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Most cases of hepatocellular carcinoma (HCC) arise with the fibrotic microenvironment where hepatic stellate cells (HSCs) and carcinoma-associated fibroblasts (CAFs) are critical components in HCC progression. Therefore, CAF normalization could be a feasible therapy for HCC. Galectin-1 (Gal-1), a β-galactoside-binding lectin, is critical for HSC activation and liver fibrosis. However, few studies has evaluated the pathological role of Gal-1 in HCC stroma and its role in hepatic CAF is unclear. Here we showed that Gal-1 mainly expressed in HCC stroma, but not cancer cells. High expression of Gal-1 is correlated with CAF markers and poor prognoses of HCC patients. In co-culture systems, targeting Gal-1 in CAFs or HSCs, using small hairpin (sh)RNAs or an therapeutic inhibitor (LLS30), downregulated plasminogen activator inhibitor-2 (PAI-2) production which suppressed cancer stem-like cell properties and invasion ability of HCC in a paracrine manner. The Gal-1-targeting effect was mediated by increased a disintegrin and metalloprotease 17 (ADAM17)-dependent TNF-receptor 1 (TNFR1) shedding/cleavage which inhibited the TNF-α → JNK → c-Jun/ATF2 signaling axis of pro-inflammatory gene transcription. Silencing Gal-1 in CAFs inhibited CAF-augmented HCC progression and reprogrammed the CAF-mediated inflammatory responses in a co-injection xenograft model. Taken together, the findings uncover a crucial role of Gal-1 in CAFs that orchestrates an inflammatory CSC niche supporting HCC progression and demonstrate that targeting Gal-1 could be a potential therapy for fibrosis-related HCC.

Purpose of the study The risk of bone fracture is high in patients with chronic kidney disease (CKD), and aggressive treatment to reduce fragility fracture risk is the major strategy. However, the outcomes of osteoporosis medications in patients with CKD remain unclear. Study design Patients with stage 3–5 CKD during 2011–2019 were enrolled. Patients were divided into two groups based on receiving osteoporosis medications (bisphosphonates, raloxifene, teriparatide or denosumab) or not. Two groups were matched at a 1:1 ratio by using propensity scores. The outcomes of interest were bone fractures, cardiovascular (CV) events and all-cause mortality. Cox proportional hazard regression models were applied to identify the risk factors. Additional stratified analyses by cumulative dose, treatment length and menopause condition were performed. Results and conclusions 67 650 patients were included. After propensity score matching, 1654 patients were included in the study and control group, respectively. The mean age was 70.2±12.4 years, and 32.0% of patients were men. After a mean follow-up of 3.9 years, the incidence rates of bone fracture, CV events and all-cause mortality were 2.0, 1.7 and 6.5 per 1000 person-months, respectively. Multivariate analysis results showed that osteoporosis medications reduced the risk of CV events (HR, 0.35; 95% CI, 0.18 to 0.71; p = 0.004), but did not alleviate the risks of bone fracture (HR, 1.48; 95% CI, 0.73 to 2.98; p = 0.28) and all-cause mortality (HR, 0.93; 95% CI, 0.67 to 1.28; p = 0.65). Stratified analysis showed that bisphosphonates users have most benefits in the reduction of CV events (HR, 0.26; 95% CI, 0.11 to 0.64; p = 0.003). In conclusion, osteoporosis medications did not reduce the risk of bone fractures, or mortality, but improved CV outcomes in patients with CKD.

Background MicroRNAs are a group of small non-coding RNAs that are involved in development and diseases such as cancer. Previously, we demonstrated that miR-335 is crucial for preventing collagen type XI alpha 1 (COL11A1)-mediated epithelial ovarian cancer (EOC) progression and chemoresistance. Here, we examined the role of miR-509-3p in EOC. Methods The patients with EOC who underwent primary cytoreductive surgery and postoperative platinum-based chemotherapy were recruited. Their clinic-pathologic characteristics were collected, and disease-related survivals were determined. The COL11A1 and miR-509-3p mRNA expression levels of 161 ovarian tumors were determined by real-time reverse transcription-polymerase chain reaction. Additionally, miR-509-3p hypermethylation was evaluated by sequencing in these tumors. The A2780CP70 and OVCAR-8 cells transfected with miR-509-3p mimic, while the A2780 and OVCAR-3 cells transfected with miR-509-3p inhibitor. The A2780CP70 cells transfected with a small interference RNA of COL11A1, and the A2780 cells transfected with a COL11A1 expression plasmid. Site-directed mutagenesis, luciferase, and chromatin immunoprecipitation assays were performed in this study. Results Low miR-509-3p levels were correlated with disease progression, a poor survival, and high COL11A1 expression levels. In vivo studies reinforced these findings and indicated that the occurrence of invasive EOC cell phenotypes and resistance to cisplatin are decreased by miR-509-3p. The miR-509-3p promoter region (p278) is important for miR-509-3p transcription regulation via methylation. The miR-509-3p hypermethylation frequency was significantly higher in EOC tumors with a low miR-509-3p expression than in those with a high miR-509-3p expression. The patients with miR-509-3p hypermethylation had a significantly shorter overall survival (OS) than those without miR-509-3p hypermethylation. Mechanistic studies further indicated that miR-509-3p transcription was downregulated by COL11A1 through a DNA methyltransferase 1 (DNMT1) phosphorylation and stability increase. Moreover, miR-509-3p targets small ubiquitin-like modifier (SUMO)-3 to regulate EOC cell growth, invasiveness, and chemosensitivity. Conclusion The miR-509-3p/DNMT1/SUMO-3 axis may be an ovarian cancer treatment target.

Circular RNAs (circRNAs), which are single-stranded RNA molecules that have individually formed into a covalently closed continuous loop, act as sponges of microRNAs to regulate transcription and translation. CircRNAs are important molecules in the field of cancer diagnosis, as growing evidence suggests that they are closely related to pathological cancer features. Therefore, they have high potential for clinical use as novel cancer biomarkers. In this article, we present our updates to CircNet (version 2.0), into which circRNAs from circAtlas and MiOncoCirc, and novel circRNAs from The Cancer Genome Atlas database have been integrated. In total, 2732 samples from 37 types of cancers were integrated into CircNet 2.0 and analyzed using several of the most reliable circRNA detection algorithms. Furthermore, target miRNAs were predicted from the full-length circRNA sequence using three reliable tools (PITA, miRanda and TargetScan). Additionally, 384 897 experimentally verified miRNA–target interactions from miRTarBase were integrated into our database to facilitate the construction of high-quality circRNA–miRNA–gene regulatory networks. These improvements, along with the user-friendly interactive web interface for data presentation, search, and visualization, showcase the updated CircNet database as a powerful, experimentally validated resource, for providing strong data support in the biomedical fields. CircNet 2.0 is currently accessible at https://awi.cuhk.edu.cn/∼CircNet.

Patients with autoimmune diseases (AD) are at increased risk of complications, which adversely affect the quality of life and prognosis. Recently, a large amount of literature suggested reduced oxidative stress by hydrogen supplements in reducing inflammation and improving prognosis. In this study, clinical trial NCT05196295, we randomly assigned different dosages (low, medium, and high) of hydrogen-rich coral calcium (HRCC) to patients with autoimmune diseases, including Rheumatoid Arthritis (RA) and Systemic Lupus Erythematosus (SLE) patients in Ming-Sheng Hospital in Taiwan (age 54 years, 80% females). Participants were measured with their baseline health biomarkers including inflammatory indicators, haematologic, urinary biomarkers, and health status prior to the trial. Clinical and follow-up assessments were collected after 1 month of the treatment. We performed analyses to compare the baseline with the changes after the HRCC treatment. Together with the groups treated with different dosages, the treatment demonstrated no adverse effects on participants’ responses to the HRCC. While the primary endpoint of the trial was met, the potential therapeutic effects of HRCC are addressed for future studies. This clinical trial of HRCC in safety response is the first-in-human study.

Background Approximately 25% of patients with early-stage breast cancer experience cancer progression throughout the disease course. Alterations in TMEM240 in breast cancer were identified and investigated to monitor treatment response and disease progression. Methods Circulating methylated TMEM240 in the plasma of breast cancer patients was used to monitor treatment response and disease progression. The Cancer Genome Atlas (TCGA) data in Western countries and Illumina methylation arrays in Taiwanese breast cancer patients were used to identify novel hypermethylated CpG sites and genes related to poor hormone therapy response. Quantitative methylation-specific PCR (QMSP), real-time reverse transcription PCR, and immunohistochemical analyses were performed to measure DNA methylation and mRNA and protein expression levels in 394 samples from Taiwanese and Korean breast cancer patients. TMEM240 gene manipulation, viability, migration assays, RNA-seq, and MetaCore were performed to determine its biological functions and relationship to hormone drug treatment response in breast cancer cells. Results Aberrant methylated TMEM240 was identified in breast cancer patients with poor hormone therapy response using genome-wide methylation analysis in the Taiwan and TCGA breast cancer cohorts. A cell model showed that TMEM240, which is localized to the cell membrane and cytoplasm, represses breast cancer cell proliferation and migration and regulates the expression levels of enzymes involved in estrone and estradiol metabolism. TMEM240 protein expression was observed in normal breast tissues but was not detected in 88.2% (67/76) of breast tumors and in 90.0% (9/10) of metastatic tumors from breast cancer patients. QMSP revealed that in 54.5% (55/101) of Taiwanese breast cancer patients, the methylation level of TMEM240 was at least twofold higher in tumor tissues than in matched normal breast tissues. Patients with hypermethylation of TMEM240 had poor 10-year overall survival (p = 0.003) and poor treatment response, especially hormone therapy response (p TMEM240 dramatically and gradually decreased and then diminished in patients without disease progression, whereas it returned and its levels in plasma rose again in patients with disease progression. Prediction of disease progression based on circulating methylated TMEM240 was found to have 87.5% sensitivity, 93.1% specificity, and 90.2% accuracy. Conclusions Hypermethylation of TMEM240 is a potential biomarker for treatment response and disease progression monitoring in breast cancer.

BACKGROUND Atrial fibrillation (AF) is an independent risk factor that increases the risk of stroke fivefold. The purpose of our study was to develop an AF predictive model by machine learning (ML) based on 3-year medical information without electrocardiograms in our database to identify AF risk among elderly patients. OBJECTIVE To examine ML approaches applied to electronic medical records in our clinical research database to identify AF risk among elderly patients. METHODS We developed a predictive model according to the Taipei Medical University clinical research database electronic medical records, including diagnostic codes, medications, and laboratory data. Classification of new-onset AF in designated 1-year interval (retrospectively passed on judicially), judged by sensitivity, specificity, F1 score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC). RESULTS A total of 2138 participants (1028 female [48.1%]; mean [SD] age 78.8 [6.8] years) with AF and 8552 random controls (after the matching process) without AF (4112 female [48.1%]; mean [SD] age 78.8 [6.8] years) were included in the model. The 1-year new-onset AF risk prediction model based on the random forest algorithm using medication and diagnostic information along with specific laboratory data attained an AUROC of 0.74 and an AUPRC of 0.89, while the specificity was 98.7%. Most ML models achieved an accuracy of approximately 0.8. CONCLUSIONS The findings of this study suggest that our ML-based model could offer acceptable discrimination in differentiating the risk of incident AF in the following year. A targeted screening approach for AF could result in a clinical choice with efficacy for prediction of the incident AF risk in elderly patients.

Enterococcus faecium is a clinically important pathogen that can cause significant morbidity and death. In this study, we aimed to develop a machine learning (ML) algorithm-based rapid susceptibility method to distinguish vancomycin-resistant E. faecium (VREfm) and vancomycin-susceptible E. faecium (VSEfm) strains. A predictive model was developed and validated to distinguish VREfm and VSEfm strains by analyzing the matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) mass spectrometry (MS) spectra of unique E. faecium isolates from different specimen types. The algorithm used 5,717 mass spectra, including 2,795 VREfm and 2,922 VSEfm mass spectra, and was externally validated with 2,280 mass spectra of isolates (1,222 VREfm and 1,058 VSEfm strains). A random forest-based algorithm demonstrated overall good classification performances for the isolates from the specimens, with mean accuracy, sensitivity, and specificity of 0.78, 0.79, and 0.77, respectively, with 10-fold cross-validation, timewise validation, and external validation. Furthermore, the algorithm provided rapid results, which would allow susceptibility prediction prior to the availability of phenotypic susceptibility results. In conclusion, an ML algorithm designed using mass spectra obtained from the routine workflow may be able to rapidly differentiate VREfm strains from VSEfm strains; however, susceptibility results must be confirmed by routine methods, given the demonstrated performance of the assay. IMPORTANCE A modified binning method was incorporated to cluster MS shifting ions into a set of representative peaks based on a large-scale MS data set of clinical VREfm and VSEfm isolates, including 2,795 VREfm and 2,922 VSEfm isolates. Predictions with the algorithm were significantly more accurate than empirical antibiotic use, the accuracy of which was 0.50, based on the local epidemiology. The algorithm improved the accuracy of antibiotic administration, compared to empirical antibiotic prescription. An ML algorithm designed using MALDI-TOF MS spectra obtained from the routine workflow accurately differentiated VREfm strains from VSEfm strains, especially in blood and sterile body fluid samples, and can be applied to facilitate the rapid and accurate clinical testing of pathogens.

The outcome of acute kidney injury (AKI) as a result of aminoglycosides (AGs) use remains uncertain in patients without prior chronic kidney disease (CKD). Therefore, we explored the outcomes of AGs use on AKI episodes associated with renal recovery and progress in patients without prior CKD in Taiwan. This was a retrospective cohort study by using the Taipei Medical University Research Database from January 2008 to December 2019. 43,259 individuals without CKD who had received parenteral AGs were enrolled. The exposed and unexposed groups underwent propensity score matching for age, gender, patients in intensive care unit/emergency admission, and covariates, except serum hemoglobin and albumin levels. We identified an exposed group of 40,547 patients who used AGs (median age, 54.4 years; 44.3% male) and an unexposed group of 40,547 patients without AG use (median age, 55.7 years; 45.5% male). There was the risk for AKI stage 1 (adjusted hazard ratio [HR] 1.34; 95% confidence interval [CI] 1.00–1.79; p = 0.05) in patients that used AGs in comparison with the control subjects. Moreover, patients using AGs were significantly associated neither with the progression to acute kidney disease (AKD) stages nor with the progression to end-stage renal disease (ESRD) on dialysis. Further analyzed, there was an increased risk of AKI episodes for serum albumin levels less than 3.0 g/dL and hemoglobin levels less than 11.6 g/dL. Among patients without prior CKD, AGs-used individuals were associated with AKI risks, especially those at relatively low albumin (

Enterococcus faecium is a clinically important pathogen that can cause significant morbidity and death. In this study, we aimed to develop a machine learning (ML) algorithm-based rapid susceptibility method to distinguish vancomycin-resistant E. faecium (VREfm) and vancomycin-susceptible E. faecium (VSEfm) strains. A predictive model was developed and validated to distinguish VREfm and VSEfm strains by analyzing the matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) mass spectrometry (MS) spectra of unique E. faecium isolates from different specimen types. The algorithm used 5,717 mass spectra, including 2,795 VREfm and 2,922 VSEfm mass spectra, and was externally validated with 2,280 mass spectra of isolates (1,222 VREfm and 1,058 VSEfm strains). A random forest-based algorithm demonstrated overall good classification performances for the isolates from the specimens, with mean accuracy, sensitivity, and specificity of 0.78, 0.79, and 0.77, respectively, with 10-fold cross-validation, timewise validation, and external validation. Furthermore, the algorithm provided rapid results, which would allow susceptibility prediction prior to the availability of phenotypic susceptibility results. In conclusion, an ML algorithm designed using mass spectra obtained from the routine workflow may be able to rapidly differentiate VREfm strains from VSEfm strains; however, susceptibility results must be confirmed by routine methods, given the demonstrated performance of the assay. IMPORTANCE A modified binning method was incorporated to cluster MS shifting ions into a set of representative peaks based on a large-scale MS data set of clinical VREfm and VSEfm isolates, including 2,795 VREfm and 2,922 VSEfm isolates. Predictions with the algorithm were significantly more accurate than empirical antibiotic use, the accuracy of which was 0.50, based on the local epidemiology. The algorithm improved the accuracy of antibiotic administration, compared to empirical antibiotic prescription. An ML algorithm designed using MALDI-TOF MS spectra obtained from the routine workflow accurately differentiated VREfm strains from VSEfm strains, especially in blood and sterile body fluid samples, and can be applied to facilitate the rapid and accurate clinical testing of pathogens.

Although cytotoxic platinum-based adjuvant chemotherapy (pACT) has been recommended for patients with completely resected early-stage (ES) non-small-cell lung cancer (ES-NSCLC), therapeutic regimens for NSCLC have evolved in the past two decades. The study was aimed to examine the effectiveness of postoperative pACT for resected ES-NSCLC patients with squamous cell carcinoma (SCC) or adenocarcinoma (ADC) according to real-world data.Inverse probability treatment weighting (IPTW) was used to adjust baseline characteristics between the group receiving pACT and those not receiving any treatment (observation, OBS) within 3 months after curative surgery. Cox regression models were used to compare overall survival (OS) and treatment failure-free survival (TFS) between the groups.Of 31,208 patients with ES-NSCLC, 4700 undergoing complete tumor resection were eligible, with a mean follow-up period of 4.5 years. The pACT (n = 2347) and OBS (n = 2353) groups were well-balanced after IPTW. The median OS differed between the pACT and OBS groups (77.2 vs. 75.5 months, adjusted hazard ratio [aHR] = 0.87, 95% confidence interval [CI] = 0.79-0.95, p = 0.003), and the 5-year survival rates were 58.2% and 55.3%, respectively (p 0.001). In the SCC group, pACT was superior to OBS in OS (75.0 vs. 57.4 months, aHR = 0.74, 95% CI = 0.62-0.88, p = 0.001) and TFS (32.7 vs. 21.8 months, aHR = 0.74, 95% CI = 0.63-0.86, p 0.001). Both OS and TFS did not differ between two groups in those with ADC.Real-world data indicated that pACT confers a survival benefit for resected ES-NSCLC patients with SCC but not ADC, which needs to be verified by a large sample of randomized controlled studies.

Background Weight‐reduction therapies, including bariatric surgery (BS), are standard treatments for severely obese patients with type 2 diabetes; however, the outcomes of these therapies are inconclusive for obese patients with chronic kidney disease (CKD). This study aimed to investigate the effects of BS or non‐surgical interventions on the estimated glomerular filtration rate (eGFR) and to determine whether BS can be recommended for renal function preservation based on body mass index (BMI) and eGFR changes in obese patients with CKD. Methods This study used data from the weight Reduction Intervention on GFR in Obese Patients with Renal Impairment‐Taipei Medical University (TMU) study, which was a large, long‐term, propensity score‐matched cohort study based on clinical data from patients who registered at weight‐reduction centres at TMU and its affiliated hospitals from 2008 to 2016. The patients were stratified according to whether they had undergone BS and into the mild, moderate, and high CKD risk groups using the Kidney Disease: Improving Global Outcomes guidelines. The primary outcome was the eGFR calculated using the Taiwan Chronic Kidney Disease‐Epidemiology Collaboration equation. Cox regression models were used to determine hazard ratios (HRs) for eGFR decreases ≥25%. Results A total of 4332 obese patients were enrolled in this study. After propensity score matching, 1620 patients, including 60.2% women, with a mean age of 36.5 (9.9) years were divided into BS or non‐surgery groups (n = 810 per group). The overall mean eGFRs increased by 4.4 (14) mL/min·1.73 m2 and decreased by 6.4 (16.0) mL/min·1.73 m2 in the BS and non‐surgery groups, respectively. The decrease in BMI in the BS and non‐surgery groups were 2.5 and 1.3 kg/m2, respectively. In the moderate/high CKD risk BS group, a significant correlation was evident between an increased eGFR and a reduced BMI (Spearman's correlation −0.229, P < 0.001). The Cox regression analysis showed that the BS group had a significantly lower risk of an eGFR decline ≥25% at 12 months [adjusted HR (aHR) 0.47, P = 0.03). After BS, obese patients with hypertension or albuminuria had significantly lower risks of eGFR declines ≥25% (aHR 0.37, P = 0.02 and aHR 0.13, P = 0.0018, respectively). Conclusions Bariatric surgery was associated with eGFR preservation in all obese patients and, particularly, in those with moderate‐to‐high CKD risks. A longer term outcome study is warranted to determine the benefits of BS for CKD patients.

A well-established lung-cancer-survival-prediction model that relies on multiple data types, multiple novel machine-learning algorithms, and external testing is absent in the literature. This study aims to address this gap and determine the critical factors of lung cancer survival. We selected non-small-cell lung cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2008 and December 2018. All patients were monitored from the index date of cancer diagnosis until the event of death. Variables, including demographics, comorbidities, medications, laboratories, and patient gene tests, were used. Nine machine-learning algorithms with various modes were used. The performance of the algorithms was measured by the area under the receiver operating characteristic curve (AUC). In total, 3714 patients were included. The best performance of the artificial neural network (ANN) model was achieved when integrating all variables with the AUC, accuracy, precision, recall, and F1-score of 0.89, 0.82, 0.91, 0.75, and 0.65, respectively. The most important features were cancer stage, cancer size, age of diagnosis, smoking, drinking status, EGFR gene, and body mass index. Overall, the ANN model improved predictive performance when integrating different data types.

Background: Little is known about the association of inferior vena cava diameter (IVCD) and left ventricular end-systolic diameter (LVESD) with mortality in patients undergoing hemodialysis (HD). Methods: The single medical center observational cohort study enrolled 241 adult chronic HD patients from 1 October 2018 to 31 December 2018. Echocardiography results of IVCD and LVESD prior to dialysis were retrieved and patients were divided into high IVCD and low IVCD groups. Patients who received HD via a tunneled cuffed catheter were excluded. Study outcomes included all-cause mortality, cardiovascular mortality, and major adverse cardiovascular events (MACE). Subgroup analyses of HD patients with high and low LVESD were also performed. Results: The incidence of all-cause mortality, cardiovascular mortality, and MACE were higher in chronic HD patients with high IVCD (p < 0.01). High IVCD patients had significantly greater all-cause mortality, cardiovascular mortality, and MACE (log-rank test; p < 0.05). High IVCD patients are also associated with an increased risk of all-cause mortality and MACE relative to low IVCD patients (aHRs, 2.88 and 3.42; 95% CIs, 1.06–7.86 and 1.73–6.77, respectively; all p < 0.05). In the subgroup analysis of patients with high or low LVESD, the high IVCD remained a significant risk factor for all-cause mortality and MACE, and the HR is especially high in the high LVESD group. Conclusions: Dilated IVCD is a risk factor for all-cause mortality and MACE in chronic HD patients. In addition, these patients with high LVESD also have a significantly higher HR of all-cause mortality and MACE.

UNSTRUCTURED This is a correction for the published manuscript: A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study (J Med Internet Res 2021;23(5):e27806). The authorship list has been corrected with an equal contribution footnote and an "Acknowledgements" section has been added to the original published manuscript.

Basal macroautophagy/autophagy has recently been found in anucleate platelets. Platelet autophagy is involved in platelet activation and thrombus formation. However, the mechanism underlying autophagy in anucleate platelets require further clarification. Our data revealed that LC3-II formation and SQSTM1/p62 degradation were noted in H(2)O(2)-activated human platelets, which could be blocked by 3-methyladenine and bafilomycin A(1), indicating that platelet activation may cause platelet autophagy. AMPK phosphorylation and MTOR dephosphorylation were also detected, and block of AMPK activity by the AMPK inhibitor dorsomorphin reversed SQSTM1 degradation and LC3-II formation. Moreover, autophagosome formation was observed through transmission electron microscopy and deconvolution microscopy. These findings suggest that platelet autophagy was induced partly through the AMPK-MTOR pathway. In addition, increased LC3-II expression occurred only in H(2)O(2)-treated Atg5(f/f) platelets, but not in H(2)O(2)-treated atg5(−/−) platelets, suggesting that platelet autophagy occurs during platelet activation. atg5(−/−) platelets also exhibited a lower aggregation in response to agonists, and platelet-specific atg5(−/−) mice exhibited delayed thrombus formation in mesenteric microvessles and decreased mortality rate due to pulmonary thrombosis. Notably, metabolic analysis revealed that sphingolipid metabolism is involved in platelet activation, as evidenced by observed several altered metabolites, which could be reversed by dorsomorphin. Therefore, platelet autophagy and platelet activation are positively correlated, partly through the interconnected network of sphingolipid metabolism. In conclusion, this study for the first time demonstrated that AMPK-MTOR signaling could regulate platelet autophagy. A novel linkage between AMPK-MTOR and sphingolipid metabolism in anucleate platelet autophagy was also identified: platelet autophagy and platelet activation are positively correlated. Abbreviations: 3-MA: 3-methyladenine; A.C.D.: citric acid/sod. citrate/glucose; ADP: adenosine diphosphate; AKT: AKT serine/threonine kinase; AMPK: AMP-activated protein kinase; ANOVA: analysis of variance; ATG: autophagy-related; B4GALT/LacCS: beta-1,4-galactosyltransferase; Baf-A1: bafilomycin A(1); BECN1: beclin 1; BHT: butylate hydrooxytoluene; BSA: bovine serum albumin; DAG: diacylglycerol; ECL: enhanced chemiluminescence; EDTA: ethylenediamine tetraacetic acid; ELISA: enzyme-linked immunosorbent assay; GALC/GCDase: galactosylceramidase; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; GBA/GluSDase: glucosylceramidase beta; GPI: glycosylphosphatidylinositol; H(2)O(2): hydrogen peroxide; HMDB: human metabolome database; HRP: horseradish peroxidase; IF: immunofluorescence; IgG: immunoglobulin G; KEGG: Kyoto Encyclopedia of Genes and Genomes; LAMP1: lysosomal associated membrane protein 1; LC-MS/MS: liquid chromatography-tandem mass spectrometry; mAb: monoclonal antibody; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MPV: mean platelet volume; MTOR: mechanistic target of rapamycin kinase; ox-LDL: oxidized low-density lipoprotein; pAb: polyclonal antibody; PC: phosphatidylcholine; PCR: polymerase chain reaction; PI3K: phosphoinositide 3-kinase; PLS-DA: partial least-squares discriminant analysis; PRP: platelet-rich plasma; Q-TOF: quadrupole-time of flight; RBC: red blood cell; ROS: reactive oxygen species; RPS6KB/p70S6K: ribosomal protein S6 kinase B; SDS: sodium dodecyl sulfate; S.E.M.: standard error of the mean; SEM: scanning electron microscopy; SGMS: sphingomyelin synthase; SM: sphingomyelin; SMPD/SMase: sphingomyelin phosphodiesterase; SQSTM1/p62: sequestosome 1; TEM: transmission electron microscopy; UGT8/CGT: UDP glycosyltransferase 8; UGCG/GCS: UDP-glucose ceramide glucosyltransferase; ULK1: unc-51 like autophagy activating kinase 1; UPLC: ultra-performance liquid chromatography; PIK3C3/VPS34: phosphatidylinositol 3-kinase catalytic subunit type 3; PtdIns3P: phosphatidylinositol-3-phosphate; WBC: white blood cell; WT: wild type

BACKGROUND More than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of the COVID-19 pandemic together with each country’s policy measures. OBJECTIVE We aimed to develop an online artificial intelligence (AI) system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heat map visualization of policy measures in 171 countries. METHODS The COVID-19 Pandemic AI System (CPAIS) integrated two data sets: the data set from the Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government, which is maintained by the University of Oxford, and the data set from the COVID-19 Data Repository, which was established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting: autoregressive integrated moving average (ARIMA), feedforward neural network (FNN), multilayer perceptron (MLP) neural network, and long short-term memory (LSTM). With regard to 1-year records (ie, whole time series data), records from the last 14 days served as the validation set to evaluate the performance of the forecast, whereas earlier records served as the training set. RESULTS A total of 171 countries that featured in both databases were included in the online system. The CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July 2020 and another peak of 6,368,591 in December 2020. A dynamic heat map with policy measures depicts changes in COVID-19 measures for each country. A total of 19 measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting; the performances of ARIMA, FNN, and the MLP neural network were not stable because their forecast accuracy was only better than LSTM for a few countries. LSTM demonstrated the best forecast accuracy for Canada, as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA (RMSE=317.53169; MAPE=0.4641688) and FNN (RMSE=181.29894; MAPE=0.2708482) demonstrated better performance for South Korea. CONCLUSIONS The CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning–based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

Laxatives are commonly prescribed for constipation management; however, they are recognized as an independent factor associated with cardiovascular diseases. Arteriovenous fistula (AVF) is the closest to the ideal model of hemodialysis (HD) vascular access and part of the cardiovascular system. Our study aims to explore the association of contact laxative use with AVF maturation outcomes in patients undergoing HD. We conducted a multi-center cohort study of 480 contact laxative users and 472 non-users who had undergone initial AVF creation. All patients were followed until the outcomes of AVF maturation were confirmed. Multivariable logistic regression models were performed to evaluate the risk of AVF maturation failure imposed by laxatives. Here, we found that patients who used contact laxatives were significantly associated with an increased risk of AVF maturation failure compared to non-users (adjusted odds ratio, 1.64; p = 0.003). Notably, the risk of AVF maturation failure increased when increasing their average daily doses and cumulative treatment days. In conclusion, our study found a significant dose- and duration-dependent relationship between contact laxative use and an increased risk of AVF maturation failure. Thus, laxatives should be prescribed with caution in this population. Further studies are needed to validate these observations and investigate the potential mechanisms.

ObjectivesTo investigate the effect of the Early Chronic Kidney Disease (CKD) Care Programme on CKD progression in patients with CKD stage I–IIIa.DesignObservational cohort study.SettingTaipei Medical University Research Database from three affiliated hospitals.ParticipantsAdult non-pregnant patients with CKD stage I–IIIa from Taipei Medical University Research Database between 1 January 2012 and 31 August 2017 were recruited. These patients were divided into Early CKD Care Programme participants (case) and non-participants (control). The models were matched by age, sex, estimated glomerular filtration rate and CKD stage with 1:2 propensity score to reduce bias between two groups.Outcome measuresThe risks of CKD stage I–IIIa progression to IIIb between Early CKD Care Programme participants and non-participants.ResultsCompared with the control group, the case group demonstrated more comorbidities and higher proportions of hypertension, diabetes mellitus, gout, dyslipidaemia, heart disease and cerebrovascular disease, but had lower risk of progression to CKD stage IIIb before and (HR 0.72; 95% CI 0.61 to 0.85) and after (adjusted HR (aHR) 0.67; 95% CI 0.55 to 0.81) adjustments. Moreover, Kaplan-Meier analysis revealed the cumulative incidence of CKD stage IIIb was significantly lower in the case group than in the control group. Finally, the programme was an independent protective factor against progression to stage IIIb, especially in patients with CKD stage IIIa before (HR 0.72; 95% CI 0.61 to 0.85) and after (aHR 0.67; 95% CI 0.55 to 0.81) adjustments.ConclusionsThe Early CKD Care Programme is an independent protective factor against progression of early CKD.

Ovarian cancer has a unique tumor microenvironment (TME) that enables cancer-associated fibroblasts (CAFs) to interact with cellular and matrix constituents and influence tumor development and migration into the peritoneal cavity. Collagen type XI alpha 1 (COL11A1) is overexpressed in CAFs; therefore this study examines its role during CAF activation in epithelial ovarian cancer (EOC). Coculturing human ovarian fibroblasts (HOFs) with high COL11A1-expressing EOC cells or exposure to the conditioned medium of these cells prompted the expression of COL11A1 and CAF phenotypes. Conversely, coculturing HOFs with low COL11A1-expressing EOC cells or COL11A1-knockdown abrogated COL11A1 overexpression and secretion, in addition to CAF activation. Increased p-SP1 expression attributed to COL11A1-mediated extracellular signal-regulated kinase activation (ERK) induced p65 translocation into the nucleus and augmented its binding to the insulin-like growth factor binding protein 2 (IGFBP2) promoter, ultimately inducing TGF-β3 activation. The CAF-cancer cell crosstalk triggered interleukin-6 release, which in turn promoted EOC cell proliferation and invasiveness. These in vitro results were confirmed by in vivo findings in a mouse model, showing that COL11A1 overexpression in EOC cells promoted tumor formation and CAF activation, which was inhibited by TGF-β3 antibody. Human tumors with high TGF-β3 levels showed elevated expression of COL11A1 and IGFBP2, which was associated with poor survival. Our findings suggest the possibility that anti-TGF-β3 treatment strategy may be effective in targeting CAFs in COL11A1-positive ovarian tumors.

The microbial colonization in the nasopharynx is a prerequisite for the onset of infectious diseases. For successful infection, pathogens should overcome host defenses as well as compete effectively with the resident microbiota. Hence, elucidating the richness and diversity of the microbiome at the site of pathogen colonization is pivotal. Here, we investigated the adenoidal tissue microbiota collected through adenoidectomy to evaluate the impact of Streptococcus pneumoniae. Prospectively, children with sleep-disordered breathing (SDB) and otitis media with effusion (OME) were enrolled. During adenoidectomy, the nasopharyngeal swab and adenoid tissues were collected to determine the pneumococcal carriage and tissue microbiota, using multiplex PCR and 16S ribosomal RNA (16S rRNA) pyrosequencing. A total of 66 pediatric patients comprising 38 children with SDB and 28 children with OME were enrolled. There was no difference between the bacterial cultures from the surface of the nasopharyngeal adenoid in the SDB and OME groups. Thirty-four samples (17 SDB and 17 OME) underwent 16S rRNA pyrosequencing and fulfilled the criteria for further analysis. The Shannon diversity index for the samples from the SDB patients was found to be higher than that observed for the samples from OME patients, although the difference was not significant (p = 0.095). The Shannon diversity index for the samples negative for the pneumococcal carriage was significantly higher than that for the samples positive for pneumococcal carriage (p = 0.038). Alloprevotella, Staphylococcus, Moraxella, and Neisseriaceae were significantly dominant in the samples positive for the pneumococcal carriage. Dialister was significantly less present in the adenoid tissue positive for the pneumococcal carriage. Streptococcus pneumoniae, one of the most common pathogens of the airway, significantly influences the composition and diversity of the microbiota in the nasopharyngeal adenoid. Thus, bacterial community analysis based on 16S rRNA pyrosequencing allows for better understanding of the relationship between the adenoidal microbial communities.

Glucoregulatory efficiency and ATP production are key regulators for neuronal plasticity and memory formation. Besides its chemotactic and neuroinflammatory functions, the CC chemokine––CCL5 displays neurotrophic activity. We found impaired learning-memory and cognition in CCL5-knockout mice at 4 months of age correlated with reduced hippocampal long-term potentiation and impaired synapse structure. Re-expressing CCL5 in knockout mouse hippocampus restored synaptic protein expression, neuronal connectivity and cognitive function. Using metabolomics coupled with FDG-PET imaging and seahorse analysis, we found that CCL5 participates in hippocampal fructose and mannose degradation, glycolysis, gluconeogenesis as well as glutamate and purine metabolism. CCL5 additionally supports mitochondrial structural integrity, purine synthesis, ATP generation, and subsequent aerobic glucose metabolism. Overexpressing CCL5 in WT mice also enhanced memory-cognition performance as well as hippocampal neuronal activity and connectivity through promotion of de novo purine and glutamate metabolism. Thus, CCL5 actions on glucose aerobic metabolism are critical for mitochondrial function which contribute to hippocampal spine and synapse formation, improving learning and memory., In addition to neuroinflammatory functions, chemokine CCL5 shows strong neurotrophic properties. In this study, we found that CCL5 facilitates hippocampal glucoregulatory efficiency. CCL5 promotes aerobic glucose metabolism, mitochondrial ATP generation and purine synthesis, improving synaptic plasticity and enhancing memory and cognition functions.

Background and Aims To evaluate the association of osteoporosis medications and outcomes of patients at risk of CKD. Method We included patients with stage 3-5 CKD from three Taipei Medical University-affiliated hospitals between January 1, 2011 and August 31, 2016. Patients were divided into two groups based on whether they received osteoporosis medications (bisphosphonates, raloxifene, teriparatide, or denosumab) and then matched at a 1:1 ratio by using propensity scores. The outcomes of interest were bone fractures, cardiovascular (CV) events, and all-cause mortality. Cox proportional hazard regression models were applied to identify the risk factors. Results A total of 39128 patients with CKD were included. After propensity score matching, 763 patients were in the study and control groups, respectively. The mean age of patients was 71.9 ± 12.5 years, and 34.4% of patients were men. After a median follow-up of 2.9 years, the incidence rates of bone fracture, CV events, and all-cause mortality were 10.4%, 7.5%, and 7.5%, respectively. Multivariate analysis results indicated that osteoporosis treatment was note associated with reduced risks of bone fracture (HR 0.85; 95% CI 0.43–1.68; P = 0.65), CV events (HR 0.82; 95% CI 0.56–1.20; P = 0.30), and all-cause mortality (HR 0.74; 95% CI 0.51–1.08; P = 0.12). Subgroup analysis demonstrated only patients with CKD stage 3 who received osteoporosis medications associated with better survival. Conclusion Osteoporosis medications did not reduce bone fractures, CV events or mortality in patients with CKD. Improved overall survival was observed in patients with CKD stage 3.

Mesenchymal stem cell (MSC) is mechanosensitive and the respond to mechanical force is pattern specific. We previously reported that oscillatory shear stress at 0.5 ± 4 dyne/cm 2 guided MSCs polarity vertical to net flow direction before apolaric stage at 30 min resulting in phosphorylation of β-catenin and inhibition of Wnt signaling . This time, we explored laminar shear stress (LS) at 0.5 dyne/cm 2 polarized MSCs by guiding F-actin orientation parallel to the flow direction before apolarity at 30 min accompanied with activation of Wnt signaling. Time-dependent microarray analysis supported cell-cell junctional complex of MSCs was the major mechanosensor on MSCs to respond 0.5 dyne/cm 2 LS. Three-dimensional immunofluorescence image confirmed LS promoting β-catenin nuclear localization during 15 min to 1 h with a peak at 30 min. Functional analysis of proteomic study on MSC with 30 min LS stimulation indicated that upregulation of β-catenin downstream proteins related to cardiovascular development, endothelial cell protection and angiogenesis . Conditioned medium from MSCs with 30 min LS stimulation improved the viability of human endothelial cells from oxidative damage . In conclusion, 0.5 dyne/cm 2 LS on MSCs for 30 min guides MSCs lack of polarity and promotes β-catenin nuclear translocation favoring Wnt activation and paracrine cardiovascular support.

Simple Summary High collagen type XI alpha 1 (COL11A1) levels are associated with tumor progression, chemoresistance, and poor patient survival in several cancer types. We examined the role of miRNAs in regulating COL11A1 expression. This study highlighted the importance of miR-335 in downregulating COL11A1-mediated ovarian tumor progression, chemoresistance, and poor survival and suggested its potential application as a therapeutic target. Abstract High collagen type XI alpha 1 (COL11A1) levels are associated with tumor progression, chemoresistance, and poor patient survival in several cancer types. MicroRNAs (miRNAs) are dysregulated in multiple cancers, including epithelial ovarian carcinoma (EOC); however, the regulation of COL11A1 by miRNAs in EOC remains unclear. We examined the role of miRNAs in regulating COL11A1 expression. We identified miR-509 and miR-335 as the candidate miRNAs through an online database search. EOC cell treatment with miR-335 mimics abrogated COL11A1 expression and suppressed cell proliferation and invasion, besides increasing the sensitivity of EOC cells to cisplatin. Conversely, treatment with miR-335 inhibitors prompted cell growth/invasiveness and chemoresistance of EOC cells. miR-335 inhibited COL11A1 transcription, thus reducing the invasiveness and chemoresistance of EOC cells via the Ets-1/MMP3 and Akt/c/EBPβ/PDK1 axes, respectively. Furthermore, it did not directly regulate PDK1 but increased PDK1 ubiquitination and degradation through COL11A1 inhibition. In vivo findings highlighted significantly decreased miR-335 mRNA expressions in EOC samples. Furthermore, patients with low miR335 levels were susceptible to advanced-stage cancer, poor response to chemotherapy, and early relapse. This study highlighted the importance of miR-335 in downregulating COL11A1-mediated ovarian tumor progression, chemoresistance, and poor survival and suggested its potential application as a therapeutic target.

Background Oral verrucous hyperplasia is commonly observed in the oral cavity of betel quid chewers and is a potential malignant disorder. However, the prognostic factors and genetic alterations of oral verrucous hyperplasia are unclear. Methods We calculate the survival rate and prognostic factors using a Kaplan-Meier analysis and Cox proportional hazards regression model. Copy number variations were analyzed using a single-nucleotide polymorphism (SNP) array. Results The 5-year disease-free and cancer-free survival rates of patients with oral verrucous hyperplasia were approximately 40% and 70%, respectively. Heavy betel quid chewing, advanced oral submucous fibrosis, and nonbuccal and nontongue lesions were risk factors for malignant transformation, whereas dysplasia did not affect outcomes. The gene amplification of CTTN, FOLR3, ORAOV1, PPFIA1, and RNF121 were associated with the poor prognosis of oral verrucous hyperplasia. Conclusion Heavy betel quid chewing, advanced oral submucous fibrosis, and nonbuccal and nontongue lesions are high-risk factors of patients with oral verrucous hyperplasia. The 5-copy number variation-associated genes could be used for early diagnosis and predicting the prognosis.

The molecular heterogeneity of gene expression profiles of glioblastoma multiforme (GBM) are the most important prognostic factors for tumor recurrence and drug resistance. Thus, the aim of this study was to identify potential target genes related to temozolomide (TMZ) resistance and GBM recurrence. The genomic data of patients with GBM from The Cancer Genome Atlas (TCGA, 154 primary and 13 recurrent tumors) and a local cohort (29 primary and 4 recurrent tumors), samples from different tumor regions from a local cohort (29 tumor and 25 peritumoral regions), and Gene Expression Omnibus data (GSE84465, single-cell RNA sequencing, 3589 cells) were included in this study. Critical gene signatures were identified based an analysis of differentially expressed genes (DEGs). DEGs were further used to evaluate gene enrichment levels among primary and recurrent GBMs and different tumor regions through gene set enrichment analysis. Protein–protein interactions (PPIs) were incorporated into gene regulatory networks to identify the affected metabolic pathways. The enrichment levels of 135 genes were identified in the peritumoral regions as being risk signatures for tumor recurrence. Fourteen genes (DVL1, PRKACB, ARRB1, APC, MAPK9, CAMK2A, PRKCB, CACNA1A, ERBB4, RASGRF1, NF1, RPS6KA2, MAPK8IP2, and PPM1A) derived from the PPI network of 135 genes were upregulated and involved in the regulation of cancer stem cell (CSC) development and relevant signaling pathways (Notch, Hedgehog, Wnt, and MAPK). The single-cell data analysis results indicated that 14 key genes were mainly expressed in oligodendrocyte progenitor cells, which could produce a CSC niche in the peritumoral region. The enrichment levels of 336 genes were identified as biomarkers for evaluating TMZ resistance in the solid tumor region. Eleven genes (ARID5A, CDC42EP3, CDKN1A, FLT3, JUNB, MAP2K3, MYBPC2, RGS14, RNASEK, TBC1D30, and TXNDC11) derived from the PPI network of 336 genes were upregulated and may be associated with a high risk of TMZ resistance, these genes were identified in both the TCGA and local cohorts. Furthermore, the expression patterns of ARID5A, CDKN1A, and MAP2K3 were identical to the gene signatures of TMZ-resistant cell lines. The identified enrichment levels of the two gene sets expressed in tumor and peritumoral regions are potentially helpful for evaluating TMZ resistance in GBM. Moreover, these key genes could be used as biomarkers, potentially providing new molecular strategies for GBM treatment.

PurposeTo compare the risk of neurodevelopmental disorders in children conceived via intracytoplasmic sperm injection (ICSI) and those conceived naturally.Materials and methodsA population-based cohort study using data retrieved from the Taipei Medical University Research Database (TMURD) from January, 2004 to August, 2016. The data included maternal pregnancy history, perinatal history and developmental follow up of their babies up to 5 years of age. The study included 23885 children, of whom 23148 were naturally conceived and 737 were conceived via ICSI. Neurodevelopmental disorders defined by 21 ICD-9-CM codes.ResultsOf the 23885 children enrolled for analysis, 2778 children were included for further subgrouping analysis after propensity matching to reduce bias from maternal factors. The single-birth group included 1752 naturally conceived (NC) children and 438 ICSI children. The multiple-birth group included 294 NC and 294 ICSI children. The risk of neurodevelopmental disorders was not increased for ICSI children in both groups (single birth: adjusted hazard ratio aHR = 0.70, 95% CI = 0.39–1.27,p= 0.243; multiple-birth group aHR = 0.77, 95% CI = 0.43–1.35,p= 0.853). In the single-birth group, multivariate analyses showed that male sex (aHR = 1.81, 95% CI = 1.29–2.54,p< 0.001), and intensive care unit (ICU) admission (aHR = 3.10, 95% CI = 1.64–5.86,p< 0.001) were risk factors for neurodevelopmental disorders. In the multiple-birth group, multivariate analyses demonstrated that ICU admission (aHR = 3.58, 95% CI = 1.82–7.04,p< 0.001), was risk factor for neurodevelopmental disorders.ConclusionOur study indicated that the use of ICSI does not associated with higher risk of neurodevelopmental disorders in the offspring. But male sex, and ICU admission do have increased risk of neurodevelopmental disorders. However, long term follow up of this cohort on health outcomes in adolescence and adulthood will strengthen the conclusions that ICSI is safe regarding offspring long term outcome.

Aberrant protein glycosylation could be a distinct surface-marker of cancer cells that influences cancer progression and metastasis because glycosylation can regulate membrane protein folding which alters receptor activation and changes epitope exposure for antibody (Ab) recognition. Carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6), a glycophosphoinositol-anchored protein, is a heavily glycosylated tumor antigen. However, the clinical significance and biological effect of CEACAM6 glycosylation has not been addressed in cancers. We recently developed an anti-CEACAM6 Ab (TMU) from an immune llama library which can be engineered to a single-domain (sd)Ab or a heavy-chain (HC)Ab. The TMU HCAb specifically recognized glycosylated CEACAM6 compared to the conventional antibodies. Using the TMU HCAb, we found that glycosylated CEACAM6 was a tumor marker associated with recurrence in early-stage OSCC (oral squamous cell carcinoma) patients. CEACAM6 promoted OSCC cell invasion, migration, cytoskeletal rearrangement, and metastasis via interaction with epidermal growth factor (EGF) receptor (EGFR) and enhancing EGFR activation, clustering and intracellular signaling cascades. These functions were modulated by N-acetylglucosaminyltransferase 5 (MGAT5) which mediated N-glycosylation at Asn256 (N256) of CEACAM6. Finally, the TMU sdAb and HCAb treatment inhibited the migration, invasion and EGF-induced signaling in CEACAM6-overexpressing cells. In conclusion, the complex N-glycosylation of CEACAM6 is critical for EGFR signaling of OSCC invasion and metastasis. Targeting glycosylated CEACAM6 with the TMU sdAb or TMU HCAb could be a feasible therapy for OSCC.

Background More than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of the COVID-19 pandemic together with each country’s policy measures. Objective We aimed to develop an online artificial intelligence (AI) system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heat map visualization of policy measures in 171 countries. Methods The COVID-19 Pandemic AI System (CPAIS) integrated two data sets: the data set from the Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government, which is maintained by the University of Oxford, and the data set from the COVID-19 Data Repository, which was established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting: autoregressive integrated moving average (ARIMA), feedforward neural network (FNN), multilayer perceptron (MLP) neural network, and long short-term memory (LSTM). With regard to 1-year records (ie, whole time series data), records from the last 14 days served as the validation set to evaluate the performance of the forecast, whereas earlier records served as the training set. Results A total of 171 countries that featured in both databases were included in the online system. The CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July 2020 and another peak of 6,368,591 in December 2020. A dynamic heat map with policy measures depicts changes in COVID-19 measures for each country. A total of 19 measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting; the performances of ARIMA, FNN, and the MLP neural network were not stable because their forecast accuracy was only better than LSTM for a few countries. LSTM demonstrated the best forecast accuracy for Canada, as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA (RMSE=317.53169; MAPE=0.4641688) and FNN (RMSE=181.29894; MAPE=0.2708482) demonstrated better performance for South Korea. Conclusions The CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning–based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

Background MicroRNAs (miRs) regulate gene expression in heart failure. Circulating miRs as biomarkers for heart failure in mitral regurgitation patients (MR) remain unexplored. Methods This case–control study enrolled 32 MR patients with heart failure, 16 asymptomatic MR patients, and 12 control subjects without heart failure. We used next generation sequencing to study the gene expression profiles in the sera, and quantitative RT-PCR to study serum and tissue miRs in the left atria. Results Next generation sequencing analysis and enrichment analysis showed that 25 miRs were differentially expressed in the sera of MR patients with heart failure compared to control subjects. The circulating miR-148b-3p (p=0.002) and miR-409-3p (p=0.010) were significantly down-regulated in the MR patients with heart failure compared to control subjects. However, only circulation miR-148b-3p was significantly down-regulated in the MR patients without heart failure compared to control subjects (p=0.009). The tissue miR-409-3p was significantly down-regulated in the MR patients with heart failure compared to 3 purchased normal controls (p=0.041). Notably, the tissue RASGRP3 mRNA, target gene of miR-409-3p, was significantly up-regulated in the MR patients with heart failure compared to normal controls (p=0.010). The tissue FRY (p=0.010) and GADD45A (p=0.010) mRNAs, target genes of miR-148b-3p, were significantly up-regulated in the MR patients with heart failure compared to normal controls. Conclusions Circulating miR-148b-3p might serve as biomarker for future development of heart failure and miR-409-3p might serve as biomarker for incident heart failure in MR patients.

Simple Summary Characterization of immunophenotypes in GBM is important for therapeutic stratification and helps predict treatment response and prognosis. However, identifying immunophenotypes of patients with GBM requires multiple laboratory experiments and is time consuming. We developed a non-invasive method to evaluate enrichment levels of CTL, aDC, Treg, and MDSC immune cells to classify immunophenotypes of GBM tumor microenvironment with radiomic features of MR imaging. Five immunophenotypes (G1–G5) of GBM can be classified with specific gene set enrichment analysis. G2 had the worst prognosis and comprised highly enriched MDSCs and lowly enriched CTLs. G3 had the best prognosis and comprised lowly enriched MDSCs and Tregs and highly enriched CTLs. Moreover, the developed radiomics models can successfully identified these two groups by immune cell subsets enriched levels prediction. Therefore, it is possible to characterize immunophenotypes of GBM and predict patient prognosis with radiomics methods. Abstract Characterization of immunophenotypes in glioblastoma (GBM) is important for therapeutic stratification and helps predict treatment response and prognosis. Radiomics can be used to predict molecular subtypes and gene expression levels. However, whether radiomics aids immunophenotyping prediction is still unknown. In this study, to classify immunophenotypes in patients with GBM, we developed machine learning-based magnetic resonance (MR) radiomic models to evaluate the enrichment levels of four immune subsets: Cytotoxic T lymphocytes (CTLs), activated dendritic cells, regulatory T cells (Tregs), and myeloid-derived suppressor cells (MDSCs). Independent testing data and the leave-one-out cross-validation method were used to evaluate model effectiveness and model performance, respectively. We identified five immunophenotypes (G1 to G5) based on the enrichment level for the four immune subsets. G2 had the worst prognosis and comprised highly enriched MDSCs and lowly enriched CTLs. G3 had the best prognosis and comprised lowly enriched MDSCs and Tregs and highly enriched CTLs. The average accuracy of T1-weighted contrasted MR radiomics models of the enrichment level for the four immune subsets reached 79% and predicted G2, G3, and the “immune-cold” phenotype (G1) according to our radiomics models. Our radiomic immunophenotyping models feasibly characterize the immunophenotypes of GBM and can predict patient prognosis.

Lung cancer is the most occurring cancer type, and its mortality rate is also the highest, among them lung adenocarcinoma (LUAD) accounts for about 40 % of lung cancer. There is an urgent need to develop a prognosis prediction model for lung adenocarcinoma. Previous LUAD prognosis studies only took single-omics data, such as mRNA or miRNA, into consideration. To this end, we proposed a deep learning-based autoencoding approach for combination of four-omics data, mRNA, miRNA, DNA methylation and copy number variations, to construct an autoencoder model, which learned representative features to differentiate the two optimal patient subgroups with a significant difference in survival (P = 4.08e-09) and good consistency index (C-index = 0.65). The multi-omics model was validated though four independent datasets, i.e. GSE81089 for mRNA (n = 198, P = 0.0083), GSE63805 for miRNA (n = 32, P = 0.018), GSE63384 for DNA methylation (n = 35, P = 0.009), and TCGA independent samples for copy number variations (n = 94, P = 0.0052). Finally, a functional analysis was performed on two survival subgroups to discover genes involved in biological processes and pathways. This is the first study incorporating deep autoencoding and four-omics data to construct a robust survival prediction model, and results show the approach is useful at predicting LUAD prognostication.

Table S1. Title: 104 common survival-related genes were identified from patients with GBM and those with LGG. Description: Summarization of Cox model results for 104 survival-relevant common genes between LGG and GBM with table. (PDF 38 kb)

Nasal microbial communities may have crucial implications for human health, including for residents of healthcare institutes (HCIs). Factors that determine the diversity of nasal microbiota in HCIs remain unclear. Herein, we used 16S rRNA amplicon sequencing to investigate the relationship between nasal and surface microbiota in three HCIs. Participants were classified into a hospitalised or nonhospitalised group based on their most recent date of hospitalisation. A total of 88 nasal samples and 83 surface samples were analysed. Dysgonomonas and Corynebacterium were the most abundant taxa in the surface and nasal samples, respectively. Significant differences were discovered in microbiota diversity among HCIs when comparing the surface and nasal samples. Fifteen taxa were identified as present in all the surface and nasal samples. SourceTracker analysis revealed that the ventilation conditions of environment might be associated with the proportion of shared microbial communities between nasal and surface. Additionally, as compared with the nonhospitalised group, the hospitalised group had a higher proportion of surface microbiota in their nasal samples, which might lead to a higher risk of human-related microorganisms or pathogens colonising the nasal cavity. The data suggest that nasal bacterial diversity could be influenced by both health status and living environment. Our results therefore highlight the importance of the indoor environment for HCI residents.

Background Recent studies have proposed several gene signatures as biomarkers for different grades of gliomas from various perspectives. However, most of these genes can only be used appropriately for patients with specific grades of gliomas. Methods In this study, we aimed to identify survival-relevant genes shared between glioblastoma multiforme (GBM) and lower-grade glioma (LGG), which could be used as potential biomarkers to classify patients into different risk groups. Cox proportional hazard regression model (Cox model) was used to extract relative genes, and effectiveness of genes was estimated against random forest regression. Finally, risk models were constructed with logistic regression. Results We identified 104 key genes that were shared between GBM and LGG, which could be significantly correlated with patients’ survival based on next-generation sequencing data obtained from The Cancer Genome Atlas for gene expression analysis. The effectiveness of these genes in the survival prediction of GBM and LGG was evaluated, and the average receiver operating characteristic curve (ROC) area under the curve values ranged from 0.7 to 0.8. Gene set enrichment analysis revealed that these genes were involved in eight significant pathways and 23 molecular functions. Moreover, the expressions of ten (CTSZ, EFEMP2, ITGA5, KDELR2, MDK, MICALL2, MAP 2 K3, PLAUR, SERPINE1, and SOCS3) of these genes were significantly higher in GBM than in LGG, and comparing their expression levels to those of the proposed control genes (TBP, IPO8, and SDHA) could have the potential capability to classify patients into high- and low- risk groups, which differ significantly in the overall survival. Signatures of candidate genes were validated, by multiple microarray datasets from Gene Expression Omnibus, to increase the robustness of using these potential prognostic factors. In both the GBM and LGG cohort study, most of the patients in the high-risk group had the IDH1 wild-type gene, and those in the low-risk group had IDH1 mutations. Moreover, most of the high-risk patients with LGG possessed a 1p/19q-noncodeletion. Conclusion In this study, we identified survival relevant genes which were shared between GBM and LGG, and those enabled to classify patients into high- and low-risk groups based on expression level analysis. Both the risk groups could be correlated with the well-known genetic variants, thus suggesting their potential prognostic value in clinical application. Electronic supplementary material The online version of this article (10.1186/s12920-019-0479-6) contains supplementary material, which is available to authorized users.

Figure S1. Phylum-level microbiota of vaginal samples. (PDF 222â kb)

Figure S4. Principal coordinate analysis (PCoA) plots of vaginal bacterial communities. The PCoA plots were generated by the unweighted UniFrac distance, weighted UniFrac distance, and Bray-Curtis distance. (PDF 426â kb)

Figure S8. Principal component analysis of 32 predictive functional modules using PICRUSt in level 3 KEGG database. (PDF 324â kb)

Table S1. Descriptive statistics of Shannon diversity and richness. Table S2. The p-values for any two groups (PDF 282â kb)

Figure S3. Boxplot of richness and Shannon diversity index in the two subgroups of the A − N− group. (PDF 206 kb)

Figure S6. Distribution of most abundant taxa across samples. (PDF 405â kb)

Figure S5. Hierarchical clustering method with Bray-Curtis metric. (PDF 368â kb)

Figure S7. Correlation network of vaginal microbiota in the Aâ +â N* group. (PDF 202â kb)

Figure S9. Boxplots of 12 predictive functional categories that differed significantly among Nugent score test groups but not between the Amsel criteria groups (Pâ

Figure S2. The Pearson correlation coefficients between richness and Shannon diversity index. (PDF 405â kb)

Background High-risk HPV subtypes are driving forces for human cancer development: HPV-16 and HPV-18 are responsible for most HPV-caused cancers. Objective This review describes the present knowledge on HR-HPV genomes coding potential for viral miRNAs. Methods HPV subtypes miRNA database, VIRmiRtar, has been constructed applying bioinformatics and a computational method, ViralMir, exploiting structural features, the presence of hairpins, and validation by comparison with RNA sequencing datasets. Results Several miRNA candidates have been localised in the genomes of high-risk HPV subtypes. Among these, HPV-16 miR-1, miR-2 and miR-3. The database contains a list of host candidate gene targets that may be responsible for the oncogenesis in the various cellular environments. Conclusion miRNA silencing therapies, based on specific cellular uptake of miRNA mimics and antagomiRs, directed towards HPV encoded miRNAs and/or microRNAs deregulated in the host cells, could be a valuable approach to support pharmaceutical interventions in the treatment of HPV dependent cancers.