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General General

Thyroid autoimmunity and adverse pregnancy outcomes: A multiple center retrospective study.

In Frontiers in endocrinology ; h5-index 55.0

BACKGROUND : The relationship between thyroid autoimmunity (TAI) and adverse pregnancy outcomes is disputable, and their dose-dependent association have not been fully clarified.

OBJECTIVE : To investigate the association and dose-dependent effect of TAI with multiple maternal and fetal-neonatal complications.

METHODS : This study is a multi-center retrospective cohort study based on singleton pregnancies of three medical college hospitals from July 2013 to October 2021. The evolution of thyroid function parameters in TAI and not TAI women were described, throughout pregnancy. The prevalences of maternal and fetal-neonatal complications were compared between the TAI and control group. Logistic regression was performed to study the risk effects and dose-dependent effects of thyroid autoantibodies on pregnancy complications, with adjustment of maternal age, BMI, gravidity, TSH concentrations, FT4 concentrations and history of infertility.

RESULTS : A total of 27408 participants were included in final analysis, with 5342 (19.49%) in the TAI group and 22066 (80.51%) in control group. TSH concentrations was higher in TAI women in baseline and remain higher before the third trimester. Positive thyroid autoantibodies were independently associated with higher risk of pregnancy-induced hypertension (OR: 1.215, 95%CI: 1.026-1.439), gestational diabetes mellitus (OR: 1.088, 95%CI: 1.001-1.183), and neonatal admission to NICU (OR: 1.084, 95%CI: 1.004-1.171). Quantitative analysis showed that increasing TPOAb concentration was correlated with higher probability of pregnancy-induced hypertension, and increasing TGAb concentration was positively correlated with pregnancy-induced hypertension, small for gestational age and NICU admission. Both TPOAb and TGAb concentration were negatively associated with neonatal birthweight.

CONCLUSION : Thyroid autoimmunity is independently associated with pregnancy-induced hypertension, gestational diabetes mellitus, neonatal lower birthweight and admission to NICU. Dose-dependent association were found between TPOAb and pregnancy-induced hypertension, and between TGAb and pregnancy-induced hypertension, small for gestational age and NICU admission.

Xu Yun, Chen Hui, Ren Meng, Gao Yu, Sun Kan, Wu Hongshi, Ding Rui, Wang Junhui, Li Zheqing, Liu Dan, Wang Zilian, Yan Li

2023

birth weight, dose dependent effect, gestational diabetes mellitus, maternal and fetal outcomes, pregnancy-induced hypertension, thyroid autoimmunity

General General

Deep learning-based EEG emotion recognition: Current trends and future perspectives.

In Frontiers in psychology ; h5-index 92.0

Automatic electroencephalogram (EEG) emotion recognition is a challenging component of human-computer interaction (HCI). Inspired by the powerful feature learning ability of recently-emerged deep learning techniques, various advanced deep learning models have been employed increasingly to learn high-level feature representations for EEG emotion recognition. This paper aims to provide an up-to-date and comprehensive survey of EEG emotion recognition, especially for various deep learning techniques in this area. We provide the preliminaries and basic knowledge in the literature. We review EEG emotion recognition benchmark data sets briefly. We review deep learning techniques in details, including deep belief networks, convolutional neural networks, and recurrent neural networks. We describe the state-of-the-art applications of deep learning techniques for EEG emotion recognition in detail. We analyze the challenges and opportunities in this field and point out its future directions.

Wang Xiaohu, Ren Yongmei, Luo Ze, He Wei, Hong Jun, Huang Yinzhen

2023

deep learning, electroencephalogram, emotion recognition, human–computer interaction, survey

Surgery Surgery

Identification of ferroptosis-related genes in ulcerative colitis: a diagnostic model with machine learning.

In Annals of translational medicine

BACKGROUND : Ulcerative colitis (UC) is an idiopathic, chronic disorder characterized by inflammation, injury, and disruption of the colonic mucosa. However, there are still many difficulties in the diagnosis and differential diagnosis of UC. An increasing amount of research has shown a connection between ferroptosis and the etiology of UC. Therefore, our study aimed to identify the key genes related to ferroptosis in UC to provide new ideas for diagnosis UC.

METHODS : Gene expression profiles of normal and UC samples were extracted from the Gene Expression Omnibus (GEO) database. By combining differentially expressed genes (DEGs), Weighted correlation network analysis (WGCNA) genes, and ferroptosis-related genes, hub genes were identified and then screened using Lasso regression. Based on the key genes, gene ontology (GO) and gene set enrichment analysis (GSEA) analyses were performed. We used NaiveBeyas, Logistic, IBk, and RandomForest algorithms to build a disease diagnosis model using the hub genes. The model was validated using GSE87473 as the validation set.

RESULTS : Gene expression matrices of GSE87466 and GSE75214 were downloaded from the GEO database, including 184 UC patients and 43 control samples. A total of 699 DEGs were obtained. From FerrDb, 565 genes related to ferroptosis were identified. The 1,513 genes with the highest absolute correlation coefficient value in the MEblue module were obtained from WGCNA analysis. Five hub genes (LCN2, MUC1, PARP8, PLIN2, and TIMP1) were identified using the Lasso regression algorithm based on the overlapped DEGs, WGCNA-identified genes, and ferroptosis-related genes. GO and GSEA analyses revealed that 5 hub genes were identified as being involved in the negative regulation of transcription by competitive promoter binding, cellular response to citrate cycle_tca_cycle, cytosolic_dna_sensing pathway, UV-A, and beta-alanine metabolism. The logistic algorithm's values of the area under the curve (AUC)were 1.000 and 0.995 for training and validation cohorts, and sensitivity is 0.962, specificity is 1.000, respectively, as determined by comparing various methods.

CONCLUSIONS : The previously described hub genes were identified as being intimately related to ferroptosis in UC and capable of distinguishing UC patients from controls. By detecting the expression of several genes, this model may aid in diagnosing UC and understanding the etiology and treatment of the disease.

Qian Rui, Tang Min, Ouyang Zichen, Cheng Honghui, Xing Sizhong

2023-Feb-28

Ulcerative colitis, bioinformatic analysis, diagnostic model, ferroptosis

Public Health Public Health

CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers.

In Frontiers in public health

COVID-19 is a novel virus that attacks the upper respiratory tract and the lungs. Its person-to-person transmissibility is considerably rapid and this has caused serious problems in approximately every facet of individuals' lives. While some infected individuals may remain completely asymptomatic, others have been frequently witnessed to have mild to severe symptoms. In addition to this, thousands of death cases around the globe indicated that detecting COVID-19 is an urgent demand in the communities. Practically, this is prominently done with the help of screening medical images such as Computed Tomography (CT) and X-ray images. However, the cumbersome clinical procedures and a large number of daily cases have imposed great challenges on medical practitioners. Deep Learning-based approaches have demonstrated a profound potential in a wide range of medical tasks. As a result, we introduce a transformer-based method for automatically detecting COVID-19 from X-ray images using Compact Convolutional Transformers (CCT). Our extensive experiments prove the efficacy of the proposed method with an accuracy of 99.22% which outperforms the previous works.

Marefat Abdolreza, Marefat Mahdieh, Hassannataj Joloudari Javad, Nematollahi Mohammad Ali, Lashgari Reza

2023

COVID-19, Compact Convolutional Transformers, Convolutional Neural Networks, deep learning, vision transformers

General General

promor: a comprehensive R package for label-free proteomics data analysis and predictive modeling.

In Bioinformatics advances

SUMMARY : We present promor, a comprehensive, user-friendly R package that streamlines label-free quantification proteomics data analysis and building machine learning-based predictive models with top protein candidates.

AVAILABILITY AND IMPLEMENTATION : promor is freely available as an open source R package on the Comprehensive R Archive Network (CRAN) (https://CRAN.R-project.org/package=promor) and distributed under the Lesser General Public License (version 2.1 or later). Development version of promor is maintained on GitHub (https://github.com/caranathunge/promor) and additional documentation and tutorials are provided on the package website (https://caranathunge.github.io/promor/).

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics Advances online.

Ranathunge Chathurani, Patel Sagar S, Pinky Lubna, Correll Vanessa L, Chen Shimin, Semmes O John, Armstrong Robert K, Combs C Donald, Nyalwidhe Julius O

2023

General General

Machine learning-based clustering to identify the combined effect of the DNA fragmentation index and conventional semen parameters on in vitro fertilization outcomes.

In Reproductive biology and endocrinology : RB&E

BACKGROUND : Previous studies have demonstrated an association between male sperm quality and assisted reproduction outcomes, focusing on the effects of individual parameters and reaching controversial conclusions. The WHO 6th edition manual highlights a new semen assay, the sperm DNA fragmentation index, for use after routine semen examination. However, the combined effect of the sperm DNA fragmentation index (DFI) and routine semen parameters remains largely unknown.

METHODS : We assessed the combined effect of the sperm DFI and conventional semen parameters on single fresh conventional IVF outcomes for infertile couples from January 1, 2017, to December 31, 2020. IVF outcomes were obtained from the cohort database follow-up records of the Clinical Reproductive Medicine Management System of the Third Affiliated Hospital of Guangzhou Medical University. An unsupervised K-means clustering method was applied to classify participants into several coexposure pattern groups. A multivariate logistic regression model was used for statistical analysis.

RESULTS : A total of 549 live births among 1258 couples occurred during the follow-up period. A linear exposure-response relationship was observed among the sperm DFI, sperm motility, and IVF outcomes. In multivariable adjustment, increased sperm DFI values and decreased sperm motility and semen concentration levels were associated with reduced odds of favourable IVF outcomes. Four coexposure patterns were generated based on the sperm DFI and the studied semen parameters, as follows: Cluster 1 (low sperm DFI values and high sperm motility and semen concentration levels), Cluster 2 (low sperm DFI values and moderate sperm motility and semen concentration levels), Cluster 3 (low sperm DFI values and low sperm motility and semen concentration levels) and Cluster 4 (high sperm DFI values and low sperm motility and semen concentration levels). Compared with those in Cluster 1, participants in Cluster 3 and Cluster 4 had lower odds of a live birth outcome, with odds ratios (95% confidence intervals [CIs]) of 0.733 (0.537, 0.998) and 0.620 (0.394, 0.967), respectively.

CONCLUSIONS : When combined with low sperm DFI values, there was no significant difference between high or moderate sperm concentration and motility levels, and both were associated with favourable IVF outcomes. Low sperm parameter levels, even when DFI values remain low, may still lead to poor IVF outcomes. Participants with high sperm DFI values and low sperm motility and semen concentration levels had the worst outcomes. Our findings offer a novel perspective for exploring the joint effects of sperm DFI and routine semen parameter values.

Peng Tianwen, Liao Chen, Ye Xin, Chen Zhicong, Li Xiaomin, Lan Yu, Fu Xin, An Geng

2023-Mar-15

Coexposure, IVF outcomes, K-means clustering, Routine semen parameters, Sperm DNA fragmentation index