic regression (LR), support vector machine (SVM), random forest classifier, naïve Bayes, k-nearest neighbor, and AdaBoost algorithm), and the classifiers were trained and tested. Among all the classifiers, the top two classifiers with the highest accuracy were LR (84.19%) and SVM (82.80%). Feature selection was performed in the six algorithms to obtain the most important variables including the right middle temporal gyrus and bilateral pallidum, which is consistent with structural and functional anomalous changes in these brain regions in PBD patients. These findings take the computer-aided diagnosis of BD a step forward.

ObjectiveTo develop and validate a DeepSurv nomogram based on radiomic features extracted from computed tomography images and clinicopathological factors, to predict the overall survival and guide individualized adjuvant chemotherapy in patients with non-small cell lung cancer (NSCLC).Patients and MethodsThis retrospective study involved 976 consecutive patients with NSCLC (training cohort, n=683; validation cohort, n=293). DeepSurv was constructed based on 1,227 radiomic features, and the risk score was calculated for each patient as the output. A clinical multivariate Cox regression model was built with clinicopathological factors to determine the independent risk factors. Finally, a DeepSurv nomogram was constructed by integrating the risk score and independent clinicopathological factors. The discrimination capability, calibration, and clinical usefulness of the nomogram performance were assessed using concordance index evaluation, the Greenwood-Nam-D’Agostino test, and decision curve analysis, respectively. The treatment strategy was analyzed using a Kaplan–Meier curve and log-rank test for the high- and low-risk groups.ResultsThe DeepSurv nomogram yielded a significantly better concordance index (training cohort, 0.821; validation cohort 0.768) with goodness-of-fit (P

Abstract Purpose The aim of this study was to explore the feasibility of 3D printing of kidney and perinephric fat based on low‐dose CT technology. Patients and Methods A total of 184 patients with stage T1 complex renal tumors who underwent laparoscopic nephrectomy were prospectively enrolled and divided into three groups: group A (conventional dose kidney and perinephric fat 3D printing group, n = 62), group B (low‐dose kidney and perinephric fat 3D printing, n = 64), and group C (conventional dose merely kidney 3D printing group, n = 58). The effective dose (ED), signal‐to‐noise ratio (SNR), and contrast‐to‐noise ratio (CNR) were determined. The 3D printing quality was evaluated using a 4‐point scale, and interobserver agreement was assessed using the intraclass correlation coefficient (ICC). Results The ED of group B was lower than that of group A, with a decrease of 55.1%. The subjective scores of 3D printing quality in all groups were 3 or 4 points. The interobserver agreement among the three observers in 3D printing quality was good (ICC = 0.84–0.92). The perioperative indexes showed that operation time (OT), warm ischemia time (WIT), estimated blood loss (EBL), and laparoscopic partial nephrectomy (LPN) conversion to laparoscopic radical nephrectomy (LRN) in groups A or B were significantly less than those in group C. LPN was more frequent in group A and group B than in group C (all p 0.017). Conclusion Low‐dose CT technology can be effectively applied to 3D printing of kidney and perinephric fat and reduce the patient's radiation dose without compromising 3D printing quality. 3D printing of kidney and perinephric fat can significantly increase the success rate of LPN and decrease OT, WIT, and EBL.

Abstract The COVID-19 pandemic overwhelms the medical resources in the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). We performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n = 1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 (n = 700) and Cohort 3 (n = 662) constructed from nine external hospitals, achieved satisfying performance for predicting ICU, MV, and death of COVID-19 patients (AUROC 0.916, 0.919, and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943, and 0.856). Both clinical and image features showed complementary roles in prediction and provided accurate estimates to the time of progression (p

This study aimed to develop a computed tomography (CT)-based radiomics model to predict the outcome of COVID-19 pneumonia. In total of 44 patients with confirmed diagnosis of COVID-19 were retrospectively enrolled in this study. The radiomics model and subtracted radiomics model were developed to assess the prognosis of COVID-19 and compare differences between the aggravate and relief groups. Each radiomic signature consisted of 10 selected features and showed good performance in differentiating between the aggravate and relief groups. The sensitivity, specificity, and accuracy of the first model were 98.1%, 97.3%, and 97.6%, respectively (AUC = 0.99). The sensitivity, specificity, and accuracy of the second model were 100%, 97.3%, and 98.4%, respectively (AUC = 1.00). There was no significant difference between the models. The radiomics models revealed good performance for predicting the outcome of COVID-19 in the early stage. The CT-based radiomic signature can provide valuable information to identify potential severe COVID-19 patients and aid clinical decisions.

Photodynamic therapy (PDT) has been widely used in cancer treatment. However, hypoxia in most solid tumors seriously restricts the efficacy of PDT. To improve the hypoxic microenvironment, we designed a novel mesoporous platinum (mPt) nanoplatform to catalyze hydrogen peroxide (H2O2) within the tumor cells in situ without an extra enzyme. During the fabrication, the carboxy terminus of the photosensitizer chlorin e6 (Ce6) was connected to the amino terminus of the bifunctional mercaptoaminopolyglycol (SH-PEG-NH2) by a condensation reaction, and then PEG-Ce6 was modified onto the mPt moiety via the mercapto terminal of SH-PEG-NH2. Material, cellular and animal experiments demonstrated that Pt@PEG-Ce6 catalyzed H2O2 to produce oxygen (O2) and that Ce6 transformed O2 to generate reactive oxygen species (ROS) upon laser irradiation. The Pt@PEG-Ce6 nanoplatform with uniform diameter presented good biocompatibility and efficient tumor accumulation. Due to the high atomic number and good near-infrared absorption for Pt, this Pt@PEG-Ce6 nanoplatform showed computed tomography (CT) and photoacoustic (PA) dual-mode imaging ability, thus providing an important tool for monitoring the tumor hypoxic microenvironment. Moreover, the Pt@PEG-Ce6 nanoplatform reduced the expression of hypoxia-inducible factor-1α (HIF-1α) and programmed death-1 (PD-1) in tumors, discussing the relationship between hypoxia, PD-1, and PDT for the first time.

Abstract Purpose Laparoscopic nephrectomy (LN) has become the preferred method for renal cell carcinoma (RCC). Adequate preoperative assessment or intraoperative navigation is key to the successful implementation of LN. The aim of this study was to evaluate the clinical application value of mixed‐reality–assisted surgical navigation (MRASN) in LN. Patients and Methods A total of 100 patients with stage T1N0M0 renal tumors who underwent laparoscopic partial nephrectomy (LPN) or laparoscopic radical nephrectomy (LRN) were prospectively enrolled and divided into a mixed‐reality‐assisted laparoscopic nephrectomy (MRALN) group (n = 50) and a non–mixed‐reality‐assisted laparoscopic nephrectomy (non‐MRALN) group (n = 50). All patients underwent renal contrast‐enhanced CT scans. The CT DICOM data of all patients in the MRALN group were imported into the mixed‐reality (MR) postprocessing workstation and underwent holographic three‐dimensional visualization (V3D) modeling and MR displayed, respectively. We adopted the Likert scale to evaluate the clinical application value of MRASN. The consistency of evaluators was assessed using the Cohen kappa coefficient (k). Results No significant differences in patient demographic indicators between the MRALN group and the non‐MRALN group (P > .05). The subjective score of MRASN clinical application value in operative plan formulation, intraoperative navigation, remote consultation, teaching guidance, and doctor‐patient communication were higher in the MRASN group than in the non‐MRASN group (all P

Abstract To date, the ability of nanoplatforms to achieve excellent therapeutic responses is hindered by short blood circulation and limited tumor accumulation/penetration. Herein, a soft mesoporous organosilica nanoplatform modified with hyaluronic acid and cyanine 5.5 are prepared, denoted SMONs-HA-Cy5.5, and comparative studies between SMONs-HA-Cy5.5 (24.2 MPa) and stiff counterparts (79.2 MPa) are conducted. Results indicate that, apart from exhibiting a twofold increase in tumor cellular uptake, the soft nanoplatforms also display a remarkable pharmacokinetic advantage, resulting in considerably improved tumor accumulation. Moreover, SMONs-HA-Cy5.5 exhibits a significantly higher tumor penetration, achieving 30-μm deeper tissue permeability in multicellular spheroids relative to the stiff counterparts. Results further reveal that the soft nanoplatforms have an easier extravasation from the tumor vessels, diffuse farther in the dense extracellular matrix, and reach deeper tumor tissues compared to the stiff ones. Specifically, the soft nanoplatforms generate a 16-fold improvement (43 vs. 2.72 μm) in diffusion distance in tumor parenchyma. Based on the significantly improved blood circulation and tumor accumulation/penetration, a soft therapeutic nanoplatform is constructed by loading photosensitizer chlorin e6 in SMONs-HA-Cy5.5. The resulting nanoplatform exhibits considerably higher therapeutic efficacy on tumors compared to the stiff ones.

The current researches have been shown high prevalence and incidence of children's teeth caries, especially for the first permanent molar, which might do a lot of harm to their general health. Fortunately, early detection and protection can reduce the difficulty of treatment and protect children's oral health. However, traditional diagnostic methods such as dentist's visual inspection and radiographic imaging diagnosis are non-automatic and time-consuming. Given the COVID-19 epidemic, these methods should not be taken into consideration, since they fail to practice social distancing and further increase the risk of infection. To address these issues, in this paper we propose a novel caries detection and assessment (UCDA) framework to achieve a new technique for fully-automated diagnosis of dental caries on the children's first permanent molar. Inspired by an efficient in-network feature pyramid and anchor boxes, the proposed UCDA framework mainly contains a backbone network that is initialized with ResNet-FPN, and two parallel task-specific subnetworks for region regression and region classification. Due to the lack of the image database, we also present a novel children's oral image database, namely “Child-OID”, which comprises 1, 368 primary school children's oral images with standard diagnostic annotations and labels, to evaluate the effectiveness of our UCDA method. Experiments on the Child-OID database demonstrate that commonly occurring caries on the first permanent molar can be more accurately detected via the proposed UCDA framework. Database and code are available at https://github.com/GipinLinn/UCDA-and-Child-OID.git.

BackgroundBy calculating cortical thickness (CT) and cortical structural covariance (SC), we aimed to investigate cortical morphology and cortical inter-regional correlation alterations in adolescent bipolar disorder type I (BD-I) and type II (BD-II) patients.MethodsT1-weighted images from 36 BD-I and 22 BD-II patients and 19 healthy controls (HCs) were processed to estimate CT. CT values of the whole brain were compared among three groups. Cortical regions showing CT differences in groups were regarded as seeds for analyzing cortical SC differences between groups. The relationship between CT and clinical indices was further assessed.ResultsBoth BD groups showed cortical thinning in several frontal and temporal areas vs. HCs, and CT showed no significant difference between two BD subtypes. Compared to HCs, both BD groups exhibited reduced SC connections between left superior frontal gyrus (SFG) and right postcentral gyrus (PCG), left superior temporal gyrus (STG) and right pars opercularis, and left STG and right PCG. Compared with HCs, decreased SC connections between left STG and right inferior parietal gyrus (IPG) and right pars opercularis and right STG were only observed in the BD-I group, and left PCG and left SFG only in the BD-II group. CT of right middle temporal gyrus was negatively correlated with number of episodes in BD-II patients.ConclusionsAdolescent BD-I and BD-II showed commonly decreased CT while presenting commonly and distinctly declined SC connections. This study provides a better understanding of cortical morphology and cortical inter-regional correlation alterations in BD and crucial insights into neuroanatomical mechanisms and pathophysiology of different BD subtypes.

Preliminary studies have shown the feasibility of deep learning (DL)-based super-resolution (SR) technique for reconstructing thick-slice/gap diagnostic MR images into high-resolution isotropic data, which would be of great significance for brain research field if the vast amount of diagnostic MRI data could be successively put into brain morphometric study. However, less evidence has addressed the practicability of the strategy, because lack of a large-sample available real data for constructing DL model. In this work, we employed a large cohort (n = 2052) of peculiar data with both low through-plane resolution diagnostic and high-resolution isotropic brain MR images from identical subjects. By leveraging a series of SR approaches, including a proposed novel DL algorithm of Structure Constrained Super Resolution Network (SCSRN), the diagnostic images were transformed to high-resolution isotropic data to meet the criteria of brain research in voxel-based and surface-based morphometric analyses. We comprehensively assessed image quality and the practicability of the reconstructed data in a variety of morphometric analysis scenarios. We further compared the performance of SR approaches to the ground truth high-resolution isotropic data. The results showed (i) DL-based SR algorithms generally improve the quality of diagnostic images and render morphometric analysis more accurate, especially, with the most superior performance of the novel approach of SCSRN. (ii) Accuracies vary across brain structures and methods, and (iii) performance increases were higher for voxel than for surface based approaches. This study supports that DL-based image super-resolution potentially recycle huge amount of routine diagnostic brain MRI deposited in sleeping state, and turning them into useful data for neurometric research.

Overlap between the pathogenesis of posterior reversible encephalopathy syndrome and that of cerebrovascular disease can confound their clinical and radiological presentations, posing a diagnostic challenge. This article presents a literature review and discussion of the clinical manifestations, pathological mechanisms, and imaging manifestations of subarachnoid hemorrhage and vasculitis leading to posterior reversible encephalopathy syndrome, coexistence of posterior reversible encephalopathy syndrome with reversible cerebral vasoconstriction syndrome, and hemorrhage and infarction secondary to posterior reversible encephalopathy syndrome. The findings show that posterior reversible encephalopathy syndrome shares some overlapping pathophysiological mechanisms with cerebrovascular disease. Importantly, neuroimaging plays an important role in identifying this entity in a timely manner and differentiating it from other diseases.

Background: Lung-RADS classification and CT signs can both help in the differential diagnosis of SPNs. The purpose of this study was to investigate the diagnostic value of these two methods and the combination of the two methods for solitary pulmonary nodules (SPNs). Methods: A total of 296 cases of SPNs were retrospectively analyzed. All the SPNs were classified according to the Lung-RADS grading version 1.1. The scores of each lesion were calculated according to their CT signs. Imaging features, such as the size and margin of the lesions, pleural traction, spiculation, lobulation, bronchial cutoff, air bronchogram, vacuoles, tumor vasculature, and cavity signs, were analyzed. The imaging results were compared with the pathology examination findings. Receiver operating characteristic (ROC) curves were applied to compare the values of the different methods in differentially diagnosing benign and malignant SPNs. Results: The sensitivity, specificity, and accuracy of Lung-RADS grading for diagnosing SPNs were 34.0%, 94.4%, and 47.6%, respectively. The area under the ROC curve (AUC) was 0.600 (p < 0.001). The sensitivity, specificity, and accuracy of the CT sign scores were 56.3%, 70.0%, and 60.5%, respectively, and the AUC was 0.657 (p < 0.001). The sensitivity, specificity, and accuracy of the combination of the two methods for diagnosing SPNs were 93.2%, 61.1%, and 83.5%, and the AUC was 0.777 (p < 0.001). Conclusion: The combination of Lung-RADS classification and CT signs significantly improved the differential diagnosis of SPNs.

Accurate epileptogenic zone (EZ) or seizure onset zone (SOZ) localization is crucial for epilepsy surgery optimization. Previous animal and human studies on epilepsy have reported that changes in blood oxygen level-dependent (BOLD) signals induced by epileptic events could be used as diagnostic markers for EZ or SOZ localization. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recording is gaining interest as a non-invasive tool for preoperative epilepsy evaluation. However, EEG-fMRI studies have reported inconsistent and ambiguous findings. Therefore, it remains unclear whether BOLD responses can be used for accurate EZ or SOZ localization. In this study, we used simultaneous EEG-fMRI recording in a rat model of 4-aminopyridine-induced acute focal seizures to assess the spatial concordance between individual BOLD responses and the SOZ. This was to determine the optimal use of simultaneous EEG-fMRI recording in the SOZ localization. We observed a high spatial consistency between BOLD responses and the SOZ. Further, dynamic BOLD responses were consistent with the regions where the seizures were propagated. These results suggested that simultaneous EEG-fMRI recording could be used as a noninvasive clinical diagnostic technique for localizing the EZ or SOZ and could be an effective tool for mapping epileptic networks.

Abstract Electric switching of non-polar bulk crystals is shown to occur when domain walls are polar in ferroelastic materials and when rough surfaces with steps on an atomic scale promote domain switching. All domains emerging from surface nuclei possess polar domain walls. The progression of domains is then driven by the interaction of the electric field with the polarity of domain boundaries. In contrast, smooth surfaces with higher activation barriers prohibit effective domain nucleation. We demonstrate the existence of an electrically driven ferroelectric hysteresis loop in a non-ferroelectric, ferroelastic bulk material.

Background: To achieve imaging report standardization and improve the quality and efficiency of the intra-interdisciplinary clinical workflow, we proposed an intelligent imaging layout system (IILS) for a clinical decision support system-based ubiquitous healthcare service, which is a lung nodule management system using medical images. Methods: We created a lung IILS based on deep learning for imaging report standardization and workflow optimization for the identification of nodules. Our IILS utilized a deep learning plus adaptive auto layout tool, which trained and tested a neural network with imaging data from all the main CT manufacturers from 11,205 patients. Model performance was evaluated by the receiver operating characteristic curve (ROC) and calculating the corresponding area under the curve (AUC). The clinical application value for our IILS was assessed by a comprehensive comparison of multiple aspects. Findings: Our IILS is clinically applicable due to the consistency with nodules detected by IILS, with its highest consistency of 0·94 and an AUC of 90·6% for malignant pulmonary nodules versus benign nodules with a sensitivity of 76·5% and specificity of 89·1%. Applying this IILS to a dataset of chest CT images, we demonstrate performance comparable to that of human experts in providing a better layout and aiding in diagnosis in 100% valid images and nodule display. The IILS was superior to the traditional manual system in performance, such as reducing the number of clicks from 14·45 ± 0·38 to 2, time consumed from 16·87 ± 0·38 s to 6·92 ± 0·10 s, number of invalid images from 7·06 ± 0·24 to 0, and missing lung nodules from 46·8% to 0%. Interpretation: This IILS might achieve imaging report standardization, and improve the clinical workflow therefore opening a new window for clinical application of artificial intelligence. Fund: The National Natural Science Foundation of China. Keywords: Lung nodule, Artificial intelligence, Deep learning algorithms, Intelligent image layout system, Standardized e-film and visualized structured report, Clinical workflow

A common failure of many cancer treatments is attributed to the resistance imparted by tumour hypoxia. Here, the authors report on the generic synthesis of small-sized hollow mesoporous organosilica nanoparticles which are designed for oxygen-independent X-ray-activated synergistic therapy.

Due to the impressive learning power, deep learning has achieved a remarkable performance in supervised hash function learning. In this paper, we propose a novel asymmetric supervised deep hashing method to preserve the semantic structure among different categories and generate the binary codes simultaneously. Specifically, two asymmetric deep networks are constructed to reveal the similarity between each pair of images according to their semantic labels. Furthermore, since the binary codes in the Hamming space also should keep the semantic affinity existing in the original space, another asymmetric pairwise loss is introduced to capture the similarity between the binary codes and real-value features. This asymmetric loss not only improves the retrieval performance, but also contributes to a quick convergence at the training phase. By taking advantage of the two-stream deep structures and two types of asymmetric pairwise functions, an alternative algorithm is designed to efficiently optimize the deep features and high-quality binary codes. Experimental results on three real-world datasets substantiate the effectiveness and superiority of our approach as compared with the state-of-the-art.

Genetic generalized epilepsy is a network disorder typically involving distributed areas identified by classical neuroanatomy. However, the finer topological relationships in terms of continuous spatial arrangement between these systems are still ambiguous. Connectome gradients provide the topological representations of human macroscale hierarchy in an abstract low-dimensional space by embedding the functional connectome into a set of axes. Leveraging connectome gradients, we systematically scrutinized abnormalities of functional connectome gradient in patients with genetic generalized epilepsy with tonic-clonic seizure (GGE-GTCS, n = 78) compared to healthy controls (HC, n = 85), and further examined the reproducibility across multiple processing configurations and in an independent validation sample (patients with GGE-GTCS, n = 28; HC, n = 31). Our findings demonstrated an extended principal gradient at different spatial scales, network-level and vertex-level, in patients with GGE-GTCS. We found consistent results across processing parameters and in validation sample. The extended principal gradient revealed the excessive functional segregation between unimodal and transmodal systems associated with duration of epilepsy and age at seizure onset in patients. Furthermore, the connectivity profile of regions with abnormal principal gradients verified the disrupted functional hierarchy revealed by gradients. Together, our findings provided a novel view of functional system hierarchy alterations, which facilitated a continuous spatial arrangement of macroscale networks, to increase our understanding of the functional connectome hierarchy in generalized epilepsy.

Purpose: We aimed to find out the distributed functional connectome of white matter in patients with functional dyspepsia (FD).Methods: 20 patients with FD and 24 age- and gender-matched healthy controls were included into the study. The functional connectome of white matter and graph theory were used to these participants. Two-sample t-test was used for the detection the abnormal graph properties in FD. Pearson correlation was used for the relationship between properties and the clinical and neuropshychological information.Results: Patients with FD and healthy controls showed small-world properties in functional connectome of white matter. Compared with healthy controls, the FD group showed decreased global properties (Cp, S, Eglobal, and Elocal). Four pairs of fiber bundles that are connected to the frontal lobe, insula, and thalamus were affected in the FD group. Duration and Pittsburgh Sleep Quality Index positively correlated with the betweenness centrality of white matter regions of interest.Conclusion: FD patients turned to a non-optimized functional organization of WM brain network. Frontal lobe, insula, and thalamus were key regions in brain information exchange of FD. It provided some novel imaging evidences for the mechanism of FD.

Brain structural covariance network (SCN) can delineate the brain synchronized alterations in a long-range time period. It has been used in the research of cognition or neuropsychiatric disorders. Recently, causal analysis of structural covariance network (CaSCN), winner-take-all and cortex–subcortex covariance network (WTA-CSSCN), and modulation analysis of structural covariance network (MOD-SCN) have expended the technology breadth of SCN. However, the lack of user-friendly software limited the further application of SCN for the research. In this work, we developed the graphical user interface (GUI) toolkit of brain structural covariance connectivity based on MATLAB platform. The software contained the analysis of SCN, CaSCN, MOD-SCN, and WTA-CSSCN. Also, the group comparison and result-showing modules were included in the software. Furthermore, a simple showing of demo dataset was presented in the work. We hope that the toolkit could help the researchers, especially clinical researchers, to do the brain covariance connectivity analysis in further work more easily.

Background: Losing one’s only child may lead to post-traumatic stress disorder (PTSD), of which re-experiencing is the core symptom. However, neuroimaging studies of sex differences in re-experiencing in the context of the trauma of losing one’s only child and PTSD are scarce; comparisons of the functional networks from the hippocampal subfields to the thalamus might clarify the neural basis.Methods: Thirty couples without any psychiatric disorder who lost their only child (non-PTSD group), 55 patients with PTSD, and 50 normal controls underwent resting-state functional magnetic resonance imaging. The functional connectivity (FC) from the hippocampal subregions to the thalamus and the correlations of FC with re-experiencing symptoms were analyzed within and between the sexes.Results: Compared with husbands without PTSD, wives without PTSD had higher re-experiencing symptoms and weaker FC between the right hippocampal cornu ammonis 3 (RCA3) and the right thalamus (RT; RCA3-RT). Moreover, only the correlation between the RCA3-RT FC and re-experiencing in wives without PTSD was significant. Among the three groups, only the RCA3-RT FC in female subjects was markedly different. Additionally, the RCA3-RT FC in wives without PTSD was remarkably lower relative to female patients with PTSD.Conclusion: Wives without PTSD who lost their only child had worse re-experiencing symptoms relative to their husbands, which was associated with the FC alteration between the hippocampal subregions and the thalamus. Importantly, the low level of the RCA3-RT FC may play a potentially protective role against the development of PTSD in wives who have lost their only child.

Whether antagonistic brain states constitute a fundamental principle of human brain organization has been debated over the past decade. Some argue that intrinsically anti-correlated brain networks in resting-state functional connectivity are an artifact of preprocessing. Others argue that anti-correlations are biologically meaningful predictors of how the brain will respond to different stimuli. Here, we investigated the co-activation patterns across the whole brain in various tasks and test whether brain regions demonstrate anti-correlated activity similar to those observed at rest. We examined brain activity in 47 task contrasts from the Human Connectome Project (N = 680) and found robust antagonistic interactions between networks. Regions of the default network exhibited the highest degree of cortex-wide negative connectivity. The negative co-activation patterns across tasks showed good correspondence to that derived from resting-state data processed with global signal regression (GSR). Interestingly, GSR-processed resting-state data was a significantly better predictor of task-induced modulation than data processed without GSR. Finally, in a cohort of 25 patients with depression, we found that task-based anti-correlations between the dorsolateral prefrontal cortex (DLPFC) and subgenual anterior cingulate cortex were associated with clinical efficacy of transcranial magnetic stimulation therapy targeting the DLPFC. Overall, our findings indicate that anti-correlations are a biologically meaningful phenomenon and may reflect an important principle of functional brain organization.

PurposeMachine learning (ML) can extract high-throughput features of images to predict disease. This study aimed to develop nomogram of multi-parametric MRI (mpMRI) ML model to predict the risk of breast cancer.MethodsThe mpMRI included non-enhanced and enhanced T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), Ktrans, Kep, Ve, and Vp. Regions of interest were annotated in an enhanced T1WI map and mapped to other maps in every slice. 1,132 features and top-10 principal components were extracted from every parameter map. Single-parametric and multi-parametric ML models were constructed via 10 rounds of five-fold cross-validation. The model with the highest area under the curve (AUC) was considered as the optimal model and validated by calibration curve and decision curve. Nomogram was built with the optimal ML model and patients’ characteristics.ResultsThis study involved 144 malignant lesions and 66 benign lesions. The average age of patients with benign and malignant lesions was 42.5 years old and 50.8 years old, respectively, which were statistically different. The sixth and fourth principal components of Ktrans had more importance than others. The AUCs of Ktrans, Kep, Ve and Vp, non-enhanced T1WI, enhanced T1WI, T2WI, and ADC models were 0.86, 0.81, 0.81, 0.83, 0.79, 0.81, 0.84, and 0.83 respectively. The model with an AUC of 0.90 was considered as the optimal model which was validated by calibration curve and decision curve. Nomogram for the prediction of breast cancer was built with the optimal ML models and patient age.ConclusionNomogram could improve the ability of breast cancer prediction preoperatively.