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BackgroundChimeric antigen receptor-modified (CAR) T-cell immunotherapy is a novel promising therapy for treatment of B-cell malignancy. Cytokine release syndrome (CRS) and infection are the most common adverse events during CAR T-cell therapy. Similar clinical presentation of concurrent CRS and infection makes it difficult to differentially diagnose and timely treat the condition.MethodsWe analyzed the features of infection events during the first 30 days after CAR T-cell infusion (CTI) in 109 patients from three clinical trials (ChiCTR-OPN-16008526, ChiCTR-OPC-16009113, ChiCTR-OPN-16009847). Based on the dynamic changes of interleukin (IL)-6 and ferritin, we proposed the “double peaks of IL-6” pattern as a feature of life-threatening infection during the first 30 days after CTI. Meanwhile, we screened candidate biomarkers from 70-biomarker panel to establish a prediction model for life-threatening infection.ResultsIn this study, 19 patients (17.4%) experienced a total of 19 infection events during the first 30 days after CAR T-cell infusion. Eleven patients (10.1%) had grade 4–5 infection, which were all bacterial infection and predominantly sepsis (N = 9). “Double peaks of IL-6” appeared in 9 out of 11 patients with life-threatening infection. The prediction model of three-cytokines (IL-8, IL-1β and interferon-γ) could predict life-threatening infection with high sensitivity (training: 100.0%; validation: 100.0%) and specificity (training: 97.6%; validation: 82.8%). On base of the aforementioned methods, we proposed a workflow for quick identification of life-threatening infection during CAR T-cell therapy.ConclusionsIn this study, we worked out two diagnostic methods for life-threatening infection during CAR T-cell therapy by analyzing inflammatory signatures, which contributed to reducing risks of infection-induced death.

Background: This meta-analysis evaluates the difference of sparing organs at risk (OAR) in different position (Prone position and Supine position) with different breathing patterns (Free breathing, FB/Deep inspiration breath hold, DIBH) for breast cancer patients receiving postoperative radiotherapy and provides a useful reference for clinical practice. Method: The relevant controlled trials of prone position versus supine position in postoperative radiotherapy for breast cancer were retrieved from the sources of PubMed, Cochrane Library, Embase, Web of Science and ClinicalTrails.gov. The principal outcome of interest was OAR doses (heart dose, left anterior descending coronary artery dose and ipsilateral lung dose) and target coverage. We mainly compared the effects of P-FB (Prone position FB) and S-FB (Supine position FB) and discussed the effects of DIBH combined with different positions on OAR dose in postoperative radiotherapy. We calculated summary standardized mean difference (SMD) and 95% confidence intervals (CI). The meta-analysis was performed using RevMan 5.4 software. Results: The analysis included 751 patients from 19 observational studies. Compared with the S-FB, the P-FB can have lower heart dose, left anterior descending coronary artery (LADCA) dose, and ipsilateral lung dose (ILL) more effectively, and the difference was statistically significant (heart dose, SMD = − 0.51, 95% CI − 0.66 ∼ − 0.36, P < .00001. LADCA dose, SMD = − 0.58, 95% CI – 0.85 ∼ − 0.31, P < .0001. ILL dose, SMD = − 2.84, 95% CI − 3.2 ∼ − 2.48, P < .00001). And there was no significant difference in target coverage between the S-FB and P-FB groups (SMD = − 0.1, 95% CI − 0.57 ∼ 0.36, P = .66). Moreover, through descriptive analysis, we found that P-DIBH (Prone position DIBH) has better sparing OAR than P-FB and S-DIBH (Supine position DIBH). Conclusion: By this meta-analysis, compared with the S-FB we found that implementation of P-FB in postoperative radiotherapy for breast cancer can reduce irradiation of heart dose, LADCA dose and ILL dose, without compromising mean dose of target coverage. Moreover, P-DIBH might become the most promising way for breast cancer patients to undergo radiotherapy.

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Mangrove actinomycetia are considered one of the promising sources for discovering novel biologically active compounds. Traditional bioactivity- and/or taxonomy-based methods are inefficient and usually result in the re-discovery of known metabolites. Thus, improving selection efficiency among strain candidates is of interest especially in the early stage of the antibiotic discovery program. In this study, an integrated strategy of combining phylogenetic data and bioactivity tests with a metabolomics-based dereplication approach was applied to fast track the selection process. A total of 521 actinomycetial strains affiliated to 40 genera in 23 families were isolated from 13 different mangrove soil samples by the culture-dependent method. A total of 179 strains affiliated to 40 different genera with a unique colony morphology were selected to evaluate antibacterial activity against 12 indicator bacteria. Of the 179 tested isolates, 47 showed activities against at least one of the tested pathogens. Analysis of 23 out of 47 active isolates using UPLC-HRMS-PCA revealed six outliers. Further analysis using the OPLS-DA model identified five compounds from two outliers contributing to the bioactivity against drug-sensitive A. baumannii. Molecular networking was used to determine the relationship of significant metabolites in six outliers and to find their potentially new congeners. Finally, two Streptomyces strains (M22, H37) producing potentially new compounds were rapidly prioritized on the basis of their distinct chemistry profiles, dereplication results, and antibacterial activities, as well as taxonomical information. Two new trioxacarcins with keto-reduced trioxacarcinose B, gutingimycin B (16) and trioxacarcin G (20), together with known gutingimycin (12), were isolated from the scale-up fermentation broth of Streptomyces sp. M22. Our study demonstrated that metabolomics tools could greatly assist classic antibiotic discovery methods in strain prioritization to improve efficiency in discovering novel antibiotics from those highly productive and rich diversity ecosystems.