Fecal microbiota transplantation: current challenges and future landscapes.

SUMMARYGiven the importance of gut microbial homeostasis in maintaining health, there has been considerable interest in developing innovative therapeutic strategies for restoring gut microbiota. One such approach, fecal microbiota transplantation (FMT), is the main "whole gut microbiome replacement" strategy and has been integrated into clinical practice guidelines for treating recurrent Clostridioides difficile infection (rCDI). Furthermore, the potential application of FMT in other indications such as inflammatory bowel disease (IBD), metabolic syndrome, and solid tumor malignancies is an area of intense interest and active research. However, the complex and variable nature of FMT makes it challenging to address its precise functionality and to assess clinical efficacy and safety in different disease contexts. In this review, we outline clinical applications, efficacy, durability, and safety of FMT and provide a comprehensive assessment of its procedural and administration aspects. The clinical applications of FMT in children and cancer immunotherapy are also described. We focus on data from human studies in IBD in contrast with rCDI to delineate the putative mechanisms of this treatment in IBD as a model, including colonization resistance and functional restoration through bacterial engraftment, modulating effects of virome/phageome, gut metabolome and host interactions, and immunoregulatory actions of FMT. Furthermore, we comprehensively review omics technologies, metagenomic approaches, and bioinformatics pipelines to characterize complex microbial communities and discuss their limitations. FMT regulatory challenges, ethical considerations, and pharmacomicrobiomics are also highlighted to shed light on future development of tailored microbiome-based therapeutics.

Impedance cardiogram based exploration of cardiac mechanisms in post-traumatic stress disorder during trauma recall.

Post-traumatic stress disorder (PTSD) is an independent risk factor for developing heart failure; however, the underlying cardiac mechanisms are still elusive. This study aims to evaluate the real-time effects of experimentally induced PTSD symptom activation on various cardiac contractility and autonomic measures. We recorded synchronized electrocardiogram and impedance cardiogram from 137 male veterans (17 PTSD, 120 non-PTSD; 48 twin pairs, 41 unpaired singles) during a laboratory-based traumatic reminder stressor. To identify the parameters describing the cardiac mechanisms by which trauma reminders can create stress on the heart, we utilized a feature selection mechanism along with a random forest classifier distinguishing PTSD and non-PTSD. We extracted 99 parameters, including 76 biosignal-based and 23 sociodemographic, medical history, and psychiatric diagnosis features. A subject/twin-wise stratified nested cross-validation procedure was used for parameter tuning and model assessment to identify the important parameters. The identified parameters included biomarkers such as pre-ejection period, acceleration index, velocity index, Heather index, and several physiology-agnostic features. These identified parameters during trauma recall suggested a combination of increased sympathetic nervous system (SNS) activity and deteriorated cardiac contractility that may increase the heart failure risk for PTSD. This indicates that the PTSD symptom activation associates with real-time reductions in several cardiac contractility measures despite SNS activation. This finding may be useful in future cardiac prevention efforts.

Moringa oleifera and Its Biochemical Compounds: Potential Multi-targeted Therapeutic Agents Against COVID-19 and Associated Cancer Progression.

The Coronavirus disease-2019 (COVID-19) pandemic is a global concern, with updated pharmacological therapeutic strategies needed. Cancer patients have been found to be more susceptible to severe COVID-19 and death, and COVID-19 can also lead to cancer progression. Traditional medicinal plants have long been used as anti-infection and anti-inflammatory agents, and Moringa oleifera (M. oleifera) is one such plant containing natural products such as kaempferol, quercetin, and hesperetin, which can reduce inflammatory responses and complications associated with viral infections and multiple cancers. This review article explores the cellular and molecular mechanisms of action of M. oleifera as an anti-COVID-19 and anti-inflammatory agent, and its potential role in reducing the risk of cancer progression in cancer patients with COVID-19. The article discusses the ability of M. oleifera to modulate NF-κB, MAPK, mTOR, NLRP3 inflammasome, and other inflammatory pathways, as well as the polyphenols and flavonoids like quercetin and kaempferol, that contribute to its anti-inflammatory properties. Overall, this review highlights the potential therapeutic benefits of M. oleifera in addressing COVID-19 and associated cancer progression. However, further investigations are necessary to fully understand the cellular and molecular mechanisms of action of M. oleifera and its natural products as anti-inflammatory, anti-COVID-19, and anti-cancer strategies.

From sleep patterns to heart rhythm: Predicting atrial fibrillation from overnight polysomnograms.

BACKGROUND: Atrial fibrillation (AF) is often asymptomatic and thus under-observed. Given the high risks of stroke and heart failure among patients with AF, early prediction and effective management are crucial. Given the prevalence of obstructive sleep apnea among AF patients, electrocardiogram (ECG) analysis from polysomnography (PSG) offers a unique opportunity for early AF prediction. Our aim is to identify individuals at high risk of AF development from single­lead ECGs during standard PSG. METHODS: We analyzed 18,782 single­lead ECG recordings from 13,609 subjects undergoing PSG at the Massachusetts General Hospital sleep laboratory. AF presence was identified using ICD-9/10 codes. The dataset included 15,913 recordings without AF history and 2054 recordings from patients diagnosed with AF between one month to fifteen years post-PSG. Data were partitioned into training, validation, and test cohorts ensuring that individual patients remained exclusive to each cohort. The test set was held out during the training process. We employed two different methods for feature extraction to build a final model for AF prediction: Extraction of hand-crafted ECG features and a deep learning method. For extraction of ECG-hand-crafted features, recordings were split into 30-s windows, and those with a signal quality index (SQI) below 0.95 were discarded. From each remaining window, 150 features were extracted from the time, frequency, time-frequency domains, and phase-space reconstructions of the ECG. A compilation of 12 statistical features summarized these window-specific features per recording, resulting in 1800 features (12 × 150). A pre-trained deep neural network from the PhysioNet Challenge 2021 was updated using transfer learning to discriminate recordings with and without AF. The model processed PSG ECGs in 16-s windows to generate AF probabilities, from which 13 statistical features were extracted. Combining 1800 features from feature extraction with 13 from the deep learning model, we performed a feature selection and subsequently trained a shallow neural network to predict future AF and evaluated its performance on the test cohort. RESULTS: On the test set, our model exhibited sensitivity, specificity, and precision of 0.67, 0.81, and 0.3, respectively, for AF prediction. Survival analysis revealed a hazard ratio of 8.36 (p-value: 1.93 × 10-52) for AF outcomes using the log-rank test. CONCLUSIONS: Our proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite modest precision, suggesting false positives. This approach could enable low-cost screening and proactive treatment for high-risk patients. Refinements, including additional physiological parameters, may reduce false positives, enhancing clinical utility and accuracy.

A Crowdsourced AI Framework for Atrial Fibrillation Detection in Apple Watch and Kardia Mobile ECGs.

Background: Atrial fibrillation (AFib) detection via mobile ECG devices is promising, but algorithms often struggle to generalize across diverse datasets and platforms, limiting their real-world applicability. Objective: This study aims to develop a robust, generalizable AFib detection approach for mobile ECG devices using crowdsourced algorithms. Methods: We developed a voting algorithm using random forest, integrating six open-source AFib detection algorithms from the PhysioNet Challenge. The algorithm was trained on an AliveCor dataset and tested on two disjoint AliveCor datasets and one Apple Watch dataset. Results: The voting algorithm outperformed the base algorithms across all metrics: the average of sensitivity (0.884), specificity (0.988), PPV (0.917), NPV (0.985), and F1-score (0.943) on all datasets. It also demonstrated the least variability among datasets, signifying its highest robustness and effectiveness in diverse data environments. Moreover, it surpassed Apple's algorithm on all metrics and showed higher specificity but lower sensitivity than AliveCor's Kardia algorithm. Conclusions: This study demonstrates the potential of crowdsourced, multi-algorithmic strategies in enhancing AFib detection. Our approach shows robust cross-platform performance, addressing key generalization challenges in AI-enabled cardiac monitoring and underlining the potential for collaborative algorithms in wearable monitoring devices.

Helicobacter pylori-derived outer membrane vesicles suppress liver autophagy: A novel mechanism for H. pylori-mediated hepatic disorder.

BACKGROUND: Helicobacter pylori outer membrane vesicles (OMVs) are nano-sized structures, which have been recently suggested to play a crucial role in H. pylori pathogenesis. There are growing evidence indicating the relationship of H. pylori infection with extra-gastroduodenal diseases, especially liver-related disorders. This study was aimed to investigate the effects of H. pylori-derived OMVs on autophagy in hepatic stellate cells (HSCs). MATERIAL AND METHODS: A selection of five clinical strains of H. pylori with different virulence genotypes were included. The OMVs were isolated by ultracentrifugation and characterized by scanning electron microscopy (SEM) and dynamic light scattering (DLS). The protein concentration of OMVs was measured by BCA assay. MTT assay was used to determine the viability of LX-2 cells (human HSCs) treated with OMVs. The expression level of MTOR, AKT, PI3K, BECN1, ATG16 and LC3B genes was assessed in OMVs-treated LX-2 cells using quantitative real-time PCR. Moreover, immunocytochemistry was performed to evaluate the protein expression of MTOR and LC3B autophagy markers. RESULTS: H. pylori strains produced round shape nano-vesicles ranging from 50 to 500 nm. Treatment of HSCs with H. pylori-derived OMVs at concentration of 10 µg/mL for 24 h significantly elevated the expression of autophagy inhibitory markers (PI3K, AKT, and MTOR) and suppressed the mRNA expression level of autophagy core proteins (BECN1, ATG16 and LC3B). Immunocytochemistry also presented a substantial reduction in the concentration of LC3B autophagy core protein, and a marked elevation in the amount of MTOR autophagy inhibitory protein. CONCLUSION: This study revealed that H. pylori-derived OMVs could potentially suppress autophagy flux in HSCs as a novel mechanism for H. pylori-mediated liver autophagy impairment and liver disease development. Further studies are required to elucidate the exact role of OMV-carried contents in liver autophagy, and liver-associated disorders.

Association between PTSD and Impedance Cardiogram-based contractility metrics during trauma recall: A controlled twin study.

Post-traumatic stress disorder (PTSD) is an independent risk factor for incident heart failure, but the underlying cardiac mechanisms remained elusive. Impedance cardiography (ICG), especially when measured during stress, can help understand the underlying psychophysiological pathways linking PTSD with heart failure. We investigated the association between PTSD and ICG-based contractility metrics (pre-ejection period (PEP) and Heather index (HI)) using a controlled twin study design with a laboratory-based traumatic reminder stressor. PTSD status was assessed using structured clinical interviews. We acquired synchronized electrocardiograms and ICG data while playing personalized-trauma scripts. Using linear mixed-effects models, we examined twins as individuals and within PTSD-discordant pairs. We studied 137 male veterans (48 pairs, 41 unpaired singles) from Vietnam War Era with a mean (standard deviation) age of 68.5(2.5) years. HI during trauma stress was lower in the PTSD vs. non-PTSD individuals (7.2 vs. 9.3 [ohm/s2 ], p = .003). PEP reactivity (trauma minus neutral) was also more negative in PTSD vs. non-PTSD individuals (-7.4 vs. -2.0 [ms], p = .009). The HI and PEP associations with PTSD persisted for adjusted models during trauma and reactivity, respectively. For within-pair analysis of eight PTSD-discordant twin pairs (out of 48 pairs), PTSD was associated with lower HI in neutral, trauma, and reactivity, whereas no association was found between PTSD and PEP. PTSD was associated with reduced HI and PEP, especially with trauma recall stress. This combination of increased sympathetic activation and decreased cardiac contractility combined may be concerning for increased heart failure risk after recurrent trauma re-experiencing in PTSD.

Diabetes as one of the long-term COVID-19 complications: from the potential reason of more diabetic patients' susceptibility to COVID-19 to the possible caution of future global diabetes tsunami.

According to recent researches, people with diabetes mellitus (type 1 and 2) have a higher incidence of coronavirus disease 2019 (COVID-19), which is caused by a SARS-CoV-2 infection. In this regard, COVID-19 may make diabetic patients more sensitive to hyperglycemia by modifying the immunological and inflammatory responses and increasing reactive oxygen species (ROS) predisposing the patients to severe COVID-19 and potentially lethal results. Actually, in addition to COVID-19, diabetic patients have been demonstrated to have abnormally high levels of inflammatory cytokines, increased virus entrance, and decreased immune response. On the other hand, during the severe stage of COVID-19, the SARS-CoV-2-infected patients have lymphopenia and inflammatory cytokine storms that cause damage to several body organs such as ß cells of the pancreas which may make them as future diabetic candidates. In this line, the nuclear factor kappa B (NF-κB) pathway, which is activated by a number of mediators, plays a substantial part in cytokine storms through various pathways. In this pathway, some polymorphisms also make the individuals more competent to diabetes via infection with SARS-CoV-2. On the other hand, during hospitalization of SARS-CoV-2-infected patients, the use of some drugs may unintentionally lead to diabetes in the future via increasing inflammation and stress oxidative. Thus, in this review, we will first explain why diabetic patients are more susceptible to COVID-19. Second, we will warn about a future global diabetes tsunami via the SARS-CoV-2 as one of its long-term complications.

COVID-19 related stigma among the general population in Iran.

BACKGROUND: COVID-19 related stigma has been identified as a critical issue since the beginning of the pandemic. We developed a valid and reliable questionnaire to measure COVID-19 related enacted stigma, inflicted by the non-infected general population. We applied the questionnaire to measure COVID-19 related enacted stigma among Tehran citizens from 27 to 30 September 2020. METHODS: A preliminary questionnaire with 18 items was developed. The total score ranged from 18 to 54; a higher score indicated a higher level of COVID-19 related stigma. An expert panel assessed the face and content validity. Of 1637 randomly recruited Tehran citizens without a history of COVID-19 infection, 1064 participants consented and were interviewed by trained interviewers by phone. RESULTS: Item content validity index (I-CVI), Item content validity ratio (I-CVR), and Item face validity index (I-FVI) were higher than 0.78 for all 18 items. The content and face validity were established with a scale content validity index (S-CVI) of 0.90 and a scale face validity index (S-CVI) of 93.9%, respectively. Internal consistency of the questionnaire with 18 items was confirmed with Cronbach's alpha of 0.625. Exploratory factor analysis revealed five latent variables, including "blaming", "social discrimination", "dishonor label", "interpersonal contact", and "retribution and requital attitude". The median of the stigma score was 24 [25th percentile: 22, 75the percentile: 28]. A large majority (86.8%) of participants reported a low level of stigma with a score below 31. None of the participants showed a high level of stigma with a score above 43. We found that the higher the educational level the lower the participant's stigma score. CONCLUSION: We found a low level of stigmatizing thoughts and behavior among the non-infected general population in Tehran, which may be due to the social desirability effect, to the widespread nature of COVID-19, or to the adaptation to sociocultural diversity of the large city.

Age, sex and race bias in automated arrhythmia detectors.

Despite the recent explosion of machine learning applied to medical data, very few studies have examined algorithmic bias in any meaningful manner, comparing across algorithms, databases, and assessment metrics. In this study, we compared the biases in sex, age, and race of 56 algorithms on over 130,000 electrocardiograms (ECGs) using several metrics and propose a machine learning model design to reduce bias. Participants of the 2021 PhysioNet Challenge designed and implemented working, open-source algorithms to identify clinical diagnosis from 2- lead ECG recordings. We grouped the data from the training, validation, and test datasets by sex (male vs female), age (binned by decade), and race (Asian, Black, White, and Other) whenever possible. We computed recording-wise accuracy, area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), F-measure, and the Challenge Score for each of the 56 algorithms. The Mann-Whitney U and the Kruskal-Wallis tests assessed the performance differences of algorithms across these demographic groups. Group trends revealed similar values for the AUROC, AUPRC, and F-measure for both male and female groups across the training, validation, and test sets. However, recording-wise accuracies were 20% higher (p < 0.01) and the Challenge Score 12% lower (p = 0.02) for female subjects on the test set. AUPRC, F-measure, and the Challenge Score increased with age, while recording-wise accuracy and AUROC decreased with age. The results were similar for the training and test sets, but only recording-wise accuracy (12% decrease per decade, p < 0.01), Challenge Score (1% increase per decade, p < 0.01), and AUROC (1% decrease per decade, p < 0.01) were statistically different on the test set. We observed similar AUROC, AUPRC, Challenge Score, and F-measure values across the different race categories. But, recording-wise accuracies were significantly lower for Black subjects and higher for Asian subjects on the training (31% difference, p < 0.01) and test (39% difference, p < 0.01) sets. A top performing model was then retrained using an additional constraint which simultaneously minimized differences in performance across sex, race and age. This resulted in a modest reduction in performance, with a significant reduction in bias. This work provides a demonstration that biases manifest as a function of model architecture, population, cost function and optimization metric, all of which should be closely examined in any model.

HIV/AIDS policy-making in Iran: A stakeholder analysis.

INTRODUCTION: The present study aims to identify and analyze HIV/AIDS stakeholders in Iran. METHODS: This qualitative stakeholder analysis was conducted in 2018 nationwide, both retrospectively and prospectively. Purposive sampling was applied and followed by snowball sampling until data saturation. Data were analyzed using framework analysis. Also, MAXQDA (Version 11) and Policy Maker software (version 4) was applied. FINDINGS: A total of 44 stakeholders were identified and categorized into 23 active and 21 inactive stakeholders. The Ministry of Education and Iran Broadcasting have moderate participation in this regard. Supreme Council of Health and NGOs have low participation. The Ministry of Health (MoH), State Welfare Organization, Blood Transfusion Organization, and the State Prisons are interested in HIV/AIDS policymaking. The MoH is the main body responsible for the stewardship of HIV/AIDS in Iran but does not have enough authority to handle the issue. CONCLUSION: Considering multidimensional nature of HIV/AIDS, there are many stakeholders regarding HIV/AIDS control. The process of HIV/AIDS -policy making is fragmented in Iran. Despite multiple active and potential stakeholders in this field, there is no integrated system to involve all stakeholders in the process of HIV/AIDS policy-making. Therefore, given the importance of the issue, an upstream entity is needed to coordinate and mobilize all stakeholders associated with managing and controlling HIV/AIDS.

Convergent effects of a functional C3 variant on brain atrophy, demyelination, and cognitive impairment in multiple sclerosis.

BACKGROUND: Complement system activation products are present in areas of neuroinflammation, demyelination, and neurodegeneration in brains of patients with multiple sclerosis (MS). C3 is a central element in the activation of complement cascades. A common coding variant in the C3 gene (rs2230199, C3R102G) affects C3 activity. OBJECTIVES: To assess the effects of rs2230199 on MS severity using clinical, cognitive, and imaging measures. METHODS: In total, 161 relapse-onset MS patients (Expanded Disability Status Scale (EDSS) ≤ 6) underwent physical assessments, cognitive tests (Paced Auditory Serial Addition Test (PASAT), Symbol Digit Modalities Test (SDMT), and California Verbal Learning Test (CVLT)), and magnetic resonance imaging (MRI). Lesion volumes were quantified semi-automatically. Voxel-wise analyses were performed to assess the effects of rs2230199 genotype on gray matter (GM) atrophy ( n = 155), white matter (WM) fractional anisotropy (FA; n = 105), and WM magnetization transfer ratio (MTR; n = 90). RESULTS: While rs2230199 minor-allele dosage (C3-102G) showed no significant effect on EDSS and Multiple Sclerosis Functional Composite (MSFC), it was associated with worse cognitive performance ( p = 0.02), lower brain parenchymal fraction ( p = 0.003), and higher lesion burden ( p = 0.02). Moreover, voxel-wise analyses showed lower GM volume in subcortical structures and insula, and lower FA and MTR in several WM areas with higher copies of rs2230199 minor allele. CONCLUSION: C3-rs2230199 affects white and GM damage as well as cognitive impairment in MS patients. Our findings support a causal role for complement system activity in the pathophysiology of MS.

Two patients with the heterozygous R189H mutation in ACTA2 and Complex congenital heart defects expands the cardiac phenotype of multisystemic smooth muscle dysfunction syndrome.

De novo heterozygous mutations changing R179 to histidine, leucine, or cysteine in the ACTA2 gene are associated with Multisystemic Smooth Muscle Dysfunction Syndrome (MSMDS). Characteristic hallmarks of this condition, caused only by these specific ACTA2 mutations, are congenital mydriasis (mid-dilated, non-reactive pupils), a large persistent ductus arteriosus (PDA), aortic aneurysms evolving during childhood, and cerebrovascular anomalies. We describe two patients, a 3-day-old newborn and a 26-year-old woman, with this unique mutation in association with a huge PDA and an aorto-pulmonary window. In addition, one showed a coarctation of the aortic arch and the other a complete interruption of the aortic arch type A; thereby expanding the spectrum of cardiac congenital heart defect of this syndrome. Each patient displayed a huge PDA and an extra-cardiovascular phenotype consistent with MSMDS. These observations exemplify that a functional alpha 2 smooth muscle actin is necessary for proper cardiovascular organ development, and demonstrate that a very exceptional congenital heart defect (aortopulmonary window) can be caused by a mutation in a gene encoding a contractile protein of vascular smooth muscle cells. © 2017 Wiley Periodicals, Inc.

Adsorption photobioreactor as a co-treatment system for ammonium and phosphate removal by the response surface method.

The co-treatment system of photosynthetic microalgae Chlorella vulgaris and adsorption was investigated as a possible combination of symbiotic mixed culture for the simultaneous removal of nutrients (ammonium and phosphate) and organic contaminants. In this study, response surface methodology for experimental design and optimization was used. For experiment operation, two factorial designs containing five chemical oxygen demand influent (CODin) concentrations (100, 200, 400, 600 and 700 mg l-1) and hydraulic retention times (0.63, 1, 1.75, 2.5 and 2.88 d) were applied. The co-treatment system performed successfully in removing both nutrients (nitrogen and phosphate) and COD, showing around 88%, 75% and 48% removal for the maximum level, respectively. The adsorption-photobioreactor (APBR) displayed superior performance of the microalgae growth rate compared to the photobioreactor. Also, the adsorption capacity (the uptake of COD) has been analysed with the first-order equation. The results showed that the experimental data of the APBR fit well with the model.

Comparative experimental study on fouling mechanisms in nano-porous membrane: cheese whey ultrafiltration as a case study.

Determination of fouling mechanisms and accurate quantitative prediction of nano-porous membrane behavior are of great interest in membrane processes. This work has focused on a comprehensive comparison of two classical and new fouling models. Different operational conditions were tested to analyze the level of agreement of these models with experimental observation. Whey solutions of 8, 0.8 and 0.5 g/L were ultrafiltered in transmembrane pressures (TMPs) of 300 and 500 KPa through a synthesized polyethersulfone/copolymer blend membrane. Fouling mechanisms and the effect of different combinations of TMPs and protein concentrations were determined and analyzed by fitting the experimental data to different models. Based on the results obtained from classical models, it was found that the predictions of the cake layer formation model were quite acceptable, followed by the intermediate blocking model. The new combined pore blockage-cake filtration model, however, was found to be very successful in predicting the flux decline over time for every operational condition tested, with all relative errors of prediction less than 5%. The latter also showed a good performance in the transition from the pore blockage mechanism to cake layer formation.

A new strategy for making a sensitive sensor for aspirin drug: first-principles investigations by using pure and metal-doped BN nano-heterostructures.

The present study used the DFT method to investigate aspirin's intermolecular interactions with boron nitride (BN) nanotubes modified with aluminum, gallium, and zinc. Our experiments obtained adsorption energy of -40.4 kJ/mol for aspirin on BN nanotubes. By doping each of the above metals on the surface of the BN nanotube, the aspirin adsorption energy increased dramatically. For BN nanotubes doped with Al, Ga, and Zn, this energy was -255, -251, and -250 kJ/mol. Thermodynamic analyses proved that all surface adsorptions are exothermic and spontaneous. Nanotubes' electronic structures and dipole moments have been examined following aspirin adsorption. In addition, AIM analysis has been performed for all systems in order to understand how the links were formed. According to the obtained results, BN nanotubes doped with metals, as mentioned previously, have a very high electron sensitivity to aspirin. These nanotubes can therefore be used to manufacture aspirin-sensitive electrochemical sensors.Communicated by Ramaswamy H. Sarma.

A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising.

OBJECTIVE: Gaussian Processes (GP)-based filters, which have been effectively used for various applications including electrocardiogram (ECG) filtering can be computationally demanding and the choice of their hyperparameters is typically ad hoc. METHODS: We develop a data-driven GP filter to address both issues, using the notion of the ECG phase domain -- a time-warped representation of the ECG beats onto a fixed number of samples and aligned R-peaks, which is assumed to follow a Gaussian distribution. Under this assumption, the computation of the sample mean and covariance matrix is simplified, enabling an efficient implementation of the GP filter in a data-driven manner, with no ad hoc hyperparameters. The proposed filter is evaluated and compared with a state-of-the-art wavelet-based filter, on the PhysioNet QT Database. The performance is evaluated by measuring the signal-to-noise ratio (SNR) improvement of the filter at SNR levels ranging from -5 to 30dB, in 5dB steps, using additive noise. For a clinical evaluation, the error between the estimated QT-intervals of the original and filtered signals is measured and compared with the benchmark filter. RESULTS: It is shown that the proposed GP filter outperforms the benchmark filter for all the tested noise levels. It also outperforms the state-of-the-art filter in terms of QT-interval estimation error bias and variance. CONCLUSION: The proposed GP filter is a versatile technique for preprocessing the ECG in clinical and research applications, is applicable to ECG of arbitrary lengths and sampling frequencies, and provides confidence intervals for its performance.

Autophagy induced by Helicobacter Pylori infection can lead to gastric cancer dormancy, metastasis, and recurrence: new insights.

According to the findings of recent research, Helicobacter Pylori (H. pylori) infection is not only the primary cause of gastric cancer (GC), but it is also linked to the spread and invasion of GC through a number of processes and factors that contribute to virulence. In this study, we discussed that H. pylori infection can increase autophagy in GC tumor cells, leading to poor prognosis in such patients. Until now, the main concerns have been focused on H. pylori's role in GC development. According to our hypothesis, however, H. pylori infection may also lead to GC dormancy, metastasis, and recurrence by stimulating autophagy. Therefore, understanding how H. pylori possess these processes through its virulence factors and various microRNAs can open new windows for providing new prevention and/or therapeutic approaches to combat GC dormancy, metastasis, and recurrence which can occur in GC patients with H. pylori infection with targeting autophagy and eradicating H. pylori infection.

Multimodal Mental Health Digital Biomarker Analysis From Remote Interviews Using Facial, Vocal, Linguistic, and Cardiovascular Patterns.

OBJECTIVE: Psychiatric evaluation suffers from subjectivity and bias, and is hard to scale due to intensive professional training requirements. In this work, we investigated whether behavioral and physiological signals, extracted from tele-video interviews, differ in individuals with psychiatric disorders. METHODS: Temporal variations in facial expression, vocal expression, linguistic expression, and cardiovascular modulation were extracted from simultaneously recorded audio and video of remote interviews. Averages, standard deviations, and Markovian process-derived statistics of these features were computed from 73 subjects. Four binary classification tasks were defined: detecting 1) any clinically-diagnosed psychiatric disorder, 2) major depressive disorder, 3) self-rated depression, and 4) self-rated anxiety. Each modality was evaluated individually and in combination. RESULTS: Statistically significant feature differences were found between psychiatric and control subjects. Correlations were found between features and self-rated depression and anxiety scores. Heart rate dynamics provided the best unimodal performance with areas under the receiver-operator curve (AUROCs) of 0.68-0.75 (depending on the classification task). Combining multiple modalities provided AUROCs of 0.72-0.82. CONCLUSION: Multimodal features extracted from remote interviews revealed informative characteristics of clinically diagnosed and self-rated mental health status. SIGNIFICANCE: The proposed multimodal approach has the potential to facilitate scalable, remote, and low-cost assessment for low-burden automated mental health services.

Forecasting High-Risk Behavioral and Medical Events in Children with Autism through Analysis of Digital Behavioral Records.

Individuals with Autism Spectrum Disorder may display interfering behaviors that limit their inclusion in educational and community settings, negatively impacting their quality of life. These behaviors may also signal potential medical conditions or indicate upcoming high-risk behaviors. This study explores behavior patterns that precede high-risk, challenging behaviors or seizures the following day. We analyzed an existing dataset of behavior and seizure data from 331 children with profound ASD over nine years. We developed a deep learning-based algorithm designed to predict the likelihood of aggression, elopement, and self-injurious behavior (SIB) as three high-risk behavioral events, as well as seizure episodes as a high-risk medical event occurring the next day. The proposed model attained accuracies of 78.4%, 80.68%, 85.43%, and 69.95% for predicting the next-day occurrence of aggression, SIB, elopement, and seizure episodes, respectively. The results were proven significant for more than 95% of the population for all high-risk event predictions using permutation-based statistical tests. Our findings emphasize the potential of leveraging historical behavior data for the early detection of high-risk behavioral and medical events, paving the way for behavioral interventions and improved support in both social and educational environments.