scEVOLVE: cell-type incremental annotation without forgetting for single-cell RNA-seq data.

The evolution in single-cell RNA sequencing (scRNA-seq) technology has opened a new avenue for researchers to inspect cellular heterogeneity with single-cell precision. One crucial aspect of this technology is cell-type annotation, which is fundamental for any subsequent analysis in single-cell data mining. Recently, the scientific community has seen a surge in the development of automatic annotation methods aimed at this task. However, these methods generally operate at a steady-state total cell-type capacity, significantly restricting the cell annotation systems'capacity for continuous knowledge acquisition. Furthermore, creating a unified scRNA-seq annotation system remains challenged by the need to progressively expand its understanding of ever-increasing cell-type concepts derived from a continuous data stream. In response to these challenges, this paper presents a novel and challenging setting for annotation, namely cell-type incremental annotation. This concept is designed to perpetually enhance cell-type knowledge, gleaned from continuously incoming data. This task encounters difficulty with data stream samples that can only be observed once, leading to catastrophic forgetting. To address this problem, we introduce our breakthrough methodology termed scEVOLVE, an incremental annotation method. This innovative approach is built upon the methodology of contrastive sample replay combined with the fundamental principle of partition confidence maximization. Specifically, we initially retain and replay sections of the old data in each subsequent training phase, then establish a unique prototypical learning objective to mitigate the cell-type imbalance problem, as an alternative to using cross-entropy. To effectively emulate a model that trains concurrently with complete data, we introduce a cell-type decorrelation strategy that efficiently scatters feature representations of each cell type uniformly. We constructed the scEVOLVE framework with simplicity and ease of integration into most deep softmax-based single-cell annotation methods. Thorough experiments conducted on a range of meticulously constructed benchmarks consistently prove that our methodology can incrementally learn numerous cell types over an extended period, outperforming other strategies that fail quickly. As far as our knowledge extends, this is the first attempt to propose and formulate an end-to-end algorithm framework to address this new, practical task. Additionally, scEVOLVE, coded in Python using the Pytorch machine-learning library, is freely accessible at https://github.com/aimeeyaoyao/scEVOLVE.

Differences in the severity and mortality risk factors for patients hospitalized for COVID-19 pneumonia between the early wave and the very late stage of the pandemic.

Background: Since China's dynamic zero-COVID policy is cancelled on December 7, 2022, the rapidly growing number of patients has brought a major public health challenge. This study aimed to assess whether there were differences in the severity and mortality risk factors for patients hospitalized for COVID-19 pneumonia between the early wave and the very late stage of the pandemic. Methods: A retrospective cross-sectional study was carried out using data from 223 hospitalized patients diagnosed with COVID-19 pneumonia during the Omicron surge in Xi'an People's Hospital (Xi'an Fourth Hospital) from December 8, 2022, to January 31, 2023. Univariable and multivariable logistic regression analyses were used to identify potential risk factors associated with the severity and mortality of COVID-19 pneumonia during the first wave of the pandemic after the dynamic zero-COVID policy was retracted. Differences in the severity and mortality risk factors were assessed at different stages of the pandemic, mainly from demographic, clinical manifestation, laboratory tests and radiological findings of patients on admission. Results: The mean age of the 223 participants was 71.2 ± 17.4. Compared with the patients in the initial stage of the pandemic, the most common manifestation among patients in this study was cough (90.6%), rather than fever (79.4%). Different from the initial stage of the pandemic, older age, chest tightness, elevated neutrophil-to-lymphocyte ratio (NLR), decreased albumin (ALB) level and ground glass opacification (GGO) in radiological finding were identified as severity risk factors, instead of mortality risk factors for COVID-19 patients in the very late stage of the pandemic. Arterial partial pressure of oxygen/fraction of inspired oxygen (PaO2/FiO2) ≤300 mmHg, cardiovascular disease and laboratory findings including elevated levels of D-dimer, α-hydroxybutyrate dehydrogenase (α-HBDH), total bilirubin (TBIL), alanine aminotransferase (ALT), urea nitrogen (BUN), creatinine (CR), fasting blood glucose (FBG) and decreased platelet count (PLT) were still associated with mortality in the very late stage of the pandemic. Conclusion: Monitoring continuously differences in the severity and mortality risk factors for COVID-19 patients between different stages of the pandemic could provide evidence for exploring uncharted territory in the coming post-pandemic era.

Relationships between beliefs about statins and non-adherence in inpatients from Northwestern China: a cross-sectional survey.

Background: Studies have identified patients' beliefs about medicines as an important determinant of non-adherence. However, scant data are available on the possible association between patients' beliefs and statin non-adherence among adult patients in China. The objectives of this study are to assess the prevalence of statin non-adherence, and to identify the factors associated with statin non-adherence, especially the association between inpatients' beliefs about statins and non-adherence in a tertiary hospital in the Northwestern China. Methods: A cross-sectional questionnaire-based survey was carried out in the department of cardiology and neurology between February and June 2022. The Beliefs about Medicine Questionnaire (BMQ) was used to assess patients' beliefs about statins. The Adherence to Refills and Medications Scale (ARMS) was used to assess statin adherence. Logistic regression analyses were performed to identify the factors associated with statin non-adherence. Receiver operator characteristic (ROC) was conducted to assess the performance of the logistic regression model in predicting statin non-adherence. Results: A total of 524 inpatients participated and finished the questionnaire, 426 (81.3%) inpatients were non-adherent to statin, and 229 (43.7%) inpatients expressed strong beliefs about the stain treatment necessity, while 246 (47.0%) inpatients expressed strong concerns about the potential negative effects. We found that the low necessity beliefs about statin (adjusted odds ratio [OR] and 95% confidence interval [CI], 1.607 [1.019, 2.532]; p = 0.041), prescribed rosuvastatin (adjusted OR 1.820 [1.124, 2.948]; p = 0.015) and ex-drinker (adjusted OR 0.254 [0.104, 0.620]; p = 0.003) were independent determinants of statin non-adherence. Conclusion: Statin adherence was poor in this study. The findings indicated a significant association between inpatients' lower necessity beliefs and statin non-adherence. More attention should be focused on statin non-adherence in China. Nurses and pharmacists could play an important role in patient education and patient counseling in order to improve medication adherence.

scGAD: a new task and end-to-end framework for generalized cell type annotation and discovery.

The rapid development of single-cell RNA sequencing (scRNA-seq) technology allows us to study gene expression heterogeneity at the cellular level. Cell annotation is the basis for subsequent downstream analysis in single-cell data mining. As more and more well-annotated scRNA-seq reference data become available, many automatic annotation methods have sprung up in order to simplify the cell annotation process on unlabeled target data. However, existing methods rarely explore the fine-grained semantic knowledge of novel cell types absent from the reference data, and they are usually susceptible to batch effects on the classification of seen cell types. Taking into consideration the limitations above, this paper proposes a new and practical task called generalized cell type annotation and discovery for scRNA-seq data whereby target cells are labeled with either seen cell types or cluster labels, instead of a unified 'unassigned' label. To accomplish this, we carefully design a comprehensive evaluation benchmark and propose a novel end-to-end algorithmic framework called scGAD. Specifically, scGAD first builds the intrinsic correspondences on seen and novel cell types by retrieving geometrically and semantically mutual nearest neighbors as anchor pairs. Together with the similarity affinity score, a soft anchor-based self-supervised learning module is then designed to transfer the known label information from reference data to target data and aggregate the new semantic knowledge within target data in the prediction space. To enhance the inter-type separation and intra-type compactness, we further propose a confidential prototype self-supervised learning paradigm to implicitly capture the global topological structure of cells in the embedding space. Such a bidirectional dual alignment mechanism between embedding space and prediction space can better handle batch effect and cell type shift. Extensive results on massive simulation datasets and real datasets demonstrate the superiority of scGAD over various state-of-the-art clustering and annotation methods. We also implement marker gene identification to validate the effectiveness of scGAD in clustering novel cell types and their biological significance. To the best of our knowledge, we are the first to introduce this new and practical task and propose an end-to-end algorithmic framework to solve it. Our method scGAD is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/aimeeyaoyao/scGAD.

miR-613 suppresses renal cell carcinoma proliferation, invasion and migration by regulating the AXL/AKT pathway.

In the last few decades, microRNAs (miRNAs) are possible to effectively control and treat cancer. However, the function of miR-613 in renal cell carcinoma (RCC) is not very clear up to now. Here, the direction of this research was to investigate the influence of miR-613 for the proliferation, invasion and migration of RCC, and the underlying molecular mechanism. First, the mRNA and protein expression levels of miR-613 were determined in RCC tissues and cancer cells (786-O and ACHN). Using bioinformatics and literature review, anexelekto (AXL), as the target of miR-613 in renal cell carcinoma, was screened. Phenotype experiment and mechanism experiment illustrated the targeting relationship between miR-613 and AXL in cancer cells. Furthermore, a rescue assay with AXL overexpression was performed to make a profound study whether miR-613 disturbs RCC proliferation, invasion, and migration through direct regulation of AXL. Finally, through experiment in vivo, we observe the influence of miR-613 overexpression for tumor. These results were as follows. The present findings proved, in RCC, that the production of miR-613 was at a low level. Except for this point, this current research confirmed, in RCC cells, that the upregulation of miR-613 can control proliferation, metastasis, and invasion by reducing AXL levels and controlling the phosphoinositide 3-kinase (PI3K)/AKT signaling pathway.

Appropriateness and Associated Factors of Stress Ulcer Prophylaxis for Surgical Inpatients of Orthopedics Department in a Tertiary Hospital: A Cross-Sectional Study.

Background: Stress ulcer prophylaxis (SUP) prescribed in patients admitted to surgical wards with a low risk of stress-related mucosal disease (SRMD) accounted for a considerable proportion of improper use of proton pump inhibitors (PPIs). This study aimed to analyze the appropriateness of SUP prescribing patterns and identify its associated factors in the orthopedics department of a tertiary hospital in the Northwestern China. Methods: In this cross-sectional study, information regarding the demographic and clinical characteristics of 1,200 fracture inpatients who underwent surgical operations from January 2020 to August 2021 were collected from medical records. Established criteria were used to assess the appropriateness of the prescribing pattern for SUP, and the incidence of inappropriate SUP medication was calculated. Logistic regression analyses were used to identify factors associated with inappropriate SUP medication. Results: Approximately, 42.4% of the study population was interpreted as inappropriate prescription of SUP. A total of 397 (33.1%) patients received SUP without a proper indication (overprescription), and the incidence of inappropriate SUP medication was calculated to be 43.11 per 100 patient-days. In addition, 112 (9.3%) inpatients for whom SUP was indicated did not receive SUP (underprescription). PPIs were prescribed in 96.1% of the inpatients who used acid suppression therapy (AST), and intravenous PPIs accounted for 95.3% thereof. In a multivariate logistic regression analysis, age above 65 years and prolonged hospitalization were associated with overprescription of SUP. Increased number of drugs excluding PPIs, the concurrent use of systemic corticosteroids, comorbidity of hypertension, and unemployed or retired status in inpatients were associated with a reduced likelihood of overprescription for SUP. Conversely, prolonged hospitalization, the concurrent use of systemic corticosteroids or anticoagulants, and unemployed status in inpatients were positively associated with underprescription of SUP. Conclusion: There was a high prevalence of inappropriate SUP prescription among noncritically ill inpatients of fracture who underwent surgical operations. We delineated the associated factors with inappropriate SUP medication, which indicated that more information was required for clinicians about rationality and efficiency of their prescribing practices. Effective intervention strategies should be executed by clinical pharmacists to reduce improper SUP medication.

The Impact of a National Stewardship Policy on the Usage Patterns of Key Monitoring Drugs in a Tertiary Teaching Hospital: An Interrupted Time Series Analysis.

Background: The management of Key Monitoring Drugs has become one of important aspects to control the growth of pharmaceutical expenditures in China. The first batch of the China National Key Monitoring Drugs (NKMDs) policy was released in July 2019. However, little is known about the impact of the national stewardship on the trends of NKMDs prescribing practice in hospitals, especially in the Northwestern China. Methods: We collected 8-years of monthly NKMDs usage data from a tertiary hospital between 2014 and 2021. A segmented regression model of interrupted time series (ITS) analysis was used to evaluate the Defined Daily Doses (DDDs) and spending trends of ten NMKDs in the hospital throughout the study period. The pre-implementation period was from January 2014 to November 2019 and the post-implementation period was from December 2019 to June 2021. Results: Prior to the implementation of the NKMDs policy, there was an increasing trend both in DDDs and spending for 8 of 10 NKMDs. The interventions managed by clinical pharmacists after the implementation of the national stewardship policy led to a significant decreasing trend of DDDs in the 19 months following implementation, of 430 fewer DDDs per month in total, compared to the pre-implementation period (p < 0.001). A similar decrease in spending was seen in the post-implementation period, with a trend of $4,682 less total spending on medications in those months compared to the pre-implementation trend (p = 0.003). There was a significant decrease in both monthly DDDs and spending for 6 of the 10 medications in the post-implementation period, while there was a significant increased trend both in monthly DDDs and spending on 1 medication in that period. Conclusion: Using ITS analysis, the total DDDs and spending on 10 NKMDs in this hospital indicated sustained reductions over 19 months after multidimensional interventions under the implementation of the national policy guidance. The national stewardship policy could therefore be considered an effective strategy. Additional comprehensive policies should be introduced to further improve the rational use of NKMDs.

Single-cell RNA-seq data semi-supervised clustering and annotation via structural regularized domain adaptation.

MOTIVATION: The rapid development of single-cell RNA sequencing (scRNA-seq) technologies allows us to explore tissue heterogeneity at the cellular level. The identification of cell types plays an essential role in the analysis of scRNA-seq data, which, in turn, influences the discovery of regulatory genes that induce heterogeneity. As the scale of sequencing data increases, the classical method of combining clustering and differential expression analysis to annotate cells becomes more costly in terms of both labor and resources. Existing scRNA-seq supervised classification method can alleviate this issue through learning a classifier trained on the labeled reference data and then making a prediction based on the unlabeled target data. However, such label transference strategy carries with risks, such as susceptibility to batch effect and further compromise of inherent discrimination of target data. RESULTS: In this article, inspired by unsupervised domain adaptation, we propose a flexible single cell semi-supervised clustering and annotation framework, scSemiCluster, which integrates the reference data and target data for training. We utilize structure similarity regularization on the reference domain to restrict the clustering solutions of the target domain. We also incorporates pairwise constraints in the feature learning process such that cells belonging to the same cluster are close to each other, and cells belonging to different clusters are far from each other in the latent space. Notably, without explicit domain alignment and batch effect correction, scSemiCluster outperforms other state-of-the-art, single-cell supervised classification and semi-supervised clustering annotation algorithms in both simulation and real data. To the best of our knowledge, we are the first to use both deep discriminative clustering and deep generative clustering techniques in the single-cell field. AVAILABILITYAND IMPLEMENTATION: An implementation of scSemiCluster is available from https://github.com/xuebaliang/scSemiCluster. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Direct interaction network inference for compositional data via codaloss.

16S rRNA gene sequencing and whole microbiome sequencing make it possible and stable to quantitatively analyze the composition of microbial communities and the relationship among microbial communities, microbes, and hosts. One essential step in the analysis of microbiome compositional data is inferring the direct interaction network among microbial species, bringing to light the potential underlying mechanism that regulates interaction in their communities. However, standard statistical analysis may obtain spurious results due to compositional nature of microbiome data; therefore, network recovery of microbial communities remains challenging. Here, we propose a novel loss function called codaloss for direct microbes interaction network estimation under the sparsity assumptions. We develop an alternating direction optimization algorithm to obtain sparse solution of codaloss as estimator. Compared to other state-of-the-art methods, our model makes less assumptions about the microbial networks. The simulation and real microbiome data results show that our method outperforms other methods in network inference. An implementation of codaloss is available from https://github.com/xuebaliang/Codaloss.

Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation.

As single-cell RNA sequencing technologies mature, massive gene expression profiles can be obtained. Consequently, cell clustering and annotation become two crucial and fundamental procedures affecting other specific downstream analyses. Most existing single-cell RNA-seq (scRNA-seq) data clustering algorithms do not take into account the available cell annotation results on the same tissues or organisms from other laboratories. Nonetheless, such data could assist and guide the clustering process on the target dataset. Identifying marker genes through differential expression analysis to manually annotate large amounts of cells also costs labor and resources. Therefore, in this paper, we propose a novel end-to-end cell supervised clustering and annotation framework called scAnCluster, which fully utilizes the cell type labels available from reference data to facilitate the cell clustering and annotation on the unlabeled target data. Our algorithm integrates deep supervised learning, self-supervised learning and unsupervised learning techniques together, and it outperforms other customized scRNA-seq supervised clustering methods in both simulation and real data. It is particularly worth noting that our method performs well on the challenging task of discovering novel cell types that are absent in the reference data.

Single-Cell Transcriptome Data Clustering via Multinomial Modeling and Adaptive Fuzzy K-Means Algorithm.

Single-cell RNA sequencing technologies have enabled us to study tissue heterogeneity at cellular resolution. Fast-developing sequencing platforms like droplet-based sequencing make it feasible to parallel process thousands of single cells effectively. Although a unique molecular identifier (UMI) can remove bias from amplification noise to a certain extent, clustering for such sparse and high-dimensional large-scale discrete data remains intractable and challenging. Most existing deep learning-based clustering methods utilize the mean square error or negative binomial distribution with or without zero inflation to denoise single-cell UMI count data, which may underfit or overfit the gene expression profiles. In addition, neglecting the molecule sampling mechanism and extracting representation by simple linear dimension reduction with a hard clustering algorithm may distort data structure and lead to spurious analytical results. In this paper, we combined the deep autoencoder technique with statistical modeling and developed a novel and effective clustering method, scDMFK, for single-cell transcriptome UMI count data. ScDMFK utilizes multinomial distribution to characterize data structure and draw support from neural network to facilitate model parameter estimation. In the learned low-dimensional latent space, we proposed an adaptive fuzzy k-means algorithm with entropy regularization to perform soft clustering. Various simulation scenarios and the analysis of 10 real datasets have shown that scDMFK outperforms other state-of-the-art methods with respect to data modeling and clustering algorithms. Besides, scDMFK has excellent scalability for large-scale single-cell datasets.

Deep soft K-means clustering with self-training for single-cell RNA sequence data.

Single-cell RNA sequencing (scRNA-seq) allows researchers to study cell heterogeneity at the cellular level. A crucial step in analyzing scRNA-seq data is to cluster cells into subpopulations to facilitate subsequent downstream analysis. However, frequent dropout events and increasing size of scRNA-seq data make clustering such high-dimensional, sparse and massive transcriptional expression profiles challenging. Although some existing deep learning-based clustering algorithms for single cells combine dimensionality reduction with clustering, they either ignore the distance and affinity constraints between similar cells or make some additional latent space assumptions like mixture Gaussian distribution, failing to learn cluster-friendly low-dimensional space. Therefore, in this paper, we combine the deep learning technique with the use of a denoising autoencoder to characterize scRNA-seq data while propose a soft self-training K-means algorithm to cluster the cell population in the learned latent space. The self-training procedure can effectively aggregate the similar cells and pursue more cluster-friendly latent space. Our method, called 'scziDesk', alternately performs data compression, data reconstruction and soft clustering iteratively, and the results exhibit excellent compatibility and robustness in both simulated and real data. Moreover, our proposed method has perfect scalability in line with cell size on large-scale datasets.

Evaluation of carbapenem use in a tertiary hospital: antimicrobial stewardship urgently needed.

Background: China launched a 3-year rectification scheme for the clinical use of antibiotics in 2011, and a specific scheme for carbapenem use in 2017. The aim of this study was to investigate the effects of government policies on carbapenem use, and their correlation with carbapenem-resistant Pseudomonas aeruginosa (CRPA). Methods: The study was divided into four stages: preintervention (2010), antimicrobial programme (2011-2013), post-antimicrobial programme (2014-2016) and carbapenem programme (2017). A point-score system was proposed for evaluating the rationality of carbapenem use, and evaluated based on the indications, microbial culture, single dose, interval, and duration. Any prescription without a global score of 10 points was judged as irrational. The trend was analyzed by regression analysis, and Spearman correlation analysis was used for testing the correlation. Findings: The rate of rational use of carbapenems was 29.7% in 2010, and increased by 55.5, 45.2, and 51.5% during the subsequent three stages. The rationality declined slightly during the post-antimicrobial programme (2014-2016) while the consumption of carbapenems was markedly increased. These two parameters improved slightly in 2017. Moreover, the prevalence of CRPA was significantly negatively correlated with the rate of rational carbapenem use (Coefficient = - 0.553, P < 0.05), and not with the consumption of carbapenems (P > 0.05). Conclusions: The rational application of carbapenems was related to government policies in this study, with irrational carbapenem use possibly related to the development of CRPA. The current point-score system could be a useful tool for performing assessments.

[HPLC-MS/MS determination of residual amount of 4 plant growth retardants in 6 dried root and Rhizome Herbs].

HPLC-MS/MS was applied to the determination of residual amount of plant growth retardant such as paclobutrazol, daminozide, chlormequat and mepiquat chloride in dried root and rhizome herbs. The sample was extracted twice with acetonitrile containing 0.1% formic acid. The separation was performed on a Waters Atlantis HILIC column with an elution system consisting of acetonitrile-5 mmol•L⁻¹ ammonium acetate solution with 0.1% formic acid, methanol and acetonitrile. The MS spectrum was acquired in positive mode with multiple reactions monitoring (MRM). The linear range was 6-1 500 µg•kg⁻¹, and the optimized method offered a good linear correlation (r>0.997 8), excellent precision (RSD<11%) and acceptable recovery (from 79.3% to 103.3%). Four kinds of plant growth retardant have detected in some ofhenise herbs like Ophiopogonis Radix, Angelicae Sinensis Radix, Achyranthis Bidentatae Radix, Alismatis Rhizoma, Chuanxiong Rhizama and Notoginseng Radix et Rhizama, is among the more severe cases, dwarf lilyturf, multi-effect azole detection quantity is 63.4~1 351.66 µg•kg⁻¹, and Daminozide was detected in Ophiopogonis Radix, Angelicae Sinensis Radix, Chuanxiong Rhizama, Alismatis Rhizoma.

[Detection of agent "zhuanggenling" and investigation of utilization of plant growth retardants in traditional Chinese medicine cultivation].

Plant growth retardant as one of plant growth regulator can inhibit the cell division, elongation and growth rate in shoot apical meristem (SAM), which can be reversed by gibberellin regulate the product of photosynthesis transfer to the root and rhizome part. As commonly used plant growth retardant, paclobutrazol, uniconazole, chlorocholine chloride, mepiquat chloride, choline chloride and daminozide are used to promote the growth of root and rhizome, call as "zhuanggenling", "pengdasu", "pengdaji" etc. Single or recombination of plant growth regulator is registered as pesticide, and called as pesticide "zhuanggenling" in this paper. Growth regulator which registered as a foliar fertilizer or fertilization was called agricultural fertilizer "zhuanggenling" in this paper. The author investigate the usage of "zhuanggenling" in the root and rhizome of medicinal plants cultivation from 2012 to 2014 in Sichuan province, Huangyuan town, Mianyang (Ophiopogonis Radix); Pengzhou Aoping town (Chuanxiong Rhizoma); Pengshan Xiejia town (Alismatis Rhizoma); Jiangyou Taiping town and Zhangming town (Aconiti Lateralis Radix Praeparata); Yunnan Wenshan (Notoginseng Radix et Rhizoma); Henan province, Wuzhidafeng Town (Rehmanniae Radix, Achyranthis Bidentatae Radix, Dioscoreae Rhizoma); Gansu Min county (Codonopsis Radix, Angelicae Sinensis Radix); Gansu Li county (Rhei Radix et Rhizoma). The result showed that "zhuanggenling" were applied in the most medicinal plant cultivation except Rhei Radix et Rhizoma. It has been applied widespreadly in Ophiopogonis Radix, Alismatis Rhizoma, Achyranthis Bidentatae Radix, Codonopsis Radix; Rehmanniae Radix, commonly in Angelicae Sinensis Radix application, and occasionally in Chuanxiong Rhizoma, Aconiti Lateralis Radix Praeparata, Notoginseng Radix et Rhizoma and Dioscoreae Rhizoma. In 53 collected sample from plantation areas, fifteen (28%) were pesticide "zhuanggenling", thirty-eight (72%) were pesticide "zhuanggenling". UPLC analysis results showed that 38 farmers fertilizer "zhuanggenling" content of 6 kinds of plant growth retardant. It is regarded that fertilizer "zhuanggenling" was dominant in medicinal plant cultivation, and that the plant growth retardant is added widespreadly in farm fertilizer "zhuanggenling". All evidence proves conclusively that "zhuanggenling" have been used in the proper way, whereas some others have been misused or even abused in the use regarding to type, number, use frequency. The root or rhizoma are increased to 20%-200%. But there is lack of evaluation to appraise the quality of medicinal materials from the aspects of research or industry. "zhuanggenling" has become a important Chemical control material besides fertilizer, insecticidal sterilization of pesticide

Chiral separation and determination of amino acids in real samples by LE-MEKC using Cu(II)-L-proline as chiral selector.

This work reports that Cu(II) complexes with l-proline were used as chiral additives for the enantioseparations and determination of three underivatized amino acids by ligand-exchange micellar electrokinetic chromatography (LE-MEKC). Sodium dodecylsulfate (SDS) was shown to be necessary for simultaneous separation of the enantiomeric amino acids. Separation parameters such as SDS concentrations, the Cu(II)-l-proline ratio, the concentration of the copper(II) complex at a specific Cu(II)-l-proline ratio, pH and separation voltage were investigated for the enantioseparation in order to achieve the maximum possible resolution. A good separation was achieved in the BGE composing of 10mM ammonium acetate, 10mM Cu(II) and 20mM l-proline and 30 mM SDS at pH 5.0, and an applied voltage of 15 kV performed. Under above-mentioned optimum conditions, linearity was achieved within concentration ranges of up to two orders of magnitudes for the investigated amino acids with the correlation coefficients ranging from 0.9917 to 0.9984. The proposed method has been successfully applied to the determination of amino acid enantiomers in human urine, compound amino acids injection, and amino acid oral liquid.

[Review on application of plant growth retardants in medicinal plants cultivation].

Plant growth retardants are widely used in cultivation of medicinal plant, but there is still lack of scientific guidance. In order to guide the use of plant growth retardants in medicinal plant cultivation efficiently and reasonably, this paper reviewed the mechanism, function characteristic, plant and soil residue of plant growth retardants, such as chlorocholine chloride, mepiquat chloride, paclobutrazol, unicnazle and succinic acid, and summarized the application of plant growth retardants in medicinal plants cultivation in recent years, with focus on the effect of growth and yield of the officinal organs and secondary metabolites.