Overexpressed Trophoblast Glycoprotein Contributes to Preeclampsia Development by Inducing Abnormal Trophoblast Migration and Invasion Toward the Uterine Spiral Artery.

BACKGROUND: Preeclampsia is a significant pregnancy disorder with an unknown cause, mainly attributed to impaired spiral arterial remodeling. METHODS: Using RNA sequencing, we identified key genes in placental tissues from healthy individuals and preeclampsia patients. Placenta and plasma samples from pregnant women were collected to detect the expression of TPBG (trophoblast glycoprotein). Pregnant rats were injected with TPBG-carrying adenovirus to detect preeclamptic features. HTR-8/SVneo cells transfected with a TPBG overexpression lentiviral vector were used in cell function experiments. The downstream molecular mechanisms of TPBG were explored using RNA sequencing and single-cell RNA sequencing data. TPBG expression was knocked down in the lipopolysaccharide-induced preeclampsia-like rat model to rescue the preeclampsia features. We also assessed TPBG's potential as an early preeclampsia predictor using clinical plasma samples. RESULTS: TPBG emerged as a crucial differentially expressed gene, expressed specifically in syncytiotrophoblasts and extravillous trophoblasts. Subsequently, we established a rat model with preeclampsia-like phenotypes by intravenously injecting TPBG-expressing adenoviruses, observing impaired spiral arterial remodeling, thus indicating a causal correlation between TPBG overexpression and preeclampsia. Studies with HTR-8/SVneo cells, chorionic villous explants, and transwell assays showed TPBG overexpression disrupts trophoblast/extravillous trophoblast migration/invasion and chemotaxis. Notably, TPBG knockdown alleviated the lipopolysaccharide-induced preeclampsia-like rat model. We enhanced preeclampsia risk prediction in early gestation by combining TPBG expression with established clinical predictors. CONCLUSIONS: These findings are the first to show that TPBG overexpression contributes to preeclampsia development by affecting uterine spiral artery remodeling. We propose TPBG levels in maternal blood as a predictor of preeclampsia risk. The proposed mechanism by which TPBG overexpression contributes to the occurrence of preeclampsia via its disruptive effect on trophoblast and extravillous trophoblast migration/invasion on uterine spiral artery remodeling, thereby increasing the risk of preeclampsia.

Bioinformatics analysis identifies potential autophagy key genes and immune infiltration in preeclampsia.

BACKGROUND: Preeclampsia (PE) is a disease that seriously threatens maternal and fetal health. Appropriate autophagy can shield the placenta from oxidative stress, but its role in PE is unclear. OBJECTIVE: To identify potential autophagy-related genes in PE. METHODS: Microarray datasets from the Gene Expression Omnibus database, compassing the test dataset GSE10588, along with validation datasets GSE4707 and GSE60438 GPL10558, were utilized. Differentially expressed genes (DEGs) were identified using the limma R package, intersected with autophagy-related genes. Hub genes were obtained using the Cytoscape software and analyzed via gene set enrichment analysis (GSEA). The diagnostic capability of hub genes was evaluated using receiver operating characteristic (ROC) curve analysis. Analysis of immune cell infiltration was conducted using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT methods. Placental tissues were collected from 10 normal pregnant women and 10 preeclamptic pregnant women, and the expression of hub genes was validated through immunohistochemistry and western blot analysis. RESULTS: Analysis of the microarray data identified 2224 DEGs, among which 26 were autophagy-related DEGs identified through intersection with autophagy genes. Ten hub genes were identified. Immune cell infiltration analysis suggested the potential involvement of T regulatory cells (Tregs), natural killer cells, neutrophils, and T follicular helper cells in the pathogenesis of PE. ROC curve analysis indicated promising diagnostic capabilities for EGFR and TP53. Additionally, levels of EGFR and TP53 were significantly higher in placental tissue from PE pregnancies compared to normal pregnancies. CONCLUSION: EGFR and TP53 may play a role in PE by influencing autophagy.

Evaluating large language models on a highly-specialized topic, radiation oncology physics.

Purpose: We present the first study to investigate Large Language Models (LLMs) in answering radiation oncology physics questions. Because popular exams like AP Physics, LSAT, and GRE have large test-taker populations and ample test preparation resources in circulation, they may not allow for accurately assessing the true potential of LLMs. This paper proposes evaluating LLMs on a highly-specialized topic, radiation oncology physics, which may be more pertinent to scientific and medical communities in addition to being a valuable benchmark of LLMs. Methods: We developed an exam consisting of 100 radiation oncology physics questions based on our expertise. Four LLMs, ChatGPT (GPT-3.5), ChatGPT (GPT-4), Bard (LaMDA), and BLOOMZ, were evaluated against medical physicists and non-experts. The performance of ChatGPT (GPT-4) was further explored by being asked to explain first, then answer. The deductive reasoning capability of ChatGPT (GPT-4) was evaluated using a novel approach (substituting the correct answer with "None of the above choices is the correct answer."). A majority vote analysis was used to approximate how well each group could score when working together. Results: ChatGPT GPT-4 outperformed all other LLMs and medical physicists, on average, with improved accuracy when prompted to explain before answering. ChatGPT (GPT-3.5 and GPT-4) showed a high level of consistency in its answer choices across a number of trials, whether correct or incorrect, a characteristic that was not observed in the human test groups or Bard (LaMDA). In evaluating deductive reasoning ability, ChatGPT (GPT-4) demonstrated surprising accuracy, suggesting the potential presence of an emergent ability. Finally, although ChatGPT (GPT-4) performed well overall, its intrinsic properties did not allow for further improvement when scoring based on a majority vote across trials. In contrast, a team of medical physicists were able to greatly outperform ChatGPT (GPT-4) using a majority vote. Conclusion: This study suggests a great potential for LLMs to work alongside radiation oncology experts as highly knowledgeable assistants.

SASH1: A Novel Eph Receptor Partner and Insights into SAM-SAM Interactions.

The Eph (erythropoietin-producing human hepatocellular) receptor family, the largest subclass of receptor tyrosine kinases (RTKs), plays essential roles in embryonic development and neurogenesis. The intracellular Sterile Alpha Motif (SAM) domain presents a critical structural feature that distinguishes Eph receptors from other RTKs and participates in recruiting and binding downstream molecules. This study identified SASH1 (SAM and SH3 domain containing 1) as a novel Eph receptor-binding partner through SAM-SAM domain interactions. Our comprehensive biochemical analyses revealed that SASH1 selectively interacts with Eph receptors via its SAM1 domain, displaying the highest affinity for EphA8. The high-resolution crystal structure of the EphA8-SASH1 complex provided insights into the specific intermolecular interactions between these proteins. Cellular assays confirmed that EphA8 and SASH1 co-localize and co-precipitate in mammalian cells, with cancer mutations (EphA8 R942H or G978D) impairing this interaction. We demonstrated that SAM-SAM interaction is critical for SASH1-mediated regulation of EphA8 kinase activity, shedding new light on the Eph signaling pathway and expanding our understanding of the molecular basis of the tumor suppressor gene SASH1.

Deep-learning based fast and accurate 3D CT deformable image registration in lung cancer.

BACKGROUND: Deformable Image Registration (DIR) is an essential technique required in many applications of radiation oncology. However, conventional DIR approaches typically take several minutes to register one pair of 3D CT images and the resulting deformable vector fields (DVFs) are only specific to the pair of images used, making it less appealing for clinical application. PURPOSE: A deep-learning-based DIR method using CT images is proposed for lung cancer patients to address the common drawbacks of the conventional DIR approaches and in turn can accelerate the speed of related applications, such as contour propagation, dose deformation, adaptive radiotherapy (ART), etc. METHODS: A deep neural network based on VoxelMorph was developed to generate DVFs using CT images collected from 114 lung cancer patients. Two models were trained with the weighted mean absolute error (wMAE) loss and structural similarity index matrix (SSIM) loss (optional) (i.e., the MAE model and the M+S model). In total, 192 pairs of initial CT (iCT) and verification CT (vCT) were included as a training dataset and the other independent 10 pairs of CTs were included as a testing dataset. The vCTs usually were taken 2 weeks after the iCTs. The synthetic CTs (sCTs) were generated by warping the vCTs according to the DVFs generated by the pre-trained model. The image quality of the synthetic CTs was evaluated by measuring the similarity between the iCTs and the sCTs generated by the proposed methods and the conventional DIR approaches, respectively. Per-voxel absolute CT-number-difference volume histogram (CDVH) and MAE were used as the evaluation metrics. The time to generate the sCTs was also recorded and compared quantitatively. Contours were propagated using the derived DVFs and evaluated with SSIM. Forward dose calculations were done on the sCTs and the corresponding iCTs. Dose volume histograms (DVHs) were generated based on dose distributions on both iCTs and sCTs generated by two models, respectively. The clinically relevant DVH indices were derived for comparison. The resulted dose distributions were also compared using 3D Gamma analysis with thresholds of 3 mm/3%/10% and 2 mm/2%/10%, respectively. RESULTS: The two models (wMAE and M+S) achieved a speed of 263.7±163 / 265.8±190 ms and a MAE of 13.15±3.8 / 17.52±5.8 HU for the testing dataset, respectively. The average SSIM scores of 0.987±0.006 and 0.988±0.004 were achieved by the two proposed models, respectively. For both models, CDVH of a typical patient showed that less than 5% of the voxels had a per-voxel absolute CT-number-difference larger than 55 HU. The dose distribution calculated based on a typical sCT showed differences of ≤2cGy[RBE] for clinical target volume (CTV) D95 and D5 , within ±0.06% for total lung V5 , ≤1.5cGy[RBE] for heart and esophagus Dmean , and ≤6cGy[RBE] for cord Dmax compared to the dose distribution calculated based on the iCT. The good average 3D Gamma passing rates (> 96% for 3 mm/3%/10% and > 94% for 2 mm/2%/10%, respectively) were also observed. CONCLUSION: A deep neural network-based DIR approach was proposed and has been shown to be reasonably accurate and efficient to register the initial CTs and verification CTs in lung cancer.

Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer.

PURPOSE: In some proton therapy facilities, patient alignment relies on two 2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed imaging is available. The visibility of the tumor in kV images is limited since the patient's 3D anatomy is projected onto a 2D plane, especially when the tumor is behind high-density structures such as bones. This can lead to large patient setup errors. A solution is to reconstruct the 3D CT image from the kV images obtained at the treatment isocenter in the treatment position. METHODS: An asymmetric autoencoder-like network built with vision-transformer blocks was developed. The data was collected from 1 head and neck patient: 2 orthogonal kV images (1024x1024 voxels), 1 3D CT with padding (512x512x512) acquired from the in-room CT-on-rails before kVs were taken and 2 digitally-reconstructed-radiograph (DRR) images (512x512) based on the CT. We resampled kV images every 8 voxels and DRR and CT every 4 voxels, thus formed a dataset consisting of 262,144 samples, in which the images have a dimension of 128 for each direction. In training, both kV and DRR images were utilized, and the encoder was encouraged to learn the jointed feature map from both kV and DRR images. In testing, only independent kV images were used. The full-size synthetic CT (sCT) was achieved by concatenating the sCTs generated by the model according to their spatial information. The image quality of the synthetic CT (sCT) was evaluated using mean absolute error (MAE) and per-voxel-absolute-CT-number-difference volume histogram (CDVH). RESULTS: The model achieved a speed of 2.1s and a MAE of <40HU. The CDVH showed that <5% of the voxels had a per-voxel-absolute-CT-number-difference larger than 185 HU. CONCLUSION: A patient-specific vision-transformer-based network was developed and shown to be accurate and efficient to reconstruct 3D CT images from kV images.

Alpha-2-macroglobulin is involved in the occurrence of early-onset pre-eclampsia via its negative impact on uterine spiral artery remodeling and placental angiogenesis.

BACKGROUND: Pre-eclampsia (PE) is one of the leading causes of maternal and fetal morbidity/mortality during pregnancy, and alpha-2-macroglobulin (A2M) is associated with inflammatory signaling; however, the pathophysiological mechanism by which A2M is involved in PE development is not yet understood. METHODS: Human placenta samples, serum, and corresponding clinical data of the participants were collected to study the pathophysiologic mechanism underlying PE. Pregnant Sprague-Dawley rats were intravenously injected with an adenovirus vector carrying A2M via the tail vein on gestational day (GD) 8.5. Human umbilical artery smooth muscle cells (HUASMCs), human umbilical vein endothelial cells (HUVECs), and HTR-8/SVneo cells were transfected with A2M-expressing adenovirus vectors. RESULTS: In this study, we demonstrated that A2M levels were significantly increased in PE patient serum, uterine spiral arteries, and feto-placental vasculature. The A2M-overexpression rat model closely mimicked the characteristics of PE (i.e., hypertension in mid-to-late gestation, histological and ultrastructural signs of renal damage, proteinuria, and fetal growth restriction). Compared to the normal group, A2M overexpression significantly enhanced uterine artery vascular resistance and impaired uterine spiral artery remodeling in both pregnant women with early-onset PE and in pregnant rats. We found that A2M overexpression was positively associated with HUASMC proliferation and negatively correlated with cell apoptosis. In addition, the results demonstrated that transforming growth factor beta 1 (TGFß1) signaling regulated the effects of A2M on vascular muscle cell proliferation described above. Meanwhile, A2M overexpression regressed rat placental vascularization and reduced the expression of angiogenesis-related genes. In addition, A2M overexpression reduced HUVEC migration, filopodia number/length, and tube formation. Furthermore, HIF-1α expression was positively related to A2M, and the secretion of sFLT-1 and PIGF of placental origin was closely related to PE during pregnancy or A2M overexpression in rats. CONCLUSIONS: Our data showed that gestational A2M overexpression can be considered a contributing factor leading to PE, causing detective uterine spiral artery remodeling and aberrant placental vascularization.

Artificial general intelligence for radiation oncology.

The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can process extensive texts and large vision models (LVMs) such as the Segment Anything Model (SAM) can process extensive imaging data to enhance the efficiency and precision of radiation therapy. This paper explores full-spectrum applications of AGI across radiation oncology including initial consultation, simulation, treatment planning, treatment delivery, treatment verification, and patient follow-up. The fusion of vision data with LLMs also creates powerful multimodal models that elucidate nuanced clinical patterns. Together, AGI promises to catalyze a shift towards data-driven, personalized radiation therapy. However, these models should complement human expertise and care. This paper provides an overview of how AGI can transform radiation oncology to elevate the standard of patient care in radiation oncology, with the key insight being AGI's ability to exploit multimodal clinical data at scale.

Ferritin light chain deficiency-induced ferroptosis is involved in preeclampsia pathophysiology by disturbing uterine spiral artery remodelling.

The proteomic analysis from samples of patients with preeclampsia (PE) displayed a low level of ferritin light chains (FTL), but we do not know what the significance of reduced FTL in PE pathophysiology is. To address this question, we first demonstrated that FTL was expressed in first- and third-trimester cytotrophoblasts, including extravillous trophoblasts (EVTs), of the human placenta. Furthermore, a pregnant rat model of FTL knockdown was successfully established by intravenously injecting adenoviruses expressing shRNA targeting FTL. In pregnant rats with downregulated FTL, we observed PE-like phenotypes and impaired spiral arterial remodelling, implying a causal relationship between FTL downregulation and PE. Blocking ferroptosis with ferrostatin-1 (Fer-1) significantly rescued the above PE-like phenotypes in pregnant rats with FTL knockdown. Furthermore, using trophoblast cell line and chorionic villous explant culture assays, we showed that FTL downregulation induced cell death, especially ferroptosis, resulting in defective uterine spiral artery remodelling. Eventually, this conclusion from the animal model was verified in PE patients' placental tissues. Taken together, this study revealed for the first time that FTL reduction during pregnancy triggered ferroptosis and then caused defective uterine spiral artery remodelling, thereby leading to PE.

Norcantharidin Nanostructured Lipid Carrier (NCTD-NLC) Suppresses the Viability of Human Hepatocellular Carcinoma HepG2 Cells and Accelerates the Apoptosis.

Malignant tumors have become the main cause of harm to human life and health. Development for new antitumor drugs and the exploration to drug carriers are becoming the concerned focus. In this study, we exploited our experiments to explore the effect of NCTD-NLC on liver cancer cells: the HepG2 cells cultured in vitro were given with NCTD-NLC administration; then, the estimation on cellular proliferation and apoptosis was accomplished through MTT and flow cytometry. Six hours after the administration, we performed the High Performance Liquid Chromatography (HPLC) detection to estimate the NCTD content in the heart, liver, spleen, lung, kidney and plasma of rats. Then, our outcomes showed that NCTD-NLC had a notable inhibitory effect on HepG2 cells, leading to a gradually decreased cellular viability. Cell viability was negatively correlated with NCTD-NLC concentration. Along with the concentration increasing, significantly increasing cellular apoptosis and gradually decreasing cellular viability were observed. The apoptosis rate was positively correlated with the concentration of NCTD-NLC. On the basis of the data we obtained, we found that the group with NCTD-NLC tail vein injection had an obvious advantage in drug delivery when compared with other groups. Through the tumorigenesis test to nude mice, we found that the tumor inhibition rate of the NCTD-NLC tail vein injection group had a 27.48% elevation in contrast to the NCTD gavage group, and it was also the group with the best tumor inhibition efficiency. In conclusion, the NCTD-NLC prepared in this study had a mighty inhibitory effect towards HepG2 cellular viability and an accelerating work on apoptosis. Tail vein injection of NCTD-NLC has the best drug delivery effect.

WASF2 Serves as a Potential Biomarker and Therapeutic Target in Ovarian Cancer: A Pan-Cancer Analysis.

Background: Wiskott-Aldrich syndrome protein family member 2 (WASF2) has been shown to play an important role in many types of cancer. Therefore, it is worthwhile to further study expression profile of WASF2 in human cancer, which provides new molecular clues about the pathogenesis of ovarian cancer. Methods: We used a series of bioinformatics methods to comprehensively analyze the relationship between WASF2 and prognosis, tumor microenvironment (TME), immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), and tried to find the potential biological processes of WASF2 in ovarian cancer. Biological behaviors of ovarian cancer cells were investigated through CCK8 assay, scratch test and transwell assay. We also compared WASF2 expression between epithelial ovarian cancer tissues and normal ovarian tissues by using immunohistochemical staining. Results: In the present study, we found that WASF2 was abnormally expressed across the diverse cancer and significantly correlated with overall survival (OS) and progression-free interval (PFI). More importantly, the WASF2 expression level also significantly related to the TME. Our results also showed that the expression of WASF2 was closely related to immune infiltration and immune-related genes. In addition, WASF2 expression was associated with TMB, MSI, and antitumor drugs sensitivity across various cancer types. Functional bioinformatics analysis demonstrated that the WASF2 might be involved in several signaling pathways and biological processes of ovarian cancer. A risk factor model was found to be predictive for OS in ovarian cancer based on the expression of WASF2. Moreover, in vitro experiments, it was demonstrated that the proliferative, migratory and invasive capacity of ovarian cancer cells was significantly inhibited due to WASF2 knockdown. Finally, the immunohistochemistry data confirmed that WASF2 were highly expressed in ovarian cancer. Conclusions: Our study demonstrated that WASF2 expression was associated with a poor prognosis and may be involved in the development of ovarian cancer, which might be explored as a potential prognostic marker and new targeted treatments.

Second-Trimester Cervical Shear Wave Elastography Combined With Cervical Length for the Prediction of Spontaneous Preterm Birth.

The goal of this study was to explore the value of shear wave elastography (SWE) combined with cervical length (CL) in the prediction of spontaneous preterm birth (sPTB) between 18 and 24 weeks of gestation. In this study, SWE was used to evaluate four regions of the cervix: the external and anterior lip (region A1), the external and posterior lip (region A2), the internal and anterior lip (region A3) and the internal and posterior lip (region A4). The cervical Young's modulus (YM) was compared between women who spontaneously delivered prematurely (<37 wk) and those who delivered full term. Finally, the predictive power of SWE was evaluated using receiver operating characteristic analysis. Overall, 773 patients were included in this study, of whom 60 (7.8%) had a sPTB. In the univariate analysis, prior sPTB, history of spontaneous abortion, history of cervical surgery, CL and YM at the anterior portion of both the internal and external os and the posterior portion of the internal os were associated with sPTB (p < 0.05). Multiple regression analyses were performed to develop the prediction probability for sPTB. YM and CL were independent predictors of sPTB in asymptomatic women, and the combination of YM and CL improved the ability to predict sPTB (area under the receiver operating characteristic curve = 0.98, 95% confidence interval: 0.97-0.99, p < 0.001). The interventions had relatively little impact on the outcome indicators measured. Cervical YM added to the CL may improve the predictive performance of second-trimester transvaginal ultrasound for sPTB.