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Background: To analyze vault effects of crystalline lens rise (CLR) and anterior chamber parameters (recorded by Pentacam) in highly myopic patients receiving implantable collamer lenses (ICLs), which may avoid subsequent complications such as glaucoma and cataract caused by the abnormal vault. Methods: We collected clinical data of 137 patients with highly myopic vision, who were all subsequent recipients of V4c ICLs between June 2020 and January 2021. Horizontal ciliary sulcus-to-sulcus diameter (hSTS) and CLR were measured by ultrasonic biomicroscopy (UBM), and a Pentacam anterior segment analyzer was used to measure horizontal white-to-white diameter (hWTW), anterior chamber depth (ACD), anterior chamber angle (ACA), anterior chamber volume (ACV), CLR, and postoperative vault (Year 1 and Month 1). The lens thickness (LT) was determined by optical biometry (IOL Master instrument). The predictive model was generated through multiple linear regression analyses of influential factors, such as hSTS, CLR, hWTW, ACD, ACA, ACV, ICL size, and LT. The predictive performance of the multivariate model on vault after ICL was assessed using the receiver operating characteristic (ROC) curve with area under the curve (AUC) as well as the point of tangency. Results: Average CLR assessed by UBM was lower than the average value obtained by Pentacam (0.561 vs. 0.683). Bland-Altman analysis showed a good consistency in the two measurement methods and substantial correlation (r = 0.316; P = 0.000). The ROC curve of Model 1 (postoperative Year 1) displayed an AUC of 0.847 (95% confidence interval [CI]: 74.19–95.27), with optimal threshold of 0.581 (sensitivity, 0.857; specificity, 0.724). In addition, respective values for Model 2 (postoperative Month 1) were 0.783 (95% CI: 64.94–91.64) and 0.522 (sensitivity, 0.917; specificity, 0.605). Conclusion: CLR and anterior chamber parameters are important determinants of postoperative vault after ICL placement. The multivariate regression model we constructed may serve in large part as a predictive gauge, effectively avoid postoperative complication. [ABSTRACT FROM AUTHOR]

Virus-encoded circular RNA (circRNA) participates in the immune response to viral infection, affects the human immune system, and can be used as a target for precision therapy and tumor biomarker. The coronaviruses SARS-CoV-1 and SARS-CoV-2 (SARS-CoV-1/2) that have emerged in recent years are highly contagious and have high mortality rates. In coronaviruses, little is known about the circRNA encoded by the SARS-CoV-1/2. Therefore, this study explores whether SARS-CoV-1/2 encodes circRNA and characteristics and functions of circRNA. Based on RNA-seq data of SARS-CoV-1 and SARS-CoV-2 infections, we used circRNA identification tools (circRNA_finder, find_circ and CIRI2) to identify circRNAs. The number of circRNAs encoded by SARS-CoV-1 and SARS-CoV-2 was identified as 151 and 470, respectively. It can be found that SARS-CoV-2 shows more prominent circRNA encoding ability than SARS-CoV-1. Expression analysis showed that only a few circRNAs encoded by SARS-CoV-1/2 showed high expression levels, and the positive strand produced more abundant circRNAs. Then, based on the identified SARS-CoV-1/2-encoded circRNAs, we performed circRNA identification and characterization using the previously developed CirRNAPL. Finally, target gene prediction and functional enrichment analysis were performed. It was found that viral circRNA is closely related to cancer and has a potential role in regulating host cell functions. This study studied the characteristics and functions of viral circRNA encoded by coronavirus SARS-CoV-1/2, providing a valuable resource for further research on the function and molecular mechanism of coronavirus circRNA. [ABSTRACT FROM AUTHOR]

Objectives: CD4+ T cell helper and regulatory function in human cancers has been well characterised. However, the definition of tumor‐infiltrating CD4+ T cell exhaustion and how it contributes to the immune response and disease progression in human gastric cancer (GC) remain largely unknown. Methods: A total of 128 GC patients were enrolled in the study. The expression of CD39 and PD‐1 on CD4+ T cells in the different samples was analysed by flow cytometry. GC‐infiltrating CD4+ T cell subpopulations based on CD39 expression were phenotypically and functionally assessed. The role of CD39 in the immune response of GC‐infiltrating T cells was investigated by inhibiting CD39 enzymatic activity. Results: In comparison with CD4+ T cells from the non‐tumor tissues, significantly more GC‐infiltrating CD4+ T cells expressed CD39. Most GC‐infiltrating CD39+CD4+ T cells exhibited CD45RA−CCR7− effector–memory phenotype expressing more exhaustion‐associated inhibitory molecules and transcription factors and produced less TNF‐α, IFN‐γ and cytolytic molecules than their CD39−CD4+ counterparts. Moreover, ex vivo inhibition of CD39 enzymatic activity enhanced their functional potential reflected by TNF‐α and IFN‐γ production. Finally, increased percentages of GC‐infiltrating CD39+CD4+ T cells were positively associated with disease progression and patients' poorer overall survival. Conclusion: Our study demonstrates that CD39 expression defines GC‐infiltrating CD4+ T cell exhaustion and their immunosuppressive function. Targeting CD39 may be a promising therapeutic strategy for treating GC patients. [ABSTRACT FROM AUTHOR]

Background: Circular RNAs (circRNAs) have been confirmed to play a vital role in the occurrence and development of diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for studying etiopathogenesis and treating diseases. To this end, based on the graph Markov neural network algorithm (GMNN) constructed in our previous work GMNN2CD, we further considered the multisource biological data that affects the association between circRNA and disease and developed an updated web server CircDA and based on the human hepatocellular carcinoma (HCC) tissue data to verify the prediction results of CircDA. Results: CircDA is built on a Tumarkov-based deep learning framework. The algorithm regards biomolecules as nodes and the interactions between molecules as edges, reasonably abstracts multiomics data, and models them as a heterogeneous biomolecular association network, which can reflect the complex relationship between different biomolecules. Case studies using literature data from HCC, cervical, and gastric cancers demonstrate that the CircDA predictor can identify missing associations between known circRNAs and diseases, and using the quantitative real-time PCR (RT-qPCR) experiment of HCC in human tissue samples, it was found that five circRNAs were significantly differentially expressed, which proved that CircDA can predict diseases related to new circRNAs. Conclusions: This efficient computational prediction and case analysis with sufficient feedback allows us to identify circRNA-associated diseases and disease-associated circRNAs. Our work provides a method to predict circRNA-associated diseases and can provide guidance for the association of diseases with certain circRNAs. For ease of use, an online prediction server (http://server.malab.cn/CircDA) is provided, and the code is open-sourced (https://github.com/nmt315320/CircDA.git) for the convenience of algorithm improvement. [ABSTRACT FROM AUTHOR]

Exploring microbial stress responses to drugs is crucial for the advancement of new therapeutic methods. While current artificial intelligence methodologies have expedited our understanding of potential microbial responses to drugs, the models are constrained by the imprecise representation of microbes and drugs. To this end, we combine deep autoencoder and subgraph augmentation technology for the first time to propose a model called JDASA-MRD, which can identify the potential indistinguishable responses of microbes to drugs. In the JDASA-MRD model, we begin by feeding the established similarity matrices of microbe and drug into the deep autoencoder, enabling to extract robust initial features of both microbes and drugs. Subsequently, we employ the MinHash and HyperLogLog algorithms to account intersections and cardinality data between microbe and drug subgraphs, thus deeply extracting the multi-hop neighborhood information of nodes. Finally, by integrating the initial node features with subgraph topological information, we leverage graph neural network technology to predict the microbes' responses to drugs, offering a more effective solution to the 'over-smoothing' challenge. Comparative analyses on multiple public datasets confirm that the JDASA-MRD model's performance surpasses that of current state-of-the-art models. This research aims to offer a more profound insight into the adaptability of microbes to drugs and to furnish pivotal guidance for drug treatment strategies. Our data and code are publicly available at: https://github.com/ZZCrazy00/JDASA-MRD. [ABSTRACT FROM AUTHOR]

Linear morphea, also known as linear scleroderma, is a localized form of scleroderma characterized by the presence of lesions that follow a linear distribution pattern. Apart from the typical inflammation and fibrosis of the skin, the linear subtype of morphea often affects underlying structures such as muscles and bones, which can lead to functional limitations. Lichen striatus, a linear inflammatory skin condition, primarily affects children aged 5 to 15 years. Interestingly, both diseases can exhibit lesions that follow the lines of Blaschko. Here we report a case with linear morphea following the lines of Blaschko mimicking lichen striatus in a 4‐year‐old child. This unique case represents the first documented instance of linear morphea exhibiting a precise Blaschko pattern and being successfully treated with baricitinib. The patient received oral baricitinib at a daily dosage of 2 mg for a duration of 1 year, resulting in remarkable improvement. The majority of the lesions softened, and there was no significant disease progression or occurrence of adverse events throughout the treatment period. Recognizing linear morphea at an early stage is of utmost importance in ensuring effective treatment and preventing disfiguring sequelae. Patients suspected of lichen striatus should also be closely followed and linear morphea should be excluded during the follow‐up. The recent breakthrough in the application and the safety of baricitinib in scleroderma is also reviewed. [ABSTRACT FROM AUTHOR]

Motivation In bioinformatics, multiple sequence alignment (MSA) is a crucial task. However, conventional methods often struggle with aligning ultralong sequences. To address this issue, researchers have designed MSA methods rooted in a vertical division strategy, which segments sequence data for parallel alignment. A prime example of this approach is FMAlign, which utilizes the FM-index to extract common seeds and segment the sequences accordingly. Results FMAlign2 leverages the suffix array to identify maximal exact matches, redefining the approach of FMAlign from searching for global chains to partial chains. By using a vertical division strategy, large-scale problem is deconstructed into manageable tasks, enabling parallel execution of subMSA. Furthermore, sequence-profile alignment and refinement are incorporated to concatenate subsets, yielding the final result seamlessly. Compared to FMAlign, FMAlign2 markedly augments the segmentation of sequences and significantly reduces the time while maintaining accuracy, especially on ultralong datasets. Importantly, FMAlign2 enhances existing MSA methods by conferring the capability to handle sequences reaching billions in length within an acceptable time frame. Availability and implementation Source code and datasets are available at https://github.com/malabz/FMAlign2 and https://zenodo.org/records/10435770. [ABSTRACT FROM AUTHOR]

Nowadays, building energy consumption is responsible for 30% to 40% of total energy usage in many countries. The energy performance of each building is related to many factors. Most of them are determined in design stage. Meanwhile, natural ventilation is one of the most potential passive energy conservation, especially in hot summer and cold winter climate zone. This study aims to figure out how to make the most of natural ventilation in building energy conservation in hot summer and cold winter climate zone. By comparing different building design plans, this study try to provide a method to help find the best-optimized building parameters design solutions. Six main factors are selected, including: building orientation, exterior wall masonry, window wall ratio, shading overhang depth, binds slat angel(include the situation when there is no blinds at all), exterior window structure. A seven-layer office building in hot summer and cold winter zone is modelled and simulated in Energyplus. Two building running plans are compared: (a) the air conditioning system is always on during office occupancy time; (b) the air conditioning system is off when natural ventilation can guarantee thermal comfort inside the building. The results shows, plan (b) saves more than 40% energy on different building parameters designs comparing to plan (a).