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MicroRNAs play an important role in gene regulation for many biological systems, including nicotine and alcohol addiction. However, the underlying mechanism behind miRNAs and mRNA interaction is not well characterized. Microarrays are commonly used to quantify the expression levels of mRNAs and/or miRNAs simultaneously. In this study, we performed a Bayesian network analysis to identify mRNA and miRNA interactions following perinatal exposure to nicotine and/or alcohol. We utilized three sets of microarray data to predict the regulation relationship between mRNA and miRNAs. Following perinatal alcohol exposure, we identified two miRNAs: miR-542-5p and miR-874-3p, that exhibited a strong mutual influence on several mRNA in gene regulatory pathways, mainly Axon guidance and Dopaminergic synapses. Finally, we confirmed our predicted addiction pathways based on the Bayesian network analysis with the widely used Kyoto Encyclopedia of Genes and Genomes (KEGG)-based database and identified comparable relevant miRNA-mRNA pairs. We believe the Bayesian network can provide insight into the complexity biological process related to addiction and can potentially be applied to other diseases.

Recently, a contactless method for measuring a biological signal using a video camera has garnered attention. Especially, video plethysmography, a technique for obtaining a pulse wave from a video, is useful for managing the health of people on a daily basis. However, any body movement of a person subjected to the measurement leads to the generation of irregular noise in video plethysmography and reduces the accuracy of the recorded biological information, e.g., heart rate, during the measurement. Blind source separation is a popular technique for eliminating noise from the results of video plethysmography comprising different multiple-color channels. However, it is difficult to apply this technique to a single-color video such as a near-infrared video. Herein, a new method that combines singular spectrum analysis with the circular autocorrelation function is introduced to eliminate irregular noise in single-color video plethysmography. Applying the proposed method on videos collected from 39 individuals improved the estimation accuracy of instantaneous heart rate by approximately 44% over a conventional method using a linear filter. Furthermore, the proposed method also enabled more precise estimations of the heart rate than that achieved using multi-color video plethysmography.

Maternal smoking during pregnancy is associated with developmental, cognitive, and behavioral disorders, including low birth weight, attention deficit hyperactivity disorder, learning disabilities, and drug abuse later in life. Nicotine activates the reward-driven behavior characteristic of drug abuse. Dopaminergic (DA) neurons originating from the ventral tegmental area (VTA) of the brain, which are stimulated by nicotine and other stimuli, are widely implicated in the natural reward pathway that is known to contribute to addiction. In recent years, microRNAs have been implicated in disrupting regulatory mechanisms due to their capability of targeting multiple genes and thus inducing downstream effects along many pathways. In order to investigate miRNA expression of dopaminergic neurons from the VTA, we employed patch clamping to identify and harvest both DA and non-DA neurons from rats perinatally exposed to nicotine for use in single-cell RT-qPCR. Our data indicated that miR-140-5p and miR-140-3p were upregulated in DA neurons; while miR-140-3p and miR-212 were differentially expressed in non-DA neurons. A functional enrichment analysis was also performed on our miRNA-gene prediction network and predicted that our miRNAs target genes involved in drug response and neuroplasticity.

Tumor angiogenesis is a promising target for cancer treatment, because severing the supply of oxygen and nutrients to tumors halts tumor growth. Unfortunately, many anticancer drugs, including angiogenesis inhibitors, fail in clinical trials, despite showing high efficiency during in vitro and in vivo experiments. This inconsistency from in vitro and in vivo experiments to clinical trials represents a major obstacle in drug development and cancer treatment. Therefore, we set out to demonstrate how our rapid, stable, easy-to-use three-dimensional (3-D) in vitro angiogenesis model can be used to investigate tumor formation and implement drug screening. In this study, we utilized a 3-D in vitro angiogenesis model, based on gelatin methacrylate (GelMA) hydrogel microwells, to mimic the native microenvironment of tumor angiogenesis. Using this model, we were able to quantify the immigration of endothelial cells into a cancer spheroid during the angiogenic process. Next, we tested the anti-angiogenic effect of the angiogenesis inhibitor, TNP-470, on the cancer spheroids in the model. Based on our results, we believe that this novel in vitro system can be widely used for the high-throughput screening of other anti-angiogenic drugs, and could contribute to the development of personalized medicine in the future.

Studies involving turbulent flow have been carried out in many parts of the cardiovascular system, and it has been widely reported that turbulence related to stenosis (narrowing) of arteries creates audible sounds, which may be analyzed to yield information about the nature and severity of the blockage. Results so far indicate that the high frequency content of the sounds generally increases with the degree of stenosis. In this paper, we designed and built an MEMs microphone array and a signal acquisition board to improve the detection of coronary occlusions using an approach based on the recording and analysis of isolated diastolic heart sounds associated with turbulent blood flow in occluded coronary arteries. The nonlinear dynamic analysis method based on approximate entropy has been proposed for the analysis of diastolic heart sounds from patients with single coronary occlusions, before and after stent placement procedures. The nonlinear dynamic analysis (approximate entropy) measures of the diastolic heart sounds recorded from eight patients with single coronary occlusions and two normal subjects were estimated. In addition, a spectral analysis based on the fast Fourier transform was used to estimate the energy content of the recorded signals. Results suggest the presence of high nonlinear (approximate entropy) values of diastolic heart sounds associated with coronary artery disease ([Formula: see text]) as well as significant differences in the energy content of the heart sound signals above and below 150 Hz ([Formula: see text]).

Angiogenesis is an indispensable mechanism in physiological and pathological development of tumors that requires an adequate blood supply. Therefore, understanding the angiogenesis mechanism of tumors has become an important research area to develop reliable and effective therapies for the treatment of tumors. Although several in vivo and in vitro models were developed and used to study the underlying mechanism of angiogenesis, they showed limited success. Therefore, there is an urgent need to build a stable and cost-effective three-dimensional (3D) in vitro angiogenesis model to investigate the tumor formation. In this study, we designed a 3D in vitro angiogenesis model based on gelatin methacrylate (GelMA) hydrogel microwells to mimic an in vivo-like microenvironment for co-cultured glioblastoma and endothelial cells. Our results confirmed the in vitro formation of microtubules during the angiogenic process. We believe that our cost-effective platform can be used for the high-throughput screening of anti-angiogenesis drugs and even for the development of better treatment strategies.

Glioblastoma (GBM) is the most aggressive brain tumor, with 12-15 months median survival time despite current treatment efforts. Among the alternative treatment approaches that have gained acceptance over the last decade is the use of replication-competent oncolytic adenoviruses, which are promising due to their relatively low toxicity and tumor-specific targeting. Three-dimensional (3D) tumor models can mimic the physiological microenvironment of GBM tumors and provide valuable information about the interaction between tumor cells and adenoviruses. Therefore, robust in vitro 3D tumor models are critical to investigate the mechanisms underlying tumor progression and explore the cytotoxicity effect of the adenovirus on tumor cells. In this study, we used a hydrogel microwell platform to generate in vitro 3D GBM spheroids and studied their interactions with the Delta-24-RGD adenovirus. The results showed that the cultured 3D spheroids were successfully infected by the Delta-24-RGD. A significant cell lysis was observed. Cell viability was decreased approximately 37%, 54% and 65% with 10, 50, and 100 MOIs, respectively. The infection of the Delta-24-RGD was found more effective on 3D spheroids when compared to 2D monolayer cell culture. These results implicate that our hydrogel microwell platform could provide a promising 3D model to investigate the oncolytic potential of the viruses in vitro.

Glioblastoma (GBM) is the most common form of primary brain tumor with a high infiltrative capacity, increased vascularity, and largely elusive tumor progression mechanism. The current GBM treatment methods do not increase the patient survival rate and studies using two-dimensional (2D) cell cultures and in vivo animal models to investigate GBM behavior and mechanism have limitations. Therefore, there is an increasing need for in vitro three-dimensional (3D) models that closely mimic in vivo microenvironment of the GBM tumors to understand the underlying mechanisms of the tumor progression. In this study we propose to use a 3D in vitro model to overcome these limitations, using poly (ethylene glycol) dimethyl acrylate (PEGDA) hydrogel-based microwells and co-culture GBM (U87) cells and endothelial cells (HUVEC) in the 3D microwells to provide a 3D in vitro simulation of the tumor microenvironment. Furthermore, we investigated the gene expression differences of co-cultures by quantitative real-time PCR. Our results suggested that the relative expression profiles of tumor angiogenesis markers, PECAM1/CD31, and VEGFR2, in co-cultures are consistent with in vivo GBM studies. Furthermore, we suggest that our microwell platform could provide robust and useful 3D co-culture models for high-throughput drug screening and treatment of the GBM.

Glioblastoma multiforme (GBM) is the most common and malignant primary brain tumor in adults because of its highly invasive behavior. The existing treatment for GBM, which involves a combination of resection, chemotherapy, and radiotherapy, has a very limited success rate with a median survival rate of <1 year. This is mainly because of the failure of early detection and effective treatment. We designed a novel 3-D GBM cell culture model based on microwells that could mimic in vitro environment and help to bypass the lack of suitable animal models for preclinical toxicity tests. Microwells were fabricated from simple and inexpensive polyethylene glycol material for the control of in vitro 3-D culture. We applied the 3-D micropatterning system to GBM (U-87) cells using the photolithography technique to control the cell spheroids' shape, size, and thickness. Our preliminary results suggested that uniform GBM spheroids can be formed in 3-D, and the size of these GBM spheroids depends on the size of microwells. The viability of the spheroids generated in this manner was quantitatively evaluated using live/dead assay and shown to improve over 21 days. We believe that in vitro 3-D cell culture model could help to reduce the time of the preclinical brain tumor growth studies. The proposed novel platform could be useful and cost-effective for high-throughput screening of cancer drugs and assessment of treatment responses.

The aim of this project is to improve the detection of coronary occlusions using an approach based on the recording and analysis of isolated diastolic heart sounds associated with turbulent blood flow in occluded coronary arteries. The nonlinear dynamic analysis method based on approximate entropy has been proposed for the analysis of diastolic heart sounds. A commercially available electronic stethoscope was used to record the diastolic heart sounds from patients diagnosed with or without coronary artery disease (CAD) based on their coronary angiography examination. The nonlinear dynamical analysis (approximate entropy) measures of the diastolic heart sounds recorded from 30 patients with coronary occlusions and ten normal subjects were estimated. Results suggest the presence of the high nonlinear (approximate entropy) values of diastolic heart sounds associated with CAD (p < 0.05). This approach led to a sensitivity of 77%, a specificity of 80%, and an overall accuracy of 78%. As a summary, 23 out of 30 abnormal patients and eight out of ten normal patients were correctly detected.

In this study, the effects of vasodilator drugs on the turbulent sound generation mechanisms during femoral artery stenoses were investigated using the wavelet analysis of the turbulent sounds to characterize these sounds before and after the injection of vasodilator drugs. Results showed that the injection of drugs drastically improved the diagnostic performance of the turbulent sounds in detecting stenoses by increasing the signal-to-noise ratio of the sounds. Results also suggested that the sound above 250 Hz was drastically increased in response to the injection of the vasodilator drug for the partially occluded cases. The turbulence sounds caused by partially occluded femoral arteries are directly related to the slope of baseline of blood flow and to the velocity of the flow. For the 0% occlusion case, initially, sounds were produced with the injection of drugs. However, the sounds totally disappeared when the slope of average blood flow was zero. These results show that the diagnostic performance of diastolic heart sounds associated with occluded arteries can be improved by using vasodilator drugs, which increase the acoustic energy in the first and second wavelet bandwidths due to the turbulence. The short-term Fourier transform (STFT) method was also applied to the same data base. Results using the STFT showed somewhat similar power distributions in that the acoustical power above 250 Hz was increased after the injection of drugs for the occluded cases. However, the WT method provided better time-frequency resolution than the STFT method, showing details of the change in the frequency characteristics with respect to time after the injection of drug.

Previous studies have indicated heart sounds may contain information useful in the detection of occluded coronary arteries. During diastole, coronary blood flow is maximum, and the sounds associated with turbulent blood flow through partially occluded coronary arteries should be detectable. In order to detect such sounds, recordings of diastolic heart sound segments were analyzed by using four signal processing techniques; the Fast Fourier Transform (FFT), the Autoregressive (AR), the Autoregressive Moving Average (ARMA), and the Minimum-Norm (Eigen-vector) methods. To further enhance the diastolic heart sounds and reduce background noise, an Adaptive filter was used as a preprocessor. The power ratios of the FFT method and the poles of the AR, ARMA, and Eigen-vector methods were used to diagnose patients as diseased or normal arteries using a blind protocol without prior knowledge of the actual disease states of the patients to guard against human bias. Results showed that normal and abnormal records were correctly distinguished in 56 of 80 cases using the Fast Fourier Transform (FFT), in 63 of 80 cases using the AR, in 62 of 80 cases using the ARMA method, and in 67 of 80 cases using the Eigenvector method. Among all four methods, the Eigenvector methods showed the best diagnostic performance when compared with the FFT, AR, and ARMA methods. These results confirm that high frequency acoustic energy between 300 and 800 Hz is associated with coronary stenosis.

Previous studies have indicated that coronary stenoses produce sounds due to the turbulent blood flow in these vessels [1]-[10]. Measurement of these signals forms the basis of our noninvasive approach to the detection of coronary artery disease. It is during diastole that coronary blood flow is maximum and the sounds associated with turbulent blood flow through partially occluded coronary arteries would be loudest [1]-[10]. Isolated diastolic heart sounds taken from recordings made at the patient's bedside were modeled using the autoregressive (AR) and autoregressive moving average (ARMA) methods [4], [7] after adaptive line enhancement (ALE). Decisions were made in a blind fashion without prior knowledge of whether a given recording was made before or after angioplasty. Resulting model frequency spectra showed greater high-frequency components (between 400 and 800 Hz) in preangioplasty patients, and a consistent shift in amplitude of the second pole pairs of the AR and ARMA methods with surgery. Blind assessment, based on frequency spectra and poles, correctly classified the diastolic recordings in 18 of 20 cases. These results provide strong evidence supporting our hypothesis that coronary stenoses produce detectable sounds during diastole [1]-[10].