CTISL: a dynamic stacking multi-class classification approach for identifying cell types from single-cell RNA-seq data.

MOTIVATION: Effective identification of cell types is of critical importance in single-cell RNA-sequencing (scRNA-seq) data analysis. To date, many supervised machine learning-based predictors have been implemented to identify cell types from scRNA-seq datasets. Despite the technical advances of these state-of-the-art tools, most existing predictors were single classifiers, of which the performances can still be significantly improved. It is therefore highly desirable to employ the ensemble learning strategy to develop more accurate computational models for robust and comprehensive identification of cell types on scRNA-seq datasets. RESULTS: We propose a two-layer stacking model, termed CTISL (Cell Type Identification by Stacking ensemble Learning), which integrates multiple classifiers to identify cell types. In the first layer, given a reference scRNA-seq dataset with known cell types, CTISL dynamically combines multiple cell-type-specific classifiers (i.e. support-vector machine and logistic regression) as the base learners to deliver the outcomes for the input of a meta-classifier in the second layer. We conducted a total of 24 benchmarking experiments on 17 human and mouse scRNA-seq datasets to evaluate and compare the prediction performance of CTISL and other state-of-the-art predictors. The experiment results demonstrate that CTISL achieves superior or competitive performance compared to these state-of-the-art approaches. We anticipate that CTISL can serve as a useful and reliable tool for cost-effective identification of cell types from scRNA-seq datasets. AVAILABILITY AND IMPLEMENTATION: The webserver and source code are freely available at http://bigdata.biocie.cn/CTISLweb/home and https://zenodo.org/records/10568906, respectively.

Deep neural network models for cell type prediction based on single-cell Hi-C data.

BACKGROUND: Cell type prediction is crucial to cell type identification of genomics, cancer diagnosis and drug development, and it can solve the time-consuming and difficult problem of cell classification in biological experiments. Therefore, a computational method is urgently needed to classify and predict cell types using single-cell Hi-C data. In previous studies, there is a lack of convenient and accurate method to predict cell types based on single-cell Hi-C data. Deep neural networks can form complex representations of single-cell Hi-C data and make it possible to handle the multidimensional and sparse biological datasets. RESULTS: We compare the performance of SCANN with existing methods and analyze the model by using five different evaluation metrics. When using only ML1 and ML3 datasets, the ARI and NMI values of SCANN increase by 14% and 11% over those of scHiCluster respectively. However, when using all six libraries of data, the ARI and NMI values of SCANN increase by 63% and 88% over those of scHiCluster respectively. These findings show that SCANN is highly accurate in predicting the type of independent cell samples using single-cell Hi-C data. CONCLUSIONS: SCANN enhances the training speed and requires fewer resources for predicting cell types. In addition, when the number of cells in different cell types was extremely unbalanced, SCANN has higher stability and flexibility in solving cell classification and cell type prediction using the single-cell Hi-C data. This predication method can assist biologists to study the differences in the chromosome structure of cells between different cell types.

scHiCStackL: a stacking ensemble learning-based method for single-cell Hi-C classification using cell embedding.

Single-cell Hi-C data are a common data source for studying the differences in the three-dimensional structure of cell chromosomes. The development of single-cell Hi-C technology makes it possible to obtain batches of single-cell Hi-C data. How to quickly and effectively discriminate cell types has become one hot research field. However, the existing computational methods to predict cell types based on Hi-C data are found to be low in accuracy. Therefore, we propose a high accuracy cell classification algorithm, called scHiCStackL, based on single-cell Hi-C data. In our work, we first improve the existing data preprocessing method for single-cell Hi-C data, which allows the generated cell embedding better to represent cells. Then, we construct a two-layer stacking ensemble model for classifying cells. Experimental results show that the cell embedding generated by our data preprocessing method increases by 0.23, 1.22, 1.46 and 1.61$\%$ comparing with the cell embedding generated by the previously published method scHiCluster, in terms of the Acc, MCC, F1 and Precision confidence intervals, respectively, on the task of classifying human cells in the ML1 and ML3 datasets. When using the two-layer stacking ensemble framework with the cell embedding, scHiCStackL improves by 13.33, 19, 19.27 and 14.5 over the scHiCluster, in terms of the Acc, ARI, NMI and F1 confidence intervals, respectively. In summary, scHiCStackL achieves superior performance in predicting cell types using the single-cell Hi-C data. The webserver and source code of scHiCStackL are freely available at http://hww.sdu.edu.cn:8002/scHiCStackL/ and https://github.com/HaoWuLab-Bioinformatics/scHiCStackL, respectively.

Copper-Catalyzed Asymmetric Allylation of N-Aryl Aldimines.

The copper-catalyzed enantioselective allylation reaction of N-aryl aldimines has been developed using a combination of Cu(OAc)2 and SPINOL-based phosphonamidite. This protocol significantly broadens the substrate scope, such that imines bearing various ortho-substituents on the N-aryl were converted smoothly into homoallylic amines in up to 99% yield and 98% ee. Taking advantage of the diversity of the N-aryl motif, three kinds of N-heterocyclic compounds were constructed, respectively, from the corresponding homoallylic amines in merely one step.

DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites.

DNA N4-methylcytosine (4mC) is an important epigenetic modification that plays a vital role in regulating DNA replication and expression. However, it is challenging to detect 4mC sites through experimental methods, which are time-consuming and costly. Thus, computational tools that can identify 4mC sites would be very useful for understanding the mechanism of this important type of DNA modification. Several machine learning-based 4mC predictors have been proposed in the past 3 years, although their performance is unsatisfactory. Deep learning is a promising technique for the development of more accurate 4mC site predictions. In this work, we propose a deep learning-based approach, called DeepTorrent, for improved prediction of 4mC sites from DNA sequences. It combines four different feature encoding schemes to encode raw DNA sequences and employs multi-layer convolutional neural networks with an inception module integrated with bidirectional long short-term memory to effectively learn the higher-order feature representations. Dimension reduction and concatenated feature maps from the filters of different sizes are then applied to the inception module. In addition, an attention mechanism and transfer learning techniques are also employed to train the robust predictor. Extensive benchmarking experiments demonstrate that DeepTorrent significantly improves the performance of 4mC site prediction compared with several state-of-the-art methods.

Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification.

Anti-cancer peptides (ACPs) are known as potential therapeutics for cancer. Due to their unique ability to target cancer cells without affecting healthy cells directly, they have been extensively studied. Many peptide-based drugs are currently evaluated in the preclinical and clinical trials. Accurate identification of ACPs has received considerable attention in recent years; as such, a number of machine learning-based methods for in silico identification of ACPs have been developed. These methods promote the research on the mechanism of ACPs therapeutics against cancer to some extent. There is a vast difference in these methods in terms of their training/testing datasets, machine learning algorithms, feature encoding schemes, feature selection methods and evaluation strategies used. Therefore, it is desirable to summarize the advantages and disadvantages of the existing methods, provide useful insights and suggestions for the development and improvement of novel computational tools to characterize and identify ACPs. With this in mind, we firstly comprehensively investigate 16 state-of-the-art predictors for ACPs in terms of their core algorithms, feature encoding schemes, performance evaluation metrics and webserver/software usability. Then, comprehensive performance assessment is conducted to evaluate the robustness and scalability of the existing predictors using a well-prepared benchmark dataset. We provide potential strategies for the model performance improvement. Moreover, we propose a novel ensemble learning framework, termed ACPredStackL, for the accurate identification of ACPs. ACPredStackL is developed based on the stacking ensemble strategy combined with SVM, Naïve Bayesian, lightGBM and KNN. Empirical benchmarking experiments against the state-of-the-art methods demonstrate that ACPredStackL achieves a comparative performance for predicting ACPs. The webserver and source code of ACPredStackL is freely available at http://bigdata.biocie.cn/ACPredStackL/ and https://github.com/liangxiaoq/ACPredStackL, respectively.

DeepGenGrep: a general deep learning-based predictor for multiple genomic signals and regions.

MOTIVATION: Accurate annotation of different genomic signals and regions (GSRs) from DNA sequences is fundamentally important for understanding gene structure, regulation and function. Numerous efforts have been made to develop machine learning-based predictors for in silico identification of GSRs. However, it remains a great challenge to identify GSRs as the performance of most existing approaches is unsatisfactory. As such, it is highly desirable to develop more accurate computational methods for GSRs prediction. RESULTS: In this study, we propose a general deep learning framework termed DeepGenGrep, a general predictor for the systematic identification of multiple different GSRs from genomic DNA sequences. DeepGenGrep leverages the power of hybrid neural networks comprising a three-layer convolutional neural network and a two-layer long short-term memory to effectively learn useful feature representations from sequences. Benchmarking experiments demonstrate that DeepGenGrep outperforms several state-of-the-art approaches on identifying polyadenylation signals, translation initiation sites and splice sites across four eukaryotic species including Homo sapiens, Mus musculus, Bos taurus and Drosophila melanogaster. Overall, DeepGenGrep represents a useful tool for the high-throughput and cost-effective identification of potential GSRs in eukaryotic genomes. AVAILABILITY AND IMPLEMENTATION: The webserver and source code are freely available at http://bigdata.biocie.cn/deepgengrep/home and Github (https://github.com/wx-cie/DeepGenGrep/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Efficacy and Safety of AbobotulinumtoxinA for the Treatment of Glabellar Lines in Chinese Patients: A Pivotal, Phase 3, Randomized, Double-Blind and Open-Label Phase Study.

BACKGROUND: Various botulinumtoxinA formulations are approved for glabellar lines treatment worldwide, including abobotulinumtoxinA (Dysport®). OBJECTIVES: Assess abobotulinumtoxinA superiority versus placebo and non-inferiority versus active comparator (onabotulinumtoxinA; Botox®), for the treatment of Chinese patients with moderate/severe glabellar lines. METHODS: Phase 3, randomized study (NCT02450526) comprising a double-blind (cycle 1) phase and an open-label (cycles 2-5) phase. Patients received abobotulinumtoxinA 50 units or matching placebo (5:1), active comparator (onabotulinumtoxinA 20 units) or matching placebo (5:1). In cycles 2-5, eligible patients were retreated with abobotulinumtoxinA only. Responders had glabellar lines of none/mild severity. PRIMARY ENDPOINT: responder rates at cycle 1, day 29 at maximum frown with abobotulinumtoxinA versus placebo (for superiority; by investigator's live assessment [ILA] and subject's self-assessment [SSA]), and versus active comparator (for non-inferiority; by ILA). Treatment-emergent adverse events were recorded. RESULTS: Overall, 520 patients were randomized. Superiority and non-inferiority, respectively, were demonstrated for abobotulinumtoxinA versus placebo (ILA, SSA; both p < 0.0001) and abobotulinumtoxinA versus active comparator. AbobotulinumtoxinA efficacy was maintained over open-label cycles; median time to onset of efficacy was 2.0 days. After 6 months, 17% of patients treated with abobotulinumtoxinA remained responders. AbobotulinumtoxinA was well-tolerated. Safety results were in line with the known profile of abobotulinumtoxinA; adverse events rate decreased with repeated treatment. CONCLUSIONS: After a single injection, abobotulinumtoxinA demonstrated superiority versus placebo and non-inferiority versus onabotulinumtoxinA for the treatment of moderate-to-severe glabellar lines in Chinese patients. Multiple injections of abobotulinumtoxinA demonstrated efficacy and safety in the treatment of glabellar lines in Chinese patients. LEVEL OF EVIDENCE I: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Botulinum toxin injections can be used to smooth frown lines that appear between the eyebrows (known as glabellar lines) in patients who have moderate or severe frown lines. This study looked at how injections of a botulinum toxin (abobotulinumtoxinA [aboBoNT-A]) could help with smoothing frown lines in patients from China compared with an injection of another botulinum toxin called onabotulinumtoxinA (onaBoNT-A) or placebo (saltwater, no treatment). The study included 520 patients from China, 18­65 years old, who had moderate or severe frown lines. All patients received a first injection of either aboBoNT-A, onaBoNT-A, or saltwater, and were studied for 12 weeks. After the first injection, patients could receive up to four more injections of aboBoNT-A, given at 12-week intervals, if their frown lines became moderate or severe again. Most patients (92%) had not previously received any botulinum toxin injections. The results showed that single and repeat injections of aboBoNT-A helped to smooth moderate and severe frown lines. The researchers found that after a single injection, aboBoNT-A was superior to no treatment and was similar to onaBoNT-A. Patients recorded a response to aboBoNT-A after 2 days and the response lasted for 6 months in 17% of patients. The effect on frown lines was maintained after repeat injections and aboBoNT-A was well tolerated by patients. These results suggest that aboBoNT-A is a suitable treatment for smoothing frown lines in patients from China with moderate to severe frown lines.

PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact.

Post-translational modifications (PTMs) play very important roles in various cell signaling pathways and biological process. Due to PTMs' extremely important roles, many major PTMs have been studied, while the functional and mechanical characterization of major PTMs is well documented in several databases. However, most currently available databases mainly focus on protein sequences, while the real 3D structures of PTMs have been largely ignored. Therefore, studies of PTMs 3D structural signatures have been severely limited by the deficiency of the data. Here, we develop PRISMOID, a novel publicly available and free 3D structure database for a wide range of PTMs. PRISMOID represents an up-to-date and interactive online knowledge base with specific focus on 3D structural contexts of PTMs sites and mutations that occur on PTMs and in the close proximity of PTM sites with functional impact. The first version of PRISMOID encompasses 17 145 non-redundant modification sites on 3919 related protein 3D structure entries pertaining to 37 different types of PTMs. Our entry web page is organized in a comprehensive manner, including detailed PTM annotation on the 3D structure and biological information in terms of mutations affecting PTMs, secondary structure features and per-residue solvent accessibility features of PTM sites, domain context, predicted natively disordered regions and sequence alignments. In addition, high-definition JavaScript packages are employed to enhance information visualization in PRISMOID. PRISMOID equips a variety of interactive and customizable search options and data browsing functions; these capabilities allow users to access data via keyword, ID and advanced options combination search in an efficient and user-friendly way. A download page is also provided to enable users to download the SQL file, computational structural features and PTM sites' data. We anticipate PRISMOID will swiftly become an invaluable online resource, assisting both biologists and bioinformaticians to conduct experiments and develop applications supporting discovery efforts in the sequence-structural-functional relationship of PTMs and providing important insight into mutations and PTM sites interaction mechanisms. The PRISMOID database is freely accessible at http://prismoid.erc.monash.edu/. The database and web interface are implemented in MySQL, JSP, JavaScript and HTML with all major browsers supported.

14-day famciclovir treatment significantly reduces the incidence of postherpetic neuralgia in elderly patients with herpes zoster.

WHAT IS KNOWN AND OBJECTIVE: Pain is the main symptom of herpes zoster (HZ), whilst postherpetic neuralgia (PHN) is a long-term unbearable pain, which seriously affects the quality of life of patients and is also the most intractable problem for clinicians. Early antiviral treatment is considered as a key measure to reduce acute pain and PHN. Nevertheless, most patients still have long-term pain after 7 days of antiviral treatment, and some patients will develop PHN. This study aimed to investigate whether prolonged duration of antiviral therapy could reduce HZ acute pain and the occurrence of PHN. METHODS: The outpatient data of HZ patients over 50 years old who visited the Dermatology Department from January 2016 to May 2018 were retrospectively analysed. According to the different courses of treatment of famciclovir (FCV), the patients were divided into 7-day FCV group and 14-day FCV group. The numerical rating scale (NRS) score at the first visit and on the 7th, 14th and 21st days after the start of treatment, the adverse drug reactions and the incidence of PHN were compared between the two groups. RESULTS: A total of 219 patients were involved in the analysis. For acute pain control, the 14-day FCV group was better than the 7-day FCV group. For patients with mild initial pain, there was no significant difference in NRS between the two treatments. For patients with moderate-to-severe initial pain, the NRS in the 14-day FCV group was significantly lower than that of the 7-day FCV group on the 14th and 21st days after starting treatment. PHN occurred in patients with moderate-to-severe initial pain, and the incidence was significantly lower in the 14-day FCV group than in the 7-day FCV group. There was no significant difference in the number of adverse reactions between the two groups. WHAT IS NEW AND CONCLUSION: Compared with the traditional 7-day antiviral therapy, the 14-day course of FCV can reduce the acute pain and the incidence of PHN in elderly patients with HZ, especially in patients with moderate to severe initial pain. Prolonging the course of medication did not increase the side effects.

DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites.

MOTIVATION: Proteases are enzymes that cleave target substrate proteins by catalyzing the hydrolysis of peptide bonds between specific amino acids. While the functional proteolysis regulated by proteases plays a central role in the 'life and death' cellular processes, many of the corresponding substrates and their cleavage sites were not found yet. Availability of accurate predictors of the substrates and cleavage sites would facilitate understanding of proteases' functions and physiological roles. Deep learning is a promising approach for the development of accurate predictors of substrate cleavage events. RESULTS: We propose DeepCleave, the first deep learning-based predictor of protease-specific substrates and cleavage sites. DeepCleave uses protein substrate sequence data as input and employs convolutional neural networks with transfer learning to train accurate predictive models. High predictive performance of our models stems from the use of high-quality cleavage site features extracted from the substrate sequences through the deep learning process, and the application of transfer learning, multiple kernels and attention layer in the design of the deep network. Empirical tests against several related state-of-the-art methods demonstrate that DeepCleave outperforms these methods in predicting caspase and matrix metalloprotease substrate-cleavage sites. AVAILABILITY AND IMPLEMENTATION: The DeepCleave webserver and source code are freely available at http://deepcleave.erc.monash.edu/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Cryptic Plasmid Improves Chlamydia Fitness in Different Regions of the Gastrointestinal Tract.

The cryptic plasmid is important for chlamydial colonization in the gastrointestinal tract. We used a combination of intragastric, intrajejunal, and intracolon inoculations to reveal the impact of the plasmid on chlamydial colonization in distinct regions of gastrointestinal tract. Following an intragastric inoculation, the plasmid significantly improved chlamydial colonization. At the tissue level, plasmid-positive Chlamydia produced infectious progenies throughout gastrointestinal tract. However, to our surprise, plasmid-deficient Chlamydia failed to produce infectious progenies in small intestine, although infectious progenies were eventually detected in large intestine, indicating a critical role of the plasmid in chlamydial differentiation into infectious particles in small intestine. The noninfectious status may represent persistent infection, since Chlamydia genomes proliferated in the same tissues. Following an intrajejunal inoculation that bypasses the gastric barrier, plasmid-deficient Chlamydia produced infectious progenies in small intestine but was 530-fold less infectious than plasmid-positive Chlamydia, suggesting that (i) the noninfectious status developed after intragastric inoculation might be induced by a combination of gastric and intestinal effectors and (ii) chlamydial colonization in small intestine was highly dependent on plasmid. Finally, following an intracolon inoculation, the dependence of chlamydial colonization on plasmid increased over time. Thus, we have demonstrated that the plasmid may be able to improve chlamydial fitness in different gut regions via different mechanisms, which has laid a foundation to further reveal the specific mechanisms.

Analysis of the Risk Factors for Postherpetic Neuralgia.

BACKGROUND: Postherpetic neuralgia is the most common complication of herpes zoster, affecting 30% of patients. It seriously affects the quality of life of patients and the curative effect of treatment is limited. So far, researchers do not fully understand the risk factors for postherpetic neuralgia and more research is needed. OBJECTIVE: The aim of this paper was to investigate the risk factors for postherpetic neuralgia and provide reference for clinical diagnosis and treatment. METHODS: A total of 202 inpatients with herpes zoster in the General Hospital of Tianjin Medical University were recruited as study subjects. According to the occurrence of postherpetic neuralgia, the patients were divided into the postherpetic neuralgia group and the nonpostherpetic neuralgia group. Data on age, gender, initial symptoms, clinical classification, involved nerves, pain grading, antiviral therapy, glucocorticoid use, and other clinical data of patients in the two groups were collected and statistically analyzed. Univariate and multivariate analysis methods were used to analyze the differences between the two groups and determine the influencing factors of postherpetic neuralgia. RESULTS: The univariate statistical analysis of the factors influencing postherpetic neuralgia showed that the contribution of gender, initial symptoms, general clinical classification, use of glucocorticoid, and the interval from onset to antiviral therapy were not statistically significant, while the differences in age, specific clinical classification, involved nerves, severity of pain during the acute stage, and body side of skin lesion distribution were statistically significant. Multivariate logistic regression analysis showed that gender, use of glucocorticoid, interval from onset to antiviral therapy, involved nerves, and specific clinical classification showed no statistical significance. However, there were significant differences in age, body side of skin lesion distribution, general clinical classification, and degree of pain during the acute stage of the disease. CONCLUSION: Pain during the acute stage of herpes zoster, age greater than 70 years, and serious type of skin lesion are risk factors for postherpetic neuralgia (p < 0.05, OR >1).

The Genital Tract Virulence Factor pGP3 Is Essential for Chlamydia muridarum Colonization in the Gastrointestinal Tract.

The cryptic plasmid is essential for Chlamydia muridarum dissemination from the genital tract to the gastrointestinal (GI) tract. Following intravaginal inoculation, a C. muridarum strain deficient in plasmid-encoded pGP3 or pGP4 but not pGP5, pGP7, or pGP8 failed to spread to the mouse gastrointestinal tract, although mice infected with these strains developed productive genital tract infections. pGP3- or pGP4-deficient strains also failed to colonize the gastrointestinal tract when delivered intragastrically. pGP4 regulates pGP3, while pGP3 does not affect pGP4 expression, indicating that pGP3 is critical for C. muridarum colonization of the gastrointestinal tract. Mutants deficient in GlgA, a chromosome-encoded protein regulated by pGP4, also consistently colonized the mouse gastrointestinal tract. Interestingly, C. muridarum colonization of the gastrointestinal tract positively correlated with pathogenicity in the upper genital tract. pGP3-deficient C. muridarum strains did not induce hydrosalpinx or spread to the GI tract even when delivered to the oviduct by intrabursal inoculation. Thus, the current study not only has revealed that pGP3 is a novel chlamydial colonization factor in the gastrointestinal tract but also has laid a foundation for investigating the significance of gastrointestinal Chlamydia.

Enantioselective Formal [4+1] Cycloaddition of Diazoarylacetates and the Danishefsky's Diene: Stereoselective Synthesis of (-)-1,13-Herbertenediol.

Rodium chiral diene complex-catalyzed enantioselective cycloaddition of aryl α-diazoarylacetates and electron-enriched Danishefsky-type dienes afforded highly functionalized and optically enriched cyclopentenones in excellent yields (up to 97% yield) and with good to excellent enantioselectivities (60-92% ee). (-)-1,13-Herbertenediol was successfully synthesized in an overall 25% yield employing the optically enriched cyclopentenone with an all-carbon quaternary center as the key intermediate.

PmpI antibody reduces the inhibitory effect of Vp1 on Chlamydia trachomatis infectivity.

Chlamydia trachomatis is the most common cause of bacterial sexually transmitted infections. The effect of antibiotic treatment is not satisfactory, and there is currently no vaccine to prevent C. trachomatis infection. Our results showed that Chlamydia virus CPG1 capsid protein Vp1 treatment significantly inhibited C. trachomatis growth in cell culture, and the inclusion numbers of different C. trachomatis serotypes were decreased. In addition, we conducted a preliminary investigation of the possible mechanisms behind the Vp1 inhibition effects and the C. trachomatis molecules targeted by Vp1. Using far-western blot and GST pull-down assay, we found that purified Vp1 can bind to the C. trachomatis outer membrane protein PmpI. PmpI polyclonal antibody treatment markedly reduced the inhibitory effect of Vp1 on C. trachomatis infectivity. On the basis of these experimental results, we infer that PmpI participates in the inhibitory effect of Vp1 and may be a potential receptor of Vp1 in the outer membrane of C. trachomatis. Our research provides clues regarding the molecular mechanisms underlying the interactions between chlamydia virus and chlamydia.

Chlamydia muridarum with Mutations in Chromosomal Genes tc0237 and/or tc0668 Is Deficient in Colonizing the Mouse Gastrointestinal Tract.

Chlamydiae colonize the gastrointestinal tracts of both animals and humans. However, their medical significance remains unknown. We have previously shown that wild-type Chlamydia muridarum spreads to and establishes stable colonization of the gastrointestinal tract following intravaginal inoculation. In the present study, we found that C. muridarum with mutations in chromosomal genes tc0237 and/or tc0668 was defective in spreading to the mouse gastrointestinal tract, which correlated with its attenuated pathogenicity in the upper genital tract. This correlation was more consistent than that of chlamydial pathogenicity with ascending infection in the genital tract, since attenuated C. muridarum spread significantly less to the gastrointestinal tract but maintained robust ascending infection of the upper genital tract. Transcervical inoculation further confirmed the correlation between C. muridarum spreading to the gastrointestinal tract and its pathogenicity in the upper genital tract. Finally, defective spreading of C. muridarum mutants was due to their inability to colonize the gastrointestinal tract since intragastric inoculation did not rescue the mutants' colonization. Thus, promoting C. muridarum colonization of the gastrointestinal tract may represent a primary function of the TC0237 and TC0668 proteins. Correlation of chlamydial colonization of the gastrointestinal tract with chlamydial pathogenicity in the upper genital tract suggests a potential role for gastrointestinal chlamydiae in genital tract pathogenicity.

Identification of proteins differentially expressed by Chlamydia trachomatis treated with chlamydiaphage capsid protein VP1 during intracellular growth.

Chlamydia trachomatis infection is one of the most prevalent sexually transmitted diseases. Our research pertains to the inhibitory effect and molecular mechanism of the chlamydiaphage capsid protein VP1 on the growth of Chlamydia trachomatis. In this research, the capsid protein VP1 of the guinea-pig conjunctivitis chlamydiaphage phiCPG1 was expressed, purified and identified, and then, it was applied to the cultivation of different serovars of Chlamydia trachomatis and Chlamydia psittaci. The inhibitory effect was observed in each serovar of Chlamydia trachomatis (D, E, F, G, H, I, K, and L2) and Chlamydia psittaci inoculated with VP1 protein. The inhibition affection of VP1 on the growth of Chlamydia trachomatis was caused by the changes of expressions of some related proteins including 36 proteins up-regulated and 81 proteins down-regulated in the development cycle of Ct through the label-free test, and the transcription levels of these proteins, including Hc1, pmpD, and MOMP, were confirmed by RT-PCR. It provides information that is essential for understanding the mechanism of chlamydiaphage capsid protein VP1 on chlamydia and a new direction for further clinical treatment of chlamydial infection.

A Randomized, Double-blind, Placebo-controlled Trial to Evaluate the Efficacy and Safety of Pregabalin for Postherpetic Neuralgia in a Population of Chinese Patients.

BACKGROUND AND PURPOSE: Currently, there are limited options for treatment of postherpetic neuralgia (PHN) patients in China. While pregabalin is an effective treatment option for PHN in several countries, there is limited information on its efficacy in Chinese patients. METHODS: This was an 8-week, double-blind, placebo-controlled trial in Chinese patients with PHN randomized (1:1) to pregabalin 300 mg/day or placebo. Primary efficacy endpoint was change from baseline in mean pain score (Daily Pain Rating Scale; 0 = 'no pain' to 10 = 'worst possible pain'). Secondary efficacy endpoints included change from baseline in overall pain intensity score, by visual analog scale (VAS; 0 = 'no pain' to 100 = 'worst possible pain') and daily sleep interference score (0 = 'pain does not interfere with sleep' to 10 = 'completely interferes'). RESULTS: A total of 220 patients were randomized and received treatment (111 pregabalin and 109 placebo). Improvement in mean pain score with pregabalin was significantly greater than placebo, least squares mean difference (95% CI), -0.71 (-1.08, -0.34); P = 0.0002. Improvements in VAS and sleep interference score at endpoint were significantly greater with pregabalin than placebo, least squares mean difference (95% CI), -8.18 (-11.99, -4.37); P < 0.0001, and -0.54 (-0.93, -0.14); P = 0.0079, respectively. Adverse events were consistent with current product labeling, with dizziness the most commonly reported adverse event (24.3% of pregabalin-treated patients). CONCLUSION: Pregabalin improved measures of pain and sleep, and is well tolerated in Chinese patients with PHN. These results may inform physicians treating patients with PHN in China.