The role of RNA methylation in tumor immunity and its potential in immunotherapy.

RNA methylation, a prevalent post-transcriptional modification, has garnered considerable attention in research circles. It exerts regulatory control over diverse biological functions by modulating RNA splicing, translation, transport, and stability. Notably, studies have illuminated the substantial impact of RNA methylation on tumor immunity. The primary types of RNA methylation encompass N6-methyladenosine (m6A), 5-methylcytosine (m5C), N1-methyladenosine (m1A), and N7-methylguanosine (m7G), and 3-methylcytidine (m3C). Compelling evidence underscores the involvement of RNA methylation in regulating the tumor microenvironment (TME). By affecting RNA translation and stability through the "writers", "erasers" and "readers", RNA methylation exerts influence over the dysregulation of immune cells and immune factors. Consequently, RNA methylation plays a pivotal role in modulating tumor immunity and mediating various biological behaviors, encompassing proliferation, invasion, metastasis, etc. In this review, we discussed the mechanisms and functions of several RNA methylations, providing a comprehensive overview of their biological roles and underlying mechanisms within the tumor microenvironment and among immunocytes. By exploring how these RNA modifications mediate tumor immune evasion, we also examine their potential applications in immunotherapy. This review aims to provide novel insights and strategies for identifying novel targets in RNA methylation and advancing cancer immunotherapy efficacy.

Understanding decision curve analysis in clinical prediction model research.

BACKGROUND: Many medical graduate students lack a thorough understanding of decision curve analysis (DCA), a valuable tool in clinical research for evaluating diagnostic models. METHODS: This article elucidates the concept and process of DCA through the lens of clinical research practices, exemplified by its application in diagnosing liver cancer using serum alpha-fetoprotein levels and radiomics indices. It covers the calculation of probability thresholds, computation of net benefits for each threshold, construction of decision curves, and comparison of decision curves from different models to identify the one offering the highest net benefit. RESULTS: The paper provides a detailed explanation of DCA, including the creation and comparison of decision curves, and discusses the relationship and differences between decision curves and receiver operating characteristic curves. It highlights the superiority of decision curves in supporting clinical decision-making processes. CONCLUSION: By clarifying the concept of DCA and highlighting its benefits in clinical decisionmaking, this article has improved researchers' comprehension of how DCA is applied and interpreted, thereby enhancing the quality of research in the medical field.

The basic statistical concepts and their interrelationships in diagnostic research.

BACKGROUND: Many medical postgraduate students exhibit a lack of clarity in their understanding of relevant statistical concepts during the conduct of diagnostic studies. METHODS: This article, grounded in research practice, delves into the role of understanding statistical concepts in diagnostic research. It includes an exploration of sensitivity, specificity, types of statistical errors, and their interrelationships, as well as a discussion on statistical power-an often-overlooked but crucial concept in research. RESULTS: The article elucidates these important concepts with specific examples and illustrations, and addresses an issue of inconsistency related to the receiver operating characteristic curve in research practice. CONCLUSION: By drawing analogies between basic concepts in diagnostic tests and concepts in statistics, this article helps to enhance researchers' abilities in designing and interpreting clinical diagnostic studies, thereby improving the quality of clinical diagnostic research.

The expression profiles of signature genes from CD103+LAG3+ tumour-infiltrating lymphocyte subsets predict breast cancer survival.

BACKGROUND: Tumour-infiltrating lymphocytes (TILs), including T and B cells, have been demonstrated to be associated with tumour progression. However, the different subpopulations of TILs and their roles in breast cancer remain poorly understood. Large-scale analysis using multiomics data could uncover potential mechanisms and provide promising biomarkers for predicting immunotherapy response. METHODS: Single-cell transcriptome data for breast cancer samples were analysed to identify unique TIL subsets. Based on the expression profiles of marker genes in these subsets, a TIL-related prognostic model was developed by univariate and multivariate Cox analyses and LASSO regression for the TCGA training cohort containing 1089 breast cancer patients. Multiplex immunohistochemistry was used to confirm the presence of TIL subsets in breast cancer samples. The model was validated with a large-scale transcriptomic dataset for 3619 breast cancer patients, including the METABRIC cohort, six chemotherapy transcriptomic cohorts, and two immunotherapy transcriptomic cohorts. RESULTS: We identified two TIL subsets with high expression of CD103 and LAG3 (CD103+LAG3+), including a CD8+ T-cell subset and a B-cell subset. Based on the expression profiles of marker genes in these two subpopulations, we further developed a CD103+LAG3+ TIL-related prognostic model (CLTRP) based on CXCL13 and BIRC3 genes for predicting the prognosis of breast cancer patients. CLTRP-low patients had a better prognosis than CLTRP-high patients. The comprehensive results showed that a low CLTRP score was associated with a high TP53 mutation rate, high infiltration of CD8 T cells, helper T cells, and CD4 T cells, high sensitivity to chemotherapeutic drugs, and a good response to immunotherapy. In contrast, a high CLTRP score was correlated with a low TP53 mutation rate, high infiltration of M0 and M2 macrophages, low sensitivity to chemotherapeutic drugs, and a poor response to immunotherapy. CONCLUSIONS: Our present study showed that the CLTRP score is a promising biomarker for distinguishing prognosis, drug sensitivity, molecular and immune characteristics, and immunotherapy outcomes in breast cancer patients. The CLTRP could serve as a valuable tool for clinical decision making regarding immunotherapy.

An MRI-based radiomics-clinical nomogram for the overall survival prediction in patients with hypopharyngeal squamous cell carcinoma: a multi-cohort study.

OBJECTIVE: To explore whether radiomics features extracted from pre-treatment magnetic resonance imaging (MRI) can predict the overall survival (OS) in patients with hypopharyngeal squamous cell carcinoma. METHODS: A total of 190 patients with hypopharyngeal squamous cell carcinoma were eligibly enrolled from two institutions. Radiomics features were extracted from contrast-enhanced axial T1-weighted (CE-T1WI) sequence. The least absolute shrinkage selection operator (LASSO) algorithm was applied to establish a radiomics score correlated with OS. Multivariate logistic regression analysis was applied to determine the independent risk factors, which was combined with radiomics score to build the final radiomics nomogram. RESULTS: A radiomics score with 6 CE-T1WI features for OS prediction was constructed and validated; its integration with specific clinicopathologic factors (N stage) showed a better prediction performance in the training, internal validation, and external validation cohorts (C-index 0.78, 0.75, and 0.75). Calibration curves determined a good agreement between the predicted and actual overall survival. CONCLUSIONS: The radiomics-clinical nomogram and radiomics score might be non-invasive and reliable methods for the risk stratification in patients with hypopharyngeal squamous cell carcinoma. KEY POINTS: • An MRI-based radiomics model was constructed to evaluate of OS in patients with hypopharyngeal squamous cell carcinoma. • A radiomics-clinical nomogram that combined radiomics features and clinical characteristics was established. • Multi-cohort study validated the predictive performance of the radiomics-clinical nomogram to stratify patients with high risk in clinical practice.

Machine learning-based radiomics for histological classification of parotid tumors using morphological MRI: a comparative study.

OBJECTIVES: To evaluate the effectiveness of machine learning models based on morphological magnetic resonance imaging (MRI) radiomics in the classification of parotid tumors. METHODS: In total, 298 patients with parotid tumors were randomly assigned to a training and test set at a ratio of 7:3. Radiomics features were extracted from the morphological MRI images and screened using the Select K Best and LASSO algorithm. Three-step machine learning models with XGBoost, SVM, and DT algorithms were developed to classify the parotid neoplasms into four subtypes. The ROC curve was used to measure the performance in each step. Diagnostic confusion matrices of these models were calculated for the test cohort and compared with those of the radiologists. RESULTS: Six, twelve, and eight optimal features were selected in each step of the three-step process, respectively. XGBoost produced the highest area under the curve (AUC) for all three steps in the training cohort (0.857, 0.882, and 0.908, respectively), and for the first step in the test cohort (0.826), but produced slightly lower AUCs than SVM in the latter two steps in the test cohort (0.817 vs. 0.833, and 0.789 vs. 0.821, respectively). The total accuracies of XGBoost and SVM in the confusion matrices (70.8% and 59.6%) outperformed those of DT and the radiologist (46.1% and 49.2%). CONCLUSION: This study demonstrated that machine learning models based on morphological MRI radiomics might be an assistive tool for parotid tumor classification, especially for preliminary screening in absence of more advanced scanning sequences, such as DWI. KEY POINTS: • Machine learning algorithms combined with morphological MRI radiomics could be useful in the preliminary classification of parotid tumors. • XGBoost algorithm performed better than SVM and DT in subtype differentiation of parotid tumors, while DT seemed to have a poor validation performance. • Using morphological MRI only, the XGBoost and SVM algorithms outperformed radiologists in the four-type classification task for parotid tumors, thus making these models a useful assistant diagnostic tool in clinical practice.

Application of high resolution computed tomography image assisted classification model of middle ear diseases based on 3D-convolutional neural network. / 基于3Då·ç§¯ç¥žç»ç½‘ç»œçš„ä¸­è€³ç–¾ç— é«˜åˆ†è¾¨çŽ‡CTå›¾åƒè¾ åŠ©åˆ†ç±»è¯Šæ–­æ¨¡åž‹çš„åº”ç”¨.

OBJECTIVES: Chronic suppurative otitis media (CSOM) and middle ear cholesteatoma (MEC) are the 2 most common chronic middle ear diseases. In the process of diagnosis and treatment, the 2 diseases are prone to misdiagnosis and missed diagnosis due to their similar clinical manifestations. High resolution computed tomography (HRCT) can clearly display the fine anatomical structure of the temporal bone, accurately reflect the middle ear lesions and the extent of the lesions, and has advantages in the differential diagnosis of chronic middle ear diseases. This study aims to develop a deep learning model for automatic information extraction and classification diagnosis of chronic middle ear diseases based on temporal bone HRCT image data to improve the classification and diagnosis efficiency of chronic middle ear diseases in clinical practice and reduce the occurrence of missed diagnosis and misdiagnosis. METHODS: The clinical records and temporal bone HRCT imaging data for patients with chronic middle ear diseases hospitalized in the Department of Otorhinolaryngology, Xiangya Hospital from January 2018 to October 2020 were retrospectively collected. The patient's medical records were independently reviewed by 2 experienced otorhinolaryngologist and the final diagnosis was reached a consensus. A total of 499 patients (998 ears) were enrolled in this study. The 998 ears were divided into 3 groups: an MEC group (108 ears), a CSOM group (622 ears), and a normal group (268 ears). The Gaussian noise with different variances was used to amplify the samples of the dataset to offset the imbalance in the number of samples between groups. The sample size of the amplified experimental dataset was 1 806 ears. In the study, 75% (1 355) samples were randomly selected for training, 10% (180) samples for validation, and the remaining 15% (271) samples for testing and evaluating the model performance. The overall design for the model was a serial structure, and the deep learning model with 3 different functions was set up. The first model was the regional recommendation network algorithm, which searched the middle ear image from the whole HRCT image, and then cut and saved the image. The second model was image contrast convolutional neural network (CNN) based on twin network structure, which searched the images matching the key layers of HRCT images from the cut images, and constructed 3D data blocks. The third model was based on 3D-CNN operation, which was used for the final classification and diagnosis of the 3D data block construction, and gave the final prediction probability. RESULTS: The special level search network based on twin network structure showed an average AUC of 0.939 on 10 special levels. The overall accuracy of the classification network based on 3D-CNN was 96.5%, the overall recall rate was 96.4%, and the average AUC under the 3 classifications was 0.983. The recall rates of CSOM cases and MEC cases were 93.7% and 97.4%, respectively. In the subsequent comparison experiments, the average accuracy of some classical CNN was 79.3%, and the average recall rate was 87.6%. The precision rate and the recall rate of the deep learning network constructed in this study were about 17.2% and 8.8% higher than those of the common CNN. CONCLUSIONS: The deep learning network model proposed in this study can automatically extract 3D data blocks containing middle ear features from the HRCT image data of patients' temporal bone, which can reduce the overall size of the data while preserve the relationship between corresponding images, and further use 3D-CNN for classification and diagnosis of CSOM and MEC. The design of this model is well fitting to the continuous characteristics of HRCT data, and the experimental results show high precision and adaptability, which is better than the current common CNN methods.

A logistic regression model for prediction of glioma grading based on radiomics. / 基于影像组学的logistic回归模型预测胶质瘤分级.

OBJECTIVES: Glioma is the most common intracranial primary tumor in central nervous system. Glioma grading possesses important guiding significance for the selection of clinical treatment and follow-up plan, and the assessment of prognosis. This study aims to explore the feasibility of logistic regression model based on radiomics to predict glioma grading. METHODS: Retrospective analysis was performed on 146 glioma patients with confirmed pathological diagnosis from January, 2012 to December, 2018. A total of 41 radiomics features were extracted from contrast-enhanced T1-weighted imaging (T1WI+C) lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) was used to select the most-predictive radiomics features for pathological grading and to calculate radiomics score (Rad-score) of each patient. A logistic regression model was built to explore the correlation between giloma grading and Rad-score. Receiver operating characteristic (ROC) curve was performed to evaluate the model's predictive ability with area under the curve (AUC) for the evaluation index. Hosmer-Lemeshow test was used to measure the model's predictive accuracy. RESULTS: A total of 5 imaging features selected by LASSO were used to establish a logistic regression model for predicting glioma grading. The model showed good discrimination with AUC value of 0.919. Hosmer-Lemeshow test showed no significant difference between the calibration curve and the ideal curve (P=0.808), indicating high predictive accuracy of the model. CONCLUSIONS: The logistic regression model using radiomics exhibits a relatively high accuracy for predicting glioma grading, which may serve as a complementary tool for preoperative prediction of giloma grading.

Sensorimotor and pain-related alterations of the gray matter and white matter in Type 2 diabetic patients with peripheral neuropathy.

Although diabetic peripheral neuropathy (DPN) has long been considered a disease of the peripheral nervous system, recent neuroimaging studies have shown that alterations in the central nervous system may play a crucial role in its pathogenesis. Here, we used surface-based morphometry (SBM) and tract-based spatial statistics (TBSS) to investigate gray matter (GM) and white matter (WM) differences between patients with DPN (n = 67, 44 painless and 23 painful) and healthy controls (HCs; n = 88). Compared with HCs, patients with DPN exhibited GM abnormalities in the pre- and postcentral gyrus and in several deep GM nuclei (caudate, putamen, medial pallidum, thalamus, and ventral nuclear). They also exhibited altered WM tracts (corticospinal tract, spinothalamic tract, and thalamocortical projecting fibers). These findings suggest impaired motor and somatosensory pathways in DPN. Further, patients with DPN (particularly painful DPN) exhibited morphological differences in the cingulate, insula, prefrontal cortex, and thalamus, as well as impaired WM integrity in periaqueductal WM and internal and external capsules. This suggests pain-perception/modulation pathways are altered in painful DPN. Intermodal correlation analyses found that the morphological indices of the brain regions identified by the SBM analysis were significantly correlated with the fractional anisotropy of brain regions identified by the TBSS analysis, suggesting that the GM and WM alterations were tightly coupled. Overall, our study showed sensorimotor and pain-related GM and WM alterations in patients with DPN, which might be involved in the development of DPN.

Epidemic features of coronavirus disease 2019 in Hunan Province. / 湖南省2019å† çŠ¶ç— æ¯’ç— ç–«æƒ æµè¡Œæƒ å†µ.

OBJECTIVES: To explore and analyze the epidemic features of coronavirus disease 2019 (COVID-19) in Hunan Province from January 21, 2020 to March 14, 2020, as well as to investigate the COVID-19 epidemics in each city of Hunan Province. METHODS: The epidemic data was obtained from the official website of Hunan Province's Health Commission. The data of each city of Hunan Province was analyzed separately. Spatial distribution of cumulative confirmed COVID-19 patients and the cumulative occurrence rate was drawn by ArcGIS software for each city in Hunan Province. Some regional indexes were also compared with that in the whole country. RESULTS: The first patient was diagnosed in January 21, sustained patient growth reached its plateau in around February 17. Up to March 14, the cumulative confirmed COVID-19 patients stopped at 1 018. The cumulative occurrence rate of COVID-19 patients was 0.48 per 0.1 million person. The number of cumulative severe patients was 150 and the number of cumulative dead patients was 4. The mortality rate (0.39%) and the cure rate (99.6%) in Hunan Province was significantly lower and higher respectively than the corresponding average rate in the whole country (0.90% and 96.2%, Hubei excluded). The first 3 cities in numbers of the confirmed patients were Changsha, Yueyang, and Shaoyang. While sorted by the cumulative occurrence rate, the first 3 cities in incidence were Changsha, Yueyang, and Zhuzhou. CONCLUSIONS: The epidemic of COVID-19 spread out smoothly in Hunan Province. The cities in Hunan Province implement anti-disease strategies based on specific situations on their own and keep the epidemic in the range of controllable.

[An artificial neural network model for glioma grading using image information].

OBJECTIVE: To explore the feasibility and efficacy of artificial neural network for differentiating high-grade glioma and low-grade glioma using image information.
 Methods: A total of 130 glioma patients with confirmed pathological diagnosis were selected retrospectively from 2012 to 2017. Forty one imaging features were extracted from each subjects based on 2-dimension magnetic resonance T1 weighted imaging with contrast-enhancement. An artificial neural network model was created and optimized according to the performance of feature selection. The training dataset was randomly selected half of the whole dataset, and the other half dataset was used to verify the performance of the neural network for glioma grading. The training-verification process was repeated for 100 times and the performance was averaged.
 Results: A total of 5 imaging features were selected as the ultimate input features for the neural network. The mean accuracy of the neural network for glioma grading was 90.32%, with a mean sensitivity at 87.86% and a mean specificity at 92.49%. The area under the curve of receiver operating characteristic curve was 0.9486.
 Conclusion: As a technique of artificial intelligence, neural network can reach a relatively high accuracy for the grading of glioma and provide a non-invasive and promising computer-aided diagnostic process for the pre-operative grading of glioma.

Relative contributions of acoustic temporal fine structure and envelope cues for lexical tone perception in noise.

Previous studies have shown that lexical tone perception in quiet relies on the acoustic temporal fine structure (TFS) but not on the envelope (E) cues. The contributions of TFS to speech recognition in noise are under debate. In the present study, Mandarin tone tokens were mixed with speech-shaped noise (SSN) or two-talker babble (TTB) at five signal-to-noise ratios (SNRs; -18 to +6 dB). The TFS and E were then extracted from each of the 30 bands using Hilbert transform. Twenty-five combinations of TFS and E from the sound mixtures of the same tone tokens at various SNRs were created. Twenty normal-hearing, native-Mandarin-speaking listeners participated in the tone-recognition test. Results showed that tone-recognition performance improved as the SNRs in either TFS or E increased. The masking effects on tone perception for the TTB were weaker than those for the SSN. For both types of masker, the perceptual weights of TFS and E in tone perception in noise was nearly equivalent, with E playing a slightly greater role than TFS. Thus, the relative contributions of TFS and E cues to lexical tone perception in noise or in competing-talker maskers differ from those in quiet and those to speech perception of non-tonal languages.

Lexical tone recognition in noise in normal-hearing children and prelingually deafened children with cochlear implants.

OBJECTIVE: The purpose of the present study was to investigate Mandarin tone recognition in background noise in children with cochlear implants (CIs), and to examine the potential factors contributing to their performance. DESIGN: Tone recognition was tested using a two-alternative forced-choice paradigm in various signal-to-noise ratio (SNR) conditions (i.e. quiet, +12, +6, 0, and -6 dB). Linear correlation analysis was performed to examine possible relationships between the tone-recognition performance of the CI children and the demographic factors. STUDY SAMPLE: Sixty-six prelingually deafened children with CIs and 52 normal-hearing (NH) children as controls participated in the study. RESULTS: Children with CIs showed an overall poorer tone-recognition performance and were more susceptible to noise than their NH peers. Tone confusions between Mandarin tone 2 and tone 3 were most prominent in both CI and NH children except for in the poorest SNR conditions. Age at implantation was significantly correlated with tone-recognition performance of the CI children in noise. CONCLUSIONS: There is a marked deficit in tone recognition in prelingually deafened children with CIs, particularly in noise listening conditions. While factors that contribute to the large individual differences are still elusive, early implantation could be beneficial to tone development in pediatric CI users.

The comprehensive roles of lncRNA FAM99A/FAM99B in hepatocellular carcinoma: Expressions, regulatory mechanisms and functional pathway analysis.

AIMS: The incidence and mortality of liver hepatocellular carcinoma (LIHC) were increasing year by year. The aim of this study was to investigate the comprehensive roles of lncRNA FAM99A and FAM99B in LIHC. MAIN METHODS: According to the data of TCGA and GTEx, the expression levels of FAM99A and FAM99B in LIHC were evaluated, and the overall survival (OS), disease-free survival (DFS), immune cell infiltration and tumor stage were analyzed. The subcellular localization of FAM99A and FAM99B in various cancer cell lines was predicted by lncATLAS database. In addition, we also used ENCORI, KEGG, LinkedOmics, Metascape and other databases. It was verified by in vivo and in vitro experiments. KEY FINDINGS: Compared with adjacent normal tissues, FAM99A and FAM99B were down-regulated in LIHC tissues, and significantly correlated with immune cell infiltration. With the progression of tumor stage and grade, the expression of FAM99A and FAM99B showed a decreasing trend, and the prognosis of patients were also poor. In addition, the biological functions, signaling pathways and protein interactions of FAM99A and FAM99B in LIHC were enriched to study the potential molecular mechanisms. The overlapping RNA binding proteins (RBP) of FAM99A and FAM99B mainly included CSTF2T, BCCIP, RBFOX2 and SF3B4. Finally, experiments showed that overexpression of FAM99A attenuated the proliferation, invasion, colony formation and tumor growth of LIHC cells. SIGNIFICANCE: Taken together, the above studies demonstrated that FAM99A and FAM99B had an inhibitory effect on the progression of LIHC, which might be promising diagnostic biomarkers and therapeutic targets for LIHC patients.

Comprehensive analysis of IGF2BP3 with expression features, prognosis, immune modulation and stemness in hepatocellular carcinoma and pan-cancer.

Insulin like growth factor 2 mRNA binding protein 3 (IGF2BP3) is a critical m6A reader. It encodes proteins that contain several KH domains, which are important in RNA binding, RNA synthesis and metabolism. Lots of researches have studied the malignant potential of m6A readers in tumors. However, the biological functional analysis of IGF2BP3 in hepatocellular carcinoma (HCC) and pan-cancer is not comprehensive. In this study, we used a bioinformatics approach to comprehensively analyze the significance of IGF2BP3 in HCC through analyzing its expression, mutation, prognosis, protein-protein interaction (PPI) network, functional enrichment, and the correlation with ferroptosis, stemness as well as immune modulation in HCC. IGF2BP3 presented a negative correlation with the ferroptosis molecule NFE2L2, and a positive correlation with the ferroptosis molecule SLC1A5 as well as the immune checkpoint HAVCR2. In addition, we also analyzed IGF2BP3 expression, prognosis and immune modulation in pan-cancer, revealing the prognostic value of IGF2BP3 in a variety of tumors. Finally, we verified the biological functions of IGF2BP3 in HCC through various experiments. The data showed that IGF2BP3 may enhance the proliferation, colony formation and invasion capacities of HCC cells, and IGF2BP3 is mainly positively correlated with the expression level of stemness marker SOX2. In conclusion, IGF2BP3 had a potential to be a new perspective biomarker in forecasting the immune response, ferroptosis, stemness and prognosis of HCC or even pan-cancer.

GO accelerate iron oxides formation and tetrabromobisphenol A removal enhancement in the GO loaded NZVI system.

Tetrabromobisphenol A (TBBPA) is an emerging persistent organic pollutant, which is very difficult to remove by common methods. In this study, the GO-load nanoscale zero-valent iron (NZVI/GO) was fabricated and optimized to improve the reaction rate and removal efficiency for TBBPA reliably and efficiently. The results showed that GO-load significantly reduced the self-aggregation of NZVI and the aggregate size decreased by 50.00% (1400-700 nm). Meanwhile, GO significantly improved the reaction rate kobs (1.11 ± 0.11 h-1) of TBBPA in the NZVI/GO system compared to the NZVI (0.40 ± 0.08 h-1) system, and this increment was more pronounced (177.5%) when the mass ratio of NZVI-to-GO reached 1.0 than other mass ratios. Furthermore, X-Ray Diffraction and X-ray photoelectron spectroscopy analysis suggested that the Fe2+ transformation was changed and enriched by the GO. Only magnetite (Fe3O4) was detected on the surface of NZVI, whereas the maghemite (γ-Fe2O3), hematite (α-Fe2O3), and Fe3O4 were detected on the interface of NZVI/GO, which further performed the complexation adsorption through the -OH of TBBPA. This specific complexation adsorption is another potential accelerated removal mechanism for TBBPA and intermediates within the NZVI/GO system. This research has put forward a new perspective for widening the application of TBBPA removal using the synergistic effect between GO and NZVI.

The combination of radiomics features and VASARI standard to predict glioma grade.

Background and Purpose: Radiomics features and The Visually AcceSAble Rembrandt Images (VASARI) standard appear to be quantitative and qualitative evaluations utilized to determine glioma grade. This study developed a preoperative model to predict glioma grade and improve the efficacy of clinical strategies by combining these two assessment methods. Materials and Methods: Patients diagnosed with glioma between March 2017 and September 2018 who underwent surgery and histopathology were enrolled in this study. A total of 3840 radiomic features were calculated; however, using the least absolute shrinkage and selection operator (LASSO) method, only 16 features were chosen to generate a radiomic signature. Three predictive models were developed using radiomic features and VASARI standard. The performance and validity of models were evaluated using decision curve analysis and 10-fold nested cross-validation. Results: Our study included 102 patients: 35 with low-grade glioma (LGG) and 67 with high-grade glioma (HGG). Model 1 utilized both radiomics and the VASARI standard, which included radiomic signatures, proportion of edema, and deep white matter invasion. Models 2 and 3 were constructed with radiomics or VASARI, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.937 and 0.831, respectively, which was less than that of Model 1, with an AUC of 0.966. Conclusion: The combination of radiomics features and the VASARI standard is a robust model for predicting glioma grades.

Systematic pan-cancer analysis identifies RALA as a tumor targeting immune therapeutic and prognostic marker.

Introduction: RALA is a member of the small GTPase Ras superfamily and has been shown to play a role in promoting cell proliferation and migration in most tumors, and increase the resistance of anticancer drugs such as imatinib and cisplatin. Although many literatures have studied the cancer-promoting mechanism of RALA, there is a lack of relevant pan-cancer analysis. Methods: This study systematically analyzed the differential expression and mutation of RALA in pan-cancer, including different tissues and cancer cell lines, and studied the prognosis and immune infiltration associated with RALA in various cancers. Next, based on the genes co-expressed with RALA in pan-cancer, we selected 241 genes with high correlation for enrichment analysis. In terms of pan-cancer, we also analyzed the protein-protein interaction pathway of RALA and the application of small molecule drug Guanosine-5'-Diphosphate. We screened hepatocellular cancer (HCC) to further study RALA. Results: The results indicated that RALA was highly expressed in most cancers. RALA was significantly correlated with the infiltration of B cells and macrophages, as well as the expression of immune checkpoint molecules such as CD274, CTLA4, HAVCR2 and LAG3, suggesting that RALA can be used as a kind of new pan-cancer immune marker. The main functions of 241 genes are mitosis and protein localization to nucleosome, which are related to cell cycle. For HCC, the results displayed that RALA was positively correlated with common intracellular signaling pathways such as angiogenesis and apoptosis. Discussion: In summary, RALA was closely related to the clinical prognosis and immune infiltration of various tumors, and RALA was expected to become a broad-spectrum molecular immune therapeutic target and prognostic marker for pan-cancer.

New insights into checkpoint inhibitor immunotherapy and its combined therapies in hepatocellular carcinoma: from mechanisms to clinical trials.

Hepatocellular carcinoma (HCC) is one of the most lethal tumors in China and worldwide, although first-line therapies for HCC, such as atezolizumab and bevacizumab, have been effective with good results, the researches on new therapies have attracted much attention. With the deepening research on tumor immunology, the role and operation mechanism of immune cells in the tumor microenvironment (TME) of HCC have been explained, such as programmed cell death protein 1 (PD-1) binding to ligand could cause T cell exhaustion and reduce IFN-γ T cell secretion, cytotoxic T lymphocyte 4 (CTLA-4) and CD28 mediate immunosuppression by competing for B7 protein and disrupting CD28 signal transduction pathway, which also lays the foundation for the development and application of more new immune checkpoint inhibitors (ICIs). The biological behavior of various immune checkpoints has been proved in HCC, such as PD-1, programmed cell death ligand 1 (PD-L1), CTLA-4 and so on, leading to a series of clinical trials. Currently, FDA approved nivolumab, pembrolizumab and nivolumab plus ipilimumab for the treatment of HCC. However, the treatment of ICI has the disadvantages of low response rate and many side effects, so the combination of ICIs and various other therapies (such as VEGF or VEGFR inhibition, neoadjuvant and adjuvant therapy, locoregional therapies) has been derived. Further studies on immune checkpoint mechanisms may reveal new therapeutic targets and new combination therapies in the future.