It has widely been accepted that food restriction (FR) without malnutrition has multiple health benefits. Various calorie restriction (CR) and intermittent fasting (IF) regimens have recently been reported to exert neuroprotective effects in traumatic brain injury (TBI) through variable mechanisms. However, the evidence connecting CR or IF to neuroprotection in TBI as well as current issues remaining in this research field have yet to be reviewed in literature. The objective of our review was therefore to weigh the evidence that suggests the connection between CR/IF with recovery promotion following TBI. Medline, Google Scholar and Web of Science were searched from inception to 25 February 2022. An overwhelming number of results generated suggest that several types of CR/IF play a promising role in promoting post-TBI recovery. This recovery is believed to be achieved by alleviating mitochondrial dysfunction, promoting hippocampal neurogenesis, inhibiting glial cell responses, shaping neural cell plasticity, as well as targeting apoptosis and autophagy. Further, we represent our views on the current issues and provide thoughts on the future direction of this research field.

Coiled-coil domain-containing 68 (CCDC68) is a novel secretory protein that acts as a tumor suppressor gene in several types of malignant tumors. However, the role of CCDC68 in the development of lung cancer has not been extensively studied. In the present study, to explore the biological functions of CCDC68 in NSCLC, we performed cell proliferation, viability and apoptosis assays on human lung cancer cell lines upon CCDC68 gene silencing with short hairpin RNA. The results demonstrated that following knockdown of CCDC68 expression, cell proliferation was decreased and the apoptotic rates were increased in A549 and H1299 cells. The role and mechanism of CCDC68 in malignant tumors, particularly in lung cancer, should be further explored, and CCDC68 may serve as a novel target for treatment of lung cancer.

Non-small cell lung cancer (NSCLC) poses a threat to human health and paclitaxel chemotherapy has been approved for the treatment of this type of cancer. However, resistance to treatment severely compromises the survival rate and prognosis of patients with NSCLC. The aim of the present study was to investigate the role of IL-1β in paclitaxel sensitivity of NSCLC cells and elucidate the underlying mechanism. The expression of IL-1β was found to be upregulated in NSCLC tissues and cells compared with healthy adjacent tissues and a normal epithelial cell line, respectively, as detected by reverse transcription-quantitative PCR and western blot analyses. Subsequently, Cell Counting Kit-8 assay and flow cytometry revealed that IL-1β weakened the sensitivity of A549 cells to paclitaxel. It was subsequently demonstrated that IL-1β induced A549 cell autophagy, while tunicamycin-induced autophagy increased the IL-1β expression level and weakened paclitaxel sensitivity. Thus, the results revealed that IL-1β reduced the sensitivity to paclitaxel in A549 cells by promoting autophagy and suggested that IL-1β may be of value for improving the therapeutic efficacy of paclitaxel chemotherapy in NSCLC.

Code search, which locates code snippets in large code repositories based on natural language queries entered by developers, has become increasingly popular in the software development process. It has the potential to improve the efficiency of software developers. Recent studies have demonstrated the effectiveness of using deep learning techniques to represent queries and codes accurately for code search. In specific, pre-trained models of programming languages have recently achieved significant progress in code searching. However, we argue that aligning programming and natural languages are crucial as there are two different modalities. Existing pre-train models based approaches for code search do not effectively consider implicit alignments of representations across modalities (inter-modal representation). Moreover, the existing methods do not take into account the consistency constraint of intra-modal representations, making the model ineffective. As a result, we propose a novel code search method that optimizes both intra-modal and inter-modal representation learning. The alignment of the representation between the two modalities is achieved by introducing contrastive learning. Furthermore, the consistency of intra-modal feature representation is constrained by KL-divergence. Our experimental results confirm the model’s effectiveness on seven different test datasets. This paper proposes a code search method that significantly improves existing methods. Our source code is publicly available on GitHub.1

Natural Language to SQL (NL2SQL) is a technology that converts natural language into executable SQL statements. For single table tasks, the dominant approach is a slot-filling-based model which actually performs multi-objective prediction. However, in existing multi-objective models, it is difficult to optimize multiple objectives simultaneously. Low correlation or conflict between various objectives can result in the model not being able to learn the parameters effectively. To effectively address this problem, this paper considers redesigning the sharing mechanism between multiple tasks and the specific network structure of individual tasks through a semantic aggregation approach in order to alleviate the above phenomenon.