Mechanism of Antidepressant Action of (2R,6R)-6-Hydroxynorketamine (HNK) and Its Compounds: Insights from Proteomic Analysis.

The effects of HNK, I5, and I6 on the expression of protein in hippocampus of depressed mice were studied by isobaric tags for relative and absolute quantitation (iTRAQ) to explore the mechanism of their antidepressant action. HNK, I5, and I6 were administered intragastric administration once a day in the morning for 7 days. The drug was subsequently discontinued for 7 days (without any treatment). On the 15th day, mice in each group were given the drug (1.0, 10.0, 30.0 mg/kg) intragastric stimulation and mouse hippocampal tissues were taken to perform iTRAQ to identify differentially expressed proteins, and bioinformatics was used to analyze the functional enrichment of the differentially expressed proteins. Compared with Ctr group, the number of differentially expressed proteins in HNK, I5, and I6 treatment groups was 158, 88, and 105, respectively. The three groups shared 29 differentially expressed proteins. In addition, compared with HNK group, the number of differentially expressed proteins in I5 and I6 groups was 201 and 203, respectively. A total of 47 and 56 differentially expressed proteins were co-expressed in I5 and I6 groups. Bioinformatics analysis showed that these differentially expressed proteins mainly had the functions of binding, biocatalysis, and transport, and mainly participated in cellular process, biological regulation process, biological metabolism process, and stress reaction process. GO and KEGG pathway analysis found that these differentially expressed proteins were involved long-term potentiation, G13 pathway, platelet activation pathway, and MAPK signaling pathway. HNK, I5, and I6 antidepressants are closely related to sudden stress sensitivity, stress resistance, neurotransmitter, and metabolic pathways. This study provides a scientific basis to further elucidate the mechanism and clinical application of HNK, I5, and I6 antidepressants.

Fundus image classification using Inception V3 and ResNet-50 for the early diagnostics of fundus diseases.

Purpose: We aim to present effective and computer aided diagnostics in the field of ophthalmology and improve eye health. This study aims to create an automated deep learning based system for categorizing fundus images into three classes: normal, macular degeneration and tessellated fundus for the timely recognition and treatment of diabetic retinopathy and other diseases. Methods: A total of 1,032 fundus images were collected from 516 patients using fundus camera from Health Management Center, Shenzhen University General Hospital Shenzhen University, Shenzhen 518055, Guangdong, China. Then, Inception V3 and ResNet-50 deep learning models are used to classify fundus images into three classes, Normal, Macular degeneration and tessellated fundus for the timely recognition and treatment of fundus diseases. Results: The experimental results show that the effect of model recognition is the best when the Adam is used as optimizer method, the number of iterations is 150, and 0.00 as the learning rate. According to our proposed approach we, achieved the highest accuracy of 93.81% and 91.76% by using ResNet-50 and Inception V3 after fine-tuned and adjusted hyper parameters according to our classification problem. Conclusion: Our research provides a reference to the clinical diagnosis or screening for diabetic retinopathy and other eye diseases. Our suggested computer aided diagnostics framework will prevent incorrect diagnoses caused by the low image quality and individual experience, and other factors. In future implementations, the ophthalmologists can implement more advanced learning algorithms to improve the accuracy of diagnosis.

A deep learning based framework for the classification of multi- class capsule gastroscope image in gastroenterologic diagnosis.

Purpose: The purpose of this paper is to develop a method to automatic classify capsule gastroscope image into three categories to prevent high-risk factors for carcinogenesis, such as atrophic gastritis (AG). The purpose of this research work is to develop a deep learning framework based on transfer learning to classify capsule gastroscope image into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. Method: In this research work, we proposed deep learning framework based on transfer learning to classify capsule gastroscope image into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. We used VGG- 16, ResNet-50, and Inception V3 pre-trained models, fine-tuned them and adjust hyperparameters according to our classification problem. Results: A dataset containing 380 images was collected for each capsule gastroscope image category, and divided into training set and test set in a ratio of 70%, and 30% respectively, and then based on the dataset, three methods, including as VGG- 16, ResNet-50, and Inception v3 are used. We achieved highest accuracy of 94.80% by using VGG- 16 to diagnose and classify capsule gastroscopic images into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. Our proposed approach classified capsule gastroscope image with respectable specificity and accuracy. Conclusion: The primary technique and industry standard for diagnosing and treating numerous stomach problems is gastroscopy. Capsule gastroscope is a new screening tool for gastric diseases. However, a number of elements, including image quality of capsule endoscopy, the doctors' experience and fatigue, limit its effectiveness. Early identification is necessary for high-risk factors for carcinogenesis, such as atrophic gastritis (AG). Our suggested framework will help prevent incorrect diagnoses brought on by low image quality, individual experience, and inadequate gastroscopy inspection coverage, among other factors. As a result, the suggested approach will raise the standard of gastroscopy. Deep learning has great potential in gastritis image classification for assisting with achieving accurate diagnoses after endoscopic procedures.

Quantifying invasiveness of clinical stage IA lung adenocarcinoma with computed tomography texture features.

OBJECTIVES: The study objectives were to establish and validate a nomogram for pathological invasiveness prediction in clinical stage IA lung adenocarcinoma and to help identify those potentially unsuitable for sublobar resection-based computed tomography texture features. METHOD: Patients with clinical stage IA lung adenocarcinoma who underwent surgery at Guangdong Provincial People's Hospital between January 2015 and October 2018 were retrospectively reviewed. All surgically resected nodules were pathologically classified into less-invasive and invasive cohorts. Each nodule was manually segmented, and its computerized texture features were extracted. Clinicopathological and computed tomographic texture features were compared between 2 cohorts. A nomogram for distinguishing the pathological invasiveness was established and validated. RESULTS: Among 428 enrolled patients, 249 were diagnosed with invasive pathological subtypes. Smoking status (odds ratio, 2.906; 95% confidence interval, 1.285-6.579; P = .011), mean computed tomography attenuation value (odds ratio, 1.005, 95% confidence interval, 1.002-1.007; P < .001), and entropy (odds ratio, 8.536, 95% confidence interval, 3.478-20.951; P < .001) were identified as independent predictors for pathological invasiveness by multivariate logistics regression analysis. The nomogram showed good calibration (P = .182) with an area under the curve of 0.849 when validated with testing set data. Decision curve analysis indicated the potentially clinical usefulness of the model with respect to treat-all or treat-none scenario. Compared with intraoperative frozen-section, the nomogram performed better in pathological invasiveness diagnosis (area under the curve, 0.815 vs 0.670; P = .00095). CONCLUSIONS: We established and validated a nomogram to compute the probability of invasiveness of clinical stage IA lung adenocarcinoma with great calibration, which may contribute to decisions related to resection extent.