Frequency-dependent alterations in functional connectivity in patients with Alzheimer's Disease spectrum disorders.

Background: In the spectrum of Alzheimer's Disease (AD) and related disorders, the resting-state functional magnetic resonance imaging (rs-fMRI) signals within the cerebral cortex may exhibit distinct characteristics across various frequency ranges. Nevertheless, this hypothesis has not yet been substantiated within the broader context of whole-brain functional connectivity. This study aims to explore potential modifications in degree centrality (DC) and voxel-mirrored homotopic connectivity (VMHC) among individuals with amnestic mild cognitive impairment (aMCI) and AD, while assessing whether these alterations differ across distinct frequency bands. Methods: This investigation encompassed a total of 53 AD patients, 40 aMCI patients, and 40 healthy controls (HCs). DC and VMHC values were computed within three distinct frequency bands: classical (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz), and slow-5 (0.01-0.027 Hz) for the three respective groups. To discern differences among these groups, ANOVA and subsequent post hoc two-sample t-tests were employed. Cognitive function assessment utilized the mini-mental state examination (MMSE) and Montreal Cognitive Assessment (MoCA). Pearson correlation analysis was applied to investigate the associations between MMSE and MoCA scores with DC and VMHC. Results: Significant variations in degree centrality (DC) were observed among different groups across diverse frequency bands. The most notable differences were identified in the bilateral caudate nucleus (CN), bilateral medial superior frontal gyrus (mSFG), bilateral Lobule VIII of the cerebellar hemisphere (Lobule VIII), left precuneus (PCu), right Lobule VI of the cerebellar hemisphere (Lobule VI), and right Lobule IV and V of the cerebellar hemisphere (Lobule IV, V). Likewise, disparities in voxel-mirrored homotopic connectivity (VMHC) among groups were predominantly localized to the posterior cingulate gyrus (PCG) and Crus II of the cerebellar hemisphere (Crus II). Across the three frequency bands, the brain regions exhibiting significant differences in various parameters were most abundant in the slow-5 frequency band. Conclusion: This study enhances our understanding of the pathological and physiological mechanisms associated with AD continuum. Moreover, it underscores the importance of researchers considering various frequency bands in their investigations of brain function.

Quantitative Analysis of Multimodal MRI Markers and Clinical Risk Factors for Cerebral Small Vessel Disease Based on Deep Learning.

Background: Cerebral small vessel disease lacks specific clinical manifestations, and extraction of valuable features from multimodal images is expected to improve its diagnostic accuracy. In this study, we used deep learning techniques to segment cerebral small vessel disease imaging markers in multimodal magnetic resonance images and analyze them with clinical risk factors. Methods and results: We recruited 211 lacunar stroke patients and 83 control patients. The patients' cerebral small vessel disease markers were automatically segmented using a V-shaped bottleneck network, and the number and volume were calculated after manual correction. The segmentation results of the V-shaped bottleneck network for white matter hyperintensity and recent small subcortical infarction were in high agreement with the ground truth (DSC>0.90). In small lesion segmentation, cerebral microbleed (average recall=0.778; average precision=0.758) and perivascular spaces (average recall=0.953; average precision=0.923) were superior to lacunar infarct (average recall=0.339; average precision=0.432) in recall and precision. Binary logistic regression analysis showed that age, systolic blood pressure, and total cerebral small vessel disease load score were independent risk factors for lacunar stroke (P<0.05). Ordered logistic regression analysis showed age was positively correlated with cerebral small vessel disease load score and total cholesterol was negatively correlated with cerebral small vessel disease score (P<0.05). Conclusion: Lacunar stroke patients exhibited higher cerebral small vessel disease imaging markers, and age, systolic blood pressure, and total cerebral small vessel disease score were independent risk factors for lacunar stroke patients. V-shaped bottleneck network segmentation network based on multimodal deep learning can segment and quantify various cerebral small vessel disease lesions to some extent.

Gadolinium deposition in the liver and brain in a rat model with liver fibrosis after intravenous administration of gadoxetate disodium.

Objectives: To investigate gadolinium deposition in the liver and brain in a rat model with liver fibrosis (LF) after intravenous administration of gadoxetate disodium (GD) and the histological effects of gadolinium deposition in the liver and brain. Methods: Adult male Sprague-Dawley rats were randomly assigned to one of the three groups: 1) LF group received intraperitoneal injection of carbon tetrachloride (CCl4) for 9 weeks alone; 2) LF&GD group received CCl4 and intravenous administration of GD (for 5 consecutive days); 3) GD group received olive oil and GD. Seven days after the final injection of GD, the deep cerebellar nuclei (DCN) and liver were excised to determine gadolinium concentrations via inductively coupled plasma mass spectrometry, and histologic staining was performed. Bonferroni's post-hoc test and Wilcoxon rank sum test were used to compare the differences between the three groups. Results: The concentrations of retained gadolinium in the liver in the LF&GD group (2.18 ± 0.44 µg/g) were significantly greater compared to the LF group (0.02 ± 0.01 µg/g, P < 0.001) and GD group (0.37 ± 0.11 µg/g, P < 0.001). Also, the concentrations of retained gadolinium in DCN were increased in the LF&GD group (0.13 ± 0.06 µg/g) compared to the LF group (0.01 ± 0.00 µg/g, P < 0.001) and GD group (0.06 ± 0.02 µg/g, P = 0.019). No histopathological alterations were detected in the liver and DCN between LF&GD group and LF group. Conclusions: LF aggravated gadolinium deposition in the liver and DCN after administration of GD. However, no significant acute histological alterations were observed due to gadolinium deposition.

Acoustic Levitation Synthesis of Ultrahigh-Density Spherical Nucleic Acid Architectures for Specific SERS Analysis.

Controllably regulating the electrostatic bilayer of nanogold colloids is a significant premise for synthesizing spherical nucleic acid (SNA) and building ordered plasmonic architectures. We develop a facile acoustic levitation reactor to universally synthesize SNAs with an ultra-high density of DNA strands, which is even higher than those of various state-of-the-art methods. Results reveal a new mechanism of DNA grafting via acoustic wave that can reconfigure the ligands on colloidal surfaces. The acoustic levitation reactor enables substrate-free three-dimentional (3D) spatial assembly of SNAs with controllable interparticle nanogaps through regulating DNA lengths. This kind of architecture may overcome the plasmonic enhancement limits by blocking electron tunneling and breaking electrostatic shielding in dried aggregations. Finite element simulations support the architecture with 3D spatial plasmonic hotspot matrix, and its ultrahigh surface-enhanced Raman scattering (SERS) capability is evidenced by in situ untargeted tracking of biomolecular events during photothermal stimulation (PTS)-induced cell death process. For biomarker diagnosis, the conjugation of adenosine triphosphate (ATP) aptamer onto SNAs enables in situ targeted tracking of ATP during PTS-induced cell death process. Particularly, the CD71 receptor and integrin α3ß1 protein on PL45 cell membrance could be well distinguished by label-free SERS fingerprints when using specific XQ-2d and DML-7 aptamers, respectively, to synthesize SNA architectures. Our current acoustic levitation reactor offers a new method for synthesizing SNAs and enables both targeted and untargeted SERS analysis for tracking molecular events in living systems. It promises great potentials in biochemical synthesis and sensing in future.

Altered trends of local brain function in classical trigeminal neuralgia patients after a single trigger pain.

OBJECTIVE: To investigate the altered trends of regional homogeneity (ReHo) based on time and frequency, and clarify the time-frequency characteristics of ReHo in 48 classical trigeminal neuralgia (CTN) patients after a single pain stimulate. METHODS: All patients underwent three times resting-state functional MRI (before stimulation (baseline), after stimulation within 5 s (triggering-5 s), and in the 30th min of stimulation (triggering-30 min)). The spontaneous brain activity was investigated by static ReHo (sReHo) in five different frequency bands and dynamic ReHo (dReHo) methods. RESULTS: In the five frequency bands, the number of brain regions which the sReHo value changed in classical frequency band were most, followed by slow 4 frequency band. The left superior occipital gyrus was only found in slow 2 frequency band and the left superior parietal gyrus was only found in slow 3 frequency band. The dReHo values were changed in midbrain, left thalamus, right putamen, and anterior cingulate cortex, which were all different from the brain regions that the sReHo value altered. There were four altered trends of the sReHo and dReHo, which dominated by decreased at triggering-5 s and increased at triggering-30 min. CONCLUSIONS: The duration of brain function changed was more than 30 min after a single pain stimulate, although the pain of CTN was transient. The localized functional homogeneity has time-frequency characteristic in CTN patients after a single pain stimulate, and the changed brain regions of the sReHo in five frequency bands and dReHo complemented to each other. Which provided a certain theoretical basis for exploring the pathophysiology of CTN.

Predictive value of delta radiomics in xerostomia after chemoradiotherapy in patients with stage III-IV nasopharyngeal carcinoma.

BACKGROUND: Xerostomia is one of the most common side effects in nasopharyngeal carcinoma (NPC) patients after chemoradiotherapy. To establish a Delta radiomics model for predicting xerostomia secondary to chemoradiotherapy for NPC based on magnetic resonance T1-weighted imaging (T1WI) sequence and evaluate its diagnostic efficacy. METHODS: Clinical data and Magnetic resonance imaging (MRI) data before treatment and after induction chemotherapy (IC) of 255 NPC patients with stage III-IV were collected retrospectively. Within one week after CCRT, the patients were divided into mild (92 cases) and severe (163 cases) according to the grade of xerostomia. Parotid glands in T1WI sequence images before and after IC were delineated as regions of interest for radiomics feature extraction, and Delta radiomics feature values were calculated. Univariate logistic analysis, correlation, and Gradient Boosting Decision Tree (GBDT) methods were applied to reduce the dimension, select the best radiomics features, and establish pretreatment, post-IC, and Delta radiomics xerostomia grading predictive models. The receiver operating characteristic (ROC) curve and decision curve were drawn to evaluate the predictive efficacy of different models. RESULTS: Finally, 15, 10, and 12 optimal features were selected from pretreatment, post-IC, and Delta radiomics features, respectively, and a xerostomia prediction model was constructed with AUC values of 0.738, 0.751, and 0.843 in the training set, respectively. Only age was statistically significant in the clinical data of both groups (P < 0.05). CONCLUSION: Delta radiomics can predict the degree of xerostomia after chemoradiotherapy for NPC patients and it has certain guiding significance for clinical early intervention measures.

In Situ Spatiotemporal SERS Profiling of Bacterial Quorum Sensing by Hierarchical Hydrophobic Plasmonic Arrays in Agar Medium.

A feedback inhibition effect of high autoinducer levels on metabolite secretion in Chromobacterium subtsugae (C. subtsugae) was evidenced by in situ spatiotemporal surface-enhanced Raman spectroscopy (SERS) profiling. The hierarchical hydrophobic plasmonic array in agar medium is structured by oil/water/oil (OL/W/OH) triphasic interfacial self-assembly. The hydrophobic layer acts as a "door curtain" to selectively permit adsorption of a quorum sensing (QS)-regulated fat-soluble metabolite, i.e., violacein (Vio), and significantly blocks nonspecific adsorption of water-soluble proteins, etc. The SERS profiling clearly evidences that the diffusion of N-hexanoyl-l-homoserine lactone (C6-HSL) in agar medium quickly triggers the initial synthesis of Vio in C. subtsugae CV026 but surprisingly inhibits the intrinsic synthesis of Vio in C. subtsugae ATCC31532. The latter negative response might be related to the VioS repressor of ATCC31532, which negatively controls violacein production without influencing the expression of the CviI/R QS system. Moreover, two sender-receiver systems are constructed by separately coculturing CV026 or ATCC31532 with Hafnia alvei H4 that secretes large amounts of C6-HSL. Expectedly, the cocultivation similarly triggers the initial synthesis of Vio in CV026 but seems to have a quite weak negative effect on the intrinsic synthesis in ATCC31532. In fact, the negative regulation in ATCC31532 might be affected by a diffusion-dependent concentration effect. The H4 growth and its secretion of C6-HSL are a slow and continuous process, thereby avoiding the gathering of local high concentrations. Overall, our study put forward an in situ SERS strategy as an alternative to traditional bioluminescent tools for highly sensitively analyzing the spatiotemporal communication and cooperation in live microbial colonies.

Changes in Resting-State Networks in Children with Growth Hormone Deficiency.

Purpose: Growth hormone deficiency (GHD) refers to the partial or complete lack of growth hormone. Short stature and slow growth are characteristic of patients with GHD. Previous neuroimaging studies have suggested that GHD may cause cognitive and behavioral impairments in patients. Resting-state networks (RSNs) are regions of the brain that exhibit synchronous activity and are closely related to our cognition and behavior. Therefore, the purpose of the current study was to explore cognitive and behavioral abnormalities in children with GHD by investigating changes in RSNs. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) data of 26 children with GHD and 15 healthy controls (HCs) were obtained. Independent component analysis was used to identify seven RSNs from rs-fMRI data. Group differences in RSNs were estimated using two-sample t-tests. Correlation analysis was employed to investigate the associations among the areas of difference and clinical measures. Results: Compared with HCs, children with GHD had significant differences in the salience network (SN), default mode network (DMN), language network (LN), and sensorimotor network (SMN). Moreover, within the SN, the functional connectivity (FC) value of the right posterior supramarginal gyrus was negatively correlated with the adrenocorticotropic hormone and the FC value of the left anterior inferior parietal gyrus was positively correlated with insulin-like growth factor 1. Conclusions: These results suggest that alterations in RSNs may account for abnormal cognition and behavior in children with GHD, such as decreased motor function, language withdrawal, anxiety, and social anxiety. These findings provide neuroimaging support for uncovering the pathophysiological mechanisms of GHD in children. Impact statement Children with growth hormone deficiency (GHD) generally experience cognitive and behavioral abnormalities. However, there are few neuroimaging studies on children with GHD. Moreover, prior research has not investigated the aberrant brain function in patients with GHD from the perspective of brain functional networks. Therefore, this study employed the independent component analysis method to investigate alterations within seven commonly observed resting-state networks due to GHD. The results showed that children with GHD had significant differences in the salience network, default mode network, language network, and sensorimotor network. This provides neuroimaging support for revealing the pathophysiological mechanisms of GHD in children.

Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics.

Purpose: The objective is to create a comprehensive model that integrates clinical, semantic, and radiomics features to forecast the 5-year progression-free survival (PFS) of individuals diagnosed with non-distant metastatic Nasopharyngeal Carcinoma (NPC). Methods: In a retrospective analysis, we included clinical and MRI data from 313 patients diagnosed with primary NPC. Patient classification into progressive and non-progressive categories relied on the occurrence of recurrence or distant metastasis within a 5-year timeframe. Initial screening comprised clinical features and statistically significant image semantic features. Subsequently, MRI radiomics features were extracted from all patients, and optimal features were selected to formulate the Rad-Score.Combining Rad-Score, image semantic features, and clinical features to establish a combined model Evaluation of predictive efficacy was conducted using ROC curves and nomogram specific to NPC progression. Lastly, employing the optimal ROC cutoff value from the combined model, patients were dichotomized into high-risk and low-risk groups, facilitating a comparison of 10-year overall survival (OS) between the groups. Results: The combined model showcased superior predictive performance for NPC progression, reflected by AUC values of 0.84, an accuracy rate of 81.60%, sensitivity at 0.77, and specificity at 0.81 within the training group. In the test set, the AUC value reached 0.81, with an accuracy of 74.6%, sensitivity at 0.82, and specificity at 0.66. Conclusion: The amalgamation of Rad-Score, clinical, and imaging semantic features from multi-parameter MRI exhibited significant promise in prognosticating 5-year PFS for non-distant metastatic NPC patients. The combined model provided quantifiable data for informed and personalized diagnosis and treatment planning.

Dynamic analysis of amplitude of low-frequency fluctuation in children with growth hormone deficiency.

OBJECTIVE: Growth hormone (GH) affects brain activities and promotes growth and development. GH is a peptide hormone secreted by the anterior pituitary gland and is tied to behavior and cognitive function. Growth hormone deficiency (GHD) is the most common type of pathological short stature in children. Existing studies provide evidence that GHD may impact functional brain activities. The aim of this study was to investigate dynamic local brain activity in GHD children. METHOD: In this study, we combined amplitude of low-frequency fluctuations (ALFF) and sliding-window techniques to examine the local brain activity of children with GHD. The resting-state functional magnetic resonance imaging (fMRI) data were collected from 26 children with GHD and 15 age- and sex-matched healthy controls (HC). RESULT: Our results showed significant abnormal temporal variability of dynamic ALFF in widespread regions in children with GHD, primarily in the frontal gyrus, temporal gyrus, and parietal lobule. CONCLUSION: The dALFF can capture dynamic changes in brain spontaneous activity, which are related to behavior and cognition. Based on this dynamic local brain activity, the results of this study provide a better understanding of the pathophysiological mechanism in children with GHD.

Altered brain function in classical trigeminal neuralgia patients: ALFF, ReHo, and DC static- and dynamic-frequency study.

The present study aimed to clarify the brain function of classical trigeminal neuralgia (CTN) by analyzing 77 CTN patients and age- and gender-matched 73 healthy controls (HCs) based on three frequency bands of the static and dynamic amplitude of low-frequency fluctuation, regional homogeneity, and degree centrality (sALFF, sReHo, sDC, dALFF, dReHo, and dDC). Compared to HCs, the number of altered brain regions was different in three frequency bands, and the classical frequency band was most followed by slow-4 in CTN patients. Cerrelellum_8_L (sReHo), Cerrelellum_8_R (sDC), Calcarine_R (sDC), and Caudate_R (sDC) were found only in classical frequency band, while Precuneus_L (sALFF) and Frontal_Inf_Tri_L (sReHo) were found only in slow-4 frequency band. Except for the above six brain regions, the others overlapped in the classical and slow-4 frequency bands. CTN seriously affects the mental health of patients, and some different brain regions are correlated with clinical parameters. The static and dynamic indicators of brain function were complementary in CTN patients, and the changing brain regions showed frequency specificity. Compared to slow-5 frequency band, slow-4 is more consistent with the classical frequency band, which could be valuable in exploring the pathophysiology of CTN.

Quantified instant conjugation of peptides on a nanogold surface for tunable ice recrystallization inhibition.

The adverse effects of recrystallization limit the application of cryopreservation in many fields. Peptide-based materials play an essential role in the antifreezing area because of their excellent biocompatibility and abundant ice-binding sites. Peptide-gold nanoparticle conjugates can effectively reduce time and material costs through metal-thiol interactions, but controlled modification remains an outstanding issue, which makes it difficult to elucidate the antifreezing effects of antifreeze peptides at different densities and lengths. In this study, we developed an instant peptide capping on gold nanoparticles with butanol-assisted dehydration and provided a controllable quantitative coupling within a certain range. This chemical dehydration makes it possible to fabricate peptide-gold nanoparticle conjugates in large batches at minute levels. Based on this, the influence of the peptide density and sequence length on the antifreezing behaviors of the conjugates was investigated. The results evidenced that the antifreezing property of the flexible peptide conjugated on a rigid core is related to both the density and length of the peptide. In a certain range, the density is proportional to the antifreeze, while the length is negatively correlated with it. We proposed a rapidly controllable method for synthesizing peptide-gold nanoparticle conjugates, which may provide a universal approach for the development of subsequent recrystallization-inhibiting materials.

Brain entropy changes in classical trigeminal neuralgia.

Background: Classical trigeminal neuralgia (CTN) is a common and severe chronic neuropathic facial pain disorder. The pathological mechanisms of CTN are not fully understood. Recent studies have shown that resting-state functional magnetic resonance imaging (rs-fMRI) could provide insights into the functional changes of CTN patients and the complexity of neural processes. However, the precise spatial pattern of complexity changes in CTN patients is still unclear. This study is designed to explore the spatial distribution of complexity alterations in CTN patients using brain entropy (BEN). Methods: A total of 85 CTN patients and 79 age- and sex-matched healthy controls (HCs) were enrolled in this study. All participants underwent rs-fMRI and neuropsychological evaluations. BEN changes were analyzed to observe the spatial distribution of CTN patient complexity, as well as the relationship between these changes and clinical variables. Sixteen different machine learning methods were employed to classify the CTN patients from the HCs, and the best-performing method was selected. Results: Compared with HCs, CTN patients exhibited increased BEN in the thalamus and brainstem, and decreased BEN in the inferior semilunar lobule. Further analyses revealed a low positive correlation between the average BEN values of the thalamus and neuropsychological assessments. Among the 16 machine learning methods, the Conditional Mutual Information Maximization-Random Forest (CMIM-RF) method yielded the highest area under the curve (AUC) of 0.801. Conclusions: Our study demonstrated that BEN changes in the thalamus and pons and inferior semilunar lobule were associated with CTN and machine learning methods could effectively classify CTN patients and HCs based on BEN changes. Our findings may provide new insights into the neuropathological mechanisms of CTN and have implications for the diagnosis and treatment of CTN.

Machine learning classifiers and associations of cognitive performance with hippocampal subfields in amnestic mild cognitive impairment.

Background: Neuroimaging studies have demonstrated alterations in hippocampal volume and hippocampal subfields among individuals with amnestic mild cognitive impairment (aMCI). However, research on using hippocampal subfield volume modeling to differentiate aMCI from normal controls (NCs) is limited, and the relationship between hippocampal volume and overall cognitive scores remains unclear. Methods: We enrolled 50 subjects with aMCI and 44 NCs for this study. Initially, a univariate general linear model was employed to analyze differences in the volumes of hippocampal subfields. Subsequently, two sets of dimensionality reduction methods and four machine learning techniques were applied to distinguish aMCI from NCs based on hippocampal subfield volumes. Finally, we assessed the correlation between the relative volumes of hippocampal subfields and cognitive test variables (Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment Scale (MoCA)). Results: Significant volume differences were observed in several hippocampal subfields, notably in the left hippocampus. Specifically, the volumes of the hippocampal tail, subiculum, CA1, presubiculum, molecular layer, GC-ML-DG, CA3, CA4, and fimbria differed significantly between the two groups. The highest area under the curve (AUC) values for left and right hippocampal machine learning classifiers were 0.678 and 0.701, respectively. Moreover, the volumes of the left subiculum, left molecular layer, right subiculum, right CA1, right molecular layer, right GC-ML-DG, and right CA4 exhibited the strongest and most consistent correlations with MoCA scores. Conclusion: Hippocampal subfield volume may serve as a predictive marker for aMCI. These findings underscore the sensitivity of hippocampal subfield volume to overall cognitive performance.

Static and dynamic functional connectivity combined with the triple network model in amnestic mild cognitive impairment and Alzheimer's disease.

Background: Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) are characterized by abnormal functional connectivity (FC) of default-mode network (DMN), salience network (SN), and central executive network (CEN). Static FC (sFC) and dynamic FC (dFC) combined with triple network model can better study the dynamic and static changes of brain networks, and improve its potential diagnostic value in the diagnosis of AD spectrum disorders. Methods: Differences in sFC values and dFC variability patterns among the three brain networks of the three groups (53 AD patients, 40 aMCI patients, and 40 NCs) were computed by ANOVA using Gaussian Random Field theory (GRF) correction. The correlation between FC values (sFC values and dFC variability) in the three networks and cognitive scores (MMSE and MoCA) in AD and aMCI groups was analyzed separately. Results: Within the DMN network, there were significant differences of sFC values in right/left medial superior frontal gyrus and dFC variability in left opercular part inferior frontal gyrus and right dorsolateral superior frontal gyrus among the three groups. Within the CEN network, there were significant differences of sFC values in left superior parietal gyrus. Within the SN network, there were significant differences of dFC variability in right Cerebelum_7b and left opercular part inferior frontal gyrus. In addition, there was a significant negative correlation between FC values (sFC values of CEN and dFC variability of SN) and MMSE and MoCA scores. Conclusion: It suggests that sFC, dFC combined with triple network model can be considered as potential biomarkers for AD and aMCI.

Prediction of early hematoma expansion of spontaneous intracerebral hemorrhage based on deep learning radiomics features of noncontrast computed tomography.

OBJECTIVES: Aimed to develop a nomogram model based on deep learning features and radiomics features for the prediction of early hematoma expansion. METHODS: A total of 561 cases of spontaneous intracerebral hemorrhage (sICH) with baseline Noncontrast Computed Tomography (NCCT) were included. The metrics of hematoma detection were evaluated by Intersection over Union (IoU), Dice coefficient (Dice), and accuracy (ACC). The semantic features of sICH were judged by EfficientNet-B0 classification model. Radiomics analysis was performed based on the region of interest which was automatically segmented by deep learning. A combined model was constructed in order to predict the early expansion of hematoma using multivariate binary logistic regression, and a nomogram and calibration curve were drawn to verify its predictive efficacy by ROC analysis. RESULTS: The accuracy of hematoma detection by segmentation model was 98.2% for IoU greater than 0.6 and 76.5% for IoU greater than 0.8 in the training cohort. In the validation cohort, the accuracy was 86.6% for IoU greater than 0.6 and 70.0% for IoU greater than 0.8. The AUCs of the deep learning model to judge semantic features were 0.95 to 0.99 in the training cohort, while in the validation cohort, the values were 0.71 to 0.83. The deep learning radiomics model showed a better performance with higher AUC in training cohort (0.87), internal validation cohort (0.83), and external validation cohort (0.82) than either semantic features or Radscore. CONCLUSION: The combined model based on deep learning features and radiomics features has certain efficiency for judging the risk grade of hematoma. CLINICAL RELEVANCE STATEMENT: Our study revealed that the deep learning model can significantly improve the work efficiency of segmentation and semantic feature classification of spontaneous intracerebral hemorrhage. The combined model has a good prediction efficiency for early hematoma expansion. KEY POINTS: • We employ a deep learning algorithm to perform segmentation and semantic feature classification of spontaneous intracerebral hemorrhage and construct a prediction model for early hematoma expansion. • The deep learning radiomics model shows a favorable performance for the prediction of early hematoma expansion. • The combined model holds the potential to be used as a tool in judging the risk grade of hematoma.

A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer.

Liquid Interfacial Coassembly of Plasmonic Arrays and Trace Hydrophobic Nanoplastics in Edible Oils for Robust Identification and Classification by Surface-Enhanced Raman Spectroscopy.

The ubiquity of micro-/nanoplastics poses a visible threat to the environment, aquatic organisms, and human beings and has become a global concern. Here, we proposed a liquid interface-based strategy using surface-enhanced Raman spectroscopy to coassemble nanoplastics and gold nanoparticles into a dense and homogeneous plasmonic array, thereby enabling the rapid and sensitive detection of trace nanoplastics. In addition, due to the uniqueness of the oil-water immiscible two-phase interface, we achieved ideal results for the detection of nanoplastics in a complex matrix (e.g., aqueous environment and edible oil) with a detection limit of µg/mL. With the aid of the principal component analysis algorithm, the differentiation and identification of multiple nanoplastic components (e.g., polystyrene, polyethylene, and polyethylene terephthalate) in aqueous environments and common hazards (e.g., Bap and Phe) in edible oil were achieved. Therefore, our self-assembled plasmonic arrays are expected to be used for monitoring environmental pollution and food safety.

Hemorrhagic Transformation Assessment Based on Dual Energy CT of Immediately and Twenty-Four Hours after Endovascular Thrombectomy for Acute Ischemic Stroke.

BACKGROUND: Dual-energy CT (DECT) shows good performance in differentiating hemorrhage from contrast staining (CS). However, no guidelines have standardized the post-endovascular thrombectomy (EVT) examination time. We evaluated the value of performing DECT immediately and 24 h post-EVT in the diagnosis and prediction of hemorrhagic transformation (HT). METHODS: Two readers evaluated simulated conventional CT (sCCT) images compared with a second reading with DECT, establishing the diagnosis of HT immediately and 24 h post-EVT. Another reader's diagnosis 2-7 days post-EVT using non-contrast CT was identified as the final diagnostic criteria. RESULTS: DECT performed immediately and 24 h post-EVT changed 22.4% (52/232) and 12.5% (29/232) of sCCT-based HT diagnoses, respectively (χ2 = 10.7, p < 0.05). The sensitivity, negative predictive value (NPV), and accuracy of DECT performed immediately post-EVT for predicting the final diagnosis of HT were 33.6%, 58.9%, and 65.9%, respectively, whereas those for DECT performed 24 h post-EVT were 82.4%, 84.3%, and 90.9%, respectively (χ2 = 58.0, χ2 = 42.9, χ2 = 13.6; p < 0.05). The specificity and positive predictive value were both 100.0%. Delayed HT occurred in 50.0% (78/156) and 42.2% (19/45) of patients with CS diagnosed immediately and 24 h post-EVT, respectively. CONCLUSIONS: DECT performed immediately post-EVT changed a greater proportion of real-time HT diagnoses, whereas that performed 24 h post-EVT had higher sensitivity, NPV, and accuracy in predicting the final diagnosis of HT. A substantial proportion of patients with CS diagnosed at these two post-EVT timepoints subsequently developed delayed HT.

Frequency Dependent Changes of Regional Homogeneity in Children with Growth Hormone Deficiency.

Abnormal spontaneous neural activity in children with growth hormone deficiency (GHD) has been found in previous resting-state functional magnetic resonance imaging (rs-fMRI) studies. Nevertheless, the spontaneous neural activity of GHD in different frequency bands is still unclear. Here, we combined rs-fMRI and regional homogeneity (ReHo) methods to analyze the spontaneous neural activity of 26 GHD children and 15 healthy controls (HCs) with age- and sex-matching in four frequency bands: slow-5 (0.014-0.031 Hz), slow-4 (0.031-0.081 Hz), slow-3 (0.081-0.224 Hz), and slow-2 (0.224-0.25 Hz). In the slow-5 band, GHD children compared with HCs displayed higher ReHo in the left dorsolateral part of the superior frontal gyrus, triangular part of the inferior frontal gyrus, precentral gyrus and middle frontal gyrus, and right angular gyrus, while lower ReHo in the right precentral gyrus, and several medial orbitofrontal regions. In the slow-4 band, GHD children relative to HCs revealed increased ReHo in the right middle temporal gyrus, whereas reduced ReHo in the left superior parietal gyrus, right middle occipital gyrus, and bilateral medial parts of the superior frontal gyrus. In the slow-2 band, compared with HCs, GHD children showed increased ReHo in the right anterior cingulate gyrus, and several prefrontal regions, while decreased ReHo in the left middle occipital gyrus, and right fusiform gyrus and anterior cingulate gyrus. Our findings demonstrate that regional brain activity in GHD children exhibits extensive abnormalities, and these abnormalities are related to specific frequency bands, which may provide bases for understanding its pathophysiology significance.