Intelligent water perimeter security event recognition based on NAM-MAE and distributed optic fiber acoustic sensing system.

Distributed optical acoustic sensing (DAS) based on phase-sensitive optical time-domain reflectometry can realize the distributed monitoring of multi-point disturbances along an optical fiber, thus making it suitable for water perimeter security applications. However, owing to the complex environment and the production of various noises by the system, continuous and effective recognition of disturbance signals becomes difficult. In this study, we propose a Noise Adaptive Mask-Masked Autoencoders (NAM-MAE) algorithm based on the novel mask mode of a Masked Autoencoders (MAE) and applies it to the intelligent event recognition in DAS. In this method, fewer but more accurate features are fed into the deep learning model for recognition by directly shielding the noise. Taking the fading noise generated by the system as an example, data on water perimeter security events collected in DAS underwater acoustic experiments are used. The NAM-MAE is compared with other models. The results indicate higher training accuracy and higher convergence speed of NAM-MAE than other models. Further, the final test accuracy reaches 96.6134%. It can be demonstrated that the proposed method has feasibility and superiority.

A multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes.

Objective: Lung cancer is one of the most common malignant tumors in humans. Adenocarcinoma of the lung is another of the most common types of lung cancer. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the fifinal diagnosis of many diseases. Thus, pathological diagnosis is known as the gold standard for disease diagnosis. However, the complexity of the information contained in pathology images and the increase in the number of patients far exceeds the number of pathologists, especially in the treatment of lung cancer in less-developed countries. Methods: This paper proposes a multilayer perceptron model for lung cancer histopathology image detection, which enables the automatic detection of the degree of lung adenocarcinoma infifiltration. For the large amount of local information present in lung cancer histopathology images, MLP IN MLP (MIM) uses a dual data stream input method to achieve a modeling approach that combines global and local information to improve the classifification performance of the model. In our experiments, we collected 780 lung cancer histopathological images and prepared a lung histopathology image dataset to verify the effectiveness of MIM. Results: The MIM achieves a diagnostic accuracy of 95.31% and has a precision, sensitivity, specificity and F1-score of 95.31%, 93.09%, 93.10%, 96.43% and 93.10% respectively, outperforming the diagnostic results of the common network model. In addition, a number of series of extension experiments demonstrated the scalability and stability of the MIM. Conclusions: In summary, MIM has high classifification performance and substantial potential in lung cancer detection tasks.

RVM-GSM: Classification of OCT Images of Genitourinary Syndrome of Menopause Based on Integrated Model of Local-Global Information Pattern.

Genitourinary syndrome of menopause (GSM) is a group of syndromes, including atrophy of the reproductive tract and urinary tract, and sexual dysfunction, caused by decreased levels of hormones, such as estrogen, in women during the transition to, or late stage of, menopause. GSM symptoms can gradually become severe with age and menopausal time, seriously affecting the safety, and physical and mental health, of patients. Optical coherence tomography (OCT) systems can obtain images similar to "optical slices" in a non-destructive manner. This paper presents a neural network, called RVM-GSM, to implement automatic classification tasks for different types of GSM-OCT images. The RVM-GSM module uses a convolutional neural network (CNN) and a vision transformer (ViT) to capture local and global features of the GSM-OCT images, respectively, and, then, fuses the two features in a multi-layer perception module to classify the image. In accordance with the practical needs of clinical work, lightweight post-processing is added to the final surface of the RVM-GSM module to compress the module. Experimental results showed that the accuracy rate of RVM-GSM in the GSM-OCT image classification task was 98.2%. This result is better than those of the CNN and Vit models, demonstrating the promise and potential of the application of RVM-GSM in the physical health and hygiene fields for women.

CTS-Net: A Segmentation Network for Glaucoma Optical Coherence Tomography Retinal Layer Images.

Optical Coherence Tomography (OCT) technology is essential to obtain glaucoma diagnostic data non-invasively and rapidly. Early diagnosis of glaucoma can be achieved by analyzing the thickness and shape of retinal layers. Accurate retinal layer segmentation assists ophthalmologists in improving the efficiency of disease diagnosis. Deep learning technology is one of the most effective methods for processing OCT retinal layer images, which can segment different retinal layers and effectively obtain the topological structure of the boundary. This paper proposes a neural network method for retinal layer segmentation based on the CSWin Transformer (CTS-Net), which can achieve pixel-level segmentation and obtain smooth boundaries. A Dice loss function based on boundary areas (BADice Loss) is proposed to make CTS-Net learn more features of edge regions and improve the accuracy of boundary segmentation. We applied the model to the publicly available dataset of glaucoma retina, and the test results showed that mean absolute distance (MAD), root mean square error (RMSE), and dice-similarity coefficient (DSC) metrics were 1.79 pixels, 2.15 pixels, and 92.79%, respectively, which are better than those of the compared model. In the cross-validation experiment, the ranges of MAD, RMSE, and DSC are 0.05 pixels, 0.03 pixels, and 0.33%, respectively, with a slight difference, which further verifies the generalization ability of CTS-Net.

CroReLU: Cross-Crossing Space-Based Visual Activation Function for Lung Cancer Pathology Image Recognition.

Lung cancer is one of the most common malignant tumors in human beings. It is highly fatal, as its early symptoms are not obvious. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the final diagnosis of many diseases. Therefore, pathology diagnosis is known as the gold standard for disease diagnosis. However, the complexity of the information contained in pathology images and the increase in the number of patients far outpace the number of pathologists, especially for the treatment of lung cancer in less developed countries. To address this problem, we propose a plug-and-play visual activation function (AF), CroReLU, based on a priori knowledge of pathology, which makes it possible to use deep learning models for precision medicine. To the best of our knowledge, this work is the first to optimize deep learning models for pathology image diagnosis from the perspective of AFs. By adopting a unique crossover window design for the activation layer of the neural network, CroReLU is equipped with the ability to model spatial information and capture histological morphological features of lung cancer such as papillary, micropapillary, and tubular alveoli. To test the effectiveness of this design, 776 lung cancer pathology images were collected as experimental data. When CroReLU was inserted into the SeNet network (SeNet_CroReLU), the diagnostic accuracy reached 98.33%, which was significantly better than that of common neural network models at this stage. The generalization ability of the proposed method was validated on the LC25000 dataset with completely different data distribution and recognition tasks in the face of practical clinical needs. The experimental results show that CroReLU has the ability to recognize inter- and intra-class differences in cancer pathology images, and that the recognition accuracy exceeds the extant research work on the complex design of network layers.

Transcatheter Tricuspid Valve-in-Valve Replacement Using a J-Valve System for a Failed Tricuspid Bioprosthesis.

Background: Redo operation for failed tricuspid bioprosthetic valves is associated with high morbidity and mortality. Transcatheter tricuspid valve-in-valve implantation has become an acceptable option for high-risk patients with a failed tricuspid bioprosthesis. We present a case of successful tricuspid valve-in-valve implantation using a J-valve in a failed tricuspid bioprosthesis position. Case Summary. A 48-year-old male, who had a failed tricuspid bioprosthesis, presented with right-side heart failure, right-to-left shunting at the atrial level, severe dyspnea, cyanosis, peripheral edema, hepatauxe, and ascites. After the interdisciplinary assessment, we successfully performed transcatheter tricuspid valve-in-valve implantation with the J-valve system. At 34-month postoperative follow-up, the patient had no symptoms of heart failure and the echocardiogram showed good valve position and well hemodynamic status. Conclusions: This case demonstrated that the J-valve system may be a new option for high-risk patients with a failed tricuspid bioprosthetic valve.

High Quality Nitrogen and Silicon Co-Doped Carbon Dots (N/Si-CDs) for Fe3+ Sensing.

Nitrogen and silicon co-doped carbon nanodots (N/Si-CDs) were synthesized by one-step hydrothermal carbonization of folic acid and 3-aminopropyl trimethoxy silane with the assistance of glycerol. The resultant N/Si-CDs present intense excitation-independent photoluminescence (PL) emission owing to their monodisperse sizes and specific surface states. In comparison to nitrogen single doped CDs (N-CDs), the co-doped N/Si-CDs possess better PL properties following different emission manner due to the synergistic effects of the nitrogen and silicon dopants. Besides, the functionalized surfaces of N/Si-CDs endow them with superior solubility and stability. The as-synthesized N/Si-CDs demonstrate selective and sensitive fluorescence response to Fe3+, which could be explained by the PL quenching effects of Fe3+ on N/Si-CDs. Based on this finding, the quantitative analysis of aqueous Fe3+ by N/Si-CDs was established, showing good linearity in a range of 10 nM~45 µM and good sensitivity with a detection limit of 3.8 nM, so did for the detection of Fe3+ in fetal bovine serum (FBS) spiked samples. Therefore, the N/Si-CDs as a novel fluorescence probe hold promising for monitoring environmental and clinical Fe3+.

The medicinal use of realgar (As4S4) and its recent development as an anticancer agent.

ETHNOPHARMACOLOGICAL RELEVANCE: Arsenicals have been known as poisons and paradoxically as therapeutic agents. In the early 1970s, Chinese physicians from Harbin revived the medicinal use of arsenicals as anticancer agents. Notable success was observed in the treatment of acute promyelocytic leukemia (APL) with arsenic trioxide (ATO). The FDA approved ATO injection in the year 2000 for the treatment of APL. In contrast, the clinical use of the other arsenical, realgar (As4S4), is currently much less established, though it has also long been used in medical history. According to ancient medical records and recent findings in clinical trials, realgar was found as effective as ATO, but with relatively good oral safety profiles even on chronic administration. These give realgar an advantage over ATO in maintenance treatment. Though there is increasing understanding on the mechanisms of action and metabolic profiles of ATO, similar aspects of realgar are unclear to date. MATERIALS AND METHODS: We outline the use of realgar in traditional medicines, especially in traditional Chinese medicines (TCM) from ancient times to present. The clinical and experimental observations on realgar as a therapeutic agent are described with an emphasis on those findings that may imply the rationale and future directions of realgar as a potential anticancer drug candidate. RESULTS: There is an increasing understanding in the mechanisms of action of realgar as an antileukemic agent. However, there is still sparse information on its metabolism and toxicity profiles. CONCLUSIONS: Realgar is poorly soluble in water. Recently, several types of realgar nanoparticles (NPs) have been developed. Some of these realgar NPs also possess the unique optical properties of quantum dots. The activities and bioavailability of realgar NPs are much influenced by their sizes, making realgar an interesting biomedical and pharmaceutical research candidate.

Low-dose ketamine combined with pentobarbital in a miniature porcine model for a cardiopulmonary bypass procedure: a randomized controlled study.

BACKGROUND AND OBJECTIVE: Porcine anaesthesia remains a great problem for cardiac surgery research and especially with cardiopulmonary bypass procedures. This study was designed to develop a suitable anaesthesia model in which miniature pigs could be induced smoothly and be maintained stably during and after a cardiopulmonary bypass procedure. METHODS: Thirty-one miniature pigs were randomly divided into two groups and induced using ketamine and pentobarbital (K-P group, n = 15) or pentobarbital (P group, n = 16) alone, respectively. Animals in group K-P were induced with intramuscular injections of ketamine 5 mg kg and pentobarbital 20 mg kg body weight, and those in group P were induced with pentobarbital 30 mg kg alone. After intubation and intravenous catheterization, group K-P was maintained by continuous infusion of ketamine and pentobarbital, and pentobarbital was withdrawn after cardiopulmonary bypass started. Group P received a continuous infusion of pentobarbital throughout the operation. In addition, both groups were injected hourly with midazolam and pipecuronium bolus to achieve optimal surgical conditions. RESULTS: All of the group K-P animals survived for 24 h postoperatively. Five of the group P animals died from anaesthesia-related respiratory and cardiac arrest: three after induction and two after extubation. The animals in group K-P had more stable haemodynamics and arterial blood gas indices than animals in group P. Furthermore, the percentage of animals achieving satisfactory anaesthetic effects was significantly higher in group K-P than in group P. CONCLUSION: Combination anaesthesia with low-dose ketamine and pentobarbital demonstrated superior haemodynamic and respiratory indices in comparison with pentobarbital. The combination regimen can achieve both hypnosis and analgesia effects with stable circulatory parameters.

Comparison of ketamine-pentobarbital anesthesia and fentanyl-pentobarbital anesthesia for open-heart surgery in minipigs.

The authors analyzed and compared the cardiovascular effects of two anesthetic combinations in minipigs undergoing open-heart surgery and cardiopulmonary bypass. Pigs in group K (n = 15) were anesthetized with low-dose ketamine combined with pentobarbital (5 mg per kg and 20 mg per kg, respectively, for induction; continuous intravenous infusion of 5 mg per kg per h and 10 mg per kg per h, respectively, for maintenance). Pigs in group F (n = 15) were treated with fentanyl and pentobarbital (20 microg per kg and 20 mg per kg, respectively, for induction; continuous intravenous infusion of 20 microg per kg per h and 10 mg per kg per h, respectively, for maintenance). Most pigs remained stable during the surgical procedures and survived for at least one day after surgery; two pigs in group F died during or soon after surgery. Heart rate and mean arterial pressure after bypass were significantly lower in group F than in group K, and pigs in group F required higher doses of inotropic agents to maintain cardiac function. Results suggest that for open-heart surgery and bypass in minipigs, ketamine-pentobarbital anesthesia is associated with more stable cardiovascular conditions than is fentanyl-pentobarbital anesthesia.