Alginate-chitosan coated layered double hydroxide nanocomposites for enhanced oral vaccine delivery

Layered double hydroxide nanoparticles (LDHs) have shown the excellent capability and good adjuvant function as a nanocarrier for protein antigen delivery to enhance the immune response. Furthermore, LDHs have good biocompatibility and low cytotoxicity. However, their oral vaccine delivery efficiency is limited due to acidic/enzyme degradation in the stomach and low bioavailability in the small intestine. To overcome these challenges, alginate-chitosan coated LDHs nanocomposites (ALG-CHT-LDH) have been developed and used as a carrier for oral protein vaccine delivery. The physicochemical properties of ALG-CHT-LDH have been determined by dynamic light scattering (DLS), transmission electron microscopy (TEM), and ultraviolet visible (UV-Vis) spectroscopy. Protein release properties of LDHs with/without polymer coating have been investigated at various pHs. The protein release profile of ALG-CHT-LDH nanocomposites indicated that ALG-CHT coating could partially protect protein release at the acidic condition (pH 1.2). The cellular uptake efficiency of protein delivered by ALG-CHT-LDH for the intestine cells and macrophages were studied. After alginate layer falls from ALG-CHT-LDH nanocomposite, flow cytometry analysis (FACS) data suggest that chitosan-coated LDHs significantly enhance the internalization of proteins at the Caco2 and macrophage cells

PREDICTION OF CUSTOMER CHURN FOR ABC MULTISTATE BANK USING MACHINE LEARNING ALGORITHMS

Customer churn is defined as the tendency of customers to cease doing business with a company in a given period. ABC Multistate Bank faces the challenges to hold clients. The purpose of this study is to apply machine learning algorithms to develop the most effective model for predicting bank customer churn. In this study, six supervised machine learning methods, K-Nearest Neighbors, Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost), are applied to the churn prediction model using Bank Customer Data of ABC Multistate Bank obtained from Kaggle. The results showed that XGBoost outperformed the other six classifiers, with an accuracy rate of 84.76%, an F1 score of 56.95%, and a ROC curve graph of 71.64%. The bank may use XGBoost model to accurately identify customers who are at risk of leaving, concentrate their efforts on them, and possibly make a profit. Future research should focus on various machine learning approaches for determining the most accurate models for bank customer churn datasets

Study of helical flow inducers with different thread pitches and diameters in vena cava

<div><p>Pulmonary embolism is a severe, potentially life-threatening condition. Inferior vena cava filters have been used to prevent recurrent pulmonary embolisms. However, the build-up of thrombosis in vena cava filters after deployment presents a severe problem to patients. Previous studies proposed that filters with helical flow are beneficial and capable of alleviating this problem. In this study, the hemodynamic performances of four typical helical flow inducers in the vena cava are determined using computational fluid dynamics simulations (steady-state and pulsatile flow) and compared. Pilot in vitro experiments were also conducted. The simulation results demonstrate that large-diameter inducers produce helical flow. Among inducers with identical diameter, those with a smaller thread pitch are more likely to induce increased helical flow. We also observed that the small thread pitch inducers can yield higher shear rates. Furthermore, a large diameter, small thread pitch helical flow inducer increases the time-averaged wall shear stress and reduces the oscillating shear index and relative residence time on the vessel wall in the vicinity of the helical flow inducer. In vitro experiments also verify that large diameter inducers generate a helical flow. A notable observation of this study is that the diameter is the key parameter that affects the induction of a helical flow. This study will likely provide important guidance for the design of interventional treatments and the deployment of filters associated with helical flow in the vena cava.</p></div

Geometric parameters for each studied case.

<p>Case A₁ represents the inducer of the helical filter of the previous study [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190609#pone.0190609.ref009" target="_blank">9</a>].</p

Contours of time-averaged wall shear stress (TAWSS), oscillating shear index (OSI) and relative residence time (RRT), based on pulsatile flow computations.

<p>I: Contours of TAWSS (unit: Pa) on the caval wall and inducer. II: Contours of OSI and RRT (unit: Pa^-1) for the caval wall.</p

Velocity streamlines for the four cases of in vitro experiments.

<p>I: Four types of helical flow inducers used in the in vitro experiments. II: Velocity streamlines corresponding to the four cases of the in vitro experiments.</p

Shear rates and velocity distributions for the five modes.

<p>I: Shear rates for the eight representative slices of the vena cava using the Carreau and Newton models in the steady flow computations. II: Longitudinal slice (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190609#pone.0190609.g002" target="_blank">Fig 2I</a>, S9) velocity distributions for T1 and T2 based on pulsatile flow computations.</p

Helical flow inducers, vena cava model, inlet waveform velocity, and experimental set-up scheme.

<p>I: Schematic of the four helical flow inducers (A, B, C, and D) using the different thread pitches and diameters considered herein. The figure also shows an inducer in the vena cava used for simulation and a diagram of depicting the geometry parameters. II: Inlet inferior vena cava velocity waveform velocity used in the pulsatile flow computations. III: Schematic of the experimental set-up.</p

Detection of Salmonella DNA and drug-resistance mutation by PCR-based CRISPR-lbCas12a system

Abstract Salmonella is an important foodborne pathogen, which can cause serious public health problems. Rapid and accurate detection of Salmonella infection and drug resistance mutations in patients will provide timely guidance for clinical treatment and avoid disease progression and other related clinical problems. Here, we established a highly sensitive and quick method for Salmonella and drug resistance mutation detection based on polymerase chain reaction (PCR) and CRISPR-lbCas12a system and evaluated its practicability with clinical samples. Specific CRISPR RNAs (crRNAs) and primers are designed for Salmonella DNA and parC gene S80I mutation diagnosis. CrRNAs with and without phosphorylated modification and different crRNA preparation methods are used to assess the effect on the detection system. After optimization, we detected as low as one copy of Salmonella DNA and drug resistance mutation parC S80I with the Salmonella DNA standard. For 94 clinical samples, this method also showed high sensitivity (100%, 95% CI: 84.98–100%) and specificity (98.48%, 95% CI: 90.73–99.92%) with less time (3 h) than plate culture (16 h) and conventional antimicrobial susceptibility testing (over 16 h). Besides, one parC S80I mutant strain was detected, which is consistent with the result of DNA sequencing. Taken together, we established a highly sensitive and specific method for Salmonella infection and parC S80I drug resistance mutation detection with fewer reagents and ordinary instruments. This assay has wide application prospects for fast detection of pathogen (bacterium and virus) infection, drug resistance determination, and proper treatment guidance