Radiomics-based analysis of dynamic contrast-enhanced magnetic resonance image: A prediction nomogram for lymphovascular invasion in breast cancer.

OBJECTIVE: To develop and validate a nomogram for quantitively predicting lymphovascular invasion (LVI) of breast cancer (BC) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and morphological features. METHODS: We retrospectively divided 238 patients with BC into training and validation cohorts. Radiomic features from DCE-MRI were subdivided into A1 and A2, representing the first and second post-contrast images respectively. We utilized the minimal redundancy maximal relevance filter to extract radiomic features, then we employed the least absolute shrinkage and selection operator regression to screen these features and calculate individualized radiomics score (Rad score). Through the application of multivariate logistic regression, we built a prediction nomogram that integrated DCE-MRI radiomics and MR morphological features (MR-MF). The diagnostic capabilities were evaluated by comparing C-indices and calibration curves. RESULTS: The diagnostic efficiency of the A1/A2 radiomics model surpassed that of the A1 and A2 alone. Furthermore, we incorporated the MR-MF (diffusion-weighted imaging rim sign, peritumoral edema) and optimized Radiomics into a hybrid nomogram. The C-indices for the training and validation cohorts were 0.868 (95% CI: 0.839-0.898) and 0.847 (95% CI: 0.787-0.907), respectively, indicating a good level of discrimination. Moreover, the calibration plots demonstrated excellent agreement in the training and validation cohorts, confirming the effectiveness of the calibration. CONCLUSION: This nomogram combined MR-MF and A1/A2 Radiomics has the potential to preoperatively predict LVI in patients with BC.

Iron-Catalyzed Sulfonylmethylation of Imidazo[1,2-α]pyridines with N,N-Dimethylacetamide and Sodium Sulfinates.

Functionalized imidazo[1,2-α]pyridines are important scaffolds in pharmaceuticals. Herein, we present an efficient 3-sulfonylmethylation protocol for imidazo[1,2-α]pyridines by sodium sulfinates in DMA and H2O (2:1) via an FeCl3-catalyzed three-component coupling reaction. Various sulfonylmethyl imidazo[1,2-α]pyridines were thus afforded in high yields with excellent functional group tolerance. A plausible oxidation-addition mechanism was proposed.

Retrospective evaluation of three types of expanded polytetrafluoroethylene grafts for upper limb vascular access.

Currently, three expanded polytetrafluoroethylene (ePTFE) prosthetic graft types are most commonly used for patients with end-stage kidney disease (ESKD) who require long-term vascular access for hemodialysis. However, studies comparing the three ePTFE grafts are limited. This study compared the clinical efficacy and postoperative complications of three ePTFE prosthetic graft types used for upper limb arteriovenous graft (AVG) surgery among patients with ESKD. Patients with ESKD requiring upper limb AVG surgery admitted to our center between January 2016 and September 2019 were enrolled. Overall, 282 patients who completed the 2-year follow-up were included and classified into the following three groups according to the ePTFE graft type: the GPVG group with the PROPATEN® graft, the GAVG group with the straight-type GORE® ACUSEAL, and the BVVG group with the VENAFLO® II. The patency rate and incidence of access-related complications were analyzed and compared between groups. The patients were followed up postoperatively, and data were collected at 6, 12, 18, and 24 months postoperatively. Respective to these follow-up time points, in the GPVG group, the primary patency rates were 74.29%, 65.71%, 51.43%, and 42.86%; the assisted primary patency rates were 85.71%, 74.29%, 60.00%, and 48.57%; and the secondary patency rates were 85.71%, 80.00%, 71.43%, and 60.00%. In the GAVG group, the primary patency rates were 73.03%, 53.93%, 59.42%, and 38.20%; the assisted primary patency rates were 83.15%, 68.54%, 59.55%, and 53.93%; and the secondary patency rates were 85.39%, 77.53%, 68.54%, and 62.92%, respectively. In the BVVG group, the primary patency rates were 67.24%, 53.45%, 41.38%, and 29.31%; the assisted primary patency rates were 84.48%, 67.24%, 55.17%, and 44.83%; and the secondary patency rates were 86.21%, 81.03%, 68.97%, and 60.34%, respectively. The differences in patency rates across the three grafts were not statistically significant. Overall, 18, 4, and 12 patients in the GPVG, GAVG, and BVVG groups, respectively, experienced seroma. Among the three grafts, GORE® ACUSEAL had the shortest anastomosis hemostatic time. The first cannulation times for the three grafts were GPVG at 16 (±8.2), GAVG at 4 (±4.9), and BVVG at 18 (±12.7) days. No significant difference was found in the postoperative swelling rate between the GPVG group and the other two groups. Furthermore, no statistically significant differences were found across the three graft types regarding postoperative vascular access stenosis and thrombosis, ischemic steal syndrome, pseudoaneurysm, or infection. In conclusion, no statistically significant differences in the postoperative primary, assisted primary, or secondary graft patency rates were observed among the three groups. A shorter anastomosis hemostatic time, first cannulation time, and seroma occurrence were observed with the ACUSEAL® graft than with its counterparts. The incidence of upper extremity swelling postoperatively was greater with the PROPATEN® graft than with the other grafts. No statistically significant differences were observed among the three grafts regarding the remaining complications.

Disulfidptosis­related lncRNA prognosis model to predict survival therapeutic response prediction in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer, and disulfidptosis is a newly discovered mechanism of programmed cell death. However, the effects of disulfidptosis-related lncRNAs (DR-lncRNAs) in LUAD have yet to be fully elucidated. The aim of the present study was to identify and validate a novel lncRNA-based prognostic marker that was associated with disulfidptosis. RNA-sequencing and associated clinical data were obtained from The Cancer Genome Atlas database. Univariate Cox regression and lasso algorithm analyses were used to identify DR-lncRNAs and to establish a prognostic model. Kaplan-Meier curves, receiver operating characteristic curves, principal component analysis, Cox regression, nomograms and calibration curves were used to assess the reliability of the prognostic model. Functional enrichment analysis, immune infiltration analysis, somatic mutation analysis, tumor microenvironment and drug predictions were applied to the risk model. Reverse transcription-quantitative PCR was subsequently performed to validate the mRNA expression levels of the lncRNAs in normal cells and tumor cells. These analyses enabled a DR-lncRNA prognosis signature to be constructed, consisting of nine lncRNAs; U91328.1, LINC00426, MIR1915HG, TMPO-AS1, TDRKH-AS1, AL157895.1, AL512363.1, AC010615.2 and GCC2-AS1. This risk model could serve as an independent prognostic tool for patients with LUAD. Numerous immune evaluation algorithms indicated that the low-risk group may exhibit a more robust and active immune response against the tumor. Moreover, the tumor immune dysfunction exclusion algorithm suggested that immunotherapy would be more effective in patients in the low-risk group. The drug-sensitivity results showed that patients in the high-risk group were more sensitive to treatment with crizotinib, erlotinib or savolitinib. Finally, the expression levels of AL157895.1 were found to be lower in A549. In summary, a novel DR-lncRNA signature was constructed, which provided a new index to predict the efficacy of therapeutic interventions and the prognosis of patients with LUAD.

Use of CT-derived radiomic features to preoperatively identify invasive mucinous adenocarcinoma in solitary pulmonary nodules ≤3 cm.

Objective: In this study, we aimed to utilize computed tomography (CT)-derived radiomics and various machine learning approaches to differentiate between invasive mucinous adenocarcinoma (IMA) and invasive non-mucinous adenocarcinoma (INMA) preoperatively in solitary pulmonary nodules (SPN) ≤3 cm. Methods: A total of 538 patients with SPNs measuring ≤3 cm were enrolled, categorized into either the IMA group (n = 50) or INMA group (n = 488) based on postoperative pathology. Radiomic features were extracted from non-contrast-enhanced CT scans and identified using the least absolute shrinkage and selection operator (LASSO) algorithm. In constructing radiomics-based models, logistic regression, support vector machines, classification and regression trees, and k-nearest neighbors were employed. Additionally, a clinical model was developed, focusing on CT radiological features. Subsequently, this clinical model was integrated with the most effective radiomic model to create a combined model. Performance assessments of these models were conducted, utilizing metrics such as the area under the receiver operating characteristic curve (AUC), DeLong's test, net reclassification index (NRI), and integrated discrimination improvement (IDI). Results: The support vector machine approach showed superior predictive efficiency, with AUCs of 0.829 and 0.846 in the training and test cohorts, respectively. The clinical model had AUCs of 0.760 and 0.777 in the corresponding cohorts. The combined model had AUCs of 0.847 and 0.857 in the corresponding cohorts. Furthermore, compared to the radiomic model, the combined model significantly improved performance in both the training (DeLong test P = 0.045, NRI 0.206, IDI 0.024) and test cohorts (P = 0.029, NRI 0.125, IDI 0.032), as well as compared to the clinical model in both the training (P = 0.01, NRI 0.310, IDI 0.09) and test cohorts (P = 0.047, NRI 0.382, IDI 0.085). Conclusion: the combined model exhibited excellent performance in distinguishing between IMA and INMA in SPNs ≤3 cm.

Genetic mutation and immune infiltration in embryonal tumor with multilayered rosettes.

PURPOSE: Genetic mutations stand as pivotal factors leading to the occurrence of embryonal tumor with multilayered rosettes (ETMR). This study aims to identify improved treatment approaches by unraveling the genetic drivers and immune infiltration in ETMR. METHODS: Two siblings with ETMR, treated at the General Hospital of Ningxia Medical University, were enrolled. Diagnosis involved MRI, Hematoxylin and Eosin (HE), and immunohistochemical (IHC) staining. Differentially expressed genes (DEGs) in ETMR were identified using GSE122077 and GSE14296 datasets. GO and KEGG analyses were used to determine ETMR-related pathways. Whole exome sequencing (WES) was utilized to annotate genetic variations in ETMR. Core genes, identified by protein-protein interaction (PPI), formed a diagnostic model evaluated by Logistic Regression. Single-sample Gene Set Enrichment Analysis (ssGSEA) assessed immune infiltration in ETMR, examining correlations between immune cells and core genes. RESULTS: Two siblings were diagnosed with ETMR. In ETMR, 135 DEGs were identified, of which 25 genes were annotated with 28 mutation sites. Moreover, ETMR-related pathways included cell cycle, synaptic functions, and neurodegeneration. Three ETMR-related core genes (ALB, PSMD1, and PAK2) were screened by protein-protein interaction (PPI). The diagnostic model constructed using these genes demonstrated an AUC value of 0.901 (95% CI: 0.811-0.991) in the training set, indicating accurate predictions in ETMR. Enhanced ssGSEA scores for 16 immune cells in ETMR tissues suggested a strong immune response. CONCLUSION: This study identifies diagnostic models associated with three core variant genes (ALB, PSMD1, PAK2) and enhanced immune cell activity in ETMR. It reveals crucial genetic features and significant immune responses in ETMR.

Kaempferol Improves Exercise Performance by Regulating Glucose Uptake, Mitochondrial Biogenesis, and Protein Synthesis via PI3K/AKT and MAPK Signaling Pathways.

Kaempferol is a natural flavonoid with reported bioactivities found in many fruits, vegetables, and medicinal herbs. However, its effects on exercise performance and muscle metabolism remain inconclusive. The present study investigated kaempferol's effects on improving exercise performance and potential mechanisms in vivo and in vitro. The grip strength, exhaustive running time, and distance of mice were increased in the high-dose kaempferol group (p < 0.01). Also, kaempferol reduced fatigue-related biochemical markers and increased the activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) related to antioxidant capacity. Kaempferol also increased the glycogen and adenosine triphosphate (ATP) content in the liver and skeletal muscle, as well as glucose in the blood. In vitro, kaempferol promoted glucose uptake, protein synthesis, and mitochondrial function and decreased oxidative stress in both 2D and 3D C2C12 myotube cultures. Moreover, kaempferol activated the PI3K/AKT and MAPK signaling pathways in the C2C12 cells. It also upregulated the key targets of glucose uptake, mitochondrial function, and protein synthesis. These findings suggest that kaempferol improves exercise performance and alleviates physical fatigue by increasing glucose uptake, mitochondrial biogenesis, and protein synthesis and by decreasing ROS. Kaempferol's molecular mechanism may be related to the regulation of the PI3K/AKT and MAPK signaling pathways.

Machine learning based on optimal VOI of multi-sequence MR images to predict lymphovascular invasion in invasive breast cancer.

Objectives: Lymphovascular invasion serves as a crucial prognostic indicator in invasive breast cancer, influencing treatment decisions. We aimed to develop a machine learning model utilizing optimal volumes of interest extracted from multisequence magnetic resonance images to predict lymphovascular invasion in patients with invasive breast cancer. Materials and methods: This study comprised 191 patients postoperatively diagnosed with invasive breast cancer through multi-sequence magnetic resonance imaging. Independent predictors were identified through univariate and multivariate logistic regression analyses, culminating in the construction of a clinical model. Radiomic features were extracted from multi-sequence magnetic resonance imaging images across various volume of interest scales (-2 mm, entire, +2 mm, +4 mm, and +6 mm). Subsequently, various radiomic models were developed using machine learning model algorithms, including logistic regression, support vector machine, k-nearest neighbor, gradient boosting machine, classification and regression tree, and random forest. A hybrid model was then formulated, amalgamating optimal radiomic and clinical models. Results: The area under the curve of the clinical model was 0.757. Among the radiomic models, the most efficient diagnosis was achieved by the k-nearest neighbor-based radiomics-volume of interest (+2 mm), resulting in an area under the curve of 0.780. The hybrid model, integrating the k-nearest neighbor-based radiomics-volume of interest (+2 mm), and the clinical model surpassed the individual clinical and radiomics models, exhibiting a superior area under the curve of 0.864. Conclusion: Utilizing a hybrid approach integrating clinical data and multi-sequence magnetic resonance imaging-derived radiomics models based on the multiscale tumor region volume of interest (+2 mm) proved effective in determining lymphovascular invasion status in patients with invasive breast cancer. This innovative methodology may offer valuable insights for treatment planning and disease management.

Quantification of Intratumoral Heterogeneity: Distinguishing Histological Subtypes in Clinical T1 Stage Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules on Computed Tomography.

RATIONALE AND OBJECTIVES: To quantify intratumor heterogeneity (ITH) in clinical T1 stage lung adenocarcinoma presenting as pure ground-glass nodules (pGGN) on computed tomography, assessing its value in distinguishing histological subtypes. MATERIALS AND METHODS: An ITH score was developed for quantitative measurement by integrating local radiomics features and global pixel distribution patterns. Diagnostic efficacy in distinguishing histological subtypes was evaluated using receiver operating characteristic curve analysis and area under the curve (AUC) values. The ITH score's performance was compared to those of conventional radiomics (C-radiomics), and radiological assessments conducted by experienced radiologists. RESULTS: The ITH score demonstrated excellent performance in distinguishing lepidic-predominant adenocarcinoma (LPA) from other histological subtypes of clinical T1 stage lung adenocarcinoma presenting as pGGN. It outperformed both C-radiomics and radiological findings, exhibiting higher AUCs of 0.784 (95% confidence interval [CI]: 0.742-0.826) and 0.801 (95% CI: 0.739-0.863) in the training and validation cohorts, respectively. The AUCs of C-radiomics were 0.764 (95% CI: 0.718-0.810, DeLong test, p = 0.025) and 0.760 (95% CI: 0.692-0.829, p = 0.023) and those of radiological findings were 0.722 (95% CI: 0.673-0.771, p = 0.003) and 0.754 (95% CI: 0.684-0.823, p = 0.016) in the training and validation cohorts, respectively. Subgroup analysis revealed varying diagnostic efficacy across clinical T1 stages, with the highest efficacy in the T1a stage, followed by the T1b stage, and lowest in the T1c stage. CONCLUSION: The ITH score presents a superior method for evaluating histological subtypes and distinguishing LPA from other subtypes in clinical T1 stage lung adenocarcinoma presenting as pGGN.

The role of quiescent thymic progenitors in TAL/LMO2-induced T-ALL chemotolerance.

Relapse in T-cell acute lymphoblastic leukemia (T-ALL) may signify the persistence of leukemia-initiating cells (L-ICs). Ectopic TAL1/LMO expression defines the largest subset of T-ALL, but its role in leukemic transformation and its impact on relapse-driving L-ICs remain poorly understood. In TAL1/LMO mouse models, double negative-3 (DN3; CD4-CD8-CD25+CD44-) thymic progenitors harbored L-ICs. However, only a subset of DN3 leukemic cells exhibited L-IC activity, and studies linking L-ICs and chemotolerance are needed. To investigate L-IC heterogeneity, we used mouse models and applied single-cell RNA-sequencing and nucleosome labeling techniques in vivo. We identified a DN3 subpopulation with a cell cycle-restricted profile and heightened TAL1/LMO2 activity, that expressed genes associated with stemness and quiescence. This dormant DN3 subset progressively expanded throughout leukemogenesis, displaying intrinsic chemotolerance and enrichment in genes linked to minimal residual disease. Examination of TAL/LMO patient samples revealed a similar pattern in CD7+CD1a- thymic progenitors, previously recognized for their L-IC activity, demonstrating cell cycle restriction and chemotolerance. Our findings substantiate the emergence of dormant, chemotolerant L-ICs during leukemogenesis, and demonstrate that Tal1 and Lmo2 cooperate to promote DN3 quiescence during the transformation process. This study provides a deeper understanding of TAL1/LMO-induced T-ALL and its clinical implications in therapy failure.

Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer.

Objective: This study aimed to investigate and validate the effectiveness of diverse radiomics models for preoperatively differentiating lymphovascular invasion (LVI) in clinically node-negative breast cancer (BC). Methods: This study included 198 patients diagnosed with clinically node-negative bc and pathologically confirmed LVI status from January 2018-July 2023. The training dataset consisted of 138 patients, while the validation dataset included 60. Radiomics features were extracted from multimodal magnetic resonance imaging obtained from T1WI, T2WI, DCE, DWI, and ADC sequences. Dimensionality reduction and feature selection techniques were applied to the extracted features. Subsequently, machine learning approaches, including logistic regression, support vector machine, classification and regression trees, k-nearest neighbors, and gradient boosting machine models (GBM), were constructed using the radiomics features. The best-performing radiomic model was selected based on its performance using the confusion matrix. Univariate and multivariable logistic regression analyses were conducted to identify variables for developing a clinical-radiological (Clin-Rad) model. Finally, a combined model incorporating both radiomics and clinical-radiological model features was created. Results: A total of 6195 radiomic features were extracted from multimodal magnetic resonance imaging. After applying dimensionality reduction and feature selection, seven valuable radiomics features were identified. Among the radiomics models, the GBM model demonstrated superior predictive efficiency and robustness, achieving area under the curve values (AUC) of 0.881 (0.823,0.940) and 0.820 (0.693,0.947) in the training and validation datasets, respectively. The Clin-Rad model was developed based on the peritumoral edema and DWI rim sign. In the training dataset, it achieved an AUC of 0.767 (0.681, 0.854), while in the validation dataset, it achieved an AUC of 0.734 (0.555-0.913). The combined model, which incorporated radiomics and the Clin-Rad model, showed the highest discriminatory capability. In the training dataset, it had an AUC value of 0.936 (0.892, 0.981), and in the validation dataset, it had an AUC value of 0.876 (0.757, 0.995). Additionally, decision curve analysis of the combined model revealed its optimal clinical efficacy. Conclusion: The combined model, integrating radiomics and clinical-radiological features, exhibited excellent performance in distinguishing LVI status. This non-invasive and efficient approach holds promise for aiding clinical decision-making in the context of clinically node-negative BC.

Anti-adhesion multifunctional poly(lactic-co-glycolic acid)/polydimethylsiloxane wound dressing for bacterial infection monitoring and photodynamic antimicrobial therapy.

Wound infection and adhesion are important factors affecting wound healing. Early detection of pathogen infection and reduction of wound-to-dressing adhesion are critical for improving wound healing. Herein, Ester-J, which can rapidly respond to lipase secreted by bacteria, was designed and synthesized. Then, Ester-J was co-spun with poly(lactic-co-glycolic acid) (PLGA) and polydimethylsiloxane (PDMS) to prepare a PP-EsJ hydrophobic anti-adhesion dressing with a contact angle of 140.7°. When the PP-EsJ membrane came into contact with the bacteria, the loaded Ester-J was hydrolyzed to Tph-TSF-OH, releasing bright cyan-blue fluorescence, thus providing a fluorescence switch for an early warning of infection. The detection limits of PP-EsJ for Pseudomonas aeruginosa and Staphylococcus aureus were 1.0 × 105 and 1.0 × 106 CFU/mL, respectively. Subsequently, Tph-TSF-OH released 1O2 through light irradiation, which rapidly killed P. aeruginosa and S. aureus, and accelerated wound healing. Compared with the control group, enhanced wound closure (up to 99.80 ± 1.10 %) was observed in mice treated with the PP-EsJ membrane. The PP-EsJ membrane not only effectively reduced the risk of external infection but also reduced adhesions to the skin during dressing changes. These characteristics make PP-EsJ membranes potentially useful for clinical treatment.

LncRNA PSMB8-AS1 increases glioma malignancy via the miR-382-3p/BCAT1 axis.

BACKGROUND: This study aimed to investigate the specific roles of the long non-coding RNA (lncRNA) proteasome 20S subunit beta 8 (PSMB8)-antisense RNA 1 (AS1)/microRNA (miR)-382-3p/branched-chain amino acid transaminase 1 (BCAT1) interaction network in gliomas. METHODS: Western blotting and quantitative reverse transcription-polymerase chain reaction were performed to assess the expression levels of lncRNA PSMB8-AS1, BCAT1, and miR-382-3p. Moreover, the cell proliferation, migration, and apoptosis were assessed using the cell counting kit-8, Transwell, and caspase-3 activity assays, respectively. The biological role of lncRNA PSMB8-AS1 in glioma was investigated in vivo using a xenograft mouse model. Additionally, the associations among lncRNA PSMB8-AS1, miR-382-3p, and BCAT1 were analyzed using dual-luciferase and RNA immunoprecipitation assays and bioinformatics analyses. RESULTS: Glioma cell lines and tissues exhibited overexpression of lncRNA PSMB8-AS1 and BCAT1 and low expression of miR-382-3p. Knockdown of PSMB8-AS1 remarkably repressed the tumor growth in vivo and the migration and proliferation of glioma cells in vitro. In contrast, knockdown of lncRNA PSMB8-AS1 increased the cell apoptosis. Mechanistically, PSMB8-AS1 directly targeted miR-382-3p. By sponging miR-382-3p, lncRNA PSMB8-AS1 stimulated the migration and proliferation of glioma cells and suppressed their apoptosis. Additionally, miR-382-3p directly targeted BCAT1. Inhibition of miR-382-3p reversed the antitumor effects of BCAT1 silencing on glioma progression. CONCLUSION: Our study revealed that lncRNA PSMB8-AS1 aggravated glioma malignancy by enhancing BCAT1 expression after competitively binding to miR-382-3p. Therefore, lncRNA PSMB8-AS1 may be a potential biomarker and therapeutic target for glioma treatment.

Elevated serum IL-6 and total IgEAb are associated with poor survival in natural killer/T-cell lymphoma.

Natural killer/T-cell lymphoma (NKTCL) is an aggressive and malignant condition with a high mortality rate. Prognostic factors may assist to evaluate the outcome of the disease and may also be useful in selecting appropriate therapeutic strategies for patients. The study aims to describe NKTCL in terms of its clinical features, laboratory examinations, and immunophenotypes and to analyze relevance affecting patient survival outcomes. The patients diagnosed as NKTCL in Jinling Hospital from Jan. 2012 to Dec. 2022 were reviewed retrospectively in this study basing on histopathology. The analysis was performed to evaluate overall survival (OS). A total of 125 NKTCL patients were included, which mainly affected male more than female with the onset median age of 51.00 years old (range, 14 ~ 85 y). NKTCL commonly affects the nasopharynx and upper aerodigestive tract, intestines, and skin. The median overall survival was 13.00 months (range, 2-156 m), and the 5-year survival rate was 9.8%. Under univariable analysis revealed the following factors at diagnosis age: serum total IgEAb ≥ 54.6 IU/mL, IL-6 ≥ 32.445 ng/L, elevated PINK score, smoking, and extranasopharyngeal site were statistically significant predictors for OS. Compared to the patients who received radiotherapy alone or chemotherapy alone, the patients who received combined chemoradiotherapy had longer OS. We found that IL-6 and total IgEAb were significant prognostic factors in NKTCL patients. Also, extranasopharyngeal site was correlated with advanced disease.

Lung-PNet: An Automated Deep Learning Model for the Diagnosis of Invasive Adenocarcinoma in Pure Ground-Glass Nodules on Chest CT.

BACKGROUND. Pure ground-glass nodules (pGGNs) on chest CT representing invasive adenocarcinoma (IAC) warrant lobectomy with lymph node resection. For pGGNs representing other entities, close follow-up or sublobar resection without node dissection may be appropriate. OBJECTIVE. The purpose of this study was to develop and validate an automated deep learning model for differentiation of pGGNs on chest CT representing IAC from those representing atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), and minimally invasive adenocarcinoma (MIA). METHODS. This retrospective study included 402 patients (283 women, 119 men; mean age, 53.2 years) with a total of 448 pGGNs on noncontrast chest CT that were resected from January 2019 to June 2022 and were histologically diagnosed as AAH (n = 29), AIS (n = 83), MIA (n = 235), or IAC (n = 101). Lung-PNet, a 3D deep learning model, was developed for automatic segmentation and classification (probability of IAC vs other entities) of pGGNs on CT. Nodules resected from January 2019 to December 2021 were randomly allocated to training (n = 327) and internal test (n = 82) sets. Nodules resected from January 2022 to June 2022 formed a holdout test set (n = 39). Segmentation performance was assessed with Dice coefficients with radiologists' manual segmentations as reference. Classification performance was assessed by ROC AUC and precision-recall AUC (PR AUC) and compared with that of four readers (three radiologists, one surgeon). The code used is publicly available (https://github.com/XiaodongZhang-PKUFH/Lung-PNet.git). RESULTS. In the holdout test set, Dice coefficients for segmentation of IACs and of other lesions were 0.860 and 0.838, and ROC AUC and PR AUC for classification as IAC were 0.911 and 0.842. At threshold probability of 50.0% or greater for prediction of IAC, Lung-PNet had sensitivity, specificity, accuracy, and F1 score of 50.0%, 92.0%, 76.9%, and 60.9% in the holdout test set. In the holdout test set, accuracy and F1 score (p values vs Lung-PNet) for individual readers were as follows: reader 1, 51.3% (p = .02) and 48.6% (p = .008); reader 2, 79.5% (p = .75) and 75.0% (p = .10); reader 3, 66.7% (p = .35) and 68.3% (p < .001); reader 4, 71.8% (p = .48) and 42.1% (p = .18). CONCLUSION. Lung-PNet had robust performance for segmenting and classifying (IAC vs other entities) pGGNs on chest CT. CLINICAL IMPACT. This automated deep learning tool may help guide selection of surgical strategies for pGGN management.

Assessment of Lymphovascular Invasion in Breast Cancer Using a Combined MRI Morphological Features, Radiomics, and Deep Learning Approach Based on Dynamic Contrast-Enhanced MRI.

BACKGROUND: Assessment of lymphovascular invasion (LVI) in breast cancer (BC) primarily relies on preoperative needle biopsy. There is an urgent need to develop a non-invasive assessment method. PURPOSE: To develop an effective model to assess the LVI status in patients with BC using magnetic resonance imaging morphological features (MRI-MF), Radiomics, and deep learning (DL) approaches based on dynamic contrast-enhanced MRI (DCE-MRI). STUDY TYPE: Cross-sectional retrospective cohort study. POPULATION: The study included 206 BC patients, with 136 in the training set [97 LVI(-) and 39 LVI(+) cases; median age: 51.5 years] and 70 in the test set [52 LVI(-) and 18 LVI(+) cases; median age: 48 years]. FIELD STRENGTH/SEQUENCE: 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, diffusion-weighted imaging (DWI), and DCE-MRI. ASSESSMENT: The MRI-MF model was developed with conventional MR features using logistic analyses. The Radiomic feature extraction process involved collecting data from categorized DCE-MRI datasets, specifically the first and second post-contrast images (A1 and A2). Next, a DL model was implemented to determine LVI. Finally, we established a joint diagnosis model by combining the MRI-MF, Radiomics, and DL approaches. STATISTICAL TESTS: Diagnostic performance was compared using receiver operating characteristic curve analysis, confusion matrix, and decision curve analysis. RESULTS: Rim sign and peritumoral edema features were used to develop the MRI-MF model, while six Radiomics signature from the A1 and A2 images were used for the Radiomics model. The joint model (MRI-MF + Radiomics + DL models) achieved the highest accuracy (area under the curve [AUC] = 0.857), being significantly superior to the MRI-MF (AUC = 0.724), Radiomics (AUC = 0.736), or DL (AUC = 0.740) model. Furthermore, it also outperformed the pairwise combination models: Radiomics + MRI-MF (AUC = 0.796), DL + MRI-MF (AUC = 0.796), or DL + Radiomics (AUC = 0.826). DATA CONCLUSION: The joint model incorporating MRI-MF, Radiomics, and DL approaches can effectively determine the LVI status in patients with BC before surgery. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Astragaloside IV inhibits pathological functions of gastric cancer-associated fibroblasts through regulation of the HOXA6/ZBTB12 axis.

Cancer-associated fibroblasts (CAFs) play critical roles in the tumor microenvironment and exert tumor-promoting or tumor-retarding effects on cancer development. Astragaloside IV has been suggested to rescue the pathological impact of CAFs in gastric cancer. This study aimed to investigate the potential mechanism of astragaloside IV in the regulation of CAF pathological functions in gastric cancer development. Homeobox A6 (HOXA6), and Zinc Finger and BTB Domain Containing 12 (ZBTB12) are highly expressed in gastric CAFs compared with normal fibroblasts (NFs) based on the GSE62740 dataset. We found that astragaloside IV-stimulated CAFs suppressed cell growth, migration, and invasiveness of gastric cancer cells. HOXA6 and ZBTB12 were downregulated after astragaloside IV treatment in CAFs. Further analysis revealed that HOXA6 or ZBTB12 knockdown in CAFs also exerted inhibitory effects on the malignant phenotypes of gastric cells. Additionally, HOXA6 or ZBTB12 overexpression in CAFs enhanced gastric cancer cell malignancy, which was reversed after astragaloside IV treatment. Moreover, based on the hTFtarget database, ZBTB12 is a target gene that may be transcriptionally regulated by HOXA6. The binding between HOXA6 and ZBTB12 promoter in 293T cells and CAFs was further confirmed. HOXA6 silencing also induced the downregulation of ZBTB12 mRNA and protein in CAFs. Astragaloside IV was demonstrated to regulate the expression of ZBTB12 by mediating the transcriptional activity of HOXA6. Our findings shed light on the therapeutic value of astragaloside IV for gastric cancer.

Pilonidal cyst of the scalp.

A 19-year-old male patient presented with a slow-growing mass on his scalp over a 10-year history. After resecting the mass, he was diagnosed with pilonidal cyst based on the clinical and histological findings. An unusual pilonidal disease is observed on the scalp in this report.

The latest edition of WHO and ELN guidance and a new risk model for Chinese acute myeloid leukemia patients.

Objective: Diagnosis classification and risk stratification are crucial in the prognosis prediction and treatment selection of acute myeloid leukemia (AML). Here, we used a database of 536 AML patients to compare the 4th and 5th WHO classifications and the 2017 and 2022 versions of ELN guidance. Methods: AML patients were classified according to the 4th and 5th WHO classifications, as well as the 2017 and 2022 versions of the European LeukemiaNet (ELN) guidance. Kaplan-Meier curves with log-rank tests were used for survival analysis. Results: The biggest change was that 25 (5.2%), 8 (1.6%), and 1 (0.2%) patients in the AML, not otherwise specified (NOS) group according to the 4th WHO classification, were re-classified into the AML-MR (myelodysplasia-related), KMT2A rearrangement, and NUP98 rearrangement subgroups based on the 5th WHO classification. Referring to the ELN guidance, 16 patients in the favorable group, six patients in the adverse group, and 13 patients in the intermediate group based on the 2017 ELN guidance were re-classified to the intermediate and adverse groups based on the 2022 ELN guidance. Regrettably, the Kaplan-Meier curves showed that the survival of intermediate and adverse groups could not be distinguished well according to either the 2017 or 2022 ELN guidance. To this end, we constructed a risk model for Chinese AML patients, in which the clinical information (age and gender), gene mutations (NPM1, RUNX1, SH2B3, and TP53), and fusions (CBFB::MYH11 and RUNX1::RUNX1T1) were included, and our model could help divide the patients into favorable, intermediate, and adverse groups. Conclusion: These results affirmed the clinical value of both WHO and ELN, but a more suitable prognosis model should be established in Chinese cohorts, such as the models we proposed.

Influence of Antenna Element Position Deviation on Radiation Performance of Helical Antenna Array.

The electrical performance of the feed array is degraded because of the position deviation of the array elements caused by manufacturing and processing, which cannot meet the high performance feeding requirements of large feed arrays. In this paper, a radiation field model of the helical antenna array considering the position deviation of array elements is proposed to investigate the influence law of position deviation on the electrical performance of the feed array. With the established model, the rectangular planar array and the circular array of the helical antenna with a radiating cup are discussed and the relationship between electrical performance index and position deviation is established by numerical analysis and curve fitting method. The research results show that the position deviation of the antenna array elements will lead to the rise of the sidelobe level, the deviation of the beam pointing, and the increase of the return loss. The valuable simulation results provided by this work can be used in antenna engineering, guiding antenna designers to set optimal parameters when fabricating antennae.