Establishment, characterization, and genetic profiling of patient-derived osteosarcoma cells from a patient with retinoblastoma.

Osteosarcoma is the most common malignant bone cancer in pediatric patients. Patients who respond poorly to chemotherapy experience worse clinical outcomes with a high mortality rate. The major challenge is the lack of effective drugs for these patients. To introduce new drugs for clinical approval, preclinical studies based on in vitro models must demonstrate the potency of the tested drugs, enabling the drugs to enter phase 1 clinical trials. Patient-derived cell culture is a promising testing platform for in vitro studies, as they more accurately recapitulate cancer states and genetic profiles compared to cell lines. In the present study, we established patient-derived osteosarcoma cells (PDC) from a patient who had previously been diagnosed with retinoblastoma. We identified a new variant of a germline mutation in the RB1 gene in the tissue of the patient. The biological effects of this PDC were studied to observe whether the cryopreserved PDC retained a feature of fresh PDC. The cryopreserved PDC preserved the key biological effects, including cell growth, invasive capability, migration, and mineralization, that define the conserved phenotypes compared to fresh PDC. From whole genome sequencing analysis of osteosarcoma tissue and patient-derived cells, we found that cryopreserved PDC was a minor population in the origin tissue and was selectively grown under the culture conditions. The cryopreserved PDC has a high resistance to conventional chemotherapy. This study demonstrated that the established cryopreserved PDC has the aggressive characteristics of osteosarcoma, in particular the chemoresistance phenotype that might be used for further investigation in the chemoresistant mechanism of osteosarcoma. In conclusion, the approach we applied for primary cell culture might be a promising method to generate in vitro models for functional testing of osteosarcoma.

Automatic Driver Drowsiness Detection Using Artificial Neural Network Based on Visual Facial Descriptors: Pilot Study.

Purpose: Driving while drowsy is a major cause of traffic accidents globally. Recent technologies for detection and alarm within automobiles for this condition are limited by their reliability, practicality, cost, and lack of clinical validation. In this study, we developed an early drowsiness detection algorithm and device based on the "gold standard brain biophysiological signal" and facial expression digital data. Methods: The data were obtained from 10 participants. Artificial neural networks (ANN) were adopted as the model. Composite features of facial descriptors (ie, eye aspect ratio (EAR), mouth aspect ratio (MAR), face length (FL), and face width balance (FWB)) extracted from two-second video frames were investigated. Results: The ANN combined with the EAR and MAR features had the most sensitivity (70.12%) while the ANN combined with the EAR, MAR, and FL features had the most accuracy and specificity (60.76% and 58.71%, respectively). In addition, by applying the discrete Fourier transform (DFT) to the composite features, the ANN combined with the EAR and MAR features again had the highest sensitivity (72.25%), while the ANN combined with the EAR, MAR, and FL features had the highest accuracy and specificity (60.40% and 54.10%, respectively). Conclusion: The ANN with DFT combined with the EAR, MAR, and FL offered the best performance. Our direct driver sleepiness detection system developed from the integration of biophysiological information and internal validation provides a valuable algorithm, specifically toward alertness level.

Automatic hemorrhage segmentation on head CT scan for traumatic brain injury using 3D deep learning model.

The most common cause of long-term disability and death in young adults is a traumatic brain injury. The decision for surgical intervention for craniotomy is dependent on the injury type and the patient's neurologic exam. The potential subtypes of intracranial hemorrhage that may necessitate surgical intervention include subdural hemorrhage, epidural hemorrhage, and intraparenchymal hemorrhage. We proposed a novel automatic method for segmenting the hemorrhage subtypes on a CT scan by integrated CT scan with bone window as input of a deep learning model. Brain CT scans were collected from adult patients and annotated regions of subdural hemorrhage, epidural hemorrhage, and intraparenchymal hemorrhage by neuroradiologists. Their raw DICOM images were preprocessed by two different window settings i.e., subdural and bone windows. The collected CT scans were divided into two datasets namely training and test datasets. A deep-learning model was modified to segment regions of each hemorrhage subtype. The model is a three-dimensional convolutional neural network including four parallel pathways that process the input at different resolutions. It was trained by a training dataset. After the segmentation result was produced by the deep-learning model, it was then improved in the post-processing step. The size of the segmented lesion was considered, and a region-growing algorithm was applied. We evaluated the performance of the proposed method on the test dataset. The method reached the median Dice similarity coefficients higher than 0.37 for each hemorrhage subtype. The proposed method demonstrates higher Dice similarity coefficients and improved segmentation performance compared to previously published literature.

Awareness, perception and perpetration of cyberbullying by high school students and undergraduates in Thailand.

The modern online society requires everyone, especially children and young people, to learn how to use the Internet. Cyberbullying is one misuse that can be detrimental to the cyberbullied individuals' mental health and lifestyle, and it often ends up with the victim becoming depressed, fearful of society, and in the worst cases, suicidal ideation. The aim of this study is to investigate the awareness, perception, and perpetration of cyberbullying by high school students and undergraduates to find ways to prevent cyberbullying in the future. For this cross-sectional study, data were collected in 2020 from 14 schools throughout Thailand and 4 universities in Chiang Mai, Thailand, using two-stage sampling. Chi-squared tests were used to compare differences between the groups. Of the 2,683 high school students, girls perceived cyberbullying more than boys (81.6% vs. 75.4%; p <0.001), with those from the later academic years being more aware of cyberbullying (p = 0.033) and more likely to conduct cyberbullying behavior (p = 0.027). Of the 721 undergraduates, women were more aware of cyberbullying than men (92.1% vs. 82.7%; p <0.001). The most common cause of cyberbullying was aiming to tease the target (67.6% of high school students vs. 82.5% of undergraduates). The most commonly cyberbullying victimization was sending mocking or rebuking messages (29.6% of high school students and 39.6% of undergraduates). The most popular solutions for cyberbullying were to avoid leaving a trace on social media and be with friends who accept who you are. Our findings show that most of the cyberbullying perpetrators did not consider that their actions would have serious consequences and only carried out cyberbullying because of wanting to tease their victims. This is useful information for the cyberbullying solution center, teachers, and parents to recognize how to make the students realize the effects of cyberbullying on the victims.

Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans.

Liver and bile duct cancers are leading causes of worldwide cancer death. The most common ones are hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). Influencing factors and prognosis of HCC and ICC are different. Precise classification of these two liver cancers is essential for treatment and prevention plans. The aim of this study is to develop a machine-based method that differentiates between the two types of liver cancers from multi-phase abdominal computerized tomography (CT) scans. The proposed method consists of two major steps. In the first step, the liver is segmented from the original images using a convolutional neural network model, together with task-specific pre-processing and post-processing techniques. In the second step, by looking at the intensity histograms of the segmented images, we extract features from regions that are discriminating between HCC and ICC, and use them as an input for classification using support vector machine model. By testing on a dataset of labeled multi-phase CT scans provided by Maharaj Nakorn Chiang Mai Hospital, Thailand, we have obtained 88% in classification accuracy. Our proposed method has a great potential in helping radiologists diagnosing liver cancer.

Relationship between Height-Weight Difference Index and Body-Fat Percentage Estimated by Bioelectrical Impedance Analysis in Thai Adults.

INTRODUCTION: The height-weight difference index (HWDI) is a new indicator for evaluating obesity status. While body-fat percentage (BF%) is considered to be the most accurate obesity evaluation tool, it is a more expensive method and more difficult to measure than the others. OBJECTIVE: Our objectives were to find the relationship between HWDI and BF% and to find a BF% prediction model from HWDI in relation to age and gender. METHOD: Bioelectrical impedance analysis was used to measure BF% in 2,771 healthy adult Thais. HWDI was calculated as the difference between height and weight. Pearson's correlation coefficient was used to assess the relationship between HWDI and BF%. Multiple linear and nonlinear regression analysis were used to construct the BF% prediction model. RESULTS: HWDI and BF% were found to be inverse which related to a tendency toward a linear relationship. Results of a multivariate linear regression analysis, which included HWDI and age as variables in the model, predicted BF% to be 34.508 - 0.159 (HWDI) + 0.161 (age) for men and 53.35 - 0.265 (HWDI) + 0.132 (age) for women. CONCLUSIONS: The prediction model provides an easy-to-use obesity evaluation tool that should help awareness of underweight and obesity conditions.

Sequence based human leukocyte antigen gene prediction using informative physicochemical properties.

Prediction of different classes within the human leukocyte antigen (HLA) gene family can provide insight into the human immune system and its response to viral pathogens. Therefore, it is desirable to develop an efficient and easily interpretable method for predicting HLA gene class compared to existing methods. We investigated the HLA gene prediction problem as follows: (a) establishing a dataset (HLA262) such that the sequence identity of the complete HLA dataset was reduced to 30%; (b) proposing a feature set of informative physicochemical properties that cooperate with SVM (named HLAPred) to achieve high accuracy and sensitivity (90.04% and 82.99%, respectively) compared with existing methods; and (c) analysing the informative physicochemical properties to understand the physicochemical properties and molecular mechanisms of the HLA gene family.

Physiological and transcriptional responses to high temperature in Arthrospira (Spirulina) platensis C1.

Arthrospira (Spirulina) platensis is a well-known commercial cyanobacterium that is used as a food and in feed supplements. In this study, we examined the physiological changes and whole-genome expression in A. platensis C1 exposed to high temperature. We found that photosynthetic activity was significantly decreased after the temperature was shifted from 35°C to 42°C for 2 h. A reduction in biomass production and protein content, concomitant with the accumulation of carbohydrate content, was observed after prolonged exposure to high temperatures for 24 h. Moreover, the results of the expression profiling in response to high temperature at the designated time points (8 h) revealed two distinct phases of the responses. The first was the immediate response phase, in which the transcript levels of genes involved in different mechanisms, including genes for heat shock proteins; genes involved in signal transduction and carbon and nitrogen metabolism; and genes encoding inorganic ion transporters for magnesium, nitrite and nitrate, were either transiently induced or repressed by the high temperature. In the second phase, the long-term response phase, both the induction and repression of the expression of genes with important roles in translation and photosynthesis were observed. Taken together, the results of our physiological and transcriptional studies suggest that dynamic changes in the transcriptional profiles of these thermal-responsive genes might play a role in maintaining cell homeostasis under high temperatures, as reflected in the growth and biochemical composition, particularly the protein and carbohydrate content, of A. platensis C1.

SCMCRYS: predicting protein crystallization using an ensemble scoring card method with estimating propensity scores of P-collocated amino acid pairs.

Existing methods for predicting protein crystallization obtain high accuracy using various types of complemented features and complex ensemble classifiers, such as support vector machine (SVM) and Random Forest classifiers. It is desirable to develop a simple and easily interpretable prediction method with informative sequence features to provide insights into protein crystallization. This study proposes an ensemble method, SCMCRYS, to predict protein crystallization, for which each classifier is built by using a scoring card method (SCM) with estimating propensity scores of p-collocated amino acid (AA) pairs (p=0 for a dipeptide). The SCM classifier determines the crystallization of a sequence according to a weighted-sum score. The weights are the composition of the p-collocated AA pairs, and the propensity scores of these AA pairs are estimated using a statistic with optimization approach. SCMCRYS predicts the crystallization using a simple voting method from a number of SCM classifiers. The experimental results show that the single SCM classifier utilizing dipeptide composition with accuracy of 73.90% is comparable to the best previously-developed SVM-based classifier, SVM_POLY (74.6%), and our proposed SVM-based classifier utilizing the same dipeptide composition (77.55%). The SCMCRYS method with accuracy of 76.1% is comparable to the state-of-the-art ensemble methods PPCpred (76.8%) and RFCRYS (80.0%), which used the SVM and Random Forest classifiers, respectively. This study also investigates mutagenesis analysis based on SCM and the result reveals the hypothesis that the mutagenesis of surface residues Ala and Cys has large and small probabilities of enhancing protein crystallizability considering the estimated scores of crystallizability and solubility, melting point, molecular weight and conformational entropy of amino acids in a generalized condition. The propensity scores of amino acids and dipeptides for estimating the protein crystallizability can aid biologists in designing mutation of surface residues to enhance protein crystallizability. The source code of SCMCRYS is available at http://iclab.life.nctu.edu.tw/SCMCRYS/.

RNA secondary structure prediction using conditional random fields model.

Non-coding RNAs (ncRNAs) have important biological functions in living cells dependent on their conserved secondary structures. Here, we focus on computational RNA secondary structure prediction by exploring primary sequences and complementary base pair interactions using the Conditional Random Fields (CRFs) model, which treats RNA prediction as a sequence labelling problem. Proposing suitable feature extraction from known RNA secondary structures, we developed a feature extraction based on natural RNA's loop and stem characteristics. Our CRFs models can predict the secondary structures of the test RNAs with optimal F-score prediction between 56.61 and 98.20% for different RNA families.

Inferring transcriptional gene regulation network of starch metabolism in Arabidopsis thaliana leaves using graphical Gaussian model.

BACKGROUND: Starch serves as a temporal storage of carbohydrates in plant leaves during day/night cycles. To study transcriptional regulatory modules of this dynamic metabolic process, we conducted gene regulation network analysis based on small-sample inference of graphical Gaussian model (GGM). RESULTS: Time-series significant analysis was applied for Arabidopsis leaf transcriptome data to obtain a set of genes that are highly regulated under a diurnal cycle. A total of 1,480 diurnally regulated genes included 21 starch metabolic enzymes, 6 clock-associated genes, and 106 transcription factors (TF). A starch-clock-TF gene regulation network comprising 117 nodes and 266 edges was constructed by GGM from these 133 significant genes that are potentially related to the diurnal control of starch metabolism. From this network, we found that ß-amylase 3 (b-amy3: At4g17090), which participates in starch degradation in chloroplast, is the most frequently connected gene (a hub gene). The robustness of gene-to-gene regulatory network was further analyzed by TF binding site prediction and by evaluating global co-expression of TFs and target starch metabolic enzymes. As a result, two TFs, indeterminate domain 5 (AtIDD5: At2g02070) and constans-like (COL: At2g21320), were identified as positive regulators of starch synthase 4 (SS4: At4g18240). The inference model of AtIDD5-dependent positive regulation of SS4 gene expression was experimentally supported by decreased SS4 mRNA accumulation in Atidd5 mutant plants during the light period of both short and long day conditions. COL was also shown to positively control SS4 mRNA accumulation. Furthermore, the knockout of AtIDD5 and COL led to deformation of chloroplast and its contained starch granules. This deformity also affected the number of starch granules per chloroplast, which increased significantly in both knockout mutant lines. CONCLUSIONS: In this study, we utilized a systematic approach of microarray analysis to discover the transcriptional regulatory network of starch metabolism in Arabidopsis leaves. With this inference method, the starch regulatory network of Arabidopsis was found to be strongly associated with clock genes and TFs, of which AtIDD5 and COL were evidenced to control SS4 gene expression and starch granule formation in chloroplasts.

HIV-1 CRF01_AE coreceptor usage prediction using kernel methods based logistic model trees.

The determination of HIV-1 coreceptor usage plays a major role in HIV treatment. Since Maraviroc has been used in a treatment for patients those exclusively harbor R5-tropic strains, the efficient performance of classifying HIV-1 coreceptor usage can help choose the most advantaged HIV treatment. In general, HIV-1 variants are classified as R5-tropic and X4-tropic or dual/mixed tropic based on their coreceptor usages. The classification of the coreceptor usage has been developed by using the various computational methods or genotypic algorithms based on V3 amino acid sequences. Most genotypic tools have been designed based on a data set of the HIV-1 subtype B that seemed to be reliable only for this subtype. However, the performance of these tools decreases in non-B subtypes. In this study, the support vector machine (SVM) method has been used to classify the HIV-1 coreceptor. To develop an efficient SVM classifier, we present a feature selector using the logistic model tree (LMT) method to select the most relevant positions from the V3 amino acid sequences. Our approach achieves as high as 97.8% accuracy, 97.7% specificity, and 97.9% sensitivity measured by ten-fold cross-validation on 273 sequences.

Draft genome sequence of Arthrospira platensis C1 (PCC9438).

Arthrospira platensis is a cyanobacterium that is extensively cultivated outdoors on a large commercial scale for consumption as a food for humans and animals. It can be grown in monoculture under highly alkaline conditions, making it attractive for industrial production. Here we describe the complete genome sequence of A. platensis C1 strain and its annotation. The A. platensis C1 genome contains 6,089,210 bp including 6,108 protein-coding genes and 45 RNA genes, and no plasmids. The genome information has been used for further comparative analysis, particularly of metabolic pathways, photosynthetic efficiency and barriers to gene transfer.

Prediction of the disulphide bonding state of cysteines in proteins using conditional random fields.

The formation of disulphide bonds between cysteines plays a major role in protein folding, structure, function and evolution. Many computational approaches have been used to predict the disulphide bonding state ofcysteines. In our work, we developed a novel method based on Conditional Random Fields (CRFs) to predict the disulphide bonding state from protein primary sequence, predicted secondary structures and predicted relative solvent accessibilities (all-state information). Our experiments obtain 84% accuracy, 88% precision and 94% recall, using all-state information. However, our results show essentially identical results when using protein sequence and predicted relative solvent accessibilities in the absence of secondary structure.

Differences in global gene expression in peripheral blood mononuclear cells indicate a significant role of the innate responses in progression of dengue fever but not dengue hemorrhagic fever.

BACKGROUND: Dengue virus infection causes an array of symptoms ranging from dengue fever (DF) to dengue hemorrhagic fever (DHF). The pathophysiological processes behind these 2 clinical manifestations are unclear. METHOD: In the present study, genomewide transcriptomes of peripheral blood mononuclear cells (PBMCs) collected from children with acute-phase DF (i.e., DF PBMCs) or acute-phase DHF (i.e., DHF PBMCs) were compared using microarray analysis. Results of genome screening were validated at the genomic and proteomics levels. RESULTS: DHF had stronger influences on the gene expression profile than did DF. Of the affected genes, metabolic gene expression was influenced the most. For the immune response category, 17 genes were more strongly up-regulated in DF PBMCs than in DHF PBMCs. Eight of the these 17 genes were categorized as belonging to the interferon (IFN) system. The up-regulation of IFN-related genes was accompanied by strong expression of CD59, a complement inhibitor. DHF PBMCs expressed genes involved in T and B cell activation, cytokine production, complement activation, and T cell apoptosis more strongly than did DF PBMCs. CONCLUSION: We hypothesize that, during DF, genes in the IFN system and complement inhibitor play a role in lowering virus production and reducing tissue damage. In patients with DHF, the dysfunction of immune cells, complement, and cytokines increases viral load and tissue damage.