Nanoassembly-Mediated Exendin-4 Derivatives to Decrease Renal Retention.

An Exendin-4 analogue that was conjugated with 68Ga exhibited an excellent diagnostic effect on insulinoma in clinical practice. On account of its low molecular weight and short hydration radius, 68Ga-Exendin-4 showed high accumulation in kidney tissues. Nanoparticle-mediated strategies have attracted much attention due to polyvalent properties and the size amplification effect. In this study, Exendin-4 derivatives of radionuclide nanodevices were developed and evaluated. The Exendin-4 derivatives consisting of a ternary block recombinant protein were purified by an inverse transition cycle (ITC) and allowed to self-assemble into a nanodevice under physiological conditions. Our results showed that the nanoassemblies of Exendin-4 derivatives formed homogeneous spherical nanoparticles, exhibited outstanding affinity for insulinoma cells, and could be deposited in insulinoma tissues in vivo. The nanoassembly-mediated Exendin-4 derivatives showed fivefold reduced renal retention and exhibited an outstanding tumor-suppression effect.

Ubiquitin ligase subunit FBXO9 inhibits V-ATPase assembly and impedes lung cancer metastasis.

BACKGROUND: The evolutionarily conserved protein FBXO9 acts as a substrate receptor for the SKP1-cullin-1-RBX1 ubiquitin ligase and is implicated in cancer, exhibiting either tumor-suppressive or oncogenic effects depending on the specific tumor type. However, their role in lung cancer metastasis remains unclear. METHODS: Lentiviral vectors carrying miRNA-based shRNA sequences for gene-specific knockdown were generated, and Lenti-CRISPR-Cas9 vectors containing gene-specific sgRNA sequences were designed. Gene overexpression was achieved using doxycycline-inducible lentiviral constructs, while gene knockdown or knockout cells were generated using shRNA and CRISPR-Cas9, respectively. Functional assays included migration, clonogenic survival assays, tumor sphere assays, and protein interaction studies using mass spectrometry, immunoprecipitation, and immunoblot analysis. RESULTS: This study identified FBXO9 as a crucial regulator that suppresses lung cancer cell migration, tumor sphere growth and restricts metastasis. We showed that FBXO9 facilitates the ubiquitination of the catalytic subunit A (ATP6V1A) of the Vacuolar-type H+-ATPase (V-ATPase), resulting in its interaction with the cytoplasmic chaperone HSPA8 and subsequent sequestration within the cytoplasm. This process hinders the assembly of functional V-ATPase, resulting in reduced vesicular acidification. In contrast, depletion of FBXO9 reduced ATP6V1A ubiquitination, resulting in increased V-ATPase assembly and vesicular acidification, thus promoting pro-metastatic Wnt signaling and metastasis of lung cancer cells. Furthermore, we demonstrated the effectiveness of inhibitors targeting V-ATPase in inhibiting lung cancer metastasis in a mouse model. Finally, we established a correlation between lower FBXO9 levels and poorer survival outcomes in patients with lung cancer. CONCLUSION: These findings collectively elucidate the critical role of FBXO9 in regulating V-ATPase assembly and provide a molecular basis for FBXO9's function in inhibiting lung cancer metastasis. This highlights the potential therapeutic opportunities of FBXO9 supplementation.

Machine learning-based risk prediction of hypoxemia for outpatients undergoing sedation colonoscopy: a practical clinical tool.

OBJECTIVES: Hypoxemia as a common complication in colonoscopy under sedation and may result in serious consequences. Unfortunately, a hypoxemia prediction model for outpatient colonoscopy has not been developed. Consequently, the objective of our study was to develop a practical and accurate model to predict the risk of hypoxemia in outpatient colonoscopy under sedation. METHODS: In this study, we included patients who received colonoscopy with anesthesia in Nanjing First Hospital from July to September 2021. Risk factors were selected through the least absolute shrinkage and selection operator (LASSO). Prediction models based on logistic regression (LR), random forest classifier (RFC), extreme gradient boosting (XGBoost), support vector machine (SVM), and stacking classifier (SCLF) model were implemented and assessed by standard metrics such as the area under the receiver operating characteristic curve (AUROC), sensitivity and specificity. Then choose the best model to develop an online tool for clinical use. RESULTS: We ultimately included 839 patients. After LASSO, body mass index (BMI) (coefficient = 0.36), obstructive sleep apnea-hypopnea syndrome (OSAHS) (coefficient = 1.32), basal oxygen saturation (coefficient = -0.14), and remifentanil dosage (coefficient = 0.04) were independent risk factors for hypoxemia. The XGBoost model with an AUROC of 0.913 showed the best performance among the five models. CONCLUSION: Our study selected the XGBoost as the first model especially for colonoscopy, with over 95% accuracy and excellent specificity. The XGBoost includes four variables that can be quickly obtained. Moreover, an online prediction practical tool has been provided, which helps screen high-risk outpatients with hypoxemia swiftly and conveniently.

Colonoscopy under sedation is an effective technique for the inspection and treatment of alimentary canal diseases, but hypoxemia associated with this process cannot be ignored, since prolonged or severe hypoxemia may result in several serious consequences.We wanted to develop a practical and accurate model to predict the risk of hypoxemia for outpatient colonoscopy under sedation, which could help clinicians make more accurate and objective judgments to prevent patients from being harmed.A total of 839 patients were included in our study and we constructed five machine learning models and selected the best one, which demonstrated satisfactory performance. On this basis, a user-friendly data interface has been developed for convenient application. Clinicians can log in to this interface at any time and it will automatically calculate the patient's risk of hypoxemia when entering patient information.This study offers evidence that machine learning algorithms can accurately predict the risk of hypoxemia for outpatient colonoscopy under sedation and the model we developed is a practical and interpretable tool that could be used as a clinical decision-making aid.

Online interpretable dynamic prediction models for postoperative delirium after cardiac surgery under cardiopulmonary bypass developed based on machine learning algorithms: A retrospective cohort study.

OBJECTIVE: Postoperative delirium (POD) is strongly associated with poor early and long-term prognosis in cardiac surgery patients with cardiopulmonary bypass (CPB). This study aimed to develop dynamic prediction models for POD after cardiac surgery under CPB using machine learning (ML) algorithms. METHODS: From July 2021 to June 2022, clinical data were collected from patients undergoing cardiac surgery under CPB at Nanjing First Hospital. A dataset from the same center (October 2022 to November 2022) was also used for temporal external validation. We used ML and deep learning to build models in the training set, optimized parameters in the test set, and finally validated the best model in the validation set. The SHapley Additive exPlanations (SHAP) method was introduced to explain the best models. RESULTS: Of the 885 patients enrolled, 221 (25.0%) developed POD. 22 (22.0%) of 100 validation cohort patients developed POD. The preoperative and postoperative artificial neural network (ANN) models exhibited optimal performance. The validation results demonstrated satisfactory predictive performance of the ANN model, with area under the receiver operator characteristic curve (AUROC) values of 0.776 and 0.684 for the preoperative and postoperative models, respectively. Based on the ANN algorithm, we constructed dynamic, highly accurate, and interpretable web risk calculators for POD. CONCLUSIONS: We successfully developed online interpretable dynamic ANN models as clinical decision aids to identify patients at high risk of POD before and after cardiac surgery to facilitate early intervention or care.

A convenient machine learning model to predict full stomach and evaluate the safety and comfort improvements of preoperative oral carbohydrate in patients undergoing elective painless gastrointestinal endoscopy.

BACKGROUND AND AIMS: Assessment of the patient's gastric contents is the key to avoiding aspiration incidents, however, there is no effective method to determine whether elective painless gastrointestinal endoscopy (GIE) patients have a full stomach or an empty stomach. And previous studies have shown that preoperative oral carbohydrates (POCs) can improve the discomfort induced by fasting, but there are different perspectives on their safety. This study aimed to develop a convenient, accurate machine learning (ML) model to predict full stomach. And based on the model outcomes, evaluate the safety and comfort improvements of POCs in empty- and full stomach groups. METHODS: We enrolled 1386 painless GIE patients between October 2022 and January 2023 in Nanjing First Hospital, and 1090 patients without POCs were used to construct five different ML models to identify full stomach. The metrics of discrimination and calibration validated the robustness of the models. For the best-performance model, we further interpreted it through SHapley Additive exPlanations (SHAP) and constructed a web calculator to facilitate clinical use. We evaluated the safety and comfort improvements of POCs by propensity score matching (PSM) in the two groups, respectively. RESULTS: Random Forest (RF) model showed the greatest discrimination with the area under the receiver operating characteristic curve (AUROC) 0.837 [95% confidence interval (CI): 79.1-88.2], F1 71.5%, and best calibration with a Brier score of 15.2%. The web calculator can be visited at https://medication.shinyapps.io/RF_model/. PSM results demonstrated that POCs significantly reduced the full stomach incident in empty stomach group (p < 0.05), but no differences in full stomach group (p > 0.05). Comfort improved in both groups and was more significant in empty stomach group. CONCLUSIONS: The developed convenient RF model predicted full stomach with high accuracy and interpretability. POCs were safe and comfortably improved in both groups, with more benefit in empty stomach group. These findings may guide the patients' gastrointestinal preparation.

This study is the first model utilizing advanced ML techniques based on multiple clinical variables to identify full stomach. The model is suitable for patient-rich outpatient clinics, primary hospitals, remote regions, and specific clinical settings where POCUS is not available.The developed convenient RF model predicted full stomach with high accuracy and interpretability. The test cohort AUROC was 0.837. We further established an online accessible individualized risk calculator and provided waterfall plots to increase the interpretability of each prediction.The propensity score matching (PSM) showed that preoperative oral carbohydrate (POCs) were safe and comfortably improved in both groups, with more benefit in empty stomach group. These findings may provide information for anesthesiologists to guide patients on POCs.

A novel interpretative tool for early prediction of low cardiac output syndrome after valve surgery: online machine learning models.

OBJECTIVE: Low cardiac output syndrome (LCOS) is a severe complication after valve surgery, with no uniform standard for early identification. We developed interpretative machine learning (ML) models for predicting LCOS risk preoperatively and 0.5 h postoperatively for intervention in advance. METHODS: A total of 2218 patients undergoing valve surgery from June 2019 to Dec 2021 were finally enrolled to construct preoperative and postoperative models. Logistic regression, support vector machine (SVM), random forest classifier, extreme gradient boosting, and deep neural network were executed for model construction, and the performance of models was evaluated by area under the curve (AUC) of the receiver operating characteristic and calibration curves. Our models were interpreted through SHapley Additive exPlanations, and presented as an online tool to improve clinical operability. RESULTS: The SVM algorithm was chosen for modeling due to better AUC and calibration capability. The AUCs of the preoperative and postoperative models were 0.786 (95% CI 0.729-0.843) and 0.863 (95% CI 0.824-0.902), and the Brier scores were 0.123 and 0.107. Our models have higher timeliness and interpretability, and wider coverage than the vasoactive-inotropic score, and the AUC of the postoperative model was significantly higher. Our preoperative and postoperative models are available online at http://njfh-yxb.com.cn:2022/lcos. CONCLUSIONS: The first interpretable ML tool with two prediction periods for online early prediction of LCOS risk after valve surgery was successfully built in this study, in which the SVM model has the best performance, reserving enough time for early precise intervention in critical care.

Incorporating intraoperative blood pressure time-series variables to assist in prediction of acute kidney injury after type a acute aortic dissection repair: an interpretable machine learning model.

BACKGROUND: Acute kidney injury (AKI) is a common and serious complication after the repair of Type A acute aortic dissection (TA-AAD). However, previous models have failed to account for the impact of blood pressure fluctuations on predictive performance. This study aims to develop machine learning (ML) models combined with intraoperative medicine and blood pressure time-series data to improve the accuracy of early prediction for postoperative AKI risk. METHODS: Indicators reflecting the duration and depth of hypotension were obtained by analyzing continuous mean arterial pressure (MAP) monitored intraoperatively with multiple thresholds (<65, 60, 55, 50) set in the study. The predictive features were selected by logistic regression and the least absolute shrinkage and selection operator (LASSO), and 4 ML models were built based on the above features. The performance of the models was evaluated by area under receiver operating characteristic curve (AUROC), calibration curve and decision curve analysis (DCA). Shapley additive interpretation (SHAP) was used to explain the prediction models. RESULTS: Among the indicators reflecting intraoperative hypotension, 65 mmHg showed a statistically superior difference to other thresholds in patients with or without AKI (p < .001). Among 4 models, the extreme gradient boosting (XGBoost) model demonstrated the highest AUROC: 0.800 (95% 0.683-0.917) and sensitivity: 0.717 in the testing set and was verified the best-performing model. The SHAP summary plot indicated that intraoperative urine output, cumulative time of mean arterial pressure lower than 65 mmHg outside cardiopulmonary bypass (OUT_CPB_MAP_65 time), autologous blood transfusion, and smoking were the top 4 features that contributed to the prediction model. CONCLUSION: With the introduction of intraoperative blood pressure time-series variables, we have developed an interpretable XGBoost model that successfully achieve high accuracy in predicting the risk of AKI after TA-AAD repair, which might aid in the perioperative management of high-risk patients, particularly for intraoperative hemodynamic regulation.

In this study, we combined intraoperative blood pressure time-series data for the first time to build 4 machine learning (ML) models that successfully improve the accuracy of early prediction of postoperative AKI risk, with the XGBoost model displaying the best predictive performance.We explored the impact of multiple intraoperative hypotension thresholds (MAP <65, <60, <55 < 50 mmHg) on the occurrence of postoperative AKI in patients and attempted to provide clinicians with recommendations for hemodynamic management during surgery.Our study found that 65 mmHg showed a statistically superior difference to other thresholds in patients with or without AKI after undergoing TA-AAD repair (p < .001).

Pre- and Post-Operative Online Prediction of Outcome in Patients Undergoing Endovascular Coiling after Aneurysmal Subarachnoid Hemorrhage: Visual and Dynamic Nomograms.

BACKGROUND: Aneurysmal subarachnoid hemorrhage (aSAH) causes long-term functional dependence and death. Early prediction of functional outcomes in aSAH patients with appropriate intervention strategies could lower the risk of poor prognosis. Therefore, we aimed to develop pre- and post-operative dynamic visualization nomograms to predict the 1-year functional outcomes of aSAH patients undergoing coil embolization. METHODS: Data were obtained from 400 aSAH patients undergoing endovascular coiling admitted to the People's Hospital of Hunan Province in China (2015-2019). The key indicator was the modified Rankin Score (mRS), with 3-6 representing poor functional outcomes. Multivariate logistic regression (MLR)-based visual nomograms were developed to analyze baseline characteristics and post-operative complications. The evaluation of nomogram performance included discrimination (measured by C statistic), calibration (measured by the Hosmer-Lemeshow test and calibration curves), and clinical usefulness (measured by decision curve analysis). RESULTS: Fifty-nine aSAH patients (14.8%) had poor outcomes. Both nomograms showed good discrimination, and the post-operative nomogram demonstrated superior discrimination to the pre-operative nomogram with a C statistic of 0.895 (95% CI: 0.844-0.945) vs. 0.801 (95% CI: 0.733-0.870). Each was well calibrated with a Hosmer-Lemeshow p-value of 0.498 vs. 0.276. Moreover, decision curve analysis showed that both nomograms were clinically useful, and the post-operative nomogram generated more net benefit than the pre-operative nomogram. Web-based online calculators have been developed to greatly improve the efficiency of clinical applications. CONCLUSIONS: Pre- and post-operative dynamic nomograms could support pre-operative treatment decisions and post-operative management in aSAH patients, respectively. Moreover, this study indicates that integrating post-operative variables into the nomogram enhanced prediction accuracy for the poor outcome of aSAH patients.

Assessing hypertension and diabetes knowledge, attitudes and practices among residents in Akatsi South District, Ghana using the KAP questionnaire.

Objective: Low awareness of hypertension and diabetes is a public health concern in Ghana. Assessing the general population's behaviour via knowledge, attitude, and practice (KAP) will be invaluable in these diseases, where prevention and control need a lifelong commitment to a healthy lifestyle. Hence, our goal was to assess the behaviour of Akatsi South residents towards the diseases to assist health providers in implementing tailored intervention programs. Methods: This was a population-based cross-sectional study with 150 adults (18-70 years) from November to December 2021. A semi-structured questionnaire with face-to-face interviews was used to obtain data. All variables in the model had descriptive statistics. The Chi-square (χ2) test was used to examine correlations between variables, and a value of p < 0.05 was considered statistically significant. The factors associated with checking blood sugar levels and blood pressure were determined using binary logistic regression. Results: The respondents' mean age and BMI were 32.40 years (± 12.07) and 24.98 kg/m2 (± 2.36), respectively. Only 46.67% of the respondents frequently monitor their blood pressure and 17.33% their blood glucose (at least once a year). Less than half of those surveyed had a good knowledge of hypertension (42.7%) and diabetes (32.0%), whereas nearly 3/4 had poor attitudes regarding both conditions. A binary logistic regression analysis revealed that having a good attitude toward hypertension (exp B = 2.479, p = 0.036) and diabetes (exp B = 4.547, p = 0.009) were the participants' strongest predictor of blood pressure and sugar level checks. However, being overweight (exp B = 0.046, p = 0.002,) or obese (exp B = 0.144, p = 0.034) negatively influenced the frequency with which our respondents checked their blood glucose levels. Conclusion: In the study, we found that the population generally has poor knowledge, which affects their behaviour (attitudes and practices) towards the diseases. To enable healthcare practitioners to reduce disease-associated mortality and morbidity in the future, frequent public health education and promotion about the conditions is critical to closing the knowledge gap.

Improved Soluble Expression in Escherichia coli and Easily Purified Recombinant Human Bone Morphogenetic 7-2 Fusion Protein.

BACKGROUND: Bone morphogenetic protein (BMP) is a cysteine-rich growth factor and plays a key role in early bone tissue development and bone defect repair. However, the low yield, high cost and complicated process in BMP significantly limit its clinical application. OBJECTIVE: In this study, we developed an efficient method for soluble expression and preparation of recombinant human bone morphogenetic 7-2 fusion protein (rhBMP7-2) and determined its molecular weight and biological activity. METHODS: The fusion gene for rhBMP-2 and rhBMP-7 was inserted into the pET-ELP expression vector. Correct DNA sequence was confirmed, the rhBMP7-2-ELP was transformed into Escherichia coli strain BL21 (DE3), and the rhBMP7-2 was produced in the recombinant E. coli. Recombinant BMP7-2 purify was identified using Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE). The cell proliferation and biological activity of rhBMP7-2 were measured by Cell Counting Kit-8 and Alkaline Phosphatase assay using C2C12 cells, respectively. RESULTS: The result of digestion of NdeI, BamHI and XhoI enzymes showed that the rhBMP7-2-ELP was correctly constructed. The recombinant BMP7-2 was successfully expressed in soluble form; the purified rhBMP7-2 showed biological activity and significantly promoted cell proliferation and differentiation in a dose-dependent manner. CONCLUSION: The rhBMP7-2 fusion protein with osteogenic activity was prepared through a lowcost and time-efficient method. Our preparation method presents the potential to be applied to the large-scale production of rhBMP7-2 and is expected to play a significant role in clinical treatment.

Substrate rigidity dictates colorectal tumorigenic cell stemness and metastasis via CRAD-dependent mechanotransduction.

Tumor physical microenvironment contributes greatly to the response of tumor cells. However, the mechanism of how extracellular substrate rigidity remodels colorectal cancer (CRC) cell fate and affects CRC progression remains elusive. Here, we show that F-actin regulator KIAA1211, also known as Capping protein inhibiting regulator of actin dynamics (CRAD), negatively correlates with CRC progression, stemness, and metastasis. Mechanistically, decreased CRAD in soft substrates induces Yes-associated protein (YAP) retention in the cytoplasm, restoring the repression effect on stemness markers NANOG and OCT4, thereby promoting CRC stemness and metastasis. Furthermore, CRAD deficiency promotes colorectal tumor cell softening and regulates epithelial-mesenchymal transition (EMT) states, contributing to its metastasis potential. Clinically, CRAD expression is correlated with malignant degrees and metastasis in CRC patients. Our work uncovers a role of CRAD in anticancer and mechanical signal transduction of the extracellular matrix in CRC.

Evaluation of Strategies for Decreasing Blood Glucose Using Albuminbinding Domain.

OBJECTIVE: Frequent administrations for DPPIV-resistant GLP-1 analogs are necessary to maintain the blood concentrations due to the short half-life of less than 5 minutes. However, most delivery systems that possess the ability of sustainable release of GLP-1 have drawbacks such as low yield, high cost and undesirable side effects. Therefore, we aimed to prepare a simple and efficient delivery system that could be feasibly applied to reduce blood glucose. METHODS: A novel GLP-1 delivery system (GLP-1-ELPs-SA) was prepared and characterized by circular dichroism. Furthermore, the activity and property of GLP-1-ELPs-SA were evaluated in vitro and in vivo. RESULTS: GLP-1-ELPs-SA are easily expressed in E. coli in a soluble formulation and purified through the inverse transition cycle. GLP-1-ELPs-SA spontaneously generated depot under physiological conditions. GLP-1-ELPs-SA was also found to be dispersed in the blood vessels from the depot and showed a high affinity to bind with mice (C57BL/6J) albumin, which shows that GLP-1-ELPs-SA has a long circulation time in vivo. CONCLUSION: Our delivery system could markedly decrease the clearance of recombinant proteins based on serum albumin, without substantially increasing the protein molecular weight and remarkably reducing the blood glucose within 120 h.

Correction: IR820 covalently linked with self-assembled polypeptide for photothermal therapy applications in cancer.

Correction for 'IR820 covalently linked with self-assembled polypeptide for photothermal therapy applications in cancer' by Kaizong Huang et al., Biomater. Sci., 2018, 6, 2925-2931.

IR820 covalently linked with self-assembled polypeptide for photothermal therapy applications in cancer.

Owing to the unique advantages of high specificity and minimal invasiveness, photothermal therapy (PTT) has been evidenced with great potential in cancer treatment. However, most photothermal agents present a shortage of photobleaching and nonspecific biodistribution in clinical application. In this study, we conjugated a new Indocyanine Green Dye (IR820) with self-assembled polypeptide (ELP) via chemical bonding in an aqueous environment. This preparation method could effectively avoid damaging the polypeptide. ELP-IR820 was fabricated as nanoconjugates with diameters of approximately 50 nm. The use of ELP-IR820 notably enhanced photothermal-mediated cytotoxicity on CT-26 cancer cells. We demonstrate that the ELP-IR820 nanoparticles significantly improved drug accumulation in the tumor and photothermal effect in vivo compared to the free dye and monomer ELP-IR820. ELP-IR820 nanoparticle also exhibited outstanding ability to cause prominent tumor tissue growth inhibition via the photothermal effect. No noticeable toxicity was detected for all treatment groups. These investigations broaden the application of NIR dyes as a multimodal photothermal therapy platform.

Improved antitumor activity of TRAIL fusion protein via formation of self-assembling nanoparticle.

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) has been known as a promising agent for cancer therapy due to its specific apoptosis-inducing effect on tumor cells rather than most normal cells. However, systemically delivered TRAIL suffers from a rapid clearance from the body with an extremely short half-life. Thermally responsive elastin-like polypeptides (ELPs) are a promising class of temperature sensitive biopolymers based on the structural motif found in mammalian tropoelastin and retain the advantages of polymeric drug delivery systems. We therefore expressed RGD-TRAIL fused with ELP (RGD-TRAIL-ELP) in E. coli. Purification of RGD-TRAIL-ELP was achieved by the conveniently inverse transition cycling (ITC). The purified RGD-TRAIL-ELP without any chemical conjugation was able to self-assemble into nanoparticle under physiological condition. Non-reducing SDS-PAGE results showed that trimer content of RGD-TRAIL-ELP increased 3.4-fold than RGD-TRAIL. Flow cytometry confirmed that RGD-TRAIL-ELP 3-fold enhanced apoptosis-inducing capacity than RGD-TRAIL. Single intraperitoneal injection of the RGD-TRAIL-ELP nanoparticle induced nearly complete tumor regression in the COLO-205 tumor xenograft model. Histological observation confirmed that RGD-TRAIL-ELP induced significant tumor cell apoptosis without apparent liver toxicity. These findings suggested that a great potential application of the RGD-TRAIL-ELP nanoparticle system as a safe and efficient delivery strategy for cancer therapy.

Targeted delivery and release of doxorubicin using a pH-responsive and self-assembling copolymer.

Developing a smart cancer drug delivery carrier with enhanced cancer-targeting and effective drug release in tumors is critical for efficient cancer chemotherapy. Herein, we designed a pH-responsive copolymer (PEG-ELP[VH4-70]) that entraps DOX into a hydrophobic core and self-assembles into smart DOX-loaded nanoparticles. The DOX-loaded nanoparticles were stabilized by Zn2+ and disrupted as the pH drops from 7.4 to 5.6. An in vitro study demonstrated that the DOX-loaded nanoparticle system exhibited efficient internalization, triggered the release of DOX into the cytoplasm and enabled the inhibition of tumors effectively. When used to deliver chemotherapeutics to a murine cancer model, PEG-ELP[VH4-70]/DOX accomplished a 4.8-fold suppressed effect relative to the free drug after intravenous injection. This simple strategy can promote preeminent stability for targeting hydrophobic drugs to tumor tissues.

Fused hydrophobic elastin-like-peptides (ELP) enhance biological activity of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL).

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a promising anticancer agent with tumor-selective apoptotic activity. Formation of aggregates as trimer is the prerequisite for TRAIL's function as an apoptosis inducer. However the polymerization property of TRAIL has also brought difficulties for its production. RGD-TRAIL is an integrin-targeting TRAIL mutant with enhanced apoptosisinducing activity towards tumor cells both in vitro and in vivo. When expressed in E. coli, TRAIL or its mutant RGDTRAIL usually formed inclusion bodies. Their extreme aggregation propensity for aggregation destabilizes the protein, leading to poor recovery and therefore low yield from the purification process. The low purification efficiency of TRAIL retards its industrial application and large-scale production. To avoid the above problems during RGD-TRAIL production, we employed elastin-like polypeptides (ELPs) for the fusion-expression of recombinant RGD-TRAIL. Recombinant RGD-TRAIL-ELP was expressed in a soluble form and efficiently purified from the clarified cell extracts by three rounds of inverse transition cycling (ITC). SDS-PAGE and Western blotting analyses of purified RGD-TRAIL-ELP showed that RGD-TRAIL-ELP was successfully purified and the yield was up to 10 mg/L of bacterial culture. Apoptosis assay was performed in human colorectal carcinoma cells (COLO-205) and human breast cancer cell line (MDA-MB-231) to assess the potency of the fusion protein. Fusion with hydrophobic ELP effectively enhanced RGD-TRAIL's biological activity. The higher activity and appropriate particle size of RGD-TRAIL-ELP could be used for RGD-TRAIL delivery in tumor therapy.

Enhancement predicting accuracy for elastin-like polypeptides temperature transition by back propagation neural network.

Elastin-like polypeptides (ELPs) have been widely used to promote the development of a variety of smart biomaterials. Transition temperature is a key attribute of ELPs central to ELPs researches. Therefore, it is essential to establish predictive models of transition temperature that are both computationally efficient and reliable by employing simple parameters. Back propagation neural network (BPNN), a powerful feed-forward algorithm designed to solve problems with overwhelming complexity, has been successfully used in non-linear predictor model. In this study, two pH-sensitive ELPs were expressed, purified and determined for temperature transition across a range of pH. The pH, concentration and molecular weight (MW) as well as isoelectric point (PI) and pseudo amino acid (PseAA) of these two ELPs were adopted as input parameters. Support vector machine (SVM) and back propagation neural network (BPNN) were performed respectively. We selected Lamda (λ) value by training set and evaluated predictor model by jackknife test that combined with Uniform Design (UD). According to the results of BPNN and SVM, whose mean absolute error (MAE) of BPNN model jackknife test were 4.80 and 4.95 respectively, the predictive ability of BPNN is a minor improvement over SVM. Applying Mackay's data, MAE of BPNN jackknife test was 2.02, while the MAE between experimental and predicted transition temperature was 2.30 in Mackay's predictor model. Compared with Mackay predictor model, the enhancement in the accuracy indicates that the proposed BPNN method could play a complementary role to predict ELPs transition temperature.

[De novo design, non-chromatographic purification and salt-effect of elastin-like polypeptides].

Elastin-like polypeptides (ELPs) are temperature sensitive biopolymers composed of a Val-Pro-Gly-Xaa-Gly pentapeptide repeat that derived from a structural motif found in mammalian elastin. It was a promising tag for recombinant protein purification. Here, we de novo designed a novel ELPs gene and cloned it into the modified expression vector pET-22b(+). Then, we transformed the recombinant expression vector pET-22b-ELPs into Escherichia coli BL21(DE3). Upon induction by Isopropyl beta-D-Thiogalactoside (IPTG), ELPs was expressed and purified by a non-chromatographic purification method named inverse temperature cycling. The influences of salts types and concentrations on ELPs were also determined. The results showed that the transition temperature of the [KV8F-20] decreased to 19 degrees C by 0.4 mmol/L Na2CO3. Due to its small molecular weight and sensitivity to salt, the ELPs might be a useful purification tag, which can provide a reliable and simple non-chromatographic method for purification of the recombinant protein by inverse transition cycling.