Swertiamarin relieves radiation-induced intestinal injury by limiting DNA damage.

Radiotherapy is the conventional treatment for pelvic abdominal tumors. However, it can cause some damage to the small intestine and colorectal, which are very sensitive to radiation. Radiation-induced intestinal injury (RIII) affects the prognosis of radiotherapy, causing sequelae of loss of function and long-term damage to patients' quality of life. Swertiamarin is a glycoside that has been reported to prevent a variety of diseases including but not limited to diabetes, hypertension, atherosclerosis, arthritis, malaria, and abdominal ulcers. However, its therapeutic effect and mechanism of action on RIII have not been established. We investigated whether swertiamarin has a protective effect against RIII. In this article, we use irradiator to create cellular and mouse models of radiation damage. Preventive administration of swertiamarin could reduce ROS and superoxide anion levels to mitigate the cellular damage caused by radiation. Swertiamarin also attenuated RIII in mice, as evidenced by longer survival, less weight loss and more complete intestinal barrier. We also found an increase in the relative abundance of primary bile acids in irradiated mice, which was reduced by both FXR agonists and swertiamarin, and a reduction in downstream interferon and inflammatory factors via the cGAS-STING pathway to reduce radiation-induced damage.

LILRB4 represents a promising target for immunotherapy by dual targeting tumor cells and myeloid-derived suppressive cells in multiple myeloma.

Multiple myeloma (MM) remains an incurable hematological malignancy. Despite tremendous advances in the treatment, about 10% of patients still have very poor outcomes with median overall survival less than 24 months. Our study aimed to underscore the critical mechanisms pertaining to the rapid disease progression and provide novel therapeutic selection for these ultra-high-risk patients. We utilized single-cell transcriptomic sequencing to dissect the characteristic bone marrow niche of patients with survival of less than two years (EM24). Notably, an enrichment of LILRB4high pre-matured plasma-cell cluster was observed in the patients in EM24 compared to patients with durable remission. This cluster exhibited aggressive proliferation and drug-resistance phenotype. High-level LILRB4 promoted MM clonogenicity and progression. Clinically, high expression of LILRB4 was correlated with poor prognosis in both newly diagnosed MM patients and relapsed/refractory MM patients. The ATAC-seq analysis identified that high chromosomal accessibility caused the elevation of LILRB4 on MM cells. CRISPR-Cas9 deletion of LILRB4 alleviated the growth of MM cells, inhibited the immunosuppressive function of MDSCs, and further rescued T cell dysfunction in MM microenvironment. The more infiltration of myeloid-derived suppressive cells (MDSCs) was observed in EM24 patients as well. Therefore, we innovatively generated a TCR-based chimeric antigen receptor (CAR) T cell, LILRB4-STAR-T. Cytotoxicity experiment demonstrated that LILRB4-STAR-T cells efficaciously eliminated tumor cells and impeded MDSCs function. In conclusion, our study elucidates that LILRB4 is an ideal biomarker and promising immunotherapy target for high-risk MM. LILRB4-STAR-T cell immunotherapy is promising against tumor cells and immunosuppressive tumor microenvironment in MM.

Novel Insights into the Initiation, Evolution, and Progression of Multiple Myeloma by Multi-Omics Investigation.

The evolutionary history of multiple myeloma (MM) includes malignant transformation, followed by progression to pre-malignant stages and overt malignancy, ultimately leading to more aggressive and resistant forms. Over the past decade, large effort has been made to identify the potential therapeutic targets in MM. However, MM remains largely incurable. Most patients experience multiple relapses and inevitably become refractory to treatment. Tumor-initiating cell populations are the postulated population, leading to the recurrent relapses in many hematological malignancies. Clonal evolution of tumor cells in MM has been identified along with the disease progression. As a consequence of different responses to the treatment of heterogeneous MM cell clones, the more aggressive populations survive and evolve. In addition, the tumor microenvironment is a complex ecosystem which plays multifaceted roles in supporting tumor cell evolution. Emerging multi-omics research at single-cell resolution permits an integrative and comprehensive profiling of the tumor cells and microenvironment, deepening the understanding of biological features of MM. In this review, we intend to discuss the novel insights into tumor cell initiation, clonal evolution, drug resistance, and tumor microenvironment in MM, as revealed by emerging multi-omics investigations. These data suggest a promising strategy to unravel the pivotal mechanisms of MM progression and enable the improvement in treatment, both holistically and precisely.

Monitoring Minimal Residual Disease in Patients with Multiple Myeloma by Targeted Tracking Serum M-Protein Using Mass Spectrometry (EasyM).

PURPOSE: We investigated both the clinical utilities and the prognostic impacts of the clonotypic peptide mass spectrometry (MS)-EasyM, a blood-based minimal residual disease (MRD) monitoring protocol in multiple myeloma. EXPERIMENTAL DESIGN: A total of 447 sequential serum samples from 56 patients with multiple myeloma were analyzed using EasyM. Patient-specific M-protein peptides were sequenced from diagnostic samples; sequential samples were quantified by EasyM to monitor the M-protein. The performance of EasyM was compared with serum immunofixation electrophoresis (IFE), bone marrow multiparameter flow cytometry (MFC), and next-generation flow cytometry (NGF) detection. The optimal balance of EasyM sensitivity/specificity versus NGF (10-5 sensitivity) was determined and the prognostic impact of MS-MRD status was investigated. RESULTS: Of the 447 serum samples detected and measured by EasyM, 397, 126, and 92 had time-matching results for comparison with serum IFE, MFC-MRD, and NGF-MRD, respectively. Using a dotp >0.9 as the MS-MRD positive, sensitivity was 99.6% versus IFE and 100.0% versus MFC and NGF. Using an MS negative cutoff informed by ROC analysis (<1.86% of that at diagnosis), EasyM sensitivity remained high versus IFE (88.3%), MFC (85.1%), and NGF (93.2%), whereas specificity increased to 90.4%, 55.8%, and 93.2%, respectively. In the multivariate analysis, older diagnostic age was an independent predictor for progression-free survival [PFS; high risk (HR), 3.15; 1.26-7.86], the best MS-MRD status (MS-MRD negative) was independent predictor for both PFS (HR, 0.25; 0.12-0.52) and overall survival (HR, 0.16; 0.06-0.40). CONCLUSIONS: EasyM is a highly sensitive and minimal invasive method of MRD monitoring in multiple myeloma; MS-MRD had significant predictive ability for survival outcomes.

CRIP1 involves the pathogenesis of multiple myeloma via dual-regulation of proteasome and autophagy.

BACKGROUND: Multiple myeloma (MM) is an incurable hematological malignancy of the plasma cells. The maintenance of protein homeostasis is critical for MM cell survival. Elevated levels of paraproteins in MM cells are cleared by proteasomes or lysosomes, which are independent but inter-connected with each other. Proteasome inhibitors (PIs) work as a backbone agent and successfully improved the outcome of patients; however, the increasing activity of autophagy suppresses the sensitivity to PIs treatment. METHODS: The transcription levels of CRIP1 were explored in plasma cells obtained from healthy donors, patients with newly diagnosed multiple myeloma (NDMM), and relapsed/refractory multiple myeloma (RRMM) using Gene expression omnibus datasets. Doxycycline-inducible CRIP1-shRNA and CRIP1 overexpressed MM cell lines were constructed to explore the role of CRIP1 in MM pathogenesis. Proliferation, invasion, migration, proteasome activity and autophagy were examined in MM cells with different CRIP1 levels. Co-immunoprecipitation (Co-IP) with Tandem affinity purification/Mass spectrum (TAP/MS) was performed to identify the binding proteins of CRIP1. The mouse xenograft model was used to determine the role of CRIP1 in the proliferation and drug-resistance of MM cells. FINDINGS: High CRIP1 expression was associated with unfavorable clinical outcomes in patients with MM and served as a biomarker for RRMM with shorter overall survival. In vitro and in vivo studies showed that CRIP1 plays a critical role in protein homeostasis via the dual regulation of the activities of proteasome and autophagy in MM cells. A combined analysis of RNA-seq, Co-IP and TAP/MS demonstrated that CRIP1 promotes proteasome inhibitors resistance in MM cells by simultaneously binding to de-ubiquitinase USP7 and proteasome coactivator PA200. CRIP1 promoted proteasome activity and autophagosome maturation by facilitating the dequbiquitination and stabilization of PA200. INTERPRETATION: Our findings clarified the pivotal roles of the CRIP1/USP7/PA200 complex in ubiquitin-dependent proteasome degradation and autophagy maturation involved in the pathogenesis of MM. FUNDING: A full list of funding sources can be found in the acknowledgements section.

Exosome miRNAs profiling in serum and prognostic evaluation in patients with multiple myeloma.

MicroRNAs (MiRNAs) carried by exosomes play pivotal roles in the crosstalk between cell components in the tumor microenvironment. Our study aimed at identifying the expression profile of exosomal miRNAs (exo-miRNAs) in the serum of multiple myeloma (MM) patients and investigating the regulation networks and their potential functions by integrated bioinformatics analysis. Exosomes in serum from 19 newly diagnosed MM patients and 9 healthy donors were isolated and the miRNA profile was investigated by small RNA sequencing. Differential expression of exo-miRNAs was calculated and target genes of miRNAs were predicted. CytoHubba was applied to identify the hub miRNAs and core target genes. The LASSO Cox regression model was used to develop the prognostic model, and the ESTIMATE immune score was calculated to investigate the correlation between the model and immune status in MM patients. The top six hub differentially expressed serum exo-miRNAs were identified. 513 target genes of the six hub exo-miRNAs were confirmed to be differentially expressed in MM cells in the Zhan Myeloma microarray dataset. Functional enrichment analysis indicated that these target genes were mainly involved in mRNA splicing, cellular response to stress, and deubiquitination. 13 core exo-miRNA target genes were applied to create a novel prognostic signature to provide risk stratification for MM patients, which is associated with the immune microenvironment of MM patients. Our study comprehensively investigated the exo-miRNA profiles in MM patients. A novel prognostic signature was constructed to facilitate the risk stratification of MM patients with distinct outcomes.

Single-cell profiles reveal tumor cell heterogeneity and immunosuppressive microenvironment in Waldenström macroglobulinemia.

BACKGROUND: Waldenström macroglobulinemia (WM) is a rare and incurable indolent B-cell malignancy. The molecular pathogenesis and the role of immunosuppressive microenvironment in WM development are still incompletely understood. METHODS: The multicellular ecosystem in bone marrow (BM) of WM were delineated by single-cell RNA-sequencing (scRNA-seq) and investigated the underlying molecular characteristics. RESULTS: Our data uncovered the heterogeneity of malignant cells in WM, and investigated the kinetic co-evolution of WM and immune cells, which played pivotal roles in disease development and progression. Two novel subpopulations of malignant cells, CD19+CD3+ and CD138+CD3+, co-expressing T-cell marker genes were identified at single-cell resolution. Pseudotime-ordered analysis elucidated that CD19+CD3+ malignant cells presented at an early stage of WM-B cell differentiation. Colony formation assay further identified that CD19+CD3+ malignant cells acted as potential WM precursors. Based on the findings of T cell marker aberrant expressed on WM tumor cells, we speculate the long-time activation of tumor antigen-induced immunosuppressive microenvironment that is involved in the pathogenesis of WM. Therefore, our study further investigated the possible molecular mechanism of immune cell dysfunction. A precursor exhausted CD8-T cells and functional deletion of NK cells were identified in WM, and CD47 would be a potential therapeutic target to reverse the dysfunction of immune cells. CONCLUSIONS: Our study facilitates further understanding of the biological heterogeneity of tumor cells and immunosuppressive microenvironment in WM. These data may have implications for the development of novel immunotherapies, such as targeting pre-exhausted CD8-T cells in WM.

Aberrant metabolic processes promote the immunosuppressive microenvironment in multiple myeloma.

Introduction: Multiple myeloma (MM) is still an incurable plasma cell malignancy. The efficacy of immunotherapy on MM remains unsatisfactory, and the underlying molecular mechanisms still are not fully understood. Methods: In this study, we delineated the dynamic features of immune cell in MM bone marrow (BM) along with elevated tumor cell infiltration by single-cell RNA sequencing (scRNA-seq), and investigated the underlying mechanisms on dysfunction of immune cells associated with myelomagenesis. Results: We found that immune cells were activated in those patients with low infiltration of tumor cells, meanwhile suppressed with elevated infiltration of MM cells, which facilitated MM escaping from immune surveillance. Besides PD-1, abnormal expression of PIM kinases, KLRB1 and KLRC1 were involved in the defect of immune cells in MM patients. Importantly, we found aberrant metabolic processes were associated with the immunosuppressive microenvironment in MM patients. Disordered amino acid metabolism promoted the dysfunction of cytotoxicity CD8 T cells as well as lipid metabolism disorder was associated with the dysregulation of NK and DCs in MM. As metabolic checkpoints, PIM kinases would be potential effective strategies for MM immunotherapy. Discussion: In summary, redressing the disordered metabolism should be the key points to get promising effects in immune-based therapies.

Artificial intelligence in gastric cancer: applications and challenges.

Gastric cancer (GC) is one of the most common malignant tumors with high mortality. Accurate diagnosis and treatment decisions for GC rely heavily on human experts' careful judgments on medical images. However, the improvement of the accuracy is hindered by imaging conditions, limited experience, objective criteria, and inter-observer discrepancies. Recently, the developments of machine learning, especially deep-learning algorithms, have been facilitating computers to extract more information from data automatically. Researchers are exploring the far-reaching applications of artificial intelligence (AI) in various clinical practices, including GC. Herein, we aim to provide a broad framework to summarize current research on AI in GC. In the screening of GC, AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological confirmation. In the diagnosis of GC, AI can support tumor-node-metastasis (TNM) staging and subtype classification. For treatment decisions, AI can help with surgical margin determination and prognosis prediction. Meanwhile, current approaches are challenged by data scarcity and poor interpretability. To tackle these problems, more regulated data, unified processing procedures, and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.

Automatic captioning of early gastric cancer using magnification endoscopy with narrow-band imaging.

BACKGROUND AND AIMS: The detection rate for early gastric cancer (EGC) is unsatisfactory, and mastering the diagnostic skills of magnifying endoscopy with narrow-band imaging (ME-NBI) requires rich expertise and experience. We aimed to develop an EGC captioning model (EGCCap) to automatically describe the visual characteristics of ME-NBI images for endoscopists. METHODS: ME-NBI images (n = 1886) from 294 cases were enrolled from multiple centers, and corresponding 5658 text data were designed following the simple EGC diagnostic algorithm. An EGCCap was developed using the multiscale meshed-memory transformer. We conducted comprehensive evaluations for EGCCap including the quantitative and quality of performance, generalization, robustness, interpretability, and assistant value analyses. The commonly used metrics were BLEUs, CIDEr, METEOR, ROUGE, SPICE, accuracy, sensitivity, and specificity. Two-sided statistical tests were conducted, and statistical significance was determined when P < .05. RESULTS: EGCCap acquired satisfying captioning performance by outputting correctly and coherently clinically meaningful sentences in the internal test cohort (BLEU1 = 52.434, CIDEr = 36.734, METEOR = 27.823, ROUGE = 49.949, SPICE = 35.548) and maintained over 80% performance when applied to other centers or corrupted data. The diagnostic ability of endoscopists improved with the assistance of EGCCap, which was especially significant (P < .05) for junior endoscopists. Endoscopists gave EGCCap an average remarkable score of 7.182, showing acceptance of EGCCap. CONCLUSIONS: EGCCap exhibited promising captioning performance and was proven with satisfying generalization, robustness, and interpretability. Our study showed potential value in aiding and improving the diagnosis of EGC and facilitating the development of automated reporting in the future.

The potential of prostate gland radiomic features in identifying the Gleason score.

BACKGROUND: Gleason score (GS) is one of the most critical predictors of diagnosing prostate cancer (PCa). The prostate gland, including both lesions and their microenvironment, may contain more comprehensive information about the PCa. We aimed to investigate the potential of prostate gland radiomic features in identifying Gleason scores (GS) < 7, = 7, and >7. METHODS: We retrospectively examined preoperative magnetic resonance imaging (MRI) results, clinical data, and postoperative pathological findings from 489 PCa patients. The three-dimensional (3D) and two-dimensional (2D) radiomic features were extracted from the manually segmented 3D prostate gland and its maximum 2D layer on MRI, respectively. Significant features were selected, and sequence signatures were then developed via multi-class linear regression (MLR) accordingly. Subsequently, 2D and 3D radiomic models were constructed by applying MLR to the combination of the sequence signatures, respectively. The stability of the significant features was discussed by their average ranking in the other 30 random cohorts. Based on our distance matrix algorithm, we generated different regions of interest to simulate the manual segmentation biases and discuss the model's tolerance to them. RESULTS: Our 2D model reached a C-index of 0.728 and an average area under the receiver operating characteristic curve of 0.794 in the validation cohort. The corresponding key features were stable, with an average ranking of the top 8.352% in 30 random cohorts, and the model could tolerate a segmentation boundary deviation of 2 mm without significant performance degradation. CONCLUSION: 2D prostate-gland-MRI-based radiomic features showed stable potential in identifying GS.

Roles of peptidyl-prolyl isomerase Pin1 in disease pathogenesis.

Pin1 belongs to the peptidyl-prolyl cis-trans isomerases (PPIases) superfamily and catalyzes the cis-trans conversion of proline in target substrates to modulate diverse cellular functions including cell cycle progression, cell motility, and apoptosis. Dysregulation of Pin1 has wide-ranging influences on the fate of cells; therefore, it is closely related to the occurrence and development of various diseases. This review summarizes the current knowledge of Pin1 in disease pathogenesis.

Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study.

BACKGROUND AND AIMS: Narrow-band imaging with magnifying endoscopy (ME-NBI) has shown advantages in the diagnosis of early gastric cancer (EGC). However, proficiency in diagnostic algorithms requires substantial expertise and experience. In this study, we aimed to develop a computer-aided diagnostic model for EGM (EGCM) to analyze and assist in the diagnosis of EGC under ME-NBI. METHODS: A total of 1777 ME-NBI images from 295 cases were collected from 3 centers. These cases were randomly divided into a training cohort (n = 170), an internal test cohort (ITC, n = 73), and an external test cohort (ETC, n = 52). EGCM based on VGG-19 architecture (Visual Geometry Group [VGG], Oxford University, Oxford, UK) with a single fully connected 2-classification layer was developed through fine-tuning and validated on all cohorts. Furthermore, we compared the model with 8 endoscopists with varying experience. Primary comparison measures included accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: EGCM acquired AUCs of .808 in the ITC and .813 in the ETC. Moreover, EGCM achieved similar predictive performance as the senior endoscopists (accuracy: .770 vs .755, P = .355; sensitivity: .792 vs .767, P = .183; specificity: .745 vs .742, P = .931) but better than the junior endoscopists (accuracy: .770 vs .728, P < .05). After referring to the results of EGCM, the average diagnostic ability of the endoscopists was significantly improved in terms of accuracy, sensitivity, PPV, and NPV (P < .05). CONCLUSIONS: EGCM exhibited comparable performance with senior endoscopists in the diagnosis of EGC and showed the potential value in aiding and improving the diagnosis of EGC by endoscopists.

Noninvasive Prediction of High-Grade Prostate Cancer via Biparametric MRI Radiomics.

BACKGROUND: Gleason score (GS) is a histologic prognostic factor and the basis of treatment decision-making for prostate cancer (PCa). Treatment regimens between lower-grade (GS ≤7) and high-grade (GS >7) PCa differ largely and have great effects on cancer progression. PURPOSE: To investigate the use of different sequences in biparametric MRI (bpMRI) of the prostate gland for noninvasively distinguishing high-grade PCa. STUDY TYPE: Retrospective. POPULATION: In all, 489 patients (training cohort: N = 326; test cohort: N = 163) with PCa between June 2008 and January 2018. FIELD STRENGTH/SEQUENCE: 3.0T, pelvic phased-array coils, bpMRI including T2 -weighted imaging (T2 WI) and diffusion-weighted imaging (DWI); apparent diffusion coefficient map extracted from DWI. ASSESSMENT: The whole prostate gland was delineated. Radiomic features were extracted and selected using the Kruskal-Wallis test, the minimum redundancy-maximum relevance, and the sequential backward elimination algorithm. Two single-sequence radiomic (T2 WI, DWI) and two combined (T2 WI-DWI, T2 WI-DWI-Clinic) models were respectively constructed and validated via logistic regression. STATISTICAL TESTS: The Kruskal-Wallis test and chi-squared test were utilized to evaluate the differences among variable groups. P < 0.05 determined statistical significance. The area under the receiver operating characteristic curve (AUC), specificity, sensitivity, and accuracy were used to evaluate model performance. The Delong test was conducted to compare the differences between the AUCs of all models. RESULT: All radiomic models showed significant (P < 0.001) predictive performances. Between the single-sequence radiomic models, the DWI model achieved the most encouraging results, with AUCs of 0.801 and 0.787 in the training and test cohorts, respectively. For the combined models, the T2 WI-DWI models acquired an AUC of 0.788, which was almost the same with DWI in the test cohort, and no significant difference was found between them (training cohort: P = 0.199; test cohort: P = 0.924). DATA CONCLUSION: Radiomics based on bpMRI can noninvasively identify high-grade PCa before the operation, which is helpful for individualized diagnosis of PCa. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1102-1109.

Assessing the effectiveness of artificial intelligence methods for melanoma: A retrospective review.

BACKGROUND: Artificial intelligence methods for the classification of melanoma have been studied extensively. However, few studies compare these methods under the same standards. OBJECTIVE: To seek the best artificial intelligence method for diagnosis of melanoma. METHODS: The contrast test used 2200 dermoscopic images. Image segmentations, feature extractions, and classifications were performed in sequence for evaluation of traditional machine learning algorithms. The recent popular convolutional neural network frameworks were used for transfer learning training classification. RESULTS: The region growing algorithm has the best segmentation performance, with an intersection over union of 70.06% and a false-positive rate of 17.67%. Classification performance was better with logistic regression, with a sensitivity of 76.36% and a specificity of 87.04%. The Inception V3 model (Google, Mountain View, CA) worked best in deep learning algorithms: the accuracy was 93.74%, the sensitivity was 94.36%, and the specificity was 85.64%. LIMITATIONS: There was no division in the severity of melanoma samples used in this experiment. The data set was relatively small for deep learning. CONCLUSION: The performance of traditional machine learning is satisfactory for the small data set of melanoma dermoscopic images, and the potential for deep learning in the future big data era is enormous.

A deep learning radiomics model for preoperative grading in meningioma.

OBJECTIVES: To noninvasively differentiate meningioma grades by deep learning radiomics (DLR) model based on routine post-contrast MRI. METHODS: We enrolled 181 patients with histopathologic diagnosis of meningioma who received post-contrast MRI preoperative examinations from 2 hospitals (99 in the primary cohort and 82 in the validation cohort). All the tumors were segmented based on post-contrast axial T1 weighted images (T1WI), from which 2048 deep learning features were extracted by the convolutional neural network. The random forest algorithm was used to select features with importance values over 0.001, upon which a deep learning signature was built by a linear discriminant analysis classifier. The performance of our DLR model was assessed by discrimination and calibration in the independent validation cohort. For comparison, a radiomic model based on hand-crafted features and a fusion model were built. RESULTS: The DLR signature comprised 39 deep learning features and showed good discrimination performance in both the primary and validation cohorts. The area under curve (AUC), sensitivity, and specificity for predicting meningioma grades were 0.811(95% CI, 0.635-0.986), 0.769, and 0.898 respectively in the validation cohort. DLR performance was superior over the hand-crafted features. Calibration curves of DLR model showed good agreements between the prediction probability and the observed outcome of high-grade meningioma. CONCLUSIONS: Using routine MRI data, we developed a DLR model with good performance for noninvasively individualized prediction of meningioma grades, which achieved a quantization capability superior over the hand-crafted features. This model has potential to guide and facilitate the clinical decision-making of whether to observe or to treat patients by providing prognostic information.

Gestational exposure to acrylamide inhibits mouse placental development in vivo.

Acrylamide, a carcinogen and neurotoxic substance, recently has been discovered in various heat-treated carbohydrate-rich foods. The aim of this study was to investigate the effects of acrylamide exposure on placental development. Pregnant mice received acrylamide by gavage at dosages of 0, 10, and 50 mg/kg/day from gestational days (GD) 3 until GD 8 or GD 13. The results showed that acrylamide feeding significantly decreased the numbers of viable embryos and increased the numbers of resorbed embryos on GD 13. Acrylamide exposure reduced the absolute and relative weight of placentas and embryos, and inhibited the development of ectoplacental cone (EPC) and placenta, as shown by the atrophy of EPC and reduced placental area. Acrylamide markedly reduced the numbers of labyrinth vessels. Expression levels of most placental key genes such as Esx1, Hand1, and Hand2 mRNA dramatically decreased in acrylamide-treated placentas. Furthermore, acrylamide treatment inhibited proliferation and induced apoptosis of placentas, as shown by decreased Ki67-positive cells and Bcl-2 protein, and increased the expression of Bax, cleaved-caspase-3, and cleaved-caspase-8 proteins. In conclusion, our results indicated that gestational exposure to acrylamide inhibits placental development through dysregulation of placental key gene expression and labyrinth vessels, suppression of proliferation, and apoptosis induction in mice.

Seed-based transrectal ultrasound-fluoroscopy registration method for intraoperative dosimetry analysis of prostate brachytherapy.

Prostate brachytherapy is an effective treatment option for early-stage prostate cancer. During a prostate brachytherapy procedure, transrectal ultrasound (TRUS) and fluoroscopy imaging modalities complement each other by providing good visualization of soft tissue and implanted seeds, respectively. Therefore, the registration of these two imaging modalities, which are readily available in the operating room, could facilitate intraoperative dosimetry, thus enabling physicians to implant additional seeds into the underdosed portions of the prostate while the patient is still on the operating table. It is desirable to register TRUS and fluoroscopy images by using the seeds as fiducial markers. Although the locations of all the implanted seeds can be reconstructed from three fluoroscopy images, only a fraction of these seeds can be located in TRUS images. It is challenging to register the TRUS and fluoroscopy images by using the identified seeds, since the correspondence between them is unknown. Furthermore, misdetection of nonseed structures as seeds can lead to the inclusion of spurious points in the data set. We developed a new method called iterative optimal assignment (IOA) to overcome these challenges in TRUS-fluoroscopy registration. By using the Hungarian method in an optimization framework, IOA computes a set of transformation parameters that yield the one-to-one correspondence with minimum cost. We have evaluated our registration method at varying noise levels, seed detection rates, and number of spurious points using data collected from 25 patients. We have found that IOA can perform registration with an average root mean square error of about 0.2 cm even when the seed detection rate is only 10%. We believe that IOA can offer a robust solution to seed-based TRUS-fluoroscopy registration, thus making intraoperative dosimetry possible.