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This meta-analysis examines whether differences in biomarkers, such as programmed cell death ligand 1 (PD-L1) expression and tumor mutation burden, are associated with improved response to immunotherapy and survival in patients with advanced non–small cell lung cancer (NSCLC).

Nitric oxide (NO) enhances human sperm motility and capacitation associated with increased protein phosphorylation. NO activates soluble guanylyl cyclase, but can also modify protein function covalently via S-nitrosylation of cysteine. Remarkably, this mechanism remains unexplored in sperm although they depend on post-translational protein modification to achieve changes in function required for fertilisation. Our objective was to identify targets for Snitrosylation in human sperm. Spermatozoa were incubated with NO donors and S-nitrosylated proteins were identified using the biotin switch assay and a proteomic approach using tandem mass spectrometry. 240 S-nitrosylated proteins were detected in sperm incubated with Snitrosoglutathione. Minimal levels were observed in glutathione or untreated samples. Proteins identified consistently based on multiple peptides included established targets for S-nitrosylation in other cells e.g. tubulin,, glutathione-S-transferase and heat shock proteins but also novel targets including A-kinase anchoring protein (AKAP) types 3 and 4, voltage-dependent anion-selective channel protein 3 and semenogelin 1 and 2. In situ localisation revealed S-nitrosylated targets on the post-acrosomal region of the head and throughout the flagellum. Potential targets for Snitrosylation in human sperm include physiologically significant proteins not previously reported in other cells. Their identification will provide novel insight into the mechanism of action of NO in spermatozoa.

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BackgroundIn this study, images of 2450 benign thyroid nodules and 2557 malignant thyroid nodules were collected and labeled, and an automatic image recognition and diagnosis system was established by deep learning using the YOLOv2 neural network. The performance of the system in the diagnosis of thyroid nodules was evaluated, and the application value of artificial intelligence in clinical practice was investigated.MethodsThe ultrasound images of 276 patients were retrospectively selected. The diagnoses of the radiologists were determined according to the Thyroid Imaging Reporting and Data System; the images were automatically recognized and diagnosed by the established artificial intelligence system. Pathological diagnosis was the gold standard for the final diagnosis. The performances of the established system and the radiologists in diagnosing the benign and malignant thyroid nodules were compared.ResultsThe artificial intelligence diagnosis system correctly identified the lesion area, with an area under the receiver operating characteristic (ROC) curve of 0.902, which is higher than that of the radiologists (0.859). This finding indicates a higher diagnostic accuracy (p = 0.0434). The sensitivity, positive predictive value, negative predictive value, and accuracy of the artificial intelligence diagnosis system for the diagnosis of malignant thyroid nodules were 90.5%, 95.22%, 80.99%, and 90.31%, respectively, and the performance did not significantly differ from that of the radiologists (p > 0.05). The artificial intelligence diagnosis system had a higher specificity (89.91% vs 77.98%, p = 0.026).ConclusionsCompared with the performance of experienced radiologists, the artificial intelligence system has comparable sensitivity and accuracy for the diagnosis of malignant thyroid nodules and better diagnostic ability for benign thyroid nodules. As an auxiliary tool, this artificial intelligence diagnosis system can provide radiologists with sufficient assistance in the diagnosis of benign and malignant thyroid nodules.

Microbes, similar to plants and animals, exhibit biogeographic patterns. However, in contrast with the considerable knowledge on the island biogeography of higher organisms, we know little about the distribution of microorganisms within and among islands. Here, we explored insular soil bacterial and fungal biogeography and underlying mechanisms, using soil microbiota from a group of land-bridge islands as a model system. Similar to island species-area relationships observed for many macroorganisms, both island-scale bacterial and fungal diversity increased with island area; neither diversity, however, was affected by island isolation. By contrast, bacterial and fungal communities exhibited strikingly different assembly patterns within islands. The loss of bacterial diversity on smaller islands was driven primarily by the systematic decline of diversity within samples, whereas the loss of fungal diversity on smaller islands was driven primarily by the homogenization of community composition among samples. Lower soil moisture limited within-sample bacterial diversity, whereas smaller spatial distances among samples restricted among-sample fungal diversity, on smaller islands. These results indicate that among-island differences in habitat quality generate the bacterial island species-area relationship, whereas within-island dispersal limitation generates the fungal island species-area relationship. Together, our study suggests that different mechanisms underlie similar island biogeography patterns of soil bacteria and fungi.

Autophagy is a complicated cellular mechanism that maintains cellular and tissue homeostasis and integrity via degradation of senescent, defective subcellular organelles, infectious agents, and misfolded proteins. Accumulating evidence has shown that autophagy is involved in numerous immune processes, such as removal of intracellular bacteria, cytokine production, autoantigen presentation, and survival of lymphocytes, indicating an apparent and important role in innate and adaptive immune responses. Indeed, in genome-wide association studies, autophagy-related gene polymorphisms have been suggested to be associated with the pathogenesis of several autoimmune and inflammatory disorders, such as systemic lupus erythematosus, psoriasis, rheumatoid arthritis, inflammatory bowel disease, and multiple sclerosis. In addition, conditional knockdown of autophagy-related genes in mice displayed therapeutic effects on several autoimmune disease models by reducing levels of inflammatory cytokines and autoreactive immune cells. However, the inhibition of autophagy accelerates the progress of some inflammatory and autoimmune diseases via promotion of inflammatory cytokine production. Therefore, this review will summarize the current knowledge of autophagy in immune regulation and discuss the therapeutic and pathogenic role of autophagy in autoimmune diseases to broaden our understanding of the etiopathogenesis of autoimmune diseases and shed light on autophagy-mediated therapies.

Key Points Question Can multiparametric magnetic resonance imaging (MRI) radiomic profiles be used to predict axillary lymph node metastasis (ALNM) and disease-free survival (DFS) in patients with early-stage breast cancer? Findings In this prognostic study that included 1214 patients, 2 clinical-radiomic nomograms were developed that accurately predicted ALNM and stratified patients into low-risk and high-risk groups for DFS. Meaning In this study, clinical-radiomic nomograms were useful in clinical decision-making associated with personalized selection of surgical interventions and therapeutic regimens for patients with early-stage breast cancer.