A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images.

BACKGROUND: Automatic segmentation of hepatocellular carcinoma (HCC) on computed tomography (CT) scans is in urgent need to assist diagnosis and radiomics analysis. The aim of this study is to develop a deep learning based network to detect HCC from dynamic CT images. METHODS: Dynamic CT images of 595 patients with HCC were used. Tumors in dynamic CT images were labeled by radiologists. Patients were randomly divided into training, validation and test sets in a ratio of 5:2:3, respectively. We developed a hierarchical fusion strategy of deep learning networks (HFS-Net). Global dice, sensitivity, precision and F1-score were used to measure performance of the HFS-Net model. RESULTS: The 2D DenseU-Net using dynamic CT images was more effective for segmenting small tumors, whereas the 2D U-Net using portal venous phase images was more effective for segmenting large tumors. The HFS-Net model performed better, compared with the single-strategy deep learning models in segmenting small and large tumors. In the test set, the HFS-Net model achieved good performance in identifying HCC on dynamic CT images with global dice of 82.8%. The overall sensitivity, precision and F1-score were 84.3%, 75.5% and 79.6% per slice, respectively, and 92.2%, 93.2% and 92.7% per patient, respectively. The sensitivity in tumors < 2 cm, 2-3, 3-5 cm and > 5 cm were 72.7%, 92.9%, 94.2% and 100% per patient, respectively. CONCLUSIONS: The HFS-Net model achieved good performance in the detection and segmentation of HCC from dynamic CT images, which may support radiologic diagnosis and facilitate automatic radiomics analysis.

An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction.

Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide. As key regulatory molecules in several biological processes, microRNAs (miRNAs) are potential biomarkers for cancer. Understanding the miRNA markers that can detect BC may improve survival rates and develop new targeted therapeutic strategies. To identify a circulating miRNA signature for diagnostic prediction in patients with BC, we developed an evolutionary learning-based method called BSig. BSig established a compact set of miRNAs as potential markers from 1280 patients with BC and 2686 healthy controls retrieved from the serum miRNA expression profiles for the diagnostic prediction. BSig demonstrated outstanding prediction performance, with an independent test accuracy and area under the receiver operating characteristic curve were 99.90% and 0.99, respectively. We identified 12 miRNAs, including hsa-miR-3185, hsa-miR-3648, hsa-miR-4530, hsa-miR-4763-5p, hsa-miR-5100, hsa-miR-5698, hsa-miR-6124, hsa-miR-6768-5p, hsa-miR-6800-5p, hsa-miR-6807-5p, hsa-miR-642a-3p, and hsa-miR-6836-3p, which significantly contributed towards diagnostic prediction in BC. Moreover, through bioinformatics analysis, this study identified 65 miRNA-target genes specific to BC cell lines. A comprehensive gene-set enrichment analysis was also performed to understand the underlying mechanisms of these target genes. BSig, a tool capable of BC detection and facilitating therapeutic selection, is publicly available at https://github.com/mingjutsai/BSig.

Developing a practical neurodevelopmental prediction model for targeting high-risk very preterm infants during visit after NICU: a retrospective national longitudinal cohort study.

BACKGROUND: Follow-up visits for very preterm infants (VPI) after hospital discharge is crucial for their neurodevelopmental trajectories, but ensuring their attendance before 12 months corrected age (CA) remains a challenge. Current prediction models focus on future outcomes at discharge, but post-discharge data may enhance predictions of neurodevelopmental trajectories due to brain plasticity. Few studies in this field have utilized machine learning models to achieve this potential benefit with transparency, explainability, and transportability. METHODS: We developed four prediction models for cognitive or motor function at 24 months CA separately at each follow-up visits, two for the 6-month and two for the 12-month CA visits, using hospitalized and follow-up data of VPI from the Taiwan Premature Infant Follow-up Network from 2010 to 2017. Regression models were employed at 6 months CA, defined as a decline in The Bayley Scales of Infant Development 3rd edition (BSIDIII) composite score > 1 SD between 6- and 24-month CA. The delay models were developed at 12 months CA, defined as a BSIDIII composite score < 85 at 24 months CA. We used an evolutionary-derived machine learning method (EL-NDI) to develop models and compared them to those built by lasso regression, random forest, and support vector machine. RESULTS: One thousand two hundred forty-four VPI were in the developmental set and the two validation cohorts had 763 and 1347 VPI, respectively. EL-NDI used only 4-10 variables, while the others required 29 or more variables to achieve similar performance. For models at 6 months CA, the area under the receiver operating curve (AUC) of EL-NDI were 0.76-0.81(95% CI, 0.73-0.83) for cognitive regress with 4 variables and 0.79-0.83 (95% CI, 0.76-0.86) for motor regress with 4 variables. For models at 12 months CA, the AUC of EL-NDI were 0.75-0.78 (95% CI, 0.72-0.82) for cognitive delay with 10 variables and 0.73-0.82 (95% CI, 0.72-0.85) for motor delay with 4 variables. CONCLUSIONS: Our EL-NDI demonstrated good performance using simpler, transparent, explainable models for clinical purpose. Implementing these models for VPI during follow-up visits may facilitate more informed discussions between parents and physicians and identify high-risk infants more effectively for early intervention.

Prediction of extranodal extension in head and neck squamous cell carcinoma by CT images using an evolutionary learning model.

BACKGROUND: Extranodal extension (ENE) in head and neck squamous cell carcinoma (HNSCC) correlates to poor prognoses and influences treatment strategies. Deep learning may yield promising performance of predicting ENE in HNSCC but lack of transparency and interpretability. This work proposes an evolutionary learning method, called EL-ENE, to establish a more interpretable ENE prediction model for aiding clinical diagnosis. METHODS: There were 364 HNSCC patients who underwent neck lymph node (LN) dissection with pre-operative contrast-enhanced computerized tomography images. All the 778 LNs were divided into training and test sets with the ratio 8:2. EL-ENE uses an inheritable bi-objective combinatorial genetic algorithm for optimal feature selection and parameter setting of support vector machine. The diagnostic performances of the ENE prediction model and radiologists were compared using independent test datasets. RESULTS: The EL-ENE model achieved the test accuracy of 80.00%, sensitivity of 81.13%, and specificity of 79.44% for ENE detection. The three radiologists achieved the mean diagnostic accuracy of 70.4%, sensitivity of 75.6%, and specificity of 67.9%. The features of gray-level texture and 3D morphology of LNs played essential roles in predicting ENE. CONCLUSIONS: The EL-ENE method provided an accurate, comprehensible, and robust model to predict ENE in HNSCC with interpretable radiomic features for expanding clinical knowledge. The proposed transparent prediction models are more trustworthy and may increase their acceptance in daily clinical practice.

Prognostic microRNA signature for estimating survival in patients with hepatocellular carcinoma.

OBJECTIVE: Hepatocellular carcinoma (HCC) is one of the leading cancer types with increasing annual incidence and high mortality in the USA. MicroRNAs (miRNAs) have emerged as valuable prognostic indicators in cancer patients. To identify a miRNA signature predictive of survival in patients with HCC, we developed a machine learning-based HCC survival estimation method, HCCse, using the miRNA expression profiles of 122 patients with HCC. METHODS: The HCCse method was designed using an optimal feature selection algorithm incorporated with support vector regression. RESULTS: HCCse identified a robust miRNA signature consisting of 32 miRNAs and obtained a mean correlation coefficient (R) and mean absolute error (MAE) of 0.87 ±â€…0.02 and 0.73 years between the actual and estimated survival times of patients with HCC; and the jackknife test achieved an R and MAE of 0.73 and 0.97 years between actual and estimated survival times, respectively. The identified signature has seven prognostic miRNAs (hsa-miR-146a-3p, hsa-miR-200a-3p, hsa-miR-652-3p, hsa-miR-34a-3p, hsa-miR-132-5p, hsa-miR-1301-3p and hsa-miR-374b-3p) and four diagnostic miRNAs (hsa-miR-1301-3p, hsa-miR-17-5p, hsa-miR-34a-3p and hsa-miR-200a-3p). Notably, three of these miRNAs, hsa-miR-200a-3p, hsa-miR-1301-3p and hsa-miR-17-5p, also displayed association with tumor stage, further emphasizing their clinical relevance. Furthermore, we performed pathway enrichment analysis and found that the target genes of the identified miRNA signature were significantly enriched in the hepatitis B pathway, suggesting its potential involvement in HCC pathogenesis. CONCLUSIONS: Our study developed HCCse, a machine learning-based method, to predict survival in HCC patients using miRNA expression profiles. We identified a robust miRNA signature of 32 miRNAs with prognostic and diagnostic value, highlighting their clinical relevance in HCC management and potential involvement in HCC pathogenesis.

Survival associated miRNA signature in patients with head and neck carcinomas.

Head and neck carcinoma (HNSC) is often diagnosed at advanced stage, incurring poor patient outcome. Despite of advances in chemoradiation and surgery approaches, limited improvements in survival rates of HNSC have been observed over the last decade. Accumulating evidences have demonstrated the importance of microRNAs (miRNAs) in carcinogenesis. In this context, we sought to identify a miRNA signature associated with the survival time in patients with HNSC. This study proposed a survival estimation method called HNSC-Sig that identified a miRNA signature consists of 25 miRNAs associated with the survival in 133 patients with HNSC. HNSC-Sig achieved 10-fold cross validation a mean correlation coefficient and a mean absolute error of 0.85 ± 0.01 and 0.46 ± 0.02 years, respectively, between actual and estimated survival times. The survival analysis revealed that five miRNAs, hsa-miR-3605-3p, hsa-miR-629-3p, hsa-miR-3127-5p, hsa-miR-497-5p, and hsa-miR-374a-5p, were significantly associated with prognosis in patients with HNSC. Comparing the relative expression difference of top 10 prioritized miRNAs, eight miRNAs, hsa-miR-629-3p, hsa-miR-3127-5p, hsa-miR-221-3p, hsa-miR-501-5p, hsa-miR-491-5p, hsa-miR-149-3p, hsa-miR-3934-5p, and hsa-miR-3170, were significantly expressed between cancer and normal groups. In addition, biological relevance, disease association, and target interactions of the miRNA signature were discussed. Our results suggest that identified miRNA signature have potential to serve as biomarker for diagnosis and clinical practice in HNSC.

Artificial intelligence-driven pan-cancer analysis reveals miRNA signatures for cancer stage prediction.

The ability to detect cancer at an early stage in patients who would benefit from effective therapy is a key factor in increasing survivability. This work proposes an evolutionary supervised learning method called CancerSig to identify cancer stage-specific microRNA (miRNA) signatures for early cancer predictions. CancerSig established a compact panel of miRNA signatures as potential markers from 4,667 patients with 15 different types of cancers for the cancer stage prediction, and achieved a mean performance: 10-fold cross-validation accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 84.27% ± 6.31%, 0.81 ± 0.12, 0.80 ± 0.10, and 0.80 ± 0.06, respectively. The pan-cancer analysis of miRNA signatures suggested that three miRNAs, hsa-let-7i-3p, hsa-miR-362-3p, and hsa-miR-3651, contributed significantly toward stage prediction across 8 cancers, and each of the 67 miRNAs of the panel was a biomarker of stage prediction in more than one cancer. CancerSig may serve as the basis for cancer screening and therapeutic selection..

Survival estimation in patients with stomach and esophageal carcinoma using miRNA expression profiles.

Identifying a miRNA signature associated with survival will open a new window for developing miRNA-targeted treatment strategies in stomach and esophageal cancers (STEC). Here, using data from The Cancer Genome Atlas on 516 patients with STEC, we developed a Genetic Algorithm-based Survival Estimation method, GASE, to identify a miRNA signature that could estimate survival in patients with STEC. GASE identified 27 miRNAs as a survival miRNA signature and estimated the survival time with a mean squared correlation coefficient of 0.80 ± 0.01 and a mean absolute error of 0.44 ± 0.25 years between actual and estimated survival times, and showed a good estimation capability on an independent test cohort. The miRNAs of the signature were prioritized and analyzed to explore their roles in STEC. The diagnostic ability of the identified miRNA signature was analyzed, and identified some critical miRNAs in STEC. Further, miRNA-gene target enrichment analysis revealed the involvement of these miRNAs in various pathways, including the somatotrophic axis in mammals that involves the growth hormone and transforming growth factor beta signaling pathways, and gene ontology annotations. The identified miRNA signature provides evidence for survival-related miRNAs and their involvement in STEC, which would aid in developing miRNA-target based therapeutics.

SPIKES: Identification of physicochemical properties of spike proteins across diverse host species of SARS-CoV-2.

We describe a protocol to identify physicochemical properties using amino acid sequences of spike (S) proteins of SARS-CoV-2. We present an S protein prediction technique named SPIKES, incorporating an inheritable bi-objective combinatorial genetic algorithm to determine the host species specificity. This protocol addresses the S protein amino acid sequence data collection, preprocessing, methodology, and analysis. For complete details on the use and execution of this protocol, please refer to Yerukala Sathipati et al. (2022).

MicroRNA signature for estimating the survival time in patients with bladder urothelial carcinoma.

Bladder urothelial carcinoma (BLC) is one of the most common cancers in men, and its heterogeneity challenges the treatment to cure this disease. Recently, microRNAs (miRNAs) gained promising attention as biomarkers due to their potential roles in cancer biology. Identifying survival-associated miRNAs may help identify targets for therapeutic interventions in BLC. This work aims to identify a miRNA signature that could estimate the survival in patients with BLC. We developed a survival estimation method called BLC-SVR based on support vector regression incorporated with an optimal feature selection algorithm to select a robust set of miRNAs as a signature to estimate the survival in patients with BLC. BLC-SVR identified a miRNA signature consisting of 29 miRNAs and obtained a mean squared correlation coefficient and mean absolute error of 0.79 ± 0.02 and 0.52 ± 0.32 year between actual and estimated survival times, respectively. The prediction performance of BLC-SVR had a better estimation capability than other standard regression methods. In the identified miRNA signature, 14 miRNAs, hsa-miR-432-5p, hsa-let-7e-3p, hsa-miR-652-3p, hsa-miR-629-5p, and hsa-miR-203a-3p, hsa-miR-129-5p, hsa-miR-769-3p, hsa-miR-570-3p, hsa-miR-320c, hsa-miR-642a-5p, hsa-miR-496, hsa-miR-5480-3p, hsa-miR-221-5p, and hsa-miR-7-1-3p, were found to be good biomarkers for BLC diagnosis; and the six miRNAs, hsa-miR-652-5p, hsa-miR-193b-5p, hsa-miR-129-5p, hsa-miR-143-5p, hsa-miR-496, and hsa-miR-7-1-3p, were found to be good biomarkers of prognosis. Further bioinformatics analysis of this miRNA signature demonstrated its importance in various biological pathways and gene ontology annotation. The identified miRNA signature would further help in understanding of BLC diagnosis and prognosis in the development of novel miRNA-target based therapeutics in BLC.

Tracking the amino acid changes of spike proteins across diverse host species of severe acute respiratory syndrome coronavirus 2.

Knowledge of the host-specific properties of the spike protein is of crucial importance to understand the adaptability of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) to infect multiple species and alter transmissibility, particularly in humans. Here, we propose a spike protein predictor SPIKES incorporating with an inheritable bi-objective combinatorial genetic algorithm to identify the biochemical properties of spike proteins and determine their specificity to human hosts. SPIKES identified 20 informative physicochemical properties of the spike protein, including information measures for alpha helix and relative mutability, and amino acid and dipeptide compositions, which have shown compositional difference at the amino acid sequence level between human and diverse animal coronaviruses. We suggest that alterations of these amino acids between human and animal coronaviruses may provide insights into the development and transmission of SARS-CoV-2 in human and other species and support the discovery of targeted antiviral therapies.

Evolutionary Learning-Derived Clinical-Radiomic Models for Predicting Early Recurrence of Hepatocellular Carcinoma after Resection.

BACKGROUND AND AIMS: Current prediction models for early recurrence of hepatocellular carcinoma (HCC) after surgical resection remain unsatisfactory. The aim of this study was to develop evolutionary learning-derived prediction models with interpretability using both clinical and radiomic features to predict early recurrence of HCC after surgical resection. METHODS: Consecutive 517 HCC patients receiving surgical resection with available contrast-enhanced computed tomography (CECT) images before resection were retrospectively enrolled. Patients were randomly assigned to a training set (n = 362) and a test set (n = 155) in a ratio of 7:3. Tumor segmentation of all CECT images including noncontrast phase, arterial phase, and portal venous phase was manually performed for radiomic feature extraction. A novel evolutionary learning-derived method called genetic algorithm for predicting recurrence after surgery of liver cancer (GARSL) was proposed to design prediction models for early recurrence of HCC within 2 years after surgery. RESULTS: A total of 143 features, including 26 preoperative clinical features, 5 postoperative pathological features, and 112 radiomic features were used to develop GARSL preoperative and postoperative models. The area under the receiver operating characteristic curves (AUCs) for early recurrence of HCC within 2 years were 0.781 and 0.767, respectively, in the training set, and 0.739 and 0.741, respectively, in the test set. The accuracy of GARSL models derived from the evolutionary learning method was significantly better than models derived from other well-known machine learning methods or the early recurrence after surgery for liver tumor (ERASL) preoperative (AUC = 0.687, p < 0.001 vs. GARSL preoperative) and ERASL postoperative (AUC = 0.688, p < 0.001 vs. GARSL postoperative) models using clinical features only. CONCLUSION: The GARSL models using both clinical and radiomic features significantly improved the accuracy to predict early recurrence of HCC after surgical resection, which was significantly better than other well-known machine learning-derived models and currently available clinical models.

Brachytherapy Approach Using 177Lu Conjugated Gold Nanostars and Evaluation of Biodistribution, Tumor Retention, Dosimetry and Therapeutic Efficacy in Head and Neck Tumor Model.

Brachytherapy can provide sufficient doses to head and neck squamous cell carcinoma (HNSCC) with minimal damage to nearby normal tissues. In this study, the ß--emitter 177Lu was conjugated to DTPA-polyethylene glycol (PEG) decorated gold nanostars (177Lu-DTPA-pAuNS) used in surface-enhanced Raman scattering and photothermal therapy (PTT). The accumulation and therapeutic efficacy of 177Lu-DTPA-pAuNS were compared with those of 177Lu-DTPA on an orthotopic HNSCC tumor model. The SPECT/CT imaging and biodistribution studies showed that 177Lu-DTPA-pAuNS can be accumulated in the tumor up to 15 days, but 177Lu-DTPA could not be detected at 24 h after injection. The tumor viability and growth were suppressed by injected 177Lu-DTPA-pAuNS but not nonconjugated 177Lu-DTPA, as evaluated by bioluminescent imaging. The radiation-absorbed dose of the normal organ was the highest in the liver (0.33 mSv/MBq) estimated in a 73 kg adult, but that of tumorsphere (0.5 g) was 3.55 mGy/MBq, while intravenous injection of 177Lu-DTPA-pAuNS resulted in 1.97 mSv/MBq and 0.13 mGy/MBq for liver and tumorsphere, respectively. We also observed further enhancement of tumor-suppressive effects by a combination of 177Lu-DTPA-pAuNS and PTT compared to 177Lu-DTPA-pAuNS alone. In conclusion, 177Lu-DTPA-pAuNS may be considered as a potential radiopharmaceutical agent for HNSCC brachytherapy.

Predicting the Risk Genes of Autism Spectrum Disorders.

Autism spectrum disorder (ASD) refers to a wide spectrum of neurodevelopmental disorders that emerge during infancy and continue throughout a lifespan. Although substantial efforts have been made to develop therapeutic approaches, core symptoms persist lifelong in ASD patients. Identifying the brain temporospatial regions where the risk genes are expressed in ASD patients may help to improve the therapeutic strategies. Accordingly, this work aims to predict the risk genes of ASD and identify the temporospatial regions of the brain structures at different developmental time points for exploring the specificity of ASD gene expression in the brain that would help in possible ASD detection in the future. A dataset consisting of 13 developmental stages ranging from 8 weeks post-conception to 8 years from 26 brain structures was retrieved from the BrainSpan atlas. This work proposes a support vector machine-based risk gene prediction method ASD-Risk to distinguish the risk genes of ASD and non-ASD genes. ASD-Risk used an optimal feature selection algorithm called inheritable bi-objective combinatorial genetic algorithm to identify the brain temporospatial regions for prediction of the risk genes of ASD. ASD-Risk achieved a 10-fold cross-validation accuracy, sensitivity, specificity, area under a receiver operating characteristic curve, and a test accuracy of 81.83%, 0.84, 0.79, 0.84, and 72.27%, respectively. We prioritized the temporospatial features according to their contribution to the prediction accuracy. The top identified temporospatial regions of the brain for risk gene prediction included the posteroventral parietal cortex at 13 post-conception weeks feature. The identified temporospatial features would help to explore the risk genes that are specifically expressed in different brain regions of ASD patients.

Identification of the miRNA signature associated with survival in patients with ovarian cancer.

Ovarian cancer is a major gynaecological malignant tumor associated with a high mortality rate. Identifying survival-related variants may improve treatment and survival in patients with ovarian cancer. In this work, we proposed a support vector regression (SVR)-based method called OV-SURV, which is incorporated with an inheritable bi-objective combinatorial genetic algorithm for feature selection to identify a miRNA signature associated with survival in patients with ovarian cancer. There were 209 patients with miRNA expression profiles and survival information of ovarian cancer retrieved from The Cancer Genome Atlas database. OV-SURV achieved a mean correlation coefficient of 0.77±0.01and a mean absolute error of 0.69±0.02 years using 10-fold cross-validation. Analysis of the top ranked miRNAs revealed that the miRNAs, hsa-let-7f, hsa-miR-1237, hsa-miR-98, hsa-miR-933, and hsa-miR-889, were significantly associated with the survival in patients with ovarian cancer. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that four of these miRNAs, hsa-miR-182, hsa-miR-34a, hsa-miR-342, and hsa-miR-1304, were highly enriched in fatty acid biosynthesis, and the five miRNAs, hsa-let-7f, hsa-miR-34a, hsa-miR-342, hsa-miR-1304, and hsa-miR-24, were highly enriched in fatty acid metabolism. The prediction model with the identified miRNA signature consisting of prognostic biomarkers can benefit therapeutic decision making of ovarian cancer.

Identification and Characterization of Species-Specific Severe Acute Respiratory Syndrome Coronavirus 2 Physicochemical Properties.

There is an urgent need to elucidate the underlying mechanisms of coronavirus disease (COVID-19) so that vaccines and treatments can be devised. Severe acute respiratory syndrome coronavirus 2 has genetic similarity with bats and pangolin viruses, but a comprehensive understanding of the functions of its proteins at the amino acid sequence level is lacking. A total of 4320 sequences of human and nonhuman coronaviruses was retrieved from the Global Initiative on Sharing All Influenza Data and the National Center for Biotechnology Information. This work proposes an optimization method COVID-Pred with an efficient feature selection algorithm to classify the species-specific coronaviruses based on physicochemical properties (PCPs) of their sequences. COVID-Pred identified a set of 11 PCPs using a support vector machine and achieved 10-fold cross-validation and test accuracies of 99.53% and 97.80%, respectively. These findings could provide key insights into understanding the driving forces during the course of infection and assist in developing effective therapies.

Endoscopic Images by a Single-Shot Multibox Detector for the Identification of Early Cancerous Lesions in the Esophagus: A Pilot Study.

Diagnosis of early esophageal neoplasia, including dysplasia and superficial cancer, is a great challenge for endoscopists. Recently, the application of artificial intelligence (AI) using deep learning in the endoscopic field has made significant advancements in diagnosing gastrointestinal cancers. In the present study, we constructed a single-shot multibox detector using a convolutional neural network for diagnosing different histological grades of esophageal neoplasms and evaluated the diagnostic accuracy of this computer-aided system. A total of 936 endoscopic images were used as training images, and these images included 498 white-light imaging (WLI) and 438 narrow-band imaging (NBI) images. The esophageal neoplasms were divided into three classifications: squamous low-grade dysplasia, squamous high-grade dysplasia, and squamous cell carcinoma, based on pathological diagnosis. This AI system analyzed 264 test images in 10 s, and the sensitivity, specificity, and diagnostic accuracy of this system in detecting esophageal neoplasms were 96.2%, 70.4%, and 90.9%, respectively. The accuracy of this AI system in differentiating the histological grade of esophageal neoplasms was 92%. Our system showed better accuracy in diagnosing NBI (95%) than WLI (89%) images. Our results showed the great potential of AI systems in identifying esophageal neoplasms as well as differentiating histological grades.

Clinical treatment and medication in decreasing the development of major depression caused by spinal fracture.

BACKGROUND: Chronic pain and limited activities of daily living after spinal fracture may induce the occurrence of major depression (MD); however, risk factors regarding medications, surgical intervention, and severity of fracture are unclear. We aimed to analyze risk factors of MD development after spinal fracture. METHODS: This was a retrospective database study, using the health care database of the Taiwan government. We included 11,225 patients with new spinal fracture (study group), and 33,675 matched patients without fracture (comparison group). We respectively reviewed data of each participant for 3 years to assess the development of MD. The Cox proportional hazards model was used to determine the prevalence of MD, after adjusting for patient demographics, medications, surgical interventions, spinal cord involvement, and postfracture comorbidities. RESULTS: In total, 187 fracture patients (1.7%) and 281 nonfracture patients (0.8%) developed new-onset MD (hazard ratio [HR]:1.96, (95% confidence interval [CI]: 1.63-2.36)). Spinal cord involvement (HR: 2.96, 95% CI: 2.54-3.42) and postfracture comorbidities (HR: 3.51, 95% CI: 2.86-3.97) obviously increased the risk of MD. CONCLUSIONS: Patients with spinal fracture (spinal cord involvement and postfracture comorbidities) were more likely to develop MD. Early surgical interventions (vertebroplasty) and medications (narcotics) may decrease the risk of MD.

Novel miRNA signature for predicting the stage of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer deaths worldwide. Recently, microRNAs (miRNAs) are reported to be altered and act as potential biomarkers in various cancers. However, miRNA biomarkers for predicting the stage of HCC are limitedly discovered. Hence, we sought to identify a novel miRNA signature associated with cancer stage in HCC. We proposed a support vector machine (SVM)-based cancer stage prediction method, SVM-HCC, which uses an inheritable bi-objective combinatorial genetic algorithm for selecting a minimal set of miRNA biomarkers while maximizing the accuracy of predicting the early and advanced stages of HCC. SVM-HCC identified a 23-miRNA signature that is associated with cancer stages in patients with HCC and achieved a 10-fold cross-validation accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the receiver operating characteristic curve (AUC) of 92.59%, 0.98, 0.74, 0.80, and 0.86, respectively; and test accuracy and test AUC of 74.28% and 0.73, respectively. We prioritized the miRNAs in the signature based on their contributions to predictive performance, and validated the prognostic power of the prioritized miRNAs using Kaplan-Meier survival curves. The results showed that seven miRNAs were significantly associated with prognosis in HCC patients. Correlation analysis of the miRNA signature and its co-expressed miRNAs revealed that hsa-let-7i and its 13 co-expressed miRNAs are significantly involved in the hepatitis B pathway. In clinical practice, a prediction model using the identified 23-miRNA signature could be valuable for early-stage detection, and could also help to develop miRNA-based therapeutic strategies for HCC.