Subway vehicle roofs must be inspected when entering and exiting the depot to ensure safe subway vehicle operations. This paper presents an improved method for detecting foreign objects on subway vehicle roofs based on the YOLOv7 algorithm. First, we capture images of foreign objects using a line-scan camera at the depot entrance and exit, creating a dataset of foreign roof objects. Subsequently, we address the shortcomings of the YOLOv7 algorithm by introducing the Ghost module, an improved weighted bidirectional feature pyramid network (WBiFPN), and the Wise intersection over union (WIoU) bounding-box regression loss function. These enhancements are incorporated to build the subway vehicle roof foreign object detection model based on the improved YOLOv7, which we refer to as YOLOv7-GBW. The experimental results demonstrate the practicality and usability of the proposed method. The analysis of the experimental results indicates that the YOLOv7-GBW algorithm achieves a detection accuracy of 90.29% at a speed of 54.3 frames per second (fps) with a parameter count of 15.51 million. The improved YOLOv7 model outperforms mainstream detection algorithms in terms of detection accuracy, speed, and parameter count. This finding confirms that the proposed method meets the requirements for detecting foreign objects on subway vehicle roofs. Subways are the preferred mode of transportation for the public . HoweverThe traditional status detection for subway vehicle roofs primarily relies on manual inspection . HoweverDeep learning offers end-to-end detection capabilities with strong feature learning capabilities and high efficiency. Qian et al.  proposedTherefore, referring to Wang Chensong’s method, aiming at the problems of many types of foreign bodies on the roof of metro vehicles and different sizes of foreign bodies, this paper uses the currently collected images of foreign bodies on the roof of metro vehicles as the training dataset of the algorithm model and uses the YOLOv7 algorithm with better overall performance. Then, according to the requirements of rapid and high-precision detection of the railway vehicle roof, the YOLOv7 algorithm is improved, and the improved yolov7 algorithm model is named the YOLOv7-GBW algorithm model.Integration of the Ghost Module: Drawing inspiration from the GhostNet architecture, the standard convolutions in the YOLOv7 algorithm model are replaced with Ghost modules. This modification makes the YOLOv7 algorithm model lighter and enhances its detection accuracy.Enhanced Feature Fusion: Addressing the problem of insufficient feature fusion information in the YOLOv7 algorithm model, this paper borrows from the weighted bidirectional feature pyramid network (WBiFPN) to optimize feature fusion. This optimization is achieved without significantly increasing the parameter count while making the feature fusion capabilities of the YOLOv7 algorithm model more comprehensive.Wise Intersection over Union Loss (WIoU) Function: This paper replaces the original complete intersection over union (CIoU) bounding-box loss function of the YOLOv7 algorithm model with the WIoU bounding-box loss function. This approach further improves the bounding-box regression accuracy and detection precision of the YOLOv7 algorithm model.The main improvements in this paper are as follows:These improvements collectively aim to enhance the performance of the YOLOv7 algorithm for foreign object detection on subway vehicle roofs, making it more lightweight, accurate, and capable of handling diverse object categories and sizes.The YOLOv7 algorithm model  is the lAs depicted in To facilitate different input sizes, YOLOv7 replaces the SPP  module in the neck with the SPPCSPC module, which is also a variant of the spatial pyramid pooling module. Its advantages include reducing image distortion caused by image processing and addressing the problem of feature redundancy extraction during convolution. Furthermore, it adopts the same aggregated feature pyramid network (PAFPN) structure as the YOLOX algorithm model published by Megvii Research in 2021. This structure allows for deep-level features to be transmitted from bottom to top for inter-layer feature fusion. The neck network also incorporates the ELAN-H module and the MP2 module. The ELAN-H module is wider than the ELAN module, and the primary difference between the MP2 and MP1 modules lies in the number of channels with identical structures. Finally, the head network employs the RepConv module, combining reparameterized convolutions with the model network structure to strike a balance between speed and accuracy during the training process.To reduce the parameter count of the YOLOv7 algorithm model and enhance its detection speed, we introduce the Ghost module from GhostNet  to replaThe Ghost module, as implemented in GhostNet, achieves a significant parameter reduction while preserving essential information features through a series of simple linear transformations. This approach enables the generation of critical feature maps with minimal computational overhead. ith to the (i+k)th feature map. In addition, In regular convolutions, the output feature maps often contain considerable redundant information with many features being similar. To reduce the redundancy introduced by conventional convolutions, the Ghost module in GhostNet employs straightforward linear transformations on a small subset of the original feature maps to obtain numerous feature maps that are functionally equivalent to those produced by standard convolutions. This process is represented by Equation (1).The number of parameters involved in a regular convolution is given by Equation (2), assuming an input feature map size of The neck network used in the YOLOv7 algorithm model employs the path aggregation network (PANet) structure, integrating features from different hierarchical levels through a bidirectional network operating from top to bottom and bottom to top. The neck network in the YOLOv7 algorithm model enhances the PANet structure by omitting the upsampling layer, downsampling layer (MP2 module), and concatenation layer (Concat). Consequently, the neck network in the YOLOv7 algorithm model represents an improved version of the PANet, as illustrated in 5 layer in 4 layer, which transfers features to the rightmost ELAN-H module in the P3 layer, ensuring more comprehensive feature fusion.To further enhance feature fusion performance in the YOLOv7 algorithm model, this paper draws inspiration from the BiFPN for weighted bidirectional feature pyramid fusion, offering a faster and more lightweight approach . The BiFMoreover, BiFPN reconfigures the top-down and bottom-up channels outside the forward propagation and introduces horizontal connections between features of the same size, addressing the information loss caused by excessive layer depth. Accordingly, the neck network in the YOLOv7 algorithm model improved, resulting in an enhanced WBiFPN neck network, as depicted in P4), a horizontal connection was added to link the output of the CBS module to the ELAN-H module. This enhancement enables the neck network to fuse more features, further enhancing its ability to capture fine-grained details and improving the detection accuracy of the YOLOv7 algorithm model.3P to 5P represent different feature inputs passed from the backbone network. Each output is defined in Equations (5)–(8).In the WBiFPN structure, −5 used to prevent the generation of unstable extreme values during the feature flow process.Within these equations, These equations describe the flow of features within the WBiFPN structure and how various operations are applied to achieve feature fusion and flow within the network. The weights and constants are critical in controlling and stabilizing the feature flow process.The original YOLOv7 algorithm model employed the CIoU boundary-box regression loss function. The CIoU loss function includes a penalty for the aspect ratio, which becomes ineffective when the aspect ratio of the predicted bounding box matches that of the ground truth bounding box, leading to instability in the CIoU loss function.This paper introduces an improved WIoU boundary-box loss function with a dynamic nonmonotonic focus mechanism to address this problem. This enhancement ensures the stability and effectiveness of the loss function, even when the aspect ratios of the predicted and ground truth bounding boxes are equal. The dynamic nonmonotonic focus mechanism likely enables the model to adapt better to various scenarios and object shapes, enhancing the accuracy and stability of the object detection capabilities of the YOLOv7 algorithm model.The WIoU loss function, specifically WIoU-v3 , incorpoThe WIoU loss function has undergone three iterative versions, and this paper references WIoU-v3; its expressions are illustrated in Equations (9) to (11). In Equation (11), The additional computational cost introduced by the WIoU loss function primarily arises from the calculation of focus coefficients and the averaging of IoU losses. Under the same experimental conditions, WIoU is faster than CIoU because it does not require the calculation of aspect ratios. The computational time for WIoU is 87.2% of that for CIoU.Tong et al. demonstrated the effectiveness of applying the WIoU loss function to the state-of-the-art YOLOv7 algorithm model. When used with the MS-COCO dataset, it improved the average precision at an IoU threshold of 0.75 from 53.03% to 54.50%. This outcome illustrates the positive effect of the WIoU boundary-box loss function on enhancing the performance of the YOLOv7 algorithm model.Since publicly available datasets containing images of foreign objects on subway vehicle roofs were unavailable, a significant number of such images were quickly acquired using the following procedure. First, photographs of foreign objects manually detected on Line 2 of Xi’an Metro, Line 2 of Dongguan Metro, and Line 7 of Shenzhen Metro were taken. These photographs were printed in color on paper. Subsequently, all collected foreign objects were cropped based on their contours, as demonstrated in After obtaining these roof images with foreign objects, they were annotated using annotation software . Data augmentation was applied in three ways, spatial transformations, adding noise, and color transformations, to prevent overfitting during the model training process, as described in several studies ,16,17. AThe dataset was formatted according to the standard dataset format for foreign objects on subway vehicle roofs. Finally, the dataset was divided into training, validation, and testing sets in a 7:2:1 ratio. With this, the creation of the foreign object dataset for subway vehicle roofs was completed.Ubuntu 20.04 operating system;CUDA 11.6;Python 3.9;PyTorch 1.12.1;Various dependent libraries.The hardware configuration for the experimental platform in this paper primarily includes an Intel i9-12900K CPU processor, 64 GB of RAM, and an NVIDIA RTX 3090 GPU graphics card with 24 GB of VRAM. Additionally, an environment for training and simulation experiments was established on a Dell Precision 3660 workstation comprising the following components:This environment was used to conduct experiments and simulations.Before training the algorithm model, the evaluation metrics must be defined, and the training parameters must be initialized. In terms of the evaluation metrics, this paper selected six metrics: the mean average precision at IoU 0.5 (mAP0.5), precision, recall, fps, parameter count, and floating-point operations per second (FLOPS). To better assess the effectiveness of the GBS module in enhancing the detection accuracy of the YOLOv7 algorithm model, this paper conducted experiments by replacing CBS modules in two areas of the YOLOv7 algorithm model: the backbone and neck. The modified models were designated as YOLOv7-G-B (backbone), YOLOv7-G-N (neck), and YOLOv7-G-BN (backbone and neck). To ensure the validity of the experiments, all data results have undergone no fewer than 10 repetitions of training and validation experiments. The best and worst results are excluded, and the remaining experimental values are averaged to obtain the experimental outcome. This paper presents experiments using the dataset with the original YOLOv7 algorithm model and YOLOv7-B model to evaluate the performance of adding the three-layer WBiFPN network to the neck network of the original YOLOv7 algorithm model (YOLOv7-B). To ensure the validity of the experiments, all data results have undergone no fewer than 10 repetitions of training and validation experiments. The best and worst results are excluded, and the remaining experimental values are averaged to obtain the experimental outcome. This study compares the training losses of the original YOLOv7 algorithm model with the improved YOLOv7 algorithm model (YOLOv7-W) to assess the effectiveness of replacing the CIoU bounding-box loss function with the WIoU bounding-box loss function on the performance of the YOLOv7 algorithm model. Experiments were conducted using the foreign object dataset, and the detailed results are presented in In In this study, three improvements were proposed: the Ghost module (YOLOv7-G-BN to YOLOv7-G conversion), the WBiFPN, and the WIoU bounding-box loss function. Different ablation experiments were conducted under the same experimental settings to examine the individual effects and effectiveness of these three improvement methods. These experiments aimed to investigate the differences between the original YOLOv7 algorithm model and various improvement strategies, including the addition of one, two, or all three optimization methods. To ensure the validity of the experiments, all data results have undergone no fewer than 10 repetitions of training and validation experiments. The best and worst results are excluded, and the remaining experimental values are averaged to obtain the experimental outcome. To further validate that the proposed YOLOv7-GBW algorithm model is the best-performing model, we conducted comparative experiments in the same experimental environment with five other mainstream algorithm models: the YOLOv7 algorithm model, faster region-based convolutional neural network (R-CNN) algorithm model , single-The comparative graph in Furthermore, when the vehicle roof components are significantly dirty, the SSD algorithm model provides false positives. However, the YOLOv7-GBW algorithm model proposed in this paper accurately detects and identifies all roof anomalies and demonstrates better bounding-box regression performance and higher confidence compared to the YOLOv7 algorithm model. This result further underscores the suitability of the YOLOv7-GBW algorithm model as the best choice for detecting anomalies on subway vehicle rooftops. The overall framework of the YOLOv7-GBW algorithm model is shown in In this study, we proposed an improved YOLOv7-GBW algorithm model based on the YOLOv7 algorithm model for the task of detecting anomalies on subway vehicle rooftops. First, to avoid making the improved model excessively large, we replaced some convolutional layers with Ghost convolutions. This approach significantly reduced the model size and computational parameters while enhancing recognition accuracy. Second, to improve the ability of the model to extract features at different scales and reduce feature map losses, we replaced the pyramid aggregation feature pyramid structure in the neck network of the original YOLOv7 algorithm model with an improved BiFPN (WBiFPN). This replacement increased the model inference speed and significantly improved its recognition accuracy. Additionally, to further enhance the bounding-box regression capability, we introduced the WIoU bounding-box loss function in the head network of the YOLOv7 algorithm model. This approach led to a further improvement in the overall mAP0.5 value, achieving a recognition accuracy of 90.29%.We compared the YOLOv7-GBW algorithm model with the five mainstream algorithm models: the YOLOv7, Faster R-CNN, SSD, YOLOv5s, and YOLOXs algorithm models; the YOLOv7-GBW algorithm model outperformed the others in multiple metrics, such as mAP0.5, precision, recall, parameter count, and computational FLOPS. Moreover, it maintained superior performance in detection speed (fps), improving the model.Applying the YOLOv7 algorithm model to the detection of anomalies on subway vehicle rooftops meets practical requirements. However, this algorithm still has some limitations. The images are single-channel grayscale images, which do not capture as many anomaly features as color images. Additionally, the algorithm does not consider the influence of special cases, such as overlapping and intersections of anomalies, on detection accuracy. Future research will focus on addressing these shortcomings and further optimizing the algorithm to enhance its robustness.


Salmonella enterica serovars Typhimurium and Enteritidis and is estimated to result in 77 500 deaths per year, disproportionately affecting children under 5 years of age in sub-Saharan Africa. Invasive non-typhoidal Salmonellae serovars are increasingly acquiring resistance to first-line antibiotics, thus an effective vaccine would be a valuable tool in reducing morbidity and mortality from infection. While NTS livestock vaccines are in wide use, no licensed vaccines exist for use in humans. Here, a first-in-human study of a novel vaccine (iNTS-GMMA) containing S. Typhimurium and S. Enteritidis Generalised Modules for Membrane Antigens (GMMA) outer membrane vesicles is presented.Invasive non-typhoidal Salmonellosis (iNTS) is mainly caused by Salmonella Vaccine Study in Oxford is a randomised placebo-controlled participant-observer blind phase I study of the iNTS-GMMA vaccine. Healthy adult volunteers will be randomised to receive three intramuscular injections of the iNTS-GMMA vaccine, containing equal quantities of S. Typhimurium and S. Enteritidis GMMA particles adsorbed on Alhydrogel, or an Alhydrogel placebo at 0, 2 and 6 months. Participants will be sequentially enrolled into three groups: group 1, 1:1 randomisation to low dose iNTS-GMMA vaccine or placebo; group 2, 1:1 randomisation to full dose iNTS-GMMA vaccine or placebo; group 3, 2:1 randomisation to full dose or lower dose (dependant on DSMC reviews of groups 1 and 2) iNTS-GMMA vaccine or placebo.The The primary objective is safety and tolerability of the vaccine. The secondary objective is immunogenicity as measured by O-antigen based ELISA. Further exploratory objectives will characterise the expanded human immune profile.Ethical approval for this study has been obtained from the South Central—Oxford A Research Ethics Committee (Ethics REF:22/SC/0059). Appropriate documentation and regulatory approvals have been acquired. Results will be disseminated via peer-reviewed articles and conferences.EudraCT Number: 2020-000510-14. Salmonella Vaccine Study in Oxford is a first-in-human study of a novel vaccine against invasive non-typhoidal Salmonellosis (iNTS), a neglected disease largely affecting low- and middle-income countries.This study provides the opportunity to investigate the adaptive immune response to bacterial outer membrane antigens, supporting future vaccine development efforts against this disease.The volunteers recruited to this trial may not be immunologically representative of the ultimate target population in endemic areas. In addition, the vaccination schedule in this study is based on the expected schedule in infants.In the absence of a known correlate of protection against iNTS disease, it will not be possible to determine vaccine efficacy in this trial.Salmonellae (NTS), such as Salmonella enterica serovars Enteritidis and Typhimurium, most commonly cause a self-limiting gastroenteritis that is indistinguishable from that caused by many other enteric pathogens.Salmonella (iNTS) infections are more common in children, the elderly and in the immunosuppressed, including HIV-infected individuals.Non-typhoidal Salmonella gastroenteritis or typhoidal Salmonella.The Global Burden of Disease study estimates 535 000 annual cases of iNTS globally, associated with 77 500 deaths in 2017 alone, representing a higher case fatality rate when compared with non-typhoid S. Typhimurium is implicated in approximately two-thirds of all cases, with the ST313 serotype accounting for most isolates.Salmonellae, which contribute to human host restriction and an invasive phenotype.Salmonellae being classified as WHO high priority antibiotic resistant pathogens.Of the invasive pathogens responsible for iNTS in sub-Saharan Africa, High mortality, logistical difficulties in diagnosing infection in the developing world and increasing antimicrobial resistance strongly advocate for the development of an effective vaccine.There are no currently licensed vaccines for iNTS although multiple candidates are in early phase development, including O-antigen (OAg) conjugates, oral attenuated vaccines and multiple antigen display protein-polysaccharide conjugate vaccines. A trivalent iNTS-typhoid vaccine is currently in phase I.Salmonella Enteritidis (SEn) and Salmonella Typhimurium (STm).The investigational product in this study is the iNTS-GMMA vaccine. This novel vaccine developed by GSK Vaccines for Global Health consists of outer membrane vesicles or Generalised Modules for Membrane Antigens (GMMA) of the two most common serotypes associated with invasive disease, This is the first trial to investigate the iNTS-GMMA vaccine in humans. Demonstration of safety and immunogenicity in this study will lead to progression to subsequent studies in a sub-Saharan country of high endemicity.The aim of the trial is to determine the safety of the iNTS-GMMA vaccine and study the immune response to vaccination. Primary, secondary and exploratory objectives are detailed in As this is a first-in-human vaccine trial, the primary objective is the safety and tolerability of the iNTS-GMMA vaccine which will be ascertained by the collection of solicited and unsolicited adverse events, serious adverse events, withdrawals and laboratory parameters. Solicited adverse events will be collected up to 7 days following each vaccination and include local injection site reactions and systemic symptoms. Unsolicited adverse events will be collected up to 28 days following each vaccination, and outside of this period will be recorded only if medically attended. Serious adverse events and withdrawals will be recorded throughout the study. Laboratory adverse events will be recorded by obtaining blood samples to analyse full blood count, renal and liver profile at days 0, 7, 28, 56, 63, 84, 168, 175 and 196.The secondary objective is the determination of immunogenicity of the iNTS-GMMA vaccine. Immunogenicity will be measured using SEn and STm O-antigen specific ELISA at time points days 0, 7, 28, 56, 63, 84, 168, 175, 196 and 350.Salmonella Enteritidis and Salmonella Typhimurium adsorbed onto Alhydrogel (0.35 mg AL3+/0.5 mL dose) and suspended in isotonic phosphate buffered saline. The parent bacteria have been genetically modified to increase production of outer membrane vesicles (∆tolR) and reduce the toxicity of lipid A component within the lipopolysaccharide (∆msbB and ∆pagP). The GMMA particles are filtered and purified to form the active component of the vaccine.The iNTS-GMMA vaccine consists of outer membrane vesicles or GMMA from the two most common serovars causing invasive disease, This is a first-in-human randomised placebo-controlled participant-observer blind trial of the iNTS-GMMA vaccine in healthy adults aged 18–55 years in the UK. A total of 30–42 participants will be randomised to receive three intramuscular doses of active vaccine or placebo at 0, 2 and 6 months . For fur10.1136/bmjopen-2023-072938.supp1Supplementary dataParticipants will be sequentially enrolled into three groups  with a dRandomisation of participants will be carried out by unblinded study staff who are independent from the blinded team and do not perform any postvaccination procedures (such as ongoing eligibility or safety review). A web-based randomisation system will be used.This study will be conducted observer- and participant-blind from the time of randomisation until participant unblinding which will occur once the last participant has completed their final visit. Observer and participant blinding is required to minimise the risk of bias on the reporting of adverse events following the administration of vaccine.Vaccine or placebo will be administered at 0, 2 and 6 months. Participants will be directly observed for a minimum of 60 min following vaccination and then asked to complete an e-diary of their symptoms daily for 7 days following each vaccination. An in-person postvaccination review will occur 7 and 28 days following each vaccine when participants will be reviewed for any possible adverse events. There are a total of 12 scheduled study visits and participants will be followed up for 1 year following first vaccination.Potential participants may be contacted by media advertisements, direct mail out or social media using an approved invitation letter or other approved advertising material to invite them to participate in the study. Participants will be reimbursed for their time, travel and inconvenience. Healthy adults between the ages 18–55 years inclusive will be eligible for enrolment. Individuals will be initially screened for eligibility by telephone followed by face-to-face visits at the trial centre. Screening visits will involve obtaining informed consent  approval for the trial.Undertaking non-COVID-19 research during a dynamic COVID-19 pandemic represents a major logistical challenge. The safety of participants remains paramount and good infection prevention and control practices will be followed throughout the trial. The SALVO study team will monitor cases of COVID-19 within the participant cohort and the local population and will follow current national guidelines on COVID-19 with options including switching to phone appointments, halting or extending the trial.Once the trial has been completed including analysis of data, results will be published in a peer-reviewed journal and presented at conferences. The results of this study will directly impact the appropriateness of subsequent trials with a larger sample size to begin in a sub-Saharan country of high iNTS endemicity.This study has been approved by South Central—Oxford A Research Ethics Committee on 28th April 2022.The protocol, study information booklet and recruitment materials were reviewed by a local patient consultation group who provided feedback and comments on the initial documents. Their comments led to changes in the participant-facing documents, ensuring they are easy and clear for participants to understand (please see SALVO Participant Information Sheet in 10.1136/bmjopen-2023-072938.supp3Supplementary dataStudy recruitment began in May 2022. The estimated last participant last visit will be in December 2023.Salmonella species.This will be the first phase I study investigating the iNTS-GMMA vaccine. The data generated by this trial will guide future vaccine development using this GMMA technology and may contribute to the licensure of the first vaccine against invasive non-typhoidal Salmonella disease,S. Typhimurium or S. Enteritidis. However, assessment of the immunogenicity of the iNTS-GMMA vaccine in an immunologically naïve cohort in SALVO will inform the decision to progress to a second larger phase I trial in a sub-Saharan African adult population where iNTS is endemic. This second study may provide insight into the vaccine-induced immunogenicity following pre-existing immunity in adults. Future studies will recruit African infants, in whom any future licensed vaccine is likely to be deployed. However without a known immune correlate of protection, the efficacy of the vaccine in prevention of iNTS disease cannot be determined without much larger field studies in endemic settings.iNTS disproportionately causes severe disease in children under the age of 5 years in sub-Saharan Africa, and an effective vaccine will be of greatest benefit in this vulnerable population. As the UK is a country with a low burden of non-typhoid Salmonella serovars capable of eliciting pan-protective immune responses. As a first-in-human phase I trial, the study design prioritises the safety of the participants. The dose escalation design allows investigation of the safety of the vaccine in a small cohort and at a significantly lower dose prior to escalation to the full dose vaccine. Multiple DSMC and internal safety reviews at set intervals provide further mandatory time points to formally review the safety data in addition to real-time monitoring by the study team. This is a participant-observer blind, randomised, placebo-controlled study. Blinding of the trial aims to reduce both recall and observational bias, which is intended to allow confidence in the study’s final outcome. The use of a placebo-controlled study design allows a direct comparison between active vaccine and placebo groups aiming to account for potentially confounding factors, such as the incidence of SARS-CoV-2 infection during the study.As GMMAs originate from the bacterial outer membrane, they contain both the immunodominant OAg and multiple membrane proteins in their native conformation.S. enterica serovars Typhimurium, Enteritidis and Typhi,S. Typhimurium was trialled in participants in 2009 but has not progressed further in the intervening years.SALVO will be the first clinical trial of the iNTS-GMMA vaccine. Currently, the only other iNTS-based vaccines to enter clinical trials are two trivalent vaccines covering The study protocol was prepared in accordance with the SPIRIT 2013 Checklist.


COVID-19 could develop severe respiratory symptoms in certain infected patients, especially in the patients with immune disorders. Gut microbiome and plasma metabolome act important immunological modulators in the human body and could contribute to the immune responses impacting the progression of COVID-19. However, the causal relationship between specific intestinal bacteria, metabolites and severe COVID-19 remains not clear.Based on two-sample Mendelian randomization (MR) framework, the causal effects of 131 intestinal taxa and 452 plasma metabolites on severe COVID-19 were evaluated. Single nucleotide polymorphisms (SNPs) strongly associated with the abundance of intestinal taxa and the concentration of plasma metabolites had been utilized as the instrument variables to infer whether they were causal factors of severe COVID-19. In addition, mediation analysis was conducted to find the potential association between the taxon and metabolite, and further colocalization analysis had been performed to validate the causal relationships.Howardella and Ruminiclostridium 6 with severe COVID-19, respectively. Butyrivibrio and Ruminococcus gnavus could mediate the association of myo-inositol and N-acetylalanine, respectively. In addition, Ruminococcus torques abundance was colocalized with severe COVID-19 (PP.H4 = 0.77) and the colon expression of permeability related protein RASIP1 (PP.H4 = 0.95).MR analysis identified 13 taxa and 53 metabolites, which were significantly associated with severe COVID-19 as causal factors. Mediation analysis revealed 11 mediated relationships. Myo-inositol, 2-stearoylglycerophosphocholine, and alpha-glutamyltyrosine, potentially contributed to the association of Our study highlights the potential causal relationships between gut microbiome, plasma metabolome and severe COVID-19, which potentially serve as clinical biomarkers for risk stratification and prognostication and benefit the mechanism mechanistic investigation of severe COVID-19. Corona Virus Disease 2019 (COVID-19) as a global pandemic continues to spread rapidly across the world causing serious concerns. The individuals with COVID-19 infection could develop fevers, coughing, difficulty in breathing, pneumonia and even death. The prognosis of COVID-19 could be greatly improved with the prevention from the development of severe symptoms. The severity of symptoms varied among the patients which may be attributed to immunity, a combination of basic diseases, The angiotensin-converting enzyme 2 expression and genetic factors . NeverthFaecalibacterium prausnitzii, Clostridium butyricum is decreased, while the abundance of Enterobacter and Enterococcus is increased . Linkage disequilibrium analysis is then applied to select the representative SNPs which can present other SNPs in a high linkage disequilibrium region. These selected SNPs are used as the final instrumental variables (IVs). Based on the IVs, the outcome population is stratified into two groups: the exposure group consisting of individuals who carry the risk alleles of exposures, and the control group consisting of individuals who do not carry the risk alleles. Finally, the differences of outcomes between the two groups are evaluated to infer the causal effects of exposures on outcomes. This causal inference process achieves an effect like that of Randomized Controlled Trials (RCT) and provides a higher level of clinical evidence than correlation analysis  analysis is currently the most commonly used causal inference method in the clinical research frequently using genome-wide association studies (GWAS) summary statistics data. It incorporates germline variants and infers causality based on the principle of random allocation of alleles transferred from parent to offspring at the time of gamete formation, which is less likely to be affected by confounding factors . In shoranalysis .Here, we applied MR analysis to examine the relationship between the gut microbiome, plasma metabolome and severe COVID-19. We found 13 taxa and 53 metabolites act as the causing factors associating with the severity of COVID-19. Moreover, 11 mediated relations among them had been identified. This is the first study to systematically assess the causal relationship between intestinal microbiome, plasma metabolome and the development of severe COVID-19.https://mibiogen.gcc.rug.nl/) and the plasma metabolome GWAS dataset (IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/). The gut microbiome abundance GWAS dataset investigated the relationship between whole genome SNPs and the abundance of 211 intestinal taxa in 18473 participants from 24 cohorts in European and American countries (https://www.ebi.ac.uk/gwas/).This study defined the abundance of intestinal bacteria and the concentration of plasma metabolites as the exposure factors, and severe COVID-19 as the outcome. The genetic association results of the exposure factors were derived from two datasets: the gut microbiome abundance GWAS dataset  were set to screen IVs to obtain more information.The association p values in each GWAS dataset were used to screen the IVs for each exposure. Due to set different p values as cutoff values would screen different number of IV and then led to different MR results. In our study, four p value levels  method was employed if the IV number was more than 1, while cis-MR analysis was performed and the Wald ratio model was used if the IV number was equal to 1. For polygenic MR analysis, Cochran Q test and MR-Egger’s intercept were also conducted to investigate the heterogeneity and pleiotropy of the selected IVs. Only the intestinal bacteria and plasma metabolites with MR p<0.05 and without heterogeneity and pleiotropy  were included in follow-up analysis.via mediator, and Mediation analysis is a statistical method that aims to investigate a variable whether mediates the relationship between two other variables. Through mediation MR analysis, we can construct a pathway from exposure factors to outcomes through a mediating factor, which can help elucidate the potential mechanism of exposure factors affecting outcomes. For example, if the abundance of a taxon and the concentration of a metabolite are both causally associated  with severe COVID-19, and the abundance of the taxon is also causally associated  with the concentration of the metabolite, a triangular relationship is formed. In this relationship, the taxon is the exposure factor, the metabolite is the mediator, and severe COVID-19 is the outcome. To identify the potential mediated relations, bi-directional mediation MR analysis was conducted to identify the potential relationship among the taxa and metabolites that causally associated with severe COVID-19. Firstly, univariate MR analysis was conducted by setting the abundance of selected taxa as the exposures and the concentration of selected metabolites as the outcomes. Then, the analysis was conducted in reverse by setting the concentration of selected metabolites as the exposures and the abundance of selected taxa as the outcomes. To calculate the proportion of mediation effect of the mediator, the following formula was utilized: ×βMOβEO , 24. WheSampleMR .https://www.metaboanalyst.ca/) was utilized to conduct the enrichment analysis of metabolites. For the most significant exposure in the cis-MR, further colocalization analysis and eQTL analysis were performed. GTEX database  was used to perform eQTL analysis, and the genes whose expression could be regulated by the IV of candidate intestinal bacteria in colon tissue were screened (http://locuszoom.sph.umich.edu/) was used for regional association plotting, and the flanking size was set as 50kb  of N-acetylalanine  and 2-stearoylglycerophosphocholine (multivariate MR p = 0.051) were more import than the others . Using GTEX database, we performed the eQTL analysis and identified rs35866622 (the IV of Ruminococcus torques) as a eQTL of RASIP1, NTN5, MAMSTR, SEC1P, IZUMO1, FAM83E, SPHK2 and FUT2. Colocalization analysis revealed that the abundance of Ruminococcus torques was highly colocalized with severe COVID-19  and 53 metabolites  had a potential causal relationship with severe COVID-19. Pathway analysis of the 53 identified metabolites suggested that they were significantly enriched in pathways of ascorbate and aldarate metabolism, beta oxidation of very long chain fatty acids and oxidation of branched chain fatty acids. Mediation analysis among the identified exposures found that the associations of Howardella, Ruminiclostridium 6, myo-inositol and N-acetylalanine with severe COVID-19 were likely to be mediated by one or more exposures. After multiple testing correction of cis-MR results, only Ruminococcus torques had a trend of positive causal relationship with the severe COVID-19. The increased abundance of Ruminococcus torques could be a contributor to the incidence of severe respiratory symptoms in COVID-19 patients. The results of the colocalization analysis revealed that the abundance of Ruminococcus torques and the expression of RASIP1 in colon tissue shared a causal factor and had a high colocalization probability with the occurrence of severe respiratory symptoms, implying that they both played important roles in the development of severe COVID-19.In this study, the causal relationships of gut microbiome and plasma metabolome for the severity of COVID-19 were investigated using MR analysis. We found that 13 taxa  of N-acetylalanine on severe COVID-19.A recent study indicates that gut microbiome of patients with post-acute COVID-19 syndrome are characterized by higher levels of  gnavus , which h gnavus . We alsoRuminiclostridium 6 was previously found to have a strong positive correlation with the levels of ghrelin  is a vascular-specific GTPase signaling regulator involved in a variety of functions, including the Rho signal transmission pathway. RASIP1 regulates the stability of vascular endothelial connections, which is relevant to vascular barrier function, and mediates the regulation of Rho in intrinsic barrier function through Rap1 , 42. RASFrom the 53 metabolites found to increase the risk of severe COVID-19, we pinpointed the key pathways including ascorbate and aldarate metabolism, beta oxidation of very long chain fatty acids and oxidation of branched chain fatty acids. In these signals, vitamins  have been reported responding to the risk of COVID-19 and its severity. Vitamin C is a potential antiviral agent and may improve immunity. Supplementation with high-dose vitamin C could increase the survival rates of patients with severe COVID-19 by decreasing inflammation and pathogen infectivity and viral yield, improving immune response, alleviating tissue and organ damage . NumerouIt is also worth noting that 5 metabolites  in the carnitine metabolism pathway were identified to be causal associated with severe COVID-19. Consistent with our findings, a UPLC-MS/MS-based widely targeted metabolomics study also revealed several carnitine family members are significantly reduced in severe COVID-19 patients versus healthy controls subjects and mild COVID-19 patients . CarnitiIn this study, we set 131 taxa and 452 metabolites as exposure factors, severe COVID-19 as the outcome variable, and then grouped the COVID-19 cohort according to the IVs selected for each exposure factor. Then, by analyzing the differences in the risk of severe COVID-19 between the exposed and control groups, we could infer the causal relationship between exposure factors and severe COVID-19. Our study showed that MR analysis could sort out some intestinal bacteria and metabolites that have a potential relationship with COVID-19 severity. In addition, by combining two-stage MR analysis and mediation analysis, we successfully linked gut microbiome and plasma metabolome, and constructed some pathways from intestinal bacteria to severe COVID-19 through plasma metabolites or from plasma metabolites to severe COVID-19 through intestinal bacteria. Our study provides potential biomarkers associated with severe COVID-19 and can benefit the mechanistic investigation of severe COVID-19.This study has some limitations. Firstly, our casual inference results of intestinal bacteria might be affected by confounding factors such as race, diet, and disease status. Additionally, no experiments have been conducted to validate the causal associations of this study. Therefore, mechanistic studies will be necessary in the future to verify these relationships.In conclusion, our comprehensive MR analyses identified 13 human intestinal taxa and 53 human serum metabolites potential causal associated with COVID-19 severity. We also found 11 mediated relations among the identified intestinal taxa and serum metabolites. These causal taxa and metabolites potentially served as clinical biomarkers for risk stratification and prognostication of severe COVID-19 and would benefit the mechanism mechanistic investigation of severe COVID-19.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The study was approved by the ethics committee of Institute of Clinical Pharmacology, Central South University.XL, LZ and J-QL participated in research design. HY, LZ, J-QL, SZ and WZ participated in the writing of the paper. H-XH, PX, X-HC and Y-DQ participated in the performance of the research. XL and HY prepared the figures. All authors contributed to the article and approved the submitted version.


COVID-19 was declared a pandemic in March 2020, and since then, it has not stopped spreading like wildfire in almost every corner of the world, despite the many efforts made to stem its spread. SARS-CoV-2 has one of the biggest genomes among RNA viruses and presents unique characteristics that differentiate it from other coronaviruses, making it even more challenging to find a cure or vaccine that is efficient enough. This work aims, using RNA sequencing (RNA-Seq) data, to evaluate whether the expression of specific human genes in the host can vary in different grades of disease severity and to determine the molecular origins of the differences in response to SARS-CoV-2 infection in different patients. In addition to quantifying gene expression, data coming from RNA-Seq allow for the discovery of new transcripts, the identification of alternative splicing events, the detection of allele-specific expression, and the detection of post-transcriptional alterations. For this reason, we performed differential expression analysis on different expression profiles of COVID-19 patients, using RNA-Seq data coming from NCBI public repository, and we obtained the lists of all differentially expressed genes (DEGs) emerging from 7 experimental conditions. We performed a Gene Set Enrichment Analysis (GSEA) on these genes to find possible correlations between DEGs and known disease phenotypes. We mainly focused on DEGs coming out from the analysis of the contrasts involving severe conditions to infer any possible relation between a worsening of the clinical picture and an over-representation of specific genes. Based on the obtained results, this study indicates a small group of genes that result up-regulated in the severe form of the disease. EXOSC5, MESD, REXO2, and TRMT2A genes are not differentially expressed or not present in the other conditions, being for that reason, good biomarkers candidates for the severe form of COVID-19 disease. The use of specific over-expressed genes, whether up-regulated or down-regulated, which have an individual role in each different condition of COVID-19 as a biomarker, can assist in early diagnosis.  Coronaviruses have one of the biggest genomes of any RNA virus, measuring between 27 and 32 kb. The membrane and envelope proteins are required for virus assembly, while the spike protein is required for virus entrance and recognition by the host cell (3). The major process through which the virus enters host cells is receptor-mediated endocytosis. SARS-CoV-2 infects lung AT2 alveolar respiratory tract epithelial cells using angiotensin-converting enzyme 2 (ACE2), a cell-surface receptor found in the kidney, blood vessels, and heart SARS-CoV-2 is the eighth coronavirus to infect humans, and it can cause a severe acute respiratory syndrome, especially in elderly people and people with comorbidities and immunocompromised states The different symptoms and, therefore, the different conditions of subjects with COVID-19 may be related to the activation of some genes rather than others, making the genes themselves potential biomarkers for the severity of the disease. For this reason, we decided to investigate COVID-19 disease from a genetic point of view, using RNA sequencing data from a public repository. Using Bioconductor/R programming language, we performed differential expression analysis on three datasets coming from the NCBI GEO dataset repository. As a result, we obtained the complete set of differentially expressed genes (DEGs) coming from different contrasts specifically designed to probe the changes in expression levels of all genes involved in the progression of COVID-19 through the various stages of the disease. To go deeper into genetic mechanisms beyond COVID-19 evolution, we also performed Gene Set Enrichment Analysis (GSEA) on the DEGs list produced by differential expression analysis to identify all those biological processes and pathways in which these genes are enriched.Since the onset of COVID-19 disease spread, scientists have put efforts into designing, discovering, and finding new drugs and compounds that are effective against the disease, as well as new methods able to define biomarkers involved in the pathogenesis of SARS-CoV-2 infection. Different methodologies concerning Artificial Intelligence (AI) and bioinformatic tools have been applied in the study of COVID-19 disease to screen approved pharmaceuticals against SARS-CoV2 proteins, thus finding medications for repurposing and determining whether the identified compounds may also influence human proteins whose expression in the lung changes after SARS-CoV2 infection RNA-sequencing (RNA-Seq) methodology has also been widely used in the COVID-19 disease field Researchers are developing drugs for COVID-19 treatment using various strategies. Among them, drug repurposing has successfully treated SARS-CoV-2 infection and bioinformatic methods and approaches have been demonstrated to be powerful tools in this context. Specifically, computational molecular modeling techniques have been employed to assess the potential of selected nucleoside analogs as prospective therapeutics against one of the Omicron subvariant According to the central dogma of molecular biology, which Crick first stated to explain the flow of genetic information in a biological system, all the information of a cell is contained in genes as DNA, then transcribed into RNA, and then translated into proteins The immune system's ability to fight against foreign pathogens depends on the strict control of immune gene expression. The control of RNA is a crucial stage in gene expression, and RNAs can be divided into two groups: those that code for proteins and those that do not, also called non-coding RNAs (ncRNAs) In SARS-CoV-2 infection, several studies have shown that ncRNAs and miRNAs may impact virus replication and the host immune response One of the most critical steps in characterizing transcriptomic data is the identification of biomarkers within a set of differentially expressed genes from differential expression analysis conducted on RNA-Seq data The goal of this work is the identification of differentially expressed genes in subjects with different conditions of COVID-19 disease in order to understand the role of the genes themselves as well as to identify some possible biomarkers for each disease condition. In particular, we focused on genes that were up-regulated in patients with severe COVID-19 conditions, in comparison to other groups. Moreover, we focused on the analysis of the most overexpressed genes, both up- and down-regulated, in the asymptomatic and severe COVID-19 conditions, intending to understand how the differential expression of certain genes may affect the different severity degrees of the symptoms.21.a.Experimental design b.Sequencing designc.Quality control Preliminary analysis, including2.a.Transcriptome profilingb.Differential gene expressionc.Functional profilingCore analysis, including:3.a.Visualizationb.Data integrationc.Results interpretationAdvanced analysis, including:The RNA-sequencing analysis aims at the identification of transcripts and the quantification of gene expression for each specific organism under examination and finds large application within life science research, far beyond the field of genomics alone http://www.bioconductor.org/) offers a large variety of tools specifically designed for the analysis of data coming from RNA-sequencing. Each of these tools is applied to one specific step of the general RNA-seq analysis workflow, which usually includes a preliminary data quality control phase followed by the differential expression analysis and gene set testing R programming language . We selected three datasets related to SARS-CoV-2, GSE166424, GSE178824 and GSE179448, for a total of 124 samples coming from GEO datasets and a total of 35,413 genes across all samples. We used “SARS-CoV-2″, “Human” and “RNA-Seq” as keywords to perform the search on the GEO database.Gene expression profile data have been downloaded from NCBI Gene Expression Omnibus (GEO) website . Asymptomatic patients with COVID-19 in this cohort study were primarily youth from Bangladesh, the Philippines and Burma. Dataset GSE178824 includes RNA samples collected from COVID-19 patients with different levels of severity and healthy controls. This study included 24 severe, 5 asymptomatic, 26 convalescent COVID-19 patients and 15 healthy controls. All patients tested positive for SARS-CoV-2 by PCR. Healthy controls were negative either by PCR or for antibodies to SARS-Cov-2. All severe patients were hospitalized in the intensive care unit for treatment because of hypoxia and respiratory distress or complications from pre-existing comorbidities. Among them, ten patients who died from COVID-19 complications underwent autopsy and samples were collected at the LSU Health Science Center Pathology Department. Finally, dataset GSE179448 includes samples coming from 84 COVID-19 patients and healthy control. In particular, COVID-19 patients (n = 57) were classified into: mild (outpatients); severe , more than half of whom were in intensive care (ICU), mostly sampled during the cytokine storm period, and recovered, virus-negative convalescents. We chose these three datasets for some specific reasons: first, they showed characteristics that best suited the type of study we were conducting, unlike many others that we had to discard. Moreover, the number of samples thus obtained was considered sufficient also from the point of view of statistical significance, so there was no need to add more data.KEGG and GO  hallmarks have been downloaded from the Molecular Signature Database  and then used to run Gene Set Enrichment Analysis (GSEA) R script.2.2ggplot combined with DESeq2 and EdgeR R packages. R (https://www.r-project.org) is a programming language specific for statistical computing and graphics. It is totally free and runs on different UNIX, Windows and MacOS environments. R allows the user to download and install different packages, specifically designed for particular tasks, from CRAN (https://cran.r-project.org) and Bioconductor (https://www.bioconductor.org) repositories.Exploratory analysis is a fundamental, preliminary step in data analysis aimed at investigating the quality of data under examination and giving a general overview of their main features even before starting the actual analysis. During exploratory analysis, data are modeled, transformed, and prepared for the next steps of analysis, and the results of this initial stage are usually shown in a visual and intuitive way by plotting various explanatory graphs. To perform exploratory analysis, we used 2.31."Control_Healthy" vs "Recovered"2."Control_Healthy" vs "Asymptomatic”3."Control_Healthy" vs "Mild"4."Control_Healthy" vs "Moderate"5."Control_Healthy" vs "Moderate to severe"6."Control_Healthy" vs "Severe"7."Asymptomatic " vs "Severe"Differential expression analysis is a process consisting in performing a series of statistical analyses on normalized read counts data in order to highlight any quantitative changes in expression value levels between different experimental conditions. In this work, we focused on the overexpressed genes coming out from the following comparisons between experimental groups:DESeq2 R package (http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html.). Among all the specific tools for the analysis of differential expression of RNA-Seq data, DESeq2 has been selected for some of the characteristics that make it particularly suitable for the purposes of this work. DESeq2, indeed, provides a holistic solution for gene-based analysis of RNA-seq data: the use of shrinkage estimators significantly improves the stability and reproducibility of the analysis results, avoiding the problems related to the comparison of fold changes in RNA-Seq experiments. In addition, the rlog transformation, used for the implementation of shrinkage on fold changes, improves the visualization of expression levels, for example in heatmaps, and facilitates the application of other techniques such as Principal Component Analysis and clustering. The typical approach in searching for overexpressed genes among different experimental conditions consists in testing the null hypothesis that the logarithmic fold change (LFC) for a certain gene in one of the aforementioned comparisons is exactly zero: this means that the expression level of that gene is not affected by the status change, for example from healthy to severe form of the disease. One of the outputs of differential expression analysis is a list of overexpressed genes ranked by p value, representing the most statistically significant genes out of the input gene set. DESeq2 package applies generalized linear models (GLM) on each gene to perform differential expression analysis To perform differential expression analysis, we used •link function describes how the mean, The •variance function describing how the variance, The Along with two functions:The dispersion parameter DESeq2 approach starts by creating a count matrix DESeq2 works on the raw counts matrix, using normalization factors to limit the differences in library depth, and estimates the gene-wise dispersion to fit the negative binomial model. The normalization method used is DESeq2′s median of ratios: basically, all counts are divided by sample-specific size factors determined by the median ratio of gene counts relative to geometric mean per gene; the factors taken into account are sequencing depth and RNA composition. This kind of normalization is commonly used for gene count comparisons between samples and for differential expression analysis DESeq2 differential expression analysis produces a list of all differentially expressed genes along with log2 fold changes, p values and adjusted p values, which we will perform ontologies and gene set enrichment analysis upon.Finally, Wald or Likelihood Ratio tests are performed to test the null hypothesis. Wald test  is used to check the significance of certain parameters in terms of contributions to the model: if their values are zero, they can be removed from the model. Otherwise, they need to be included. A likelihood ratio test is performed to choose between two models by comparing their log likelihoods: if this difference is statistically significant, then the less restrictive model (the one with more variables) is chosen as the one which fits the data significantly better than, the more restrictive model. In this study, we decided to apply the Wald test to our data because, with equal statistical power, the Wald test is more suitable for the purposes of this work: the Likelihood Ratio test is usually preferred in the analysis of time course experiments and its high computational cost makes it impractical to apply. The 2.4https://www.gsea-msigdb.org/gsea/index.jsp). One of the main tools used to perform GSEA is Gene Ontologies (GO): basically, GO is just like a structured vocabulary of biological terms, called GO terms, indeed. GO terms are subdivided into three macro-categories: Molecular Function (MF), Biological Process (BP), and Cellular Component (CC), and each of these terms are connected to a specific gene or protein through annotations based on literature. We used the DOSE R package DOSE produces a large set of highly customizable figures, which make the results of the analysis even more readable, and which can be found in clusterProfiler R package (https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html) was used to visualize enrichment results through a series of plots.Gene Set Enrichment Analysis (GSEA) is an analytical method aimed at highlighting groups of genes that result over-represented between two biological conditions within a larger set of genes and inferring possible associations between those genes and different disease phenotypes  with specific properties, for example it must be even, non-negative and real-valued One of the most important preliminary steps in an RNA-Seq analysis is to assess the overall similarity between samples, expressed as the distance between each pair of samples calculated over the sample distance matrix. As a correlation paradigm, we choose Spearman correlation, which is a non-parametric algorithm that compares two ranked variables of either ordinal, interval, or ratio type and describes how well they can be described using a monotonic function. Unlikely parametric RNA-Seq can allow monitoring of the expression levels of a particular gene algorithm, no further assumptions on the data set are necessary to proceed with the similarity assessment. Spearman correlation evaluates monotonic relationships (linear or non-linear): perfect Spearman correlation of + 1 or − 1 only occurs when each of the variables is a perfect monotone function of the other, as shown in Another way to examine the relationship among samples is using hierarchical clustering combined with heatmap, as shown in pvclust R package In order to assess the consistency of the obtained clusters, we decided to apply bootstrapping on them by using the Principal components analysis (PCA) is another method to visualize sample-to-sample distances. As shown in Before proceeding with the differential expression analysis, it is usually necessary filtering out all those genes showing very low counts across all libraries because they would not provide enough evidence for differential expression and could interfere with some of the statistical approximations used in the pipeline. They also add to the burden of multiple tests when estimating false discovery rates, decreasing the ability to detect differentially expressed genes. In this study, we choose to keep genes only if they are expressed at a counts-per-million (CPM) above 0.5 in at least two samples. A general rule to choose a good threshold could be identifying the CPM that corresponds to a count of 10, which in this case is about 0.5. After removing all lowly expressed genes, it is possible to look at the quality of data plotting the library size as shown in 3.2Variance Modelling at the Observational level (VOOM) method was used to estimate the mean-variance relationship to understand how the variability of RNA-Seq read counts depends on the size of the counts 1."Control_Healthy" vs "Recovered"2."Control_Healthy" vs "Asymptomatic"3."Control_Healthy" vs "Mild"4."Control_Healthy" vs "Moderate"5."Control_Healthy" vs "Moderate to severe"6."Control_Healthy" vs "Severe"7."Asymptomatic " vs "Severe"The most important part of the entire workflow is the identification of all those genes showing any difference in their expression values in a particular experimental condition. This difference is assessed both in terms of fold-change (FC) and statistical significance . In this study, we focused on the following groups:, along with the complete series of plots produced at the various stages of the analysis. Tables 1–7 •ENSG00000117899 (MESD)•ENSG00000076043 (REXO2)•ENSG00000099899 (TRMT2A)Volcano plots are a very good method to give an overview of the expression level of all genes for a specific condition in a clear and intuitive way. We decided to focus on the differentially expressed genes for “Asymptomatic” and “Severe” conditions in the next steps of the analysis. Both these conditions represent two key points in the fight against COVID-19: asymptomatic patients represent the main vector of the spread of the virus because they are difficult to identify and, as a result, are not treated properly and in time, allowing the virus to circulate without almost any control. Severe patients, on the other hand, are those most at risk, are almost always hospitalized and, unfortunately, tend to have often permanent and serious consequences, even in cases where therapy is successful. In both cases, early diagnosis is decisive in terms of both control of the spread and treatment of the disease, and it is for this reason that this study has focused mainly on the analysis of differentially expressed genes for these two particular conditions.3.3DOSE R package (https://www.rdocumentation.org/packages/DOSE/versions/2.10.6) was used with the clusterProfiler R package (https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html) to perform enrichment analysis in order to discover disease associations of high-throughput biological data. Both these packages produce different plots to show the results of the analysis. TNF-α and other inflammatory cytokines that were increased during the SARS-CoV-2 infection, thus making p38 MAPK a potential pharmacological target for COVID-19 Over-representation analysis (ORA) has been performed to determine whether genes from pre-defined gene sets, for example, those genes belonging to specific GO terms or pathway, are presently more than would be expected (over-represented) in the subset of data under examination. The results of this analysis can be visualized both as a dotplot, as shown in 4COVID-19 has a different range of clinical manifestations, on the basis of which COVID-19 patients are categorized as asymptomatic, mild, moderate, and severe. According to some data, 81 % of patients with COVID-19 had a mild or moderate condition, 14 % had severe disease, and 5 % had critical illness Researchers have been exploring different compounds and approaches for the treatment of COVID-19. One of the beneficial approaches in addition to an in vitro and in vivo investigation, is a molecular computational investigation, the in silico molecular docking. Using this approach, polyphenolics and 1,3,4-oxadiazoles have been identified as potent bioactive compounds with antiviral properties. Specifically, Taroxaz-104 shows highly potent inhibitory interactions with nCoV-RdRp-RNA, making it a promising candidate for targeting one of the potential active sites of nCoV-RdRp In our research, we selected three gene expression profiles  related to SARS-CoV-2 from NCBI GEO datasets. We focused on genes that were up-regulated in patients with severe COVID-19 condition, in comparison to other groups. We identified four up-regulated genes in the severe condition: TRMT2A, EXOSC5, REXO2, and MESD. The same four genes were noticed to be non-differentially expressed in the asymptomatic condition, and only one of these, TRMT2A, was up-regulated in the moderate one. On the other hand, these four genes are not present in the moderate to severe, mild, and recovered conditions. TRMT2A gene encodes a protein characterized as a biomarker of increased risk of recurrence in HER2 + breast cancer patients Moreover, we focused on the most overexpressed genes in severe COVID-19 patients. Our results have shown that POLR2A gene is overexpressed and up-regulated in patients with severe COVID-19 condition. This gene encodes a protein which has a crucial interaction with the virus polymerase, thus having a key role in the viral replication and in its transcriptional activity APOBEC3A  is one of the most down-regulated genes in patients with severe COVID-19 condition. Generally, the APOBEC3 (A3) family has a key role in the immune defence mechanism against viral infections by producing deadly mutations in viral genomic DNA or RNA in infected cells. In particular, A3A is significantly induced after INF-gamma stimulation HLA-DQA1 gene, which encodes the HLA class II histocompatibility antigen, DQ alpha 1 chain, appears also to be down-regulated in severe COVID-19 patients. Several studies have investigated the role of human leukocyte antigen (HLA) genes in COVID-19 disease and their association with the severity of the disease We also investigated the most overexpressed genes in the asymptomatic condition, focusing on the most up-regulated gene, as well as the most down-regulated one. TNF gene is the most up-regulated gene in the asymptomatic condition, and it is one of the genes that encode the tumour necrosis factor (TNF). TNF acts both as an adipokine and a cytokine; in the latter case, it is released by macrophages, as part of an inflammatory response, to alert the other immune cells. In our results, such as most of the literature data suggest, it is shown to be up-regulated also in the severe condition, but its p-value is dramatically lower in the asymptomatic condition compared to the severe one. This means that, in our study, this gene is more up-regulated in patients with asymptomatic COVID-19 disease, than in patients with a severe condition. Generally, an increased tumour necrosis factor α (TNF-α) characterizes a severity of the disease The most down-regulated gene in the asymptomatic condition is the KIR2DS4 gene, which encodes the human killer cell immunoglobulin-like receptor 2DS4 protein. Killer cell immunoglobulin-like receptors (KIRs) are expressed by natural killer cells (NKcs) and specific T cell subsets. In the work of Bernal et al. The pathogenesis of SARS-COV-2 infection may vary in different patients, and the genetic component of the subject certainly plays an important role. Finding genes that could be involved in the different grades of severity of the COVID-19 disease can be a useful tool in clinical practice. Being able to understand which genes are mostly involved in a severe condition of the disease could be fundamental in predicting the most harmful symptoms and to act promptly on the patient in this context.In a similar investigation, genome association studies using RNA-Seq data were conducted to identify highly expressed gene biomarkers for COVID-19 and develop a machine learning model for analysis. Through a combination of bioinformatics strategies, statistical techniques, and machine learning algorithms, potential biomarkers were extracted and analyzed, identifying 67 genes as promising biomarkers comprising 49 up-regulated and 18 down-regulated genes. Among the classifiers scrutinized, the Support Vector Machine (SVM) exhibited the highest accuracy among the classifiers tested, highlighting its potential for predicting COVID-19 based on the identified differentially expressed genes Understanding the biology and functional significance of the four obtained genes  provides insights into the molecular processes associated with disease severity in COVID-19. Integrating these potential biomarkers into clinical practice may hold several potential implications. Firstly, they can aid in risk stratification and early identification of patients at higher risk of developing severe symptoms. This knowledge can guide allocation of healthcare resources and intensify monitoring or treatment interventions for high-risk individuals. Secondly, these candidate biomarkers can be incorporated into diagnostic tests to improve accuracy in predicting disease outcomes. Thirdly, targeted therapies can be developed to modulate the pathways associated with these biomarkers, aiming to mitigate disease severity and improve patient outcomes. However, further validation studies are essential to establish the robustness and generalizability of these potential biomarker genes across diverse populations. Additionally, exploring their utility in combination with other established biomarkers or clinical indicators may enhance predictive accuracy and clinical decision-making.5The pathophysiology and severity of COVID-19 differ from patient to patient and depend partly on underlying chronic disease and risk factors. Our research presents specific hypotheses that connect host genetic diversity to symptom profiles and laboratory characteristics. RNA-Seq data analysis for transcript identification and the quantification of gene expression represents a powerful tool that finds a wide variety of applications; in this study, performing differential expression analysis on RNA-Seq data allowed us to detect and highlight all those genes involved in COVID-19 progression whose expression level may vary changing from a specific grade of disease severity to another, helping us to go deeper into the genetic causes of COVID-19. We specifically designed and analyzed 7 different contrasts to investigate all possible biological conditions related to specific disease states and infer consistent conclusions about the expression level of particular groups of genes in different stages of the disease. Our analysis produced a list of differentially expressed genes (DEGs) for each condition, upon which we also performed a Gene Set Enrichment Analysis (GSEA), to highlight any possible correlation between genes in our lists and different disease phenotypes in which our genes resulted enriched. The identification of genes that turn out to be overexpressed in a specific grade of disease severity, could help to think about them as potential biomarkers for that grade of disease severity.In particular, we focused on severe and asymptomatic conditions and performed an accurate literature search on some of the most up- and down-regulated genes in the correspondent DEGs list, which we found particularly interesting. We identified four genes being up-regulated in the severe condition and not-differentially expressed in the asymptomatic condition: TRMT2A, EXOSC5, REXO2, and MESD. Moreover, we found out that POLR2A, a gene playing a crucial role in virus polymerase, is up-regulated in severe conditions. Finally, we identified APOBEC3A as the most down-regulated gene in asymptomatic conditions: APOBEC3A family is strongly involved in immune defense mechanisms against viral infection.For future study, a more detailed investigation of the identified genes may provide a context to introduce new human gene biomarkers for different conditions of COVID-19 disease for rapid identification and treatment. In particular, having regard to the connection of many of the identified genes with other diseases, such as various types of cancer, a possible application of the obtained results could be a thorough analysis of the effects of COVID-19 infection on patients already suffering from particular types of cancer, or conversely, how the progression of a tumor can change on COVID-19 patients. Moreover, these differentially expressed genes can be effectively used in profiling digital patients for their usage in modelling and simulation platforms dealing with COVID-19. In this respect, among the potential future developments for this study, we envisage the combined use of the Universal Immune System Simulator for SARS-CoV-2 (UISS-COVID-19 for short). UISS-COVID-19 is an in silico trial platform able to simulate and predict the intricate human immune system dynamics in response to SARS-CoV-2 insult Undoubtedly, the results obtained through this in silico pipeline would require further confirmation studies to assess the credibility of the selected potential biomarkers; however, the in silico predictions could be used as input for the designing and refinement of new clinical trials.DESeq2 R package has been recognized as one of the most reliable and accredited tools among all those tested.The RNA-Seq methodology, while widely used in research, is still relatively young and continuously evolving. The ease in the application and interpretation of the results are certainly one of its strengths and among the main reasons why it is so widespread. RNA-Seq computational pipelines are many and all make use of different computer tools. For this reason, there is still no universally recognized golden standard that would increase the credibility and reliability of such techniques, as well as help less experienced users. The absence of a standard imposes a great effort on the scientific community: who is going to use for the first time an RNA-Seq pipeline has to choose between thousands of different tools, not always properly explained, and the application of the various stages that constitute the pipeline, some of which are absolutely necessary, is not immediately understood. Many studies, such as DESeq2 R package, leads to the identification of potential biomarkers through a long series of steps that we have tried to explain in as much detail as possible. We trust that, beyond the potential discovery of the candidate biomarkers presented, which represent the main objective of this work, it can also be of help to those who approach this type of analysis for the first time.In this work we presented a complete RNA-Seq pipeline which, starting from publicly available datasets and using specific tools such as Valentina Di Salvatore: Writing – original draft, Software, Methodology, Writing – review & editing. Avisa Maleki: Data curation, Writing – review & editing. Elena Crispino: Data curation, Writing – review & editing. Giulia Nicotra: Writing – review & editing. Giulia Russo: Writing – review & editing, Formal analysis. Francesco Pappalardo: Conceptualization, Supervision, Writing – original draft.The authors declare no conflict of interest.


Tinnitus is a neuropathological phenomenon caused by the recognition of external sound that does not actually exist. Existing diagnostic methods for tinnitus are rather subjective and complicated medical examination procedures. The present study aimed to diagnose tinnitus using deep learning analysis of electroencephalographic (EEG) signals while patients performed auditory cognitive tasks. We found that, during an active oddball task, patients with tinnitus could be identified with an area under the curve of 0.886 through a deep learning model (EEGNet) using EEG signals. Furthermore, using broadband (0.5 to 50 Hz) EEG signals, an analysis of the EEGNet convolutional kernel feature maps revealed that alpha activity might play a crucial role in identifying patients with tinnitus. A subsequent time-frequency analysis of the EEG signals indicated that the tinnitus group had significantly reduced pre-stimulus alpha activity compared with the healthy group. These differences were observed in both the active and passive oddball tasks. Only the target stimuli during the active oddball task yielded significantly higher evoked theta activity in the healthy group compared with the tinnitus group. Our findings suggest that task-relevant EEG features can be considered as a neural signature of tinnitus symptoms and support the feasibility of EEG-based deep-learning approach for the diagnosis of tinnitus. Tinnitus is the illusory perception of sound in the absence of an external sound . People Due to many possible causes, such as abnormality in top-down or bottom-up processes, and different symptoms, such as hearing loss or noise trauma, there is currently no universally effective clinical method for tinnitus diagnosis . At presNeuroimaging techniques are widely applied to monitor neural activity and diagnose different brain disorders. Electroencephalography (EEG) has emerged as one of the most practical techniques since it gives an insight into the temporal neuro-dynamics, it has an excellent temporal resolution (milliseconds or better), good portability, and an inexpensive set-up cost in comparison to other neuroimaging techniques, such as magnetoencephalography (MEG) or functional magnetic resonance imaging (fMRI) . Many reHere, to investigate whether patients with tinnitus can be identified using top-down or bottom-up EEG features, we used an active oddball paradigm (as a top-down directed task) in comparison to a passive oddball paradigm (as a bottom-up directed task). Task-relevant modulations may be reflected in event-related oscillations and provide the essential electrophysiological features for identifying patients with tinnitus. Thus, in the present study, we extracted top-down and bottom-up EEG cognitive signals and used them as discriminative features to identify patients with tinnitus using a novel deep-learning-based tinnitus-diagnostic tool.Recent studies used machine learning to reduce reliance on experts and mitigate the influence of personal factors in the tinnitus-diagnosis process . HoweverEleven patients with tinnitus  and 11 age-matched healthy volunteers  participated in the experiment. All patients had definite signs of chronic tinnitus, which lasted longer than 3 months but less than a year but had normal hearing otherwise. We assessed normal hearing with the following criteria: (i) the audiometric threshold was within 25 dB of the pure tone average at octave frequencies within 250–8,000 Hz; (ii) transient-evoked otoacoustic emissions (TEOAEs) were recorded in the external ear canal after stimulation with at least 5 dB signal-to-noise ratio (SNR), and distortion product otoacoustic emissions (DPOAEs) were recorded with at least 3 dB SNR; (iii) peak latencies for waves I-III were less than 2.4 ms, and for wave V were less than 6.2 ms on 90 dB normalized hearing level click-evoked auditory brainstem responses (ABR); and (iv) the tympanic membrane had a normal appearance on otoscope examination. Note, normal wave I-III latencies typically suggest intact peripheral auditory nerves , and norIn addition, we introduced the following exclusion criteria to match patients and healthy volunteers in cognitive abilities: (i) age older than 50 years; (ii) present or past diagnosis of vertigo, Meniere’s disease, noise exposure, hyperacusis, or psychiatric problems; (iii) exposure to ototoxic drugs; (iv) complex cases of tinnitus, for example, a failure of tinnitus pitch matching.To assess tinnitus severity, all patients completed a tinnitus questionnaire with a 0–10 scale  and a Korean translation of the Tinnitus Handicap Inventory of the American Tinnitus Association . The heaNote, the data in this study were previously collected and published by our group. Extended details on the participants, data acquisition, and audiometric and tinnitus tests are provided in our previous study .preattentive and automatic response to an auditory stimulus deviating from the preceding standard stimuli  in both ears of the participants. All participants performed the task in the same environment, and the acoustic intensities of all stimuli were set to 50 dB SPL (sound pressure level) using a sound level meter .The distance between the participant and the monitor was 80 cm, and visual stimuli were displayed within a visual angle of 6.5° to avoid image formation in the blind spot. The frequencies of the target stimuli in the oddball paradigm for the tinnitus group were their individual tinnitus-pitch-matched frequencies, and the target-stimulus frequency of the healthy group was 8 kHz, which was the most prominent as the individual tinnitus frequency in the tinnitus group. It has been reported that tinnitus patients respond sensitively to tinnitus sound stimuli . The staEEG signals were recorded using a BrainAmp DC amplifier  with a 32 Ag/AgCl-electrode actiCAP having a 10–10 electrode system placement. The ground was set as the AFz electrode, and the reference was set as an electrode on the tip of the nose. The impedances of the electrodes were lowered below 5 kΩ during electrode setup. EEG data were collected with a sampling frequency of 1 kHz and an analog band-pass filter of 0.5–70 Hz. An electrooculography (EOG) electrode was placed below the left eye to track eye movement artifacts. Vertical and horizontal electroocular signals were then estimated using the Fp1–EOG and F7–F8 electrode pairs, respectively. EOG artifacts were removed using an independent component analysis algorithm . The BraTwo dominant ERP components were analyzed: P300 and MMN. Depending on the regions of the brain in which the activity was most prominent , the following corresponding electrodes were chosen for analysis: for P300 (maximum peak 200–400 ms post-stimulus), four centro-parietal electrodes ; for MMN (minimum peak 100–300 ms post-stimulus), four frontal-central electrodes . All time windows were based on their grand averages while taking individual variations into account. Baseline corrections were conducted using the 500–0 ms pre-stimulus interval. The amplitudes and latencies of each peak were compared between healthy and tinnitus groups. To display the ERP components, an offline filter (0.5–30 Hz) was applied to the results.We used the Morlet wavelet transform to compute time-frequency responses . For theTo calculate the pre-stimulus total activity in the alpha band, we averaged signal power in a baseline window −400 to −100 ms prior to stimulus onset. To calculate the evoked theta activity, we measured the maximum theta power in the time window between 0 and 500 ms after stimulus onset. All time windows were selected based on their grand-averages. Baseline correction was performed on the evoked theta activity using the pre-stimulus interval −400 to −100 ms prior to stimulus onset. No baseline correction was applied to the total alpha activity since we were interested in studying effects related to the pre-stimulus  total alpha activity . Based oDeep learning has been tremendously successful, in large part because it enables the automatic learning of discriminative features from the data . RecentlFor training and evaluation of the EEGNet model to detect tinnitus patients, we used the time series of EEG data (30 electrodes) of both healthy and tinnitus groups . The arcWe trained the decoding model for up to 100 epochs, and the best model was finally selected based on the epoch with the minimum validation loss. To evaluate performance, the average of the area under the curve (AUC) in the receiver operating characteristic (ROC) curves, sensitivity, specificity, and accuracy were obtained through the 11-fold leave-one-pair-out cross-validation , in whicTo compare the EEGNet-based decoding performance against other classical machine-learning techniques, a support vector machine (SVM)  classifiTo investigate the contribution of each individual frequency band to model training, beyond assessing the classification performance of the EEGNet trained on band-limited data, we also performed a feature analysis of the convolutional layer filter of the EEGNet trained on broadband data . The conLast, to investigate the robustness and stability of the deep neural network model, we additionally assessed decoding performance using a smaller number of filters in the first convolutional layer (five instead of eight filters) of the EEGNet architecture. This result is shown in the U test was performed to compare the measures between the two groups  and to compare the AUCs between the two decoding methods of EEGNet and SVM. To statistically assess decoding performances, we evaluated whether the AUC was statistically significantly higher than the chance level using Wilcoxon signed-rank tests (Z scores). All analysis and statistical processing were performed using MATLAB , Python (Python Software Foundation) or SPSS Statistics .The independent-sample Mann–Whitney U = 16, p < 0.005; U = 54, n.s.; Significantly higher P300 amplitudes were observed in healthy controls than patients with tinnitus during the active oddball task  and stronger evoked theta activity  compared with the tinnitus group. For the standard stimuli, the healthy group exhibited salient pre-stimulus alpha activity compared with the tinnitus group , but the two groups had no significant differences in evoked theta activity.In the active oddball task, we observed significant differences in both pre-stimulus alpha and evoked theta activities between the two groups . For the2, tinnitus group, 0.958μV2; U = 21, p < 0.01), but the two groups had no significant differences in evoked theta activity. For the standard stimuli, the healthy group exhibited salient pre-stimulus alpha activity compared with the tinnitus group , but the two groups had no significant differences in evoked theta activity  was achieved using the EEG alpha band . The associated confusion matrix of the EEGNet model is provided in Z = 2.934, p < 0.005) was also achieved using the EEG alpha band. On the other hand, for the target stimuli in the passive oddball task, the highest AUC of 0.819 ± 0.068  was achieved using the EEG broadband. For the standard stimuli in the passive oddball task, the highest AUC of 0.807 ± 0.058  was achieved using the EEG alpha activity.The EEGNet model effectively discriminated patients with tinnitus from healthy controls, most often achieving the best performance using spectral features in the alpha band. For example, when using the entire trial time period, for the target stimuli in the active oddball task, the highest AUC of 0.886 ± 0.042 , and different time periods  are provided in Z = 2.934, p < 0.005) for the target stimulus in the active oddball task  for the target stimulus in the active oddball task , false positive (N = 223), true negative (N = 533), and false negative (N = 124). Patterns in the time-frequency plots revealed that the misclassified EEG trials clearly lacked the prominent features of the correctly classified EEG trials.We compared the time-frequency representations of the correctly classified versus misclassified EEG trials in a sample pair of healthy/patient subjects . After tThis study demonstrated that human EEG signals provide promising tinnitus identification features that enable practical tinnitus-diagnostic applications. Based on the EEG spectral analysis, we observed different behaviors of pre-stimulus alpha and evoked theta activities during task performance between healthy controls and patients with tinnitus, suggesting that these spectral components may be crucial features for EEG-based diagnosis of tinnitus. It is noteworthy that the tinnitus group showed significantly reduced pre-stimulus alpha activity compared with healthy participants. Since pre-stimulus alpha activity might reflect the top-down preparation for upcoming stimuli , the redIn agreement with the EEG spectral analysis, the spectral analysis of the first convolutional layer of the EEGNet model further implicated the EEG alpha band as the most decisive feature for the classification of patients with tinnitus . FurtherTo investigate whether the healthy and tinnitus groups could be distinguished even before stimulus presentation, the EEGNet model was also trained and evaluated by dividing the EEG trial data into corresponding time segments . For theThe SVM-based AUC based on the alpha band (0.759) was not significantly different from that of the removal of the alpha band (0.73) . This obSymptoms of tinnitus have been linked to hyperactivity and reorganization of the auditory central nervous system  with theThus, the present deep-learning method of EEG-based tinnitus diagnosis demonstrated its capability of extracting and harnessing interpretable EEG features generally corresponding to known neurophysiological observations. The highest AUC in the alpha band  can be aU tests and Wilcoxon signed-rank tests), the limited statistical power should be carefully considered when interpreting our findings. Several data-augmentation methods, such as generative adversarial networks . The patients/participants provided their written informed consent to participate in this study.B-KM conceptualized the study and designed the experimental paradigm to identify tinnitus symptoms. E-SH, H-SK, and B-KM performed the experiment and analyzed the data. E-SH, H-SK, SH, DP, and B-KM wrote the main manuscript text and reviewed the manuscript. All authors contributed to the article and approved the submitted version.


Cystoceles are the most common prolapses. Limitation of the use of synthetic mesh has led to the comeback of native tissue repair procedures. We have developed a new transobturator technique with native tissue based on a mix of a vaginal plastron technique and the transobturator procedure. We present the functional and anatomical mid-term results. In this retrospective study, the vaginal plastron technique and the transobturator procedure were performed in 32 patients. Functional assessment with several validated quality-of-life questionnaires  and anatomical evaluation with pelvic examination were performed at 1, 6, and 12 months after surgery. The anatomical success rate was 94.4% at 12 months. There was one Clavien–Dindo grade 2 postoperative complication (one urinary tract infection). All of the quality-of-life scores were statistically significantly improved at one year follow-up. The transobturator technique combined with the vaginal plastron seems to be a promising, effective, innovative, and relevant technique for the repair of high-stage cystoceles. The vaginal patch plastron with six vaginal fixations elaborated by Crépin and Cosson is based on a vaginal strip still attached on the bladder with suspension to the tendinous arch of the pelvic fascia . Short-tAnterior sacrospinous fixation consisting of an anterior suspension to the sacrospinous ligament seems rather relevant, but mid- and long-term data are missing .Since the recent ban on using transvaginal mesh for prolapse repair by the FDA , surgicaRecently, new transobturator techniques have been described ,7,8 withWe previously performed a transobturator cystocele technique with mesh with a long-term cure rate of 90%  [We have developed a new transobturator technique with native tissue based on a mix of a vaginal plastron technique and the transobturator procedure. The advantages of vaginal plastron may combine bladder support and suspension with the transobturator fixation of the vaginal plastron. This technique may be suitable for the treatment of the median cystocele  as well as for the treatment of the paravaginal repair .We propose a functional and anatomical mid-term retrospective series of this new transobturator technique with native tissue.A single-center retrospective study of women with cystocele undergoing surgical repair by the transobturator technique with native tissue was conducted from September 2020 to March 2023 in accordance with the Declaration of Helsinki. This study was approved by the local Institutional Review Board  for studies involving humans. The ClinicalTrials.gov identifier was NCT05741567. All patients with symptomatic cystocele prolapse stage II or higher according to the Pelvic Organ Prolapse Quantification system (POP-Q) were invited to participate in the study. Consenting women agreed to provide baseline and follow-up data at 1, 6, and 12 months postoperatively on symptoms, anatomical correction, and quality of life, using specific questionnaires. If indicated, a concomitant posterior colporrhaphy with native tissue and/or a Richter’s sacrospinofixation could be performed. Vaginal hysterectomy was performed only in the case of symptomatic abnormalities confirmed via preoperative ultrasound . No other mesh or sling (such as TVT or TOT) was used during the procedure. A preoperative urodynamic assessment was systematically performed.The Patient Global Impression of Improvement (PGI-I) questionnaire for urogenital prolapse . This isThe Short Form 12 (SF-12) questionnaire . This isThe Pelvic Floor Distress Inventory-20 (PFDI-20) questionnaire . This isThe Pelvic Floor Impact Questionnaire-7 (PFIQ-7) questionnaire . This isThe short form of the Pelvic Organ Prolapse/Urinary Incontinence Sexual Questionnaire (PISQ-12) . This isGlobal pain was assessed using a 10 cm-VAS.The primary objective was to assess the degree of improvement after prolapse surgery using short- and mid-term health-related quality-of-life (HRQOL) questionnaires at 1, 6, and 12 months. We used the following validated quality-of-life questionnaires:The secondary objective was objective anatomical success by clinical examination using the POP-Q classification. Cystocele stage II or higher (Ba > −1) was considered a failure of the surgical procedure.A preoperative ultrasound was always performed to check for the presence or absence of uterine abnormalities. All procedures were performed under general anesthesia or locoregional anesthesia by an experienced urogynecologic surgeon (GC). The following surgical technical protocol was used, based on the technique described by Chabanon-Pouget et al. :Step 1: Incision of the vaginal plastron (diamond shape) after deep hydrodissection of the anterior vaginal wall. In order to avoid postoperative mucoceles, especially in non-menopausal women, the superficial epithelium from the plastron is destroyed by coagulation using a monopolar diathermy instrument or de-epidermized with a cold knife.Step 2: Pubocervical fascia dissection and opening of both paravaginal spaces by blunt dissection towards the fascia of the obturator internus muscle.Step 3: Two skin incisions  are fixed into the lateral part of the vaginal plastron.Step 4: Two non-absorbable sutures  is passed through the superior left skin incision and the obturator membrane towards the paravaginal space. The free end of the first suture is threaded through the open hole of the tunneller, which is removed by a reverse rotation. The tunneller is immediately reinserted subcutaneously through the same skin incision to the second inferior skin incision  . A p-value less than 0.05 was considered significant.Statistical analysis was performed using the McNemar test and Student’s Thirty-two patients were included in this study . PatientAccording to the POP-Q classification, the anatomical success rate concerning cystocele repair was 94.4% at 12 months . One patAccording to the global index PGI-I, there was a significant improvement at 1, 6, and 12 months. The improvement in quality-of-life indicators was statistically significant at 12 months for SF-12, PFIQ-7, and PFDI-20. An assessment of the pain at the level of the operated area showed the virtual absence of pain. Recovery of sexuality was considered satisfactory in sexually active patients, with a significant improvement in the PISQ12 score at 12 months .Anterior vaginal wall prolapse or cystoceles are the most common prolapses. The pathophysiology is complex, and cystoceles may be secondary to vaginal defects. These defects may be central or lateral  and often combined. Several surgical techniques have been described via the vaginal route: anterior colporrhaphy, vaginal wall flap, and paravaginal repair ,3,4,5. HTherefore, there has been a return to autologous techniques. The transobturator route is well known to urogynecologists . Some transobturator techniques have recently been published. Kalis et al.  describeThe interest of the transobturator approach in combination with the vaginal patch is to repair the lateral defect  on the one hand and, on the other hand, to repair the central defect . If indicated, a surgeon may add additional surgical procedures, such as vaginal hysterectomy , RichterIn our study, functional and anatomic mid-term results were very good, with no peri-nor postoperative complications. The transobturator technique is quick and easy to learn. Moreover, it is possible that there may be a TOT effect of our technique, as the frequency of SUI decreased after the procedure.®, AMI, Feldkirch, Austria), which is a reusable instrument made of stainless steel for tunnelling during the treatment of female stress urinary incontinence with the suburethral slings. The Food and Drug Administration reclassified surgical instrumentation for use with urogynecologic surgical mesh from class I  to class II  and identified them as ‘‘specialized surgical instrumentation for use with urogynecologic surgical mesh’’ [We used the outside-in tunneller . The strengths lie in a new, easy, and promising surgical technique with relevant functional and anatomical long-term results. We chose several validated QoL questionnaires to assess all components of quality of life.In a recent meta-analysis comparing native tissue repair versus transvaginal mesh interventions for the treatment of anterior vaginal prolapse, Capobianco et al.  showed tNevertheless, other prospective studies with larger cohorts are needed to confirm the interest of the transobturator approach as a relevant option for the correction of cystoceles.