Prevention of aspirin-mediated secondary toxicity by combined treatment of carotenoids in macrophages.

Upon understanding the boosting role of carotenoids on the endogenous anti-inflammatory system, it is vital to explore their role in reducing the use of high doses of non-steroidal anti-inflammatory drug (NSAIDs), and their mediated secondary toxicity during the treatment of chronic diseases. The current study investigates the carotenoids potential on inhibition of secondary complications induced by NSAIDs, aspirin (ASA) against lipopolysaccharide (LPS) stimulated inflammation. Initially, this study evaluated a minimal cytotoxic dose of ASA and carotenoids (ß-carotene, BC/lutein, LUT/astaxanthin, AST/fucoxanthin FUCO) in Raw 264.7, U937, and peripheral blood mononuclear cells (PBMCs). In all three cells, carotenoids + ASA treatment reduced the LDH release, NO, and PGE2 efficiently than an equivalent dose of carotenoid or ASA treated alone. Based on cytotoxicity and sensitivity results, RAW 264.7 cells were selected for further cell-based assay. Among carotenoids, FUCO + ASA exhibited an efficient reduction of LDH release, NO, and PGE2 than the other carotenoids (BC + ASA, LUT + ASA, and AST + ASA) treatment. FUCO + ASA combination decreased LPS/ASA induced oxidative stress, pro-inflammatory mediators (iNOS, COX-2, and NF-κB), and cytokines (IL-6, TNF-α, and IL-1ß) efficiently. Further, apoptosis was inhibited by 69.2% in FUCO + ASA, and 46.7% in ASA than LPS treated cells. A drastic decrease in intracellular ROS generation with the increase in GSH was observed in FUCO + ASA compared to LPS/ASA groups. The results documented on the low dose of ASA with a relative physiological concentration of FUCO suggested greater importance for alleviating secondary complications and optimize prolonged chronic disease treatments with NSAID's associated side effects. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03632-w.

Can metagenomics unravel the impact of oral bacteriome in human diseases?

Oral microbial ecosystems are vital in maintaining the health of the oral cavity and the entire body. Oral microbiota is associated with the progression of oral diseases such as dental caries, periodontal diseases, head and neck cancer, and several systemic diseases such as cardiovascular disease, rheumatoid arthritis, adverse pregnancy outcomes, diabetes, lung infection, colorectal cancer, and pancreatic cancer. Buccal mucosa, tongue dorsum, hard palate, saliva, palatine tonsils, throat, keratinized gingiva, supra-gingival plaque, subgingival plaque, dentures, and lips are microbial habitats of the oral cavity. Porphyromonas gingivalis may have a role in the development of periodontal diseases, oral cancer, diabetes, and atherosclerotic disease. Fusobacterium nucleatum showed a higher abundance in periodontal diseases, oral and colon cancer, adverse pregnancy outcomes, diabetes, and rheumatoid arthritis. The higher abundance of Prevotella intermedia is typical in periodontal diseases, rheumatoid arthritis, and adverse pregnancy outcome. S. salivarius displayed higher abundance in both dental caries and OSCC. Oral bacteria may influence systemic diseases through inflammation by releasing pro inflammatory cytokines. Identification of oral bacteria using culture-dependent approaches and next-generation sequencing-based metagenomic approaches is believed to significantly identify the therapeutic targets and non-invasive diagnostic indicators in different human diseases. Oral bacteria in saliva could be exploited as a non-invasive diagnostic indicator for the early detection of oral and systemic disorders. Other therapeutic approaches such as the use of probiotics, green tea polyphenol, cold atmospheric plasma (CAP) therapy, antimicrobial photodynamic therapy, and antimicrobial peptides are used to inhibit the growth of biofilm formation by oral bacteria.

Porphyromonas gingivalis may have a role in the development of periodontal diseases, oral cancer, diabetes, and atherosclerotic diseaseFusobacterium nucleatum showed a higher abundance in periodontal diseases, oral and colon cancer, adverse pregnancy outcomes, diabetes, and rheumatoid arthritisOral bacteria may influence systemic diseases through inflammation by releasing pro inflammatory cytokines.Identification of oral bacteria in saliva may be used as a non-invasive diagnostic indicator for the early detection of oral and systemic disorders.

Digitization and Analysis of Capnography Using Image Processing Technique.

The study of carbon dioxide expiration is called capnometry. The graphical representation of capnometry is called capnography. There is a growing interest in the usage of capnography as the usage has expanded toward the study of metabolism, circulation, lung perfusion and diffusion, quality of spontaneous respiration, and patency of airways outside of its typical usage in the anesthetic and emergency medicine field. The parameters of the capnograph could be classified as carbon dioxide (CO2) concentration and time points and coordinates, slopes angle, volumetric studies, and functional transformation of wave data. Up to date, there is no gold standard device for the calculation of the capnographic parameters. Capnography digitization using the image processing technique could serve as an option. From the algorithm we developed, eight identical breath waves were tested by four investigators. The values of the parameters chosen showed no significant difference between investigators. Although there were no significant differences between any of the parameters tested, there were a few related parameters that were not calculable. Further testing after refinement of the algorithm could be done. As more capnographic parameters are being derived and rediscovered by clinicians and researchers alike for both lung and non-lung-related diseases, there is a dire need for data analysis and interpretation. Although the proposed algorithm still needs minor refinements and further large-scale testing, we proposed that the digitization of the capnograph via image processing technique could serve as an intellectual option as it is fast, convenient, easy to use, and efficient.

On Analyzing Capnogram as a Novel Method for Screening COVID-19: A Review on Assessment Methods for COVID-19.

In November 2019, the novel coronavirus disease COVID-19 was reported in Wuhan city, China, and was reported in other countries around the globe. COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Strategies such as contact tracing and a vaccination program have been imposed to keep COVID-19 under control. Furthermore, a fast, noninvasive and reliable testing device is needed urgently to detect COVID-19, so that contact can be isolated and ringfenced before the virus spreads. Although the reverse transcription polymerase chain reaction (RT-PCR) test is considered the gold standard method for the diagnosis of SARS-CoV-2 infection, this test presents some limitations which cause delays in detecting the disease. The antigen rapid test (ART) test, on the other hand, is faster and cheaper than PCR, but is less sensitive, and may limit SARS-CoV-2 detection. While other tests are being developed, accurate, noninvasive and easy-to-use testing tools are in high demand for the rapid and extensive diagnosis of the disease. Therefore, this paper reviews current diagnostic methods for COVID-19. Following this, we propose the use of expired carbon dioxide (CO2) as an early screening tool for SARS-CoV-2 infection. This system has already been developed and has been tested on asthmatic patients. It has been proven that expired CO2, also known as capnogram, can help differentiate between respiratory conditions and, therefore, could be used to detect SARS-CoV-2 infection, as it causes respiratory tract-related diseases.

Effect of short-term ketogenic diet on end-tidal carbon dioxide.

BACKGROUND & AIMS: Previous studies have shown that end-tidal carbon dioxide (EtCO2) is lower with the presence of supraphysiological ketones as in the case of chronic ketogenic diet (KD) and diabetic ketoacidosis (DKA). This study aimed to determine changes in EtCO2 upon short term KD. METHODS: Healthy subjects were screened not to have conditions that exerts abnormal EtCO2 nor contraindicated for KD. Subjects underwent seven days of KD while the EtCO2 and blood ketone (beta-hydroxybutyrate; ß-OHB) parameters were sampled at day zero (t0) and seven (t7) of ketosis respectively. Statistically, the t-test and Pearson's coefficient were conducted to determine the changes and correlation of both parameters. RESULTS: 12 subjects completed the study. The mean score ± standard deviation (SD) for EtCO2 were 35.08 ± 3.53 and 35.67 ± 3.31 mm Hg for t0 and t7 respectively. The mean score ±SD for ß-OHB were 0.07 ± 0.08 and 0.87 ± 0.84 mmol/L for t0 and t7 respectively. There was no significant difference of EtCO2 between the period of study (p > 0.05) but the ß-OHB increased during t7 (p < 0.05). There was also no correlation between the parameters. CONCLUSIONS: These findings suggest that EtCO2 may not be utilized to determine short term nutritional ketosis.

Fusion of heart rate variability and salivary cortisol for stress response identification based on adverse childhood experience.

Adverse childhood experiences have been suggested to cause changes in physiological processes and can determine the magnitude of the stress response which might have a significant impact on health later in life. To detect the stress response, biomarkers that represent both the Autonomic Nervous System (ANS) and Hypothalamic-Pituitary-Adrenal (HPA) axis are proposed. Among the available biomarkers, Heart Rate Variability (HRV) has been proven as a powerful biomarker that represents ANS. Meanwhile, salivary cortisol has been suggested as a biomarker that reflects the HPA axis. Even though many studies used multiple biomarkers to measure the stress response, the results for each biomarker were analyzed separately. Therefore, the objective of this study is to propose a fusion of ANS and HPA axis biomarkers in order to classify the stress response based on adverse childhood experience. Electrocardiograph, blood pressure (BP), pulse rate (PR), and salivary cortisol (SCort) measures were collected from 23 healthy participants; 11 participants had adverse childhood experience while the remaining 12 acted as the no adversity control group. HRV was then computed from the ECG and the HRV features were extracted. Next, the selected HRV features were combined with the other biomarkers using Euclidean distance (ed) and serial fusion, and the performance of the fused features was compared using Support Vector Machine. From the result, HRV-SCort using Euclidean distance achieved the most satisfactory performance with 80.0% accuracy, 83.3% sensitivity, and 78.3% specificity. Furthermore, the performance of the stress response classification of the fused biomarker, HRV-SCort, outperformed that of the single biomarkers: HRV (61% Accuracy), Cort (59.4% Accuracy), BP (78.3% accuracy), and PR (53.3% accuracy). From this study, it was proven that the fused biomarkers that represent both ANS and HPA (HRV-SCort) able to demonstrate a better classification performance in discriminating the stress response. Furthermore, a new approach for classification of stress response using Euclidean distance and SVM named as ed-SVM was proven to be an effective method for the HRV-SCort in classifying the stress response from PASAT. The robustness of this method is crucial in contributing to the effectiveness of the stress response measures and could further be used as an indicator for future health. Graphical abstract ᅟ.

Assessment of newly developed real-time human respiration carbon dioxide measurement device for management of asthma outside of hospital.

BACKGROUND: Assessment of asthma outside of the hospital using a patient independent device is highly in demand due to the limitation of existing devices, which are manual and unreliable if patients are not cooperative. OBJECTIVE: The study aims to verify the use of newly developed human respiration, carbon dioxide (CO2) measurement device for the management of asthma outside of the hospital. METHOD: The data were collected from 60 subjects aged between 18-35 years via convenience sampling method reported in UTM Health Center using the device. Furthermore, the data were normalized and analyzed using descriptive statistics, t-test, and area (Az) under receiver operating characteristic curve (ROC). RESULT: Findings revealed that the normalized mean values of end-tidal carbon dioxide (EtCO2), Hjorth Activity (HA), and respiratory rate (RR) were lower in asthmatic compared with healthy subjects with minimum deviation from the mean. In addition, each parameter was found to significantly differ statistically for asthma and non-asthma with p< 0.05. Furthermore, the Az shows the strong association for the screening of asthma and non-asthma with an average of 0.71 (95% CI: 0.57-0.83), 0.77 (95% CI: 0.64-0.90), and 0.83 (95% CI: 0.73-0.94) for RR, EtCO2, and HA, respectively. CONCLUSIONS: This study demonstrates that the newly developed handheld human respiration CO2 measurement device may possibly be used as an effort-independent asthma management method outside of the hospital.

Fetal heart rate monitoring device using condenser microphone sensor: Validation and comparison to standard devices.

BACKGROUND: Fetal heart rate (FHR) monitoring device is highly demanded to assess the fetus health condition in home environments. Conventional standard devices such as ultrasonography and cardiotocography are expensive, bulky and uncomfortable and consequently not suitable for long-term monitoring. Herein, we report a device that can be used to measure fetal heart rate in clinical and home environments. METHODS: The proposed device measures and displays the FHR on a screen liquid crystal display (LCD). The device consists of hardware that comprises condenser microphone sensor, signal conditioning, microcontroller and LCD, and software that involves the algorithm used for processing the conditioned fetal heart signal prior to FHR display. The device's performance is validated based on analysis of variance (ANOVA) test. RESULTS: FHR data was recorded from 22 pregnant women during the 17th to 37th week of gestation using the developed device and two standard devices; AngelSounds and Electronic Stethoscope. The results show that F-value (1.5) is less than F𝑐𝑟𝑖𝑡, (3.1) and p-value (p> 0.05). Accordingly, there is no significant difference between the mean readings of the developed and existing devices. Hence, the developed device can be used for monitoring FHR in clinical and home environments.

Real-time human respiration carbon dioxide measurement device for cardiorespiratory assessment.

The development of a human respiration carbon dioxide (CO2) measurement device to evaluate cardiorespiratory status inside and outside a hospital setting has proven to be a challenging area of research over the few last decades. Hence, we report a real-time, user operable CO2 measurement device using an infrared CO2 sensor (Arduino Mega2560) and a thin film transistor (TFT, 3.5″), incorporated with low pass (cut-off frequency, 10 Hz) and moving average (span, 8) filters. The proposed device measures features such as partial end-tidal carbon dioxide (EtCO2), respiratory rate (RR), inspired carbon dioxide (ICO2), and a newly proposed feature-Hjorth activity-that annotates data with the date and time from a real-time clock, and is stored onto a secure digital (SD) card. Further, it was tested on 22 healthy subjects and the performance (reliability, validity and relationship) of each feature was established using (1) an intraclass correlation coefficient (ICC), (2) standard error measurement (SEM), (3) smallest detectable difference (SDD), (4) Bland-Altman plot, and (5) Pearson's correlation (r). The SEM, SDD, and ICC values for inter- and intra-rater reliability were less than 5% and more than 0.8, respectively. Further, the Bland-Altman plot demonstrates that mean differences ± standard deviations for a set limit were 0.30 ± 0.77 mmHg, -0.34 ± 1.41 mmHg and 0.21 ± 0.64 breath per minute (bpm) for CO2, EtCO2 and RR. The findings revealed that the developed device is highly reliable, providing valid measurements for CO2, EtCO2, ICO2 and RR, and can be used in clinical settings for cardiorespiratory assessment. This research also demonstrates that EtCO2 and RR (r, -0.696) are negatively correlated while EtCO2 and activity (r, 0.846) are positively correlated. Thus, simultaneous measurement of these features may possibly assist physicians in understanding the subject's cardiopulmonary status. In future, the proposed device will be tested with asthmatic patients for use as an early screening tool outside a hospital setting.

Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III.

This paper presents a method that able to predict the paroxysmal atrial fibrillation (PAF). The method uses shorter heart rate variability (HRV) signals when compared to existing methods, and achieves good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to electrically stabilize and prevent the onset of atrial arrhythmias with different pacing techniques. We propose a multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm III for optimizing the baseline PAF prediction system, that consists of the stages of pre-processing, HRV feature extraction, and support vector machine (SVM) model. The pre-processing stage comprises of heart rate correction, interpolation, and signal detrending. After that, time-domain, frequency-domain, non-linear HRV features are extracted from the pre-processed data in feature extraction stage. Then, these features are used as input to the SVM for predicting the PAF event. The proposed optimization algorithm is used to optimize the parameters and settings of various HRV feature extraction algorithms, select the best feature subsets, and tune the SVM parameters simultaneously for maximum prediction performance. The proposed method achieves an accuracy rate of 87.7%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 min to just 5 min (a reduction of 83%). Furthermore, another significant result is the sensitivity rate, which is considered more important that other performance metrics in this paper, can be improved with the trade-off of lower specificity.

Paroxysmal atrial fibrillation prediction method with shorter HRV sequences.

This paper proposes a method that predicts the onset of paroxysmal atrial fibrillation (PAF), using heart rate variability (HRV) segments that are shorter than those applied in existing methods, while maintaining good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to stabilize (electrically) and prevent the onset of atrial arrhythmias with different pacing techniques. We investigate the effect of HRV features extracted from different lengths of HRV segments prior to PAF onset with the proposed PAF prediction method. The pre-processing stage of the predictor includes QRS detection, HRV quantification and ectopic beat correction. Time-domain, frequency-domain, non-linear and bispectrum features are then extracted from the quantified HRV. In the feature selection, the HRV feature set and classifier parameters are optimized simultaneously using an optimization procedure based on genetic algorithm (GA). Both full feature set and statistically significant feature subset are optimized by GA respectively. For the statistically significant feature subset, Mann-Whitney U test is used to filter non-statistical significance features that cannot pass the statistical test at 20% significant level. The final stage of our predictor is the classifier that is based on support vector machine (SVM). A 10-fold cross-validation is applied in performance evaluation, and the proposed method achieves 79.3% prediction accuracy using 15-minutes HRV segment. This accuracy is comparable to that achieved by existing methods that use 30-minutes HRV segments, most of which achieves accuracy of around 80%. More importantly, our method significantly outperforms those that applied segments shorter than 30 minutes.

Labview Based ECG Patient Monitoring System for Cardiovascular Patient Using SMTP Technology.

This paper leads to developing a Labview based ECG patient monitoring system for cardiovascular patient using Simple Mail Transfer Protocol technology. The designed device has been divided into three parts. First part is ECG amplifier circuit, built using instrumentation amplifier (AD620) followed by signal conditioning circuit with the operation amplifier (lm741). Secondly, the DAQ card is used to convert the analog signal into digital form for the further process. Furthermore, the data has been processed in Labview where the digital filter techniques have been implemented to remove the noise from the acquired signal. After processing, the algorithm was developed to calculate the heart rate and to analyze the arrhythmia condition. Finally, SMTP technology has been added in our work to make device more communicative and much more cost-effective solution in telemedicine technology which has been key-problem to realize the telediagnosis and monitoring of ECG signals. The technology also can be easily implemented over already existing Internet.

Newborn seizure detection based on heart rate variability.

In this paper, we investigate the use of heart rate variability (HRV) for automatic newborn seizure detection. The proposed method consists of a sequence of processing steps, namely, obtaining HRV from the ECG, extracting a discriminating HRV feature set, selecting an optimal subset from the full feature set, and, finally, classifying the HRV into seizure/nonseizure using a supervised statistical classifier. Due to the fact that HRV signals are nonstationary, a set of time-frequency features from the newborn HRV is proposed and extracted. In order to achieve efficient HRV-based automatic newborn seizure detection, a two-phase wrapper-based feature selection technique is used to select the feature subset with minimum redundancy and maximum class discriminability. Tested on ECG recordings obtained from eight newborns with identified EEG seizure, the proposed HRV-based neonatal seizure detection algorithm achieved 85.7% sensitivity and 84.6% specificity. These results suggest that the HRV is sensitive to changes in the cardioregulatory system induced by the seizure, and therefore, can be used as a basis for an automatic seizure detection.

Combining newborn EEG and HRV information for automatic seizure detection.

We propose a new seizure detection framework based on combination of information extracted from newborn multi-channel electroencephalogram (EEG) and heart rate variability (HRV). Two approaches are investigated for the combination of EEG and HRV, namely; feature fusion and classifier/decision fusion. The feature fusion was performed by concatenating the features vectors extracted from the EEG and the HRV signals while the classifier fusion was accomplished by fusing the independent decisions from individual classifiers of EEG and HRV. Both proposed schemes consist of a sequence of processing steps, namely; preprocessing, feature extraction, feature selection and finally the combination. We have shown that both proposed approaches lead to improved performance of newborn seizure detection compared to either EEG or HRV based seizure detectors.

Time-frequency analysis of heart rate variability for neonatal seizure detection.

The ECG has been much neglected in automatic seizure detection in the newborn. Changes in heart rate and ECG rhythm are often found in animal and adult patients with seizure. However, little is known about heart rate variability (HRV) changes in human neonate during seizure. Results of ongoing time-frequency research are presented here with the aim to compare the performance of various time-frequency distributions (TFDs) when applied to HRV time series for non-seizure and seizure newborns. The TFDs studied are the Wigner-Ville (WVD), the Spectrogram (SP), the Choi-Williams (CWD) and the Modified B (MBD) distributions. Based on our preliminary results, our current conclusion is MBD outperforms other TFDs in terms of time-frequency resolution, cross-terms suppression and to represent the newborn HRV signals of non-seizure and seizure which are closely-spaced components in the time-frequency domain.