Novel Computer-Aided Diagnosis Schemes for Radiological Image Analysis

The computer-aided diagnosis (CAD) scheme is a powerful tool in assisting clinicians (e.g., radiologists) to interpret medical images more accurately and efficiently. In developing high-performing CAD schemes, classic machine learning (ML) and deep learning (DL) algorithms play an essential role because of their advantages in capturing meaningful patterns that are important for disease (e.g., cancer) diagnosis and prognosis from complex datasets. This dissertation, organized into four studies, investigates the feasibility of developing several novel ML-based and DL-based CAD schemes for different cancer research purposes. The first study aims to develop and test a unique radiomics-based CT image marker that can be used to detect lymph node (LN) metastasis for cervical cancer patients. A total of 1,763 radiomics features were first computed from the segmented primary cervical tumor depicted on one CT image with the maximal tumor region. Next, a principal component analysis algorithm was applied on the initial feature pool to determine an optimal feature cluster. Then, based on this optimal cluster, machine learning models (e.g., support vector machine (SVM)) were trained and optimized to generate an image marker to detect LN metastasis. The SVM based imaging marker achieved an AUC (area under the ROC curve) value of 0.841 ± 0.035. This study initially verifies the feasibility of combining CT images and the radiomics technology to develop a low-cost image marker for LN metastasis detection among cervical cancer patients. In the second study, the purpose is to develop and evaluate a unique global mammographic image feature analysis scheme to identify case malignancy for breast cancer. From the entire breast area depicted on the mammograms, 59 features were initially computed to characterize the breast tissue properties in both the spatial and frequency domain. Given that each case consists of two cranio-caudal and two medio-lateral oblique view images of left and right breasts, two feature pools were built, which contain the computed features from either two positive images of one breast or all the four images of two breasts. For each feature pool, a particle swarm optimization (PSO) method was applied to determine the optimal feature cluster followed by training an SVM classifier to generate a final score for predicting likelihood of the case being malignant. The classification performances measured by AUC were 0.79±0.07 and 0.75±0.08 when applying the SVM classifiers trained using image features computed from two-view and four-view images, respectively. This study demonstrates the potential of developing a global mammographic image feature analysis-based scheme to predict case malignancy without including an arduous segmentation of breast lesions. In the third study, given that the performance of DL-based models in the medical imaging field is generally bottlenecked by a lack of sufficient labeled images, we specifically investigate the effectiveness of applying the latest transferring generative adversarial networks (GAN) technology to augment limited data for performance boost in the task of breast mass classification. This transferring GAN model was first pre-trained on a dataset of 25,000 mammogram patches (without labels). Then its generator and the discriminator were fine-tuned on a much smaller dataset containing 1024 labeled breast mass images. A supervised loss was integrated with the discriminator, such that it can be used to directly classify the benign/malignant masses. Our proposed approach improved the classification accuracy by 6.002%, when compared with the classifiers trained without traditional data augmentation. This investigation may provide a new perspective for researchers to effectively train the GAN models on a medical imaging task with only limited datasets. Like the third study, our last study also aims to alleviate DL models’ reliance on large amounts of annotations but uses a totally different approach. We propose employing a semi-supervised method, i.e., virtual adversarial training (VAT), to learn and leverage useful information underlying in unlabeled data for better classification of breast masses. Accordingly, our VAT-based models have two types of losses, namely supervised and virtual adversarial losses. The former loss acts as in supervised classification, while the latter loss works towards enhancing the model’s robustness against virtual adversarial perturbation, thus improving model generalizability. A large CNN and a small CNN were used in this investigation, and both were trained with and without the adversarial loss. When the labeled ratios were 40% and 80%, VAT-based CNNs delivered the highest classification accuracy of 0.740±0.015 and 0.760±0.015, respectively. The experimental results suggest that the VAT-based CAD scheme can effectively utilize meaningful knowledge from unlabeled data to better classify mammographic breast mass images. In summary, several innovative approaches have been investigated and evaluated in this dissertation to develop ML-based and DL-based CAD schemes for the diagnosis of cervical cancer and breast cancer. The promising results demonstrate the potential of these CAD schemes in assisting radiologists to achieve a more accurate interpretation of radiological images

Vitexin attenuates smoke inhalation induced acute lung injury in rats by inhibiting oxidative stress via PKC β/p66Shc signaling pathway

Purpose: To investigate the protective effect of vitexin on smoke inhalation-induced acute lung injury (SI-ALI), and the underlying mechanism of action.Methods: The ALI rat model was established by inhalation of smoke in a closed smoke chamber. Survival rate, arterial blood gas analysis, wet-to-dry weight ratio of lung tissues, bronchoalveolar lavage fluid protein concentration, lung tissue histology, and oxidative stress and inflammation level were evaluated. Expressions of protein kinase C β (PKC β), p66Shc, and phosphorylated p66Shc were determined by western blot or quantitative reverse transcription-polymerase chain reaction.Results: Compared with smoke inhalation group, vitexin alleviated the decline in arterial partial pressure of oxygen (p < 0.05), reduced lung tissue exudation and pathological lung tissue damage, inhibited the expression of PKC β/p66Shc signaling pathway proteins, downregulated the level of oxidative stress and inflammation, and ultimately improved the survival rate in SI-ALI rats (p < 0.05).Conclusion: Vitexin attenuates SI-ALI in rats by alleviating oxidative stress via inhibition of PKC β/p66Shc signaling pathway. Thus, this compound is a potential agent for the treatment of SI-ALI

Computational Optogenetics: Empirically-Derived Voltage- and Light-Sensitive Channelrhodopsin-2 Model

Channelrhodospin-2 (ChR2), a light-sensitive ion channel, and its variants have emerged as new excitatory optogenetic tools not only in neuroscience, but also in other areas, including cardiac electrophysiology. An accurate quantitative model of ChR2 is necessary for in silicoprediction of the response to optical stimulation in realistic tissue/organ settings. Such a model can guide the rational design of new ion channel functionality tailored to different cell types/tissues. Focusing on one of the most widely used ChR2 mutants (H134R) with enhanced current, we collected a comprehensive experimental data set of the response of this ion channel to different irradiances and voltages, and used these data to develop a model of ChR2 with empirically-derived voltage- and irradiance- dependence, where parameters were fine-tuned via simulated annealing optimization. This ChR2 model offers: 1) accurate inward rectification in the current-voltage response across irradiances; 2) empirically-derived voltage- and light-dependent kinetics (activation, deactivation and recovery from inactivation); and 3) accurate amplitude and morphology of the response across voltage and irradiance settings. Temperature-scaling factors (Q10) were derived and model kinetics was adjusted to physiological temperatures. Using optical action potential clamp, we experimentally validated model-predicted ChR2 behavior in guinea pig ventricular myocytes. The model was then incorporated in a variety of cardiac myocytes, including human ventricular, atrial and Purkinje cell models. We demonstrate the ability of ChR2 to trigger action potentials in human cardiomyocytes at relatively low light levels, as well as the differential response of these cells to light, with the Purkinje cells being most easily excitable and ventricular cells requiring the highest irradiance at all pulse durations. This new experimentally-validated ChR2 model will facilitate virtual experimentation in neural and cardiac optogenetics at the cell and organ level and provide guidance for the development of in vivo tools

Mesenchymal Stem Cells Modified with Heme Oxygenase-1 Have Enhanced Paracrine Function and Attenuate Lipopolysaccharide-Induced Inflammatory and Oxidative Damage in Pulmonary Microvascular Endothelial Cells

Background/Aims: Bone marrow-derived mesenchymal stem cell (BM-MSC) transplantation has therapeutic effects on endothelial damage during acute lung injury (ALI). Heme oxygenase-1 (HO-1) can restore homeostasis and implement cytoprotective defense functions in many pathologic states. Therefore, we explored whether transduction of HO-1 into BM-MSCs (MSCs-HO-1) would have an increased beneficial effect on lipopolysaccharide (LPS)-induced inflammatory and oxidative damage in human pulmonary microvascular endothelial cells (PVECs). Methods: MSCs were isolated from rat bone marrow and transfected with the HO-1 gene by a lentivirus vector. The phenotype and multilineage differentiation of MSCs were assessed. MSCs or MSCs-HO-1 were co-cultured with PVECs using a transwell system, and LPS was added to induce PVEC injury. The production of reactive oxygen species (ROS), and the activities of lipid peroxide (LPO), malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione peroxidase (GPx) in PVECs were determined by flow cytometry and colorimetric assays, respectively. The levels of human PVEC-derived tumor necrosis factor-α (TNF-α), interleukin (IL)-1β and IL-6 in the supernatants of the co-culture system, and the activity of nuclear transcription factor-κB and NF-E2-related factor 2 (Nrf2) in PVECs were examined by enzyme-linked immunosorbent assay (ELISA). The mRNA expression of TNF-α, IL-1β and IL-6 in PVECs was detected by quantitative real-time polymerase chain reaction (qRT-PCR), HO-1 expression and enzymatic activity in PVECs and the influence of zinc protoporphyrin (ZnPP) or HO-1 small interfering RNA on the above inflammatory and oxidative stress markers were evaluated. In addition, the expression of rat MSC-derived hepatocyte growth factor (HGF) and IL-10 was determined by ELISA and qRT-PCR. Results: MSCs showed no significant changes in phenotype or multilineage differentiation after transduction. LPS strongly increased the production of inflammatory and oxidative stress indicators, as well as decreased the levels of antioxidant components and the activity of Nrf2 in PVECs. MSC co-cultivation ameliorated these detrimental effects in PVECs and MSCs-HO-1 further improved the damage to PVECs induced by LPS when compared with MSCs alone. The beneficial effects of MSCs-HO-1 were dependent on HO-1 overexpression and may be attributed to the enhanced paracrine production of HGF and IL-10. Conclusion: MSCs-HO-1 have an enhanced ability to improve LPS-induced inflammatory and oxidative damage in PVECs, and the mechanism may be partially associated with the enhanced paracrine function of the stem cells. These data encourage further testing of the beneficial effects of MSCs-HO-1 in ALI animal models

Cross-cultural metathemes of Chinese and Japanese university students' perspective on parental care

IntroductionDue to declining birthrates and aging populations, parental care is going to place a greater burden on younger generations in the future, especially in East Asia where it is more common for children to provide care regardless of whether there is a national long-term care insurance program. Therefore, it has become important to understand the younger generation's views on parental care.MethodsAn explorative, metathematic qualitative study design was used. Data collection relied on semi-structured interviews, of which 19 Chinese and 19 Japanese university students were conducted from December 2021 to July 2022 using a snowball sampling method. Metatheme analysis was then used to identify broad cross-cultural metathemes and inter-relationships on parental care.ResultsThree parental care metathemes were identified for the perspectives of parental care: distrust of leaving parental care to others, responsibility to care for their parents, and importance of parent-child interactions about parental care.ConclusionTo improve social support for care, both countries must improve long-term care service delivery and healthcare systems and ensure that there is a trusting relationship between healthcare professionals and the public. Governments should also ensure that adult children receive assistance to balance their work, life, and parental care responsibilities. The findings provide several practical suggestions for improving healthcare systems in China and Japan through the younger generations' views

Developing a Novel Image Marker to Predict the Responses of Neoadjuvant Chemotherapy (NACT) for Ovarian Cancer Patients

Objective: Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the patients' responses to NACT varies significantly among different subgroups. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy response prediction of the NACT at an early stage. Methods: For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. Using this cluster as the input, an SVM based classifier was developed and optimized to create a final marker, indicating the likelihood of the patient being responsive to the NACT treatment. To validate this scheme, a total of 42 ovarian cancer patients were retrospectively collected. A nested leave-one-out cross-validation was adopted for model performance assessment. Results: The results demonstrate that the new method yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.745. Meanwhile, the model achieved overall accuracy of 76.2%, positive predictive value of 70%, and negative predictive value of 78.1%. Conclusion: This study provides meaningful information for the development of radiomics based image markers in NACT response prediction

Evaluating the Effectiveness of 2D and 3D Features for Predicting Tumor Response to Chemotherapy

2D and 3D tumor features are widely used in a variety of medical image analysis tasks. However, for chemotherapy response prediction, the effectiveness between different kinds of 2D and 3D features are not comprehensively assessed, especially in ovarian cancer-related applications. This investigation aims to accomplish such a comprehensive evaluation. For this purpose, CT images were collected retrospectively from 188 advanced-stage ovarian cancer patients. All the metastatic tumors that occurred in each patient were segmented and then processed by a set of six filters. Next, three categories of features, namely geometric, density, and texture features, were calculated from both the filtered results and the original segmented tumors, generating a total of 1595 and 1403 features for the 3D and 2D tumors, respectively. In addition to the conventional single-slice 2D and full-volume 3D tumor features, we also computed the incomplete-3D tumor features, which were achieved by sequentially adding one individual CT slice and calculating the corresponding features. Support vector machine (SVM) based prediction models were developed and optimized for each feature set. 5-fold cross-validation was used to assess the performance of each individual model. The results show that the 2D feature-based model achieved an AUC (area under the ROC curve [receiver operating characteristic]) of 0.84+-0.02. When adding more slices, the AUC first increased to reach the maximum and then gradually decreased to 0.86+-0.02. The maximum AUC was yielded when adding two adjacent slices, with a value of 0.91+-0.01. This initial result provides meaningful information for optimizing machine learning-based decision-making support tools in the future

Effects of livestock overgrazing on the relationships between plant and microbial diversity across the temperate steppes in northern China

11 páginas.- 3 figuras.- 3 tablas.- 57 referencias.-Livestock overgrazing has led to worldwide grassland degradation, posing a significant threat to plant and soil microbial diversity. However, little is known about whether livestock overgrazing influences plant and soil microbial diversity linkages. We examined relationships between plant and soil microbial beta diversity in eight pairs of ungrazed and overgrazed sites across temperate steppes in northern China. Our results revealed a positive correlation between plant and microbial beta diversity across ungrazed grasslands, and overgrazing did not change this relationship. However, different mechanisms underlay the correlations between plant and microbial beta diversity in ungrazed and overgrazed grasslands. In ungrazed grasslands, plant and microbial diversity associations were maintained mainly due to their similar responses to the shared environmental factors. While in overgrazed grasslands, the maintenance of plant and microbial diversity associations was primarily due to their functional associations. Furthermore, the positive links between plant species and microbial taxa increased in overgrazed grasslands, indicating that more soil microbial taxa form close associations with plant species in overgrazed grasslands. Our work provides new insights regarding the mechanisms of plant and microbial communities that associate under different ecological contexts, ultimately suggesting that the functional associations of plant and microbial communities are tighter as grazing intensifies in grasslands.The work was made possible by the National Natural Science Founda-tion of China (No. 32271642, 32061143027). M.D-B. acknowledges support from the Spanish Ministry of Science and Innovation for the I+D+i project PID2020-115813RA-I00 funded by MCIN/AEI/10.13039/501100011033. M.D-B. is also supported by a project of the Fondo Europeo de Desarrollo Regional (FEDER) and the Consejería de Transformación Económica, Industria, Conocimiento y Universidades of the Junta de Andalucía (FEDER Andalucía 2014-2020 Objetivo temático“01 - Refuerzo de la investigación, el desarrollo tecnológico y la innovación”) associated with the research project P20_00879 (ANDABIOMA).Peer reviewe