A novel multitrophic biofloc technology for duckweed and Megalobrama amblycephala integrated culture: Improving nutrient utilization and animal welfare.

Biofloc technology (BFT) is an eco-friendly aquaculture model that utilizes zero-exchange water. In this study, we investigated the integration of duckweed into BFT in an effort to enhance nitrogen, phosphorus, and carbon utilization and to improve animal welfare for cultivating Megalobrama amblycephala. The experiment spanned 75 days, comparing a group of M. amblycephala supplemented with duckweed (DM) to a control group (CG) with no supplementation, where duckweed consumption relied solely on the feeding behavior of the fish. The concentrations of nitrate, total nitrogen, and phosphorus accumulation were lower in the DM than in the CG from day 45 onwards, with differences of 16.19, 26.90, and 1.45 mg/L, respectively, at the end of the experiment. The DM showed simultaneous increases of 5.77, 11.20, and 5.07 % in the absolute utilization of nitrogen, phosphorus, and carbon, respectively. The abundance of TM7a (10.27 %), linked to nitrate absorption, became the dominant genus in the water of the DM. Additionally, the abundance of Cetobacterium, associated with carbohydrate digestion, was significantly higher in gut of the DM (23.83 %) than in the gut of CG (1.24 %, P < 0.05). Supplementing the diet of M. amblycephala with duckweed improved digestion and antioxidant enzyme activity. Transcriptome data showed that duckweed supplementation resulted in an increase in the expression of genes related to protein digestion and absorption and carbohydrate metabolism in M. amblycephala, and analysis of the significantly enriched pathways further supported improved antioxidant capacity. Based on the above results, we concluded that as M. amblycephala consumes more duckweed, the differences in nitrogen and phosphorus levels between the DM and CG would continue to increase, along with a simultaneous increase in fixed carbon. Thus, this study achieved the goal of recycling BFT resources and improving animal welfare by integrating duckweed.

Study on Molecular Information Intelligent Diagnosis and Treatment of Bladder Cancer on Pathological Tissue Image.

Background: Molecular information about bladder cancer is significant for treatment and prognosis. The immunohistochemistry (IHC) method is widely used to analyze the specific biomarkers to determine molecular subtypes. However, procedures in IHC and plenty of reagents are time and labor-consuming and expensive. This study established a computer-aid diagnosis system for predicting molecular subtypes, p53 status, and programmed death-ligand 1 (PD-L1) status of bladder cancer with pathological images. Materials and Methods: We collected 119 muscle-invasive bladder cancer (MIBC) patients who underwent radical cystectomy from January 2016 to September 2018. All the pathological sections are scanned into digital whole slide images (WSIs), and the IHC results of adjacent sections were recorded as the label of the corresponding slide. The tumor areas are first segmented, then molecular subtypes, p53 status, and PD-L1 status of those tumor-positive areas would be identified by three independent convolutional neural networks (CNNs). We measured the performance of this system for predicting molecular subtypes, p53 status, and PD-L1 status of bladder cancer with accuracy, sensitivity, and specificity. Results: For the recognition of molecular subtypes, the accuracy is 0.94, the sensitivity is 1.00, and the specificity is 0.909. For PD-L1 status recognition, the accuracy is 0.897, the sensitivity is 0.875, and the specificity is 0.913. For p53 status recognition, the accuracy is 0.846, the sensitivity is 0.857, and the specificity is 0.750. Conclusion: Our computer-aided diagnosis system can provide a novel and simple assistant tool to obtain the molecular subtype, PD-L1 status, and p53 status. It can reduce the workload of pathologists and the medical cost.