Non-medullary Thyroid Cancer Susceptibility Genes: Evidence and Disease Spectrum.

BACKGROUND: The prevalence of non-medullary thyroid cancer (NMTC) is increasing worldwide. Although most NMTCs grow slowly, conventional therapies are less effective in advanced tumors. Approximately 5-15% of NMTCs have a significant germline genetic component. Awareness of the NMTC susceptibility genes may lead to earlier diagnosis and better cancer prevention. OBJECTIVE: The aim of this study was to provide the current panorama of susceptibility genes associated with NMTC and the spectrum of diseases associated with these genes. METHODS: Twenty-five candidate genes were identified by searching for relevant studies in PubMed. Each candidate gene was carefully checked using six authoritative genetic resources: ClinGen, National Comprehensive Cancer Network guidelines, Online Mendelian Inheritance in Man, Genetics Home Reference, GeneCards, and Gene-NCBI, and a validated natural language processing (NLP)-based literature review protocol was used to further assess gene-disease associations where there was ambiguity. RESULTS: Among 25 candidate genes, 10 (APC, DICER1, FOXE1, HABP2, NKX2-1, PRKAR1A, PTEN, SDHB, SDHD, and SRGAP1) were verified among the six genetic resources. Two additional genes, CHEK2 and SEC23B, were verified using the NLP protocol. Seventy-nine diseases were found to be associated with these 12 NMTC susceptibility genes. The following diseases were associated with more than one NMTC susceptibility gene: colorectal cancer, breast cancer, gastric cancer, kidney cancer, gastrointestinal stromal tumor, paraganglioma, pheochromocytoma, and benign skin conditions. CONCLUSION: Twelve genes predisposing to NMTC and their associated disease spectra were identified and verified. Clinicians should be aware that patients with certain pathogenic variants may require more aggressive surveillance beyond their thyroid cancer risk.

Natural language processing to facilitate breast cancer research and management.

The medical literature has been growing exponentially, and its size has become a barrier for physicians to locate and extract clinically useful information. As a promising solution, natural language processing (NLP), especially machine learning (ML)-based NLP is a technology that potentially provides a promising solution. ML-based NLP is based on training a computational algorithm with a large number of annotated examples to allow the computer to "learn" and "predict" the meaning of human language. Although NLP has been widely applied in industry and business, most physicians still are not aware of the huge potential of this technology in medicine, and the implementation of NLP in breast cancer research and management is fairly limited. With a real-world successful project of identifying penetrance papers for breast and other cancer susceptibility genes, this review illustrates how to train and evaluate an NLP-based medical abstract classifier, incorporate it into a semiautomatic meta-analysis procedure, and validate the effectiveness of this procedure. Other implementations of NLP technology in breast cancer research, such as parsing pathology reports and mining electronic healthcare records, are also discussed. We hope this review will help breast cancer physicians and researchers to recognize, understand, and apply this technology to meet their own clinical or research needs.

Effects of bump/pit on the radiative properties of small particles.

We use the particle superposition model to create bumps or pits on the surface of small particles for the purpose of simulating the roughness of the particles. Four different models are introduced to show the bump/pit effect on the radiative properties of the host particle. The results show that surface roughness plays an important role in the light scattering properties of small particles. Different roughened models behave differently.

Chronic Low-Grade Inflammation and Brain Structure in the Middle-Aged and Elderly Adults.

Low-grade inflammation (LGI) mainly acted as the mediator of the association of obesity and inflammatory diet with numerous chronic diseases, including neuropsychiatric diseases. However, the evidence about the effect of LGI on brain structure is limited but important, especially in the context of accelerating aging. This study was then designed to close the gap, and we leveraged a total of 37,699 participants from the UK Biobank and utilized inflammation score (INFLA-score) to measure LGI. We built the longitudinal relationships of INFLA-score with brain imaging phenotypes using multiple linear regression models. We further analyzed the interactive effects of specific covariates. The results showed high level inflammation reduced the volumes of the subcortex and cortex, especially the globus pallidus (ß [95% confidence interval] = -0.062 [-0.083, -0.041]), thalamus (-0.053 [-0.073, -0.033]), insula (-0.052 [-0.072, -0.032]), superior temporal gyrus (-0.049 [-0.069, -0.028]), lateral orbitofrontal cortex (-0.047 [-0.068, -0.027]), and others. Most significant effects were observed among urban residents. Furthermore, males and individuals with physical frailty were susceptive to the associations. The study provided potential insights into pathological changes during disease progression and might aid in the development of preventive and control targets in an age-friendly city to promote great health and well-being for sustainable development goals.

Associations of long-term exposure to low-level PM2.5 and brain disorders in 260,922 middle-aged and older adults.

Long-term exposure to high-level ambient PM2.5 was associated with increased risks of brain disorders, while the associations remain uncertain when the exposure is lower than current air quality standards in numerous countries. This study aimed to assess the effects of PM2.5 exposure on the brain system in the population with annual mean concentrations ≤15 µg/m3. We analyzed data from 260,922 participants without preexisting brain diseases at baseline in the UK Biobank. The geographical distribution of PM2.5 in 2010 was estimated by a land use regression model and linked with individual residential address. We investigated associations of ambient PM2.5 with incident neurological (dementia, Parkinson's diseases [PD], epilepsy, and migraine) and psychiatric (major depressive disorder [MDD] and anxiety disorder) diseases through Cox proportional hazard models. We further estimated the links with brain imaging phenotypes by neuroimaging analysis. Results showed that in the population with PM2.5 concentrations ≤15 µg/m3, each interquartile range (IQR, 1.28 µg/m3) increment in PM2.5 was related to incidence risks of dementia, epilepsy, migraine, MDD, and anxiety disorder with hazard ratios of 1.08 (95% confidence interval [CI]: 1.03, 1.13), 1.12 (1.05, 1.20), 1.07 (1.00, 1.13), 1.06 (1.03, 1.09), and 1.05 (1.02, 1.08), respectively. We did not observe a significant association with PD. The association with dementia was stronger among the population with poor cardiovascular health (measured by Life's Essential 8) than the counterpart (P for interaction = 0.037). Likewise, per IQR increase was associated with specific brain imaging phenotypes, including volumes of total brain (ß = -0.036; 95% CI: -0.050, -0.022), white matter (-0.030; -0.046, -0.014), grey matter (-0.030; -0.042, -0.017), respectively. The findings suggest long-term exposure to ambient PM2.5 at low-level still has an adverse impact on the neuro-psychiatric systems. The brain-relevant epidemiological assessment suggests that each country should update the standard for ambient PM2.5 following the World Health Organization Air Quality Guidelines 2021.

Associations of Triglycerides and Atherogenic Index of Plasma with Brain Structure in the Middle-Aged and Elderly Adults.

Triglyceride (TG) and atherogenic index of plasma (AIP) have been acknowledged to be risk factors for vascular insults, but their impacts on the brain system remain elusive. To fill in some gaps, we investigated associations of TG and AIP with brain structure, leveraging the UK Biobank database. TG and high-density lipoprotein cholesterol (HDL-C) were examined at baseline and AIP was calculated as log (TG/HDL-C). We build several linear regression models to estimate associations of TG and AIP with volumes of brain grey matter phenotypes. Significant inverse associations of TG and AIP with volumes of specific subcortical traits were observed, among which TG and AIP were most significantly associated with caudate nucleus (TG: ß [95% confidence interval CI] = -0.036 [-0.051, -0.022], AIP: -0.038 [-0.053, -0.023]), thalamus (-0.029 [-0.042, -0.017], -0.032 [-0.045, -0.019]). Higher TG and AIP were also considerably related with reduced cortical structure volumes, where two most significant associations of TG and AIP were with insula (TG: -0.035 [-0.048, -0.022], AIP: -0.038 [-0.052, -0.025]), superior temporal gyrus (-0.030 [-0.043, -0.017], -0.033 [-0.047, -0.020]). Modification effects of sex and regular physical activity on the associations were discovered as well. Our findings show adverse associations of TG and AIP with grey matter volumes, which has essential public health implications for early prevention in neurodegenerative diseases.

Global burden of enteric infections related foodborne diseases, 1990-2021: findings from the Global Burden of Disease Study 2021.

Background: Understanding the global burden of enteric infections is crucial for prioritizing control strategies for foodborne and waterborne diseases. This study aimed to assess the global burden of enteric infections in 2021 and identify risk factors from One Health aspects. Methods: Leveraging the Global Burden of Disease (GBD) 2021 database, the incidence, disability-adjusted life years (DALYs), and deaths of enteric infections and the subtypes were estimated, including diarrheal diseases, typhoid and paratyphoid fever, invasive non-typhoidal Salmonella (iNTS) infections, and other intestinal infectious diseases. The estimates were quantified by absolute number, age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR) and age-standardized DALY rate with 95% uncertainty intervals (UIs). Thirteen pathogens and three risk factors associated with diarrheal diseases were analyzed. Results: In 2021, the global age-standardized DALY rate of enteric infections was 1020.15 per 100,000 popultion (95% UI: 822.70-1259.39 per 100,000 population) with an estimated annual percentage change (EAPC) of -4.11% (95% confidence interval: -4.31% to -3.90%) in 1990-2021. A larger burden was observed in regions with lower Socio-demographic index (SDI) levels. Diarrheal disease was the most serious subtype with Western Sub-Saharan Africa exhibiting the highest age-standardized DALY rate (2769.81 per 100,000 population, 95% UI: 1976.80-3674.41 per 100,000 population). Children under 5 and adults over 65 years suffered more from diarrheal diseases with the former experiencing the highest global age-standardized DALY rate (9382.46 per 100,000 population, 95% UI: 6771.76-13,075.12 per 100,000 population). Rotavirus remained the leading cause of diarrheal diseases despite a cross-year decline in the observed age-standardized DALY rate. Unsafe water, sanitation, and handwashing contributed most to the disease burden. Conclusion: The reduced burden of enteric infections suggested the effectiveness of previous control strategies; however, more efforts should be made in vulnerable regions and populations through a One Health approach.

Incidence trends and disparities in Helicobacter pylori related malignancy among US adults, 2000-2019.

Background: Helicobacter pylori (H. pylori) is closely related to the carcinogenesis of gastric cancer (GC) and gastric non-Hodgkin lymphoma (NHL). However, the systemic trend analysis in H. pylori-related malignancy is limited. We aimed to determine the national incidence trend in non-cardia GC, cardia GC, and gastric NHL in the US during 2000-2019. Method: In this population-based study, we included 186,769 patients with a newly diagnosed H. pylori-related malignancy, including non-cardia GC, cardia GC, and gastric NHL from the Surveillance, Epidemiology, and End Results (SEER) Registry from January 1, 2000 to December 31, 2019. We determined the age-adjusted incidence of three H. pylori-related malignancies respectively. Average annual percentage change (AAPC) in 2000-2019 was calculated to describe the incidence trends. Analyses were stratified by sex, age, race and ethnicity, geographic location and SEER registries. We also determined the 5-year incidence (during 2015-2019) by SEER registries to examine the geographic variance. Results: The incidence in non-cardia GC and gastric NHL significantly decreased during 2000-2019, while the rate plateaued for cardia GC (AAPCs, -1.0% [95% CI, -1.1%-0.9%], -2.6% [95% CI, -2.9%-2.3%], and -0.2% [95% CI, -0.7%-0.3%], respectively). For non-cardia GC, the incidence significantly increased among individuals aged 20-64 years (AAPC, 0.8% [95% CI, 0.6-1.0%]). A relative slower decline in incidence was also observed for women (AAPC, -0.4% [95% CI, -0.6%-0.2%], P for interaction < 0.05). The incidence of cardia GC reduced dramatically among Hispanics (AAPC, -0.8% [95% CI, -1.4%-0.3%]), however it increased significantly among nonmetropolitan residents (AAPC, 0.8% [95% CI, 0.4-1.3%]). For gastric NHL, the decreasing incidence were significantly slower for those aged 20-64 years (AAPC, -1.5% [95% CI, -1.9-1.1%]) and Black individuals (AAPC, -1.3% [95% CI, -1.9-1.1%]). Additionally, the highest incidence was observed among Asian and the Black for non-cardia GC, while Whites had the highest incidence of cardia GC and Hispanics had the highest incidence of gastric NHL (incidence rate, 8.0, 8.0, 3.1, and 1.2, respectively) in 2019. Geographic variance in incidence rates and trends were observed for all three H. pylori-related malignancies. The geographic disparities were more pronounced for non-cardia GC, with the most rapid decline occurring in Hawaii (AAPC, -4.5% [95% CI, -5.5-3.6%]) and a constant trend in New York (AAPC 0.0% [95% CI, -0.4-0.4%]), the highest incidence in Alaska Natives, and the lowest incidence among Iowans (14.3 and 2.3, respectively). Conclusion: The incidence of H. pylori-related cancer declined dramatically in the US between 2000 and 2019, with the exception of cardia GC. For young people, a rising trend in non-cardia GC was noted. Existence of racial/ethnic difference and geographic diversity persists. More cost-effective strategies of detection and management for H. pylori are still in demand.

Disease Spectrum of Breast Cancer Susceptibility Genes.

BACKGROUND: Pathogenic variants in cancer susceptibility genes can increase the risk of a spectrum of diseases, which clinicians must manage for their patients. We evaluated the disease spectrum of breast cancer susceptibility genes (BCSGs) with the aim of developing a comprehensive resource of gene-disease associations for clinicians. METHODS: Twelve genes (ATM, BARD1, BRCA1, BRCA2, CDH1, CHEK2, NF1, PALB2, PTEN, RECQL, STK11, and TP53), all of which have been conclusively established as BCSGs by the Clinical Genome Resource (ClinGen) and/or the NCCN guidelines, were investigated. The potential gene-disease associations for these 12 genes were verified and evaluated based on six genetic resources (ClinGen, NCCN, OMIM, Genetics Home Reference, GeneCards, and Gene-NCBI) and an additional literature review using a semiautomated natural language processing (NLP) abstract classification procedure. RESULTS: Forty-two diseases were found to be associated with one or more of the 12 BCSGs for a total of 86 gene-disease associations, of which 90% (78/86) were verified by ClinGen and/or NCCN. Four gene-disease associations could not be verified by either ClinGen or NCCN but were verified by at least three of the other four genetic resources. Four gene-disease associations were verified by the NLP procedure alone. CONCLUSION: This study is unique in that it systematically investigates the reported disease spectrum of BCSGs by surveying multiple genetic resources and the literature with the aim of developing a single consolidated, comprehensive resource for clinicians. This innovative approach provides a general guide for evaluating gene-disease associations for BCSGs, potentially improving the clinical management of at-risk individuals.

Disease spectrum of gastric cancer susceptibility genes.

Pathogenic variants in germline cancer susceptibility genes can increase the risk of a large number of diseases. Our study aims to assess the disease spectrum of gastric cancer susceptibility genes and to develop a comprehensive resource of gene-disease associations for clinicians. Twenty-seven potential germline gastric cancer susceptibility genes were identified from three review articles and from six commonly used genetic information resources. The diseases associated with each gene were evaluated via a semi-structured review of six genetic resources and an additional literature review using a natural language processing (NLP)-based procedure. Out of 27 candidate genes, 13 were identified as gastric cancer susceptibility genes (APC, ATM, BMPR1A, CDH1, CHEK2, EPCAM, MLH1, MSH2, MSH6, MUTYH-Biallelic, PALB2, SMAD4, and STK11). A total of 145 gene-disease associations (with 45 unique diseases) were found to be associated with these 13 genes. Other gastrointestinal cancers were prominent among identified associations, with 11 of 13 gastric cancer susceptibility genes also associated with colorectal cancer, eight genes associated with pancreatic cancer, and seven genes associated with small intestine cancer. Gastric cancer susceptibility genes are frequently associated with other diseases as well as gastric cancer, with potential implications for how carriers of these genes are screened and managed. Unfortunately, commonly used genetic resources provide heterogeneous information with regard to these genes and their associated diseases, highlighting the importance of developing guides for clinicians that integrate data across available resources and the medical literature.

A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization.

We present the baseline regularization model for computational drug repurposing using electronic health records (EHRs). In EHRs, drug prescriptions of various drugs are recorded throughout time for various patients. In the same time, numeric physical measurements (e.g., fasting blood glucose level) are also recorded. Baseline regularization uses statistical relationships between the occurrences of prescriptions of some particular drugs and the increase or the decrease in the values of some particular numeric physical measurements to identify potential repurposing opportunities.

Validation of a Semiautomated Natural Language Processing-Based Procedure for Meta-Analysis of Cancer Susceptibility Gene Penetrance.

PURPOSE: Quantifying the risk of cancer associated with pathogenic mutations in germline cancer susceptibility genes-that is, penetrance-enables the personalization of preventive management strategies. Conducting a meta-analysis is the best way to obtain robust risk estimates. We have previously developed a natural language processing (NLP) -based abstract classifier which classifies abstracts as relevant to penetrance, prevalence of mutations, both, or neither. In this work, we evaluate the performance of this NLP-based procedure. MATERIALS AND METHODS: We compared the semiautomated NLP-based procedure, which involves automated abstract classification and text mining, followed by human review of identified studies, with the traditional procedure that requires human review of all studies. Ten high-quality gene-cancer penetrance meta-analyses spanning 16 gene-cancer associations were used as the gold standard by which to evaluate the performance of our procedure. For each meta-analysis, we evaluated the number of abstracts that required human review (workload) and the ability to identify the studies that were included by the authors in their quantitative analysis (coverage). RESULTS: Compared with the traditional procedure, the semiautomated NLP-based procedure led to a lower workload across all 10 meta-analyses, with an overall 84% reduction (2,774 abstracts v 16,941 abstracts) in the amount of human review required. Overall coverage was 93%-we are able to identify 132 of 142 studies-before reviewing references of identified studies. Reasons for the 10 missed studies included blank and poorly written abstracts. After reviewing references, nine of the previously missed studies were identified and coverage improved to 99% (141 of 142 studies). CONCLUSION: We demonstrated that an NLP-based procedure can significantly reduce the review workload without compromising the ability to identify relevant studies. NLP algorithms have promising potential for reducing human efforts in the literature review process.

Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes.

PURPOSE: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools that help to monitor and prioritize the literature to understand the clinical implications of pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance-risk of cancer for germline mutation carriers-or prevalence of germline genetic mutations. MATERIALS AND METHODS: We conducted literature searches in PubMed and retrieved paper titles and abstracts to create an annotated data set for training and evaluating the two machine learning classification models. Our first model is a support vector machine (SVM) which learns a linear decision rule on the basis of the bag-of-ngrams representation of each title and abstract. Our second model is a convolutional neural network (CNN) which learns a complex nonlinear decision rule on the basis of the raw title and abstract. We evaluated the performance of the two models on the classification of papers as relevant to penetrance or prevalence. RESULTS: For penetrance classification, we annotated 3,740 paper titles and abstracts and evaluated the two models using 10-fold cross-validation. The SVM model achieved 88.93% accuracy-percentage of papers that were correctly classified-whereas the CNN model achieved 88.53% accuracy. For prevalence classification, we annotated 3,753 paper titles and abstracts. The SVM model achieved 88.92% accuracy and the CNN model achieved 88.52% accuracy. CONCLUSION: Our models achieve high accuracy in classifying abstracts as relevant to penetrance or prevalence. By facilitating literature review, this tool could help clinicians and researchers keep abreast of the burgeoning knowledge of gene-cancer associations and keep the knowledge bases for clinical decision support tools up to date.