Speed-enhanced scattering compensation method with sub-Nyquist sampling.

A rapid feedback-based scattering compensation method is particularly important for guiding light precisely within turbid tissues, especially the dynamic tissues. However, the huge number of measurements that come from the underutilization of the signal frequency channel greatly limits the modulation speed. This paper introduces a rapid compensation method with the sub-Nyquist sampling which improves the channel utilization and the speed of wavefront shaping. The number of measurements is reduced to ∼1500 with 32 × 32 freedom, and the PBR of the focus reaches ∼200. The system performances are demonstrated by focusing the light through brain slices of different thicknesses.

End-to-End Deep-Learning-Based Diagnosis of Benign and Malignant Orbital Tumors on Computed Tomography Images.

Determining the nature of orbital tumors is challenging for current imaging interpretation methods, which hinders timely treatment. This study aimed to propose an end-to-end deep learning system to automatically diagnose orbital tumors. A multi-center dataset of 602 non-contrast-enhanced computed tomography (CT) images were prepared. After image annotation and preprocessing, the CT images were used to train and test the deep learning (DL) model for the following two stages: orbital tumor segmentation and classification. The performance on the testing set was compared with the assessment of three ophthalmologists. For tumor segmentation, the model achieved a satisfactory performance, with an average dice similarity coefficient of 0.89. The classification model had an accuracy of 86.96%, a sensitivity of 80.00%, and a specificity of 94.12%. The area under the receiver operating characteristics curve (AUC) of the 10-fold cross-validation ranged from 0.8439 to 0.9546. There was no significant difference on diagnostic performance of the DL-based system and three ophthalmologists (p > 0.05). The proposed end-to-end deep learning system could deliver accurate segmentation and diagnosis of orbital tumors based on noninvasive CT images. Its effectiveness and independence from human interaction allow the potential for tumor screening in the orbit and other parts of the body.

Contextual Fear Learning and Extinction in the Primary Visual Cortex of Mice.

Fear memory contextualization is critical for selecting adaptive behavior to survive. Contextual fear conditioning (CFC) is a classical model for elucidating related underlying neuronal circuits. The primary visual cortex (V1) is the primary cortical region for contextual visual inputs, but its role in CFC is poorly understood. Here, our experiments demonstrated that bilateral inactivation of V1 in mice impaired CFC retrieval, and both CFC learning and extinction increased the turnover rate of axonal boutons in V1. The frequency of neuronal Ca2+ activity decreased after CFC learning, while CFC extinction reversed the decrease and raised it to the naïve level. Contrary to control mice, the frequency of neuronal Ca2+ activity increased after CFC learning in microglia-depleted mice and was maintained after CFC extinction, indicating that microglial depletion alters CFC learning and the frequency response pattern of extinction-induced Ca2+ activity. These findings reveal a critical role of microglia in neocortical information processing in V1, and suggest potential approaches for cellular-based manipulation of acquired fear memory.

A multi-feature deep learning system to enhance glaucoma severity diagnosis with high accuracy and fast speed.

Glaucoma is the leading cause of irreversible blindness, and the early detection and timely treatment are essential for glaucoma management. However, due to the interindividual variability in the characteristics of glaucoma onset, a single feature is not yet sufficient for monitoring glaucoma progression in isolation. There is an urgent need to develop more comprehensive diagnostic methods with higher accuracy. In this study, we proposed a multi- feature deep learning (MFDL) system based on intraocular pressure (IOP), color fundus photograph (CFP) and visual field (VF) to classify the glaucoma into four severity levels. We designed a three-phase framework for glaucoma severity diagnosis from coarse to fine, which contains screening, detection and classification. We trained it on 6,131 samples from 3,324 patients and tested it on independent 240 samples from 185 patients. Our results show that MFDL achieved a higher accuracy of 0.842 (95 % CI, 0.795-0.888) than the direct four classification deep learning (DFC-DL, accuracy of 0.513 [0.449-0.576]), CFP-based single-feature deep learning (CFP-DL, accuracy of 0.483 [0.420-0.547]) and VF-based single-feature deep learning (VF-DL, accuracy of 0.725 [0.668-0.782]). Its performance was statistically significantly superior to that of 8 juniors. It also outperformed 3 seniors and 1 expert, and was comparable with 2 glaucoma experts (0.842 vs 0.854, p = 0.663; 0.842 vs 0.858, p = 0.580). With the assistance of MFDL, junior ophthalmologists achieved statistically significantly higher accuracy performance, with the increased accuracy ranged from 7.50 % to 17.9 %, and that of seniors and experts were 6.30 % to 7.50 % and 5.40 % to 7.50 %. The mean diagnosis time per patient of MFDL was 5.96 s. The proposed model can potentially assist ophthalmologists in efficient and accurate glaucoma diagnosis that could aid the clinical management of glaucoma.

Long-term microglial phase-specific dynamics during single vessel occlusion and recanalization.

Vascular occlusion leading to brain dysfunctions is usually considered evoking microglia-induced inflammation response. However, it remains unclear how microglia interact with blood vessels in the development of vascular occlusion-related brain disorders. Here, we illuminate long-term spatiotemporal dynamics of microglia during single vessel occlusion and recanalization. Microglia display remarkable response characteristics in different phases, including acute reaction, rapid diffusion, transition and chronic effect. Fibrinogen-induced microglial cluster promotes major histocompatibility complex II (MHCII) expression. Microglial soma represents a unique filament-shape migration and has slower motility compared to the immediate reaction of processes to occlusion. We capture proliferative microglia redistribute territory. Microglial cluster resolves gradually and microglia recover to resting state both in the morphology and function in the chronic effect phase. Therefore, our study offers a comprehensive analysis of spatiotemporal dynamics of microglia and potential mechanisms to both vessel occlusion and recanalization. Microglial phase-specific response suggests the morphological feature-oriented phased intervention would be an attractive option for vascular occlusion-related diseases treatments.

A Structure-Related Fine-Grained Deep Learning System With Diversity Data for Universal Glaucoma Visual Field Grading.

Purpose: Glaucoma is the main cause of irreversible blindness worldwide. However, the diagnosis and treatment of glaucoma remain difficult because of the lack of an effective glaucoma grading measure. In this study, we aimed to propose an artificial intelligence system to provide adequate assessment of glaucoma patients. Methods: A total of 16,356 visual fields (VFs) measured by Octopus perimeters and Humphrey Field Analyzer (HFA) were collected, from three hospitals in China and the public Harvard database. We developed a fine-grained grading deep learning system, named FGGDL, to evaluate the VF loss, compared to ophthalmologists. Subsequently, we discuss the relationship between structural and functional damage for the comprehensive evaluation of glaucoma level. In addition, we developed an interactive interface and performed a cross-validation study to test its auxiliary ability. The performance was valued by F1 score, overall accuracy and area under the curve (AUC). Results: The FGGDL achieved a high accuracy of 85 and 90%, and AUC of 0.93 and 0.90 for HFA and Octopus data, respectively. It was significantly superior (p < 0.01) to that of medical students and nearly equal (p = 0.614) to that of ophthalmic clinicians. For the cross-validation study, the diagnosis accuracy was almost improved (p < 0.05). Conclusion: We proposed a deep learning system to grade VF of glaucoma with a high detection accuracy, for effective and adequate assessment for glaucoma patients. Besides, with the convenient and credible interface, this system can promote telemedicine and be used as a self-assessment tool for patients with long-duration diseases.

[The clinical analysis of cervical lymph node metastases from squamous cell carcinoma of the larynx and hypopharynx treated with total laryngectomy].

OBJECTIVE: To detect the distribution and characteristics of lymph nodes metastasis in patients of laryngeal and hypopharyngeal cancer. METHOD: A retrospective review of 129 patients who had radical neck dissection, including first recurrence in cervical lymph nodes after laryngectomy, was performed to analyze the cervical lymph node metastases of different types of laryngeal and hypopharyngeal cancer treated by total laryngectomy. RESULT: The patients of the hypopharyngeal and supraglottic cancer had a tendency of cervical lymph nodes metastasis in their early stage. The rate of the fusional lymph nodes was high in hypopharyngeal cancer, and so as to the rate of the positive lymph nodes in their level IV. In our study the percentage of the poor-differentiated squamous cell carcinoma was relatively high in hypopharyngeal and supraglottic cancer than in glottic cancer. And there was no statistical difference in stages of the primary tumour between groups of neck dissection with laryngectomy simultaneously and of first recurrence in lymph nodes after laryngectomy. CONCLUSION: The latent lymph nodes metastasis and preventive neck dissection should be considered in patients of hypopharyngeal and supraglottic cancer in or above stages of T2, especially in patients with poor-differentiated squamous cell carcinoma, though the sign of positive lymph nodes was not evident.