Imaging Screening Research Articles

Epidemiology, aetiology and diagnosis of congenital hearing loss via hearing screening of 153 913 newborns.

Congenital hearing loss (HL), one of the most common paediatric chronic conditions, significantly affects speech and language development. Its early diagnosis and medical intervention can be achieved via newborn hearing screening. However, data on the prevalence and aetiology of congenital HL in infants who fail newborn hearing screening are limited. The sample population included 153 913 infants who underwent newborn hearing screening, and the prevalence of congenital HL, defined as moderate to profound bilateral HL (BHL) or unilateral HL (UHL) (≥40 dB HL), in one prefecture of Japan was measured to minimize the loss-to-follow-up rate, a common factor affecting the screening procedure. Comprehensive aetiological investigation, including physiology, imaging, genetic tests, and congenital cytomegalovirus screening, was performed on children diagnosed with congenital HL. The calculated prevalence of congenital HL was 1.62 per 1000 newborns (bilateral, 0.84; unilateral, 0.77). More than half of the cases with congenital bilateral or severe to profound UHL showed genetic aetiology or cochlear nerve deficiency (CND), respectively. Approximately 4% and 6% of the cases of congenital BHL and UHL were associated with congenital cytomegalovirus infection and auditory neuropathy spectrum disorder, respectively. This is an epidemiological and comprehensive aetiological study of congenital HL, as determined via newborn hearing screening according to its severity and laterality, in a large-scale general population of a developed country. Our findings can serve as a reference for optimizing care and intervention options for children with HL and their families.

Deep learning-assisted concentration gradient generation for the study of 3D cell cultures in hydrogel beads of varying stiffness.

The study of dose-response relationships underpins analytical biosciences. Droplet microfluidics platforms can automate the generation of microreactors encapsulating varying concentrations of an assay component, providing datasets across a large chemical space in a single experiment. A classical method consists in varying the flow rate of multiple solutions co-flowing into a single microchannel (producing different volume fractions) before encapsulating the contents into water-in-oil droplets. This process can be automated through controlling the pumping elements but lacks the ability to adapt to unpredictable experimental scenarios, often requiring constant human supervision. In this paper, we introduce an image-based, closed-loop control system for assessing and adjusting volume fractions, thereby generating unsupervised, uniform concentration gradients. We trained a shallow convolutional neural network to assess the position of the laminar flow interface between two co-flowing fluids and used this model to adjust flow rates in real-time. We apply the method to generate alginate microbeads in which HEK293FT cells could grow in three dimensions. The stiffnesses ranged from 50Pa to close to 1kPa in Young modulus and were encoded with a fluorescent marker. We trained deep learning models based on the YOLOv4 object detector to efficiently detect both microbeads and multicellular spheroids from high-content screening images. This allowed us to map relationships between hydrogel stiffness and multicellular spheroid growth.

Pharmacological expansion of type 2 alveolar epithelial cells promotes regenerative lower airway repair

Type 2 alveolar epithelial cells (AEC2s) are stem cells in the adult lung that contribute to lower airway repair. Agents that promote the selective expansion of these cells might stimulate regeneration of the compromised alveolar epithelium, an etiology-defining event in several pulmonary diseases. From a high-content imaging screen of the drug repurposing library ReFRAME, we identified that dipeptidyl peptidase 4 (DPP4) inhibitors, widely used type 2 diabetes medications, selectively expand AEC2s and are broadly efficacious in several mouse models of lung damage. Mechanism of action studies revealed that the protease DPP4, in addition to processing incretin hormones, degrades IGF-1 and IL-6, essential regulators of AEC2 expansion whose levels are increased in the luminal compartment of the lung in response to drug treatment. To selectively target DPP4 in the lung with sufficient drug exposure, we developed NZ-97, a locally delivered, lung persistent DPP4 inhibitor that broadly promotes efficacy in mouse lung damage models with minimal peripheral exposure and good tolerability. This work reveals DPP4 as a central regulator of AEC2 expansion and affords a promising therapeutic approach to broadly stimulate regenerative repair in pulmonary disease.

Advancing preclinical drug evaluation through automated 3D imaging for high-throughput screening with kidney organoids

High-throughput drug screening is crucial for advancing healthcare through drug discovery. However, a significant limitation arises from available in vitro models using conventional 2D cell culture, which lack the proper phenotypes and architectures observed in three-dimensional (3D) tissues. Recent advancements in stem cell biology have facilitated the generation of organoids—3D tissue constructs that mimic human organs in vitro. Kidney organoids, derived from human pluripotent stem cells, represent a significant breakthrough in disease representation. They encompass major kidney cell types organized within distinct nephron segments, surrounded by stroma and endothelial cells. This tissue allows for the assessment of structural alterations such as nephron loss, a characteristic of chronic kidney disease. Despite these advantages, the complexity of 3D structures has hindered the use of organoids for large-scale drug screening, and the drug screening pipelines utilizing these complex in vitro models remain to be established for high-throughput screening. In this study, we address the technical limitations of kidney organoids through fully automated 3D imaging, aided by a machine-learning approach for automatic profiling of nephron segment-specific epithelial morphometry. Kidney organoids were exposed to the nephrotoxic agent cisplatin to model severe acute kidney injury. An U.S. Food and Drug Administration (FDA)-approved drug library was tested for therapeutic and nephrotoxicity screening. The fully automated pipeline of 3D image acquisition and analysis identified nephrotoxic or therapeutic drugs during cisplatin chemotherapy. The nephrotoxic potential of these drugs aligned with previous in vivo and human reports. Additionally, Imatinib, a tyrosine kinase inhibitor used in hematological malignancies, was identified as a potential preventive therapy for cisplatin-induced kidney injury. Our proof-of-concept report demonstrates that the automated screening process, using 3D morphometric assays with kidney organoids, enables high-throughput screening for nephrotoxicity and therapeutic assessment in 3D tissue constructs.

Machine learning based on the EEG and structural MRI can predict different stages of vascular cognitive impairment.

Vascular cognitive impairment (VCI) is a major cause of cognitive impairment in the elderly and a co-factor in the development and progression of most neurodegenerative diseases. With the continuing development of neuroimaging, multiple markers can be combined to provide richer biological information, but little is known about their diagnostic value in VCI. A total of 83 subjects participated in our study, including 32 patients with vascular cognitive impairment with no dementia (VCIND), 21 patients with vascular dementia (VD), and 30 normal controls (NC). We utilized resting-state quantitative electroencephalography (qEEG) power spectra, structural magnetic resonance imaging (sMRI) for feature screening, and combined them with support vector machines to predict VCI patients at different disease stages. The classification performance of sMRI outperformed qEEG when distinguishing VD from NC (AUC of 0.90 vs. 0,82), and sMRI also outperformed qEEG when distinguishing VD from VCIND (AUC of 0.8 vs. 0,0.64), but both underperformed when distinguishing VCIND from NC (AUC of 0.58 vs. 0.56). In contrast, the joint model based on qEEG and sMRI features showed relatively good classification accuracy (AUC of 0.72) to discriminate VCIND from NC, higher than that of either qEEG or sMRI alone. Patients at varying stages of VCI exhibit diverse levels of brain structure and neurophysiological abnormalities. EEG serves as an affordable and convenient diagnostic means to differentiate between different VCI stages. A machine learning model that utilizes EEG and sMRI as composite markers is highly valuable in distinguishing diverse VCI stages and in individually tailoring the diagnosis.

Development and validation of an imageless machine-learning algorithm for the initial screening of prostate cancer.

Prostate specific antigen (PSA) testing is a low-cost screening method for prostate cancer (PCa). However, its accuracy is limited. While progress is being made using medical imaging for PCa screening, PSA testing can still be improved as an easily accessible first step in the screening process. We aimed to develop and validate a new model by further personalizing the analysis of PSA with demographic, medical history, lifestyle parameters, and digital rectal examination (DRE) results. Using data from 34,224 patients in the screening arm of the PLCO trial (22,188 for the training set and 12,036 for the validation set), we applied a gradient-boosting model whose features (Model 1) were one PSA value and the personal variables available in the PLCO trial except those that signaled an ex-anteassumption of PCa. A second algorithm (Model 2) included a DRE result. The primary outcome was the occurrence of PCa, while the aggressiveness of PCa was a secondary outcome. ROC analyses were used to compare both models to other initial screening tests. The areas under the curve(AUC) for Model 2 was 0.894 overall and 0.908 for patients with a suspicious DRE, compared to 0.808 for PSA for patients with a suspicious DRE. The AUC for Model 1 was 0.814 compared to 0.821 for PSA. Model 2 predicted 58% more high-risk PCa than PSA ≥4 combined with an abnormal DRE and had a positive predictive value of 74.7% (vs. 50.6%). Personalizing the interpretation of PSA values and DRE results with a gradient-boosting model showed promising results as a potential novel, low-cost method for the initial screening of PCa. The importance of DRE, when included in such a model, was also highlighted.

X-ray excited broadband blue emitting Lu2(1-x)Bi2xO3-PMMA thin films for high resolution imaging

In this paper, cubic-phase Lu2O3:Bi3+ nanoparticles, with a range of sizes between 110 and 180 nm, were obtained by a combination of co-precipitation and thermal annealing techniques. For these nanoparticles upon UV or X-ray excitation, strong broadband emission peaks at 410 and 480 nm corresponding to Bi3+ occupying S6 and C2 (Lu3+(S6) and Lu3+(C2)) sites, respectively, can be observed. On the basis of the concentration-dependent luminescence intensity ratios (I480(C2)/I410(S6)) of Bi3+(C2) to Bi3+(S6), the energy transfer from Bi3+(S6) to Bi3+(C2) can be confirmed at high Bi3+ concentration. In particular, their X-ray absorption coefficient, emission wavelength, afterglow level (60 ppm) and response time (465 ns) are almost comparable to those of the commercial Bi4Ge3O12 crystals. By the means of dispersing Lu1.98Bi0.02O3 nanoscintillators into organic polymer (PMMA)-acetone solution, the Lu1.98Bi0.02O3-PMMA flexible composite films exhibiting rapid response to X-ray photons, high X-ray sensitivity and radiation resistance, good mechanical performance and excellent environmental stability were prepared using a spin-coating technique. On the surface of X-ray imaging screen formed by the flexible dense composite films, the spatial resolution can reach to 6.0 lp/mm at a safe irradiation dose (4.6 μGy). Meanwhile, for the imaging screen using the flexible composite films after bended for thousands of times, we can observe clear image of each reed gap (300 μm) of the music box. For the Lu2O3:Bi3+-PMMA composite films, our results suggest that not only their scintillation properties are almost comparable to those of the commercial broadband Bi4Ge3O12 crystals, but also they might be applied in large-area and non-planar X-ray imaging.

Gazing Beyond the Horizon: A Systematic Review Unveiling the Theranostic Potential of Quantum Dots in Alzheimer's Disease.

Alzheimer's disease (AD), a neurodegenerative disorder characterized by cognitive decline, poses a significant healthcare challenge worldwide. The accumulation of amyloid-beta (Aβ) plaques and hyperphosphorylated tau protein drives neuronal degeneration and neuroinflammation, perpetuating disease progression. Despite advancements in understanding the cellular and molecular mechanisms, treatment hurdles persist, emphasizing the need for innovative intervention strategies. Quantum dots (QDs) emerge as promising nanotechnological tools with unique photo-physical properties, offering advantages over conventional imaging modalities. This systematic review endeavors to elucidate the theranostic potential of QDs in AD by synthesizing preclinical and clinical evidence. A comprehensive search across electronic databases yielded 20 eligible studies investigating the diagnostic, therapeutic, or combined theranostic applications of various QDs in AD. The findings unveil the diverse roles of QDs, including inhibiting Aβ and tau aggregation, modulating amyloidogenesis pathways, restoring membrane fluidity, and enabling simultaneous detection of AD biomarkers. The review highlights the potential of QDs in targeting multiple pathological hallmarks, delivering therapeutic payloads across the blood-brain barrier, and facilitating real-time imaging and high-throughput screening. While promising, challenges such as biocompatibility, surface modifications, and clinical translation warrant further investigation. This systematic review provides a comprehensive synthesis of the theranostic potential of QDs in AD, paving the way for translational research and clinical implementation.

Deep learning for automated trash screen blockage detection using cameras: Actionable information for flood risk management

ABSTRACT Trash screens are used to prevent debris from entering critical parts of rivers. However, debris can accumulate on the screen and generate floods. This makes their monitoring critical both for maintenance and flood modeling purposes (e.g., local forecasts may change because the trash screen is blocked). We developed three novel deep learning methods for trash screen maintenance management consisting of automatically detecting trash screen blockage using cameras: a method based on image classification, a method based on image similarity matching, and a method based on anomaly detection. To facilitate their use by end users, these methods are designed so that they can be directly applied to any new trash screen camera installed by the end users. We have built a new dataset of labeled trash screen images to train and evaluate the efficiency of our methods, in terms of both accuracy and implications for end users. This dataset consists of 80,452 trash screen images from 54 cameras installed by the Environment Agency (UK). This work demonstrates that trash screen blockage detection can be automated using trash screen cameras and deep learning, which could have an impact on both trash screen management and flood modeling.

The performance of artificial intelligence in prostate magnetic resonance imaging screening

Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.

Myocardial scar and left ventricular ejection fraction classification for electrocardiography image using multi-task deep learning

Myocardial scar (MS) and left ventricular ejection fraction (LVEF) are vital cardiovascular parameters, conventionally determined using cardiac magnetic resonance (CMR). However, given the high cost and limited availability of CMR in resource-constrained settings, electrocardiograms (ECGs) are a cost-effective alternative. We developed computer vision-based multi-task deep learning models to analyze 12-lead ECG 2D images, predicting MS and LVEF < 50%. Our dataset comprises 14,052 ECGs with clinical features, utilizing ground truth labels from CMR. Our top-performing model achieved AUC values of 0.838 (95% CI 0.812–0.862) for MS and 0.939 (95% CI 0.921–0.954) for LVEF < 50% classification, outperforming cardiologists. Moreover, MS predictions in a prevalence-specific test dataset recorded an AUC of 0.812 (95% CI 0.810–0.814). Extracted 1D signals from ECG images yielded inferior performance, compared to the 2D approach. In conclusion, our results demonstrate the potential of computer-based MS and LVEF < 50% classification from ECG scan images in clinical screening offering a cost-effective alternative to CMR.

Can artificial intelligence detect type 2 diabetes in women by evaluating the pectoral muscle on tomosynthesis: diagnostic study

ObjectivesThis retrospective single-center analysis aimed to evaluate whether artificial intelligence can detect type 2 diabetes mellitus by evaluating the pectoral muscle on digital breast tomosynthesis (DBT).Material methodAn analysis of 11,594 DBT images of 287 consecutive female patients (mean age 60, range 40–77 years) was conducted using convolutional neural networks (EfficientNetB5). The inclusion criterion was left-sided screening images with unsuspicious interpretation who also had a current glycosylated hemoglobin A1c (HBA1c) % value. The exclusion criteria were inadequate imaging, history of breast cancer, and/or diabetes mellitus. HbA1c values between 5.6 and 6.4% were categorized as prediabetic, and those with values ≥ 6.5% were categorized as diabetic. A recorded HbA1c ≤ 5.5% served as the control group. Each group was divided into 3 subgroups according to age. Images were subjected to pattern analysis parameters then cropped and resized in a format to contain only pectoral muscle. The dataset was split into 85% for training and 15% for testing the model’s performance. The accuracy rate and F1-score were selected as performance indicators.ResultsThe training process was concluded in the 15th epoch, each comprising 1000 steps, with an accuracy rate of 92% and a loss of only 0.22. The average specificity and sensitivity for all 3 groups were 95%. The F1-score was 0.95. AUC-ROC was 0.995. PPV was 94%, and NPV was 98%.ConclusionOur study presented a pioneering approach, applying deep learning for the detection of diabetes mellitus status in women using pectoral muscle images and was found to function with an accuracy rate of 92%.Critical relevance statementAI can differentiate pathological changes within pectoral muscle tissue by assessing radiological images and maybe a potential diagnostic tool for detecting diabetes mellitus and other diseases that affect muscle tissues.Key points• AI may have an opportunistic use as a screening exam for diabetes during digital breast tomosynthesis.• This technique allows for early and non-invasive detection of diabetes mellitus by AI.• AI may have broad applications in detecting pathological changes within muscle tissue.Graphical

AMS-U-Net: automatic mass segmentation in digital breast tomosynthesis via U-Net.

The objective of this study was to develop a fully automatic mass segmentation method called AMS-U-Net for digital breast tomosynthesis (DBT), a popular breast cancer screening imaging modality. The aim was to address the challenges posed by the increasing number of slices in DBT, which leads to higher mass contouring workload and decreased treatment efficiency. The study used 50 slices from different DBT volumes for evaluation. The AMS-U-Net approach consisted of four stages: image pre-processing, AMS-U-Net training, image segmentation, and post-processing. The model performance was evaluated by calculating the true positive ratio (TPR), false positive ratio (FPR), F-score, intersection over union (IoU), and 95% Hausdorff distance (pixels) as they are appropriate for datasets with class imbalance. The model achieved 0.911, 0.003, 0.911, 0.900, 5.82 for TPR, FPR, F-score, IoU, and 95% Hausdorff distance, respectively. The AMS-U-Net model demonstrated impressive visual and quantitative results, achieving high accuracy in mass segmentation without the need for human interaction. This capability has the potential to significantly increase clinical efficiency and workflow in DBT for breast cancer screening.

Abstract 4798: MRI screening for brain metastases (BrM) versus SYMptom-directed brain imaging for patients with triple negative (TN) or HER2+ metastatic breast cancer (MBC): An ad-hoc interim analysis of a randomized phase II pilot study

Abstract Background: Patients with HER2+ or TN MBC are at high risk of developing BrM, but the role of MRI screening for early detection of asymptomatic BrM has not yet been established. Methods: We conducted a multicentre phase II pilot study, which randomized patients with TN or HER2+ MBC to 1 year of BrM screening (contrast enhanced brain MRI at baseline, 4, 8, and 12 months) versus a control arm of symptom directed brain imaging (MRI only if symptoms of BrM develop). The primary goal was to determine the feasibility of a future randomized trial of BrM screening versus symptom-directed imaging in this patient population. Key inclusion criteria were age ≥18; TN MBC diagnosed ≤12 weeks prior to study entry or HER2+ MBC with no restrictions regarding time of diagnosis; no symptoms of BrM or known asymptomatic BrM; ECOG ≥ 1 and no MRI contraindications. Overall (EORTC QLQ BN20) and neurologic-specific (FACT-BR tools) quality-of-life as well as cancer-related anxiety (NCI PRO-CTCAE) were assessed at baseline, 6 and 15 months. Criteria rendering a future trial “not feasible” were defined a-priori as: &amp;lt;30% of eligible patients enroll in the study, &amp;lt;50% complete the study protocol, and/or &amp;gt;50% of patients allocated to the control arm during the 1 year study period are screened for asymptomatic BrM with CT or MRI. Results: Between 2018 and 2023, 44 patients from 3 participating centres enrolled in the study. Thirty-five participants (80%) had HER2+ and 9 (20%) had TN MBC. Of the 22 patients randomized to each of the study arms; 3 patients (8%) in each arm withdrew prior to study completion. Reasons for withdrawal included claustrophobia (n=3) or discontent with their randomised arm (n=3) or follow-up requirements (n=1). Metrics regarding the proportion of eligible patients who enrolled in the study were only collected from the Sunnybrook Odette Cancer Centre site; of 91 patients approached, 38 (42%) enrolled in the study. Within the 12-month study period, the incidence of BrM among patients in the screening arm was n=3 (14%) versus n=4 (18%) in the control arm. To-date among patients in the study arm, 11 (50%) completed the 12-month screening protocol, 5 (23%) remain on study and 6 (27%) did not complete the protocol due to either death or early study withdrawal. Among patients in the control arm, 10 (45%) completed 12-month follow-up, 4 (18%) remain on study and 8 (36%) did not complete follow-up due to either death or early study withdrawal. In total, 13 patients (59%) in the control arm had brain imaging, 4 in the absence of neurological symptoms. Conclusion: A large randomized trial to investigate the utility of MRI-based surveillance for asymptomatic detection of BrM in the proposed patient population is likely feasible, although episodic brain imaging in the control arm was common. Analysis of patient reported outcomes is ongoing. Citation Format: Katarzyna Joanna Jerzak, Megan Shum, Gregory Pond, Orit Freedman, Gursimran Chandhoke, Tatiana Conrad, Priscilla Brastianos, Gregory Stanisz, Arjun Sahgal, Ellen Warner. MRI screening for brain metastases (BrM) versus SYMptom-directed brain imaging for patients with triple negative (TN) or HER2+ metastatic breast cancer (MBC): An ad-hoc interim analysis of a randomized phase II pilot study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4798.

Screening of Oral Squamous Cell Carcinoma Through Color Intensity-Based Textural Features.

Background Early screening and diagnosis of oral squamous cell carcinoma (OSCC) has always been a major challenge for pathologists. Artificial intelligence (AI)-assisted screening tools can serve as an adjunct for the objective interpretation of Papanicolaou (PAP)-stained oral smears. Aim This study aimed to develop a handy and sensitive computer-assisted AItool based on color-intensity textural features to be applied to cytologic images for screening and diagnosis of OSCC. Methodology The study included two groups consisting of 80 OSCC subjects and 80 control groups. PAP-stained smears were collected from both groups. The smears were analyzed in Matlab software computed data and color intensity-based textural features such as entropy, contrast, energy, homogeneity, and correlation,were quantitatively extracted. Results In this study, a statistically significant difference was noted for entropy, energy, correlation, contrast, and homogeneity. It was found that entropy and contrast were found to be higher with a decrease in homogeneity, correlation, and energy in OSCC when compared to the control group. Receiver operating characteristiccurve analysis was done and accuracy, sensitivity, and specificity were found to be 88%, 91%, and 81%, respectively. Conclusion The gray-level co-occurrence matrix (GLCM) color intensity-based textural features play a significant role in differentiating dysplastic and normal cells in the diagnosis of OSCC. Computer-aided textural analysis has the potential to aid in the early detection of oral cancer, which can lead to improved clinical outcomes.

Screening, risk stratification and CT imaging for assessment of coronary artery disease

Early treatment of cardiovascular risk factors and characterization of coronary plaques is essential to collect prognostic information about coronary artery disease (CAD) and prevent cardiovascular events. Discussion of the most important risk factors of CAD, basic diagnostic of CAD, prevention, and prognostic factors of CAD with focus on cardiac computed tomography (CT). Prevalence and prognostic impact of CAD risk factors; description of specific assessment of risk profiles; estimation of pretest probability; conventional prevention of CAD; prognostic assessment of CAD using the Calcium Scoring and coronary CT angiography. Assessment of risk profiles and estimation of pretest probability for obstructive coronary stenosis necessitates athorough evaluation of medical history and laboratory values. The composition and extent of calcified and noncalcified plaques in CT exams based on the criteria of the Coronary Artery Disease-Reporting and Data System give important prognostic information about the risk of cardiovascular events, which increases with high plaque burden and vice versa. Initial imaging with CT for evaluation of CAD leads to areduction of invasive coronary angiographies and catheter-associated complications. Besides early detection of cardiovascular risk factors, the additional assessment of plaque burden and significant stenosis in CT gives further prognostic information to facilitate effective therapies to prevent cardiovascular events and in the case of low plaque burden avoid invasive coronary angiography. However, systmatic screening using Calcium Scoring is not established yet due to insufficient data, although it could potentially be used for an early risk stratification in patients with multiple risk factors.

Artistic transformations of the image of father in the Ukrainian cinema during the Stalinist era

The purpose of the article is to study the peculiarities of artistic interpretations of the image of father in Ukrainian cinema during the Stalinist era, with special attention to the socio-cultural and political factors of the period, which fundamentally influenced the trends in the interpretations of the screen image of father.&#x0D; The methodology. On the example of the films “Ivan” (1932), “Wonderful Garden” (1935), “Aerograd” (1935), “Guerillas in the steps of Ukraine” (1942), “Third strike” (1948) historical factors were analyzed that fundamentally determined the peculiarities of the screen character of father during the years of Soviet repressions. In the article, a definition of the concept of paternalism in the context of the theory of law and statecraft is provided. Also in the article Ukrainian films of the Stalinist era were studied, in which the paternal concept “The state is father of its citizens” was fully manifested. Socio-cultural analysis of films, in which the figure of the genetic father was relegated to the background by Soviet ideology, and the role of the main educator of children was taken over by representatives of the Russian imperial authorities, was carried out. The paternal figure is considered in the broadest symbolic context — when the paternal role for a citizens can be played by an entire social institution or the apparatus of state power.&#x0D; The results. The period of Joseph Stalin turned out to be one of the most difficult in Ukrainian history. In those years, the Ukrainian nation faced numerous political cataclysms: industrialization, the Holodomor, deportation to remote regions of the USSR, repressions and World War II. However, the cinematography presented a completely transformed picture of reality, according to which the Bolshevik government was a kind and caring father for Ukrainians, but not a cruel tyrant. Nevertheless, even in such a tragic historical period, national directors were able to reflect in propagandistic films the longing of Ukrainian society for the traditional family system, and by biological parents in particular.&#x0D; The scientific novelty. The main attention is focused on the essential characteristics of the archetypal image of father and it makes it possible to analyze many Ukrainian classic films in a new scientific context.&#x0D; The practical significance. The article can contribute to further research in the field of several humanitarian disciplines: film studies, cultural studies, art studies, sociology and History of Ukraine.

Label-free Raman imaging for screening of anti-inflammatory function food

Natural bioactive compounds and plant constituents are considered to have a positive anti-inflammatory effect. This study aimed to establish a screening technique for anti-inflammatory function in foods based on label-free Raman imaging. A visible anti-inflammatory analysis method based on coherent anti-Stokes Raman scattering (CARS) was established with an LPS-induced RAW264.7 cell model. Dynamic changes in proteins and lipids were determined at laser pump light wavelengths of 2956 cm−1 and 2856 cm−1, respectively. The method was applied to a plant-based formula (JC) with anti-inflammatory activity. Q-TOF-MS and HPLC analyses revealed the main active constituents of JC as quercetin, kaempferol, l-glutamine, and sodium copper chlorophyllin. In in vitro and in vivo verification experiments, JC showed significant anti-inflammatory activity by regulating the TLR4/NF-κB pathway. In conclusion, this study successfully established a label-free and visible method for screening anti-inflammatory constituents in plant-based food products, which will facilitate the evaluation of functional foods.

Deep learning analysis of epicardial adipose tissue to predict cardiovascular risk in heavy smokers

BackgroundHeavy smokers are at increased risk for cardiovascular disease and may benefit from individualized risk quantification using routine lung cancer screening chest computed tomography. We investigated the prognostic value of deep learning-based automated epicardial adipose tissue quantification and compared it to established cardiovascular risk factors and coronary artery calcium.MethodsWe investigated the prognostic value of automated epicardial adipose tissue quantification in heavy smokers enrolled in the National Lung Screening Trial and followed for 12.3 (11.9–12.8) years. The epicardial adipose tissue was segmented and quantified on non-ECG-synchronized, non-contrast low-dose chest computed tomography scans using a validated deep-learning algorithm. Multivariable survival regression analyses were then utilized to determine the associations of epicardial adipose tissue volume and density with all-cause and cardiovascular mortality (myocardial infarction and stroke).ResultsHere we show in 24,090 adult heavy smokers (59% men; 61 ± 5 years) that epicardial adipose tissue volume and density are independently associated with all-cause (adjusted hazard ratios: 1.10 and 1.38; P < 0.001) and cardiovascular mortality (adjusted hazard ratios: 1.14 and 1.78; P < 0.001) beyond demographics, clinical risk factors, body habitus, level of education, and coronary artery calcium score.ConclusionsOur findings suggest that automated assessment of epicardial adipose tissue from low-dose lung cancer screening images offers prognostic value in heavy smokers, with potential implications for cardiovascular risk stratification in this high-risk population.

Antimicrobial peptide A9K as a gene delivery vector in cancer cells

Designed peptides are promising biomaterials for biomedical applications. The amphiphilic cationic antimicrobial peptide (AMP), A9K, can self-assemble into nano-rod structures and has shown cancer cell selectivity and could therefore be a promising candidate for therapeutic delivery into cancer cells. In this paper, we investigate the selectivity of A9K for cancer cell models, examining its effect on two human cancer cell lines, A431 and HCT-116. Little or no activity was observed on the control, human dermal fibroblasts (HDFs). In the cancer cell lines the peptide inhibited cellular growth through changes in mitochondrial morphology and membrane potential while remaining harmless towards HDFs. In addition, the peptide can bind to and protect nucleic acids while transporting them into both 2D cultures and 3D spheroids of cancer cells. A9K showed high efficiency in delivering siRNA molecules into the centre of the spheroids. A9K was also explored in vivo, using a zebrafish (Danio rerio) development toxicity assay, showing that the peptide is safe at low doses. Finally, a high-content imaging screen, using RNA interference (RNAi) targeted towards cellular uptake, in HCT-116 cells was carried out. Our findings suggest that active cellular uptake is involved in peptide internalisation, mediated through clathrin-mediated endocytosis. These new discoveries make A9K attractive for future developments in clinical and biotechnological applications.