Diagnostics

Thermo Fisher Scientific | qPCR

Discover a versatile, easy‑to‑use, and efficient real‑time PCR system. Powered by proven technology, the Applied Biosystems™ QuantStudio™ 7 Pro Dx Real‑Time PCR System is a clinical molecular diagnostics platform that enables next‑level usability with a smart qPCR workflow, from setup to report.

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Sartorius - Infectious Disease

Since the onset of the pandemic, massive global demand for laboratory consumables has grown, and the need for pipette tips has more than tripled as a result. By prioritizing the manufacture of filter tips, converting existing production lines and running production 24/7, Sartorius could significantly increase output within a short period of time. Our ability to serve customers will be further improved as our second expansion nears completion in Kajaani, Finland.  

In this video, Matti Pilviö, Head of Operations, and Niklas Forsman, Regional Business Manager, provide insights into the measures that have - and will - be taken to help laboratories worldwide maintain operations.

Discover how Sartorius has significantly increased manufacturing output of filter tips in the following ways:
 

Prioritizing filter tip manufacturing
Introduction of new production lines
Running production 24/7

Sartorius Tip Manufacturing Response to COVID-19 Pandemic

Feiyuebio ELISA test

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Aging (Aging-US)

Aging-US published this trending research paper as the cover for Volume 14, Issue 14, entitled, "Aging the brain: multi-region methylation principal component based clock in the context of Alzheimer’s disease" by researchers from Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT; Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL; Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA; Department of Biostatistics, Fielding School of Public Health, UCLA, Los Angeles, CA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT; Alzheimer’s Disease Research Center, Yale University School of Medicine, New Haven, CT; VA Connecticut Healthcare System, West Haven, CT; Department of Pathology, Yale University School of Medicine, New Haven, CT; Altos Labs, San Diego Institute of Science, San Diego, CA.

DOI - https://doi.org/10.18632/aging.204196

Corresponding authors - Albert T. Higgins-Chen - a.higginschen@yale.edu, and Morgan E. Levine - morgan.levine@yale.edu

Abstract

Alzheimer’s disease (AD) risk increases exponentially with age and is associated with multiple molecular hallmarks of aging, one of which is epigenetic alterations. Epigenetic age predictors based on 5’ cytosine methylation (DNAm), or epigenetic clocks, have previously suggested that epigenetic age acceleration may occur in AD brain tissue. Epigenetic clocks are promising tools for the quantification of biological aging, yet we hypothesize that investigation of brain aging in AD will be assisted by the development of brain-specific epigenetic clocks. Therefore, we generated a novel age predictor termed PCBrainAge that was trained solely in cortical samples. This predictor utilizes a combination of principal components analysis and regularized regression, which reduces technical noise and greatly improves test-retest reliability. To characterize the scope of PCBrainAge’s utility, we generated DNAm data from multiple brain regions in a sample from the Religious Orders Study and Rush Memory and Aging Project. PCBrainAge captures meaningful heterogeneity of aging: Its acceleration demonstrates stronger associations with clinical AD dementia, pathologic AD, and APOE ε4 carrier status compared to extant epigenetic age predictors. It further does so across multiple cortical and subcortical regions. Overall, PCBrainAge’s increased reliability and specificity makes it a particularly promising tool for investigating heterogeneity in brain aging, as well as epigenetic alterations underlying AD risk and resilience.

Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.204196

Press release - https://www.aging-us.com/news_room/aging-the-brain-multi-region-methylation-principal-component-based-clock-in-the-context-of-alzheimers-disease

Keywords - aging, epigenetic clocks, unsupervised machine learning, brain, Alzheimer’s, age acceleration

About Aging-US

Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways.

Please visit our website at https://www.Aging-US.com​​ and connect with us:

SoundCloud - https://soundcloud.com/Aging-Us
Facebook - https://www.facebook.com/AgingUS/
Twitter - https://twitter.com/AgingJrnl
Instagram - https://www.instagram.com/agingjrnl/
YouTube - https://www.youtube.com/agingus​
LinkedIn - https://www.linkedin.com/company/aging/
Pinterest - https://www.pinterest.com/AgingUS/

Media Contact
18009220957
MEDIA@IMPACTJOURNALS.COM

Trending With Impact: PCBrainAge in the Context of Alzheimer’s Disease

Feiyuebio Antibodies - ELISA - Protein

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Oncotarget

Dr. Robert Hsu from the Department of Internal Medicine, Division of Medical Oncology, Norris Comprehensive Cancer Center and Hospital, University of Southern California, describes a recent research paper he co-authored that was published by Oncotarget, entitled, “Molecular characterization of Kita-Kyushu lung cancer antigen (KK-LC-1) expressing carcinomas.”

DOI - https://doi.org/10.18632/oncotarget.28132

Correspondence to - Jorge J. Nieva - jorge.nieva@med.usc.edu

Abstract

Cancer/testis antigens (CTAs) are strongly expressed in some solid tumors but minimally expressed in normal tissue, making them appealing therapeutic targets. KK-LC-1 (CXorf61) has cytoplasmic expression in gastric, breast, and lung cancer. We characterized the molecular subtypes of non-small cell lung cancer (NSCLC) expressing KK-LC-1 to inform rational clinical trials of T-cell receptor therapy (TCR-T) targeting KK-LC-1. 9790 NSCLC tumors that underwent whole transcriptome sequencing (Illumina NovaSeq) and NextGen DNA sequencing (NextSeq, 592 Genes and NovaSEQ, WES) at Caris Life Sciences (Phoenix, AZ) were analyzed. Tumors were split into quartiles based on KK-LC-1 expression and pathological and molecular differences were investigated. Adenocarcinoma had significantly higher KK-LC-1 expression than squamous cell carcinoma (median, 3.25 vs. 1.17 transcripts per million (TPM), p < 0.0001). Tumors with the highest quartile of KK-LC-1 expression had a greater proportion of tumors with high tumor mutation burden (TMB) (≥10 mutations per megabase; 44% vs. 28% in Q1, p < 0.001). Increased KK-LC-1 expression was associated with increased M1 macrophage abundance. Higher levels of KK-LC-1 expression were seen in pan-wild type and KRAS mutated tumors and associated with high TMB. TCR-T therapy directed against KK-LC-1 should be considered in patients whose clinical features reflect these characteristics.

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Press release - https://www.oncotarget.com/news/pr/kita-kyushu-lung-cancer-antigen-kk-lc-1-expressing-carcinomas/

Keywords - lung cancer, tumor microenvironment, diagnostic biomarkers, biomarkers for immunotherapy, cancer testis antigen

About Oncotarget

Oncotarget is a peer-reviewed, open access biomedical journal covering research on all aspects of oncology.

To learn more about Oncotarget, please visit https://www.oncotarget.com and connect with us:

SoundCloud - https://soundcloud.com/oncotarget
Facebook - https://www.facebook.com/Oncotarget/
Twitter - https://twitter.com/oncotarget
Instagram - https://www.instagram.com/oncotargetjrnl/
YouTube - https://www.youtube.com/OncotargetYouTube
LinkedIn - https://www.linkedin.com/company/oncotarget
Pinterest - https://www.pinterest.com/oncotarget/
Reddit - https://www.reddit.com/user/Oncotarget/

Oncotarget is published by Impact Journals, LLC: https://www.ImpactJournals.com

Media Contact
MEDIA@IMPACTJOURNALS.COM
18009220957

Behind the Study: Molecular Characterization of Lung Carcinomas Expressing KK-LC-1

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Dr. Liang-Kung Chen from the National Yang-Ming Chiao-Tung University, Taipei, Taiwan details a research paper he co-authored that was published by Aging (Aging-US) in Volume 14, Issue 3, entitled, “Predicting neuropsychiatric symptoms of persons with dementia in a day care center using a facial expression recognition system.”

DOI - https://doi.org/10.18632/aging.203869

Corresponding author - Liang-Kung Chen - lkchen2@vghtpe.gov.tw

Video transcript - https://aging-us.net/2022/06/28/behind-the-study-facial-expression-recognition-predicts-neuropsychiatric-symptoms-of-dementia/

Abstract

Background: Behavioral and psychological symptoms of dementia (BPSD) affect 90% of persons with dementia (PwD), resulting in various adverse outcomes and aggravating care burdens among their caretakers. This study aimed to explore the potential of artificial intelligence-based facial expression recognition systems (FERS) in predicting BPSDs among PwD.

Methods: A hybrid of human labeling and a preconstructed deep learning model was used to differentiate basic facial expressions of individuals to predict the results of Neuropsychiatric Inventory (NPI) assessments by stepwise linear regression (LR), random forest (RF) with importance ranking, and ensemble method (EM) of equal importance, while the accuracy was determined by mean absolute error (MAE) and root-mean-square error (RMSE) methods.

Results: Twenty-three PwD from an adult day care center were enrolled with ≥ 11,500 FERS data series and 38 comparative NPI scores. The overall accuracy was 86% on facial expression recognition. Negative facial expressions and variance in emotional switches were important features of BPSDs. A strong positive correlation was identified in each model (EM: r = 0.834, LR: r = 0.821, RF: r = 0.798 by the patientwise method; EM: r = 0.891, LR: r = 0.870, RF: r = 0.886 by the MinimPy method), and EM exhibited the lowest MAE and RMSE.

Conclusions: FERS successfully predicted the BPSD of PwD by negative emotions and the variance in emotional switches. This finding enables early detection and management of BPSDs, thus improving the quality of dementia care.

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Keywords - aging, artificial intelligence, behavioral and psychological symptoms of dementia, dementia, facial expression recognition system, machine learning

About Aging-US

Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways.

Please visit our website at http://www.Aging-US.com​​ or connect with us:

SoundCloud - https://soundcloud.com/Aging-Us
Facebook - https://www.facebook.com/AgingUS/
Twitter - https://twitter.com/AgingJrnl
Instagram - https://www.instagram.com/agingjrnl/
YouTube - https://www.youtube.com/agingus​
LinkedIn - https://www.linkedin.com/company/aging/
Pinterest - https://www.pinterest.com/AgingUS/

Aging-US is published by Impact Journals, LLC: http://www.ImpactJournals.com​​

Media Contact
18009220957
MEDIA@IMPACTJOURNALS.COM

Behind the Study: Facial Expression Recognition Predicts Neuropsychiatric Symptoms of Dementia

Teach Me in 10

In this Teach me In 10 episode we are joined by Nicky Keay, a leading expert in endocrinology and honorary clinical lecturer in medicine at UCL, honorary fellow in the Department of Sport and Exercise Sciences at Durham University, and former research fellow at St. Thomas’ Hospital. 

Dr. Keay will be teaching us about personalizing female hormone health, and how applying artificial intelligence to modeling female hormones enables women to access hormone intelligence at their fingertips.

Diagnostics

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