Liposome-trimethyl chitosan nanoparticles codeliver insulin and siVEGF to treat corneal alkali burns by inhibiting ferroptosis.

Alkali burns are potentially blinding corneal injuries. Due to the lack of available effective therapies, the prognosis is poor. Thus, effective treatment methods for corneal alkali burns are urgently needed. Codelivery nanoparticles (NPs) with characteristics such as high bioavailability and few side effects have been considered effective therapeutic agents for ocular diseases. In this study, we designed a new combination therapy using liposomes and trimethyl chitosan (TMC) for the codelivery of insulin (INS) and vascular endothelial growth factor small interfering RNA (siVEGF) to treat alkali-burned corneas. We describe the preparation and characterization of siVEGF-TMC-INS-liposome (siVEGF-TIL), drug release characteristics, intraocular tracing, pharmacodynamics, and biosafety. We found that siVEGF-TIL could inhibit oxidative stress, inflammation, and the expression of VEGF in vitro and effectively maintained corneal transparency, accelerated epithelialization, and inhibited corneal neovascularization (CNV) in vivo. Morever, we found that the therapeutic mechanism of siVEGF-TIL is possibly relevant to the inhibition of the ferroptosis signaling pathway by metabolomic analysis. In general, siVEGF-TIL NPs could be a safe and effective therapy for corneal alkali burn.

Using Convolutional Neural Network as a Statistical Algorithm to Explore the Therapeutic Effect of Insulin Liposomes on Corneal Inflammation.

Aiming at the disadvantages of easy recurrence of keratitis, difficult eradication by surgery, and easy bacterial resistance, insulin-loaded liposomes were prepared, and convolutional neural network was used as a statistical algorithm to build SD rat corneal inflammation model and study insulin-loaded liposomes, alleviating effect on corneal inflammatory structure in SD rats. The INS/PFOB@LIP was developed by means of thin-film dispersive phacoemulsification, its structure was monitored using a transmission electron microscope, particle size and appearance potential were monitored using a Malvern particle sizer, and ultraviolet consumption spectrum was monitored using a UV spectrophotometer. The encapsulation rate, drug loading, and distribution of insulin liposomes in rat corneal inflammatory model were measured and calculated. The cytotoxicity of liposome materials was evaluated by CCK-8 assay, and the toxic effects of insulin and insulin liposomes on cells were detected. The cornea of SD rats was burned with NaOH solution (1 mol/L), and the SD rat corneal inflammation model was created. The insulin liposome was applied to the corneal inflammation model, and the therapeutic effect of insulin liposome on corneal inflammation was evaluated by slit lamp, corneal immunohistochemistry, corneal HE staining, and corneal Sirius red staining. Insulin-loaded liposomes were successfully constructed with an average particle size of (130.69 ± 3.87) nm and a surface potential of (-38.24 ± 2.57) mV. The encapsulation rate of insulin liposomes was (48.89 ± 1.24)%, and the drug loading rate was (24.45 ± 1.24)%. The SD rat corneal inflammation model was successfully established. After insulin liposome treatment, the staining area of corneal fluorescein sodium was significantly reduced, the corneal epithelium was significantly thickened, the content of corneal collagen was increased, the expression of inflammatory factors was significantly reduced, and new blood vessels (corneal neovascularization, CNV) growth was inhibited.

Urea derivative organocatalyzed enantioselective Friedel-Crafts alkylation of α-naphthols with isatins.

An organocatalytic enantioselective Friedel-Crafts alkylation of α-naphthols with isatin derivatives was catalyzed by Takemoto-type urea catalyst to give optical active 3-(naphthalen-2-yl)-3-hydroxy-2-oxindoles in 75%-92% yields with up to 95% enantiomeric excess (ee) value. The catalyst type and the substrate scope were broadened in this methodology.

Shenjinhuoxue Mixture Attenuates Inflammation, Pain, and Cartilage Degeneration by Inhibiting TLR-4 and NF-κB Activation in Rats with Osteoarthritis: A Synergistic Combination of Multitarget Active Phytochemicals.

Osteoarthritis (OA), a highly prevalent chronic joint disease, involves a complex network of inflammatory mediators that not only triggers pain and cartilage degeneration but also accelerates disease progression. Traditional Chinese medicinal shenjinhuoxue mixture (SHM) shows anti-inflammatory and analgesic effects against OA with remarkable clinical efficacy. This study explored the mechanism underlying anti-OA properties of SHM and evaluated its efficacy and safety via in vivo experiments. Through network pharmacology and published literature, we identified the key active phytochemicals in SHM, including ß-sitosterol, oleanolic acid, licochalcone A, quercetin, isorhamnetin, kaempferol, morusin, lupeol, and pinocembrin; the pivotal targets of which are TLR-4 and NF-κB, eliciting anti-OA activity. These phytochemicals can enter the active pockets of TLR-4 and NF-κB with docking score ≤ -3.86 kcal/mol, as shown in molecular docking models. By using surface plasmon resonance assay, licochalcone A and oleanolic acid were found to have good TLR-4-binding affinity. In OA rats, oral SHM at mid and high doses (8.72 g/kg and 26.2 g/kg) over 6 weeks significantly alleviated mechanical and thermal hyperalgesia (P < 0.0001). Accordingly, the expression of inflammatory mediators (TLR-4, interleukin (IL-) 1 receptor-associated kinase 1 (IRAK1), NF-κB-p65, tumor necrosis factor (TNF-) α, IL-6, and IL-1ß), receptor activator of the NF-κB ligand (RANKL), and transient receptor potential vanilloid 1 (TRPV1) in the synovial and cartilage tissue of OA rats was significantly decreased (P < 0.05). Moreover, pathological observation illustrated amelioration of cartilage degeneration and joint injury. In chronic toxicity experiment of rats, SHM at 60 mg/kg demonstrated the safety. SHM had an anti-inflammatory effect through a synergistic combination of active phytochemicals to attenuate pain and cartilage degeneration by inhibiting TLR-4 and NF-κB activation. This study provided the experimental foundation for the development of SHM into a more effective dosage form or new drugs for OA treatment.

Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification.

The worldwide spread of coronavirus disease (COVID-19) has become a threat to global public health. It is of great importance to rapidly and accurately screen and distinguish patients with COVID-19 from those with community-acquired pneumonia (CAP). In this study, a total of 1,658 patients with COVID-19 and 1,027 CAP patients underwent thin-section CT and were enrolled. All images were preprocessed to obtain the segmentations of infections and lung fields. A set of handcrafted location-specific features was proposed to best capture the COVID-19 distribution pattern, in comparison to the conventional CT severity score (CT-SS) and radiomics features. An infection size-aware random forest method (iSARF) was proposed for discriminating COVID-19 from CAP. Experimental results show that the proposed method yielded its best performance when using the handcrafted features, with a sensitivity of 90.7%, a specificity of 87.2%, and an accuracy of 89.4% over state-of-the-art classifiers. Additional tests on 734 subjects, with thick slice images, demonstrates great generalizability. It is anticipated that our proposed framework could assist clinical decision making.

Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT.

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE), AUC, precision and F1-score achieved by our method are 91.79%, 93.05%, 89.95%, 96.35%, 93.10% and 93.07%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning.

Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of infected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting high-dimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the number of training data.

Validation of the Key Active Ingredients and Anti-Inflammatory and Analgesic Effects of Shenjin Huoxue Mixture Against Osteoarthritis by Integrating Network Pharmacology Approach and Thin-Layer Chromatography Analysis.

BACKGROUND: Shenjin Huoxue Mixture (SHM), a classic traditional herb mixture has shown significant clinical efficacy against osteoarthritis (OA). Our previous experimental study has confirmed its anti-inflammatory and analgesic effect on acute soft tissue injury in rats, with the compound of glycyrrhizinate in SHM identified and the content of paeoniflorin in SHM determined by high-performance liquid chromatography (HPLC). However, the components and its pharmacological mechanisms of SHM against OA have not been systematically elucidated yet. Thus this study aimed to predict the key active ingredients and potential pharmacological mechanisms of SHM in the treatment of OA by network pharmacology approach and thin-layer chromatography (TLC) validation. METHODS: The active ingredients of SHM and their targets, as well as OA-related targets, were identified from databases. The key active ingredients were defined and ranked by the number of articles retrieved in PubMed using the keyword "(the active ingredients [Title/Abstract]) AND Osteoarthritis[Title/Abstract] ", and validated partially by TLC. The pharmacological mechanisms of SHM against OA were displayed by GO term and Reactome pathway enrichment analysis with Discovery Studio 3.0 software docking to testing the reliability. RESULTS: Finally, 16 key active ingredients were identified and ranked, including quercetin validated through TLC. Inflammatory response, IL-6 signaling pathway and toll-like receptor (TLR) cascades pathway were predicted as the main pharmacological mechanisms of SHM against OA. Especially, 12 out of 16 key active ingredients, including validated quercetin, were well docked to IL-6 proteins. CONCLUSION: Our results confirmed the anti-inflammatory and analgesic effect of SHM against OA through multiple components, multiple targets and multiple pathways, which revealed the theoretical basis of SHM against OA and may provide a new drug option for treating OA.

Predicting Brain Age of Healthy Adults Based on Structural MRI Parcellation Using Convolutional Neural Networks.

Structural magnetic resonance imaging (MRI) studies have demonstrated that the brain undergoes age-related neuroanatomical changes not only regionally but also on the network level during the normal development and aging process. In recent years, many studies have focused on estimating age using structural MRI measurements. However, the age prediction effects on different structural networks remain unclear. In this study, we established age prediction models based on common structural networks using convolutional neural networks (CNN) with data from 1,454 healthy subjects aged 18-90 years. First, based on the reference map of CorticalParcellation_Yeo2011, we obtained structural network images for each subject, including images of the following: the frontoparietal network (FPN), the dorsal attention network (DAN), the default mode network (DMN), the somatomotor network (SMN), the ventral attention network (VAN), the visual network (VN), and the limbic network (LN). Then, we built a 3D CNN model for each structural network using a large training dataset (n = 1,303) and the predicted ages of the subjects in the test dataset (n = 151). Finally, we estimated the age prediction performance of CNN compared with Gaussian process regression (GPR) and relevance vector regression (RVR). The results of CNN showed that the FPN, DAN, and DMN exhibited the optimal age prediction accuracies with mean absolute errors (MAEs) of 5.55 years, 5.77 years, and 6.07 years, respectively, and the other four networks, i.e., the SMN, VAN, VN, and LN, tended to have larger MAEs of more than 8 years. With respect to GPR and RVR, the top three prediction accuracies were still from the FPN, DAN, and DMN; moreover, CNN made more precise predictions than GPR and RVR for these three networks. Our findings suggested that CNN has the optimal age prediction performance, and our age prediction model can be potentially used for brain disorder diagnosis according to age prediction differences.

A new genus of Rhaphidophorinae (Orthoptera, Rhaphidophoridae) from China.

In this article, a new genus Sinorhaphidophora gen. nov., three new combinations: Sinorhaphidophora hainanensis (Bian Shi, 2016) comb. nov., Neorhaphidophora brevispinula (Bian, Zhu Shi, 2017) comb. nov., Neorhaphidophora longispinula (Bian, Zhu Shi, 2017) comb. nov. and three new species of Rhaphidophora Audinet-Serville, 1838: Rhaphidophora quadridentata sp. nov., Rhaphidophora setiformis sp. nov., Rhaphidophora wuzhishanensis sp. nov. are proposed. Keys to all genera of Rhaphidophorinae and all species of Rhaphidophora from China are given. And the female of Rhaphidophora sichuanensis Liu Zhang, 2002 is also added.

[PK/PD of vancomycin in patients with severe acute pancreatitis combined with augmented renal clearance].

OBJECTIVE: To evaluate the serum trough concentration and the pharmacokinetics/pharmacodynamics (PK/PD) of vancomycin in patients with severe acute pancreatitis (SAP), and analyze the effect of vancomycin continuous infusion for optimizing the characteristics of its PK/PD. METHODS: The inhospital patients with SAP received vancomycin treatment and admitted to emergency intensive care unit (EICU) of Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2011 to December 2016 were enrolled. Steady-state trough concentrations of vancomycin from patients were collected retrospectively. The SAP patients were divided into augmented renal clearance (ARC) and non-ARC groups, as well as systemic inflammatory response syndrome (SIRS) and non-SIRS groups according to the patients with or without symptom above. Adjustments of increased dosage or 24-hour continuous infusion or increase vancomycin dose were made for patients if the steady-state trough concentrations fell below the target level. Steady state trough concentration for vancomycin intermittent infusion or steady state concentration for vancomycin continuous infusion was determined by the fluorescence polarization immunoassay method. PK parameters of vancomycin were calculated using the Bayesian estimator and the area under the serum drug concentration-time curve (AUCc-t), the minimum inhibitory concentration (MIC) and AUCc-t/MIC was recorded and calculated. RESULTS: The steady state trough concentration or steady state concentration from 61 patients with SAP were collected with mean steady state trough concentration of vancomycin of (7.7±4.4) mg/L, which was significantly lower than standard concentration (15 mg/L, P < 0.001). Apparent volume of distribution (Vd) and clearance of vancomycin was (1.06±0.26) L/kg and (8.9±2.8) L/h. The serum steady state trough concentration of vancomycin in ARC group (n = 33) was significantly lower than that in non-ARC group (n = 28; mg/L: 6.7±3.5 vs. 8.2±4.1, P < 0.01), clearance was significantly increased (L/h: 9.8±2.9 vs. 7.7±2.2, P < 0.01). Compared with non-SIRS group (n = 31), the serum steady state trough concentration of vancomycin in SIRS group (n = 30) was significantly lowered (mg/L: 6.1±3.2 vs. 13.0±4.2, P < 0.01), and clearance was significantly increased (L/h: 9.4±2.0 vs. 7.1±2.1, P < 0.05). Compared with the only increasing vancomycin dose group (n = 29), vancomycin continuous infusion for 24 hours (n = 21) could significantly reduce daily dosage (mg/kg: 13.6±3.9 vs. 19.1±3.5, P < 0.01), increase the serum trough concentration (mg/L: 18.1±7.0 vs. 12.6±5.3, P < 0.01), and improve the AUCc-t/MIC. CONCLUSIONS: The serum trough concentration of vancomycin was significantly reduced in SAP patients with ARC. The more serious of the SIRS is, the lower the vancomycin trough concentration is. Vancomycin 24-hour continuous infusion could optimize the PK/PD parameters, decrease the daily dose, increase the clinical effect, and reduce the bacterial resistance.

[Pharmacokinetics of vancomycin in patients with severe acute pancreatitis and its influencing factors: analysis of 7 years data].

OBJECTIVE: To observe the change of the serum trough concentration and its pharmacokinetics of vancomycin in patients with severe acute pancreatitis (SAP), and to analyze the factors influencing vancomycin concentration. METHODS: A retrospective analysis was conducted. Steady-state trough concentrations of vancomycin from patients (18-80 years old) with SAP concomitantly with G+ infection admitted to Intensive Care Unit (ICU) of Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2010 to December 2016 were enrolled. According to the usage time of vancomycin, the patients with SAP were divided into early group (onset within 21 days), middle group (onset between 21-28 days) and late group (onset over 28 days). The gender, age, body weight, clinical diagnosis, acute physiology and chronic health evaluation II (APACHE II) score, renal function, and the pharmacokinetic parameters were recorded. Influencing factors of vancomycin was analyzed by multiple linear regression and stepwise regression. RESULTS: Fifty-eight patients were enrolled who contained 134 times trough concentrations of vancomycin. There were 41 patients enrolled and 61 times of trough concentrations in the early group, 24 patients enrolled and 33 times of trough concentrations in the middle group, and 28 patients enrolled and 40 times of trough concentrations in the late group. There was no significant difference in gender, age, body weight, serum creatinine, creatinine clearance (CCr), albumin, APACHE II score among the three groups. There was significantly difference in the duration from the onset time to vancomycin administration between early, middle groups and late group (days: 15.9±3.2, 23.3±2.2 vs. 35.0±6.7, both P < 0.05). The positive liquid balance in early group was lower than that of late group (mL: 1 565.2±3 132.1 vs. 3 675.1±3 411.5, P < 0.01), while it was increased in the middle group as compared with that of late group (mL: 5 078.7±3 892.4 vs. 3 675.1±3 411.5, P < 0.05). The average daily dose of vancomycin in the early, middle and late groups were (14.7±5.0), (15.0±2.8), (17.0±4.2) mg/kg, respectively, and there was no significant difference (P > 0.05). Compared with the standard concentration (15 mg/L) of vancomycin, the serum trough concentration of vancomycin was significantly reduced in SAP patients [(7.5±4.3) mg/L, P < 0.01]. Apparent volume of distribution (Vd) was (72.4±15.4) L, and clearance rate (CL) was (9.0±2.8) L/h. According to the Bayesian, the serum trough concentration of vancomycin was significantly reduced in early group and middle group compared with late group (mg/L: 5.0±2.1, 7.3±2.5 vs. 11.5±5.1, both P < 0.01), CL was significantly increased (L/h: 10.5±3.0, 8.1±1.9 vs. 7.4±1.9, both P < 0.05), and Vd was significantly increased in early group compared with late group (L: 73.7±15.5 vs. 71.0±12.6, P < 0.05). It was shown by multiple linear regression analysis that there was strong relationship between serum trough concentration and the serum creatinine, CCr, average daily dose and the starting time of vancomycin treatment (r value were 0.449, -0.318, 0.373, 0.763, respectively, all P < 0.05). CONCLUSIONS: The serum trough concentration of vancomycin was significantly reduced in SAP patients. And the earlier usage of vancomycin, the lower of the trough concentration is. Therefore, higher dosage regimen was needed to ensure the clinical effect, and reduce the bacterial resistance.

Pre-Treatment Serum C-Reactive Protein Level Is An Independent Risk Factor for Development of Nephrotoxicity in Patients Receiving High-Dose Vancomycin.

High-dose vancomycin treatment increases the likelihood of vancomycin-related nephrotoxicity. C-reactive protein (CRP) is a sensitive marker of systemic inflammation. In this study, we evaluated the pre-treatment serum CRP level as a risk factor of the development of nephrotoxicity in patients receiving high total daily doses (>2.5 g) of vancomycin. Data extracted from medical records for 174 patients who received total daily doses of >2.5 g of intravenous vancomycin for a minimum of 48 h and had their serum CRP level and erythrocyte sedimentation rate tested within 24 h before vancomycin treatment were subject to final analyses. Univariate analyses showed that patients who developed nephrotoxicity during vancomycin treatment had significantly higher median vancomycin serum concentration, duration of vancomycin treatment, and the serum CRP level within 24 h before vancomycin treatment than the non-nephrotoxicity group. Multivariate logistic regression analysis showed that after adjustment for potential confounders, median vancomycin serum concentration, duration of treatment, serum CRP level within 24 h before vancomycin treatment, and nephrotoxic medication were found significantly associated with the development of nephrotoxicity. This was confirmed by multivariate hazard ratio analysis after adjustment for potential confounders. In conclusion, this study provides the first evidence supporting the fact that the serum CRP level within 24 h before vancomycin treatment is an independent risk factor for the development of nephrotoxicity in patients receiving total daily doses of >2.5 g of vancomycin. Therefore, the serum CRP level within 24 h before vancomycin treatment could be a potential biomarker or prognostic factor for the development of vancomycin nephrotoxicity.

The pharmacokinetics of vancomycin in patients with severe acute pancreatitis.

OBJECTIVE: The aim of this study was to evaluate the pharmacokinetics of vancomycin in patients with severe acute pancreatitis (SAP). METHODS: Sixty-seven patients with SAP were included. The FPIA method was used to measure vancomycin serum trough concentrations, and the pharmacokinetic parameters were calculated using the Bayesian estimator. Comparisons of mean values were analyzed using SPSS 11.0. RESULTS: The average daily dose of vancomycin was 15.0 ± 3.7 mg/kg (q 12 h). Sixty-seven trough concentrations were collected. Compared with the recommended standard vancomycin trough concentration (15 mg/L), SAP patients had significantly lower vancomycin trough concentrations (6.1 ± 3.0 mg/L; p < 0.0001) while the volume of distribution (Vd) and clearance (CL) of vancomycin were significantly increased. Multiple regression analysis revealed that vancomycin trough concentration was strongly correlated not only with age and albumin but also with the duration from SAP onset to vancomycin therapy (p < 0.0001). Stepwise regression analysis revealed that the duration was the most important variable for vancomycin trough concentration (r (2) = 0.456). The relationships between vancomycin trough concentrations and the duration were further evaluated after the 67 patients were stratified into two groups according to the duration from SAP onset to vancomycin therapeutic drug monitoring (TDM) within or over 4 weeks. Early group had much lower trough concentrations compared to late group, and the CL was also significantly increased in the early group. Of these 67 patients, 24 patients made vancomycin dosage adjustment (increased to 18.5 ± 3.9 mg/kg, q 12 h) and the average trough concentrations increased to 12.6 ± 3.8 mg/L. CONCLUSIONS: The serum trough concentration of vancomycin was significantly reduced in SAP patients. Higher dosage regimens are needed to ensure the clinical effect.