Exploring the value of multiple preprocessors and classifiers in constructing models for predicting microsatellite instability status in colorectal cancer.

Approximately 15% of patients with colorectal cancer (CRC) exhibit a distinct molecular phenotype known as microsatellite instability (MSI). Accurate and non-invasive prediction of MSI status is crucial for cost savings and guiding clinical treatment strategies. The retrospective study enrolled 307 CRC patients between January 2020 and October 2022. Preoperative images of computed tomography and postoperative status of MSI information were available for analysis. The stratified fivefold cross-validation was used to avoid sample bias in grouping. Feature extraction and model construction were performed as follows: first, inter-/intra-correlation coefficients and the least absolute shrinkage and selection operator algorithm were used to identify the most predictive feature subset. Subsequently, multiple discriminant models were constructed to explore and optimize the combination of six feature preprocessors (Box-Cox, Yeo-Johnson, Max-Abs, Min-Max, Z-score, and Quantile) and three classifiers (logistic regression, support vector machine, and random forest). Selecting the one with the highest average value of the area under the curve (AUC) in the test set as the radiomics model, and the clinical screening model and combined model were also established using the same processing steps as the radiomics model. Finally, the performances of the three models were evaluated and analyzed using decision and correction curves.We observed that the logistic regression model based on the quantile preprocessor had the highest average AUC value in the discriminant models. Additionally, tumor location, the clinical of N stage, and hypertension were identified as independent clinical predictors of MSI status. In the test set, the clinical screening model demonstrated good predictive performance, with the average AUC of 0.762 (95% confidence interval, 0.635-0.890). Furthermore, the combined model showed excellent predictive performance (AUC, 0.958; accuracy, 0.899; sensitivity, 0.929) and favorable clinical applicability and correction effects. The logistic regression model based on the quantile preprocessor exhibited excellent performance and repeatability, which may further reduce the variability of input data and improve the model performance for predicting MSI status in CRC.

Generation of dual-attribute iTNK cells from hPSCs for cancer immunotherapy.

Dual-attribute immune cells possess advantageous features of cytotoxic T cells and natural killer (NK) cells and hold promise for advancing immunotherapy. Dual-attribute cell types such as invariant natural killer T cells, induced T-to-NK cells, and cytokine-induced killer cells have demonstrated efficacy and safety in preclinical and clinical studies. However, their limited availability hinders their widespread application. Human pluripotent stem cells (hPSCs) offer an ideal source. Here, we generate dual-attribute induced T-NK (iTNK) cells from hPSCs, expressing markers of both cytotoxic T and NK cells. Single-cell RNA and T cell receptor (TCR) sequencing analyses reveal that iTNK cells expressed signature genes associated with both NK and T cells and displayed a diverse TCR repertoire. iTNK cells release cytotoxic mediators, exert cytotoxicity against diverse tumor cell lines, and inhibit tumor growth in vivo. By harnessing adaptive and innate immune responses, hPSC-derived iTNK cells offer promising strategies for cancer immunotherapy.

TOX: a potential new immune checkpoint in cancers by pancancer analysis.

BACKGROUND: Thymocyte selection-associated HMG-BOX (TOX) belongs to a family of transcription factors containing a highly conserved region of the high mobility group box (HMG-Box). A growing body of research has shown that TOX is involved in the occurrence and development of tumors and promotes T-cell exhaustion. We assessed the role of TOX with The Cancer Genome Atlas (TCGA) Pancancer Data. METHODS: TOX expression was examined with RNA-seq data from the TCGA and Genotype-Tissue Expression (GTEx) databases. The genetic alteration status and protein level of TOX were analyzed using databases, including the Human Protein Atlas (HPA), GeneCards, and STRING. The prognostic significance was estimated with survival data from the TCGA. Moreover, R software was used for enrichment analysis of TOX. The relationship between TOX and immune cell infiltration was assessed with the Tumor Immune Estimation Resource (TIMER) 2.0 database and the "CIBERSORT" method. The correlation between TOX and immune checkpoints was further explored. Immunohistochemical analysis was used to further verify the difference in TOX expression between cancerous and paracancerous tissues, and cell viability was evaluated using a CCK-8 assay. RESULTS: In most cancer types in the TCGA cohort, differential TOX expression was observed. The genetic alteration status and protein level of TOX were examined, and the prognosis of cancers was associated with TOX expression. Moreover, TOX levels were closely related to different immune-related pathways, immune cell infiltration and immune checkpoints. Additionally, significant differences in TOX expression between several cancerous and paracancerous tissues were validated. Furthermore, TOX clearly impacted the viability of cancer cells. CONCLUSIONS: TOX, a potential biomarker for cancer, may be involved in the regulation of the immune microenvironment and can be used for new targeted drugs.

Ductile P-Type AgCu(Se,S,Te) Thermoelectric Materials.

Ductile inorganic thermoelectric (TE) materials open a new approach to develop high-performance flexible TE devices. N-type Ag2(S,Se,Te) and p-type AgCu(Se,S,Te) pseudoternary solid solutions are two typical categories of ductile inorganic TE materials reported so far. Comparing with the Ag2(S,Se,Te) pseudoternary solid solutions, the phase composition, crystal structure, and physical properties of AgCu(Se,S,Te) pseudoternary solid solutions are more complex, but their relationships are still ambiguous now. In this work, via systematically investigating the phase composition, crystal structure, mechanical, and TE properties of about 60 AgCu(Se,S,Te) pseudoternary solid solutions, the comprehensive composition-structure-property phase diagrams of the AgCuSe-AgCuS-AgCuTe pseudoternary system is constructed. By mapping the complex phases, the "ductile-brittle" and "n-p" transition boundaries are determined and the composition ranges with high TE performance and inherent ductility are illustrated. On this basis, high performance p-type ductile TE materials are obtained, with a maximum zT of 0.81 at 340 K. Finally, flexible in-plane TE devices are prepared by using the AgCu(Se,S,Te)-based ductile TE materials, showing high output performance that is superior to those of organic and inorganic-organic hybrid flexible devices.

Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases.

OBJECTIVE: Existing approaches to fairness evaluation often overlook systematic differences in the social determinants of health, like demographics and socioeconomics, among comparison groups, potentially leading to inaccurate or even contradictory conclusions. This study aims to evaluate racial disparities in predicting mortality among patients with chronic diseases using a fairness detection method that considers systematic differences. METHODS: We created five datasets from Mass General Brigham's electronic health records (EHR), each focusing on a different chronic condition: congestive heart failure (CHF), chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), chronic liver disease (CLD), and dementia. For each dataset, we developed separate machine learning models to predict 1-year mortality and examined racial disparities by comparing prediction performances between Black and White individuals. We compared racial fairness evaluation between the overall Black and White individuals versus their counterparts who were Black and matched White individuals identified by propensity score matching, where the systematic differences were mitigated. RESULTS: We identified significant differences between Black and White individuals in age, gender, marital status, education level, smoking status, health insurance type, body mass index, and Charlson comorbidity index (p-value < 0.001). When examining matched Black and White subpopulations identified through propensity score matching, significant differences between particular covariates existed. We observed weaker significance levels in the CHF cohort for insurance type (p = 0.043), in the CKD cohort for insurance type (p = 0.005) and education level (p = 0.016), and in the dementia cohort for body mass index (p = 0.041); with no significant differences for other covariates. When examining mortality prediction models across the five study cohorts, we conducted a comparison of fairness evaluations before and after mitigating systematic differences. We revealed significant differences in the CHF cohort with p-values of 0.021 and 0.001 in terms of F1 measure and Sensitivity for the AdaBoost model, and p-values of 0.014 and 0.003 in terms of F1 measure and Sensitivity for the MLP model, respectively. DISCUSSION AND CONCLUSION: This study contributes to research on fairness assessment by focusing on the examination of systematic disparities and underscores the potential for revealing racial bias in machine learning models used in clinical settings.

Prognosis Prediction of Diffuse Large B-Cell Lymphoma in 18F-FDG PET Images Based on Multi-Deep-Learning Models.

Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one of the most challenging and complicated diseases because of its considerable variation in clinical behavior, response to therapy, and prognosis. Radiomic features from medical images, such as PET images, have become one of the most valuable features for disease classification or prognosis prediction using learning-based methods. In this paper, a new flexible ensemble deep learning model is proposed for the prognosis prediction of the DLBCL in 18F-FDG PET images. This study proposes the multi-R-signature construction through selected pre-trained deep learning models for predicting progression-free survival (PFS) and overall survival (OS). The proposed method is trained and validated on two datasets from different imaging centers. Through analyzing and comparing the results, the prediction models, including Age, Ann abor stage, Bulky disease, SUVmax, TMTV, and multi-R-signature, achieve the almost best PFS prediction performance (C-index: 0.770, 95% CI: 0.705-0.834, with feature adding fusion method and C-index: 0.764, 95% CI: 0.695-0.832, with feature concatenate fusion method) and OS prediction (C-index: 0.770 (0.692-0.848) and 0.771 (0.694-0.849)) on the validation dataset. The developed multiparametric model could achieve accurate survival risk stratification of DLBCL patients. The outcomes of this study will be helpful for the early identification of high-risk DLBCL patients with refractory relapses and for guiding individualized treatment strategies.

Tumor-derived Prevotella intermedia aggravates gastric cancer by enhancing Perilipin 3 expression.

The indigenous microbial milieu within tumorous tissues exerts a pivotal influence on the genesis and advancement of gastric cancer (GC). This investigation scrutinizes the functions and molecular mechanisms attributed to Prevotella intermedia in the malignant evolution of GC. Isolation of P. intermedia from paired GC tissues was undertaken. Quantification of P. intermedia abundance in 102 tissues was accomplished using quantitative real-time PCR (qRT-PCR). Assessment of the biological effects of P. intermedia on GC cells was observed using culture medium supernatant. Furthermore, the protein profile of GC cells treated with tumor-derived P. intermedia was examined through label-free protein analysis. The functionality of perilipin 3 (PLIN3) was subsequently confirmed using shRNA. Our investigation revealed that the relative abundance of P. intermedia in tumor tissues significantly surpassed that of corresponding healthy tissues. The abundance of P. intermedia exhibited correlations with tumor differentiation (p = 0.006), perineural invasion (p = 0.004), omentum majus invasion (p = 0.040), and the survival duration of GC patients (p = 0.042). The supernatant derived from tumor-associated P. intermedia bolstered the proliferation, clone formation, migration, and invasion of GC cells. After indirect co-cultivation with tumor-derived P. intermedia, dysregulation of 34 proteins, including PLIN3, was discerned in GC cells. Knockdown of PLIN3 mitigated the malignancy instigated by P. intermedia in GC cells. Our findings posit that P. intermedia from the tumor microenvironment plays a substantial role in the malignant progression of GC via the modulation of PLIN3 expression. Moreover, the relative abundance of P. intermedia might serve as a potential biomarker for the diagnosis and prognosis of GC.

Borrmann Type IV Gastric Cancer: Computed Tomography Features and Corresponding Pathological Findings.

OBJECTIVE: We aimed to analyze the association between computed tomography (CT) features and the corresponding pathological findings in Borrmann type IV (BT-4) gastric cancers and explore the pathological basis of the characteristic CT features. METHODS: This retrospective study included 84 patients with BT-4 gastric cancers who underwent contrast-enhanced CT and surgical resection. Preoperative CT features were evaluated, including the major location, range, circumferential invasion, perigastric fat infiltration, enlarged lymph nodes, layered enhancement, degree of enhancement, and peak enhanced phase. Postoperative pathological findings were also recorded. Differences in CT features according to different World Health Organization types, surgical margin, adjacent organ invasion, and peritoneal status were assessed using the χ 2 or Fisher exact test (n < 5). RESULTS: The most common World Health Organization type of BT-4 gastric cancer was poorly cohesive carcinoma (65.5%), which tended to show circumferential invasion, fewer enlarged lymph nodes, and layered enhancement. Although 82 patients with BT-4 gastric cancer (97.6%) had positive lymph nodes, only 26 (31.0%) had enlarged lymph nodes. Lesions originating from the gastroesophageal junction had a higher rate of positive margins ( P < 0.05). Adjacent organ invasion was more likely to occur in lesions with perigastric fat infiltration ( P < 0.05). Patients with circumferential invasion tended to show peritoneal metastasis ( P < 0.05). CONCLUSIONS: The characteristic CT features of BT-4 gastric cancer may be attributed to the corresponding pathological findings. Recognizing the association between CT features and pathological findings may help evaluate the aggressiveness of BT-4 gastric cancers.

Protective effect of novel angiotensin receptor neprilysin inhibitor S086 on target organ injury in spontaneously hypertensive rats.

BACKGROUND: Hypertension is a clinical syndrome characterized by elevated systemic arterial blood pressure associated with injury to the heart, kidney, brain, and other organs. Angiotensin receptor neprilysin inhibitors (ARNi), including angiotensin receptor blockers (ARBs) and neprilysin inhibitors (NEPi), have been shown to be safe and effective at reducing blood pressure and alleviating development of target organ injury. This study was used to develop S086 as a novel ARNi and conducted preclinical studies in animal models to evaluate the protective effects of S086 on target organs. METHODS: This study used a 14-month-old spontaneously hypertensive rat (SHR) model to evaluate the protective effects of S086 on the cardiovascular system and organs such as heart and kidney by blood pressure monitoring, urine and blood examination, pathological examination, and immunological index detection. RESULTS: After administering S086 orally to the SHR, their blood pressure and levels of renal injury indicators such as serum creatinine and urinary microalbumin were reduced, and myocardial cell necrosis and cardiac fibrosis of the heart were significantly improved. In addition, there were also significantly improvements in the histological lesions of blood vessels and the kidneys. CONCLUSIONS: The findings showed that S086 effectively reduced the blood pressure of SHR and had effects on alleviating development of heart, blood vessels and kidney.

CircUSP10 promotes liver cancer progression by regulating miR-211-5p/TCF12/EMT signaling pathway.

There is no precise diagnosis or prognosis for liver cancer (LC) using a single biomarker. Circular RNAs (circRNAs) contribute to the pathogenesis of different cancers, but their role in LC is not entirely understood. In this study, circUSP10, an aberrantly expressed circRNA in LC, was screened using the Gene Expression Omnibus database, and its tissue-specific expression was verified using qRT-PCR. In vitro, functional assays and nude mouse tumorigenesis models were used to investigate circUSP10 role in LC. RNA immunoprecipitation and dual-luciferase reporter assays were performed to study the mechanistic relationship between circUSP10, miR-211-5p, and transcription factor 12 (TCF12). We found that circUSP10 expression was upregulated in LC tissues and cells. CircUSP10 expression was linked to tumor size and tumor node metastasis stage and negatively correlated with LC prognosis. In vitro assays confirmed circUSP10-mediated proliferation, migration, and invasion of LC cells and their association with the epithelial-mesenchymal transition (EMT) pathway. Mechanistically, circUSP10 adsorbed miR-211-5p, which regulated TCF12 and promoted tumorigenesis via the EMT signaling pathway. Therefore, our results suggest that circUSP10 may promote LC progression by modulating the miR-211-5p/TCF12/EMT signaling cascade and may serve as a potential biomarker for LC diagnosis and prognosis.

Adamantoyl chloride inhibited replication of the largemouth bass virus via enhanced immunity and inhibition of apoptosis.

The largemouth bass virus (LMBV) is a commonly encountered pathogen in aquaculture and presents significant challenges to development of the largemouth bass industry due to the lack of effective treatment methods. Here, the inhibitory potential and underlying mechanisms of adamantoyl chloride (AdCl) against LMBV were assessed both in vitro and in vivo. The results showed that AdCl (IC50 = 72.35 µM) significantly inhibited replication of LMBV in epithelioma papulosum cyprini (EPC) cells. The results of the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide and cytopathic effect (CPE) assays confirmed that AdCl inhibited replication of LMBV in EPC cells and significantly reduced the CPE effect, respectively. As a potential mechanism, AdCl inhibited apoptosis as determined by fluorescence and transmission electron microscopy. The results of flow cytometry showed that the apoptosis rate was decreased by 69 % in the AdCl-treated group as compared to the LMBV-infected group. Additionally, AdCl inhibited viral release. In vivo, the survival rate was 16.2 % higher in the AdCl-treated group as compared to the LMBV-infected group (26.9 % vs. 10.7 %, respectively). Additionally, the results of quantitative reverse transcription polymerase chain reaction (RT-qPCR) showed that AdCl significantly reduced the viral load of the fish liver, spleen, and kidneys at 3, 6, and 9 days postinfection. In addition, RT-qPCR analysis found that AdCl upregulated expression of immune-related genes to suppress replication of LMBV. Collectively, these results confirmed the anti-LMBV activities of AdCl for use in the aquaculture industry.

Daily level examination indicates that positive affect, but not negative affect, is associated with alcohol and marijuana use among adolescents and young adults.

OBJECTIVE: Although there is growing evidence that alcohol use at the daily level is associated with positive but not negative affect, results are mixed when examining marijuana use and simultaneous alcohol and marijuana (SAM) use (i.e., use with overlapping effects). In addition, less is known about these daily level associations among diverse samples of adolescents and young adults. The present study will address these gaps. METHOD: Participants (N = 1,006; 57.3% female, 43.1% White, Non-Hispanic, ages 15-25; Mage = 20.0) were part of a study on substance use that consisted of a 3-week ecological momentary assessment (EMA) burst design (eight surveys per week, up to 2×/day) that was repeated quarterly over a 12-month period. RESULTS: Within-person results indicated that on days with elevated positive affect, participants reported consuming more drinks, whereas positive affect was not significant for hours high from marijuana. In addition, on days with elevated negative affect, participants reported fewer hours high from marijuana. No association was found between negative affect and number of drinks. Finally, within-person results indicated that on alcohol or marijuana days with elevated positive affect, individuals were more likely to report SAM use. There was no association found between negative affect and SAM use. CONCLUSIONS: Results have implications such that in-the-moment interventions for alcohol and SAM use may be more salient when individuals have higher positive affect than average, whereas such interventions may be more relevant for marijuana use when negative affect is lower compared to average levels. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A pan-cancer analysis for the oncogenic role of cyclin-dependent kinase inhibitor 1B in human cancers.

BACKGROUND: Human health and life are threatened by cancer with high morbidity and mortality worldwide. In many experiments, CDKN1B level is associated with cancer risk, Nevertheless, no pan-cancer analysis has been conducted on CDKN1B in human cancers. METHODS: With the help of bioinformatics, a pan-cancer analysis was conducted on the expression levels of CDKN1B in cancer tissues and adjacent tissues from the TCGA, CPTAC and GEO databases. The CDKN1B expression levels in tumor patients was further validated using immunohistochemistry (IHC) and quantitative real-time PCR. RESULTS: In the study, we first investigated the cancer-related roles of CDKN1B's in 40 tumors with malignancy. The CDKN1B gene encodes the p27Kip1 protein, which can block the production cyclin-dependent kinase (CDK), which is obviously related to the function and survival of cancer cells and alters the prognosis of cancer patients. Furthermore, CDKN1B function requires both protein processing and RNA metabolism. Additionally, the elevated expression of the CDKN1B gene and protein was validated in several cancer tissues from the patients. CONCLUSIONS: These results showed that the levels of CDKN1B were considerably different in a number of cancer tissues, offering a potential future target for cancer therapy.

Value of dual-layer spectral detector computed tomography in the diagnosis of benign/malignant solid solitary pulmonary nodules and establishment of a prediction model.

Objective: This study aimed to investigate the role of spectral detector computed tomography (SDCT) quantitative parameters and their derived quantitative parameters combined with lesion morphological information in the differential diagnosis of solid SPNs. Methods: This retrospective study included basic clinical data and SDCT images of 132 patients with pathologically confirmed SPNs (102 and 30 patients in the malignant and benign groups, respectively). The morphological signs of SPNs were evaluated and the region of interest (ROI) was delineated from the lesion to extract and calculate the relevant SDCT quantitative parameters, and standardise the process. Differences in qualitative and quantitative parameters between the groups were statistically analysed. A receiver operating characteristic (ROC) curve was constructed to evaluate the efficacy of the corresponding parameters in the diagnosis of benign and malignant SPNs. Statistically significant clinical data, CT signs and SDCT quantitative parameters were analysed using multivariate logistic regression to determine the independent risk factors for predicting benign and malignant SPNs, and the best multi-parameter regression model was established. Inter-observer repeatability was assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. Results: Malignant SPNs differed from benign SPNs in terms of size, lesion morphology, short spicule sign, and vascular enrichment sign (P< 0.05). The SDCT quantitative parameters and their derived quantitative parameters of malignant SPNs (SAR40keV, SAR70keV, Δ40keV, Δ70keV, CER40keV, CER70keV, NEF40keV, NEF70keV, λ, NIC, NZeff) were significantly higher than those of benign SPNs (P< 0.05). In the subgroup analysis, most parameters could distinguish between benign and adenocarcinoma groups (SAR40keV, SAR70keV, Δ40keV, Δ70keV, CER40keV, CER70keV, NEF40keV, NEF70keV, λ, NIC, and NZeff), and between benign and squamous cell carcinoma groups (SAR40keV, SAR70keV, Δ40keV, Δ70keV, NEF40keV, NEF70keV, λ, and NIC). However, there were no significant differences between the parameters in the adenocarcinoma and squamous cell carcinoma groups. ROC curve analysis indicated that NIC, NEF70keV, and NEF40keV had higher diagnostic efficacy for differentiating benign and malignant SPNs (area under the curve [AUC]:0.869, 0.854, and 0.853, respectively), and NIC was the highest. Multivariate logistic regression analysis showed that size (OR=1.138, 95% CI 1.022-1.267, P=0.019), Δ70keV (OR=1.060, 95% CI 1.002-1.122, P=0.043), and NIC (OR=7.758, 95% CI 1.966-30.612, P=0.003) were independent risk factors for the prediction of benign and malignant SPNs. ROC curve analysis showed that the AUC of size, Δ70keV, NIC, and a combination of the three for differential diagnosis of benign and malignant SPNs were 0.636, 0.846, 0.869, and 0.903, respectively. The AUC for the combined parameters was the largest, and the sensitivity, specificity, and accuracy were 88.2%, 83.3% and 86.4%, respectively. The SDCT quantitative parameters and their derived quantitative parameters in this study exhibited satisfactory inter-observer repeatability (ICC: 0.811-0.997). Conclusion: SDCT quantitative parameters and their derivatives can be helpful in the differential diagnosis of benign and malignant solid SPNs. The quantitative parameter, NIC, is superior to the other relevant quantitative parameters and when NIC is combined with lesion size and Δ70keV value for comprehensive diagnosis, the efficacy could be further improved.

Prognostic value of 18F-FDG lesion dissemination features in patients with peripheral T-cell lymphoma (PTCL).

PURPOSE: To explore the prognostic value of the distance between the two lesions that were farthest apart (Dmax) on baseline 18F-FDG PET/CT in peripheral T lymphoma (PTCL) and establish a new prognostic model for predicting the survival outcomes of patients with PTCL. METHODS: In this study, a retrospective analysis of 95 patients with PTCL who underwent baseline 18F-FDG PET/CT was performed to assess the predictive value of Dmax. The total metabolic tumour volume (TMTV), total lesion glycolysis (TLG), standardized uptake value (SUV), and Dmax were calculated with LIFEx software. Progression-free survival (PFS) and overall survival (OS) were used as endpoints. The prognostic model was developed based on the results of the multivariate analysis. The time-dependent area under the ROC curve (tdAUC), calibration curves, Harrell C-index, and decision curve analysis (DCA) were used to assess the model. RESULTS: Patients were followed up for a median of 17.0 months. Multivariate analysis showed that bone marrow biopsy (BMB) and Dmax were independent predictors of PFS (HR: 1.889, P = 0.039; HR: 1.965, P = 0.047) and OS (HR: 1.923, P = 0.031; HR: 1.982, P = 0.034). The model consisting of Dmax, TMTV, and BMB had substantial prognostic value for survival outcomes of PTCL and could successfully identify four groups of patients with significantly different prognoses (χ2 = 13.731, P = 0.003 for PFS; χ2 = 11.841, P = 0.008 for OS). The tdAUC, C-index, calibration curves, and DCA supported that the model was superior to the prognostic index for T-cell lymphoma (PIT) and International Prognostic Index (IPI) scores. CONCLUSION: BMB and Dmax were independent predictors of PTCL in our study. Moreover, a prognostic model based on the Dmax, TMTV, and BMB could be useful for predicting the survival outcomes of patients with PTCL.

Nocardia seriolae mediates liver granulomatous chronic inflammation in Micropterus salmoides through pyroptosis.

Granulomatous diseases caused by Nocardia seriously endanger the health of cultured fish. These bacteria are widely distributed, but prevention and treatment methods are very limited. Chronic granulomatous inflammation is an important pathological feature of Nocardia infection. However, the molecular mechanisms of granuloma formation and chronic inflammation are still unclear. Constructing a granuloma infection model of Nocardia is the key to exploring the pathogenesis of the disease. In this study, we established a granuloma model in the liver of largemouth bass (Micropterus salmoides) and assessed the infection process of Nocardia seriolae at different concentrations by analysing relevant pathological features. By measuring the expression of pro-inflammatory cytokines, transcription factors and a pyroptosis-related protein, we revealed the close relationship between pyroptosis and chronic inflammation of granulomas. We further analysed the immunofluorescence results and the expression of pyroptosis-related protein of macrophage infected by N. seriolae and found that N. seriolae infection induced macrophage pyroptosis in vitro. These results were proved by flow cytometry analysis of infection experiment in vivo. Our results indicated that the pyroptosis effect may be the key to inducing chronic inflammation in the fish liver and further mediating granuloma formation. In this study, we explored the molecular mechanism underlying chronic inflammation of granulomas and developed research ideas for understanding the occurrence and development of granulomatous diseases in fish.

Application of preoperative CT texture analysis in papillary gastric adenocarcinoma.

BACKGROUND: This study aimed to analyze the ability of computed tomography (CT) texture analysis to discriminate papillary gastric adenocarcinoma (PGC) and to explore the diagnostic efficacy of multivariate models integrating clinical information and CT texture parameters for discriminating PGCs. METHODS: This retrospective study included 20 patients with PGC and 80 patients with tubular adenocarcinoma (TAC). The clinical data and CT texture parameters based on the arterial phase (AP) and venous phase (VP) of all patients were collected and analyzed. Two CT signatures based on the AP and VP were built with the optimum features selected by the least absolute shrinkage and selection operator method. The performance of CT signatures was tested by regression analysis. Multivariate models based on regression analysis and the support vector machine (SVM) algorithm were established. The diagnostic performance of the established nomogram based on regression analysis was evaluated by receiver operating characteristic curve analysis. RESULTS: Thirty-two and fifteen CT texture parameters extracted from AP and VP CT images, respectively, differed significantly between PGCs and TACs (all p < 0.05). The diagnostic performance of CT signatures based on the AP and VP achieved AUCs of 0.873 and 0.859 in distinguishing PGCs. Multivariate models that integrated two CT signatures and age based on regression analysis and the SVM algorithm showed favorable performance in preoperatively predicting PGCs (AUC = 0.922 and 0.914, respectively). CONCLUSION: CT texture analysis based multivariate models could preoperatively predict PGCs with satisfactory diagnostic efficacy.

Screening of ferroptosis-related genes with prognostic effect in colorectal cancer by bioinformatic analysis.

Colorectal cancer (CRC) remains a common malignant tumor of digestive tract with high incidence rate and high mortality in the worldwide. The current clinical treatments of CRC often fail to achieve satisfactory results. Searching for more effective prediction or prognosis biomarkers, or developing more targeted therapeutic schedule may help to improve the outcomes of CRC patients. Here, we tried to study the effect of ferroptosis-related genes on CRC prognosis and make it clearer that ferroptosis has connection with immune environment. First, we obtained gene expression data of CRC and normal tissues, as well as corresponding clinical data from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were intersected with ferroptosis-related gene set downloaded from FerrDb database, and 93 abnormally expressed ferroptosis-related genes were obtained. Then, these genes were analyzed for functional enrichment. Univariate Cox regression and multivariate Cox regression analyses were performed to establish prognostic model based on ferroptosis-related genes. In the process of exploring the correlation between prognostic genes and immune infiltration, we found that these genes were closely related to B cells, CD8+ T cells, CD4+ T cells, macrophages and other cells in CRC. In addition, we found a large proportion of plasma cells and macrophages in TCGA-COADREAD. Finally, a prognostic nomogram of ferroptosis-related genes was established, including age, sex, grade and other predicted values. To summary, we established a prognostic model of colorectal cancer (CRC) based on ferroptosis-related genes and further explored the relationship between these genes with immune microenvironment.

Radiomics features based on internal and marginal areas of the tumor for the preoperative prediction of microsatellite instability status in colorectal cancer.

Objectives: To explore whether the preoperative CT radiomics can predict the status of microsatellite instability (MSI) in colorectal cancer (CRC) patients and identify the region with the most stable and high-efficiency radiomics features. Methods: This retrospective study involved 230 CRC patients with preoperative computed tomography scans and available MSI status between December 2019 and October 2021. Image segmentation and radiomic feature extraction were performed as follows. First, slices with the maximum tumor area (region of interest, ROI) were manually contoured. Subsequently, each ROI was shrunk inward by 1, 2, and 3 mm, respectively, where the remaining ROIs were considered as the internal region of the tumor (named as IROI1, IROI2, and IROI3), and the shrunk regions were considered as marginal regions of the tumor (named as MROI1, MROI2, and MROI3). Finally, radiomics features were extracted from each of the ROI. The intraclass correlation coefficient and least absolute shrinkage and selection operator method were used to choose the most reliable and relevant features of MSI status. Clinical, radiomics, and combined clinical radiomics models have been established. Calibration curve and decision curve analyses (DCA) were generated to explore the correction effect and assess the clinical applicability of the above models, respectively. Results: In the testing cohort, the radiomics model based on IROI3 yielded the highest average area under the curve (AUC) value of 0.908, compared with the remaining radiomics models. Additionally, hypertension and N stage were considered as clinically independent factors of MSI status. The combined clinical radiomics model achieved excellent diagnostic efficacy (AUC: 0.928; sensitivity: 0.840; specificity: 0.867) in the testing cohort, as well as favorable calibration and clinical utility by calibration curve and DCA analyses. Conclusions: The IROI3 model, which is based on a 3-mm shrink in the largest areas of the tumor, could noninvasively reflect the heterogeneity and genetic instability within the tumor. This suggests that it is an important biomarker for the preoperative prediction of MSI status. The model can extract more robust and effective radiomics features, which lays a foundation for the radiomics study of hollow organs, such as in CRC.

With the Increasing Health Awareness: Clinical and Computed Tomography Findings in Primary Pulmonary Mucosa-Associated Lymphoid Tissue Lymphoma Lymphoma.

OBJECTIVE: The objective of this study is to investigate the clinical manifestations, computed tomography (CT) findings, and prognosis of primary pulmonary mucosa-associated lymphoid tissue lymphoma (MALToma) with the improved health awareness nowadays. METHODS: Clinical data and CT images of patients with pathologically confirmed MALToma from October 2012 to October 2021 were retrospectively analyzed. RESULTS: Eighteen MALToma patients were included in the study (8 men and 10 women; mean age, 56.17 years; range, 33-73 years). Six men had a long-term history of smoking (>20 years). One patient had an autoimmune disease (Sjögren syndrome). Eight patients (44.44%) were asymptomatic, and 10 (55.56%) had mild chest or systemic symptoms. Most lesions (88.89%) were subpleural or located along the bronchovascular tree. According to the CT characteristics, the lesions were divided into 4 groups: nodular type (n = 8; 44.44%, including 5 solid nodules and 3 ground-glass nodules), mass type (n = 4, 22.22%), patch or consolidate type (n = 5, 27.78%), and mixed type (n = 1, 5.56%). Air bronchogram (11/18, 61.11%) and angiogram sign (8/16, 50%) were the most frequent CT findings and may help differential diagnosis. The patients were misdiagnosed with lung cancer (n = 11, 61.11%), inflammatory or chronic inflammatory disease (n = 4, 22.22%), bronchiectasis accompanied by infection (n = 1, 5.56%), diffuse interstitial lung disease (n = 1, 5.56%), and granuloma or tuberculosis (n = 1, 5.56%). The prognosis of all patients was favorable; 1 patient was lost to follow-up. CONCLUSIONS: Nowadays, patients with MALToma are usually asymptomatic or paucisymptomatic. Nodular lesions, including ground-glass nodules, are the most common presentation. Mucosa-associated lymphoid tissue lymphoma is most likely misdiagnosed as lung cancer. Lesions located in the subpleural areas or along the bronchovascular tree and presenting with an air bronchogram or angiogram sign could indicate a MALToma diagnosis.