Clinical characteristics and risk factors in patients with SARS-CoV-2 Omicron variant infection complicated with cardiovascular diseases.

Objective: To investigate the clinical characteristics and risk factors of patients with SARS-CoV-2 Omicron variant infection complicated with cardiovascular diseases. Methods: A retrospective analysis of general clinical data was conducted on patients with SARS-CoV-2 omicron infection complicated with hypertension, coronary heart disease, and heart failure admitted to one hospital in Guangdong Province from December 1, 2022, to February 28, 2023. Clinical symptoms, laboratory tests, imaging examinations, treatment, and clinical outcomes were collected. Multivariate logistic regression analysis was used to analyze the risk factors for mortality in patients with SARS-CoV-2 Omicron variant infection complicated with cardiovascular diseases. ROC curves were drawn to evaluate the predictive value of CRP, D-dimer, and CK-MB in predicting the risk of death. Results: A total of 364 confirmed cases were included, divided into the asymptomatic group, mild to moderate group, and severe to critically ill group based on the symptoms of COVID-19. There were 216 males (59.34%) and 148 females (40.66%), with a median age of 75 years. The differences between the three groups in terms of sex and age were statistically significant (p < 0.05). The top three underlying diseases were hypertension (288 cases, 79.12%), coronary heart disease (100 cases, 27.47%), and diabetes (84 cases, 23.08%). The differences in unvaccinated and triple-vaccinated patients among the three groups were statistically significant (p < 0.05). The common respiratory symptoms were cough in 237 cases (65.11%) and sputum production in 199 cases (54.67%). In terms of laboratory tests, there were statistically significant differences in neutrophils, lymphocytes, red blood cells, C-reactive protein, D-dimer, aspartate aminotransferase, and creatinine among the three groups (p < 0.05). In imaging examinations, there were statistically significant differences among the three groups in terms of unilateral pulmonary inflammation, bilateral pulmonary inflammation, and bilateral pleural effusion (p < 0.05). There were statistically significant differences among the three groups in terms of antibiotic treatment, steroid treatment, oxygen therapy, nasal cannula oxygen inhalation therapy, non-invasive ventilation, and tracheal intubation ventilation (p < 0.05). Regarding clinical outcomes, there were statistically significant differences among the three groups in terms of mortality (p < 0.05). Multivariate logistic regression analysis showed that CRP (OR = 1.012, 95% CI = 1.004-1.019) and D-dimer (OR = 1.117, 95% CI = 1.021-1.224) were independent risk factors for patient mortality. The predictive value of CRP, D-dimer, and CK-MB for the risk of death was assessed. D-dimer had the highest sensitivity (95.8%) in predicting patient mortality risk, while CRP had the highest specificity (84.4%). Conclusion: For patients with COVID-19 and concomitant cardiovascular diseases without contraindications, early administration of COVID-19 vaccines and booster shots can effectively reduce the mortality rate of severe cases. Monitoring biomarkers such as CRP, D-dimer, and CK-MB and promptly providing appropriate care can help mitigate the risk of mortality in patients.

Body composition and inflammation variables as the potential prognostic factors in epithelial ovarian cancer treated with Olaparib.

Background: Epithelial ovarian cancer (EOC) is a significant cause of mortality among gynecological cancers. While Olaparib, a PARP inhibitor, has demonstrated efficacy in EOC maintenance therapy, individual responses vary. This study aims to assess the prognostic significance of body composition and systemic inflammation markers in EOC patients undergoing initial Olaparib treatment. Methods: A retrospective analysis was conducted on 133 EOC patients initiating Olaparib therapy. Progression-free survival (PFS) was assessed through Kaplan-Meier analysis and Cox proportional hazards regression. Pre-treatment computed tomography images were utilized to evaluate body composition parameters including subcutaneous adipose tissue index (SATI), visceral adipose tissue index (VATI), skeletal muscle area index (SMI), and body mineral density (BMD). Inflammatory markers, such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), serum albumin, and hemoglobin levels, were also measured. Results: The median follow-up duration was 16 months (range: 5-49 months). Survival analysis indicated that high SATI, high VATI, high SMI, high BMD, low NLR, and low PLR were associated with decreased risk of disease progression (all p < 0.05). Multivariate analysis identified several factors independently associated with poor PFS, including second or further lines of therapy (HR = 2.16; 95% CI = 1.09-4.27, p = 0.027), low VATI (HR = 3.79; 95% CI = 1.48-9.70, p = 0.005), low SMI (HR = 2.52; 95% CI = 1.11-5.72, p = 0.027), low BMD (HR = 2.36; 95% CI = 1.22-4.54, p = 0.010), and high NLR (HR = 0.31; 95% CI = 0.14-0.69, p = 0.004). Subgroup analysis in serous adenocarcinoma patients revealed distinct prognostic capabilities of SATI, VATI, SMI, PLR, and NLR. Conclusion: Body composition and inflammation variables hold promise as predictors of therapeutic response to Olaparib in EOC patients. Understanding their prognostic significance could facilitate tailored treatment strategies, potentially improving patient outcomes.

Progesterone Enhances Niraparib Efficacy in Ovarian Cancer by Promoting Palmitoleic-Acid-Mediated Ferroptosis.

Poly (adenosine 5'-diphosphate-ribose) polymerase inhibitors (PARPi) are increasingly important in the treatment of ovarian cancer. However, more than 40% of BRCA1/2-deficient patients do not respond to PARPi, and BRCA wild-type cases do not show obvious benefit. In this study, we demonstrated that progesterone acted synergistically with niraparib in ovarian cancer cells by enhancing niraparib-mediated DNA damage and death regardless of BRCA status. This synergy was validated in an ovarian cancer organoid model and in vivo experiments. Furthermore, we found that progesterone enhances the activity of niraparib in ovarian cancer through inducing ferroptosis by up-regulating palmitoleic acid and causing mitochondrial damage. In clinical cohort, it was observed that progesterone prolonged the survival of patients with ovarian cancer receiving PARPi as second-line maintenance therapy, and high progesterone receptor expression combined with low glutathione peroxidase 4 (GPX4) expression predicted better efficacy of PARPi in patients with ovarian cancer. These findings not only offer new therapeutic strategies for PARPi poor response ovarian cancer but also provide potential molecular markers for predicting the PARPi efficacy.

Chloramphenicol-imprinted polychitosan bounded with carbon dots as fluorescent sensor, dispersive sorbent, and drug carrier.

Chitosan, an abundant natural polysaccharide, was conjugated with carbon dots (CDs) and self-polymerized with chloramphenicol (CAP) templates to synthesize CD-incorporated and molecularly CAP-imprinted polychitosan (CD-MIC). The CD-MIC was used for fluorescent sensing, dispersive sorption, and dosage release of CAP at different pH levels. The sphere of action mechanism, approved by emission and excitation fluorescence, UV-Vis absorption, and fluorescence lifetime measurements, regulated the fluorescence static quenching. By the Perrin model, the quenching extent was linearly correlated to CAP within 0.17 - 33.2 µM (LOD = 37 nM) at pH 7.0. With an imprinting factor of 3.1, the CD-MIC was more selective for CAP than CD, although it was less sensitive to CAP. The recoveries of 5.0 µM CAP from milk matrix were 95% (RSD = 2.3%) for CD-MIC probes and 62% (RSD = 4.5%) for CD. The Langmuir and pseudo-second-order models preferably described the isothermal and kinetic sorptions of CAP into the imprinted cavities in CD-MICs, respectively. The Weber - Morris kinetic model showed three stages involved in intraparticle diffusion, which was pH-dependent and gradually arduous at the later stage, and showed external diffusion partly engaged in the diffusion mechanism. The 20 - 70% of CAP formulated in CAP-embedded CD-MICs were released in 8 - 48 h. The release percentage was lower at pH 7.0 than at pH 5.0 and 9.0, but the equilibrium time was shorter. At pH 7.0, the release percentage reached 45% at 10 min and slowly increased to 51% at 24 h.

Prior Clinico-Radiological Features Informed Multi-Modal MR Images Convolution Neural Network: A novel deep learning framework for prediction of lymphovascular invasion in breast cancer.

BACKGROUND: Current methods utilizing preoperative magnetic resonance imaging (MRI)-based radiomics for assessing lymphovascular invasion (LVI) in patients with early-stage breast cancer lack precision, limiting the options for surgical planning. PURPOSE: This study aimed to develop a sophisticated deep learning framework called "Prior Clinico-Radiological Features Informed Multi-Modal MR Images Convolutional Neural Network (PCMM-Net)" to improve the accuracy of LVI prediction in breast cancer. By incorporating multiparameter MRI and prior clinical knowledge, PCMM-Net should enhance the precision of LVI assessment. METHODS: A total of 341 patients with breast cancer were randomly divided into training and validation groups at a ratio of 7:3. Imaging features were extracted from T1-weighted, T2-weighted, and contrast-enhanced T1-weighted MRI sequences. Stepwise univariate and multivariate logistic regression were employed to establish a clinico-radiological model for LVI prediction. The radiomics model was built using redundancy and the least absolute shrinkage and selection operator. Then, two deep learning frameworks were developed: the Multi-Modal MR Images Convolutional Neural Network (MM-Net), which does not consider prior radiological features, and PCMM-Net, which incorporates multiparameter MRI and prior clinical knowledge. Receiver operating characteristic curves were used, and the corresponding areas under the curves (AUCs) were calculated for evaluation. RESULTS: PCMM-Net achieved the highest AUC of 0.843. The clinico-radiological features displayed the lowest AUC value of 0.743, followed by MM-Net with an AUC of 0.774, and radiomics with an AUC of 0.795. CONCLUSIONS: This study introduces PCMM-Net, an innovative deep learning framework that integrates prior clinico-radiological features for accurate LVI prediction in breast cancer. PCMM-Net demonstrates excellent diagnostic performance and facilitates the application of precision medicine.

Comparison of preoperative CT- and MRI-based multiparametric radiomics in the prediction of lymph node metastasis in rectal cancer.

Objective: To compare computed tomography (CT)- and magnetic resonance imaging (MRI)-based multiparametric radiomics models and validate a multi-modality, multiparametric clinical-radiomics nomogram for individual preoperative prediction of lymph node metastasis (LNM) in rectal cancer (RC) patients. Methods: 234 rectal adenocarcinoma patients from our retrospective study cohort were randomly selected as the training (n = 164) and testing (n = 70) cohorts. The radiomics features of the primary tumor were extracted from the non-contrast enhanced computed tomography (NCE-CT), the enhanced computed tomography (CE-CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. Three kinds of models were constructed based on training cohort, including the Clinical model (based on the clinical features), the radiomics models (based on NCE-CT, CE-CT, T2WI, CE-T1WI, CT, MRI, CT combing with MRI) and the clinical-radiomics models (based on CT or MRI radiomics model combing with clinical data) and Clinical-IMG model (based on CT and MRI radiomics model combing with clinical data). The performances of the 11 models were evaluated via the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity in the training and validation cohort. Differences in the AUCs among the 11 models were compared using DeLong's test. Finally, the optimal model (Clinical-IMG model) was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis (DCA). Results: The MRI radiomics model in the validation cohort significantly outperformed than CT radiomics model (AUC, 0.785 vs. 0.721, p<0.05). The Clinical-IMG nomogram had the highest prediction efficiency than all other predictive models (p<0.05), of which the AUC was 0.947, the sensitivity was 0.870 and the specificity was 0.884. Conclusion: MRI radiomics model performed better than both CT radiomics model and Clinical model in predicting LNM of RC. The clinical-radiomics nomogram that combines the radiomics features obtained from both CT and MRI along with preoperative clinical characteristics exhibits the best diagnostic performance.

Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI for Predicting Lymphovascular Invasion in Invasive Breast Cancer.

RATIONALE AND OBJECTIVES: Treatment strategies for invasive breast cancer require accurate lymphovascular invasion (LVI) predictions. This study aimed to investigate the effectiveness of delta radiomics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for assessing LVI and develop a nomogram to aid treatment decisions. MATERIALS AND METHODS: Overall, 293 patients with resectable invasive breast cancer underwent preoperative DCE-MRI. Radiomic features were extracted from pre-contrast (A0), first post-contrast (A1), and subtracted images of A0 and A1. Three radiomics models were developed using several data analyses; logistic analyses were performed to identify radiological features to predict the LVI status. A hybrid model integrating both radiological features and optimal radiomics was developed. Receiver operating characteristic analysis was employed to evaluate model performance, using the area under the curve (AUC) as a quantitative metric for discriminative ability. RESULTS: In the test set, the Radiomics-Delta model, with 17 radiomic features, had an AUC of 0.781 and accuracy of 0.705. Radiomics-A0, with 10 features, had an AUC of 0.619 and accuracy of 0.523, while Radiomics-A1, with 8 features, had an AUC of 0.715 and accuracy of 0.591. The hybrid model exhibited better performance, with an AUC of 0.868 and accuracy of 0.875, than the radiological and Radiomics-Delta models, with an AUC of 0.759 and 0.781, respectively, and accuracy of 0.773 and 0.705, respectively. CONCLUSION: Compared to Radiomics-A0 and Radiomics-A1, Radiomics-Delta demonstrated superior performance. Moreover, the hybrid model incorporating Radiomics-Delta and radiological features exhibited excellent performance in determining the LVI status in cases of invasive breast cancer.

Predicting pathological response to neoadjuvant or conversion chemoimmunotherapy in stage IB-III non-small cell lung cancer patients using radiomic features.

BACKGROUND: To develop a radiomics model based on chest computed tomography (CT) for the prediction of a pathological complete response (pCR) after neoadjuvant or conversion chemoimmunotherapy (CIT) in patients with non-small cell lung cancer (NSCLC). METHODS: Patients with stage IB-III NSCLC who received neoadjuvant or conversion CIT between September 2019 and July 2021 at Hunan Cancer Hospital, Xiangya Hospital, and Union Hospital were retrospectively collected. The least absolute shrinkage and selection operator (LASSO) were used to screen features. Then, model 1 (five radiomics features before CIT), model 2 (four radiomics features after CIT and before surgery) and model 3 were constructed for the prediction of pCR. Model 3 included all nine features of model 1 and 2 and was later named the neoadjuvant chemoimmunotherapy-related pathological response prediction model (NACIP). RESULTS: This study included 110 patients: 77 in the training set and 33 in the validation set. Thirty-nine (35.5%) patients achieved a pCR. Model 1 showed area under the curve (AUC) = 0.65, 64% accuracy, 71% specificity, and 50% sensitivity, while model 2 displayed AUC = 0.81, 73% accuracy, 62% specificity, and 92% sensitivity. In comparison, NACIP yielded a good predictive value, with an AUC of 0.85, 81% accuracy, 81% specificity, and 83% sensitivity in the validation set. CONCLUSION: NACIP may be a potential model for the early prediction of pCR in patients with NSCLC treated with neoadjuvant/conversion CIT.

CT radiomics features of meso-esophageal fat in predicting overall survival of patients with locally advanced esophageal squamous cell carcinoma treated by definitive chemoradiotherapy.

OBJECTIVE: To investigate the value of CT radiomics features of meso-esophageal fat in the overall survival (OS) prediction of patients with locally advanced esophageal squamous cell carcinoma (ESCC). METHODS: A total of 166 patients with locally advanced ESCC in two medical centers were retrospectively analyzed. The volume of interest (VOI) of meso-esophageal fat and tumor were manually delineated on enhanced chest CT using ITK-SNAP. Radiomics features were extracted from the VOIs by Pyradiomics and then selected using the t-test, the Cox regression analysis, and the least absolute shrinkage and selection operator. The radiomics scores of meso-esophageal fat and tumors for OS were constructed by a linear combination of the selected radiomic features. The performance of both models was evaluated and compared by the C-index. Time-dependent receiver operating characteristic (ROC) analysis was employed to analyze the prognostic value of the meso-esophageal fat-based model. A combined model for risk evaluation was constructed based on multivariate analysis. RESULTS: The CT radiomic model of meso-esophageal fat showed valuable performance for survival analysis, with C-indexes of 0.688, 0.708, and 0.660 in the training, internal, and external validation cohorts, respectively. The 1-year, 2-year, and 3-year ROC curves showed AUCs of 0.640-0.793 in the cohorts. The model performed equivalently compared to the tumor-based radiomic model and performed better compared to the CT features-based model. Multivariate analysis showed that meso-rad-score was the only factor associated with OS. CONCLUSIONS: A baseline CT radiomic model based on the meso-esophagus provide valuable prognostic information for ESCC patients treated with dCRT.

A novel immune-related model to predict prognosis and responsiveness to checkpoint and angiogenesis blockade therapy in advanced renal cancer.

Background: Immune checkpoint blockade (ICB) and anti-angiogenic drug combination has prolonged the survival of patients with advanced renal cell carcinoma (RCC). However, not all patients receive clinical benefits from this intervention. In this study, we aimed to establish a promising immune-related prognostic model to stratify the patients responding to ICB and anti-angiogenic drug combination and facilitate the development of personalized therapies for patients with RCC. Materials and methods: Based on clinical annotations and RNA-sequencing (RNA-seq) data of 407 patients with advanced RCC from the IMmotion151 cohort, nine immune-associated differentially expressed genes (DEGs) between responders and non-responders to atezolizumab (anti-programmed death-ligand 1 antibody) plus bevacizumab (anti-vascular endothelial growth factor antibody) treatment were identified via weighted gene co-expression network analysis. We also conducted single-sample gene set enrichment analysis to develop a novel immune-related risk score (IRS) model and further estimate the prognosis of patients with RCC by predicting their sensitivity to chemotherapy and responsiveness to immunotherapy. IRS model was further validated using the JAVELIN Renal 101 cohort, the E-MTAB-3218 cohort, the IMvigor210 and GSE78220 cohort. Predictive significance of the IRS model for advanced RCC was assessed using receiver operating characteristic curves. Results: The IRS model was constructed using nine immune-associated DEGs: SPINK5, SEMA3E, ROBO2, BMP5, ORM1, CRP, CTSE, PMCH and CCL3L1. Advanced RCC patients with high IRS had a high risk of undesirable clinical outcomes (hazard ratio = 1.91; 95% confidence interval = 1.43-2.55; P < 0.0001). Transcriptome analysis revealed that the IRS-low group exhibited significantly high expression levels of CD8+ T effectors, antigen-processing machinery, and immune checkpoints, whereas the epithelial-mesenchymal transition pathway was enriched in the IRS-high group. IRS model effectively differentiated the responders from non-responders to ICB combined with angiogenesis blockade therapy or immunotherapy alone, with area under the curve values of 0.822 in the IMmotion151 cohort, 0.751 in the JAVELIN Renal 101 cohort, and 0.776 in the E-MTAB-3218 cohort. Conclusion: IRS model is a reliable and robust immune signature that can be used for patient selection to optimize the efficacy of ICB plus anti-angiogenic drug therapies in patients with advanced RCC.

MRI-Based Radiomic Models Outperform Radiologists in Predicting Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.

RATIONALE AND OBJECTIVES: The 15%-27% of patients with locally advanced rectal cancer (LARC) achieved pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) and could avoid proctectomy. We aimed to investigate the effectiveness of treatment response prediction using MRI-based pre-, post-, and delta-radiomic features for LARC patients treated with nCRT and to compare these radiomic models with radiologists' visual assessment. MATERIALS AND METHODS: A total of 126 patients with LARC who received nCRT before surgery were included and randomly divided into a training set (n = 84) and a validation set (n = 42). 250 radiomic features were extracted from T2-weighted images from pre- and post-nCRT MRI. Pearson correlation analysis and AONVA or Relief were used to identify radiomic descriptors associated with pCR. Five machine-learning classifiers were compared to construct radiomic models. The radiomic nomogram was built via multivariate logistic regression analysis. Two senior radiologists independently rated tumor regression grades and compared with radiomic models. Area under the curve (AUC) of the models and pooled observers were compared by using the DeLong test. RESULTS: The optimal pre-, post-, and delta-radiomic models yielded an AUC of 0.717 (95% CI: 0.639-0.795), 0.805 (95%CI: 0.736-0.874), and 0.724 (95%CI: 0.648-0.800), respectively. The radiomic nomogram based on pre-nCRT cN stage, pre-nCRT radscore, and post-nCRT radscore achieved an AUC of 0.852 (95%CI: 0.774-0.930), which was higher than the single radiomic models and pooled readers (all p < 0.05). CONCLUSIONS: The radiomic nomogram is an effective and invasive tool to predict pCR in LARC patients after nCRT, which outperforms radiologists.

Conjugated hypercrosslinked polymers imprinted with 3,5-dinitrosalicylic acid for the fluorescent determination of α-amylase activity.

The discovery of a series of coupling reactions between various building blocks has driven the development of porous organic polymers, but the common usage of expensive and air-sensitive organometallic catalysts and complex procedures in harsh syntheses has limited their expansion. A microporous hypercrosslinked polymer (HCP) was synthesized by polymerizing a naphthalene monomer and a 1,4-dimethoxybenzene crosslinker using Friedel-Crafts alkylation over an FeCl3 catalyst and imprinted with 3,5-dinitrosalicylic acid (DNS). The DNS-molecularly-imprinted HCPs (MIHCPs) were characterized as having IUPAC Type I mesoporosity, a specific surface area of 1134 m2 g-1, a monolayer adsorption capacity of 116 cm2 g-1, pore sizes ranging from 5 to 8.5 Å, amorphous frameworks, and distinctive absorption and emission bands by N2 adsorption/desorption analyses, scanning and transmission electron microscopies, and FTIR, UV-Vis, and fluorescence spectrometries. The π-conjugated imprinted framework endowed the MIHCPs with 405-nm fluorescent emission at a 330-nm excitation and dynamic quenching, which was confirmed by changes in fluorescence lifetime and followed a linear Stern-Volmer plot against 1.0-200 µM DNS template molecules under optimized conditions of a pH 7.0 buffer, an MIHCP concentration of 125 µg mL-1, and a 3.0-min equilibration time. The MIHCPs exhibited a high imprinted factor of 8.7 against nonimprinted HCP and a selectivity of 8.63 against reduced DNS, which enabled fluorometric detection of DNS molecules produced by the hydrolysis of starch with microbial, salivary, and pancreatic α-amylases and the subsequent redox incubation with the DNS oxidant. The fluorometric measurement of α-amylase activity was higher in accuracy and precision (RSD: 2.6-2.8% vs. 3.9-4.0%) than conventional UV-Vis spectrometry because the excellent fluorescent sensitivity and imprinting selectivity of the MIHCP probes enabled the use of higher dilution factors with weaker matrix effects.

Fluorescence determination of tannic acid imprinted in conjugated hypercrosslinked polymers by Friedel-Crafts acylation.

A molecularly imprinted hypercrosslinked polymer (HCP) was synthesized from the polymerization of mesitylene monomer, terephthaloyl chloride crosslinker, and tannic acid (TA) template through FeCl3-catalyzed Friedel-Crafts acylation. The TA-imprinted HCP (TAHCP) was capable of IUPAC Type I mesoporosity, with specific surface area of 1258 m2 g-1, monolayer adsorption capacity of 289 cm2 g-1, pore sizes ranging from 4.4 to 12.6 Å, amorphous morphology, and characteristic absorption and emission bands. The extended π-conjugation framework of TAHCP was endowed with 385-nm fluorescent emission at 310-nm excitation. The fluorescence intensity of TAHCP could be dynamically quenched by TA and was linearly correlated with 20-1000 nM TA concentrations on the Stern-Volmer plot in the optimized conditions of pH 5.5 buffer, 100 µg mL-1 TAHCP, and 3.5 min equilibrium. The relative standard deviation (RSD) for 50 nM TA was 3.4% (n = 5), and the limit of detection was 6.2 nM based on the 3σ of the TA blanks). For 50nM TA, the imprinted factor was calculated to be 7.8, and the selectivity for 250 nM interferents, including ions, organic acids, saccharides, amino acids, and caffeine, which are commonly found in beverages, was 7.5-9.5, except for gallic acid (1.2). The recoveries of TA spiked in tea and juice beverages at three levels (10-150 nM) were 93.6-101.9% (RSD = 3.6-4.3%).

Prediction of therapeutic response of unresectable hepatocellular carcinoma to hepatic arterial infusion chemotherapy based on pretherapeutic MRI radiomics and Albumin-Bilirubin score.

PURPOSE: To construct and validate a combined nomogram model based on magnetic resonance imaging (MRI) radiomics and Albumin-Bilirubin (ALBI) score to predict therapeutic response in unresectable hepatocellular carcinoma (HCC) patients treated with hepatic arterial infusion chemotherapy (HAIC). METHODS: The retrospective study was conducted on 112 unresectable HCC patients who underwent pretherapeutic MRI examinations. Patients were randomly divided into training (n = 79) and validation cohorts (n = 33). A total of 396 radiomics features were extracted from the volume of interest of the primary lesion by the Artificial Kit software. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify optimal radiomic features. After feature selection, three models, including the clinical, radiomics, and combined models, were developed to predict the non-response of unresectable HCC to HAIC treatment. The performance of these models was evaluated by the receiver operating characteristic curve. According to the most efficient model, a nomogram was established, and the performance of which was also assessed by calibration curve and decision curve analysis. Kaplan-Meier curve and log-rank test were performed to evaluate the Progression-free survival (PFS). RESULTS: Using the LASSO regression, we ultimately selected three radiomics features from T2-weighted images to construct the radiomics score (Radscore). Only the ALBI score was an independent factor associated with non-response in the clinical model (P = 0.033). The combined model, which included the ALBI score and Radscore, achieved better performance in the prediction of non-response, with an AUC of 0.79 (95% CI 0.68-0.90) and 0.75 (95% CI 0.58-0.92) in the training and validation cohorts, respectively. The nomogram based on the combined model also had good discrimination and calibration (P = 0.519 for the training cohort and P = 0.389 for the validation cohort). The Kaplan-Meier analysis also demonstrate that the high-score patients had significantly shorter PFS than the low-score patients (P = 0.031) in the combined model, with median PFS 6.0 vs 9.0 months. CONCLUSION: The nomogram based on the combined model consisting of MRI radiomics and ALBI score could be used as a biomarker to predict the therapeutic response of unresectable HCC after HAIC.

Enhanced stent visualization system for percutaneous coronary intervention in patients with chronic kidney disease: effects on contrast media volume and radiation exposure.

OBJECTIVE: We aimed to assess the impact of using enhanced stent visualization (ESV) systems on contrast media volume and radiation dose in percutaneous coronary intervention (PCI), especially for patients with chronic kidney disease (CKD). BACKGROUND: Coronary heart disease (CHD) is associated with chronic kidney disease (CKD), as they share a similar pathological pathway. In addition, the iodinated contrast media used for angiography is a risk factor for contrast-associated acute kidney injury (CA-AKI), which could aggravate the progression of CKD. We hypothesized that ESV systems have the potential to reduce the use of contrast media as well as the radiation dose; however, few studies have reported the impact on contrast media with the use of ESV systems. METHODS: We retrospectively collected 124 patients with acute coronary syndrome who underwent PCI from May 2020 to July 2021. The patients were divided into the ESV-guided group (n = 64) and angiography-guided group (n = 60). Procedural parameters, including contrast media volume, radiation exposure (in Air Kerma-AK and Dose Area Product-DAP), number of cines, cine frames, fluoroscopy and procedure time, were recorded and analysed. RESULTS: The groups were comparable regarding the patient characteristics. There was a significant reduction in contrast media volume (174.7 ± 29.6 ml vs.132.6 ± 22.3 ml, p = 0.0001), radiation exposure (776 (499 - 1200) mGy vs. 1065 (791 - 1603) mGy, p = 0.002 in AK; 43 (37 - 73) Gycm2 vs. 80 (64 - 133) Gycm2, p = 0.030 in DAP) and procedure time (53.06 ± 21.20 min vs. 72.00 ± 30.55 min, p = 0.01) with the use of ESV systems. Similar results were observed in the subgroup analysis for the patients with CKD. CONCLUSION: This study suggested that the use of ESV is associated with reduced contrast media usage, radiation dose and procedure time during PCI. The same results were observed in a subgroup analysis in patients with CKD, and this shows that ESV-guided PCI has the potential to reduce renal impairment and mitigate the progression of CKD for those CHD patients with CKD.

Elevated Nitrite/Nitrate Ratio as a Potential Biomarker for the Differential Diagnosis of Pleural Effusions.

Pleural effusions (PEs) are common in clinical practice and can be due to many different underlying diseases such as cancer, congestive heart failure, or pneumonia. An accurate differential diagnostic categorization is essential, as the treatment and prognosis of PEs largely depend on its cause. In this study, we tested the hypothesis that nitrite and nitrate concentrations in PEs are associated with the inflammation and infection conditions. We therefore measured the nitrite and nitrate levels in 143 PE samples using a sensitive liquid chromatography-tandem mass spectrometry method and investigated their diagnostic potential in differentiating PEs. The results showed that nitrite concentrations and nitrite/nitrate ratios were higher in exudates than in transudates (NO2-: 2.12 vs. 1.49 µM; NO2-/NO3-: 23.3 vs. 14.0). Both the nitrite concentrations and the nitrite/nitrate ratios were positively correlated with the three Light's criteria. Moreover, the receiver operating characteristic curve analysis revealed that the nitrite/nitrate ratio with an area under the curve of 0.71 could be a potential diagnostic biomarker in separating infectious PEs (IPEs) from other types of PEs. Taken together, the nitrite/nitrate ratio not only reflected the statuses of inflammation, but also the nitrate reduction by pathogenic bacteria infection in the pleural cavity. The nitrite/nitrate ratio could be a better biomarker in the differential diagnosis of PEs than the nitrite concentration alone.

MRI-Based Radiomics and Urine Creatinine for the Differentiation of Renal Angiomyolipoma With Minimal Fat From Renal Cell Carcinoma: A Preliminary Study.

Objectives: Standard magnetic resonance imaging (MRI) techniques are different to distinguish minimal fat angiomyolipoma (mf-AML) with minimal fat from renal cell carcinoma (RCC). Here we aimed to evaluate the diagnostic performance of MRI-based radiomics in the differentiation of fat-poor AMLs from other renal neoplasms. Methods: A total of 69 patients with solid renal tumors without macroscopic fat and with a pathologic diagnosis of RCC (n=50) or mf-AML (n=19) who underwent conventional MRI and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) were included. Clinical data including age, sex, tumor location, urine creatinine, and urea nitrogen were collected from medical records. The apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were measured from renal tumors. We used the ITK-SNAP software to manually delineate the regions of interest on T2-weighted imaging (T2WI) and IVIM-DWI from the largest cross-sectional area of the tumor. We extracted 396 radiomics features by the Analysis Kit software for each MR sequence. The hand-crafted features were selected by using the Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO). Diagnostic models were built by logistic regression analysis. Receiver operating characteristic curve analysis was performed using five-fold cross-validation and the mean area under the curve (AUC) values were calculated and compared between the models to obtain the optimal model for the differentiation of mf-AML and RCC. Decision curve analysis (DCA) was used to evaluate the clinical utility of the models. Results: Clinical model based on urine creatinine achieved an AUC of 0.802 (95%CI: 0.761-0.843). IVIM-based model based on f value achieved an AUC of 0.692 (95%CI: 0.627-0.757). T2WI-radiomics model achieved an AUC of 0.883 (95%CI: 0.852-0.914). IVIM-radiomics model achieved an AUC of 0.874 (95%CI: 0.841-0.907). Combined radiomics model achieved an AUC of 0.919 (95%CI: 0.894-0.944). Clinical-radiomics model yielded the best performance, with an AUC of 0.931 (95%CI: 0.907-0.955). The calibration curve and DCA confirmed that the clinical-radiomics model had a good consistency and clinical usefulness. Conclusion: The clinical-radiomics model may be served as a noninvasive diagnostic tool to differentiate mf-AML with RCC, which might facilitate the clinical decision-making process.

Simultaneous determination of areca nut- and tobacco-specific alkaloids in saliva by LC-MS/MS: Distribution and transformation of alkaloids in oral cavity.

Areca nut and tobacco are frequently used in combination. Cigarette smoking and betel quid (BQ) chewing habits impose greater oral cancer risk than either habit alone. Saliva is a better noninvasive diagnostic material as it is in direct contact with oral mucosa and cancerous lesions. This study describes the application of isotope-dilution LC-MS/MS for simultaneous quantitation of five areca nut-specific alkaloids (ASAs) and three tobacco-specific alkaloids (TSAs) in human saliva. With this method, we demonstrate that the distribution of ASAs vary significantly in smokers who chew BQ habitually, due to the hydrolysis of ASAs and metabolic activity in the oral cavity. The alkaline condition formed due to slaked lime in BQ, plays an important role in the distribution of ASAs and TSAs, by catalyzing the hydrolysis of ester forms of ASAs to their respective carboxylic acid forms besides facilitating the TSA (i.e., nicotine) absorption in the oral cavity. Moreover, our results reveal that oral mucosa rather than saliva contributes to the metabolism of ASAs at oral cavity. Less than 2.1% of ASAs were metabolized by saliva, as determined by in vitro test. Our findings may provide a better insight into the pathobiology of oral carcinogenesis due to BQ chewing.

Radiomic Score as a Potential Imaging Biomarker for Predicting Survival in Patients With Cervical Cancer.

OBJECTIVES: Accurate prediction of prognosis will help adjust or optimize the treatment of cervical cancer and benefit the patients. We aimed to investigate the incremental value of radiomics when added to the FIGO stage in predicting overall survival (OS) in patients with cervical cancer. METHODS: This retrospective study included 106 patients with cervical cancer (FIGO stage IB1-IVa) between October 2017 and May 2019. Patients were randomly divided into a training cohort (n = 74) and validation cohort (n = 32). All patients underwent contrast-enhanced computed tomography (CT) prior to treatment. The ITK-SNAP software was used to delineate the region of interest on pre-treatment standard-of-care CT scans. We extracted 792 two-dimensional radiomic features by the Analysis Kit (AK) software. Pearson correlation coefficient analysis and Relief were used to detect the most discriminatory features. The radiomic signature (i.e., Radscore) was constructed via Adaboost with Leave-one-out cross-validation. Prognostic models were built by Cox regression model using Akaike information criterion (AIC) as the stopping rule. A nomogram was established to individually predict the OS of patients. Patients were then stratified into high- and low-risk groups according to the Youden index. Kaplan-Meier curves were used to compare the survival difference between the high- and low-risk groups. RESULTS: Six textural features were identified, including one gray-level co-occurrence matrix feature and five gray-level run-length matrix features. Only the FIGO stage and Radscore were independent risk factors associated with OS (p < 0.05). The C-index of the FIGO stage in the training and validation cohorts was 0.703 (95% CI: 0.572-0.834) and 0.700 (95% CI: 0.526-0.874), respectively. Correspondingly, the C-index of Radscore was 0.794 (95% CI: 0.707-0.880) and 0.754 (95% CI: 0.623-0.885). The incorporation of the FIGO stage and Radscore achieved better performance, with a C-index of 0.830 (95% CI: 0.738-0.922) and 0.772 (95% CI: 0.615-0.929), respectively. The nomogram based on the FIGO stage and Radscore could individually predict the OS probability with good discrimination and calibration. The high-risk patients had shorter OS compared with the low-risk patients (p < 0.05). CONCLUSION: Radiomics has the potential for noninvasive risk stratification and may improve the prediction of OS in patients with cervical cancer when added to the FIGO stage.

Diltiazem-imprinted porphyrinic covalent organic frameworks as solid-phase extractants and fluorescent sensors.

Diltiazem, which is a calcium channel blocker, is involved in the formation of covalent organic frameworks (COFs) through the Schiff base reaction of tetrakis (4-aminophenyl)-porphine (TAPP) and dihydroxynaphthalene-dicarbaldehyde (DHNDC) and the next enol-to-keto tautomerization. The diltiazem-imprinted COFs (DICOFs) were optimally formed using Sc(OTf)3 as the catalyst, TAPP/DHNDC/diltiazem in a molar ratio of 2/3/4, N-methylpyrrolidone/mesitylene (v/v = 3/5) as the porogen, and a 1-h reaction with a high imprinting factor of 10.5 compared to the nonimprinted counterparts (NICOFs). The optimized DICOF exhibited a more amorphous XRD pattern, a larger surface area (1650 vs. 930 m2/g), a larger pore volume (1.33 vs. 0.75 cm3/g), and a finer porous SEM feature than NICOF. The selectivity of NICOF toward diltiazem and diazepam at 250 nM (α = 1.03, RSD = 1.3%) was smaller than the selectivity of DICOF (α = 2.94, RSD = 1.6%). The diltiazem samples (5.0-300 ng mL-1) dynamically quenched the fluorescence of 15 µg/mL DICOF in 50 mM phosphate buffer at pH 6.5 at 8.0 min equilibrium; thus, Stern-Volmer plots were linearly constructed for sensing diltiazem with an LOD of 3.4 ng mL-1 and an LOQ of 10.2 ng mL-1. According to the plots, 30 ng mL-1 diltiazem solutions that were diluted from 30 mg-specified tablets had an average measured concentration of 29.5 ng mL-1 (σ = 1.3% and n = 5). In addition to application as fluorescent sensors, DICOFs (30 mg) could be used as dispersive extractants to recover 95.2% of 0.6 ng mL-1 diltiazem from 25 mL phosphate buffer with quadruplicate uses of 0.5 mL methanol/acetic acid (v/v = 9/1) as the eluent. Langmuir and pseudo-second-order models were fitted to the isothermal and kinetic sorption mechanisms, respectively. The maximum sorption capacity of DICOF was ten times larger than that of NICOF (156 vs. 15.2 mg/g). The interday recoveries of 0.6 ng mL-1 spiked in 20-fold diluted human urine, and 60-fold diluted human serum were 93.2% and 90.6%, respectively.