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Breast cancer is a global concern as a leading cause of death for women. Early and precise diagnosis can be vital in handling the disease efficiently. Breast cancer subtyping based on estrogen receptor (ER) status is crucial for determining prognosis and treatment. This study uses metabolomics data from plasma samples to detect metabolite biomarkers that could distinguish ER-positive from ER-negative breast cancers in a non-invasive manner. The dataset includes demographic information, ER status, and metabolite levels from 188 breast cancer patients and 73 healthy controls. Recursive Feature Elimination (RFE) with a Random Forest (RF) classifier identified an optimal subset of 30 features-29 biomarkers and age-that achieved the highest area under the curve (AUC). To address the class imbalance, Gaussian noise-based augmentation and Adaptive Synthetic Oversampling (ADASYN) were applied, ensuring balanced representation during training. Four machine learning (ML) algorithms-Random Forest, Support Vector Classifier (SVC), XGBoost, and Logistic Regression (LR)-were evaluated using grid search. The Random Forest classifier emerged as the top performer, achieving an AUC of 0.95 and an accuracy of 93%. These results suggest that ML has great promise for identifying specific metabolites linked to ER expression, paving the development of a novel analytical tool that can minimize current challenges in identifying ER status, and improve the precision of breast cancer subtyping.

Breast cancer remains a major public health concern, and early detection is crucial for improving survival rates. Metabolomics offers the potential to develop non-invasive screening and diagnostic tools based on metabolic biomarkers. However, the inherent complexity of metabolomic datasets and the high dimensionality of biomarkers complicates the identification of diagnostically relevant features, with multiple studies demonstrating limited consensus on the specific metabolites involved. Unlike previous studies that rely on singular feature selection techniques such as Partial Least Square (PLS) or LASSO regression, this research combines supervised and unsupervised machine learning methods with random sampling strategies, offering a more robust and interpretable approach to feature selection. This study aimed to identify a parsimonious and robust set of biomarkers for breast cancer diagnosis using metabolomics data. Plasma samples from 185 breast cancer patients and 53 controls (from the Cooperative Human Tissue Network, USA) were analyzed. This study also overcomes the common issue of dataset imbalance by using propensity score matching (PSM), which ensures reliable comparisons between cancer and control groups. We employed Univariate Naïve Bayes, L2-regularized Support Vector Classifier (SVC), Principal Component Analysis (PCA), and feature engineering techniques to refine and select the most informative features. Our best-performing feature set comprised 11 biomarkers, including 9 metabolites (SM(OH) C22:2, SM C18:0, C0, C3OH, C14:2OH, C16:2OH, LysoPC a C18:1, PC aa C36:0 and Asparagine), a metabolite ratio (Kynurenine-to-Tryptophan), and 1 demographic variable (Age), achieving an area under the ROC curve (AUC) of 98%. These results demonstrate the potential for a robust, cost-effective, and non-invasive breast cancer screening and diagnostic tool, offering significant clinical value for early detection and personalized patient management.

Background: Substantial evidence indicates trace amines can induce vasoconstriction independently of noradrenaline release. However, the mechanism underlying noradrenaline-independent vasoconstrictor responses to trace amines has not yet been established. This study evaluates the role of trace amine-associated receptor 1 (TAAR1) and other biogenic amine receptors in mediating β-phenylethylamine and the TAAR-1 selective agonist RO5256390-induced vasoconstriction., Methods: Vasoconstrictor responses to β-PEA and the TAAR1-selective agonist, RO5256390 were assessed in vitro in endothelium-denuded aortic rings and third-order mesenteric arteries of male Sprague Dawley rats., Results: β-PEA and RO5256390 induced concentration-dependent vasoconstriction of aortic rings but not third-order mesenteric arteries. Vasoconstrictor responses in aortic rings were insensitive to antagonists of 5-HT. The murine-selective TAAR1 antagonist, EPPTB, had no effect on either β-PEA or RO5256390-induced vasoconstriction. The α 1 -adrenoceptor antagonist, prazosin, and the α 2 -adrenoceptor antagonist, yohimbine, induced a shift of the β-PEA concentration response curve too small to be ascribed to antagonism of α 1 -or α 2 -adrenoceptors, respectively. The α 2 -adrenoceptor antagonist atipamezole had no effect on β-PEA or RO5256390-induced vasoconstriction., Conclusion: Vasoconstrictor responses to trace amines are not mediated by classical biogenic amine neurotransmitter receptors. Insensitivity of β-PEA vasoconstrictor responses to EPPTB, may be explained by its low affinity for rat rather than murine TAAR1. Therefore, TAAR1 remains the most likely candidate receptor mediating vasoconstrictor responses to trace amines and that prazosin and yohimbine have low affinity for TAAR1., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Parkinson's disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assists in recognizing PD with good accuracy. The decorrelation function reduces the nonlinear correlation between features and bias in order to learn bias-invariant features. The publicly available Parkinson's Progression Markers Initiative (PPMI) dataset, referred to as a single-scanner imbalanced dataset in this study, was used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new decorrelated convolutional neural network (DcCNN) framework by applying decorrelation-based optimization to convolutional neural networks (CNNs). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN models perform significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner-invariant features without significantly impacting the performance of a model. The obtained dataset is a multiscanner dataset, which leads to scanner bias due to the differences in acquisition protocols and scanners. The multiscanner dataset is a combination of two publicly available datasets, namely, PPMI and FTLDNI-the frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed feature extraction-DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multiscanner dataset for differentiating PD from a healthy control, which is superior to the DcCNN model trained on a single-scanner imbalanced dataset.

A high percentage of asthma patients have symptoms that are not well controlled, despite effective drugs being available. One potential reason for this may be that poor inhaler technique limits the dose delivered to the lungs, thereby reducing the therapeutic efficacy. The aim of this study was to assess the prevalence of poor inhaler technique in an asthma patient population and to probe the impact of various demographic parameters on technique quality. This study was conducted at community pharmacies across Wales, UK. Patients diagnosed with asthma and 12 years or older were invited to participate. An aerosol inhalation monitor (AIM, Vitalograph ® ) was used to measure the quality of patient inhaler technique. A total of 295 AIM assessments were carried out. There were significant differences in the quality of inhaler technique across the different inhaler types ( p < 0.001, Chi squared). The best technique was associated with dry-powder inhalers (DPI devices, 58% of 72 having good technique), compared with pressurized metered-dose inhalers (pMDI) or pMDIs with a spacer device (18% of 174 and 47% of 49 AIM assessments, respectively). There were some significant associations between gender, age, and quality of inhaler technique, as determined with adjusted odds ratios. It seems that the majority of asthmatic patients were not using their inhalers appropriately. We recommend that healthcare professionals place more emphasis on assessing and correcting inhaler technique, as poor inhaler technique might be responsible for the observed lack of symptom control in the asthma patient population.

The role of the community pharmacist has evolved to include the provision of more clinical services for patients. Those people who have stable chronic conditions will be managed in community pharmacies. This qualitative study used semi-structured in-depth interviews to understand the potential of providing additional patient-centred care for patients with stable chronic conditions in community pharmacies and identify potential limitations of this approach. Participants were recruited from Welsh Government, Local Health Boards (LHBS), Community Pharmacy Wales (CPW) and the Royal Pharmaceutical Society Wales (RPSW). The interviews were audio-recorded, transcribed verbatim, and analysed thematically. Eight interviews were conducted. The identified themes were as follows: (1) inconsistency and bureaucracy in commissioning pharmacy services; (2) availability of funding and resources; (3) disagreement and uncertainty about the contribution of the community pharmacy sector; (4) continuity of patient medical information and fragmented care; (5) accessibility, capacity and facilities in community pharmacy; (6) pharmacy education and clinical expertise, and (7) patient acceptability. It was clear that the potential benefit of managing stable chronic diseases in community pharmacies was recognised; however, several limitations expressed by stakeholders of pharmacy services need to be considered prior to moving forward.