Requirement of endogenous tumor necrosis factor/cachectin for recovery from experimental peritonitis

By intrasplenic immunization we raised a rat mAb (mAb V1q; IgG2a, kappa) with a potent neutralizing activity against natural mouse TNF (1 microgram/ml mAb V1q/100 U/ml TNF). mAb V1q was used to study the role of endogenous TNF in experimental peritonitis induced by sublethal cecal ligation and puncture. mAb V1q persisted for over 5 days in the serum of mice injected with 100 micrograms of the antibody and, therefore, proved useful for in vivo experiments. As little as 20 micrograms mAb V1q/mouse prevented lethal shock of the animals by 400 micrograms LPS/mouse. In sublethal cecal ligation and puncture i.p. injection of mAb V1q directly and up to 8 h after induction of experimental peritonitis lead to death of the animals within 1 to 3 days. The lethal effect of mAb V1q was compensated by injection of recombinant mouse TNF. Similar mAb V1q effects as in immunocompetent mice were shown in severe combined immune deficiency mice deficient of mature functional B and T cells. Taken together, these data suggest that during the early phase of peritonitis endogenous TNF may stimulate nonlymphoid cells such as granulocytes, macrophages, platelets, and fibroblasts to ingest bacteria and to localize inflammation, respectively. These beneficial effects of TNF may determine survival. Thus, our data may have implications for the therapeutic management of a beginning peritonitis

Induction of IL 2 receptor expression and cytotoxicity of thymocytes by stimulation with TCF1

We investigated the role of T cell cytotoxicity inducing factor 1 (TCF1) in the induction of a cytotoxic T cell response. We found that help-deficient thymocyte cultures supplied with saturating amounts of purified IL 2 did not develop CTL in a 5-day culture. The expression of cytotoxicity was dependent on the addition of TCF1 derived from the T cell hybridoma K15. TCF1 also induced proliferation of thymocytes in the presence of IL 2. Only the PNA- thymocyte subpopulation responded to TCF1 with proliferation and cytotoxicity in the presence of IL 2. The monokine IL 1 also induced proliferation in this subpopulation but failed to induce cytotoxicity. IL 1 was further distinguished from TCF1 by inhibition of IL 1-induced but not TCF1-induced proliferation by anti-IL 1 antibodies. In addition, using anti-IL 2 receptor antibodies (AMT 13), we showed that TCF1 in the presence of IL 2 substantially increased IL 2 receptor expression in thymocytes. IL 1 had the same effect on induction of IL 2 receptor expression as TCF1. Because some effects of IL 1 and TCF1 are distinct and some overlap, we discuss whether IL 1 and TCF1 induce different subsets of PNA- thymocytes

Influenza Virus Infection in Guinea Pigs Raised as Livestock, Ecuador

To determine whether guinea pigs are infected with influenza virus in nature, we conducted a serologic study in domestic guinea pigs in Ecuador. Detection of antibodies against influenza A and B raises the question about the role of guinea pigs in the ecology and epidemiology of influenza virus in the region

Joint-Individual Fusion Structure with Fusion Attention Module for Multi-Modal Skin Cancer Classification

Most convolutional neural network (CNN) based methods for skin cancer classification obtain their results using only dermatological images. Although good classification results have been shown, more accurate results can be achieved by considering the patient's metadata, which is valuable clinical information for dermatologists. Current methods only use the simple joint fusion structure (FS) and fusion modules (FMs) for the multi-modal classification methods, there still is room to increase the accuracy by exploring more advanced FS and FM. Therefore, in this paper, we design a new fusion method that combines dermatological images (dermoscopy images or clinical images) and patient metadata for skin cancer classification from the perspectives of FS and FM. First, we propose a joint-individual fusion (JIF) structure that learns the shared features of multi-modality data and preserves specific features simultaneously. Second, we introduce a fusion attention (FA) module that enhances the most relevant image and metadata features based on both the self and mutual attention mechanism to support the decision-making pipeline. We compare the proposed JIF-MMFA method with other state-of-the-art fusion methods on three different public datasets. The results show that our JIF-MMFA method improves the classification results for all tested CNN backbones and performs better than the other fusion methods on the three public datasets, demonstrating our method's effectiveness and robustnessComment: submitted to Pattern Recognition journal before 202