The Course of SARS-COV2 Infection Was Not Severe in a Crohn\u27s Patient Who Administered Maintenance Anti-TNF Therapy Overlapping the Early Pre-Symptomatic Period of Infection.

The Inflammatory Bowel Disease (IBD) population, which may require treatment with immunosuppressive medications, may be uniquely vulnerable to COVID-19 infection. In fact, there is some evidence these medications may inhibit the cytokine storm that is theorized to cause a rapid decline seen in COVID-19. In addition, the digestive symptoms of COVID-19 can be difficult to distinguish from the activation of IBD. We present an interesting case of a Crohn\u27s patient inadvertently administering anti-cytokine therapy during the pre-symptomatic period of COVID-19 infection. Immune suppression during early infection with SARS-COV2 risks a poor immune response to the virus and could theoretically result in a more severe course of infection

Online Game Addiction to Adolescent Adaptation Ability in Sikumana Village, Kupang City

Abstract. Online games are games that are accessed online by many players using the internet network. Excessive use of online games can lead to online game addiction. Teens who spend their free time playing online games have the opportunity to experience online gaming. This study aims to see the effect of online game addiction on self-efficacy in adolescents in Sikumana Village, Kupang City. The sampling technique in this study used the Slovin formula with 100 adolescents and 17-21 years old as many as subjects. This hypothesis reveals that online game addiction has a negative effect on adolescents in Sikumana Village, Kupang City. The analysis used in the study used simple regression analysis. This is indicated by a constant value of 28,432; this means that if online game addiction (X) is 0, then the self-value is 28,432. The regression coefficient for the online game addiction variable (X) is - 0.104, meaning that if online game addiction increases by 1%, then self will decrease by 0.104

COngenital heart disease and the Diagnostic yield with Exome sequencing (CODE Study): prospective cohort study and systematic review

OBJECTIVES: To determine the yield of antenatal exome sequencing (ES) over chromosome microarray (CMA) / conventional karyotyping in; (i) any prenatally diagnosed congenital heart disease (CHD); (ii) isolated CHD; (iii) multi‐system CHD and; (iv) CHD by phenotypic subgroup. / METHODS: A prospective cohort study of 197 trios undergoing ES following CMA/karyotype because CHD was identified prenatally and a systematic review of the literature was performed. MEDLINE, EMBASE and CINAHL (2000–Oct 2019) databases were searched electronically. Selected studies included those with; (i) >3 cases; (ii) initiation of testing based upon a prenatal phenotype only and; (iii) where CMA/karyotyping was negative. PROSPERO No. CRD42019140309. / RESULTS: In our cohort ES gave an additional diagnostic yield in; (i) all CHD; (ii) isolated CHD and; (iii) multi‐system CHD of 12.7% (n=25/197), 11.5% (n=14/122) and 14.7% (n=11/75) (p=0.81). The pooled incremental yields for the aforementioned categories from 18‐studies (n=636) were 21% (95% CI, 15‐27%), 11% (95% CI, 7‐15%) and 37% (95% CI, 18%‐56%) respectively. This did not differ significantly when sub‐analyses were limited to studies including >20 cases. In instances of multi‐system CHD in the primary analysis, the commonest extra‐cardiac anomalies associated with a pathogenic variant were those affecting the genitourinary system 44.2% (n=23/52). Cardiac shunt lesions had the greatest incremental yield, 41% (95% CI, 19‐63%), followed by right‐sided lesions 26% (95% CI, 9‐43%). In the majority of instances pathogenic variants occurred de novo and in autosomal dominant (monoallelic) disease genes (68/96; 70.8%). The commonest monogenic syndrome identified was Kabuki syndrome (n=19/96; 19.8%). / CONCLUSIONS: Despite the apparent incremental yield of prenatal exome sequencing in congenital heart disease, the routine application of such a policy would require the adoption of robust bioinformatic, clinical and ethical pathways. Whilst the greatest yield is with multi‐system anomalies, consideration may also be given to performing ES in the presence of isolated cardiac abnormalities

COngenital heart disease and the Diagnostic yield with Exome sequencing (CODE) study: prospective cohort study and systematic review.

OBJECTIVE: To determine the incremental yield of antenatal exome sequencing (ES) over chromosomal microarray analysis (CMA) or conventional karyotyping in prenatally diagnosed congenital heart disease (CHD). METHODS: A prospective cohort study of 197 trios undergoing ES following CMA or karyotyping owing to CHD identified prenatally and a systematic review of the literature were performed. MEDLINE, EMBASE, CINAHL and ClinicalTrials.gov (January 2000 to October 2019) databases were searched electronically for studies reporting on the diagnostic yield of ES in prenatally diagnosed CHD. Selected studies included those with more than three cases, with initiation of testing based upon prenatal phenotype only and that included cases in which CMA or karyotyping was negative. The incremental diagnostic yield of ES was assessed in: (1) all cases of CHD; (2) isolated CHD; (3) CHD associated with extracardiac anomaly (ECA); and (4) CHD according to phenotypic subgroup. RESULTS: In our cohort, ES had an additional diagnostic yield in all CHD, isolated CHD and CHD associated with ECA of 12.7% (25/197), 11.5% (14/122) and 14.7% (11/75), respectively (P = 0.81). The corresponding pooled incremental yields from 18 studies (encompassing 636 CHD cases) included in the systematic review were 21% (95% CI, 15-27%), 11% (95% CI, 7-15%) and 37% (95% CI, 18-56%), respectively. The results did not differ significantly when subanalysis was limited to studies including more than 20 cases, except for CHD associated with ECA, in which the incremental yield was greater (49% (95% CI, 17-80%)). In cases of CHD associated with ECA in the primary analysis, the most common extracardiac anomalies associated with a pathogenic variant were those affecting the genitourinary system (23/52 (44.2%)). The greatest incremental yield was in cardiac shunt lesions (41% (95% CI, 19-63%)), followed by right-sided lesions (26% (95% CI, 9-43%)). In the majority (68/96 (70.8%)) of instances, pathogenic variants occurred de novo and in autosomal dominant (monoallelic) disease genes. The most common (19/96 (19.8%)) monogenic syndrome identified was Kabuki syndrome. CONCLUSIONS: There is an apparent incremental yield of prenatal ES in CHD. While the greatest yield is in CHD associated with ECA, consideration could also be given to performing ES in the presence of an isolated cardiac abnormality. A policy of routine application of ES would require the adoption of robust bioinformatic, clinical and ethical pathways. Copyright © 2020 ISUOG. Published by John Wiley & Sons Ltd

The incremental yield of prenatal exome sequencing over chromosome microarray for congenital heart abnormalities:A systematic review and meta-analysis

ObjectivesTo determine the incremental yield of prenatal exome sequencing (PES) over standard testing in fetuses with an isolated congenital heart abnormality (CHA), CHA associated with extra-cardiac malformations (ECMs) and CHA dependent upon anatomical subclassification.MethodsA systematic review of the literature was performed using MEDLINE, EMBASE, Web of Science and grey literature January 2010-February 2023. Studies were selected if they included greater than 20 cases of prenatally diagnosed CHA when standard testing (QF-PCR/chromosome microarray/karyotype) was negative. Pooled incremental yield was determined. PROSPERO CRD 42022364747.ResultsOverall, 21 studies, incorporating 1957 cases were included. The incremental yield of PES (causative pathogenic and likely pathogenic variants) over standard testing was 17.4% (95% CI, 13.5%-21.6%), 9.3% (95% CI, 6.6%-12.3%) and 35.9% (95% CI, 21.0%-52.3%) for all CHAs, isolated CHAs and CHAs associated with ECMs. The subgroup with the greatest yield was complex lesions/heterotaxy; 35.2% (95% CI 9.7%-65.3%). The most common syndrome was Kabuki syndrome (31/256, 12.1%) and most pathogenic variants occurred de novo and in autosomal dominant (monoallelic) disease causing genes (114/224, 50.9%).ConclusionThe likelihood of a monogenic aetiology in fetuses with multi-system CHAs is high. Clinicians must consider the clinical utility of offering PES in selected isolated cardiac lesions.What is already known?Congenital heart abnormalities are the most commonly occurring congenital anomalies and can be associated with chromosomal or monogenic conditions. With the increasing use of fetal sequencing, there is a need to define the association between monogenic conditions and specific cardiac abnormalities, particularly when isolated to facilitate triaging for prenatal sequencing.What does this study add?The incremental yield of prenatal exome sequencing over and above chromosome microarray for congenital heart abnormalities is 9.3% in isolated lesions and 35.2% in the presence of complex lesions/heterotaxy. Clinicians should consider the clinical utility of offering prenatal exome sequencing in selected isolated cardiac lesions dependent on resources available

The incremental yield of prenatal exome sequencing over chromosome microarray for congenital heart abnormalities:A systematic review and meta-analysis

ObjectivesTo determine the incremental yield of prenatal exome sequencing (PES) over standard testing in fetuses with an isolated congenital heart abnormality (CHA), CHA associated with extra-cardiac malformations (ECMs) and CHA dependent upon anatomical subclassification.MethodsA systematic review of the literature was performed using MEDLINE, EMBASE, Web of Science and grey literature January 2010-February 2023. Studies were selected if they included greater than 20 cases of prenatally diagnosed CHA when standard testing (QF-PCR/chromosome microarray/karyotype) was negative. Pooled incremental yield was determined. PROSPERO CRD 42022364747.ResultsOverall, 21 studies, incorporating 1957 cases were included. The incremental yield of PES (causative pathogenic and likely pathogenic variants) over standard testing was 17.4% (95% CI, 13.5%-21.6%), 9.3% (95% CI, 6.6%-12.3%) and 35.9% (95% CI, 21.0%-52.3%) for all CHAs, isolated CHAs and CHAs associated with ECMs. The subgroup with the greatest yield was complex lesions/heterotaxy; 35.2% (95% CI 9.7%-65.3%). The most common syndrome was Kabuki syndrome (31/256, 12.1%) and most pathogenic variants occurred de novo and in autosomal dominant (monoallelic) disease causing genes (114/224, 50.9%).ConclusionThe likelihood of a monogenic aetiology in fetuses with multi-system CHAs is high. Clinicians must consider the clinical utility of offering PES in selected isolated cardiac lesions.What is already known?Congenital heart abnormalities are the most commonly occurring congenital anomalies and can be associated with chromosomal or monogenic conditions. With the increasing use of fetal sequencing, there is a need to define the association between monogenic conditions and specific cardiac abnormalities, particularly when isolated to facilitate triaging for prenatal sequencing.What does this study add?The incremental yield of prenatal exome sequencing over and above chromosome microarray for congenital heart abnormalities is 9.3% in isolated lesions and 35.2% in the presence of complex lesions/heterotaxy. Clinicians should consider the clinical utility of offering prenatal exome sequencing in selected isolated cardiac lesions dependent on resources available

Classification and risk stratification of invasive breast carcinomas using a real-time quantitative RT-PCR assay

INTRODUCTION: Predicting the clinical course of breast cancer is often difficult because it is a diverse disease comprised of many biological subtypes. Gene expression profiling by microarray analysis has identified breast cancer signatures that are important for prognosis and treatment. In the current article, we use microarray analysis and a real-time quantitative reverse-transcription (qRT)-PCR assay to risk-stratify breast cancers based on biological 'intrinsic' subtypes and proliferation. METHODS: Gene sets were selected from microarray data to assess proliferation and to classify breast cancers into four different molecular subtypes, designated Luminal, Normal-like, HER2+/ER-, and Basal-like. One-hundred and twenty-three breast samples (117 invasive carcinomas, one fibroadenoma and five normal tissues) and three breast cancer cell lines were prospectively analyzed using a microarray (Agilent) and a qRT-PCR assay comprised of 53 genes. Biological subtypes were assigned from the microarray and qRT-PCR data by hierarchical clustering. A proliferation signature was used as a single meta-gene (log(2 )average of 14 genes) to predict outcome within the context of estrogen receptor status and biological 'intrinsic' subtype. RESULTS: We found that the qRT-PCR assay could determine the intrinsic subtype (93% concordance with microarray-based assignments) and that the intrinsic subtypes were predictive of outcome. The proliferation meta-gene provided additional prognostic information for patients with the Luminal subtype (P = 0.0012), and for patients with estrogen receptor-positive tumors (P = 3.4 × 10(-6)). High proliferation in the Luminal subtype conferred a 19-fold relative risk of relapse (confidence interval = 95%) compared with Luminal tumors with low proliferation. CONCLUSION: A real-time qRT-PCR assay can recapitulate microarray classifications of breast cancer and can risk-stratify patients using the intrinsic subtype and proliferation. The proliferation meta-gene offers an objective and quantitative measurement for grade and adds significant prognostic information to the biological subtypes

The molecular portraits of breast tumors are conserved acress microarray platforms

Background Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. Results A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. Conclusion This study validates the breast tumor intrinsic subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile

The molecular portraits of breast tumors are conserved across microarray platforms

BACKGROUND: Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. RESULTS: A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. CONCLUSION: This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile