Lightweight neural network for Alzheimer's disease classification using multi-slice sMRI.

Alzheimer's disease (AD) is a progressive neurodegenerative disease. Early detection and intervention are crucial in preventing the progression of AD. To achieve efficient and scalable AD auto-detection based on structural Magnetic Resonance Imaging (sMRI), a lightweight neural network using multi-slice sMRI is proposed in this paper. The backbone for feature extraction is based on ShuffleNet V1 architecture, which is effective for overcoming the limitations posed by limited sMRI data and resource-restricted devices. In addition, we incorporate Efficient Channel Attention (ECA) to capture cross-channel interaction information, enabling us to effectively enhance features of disease associated brain regions. To optimize the model, we employ both cross entropy loss and triplet loss functions to constrain the predicted probabilities to the ground-truth labels, and to ensure appropriate representation of distances between different classes in the learned features. Experimental results show that the classification accuracies of our method for AD vs. CN, AD vs. MCI, and MCI vs. CN classification tasks are 95.00%, 87.50%, and 85.62% respectively. Our method utilizes only 3.42 M parameters and 6.08G FLOPs, while maintaining a comparable level of performance compared to the other 5 latest lightweight methods. This model design is computationally efficient, allowing it to process large amounts of data quickly and accurately in a timely manner. Additionally, it has the potential to advance the intelligent detection of Alzheimer's disease on devices with limited computing capabilities.

TIGAR Protects Cochlear Hair Cells against Teicoplanin-Induced Damage.

Teicoplanin is a glycopeptide antibiotic used to treat severe staphylococcal infections. It has been claimed that teicoplanin possesses ototoxic potential, although its toxic effects on cochlear hair cells (HCs) remain unknown. The TP53-induced glycolysis and apoptosis regulator (TIGAR) plays a crucial role in promoting cell survival. Prior research has demonstrated that TIGAR protects spiral ganglion neurons against cisplatin damage. However, the significance of TIGAR in damage to mammalian HCs has not yet been investigated. In this study, firstly, we discovered that teicoplanin caused dose-dependent cell death in vitro in both HEI-OC1 cells and cochlear HCs. Next, we discovered that HCs and HEI-OC1 cells treated with teicoplanin exhibited a dramatically decrease in TIGAR expression. To investigate the involvement of TIGAR in inner ear injury caused by teicoplanin, the expression of TIGAR was either upregulated via recombinant adenovirus or downregulated by shRNA in HEI-OC1 cells. Overexpression of TIGAR increased cell viability, decreased apoptosis, and decreased intracellular reactive oxygen species (ROS) level, whereas downregulation of TIGAR decreased cell viability, exacerbated apoptosis, and elevated ROS level following teicoplanin injury. Finally, antioxidant therapy with N-acetyl-L-cysteine decreased ROS level, prevented cell death, and restored p38/phosphorylation-p38 expression levels in HEI-OC1 cells injured by teicoplanin. This study demonstrates that TIGAR may be a promising novel target for the prevention of teicoplanin-induced ototoxicity.

Prototype-guided multi-scale domain adaptation for Alzheimer's disease detection.

Alzheimer's disease (AD) is the most common form of dementia and there is no effective treatment currently. Using artificial intelligence technology to assist the diagnosis and intervention as early as possible is of great significance to delay the development of AD. Structural Magnetic Resonance Imaging (sMRI) has shown great practical values on computer-aided AD diagnosis. Affected by data from different sources or acquisition domains in realistic scenarios, MRI data often suffer from domain shift problem. In this paper, we propose a deep Prototype-Guided Multi-Scale Domain Adaptation (PMDA) framework to handle MRI data with domain shift problem, and realize automatic auxiliary diagnosis of AD, Mild Cognitive Impairment (MCI) and Cognitively Normal (CN). PMDA is composed of three modules: (1) MRI multi-scale feature extraction module combines the advantages of 3D convolution and self-attention to effectively extract multi-scale features in high-dimensional space, (2) Prototype Maximum Density Divergence (Pro-MDD) module adopts prototype learning to constrain the feature outlier samples in a mini-batch when MDD is used to align source domain and target domain, and (3) Adversarial Domain Adaptation module is applied to achieve global feature alignment of the source domain and target domain and co-training two distinctive discriminators to mitigate the over-fitting issue. Experiments have been performed on 3T and 1.5T sMRI with domain shift in ADNI dataset. The experimental results demonstrated that the proposed framework PMDA outperforms supervised learning methods and several state-of-the-art domain adaptation methods and achieves a superior accuracy of 92.11%, 76.01% and 82.37% on AD vs. CN, AD vs. MCI, and MCI vs. CN tasks, respectively.

Discriminative Analysis of Migraine without Aura: Using Functional and Structural MRI with a Multi-Feature Classification Approach.

Magnetic resonance imaging (MRI) is by nature a multi-modality technique that provides complementary information about different aspects of diseases. So far no attempts have been reported to assess the potential of multi-modal MRI in discriminating individuals with and without migraine, so in this study, we proposed a classification approach to examine whether or not the integration of multiple MRI features could improve the classification performance between migraine patients without aura (MWoA) and healthy controls. Twenty-one MWoA patients and 28 healthy controls participated in this study. Resting-state functional MRI data was acquired to derive three functional measures: the amplitude of low-frequency fluctuations, regional homogeneity and regional functional correlation strength; and structural MRI data was obtained to measure the regional gray matter volume. For each measure, the values of 116 pre-defined regions of interest were extracted as classification features. Features were first selected and combined by a multi-kernel strategy; then a support vector machine classifier was trained to distinguish the subjects at individual level. The performance of the classifier was evaluated using a leave-one-out cross-validation method, and the final classification accuracy obtained was 83.67% (with a sensitivity of 92.86% and a specificity of 71.43%). The anterior cingulate cortex, prefrontal cortex, orbitofrontal cortex and the insula contributed the most discriminative features. In general, our proposed framework shows a promising classification capability for MWoA by integrating information from multiple MRI features.

Multimodal MRI-Based Classification of Trauma Survivors with and without Post-Traumatic Stress Disorder.

Post-traumatic stress disorder (PTSD) is a debilitating psychiatric disorder. It can be difficult to discern the symptoms of PTSD and obtain an accurate diagnosis. Different magnetic resonance imaging (MRI) modalities focus on different aspects, which may provide complementary information for PTSD discrimination. However, none of the published studies assessed the diagnostic potential of multimodal MRI in identifying individuals with and without PTSD. In the current study, we investigated whether the complementary information conveyed by multimodal MRI scans could be combined to improve PTSD classification performance. Structural and resting-state functional MRI (rs-fMRI) scans were conducted on 17 PTSD patients, 20 trauma-exposed controls without PTSD (TEC) and 20 non-traumatized healthy controls (HC). Gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and regional homogeneity were extracted as classification features, and in order to integrate the information of structural and functional MRI data, the extracted features were combined by a multi-kernel combination strategy. Then a support vector machine (SVM) classifier was trained to distinguish the subjects at individual level. The performance of the classifier was evaluated using the leave-one-out cross-validation (LOOCV) method. In the pairwise comparison of PTSD, TEC, and HC groups, classification accuracies obtained by the proposed approach were 2.70, 2.50, and 2.71% higher than the best single feature way, with the accuracies of 89.19, 90.00, and 67.57% for PTSD vs. HC, TEC vs. HC, and PTSD vs. TEC respectively. The proposed approach could improve PTSD identification at individual level. Additionally, it provides preliminary support to develop the multimodal MRI method as a clinical diagnostic aid.

[Head and Neck Tumor Segmentation Based on Augmented Gradient Level Set Method].

To realize the accurate positioning and quantitative volume measurement of tumor in head and neck tumor CT images, we proposed a level set method based on augmented gradient. With the introduction of gradient information in the edge indicator function, our proposed level set model is adaptive to different intensity variation, and achieves accurate tumor segmentation. The segmentation result has been used to calculate tumor volume. In large volume tumor segmentation, the proposed level set method can reduce manual intervention and enhance the segmentation accuracy. Tumor volume calculation results are close to the gold standard. From the experiment results, the augmented gradient based level set method has achieved accurate head and neck tumor segmentation. It can provide useful information to computer aided diagnosis.

[Mitochondrial 12S rRNA variants studies in 456 subjects with hearing loss in seven schools for deaf and mutes in Zhejiang province].

OBJECTIVE: To investigate mutational spectrum and frequency of the mitochondrial 12S rRNA gene in Chinese subjects with aminoglycoside-induced and non-syndromic hearing loss. METHODS: Total of 456 subjects with non-syndromic hearing loss were recruited from seven schools for deaf-mutes in Zhejiang province. Genomic DNA was extracted from the whole blood, and then the DNA fragment was amplified spanning the 12S rRNA gene, followed by sequencing and analyzed. RESULTS: Thirty-one variants were identified by mutation analysis of 12S rRNA gene in these subjects. The frequency of the known 1555A > G mutation was 4.4% (20/456). Prevalence of other putative deafness-associated mutation at positions 961 and 1095 were 2.0% (9/456) and 0.7% (3/456) respectively. Furthermore, the 1027A > G, 1109T > C and 1431G > A variants conferred increased sensitivity to ototoxic drugs or non-syndromic deafness as they were absent in 449 Chinese controls and localized at highly conserved nucleotides of this 12S rRNA gene. Moreover, clinical data showed a wide range of age-of-onset, variety of severity and various audiometric configurations in subjects carrying the 1555A > G mutation. CONCLUSIONS: Our data demonstrated that the mitochondrial 12S rRNA gene is the hot spot for mutations associated with aminoglycoside ototoxicity and non-syndromic hearing loss. Nuclear modifier genes, mitochondrial haplotypes and environmental factors might play a role in the phenotypic manifestation of these mutations.