Effects of Lactobacillus acidophilus AC on the growth, intestinal flora and metabolism of zebrafish (Danio rerio).

The intensive aquaculture model has resulted in a heightened prevalence of diseases among farmed animals. It is imperative to identify healthy and efficacious alternatives to antibiotics for the sustainable progression of aquaculture. In this investigation, a strain of Lactobacillus acidophilus AC was introduced into the cultural water at varying concentrations (105 CFU/mL, 106 CFU/mL, 107 CFU/mL) to nourish zebrafish (Danio rerio). The findings revealed that L. acidophilus AC effectively increased the growth performance of zebrafish, improved the ion exchange capacity of gills, and enhanced hepatic antioxidant and immune-enzyme activities. Furthermore, L. acidophilus AC notably enhanced the intestinal morphology and augmented the activity of digestive enzymes within the intestinal tract. Analysis of intestinal flora revealed that L. acidophilus AC exerted a significant impact on the intestinal flora community, manifested by a reduction in the relative abundance of Burkholderiales, Candidatus_Saccharibacteria_bacterium, and Sutterellaceae, coupled with an increase in the relative abundance of Cetobacterium. Metabolomics analysis demonstrated that L. acidophilus AC significantly affected intestinal metabolism of zebrafish. PG (i-19:0/PGE2) and 12-Hydroxy-13-O-d-glucuronoside-octadec-9Z-enoate were the metabolites with the most significant up- and down-regulation folds, respectively. Finally, L. acidophilus AC increased the resistance of zebrafish to Aeromonas hydrophila. In conclusion, L. acidophilus AC was effective in enhancing the health and immunity of zebrafish. Thus, our findings suggested that L. acidophilus AC had potential applications and offered a reference for its use in aquaculture.

[Corona virus disease 2019 lesion segmentation network based on an adaptive joint loss function].

Corona virus disease 2019 (COVID-19) is an acute respiratory infectious disease with strong contagiousness, strong variability, and long incubation period. The probability of misdiagnosis and missed diagnosis can be significantly decreased with the use of automatic segmentation of COVID-19 lesions based on computed tomography images, which helps doctors in rapid diagnosis and precise treatment. This paper introduced the level set generalized Dice loss function (LGDL) in conjunction with the level set segmentation method based on COVID-19 lesion segmentation network and proposed a dual-path COVID-19 lesion segmentation network (Dual-SAUNet++) to address the pain points such as the complex symptoms of COVID-19 and the blurred boundaries that are challenging to segment. LGDL is an adaptive weight joint loss obtained by combining the generalized Dice loss of the mask path and the mean square error of the level set path. On the test set, the model achieved Dice similarity coefficient of (87.81 ± 10.86)%, intersection over union of (79.20 ± 14.58)%, sensitivity of (94.18 ± 13.56)%, specificity of (99.83 ± 0.43)% and Hausdorff distance of 18.29 ± 31.48 mm. Studies indicated that Dual-SAUNet++ has a great anti-noise capability and it can segment multi-scale lesions while simultaneously focusing on their area and border information. The method proposed in this paper assists doctors in judging the severity of COVID-19 infection by accurately segmenting the lesion, and provides a reliable basis for subsequent clinical treatment.

[Research progress in lung parenchyma segmentation based on computed tomography].

Lung diseases such as lung cancer and COVID-19 seriously endanger human health and life safety, so early screening and diagnosis are particularly important. computed tomography (CT) technology is one of the important ways to screen lung diseases, among which lung parenchyma segmentation based on CT images is the key step in screening lung diseases, and high-quality lung parenchyma segmentation can effectively improve the level of early diagnosis and treatment of lung diseases. Automatic, fast and accurate segmentation of lung parenchyma based on CT images can effectively compensate for the shortcomings of low efficiency and strong subjectivity of manual segmentation, and has become one of the research hotspots in this field. In this paper, the research progress in lung parenchyma segmentation is reviewed based on the related literatures published at domestic and abroad in recent years. The traditional machine learning methods and deep learning methods are compared and analyzed, and the research progress of improving the network structure of deep learning model is emphatically introduced. Some unsolved problems in lung parenchyma segmentation were discussed, and the development prospect was prospected, providing reference for researchers in related fields.

Antipsychotic Drug Trifluoperazine Suppresses Colorectal Cancer by Inducing G0/G1 Arrest and Apoptosis.

Repurposing existing drugs for cancer treatment is an effective strategy. An approved antipsychotic drug, trifluoperazine (TFP), has been reported to have potential anticancer effects against several cancer types. Here, we investigated the effect and molecular mechanism of TFP in colorectal cancer (CRC). In vitro studies showed that TFP induced G0/G1 cell cycle arrest to dramatically inhibit CRC cell proliferation through downregulating cyclin-dependent kinase (CDK) 2, CDK4, cyclin D1, and cyclin E and upregulating p27. TFP also induced apoptosis, decreased mitochondrial membrane potential, and increased reactive oxygen species levels in CRC cells, indicating that TFP induced mitochondria-mediated intrinsic apoptosis. Importantly, TFP significantly suppressed tumor growth in two CRC subcutaneous tumor models without side effects. Interestingly, TFP treatment increased the expression levels of programmed death-1 ligand 1 (PD-L1) in CRC cells and programmed death-1 (PD-1) in tumor-infiltrating CD4+ and CD8+ T cells, implying that the combination of TFP with an immune checkpoint inhibitor, such as an anti-PD-L1 or anti-PD-1 antibody, might have synergistic anticancer effects. Taken together, our study signifies that TFP is a novel treatment strategy for CRC and indicates the potential for using the combination treatment of TFP and immune checkpoint blockade to increase antitumor efficiency.

The antipsychotic agent trifluoperazine hydrochloride suppresses triple-negative breast cancer tumor growth and brain metastasis by inducing G0/G1 arrest and apoptosis.

Women with aggressive triple-negative breast cancer (TNBC) are at high risk of brain metastasis, which has no effective therapeutic option partially due to the poor penetration of drugs across the blood-brain barrier. Trifluoperazine (TFP) is an approved antipsychotic drug with good bioavailability in brain and had shown anticancer effect in several types of cancer. It drives us to investigate its activities to suppress TNBC, especially the brain metastasis. In this study, we chose three TNBC cell lines MDA-MB-468, MDA-MB-231, and 4T1 to assess its anticancer activities along with the possible mechanisms. In vitro, it induced G0/G1 cell cycle arrest via decreasing the expression of both cyclinD1/CDK4 and cyclinE/CDK2, and stimulated mitochondria-mediated apoptosis. In vivo, TFP suppressed the growth of subcutaneous xenograft tumor and brain metastasis without causing detectable side effects. Importantly, it prolonged the survival of mice bearing brain metastasis. Immunohistochemical analysis of Ki67 and cleaved caspase-3 indicated TFP could suppress the growth and induce apoptosis of cancer cells in vivo. Taken together, TFP might be a potential available drug for treating TNBC with brain metastasis, which urgently needs novel treatment options.