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Dendritic cells (DCs) are first responders of the innate immune system that integrate signals from external stimuli to direct context-specific immune responses. Current models suggest that an active switch from mitochondrial metabolism to glycolysis accompanies DC activation to support the anabolic requirements of DC function. We show that early glycolytic activation is a common program for both strong and weak stimuli, but that weakly activated DCs lack long-term HIF-1α-dependent glycolytic reprogramming and retain mitochondrial oxidative metabolism. Early induction of glycolysis is associated with activation of AKT, TBK, and mTOR, and sustained activation of these pathways is associated with long-term glycolytic reprogramming. We show that inhibition of glycolysis impaired maintenance of elongated cell shape, DC motility, CCR7 oligomerization, and DC migration to draining lymph nodes. Together, our results indicate that early induction of glycolysis occurs independent of pro-inflammatory phenotype, and that glycolysis supports DC migratory ability regardless of mitochondrial bioenergetics. The activation of dendritic cells (DC) is associated with a metabolic switch from oxidative to glycolytic metabolism. Here, the authors show that both strong and weak stimuli cause an immediate increase in glycolysis, but only strong stimuli induce long-term glycolytic reprogramming.

While Th2 adaptive immunity has long been considered to orchestrate type 2 inflammation in the allergic lung, group 2 innate lymphoid cells (ILC2s), with the ability to produce a similar profile of type 2 cytokines, likely participate in lung inflammation in allergic asthma. ILC2s are also implicated in sex disparities in asthma, supported by data from murine models showing they are inhibited by male sex hormones. Moreover, larger numbers of ILC2s are present in the lungs of female mice and are correlated with greater type 2 inflammation. Lung ILC2s exhibit intriguing memory-like responses, though whether these differ in males and females does not appear to have been addressed. We have examined type 2 lung inflammation in adult male and female Balb/c mice following delivery of IL-33 to the lung. While the number of ILC2s was elevated equally in males and females four weeks after exposure to IL-33, ILC2s from female mice expressed higher levels of ST2, the IL-33 cognate receptor subunit, and a larger proportion of ILC2s from females expressed the IL-25 receptor (IL-25R), which has previously been linked to memory-like ILC2 responses in mice. Our data show that the subset of ILC2s expressing IL-25R, upon activation, was more likely to produce IL-5 and IL-13. Moreover, STAT6 was absolutely required for enhanced responsiveness in this model system. Altogether, our data show that enhanced type 2 inflammation in females is linked to durable changes in ILC2 subsets with the ability to respond more robustly, in a STAT6-dependent manner, upon secondary activation by innate epithelial-derived cytokines.

Cardiovascular disease (CVD) is rising as a significant concern for the healthcare sector around the world. Researchers have applied multiple traditional approaches to making healthcare systems find new solutions for the CVD concern. Artificial Intelligence (AI) and blockchain are emerging approaches that may be integrated into the healthcare sector to help responsible and secure decision-making in dealing with CVD concerns. Secure CVD information is needed while dealing with confidential patient healthcare data, especially with a decentralized blockchain technology (BCT) system that requires strong encryption. However, AI and blockchain-empowered approaches could make people trust the healthcare sector, mainly in diagnosing areas like cardiovascular care. This research proposed an explainable AI (XAI) approach entangled with BCT that enhances healthcare interpretability and responsibility to cardiovascular health medical experts. XAI is significant in addressing cardiovascular prediction issues and offers potential solutions for complex communication and decision-making in cardiovascular care. The proposed approach performs better, with the highest accuracy of 97.12% compared to earlier methods. This achievement shows its ability to tackle complex issues, accessible during healthcare sector communication and decision processes.

Respiratory syncytial virus (RSV) causes millions of hospitalizations and thousands of deaths per year globally. Early-life RSV infection is also associated with the subsequent development of wheezing and asthma, which exhibits sex-related disparities in incidence, epidemiology, and morbidity. The mechanisms that underlie these sex-specific effects are not clear. We have developed a combined infection-allergy model in which 10-day old mice are infected with RSV and subsequently exposed to a common allergen, house dust mite (HDM). We show that early-life exposure to RSV enhanced allergic lung inflammation upon HDM exposure 10 days after viral infection. Early-life RSV infection increased levels of the innate cytokine, IL-33, in the lung 6h following HDM exposure. Accumulation of CD11c med eosinophils and group 2 innate lymphoid cells was more prominent in the lungs of female mice exposed to both RSV and HDM. Moreover, the numbers of IL-13 + T cells (both CD4 + and CD8 + ) in the lung were significantly increased in mice exposed to both RSV infection and HDM, although the expression of ST2 (the cognate receptor for IL-33) was not linked to T cell cytokine production. Inflammatory responses were maintained when the interval between RSV infection and HDM exposure was extended to one month. Thus, our results show that early exposure to RSV increased numbers of innate cells as well as T cells in response to a common allergen, whether delivered within days or after several weeks of viral infection and that most responses were enhanced in female mice. Our work highlights sex-specific impact of early-life viral infection on the developing lung, and suggests possible mechanisms to explain the subsequent predisposition to enhanced allergic responses long after viral clearance.

Sex differences in asthma prevalence are well-documented but poorly understood. Murine models have contributed to our understanding of mechanisms that could regulate this sex disparity, though the majority of these studies have examined responses present after Th2 adaptive immunity is established. We have now investigated how sex influences acute activation of innate cell populations in the lung upon initial exposure to the model antigen, ovalbumin (OVA), in the presence of IL-33 (OVA+IL-33), to prime the lungs for type 2 immunity. We also examined how inflammatory responses induced by OVA+IL-33 were altered in mice lacking the STAT6 transcription factor, which is activated by IL-13, an effector cytokine of IL-33. Our data demonstrate that type 2 inflammation induced by OVA+IL-33 was more severe in female mice compared to males. Females exhibited greater cytokine and chemokine production, eosinophil influx and activation, macrophage polarization to the alternatively activated phenotype, and expansion of group 2 innate lymphoid cells (ILC2s). While increases in ILC2s and eosinophils were largely of STAT6 in both males and females, many other responses were STAT6-dependent only in female mice. Our findings indicate that a subset of type 2 inflammatory responses induced by OVA+IL-33 require STAT6 in both males and females and that enhanced type 2 inflammation in females, compared to males, is associated with greater IL-13 protein production. Our findings suggest blunted IL-13 production in males may protect against type 2 inflammation initiated by OVA+IL-33 delivery to the lung.

Epilepsy is a neurological disorder in which patients experience recurrent seizures, with the frequency of occurrence more than twice a day, which highly affects a patient's life. In recent years, multiple researchers have proposed multiple machine learning and deep learning-based methods to predict the onset of seizures using electroencephalogram (EEG) signals before they occur; however, robust preprocessing to mitigate the effect of noise, channel selection to reduce dimensionality, and feature extraction remain challenges in accurate prediction. This study proposes a novel method for accurately predicting epileptic seizures. In the first step, a Butterworth filter is applied, followed by a wavelet and a Fourier transform for the denoising of EEG signals. A non-overlapping window of 15 s is selected to segment the EEG signals, and an optimal spatial filter is applied to reduce the dimensionality. Handcrafted features, including both time and frequency domains, have been extracted and concatenated with the customized one-dimensional convolutional neural network-based features to form a comprehensive feature vector. It is then fed into three classifiers, including support vector machines, random forest, and long short-term memory (LSTM) units. The output of these classifiers is then fed into the model-agnostic meta learner ensemble classifier with LSTM as the base classifier for the final prediction of interictal and preictal states. The proposed methodology is trained and tested on the publicly available CHB-MIT dataset while achieving 99.34% sensitivity, 98.67% specificity, and a false positive alarm rate of 0.039. The proposed method not only outperforms the existing methods in terms of sensitivity and specificity but is also computationally efficient, making it suitable for real-time epileptic seizure prediction systems.

Fetal hypoxia is a condition characterized by a lack of oxygen supply in a developing fetus in the womb. It can cause potential risks, leading to abnormalities, birth defects, and even mortality. Cardiotocograph (CTG) monitoring is among the techniques that can detect any signs of fetal distress, including hypoxia. Due to the critical importance of interpreting the results of this test, it is essential to accompany these tests with the evolving available technology to classify cases of hypoxia into three cases: normal, suspicious, or pathological. Furthermore, Machine Learning (ML) is a blossoming technique constantly developing and aiding in medical studies, particularly fetal health prediction. Notwithstanding the past endeavors of health providers to detect hypoxia in fetuses, implementing ML and Deep Learning (DL) techniques ensures more timely and precise detection of fetal hypoxia by efficiently and accurately processing complex patterns in large datasets. Correspondingly, this review paper aims to explore the application of artificial intelligence models using cardiotocographic test data. The anticipated outcome of this review is to introduce guidance for future studies to enhance accuracy in detecting cases categorized within the suspicious class, an aspect that has encountered challenges in previous studies that holds significant implications for obstetricians in effectively monitoring fetal health and making informed decisions.

Background Musculoskeletal (MSK) conditions are the leading cause of disability worldwide. In MSK care, access to physical therapy is a major issue. The effectiveness of telerehabilitation phone assessment in improving access to care for MSK conditions has not yet been established in Saudi Arabia. Purpose To compare the effect of using a telerehabilitation phone with face-to-face care initial assessment on access to care, number of sessions and goals achievement at discharge. Methods This is a retrospective unmatched case-control analysis of a total of 724 Epic ® Electronic Medical Records guided by the Evidence standards framework for digital health technologies. Closed referrals from January 1st, 2022, to August 4th, 2022, for adults referred to Physical Therapy for MSK conditions were included. Participants who received a phone assessment were compared to those who received face-to-face care. The t-test was used to compare the means of the lead time, days to second appointment and number of treatment sessions. Univariate logistic regression was conducted to obtain the odds ratio of the outcome factors in the phone group. Statistical significance was set at p ≤ .05. Additionally,we compared the percentage of goal achievement at discharge. Results The lead time in days was significantly lower for the phone group (3.82 ± 5.36) compared to the face-to-face group (15.65 ± 17.71) p < 0.0001. Longer lead times from referral to first appointment were less likely to be a phone appointment and more likely to be a face-to-face appointment p < 0.001. However, the time from the first to the second appointment was significantly longer for the phone group p<0.0001. There was no significant difference in the number of appointments between both groups. The majority of the patients in both groups achieved all therapy goals (over 80% for phone and over 75% for face-to-face). Conclusion Initial telerehabilitation phone assessments compared to face-to-face care were associated with improved access to care, and there was no difference in the number of therapy sessions associated with achieving treatment goals. Future research is needed to determine the clinical effectiveness of phone initial assessments in the management of musculoskeletal conditions.

Atrial Fibrillation (AF) is a cardiac disorder characterized by irregular heart rhythms, typically diagnosed using Electrocardiogram (ECG) signals. In remote regions with limited healthcare personnel, automated AF detection is extremely important. Although recent studies have explored various machine learning and deep learning approaches, challenges such as signal noise and subtle variations between AF and other cardiac rhythms continue to hinder accurate classification. In this study, we propose a novel framework that integrates robust preprocessing, comprehensive feature extraction, and an ensemble classification strategy. In the first step, ECG signals are divided into equal-sized segments using a 5-s sliding window with 50% overlap, followed by bandpass filtering between 0.5 and 45 Hz for noise removal. After preprocessing, both time and frequency-domain features are extracted, and a custom one-dimensional Convolutional Neural Network—Bidirectional Long Short-Term Memory (1D CNN-BiLSTM) architecture is introduced. Handcrafted and automated features are concatenated into a unified feature vector and classified using Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) models. A Quantum Genetic Algorithm (QGA) optimizes weighted averages of the classifier outputs for multi-class classification, distinguishing among AF, noisy, normal, and other rhythms. Evaluated on the PhysioNet 2017 Cardiology Challenge dataset, the proposed method achieved an accuracy of 94.40% and an F1-score of 92.30%, outperforming several state-of-the-art techniques.

Developmental care in neonatal intensive care units (NICUs) promotes neurological and functional development in premature and critically ill infants. With 15 million preterm births annually, creating a supportive and therapeutic environment is critical. This care focuses on five core areas: routine daily care, family-centered care, healthy environmental modifications, pain management, and sleep enhancement. These practices aim to reduce environmental stressors, promote growth, and improve health outcomes. However, NICUs face challenges, including lack of resources, inadequate training, environmental stressors, and emotional burden on families. Addressing these challenges requires targeted strategies such as advanced staff training, environmental modifications to reduce noise and light, family engagement in caregiving, and the use of innovative technologies to improve monitoring and pain management. These strategies contribute to a sustainable, comprehensive model of care that supports infant development, promotes family engagement, and improves long-term outcomes for neonates in critical care.