5-Aza-2'-Deoxycytidine Ameliorates Choroidal Neovascularization by Inhibiting the Wnt/ß-Catenin Signaling Pathway.

Purpose: Choroidal neovascularization (CNV) can constitute the final pathology of many ocular diseases and result in severe vision loss. Studies have demonstrated that DNA methylation is critical in retinal development, aging, and disorders. The current work investigated the effects and underlying mechanism of 5-Aza-2'-deoxycytidine (5-aza-dC), a suppressor of DNA methylation, in the pathological progression of CNV. Methods: The DNA methylation profiles of retinal pigment epithelial (RPE)/choroidal complexes in normal and laser-induced CNV mice were assessed by Arraystar Mouse RefSeq Promoter Arrays. The CNV area and blood flow density and intensity were observed by optical coherence tomography angiography, and fluorescence leakage was examined by fundus fluorescein angiography in CNV mice with systemic administration of 5-aza-dC. The effects of 5-aza-dC on the biological functions of bEnd.3 cells were estimated by related assays. Notum gene promoter methylation was measured using bisulfite sequencing PCR. Methyltransferases and Wnt signaling-related genes were detected in animal and cell culture experiments by real-time PCR and immunoblot. Results: Methyltransferases were upregulated, but Notum (a secretion inhibitor of Wnt signaling) was downregulated in the RPE/choroidal complexes of mice with experimental CNV. Intraperitoneal injection of 5-aza-dC inactivated the Wnt pathway and ameliorated the lesion area and the intensity and density of blood flow, as well as the degree of leakage in CNV. In vitro, vascular endothelial growth factor A (VEGFA) stimulation promoted methyltransferases expression and suppressed Notum expression, consequently activating Wnt signaling, whereas exogenous 5-aza-dC reversed VEGFA-induced hyperpermeability, proliferation, migration, and tube formation in bEnd.3 cells via demethylation of Notum promoter. Conclusions: We observed that 5-aza-dC attenuates the growth of CNV by inhibiting the Wnt signaling pathway via promoter demethylation of the Wnt antagonist Notum. These findings provide a theoretical basis for methylation-based treatment with the Notum gene as a potential target for CNV treatment.

Mutational signature and prognosis in adenocarcinoma of the bladder.

Adenocarcinoma of the bladder is a rare urinary bladder carcinoma with limited therapy options due to lack of molecular characterization. Here, we aimed to reveal the mutational and transcriptomic landscapes of adenocarcinoma of the bladder and assess any relationship with prognosis. Between February 2015 and June 2021, a total of 23 patients with adenocarcinoma of the bladder were enrolled. These included 16 patients with primary bladder adenocarcinomas and seven patients with urachal adenocarcinoma. Whole exome sequencing (16 patients), whole genome sequencing (16 patients), bulk RNA sequencing (RNA-seq) (19 patients), and single-cell RNA-seq (5 patients) were conducted for the specimens. Correlation analysis, survival analysis, and t-tests were also performed. Prevalent T>A substitutions were observed among somatic mutations, and major trinucleotide contexts included 5'-CTC-3' and 5'-CTG-3'. This pattern was mainly contributed by COSMIC signature 22 related to chemical carcinogen exposure (probably aristolochic acid), which has not been reported in bladder adenocarcinoma. Moreover, genes with copy number changes were also enriched in the KEGG term 'chemical carcinogenesis'. Transcriptomic analysis suggested high immune cell infiltration and luminal-like features in the majority of samples. Interestingly, a small fraction of samples with an APOBEC-derived mutational signature exhibited a higher risk of disease progression compared with samples with only a chemical carcinogen-related signature, confirming the molecular and prognostic heterogeneity of bladder adenocarcinoma. This study presents mutational and transcriptomic landscapes of bladder adenocarcinoma, and indicates that a chemical carcinogen-related mutational signature may be related to a better prognosis compared with an APOBEC signature in adenocarcinoma of the bladder. © 2024 The Pathological Society of Great Britain and Ireland.

A carboxy-terminal ubiquitylation site regulates androgen receptor activity.

Degradation of unliganded androgen receptor (AR) in prostate cancer cells can be prevented by proteasome inhibition, but this is associated with only modest increases in polyubiquitylated AR. An inhibitor (VLX1570) of the deubiquitylases associated with the proteasome did not increase ubiquitylation of unliganded AR, indicating that AR is not targeted by these deubiquitylases. We then identified a series of AR ubiquitylation sites, including a not previously identified site at K911, as well as methylation sites and previously identified phosphorylation sites. Mutagenesis of K911 increases AR stability, chromatin binding, and transcriptional activity. We further found that K313, a previously reported ubiquitylation site, could also be methylated and acetylated. Mutagenesis of K313, in combination with K318, increases AR transcriptional activity, indicating that distinct posttranslational modifications at K313 differentially regulate AR activity. Together these studies expand the spectrum of AR posttranslational modifications, and indicate that the K911 site may regulate AR turnover on chromatin.

Endoloop pre-ligation assisted resection of a giant Brunner's gland adenoma of the duodenal bulb.

Brunner's gland adenoma (BGA), also known as Brunneroma or polypoid hamartoma, is a rare benign duodenal tumor that proliferates from Brunner's glands of the duodenum. They are usually asymptomatic and discovered by chance during endoscopy. Some giant lesions can sometimes present with chronic abdominal pain, nausea, vomiting, and anemia, including gastrointestinal bleeding and obstructive symptoms, and need to be resected by surgery or endoscopy. Here we report a giant BGA that was easily and safely removed by Endoloop pre-ligation assisted resection.

High-Speed and Accurate Diagnosis of Gastrointestinal Disease: Learning on Endoscopy Images Using Lightweight Transformer with Local Feature Attention.

In response to the pressing need for robust disease diagnosis from gastrointestinal tract (GIT) endoscopic images, we proposed FLATer, a fast, lightweight, and highly accurate transformer-based model. FLATer consists of a residual block, a vision transformer module, and a spatial attention block, which concurrently focuses on local features and global attention. It can leverage the capabilities of both convolutional neural networks (CNNs) and vision transformers (ViT). We decomposed the classification of endoscopic images into two subtasks: a binary classification to discern between normal and pathological images and a further multi-class classification to categorize images into specific diseases, namely ulcerative colitis, polyps, and esophagitis. FLATer has exhibited exceptional prowess in these tasks, achieving 96.4% accuracy in binary classification and 99.7% accuracy in ternary classification, surpassing most existing models. Notably, FLATer could maintain impressive performance when trained from scratch, underscoring its robustness. In addition to the high precision, FLATer boasted remarkable efficiency, reaching a notable throughput of 16.4k images per second, which positions FLATer as a compelling candidate for rapid disease identification in clinical practice.

Dissecting transcription of the 8q24-MYC locus in prostate cancer recognizes the equilibration between androgen receptor direct and indirect dual-functions.

BACKGROUND: Androgen receptor (AR) activation and repression dual-functionality only became known recently and still remains intriguing in prostate cancer (PCa). MYC is a prominent oncogene that functionally entangles with AR signaling in PCa. Further exploration of AR regulatory mechanisms on MYC gene transcription bears clinical and translation significance. METHODS: Bioinformatics analysis of PCa cell line and clinical RNA-Seq and ChIP-Seq (chromatin immunoprecipitation-sequencing) datasets to anchor interactions of AR and MYC transcriptional networks. ChIP-qPCR and 3C (chromosome conformation capture) analyses to probe MYC distal regulation by AR binding sites (ABSs). CRISPR/Cas9-mediated genome-editing to specify functions of ABS within the 8q24-MYC locus on androgen-mediated MYC transcription. Global FoxA1 and HoxB13 distribution profiling to advance AR transcriptional mechanisms. RESULTS: Here we recognize AR bi-directional transcription mechanisms by exploiting the prominent 8q24-MYC locus conferring androgen hyper-sensitivity. At ~ 25 Kb downstream of the MYC gene, we identified an undefined ABS, P10. By chromatin analyses, we validated androgen-dependent spatial interaction between P10 and MYC-Promoter (MYC-Pro) and temporal epigenetic repression of these MYC-proximal elements. We next designed a CRISPR/Cas9-mediated double genomic knock-out (KO) strategy to show that P10-KO slightly lessened androgen-elicited MYC transrepression in LNCaP-AR cells. In similar genomic editing assays, androgen-mediated MYC repression became slightly deepened upon KO of P11, an ABS in the PVT1 gene locus highly enriched in AR-binding motifs and peaks. We also investigated multiple ABSs in the established PCAT1 super-enhancer that distally interacts with MYC-Pro for transactivation, with each KO pool consistently shown to relieve androgen-elicited MYC repression. In the end, we systemically assessed androgen effects in the 8q24-MYC locus and along PCa genome to generalize H3K27ac and BRD4 re-distribution from pioneer factors (FoxA1 and HoxB13) to AR sites. CONCLUSION: Together, we reconciled these observations by unifying AR dual-functions that are mechanistically coupled to and equilibrated by co-factor redistribution.

Development and validation of a nomogram for predicting internal mammary sentinel node metastasis in breast cancer patients.

OBJECTIVE: Internal mammary nodes are important in breast cancer prognosis, but their diagnosis is often missed in clinical practice, leading to inaccurate staging and treatment. We developed a validated nomogram to predict the presence of internal mammary sentinel nodes (IMSN) metastasis. METHODS: A total of 864 sequential IMSN biopsy procedures from a prospective studies database of 1505 cases were used for model development and validation. Multivariable logistic regression was performed on 519 sequential IMSN biopsy procedures from multi-center data between August 2018 and July 2022 to predict the presence of IMSN metastasis. A nomogram was developed based on the logistic regression model and subsequently applied to 345 sequential IMSN biopsy procedures from single-center data between November 2011 and July 2018. The model's discrimination was assessed using the area under the receiver operating characteristic curve. RESULTS: The overall frequency of IMSN metastasis was 17.0% in our study. A predictive model for IMSN metastasis was constructed using tumor size, tumor location, lymphovascular invasion, the number of positive axillary nodes (P < 0.05 for all variables in multivariate analysis), and histological grade (P < 0.05 only in univariate analysis). The nomogram was accurate, with a concordance index of 0.84 in the bootstrapping analysis and an area under the receiver operating characteristic curve of 0.80 in the validation population. CONCLUSION: Our nomogram provides an accurate and validated multivariable predictive model for estimating the individual likelihood of having IMSN metastasis. This may be useful for personalized treatment decisions regarding internal mammary radiotherapy in breast cancer patients.

PORCN promotes hepatocellular carcinogenesis via Wnt/ß-catenin-dependent manner.

Background: Porcupine O-acyltransferase (PORCN), a membrane-bound O-acyltransferase, is crucial in Wnt ligand palmitoylation. However, the roles of PORCN in the development of hepatocellular carcinoma (HCC) remain unknown. Methods: Western blot, real-time quantitative polymerase chain reaction (RT-qPCR) assays, and The Cancer Genome Atlas (TCGA) database were used to study the expression and prognostic values of PORCN in patients with HCC. Following this, Cell Counting Kit-8 (CCK-8), wound-healing tests, Transwell assay, and a xenograft mouse model were employed to examine the effect of PORCN on HCC cells. Finally, the underlying molecular mechanisms involved in cell proliferation and migration caused by PORCN were identified. Results: The protein and messenger RNA (mRNA) levels of PORCN in HCC tissues were higher than those of adjacent normal tissues. The analysis of TCGA database indicated that patients with higher PORCN expression had a lower overall survival (OS) rate. Overexpression of PORCN could promote the proliferation and migration abilities of HCC cells both in vitro and in vivo. Gene set enrichment analysis (GSEA) showed that the effect of PORCN on the biological characteristics of HCC cells mainly centered on the Wnt-ß-catenin signaling pathway. Mechanically, immunofluorescence staining and subcellular protein fraction assays showed that PORCN could induce epithelial-mesenchymal transition (EMT) by promoting the translocation of ß-catenin from the cytoplasm to nucleus, ultimately promoting the progression of HCC. Conclusions: The findings of this study suggest that PORCN can promote HCC cell proliferation and migration by stimulating the Wnt-ß-catenin signaling pathway. Therefore, PORCN may be a promising therapeutic target for HCC.

Androgen receptor and MYC transcriptomes are equilibrated in multilayer regulatory circuitries in prostate cancer.

BACKGROUND: The discovery of androgen receptor (AR) having transrepression effects completes the circle of its functionalities as a typical transcription factor, which intrinsically bears dual functions of activation and repression linked to co-factor competition and redistribution. Indeed, AR dual functions are exemplified by locus-wide regulation of the oncogenic 8q24-MYC region. METHODS: RT-qPCR assay and public RNA-profiling datasets were used to assess MYC transcription in androgen-sensitive cell lines. Public ChIP-seq and RNA-Seq datasets were computed to evaluate AR-MYC direct and indirect signatures. Gene sets in typical MYC and AR pathways were monitored to validate their cross-talks. Bio-informatics and chromosome conformation capture (3C) assay were performed in the AR gene locus to examine androgen-elicited distal regulation. Finally, co-factor re-distribution were globally tracked between AR and MYC binding sites. RESULTS: In this report, we found MYC responded negatively to androgen with hypersensitivity, rivaling AR natural functions as an innate androgen effector. Furthermore, both direct and indirect AR and MYC transcriptional programs were actively in equilibration. With established androgen-mediated versus MYC-mediated gene subsets, we validated AR and MYC pathways were both bidirectional and extensively entangled. In addition, we determined that the AR gene locus resembled the MYC gene region and both loci were androgen-repressed via epigenetics and chromatin architectural alterations. Significantly, transcriptional factor profiling along the prostate cancer (PCa) genome exposed that PCa transcriptomes were dynamically equilibrated between AR-binding site and MYC-binding site. CONCLUSION: Together, our findings stratified AR-MYC interactions that are extensively wired and intricately organized to compensate for essential PCa transcriptional programs and neutralize excessive signaling.

An Actinic Keratosis Auxiliary Diagnosis Method Based on an Enhanced MobileNet Model.

Actinic keratosis (AK) is a common precancerous skin lesion with significant harm, and it is often confused with non-actinic keratoses (NAK). At present, the diagnosis of AK mainly depends on clinical experience and histopathology. Due to the high difficulty of diagnosis and easy confusion with other diseases, this article aims to develop a convolutional neural network that can efficiently, accurately, and automatically diagnose AK. This article improves the MobileNet model and uses the AK and NAK images in the HAM10000 dataset for training and testing after data preprocessing, and we performed external independent testing using a separate dataset to validate our preprocessing approach and to demonstrate the performance and generalization capability of our model. It further compares common deep learning models in the field of skin diseases (including the original MobileNet, ResNet, GoogleNet, EfficientNet, and Xception). The results show that the improved MobileNet has achieved 0.9265 in accuracy and 0.97 in Area Under the ROC Curve (AUC), which is the best among the comparison models. At the same time, it has the shortest training time, and the total time of five-fold cross-validation on local devices only takes 821.7 s. Local experiments show that the method proposed in this article has high accuracy and stability in diagnosing AK. Our method will help doctors diagnose AK more efficiently and accurately, allowing patients to receive timely diagnosis and treatment.

Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study.

BACKGROUND: Determining the grade and molecular marker status of intramedullary gliomas is important for assessing treatment outcomes and prognosis. Invasive biopsy for pathology usually carries a high risk of tissue damage, especially to the spinal cord, and there are currently no non-invasive strategies to identify the pathological type of intramedullary gliomas. Therefore, this study aimed to develop a non-invasive machine learning model to assist doctors in identifying the intramedullary glioma grade and mutation status of molecular markers. METHODS: A total of 461 patients from two institutions were included, and their sagittal (SAG) and transverse (TRA) T2-weighted magnetic resonance imaging scans and clinical data were acquired preoperatively. We employed a transformer-based deep learning model to automatically segment lesions in the SAG and TRA phases and extract their radiomics features. Different feature representations were fed into the proposed neural networks and compared with those of other mainstream models. RESULTS: The dice similarity coefficients of the Swin transformer in the SAG and TRA phases were 0.8697 and 0.8738, respectively. The results demonstrated that the best performance was obtained in our proposed neural networks based on multimodal fusion (SAG-TRA-clinical) features. In the external validation cohort, the areas under the receiver operating characteristic curve for graded (WHO I-II or WHO III-IV), alpha thalassemia/mental retardation syndrome X-linked (ATRX) status, and tumor protein p53 (P53) status prediction tasks were 0.8431, 0.7622, and 0.7954, respectively. CONCLUSIONS: This study reports a novel machine learning strategy that, for the first time, is based on multimodal features to predict the ATRX and P53 mutation status and grades of intramedullary gliomas. The generalized application of these models could non-invasively provide more tumor-specific pathological information for determining the treatment and prognosis of intramedullary gliomas.

Data-Driven Assisted Decision Making for Surgical Procedure of Hepatocellular Carcinoma Resection and Prognostic Prediction: Development and Validation of Machine Learning Models.

Background: Currently, surgical decisions for hepatocellular carcinoma (HCC) resection are difficult and not sufficiently personalized. We aimed to develop and validate data driven prediction models to assist surgeons in selecting the optimal surgical procedure for patients. Methods: Retrospective data from 361 HCC patients who underwent radical resection in two institutions were included. End-to-end deep learning models were built to automatically segment lesions from the arterial phase (AP) of preoperative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Clinical baseline characteristics and radiomic features were rigorously screened. The effectiveness of radiomic features and radiomic-clinical features was also compared. Three ensemble learning models were proposed to perform the surgical procedure decision and the overall survival (OS) and recurrence-free survival (RFS) predictions after taking different solutions, respectively. Results: SegFormer performed best in terms of automatic segmentation, achieving a Mean Intersection over Union (mIoU) of 0.8860. The five-fold cross-validation results showed that inputting radiomic-clinical features outperformed using only radiomic features. The proposed models all outperformed the other mainstream ensemble models. On the external test set, the area under the receiver operating characteristic curve (AUC) of the proposed decision model was 0.7731, and the performance of the prognostic prediction models was also relatively excellent. The application web server based on automatic lesion segmentation was deployed and is available online. Conclusions: In this study, we developed and externally validated the surgical decision-making procedures and prognostic prediction models for HCC for the first time, and the results demonstrated relatively accurate predictions and strong generalizations, which are expected to help clinicians optimize surgical procedures.

Improved prognostic prediction model for liver cancer based on biomarker data screened by combined methods.

Liver cancer is a common cause of death from cancer in the population, with the 4th highest mortality rate from cancer worldwide. The high recurrence rate of hepatocellular carcinoma after surgery is an important cause of high mortality among patients. In this paper, based on eight scheduled core markers of liver cancer, an improved feature screening algorithm was proposed based on the analysis of the basic principles of the random forest algorithm, and the system was finally applied to liver cancer prognosis prediction to improve the prediction of biomarkers for liver cancer recurrence, and the impact of different algorithmic strategies on the prediction accuracy was compared and analyzed. The results showed that the improved feature screening algorithm was able to reduce the feature set by about 50% while ensuring that the prediction accuracy was reduced within 2%.

Cardiac-Specific Expression of Cre Recombinase Leads to Age-Related Cardiac Dysfunction Associated with Tumor-like Growth of Atrial Cardiomyocyte and Ventricular Fibrosis and Ferroptosis.

Transgenic expression of Cre recombinase driven by a specific promoter is normally used to conditionally knockout a gene in a tissue- or cell-type-specific manner. In αMHC-Cre transgenic mouse model, expression of Cre recombinase is controlled by the myocardial-specific α-myosin heavy chain (αMHC) promoter, which is commonly used to edit myocardial-specific genes. Toxic effects of Cre expression have been reported, including intro-chromosome rearrangements, micronuclei formation and other forms of DNA damage, and cardiomyopathy was observed in cardiac-specific Cre transgenic mice. However, mechanisms associated with Cardiotoxicity of Cre remain poorly understood. In our study, our data unveiled that αMHC-Cre mice developed arrhythmias and died after six months progressively, and none of them survived more than one year. Histopathological examination showed that αMHC-Cre mice had aberrant proliferation of tumor-like tissue in the atrial chamber extended from and vacuolation of ventricular myocytes. Furthermore, the αMHC-Cre mice developed severe cardiac interstitial and perivascular fibrosis, accompanied by significant increase of expression levels of MMP-2 and MMP-9 in the cardiac atrium and ventricular. Moreover, cardiac-specific expression of Cre led to disintegration of the intercalated disc, along with altered proteins expression of the disc and calcium-handling abnormality. Comprehensively, we identified that the ferroptosis signaling pathway is involved in heart failure caused by cardiac-specific expression of Cre, on which oxidative stress results in cytoplasmic vacuole accumulation of lipid peroxidation on the myocardial cell membrane. Taken together, these results revealed that cardiac-specific expression of Cre recombinase can lead to atrial mesenchymal tumor-like growth in the mice, which causes cardiac dysfunction, including cardiac fibrosis, reduction of the intercalated disc and cardiomyocytes ferroptosis at the age older than six months in mice. Our study suggests that αMHC-Cre mouse models are effective in young mice, but not in old mice. Researchers need to be particularly careful when using αMHC-Cre mouse model to interpret those phenotypic impacts of gene responses. As the Cre-associated cardiac pathology matched mostly to that of the patients, the model could also be employed for investigating age-related cardiac dysfunction.

Application of 3D printed pelvic fracture related urethra and surrounding tissue as preoperative planning model.

OBJECTIVE: Urethral stenosis caused by pelvic fracture urethral injury (PFUI) is a complex urological disease, especially for the redo cased. However, to find the proximal end of the posterior urethra, and to avoid injury to the rectum and to forecast to remove the inferior pubic margin are two key points for a successful surgery. These steps can be challenging for even the most experienced urologists. This study is to describe a new technique for understanding the three-dimensional (3D) anatomy of the urethra, which will also aid in surgical planning and simplify urethroplasty. MATERIALS AND METHODS: Three patients underwent routine urethroscopy, X ray urethrography and contrast CT urethrography. The 3D images were then reconstructed, and the data were transmitted to a 3D printer. 3D models were printed with polyacrylic acid to simulate the anatomical structure and relationship of urethral stenosis with pubic symphysis and rectum. Various diagnosis methods were compared with the condition in surgery. The patients and trainee questionnaires were performed. RESULTS: Three models of urethral CT were obtained. These models were presented to patients and trainee doctors along with routine urethroscopy, urethrography, and urethral CT. The scores of patients and trainee question forms demonstrated that the 3D printed urethral stenosis model of pelvic fracture has obvious advantages in urethral adjacency and ease of understanding. The 3D printed urethras were easy to show the pubic symphysis and simulate its excision and exposure of urethra. The model could show the precise distance from urethra to rectum to prevent the rectum injury in surgery. CONCLUSIONS: 3D printing technology can be applied to the preoperative evaluation of urethral stenosis caused by PFUI. It can be auxiliary to understand the anatomical structure of the posterior urethra, the direction of urethral displacement, protecting the rectum and the forecasting for pubectomy. It is especially helpful for the accurate preoperative planning of some complex urethral stenosis and redo cases.

Deep learning drives efficient discovery of novel antihypertensive peptides from soybean protein isolate.

As a potential and effective substitute for the drugs of antihypertension, the food-derived antihypertensive peptides have arisen great interest in scholars recently. However, the traditional screening methods for antihypertensive peptides are at considerable expense and laborious, which blocks the exploration of available antihypertensive peptides. In our study, we reported the use of a protein-specific deep learning model called ProtBERT to screen for antihypertensive peptides. Compared to other deep learning models, ProrBERT reached the highest the area under the receiver operating characteristic curve (AUC) value of 0.9785. In addition, we used ProtBERT to screen candidate peptides in soybean protein isolate (SPI), followed by molecular docking and in vitro validation, and eventually found that peptides LVPFGW (IC50 = 20.63 µM), VSFPVL (2.57 µM), and VLPF (5.78 µM) demonstrated the good antihypertensive activity. Deep learning such as ProtBERT will be a useful tool for the rapid screening and identification of antihypertensive peptides.

The Prognostic Significance of FKBP1A and Its Related Immune Infiltration in Liver Hepatocellular Carcinoma.

Liver hepatocellular carcinoma (LIHC) remains a global health challenge with poor prognosis and high mortality. FKBP1A was first discovered as a receptor for the immunosuppressant drug FK506 in immune cells and is critical for various tumors and cancers. However, the relationships between FKBP1A expression, cellular distribution, tumor immunity, and prognosis in LIHC remain unclear. Here, we investigated the expression level of FKBP1A and its prognostic value in LIHC via multiple datasets including ONCOMINE, TIMER, GEPIA, UALCAN, HCCDB, Kaplan-Meier plotter, LinkedOmics, and STRING. Human liver tissue microarray was employed to analyze the characteristics of FKBP1A protein including the expression level and pathological alteration in cellular distribution. FKBP1A expression was significantly higher in LIHC and correlated with tumor stage, grade and metastasis. The expression level of the FKBP1A protein was also increased in LIHC patients along with its accumulation in endoplasmic reticulum (ER). High FKBP1A expression was correlated with a poor survival rate in LIHC patients. The analysis of gene co-expression and the regulatory pathway network suggested that FKBP1A is mainly involved in protein synthesis, metabolism and the immune-related pathway. FKBP1A expression had a significantly positive association with the infiltration of hematopoietic immune cells including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. Moreover, M2 macrophage infiltration was especially associated with a poor survival prognosis in LIHC. Furthermore, FKBP1A expression was significantly positively correlated with the expression of markers of M2 macrophages and immune checkpoint proteins such as PD-L1, CTLA-4, LAG3 and HAVCR2. Our study demonstrated that FKBP1A could be a potential prognostic target involved in tumor immune cell infiltration in LIHC.

Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgery.

Preoperative prediction of recurrence outcome in hepatocellular carcinoma (HCC) facilitates physicians' clinical decision-making. Preoperative imaging and related clinical baseline data of patients are valuable for evaluating prognosis. With the widespread application of machine learning techniques, the present study proposed the ensemble learning method based on efficient feature representations to predict recurrence outcomes within three years after surgery. Radiomics features during arterial phase (AP) and clinical data were selected for training the ensemble models. In order to improve the efficiency of the process, the lesion area was automatically segmented by 3D U-Net. It was found that the mIoU of the segmentation model was 0.8874, and the Light Gradient Boosting Machine (LightGBM) was the most superior, with an average accuracy of 0.7600, a recall of 0.7673, a F1 score of 0.7553, and an AUC of 0.8338 when inputting radiomics features during AP and clinical baseline indicators. Studies have shown that the proposed strategy can relatively accurately predict the recurrence outcome within three years, which is helpful for physicians to evaluate individual patients before surgery.

Synthesis of Prussian Blue Nanoparticles and Their Antibacterial, Antiinflammation and Antitumor Applications.

In recent years, Prussian blue nanoparticles (PBNPs), also named Prussian blue nano-enzymes, have been shown to demonstrate excellent multi-enzyme simulation activity and anti-inflammatory properties, and can be used as reactive oxygen scavengers. Their good biocompatibility and biodegradability mean that they are ideal candidates for in vivo use. PBNPs are highly efficient electron transporters with oxidation and reduction activities. PBNPs also show considerable promise as nano-drug carriers and biological detection sensors owing to their huge specific surface area, good chemical characteristics, and changeable qualities, which might considerably increase the therapeutic impact. More crucially, PBNPs, as therapeutic and diagnostic agents, have made significant advances in biological nanomedicine. This review begins with a brief description of the synthesis methods of PBNPs, then focuses on the applications of PBNPs in tissue regeneration and inflammation according to the different properties of PBNPs. This article will provide a timely reference for further study of PBNPs as therapeutic agents.

MVI-Mind: A Novel Deep-Learning Strategy Using Computed Tomography (CT)-Based Radiomics for End-to-End High Efficiency Prediction of Microvascular Invasion in Hepatocellular Carcinoma.

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) directly affects a patient's prognosis. The development of preoperative noninvasive diagnostic methods is significant for guiding optimal treatment plans. In this study, we investigated 138 patients with HCC and presented a novel end-to-end deep learning strategy based on computed tomography (CT) radiomics (MVI-Mind), which integrates data preprocessing, automatic segmentation of lesions and other regions, automatic feature extraction, and MVI prediction. A lightweight transformer and a convolutional neural network (CNN) were proposed for the segmentation and prediction modules, respectively. To demonstrate the superiority of MVI-Mind, we compared the framework's performance with that of current, mainstream segmentation, and classification models. The test results showed that MVI-Mind returned the best performance in both segmentation and prediction. The mean intersection over union (mIoU) of the segmentation module was 0.9006, and the area under the receiver operating characteristic curve (AUC) of the prediction module reached 0.9223. Additionally, it only took approximately 1 min to output a prediction for each patient, end-to-end using our computing device, which indicated that MVI-Mind could noninvasively, efficiently, and accurately predict the presence of MVI in HCC patients before surgery. This result will be helpful for doctors to make rational clinical decisions.