Markovian Approach for Exploring Competitive Diseases with Heterogeneity-Evidence from COVID-19 and Influenza in China.

Due to the complex interactions between multiple infectious diseases, the spreading of diseases in human bodies can vary when people are exposed to multiple sources of infection at the same time. Typically, there is heterogeneity in individuals' responses to diseases, and the transmission routes of different diseases also vary. Therefore, this paper proposes an SIS disease spreading model with individual heterogeneity and transmission route heterogeneity under the simultaneous action of two competitive infectious diseases. We derive the theoretical epidemic spreading threshold using quenched mean-field theory and perform numerical analysis under the Markovian method. Numerical results confirm the reliability of the theoretical threshold and show the inhibitory effect of the proportion of fully competitive individuals on epidemic spreading. The results also show that the diversity of disease transmission routes promotes disease spreading, and this effect gradually weakens when the epidemic spreading rate is high enough. Finally, we find a negative correlation between the theoretical spreading threshold and the average degree of the network. We demonstrate the practical application of the model by comparing simulation outputs to temporal trends of two competitive infectious diseases, COVID-19 and seasonal influenza in China.

A CT-based radiomics nomogram for predicting histologic grade and outcome in chondrosarcoma.

OBJECTIVE: The preoperative identification of tumor grade in chondrosarcoma (CS) is crucial for devising effective treatment strategies and predicting outcomes. The study aims to build and validate a CT-based radiomics nomogram (RN) for the preoperative identification of tumor grade in CS, and to evaluate the correlation between the RN-predicted tumor grade and postoperative outcome. METHODS: A total of 196 patients (139 in the training cohort and 57 in the external validation cohort) were derived from three different centers. A clinical model, radiomics signature (RS) and RN (which combines significant clinical factors and RS) were developed and validated to assess their ability to distinguish low-grade from high-grade CS with area under the curve (AUC). Additionally, Kaplan-Meier survival analysis was applied to examine the association between RN-predicted tumor grade and recurrence-free survival (RFS) of CS. The predictive accuracy of the RN was evaluated using Harrell's concordance index (C-index), hazard ratio (HR) and AUC. RESULTS: Size, endosteal scalloping and active periostitis were selected to build the clinical model. Three radiomics features, based on CT images, were selected to construct the RS. Both the RN (AUC, 0.842) and RS (AUC, 0.835) were superior to the clinical model (AUC, 0.776) in the validation set (P = 0.003, 0.040, respectively). A correlation between Nomogram score (Nomo-score, derived from RN) and RFS was observed through Kaplan-Meier survival analysis in the training and test cohorts (log-rank P < 0.050). Patients with high Nomo-score tumors were 2.669 times more likely to suffer recurrence than those with low Nomo-score tumors (HR, 2.669, P < 0.001). CONCLUSIONS: The CT-based RN performed well in predicting both the histologic grade and outcome of CS.

PET Imaging of Fibroblast Activation Protein in Various Cancers Using [18F]AlF­NOTA­FAPI­04: Comparison with 18F-FDG in a Single-Center, Prospective Study.

RATIONALE AND OBJECTIVES: Targeting fibroblast-activation protein is a newer diagnostic approach for the visualization of tumor stroma, and a novel aluminum-[18F] fluoride (Al18F)-labeled fibroblast-activation protein inhibitor-4 (FAPI-04), hereafter [18F] AlF-NOTA-FAPI-04, presents a promising alternative to gallium 68 (68Ga)-labeled FAPI owing to its relatively longer half-life. This study sought to evaluate the clinical usefulness of [18F] AlF-NOTA-FAPI-04 PET/CT for the diagnosis of various types of cancer, compared to [18F] FDG PET/CT. MATERIALS AND METHODS: In this prospective study conducted from October 2021 to January 2024, a total of 148 patients with 16 different tumor entities underwent contemporaneous 18F-FDG and 18F-FAPI-04 PET/CT either for an initial assessment or for recurrence detection. Uptake of 18F-FDG and 18F-FAPI-04 was quantified by the maximum standard uptake value (SUV max). Diagnostic sensitivity, specificity, and accuracy were compared by using the McNemar test between these two imaging agents. RESULTS: 18F-FAPI-04 PET/CT could clearly depict 16 different types of cancer with excellent image contrast, thereby leading to a higher detection rate of primary tumors than did 18F-FDG PET/CT (98.06% vs. 81.55%, P＜0.001). In per-lymph node analysis, the sensitivity, specificity, and accuracy in the diagnosis of metastatic lymph nodes were 92.44%, 90.44%, and 91.56%, respectively, which was much higher than that 18F-FDG PET/CT (80.23%, 79.41%, and 79.87%, respectively). Meanwhile, 18F-FAPI-04 PET/CT outperformed 18F-FDG PET/CT in identifying more suspected distant metastases (86.57% vs. 74.13%, P＜0.001). Furthermore, 18F-FAPI-04 PET/CT upgraded tumor staging in 36/101 patients (35.6%), and detected tumor recurrence or metastases in 43/47 patients (91.49%). CONCLUSION: Our findings demonstrated that primary and metastatic lesions in patients with various types of malignant tumors are well-visualized on 18F-FAPI-04 PET/CT, which exhibited a superior diagnostic performance than 18F-FDG PET/CT. Moreover, 18F-FAPI-04 PET/CT is a promising tool for tumor staging and follow-up of various malignancies.

Effects of Salt Stress on Physiological and Agronomic Traits of Rice Genotypes with Contrasting Salt Tolerance.

Salinity is one of the major constraints to crop production. Rice is a main staple food and is highly sensitive to salinity. This study aimed to elucidate the effects of salt stress on physiological and agronomic traits of rice genotypes with contrasting salt tolerance. Six contrasting rice genotypes (DJWJ, JFX, NSIC, HKN, XD2H and HHZ), including three salt-tolerant and three salt-sensitive rice genotypes, were grown under two different salt concentrations (0 and 100 mmol L-1 NaCl solution). The results showed that growth, physiological and yield-related traits of both salt-sensitive and salt-tolerant rice were significantly affected by salt stress. In general, plant height, tiller number, dry weight and relative growth rate showed 15.7%, 11.2%, 25.2% and 24.6% more reduction in salt-sensitive rice than in salt-tolerant rice, respectively. On the contrary, antioxidant enzyme activity (superoxide dismutase, peroxidase, catalase), osmotic adjustment substances (proline, soluble protein, malondialdehyde (MDA)) and Na+ content were significantly increased under salt stress, and the increase was far higher in salt-tolerant rice except for MDA. Furthermore, grain yield and yield components significantly decreased under salt stress. Overall, the salt-sensitive rice genotypes showed a 15.3% greater reduction in grain yield, 5.1% reduction in spikelets per panicle, 7.4% reduction in grain-filling percentage and 6.1% reduction in grain weight compared to salt-tolerant genotypes under salt stress. However, a modest gap showed a decline in panicles (22.2% vs. 22.8%) and total spikelets (45.4% vs. 42.1%) between salt-sensitive and salt-tolerant rice under salinity conditions. This study revealed that the yield advantage of salt-tolerant rice was partially caused by more biomass accumulation, growth rate, strong antioxidant capacity and osmotic adjustment ability under salt stress, which contributed to more spikelets per panicle, high grain-filling percentage and grain weight. The results of this study could be helpful in understanding the physiological mechanism of contrasting rice genotypes' responses to salt stress and to the breeding of salt-tolerant rice.

A preoperative CT-based deep learning radiomics model in predicting the stage, size, grade and necrosis score and outcome in localized clear cell renal cell carcinoma: A multicenter study.

BACKGROUND AND PURPOSE: The Stage, Size, Grade and Necrosis (SSIGN) score is the most commonly used prognostic model in clear cell renal cell carcinoma (ccRCC) patients. It is a great challenge to preoperatively predict SSIGN score and outcome of ccRCC patients. The aim of this study was to develop and validate a CT-based deep learning radiomics model (DLRM) for predicting SSIGN score and outcome in localized ccRCC. METHODS: A multicenter 784 (training cohort/ test 1 cohort / test 2 cohort, 475/204/105) localized ccRCC patients were enrolled. Radiomics signature (RS), deep learning signature (DLS), and DLRM incorporating radiomics and deep learning features were developed for predicting SSIGN score. Model performance was evaluated with area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival analysis was used to assess the association of the model-predicted SSIGN with cancer-specific survival (CSS). Harrell's concordance index (C-index) was calculated to assess the CSS predictive accuracy of these models. RESULTS: The DLRM achieved higher micro-average/macro-average AUCs (0.913/0.850, and 0.969/0.942, respectively in test 1 cohort and test 2 cohort) than the RS and DLS did for the prediction of SSIGN score. The CSS showed significant differences among the DLRM-predicted risk groups. The DLRM achieved higher C-indices (0.827 and 0.824, respectively in test 1 cohort and test 2 cohort) than the RS and DLS did in predicting CSS for localized ccRCC patients. CONCLUSION: The DLRM can accurately predict the SSIGN score and outcome in localized ccRCC.

Robust deep learning-based PET prognostic imaging biomarker for DLBCL patients: a multicenter study.

OBJECTIVE: To develop and independently externally validate robust prognostic imaging biomarkers distilled from PET images using deep learning techniques for precise survival prediction in patients with diffuse large B cell lymphoma (DLBCL). METHODS: A total of 684 DLBCL patients from three independent medical centers were included in this retrospective study. Deep learning scores (DLS) were generated from PET images using deep convolutional neural network architecture known as VGG19 and DenseNet121. These DLSs were utilized to predict progression-free survival (PFS) and overall survival (OS). Furthermore, multiparametric models were designed based on results from the Cox proportional hazards model and assessed through calibration curves, concordance index (C-index), and decision curve analysis (DCA) in the training and validation cohorts. RESULTS: The DLSPFS and DLSOS exhibited significant associations with PFS and OS, respectively (P<0.05) in the training and validation cohorts. The multiparametric models that incorporated DLSs demonstrated superior efficacy in predicting PFS (C-index: 0.866) and OS (C-index: 0.835) compared to competing models in training cohorts. In external validation cohorts, the C-indices for PFS and OS were 0.760 and. 0.770 and 0.748 and 0.766, respectively, indicating the reliable validity of the multiparametric models. The calibration curves displayed good consistency, and the decision curve analysis (DCA) confirmed that the multiparametric models offered more net clinical benefits. CONCLUSIONS: The DLSs were identified as robust prognostic imaging biomarkers for survival in DLBCL patients. Moreover, the multiparametric models developed in this study exhibited promising potential in accurately stratifying patients based on their survival risk.

A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study.

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

Clinical application of AI-based PET images in oncological patients.

Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.

Synthesis of n-butyl acetate via reactive distillation column using Candida Antarctica lipase as catalyst.

The reactive distillation process for the synthesis of n-butyl acetate via transesterification of ethyl acetate with n-butyl alcohol catalyzed by immobilized lipase was simulated and experimentally tested in this work. Based on the reaction kinetics, a reactive distillation process model was developed. The effects of theoretical stages number in the reaction section, the rectifying section and stripping section, reflux ratio, feed molar ratio and relative feed position on the transesterification distillation process were investigated. The transesterification of ethyl acetate with n-butyl alcohol was carried out in a small-scale reactive distillation column. The results showed that the optimal operating conditions are as follows: reaction section stages were 13, rectifying section stages were six, stripping section stages were five, reflux ratio was 1, mole ratio of ethyl acetate and n-butanol was 3:1, the feeding positions of n-butanol and ethyl acetate were at the top and bottom of the reaction section, respectively. Compared to the batch reaction with only 60% conversion of n-butanol, the reactive distillation column can improve the conversion of n-butanol (up to 93.6%).At the same time, the experiment verified that the conversion of n-butanol could still reach 72.5%, after the lipase-loaded packing storage in the reaction system at 70 °C for 120 days.

Association of sirtuins with clinicopathological parameters and overall survival in gastric cancer.

To evaluate the associations of sirtuins (SIRT1-7) with clinicopathological parameters in gastric cancer, sirtuins expression profile in NCBI GEO datasets, GSE62254 and GSE15459, was integrated and analyzed. The results suggested that SIRT4, SIRT6, and SIRT7 were associated with Lauren classification and SIRT3-5 were associated with pStage in gastric cancer. Then an online database derived from 1,065 gastric cancer cases, Kaplan-Meier plotter, was used to explore the associations of the mRNA levels of sirtuins with overall survival in gastric cancer. Survival curves generated from Kaplan-Meier plotter suggested that high expression of SIRT1 mRNA was favorable for overall survival in gastric cancer (SIRT1: HR = 0.64, 95% CI = 0.54-0.76, P = 2.2E-07), high expressions of SIRT2-4 and SIRT6-7 were poor for overall survival (SIRT2: HR = 2.31, 95% CI = 1.87-2.87, P = 3.6E-15; SIRT3: HR = 1.99, 95% CI = 1.62-2.45, P = 2.6E-11; SIRT4: HR = 1.41, 95% CI = 1.19-1.68, P = 6.6E-05; SIRT6: HR = 2.02, 95% CI = 1.66-2.47, P = 1.7E-12; SIRT7: HR = 1.96, 95% CI = 1.63-2.35, P = 2.7E-13), whereas no significant association existed between SIRT5 mRNA expression and overall survival. Further analyses stratified by gender, stages, Lauren classification, differentiation, treatment, and HER2 status were also performed. In summary, high SIRT1 mRNA level was associated with better overall survival, SIRT2-4 and 6-7 were associated with poor overall survival, whereas SIRT5 did not show significant association with overall survival in gastric cancer.

Detection of Ligand-activated G Protein-coupled Receptor Internalization by Confocal Microscopy.

Confocal laser scanning microscopy (CLSM) is an optical imaging technique for high-contrast imaging. It is a powerful approach to visualize fluorescent fusion proteins, such as green fluorescent protein (GFP), to determine their expression, localization, and function. The subcellular localization of target proteins is important for identification, characterization, and functional analyses. Internalization is one of the predominant mechanisms controlling G protein-coupled receptor (GPCR) signaling to ensure the appropriate cellular responses to stimuli. Here, we describe an experimental method to detect the subcellular localization and internalization of GPCR in HEK293 cells with confocal microscopy. In addition, this experiment provides some details about cell culture and transfection. This protocol is compatible with a variety of widely available fluorescent markers and is applicable to the visualization of the subcellular localization of a majority of proteins, as well as of the internalization of GPCR. This technique should enable researchers to efficiently manipulate GPCR gene expression in mammalian cell lines and should facilitate studies on GPCR subcellular localization and internalization.

Predictive value of GSTP1 Ile105Val polymorphism in clinical outcomes of chemotherapy in gastric and colorectal cancers: a systematic review and meta-analysis.

PURPOSE: Gastric and colorectal cancers remain the major causes of cancer-related death with a bad prognosis. Up to now, platinum combined with fluoropyrimidines has been most commonly used in chemotherapy regimens of gastric and colorectal cancers. Recently, a series of studies have been conducted to investigate the associations of biomarkers, such as GSTP1 Ile105Val polymorphism, with the chemotherapy efficacy in gastric and colorectal cancers; however, the results were not consistent and inconclusive. Here, we performed a systematic review and meta-analysis to summarize the associations of GSTP1 Ile105Val polymorphism with the chemotherapy efficacy in gastric and colorectal cancers. METHODS: A systematic review was conducted to search relevant studies in English databases (PubMed, ISI Web of Science, and EMBASE) up to November 30, 2015. The pooling ORs or HRs were used to assess the strength of the associations of GSTP1 Ile105Val polymorphism with clinical outcomes such as tumor response, toxicity, progression-free survival (PFS), and overall survival (OS). RESULTS: Forty-one papers containing 8169 cases were finally included in the present meta-analysis study. Of which, 28 articles were performed in colorectal cancers, one in gastrointestinal carcinoma (gastric and colon cancer), 11 in gastric cancers, and one in colorectal and gastroesophageal cancers. After pooling all the eligible studies, we identified significant associations of GSTP1 Ile105Val polymorphism with chemotherapy-related tumor response (G vs. A: OR 1.697, 95 % CI 1.191-2.418; GG vs. AA: OR 2.804, 95 % CI 1.414-5.560; AG vs. AA: OR 1.540, 95 % CI 1.011-2.347; GG vs. AAAG: OR 2.139, 95 % CI 1.256-3.641), PFS (GG vs. AA, HR 0.640, 95 % CI 0.455-0.900; AGGG vs. AA: HR 0.718, 95 % CI 0.562-0.919), and OS (AG vs. AA: HR 0.857, 95 % CI 0.746-0.986; GG vs. AA: HR 0.679, 95 % CI 0.523-0.882; AGGG vs. AA: HR 0.663, 95 % CI 0.542-0.812) in gastric and colorectal cancers and no significant association was found between the polymorphism with toxicity. CONCLUSIONS: GSTP1 Ile105Val polymorphism was associated with tumor response, PFS, and OS in gastric and colorectal cancers after chemotherapy.