Puccinia suaveolens Causing Leaf Rust on Cirsium setosum in China.

Thistle, Cirsium setosum (Willd.) M. Bieb., is widely distributed in China as a common weed in fields. It is also used as a traditional Chinese medicine for cooling blood, stopping bleeding, dispelling stasis, detoxifying, and resolving carbuncle. In 2023, we found a rust disease on plants of Cirsium setosum in the experimental field of Hebei Agricultural University, Baoding, Hebei Province, China, with incidence of 15% - 25% (Fig. S1 A, B). The diseased leaves turned yellow, and the leaf edges were slightly rolled. The yellow, oil-like pycnia and pycniospores covered the baxial surface of leaves, and brown pustules were produced after 2-3 weeks. On the adaxial surface of the leaves, the brown rust pustules were mainly along the leaf veins. Stems were also be infected later, and dark pustules were scattered. The diseased plants were relatively short and small, and produced relatively small or no flowers compared to healthy plants. A total of 100 plants with typical leaf rust symptoms and signs were collected. To confirm the pathogenicity, healthy plants of thistle were sprayed with 5 ml of urediospores suspension (2.6×105/ml), and plants sprayed with sterile distilled water were treated as control. The sprayed plants were incubated under high moist conditions at 18°C for 24 h, and the inoculated plants were grown at 20°C in a greenhouse. Ten days after inoculation, the plants inoculated with urediniospores showed rust symptoms with uredinia and urediniospores on the leaves (Fig. S1 C), while the control plants were healthy. For morphological characterization, urediospores were picked from the naturally infected plants and placed in a drop of sterile water on a glass slide using a sterile needle, and observed and measured under a microscope. Urediospores were nearly spherical, brown-yellow, and measured 15 - 25 µm in diameter (n=100) (Fig. S1 D). Telia were scattered on the baxial surface of the naturally infected leaves, and teliospores were oval, yellow-brown, double-celled, with very short hyaline pedicels, and measured 15-20 × 15-30 µm (n=100) (Fig. S1 E). For molecular characterization, about 200 µg of urediniospores was collected and placed in a 1.5 ml sterile centrifuge tube, and genomic DNA was extracted using the cetyl-trimethylammonium bromide method (Gawel et al. 1991). The internal transcribed spacer (ITS) region of the rDNA and the D1/D2 domain were amplified using primer pairs ITS1/ITS4 (White et al. 1990) and NL1/NL4 (Borhani et al. 2013) in polymerase chain reaction (PCR), respectively. The PCR products were sequenced, and their sequences were aligned and compared with those deposited in GenBank. The obtained sequences were deposited in GenBank (OR600240 for ITS and OR598614 for D1/D2), which were 100% identical with 100% coverage to the ITS sequence (ON063373.1) and the D1/D2 sequence (ON063379.1) of Puccinia suaveolens (Menzies 1953). Based on the morphological characteristics and DNA sequences, the isolates were identified as P. suaveolens (Fig. S1 and Fig. S2). Thistle rust caused by Puccinia obtegens has been reported in some other parts of China (Zhang 2012). To the best of our knowledge, this is the first report of P. suaveolens causing leaf rust on C. setosum in China. This discovery is helpful for control of leaf rust on thistle grown for Chines medicine and other purposes, and the rust species could be used for biological control of thistle as a weed in crop fields.

Prediction of Membrane Protein Amphiphilic Helix Based on Horizontal Visibility Graph and Graph Convolution Network.

Membrane protein amphiphilic helices play an important role in many biological processes. Based on the graph convolution network and the horizontal visibility graph the prediction method of membrane protein amphiphilic helix structure is proposed in this paper. The new dataset of amphiphilic helix is constructed. In this paper, we propose the novel feature extraction method, which characterize the amphiphilicity of membrane protein. We also extract three commonly used protein features together with the new features as protein node features. The neighbor information and long-distance dependence information of proteins are further extracted by sliding window and bidirectional long-short term memory network respectively. From the perspective of horizontal visibility algorithm, we transform protein sequences into complex networks to obtain the graph features of proteins. Then, graph convolutional network model is employed to predict the amphiphilic helix structure of membrane protein. A rigorous ten-fold cross-validation shows that the proposed method outperforms other AH prediction methods on the newly constructed dataset.

Virulence and molecular genetic diversity, variation, and evolution of the Puccinia triticina population in Hebei Province of China from 2001 to 2010.

Wheat leaf rust, caused by Puccinia triticina, is one of the most important fungal diseases of wheat in China. However, little is known about the dynamic changes of population structure and genetic diversity of P. triticina during a period of time. In this study, 247 isolates of P. triticina collected from Hebei Province from 2001 to 2010 were tested on 36 Thatcher near-isogenic lines for virulence diversity and detected by 21 pairs of Expressed Sequence Tag derived Simple Sequence Repeat (EST-SSR) primers for genetic diversity. A total of 204 isolates were successfully identified as 164 races, and THTT, THST, PHRT, THTS, and PHTT were the most common races in Hebei Province from 2001 to 2010. The cluster analysis based on virulence showed that P. triticina has a rich virulence polymorphism, which had a certain correlation with the years, while the cluster analysis based on EST-SSR showed that the genetic diversity of the P. triticina population was significantly different between years in Hebei Province from 2001 to 2010. In addition, the population structure of P. triticina may have changed greatly in 2007 and 2009, which was significantly different from that of 2001-2006 on either virulence or genetic characteristics. The variation frequency of the population structure had an increasing trend during this period. From 2001 to 2010, there was a certain degree of gene flow among the P. triticina populations. No significant correlation was found between virulence and molecular polymorphism. The genetic differentiation analysis of the 10 tested populations (each year as a population) showed that the coefficient of genetic differentiation (Gst) was 0.27, indicating that there was a certain genetic differentiation among or within populations of P. triticina in Hebei Province. The genetic variation within populations (73.08%) was higher than that among populations (26.92%), which indicated that the genetic variations were mainly found within populations. Our study provides the foundation for a better understanding of the population structure change and genetic diversity of P. triticina over a period in Hebei Province of China.

Evaluation of the resistance to Chinese predominant races of Puccinia triticina and analysis of effective leaf rust resistance genes in wheat accessions from the U.S. National Plant Germplasm System.

Puccinia triticina, which is the causative agent of wheat leaf rust, is widely spread in China and most other wheat-planting countries around the globe. Cultivating resistant wheat cultivars is the most economical, effective, and environmentally friendly method for controlling leaf rust-caused yield damage. Exploring the source of resistance is very important in wheat resistance breeding programs. In order to explore more effective resistance sources for wheat leaf rust, the resistance of 112 wheat accessions introduced from the U.S. National Plant Germplasm System were identified using a mixture of pathogenic isolates of THTT, THTS, PHTT, THJT and THJS which are the most predominant races in China. As a result, all of these accessions showed high resistance at seedling stage, of which, ninety-nine accessions exhibited resistance at adult plant stage. Eleven molecular markers of eight effective leaf rust resistance genes in China were used to screen the 112 accessions. Seven effective leaf rust resistance genes Lr9, Lr19, Lr24, Lr28, Lr29, Lr38 and Lr45 were detected, except Lr47. Twenty-three accessions had only one of those seven effective leaf rust resistance gene. Eleven accessions carried Lr24+Lr38, and 7 accessions carried Lr9+Lr24+Lr38, Lr24+Lr38+Lr45, Lr24+Lr29+Lr38 and Lr19+Lr38+Lr45 respectively. The remaining seventy-one accessions had none of those eight effective leaf rust resistance genes. This study will provide theoretical guidance for rational utilization of these introduted wheat accessions directly or for breeding the resistant wheat cultivars.

Risk of gestational hypertension and preeclampsia in pregnant women with new onset blood pressure of 120-129/≤89 mmHg: a meta-analysis of prospective studies.

BACKGROUND: To explore whether elevated blood pressure and stage 1 hypertension were risks to GH and PE occurrence by a meta-analysis. METHODS: Electronic databases were searched systematically up to Dec 2020. Combined risk ratio (RR) and 95%CI were used to evaluate the relationship between two blood pressure groups and GH and PE. RESULTS: Seven studies were included in this study. In elevated blood pressure group, the overall RR of GH group is 1.93 and the combined RR is 1.98 in PE group. CONCLUSION: Elevated BP and stage 1 hypertension were risk factors of GH and PE occurrence in pregnant women.

A Cascade Flexible Neural Forest Model for Cancer Subtypes Classification on Gene Expression Data.

The correct classification of cancer subtypes is of great significance for the in-depth study of cancer pathogenesis and the realization of accurate treatment for cancer patients. In recent years, the classification of cancer subtypes using deep neural networks and gene expression data has become a hot topic. However, most classifiers may face the challenges of overfitting and low classification accuracy when dealing with small sample size and high-dimensional biological data. In this paper, the Cascade Flexible Neural Forest (CFNForest) Model was proposed to accomplish cancer subtype classification. CFNForest extended the traditional flexible neural tree structure to FNT Group Forest exploiting a bagging ensemble strategy and could automatically generate the model's structure and parameters. In order to deepen the FNT Group Forest without introducing new hyperparameters, the multilayer cascade framework was exploited to design the FNT Group Forest model, which transformed features between levels and improved the performance of the model. The proposed CFNForest model also improved the operational efficiency and the robustness of the model by sample selection mechanism between layers and setting different weights for the output of each layer. To accomplish cancer subtype classification, FNT Group Forest with different feature sets was used to enrich the structural diversity of the model, which make it more suitable for processing small sample size datasets. The experiments on RNA-seq gene expression data showed that CFNForest effectively improves the accuracy of cancer subtype classification. The classification results have good robustness.

A laminar augmented cascading flexible neural forest model for classification of cancer subtypes based on gene expression data.

BACKGROUND: Correctly classifying the subtypes of cancer is of great significance for the in-depth study of cancer pathogenesis and the realization of personalized treatment for cancer patients. In recent years, classification of cancer subtypes using deep neural networks and gene expression data has gradually become a research hotspot. However, most classifiers may face overfitting and low classification accuracy when dealing with small sample size and high-dimensional biology data. RESULTS: In this paper, a laminar augmented cascading flexible neural forest (LACFNForest) model was proposed to complete the classification of cancer subtypes. This model is a cascading flexible neural forest using deep flexible neural forest (DFNForest) as the base classifier. A hierarchical broadening ensemble method was proposed, which ensures the robustness of classification results and avoids the waste of model structure and function as much as possible. We also introduced an output judgment mechanism to each layer of the forest to reduce the computational complexity of the model. The deep neural forest was extended to the densely connected deep neural forest to improve the prediction results. The experiments on RNA-seq gene expression data showed that LACFNForest has better performance in the classification of cancer subtypes compared to the conventional methods. CONCLUSION: The LACFNForest model effectively improves the accuracy of cancer subtype classification with good robustness. It provides a new approach for the ensemble learning of classifiers in terms of structural design.

Identification and Predictions Regarding the Biosynthesis Pathway of Polyene Macrolides Produced by Streptomyces roseoflavus Men-myco-93-63.

A group of polyene macrolides mainly composed of two constituents was isolated from the fermentation broth of Streptomyces roseoflavus Men-myco-93-63, which was isolated from soil where potato scabs were repressed naturally. One of these macrolides was roflamycoin, which was first reported in 1968, and the other was a novel compound named Men-myco-A, which had one methylene unit more than roflamycoin. Together, they were designated RM. This group of antibiotics exhibited broad-spectrum antifungal activities in vitro against 17 plant-pathogenic fungi, with 50% effective concentrations (EC50) of 2.05 to 7.09 µg/ml and 90% effective concentrations (EC90) of 4.32 to 54.45 µg/ml, which indicates their potential use in plant disease control. Furthermore, their biosynthetic gene cluster was identified, and the associated biosynthetic assembly line was proposed based on a module and domain analysis of polyketide synthases (PKSs), supported by findings from gene inactivation experiments.IMPORTANCEStreptomyces roseoflavus Men-myco-93-63 is a biocontrol strain that has been studied in our laboratory for many years and exhibits a good inhibitory effect in many crop diseases. Therefore, the identification of antimicrobial metabolites is necessary and our main objective. In this work, chemical, bioinformatic, and molecular biological methods were combined to identify the structures and biosynthesis of the active metabolites. This work provides a new alternative agent for the biological control of plant diseases and is helpful for improving both the properties and yield of the antibiotics via genetic engineering.

First Report of Alternaria alternata Causing Leaf Spot on Avena nuda in Zhangbei, China.

Naked oats (Avena nuda L.) is an independent species of Avena, which can be used as both food and forage for rich nutritional value. In August 2019, leaf spot was observed at a naked oats planting base in Zhangbei County, Zhangjiakou City, Hebei Province. The incidence of disease was 40% to 50%. The symptoms of the lesions were chlorosis and gradually developing light brown spots with light yellow halos. The spots were irregular, enlarged and even coalesced to form large areas of necrosis on leaves. To identify the pathogen, twenty symptomatic leaves were collected, and one disease spot was isolated from each samples. Small square leaf pieces (3 to 5 mm) were excised from the junction of diseased and healthy tissues with a sterile scalpel and were sterilized with 75% alcohol for 30s, 0.1% mercuric chloride solution for 1 min, and then rinsed three times with sterile water, then transferred cultured on potato dextrose agar (PDA) at 25°C for 7 days. Four fungal isolates were obtained and purified by single-spore isolation method. All fungi have the same morphology and no other fungi were isolated. Colonies of the isolates had round margins, and thick fluffy aerial mycelia with brown coloration after 7 days on PDA. Conidiophores were brown, straight or flexuous, septate, single or in clusters. Conidia were obclavate or oval, dark brown, and size ranging from 4.61 to 15.68 × 6.61 to 35.49µm (n=100), with longitudinal and transverse septa varying from 1 to 3 and 1 to 7, respectively. The transverse median septum of the central section was especially thick. On the basis of morphological characteristics, the isolates were identified as Alternaria spp. (Simmons 2007). To further assess the identity of the species, the genomic DNA of pathogenic isolate (YM3) was extracted by CTAB protocol. The ribosomal DNA internal transcribed spacer (ITS) region, the glyceraldehyde-3-phosphate dehydrogenase (GAPDH), the RNA polymerase II second largest subunit (RPB2), and the plasma membrane ATPase genes were amplified and sequenced with primers ITS1/4, gpd1/2, RPB2-6F/7cR and ATPDF1/ATPDR1 respectively (Nishikawa and Nakashima 2015; Woudenberg et al. 2015). Sequences of ITS, GAPDH, RPB2 and ATPase (MN646900, MT233043, MT233042, MN640794) of the isolate was 99.82%, 99.68%, 100% and 99.51% similar to the fungus A. alternata (MK461082.1, MK451978, KP124770.1, MK804115). A neighbor-joining phylogenetic tree was constructed by combining all sequenced loci in MEGA7. The isolate YM3 clustered in the A. alternata clade with 100% bootstrap support. Therefore, the pathogen was identified as A. alternata based on the morphological characteristics and molecular identification. A pathogenicity test of the A. alternata isolates was performed by placing mycelial disks (5 mm) with conidia on the surface of the first unfolding leaves of naked oats. Each leaf was inoculated with three disks. The pathogenicity test was repeated four times, and 10 leaves were inoculated in each repetition, while sterile PDA was used as the control. All treated plants were placed in a moist chamber (25°C, 16-h light and 8-h dark period). Leaf spot symptoms developed on the inoculated plants about 10 days post inoculation while all control plants remained healthy. The similar isolates were re-isolated from the inoculated and infected leaves and identified as A. alternata by DNA sequencing, fulfilling Koch's postulates. It has been reported that A. alternata can cause leaf spots on A. Sativa(Chen et al. 2020). However, to our knowledge, this is the first report of A. alternata causing leaf spots on A. nuda in China. It can be concluded that A. alternata can cause leaf spot disease of oats (A. Sativa and A. nuda). The spots disease is worthy of our attention for its harm to the production of oats.

Race and Virulence Analysis of Puccinia triticina in China During 2011 to 2013.

Wheat leaf rust, caused by Puccinia triticina, is a common fungal disease of wheat in China. In order to identify races and determine the individual virulence of isolates in different wheat-growing regions in China, leaf rust samples collected from 18 provinces in 2011 to 2013 were tested on 37 Thatcher near-isogenic lines each carrying a different single leaf rust resistance gene. A total of 158 races were identified. Races THTT (19.5%), THTS (16.9%), PHTT (7.7%), THJS (5.0%), THJT (4.2%), and PHTS (4.0%) were the most predominant races in 2011 to 2013. All of these races were avirulent to resistance genes Lr9 and Lr24. The two most frequent races, THTT and THTS, were widely distributed. The frequencies of the isolates with virulence to Lr1, Lr2c, Lr3, Lr16, Lr26, Lr17, LrB, Lr10, Lr14a, Lr3bg, Lr14b, Lr33, Lr37, and Lr50 exceeded 90%. Frequencies of virulence to Lr2a, Lr3ka, Lr11, Lr30, Lr2b, and Lr32 exceeded 70% but were less than 90%. Frequencies of virulence to Lr18, Lr21, Lr15, Lr23, Lr33+34, Lr36, Lr39, and Lr44 were below 70%, whereas the frequency of virulence to Lr25 was less than 1%. All isolates were avirulent to Lr9, Lr19, Lr24, Lr28, Lr42, Lr29, Lr38, and Lr47. The identified races and individual virulence frequencies provide a basis for selection of effective leaf rust resistance genes for use in breeding programs and can also provide information for the study of race evolution of P. triticina.

Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and LDA.

BACKGROUND: Apoptosis, also called programmed cell death, refers to the spontaneous and orderly death of cells controlled by genes in order to maintain a stable internal environment. Identifying the subcellular location of apoptosis proteins is very helpful in understanding the mechanism of apoptosis and designing drugs. Therefore, the subcellular localization of apoptosis proteins has attracted increased attention in computational biology. Effective feature extraction methods play a critical role in predicting the subcellular location of proteins. RESULTS: In this paper, we proposed two novel feature extraction methods based on evolutionary information. One of the features obtained the evolutionary information via the transition matrix of the consensus sequence (CTM). And the other utilized the evolutionary information from PSSM based on absolute entropy correlation analysis (AECA-PSSM). After fusing the two kinds of features, linear discriminant analysis (LDA) was used to reduce the dimension of the proposed features. Finally, the support vector machine (SVM) was adopted to predict the protein subcellular locations. The proposed CTM-AECA-PSSM-LDA subcellular location prediction method was evaluated using the CL317 dataset and ZW225 dataset. By jackknife test, the overall accuracy was 99.7% (CL317) and 95.6% (ZW225) respectively. CONCLUSIONS: The experimental results show that the proposed method which is hopefully to be a complementary tool for the existing methods of subcellular localization, can effectively extract more abundant features of protein sequence and is feasible in predicting the subcellular location of apoptosis proteins.

Puccinia triticina pathotypes THTT and THTS display complex transcript profiles on wheat cultivar Thatcher.

BACKGROUND: Wheat leaf rust is an important disease worldwide. Understanding the pathogenic molecular mechanism of Puccinia triticina Eriks. (Pt) and the inconstant toxic region is critical for managing the disease. The present study aimed to analyze the pathogenic divergence between Pt isolates. RESULTS: Total RNA was extracted from the wheat cultivar Thatcher infected by two Pt isolates, Tc361_1 (THTT) and Tc284_2 (THTS), at 144 h post inoculation (hpi). The mRNA was then sequenced, and a total of 2784 differentially expressed genes (DEGs) were detected. Forty-five genes were specifically expressed in THTT; these genes included transcription initiation factors and genes with transmembrane transporter activity and other genes. Twenty-six genes were specifically expressed in THTS, including genes with GTPase activity, ABC transporters and other genes. Fifty-four differentially expressed candidate effectors were screened from the two isolates. Two candidate effectors were chosen and validated on tobacco, and the results showed that they could inhibit necrosis induced by BAX. qRT-PCR of 12 significant DEGs was carried out to validate that the results are similar to those of RNA-seq at 144 hpi, to show the expression levels of these DEGs in the early stage and to elucidate the differences in expression between the two Pt pathotypes. CONCLUSION: The results obtained in this study showed that although the two pathotypes of THTT and THTS contribute similar virulence to wheat, there are a large number of genes participate in the interaction with the susceptible wheat cultivar Thatcher, and revealed the pathogenicity of rust is very complicated.

Race and Virulence Analysis of Puccinia triticina in China in 2014 and 2015.

Wheat leaf rust, caused by Puccinia triticina, is an important fungal disease of wheat in China. To study races of the pathogen in China, leaf rust samples were collected from 14 provinces in 2014 and 15 provinces in 2015. From the samples, 494 single-uredinial isolates were derived from the 2014 collection and 649 from the 2015 collection. These isolates were tested on 40 near-isogenic lines of Thatcher carrying single leaf rust resistance genes. From the isolates, 84 races were identified in 2014 and 65 races in 2015. Races THTT (22.1%), THTS (19.6%), THJT (8.7%), PHTT (4.9%), and PHJT (3.6%) were the most common races in 2014, and THTT (28.4%), THTS (12.8%), THJT (11.6%), THJS (9.9%), and PHTT (9.7%) were the most frequent in 2015. All of these races were avirulent to resistance genes Lr9 and Lr24. THTT and THTS, the most frequent races in both years, were widely distributed throughout the country. The frequencies of isolates with virulence to Lr1, Lr2a, Lr2c, Lr3, Lr16, Lr26, Lr11, Lr17, LrB, Lr10, Lr14a, Lr2b, Lr3bg, Lr14b, Lr32, Lr33, and Lr50 were over 80%, whereas the frequencies of virulence to Lr9, Lr19, Lr25, Lr28, Lr29, and Lr47 were less than 3.5%. In the present study, all isolates were avirulent to Lr24 and Lr38. The race analysis and individual virulence frequencies provide guidance to breeders in choosing leaf rust resistance genes for use in breeding programs.

A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data.

BACKGROUND: Cancer subtype classification attains the great importance for accurate diagnosis and personalized treatment of cancer. Latest developments in high-throughput sequencing technologies have rapidly produced multi-omics data of the same cancer sample. Many computational methods have been proposed to classify cancer subtypes, however most of them generate the model by only employing gene expression data. It has been shown that integration of multi-omics data contributes to cancer subtype classification. RESULTS: A new hierarchical integration deep flexible neural forest framework is proposed to integrate multi-omics data for cancer subtype classification named as HI-DFNForest. Stacked autoencoder (SAE) is used to learn high-level representations in each omics data, then the complex representations are learned by integrating all learned representations into a layer of autoencoder. Final learned data representations (from the stacked autoencoder) are used to classify patients into different cancer subtypes using deep flexible neural forest (DFNForest) model.Cancer subtype classification is verified on BRCA, GBM and OV data sets from TCGA by integrating gene expression, miRNA expression and DNA methylation data. These results demonstrated that integrating multiple omics data improves the accuracy of cancer subtype classification than only using gene expression data and the proposed framework has achieved better performance compared with other conventional methods. CONCLUSION: The new hierarchical integration deep flexible neural forest framework(HI-DFNForest) is an effective method to integrate multi-omics data to classify cancer subtypes.

Novel Automatic Epilepsy Detection Method Multi-weight Transition Network.

The automatic diagnosis of epilepsy using Electroencephalogram (EEG) signals had always been an important research direction. A novel automatic epilepsy detection method based on multi-weight transition network was proposed in this paper. The epileptic EEG signal was first transformed into complex network according to our proposed multi-weight transition network algorithm. Then, based on the statistical characteristics of the multi-weight transition network, the degree of network and the local entropy of network were extracted as features. Finally, the extracted features and support vector machines (SVM) were combined to classify epileptic seizure and non-seizure signals, and the classification performance was evaluated by k-fold cross validation. Seven different experimental cases were tested. The experimental results indicate that the algorithm had high classification accuracy for all cases.

Therapeutic potential of artesunate in experimental autoimmune myasthenia gravis by upregulated T regulatory cells and regulation of Th1/Th2 cytokines.

Artesunate is a semi-synthetic derivative of a Chinese herb named Artemisia annua L. that is commonly used as an antimalarial agent in the history of traditional Chinese medicine. Many studies have reported artesunate possesses anti-inflammatory and immunoregulation properties. The present study was conducted to explore whether artesunate was effective in experimental autoimmune myasthenia gravis (EAMG) in Lewis rats. Our data showed that artesunate could improve the clinical symptoms and suppress the development of EAMG. Artesunate exerted its immunomodulatory effects by inhibiting lymphocyte proliferation and the expression of costimulatory molecules CD86, modulating Th1/Th2 cytokine expression levels, and enhancing the level of Treg cells. The final result of administration of artesunate was the decreased synthesis of anti-R97-116 IgG, IgG2a, and IgG2b antibodies. The treatment effect of artesunate was more obvious at dose of 10 mg/kg. These date suggest that artesunate might be a potential drug for the treatment of human myasthenia gravis (MG).

A reliable and validated LC-MS/MS method for the simultaneous quantification of 4 cannabinoids in 40 consumer products.

In the past 50 years, Cannabis sativa (C. sativa) has gone from a substance essentially prohibited worldwide to one that is gaining acceptance both culturally and legally in many countries for medicinal and recreational use. As additional jurisdictions legalize Cannabis products and the variety and complexity of these products surpass the classical dried plant material, appropriate methods for measuring the biologically active constituents is paramount to ensure safety and regulatory compliance. While there are numerous active compounds in C. sativa the primary cannabinoids of regulatory and safety concern are (-)-Δ9-tetrahydrocannabinol (THC), cannabidiol (CBD), and their respective acidic forms THCA-A and CBDA. Using the US Food and Drug Administration (FDA) bioanalytical method validation guidelines we developed a sensitive, selective, and accurate method for the simultaneous analysis CBD, CBDA, THC, and THCA-A in oils and THC & CBD in more complex matrices. This HPLC-MS/MS method was simple and reliable using standard sample dilution and homogenization, an isocratic chromatographic separation, and a triple quadrupole mass spectrometer. The lower limit of quantification (LLOQ) for analytes was 0.195 ng/mL over a 0.195-50.0 ng/mL range of quantification with a coefficient of correlation of >0.99. Average intra-day and inter-day accuracies were 94.2-112.7% and 97.2-110.9%, respectively. This method was used to quantify CBD, CBDA, THC, and THCA-A in 40 commercial hemp products representing a variety of matrices including oils, plant materials, and creams/cosmetics. All products tested met the federal regulatory restrictions on THC content in Canada (<10 µg/g) except two, with concentrations of 337 and 10.01 µg/g. With respect to CBD, the majority of analyzed products contained low CBD levels and a CBD: CBDA ratio of <1.0. In contrast, one product contained 8,410 µg/g CBD and a CBD: CBDA ratio of >1,000 (an oil-based product). Overall, the method proved amenable to the analysis of various commercial products including oils, creams, and plant material and may be diagnostically indicative of adulteration with non-hemp C. sativa, specialized hemp cultivars, or unique manufacturing methods.

Comparative pharmacokinetics and safety assessment of transdermal berberine and dihydroberberine.

The natural alkaloid berberine has been ascribed numerous health benefits including lipid and cholesterol reduction and improved insulin sensitivity in diabetics. However, oral (PO) administration of berberine is hindered by poor bioavailability and increasing dose often elicits gastro-intestinal side effects. To overcome the caveats associated with oral berberine, we developed transdermal (TD) formulations of berberine (BBR) and the berberine precursor dihydroberberine (DHB). These formulations were compared to oral BBR using pharmacokinetics, metabolism, and general safety studies in vivo. To complete this work, a sensitive quantitative LC-MS/MS method was developed and validated according the FDA guidelines for bioanalytical methods to simultaneously measure berberine, simvastatin, and simvastatin hydroxy acid with relative quantification used for the berberine metabolite demethylene berberine glucuronide (DBG). Acute pharmacokinetics in Sprague-Dawley rats demonstrated a statistically relevant ranking for berberine bioavailability based upon AUC0-8 as DHB TD > BBR TD >> BBR PO with similar ranking for the metabolite DBG, indicating that transdermal administration achieves BBR levels well above oral administration. Similarly, chronic administration (14 days) resulted in significantly higher levels of circulating BBR and DBG in DHB TD treated animals. Chronically treated rats were given a single dose of simvastatin with no observed change in the drugs bioavailability compared with control, suggesting the increased presence of BBR had no effect on simvastatin metabolism. This observation was further supported by consistent CYP3A4 expression across all treatment groups. Moreover, no changes in kidney and liver biomarkers, including alanine aminotransferase and alkaline phosphatase, were observed between treatment formats, and confirming previous reports that BBR has no effect on HMG-CoA expression. This study supports the safe use of transdermal compositions that improve on the poor bioavailability of oral berberine and have the potential to be more efficacious in the treatment of dyslipidemia or hypercholesterolemia.