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In contrast to the well-established genomic 5-methylcytosine (5mC), the existence of N6-methyladenine (6 mA) in eukaryotic genomes was discovered only recently. Initial studies found that it was actively regulated in cancer cells, suggesting its involvement in the process of carcinogenesis. However, the contribution of 6 mA in tongue squamous cell carcinoma (TSCC) still remains uncharacterized. In this study, a pan-cancer type analysis was first performed, which revealed enhanced 6 mA metabolism in diverse cancer types. The study was then focused on the regulation of 6 mA metabolism, as well as its effects on TSCC cells. To these aspects, genome 6 mA level was found greatly increased in TSCC tissues and cultured cells. By knocking down 6 mA methylases N6AMT1 and METTL4, the level of genomic 6 mA was decreased in TSCC cells. This led to suppressed colony formation and cell migration. By contrast, knockdown of 6 mA demethylase ALKBH1 resulted in an increased 6 mA level, enhanced colony formation, and cell migration. Further study suggested that regulation of the NF-κB pathway might contribute to the enhanced migration of TSCC cells. Therefore, in the case of TSCC, we have shown that genomic 6 mA modification is involved in the proliferation and migration of cancer cells.

RT-LAMP detection of SARS-CoV-2 has been shown to be a valuable approach to scale up COVID-19 diagnostics and thus contribute to limiting the spread of the disease. Here we present the optimization of highly cost-effective in-house produced enzymes, and we benchmark their performance against commercial alternatives. We explore the compatibility between multiple DNA polymerases with high strand-displacement activity and thermostable reverse transcriptases required for RT-LAMP. We optimize reaction conditions and demonstrate their applicability using both synthetic RNA and clinical patient samples. Finally, we validate the optimized RT-LAMP assay for the detection of SARS-CoV-2 in unextracted heat-inactivated nasopharyngeal samples from 184 patients. We anticipate that optimized and affordable reagents for RT-LAMP will facilitate the expansion of SARS-CoV-2 testing globally, especially in sites and settings where the need for large scale testing cannot be met by commercial alternatives.

Background The common treatment for pediatric cataracts is to replace the cloudy lens with an artificial one. However, patients may suffer complications (severe lens proliferation into the visual axis and abnormal high intraocular pressure; SLPVA and AHIP) within 1 year after surgery and factors causing these complications are unknown. Methods Apriori algorithm is employed to find association rules related to complications. We use random forest (RF) and Naïve Bayesian (NB) to predict the complications with datasets preprocessed by SMOTE (synthetic minority oversampling technique). Genetic feature selection is exploited to find real features related to complications. Results Average classification accuracies in three binary classification problems are over 75%. Second, the relationship between the classification performance and the number of random forest tree is studied. Results show except for gender and age at surgery (AS); other attributes are related to complications. Except for the secondary IOL placement, operation mode, AS and area of cataracts; other attributes are related to SLPVA. Except for the gender, operation mode, and laterality; other attributes are related to the AHIP. Next, the association rules related to the complications are mined out. Then additional 50 data were used to test the performance of RF and NB, both of then obtained the accuracies of over 65% for three classification problems. Finally, we developed a webserver to assist doctors. Conclusions The postoperative complications of pediatric cataracts patients can be predicted. Then the factors related to the complications are found. Finally, the association rules that is about the complications can provide reference to doctors. Electronic supplementary material The online version of this article (10.1186/s12967-018-1758-2) contains supplementary material, which is available to authorized users.

Given the challenges in exploring lifelong therapy with little side effect for human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS) cases, there is increasing interest in developing traditional Chinese medicine (TCM) treatments based on specific TCM syndrome. However, there are few objective and biological evidences for classification and diagnosis of HIV/AIDS TCM syndromes to date. In this study, iTRAQ-2DLC-MS/MS coupled with bioinformatics were firstly employed for comparative proteomic profiling of top popular TCM syndromes of HIV/AIDS: accumulation of heat-toxicity (AHT) and Yang deficiency of spleen and kidney (YDSK). It was found that for the two TCM syndromes, the identified differential expressed proteins (DEPs) as well as their biological function distributions and participation in signaling pathways were significantly different, providing biological evidence for the classification of HIV/AIDS TCM syndromes. Furthermore, the TCM syndrome-specific DEPs were confirmed as biomarkers based on western blot analyses, including FN1, GPX3, KRT10 for AHT and RBP4, ApoE, KNG1 for YDSK. These biomarkers also biologically linked with the specific TCM syndrome closely. Thus the clinical and biological basis for differentiation and diagnosis of HIV/AIDs TCM syndromes were provided for the first time, providing more opportunities for stable exertion and better application of TCM efficacy and superiority in HIV/AIDS treatment.

There are many image classification methods, but it remains unclear which methods are most helpful for analyzing and intelligently identifying ophthalmic images. We select representative slit-lamp images which show the complexity of ocular images as research material to compare image classification algorithms for diagnosing ophthalmic diseases. To facilitate this study, some feature extraction algorithms and classifiers are combined to automatic diagnose pediatric cataract with same dataset and then their performance are compared using multiple criteria. This comparative study reveals the general characteristics of the existing methods for automatic identification of ophthalmic images and provides new insights into the strengths and shortcomings of these methods. The relevant methods (local binary pattern +SVMs, wavelet transformation +SVMs) which achieve an average accuracy of 87% and can be adopted in specific situations to aid doctors in preliminarily disease screening. Furthermore, some methods requiring fewer computational resources and less time could be applied in remote places or mobile devices to assist individuals in understanding the condition of their body. In addition, it would be helpful to accelerate the development of innovative approaches and to apply these methods to assist doctors in diagnosing ophthalmic disease.

Using artificial intelligence (AI) to prevent and treat diseases is an ultimate goal in computational medicine. Although AI has been developed for screening and assisted decision-making in disease prevention and management, it has not yet been validated for systematic application in the clinic. In the context of rare diseases, the main strategy has been to build specialized care centres; however, these centres are scattered and their coverage is insufficient, which leaves a large proportion of rare-disease patients with inadequate care. Here, we show that an AI agent using deep learning, and involving convolutional neural networks for diagnostics, risk stratification and treatment suggestions, accurately diagnoses and provides treatment decisions for congenital cataracts in an in silico test, in a website-based study, in a ‘finding a needle in a haystack’ test and in a multihospital clinical trial. We also show that the AI agent and individual ophthalmologists perform equally well. Moreover, we have integrated the AI agent with a cloud-based platform for multihospital collaboration, designed to improve disease management for the benefit of patients with rare diseases. An artificial intelligence agent integrated with a cloud-based platform for multihospital collaboration performs equally as well as ophthalmologists in the diagnosis of congenital cataracts in a series of online tests and a multihospital clinical trial.

The experimental data of lean blowout fuel/air ratio of a rectangular swirl cup combustor with different inlet temperatures was obtained at atmospheric pressure condition. Numerical simulations both burning and non-burning were performed corresponding to the experimental data at lean blowout. Results indicated that the size of the recirculation region in the primary zone was obviously smaller when burning than non-burning, but the locations of the cores of their recirculation regions were almost the same. The increase of inlet air temperature didn’t mean the rise of the temperature of recirculation region core. The location of the maximum temperature in the primary zone was not the same as that one of the core temperature of the recirculation region. Further more, the reasons were analyzed how the lean blowout fuel/air ratio changed with the inlet temperature increasing under the actions of factors both positive and negative to combustion, and this would be helpful to deepen the understanding of the lean blowout process of swirl cup combustor.

An experimental investigation has been performed to study the unsteady pressure fluctuation of rotor tip region in high pressure stage of a vaneless counter-rotating turbine. The experiment is carried out on a blow-down short duration turbine facility. The investigation indicates that the blow-down short duration turbine facility is capable of substituting continuous turbine facilities in most turbine testing. Through this experimental investigation, a distinct blade-to-blade variation is observed. The results indicate that the combined effects of vane wake, tip leakage flow, complicated wave systems and rotor wake induce the remarkable blade-to-blade variations. The results also show that the unsteady effect is intensified along the flow direction.

Effect of the addition of six different anions on the absorption and fluorescence spectra of acridine yellow G (AYG) was examined. Only the F− anion could induce a visible color change observable with naked eye and a strong fluorescence quenching with K SV of 8.3 × 104 mol−1 L in CH3CN solutions. Calculated results of the interaction between the F− anion and acridine yellow G using the B3LYP/6-31G(d) program showed that the intermolecular charge transfer through the formation of an H-bond between AYG and F− is an essential sensor mechanism.