New Insight of microRNAs & short interfering RNA in Treatment of COVID-19; a Narrative Review

Since 31 December 2019, the coronavirus disease 2019 (COVID-19) resulted in a state of hyperinflammation syndrome and multiorgan failure. In areas with pandemic outbreaks, despite several emerging vaccines, supportive treatments to mitigate fatality rates were required. Growing evidence suggests that several small RNAs such as microRNAs (miRNAs) and short interfering RNA (siRNA) could be candidates for the treatment of COVID-19 by inhibiting the expression of crucial virus genes. small RNAs by binding to the 3′-untranslated region (UTR) or 5′-UTR of viral RNA play an important role in COVID-19-host interplay and viral replication. In this review, the authors sought to specify the efficacy and safety of miRNAs and siRNA expressions of patients with COVID-19, which has an axial role in the pathogenesis of human diseases

Preparation of Enteric Coated Pellets Containing Lansoprazole Using Extrusion/Spheronization Technique

Lansoprazole degrades rapidly in an aqueous solution at low pH values. Degradation rate increases at pH values below 4 .The aim of this study was development of enteric coated pellets containing Lansoprazole by an extrusion/ spheronization technique. Eight different formulations based on lactose and six different formulations based on mannitol, consisting of different portions of other excipients including sucrose, hydroxy propyl methyl cellulose, magnesium carbonate, and sodium lauryl sulfate have been prepared. Feret diameter, shape factor, and amount of drug released were determined for each formulation. Among different formulations, F14, which consists of mannitol, sucrose, HPMC, talc, magnesium carbonate, and lansoprazole, is considered to be the best formulation. Six other different formulations for the preparation of enteric coatings based on Eudragit S100, Eudragit L100, triethyl citrate, and talc were prepared and coating procedure on pellets (F14) was performed using coating pan. In vitro drug release tests in acidic media (pH=1.2) and buffer media (pH=6.8) were performed for pellets, coated with each coating formulation and dissolution profile for each formulation was prepared. The pellets coated by formulation F.C 4, consisting of Eudragit L 100, triethylcitrate, and talc, showed a proper in vitro drug release profile. Accelerated stability tests were performed on coated pellets according to USP, and data suggested that pellets will be stable for 2 year

Applied the additive hazard model to predict the survival time of patient with diffuse large B- cell lymphoma and determine the effective genes, using microarray data

Background: Recent studies have shown that effective genes on survival time of cancer patients play an important role as a risk factor or preventive factor. Present study was designed to determine effective genes on survival time for diffuse large B-cell lymphoma patients and predict the survival time using these selected genes. Materials & Methods: Present study is a cohort study was conducted on 40 patients with diffuse large B-cell lymphoma. For these patients, 2042 gene expression was measured. In order to predict the survival time, the composition of the semi-parametric additive survival model with two gene selection methods elastic net and lasso were used. Two methods were evaluated by plotting area under the ROC curve over time and calculating the integral of this curve. Results: Based on our findings, the elastic net method identified 10 genes, and Lasso-Cox method identified 7 genes. GENE3325X increased the survival time (P=0.006), Whereas GENE3980X and GENE377X reduced the survival time (P=0.004). These three genes were selected as important genes in both methods. Conclusion: This study showed that the elastic net method outperformed the common Lasso method in terms of predictive power. Moreover, apply the additive model instead Cox regression and using microarray data is usable way for predict the survival time of patients

Anti-CD44 and EGFR Dual-Targeted Solid Lipid Nanoparticles for Delivery of Doxorubicin to Triple-Negative Breast Cancer Cell Line: Preparation, Statistical Optimization, and In Vitro Characterization

Background. Despite being more aggressive than other types of breast cancer, there is no suitable treatment for triple-negative breast cancer (TNBC). Here, we designed doxorubicin-containing solid lipid nanoparticles (SLNs) decorated with anti-EGFR/CD44 dual-RNA aptamers, which are overexpressed in TNBC. For more efficiency in the nuclear delivery of doxorubicin, dexamethasone (Dexa) was chemically attached to the surface of nanoparticles. Methods. To prepare the cationic SLNs, 6-lauroxyhexyl BOC-ornithine (LHON) was synthesized and was chemically attached to dexamethasone to form Dexa-LHON complexes. The doxorubicin-containing SLNs were prepared via double emulsification (w/o/w) and the solvent evaporation technique. The preparation of SLNs was statistically optimized using the central composite response surface methodology. Independent factors were the GMS/lecithin concentration ratio and the amount of Tween 80, while responses considered were particle size, polydispersity index, and entrapment efficiency of the nanoparticles. The optimized nanoparticles were studied morphologically using transmission electron microscopy, and in vitro release of doxorubicin from nanoparticles was studied in phosphate-buffered saline. Then, the designated aptamers were attached to the surface of nanoparticles using electrostatic interactions, and their cytotoxicity was assessed in vitro. Results. The size, PDI, zeta potential, EE%, and LE% of the prepared nanoparticles were 101±12.6 nm, 0.341±0.005, +13.6±1.83 mV, 69.98±7.54%, and 10.2±1.06%, respectively. TEM images revealed spherical nanoparticles with no sign of aggregation. In vitro release study exhibited that 96.1±1.97% of doxorubicin was released within 48 h of incubation. The electrostatic attachment of the designated aptamers to the nanoparticles’ surface was confirmed by reducing the zeta potential to −15.6±2.07 mV. The in vitro experiments revealed that the SLNs/DOX/Dexa/CD44 or EGFR aptamers were substantially more successful than SLNs/DOX/Dexa at inhibiting cell proliferation. Using the MDA-MB-468 cell line, we discovered that SLN/DOX/Dexa/CD44/EGFR aptamers were more effective than other constructs in inhibiting cell proliferation (p<0.001). The reduction of cell viability using this construct suggests that targeting numerous proliferation pathways is effective. Conclusion. Overall, the finding of this investigation suggested that SLNs/DOX/Dexa/CD44/EGFR could be a promising new enhanced anticancer delivery system and deserved further preclinical consideration

Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression

The Support Vector Regression (SVR) model has been broadly used for response prediction. However, few researchers have used SVR for survival analysis. In this study, a new SVR model is proposed and SVR with different kernels and the traditional Cox model are trained. The models are compared based on different performance measures. We also select the best subset of features using three feature selection methods: combination of SVR and statistical tests, univariate feature selection based on concordance index, and recursive feature elimination. The evaluations are performed using available medical datasets and also a Breast Cancer (BC) dataset consisting of 573 patients who visited the Oncology Clinic of Hamadan province in Iran. Results show that, for the BC dataset, survival time can be predicted more accurately by linear SVR than nonlinear SVR. Based on the three feature selection methods, metastasis status, progesterone receptor status, and human epidermal growth factor receptor 2 status are the best features associated to survival. Also, according to the obtained results, performance of linear and nonlinear kernels is comparable. The proposed SVR model performs similar to or slightly better than other models. Also, SVR performs similar to or better than Cox when all features are included in model

Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression

The Support Vector Regression (SVR) model has been broadly used for response prediction. However, few researchers have used SVR for survival analysis. In this study, a new SVR model is proposed and SVR with different kernels and the traditional Cox model are trained. The models are compared based on different performance measures. We also select the best subset of features using three feature selection methods: combination of SVR and statistical tests, univariate feature selection based on concordance index, and recursive feature elimination. The evaluations are performed using available medical datasets and also a Breast Cancer (BC) dataset consisting of 573 patients who visited the Oncology Clinic of Hamadan province in Iran. Results show that, for the BC dataset, survival time can be predicted more accurately by linear SVR than nonlinear SVR. Based on the three feature selection methods, metastasis status, progesterone receptor status, and human epidermal growth factor receptor 2 status are the best features associated to survival. Also, according to the obtained results, performance of linear and nonlinear kernels is comparable. The proposed SVR model performs similar to or slightly better than other models. Also, SVR performs similar to or better than Cox when all features are included in model

Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran

Abstract Background The high number of COVID-19 deaths is a serious threat to the world. Demographic and clinical biomarkers are significantly associated with the mortality risk of this disease. This study aimed to implement Generalized Neural Additive Model (GNAM) as an interpretable machine learning method to predict the COVID-19 mortality of patients. Methods This cohort study included 2181 COVID-19 patients admitted from February 2020 to July 2021 in Sina and Besat hospitals in Hamadan, west of Iran. A total of 22 baseline features including patients' demographic information and clinical biomarkers were collected. Four strategies including removing missing values, mean, K-Nearest Neighbor (KNN), and Multivariate Imputation by Chained Equations (MICE) imputation methods were used to deal with missing data. Firstly, the important features for predicting binary outcome (1: death, 0: recovery) were selected using the Random Forest (RF) method. Also, synthetic minority over-sampling technique (SMOTE) method was used for handling imbalanced data. Next, considering the selected features, the predictive performance of GNAM for predicting mortality outcome was compared with logistic regression, RF, generalized additive model (GAMs), gradient boosting decision tree (GBDT), and deep neural networks (DNNs) classification models. Each model trained on fifty different subsets of a train-test dataset to ensure a model performance. The average accuracy, F1-score and area under the curve (AUC) evaluation indices were used for comparison of the predictive performance of the models. Results Out of the 2181 COVID-19 patients, 624 died during hospitalization and 1557 recovered. The missing rate was 3 percent for each patient. The mean age of dead patients (71.17 ± 14.44 years) was statistically significant higher than recovered patients (58.25 ± 16.52 years). Based on RF, 10 features with the highest relative importance were selected as the best influential features; including blood urea nitrogen (BUN), lymphocytes (Lym), age, blood sugar (BS), serum glutamic-oxaloacetic transaminase (SGOT), monocytes (Mono), blood creatinine (CR), neutrophils (NUT), alkaline phosphatase (ALP) and hematocrit (HCT). The results of predictive performance comparisons showed GNAM with the mean accuracy, F1-score, and mean AUC in the test dataset of 0.847, 0.691, and 0.774, respectively, had the best performance. The smooth function graphs learned from the GNAM were descending for the Lym and ascending for the other important features. Conclusions Interpretable GNAM can perform well in predicting the mortality of COVID-19 patients. Therefore, the use of such a reliable model can help physicians to prioritize some important demographic and clinical biomarkers by identifying the effective features and the type of predictive trend in disease progression

Development of Perphenazine-Loaded Solid Lipid Nanoparticles: Statistical Optimization and Cytotoxicity Studies

Objective. Perphenazine (PPZ), as a typical antipsychotic medical substance, has the same effectiveness compared to atypical antipsychotic medications for the treatment of schizophrenia. Despite the lipophilic essence, PPZ encounters limited bioavailability caused by the first-pass metabolism following oral administration. In the present study, PPZ-containing solid lipid nanoparticles (PPZ-SLNs) were prepared and optimized based on different factors, including lipid and surfactant amount, to develop appropriate and safe novel oral dosage forms of PPZ. Methods. The solvent emulsification-evaporation method was utilized to form SLNs by using soybean lecithin, glycerol monostearate (GMS), and Tween 80. Statistical optimization was done by the Box-Behnken design method to achieve formulation with optimized particle size, entrapment efficiency, and zeta potential. Also, transmission electron microscopy, in vitro release behavior, differential scanning calorimetry (DSC), and powder X-ray diffractometry (P-XRD) studies and cytotoxicity studies were assessed. Results. Optimization exhibited the significant effect of various excipients on SLN characteristics. Our finding indicated that the mean particle size, zeta potential, and entrapment efficiency of optimized PPZ-SLN were, respectively, 104±3.92 nm, −28±2.28 mV, and 83%±1.29. Drug release of PPZ-SLN was observed to be greater than 90% for 48 h that emphasized a sustained-release pattern. The DSC and P-XRD studies revealed the amorphous state of PPZ-SLN. FTIR spectra showed no incompatibility between the drug and the lipid. Performing cytotoxicity studies indicated no significant cytotoxicity on HT-29 cell culture. Conclusion. Our study suggests that PPZ-SLNs can make a promising vehicle for a suitable therapy of schizophrenia for the oral drug delivery system