A Smart pH-Sensitive Delivery System for Enhanced Anticancer Efficacy via Paclitaxel Endosomal Escape

Micelles are highly attractive nano-drug delivery systems for targeted cancer therapy. While they have been demonstrated to significantly alleviate the side-effects of their cargo drugs, the therapy outcomes are usually suboptimal partially due to ineffective drug release and endosome entrapment. Stimulus-responsive nanoparticles have allowed controlled drug release in a smart fashion, and we want to use this concept to design novel micelles. Herein, we reported pH-sensitive paclitaxel (PTX)-loaded poly (ethylene glycol)-phenylhydrazone-dilaurate (PEG-BHyd-dC12) micelles (PEG-BHyd-dC12/PTX). The micelles were spherical, with an average particle size of ∼135 nm and a uniform size distribution. The pH-responsive properties of the micelles were certified by both colloidal stability and drug release profile, where the particle size was strikingly increased accompanied by faster drug release as pH decreased from 7.4 to 5.5. As a result, the micelles exhibited much stronger cytotoxicity than the pH-insensitive counterpart micelles against various types of cancer cells due to the hydrolysis of the building block polymers and subsequent rapid PTX release. Overall, these results demonstrate that the PEG-BHyd-dC12 micelle is a promising drug delivery system for cancer therapy

To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now

The recent advances in diffusion models (DMs) have revolutionized the generation of complex and diverse images. However, these models also introduce potential safety hazards, such as the production of harmful content and infringement of data copyrights. Although there have been efforts to create safety-driven unlearning methods to counteract these challenges, doubts remain about their capabilities. To bridge this uncertainty, we propose an evaluation framework built upon adversarial attacks (also referred to as adversarial prompts), in order to discern the trustworthiness of these safety-driven unlearned DMs. Specifically, our research explores the (worst-case) robustness of unlearned DMs in eradicating unwanted concepts, styles, and objects, assessed by the generation of adversarial prompts. We develop a novel adversarial learning approach called UnlearnDiff that leverages the inherent classification capabilities of DMs to streamline the generation of adversarial prompts, making it as simple for DMs as it is for image classification attacks. This technique streamlines the creation of adversarial prompts, making the process as intuitive for generative modeling as it is for image classification assaults. Through comprehensive benchmarking, we assess the unlearning robustness of five prevalent unlearned DMs across multiple tasks. Our results underscore the effectiveness and efficiency of UnlearnDiff when compared to state-of-the-art adversarial prompting methods. Codes are available at https://github.com/OPTML-Group/Diffusion-MU-Attack. WARNING: This paper contains model outputs that may be offensive in nature.Comment: Codes are available at https://github.com/OPTML-Group/Diffusion-MU-Attac

Oral diet management for carcinoma at the base of tongue with radiotherapy and chemotherapy associated dysphagia: a case report

IntroductionTongue cancer is one of the common malignancy of the head and neck, and directly impacts chewing, swallowing, and other eating activities. Based on the evidence-based guidelines and clinical management, this paper presents nutrition management experience of a patient with tongue cancer who had a dysphagia and feeding reflux while undergoing radiotherapy and chemotherapy.MethodsNutritional risk screening and comprehensive nutritional assessment were performed based on the patient’s medical history, and personalized nutritional programs were developed under the guidance of the clinical pharmaceutical consensus of parenteral nutrition and nutritional treatment guidelines for patients with tumors during radiotherapy. For the management of oral feeding, the patient’s swallowing function was evaluated to manage oral feeding. Thickening powders were used to improve the consistency of the patient’s food, which successfully achieved oral feeding of the patient.ResultsThe patient finally ate five meals a day by mouth, and energy requirements were met using industrialized nutritional supplements, and homogenized food was added in between the meals. The energy provided by enteral nutrition can reached approximately 60–75%. The patient’s weight and albumin levels had increased significantly at the time of discharge.DiscussionThe nutritional management of patients with dysphagia should be jointly managed by clinicians, nurses, nutritionists, and family members to effectively improve the quality of life (QOL) and nutritional status of patients. To ensure adequate nutritional supply, appropriate swallowing training may delay the deterioration of the chewing function and improve the eating experience of such patients

Exploring the supersymmetric U(1)B−L×_{B-L} \times U(1)R_{R} model with dark matter, muon g−2g-2 and Z′Z^\prime mass limits

We study the low scale predictions of supersymmetric standard model extended by $U(1)_{B-L}\times U(1)_{R}$ symmetry, obtained from $SO(10)$ breaking via a left-right supersymmetric model, imposing universal boundary conditions. Two singlet Higgs fields are responsible for the radiative $U(1)_{B-L}\times U(1)_{R}$ symmetry breaking, and a singlet fermion $S$ is introduced to generate neutrino masses through inverse seesaw mechanism. The lightest neutralino or sneutrino emerge as dark matter candidates, with different low scale implications. We find that the composition of the neutralino LSP changes considerably depending on the neutralino LSP mass, from roughly half $U(1)_R$ bino, half MSSM bino, to singlet higgsino, or completely dominated by MSSM higgsino. The sneutrino LSP is statistically much less likely, and when it occurs it is a 50-50 mixture of right-handed sneutrino and the scalar $\tilde S$. Most of the solutions consistent with the relic density constraint survive the XENON 1T exclusion curve for both LSP cases. We compare the two scenarios and investigate parameter space points and find consistency with the muon anomalous magnetic moment only at the edge of $2\sigma$ deviation from the measured value. However, we find that the sneutrino LSP solutions could be ruled out completely by strict reinforcement of the recent $Z^\prime$ mass bounds. We finally discuss collider prospects for testing the model

A data-driven based decomposition?integration method for remanufacturing cost prediction of end-of-life products

Remanufacturing cost prediction is conducive to visually judging the remanufacturability of end-of-life (EOL) products from economic perspective. However, due to the randomness, non-linearity of remanufacturing cost and the lack of sufficient data samples. The general method for predicting the remanufacturing cost of EOL products is very low precision. To this end, a data-driven based decomposition–integration method is proposed to predict remanufacturing cost of EOL products. The approach is based on historical remanufacturing cost data to build a model for prediction. First of all, the remanufacturing cost of individual EOL product is arranged as a time series in reprocessing order. The Improved Local Mean Decomposition (ILMD) is employed to decompose remanufacturing cost time series data into several components with smooth, periodic fluctuation and use this as input. BP neural network based on Particle Swarm Optimization (PSO-BP) algorithm is utilized to predict the cost of each component. Finally, the predicted components are added to obtain the final prediction result. To illustrate and verify the feasibility of the proposed method, the remanufacturing cost of DH220 excavator is applied as the sample data, and empirical results show that the proposed model is statistically superior to other benchmark models owing to its high prediction accuracy and less computation time. And proposed method can be utilized as an effective tool to analyze and predict remanufacturing cost of EOL products

A Novel Virtual Vector Modulation-based scheme of Model Power Predictive for VIENNA Rectifier

When the finite control set model predictive(FCS-MPC) algorithm is applied to the three-level converter, there are problems such as large current harmonics, high requirements for the computing efficiency of the micro-controller, complex multi-objective optimization and limited output vector switching. In additional, the mismatch of inductance parameter may directly affect the observation accuracy of FCS-MPC. Furthermore, due to the limitation of finite set model prediction, it leads to the switching operation is not constant and the decrease of the grid-connected current quality. In this regard, an improved model predictive direct power control based on the combined virtual vector modulation (MPDPC-VM) is proposed by considering the influence of the filter inductance parameter mismatch. The finite control set and restricted vector switching of the Vienna rectifier are modeled to avoid excessive voltage jumps, and the predicted values of input power is obtained by the sliding-mode control (SMC) strategy. Then, a linear synthesis method of virtual vector modulation-based scheme is proposed, which increases the number of the available voltage vectors in a single switching period from 8 to 19. The grid-connected current ripple is improved by reducing the error between the expected voltage vector and the available voltage vector. Finally, the model reference adaptive system (MRAS) method is applied to improve the working reliability and reduce the influence of mismatching of inductance parameters. Extensive simulation and matching experimental results is given to demonstrate the validity of the proposed strategy under steady-state and transient responses conditions compared against the existing FCS-MPC

HLungDB: an integrated database of human lung cancer research

The human lung cancer database (HLungDB) is a database with the integration of the lung cancer-related genes, proteins and miRNAs together with the corresponding clinical information. The main purpose of this platform is to establish a network of lung cancer-related molecules and to facilitate the mechanistic study of lung carcinogenesis. The entries describing the relationships between molecules and human lung cancer in the current release were extracted manually from literatures. Currently, we have collected 2585 genes and 212 miRNA with the experimental evidences involved in the different stages of lung carcinogenesis through text mining. Furthermore, we have incorporated the results from analysis of transcription factor-binding motifs, the promoters and the SNP sites for each gene. Since epigenetic alterations also play an important role in lung carcinogenesis, genes with epigenetic regulation were also included. We hope HLungDB will enrich our knowledge about lung cancer biology and eventually lead to the development of novel therapeutic strategies. HLungDB can be freely accessed at http://www.megabionet.org/bio/hlung

Spectrum of Oncogenic Driver Mutations in Lung Adenocarcinomas from East Asian Never Smokers

PURPOSE:We previously showed that 90% (47 of 52; 95% CI, 0.79 to 0.96) of lung adenocarcinomas from East Asian never-smokers harbored well-known oncogenic mutations in just four genes: EGFR, HER2, ALK, and KRAS. Here, we sought to extend these findings to more samples and identify driver alterations in tumors negative for these mutations. EXPERIMENTAL DESIGN:We have collected and analyzed 202 resected lung adenocarcinomas from never smokers seen at Fudan University Shanghai Cancer Center. Since mutations were mutually exclusive in the first 52 examined, we determined the status of EGFR, KRAS, HER2, ALK, and BRAF in stepwise fashion as previously described. Samples negative for mutations in these 5 genes were subsequently examined for known ROS1 fusions by RT-PCR and direct sequencing. RESULTS:152 tumors (75.3%) harbored EGFR mutations, 12 (6%) had HER2 mutations, 10 (5%) had ALK fusions all involving EML4 as the 5' partner, 4 (2%) had KRAS mutations, and 2 (1%) harbored ROS1 fusions. No BRAF mutation were detected. CONCLUSION:The vast majority (176 of 202; 87.1%, 95% CI: 0.82 to 0.91) of lung adenocarcinomas from never smokers harbor mutant kinases sensitive to available TKIs. Interestingly, patients with EGFR mutant patients tend to be older than those without EGFR mutations (58.3 Vs 54.3, P = 0.016) and patient without any known oncogenic driver tend to be diagnosed at a younger age (52.3 Vs 57.9, P = 0.013). Collectively, these data indicate that the majority of never smokers with lung adenocarcinoma could benefit from treatment with a specific tyrosine kinase inhibitor

ATR/Chk1/Smurf1 pathway determines cell fate after DNA damage by controlling RhoB abundance

该研究论文首次发现Smurf1可以作为RhoB的E3泛素连接酶，并位于ATR/Chk1信号通路下游参与介导单链DNA损伤引起的细胞凋亡，也为进一步的抗肿瘤药物的研发提供了可靠的细胞生物学和分子生物学依据。 　　该论文的共同第一作者为生命科学学院2009级博士生郭磊、2011级博士生王梅林和2013级博士生吴勤刚。​ATM- and RAD3-related (​ATR)/​Chk1 and ​ataxia–telangiectasia mutated (​ATM)/​Chk2 signalling pathways play critical roles in the DNA damage response. Here we report that the E3 ubiquitin ligase ​Smurf1 determines cell apoptosis rates downstream of DNA damage-induced ​ATR/​Chk1 signalling by promoting degradation of ​RhoB, a small GTPase recognized as tumour suppressor by promoting death of transformed cells. We show that ​Smurf1 targets ​RhoB for degradation to control its abundance in the basal state. DNA damage caused by ultraviolet light or the alkylating agent ​methyl methanesulphonate strongly activates ​Chk1, leading to phosphorylation of ​Smurf1 that enhances its self-degradation, hence resulting in a ​RhoB accumulation to promote apoptosis. Suppressing ​RhoB levels by overexpressing ​Smurf1 or blocking ​Chk1-dependent ​Smurf1 self-degradation significantly inhibits apoptosis. Hence, our study unravels a novel ​ATR/​Chk1/​Smurf1/​RhoB pathway that determines cell fate after DNA damage, and raises the possibility that aberrant upregulation of ​Smurf1 promotes tumorigenesis by excessively targeting ​RhoB for degradation