The purpose of this experiment was to investigate the effects of Litsea cubeba essential oil (LCO) on the growth performance, blood antioxidation, immune function, apparent digestibility of nutrients, and fecal microflora in fattening pigs. A total of 120 pigs were randomly assigned to five groups, with six replicate pens per treatment and four pigs per pen, and they were fed basal diet, chlortetracycline (CTC), and low-, medium-, and high-concentration LCO. The results of the study showed that compared with the control treatment and CTC addition treatment of the basic diet, the catalase level in the serum of the pigs treated with 500 mg/kg LCO in the diet of finishing pigs was significantly increased (p < 0.05). The apparent digestibility of crude protein, crude ash, and calcium in pigs with different levels of LCO was significantly increased compared with the control treatments fed the basal diet (p < 0.05). In addition, compared with the control treatment fed the basal diet and the treatment with CTC, the apparent digestibility of ether extract in pigs treated with medium-dose LCO was significantly increased (p < 0.05), and the apparent digestibility of pigs was significantly increased after the addition of low-dose LCO (p < 0.05). Among the genera, the percentage abundance of SMB53 (p &lt; 0.05) was decreased in the feces of the CTC group when compared to that in the medium-LCO group. At the same time, the relative abundance of L7A_E11 was markedly decreased in the feces of the control and medium- and high-concentration LCO group than that in the CTC group (p &lt; 0.05). In conclusion, adding the level of 250 mg/kg LCO in the diet of pig could improve the growth performance and blood physiological and biochemical indicators of pigs, improve the antioxidant level of body and the efficiency of digestion and absorption of nutrients, and show the potential to replace CTC.

Data quality control and preprocessing are often the first step in processing next-generation sequencing (NGS) data of tumors. Not only can it help us evaluate the quality of sequencing data, but it can also help us obtain high-quality data for downstream data analysis. However, by comparing data analysis results of preprocessing with Cutadapt, FastP, Trimmomatic, and raw sequencing data, we found that the frequency of mutation detection had some fluctuations and differences, and human leukocyte antigen (HLA) typing directly resulted in erroneous results. We think that our research had demonstrated the impact of data preprocessing steps on downstream data analysis results. We hope that it can promote the development or optimization of better data preprocessing methods, so that downstream information analysis can be more accurate.

Metastatic cancers require further diagnosis to determine their primary tumor sites. However, the tissue-of-origin for around 5% tumors could not be identified by routine medical diagnosis according to a statistics in the United States. With the development of machine learning techniques and the accumulation of big cancer data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), it is now feasible to predict cancer tissue-of-origin by computational tools. Metastatic tumor inherits characteristics from its tissue-of-origin, and both gene expression profile and somatic mutation have tissue specificity. Thus, we developed a computational framework to infer tumor tissue-of-origin by integrating both gene mutation and expression (TOOme). Specifically, we first perform feature selection on both gene expressions and mutations by a random forest method. The selected features are then used to build up a multi-label classification model to infer cancer tissue-of-origin. We adopt a few popular multiple-label classification methods, which are compared by the 10-fold cross validation process. We applied TOOme to the TCGA data containing 7,008 non-metastatic samples across 20 solid tumors. Seventy four genes by gene expression profile and six genes by gene mutation are selected by the random forest process, which can be divided into two categories: (1) cancer type specific genes and (2) those expressed or mutated in several cancers with different levels of expression or mutation rates. Function analysis indicates that the selected genes are significantly enriched in gland development, urogenital system development, hormone metabolic process, thyroid hormone generation prostate hormone generation and so on. According to the multiple-label classification method, random forest performs the best with a 10-fold cross-validation prediction accuracy of 96%. We also use the 19 metastatic samples from TCGA and 256 cancer samples downloaded from GEO as independent testing data, for which TOOme achieves a prediction accuracy of 89%. The cross-validation validation accuracy is better than those using gene expression (i.e., 95%) and gene mutation (53%) alone. In conclusion, TOOme provides a quick yet accurate alternative to traditional medical methods in inferring cancer tissue-of-origin. In addition, the methods combining somatic mutation and gene expressions outperform those using gene expression or mutation alone.

Suitable fermentation substrates and fermentation modes can effectively improve the fermentation ethanol yield. In this study, we optimised the hydrothermal pretreatment conditions by orthogonal optimisation using waste tissue paper as substrate. These conditions consisted of 50 min duration in a high-pressure reactor with pure water as solvent at a temperature of 160 °C. The biomass to water ratio was maintained at a constant level. The cellulose content of the pretreated TP was 81.19 ± 4.06%, which was an increase of 21.59% compared to the blank control. The 72 h reducing sugar yield of pretreated TP was 0.61 g sugar/g paper, which was 38.64% higher than that of untreated TP. Subsequently, the pretreated TP was fermented under optimal conditions. The mixed group of Saccharomyces cerevisiae and Candida shehatae (SC) showed a distributed saccharification fermentation pattern, with an ethanol yield of 28.11 g/L in 72 h. On the other hand, the single Saccharomyces cerevisiae (S) exhibited a homobloc saccharification fermentation pattern, with an ethanol yield of 35.15 g/L in 72 h.

1,3,5-triazine derivatives, also called s-triazines, are a series of containing-nitrogen heterocyclic compounds that play an important role in anticancer drug design and development. To date, three s-triazine derivatives, including altretamine, gedatolisib, and enasidenib, have already been approved for refractory ovarian cancer, metastatic breast cancer, and leukemia therapy, respectively, demonstrating that the s-triazine core is a useful scaffold for the discovery of novel anticancer drugs. In this review, we mainly focus on s-triazines targeting topoisomerases, tyrosine kinases, phosphoinositide 3-kinases, NADP+-dependent isocitrate dehydrogenases, and cyclin-dependent kinases in diverse signaling pathways, which have been extensively studied. The medicinal chemistry of s-triazine derivatives as anticancer agents was summarized, including discovery, structure optimization, and biological applications. This review will provide a reference to inspire new and original discoveries.

Carcinoma of unknown primary (CUP) is a type of metastatic cancer, the primary tumor site of which cannot be identified. CUP occupies approximately 5% of cancer incidences in the United States with usually unfavorable prognosis, making it a big threat to public health. Traditional methods to identify the tissue-of-origin (TOO) of CUP like immunohistochemistry can only deal with around 20% CUP patients. In recent years, more and more studies suggest that it is promising to solve the problem by integrating machine learning techniques with big biomedical data involving multiple types of biomarkers including epigenetic, genetic, and gene expression profiles, such as DNA methylation. Different biomarkers play different roles in cancer research; for example, genomic mutations in a patient’s tumor could lead to specific anticancer drugs for treatment; DNA methylation and copy number variation could reveal tumor tissue of origin and molecular classification. However, there is no systematic comparison on which biomarker is better at identifying the cancer type and site of origin. In addition, it might also be possible to further improve the inference accuracy by integrating multiple types of biomarkers. In this study, we used primary tumor data rather than metastatic tumor data. Although the use of primary tumors may lead to some biases in our classification model, their tumor-of-origins are known. In addition, previous studies have suggested that the CUP prediction model built from primary tumors could efficiently predict TOO of metastatic cancers (Lal et al., 2013; Brachtel et al., 2016). We systematically compared the performances of three types of biomarkers including DNA methylation, gene expression profile, and somatic mutation as well as their combinations in inferring the TOO of CUP patients. First, we downloaded the gene expression profile, somatic mutation and DNA methylation data of 7,224 tumor samples across 21 common cancer types from the cancer genome atlas (TCGA) and generated seven different feature matrices through various combinations. Second, we performed feature selection by the Pearson correlation method. The selected features for each matrix were used to build up an XGBoost multi-label classification model to infer cancer TOO, an algorithm proven to be effective in a few previous studies. The performance of each biomarker and combination was compared by the 10-fold cross-validation process. Our results showed that the TOO tracing accuracy using gene expression profile was the highest, followed by DNA methylation, while somatic mutation performed the worst. Meanwhile, we found that simply combining multiple biomarkers does not have much effect in improving prediction accuracy.