Antiarrhythmic preferences and outcomes post DC cardioversion for atrial fibrillation, an Australian rural perspective.

INTRODUCTION: Direct current cardioversion (DCCV) remains one of the recommended management strategies for symptomatic atrial fibrillation (AF). Antiarrhythmic drugs (AAD) are prescribed post procedure to maintain sinus rhythm (SR). Limited literature exists on the AAD prescribing practices and their efficacy, post-DCCV in rural Australia. OBJECTIVE: The primary aim was to determine the preferred AAD post-DCCV and the factors affecting AAD prescribing practices. The secondary aim was to assess the efficacy of the AAD in maintaining SR. DESIGN: A retrospective observational audit of patients with non-valvular AF who underwent successful elective DCCV for symptomatic AF, during 2015-2020 at a regional hospital in New South Wales (NSW) (Dubbo Base Hospital). Patients were followed up for a duration of 12 months post-DCCV. RESULTS: 233 patients underwent successful DCCV during the study duration. Amiodarone was the preferred AAD of choice post-DCCV followed by sotalol and flecainide, respectively (36.5% vs. 27.8% vs. 1.3%). 35.2% patients were not prescribed AAD. Amiodarone and sotalol had similar but modest efficacies and neither were superior to no AAD, in maintaining SR 12 months post-DCCV (AF recurrence rate 61.5% vs. 68.2% vs. 71.6% respectively, p = 0.37). Antecedent cerebrovascular accident (CVA), pulmonary disease, smoking, prior treatment with digoxin, diuretics and left ventricular (LV) dysfunction were factors that influenced AAD prescribing practices. CONCLUSION: The study demonstrates equal efficacies of amiodarone, sotalol and no AAD in maintaining SR 12 months post-DCCV. Prescribing practices post-DCCV at Dubbo Base Hospital differ from observed national trends and guidelines. AAD prescription requires a multifaceted approach with a key consideration to prioritise safety over efficacy, being mindful of challenges in delivering optimal healthcare in a rural setting.

CDNA-SNN: A New Spiking Neural Network for Pattern Classification Using Neuronal Assemblies.

Spiking neural networks (SNNs) mimic their biological counterparts more closely than their predecessors and are considered the third generation of artificial neural networks. It has been proven that networks of spiking neurons have a higher computational capacity and lower power requirements than sigmoidal neural networks. This article introduces a new type of SNN that draws inspiration and incorporates concepts from neuronal assemblies in the human brain. The proposed network, termed as class-dependent neuronal activation-based SNN (CDNA-SNN), assigns each neuron learnable values known as CDNAs which indicate the neuron's average relative spiking activity in response to samples from different classes. A new learning algorithm that categorizes the neurons into different class assemblies based on their CDNAs is also presented. These neuronal assemblies are trained via a novel training method based on spike-timing-dependent plasticity (STDP) to have high activity for their associated class and low firing rate for other classes. Also, using CDNAs, a new type of STDP that controls the amount of plasticity based on the assemblies of pre-and postsynaptic neurons is proposed. The performance of CDNA-SNN is evaluated on five datasets from the University of California, Irvine (UCI) machine learning repository, as well as Modified National Institute of Standards and Technology (MNIST) and Fashion MNIST, using nested cross-validation (N-CV) for hyperparameter optimization. Our results show that CDNA-SNN significantly outperforms synaptic weight association training (SWAT) ( p 0.0005) and SpikeProp ( p 0.05) on 3/5 and self-regulating evolving spiking neural (SRESN) ( p 0.05) on 2/5 UCI datasets while using the significantly lower number of trainable parameters. Furthermore, compared to other supervised, fully connected SNNs, the proposed SNN reaches the best performance for Fashion MNIST and comparable performance for MNIST and neuromorphic-MNIST (N-MNIST), also utilizing much less (1%-35%) parameters.

Chemoinformatic regression methods and their applicability domain.

The growing interest in chemoinformatic model uncertainty calls for a summary of the most widely used regression techniques and how to estimate their reliability. Regression models learn a mapping from the space of explanatory variables to the space of continuous output values. Among other limitations, the predictive performance of the model is restricted by the training data used for model fitting. Identification of unusual objects by outlier detection methods can improve model performance. Additionally, proper model evaluation necessitates defining the limitations of the model, often called the applicability domain. Comparable to certain classifiers, some regression techniques come with built-in methods or augmentations to quantify their (un)certainty, while others rely on generic procedures. The theoretical background of their working principles and how to deduce specific and general definitions for their domain of applicability shall be explained.

Plantar pressures in people with midfoot osteoarthritis: cross-sectional findings from the Clinical Assessment Study of the Foot.

BACKGROUND: Midfoot osteoarthritis (OA) is a common condition, however its aetiology is not well understood. Understanding how plantar pressures differ between people with and without midfoot OA may provide insight into the aetiology and how best to manage this condition. RESEARCH QUESTION: To compare plantar pressures between people with and without symptomatic radiographic midfoot OA. METHODS: This was a cross-sectional study of adults aged ≥ 50 years registered with four UK general practices who reported foot pain in the past year. Symptomatic radiographic midfoot OA was defined as midfoot pain in the last four weeks, combined with radiographic OA in one or more midfoot joints. Cases were matched 1:1 for sex and age ( ± 5 years) to controls. Peak plantar pressure and maximum force in 10 regions of the foot were determined using a pressure platform (RSscan International, Olen, Belgium) and compared between the groups using independent samples t-tests and effect sizes (Cohen's d). RESULTS: We included 61 midfoot OA cases (mean age 67.0, SD 8.1, 31 males, 30 females) and matched these to 61 controls (mean age 66.0, SD 7.9). Midfoot OA cases displayed greater force (d=0.79, medium effect size, p = <0.001) and pressure at the midfoot (d=0.70, medium effect size, p = <0.001), greater force at the fourth metatarsophalangeal (MTP) joint (d=0.28, small effect size, p = 0.13), and fifth MTP joint (d=0.37, small effect size, p = 0.10) and greater pressure at the fifth MTP joint (d=0.34, small effect size, p = 0.13). They also displayed lower force (d=0.40, small effect size, p = 0.02) and pressure at the hallux (d=0.50, medium effect size, p = <0.001) and lower force (d=0.54, medium effect size, p = <0.001) and pressure at the lesser toes (d=0.48, small effect size, p = <0.001) compared with controls. SIGNIFICANCE: Midfoot OA appears to be associated with lowering of the medial longitudinal arch, greater lateral push off and less propulsion at toe off. Longitudinal studies are needed to establish causal relationships.

The problem of antimalarial resistance and its implications for drug discovery.

INTRODUCTION: Malaria remains a devastating infectious disease with hundreds of thousands of casualties each year. Antimalarial drug resistance has been a threat to malaria control and elimination for many decades and is still of concern today. Despite the continued effectiveness of current first-line treatments, namely artemisinin-based combination therapies, the emergence of drug-resistant parasites in Southeast Asia and even more alarmingly the occurrence of resistance mutations in Africa is of great concern and requires immediate attention. AREAS COVERED: A comprehensive overview of the mechanisms underlying the acquisition of drug resistance in Plasmodium falciparum is given. Understanding these processes provides valuable insights that can be harnessed for the development and selection of novel antimalarials with reduced resistance potential. Additionally, strategies to mitigate resistance to antimalarial compounds on the short term by using approved drugs are discussed. EXPERT OPINION: While employing strategies that utilize already approved drugs may offer a prompt and cost-effective approach to counter antimalarial drug resistance, it is crucial to recognize that only continuous efforts into the development of novel antimalarial drugs can ensure the successful treatment of malaria in the future. Incorporating resistance propensity assessment during this developmental process will increase the likelihood of effective and enduring malaria treatments.

Deep neural networks can differentiate thyroid pathologies on infrared hyperspectral images.

BACKGROUND AND OBJECTIVE: The thyroid is a gland responsible for producing important body hormones. Several pathologies can affect this gland, such as thyroiditis, hypothyroidism, and thyroid cancer. The visual histological analysis of thyroid specimens is a valuable process that enables pathologists to detect diseases with high efficiency, providing the patient with a better prognosis. Existing computer vision systems developed to aid in the analysis of histological samples have limitations in distinguishing pathologies with similar characteristics or samples containing multiple diseases. To overcome this challenge, hyperspectral images are being studied to represent biological samples based on their molecular interaction with light. METHODS: In this study, we address the acquisition of infrared absorbance spectra from each voxel of histological specimens. This data is then used for the development of a multiclass fully-connected neural network model that discriminates spectral patterns, enabling the classification of voxels as healthy, cancerous, or goiter. RESULTS: Through experiments using the k-fold cross-validation protocol, we obtained an average accuracy of 93.66 %, a sensitivity of 93.47 %, and a specificity of 96.93 %. Our results demonstrate the feasibility of using infrared hyperspectral imaging to characterize healthy tissue and thyroid pathologies using absorbance measurements. The proposed deep learning model has the potential to improve diagnostic efficiency and enhance patient outcomes.

Homozygous ALS-linked mutations in TARDBP/TDP-43 lead to hypoactivity and synaptic abnormalities in human iPSC-derived motor neurons.

Cytoplasmic mislocalization and aggregation of the RNA-binding protein TDP-43 is a pathological hallmark of the motor neuron (MN) disease amyotrophic lateral sclerosis (ALS). Furthermore, while mutations in TARDBP (encoding TDP-43) have been associated with ALS, the pathogenic consequences of these mutations remain poorly understood. Using CRISPR-Cas9, we engineered two homozygous knock-in induced pluripotent stem cell lines carrying mutations in TARDBP encoding TDP-43A382T and TDP-43G348C, two common yet understudied ALS TDP-43 variants. Motor neurons (MNs) differentiated from knock-in iPSCs had normal viability and displayed no significant changes in TDP-43 subcellular localization, phosphorylation, solubility, or aggregation compared with isogenic control MNs. However, our results highlight synaptic impairments in both TDP-43A382T and TDP-43G348C MN cultures, as reflected in synapse abnormalities and alterations in spontaneous neuronal activity. Collectively, our findings suggest that MN dysfunction may precede the occurrence of TDP-43 pathology and neurodegeneration in ALS and further implicate synaptic and excitability defects in the pathobiology of this disease.

In vivo evaluation of cortical thickness with magnetic resonance imaging in patients with bipolar disorder

Changes in cortical thickness can be related to neuropsychiatric disorders and neurodegenerative processes. Previous studies have been conducted to characterize the pattern of changes in cortical thickness in several psychiatric diseases. The aim of the present study was to evaluate changes in cortical thickness with magnetic resonance imaging (MRI) in patients with bipolar disorder. Twenty-seven patients with bipolar disorder (14 male, 36.0 ± 16.2 years old; 13 female, 41.6 ± 10.7 years old) and 40 healthy controls (16 male, 36.0 ± 10.5 years old; 24 female, 37.0 ± 4.7 years old) underwent 3T MRI. Sagittal T1-weighted magnetization prepared rapid acquisition gradient echo images were acquired (voxel size, 1.33 mm³; 128 slices; in-plane matrix resolution, 256 × 256; flip angle, 7º; repetition time, 2530 ms; echo time, 3.39 ms; inversion time, 1100 ms). Cortical segmentation was performed using FreeSurfer 4.0.5. The results indicated both thinning and thickening of the cerebral cortex in patients with bipolar disorder compared with controls, depending on gender. Significant (p < .01) thickening was observed in the right hemisphere superior-parietal cortex in female patients with bipolar disorder, and significant (p < .05) thinning was observed in the left hemisphere caudal-anterior cingulate in male patients with bipolar disorder. The other regions did not show significant differences. The results suggest that an analysis of cortical thickness with MRI in patients with bipolar disorder may allow identification of areas that may be morphologically changed compared with controls. Demonstration of these alterations will elucidate the pathophysiology of bipolar disorder and may contribute to better therapies for this disorder.