Suboptimal health among Chinese middle school students may be associated with psychological symptoms and sleep duration: a cross-sectional survey in China.

Background: Suboptimal health status (SHS) is an intermediate status between ideal heath and illness, it is characterized by the perception of health complaints, general weakness, decreased immunity and low energy. An increasing number of Chinese middle school students are suffering from psychological symptoms (PS), particularly anxiety and depression. The relationship between SHS and PS is unclear in adolescents.

Extension of multi-site analogue series with potent compounds using a bidirectional transformer-based chemical language model.

Generating potent compounds for evolving analogue series (AS) is a key challenge in medicinal chemistry. The versatility of chemical language models (CLMs) makes it possible to formulate this challenge as an off-the-beaten-path prediction task. In this work, we have devised a coding and tokenization scheme for evolving AS with multiple substitution sites (multi-site AS) and implemented a bidirectional transformer to predict new potent analogues for such series.

Apatinib combined with camrelizumab in the treatment of recurrent/metastatic nasopharyngeal carcinoma: a prospective multicenter phase II study.

Background: Preclinical studies demonstrated that immune checkpoint inhibitors combined with antiangiogenic drugs have a synergistic anti-tumor effect. This present phase II trial aimed to evaluate the efficacy and safety of apatinib combined with camrelizumab in patients with recurrent/metastatic nasopharyngeal carcinoma (RM-NPC).

Methods: Patients with RM-NPC were administered with apatinib at 250 mg orally once every day and with camrelizumab at 200 mg intravenous infusion every 2 weeks until the disease progressed or toxicity became unacceptable.

Meta-learning for transformer-based prediction of potent compounds.

For many machine learning applications in drug discovery, only limited amounts of training data are available. This typically applies to compound design and activity prediction and often restricts machine learning, especially deep learning. For low-data applications, specialized learning strategies can be considered to limit required training data.

Designing highly potent compounds using a chemical language model.

Compound potency prediction is a major task in medicinal chemistry and drug design. Inspired by the concept of activity cliffs (which encode large differences in potency between similar active compounds), we have devised a new methodology for predicting potent compounds from weakly potent input molecules. Therefore, a chemical language model was implemented consisting of a conditional transformer architecture for compound design guided by observed potency differences.

Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies.

While synergistic drug combinations are more effective at fighting tumors with complex pathophysiology, preference compensating mechanisms, and drug resistance, the identification of novel synergistic drug combinations, especially complex higher-order combinations, remains challenging due to the size of combination space. Even though certain computational methods have been used to identify synergistic drug combinations of traditional and screening tests, the majority of previously published work has focused on predicting synergistic drug pairs for specific types of cancer and paid little attention to the sophisticated high-order combinations. The main objective of this study is to develop a deep learning-based approach that integrated multi-omics data to predict novel synergistic multi-drug combinations (DeepMDS) in a given cell line.

DeepCubist: Molecular Generator for Designing Peptidomimetics based on Complex three-dimensional scaffolds.

Mimicking bioactive conformations of peptide segments involved in the formation of protein-protein interfaces with small molecules is thought to represent a promising strategy for the design of protein-protein interaction (PPI) inhibitors. For compound design, the use of three-dimensional (3D) scaffolds rich in sp3-centers makes it possible to precisely mimic bioactive peptide conformations. Herein, we introduce DeepCubist, a molecular generator for designing peptidomimetics based on 3D scaffolds.

Prediction model for synergistic anti-tumor multi-compound combinations from traditional Chinese medicine based on extreme gradient boosting, targets and gene expression data.

Traditional Chinese medicine (TCM) is characterized by synergistic therapeutic effect involving multiple compounds and targets, which provide potential new therapy for the treatment of complex cancer conditions. However, the main contributors and the underlying mechanisms of synergistic TCM cancer therapies remain largely undetermined. Machine learning now provides a new approach to determine synergistic compound combinations from complex components of TCM.

A Strategy for the Effective Optimization of Pharmaceutical Formulations Based on Parameter-Optimized Support Vector Machine Model.

Engineering pharmaceutical formulations is governed by a number of variables, and the finding of the optimal preparation is intricately linked to the exploration of a multiparametric space through a variety of optimization tasks. As a result, making such optimization activities simpler is a significant undertaking. For the purposes of this study, we suggested a prediction model that was based on least square support vector machine (LSSVM) and whose parameters were optimized using the particle swarm optimization algorithm (PSO-LSSVM model).

Loss of PDK4 expression promotes proliferation, tumorigenicity, motility and invasion of hepatocellular carcinoma cells.

Although the roles and underlying mechanisms of other PDK family members (i.e., PDK1, PDK2 and PDK3) in tumor progression have been extensively investigated and are well understood, the functions and underlying molecular mechanisms of pyruvate dehydrogenase kinase 4 (PDK4) in the tumorigenesis and progression of various cancers [including hepatocellular carcinoma (HCC)] remain largely unknown.

miR-19 regulates the expression of interferon-induced genes and MHC class I genes in human cancer cells.

MicroRNA-19 (miR-19) is identified as the key oncogenic component of the miR-17-92 cluster. When we explored the functions of the dysregulated miR-19 in lung cancer, microarray-based data unexpectedly demonstrated that some immune and inflammatory response genes (i.e.

Optimization Strategy for Generating Gene-edited Tibet Minipigs by Synchronized Oestrus and Cytoplasmic Microinjection.

The Tibet minipig is a rare highland pig breed worldwide and has many applications in biomedical and agricultural research. However, Tibet minipigs are not like domesticated pigs in that their ovulation number is low, which is unfavourable for the collection of zygotes. Partly for this reason, few studies have reported the successful generation of genetically modified Tibet minipigs by zygote injection.

Loss of Cirbp expression is correlated with the malignant progression and poor prognosis in nasopharyngeal carcinoma.

The correlation of cold-inducible RNA-binding protein (Cirbp) expression with clinicopathological features including patient prognosis in nasopharyngeal carcinoma (NPC) was investigated. The expression of Cirbp in NPC cell lines and tissue specimens was examined by qRT-PCR or immunohistochemistry (IHC). Immunohistochemistry (IHC) results showed that high Cirbp expression was detected in 61 of 61 non-cancerous nasopharyngeal squamous epithelial biopsies, whereas the significantly reduced expression of Cirbp was observed in NPC specimens.

miR-26a promotes hepatocellular carcinoma invasion and metastasis by inhibiting PTEN and inhibits cell growth by repressing EZH2.

A previous study revealed that therapeutic miR-26a delivery suppresses tumorigenesis in a murine liver cancer model, whereas we found that forced miR-26a expression increased hepatocellular carcinoma (HCC) cell migration and invasion, which prompted us to characterize the causes and mechanisms underlying enhanced invasion due to ectopic miR-26a expression. Gain-of-function and loss-of-function experiments demonstrated that miR-26a promoted migration and invasion of BEL-7402 and HepG2 cells in vitro and positively modulated matrix metalloproteinase (MMP)-1, MMP-2, MMP-9, and MMP-10 expression. In addition, exogenous miR-26a expression significantly enhanced the metastatic ability of HepG2 cells in vivo.

Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance.

Drug-induced liver injury (DILI), one of the most common adverse effects, leads to drug development failure or withdrawal from the market in most cases, showing an emerging challenge that is to accurately predict DILI in the early stage. Recently, the vast amount of gene expression data provides us valuable information for distinguishing DILI on a genomic scale. Moreover, the deep learning algorithm is a powerful strategy to automatically learn important features from raw and noisy data and shows great success in the field of medical diagnosis.

Abnormalities of hair structure and skin histology derived from CRISPR/Cas9-based knockout of phospholipase C-delta 1 in mice.

Background: Hairless mice have been widely applied in skin-related researches, while hairless pigs will be an ideal model for skin-related study and other biomedical researches because of the similarity of skin structure with humans. The previous study revealed that hairlessness phenotype in nude mice is caused by insufficient expression of phospholipase C-delta 1 (PLCD1), an essential molecule downstream of Foxn1, which encouraged us to generate PLCD1-deficient pigs. In this study, we plan to firstly produce PLCD1 knockout (KO) mice by CRISPR/Cas9 technology, which will lay a solid foundation for the generation of hairless PLCD1 KO pigs.