The association between ambient air pollution and colorectal cancer: a Mendelian randomization study.

Mounting epidemiology studies have reported the potential associations between ambient air pollution exposure and colorectal cancer (CRC). However, the genetic association between ambient air pollution and CRC remains unclear. Using the Genome-wide association study (GWAS) data from UK biobank, we explored the genetic association of CRC (5,657 cases and 372,016 controls) with four ambient air pollutants (PM2.5, PM10, NO2, NOx; n = 423,796 to 456,380) under the framework of Mendelian randomization (MR). Our results revealed a significant association between long-term NO2 exposure (per 10 µg/m3) and increased CRC risk, with an odds ratio (OR) of 1.02 (95% confidence interval [CI]: 1.00-1.03), while no statistical association was found between CRC risk and the other air pollutants. Sensitivity analysis confirmed the robustness of the results. It is imperative to consider the impact of air pollution, particularly NO2, in mitigating the risk of CRC.

The chemical recognition of hydrogen fluoride via B24N24 nanocage: quantum chemical approach.

CONTEXT: Ab initio calculations were employed in this investigation to scrutinize the adsorption characteristics of a linear chain (HF)n on a BN nanocage (B24N24), wherein the chain lengths varied (n = 1, 2, 3, and 4). The overarching aim was to assess the efficiency of this setup in detecting and adhering to (HF)n under both liquid and gaseous scenarios. This study encompassed an array of aspects, encompassing adsorption energy, optimal configuration determination, work function analysis, and charge exchange assessment. Furthermore, an exploration was conducted into the impact of HF linear chain dimensions on electrical attributes and adsorption energy. According to the values of adsorption energy, the dimer form of HF adsorbed onto BN nanocages displayed the highest stability. METHODS: This scrutiny was undertaken utilizing density functional theory (DFT), employing the B3LYP functional and the 6-31 + + G(d,p) basis set. Notably, the choice of the 6-31 + + G(d,p) basis set is particularly apt for delving into nanostructure analyses. The HOMO-LUMO energy gap was significantly reduced by (HF)n upon adsorption onto the nanocage, falling from 6.48 to 5.43 eV and enhancing electrical conductivity as a result. Additionally, BN nanocages may be used as sensors to find (HF)n among other environmental pollutants.

A radiomics signature associated with underlying gene expression pattern for the prediction of prognosis and treatment response in hepatocellular carcinoma.

PURPOSE: Identifying robust prognosis and treatment efficiency predictive biomarkers of hepatocellular carcinoma (HCC) is challenging. The purpose of this study is to develop a radiomics approach for predicting the overall survival (OS) based on pretreatment CT images and to explore the radiomic-associated key genes. METHODS: Patients with pathologically or clinically proven HCC from three data sets were retrospectively included in this study. The institute internal data that received transarterial chemoembolization (TACE) treatment was used as the training set to construct the radiomics signature to predict OS by the least absolute shrinkage and selection operator COX (LASSO-COX) regression algorithms. The model was externally tested in 41 patients from The Cancer Genome Atlas (TCGA) with available CT images. Area under the receiver operating characteristics curve (AUC) and the log-rank test were used for survival analysis based on high versus low radiomics score. RNA sequencing data of TCGA and Gene Expression Omnibus (GEO) public database were used for gene expression analysis. RESULTS: A total of 752 patients were divided into the Radiomics cohort (n = 267), the TCGA cohort (n = 338) and GEO cohort (n = 147). The rad-score divided patients into high and low risk groups, with significant survival differences (P < 0.0001 and P = 0.0055) in the training and external test set. The AUC for 5 years' OS were 0.730 and 0.695, respectively. Seven OS-related genes (SPP1, GJA5, GJA4, INMT, PDZD4, ALDOA and MAFG) were identified, all of which were related with TACE efficiency, except for MAFG (P greater than 0.05). CONCLUSIONS: CT-radiomics signature could effectively predict the prognosis and treatment response of HCC, which were also associated with the tumor microenvironment heterogeneity.

Expression Improvement of Recombinant Plasmids of the Interleukin-7 Gene in Chitosan-Derived Nanoparticles and Their Elevation of Mice Immunity.

To investigate a safe and effective approach for enhancing the in vivo expression of recombinant genes and improving the systemic immunity of animals against infectious diseases, we employed the interleukin-7 (IL-7) gene from Tibetan pigs to construct a recombinant eukaryotic plasmid (VRTPIL-7). We first examined VRTPIL-7's bioactivity on porcine lymphocytes in vitro and then encapsulated it with polyethylenimine (PEI), chitosan copolymer (CS), PEG-modified galactosylated chitosan (CS-PEG-GAL) and methoxy poly (ethylene glycol) (PEG) and PEI-modified CS (CS-PEG-PEI) nanoparticles using the ionotropic gelation technique. Next, we intramuscularly or intraperitoneally injected mice with various nanoparticles containing VRTPIL-7 to evaluate their immunoregulatory effects in vivo. We observed a significant increase in neutralizing antibodies and specific IgG levels in response to the rabies vaccine in the treated mice compared to the controls. Treated mice also exhibited increased leukocytes, CD8+ and CD4+ T lymphocytes, and elevated mRNA levels of toll-like receptors (TLR1/4/6/9), IL-1, IL-2, IL-4, IL-6, IL-7, IL-23, and transforming growth factor-beta (TGF-ß). Notably, the recombinant IL-7 gene encapsulated in CS-PEG-PEI induced the highest levels of immunoglobulins, CD4+ and CD8+ T cells, TLRs, and cytokines in the mice's blood, suggesting that chitosan-PEG-PEI may be a promising carrier for in vivo IL-7 gene expression and enhanced innate and adaptive immunity for the prevention of animal diseases.

Monitoring of COS, SO2, H2S, and CS2 gases by Al24P24 nanoclusters: a DFT inspection.

Through utilizing density functional theory (DFT), the current work investigates the potential uses of Al24P24 fullerene for detecting CS2, H2S, SO2, and COS. The interaction order for the stability of these gases was SO2 > H2S > COS > CS2. The moment of electric dipole and molecules' adsorption energy seems correlated. Al24P24 fullerene is regarded as an electronic sensor of the Ф-type for detecting SO2 and CS2. According to the findings, CS2 and SO2 might act as Al24P24 fullerenes when H2S is present. Nevertheless, we cannot presume it to be a COS and H2S sensor of Ф-type. At room temperature, the fullerene of Al24P24 has a quick recovery time of 0.50 µs and 0.17 s in CS2 and SO2 desorption from the surface. It can thus be inferred that it has the ability to function in moist media.

Clinical-radiomics predictors to identify the suitability of transarterial chemoembolization treatment in intermediate-stage hepatocellular carcinoma: A multicenter study.

BACKGROUND: Although transarterial chemoembolization (TACE) is the first-line therapy for intermediate-stage hepatocellular carcinoma (HCC), it is not suitable for all patients. This study aimed to determine how to select patients who are not suitable for TACE as the first treatment choice. METHODS: A total of 243 intermediate-stage HCC patients treated with TACE at three centers were retrospectively enrolled, of which 171 were used for model training and 72 for testing. Radiomics features were screened using the Spearman correlation analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. Subsequently, a radiomics model was established using extreme gradient boosting (XGBoost) with 5-fold cross-validation. The Shapley additive explanations (SHAP) method was used to visualize the radiomics model. A clinical model was constructed using univariate and multivariate logistic regression. The combined model comprising the radiomics signature and clinical factors was then established. This model's performance was evaluated by discrimination, calibration, and clinical application. Generalization ability was evaluated by the testing cohort. Finally, the model was used to analyze overall and progression-free survival of different groups. RESULTS: A third of the patients (81/243) were unsuitable for TACE treatment. The combined model had a high degree of accuracy as it identified TACE-unsuitable cases, at a sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of 0.759, 0.885, 0.906 [95% confidence interval (CI): 0.859-0.953] in the training cohort and 0.826, 0.776, and 0.894 (95% CI: 0.815-0.972) in the testing cohort, respectively. CONCLUSIONS: The high degree of accuracy of our clinical-radiomics model makes it clinically useful in identifying intermediate-stage HCC patients who are unsuitable for TACE treatment.

Porcine Interleukin-17 and 22 Co-Expressed by Yarrowia lipolytica Enhance Immunity and Increase Protection against Bacterial Challenge in Mice and Piglets

Drug resistance in economic animals to pathogens is a matter of widespread concern due to abuse of antibiotics. In order to develop a safe and economical immunopotentiator to raise the immunity and antibacterial response as a replacement for antibiotics, a recombinant yeast co-expressing pig interleukin-17 (IL-17) and IL-22 was constructed and designated as Po1h-pINA1297-IL-17/22. To evaluate the immunoregulator activities of Po1h-pINA1297-IL-17/22, two experiment groups (oral inoculation with Po1h-pINA1297 or Po1h-pINA1297-IL-17/22) and a negative control group (PBS) were set up using 4-week-old female BALB/c mice (10/group). The level of cytokines, including IL-2, IL-4, IL-10, and IFN-γ, were detected by ELISA, and the circulating CD4+ and CD8+ lymphocytes were quantified by flow cytometry. The IgG and secretory IgA (SIgA) levels in both small intestine and fecal matter were also measured by ELISA. The results indicated that the IgG antibody titer and SIgA concentration increased significantly in the Po1h-pINA1297-IL17/22 group in comparison with the controls (p < 0.05) and so did the cytokine levels in the serum (IL-2, IL-4, IL-10, and IFN-γ). In addition, CD4+ and CD8+ T cells were also obviously elevated in the Po1h-pINA1297-IL17/22 group on 35th day (p < 0.05). After challenge with pathogenic Salmonella typhimurium, the Po1h-pINA1297-IL17/22 group showed a relatively higher survival rate without obvious infectious symptoms. On the contrary, the mortality of control group reached 80% due to bacterial infection. As for the piglet experiment, 30 healthy 7-day piglets were similarly attributed into three groups. The oral inoculation of piglets with Po1h-pINA1297-IL17/22 also markedly improved the growth performance and systemic immunity (up-regulations of IL-4, IL-6, IL-15, IL-17, IL-22, and IL-23). Overall, the results indicated that Po1h-pINA1297-IL17/22 effectively promoted the humoral and cellular immunity against bacterial infection. These proved the promising potential of Po1h-pINA1297-IL-17/22 to be a potent immunopotentiator for the prevention of microbial pathogen infections.

Co-Expression of Pig IL-2 and Fusion Bovine Cathelicidin Gene by Recombinant Plasmids in Yeast and Their Promotion of Mouse Antibacterial Defense.

In order to develop an effective and safe immunomodulator to enhance the antimicrobial bioactivity and immunity of animals against infectious bacterial diseases, a recombinant plasmid pGAPZαA-IL2-B co-expressing pig interleukin-2 (PIL-2) and fused bovine cathelicidin (FBC) genes were constructed using the 2A self-cleavage technique. After being expressed in Pichia pastoris strain SMD1168, the recombinant yeast was administered orally to 5-week-old female ICR mice. The control mice were similarly dosed with P. pastoris with a blank plasmid or FBC recombinant plasmid alone. At 28 days post-treatment, the mice were challenged intraperitoneally with virulent strains of either E. coli or S. aureus. Compared with the control groups, the mice that received recombinant yeast co-expressing PIL-2/FBC manifested significant increases in the number of leukocytes, CD4+ and CD8+ T cells, IgG, and the gene expressions of TLRs(TLR1,4,6,9), antimicrobial peptides(CRP4 and CRAMP) and cytokines (IL-2, 4, 6, 7, 12, 15, 23, IFN-γ, and TNF-α) in the blood. Furthermore, the treated mice displayed significantly higher survival than the other two control groups after the challenge. These results suggest that the antimicrobial activity and immunity of animals can be effectively enhanced by the in vivo co-expression of IL-2 and the FBS gene, which can facilitate the development of new immunopotentiation molecules to overcome the infection of antibiotic-resistant bacteria.

Radioimmunotherapy study of 131I-labeled Atezolizumab in preclinical models of colorectal cancer.

BACKGROUND: Programmed cell death 1 ligand 1(PD-L1) is overexpressed in many tumors. The radionuclide-labeled anti-PD-L1 monoclonal antibody can be used for imaging and therapy of PD-L1 overexpressing cancer. Here, we described 131I-labeled Atezolizumab (131I-Atezolizumab, targeting PD-L1) as a therapeutic agent for colorectal cancer with PD-L1 overexpression. METHODS: 131I-Atezolizumab was prepared by the Iodogen method. The expression levels of PD-L1 in different human colorectal cells were determined by flow cytometry, western blot and cell binding assay. The immunoreactivity of 131I-Atezolizumab to PD-L1 high-expressing cells was determined by immunoreactive fraction. The killing abilities of different concentrations of 131I-Atezolizumab on cells with high and low expression of PD-L1 were detected by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method. Cerenkov luminescence imaging (CLI) and radioimmunotherapy (RIT) of 131I-Atezolizumab were performed on two human colorectal cancer models. The distribution and tumor targeting of 131I-Atezolizumab were evaluated by imaging. Tumor volume and survival time were used as indicators to evaluate the anti-tumor effect of 131I-Atezolizumab. RESULTS: The expression level of PD-L1 in vitro determined by the cell binding assay was related to the data of flow cytometry and western blot. 131I-Atezolizumab can specifically bind to PD-L1 high-expressing cells in vitro to reflect the expression level of PD-L1. Immunoreactive fraction of PD-L1 high-expressing RKO cells with 131I-Atezolizumab was 52.2%. The killing ability of 131I-Atezolizumab on PD-L1 high-expressing cells was higher than that of low-expressing cells. CLI proved that the specific uptake level of tumors depends on the expression level of PD-L1. Effect of 131I-Atezolizumab RIT showed an activity-dependent tumor suppressor effect on RKO tumor-bearing mice with high PD-L1 expression. 131I-Atezolizumab (37 MBq) can improve the median survival time of mice (34 days), compared to untreated mice (27 days) (P = 0.027). Although a single activity(37 MBq) of 131I-Atezolizumab also inhibited the tumors of HCT8 tumor-bearing mice with low PD-L1 expression (P < 0.05), it could not prolong the survival of mice(P = 0.29). CONCLUSION: 131I-Atezolizumab can be used as a CLI agent for screening PD-L1 expression levels. It may be used as a radioimmunotherapy drug target for PD- L1 overexpressing tumors.

A nomogram model based on MRI and radiomic features developed and validated for the evaluation of lymph node metastasis in patients with rectal cancer.

PURPOSE: The aim of this study was to develop and validate a nomogram model to evaluate lymph node metastasis (LNM) in patients with rectal cancer (RC). METHODS: A total of 162 patients with RC were included in the study. The MRI reported model, the Radscore model, and the Complex model were constructed using the logistics regression (LR) algorithm. The DeLong test and decision curve analysis (DCA) were used to compare the prediction performance and clinical utility of these models. The nomogram model was constructed to visualize the prediction results of the best model. Model performance was evaluated in the training and validation groups, and the calibration curve and Hosmer-Lemeshow goodness of fit test were used to evaluate the calibration. RESULT: All three models constructed by the LR algorithm were good at identifying LNM. The DeLong test and the DCA results showed that the Complex model outperformed the MRI reported model and the Radscore model in relation to their predictive performance and clinical utility. The nomogram of the Complex model had an area under the curve (AUC) of 0.902 (95% confidence interval (CI) 0.848-0.957) in the training group and an AUC of 0.891 (95% CI 0.799-0.983) in the validation group. Meanwhile, the nomogram showed good calibration. CONCLUSION: The nomogram model constructed based on T2WI radiomics and MRI reported had good diagnostic efficacies for LNM in patients with RC, and provided a new auxiliary method for accurate and individualized clinical management.

An unusual sternalis with variation of the contralateral sternocleidomastoid muscle: a case report.

PURPOSE: To report a previously undocumented variant of sternalis. METHODS: An unusual muscle was observed during routine dissection. RESULTS: The sternalis muscle located in the right thoracic region originated from the superior portion of the rectus abdominis sheath and 5-6th costal cartilages, crossed the midline and attached at the sternum. The muscle fibers then ascended with the left sternocleidomastoid muscle as an additional fasciculus, of which the superior ends were finally terminated at the left mastoid process. The sternalis muscle of the thoracic region was innervated by the anterior cutaneous branches of right intercostal nerve, while the additional fasciculus ascended with the left sternocleidomastoid muscle was innervated by the branches of left accessory nerve. CONCLUSIONS: This study presents a unilateral sternalis muscle with the contralateral sternocleidomastoid variation. It will enhance the exhaustive classification of sternalis, and provide significant information to radiologists, angiologists and surgeons for better interpretation of images and safer interventions.

The Machine Learning Model for Distinguishing Pathological Subtypes of Non-Small Cell Lung Cancer.

Purpose: Machine learning models were developed and validated to identify lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) using clinical factors, laboratory metrics, and 2-deoxy-2[18F]fluoro-D-glucose ([18F]F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomic features. Methods: One hundred and twenty non-small cell lung cancer (NSCLC) patients (62 LUAD and 58 LUSC) were analyzed retrospectively and randomized into a training group (n = 85) and validation group (n = 35). A total of 99 feature parameters-four clinical factors, four laboratory indicators, and 91 [18F]F-FDG PET/CT radiomic features-were used for data analysis and model construction. The Boruta algorithm was used to screen the features. The retained minimum optimal feature subset was input into ten machine learning to construct a classifier for distinguishing between LUAD and LUSC. Univariate and multivariate analyses were used to identify the independent risk factors of the NSCLC subtype and constructed the Clinical model. Finally, the area under the receiver operating characteristic curve (AUC) values, sensitivity, specificity, and accuracy (ACC) was used to validate the machine learning model with the best performance effect and Clinical model in the validation group, and the DeLong test was used to compare the model performance. Results: Boruta algorithm selected the optimal subset consisting of 13 features, including two clinical features, two laboratory indicators, and nine PEF/CT radiomic features. The Random Forest (RF) model and Support Vector Machine (SVM) model in the training group showed the best performance. Gender (P=0.018) and smoking status (P=0.011) construct the Clinical model. In the validation group, the SVM model (AUC: 0.876, ACC: 0.800) and RF model (AUC: 0.863, ACC: 0.800) performed well, while Clinical model (AUC:0.712, ACC: 0.686) performed moderately. There was no significant difference between the RF and Clinical models, but the SVM model was significantly better than the Clinical model. Conclusions: The proposed SVM and RF models successfully identified LUAD and LUSC. The results indicate that the proposed model is an accurate and noninvasive predictive tool that can assist clinical decision-making, especially for patients who cannot have biopsies or where a biopsy fails.

Quantitative dual-energy computed tomography texture analysis predicts the response of primary small hepatocellular carcinoma to radiofrequency ablation.

BACKGROUND: Radiofrequency ablation (RFA) is one of the effective therapeutic modalities in patients with hepatocellular carcinoma (HCC). However, there is no proper method to evaluate the HCC response to RFA. This study aimed to establish and validate a clinical prediction model based on dual-energy computed tomography (DECT) quantitative-imaging parameters, clinical variables, and CT texture parameters. METHODS: We enrolled 63 patients with small HCC. Two to four weeks after RFA, we performed DECT scanning to obtain DECT-quantitative parameters and to record the patients' clinical baseline variables. DECT images were manually segmented, and 56 CT texture features were extracted. We used LASSO algorithm for feature selection and data dimensionality reduction; logistic regression analysis was used to build a clinical model with clinical variables and DECT-quantitative parameters; we then added texture features to build a clinical-texture model based on clinical model. RESULTS: A total of six optimal CT texture analysis (CTTA) features were selected, which were statistically different between patients with or without tumor progression (P < 0.05). When clinical variables and DECT-quantitative parameters were included, the clinical models showed that albumin-bilirubin grade (ALBI) [odds ratio (OR) = 2.77, 95% confidence interval (CI): 1.35-6.65, P = 0.010], λAP (40-100 keV) (OR = 3.21, 95% CI: 3.16-5.65, P = 0.045) and ICAP (OR = 1.25, 95% CI: 1.01-1.62, P = 0.028) were associated with tumor progression, while the clinical-texture models showed that ALBI (OR = 2.40, 95% CI: 1.19-5.68, P = 0.024), λAP (40-100 keV) (OR = 1.43, 95% CI: 1.10-2.07, P = 0.019), and CTTA-score (OR = 2.98, 95% CI: 1.68-6.66, P = 0.001) were independent risk factors for tumor progression. The clinical model, clinical-texture model, and CTTA-score all performed well in predicting tumor progression within 12 months after RFA (AUC = 0.917, 0.962, and 0.906, respectively), and the C-indexes of the clinical and clinical-texture models were 0.917 and 0.957, respectively. CONCLUSIONS: DECT-quantitative parameters, CTTA, and clinical variables were helpful in predicting HCC progression after RFA. The constructed clinical prediction model can provide early warning of potential tumor progression risk for patients after RFA.

Prediction model based on 18F-FDG PET/CT radiomic features and clinical factors of EGFR mutations in lung adenocarcinoma.

The aim of this study was to build a prediction model for epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma. A retrospective analysis was performed on 88 patients with lung adenocarcinoma. All patients underwent an 18F-FDG PET/CT scan and genetic testing of EGFR before the treatment. In the training set, the radiomic features and clinical factors were screened out, and model-1 based on CT radiomic features, model-2 based on PET radiomic features, model-3 based on clinical factors, and model-4 based on radiomic features combined with clinical factors were established, respectively. The performance of the prediction model was assessed by area under the receiver operating characteristic (ROC) curve (AUC). The DeLong test was used to compare the performance of the models to screen out the optimal model, and then built the nomogram of the optimal model. The effect and clinical utility of the nomogram was verified in the validation cohort. In our analysis, model-4 was superior to the other prediction models in identifying EGFR mutations. The AUC was 0.864 (95% CI: 0.777-0.950), with a sensitivity of 0.714 and a specificity of 0.784. The nomogram of model-4 was established. In the validation cohort, the concordance index (C-index) value of the calibration curve of the nomogram model was 0.778 (95%CI: 0.585-0.970), and the nomogram had a good clinical utility. We demonstrated that the model based on 18F-FDG PET/CT radiomic features combined with clinical factors could predict EGFR mutations in lung adenocarcinoma, which was expected to be an important supplement to molecular diagnosis.

Artificial Intelligent Multi-Modal Point-of-Care System for Predicting Response of Transarterial Chemoembolization in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) ranks the second most lethal tumor globally and is the fourth leading cause of cancer-related death worldwide. Unfortunately, HCC is commonly at intermediate tumor stage or advanced tumor stage, in which only some palliative treatment can be used to offer a limited overall survival. Due to the high heterogeneity of the genetic, molecular, and histological levels, HCC makes the prediction of preoperative transarterial chemoembolization (TACE) efficacy and the development of personalized regimens challenging. In this study, a new multi-modal point-of-care system is employed to predict the response of TACE in HCC by a concept of integrating multi-modal large-scale data of clinical index and computed tomography (CT) images. This multi-modal point-of-care predicting system opens new possibilities for predicting the response of TACE treatment and can help clinicians select the optimal patients with HCC who can benefit from the interventional therapy.

18F-FDG texture analysis predicts the pathological Fuhrman nuclear grade of clear cell renal cell carcinoma.

PURPOSE: This article analyzes the image heterogeneity of clear cell renal cell carcinoma (ccRCC) based on positron emission tomography (PET) and positron emission tomography-computed tomography (PET/CT) texture parameters, and provides a new objective quantitative parameter for predicting pathological Fuhrman nuclear grading before surgery. METHODS: A retrospective analysis was performed on preoperative PET/CT images of 49 patients whose surgical pathology was ccRCC, 27 of whom were low grade (Fuhrman I/II) and 22 of whom were high grade (Fuhrman III/IV). Radiological parameters and standard uptake value (SUV) indicators on PET and computed tomography (CT) images were extracted by using the LIFEx software package. The discriminative ability of each texture parameter was evaluated through receiver operating curve (ROC). Binary logistic regression analysis was used to screen the texture parameters with distinguishing and diagnostic capabilities and whose area under curve (AUC) > 0.5. DeLong's test was used to compare the AUCs of PET texture parameter model and PET/CT texture parameter model with traditional maximum standardized uptake value (SUVmax) model and the ratio of tumor SUVmax to liver SUVmean (SUL)model. In addition, the models with the larger AUCs among the SUV models and texture models were prospectively internally verified. RESULTS: In the ROC curve analysis, the AUCs of SUVmax model, SUL model, PET texture parameter model, and PET/CT texture parameter model were 0.803, 0.819, 0.873, and 0.926, respectively. The prediction ability of PET texture parameter model or PET/CT texture parameter model was significantly better than SUVmax model (P = 0.017, P = 0.02), but it was not better than SUL model (P = 0.269, P = 0.053). In the prospective validation cohort, both the SUL model and the PET/CT texture parameter model had good predictive ability, and the AUCs of them were 0.727 and 0.792, respectively. CONCLUSION: PET and PET/CT texture parameter models can improve the prediction ability of ccRCC Fuhrman nuclear grade; SUL model may be the more accurate and easiest way to predict ccRCC Fuhrman nuclear grade.

"Resurrected" human-source urate oxidase with high uricolytic activity and stability.

As evidences showed that UOX(Gene ID: 391051), a single pseudogene formed after multiple mutations during human evolution, could be transcribed to mature mRNA and translated to two short peptides, we hypothesized that urate oxidase with higher homology with deduced human urate oxidase (dHU) might have lower immunogenicity. In this work, we constructed a "resurrected" human-source urate oxidase (rHU19) based on dHU. It obtained better uricolytic activity (8.29 U/mg) and catalytic efficiency (3.32 s-1 µM-1) compared with wild porcine urate oxidase (wPU) and FDA-approved porcine-baboon chimera (PBC). Maintaining high homology with dHU (93.75 %), rHU19 could be more suitable for the treatment of gout and hyperuricemia theoretically.

Systematic review & meta-analysis of positron emission tomography/computed tomography and bone scan in the diagnosis of prostate lesions.

BACKGROUND: To date, the results of studies into the effectiveness of positron emission tomography (PET) combined with computed tomography (CT) and bone scan (BS) in the diagnosis of malignant prostate lesions have been inconsistent, and the advantages and disadvantages of the two methods cannot be accurately judged. METHODS: Articles were retrieved from the China National Knowledge Infrastructure (CNKI) database, Wan Fang Medical Network, PubMed, Excerpta Medica data BASE (EMBASE), Medline, and Cochrane database. The keywords used in the search were: 68Ga-prostate specific membrane antibody (68Ga-PSMA), PET/CT, prostate lesions, prostate adenocarcinoma, bone metastasis, and BS. RESULTS: Ultimately, 3 publications were selected for inclusion in the meta-analysis. A total of 215 patients were considered in the 3 articles that met the inclusion criteria. All of the included articles were small sample studies, with sample sizes ranging from 28 to 113 cases. In this study, from the 3 randomized controlled trials, only 2 (66.67%) randomized controls described the correct randomized allocation method, and only 1 (33.33%) described the hidden allocation scheme in detail. The highest sensitivity for 68Ga-PSMA PET/CT was 0.96, with 95% CI: 0.87, 1.00, and the highest specificity was 1.00, with 95% CI: 0.96, 1.00. The highest sensitivity and specificity of BS were 0.92 with 95% CI: 0.81, 0.98 and 0.96 with 95% CI: 0.78, 1.00, respectively. The results of meta-analysis of 68Ga-PSMA PET/CT diagnosis with confirmation by surgical and histopathological examination showed that the area under the summary receiver operating characteristics (SROC) curve (AUC) =0.826 and standard error (SE) (AUC) =0.0425. The results of meta-analysis of BS diagnosis with confirmation by surgical and histopathological examination showed that the area under the SROC curve (AUC) =0.714 and SE (AUC) =0.0034. DISCUSSION: The meta-analysis showed that 68Ga-PSMA PET/CT has clear advantages over BS in the diagnosis of bone metastases of malignant prostate tumors, and could improve the diagnostic accuracy of bone metastases.

lncRNA-SOX2OT promotes hepatocellular carcinoma invasion and metastasis through miR-122-5p-mediated activation of PKM2.

Tumor cells primarily utilize aerobic glycolysis for energy production, a phenomenon known as the Warburg effect, but the involvement of Warburg effect in liver cancer cell metastasis is not well understood. In present study, our results indicate a positive correlation between glucose metabolism level and metastatic potential of hepatocellular carcinoma (HCC). We also observed that a long noncoding RNA-SOX2OT (lncRNA-SOX2OT) can not only increase the metastatic potential of HCC but also promote a pyruvate kinase M2 (PKM2)-mediated activation of glucose metabolism. Inhibition of PKM2 in HCC cells greatly compromises lncRNA-SOX2OT in promoting Warburg effect and metastasis. Furthermore, miR-122-5p was found being a direct target of lncRNA-SOX2OT in regulating PKM2 expression. Thus, our findings reveal that lncRNA-SOX2OT, a regulator of PKM2, could predispose HCC patients to metastases and may serve as a candidate for metastatic prediction and therapies in HCC patients.