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Neil Fawkes, Evi Tselenti, Neil Shah, Victoria Lappin, Neil Smith, Arjun Narasimhan, Adam B Smith Reckitt Benckiser Health Care Ltd., Hull, HU8 7DS, UKCorrespondence: Neil FawkesReckitt Benckiser Health Care Ltd., Dansom Lane, Hull, HU8 7DS, UKEmail neil.fawkes@rb.comBackground: Skin sensitivity characteristics and triggers have been identified in populations in previous studies. However, few have compared these characteristics among self-reported sensitive skin.Objective: The aim of the study was to evaluate and compare specific intrinsic and extrinsic triggers of skin sensitivity between individuals with self-reported sensitive skin and non-sensitive skin.Methods: A systematic literature review was undertaken to identify intrinsic and extrinsic factors associated with sensitive skin. A 167-item survey was developed on the basis of the literature review. The survey was completed online by a sample of adult participants drawn from the general United Kingdom population. Participants also completed sociodemographic and self-reported health questions.Results: A total of 3050 surveys were completed: 1526 participants with self-reported skin sensitivity and 1524 participants not reporting skin sensitivity. There was a decrease in self-reported skin sensitivity with increasing age (p< 0.05), and proportionally more women reported sensitive skin. Smoking also led to a higher frequency of sensitive skin. All signs and symptoms of sensitive skin, such as itch, dryness/flakiness, roughness and flushing/blushing were more commonly reported by those with self-reported sensitive skin. These were frequently reported in association with external factors (cold/windy weather, clothes and fabrics), as well as internal factors such as pre-existing skin conditions and atopy.Conclusion: The study evaluated self-reported sensitive skin against a non-sensitive skin in order to identify common inherent and external triggers to distinguish between these groups in a large general population study in the United Kingdom. The key symptoms and signs of this syndrome identified in the literature were confirmed to be reported significantly more when compared with those without sensitive skin. However, no correlation or pattern of symptomology could be identified, reinforcing the complexity of this condition. Given the strong differentiation from the non-sensitive group, the results of this research could be utilised for the development of a clinically meaningful screening tool.Keywords: sensitive skin, smoking, age, asthma, atopy

The development of World Health Organization (WHO) guidelines is multidisciplinary and includes input from a range of experts such as policy-makers, programme managers, civil society and health workers in the [...]

Systemic lupus erythematosus (SLE) is the prototype of an autoimmune disease with various manifestations in the skin and several other organs. Subacute cutaneous lupus erythematosus may present with annular and psoriasiform lesions. There have been case reports of pustular lesions in SLE. Herein, we describe a known case of SLE, who developed pustular and crusted lesions which complicated the course of the disease. We considered Acute Generalised Exanthematous Pustulosis, Subcorneal Pustular Dermatosis, Pustular Psoriasis, Pustular Vasculitis, and Pustular Folliculitis as our initial differential diagnosis. A potassium hydroxide mount from crusted lesions showed scabies mites. With these findings, a diagnosis of SLE complicated with crusted scabies was made. She was managed with multiple systemic immunosuppressant and antiscabetic measures following which clinical improvement was seen with remission of her lesions.

Abstract Background Perfluoroalkyl and polyfluoroalkyl substances (PFAS) exposure has been associated with metabolic diseases, however, the underlying molecular pathogenesis remains to be understood. Integrated PFAS and lipidomic analysis has the potential to identify alterations in lipid metabolism pathways for exposome research. Methods A targeted LC-MS/MS method was developed for the quantification of 14 PFAS from human plasma samples (n = 96). Concurrently, high coverage lipidomics was conducted for the quantification of 665 lipid species in the same plasma samples. Linear regression models were implemented to study the association of PFAS with plasma lipidome. Results Women had lower levels of PFAS compared to men and Asian-Indians had lower levels of PFAS compared to both Chinese and Malay subjects. PFAS were positively associated with a number of lipid species from lysophospholipid, ceramide and triacylglycerol lipid classes. Phosphatidylinositol, acylcarnitine and sphingosine-1-phosphate were negatively associated with PFAS. Association studies revealed both shared and distinct relationship of PFAS with plasma lipids. Conclusions We demonstrate that the circulating levels of PFAS vary with age, ethnicity and sex within a multi-ethnic Asian population with potential implications in future biomonitoring and mitigation. Our comprehensive lipidomics methodology and association studies enabled us to characterize the relationship of circulating PFAS and lipidomic profiles. These results will help in better understanding of the molecular basis of PFAS exposure on human health outcomes.

Abstract Large ancient DNA (aDNA) studies offer the chance to examine genomic changes over time, providing direct insights into human evolution. While recent studies have used time-stratified aDNA for selection scans, most focus on single-locus methods. We conducted a multi-locus genotype scan on 708 samples spanning 7000 years of European history. We show that the G12 statistic, originally designed for unphased diploid data, can effectively detect selection in aDNA processed to create ‘pseudo-haplotypes’. In simulations and at known positive control loci (e.g., lactase persistence), G12 outperforms the allele frequency-based selection statistic, SweepFinder2, previously used on aDNA. Applying our approach, we identified 14 candidate regions of selection across four time periods, with half the signals detectable only in the earliest period. Our findings suggest that selective events in European prehistory, including from the onset of animal domestication, have been obscured by neutral processes like genetic drift and demographic shifts such as admixture.

Abstract Purpose Tuberculosis (TB) is a widespread infectious disease that requires early detection for effective treatment and control. This study aims to improve TB detection using cough audio analysis, comparing the performance of capsule networks to other deep learning models. Methods We used cough audio recordings from 1105 individuals with a new or worsening cough for at least two weeks, totaling 9772 recordings. These recordings were processed into spectral images, and HOG features were extracted. Various models, including Capsule Networks + FCNN, CNN, VGG16, and ResNet50 were trained and evaluated. Results Capsule Networks + FCNN achieved the best performance with an accuracy of 0.97, sensitivity of 0.98, specificity of 0.96, F1 score of 0.97, and precision of 0.97, outperforming other models. This attribute is due to the model’s ability to learn complex features from spectral images. Conclusions This study concludes that Capsule Networks are more effective than typical CNN-based models in diagnosing TB from cough audio. This suggests that advanced deep learning frameworks could significantly enhance TB screening accuracy, especially in resource-limited areas.

Abstract Background Phenotypic age (PhenoAge), a widely used marker of biological aging, has been shown to be a robust predictor of all-cause mortality and morbidity in different populations. Existing studies on biological aging have primarily focused on individual domains, resulting in a lack of a comprehensive understanding of the multi-systemic dysregulation that occurs in aging. Methods PhenoAge was evaluated based on a linear combination of chronological age (CA) and 9 clinical biomarkers in 952 multi-ethnic Asian women of reproductive age. Phenotypic age acceleration (PhenoAgeAccel), an aging biomarker, represents PhenoAge after adjusting for CA. This study conducts an in-depth association analysis of PhenoAgeAccel with clinical, nutritional, lipidomic, gut microbiome, and genetic factors. Results Higher adiposity, glycaemia, plasma saturated fatty acids, kynurenine pathway metabolites, GlycA, riboflavin, nicotinamide, and insulin-like growth factor binding proteins were positively associated with PhenoAgeAccel. Conversely, a healthier diet and higher levels of pyridoxal phosphate, all-trans retinol, betaine, tryptophan, glutamine, histidine, apolipoprotein B, and insulin-like growth factors were inversely associated with PhenoAgeAccel. Lipidomic analysis found 132 lipid species linked to PhenoAgeAccel, with PC(O-36:0) showing the strongest positive association and CE(24:5) demonstrating the strongest inverse association. A genome-wide association study identified rs9864994 as the top genetic variant (P = 5.69E-07) from the ZDHHC19 gene. Gut microbiome analysis revealed that Erysipelotrichaceae UCG-003 and Bacteroides vulgatus were inversely associated with PhenoAgeAccel. Integrative network analysis of aging-related factors underscored the intricate links among clinical, nutritional and lipidomic variables, such as positive associations between kynurenine pathway metabolites, amino acids, adiposity, and insulin resistance. Furthermore, potential mediation effects of blood biomarkers related to inflammation, immune response, and nutritional and energy metabolism were observed in the associations of diet, adiposity, genetic variants, and gut microbial species with PhenoAgeAccel. Conclusions Our findings provide a comprehensive analysis of aging-related factors across multiple platforms, delineating their complex interconnections. This study is the first to report novel signatures in lipidomics, gut microbiome and blood biomarkers specifically associated with PhenoAgeAccel. These insights are invaluable in understanding the molecular and metabolic mechanisms underlying biological aging and shed light on potential interventions to mitigate accelerated biological aging by targeting modifiable factors.

Parkinson's disease (PD) is the second most prevalent neurological disorder, predominantly affecting older people. With no existing cure, the early detection of PD, where symptoms are not entirely evident but indicative of the disease's onset, is critical. This study aims to design and develop AI-based diagnostic methods that can detect these early signs of PD with high accuracy, thereby facilitating more effective disease management. This study focuses on developing a method that not only identifies PD at an early stage but also provides clinicians with a tool to interpret the decisions taken by the AI models to avoid misdiagnosis. In this study, a T2-weighted 3D Magnetic resonance imaging (MRI) dataset is used to analyze detailed morphological, textural, and structural changes. The MRI scans are pre-processed using brain extraction, image registration, bias correction, normalization, and segmentation processes. Upon segmentation, feature extraction was applied to the segmented subcortical regions using radiomics tools, resulting in the extraction of 107 features. The top 20 features were selected through Pearson’s correlation, recursive feature elimination, and a ranking model, which are responsible for the ML model’s class prediction. Statistical validation of these features was also performed using Analysis of Variance (ANOVA), pairwise t-tests, and Kruskal-Wallis H-tests to ensure that the identified 20 features were dominant for the prediction. Based on the identified features, several Machine Learning (ML) models were used to identify the best classifier for the provided real-world MRI scans. The Gradient Boosting (GB) algorithm achieved better prediction accuracy among the compared models. Incorporating the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalances significantly improved the model's performance, boosting accuracy to 96.8 % from 87 %. Further, multiple Explainable Artificial Intelligence (XAI) techniques were deployed to enhance the transparency and interpretability of the models. These techniques provide insights into how each identified feature influences predictions by the classifier, assisting clinicians in making trustworthy decisions when planning diagnosis and treatment measures.

Abstract Parkinson’s disease (PD) is one of the most common neurodegenerative disorders that affect the quality of human life of millions of people throughout the world. The probability of getting affected by this disease increases with age, and it is common among the elderly population. Early detection can help in initiating medications at an earlier stage. It can significantly slow down the progression of this disease, assisting the patient to maintain a good quality of life for a more extended period. Magnetic resonance imaging (MRI)-based brain imaging is an area of active research that is used to diagnose PD disease early and to understand the key biomarkers. The prior research investigations using MRI data mainly focus on volume, structural, and morphological changes in the basal ganglia (BG) region for diagnosing PD. Recently, researchers have emphasized the significance of studying other areas of the human brain for a more comprehensive understanding of PD and also to analyze changes happening in brain tissue. Thus, to perform accurate diagnosis and treatment planning for early identification of PD, this work focuses on learning the onset of PD from images taken from whole-brain MRI using a novel 3D-convolutional neural network (3D-CNN) deep learning architecture. The conventional 3D-Resent deep learning model, after various hyper-parameter tuning and architectural changes, has achieved an accuracy of 90%. In this work, a novel 3D-CNN architecture was developed, and after several ablation studies, the model yielded results with an improved accuracy of 93.4%. Combining features from the 3D-CNN and 3D ResNet models using Canonical Correlation Analysis (CCA) resulted in 95% accuracy. For further enhancements of the model performance, feature fusion with optimization was employed, utilizing various optimization techniques. Whale optimization based on a biologically inspired approach was selected on the basis of a convergence diagram. The performance of this approach is compared to other methods and has given an accuracy of 97%. This work represents a critical advancement in improving PD diagnosis techniques and emphasizing the importance of deep nested 3D learning and bio-inspired feature selection.

Abstract Background Despite routine echocardiographic surveillance for childhood cancer survivors, the ability to predict cardiomyopathy risk in individual patients is limited. We explored the feasibility and optimal processes for machine learning-enhanced cardiomyopathy prediction in survivors using serial echocardiograms from five centers. Methods We designed a series of deep convolutional neural networks (DCNNs) for prediction of cardiomyopathy (shortening fraction ≤ 28% or ejection fraction ≤ 50% on two occasions) for at-risk survivors ≥ 1-year post initial cancer therapy. We built DCNNs with four subsets of echocardiographic data differing in timing relative to case (survivor who developed cardiomyopathy) index diagnosis and two input formats (montages) with differing image selections. We used holdout subsets in a 10-fold cross-validation framework and standard metrics to assess model performance (e.g., F1-score, area under the precision-recall curve [AUPRC]). Performance of the input formats was compared using a combined 5 × 2 cross-validation F-test. Results The dataset included 542 pairs of montages: 171 montage pairs from 45 cases at time of cardiomyopathy diagnosis or pre-diagnosis and 371 pairs from 70 at-risk survivors who didn’t develop cardiomyopathy during follow-up (non-case). The DCNN trained to distinguish between non-case and time of cardiomyopathy diagnosis or pre-diagnosis case montages achieved an AUROC of 0.89 ± 0.02, AUPRC 0.83 ± 0.03, and F1-score: 0.76 ± 0.04. When limited to smaller subsets of case data (e.g., ≥ 1 or 2 years pre-diagnosis), performance worsened. Model input format did not impact performance accuracy across models. Conclusions This methodology is a promising first step toward development of a DCNN capable of accurately differentiating pre-diagnosis versus non-case echocardiograms to predict survivors more likely to develop cardiomyopathy. Graphical Abstract