Adrenal pheochromocytoma impacts three main pathways: cysteine-methionine, pyrimidine, and tyrosine metabolism. / è‚¾ä¸Šè ºå—œé“¬ç»†èƒžç˜¤å½±å“ä¸‰æ¡ä¸»è¦ä»£è°¢é€šè·¯ï¼šåŠèƒ±æ°¨é ¸-è›‹æ°¨é ¸ä»£è°¢ã€å˜§å•¶ä»£è°¢å’Œé ªæ°¨é ¸ä»£è°¢é€šè·¯.

Pheochromocytomas and paragangliomas (PPGLs) cause symptoms by altering the circulation levels of catecholamines and peptide hormones. Currently, the diagnosis of PPGLs relies on diagnostic imaging and the detection of catecholamines. In this study, we used ultra-performance liquid chromatography (UPLC)/quadrupole time-of-flight mass spectrometry (Q-TOF MS) analysis to identify and measure the perioperative differential metabolites in the plasma of adrenal pheochromocytoma patients. We identified differentially expressed genes by comparing the transcriptomic data of pheochromocytoma with the normal adrenal medulla. Through conducting two steps of metabolomics analysis, we identified 111 differential metabolites between the healthy group and the patient group, among which 53 metabolites were validated. By integrating the information of differential metabolites and differentially expressed genes, we inferred that the cysteine-methionine, pyrimidine, and tyrosine metabolism pathways were the three main metabolic pathways altered by the neoplasm. The analysis of transcription levels revealed that the tyrosine and cysteine-methionine metabolism pathways were downregulated in pheochromocytoma, whereas the pyrimidine pathway showed no significant difference. Finally, we developed an optimized diagnostic model of two metabolites, L-dihydroorotic acid and vanylglycol. Our results for these metabolites suggest that they may serve as potential clinical biomarkers and can be used to supplement and improve the diagnosis of pheochromocytoma.

Adrenal pheochromocytoma impacts three main pathways: cysteine-methionine, pyrimidine, and tyrosine metabolism. / è‚¾ä¸Šè ºå—œé“¬ç»†èƒžç˜¤å½±å“ä¸‰æ¡ä¸»è¦ä»£è°¢é€šè·¯ï¼šåŠèƒ±æ°¨é ¸-è›‹æ°¨é ¸ä»£è°¢ã€å˜§å•¶ä»£è°¢å’Œé ªæ°¨é ¸ä»£è°¢é€šè·¯.

Pheochromocytomas and paragangliomas (PPGLs) cause symptoms by altering the circulation levels of catecholamines and peptide hormones. Currently, the diagnosis of PPGLs relies on diagnostic imaging and the detection of catecholamines. In this study, we used ultra-performance liquid chromatography (UPLC)/quadrupole time-of-flight mass spectrometry (Q-TOF MS) analysis to identify and measure the perioperative differential metabolites in the plasma of adrenal pheochromocytoma patients. We identified differentially expressed genes by comparing the transcriptomic data of pheochromocytoma with the normal adrenal medulla. Through conducting two steps of metabolomics analysis, we identified 111 differential metabolites between the healthy group and the patient group, among which 53 metabolites were validated. By integrating the information of differential metabolites and differentially expressed genes, we inferred that the cysteine-methionine, pyrimidine, and tyrosine metabolism pathways were the three main metabolic pathways altered by the neoplasm. The analysis of transcription levels revealed that the tyrosine and cysteine-methionine metabolism pathways were downregulated in pheochromocytoma, whereas the pyrimidine pathway showed no significant difference. Finally, we developed an optimized diagnostic model of two metabolites, L-dihydroorotic acid and vanylglycol. Our results for these metabolites suggest that they may serve as potential clinical biomarkers and can be used to supplement and improve the diagnosis of pheochromocytoma.

Alternative polyadenylation quantitative trait methylation mapping in human cancers provides clues into the molecular mechanisms of APA.

Genetic variants are involved in the orchestration of alternative polyadenylation (APA) events, while the role of DNA methylation in regulating APA remains unclear. We generated a comprehensive atlas of APA quantitative trait methylation sites (apaQTMs) across 21 different types of cancer (1,612 to 60,219 acting in cis and 4,448 to 142,349 in trans). Potential causal apaQTMs in non-cancer samples were also identified. Mechanistically, we observed a strong enrichment of cis-apaQTMs near polyadenylation sites (PASs) and both cis- and trans-apaQTMs in proximity to transcription factor (TF) binding regions. Through the integration of ChIP-signals and RNA-seq data from cell lines, we have identified several regulators of APA events, acting either directly or indirectly, implicating novel functions of some important genes, such as TCF7L2, which is known for its involvement in type 2 diabetes and cancers. Furthermore, we have identified a vast number of QTMs that share the same putative causal CpG sites with five different cancer types, underscoring the roles of QTMs, including apaQTMs, in the process of tumorigenesis. DNA methylation is extensively involved in the regulation of APA events in human cancers. In an attempt to elucidate the potential underlying molecular mechanisms of APA by DNA methylation, our study paves the way for subsequent experimental validations into the intricate biological functions of DNA methylation in APA regulation and the pathogenesis of human cancers. To present a comprehensive catalog of apaQTM patterns, we introduce the Pancan-apaQTM database, available at https://pancan-apaqtm-zju.shinyapps.io/pancanaQTM/.

OpenNAU: An open-source platform for normalizing, analyzing, and visualizing cancer untargeted metabolomics data.

Objective: As an important part of metabolomics analysis, untargeted metabolomics has become a powerful tool in the study of tumor mechanisms and the discovery of metabolic markers with high-throughput spectrometric data which also poses great challenges to data analysis, from the extraction of raw data to the identification of differential metabolites. To date, a large number of analytical tools and processes have been developed and constructed to serve untargeted metabolomics research. The different selection of analytical tools and parameter settings lead to varied results of untargeted metabolomics data. Our goal is to establish an easily operated platform and obtain a repeatable analysis result. Methods: We used the R language basic environment to construct the preprocessing system of the original data and the LAMP (Linux+Apache+MySQL+PHP) architecture to build a cloud mass spectrum data analysis system. Results: An open-source analysis software for untargeted metabolomics data (openNAU) was constructed. It includes the extraction of raw mass data and quality control for the identification of differential metabolic ion peaks. A reference metabolomics database based on public databases was also constructed. Conclusions: A complete analysis system platform for untargeted metabolomics was established. This platform provides a complete template interface for the addition and updating of the analysis process, so we can finish complex analyses of untargeted metabolomics with simple human-computer interactions. The source code can be downloaded from https://github.com/zjuRong/openNAU.

TP53 mutation is enriched in colorectal cancer liver metastasis in the context of polyclonal seeding.

Cancer metastasis accounts for the majority of cancer motility burden. For colorectal cancer (CRC), the liver is the most common site of distant metastasis. It is still little known that cancer genomic mutations, which are a cell-intrinsic and heritable property, are enriched in CRC liver metastasis. Here, we try to answer the question in the context of polyclonal seeding. In this study, we sequenced 18 pairs of colorectal cancer primary tumors and their matched liver metastasis samples. Together with public available sequencing data, we compared the mutations in 113 primary and metastasis pairs. The TP53 mutation variant allele frequency (VAF) was significantly increased in metastasis compared to the paired primary tumor, although most of the frequently observed mutations in liver metastasis foci were concordant with their matched CRC primary tumors. The results support late metastasis and polyclonal seeding. Consequently, we quantitatively compared the intratumor heterogeneity (ITH) between primary and metastasis tumors, and with the help of in silico metastasis simulation, we inferred that more than 10 cells take part in the CRC liver metastasis.

Desmoplastic Small Round Cell Tumor in a Pregnant Woman: A Case Report and Literature Review.

Background: Desmoplastic small round cell tumor (DSRCT) is an aggressive malignant tumor commonly found in young men; most occurs in the abdominal cavity. Here we conducted an in-depth analysis of a pregnant patient in our hospital and explored all the case information in the literature on small round cell carcinoma of women. Case presentation: A 27-year-old pregnant woman underwent tumor resection in our hospital at 29 weeks gestational age for a large progressive shoulder lump. The postoperative pathology showed that the mass was a DSRCT. Genetic testing found no fusion gene. At 36 weeks gestation, a painful mass was found in the breast and proved to be a metastatic focus of the desmoplastic small round cell tumor. Twenty days after a successful cesarean section at 40 weeks gestation, she received the VAC-IE chemotherapy regimen, successfully completed the first course, but when awaiting the next chemotherapy, unfortunately, the patient died during follow-up due to tumor recurrence and metastasis. Conclusion: The treatment of DSRCT in pregnant women requires a multidisciplinary consultation, and the treatment and examination during pregnancy are subject to many constraints, which may have a negative impact on the patient's prognosis. Also, a review of the literature found that there is still no standard treatment protocol for DSRCT, and its prognosis in female patients is independent of age and tissue origin.

Mutant CDKN2A regulates P16/p14 expression by alternative splicing in renal cell carcinoma metastasis.

OBJECTIVE: Metastatic renal cell carcinoma (mRCC) is the important factor for patient mortality, meanwhile gene mutation constantly changes cancer prognosis in tumor process. Exploring the driver mutation in mRCC process become more and more important. MATERIALS AND METHODS: We obtained the 15 paired primary and metastatic mRCC samples and analyzed specific mutation genes in the metastatic foci (SMGs) by next generation sequencing. Moreover, we explored the Correlated networks, Pathway and Gene Ontology (GO) enrichment results, prediction analysis of AS sites and prognosis of survival. RESULTS: We identify EPCAM, TMEM127, EZH2, EXT1, CDKN2A, PRF1, AIP, CDK4, PRKARIA as SMGs and find that CDKN2A mutation sites affect the prognosis of mRCC by altering splicing elements. Based on the differential analysis for SMGs in KIRC, we found that EPCAM, PRF1 and EZH2 were differential expression in both primary tumors with metastasis compared to primary tumors without metastasis or metastatic tissues. By the AS prediction analysis, we suggest that CDKN2A mutation sites play an important role for RCC metastasis by affecting the p16/p14 expression. CONCLUSIONS: The SMGs could provide new molecular cues associated with tumor metastasis and have potential clinical implications for cancer prognosis and treatment. Definitive conclusions await further validation and follow up.

Evaluation of the Current Therapeutic Approaches for COVID-19: A Systematic Review and a Meta-analysis.

Background: Limited data on the efficacy and safety of currently applied COVID-19 therapeutics and their impact on COVID-19 outcomes have raised additional concern. Objective and Methods: To estimate the efficacy and safety of COVID-19 therapeutics, we performed meta-analyses of the studies reporting clinical features and treatments of COVID-19 published from January 21 to September 6, 2020. Results: We included 136 studies that involved 102,345 COVID-19 patients. The most prevalent treatments were antibiotics (proportion: 0.59, 95% CI: [0.51, 0.67]) and antivirals (proportion: 0.52, 95% CI: [0.44, 0.60]). The combination of lopinavir/ritonavir and Arbidol was the most effective in treating COVID-19 (standardized mean difference (SMD) = 0.68, 95% CI: [0.15, 1.21]). The use of corticosteroids was associated with a small clinical improvement (SMD = -0.40, 95% CI: [-0.85, -0.23]), but with a higher risk of disease progression and death (mortality: RR = 9.26, 95% CI: [4.81, 17.80]; hospitalization length: RR = 1.54, 95% CI: [1.39, 1.72]; severe adverse events: RR = 2.65, 95% CI: [2.09, 3.37]). The use of hydroxychloroquine was associated with a higher risk of death (RR = 1.68, 95% CI: [1.18, 2.38]). The combination of lopinavir/ritonavir, ribavirin, and interferon-ß (RR = 0.34, 95% CI: [0.22, 0.54]); hydroxychloroquine (RR = 0.58, 95% CI: [0.39, 0.58]); and lopinavir/ritonavir (RR = 0.72, 95% CI: [0.56, 0.91]) was associated with reduced hospitalization length. Hydrocortisone (RR = 0.05, 95% CI: [0.03, 0.10]) and remdesivir (RR = 0.74, 95% CI: [0.62, 0.90]) were associated with lower incidence of severe adverse events. Dexamethasone was not significant in reducing disease progression (RR = 0.45, 95% CI: [0.16, 1.25]) and mortality (RR = 0.90, 95% CI: [0.70, 1.16]). The estimated combination of corticosteroids with antivirals was associated with a better clinical improvement than antivirals alone (SMD = -1.09, 95% CI: [-1.64, -0.53]). Conclusion: Antivirals are safe and effective in COVID-19 treatment. Remdesivir cannot significantly reduce COVID-19 mortality and hospitalization length, while it is associated with a lower incidence of severe adverse events. Corticosteroids could increase COVID-19 severity, but it could be beneficial when combined with antivirals. Our data are potentially valuable for the clinical treatment and management of COVID-19 patients.

Computational Identification of Tumor Suppressor Genes Based on Gene Expression Profiles in Normal and Cancerous Gastrointestinal Tissues.

Cancer prevails in various gastrointestinal (GI) organs, such as esophagus, stomach, and colon. However, the small intestine has an extremely low cancer risk. It is interesting to investigate the molecular cues that could explain the significant difference in cancer incidence rates among different GI tissues. Using several large-scale normal and cancer tissue genomics datasets, we compared the gene expression profiling between small intestine and other GI tissues and between GI cancers and normal tissues. We identified 17 tumor suppressor genes (TSGs) which showed significantly higher expression levels in small intestine than in other GI tissues and significantly lower expression levels in GI cancers than in normal tissues. These TSGs were mainly involved in metabolism, immune, and cell growth signaling-associated pathways. Many TSGs had a positive expression correlation with survival prognosis in various cancers, confirming their tumor suppressive function. We demonstrated that the downregulation of many TSGs was associated with their hypermethylation in cancer. Moreover, we showed that the expression of many TSGs inversely correlated with tumor purity and positively correlated with antitumor immune response in various cancers, suggesting that these TSGs may exert their tumor suppressive function by promoting antitumor immunity. Furthermore, we identified a transcriptional regulatory network of the TSGs and their master transcriptional regulators (MTRs). Many of MTRs have been recognized as tumor suppressors, such as HNF4A, ZBTB7A, p53, and RUNX3. The TSGs could provide new molecular cues associated with tumorigenesis and tumor development and have potential clinical implications for cancer diagnosis, prognosis, and treatment.

Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies.

WHAT IS KNOWN AND OBJECTIVE: Drug-drug interactions (DDI) are frequent causes of adverse clinical drug reactions. Efforts have been directed at the early stage to achieve accurate identification of DDI for drug safety assessments, including the development of in silico predictive methods. In particular, similarity-based in silico methods have been developed to assess DDI with good accuracies, and machine learning methods have been employed to further extend the predictive range of similarity-based approaches. However, the performance of a developed machine learning method is lower than expectations partly because of the use of less diverse DDI training data sets and a less optimal set of similarity measures. METHOD: In this work, we developed a machine learning model using support vector machines (SVMs) based on the literature-reported established set of similarity measures and comprehensive training data sets. The established similarity measures include the 2D molecular structure similarity, 3D pharmacophoric similarity, interaction profile fingerprint (IPF) similarity, target similarity and adverse drug effect (ADE) similarity, which were extracted from well-known databases, such as DrugBank and Side Effect Resource (SIDER). A pairwise kernel was constructed for the known and possible drug pairs based on the five established similarity measures and then used as the input vector of the SVM. RESULT: The 10-fold cross-validation studies showed a predictive performance of AUROC >0.97, which is significantly improved compared with the AUROC of 0.67 of an analogously developed machine learning model. Our study suggested that a similarity-based SVM prediction is highly useful for identifying DDI. CONCLUSION: in silico methods based on multifarious drug similarities have been suggested to be feasible for DDI prediction in various studies. In this way, our pairwise kernel SVM model had better accuracies than some previous works, which can be used as a pharmacovigilance tool to detect potential DDI.

Correction to: The Cancer Omics Atlas: an integrative resource for cancer omics annotations.

Following publication of the original article [1], the authors reported an error in Fig. 1A.

The Cancer Omics Atlas: an integrative resource for cancer omics annotations.

BACKGROUND: The Cancer Genome Atlas (TCGA) is an important data resource for cancer biologists and oncologists. However, a lack of bioinformatics expertise often hinders experimental cancer biologists and oncologists from exploring the TCGA resource. Although a number of tools have been developed for facilitating cancer researchers to utilize the TCGA data, these existing tools cannot fully satisfy the large community of experimental cancer biologists and oncologists without bioinformatics expertise. METHODS: We developed a new web-based tool The Cancer Omics Atlas (TCOA, http://tcoa.cpu.edu.cn ) for fast and straightforward querying of TCGA "omics" data. RESULTS: TCOA provides the querying of gene expression, somatic mutations, microRNA (miRNA) expression, protein expression data based on a single molecule or cancer type. TCOA also provides the querying of expression correlation between gene pairs, miRNA pairs, gene and miRNA, and gene and protein. Moreover, TCOA provides the querying of the associations between gene, miRNA, or protein expression and survival prognosis in cancers. In addition, TCOA displays transcriptional profiles across various human cancer types based on the pan-cancer analysis. Finally, TCOA provides the querying of molecular profiles for 2877 immune-related genes in human cancers. These immune-related genes include those that are established or promising targets for cancer immunotherapy such as CTLA4, PD1, PD-L1, PD-L2, IDO1, LAG3, and TIGIT. CONCLUSIONS: TCOA is a useful tool that supplies a number of unique and new functions complementary to the existing tools to facilitate exploration of the TCGA resource.

Nursing interventions in depressed children with low serum levels of BDNF.

The purpose of this study was to investigate the correlation between brain-derived neurotrophic factor (BDNF) in serum and depression in children, and explore the effects of different nursing protocols on patients with low levels of BDNF. We recruited 128 children with depression and 50 healthy subjects. Compared with healthy controls, the mRNA and protein levels of BDNF in serum were lower in children with depression (p<0.01). We selected 60 depressed children with low BDNF and randomly divided them in two groups: comprehensive nursing (n=30) and regular nursing (n=30). Compared to healthy children, there was a significant increase in Hamilton depression (HAMD) scores in patients with depression in childhood (p<0.01). After treatment, BDNF protein expression was higher in the comprehensive nursing group than that in the regular nursing group (p<0.05). Also, the HAMD score in the comprehensive nursing group was significantly lower than that in the regular nursing group (p<0.05). Compliance to treatment and quality of life after treatment improved in the comprehensive nursing group compared with the regular nursing group (p<0.05). Overall, a decrease in BDNF expression is closely correlated with depression, and comprehensive nursing care can significantly ameliorate the depression symptoms in pediatric patients, increase the BDNF expression, and improve compliance and quality of life. These results provide theoretical and practical significance for clinical nursing care of patients with depression in childhood.

A Pharmacokinetic-Pharmacodynamic Model of Tamoxifen and Endoxifen to Predict Their Distribution and Effects on Inhibition of Tumor Growth.

BACKGROUND: Tamoxifen is widely used in the therapy for breast cancer and has three major metabolites, N-desmethyltamoxifen, 4-hydroxytamoxifen, and endoxifen. Endoxifen has played a major role in the inhibition of tumor growth of breast cancer and the tumor growth is related to endoxifen concentration. OBJECTIVES: The aim of this study was to develop a pharmacokinetic-pharmacodynamic model to predict the distribution of tamoxifen and endoxifen quantitatively, and to discover the anti-tumor effect patterns of tamoxifen and endoxifen. METHODS: The pharmacokinetic-pharmacodynamic model was established by integrating a four compartments pharmacokinetics model and a pharmacodynamic model, the first one include central compartment and peripheral compartment both of which contain tamoxifen and endoxifen. The parameters of the model were calculated by the values of plasma concentrations and the tumor growth data before and after the administration of tamoxifen. RESULTS: The transport rate k42 (6.0003) of endoxifen from the peripheral compartment to the central compartment and the metabolism rate k34 (0.0031) from tamoxifen to endoxifen in the peripheral compartment were proven to be significant, which showed that tamoxifen and endoxifen are mainly distributed in the central compartment. The model provided reasonable predictions of tumor growth, which was inhibited after the administration and varies with the concentration of endoxifen. CONCLUSION: We established a PK-PD model of tamoxifen and endoxifen to predict the tumor growth. The parameters of the pharmacodynamic model, which characterized the tumor growth, revealed the patterns of tamoxifen's anti-tumor functions. The PK-PD model successfully provided illustration for the pharmacokinetics of tamoxifen and endoxifen, and predicted the inhibition effect of endoxifen on the tumor growth.

Transcriptional landscape of human cancers.

The homogeneity and heterogeneity in somatic mutations, copy number alterations and methylation across different cancer types have been extensively explored. However, the related exploration based on transcriptome data is lacking. In this study we explored gene expression profiles across 33 human cancer types using The Cancer Genome Atlas (TCGA) data. We identified consistently upregulated genes (such as E2F1, EZH2, FOXM1, MYBL2, PLK1, TTK, AURKA/B and BUB1) and consistently downregulated genes (such as SCARA5, MYOM1, NKAPL, PEG3, USP2, SLC5A7 and HMGCLL1) across various cancers. The dysregulation of these genes is likely to be associated with poor clinical outcomes in cancer. The dysregulated pathways commonly in cancers include cell cycle, DNA replication, repair, and recombination, Notch signaling, p53 signaling, Wnt signaling, TGFß signaling, immune response etc. We also identified genes consistently upregulated or downregulated in highly-advanced cancers compared to lowly-advanced cancers. The highly (low) expressed genes in highly-advanced cancers are likely to have higher (lower) expression levels in cancers than in normal tissue, indicating that common gene expression perturbations drive cancer initiation and cancer progression. In addition, we identified a substantial number of genes exclusively dysregulated in a single cancer type or inconsistently dysregulated in different cancer types, demonstrating the intertumor heterogeneity. More importantly, we found a number of genes commonly dysregulated in various cancers such as PLP1, MYOM1, NKAPL and USP2 which were investigated in few cancer related studies, and thus represent our novel findings. Our study provides comprehensive portraits of transcriptional landscape of human cancers.

PLK1, A Potential Target for Cancer Therapy.

Polo-like kinase 1 (PLK1) plays an important role in the initiation, maintenance, and completion of mitosis. Dysfunction of PLK1 may promote cancerous transformation and drive its progression. PLK1 overexpression has been found in a variety of human cancers and was associated with poor prognoses in cancers. Many studies have showed that inhibition of PLK1 could lead to death of cancer cells by interfering with multiple stages of mitosis. Thus, PLK1 is expected to be a potential target for cancer therapy. In this article, we examined PLK1's structural characteristics, its regulatory roles in cell mitosis, PLK1 expression, and its association with survival prognoses of cancer patients in a wide variety of cancer types, PLK1 interaction networks, and PLK1 inhibitors under investigation. Finally, we discussed the key issues in the development of PLK1-targeted cancer therapy.

TP53 mutations, expression and interaction networks in human cancers.

Although the associations of p53 dysfunction, p53 interaction networks and oncogenesis have been widely explored, a systematic analysis of TP53 mutations and its related interaction networks in various types of human cancers is lacking. Our study explored the associations of TP53 mutations, gene expression, clinical outcomes, and TP53 interaction networks across 33 cancer types using data from The Cancer Genome Atlas (TCGA). We show that TP53 is the most frequently mutated gene in a number of cancers, and its mutations appear to be early events in cancer initiation. We identified genes potentially repressed by p53, and genes whose expression correlates significantly with TP53 expression. These gene products may be especially important nodes in p53 interaction networks in human cancers. This study shows that while TP53-truncating mutations often result in decreased TP53 expression, other non-truncating TP53 mutations result in increased TP53 expression in some cancers. Survival analyses in a number of cancers show that patients with TP53 mutations are more likely to have worse prognoses than TP53-wildtype patients, and that elevated TP53 expression often leads to poor clinical outcomes. We identified a set of candidate synthetic lethal (SL) genes for TP53, and validated some of these SL interactions using data from the Cancer Cell Line Project. These predicted SL genes are promising candidates for experimental validation and the development of personalized therapeutics for patients with TP53-mutated cancers.

Core-shell microstructured nanocomposites for synergistic adjustment of environmental temperature and humidity.

The adjustment of temperature and humidity is of great importance in a variety of fields. Composites that can perform both functions are prepared by mixing phase change materials (PCMs) with hygroscopic materials. However, the contact area between the adsorbent and humid air is inevitably decreased in such structures, which reduces the number of mass transfer channels for water vapor. An approach entailing the increase in the mass ratio of the adsorbent is presented here to improve the adsorption capacity. A core-shell CuSO4/polyethylene glycol (PEG) nanomaterial was developed to satisfy the conflicting requirements of temperature control and dehumidification. The results show that the equilibrium adsorption capacity of the PEG coating layer was enhanced by a factor of 188 compared with that of the pure PEG powder. The coating layer easily concentrates vapor, providing better adsorption properties for the composite. Furthermore, the volume modification of the CuSO4 matrix was reduced by 80% by the PEG coated layer, a factor that increases the stability of the composite. For the phase change process, the crystallization temperature of the coating layer was adjusted between 37.2 and 46.3 °C by interfacial tension. The core-shell CuSO4/PEG composite reported here provides a new general approach for the simultaneous control of temperature and humidity.

Steady-state equation of water vapor sorption for CaCl2-based chemical sorbents and its application.

Green CaCl2-based chemical sorbent has been widely used in sorption refrigeration, air purification and air desiccation. Methods to improve the sorption rate have been extensively investigated, but the corresponding theoretical formulations have not been reported. In this paper, a sorption system of solid-liquid coexistence is established based on the hypothesis of steady-state sorption. The combination of theoretical analysis and experimental results indicates that the system can be described by steady-state sorption process. The steady-state sorption equation, µ = (η - γT) , was obtained in consideration of humidity, temperature and the surface area. Based on engineering applications and this equation, two methods including an increase of specific surface area and adjustment of the critical relative humidity (γ) for chemical sorbents, have been proposed to increase the sorption rate. The results indicate that the CaCl2/CNTs composite with a large specific surface area can be obtained by coating CaCl2 powder on the surface of carbon nanotubes (CNTs). The composite reached sorption equilibrium within only 4 h, and the sorption capacity was improved by 75% compared with pure CaCl2 powder. Furthermore, the addition of NaCl powder to saturated CaCl2 solution could significantly lower the solution's γ. The sorption rate was improved by 30% under the same environment.

Molecular cloning and heterologous expression analysis of JrVTE1 gene from walnut (Juglans regia).

Tocopherol cyclase (VTE1) plays a key role in promoting the production of γ-tocopherol and improving total tocopherol content in photosynthetic organisms. Walnut is an important source of tocopherols in the human diet, and γ-tocopherol is the major tocopherol compound in walnut kernels. In this study, a full-length cDNA of the VTE1 gene was isolated from walnut using RT-PCR and RACE, and designated as JrVTE1. The full-length cDNA of the JrVTE1 gene contained a 1353-bp open-reading frame encoding a 451-amino-acid protein with a calculated molecular weight of 49.5 kDa. The deduced JrVTE1 protein had a considerable homology with other plant VTE1s and belonged to the tocopherol cyclase family. Functional characterization of JrVTE1 by heterologous expression was carried out in E. coli BL21 (DE3) and microshoot lines of the fruit trees jujube (Zizyphus jujuba var. spinosa) and pear (Pyrus communis) cultivar 'Old Home'. JrVTE1 in E. coli expressed as a 50 kDa protein, as expected. One or two copies of the transferred JrVTE1 gene were detected in the genomes of representative transgenic lines (from the initial transgenic plants) of jujube and pear by gel blots analysis. Over-expression of JrVTE1 in jujube and pear resulted in an accumulation of tocopherol and a shift in tocopherol composition in leaf, root and stem tissues. In the transgenic jujube, the total tocopherol content increased by 29.8 µg/g in the stems of line J3, 43.7 and 22.5 µg/g in the roots and leaves of line J1, respectively, whereas in the transgenic pear it increased by 47.3 µg/g in the leaf of line P3, and 16.7 and 10.4 µg/g in roots and stems of line P9, respectively. In the examined tissues of transgenic plants, the highest accumulation rate was the γ-tocopherol. These results indicate that JrVTE1 is one of the rate-limiting enzymes for tocopherol production and could be used to improve the tocopherol content of tree crops through genetic engineering.