Pregnancy has been shown to be times in a woman’s life particularly prone to mental health issues, however a substantial percentage of mothers report subclinical perinatal mental health symptoms that go undetected. Experiences of prenatal trauma, such as the COVID-19 pandemic, may exacerbate vulnerability to negative health outcomes for pregnant women and their infants. We aimed to examine the role of: 1) anxiety, depression, and stress related to COVID-19 in predicting the quality of antenatal attachment; 2) perceived social support and COVID-19 appraisal in predicting maternal anxiety and depression.A sample of 150 UK expectant women were surveyed during the COVID-19 pandemic. Questions included demographics, pregnancy details, and COVID-19 appraisal. Validated measures were used to collect self-reported maternal antenatal attachment (MAAS), symptoms of anxiety (STAI), depression (BDI-II), and stress related to the psychological impact of COVID-19 (IES-r).We found that the pandemic has affected UK expectant mothers’ mental health by increasing prevalence of depression (47%), anxiety (60%) and stress related to the psychological impact of COVID-19 (40%). Women for whom COVID-19 had a higher psychological impact were more likely to suffer from depressive  and anxiety symptoms . High depressive symptoms were associated with reduced attachment to the unborn baby . Whilst women who appraised the impact of COVID-19 to be more negative showed higher levels of anxiety , higher social support acted as a protective factor and was associated with lower anxiety .The current findings demonstrate that direct experience of prenatal trauma, such as the one experienced during the COVID-19 pandemic, significantly amplifies mothers’ vulnerability to mental health symptoms and impairs the formation of a positive relationship with their unborn baby. Health services should prioritise interventions strategies aimed at fostering support for pregnant women.The online version contains supplementary material available at 10.1186/s12884-022-04387-7. Research shows that in times of natural disasters, e.g. pandemics, there is an increased risk in mental health issues such as Post-Traumatic Stress Disorder (PTSD), depression, and anxiety . Among tLife changing events may render individuals particularly vulnerable to the impact of disasters. In particular, pregnancy has been extensively shown to be times in a woman’s life especially prone to mental health issues –4. AmongMaternal mental health difficulties, such as depressive or anxiety symptoms, can also have an immediate impact, by interfering and impairing the formation of a positive relationship with the fetus . MaternaThere exists also evidence that factors from contextual domains, i.e. social support, are related to maternal antenatal attachment . While aIn the present study, we sought to examine the effects of the COVID-19 pandemic on prenatal mental health, antenatal attachment and social support. According to the intergenerational transition hypothesis (e.g. ), directRecent evidence shows that higher social support is associated with lower mental health symptoms, thus suggesting that support may act as a protective factor, particularly for those who appraise the impact of COVID-19 to be more negative . In lineParticipants were 150 pregnant individuals who took part in the Wellbeing During Pregnancy Study. Online surveys  were completed between April 2020 and January 2021. Pregnant individuals were recruited through social media (Facebook and Instagram) advertisements and word of mouth. Participant inclusion criteria were: Over 18 years of age, UK resident, English speaking, and pregnant. At the time of the survey, participants had an average age of 31.06 years (SD = 4.64) and had spent an average of 16.55 years in education (SD = 4.50). The participants’ ethnic background was predominantly white (93%). The majority of participants were married or cohabiting (95%). Twelve percent of participants completed the online survey whilst they were in their first trimester of pregnancy, 40% were in their second trimester, and 48% were in their third trimester of pregnancy.N = 75) from unpublished online research conducted before the COVID-19 pandemic was used to compare means of the BDI-II scores collected in the current study. The mean age of these participants we 32.71 years (SD = 3.83) and had spent an average of 18.1 years in education (SD = 2.64). The participants’ ethnic background was predominantly white (87%), and the majority of them was married or cohabiting (76%). Further details of the data and additional analysis are presented in An additional dataset . We then generated scores for maternal antenatal attachment under the assumption that a change of 1 for either depression or anxiety was associated with a change of - 0.25 in maternal antenatal attachment. The final step in our data generation process was to add Gaussian noise  to the maternal antenatal attachment. For an example of the simulated data please see Fig. 1S in the Before taking part in the study, participants were provided with an electronic information sheet and consent form and were asked to tick a box to confirm consent. At the end of the survey, an electronic debrief with signposting to relevant support information was included. Participants had the option to enter a draw to win one of three £20 vouchers.Pregnancy-related demographic questions were asked at the beginning of the survey  . The queMaternal antenatal attachment was assessed using the Maternal Antenatal Attachment Scale (MAAS) . This seThe Revised Impact of Event Scale (IES-r) questionnaire is a self-report measure of stress reactions after traumatic event. The revised version has seven additional questions and a scoring range between 0 and 88 . In the During the survey, participants were asked specific questions in relation to the impact of COVID-19 on their relationships and lives . These iT-test comparing women in their first, second and third trimester did not reveal any significant difference between these groups on the MAAS quality of attachment score  = -1.308, p = .204; second vs third trimester, t(110.55) = -.191, p = .849; first vs third trimester, t (22.89) = -1.233, p = .231). A t-test comparing women with high- and low-risk pregnancies did not reveal any significant differences between these two groups on the MAAS quality of attachment score, t(56.63) = –1.746, p = .086.Descriptive analyses were conducted to identify prevalence rates of depression (BDI-II), anxiety (STAI-State and STAI-Trait), and psychological impact of COVID-19 (IES-r). Bivariate correlations were conducted to identify relationships between variables to inform inferential statistics. We conducted bivariate correlations between the key demographic and pregnancy information  and the MAAS quality of attachment score to determine if any of these variables contributed to the outcome measure. There were no significant correlations between the MAAS quality of attachment score and participant age and years in education. To investigate the relationship between our variables of interest we carried out a series of Bayesian linear models using the Bayesian Regression Models using Stan (brms)  package Seventy-three out of the 137 women that completed the BDI-II (53%) reported minimal depressive symptoms (scored 13 or below). However, 64 women reported a score > 13 on the BDI-II which indicates the presence of mild or above depressive symptomatology. Specifically, 19% of the sample reported mild depression (scores 14-19), 20% reported moderate depression (scores 20-28), and 7% of participants reported high depression (scored 29 or above). In relation to anxiety symptoms, 60% of participants (90 out of 150 women that completed the STAI-State) had scores above the cut-off based on state anxiety scores (scores of 40 and above) and 62% of participants (90 out of 145 women that completed the STAI-Trait) had scores above the cut-off based on trait anxiety scores. Finally, out of the 126 women that completed the IES-r, 50 participants (40%) reported symptoms associated with post-traumatic stress disorder (a score of ⩾24), suggesting that that the COVID-19 pandemic had a moderate-to-severe psychological impact on their lives.We also ran additional analysis to compare the depression scores in the current study with data from unpublished research conducted by the senior member of the authorship team before the COVID-19 pandemic. These analyses show that depression scores in the group of women expecting a baby during the second lockdown were significantly higher than the depression scores of women that were pregnant before the COVID-19 pandemic. Statistical details of these analyses are included in To investigate our first hypothesis, we examined whether STAI-State (anxiety henceforth) and BDI-II (depression henceforth) scores were predictive of the quality of maternal antenatal attachment. As highlighted in Fig. Next, we aimed to investigate the potential effect of the psychological impact of the COVID-19 pandemic see Fig. . We fittWe found that the psychological impact of COVID-19 increased both depression  and anxiety  symptoms. The evidence for a direct effect on the quality of maternal attachment is more mixed , given that the estimated model fits are not reliably < 0.Our second hypothesis aimed to test the effect of perceived social support and COVID-19 appraisal on maternal anxiety and depression. We modelled both relationships independently, allowing for a potential interaction between the predictors. The modelling results are displayed in Fig. With this study, we set out to investigate prenatal maternal wellbeing during the COVID-19 pandemic. The aim of our investigation was twofold; 1) to examine the role of mental health symptoms of anxiety, depression and stress reaction due to COVID-19 in predicting the quality of maternal antenatal attachment to the fetus; 2) to examine the role of perceived social support and COVID-19 appraisal in predicting maternal anxiety and depression. According to the evidence that prenatal trauma heightens the risk of clinically significant antenatal mental health symptoms , we expeOur results show that expectant women with higher depressive symptoms reported feeling less attached to their unborn baby. In line with previous studies demonstrating that women with higher depressive symptoms also have lower levels of maternal antenatal attachment  during their pregnancy. Previous research has demonstrated that increased stress can exacerbate symptoms of depression and anxiety during the perinatal period  and moreIt is also well established that poor social support is linked to vulnerability to depression and anxiety disorders , 51. ThePrevious research has demonstrated that frequent support from pregnancy to the postnatal period, such as the one provided through continuity of care, weakens the association between high levels of stress experienced by women in the context of a natural disaster and mental health symptoms , 54. SimOverall, our research shows that COVID-19 has affected the mental health of UK expectant women, with about 40-60% of them reporting symptoms of depression, anxiety and post-traumatic stress disorder. Given that the usual reported incidence of depression and anxiety during pregnancy are around 17% and 23%, respectively , 6, our It is also worth noting that, participants with a history of poor mental health reported higher depressive symptoms during pregnancy compared to participants that did not report previous mental health problems. In addition, those with high-risk pregnancies scored higher in prenatal anxiety and depression see . Prior hImportantly, a recent study that looked at maternal postnatal mental health during the COVID-19 pandemic showed that 43 and 61% of women self-reported symptoms associated with clinically relevant depression and anxiety, respectively . It is sOur research also presents other limitations. One of these is that we collected data only from mothers-to-be. Research has suggested that paternal perinatal mental health symptoms can exacerbate maternal symptoms  and predOur research emphasises the need for heightened support from the beginning of pregnancy and for all mothers-to-be. The high rates of women reporting mental health symptoms of depression and anxiety during the COVID-19 pandemic highlighted by our study suggest that expectant women have been increasingly facing mental health difficulties that significantly interfere and impair the formation of a positive relationship with their unborn baby, and can potentially impact on childbirth outcome as well as infant and child long-term development. Importantly, we show that direct experience of prenatal trauma, such as the one experienced during the COVID-19 pandemic, can significantly amplify vulnerability to these mental health issues. Nonetheless, our study also highlights the protective role of social support on negative mental health outcomes, thus informing about intervention strategies that can protect maternal wellbeing during pregnancy and beyond.Additional file 1.


This work develops a robust classifier for a COVID-19 pre-screening model from crowdsourced cough sound data. The crowdsourced cough recordings contain a variable number of coughs, with some input sound files more informative than the others. Accurate detection of COVID-19 from the sound datasets requires overcoming two main challenges (i) the variable number of coughs in each recording and (ii) the low number of COVID-positive cases compared to healthy coughs in the data. We use two open datasets of crowdsourced cough recordings and segment each cough recording into non-overlapping coughs. The segmentation enriches the original data without oversampling by splitting the original cough sound files into non-overlapping segments. Splitting the sound files enables us to increase the samples of the minority class (COVID-19) without changing the feature distribution of the COVID-19 samples resulted from applying oversampling techniques. Each cough sound segment is transformed into six image representations for further analyses. We conduct extensive experiments with shallow machine learning, Convolutional Neural Network (CNN), and pre-trained CNN models. The results of our models were compared to other recently published papers that apply machine learning to cough sound data for COVID-19 detection. Our method demonstrated a high performance using an ensemble model on the testing dataset with area under receiver operating characteristics curve = 0.77, precision = 0.80, recall = 0.71, F1 measure = 0.75, and Kappa = 0.53. The results show an improvement in the prediction accuracy of our COVID-19 pre-screening model compared to the other models. This situation mandates prompt and precise identification of COVID-19 through frequent and widespread testing to prevent community outbreaks. The world health organization (WHO) has identified and updated several symptoms of COVID-19, such as high temperature, coughing, and breathing difficulties2. However, these symptoms are common for several respiratory diseases and not necessarily unique to COVID-19, rendering it difficult for patients to self-assess. The gold-standard method for diagnosing COVID-19 uses reverse transcription-polymerase chain reaction (rRT-PCR) in nasopharyngeal (NP) swabs. However, sample collection with the NP swab is an invasive method and is not ideal for screening, prognostics, and longitudinal monitoring purposes, given that it requires close contact between healthcare providers and patients. This contact introduces a significant risk of viral transmission to healthcare providers and other patients and burdens healthcare systems. The longitudinal monitoring and early pre-screening of individuals suspicious of COVID-19 could be improved substantially with new non-invasive and easy-to-implement approaches that can be carried out efficiently at a low-cost by patients themselves without professional help.From what we know about COVID-19, more than 40% of infected people show no to very moderate symptoms, significantly contributing to the disease's non-intentional spreadGiven the difficulties and bottlenecks experienced so far around the world with the implementation of widespread testing, the ideal test procedure would, while maintaining a high level of accuracy (sensitivity and specificity), (a) allow patients to self-assess without the need for physical contact with healthcare professionals, (b) bring down the cost per test substantially , (c) eliminate the dependency of diagnostic kits on scarce materials, manufacturing capacity, and supply chain bottlenecks, and (d) be rapidly deployable around the world without dependency on logistics and physical distribution bottlenecks. In this work, we pursue a digital method of COVID-19 testing based on audio recordings that would satisfy all four criteria. It is noted that the cough-based COVID-19 testing does not aim primarily to replace gold-standard diagnostic methods but to be used as a longitudinal and pre-screening approach for better management of COVID-19.6. Since cough sounds can easily be converted to information signals and stored as digital files, and they may allow us to create a purely digital COVID-19 testing and offer enormous economic benefits. In essence, a purely digital COVID-19 testing is an 'information good' that benefits from the many favourable economics of information goods, such as zero marginal cost of production and distribution8. If the user can employ the digital test without professional supervision, it becomes instantly usable anywhere, anytime, and by anyone. Therefore, the benefits of a digital COVID-19 test are significant enough to merit its pursuit.Computer representations of sound data may reveal information that is not detectable by humans. There is also mounting evidence that machine learning methods can detect COVID-19 signatures in cough sounds12. If enough data is available, artificial intelligence (AI) techniques would be leveraged to design and deploy COVID-19 detection models. However, publicly available datasets of cough sounds containing a substantial number of COVID-19 positive cases are limited, so data availability represents a bottleneck for training such machine learning models. The work illustrated in Imran et al.10 provided a proof-of-concept COVID-19 pre-screening model. They used smartphones to collect cough recordings from a limited number of COVID-positive patients (70 patients) as well as cough recordings from healthy people (247 samples) and patients with other pathological diseases (96 bronchitis and 130 pertussis patients). The coughs were then used to train three machine learning models to detect coughs of COVID-positive patients. The data and trained models for this study are not publicly available.Several studies have collected and analyzed cough sound data for COVID-19 pre-screening testing using mobile devices and Web technology9. Neither the dataset nor the models developed are publicly available. A publicly and verified crowdsourced COVID-19 cough dataset was presented by Sharma et al.11. Although the dataset contains more than 14,000 unique subjects, less than 10% are identified as COVID-19 positive. Such highly imbalanced datasets have limitations to be used for training machine learning models for pre-screening the patients. Another dataset of cough sounds collected from media interviews of COVID-19 patients was presented, known as NoCoCoDa12. This database has cough sounds from only ten unique subjects, which is very limited to train machine learning algorithms. Another publicly available dataset of 121 segmented cough samples was collected from 16 patients4. The data also contains clinical annotation, which is accurate given its collection at a hospital under supervision. The cough samples were pre-processed and labelled with COVID-19 status acquired from polymerase chain reaction (PCR) testing, along with patient demographics . More details on the datasets presented above can be found in the “11.In another study, a crowdsourced database that contained more than 10,000 cough samples (at the time of writing) was collected from 7000 unique users, 235 of whom self-declared to have been diagnosed with COVID-1913. Support Vector Machine (SVM), Neural Network (NN), and K-Nearest Neighbour (KNN) algorithms have been utilized to analyze cough and breath sound recorded using smart-phones15 for different diseases, such as chronic obstructive pulmonary disease (COPD)16, tuberculosis17, and respiratory disorders like asthma and pneumonia. Nonnegative Matrix Factorization (NMF), SVM, CNN, logistic regression algorithms were used to extract features to analyze speech and cough sounds24. The features included the number of peaks in the energy envelope of the cough signal and the power ratio of the two frequency bands of the second phase of the cough signal. The results showed that these features could classify dry and wet coughs, enabling the identification of associated diseases. Mobile applications and Web services were developed for COVID-19 cough sound data collection and pre-screening28. However, none of the datasets are publicly available for replication.Coughing is a common symptom for over one hundred pathological conditions3 collected a large dataset of microbiologically confirmed COVID-19 cough sound from 3621 individuals, of which 2001 had tested positive. While voice and breathing sounds were collected and manually verified, only the cough sounds were used for model training. They applied a CNN model and showed a statistically significant signal predicting the COVID-19 status with the area under the receiver operating characteristic curve (AUC = 0.72). Due to the imbalanced nature of the collected dataset (more negative vs. positive cases), the authors performed two data augmentations to enrich the minority class (COVID-positive) by adding external background environment sounds from the ESC-50 dataset29 creating different time and frequency masking of the input spectrogram30. Furthermore, the authors randomly sampled 2-s overlapped segments from the entire cough segment and used short-term magnitude spectrograms as input to the CNN model.Bagad et al.31. They collected a balanced audio COVID-19 cough dataset (not publicly available) with 5320 patients. They developed a CNN-based speech processing framework that leverages acoustic features to pre-screen COVID-19 from cough recordings. Cough recordings were transformed with Mel Frequency Cepstral Coefficient (MFCC) and trained with a CNN ensemble model. The ensembled model was composed of a CNN model trained on the Poisson transformed MFCC layer representing the patient's muscular degradation. Three parallel pre-trained ResNet50 models tuned on speech recordings representing the patient's vocal cord, sentiment, lungs and respiratory tract characteristics. The results showed that the CNN model achieved COVID-19 sensitivity of 98.5% with a specificity of 94.2% (AUC: 0.97).A study conducted by MIT researchers claimed that COVID-19 patients, specifically asymptomatic patients, could be accurately identified from a forced-cough cell phone recording using CNN models32, and implemented the ensemble model.Moreover, for asymptomatic patients, the trained model achieved a sensitivity of 100% with a specificity of 83.2%. The CNN model was trained on 4,256 patients and tested on 1,064 patients. Each split input cough recording was split into 6-s audio segments, padded as needed, processed with the MFCC module9. The authors used cough and breathing sounds to identify COVID-19 distinguished from sounds from asthma patients or healthy people. The librosa module was used as the primary audio processing library, while VGGish was used to automatically extract audio features in addition to the various handcrafted features33. The handcrafted and VGGish extracted features were utilized in shallow machine learning algorithms . The results showed that this model could differentiate between cough and breathing sounds of COVID-19 patients and healthy users or patients with asthma (AUC = 0.8).To achieve better automation in voice/cough feature extraction, a large-scale crowdsourced dataset of respiratory sounds was collected to aid the detection of COVID-19There are several challenges and limitations associated with the previous studies. The main challenge is data availability and quality. Even though some datasets are publicly available, the datasets are naturally limited in COVID-positive samples compared to the negative samples. Moreover, the nature of the crowdsourced data does not guarantee any noise-free recordings. The crowdsourced cough sounds may include prolonged silence periods or significant background noise, making it challenging for any machine learning model to identify valuable patterns related to COVID-19. Previous studies have used an overlapped sliding window approach to segment the cough sound files and, consequently, enrich the data of limited COVID-positive samples. The overlapped sliding window size may significantly impact the machine learning model results as it may accumulate sound information unrelated to the cough (silence) if the window size is relatively long. If the window size is small, the machine learning model may learn repetitive patterns that might not necessarily correlate with COVID-19. The previous studies based their analysis on either the MFCC or Spectrogram of the sound files and did not explore other features or representations of the cough sound files. Moreover, the lack of fully automated feature extraction limits the ability of machine learning models to learn from diverse features that may identify COVID-19.11 and segment the cough sound recordings into individual non-overlapped segments to enrich the COVID-positive records. We process each recorded cough for the first time to generate multiple representations and extract automated features per record. We then employ the generated feature library to develop and examine several shallow and deep learning models. The high-performance models are selected and further aggregated into an ensemble of classifiers to produce a robust classifier to identify COVID-19 from cough recordings. We used the kappa statistic to incorporate high-rank classifiers without favouring any of the classes34.In this work, we utilize a crowdsourced cough dataset with diverse length, pacing, number of coughs, and stochastic background noise from publicly available dataThe presented work identifies COVID-19 from cough sound recordings. The main challenge faced in this work is how to utilize a crowdsourced cough dataset with diverse length, pacing, number of coughs, and stochastic background noise from publicly available COVID-19 cough sounds. We provide a practical solution that segments the cough sound recordings into individual non-overlapped segments to enrich the COVID-19 positive records. We process each recorded cough to generate multiple representations and extract automated features per record. We then employ the generated feature library to develop and examine several shallow and deep learning models.The high-performance models are selected and aggregated into an ensemble of classifiers to produce a robust classifier and identify COVID-19 patients from their cough recordings. In addition, we used the kappa statistic to incorporate high-rank classifiers without favouring any of the classes. Finally, we show the significance of the proposed classification method by comparing the proposed method to recent related works. The proposed method outperforms compared to other complicated methods.The methods developed so far segmented the cough sound recordings into overlapped segments of unjustified length and padded the resulted segments as needed. This type of segmentation introduced undesired frequencies and led to misleading classification results. Our method was deployed into a Web App to identify COVID-19 patients from cough sounds that signal the work's potential practical significance.There is a legitimate need for the proposed predictive models based on shallow and deep learning, wherein these models use non-medical secondary data to identify health-related conditions such as COVID-19. These predictive models can be used in large-scale real-world settings. The results on real-world datasets are promising and motivate further investigations into secondary data analysis for identification of other health-related conditions".31, there is no comparative information presentable in this regard. As most of the classification results are close to a random chance on average across all features of the classifiers, we do not proceed with shallow learning models in the final ensemble.Here we illustrate the shallow and deep learning experimentation results on the target cough sound data extracted from crowdsourced recordings. The goal is to identify COVID-19 patients from just one cough. Table Tables Tables Tables 31. However, we set the threshold of all classifiers at 0.5 to eliminate the bias to a specific class (COVID-19 versus non-COVID-19). Our results are closest to the study24, where they used log-Mel spectrogram from cough sounds to train a ResNet18 CNN model and manipulated the model threshold toward producing the sensitivity of 0.9. The study of Laguarta et al.31 used four ResNet 50 CNN pre-trained models trained on muscular degradation and vocal cords, where the threshold manipulation was done on MFCC features to achieve AUC = 0.97.Table 35 (dynamic behaviour of the cough sound such as start-stop sequence or pauses). Several shallow  and deep machine learning models were trained to detect COVID-19 status (either positive or negative) using the kappa statistic (> = 0.2) to select candidate classifiers and create an ensemble model to identify COVID-19 status with better accuracy compared to individual models. Because there is a high degree of overlap between the class features, we did not reach an accuracy above 90%. However, this unbiased classification threshold ensures the minimal dependency of the predictive model on the type and pattern of classifiers. Future work can emphasize learning the similarity and difference among class labels and avoid or minimize excessive false positive (waste or resources) or false negative (untreated COVID-19 patient) results. The design and deployment of a mobile and Web app to longitudinally collect and analyze cough sounds can further support informing subjects about the algorithm's performance for their COVID-19 pre-screening.This work contributes to the crucial project of developing a purely digital COVID-19 diagnostic test by applying machine learning methods to analyze cough recordings. We developed a new technique to enrich crowdsourced cough sound samples by splitting/isolating the cough sound into non-overlapping coughs and extracting six different representations from each cough sound. It is assumed that there is a negligible information loss or frequency distortion due to the segmentation37, a new neural network model based on discrete events (spikes) representation over time, rather than continuous values representation used in the convolutional neural network. SNN showed considerable success in discrete event detection such as tinnitus37 . Therefore, we utilize the SNN model to identify COVID-19 vs non-COVID-19 directly from the coughing sound. Furthermore, utilizing the SNN model would help us prevent any information loss (due to quantization error) when segmenting the sound files into none overlapping segments and further converting each segment into different visual representations .One of the recent developments in computational neuroscience is the utilization of the spiking neural network (SNN)39 to extract information from coughing and breathing sounds. The sliding window technique is sufficient if the dataset is noise-free. The noise may include a prolonged pause period and background noise. The sliding window may capture the dynamics of the sound signal. For instance, the sliding window technique can capture the number of coughs per 'unit time' and the time between two consecutive coughs. The dynamics of the cough sound signal may positively impact the successful detection of COVID-19 cases. However, the width of the sliding window may differ based on the quality of the cough sound. When the sliding window is relatively small, the dynamics of the cough sound may not be correctly captured, which causes misleading results. The longer the sliding window length, the less the dynamics of the cough sound are captured.Other studies used a sliding window (2 to 6 s)11. Table An ensemble of machine learning models implemented in this study uses crowdsourced cough recordings to identify COVID-19. We randomly and manually verified 30% of the cough sound files in both datasets as a safe-guard. Our verification test agrees with the ones done in previous studies40 (either learning and pooling different classifiers on the same dataset or using bagging or boosting methods for ensemble learning), we focus on investigating different extracted features of a single cough sound to enhance the identification of COVID-19 status without oversampling or sliding window techniques. The research hypothesis necessitates the following requirements for a successful solution: The first requirement is to enrich the original data without oversampling by splitting the original cough sound files into non-overlapping segments. Splitting the sound files allows us to increase the sample size of the minority class (COVID-19) without changing the feature distribution resulted from applying oversampling techniques. The second requirement is to use an ensemble of classifiers that act independently on each extracted information signal and utilize the value of 0.5 as a threshold to decide on the input feature classes (COVID-19 positive vs. negative). This provides those classifiers and classifier ensembles that do not favour one class over the other. The third requirement is to implement a robust inclusion/exclusion criterion to include or exclude a classifier in an ensemble.Our main goal is to learn from multiple representations of crowdsourced cough sounds to identify COVID-19 patients. More specifically, we aim to extract and integrate multiple information signals from a single cough sound to identify COVID-positive versus negative patients with adequate accuracy in a classifier without bias toward a specific population. While ensemble learning is a standard method to integrate multiple information signals34 (the inclusion/exclusion criterion) of each classifier to include it into an ensemble for producing a more robust classifier. The range of the Kappa statistic is . It is interpreted as follows: values ≤ 0 imply no agreement , 0.01–0.20 as none to a slight agreement, 0.21–0.40 as fair, 0.41– 0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1.00 as almost perfect agreement .This study utilizes several classification evaluation metrics, including AUC, accuracy (ACC), precision, recall, harmonic mean (F1), and Kappa statistic. The Kappa statistic is used as a reliability measure41. This module is used as a universal tool for sound data tokenization, functioning based on finding where an acoustic activity occurs in an audio stream followed by isolating the equivalent slice of the audio signal. Figure Figure 33. The six measures for each isolated segment are Mel spectrum, power spectrum spectrogram, chroma, tonal, and MFCC, all based on the Mel frequency scale. Figure Each isolated cough sound enters the measuring module following the audio splitting step to generate six different independent frequency measures (representations of the same cough sound). Each measure is converted into a reasonable resolution image (432*288 pixels) for further analysis. The Mel frequency scale is a standard audio signal representation offering a rough human frequency perception model42 for feature extraction in audio signals, we extract features from these images using Vgg16 architecture and subject them to several shallow and deep learning models. Following the segmenting of all the positive and negative cough sound files for all participants in both datasets used in this study, we reached a total of 638 COVID-positive and 8248 negative cough sounds. We used all the 638 positive cough sounds while randomly selecting 638 negative coughing sounds to create a balanced dataset (1276 cough sound samples) for training and testing purposes. The data was divided into 80% for training (1020 images for each measure) and 20% for testing all the machine learning classifiers used in this study (256 images for each measure).Inspired by the Vggish's modelWe experiment with several traditional  machine learning models, including Naïve Bayes, logistic regression, k-nearest neighbours, random forest, stochastic gradient descent, extreme gradient boosting, and support vector machine. Figure


Quantitative polymerase chain reaction (PCR) evaluated EGF/EGFR gene expression using the SYBR Green Master Mix detection system. The results showed that EGF expression in the peri-implant crevicular fluid was statistically different. There was a higher EGF expression for group C  (p = 0.04) than for the other groups. Regarding EGFR, there was no statistical difference among the groups (p = 0.56). It was concluded that low levels of EGF gene expression in the peri-implant crevicular fluid are related to the development of peri-implant mucositis and the absence of mucosae sealing. There was no correlation between EGFR gene expression with health or mucositis.This study aimed to evaluate the correlation between epidermal growth factor (EGF) and receptor (EGFR) levels in different clinical stages of dental implant rehabilitation and trace mucositis development’s biological profile. Thirty-six participants from the Specialization in Implant Dentistry, Universidade Federal Fluminense, Brazil, were included in the study and underwent sample collection: inside the alveolar socket, immediately before implant placement (Group 1,  Implant therapy has been widely applied in oral rehabilitation for many years, with predictable long-term results. The longevity and functionality of dental implants depend on the new bone formation around the implant body and the establishment of a soft tissue barrier called mucosal sealing that protects the underlying peri-implant structures and the implant itself. However, the presence of peri-implant mucosal inflammation (mucositis) can lead to loss of mucosal sealing and possible pathological bone resorption (peri-implantitis), culminating in implant loss .In recent years, peri-implant diseases have become increasingly present in daily clinical practice. Despite differences in diagnosis, peri-implant mucositis is estimated to have a prevalence ranging from 50% to 80%, while peri-implantitis affects approximately 12–40% of implants . In addiPeri-implant mucositis was defined by the Consensus of the 6th European Periodontics Workshop (WEP) as the presence of inflammation in the peri-implant mucosa without signs of loss of supporting bone ,4. SubseIn this context, and with the increasing incidence of mucositis after implant placement, numerous studies have explored the biological characteristics of peri-implant mucositis that may be associated with the development or permanence of the disease. However, most of these studies focused on the exacerbated inflammatory response associated with the clinical pattern of mucositis, including analysis of interleukins, metalloproteinases, and different cytokines related to tissue destruction present in both peri-implant tissue and peri-implant crevicular fluid. As a result, most of these studies showed a high pattern of these pro-inflammatory markers associated with different patterns of tissue disruption around the implant ,8,9.However, studies of the characteristics of the peri-implant mucosa in health conditions, and its relationship with the formation of the mucosal seal, have shown peculiar characteristics present only in this tissue ,10. In aConsidering that, for the formation of the oral epithelium, the activity and expression of genes that enable tissue proliferation and stabilization of regenerative aspects is necessary, a study by Kim et al. (2012)  investigEpidermal Growth Factor (EGF) is a 53-amino acid polypeptide isolated initially from mouse salivary glands. The discovery of EGF was preceded by its ability to stimulate epithelial growth and differentiation after administration in newborn mice. The interaction of EGF with its receptor (EGFR) is known to trigger complex biochemical processes that lead to cell cycle progression. EGF is responsible for numerous primordial functions for wound healing in the oral mucosa since its functions include stimuli for cell proliferation, migration, and repopulation . Low EGFMolecular studies can help to understand the structural and biological properties of mucosal sealing and its interaction with a dental implant, showing mechanisms that orchestrate the integrity of the peri-implant soft tissue interface. However, data from human studies are still scarce . TherefoThis research was conducted after the project submission and the respective In-formed Consent Form to the Research Ethics Committee (CEP) of the Hospital Universitário Antônio Pedro-School of Medicine, Universidade Federal Fluminense, having been approved by the number 2,455,991, in accordance with the provisions of Resolution 466/2012 and its complements to the National Health Council and Resolution 441/2011, addressing the inclusion of a biorepository.All clinical and radiographic procedures were performed at the Clinic of the Specialization in Implant Dentistry, School of Dentistry, Universidade Federal Fluminense. Laboratory procedures were performed at the Clinical Research Unit.Inclusion Criteria: Healthy research participants were included; non-smokers; with and without previous history of periodontal disease, indicating installation of an endosseous implant (groups 1 and 2) or with already installed endosseous implants (groups 3 and 4), external hexagon; independent and lucid elderly, not dependent on other people/caregivers, and the informed consent form signed.In groups 3 and 4, the research participants were required to have total fixed metal–plastic prostheses supported by mandibular screws installed for at least one year.Exclusion Criteria: Report during anamnesis of systemic impairment , use of resorptive drugs and hormone replacement treatment, use of antibiotics, anti-inflammatory drugs, and mouthwash three months before the start of the study, and those considered vulnerable, according to Resolution 466/12.In groups 3 and 4, research participants diagnosed with peri-implantitis and those who underwent peri-implant maintenance for at least 6 months were excluded.A total of 36 participants were included in this study and divided into 4 groups, considering the bone healing period and load application: At dental implant installation (without loading) and assessment during the new bone formation: Groups 1 and 2. With load application on dental implants for at least 6 months: Groups 3 and 4.A total of 10 participants were selected from the patients of the Universidade Federal Fluminense, Implant Dentistry Specialization course from March 2017 to April 2019. This number of participants was based on performing the sample calculation with 80% power.These participants were all candidates for the installation of endosseous dental implants and were submitted to clinical/tomographic planning, according to the Implant Dentistry Specialization course protocol.® , placed at the level of the alveolar bone crest.All participants were submitted to the installation of endosseous dental implant by the two-stage surgical technique, according to the protocol recommended by Misch et al. (2008) . All impAll surgical procedures were performed by the same surgeon involved in the research (according to the instrumentation/milling protocol recommended by the manufacturer).Clinical and radiographic analyzes were performed at two different times, as described below:Group 1 : clinical examination was performed after the dental implant placement, considering the primary stability and the immediate torque obtained. Digital periapical radiographs coupled to a radiographic positioner with an acrylic guide were used.Group 2 : it was performed with exposure of the implant 3 months after the installation surgery. Fifteen days after reopening, the implants were submitted to secondary stability measurement. Peri-implant tissue was clinically evaluated by mucosal staining and bleeding. The presence of implant mobility was also analyzed after the bone healing period.All patients under treatment for peri-implant supportive therapy, in the period 2017–2019, at the Clinic of the Specialization in Implant Dentistry, School of Dentistry, Universidade Federal Fluminense, were considered possible volunteers to participate in the research.Considering the inclusion and exclusion criteria, from a total of 42 patients, 26 participants were selected from these research groups, who after clinical evaluations were subdivided into 8 healthy participants (group 3—control) and 18 participants with peri-implant mucositis (group 4—mucositis).A single researcher, calibrated through the kappa test, performed all the clinical steps of this study. The mandibular screwed implant-supported fixed total prostheses (hybrid denture) were removed, and all peri-implant implants and tissues were examined. Peri-implant clinical examination was performed on the mesial, distal, buccal, and lingual surfaces of each implant using a periodontal probe . Additionally, the distance from the edge of the pros-thesis to the mucosa was evaluated using a castroviejo dry-tip spectrometer .The following clinical parameters were considered: peri-implant probing depth; bleeding on probing and/or suppuration; spontaneous bleeding, presence of mobility; the presence of plaque in the prosthesis and implants; keratinized tissue band; peri-implant biotype; mucosal color change; the presence of swollen area; exposure of implant threads; percussion sensitivity; and implant function time. The diagnosis of the regions analyzed was based on the parameters shown in According to the diagnosis, participants in this part of the research were divided into two groups: control group, characterized by peri-implant health in all implants, and mucositis group, characterized by the presence of peri-implant mucositis in at least 1 implant.Radiographic exam: Radiographic examination consisted of digital periapical radiography using the Indicator Digital Shick Elite radiographic cone positioner—Indusbello . All radiographic shots were performed on the same X-ray machine DABI ATLANTE Spectro 70×  with the KODAK RVG5100 Digital Radiography System sensor  and using the KODAK imaging program Software , through a single operator. At the time of the radiographic examination, the participants wore a lead apron and a thyroid protector, complying with Federal Ordinance 453/98 (1 June 1998).Physiological bone loss: physiological bone loss was characterized considering the normal bone loss of one millimeter during the first year after implant installation and 0.2 mm per subsequent year according to the bone healing period [Patients diagnosed with peri-implantitis were referred for specific treatment at the Implant Dentistry Clinic.All research participants with a diagnosis of peri-implant mucositis were referred for treatment in Peri-implant Supportive Therapy at the Implant Dentistry Specialization Course.Diagnosis of periodontitis history was based on Berglundh et al.’s (2018) study  and it wFor the laboratory analysis, peri-implant crevicular fluid (PICF) was collected in the 4 different research phases. In Group 1, the collection was performed in the surgical socket before implant placement. For the groups submitted to the load, the place chosen for sample collection was the site with the highest degree of disease of each participant. In cases where all implants were healthy, the location of the implant closest to the midline was collected .The mandibular screwed implant-supported fixed total fixed prosthesis (hybrid denture) was removed, and after relative isolation with a cotton roller, the implant surface in the collection area was dried. An absorbent paper filter  was inserted into the peri-implant groove for 60 s and immediately thereafter submerged in 1 mL TRIzol reagent and stored at −80 °C.® . RNA integrity was confirmed and run on electrophoresis, agarose gel stained with 1.2% SYBR Stain® . RNA purity was confirmed by spectrophotometer absorbance ratio 260/280 and estimated RNA amount at 260 nm . The reverse transcription PCR (RT-PCR) reaction was performed for complementary DNA synthesis (cDNA) from 300 ng RNA using the ImProm-II Reverse Transcription System™  according to the manufacturer’s protocol. Blank control (RT-PCR without RNA matrix) and RT reactions (PCR reactions without reverse transcription) were performed together with all RT-PCRs. Quantitative PCR (qPCR) reactions were performed on the MxPro-Mx3005P software  using the SYBR Green Master Mix  with 1.5 µL of cDNA in each reaction. qPCR used activation at 95 °C for 10 min, followed by 40 cycles of denaturation and prolongation (95 °C for 15 s and 60 °C for 1 min). EGF specific initiators, EGFR were made based on BLAST data (http://blast.ddbj.nig.ac.jp/top-j.html accessed in 30 April 2019). The Livak method (2-ΔΔCT) determined the relative quantification of these gene expressions. The values were normalized to constitutive expression of β-actin. (forward 5′-AAT TAC GAG CTG CGT GTG G-3′ /reverse 5′-AGA GCG CAG GTA GGA TAG CA-3′).Total mRNA was extracted from the samples by the conventional TRIzol method . DNase treatment to digest genomic DNA that could lead to false-positive results was performed using DNA-freeDNaset-test) e non-normal (Mann–Whitney). Nominal variables were assessed by the chi-square test, including the odds ratio assessment with a 95% confidence interval. The Kruskal–Wallis test compared the 4 research groups simultaneously. The p-value < 0.05 was considered statistically significant. Gene expression analysis considered the normal distribution pattern, using the Mann Whitney test in the comparison between groups. Statistical analyzes were performed using the STATA11.1 software .Numerical variables were expressed as mean ± standard deviation and subjected to the Normality Test (Shapiro–Wilk Test): normal  women and 5 (50%) men, and only 1 (10%) participant was hypertensive. No participant had a history of periodontitis (0%).Clinical results showed the absence of percussive and spontaneous pain, absence of peri-implant mucosal discoloration, and any other signs and symptoms of peri-implant inflammation and all had primary and secondary stability.Considering the clinical and radiographic aspects, the regions studied at both intervals, before implant installation and after bone healing, showed no clinical signs of inflammation or peri-implant pathological bone loss.Considering the inclusion and exclusion criteria previously described, from a total of 42 patients, 4 were excluded due to systemic impairment, 6 due to smoking, 3 due to a change of address (state), and 3 due to the diagnosis of peri-implantitis, totaling 26 participants.Considering gender, 16 (61.5%) participants were men and 10 (38.5%) women with a mean age of 65.5 ± 7.99 years. Based on the clinical parameters, no difference was found between the control and mucositis groups . No radip > 0.05), being considered similar for the statistical comparison relative to other parameters.The comparison between the clinical characteristics in the research groups showed that although the groups without load (1 and 2), groups 3 and 4 had different participants and were analyzed in different periods after implant installation, they did not present a statistically significant difference, considering age, gender, and history of periodontitis (p = 0.04) (p = 0.04). Considering EGFR gene expression levels, the results showed no statistically significant differences among the groups (p = 0.56) , that the expression of EGF mRNA in the peri-implant crevicular fluid (PICF) was significantly higher in group 3  compared to the other groups ( = 0.04) . The Kru = 0.56) .According to clinical results and EGF expression, it can be observed that the increase in EGF is compatible with the presence of mucosal sealing, and its decrease in implant function is associated with the presence of mucositis .Peri-implant mucositis is clinically characterized by signs compatible with an inflammatory response in the mucosa surrounding the implant. The rates of this disease have been growing significantly in recent years, stimulating studies looking for its etiology and main treatments. One consequence of mucositis is the loss of peri-implant mucosal architecture and integrity, suggesting an instability associated with growth factors responsible for tissue maintenance around implants. However, there is little research on the function or formation of soft tissue sealing around dental implants, and the biological characterization of this interface remains unclear . Thus, tPeri-implant mucositis is mainly caused by the formation of a bacterial biofilm, which triggers an exacerbated local inflammatory response, leading to the destruction of peri-implant soft tissues ,15. TherJust as prevention and treatment of peri-implant mucositis are related to long-term success in implantology, mucositis is the obvious precursor of peri-implantitis, a significant cause of implant loss . TherefoAnother critical issue in the study of peri-implant diseases is the representativeness of peri-implant crevicular fluid, which has been characterized as a promising means for detecting peri-implant activity . BiochemThe mucosa around the implants forms a seal that is similar to the junctional epithelium in the periodontium. This peri-implant union is composed of three types of epithelium: peri-implant epithelium, peri-implant sulcular epithelium, and oral epithelium ,20. The Considering the natural biology of peri-implant mucosal seal formation and considering that the integrity of this seal is associated with the maintenance of peri-implant health, as it is the main barrier to external insults, our study investigated the expression of markers related to mucosal regeneration before and after the formation of this seal, culminating in the health transition to peri-implant mucositis, characterized by the loss of the seal. The mRNA expressed by cells in the FCPI showed that EGF and EGFR gene expression during implant installation and two weeks after dental implant exposure surgery showed no statistically significant difference. This result is closely related to the fact that the mucosal sealing was not completely formed during these two ICF collection periods. Its formation begins from the oral epithelium two–three weeks after the implant contact with the oral epithelium . In our However, it became evident that healthy peri-implant regions in implants subjected to loading showed a significant increase in EGF gene expression. This fact may be based on numerous justifications. First, healthy peri-implant tissues are characterized by the presence of an intact mucosal seal that possibly retains its architecture and function due to the activity of growth factors such as EGF, closely associated with epithelial regenerative capacity. Another explanation, which we consider plausible, is that the presence of load on the implant may cause tissue microtrauma that needs a constant regenerative response to maintain mucosal integrity, different from what occurs in the newly unloaded implant, thus presenting low EGF levels compared to healthy tissues around prosthetic-connected implants. Perhaps there is a positive regulation between mechanical implant functionality, mucosal seal formation, and high EGF levels.On the contrary, we observe that when peri-implant tissue loses mucosal sealing in implants subjected to loading due to the presence of exacerbated mucosal inflammation characteristic of peri-implant mucositis, EGF levels fall dramatically in the PICF. This fact may represent an influence of the destructive tissue inflammatory response in the peri-implant, culminating in the destruction of cells capable of producing EGF and maintaining tissue integrity, thus activating a cascade effect of mucosal sealing regenerative incapacity coupled with the high inflammatory response. At this point, the indication of some therapeutic techniques is justified in order to prevent the development of peri-implantitis and implant loss. Del Amo et al. (2016)  and RenvEGF’s biological activities depend on its binding to a specific cell membrane receptor, through which it exerts a potent mitogenic effect on most epithelial cells, fibroblasts, and endothelial cells . EGF-recOther interesting finds were from Óbice et al. (2019) . The autBased on the regenerative potential of EGF, numerous therapies have been suggested and tested for wound healing and increased tissue regeneration. EGF is one of the most significant growth factors in therapies involving Platelet Rich Plasma for bone regeneration, being applied in the dental clinic for different purposes. In 2019, Pansani et al.  evaluateHowever, so far, few studies have investigated the peri-implant adhesion structures and their biology, which is necessary to maintain peri-implant health and implant longevity. This study is the first to describe differences in EGF expression before and after mucosal seal formation and its association with the presence of peri-implant mucositis. Some limitations found were the impossibility of characterizing peri-implant mucosal sealing, considering that the work was performed in humans, the lack of information on EGF levels after treatment of peri-implant mucositis, and the number of participants in the research. Other limitations include the follow-up of the same participant from implant placement (group 1) to the possible development of mucositis (group4). However, it would limit the number of samples and make the study execution time unpredictable. Maybe this model can be reproduced in animal studies.Dental research in the field of genetics has been introduced in universities as a vital part of developing a thriving and strong educational system in its three pillars—Education, Research, and Extension—and studies are growing as new faculties of dentistry incorporate and mature a solid research profile in their educational programs , openingFrom the results obtained in our work, as well as the clinical experience of the present day, the success of implant-supported prosthetic rehabilitation has become more straightforward and predictable. However, there is still a range of individuals for whom the prediction is not so simple. The patient’s susceptibility to developing exacerbated pro-inflammatory reactions may, in part, be an etiological factor in the failure of dental implants and surrounding soft tissues. Specifically, in these cases and perhaps in general, if possible, EGF and EGF-R levels may contribute to the optimization of mucosal seal results around implants and prosthetic components. Future human clinical studies, including these, among other molecules, may play a key role in their influence on regenerative processes and peri-implant health.Based on our results, the low levels of EGF gene expression in the peri-implant crevicular fluid are related to the development of peri-implant mucositis and the absence of mucosae sealing. Futures therapies based on EGF clinical application in mucositis treatment should be investigated in futures researches. There was no correlation between EGFR gene expression and health or mucositis.


In summary, our results declare that mining the selective remodeling of DNA methylation in promoter regions can identify robust gene-level associations with phenotype, and the characteristic remodeling of a given gene’s promoter region can reflect the essence of disease.Epigenetics is an essential biological frontier linking genetics to the environment, where DNA methylation is one of the most studied epigenetic events. In recent years, through the epigenome-wide association study (EWAS), researchers have identified thousands of phenotype-related methylation sites. However, the overlaps of identified phenotype-related DNA methylation sites between various studies are often quite small, and it might be due to the fact that methylation remodeling has a certain degree of randomness within the genome. Thus, the identification of robust gene-phenotype associations is crucial to interpreting pathogenesis. How to integrate the methylation values of different sites on the same gene and to mine the DNA methylation at the gene level remains a challenge. A recent study found that the DNA methylation difference of the gene body and promoter region has a strong correlation with gene expression. In this study, we proposed a Statistical difference of DNA Methylation between Promoter and Other Body Region (SIMPO) algorithm to extract DNA methylation values at the gene level. First, by choosing to smoke as an environmental exposure factor, our method led to significant improvements in gene overlaps (from 5 to 17%) between different datasets. In addition, the biological significance of phenotype-related genes identified by SIMPO algorithm is comparable to that of the traditional probe-based methods. Then, we selected two disease contents  to show that the biological efficiency of disease-related gene identification increased from 15.43 to 44.44% ( Epigenetics is a branch of genetics that studies the heritable changes in gene expression without changing the nucleotide sequence of a gene , includiSimilar to GWAS (genome-wide association study), EWAS can compare variations between patients and healthy people at the DNA methylation level and associate epigenetic variations with complex diseases as well as interpret the pathogenesis of complex diseases at the epigenetic level . EWAS opIn the detection of clinical samples, the human DNA methylation chip is a common method for high-throughput EWAS analysis. The current widely used methylation chip is the Illumina 450 K BeadChip . HoweverAnother example is the identification of Parkinson’s disease–related DNA methylation probes based on three independent EWASs, corresponding to 194 genes . Unfortup-value < 2.2e–16)  . This re2.2e–16) .Based on the above correlation, this study proposed the statistical difference of DNA Methylation between Promoter and Other Body Region (SIMPO) algorithm to mine gene-level DNA methylation associations with phenotype. It showed the robustness of SIMPO-identified differential genes in the same dataset and between different datasets through three smoking phenotype-related DNA methylation datasets. The results also showed that the biological efficacy of SIMPO-identified differential genes is comparable to those predicted by traditional probe-based methods. In addition, we further applied the SIMPO algorithm to predict insulin resistance (IR)– and Parkinson’s disease (PD)–associated genes and revealed the biological significance of corresponding genes.First, this study collected transcription and DNA methylation data from the MESA (Multi-Ethnic Study of Atherosclerosis) Epigenomics and Transcriptomics Study. This study has been launched to investigate potential gene expression regulatory methylation sites in humans by examining the association between CpG methylation and gene expression in purified human monocytes from 1,202 individuals (ranging 44∼83 years of age) and proved that blood monocyte transcriptome and epigenome can reveal loci associated with human age . We downNext, we used three smoking phenotype-related DNA methylation datasets to test the robustness of the SIMPO algorithm. Previous studies have found that smoking is associated with a variety of diseases by affecting DNA methylation and causing abnormal gene expression . For exaData of IR-related DNA methylation BeadChip analyzed in this study were also downloaded from the NCBI GEO database (GEO accession: GSE115278). This dataset uses Illumina HumanMethylation450 BeadChip’s GPL16304 platform and contains DNA methylation data of peripheral white blood cells collected from 74 HOMA-IR  >3, and 258 HOMA-IR ≤ 3 individuals. Furthermore, based on this data, a rigorous statistical analysis revealed that 478 CpGs showed a differential methylation pattern between individuals with HOMA-IR ≤ 3 and >3 .Two PD-related DNA methylation datasets were downloaded from the NCBI GEO database (GEO accession: GSE72774 and GSE111629). These dataset use Illumina HumanMethylation450 BeadChip’s GPL13534 platform. GSE72774 contains DNA methylation data of whole blood collected from 289 individuals with PD and 219 control samples; then, these researchers obtained 82 genome-wide significant CpGs of PD . Whole bt-test is used in SIMPO, and the degree of difference (SIMPO score) is used to characterize the DNA methylation remodeling of corresponding genes:Previous research found that the DNA methylation difference between the promoter region and the body region is highly related to the expression level of the gene . The inpt-test and the SIMPO score relates to the number of probes, in order to ensure the reliability of the SIMPO score, we only selected genes with other region-located and promoter region-located probes greater than or equal to five for further calculation.Here, t-test  of a certain gene is less than 0.05, we predict that this gene is significantly associated with the corresponding phenotype . Next, we calculated the differences of gene SIMPO scores between the phenotypic individuals and control groups based on the adjusted t-test . In thishenotype . Throughhttp://www.disgenet.org) and the SCG-Drug database (http://zhanglab.hzau.edu.cn/scgdrug)  . DisGeNEscgdrug) . DisGeNEscgdrug) . SimilarIn order to verify the robustness of SIMPO algorithm in the same dataset, this study added random noise of 0.1–1° to the DNA methylation beta value of each probe. Firstly, because the range of normalized DNA methylation beta is −1 to 1, we generated the random numbers in the range of −1 to 1 through the Python random module. Next, we multiplied the random number by 0.1–1 and obtained the random noise values of about 0.1–1. Third, we added random noise values to the original DNA methylation beta values and received the new beta values. We further used these new DNA methylation beta values of probes in the SIMPO score calculation.p-values of t-test based on SIMPO scores, and KEGG pathway gene sets were downloaded from the Molecular Signatures Database . GSEA calculations are performed based on the R packages of “dplyr” and “GSEABase.” In addition, we performed the KEGG pathway enrichment analyses for the IR-associated gene sets by using the Enrichr database (https://amp.pharm.mssm.edu/Enrichr/).We enriched the KEGG pathway of PD-associated differential genes through GSEA  . The ranp-value of 0.05 as the threshold for determining the significant correlation between DNA methylation and mRNA transcription. When the p-value of Spearman correlation test is less than or equal to 0.05, we think the SIMPO scores of genes are significantly related to the average mRNA transcription. The results are shown in The DNA methylation feature (SIMPO score) of each gene was extracted based on the SIMPO algorithm, and the Spearman correlation test was used to test the correlation between the SIMPO score and mRNA transcription average of each gene in 1,202 samples (GSE56045 and GSE56046 datasets). In this study, we used a commonly accepted t-test). The numbers of differential genes  obtained from these three smoking DNA methylation datasets are shown in Based on the SIMPO algorithm and traditional probe-based algorithm, DNA methylation features of different genes of smokers and healthy people were obtained. Then, the significantly associated probes/genes of smoking were predicted through differential analysis  and the SIMPO algorithm were compared. The results are shown in p-values. For the same gene, this study intended to select the average, the max, and the min p-value to represent the correlation between this gene and the smoking phenotype. The intersections of top N genes between dataset pairs were then used to show the robustness of the traditional method between different datasets. The results are shown in p-value probe-selected method is the most robust among different datasets. However, the proportion of intersections is relatively small at only about 5%.For traditional probe-based methods, because the same gene often contains multiple methylation probes, the same gene will get multiple smoking-related Comparing the intersection ratio of differential genes/probes derived from at least two smoking-related datasets identified by DMGs and SIMPO showed that the robustness of the SIMPO algorithm  was significantly due to traditional EWAS . In the p-value ≤ 0.05). Among them, 168 (20.31%) genes are known tobacco use disorder-related genes. Based on the same background gene sets , DMGs method can identify 4,018 significantly associated genes, of which 19.59% (787) are known tobacco use disorder-related genes, slightly lower than SIMPO-TSS200. Similarly, based on the same background gene sets of the corresponding SIMPO algorithm, the proportions of known tobacco use disorder-related genes obtained by the SIMPO-TSS200 and TSS1500 (23.37%) algorithms are higher than DMGs (21.86%) for the GSE50660 dataset; the proportions of these three SIMPO algorithms  are higher than DMGs  for the GSE53045 dataset; the proportions of SIMPO-TSS200 algorithm (19.62%) are higher than DMGs (18.62%) for the GSE85210 dataset  with smoking phenotype-related genes that were identified by the traditional probe-based method (DMGs) and the SIMPO algorithm. The results are shown in  dataset . In summIn the above analyses, we analyzed a set of samples  that contained both transcriptome and DNA methylation data and showed that the SIMPO scores of ∼40% of genes were significantly correlated with mRNA expression values, proving that SIMPO scores and mRNA expression of genes have good correlations. Next, we used three smoking-related DNA methylation datasets to validate the robustness of the SIMPO algorithm. The results showed that the robustness of the SIMPO is significantly better than the traditional probe-based methods for the same datasets and between different datasets. Finally, by comparing with known tobacco use disorder-associated genes, it is proved that the biological significance of phenotype-related genes identified by SIMPO algorithm is comparable to that of the traditional probe-based methods. Therefore, we will use SIMPO-TSS1500 as a representative of SIMPO algorithm for the following analyses. In summary, the SIMPO algorithm has good robustness and biological efficacy and can be further applied to phenotype or disease research in the field of epigenetic biology.t-test was applied to identify differential genes between individuals with HOMA-IR ≤ 3 and > 3. As a result, 990 IR-associated genes were predicted by SIMPO-TSS1500 .In this study, the SIMPO-TSS1500 algorithm was used to mine gene-level methylation remodeling pattern for IR-related dataset (GEO accession: GSE115278), and then -TSS1500 . On the -TSS1500 . Because-TSS1500 , we spec-TSS1500 , which ip-values of the differential genes obtained by the t-test, from small p-value (most significant) to large p-value (least significant), we obtained the top 100 ∼ top 1,000 gene sets related to IR. Similarly, through the probe-based method, we also obtained the top 100 ∼ top 1,000 gene sets. It is worth reminding that when a gene corresponds to multiple probes, we use the probe with the smallest p-value to represent this gene and to rank. Based on the KEGG pathway enrichment of the Enrichr database, the results showed that multiple top N gene sets identified by SIMPO-TSS1500 were enriched in diabetes-related KEGG pathways , it was found that four pathways are related to nervous system diseases, including Alzheimer’s disease, Inositol phosphate metabolism, phosphatidylinositol signaling system, and purine metabolism.Then, this study enriched the KEGG pathway for SIMPO-TSS1500-predicted differential gene sets of PDs through GSEA. The results are shown in SCG-Drug and DisGeNET databases collected gene-disease associations from multiple sources, and both annotated the credibility scores of gene-disease associations. This study compared SIMPO-TSS1500-predicted PD-related differential gene sets with the top 10% scored PD pathogenic genes recorded in DisGeNET and SCG-Drug. The intersections of SIMPO-TSS1500-predicted gene sets with known PD-causing genes were significantly higher than the background databases  Supplem. The aboIn recent years, through EWAS, researchers have identified thousands of phenotype-related differential methylation sites. However, since the same gene may contain hundreds of methylation sites, the DNA methylation beta values at different sites vary widely. Furthermore, DNA methylation remodeling has a certain degree of randomness on the genome. As a result, for multiple EWASs focused on the same phenotype, the intersections of identified differential methylation site are small, which makes it challenging to identify phenotype-associated genes and analyze epigenetic mechanisms. Therefore, how to integrate the methylation values of different sites on the same gene and identify robust gene-level associations with phenotype becomes a challenge in the epigenetics field.In this study, by analyzing a set of individual samples containing both transcriptome and DNA methylome data, it was found that the SIMPO scores of ∼40% genes were significantly correlated with transcription of mRNA, demonstrating that the SIMPO scores of genes have a reasonable correlation with gene expression. Then, three DNA methylation datasets related to the smoking phenotype were used to test the robustness of SIMPO algorithm. The results showed that the robustness of the SIMPO algorithm in the same dataset and between different datasets was significantly better than the traditional EWAS method. Finally, through comparing with known tobacco use disorder pathogenic genes, it is proved that the biological significance of phenotype-related genes identified by SIMPO algorithm is comparable to that of the traditional probe-based methods. Next, we further applied the SIMPO-TSS1500 algorithm to predict IR- and PD-associated genes and proved the biological significance of corresponding genes. On the other hand, our research group previously applied the SIMPO algorithm to the prediction of disease-related biomarkers. As a result, these studies successfully identified several DNA methylation biomarkers related to the onset of type 2 diabetes and colorectal cancer and were supported by clinical trials . In summdrosophila). Therefore, the exploration of the effectiveness of the SIMPO algorithm in other species will be the focus of our future research. As a supplement of the traditional methods, SIMPO algorithm provides a new insight for assessing the degree of gene methylation. The different analysis methods of methylation chip can help us better understand the gene-level methylation associations with phenotype, providing a different perspective in exploring the biological issues. We believe that the combination of these methods can help us understand the regulation mechanism of gene methylation more deeply and solve scientific problems more effectively.However, the SIMPO algorithm still has some shortcomings. In order to ensure the stability of the SIMPO algorithm, it requires that the promoter region and other regions of a gene contain five or more probes to obtain a SIMPO score. Therefore, a large number of genes containing a small number of probes will be lost. At present, the number of genes that can be calculated by SIMPO-TSS200 and SIMPO-TSS1500 is only about 5,000, and the number of genes that can be calculated by SIMPO-TSS200 and 1500 is only about 10,000. It is much smaller than the number of genes contained in the human genome. As a result, some critical functional genes have been missed for the current SIMPO algorithm. Fortunately, with the popularity of the Illumina 850K BeadChip in EWAS, which contains more than 850,000 probes, the number of genes that the SIMPO algorithm can calculate will increase significantly. In addition, the effectiveness of the SIMPO algorithm is only verified in DNA methylation data of human at this stage, excluding other common model animals (such as mouse, rat, and


AimDuring the COVID-19 pandemic, there has been worldwide cancellation of elective surgeries to protect patients from nosocomial viral transmission and peri-operative complications. With the unfolding situation, there is a definite need for an exit strategy to reinstate elective services. Therefore, more literature evidence supporting exit plans for elective surgical services is imperative to adopt a safe working principle. This study aims to provide evidence for safe elective surgical practice during the pandemic.MethodologyThis single centre, prospective, observational study included adult patients who were admitted and underwent elective surgical procedures in the trust’s COVID-free environment at the Birmingham Treatment Centre between May 19 and July 14, 2020. Data were collected on demographic parameters, peri-operative variables, surgical specialities, COVID-19 reverse transcription polymerase chain reaction (RT-PCR) testing results, post-operative complications and mortality. The study also highlighted the protocols it followed for the elective services during the pandemic.ResultsA total of 303 patients were included with mean age of 49.9 years (SD 16.5) comprising of 59% (178) female and 41% (125) male. They were classified according to the American Society of Anaesthesiologist Grade, different surgical specialities and types of anaesthesia used. Ninety-six percent (96%) of patients were discharged on the same day. Hundred percent (100%) compliance with pre-operative COVID-19 RT-PCR testing was maintained. There was no 30-day mortality or major respiratory complications.ConclusionCareful patient selection, simultaneous involvement of the pre-assessment and anaesthetic team, strict adherence to peri-operative protocols and delivering vigilant post-operative care for COVID-19 infection can help provide safe elective surgical services if the community transmission is under reasonable control. However, it is particularly important to maintain COVID-free safe environment for such procedures. World Health Organization declared COVID-19 infection as a pandemic on March 11, 2020 and soon it outspread to different continents quickly . TherefoPatients undergoing surgery are particularly susceptible to COVID-19 exposure in the hospital environment and could be vulnerable to further post-operative complications owing to immunosuppressive responses following surgery and pro-inflammatory cytokine surge ,13. TherGlobally as of June 27, 2020, there have been 9,653,048 confirmed cases of COVID-19, including 491,128 deaths, reported by the World Health Organization . The UniWe have reported on a prospective study involving adult elective surgeries performed in a COVID-19-free hospital environment during the COVID-19 pandemic. This study aims to show that meticulous patient selection involving adequate pre-assessment and strict compliance to peri-operative protocols, can be a safe way to resume elective surgeries in a designated COVID-19-free environment.This prospective study included patients who underwent elective surgical procedures in the trust’s COVID-free environment at Birmingham Treatment Centre between May 19 and July 14, 2020. This observational study collected single centre data without any change to the set clinical practice. We only included adult patients, excluding all non-elective cases. The project was registered with the trust’s Clinical Effectiveness Department and was conducted as per the local clinical governance protocols. We maintained full compliance to the standards of strengthening the reporting of observational studies in epidemiology (STROBE) statement .The British Orthopaedic Association outlined the concept of ‘Green Pathway’ in their initial guidelines on the resumption of elective services . TheoretAs per the locally implemented protocol, all the healthcare personnel used specific scrubs while at the ‘Green Corridors’ and those scrubs were disinfected after each session. There was absolutely restricted use of these uniforms beyond the clinical corridors. Adequate and multiple handwashing, sanitization and use of shower facilities were encouraged to reduce nosocomial transmission. Furthermore, symptomatic screening of the health care personnel was reinforced. For any prodromal symptoms and suspicion, staff were tested as per NHS testing protocols and self-isolation was advised as per government guidelines . Staff oA total of 303 consecutive adult patients aged 18 and over who underwent an elective surgical procedure in the COVID-free environment were included in the study. This study includes patients from various specialities including orthopaedic surgery, urology, general surgery, plastic surgery, gynaecology, and ENT  surgery. The patients were meticulously selected through a well-designed pathway. Surgical teams from respective specialities worked synchronously with the anaesthetic and pre-assessment team to screen through the long waiting list to select safe and suitable patients in whom benefit from surgery would be more than the potential risk. All cases were double-checked by one of the designated senior speciality members who finalised the operating lists. Cases were also categorised as per the urgency and complexity. Length of hospital stay following these surgeries was also considered while finalising these lists with priority for day cases. These cases were anticipated for the least complications. Therefore, the anaesthetic and pre-assessment team screened those lists. Stratification of risk factors was also carried out keeping in mind diabetic status, age more than 70 years, presence of ischaemic heart disease, significant chronic lung disease and BAME  risk assessments. Special attention was given whether patients were on steroids or immunosuppressants and suffering from any condition lowering immunity.The listed patients were either contacted or reviewed by the operating team to establish the suitability for the surgery and to explain the procedure once again. They were also re-consented explaining all the associated risks once again. Patients were screened for any recent clinical features of a COVID-19 infection like fever, cough, dyspnoea, myalgia, anosmia, or other respiratory symptoms . MoreoveAs national guidance was introduced, the pre-operative assessment included a clear directive for self-isolation for at least two weeks before and two weeks after the surgery. As a prospective model of the study, we have been obliged to adhere to all the latest guidance from the Royal College of Surgeons England . Since ADemographic parameter of these patients was noted with the American Society of Anaesthesiologist (ASA) Grade classification. We also documented different types of anaesthetic procedures noted during the surgical procedure.Apart from routine post-operative follow-up, they were contacted over the telephone with a validated questionnaire after two weeks of the surgical procedure. The primary outcome of the study was to assess the 30-day mortality rate. We also measured secondary outcomes considering post-operative infection rate and incidence of swab positive results for COVID-19, the necessity for hospital re-admission in the high dependency units/intensive care units and incidence of COVID-19-related respiratory complications. Prospectively, we also checked the electronic medical records  from our hospital database and GP  records to track if there were any complications, hospital reattendances, or GP visits.Operating protocolsStandard operating protocols for elective surgery were followed with an additional checklist for COVID-19 swab result (ensuring negative result), a new consent form explaining COVID-19-related complications and also checking for any symptoms of COVID-19. No changes were adopted to the laminar airflow system and sterilization protocols. PPE checks and extra cleanliness were taken care of.We took measures to minimize the healthcare-related exposure to all the staff members during the peri-operative procedures. Particular attention was given towards in-theatre intubation and extubation, limiting staff members during surgical and aerosol-generating procedures, ensuring sufficient air changes between the procedures; thereby minimizing aerosol contamination and potential transmission. The patients were transferred to the recovery area with an appropriate surgical mask. The recovery area consists of isolated bays or separate cubicles to ensure adequate separation of the patients. Hence, this helped to reduce the chance of nosocomial transmission. The theatre team members ensured that they wore adequate protective equipment (PPEs) during perioperative care. This included protective double gloves, fluid repellent gowns, eye protection and masks according to their FIT testing. Regular FIT testing was carried out for all the staff members to make sure that they are at par with the safety benchmark before allocating them in these ‘Green Corridors’.The demographic profile of the study population is summarized in Table Of the 303 patients included in the study, 178 (59%) were women and 125 (41%) were men. Comorbidities were assessed during the anaesthetic pre-assessment and comprehensively we had used ASA Grading to classify patients. Thirty-seven percent (37%) (112) of the patients included had an ASA score of I, 55.8% (169) had an ASA of II, 7.2% (22) had an ASA of III  of the patients responded to the telephone follow-up questionnaire. However, we have gone through the electronic medical care record of all the patients. We have also tracked all the patients till 30 days from the surgery. We are not aware of any COVID-19 positive cases post-operatively.The study reports the management and overall outcome of a series of 303 consecutive patients who underwent elective surgical procedures combining different specialities during the COVID‐19 pandemic. The outcomes show that the peri‐operative management plan for elective surgical procedures which we accomplished and delivered during this crucial time was safe and productive.Because of the rapidly evolving pattern of the pandemic, the intra-operative and post-operative risks and outcomes for planned surgical procedures amidst the COVID-19 pandemic are partly understood. Few early studies reported that morbidity and mortality for surgical patients with added COVID‐19 infection might be significantly high. At the very onset of the pandemic, Lei et al. reported in a series of 34 patients from Wuhan who underwent elective surgery and were subsequently found to be COVID‐19 positive, 15 (44%) were admitted to intensive care and seven (20%) died . In thatKane et al.  from MidGammeri et al.  reportedOn the other hand, considering pre-operative RT-PCR testing, Kader et al.  have shoIt is definitely a matter of concern for nosocomial transmission of the COVID-19 infection, particularly, when we are bringing patients for elective or non-acute procedures. Columbia University Irving Medical Centre of the New York-Presbyterian Hospital have conducted a study to find out the incidence of nosocomial COVID-19 infection from March 1 till April 27, 2020, in two separate patient-care units restricted to COVID-19-negative patients (a cardiothoracic ICU and a regular floor unit). Hospital-acquired nosocomial transmission and infection with COVD-19 occurred in 0 to 2% of 311 patients . These hCárdenas-Camarena et al.  publisheKarayiannis et al.  publisheIt is a known fact that COVID-19 infection and related complications are going to be a continuing risk factor for the upcoming future. On the other hand, the resumption of elective surgeries is almost a unanimous perception for all the nations which have passed the crucial waves of the pandemic. There could be several factors to be addressed when considering the reinstatement of elective services. Every patient needs to be individualised while considering surgical pre-assessment. Adequate consideration must be given to the factors like age, associated co-morbid conditions, potential for ongoing and previous exposure to the virus infection and any factor that might be contributing to an immunocompromised state. The threshold for risk stratification and anticipation of potential complications needs to be realistic while planning the elective surgery.The result of this study is providing evidence for reinstating elective surgical procedures with a balance of risk stratification and anticipated complications. However, there is a need for a safe environment and robust peri-operative protocol which ensures minimum chance for nosocomial transmission. The locally implemented peri-operative management protocols for elective surgeries, that we followed during the pandemic with compliance to the Royal College and NHS guidelines, have been proven to be safe and can be considered as a framework for the resumption of elective services in near future.We acknowledge the weakness of this single centre study with a moderate sample size. However, the characteristics of the study cohort and the outcomes are keeping in line with the available resources and literature at the time of writing. Certainly, there is a need for more evidence before establishing universal standards for elective procedures soon.Careful patient selection, the simultaneous involvement of the pre-assessment team and anaesthetic teams and strict adherence to the peri-operative protocols can help provide elective surgical services safely if the community transmission of the virus is under reasonable control. Moreover, delivering vigilant post-operative care for COVID-19 infection is crucial for maintaining the chain of safety. However, it is particularly important to maintain a COVID-free safe environment for such elective procedures.