Generalization challenges in electrocardiogram deep learning: insights from dataset characteristics and attention mechanism.

Aim: Deep learning's widespread use prompts heightened scrutiny, particularly in the biomedical fields, with a specific focus on model generalizability. This study delves into the influence of training data characteristics on the generalization performance of models, specifically in cardiac abnormality detection. Materials & methods: Leveraging diverse electrocardiogram datasets, models are trained on subsets with varying characteristics and subsequently compared for performance. Additionally, the introduction of the attention mechanism aims to improve generalizability. Results: Experiments reveal that using a balanced dataset, just 1% of a large dataset, leads to equal performance in generalization tasks, notably in detecting cardiology abnormalities. Conclusion: This balanced training data notably enhances model generalizability, while the integration of the attention mechanism further refines the model's ability to generalize effectively.

This study tackles a common problem for deep learning models: they often struggle when faced with new, unfamiliar data that they have not been trained on. This phenomenon is also known as performance drop in out-of-distribution generalization. This reduced performance on out-of-distribution generalization is a key focus of the research, aiming to improve the models' ability to handle diverse data sets beyond their training data.The study examines how the characteristics of the dataset used to train deep learning models affect their ability to detect abnormal heart activities when applied to new, unseen data. Researchers trained these models using various sets of electrocardiogram (ECG) data and then evaluated their performance in identifying abnormalities. They also introduced an attention mechanism to enhance the models' learning capabilities. The attention mechanism in deep learning is like a spotlight that helps the model focus on important information while ignoring less relevant details.The findings were particularly noteworthy. Despite being trained on a small, well-balanced subset of a larger dataset, the models excelled in detecting heart abnormalities in new, unfamiliar data. This training method significantly improved the models' generalization and performance with unseen data. Furthermore, integrating the attention mechanism substantially enhanced the models' ability to generalize effectively on new information.

Efficient Edge-AI Models for Robust ECG Abnormality Detection on Resource-Constrained Hardware.

This study introduces two models, ConvLSTM2D-liquid time-constant network (CLTC) and ConvLSTM2D-closed-form continuous-time neural network (CCfC), designed for abnormality identification using electrocardiogram (ECG) data. Trained on the Telehealth Network of Minas Gerais (TNMG) subset dataset, both models were evaluated for their performance, generalizability capacity, and resilience. They demonstrated comparable results in terms of F1 scores and AUROC values. The CCfC model achieved slightly higher accuracy, while the CLTC model showed better handling of empty channels. Remarkably, the models were successfully deployed on a resource-constrained microcontroller, proving their suitability for edge device applications. Generalization capabilities were confirmed through the evaluation on the China Physiological Signal Challenge 2018 (CPSC) dataset. The models' efficient resource utilization, occupying 70.6% of memory and 9.4% of flash memory, makes them promising candidates for real-world healthcare applications. Overall, this research advances abnormality identification in ECG data, contributing to the progress of AI in healthcare.

S4D-ECG: A Shallow State-of-the-Art Model for Cardiac Abnormality Classification.

PURPOSE: This study introduces an algorithm specifically designed for processing unprocessed 12-lead electrocardiogram (ECG) data, with the primary aim of detecting cardiac abnormalities. METHODS: The proposed model integrates Diagonal State Space Sequence (S4D) model into its architecture, leveraging its effectiveness in capturing dynamics within time-series data. The S4D model is designed with stacked S4D layers for processing raw input data and a simplified decoder using a dense layer for predicting abnormality types. Experimental optimization determines the optimal number of S4D layers, striking a balance between computational efficiency and predictive performance. This comprehensive approach ensures the model's suitability for real-time processing on hardware devices with limited capabilities, offering a streamlined yet effective solution for heart monitoring. RESULTS: Among the notable features of this algorithm is its strong resilience to noise, enabling the algorithm to achieve an average F1-score of 81.2% and an AUROC of 95.5% in generalization. The model underwent testing specifically on the lead II ECG signal, exhibiting consistent performance with an F1-score of 79.5% and an AUROC of 95.7%. CONCLUSION: It is characterized by the elimination of pre-processing features and the availability of a low-complexity architecture that makes it suitable for implementation on numerous computing devices because it is easily implementable. Consequently, this algorithm exhibits considerable potential for practical applications in analyzing real-world ECG data. This model can be placed on the cloud for diagnosis. The model was also tested on lead II of the ECG alone and has demonstrated promising results, supporting its potential for on-device application.

Seizure detection with reduced electroencephalogram channels: research trends and outlook.

Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.

A miniaturized and low-energy subcutaneous optical telemetry module for neurotechnology.

Objective.This study presents a proof-of-concept optical telemetry module that leverages a single light-emitting diode (LED) to transmit data at a high bit rate while consuming low power and occupying a small area. Our experiments showed that we could achieve 108 Mbit s-1and 54 Mbit s-1back telemetry data rates for tissue thicknesses of 3 mm and 8 mm, respectively.Approach.The proposed module is designed to be powered by near-field coupling and achieve bidirectional communication by low-speed downlink from near-field communication. It aims to minimize the size of the implant while providing reliable transmission that meets the requirements of high-speed wireless communication from a multi-electrode array neurotechnology implant outside the body.Results.The power consumption of the module is 1.57 mW, including the power consumption of related circuits, resulting in an efficiency of 14.5 pJ bit-1, at a tissue thickness of 3 mm and a data rate of 108 Mbit. The use of an optical lens, combined with tissue scattering effect and optimized emission angle, makes the module robust to misalignments of up to ±5 mm and ±15° between the implantable and external units. The LED in the implantable unit is only 0.98 × 0.98 × 0.6 mm3, and the testing module is composed of discrete components and laboratory instruments.Significance.This work aims to show how it is possible to strike a balance between a small, reliable, and high-bit-rate data uplink between a neural implant and its proximal, wirelessly connected external unit. This optical telemetry module has the potential to be integrated into a significantly miniaturized system through an application-specific integrated circuit and can support up to 1000 channels of neural recordings, each sampled at 9 kSps with a 12-bit readout resolution.

Weak self-supervised learning for seizure forecasting: a feasibility study.

This paper proposes an artificial intelligence system that continuously improves over time at event prediction using initially unlabelled data by using self-supervised learning. Time-series data are inherently autocorrelated. By using a detection model to generate weak labels on the fly, which are concurrently used as targets to train a prediction model on a time-shifted input data stream, this autocorrelation can effectively be harnessed to reduce the burden of manual labelling. This is critical in medical patient monitoring, as it enables the development of personalized forecasting models without demanding the annotation of long sequences of physiological signal recordings. We perform a feasibility study on seizure prediction, which is identified as an ideal test case, as pre-ictal brainwaves are patient-specific, and tailoring models to individual patients is known to improve forecasting performance significantly. Our self-supervised approach is used to train individualized forecasting models for 10 patients, showing an average relative improvement in sensitivity by 14.30% and a reduction in false alarms by 19.61% in early seizure forecasting. This proof-of-concept on the feasibility of using a continuous stream of time-series neurophysiological data paves the way towards a low-power neuromorphic neuromodulation system.

A Multimodal AI System for Out-of-Distribution Generalization of Seizure Identification.

Artificial intelligence (AI) and health sensory data-fusion hold the potential to automate many laborious and time-consuming processes in hospitals or ambulatory settings, e.g. home monitoring and telehealth. One such unmet challenge is rapid and accurate epileptic seizure annotation. An accurate and automatic approach can provide an alternative way to label seizures in epilepsy or deliver a substitute for inaccurate patient self-reports. Multimodal sensory fusion is believed to provide an avenue to improve the performance of AI systems in seizure identification. We propose a state-of-the-art performing AI system that combines electroencephalogram (EEG) and electrocardiogram (ECG) for seizure identification, tested on clinical data with early evidence demonstrating generalization across hospitals. The model was trained and validated on the publicly available Temple University Hospital (TUH) dataset. To evaluate performance in a clinical setting, we conducted non-patient-specific pseudo-prospective inference tests on three out-of-distribution datasets, including EPILEPSIAE (30 patients) and the Royal Prince Alfred Hospital (RPAH) in Sydney, Australia (31 neurologists-shortlisted patients and 30 randomly selected). Our multimodal approach improves the area under the receiver operating characteristic curve (AUC-ROC) by an average margin of 6.71% and 14.42% for deep learning techniques using EEG-only and ECG-only, respectively. Our model's state-of-the-art performance and robustness to out-of-distribution datasets show the accuracy and efficiency necessary to improve epilepsy diagnoses. To the best of our knowledge, this is the first pseudo-prospective study of an AI system combining EEG and ECG modalities for automatic seizure annotation achieved with fusion of two deep learning networks.

AFExNet: An Adversarial Autoencoder for Differentiating Breast Cancer Sub-Types and Extracting Biologically Relevant Genes.

Technological advancements in high-throughput genomics enable the generation of complex and large data sets that can be used for classification, clustering, and bio-marker identification. Modern deep learning algorithms provide us with the opportunity of finding most significant features in such huge dataset to characterize diseases (e.g., cancer) and their sub-types. Thus, developing such deep learning method, which can successfully extract meaningful features from various breast cancer sub-types, is of current research interest. In this paper, we develop dual stage (unsupervised pre-training and supervised fine-tuning) neural network architecture termed AFExNet based on adversarial auto-encoder (AAE) to extract features from high dimensional genetic data. We evaluated the performance of our model through twelve different supervised classifiers to verify the usefulness of the new features using public RNA-Seq dataset of breast cancer. AFExNet provides consistent results in all performance metrics across twelve different classifiers which makes our model classifier independent. We also develop a method named 'TopGene' to find highly weighted genes from the latent space which could be useful for finding cancer bio-markers. Put together, AFExNet has great potential for biological data to accurately and effectively extract features. Our work is fully reproducible and source code can be downloaded from Github: https://github.com/NeuroSyd/breast-cancer-sub-types.

A comparative study of AI systems for epileptic seizure recognition based on EEG or ECG.

The majority of studies for automatic epileptic seizure (ictal) detection are based on electroencephalogram (EEG) data, but electrocardiogram (ECG) presents a simpler and more wearable alternative for long-term ambulatory monitoring. To assess the performance of EEG and ECG signals, AI systems offer a promising way forward for developing high performing models in securing both a reasonable sensitivity and specificity. There are crucial needs for these AI systems to be developed with more clinical relevance and inference generalization. In this work, we implement an ECG-specific convolutional neural network (CNN) model with residual layers and an EEG-specific convolutional long short-term memory (ConvLSTM) model. We trained, validated, and tested these models on a publicly accessible Temple University Hospital (TUH) dataset for reproducibility and performed a non-patient-specific inference-only test on patient EEG and ECG data of The Royal Prince Alfred Hospital (RPAH) in Sydney, Australia. We selected 31 adult patients to balance groups with the following seizure types: generalized, frontal, frontotemporal, temporal, parietal, and unspecific focal epilepsy. Our tests on both EEG and ECG of these patients achieve an AUC score of 0.75. Our results show ECG outperforms EEG with an average improvement of 0.21 and 0.11 AUC score in patients with frontal and parietal focal seizures, respectively.Clinical relevance-Prior research has demonstrated the value of using ECG for seizure documentation. It is believed that specific epileptic foci (seizure origin) may involve network inputs to the autonomic nervous system. Our result indicates that ECG could outperform EEG for individuals with specific seizure origin, particularly in the frontal and parietal lobes.

A case report of surgical repair of cleft sternum in a child.

BACKGROUND: Sternal cleft is a rare congenital chest wall defect, occurring in only 1 in 100,000 live births, and very few cases have been described in the literature. Surgery is indicated to protect the heart and major vessels. This study provides a clinical case presentation and literature review of sternal cleft. METHODS: This is a review of a case presenting with chest wall defects. The patient underwent a primary cleft closure at Children's Hospital No. 2. All perioperative data were collected and presented. CASE PRESENTATION: A healthy 3-year-old girl was admitted to Children's Hospital No. 2 with an abnormal chest shape, observed by her mother. An inverted "U"-shaped defect of the sternum was visible, and the extent of the defect could be observed by chest X-ray and spiral computed tomography (CT) imaging of the chest. After the diagnosis was confirmed, the patient was prepared for primary closure surgery. We achieved primary closure, the patient discontinued oxygen 5 days after surgery, and the patient was discharged 14 days after surgery. CONCLUSION: Chest wall malformations can present with various phenotypes, although congenital sternal cleft is a rare anomaly. This defect is often asymptomatic. Depending on the size of the defect, a sternal cleft may be treated or monitored. The optimal treatment during early life is surgical repair to achieve primary closure.

Seizure Susceptibility Prediction in Uncontrolled Epilepsy.

Epileptic seizure forecasting, combined with the delivery of preventative therapies, holds the potential to greatly improve the quality of life for epilepsy patients and their caregivers. Forecasting seizures could prevent some potentially catastrophic consequences such as injury and death in addition to several potential clinical benefits it may provide for patient care in hospitals. The challenge of seizure forecasting lies within the seemingly unpredictable transitions of brain dynamics into the ictal state. The main body of computational research on determining seizure risk has been focused solely on prediction algorithms, which involves a challenging issue of balancing sensitivity and false alarms. There have been some studies on identifying potential biomarkers for seizure forecasting; however, the questions of "What are the true biomarkers for seizure prediction" or even "Is there a valid biomarker for seizure prediction?" are yet to be fully answered. In this paper, we introduce a tool to facilitate the exploration of the potential biomarkers. We confirm using our tool that interictal slowing activities are a promising biomarker for epileptic seizure susceptibility prediction.

On the Inhibitability of Natural Products Isolated from Tetradium ruticarpum towards Tyrosine Phosphatase 1B (PTP1B) and α-Glucosidase (3W37): An In Vitro and In Silico Study.

Folk experiences suggest natural products in Tetradium ruticarpum can be effective inhibitors towards diabetes-related enzymes. The compounds were experimentally isolated, structurally elucidated, and tested in vitro for their inhibition effects on tyrosine phosphatase 1B (PTP1B) and α-glucosidase (3W37). Density functional theory and molecular docking techniques were utilized as computational methods to predict the stability of the ligands and simulate interaction between the studied inhibitory agents and the targeted proteins. Structural elucidation identifies two natural products: 2-heptyl-1-methylquinolin-4-one (1) and 3-[4-(4-methylhydroxy-2-butenyloxy)-phenyl]-2-propenol (2). In vitro study shows that the compounds (1 and 2) possess high potentiality for the inhibition of PTP1B (IC50 values of 24.3 ± 0.8, and 47.7 ± 1.1 µM) and α-glucosidase (IC50 values of 92.1 ± 0.8, and 167.4 ± 0.4 µM). DS values and the number of interactions obtained from docking simulation highly correlate with the experimental results yielded. Furthermore, in-depth analyses of the structure-activity relationship suggest significant contributions of amino acids Arg254 and Arg676 to the conformational distortion of PTP1B and 3W37 structures overall, thus leading to the deterioration of their enzymatic activity observed in assay-based experiments. This study encourages further investigations either to develop appropriate alternatives for diabetes treatment or to verify the role of amino acids Arg254 and Arg676.

Epileptic Seizure Classification With Symmetric and Hybrid Bilinear Models.

Epilepsy affects nearly [Formula: see text] of the global population, of which two thirds can be treated by anti-epileptic drugs and a much lower percentage by surgery. Diagnostic procedures for epilepsy and monitoring are highly specialized and labour-intensive. The accuracy of the diagnosis is also complicated by overlapping medical symptoms, varying levels of experience and inter-observer variability among clinical professions. This paper proposes a novel hybrid bilinear deep learning network with an application in the clinical procedures of epilepsy classification diagnosis, where the use of surface electroencephalogram (sEEG) and audiovisual monitoring is standard practice. Hybrid bilinear models based on two types of feature extractors, namely Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are trained using Short-Time Fourier Transform (STFT) of one-second sEEG. In the proposed hybrid models, CNNs extract spatio-temporal patterns, while RNNs focus on the characteristics of temporal dynamics in relatively longer intervals given the same input data. Second-order features, based on interactions between these spatio-temporal features are further explored by bilinear pooling and used for epilepsy classification. Our proposed methods obtain an F1-score of [Formula: see text] on the Temple University Hospital Seizure Corpus and [Formula: see text] on the EPILEPSIAE dataset, comparing favourably to existing benchmarks for sEEG-based seizure type classification. The open-source implementation of this study is available at https://github.com/NeuroSyd/Epileptic-Seizure-Classification.

Effects of familial and non-familial warmth during childhood and adolescence on sexual-orientation disparities in alcohol use trajectories and disorder during emerging adulthood.

BACKGROUND: We investigated sexual-orientation differences in typologies of self-reported familial and non-familial warmth in childhood (before age 11) and adolescence (ages 11-17); and tested whether warmth explained sexual minority emerging adults' (ages 18-25) heightened odds of having heavier alcohol use trajectories (AUTs) and heightened risk for past-year alcohol use disorder (AUD) compared to completely heterosexuals. METHODS: Using self-reported data from the U.S.-based Growing Up Today Study cohort, latent class analyses identified typologies of familial and non-familial warmth during childhood and adolescence. Multivariable regression models tested our objectives. RESULTS: Six warmth classes emerged, including: High-High (i.e., high familial and high non-familial warmth, respectively); High-Moderate; Moderate-Moderate; Moderate-Occasional; Occasional-Occasional; and Low-Low. Among women, sexual minorities had higher odds than completely heterosexuals of being in the Moderate-Moderate, Moderate-Occasional, and Occasional-Occasional versus the High-High warmth class. There were not significant associations between sexual orientation and warmth classes for men. Lower warmth classes were generally associated with greater past-year AUD, and mediated heightened disparities in AUD for sexual minority women versus completely heterosexual women (4.3% mediated), but not among men. Warmth classes were generally unassociated with AUTs, and did not mediate sexual-orientation differences in AUTs. CONCLUSIONS: Lower warmth was associated with greater alcohol-related problems, but not alcohol use itself. Warmth explained a small proportion of AUD disparities for sexual minority women-but not for men.

Opportunities for Electroceuticals in Epilepsy.

Epilepsy is a neurological disorder that affects ∼1% of the world population. Nearly 30% of epilepsy patients suffer from pharmacoresistant epilepsy that cannot be treated with antiepileptic drugs. Depending on seizure type, a diverse range of therapies are available, including surgery, vagus nerve stimulation, and deep brain stimulation. We review the sensing and stimulation technologies most used in neurological disorders, and provide a vision of minimally invasive electroceuticals to enable accurate forecasting of epileptic seizures and therapy. The use of such systems could potentially help patients to prevent injuries and, in combination with an intervention mechanism, could provide a method of suppressing seizures in epileptic patients.

Semi-supervised Seizure Prediction with Generative Adversarial Networks.

Many outstanding studies have reported promising results in seizure prediction that is considered one of the most challenging predictive data analysis. This is mainly because electroencephalogram (EEG) bio-signal intensity is very small, in µV range, and there are significant sensing difficulties given physiological and non-physiological artifacts. In this article, we propose an approach that can make use of not only labeled EEG signals but also the unlabeled ones which are more accessible. We also suggest the use of data fusion to further improve the seizure prediction accuracy. Data fusion in our vision includes EEG signals, cardiogram signals, body temperature and time. We use the short-time Fourier transform on 28-s EEG windows as a pre-processing step. A generative adversarial network (GAN) is trained in an unsupervised manner where information of seizure onset is disregarded. The trained Discriminator of the GAN is then used as feature extractor. Features generated by the feature extractor are classified by two fully-connected layers (can be replaced by any classifier) for the labeled EEG signals. This semi-supervised seizure prediction method achieves area under the operating characteristic curve (AUC) of 77.68% and 75.47% for the CHBMIT scalp EEG dataset and the Freiburg Hospital intracranial EEG dataset, respectively. Unsupervised training without the need of labeling is important because not only it can be performed in real-time during EEG signal recording, but also it does not require feature engineering effort for each patient.

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram.

Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic seizures. Many outstanding studies have reported great results in providing sensible indirect (warning systems) or direct (interactive neural stimulation) control over refractory seizures, some of which achieved high performance. However, to achieve high sensitivity and a low false prediction rate, many of these studies relied on handcraft feature extraction and/or tailored feature extraction, which is performed for each patient independently. This approach, however, is not generalizable, and requires significant modifications for each new patient within a new dataset. In this article, we apply convolutional neural networks to different intracranial and scalp electroencephalogram (EEG) datasets and propose a generalized retrospective and patient-specific seizure prediction method. We use the short-time Fourier transform on 30-s EEG windows to extract information in both the frequency domain and the time domain. The algorithm automatically generates optimized features for each patient to best classify preictal and interictal segments. The method can be applied to any other patient from any dataset without the need for manual feature extraction. The proposed approach achieves sensitivity of 81.4%, 81.2%, and 75% and a false prediction rate of 0.06/h, 0.16/h, and 0.21/h on the Freiburg Hospital intracranial EEG dataset, the Boston Children's Hospital-MIT scalp EEG dataset, and the American Epilepsy Society Seizure Prediction Challenge dataset, respectively. Our prediction method is also statistically better than an unspecific random predictor for most of the patients in all three datasets.

Sexual-orientation differences in alcohol use trajectories and disorders in emerging adulthood: results from a longitudinal cohort study in the United States.

AIMS: We estimated sexual-orientation differences in alcohol use trajectories during emerging adulthood, and tested whether alcohol use trajectories mediated sexual-orientation differences in alcohol use disorders (AUDs). DESIGN: Longitudinal self-reported survey data from the Growing Up Today Study. SETTING: United States. PARTICIPANTS: A total of 12 493 participants aged 18-25 during the 2003, 2005, 2007 or 2010 surveys. MEASUREMENTS: Stratified by gender, longitudinal latent class analyses estimated alcohol use trajectories (using past-year frequency, quantity and binge drinking from 2003 to 2010). Multinomial logistic regression tested differences in trajectory class memberships by sexual orientation [comparing completely heterosexual (CH) participants with sexual-minority subgroups: mainly heterosexual (MH), bisexual (BI) and gay/lesbian (GL) participants]. Modified Poisson regression and mediation analyses tested whether trajectories explained sexual-orientation differences in AUDs (past-year DSM-IV abuse/dependence in 2010). FINDINGS: Six alcohol use trajectory classes emerged for women and five for men: these included heavy (23.5/36.9% of women/men), moderate (31.8/26.4% of women/men), escalation to moderately heavy (9.7/12.0% of women/men), light (17.0% for women only), legal (drinking onset at age 21; 11.1/15.7% of women/men) and non-drinkers (7.0/9.1% of women/men). Compared with CH women, MH and BI women had higher odds of being heavy, moderate, escalation to moderately heavy and light drinkers versus non-drinkers (odds ratios = 2.02-3.42; P-values < 0.01-0.04). Compared with CH men, MH men had higher odds of being heavy, moderate and legal drinkers versus non-drinkers (odds ratios = 2.24-3.34; P-values < 0.01-0.01). MH men and women, BI women and GLs had higher risk of AUDs in 2010 than their same-gender CH counterparts (risk ratios = 1.34-2.17; P-values < 0.01). Alcohol use trajectories mediated sexual-orientation differences in AUDs for MH and GL women (proportion of effect mediated = 30.8-31.1%; P-values < 0.01-0.02), but not for men. CONCLUSIONS: In the United States, throughout emerging adulthood, several sexual-minority subgroups appear to have higher odds of belonging to heavier alcohol use trajectories than completely heterosexuals. These differences partially explained the higher risk of alcohol use disorders among mainly heterosexual and gay/lesbian women but not among sexual-minority men.

A systematic review of measures of HIV/AIDS stigma in paediatric HIV-infected and HIV-affected populations.

INTRODUCTION: HIV-related stigma impacts the quality of life and care management of HIV-infected and HIV-affected individuals, but how we measure stigma and its impact on children and adolescents has less often been described. METHODS: We conducted a systematic review of studies that measured HIV-related stigma with a quantitative tool in paediatric HIV-infected and HIV-affected populations. RESULTS AND DISCUSSION: Varying measures have been used to assess stigma in paediatric populations, with most studies utilizing the full or variant form of the HIV Stigma Scale that has been validated in adult populations and utilized with paediatric populations in Africa, Asia and the United States. Other common measures included the Perceived Public Stigma Against Children Affected by HIV, primarily utilized and validated in China. Few studies implored item validation techniques with the population of interest, although scales were used in a different cultural context from the origin of the scale. CONCLUSIONS: Many stigma measures have been used to assess HIV stigma in paediatric populations, globally, but few have implored methods for cultural adaptation and content validity.

What is in a label? Multiple meanings of 'MSM' among same-gender-loving Black men in Mississippi.

Men who have sex with men (MSM) and other same-gender-loving (SGL) men continue to be disproportionately affected by HIV and AIDS, particularly among the Black population. Innovative strategies are needed to support the health of this community; however, public health efforts primarily approach MSM as a monolithic population erasing the diverse identities, practices, and sexualities within and beyond this category. To better understand diversity within MSM in a geographic region with the largest proportion of Black Americans in the U.S.A. and among the most heavily affected by the epidemic, the Deep South, we conducted four focus groups (n = 29) with Black men who reported having sex with other men residing in Jackson, Mississippi. Results suggest multiple overlapping usages of MSM as identity and behaviour, reflecting internalisation of behavioural categories and co-creation of identities unique to the Black community. These narratives contribute to the literature by documenting the evolving understandings of the category 'MSM' among Black men to reflect intersections between race, socioeconomic status, sexual behaviour, sexuality, subjectivities, and social context. Findings suggest the current monolithic approach to treating MSM may limit public health efforts in developing effective HIV prevention and promotion programmes targeting SGL Black men in the Deep South.