Aim: This study explored whether inherited variants in genes causing the hereditary neuropathy condition Charcot–Marie–Tooth disease are associated with sensitivity to paclitaxel-induced peripheral neuropathy (PN). Patients & methods: Hereditary neuropathy genes previously associated with risk of paclitaxel-induced PN were sequenced in paclitaxel-treated patients. Eight putative genetic predictors in five hereditary neuropathy genes ( ARHGEF10, SBF2, FGD4, FZD3 and NXN) were tested for association with PN sensitivity after accounting for systemic exposure and clinical variables. Results: FZD3 rs7833751, a proxy for rs7001034, decreased PN sensitivity (additive model, β = -0.41; 95% CI: -0.66 to -0.17; p = 0.0011). None of the other genetic predictors were associated with PN sensitivity. Conclusion: Our results support prior evidence that FZD3 rs7001034 is protective of PN and may be useful for individualizing paclitaxel treatment to prevent PN.

ObjectiveThis systematic review aimed to assess methods used to relate repeated mammographic images to breast cancer risk, including the time from mammogram to diagnosis of breast cancer, and methods for analysis of data from either one or both breasts (averaged or assessed individually).DesignA systematic review was performed.SettingThe databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021 to extract published articles in English describing the relationship of change in mammographic features with risk of breast cancer.ParticipantsWomen with mammogram images.Main outcome measureBreast cancer incidence.ResultsTwenty articles were included in the final review. We found that BIRADs and Cumulus were most commonly used for classifying mammographic density and automated assessment was used on more recent digital mammograms. Time between mammograms varied from 1 to median of 4.1 years, and only 9 of the studies used more than 2 mammograms to quantify features. One study used a prediction horizon of 5 and 10 years, one used 5 years only and another 10 years only, while in the others the prediction horizon was not clearly defined with investigators using the next screening mammogram.ConclusionThis review provided an updated overview of the state of the art and revealed research gaps; based on these, we provide recommendations for future studies using repeated measure methods for mammogram images to make the use of accumulating image data. By following these recommendations, we expect to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.Article summaryStrengths and limitations of the studyTo the best of our knowledge, this is the most recent systematic review on the topic of using multiple mammogram images to define risk of breast cancer.This review was performed strictly following systematic review guidelines including a medical librarian with expertise in searching, multiple independent reviewers involved in study selection and data extraction, and reporting following PRISMA 2020 guidelines.Due to heterogeneity of methods for assessment and classification (categorical and continuous) of mammographic features including breast density and time to breast cancer, we did not perform risk of bias or conduct a meta-analysis.Few studies looked at repeated measures of non-density features.

Purpose It may be important for women to have mammograms at different points in time to track changes in breast density, as fluctuations in breast density can affect breast cancer risk. This systematic review aimed to assess methods used to relate repeated mammographic images to breast cancer risk. Methods The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021. Eligibility criteria included published articles in English describing the relationship of change in mammographic features with risk of breast cancer. Risk of bias was assessed using the Quality in Prognostic Studies tool. Results Twenty articles were included. The Breast Imaging Reporting and Data System and Cumulus were most commonly used for classifying mammographic density and automated assessment was used on more recent digital mammograms. Time between mammograms varied from 1 year to a median of 4.1, and only nine of the studies used more than two mammograms. Several studies showed that adding change of density or mammographic features improved model performance. Variation in risk of bias of studies was highest in prognostic factor measurement and study confounding. Conclusion This review provided an updated overview and revealed research gaps in assessment of the use of texture features, risk prediction, and AUC. We provide recommendations for future studies using repeated measure methods for mammogram images to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.

This systematic review aimed to assess the methods used to classify mammographic breast parenchymal features in relation to prediction of future breast cancer including the time from mammogram to diagnosis of breast cancer, and methods for the identification of texture features and selection of features for inclusion in analysis. The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov. were searched through October 2021 to extract published articles in English describing the relationship of parenchymal texture features with risk of breast cancer. Twenty-eight articles published since 2016 were included in the final review. Of these, 7 assessed texture features from film mammograms images, 3 did not report details of the image used, and the others used full field mammograms from Hologic, GE and other manufacturers. The identification of parenchymal texture features varied from using a predefined list to machine-driven identification. Reduction in number of features chosen for analysis in relation to cancer incidence then varied across statistical approaches and machine learning methods. The variation in approach and number of features identified for inclusion in analysis precluded generating a quantitative summary or meta-analysis of the value of these to improve predicting risk of future breast cancers. This updated overview of the state of the art revealed research gaps; based on these, we provide recommendations for future studies using parenchymal features for mammogram images to make use of accumulating image data, and external validation of prediction models that extend to 5 and 10 years to guide clinical risk management. By following these recommendations, we expect to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.

Background: The cytoplasmic pattern recognition receptor absent in melanoma 2 (AIM2) detects cytosolic DNA and activates the inflammasome, resulting in the production of pro-inflammatory cytokines and inducing pyroptotic cell death. Recent research has highlighted AIM2's role in PANoptosis and host defence. Acute liver injury resulting from acetaminophen (APAP) overdose involves various critical events such as APAP metabolite protein adduct formation, mitochondrial dysfunction, oxidant stress, peroxynitrite formation, and nuclear DNA fragmentation. However, the role of AIM2 in APAP-induced hepatoxicity remains unclear. Results: we discovered that AIM2 negatively regulates the pathogenesis of liver damage induced by APAP in aged mice, independent of inflammasome activation. AIM2-deficient aged mice displayed increased lipid accumulation and hepatic triglycerides compared to wild-type mice. Moreover, AIM2 knockout mice with APAP overdose experienced more severe liver injury, worse mitochondrial stability, greater glutathione depletion, reduced autophagy, and higher levels of phosphorylated c-Jun N-terminal kinase (JNK) and extracellular signal-regulated kinase (ERK). Additionally, we found that AIM2 localizes in mitochondria and its overexpression in mouse hepatocytes enhances autophagy while reducing JNK phosphorylation. Notably, induction of autophagy through rapamycin administration reduced serum alanine aminotransferase levels and the necrotic liver area in AIM2-deficient aged mice with APAP overdose. Mechanistically, AIM2 deficiency promoted acute liver damage induced by APAP and an inflammatory response by increasing oxidative stress and the phosphorylation of JNK and ERK in aged mice. Conclusions: AIM2 regulates autophagy and lipid peroaxidation, making it a promising therapeutic target for the treatment of age-related acute liver damage.