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Diazotrophic cyanobacteria can fix nitrogen gas (N2) but are usually scarce in nitrogen-limited coastal waters, which poses an apparent ecological paradox. One hypothesis is that high salinities (> 10 g/L NaCl) may inhibit cyanobacterial N2 fixation. However, here we show that N2 fixation in a unicellular coastal cyanobacterium exclusively depends on sodium ions and is inhibited at low NaCl concentrations (< 4 g/L). In the absence of Na+, cells of Cyanothece sp. ATCC 51142 (recently reclassified as Crocosphaera subtropica) upregulate the expression of nifHDK genes and synthesise a higher amount of nitrogenase, but do not fix N2 and do not grow. We find that the loss of N2-fixing ability in the absence of Na+ is due to insufficient ATP supply. Additional experiments suggest that N2 fixation in this organism is driven by sodium energetics and mixed-acid fermentation, rather than proton energetics and aerobic respiration, even though cells were cultured aerobically. Further work is needed to clarify the underlying mechanisms and whether our findings are relevant to other coastal cyanobacteria. High salinities are thought to inhibit nitrogen fixation by cyanobacteria in coastal waters. In contrast, Tang et al. show that nitrogen fixation in a coastal cyanobacterium requires sodium ions and is apparently driven by sodium energetics and mixed-acid fermentation. [ABSTRACT FROM AUTHOR]

Background: Correctly distinguishing between benign and malignant pulmonary nodules can avoid unnecessary invasive procedures. This study aimed to construct a deep learning radiomics clinical nomogram (DLRCN) for predicting malignancy of pulmonary nodules. Methods: One thousand and ninety‐eight patients with 6–30 mm pulmonary nodules who received histopathologic diagnosis at 3 centers were included and divided into a primary cohort (PC), an internal test cohort (I‐T), and two external test cohorts (E‐T1, E‐T2). The DLRCN was built by integrating adipose tissue radiomics features, intranodular and perinodular deep learning features, and clinical characteristics for diagnosing malignancy of pulmonary nodules. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. The performance of DLRCN was assessed with respect to its calibration curve, area under the curve (AUC), and decision curve analysis (DCA). Furthermore, we compared it with three radiologists. The net reclassification improvement (NRI), integrated discrimination improvement (IDI), and subgroup analysis were also taken into account. Results: The incorporation of adipose tissue radiomics features led to significant NRI and IDI (NRI = 1.028, p < 0.05, IDI = 0.137, p < 0.05). In the I‐T, E‐T1, and E‐T2, the AUCs of DLRCN were 0.946 (95% CI: 0.936, 0.955), 0.948 (95% CI: 0.933, 0.963) and 0.962 (95% CI: 0.945, 0.979), The calibration curve revealed good predictive accuracy between the actual probability and predicted probability (p > 0.05). DCA showed that the DLRCN was clinically useful. Under equal specificity, the sensitivity of DLRCN increased by 8.6% compared to radiologist assessments. The subgroup analysis conducted on adipose tissue radiomics features further demonstrated their supplementary value in determining the malignancy of pulmonary nodules. Conclusion: The DLRCN demonstrated good performance in predicting the malignancy of pulmonary nodules, which was comparable to radiologist assessments. The adipose tissue radiomics features have notably enhanced the performance of DLRCN. [ABSTRACT FROM AUTHOR]

Triple‐negative breast cancer (TNBC), a disease with a poor prognosis and high mortality, is difficult to targeted therapy, due to the metastatic nature of TNBC cells and their lack of estrogen, progesterone, and Her‐2/Neu receptor targets. Here, a novel pretargeting strategy involving self‐assembled peptides and a bioorthogonal reaction are described. This process generates nanofibers on TNBC cell surface that prevent them migration and invasion and deliver untargeted anticancer drugs (e.g., doxorubicin) that synergistically eliminates TNBC cells. The self‐assembled peptide contains four modules: i) an aromatic group‐rich tetraphenylethylene (TPE) unit for promoting self‐assembly and nanofiber formation, ii) a GFFY peptide motif that supports self‐assembly of nanofibers into peptide scaffolds, iii) a targeting RGD motif that binding with the abundant integrin molecules present on TNBC cell surface, and iv) an azide group acting as the bioorthogonal reaction site that reacts with drugs or probes containing the dibenzocyclooctyne (DBCO) group. Results of in vitro and in vivo experiments indicate that the self‐assembled peptide inhibits tumor invasion and metastasis, while also pretargeting and killing TNBC cells with superior effectiveness and lower toxicity to that of current chemotherapies. In conclusion, this novel bioorthogonal pretargeting strategy holds promise as an effective anticancer therapeutic approach. [ABSTRACT FROM AUTHOR]