The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This article serves as a tutorial and perspective showing how to apply the lessons learned from several decades of research in deep learning, gradient descent, backpropagation, and neuroscience to biologically plausible spiking neural networks (SNNs). We also explore the delicate interplay between encoding data as spikes and the learning process; the challenges and solutions of applying gradient-based learning to SNNs; the subtle link between temporal backpropagation and spike timing-dependent plasticity; and how deep learning might move toward biologically plausible online learning. Some ideas are well accepted and commonly used among the neuromorphic engineering community, while others are presented or justified for the first time here. A series of companion interactive tutorials complementary to this article using our Python package, snnTorch , are also made available: https://snntorch.readthedocs.io/en/latest/tutorials/index.html.

Cardiovascular disease (CVD) is the leading cause of death worldwide. The human body is populated by a diverse community of microbes, dominated by bacteria, but also including viruses and fungi. The largest and most complex of these communities is located in the gastrointestinal system and, with its associated genome, is known as the gut microbiome. Gut microbiome perturbations and related dysbiosis have been implicated in the progression and pathogenesis of CVD, including atherosclerosis, hypertension, and heart failure. Although there have been advances in the characterization and analysis of the gut microbiota and associated bacterial metabolites, the exact mechanisms through which they exert their action are not well understood. This review will focus on the role of the gut microbiome and associated functional components in the development and progression of atherosclerosis. Potential treatments to alter the gut microbiome to prevent or treat atherosclerosis and CVD are also discussed.

Aims: Machine learning (ML) is widely believed to be able to learn complex hidden interactions from the data and has the potential in predicting events such as heart failure (HF) readmission and death. Recent studies have revealed conflicting results likely due to failure to take into account the class imbalance problem commonly seen with medical data. We developed a new ML approach to predict 30 day HF readmission or death and compared the performance of this model with other commonly used prediction models. Methods and results: We identified all Western Australian patients aged above 65 years admitted for HF between 2003 and 2008 in the linked Hospital Morbidity Data Collection. Taking into consideration the class imbalance problem, we developed a multi-layer perceptron (MLP)-based approach to predict 30 day HF readmission or death and compared the predictive performances using the performance metrics, that is, area under the receiver operating characteristic curve (AUC), area under the precision–recall curve (AUPRC), sensitivity and specificity with other ML and regression models. Out of the 10 757 patients with HF, 23.6% were readmitted or died within 30 days of hospital discharge. We observed an AUC of 0.55, 0.53, 0.58, and 0.54 while an AUPRC of 0.39, 0.38, 0.46, and 0.38 for weighted random forest, weighted decision trees, logistic regression, and weighted support vector machines models, respectively. The MLP-based approach produced the highest AUC (0.62) and AUPRC (0.46) with 48% sensitivity and 70% specificity. Conclusions: We show that for the medical data with class imbalance, the proposed MLP-based approach is superior to other ML and regression techniques for the prediction of 30 day HF readmission or death.

Introduction: Surveys conducted internationally have found widespread interest in artificial intelligence (AI) amongst medical students. No similar surveys have been conducted in Western Australia (WA) and it is not known how medical students in WA feel about the use of AI in healthcare or their understanding of AI. We aim to assess WA medical students’ attitudes towards AI in general, AI in healthcare, and the inclusion of AI education in the medical curriculum. Methods: A digital survey instrument was developed based on a review of available literature and consultation with subject matter experts. The survey was piloted with a group of medical students and refined based on their feedback. We then sent this anonymous digital survey to all medical students in WA (approximately 1539 students). Responses were open from the 7^th of September 2021 to the 7^th of November 2021. Students’ categorical responses were qualitatively analysed, and free text comments from the survey were qualitatively analysed using open coding techniques. Results: Overall, 134 students answered one or more questions (8.9% response rate). The majority of students (82.0%) were 20–29 years old, studying medicine as a postgraduate degree (77.6%), and had started clinical rotations (62.7%). Students were interested in AI (82.6%), self-reported having a basic understanding of AI (84.8%), but few agreed that they had an understanding of the basic computational principles of AI (33.3%) or the limitations of AI (46.2%). Most students (87.5%) had not received teaching in AI. The majority of students (58.6%) agreed that AI should be part of medical training and most (72.7%) wanted more teaching focusing on AI in medicine. Medical students appeared optimistic regarding the role of AI in medicine, with most (74.4%) agreeing with the statement that AI will improve medicine in general. The majority (56.6%) of medical students were not concerned about the impact of AI on their job security as a doctor. Students selected radiology (72.6%), pathology (58.2%), and medical administration (44.8%) as the specialties most likely to be impacted by AI, and psychiatry (61.2%), palliative care (48.5%), and obstetrics and gynaecology (41.0%) as the specialties least likely to be impacted by AI. Qualitative analysis of free text comments identified the use of AI as a tool, and that doctors will not be replaced as common themes. Conclusion: Medical students in WA appear to be interested in AI. However, they have not received education about AI and do not feel they understand its basic computational principles or limitations. AI appears to be a current deficit in the medical curriculum in WA, and most students surveyed were supportive of its introduction. These results are consistent with previous surveys conducted internationally.

Whether circulating sex hormones modulate mortality and cardiovascular disease (CVD) risk in aging men is controversial. To clarify associations of sex hormones with these outcomes. Systematic literature review to July 2019, with bridge searches to March 2024. Prospective cohort studies of community-dwelling men with sex steroids measured using mass spectrometry and at least 5 years of follow-up. Independent variables were testosterone, sex hormone-binding globulin (SHBG), luteinizing hormone (LH), dihydrotestosterone (DHT), and estradiol concentrations. Primary outcomes were all-cause mortality, CVD death, and incident CVD events. Covariates included age, body mass index, marital status, alcohol consumption, smoking, physical activity, hypertension, diabetes, creatinine concentration, ratio of total to high-density lipoprotein cholesterol, and lipid medication use. Nine studies provided individual participant data (IPD) (255 830 participant-years). Eleven studies provided summary estimates (n = 24 109). Two-stage random-effects IPD meta-analyses found that men with baseline testosterone concentrations below 7.4 nmol/L (<213 ng/dL), LH concentrations above 10 IU/L, or estradiol concentrations below 5.1 pmol/L had higher all-cause mortality, and those with testosterone concentrations below 5.3 nmol/L (<153 ng/dL) had higher CVD mortality risk. Lower SHBG concentration was associated with lower all-cause mortality (median for quintile 1 [Q1] vs. Q5, 20.6 vs. 68.3 nmol/L; adjusted hazard ratio [HR], 0.85 [95% CI, 0.77 to 0.95]) and lower CVD mortality (adjusted HR, 0.81 [CI, 0.65 to 1.00]). Men with lower baseline DHT concentrations had higher risk for all-cause mortality (median for Q1 vs. Q5, 0.69 vs. 2.45 nmol/L; adjusted HR, 1.19 [CI, 1.08 to 1.30]) and CVD mortality (adjusted HR, 1.29 [CI, 1.03 to 1.61]), and risk also increased with DHT concentrations above 2.45 nmol/L. Men with DHT concentrations below 0.59 nmol/L had increased risk for incident CVD events. Observational study design, heterogeneity among studies, and imputation of missing data. Men with low testosterone, high LH, or very low estradiol concentrations had increased all-cause mortality. SHBG concentration was positively associated and DHT concentration was nonlinearly associated with all-cause and CVD mortality. Medical Research Future Fund, Government of Western Australia, and Lawley Pharmaceuticals. (PROSPERO: CRD42019139668).

Sodium-glucose cotransporter 2 (SGLT2) inhibitors are a unique class of oral anti-hyperglycaemic medications that act to reduce glucose reabsorption in the renal proximal tubules, thereby enhancing urinary glucose excretion. Large randomized placebo-controlled trials in people with diabetes at high cardiovascular risk have demonstrated that SGLT2 inhibitors reduce heart failure hospitalization within months of commencing therapy. These findings are of considerable interest, as diabetes is associated with an increased risk of both heart failure with reduced ejection fraction and heart failure with preserved ejection fraction. In addition, left ventricular (LV) hypertrophy and impaired diastolic function is thought to be more prevalent in people with diabetes. Although many hypotheses have been proposed, the underlying mechanisms through which SGLT2 inhibitors reduce the risk of heart failure in people with diabetes are not fully understood. Given the rapid reduction in heart failure hospitalization, it is conceivable that the benefits of SGLT2 inhibitors are due to favourable haemodynamic and metabolic effects on LV function. Several clinical studies have been conducted to investigate the effect of SGLT2 inhibitors on LV structure and function and have found that LV mass index and diastolic function improve following SGLT2 inhibitor therapy in people with type 2 diabetes. If these findings are confirmed in future studies utilizing novel cardiac imaging modalities and large randomized controlled trials, then this will bring new hope for the prevention and management of heart failure with preserved ejection fraction, for which no current treatments have been shown to reduce mortality. At the present time, SGLT2 inhibitors are indicated for the treatment of type 2 diabetes; however, the results of ongoing trials in participants with heart failure but without diabetes are eagerly awaited. The purpose of this review is to summarize current knowledge regarding the effects of SGLT2 inhibitors on LV function, particularly the findings from clinical studies, proposed biological mechanisms, and future directions.