Introduction: 

A number of genetic-association studies have identified genes contributing to ankylosing spondylitis (AS) susceptibility but such approaches provide little information as to the gene activity changes occurring during the disease process. Transcriptional profiling generates a 'snapshot' of the sampled cells' activity and thus can provide insights into the molecular processes driving the disease process. We undertook a whole-genome microarray approach to identify candidate genes associated with AS and validated these gene-expression changes in a larger sample cohort.


Methods: 

A total of 18 active AS patients, classified according to the New York criteria, and 18 gender- and age-matched controls were profiled using Illumina HT-12 whole-genome expression BeadChips which carry cDNAs for 48,000 genes and transcripts. Class comparison analysis identified a number of differentially expressed candidate genes. These candidate genes were then validated in a larger cohort using qPCR-based TaqMan low density arrays (TLDAs).


Results: 

A total of 239 probes corresponding to 221 genes were identified as being significantly different between patients and controls with a P-value <0.0005 (80% confidence level of false discovery rate). Forty-seven genes were then selected for validation studies, using the TLDAs. Thirteen of these genes were validated in the second patient cohort with 12 downregulated 1.3- to 2-fold and only 1 upregulated (1.6-fold). Among a number of identified genes with well-documented inflammatory roles we also validated genes that might be of great interest to the understanding of AS progression such as SPOCK2 (osteonectin) and EP300, which modulate cartilage and bone metabolism.


Conclusions:

We have validated a gene expression signature for AS from whole blood and identified strong candidate genes that may play roles in both the inflammatory and joint destruction aspects of the disease.

Whole blood transcriptional profiling in ankylosing spondylitis identifies novel candidate genes that might contribute to the inflammatory and tissue-destructive disease aspects

Ankylosing spondylitis (AS) is associated with an increased risk of atherosclerotic cardiovascular disease (ACD). The atherogenic index of plasma (AIP), which is the logarithmic transformation of the plasma triglyceride (TG) level to the high-density lipoprotein level (HDL) ratio, has been suggested to be a novel marker in the identification of atherosclerosis risk. Therefore, this study aims to determine if the AIP can act as an accurate marker for the detection of subclinical atherosclerosis. Fifty-two male patients with AS and 52 age-, gender-, and body mass index (BMI)-matched healthy control subjects were included in the study. For each patient, AIP and total cholesterol (TC)/HDL values were calculated and carotid artery intima-media thickness (cIMT) was measured. The mean (SD) cIMT and median (range) AIP values for AS patients were higher than that of the healthy control subjects (0.60 ± 0.18 vs. 0.51 ± 0.10, p = 0.003 and 0.23 [− 0.32 to 0.85] vs. 0.09 [− 0.53 to 0.49], p = 0.007, respectively). A positive correlation was found between the patients’ cIMT and AIP values (r = 0.307, p = 0.002) and TC/HDL values (r = 0.241, p = 0.014). Regression analysis revealed an independent association between the subclinical atherosclerosis and AIP (beta [β] = 0.309, p = 0.002). There were no independent correlations between subclinical atherosclerosis and TC (β = 0.245, p = 0.065), TG (β = 0.185, p = 0.515), HDL (β = 0.198, p = 0.231), TC/HDL (β = 0.032, p = 0.862), and low-density lipoprotein (LDL) (β = 0.151, p = 0.246). A strong and independent correlation exists between AIP and cIMT values. Therefore, the AIP could serve as a better marker than the TC/HDL ratio for the detection of subclinical atherosclerosis in AS patients.

Atherogenic index of plasma: a useful marker for subclinical atherosclerosis in ankylosing spondylitis : AIP associate with cIMT in AS.

Abstract Traditional Chinese medicine (TCM) has been practiced for thousands of years with clinical efficacy. Natural products and their effective agents such as artemisinin and paclitaxel have saved millions of lives worldwide. Artificial intelligence is being increasingly deployed in TCM. By summarizing the principles and processes of deep learning and traditional machine learning algorithms, analyzing the application of machine learning in TCM, reviewing the results of previous studies, this study proposed a promising future perspective based on the combination of machine learning, TCM theory, chemical compositions of natural products, and computational simulations based on molecules and chemical compositions. In the first place, machine learning will be utilized in the effective chemical components of natural products to target the pathological molecules of the disease which could achieve the purpose of screening the natural products on the basis of the pathological mechanisms they target. In this approach, computational simulations will be used for processing the data for effective chemical components, generating datasets for analyzing features. In the next step, machine learning will be used to analyze the datasets on the basis of TCM theories such as the superposition of syndrome elements. Finally, interdisciplinary natural product-syndrome research will be established by unifying the results of the two steps outlined above, potentially realizing an intelligent artificial intelligence diagnosis and treatment model based on the effective chemical components of natural products under the guidance of TCM theory. This perspective outlines an innovative application of machine learning in the clinical practice of TCM based on the investigation of chemical molecules under the guidance of TCM theory. 

/pdf/machine-learning-in-tcm-with-natural-products-and-molecules-2p98irov.pdf

Machine learning in TCM with natural products and molecules: current status and future perspectives

Overweight produces oxidative stress (OS) on the articular cartilage, with the subsequent risk of developing knee osteoarthritis (OA). Associations between genetic polymorphisms related to OS and OA have been reported, but it is currently unknown whether there exist interactions among them that affect OA development. To identify and evaluate interactions between multiple SNPs related to OS in Mexican knee OA patients. Ninety-two knee OA patients were included in the study, which were compared to 147 healthy controls. Nine variants of six genes (PEPD, AGER, IL6, ADIPOQ, PON1, and CA6) related to OS were genotyped in both study groups through the OpenArray system. Epistasis was analyzed with the multifactor dimensionality reduction (MDR) method. The MDR analysis revealed a significant interaction (p = 0.0107) between polymorphisms rs1501299 (ADIPOQ) and rs662 (PON1), with an entropy value of 9.84%; in addition, high and low risk genotypes were identified between these two polymorphisms. The effect of the interaction between rs1501299 (ADIPOQ) and rs662 (PON1) polymorphisms seems to play an important role in OA pathogenesis; so the epistasis analysis may provide an excellent tool for identifying individuals at high risk for developing OA.

Epistasis between ADIPOQ rs1501299 and PON1 rs662 polymorphisms is potentially associated with the development of knee osteoarthritis

CYP2C19, PON1, and ABCB1 gene polymorphisms in Han and Uygur populations with coronary artery disease in Northwestern Xinjiang, China, From 2014 Through 2019.

Correlation of PON1 polymorphisms with ankylosing spondylitis susceptibility: A case-control study in Chinese Han population.

Background Stroke is the second leading cause of death worldwide, causing a considerable disease burden. Ischemic stroke is more frequent, but haemorrhagic stroke is responsible for more deaths. The clinical management and treatment are different, and it is advantageous to classify their risk as early as possible for disease prevention. Furthermore, retinal characteristics have been associated with stroke and can be used for stroke risk estimation. This study investigated machine learning approaches to retinal images for risk estimation and classification of ischemic and haemorrhagic stroke. Study design A case-control study was conducted in the Shenzhen Traditional Chinese Medicine Hospital. According to the computerized tomography scan (CT) or magnetic resonance imaging (MRI) results, stroke patients were classified as either ischemic or hemorrhage stroke. In addition, a control group was formed using non-stroke patients from the hospital and healthy individuals from the community. Baseline demographic and medical information was collected from participants' hospital medical records. Retinal images of both eyes of each participant were taken within 2 weeks of admission. Classification models using a machine-learning approach were developed. A 10-fold cross-validation method was used to validate the results. Results 711 patients were included, with 145 ischemic stroke patients, 86 haemorrhagic stroke patients, and 480 controls. Based on 10-fold cross-validation, the ischemic stroke risk estimation has a sensitivity and a specificity of 91.0% and 94.8%, respectively. The area under the ROC curve for ischemic stroke is 0.929 (95% CI 0.900 to 0.958). The haemorrhagic stroke risk estimation has a sensitivity and a specificity of 93.0% and 97.1%, respectively. The area under the ROC curve is 0.951 (95% CI 0.918 to 0.983). Conclusion A fast and fully automatic method can be used for stroke subtype risk assessment and classification based on fundus photographs alone.

Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis

Background A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite the clinicians-in-the-loop DL approach having great potential, no study has systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. Objective We systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. Methods PubMed, Embase, IEEEXplore, and the Cochrane Library were searched for studies published between January 1, 2012, and December 7, 2021. Any type of study design was permitted that focused on comparing unassisted clinicians and DL-assisted clinicians in cancer identification using medical imaging. Studies using medical waveform-data graphics material and those investigating image segmentation rather than classification were excluded. Studies providing binary diagnostic accuracy data and contingency tables were included for further meta-analysis. Two subgroups were defined and analyzed, including cancer type and imaging modality. Results In total, 9796 studies were identified, of which 48 were deemed eligible for systematic review. Twenty-five of these studies made comparisons between unassisted clinicians and DL-assisted clinicians and provided sufficient data for statistical synthesis. We found a pooled sensitivity of 83% (95% CI 80%-86%) for unassisted clinicians and 88% (95% CI 86%-90%) for DL-assisted clinicians. Pooled specificity was 86% (95% CI 83%-88%) for unassisted clinicians and 88% (95% CI 85%-90%) for DL-assisted clinicians. The pooled sensitivity and specificity values for DL-assisted clinicians were higher than for unassisted clinicians, at ratios of 1.07 (95% CI 1.05-1.09) and 1.03 (95% CI 1.02-1.05), respectively. Similar diagnostic performance by DL-assisted clinicians was also observed across the predefined subgroups. Conclusions The diagnostic performance of DL-assisted clinicians appears better than unassisted clinicians in image-based cancer identification. However, caution should be exercised, because the evidence provided in the reviewed studies does not cover all the minutiae involved in real-world clinical practice. Combining qualitative insights from clinical practice with data-science approaches may improve DL-assisted practice, although further research is required. Trial Registration PROSPERO CRD42021281372; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=281372

Unassisted Clinicians Versus Deep Learning–Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis

Background: Coronary heart disease (CHD) is the leading cause of death worldwide, constituting a growing health and social burden. People with cardiometabolic disorders are more likely to develop CHD. Retinal image analysis is a novel and noninvasive method to assess microvascular function. We aim to investigate whether retinal images can be used for CHD risk estimation for people with cardiometabolic disorders. Methods: We have conducted a case–control study at Shenzhen Traditional Chinese Medicine Hospital, where 188 CHD patients and 128 controls with cardiometabolic disorders were recruited. Retinal images were captured within two weeks of admission. The retinal characteristics were estimated by the automatic retinal imaging analysis (ARIA) algorithm. Risk estimation models were established for CHD patients using machine learning approaches. We divided CHD patients into a diabetes group and a non-diabetes group for sensitivity analysis. A ten-fold cross-validation method was used to validate the results. Results: The sensitivity and specificity were 81.3% and 88.3%, respectively, with an accuracy of 85.4% for CHD risk estimation. The risk estimation model for CHD with diabetes performed better than the model for CHD without diabetes. Conclusions: The ARIA algorithm can be used as a risk assessment tool for CHD for people with cardiometabolic disorders.

/pdf/risk-assessment-of-chd-using-retinal-images-with-machine-3nfo1se6.pdf

Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders

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Papers

Correlation of PON1 polymorphisms with ankylosing spondylitis susceptibility: A case-control study in Chinese Han population.

Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis

Unassisted Clinicians Versus Deep Learning–Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis

Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders