Complete breast autoamputation: Clinical image

The Antimicrobial Peptide Merecidin Inhibit the Metastasis of Triple-Negative Breast Cancer by Obstructing EMT via miR-30d-5p/Vimentin

Purpose: To investigate the inhibitory effect of antimicrobial peptide merecidin on triple-negative breast cancer (TNBC) and the mechanism of inhibiting epithelial-mesenchymal transformation (EMT) by regulating miR-30d-5p/vimentin. Methods: TNBC cell lines (MDA-MB-231, MDA-MB-468) were treated with merecidin to assess proliferation, migration, invasion ability, and EMT. Confocal laser localization was used to examine the role of merecidin and TNBC cells. The relationship between merecidin and miR-30d-5p was determined through RT-qPCR and dual-luciferase reporter gene, and the relationship between merecidin and vimentin was verified through pulling down the experiment. The effects of miR-30d-5p on the migration and invasion ability of TNBC cells were confirmed through scratch and transwell experiments. Vimentin levels, tumor volume, shape, size, and weight were observed in the MDA-MB-231 subcutaneous tumor model in nude mice. Results: merecidin inhibited the proliferation, migration, invasion, and EMT of TNBC cells. merecidin was primarily located in the cytoplasm of TNBC cells, and the expression of miR-30d-5p was low in TNBC cells. merecidin significantly up-regulated the expression of miR-30d-5p. miR-30d-5p negatively regulated vimentin. merecidin could bind to vimentin in vitro. miR-30d-5p inhibited the migration and invasion ability of TNBC cells, while vimentin promoted their migration and invasion ability. Down-regulation of miR-30d-5p or overexpression of vimentin partially counteracted the inhibitory effects of merecidin on TNBC cell migration, invasion ability, and EMT. In nude mouse tumor models, merecidin significantly suppressed tumor growth. Conclusion: Merecidin effectively blocks the EMT process and inhibits the migration and invasion of TNBC cells by regulating miR-30d-5p/vimentin.

Breast-Conserving Surgery in Triple-Negative Breast Cancer: A Retrospective Cohort Study

Objectives

The aim of the study is to evaluate the efficacy and prognosis of neoadjuvant chemotherapy (NAC) combined with breast-conserving surgery (BCS) in treating triple-negative breast cancer (TNBC) and analyze the influencing factors and predictors of the efficiency and prognosis of NAC.

Methods

A retrospective cohort study was conducted by dividing patients into two groups according to two different therapy methods. With BCS as the exposure factor, 46 cases were assigned to the exposed group and 80 cases to the nonexposed group. We compare the difference in operation-related indicators, postoperative complications, local recurrence rate, distant metastasis rate, and overall survival (OS) rate between the two groups. The factors affecting the efficiency and prognosis of NAC were analyzed by binary logistic regression, and the optimal cutoff value was determined by the area under the ROC curve (AUC). The survival curve was plotted, and the univariate log-rank test was performed to analyze the difference in OS between the two groups. The influencing factors of OS were analyzed by the Cox risk regression model.

Results

NAC + BCS resulted in significantly less intraoperative blood loss, lower incidence of postoperative complications, and shorter operative time and length of hospital stay than that in NAC (P < 0.05). There was no significant difference in local recurrence, distant metastasis, or OS between the two groups (P > 0.05). Multivariate analysis showed that the clinical stage I and Ki-67 high expression were independent protective factors of the efficacy of NAC. The high expression of Ki-67 and nondecline expression of Ki-67 were independent risk factors of prognosis. Ki-67 high expression was an independent risk factor of OS (P < 0.05). The ROC curve showed that the AUC of Ki-67 for NAC efficacy, prognosis, and OS were 0.706, 0.820, and 0.687, respectively, with optimal cutoff values of 25.5%, 29.0%, and 32.5%, respectively. Survival analysis showed that the OS of patients with NAC + BCS was 73.9% and NAC + MRM was 70.0% (P > 0.05). In the low expression subgroup of Ki-67, the OS of the two groups were 100.0% and 77.8%, respectively (P=0.060). In the high expression subgroup of Ki-67, the OS of the two groups were 53.8% and 63.6%, respectively (P=0.419).

Conclusions

NAC + BCS is a good method for treating TNBC, which has an obvious short-term effect and a good long-term prognosis. Clinical stage I and the high expression of Ki-67 are independent protective factors for the efficacy of NAC. The high expression of Ki-67 and nondecline expression of Ki-67 are independent risk factors of prognosis. Ki-67 is a potential predictor for the efficacy, prognosis, and OS of NAC in TNBC patients. The high expression of Ki-67 indicates better NAC efficacy, a poorer prognosis, and a lower OS.

Application of Deep Learning to Reduce the Rate of Malignancy Among BI-RADS 4A Breast Lesions Based on Ultrasonography

The aim of the work described here was to develop an ultrasound (US) image-based deep learning model to reduce the rate of malignancy among breast lesions diagnosed as category 4A of the Breast Imaging-Reporting and Data System (BI-RADS) during the pre-operative US examination. A total of 479 breast lesions diagnosed as BI-RADS 4A in pre-operative US examination were enrolled. There were 362 benign lesions and 117 malignant lesions confirmed by postoperative pathology with a malignancy rate of 24.4%. US images were collected from the database server. They were then randomly divided into training and testing cohorts at a ratio of 4:1. To correctly classify malignant and benign tumors diagnosed as BI-RADS 4A in US, four deep learning models, including MobileNet, DenseNet121, Xception and Inception V3, were developed. The performance of deep learning models was compared using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Meanwhile, the robustness of the models was evaluated by five-fold cross-validation. Among the four models, the MobileNet model turned to be the optimal model with the best performance in classifying benign and malignant lesions among BI-RADS 4A breast lesions. The AUROC, accuracy, sensitivity, specificity, PPV and NPV of the optimal model in the testing cohort were 0.897, 0.913, 0.926, 0.899, 0.958 and 0.784, respectively. About 14.4% of patients were expected to be upgraded to BI-RADS 4B in US with the assistance of the MobileNet model. The deep learning model MobileNet can help to reduce the rate of malignancy among BI-RADS 4A breast lesions in pre-operative US examinations, which is valuable to clinicians in tailoring treatment for suspicious breast lesions identified on US.

The dynamic alteration of transcriptional regulation by crucial TFs during tumorigenesis of gastric cancer

Background

The mechanisms of Gastric cancer (GC) initiation and progression are complicated, at least partly owing to the dynamic changes of gene regulation during carcinogenesis. Thus, investigations on the changes in regulatory networks can improve the understanding of cancer development and provide novel insights into the molecular mechanisms of cancer.

Methods

Differential co-expression analysis (DCEA), differential gene regulation network (GRN) modeling and differential regulation analysis (DRA) were integrated to detect differential transcriptional regulation events between gastric normal mucosa and cancer samples based on GSE54129 dataset. Cytological experiments and IHC staining assays were used to validate the dynamic changes of CREB1 regulated targets in different stages.

Results

A total of 1955 differentially regulated genes (DRGs) were identified and prioritized in a quantitative way. Among the top 1% DRGs, 14 out of 19 genes have been reported to be GC relevant. The four transcription factors (TFs) among the top 1% DRGs, including CREB1, BPTF, GATA6 and CEBPA, were regarded as crucial TFs relevant to GC progression. The differentially regulated links (DRLs) around the four crucial TFs were then prioritized to generate testable hypotheses on the differential regulation mechanisms of gastric carcinogenesis. To validate the dynamic alterations of gene regulation patterns of crucial TFs during GC progression, we took CREB1 as an example to screen its differentially regulated targets by using cytological and IHC staining assays. Eventually, TCEAL2 and MBNL1 were proved to be differentially regulated by CREB1 during tumorigenesis of gastric cancer.

Conclusions

By combining differential networking information and molecular cell experiments verification, testable hypotheses on the regulation mechanisms of GC around the core TFs and their top ranked DRLs were generated. Since TCEAL2 and MBNL1 have been reported to be potential therapeutic targets in SCLC and breast cancer respectively, their translation values in GC are worthy of further investigation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-022-00468-7.

TAP Block Prior to Open Ventral Hernia Repair Improves Surgical Outcome

Background

Ventral hernias commonly affect patients after major abdominal surgery. To reduce postoperative pain, the effects of the transversus abdominis plane (TAP) block, epidural analgesia and medication-only protocol have been investigated. The primary outcome was the cumulative dosage of opioids (morphine milligram equivalents MME), of acetaminophen and diclofenac for postoperative pain control on postoperative day (POD) 0, 1, and 2. Secondary outcomes were length of stay (LOS) and the pain scale rating using the numeric rating scale (NRS) on POD 0, 1, and 2.

Methods

The data were retrospectively extracted from the charts of the patients admitted for a surgical operation for OVHR from January 2015 to December 2019.

Results

Patients receiving medication-only analgesia had longer LOS (mean 6.1 days; p < 0.00001). Cumulative opioid consumption was significantly lower at 24 and 48 h after surgery in the TAP block group than in the other groups (mean MME 1.9 mg and 0.7 mg, respectively; p < 0.05). The cumulative consumption of diclofenac was significantly lower in the TAP block group than in the others (44.1 mg; p ≤ 0.00001 on POD 1; 4.4 mg; p = 0.03 on POD 2). TAP block is more effective in pain control in POD 0 (mean NRS 5.4; p < 0.00001), POD 1 (mean NRS 6.1; p = 0.006), and POD 2 (mean NRS 4.9; p = 0.001) if it is performed after adopting the retromuscular technique.

Conclusions

The comparison between the medication-only technique, epidural, and TAP block demonstrated the superiority of the last one for the aims considered in this study.

Multimodal medical image fusion based on content-based decomposition and PCA-Sigmoid

OBJECTIVE

The objective of any multimodal medical image fusion algorithm is to assist a radiologist for better decision-making during the diagnosis and therapy by integrating the anatomical (magnetic resonance imaging) and functional (positron emission tomography/single-photon emission computed tomography) information.

METHODS

We proposed a new medical image fusion method based on content-based decomposition, principal component analysis (PCA), and sigmoid function. We considered empirical wavelet transform (EWT) for content-based decomposition purposes since it can preserve crucial medical image information such as edges and corners. PCA is used to obtain initial weights corresponding to each detail layer.

RESULTS

In our experiments, we found that direct usage of PCA for detail layer fusion introduces severe artifacts into the fused image due to weight scaling issues. In order to tackle this, we considered using the sigmoid function for better weight scaling. We considered 24 pairs of MRI-PET and 24 pairs of MRI-SPECT images for fusion and the results are measured using four significant quantitative metrics.

CONCLUSION

Finally, we compared our proposed method with other state-of-the-art transform-based fusion approaches, using traditional and recent performance measures. An appreciable improvement is observed in both qualitative and quantitative results compared to other fusion methods.

Unsupervised end-to-end Brain Tumor Magnetic Resonance Image Registration using RBCNN: Rigid Transformation, B-Spline Transformation and Convolutional Neural Network

BACKGROUND

Image registration is the process of aligning two or more images in a single coordinate. Now a days, medical image registration plays a significant role in computer assisted disease diagnosis, treatment, and surgery. The different modalities available in the medical image makes medical image registration as an essential step in Computer Assisted Diagnosis(CAD), Computer-Aided Therapy (CAT) and Computer-Assisted Surgery (CAS). Problem definition: Recently many learning based methods were employed for disease detection and classification but those methods were not suitable for real time due to delayed response and need of pre alignment,labeling.

METHOD

The proposed research constructed a deep learning model with Rigid transform and B-Spline transform for medical image registration for an automatic brain tumour finding. The proposed research consists of two steps. First steps uses Rigid transformation based Convolutional Neural Network and the second step uses B-Spline transform based Convolutional Neural Network. The model is trained and tested with 3624 MR (Magnetic Resonance) images to assess the performance. The researchers believe that MR images helps in success the treatment of brain tumour people.

RESULT

The result of the proposed method is compared with the Rigid Convolutional Neural Network (CNN), Rigid CNN + Thin-Plat Spline (TPS), Affine CNN, Voxel morph, ADMIR (Affine and Deformable Medical Image Registration) and ANT(Advanced Normalization Tools) using DICE score, average symmetric surface distance (ASD), and Hausdorff distance.

CONCLUSION

The RBCNN model will help the physician to automatically detect and classify the brain tumor quickly(18 Sec) and efficiently with out doing any pre-alignment and labeling.

Pan-cancer analysis of the prognostic value of C12orf75 based on data mining

The differential expression of chromosome 12 open reading frame 75 (C12orf75) is closely related with cancer progression. Here, we studied the expression levels of C12orf75 and investigated its prognostic value in various cancers across distinct datasets including ONCOMINE, PrognoScan, GEPIA, and TCGA. The correlation between genetic alteration of C12orf75 and immune infiltration was investigated using the cBioPortal and TIMER databases. RNA interference was used to verify the influence of C12orf75 knockdown on the biological phenotype of hepatocellular carcinoma cells. C12orf75 showed increased expression in most tested human cancers. The increased expression of C12orf75 was related with a poor prognosis in urothelial bladder carcinoma and hepatocellular liver carcinoma, but it was surprisingly converse in renal papillary cell carcinoma. In urothelial bladder carcinoma and hepatocellular liver carcinoma, we observed positive correlations between the expression of C12orf75 and the infiltration of immune cells, including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. The knockdown of C12orf75 in hepatocellular carcinoma cells suppressed the proliferation, migration, and invasion and arrested the cell cycle. This is the first report C12orf75 has potential as a prognostic biomarker and therapeutic target for molecularly targeted drugs in urothelial bladder carcinoma, hepatocellular liver carcinoma, and renal papillary cell carcinoma.

Conditional survival and hazards of death for peripheral T-cell lymphomas

Typically, peripheral T-cell lymphoma (PTCLs) prognosis is estimated using overall survival before treatment. However, these estimates cannot show how prognosis evolves with the changing hazard rate over time. Patients (n = 650) with newly diagnosed PTCLs were enrolled retrospectively. After a median follow-up of 5.4 years, angioimmunoblastic T-cell lymphoma, peripheral T-cell lymphoma, not otherwise specified (PTCL, NOS) and NK/T cell lymphoma had initially lower 3-year conditional overall survival (COS3; i.e., the 3-year conditional overall survival was defined as the probability of surviving an additional 3 years) and higher hazards of death (26–44.3%). However, after 2 years, the COS3 increased and the death risk decreased over time, whereas anaplastic lymphoma kinase-positive anaplastic large-cell lymphoma constantly had a lower risk over time (0–19.5%). For patients with complete remission after initial treatment, prognosis varied by histological subtypes, with PTCL, NOS having a negative impact. Our data suggested that the risk stratification using the International Prognostic Index might not accurately predict the COS3 for survivors of PTCLs. The COS3 provided time-dependent prognostic information for PTCLs, representing a possible surrogate prognosis indicator for long-term survivors after systemic chemotherapy.