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The NAD-dependent deacetylase sirtuin 1 (SIRT1), a member of the mammalian sirtuin family, plays a pivotal role in deacetylating histone and nonhistone proteins. Recently, it has been reported that SIRT1 is upregulated in various kinds of tumors and is associated with cell growth and metastasis. However, the factors and molecular mechanism regulating its cellular levels remain to be clarified. Here, we reported that the E3 ubiquitin ligase SMURF2 interacts with SIRT1 and mediates its ubiquitination and degradation. Depletion of SMURF2 leads to SIRT1 upregulation and induces the tumor formation and growth of colorectal cancer in vitro and in vivo. Furthermore, we show a negative correlation between SIRT1 and SMURF2 expression in human colorectal cancer. Thus, we propose a novel mechanism of colorectal tumorigenesis via SIRT1 regulation by SMURF2, which could potentially give rise to a new strategy for the treatment of colorectal cancer.

Background Minimally invasive modifications of inguinal lymphadenectomy (IL), including laparoscopic IL (LIL) and robotic-assisted IL (RAIL), have been utilized for penile cancer. Comparative study is necessary to guide the decision about which minimally invasive technique to select for IL. Therefore we compared RAIL with LIL performed via an antegrade approach in terms of perioperative outcomes. Methods We conducted a retrospective study of 43 patients who underwent RAIL (n = 20) or LIL (n = 23) for penile cancer from 2016 to 2020. The key surgical procedures and techniques are described. Complications were graded by the Clavien-Dindo classification, and operative time, estimated blood loss (EBL), lymph nodal yield, nodal positivity, postoperative drain duration, and disease recurrence during follow-up were assessed. Categorical variables were compared using chi-squared whereas continuous variables were compared by t-tests. Results The operative time for RAIL was significantly shorter than that of LIL (median 83 vs 95 min). Significantly less blood loss was reported with RAIL than with LIL (median 10 vs 35 ml). Lymph node yield, pathological positive nodes, the hospital stay, postoperative drain duration, postoperative complications and recurrence were similar for RAIL and LIL. Conclusions For patients with penile cancer, perioperative outcomes of RAIL and LIL were similar, but there was less blood loss, a shorter operative time for robotic cases.

Testes-specific protease 50 (TSP50) is normally expressed in testes and abnormally expressed in breast cancer, but whether TSP50 is expressed in colorectal carcinoma (CRC) and its clinical significance is unclear. We aimed to detect TSP50 expression in CRC, correlate it with clinicopathological factors, and assess its potential diagnostic and prognostic value. TSP50 mRNAs and proteins were detected in 7 CRC cell lines and 8 CRC specimens via RT-PCR and Western blot analysis. Immunohistochemical analysis of TSP50, p53 and carcinoembryonic antigen (CEA) with tissue microarrays composed of 95 CRCs, 20 colorectal adenomas and 20 normal colorectal tissues were carried out and correlated with clinicopathological characteristics and disease-specific survival for CRC patients. There was no significant correlation between the expression levels of TSP50 and p53 (P = 0.751) or CEA (P = 0.663). Abundant expression of TSP50 protein was found in CRCs (68.4%) while it was poorly expressed in colorectal adenomas and normal tissues (P<0.0001). Thus, CRCs can be distinguished from them with high specificity (92.5%) and positive predictive value (PPV, 95.6%). The survival of CRC patients with high TSP50 expression was significantly shorter than that of the patients with low TSP50 expression (P = 0.010), specifically in patients who had early-stage tumors (stage I and II; P = 0.004). Multivariate Cox regression analysis indicated that high TSP50 expression was a statistically significant independent risk factor (hazard ratio  = 2.205, 95% CI = 1.214-4.004, P = 0.009). Our data demonstrate that TSP50 is a potential effective indicator of poor survival for CRC patients, especially for those with early-stage tumors.

The objectives of this study were to detect the immunogenicity of a lipopolysaccharide (LPS) binding protein (LBP) mimic epitope peptide vaccine and evaluate its effect on controlling excessive and uncontrolled inflammatory reactions in mice with acute lung injury. The vaccine was prepared by mixing self-made LBP mimic epitope multiple antigen peptide (MAP) and Freund adjuvants in a proper proportion. Healthy mice were inoculated with the vaccine and the dynamic changes of anti-MAP antibody were measured using ELISA. Anti-MAP antibody was prepared from the immune serum of the mice based on the standard antibody preparation program. Western blot assay was used to identify LBP specificity of anti-MAP antibody. Anti-MAP antibody bioactivity was analyzed using in vitro binding activity test. Following the vaccine inoculation, the mice were injected with LPS to induce acute lung injury. Anti-MAP antibody prepared was also used to immune the mice with LPS-induced acute lung injury. TNF-α and IL-1β contents in serum and lung tissue homogenate were measured using double antibody sandwiched ELISA at different time-points after LPS challenge. At 8 h time-point, total white blood cell counts, polymorphonuclear leucocyte count and protein content in bronchoalveolar lavage fluid were measured and pulmonary morphological changes were evaluated. The antibody titer was gradually rising, reaching to its peak at 8th week and lasting to the tenth week. The antibody possessed strong immunogenicity, high specificity and favorable biologic activity. Whole range inoculation of LBP mimic epitope peptide vaccine or anti-MAP antibody intervention partly eliminates LPS-mediated acute lung injury. In conclusion, LBP mimic epitope peptide vaccine successfully induced highly specific antibody with high bioactivity in mice. LBP mimic epitope peptide vaccine and anti-MAP antibody inhibited excessive and uncontrolled inflammatory reactions from LPS-mediated acute lung injury in mice.

Background As it is often difficult for a transplant pathologist to make a definite diagnosis of acute cellular rejection (ACR) by routine morphological analysis of liver allograft biopsy, supplementary methods and objective markers are needed to facilitate this determination. Methods To evaluate the diagnostic value of cytotoxic molecules in ACR episodes, immunohistochemical staining for perforin, granzyme B and T-cell intracellular antigen-1 (TIA-1) were performed in liver allograft biopsies. The positive cells in the portal tract area and lobules were counted separately to investigate the distribution of the cytotoxic molecules. Results The immunohistochemical study showed that the overall positive rates for the three markers were not significantly different between the ACR and non-ACR groups. However, in the portal tract area, perforin-, granzyme B- and TIA-1-positive cells in the ACR group were significantly more than those in the non-ACR groups. In the lobules, perforin- and granzyme B-positive cells in the ACR group were significantly more than those in the biliary complication and opportunistic infection groups, while TIA-1-positive cells was significantly fewer than those in non-ACR groups. The numbers of positive cells in the portal tract area correlated with the rejection activity index of ACR. Conclusions These results indicate that, though the overall positive rates have nonsense in ACR diagnosis, the quantification and local distribution analysis of cytotoxic molecule positive cells in liver tissue is helpful for differential diagnosis and severity evaluation of ACR following liver transplantation. Virtual slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/2292255038100487

We reported one case of penile angiosarcoma in a 49-year-old patient who had been misdiagnosed as syphilis. Based on our experience, we suggest that if sexually transmitted diseases and Peyronie’s disease can be ruled out, diagnosis of the penile angiosarcoma be warranted if unexplained rash, ulceration on the penis, and induration in corpus cavernosum are observed. It should be noted that if the anti-inflammatory or anti-viral treatment is prescribed for some time but the lesion does not disappear or even gets worse, the penile angiosarcoma should be suspected and penile biopsy is necessary.

Objectives To develop and validate a CT-based deep learning radiomics nomogram (DLRN) for outcome prediction in clear cell renal cell carcinoma (ccRCC), and its performance was compared with the Stage, Size, Grade, and Necrosis (SSIGN) score, the University of California, Los Angeles, Integrated Staging System (UISS), the Memorial Sloan-Kettering Cancer Center (MSKCC), and the International Metastatic Renal Cell Database Consortium (IMDC). Methods A multicenter of 799 localized (training/ test cohort, 558/241) and 45 metastatic ccRCC patients were studied. A DLRN was developed for predicting recurrence-free survival (RFS) in localized ccRCC patients, and another DLRN was developed for predicting overall survival (OS) in metastatic ccRCC patients. The performance of the two DLRNs was compared with that of the SSIGN, UISS, MSKCC, and IMDC. Model performance was assessed with Kaplan-Meier curves, time-dependent area under the curve (time-AUC), Harrell’s concordance index (C-index), and decision curve analysis (DCA). Results In the test cohort, the DLRN achieved higher time-AUCs (0.921, 0.911, and 0.900 for 1, 3, and 5 years, respectively), C-index (0.883), and net benefit than SSIGN and UISS in predicting RFS for localized ccRCC patients. The DLRN provided higher time-AUCs (0.594, 0.649, and 0.754 for 1, 3, and 5 years, respectively) than MSKCC and IMDC in predicting OS for metastatic ccRCC patients. Conclusions The DLRN can accurately predict outcomes and outperformed the existing prognostic models in ccRCC patients. Clinical relevance statement This deep learning radiomics nomogram may facilitate individualized treatment, surveillance, and adjuvant trial design for patients with clear cell renal cell carcinoma. Key Points • SSIGN, UISS, MSKCC, and IMDC may be insufficient for outcome prediction in ccRCC patients. • Radiomics and deep learning allow for the characterization of tumor heterogeneity. • The CT-based deep learning radiomics nomogram outperforms the existing prognostic models in ccRCC outcome prediction.

Aerosol acidity (or pH) plays a crucial role in atmospheric chemistry, influencing the interaction of air pollutants with ecosystems and climate. Aerosol pH shows large temporal variations, while the driving factors of chemical profiles versus meteorological conditions are not fully understood due to their intrinsic complexity. Here, we propose a new framework to quantify factor importance, which incorporated an interpretive structural modeling (ISM) approach and time series analysis. In particular, a hierarchical influencing factor relationship is established based on the multiphase buffer theory with ISM. A long-term (2018–2023) observation dataset in Changzhou, China, is analyzed with this framework. We found the pH temporal variation is dominated by the seasonal and random variations, while the long-term pH trend varies little despite the large emission changes. This is an overall effect of decreasing PM2.5, increasing temperature and increased alkali-to-acid ratios. Temperature is the controlling factor of pH seasonal variations, through influencing the multiphase effective acid dissociation constant Ka∗, non-ideality cni and gas–particle partitioning. Random variations are dominated by the aerosol water contents through Ka∗ and chemical profiles through cni. This framework provides quantitative understanding of the driving factors of aerosol acidity at different levels, which is important in acidity-related process studies and policy-making.

Background Drug resistance reflects the evolution of tumors and represents the leading factor behind recurrence and death. Lenvatinib is the first-line therapy for hepatocellular carcinoma (HCC), but its effectiveness is limited by rapid development of resistance. Therefore, we aimed to identify lenvatinib resistance-related genes and assess their influence on prognosis and treatment response in HCC. Methods The GSE186191 dataset served as the discovery cohort to identify lenvatinib resistance-related genes. A Venn diagram analysis delineated the intersection between lenvatinib resistance-related genes and prognostic-associated genes derived from The Cancer Genome Atlas (TCGA) database. The LASSO Cox regression model was implemented to construct a multigene signature in the TCGA cohort. A nomogram was built by integrating the TNM stage and our prognostic model. The gene signature and nomogram were further validated using HCC patients from the International Cancer Genome Consortium (ICGC) cohort. Mutation signatures, therapeutic response, functional enrichment, and immune profile analyses were performed in the two groups. Two lenvatinib-resistant (LR) HCC cells were established using a concentration gradient increment method. PFKFB4 expression was detected via qRT-PCR and western blot assay. The CCK-8 assay and flow cytometry were utilized to evaluate the proliferation and apoptosis of LR cells under different interventions. Results We developed a lenvatinib resistance-related gene signature (ALPK3, SLC2A2, CTSV, and PFKFB4), and demonstrated that’s a precise, independent, and specific prognostic model for HCC patients. High-risk patients were characterized by a predisposition to TP53 mutations, aggressive tumor features, and treatment resistance. The risk score was significantly associated with immune cell infiltration, immune checkpoint expression, angiogenesis, and tumor stemness. PFKFB4 was overexpressed in LR cells, and its knockdown significantly enhances the antiproliferative and pro-apoptotic effects of lenvatinib on resistant cells. Conclusions The lenvatinib resistance-related prognostic signature exhibits strong predictive power for prognosis in HCC patients and may serve as an effective tool for guiding treatment decisions. PFKFB4 promotes tumor progression and lenvatinib resistance, highlighting its potential as a novel therapeutic target for HCC. Clinical trial number Not applicable.

The accurate classification of traffic data is challenging for network management and security, especially in imbalanced situations. The limitation of the existing convolutional neural networks is that they have problems such as overfitting, instability, and poor generalization when used to classify imbalanced datasets. In this paper, we propose a new imbalanced encrypted traffic classification model. The proposed model is based on the improved convolutional block attention module (CBAM) and re-weighted cross-entropy focal loss (CEFL) function. The model exploits the redefined imbalance degree to construct a weight function, which is used to reassign the weights of the categories. The improved CBAM based on the redefined imbalance degree can make the model pay more attention to the characteristics of the minority samples, and increase the representation ability of these samples. The re-weighted CEFL loss function can be used to expand the effective loss gap between minority and majority samples. The method is validated on the public ISCX Tor 2016 dataset. The experimental results show that the performance of the new classification model is better than the baseline methods, and the proposed method can remarkably push the precision of the minority categories to 93.28% (14.63%↑), recall to 91.71% (16.98%↑), and F1 score to 92.49% (16.23%↑).