COT-07 HEREDITARY BRAIN TUMOR DISEASE MEDICINE IN THE CANCER GENOME MEDICAL AGE-IMPORTANCE OF MULTIDISCIPLINARY COLLABORATION AND GENETIC COUNSELING

Abstract INTRODUCTION Recently, the cancer genome medical system has been rapidly promoted in Japan, such as cancer related gene panel test as NCC oncopanel and Fonduation One being covered by insurance. At the same time, there is a great deal of attention now on hereditary tumor diseases caused by abnormalities in cancer-related genes and their medical treatment. Von Hippel-Lindau (VHL) disease and tuberous sclerosis (TSC) are one such hereditary tumor disease, and develop tumors in multiple organs such as kidney tumors and lung tumors as well as brain tumors. The University of Tokyo Hospital has been developing an in-hospital medical care system for VHL disease and TSC. [VHL disease] If VHL disease is diagnosed at an early stage and strives for early detection and treatment of tumor, it is said that the prognosis is not largely different from normal people. Therefore, our hospital opened a specialized outpatient for VHL disease from 2012, and has carried out genetic diagnosis of VHL disease and medical treatment based on it. [TSC] Recently, the use of Afinitor for SEGA was covered by insurance in Japan, and a system in which multiple departments involved in our hospital cooperated was established, and regular meetings were held to determine treatment policies. DISCUSSION It was the development of genetic counseling system and multidisciplinary cooperation system that was important in any disease. Genetic counseling helps patients with illness and their families to genetically understand and adapt to illness. In the case of hereditary diseases, the practice of genetic counseling was very useful. In addition, since it is a disease involving multiple organs, close cooperation with other in-hospital clinics has been extremely useful for early detection and treatment of patient’s lesions.

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Genetic Analysis of Gorlin Syndrome Using a Multiplex PCR Gene Panel, and Its Potential Clinical Utility for Liquid Biopsy

10.21203/rs.3.rs-676760/v1 ◽

2021 ◽

Author(s):

Yuriko Nakamura ◽

Shoko Onodera ◽

Kazuhiko Hashimoto ◽

Masayuki Takano ◽

Akira Katakura ◽

...

Keyword(s):

Clinical Symptoms ◽

Low Cost ◽

Genetic Diagnosis ◽

Gene Mutations ◽

Gene Panel ◽

Hereditary Diseases ◽

Gorlin Syndrome ◽

Prediction Tools ◽

Multiple Organs ◽

Target Ratio

Abstract Background: Gorlin syndrome is an autosomal dominant, rare disease caused by mutations in PTCH1, PTCH2, and SUFU with various symptoms in multiple organs making early diagnosis challenging. In this study, we generated a Gorlin syndrome gene panel that could help to overcome the difficulties in diagnosing Gorlin syndrome using a single test. Results: This gene panel is time- and cost-efficient and highly reliable with a high-quality score of 30, on-target ratio, and coverage depth, and could detect more mutations than whole-exome sequencing of the same patient. Although the current in silico prediction tools have a limited genetic database of gene mutations in rare hereditary diseases, five prediction tools were used to identify pathological mutations. Pathogenic gene mutations were detected not only in PTCH1 but also in PTCH2 in five out of 12 patients with Gorlin syndrome diagnosed based on clinical symptoms. Conclusions: Using this gene panel, we showed the same gene mutation in the patients and their asymptomatic relatives; hence, it has enabled a highly reliable genetic diagnosis of Gorlin syndrome at a low cost requiring only blood sample.

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Advances in Genetic Diagnosis of Kallmann Syndrome and Genetic Interruption

Reproductive Sciences ◽

10.1007/s43032-021-00638-8 ◽

2021 ◽

Author(s):

Yujun Liu ◽

Xu Zhi

Keyword(s):

Prenatal Diagnosis ◽

Genetic Testing ◽

Early Stage ◽

Hypogonadotropic Hypogonadism ◽

Genetic Diagnosis ◽

Kallmann Syndrome ◽

Hereditary Diseases ◽

Congenital Defects ◽

Unilateral Renal Agenesis ◽

Congenital Hypogonadotropic Hypogonadism

AbstractKallmann syndrome (KS) is a rare hereditary disease with high phenotypic and genetic heterogeneity. Congenital hypogonadotropic hypogonadism and hyposmia/anosmia are the two major characterized phenotypes of KS. Besides, mirror movements, dental agenesis, digital bone abnormalities, unilateral renal agenesis, midline facial defects, hearing loss, and eye movement abnormalities can also be observed in KS patients. Because of the phenotypic heterogeneity, genetic diagnosis become increasingly valuable to distinguish KS from other disorders including normosmic congenital hypogonadotropic hypogonadism, constitutional delay of growth and puberty, CHARGE syndrome, and functional hypogonadotropic hypogonadism. Application of next-generation sequencing has promoted the discovery of novel pathogenic genes in KS pedigrees. Prenatal diagnosis is an effective method in clinical settings to decrease birth defects and block transmission of genetic disorders. However, pregnant women may suffer from physical and psychological distress when fetuses are diagnosed with congenital defects. Preimplantation genetic testing (PGT) is a prospective approach during the in vitro fertilization process that helps to interrupt transmission of hereditary diseases to offspring at an early stage. Thus, genetic testing and counseling are recommended to KS patients with family histories, prenatal diagnosis and PGT are considered to be useful options.

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Semi-Automatic Method for Early Detection of Xylella fastidiosa in Olive Trees Using UAV Multispectral Imagery and Geostatistical-Discriminant Analysis

Remote Sensing ◽

10.3390/rs13010014 ◽

2020 ◽

Vol 13 (1) ◽

pp. 14

Author(s):

Annamaria Castrignanò ◽

Antonella Belmonte ◽

Ilaria Antelmi ◽

Ruggiero Quarto ◽

Francesco Quarto ◽

...

Keyword(s):

Discriminant Analysis ◽

Early Detection ◽

Plant Pathogens ◽

Early Stage ◽

Xylella Fastidiosa ◽

Mediterranean Countries ◽

Parametric Classification ◽

Aerial Vehicle ◽

Olive Groves ◽

Olive Trees

Xylella fastidiosa subsp. pauca (Xfp) is one of the most dangerous plant pathogens in the world. Identified in 2013 in olive trees in south–eastern Italy, it is spreading to the Mediterranean countries. The bacterium is transmitted by insects that feed on sap, and causes rapid wilting in olive trees. The paper explores the use of Unmanned Aerial Vehicle (UAV) in combination with a multispectral radiometer for early detection of infection. The study was carried out in three olive groves in the Apulia region (Italy) and involved four drone flights from 2017 to 2019. To classify Xfp severity level in olive trees at an early stage, a combined method of geostatistics and discriminant analysis was implemented. The results of cross-validation for the non-parametric classification method were of overall accuracy = 0.69, mean error rate = 0.31, and for the early detection class of accuracy 0.77 and misclassification probability 0.23. The results are promising and encourage the application of UAV technology for the early detection of Xfp infection.

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Automated Assay of a Four-Protein Biomarker Panel for Improved Detection of Ovarian Cancer

Cancers ◽

10.3390/cancers13020325 ◽

2021 ◽

Vol 13 (2) ◽

pp. 325

Author(s):

Christopher Walker ◽

Tuan-Minh Nguyen ◽

Shlomit Jessel ◽

Ayesha B. Alvero ◽

Dan-Arin Silasi ◽

...

Keyword(s):

Ovarian Cancer ◽

Early Detection ◽

Predictive Value ◽

Early Stage ◽

Ca 125 ◽

Healthy Controls ◽

Classification Models ◽

Serum Samples ◽

Protein Biomarker ◽

Biomarker Panel

Background: Mortality from ovarian cancer remains high due to the lack of methods for early detection. The difficulty lies in the low prevalence of the disease necessitating a significantly high specificity and positive-predictive value (PPV) to avoid unneeded and invasive intervention. Currently, cancer antigen- 125 (CA-125) is the most commonly used biomarker for the early detection of ovarian cancer. In this study we determine the value of combining macrophage migration inhibitory factor (MIF), osteopontin (OPN), and prolactin (PROL) with CA-125 in the detection of ovarian cancer serum samples from healthy controls. Materials and Methods: A total of 432 serum samples were included in this study. 153 samples were from ovarian cancer patients and 279 samples were from age-matched healthy controls. The four proteins were quantified using a fully automated, multi-analyte immunoassay. The serum samples were divided into training and testing datasets and analyzed using four classification models to calculate accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Results: The four-protein biomarker panel yielded an average accuracy of 91% compared to 85% using CA-125 alone across four classification models (p = 3.224 × 10−9). Further, in our cohort, the four-protein biomarker panel demonstrated a higher sensitivity (median of 76%), specificity (median of 98%), PPV (median of 91.5%), and NPV (median of 92%), compared to CA-125 alone. The performance of the four-protein biomarker remained better than CA-125 alone even in experiments comparing early stage (Stage I and Stage II) ovarian cancer to healthy controls. Conclusions: Combining MIF, OPN, PROL, and CA-125 can better differentiate ovarian cancer from healthy controls compared to CA-125 alone.

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Health Insurance Status as a Predictor of Mode of Colon Cancer Detection but Not Stage at Diagnosis: Implications for Early Detection

Public Health Reports ◽

10.1177/0033354921999173 ◽

2021 ◽

pp. 003335492199917

Author(s):

Lindsey A. Jones ◽

Katherine C. Brewer ◽

Leslie R. Carnahan ◽

Jennifer A. Parsons ◽

Blase N. Polite ◽

...

Keyword(s):

Colon Cancer ◽

Health Insurance ◽

Early Detection ◽

Late Stage ◽

Early Stage ◽

Insurance Status ◽

Stage At Diagnosis ◽

Health Insurance Status ◽

Percentage Point Increase ◽

Point Increase

Objective For colon cancer patients, one goal of health insurance is to improve access to screening that leads to early detection, early-stage diagnosis, and polyp removal, all of which results in easier treatment and better outcomes. We examined associations among health insurance status, mode of detection (screen detection vs symptomatic presentation), and stage at diagnosis (early vs late) in a diverse sample of patients recently diagnosed with colon cancer from the Chicago metropolitan area. Methods Data came from the Colon Cancer Patterns of Care in Chicago study of racial and socioeconomic disparities in colon cancer screening, diagnosis, and care. We collected data from the medical records of non-Hispanic Black and non-Hispanic White patients aged ≥50 and diagnosed with colon cancer from October 2010 through January 2014 (N = 348). We used logistic regression with marginal standardization to model associations between health insurance status and study outcomes. Results After adjusting for age, race, sex, and socioeconomic status, being continuously insured 5 years before diagnosis and through diagnosis was associated with a 20 (95% CI, 8-33) percentage-point increase in prevalence of screen detection. Screen detection in turn was associated with a 15 (95% CI, 3-27) percentage-point increase in early-stage diagnosis; however, nearly half (47%; n = 54) of the 114 screen-detected patients were still diagnosed at late stage (stage 3 or 4). Health insurance status was not associated with earlier stage at diagnosis. Conclusions For health insurance to effectively shift stage at diagnosis, stronger associations are needed between health insurance and screening-related detection; between screening-related detection and early stage at diagnosis; or both. Findings also highlight the need to better understand factors contributing to late-stage colon cancer diagnosis despite screen detection.

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Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network

Applied Sciences ◽

10.3390/app11041574 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1574

Author(s):

Shabana Urooj ◽

Satya P. Singh ◽

Areej Malibari ◽

Fadwa Alrowais ◽

Shaeen Kalathil

Keyword(s):

Neural Network ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Early Detection ◽

Differential Evolution ◽

Early Stage ◽

Selection Process ◽

Parameter Tuning ◽

Wavelet Neural Network ◽

Tuning Parameter

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC).

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Simple and fast isolation of circulating exosomes with chitosan modified shuttle flow microchip towards breast cancer diagnosis

Lab on a Chip ◽

10.1039/d0lc01311k ◽

2021 ◽

Author(s):

Wenwen Chen ◽

Rongkai Cao ◽

Wentao Su ◽

xu zhang ◽

Yuhai Xu ◽

...

Keyword(s):

Breast Cancer ◽

Medical Treatment ◽

Cancer Diagnosis ◽

Biological Samples ◽

Early Stage ◽

Breast Cancer Diagnosis ◽

Early Stage Cancer ◽

Tumor Prognosis ◽

Huge Challenge

Tumor-derived exosomes have been recognized as promising biomarkers for early-stage cancer diagnosis, tumor prognosis monitoring and individual medical treatment. However, separating exosomes from trace biological samples is a huge challenge...

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Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms

Remote Sensing ◽

10.3390/rs13101975 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1975

Author(s):

Lin Wang ◽

Yuzhen Zhou ◽

Qiao Hu ◽

Zhenghong Tang ◽

Yufeng Ge ◽

...

Keyword(s):

Early Detection ◽

Early Development ◽

Spatial Resolution ◽

Early Stage ◽

Semantic Segmentation ◽

Development Stage ◽

Unmanned Aircraft ◽

Economic Losses ◽

Juniperus Virginiana ◽

Segmentation Algorithms

Woody plant encroachment into grasslands ecosystems causes significantly ecological destruction and economic losses. Effective and efficient management largely benefits from accurate and timely detection of encroaching species at an early development stage. Recent advances in unmanned aircraft systems (UAS) enabled easier access to ultra-high spatial resolution images at a centimeter level, together with the latest machine learning based image segmentation algorithms, making it possible to detect small-sized individuals of target species at early development stage and identify them when mixed with other species. However, few studies have investigated the optimal practical spatial resolution of early encroaching species detection. Hence, we investigated the performance of four popular semantic segmentation algorithms (decision tree, DT; random forest, RF; AlexNet; and ResNet) on a multi-species forest classification case with UAS-collected RGB images in original and down-sampled coarser spatial resolutions. The objective of this study was to explore the optimal segmentation algorithm and spatial resolution for eastern redcedar (Juniperus virginiana, ERC) early detection and its classification within a multi-species forest context. To be specific, firstly, we implemented and compared the performance of the four semantic segmentation algorithms with images in the original spatial resolution (0.694 cm). The highest overall accuracy was 0.918 achieved by ResNet with a mean interaction over union at 85.0%. Secondly, we evaluated the performance of ResNet algorithm with images in down-sampled spatial resolutions (1 cm to 5 cm with 0.5 cm interval). When applied on the down-sampled images, ERC segmentation performance decreased with decreasing spatial resolution, especially for those images coarser than 3 cm spatial resolution. The UAS together with the state-of-the-art semantic segmentation algorithms provides a promising tool for early-stage detection and localization of ERC and the development of effective management strategies for mixed-species forest management.

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Reconsidering Early Detection in Countering Violent Extremism (CVE) by Local Frontline Professionals

Proceedings ◽

10.3390/proceedings2021077012 ◽

2021 ◽

Vol 77 (1) ◽

pp. 12

Author(s):

Annemarie van de Weert

Keyword(s):

Early Detection ◽

The Netherlands ◽

Political Violence ◽

Social Innovation ◽

Early Stage ◽

Violent Behavior ◽

Research Centre ◽

Access To Justice ◽

Local Approach ◽

Performative Power

In recent years, the fight against terrorism and political violence has focused more on anticipating the threats that they pose. Therefore, early detection of ideas by local professionals has become an important part of the preventive approach in countering radicalization. Frontline workers who operate in the arteries of society are encouraged to identify processes towards violent behavior at an early stage. To date, however, little is known about how these professionals take on this screening task at their own discretion. Research from the Netherlands suggests that subjective assessment appears to exist. This is due to the absence of a clear norm for preliminary judgments. However, such an approach affects prejudice or administrative arbitrariness, which may cause side effects due to unjustified profiling. The publications about the Dutch case are inspired by the concept of “performativity”, (de Graaf, B., & de Graaff, B. G. J. (2010). Bringing politics back in: The introduction of the ‘performative power’ of counterterrorism. Critical Studies on Terrorism, 3(2), pp. 261–275.) which points to a distinct relationship between the performative power of counterterrorism instruments and the effectiveness of the local approach. Performativity contends that the overall effect of the policy in question is not necessarily determined by the policy measures and their intended results, as such, but more by the way in which they are presented and perceived. This means that, in order to create an equitable approach, governments, whether local or national, should focus more on the actual practice performed by frontline practitioners. The focus on practices is part of a larger project, entitled ‘Gatekeepers of Justice’ (See: https://www.internationalhu.com/research/access-to-justice), by the Research Group Access2Justice (Research Centre of Social Innovation at Utrecht University of Applied Science), led by professor Quirine Eijkman, Deputy President of the Netherlands Institute for Human Rights.

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