ChatGPT-A promising generative AI tool and its implications for cancer care.

PLAIN LANGUAGE SUMMARY: Since its launch, ChatGPT has taken the internet by storm and has the potential to be used broadly in the health care system, particularly in a setting such as medical oncology. ChatGPT is well suited to review and extract key content from records of patients with cancer, interpret next-generation sequencing reports, and offer a list of potential clinical trial options.

Putting the "mi" in omics: discovering miRNA biomarkers for pediatric precision care.

In the past decade, growing interest in micro-ribonucleic acids (miRNAs) has catapulted these small, non-coding nucleic acids to the forefront of biomarker research. Advances in scientific knowledge have made it clear that miRNAs play a vital role in regulating cellular physiology throughout the human body. Perturbations in miRNA signaling have also been described in a variety of pediatric conditions-from cancer, to renal failure, to traumatic brain injury. Likewise, the number of studies across pediatric disciplines that pair patient miRNA-omics with longitudinal clinical data are growing. Analyses of these voluminous, multivariate data sets require understanding of pediatric phenotypic data, data science, and genomics. Use of machine learning techniques to aid in biomarker detection have helped decipher background noise from biologically meaningful changes in the data. Further, emerging research suggests that miRNAs may have potential as therapeutic targets for pediatric precision care. Here, we review current miRNA biomarkers of pediatric diseases and studies that have combined machine learning techniques, miRNA-omics, and patient health data to identify novel biomarkers and potential therapeutics for pediatric diseases. IMPACT: In the following review article, we summarized how recent developments in microRNA research may be coupled with machine learning techniques to advance pediatric precision care.

Adoption of Ultrasound-guided Polymer Nanocarriers in Tumor Chemoradiotherapy and Oxidation Treatment.

The research was aimed at discussing the effectiveness of ultrasound-guided polymer nanocarriers in the clinical treatment of tumors by chemoradiotherapy and oxidation treatment. Twenty female Balb/cAnN (BALB/C) mice were selected as the research objects in the experiment. These mice were set up as tumor-bearing mice, and then ultrasound-guided polymers with different doses, including polyethylene glycol-poly 2-bromoethyl methacrylate (PEG-PBEMA) (Micelle group), free small molecules called l-ascorbyl palmitate (PA) (PA group), PA-micelle micellar particles (PA-Micelle group) prepared in the research, and phosphate buffer solution (PBS) (PBS group) were adopted. Besides, the growth of mice was recorded and compared after each operation. Meanwhile, different concentrations of PA-Micelle micellar particles and free small molecules of PA were added to the breast cancer cells of mice, and the concentration changes of glutathione (GSH) were detected to test the oxidation treatment ability of this method. According to the results of the experiment, the tumor volume of mice in the PA-Micelle group prepared in the research was the smallest followed by the PA group, and the tumor volume of mice in the Micelle group was the third smallest. The mice in the PBS group had the largest tumors among mice in all four groups. In oxidation treatment, the GSH concentration of mice in the PA-Micelle group was the lowest, while the GSH concentration of mice in the PA group was almost unchanged. The results of this experiment proved that the therapeutic effect of polymer nanocarriers in tumor chemotherapy and oxidation treatment was more significant than in traditional drug treatment.

Saliva microRNA Profile in Children with and without Severe SARS-CoV-2 Infection.

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) may impair immune modulating host microRNAs, causing severe disease. Our objectives were to determine the salivary miRNA profile in children with SARS-CoV-2 infection at presentation and compare the expression in those with and without severe outcomes. Children <18 years with SARS-CoV-2 infection evaluated at two hospitals between March 2021 and February 2022 were prospectively enrolled. Severe outcomes included respiratory failure, shock or death. Saliva microRNAs were quantified with RNA sequencing. Data on 197 infected children (severe = 45) were analyzed. Of the known human miRNAs, 1606 (60%) were measured and compared across saliva samples. There were 43 miRNAs with ≥2-fold difference between severe and non-severe cases (adjusted p-value < 0.05). The majority (31/43) were downregulated in severe cases. The largest between-group differences involved miR-4495, miR-296-5p, miR-548ao-3p and miR-1273c. These microRNAs displayed enrichment for 32 gene ontology pathways including viral processing and transforming growth factor beta and Fc-gamma receptor signaling. In conclusion, salivary miRNA levels are perturbed in children with severe COVID-19, with the majority of miRNAs being down regulated. Further studies are required to validate and determine the utility of salivary miRNAs as biomarkers of severe COVID-19.

Hypoxia-Inducible Factor 1 alpha (HIF-1α)/Vascular Endothelial Growth Factor (VEGF) Pathway Participates in Angiogenesis of Myocardial Infarction in Muscone-Treated Mice: Preliminary Study.

BACKGROUND Angiogenesis plays a crucial role in myocardial infarction (MI) treatment by ameliorating myocardial remodeling, thus improving cardiac function and preventing heart failure. Muscone has been reported to have beneficial effects on cardiac remodeling in MI mice. However, the effects of muscone on angiogenesis in MI mice and its underlying mechanisms remain unknown. MATERIAL AND METHODS Mice were randomly divided into sham, MI, and MI+muscone groups. The MI mouse model was established by ligating the left anterior descending coronary artery. Mice in the sham group received the same procedure except for ligation. Mice were administered muscone or an equivalent volume of saline for 4 consecutive weeks. Cardiac function was evaluated by echocardiograph after MI for 2 and 4 weeks. Four weeks later, all mice were sacrificed and Masson's trichrome staining was used to assess myocardial fibrosis. Isolectin B4 staining was applied to evaluate the angiogenesis in mouse hearts. Immunohistochemistry, Western blot analysis, and quantitative real-time polymerase chain reaction (qPCR) were performed to analyze expression levels of HIF-1a and its downstream genes. RESULTS Compared with the MI group, muscone treatment significantly improved cardiac function and reduced myocardial fibrosis. Moreover, muscone enhanced angiogenesis in the peri-infarct region and p-VEGFR2 expression in the vascular endothelial cells. Western blot analysis and qPCR showed that muscone upregulated expression levels of HIF-1a and VEGFA. CONCLUSIONS Muscone improved cardiac function in MI mice through augmented angiogenesis. The potential mechanism of muscone treatment in regulating angiogenesis of MI mice was upregulating expression levels of HIF-1α and VEGFA.

Leveraging auxiliary measures: a deep multi-task neural network for predictive modeling in clinical research.

BACKGROUND: Accurate predictive modeling in clinical research enables effective early intervention that patients are most likely to benefit from. However, due to the complex biological nature of disease progression, capturing the highly non-linear information from low-level input features is quite challenging. This requires predictive models with high-capacity. In practice, clinical datasets are often of limited size, bringing danger of overfitting for high-capacity models. To address these two challenges, we propose a deep multi-task neural network for predictive modeling. METHODS: The proposed network leverages clinical measures as auxiliary targets that are related to the primary target. The predictions for the primary and auxiliary targets are made simultaneously by the neural network. Network structure is specifically designed to capture the clinical relevance by learning a shared feature representation between the primary and auxiliary targets. We apply the proposed model in a hypertension dataset and a breast cancer dataset, where the primary tasks are to predict the left ventricular mass indexed to body surface area and the time of recurrence of breast cancer. Moreover, we analyze the weights of the proposed neural network to rank input features for model interpretability. RESULTS: The experimental results indicate that the proposed model outperforms other different models, achieving the best predictive accuracy (mean squared error 199.76 for hypertension data, 860.62 for Wisconsin prognostic breast cancer data) with the ability to rank features according to their contributions to the targets. The ranking is supported by previous related research. CONCLUSION: We propose a novel effective method for clinical predictive modeling by combing the deep neural network and multi-task learning. By leveraging auxiliary measures clinically related to the primary target, our method improves the predictive accuracy. Based on featue ranking, our model is interpreted and shows consistency with previous studies on cardiovascular diseases and cancers.

Novel trigenic CACNA1C/DES/MYPN mutations in a family of hypertrophic cardiomyopathy with early repolarization and short QT syndrome.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) patients with early repolarization (ER) pattern are at higher risk of ventricular arrhythmia, yet the genetic background of this situation has not been well investigated. Here we report novel trigenic mutations detected in a Chinese family of obstructive HCM with ER and short QT syndrome (SQTS). METHODS: Proband and family members underwent detailed medical assessments. DNAs were extracted from peripheral blood leukocytes for genetic screening with next generation method. The functional characterization of the mutation was conducted in TSA201 cells with patch-clamp experiment. RESULTS: The proband was a 52-year-old male who had a ER pattern ECG in inferioral-lateral leads with atrioventricular block and QTc of 356 ms. He also suffered from severe left ventricular hypertrophy and dysfunction. Targeted sequencing revealed trigenic mutations: c.700G>A/p.E234K in DES, c.2966G>A/p.R989H in MYPN, and c.5918G>C/p.R1973P in CACNA1C. All mutations were also detected in his daughter with ER and mild myocardium hypertrophy. The CACNA1C-R1973P mutation caused significant reduction (68.4%) of ICa compared to CACNA1C-WT (n = 14 and 14, P < 0.05). The computer modeling showed that all 3 mutations were highly disease-causing. The proband received the CRT-D (cardiac resynchronizing therapy) implantation, which lowered the left ventricular outflow tract gradient (LVOTG, 124 mmHg pre vs. 27 mmHg post) and restored the LV function (LVEF 40% pre vs. 63% post). CONCLUSIONS: The study reveals a novel CACNA1C mutation underlying the unique ER pattern ECGs with SQTS. It also shows the rare trigenic mutations are the pathogenic substrates for the complicated clinical manifestation in HCM patients.

Cse4 is part of an octameric nucleosome in budding yeast.

The budding yeast CenH3 histone variant Cse4 localizes to centromeric nucleosomes and is required for kinetochore assembly and chromosome segregation. The exact composition of centromeric Cse4-containing nucleosomes is a subject of debate. Using unbiased biochemical, cell-biological, and genetic approaches, we have tested the composition of Cse4-containing nucleosomes. Using micrococcal nuclease-treated chromatin, we find that Cse4 is associated with the histones H2A, H2B, and H4, but not H3 or the nonhistone protein Scm3. Overexpression of Cse4 rescues the lethality of a scm3 deletion, indicating that Scm3 is not essential for the formation of functional centromeric chromatin. We also find that octameric Cse4 nucleosomes can be reconstituted in vitro. Furthermore, Cse4-Cse4 dimerization occurs in vivo at the centromeric nucleosome, and this requires the predicted Cse4-Cse4 dimerization interface. Taken together, our experimental evidence supports the model that the Cse4 nucleosome is an octamer, containing two copies each of Cse4, H2A, H2B, and H4.

TEAK: topology enrichment analysis framework for detecting activated biological subpathways.

To mine gene expression data sets effectively, analysis frameworks need to incorporate methods that identify intergenic relationships within enriched biologically relevant subpathways. For this purpose, we developed the Topology Enrichment Analysis frameworK (TEAK). TEAK employs a novel in-house algorithm and a tailor-made Clique Percolation Method to extract linear and nonlinear KEGG subpathways, respectively. TEAK scores subpathways using the Bayesian Information Criterion for context specific data and the Kullback-Leibler divergence for case-control data. In this article, we utilized TEAK with experimental studies to analyze microarray data sets profiling stress responses in the model eukaryote Saccharomyces cerevisiae. Using a public microarray data set, we identified via TEAK linear sphingolipid metabolic subpathways activated during the yeast response to nitrogen stress, and phenotypic analyses of the corresponding deletion strains indicated previously unreported fitness defects for the dpl1Δ and lag1Δ mutants under conditions of nitrogen limitation. In addition, we studied the yeast filamentous response to nitrogen stress by profiling changes in transcript levels upon deletion of two key filamentous growth transcription factors, FLO8 and MSS11. Via TEAK we identified a nonlinear glycerophospholipid metabolism subpathway involving the SLC1 gene, which we found via mutational analysis to be required for yeast filamentous growth.

Mechanical evolution of metastatic cancer cells in three-dimensional microenvironment.

Cellular biomechanics plays critical roles in cancer metastasis and tumor progression. Existing studies on cancer cell biomechanics are mostly conducted in flat 2D conditions, where cells' behavior can differ considerably from those in 3D physiological environments. Despite great advances in developing 3D in vitro models, probing cellular elasticity in 3D conditions remains a major challenge for existing technologies. In this work, we utilize optical Brillouin microscopy to longitudinally acquire mechanical images of growing cancerous spheroids over the period of eight days. The dense mechanical mapping from Brillouin microscopy enables us to extract spatially resolved and temporally evolving mechanical features that were previously inaccessible. Using an established machine learning algorithm, we demonstrate that incorporating these extracted mechanical features significantly improves the classification accuracy of cancer cells, from 74% to 95%. Building on this finding, we have developed a deep learning pipeline capable of accurately differentiating cancerous spheroids from normal ones solely using Brillouin images, suggesting the mechanical features of cancer cells could potentially serve as a new biomarker in cancer classification and detection.

Optimal structural inference of signaling pathways from unordered and overlapping gene sets.

MOTIVATION: A plethora of bioinformatics analysis has led to the discovery of numerous gene sets, which can be interpreted as discrete measurements emitted from latent signaling pathways. Their potential to infer signaling pathway structures, however, has not been sufficiently exploited. Existing methods accommodating discrete data do not explicitly consider signal cascading mechanisms that characterize a signaling pathway. Novel computational methods are thus needed to fully utilize gene sets and broaden the scope from focusing only on pairwise interactions to the more general cascading events in the inference of signaling pathway structures. RESULTS: We propose a gene set based simulated annealing (SA) algorithm for the reconstruction of signaling pathway structures. A signaling pathway structure is a directed graph containing up to a few hundred nodes and many overlapping signal cascades, where each cascade represents a chain of molecular interactions from the cell surface to the nucleus. Gene sets in our context refer to discrete sets of genes participating in signal cascades, the basic building blocks of a signaling pathway, with no prior information about gene orderings in the cascades. From a compendium of gene sets related to a pathway, SA aims to search for signal cascades that characterize the optimal signaling pathway structure. In the search process, the extent of overlap among signal cascades is used to measure the optimality of a structure. Throughout, we treat gene sets as random samples from a first-order Markov chain model. We evaluated the performance of SA in three case studies. In the first study conducted on 83 KEGG pathways, SA demonstrated a significantly better performance than Bayesian network methods. Since both SA and Bayesian network methods accommodate discrete data, use a 'search and score' network learning strategy and output a directed network, they can be compared in terms of performance and computational time. In the second study, we compared SA and Bayesian network methods using four benchmark datasets from DREAM. In our final study, we showcased two context-specific signaling pathways activated in breast cancer. AVAILABILITY: Source codes are available from http://dl.dropbox.com/u/16000775/sa_sc.zip.

Isoform-level microRNA-155 target prediction using RNA-seq.

Computational prediction of microRNA targets remains a challenging problem. The existing rule-based, data-driven and expression profiling approaches to target prediction are mostly approached from the gene-level. The increasing availability of RNA-seq data provides a new perspective for microRNA target prediction on the isoform-level. We hypothesize that the splicing isoform is the ultimate effector in microRNA targeting and that the proposed isoform-level approach is capable of predicting non-dominant isoform targets as well as their targeting regions that are otherwise invisible to many existing approaches. To test the hypothesis, we used an iterative expectation maximization (EM) algorithm to quantify transcriptomes at the isoform-level. The performance of the EM algorithm in transcriptome quantification was examined in simulation studies using FluxSimulator. We used joint evidence from isoform-level down-regulation and seed enrichment to predict microRNA-155 targets. We validated our computational approach using results from 149 in-house performed in vitro 3'-UTR assays. We also augmented the splicing database using exon-exon junction evidence, and applied the EM algorithm to predict and quantify 1572 cell line specific novel isoforms. Combined with seed enrichment analysis, we predicted 51 novel microRNA-155 isoform targets. Our work is among the first computational studies advocating the isoform-level microRNA target prediction.

Adaptive coherent weighted averaging algorithm for enhancement of photoacoustic tomography images of brain.

One common method to improve the low signal-to-noise ratio of the photoacoustic (PA) signal generated from weak absorbers or absorbers located in deep tissue is to acquire signal multiple times from the same region and perform averaging. However, pulse-to-pulse laser fluctuations together with differences in the beam profile of the pulses create undeterministic multiple scattering processes in the tissue. This phenomenon consequently induces a spatiotemporal displacement in the PA signal samples which in turn deteriorates the effectiveness of signal averaging. Here, we present an adaptive coherent weighted averaging algorithm to adjust the locations and values of PA signal samples for more efficient signal averaging. The proposed method is evaluated in a linear array-based PA imaging setup of ex vivo sheep brain.

An uncertainty-aware deep learning architecture with outlier mitigation for prostate gland segmentation in radiotherapy treatment planning.

PURPOSE: Task automation is essential for efficient and consistent image segmentation in radiation oncology. We report on a deep learning architecture, comprising a U-Net and a variational autoencoder (VAE) for automatic contouring of the prostate gland incorporating interobserver variation for radiotherapy treatment planning. The U-Net/VAE generates an ensemble set of segmentations for each image CT slice. A novel outlier mitigation (OM) technique was implemented to enhance the model segmentation accuracy. METHODS: The primary source dataset (source_prim) consisted of 19 200 CT slices (from 300 patient planning CT image datasets) with manually contoured prostate glands. A smaller secondary source dataset (source_sec) comprised 640 CT slices (from 10 patient CT datasets), where prostate glands were segmented by 5 independent physicians on each dataset to account for interobserver variability. Data augmentation via random rotation (<5 degrees), cropping, and horizontal flipping was applied to each dataset to increase sample size by a factor of 100. A probabilistic hierarchical U-Net with VAE was implemented and pretrained using the augmented source_prim dataset for 30 epochs. Model parameters of the U-Net/VAE were fine-tuned using the augmented source_sec dataset for 100 epochs. After the first round of training, outlier contours in the training dataset were automatically detected and replaced by the most accurate contours (based on Dice similarity coefficient, DSC) generated by the model. The U-Net/OM-VAE was retrained using the revised training dataset. Metrics for comparison included DSC, Hausdorff distance (HD, mm), normalized cross-correlation (NCC) coefficient, and center-of-mass (COM) distance (mm). RESULTS: Results for U-Net/OM-VAE with outliers replaced in the training dataset versus U-Net/VAE without OM were as follows: DSC = 0.82 ± 0.01 versus 0.80 ± 0.02 (p = 0.019), HD = 9.18 ± 1.22 versus 10.18 ± 1.35 mm (p = 0.043), NCC = 0.59 ± 0.07 versus 0.62 ± 0.06, and COM = 3.36 ± 0.81 versus 4.77 ± 0.96 mm over the average of 15 contours. For the average of 15 highest accuracy contours, values were as follows: DSC = 0.90 ± 0.02 versus 0.85 ± 0.02, HD = 5.47 ± 0.02 versus 7.54 ± 1.36 mm, and COM = 1.03 ± 0.58 versus 1.46 ± 0.68 mm (p < 0.03 for all metrics). Results for the U-Net/OM-VAE with outliers removed were as follows: DSC = 0.78 ± 0.01, HD = 10.65 ± 1.95 mm, NCC = 0.46 ± 0.10, COM = 4.17 ± 0.79 mm for the average of 15 contours, and DSC = 0.88 ± 0.02, HD = 7.00 ± 1.17 mm, COM = 1.58 ± 0.63 mm for the average of 15 highest accuracy contours. All metrics for U-Net/VAE trained on the source_prim and source_sec datasets via pretraining, followed by fine-tuning, show statistically significant improvement over that trained on the source_sec dataset only. Finally, all metrics for U-Net/VAE with or without OM showed statistically significant improvement over those for the standard U-Net. CONCLUSIONS: A VAE combined with a hierarchical U-Net and an OM strategy (U-Net/OM-VAE) demonstrates promise toward capturing interobserver variability and produces accurate prostate auto-contours for radiotherapy planning. The availability of multiple contours for each CT slice enables clinicians to determine trade-offs in selecting the "best fitting" contour on each CT slice. Mitigation of outlier contours in the training dataset improves prediction accuracy, but one must be wary of reduction in variability in the training dataset.

A new architecture combining convolutional and transformer-based networks for automatic 3D multi-organ segmentation on CT images.

PURPOSE: Deep learning-based networks have become increasingly popular in the field of medical image segmentation. The purpose of this research was to develop and optimize a new architecture for automatic segmentation of the prostate gland and normal organs in the pelvic, thoracic, and upper gastro-intestinal (GI) regions. METHODS: We developed an architecture which combines a shifted-window (Swin) transformer with a convolutional U-Net. The network includes a parallel encoder, a cross-fusion block, and a CNN-based decoder to extract local and global information and merge related features on the same scale. A skip connection is applied between the cross-fusion block and decoder to integrate low-level semantic features. Attention gates (AGs) are integrated within the CNN to suppress features in image background regions. Our network is termed "SwinAttUNet." We optimized the architecture for automatic image segmentation. Training datasets consisted of planning-CT datasets from 300 prostate cancer patients from an institutional database and 100 CT datasets from a publicly available dataset (CT-ORG). Images were linearly interpolated and resampled to a spatial resolution of (1.0 × 1.0× 1.5) mm3 . A volume patch (192 × 192 × 96) was used for training and inference, and the dataset was split into training (75%), validation (10%), and test (15%) cohorts. Data augmentation transforms were applied consisting of random flip, rotation, and intensity scaling. The loss function comprised Dice and cross-entropy equally weighted and summed. We evaluated Dice coefficients (DSC), 95th percentile Hausdorff Distances (HD95), and Average Surface Distances (ASD) between results of our network and ground truth data. RESULTS: SwinAttUNet, DSC values were 86.54 ± 1.21, 94.15 ± 1.17, and 87.15 ± 1.68% and HD95 values were 5.06 ± 1.42, 3.16 ± 0.93, and 5.54 ± 1.63 mm for the prostate, bladder, and rectum, respectively. Respective ASD values were 1.45 ± 0.57, 0.82 ± 0.12, and 1.42 ± 0.38 mm. For the lung, liver, kidneys and pelvic bones, respective DSC values were: 97.90 ± 0.80, 96.16 ± 0.76, 93.74 ± 2.25, and 89.31 ± 3.87%. Respective HD95 values were: 5.13 ± 4.11, 2.73 ± 1.19, 2.29 ± 1.47, and 5.31 ± 1.25 mm. Respective ASD values were: 1.88 ± 1.45, 1.78 ± 1.21, 0.71 ± 0.43, and 1.21 ± 1.11 mm. Our network outperformed several existing deep learning approaches using only attention-based convolutional or Transformer-based feature strategies, as detailed in the results section. CONCLUSIONS: We have demonstrated that our new architecture combining Transformer- and convolution-based features is able to better learn the local and global context for automatic segmentation of multi-organ, CT-based anatomy.

Effect of ultrasound-guided lumbar square muscle block on stress response in patients undergoing radical gastric cancer surgery.

BACKGROUND: Radical surgery is a common treatment for patients with gastric cancer; however, it can lead to postoperative complications and intestinal barrier dysfunction. Ultrasound-guided quadratus lumborum block is often used for postoperative analgesia, but its effects on stress response and intestinal barrier function are not well understood. AIM: To investigate the effects of an ultrasound-guided quadratus lumborum block on stress response and intestinal barrier function in patients undergoing radical surgery for gastric cancer. METHODS: A total of 100 patients undergoing radical surgery for gastric cancer were randomly categorized into observation and control groups. Plasma adrenaline and cortisol levels, intestinal mucosal barrier indexes, and complication rates were compared between the two groups before, during, and 1 day after surgery. RESULTS: The observation group had significantly lower plasma adrenaline and cortisol levels during surgery and at 1 day postoperatively than that of the control group (P < 0.05). Additionally, intestinal barrier indexes (endotoxin and D-dimer) at 1 day postoperatively were significantly lower in the observation group than in the control group (P < 0.05). CONCLUSION: Ultrasound-guided quadratus lumborum block could reduce stress response, protect intestinal barrier function, and decrease the incidence of complications in patients undergoing radical surgery for gastric cancer. This technique has the potential for clinical applications.

Transcriptome and targetome analysis in MIR155 expressing cells using RNA-seq.

Previous studies have demonstrated the utility of microarray expression analysis to identify potential microRNA targets. Nevertheless, technical limitations intrinsic to this platform constrain its ability to fully exploit the potential of assessing transcript level changes to explore microRNA targetomes. High-throughput multiplexed Illumina-based next-generation sequencing (NGS) provides a digital readout of absolute transcript levels and imparts a higher level of accuracy and dynamic range than microarray platforms. We used Illumina NGS to analyze transcriptome changes induced by the human microRNA MIR155. This analysis resulted in a larger inferred targetome than similar studies carried out using microarray platforms. A comparison with 3' UTR reporter data demonstrated general concordance between NGS and corresponding 3' UTR reporter results. Nonharmonious results were investigated more deeply using transcript structure information assembled from the NGS data. This analysis revealed that transcript structure plays a substantial role in mitigated targeting and in frank targeting failures. With its high level of accuracy, its broad dynamic range, its utility in assessing transcript structure, and its capacity to accurately interrogate global direct and indirect transcriptome changes, NGS is a useful tool for investigating the biology and mechanisms of action of microRNAs.

Analysis of the Application Value of Echocardiography Combined with CK-MB, Alb, and CysC in the Prognosis Assessment of Patients with Chronic HF.

In order to evaluate the diagnostic and prognostic value of echocardiography combined with serum creatine kinase-MB (CK-MB), albumin (Alb), and cystatin C (CysC) in patients with chronic heart failure (HF), 93 patients diagnosed with chronic HF in our hospital from March 2019 to January 2020 are retrospectively analyzed and included in the HF group. Another 100 healthy subjects who come to our hospital for general physical examination are selected as the control group. Echocardiography is used to detect the cardiac parameters of each group. The experimental results show that echocardiography parameters combined with CK-MB, Alb, and CysC have high application value in diagnosis and evaluation of patients with chronic HF, which can provide theoretical basis for improving the prognosis of patients with chronic HF through real-time monitoring of the above indicators.