Challenges and limitations in applying radiomics to PET imaging: Possible opportunities and avenues for research.

Radiomics, the high-throughput extraction of quantitative imaging features from medical images, holds immense potential for advancing precision medicine in oncology and beyond. While radiomics applied to positron emission tomography (PET) imaging offers unique insights into tumor biology and treatment response, it is imperative to elucidate the challenges and constraints inherent in this domain to facilitate their translation into clinical practice. This review examines the challenges and limitations of applying radiomics to PET imaging, synthesizing findings from the last five years (2019-2023) and highlights the significance of addressing these challenges to realize the full clinical potential of radiomics in oncology and molecular imaging. A comprehensive search was conducted across multiple electronic databases, including PubMed, Scopus, and Web of Science, using keywords relevant to radiomics issues in PET imaging. Only studies published in peer-reviewed journals were eligible for inclusion in this review. Although many studies have highlighted the potential of radiomics in predicting treatment response, assessing tumor heterogeneity, enabling risk stratification, and personalized therapy selection, various challenges regarding the practical implementation of the proposed models still need to be addressed. This review illustrates the challenges and limitations of radiomics in PET imaging across various cancer types, encompassing both phantom and clinical investigations. The analyzed studies highlight the importance of reproducible segmentation methods, standardized pre-processing and post-processing methodologies, and the need to create large multicenter studies registered in a centralized database to promote the continuous validation and clinical integration of radiomics into PET imaging.

Radiomics Analysis of Preprocedural CT Imaging for Outcome Prediction after Transjugular Intrahepatic Portosystemic Shunt Creation.

PURPOSE: To evaluate the role of radiomics in preoperative outcome prediction in cirrhotic patients who underwent transjugular intrahepatic portosystemic shunt (TIPS) using "controlled expansion covered stents". MATERIALS AND METHODS: This retrospective institutional review board-approved study included cirrhotic patients undergoing TIPS with controlled expansion covered stent placement. From preoperative CT images, the whole liver was segmented into Volumes of Interest (VOIs) at the unenhanced and portal venous phase. Radiomics features were extracted, collected, and analyzed. Subsequently, receiver operating characteristic (ROC) curves were drawn to assess which features could predict patients' outcomes. The endpoints studied were 6-month overall survival (OS), development of hepatic encephalopathy (HE), grade II or higher HE according to West Haven Criteria, and clinical response, defined as the absence of rebleeding or ascites. A radiomic model for outcome prediction was then designed. RESULTS: A total of 76 consecutive cirrhotic patients undergoing TIPS creation were enrolled. The highest performances in terms of the area under the receiver operating characteristic curve (AUROC) were observed for the "clinical response" and "survival at 6 months" outcome with 0.755 and 0.767, at the unenhanced and portal venous phase, respectively. Specifically, on basal scans, accuracy, specificity, and sensitivity were 66.42%, 63.93%, and 73.75%, respectively. At the portal venous phase, an accuracy of 65.34%, a specificity of 62.38%, and a sensitivity of 74.00% were demonstrated. CONCLUSIONS: A pre-interventional machine learning-based CT radiomics algorithm could be useful in predicting survival and clinical response after TIPS creation in cirrhotic patients.

PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI.

PURPOSE: High PSMA expression might be correlated with structural characteristics such as growth patterns on histopathology, not recognized by the human eye on MRI images. Deep structural image analysis might be able to detect such differences and therefore predict if a lesion would be PSMA positive. Therefore, we aimed to train a neural network based on PSMA PET/MRI scans to predict increased prostatic PSMA uptake based on the axial T2-weighted sequence alone. MATERIAL AND METHODS: All patients undergoing simultaneous PSMA PET/MRI for PCa staging or biopsy guidance between April 2016 and December 2020 at our institution were selected. To increase the specificity of our model, the prostatic beds on PSMA PET scans were dichotomized in positive and negative regions using an SUV threshold greater than 4 to generate a PSMA PET map. Then, a C-ENet was trained on the T2 images of the training cohort to generate a predictive prostatic PSMA PET map. RESULTS: One hundred and fifty-four PSMA PET/MRI scans were available (133 [68Ga]Ga-PSMA-11 and 21 [18F]PSMA-1007). Significant cancer was present in 127 of them. The whole dataset was divided into a training cohort (n = 124) and a test cohort (n = 30). The C-ENet was able to predict the PSMA PET map with a dice similarity coefficient of 69.5 ± 15.6%. CONCLUSION: Increased prostatic PSMA uptake on PET might be estimated based on T2 MRI alone. Further investigation with larger cohorts and external validation is needed to assess whether PSMA uptake can be predicted accurately enough to help in the interpretation of mpMRI.

Development and Implementation of an Innovative Framework for Automated Radiomics Analysis in Neuroimaging.

Radiomics represents an innovative approach to medical image analysis, enabling comprehensive quantitative evaluation of radiological images through advanced image processing and Machine or Deep Learning algorithms. This technique uncovers intricate data patterns beyond human visual detection. Traditionally, executing a radiomic pipeline involves multiple standardized phases across several software platforms. This could represent a limit that was overcome thanks to the development of the matRadiomics application. MatRadiomics, a freely available, IBSI-compliant tool, features its intuitive Graphical User Interface (GUI), facilitating the entire radiomics workflow from DICOM image importation to segmentation, feature selection and extraction, and Machine Learning model construction. In this project, an extension of matRadiomics was developed to support the importation of brain MRI images and segmentations in NIfTI format, thus extending its applicability to neuroimaging. This enhancement allows for the seamless execution of radiomic pipelines within matRadiomics, offering substantial advantages to the realm of neuroimaging.

Biodistribution Assessment of a Novel 68Ga-Labeled Radiopharmaceutical in a Cancer Overexpressing CCK2R Mouse Model: Conventional and Radiomics Methods for Analysis.

The aim of the present study consists of the evaluation of the biodistribution of a novel 68Ga-labeled radiopharmaceutical, [68Ga]Ga-NODAGA-Z360, injected into Balb/c nude mice through histopathological analysis on bioptic samples and radiomics analysis of positron emission tomography/computed tomography (PET/CT) images. The 68Ga-labeled radiopharmaceutical was designed to specifically bind to the cholecystokinin receptor (CCK2R). This receptor, naturally present in healthy tissues such as the stomach, is a biomarker for numerous tumors when overexpressed. In this experiment, Balb/c nude mice were xenografted with a human epidermoid carcinoma A431 cell line (A431 WT) and overexpressing CCK2R (A431 CCK2R+), while controls received a wild-type cell line. PET images were processed, segmented after atlas-based co-registration and, consequently, 112 radiomics features were extracted for each investigated organ / tissue. To confirm the histopathology at the tissue level and correlate it with the degree of PET uptake, the studies were supported by digital pathology. As a result of the analyses, the differences in radiomics features in different body districts confirmed the correct targeting of the radiopharmaceutical. In preclinical imaging, the methodology confirms the importance of a decision-support system based on artificial intelligence algorithms for the assessment of radiopharmaceutical biodistribution.

A Critical Analysis of the Robustness of Radiomics to Variations in Segmentation Methods in 18F-PSMA-1007 PET Images of Patients Affected by Prostate Cancer.

BACKGROUND: Radiomics shows promising results in supporting the clinical decision process, and much effort has been put into its standardization, thus leading to the Imaging Biomarker Standardization Initiative (IBSI), that established how radiomics features should be computed. However, radiomics still lacks standardization and many factors, such as segmentation methods, limit study reproducibility and robustness. AIM: We investigated the impact that three different segmentation methods (manual, thresholding and region growing) have on radiomics features extracted from 18F-PSMA-1007 Positron Emission Tomography (PET) images of 78 patients (43 Low Risk, 35 High Risk). Segmentation was repeated for each patient, thus leading to three datasets of segmentations. Then, feature extraction was performed for each dataset, and 1781 features (107 original, 930 Laplacian of Gaussian (LoG) features, 744 wavelet features) were extracted. Feature robustness and reproducibility were assessed through the intra class correlation coefficient (ICC) to measure agreement between the three segmentation methods. To assess the impact that the three methods had on machine learning models, feature selection was performed through a hybrid descriptive-inferential method, and selected features were given as input to three classifiers, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF), AdaBoost and Neural Networks (NN), whose performance in discriminating between low-risk and high-risk patients have been validated through 30 times repeated five-fold cross validation. CONCLUSIONS: Our study showed that segmentation methods influence radiomics features and that Shape features were the least reproducible (average ICC: 0.27), while GLCM features the most reproducible. Moreover, feature reproducibility changed depending on segmentation type, resulting in 51.18% of LoG features exhibiting excellent reproducibility (range average ICC: 0.68-0.87) and 47.85% of wavelet features exhibiting poor reproducibility that varied between wavelet sub-bands (range average ICC: 0.34-0.80) and resulted in the LLL band showing the highest average ICC (0.80). Finally, model performance showed that region growing led to the highest accuracy (74.49%), improved sensitivity (84.38%) and AUC (79.20%) in contrast with manual segmentation.

Radiomics Analyses to Predict Histopathology in Patients with Metastatic Testicular Germ Cell Tumors before Post-Chemotherapy Retroperitoneal Lymph Node Dissection.

BACKGROUND: The identification of histopathology in metastatic non-seminomatous testicular germ cell tumors (TGCT) before post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) holds significant potential to reduce treatment-related morbidity in young patients, addressing an important survivorship concern. AIM: To explore this possibility, we conducted a study investigating the role of computed tomography (CT) radiomics models that integrate clinical predictors, enabling personalized prediction of histopathology in metastatic non-seminomatous TGCT patients prior to PC-RPLND. In this retrospective study, we included a cohort of 122 patients. METHODS: Using dedicated radiomics software, we segmented the targets and extracted quantitative features from the CT images. Subsequently, we employed feature selection techniques and developed radiomics-based machine learning models to predict histological subtypes. To ensure the robustness of our procedure, we implemented a 5-fold cross-validation approach. When evaluating the models' performance, we measured metrics such as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, and F-score. RESULT: Our radiomics model based on the Support Vector Machine achieved an optimal average AUC of 0.945. CONCLUSIONS: The presented CT-based radiomics model can potentially serve as a non-invasive tool to predict histopathological outcomes, differentiating among fibrosis/necrosis, teratoma, and viable tumor in metastatic non-seminomatous TGCT before PC-RPLND. It has the potential to be considered a promising tool to mitigate the risk of over- or under-treatment in young patients, although multi-center validation is critical to confirm the clinical utility of the proposed radiomics workflow.

In Vivo Identification of H3K9me2/H3K79me3 as an Epigenetic Barrier to Carcinogenesis.

The highly dynamic nature of chromatin's structure, due to the epigenetic alterations of histones and DNA, controls cellular plasticity and allows the rewiring of the epigenetic landscape required for either cell differentiation or cell (re)programming. To dissect the epigenetic switch enabling the programming of a cancer cell, we carried out wide genome analysis of Histone 3 (H3) modifications during osteogenic differentiation of SH-SY5Y neuroblastoma cells. The most significant modifications concerned H3K27me2/3, H3K9me2, H3K79me1/2, and H3K4me1 that specify the process of healthy adult stem cell differentiation. Next, we translated these findings in vivo, assessing H3K27, H3K9, and H3K79 methylation states in biopsies derived from patients affected by basalioma, head and neck carcinoma, and bladder tumors. Interestingly, we found a drastic decrease in H3K9me2 and H3K79me3 in cancer specimens with respect to their healthy counterparts and also a positive correlation between these two epigenetic flags in all three tumors. Therefore, we suggest that elevated global levels of H3K9me2 and H3K79me3, present in normal differentiated cells but lost in malignancy, may reflect an important epigenetic barrier to tumorigenesis. This suggestion is further corroborated, at least in part, by the deranged expression of the most relevant H3 modifier enzymes, as revealed by bioinformatic analysis. Overall, our study indicates that the simultaneous occurrence of H3K9me2 and H3K79me3 is fundamental to ensure the integrity of differentiated tissues and, thus, their combined evaluation may represent a novel diagnostic marker and potential therapeutic target.

Artificial Intelligence for Classifying the Relationship between Impacted Third Molar and Mandibular Canal on Panoramic Radiographs.

The purpose of this investigation was to evaluate the diagnostic performance of two convolutional neural networks (CNNs), namely ResNet-152 and VGG-19, in analyzing, on panoramic images, the rapport that exists between the lower third molar (MM3) and the mandibular canal (MC), and to compare this performance with that of an inexperienced observer (a sixth year dental student). Utilizing the k-fold cross-validation technique, 142 MM3 images, cropped from 83 panoramic images, were split into 80% as training and validation data and 20% as test data. They were subsequently labeled by an experienced radiologist as the gold standard. In order to compare the diagnostic capabilities of CNN algorithms and the inexperienced observer, the diagnostic accuracy, sensitivity, specificity, and positive predictive value (PPV) were determined. ResNet-152 achieved a mean sensitivity, specificity, PPV, and accuracy, of 84.09%, 94.11%, 92.11%, and 88.86%, respectively. VGG-19 achieved 71.82%, 93.33%, 92.26%, and 85.28% regarding the aforementioned characteristics. The dental student's diagnostic performance was respectively 69.60%, 53.00%, 64.85%, and 62.53%. This work demonstrated the potential use of deep CNN architecture for the identification and evaluation of the contact between MM3 and MC in panoramic pictures. In addition, CNNs could be a useful tool to assist inexperienced observers in more accurately identifying contact relationships between MM3 and MC on panoramic images.

Long-term follow-up of rituximab plus bendamustine and cytarabine in older patients with newly diagnosed MCL.

The combination of rituximab, bendamustine, and low-dose cytarabine (R-BAC) has been studied in a phase 2 prospective multicenter study from Fondazione Italiana Linfomi (RBAC500). In 57 previously untreated elderly patients with mantle cell lymphoma (MCL), R-BAC was associated with a complete remission rate of 91% and 2-year progression-free survival (PFS) of 81% (95% confidence interval [CI], 68-89). Here, we report the long-term survival outcomes, late toxicities, and results of minimal residual disease (MRD) evaluation. After a median follow-up of 86 months (range, 57-107 months), the median overall survival (OS) and PFS were not reached. The 7-year PFS and OS rates were 55% (95% CI, 41-67), and 63% (95% CI, 49-74), respectively. Patients who responded (n = 53) had a 7-year PFS of 59% (95% CI, 44-71), with no relapse or progression registered after the sixth year. In the multivariate analysis, blastoid/pleomorphic morphology was the strongest adverse predictive factor for PFS (P = .04). Patients with an end of treatment negative MRD had better, but not significant, outcomes for both PFS and OS than patients with MRD-positive (P = 0.148 and P = 0.162, respectively). There was no signal of late toxicity or an increase in secondary malignancies during the prolonged follow-up. In conclusion, R-BAC, which was not followed by maintenance therapy, showed sustained efficacy over time in older patients with MCL. Survival outcomes compare favorably with those of other immunochemotherapy regimens (with or without maintenance), including combinations of BTK inhibitors upfront. This study was registered with EudraCT as 2011-005739-23 and at www.clinicaltrials.gov as #NCT01662050.

An Overview of In Vitro Assays of 64Cu-, 68Ga-, 125I-, and 99mTc-Labelled Radiopharmaceuticals Using Radiometric Counters in the Era of Radiotheranostics.

Radionuclides are unstable isotopes that mainly emit alpha (α), beta (ß) or gamma (γ) radiation through radiation decay. Therefore, they are used in the biomedical field to label biomolecules or drugs for diagnostic imaging applications, such as positron emission tomography (PET) and/or single-photon emission computed tomography (SPECT). A growing field of research is the development of new radiopharmaceuticals for use in cancer treatments. Preclinical studies are the gold standard for translational research. Specifically, in vitro radiopharmaceutical studies are based on the use of radiopharmaceuticals directly on cells. To date, radiometric ß- and γ-counters are the only tools able to assess a preclinical in vitro assay with the aim of estimating uptake, retention, and release parameters, including time- and dose-dependent cytotoxicity and kinetic parameters. This review has been designed for researchers, such as biologists and biotechnologists, who would like to approach the radiobiology field and conduct in vitro assays for cellular radioactivity evaluations using radiometric counters. To demonstrate the importance of in vitro radiopharmaceutical assays using radiometric counters with a view to radiogenomics, many studies based on 64Cu-, 68Ga-, 125I-, and 99mTc-labeled radiopharmaceuticals have been revised and summarized in this manuscript.

Proton boron capture therapy (PBCT) induces cell death and mitophagy in a heterotopic glioblastoma model.

Despite aggressive therapeutic regimens, glioblastoma (GBM) represents a deadly brain tumor with significant aggressiveness, radioresistance and chemoresistance, leading to dismal prognosis. Hypoxic microenvironment, which characterizes GBM, is associated with reduced therapeutic effectiveness. Moreover, current irradiation approaches are limited by uncertain tumor delineation and severe side effects that comprehensively lead to unsuccessful treatment and to a worsening of the quality of life of GBM patients. Proton beam offers the opportunity of reduced side effects and a depth-dose profile, which, unfortunately, are coupled with low relative biological effectiveness (RBE). The use of radiosensitizing agents, such as boron-containing molecules, enhances proton RBE and increases the effectiveness on proton beam-hit targets. We report a first preclinical evaluation of proton boron capture therapy (PBCT) in a preclinical model of GBM analyzed via µ-positron emission tomography/computed tomography (µPET-CT) assisted live imaging, finding a significant increased therapeutic effectiveness of PBCT versus proton coupled with an increased cell death and mitophagy. Our work supports PBCT and radiosensitizing agents as a scalable strategy to treat GBM exploiting ballistic advances of proton beam and increasing therapeutic effectiveness and quality of life in GBM patients.

A novel tumour enhancer function of Insulin-like growth factor II mRNA-binding protein 3 in colorectal cancer.

CRC cells evolve a variety of strategies to limit or circumvent apoptosis cell death. RNA binding proteins (RBPs) regulate many of the molecular mechanisms that underlie the development of cancer. The insulin-like growth factor II mRNA-binding proteins (IMP) family are oncofoetal RBPs, consisting of IMP1, IMP2 and IMP3, which have an important role in RNA metabolism. IMP3 is highly expressed in colorectal cancer (CRC) tissue, where its expression often correlates with poor prognosis. However, the role of IMP3 in CRC is not fully understood. IMP3 expression was analysed using a public database and by Western blotting and immunohistochemistry in human colon samples derived from patients with sporadic CRC and healthy subjects. To address whether IMP3 controls cancer cell survival, we analysed cell death pathways in in vitro and in vivo experiments after IMP3 downregulation by siRNA or an antisense oligonucleotide. IMP3 was highly expressed in CRC samples compared to normal control tissues. The knockdown of IMP3 enhanced a caspase-independent cell death in CRC cell lines. Furthermore, the treatment of CRC cells with IMP3 siRNA did not alter the expression of GSDMD, GPX-4 and the activated form of RIP3, three key molecules that govern pyroptosis, ferroptosis and necroptosis, respectively. Abrogation of IMP3 in CRC significantly reduced Bcl-2 and Bcl-xL mRNA and was associated with an altered mitochondrial membrane potential that allowed the nuclear migration of the apoptosis-inducing factor (AIF). Moreover, specific immunoprecipitation experiments on CRC human cell lines indicated that IMP3 binds Bcl-2 and Bcl-xL mRNA, suggesting that IMP3 acts as a regulator of the intrinsic apoptotic pathway through the surveillance of anti-apoptotic Bcl mRNA metabolism. Finally, we showed that IMP3 block inhibited the growth of CRC cell lines in vivo after transplantation into immunodeficient mice. Altogether, these data support a novel role for IMP3 in controlling the intrinsic caspase-independent apoptotic pathway in CRC.

Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics?

The aim of this study was to investigate the usefulness of radiomics in the absence of well-defined standard guidelines. Specifically, we extracted radiomics features from multicenter computed tomography (CT) images to differentiate between the four histopathological subtypes of non-small-cell lung carcinoma (NSCLC). In addition, the results that varied with the radiomics model were compared. We investigated the presence of the batch effects and the impact of feature harmonization on the models' performance. Moreover, the question on how the training dataset composition influenced the selected feature subsets and, consequently, the model's performance was also investigated. Therefore, through combining data from the two publicly available datasets, this study involves a total of 152 squamous cell carcinoma (SCC), 106 large cell carcinoma (LCC), 150 adenocarcinoma (ADC), and 58 no other specified (NOS). Through the matRadiomics tool, which is an example of Image Biomarker Standardization Initiative (IBSI) compliant software, 1781 radiomics features were extracted from each of the malignant lesions that were identified in CT images. After batch analysis and feature harmonization, which were based on the ComBat tool and were integrated in matRadiomics, the datasets (the harmonized and the non-harmonized) were given as an input to a machine learning modeling pipeline. The following steps were articulated: (i) training-set/test-set splitting (80/20); (ii) a Kruskal-Wallis analysis and LASSO linear regression for the feature selection; (iii) model training; (iv) a model validation and hyperparameter optimization; and (v) model testing. Model optimization consisted of a 5-fold cross-validated Bayesian optimization, repeated ten times (inner loop). The whole pipeline was repeated 10 times (outer loop) with six different machine learning classification algorithms. Moreover, the stability of the feature selection was evaluated. Results showed that the batch effects were present even if the voxels were resampled to an isotropic form and whether feature harmonization correctly removed them, even though the models' performances decreased. Moreover, the results showed that a low accuracy (61.41%) was reached when differentiating between the four subtypes, even though a high average area under curve (AUC) was reached (0.831). Further, a NOS subtype was classified as almost completely correct (true positive rate ~90%). The accuracy increased (77.25%) when only the SCC and ADC subtypes were considered, as well as when a high AUC (0.821) was obtained-although harmonization decreased the accuracy to 58%. Moreover, the features that contributed the most to models' performance were those extracted from wavelet decomposed and Laplacian of Gaussian (LoG) filtered images and they belonged to the texture feature class.. In conclusion, we showed that our multicenter data were affected by batch effects, that they could significantly alter the models' performance, and that feature harmonization correctly removed them. Although wavelet features seemed to be the most informative features, an absolute subset could not be identified since it changed depending on the training/testing splitting. Moreover, performance was influenced by the chosen dataset and by the machine learning methods, which could reach a high accuracy in binary classification tasks, but could underperform in multiclass problems. It is, therefore, essential that the scientific community propose a more systematic radiomics approach, focusing on multicenter studies, with clear and solid guidelines to facilitate the translation of radiomics to clinical practice.

CT Rendering and Radiomic Analysis in Post-Chemotherapy Retroperitoneal Lymph Node Dissection for Testicular Cancer to Anticipate Difficulties for Young Surgeons.

Post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) in non-seminomatous germ-cell tumor (NSTGCTs) is a complex procedure. We evaluated whether 3D computed tomography (CT) rendering and their radiomic analysis help predict resectability by junior surgeons. The ambispective analysis was performed between 2016-2021. A prospective group (A) of 30 patients undergoing CT was segmented using the 3D Slicer software while a retrospective group (B) of 30 patients was evaluated with conventional CT (without 3D reconstruction). CatFisher's exact test showed a p-value of 0.13 for group A and 1.0 for Group B. The difference between the proportion test showed a p-value of 0.009149 (IC 0.1-0.63). The proportion of the correct classification showed a p-value of 0.645 (IC 0.55-0.87) for A, and 0.275 (IC 0.11-0.43) for Group B. Furthermore, 13 shape features were extracted: elongation, flatness, volume, sphericity, and surface area, among others. Performing a logistic regression with the entire dataset, n = 60, the results were: Accuracy: 0.7 and Precision: 0.65. Using n = 30 randomly chosen, the best result obtained was Accuracy: 0.73 and Precision: 0.83, with a p-value: 0.025 for Fisher's exact test. In conclusion, the results showed a significant difference in the prediction of resectability with conventional CT versus 3D reconstruction by junior surgeons versus experienced surgeons. Radiomic features used to elaborate an artificial intelligence model improve the prediction of resectability. The proposed model could be of great support in a university hospital, allowing it to plan the surgery and to anticipate complications.

Anti-Arthritic and Anti-Cancer Activities of Polyphenols: A Review of the Most Recent In Vitro Assays.

Polyphenols have gained widespread attention as they are effective in the prevention and management of various diseases, including cancer diseases (CD) and rheumatoid arthritis (RA). They are natural organic substances present in fruits, vegetables, and spices. Polyphenols interact with various kinds of receptors and membranes. They modulate different signal cascades and interact with the enzymes responsible for CD and RA. These interactions involve cellular machinery, from cell membranes to major nuclear components, and provide information on their beneficial effects on health. These actions provide evidence for their pharmaceutical exploitation in the treatment of CD and RA. In this review, we discuss different pathways, modulated by polyphenols, which are involved in CD and RA. A search of the most recent relevant publications was carried out with the following criteria: publication date, 2012-2022; language, English; study design, in vitro; and the investigation of polyphenols present in extra virgin olive, grapes, and spices in the context of RA and CD, including, when available, the underlying molecular mechanisms. This review is valuable for clarifying the mechanisms of polyphenols targeting the pathways of senescence and leading to the development of CD and RA treatments. Herein, we focus on research reports that emphasize antioxidant properties.

Compromiso del parénquima pulmonar secundaria a la inyección subcutánea de geles de silicona: reporte de caso

El compromiso del parénquima pulmonar secundario a la inyección subcutánea de geles de silicona es un cuadro de presentación excepcional, que se presenta con mayor frecuencia en mujeres entre los 22 a 55 años. Aunque se han planteado distintas teorías sobre su etiología, esta se desconoce y puede presentar complicaciones locales, sistémicas e incluso tener un desenlace fatal. Son pocos los casos reportados en América del Sur, y en el Perú no existe reporte alguno de esta singular entidad. Se presenta el caso de un paciente varón de 28 años, transexual, previamente sano, quien tras la inyección subcutánea de geles de silicona en la región glútea, en forma ilegal y por una persona que no es profesional de la salud, manifestó, dentro de las primeras 24 horas, un cuadro clínico caracterizado por dificultad respiratoria progresiva y dolor torácico punzante de aproximadamente 7 de 10 en la escala del dolor. Al ingresar a emergencias se evidenció de manera objetiva insuficiencia respiratoria, ya que el paciente presentó una saturación de oxígeno del 72 % a Fio₂: 21 %, asimismo, el compromiso del parénquima pulmonar tanto en la tomografía y radiografía de tórax con signos muy sugerentes de esta patología. Mediante la prueba ARN para SARS-CoV-2 en la muestra respiratoria por RT-PCR en tiempo real se descartó la neumonía secundaria al virus SARS-CoV-2, igualmente se excluyó la patología por inmunosupresión y tromboembolismo pulmonar. Debido a que no existe un tratamiento estandarizado se le brindó las medidas de soporte pertinentes, tales como administración de oxígeno suplementario a bajo flujo por cánula binasal, corticoides sistémicos vía endovenosa, además de antibioticoterapia, y se logró una evolución favorable con resolución del cuadro clínico inicial. Después de 10 días de tratamiento intrahospitalario el paciente fue dado de alta.

Pulmonary parenchymal involvement secondary to the subcutaneous injection of silicone gels is an unusual condition which occurs more frequently in women aged between 22 and 55 years. Although different theories have been put forward about its etiology, it is unknown and the condition may cause local and systemic complications and even have a fatal outcome. Few cases have been reported in South America and there is no report of this unique entity in Peru. We present the case of a previously healthy 28-year-old male transgender patient who, after an illegal subcutaneous injection of silicone gels in the gluteal region given by a non-healthcare professional, showed progressive respiratory distress and stabbing chest pain of approximately 7 out of 10 on the pain scale within the first 24 hours. Upon admission to the emergency room, respiratory failure was objectively evidenced since the patient had an oxygen saturation of 72 % at a FiO₂ of 21 %, as well as pulmonary parenchymal involvement both in the CT scan and chest X-ray with signs highly suggestive of this pathology. Using a SARS-CoV-2 RNA real-time RT-PCR test performed on a respiratory specimen, COVID pneumonia, immunodeficiency disorders and pulmonary embolism were ruled out. Since there is no standard treatment, the patient was given relevant support measures such as the administration of supplemental oxygen at a low flow rate by binasal cannula, intravenous systemic corticosteroids and antibiotic therapy, thus achieving good progress with resolution of the initial clinical presentation. Then, after 10 days of intrahospital treatment, the patient was discharged.

Mirabegron relaxes arteries from human visceral adipose tissue through antagonism of α1-adrenergic receptors.

AIM: As inadequate perfusion has emerged as a key determinant of adipose tissue dysfunction in obesity, interest has grown regarding possible pharmacological interventions to prevent this process. Mirabegron has proved to improve insulin sensitivity and glucose homeostasis in obese humans via stimulation of ß3-adrenoceptors which also seem to mediate endothelium-dependent vasodilation in disparate human vascular beds. We characterized, therefore, the vasomotor function of mirabegron in human adipose tissue arteries and the underlying mechanisms. METHODS: Small arteries (116-734 µm) isolated from visceral adipose tissue were studied ex vivo in a wire myograph. After vessels had been contracted, changes in vascular tone in response to mirabegron were determined under different conditions. RESULTS: Mirabegron did not elicit vasorelaxation in vessels contracted with U46619 or high-K+ (both P > 0.05). Notably, mirabegron markedly blunted the contractile effect of the α1-adrenergic receptor agonist phenylephrine (P < 0.001) either in presence or absence of the vascular endothelium. The anti-contractile action of mirabegron on phenylephrine-induced vasoconstriction was not influenced by the presence of the selective ß3-adrenoceptor blocker L-748,337 (P < 0.05); lack of involvement of ß3-adrenoceptors was further supported by absent vascular staining for them at immunohistochemistry. CONCLUSIONS: Mirabegron induces endothelium-independent vasorelaxation in arteries from visceral adipose tissue, likely through antagonism of α1-adrenoceptors.

matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model.

Radiomics aims to support clinical decisions through its workflow, which is divided into: (i) target identification and segmentation, (ii) feature extraction, (iii) feature selection, and (iv) model fitting. Many radiomics tools were developed to fulfill the steps mentioned above. However, to date, users must switch different software to complete the radiomics workflow. To address this issue, we developed a new free and user-friendly radiomics framework, namely matRadiomics, which allows the user: (i) to import and inspect biomedical images, (ii) to identify and segment the target, (iii) to extract the features, (iv) to reduce and select them, and (v) to build a predictive model using machine learning algorithms. As a result, biomedical images can be visualized and segmented and, through the integration of Pyradiomics into matRadiomics, radiomic features can be extracted. These features can be selected using a hybrid descriptive-inferential method, and, consequently, used to train three different classifiers: linear discriminant analysis, k-nearest neighbors, and support vector machines. Model validation is performed using k-fold cross-Validation and k-fold stratified cross-validation. Finally, the performance metrics of each model are shown in the graphical interface of matRadiomics. In this study, we discuss the workflow, architecture, application, future development of matRadiomics, and demonstrate its working principles in a real case study with the aim of establishing a reference standard for the whole radiomics analysis, starting from the image visualization up to the predictive model implementation.