From Sleep Patterns to Heart Rhythms: 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. Importantly, obstructive sleep apnea is highly prevalent among AF patients (60-90%); therefore, electrocardiogram (ECG) analysis from polysomnography (PSG), a standard diagnostic tool for subjects with suspected sleep apnea, presents a unique opportunity for the early prediction of AF. Our goal is to identify individuals at a high risk of developing AF in the future from a single-lead ECG recorded during standard PSGs. Methods: We analyzed 18,782 single-lead ECG recordings from 13,609 subjects at Massachusetts General Hospital, identifying AF presence using ICD-9/10 codes in medical records. Our dataset comprises 15,913 recordings without a medical record for AF and 2,056 recordings from patients who were first diagnosed with AF between 1 day to 15 years after the PSG recording. The PSG data were partitioned into training, validation, and test cohorts. In the first phase, a signal quality index (SQI) was calculated in 30-second windows and those with SQI < 0.95 were removed. From each remaining window, 150 hand-crafted features were extracted from 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 1,800 features. We then updated a pre-trained deep neural network and data from the PhysioNet Challenge 2021 using transfer-learning to discriminate between recordings with and without AF using the same Challenge data. The model was applied to the PSG ECGs in 16-second windows to generate the probability of AF for each window. From the resultant probability sequence, 13 statistical features were extracted. Subsequently, we trained a shallow neural network to predict future AF using the extracted ECG and probability features. Results: On the test set, our model demonstrated a sensitivity of 0.67, specificity of 0.81, and precision of 0.3 for predicting AF. Further, survival analysis for AF outcomes, using the log-rank test, revealed a hazard ratio of 8.36 (p-value of 1.93 × 10 -52 ). Conclusions: Our proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite a modest precision indicating the presence of false positive cases. This approach could potentially enable low-cost screening and proactive treatment for high-risk patients. Ongoing refinement, such as integrating additional physiological parameters could significantly reduce false positives, enhancing its clinical utility and accuracy.

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.

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.

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.

Off-body Sleep Analysis for Predicting Adverse Behavior in Individuals with Autism Spectrum Disorder.

Poor sleep quality in Autism Spectrum Disorder (ASD) individuals is linked to severe daytime behaviors. This study explores the relationship between a prior night's sleep structure and its predictive power for next-day behavior in ASD individuals. The motion was extracted using a low-cost near-infrared camera in a privacy-preserving way. Over two years, we recorded overnight data from 14 individuals, spanning over 2,000 nights, and tracked challenging daytime behaviors, including aggression, self-injury, and disruption. We developed an ensemble machine learning algorithm to predict next-day behavior in the morning and the afternoon. Our findings indicate that sleep quality is a more reliable predictor of morning behavior than afternoon behavior the next day. The proposed model attained an accuracy of 74% and a F1 score of 0.74 in target-sensitive tasks and 67% accuracy and 0.69 F1 score in target-insensitive tasks. For 7 of the 14, better-than-chance balanced accuracy was obtained (p-value<0.05), with 3 showing significant trends (p-value<0.1). These results suggest off-body, privacy-preserving sleep monitoring as a viable method for predicting next-day adverse behavior in ASD individuals, with the potential for behavioral intervention and enhanced care in social and learning settings.

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.

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.

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.

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.

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.

Fighting against amyotrophic lateral sclerosis (ALS) with flavonoids: a computational approach to inhibit superoxide dismutase (SOD1) mutant aggregation.

Protein aggregation is a biological process that occurs when proteins misfold. Misfolding and aggregation of human superoxide dismutase (hSOD1) cause a neurodegenerative disease called amyotrophic lateral sclerosis (ALS). Among the mutations occurring, targeting the E21K mutation could be a good choice to understand the pathological mechanism of SOD1 in ALS, whereof it significantly reduces life hopefulness in patients. Naturally occurring polyphenolic flavonoids have been suggested as a way to alleviate the amyloidogenic behavior of proteins. In this study, computational tools were used to identify promising flavonoid compounds that effectively inhibit the pathogenic behavior of the E21K mutant. Initial screening identified Pelargonidin, Curcumin, and Silybin as promising leads. Molecular dynamics (MD) simulations showed that the binding of flavonoids to the mutated SOD1 caused changes in the protein stability, hydrophobicity, flexibility, and restoration of lost hydrogen bonds. Secondary structure analysis indicated that the protein destabilization and the increased propensity of ß-sheet caused by the mutation were restored to the wild-type state upon binding of flavonoids. Free energy landscape (FEL) analysis was also used to differentiate aggregation, and results showed that Silybin followed by Pelargonidin had the most therapeutic efficacy against the E21K mutant SOD1. Therefore, these flavonoids hold great potential as highly effective inhibitors in mitigating ALS's fatal and insuperable effects.Communicated by Ramaswamy H. Sarma.

Characterization of gut microbiome composition in Iranian patients with nonalcoholic fatty liver disease and nonalcoholic steatohepatitis.

Gut microbiota dysbiosis is intimately associated with development of non-alcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH). Nevertheless, the gut microbial community during the course of NAFLD and NASH is yet to be comprehensively profiled. This study evaluated alterations in fecal microbiota composition in Iranian patients with NAFLD and NASH compared with healthy individuals. This cross-sectional study enrolled 15 NAFLD, 15 NASH patients, and 20 healthy controls, and their clinical parameters were examined. The taxonomic composition of the fecal microbiota was determined by sequencing the V3-V4 region of 16S rRNA genes of stool samples. Compared to the healthy controls, NAFLD and NASH patients presented reduced bacterial diversity and richness. We noticed a reduction in the relative abundance of Bacteroidota and a promotion in the relative abundance of Proteobacteria in NAFLD and NASH patients. L-histidine degradation I pathway, pyridoxal 5'-phosphate biosynthesis I pathway, and superpathway of pyridoxal 5'-phosphate biosynthesis and salvage were more abundant in NAFLD patients than in healthy individuals. This study examined fecal microbiota dysbiosis in NAFLD and NASH patients and presented consistent results to European countries. These condition- and ethnicity-specific data could provide different diagnostic signatures and therapeutic targets.

Using HIPAA (Health Insurance Portability and Accountability Act)-Compliant Transcription Services for Virtual Psychiatric Interviews: Pilot Comparison Study.

BACKGROUND: Automatic speech recognition (ASR) technology is increasingly being used for transcription in clinical contexts. Although there are numerous transcription services using ASR, few studies have compared the word error rate (WER) between different transcription services among different diagnostic groups in a mental health setting. There has also been little research into the types of words ASR transcriptions mistakenly generate or omit. OBJECTIVE: This study compared the WER of 3 ASR transcription services (Amazon Transcribe [Amazon.com, Inc], Zoom-Otter AI [Zoom Video Communications, Inc], and Whisper [OpenAI Inc]) in interviews across 2 different clinical categories (controls and participants experiencing a variety of mental health conditions). These ASR transcription services were also compared with a commercial human transcription service, Rev (Rev.Com, Inc). Words that were either included or excluded by the error in the transcripts were systematically analyzed by their Linguistic Inquiry and Word Count categories. METHODS: Participants completed a 1-time research psychiatric interview, which was recorded on a secure server. Transcriptions created by the research team were used as the gold standard from which WER was calculated. The interviewees were categorized into either the control group (n=18) or the mental health condition group (n=47) using the Mini-International Neuropsychiatric Interview. The total sample included 65 participants. Brunner-Munzel tests were used for comparing independent sets, such as the diagnostic groupings, and Wilcoxon signed rank tests were used for correlated samples when comparing the total sample between different transcription services. RESULTS: There were significant differences between each ASR transcription service's WER (P<.001). Amazon Transcribe's output exhibited significantly lower WERs compared with the Zoom-Otter AI's and Whisper's ASR. ASR performances did not significantly differ across the 2 different clinical categories within each service (P>.05). A comparison between the human transcription service output from Rev and the best-performing ASR (Amazon Transcribe) demonstrated a significant difference (P<.001), with Rev having a slightly lower median WER (7.6%, IQR 5.4%-11.35 vs 8.9%, IQR 6.9%-11.6%). Heat maps and spider plots were used to visualize the most common errors in Linguistic Inquiry and Word Count categories, which were found to be within 3 overarching categories: Conversation, Cognition, and Function. CONCLUSIONS: Overall, consistent with previous literature, our results suggest that the WER between manual and automated transcription services may be narrowing as ASR services advance. These advances, coupled with decreased cost and time in receiving transcriptions, may make ASR transcriptions a more viable option within health care settings. However, more research is required to determine if errors in specific types of words impact the analysis and usability of these transcriptions, particularly for specific applications and in a variety of populations in terms of clinical diagnosis, literacy level, accent, and cultural origin.

Corrigendum to "Helicobacter pylori infection contributes to the expression of Alzheimer's disease-associated risk factors and neuroinflammation" [Heliyon 9(9) (September 2023) e19607].

[This corrects the article DOI: 10.1016/j.heliyon.2023.e19607.].

Helicobacter pylori infection contributes to the expression of Alzheimer's disease-associated risk factors and neuroinflammation.

Over time, mounting evidence has demonstrated extra-gastric manifestations of Helicobacter pylori infection. As such, a number of studies demonstrated the potential contribution of H. pylori infection to the incidence and progression of Alzheimer's disease (AD). Considering unanswered questions regarding the effect of H. pylori infection on brain activity, we sought to investigate the impact of H. pylori infection on the expression of AD-associated risk factors. We used two H. pylori clinical strains obtained from two patients with peptic ulcer and evaluated their influence on the expression level of AD-associated genes (APP, ApoE2, ApoE4, ABCA7, BIN1, Clu, CD33) and genes for inflammatory markers (TLR-4, IL-8, TNF-α) by RT-qPCR in human glioblastoma (U87MG) and astrocyte (1321N1) cell lines. The expression of inflammatory cytokines was further assessed by ELISA assay. The exposure of U97MG and 1321N1 cells to H. pylori strains resulted in a significant enhancement in the expression level of the risk allele ApoE4, while reducing the expression of the protective allele ApoE2. H. pylori infection remarkably increased the expression level of main AD-associated risk genes, and also pro-inflammatory cytokines. Furthermore, we noticed a substantial elevation in the mRNA expression level of transmembrane receptor TLR-4 following H. pylori infection. Our findings presented the potential for H. pylori to stimulate the expression of AD-associated risk genes and trigger neuroinflammation in the brain tissue. This, in principle, leads to the recommendation that AD patients should perhaps test for H. pylori infection and receive treatments upon positive detection.

Heart murmur detection from phonocardiogram recordings: The George B. Moody PhysioNet Challenge 2022.

Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs, who may need follow-up diagnostic screening and treatment for abnormal cardiac function. However, experts are needed to interpret the heart sounds, limiting the accessibility of cardiac auscultation in resource-constrained environments. Therefore, the George B. Moody PhysioNet Challenge 2022 invited teams to develop algorithmic approaches for detecting heart murmurs and abnormal cardiac function from phonocardiogram (PCG) recordings of heart sounds. For the Challenge, we sourced 5272 PCG recordings from 1452 primarily pediatric patients in rural Brazil, and we invited teams to implement diagnostic screening algorithms for detecting heart murmurs and abnormal cardiac function from the recordings. We required the participants to submit the complete training and inference code for their algorithms, improving the transparency, reproducibility, and utility of their work. We also devised an evaluation metric that considered the costs of screening, diagnosis, misdiagnosis, and treatment, allowing us to investigate the benefits of algorithmic diagnostic screening and facilitate the development of more clinically relevant algorithms. We received 779 algorithms from 87 teams during the Challenge, resulting in 53 working codebases for detecting heart murmurs and abnormal cardiac function from PCG recordings. These algorithms represent a diversity of approaches from both academia and industry, including methods that use more traditional machine learning techniques with engineered clinical and statistical features as well as methods that rely primarily on deep learning models to discover informative features. The use of heart sound recordings for identifying heart murmurs and abnormal cardiac function allowed us to explore the potential of algorithmic approaches for providing more accessible diagnostic screening in resource-constrained environments. The submission of working, open-source algorithms and the use of novel evaluation metrics supported the reproducibility, generalizability, and clinical relevance of the research from the Challenge.

Multimodal mental health assessment with remote interviews using facial, vocal, linguistic, and cardiovascular patterns.

Objective: The current clinical practice of psychiatric evaluation suffers from subjectivity and bias, and requires highly skilled professionals that are often unavailable or unaffordable. Objective digital biomarkers have shown the potential to address these issues. In this work, we investigated whether behavioral and physiological signals, extracted from remote interviews, provided complimentary information for assessing psychiatric disorders. Methods: Time series of multimodal features were derived from four conceptual modes: facial expression, vocal expression, linguistic expression, and cardiovascular modulation. The features were extracted from simultaneously recorded audio and video of remote interviews using task-specific and foundation models. Averages, standard deviations, and hidden Markov model-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 controls and subjects with mental health conditions. Correlations were found between features and self-rated depression and anxiety scores. Visual heart rate dynamics achieved 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 achieved AUROCs of 0.72-0.82. Features from task-specific models outperformed features from foundation models. 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 objective, remote, and low-cost assessment for low-burden automated mental health services.

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.

Pyrosequencing analysis for rapid and accurate detection of clarithromycin resistance-associated mutations in Iranian Helicobacter pylori isolates.

BACKGROUND: Treatment of Helicobacter pylori (H. pylori) infection has become challenging following the development of primary antibiotic resistance. A primary therapeutic regimen for H. pylori eradication includes clarithromycin; however, the presence of point mutations within the 23S rRNA sequence of H. pylori contributes to clarithromycin resistance and eradication failure. Thus, we aimed to develop a rapid and precise method to determine clarithromycin resistance-related point mutations using the pyrosequencing method. METHODS AND RESULTS: H. pylori was isolated from 82 gastric biopsy samples and minimal inhibitory concentration (MIC) was evaluated using the agar dilution method. Clarithromycin resistance-associated point mutations were detected by Sanger sequencing, from which 11 isolates were chosen for pyrosequencing. Our results demonstrated a 43.9% (36/82) prevalence in resistance to clarithromycin. The A2143G mutation was detected in 8.3% (4/48) of H. pylori isolates followed by A2142G (6.2%), C2195T (4.1%), T2182C (4.1%), and C2288T (2%). Although the C2195T mutation was only detected by Sanger sequencing, the overall results from pyrosequencing and Sanger sequencing platforms were comparable. CONCLUSIONS: Pyrosequencing could be used as a rapid and practical platform in clinical laboratories to determine the susceptibility profile of H. pylori isolates. This might pave the way for efficient H. pylori eradication upon detection.