Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis

Confounding bias is a crucial problem when applying machine learning to practice, especially in clinical practice. We consider the problem of learning representations independent to multiple biases. In literature, this is mostly solved by purging the bias information from learned representations. We however expect this strategy to harm the diversity of information in the representation, and thus limiting its prospective usage (e.g., interpretation). Therefore, we propose to mitigate the bias while keeping almost all information in the latent representations, which enables us to observe and interpret them as well. To achieve this, we project latent features onto a learned vector direction, and enforce the independence between biases and projected features rather than all learned features. To interpret the mapping between projected features and input data, we propose projection-wise disentangling: a sampling and reconstruction along the learned vector direction. The proposed method was evaluated on the analysis of 3D facial shape and patient characteristics (N=5011). Experiments showed that this conceptually simple method achieved state-of-the-art fair prediction performance and interpretability, showing its great potential for clinical applications.Comment: Accepted at MICCAI 202

AI-based association analysis for medical imaging using latent-space geometric confounder correction

AI has greatly enhanced medical image analysis, yet its use in epidemiological population imaging studies remains limited due to visualization challenges in non-linear models and lack of confounder control. Addressing this, we introduce an AI method emphasizing semantic feature interpretation and resilience against multiple confounders. Our approach's merits are tested in three scenarios: extracting confounder-free features from a 2D synthetic dataset; examining the association between prenatal alcohol exposure and children's facial shapes using 3D mesh data; exploring the relationship between global cognition and brain images with a 3D MRI dataset. Results confirm our method effectively reduces confounder influences, establishing less confounded associations. Additionally, it provides a unique visual representation, highlighting specific image alterations due to identified correlations.Comment: 18 pages; 7 figure

Hypolipidemic effect of aqueous leaf extract of carmona microphylla G Don

Purpose: To investigate the hypolipidemic effects of the aqueous leaf extract of Carmona microphylla (Lam.) G. Don. (CAE) in vitro and in vivo.Methods: The lipid-lowering effect of CAE was investigated in oleic acid (OA)-induced steatosis in HepG2 liver cells, as well as in high-fat diet (HFD)- and triton WR-1339 (TRI)-induced hyperlipidemic mice. The levels of intracellular, serum and/or hepatic total cholesterol (TC); triglyceride (TG); low density lipoprotein-cholesterol (LDL-c); high density lipoprotein-cholesterol (HDL-c); hepatic superoxide dismutase (SOD) activity and malondialdehyde (MDA) were determined by oil-red O staining and appropriate kits.Results: Treatment with CAE inhibited lipid accumulation in HepG2 cells and reduced the elevated levels of serum TC, TG and LDL-c as well as hepatic TC and TG in hyperlipidemic mice induced by HFD. CAE administration also significantly decreased arteriosclerosis index (AI) and LDL-c/HDL-c ratio, but improved oxidative status as revealed by increased hepatic SOD activity and decreased MDA level. The lipid ameliorating and antioxidative effects of CAE (600 mg/kg) were comparable to those of the standard lipid-lowering drug, sivastatin (5 mg/kg).Conclusion: These results suggest that C. microphylla aqueous extract (CAE) protects against hyperlipidemia induced by HFD in mice and may find therapeutic application in hyperlipidemic patients.Keywords: Carmona microphylla, Hyperlipidemia, Atherosclerosis, Oxidative stress, Sivastatin, Lipidlowerin

Automatic Lumen Segmentation in Intravascular Optical Coherence Tomography Images Using Level Set

<p> Automatic lumen segmentation from intravascular optical coherence tomography (IVOCT) images is an important and fundamental work for diagnosis and treatment of coronary artery disease. However, it is a very challenging task due to irregular lumen caused by unstable plaque and bifurcation vessel, guide wire shadow, and blood artifacts. To address these problems, this paper presents a novel automatic level set based segmentation algorithm which is very competent for irregular lumen challenge. Before applying the level set model, a narrow image smooth filter is proposed to reduce the effect of artifacts and prevent the leakage of level set meanwhile. Moreover, a divide-and-conquer strategy is proposed to deal with the guide wire shadow. With our proposed method, the influence of irregular lumen, guide wire shadow, and blood artifacts can be appreciably reduced. Finally, the experimental results showed that the proposed method is robust and accurate by evaluating 880 images from 5 different patients and the average DSC value was 98.1% +/- 1.1%.</p

A rapid VEGF-gene-sequence photoluminescence detector for osteoarthritis

Osteoarthritis (OA) has become a serious problem to the human society for years due to its high economic burden, disability, pain, and severe impact on the patient’s lifestyle. The importance of current clinical imaging modalities in the assessment of the onset and progression of OA is well recognized by clinicians, but these modalities can only detect OA in the II stage with significant structural deterioration and clinical symptoms. Blood vessel formation induced by vascular endothelial growth factor (VEGF) occurs in the early stage and throughout the entire course of OA, enables VEGF relating gene sequence to act as a biomarker in the field of early diagnosis and monitoring of the disease. Here in, a facile rapid detection of VEGF relating ssDNA sequence was developed, in which manganese-based zeolitic imidazolate framework nanoparticles (Mn-ZIF-NPs) were synthesized by a simple coprecipitation strategy, followed by the introduction and surficial absorption of probe ssDNAs and the CRISPR/Cas12a system components. Furthermore, fluorescence experiments demonstrated that the biosensor displayed a low detection limit of 2.49 nM, a good linear response to the target ssDNA ranging from 10 nM to 500 nM, and the ability of distinguishing single nucleotide polymorphism. This finding opens a new window for the feasible and rapid detection of ssDNA molecules for the early diagnose of OA

Protection of Human Umbilical Vein Endothelial Cells against Oxidative Stress by MicroRNA-210

Oxidative stress induces endothelial cell apoptosis and promotes atherosclerosis development. MicroRNA-210 (miR-210) is linked with apoptosis in different cell types. This study aimed to investigate the role of miR-210 in human umbilical vein endothelial cells (HUVECs) under oxidative stress and to determine the underlying mechanism. HUVECs were treated with different concentrations of hydrogen peroxide (H2O2), and cell viability was evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay and ATP assay. To evaluate the role of miR-210 in H2O2-mediated apoptosis, gain-and-loss-of-function approaches were used, and the effects on apoptosis and reactive oxygen species (ROS) level were assayed using flow cytometry. Moreover, miR-210 expression was detected by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), and expression of the following apoptosis-related genes was assessed by qRT-PCR and Western blot at the RNA and protein level, respectively: caspase-8-associated protein 2 (CASP8AP2), caspase-8, and caspase-3. The results showed that H2O2 induced apoptosis in HUVECs in a dose-dependent manner and increased miR-210 expression. Overexpression of miR-210 inhibited apoptosis and reduced ROS level in HUVECs treated with H2O2. Furthermore, miR-210 downregulated CASP8AP2 and related downstream caspases at protein level. Thus, under oxidative stress, miR-210 has a prosurvival and antiapoptotic effect on HUVECs by reducing ROS generation and downregulating the CASP8AP2 pathway

Position Measurement Based on Fisheye Imaging

For the omnidirectional measurement, the collected images of large-angle fisheye lens need to be corrected and spliced before next procedure, which is complicated and inaccurate. In this paper, a direct position measurement method based on fisheye imaging is proposed for large-angle imaging without any image correcting and splicing. A nonlinear imaging system of fisheye lens is used to acquire the sequence images based on its distortion model, and the critical distortion features of the sequence images are extracted, which contains the position information. And a BP neural network is trained with the extracted image features of previous standard experimental dataset. Finally, the trained BP neural network is employed to measure the object’s distance. Experimental results demonstrate show that the proposed method achieves simple close-object distance measurement with high robustness and a measurement error of ±0.5cm. The proposed method overcomes the shortcomings of conventional measurement methods and expands the fisheye applications filed for omnidirectional measurement