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Biomedical Engineering Online Jun 2024The monitoring and analysis of quasi-periodic biological signals such as electrocardiography (ECG), intracranial pressure (ICP), and cerebral blood flow velocity (CBFV)... (Comparative Study)
Comparative Study
BACKGROUND
The monitoring and analysis of quasi-periodic biological signals such as electrocardiography (ECG), intracranial pressure (ICP), and cerebral blood flow velocity (CBFV) waveforms plays an important role in the early detection of adverse patient events and contributes to improved care management in the intensive care unit (ICU). This work quantitatively evaluates existing computational frameworks for automatically extracting peaks within ICP waveforms.
METHODS
Peak detection techniques based on state-of-the-art machine learning models were evaluated in terms of robustness to varying noise levels. The evaluation was performed on a dataset of ICP signals assembled from 700 h of monitoring from 64 neurosurgical patients. The groundtruth of the peak locations was established manually on a subset of 13, 611 pulses. Additional evaluation was performed using a simulated dataset of ICP with controlled temporal dynamics and noise.
RESULTS
The quantitative analysis of peak detection algorithms applied to individual waveforms indicates that most techniques provide acceptable accuracy with a mean absolute error (MAE) ms without noise. In the presence of a higher noise level, however, only kernel spectral regression and random forest remain below that error threshold while the performance of other techniques deteriorates. Our experiments also demonstrated that tracking methods such as Bayesian inference and long short-term memory (LSTM) can be applied continuously and provide additional robustness in situations where single pulse analysis methods fail, such as missing data.
CONCLUSION
While machine learning-based peak detection methods require manually labeled data for training, these models outperform conventional signal processing ones based on handcrafted rules and should be considered for peak detection in modern frameworks. In particular, peak tracking methods that incorporate temporal information between successive periods of the signals have demonstrated in our experiments to provide more robustness to noise and temporary artifacts that commonly arise as part of the monitoring setup in the clinical setting.
Topics: Intracranial Pressure; Humans; Signal Processing, Computer-Assisted; Monitoring, Physiologic; Machine Learning; Algorithms; Cerebrovascular Circulation; Signal-To-Noise Ratio
PubMed: 38915091
DOI: 10.1186/s12938-024-01245-9 -
Nature Communications Jun 2024Idling brain activity has been proposed to facilitate inference, insight, and innovative problem-solving. However, it remains unclear how and when the idling brain can...
Idling brain activity has been proposed to facilitate inference, insight, and innovative problem-solving. However, it remains unclear how and when the idling brain can create novel ideas. Here, we show that cortical offline activity is both necessary and sufficient for building unlearned inferential knowledge from previously acquired information. In a transitive inference paradigm, male C57BL/6J mice gained the inference 1 day after, but not shortly after, complete training. Inhibiting the neuronal computations in the anterior cingulate cortex (ACC) during post-learning either non-rapid eye movement (NREM) or rapid eye movement (REM) sleep, but not wakefulness, disrupted the inference without affecting the learned knowledge. In vivo Ca imaging suggests that NREM sleep organizes the scattered learned knowledge in a complete hierarchy, while REM sleep computes the inferential information from the organized hierarchy. Furthermore, after insufficient learning, artificial activation of medial entorhinal cortex-ACC dialog during only REM sleep created inferential knowledge. Collectively, our study provides a mechanistic insight on NREM and REM coordination in weaving inferential knowledge, thus highlighting the power of idling brain in cognitive flexibility.
Topics: Animals; Sleep, REM; Male; Mice, Inbred C57BL; Prefrontal Cortex; Learning; Mice; Gyrus Cinguli; Wakefulness; Sleep, Slow-Wave; Knowledge; Entorhinal Cortex; Neurons
PubMed: 38914541
DOI: 10.1038/s41467-024-48816-x -
Ageing Research Reviews Jun 2024Exergame-based training is currently considered a more promising training approach than conventional physical and/or cognitive training. (Review)
Review
BACKGROUND
Exergame-based training is currently considered a more promising training approach than conventional physical and/or cognitive training.
OBJECTIVES
This study aimed to provide quantitative evidence on dose-response relationships of specific exercise and training variables (training components) of exergame-based training on cognitive functioning in middle-aged to older adults (MOA).
METHODS
We conducted a systematic review with meta-analysis including randomized controlled trials comparing the effects of exergame-based training to inactive control interventions on cognitive performance in MOA.
RESULTS
The systematic literature search identified 22,928 records of which 31 studies were included. The effectiveness of exergame-based training was significantly moderated by the following training components: body position for global cognitive functioning, the type of motor-cognitive training, training location, and training administration for complex attention, and exercise intensity for executive functions.
CONCLUSION
The effectiveness of exergame-based training was moderated by several training components that have in common that they enhance the ecological validity of the training (e.g., stepping movements in a standing position). Therefore, it seems paramount that future research focuses on developing innovative novel exergame-based training concepts that incorporate these (and other) training components to enhance their ecological validity and transferability to clinical practice. We provide specific evidence-based recommendations for the application of our research findings in research and practical settings and identified and discussed several areas of interest for future research.
PROSPERO REGISTRATION NUMBER
CRD42023418593; prospectively registered, date of registration: 1 May 2023.
PubMed: 38914262
DOI: 10.1016/j.arr.2024.102385 -
PloS One 2024Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease...
BACKGROUND
Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling.
METHODS
To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures. We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member.
DISCUSSION
Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.
Topics: Humans; Mozambique; Developing Countries; Guatemala; Pakistan; India; Focus Groups; Female; Infant; Social Interaction; Male; Communicable Diseases; Rural Population; Research Design
PubMed: 38913670
DOI: 10.1371/journal.pone.0301638 -
PloS One 2024Advanced Persistent Threat (APT) attacks are causing a lot of damage to critical organizations and institutions. Therefore, early detection and warning of APT attack...
Advanced Persistent Threat (APT) attacks are causing a lot of damage to critical organizations and institutions. Therefore, early detection and warning of APT attack campaigns are very necessary today. In this paper, we propose a new approach for APT attack detection based on the combination of Feature Intelligent Extraction (FIE) and Representation Learning (RL) techniques. In particular, the proposed FIE technique is a combination of the Bidirectional Long Short-Term Memory (BiLSTM) deep learning network and the Attention network. The FIE combined model has the function of aggregating and extracting unusual behaviors of APT IPs in network traffic. The RL method proposed in this study aims to optimize classifying APT IPs and normal IPs based on two main techniques: rebalancing data and contrastive learning. Specifically, the rebalancing data method supports the training process by rebalancing the experimental dataset. And the contrastive learning method learns APT IP's important features based on finding and pulling similar features together as well as pushing contrasting data points away. The combination of FIE and RL (abbreviated as the FIERL model) is a novel proposal and innovation and has not been proposed and published by any research. The experimental results in the paper have proved that the proposed method in the paper is correct and reasonable when it has shown superior efficiency compared to some other studies and approaches over 5% on all measurements.
Topics: Deep Learning; Humans; Computer Security; Neural Networks, Computer; Algorithms
PubMed: 38913651
DOI: 10.1371/journal.pone.0305618 -
Psicologia, Reflexao E Critica :... Jun 2024Body image is the mental representation of the body and can be influenced by cognitive, biological, behavioral, sociocultural, and environmental factors. University... (Review)
Review
BACKGROUND
Body image is the mental representation of the body and can be influenced by cognitive, biological, behavioral, sociocultural, and environmental factors. University students often encounter challenges related to it.
OBJECTIVE
This systematic review examined interventions aimed at holistically developing a positive body image within this population.
METHODS
The PRISMA 2020 guidelines and the PICO method were employed to identify, select, assess, and synthesize studies. The consulted databases included Scopus, Web of Science, and PsycINFO, with inclusion criteria targeting body image interventions for university students aged 18 to 39. Study quality was evaluated using the QATSDD tool.
RESULTS
Twenty-one relevant studies were identified, primarily from the United States, mostly employing quantitative methods, with a focus on female participants. Various intervention strategies were utilized, including cognitive-behavioral approaches, media literacy, and physical/resistance training, with a growing use of technology like mobile applications. The majority of studies reported effective outcomes, such as reduced body dissatisfaction and increased self-esteem following interventions. Nevertheless, literature gaps were identified, such as the scarcity of formative interventions and limited use of qualitative approaches.
CONCLUSION
While technology in interventions offers promising opportunities, careful assessments and judicious selection of evaluation instruments are fundamental for reliable results. Future research should focus on addressing identified gaps, such as exploring more formative interventions and incorporating qualitative methodologies to provide a more comprehensive understanding of the effectiveness of body image interventions among university students.
PubMed: 38913140
DOI: 10.1186/s41155-024-00307-0 -
Heliyon Jun 2024Early cancer detection and treatment depend on the discovery of specific genes that cause cancer. The classification of genetic mutations was initially done manually....
Early cancer detection and treatment depend on the discovery of specific genes that cause cancer. The classification of genetic mutations was initially done manually. However, this process relies on pathologists and can be a time-consuming task. Therefore, to improve the precision of clinical interpretation, researchers have developed computational algorithms that leverage next-generation sequencing technologies for automated mutation analysis. This paper utilized four deep learning classification models with training collections of biomedical texts. These models comprise bidirectional encoder representations from transformers for Biomedical text mining (BioBERT), a specialized language model implemented for biological contexts. Impressive results in multiple tasks, including text classification, language inference, and question answering, can be obtained by simply adding an extra layer to the BioBERT model. Moreover, bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) have been leveraged to produce very good results in categorizing genetic mutations based on textual evidence. The dataset used in the work was created by Memorial Sloan Kettering Cancer Center (MSKCC), which contains several mutations. Furthermore, this dataset poses a major classification challenge in the Kaggle research prediction competitions. In carrying out the work, three challenges were identified: enormous text length, biased representation of the data, and repeated data instances. Based on the commonly used evaluation metrics, the experimental results show that the BioBERT model outperforms other models with an F1 score of 0.87 and 0.850 MCC, which can be considered as improved performance compared to similar results in the literature that have an F1 score of 0.70 achieved with the BERT model.
PubMed: 38912449
DOI: 10.1016/j.heliyon.2024.e32279 -
International Journal of Women's Health 2024Research demonstrates resistance training is not only safe but also beneficial for pregnant women. However, exercise recommendations for pregnant women still minimize... (Review)
Review
Research demonstrates resistance training is not only safe but also beneficial for pregnant women. However, exercise recommendations for pregnant women still minimize the importance of resistance exercise and provide minimal guidance. With a large increase in strength-focused sports among women, it is critical to re-evaluate the risk/benefit ratio of these exercises and ensure the latest recommendations reflect the latest clinical research. The purpose of this review is to highlight the safety and benefits of resistance training for both maternal and fetal health, particularly focusing on recent work. Relevant research involving resistance training during pregnancy was accessed and analyzed via a quasi-systematic search. Results demonstrate that appropriate prenatal resistance training can help alleviate some of the common symptoms of pregnancy, such as fatigue, back pain, and poor mental health. Resistance exercise can assist with glucose control in gestational diabetes mellitus, as well as decrease the risk of infant macrosomia and childhood metabolic dysfunction associated with uncontrolled gestational diabetes. Resistance training can also increase the likelihood of a vaginal delivery, which is beneficial for both mother and baby. Concerning fetal health, resistance training increases uterine blood flow, decreases the risk of neonatal macrosomia, and improves cognitive function and metabolic health in childhood. As with all forms of exercise, pregnant women should avoid resistance exercises that involve the supine position for extended bouts of time, trauma (or risk of trauma) to the abdomen, ballistic movements, movements that rely heavily on balance, and conditions that prohibit appropriate temperature control. With these considerations in mind, resistance training's benefits far surpass the lack of risk to the fetus. Resistance training is a safe and effective way to improve and maintain physical fitness during pregnancy and represents no risk to fetal health and development. Thus, healthcare providers should recommend resistance training for pregnant women.
PubMed: 38912201
DOI: 10.2147/IJWH.S462591 -
Iranian Journal of Public Health May 2024Compared with able-bodied people, speech disabilities are more prone to various mental health problems. We aimed to explore the impact of positive psychology-based...
BACKGROUND
Compared with able-bodied people, speech disabilities are more prone to various mental health problems. We aimed to explore the impact of positive psychology-based intervention strategies on emotional cognition, mental health, and recovery of speech function in speech disabilities.
METHODS
In May 2023, 306 cases of speech disabilities were selected from 112 village committees and 129 neighborhood committees in Jingmen City, China. The control group was given routine speech rehabilitation training, and the observation group was given an intervention strategies-based on positive psychology based on the above training. The Symptom Checklist-90 (SCL-90), Chinese Facial Emotion Test (CFET), Comprehensive Function Assessment for Disabled Children (CFADC), and Boston Diagnostic Aphasia Examination (BDAE) were used to evaluate the two groups of patients before and after intervention.
RESULTS
After the intervention, the mental state scores (psychotic, obsessive-compulsive symptoms, somatization, paranoia, terror, hostility, anxiety, and depression) of the observation group were lower than those of the control group (<0.05). The correct emotional scores in the observation group were higher than those in the control group were. However, the remote error scores of the observation group were lower than those of the control group were. The difference was also statistically significant (<0.05). The cognitive function score, speech function score, and BDAE score (retelling, writing, fluency, and reading comprehension) of the observation group were all higher than those of the control group (<0.05).
CONCLUSION
The intervention strategies-based on positive psychology could promote the improvement of health problems and speech function in speech disabilities.
PubMed: 38912140
DOI: 10.18502/ijph.v53i5.15588 -
Wellcome Open Research 2024The Mental Capacity Act 2005 of England and Wales is a ground-breaking piece of legislation with reach into healthcare, social care and legal settings. Professionals...
BACKGROUND
The Mental Capacity Act 2005 of England and Wales is a ground-breaking piece of legislation with reach into healthcare, social care and legal settings. Professionals have needed to develop skills to assess mental capacity and handle malign influence, but it is unclear how assessments are implemented in real world settings. Our previously reported survey found professionals juggling competing resources in complex systems, often struggling to stay up to date with law.The current follow-up study uses one-to-one interviews of professionals to characterise in detail six areas of uncertainty faced when assessing mental capacity, whilst suggesting ways to make improvements.
METHODS
Forty-four healthcare, social care and legal professionals were interviewed, using a semi-structured topic guide. Transcripts were analysed using framework analysis: a qualitative technique built to investigate healthcare policy.
RESULTS
Our topic guide generated 21 themes. In relation to the six areas of uncertainty: 1) Many participants stressed the importance of capturing a holistic view, adding that their own profession was best-placed for this - although a medical diagnosis was often needed. 2) The presumption of capacity was a laudable aim, though not always easy to operationalise and occasionally being open to abuse. 3) There was cautious interest in psychometric testing, providing a cognitive context for decisions. 4) Undue influence was infrequent, but remained under-emphasised in training. 5) Multi-professional assessments were common, despite doubts about fitting these within local resources and the law. 6) Remote assessment was generally acceptable, if inadequate for identifying coercion.
CONCLUSIONS
Practical constraints and competing demands were reported by professionals working within real world systems. Assessment processes must be versatile, equally applicable in routine and emergency settings, across diverse decisional types, for both generalist and specialist assessors, and able to handle coercion. Recognising these challenges will guide development of best practices in assessment and associated policy.
PubMed: 38911900
DOI: 10.12688/wellcomeopenres.20952.1