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Cellular alignment plays a pivotal role in several human tissues, including skeletal muscle, spinal cord and tendon. Various techniques have been developed to control cellular alignment using 3D biomaterials. However, the majority of 3D-aligned scaffolds require invasive surgery for implantation. In contrast, injectable hydrogels provide a non-invasive delivery method, gaining considerable attention for the treatment of diverse conditions, including osteochondral lesions, volumetric muscle loss, and traumatic brain injury.We engineered a biomimetic hydrogel with magnetic responsiveness by combining gellan gum, hyaluronic acid, collagen, and magnetic nanoparticles (MNPs). Collagen type I was paired with MNPs to form magnetic collagen bundles (MCollB), allowing the orientation control of these bundles within the hydrogel matrix through the application of a remote low-intensity magnetic field. This resulted in the creation of an anisotropic architecture. The hydrogel mechanical properties were comparable to those of human soft tissues, such as skeletal muscle, and proof of the aligned hydrogel concept was demonstrated.In vitro findings confirmed the absence of toxicity and pro-inflammatory effects. Notably, an increased fibroblast cell proliferation and pro-regenerative activation of macrophages were observed. The in-vivo study further validated the hydrogel biocompatibility and demonstrated the feasibility of injection with rapid in situ gelation. Consequently, this magnetically controlled injectable hydrogel exhibits significant promise as a minimally invasive, rapid gelling and effective treatment for regenerating various aligned human tissues.

Phonocardiogram (PCG) signals generated by the heart contain information about heart conditions. This review examines how PCG analysis identifies and diagnoses heart issues. We studied traditional signal processing and artificial intelligence techniques and provided a complete picture of the current state of this field. Adhering to the systematic review guidelines, our comprehensive review covers 103 studies from reputed journals. It includes Machine Learning (ML) and Deep Learning (DL) techniques used to develop the computer-aided diagnostic tools using PCG signals. This review evaluates the strengths and weaknesses of various ML and DL methods, emphasizing their effectiveness in diagnosing several abnormalities. Additionally, we examine the obstacles and challenges limiting the widespread adoption of PCG-based diagnostic systems in clinical settings. We outline a plan for future research to develop improved versions of PCG analysis models. These models will be more robust, precise, and user-friendly. They will improve cardiovascular care by enabling machines to screen for problems automatically and intelligently.

Deep learning methods have shown promise for automated medical image analysis tasks. However, class imbalance is a common challenge that can negatively impact model performance, especially for tasks with minority classes that are clinically significant. This study aims to address this challenge through a novel hyperparameter optimization technique for training convolutional neural networks on imbalanced data. We developed a custom Convolutional Neural Network (CNN) architecture and introduced a Tangent Optimization Algorithm (TOA) based on the trigonometric properties of the tangent function. The TOA optimizes hyperparameters during training without requiring data preprocessing or augmentation steps. We applied our approach to classifying B-mode ultrasound carotid artery plaque images as symptomatic or asymptomatic using a dataset with significant class imbalance. On k-fold cross-validation, our method achieved an average accuracy of 98.82%, a sensitivity of 99.41%, and a specificity of 95.74%. The proposed optimization technique provides a computationally efficient and interpretable solution for training deep learning models on unbalanced medical image datasets.

Automated segmentation of histological structures in microscopy images is a crucial step in computer-aided diagnosis framework. However, this task remains a challenging problem due to issues like overlapping and touching objects, shape variation, and background complexity. In this work, we present a novel and effective approach for instance segmentation through the synergistic combination of two deep learning networks (detection and segmentation models) with active shape models. Our method, called softmax-driven active shape model (SD-ASM), uses information from deep neural networks to initialize and evolve a dynamic deformable model. The detection module enables treatment of individual objects separately, while the segmentation map precisely outlines boundaries. We conducted extensive tests using various state-of-the-art architectures on two standard datasets for segmenting crowded objects like cell nuclei - MoNuSeg and CoNIC. The proposed SD-ASM consistently outperformed reference methods, achieving up to 8.93% higher Aggregated Jaccard Index (AJI) and 9.84% increase in Panoptic Quality (PQ) score compared to segmentation networks alone. To emphasize versatility, we also applied SD-ASMs to segment hepatic steatosis and renal tubules, where individual structure identification is critical. Once again, integration of SD-ASM with deep models enhanced segmentation accuracy beyond prior works by up to 6.2% in AJI and 38% decrease in Hausdorff Distance. The proposed approach demonstrates effectiveness in accurately segmenting touching objects across multiple clinical scenarios.

Abstract Prion disease is a fatal neurodegenerative disorder characterized by accumulation of an abnormal prion protein (PrPSc) in the central nervous system. To identify PrPSc aggregates for diagnostic purposes, pathologists use immunohistochemical staining of prion protein antibodies on tissue samples. With digital pathology, artificial intelligence can now analyze stained slides. In this study, we developed an automated pipeline for the identification of PrPSc aggregates in tissue samples from the cerebellar and occipital cortex. To the best of our knowledge, this is the first framework to evaluate PrPSc deposition in digital images. We used two strategies: a deep learning segmentation approach using a vision transformer, and a machine learning classification approach with traditional classifiers. Our method was developed and tested on 64 whole slide images from 41 patients definitively diagnosed with prion disease. The results of our study demonstrated that our proposed framework can accurately classify WSIs from a blind test set. Moreover, it can quantify PrPSc distribution and localization throughout the brain. This could potentially be extended to evaluate protein expression in other neurodegenerative diseases like Alzheimer's and Parkinson's. Overall, our pipeline highlights the potential of AI-assisted pathology to provide valuable insights, leading to improved diagnostic accuracy and efficiency.

Recent years have ushered in a transformative era in in vitro modeling with the advent of organoids, three-dimensional structures derived from stem cells or patient tumor cells. Still, fully harnessing the potential of organoids requires advanced imaging technologies and analytical tools to quantitatively monitor organoid growth. Optical coherence tomography (OCT) is a promising imaging modality for organoid analysis due to its high-resolution, label-free, non-destructive, and real-time 3D imaging capabilities, but accurately identifying and quantifying organoids in OCT images remain challenging due to various factors. Here, we propose an automatic deep learning-based pipeline with convolutional neural networks that synergistically includes optimized preprocessing steps, the implementation of a state-of-the-art deep learning model, and ad-hoc postprocessing methods, showcasing good generalizability and tracking capabilities over an extended period of 13 days. The proposed tracking algorithm thoroughly documents organoid evolution, utilizing reference volumes, a dual branch analysis, key attribute evaluation, and probability scoring for match identification. The proposed comprehensive approach enables the accurate tracking of organoid growth and morphological changes over time, advancing organoid analysis and serving as a solid foundation for future studies for drug screening and tumor drug sensitivity detection based on organoids.

Intracranial hematoma due to traumatic brain injury is a serious health concern with rates of morbidity and mortality that are increasing worldwide. Manual identification is slow, subject to observer variabilities, and the existing automated techniques for intracranial hematoma detection in non-contrast computed tomography images cannot effectively detect multiple lesions of irregular sizes and shapes. Therefore, a computer-aided system using different window settings, YOLOv5s, cascaded attention module, and spatial pyramid pooling-fast is proposed to detect hematoma types, namely acute intraparenchymal, intraventricular, subdural, epidural, subarachnoid, and chronic subdural. Firstly, the computed tomography images are pre-processed using a window-based stacking approach wherein a three-channel image is generated by stacking grayscale images obtained with the aid of multiple window settings, i.e, brain, bone, and subdural. Secondly, a cascaded attention module is constructed in the neck of the YOLOv5s model to improve its detection performance by placing the convolution block attention module in serial with the efficient channel attention module. The cascaded attention module enriches the feature representation of various hematoma types in complex backgrounds especially when they are small and inconspicuous. The spatial pyramid pooling is replaced by a spatial pyramid pooling-fast to reduce the computational parameters and accelerate the feature fusion ability. The proposed deep learning model is trained, validated, and tested with 15,921 images from the brain haemorrhage extended dataset and it achieved overall precision, recall, F1-score, and mean average precision at 0.5, and mean average precision at 0.5:0.95 of 0.935, 0.908, 0.921, 0.943 and 0.65 respectively. The experimental results show that in comparison to the original YOLOv5s model and state-of-the-art methods, the model was able to localize and classify the acute or chronic instances of five hematoma subtypes in an individual image with improved precision and recall values. Hence the proposed system can be used in hospitals for the early and accurate detection of hematoma.

One of the foremost causes of death in males worldwide is prostate cancer. The identification, detection and diagnosis of the same is very crucial in saving lives. In this paper, we present an efficient gland segmentation model using digital histopathology and deep learning. These methods have the potential to revolutionize medicine by identifying hidden patterns within the image. The recent improvements in data acquisition, processing and analysis of Deep Learning Models has made Artificial Intelligence driven healthcare a very lucrative area, in terms of data inference and delivering meaningful insights. This study presents an automated method for segmenting histopathological images of human prostate glands. The main focus is developing new methods for segmenting histopathological images of prostate gland using a multi-channel algorithm with an attention mechanism to detect important areas. We compare our results with a host of contemporary techniques and show that our method performs better at the segmentation task for histopathological imagery. Our method is able to delineate gland and background parts with an average Dice-coefficient of 0.9168. In this attention-based model we propose for semantic segmentation of prostate glands the potential to provide accurate segmentation versus tumor features, which has significant implications for medical screening applications.

Background: It has been reported that coronavirus disease 2019 (COVID-19) vaccines could provoke flu-like symptoms and changes in menstrual cycles in some women, probably as a result of the immune response caused by the vaccination itself. Altered local immunity and inflammatory processes are found in women suffering from adenomyosis and endometriosis, this having a role in the typical symptomatic manifestations. This study aims to investigate the prevalence of side effects like abnormal uterine bleeding (AUB) or pain following the administration of COVID-19 vaccines in women with adenomyosis or endometriosis. Methods: A retrospective cohort study was performed on 172 patients referring to our Chronic Pelvic Pain Center, who underwent COVID-19 vaccination. A hystorical cohort of sixty-three non-vaccinated women was used as control. We collected anamnestic data and each woman scored menstrual pain, chronic pelvic pain, and the occurrence of AUB before and after vaccination. Results: Among control women, no one showed AUB, and only 2 women experienced a slight worsening of menstrual pain. Side effects were observed in 29/172 (16.8%) of COVID-19 vaccinated women, independent of whether they were on hormone therapy. In comparison to asymptomatic, symptomatic women had a higher prevalence of adenomyosis (82.7% vs 63.6%), adenomyosis being present in 100% of the 6 women with intermenstrual bleeding, in 79% of the 19 women with heavy menstrual bleeding and in 81% of the 16 women with pain worsening. Nine out of 55 (16.3%) women with endometrioma experienced side effects. Among these, the 3 women that were not on hormone therapy experienced a huge increase in endometrioma volume from +208% to +806%. In one case emergency surgery was necessary for endometrioma rupture and hemoperitoneum. Conclusions: In our sample, adenomyosis appears strictly related to the manifestation of post-vaccine side effects. Hormone therapy seems to be insufficient to protect from post-vaccine symptoms, but the increase in endometrioma volume was observed only in cases without hormone therapy.

SARS-CoV-2 is inactivated in aerosol (its primary mode of transmission) by means of radiated microwaves at frequencies that have been experimentally determined. Such frequencies are best predicted by the mathematical model suggested by Taylor, Margueritat and Saviot. The alignment between such mathematical prediction and the outcomes of our experiments serves to reinforce the efficacy of the radiated microwave technology and its promise in mitigating the transmission of SARS-CoV-2 in its naturally airborne state.

Coronaviruses are a family of viruses that cause disease in mammals and birds. In humans, coronaviruses cause infections on the respiratory tract that can be fatal. These viruses can cause both mild illnesses such as the common cold and lethal illnesses such as SARS, MERS, and COVID-19. Air transmission represents the principal mode by which people become infected by SARS-CoV-2. To reduce the risks of air transmission of this powerful pathogen, we devised a method of inactivation based on the propagation of electromagnetic waves in the area to be sanitized. We optimized the conditions in a controlled laboratory environment mimicking a natural airborne virus transmission and consistently achieved a 90% (tenfold) reduction of infectivity after a short treatment using a Radio Frequency (RF) wave emission with a power level that is safe for people according to most regulatory agencies, including those in Europe, USA, and Japan. To the best of our knowledge, this is the first time that SARS-CoV-2 has been shown to be inactivated through RF wave emission under conditions compatible with the presence of human beings and animals. Additional in-depth studies are warranted to extend the results to other viruses and to explore the potential implementation of this technology in different environmental conditions.

Lignin is the second most abundant polymer in nature, which is also widely generated during biomass fractionation in lignocellulose biorefineries. At present, most of technical lignin is simply burnt for energy supply although it represents the richest natural source of aromatics, and thus it is a promising feedstock for generation of value-added compounds. Lignin is heterogeneous in composition and recalcitrant to degradation, with this substantially hampering its use. Notably, microbes have evolved particular enzymes and specialized metabolic pathways to degrade this polymer and metabolize its various aromatic components. In recent years, novel pathways have been designed allowing to establish engineered microbial cell factories able to efficiently funnel the lignin degradation products into few metabolic intermediates, representing suitable starting points for the synthesis of a variety of valuable molecules. This review focuses on recent success cases (at the laboratory/pilot scale) based on systems metabolic engineering studies aimed at generating value-added and specialty chemicals, with much emphasis on the production of cis,cis-muconic acid, a building block of recognized industrial value for the synthesis of plastic materials. The upgrade of this global waste stream promises a sustainable product portfolio, which will become an industrial reality when economic issues related to process scale up will be tackled.

Abstract Objective To identify the impact of segmentation methods and intensity discretization on radiomic features (RFs) extraction from 68Ga-DOTA-TOC PET images in patients with neuroendocrine tumors. Methods Forty-nine patients were retrospectively analyzed. Tumor contouring was performed manually by four different operators and with a semi-automatic edge-based segmentation (SAEB) algorithm. Three SUVmax fixed thresholds (20, 30, 40%) were applied. Fifty-one RFs were extracted applying two different intensity rescale factors for gray-level discretization: one absolute (AR60 = SUV from 0 to 60) and one relative (RR = min-max of the VOI SUV). Dice similarity coefficient (DSC) was calculated to quantify segmentation agreement between different segmentation methods. The impact of segmentation and discretization on RFs was assessed by intra-class correlation coefficients (ICC) and the coefficient of variance (COV L ). The RFs’ correlation with volume and SUVmax was analyzed by calculating Pearson’s correlation coefficients. Results DSC mean value was 0.75 ± 0.11 (0.45–0.92) between SAEB and operators and 0.78 ± 0.09 (0.36–0.97), among the four manual segmentations. The study showed high robustness (ICC > 0.9): (a) in 64.7% of RFs for segmentation methods using AR60, improved by applying SUVmax threshold of 40% (86.5%); (b) in 50.9% of RFs for different SUVmax thresholds using AR60; and (c) in 37% of RFs for discretization settings using different segmentation methods. Several RFs were not correlated with volume and SUVmax. Conclusions RFs robustness to manual segmentation resulted higher in NET 68Ga-DOTA-TOC images compared to 18F-FDG PET/CT images. Forty percent SUVmax thresholds yield superior RFs stability among operators, however leading to a possible loss of biological information. SAEB segmentation appears to be an optimal alternative to manual segmentation, but further validations are needed. Finally, discretization settings highly impacted on RFs robustness and should always be stated.

In the presence of gestational diabetes mellitus (GDM), the fetus is exposed to a hyperinsulinemia environment. This environment can cause a wide range of metabolic and fetal cardiac structural alterations. Fetal myocardial hypertrophy predominantly affecting the interventricular septum possesses a morphology of disarray similar to hypertrophic cardiomyopathy, and may be present in some GDM neonates after birth. Myocardial thickness may increase in GDM fetuses independent of glycemic control status and fetal weight. Fetal echocardiography performed on fetuses with GDM helps in assessing cardiac structure and function, and to diagnose myocardial hypertrophy. There are few studies in the literature which have established evidence for morphologic variation associated with cardiac hypertrophy among fetuses of GDM mothers. In this study, fetal ultrasound images of normal, pregestational diabetes mellitus (preGDM) and GDM mothers were used to develop a computer aided diagnostic (CAD) tool. We proposed a new method called local preserving class separation (LPCS) framework to preserve the geometrical configuration of normal and preGDM/GDM subjects. The generated shearlet based texture features under LPCS framework showed promising results compared with deep learning algorithms. The proposed method achieved a maximum accuracy of 98.15% using a support vector machine (SVM) classifier. Hence, this paradigm can be helpful to physicians in detecting fetal myocardial hypertrophy in preGDM/GDM mothers.

Background: Laser-Doppler Vibrometry (LDV) is a laser-based technique that allows measuring the motion of moving targets with high spatial and temporal resolution. To demonstrate its use for the measurement of carotid-femoral pulse wave velocity, a prototype system was employed in a clinical feasibility study. Data were acquired for analysis without prior quality control. Real-time application, however, will require a real-time assessment of signal quality. In this study, we (1) use template matching and matrix profile for assessing the quality of these previously acquired signals; (2) analyze the nature and achievable quality of acquired signals at the carotid and femoral measuring site; (3) explore models for automated classification of signal quality.Methods: Laser-Doppler Vibrometry data were acquired in 100 subjects (50M/50F) and consisted of 4–5 sequences of 20-s recordings of skin displacement, differentiated two times to yield acceleration. Each recording consisted of data from 12 laser beams, yielding 410 carotid-femoral and 407 carotid-carotid recordings. Data quality was visually assessed on a 1–5 scale, and a subset of best quality data was used to construct an acceleration template for both measuring sites. The time-varying cross-correlation of the acceleration signals with the template was computed. A quality metric constructed on several features of this template matching was derived. Next, the matrix-profile technique was applied to identify recurring features in the measured time series and derived a similar quality metric. The statistical distribution of the metrics, and their correlates with basic clinical data were assessed. Finally, logistic-regression-based classifiers were developed and their ability to automatically classify LDV-signal quality was assessed.Results: Automated quality metrics correlated well with visual scores. Signal quality was negatively correlated with BMI for femoral recordings but not for carotid recordings. Logistic regression models based on both methods yielded an accuracy of minimally 80% for our carotid and femoral recording data, reaching 87% for the femoral data.Conclusion: Both template matching and matrix profile were found suitable methods for automated grading of LDV signal quality and were able to generate a quality metric that was on par with the signal quality assessment of the expert. The classifiers, developed with both quality metrics, showed their potential for future real-time implementation.

In digital pathology, the final appearance of digitized images is affected by several factors, resulting in stain color and intensity variation. Stain normalization is an innovative solution to overcome stain variability. However, the validation of color normalization tools has been assessed only from a quantitative perspective, through the computation of similarity metrics between the original and normalized images. To the best of our knowledge, no works investigate the impact of normalization on the pathologist’s evaluation.The objective of this paper is to propose a multi-tissue (i.e., breast, colon, liver, lung, and prostate) and multi-center qualitative analysis of a stain normalization tool with the involvement of pathologists with different years of experience. Two qualitative studies were carried out for this purpose: (i) a first study focused on the analysis of the perceived image quality and absence of significant image artifacts after the normalization process; (ii) a second study focused on the clinical score of the normalized image with respect to the original one.The results of the first study prove the high quality of the normalized image with a low impact artifact generation, while the second study demonstrates the superiority of the normalized image with respect to the original one in clinical practice.The normalization process can help both to reduce variability due to tissue staining procedures and facilitate the pathologist in the histological examination. The experimental results obtained in this work are encouraging and can justify the use of a stain normalization tool in clinical routine.

Difficulty in controlling SARS-CoV-2 transmission made the ability to inactivate viruses in aerosols and fomites to be an important and attractive risk reduction measure. Evidence that light frequencies have the ability to inhibit microorganisms has already been reported by many studies which, however, focused on ultraviolet (UV) wavelengths, which are known to induce potential injury in humans. In the present study, the effect on suspensions of SARS-CoV-2 of a Light Emitting Diode (LED) device capable of radiating frequencies in the non-hazardous visible light spectrum (VIS) was investigated. In order to evaluate the efficiency of viral inactivation, plaque assay and western blot of viral proteins were performed. The observed results showed a significant reduction in infectious particles that had been exposed to the LED irradiation of visible light. Furthermore, the analysis of the intracellular expression of viral proteins confirmed the inactivating effect of this irradiation technology. This in vitro study revealed for the first time the inactivation of SARS-CoV-2 through LED irradiation with multiple wavelengths of the visible spectrum. However additional and more in-depth studies can aim to demonstrate the data obtained during these experiments in different matrices, in mutable environmental conditions and on other respiratory viruses such as the influenza virus. The type of LED technology can decisively contribute on reducing virus transmission through the continuous sanitation of common environments without risks for humans and animals.

Canine cutaneous round cell tumors (RCT) represent one of the routine diagnostic challenges for veterinary pathologists. Computer-aided approaches are developed to overcome these restrictions and to increase accuracy and consistency of diagnosis. These systems are also of high benefit reducing errors when a large number of cases are screened daily. In this study we describe ARCTA (Automated Round Cell Tumors Assessment), a fully automated algorithm for cutaneous RCT classification and mast cell tumors grading in canine histopathological images. ARCTA employs a deep learning strategy and was developed on 416 RCT images and 213 mast cell tumors images. In the test set, our algorithm exhibited an excellent classification performance in both RCT classification (accuracy: 91.66%) and mast cell tumors grading (accuracy: 100%). Misdiagnoses were encountered for histiocytomas in the train set and for melanomas in the test set. For mast cell tumors the reduction of a grade was observed in the train set, but not in the test set. To the best of our knowledge, the proposed model is the first fully automated algorithm in histological images specifically developed for veterinary medicine. Being very fast (average computational time 2.63 s), this algorithm paves the way for an automated and effective evaluation of canine tumors.

Aim: This work aims to evaluate whether the radiomic features extracted by 68Ga-DOTATOC-PET/CT of two patients are associated with the response to peptide receptor radionuclide therapy (PRRT) in patients affected by neuroendocrine tumor (NET).Methods: This is a pilot report in two NET patients who experienced a discordant response to PRRT (responder vs. non-responder) according to RECIST1.1. The patients presented with liver metastasis from the rectum and pancreas G3-NET, respectively. Whole-body total-lesion somatostatin receptor-expression (TLSREwb-50) and somatostatin receptor-expressing tumor volume (SRETV wb-50) were obtained in pre- and post-PRRT PET/CT. Radiomic analysis was performed, extracting 38 radiomic features (RFs) from the patients' lesions. The Mann–Whitney test was used to compare RFs in the responder patient vs. the non-responder patient. Pearson correlation and principal component analysis (PCA) were used to evaluate the correlation and independence of the different RFs.Results: TLSREwb-50 and SRETVwb-50 modifications correlate with RECIST1.1 response. A total of 28 RFs extracted on pre-therapy PET/CT showed significant differences between the two patients in the Mann–Whitney test (p < 0.05). A total of seven second-order features, with poor correlation with SUVmax and PET volume, were identified by the Pearson correlation matrix. Finally, the first two PCA principal components explain 83.8% of total variance.Conclusion: TLSREwb-50 and SRETVwb-50 are parameters that might be used to predict and to assess the PET response to PRRT. RFs might have a role in defining inter-patient heterogeneity and in the prediction of therapy response. It is important to implement future studies with larger and more homogeneous patient populations to confirm the efficacy of these biomarkers.

Background: Recently, deep learning has rapidly become the methodology of choice in digital pathology image analysis. However, due to the current challenges of digital pathology (color stain variability, large images, etc.), specific pre-processing steps are required to train a reliable deep learning model. Method: In this work, there are two main goals: i) present a fully automated pre-processing algorithm for a smart patch selection within histopathological images, and ii) evaluate the impact of the proposed strategy within a deep learning framework for the detection of prostate and breast cancer. The proposed algorithm is specifically designed to extract patches only on informative regions (i.e., high density of nuclei), most likely representative of where cancer can be detected. Results: Our strategy was developed and tested on 1000 hematoxylin and eosin (H&E) stained images of prostate and breast tissue. By combining a stain normalization step and a segmentation-driven patch extraction, the proposed approach is capable of increasing the performance of a computer-aided diagnosis (CAD) system for the detection of prostate cancer (18.61% accuracy improvement) and breast cancer (17.72% accuracy improvement). Conclusion: We strongly believe that the integration of the proposed pre-processing steps within deep learning frameworks will allow the achievement of robust and reliable CAD systems. Being based on nuclei detection, this strategy can be easily extended to other glandular tissues (e.g., colon, thyroid, pancreas, etc.) or staining methods (e.g., PAS).

Magnetic resonance imaging (MRI) has a growing role in the clinical workup of prostate cancer. However, manual three-dimensional (3D) segmentation of the prostate is a laborious and time-consuming task. In this scenario, the use of automated algorithms for prostate segmentation allows us to bypass the huge workload of physicians. In this work, we propose a fully automated hybrid approach for prostate gland segmentation in MR images using an initial segmentation of prostate volumes using a custom-made 3D deep network (VNet-T2), followed by refinement using an Active Shape Model (ASM). While the deep network focuses on three-dimensional spatial coherence of the shape, the ASM relies on local image information and this joint effort allows for improved segmentation of the organ contours. Our method is developed and tested on a dataset composed of T2-weighted (T2w) MRI prostatic volumes of 60 male patients. In the test set, the proposed method shows excellent segmentation performance, achieving a mean dice score and Hausdorff distance of 0.851 and 7.55 mm, respectively. In the future, this algorithm could serve as an enabling technology for the development of computer-aided systems for prostate cancer characterization in MR imaging.

The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child’s outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field.

Hypertrophic cardiomyopathy (HCM) is a genetic disorder that exhibits a wide spectrum of clinical presentations, including sudden death. Early diagnosis and intervention may avert the latter. Left ventricular hypertrophy on heart imaging is an important diagnostic criterion for HCM, and the most common imaging modality is heart ultrasound (US). The US is operator-dependent, and its interpretation is subject to human error and variability. We proposed an automated computer-aided diagnostic tool to discriminate HCM from healthy subjects on US images. We used a local directional pattern and the ResNet-50 pretrained network to classify heart US images acquired from 62 known HCM patients and 101 healthy subjects. Deep features were ranked using Student’s t-test, and the most significant feature (SigFea) was identified. An integrated index derived from the simulation was defined as 100·log10(SigFea/2) in each subject, and a diagnostic threshold value was empirically calculated as the mean of the minimum and maximum integrated indices among HCM and healthy subjects, respectively. An integrated index above a threshold of 0.5 separated HCM from healthy subjects with 100% accuracy in our test dataset.

We performed next-generation sequencing (NGS), phylogenetic analysis, gene flows, and N- and O-glycosylation prediction on SARS-CoV-2 genomes collected from lab-confirmed cases from different Italian regions. To this end, a total of 111 SARS-CoV-2 genomes collected in Italy between 29 January and 27 March 2020 were investigated. The majority of the genomes belonged to lineage B.1, with some descendant lineages. The gene flow analysis showed that the spread occurred mainly from the north to the center and to the south of Italy, as confirmed by epidemiological data. The mean evolutionary rate estimated here was 8.731 × 10−4 (95% highest posterior density, HPD intervals 5.809 × 10−4 to 1.19 × 10−3), in line with values reported by other authors. The dated phylogeny suggested that SARS-CoV-2 lineage B.1 probably entered Italy between the end of January and early February 2020. Continuous molecular surveillance is needed to trace virus circulation and evolution.

This work describes the development of an injectable nanocomposite system based on a chitosan thermosensitive hydrogel combined with liposomes for regenerative medicine applications. Liposomes with good physicochemical properties are prepared and embedded within the chitosan network. The resulting nanocomposite hydrogel is able to provide a controlled release of the content from liposomes, which are able to interact with cells and be internalized. The cellular uptake is enhanced by the presence of a chitosan coating, and cells incubated with liposomes embedded within thermosensitive hydrogels displayed a higher cell uptake compared to cells incubated with liposomes alone. Furthermore, the gelation temperature of the system resulted to be equal to 32.6 °C; thus, the system can be easily injected in the target site to form a hydrogel at physiological temperature. Given the peculiar performance of the selected systems, the resulting thermosensitive hydrogels are a versatile platform and display potential applications as controlled delivery systems of liposomes for tissue regeneration.

Abstract Background Accurate nuclei detection and segmentation in histological images is essential for many clinical purposes. While manual annotations are time-consuming and operator-dependent, full automated segmentation remains a challenging task due to the high variability of cells intensity, size and morphology. Most of the proposed algorithms for the automated segmentation of nuclei were designed for specific organ or tissues. Results The aim of this study was to develop and validate a fully multiscale method, named MANA (Multiscale Adaptive Nuclei Analysis), for nuclei segmentation in different tissues and magnifications. MANA was tested on a dataset of H&E stained tissue images with more than 59,000 annotated nuclei, taken from six organs (colon, liver, bone, prostate, adrenal gland and thyroid) and three magnifications (10×, 20×, 40×). Automatic results were compared with manual segmentations and three open-source software designed for nuclei detection. For each organ, MANA obtained always an F1-score higher than 0.91, with an average F1 of 0.9305 ± 0.0161. The average computational time was about 20 s independently of the number of nuclei to be detected (anyway, higher than 1000), indicating the efficiency of the proposed technique. Conclusion To the best of our knowledge, MANA is the first fully automated multi-scale and multi-tissue algorithm for nuclei detection. Overall, the robustness and versatility of MANA allowed to achieve, on different organs and magnifications, performances in line or better than those of state-of-art algorithms optimized for single tissues.

Aim: To evaluate if conventional Positron emission tomography (PET) parameters and radiomic features (RFs) extracted by 18F-FDG-PET/CT can differentiate among different histological subtypes of lung neuroendocrine neoplasms (Lu-NENs). Methods: Forty-four naïve-treatment patients on whom 18F-FDG-PET/CT was performed for histologically confirmed Lu-NEN (n = 46) were retrospectively included. Manual segmentation was performed by two operators allowing for extraction of four conventional PET parameters (SUVmax, SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG)) and 41 RFs. Lu-NENs were classified into two groups: lung neuroendocrine tumors (Lu-NETs) vs. lung neuroendocrine carcinomas (Lu-NECs). Lu-NETs were classified according to histological subtypes (typical (TC)/atypical carcinoid (AC)), Ki67-level, and TNM staging. The least absolute shrink age and selection operator (LASSO) method was used to select the most predictive RFs for classification and Pearson correlation analysis was performed between conventional PET parameters and selected RFs. Results: PET parameters, in particular, SUVmax (area under the curve (AUC) = 0.91; cut-off = 5.16) were higher in Lu-NECs vs. Lu-NETs (p < 0.001). Among RFs, HISTO_Entropy_log10 was the most predictive (AUC = 0.90), but correlated with SUVmax/SUVmean (r = 0.95/r = 0.94, respectively). No statistical differences were found between conventional PET parameters and RFs (p > 0.05) and TC vs. AC classification. Conventional PET parameters were correlated with N+ status in Lu-NETs. Conclusion: In our study, conventional PET parameters were able to distinguish Lu-NECs from Lu-NETs, but not TC from AC. RFs did not provide additional information.

Introduction: Carotid artery atherosclerosis is an established risk factor for cerebrovascular events. Core lipid-rich plaques are considered at a higher risk of embolization compared to fibrous or calcified lesions. Contrast enhanced ultrasound (CEUS) is effective for studying carotid plaques, providing a virtual histology [1]. Here we assess the behavior of non-surgical carotid plaques in terms of lipid variation over time. Methods: Eleven patients were enrolled (University of Turin) with a 50–69% (ECST) carotid asymptomatic stenosis. Seven patients were on statin therapy. All patients signed an informed consent and underwent standard carotid ultrasound (MyLab25 Gold, Esaote). A 1.5ml bolus of SonoVue (Bracco Spa) was injected; then a 5ml saline flush. Post-contrast Bmode images (180s after injection) were saved and analyzed offline. All patients repeated this protocol after 6 months. The plaques were segmented, intensity normalized [2], and characterized according to a previous method [3]. Results: We evaluated small cohorts according to lipid concentration changes, identifying four categories. One patient had a plaque showing no lipid variation; four patients showed a slight decrease; four patients a remarkable decrease; two patients an increase. Seven patients with a decrease in lipid concentration were on statin therapy, while both patients with an increase in lipids were not. Conclusions: A method for asymptomatic carotid plaque characterization using CEUS is presented. We focused on plaque lipid variations and the possible influence of statin therapy. We showed that carotid plaques are rarely stable, but rather continuously change composition over time and how statins could play an important role in this process.

Eye Movement Desensitization and Reprocessing (EMDR) is a psychotherapeutic treatment resolving emotional distress caused by traumatic events. With EMDR, information processing is facilitated by eye movements (EM) during the recall of a traumatic memory (RECALL). The aim of this study is to investigate the effects of ocular movements of EMDR on the hemodynamics of the prefrontal cortex (PFC).Two groups were recruited: a trial group (wEM) received a complete EMDR treatment, whereas a control group (woEM) received a therapy without EM. PFC hemodynamics was monitored by near-infrared spectroscopy during RECALL and during focusing on the worst image of the trauma (pre-RECALL). The parameters of oxy- (oxy-Hb), and deoxy-hemoglobin (deoxy-Hb) were acquired and analyzed in time domain, by calculating the slope within pre-RECALL and RECALL periods, and in the frequency domain, by calculating the mean power of oxy-Hb and deoxy-Hb in the very-low frequency (VLF, 20-40 mHz) and low frequency (LF, 40-140 mHz) bandwidths. We compared pre-RECALL with RECALL periods within subjects, and pre-RECALL and RECALL parameters of wEM with the corresponding of woEM.An effect of group on mean slope of oxy-Hb and deoxy-Hb in pre-RECALL and oxy-Hb in RECALL periods was observed. wEM showed a lower percentage of positive angular coefficients during pre-RECALL with respect to RECALL, on the opposite of woEM. In the frequency domain, wEM had significant difference in oxy-Hb and deoxy-Hb LF of left hemisphere, whereas woEM showed no difference.We observed the effect of EM on PFC oxygenation during EMDR, since wEM subjects showed a mean increase of oxy-Hb during RECALL and a decrease during pre-RECALL, as opposed to woEM. Frequency analysis evidenced a reduction of activity of sympathetic nervous system in wEM group during pre-RECALL. Our outcomes revealed a different hemodynamics induced by eye movements in wEM with respect to woEM group.

Abnormalities in B-mode ultrasound images of the patellar tendon often take place in asymptomatic athletes but it is still not clear if these modifications forego or can predict the development of tendinopathy. Subclinical tendinopathy can be arbitrarily defined as either (1) the presence of light structural changes in B-mode ultrasound images in association with mild neovascularization (determined with Power Doppler images) or (2) the presence of moderate/severe structural changes with or without neovascularization. Up to now, the structural changes and neovascularization of the tendon are evaluated qualitatively by visual inspection of ultrasound images. The aim of this study is to investigate the capability of a quantitative texture-based approach to determine tendon abnormality of “pallapugno” players. B-mode ultrasound images of the patellar tendon were acquired in 14 players and quantitative texture parameters were calculated within a Region of Interest (ROI) of both the non-dominant and the dominant tendon. A total of 90 features were calculated for each ROI, including 6 first-order descriptors, 24 Haralick features, and 60 higher-order spectra and entropy features. These features on the dominant and non-dominant side were used to perform a multivariate linear regression analysis (MANOVA) and our results show that the descriptors can be effectively used to determine tendon abnormality and, more importantly, the occurrence of subclinical tendinopathy.

Near-infrared spectroscopy (NIRS) is a noninvasive system for the real-time monitoring of the concentration of oxygenated (O2Hb) and reduced (HHb) hemoglobin in the brain cortex. O2Hb and HHb concentrations vary in response to cerebral autoregulation. Sixty-eight women (14 migraineurs without aura, 49 migraineurs with aura, and 5 controls) performed breath-holding and hyperventilation during NIRS recordings. Signals were processed using the Choi-Williams time-frequency transform in order to measure the power variation of the very-low frequencies (VLF: 20–40 mHz) and of the low frequencies (LF: 40–140 mHz). Results showed that migraineurs without aura present different LF and VLF power levels than controls and migraineurs with aura. The accurate power measurement of the time-frequency analysis allowed for the discrimination of the subjects' hemodynamic patterns. The time-frequency analysis of NIRS signals can be used in clinical practice to assess cerebral hemodynamics.