A semi-automatic deep learning model based on biparametric MRI scanning strategy to predict bone metastases in newly diagnosed prostate cancer patients.

Objective: To develop a semi-automatic model integrating radiomics, deep learning, and clinical features for Bone Metastasis (BM) prediction in prostate cancer (PCa) patients using Biparametric MRI (bpMRI) images. Methods: A retrospective study included 414 PCa patients (BM, n=136; NO-BM, n=278) from two institutions (Center 1, n=318; Center 2, n=96) between January 2016 and December 2022. MRI scans were confirmed with BM status via PET-CT or ECT pre-treatment. Tumor areas on bpMRI images were delineated as tumor's region of interest (ROI) using auto-delineation tumor models, evaluated with Dice similarity coefficient (DSC). Samples were auto-sketched, refined, and used to train the ResNet BM prediction model. Clinical, radiomics, and deep learning data were synthesized into the ResNet-C model, evaluated using receiver operating characteristic (ROC). Results: The auto-segmentation model achieved a DSC of 0.607. Clinical BM prediction's internal validation had an accuracy (ACC) of 0.650 and area under the curve (AUC) of 0.713; external cohort had an ACC of 0.668 and AUC of 0.757. The deep learning model yielded an ACC of 0.875 and AUC of 0.907 for the internal, and ACC of 0.833 and AUC of 0.862 for the external cohort. The Radiomics model registered an ACC of 0.819 and AUC of 0.852 internally, and ACC of 0.885 and AUC of 0.903 externally. ResNet-C demonstrated the highest ACC of 0.902 and AUC of 0.934 for the internal, and ACC of 0.885 and AUC of 0.903 for the external cohort. Conclusion: The ResNet-C model, utilizing bpMRI scanning strategy, accurately assesses bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients, facilitating precise treatment planning and improving patient prognoses.

Gamma-aminobutyric acid (GABA) improves salinity stress tolerance in soybean seedlings by modulating their mineral nutrition, osmolyte contents, and ascorbate-glutathione cycle.

BACKGROUND: In plants, GABA plays a critical role in regulating salinity stress tolerance. However, the response of soybean seedlings (Glycine max L.) to exogenous gamma-aminobutyric acid (GABA) under saline stress conditions has not been fully elucidated. RESULTS: This study investigated the effects of exogenous GABA (2 mM) on plant biomass and the physiological mechanism through which soybean plants are affected by saline stress conditions (0, 40, and 80 mM of NaCl and Na2SO4 at a 1:1 molar ratio). We noticed that increased salinity stress negatively impacted the growth and metabolism of soybean seedlings, compared to control. The root-stem-leaf biomass (27- and 33%, 20- and 58%, and 25- and 59% under 40- and 80 mM stress, respectively]) and the concentration of chlorophyll a and chlorophyll b significantly decreased. Moreover, the carotenoid content increased significantly (by 35%) following treatment with 40 mM stress. The results exhibited significant increase in the concentration of hydrogen peroxide (H2O2), malondialdehyde (MDA), dehydroascorbic acid (DHA) oxidized glutathione (GSSG), Na+, and Cl- under 40- and 80 mM stress levels, respectively. However, the concentration of mineral nutrients, soluble proteins, and soluble sugars reduced significantly under both salinity stress levels. In contrast, the proline and glycine betaine concentrations increased compared with those in the control group. Moreover, the enzymatic activities of ascorbate peroxidase, monodehydroascorbate reductase, glutathione reductase, and glutathione peroxidase decreased significantly, while those of superoxide dismutase, catalase, peroxidase, and dehydroascorbate reductase increased following saline stress, indicating the overall sensitivity of the ascorbate-glutathione cycle (AsA-GSH). However, exogenous GABA decreased Na+, Cl-, H2O2, and MDA concentration but enhanced photosynthetic pigments, mineral nutrients (K+, K+/Na+ ratio, Zn2+, Fe2+, Mg2+, and Ca2+); osmolytes (proline, glycine betaine, soluble sugar, and soluble protein); enzymatic antioxidant activities; and AsA-GSH pools, thus reducing salinity-associated stress damage and resulting in improved growth and biomass. The positive impact of exogenously applied GABA on soybean plants could be attributed to its ability to improve their physiological stress response mechanisms and reduce harmful substances. CONCLUSION: Applying GABA to soybean plants could be an effective strategy for mitigating salinity stress. In the future, molecular studies may contribute to a better understanding of the mechanisms by which GABA regulates salt tolerance in soybeans.

A machine learning radiomics model based on bpMRI to predict bone metastasis in newly diagnosed prostate cancer patients.

OBJECTIVES: To develop and evaluate a machine learning radiomics model based on biparametric magnetic resonance imaging MRI (bpMRI) to predict bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients. METHODS: We retrospectively analyzed bpMRI scans of PCa patients from multiple centers between January 2016 and October 2021. 348 PCa patients were recruited from two institutions for this study. The first institution contributed 284 patients, stratified and randomly divided into training and internal validation cohorts at a 7:3 ratio. The remaining 64 patients were sourced from the second institution and comprised the external validation cohort. Radiomics features were extracted from axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) tumor regions. We developed the radiomics prediction model for BM in the training cohort and validated it in the internal and external validation cohorts. As a benchmark, we trained the logistic regression model with lasso feature reduction (LFR-LRM) in the training cohort and further compared it with Naive Bayes, eXtreme Gradient Boosting (XGboost), Random Forest (RF), GBDT, SVM, Adaboost, and KNN algorithms and validated in both the internal and external cohorts. The performance of several predictive models was assessed by receiver operating characteristic (ROC). RESULTS: The LFR-LRM model achieved an area under the receiver operating characteristic curve (AUC) of 0.89 (95% CI: 0.822-0.974) and an accuracy of 0.828 (95% CI: 0.713-0.911). The AUC and accuracy in external validation were 0.866 (95% CI: 0.784-0.948) and 0.769 (95% CI: 0.648-0.864), respectively. The RF and XGBoost models outperformed the LFR-LRM, with AUCs of 0.907 (95% CI: 0.863-0.949) and 0.928 (95% CI: 0.882-0.974) and accuracies of 0.831 (95% CI: 0.727-0.907) and 0.884 (95% CI: 0.792-0.946). External validation for these models yielded AUCs and accuracies of 0.911 (95% CI: 0.861-0.966), 0.921 (95% CI: 0.889-0.953), and 0.846 (95% CI: 0.735-0.923) and 0.876 (95% CI: 0.771-0.945), respectively. CONCLUSIONS: The XGboost machine learning model is more accurate than LFR-LRM for predicting BM in patients with newly confirmed PCa.

Oleanolic acid inhibits cervical cancer Hela cell proliferation through modulation of the ACSL4 ferroptosis signaling pathway.

Cervical cancer remains the leading cause of cancerous death among women worldwide. Oleanolic acid (OA) is a substance that occurs naturally in the leaves, fruits, and rhizomes of plants and has anti-cancer activity. In this study, tumor-bearing mice were used as the animal model and Hela cells were used as cellular model. In vivo experiments have showed that OA significantly reduced the size and mass of cervical cancer tumors in mice. In vitro experiments have showed that OA significantly reduced the viability and proliferative capacity of Hela cells. In both in vivo and in vitro assays, OA increased the oxidative stress levels and Fe2+ content, and increased the expression of ferroptosis-related proteins. We found that ACSL4 was highly expressed in both xenograft models and cervical carcinoma cells with OA treatment. Further use of siRNA to interfere with ACSL4 expression in cervical cancer cells revealed that the inhibitory effect of OA on cell viability and proliferative capacity was counteracted, while a decrease in ROS levels and GPX4 was detected, suggesting that OA activated ferroptosis in Hela cells by promoting ACSL4 expression, thereby reducing the survival rate of Hela cells. Therefore, promotion of ACSL4-dependent ferroptosis by OA may be a potential approach for the treatment of cervical cancer.

Diagnosis of Thyroid Nodules Based on Local Non-quantitative Multi-Directional Texture Descriptor with Rotation Invariant Characteristics for Ultrasound Image.

The traditional texture feature lacks the directional analysis of graphical element, so it could not better distinguish the thyroid nodule texture image formed by the rotation of graphical element. A non-quantifiable local feature is adopted in this paper to design a robust texture descriptor so as to enhance the robustness of the texture classification in the rotation and scale changes, which can improve the diagnostic accuracy of thyroid nodules in ultrasound images. First of all, the concept of local feature with rotational symmetry is introduced. It is found that many rotation invariant local features are rotational symmetric to a certain degree. Therefore, we propose a novel local feature to describe the rotation invariant properties of the texture. In order to deal with the change of rotation and scale of ultrasound thyroid nodules in image, Pairwise rotation-invariant spatial context feature is adopted to analyze the texture feature, which can combine with the scale information without increasing the dimension of the local feature. The fadopted local features have strong robustness to rotation and gray intensity variation. The experimental results show that our proposed method outperforms the existing algorithms on thyroid ultrasound data sets, which greatly improve the Diagnosis accuracy of thyroid nodules.

Evaluation of the impact of passive smoke on arterial elasticity via echo-tracking technology in a rabbit model.

OBJECTIVES: The purpose of this study was to evaluate the elasticity of the abdominal aorta in passively smoking rabbits using echo-tracking technology and pathologic examination. METHODS: Fifty-four male New Zealand White rabbits were randomly divided into a passive smoking group and a normal control group. The elasticity indicators for the abdominal aorta of the rabbits were measured by means of echo tracking, which was performed before and 1, 2, and 3 months after passive smoking. Measured indicators included the pressure-strain elastic modulus, stiffness, arterial compliance, augmentation index, and pulse wave velocity. After the completion of the in vivo measurements, rabbits were euthanized randomly, and the corresponding arterial sites were resected for pathologic examination and in vitro measurement of vascular elasticity. RESULTS: The echo-tracking technology used in our research proved that the elastic modulus, stiffness, and pulse wave velocity gradually increased with time by passive smoking, whereas arterial compliance decreased by passive smoking. Pathologic examination and in vitro measurements were performed and further confirmed the observed in vivo results. CONCLUSIONS: Passive smoking can injure arteries and reduce arterial elasticity. Echo-tracking technology is an accurate, noninvasive, and reliable method for analysis of the impact of passive smoking on arterial elasticity and detection arterial injury, which also can provide a new instructional basis for prevention and treatment of several arterial diseases.