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Background: Klippel-Feil syndrome (KFS) represents a rare anomaly characterized by congenital fusion of the cervical vertebrae. The underlying molecular etiology remains largely unknown because of the genetic and phenotypic heterogeneity.Methods: We consecutively recruited a Chinese cohort of 37 patients with KFS. The clinical manifestations and radiological assessments were analyzed and whole-exome sequencing (WES) was performed. Additionally, rare variants in KFS cases and controls were compared using genetic burden analysis.Results: We primarily examined rare variants in five reported genes (GDF6, MEOX1, GDF3, MYO18B and RIPPLY2) associated with KFS and detected three variants of uncertain significance in MYO18B. Based on rare variant burden analysis of 96 candidate genes related to vertebral segmentation defects, we identified BAZ1B as having the highest probability of association with KFS, followed by FREM2, SUFU, VANGL1 and KMT2D. In addition, seven patients were proposed to show potential oligogenic inheritance involving more than one variants in candidate genes, the frequency of which was significantly higher than that in the in-house controls.Conclusions: Our study presents an exome-sequenced cohort and identifies five novel genes potentially associated with KFS, extending the spectrum of known mutations contributing to this syndrome. Furthermore, the genetic burden analysis provides further evidence for potential oligogenic inheritance of KFS. [ABSTRACT FROM AUTHOR]

Objective: Alzheimer's disease (AD) is a type of neurological illness that significantly impacts individuals' daily lives. In the intelligent diagnosis of AD, 3D networks require larger computational resources and storage space for training the models, leading to increased model complexity and training time. On the other hand, 2D slices analysis may overlook the 3D structural information of MRI and can result in information loss. Approach: We propose a multi-slice attention fusion and multi-view personalized fusion lightweight network for automated AD diagnosis. It incorporates a multi-branch lightweight backbone to extract features from sagittal, axial, and coronal view of MRI, respectively. In addition, we introduce a novel multi-slice attention fusion module, which utilizes a combination of global and local channel attention mechanism to ensure consistent classification across multiple slices. Additionally, a multi-view personalized fusion module is tailored to assign appropriate weights to the three views, taking into account the varying significance of each view in achieving accurate classification results. To enhance the performance of the multi-view personalized fusion module, we utilize a label consistency loss to guide the model's learning process. This encourages the acquisition of more consistent and stable representations across all three views. Main results: The suggested strategy is efficient in lowering the number of parameters and FLOPs, with only 3.75M and 4.45G respectively, and accuracy improved by 10.5% to 14% in three tasks. Moreover, in the classification tasks of AD vs. CN, AD vs. MCI and MCI vs. CN, the accuracy of the proposed method is 95.63%, 86.88% and 85.00%, respectively, which is superior to the existing methods. Conclusions: The results show that the proposed approach not only excels in resource utilization, but also significantly outperforms the four comparison methods in terms of accuracy and sensitivity, particularly in detecting early-stage AD lesions. It can precisely capture and accurately identify subtle brain lesions, providing crucial technical support for early intervention and treatment. [ABSTRACT FROM AUTHOR]