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Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer biology, and immunology, with object detection, image feature extraction, classification, and segmentation applications. Advancements in deep learning (DL) research have been a critical factor in advancing computer techniques for biomedical image analysis and data mining. A significant improvement in the accuracy of cell detection and segmentation algorithms has been achieved as a result of the emergence of open-source software and innovative deep neural network architectures. Automated cell segmentation now enables the extraction of quantifiable cellular and spatial features from microscope images of cells and tissues, providing critical insights into cellular organization in various diseases. This review aims to examine the latest AI and DL techniques for cell analysis and data mining in microscopy images, aid the biologists who have less background knowledge in AI and machine learning (ML), and incorporate the ML models into microscopy focus images.

Targeted Radionuclide Therapy (TRT) is a medical technique exploiting radionuclides to combat cancer growth and spread. TRT requires a supply of radionuclides that are currently produced by either cyclotrons or nuclear research reactors. In this context, the ISOLPHARM project investigates the production of innovative radionuclides for medical applications. This production will be based on the forthcoming SPES facility at the Legnaro National Laboratories (LNL) of the National Institute for Nuclear Physics (INFN), an ISOL facility where high-purity radioactive beams will be used to produce carrier-free radiopharmaceuticals. Previous studies demonstrated that a significant amount of 111 Ag, an innovative β/γ emitter suitable for TRT with theranostic applications, can be obtained at the SPES facility. The present work describes the first imaging study on phantoms with 111 Ag performed by the ISOLPHARM collaboration. This is a fundamental step to pave the way for the upcoming in vivo studies on the 111 Ag-based radiopharmaceutical currently being developed. The imaging potential of this radionuclide was investigated by acquiring phantom images with Cerenkov Luminescence Imaging (CLI) and digital autoradiography (ARG)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Gastric cancer (GC) is the second most common cause of cancer-related death worldwide and a serious public health concern. This high death rate is mostly caused by late-stage diagnoses, which lead to poor treatment outcomes. Radiation immunotherapy and targeted therapies are becoming increasingly popular in GC treatment, in addition to surgery and systemic chemotherapy. In this review, we have focused on both in vitro and in vivo research, which presents a summary of recent developments in targeted therapies for gastric cancer. We explore targeted therapy approaches, including integrin receptors, HER2, Claudin 18, and glutathione-responsive systems. For instance, therapies targeting the integrin receptors such as the αvβ3 and αvβ5 integrins have shown promise in enhancing diagnostic precision and treatment efficacy. Furthermore, nanotechnology provides novel approaches to targeted drug delivery and imaging. These include glutathione-responsive nanoplatforms and cyclic RGD peptide-conjugated nanoparticles. These novel strategies seek to reduce systemic toxicity while increasing specificity and efficacy. To sum up, the review addresses the significance of personalized medicine and advancements in gastric cancer-targeted therapies. It explores potential methods for enhancing gastric cancer prognosis and treatment in the future.

Rheumatoid arthritis (RA) is a systemic autoimmune disorder caused by inflammation of cartilaginous diarthrodial joints that destroys joints and cartilage, resulting in synovitis and pannus formation. Timely detection and effective management of RA are pivotal for mitigating inflammatory arthritis consequences, potentially influencing disease progression. Nuclear medicine using radiolabeled targeted vectors presents a promising avenue for RA diagnosis and response to treatment assessment. Radiopharmaceutical such as technetium-99m ( 99m Tc), combined with single photon emission computed tomography (SPECT) combined with CT (SPECT/CT), introduces a more refined diagnostic approach, enhancing accuracy through precise anatomical localization, representing a notable advancement in hybrid molecular imaging for RA evaluation. This comprehensive review discusses existing research, encompassing in vitro, in vivo, and clinical studies to explore the application of 99m Tc radiolabeled targeting vectors with SPECT imaging for RA diagnosis. The purpose of this review is to highlight the potential of this strategy to enhance patient outcomes by improving the early detection and management of RA.

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.

In cervical biopsies, for diagnosis of Human Papilloma Virus (HPV) related conditions, the immunohistochemical staining for p16 has a diagnostic value only if diffusely and strongly positive, pattern named "block-like". "Weak and/or focal (w/f) p16 expression" is commonly considered nonspecific. In our previous study, we demonstrated the presence of high-risk HPV (hrHPV) DNA by LiPa method in biopsies showing w/f p16 positivity. The aim of the present study was to investigate the presence of hrHPV-DNA by CISH in the areas showing w/f p16 expression. We assessed the presence of hrHPV16, 18, 31, 33, 51 by CISH in a group of 20 cervical biopsies showing w/f p16 expression, some with increased Ki67, and in 10 cases of block-like expression, employed as control. The immunohistochemical p16 expression was also assessed by digital pathology. hrHPV-CISH nuclear positivity was encountered in 12/20 cases of w/f p16 expression (60%). Different patterns of nuclear positivity were identified, classified as punctate, diffuse and mixed, with different epithelial distributions. Our results, albeit in a limited casuistry, show the presence of HPV in an integrated status highlighted by CISH in w/f p16 positive cases. This could suggest the necessity of a careful follow-up of the patients with "weak" and/or "focal" immunohistochemical patterns of p16, mainly in cases of increased Ki67 cell proliferation index, supplemented with molecular biology examinations.

The aim of the present study consists of the evaluation of the biodistribution of a novel 68 Ga-labeled radiopharmaceutical, [ 68 Ga]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 68 Ga-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.

Resveratrol is a polyphenolic compound that has gained considerable attention in the past decade due to its multifaceted therapeutic potential, including anti-inflammatory and anticancer properties. However, its anticancer efficacy is impeded by low water solubility, dose-limiting toxicity, low bioavailability, and rapid hepatic metabolism. To overcome these hurdles, various nanoparticles such as organic and inorganic nanoparticles, liposomes, polymeric nanoparticles, dendrimers, solid lipid nanoparticles, gold nanoparticles, zinc oxide nanoparticles, zeolitic imidazolate frameworks, carbon nanotubes, bioactive glass nanoparticles, and mesoporous nanoparticles were employed to deliver resveratrol, enhancing its water solubility, bioavailability, and efficacy against various types of cancer. Resveratrol-loaded nanoparticle or resveratrol-conjugated nanoparticle administration exhibits excellent anticancer potency compared to free resveratrol. This review highlights the latest developments in nanoparticle-based delivery systems for resveratrol, focusing on the potential to overcome limitations associated with the compound's bioavailability and therapeutic effectiveness.

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.

Radionuclides are unstable isotopes that mainly emit alpha (α), beta (β) 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 β- 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 64 Cu-, 68 Ga-, 125 I-, and 99m Tc-labeled radiopharmaceuticals have been revised and summarized in this manuscript.

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.

Streptomyces coelicolor is a model organism for studying streptomycetes. This genus possesses relevant medical and economical roles, because it produces many biologically active metabolites of pharmaceutical interest, including the majority of commercialized antibiotics. In this bioinformatic study, the transcriptome of S. coelicolor has been analyzed to identify novel RNA species and quantify the expression of both annotated and novel transcripts in solid and liquid growth medium cultures at different times. The major characteristics disclosed in this study are: (i) the diffuse antisense transcription; (ii) the great abundance of transfer-messenger RNAs (tmRNA); (iii) the abundance of rnpB transcripts, paramount for the RNase-P complex; and (iv) the presence of abundant fragments derived from pre-ribosomal RNA leader sequences of unknown biological function. Overall, this study extends the catalogue of ncRNAs in S. coelicolor and suggests an important role of non-coding transcription in the regulation of biologically active molecule production., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The 64Cu-labeled chelator was analyzed in vivo by positron emission tomography (PET) imaging to evaluate its biodistribution in a murine model at different acquisition times. For this purpose, nine 6-week-old female Balb/C nude strain mice underwent micro-PET imaging at three different time points after 64Cu-labeled chelator injection. Specifically, the mice were divided into group 1 (acquisition 1 h after [64Cu] chelator administration, n = 3 mice), group 2 (acquisition 4 h after [64Cu]chelator administration, n = 3 mice), and group 3 (acquisition 24 h after [64Cu] chelator administration, n = 3 mice). Successively, all PET studies were segmented by means of registration with a standard template space (3D whole-body Digimouse atlas), and 108 radiomics features were extracted from seven organs (namely, heart, bladder, stomach, liver, spleen, kidney, and lung) to investigate possible changes over time in [64Cu]chelator biodistribution. The one-way analysis of variance and post hoc Tukey Honestly Significant Difference test revealed that, while heart, stomach, spleen, kidney, and lung districts showed a very low percentage of radiomics features with significant variations (p-value < 0.05) among the three groups of mice, a large number of features (greater than 60% and 50%, respectively) that varied significantly between groups were observed in bladder and liver, indicating a different in vivo uptake of the 64Cu-labeled chelator over time. The proposed methodology may improve the method of calculating the [64Cu]chelator biodistribution and open the way towards a decision support system in the field of new radiopharmaceuticals used in preclinical imaging trials.

Background: Prostate volume, as determined by magnetic resonance imaging (MRI), is a useful biomarker both for distinguishing between benign and malignant pathology and can be used either alone or combined with other parameters such as prostate-specific antigen., Purpose: This study compared different deep learning methods for whole-gland and zonal prostate segmentation., Study Type: Retrospective., Population: A total of 204 patients (train/test = 99/105) from the PROSTATEx public dataset., Field Strength/sequence: A 3 T, TSE T 2 -weighted., Assessment: Four operators performed manual segmentation of the whole-gland, central zone + anterior stroma + transition zone (TZ), and peripheral zone (PZ). U-net, efficient neural network (ENet), and efficient residual factorized ConvNet (ERFNet) were trained and tuned on the training data through 5-fold cross-validation to segment the whole gland and TZ separately, while PZ automated masks were obtained by the subtraction of the first two., Statistical Tests: Networks were evaluated on the test set using various accuracy metrics, including the Dice similarity coefficient (DSC). Model DSC was compared in both the training and test sets using the analysis of variance test (ANOVA) and post hoc tests. Parameter number, disk size, training, and inference times determined network computational complexity and were also used to assess the model performance differences. A P < 0.05 was selected to indicate the statistical significance., Results: The best DSC (P < 0.05) in the test set was achieved by ENet: 91% ± 4% for the whole gland, 87% ± 5% for the TZ, and 71% ± 8% for the PZ. U-net and ERFNet obtained, respectively, 88% ± 6% and 87% ± 6% for the whole gland, 86% ± 7% and 84% ± 7% for the TZ, and 70% ± 8% and 65 ± 8% for the PZ. Training and inference time were lowest for ENet., Data Conclusion: Deep learning networks can accurately segment the prostate using T 2 -weighted images., Evidence Level: 4 TECHNICAL EFFICACY: Stage 2., (© 2021 International Society for Magnetic Resonance in Medicine.)

Diagnosis of ascending thoracic aortic aneurysm (ATAA) is based on the measurement of the maximum aortic diameter, but size is not a good predictor of the risk of adverse events. There is growing interest in the development of novel image-derived risk strategies to improve patient risk management towards a highly individualized level. In this study, the feasibility and efficacy of deep learning for the automatic segmentation of ATAAs was investigated using UNet, ENet, and ERFNet techniques. Specifically, CT angiography done on 72 patients with ATAAs and different valve morphology (i.e., tricuspid aortic valve, TAV, and bicuspid aortic valve, BAV) were semi-automatically segmented with Mimics software (Materialize NV, Leuven, Belgium), and then used for training of the tested deep learning models. The segmentation performance in terms of accuracy and time inference were compared using several parameters. All deep learning models reported a dice score higher than 88%, suggesting a good agreement between predicted and manual ATAA segmentation. We found that the ENet and UNet are more accurate than ERFNet, with the ENet much faster than UNet. This study demonstrated that deep learning models can rapidly segment and quantify the 3D geometry of ATAAs with high accuracy, thereby facilitating the expansion into clinical workflow of personalized approach to the management of patients with ATAAs., Competing Interests: Conflict of interestAlbert Comelli declares that he has no conflict of interest. Navdeep Dahiya declares that he has no conflict of interest. Alessandro Stefano declares that he has no conflict of interest. Viviana Benfante declares that she has no conflict of interest. Giovanni Gentile declares that he has no conflict of interest. Valentina Agnese declares that she has no conflict of interest. Giuseppe M Raffa declares that he has no conflict of interest. Michele Pilato declares that he has no conflict of interest. Anthony Yezzi declares that he has no conflict of interest. Giovanni Petrucci declares that he has no conflict of interest. Salvatore Pasta declares that he has no conflict of interest., (© Korean Society of Medical and Biological Engineering 2020.)

Background: The aim of this work is to identify an automatic, accurate, and fast deep learning segmentation approach, applied to the parenchyma, using a very small dataset of high-resolution computed tomography images of patients with idiopathic pulmonary fibrosis. In this way, we aim to enhance the methodology performed by healthcare operators in radiomics studies where operator-independent segmentation methods must be used to correctly identify the target and, consequently, the texture-based prediction model., Methods: Two deep learning models were investigated: (i) U-Net, already used in many biomedical image segmentation tasks, and (ii) E-Net, used for image segmentation tasks in self-driving cars, where hardware availability is limited and accurate segmentation is critical for user safety. Our small image dataset is composed of 42 studies of patients with idiopathic pulmonary fibrosis, of which only 32 were used for the training phase. We compared the performance of the two models in terms of the similarity of their segmentation outcome with the gold standard and in terms of their resources' requirements., Results: E-Net can be used to obtain accurate (dice similarity coefficient = 95.90%), fast (20.32 s), and clinically acceptable segmentation of the lung region., Conclusions: We demonstrated that deep learning models can be efficiently applied to rapidly segment and quantify the parenchyma of patients with pulmonary fibrosis, without any radiologist supervision, in order to produce user-independent results.