Objective: To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using 18 F-fluorodeoxyglucose ( 18 F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC. Materials and Methods: This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent 18 F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models. Results: In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46–1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status. Conclusion Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from 18 F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.

Objective: Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and validate radiomics prediction models to distinguish patients with JME from healthy controls (HCs), and to evaluate the feasibility of a radiomics approach using MRI for diagnosing JME. Materials and Methods: A total of 97 JME patients (25.6 ± 8.5 years; female, 45.5%) and 32 HCs (28.9 ± 11.4 years; female, 50.0%) were randomly split (7:3 ratio) into a training (n = 90) and a test set (n = 39) group. Radiomic features were extracted from 22 regions of interest in the brain using the T1-weighted MRI based on clinical evidence. Predictive models were trained using seven modeling methods, including a light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, with radiomics features in the training set. The performance of the models was validated and compared to the test set. The model with the highest area under the receiver operating curve (AUROC) was chosen, and important features in the model were identified. Results: The seven tested radiomics models, including light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, showed AUROC values of 0.817, 0.807, 0.783, 0.779, 0.767, 0.762, and 0.672, respectively. The light gradient boosting machine with the highest AUROC, albeit without statistically significant differences from the other models in pairwise comparisons, had accuracy, precision, recall, and F1 scores of 0.795, 0.818, 0.931, and 0.871, respectively. Radiomic features, including the putamen and ventral diencephalon, were ranked as the most important for suggesting JME. Conclusion Radiomic models using MRI were able to differentiate JME from HCs.

Background/Aims: We aim to evaluate the differences in the microbiome of responders and non-responders, as well as predict the response to probiotic therapy, based on fecal microbiome data in patients with diarrhea-predominant irritable bowel syndrome (IBS-D). Methods: A multi-strain probiotics that contains Lactobacillus acidophilus (KCTC 11906BP), Lactobacillus plantarum (KCTC11867BP), Lactobacillus rhamnosus (KCTC 11868BP), Bifidobacterium breve (KCTC 11858BP), Bifidobacterium lactis (KCTC 11903BP), Bifidobacterium longum (KCTC 11860BP), and Streptococcus thermophilus (KCTC 11870BP) were used. Patients were categorized into probiotic and placebo groups, and fecal samples were collected from all patients before and at the end of 8 weeks of treatment. The probiotic group was further divided into responders and non-responders. Responders were defined as patients who experienced adequate relief of overall irritable bowel syndrome symptoms after probiotic therapy. Fecal microbiota were investigated using Illumina MiSeq and analyzed using the EzBioCloud 16S database and microbiome pipeline (https://www.EZbiocloud.net). Results: There was no significant difference in the alpha and beta diversity between the responder and non-responder groups. The abundances of the phylum Proteobacteria and genus Bacteroides significantly decreased after probiotic treatment. Bifidobacterium bifidum, Pediococcus acidilactici, and Enterococcus faecium showed a significantly higher abundance in the probiotic group after treatment compared to the placebo group. Enterococcus faecalis and Lactococcus lactis were identified as biomarkers of non-response to probiotics. The abundance of Fusicatenibacter saccharivorans significantly increased in the responders after treatment. Conclusions Probiotic treatment changes some composition of fecal bacteria in patients with IBS-D. E. faecalis and L. lactis may be prediction biomarkers for non-response to probiotics. Increased abundance of F. sccharivorans is correlated to symptom improvement by probiotics in patients with IBS-D.

Objective: The number of deaths due to acute poisoning (AP) is on the increase. It is crucial to predict AP patient mortality to identify those requiring intensive care for providing appropriate patient care as well as preserving medical resources. The aim of this study is to predict the risk of in-hospital mortality associated with AP using an artificial neural network (ANN) model. Methods: In this multicenter retrospective study, ANN and logistic regression models were constructed using the clinical and laboratory data of 1,304 patients seeking emergency treatment for AP. The ANN model was first trained on 912/1,304 (70%) randomly selected patients and then tested on the remaining 392/1,304 (30%). Receiver operating characteristic curve analysis was used to evaluate the mortality prediction of the two models. Results: Age, endotracheal intubation status, and intensive care unit admission were significant predictors of mortality in patients with AP in the multivariate logistic regression model. The ANN model indicated age, Glasgow Coma Scale, intensive care unit admission, and endotracheal intubation status were critical factors among the 12 independent variables related to in-hospital mortality. The area under the receiver operating characteristic curve for mortality prediction was significantly higher in the ANN model compared to the logistic regression model. Conclusion This study establishes that the ANN model could be a valuable tool for predicting the risk of death following AP. Thus, it may facilitate effective patient triage and improve the outcomes.

Objective: The number of deaths due to acute poisoning (AP) is on the increase. It is crucial to predict AP patient mortality to identify those requiring intensive care for providing appropriate patient care as well as preserving medical resources. The aim of this study is to predict the risk of in-hospital mortality associated with AP using an artificial neural network (ANN) model. Methods: In this multicenter retrospective study, ANN and logistic regression models were constructed using the clinical and laboratory data of 1,304 patients seeking emergency treatment for AP. The ANN model was first trained on 912/1,304 (70%) randomly selected patients and then tested on the remaining 392/1,304 (30%). Receiver operating characteristic curve analysis was used to evaluate the mortality prediction of the two models. Results: Age, endotracheal intubation status, and intensive care unit admission were significant predictors of mortality in patients with AP in the multivariate logistic regression model. The ANN model indicated age, Glasgow Coma Scale, intensive care unit admission, and endotracheal intubation status were critical factors among the 12 independent variables related to in-hospital mortality. The area under the receiver operating characteristic curve for mortality prediction was significantly higher in the ANN model compared to the logistic regression model. Conclusion This study establishes that the ANN model could be a valuable tool for predicting the risk of death following AP. Thus, it may facilitate effective patient triage and improve the outcomes.

BACKGROUND AND OBJECTIVES: Several recent studies have claimed that cancer cells can be reprogrammed into induced pluripotent stem cells (iPSCs). However, in most cases, cancer cells seem to be resistant to cellular reprogramming. Furthermore, the underlying mechanisms of limited reprogramming in cancer cells are largely unknown. Here, we identified the candidate barrier genes and their target genes at the early stage of reprogramming for investigating cancer reprogramming.METHODS: We tried induction of pluripotency in normal human fibroblasts (BJ) and both human benign (MCF10A) and malignant (MCF7) breast cancer cell lines using a classical retroviral reprogramming method. We conducted RNA-sequencing analysis to compare the transcriptome of the three cell lines at early stage of reprogramming.RESULTS: We could generate iPSCs from BJ, whereas we were unable to obtain iPSCs from cancer cell lines. To address the underlying mechanism of limited reprogramming in cancer cells, we identified 29 the candidate barrier genes based on RNA-sequencing data. In addition, we found 40 their target genes using Cytoscape software.CONCLUSIONS: Our data suggest that these genes might one of the roadblock for cancer cell reprogramming. Furthermore, we provide new insights into application of iPSCs technology in cancer cell field for therapeutic purposes.
Breast Neoplasms ; Cell Line ; Cellular Reprogramming ; Fibroblasts ; Humans ; Induced Pluripotent Stem Cells ; Methods ; Transcriptome ; Zidovudine

OBJECTIVES: Atrophic vaginitis (AV), which is common in postmenopausal women, is characterized by vaginal dryness, dyspareunia, and discomfort. There are a variety of therapeutic agents for the treatment of AV, besides hormone replacement therapy. We performed this systematic review to compare the effectiveness of various therapies for symptom improvement in AV patients. METHODS: We searched the Cochrane Library, EMBASE, MEDLINE, and other literature (Google Scholar, Web of Science, and hand search) for studies published between January 2010 and March 2015. AV was evaluated by the following outcomes: vaginal pH, dyspareunia, vaginal dryness, or cytological change (endometrial thickness, percentages of superficial cells and parabasal cells). They measured treatment efficacy with various outcomes pertaining to AV symptoms. RESULTS: Meta-analysis suggested that ospemifene was effective against dyspareunia, vaginal dryness, endometrial thickness, and percentage changes in superficial and parabasal cells. Vaginal pH was most affected by soy isoflavone vaginal gel. Ospemifene was effective for AV symptoms. CONCLUSIONS: This systematic review compared the effects of several therapeutic agents on symptoms of AV through a network meta-analysis. This study provides objective evidence for clinical treatment and efficacy management in AV.
Atrophic Vaginitis ; Dyspareunia ; Female ; Hand ; Hormone Replacement Therapy ; Humans ; Hydrogen-Ion Concentration ; Postmenopause ; Treatment Outcome ; Vagina ; Vaginal Creams, Foams, and Jellies

OBJECTIVE: To determine whether transplanted human adipose tissue derived stem cells (hATSCs) can survive and increase the amount of proteoglycans in degenerated intervertebral disc. METHOD: Lumbar disc degeneration was induced in thirty New Zealand white rabbits by injection of chondroitinase ABC(R). After 2 weeks, hATSCs were transplanted in degenerated disc in hATSCs group. Control group received phosphate buffered saline. The histologic grading and height of disc were measured at 2, 4, and 8 weeks after transplantation. The viability of donor cells was identified by using beta-Actin gene polymerase chain reaction (PCR). RESULTS: 4 and 8 weeks after hATSCs transplantation, the histologic grading showed significantly high score in hATSCs group (p<0.05), but the amount of proteoglycans was not significantly different between the two groups. The change of disc height was not significantly increased in hATSCs group. In the beta-Actin gene PCR analysis, positive signal in the hATSCs group was observed. CONCLUSION: hATSCs transplantation may be useful in decelerating disc degeneration in experimental models and provide new hopes for treatment of degenerative disc disease in humans
Actins ; Adipose Tissue* ; Hope ; Humans* ; Intervertebral Disc ; Intervertebral Disc Degeneration ; Mesenchymal Stromal Cells* ; Models, Theoretical ; Polymerase Chain Reaction ; Proteoglycans ; Rabbits ; Stem Cells ; Tissue Donors

Patients with ankylosing spondylitis are more susceptible to spine fractures than healthy people. Because of their underlying back pain, vertebral fracture induced pain is not easily controlled by bed rest, physical therapy and medications. Thus, new treatment methods should be introduced. We report a 63 year-old man with ankylosing spondylitis who fell down 4 weeks ago and suffered a fracture of T6 spine. He complained of a mid thoracic pain. Although he was treated with bed rest, physical therapy, second-line bisphosphonate agents, the pain persisted. Therefore, intravenous zoledronate which was approved for palliative treatment of bone metastases in patients with breast cancer, 4 mg was administrated. The severity of pain was decreased by half within 2 days and sustained over 4 weeks.
Back Pain ; Bed Rest ; Breast Neoplasms ; Humans ; Middle Aged ; Neoplasm Metastasis ; Palliative Care ; Spine ; Spondylitis, Ankylosing*