Change in Nfkb/Nrf2/Bax Levels by High Monomeric Polyphenols Berries Extract (HMPBE) in Acute and Chronic Secondary Brain Damage.

BACKGROUND/AIMS: High Monomeric Polyphenols Berries Extract (HMPBE) is a formula highly rich in polyphenols clinically proven to enhance learning and memory. It is currently used to enhances cognitive performance including accuracy, working memory and concentration. METHODS: Here, we investigated for the first time the beneficial effects of HMPBE in a mouse model of acute and chronic traumatic brain injury (TBI). RESULTS: HMPBE, at the dose of 15 mg/kg was able to reduce histological alteration as well as inflammation and lipid peroxidation. HMPBE ameliorate TBI by improving Nrf-2 pathway, reducing Nf-kb nuclear translocation and apoptosis, and ameliorating behavioral alteration such as anxiety and depression. Moreover, in the chronic model of TBI, HMPBE administration restored the decline of Tyrosine Hydroxylase (TH) and dopamine transporter (DAT) and the accumulation of a-synuclein into the midbrain region. This finding correlates the beneficial effect of HMPBE administration with the onset of parkinsonism related to traumatic brain damage. CONCLUSION: The data may open a window for developing new support strategies to limit the neuroinflammation event of acute and chronic TBI.

Determination of osteopontin in monitoring retinal damage in metabolic syndrome.

Metabolic syndrome (MetS) is becoming an increasing public health challenge. Many of the individual components of MetS are associated with ocular changes, but it is not yet clear what the association is. It is known that MetS can lead to diabetes and hence its consequences such as retinopathy. Osteopontin (OPN) is a phosphoglycoprotein that appears to be implicated in diabetic retinopathy. Given the involvement of OPN in retinal damage, the aim of this research was to evaluate OPN expression and its variation over time in a model of MetS induced by 30% fructose consumption for 1, 2 and 3 months. The weight of the animals and the consumption of food and fructose/water were evaluated during the experiment. The results showed a time-dependent increase in weight and liquid consumption in animals treated with fructose, while there was no significant difference in food consumption. Subsequently, the biochemical parameters confirmed that the animals treated with fructose, over time, underwent alterations like those found in patients with MetS. We then moved on to the evaluation of OPN and microglia. In both cases, we observed a time-dependent increase in OPN and Iba-1 in fructose consumption. Furthermore, the results showed a gradual loss of ZO-1 and occludin levels over time. Thus identification of OPN in patients with MetS could be used as an early marker of retinal damage, and this could help to prevent the complications related to the progression of this pathology.

Impaired mitochondrial quality control in fibromyalgia: Mechanisms involved in skeletal muscle alteration.

Fibromyalgia (FMS) is a persistent syndrome marked by widespread musculoskeletal pain and behavioural symptoms. Given the hypothesis linking FMS aetiology to mitochondrial dysfunction and oxidative stress, we examined the biochemical correlation among these factors by studying specific proteins associated with mitochondrial homeostasis in muscle. Additionally, this study investigated the role of Boswellia serrata gum resin extract (BS), known for its various functions, including the potent induction of antioxidant enzymes, in determining protective or reparative mechanisms in the muscle cells. Sprague-Dawley rats were injected with reserpine to induce FMS. These animals exhibited moderate changes in hind limb skeletal muscles, experiencing mobility difficulties. Additionally, there were noteworthy morphological and ultrastructural alterations, along with the expression of myogenin, mitochondrial enzymes and oxidative stress markers in the gastrocnemius muscle. Interestingly, BS demonstrated a reduction in spontaneous motor activity difficulties. Moreover, BS showed a positive impact on musculoskeletal morphostructural aspects, as well as a decrease in oxidative stress and mitochondrial alterations. In particular, BS restored the mRNA expression of citrate synthase and cytochrome-c oxidase subunit II and the activity of electron transfer chain complexes. BS also influenced mitochondrial biogenesis, upregulating PGC-1α expression and the related transcription factors (Nrf1, Tfam, Nrf2, FOXO3a, SIRT3, GCLC, NQO1, SOD2 and GPx4), oxidative stress (lipid peroxidation, GSH levels and GSH-Px activity) and mitochondrial dynamics and function (Mnf2 expression and CoQ10 levels). Overall, this study underlined the key role of the mitochondrial alteration in FMS and that BS had a very high antioxidant effect in these organelles and also in the cells.

Risk Assessment and Radiomics Analysis in Magnetic Resonance Imaging of Pancreatic Intraductal Papillary Mucinous Neoplasms (IPMN).

Intraductal papillary mucinous neoplasms (IPMNs) are a very common incidental finding during patient radiological assessment. These lesions may progress from low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and even pancreatic cancer. The IPMN progression risk grows with time, so discontinuation of surveillance is not recommended. It is very important to identify imaging features that suggest LGD of IPMNs, and thus, distinguish lesions that only require careful surveillance from those that need surgical resection. It is important to know the management guidelines and especially the indications for surgery, to be able to point out in the report the findings that suggest malignant degeneration. The imaging tools employed for diagnosis and risk assessment are Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) with contrast medium. According to the latest European guidelines, MRI is the method of choice for the diagnosis and follow-up of patients with IPMN since this tool has a highest sensitivity in detecting mural nodules and intra-cystic septa. It plays a key role in the diagnosis of worrisome features and high-risk stigmata, which are associated with IPMNs malignant degeneration. Nowadays, the main limit of diagnostic tools is the ability to identify the precursor of pancreatic cancer. In this context, increasing attention is being given to artificial intelligence (AI) and radiomics analysis. However, these tools remain in an exploratory phase, considering the limitations of currently published studies. Key limits include noncompliance with AI best practices, radiomics workflow standardization, and clear reporting of study methodology, including segmentation and data balancing. In the radiological report it is useful to note the type of IPMN so as the morphological features, size, rate growth, wall, septa and mural nodules, on which the indications for surveillance and surgery are based. These features should be reported so as the surveillance time should be suggested according to guidelines.

Preliminary data on artificial intelligence tool in magnetic resonance imaging assessment of degenerative pathologies of lumbar spine.

PURPOSE: To evaluate the ability of an artificial intelligence (AI) tool in magnetic resonance imaging (MRI) assessment of degenerative pathologies of lumbar spine using radiologist evaluation as a gold standard. METHODS: Patients with degenerative pathologies of lumbar spine, evaluated with MRI study, were enrolled in a retrospective study approved by local ethical committee. A comprehensive software solution (CoLumbo; SmartSoft Ltd., Varna, Bulgaria) designed to label the segments of the lumbar spine and to detect a broad spectrum of degenerative pathologies based on a convolutional neural network (CNN) was employed, utilizing an automatic segmentation. The AI tool efficacy was compared to data obtained by a senior neuroradiologist that employed a semiquantitative score. Chi-square test was used to assess the differences among groups, and Spearman's rank correlation coefficient was calculated between the grading assigned by radiologist and the grading obtained by software. Moreover, agreement was assessed between the value assigned by radiologist and software. RESULTS: Ninety patients (58 men; 32 women) affected with degenerative pathologies of lumbar spine and aged from 60 to 81 years (mean 66 years) were analyzed. Significant correlations were observed between grading assigned by radiologist and the grading obtained by software for each localization. However, only when the localization was L2-L3, there was a good correlation with a coefficient value of 0.72. The best agreements were obtained in case of L1-L2 and L2-L3 localizations and were, respectively, of 81.1% and 72.2%. The lowest agreement of 51.1% was detected in case of L4-L5 locations. With regard canal stenosis and compression, the highest agreement was obtained for identification of in L5-S1 localization. CONCLUSIONS: AI solution represents an efficacy and useful toll degenerative pathologies of lumbar spine to improve radiologist workflow.

Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging.

PURPOSE: To assess the efficacy of radiomics features, obtained by magnetic resonance imaging (MRI) with hepatospecific contrast agent, in pre-surgical setting, to predict RAS mutational status in liver metastases. METHODS: Patients with MRI in pre-surgical setting were enrolled in a retrospective study. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. The features were extracted considering the agreement with the Imaging Biomarker Standardization Initiative (IBSI). Balancing was performed through synthesis of samples for the underrepresented classes using the self-adaptive synthetic oversampling (SASYNO) approach. Inter- and intraclass correlation coefficients (ICC) were calculated to assess the between-observer and within-observer reproducibility of all radiomics characteristics. For continuous variables, nonparametric Wilcoxon-Mann-Whitney test was utilized. Benjamini and Hochberg's false discovery rate (FDR) adjustment for multiple testing was used. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. Moreover, features selection were performed before and after a normalized procedure using two different methods (3-sigma and z-score). McNemar test was used to assess differences statistically significant between dichotomic tables. All statistical procedures were done using MATLAB R2021b Statistics and Machine Toolbox (MathWorks, Natick, MA, USA). RESULTS: Seven normalized radiomics features, extracted from arterial phase, 11 normalized radiomics features, from portal phase, 12 normalized radiomics features from hepatobiliary phase and 12 normalized features from T2-W SPACE sequence were robust predictors of RAS mutational status. The multivariate analysis increased significantly the accuracy in RAS prediction when a LRM was used, combining 12 robust normalized features extracted by VIBE hepatobiliary phase reaching an accuracy of 99%, a sensitivity 97%, a specificity of 100%, a PPV of 100% and a NPV of 98%. No statistically significant increase was obtained, considering the tested classifiers DT, KNN and SVM, both without normalization and with normalization methods. CONCLUSIONS: Normalized approach in MRI radiomics analysis allows to predict RAS mutational status.

Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction.

PURPOSE: To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases. METHODS: Patient selection in a retrospective study was carried out from January 2018 to May 2021 considering the following inclusion criteria: patients subjected to surgical resection for liver metastases; proven pathological liver metastases; patients subjected to enhanced CT examination in the presurgical setting with a good quality of images; and RAS assessment as standard reference. A total of 851 radiomics features were extracted using the PyRadiomics Python package from the Slicer 3D image computing platform after slice-by-slice segmentation on CT portal phase by two expert radiologists of each individual liver metastasis performed first independently by the individual reader and then in consensus. Balancing technique was performed, and inter- and intraclass correlation coefficients were calculated to assess the between-observer and within-observer reproducibility of features. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers were considered. Moreover, features selection was performed before and after a normalized procedure using two different methods (3-sigma and z-score). RESULTS: Seventy-seven liver metastases in 28 patients with a mean age of 60 years (range 40-80 years) were analyzed. The best predictors, at univariate analysis for both normalized procedures, were original_shape_Maximum2DDiameter and wavelet_HLL_glcm_InverseVariance that reached an accuracy of 80%, an AUC ≥ 0.75, a sensitivity ≥ 80% and a specificity ≥ 70% (p value < < 0.01). However, a multivariate analysis significantly increased the accuracy in RAS prediction when a linear regression model (LRM) was used. The best performance was obtained using a LRM combining linearly 12 robust features after a z-score normalization procedure: AUC of 0.953, accuracy 98%, sensitivity 96%, specificity of 100%, PPV 100% and NPV 96% (p value < < 0.01). No statistically significant increase was obtained considering the tested machine learning both without normalization and with normalization methods. CONCLUSIONS: Normalized approach in CT radiomics analysis allows to predict RAS mutational status in colorectal liver metastases patients.

A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.

OBJECTIVE: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer. METHODS: From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered. RESULTS: The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set. CONCLUSIONS: The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.

Açaí berry ameliorates cognitive impairment by inhibiting NLRP3/ASC/CASP axis in STZ-induced diabetic neuropathy in mice.

Diabetes complications such as diabetic peripheral neuropathy (DPN) are linked to morbidity and mortality. Peripheral nerve damages in DPN are accompanied by discomfort, weakness, and sensory loss. Some drugs may demonstrate their therapeutic promise by reducing neuroinflammation, but they have side effects. Based on these considerations, the objective of this study was to examine the beneficial properties of açaí berry in a mouse model of DPN generated by injection of streptozotocin (STZ). Açaí berry was given orally to diabetic and control mice every day beginning 2 wk after STZ injection. The animals were euthanized after 16 wk, and tissues from the spinal cord and sciatic nerve and urine were taken. Our findings showed that daily treatment of açaí berry at a dose of 500 mg/kg was able to prevent behavioral changes as well as mast cell activation and nerve deterioration via NOD-like receptor family pyrin-domain-containing-3 (NLRP3)/apoptosis-associated speck-like protein containing a card (ASC)/caspase (CASP) regulation after diabetes induction.NEW & NOTEWORTHY Our research shows that açaí berry reduces mast cells degranulation and histological damage in diabetic neuropathy, improves physiological defense against reactive oxygen species, modulates the NLRP3/ASC/CASP axis, and ameliorates inflammation and oxidative stress. Diet could help treatment for diabetic peripheral neuropathy.

Oligo-metastatic neoPlasms from the gastro-intestinal tract: iDentIfiCaTIon of cliNical and molecular drivers: the PREDICTION study.

BACKGROUND: Metastatic disease in tumors originating from the gastrointestinal tract can exhibit varying degrees of tumor burden at presentation. Some patients follow a less aggressive disease course, characterized by a limited number of metastatic sites, referred to as "oligo-metastatic disease" (OMD). The precise biological characteristics that define the oligometastatic behavior remain uncertain. In this study, we present a protocol designed to prospectively identify OMD, with the aim of proposing novel therapeutic approaches and monitoring strategies. METHODS: The PREDICTION study is a monocentric, prospective, observational investigation. Enrolled patients will receive standard treatment, while translational activities will involve analysis of the tumor microenvironment and genomic profiling using immunohistochemistry and next-generation sequencing, respectively. The first primary objective (descriptive) is to determine the prevalence of biological characteristics in OMD derived from gastrointestinal tract neoplasms, including high genetic concordance between primary tumors and metastases, a significant infiltration of T lymphocytes, and the absence of clonal evolution favoring specific driver genes (KRAS and PIK3CA). The second co-primary objective (analytic) is to identify a prognostic score for true OMD, with a primary focus on metastatic colorectal cancer. The score will comprise genetic concordance (> 80%), high T-lymphocyte infiltration, and the absence of clonal evolution favoring driver genes. It is hypothesized that patients with true OMD (score 3+) will have a lower rate of progression/recurrence within one year (20%) compared to those with false OMD (80%). The endpoint of the co-primary objective is the rate of recurrence/progression at one year. Considering a reasonable probability (60%) of the three factors occurring simultaneously in true OMD (score 3+), using a significance level of α = 0.05 and a test power of 90%, the study requires a minimum enrollment of 32 patients. DISCUSSION: Few studies have explored the precise genetic and biological features of OMD thus far. In clinical settings, the diagnosis of OMD is typically made retrospectively, as some patients who undergo intensive treatment for oligometastases develop polymetastatic diseases within a year, while others do not experience disease progression (true OMD). In the coming years, the identification of true OMD will allow us to employ more personalized and comprehensive strategies in cancer treatment. TRIAL REGISTRATION: ClinicalTrials.gov ID NCT05806151.

Inhibiting IL-6 in medicine: a new twist to sustain inhibition of his cytokine tin the therapy of Pulmonary Arterial Hypertension.

Pulmonary arterial hypertension (PAH) is a chronic, progressive disease characterized by an increase in blood pressure in the lungs' arteries. It can occur in a variety of species, including humans, dogs, cats, and horses. To date, PAH has a high mortality rate in both veterinary and human medicine, often due to complications such as heart failure. The complex pathological mechanisms of PAH involve multiple cellular signalling pathways at various levels. IL-6 is a powerful pleiotropic cytokine that regulates several phases of immune response, inflammation, and tissue remodelling. The hypothesis of this study was that the use of an IL-6 antagonist in PAH could interrupt or mitigate the cascade of events that leads to the progression of the disease and the worsening of clinical outcome, as well as tissue remodelling. In this study, we used two pharmacological protocols with an IL-6 receptor antagonist in a monocrotaline-induced PAH model in rats. Our results showed that the use of an IL-6 receptor antagonist had a significant protective effect, ameliorating both haemodynamic parameters, lung and cardiac function, tissue remodelling, and the inflammation associated with PAH. The results of this study suggest that the inhibition IL-6 could be a useful pharmacological strategy in PAH, in both human and veterinary medicine.

Artificial intelligence and radiation effects on brain tissue in glioblastoma patient: preliminary data using a quantitative tool.

PURPOSE: The quantification of radiotherapy (RT)-induced functional and morphological brain alterations is fundamental to guide therapeutic decisions in patients with brain tumors. The magnetic resonance imaging (MRI) allows to define structural RT-brain changes, but it is unable to evaluate early injuries and to objectively quantify the volume tissue loss. Artificial intelligence (AI) tools extract accurate measurements that permit an objective brain different region quantification. In this study, we assessed the consistency between an AI software (Quibim Precision® 2.9) and qualitative neruroradiologist evaluation, and its ability to quantify the brain tissue changes during RT treatment in patients with glioblastoma multiforme (GBM). METHODS: GBM patients treated with RT and subjected to MRI assessment were enrolled. Each patient, pre- and post-RT, undergoes to a qualitative evaluation with global cerebral atrophy (GCA) and medial temporal lobe atrophy (MTA) and a quantitative assessment with Quibim Brain screening and hippocampal atrophy and asymmetry modules on 19 extracted brain structures features. RESULTS: A statistically significant strong negative association between the percentage value of the left temporal lobe and the GCA score and the left temporal lobe and the MTA score was found, while a moderate negative association between the percentage value of the right hippocampus and the GCA score and the right hippocampus and the MTA score was assessed. A statistically significant strong positive association between the CSF percentage value and the GCA score and a moderate positive association between the CSF percentage value and the MTA score was found. Finally, quantitative feature values showed that the percentage value of the cerebro-spinal fluid (CSF) statistically differences between pre- and post-RT. CONCLUSIONS: AI tools can support a correct evaluation of RT-induced brain injuries, allowing an objective and earlier assessment of the brain tissue modifications.

Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment.

OBJECTIVE: The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated. METHODS: The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed. RESULTS: The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence. CONCLUSIONS: The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.

Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome.

OBJECTIVE: The objective of the study was to evaluate the accuracy of radiomics features obtained by MR images to predict Breast Cancer Histological Outcome. METHODS: A total of 217 patients with malignant lesions were analysed underwent MRI examinations. Considering histological findings as the ground truth, four different types of findings were used in both univariate and multivariate analyses: (1) G1 + G2 vs G3 classification; (2) presence of human epidermal growth factor receptor 2 (HER2 + vs HER2 -); (3) presence of the hormone receptor (HR + vs HR -); and (4) presence of luminal subtypes of breast cancer. RESULTS: The best accuracy for discriminating HER2 + versus HER2 - breast cancers was obtained considering nine predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 88% on validation set). The best accuracy for discriminating HR + versus HR - breast cancers was obtained considering nine predictors by T2-weighted subtraction images and a decision tree (accuracy of 90% on validation set). The best accuracy for discriminating G1 + G2 versus G3 breast cancers was obtained considering 16 predictors by early phase T1-weighted subtraction images in a linear regression model with an accuracy of 75%. The best accuracy for discriminating luminal versus non-luminal breast cancers was obtained considering 27 predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 94% on validation set). CONCLUSIONS: The combination of radiomics analysis and artificial intelligence techniques could be used to support physician decision-making in prediction of Breast Cancer Histological Outcome.

Structured reporting of computed tomography in the polytrauma patient assessment: a Delphi consensus proposal.

OBJECTIVES: To develop a structured reporting (SR) template for whole-body CT examinations of polytrauma patients, based on the consensus of a panel of emergency radiology experts from the Italian Society of Medical and Interventional Radiology. METHODS: A multi-round Delphi method was used to quantify inter-panelist agreement for all SR sections. Internal consistency for each section and quality analysis in terms of average inter-item correlation were evaluated by means of the Cronbach's alpha (Cα) correlation coefficient. RESULTS: The final SR form included 118 items (6 in the "Patient Clinical Data" section, 4 in the "Clinical Evaluation" section, 9 in the "Imaging Protocol" section, and 99 in the "Report" section). The experts' overall mean score and sum of scores were 4.77 (range 1-5) and 257.56 (range 206-270) in the first Delphi round, and 4.96 (range 4-5) and 208.44 (range 200-210) in the second round, respectively. In the second Delphi round, the experts' overall mean score was higher than in the first round, and standard deviation was lower (3.11 in the second round vs 19.71 in the first round), reflecting a higher expert agreement in the second round. Moreover, Cα was higher in the second round than in the first round (0.97 vs 0.87). CONCLUSIONS: Our SR template for whole-body CT examinations of polytrauma patients is based on a strong agreement among panel experts in emergency radiology and could improve communication between radiologists and the trauma team.

Endocrine Disruptor Compounds in Environment: Focus on Women's Reproductive Health and Endometriosis.

Endometriosis is an estrogen-dependent gynecologic illness that has long-term effects on a woman's fertility, physical health, and overall quality of life. Growing evidence suggests that endocrine-disrupting chemicals (EDCs) may be etiologically involved in the development and severity of the disease. We consider the available human evidence on EDCs and endometriosis, limiting ourselves to studies that have individually assessed chemical amounts in women. Dioxins, BPA, Phthalates, and other endocrine disruptors, like DDT, are among the evidence indicating an environmental etiology for endometriosis. Collectively, this review describes how environmental toxins are linked to lower fertility in women, as well as a number of reproductive diseases, focusing on the pathology of endometriosis and its treatments. Importantly, this review can be used to investigate techniques for preventing the negative effects of EDC exposure.

Fisetin, a Natural Polyphenol, Ameliorates Endometriosis Modulating Mast Cells Derived NLRP-3 Inflammasome Pathway and Oxidative Stress.

A chronic, painful, and inflammatory condition known as endometriosis is defined by the extra-uterine development of endometrial tissue. The aim of this study was to evaluate the beneficial effects of fisetin, a naturally occurring polyphenol that is frequently present in a variety of fruits and vegetables. Uterine fragments were injected intraperitoneally to cause endometriosis, and fisetin was given orally every day. At 14 days of treatment, laparotomy was performed, and the endometrial implants and peritoneal fluids were collected for histological, biochemical, and molecular analyses. Rats subjected to endometriosis presented important macroscopic and microscopic changes, increased mast cell (MC) infiltration, and fibrosis. Fisetin treatment reduced endometriotic implant area, diameter, and volumes, as well as histological alterations, neutrophil infiltration, cytokines release, the number of MCs together with the expression of chymase and tryptase, and diminished α smooth muscle actin (α-sma) and transforming growth factor beta (TGF ß) expressions. In addition, fisetin was able to reduce markers of oxidative stress as well as nitrotyrosine and Poly ADP ribose expressions and increase apoptosis in endometrial lesions. In conclusion, fisetin could represent a new therapeutic strategy to control endometriosis perhaps by targeting the MC-derived NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome pathway and oxidative stress.

Aggravation of TGFß1-Smad Pathway and Autoimmune Myocarditis by Fungicide (Tebuconazole) Exposure.

Myocarditis is an inflammatory cardiac disorder and the primary cause of heart failure in young adults. Its origins can be attributed to various factors, including bacterial or viral infections, exposure to toxins or drugs, endocrine disruptors (EDs), and autoimmune processes. Tebuconazole (TEB), which is a member of the triazole fungicide family, is utilized to safeguard agricultural crop plants against fungal pathogens. Although TEB poses serious threats to mammal health, the information about how it induces toxic effects through various pathways, particularly in autoimmune diseases, are still limited. Thus, the aim of this paper was to evaluate the effect of TEB exposure in autoimmune myocarditis (AM). To induce AM, rats were immunized with porcine cardiac myosin and exposed to TEB for 21 days. Thereafter, animals were sacrificed, and histological, biochemical, and molecular analyses were performed. TEB exposure increased heart weight, systolic blood pressure and heart rate already augmented by AM. Additionally, it significantly increased creatine phosphokinase heart (CK-MB), creatine phosphokinase (CPK), cardiac troponin T (cTnT), and cardiac troponin I (cTnI), as compared to the control. From the histological perspective, TEB exacerbates the histological damage induced by AM (necrosis, inflammation and cell infiltration) and increased fibrosis and collagen deposition. TEB exposure strongly increased pro-inflammatory cytokines and prooxidant levels (O2-, H2O2, NO2-, lipid peroxidation) and reduced antioxidant enzyme levels, which were already dysregulated by AM. Additionally, TEB increased NOX-4 expression and the TGFß1-Smads pathway already activated by AM. Overall, our results showed that TEB exposure strongly aggravated the cardiotoxicity induced by AM.

Modulation of the Proliferative Pathway, Neuroinflammation and Pain in Endometriosis.

Endometriosis is a chronic disease characterized by pelvic inflammation. This study aimed at investigating the molecular mechanisms underlying the pathology and how they can be modulated by the administration of a natural compound, Actaea racemosa (AR). We employed an in vivo model of endometriosis in which rats were intraperitoneally injected with uterine fragments from donor animals. During the experiment, rats were monitored by abdominal high-frequency ultrasound analysis. AR was able to reduce the lesion's size and histological morphology. From a molecular point of view, AR reduced hyperproliferation, as shown by Ki-67 and PCNA expression and MAPK phosphorylation. The impaired apoptosis pathway was also restored, as shown by the TUNEL assay and RT-PCR for Bax, Bcl-2, and Caspase levels. AR also has important antioxidant (reduced Nox expression, restored SOD activity and GSH levels, and reduced MPO activity and MDA levels) and anti-inflammatory (reduced cytokine levels) properties. Moreover, AR demonstrated its ability to reduce the pain-like behaviors associated with the pathology, the neuro-sensitizing mediators (c-FOS and NGF) expression, and the related central astrogliosis (GFAP expression in the spinal cord, brain cortex, and hippocampus). Overall, our data showed that AR was able to manage several pathways involved in endometriosis suppression.

Inhibition of Fatty Acid Amide Hydrolase (FAAH) Regulates NF-kb Pathways Reducing Bleomycin-Induced Chronic Lung Inflammation and Pulmonary Fibrosis.

The deadly interstitial lung condition known as idiopathic pulmonary fibrosis (IPF) worsens over time and for no apparent reason. The traditional therapy approaches for IPF, which include corticosteroids and immunomodulatory drugs, are often ineffective and can have noticeable side effects. The endocannabinoids are hydrolyzed by a membrane protein called fatty acid amide hydrolase (FAAH). Increasing endogenous levels of endocannabinoid by pharmacologically inhibiting FAAH results in numerous analgesic advantages in a variety of experimental models for pre-clinical pain and inflammation. In our study, we mimicked IPF by administering intratracheal bleomycin, and we administered oral URB878 at a dose of 5 mg/kg. The histological changes, cell infiltration, pro-inflammatory cytokine production, inflammation, and nitrosative stress caused by bleomycin were all reduced by URB878. Our data clearly demonstrate for the first time that the inhibition of FAAH activity was able to counteract not only the histological alteration bleomycin-induced but also the cascade of related inflammatory events.