Comparative efficacy and safety of greenlight and thulium laser vaporization techniques for benign prostatic hyperplasia: a systematic review and meta-analysis.

This meta-analysis evaluates the efficacy and safety of greenlight (PVP) and thulium laser vaporization (ThuVAP) in Benign Prostatic Hyperplasia (BPH) treatment. A systematic literature search was conducted in databases including PubMed, Cochrane Library, EMBASE, CNKI, Wangfang, and VIP in November 2023. Following the PRISMA guidelines, a systematic review and meta-analysis of the primary outcomes of interest were performed. The review was prospectively registered on PROSPERO under the registration number CRD42023491316. A total of 13 studies were included. The results of the meta-analysis showed that compared to PVP, ThuVAP had a shorter operation time (MD: 8.56, 95% CI: 4.10 ~ 13.03, p = 0.0002), and higher postoperative transfusion (OR:0.26, 95% CI: 0.10 ~ 0.64, p = 0.004). However, no significant differences were observed between the two groups in terms of length of stay (MD: -0.32, 95% CI: -0.78 ~ 0.14, p = 0.17), catherization time (MD: 0.03, 95% CI: -0.13 ~ 0.19, p = 0.73), international prostate symptom score improvement (MD: 0.23, 95% CI: -0.36 ~ 0.81, p = 0.45), quality of life improvement (MD: 0.04, 95% CI: -0.04 ~ 0.12, p = 0.29), maximum urinary flow rate improvement (MD: -0.59, 95% CI: -1.42 ~ 0.24, p = 0.16), postvoid residual urine volume improvement (MD: 1.04, 95% CI: -6.63 ~ 8.71, p = 0.79), overall postoperative complications (OR:1.15, 95% CI: 0.65 ~ 2.03, p = 0.63), postoperative bleeding (OR:1.18, 95%  CI: 0.67 ~ 2.07, p = 0.56), re-peration (OR:0.55, 95% CI: 0.16 ~ 1.95, p = 0.35), urethral stricture (OR:0.90, 95% CI: 0.46 ~ 1.75, p = 0.75), and urinary incontinence (OR:1.07, 95% CI: 0.64 ~ 1.78, p = 0.80). The results of subgroup analysis showed that the results of comparing thulium vaporesection or vapoenucleation with PVP were consistent with the results of the pooled analysis. Both greenlight and thulium laser vaporization are effective and safe, with comparable surgical and functional outcomes. The choice between these methods should be based on patient-specific factors.

Lymph node metastasis detection using artificial intelligence in T1 colorectal cancer: A comprehensive systematic review.

We systematically reviewed the application of artificial intelligence (AI) in predicting lymph node metastasis (LNM) in T1 colorectal cancer (CRC). Thirteen studies with 8417 patients were included. AI demonstrated high potential in predicting LNM with sensitivity, specificity, and AUC ranging from 0.561 to 1.0, 0.45 to 1.0, and 0.717 to 1.0, respectively, reducing unnecessary surgeries by approximately 70%.

Icariin Ameliorates D-galactose-induced Cell Injury in Neuron-like PC12 Cells by Inhibiting MPTP Opening.

OBJECTIVE: Icariin (ICA) has a good neuroprotective effect and can upregulate neuronal basal autophagy in naturally aging rats. Mitochondrial dysfunction is associated with brain aging-related neurodegenerative diseases. Abnormal opening of the mitochondrial permeability transition pore (mPTP) is a crucial factor in mitochondrial dysfunction and is associated with excessive autophagy. This study aimed to explore that ICA protects against neuronal injury by blocking the mPTP opening and down-regulating autophagy levels in a D-galactose (D-gal)-induced cell injury model. METHODS: A cell model of neuronal injury was established in rat pheochromocytoma cells (PC12 cells) treated with 200 mmol/L D-gal for 48 h. In this cell model, PC12 cells were pre-treated with different concentrations of ICA for 24 h. MTT was used to detect cell viability. Senescence associated ß-galactosidase (SA-ß-Gal) staining was used to observe cell senescence. Western blot analysis was performed to detect the expression levels of a senescence-related protein (p21), autophagy markers (LC3B, p62, Atg7, Atg5 and Beclin 1), mitochondrial fission and fusion-related proteins (Drp1, Mfn2 and Opa1), and mitophagy markers (Pink1 and Parkin). The changes of autophagic flow were detected by using mRFP-GFP-LC3 adenovirus. The intracellular ultrastructure was observed by transmission electron microscopy. Immunofluorescence was used to detect mPTP, mitochondrial membrane potential (MMP), mitochondrial reactive oxygen species (mtROS) and ROS levels. ROS and apoptosis levels were detected by flow cytometry. RESULTS: D-gal treatment significantly decreased the viability of PC12 cells, and markedly increased the SA-ß-Gal positive cells as compared to the control group. With the D-gal stimulation, the expression of p21 was significantly up-regulated. Furthermore, D-gal stimulation resulted in an elevated LC3B II/I ratio and decreased p62 expression. Meanwhile, autophagosomes and autolysosomes were significantly increased, indicating abnormal activation of autophagy levels. In addition, in this D-gal-induced model of cell injury, the mPTP was abnormally open, the ROS generation was continuously increased, the MMP was gradually decreased, and the apoptosis was increased. ICA effectively improved mitochondrial dysfunction to protect against D-gal-induced cell injury and apoptosis. It strongly inhibited excessive autophagy by blocking the opening of the mPTP. Cotreatment with ICA and an mPTP inhibitor (cyclosporin A) did not ameliorate mitochondrial dysfunction. However, the protective effects were attenuated by cotreatment with ICA and an mPTP activator (lonidamine). CONCLUSION: ICA inhibits the activation of excessive autophagy and thus improves mitochondrial dysfunction by blocking the mPTP opening.

Icariin improves oxidative stress injury during ischemic stroke via inhibiting mPTP opening.

BACKGROUND: Ischemic stroke presents a significant threat to human health due to its high disability rate and mortality. Currently, the clinical treatment drug, rt-PA, has a narrow therapeutic window and carries a high risk of bleeding. There is an urgent need to find new effective therapeutic drugs for ischemic stroke. Icariin (ICA), a key ingredient in the traditional Chinese medicine Epimedium, undergoes metabolism in vivo to produce Icaritin (ICT). While ICA has been reported to inhibit neuronal apoptosis after cerebral ischemia-reperfusion (I/R), yet its underlying mechanism remains unclear. METHODS: PC-12 cells were treated with 200 µM H2O2 for 8 h to establish a vitro model of oxidative damage. After administration of ICT, cell viability was detected by Thiazolyl blue tetrazolium Bromide (MTT) assay, reactive oxygen species (ROS) and apoptosis level, mPTP status and mitochondrial membrane potential (MMP) were detected by flow cytometry and immunofluorescence. Apoptosis and mitochondrial permeability transition pore (mPTP) related proteins were assessed by Western blotting. Middle cerebral artery occlusion (MCAO) model was used to establish I/R injury in vivo. After the treatment of ICA, the neurological function was scored by ZeaLonga socres; the infarct volume was observed by 2,3,5-Triphenyltetrazolium chloride (TTC) staining; HE and Nissl staining were used to detect the pathological state of the ischemic cortex; the expression changes of mPTP and apoptosis related proteins were detected by Western blotting. RESULTS: In vitro: ICT effectively improved H2O2-induced oxidative injury through decreasing the ROS level, inhibiting mPTP opening and apoptosis. In addition, the protective effects of ICT were not enhanced when it was co-treated with mPTP inhibitor Cyclosporin A (CsA), but reversed when combined with mPTP activator Lonidamine (LND). In vivo: Rats after MCAO shown cortical infarct volume of 32-40%, severe neurological impairment, while mPTP opening and apoptosis were obviously increased. Those damage caused was improved by the administration of ICA and CsA. CONCLUSIONS: ICA improves cerebral ischemia-reperfusion injury by inhibiting mPTP opening, making it a potential candidate drug for the treatment of ischemic stroke.

IRF4 induces M1 macrophage polarization and aggravates ulcerative colitis progression by the Bcl6-dependent STAT3 pathway.

Ulcerative colitis (UC) is an idiopathic chronic intestinal inflammation. An increasing body of evidence shows that macrophages play an important role in the pathogenesis of UC. Interferon regulatory factor 4 (IRF4) is crucial for the development of autoimmune diseases via regulating immune cells. This research was designed to explore the function of IRF4 in UC and its association with macrophage polarization. The in vitro model of UC was established by stimulating colonic epithelial cells with tumor necrosis factor α (TNF-α). A mouse model of UC was constructed by injecting C57BL/6 mice with dextran sulfate sodium salt. Flow cytometry was used to assess percentage of CD11b+ CD86+ and CD11b+ CD206+ cells in bone marrow macrophages. Occult blood tests were used to detect hematochezia. Hematoxylin and eosin staining assay was used to assess colon pathological changes. Enzyme-linked immunosorbent assay (ELISA) was used to detect concentrations of inflammatory cytokines. The interaction of IRF4 and B-cell lymphoma 6 (Bcl6) was confirmed using GST pull-down and coimmunoprecipitation assays. Our findings revealed that IRF4 promoted cell apoptosis and stimulated M1 macrophage polarization in vitro. Furthermore, IRF4 aggravated symptoms of the mouse model of UC and aggravated M1 macrophage polarization in vivo. IRF4 negatively regulated Bcl6 expression. Downregulation of Bcl6 promoted apoptosis and M1 macrophage polarization in the presence of IRF4 in vitro and in vivo. Moreover, Bcl6 positively mediated the Janus kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) signaling pathway. In conclusion, IRF4 aggravated UC progression through promoting M1 macrophage polarization via Bcl6/JAK2/STAT3 pathway. These findings suggested that IRF4 might be a good target to competitively inhibit or to treat with UC.

USP29 activation mediated by FUBP1 promotes AURKB stability and oncogenic functions in gastric cancer.

BACKGROUND: Gastric cancer is a highly prevalent cancer type and the underlying molecular mechanisms are not fully understood. Ubiquitin-specific peptidase (USP) 29 has been suggested to regulate cell fate in several types of cancer, but its potential role in gastric carcinogenesis remains unclear. METHODS: The expression of USP29 in normal and gastric cancer tissues was analyzed by bioinformatics analysis, immunohistochemistry and immunoblot. Gene overexpression, CRISPR-Cas9 technology, RNAi, and Usp29 knockout mice were used to investigate the roles of USP29 in cell culture, xenograft, and benzo[a]pyrene (BaP)-induced gastric carcinogenesis models. We then delineated the underlying mechanisms using mass spectrometry, co-immunoprecipitation (Co-IP), immunoblot, ubiquitination assay, chromatin immunoprecipitation (ChIP), quantitative real-time PCR (qRT-PCR), and luciferase assays. RESULTS: In this study, we found that USP29 expression was significantly upregulated in gastric cancers and associated with poor patient survival. Ectopic expression of USP29 promoted, while depletion suppressed the tumor growth in vitro and in vivo mouse model. Mechanistically, transcription factor far upstream element binding protein 1 (FUBP1) directly activates USP29 gene transcription, which then interacts with and stabilizes aurora kinase B (AURKB) by suppressing K48-linked polyubiquitination, constituting a FUBP1-USP29-AURKB regulatory axis that medicates the oncogenic role of USP29. Importantly, systemic knockout of Usp29 in mice not only significantly decreased the BaP-induced carcinogenesis but also suppressed the Aurkb level in forestomach tissues. CONCLUSIONS: These findings uncovered a novel FUBP1-USP29-AURKB regulatory axis that may play important roles in gastric carcinogenesis and tumor progression, and suggested that USP29 may become a promising drug target for cancer therapy.

N6-methyladenosine methylation analysis of long noncoding RNAs and mRNAs in 5-FU-resistant colon cancer cells.

N6 methyladenosine (m6A), methylation at the sixth N atom of adenosine, is the most common and abundant modification in mammalian mRNAs and non-coding RNAs. Increasing evidence shows that the alteration of m6A modification level could regulate tumour proliferation, metastasis, self-renewal, and immune infiltration by regulating the related expression of tumour genes. However, the role of m6A modification in colorectal cancer (CRC) drug resistance is unclear. Here, MeRIP-seq and RNA-seq techniques were utilized to obtain mRNA, lncRNA expression, and their methylation profiles in 5-Fluorouracil (5-FU)-resistant colon cancer HCT-15 cells and control cells. In addition, we performed detailed bioinformatics analysis as well as in vitro experiments of lncRNA to explore the function of lncRNA with differential m6A in CRC progression and drug resistance. In this study, we obtained the m6A methylomic landscape of CRC cells and resistance group cells by MeRIP-seq and RNA-seq. We identified 3698 differential m6A peaks, of which 2224 were hypermethylated, and 1474 were hypomethylated. Among the lncRNAs, 60 were hypermethylated, and 38 were hypomethylated. GO and KEGG analysis annotations showed significant enrichment of endocytosis and MAPK signalling pathways. Moreover, knockdown of lncRNA ADIRF-AS1 and AL139035.1 promoted CRC proliferation and invasive metastasis in vitro. lncRNA- mRNA network showed that ADIRF-AS1 and AL139035.1 May play a key role in regulating drug resistance formation. We provide the first m6A methylation profile in 5-FU resistance CRC cells and analyse the functions of differential m6A-modified mRNAs and lncRNAs. Our results indicated that differential m6A RNAs were significantly associated with MAPK signalling and endocytosis after induction of 5-FU resistance. Knockdown of LncRNA ADIRF-AS1 and AL139035.1 promotes CRC progression and might be critical in regulating drug resistance formation.

We outline the first m6A methylation profile of mRNA and lncRNA in CRC cells involved in 5-FU resistance.This study sought to identify the critical genes that produced 5-FU resistance by analysing the functions of differentially m6A-modified mRNAs and lncRNAs.

An artificial intelligence-assisted diagnosis modeling software (AIMS) platform based on medical images and machine learning: a development and validation study.

Background: Supervised machine learning methods [both radiomics and convolutional neural network (CNN)-based deep learning] are usually employed to develop artificial intelligence models with medical images for computer-assisted diagnosis and prognosis of diseases. A classical machine learning-based modeling workflow involves a series of interconnected components and various algorithms, but this makes it challenging, tedious, and labor intensive for radiologists and researchers to build customized models for specific clinical applications if they lack expertise in machine learning methods. Methods: We developed a user-friendly artificial intelligence-assisted diagnosis modeling software (AIMS) platform, which supplies standardized machine learning-based modeling workflows for computer-assisted diagnosis and prognosis systems with medical images. In contrast to other existing software platforms, AIMS contains both radiomics and CNN-based deep learning workflows, making it an all-in-one software platform for machine learning-based medical image analysis. The modular design of AIMS allows users to build machine learning models easily, test models comprehensively, and fairly compare the performance of different models in a specific application. The graphical user interface (GUI) enables users to process large numbers of medical images without programming or script addition. Furthermore, AIMS also provides a flexible image processing toolkit (e.g., semiautomatic segmentation, registration, morphological operations) to rapidly create lesion labels for multiphase analysis, multiregion analysis of an individual tumor (e.g., tumor mass and peritumor), and multimodality analysis. Results: The functionality and efficiency of AIMS were demonstrated in 3 independent experiments in radiation oncology, where multiphase, multiregion, and multimodality analyses were performed, respectively. For clear cell renal cell carcinoma (ccRCC) Fuhrman grading with multiphase analysis (sample size =187), the area under the curve (AUC) value of the AIMS was 0.776; for ccRCC Fuhrman grading with multiregion analysis (sample size =177), the AUC value of the AIMS was 0.848; for prostate cancer Gleason grading with multimodality analysis (sample size =206), the AUC value of the AIMS was 0.980. Conclusions: AIMS provides a user-friendly infrastructure for radiologists and researchers, lowering the barrier to building customized machine learning-based computer-assisted diagnosis models for medical image analysis.

Saponins from Panax japonicus improve neuronal mitochondrial injury of aging rats.

CONTEXT: Panax japonicus is the dried rhizome of Panax japonicus C.A. Mey. (Araliaceae). Saponins from Panax japonicus (SPJ) exhibit anti-oxidative and anti-aging effects. OBJECTIVE: We evaluated the neuroprotective effects of SPJ on aging rats. MATERIALS AND METHODS: Sprague-Dawley rats (18-months-old) were randomly divided into aging and SPJ groups (n = 8). Five-month-old rats were taken as the adult control (n = 8). The rats were fed a normal chow diet or the SPJ-containing diet (10 or 30 mg/kg) for 4 months. An in vitro model was established by d-galactose (d-Gal) in the SH-SY5Y cell line and pretreated with SPJ (25 and 50 µg/mL). The neuroprotection of SPJ was evaluated via Nissl staining, flow cytometry, transmission electron microscopy and Western blotting in vivo and in vitro. RESULTS: SPJ improved the neuronal degeneration and mitochondrial morphology that are associated with aging. Meanwhile, SPJ up-regulated the protein levels of mitofusin 2 (Mfn2) and optic atrophy 1 (Opa1) and down-regulated the protein level of dynamin-like protein 1 (Drp1) in the hippocampus of aging rats (p < 0.05 or p < 0.01 vs. 22 M). The in vitro studies also demonstrated that SPJ attenuated d-Gal-induced cell senescence concomitant with the improvement in mitochondrial function; SPJ, also up-regulated the Mfn2 and Opa1 protein levels, whereas the Drp1 protein level (p < 0.05 or p < 0.01 vs. d-Gal group) was down-regulated. DISCUSSION AND CONCLUSIONS: Further research on the elderly population will contribute to the development and utilization of SPJ for the treatment of neurodegenerative disorders.

Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma.

Objective: This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC). Methods: A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC). Results: The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039). Conclusion: The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.

New insights into nanosystems for non-small-cell lung cancer: diagnosis and treatment.

Lung cancer is caused by a malignant tumor that shows the fastest growth in both incidence and mortality and is also the greatest threat to human health and life. At present, both in terms of incidence and mortality, lung cancer is the first in male malignant tumors, and the second in female malignant tumors. In the past two decades, research and development of antitumor drugs worldwide have been booming, and a large number of innovative drugs have entered clinical trials and practice. In the era of precision medicine, the concept and strategy of cancer from diagnosis to treatment are experiencing unprecedented changes. The ability of tumor diagnosis and treatment has rapidly improved, the discovery rate and cure rate of early tumors have greatly improved, and the overall survival of patients has benefited significantly, with a tendency to transform to a chronic disease with tumor. The emergence of nanotechnology brings new horizons for tumor diagnosis and treatment. Nanomaterials with good biocompatibility have played an important role in tumor imaging, diagnosis, drug delivery, controlled drug release, etc. This article mainly reviews the advancements in lipid-based nanosystems, polymer-based nanosystems, and inorganic nanosystems in the diagnosis and treatment of non-small-cell lung cancer (NSCLC).

MicroRNA-146a-5p induces cell cycle arrest and enhances apoptosis in gastric cancer via targeting CDC14A.

Objective: The present study was designed to investigate the expression of miRNA-146a-5p in gastric cancer (GC) tissues and the paired nonmalignant counterparts, to explore the influences of miRNA-146a-5p on the cell biological behavior of MKN-28 cells (highly metastatic human gastric cancer cells), and to identify the function of abnormal expression of its target gene cell division cycle 14 homolog A (CDC14A) in GC. Methods: We detected the expression of miRNA-146a-5p in formalin-fixed and paraffin-embedded (FFPE) GC tissues through microarray and quantitative real-time polymerase chain reaction (qRT-PCR). Then, we employed cell counting kit-8 (CCK-8) assays, cell cycle assays, and apoptosis analysis to uncover the latent function of miRNA-146a-5p in MKN-28 human GC cells. We also validated the target of miRNA-146a-5p via luciferase reporter assays. Results: miRNA-146a-5p levels were examined in the majority of primary GC tissues and several GC cell lines. As a result, miRNA-146a-5p levels were significantly declined in the GC tissues and cells. In addition, miRNA-146a-5p demonstrated a straight act on its 3'-untranslated region (3'-UTR) of CDC14A mRNA, accordingly decreasing the contents of CDC14A mRNA as well as its protein expression. An inverse correlation between CDC14A and miRNA-146a-5p was observed. Conclusion: The data suggest miRNA-146a-5p may contribute to inducing cell cycle arrest as well as prompting GC cell apoptosis via directly targeting CDC14A. Therefore, miRNA-146a-5p may be a potential indicator of the occurrence and development of GC.

Using radiomics based on multicenter magnetic resonance images to predict isocitrate dehydrogenase mutation status of gliomas.

Background: Isocitrate dehydrogenase (IDH) mutation status is an important biomarker for the treatment strategy selection and prognosis evaluation of glioma. The purpose of this study is to predict the IDH mutation status of gliomas based on multicenter magnetic resonance (MR) images using radiomic models, which were composed from the selected radiomics features and logistic regression (LR), support vector machine (SVM), and LR least absolute shrinkage and selection operator (LASSO) classifiers. Methods: We retrospectively reviewed the medical records of 205 patients with gliomas. We enrolled 78 patients from Shandong Provincial Hospital from January 2018 to December 2019 as testing sets and 127 patients from The Cancer Genome Atlas (TCGA) as training sets. Preoperative MR images were stratified according to their IDH status, and the participants formed a consecutive and random series. Four MR modalities, including T1C, T2, T1 fluid-attenuated inversion recovery (FLAIR), and T2 FLAIR, were used for analysis. Five-fold cross-validation was adopted to train the models, and the models' performances were verified through the testing set. Tumor volumes of interest (VOI) were delineated on the 4 MR modalities. A total of 428 radiomics features were extracted. Two feature selection algorithms, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE), were used to select radiomics features. These features were fed into 3 machine learning classifiers, which were LR, SVM, and LR LASSO, to construct prediction models. The accuracy (ACC), sensitivity (SEN), specificity (SPEC), and area under the curve (AUC) were applied to measure the predictive performance of the radiomics models. Results: The LR (SVM and LR LASSO) classifier predicted IDH mutation status with an average testing set ACC of 80.77% (80.64% and 80.41%), a SEN of 73.68% (84.21% and 89.47%), a SPEC of 87.50% (67.50% and 62.50%), and an AUC of 0.8572 (0.8217 and 0.8164). Conclusions: The radiomics models based on MR modalities demonstrated the potential to be used as tools across different data sets for the noninvasive prediction of the IDH mutation status in glioma.

Multidisciplinary team diagnosis and treatment of pancreatic cancer: Current landscape and future prospects.

The pathogenesis of pancreatic cancer has not been completely clear, there is no highly sensitive and specific detection method, so early diagnosis is very difficult. Despite the rapid development of tumor diagnosis and treatment, it is difficult to break through in the short term and the overall 5-year survival rate of pancreatic cancer is less than 8%. In the face of the increasing incidence of pancreatic cancer, in addition to strengthening basic research, exploring its etiology and pathogenesis, it is urgent to optimize the existing diagnosis and treatment methods through standard multidisciplinary team (MDT), and formulate personalized treatment plan to achieve the purpose of improving the curative effect. However, there are some problems in MDT, such as insufficient understanding and enthusiasm of some doctors, failure to operate MDT according to the system, lack of good communication between domestic and foreign peers, and lack of attention in personnel training and talent echelon construction. It is expected to protect the rights and interests of doctors in the future and ensure the continuous operation of MDT. To strengthen the research on the diagnosis and treatment of pancreatic cancer, MDT can try the Internet +MDT mode to improve the efficiency of MDT.

Digital light processing-bioprinted poly-NAGA-GelMA-based hydrogel lenticule for precise refractive errors correction.

Refractive disorder is the most prevalent cause of visual impairment worldwide. While treatment of refractive errors can bring improvement to quality of life and socio-economic benefits, there is a need for individualization, precision, convenience, and safety with the chosen method. Herein, we propose using pre-designed refractive lenticules based on poly-NAGA-GelMA (PNG) bio-inks photo-initiated by digital light processing (DLP)-bioprinting for correcting refractive errors. DLP-bioprinting allows PNG lenticules to have individualized physical dimensions with precision achievable to 10µm (µm). Material characteristics of PNG lenticules in tests included optical and biomechanical stability, biomimetical swelling and hydrophilic capability, nutritional and visual functionality, supporting its suitability as stromal implants. Cytocompatibility distinguished by morphology and function of corneal epithelial, stromal, and endothelial cells on PNG lenticules suggested firm adhesion, over 90% viability, phenotypic maintenance instead of excessive keratocyte-myofibroblast transformation.In-vitroimmune response analyzed by illumina RNA sequencing in human peripheral blood mononuclear cells indicated that PNG lenticules activated type-2 immunity, facilitating tissue regeneration and suppressing inflammation.In-vivoperformance assessed using intrastromal keratoplasty models in New Zealand white rabbits illustrated that implantation of PNG lenticules maintained stable optical pathway, induced controlled stromal bio-integration and regeneration, avoided complications such as stromal melt, interface scarring, etc, but exerted no adverse effects on the host. Postoperative follow-up examination on intraocular pressure, corneal sensitivity, and tear production remained unaffected by surgery up to 1-month post-implantation of PNG lenticules. DLP-bioprinted PNG lenticule is a bio-safe and functionally effective stromal implants with customizable physical dimensions, providing potential therapeutic strategies in correction of refractive errors.

Estrogen-dependent activation of NCOA3 couples with p300 and NF-κB to mediate antiapoptotic genes in ER-positive breast cancer cells.

Circumvention of apoptosis by the elevation of antiapoptotic proteins is an important cause of carcinogenesis. The induction of antiapoptotic genes, including B-cell CLL/lymphoma 2 (BCL2), BCL2 related protein A1 (BCL2A1), BCL2 like 1 (BCL2L1), BCL2L2, and myeloid cell leukemia 1 (MCL1), has been observed in multiple cancers, including breast cancer. However, the underlying mechanisms of their overexpression are still being investigated. Here, we revealed that BCL2, BCL2A1, BCL2L2, and MCL1 but not BCL2L1 were overexpressed in estrogen receptor (ER)-positive breast cancer cells and clinical biopsies. Stimulation with estrogen in ER-positive cell lines resulted in a dose-dependent increase in BCL2, BCL2A1, BCL2L2, and MCL1 mRNA levels. Molecular investigation revealed that nuclear factor kappa B (NF-κB) recruited histone acetyltransferase p300 and nuclear receptor coactivator 3 (NCOA3) to form a transcriptional complex. This complex docked the promoters of BCL2, BCL2A1, BCL2L2, and MCL1 and activated their expression. Interestingly, estrogen exposure dose-dependently activated NCOA3. Depletion of the NCOA3-p300-NF-κB components or blockage of NCOA3 function with inhibitors (gossypol and bufalin) in ER-positive cells suppressed BCL2, BCL2A1, BCL2L2, and MCL1 expression, while also decreasing cell viability, colony formation, cell invasion, and tumor growth. Collectively, our results demonstrate an upstream signaling that activates four antiapoptotic genes in ER-positive breast cancer cells. Importantly, our results also imply that targeting NCOA3 or blocking the assembly of the NCOA3-p300-NF-κB complex may be promising therapeutic strategies for treating ER-positive breast cancer.

Dual attention guided multiscale neural network trained with curriculum learning for noninvasive prediction of Gleason Grade Group from MRI.

BACKGROUND: The Gleason Grade Group (GG) is essential in assessing the malignancy of prostate cancer (PCa) and is typically obtained by invasive biopsy procedures in which sampling errors could lead to inaccurately scored GGs. With the gradually recognized value of bi-parametric magnetic resonance imaging (bpMRI) in PCa, it is beneficial to noninvasively predict GGs from bpMRI for early diagnosis and treatment planning of PCa. However, it is challenging to establish the connection between bpMRI features and GGs. PURPOSE: In this study, we propose a dual attention-guided multiscale neural network (DAMS-Net) to predict the 5-scored GG from bpMRI and design a training curriculum to further improve the prediction performance. METHODS: The proposed DAMS-Net incorporates a feature pyramid network (FPN) to fully extract the multiscale features for lesions of varying sizes and a dual attention module to focus on lesion and surrounding regions while avoiding the influence of irrelevant ones. Furthermore, to enhance the differential ability for lesions with the inter-grade similarity and intra-grade variation in bpMRI, the training process employs a specially designed curriculum based on the differences between the radiological evaluations and the ground truth GGs. RESULTS: Extensive experiments were conducted on a private dataset of 382 patients and the public PROSTATEx-2 dataset. For the private dataset, the experimental results showed that the proposed network performed better than the plain baseline model for GG prediction, achieving a mean quadratic weighted Kappa (Kw ) of 0.4902 and a mean positive predictive value of 0.9098 for predicting clinically significant cancer (PPVGG>1 ). With the application of curriculum learning, the mean Kw and PPVGG>1 further increased to 0.5144 and 0.9118, respectively. For the public dataset, the proposed method achieved state-of-the-art results of 0.5413 Kw and 0.9747 PPVGG>1 . CONCLUSION: The proposed DAMS-Net trained with curriculum learning can effectively predict GGs from bpMRI, which may assist clinicians in early diagnosis and treatment planning for PCa patients.

Automated quantitative assessment of pediatric blunt hepatic trauma by deep learning-based CT volumetry.

BACKGROUND: To develop an end-to-end deep learning method for automated quantitative assessment of pediatric blunt hepatic trauma based on contrast-enhanced computed tomography (CT). METHODS: This retrospective study included 170 children with blunt hepatic trauma between May 1, 2015, and August 30, 2021, who had undergone contrast-enhanced CT. Both liver parenchyma and liver trauma regions were manually segmented from CT images. Two deep convolutional neural networks (CNNs) were trained on 118 cases between May 1, 2015, and December 31, 2019, for liver segmentation and liver trauma segmentation. Liver volume and trauma volume were automatically calculated based on the segmentation results, and the liver parenchymal disruption index (LPDI) was computed as the ratio of liver trauma volume to liver volume. The segmentation performance was tested on 52 cases between January 1, 2020, and August 30, 2021. Correlation analysis among the LPDI, trauma volume, and the American Association for the Surgery of Trauma (AAST) liver injury grade was performed using the Spearman rank correlation. The performance of severity assessment of pediatric blunt hepatic trauma based on the LPDI and trauma volume was evaluated using receiver operating characteristic (ROC) analysis. RESULTS: The Dice, precision, and recall of the developed deep learning framework were 94.75, 94.11, and 95.46% in segmenting the liver and 72.91, 72.40, and 76.80% in segmenting the trauma regions. The LPDI and trauma volume were significantly correlated with AAST grade (rho = 0.823 and rho = 0.831, respectively; p < 0.001 for both). The area under the ROC curve (AUC) values for the LPDI and trauma volume to distinguish between high-grade and low-grade pediatric blunt hepatic trauma were 0.942 (95% CI, 0.882-1.000) and 0.952 (95% CI, 0.895-1.000), respectively. CONCLUSIONS: The developed end-to-end deep learning method is able to automatically and accurately segment the liver and trauma regions from contrast-enhanced CT images. The automated LDPI and liver trauma volume can act as objective and quantitative indexes to supplement the current AAST grading of pediatric blunt hepatic trauma.

Preparation of curcumin-loaded cochleates: characterisation, stability and antioxidant properties.

Curcumin (CUR) has a wide range of applications in functional foods. However, it has some disadvantages such as poor water solubility and stability. To solve these problems, CUR was encapsulated into cochleates with an encapsulation efficiency of 83.66% and a diameter of about 403.9 nm. The study found that the stability of CUR-loaded cochleates (CUR-Cochs) was improved when compared with that of the curcumin-loaded liposomes (CUR-Lipos). Additionally, it showed that the DPPH scavenging ability of CUR-Cochs was equivalent to free CUR. In addition, 3 µM CUR-Cochs could significantly reduce the MDA content and the LDH release, and increased SOD activity in the H2O2 induced NIH3T3 cell oxidative damage model. The expression of Nrf2 and NQO1 proteins were obviously increased in the CUR-Cochs group, indicating that CUR-Cochs could effectively reduce NIH3T3 cell damage caused by H2O2. The CUR-Cochs could improve the stability of CUR with a desired anti-oxidation ability, which may mean that it is feasible for it to be applied further in functional foods.

Development of an ultrasound-based radiomics nomogram to preoperatively predict Ki-67 expression level in patients with breast cancer.

Objective: To develop and validate a radiomics nomogram that could incorporate clinicopathological characteristics and ultrasound (US)-based radiomics signature to non-invasively predict Ki-67 expression level in patients with breast cancer (BC) preoperatively. Methods: A total of 328 breast lesions from 324 patients with BC who were pathologically confirmed in our hospital from June 2019 to October 2020 were included, and they were divided into high Ki-67 expression level group and low Ki-67 expression level group. Routine US and shear wave elastography (SWE) were performed for each lesion, and the ipsilateral axillary lymph nodes (ALNs) were scanned for abnormal changes. The datasets were randomly divided into training and validation cohorts with a ratio of 7:3. Correlation analysis and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomics features obtained from gray-scale US images of BC patients, and each radiomics score (Rad-score) was calculated. Afterwards, multivariate logistic regression analysis was used to establish a radiomics nomogram based on the radiomics signature and clinicopathological characteristics. The prediction performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC), the calibration curve, and decision curve analysis (DCA) using the results of immunohistochemistry as the gold standard. Results: The radiomics signature, consisted of eight selected radiomics features, achieved a nearly moderate prediction efficacy with AUC of 0.821 (95% CI:0.764-0.880) and 0.713 (95% CI:0.612-0.814) in the training and validation cohorts, respectively. The radiomics nomogram, incorporating maximum diameter of lesions, stiff rim sign, US-reported ALN status, and radiomics signature showed a promising performance for prediction of Ki-67 expression level, with AUC of 0.904 (95% CI:0.860-0.948) and 0.890 (95% CI:0.817-0.964) in the training and validation cohorts, respectively. The calibration curve and DCA indicated promising consistency and clinical applicability. Conclusion: The proposed US-based radiomics nomogram could be used to non-invasively predict Ki-67 expression level in BC patients preoperatively, and to assist clinicians in making reliable clinical decisions.