Analysis of the risk factors of the radiation-induced encephalopathy in nasopharyngeal carcinoma: A retrospective cohort study.

To analyze the related factors of radiation-induced encephalopathy in nasopharyngeal carcinoma (NPC) to identify the risk factors and their clinical significance. This retrospective cohort study included 707 NPC patients. They had undergone conventional and enhanced computed tomography or magnetic resonance imaging scans. They were divided into the radiation-induced encephalopathy group and the no encephalopathy group according to the imaging examination. Detailed clinical information was collected. The incidence of radiation-induced encephalopathy in NPC was 22.2%, in which 124 were radiation-induced encephalopathy and 33 were reirradiation patients. We found that age, pathological type, radiation method, hypertension, radiation course, relapse, carotid/cerebral arteriosclerosis, clinical stage, and radiotherapy dose were statistically significant between the two groups (p < 0.05). Multiple logistic regression showed that clinical stage, age, radiotherapy method, hypertension, carotid/cerebral arteriosclerosis, and radiation courses after a reoccurrence of NPC were risk factors for radiation-induced encephalopathy. The more advanced the clinical stage was and the older the patient, the greater the risk. Radiotherapy method, radiation course, hypertension, carotid/cerebral arteriosclerosis, age, and clinical stage were the risk factors associated with radiation-induced encephalopathy in NPC.

Meta-analysis of the value of dual-energy computed tomography in the diagnosis of anterior cruciate ligament injuries of the knee.

BACKGROUND: This meta-analysis assessed the efficacy of dual-energy computed tomography (DECT) in the diagnosis of anterior cruciate ligament (ACL) injuries. METHODS: The literature search was performed up to December 8, 2023, and included a comprehensive examination of several databases: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP. Diagnostic metrics sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and a summary receiver operating characteristic (SROC) were determined using a bivariate model analysis. Heterogeneity within the data was explored through subgroup analyses, which considered variables including geographical region, use of magnetic resonance imaging (MRI), arthroscopy, and study design. RESULTS: The analysis included ten studies encompassing 544 patients. DECT demonstrated substantial diagnostic utility for ACL injuries of the knee, with a sensitivity of 0.91 (95% confidence interval [CI]: 0.88-0.94), a specificity of 0.90 (95% CI: 0.81-0.95), a PLR of 9.20 (95% CI: 4.50-19.00), a NLR of 0.10 (95% CI: 0.06-0.14), a DOR of 97.00 (95% CI: 35.00-268.00), and an area under the curve (AUC) of 0.95 (95% CI: 0.93-0.97). The subgroup analyses consistently showed high diagnostic precision for ACL injuries across Asian population (sensitivity: 0.91, specificity: 0.91, PLR: 9.90, NLR: 0.09, DOR: 105.00, AUC: 0.96), in MRI subgroup (sensitivity: 0.85, specificity: 0.94, PLR: 9.57, NLR: 0.18, DOR: 56.00, AUC: 0.93), in arthroscopy subgroup (sensitivity: 0.92, specificity: 0.89, PLR: 8.40, NLR: 0.09, DOR: 94.00, AUC: 0.95), for prospective studies (sensitivity: 0.92, specificity: 0.88, PLR: 7.40, NLR: 0.09, DOR: 78.00, AUC: 0.95), and for retrospective studies (sensitivity: 0.91, specificity: 0.93, AUC: 0.93). CONCLUSION: DECT exhibits a high value in diagnosing ACL injuries. The significant diagnostic value of DECT provides clinicians with a powerful tool that enhances the accuracy and efficiency of diagnosis and optimizes patient management and treatment outcomes.

Resting-state neural activity and cerebral blood flow alterations in type 2 diabetes mellitus: Insights from hippocampal subfields.

OBJECTIVE: In this study, multimodal magnetic resonance imaging (MRI) imaging was used to deeply analyze the changes of hippocampal subfields perfusion and function in patients with type 2 diabetes mellitus (T2DM), aiming to provide image basis for the diagnosis of hippocampal-related nerve injury in patients with T2DM. METHODS: We recruited 35 patients with T2DM and 40 healthy control subjects (HCs). They underwent resting-state functional MRI (rs-fMRI), arterial spin labeling (ASL) scans, and a series of cognitive tests. Then, we compared the differences of two groups in the cerebral blood flow (CBF) value, amplitude of low-frequency fluctuation (ALFF) value, and regional homogeneity (ReHo) value of the bilateral hippocampus subfields. RESULTS: The CBF values of cornu ammonis area 1 (CA1), dentate gyrus (DG), and subiculum in the right hippocampus of T2DM group were significantly lower than those of HCs. The ALFF values of left hippocampal CA3, subiculum, and bilateral hippocampus amygdala transition area (HATA) were higher than those of HCs in T2DM group. The ReHo values of CA3, DG, subiculum, and HATA in the left hippocampus of T2DM group were higher than those of HCs. In the T2DM group, HbAc1 and FINS were negatively correlated with imaging characteristics in some hippocampal subregions. CONCLUSION: This study indicates that T2DM patients had decreased perfusion in the CA1, DG, and subiculum of the right hippocampus, and the right hippocampus subiculum was associated with chronic hyperglycemia. Additionally, we observed an increase in spontaneous neural activity within the left hippocampal CA3, subiculum, and bilateral HATA regions, as well as an enhanced local neural coordination in the left hippocampal CA3, DG, HATA, and subiculum among patients with type 2 diabetes, which may reflect an adaptive compensation for cognitive decline. However, this compensation may decline with the exacerbation of metabolic disorders.

Cortical Morphological Networks Differ Between Gyri and Sulci.

This study explored how the human cortical folding pattern composed of convex gyri and concave sulci affected single-subject morphological brain networks, which are becoming an important method for studying the human brain connectome. We found that gyri-gyri networks exhibited higher morphological similarity, lower small-world parameters, and lower long-term test-retest reliability than sulci-sulci networks for cortical thickness- and gyrification index-based networks, while opposite patterns were observed for fractal dimension-based networks. Further behavioral association analysis revealed that gyri-gyri networks and connections between gyral and sulcal regions significantly explained inter-individual variance in Cognition and Motor domains for fractal dimension- and sulcal depth-based networks. Finally, the clinical application showed that only sulci-sulci networks exhibited morphological similarity reductions in major depressive disorder for cortical thickness-, fractal dimension-, and gyrification index-based networks. Taken together, these findings provide novel insights into the constraint of the cortical folding pattern to the network organization of the human brain.

Functional connectivity density aberrance in type 2 diabetes mellitus with and without mild cognitive impairment.

Purpose: The objective of this study was to investigate alterations in functional connectivity density (FCD) mapping and their impact on functional connectivity (FC) among individuals diagnosed with Type 2 diabetes mellitus (T2DM) across different cognitive states. Moreover, the study sought to explore the potential association between aberrant FCD/FC patterns and clinical or cognitive variables. Methods: A total of 211 participants were recruited for this study, consisting of 75 healthy controls (HCs), 89 T2DM patients with normal cognitive function (DMCN), and 47 T2DM patients with mild cognitive impairment (DMCI). The study employed FCD analysis to pinpoint brain regions exhibiting significant FCD alterations. Subsequently, these regions showing abnormal FCD served as seeds for FC analysis. Exploratory partial correlations were conducted to explore the relationship between clinical biochemical indicators, neuropsychological test scores, and altered FCD or FC. Results: The FCD analysis revealed an increased trend in global FCD (gFCD), local FCD (lFCD), and long-range FCD (lrFCD) within the bilateral supramarginal gyrus (SMG) among individuals with DMCN. Additionally, significant lFCD alterations were observed in the right inferior frontal gyrus and left precuneus when comparing DMCN to HCs and DMCI. Conclusion: When comparing individuals with T2DM and healthy controls (HCs), it was revealed that DMCN exhibited significant improvements in FCD. This suggests that the brain may employ specific compensatory mechanisms to maintain normal cognitive function at this stage. Our findings provide a novel perspective on the neural mechanisms involved in cognitive decline associated with T2DM.

Cross-ancestry genome-wide association studies of brain imaging phenotypes.

Genome-wide association studies of brain imaging phenotypes are mainly performed in European populations, but other populations are severely under-represented. Here, we conducted Chinese-alone and cross-ancestry genome-wide association studies of 3,414 brain imaging phenotypes in 7,058 Chinese Han and 33,224 white British participants. We identified 38 new associations in Chinese-alone analyses and 486 additional new associations in cross-ancestry meta-analyses at P < 1.46 × 10-11 for discovery and P < 0.05 for replication. We pooled significant autosomal associations identified by single- or cross-ancestry analyses into 6,443 independent associations, which showed uneven distribution in the genome and the phenotype subgroups. We further divided them into 44 associations with different effect sizes and 3,557 associations with similar effect sizes between ancestries. Loci of these associations were shared with 15 brain-related non-imaging traits including cognition and neuropsychiatric disorders. Our results provide a valuable catalog of genetic associations for brain imaging phenotypes in more diverse populations.

A study on the association between prefrontal functional connectivity and non-suicidal self-injury in adolescents with depression.

Objective: Among adolescents with depression, the occurrence of non-suicidal self-injury (NSSI) behavior is prevalent, constituting a high-risk factor for suicide. However, there has been limited research on the neuroimaging mechanisms underlying adolescent depression and NSSI behavior, and the potential association between the two remains unclear. Therefore, this study aims to investigate the alterations in functional connectivity (FC) of the regions in the prefrontal cortex with the whole brain, and elucidates the relationship between these alterations and NSSI behavior in adolescents with depression. Methods: A total of 68 participants were included in this study, including 35 adolescents with depression and 33 healthy controls. All participants underwent assessments using the 17-item Hamilton Depression Rating Scale (17-HAMD) and the Ottawa Self-Harm Inventory. In addition, functional magnetic resonance imaging (fMRI) data of the participants' brains were collected. Subsequently, the FCs of the regions in the prefrontal cortex with the whole brain was calculated. The FCs showing significant differences were then subjected to correlation analyses with 17-HAMD scores and NSSI behavior scores. Result: Compared to the healthy control group, the adolescent depression group exhibited decreased FCs in several regions, including the right frontal eye field, left dorsolateral prefrontal cortex, right orbitofrontal cortex, left insula and right anterior cingulate coetex. The 17-HAMD score was positively correlated with the frequency of NSSI behavior within 1 year (rs = 0.461, p = 0.005). The FC between the right anterior cingulate cortex and the right precuneus showed a negative correlation with the 17-HAMD scores (rs = -0.401, p = 0.023). Additionally, the FC between the right orbitofrontal cortex and the right insula, demonstrated a negative correlation with the frequency of NSSI behavior within 1 year (rs = -0.438, p = 0.012, respectively). Conclusion: Adolescents with depression showed decreased FCs of the prefrontal cortex with multiple brain regions, and some of these FCs were associated with the NSSI frequency within 1 year. This study provided neuroimaging evidence for the neurophysiological mechanisms underlying adolescent depression and its comorbidity with NSSI behavior.

Structural characteristics of amygdala subregions in type 2 diabetes mellitus.

Type 2 diabetes mellitus (T2DM) patients often suffer from depressive symptoms, which seriously affect cooperation in treatment and nursing. The amygdala plays a significant role in depression. This study aims to explore the microstructural alterations of the amygdala in T2DM and to investigate the relationship between the alterations and depressive symptoms. Fifty T2DM and 50 healthy controls were included. Firstly, the volumes of subcortical regions and subregions of amygdala were calculated by FreeSurfer. Covariance analysis (ANCOVA) was conducted between the two groups with covariates of age, sex, and estimated total intracranial volume to explore the differences in volume of subcortical regions and subregions of amygdala. Furthermore, the structural covariance within the amygdala subregions was performed. Moreover, we investigate the correlation between depressive symptoms and the volume of subcortical regions and amygdala subregions in T2DM. We observed a reduction in the volume of the bilateral cortico-amygdaloid transition area, left basal nucleus, bilateral accessory basal nucleus, left anterior amygdaloid area of amygdala, the left thalamus and left hippocampus in T2DM. T2DM patients showed decreased structural covariance connectivity between left paralaminar nucleus and the right central nucleus. Moreover, there was a negative correlation between self-rating depression scale scores and the volume of the bilateral cortico-amygdaloid transition area in T2DM. This study reveals extensive structural alterations in the amygdala subregions of T2DM patients. The reduction in the volume of the bilateral cortico-amygdaloid transition area may be a promising imaging marker for early recognition of depressive symptoms in T2DM.

Construction of a Female Sterility Maintaining System Based on a Novel Mutation of the MEL2 Gene.

BACKGROUND: Hybrid rice has significant yield advantage and stress tolerance compared with inbred rice. However, production of hybrid rice seeds requires extensive manual labors. Currently, hybrid rice seeds are produced by crosspollination of male sterile lines by fertile paternal lines. Because seeds from paternal lines can contaminate the hybrid seeds, mechanized production by mixed-seeding and mixed-harvesting is difficult. This problem can be solved if the paternal line is female sterile. RESULTS: Here we identified a female infertile mutant named h569 carrying a novel mutation (A1106G) in the MEL2 gene that was previously reported to regulate meiosis entry both in male and female organs. h569 mutant is female infertile but male normal, suggesting that MEL2 regulates meiosis entry in male and female organs through distinct pathways. The MEL2 gene and h569 mutant gave us tools to construct female sterility maintaining systems that can be used for propagation of female sterile lines. We connected the wild-type MEL2 gene with pollen-killer gene ZmAA1 and seed-marker gene DsRed2 in one T-DNA cassette and transformed it into ZZH1607, a widely used restorer line. Transgenic line carrying a single transgene inserted in an intergenic region was selected to cross with h569 mutant. F2 progeny carrying homozygous A1106G mutation and hemizygous transgene displayed 1:1 segregation of fertile and infertile pollen grains and 1:1 segregation of fluorescent and non-fluorescent seeds upon self-fertilization. All of the non-fluorescent seeds generated female infertile plants, while the fluorescent seeds generated fertile plants that reproduced in the way as their previous generation. CONCLUSIONS: These results indicated that the female sterility maintaining system constructed in the study can be used to breed and propagate paternal lines that are female infertile. The application of this system will enable mechanized production of hybrid rice seed by using the mixed-seeding and mixed harvesting approach, which will significantly reduce the cost in hybrid rice seed production.

Use of Computed Tomography-Based Texture Analysis to Differentiate Benign From Malignant Salivary Gland Lesions.

OBJECTIVE: Salivary gland lesions show overlapping morphological findings and types of time/intensity curves. This research aimed to evaluate the role of 2-phase multislice spiral computed tomography (MSCT) texture analysis in differentiating between benign and malignant salivary gland lesions. METHODS: In this prospective study, MSCT was carried out on 90 patients. Each lesion was segmented on axial computed tomography (CT) images manually, and 33 texture features and morphological CT features were assessed. Logistic regression analysis was used to confirm predictors of malignancy ( P < 0.05 was considered to be statistically significant), followed by receiver operating characteristics analysis to assess the diagnostic performance. RESULTS: Univariate logistic regression analysis revealed that morphological CT features (shape, size, and invasion of adjacent tissues) and 17 CT texture parameters had significant differences between benign and malignant lesions ( P < 0.05). Multivariate binary logistic regression demonstrated that shape, invasion of adjacent tissues, entropy, and inverse difference moment were independent factors for malignant tumors. The diagnostic accuracy values of multivariate binary logistic models based on morphological parameters, CT texture features, and a combination of both were 87.8%, 90%, and 93.3%, respectively. CONCLUSIONS: Two-phase MSCT texture analysis was conducive to differentiating between malignant and benign neoplasms in the salivary gland, especially when combined with morphological CT features.

Hybrid Multimodality Fusion with Cross-Domain Knowledge Transfer to Forecast Progression Trajectories in Cognitive Decline.

Magnetic resonance imaging (MRI) and positron emission tomography (PET) are increasingly used to forecast progression trajectories of cognitive decline caused by preclinical and prodromal Alzheimer's disease (AD). Many existing studies have explored the potential of these two distinct modalities with diverse machine and deep learning approaches. But successfully fusing MRI and PET can be complex due to their unique characteristics and missing modalities. To this end, we develop a hybrid multimodality fusion (HMF) framework with cross-domain knowledge transfer for joint MRI and PET representation learning, feature fusion, and cognitive decline progression forecasting. Our HMF consists of three modules: 1) a module to impute missing PET images, 2) a module to extract multimodality features from MRI and PET images, and 3) a module to fuse the extracted multimodality features. To address the issue of small sample sizes, we employ a cross-domain knowledge transfer strategy from the ADNI dataset, which includes 795 subjects, to independent small-scale AD-related cohorts, in order to leverage the rich knowledge present within the ADNI. The proposed HMF is extensively evaluated in three AD-related studies with 272 subjects across multiple disease stages, such as subjective cognitive decline and mild cognitive impairment. Experimental results demonstrate the superiority of our method over several state-of-the-art approaches in forecasting progression trajectories of AD-related cognitive decline.

Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI.

Brain structural MRI has been widely used for assessing future progression of cognitive impairment (CI) based on learning-based methods. Previous studies generally suffer from the limited number of labeled training data, while there exists a huge amount of MRIs in large-scale public databases. Even without task-specific label information, brain anatomical structures provided by these MRIs can be used to boost learning performance intuitively. Unfortunately, existing research seldom takes advantage of such brain anatomy prior. To this end, this paper proposes a brain anatomy-guided representation (BAR) learning framework for assessing the clinical progression of cognitive impairment with T1-weighted MRIs. The BAR consists of a pretext model and a downstream model, with a shared brain anatomy-guided encoder for MRI feature extraction. The pretext model also contains a decoder for brain tissue segmentation, while the downstream model relies on a predictor for classification. We first train the pretext model through a brain tissue segmentation task on 9,544 auxiliary T1-weighted MRIs, yielding a generalizable encoder. The downstream model with the learned encoder is further fine-tuned on target MRIs for prediction tasks. We validate the proposed BAR on two CI-related studies with a total of 391 subjects with T1-weighted MRIs. Experimental results suggest that the BAR outperforms several state-of-the-art (SOTA) methods. The source code and pre-trained models are available at https://github.com/goodaycoder/BAR.