An implantable sustained-release chemotherapy delivery system for the treatment of breast cancer

The influence of chitosan on the degradation characteristics of paclitaxel-loaded Poly (Lactic-co-Glycolic Acid) (PLGA) rod-shaped implants was investigated and modeled. The implant was designed for sustained release of the hydrophobic chemotherapeutic paclitaxel (PTX) intramuscularly or subcutaneously. In this study, integration of PTX and PLGA was achieved via a solvent evaporation method resulting in a solid dispersion of the substances. To customize degradation of the implants and secure delivery of high doses of PTX, chitosan and the PLGA-PTX blend were mixed in a 30:70 mass ratio. Cryomilling was utilized to create the chitosan-PLGA-PTX mixture, due to its proven effectiveness in producing homogenous blends. Implants were then fabricated into rods by injection molding and characterized in terms of content uniformity, morphology, and thermal property. The integrity of the blends was ascertained via x-ray diffraction, while miscibility between the drug and excipients was established by differential scanning calorimetry. In vitro drug release was studied in a phosphate buffer of pH 7.4 and measured by ultraviolet-visible spectrophotometry (UV-Vis). Meanwhile, the degradation rate was determined by quantifying mass loss at various points in 30 days. This study revealed the chitosan blended PTX-loaded PLGA implant possesses a longer, yet steadier, sustained drug release behavior than the PTX-loaded PLGA implant. The results suggest that introducing chitosan into PLGA implants through this fabrication method could be integrated to regulate and control the degradation rate of PLGA implants

SERS Tags: Novel Optical Nanoprobes for Bioanalysis

CONTENTS1. Introduction1.1. Fundamental Theory of Surface-Enhanced Raman Scattering1.2. Optical Properties of SERS Tags2. Synthesis of SERS Tags2.1. Noble Metal Nanosubstrates2.1.1. Single Particle-Based SERS Substrates2.1.2. Nanoparticle Cluster-Based Substrates2.2. Raman Reporter Molecules2.2.1. Selection Principles and Reporter Types2.2.2. Self-Assembled Monolayer Coverage Strategy2.3. Surface Coating for Protection2.3.1. Biomolecule Coating2.3.2. Polymer Coating2.3.3. Liposome Coating2.3.4. Silica Coating2.4. Attachment of Targeting Molecules3. Bioanalysis Applications3.1. Ionic and Molecular Detection3.2. Pathogen Detection3.3. Live-Cell Imaging3.3.1. Cancer Marker Detection3.3.2. Intercellular Microenvironment Sensing3.4. Tissue SERS Imaging3.5. In Vivo SERS Imaging4. Challenges and Perspectives4.1. Reproducible Signal of SERS Tags4.1.1. Precisely Controlled Hot Spots for Nanosubstrates4.1.2. Calibration of SERS Intensities and Enhancements4.2. Improving Multiplexing Capability4.3. Reduced Size for Subcellular Imaging4.4. Development of Multifunctional Nanoplatforms4.4.1. Magnetic SERS Dots4.4.2. Multimodal Imaging Dots4.4.3. SERS Tag-Based Therapeutic Systems4.5. Biocompatibility5. Conclusions and Remarks</ul

Recovery type a posteriori error estimation of an adaptive finite element method for Cahn--Hilliard equation

In this paper, we derive a novel recovery type a posteriori error estimation of the Crank-Nicolson finite element method for the Cahn--Hilliard equation. To achieve this, we employ both the elliptic reconstruction technique and a time reconstruction technique based on three time-level approximations, resulting in an optimal a posteriori error estimator. We propose a time-space adaptive algorithm that utilizes the derived a posteriori error estimator as error indicators. Numerical experiments are presented to validate the theoretical findings, including comparing with an adaptive finite element method based on a residual type a posteriori error estimator.Comment: 36 pages, 7 figure

Dynamics of spatial flexible multibody systems with clearance and lubricated spherical joints

A computational methodology for analysis of spatial flexible multibody systems, considering the effects of the clearances and lubrication in the system spherical joints, is presented. The dry contact forces are evaluated through a Hertzian-based contact law, which includes a damping term representing the energy dissipation. The frictional forces are evaluated using a modified Coulomb’s friction law. In the case of lubricated joints, the resulting lubricant forces are derived from the corresponding Reynolds’ equation. An absolute nodal formulation is utilized in flexible body formulation. The generalized-α method is used to solve the resulting equations of motion. The effectiveness of the methodology is demonstrated by two numerical examples.Fundação para a Ciência e a Tecnologia (FCT

Task Difficulty Aware Parameter Allocation & Regularization for Lifelong Learning

Parameter regularization or allocation methods are effective in overcoming catastrophic forgetting in lifelong learning. However, they solve all tasks in a sequence uniformly and ignore the differences in the learning difficulty of different tasks. So parameter regularization methods face significant forgetting when learning a new task very different from learned tasks, and parameter allocation methods face unnecessary parameter overhead when learning simple tasks. In this paper, we propose the Parameter Allocation & Regularization (PAR), which adaptively select an appropriate strategy for each task from parameter allocation and regularization based on its learning difficulty. A task is easy for a model that has learned tasks related to it and vice versa. We propose a divergence estimation method based on the Nearest-Prototype distance to measure the task relatedness using only features of the new task. Moreover, we propose a time-efficient relatedness-aware sampling-based architecture search strategy to reduce the parameter overhead for allocation. Experimental results on multiple benchmarks demonstrate that, compared with SOTAs, our method is scalable and significantly reduces the model's redundancy while improving the model's performance. Further qualitative analysis indicates that PAR obtains reasonable task-relatedness.Comment: Accepted by CVPR2023. Code is available at https://github.com/WenjinW/PA

Dataless text classification with descriptive LDA

Manually labeling documents for training a text classifier is expensive and time-consuming. Moreover, a classifier trained on labeled documents may suffer from overfitting and adaptability problems. Dataless text classification (DLTC) has been proposed as a solution to these problems, since it does not require labeled documents. Previous research in DLTC has used explicit semantic analysis of Wikipedia content to measure semantic distance between documents, which is in turn used to classify test documents based on nearest neighbours. The semantic-based DLTC method has a major drawback in that it relies on a large-scale, finely-compiled semantic knowledge base, which is difficult to obtain in many scenarios. In this paper we propose a novel kind of model, descriptive LDA (DescLDA), which performs DLTC with only category description words and unlabeled documents. In DescLDA, the LDA model is assembled with a describing device to infer Dirichlet priors from prior descriptive documents created with category description words. The Dirichlet priors are then used by LDA to induce category-aware latent topics from unlabeled documents. Experimental results with the 20Newsgroups and RCV1 datasets show that: (1) our DLTC method is more effective than the semantic-based DLTC baseline method; and (2) the accuracy of our DLTC method is very close to state-of-the-art supervised text classification methods. As neither external knowledge resources nor labeled documents are required, our DLTC method is applicable to a wider range of scenarios

Learning user and product distributed representations using a sequence model for sentiment analysis

In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-The-Art results on these benchmark datasets

A Meta-analysis of Major Complications between Traditional Pacemakers and Leadless Pacemakers

Objectives: We aim to compare the major complications between leadless pacemakers and traditional pacemakers. Background: Leadless pacemakers, which are increasingly used in clinical practice, have several advantages compared with traditional pacemakers in avoiding pocket- and lead-related complications. However, the clinical effect of leadless pacemakers remains controversial. Methods: PubMed, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), the CNKI database, and the Wanfang database were searched from July 2013 to December 2019. Studies comparing leadless pacemakers and traditional pacemakers were included. The primary end point was major complications. The secondary end points were cardiac perforation/pericardial effusion, device revision or extraction, loss of device function, and death. Results: Six studies fulfilled the inclusion criteria. Only four of the six studies reported data on major complications. Leadless pacemakers were associated with a lower incidence of major complications (risk ratio 0.33, 95% confidence interval 0.25–0.44, P<0.00001, I 2 =49%). We extracted data on cardiac perforation/pericardial effusion, device revision or extraction, loss of device function, and death from six studies. Our meta-analysis showed that leadless pacemakers have a higher risk of cardiac perforation or pericardial effusion (risk ratio 4.28, 95% confidence interval 1.66–11.08, P=0.003, I 2 =0%). No statistically significant differences were found for mortality, device revision or extraction, and loss of device function. Conclusion: Compared with traditional pacemakers, leadless pacemakers have a significantly decreased risk of major complications, but have a higher risk of cardiac perforation or pericardial effusion

Two-Link Flexible Manipulator Modeling and Tip Trajectory Tracking Based on Absolute Nodal Coordinate Method

Abstract It has been demonstrated that the absolute nodal coordinate formulation (ANCF) proposed recently in literature can be used to exactly describe the flexible multibody system unlike traditional methods such as the floating coordinate method and assumed mode method. Therefore, in this paper a new dynamic modeling technique for a two-link flexible manipulator based on absolute nodal coordinate method is proposed. The link shear effect was taken into account by using the 2D ANCF shear beam element. The resulting state equation can be explicitly described by generalized coordinate since the system mass matrix is constant in the ANCF framework. The proposed method is validated through the two-link flexible manipulator tip circle and square trajectory tracking control simulations by using a simple PD controller. To improve computational efficiency, the invariant matrix method and the Broyden quasi-Newton method are introduced. To improve the tracking accuracy, different PD parameters in different simulation periods are used. The simulation results indicate that modeling and controlling the flexible manipulator based on the ANCF is effective

Ferroptosis in COVID-19-related liver injury: A potential mechanism and therapeutic target

The outbreak and worldwide spread of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a threat to global public health. SARS-CoV-2 infection not only impacts the respiratory system but also causes hepatic injury. Ferroptosis, a distinct iron-dependent form of non-apoptotic cell death, has been investigated in various pathological conditions, such as cancer, ischemia/reperfusion injury, and liver diseases. However, whether ferroptosis takes part in the pathophysiological process of COVID-19-related liver injury has not been evaluated yet. This review highlights the pathological changes in COVID-19-related liver injury and presents ferroptosis as a potential mechanism in the pathological process. Ferroptosis, as a therapeutic target for COVID-19-related liver injury, is also discussed. Discoveries in these areas will improve our understanding of strategies to prevent and treat hepatic injuries caused by COVID-19