Orally Ingested Micro- and Nano-Plastics: A Hidden Driver of Inflammatory Bowel Disease and Colorectal Cancer.

Micro- and nano-plastics (MNPLs) can move along the food chain to higher-level organisms including humans. Three significant routes for MNPLs have been reported: ingestion, inhalation, and dermal contact. Accumulating evidence supports the intestinal toxicity of ingested MNPLs and their role as drivers for increased incidence of colorectal cancer (CRC) in high-risk populations such as inflammatory bowel disease (IBD) patients. However, the mechanisms are largely unknown. In this review, by using the leading scientific publication databases (Web of Science, Google Scholar, Scopus, PubMed, and ScienceDirect), we explored the possible effects and related mechanisms of MNPL exposure on the gut epithelium in healthy conditions and IBD patients. The summarized evidence supports the idea that oral MNPL exposure may contribute to intestinal epithelial damage, thus promoting and sustaining the chronic development of intestinal inflammation, mainly in high-risk populations such as IBD patients. Colonic mucus layer disruption may further facilitate MNPL passage into the bloodstream, thus contributing to the toxic effects of MNPLs on different organ systems and platelet activation, which may, in turn, contribute to the chronic development of inflammation and CRC development. Further exploration of this threat to human health is warranted to reduce potential adverse effects and CRC risk.

Multi-omics staging of locally advanced rectal cancer predicts treatment response: a pilot study.

Treatment response assessment of rectal cancer patients is a critical component of personalized cancer care and it allows to identify suitable candidates for organ-preserving strategies. This pilot study employed a novel multi-omics approach combining MRI-based radiomic features and untargeted metabolomics to infer treatment response at staging. The metabolic signature highlighted how tumor cell viability is predictively down-regulated, while the response to oxidative stress was up-regulated in responder patients, showing significantly reduced oxoproline values at baseline compared to non-responder patients (p-value < 10-4). Tumors with a high degree of texture homogeneity, as assessed by radiomics, were more likely to achieve a major pathological response (p-value < 10-3). A machine learning classifier was implemented to summarize the multi-omics information and discriminate responders and non-responders. Combining all available radiomic and metabolomic features, the classifier delivered an AUC of 0.864 (± 0.083, p-value < 10-3) with a best-point sensitivity of 90.9% and a specificity of 81.8%. Our results suggest that a multi-omics approach, integrating radiomics and metabolomic data, can enhance the predictive value of standard MRI and could help to avoid unnecessary surgical treatments and their associated long-term complications.

Impact of Maternal Lifestyle and Dietary Habits during Pregnancy on Newborn Metabolic Profile.

Expanded newborn screening (NBS) is a preventive program that allows for the early identification of over 40 congenital endocrine-metabolic diseases by analyzing dried blood spot samples collected from the newborn's heel within 48-72 h of birth. The determination of amino acids and acyl-carnitines by Flow Injection Analysis Tandem Mass Spectrometry (FIA-MS/MS) may also highlight metabolic alterations resulting from external factors, such as maternal nutrition. In the present study, we developed a questionnaire to investigate the eating habits of 109 women during pregnancy and statistically correlated the results from the investigation on dietary habits with the data obtained by the NBS laboratory of Abruzzo region (Italy). Parameters such as smoking, physical activity, and the intake of iodized salt, drugs, and supplements were analyzed. This study aimed to highlight how maternal lifestyle, diet, and drug intake during pregnancy may affect the neonatal metabolic profile, possibly generating false positive or false negative results in the NBS test. The results pointed out how the knowledge of maternal nutrition and lifestyle may also be precious in preventing misinterpretations of the neonatal metabolic profile, thereby reducing unnecessary stress for newborns and their parents and limiting costs for the health system.

MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 - Early Breast Cancer Patients.

Oncotype Dx Recurrence Score (RS) has been validated in patients with ER + /HER2 - invasive breast carcinoma to estimate patient risk of recurrence and guide the use of adjuvant chemotherapy. We investigated the role of MRI-based radiomics features extracted from the tumor and the peritumoral tissues to predict the risk of tumor recurrence. A total of 62 patients with biopsy-proved ER + /HER2 - breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS > 25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine learning algorithm. PLS ß-weights of radiomics features included the 5% features with the largest ß-weights in magnitude (top 5%). Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The exploratory analysis for the complete dataset revealed an average absolute correlation among features of 0.51. The nCV framework delivered an AUC of 0.76 (p = 1.1∙10-3). When combining "early" and "peak" DCE images of only T or TST, a tendency toward statistical significance was obtained for TST with an AUC of 0.61 (p = 0.05). The 47 features included in the top 5% were balanced between T and TST (23 and 24, respectively). Moreover, 33/47 (70%) were texture-related, and 25/47 (53%) were derived from high-resolution images (1 mm). A radiomics-based machine learning approach shows the potential to accurately predict the recurrence risk in early ER + /HER2 - breast cancer patients.

Circulating Extracellular Vesicles: Their Role in Patients with Abdominal Aortic Aneurysm (AAA) Undergoing EndoVascular Aortic Repair (EVAR).

Abdominal aortic aneurysm (AAA) is a frequent aortic disease. If the diameter of the aorta is larger than 5 cm, an open surgical repair (OSR) or an endovascular aortic repair (EVAR) are recommended. To prevent possible complications (i.e., endoleaks), EVAR-treated patients need to be monitored for 5 years following the intervention, using computed tomography angiography (CTA). However, this radiological method involves high radiation exposure in terms of CTA/year. In such a context, the study of peripheral-blood-circulating extracellular vesicles (pbcEVs) has great potential to identify biomarkers for EVAR complications. We analyzed several phenotypes of pbcEVs using polychromatic flow cytometry in 22 patients with AAA eligible for EVAR. From each enrolled patient, peripheral blood samples were collected at AAA diagnosis, and after 1, 6, and 12 months following EVAR implantation, i.e. during the diagnostic follow-up protocol. Patients developing an endoleak displayed a significant decrease in activated-platelet-derived EVs between the baseline condition and 6 months after EVAR intervention. Furthermore, we also observed, that 1 month after EVAR implantation, patients developing an endoleak showed higher concentrations of activated-endothelial-derived EVs than patients who did not develop one, suggesting their great potential as a noninvasive and specific biomarker for early identification of EVAR complications.

Conspicuity and muscle-invasiveness assessment for bladder cancer using VI-RADS: a multi-reader, contrast-free MRI study to determine optimal b-values for diffusion-weighted imaging.

OBJECTIVE: To (1) compare bladder cancer (BC) muscle invasiveness among three b-values using a contrast-free approach based on Vesical Imaging-Reporting and Data System (VI-RADS), to (2) determine if muscle-invasiveness assessment is affected by the reader experience, and to (3) compare BC conspicuity among three b-values, qualitatively and quantitatively. METHODS: Thirty-eight patients who underwent a bladder MRI on a 3.0-T scanner were enrolled. The gold standard was histopathology report following transurethral resection of BC. Three sets of images, including T2w and different b-values for DWI, set 1 (b = 1000 s/mm2), set 2 (b = 1500 s/mm2), and set 3 (b = 2000 s/mm2), were reviewed by three differently experienced readers. Descriptive statistics and Intraclass Correlation Coefficient (ICC) were calculated. Comparisons among readers and DWI sets were performed with the Wilcoxon test. Receiver operating characteristic (ROC) analysis was performed. Areas under the curves (AUCs) and pairwise comparison were calculated. RESULTS: AUCs of muscle-invasiveness assessment ranged from 0.896 to 0.984 (reader 1), 0.952-0.968 (reader 2), and 0.952-0.984 (reader 3) without significant differences among different sets and readers (p > 0.05). The mean conspicuity qualitative scores were higher in Set 1 (2.21-2.33), followed by Set 2 (2-2.16) and Set 3 (1.82-2.14). The quantitative conspicuity assessment showed that mean normalized intensity of tumor was significantly higher in Set 2 (4.217-4.737) than in Set 1 (3.923-4.492) and Set 3 (3.833-3.992) (p < 0.05). CONCLUSION: Muscle invasiveness can be assessed with high accuracy using a contrast-free protocol with T2W and DWI, regardless of reader's experience. b = 1500 s/mm2 showed the best tumor delineation, while b = 1000 s/mm2 allowed for better tumor-wall interface assessment.

A Dedicated Tool for Presurgical Mapping of Brain Tumors and Mixed-Reality Navigation During Neurosurgery.

Brain tumor surgery requires a delicate tradeoff between complete removal of neoplastic tissue while minimizing loss of brain function. Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) have emerged as valuable tools for non-invasive assessment of human brain function and are now used to determine brain regions that should be spared to prevent functional impairment after surgery. However, image analysis requires different software packages, mainly developed for research purposes and often difficult to use in a clinical setting, preventing large-scale diffusion of presurgical mapping. We developed a specialized software able to implement an automatic analysis of multimodal MRI presurgical mapping in a single application and to transfer the results to the neuronavigator. Moreover, the imaging results are integrated in a commercially available wearable device using an optimized mixed-reality approach, automatically anchoring 3-dimensional holograms obtained from MRI with the physical head of the patient. This will allow the surgeon to virtually explore deeper tissue layers highlighting critical brain structures that need to be preserved, while retaining the natural oculo-manual coordination. The enhanced ergonomics of this procedure will significantly improve accuracy and safety of the surgery, with large expected benefits for health care systems and related industrial investors.

Dynamic susceptibility MR perfusion imaging of the brain: not a question of contrast agent molarity.

PURPOSE: Dynamic susceptibility contrast (DSC) perfusion-weighted MR imaging (PWI) is increasingly used in clinical neuroimaging for a range of conditions. More highly concentrated GBCAs (e.g., gadobutrol) are often preferred for DSC imaging because it is thought that more Gd is present in the volume of interest during first pass for a given equivalent injection rate. However, faster injection of a less viscous GBCA (e.g., gadoteridol) might generate a more compact and narrower contrast bolus thus obviating any perceived benefit of higher Gd concentration. This preliminary study aimed to analyze and compare DSC examinations in the healthy brain hemisphere of patients with brain tumors using gadobutrol and gadoteridol administered at injection rates of 4 and 6 mL/s. METHODS: Thirty-nine brain tumor patients studied with DSC-PWI were evaluated. A simplified gamma-variate model function was applied to calculate the mean peak, area under the curve (AUC), and full-width at half-maximum (FHWM) of concentration-time curves derived from ΔR2* signals at four different regions-of-interest (ROIs). Qualitative assessment of the derived CBV maps was also performed independently by 2 neuroradiologists. RESULTS: No qualitative or quantitative differences between the two GBCAs were observed when administered at a flow rate of 4 mL/s. At a flow rate of 6 mL/s, gadoteridol showed lower FWHM values. CONCLUSION: Gadobutrol and gadoteridol are equivalent for clinical assessment of qualitative CBV maps and quantitative perfusion parameters (FHWM) at a flow rate of 4 mL/s. At 6 mL/s, gadoteridol produces a narrower bolus shape and potentially improves quantitative assessment of perfusion parameters.

MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer.

Neoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (≥ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response after CRT. The use of pre-treatment MRI as predictive biomarker could help to increase the chance of organ preservation by tailoring the neoadjuvant treatment. We present a novel machine learning model combining pre-treatment MRI-based clinical and radiomic features for the early prediction of treatment response in LARC patients. MRI scans (3.0 T, T2-weighted) of 72 patients with LARC were included. Two readers independently segmented each tumor. Radiomic features were extracted from both the "tumor core" (TC) and the "tumor border" (TB). Partial least square (PLS) regression was used as the multivariate, machine learning, algorithm of choice and leave-one-out nested cross-validation was used to optimize hyperparameters of the PLS. The MRI-Based "clinical-radiomic" machine learning model properly predicted the treatment response (AUC = 0.793, p = 5.6 × 10-5). Importantly, the prediction improved when combining MRI-based clinical features and radiomic features, the latter extracted from both TC and TB. Prospective validation studies in randomized clinical trials are warranted to better define the role of radiomics in the development of rectal cancer precision medicine.

Tumor detectability and conspicuity comparison of standard b1000 and ultrahigh b2000 diffusion-weighted imaging in rectal cancer.

PURPOSE: To compare tumor detectability and conspicuity of standard b = 1000 s/mm2 (b1000) versus ultrahigh b = 2000 s/mm2 (b2000) diffusion-weighted imaging (DWI) in rectal cancer. METHODS: Fifty-five patients for a total of 81 3T DWI-MR scans were retrospectively evaluated by two differently experienced readers. A comparison between b1000 and b2000 for tumor detectability and conspicuity was performed. The conspicuity was qualitatively and quantitatively assessed by using three-point scale and whole tumor volume manual delineation, respectively. Receiver-operating characteristic curve (ROC) with area under the curve (AUC) analysis provided diagnostic accuracy in tumor detectability of restaging MR scans. Qualitative scores and quantitative features including mean signal intensity, variance, 10th percentile and 90th percentile, were compared using the Wilcoxon test. Interobserver agreement (IOA) for qualitative and quantitative data was calculated using Cohen's Kappa and intraclass correlation coefficient (ICC) respectively. RESULTS: Diagnostic accuracy was comparable between b1000 and b2000 for both readers (p > 0.05). Overall quality scores were significantly better for b2000 than b1000 (2.29 vs 1.65 Reader 1, p = 0.01; 2.18 vs 1.69 Reader 2, p = 0.04). IOA was equally good for both b values (k = 0.86 b1000, k = 0.86 b2000). Quantitative analysis revealed more uniform signal (measured in variance) of b2000 in both healthy surrounding tissue (p < 0.05) and tumor (p < 0.05), with less outliers (measured using 10th and 90th percentile). Additionally, b2000 offered lower mean signal intensity in tissue sorrounding the tumor (p < 0.05). Finally, ICC improved from 0.92 (b1000) to 0.97 (b2000). CONCLUSION: Ultrahigh b value (b2000) may improve rectal cancer conspicuity and introbserver agreement maintaining comparable diagnostic accuracy to standard b1000.

Aortic valve bypass surgery in severe aortic valve stenosis: Insights from cardiac and brain magnetic resonance imaging.

OBJECTIVE: To investigate and describe the distribution of aortic and cerebral blood flow (CBF) in patients with severe valvular aortic stenosis (AS) before and after aortic valve bypass (AVB) surgery. METHODS: We enrolled 10 consecutive patients who underwent AVB surgery for severe AS. Cardiovascular magnetic resonance imaging (CMR) and brain magnetic resonance imaging were performed as baseline before surgery and twice after surgery. Quantitative flow measurements were obtained using 1.5-T magnetic resonance imaging (MRI) scanner phase-contrast images of the ascending aorta, descending thoracic aorta (3 cm proximally and distally from the conduit-to-aorta anastomosis), and ventricular outflow portion of the conduit. The evaluation of CBF was performed using 3.0-T MRI scanner arterial spin labeling (ASL) through sequences acquired at the gray matter, dorsal default-mode network, and sensorimotor levels. RESULTS: Conduit flow, expressed as the percentage of total antegrade flow through the conduit, was 63.5 ± 8% and 67.8 ± 7% on early and mid-term postoperative CMR, respectively (P < .05). Retrograde perfusion from the level of the conduit insertion in the descending thoracic aorta toward the aortic arch accounted for 6.9% of total cardiac output and 11% of total conduit flow. We did not observe any significant reduction in left ventricular stroke volume at postoperative evaluation compared with preoperative evaluation (P = .435). No differences were observed between preoperative and postoperative CBF at the gray matter, dorsal default-mode network, and sensorimotor levels (P = .394). CONCLUSIONS: After AVB surgery in patients with severe AS, cardiac output is split between the native left ventricular outflow tract and the apico-aortic bypass, with two-thirds of the total antegrade flow passing through the latter and one-third passing through the former. In our experience, CBF assessment confirms that the flow redistribution does not jeopardize cerebral blood supply.