HCA-DAN: hierarchical class-aware domain adaptive network for gastric tumor segmentation in 3D CT images

BACKGROUND

Accurate segmentation of gastric tumors from CT scans provides useful image information for guiding the diagnosis and treatment of gastric cancer. However, automated gastric tumor segmentation from 3D CT images faces several challenges. The large variation of anisotropic spatial resolution limits the ability of 3D convolutional neural networks (CNNs) to learn features from different views. The background texture of gastric tumor is complex, and its size, shape and intensity distribution are highly variable, which makes it more difficult for deep learning methods to capture the boundary. In particular, while multi-center datasets increase sample size and representation ability, they suffer from inter-center heterogeneity.

METHODS

In this study, we propose a new cross-center 3D tumor segmentation method named Hierarchical Class-Aware Domain Adaptive Network (HCA-DAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale context features from the CT images with anisotropic resolution, and a hierarchical class-aware domain alignment (HCADA) module for adaptively aligning multi-scale context features across two domains by integrating a class attention map with class-specific information. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers and validate its segmentation performance in both in-center and cross-center test scenarios.

RESULTS

Our baseline segmentation network (i.e., AsTr) achieves best results compared to other 3D segmentation models, with a mean dice similarity coefficient (DSC) of 59.26%, 55.97%, 48.83% and 67.28% in four in-center test tasks, and with a DSC of 56.42%, 55.94%, 46.54% and 60.62% in four cross-center test tasks. In addition, the proposed cross-center segmentation network (i.e., HCA-DAN) obtains excellent results compared to other unsupervised domain adaptation methods, with a DSC of 58.36%, 56.72%, 49.25%, and 62.20% in four cross-center test tasks.

CONCLUSIONS

Comprehensive experimental results demonstrate that the proposed method outperforms compared methods on this multi-center database and is promising for routine clinical workflows.

Decreased brain iron deposition based on quantitative susceptibility mapping correlates with reduced neurodevelopmental status in children with autism spectrum disorder

To investigate potential correlations between the susceptibility values of certain brain regions and the severity of disease or neurodevelopmental status in children with autism spectrum disorder (ASD), 18 ASD children and 15 healthy controls (HCs) were recruited. The neurodevelopmental status was assessed by the Gesell Developmental Schedules (GDS) and the severity of the disease was evaluated by the Autism Behavior Checklist (ABC). Eleven brain regions were selected as regions of interest and the susceptibility values were measured by quantitative susceptibility mapping. To evaluate the diagnostic capacity of susceptibility values in distinguishing ASD and HC, the receiver operating characteristic (ROC) curve was computed. Pearson and Spearman partial correlation analysis were used to depict the correlations between the susceptibility values, the ABC scores, and the GDS scores in the ASD group. ROC curves showed that the susceptibility values of the left and right frontal white matter had a larger area under the curve in the ASD group. The susceptibility value of the right globus pallidus was positively correlated with the GDS-fine motor scale score. These findings indicated that the susceptibility value of the right globus pallidus might be a viable imaging biomarker for evaluating the neurodevelopmental status of ASD children.

White matter structural changes before and after microvascular decompression for hemifacial spasm

Hemifacial spasm (HFS) is a syndrome characterized by involuntary contractions of the facial muscles innervated by the ipsilateral facial nerve. Currently, microvascular decompression (MVD) is an effective treatment for HFS. Diffusion weighted imaging (DWI) is a non-invasive advanced magnetic resonance technique that allows us to reconstruct white matter (WM) virtually based on water diffusion direction. This enables us to model the human brain as a complex network using graph theory. In our study, we recruited 32 patients with HFS and 32 healthy controls to analyze and compare the topological organization of whole-brain white matter networks between the groups. We also explored the potential relationships between altered topological properties and clinical outcomes. Compared to the HC group, the white matter network was disrupted in both preoperative and postoperative groups of HFS patients, mainly located in the somatomotor network, limbic network, and default network (All P < 0.05, FDR corrected). There was no significant difference between the preoperative and postoperative groups (P > 0.05, FDR corrected). There was a correlation between the altered topological properties and clinical outcomes in the postoperative group of patients (All P < 0.05, FDR corrected). Our findings indicate that in HFS, the white matter structural network was disrupted before and after MVD, and that these alterations in the postoperative group were correlated with the clinical outcomes. White matter alteration here described may subserve as potential biomarkers for HFS and may help us identify patients with HFS who can benefit from MVD and thus can help us make a proper surgical patient selection.

Associations of quantitative susceptibility mapping with cortical atrophy and brain connectome in Alzheimer's disease: A multi-parametric study

Aberrant susceptibility due to iron level abnormality and brain network disconnections are observed in Alzheimer's disease (AD), with disrupted iron homeostasis hypothesized to be linked to AD pathology and neuronal loss. However, whether associations exist between abnormal quantitative susceptibility mapping (QSM), brain atrophy, and altered brain connectome in AD remains unclear. Based on multi-parametric brain imaging data from 30 AD patients and 26 healthy controls enrolled at the China-Japan Friendship Hospital, we investigated the abnormality of the QSM signal and volumetric measure across 246 brain regions in AD patients. The structural and functional connectomes were constructed based on diffusion MRI tractography and functional connectivity, respectively. The network topology was quantified using graph theory analyses. We identified seven brain regions with both reduced cortical thickness and abnormal QSM (p < 0.05) in AD, including the right superior frontal gyrus, left superior temporal gyrus, right fusiform gyrus, left superior parietal lobule, right superior parietal lobule, left inferior parietal lobule, and left precuneus. Correlations between cortical thickness and network topology computed across patients in the AD group resulted in statistically significant correlations in five of these regions, with higher correlations in functional compared to structural topology. We computed the correlation between network topological metrics, QSM value and cortical thickness across regions at both individual and group-averaged levels, resulting in a measure we call spatial correlations. We found a decrease in the spatial correlation of QSM and the global efficiency of the structural network in AD patients at the individual level. These findings may provide insights into the complex relationships among QSM, brain atrophy, and brain connectome in AD.

[The influence of Ras-associated binding protein 23 knockdown on the migration and invasion of esophageal squamous cell carcinoma cells and its mechanism]

Objective: To investigate the role and the mechanism of Ras-associated binding protein23 (RAB23) in the migration and invasion of esophageal squamous cell carcinoma (ESCC) cells. Methods: RAB23 mRNA levels were measured in 16 pairs of ESCC and adjacent normal tissues via real-time polymerase chain reactions. RAB23 mRNA levels in the ESCC and adjacent normal tissues of dataset GSE20347 deposited in the Gene Expression Omnibus (GEO) database were also analyzed. Immunohistochemistry (IHC) was used to detect the RAB23 protein expressions in 106 pairs of ESCC and adjacent normal tissues, as well as in the lymph glands and primary tumor tissues of 33 patients with positive lymph nodes and 10 patients with negative lymph nodes. Endogenous RAB23 expression was transiently depleted using siRNAs (si-NC, si-RAB23-1, and si-RAB23-9) or stably reduced using shRNAs (sh-NC and sh-RAB23) in ESCC KYSE30 and KYSE150 cells, and the knockdown efficiency was tested using Western blot assays. Cell counting kit-8 assays and mouse xenograft models were used to test the proliferation of ESCC cells. Transwell assays and tail vein-pulmonary metastasis models in immunocompromised mice were used to examine the migration and invasion of ESCC cells. Cell adhesion assays were used to test the adhesion of ESCC cells. RNA-seq assays were used to analyze how RAB23 knockdown influenced the expression profile of ESCC cells and the implicated signal pathways were confirmed using Western blot assays. Results: The RAB23 mRNA expression in 16 cases of ESCC tissues was 0.009 7±0.008 9, which was markedly higher than that in adjacent normal tissues (0.003 2±0.003 7, P=0.006). GEO analysis on RAB23 expressions in ESCC and adjacent normal tissues showed that the RAB23 mRNA level in ESCC tissues (4.30±0.25) was remarkably increased compared with their normal counterparts (4.10±0.17, P=0.037). Among the 106 pairs of ESCC and tumor-adjacent normal tissues, 51 cases exhibited low expression of RAB23 and 55 cases showed high expression of RAB23, whereas in the paired tumor-adjacent normal tissues 82 cases were stained weakly and 24 strongly for RAB23 protein. These results indicated that RAB23 expression was markedly increased in ESCC tissues (P<0.001). Additionally, only 1 out of 33 primary ESCC tissues with positive lymph nodes showed low RAB23 protein expression. On the other hand, 7 samples of primary ESCC tissues with negative lymph nodes were stained strongly for RAB23 while its level in the other 3 samples was weak. These results showed that RAB23 expression was remarkably increased in primary ESCC tissues with positive lymph nodes compared with those with negative lymph nodes (P=0.024). Further tests showed that 32 out of 33 positive lymph nodes were stained strongly for RAB23, whereas no negative lymph nodes (n=10) exhibited high expression of RAB23 (P<0.001). Both transient and stable knockdown of endogenous RAB23 expression failed to cause detectable changes in the proliferation of KYSE30 cells in vitro and in vivo, but attenuated the migration and invasion of KYSE30 cells as well as the invasion of KYSE150 cells. RAB23 knockdown was found to significantly decrease the number of adhesive KYSE30 cells in the sh-RAB23 group (313.75±89.34) compared with control cells in the sh-NC group (1 030.75±134.29, P<0.001). RAB23 knockdown was also found to significantly decrease the number of adhesive KYSE150 cells in the sh-RAB23 group (710.5±31.74) compared with the number of control cells in the sh-NC group (1 005.75±61.09, P<0.001). RNA-seq assays demonstrated that RAB23 knockdown using two siRNAs targeting RAB23 mRNA markedly impaired focal adhesion-related signal pathways, and decreased the levels of phosphorylated FAK (p-FAK) and phosphorylated paxillin (p-paxillin) in KYSE30 and KYSE150 cells. Conclusions: Significantly increased RAB23 in ESCC tissues positively correlates with lymph node metastasis. Depleted RAB23 expression attenuates focal adhesion-related signal pathways, thus impairing the invasion, metastasis, and adhesion of ESCC cells.

Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults. This study aimed to construct immune-related long non-coding RNAs (lncRNAs) signature and radiomics signature to probe the prognosis and immune infiltration of GBM patients.

METHODS

We downloaded GBM RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) project database, and MRI data were obtained from The Cancer Imaging Archive (TCIA). Then, we conducted a cox regression analysis to establish the immune-related lncRNAs signature and radiomics signature. Afterward, we employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways. Besides, we used CIBERSORT to estimate the abundance of tumor-infiltrating immune cells (TIICs). Furthermore, we investigated the relationship between the immune-related lncRNAs signature, radiomics signature and immune checkpoint genes. Finally, we constructed a multifactors prognostic model and compared it with the clinical prognostic model.

RESULTS

We identified four immune-related lncRNAs and two radiomics features, which show the ability to stratify patients into high-risk and low-risk groups with significantly different survival rates. The risk score curves and Kaplan-Meier curves confirmed that the immune-related lncRNAs signature and radiomics signature were a novel independent prognostic factor in GBM patients. The GSEA suggested that the immune-related lncRNAs signature were involved in L1 cell adhesion molecular (L1CAM) interactions and the radiomics signature were involved signaling by Robo receptors. Besides, the two signatures was associated with the infiltration of immune cells. Furthermore, they were linked with the expression of critical immune genes and could predict immunotherapy's clinical response. Finally, the area under the curve (AUC) (0.890,0.887) and C-index (0.737,0.817) of the multifactors prognostic model were greater than those of the clinical prognostic model in both the training and validation sets, indicated significantly improved discrimination.

CONCLUSIONS

We identified the immune-related lncRNAs signature and tradiomics signature that can predict the outcomes, immune cell infiltration, and immunotherapy response in patients with GBM.

[Clinical implications of Naples prognostic scores in patients with resectable Siewert type II-III adenocarcinoma of the esophagogastric junction]

Objective: To evaluate the clinical value of preoperative Naples prognostic scores (NPS) in patients with resectable Siewert type II-III esophagogastric junction adenocarcinoma (AEG). Methods: In this retrospective observational study we collected and analyzed relevant data of patients with Siewert Type II-III AEG treated in the Department of Gastric Cancer, Tianjin Medical University Cancer Institute and Hospital from January 2014 to December 2018. NPS were calculated using preoperative albumin concentration, total cholesterol concentration, neutrophil/lymphocyte ratio, and lymphocyte/monocyte ratio and used to allocate patients into three groups: NTS-0 (0 points), NTS-1 (1-2 points) and NTS-2 (3-4 points). Kaplan-Meier was used to calculate disease-free survival (DFS) and overall survival (OS) in each NPS group and the log-rank test to compare these groups. Univariate and multivariate survival analyes were performed using the Cox regression model. Time-dependent receiver operating characteristic curves were constructed to compare the relationships between four commonly used tools for evaluating inflammatory responses and nutritional status:NPS, systemic inflammatory response scores, nutrient control status (CONUT), and prognostic nutrition index (PNI). Results: The study cohort comprised 221 patients with AEG of median age 63.0 (36.0-87.0) years. There were 190 men (86.0%) and 31 women (14.0%). As to pTNM stage, 47 patients (21.3%) had Stage I disease, 68 (30.8%) Stage II, and 106 (48.0%) Stage III. One hundred and forty-seven patients (66.5%) had Siewert Type II disease and 74 (33.5%) Siewert type III. There were 45 patients (20.4%) in the NPS-0, 142 (64.2%) in the NPS-1 and 34 (15.4%) in the NPS-2 groups. Higher NPS scores were significantly associated with older patients (χ²=5.056, P=0.027) and higher TNM stages (H=5.204,P<0.001). The median follow-up was 39 (6-105) months; 16 patients (7.2%) were lost to follow-up. The median OS in the NPS-0, NPS-1, and NPS-2 groups were 78.4, 63.1, and 37.0 months, respectively; these differences are statistically significant (P=0.021). Univariate and multivariate Cox regression analysis identified the following as independently and significantly associated with OS in patients with Siewert Type II-III: TNM stage (Stage II: HR=2.182, 95%CI: 1.227-3.878, P=0.008; Stage III: HR=3.534, 95%CI: 1.380-6.654, P<0.001), tumor differentiation (G3: HR=1.995, 95%CI: 1.141-3.488, P=0.015), vascular invasion (HR=2.172, 95%CI: 1.403-3.363, P<0.001), adjuvant chemotherapy (HR=0.326, 95%CI: 0.200-0.531, P<0.001), NPS (NPS-1: HR=2.331, 95%CI: 1.371-3.964, P=0.002; NPS-2: HR=2.494, 95%CI: 1.165-5.341, P=0.019), SIS group (NPS-1: HR=2.170, 95%CI: 1.244-3.784, P=0.006; NPS-2: HR=2.291, 95%CI: 1.052-4.986, P=0.037), and CONUT (HR=1.597, 95% CI: 1.187-2.149, P=0.038). The median DFS in the NPS-0, NPS-1, and NPS-2 groups was 68.6, 52.5, and 28.3 months, respectively; these differences are statistically significant (P=0.009). Univariate and multivariate Cox regression analysis identified the following as independently and significantly associated with DFS in patients with Siewert Type II-III AEG: TNM stage (StageⅡ: HR=2.789, 95%CI:1.210-6.428, P=0.016; Stage III: HR=10.721, 95%CI:4.709-24.411, P<0.001), adjuvant chemotherapy (HR=0.640, 95% CI: 0.432-0.946, P=0.025), and NPS (NPS-1: HR=1.703, 95%CI: 1.043-2.782, P=0.033; NPS-2: HR=3.124, 95%CI:1.722-5.666, P<0.001). Time-dependent receiver operating characteristic curves showed that NPS was more accurate in predicting OS and DFS in patients with Siewert Type II-III AEG than were systemic inflammatory response scores, CONUT, or PNI scores. Conclusion: NPS is associated with age and TNM stage, is an independent prognostic factor in patients who have undergone resection of Siewert type II-III AEG, and is better than SIS, CONUT, or PNI in predicting survival.

Relationship between topological efficiency of white matter structural connectome and plasma biomarkers across the Alzheimer's disease continuum

Both plasma biomarkers and brain network topology have shown great potential in the early diagnosis of Alzheimer's disease (AD). However, the specific associations between plasma AD biomarkers, structural network topology, and cognition across the AD continuum have yet to be fully elucidated. This retrospective study evaluated participants from the Sino Longitudinal Study of Cognitive Decline cohort between September 2009 and October 2022 with available blood samples or 3.0-T MRI brain scans. Plasma biomarker levels were measured using the Single Molecule Array platform, including β-amyloid (Aβ), phosphorylated tau181 (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). The topological structure of brain white matter was assessed using network efficiency. Trend analyses were carried out to evaluate the alterations of the plasma markers and network efficiency with AD progression. Correlation and mediation analyses were conducted to further explore the relationships among plasma markers, network efficiency, and cognitive performance across the AD continuum. Among the plasma markers, GFAP emerged as the most sensitive marker (linear trend: t = 11.164, p = 3.59 × 10-24 ; quadratic trend: t = 7.708, p = 2.25 × 10-13 ; adjusted R2  = 0.475), followed by NfL (linear trend: t = 6.542, p = 2.9 × 10-10 ; quadratic trend: t = 3.896, p = 1.22 × 10-4 ; adjusted R2  = 0.330), p-tau181 (linear trend: t = 8.452, p = 1.61 × 10-15 ; quadratic trend: t = 6.316, p = 1.05 × 10-9 ; adjusted R2  = 0.346) and Aβ42/Aβ40 (linear trend: t = -3.257, p = 1.27 × 10-3 ; quadratic trend: t = -1.662, p = 9.76 × 10-2 ; adjusted R2  = 0.101). Local efficiency decreased in brain regions across the frontal and temporal cortex and striatum. The principal component of local efficiency within these regions was correlated with GFAP (Pearson's R = -0.61, p = 6.3 × 10-7 ), NfL (R = -0.57, p = 6.4 × 10-6 ), and p-tau181 (R = -0.48, p = 2.0 × 10-4 ). Moreover, network efficiency mediated the relationship between general cognition and GFAP (ab = -0.224, 95% confidence interval [CI] = [-0.417 to -0.029], p = .0196 for MMSE; ab = -0.198, 95% CI = [-0.42 to -0.003], p = .0438 for MOCA) or NfL (ab = -0.224, 95% CI = [-0.417 to -0.029], p = .0196 for MMSE; ab = -0.198, 95% CI = [-0.42 to -0.003], p = .0438 for MOCA). Our findings suggest that network efficiency mediates the association between plasma biomarkers, specifically GFAP and NfL, and cognitive performance in the context of AD progression, thus highlighting the potential utility of network-plasma approaches for early detection, monitoring, and intervention strategies in the management of AD.

Discriminative analysis of schizophrenia patients using an integrated model combining 3D CNN with 2D CNN: A multimodal MR image and connectomics analysis

OBJECTIVE

Few studies have applied deep learning to the discriminative analysis of schizophrenia (SZ) patients using the fusional features of multimodal MRI data. Here, we proposed an integrated model combining a 3D convolutional neural network (CNN) with a 2D CNN to classify SZ patients.

METHOD

Structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data were acquired for 140 SZ patients and 205 normal controls. We computed structural connectivity (SC) from the sMRI data as well as functional connectivity (FC), amplitude of low-frequency fluctuation (ALFF), and regional homogeneity (ReHo) from the rs-fMRI data. The 3D images of T1, ReHo, and ALFF were used as the inputs for the 3D CNN model, while the SC and FC matrices were used as the inputs for the 2D CNN model. Moreover, we added squeeze and excitation blocks (SE-blocks) to each layer of the integrated model and used a support vector machine (SVM) to replace the softmax classifier.

RESULTS

The integrated model proposed in this study, using the fusional features of the T1 images, and the matrices of FC, showed the best performance. The use of the SE-blocks and SVM classifiers significantly improved the performance of the integrated model, in which the accuracy, sensitivity, specificity, area under the curve, and F1-score were 89.86%, 86.21%, 92.50%, 89.35%, and 87.72%, respectively.

CONCLUSIONS

Our findings indicated that an integrated model combining 3D CNN with 2D CNN is a promising method to improve the classification performance of SZ patients and has potential for the clinical diagnosis of psychiatric diseases.

Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme

Objective

To compare the performance of radiomics-based machine learning survival models in predicting the prognosis of glioblastoma multiforme (GBM) patients.

Methods

131 GBM patients were included in our study. The traditional Cox proportional-hazards (CoxPH) model and four machine learning models (SurvivalTree, Random survival forest (RSF), DeepSurv, DeepHit) were constructed, and the performance of the five models was evaluated using the C-index.

Results

After the screening, 1792 radiomics features were obtained. Seven radiomics features with the strongest relationship with prognosis were obtained following the application of the least absolute shrinkage and selection operator (LASSO) regression. The CoxPH model demonstrated that age (HR = 1.576, p = 0.037), Karnofsky performance status (KPS) score (HR = 1.890, p = 0.006), radiomics risk score (HR = 3.497, p = 0.001), and radiomics risk level (HR = 1.572, p = 0.043) were associated with poorer prognosis. The DeepSurv model performed the best among the five models, obtaining C-index of 0.882 and 0.732 for the training and test set, respectively. The performances of the other four models were lower: CoxPH (0.663 training set / 0.635 test set), SurvivalTree (0.702/0.655), RSF (0.735/0.667), DeepHit (0.608/0.560).

Conclusion

This study confirmed the superior performance of deep learning algorithms based on radiomics relative to the traditional method in predicting the overall survival of GBM patients; specifically, the DeepSurv model showed the best predictive ability.

Evaluation of whole-brain oxygen metabolism in Alzheimer's disease using QSM and quantitative BOLD

OBJECTIVE

The objective of this study was to evaluate the whole-brain pattern of oxygen extraction fraction (OEF), cerebral blood flow (CBF), and cerebral metabolic rate of oxygen consumption (CMRO2) perturbation in Alzheimer's disease (AD) and investigate the relationship between regional cerebral oxygen metabolism and global cognition.

METHODS

Twenty-six AD patients and 25 age-matched healthy controls (HC) were prospectively recruited in this study. Mini-Mental State Examination (MMSE) was used to evaluate cognitive status. We applied the QQ-CCTV algorithm which combines quantitative susceptibility mapping and quantitative blood oxygen level-dependent models (QQ) for OEF calculation. CBF map was computed from arterial spin labeling and CMRO2 was generated based on Fick's principle. Whole-brain and regional OEF, CBF, and CMRO2 analyses were performed. The associations between these measures in substructures of deep brain gray matter and MMSE scores were assessed.

RESULTS

Whole brain voxel-wise analysis showed that CBF and CMRO2 values significantly decreased in AD predominantly in the bilateral angular gyrus, precuneus gyrus and parieto-temporal regions. Regional analysis showed that CBF value decreased in the bilateral caudal hippocampus and left rostral hippocampus and CMRO2 value decreased in left caudal and rostral hippocampus in AD patients. Considering all subjects in the AD and HC groups combined, the mean CBF and CMRO2 values in the bilateral hippocampus positively correlated with the MMSE score.

CONCLUSION

CMRO2 mapping with the QQ-CCTV method - which is readily available in MR systems for clinical practice - can be a potential biomarker for AD. In addition, CMRO2 in the hippocampus may be a useful tool for monitoring cognitive impairment.

Prognostic value of multiphase CT angiography: estimated infarct core volume in the patients with acute ischaemic stroke after mechanical thrombectomy

BACKGROUND AND PURPOSE

Recent studies reported the feasibility of quantifying a reliable infarct core (IC) volume using multiphase computed tomography (mCTA) based on deep learning, however its prognostic value was not fully clarified. Therefore, we aimed to evaluate the prognostic value of mCTA-estimated IC volume in patients with acute ischemic stroke (AIS) after mechanical thrombectomy (MT).

MATERIALS AND METHODS

We retrospectively reviewed patients who underwent mCTA and MT for large vessel occlusion in middle cerebral artery and (or) internal carotid artery within 6 hours after symptom onset between January 2018 and November 2019. Patients were dichotomized into good (modified Rankin Scale [mRS] score, 0-2) and poor (mRS, 3-6) outcome groups. mCTA-estimated IC volume were generated based on a multi-scale three-dimensional convolutional neural network. Univariate, multivariate logistic regression and receiver operating characteristic (ROC) curve analyses were used to identify the independent variables, and evaluate their performances in predicting the clinical outcome.

RESULTS

Of 44 included patients, 27 (61.4%) patients achieved good outcome. National Institutes of Health Stroke Scale scores at admission [NIHSSpre] (odds ratio [OR], 1.191; 95%confidence interval [CI], 1.028-1.379; P=0.020) and mCTA-estimated IC volume (OR, 1.076; 95%CI, 1.016-1.140; P=0.013) were found to be independently associated with functional outcome in patients with AIS after MT. After integrating NIHSSpre and mCTA-estimated IC volume, optimal performance (area under the ROC curve, 0.874; 95%CI, 0.739-0.954) could be obtained in predicting the clinical outcome.

CONCLUSIONS

mCTA-estimated IC volume might be promising for predicting the prognosis, and assisting in making individualized treatment decision in patients with AIS.

An apical Phe-His pair defines the Orai1-coupling site and its occlusion within STIM1

Ca2+ signal-generation through inter-membrane junctional coupling between endoplasmic reticulum (ER) STIM proteins and plasma membrane (PM) Orai channels, remains a vital but undefined mechanism. We identify two unusual overlapping Phe-His aromatic pairs within the STIM1 apical helix, one of which (F394-H398) mediates important control over Orai1-STIM1 coupling. In resting STIM1, this locus is deeply clamped within the folded STIM1-CC1 helices, likely near to the ER surface. The clamped environment in holo-STIM1 is critical-positive charge replacing Phe-394 constitutively unclamps STIM1, mimicking store-depletion, negative charge irreversibly locks the clamped-state. In store-activated, unclamped STIM1, Phe-394 mediates binding to the Orai1 channel, but His-398 is indispensable for transducing STIM1-binding into Orai1 channel-gating, and is spatially aligned with Phe-394 in the exposed Sα2 helical apex. Thus, the Phe-His locus traverses between ER and PM surfaces and is decisive in the two critical STIM1 functions-unclamping to activate STIM1, and conformational-coupling to gate the Orai1 channel.

Shedding light on ORAI1 channel with genetic code expansion

Genetic code expansion technology has been widely applied to control protein activity and biological systems by taking advantage of an amber stop codon suppressor tRNA and orthogonal aminoacyl-tRNA synthetase pair. With this chemical biology approach, Maltan et al. incorporated photocrosslinking unnatural amino acids (UAAs) into the transmembrane domains of ORAI1 to enable UV light-inducible calcium influx across the plasma membrane, mechanistic interrogation of the calcium release-activated calcium (CRAC) channel at the single amino acid level, and remote control of downstream calcium-modulated signaling in mammalian cells.

USBDAN: Unsupervised Scale-aware and Boundary-aware Domain Adaptive Network for Gastric Tumor Segmentation

Accurate segmentation of gastric tumors from computed tomography (CT) images provides useful image information for guiding the diagnosis and treatment of gastric cancer. Researchers typically collect datasets from multiple medical centers to increase sample size and representation, but this raises the issue of data heterogeneity. To this end, we propose a new cross-center 3D tumor segmentation method named unsupervised scale-aware and boundary-aware domain adaptive network (USBDAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale features from the CT images with anisotropic resolution, and a scale-aware and boundary-aware domain alignment (SaBaDA) module for adaptively aligning multi-scale features between two domains and enhancing tumor boundary drawing based on location-related information drawn from each sample across all domains. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers. Our results demonstrate that the proposed method outperforms several state-of-the-art methods.

Investigation of functional connectivity in Bell's palsy using functional magnetic resonance imaging: prospective cross-sectional study

Background

The most common cause of lower motor neuron facial palsy is Bell's palsy (BP). BP results in partial or complete inability to automatically move the facial muscles on the affected side and, in some cases, to close the eyelids, which can cause permanent eye damage. This study investigated changes in brain function and connectivity abnormalities in patients with BP.

Methods

This study included 46 patients with unilateral BP and 34 healthy controls (HCs). Resting-state brain functional magnetic resonance imaging (fMRI) images were acquired, and Toronto Facial Grading System (TFGS) scores were obtained for all participants. The fractional amplitude of low-frequency fluctuation (fALFF) was estimated, and the relationship between the TFGS and fALFF was determined using correlation analysis for brain regions with changes in fALFF in those with BP versus HCs. Brain regions associated with TFGS were used as seeds for further functional connectivity (FC) analysis; relationships between FC values of abnormal areas and TFGS scores were also analyzed.

Results

Activation of the right precuneus, right angular gyrus, left supramarginal gyrus, and left middle occipital gyrus was significantly decreased in the BP group. fALFF was significantly higher in the right thalamus, vermis, and cerebellum of the BP group compared with that in the HC group (P<0.05). The FC between the left middle occipital gyrus and right angular gyrus, left precuneus, and right middle frontal gyrus increased sharply, but decreased in the left angular gyrus, left posterior cingulate gyrus, left middle frontal gyrus, inferior cerebellum, and left middle temporal gyrus. Furthermore, the fALFF in the left middle occipital gyrus was negatively correlated with TFGS score (R=0.144; P=0.008).

Conclusions

The pathogenesis of BP is closely related to functional reorganization of the cerebral cortex. Patients with BP have altered fALFF activity in cortical regions associated with facial motion feedback monitoring.

Mouth breathing induces condylar remodelling and chondrocyte apoptosis via both the extrinsic and mitochondrial pathways in male adolescent rats

The prevalence of mouth breathing is high in children and adolescents. It causes various changes to the respiratory tract and, consequently, craniofacial growth deformities. However, the underlying mechanisms contributing to these effects are obscure. Herein, we aimed to study the effects of mouth breathing on chondrocyte proliferation and death in the condylar cartilage and morphological changes in the mandible and condyle. Additionally, we aimed to elucidate the mechanisms underlying chondrocyte apoptosis and investigate any variations in the related pathways. Subchondral bone resorption and decreased condylar cartilage thickness were observed in mouth-breathing rats; further, mRNA expression levels of Collagen II, Aggrecan, and Sox 9 were lower in the mouth breathing group, while those of matrix metalloproteinase 9 increased. TdT-mediated dUTP nick end labelling staining and immunohistochemistry analyses showed that apoptosis occurred in the proliferative and hypertrophic layers of cartilage in the mouth breathing group. TNF, BAX, cytochrome c, and cleaved-caspase-3 were highly expressed in the condylar cartilage of the mouth-breathing rats. These results suggest that mouth breathing leads to subchondral bone resorption, cartilage layer thinning, and cartilage matrix destruction, inducing chondrocyte apoptosis via both the extrinsic and mitochondrial apoptosis pathways.

Effects of repetitive transcranial magnetic stimulation and their underlying neural mechanisms evaluated with magnetic resonance imaging-based brain connectivity network analyses

Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain modulation and rehabilitation technique used in patients with neuropsychiatric diseases. rTMS can structurally remodel or functionally induce activities of specific cortical regions and has developed to an important therapeutic method in such patients. Magnetic resonance imaging (MRI) provides brain data that can be used as an explanation tool for the neural mechanisms underlying rTMS effects; brain alterations related to different functions or structures may be reflected in changes in the interaction and influence of brain connections within intrinsic specific networks. In this review, we discuss the technical details of rTMS and the biological interpretation of brain networks identified with MRI analyses, comprehensively summarize the neurobiological effects in rTMS-modulated individuals, and elaborate on changes in the brain network in patients with various neuropsychiatric diseases receiving rehabilitation treatment with rTMS. We conclude that brain connectivity network analysis based on MRI can reflect alterations in functional and structural connectivity networks comprising adjacent and separated brain regions related to stimulation sites, thus reflecting the occurrence of intrinsic functional integration and neuroplasticity. Therefore, MRI is a valuable tool for understanding the neural mechanisms of rTMS and practically tailoring treatment plans for patients with neuropsychiatric diseases.

Corrigendum: Discriminative analysis of schizophrenia patients using graph convolutional networks: a combined multimodal MRI and connectomics analysis

[This corrects the article DOI: 10.3389/fnins.2023.1140801.].

Personalized Functional Connectivity based Spatio-Temporal Aggregated Attention Network for MCI Identification

Functional connectivity (FC) networks derived from resting-state magnetic resonance image (rs-fMRI) are effective biomarkers for identifying mild cognitive impairment (MCI) patients. However, most FC identification methods simply extract features from group-averaged brain templates, and neglect inter-subject functional variations. Furthermore, the existing methods generally concentrate on spatial correlation among brain regions, resulting in the inefficient capture of the fMRI temporal features. To address these limitations, we propose a novel personalized functional connectivity based dual-branch graph neural network with spatio-temporal aggregated attention (PFC-DBGNN-STAA) for MCI identification. Specifically, a personalized functional connectivity (PFC) template is firstly constructed to align 213 functional regions across samples and generate discriminative individualized FC features. Secondly, a dual-branch graph neural network (DBGNN) is conducted by aggregating features from the individual- and group-level templates with the cross-template FC, which is beneficial to improve the feature discrimination by considering dependency between templates. Finally, a spatio-temporal aggregated attention (STAA) module is investigated to capture the spatial and dynamic relationships between functional regions, which solves the limitation of insufficient temporal information utilization. We evaluate our proposed method on 442 samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and achieve the accuracies of 90.1%, 90.3%, 83.3% for normal control (NC) vs. early MCI (EMCI), EMCI vs. late MCI (LMCI), and NC vs. EMCI vs. LMCI classification tasks, respectively, indicating that our method boosts MCI identification performance and outperforms state-of-the-art methods.

Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis

Introduction

Recent studies in human brain connectomics with multimodal magnetic resonance imaging (MRI) data have widely reported abnormalities in brain structure, function and connectivity associated with schizophrenia (SZ). However, most previous discriminative studies of SZ patients were based on MRI features of brain regions, ignoring the complex relationships within brain networks.

Methods

We applied a graph convolutional network (GCN) to discriminating SZ patients using the features of brain region and connectivity derived from a combined multimodal MRI and connectomics analysis. Structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 140 SZ patients and 205 normal controls. Eighteen types of brain graphs were constructed for each subject using 3 types of node features, 3 types of edge features, and 2 brain atlases. We investigated the performance of 18 brain graphs and used the TopK pooling layers to highlight salient brain regions (nodes in the graph).

Results

The GCN model, which used functional connectivity as edge features and multimodal features (sMRI + fMRI) of brain regions as node features, obtained the highest average accuracy of 95.8%, and outperformed other existing classification studies in SZ patients. In the explainability analysis, we reported that the top 10 salient brain regions, predominantly distributed in the prefrontal and occipital cortices, were mainly involved in the systems of emotion and visual processing.

Discussion

Our findings demonstrated that GCN with a combined multimodal MRI and connectomics analysis can effectively improve the classification of SZ at an individual level, indicating a promising direction for the diagnosis of SZ patients. The code is available at https://github.com/CXY-scut/GCN-SZ.git.

Bacterial antibiotic resistance among cancer inpatients in China: 2016-20

BACKGROUND

The incidence of infections among cancer patients is as high as 23.2-33.2% in China. However, the lack of information and data on the number of antibiotics used by cancer patients is an obstacle to implementing antibiotic management plans.

AIM

This study aimed to investigate bacterial infections and antibiotic resistance in Chinese cancer patients to provide a reference for the rational use of antibiotics.

DESIGN

This was a 5-year retrospective study on the antibiotic resistance of cancer patients.

METHODS

In this 5-year surveillance study, we collected bacterial and antibiotic resistance data from 20 provincial cancer diagnosis and treatment centers and three specialized cancer hospitals in China. We analyzed the resistance of common bacteria to antibiotics, compared to common clinical drug-resistant bacteria, evaluated the evolution of critical drug-resistant bacteria and conducted data analysis.

FINDINGS

Between 2016 and 2020, 216 219 bacterial strains were clinically isolated. The resistance trend of Escherichia coli and Klebsiella pneumoniae to amikacin, ciprofloxacin, cefotaxime, piperacillin/tazobactam and imipenem was relatively stable and did not significantly increase over time. The resistance of Pseudomonas aeruginosa strains to all antibiotics tested, including imipenem and meropenem, decreased over time. In contrast, the resistance of Acinetobacter baumannii strains to carbapenems increased from 4.7% to 14.7%. Methicillin-resistant Staphylococcus aureus (MRSA) significantly decreased from 65.2% in 2016 to 48.9% in 2020.

CONCLUSIONS

The bacterial prevalence and antibiotic resistance rates of E. coli, K. pneumoniae, P. aeruginosa, A. baumannii, S. aureus and MRSA were significantly lower than the national average.

Reconfiguration of brain network dynamics underlying spatial deficits in subjective cognitive decline

Brain dynamics and the associations with spatial navigation in individuals with subjective cognitive decline (SCD) remain unknown. In this study, a hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging data in a cohort of 80 SCD and 77 normal control (NC) participants. By HMM, 12 states with distinct brain activity were identified. The SCD group showed increased fractional occupancy in the states with less activated ventral default mode, posterior salience, and visuospatial networks, while decreased fractional occupancy in the state with general network activation. The SCD group also showed decreased probabilities of transition into and out of the state with general network activation, suggesting an inability to dynamically upregulate and downregulate brain network activity. Significant correlations between brain dynamics and spatial navigation were observed. The combined features of spatial navigation and brain dynamics showed an area under the curve of 0.854 in distinguishing between SCD and NC. The findings may provide exploratory evidence of the reconfiguration of brain network dynamics underlying spatial deficits in SCD.

Altered coupling of resting-state cerebral blood flow and functional connectivity in Meige syndrome

Introduction

Meige syndrome (MS) is an adult-onset segmental dystonia disease, mainly manifested as blepharospasm and involuntary movement caused by dystonic dysfunction of the oromandibular muscles. The changes of brain activity, perfusion and neurovascular coupling in patients with Meige syndrome are hitherto unknown.

Methods

Twenty-five MS patients and thirty age- and sex-matched healthy controls (HC) were prospectively recruited in this study. All the participants underwent resting-state arterial spin labeling and blood oxygen level-dependent examinations on a 3.0 T MR scanner. The measurement of neurovascular coupling was calculated using cerebral blood flow (CBF)-functional connectivity strength (FCS) correlations across the voxels of whole gray matter. Also, voxel-wised analyses of CBF, FCS, and CBF/FCS ratio images between MS and HC were conducted. Additionally, CBF and FCS values were compared between these two groups in selected motion-related brain regions.

Results

MS patients showed increased whole gray matter CBF-FCS coupling relative to HC (t = 2.262, p = 0.028). In addition, MS patients showed significantly increased CBF value in middle frontal gyrus and bilateral precentral gyrus.

Conclusion

The abnormal elevated neurovascular coupling of MS may indicate a compensated blood perfusion in motor-related brain regions and reorganized the balance between neuronal activity and brain blood supply. Our results provide a new insight into the neural mechanism underlying MS from the perspective of neurovascular coupling and cerebral perfusion.

Liquid metal injected from interstitial channels for inhibiting subcutaneous hepatoma growth and improving MRI/MAT image contrast

Objective

Liquid metal (LM) nowadays is considered a new biomedical material for medical treatment. The most common application of LM in medical therapy is taking LM as a carrier for oncology therapeutics. However, the feasibility and direct effect of LM in tumor treatment are still unknown, and how to delineate the negative resection margin (NRM) of the tumor is also a crucial problem in surgery. We aimed to inject LM into interstitial channels of extremities of mice to overlay the surface of the primary tumor to investigate the effect of LM on inhibiting tumor growth and highlight the NRM of the tumor.

Methods

In this study, all 50 BALB/c-nude female mice were used to construct the transplanted HepG2-type hepatocellular carcinoma model. One week after the establishment of the model, the mice were divided into three groups, named LM group, PBS group and Control group by injecting different liquid materials into the forelimb interstitial channel of the mice. T2WI image on MRI and Magneto-acoustic tomography (MAT) were used to show the distribution of LM and PBS in vivo. The group comparisons of tumor growth and blood tests were evaluated by one-way ANOVA and post-hoc analysis. And the biocompatibility of LM to BALB/c nude mice was evaluated by histopathological analysis of LM group and control group.

Results

The volume change ratio of tumor was significantly lower in LM group than in PBS and Control group after 10 days of grouping. Compared with PBS and Control group, the main indexes of blood tests in LM group were significantly lower and close to normal level. In addition, the distribution of LM in vivo could be clearly observed under T2WI anatomic images and the crossprofile of the tumor in MAT. LM also has a obvious contrast in MRI T2WI and enhanced the amplitude of imaging signal in MAT.

Conclusion

LM may inhibit the growth of transplanted hepatoma tumor through tumor encapsulation. In vivo, tumor imaging and LM distribution imaging were achieved by MRI T2WI, which verified that LM injected with interstitial injection made the NRM of tumor more prominent and had the potential of being MRI contrast agent. At the same time, LM could also be a new conductive medium to improve the imaging quality of MAT. Moreover, LM performed mild biocompatibility.

Current census of oncology critical care medicine in China

OBJECTIVE

The purposes of this survey were to show the current situation of oncology critical care medicine in China by questionnaire, to understand the resource distribution of oncology critical care medicine and to analyze and evaluate the existing resources and reserve capacity of oncology critical care medicine in China.

METHODS

We conducted the survey mainly in the form of an online questionnaire. The Committee of Cancer Critical Care Medicine of the Chinese Anticancer Association (CACA) initiated the survey on 1 November 2017, and 36 member hospitals nationwide participated in the survey. The questionnaire included 10 items: investigator information, hospital information, general information of oncology critical care department, staffing of oncology critical care department, management in oncology critical care department, technical skills in oncology critical care department, patient source in oncology critical care department, equipment configuration in oncology critical care department, special skills in oncology critical care department and summary of the information.

RESULTS

The survey results included information from 28 member units, all of which were tertiary hospitals, distributed in 20 provinces and 4 direct-controlled municipalities. The results are as follows. (i) The total ratio of beds in the oncology critical care department to hospital beds was 1.06%, and the average number of beds in the oncology critical care department was 16.36. (ii) The ratio of physicians in the oncology critical care department to beds was ∼0.62:1, and the ratio of nurses to beds was ∼1.98:1. (iii) According to the census of the population and gross domestic product (GDP) of different regions conducted by the State Statistics Bureau in 2017, the ratio of beds in the oncology critical care department for tumor patients to the population was 4.55 beds per 10 million people, and the ratio of beds in the oncology critical care department to GDP was 8.00 beds per RMB 100 billion, on average. (iv) According to the requirements of the guidelines for the development and management of critical care medicine in China, the facilities in departments of oncology critical care medicine meet the requirements, and the technical skills of medical staff are competent.

CONCLUSION

The development of oncology critical care in China is becoming better, but there is still a certain gap compared with the intensive care unit standards in China and the average level of the nationwide. The development of oncology critical care medicine is urgent.

Application of pseudocontinuous arterial spin labeling perfusion imaging in children with autism spectrum disorders

Introduction

Pseudocontinuous Arterial Spin Labeling (pCASL) perfusion imaging allows non-invasive quantification of regional cerebral blood flow (CBF) as part of a multimodal magnetic resonance imaging (MRI) protocol. This study aimed to compare regional CBF in autism spectrum disorders (ASD) individuals with their age-matched typically developing (TD) children using pCASL perfusion imaging.

Materials and methods

This cross-sectional study enrolled 17 individuals with ASD and 13 TD children. All participants underwent pCASL examination on a 3.0 T MRI scanner. Children in two groups were assessed for clinical characteristics and developmental profiles using Autism Behavior Checklist (ABC) and Gesell development diagnosis scale (GDDS), respectively. We compared CBF in different cerebral regions of ASD and TD children. We also assessed the association between CBF and clinical characteristics/developmental profile.

Results

Compared with TD children, individuals with ASD demonstrated a reduction in CBF in the left frontal lobe, the bilateral parietal lobes, and the bilateral temporal lobes. Within the ASD group, CBF was significantly higher in the right parietal lobe than in the left side. Correlation analysis of behavior characteristics and CBF in different regions showed a positive correlation between body and object domain scores on the ABC and CBF of the bilateral occipital lobes, and separately, between language domain scores and CBF of the left frontal lobe. The score of the social and self-help domain was negatively correlated with the CBF of the left frontal lobe, the left parietal lobe, and the left temporal lobe.

Conclusion

Cerebral blood flow was found to be negatively correlated with scores in the social and self-help domain, and positively correlated with those in the body and object domain, indicating that CBF values are a potential MRI-based biomarker of disease severity in ASD patients. The findings may provide novel insight into the pathophysiological mechanisms of ASD.

MC-ViT: Multi-path cross-scale vision transformer for thymoma histopathology whole slide image typing

OBJECTIVES

Accurate histological typing plays an important role in diagnosing thymoma or thymic carcinoma (TC) and predicting the corresponding prognosis. In this paper, we develop and validate a deep learning-based thymoma typing method for hematoxylin & eosin (H&E)-stained whole slide images (WSIs), which provides useful histopathology information from patients to assist doctors for better diagnosing thymoma or TC.

METHODS

We propose a multi-path cross-scale vision transformer (MC-ViT), which first uses the cross attentive scale-aware transformer (CAST) to classify the pathological information related to thymoma, and then uses such pathological information priors to assist the WSIs transformer (WT) for thymoma typing. To make full use of the multi-scale (10×, 20×, and 40×) information inherent in a WSI, CAST not only employs parallel multi-path to capture different receptive field features from multi-scale WSI inputs, but also introduces the cross-correlation attention module (CAM) to aggregate multi-scale features to achieve cross-scale spatial information complementarity. After that, WT can effectively convert full-scale WSIs into 1D feature matrices with pathological information labels to improve the efficiency and accuracy of thymoma typing.

RESULTS

We construct a large-scale thymoma histopathology WSI (THW) dataset and annotate corresponding pathological information and thymoma typing labels. The proposed MC-ViT achieves the Top-1 accuracy of 0.939 and 0.951 in pathological information classification and thymoma typing, respectively. Moreover, the quantitative and statistical experiments on the THW dataset also demonstrate that our pipeline performs favorably against the existing classical convolutional neural networks, vision transformers, and deep learning-based medical image classification methods.

CONCLUSION

This paper demonstrates that comprehensively utilizing the pathological information contained in multi-scale WSIs is feasible for thymoma typing and achieves clinically acceptable performance. Specifically, the proposed MC-ViT can well predict pathological information classes as well as thymoma types, which show the application potential to the diagnosis of thymoma and TC and may assist doctors in improving diagnosis efficiency and accuracy.

Does filter pore size introduce bias in DNA sequence-based plankton community studies?

The cell size of microbial eukaryotic plankton normally ranges from 0.2 to 200 μm. During the past decade, high-throughput sequencing of DNA has been revolutionizing their study on an unprecedented scale. Nonetheless, it is currently unclear whether we can accurately, effectively, and quantitatively depict the microbial eukaryotic plankton community using size-fractionated filtration combined with environmental DNA (eDNA) molecular methods. Here we assessed the microbial eukaryotic plankton communities with two filtering strategies from two subtropical reservoirs, that is one-step filtration (0.2–200 μm) and size-fractionated filtration (0.2–3 and 3–200 μm). The difference of 18S rRNA gene copy abundance between the two filtering treatments was less than 50% of the 0.2–200 μm microbial eukaryotic community for 95% of the total samples. Although the microbial eukaryotic plankton communities within the 0.2–200 μm and the 0.2–3 and 3–200 μm size fractions had approximately identical 18S rRNA gene copies, there were significant differences in their community composition. Furthermore, our results demonstrate that the systemic bias introduced by size-fractionation filtration has more influence on unique OTUs than shared OTUs, and the significant differences in abundance between the two eukaryotic plankton communities largely occurred in low-abundance OTUs in specific seasons. This work provides new insights into the use of size-fractionation in molecular studies of microbial eukaryotes populating the plankton.

Altered cerebral blood flow in patients with spinocerebellar degeneration

Objectives

Spinocerebellar degeneration (SCD) comprises a multitude of disorders with sporadic and hereditary forms, including spinocerebellar ataxia (SCA). Except for progressive cerebellar ataxia and structural atrophy, hemodynamic changes have also been observed in SCD. This study aimed to explore the whole-brain patterns of altered cerebral blood flow (CBF) and its correlations with disease severity and psychological abnormalities in SCD via arterial spin labeling (ASL).

Methods

Thirty SCD patients and 30 age- and sex-matched healthy controls (HC) were prospectively recruited and underwent ASL examination on a 3.0T MR scanner. The Scale for Assessment and Rating of Ataxia (SARA) and the International Cooperative Ataxia Rating Scale (ICARS) scores were used to evaluate the disease severity in SCD patients. Additionally, the status of anxiety, depression and sleep among all patients were, respectively, evaluated by the Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS) and Self-Rating Scale of Sleep (SRSS). We compared the whole-brain CBF value between SCD group and HC group at the voxel level. Then, the correlation analyses between CBF and disease severity, and psychological abnormalities were performed on SCD group.

Results

Compared with HC, SCD patients demonstrated decreased CBF value in two clusters (FWE corrected P < 0.05), covering bilateral dentate and fastigial nuclei, bilateral cerebellar lobules I-IV, V and IX, left lobule VI, right lobule VIIIb, lobules IX and X of the vermis in the cerebellar Cluster 1 and the dorsal part of raphe nucleus in the midbrain Cluster 2. The CBF of cerebellar Cluster 1 was negatively correlated with SARA scores (Spearman’s rho = –0.374, P = 0.042) and SDS standard scores (Spearman’s rho = –0.388, P = 0.034), respectively. And, the CBF of midbrain Cluster 2 also had negative correlations with SARA scores (Spearman’s rho = –0.370, P = 0.044) and ICARS scores (Pearson r = –0.464, P = 0.010).

Conclusion

The SCD-related whole-brain CBF changes mainly involved in the cerebellum and the midbrain of brainstem, which are partially overlapped with the related function cerebellar areas of hand, foot and tongue movement. Decreased CBF was related to disease severity and depression status in SCD. Therefore, CBF may be a promising neuroimaging biomarker to reflect the severity of SCD and suggest mental changes.

Brain morphological alterations and their correlation to tumor differentiation and duration in patients with lung cancer after platinum chemotherapy

Objective

Chemotherapy-related brain impairments and changes can occur in patients with lung cancer after platinum chemotherapy and have a substantial impact on survivors’ quality of life. Therefore, it is necessary to understand the brain neuropathological alterations and response mechanisms to provide a theoretical basis for rehabilitation strategies. This study aimed to investigate the related brain morphological changes and clarified their correlation with clinical and pathological indicators in patients with lung cancer after platinum chemotherapy.

Methods

Overall, 28 patients with chemotherapy, 56 patients without chemotherapy, and 41 healthy controls were categorized in three groups, matched for age, sex, and years of education, and included in the cross-sectional comparison of brain volume and cortical thickness. 14 matched patients before and after chemotherapy were subjected to paired comparison for longitudinal observation of brain morphological changes. Three-dimensional T1-weighted images were acquired from all participants, and quantitative parameters were calculated using the formula of the change from baseline. Correlation analysis was performed to evaluate the relationship between abnormal morphological indices and clinical information of patients.

Results

Brain regions with volume differences among the three groups were mainly distributed in frontal lobe and limbic cortex. Additionally, significant differences in cerebrospinal fluid were observed in most ventricles, and the main brain regions with cortical thickness differences were the gyrus rectus and medial frontal cortex of the frontal lobe, transverse temporal gyrus of the temporal lobe, insular cortex, anterior insula, and posterior insula of the insular cortex. According to the paired comparison, decreased brain volumes in the patients after chemotherapy appeared in some regions of the frontal, parietal, temporal, and occipital lobes; limbic cortex; insular cortex; and lobules VI-X and decreased cortical thickness in the patients after chemotherapy was found in the frontal, temporal, limbic, and insular cortexes. In the correlation analysis, only the differentiation degree of the tumor and duration after chemotherapy were significantly correlated with imaging indices in the abnormal brain regions.

Conclusions

Our findings illustrate the platinum-related brain reactivity morphological alterations which provide more insights into the neuropathological mechanisms of patients with lung cancer after platinum chemotherapy and empirical support for the details of brain injury related to cancer and chemotherapy.

MRI-based radiomics analysis in differentiating solid non-small-cell from small-cell lung carcinoma: a pilot study

AIM

To investigate the ability of a T2-weighted (W) magnetic resonance imaging (MRI)-based radiomics signature to differentiate solid non-small-cell lung carcinoma (NSCLC) from small-cell lung carcinoma (SCLC).

MATERIALS AND METHODS

The present retrospective study enrolled 152 eligible patients (NSCLC = 125, SCLC = 27). All patients underwent MRI using a 3 T scanner and radiomics features were extracted from T2W MRI. The least absolute shrinkage and selection operator (LASSO) logistic regression model was used to identify the optimal radiomics features for the construction of a radiomics model to differentiate solid NSCLC from SCLC. Threefold cross validation repeated 10 times was used for model training and evaluation. The conventional MRI morphology features of the lesions were also evaluated. The performance of the conventional MRI morphological features, and the radiomics signature model and nomogram model (combining radiomics signature with conventional MRI morphological features) was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

Five optimal features were chosen to build a radiomics signature. There was no significant difference in age, gender, and the largest diameter. The radiomics signature and conventional MRI morphological features (only pleural indentation and lymph node enlargement) were independent predictive factors for differentiating solid NSCLC from SCLC. The area under the ROC curves (AUCs) for MRI morphological features, and the radiomics model, and nomogram model was 0.69, 0.85, and 0.90 (ROC), respectively.

CONCLUSIONS

The T2W MRI-based radiomics signature is a potential non-invasive approach for distinguishing solid NSCLC from SCLC.

Detection of Appearance and Behavior Anomalies in Stationary Camera Videos Using Convolutional Neural Networks

The automatic detection and tracking of appearance and behavior anomalies in video surveillance systems is one of the promising areas for the development and implementation of artificial intelligence. In this paper, we present a formalization of these problems. Based on the proposed generalization, a detection and tracking algorithm that uses the tracking-by-detection paradigm and convolutional neural networks (CNNs) is developed. At the first stage, people are detected using the YOLOv5 CNN and are marked with bounding boxes. Then, their faces in the selected regions are detected and the presence or absence of face masks is determined. Our approach to face-mask detection also uses YOLOv5 as a detector and classifier. For this problem, we generate a training dataset by combining the Kaggle dataset and a modified Wider Face dataset, in which face masks were superimposed on half of the images. To ensure a high accuracy of tracking and trajectory construction, the CNN features of the images are included in a composite descriptor, which also contains geometric and color features, to describe each person detected in the current frame and compare this person with all people detected in the next frame. The results of the experiments are presented, including some examples of frames from processed video sequences with visualized trajectories for loitering and falls.

Neural tissue engineering: From bioactive scaffolds and in situ monitoring to regeneration

Peripheral nerve injury is a large-scale problem that annually affects more than several millions of people all over the world. It remains a great challenge to effectively repair nerve defects. Tissue engineered nerve guidance conduits (NGCs) provide a promising platform for peripheral nerve repair through the integration of bioactive scaffolds, biological effectors, and cellular components. Herein, we firstly describe the pathogenesis of peripheral nerve injuries at different orders of severity to clarify their microenvironments and discuss the clinical treatment methods and challenges. Then, we discuss the recent progress on the design and construction of NGCs in combination with biological effectors and cellular components for nerve repair. Afterward, we give perspectives on imaging the nerve and/or the conduit to allow for the in situ monitoring of the nerve regeneration process. We also cover the applications of different postoperative intervention treatments, such as electric field, magnetic field, light, and ultrasound, to the well-designed conduit and/or the nerve for improving the repair efficacy. Finally, we explore the prospects of multifunctional platforms to promote the repair of peripheral nerve injury.

Detection of Brucella spp. during a serosurvey of pig-hunting and regional pet dogs in eastern Australia

Brucellosis is a zoonotic disease with worldwide distribution. Brucella suis serotype 1 is thought to be maintained in the Australian feral pig population, with disease prevalence higher in Queensland (Qld) than New South Wales (NSW). Pig hunting is a popular recreational activity in rural Qld and NSW, with feral pigs in these states thought to carry B. suis. Brucellosis associated with B. suis has been diagnosed in dogs engaged in pig hunting in some of these areas. A total of 431 dogs from northern Qld and north‐west NSW were recruited. Two distinct cohorts of clinically healthy dogs were tested – (1) 96 dogs from central, north and far north Queensland actively engaged in pig‐hunting and (2) 335 dogs from rural and remote north‐west NSW that were primarily companion (non‐pig hunting) animals. Serum samples were tested for antibodies to Brucella spp. using the Rose Bengal test (RBT) test followed by complement fixation testing (CFT) for RBT‐positive samples. A subset of samples was retested using RBT and CFT. Seven dogs were considered seropositive for B. suis from Qld and remote NSW, including 4/96 (4.2%; 95% CI 3.5% to 4.3%) from the pig‐hunting cohort and 3/335 (0.9%) from the regional pet dog cohort. The use of RBT and CFT in dogs to detect anti‐Brucella antibodies requires validation. Veterinarians treating pig‐hunting dogs and physicians treating pig hunters in central, north and far north Qld need to be aware of the zoonotic risk posed by B. suis to these groups.

Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome

An emerging trend is to use regression‐based machine learning approaches to predict cognitive functions at the individual level from neuroimaging data. However, individual prediction models are inherently influenced by the vast options for network construction and model selection in machine learning pipelines. In particular, the brain white matter (WM) structural connectome lacks a systematic evaluation of the effects of different options in the pipeline on predictive performance. Here, we focused on the methodological evaluation of brain structural connectome‐based predictions. For network construction, we considered two parcellation schemes for defining nodes and seven strategies for defining edges. For the regression algorithms, we used eight regression models. Four cognitive domains and brain age were targeted as predictive tasks based on two independent datasets (Beijing Aging Brain Rejuvenation Initiative [BABRI]: 633 healthy older adults; Human Connectome Projects in Aging [HCP‐A]: 560 healthy older adults). Based on the results, the WM structural connectome provided a satisfying predictive ability for individual age and cognitive functions, especially for executive function and attention. Second, different parcellation schemes induce a significant difference in predictive performance. Third, prediction results from different data sets showed that dMRI with distinct acquisition parameters may plausibly result in a preference for proper fiber reconstruction algorithms and different weighting options. Finally, deep learning and Elastic‐Net models are more accurate and robust in connectome‐based predictions. Together, significant effects of different options in WM network construction and regression algorithms on the predictive performances are identified in this study, which may provide important references and guidelines to select suitable options for future studies in this field.

Identification of a STIM1 Splicing Variant that Promotes Glioblastoma Growth

Deregulated store-operated calcium entry (SOCE) mediated by aberrant STIM1-ORAI1 signaling is closely implicated in cancer initiation and progression. Here the authors report the identification of an alternatively spliced variant of STIM1, designated STIM1β, that harbors an extra exon to encode 31 additional amino acids in the cytoplasmic domain. STIM1β, highly conserved in mammals, is aberrantly upregulated in glioma tissues to perturb Ca²⁺ signaling. At the molecular level, the 31-residue insertion destabilizes STIM1β by perturbing its cytosolic inhibitory domain and accelerating its activation kinetics to efficiently engage and gate ORAI calcium channels. Functionally, STIM1β depletion affects SOCE in glioblastoma cells, suppresses tumor cell proliferation and growth both in vitro and in vivo. Collectively, their study establishes a splicing variant-specific tumor-promoting role of STIM1β that can be potentially targeted for glioblastoma intervention.

Radiomics of dual-energy computed tomography for predicting progression-free survival in patients with early glottic cancer

Aim: This study aimed to predict progression-free survival (PFS) in patients with early glottic cancer using radiomic features on dual-energy computed tomography iodine maps. Methods: Radiomic features were extracted from arterial and venous phase iodine maps, and radiomic risk scores were determined by univariate Cox proportional hazards regression analysis and least absolute shrinkage and selection operator regression with tenfold cross-validation. The Kaplan-Meier method was used to evaluate the association between radiomic risk scores and PFS. Results: Patients were stratified into low-risk and high-risk groups using radiomics, the PFS corresponding rates with statistical significance between the two groups. The high-risk group showed better survival, benefiting from laryngectomy. Conclusion: Radiomics could provide a promising biomarker for predicting the PFS of early glottic cancer patients.

Cerebral Blood Flow Pattern Changes in Unilateral Sudden Sensorineural Hearing Loss

Objective

This study analyzed the differences in the cerebral blood flow (CBF) between unilateral Sudden Sensorineural Hearing Loss (SSNHL) patients and healthy controls (HCs). We also investigated CBF differences in auditory-related areas in patients with left- and right-sided SSNHL (lSSNHL and rSSNHL) and HCs. We further explore the correlation between unilateral SSNHL characteristics and changes in the CBF.

Methods

36 patients with unilateral SSNHL (15 males and 21 females, 40.39 ± 13.42 years) and 36 HCs (15 males and 21 females, 40.39 ± 14.11 years) were recruited. CBF images were collected and analyzed using arterial spin labeling (ASL). CereFlow software was used for the post-processing of the ASL data to obtain the CBF value of 246 subregions within brainnetome atlas (BNA). The Two-sample t-test was used to compare CBF differences between SSNHL patients and HCs. One-way ANOVA or Kruskal-Wallis test was used to compare the CBF difference of auditory-related areas among the three groups (lSSNHL, rSSNHL, and HCs). Then, the correlation between CBF changes and specific clinical characteristics were calculated.

Results

The SSNHL patients exhibited decreased CBF in the bilateral middle frontal gyrus (MFG, MFG_7_1 and MFG_7_3), the contralateral precentral gyrus (PrG, PrG_6_3) and the bilateral superior parietal lobule (SPL, bilateral SPL_5_1, SPL_5_2, and ipsilateral SPL_5_4), p &lt; 0.0002. Compared with HCs, unilateral SSNHL patients exhibited increased rCBF in the bilateral orbital gyrus (OrG, OrG_6_5), the bilateral inferior temporal gyrus (ITG, contralateral ITG_7_1 and bilateral ITG_7_7), p &lt; 0.0002. lSSNHL showed abnormal CBF in left BA21 caudal (p = 0.02) and left BA37 dorsolateral (p = 0.047). We found that the CBF in ipsilateral MFG_7_1 of SSNHL patients was positively correlated with tinnitus Visual Analog Scale (VAS) score (r = 0.485, p = 0.008).

Conclusion

Our preliminary study explored CBF pattern changes in unilateral SSNHL patients in auditory-related areas and non-auditory areas, suggesting that there may exist reduced attention and some sensory compensation in patients with SSNHL. These findings could advance our understanding of the potential pathophysiology of unilateral SSNHL.

Liquid metal biomaterials for biomedical imaging

Liquid metals (LMs) not only retain the basic properties of metallic biomaterials, such as high thermal conductivity and high electrical conductivity, but also possess flexibility, flowability, deformability, plasticity, good adhesion, and so on. Therefore, they open many possibilities of extending soft metals into biomedical sciences including biomedical imaging. One of the special properties of LMs is that they can provide a controllable material system in which the electrical, thermal, mechanical, and chemical properties can be controlled on a large scale. This paper reviews the preparation and characteristics of LM-based biomaterials classified into four categories: LM micro/nanoparticles, surface modified LM droplets, LM composites with inorganic substances, and LM composites with organic polymers. Besides, considering the most important requirement for biomaterials is biocompatibility, the paper also analyzes the toxicity results of various LM biomaterials when used in the biomedical area, from different levels including body weight measurement, histology evaluation, and blood biochemistry tests. Next, the applications of LMs in X-ray, CT, MRI, photoacoustic imaging, and molecular imaging are introduced in detail. And finally, the challenges and opportunities of their application in medical imaging are also discussed.

Multi-center sparse learning and decision fusion for automatic COVID-19 diagnosis

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a sharp increase in hospitalized patients with multi-organ disease pneumonia. Early and automatic diagnosis of COVID-19 is essential to slow down the spread of this epidemic and reduce the mortality of patients infected with SARS-CoV-2. In this paper, we propose a joint multi-center sparse learning (MCSL) and decision fusion scheme exploiting chest CT images for automatic COVID-19 diagnosis. Specifically, considering the inconsistency of data in multiple centers, we first convert CT images into histogram of oriented gradient (HOG) images to reduce the structural differences between multi-center data and enhance the generalization performance. We then exploit a 3-dimensional convolutional neural network (3D-CNN) model to learn the useful information between and within 3D HOG image slices and extract multi-center features. Furthermore, we employ the proposed MCSL method that learns the intrinsic structure between multiple centers and within each center, which selects discriminative features to jointly train multi-center classifiers. Finally, we fuse these decisions made by these classifiers. Extensive experiments are performed on chest CT images from five centers to validate the effectiveness of the proposed method. The results demonstrate that the proposed method can improve COVID-19 diagnosis performance and outperform the state-of-the-art methods.