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The COVID-19 pandemic continues to influence every aspect of human life across the globe. It was reported that vascular angiogenesis of COVID-19 was elevated in patients with equally severe influenza virus infection. In this issue of AJNMMI, Farolfi et al. reported that there was lung uptake not related to prostate cancer in almost all COVID-19 patients who performed (68)Ga-PSMA-11 PET/CT scans and most of the lung uptake lesions were matched with typical CT patterns of COVID-19. With the advantages of having various tracers for whole-body imaging, PET provides opportunities to study the mechanism of COVID-19 from different aspects and obtain patterns of extrapulmonary lesions in COVID-19, which helps explore more effective treatments for the patients. This case series opened the door to many future studies. Furthermore, such a multi-national/multi-institutional collaboration in the pandemic truly encouraged us that science is indeed without borders.

This perspective briefly reviewed the applications of (18)F-FDG PET/CT in the clinical management of lymphoma and the need for lesion segmentation in those applications. It discussed the limitations of existing segmentation technologies and the great potential of using deep learning convolutional neural network (DLCNN) to accomplish automatic lymphoma segmentation and characterizations. Finally, the authors shared perspectives on the technical challenges that need to be addressed to fully unleash the potential of DLCNN and (18)F-FDG PET/CT in the diagnosis, prognosis, and treatment of lymphoma.

Additional file 1: Table S1. Characterization of PLGA nanoparticles (PLGA NPs), DDAB/PLGA NPs, and DDAB/PLGA Nano-vaccines (OVA@DDAB/PLGA Nv) (Mean �� STD). Figure S1. Cytotoxicity of OVA@DDAB/PLGA Nv. Figure S2. Expression of co-stimulatory molecules from DCs stimulated with different formulations. (a) the gating strategies of flow cytometry of activated DC cells. (b-d) Percentages of CD40+ CD11c+ (b), CD86+ CD11c+ (c) and MHC II+ CD11c+ (d) cells were analyzed by flow cytometry. Figure S3. (a) The changes of p38 MAPK, p-AKT and p-ERK phosphorylation after being stimulated with different formulation DCs for 6 h and 12 h. (b) Change of the p38 MAPK, p-AKT and p-ERK phosphorylation level by stimulated DCs with different concentration of DDAB-PLGA Nv. (c) DDAB-PLGA Nv increased the binding of MKK3 to its substrate p38��. Figure S4. The radiochemical purity of [89Zr]-Df-Bz-NCS-OVA incubated at room temperature, saline (37���) and fresh serum (37���) over the course of 14 days. Figure S5. The proportion of antigen-carrying cells in different LNs as analyzed by flow cytometry. Three mice were analyzed in every group (n = 3), and data are the mean �� SEM and representative of three independent experiments. Differences between two groups were tested using an unpaired, two-tailed Student���s t-test. Differences among multiple groups were tested with one-way ANOVA followed by Tukey���s multiple comparison. Significant differences between groups are expressed as follows: *P < 0.05, **P < 0.01, or ***P < 0.001. Figure S6. The proportion of antigen-carrying cells in different LNs as analyzed by immumohistochemical staining. The data were analyzed by automatic multispectral imaging system (PerkinElmer Vectra II). Three mice were analyzed in every group (n = 3), and data are the mean �� SEM and representative of three independent experiments. Differences between two groups were tested using an unpaired, two-tailed Student���s t-test. Differences among multiple groups were tested with one-way ANOVA followed by Tukey���s multiple comparison. Significant differences between groups are expressed as follows: *P < 0.05, **P < 0.01, or ***P < 0.001. Figure S7. Activation and maturation of BMDCs in LNs in vivo. (a���c) Expression of activation markers (CD86, MHC I and MHC II) of DCs in draining LNs.Three mice were analyzed in every group (n = 3), and data are the mean �� SEM and representative of three independent experiments. Differences between two groups were tested using an unpaired, two-tailed Student���s t-test. Differences among multiple groups were tested with one-way ANOVA followed by Tukey���s multiple comparison. Significant differences between groups are expressed as follows: *P < 0.05, **P < 0.01, or ***P < 0.001. Figure S8. The OVA@DDAB/PLGA Nv induced the formation of germinal centers in draining LNs. (a) The gating strategies of flow cytometry of germinal center and follicular helper CD4+ T cells. (b) The count of germinal center (GL-7hi B220+ cells) and (c) the follicular helper CD4+ T cells (Tfh, CXCR5hi PD-1hi CD4+ T cells) in draining LNs were analyzed by flow cytometry. Figure S9. Germinal centers in draining LNs determined by immunohistochemical staining. The data were analyzed by automatic multispectral imaging system (PerkinElmer Vectra II). Three mice were analyzed in every group (n = 3), and data are the mean �� SEM and representative of three independent experiments. Differences between two groups were tested using an unpaired, two-tailed Student���s t-test. Differences among multiple groups were tested with one-way ANOVA followed by Tukey���s multiple comparison. Significant differences between groups are expressed as follows: *P < 0.05, **P < 0.01, or ***P < 0.001. Figure S10. OVA@DDAB/PLGA Nv induced splenocyte activation. (a) The gating strategies of flow cytometry of splenocyte activation. (b, c, d) The activation of CD4+ T cells (b) CD8+ T cells (c) and B cells (d) from mice after being immunized with different vaccine formulations were measured by flow cytometry. Figure S11. Effects of different vaccines on memory T cell responses. (a) The gating strategies of flow cytometry of memory T cell. (b) Effector memory (CD44hi CD62Llow) andcentral memory (CD44hi CD62Lhi) in CD4+ and CD8+ T cells were measured by flow cytometry. Figure S12. Effects of different vaccines on CTL response. (a) The gating strategies of flow cytometry of CTL cells. The expression of Perforin (b), CD107 (c), and FasL (d) on CD8+ T cell in splenocytes were measured by flow cytometry. Figure S13. In vivo toxicity evaluation of DDAB-PLGA Nv. Hematological analysis of treated mice after 35 days. The range marked by dotted lines represents the normal range of different biosafety indicators. The determination of serum biochemistry of urea nitrogen (BUN) (a), aspartate transaminase (AST) (b), alanine aminotransferase (ALT) (c), alkaline phosphatase (ALP) (d), and lactate dehydrogenase (LDH). Three mice were analyzed in every group (n = 3), and data are the mean �� SEM and representative of three independent experiments. Differences between two groups were tested using an unpaired, two-tailed Student���s t-test. Differences among multiple groups were tested with one-way ANOVA followed by Tukey���s multiple comparison. Significant differences between groups are expressed as follows: *P < 0.05, **P < 0.01, or ***P < 0.001.