Advances on microRNA in regulating mammalian skeletal muscle development.

MicroRNA (miRNA) is a class of short non-coding RNA, which is about 22 bp in length. In mammals, miRNA exerts its funtion through binding with the 3°-UTR region of target genes and inhibiting their translation. Skeletal muscle development is a complex event, including: proliferation, migration and differentiation of skeletal muscle stem cells; proliferation, differentiation and fusion of myocytes; as well as hypertrophy, energy metabolism and conversion of muscle fiber types. The miRNA plays important roles in all processes of skeletal muscle development through targeting the key factors of different stages. Herein we summarize the miRNA related to muscle development, providing a better understanding of the skeletal muscle development.

A review for cell and particle tracking on microscopy images using algorithms and deep learning technologies.

Time-lapse microscopy images generated by biological experiments have been widely used for observing target activities, such as the motion trajectories and survival states. Based on these observations, biologists can conclude experimental results or present new hypotheses for several biological applications, i.e. virus research or drug design. Many methods or tools have been proposed in the past to observe cell and particle activities, which are defined as single cell tracking and single particle tracking problems, by using algorithms and deep learning technologies. In this article, a review for these works is presented in order to summarize the past methods and research topics at first, then points out the problems raised by these works, and finally proposes future research directions. The contributions of this article will help researchers to understand past development trends and further propose innovative technologies.

Machine Learning Approaches to Predict In-Hospital Mortality among Neonates with Clinically Suspected Sepsis in the Neonatal Intensive Care Unit.

BACKGROUND: preterm and critically ill neonates often experience clinically suspected sepsis during their prolonged hospitalization in the neonatal intensive care unit (NICU), which can be the initial sign of final adverse outcomes. Therefore, we aimed to utilize machine learning approaches to predict neonatal in-hospital mortality through data-driven learning. METHODS: a total of 1095 neonates who experienced clinically suspected sepsis in a tertiary-level NICU in Taiwan between August 2017 and July 2020 were enrolled. Clinically suspected sepsis was defined based on clinical features and laboratory criteria and the administration of empiric antibiotics by clinicians. The variables used for analysis included patient demographics, clinical features, laboratory data, and medications. The machine learning methods used included deep neural network (DNN), k-nearest neighbors, support vector machine, random forest, and extreme gradient boost. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: the final in-hospital mortality of this cohort was 8.2% (90 neonates died). A total of 765 (69.8%) and 330 (30.2%) patients were randomly assigned to the training and test sets, respectively. Regarding the efficacy of the single model that most accurately predicted the outcome, DNN exhibited the greatest AUC (0.923, 95% confidence interval [CI] 0.953-0.893) and the best accuracy (95.64%, 95% CI 96.76-94.52%), Cohen's kappa coefficient value (0.74, 95% CI 0.79-0.69) and Matthews correlation coefficient value (0.75, 95% CI 0.80-0.70). The top three most influential variables in the DNN importance matrix plot were the requirement of ventilator support at the onset of suspected sepsis, the feeding conditions, and intravascular volume expansion. The model performance was indistinguishable between the training and test sets. CONCLUSIONS: the DNN model was successfully established to predict in-hospital mortality in neonates with clinically suspected sepsis, and the machine learning algorithm is applicable for clinicians to gain insights and have better communication with families in advance.

Development of a real-time quantitative reverse transcriptase PCR assay for detection of the Friend leukemia virus load in murine plasma.

Friend leukemia virus (FLV), a murine retrovirus, has been used as a model for elucidation of human immunodeficiency virus (HIV) immunopathogenesis and evaluation of anti-HIV drug effects for several decades. However, no method for direct detection of the plasma viral load has yet been reported. In this study, a TaqMan real-time quantitative reverse transcriptase PCR (qRT-PCR) assay was established for the rapid detection and quantitation of FLV. Measurement of the absolute FLV load was achieved through synthesis of a standard RNA from within the FLV envelope gene for generation of a standard curve. The assay allows quantitation over a range from 20 to 2 x 10(8) RNA copies per reaction in a two-step real-time quantitative reverse transcriptase PCR protocol. The relationships between the initially injected FLV dose and the plasma FLV load and spleen index were explored. Following this, the in vivo effects of zidovudine, adefovir dipivoxil, and entecavir on mice infected with FLV were evaluated. The results showed that the plasma FLV load was not proportional to the spleen index over the same FLV injection dosage series, although a trend was observed. When evaluated using plasma viral load, high dose (15 mg/(kg d)) adefovir dipivoxil was capable of significant inhibition of FLV replication in mice. The qRT-PCR assay described here allows specific, sensitive and direct detection of FLV and may also provide more precise measurement of FLV load.

Embedded-Based Graphics Processing Unit Cluster Platform for Multiple Sequence Alignments.

High-end graphics processing units (GPUs), such as NVIDIA Tesla/Fermi/Kepler series cards with thousands of cores per chip, are widely applied to high-performance computing fields in a decade. These desktop GPU cards should be installed in personal computers/servers with desktop CPUs, and the cost and power consumption of constructing a GPU cluster platform are very high. In recent years, NVIDIA releases an embedded board, called Jetson Tegra K1 (TK1), which contains 4 ARM Cortex-A15 CPUs and 192 Compute Unified Device Architecture cores (belong to Kepler GPUs). Jetson Tegra K1 has several advantages, such as the low cost, low power consumption, and high applicability, and it has been applied into several specific applications. In our previous work, a bioinformatics platform with a single TK1 (STK platform) was constructed, and this previous work is also used to prove that the Web and mobile services can be implemented in the STK platform with a good cost-performance ratio by comparing a STK platform with the desktop CPU and GPU. In this work, an embedded-based GPU cluster platform will be constructed with multiple TK1s (MTK platform). Complex system installation and setup are necessary procedures at first. Then, 2 job assignment modes are designed for the MTK platform to provide services for users. Finally, ClustalW v2.0.11 and ClustalWtk will be ported to the MTK platform. The experimental results showed that the speedup ratios achieved 5.5 and 4.8 times for ClustalW v2.0.11 and ClustalWtk, respectively, by comparing 6 TK1s with a single TK1. The MTK platform is proven to be useful for multiple sequence alignments.

Modified Glasgow prognostic score is an independent prognostic factor in patients with cervical cancer undergoing chemoradiotherapy.

OBJECTIVES: There is increasing evidence that the presence of an inflammation-based prognostic score (modified Glasgow prognostic score, mGPS) could predict survival in patients with advanced cancer. The aim of this study was to investigate the prognostic value of mGPS in patients with cervical cancer. METHODS: We included 238 consecutive patients with cervical cancer in our study. The albumin and serum C-reactive protein (CRP) were measured before initiation of treatment. The relationships between the mGPS and other clinical parameters including body mass index (BMI), white blood cell count, lymphocyte, platelet, hemoglobin, total bilirubin, aspartate aminotransferase (AST), alanine aminotransferase (ALT) and lactate dehydrogenase (LDH) were analyzed. Overall survival (OS) and progression-free survival (PFS) were calculated. Significant prognostic factors were identified using univariate and multivariate analyses. RESULTS: The 5-year OS rate for all patients was 52.1% and 5-year PFS rate was 42.3%. Patients with mGPS of 0, 1 and 2 were 138, 71, 29, respectively. Higher mGPS was related to more advanced disease, including higher FIGO stage, lymph node metastases and lower lymphocyte counts, BMI and hemoglobin level. Performance status (PS), FIGO stage, lymph nodal status and mGPS were independent prognostic indicators for OS and PFS in the multivariate analysis. CONCLUSIONS: Higher mGPS is associated with advanced cervical cancer. The mGPS is an easily measurable biomarker which can be used in combination with conventional FIGO stage to predict survival in patients with cervical cancer undergoing chemoradiotherapy.