Online Adaptive Proton Therapy Facilitated by Artificial Intelligence-Based Autosegmentation in Pencil Beam Scanning Proton Therapy.

PURPOSE: Online adaptive proton therapy (oAPT) is essential to address interfractional anatomical changes in patients receiving pencil beam scanning proton therapy. Artificial intelligence (AI)-based autosegmentation can increase the efficiency and accuracy. Linear energy transfer (LET)-based biological effect evaluation can potentially mitigate possible adverse events caused by high LET. New spot arrangement based on the verification computed tomography (vCT) can further improve the replan quality. We propose an oAPT workflow that incorporates all these functionalities and validate its clinical implementation feasibility with patients with prostate cancer. METHODS AND MATERIALS: AI-based autosegmentation tool AccuContour (Manteia) was seamlessly integrated into oAPT. Initial spot arrangement tool on the vCT for reoptimization was implemented using raytracing. An LET-based biological effect evaluation tool was developed to assess the overlap region of high dose and high LET in selected organs at risk. Eleven patients with prostate cancer were retrospectively selected to verify the efficacy and efficiency of the proposed oAPT workflow. The time cost of each component in the workflow was recorded for analysis. RESULTS: The verification plan showed significant degradation of the clinical target volume coverage and rectum and bladder sparing due to the interfractional anatomical changes. Reoptimization on the vCT resulted in great improvement of the plan quality. No overlap regions of high dose and high LET distributions were observed in bladder or rectum in replans. Three-dimensional γ analyses in patient-specific quality assurance confirmed the accuracy of the replan doses before delivery (γ passing rate, 99.57% ± 0.46%) and after delivery (98.59% ± 1.29%). The robustness of the replans passed all clinical requirements. The average time for the complete execution of the workflow was 9.12 ± 0.85 minutes, excluding manual intervention time. CONCLUSIONS: The AI-facilitated oAPT workflow demonstrated to be both efficient and effective by generating a replan that significantly improved the plan quality in prostate cancer treated with pencil beam scanning proton therapy.

Benchmarking a Foundation Large Language Model on its Ability to Relabel Structure Names in Accordance With the American Association of Physicists in Medicine Task Group-263 Report.

PURPOSE: To introduce the concept of using large language models (LLMs) to relabel structure names in accordance with the American Association of Physicists in Medicine Task Group-263 standard and to establish a benchmark for future studies to reference. METHODS AND MATERIALS: Generative Pretrained Transformer (GPT)-4 was implemented within a Digital Imaging and Communications in Medicine server. Upon receiving a structure-set Digital Imaging and Communications in Medicine file, the server prompts GPT-4 to relabel the structure names according to the American Association of Physicists in Medicine Task Group-263 report. The results were evaluated for 3 disease sites: prostate, head and neck, and thorax. For each disease site, 150 patients were randomly selected for manually tuning the instructions prompt (in batches of 50), and 50 patients were randomly selected for evaluation. Structure names considered were those that were most likely to be relevant for studies using structure contours for many patients. RESULTS: The per-patient accuracy was 97.2%, 98.3%, and 97.1% for prostate, head and neck, and thorax disease sites, respectively. On a per-structure basis, the clinical target volume was relabeled correctly in 100%, 95.3%, and 92.9% of cases, respectively. CONCLUSIONS: Given the accuracy of GPT-4 in relabeling structure names as presented in this work, LLMs are poised to become an important method for standardizing structure names in radiation oncology, especially considering the rapid advancements in LLM capabilities that are likely to continue.

Exploration of the molecular characteristics and potential clinical significance of shared immune-related genes between preterm preeclampsia and term preeclampsia.

BACKGROUND: Preeclampsia is a severe obstetric disorder that significantly affects the maternal and neonatal peri-partum safety and long-term quality of life. However, there is limited research exploring the common mechanisms and potential clinical significance between early-onset preeclampsia and full-term preeclampsia from an immunological perspective. METHODS: In this study, data analysis was conducted. Initially, immune-related co-expressed genes involving both subtypes of preeclampsia were identified through Weighted Gene Co-expression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were further employed to investigate the shared pathways regulated by immune-related genes. Binary logistic regression identified co-expressed genes with diagnostic value for preeclampsia, and a diagnostic model was constructed. Gene Set Enrichment Analysis (GSEA) predicted the potential biological functions of the selected genes. Lasso and Cox regression analyses identified genes closely associated with gestational duration, and a risk score model was established. A 4-gene feature, immune-related gene model for predicting the risk of preterm birth in preeclamptic pregnant women, was developed and validated through qPCR experiments. Immune cell infiltration analysis determined differences in immune cell infiltration between the two subtypes of preeclampsia. RESULTS: This study identified 4 immune-related co-expressed genes (CXCR6, PIK3CB, IL1RAP, and OSMR). Additionally, diagnostic and preterm birth risk prediction models for preeclampsia were constructed based on these genes. GSEA analysis suggested the involvement of these genes in the regulation of galactose metabolism, notch signaling pathway, and RIG-I like receptor signaling pathway. Immune pathway analysis indicated that the activation of T cell co-inhibition could be a potential intervention target for immunotherapy in early-onset preeclampsia. CONCLUSION: Our study provides promising insights into immunotherapy and mechanistic research for preeclampsia, discovering novel diagnostic and intervention biomarkers, and offering personalized diagnostic tools for preeclampsia.

Nanoemulsion based lipid nanoparticles for effective demethylcantharidin delivery to cure liver cancer.

Demethylcantharidin (DEM) is a widely used antitumor drug; however, its poor tumor targeting and serious organotoxicity limit its application. The aim of this study was to develop a new drug delivery system for efficient delivery of DEM. Nanoemulsion based lipid nanoparticles containing demethylcantharidin (DNLNs) were prepared by loading nanoemulsions into lipid nanoparticles. The cells proliferation, apoptosis, cycle, and uptake were investigated by Cell counting kit-8 (CCK-8), flow cytometry, and in situ fluorescence assays, respectively. Then, we established the H22 tumor-bearing mouse model to evaluate the antitumor efficacy of DNLNs and further studied its organ toxicity and distribution. DNLNs significantly inhibited the proliferation and promoted apoptosis of H22 cells, and H22 cells could take up more DNLNs. Compared with DEM, DNLNs had certain tumor-targeting properties, and the tumor inhibition rate increased by 23.24%. Moreover, DNLNs can increase white blood cell count and reduce organ toxicity. This study paves the way for nanoemulsion-based lipid nanoparticle (NLNs)-efficient DEM delivery to treat liver cancer.

Predicting Bloodstream Infection in High-risk Groups of Liver Failure Patients with Artificial Liver Support System.

Background: Liver failure is a rare, life-threatening disease that has a high mortality rate and affects many organ systems. Bloodstream bacterial infection has played a key role in liver failure patients with plasma exchange-centered artificial liver support systems, but the predicted risk factors of infection have not been fully understood. Objective: We aimed to predict bloodstream bacterial infection in high-risk groups of liver failure patients during a plasma exchange-centered artificial liver support system. Design: This was a prospective cohort study. Setting: This study was performed in Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School. Participants: 118 liver failure patients with plasma exchange-centered artificial liver support system therapy from Nanjing Drum Tower Hospital from November 2019 to November 2020 were selected. Interventions: We used a stepwise binary logistic regression model to select the optimal risk factors of infection with minimum Akaike information criterion, and the Nomogram prognostic model for bloodstream infection was constructed for visualization. Primary Outcome Measures: Risk factors of bloodstream infection (2) predictive accuracy of the constructed nomogram model. Results: Among the 118 liver failure patients, 22 (18.64%) were diagnosed with bloodstream bacterial infection. The univariable and multivariate logistic regression analyses suggested that culture level, glucocorticoids use, number of punctures, blood platelet counts, white blood cell counts, and indwelling catheter time were the sex predictors of bloodstream infection for liver failure patients during plasma exchange-centered artificial liver support system (P = .042, P = .013, P = .025, P = .003, P = .024 and P = .026). The nomogram predictive model was established with high prediction accuracy, of which the area under the curve was 0.935 (95% confidence interval: 0.884-0.986), the sensitivity was 0.955, and the specificity was 0.854. Conclusion: The constructed nomogram prognostic model can recognize the risk factors and accurately predict bloodstream infection for liver failure patients during plasma exchange-centered artificial liver support system.

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.

Oxidation of Primary Alcohols and Aldehydes to Carboxylic Acids with 1-Hydroxycyclohexyl Phenyl Ketone.

The oxidation of primary alcohols to the corresponding carboxylic acids is one of the fundamental and useful reactions in organic synthesis. In this paper, we report our comprehensive results toward the oxidation of primary alcohols and aldehydes to acids via hydride transfer reactions mediated by 1-hydroxycyclohexyl phenyl ketone. Under the strong basic conditions of sodium tert-butoxide, the room temperature oxidations tolerate a range of functional groups, including vulnerable tert-butanesulfinamides, amines, sulfides, olefins, and heterocycles, and provide good to excellent yields. Most importantly, our oxidation procedure can be applied to chemoselective oxidation of primary alcohols and aldehydes to carboxylic acids in the presence of secondary alcohols.

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.

Total Synthesis of Immunosuppressive Mycestericin E and G Enabled by a Highly Stereoselective Nitroso-Ene Cyclization.

This report describes a streamlined synthesis of immunosuppressive mycestericin E and G through a highly stereoselective nitroso-ene cyclization in 11-12 steps using readily available materials. The stereochemical outcome in the formation of a Nα-quaternary stereogenic center is rationalized by a trajectory based on the polar diradical intermediate and subsequent hydrogen transfer. Julia olefination offers a facile chain elongation method that presents a viable strategy for structural derivatization in future medicinal applications.

Specialized cis-Acting RNA Elements Balance Genome Cyclization to Ensure Efficient Replication of Yellow Fever Virus.

Genome cyclization is essential for viral RNA (vRNA) replication of the vertebrate-infecting flaviviruses, and yet its regulatory mechanisms are not fully understood. Yellow fever virus (YFV) is a notorious pathogenic flavivirus. Here, we demonstrated that a group of cis-acting RNA elements in YFV balance genome cyclization to govern efficient vRNA replication. It was shown that the downstream of the 5'-cyclization sequence hairpin (DCS-HP) is conserved in the YFV clade and is important for efficient YFV propagation. By using two different replicon systems, we found that the function of the DCS-HP is determined primarily by its secondary structure and, to a lesser extent, by its base-pair composition. By combining in vitro RNA binding and chemical probing assays, we found that the DCS-HP orchestrates the balance of genome cyclization through two different mechanisms, as follows: the DCS-HP assists the correct folding of the 5' end in a linear vRNA to promote genome cyclization, and it also limits the overstabilization of the circular form through a potential crowding effect, which is influenced by the size and shape of the DCS-HP structure. We also provided evidence that an A-rich sequence downstream of the DCS-HP enhances vRNA replication and contributes to the regulation of genome cyclization. Interestingly, diversified regulatory mechanisms of genome cyclization, involving both the downstream of the 5'-cyclization sequence (CS) and the upstream of the 3'-CS elements, were identified among different subgroups of the mosquito-borne flaviviruses. In summary, our work highlighted how YFV precisely controls the balance of genome cyclization to ensure viral replication. IMPORTANCE Yellow fever virus (YFV), the prototype of the Flavivirus genus, can cause devastating yellow fever disease. Although it is preventable by vaccination, there are still tens of thousands of yellow fever cases per year, and no approved antiviral medicine is available. However, the understandings about the regulatory mechanisms of YFV replication are obscure. In this study, by a combination of bioinformatics, reverse genetics, and biochemical approaches, it was shown that the downstream of the 5'-cyclization sequence hairpin (DCS-HP) promotes efficient YFV replication by modulating the conformational balance of viral RNA. Interestingly, we found specialized combinations for the downstream of the 5'-cyclization sequence (CS) and upstream of the 3'-CS elements in different groups of the mosquito-borne flaviviruses. Moreover, possible evolutionary relationships among the various downstream of the 5'-CS elements were implied. This work highlighted the complexity of RNA-based regulatory mechanisms in the flaviviruses and will facilitate the design of RNA structure-targeted antiviral therapies.

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.

Comparison of different modified operations in the reduced uteroplacental perfusion pressure rat model of preeclampsia.

INTRODUCTION: Animal models are indispensable tools in studying the mechanisms underlying the diseases. Rat models with reduced uterine perfusion pressure (RUPP) were able to mimic the pathophysiological traits of placental ischemia and hypoxia in preeclampsia (PE). However, ischemic injury can lead to a cascade of damage to lower limb ischemia in RUPP. Therefore, the aim of our study was to compare three modified surgical procedures of reducing uteroplacental perfusion pressure, and to provide a reference for the recognition of different PE phenotypes in the future. MATERIAL AND METHODS: To establish a specific uteroplacental malperfusion model of PE in rats, we bilaterally ligated uterine vessels (UU), ovarian vessels distal to ovarian branches (OO), or both (sRUPP) at 13.5 days post coitum. 21 Sprague-Dawley rats in total were used and were divided into four groups: Sham (n = 4), UU (n = 6), OO (n = 5) and sRUPP (n = 8). RESULTS: The results showed that the OO and sRUPP groups could successfully mimic the phenotypes of PE while UU group not. Then, autophagy, apoptosis, and synthesis of unsaturated fatty acids were increased in both the OO and sRUPP groups compared with the Sham group, while inflammation were not statistically different. CONCLUSIONS: The OO and sRUPP groups could successfully establish the rat model of PE while the UU group not. Notably, between the OO and sRUPP groups, the OO group has a higher fetal survival rate and might be more suitable for studying fetal-related questions, while the sRUPP group has a heavier phenotypic profile and is more suitable for studying maternal phenotypes related to PE.

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.

Synthesis of C2-Carbonyl Indoles via Visible Light-Induced Oxidative Cleavage of an Aminomethylene Group.

A strategy for photochemical oxidative cleavage of the aminomethylene group at the C2 position of indole was developed to synthesize C2-carbonyl indoles. The reaction was initiated by the photochemical oxidation of N1, followed by a water-assisted concerted H-shift by abstracting hydrogen from aminomethylene. Bromopyridine was discovered to play dual roles as an oxidant for the regeneration of photocatalysts and as an accelerant for the single-electron transfer process.

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.

Decatungstate Catalyzed Photochemical Acetylation of C(sp3)-H Bonds.

A direct acetylation of inert C(sp3)-H bonds was developed that was catalyzed by decatungstate under visible light irradiation and was followed by radical addition-disassociation with phenylsulfonyl ethanone oxime. The reaction displays site-selectivity in multiple C(sp3)-H bonds without prefunctionalization and directing groups. Various functional groups are well-tolerated and natural molecules are structurally feasible. CF3-modified phenylsulfonyl ethanone oxime was discovered to be necessary for enhancing the electrophilicity of imine and lowering the C-S bond cleavage energy.