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Hypoxic–Ischemic Encephalopathy (HIE) occurs in patients who experience a decreased flow of blood and oxygen to the brain, with the optimal window for effective treatment being within the first six hours of life. This puts a significant demand on medical professionals to accurately and effectively grade the severity of the HIE present, which is a time-consuming and challenging task. This paper proposes a novel workflow for background EEG grading, implementing a blend of Digital Signal Processing (DSP) and Machine-Learning (ML) techniques. First, the EEG signal is transformed into an amplitude and frequency modulated audio spectrogram, which enhances its relevant signal properties. The difference between EEG Grades 1 and 2 is enhanced. A convolutional neural network is then designed as a regressor to map the input image into an EEG grade, by utilizing an optimized rounding module to leverage the monotonic relationship among the grades. Using a nested cross-validation approach, an accuracy of 89.97% was achieved, in particular improving the AUC of the most challenging grades, Grade 1 and Grade 2, to 0.98 and 0.96. The results of this study show that the proposed representation and workflow increase the potential for background grading of EEG signals, increasing the accuracy of grading background patterns that are most relevant for therapeutic intervention, across large windows of time.

The study proposes a novel method to empower healthcare professionals to interact and leverage AI decision support in an intuitive manner using auditory senses. The method’s suitability is assessed through acoustic detection of the presence of neonatal seizures in electroencephalography (EEG). Neurophysiologists use EEG recordings to identify seizures visually. However, neurophysiological expertise is expensive and not available 24/7, even in tertiary hospitals. Other neonatal and pediatric medical professionals (nurses, doctors, etc.) can make erroneous interpretations of highly complex EEG signals. While artificial intelligence (AI) has been widely used to provide objective decision support for EEG analysis, AI decisions are not always explainable. This work developed a solution to combine AI algorithms with a human-centric intuitive EEG interpretation method. Specifically, EEG is converted to sound using an AI-driven attention mechanism. The perceptual characteristics of seizure events can be heard using this method, and an hour of EEG can be analysed in five seconds. A survey that has been conducted among targeted end-users on a publicly available dataset has demonstrated that not only does it drastically reduce the burden of reviewing the EEG data, but also the obtained accuracy is on par with experienced neurophysiologists trained to interpret neonatal EEG. It is also shown that the proposed communion of a medical professional and AI outperforms AI alone by empowering the human with little or no experience to leverage AI attention mechanisms to enhance the perceptual characteristics of seizure events.

The expense and time required for in vivo reproductive and developmental toxicity studies have driven the development of in vitro alternatives. Here, we used a new in vitro split luciferase‐based assay to screen a library of 177 toxicants for inhibitors of apoptosome formation. The apoptosome contains seven Apoptotic Protease‐Activating Factor‐1 (Apaf‐1) molecules and induces cell death by activating caspase‐9. Apaf‐1‐dependent caspase activation also plays an important role in CNS development and spermatogenesis. In the in vitro assay, Apaf‐1 fused to an N‐terminal fragment of luciferase binds to Apaf‐1 fused to a C‐terminal fragment of luciferase and reconstitutes luciferase activity. Our assay indicated that pentachlorophenol (PCP) inhibits apoptosome formation, and further investigation revealed that PCP binds to cytochrome c. PCP is a wood preservative that reduces male fertility by ill‐defined mechanisms. Although the data show that PCP inhibited apoptosome formation, the concentration required suggests that other mechanisms may be more important for PCP's effects on spermatogenesis. Nonetheless, the data demonstrate the utility of the new assay in identifying apoptosome inhibitors, and we suggest that the assay may be useful in screening for reproductive and developmental toxicants. Oligomerization of Apoptotic Protease‐Activating Factor‐1 fused to luciferase fragments reconstitutes luciferase activity and generates light during apoptosome formation. Here, this luciferase‐based assay was used to screen a toxicant library. Pentachlorophenol was identified as an inhibitor of apoptosome formation, and subsequent experiments showed it acts by directly targeting cytochrome c.

The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort.

This comprehensive review aims to provide a thorough overview of the vital role that extracellular vesicles (EVs) play in endothelial dysfunction, particularly emphasizing how physiological factors—such as sex and aging—along with significant cardiovascular risk factors, influence this process. The review covers studies ranging from the first description of EVs in 1945 to contemporary insights into their biological roles in intercellular signaling and endothelial dysfunction. A comprehensive analysis of peer-reviewed articles and reviews indexed in the PubMed database was conducted to compile the information. Initially, Medical Subject Headings (MeSH) terms included keywords aimed at providing general knowledge about the role of EVs in the regulation of endothelial signaling, such as “extracellular vesicles”, “endothelium”, and “intercellular signaling”. Subsequently, terms related to the pathophysiological implications of EV interactions with endothelial dysfunction and cardiovascular disease were added, including “cardiovascular disease”, “sex”, “aging”, “atherosclerosis”, “obesity”, and “diabetes”. Additionally, the potential applications of EVs in cardiovascular disease were explored using the MeSH terms “extracellular vesicles”, “cardiovascular disease”, “biomarker”, and “therapeutic strategy”. The results of this bibliographical review reveal that EVs have the capacity to induce various cellular responses within the cardiovascular system and play a significant role in the complex landscape of endothelial dysfunction and cardiovascular disease. The composition of the EV cargo is subject to modification by pathophysiological conditions such as sex, aging, and cardiovascular risk factors, which result in a complex regulatory influence on endothelial function and neighboring cells when released from a dysfunctional endothelium. Moreover, the data suggest that this field still requires further exploration, as EV biology is continuously evolving, presenting a dynamic and engaging area for research. A deeper understanding of the molecular cargo involved in EV–endothelium interactions could yield valuable biomarkers for monitoring cardiovascular disease progression and facilitate the development of innovative bioengineered therapeutic strategies to enhance patient outcomes.

Genomic balance: underweight as a mirror image of obesity Underweight and obese phenotypes can both pose health risks. But whereas obesity has been associated with a number of genetic variants, little is known about the genetic basis of underweight. A large-scale screen of data from 28 cytogenetic centres in Europe and North America now shows that being underweight is frequently associated with duplication of a short region on chromosome 16. Deletion of this same chromosomal region has previously been associated with obesity. The observed associated phenotypes are opposites, or mirrors, of those reported in carriers of deletions at this locus, and correlate with changes in transcript levels for genes within the duplication but not within the adjacent regions. The suggestion is that severe obesity and being underweight could have mirror etiologies, possibly through contrasting effects on energy balance. Both obesity and being underweight have been associated with increased mortality 1 , 2 . Underweight, defined as a body mass index (BMI) ≤ 18.5 kg per m 2 in adults and ≤ −2 standard deviations from the mean in children, is the main sign of a series of heterogeneous clinical conditions including failure to thrive 3 , 4 , 5 , feeding and eating disorder and/or anorexia nervosa 6 , 7 . In contrast to obesity, few genetic variants underlying these clinical conditions have been reported 8 , 9 . We previously showed that hemizygosity of a ∼600-kilobase (kb) region on the short arm of chromosome 16 causes a highly penetrant form of obesity that is often associated with hyperphagia and intellectual disabilities 10 . Here we show that the corresponding reciprocal duplication is associated with being underweight. We identified 138 duplication carriers (including 132 novel cases and 108 unrelated carriers) from individuals clinically referred for developmental or intellectual disabilities (DD/ID) or psychiatric disorders, or recruited from population-based cohorts. These carriers show significantly reduced postnatal weight and BMI. Half of the boys younger than five years are underweight with a probable diagnosis of failure to thrive, whereas adult duplication carriers have an 8.3-fold increased risk of being clinically underweight. We observe a trend towards increased severity in males, as well as a depletion of male carriers among non-medically ascertained cases. These features are associated with an unusually high frequency of selective and restrictive eating behaviours and a significant reduction in head circumference. Each of the observed phenotypes is the converse of one reported in carriers of deletions at this locus. The phenotypes correlate with changes in transcript levels for genes mapping within the duplication but not in flanking regions. The reciprocal impact of these 16p11.2 copy-number variants indicates that severe obesity and being underweight could have mirror aetiologies, possibly through contrasting effects on energy balance.

The use of multidetector computed tomography (MDCT) in routine abdominal explorations has increased the detection of the nutcracker phenomenon, defined as left renal vein (LRV) compression by adjacent anatomic structures. The embryology and anatomy of the nutcracker phenomenon are relevant as a background for the nutcracker syndrome, a rare cause of hematuria as well as other symptoms. MDCT examples of collateral renal vein circulation (gonadal, ureteric, azygous, lumbar, capsular) and aortomesenteric (anterior) and retroaortic (posterior) nutcracker phenomena in patients with no urologic complaint are shown as well as studies performed on patients with gross hematuria of uncertain origin. Incidental observation of collateral veins draining the LRV in abdominal MDCT explorations of asymptomatic patients may be a sign of a compensating nutcracker phenomenon. Imbalance between LRV compression and development of collateral circulation may lead to symptomatic nutcracker syndrome.

Significant training is required to visually interpret neonatal EEG signals. This study explores alternative sound-based methods for EEG interpretation which are designed to allow for intuitive and quick differentiation between healthy background activity and abnormal activity such as seizures. A novel method based on frequency and amplitude modulation (FM/AM) is presented. The algorithm is tuned to facilitate the audio domain perception of rhythmic activity which is specific to neonatal seizures. The method is compared with the previously developed phase vocoder algorithm for different time compressing factors. A survey is conducted amongst a cohort of non-EEG experts to quantitatively and qualitatively examine the performance of sound-based methods in comparison with the visual interpretation. It is shown that both sonification methods perform similarly well, with a smaller inter-observer variability in comparison with visual. A post-survey analysis of results is performed by examining the sensitivity of the ear to frequency evolution in audio.

This paper proposes and implements an intuitive and pervasive solution for neonatal EEG monitoring assisted by sonification and deep learning AI that provides information about neonatal brain health to all neonatal healthcare professionals, particularly those without EEG interpretation expertise. The system aims to increase the demographic of clinicians capable of diagnosing abnormalities in neonatal EEG. The proposed system uses a low-cost and low-power EEG acquisition system. An Android app provides single-channel EEG visualization, traffic-light indication of the presence of neonatal seizures provided by a trained, deep convolutional neural network and an algorithm for EEG sonification, designed to facilitate the perception of changes in EEG morphology specific to neonatal seizures. The multifaceted EEG interpretation framework is presented and the implemented mobile platform architecture is analyzed with respect to its power consumption and accuracy.

En el contexto del debate sobre la calidad de las intervenciones educativas, este libro presenta el proceso de investigación científica como una competencia profesional esencial al servicio de la mejora de las prácticas de enseñanza y aprendizaje. Dirigido a estudiantes, profesores y profesionales que intervienen en los diversos ámbitos de la educación, centra su atención en el importante papel que la investigación, y particularmente las evidencias que proporciona, puede tener para ayudar a orientar las decisiones en relación a dos momentos centrales: la fundamentación y la evaluación de las prácticas en el ejercicio de la actividad profesional.