In Scientific data

Poon Charissa, Teikari Petteri, Rachmadi Muhammad Febrian, Skibbe Henrik, Hynynen Kullervo

2023-Mar-17

In NeuroImage ; h5-index 117.0

Werdiger Freda, Visser Milanka, Bivard Andrew, Li Xingjuan, Gotla Sunay, Sharobeam Angelos, Valente Michael, Beharry James, Yogendrakumar Vignan, Parsons Mark

2023-Mar-16

Clot Detection, Computed Tomography, Ischemic Stroke, Machine Learning

In Phytomedicine : international journal of phytotherapy and phytopharmacology

BACKGROUND : New therapeutic approaches are required to improve the outcomes of lung cancer (LC), a leading cause of cancer-related deaths worldwide. Chinese herbal medicine formulae widely used in China provide a unique opportunity for improving LC treatment, and the Shuang-Huang-Sheng-Bai (SHSB) formula is a typical example. However, the underlying mechanisms of action remains unclear.

PURPOSE : This study aimed to confirm the efficacy of SHSB against lung adenocarcinoma (LUAD), which is a major histological type of LC, unveil the downstream targets of this formula, and assess the clinical relevance and biological roles of the newly identified target.

METHODS : An experimental metastasis mouse model and a subcutaneous xenograft mouse model were used to evaluate the anti-cancer activity of SHSB. Multi-omics profiling of subcutaneous tumors and metabolomic profiling of sera were performed to identify downstream targets, especially the metabolic targets of SHSB. A clinical trial was conducted to verify the newly identified metabolic targets in patients. Next, the metabolites and enzymes engaged in the metabolic pathway targeted by SHSB were measured in clinical samples. Finally, routine molecular experiments were performed to decipher the biological functions of the metabolic pathways targeted by SHSB.

RESULTS : Oral SHSB administration showed overt anti-LUAD efficacy as revealed by the extended overall survival of the metastasis model and impaired growth of implanted tumors in the subcutaneous xenograft model. Mechanistically, SHSB administration altered protein expression in the post-transcriptional layer and modified the metabolome of LUAD xenografts. Integrative analysis demonstrated that SHSB markedly inhibited acetyl-CoA synthesis in tumors by post-transcriptionally downregulating ATP-citrate lyase (ACLY). Consistently, our clinical trial showed that oral SHSB administration declined serum acetyl-CoA levels of patients with LC. Moreover, acetyl-CoA synthesis and ACLY expression were both augmented in clinical LUAD tissues of patients, and high intratumoral ACLY expression predicted a detrimental prognosis. Finally, we showed that ACLY-mediated acetyl-CoA synthesis is essential for LUAD cell growth by promoting G1/S transition and DNA replication.

CONCLUSION : Limited downstream targets of SHSB for LC treatment have been reported in previous hypothesis-driven studies. In this study, we conducted a comprehensive multi-omics investigation and demonstrated that SHSB exerted its anti-LUAD efficacy by actively and post-transcriptionally modulating protein expression and particularly restraining ACLY-mediated acetyl-CoA synthesis.

Liu Dan, Dong Changsheng, Wang Fengying, Liu Wei, Jin Xing, Qi Sheng-Lan, Liu Lei, Jin Qiang, Wang Siliang, Wu Jia, Wang Congcong, Yang Jing, Deng Haibin, Cai Yuejiao, Yang Lu, Qin Jingru, Zhang Chengcheng, Yang Xi, Wang Ming-Song, Yu Guanzhen, Xue Yu-Wen, Wang Zhongqi, Ge Guang-Bo, Xu Zhenye, Chen Wen-Lian

2023-Feb-26

ACLY, Acetyl-CoA, Chinese herbal medicine formula, Multi-omics, Shuang-Huang-Sheng-Bai

In Gynecologic oncology ; h5-index 67.0

STUDY OBJECTIVE : There is scant research identifying thematic trends within medical research. This work may provide insight into how a given field values certain topics. We assessed the feasibility of using a machine learning approach to determine the most common research themes published in Gynecologic Oncology over a thirty-year period and to subsequently evaluate how interest in these topics changed over time.

METHODS : We retrieved the abstracts of all original research published in Gynecologic Oncology from 1990 to 2020 using PubMed. Abstract text was processed through a natural language processing algorithm and clustered into topical themes using latent Dirichlet allocation (LDA) prior to manual labeling. Topics were investigated for temporal trends.

RESULTS : We retrieved 12,586 original research articles, of which 11,217 were evaluable for subsequent analysis. Twenty-three research topics were selected at the completion of topic modeling. The topics of basic science genetics, epidemiologic methods, and chemotherapy experienced the greatest increase over the time period, while postoperative outcomes, reproductive age cancer management, and cervical dysplasia experienced the greatest decline. Interest in basic science research remained relatively constant. Topics were additionally reviewed for words indicative of either surgical or medical therapy. Both surgical and medical topics saw increasing interest, with surgical topics experiencing a greater increase and representing a higher proportion of published topics.

CONCLUSIONS : Topic modeling, a type of unsupervised machine learning, was successfully used to identify trends in research themes. The application of this technique provided insight into how the field of gynecologic oncology values the components of its scope of practice and therefore how it may choose to allocate grant funding, disseminate research, and participate in the public discourse.

Grubbs Allison E, Sinha Nikita, Garg Ravi, Barber Emma L

2023-Mar-16

Machine learning, Research trends

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : MRI is considered the gold standard for diagnosing anterior disc displacement (ADD), the most common temporomandibular joint (TMJ) disorder. However, even highly trained clinicians find it difficult to integrate the dynamic nature of MRI with the complicated anatomical features of the TMJ. As the first validated study for MRI-based automatic TMJ ADD diagnosis, we propose a clinical decision support engine that diagnoses TMJ ADD using MR images and provides heat maps as the visualized rationale of diagnostic predictions using explainable artificial intelligence.

METHODS : The engine builds on two deep learning models. The first deep learning model detects a region of interest (ROI) containing three TMJ components (i.e., temporal bone, disc, and condyle) in the entire sagittal MR image. The second deep learning model classifies TMJ ADD into three classes (i.e., normal, ADD without reduction, and ADD with reduction) within the detected ROI. In this retrospective study, the models were developed and tested on the dataset acquired between April 2005 to April 2020. The additional independent dataset acquired at a different hospital between January 2016 to February 2019 was used for the external test of the classification model. Detection performance was assessed by mean average precision (mAP). Classification performance was assessed by the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and Youden's index. 95% confidence intervals were calculated via non-parametric bootstrap to assess the statistical significance of model performances.

RESULTS : The ROI detection model achieved mAP of 0.819 at 0.75 intersection over union (IoU) thresholds in the internal test. In internal and external tests, the ADD classification model achieved AUROC values of 0.985 and 0.960, sensitivities of 0.950 and 0.926, and specificities of 0.919 and 0.892, respectively.

CONCLUSIONS : The proposed explainable deep learning-based engine provides clinicians with the predictive result and its visualized rationale. The clinicians can make the final diagnosis by integrating primary diagnostic prediction obtained from the proposed engine with the patient's clinical examination findings.

Yoon Kyubaek, Kim Jae-Young, Kim Sun-Jong, Huh Jong-Ki, Kim Jin-Woo, Choi Jongeun

2023-Mar-05

Anterior disc displacement, Clinical decision support system, Deep learning, Diagnosis, Explainable artificial intelligence, Temporomadibular joint

In Journal of applied clinical medical physics ; h5-index 28.0

PURPOSE : Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of surgical treatment.

METHODS : Recently, the convolutional neural networks (CNN) have been proved to be efficient for the task of medical image segmentation. The paper proposes an automatic deep learning-based system for liver hepatic vessels segmentation of Computed Tomography (CT) datasets from different sources. The proposed work focuses on the combination of different steps; it starts by a preprocessing step to improve the vessels appearance within the liver region of interest in the CT scans. Coherence enhancing diffusion filtering (CED) and vesselness filtering methods are used to improve vessels contrast and intensity homogeneity. The proposed U-net based network architecture is implemented with modified residual block to include concatenation skip connection. The effect of enhancement using filtering step was studied. Also, the effect of data mismatch used in training and validation is studied.

RESULTS : The proposed method is evaluated using many CT datasets. Dice similarity coefficient (DSC) is used to evaluate the method. The average DSC score achieved a score 79%.

CONCLUSIONS : The proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning.

Alirr Omar Ibrahim, Rahni Ashrani Aizzuddin Abd

2023-Mar-18

CED, U-net, abdominal CT, deep learning, residual block, vasculature segmentation