Tumor-associated macrophages (TAMs) play an important role in the progression and prognostication of numerous cancers. However, the role and clinical significance of TAM markers in oral squamous cell carcinoma (OSCC) has not been elucidated. The present study was designed to investigate the correlation between the expression of TAM markers and pathological features in OSCC by tissue microarray. Tissue microarrays containing 16 normal oral mucosa, 6 oral epithelial dysplasia, and 43 OSCC specimens were studied by immunohistochemistry. We observed that the protein expression of the TAM markers CD68 and CD163 as well as the cancer stem cell (CSC) markers ALDH1, CD44, and SOX2 increased successively from the normal oral mucosa to OSCC. The expressions of CD68 and CD163 were significantly associated with lymph node status, and SOX2 was significantly correlated with pathological grade and lymph node status, whereas ALDH1 was correlated with tumor stage. Furthermore, CD68 was significantly correlated with CD163, SOX2, and ALDH1 (P < 0.05). Kaplan-Meier analysis revealed that OSCC patients overexpressing CD163 had significantly worse overall survival (P < 0.05). TAM markers are associated with cancer stem cell marker and OSCC overall survival, suggesting their potential prognostic value in OSCC.

The 2019 novel coronavirus, SARS-CoV-2, is an emerging pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors like diabetes. A critical question for treatment and protection is why it appears that the severity of infection may correlate with the initial level of virus exposure. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interactions, screen potential therapies, and identify potential biomarkers that differentiate response dynamics. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce a prototype of a multiscale model of SARS-CoV-2 dynamics in lung and intestinal tissue that will be iteratively refined. The first prototype model was built and shared internationally as open source code and interactive, cloud-hosted executables in under 12 hours. In a sustained community effort, this model will integrate data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health.

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The direct production of light α‐olefins (C2=‐C4=) from CO2 is of great importance as this process can convert the greenhouse gas into the desired chemicals. In this study, the crucial roles of Na and Mn promoter in CO2 hydrogenation to produce light α‐olefins via the Fischer‐Tropsch synthesis (FTS) over Fe3O4‐based catalysts are investigated. The results indicate that both Na and Mn promoter can enhance the reducibility of Fe3O4. In situ XPS and DFT calculations show that Na facilitates the reduction by electron donation from Na to Fe as the oxygen vacancy formation energy is reduced by Na. In contrast, Mn promotes the reduction by the presence of oxygen vacancy in MnO as the oxygen in Fe oxide can spillover to the vacancy in MnO spontaneously. For un‐promoted Fe3O4 catalysts, CO2 hydrogenation dominantly produces light n‐paraffins. The addition of Na remarkably shifts the selectivity to light α‐olefins with a sharp decline in the selectivity to light n‐paraffins, which is attributed to the electron donation from Na to Fe resulting in the promoted CO dissociation and the favorable β‐H abstraction of surface short alkyl‐metal intermediates. The addition of Mn into Na‐containing Fe3O4 catalysts can obviously further enhance the selectivity to light α‐olefins as the spatial hindrance of Mn suppresses the chain growth to increase the amount of surface short alkyl‐metal intermediates.

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BackgroundmicroRNAs are small noncoding RNAs that modulate a variety of cellular processes by regulating multiple targets, which can promote or inhibit the development of malignant behaviors. Accumulating evidence suggests miR-24 plays important roles in human carcinogenesis. However, its precise biological role remains largely elusive. This study examined the role of miR-24 in gastric cancer (GC).MethodsThe expression of miR-24 in GC tissues compared with matched non-tumor tissues and GC cells was detected by qRT-PCR. Synthetic short single or double stranded RNA oligonucleotides and lentiviral vectors were used to regulate miR-24 expression in GC cells to investigate its function in vitro and in vivo.ResultsmiR-24 was significantly downregulated in GC tissues compared with matched non-tumor tissues and was associated with tumor differentiation. Ectopic expression of miR-24 in SGC-7901 GC cells suppressed cell proliferation, migration and invasion in vitro as well as tumorigenicity in vivo by inducing cell cycle arrest in G0/G1 phase and promoting cell apoptosis. Furthermore, we identified RegIV as a target of miR-24 and demonstrated that miR-24 regulated RegIV expression via binding its 3′ untranslated region.ConclusionsmiR-24 functions as a novel tumor suppressor in GC and the anti-oncogenic activity may involve its inhibition of the target gene RegIV. These findings suggest the possibility for miR-24 as a therapeutic target in GC.

BackgroundH. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are essential for understanding the infection mechanism of the formidable pathogen M. tuberculosis H37Rv. Computational prediction is an important strategy to fill the gap in experimental H. sapiens-M. tuberculosis H37Rv PPI data. Homology-based prediction is frequently used in predicting both intra-species and inter-species PPIs. However, some limitations are not properly resolved in several published works that predict eukaryote-prokaryote inter-species PPIs using intra-species template PPIs.ResultsWe develop a stringent homology-based prediction approach by taking into account (i) differences between eukaryotic and prokaryotic proteins and (ii) differences between inter-species and intra-species PPI interfaces. We compare our stringent homology-based approach to a conventional homology-based approach for predicting host-pathogen PPIs, based on cellular compartment distribution analysis, disease gene list enrichment analysis, pathway enrichment analysis and functional category enrichment analysis. These analyses support the validity of our prediction result, and clearly show that our approach has better performance in predicting H. sapiens-M. tuberculosis H37Rv PPIs. Using our stringent homology-based approach, we have predicted a set of highly plausible H. sapiens-M. tuberculosis H37Rv PPIs which might be useful for many of related studies. Based on our analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent homology-based approach, we have discovered several interesting properties which are reported here for the first time. We find that both host proteins and pathogen proteins involved in the host-pathogen PPIs tend to be hubs in their own intra-species PPI network. Also, both host and pathogen proteins involved in host-pathogen PPIs tend to have longer primary sequence, tend to have more domains, tend to be more hydrophilic, etc. And the protein domains from both host and pathogen proteins involved in host-pathogen PPIs tend to have lower charge, and tend to be more hydrophilic.ConclusionsOur stringent homology-based prediction approach provides a better strategy in predicting PPIs between eukaryotic hosts and prokaryotic pathogens than a conventional homology-based approach. The properties we have observed from the predicted H. sapiens-M. tuberculosis H37Rv PPI network are useful for understanding inter-species host-pathogen PPI networks and provide novel insights for host-pathogen interaction studies.ReviewersThis article was reviewed by Michael Gromiha, Narayanaswamy Srinivasan and Thomas Dandekar.