Optical Coherence Tomography (OCT) is a newly established imaging technology, now widely adopted in various medical settings such as ophthalmology and dermatology, though to a lesser but emerging extent in dentistry. Its conventional acceptance for den-tistry, particularly, is hindered by speckle noise, inherent in the methodology of image capture. A degraded signal-to-noise ratio accentuates ambiguity in feature extraction and contributes to the introduction of artefacts. This ultimately impacts its clinical utility where clear diagnostic detail is sort. This paper proposes a deep learning based denoising technique for OCT images. The approach is an unsupervised denoising framework in which the training data was created from one OCT image. This ensures fast processing as it is focused on essential data removal. Additionally, there are limited clean datasets for OCT available. The approach was analysed quan-titatively and visually against state-of-the-art denoising algorithms. The experimental results show that the approach verifiably removes speckle noise. The method improved the PSNR (dB) by 23.5, CNR (dB) by 7.7 and ENL (dB) by 585.5.
{"title":"Framework of Unsupervised based Denoising for Optical Coherence Tomography","authors":"Hanya Ahmed, Qianni Zhang, R. Donnan, A. Alomainy","doi":"10.1145/3563737.3563741","DOIUrl":"https://doi.org/10.1145/3563737.3563741","url":null,"abstract":"Optical Coherence Tomography (OCT) is a newly established imaging technology, now widely adopted in various medical settings such as ophthalmology and dermatology, though to a lesser but emerging extent in dentistry. Its conventional acceptance for den-tistry, particularly, is hindered by speckle noise, inherent in the methodology of image capture. A degraded signal-to-noise ratio accentuates ambiguity in feature extraction and contributes to the introduction of artefacts. This ultimately impacts its clinical utility where clear diagnostic detail is sort. This paper proposes a deep learning based denoising technique for OCT images. The approach is an unsupervised denoising framework in which the training data was created from one OCT image. This ensures fast processing as it is focused on essential data removal. Additionally, there are limited clean datasets for OCT available. The approach was analysed quan-titatively and visually against state-of-the-art denoising algorithms. The experimental results show that the approach verifiably removes speckle noise. The method improved the PSNR (dB) by 23.5, CNR (dB) by 7.7 and ENL (dB) by 585.5.","PeriodicalId":127021,"journal":{"name":"Proceedings of the 7th International Conference on Biomedical Signal and Image Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131815037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Electrocardiographic imaging (ECGI) is a non-invasive technique for reconstructing cardiac electrical activity from body surface electrocardiogram (ECG) signals. The collection of ECG signals from the body surface is a vital stage in ECGI. Placing and fastening electrodes in conventional ECG electrode acquisition equipment is a time-consuming and complex process. It is not uncommon for the acquired signal to be of poor quality or to be completely absent due to insufficient contact between certain electrodes and the surface skin. The study recommends the development of a novel electrode shirt for ECGI. The electrode shirt optimizes the multi-channel ECG acquisition efficiency; the electrode holders aid in the selection and adjustment of electrode placements flexibly; and the elasticity of the fabric and the presence of airbag columns ensures optimal skin-to-electrode contact. About 280 body surface ECG signals of 16 male volunteers were analyzed in this study. The electrodes shirt described above achieves optimal signal quality in a variety of body shapes with no electrodes slipping off.
{"title":"The electrodes shirt design for ECG imaging","authors":"Yadan Zhang, Xin Lian, Jian Wu","doi":"10.1145/3563737.3563753","DOIUrl":"https://doi.org/10.1145/3563737.3563753","url":null,"abstract":"Electrocardiographic imaging (ECGI) is a non-invasive technique for reconstructing cardiac electrical activity from body surface electrocardiogram (ECG) signals. The collection of ECG signals from the body surface is a vital stage in ECGI. Placing and fastening electrodes in conventional ECG electrode acquisition equipment is a time-consuming and complex process. It is not uncommon for the acquired signal to be of poor quality or to be completely absent due to insufficient contact between certain electrodes and the surface skin. The study recommends the development of a novel electrode shirt for ECGI. The electrode shirt optimizes the multi-channel ECG acquisition efficiency; the electrode holders aid in the selection and adjustment of electrode placements flexibly; and the elasticity of the fabric and the presence of airbag columns ensures optimal skin-to-electrode contact. About 280 body surface ECG signals of 16 male volunteers were analyzed in this study. The electrodes shirt described above achieves optimal signal quality in a variety of body shapes with no electrodes slipping off.","PeriodicalId":127021,"journal":{"name":"Proceedings of the 7th International Conference on Biomedical Signal and Image Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133519793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Brain-computer interfaces (BCIs) are systems that provide the connection between the brain and the external device. With the development of BCIs, understanding the basic mechanism like the BCI-induced brain plasticity enables the practical design of training sessions. We recorded neural activity over five weeks during the motor imagery task and the speech task under our spike-based BCI system involving an individual with tetraplegia. Pairwise correlations between neurons were calculated to quantify brain plasticity for the neural ensemble. The sharply increasing correlated activity was observed at the initial training phase and then declined and remained relatively low for both tasks. Our results also demonstrate the BCI-induced neural plasticity during short-time training sessions for the motor imagery task and the speech task. Moreover, a stronger correlation was found during the speech task than during the motor imagery task, suggesting task-induced changes in neural connectivity. Our findings provide important insights into human brain plasticity and thus benefit the design of clinical neuroprosthetics systems.
{"title":"Learning-induced Neural Plasticity in the Primary Motor Cortex during the Motor Imagery Task and the Speech Task","authors":"Dongrong Lai, Zijun Wan, Feixiao Ren, Hongjie Jiang, Kedi Xu","doi":"10.1145/3563737.3563752","DOIUrl":"https://doi.org/10.1145/3563737.3563752","url":null,"abstract":"Brain-computer interfaces (BCIs) are systems that provide the connection between the brain and the external device. With the development of BCIs, understanding the basic mechanism like the BCI-induced brain plasticity enables the practical design of training sessions. We recorded neural activity over five weeks during the motor imagery task and the speech task under our spike-based BCI system involving an individual with tetraplegia. Pairwise correlations between neurons were calculated to quantify brain plasticity for the neural ensemble. The sharply increasing correlated activity was observed at the initial training phase and then declined and remained relatively low for both tasks. Our results also demonstrate the BCI-induced neural plasticity during short-time training sessions for the motor imagery task and the speech task. Moreover, a stronger correlation was found during the speech task than during the motor imagery task, suggesting task-induced changes in neural connectivity. Our findings provide important insights into human brain plasticity and thus benefit the design of clinical neuroprosthetics systems.","PeriodicalId":127021,"journal":{"name":"Proceedings of the 7th International Conference on Biomedical Signal and Image Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127570932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the image reconstruction of computed tomography (CT), the algebra reconstruction technique (ART) has many virtues that the analytic reconstruction algorithms don't have. In this paper, a simplified weighting factor calculation method is proposed. All voxels are firstly projected at a scanning angle, and the voxels and the rays are classified on the basis of the distance between the voxel's projection and the ray's projection. For the reason that the voxel mainly contributes to the projection value of the nearest ray, the weighting factors of the voxels to the ray's projection value in the same classification is approximately 1. Then the reconstruction equations are obtained at a scanning angle. Finally, based on the proposed simplified weighting factor calculation method, a fast ART algorithm for CT image reconstruction is developed. The simulation results show the validity of the proposed fast ART algorithm.
{"title":"Fast ART Algorithm Based on Simplified Weighting Factor Calculation","authors":"Xianchao Wang, Yong Zhang, Yu Dai","doi":"10.1145/3563737.3563738","DOIUrl":"https://doi.org/10.1145/3563737.3563738","url":null,"abstract":"In the image reconstruction of computed tomography (CT), the algebra reconstruction technique (ART) has many virtues that the analytic reconstruction algorithms don't have. In this paper, a simplified weighting factor calculation method is proposed. All voxels are firstly projected at a scanning angle, and the voxels and the rays are classified on the basis of the distance between the voxel's projection and the ray's projection. For the reason that the voxel mainly contributes to the projection value of the nearest ray, the weighting factors of the voxels to the ray's projection value in the same classification is approximately 1. Then the reconstruction equations are obtained at a scanning angle. Finally, based on the proposed simplified weighting factor calculation method, a fast ART algorithm for CT image reconstruction is developed. The simulation results show the validity of the proposed fast ART algorithm.","PeriodicalId":127021,"journal":{"name":"Proceedings of the 7th International Conference on Biomedical Signal and Image Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124913657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Magnetic resonance imaging is now a relatively mature technique, with a variety of sequences with different functions have derived from conventional magnetic resonance imaging, each with its own advantages in monitoring brain function, detecting oedema or infarction, identifying tumours and assessing prognosis. This paper will review the principles of functional magnetic resonance imaging, diffusion weighted imaging and perfusion weighted imaging and their applications in the diagnosis of brain diseases, with the aim of providing ideas for the selection of diagnostic methods for brain diseases, and put forward the further development direction of diagnostic technology
{"title":"Advances in the Application of Magnetic Resonance Imaging in the Diagnosis of Brain Diseases","authors":"Qingyun Lin","doi":"10.1145/3563737.3563739","DOIUrl":"https://doi.org/10.1145/3563737.3563739","url":null,"abstract":"Magnetic resonance imaging is now a relatively mature technique, with a variety of sequences with different functions have derived from conventional magnetic resonance imaging, each with its own advantages in monitoring brain function, detecting oedema or infarction, identifying tumours and assessing prognosis. This paper will review the principles of functional magnetic resonance imaging, diffusion weighted imaging and perfusion weighted imaging and their applications in the diagnosis of brain diseases, with the aim of providing ideas for the selection of diagnostic methods for brain diseases, and put forward the further development direction of diagnostic technology","PeriodicalId":127021,"journal":{"name":"Proceedings of the 7th International Conference on Biomedical Signal and Image Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133433516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Motor commands for the elbow and wrist generally arise from contralateral motor cortex. However, ipsilateral motor cortex also shows correlated responses despite the lack of direct connection. To investigate the activities of primary motor cortex (M1) neurons to bilateral movements, we recorded neural responses to attempted unilateral and bilateral movements of elbows and wrists. The intracortical electrode arrays are implanted in left M1 area correlated with elbow and shoulder. Then we investigated the classification accuracy and the possibility of controlling bilateral exoskeletons through Brain-Computer Interface (BCI). We found left M1 neurons encode both unilateral and bilateral movements of both elbows and wrists. The bilateral movement encoding patterns were more correlated with contralateral movement than ipsilateral movement. In addition, we got high classification accuracy and found the participant can control exoskeletons well in online experiments.
{"title":"The Primary Motor Cortex Represents Unilateral and Bilateral Movements of Elbows and Wrists – a Pilot Study","authors":"Zijun Wan, Dongrong Lai, Feixiao Ren, Weidong Chen, Kedi Xu","doi":"10.1145/3563737.3563758","DOIUrl":"https://doi.org/10.1145/3563737.3563758","url":null,"abstract":"Motor commands for the elbow and wrist generally arise from contralateral motor cortex. However, ipsilateral motor cortex also shows correlated responses despite the lack of direct connection. To investigate the activities of primary motor cortex (M1) neurons to bilateral movements, we recorded neural responses to attempted unilateral and bilateral movements of elbows and wrists. The intracortical electrode arrays are implanted in left M1 area correlated with elbow and shoulder. Then we investigated the classification accuracy and the possibility of controlling bilateral exoskeletons through Brain-Computer Interface (BCI). We found left M1 neurons encode both unilateral and bilateral movements of both elbows and wrists. The bilateral movement encoding patterns were more correlated with contralateral movement than ipsilateral movement. In addition, we got high classification accuracy and found the participant can control exoskeletons well in online experiments.","PeriodicalId":127021,"journal":{"name":"Proceedings of the 7th International Conference on Biomedical Signal and Image Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115094272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Discovering the source of a patient's fever without clinically localised signs can be a daunting task for doctors. In particular for leukaemia patients with fever of unknown origin, fast discovering the source of the fever is a formidable challenge, as this population has the potential to lead to fever in many different situations. In this paper, we applied XGBoost algorithm to predict the pathogenic infections from a big data repository of leukemia patients with fever of unknown origin (FUO) and compared the performance with other machine learning algorithms. Our results illustrates that those machine learning algorithms achieves good performance. In particular, the XGBoost obtains the best performance with an area under receiving-operating-characteristics curve (AUC) of 0.8376 and F1-score of 0.7034. Compared with existing literature, our experiment provides new insights for doctors to determine the cause of fever in leukemia patients.
{"title":"XGBoost Prediction of Infection of Leukemia Patients with Fever of Unknown Origin","authors":"Yan Li, Yanhui Song, Fei Ma","doi":"10.1145/3563737.3563761","DOIUrl":"https://doi.org/10.1145/3563737.3563761","url":null,"abstract":"Discovering the source of a patient's fever without clinically localised signs can be a daunting task for doctors. In particular for leukaemia patients with fever of unknown origin, fast discovering the source of the fever is a formidable challenge, as this population has the potential to lead to fever in many different situations. In this paper, we applied XGBoost algorithm to predict the pathogenic infections from a big data repository of leukemia patients with fever of unknown origin (FUO) and compared the performance with other machine learning algorithms. Our results illustrates that those machine learning algorithms achieves good performance. In particular, the XGBoost obtains the best performance with an area under receiving-operating-characteristics curve (AUC) of 0.8376 and F1-score of 0.7034. Compared with existing literature, our experiment provides new insights for doctors to determine the cause of fever in leukemia patients.","PeriodicalId":127021,"journal":{"name":"Proceedings of the 7th International Conference on Biomedical Signal and Image Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124923195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Despite the ever-increasing demand for online spike sorting to be applied in various closed-loop neuromodulation experiments and treatments, the performance and bandwidth are still constrained by the strict requirement for time complexity. Initiatives for improving online spike sorting performance mostly started with the implementation and designing of sorting algorithms, assuming standardized data preprocessing operations are applicable to all cases and separable for evaluating sorting performance. However, we postulated that the cutoff frequency of the high-pass filter could affect the sorting performance, given that spike waveforms are informative in a broad band and would be distorted if the frequency characteristics of the filter and noise do not match. Based on this rationale, we have evaluated how cutoff frequency affects the spike sorting performance on both the synthetic and real datasets. It was demonstrated that, the cutoff frequency can have a huge impact on the sorting performance. Further, this impact was noise-dependent. For neural signals with homogeneous noise, the cutoff frequency would lead to greater disparity when the signal-noise ratio decreased. While for signals with different types of noise, when the noise was subject to a “1/f” power spectrum, higher cutoff frequencies would render better performance. However, lower cutoff frequencies were advantageous when the noise deviated from the “1/f” noise. Therefore, according to the evaluation, when the cutoff frequency of the high-pass filter was adaptively switchable, the spike sorting performance would be enhanced while sidestepping the challenges in designing sorting algorithms.
{"title":"Evaluation of Different Cutoff Frequencies of High-pass Filter for Online Spike Sorting","authors":"Yuxiao Ning, Yiwei Zhang, Tianyu Zheng, Shaomin Zhang","doi":"10.1145/3563737.3563754","DOIUrl":"https://doi.org/10.1145/3563737.3563754","url":null,"abstract":"Despite the ever-increasing demand for online spike sorting to be applied in various closed-loop neuromodulation experiments and treatments, the performance and bandwidth are still constrained by the strict requirement for time complexity. Initiatives for improving online spike sorting performance mostly started with the implementation and designing of sorting algorithms, assuming standardized data preprocessing operations are applicable to all cases and separable for evaluating sorting performance. However, we postulated that the cutoff frequency of the high-pass filter could affect the sorting performance, given that spike waveforms are informative in a broad band and would be distorted if the frequency characteristics of the filter and noise do not match. Based on this rationale, we have evaluated how cutoff frequency affects the spike sorting performance on both the synthetic and real datasets. It was demonstrated that, the cutoff frequency can have a huge impact on the sorting performance. Further, this impact was noise-dependent. For neural signals with homogeneous noise, the cutoff frequency would lead to greater disparity when the signal-noise ratio decreased. While for signals with different types of noise, when the noise was subject to a “1/f” power spectrum, higher cutoff frequencies would render better performance. However, lower cutoff frequencies were advantageous when the noise deviated from the “1/f” noise. Therefore, according to the evaluation, when the cutoff frequency of the high-pass filter was adaptively switchable, the spike sorting performance would be enhanced while sidestepping the challenges in designing sorting algorithms.","PeriodicalId":127021,"journal":{"name":"Proceedings of the 7th International Conference on Biomedical Signal and Image Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128334076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent decades, the unhealthy diet and sedentary lifestyles of people are taking their toll on growing cases of metabolic diseases worldwide, one of them being Nonalcoholic Fatty Liver Disease (NAFLD). This disease has become one of the most sophisticated medical and physiological puzzles because of its convoluted mechanisms of progression.Existing gene expression analysis methods like microarray or RNA-sequencing are unable to resolve the complex mechanisms of progression of non-alcoholic fatty liver disease (NAFLD) due to insufficient accuracy and lack of phenotypic data. Particularly, incomplete phenotypic data in public liver gene expression cohorts have cumbered many studies on the progression of NAFLD. To address this issue, the cutting-edge pseudotime analysis is adopted to estimate liver health status in human liver gene expression data. The identified DE genes separate the NAFLD patients and the healthy controls in hierarchical clustering, and their related biological pathways are highly relevant to liver signaling and injury, implying the close relationship between the DE gene expressions and NAFLD. What's more, the pseudotime analysis we conducted simulates the deterioration of NAFLD by using liver fat percent to represent NAFLD severity and aligning the candidate samples on the estimated trajectory according to their respective gene expression and covariates; we verified the pseudotime model using another microarray cohort.
{"title":"Study on fatty liver based on Pseudotime analysis","authors":"Yunheng Wu, Meixue Li","doi":"10.1145/3563737.3563744","DOIUrl":"https://doi.org/10.1145/3563737.3563744","url":null,"abstract":"In recent decades, the unhealthy diet and sedentary lifestyles of people are taking their toll on growing cases of metabolic diseases worldwide, one of them being Nonalcoholic Fatty Liver Disease (NAFLD). This disease has become one of the most sophisticated medical and physiological puzzles because of its convoluted mechanisms of progression.Existing gene expression analysis methods like microarray or RNA-sequencing are unable to resolve the complex mechanisms of progression of non-alcoholic fatty liver disease (NAFLD) due to insufficient accuracy and lack of phenotypic data. Particularly, incomplete phenotypic data in public liver gene expression cohorts have cumbered many studies on the progression of NAFLD. To address this issue, the cutting-edge pseudotime analysis is adopted to estimate liver health status in human liver gene expression data. The identified DE genes separate the NAFLD patients and the healthy controls in hierarchical clustering, and their related biological pathways are highly relevant to liver signaling and injury, implying the close relationship between the DE gene expressions and NAFLD. What's more, the pseudotime analysis we conducted simulates the deterioration of NAFLD by using liver fat percent to represent NAFLD severity and aligning the candidate samples on the estimated trajectory according to their respective gene expression and covariates; we verified the pseudotime model using another microarray cohort.","PeriodicalId":127021,"journal":{"name":"Proceedings of the 7th International Conference on Biomedical Signal and Image Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124758073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Significant advances have been made in cancer immunotherapy in recent years. However, few patients with ovarian cancer have benefited from immunotherapy and the prognosis is usually poor. A major mechanism by which cancer is rarely detected by immune cells is the downregulation of major histocompatibility complex class I (MHC-I), which leads to reduced recognition and cytotoxicity of cytotoxic T cells. What we were investigating is to find regulators that can modulate the expression of MHC-I molecules in ovarian cancer. Improving MHC-I expression may help patients to have a good prognosis. By analyzing tumoral transcriptional sequencing data and combined with clinical effects, we identified STAT1 as a potential MHC-I regulator in ovarian cancer cells. The “wet-lab” experiments confirmed the role of STAT1 in the expression of MHC-I molecules. Our study suggested that STAT1 could be a target to promote immunotherapeutic response by promoting the expression of MHC-I molecule in ovarian cancer cells.
{"title":"STAT1 Regulates the Expression of MHC-I Class Molecules in Ovarian Cancer","authors":"Yu-Ning Fan, Chunbo He","doi":"10.1145/3563737.3563746","DOIUrl":"https://doi.org/10.1145/3563737.3563746","url":null,"abstract":"Significant advances have been made in cancer immunotherapy in recent years. However, few patients with ovarian cancer have benefited from immunotherapy and the prognosis is usually poor. A major mechanism by which cancer is rarely detected by immune cells is the downregulation of major histocompatibility complex class I (MHC-I), which leads to reduced recognition and cytotoxicity of cytotoxic T cells. What we were investigating is to find regulators that can modulate the expression of MHC-I molecules in ovarian cancer. Improving MHC-I expression may help patients to have a good prognosis. By analyzing tumoral transcriptional sequencing data and combined with clinical effects, we identified STAT1 as a potential MHC-I regulator in ovarian cancer cells. The “wet-lab” experiments confirmed the role of STAT1 in the expression of MHC-I molecules. Our study suggested that STAT1 could be a target to promote immunotherapeutic response by promoting the expression of MHC-I molecule in ovarian cancer cells.","PeriodicalId":127021,"journal":{"name":"Proceedings of the 7th International Conference on Biomedical Signal and Image Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117105523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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