Improving thermal stability and efficacy of BCNU in treating glioma cells using PAA-functionalized graphene oxide

Yu-Jen Lu1,2,#, Hung-Wei Yang1,#, Sheng-Che Hung3, Chiung-Yin Huang2, Shin-Ming Li4, Chen-Chi M Ma4, Pin-Yuan Chen2, Hong-Chieh Tsai2, Kuo-Chen Wei2, Jyh-Ping Chen1 1Department of Chemical and Materials Engineering, Chang Gung University, Kwei-San, Taoyuan, Taiwan; 2Department of Neurosurgery, Chang Gung Memorial Hospital, Kwei-San, Taoyuan, Taiwan; 3Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan; 4Department of Chemical Engineering, National Tsing Hua University, Hsinchu, Taiwan#These authors contributed equally to this workBackground: 1,3-bis(2-chloroethyl)-1-nitrosourea (BCNU), a commercial chemotherapeutic drug for treating malignant brain tumors, has poor thermal stability and a short half-life. Immobilization of BCNU on a nanocarrier might increase the thermal stability of BCNU and extend its half-life.Methods: Nanosized graphene oxide (GO) could be modified by polyacrylic acid (PAA) to improve the aqueous solubility and increase the cell penetration efficacy of the nanocarrier. PAA–GO intended as a drug carrier for BCNU was prepared and characterized in this study. The size and thickness of PAA–GO was investigated by transmission electron microscopy and atomic force microscopy, and the presence of PAA functional groups was confirmed by electron spectroscopy for chemical analysis and thermogravimetric analysis. BCNU was conjugated to PAA–GO by covalent binding for specific killing of cancer cells, which could also enhance the thermal stability of the drug.Results: Single layer PAA–GO (about 1.9 nm) with a lateral width as small as 36 nm was successfully prepared. The optimum drug immobilization condition was by reacting 0.5 mg PAA–GO with 0.4 mg BCNU, and the drug-loading capacity and residual drug activity were 198 µg BCNU/mg PAA–GO and 70%, respectively. This nanocarrier significantly prolonged the half-life of bound BCNU from 19 to 43 hours compared with free drug and showed efficient intracellular uptake by GL261 cancer cells. The in vitro anticancer efficacy of PAA–GO–BCNU was demonstrated by a 30% increase in DNA interstrand cross-linking and a 77% decrease in the IC50 value toward GL261 compared with the same dosage of free drug.Conclusion: Nanosized PAA–GO serves as an efficient BCNU nanocarrier by covalent binding. This nanocarrier will be a promising new vehicle for an advanced drug delivery system in cancer therapy.Keywords: graphene oxide, BCNU, glioma cells, drug delivery, thermal stabilit

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early; the treatment will be relatively easy; which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However; the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology; this research solves the problem that the original cutting technology cannot extract certain single teeth; and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN); which can identify caries and restorations from the bitewing images. Moreover; it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image; which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization; (2) a dental image cropping procedure to obtain individually separated tooth samples; and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks; namely; AlexNet; GoogleNet; Vgg19; and ResNet50; experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%; respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion

Tooth Position Determination by Automatic Cutting and Marking of Dental Panoramic X-ray Film in Medical Image Processing

This paper presents a novel method for automatic segmentation of dental X-ray images into single tooth sections and for placing every segmented tooth onto a precise corresponding position table. Moreover, the proposed method automatically determines the tooth’s position in a panoramic X-ray film. The image-processing step incorporates a variety of image-enhancement techniques, including sharpening, histogram equalization, and flat-field correction. Moreover, image processing was implemented iteratively to achieve higher pixel value contrast between the teeth and cavity. The next image-enhancement step is aimed at detecting the teeth cavity and involves determining the segment and points separating the upper and lower jaw, using the difference in pixel values to cut the image into several equal sections and then connecting each cavity feature point to extend a curve that completes the description of the separated jaw. The curve is shifted up and down to look for the gap between the teeth, to identify and address missing teeth and overlapping. Under FDI World Dental Federation notation, the left and right sides receive eight-code sequences to mark each tooth, which provides improved convenience in clinical use. According to the literature, X-ray film cannot be marked correctly when a tooth is missing. This paper utilizes artificial center positioning and sets the teeth gap feature points to have the same count. Then, the gap feature points are connected as a curve with the curve of the jaw to illustrate the dental segmentation. In addition, we incorporate different image-processing methods to sequentially strengthen the X-ray film. The proposed procedure had an 89.95% accuracy rate for tooth positioning. As for the tooth cutting, where the edge of the cutting box is used to determine the position of each tooth number, the accuracy of the tooth positioning method in this proposed study is 92.78%

RNASEQR—a streamlined and accurate RNA-seq sequence analysis program

Next-generation sequencing (NGS) technologies-based transcriptomic profiling method often called RNA-seq has been widely used to study global gene expression, alternative exon usage, new exon discovery, novel transcriptional isoforms and genomic sequence variations. However, this technique also poses many biological and informatics challenges to extracting meaningful biological information. The RNA-seq data analysis is built on the foundation of high quality initial genome localization and alignment information for RNA-seq sequences. Toward this goal, we have developed RNASEQR to accurately and effectively map millions of RNA-seq sequences. We have systematically compared RNASEQR with four of the most widely used tools using a simulated data set created from the Consensus CDS project and two experimental RNA-seq data sets generated from a human glioblastoma patient. Our results showed that RNASEQR yields more accurate estimates for gene expression, complete gene structures and new transcript isoforms, as well as more accurate detection of single nucleotide variants (SNVs). RNASEQR analyzes raw data from RNA-seq experiments effectively and outputs results in a manner that is compatible with a wide variety of specialized downstream analyses on desktop computers

Microcrystalline-Silicon-Oxide-Based N-Type Reflector Structure in Micromorph Tandem Solar Cells

N-type microcrystalline silicon oxide thin films (n-c-SiO:H) have been deposited by VHF-PECVD (40 MHz) with reactant gas mixtures of CO2/SiH4 and H2. N-c-SiO thin films exhibiting low refractive index value (n600nm∼2), and medium/high conductivity (≧10−9 S/cm) are suitable to be used as an “n-type reflector” in micromorph tandem solar cells. Transmission electron microscopy (TEM) results show that microstructures of n-c-SiO:H thin films contain nanocrystalline Si particles, which are randomly embedded in the a-SiO matrix. This specific microstructure provides n-c-SiO:H thin films excellent optoelectronic properties; therefore, n-c-SiO:H thin films are appropriate candidates for “n-type reflector” structures in Si tandem solar cells

Large Renal Cell Carcinoma with Multiple Metastases Presenting as Low Back Pain

Renal cell carcinoma (RCC) is a highly lethal genitourinary malignancy due to infrequent early diagnosis, which responds poorly to conventional immune therapy, chemotherapy or radiotherapy in metastatic disease. There have been great advances in the understanding of the molecular mechanisms of RCC in recent years, especially with regards to dysfunction of the Von Hippel–Lindau tumor suppressor gene in clear cell type RCC. This dysfunction has been shown to result in the activation of downstream angiogenesis related genes such as vascular endothelial growth factor. Treatment targeting this molecular pathway has been shown to improve patient prognosis and has become the standard treatment for metastatic clear cell RCC. Herein, we report a case of advanced and metastatic RCC initially presenting as low-back pain, but without the typical symptoms. Physical, laboratory and imaging examinations revealed a large left kidney tumor and multiple metastases to the liver and bones. The patient received oral tyrosine kinase inhibitor target therapy with a good tumor response. Treatment of RCC has entered the era of molecular target therapy, however some issues still need to be addressed, such as the safety issue of tyrosine kinase inhibitor and role of cytoreductive surgery. We also review related articles and have some discussion

Non-invasive focused ultrasound-based synergistic treatment of brain tumors

Among the primary brain tumors, gliomas are the most commonly found. In the United States, these tumors account for approximately 78% of the new cases of primary malignant brain and central nervous system (CNS) tumors diagnosed annually. Glioblastoma multiforme (GBM) is the most common and aggressive type of glioma, with the poorest survival duration. Although surgical techniques, radiotherapy and chemotherapy have improved, the prognoses of patients with glioblastomas are still poor (∼1 year), which is largely because of the spread of tumor cells to other regions of the brain. Intravenous administration of chemotherapy drugs is the standard procedure for treat these spreaded cells, but the treatment effect is limited because of the adverse side effect and the delivery blockage from the blood–brain barrier (BBB). Thus, developing an effective treatment system to conquer this blockage will be beneficial for those who suffer from malignant brain tumor. Focused ultrasound (FUS) sonication in the presence of microbubbles (MB) may be able to promote the effectiveness of drug delivery, and help to overcome the blockage. The oscillation and destruction of microbubbles as well as microstreaming and radiation forces are the key factors capable of creating the transient rupture of the vascular barriers and subsequent increase in the tumor's vascular permeability. The success of FUS BBB disruption in delivering a variety of therapeutic molecules into brain tumors has recently been demonstrated in an animal model. In this paper the authors review a number of critical studies that have demonstrated successful outcomes, including enhancement of the delivery of traditional clinically used chemotherapeutic agents or application of novel nanocarrier designs for actively transporting drugs, or extending drug half-lives to significantly improve treatment efficacy in preclinical animal models