LPAR6 Participates in Neuropathic Pain by Mediating Astrocyte Cells via ROCK2/NF-κB Signal Pathway.

Neuropathic pain (NPP) is a common type of chronic pain. Glial cells, including astrocytes (AS), are believed to play an important role in the progression of NPP. AS cells can be divided into various types based on their expression profiles, among which A1 and A2 types have clear functions. A1-type AS cells are neurotoxic, while A2-type AS cells exert neuroprotective functions. Some types of lysophosphatidic acid receptors (LPAR) have been shown to play a role in NPP. However, it remains unclear how AS cells and LPAR6 affect the occurrence and progression of NPP. In this study, we established a mouse model of chronic constriction injury (CCI) to simulate NPP. It was found that the expression of LPAR6 in AS cells of the spinal dorsal horn was increased in the CCI model, and the thresholds of mechanical and thermal pain were elevated after knocking out LPAR6, indicating that LPAR6 and AS cells participated in the occurrence of NPP. The experiment involved culturing primary AS cells and knocking down LPAR6 by Lentivirus. The results showed that the NF-κB signal pathway was activated and the number of A1-type AS cells increased in the CCI model. However, LPAR6 knockdown inhibited the NF-κB signal pathway and A1-type AS cells. The results of the mRNA sequencing and immunoprecipitation test indicate an interaction between LPAR6 and ROCK2. Inhibiting ROCK2 by Y-27632 increased mechanical and thermal pain thresholds and alleviated NPP at the molecular level. The study presents evidence that LPAR6 activates the NF-κB pathway through ROCK2 and contributes to the progression of NPP by increasing A1-type AS and decreasing A2-type AS. This suggests that LPAR6 could be a potential therapeutic target for alleviating NPP. Clinical applications that are successful can offer new therapeutic options, enhance the quality of life for patients, and potentially uncover new mechanisms for pain modulation.

PRMT6 deficiency or inhibition alleviates neuropathic pain by decreasing glycolysis and inflammation in microglia.

Microglia induced chronic inflammation is the critical pathology of Neuropathic pain (NP). Metabolic reprogramming of macrophage has been intensively reported in various chronic inflammation diseases. However, the metabolic reprogramming of microglia in chronic pain remains to be elusive. Here, we reported that immuno-metabolic markers (HIF-1α, PKM2, GLUT1 and lactate) were related with increased expression of PRMT6 in the ipsilateral spinal cord dorsal horn of the chronic construction injury (CCI) mice. PRMT6 deficiency or prophylactic and therapeutic intrathecal administration of PRMT6 inhibitor (EPZ020411) ameliorated CCI-induced NP, inflammation and glycolysis in the ipsilateral spinal cord dorsal horn. PRMT6 knockout or knockdown inhibited LPS-induced inflammation, proliferation and glycolysis in microglia cells. While PRMT6 overexpression exacerbated LPS-induced inflammation, proliferation and glycolysis in BV2 cells. Recent research revealed that PRMT6 could interact with and methylate HIF-1α, which increased HIF-1α protein stability. In sum, increased expression of PRMT6 exacerbates NP progress by increasing glycolysis and neuroinflammation through interacting with and stabilizing HIF-1α in a methyltransferase manner, which outlines novel pathological mechanism and drug target for NP.

HSV-1 reactivation results in post-herpetic neuralgia by upregulating Prmt6 and inhibiting cGAS-STING.

Chronic varicella zoster virus (VZV) infection induced neuroinflammatory condition is the critical pathology of postherpetic neuralgia (PHN). The immune escape mechanism of VZV remains to be elusive. Due to mice have no VZV infection receptor, herpes simplex virus type 1 (HSV-1) infection is a well-established PHN mice model. Transcriptional expression analysis identified that the protein arginine methyltransferases 6 (Prmt6) was upregulated upon HSV-1 infection, which was further confirmed by immunofluorescence staining in spinal dorsal horn. Prmt6 deficiency decreased HSV-1-induced neuroinflammation and PHN by enhancing antiviral innate immunity and decreasing HSV-1 load in vivo and in vitro. Overexpression of Prmt6 in microglia dampened antiviral innate immunity and increased HSV-1 load. Mechanistically, Prmt6 methylated and inactivated STING, resulting in reduced phosphorylation of TANK binding kinase-1 (TBK1) and interferon regulatory factor 3 (IRF3), diminished production of type I interferon (IFN-I) and antiviral innate immunity. Furthermore, intrathecal or intraperitoneal administration of the Prmt6 inhibitor EPZ020411 decreased HSV-1-induced neuroinflammation and PHN by enhancing antiviral innate immunity and decreasing HSV-1 load. Our findings revealed that HSV-1 escapes antiviral innate immunity and results in PHN by upregulating Prmt6 expression and inhibiting cGAS-STING pathway, providing novel insights and a potential therapeutic target for PHN.

Macrophage ß2-AR activation amplifies inflammation in wound healing by upregulating Trem1 via the cAMP/PKA/CREB pathway.

BACKGROUND: Inflammation is an important part of the wound healing process. The stress hormone epinephrine has been demonstrated to modulate the inflammatory response via its interaction with ß2-adrenergic receptor (ß2-AR). However, the precise molecular mechanism through which ß2-AR exerts its influence on inflammation during the wound healing process remains an unresolved question. METHODS: Transcriptome datasets of wound and macrophages from the GEO database were reanalyzed using bioinformatics. The role of ß2-AR in wound healing was explored by a mouse hind paw plantar wound model, and histological analyses were performed to assess wound healing. In vivo and in vitro assays were performed to elucidate the role of ß2-AR on the inflammatory response. Triggering receptor expressed on myeloid cells 1 (Trem1) was knocked down with siRNA on RAW cells and western blot and qPCR assays were performed. RESULTS: Trem1 was upregulated within 24 h of wounding, and macrophage ß2-AR activation also upregulated Trem1. In vivo experiments demonstrated that ß2-AR agonists impaired wound healing, accompanied by upregulation of Trem1 and activation of cAMP/PKA/CREB pathway, as well as by a high level of pro-inflammatory cytokine production. In vitro experiments showed that macrophage ß2-AR activation amplified LPS-induced inflammation, and knockdown of Trem1 reversed this effect. Using activator and inhibitor of cAMP, macrophage ß2-AR activation was confirmed to upregulate Trem1 via the cAMP/PKA/CREB pathway. CONCLUSION: Our study found that ß2-AR agonists increase Trem1 expression in wounds, accompanied by amplification of the inflammatory response, impairing wound healing. ß2-AR activation in RAW cells induces Trem1 upregulation via the cAMP/PKA/CREB pathway and amplifies LPS-induced inflammatory responses.

New insights into the downregulation of cytochrome P450 2E1 via nuclear factor κB-dependent pathways in immune-mediated liver injury.

The extent of immune-mediated hepatic damage (such as in viral hepatitis) is characterised by the downregulation of cytochrome P450s (CYPs), a class of drug-metabolising enzymes. However, whether this downregulation aids liver cells in maintaining their homeostasis or whether the damage is aggravated remains largely unexplored. Herein, we evaluated the effects of phosphorylation mediated by the protein kinase C (PKC)/cAMP-response element binding protein (CREB) and nitration mediated by inducible nitric oxide synthase (iNOS) on the downregulation of CYP2E1 during immune-mediated liver injury. Additionally, we investigated the regulatory mechanism mediated by the nuclear factor κB (NF-κB). The rat model of immune-mediated liver injury was replicated by administering a single i.v. injection of Bacillus Calmette-Guerin (BCG, 125 mg/kg) vaccine and three i.p. injections of ammonium pyrrolidine dithiocarbamate (25, 50, 100 mg/kg/d, days 11, 12, and 13); blood was then collected on day 14. Subsequently, the livers were extracted to identify the different pharmacokinetic and biochemical indicators involved in the process. Our study reports new findings on the dependence between PKC-mediated CREB phosphorylation in the anti-inflammatory pathway and nitration emergency induced by iNOS in pro-inflammatory pathways in the NF-κB pathway. The interaction of these two pathways leads to the downregulation and recovery of CYP2E1, thus alleviating inflammation and nitration stress. Our results confirm that BCG-mediated downregulation of CYP2E1 is linked to iNOS-induced nitration and PKC/NF-κB-mediated CREB phosphorylation, and that NF-κB is an important molecular target in this process. These findings suggest that the downregulation of CYP2E1 may be an autonomous process characteristic of liver cells, helping them adapt to environmental changes, alleviate further hypoxia in inflamed tissues, and minimise exposure to toxic and harmful metabolites.

Hippophae rhamnoides reverses decreased CYP2D6 expression in rats with BCG-induced liver injury.

In this study, we investigated the effect of Hippophae rhamnoides L. (HRP) on the activity of CYP2D6 via the CAMP/PKA/NF-κB pathway in rats with Bacille Calmette-Guerin (BCG)-induced immunological liver injury. BCG (125 mg/kg) was injected to establish the rat model of liver injury. HRP was administered intragastrically for one week as the intervention drug. Proteomics techniques were used to analyze protein expression levels, obtaining a comprehensive understanding of the liver injury process. ELISA or western blotting was used to detect specific protein levels. Dextromethorphan was detected using high-performance liquid chromatography to reflect the metabolic activity of CYP2D6. BCG downregulated the expression of CYP2D6, cAMP, PKA, IκB, and P-CREB and upregulated that of NF-κB, IL-1ß, TNF-α, and CREB in the liver; HRP administration reversed these effects. Therefore, HRP may restore the metabolic function of the liver by reversing the downregulation of CYP2D6 through inhibition of NF-κB signal transduction and regulation of the cAMP/PKA/CREB/CYP2D6 pathway. These findings highlight the role of HRP as an alternative clinical drug for treating hepatitis B and other immune-related liver diseases.

A denoised multi-omics integration framework for cancer subtype classification and survival prediction.

The availability of high-throughput sequencing data creates opportunities to comprehensively understand human diseases as well as challenges to train machine learning models using such high dimensions of data. Here, we propose a denoised multi-omics integration framework, which contains a distribution-based feature denoising algorithm, Feature Selection with Distribution (FSD), for dimension reduction and a multi-omics integration framework, Attention Multi-Omics Integration (AttentionMOI) to predict cancer prognosis and identify cancer subtypes. We demonstrated that FSD improved model performance either using single omic data or multi-omics data in 15 The Cancer Genome Atlas Program (TCGA) cancers for survival prediction and kidney cancer subtype identification. And our integration framework AttentionMOI outperformed machine learning models and current multi-omics integration algorithms with high dimensions of features. Furthermore, FSD identified features that were associated to cancer prognosis and could be considered as biomarkers.

Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level.

Aphis gossypii Glover is a major insect pest in cotton production, which can cause yield reduction in severe cases. In this paper, we proposed the A. gossypii infestation monitoring method, which identifies the infestation level of A. gossypii at the cotton seedling stage, and can improve the efficiency of early warning and forecasting of A. gossypii, and achieve precise prevention and cure according to the predicted infestation level. We used smartphones to collect A. gossypii infestation images and compiled an infestation image data set. And then constructed, trained, and tested three different A. gossypii infestation recognition models based on Faster Region-based Convolutional Neural Network (R-CNN), You Only Look Once (YOLO)v5 and single-shot detector (SSD) models. The results showed that the YOLOv5 model had the highest mean average precision (mAP) value (95.7%) and frames per second (FPS) value (61.73) for the same conditions. In studying the influence of different image resolutions on the performance of the YOLOv5 model, we found that YOLOv5s performed better than YOLOv5x in terms of overall performance, with the best performance at an image resolution of 640×640 (mAP of 96.8%, FPS of 71.43). And the comparison with the latest YOLOv8s showed that the YOLOv5s performed better than the YOLOv8s. Finally, the trained model was deployed to the Android mobile, and the results showed that mobile-side detection was the best when the image resolution was 256×256, with an accuracy of 81.0% and FPS of 6.98. The real-time recognition system established in this study can provide technical support for infestation forecasting and precise prevention of A. gossypii.

Machine Learning-Based Screening of Risk Factors and Prediction of Deep Vein Thrombosis and Pulmonary Embolism After Hip Arthroplasty.

Prophylactic anticoagulation is a standard strategy for patients undergoing total hip arthroplasty (THA) to prevent deep venous thromboembolism (DVT) and pulmonary embolism (PE). Nevertheless, some patients still experience these complications during their hospital stay. Current risk assessment methods like the Caprini and Geneva scores are not specifically designed for THA and may not accurately predict DVT or PE postoperatively. This study used machine learning techniques to establish models for early diagnosis of DVT and PE in patients undergoing THA. Data were collected from 1481 patients who received perioperative prophylactic anticoagulation. Model establishment and parameter tuning were performed using a training set and evaluated using a test set. Among the models, extreme gradient boosting (XGBoost) performed the best, with an area under the receiver operating characteristic curve (AUC) of 0.982, sensitivity of 0.913, and specificity of 0.998. The main features used in the XGBoost model were direct and indirect bilirubin, partial activation prothrombin time, prealbumin, creatinine, D-dimer, and C-reactive protein. Shapley Additive Explanations analysis was conducted to further analyze these features. This study presents a model for early diagnosis DVT or PE after THA and demonstrates bilirubin could be a potential predictor in the assessment of DVT or PE. Compared to traditional risk assessment, XGBoost has a high sensitivity and specificity to predict DVT and PE in the clinical setting. Furthermore, the results of this study were converted into a web calculator that can be used in clinical practice.

Lyn-mediated glycolysis enhancement of microglia contributes to neuropathic pain through facilitating IRF5 nuclear translocation in spinal dorsal horn.

The pro-inflammatory phenotype of microglia usually induces neuroinflammatory reactions in neuropathic pain. Glycometabolism shift to glycolysis can promote the pro-inflammatory phenotype transition of microglia. The omics data analysis suggest a critical role for Lyn dysregulation in neuropathic pain. The present study aimed at exploring the mechanism of Lyn-mediated glycolysis enhancement of microglia in neuropathic pain. Neuropathic pain model was established by chronic constriction injury (CCI), then pain thresholds and Lyn expression were measured. Lyn inhibitor Bafetinib and siRNA-lyn knockdown were administrated intrathecally to evaluate the effects of Lyn on pain thresholds, glycolysis and interferon regulatory factor 5 (IRF5) nuclear translocation of microglia in vivo and in vitro. ChIP was carried out to observe the binding of transcription factors SP1, PU.1 to glycolytic gene promoters by IRF5 knockdown. Finally, the relationship between glycolysis and pro-inflammatory phenotype transition of microglia was evaluated. CCI led to the upregulation of Lyn expression and glycolysis enhancement in microglia of spinal dorsal horn. Bafetinib or siRNA-lyn knockdown intrathecally alleviated pain hyperalgesia, suppressed glycolysis enhancement and inhibited nuclear translocation of IRF5 in CCI mice. Also, IRF5 promoted the binding of transcription factors SP1, PU.1 to glycolytic gene promoters, and then the enhanced glycolysis facilitated the proliferation and pro-inflammatory phenotype transition of microglia and contributed to neuropathic pain. Lyn-mediated glycolysis enhancement of microglia contributes to neuropathic pain through facilitating IRF5 nuclear translocation in spinal dorsal horn.

Sitobion miscanthi L type symbiont enhances the fitness and feeding behavior of the host grain aphid.

BACKGROUND: Symbiotic bacteria affect physiology and ecology of insect hosts. The Sitobion miscanthi L type symbiont (SMLS) is a recently discovered and widely distributed secondary symbiont in the grain aphid Sitobion miscanthi Takahashi in China. RESULTS: In this study, SMLS-infected (SI) and SMLS-uninfected (SU) aphid strains were obtained from field population. The artificially SMLS-re-infected (SRI) strain was established by injecting SU aphids with the SI strain hemolymph containing SMLS. The SRI and SU strains had identical genetic backgrounds and similar microbial community structures. Compared with the SU strain, adult longevity, survival rate, and fecundity were significantly greater in the SRI strain (biological fitness of 1.48). Moreover, the SRI strain spent more time ingesting phloem than the SU strain. A comparative transcriptome analysis indicated that reproduction- and longevity-related genes were more highly expressed in the SRI strain than in the SU strain. CONCLUSION: The findings indicated that the infection with SMLS enhanced the Sitobion miscanthi fitness and feeding behavior. The beneficial effect of the SMLS on hosts could explain why it frequently infects the field populations in the grain aphid Sitobion miscanthi Takahashi in China. © 2022 Society of Chemical Industry.

Prognosis and pain dissection of novel signatures in kidney renal clear cell carcinoma based on fatty acid metabolism-related genes.

Renal cell carcinoma (RCC) is a malignant tumor that is characterized by the accumulation of intracellular lipid droplets. The prognostic value of fatty acid metabolism-related genes (FMGs) in RCC remains unclear. Alongside this insight, we collected data from three RCC cohorts, namely, The Cancer Genome Atlas (TCGA), E-MTAB-1980, and GSE22541 cohorts, and identified a total of 309 FMGs that could be associated with RCC prognosis. First, we determined the copy number variation and expression levels of these FMGs, and identified 52 overall survival (OS)-related FMGs of the TCGA-KIRC and the E-MTAB-1980 cohort data. Next, 10 of these genes-FASN, ACOT9, MID1IP1, CYP2C9, ABCD1, CPT2, CRAT, TP53INP2, FAAH2, and PTPRG-were identified as pivotal OS-related FMGs based on least absolute shrinkage and selection operator and Cox regression analyses. The expression of some of these genes was confirmed in patients with RCC by immunohistochemical analyses. Kaplan-Meier analysis showed that the identified FMGs were effective in predicting the prognosis of RCC. Moreover, an optimal nomogram was constructed based on FMG-based risk scores and clinical factors, and its robustness was verified by time-dependent receiver operating characteristic analysis, calibration curve analysis, and decision curve analysis. We have also described the biological processes and the tumor immune microenvironment based on FMG-based risk score classification. Given the close association between fatty acid metabolism and cancer-related pain, our 10-FMG signature may also serve as a potential therapeutic target with dual effects on ccRCC prognosis and cancer pain and, therefore, warrants further investigation.

Calcitriol ameliorates renal injury with high-salt diet-induced hypertension by upregulating GLIS2 expression and AMPK/mTOR-regulated autophagy.

The goal of the present study was to investigate the protective effect of calcitriol on high-salt diet-induced hypertension. The hypertension rat model was established by a long-term high-salt diet (8% NaCl). Rats were treated with calcitriol, losartan, or their combination. Histological staining was used to confirm renal pathology. Global transcriptome analysis of renal tissues was performed, and the mechanism of the therapeutic effect of calcitriol was analysed by functional annotation and pathway analysis of the differentially expressed genes (DEGs) as well as by Western blotting analysis. The core genes for potential therapeutic regulation were identified through the coexpression gene network. For in vitro HK-2 cell experiments, small interfering RNA (siRNA) was used to knockdown key a transcription factor (TF) Glis2 to validate the therapeutic target of calcitriol. MAPK1 and CXCL12 expression was downregulated and the apoptosis pathway was significantly enriched by calcitriol treatment. The western blotting results showed that calcitriol treatment increased AMPK phosphorylation and decreased downstream mTOR phosphorylation, which was accompanied by a decrease in autophagy protein p62 expression and an increase in LC3-II/I expression. GLIS2 was identified as a specific therapeutic target for calcitriol. GLIS2 expression was upregulated by calcitriol and confirmed by HK-2 cells in vitro. Our omics data show that calcitriol can alleviate oxidative stress and fibrosis. Moreover, calcitriol can regulate the CXCL12/ERK1/2 cascade to inhibit the inflammatory response and renal cell apoptosis and induce renal autophagy through the AMPK/mTOR pathway. Our study partially elucidate the pathogenesis and treatment mechanism underlying hypertension, and provide new insights into the treatment of hypertension.

Calcitriol ameliorates damage in high-salt diet-induced hypertension: Evidence of communication with the gut-kidney axis.

Several studies have established a link between high-salt diet, inflammation, and hypertension. Vitamin D supplementation has shown anti-inflammatory effects in many diseases; gut microbiota is also associated with a wide variety of cardiovascular diseases, but potential role of vitamin D and gut microbiota in high-salt diet-induced hypertension remains unclear. Therefore, we used rats with hypertension induced by a high-salt diet as the research object and analyzed the transcriptome of their tissues (kidney and colon) and gut microbiome to conduct an overall analysis of the gut-kidney axis. We aimed to confirm the effects of high salt and calcitriol on the gut-kidney immune system and the composition of the intestinal flora. We demonstrate that consumption of a high-salt diet results in hypertension and inflammation in the colon and kidney and alteration of gut microbiota composition and function. High-salt diet-induced hypertension was found to be associated with seven microbial taxa and mainly associated with reduced production of the protective short-chain fatty acid butyrate. Calcitriol can reduce colon and kidney inflammation, and there are gene expression changes consistent with restored intestinal barrier function. The protective effect of calcitriol may be mediated indirectly by immunological properties. Additionally, the molecular pathways of the gut microbiota-mediated blood pressure regulation may be related to circadian rhythm signals, which needs to be further investigated. An innovative association analysis of the microbiota may be a key strategy to understanding the association between gene patterns and host.

Prediction of response after cardiac resynchronization therapy with machine learning.

AIMS: Nearly one third of patients receiving cardiac resynchronization therapy (CRT) suffer non-response. We intend to develop predictive models using machine learning (ML) approaches and easily attainable features before CRT implantation. METHODS AND RESULTS: The baseline characteristics of 752 CRT recipients from two hospitals were retrospectively collected. Nine ML predictive models were established, including logistic regression (LR), elastic network (EN), lasso regression (Lasso), ridge regression (Ridge), neural network (NN), support vector machine (SVM), random forest (RF), XGBoost and k-nearest neighbour (k-NN). Sensitivity, specificity, precision, accuracy, F1, log-loss, area under the receiver operating characteristic (AU-ROC), and average precision (AP) of each model were evaluated. AU-ROC was compared between models and the latest guidelines. Six models had an AU-ROC value above 0.75. The LR, EN and Ridge models showed the highest overall predictive power compared with other models with AU-ROC at 0.77. The XGBoost model reached the highest sensitivity at 0.72, while the highest specificity was achieved by Ridge model at 0.92. All ML models achieved higher AU-ROCs that those derived from the latest guidelines (all P < 0.05). The effect size analysis identified left bundle branch block, left ventricular end-systolic diameter, and history of percutaneous coronary intervention as the most crucial predictors of CRT response. An online tool to facilitate the prediction of CRT response is freely available at http://www.crt-response.com/. CONCLUSIONS: ML algorithms produced efficient predictive models for evaluation of CRT response with features before implantation. Tools developed accordingly could improve the selection of CRT candidates and reduce the incidence of non-response.

Risk factors and socio-economic burden in pancreatic ductal adenocarcinoma operation: a machine learning based analysis.

BACKGROUND: Surgical resection is the major way to cure pancreatic ductal adenocarcinoma (PDAC). However, this operation is complex, and the peri-operative risk is high, making patients more likely to be admitted to the intensive care unit (ICU). Therefore, establishing a risk model that predicts admission to ICU is meaningful in preventing patients from post-operation deterioration and potentially reducing socio-economic burden. METHODS: We retrospectively collected 120 clinical features from 1242 PDAC patients, including demographic data, pre-operative and intra-operative blood tests, in-hospital duration, and ICU status. Machine learning pipelines, including Supporting Vector Machine (SVM), Logistic Regression, and Lasso Regression, were employed to choose an optimal model in predicting ICU admission. Ordinary least-squares regression (OLS) and Lasso Regression were adopted in the correlation analysis of post-operative bleeding, total in-hospital duration, and discharge costs. RESULTS: SVM model achieved higher performance than the other two models, resulted in an AU-ROC of 0.80. The features, such as age, duration of operation, monocyte count, and intra-operative partial arterial pressure of oxygen (PaO2), are risk factors in the ICU admission. The protective factors include RBC count, analgesic pump dexmedetomidine (DEX), and intra-operative maintenance of DEX. Basophil percentage, duration of the operation, and total infusion volume were risk variables for staying in ICU. The bilirubin, CA125, and pre-operative albumin were associated with the post-operative bleeding volume. The operation duration was the most important factor for discharge costs, while pre-lymphocyte percentage and the absolute count are responsible for less cost. CONCLUSIONS: We observed that several new indicators such as DEX, monocyte count, basophil percentage, and intra-operative PaO2 showed a good predictive effect on the possibility of admission to ICU and duration of stay in ICU. This work provided an essential reference for indication in advance to PDAC operation.

[The design of the software system for the interpretation and guidance of drug locus related to drug treatment].

Based on the pharmacogenomics theory, this study developed a software system for interpretation of drug gene loci and guidance on clinical safe medication with the purpose of providing clinical guidance on the safety and effectiveness of drug use through accurate and efficient detection and interpretation of drug gene loci. The system infrastructure was built on a service-oriented architecture (SOA) design and Docker container virtualization approach to achieve a rapid and automatic interpretation of genetic results and best available drugs. The front end was established on HTML5 and JavaScript to realize visualization of analysis results and user interaction. The system was tested and validated to show robust performance which is reliable in clinical use. It will show high impact on the development of pharmacogenomics and clinical practice of patients with personalized medicine.

Comparison and development of machine learning tools in the prediction of chronic kidney disease progression.

BACKGROUND: Urinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD). However, the current procedure for determining the severity of CKD is completed through evaluating 24-h urinary protein, which is inconvenient during follow-up. OBJECTIVE: To quickly predict the severity of CKD using more easily available demographic and blood biochemical features during follow-up, we developed and compared several predictive models using statistical, machine learning and neural network approaches. METHODS: The clinical and blood biochemical results from 551 patients with proteinuria were collected. Thirteen blood-derived tests and 5 demographic features were used as non-urinary clinical variables to predict the 24-h urinary protein outcome response. Nine predictive models were established and compared, including logistic regression, Elastic Net, lasso regression, ridge regression, support vector machine, random forest, XGBoost, neural network and k-nearest neighbor. The AU-ROC, sensitivity (recall), specificity, accuracy, log-loss and precision of each of the models were evaluated. The effect sizes of each variable were analysed and ranked. RESULTS: The linear models including Elastic Net, lasso regression, ridge regression and logistic regression showed the highest overall predictive power, with an average AUC and a precision above 0.87 and 0.8, respectively. Logistic regression ranked first, reaching an AUC of 0.873, with a sensitivity and specificity of 0.83 and 0.82, respectively. The model with the highest sensitivity was Elastic Net (0.85), while XGBoost showed the highest specificity (0.83). In the effect size analyses, we identified that ALB, Scr, TG, LDL and EGFR had important impacts on the predictability of the models, while other predictors such as CRP, HDL and SNA were less important. CONCLUSIONS: Blood-derived tests could be applied as non-urinary predictors during outpatient follow-up. Features in routine blood tests, including ALB, Scr, TG, LDL and EGFR levels, showed predictive ability for CKD severity. The developed online tool can facilitate the prediction of proteinuria progress during follow-up in clinical practice.

Transgenic Cry1Ac cotton does not affect the development and fecundity of Chrysoperla carnea.

The development and fecundity of the predator Chrysoperla carnea Stephens (Neuroptera: Chrysopidae) were assessed by feeding Aphis gossypii Glover (Hemiptera: Aphididae) that had been reared on transgenic Bacillus thuringiensis (Bt) cotton SGK321 and a non-Bt cotton control (SY321) for two successive generations. We found no significant differences in the developmental stage duration, stage survival, or egg hatch rate between C. carnea fed A. gossypii reared on the Bt and non-Bt cotton. The fecundity per female over a 25-day observation period was very similar between treatments; for C. carnea fed A. gossypii reared on SGK321 vs. SY321, the amount of eggs laid was not significantly different in both generations. Furthermore, a population dynamics of A. gossypii and lacewing (mainly C. carnea) were highly similar in the SGK321 and SY321 treatments during 2016-2017. These results suggest that Bt cotton does not have a significantly negative or positive effect on C. carnea in terms of development, survival, fecundity, or population dynamics.

Parameter and state estimator for state space models.

This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.