Characterization of vestibular schwannoma tissues using liquid ...
Is It Cancer? A Combination Non-invasive Approach To Determine If Indeterminate Pulmonary Nodules Are Cancerous
Early cancer detection is generally considered an effective strategy for reducing patient mortality. In particular, screening programs for lung cancer, the most common cause of death worldwide, have significantly reduced mortality rates. One screening method is called low dose computed tomography (LD-CT). This approach detects a large fraction of lung nodules, abnormal growths in lung tissue, which are usually benign. However, a fraction of these nodules are so-called indeterminate pulmonary nodules (IPNs) and have an intermediate cancer risk. They are called indeterminate because they may or may not be cancerous. To definitively detemine whether a particular IPN is cancerous, invasive biopsy procedures are necessary; thus, a better method of differentiating cancerous IPNs from benign ones—a method which does not involve invasive procedures—is desirable. To address this need, Dr. Paul Lampe, a professor in the Translational Research Program, and Dr. Paul Kinahan, a professor of Radiology at the University of Washington, developed a noninvasive approach to more accurately determine whether IPNs are malignant or not: the PSR (plasma, semantic, radiomic) risk prediction model. This approach extracts information using: 1.) Plasma samples for identification of biomarkers, 2.) Semantic features (common characteristics of a tumor using a structured reporting format) observed by radiologists in CT images and 3.) Radiomic features (identifies image texture and other features that are not apparent to the human eye) analyzed by algorithms. Their results describing the method were recently published in Cancers.
The participants of this study were enrolled in the Fred Hutchinson Lung Cancer Early Detection and Prevention Clinic (LCEDPC). They were divided into two cohorts: FH1 and FH2. The FH1 cohort included 69 subjects with non-small cell lung cancer (NSCLC, case group) and 66 with benign nodules (control group) and this cohort was matched by age, gender, and cigarette pack years. Meanwhile, the FH2 cohort included 71 subjects with NSCLC and 78 controls and was unmatched by age, gender, and pack years. The FH1 cohort was used to generate the PSR risk prediction model. The FH2 cohort was used to test the predicted model trained in FH1.
To begin with, the authors looked for upregulation of biomarkers in the plasma samples. The authors noticed that several proteins were upregulated in the case group of the FH1 cohort, and several other proteins exhibited glycan modifications. Further, the autoantibody-antigen complex analysis revealed higher levels of IgG and IgM in the case group of the FH1 cohort when compared with the control group. These findings were validated in the FH2 cohort. The results showed that a variety of biomarkers were elevated in the plasma of patients with malignant IPNs in two independent cohorts.
For the semantic features, an experienced thoracic radiologist, blinded to clinical and histologic findings, reviewed the CT images of all the participants. For the radiomic features, the authors used PyRadiomics, an open-source package for radiomic data analysis with pre- and post processing steps that were extensively evaluated to improve repeatability and reproducabilty.
Finally, the authors generated the PSR risk model prediction using a panel of nine biomarkers: five plasma markers (ALPL, TNFRSF8, WNT5B, RGL1-IgG, and WNT10A-IgG), three semantic features (smooth margin, spiculated margin, and part-solid nodule density), and one radiomic feature ("least axis length"). This model yielded an area under the curve (AUC) value of 0.98 for the case group of the FH1 cohort and an AUC of 0.85 for the case group of the FH2 cohort (AUC is a statistic model used to determine how good a model is at classifying between two groups, in this case, case and control group. AUC closer to 1, the better the model is at classifying IPNs). The authors then incorporated known clinical risk factors such as age, gender, and pack-years into their PSR model. The AUC of their risk prediction model improved to 0.90 and was more accurate than a well-characterized clinical risk prediction model from Mayo Clinic (AUC = 0.80).
The PSR risk model prediction demonstrates the promise of a noninvasive approach for assessing the risk of IPNs. Based on their risk prediction model, the authors aim to assign a high or low risk score to each individual with IPN. The high-risk prediction score would identify patients who require immediate diagnostic follow-up. Low-risk prediction scores would indicate patients who can be monitored noninvasively with repeated imaging.
This spotlighted research was supported by the National Institutes of Health.
Fred Hutch/University of Washington/Seattle Children's Cancer Consortium members Drs. Paul Lampe and Paul Kinahan contributed to this work.
Lastwika KJ, Wu W, Zhang Y, Ma N, Zečević M, Pipavath SNJ, Randolph TW, Houghton AM, Nair VS, Lampe PD, Kinahan PE. Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment. Cancers (Basel). 2023 Jun 29;15(13):3418. Doi: 10.3390/cancers15133418.W
New Study Finds Blocking Histones Using Antibodies Alleviates Lung Fibrosis
Lung fibrosis is a debilitating disease affecting nearly 250,000 people in the U.S. Alone with 50,000 new cases reported each year. There is currently no cure and limited available treatment options, underscoring the pressing need to better understand why people get this disease.
In a new study, Boston University Chobanian & Avedisian School of Medicine researchers have identified that abnormal interactions between different cell types, particularly platelets and lung immune cells, promote lung fibrosis.
According to the researchers, this study highlights how different cell types work together in lung fibrosis. Platelets are cells that normally form blood clots, but in lung fibrosis they become involved in immune cell functions that end up attacking healthy cells and damaging the lung. While the immune system is supposed to protect us from viruses and bacteria, in patients with lung fibrosis it harms their own body.
BU researchers found that chromosomal structural support proteins termed histones, released by immune cells called neutrophils, initiate this aberrant interaction by activating the production of the immune mediator, transforming growth factor β 1 (TGFβ1), from platelets. TGFβ1 in turn blocks the release of another mediator, interleukin-27 (IL-27), from specialized immune cells called macrophages. This inhibition prevents IL-27 from suppressing fibrosis.
"Although histones have previously been implicated in fibrosis, how they mediate the development of the disease is not completely understood. Our findings provide novel mechanistic insights into how the altered interactions between different cell types contributes to histone-mediated fibrosis development," explain Arjun Sharma, one of the first authors, and Markus Bosmann, MD, the corresponding author and associate professor of medicine and pathology & laboratory medicine.
An initial investigation using samples from patients with idiopathic pulmonary fibrosis revealed higher histone release in those patients compared to healthy individuals. Subsequently, using an experimental model of lung fibrosis initiated by bleomycin-induced lung injury, neutrophils were established as a major source of histones.
Further experiments including screening lung airway fluid to assess levels of immune mediators after blocking histones, tissue staining and genetic deletion, all identified TGFβ1 and IL-27 as key downstream molecular mediators of histones during fibrosis. "This study helps bridge a crucial knowledge gap by elucidating the role of three key proteins—histones, TGFβ1, and IL-27 in fibrosis development, opening up avenues for new therapies," says Bosmann.
These findings appear online in the journal Proceedings of the National Academy of Sciences.
More information: Riehl, Dennis R. Et al, Externalized histones fuel pulmonary fibrosis via a platelet-macrophage circuit of TGFβ1 and IL-27, Proceedings of the National Academy of Sciences (2023). DOI: 10.1073/pnas.2215421120. Doi.Org/10.1073/pnas.2215421120
Citation: New study finds blocking histones using antibodies alleviates lung fibrosis (2023, September 26) retrieved 26 September 2023 from https://medicalxpress.Com/news/2023-09-blocking-histones-antibodies-alleviates-lung.Html
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Medications And Their Potential To Cause Increase In 'Lung Nodule'
List of Drugs that may cause 'Lung nodule'Advertisement
Updated on August 25, 2023 This page features an assortment of drug(s) that could potentially trigger 'Lung nodule' as a Side-effect or adverse response. It is not uncommon for medications to have some tolerable mild side effects. Do remember that these listed medication(s) only represents individual medications that could be part of a larger combination therapy. Please keep in mind that this list of drug(s) is intended to serve as an information resource and should not be a substitute to professional medical advice. If you have concerns about 'Lung nodule', we advise that you speak with a healthcare professional. Similar to 'Lung nodule,' there are other symptoms or signs that might more accurately describe your side effect. They are detailed below for your convenience. If any of these additional symptom(s) align more closely with your experience, you can choose them to determine potential medications that could be responsible.Advertisement
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