In Current research in food science

Zhang Haoyang, Zhang Dachuan, Wei Zhisheng, Li Yan, Wu Shaji, Mao Zhiheng, He Chunmeng, Ma Haorui, Zeng Xin, Xie Xiaoling, Kou Xingran, Zhang Bingwen

2023

Big data, Deep learning, Foodinformatics, Machine learning, Natural language processing

In Frontiers in cell and developmental biology

Alatawneh Rawan, Salomon Yahel, Eshel Reut, Orenstein Yaron, Birnbaum Ramon Y

2023

convolution neuronal networks, deep-learning, inhibitory interneuron progenitors, non-active enhancers, predicted TF motifs, repressed enhancers

In Journal of medical imaging (Bellingham, Wash.)

PURPOSE : We perform anatomical landmarking for craniomaxillofacial (CMF) bones without explicitly segmenting them. Toward this, we propose a simple, yet efficient, deep network architecture, called relational reasoning network (RRN), to accurately learn the local and the global relations among the landmarks in CMF bones; specifically, mandible, maxilla, and nasal bones.

APPROACH : The proposed RRN works in an end-to-end manner, utilizing learned relations of the landmarks based on dense-block units. For a given few landmarks as input, RRN treats the landmarking process similar to a data imputation problem where predicted landmarks are considered missing.

RESULTS : We applied RRN to cone-beam computed tomography scans obtained from 250 patients. With a fourfold cross-validation technique, we obtained an average root mean squared error of < 2    mm per landmark. Our proposed RRN has revealed unique relationships among the landmarks that help us in inferring informativeness of the landmark points. The proposed system identifies the missing landmark locations accurately even when severe pathology or deformations are present in the bones.

CONCLUSIONS : Accurately identifying anatomical landmarks is a crucial step in deformation analysis and surgical planning for CMF surgeries. Achieving this goal without the need for explicit bone segmentation addresses a major limitation of segmentation-based approaches, where segmentation failure (as often is the case in bones with severe pathology or deformation) could easily lead to incorrect landmarking. To the best of our knowledge, this is the first-of-its-kind algorithm finding anatomical relations of the objects using deep learning.

Torosdagli Neslisah, Anwar Syed, Verma Payal, Liberton Denise K, Lee Janice S, Han Wade W, Bagci Ulas

2023-Mar

anatomical landmarking, craniomaxillofacial bones, deep relational learning, relational reasoning, surgical modeling

In Frontiers in microbiology

Yang Hechen, Li Chen, Zhao Xin, Cai Bencheng, Zhang Jiawei, Ma Pingli, Zhao Peng, Chen Ao, Jiang Tao, Sun Hongzan, Teng Yueyang, Qi Shouliang, Huang Xinyu, Grzegorzek Marcin

2023

deep learning, environmental microorganism, image analysis, image dataset construction, multiple object detection

In Translational pediatrics

BACKGROUND : The incidence of clinical adverse events after tetralogy of Fallot (TOF) repair remains high. This study was performed to explore risk factors for adverse events and develop a prediction model through machine learning (ML) to forecast the incidence of clinical adverse events after TOF repair.

METHODS : A total of 281 participants who were treated with cardiopulmonary bypass (CPB) at our hospital from January 2002 to January 2022 were included in the study. Risk factors for adverse events were explored by composite and comprehensive analyses. Five artificial intelligence (AI) models were used for ML to build prediction models and screen out the model with the best performance in predicting adverse events.

RESULTS : CPB time, differential pressure of the right ventricular outflow tract (RVOTDP or DP), and transannular patch repair were identified as the main risk factors for adverse events. The reference point for CPB time was 116.5 minutes and that for right ventricular (RV) outflow tract differential pressure was 70 mmHg. SPO₂ was a protective factor, with a reference point of 88%. By integrating the results for the training and validation cohorts, we confirmed that, among all models, the logistic regression (LR) model and Gaussian Naive Bayes (GNB) model were stable, showing good discrimination, calibration and clinical practicability. The dynamic nomogram can be used as a predictive tool for clinical application.

CONCLUSIONS : Differential pressure of the RV outflow tract, CPB time, and transannular patch repair are risk factors, and SPO₂ is a protective factor for adverse events after complete TOF repair. In this study, models developed by ML were established to predict the incidence of adverse events.

Xi Linyun, Xiang Ming, Wu Chun, Pan Zhengxia, Dai Jiangtao, Wang Gang, Li Hongbo, An Yong, Li Yonggang, Zhang Yuan, Wei Xiaoqin, He Dawei, Wang Quan

2023-Feb-28

Tetralogy of Fallot (TOF), adverse events, artificial intelligence (AI), machine learning (ML)

In Frontiers in immunology ; h5-index 100.0

OBJECTIVE : An analysis of the relationship between rheumatoid arthritis (RA) and copper death-related genes (CRG) was explored based on the GEO dataset.

METHODS : Based on the differential gene expression profiles in the GSE93272 dataset, their relationship to CRG and immune signature were analysed. Using 232 RA samples, molecular clusters with CRG were delineated and analysed for expression and immune infiltration. Genes specific to the CRGcluster were identified by the WGCNA algorithm. Four machine learning models were then built and validated after selecting the optimal model to obtain the significant predicted genes, and validated by constructing RA rat models.

RESULTS : The location of the 13 CRGs on the chromosome was determined and, except for GCSH. LIPT1, FDX1, DLD, DBT, LIAS and ATP7A were expressed at significantly higher levels in RA samples than in non-RA, and DLST was significantly lower. RA samples were significantly expressed in immune cells such as B cells memory and differentially expressed genes such as LIPT1 were also strongly associated with the presence of immune infiltration. Two copper death-related molecular clusters were identified in RA samples. A higher level of immune infiltration and expression of CRGcluster C2 was found in the RA population. There were 314 crossover genes between the 2 molecular clusters, which were further divided into two molecular clusters. A significant difference in immune infiltration and expression levels was found between the two. Based on the five genes obtained from the RF model (AUC = 0.843), the Nomogram model, calibration curve and DCA also demonstrated their accuracy in predicting RA subtypes. The expression levels of the five genes were significantly higher in RA samples than in non-RA, and the ROC curves demonstrated their better predictive effect. Identification of predictive genes by RA animal model experiments was also confirmed.

CONCLUSION : This study provides some insight into the correlation between rheumatoid arthritis and copper mortality, as well as a predictive model that is expected to support the development of targeted treatment options in the future.

Zhou Yu, Li Xin, Ng Liqi, Zhao Qing, Guo Wentao, Hu Jinhua, Zhong Jinghong, Su Wenlong, Liu Chaozong, Su Songchuan

2023

copper death, immune infiltration, machine learning, predictive models, rheumatoid arthritis