Cytoplasmic Expression of the EGFL6 Protein Is an Independent Prognostic Factor for Shortened Patient Survival in Human Hepatocellular Carcinoma.

BACKGROUND/AIM: Hepatocellular carcinoma (HCC) is the most common primary liver tumor and the second leading cause of cancer-related deaths worldwide. The current study aimed to investigate the clinical relevance of the epidermal growth factor-like domain multiple 6 (EGFL6) expression in HCC and to evaluate whether the expression of EGFL6 in HCC has diagnostic and prognostic significance. PATIENTS AND METHODS: This study aimed to investigate EGFL6 protein expression levels in 260 HCC tissue specimens using immunohistochemical analyses. The immunohistochemical study demonstrated strong EGFL6 expression in the cytoplasm of non-tumor or normal hepatocytes. RESULTS: The findings revealed that 98 patients exhibited low EGFL6 expression, while 162 patients displayed high EGFL6 expression. We explored the associations between cytoplasmic EGFL6 expression and the clinicopathological features of HCC. Decreased cytoplasmic EGFL6 expression exhibited significant correlations with worse cellular differentiation, higher T classification, vascular invasion, higher stage, and tumor recurrence. Survival analyses, using Kaplan-Meier survival curves for HCC patients, revealed that those with reduced cytoplasmic EGFL6 expression experienced significantly worse disease-free survival (DFS) and disease-specific survival (DSS). Univariate and multivariate analyses identified EGFL6 as an independent predictor for decreased expression, differentiation grade, vascular invasion, stage, or recurrence in cases of DFS or DSS in HCC. CONCLUSION: This study represents, to the best of our knowledge, the first investigation into the expression of EGFL6 protein in HCC. Taken together, our findings strongly suggest that EGFL6 likely plays a crucial role in the pathogenesis of HCC and indicates that targeting EGFL6 could be a promising therapeutic strategy.

Translating Risk Ratios, Baseline Incidence, and Proportions Diseased to Correlations and Chi-Squared Statistics: Simulation Epidemiology.

Background In a population, when a disease is causing a symptom, the overall symptom incidence can be determined by proportions diseased, baseline symptom incidence, and risk ratios of developing the symptom due to the disease. There are various measures of association, including risk ratios. How risk ratios are linked to other measures of association, such as correlation coefficients and chi-squared statistics, has not been explicitly discussed. This study aims to demonstrate their connection via equations and simulations, assuming one disease causes symptoms. Methods The equations for correlation coefficients and chi-square statistics were rewritten using epidemiological measures: proportions diseased, baseline symptom incidence, and risk ratios. Simulations were conducted to test the accuracy of the equations. The baseline symptom incidence and the proportions diseased were assumed to be 0.05, 0.1, 0.2, 0.4, or 0.8. The risk ratios were assumed to be 0.5, 1, 2, 5, 10, and 25. Another disease that correlates with this disease was created (correlation = 0, 0.3, or 0.7). For each combination of symptom incidence, proportions diseased, risk ratios, and between-disease correlations, 10,000 subjects were simulated. The correlation coefficients and chi-squared statistics were approximated with epidemiologic measures and their interaction terms. R-squared was used to assess the importance of the epidemiologic measures. Results In the simulations, the overall symptom incidence, correlation coefficients, and chi-squared statistics between the disease and symptoms could be fully explained by the epidemiologic measures in the equations (R-squared = 1). When approximating correlation coefficients and chi-squared statistics with individual measures or their interaction terms, the importance of these measures depended on whether the at-risk incidence reached 1 or not. The numbers in the four cells in the contingency table predicted correlation coefficients, or chi-squared statistics, with different R-squared. Conclusion To our knowledge, this is the first study to translate the three epidemiologic measures (risk ratios, baseline symptom incidence, and proportions diseased) into correlation coefficients and chi-squared statistics. However, chi-squared statistics also depend on sample sizes. This study also provides a platform for developing teaching cases for students to investigate the causal relationship between diseases and symptoms or exposure and outcomes.

Correlation Between Low Cytoplasmic Expression of XBP1 and the Likelihood of Surviving Hepatocellular Carcinoma.

BACKGROUND/AIM: Our objectives in this study were to (i) evaluate the clinical significance of X-box-binding protein 1 (XBP1) expression in cases of hepatocellular carcinoma (HCC) and (ii) assess the potential of XBP1 to be used as a prognostic biomarker. PATIENTS AND METHODS: The expression of XBP1 protein in 267 HCC tissue specimens was measured using immunohistochemistry in order to characterize the associations among XBP1 expression, clinicopathological factors and survival outcomes. Survival analysis using follow-up data was used to assess the prognostic value of XBP1 in cases of HCC. Immunohistochemistry revealed a significant decrease in cytoplasmic XBP1 protein expression in HCC tumor tissue. RESULTS: Immunoreactivity results showed that low cytoplasmic XBP1 expression was significantly associated with vascular invasion, as well as poor 5-year overall survival and long-term disease-specific (DSS) and disease-free (DFS) survival rates. Kaplan-Meier survival curves further confirmed a significant association between low cytoplasmic XBP1 protein expression and poor DSS and DFS. Univariate and multivariate analyses revealed that XBP1 expression, tumor differentiation, vascular invasion, tumor stage, and the rate of recurrence were linked to DSS, while low cytoplasmic XBP1 expression remained an independent predictor of poor DSS. Our analysis also revealed that XBP1 expression, tumor differentiation, vascular invasion, and T classification were linked to DFS, while low cytoplasmic XBP1 expression remained an independent predictor of poor DFS. CONCLUSION: Low cytoplasmic XBP1 protein expression may play an important role in the pathogenesis of HCC, which suggests that XBP1 could potentially be targeted to benefit therapeutic strategies for HCC.

Impact of LINC00312 gene polymorphism coupled with habitual risks on buccal mucosa cancer.

Oral squamous cell carcinoma (OSCC) is a prevalent and lethal malignancy with a diverse etiology. LINC00312 is a long intergenic non-coding RNA that functions as a signal hub to regulate the progression and treatment of head and neck cancer. The aim of this study was to evaluate the effect of LINC00312 single nucleotide polymorphisms (SNPs) on the development of oral cancer. Two LINC00312 SNPs, namely rs12497104 and rs164966, were investigated among 469 male patients with cancer of buccal mucosa and 1194 gender- and age-matched controls. No significant correlation was observed between these two SNPs and the occurrence of OSCC in the case and control groups. While assessing the clinicopathological features, carriers of at least one minor allele of rs164966 (GA and GG) were less prone to develop lymph node metastasis (adjusted odds ratio [AOR], 0.666; 95% confidence interval [CI], 0.447-0.991; p=0.045) in comparison with homozygous carriers of the major allele (AA). Subsequent stratifying surveys revealed that this genetic association with nodal spread was seen only in cases who habitually chewed betel quid (AOR, 0.616; 95% CI, 0.386-0.985; p=0.042) or smoked cigarettes (AOR, 0.612; 95% CI, 0.393-0.953; p=0.029), but undetected in cases free of these main behavioral risks. Our results indicate an interactivity of LINC00312 rs164966 with lifestyle-related risks on modulating OSCC progression.

Mental Illness Diagnoses May Not Cause All Mental Symptoms: A Simulation Study for Major Depressive Episodes, Dysthymic Disorder, and Manic Episodes.

Objectives This study aims to understand the statistical significance of the associations between diagnoses and symptoms based on simulations that have been used to understand the interpretability of mental illness diagnoses. Methods The symptoms for the diagnosis of major depressive episodes, dysthymic disorder, and manic episodes were extracted from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR, American Psychiatric Association, Philadelphia, Pennsylvania). Without real-world symptom data, we simulated populations using various combinations of symptom prevalence and correlations. Assuming symptoms occurred with similar prevalence and correlations, for each combination of symptom prevalence (0.05, 0.1, 0.3, 0.5, and 0.7) and correlation (0, 0.1, 0.4, 0.7, and 0.9), 100 cohorts with 10,000 individuals were randomly created. Diagnoses were made according to the DSM-IV-TR criteria. The associations between the diagnoses and their input symptoms were quantified with odds ratios and correlation coefficients. P-values from 100 cohorts for each combination of symptom prevalence and correlation were summarized. Results Three mental illness diagnoses were not significantly correlated with their own symptoms in all simulations, particularly when symptoms were not correlated, except for the symptom in the major criteria of major depressive episodes or dysthymic disorder. The symptoms for the diagnosis of major depressive episodes and dysthymic disorder were significantly correlated with these two diagnoses in some simulations, assuming 0.1, 0.4, 0.7, or 0.9 symptom correlations, except for one symptom. The overlap in the input symptoms for the diagnosis of major depressive episodes and dysthymic disorder also leads to significant correlations between these two diagnoses, assuming 0.1, 0.4, 0.7, and 0.9 correlations between input symptoms. Manic episodes are not significantly associated with the input symptoms of major depressive episodes and dysthymic disorder. Conclusion There are challenges to establish the causation between psychiatric symptoms and mental illness diagnoses. There is insufficient prevalence and incidence data to show all psychiatric symptoms exist or can be observed in patients. The diagnostic accuracy of symptoms to detect a disease cause is far from perfect. Assuming the symptoms of three mood disorders may present in patients, three diagnoses are not significantly associated with all psychiatric symptoms used to diagnose them. The diagnostic criteria of the three diagnoses have not been designed to guarantee significant associations between symptoms and diagnoses. Because statistical associations are important for making causal inferences, there may be a lack of causation between diagnoses and symptoms. Previous research has identified factors that lead to insignificant associations between diagnoses and symptoms, including biases due to data processing and a lack of epidemiological evidence to support the design of mental illness diagnostic criteria.

Investigation of the impact of Globo-H expression on the progression of gastric cancer.

Globo-H (GH), a globo-series glycosphingolipid antigen that is synthesized by key enzymes ß1,3-galactosyltransferase V (ß3GalT5), fucosyltransferase (FUT) 1 and 2, is highly expressed on a variety of epithelial cancers rendering it a promising target for cancer immunotherapy. GH-targeting antibody-drug conjugate has been demonstrated an excellent tumor growth inhibition potency in animal models across multiple cancer types including Gastric cancer (GC). This study aims to further investigate the GH roles in GC. Significant correlations were observed between high mRNA expression of GH-synthetic key enzymes and worse overall survival (OS)/post-progression survival for GC patients based on the data from "Kaplan-Meier plotter" database (n=498). The level of GH expression was evaluated in clinical adenocarcinoma samples from 105 patients with GC by immunohistochemistry based on H-score. GH expression (H score ≥ 20; 33.3%) was significantly associated with a poor disease specific survival (DSS) and invasiveness in all samples with P=0.029 and P=0.013, respectively. In addition, it is also associated with shorter DSS and OS in poorly differentiated tumors with P=0.033 and P=0.045, respectively. Particularly, with patients ≥ 65 years of age, GH expression is also significantly associated with the stages (P=0.023), differentiation grade (P=0.038), and invasiveness (P=0.026) of the cancer. Sorted GC NCI-N87 cells with high level of endogenous GH showed higher proliferative activity compared with low-GH-expressing cells based on PCNA expression. Micro-western array analysis on high-GH-expressing GC cells indicated an upregulation in HER2-related signaling proteins including phospho-AKT/P38/JNK and Cyclin D1/Cyclin E1 proteins. Moreover, GH level was shown to be correlated with expression of total HER2 and caveolin-1 in GC cells. Immunoprecipitation study suggested that there are potential interactions among GH, caveolin-1, and HER2. In conclusions, GH level was significantly associated with the worse survival and disease progression in GC patients, especially in older patients. Enhanced cell proliferation activity through interactions among GH, HER2, and caveolin-1 interactions may contribute to GH induced tumor promotion signaling in GC. GH-targeting therapy may be a viable option for the treatment of GC patients.

The Upper Limits of Risk Ratios and Recommendations for Reporting Risk Ratios, Odds Ratios, and Rate Ratios.

Background Relative measures, including risk ratios (RRs) and odds ratios (ORs), are reported in many epidemiological studies. RRs represent how many times a condition is likely to develop when exposed to a risk factor. The upper limit of RRs is the multiplicative inverse of the baseline incidence. Ignoring the upper limits of RRs can lead to reporting exaggerated relative effect sizes. Objectives This study aims to demonstrate the importance of such upper limits for effect size reporting via equations, examples, and simulations and provide recommendations for the reporting of relative measures. Methods Equations to calculate RRs and their 95% confidence intervals (CIs) were listed. We performed simulations with 10,000 simulated subjects and three population variables: proportions at risk (0.05, 0.1, 0.3, 0.5, and 0.8), baseline incidence (0.05, 0.1, 0.3, 0.5, and 0.8), and RRs (0.5, 1.0, 5.0, 10.0, and 25.0). Subjects were randomly assigned with a risk based on the set of proportions-at-risk values. A disease occurred based on the baseline incidence among those not at risk. The incidence of those at risk was the product of the baseline incidence and the RRs. The 95% CIs of RRs were calculated according to Altman. Results The calculation of RR 95% CIs is not connected to the RR upper limits in equations. The RRs in the simulated populations at risk could reach the upper limits of RRs: multiplicative inverse of the baseline incidence. The upper limits to the derived RRs were around 1.25, 2, 3.3, 10, and 20, when the assumed baseline incidence rates were 0.8, 0.5, 0.3, 0.2, and 0.05, respectively. We demonstrated five scenarios in which the RR 95% CIs might exceed the upper limits. Conclusions Statistical significance does not imply the RR 95% CIs not exceeding the upper limits of RRs. When reporting RRs or ORs, the RR upper limits should be assessed. The rate ratio is also subject to a similar upper limit. In the literature, ORs tend to overestimate effect sizes. It is recommended to correct ORs that aim to approximate RRs assuming outcomes are rare. A reporting guide for relative measures, RRs, ORs, and rate ratios, is provided. Researchers are recommended to report whether the 95% CIs of relative measures, RRs, ORs, and rate ratios, overlap with the range of upper limits and discuss whether the relative measure estimates may exceed the upper limits.

Critical Hierarchical Appraisal and Reporting Tool for Composite Measures (CHAOS).

Background Composite measures are often used to represent certain concepts that cannot be measured with single variables and can be used as diagnoses, prognostic factors, or outcomes in clinical or health research. For example, frailty is a diagnosis confirmed based on the number of age-related symptoms and has been used to predict major health outcomes. However, undeclared assumptions and problems are prevalent among composite measures. Thus, we aim to propose a reporting guide and an appraisal tool for identifying these assumptions and problems. Methods We developed this reporting and assessment tool based on evidence and the consensus of experts pioneering research on index mining and syndrome mining. We designed a development framework for composite measures and then tested and revised it based on several composite measures commonly used in medical research, such as frailty, body mass index (BMI), mental illness diagnoses, and innovative indices mined for mortality prediction. We extracted review questions and reporting items from various issues identified by the development framework. This panel reviewed the identified issues, considered other aspects that might have been neglected in previous studies, and reached a consensus on the questions to be used by the reporting and assessment tool. Results We selected 19 questions in seven domains for reporting or critical assessment. Each domain contains review questions for authors and readers to critically evaluate the interpretability and validity of composite measures, which include candidate variable selection, variable inclusion and assumption declaration, data processing, weighting scheme, methods to aggregate information, composite measure interpretation and justification, and recommendations on the use. Conclusions For all seven domains, interpretability is central with respect to composite measures. Variable inclusion and assumptions are important clues to show the connection between composite measures and their theories. This tool can help researchers and readers understand the appropriateness of composite measures by exploring various issues. We recommend using this Critical Hierarchical Appraisal and repOrting tool for composite measureS (CHAOS) along with other critical appraisal tools to evaluate study design or risk of bias.

Endoscopic Ultrasound Appearance of Jejunal Ectopic Pancreas Mimicking Metastatic Nodule in a Cancer Patient.

The ectopic pancreas is a benign subepithelial tumor (SET) mostly found incidentally in the stomach and duodenum. Here, we present computed tomography (CT) scans and endoscopic ultrasound (EUS) images from a 71-year-old Taiwanese man newly diagnosed with colonic adenocarcinoma. CT examination revealed a mural nodule in the proximal jejunum, with good enhancement after IV contrast medium administration. Push enteroscopy was performed to localize the lesion and evaluate its nature, and a 1 cm subepithelial lesion was found. The lesion appeared hyperechoic within the submucosal layer of the bowel wall on endoscopic ultrasound examination. A tattoo was performed, and the lesion was removed during the resection of colon cancer. The histopathology confirmed the presence of pancreatic tissue inside. As far as we know, this is the first description in the literature of an endoscopic ultrasound finding of a jejunal ectopic pancreas.

Frailty does not cause all frail symptoms: United States Health and Retirement Study.

BACKGROUND: Frailty is associated with major health outcomes. However, the relationships between frailty and frailty symptoms haven't been well studied. This study aims to show the associations between frailty and frailty symptoms. METHODS: The Health and Retirement Study (HRS) is an ongoing longitudinal biannual survey in the United States. Three of the most used frailty diagnoses, defined by the Functional Domains Model, the Burden Model, and the Biologic Syndrome Model, were reproduced according to previous studies. The associations between frailty statuses and input symptoms were assessed using odds ratios and correlation coefficients. RESULTS: The sample sizes, mean ages, and frailty prevalence matched those reported in previous studies. Frailty statuses were weakly correlated with each other (coefficients = 0.19 to 0.38, p < 0.001 for all). There were 49 input symptoms identified by these three models. Frailty statuses defined by the three models were not significantly correlated with one or two symptoms defined by the same models (p > 0.05 for all). One to six symptoms defined by the other two models were not significantly correlated with each of the three frailty statuses (p > 0.05 for all). Frailty statuses were significantly correlated with their own bias variables (p < 0.05 for all). CONCLUSION: Frailty diagnoses lack significant correlations with some of their own frailty symptoms and some of the frailty symptoms defined by the other two models. This finding raises questions like whether the frailty symptoms lacking significant correlations with frailty statuses could be included to diagnose frailty and whether frailty exists and causes frailty symptoms.

Diagnostic accuracy of symptoms for an underlying disease: a simulation study.

Symptoms have been used to diagnose conditions such as frailty and mental illnesses. However, the diagnostic accuracy of the numbers of symptoms has not been well studied. This study aims to use equations and simulations to demonstrate how the factors that determine symptom incidence influence symptoms' diagnostic accuracy for disease diagnosis. Assuming a disease causing symptoms and correlated with the other disease in 10,000 simulated subjects, 40 symptoms occurred based on 3 epidemiological measures: proportions diseased, baseline symptom incidence (among those not diseased), and risk ratios. Symptoms occurred with similar correlation coefficients. The sensitivities and specificities of single symptoms for disease diagnosis were exhibited as equations using the three epidemiological measures and approximated using linear regression in simulated populations. The areas under curves (AUCs) of the receiver operating characteristic (ROC) curves was the measure to determine the diagnostic accuracy of multiple symptoms, derived by using 2 to 40 symptoms for disease diagnosis. With respect to each AUC, the best set of sensitivity and specificity, whose difference with 1 in the absolute value was maximal, was chosen. The results showed sensitivities and specificities of single symptoms for disease diagnosis were fully explained with the three epidemiological measures in simulated subjects. The AUCs increased or decreased with more symptoms used for disease diagnosis, when the risk ratios were greater or less than 1, respectively. Based on the AUCs, with risk ratios were similar to 1, symptoms did not provide diagnostic values. When risk ratios were greater or less than 1, maximal or minimal AUCs usually could be reached with less than 30 symptoms. The maximal AUCs and their best sets of sensitivities and specificities could be well approximated with the three epidemiological and interaction terms, adjusted R-squared ≥ 0.69. However, the observed overall symptom correlations, overall symptom incidence, and numbers of symptoms explained a small fraction of the AUC variances, adjusted R-squared ≤ 0.03. In conclusion, the sensitivities and specificities of single symptoms for disease diagnosis can be explained fully by the at-risk incidence and the 1 minus baseline incidence, respectively. The epidemiological measures and baseline symptom correlations can explain large fractions of the variances of the maximal AUCs and the best sets of sensitivities and specificities. These findings are important for researchers who want to assess the diagnostic accuracy of composite diagnostic criteria.

Improved Delivery Performance of n-Butylidenephthalide-Polyethylene Glycol-Gold Nanoparticles Efficient for Enhanced Anti-Cancer Activity in Brain Tumor.

n-butylidenephthalide (BP) has been verified as having the superior characteristic of cancer cell toxicity. Furthermore, gold (Au) nanoparticles are biocompatible materials, as well as effective carriers for delivering bio-active molecules for cancer therapeutics. In the present research, Au nanoparticles were first conjugated with polyethylene glycol (PEG), and then cross-linked with BP to obtain PEG-Au-BP nanodrugs. The physicochemical properties were characterized through ultraviolet-visible spectroscopy (UV-Vis), Fourier-transform infrared spectroscopy (FTIR), and dynamic light scattering (DLS) to confirm the combination of PEG, Au, and BP. In addition, both the size and structure of Au nanoparticles were observed through scanning electron microscopy (SEM) and transmission electron microscopy (TEM), where the size of Au corresponded to the results of DLS assay. Through in vitro assessments, non-transformed BAEC and DBTRG human glioma cells were treated with PEG-Au-BP drugs to investigate the tumor-cell selective cytotoxicity, cell uptake efficiency, and mechanism of endocytic routes. According to the results of MTT assay, PEG-Au-BP was able to significantly inhibit DBTRG brain cancer cell proliferation. Additionally, cell uptake efficiency and potential cellular transportation in both BAEC and DBTRG cell lines were observed to be significantly higher at 2 and 24 h. Moreover, the mechanisms of endocytosis, clathrin-mediated endocytosis, and cell autophagy were explored and determined to be favorable routes for BAEC and DBTRG cells to absorb PEG-Au-BP nanodrugs. Next, the cell progression and apoptosis of DBTRG cells after PEG-Au-BP treatment was investigated by flow cytometry. The results show that PEG-Au-BP could remarkably regulate the DBTRG cell cycle at the Sub-G1 phase, as well as induce more apoptotic cells. The expression of apoptotic-related proteins in DBTRG cells was determined through Western blotting assay. After treatment with PEG-Au-BP, the apoptotic cascade proteins p21, Bax, and Act-caspase-3 were all significantly expressed in DBTRG brain cancer cells. Through in vivo assessments, the tissue morphology and particle distribution in a mouse model were examined after a retro-orbital sinus injection containing PEG-Au-BP nanodrugs. The results demonstrate tissue integrity in the brain (forebrain, cerebellum, and midbrain), heart, liver, spleen, lung, and kidney, as they did not show significant destruction due to PEG-Au-BP treatment. Simultaneously, the extended retention period for PEG-Au-BP nanodrugs was discovered, particularly in brain tissue. The above findings identify PEG-Au-BP as a potential nanodrug for brain cancer therapies.

Why Mental Illness Diagnoses Are Wrong: A Pilot Study on the Perspectives of the Public.

Background: Mental illness diagnostic criteria are made based on assumptions. This pilot study aims to assess the public's perspectives on mental illness diagnoses and these assumptions. Methods: An anonymous survey with 30 questions was made available online in 2021. Participants were recruited via social media, and no personal information was collected. Ten questions focused on participants' perceptions regarding mental illness diagnoses, and 20 questions related to the assumptions of mental illness diagnoses. The participants' perspectives on these assumptions held by professionals were assessed. Results: Among 14 survey participants, 4 correctly answered the relationships of 6 symptom pairs (28.57%). Two participants could not correctly conduct the calculations involved in mood disorder diagnoses (14.29%). Eleven (78.57%) correctly indicated that 2 or more sets of criteria were available for single diagnoses of mental illnesses. Only 1 (7.14%) correctly answered that the associations between symptoms and diagnoses were supported by including symptoms in the diagnostic criteria of the diagnoses. Nine (64.29%) correctly answered that the diagnosis variances were not fully explained by their symptoms. The confidence of participants in the major depressive disorder diagnosis and the willingness to take medications for this diagnosis were the same (mean = 5.50, standard deviation [SD] = 2.31). However, the confidence of participants in the symptom-based diagnosis of non-solid brain tumor was significantly lower (mean = 1.62, SD = 2.33, p < 0.001). Conclusion: Our study found that mental illness diagnoses are wrong from the perspectives of the public because our participants did not agree with all the assumptions professionals make about mental illness diagnoses. Only a minority of our participants obtained correct answers to the calculations involved in mental illness diagnoses. In the literature, neither patients nor the public have been engaged in formulating the diagnostic criteria of mental illnesses.

YAP1 regulates the self-organized fate patterning of hESC-derived gastruloids.

The gastrulation process relies on complex interactions between developmental signaling pathways that are not completely understood. Here, we interrogated the contribution of the Hippo signaling effector YAP1 to the formation of the three germ layers by analyzing human embryonic stem cell (hESC)-derived 2D-micropatterned gastruloids. YAP1 knockout gastruloids display a reduced ectoderm layer and enlarged mesoderm and endoderm layers compared with wild type. Furthermore, our epigenome and transcriptome analysis revealed that YAP1 attenuates Nodal signaling by directly repressing the chromatin accessibility and transcription of key genes in the Nodal pathway, including the NODAL and FOXH1 genes. Hence, in the absence of YAP1, hyperactive Nodal signaling retains SMAD2/3 in the nuclei, impeding ectoderm differentiation of hESCs. Thus, our work revealed that YAP1 is a master regulator of Nodal signaling, essential for instructing germ layer fate patterning in human gastruloids.

Arecoline N-oxide initiates oral carcinogenesis and arecoline N-oxide mercapturic acid attenuates the cancer risk.

Arecoline N-oxide (ANO), an oxidative metabolite of the areca nut, is a predictable initiator in carcinogenesis. The mechanisms of arecoline metabolites in human cancer specimens is still limited. This present study aims to estimate the oral squamous cell carcinoma (OSCC) inductive activity between arecoline metabolites in human cancer specimens/OSCC cells. We have collected 22 pairs (tumor and non-tumor part) of patient's specimens and checked for clinical characteristics. The identification of arecoline and its metabolites levels by using LC-MS/MS. The NOD/SCID mice model was used to check the OSCC inductive activity. The tumor part of OSCC samples exhibited higher levels of arecoline and ANO. Besides, ANO treated mice accelerates the NOTCH1, IL-17a and IL-1ß expressions compared to the control mice. ANO exhibited higher cytotoxicity, intracellular ROS levels and decline in antioxidant enzyme levels in OC-3 cells. The protein expression of NOTCH1 and proliferation marker levels are significantly lower in NOM treated cells. Overall, ANO induced initial stage carcinogenesis in the oral cavity via inflammation, ROS and depletion of antioxidant enzymes. Arecoline N-oxide mercapturic acid (NOM) attenuates the initiation of oral carcinogenesis.

Simulation study to demonstrate biases created by diagnostic criteria of mental illnesses: major depressive episodes, dysthymia, and manic episodes.

OBJECTIVES: Composite diagnostic criteria alone are likely to create and introduce biases into diagnoses that subsequently have poor relationships with input symptoms. This study aims to understand the relationships between the diagnoses and the input symptoms, as well as the magnitudes of biases created by diagnostic criteria and introduced into the diagnoses of mental illnesses with large disease burdens (major depressive episodes, dysthymic disorder, and manic episodes). SETTINGS: General psychiatric care. PARTICIPANTS: Without real-world data available to the public, 100 000 subjects were simulated and the input symptoms were assigned based on the assumed prevalence rates (0.05, 0.1, 0.3, 0.5 and 0.7) and correlations between symptoms (0, 0.1, 0.4, 0.7 and 0.9). The input symptoms were extracted from the diagnostic criteria. The diagnostic criteria were transformed into mathematical equations to demonstrate the sources of biases and convert the input symptoms into diagnoses. PRIMARY AND SECONDARY OUTCOMES: The relationships between the input symptoms and diagnoses were interpreted using forward stepwise linear regressions. Biases due to data censoring or categorisation introduced into the intermediate variables, and the three diagnoses were measured. RESULTS: The prevalence rates of the diagnoses were lower than those of the input symptoms and proportional to the assumed prevalence rates and the correlations between the input symptoms. Certain input or bias variables consistently explained the diagnoses better than the others. Except for 0 correlations and 0.7 prevalence rates of the input symptoms for the diagnosis of dysthymic disorder, the input symptoms could not fully explain the diagnoses. CONCLUSIONS: There are biases created due to composite diagnostic criteria and introduced into the diagnoses. The design of the diagnostic criteria determines the prevalence of the diagnoses and the relationships between the input symptoms, the diagnoses, and the biases. The importance of the input symptoms has been distorted largely by the diagnostic criteria.

Opportunities and Challenges From Leading Trends in a Biomonitoring Project: Canadian Health Measures Survey 2007-2017.

Background: Biomonitoring can be conducted by assessing the levels of chemicals in human bodies and their surroundings, for example, as was done in the Canadian Health Measures Survey (CHMS). This study aims to report the leading increasing or decreasing biomarker trends and determine their significance. Methods: We implemented a trend analysis for all variables from CHMS biomonitoring data cycles 1-5 conducted between 2007 and 2017. The associations between time and obesity were determined with linear regressions using the CHMS cycles and body mass index (BMI) as predictors. Results: There were 997 unique biomarkers identified and 86 biomarkers with significant trends across cycles. Nine of the 10 leading biomarkers with the largest decreases were environmental chemicals. The levels of 1,2,3-trimethyl benzene, dodecane, palmitoleic acid, and o-xylene decreased by more than 60%. All of the 10 chemicals with the largest increases were environmental chemicals, and the levels of 1,2,4-trimethylbenzene, nonanal, and 4-methyl-2-pentanone increased by more than 200%. None of the 20 biomarkers with the largest increases or decreases between cycles were associated with BMI. Conclusions: The CHMS provides the opportunity for researchers to determine associations between biomarkers and time or BMI. However, the unknown causes of trends with large magnitudes of increase or decrease and their unclear impact on Canadians' health present challenges. We recommend that the CHMS plan future cycles on leading trends and measure chemicals with both human and environmental samples.