Recal

INTRODUCTION: Chagas disease is a severe parasitic illness that is prevalent in Latin America and often goes unaddressed. Early detection and treatment are critical in preventing the progression of the illness and its associated life-threatening complications. In recent years, machine learning algorithms have emerged as powerful tools for disease prediction and diagnosis. METHODS: In this study, we developed machine learning algorithms to predict the risk of Chagas disease based on five general factors: age, gender, history of living in a mud or wooden house, history of being bitten by a triatomine bug, and family history of Chagas disease...

OBJECTIVE: Machine learning (ML) is increasingly employed to diagnose medical conditions, with algorithms trained to assign a single label using a black-box approach. We created an ML approach using deep learning that generates outcomes that are transparent and in line with clinical, diagnostic rules. We demonstrate our approach for autism spectrum disorders (ASD), a neurodevelopmental condition with increasing prevalence. METHODS: We use unstructured data from the Centers for Disease Control and Prevention (CDC) surveillance records labeled by a CDC-trained clinician with ASD A1-3 and B1-4 criterion labels per sentence and with ASD cases labels per record using Diagnostic and Statistical Manual of Mental Disorders (DSM5) rules...

OBJECTIVE: Scalp high-frequency oscillations (sHFOs) are a promising non-invasive biomarker of epilepsy. However, the visual marking of sHFOs is a time-consuming and subjective process, existing automatic detectors based on single-dimensional analysis have difficulty with accurately eliminating artifacts and thus do not provide sufficient reliability to meet clinical needs. Therefore, we propose a high-performance sHFOs detector based on a deep learning algorithm. METHODS: An initial detection module was designed to extract candidate high-frequency oscillations...

Despite the salient experience of encoding threatening events, these memories are prone to distortions and often non-veridical from encoding to recall. Further, threat has been shown to preferentially disrupt the binding of event details and enhance goal-relevant information. While extensive work has characterised distinctive features of emotional memory, research has not fully explored the influence threat has on temporal memory, a process putatively supported by the binding of event details into a temporal context...

We investigated the association between pre-COVID-19 memory function and (a) receipt of a COVID-19 test and (b) incidence of COVID-19 using the COVID-19 Questionnaire Study (CQS) of the Canadian Longitudinal Study on Aging (CLSA). The CQS included 28,565 middle-aged and older adults. We regressed receipt of a COVID-19 test on participants' immediate and delayed recall memory scores and re-ran the regression models with COVID-19 incidence as the outcome. All regression models were adjusted for sociodemographic, lifestyle, and health covariates...

This study aimed to assess the consumption of ultra-processed foods (UPF) and identify their association with obesity and abdominal obesity in adult women of reproductive age living in situations of social vulnerability in Maceió, Northeastern Brazil. This was a cross-sectional study carried out between October 2020 and May 2021. An anthropometric evaluation was carried out to assess obesity and abdominal obesity. A dietary assessment was also conducted using a 24-h food recall to determine the calorie intake from UPF...

Mental Status Assessment (MSA) holds significant importance in psychiatry. In recent years, several studies have leveraged Electroencephalogram (EEG) technology to gauge an individual's mental state or level of depression. This study introduces a novel multi-tier ensemble learning approach to integrate multiple EEG bands for conducting mental state or depression assessments. Initially, the EEG signal is divided into eight sub-bands, and then a Long Short-Term Memory (LSTM)-based Deep Neural Network (DNN) model is trained for each band...

The banking sector's shift from traditional physical locations to digital channels has offered customers unprecedented convenience and increased the risk of fraud for customers and institutions alike. In this study, we discuss the pressing need for robust fraud detection & prevention systems in the context of evolving technological environments. We introduce a graph-based machine learning model that is specifically designed to detect fraudulent activity in various types of banking operations, such as credit card transactions, debit card transactions, and online banking transactions...

Understanding the impacts of environmental exposures on health outcomes during development is an important area of research for plenty of reasons. Collecting retrospective and prospective residential history can enrich observational studies through eventual linkages to external sources. Augmenting participant health outcome data with environmental data can better inform on the role of the environment, thereby enhancing prevention and intervention efforts. However, collecting the geospatial information needed for this type of research can be difficult, especially when data are collected directly from participants...

This research project explored into the intricacies of road traffic accidents severity in the UK, employing a potent combination of machine learning algorithms, econometric techniques, and traditional statistical methods to analyse longitudinal historical data. Our robust analysis framework includes descriptive, inferential, bivariate, multivariate methodologies, correlation analysis: Pearson's and Spearman's Rank Correlation Coefficient, multiple logistic regression models, Multicollinearity Assessment, and Model Validation...

Highly repetitive adverse environmental conditions are encountered by plants multiple times during their lifecycle. These repetitive encounters with stresses provide plants an opportunity to remember and recall the experiences of past stress-associated responses, resulting in better adaptation towards those stresses. In general, this phenomenon is known as plant stress memory. According to our current understanding, epigenetic mechanisms play a major role in plants stress memory through DNA methylation, histone, and chromatin remodeling, and modulating non-coding RNAs...

A considerable proportion of women subjectively perceive a detriment to their cognitive capacity during pregnancy, with decreased memory functions being the most frequently self-reported concerns. However, objective investigation of these perceived cognitive deficits has yielded inconsistent results. This study focused on memory functions during late pregnancy using multiple tasks designed to assess various memory indices, for example, working memory, learning rate, immediate recall, proactive and retroactive interference, delayed recall, retrieval efficiency, visuospatial constructional ability, recognition, and executive function...

Sustainability of a product or device is currently primarily related to its environmental footprint. Here, a wider concept of sustainability is introduced for medical devices and their components in healthcare provision. Such devices sustain healthcare and patient wellbeing due to their quality specifications for material composition, product design and performance. The term quality must be intended in the most comprehensive term, including purity and biocompatibility of materials, device reliability, limited number of recalls and reduced risks as well as acceptability for patients...

BACKGROUND: Intake of a diversified diet is key to the prevention of malnutrition among children as it results in improved intake of energy and micronutrients, which are deemed critical for better nutritional status of children. This study assessed minimum dietary diversity (MDD) and its determinants among children aged 6-23 months in the Sagnarigu Municipality of Ghana. METHODS: This was an analytical cross-sectional study, carried out in the Sagnarigu Municipality, Ghana and involved 369 mother-child pairs selected through a systematic random sampling...

BACKGROUND: Self-glazed zirconia (SZ) restorations are made by a novel additive three-dimensional gel deposition approach, which are suitable for a straightforward completely digital workflow. SZ has recently been used as minimally invasive veneer, but its clinical outcomes have not been clarified yet. This study aimed to evaluate the preliminary clinical outcomes of SZ veneers compared with the widely used lithium disilicate glass-ceramic veneers made by either pressing (PG) or milling (MG) process...

Dreaming is a universal human behavior that has inspired searches for meaning across many disciplines including art, psychology, religion, and politics, yet its function remains poorly understood. Given the suggested role of sleep in emotional memory processing, we investigated whether reported overnight dreaming and dream content are associated with sleep-dependent changes in emotional memory and reactivity, and whether dreaming plays an active or passive role. Participants completed an emotional picture task before and after a full night of sleep and they recorded the presence and content of their dreams upon waking in the morning...

A bridge disease identification approach based on an enhanced YOLO v3 algorithm is suggested to increase the accuracy of apparent disease detection of concrete bridges under complex backgrounds. First, the YOLO v3 network structure is enhanced to better accommodate the dense distribution and large variation of disease scale characteristics, and the detection layer incorporates the squeeze and excitation (SE) networks attention mechanism module and spatial pyramid pooling module to strengthen the semantic feature extraction ability...

Agriculture plays a pivotal role in the economic development of a nation, but, growth of agriculture is affected badly by the many factors one such is plant diseases. Early stage prediction of these disease is crucial role for global health and even for game changers the farmer's life. Recently, adoption of modern technologies, such as the Internet of Things (IoT) and deep learning concepts has given the brighter light of inventing the intelligent machines to predict the plant diseases before it is deep-rooted in the farmlands...

IMPORTANCE: Older women with fecal incontinence (FI) who underwent diet modification intervention (DMI) showed significant improvement in FI symptoms. It is unclear whether improvement in symptoms was associated with objective changes in dietary intake quality. OBJECTIVES: The primary aim was to determine if improvement in overall dietary intake quality was associated with improvement in FI symptoms. Our secondary aim was to evaluate whether individual food group consumption changes were associated with changes in FI symptoms...

Self-representations guide and shape our thoughts and behaviour. People usually exhibit inherent biases in perception, attention, and memory to favour the information associated with themselves over that associated with others. The present study explored the phenomenon of self-bias in working memory (WM), specifically how self-referential processing impacts WM precision. Four precision-based experiments were conducted to assess the recall precision of self-referential items and items associated with other social agents...