Early detection and nonintrusive assessment of inflammatory bowel disease (IBD) remain an unmet clinical challenge. Spectral computed tomography (CT) presents a potential modality for gastrointestinal (GI) imaging; however, clinical CT contrast agents are unable to achieve targeted detection of IBD in spectral CT imaging. In this study, we developed neodymium-hyaluronic acid nanoparticles (Nd-HA NPs) as novel contrast agents for spectral CT imaging of IBD. Nd-HA NPs were synthesized by conjugating HA units with lanthanide complex neodymium-diethylenetriamine-pentaacetic acid (Nd-DTPA). The physical properties, biotoxicity, and CT imaging ability of Nd-HA NPs were systematically evaluated in vitro. Subsequently, the applicability of Nd-HA NPs for GI tract imaging was assessed in both healthy and colitis mouse models. Nd-HA NPs exhibited excellent stability, biocompatibility, and potent x-ray attenuation property in vitro as novel spectral CT contrast agents. Attributed to HA's high affinity for cluster of differentiation 44 receptor, which is abundantly expressed at inflammatory sites, Nd-HA NPs successfully achieved targeted spectral CT imaging of IBD, and showed greater accumulation in the lesions of colitis mice compared with the clinical contrast agent iohexol. More importantly, after oral administration of Nd-HA NPs, the CT values of GI tract in healthy mice, 2.5% DSS-induced mice (moderate colitis), and 5% DSS-induced mice (severe colitis) were 90.19, 140.99, and 264.07 HU, respectively, with statistically significant difference (P < 0.001). These results indicated that Nd-HA NPs had the potential to realize severity assessment of IBD in spectral CT imaging, which was further confirmed by inductively coupled plasma optical emission spectrometry analysis and histopathological evaluation. The study suggested that Nd-HA NPs could serve as effective spectral CT contrast agents, enabling noninvasive early detection and severity assessment of IBD.

Selective detection of inflammatory bowel disease (IBD) could relieve the suffering of patients and reduce the risk of developing colorectal cancer. Spectral computed tomography (CT) imaging, an advanced CT modality, has the potential to achieve accurate diagnosis of gastrointestinal diseases with favorable soft-tissue contrast and unique k-edge imaging capacity. However, clinically available CT contrast agents exhibited suboptimal performance in spectral CT imaging and poor specificity for inflammatory lesions. Therefore, we developed hyaluronic acid-functionalized neodymium vanadate nanoparticles (HA-NdVO4 NPs) as biocompatible, CD44-targeted contrast agents to realize the specific detection of IBD. Leveraging higher k-edge value of neodymium, HA-NdVO4 NPs exhibited superior spectral CT imaging compared to clinically utilized iohexol, enabling clearer delineation and examination of gastrointestinal structures. Most HA-NdVO4 NPs were cleared within 24 h in healthy mice. Nevertheless, obvious nanoparticle accumulation and pronounced CT enhancement were observed in the inflamed colon region of colitis mice 24 h post administration, suggesting the excellent inflammation targeting ability of HA-NdVO4 NPs. Notably, spectral CT imaging provided brighter images and enhanced contrast at low monochromatic energies (40-60 keV), further improving the detection sensitivity of HA-NdVO4 NPs for IBD. Therefore, we believe that this contrast agent could advance the selective detection of IBD via spectral CT imaging.

INTRODUCTION:Immune checkpoint inhibitors (ICIs) have been widely used as an important approach to treat tumors. However, about 30% patients receiving ICIs develop immune-related dermatologic toxicities (IDTs), which may lead to treatment interruption and poor prognosis. OBJECTIVES:This evidence implementation project was carried out in the Department of Radiation Oncology at Nanfang Hospital in Guangzhou, China and aimed to improve the management of IDTs in cancer patients treated with PD-1/PD-L1 inhibitors. METHODS:The project used the JBI Evidence Implementation Framework, which is based on an audit and feedback process. Criteria for audits were derived from JBI Best Practice Evidence Summaries. The baseline audit was used to identify gaps in compliance with best practices and barriers and facilitators of implementation. After the implementation of change strategies, a follow-up audit was conducted to measure changes in compliance. RESULTS:Gaps between evidence and practice were noted for six of the criteria, and five barriers to implementation were identified. The follow-up audit revealed that compliance with audit criteria 1-5, 7, and 8 reached 100%, while criterion 6 partially improved from 36.7% to 50%. CONCLUSIONS:The results indicate that this project improved compliance with evidence-based practices for the management of IDTs by standardizing clinical practices and improving the quality of nursing management. SPANISH ABSTRACT:http://links.lww.com/IJEBH/A550.

Chemotherapy failure caused by adriamycin (ADM) and imatinib (IM) resistance remains a critical challenge in the treatment of chronic myeloid leukemia (CML). In this study, a novel compound 4, 4’-(selenophene-2, 5-diyl)bis(3-fluorophenol) (Se-1) with estrogen receptor regulation and selenium anticancer activity was applied to reverse drug resistance of CML. Se-1 exhibited superior inhibitory activity against resistant K562/ADM cells compared to sensitive K562 cells. The growth of K562/ADM in xenograft mouse was suppressed by Se-1 treatment. The anti-leukemia mechanism of Se-1 was tested by western-blot, flow cytometry, molecular docking and fluorescence imaging. The apoptosis rate was increasing after Se-1 treatment, meanwhile proteins of Cleaved PARP and Cleaved Caspase3 were up-regulated and Bcl-2 was down-regulated. In addition, the autophagy was activated through increasing of autophagy vesicles and proteins of LC3-II and P62, and inactivating of mTOR protein. Moreover, estrogen receptor α (ERα), ERK and P38 were activated, the proteins of PI3K and AKT1 were decreased. Overall, Se-1 exerted anti-CML effects through multi-mechanism interaction, which was expected to advance the research in reversing ADM and IM resistance of chronic myeloid leukemia.

Objective: Gastritis, a global inflammatory disorder, progresses from symptomatic discomfort to potentially malignant changes. Existing staging systems (e.g., OLGA) focus on cancer risk but ignore modifiable factors like inflammation markers and Helicobacter pylori infection. We developed a Nomogram model based on baseline data, inflammatory markers and infectious pathogens for predicting the prognosis of gastritis patients and validating it. Methods: Retrospectively collect the clinical data of patients diagnosed with gastritis, including baseline characteristics, inflammatory markers, and pathogenic infection test results. Univariate and multivariate analyses were performed to identify independent risk factors associated with the prognosis of gastritis patients, based on which a Nomogram prediction model was constructed. The model's accuracy, calibration, and discriminative ability were internally validated using the concordance index (C-index), calibration curve, and the area under the receiver operating characteristic curve (AUC). Results: Among the 185 patients in the training set, 43 (23.24%) had poor treatment outcomes, while in the validation set of 79 patients, 18 (22.78%) exhibited poor treatment outcomes. No statistically significant differences were observed between the training and validation sets in terms of the incidence of poor treatment outcomes, baseline characteristics, or inflammatory and infectious markers parameters (p > 0.05). Univariate analysis revealed significant differences (p < 0.05) between the poor-outcome and favorable-outcome groups in dietary score, white blood cell count, neutrophil percentage, lymphocyte percentage, C-reactive protein (CRP) level, erythrocyte sedimentation rate (ESR), serum albumin level, and H. pylori infection status. Multivariate logistic regression analysis identified dietary score, neutrophil proportion, CRP, ESR, serum albumin level, and H. pylori infection as independent risk factors for poor endoscopic treatment outcome (p < 0.05). Subsequently, a nomogram prediction model was constructed. The model demonstrated good calibration and fit between predicted and actual outcomes in both the training and validation sets. ROC curve analysis showed that the nomogram model achieved AUC values of 0.808 in the training set and 0.800 in the validation set for predicting gastritis prognosis. Conclusion: The Nomogram model constructed in this study based on baseline data, inflammation indicators and infectious pathogens can effectively predict the prognosis of patients with gastritis, which can provide a powerful reference for clinical individualized treatment decision-making.

To develop a nomogram-based risk prediction model utilizing patients' clinical characteristics for post-endoscopic submucosal dissection hemorrhage, and to evaluate its clinical utility. A total of 250 patients who developed postprocedural hemorrhage after endoscopic submucosal dissection (ESD) for gastrointestinal tumors at our institution (2022–2024) were enrolled. Enrichment criteria included at least one high-risk factor for complications (e.g., lesion size > 10 mm, antithrombotic medication use, or comorbid diabetes/hypertension) or early post-ESD symptoms suggestive of complications. Patients were randomly divided into a training set (n = 175) and a validation set (n = 75) at a 7:3 ratio. In the training set, multivariate logistic regression identified independent prognostic risk factors to construct the nomogram. Model performance was assessed via receiver operating characteristic (ROC) curves and calibration plots, with external validation performed in the validation set. Hemorrhage occurred in 70/175 cases (40.00

Spectral computed tomography (CT) imaging as an advanced and non-invasive technique is of importance in the diagnosis of disease. Therefore, it is significant to develop safe and high-performance contrast agents for spectral CT imaging. Herein, we synthesized a small-molecule erbium chelate (Er-DOTA dimeglumine), with the advantages of favorable colloidal and structure stability, good biocompatibility and biosafety, and sensitive spectral CT imaging ability in vitro and in vivo. Erbium chelate exhibits strong X-ray attenuation capability and the energy-dependent attenuation in the range of 40-160 keV due to the high K-edge value of Er (57.5 keV). Especially, the slope of the Hounsfield unit (HU) curve for erbium chelate is 1.5 times that of iohexol at 50 keV, and 5.6 times that of iohexol at 130 keV. We then applied erbium chelate in the in vivo spectral CT imaging of healthy mice and DSSinduced colitis mice. It is found that erbium chelate is rapidly metabolized from the intestinal tract in healthy mice within 12 h due to favorable biocompatibility, while it is enriched and displays brighter signals in the inflammatory site of colon in colitis mice. Specifically, the CT value in the large intestines of colitis mice 12 h after erbium chelate administration is 53.0, which is much higher than that of healthy mice (8.3), showing great potential for sensitive and accurate diagnosis of inflammatory bowel disease. Moreover, compared with the clinically used contrast agent iohexol, erbium chelate shows better spectral CT imaging performance in both healthy and colitis mice at low to high energy settings. Superior CT imaging is also observed in CT26 tumor-bearing mice after administration with erbium chelate in comparison to iohexol. In summary, the small-molecule erbium chelate is expected to be a safe and highperformance contrast agent for spectral CT imaging to promote the diagnosis of gastrointestinal diseases. (c) 2024 Chinese Society of Rare Earths. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

ObjectiveTo investigate the value of preoperative prediction of risk factors for recurrence of operable cervical cancer based on the radiomics features of biparametric magnetic resonance imaging (bp-MRI) combined with clinical features.MethodA retrospective collection of cervical cancer cases undergoing radical hysterectomy + pelvic and/or para-aortic lymph node dissection at the Affiliated Hospital of North Sichuan Medical College was conducted. Region of interest (ROI) was outlined using the 3D Slicer software, and radiomics after feature extraction and feature screening was performed using the least absolute shrinkage and selection operator (LASSO) algorithm. Logistic regression algorithms were used to construct a fusion clinical-radiomics model to visualize nomograms. Receiver operating characteristic (ROC), DeLong test, calibration curve (CC), and decision curve (DC) were used to evaluate the predictive performance and clinical benefit of the model.ResultA total of 99 patients with cervical cancer were included in this study, with 79 and 20 cases in the training and test groups, respectively. Seventeen key features were selected for radiomics model construction. Three clinical features were screened to construct a clinical model. A fusion model of the radiomics model combined with the clinical model was constructed. The area under the curve (AUC) values in the training group were 0.710 (95% CI 0.602–0.819), 0.892 (95% CI 0.826–0.958), and 0.906 (95% CI 0.842–0.970), for the comparative clinical model, radiomics model, and fusion model, respectively, and the AUC values in the testing group were 0.620 (95% CI 0.366–0.874), 0.860 (95% CI 0.677–1.000), and 0.880 (95% CI 0.690–1.000), respectively. The DeLong test showed a statistically significant difference between the AUC values of the fusion model and the clinical model (p < 0.05). Decision curve analysis (DCA) showed that the fusion model had the greatest net benefit when the threshold probability was approximately 0.5.ConclusionThe fusion model constructed based on bp-MRI radiomics features combined with clinical features provides an important reference for predicting the risk status of recurrence in operable cervical cancer. The findings of this study are preliminary exploratory results, and further large-scale, multicenter studies are needed to validate these findings.

Background:The macrotrabecular-massive subtype of hepatocellular carcinoma (MTM-HCC) is a special histological type of hepatocellular carcinoma (HCC), characterized by aggressive behavior and poor prognosis. Identification of MTM-HCC during pretreatment assessments may carry significant prognostic and therapeutic implications. This study aimed to develop a diagnostic prediction model for MTM-HCC using preoperative contrast-enhanced computed tomography (CECT) features and machine learning (ML) algorithms. Methods:Patients diagnosed with HCC who underwent liver resection surgery and preoperative CECT between October 2018 and December 2021 were retrospectively enrolled. These patients were subsequently categorized into MTM-HCC and non-MTM-HCC groups. Independent predictors of the MTM subtype were identified through multivariable logistic regression analyses. Clinical and CECT features were then selected to construct predictive models using ML algorithms. The performance of each model was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). Furthermore, Kaplan-Meier survival curves were employed to evaluate the association with early recurrence. Results:The final study cohort comprised 122 patients diagnosed with HCC (mean age: 53±10 years; 102 males), of which 46 tumors (37.7%) were classified as MTM-HCC. Multivariable analyses identified substantial necrosis as an independent predictor of MTM-HCC [odds ratio (OR) =2.23, 95% confidence interval (CI): 1.04-5.05; P=0.04]. The clinicoradiological histogram model, which integrated clinical imaging features and whole-tumor computed tomography (CT) histogram features, demonstrated AUC values of 0.917 (95% CI: 0.862-0.973) and 0.853 (95% CI: 0.728-0.977) in the training and test sets, respectively. Additionally, Kaplan-Meier survival curves indicated that substantial necrosis (P<0.01) was significantly associated with early recurrence. Conclusions:Substantial necrosis on CECT was an independent predictor of MTM-HCC and was associated with early recurrence of HCC. Meanwhile, the clinicoradiological histogram model based on preoperative CECT was shown to be useful in predicting MTM-HCC.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease that affects 5 million people globally. YTH N6-methyladenosine RNA binding protein C (YTHDC1) is a critical regulator in various biological processes, yet its role in UC remains undefined. This study aims to investigate the regulatory function of YTHDC1 in UC pathogenesis. An in vivo UC model was established in C57BL/6 mice using 3% dextran sulfate sodium (DSS). To establish an in vitro UC model, Caco-2 cells were exposed to 10 μg/mL lipopolysaccharide (LPS). Quantitative real-time PCR (qRT-PCR) was employed to detect mRNA expression levels of YTHDC1 and X-box binding protein 1 (XBP1). Protein expression was analyzed by Western blot analysis assay. Haematoxylin and eosin staining was used to assess colonic pathology. Enzyme-linked immunosorbent assay was performed to measure the levels of TNF-α, IL-6, and IL-1β. Immunohistochemistry assay was used to determine the LC3II-positive expression rate. m6A methylated RNA immunoprecipitation assay and RNA immunoprecipitation assay were used to analyze the association between YTHDC1 and XBP1. An actinomycin D assay was performed to evaluate the effect of YTHDC1 overexpression on XBP1 mRNA stability. The study showed YTHDC1 and XBP1 expression were downregulated in colonic tissues from UC patients. Overexpression of YTHDC1 increased colon length and body weight in DSS-induced mice and inhibited DSS-triggered production of TNF-α, IL-6, and IL-1β. Additionally, the upregulation of YTHDC1 counteracted the suppressive impact of DSS treatment on autophagy in colon tissues. In Caco-2 cells, LPS treatment promoted TNF-α, IL-6, and IL-1β production, whereas these effects were attenuated after YTHDC1 overexpression. Moreover, LPS-induced Caco-2 cells showed decreases in the ratio of LC3II to LC3I and beclin1 protein expression and an increase in P62 protein expression, however, YTHDC1 overexpression relieved these effects. In addition, the results showed that the treatment with AMPK inhibit or attenuated YTHDC1 overexpression-induced effects in LPS-treated Caco-2 cells. Furthermore, YTHDC1 was found to stabilize the expression of XBP1 and activate the AMPK/mTOR pathway. Thus, YTHDC1 overexpression inhibited inflammation and promoted autophagy to ameliorate UC through the XBP1/AMPK/mTOR pathway. These findings highlight YTHDC1 as apotential therapeutic target for UC treatment.

Overexpression of the proviral integration site for Moloney murine leukemia virus (PIM) kinase 1 (PIM1) has emerged as a pivotal factor in multiple myeloma (MM) progression, positioning PIM1 as a promising target for novel therapeutic interventions. In response, this study developed a robust virtual screening protocol that integrates deep learning-based drug screening with docking-based approaches to systematically identify potential PIM1 inhibitors. This strategy led to the identification of a compound with potent inhibitory activity against PIM1, evidenced by an IC50 of 307 nM in a homogeneous time-resolved fluorescence (HTRF) bioassay. Moreover, compound 1 effectively suppressed the proliferation of MM.1S and NCI-H929 cells. Detailed molecular dynamics simulations further elucidated the binding interactions between the compound and PIM1, offering critical insights into its mechanism of action.

Introduction This study evaluated the efficacy of radiomic analysis with optimal volumes of interest (VOIs) on computed tomography images to preoperatively differentiate invasive mucinous adenocarcinoma (IMA) from non-mucinous adenocarcinoma (non-IMA) in patients with incidental pulmonary nodules (IPNs). Methods This multicenter, large-scale retrospective study included 1383 patients with IPNs, 110 (8%) of whom were pathologically diagnosed with IMA postoperatively. Radiomic features were extracted from multi-scale VOI subgroups (VOI −2 mm , VOI entire , VOI + 2 mm , and VOI + 4 mm ). Resampling methods, specifically, the synthetic minority oversampling technique, addressed the imbalance between the majority (IMA) and minority (non-IMA) groups. Radiomic features were identified using the least absolute shrinkage and selection operator algorithm. Radscores were calculated by linearly combining the selected features with their weights. A combined nomogram integrating the optimal VOI-based radiomic model with the image-finding classifier was constructed. Results Bubble lucency and lower lobe predominance were significant in establishing an image-finding classifier to differentiate between IMA and non-IMA in IPNs, achieving an area under the curve (AUC) value of 0.684 (0.568-0.801). Across all radiomic models, IMA had a higher Radscore than did non-IMA. Specifically, the VOI + 2 mm-based radiomic model exhibited the highest performance, with an AUC of 0.832 (0.753-0.911). The combined nomogram outperformed the recognized image-finding classifier and radiomic models, achieving an AUC of 0.850 (0.776-0.925). Conclusion A nomogram that combines a recognized image-finding classifier with an optimal VOI-based radiomic model effectively predicts IMA in IPNs, aiding physicians in developing comprehensive treatment strategies.

Spectral CT can overcome the shortcomings of conventional CT, however, clinically used iodized contrast agents are not ideal for spectral CT imaging, which calls for the development of novel contrast agent with high attenuation coefficient. In this study, a small molecule lanthanide chelate Ytterbium-tetraazacyclododecane1,4,7,10-tetraacetic acid (Yb-DOTA) was prepared by a simple and green one-step method, and it had multiadvantages such as good stability, excellent biocompatibility and high performance of spectral CT imaging in vitro and in vivo. In addition, Yb-DOTA had more prominent ability to visualize renal tissue, GI tract, the heart and venous system in both conventional and spectral CT imaging compared with clinical iohexol. Moreover, higher tissue contrast and enhanced sensitivity were achieved at high monochromatic energies (70-150 key) after Yb-DOTA administration, indicating its superior spectral CT imaging performance. Yb-DOTA is expected to be a novel contrast agent to enhance the diagnostic sensitivity and accuracy.

Background Non-intrusive imaging of gastrointestinal (GI) tract using computed tomography (CT) contrast agents is of the most significant issues in the diagnosis and treatment of GI diseases. Moreover, spectral CT, which can generate monochromatic images to display the X-ray attenuation characteristics of contrast agents, provides a better imaging sensitivity for diagnose inflammatory bowel disease (IBD) than convention CT imaging. Methods Herein, a convenient and one-pot synthesis method is provided for the fabrication of small-molecule lanthanide complex Holmium-tetraazacyclododecane-1, 4, 7, 10-tetraacetic acid (Ho-DOTA) as a biosafe and high-performance spectral CT contrast agent for GI imaging with IBD. In vivo CT imaging was administered with both healthy mice and colitis mice induced by dextran sodium sulfate. Results We found that Ho-DOTA accumulated in inflammation sites of large intestines and produced high CT contrast compared with healthy mice. Both in vitro and in vivo experimental results also showed that Ho-DOTA provided much more diagnostic sensitivity and accuracy due to the excellent X-ray attenuation characteristics of Ho-DOTA compared with clinical iodinate agent. Furthermore, the proposed contrast media could be timely excreted from the body via the urinary and digestive system, keeping away from the potential side effects due to long-term retention in vivo. Conclusion Accordingly, Ho-DOTA with excellent biocompatibility can be useful as a potential high-performance spectral CT contrast agent for further clinical imaging of gastrointestinal tract and diagnosis of intestinal system diseases. Graphical Abstract

BackgroundAccurate identification of extrahepatic cholangiocarcinoma (ECC) from an image is challenging because of the small size and complex background structure. Therefore, considering the limitation of manual delineation, it's necessary to develop automated identification and segmentation methods for ECC. The aim of this study was to develop a deep learning approach for automatic identification and segmentation of ECC using MRI.MethodsWe recruited 137 ECC patients from our hospital as the main dataset (C1) and an additional 40 patients from other hospitals as the external validation set (C2). All patients underwent axial T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). Manual delineations were performed and served as the ground truth. Next, we used 3D VB-Net to establish single-mode automatic identification and segmentation models based on T1WI (model 1), T2WI (model 2), and DWI (model 3) in the training cohort (80% of C1), and compared them with the combined model (model 4). Subsequently, the generalization capability of the best models was evaluated using the testing set (20% of C1) and the external validation set (C2). Finally, the performance of the developed models was further evaluated.ResultsModel 3 showed the best identification performance in the training, testing, and external validation cohorts with success rates of 0.980, 0.786, and 0.725, respectively. Furthermore, model 3 yielded an average Dice similarity coefficient (DSC) of 0.922, 0.495, and 0.466 to segment ECC automatically in the training, testing, and external validation cohorts, respectively.ConclusionThe DWI-based model performed better in automatically identifying and segmenting ECC compared to T1WI and T2WI, which may guide clinical decisions and help determine prognosis.

Isocitrate dehydrogenases (IDH) catalyze the oxidative decarboxylation of isocitrate to 2-oxoglutarate. IDH1 mutation has been reported in various tumors especially Cholangiocarcinoma, while the IDH1_R132H is reported to be the most common mutation of IDH1. IDH1_R132H inhibitors are effective anti-cancer drugs and have shown significant therapeutic effects in clinical. In this study, two novel natural compounds were identified to combine respectively with IDH1_R132H with a stronger binding force with conductive to interaction energy. They also showed low toxicity potential. Molecular dynamics simulation analysis demonstrated that the candidate ligands-IDH1_R132H complexes is stable in natural circumstances with favorable potential energy. Thus, Styraxlignolide F and Tremulacin were screened as promising IDH1_R132H inhibitors. We provide a solid foundation for the design and development of IDH1_R132H targeted drugs.

ABSTRACT:A 79-year-old man with rising prostate-specific antigen of 3.2 ng/mL and diagnosis of metastatic castration-resistant prostate cancer received abiraterone and prednisone for treatment regarding prostate-specific membrane antigen (PSMA)-avid bone lesions on PET/CT. Four months later, a follow-up 18 F-DCFPyL PET/CT demonstrated new and increased multifocal PSMA-avid osseous and liver lesions, whereas prostate-specific antigen was stabled at 3.1 ng/mL. Biopsy of liver lesion showed metastasis from a primary pancreatic acinar cell carcinoma. Retrospectively, PSMA-avid pancreatic body lesion was identified on both PSMA PET/CT scans. This case illustrated that any above background PSMA uptake in the pancreas warrants suspicion for malignancy.

ABSTRACT:A 76-year-old man with biopsy-proven metastatic papillary thyroid cancer in a mediastinal nodule status post total thyroidectomy is on surveillance. The patient also had prostate cancer and received prostatectomy and androgen deprivation treatment. An 18 F-fluciclovine PET revealed avid lesions in the mediastinal nodule and a sclerotic focus at L5 with concurrent prostate-specific antigen level of 0.4 ng/mL. The L5 lesion was later biopsied and confirmed as metastasis from prostate cancer. A 68 Ga-PSMA-11 PET 2 months later showed avid radiotracer uptake within L5 metastasis but not the mediastinal nodule. The patient received radiation therapy to the L5 lesion and responded well.

Object To identify novel targets for the diagnosis, treatment and prognosis of cholangiocarcinoma, we screen ideal lead compounds and preclinical drug candidates with MYC inhibitory effect from the ZINC database, and verify the therapeutic effect of Dhea and 2-14,15-Eg on cholangiocarcinoma. Methods The gene expression profiles of GSE132305, GSE89749, and GSE45001 were obtained respectively from the Gene Expression Omnibus database. The DEGs were identified by comparing the gene expression profiles of cholangiocarcinoma and normal tissues. GO, KEGG analysis and PPI network analyses were performed. LibDock, ADME and toxicity prediction, molecular docking and molecular dynamics simulations were used to identify potential inhibitors of MYC. Moreover, in vitro, MTT assay, colony-forming assay, the scratch assay and Western blotting were performed to verify the therapeutic effect of Dhea and 2-14,15-Eg. Results PPI network analysis showed that ALB, MYC, APOB, IGF1 and KNG1 were hub genes, of which MYC was mainly studied in this study. A battery of computer-aided virtual techniques showed that Dhea and 2-14,15-Eg have lower rodent carcinogenicity, Ames mutagenicity, developmental toxicity potential, and high tolerance to cytochrome P4502D6, as well as could exist stably in natural circumstances. In vitro assays showed that Dhea and 2-14,15-Eg inhibited cholangiocarcinoma cellular viability, proliferation, and migration inhibiting expression of MYC. Conclusion This study suggested that Dhea and 2-14,15-Eg were novel potential inhibitors of MYC targeting, as well as are a promising drug in dealing with cholangiocarcinoma and have a perspective application.

Innovations and discoveries in cancer therapeutics have improved survival rates in patients with various types of malignancies. At the same time, physicians are identifying an increased number of patients with treatment-related cardiotoxicity. It is imperative that physicians recognize early treatment-related adverse effects to determine the safest therapeutic options for patients with cancer. This manuscript evaluates the role of cardiovascular imaging and biomarkers in identifying cardiotoxicity trigged by various chemotherapeutic agents and summarizes expert consensus statements regarding cardiotoxicity monitoring.