Specific Imaging Research Articles

Standardized Response Assessment in Patients with Advanced Cholangiocarcinoma Treated with Personalized Therapy

Background/Objectives: Current guidelines recommend Cisplatin/Gemcitabine/Durvalumab as first-line treatment for inoperable or recurrent cholangiocarcinoma (CCA). Molecular tumor boards (MTB) have the expertise to support organ-specific tumor boards with evidence-based treatment recommendations for subsequent lines of treatment, based on genomic tumor data and scientific evidence. This study evaluates the adoption of an MTB at a comprehensive cancer center in Germany and whether actionable genetic alterations are associated with specific imaging phenotypes. Methods: Patients with CCA referred to MTB were enrolled from May 2019 to September 2021. For comparison, a cohort of patients from a second center was included. Data on treatment recommendations, regimens, and survival were collected from prospective registries. Baseline and follow-up contrast-enhanced CT were analyzed according to RECIST 1.1. The chi-square test and t-test were used to compare categorical and continuous variables. Results: 583 patients were referred to the MTB, and 92 patients (47 female/51%) with a mean age of 60.3 ± 11.2 were referred for CCA treatment. 65/92 patients harbored 1–3 targetable mutations. Liver metastases were more frequently observed in patients with targetable mutations (84% vs. 62%). Metastasis to the liver and lung was associated with increased sums of diameters (93 mm and 111 mm vs. 40/73 mm in patients with no liver/lung metastasis). The number of metastases in individual organs was unrelated to treatment targets. Follow-up was available for 25 patients with a median time until imaging progression of 23 weeks. Progression occurred as target progression in 63%, nontarget progression in 13%, and appearance of new lesions in 63%. Conclusions: Most patients with CCA harbored targetable mutations, some were related to disease patterns on imaging. The pattern of treatment response and progression was as diverse as the metastatic spread.

Development of a new VMAT QA framework for Mobius3D using control-point specific EPID images

PurposeThis study presents novel quality assurance (QA) approach for volumetric modulated arc therapy (VMAT) that leverages frame-by-frame electronic portal imaging device (EPID) images integrated into Mobius3D for accurate three-dimensional dose calculations.MethodsSequential EPID images for VMAT plans were acquired every 0.4-second by iView system and processed through iterative deconvolution to mitigate blurring from photon scattering. Deconvolved images were binarized to define multi-leaf collimator (MLC) positions. Pre-acquired box fluences determined optimal threshold for binarization and adjusted for detector shift depending on gantry and collimator angles. Sequential EPID images were re-scaled using pixel scaling factor (PSF) and converted to monitor unit (MU) proportional values. Generated EPID-based log file, including control-point specific MLC and monitor units (MU) information, were analyzed in Mobius3D for Gamma passing rate (GPR) of VMAT plans from 18 patients. Plan complexity indices were calculated and correlated with GPR.ResultsClinically appropriate threshold was defined to be 20000 that can extract accurate MLC data from the deconvolved binarized EPID images. Positional deviations due to gantry and collimator rotations were observed to be up to 4.5 pixels. Recalibrated EPID pixel values showed linearity with MU regardless of changes in dose rate. Consequently, average GPR for 18 patients evaluated using Mobius3D reached 95.2% ± 3.7%%, based on 3% dose difference and 3mm distance-to-agreement criterion. It was found that two plan complexity indices showed statistically significant correlation with GPR.ConclusionThis study successfully implemented novel measurement-based VMAT QA framework based on control-point specific EPID, based upon accurate MLC and MU data at each frame.

Probing Deep Hydrogen Polysulfides Fluctuations In Vivo by Engineering Activatable Fluorescence Reporters with Second Near-Infrared Window Emission.

Deep and comprehensive understanding of the intricate biochemical processes mediated by H2Sn (n ≥ 1), but not H2S, is of paramount importance. A few fluorescent probes have been developed with the intention of addressing this issue. However, there is currently no evidence of any activatable NIR-II fluorescent probes for H2Sn-specific imaging over H2S. In this study, we developed the inaugural H2Sn-activated NIR-II probe for specific and deep imaging of H2Sn through a series of molecular engineering strategies. Strong electron-absorbing moieties enabled the redshift of the emission of the designed reporter, while leaving groups with different dissociation capacities were responsible for modulating the selective and sensitive responsiveness to H2Sn over H2S. By using this activatable specific probe for deep tissue mapping of endogenous H2Sn fluctuations, accurate identification of acute inflammation in animal models was achieved. It is anticipated that the advancement of this activatable NIR-II probe will facilitate a more profound comprehension of the physiological implications of H2Sn.

Features Associated With Different Inflammatory Phenotypes of Calcium Pyrophosphate Deposition Disease: A Study Using Data From the International American College of Rheumatology/EULAR Calcium Pyrophosphate Deposition Classification Criteria Cohort.

The study objective was to examine the disease, demographic, and imaging features associated with different inflammatory phenotypes of calcium pyrophosphate deposition (CPPD) disease, ie, recurrent acute calcium pyrophosphate (CPP) crystal arthritis, chronic CPP crystal inflammatory arthritis, and crowned dens syndrome (CDS). Data from an international cohort (assembled from 25 sites in 7 countries for the development and validation of the 2023 CPPD classification criteria from the American College of Rheumatology/EULAR) that met the criteria were included. Three cross-sectional studies were conducted to determine the phenotypic characteristics of recurrent acute CPP crystal arthritis, chronic CPP crystal inflammatory arthritis, and CDS. Multivariable logistic regression analysis was used to calculate adjusted odds ratio (aOR) and 95% confidence interval (CI) to examine the association between potential risk factors and the inflammatory phenotype. Among the 618 people included (56% female; mean age [standard deviation] 74.0 [11.9] years), 602 (97.4%) had experienced acute CPP crystal arthritis, 332 (53.7%) had recurrent acute arthritis, 158 (25.6%) had persistent inflammatory arthritis, and 45 (7.3%) had had CDS. Recurrent acute CPP crystal arthritis associated with longer disease duration (aOR 2.88 [95% CI 2.00-4.14]). Chronic CPP crystal inflammatory arthritis was associated with acute wrist arthritis (aOR 2.92 [95% CI 1.81-4.73]), metacarpophalangeal joint osteoarthritis (aOR 1.87 [95% CI 1.17-2.97]), and scapho-trapezo-trapezoid (STT) joint osteoarthritis (aOR 1.83 [95% CI 1.15-2.91]), and it was negatively associated with either metabolic or familial risk for CPPD (aOR 0.60 [95% CI 0.37-0.96]). CDS was associated with male sex (aOR 2.35 [95% CI 1.21-4.59]), STT joint osteoarthritis (aOR 2.71 [95% CI 1.22-6.05]), and more joints affected with chondrocalcinosis (aOR 1.46 [95% CI 1.15-1.85]). CPPD disease encompasses acute and chronic inflammatory phenotypes, each with specific clinical and imaging features that need to be considered in the diagnostic workup.

Deep phenotyping the right ventricle to establish translational MRI biomarkers for characterization of adaptive and maladaptive states in pulmonary hypertension

Deep phenotyping the right ventricle (RV) is essential for understanding the mechanisms of adaptive and maladaptive RV responses to pulmonary hypertension (PH). In this study, feature selection coupled with machine learning classification/ranking of specific cardiac magnetic resonance imaging (MRI) features from cine-MRI, flow-sensitized, and extracellular-volume techniques were used to assess RV remodelling in monocrotaline (MCT) and Sugen hypoxia (SuHx) PH rats. Early physiological changes associated with RV adaptation were detected along with prediction of RV maladaptive outcomes. Key adaptation features included haemodynamic alterations of pulmonary blood flow ejection and wave reflection, mild RV dilatation, progressive RV hypertrophy with subtle extracellular volume growth of RV wall. A dominant component of maladaptation was the extracellular matrix increase at RV insertion points and septum, observations compatible with histopathologic and RNA-sequencing results. The upregulation of mammalian target of rapamycin (mTOR) paralleled by AMP-activated protein kinase (AMPK) deactivation was seen at 4-week MCT and 8-week SuHx, along with reduced sarcoplasmic/endoplasmic reticulum Ca2+ATPase (SERCA2) expression, strongly associated with the RV systolic malfunction seen at this stage in vivo. The here established MRI features can serve as potential imaging biomarkers to evaluate PH treatment efficacy in preclinical studies and build up translational markers for the PH clinic.

Organotropic Engineering of Luminescent Gold Nanoclusters for In Vivo Imaging of Lung Orthotopic Tumors.

Gold nanoclusters (AuNCs) are emerging as promising functional probes for bioapplications. However, because of rapid renal clearance, it is a challenge to tailor their biofate and improve their disease-targeting ability in vivo. Herein, we report an efficient strategy to tailor their organotropic actions by rationally designing AuNC assemblies. The nanocluster assembly is established based on the moderate electrostatic interaction or strong coordination between AuNCs, enabled by solely chitosan (CS) or the coadded chelating metal ions (e.g., Gd3+). We show that AuNCs-CS is rapidly excreted into urine, while further coordination of Gd3+ confers assemblies with liver and lung accumulation capabilities, dependent on Gd3+ contents. The organotropic actions are unraveled to result from their tunable stability in vivo and binding capability to cells/proteins. We also demonstrate that lung-targeting assemblies can enable specific NIR-II luminescence imaging of lung orthotopic tumors, which cannot be realized by employing discrete AuNCs. We anticipate that these findings will offer insights into the design principles of metal nanocluster probes and related bioapplications.

Distinguishing ABCA4 from PRPH2-related disease: qualitative analysis of examination and imaging features

ABSTRACT Introduction ABCA4 and PRPH2-related diseases are both phenotypically heterogeneous and clinically difficult to differentiate. There may be examination and imaging features that can aid in establishing a clinical diagnosis. Methods A single-center, retrospective, consecutive case series including patients with a molecular confirmation of pathologic variants in either the ABCA4 or PRPH2 were included. Chi-square analysis, Fisher exact test, and Student’s t-test comparing prevalence of specific examination and imaging features between ABCA4 and PRPH2 Results Of the 127 eyes from 64 patients included, the ABCA4 group was more significantly associated with peripapillary sparing on both fundus imaging (73% vs. 40%; p = 0.006) and FAF (71% vs. 44%; p = 0.025), macular (64% vs. 12%; p < 0.001) and peripheral pisciform flecks (22% vs. 3.6%; p = 0.025). The PRPH2 group was more highly associated with macular chorioretinal atrophy (86% vs. 55%; p = 0.003). Conclusions Peripapillary sparing and pisciform flecks are more highly associated with ABCA4-related disease, while macular chorioretinal atrophy is more highly associated with PRPH2-related disease. Logistic regression demonstrates that bull’s eye maculopathy and macular flecks are predictive of the ABCA4 genotype.

Minimal data poisoning attack in federated learning for medical image classification: An attacker perspective

The privacy-sensitive nature of medical image data is often bounded by strict data sharing regulations that necessitate the need for novel modeling and analysis techniques. Federated learning (FL) enables multiple medical institutions to collectively train a deep neural network without sharing sensitive patient information. In addition, FL uses its collaborative approach to address challenges related to the scarcity and non-uniform distribution of heterogeneous medical domain data. Nevertheless, the data-opaque nature and distributed setup make FL susceptible to data poisoning attacks. There are diverse FL data poisoning attacks for classification models on natural image data in the literature. But their primary focus is on the impact of the attack and they do not consider the attack budget and attack visibility. The attack budget is essential for adversaries to optimize resource utilization in real-world scenarios, which determines the number of manipulations or perturbations they can apply. Simultaneously, attack visibility is crucial to ensure covert execution, allowing attackers to achieve their objectives without triggering detection mechanisms. Generally, an attacker’s aim is to create maximum attack impact with minimal resources and low visibility. So, considering these three entities can effectively comprehend the adversary’s perspective in designing an attack for real-world scenarios. Further, data poisoning attacks on medical images are challenging compared to natural images due to the subjective nature of medical data. Hence, we develop an attack with a low budget, low visibility, and high impact for medical image classification in FL. We propose a federated learning attention guided minimal attack (FL-AGMA), that uses class attention maps to identify specific medical image regions for perturbation. We introduce image distortion degree (IDD) as a metric to assess the attack budget. Also, we develop a feedback mechanism to regulate the attack coefficient for low attack visibility. Later, we optimize the attack budget by adaptively changing the IDD based on attack visibility. We extensively evaluate three large-scale datasets, namely, Covid-chestxray, Camelyon17, and HAM10000, covering three different data modalities. We observe that our FL-AGMA method has resulted in 44.49% less test accuracy with only 24% of IDD attack budget and lower attack visibility compared to the other attacks.

An automated toolbox for microcalcification cluster modeling for mammographic imaging.

Mammographic imaging is essential for breast cancer detection and diagnosis. In addition to masses, calcifications are of concern and the early detection of breast cancer also heavily relies on the correct interpretation of suspicious microcalcification clusters. Even with advances in imaging and the introduction of novel techniques such as digital breast tomosynthesis and contrast-enhanced mammography, a correct interpretation can still be challenging given the subtle nature and large variety of calcifications. Computer simulated lesion models can serve to develop, optimize, or improve imaging techniques. In addition to their use in comparative (virtual clinical trial) detection experiments, these models have potential application in training deep learning models and in the understanding and interpretation of breast lesions. Existing simulation methods, however, often lack the capacity to model the diversity occurring in breast lesions or to generate models relevant for a specific case. This study focuses on clusters of microcalcifications and introduces an automated, flexible toolbox designed to generate microcalcification cluster models customized to specific tasks. The toolbox allows users to control a large number of simulation parameters related to model characteristics such as lesion size, calcification shape, or number of microcalcifications per cluster. This leads to the capability of creating models that range from regular to complex clusters. Based on the input parameters, which are either tuned manually or pre-set for a specific clinical type, different sets of models can be simulated depending on the use case. Two lesion generation methods are described. The first method generates three-dimensional microcalcification clusters models based on geometrical shapes and transformations. The second method creates two-dimensional (2D) microcalcification cluster models for a specific 2D mammographic image. This novel method employs radiomics analysis to account for local textures, ensuring the simulated microcalcification cluster is appropriately integrated within the existing breast tissue. The toolbox is implemented in the Python language and can be conveniently run through a Jupyter Notebook interface, openly accessible at https://gitlab.kuleuven.be/medphysqa/deploy/breast-calcifications. Validation studies performed by radiologists assessed the level of malignancy and realism of clusters tuned with specific parameters and inserted in mammographic images. The flexibility of the toolbox with multiple simulation methods is illustrated, as well as the compatibility with different simulation frameworks and image types. The automation allows for the straightforward and fast generation of diverse microcalcification cluster models. The generated models are most likely applicable for various tasks as they can be configured in a variety of ways and inserted in different types of mammographic images of multiple acquisition systems. Validation studies confirmed the capacity to simulate realistic clusters and capture clinical properties when tuned with appropriate parameter settings. This simulation toolbox offers a flexible means of simulating microcalcification cluster models with potential use in both technical and clinical research in mammography imaging. The 3D generation methods allow for specifying many characteristics regarding the calcification shape and cluster architecture, and the 2D generation method presents a novel manner to create microcalcification clusters tailored to existing breast textures.

Redox-switchable multicolor luminescent polymers for theragnosis of osteoarthritis

Nonaromatic and nonconjugated fluorescent materials have garnered increasing attention in recent years. However, most non-classical chromophores are derived from electro-rich nitrogen and oxygen atoms, which suffer from short emission wavelengths, low efficiency, limited responsiveness, and obscure luminescence mechanisms. Here we present an emission mechanism in bioactive polycysteine, an aliphatic polymer that displays polymerization- and aggregation-induced emission, high quantum yield, and multicolor emission properties. We show that the hydrogen atoms bonded to the sulfur atoms play a crucial role in luminescence. This enables reversible modulation of polymer fluorescence under reducing and oxidizing conditions, facilitating specific imaging and quantitative detection of redox species in cells and in vivo. Furthermore, the polymer exhibits better anti-inflammatory and antioxidative activities compared to first-line clinical antioxidants, offering a promising platform for in vivo theragnosis of diseases such as osteoarthritis.

Endogenous Telomerase-Activated Fluorescent Probes for Specific Detection and Imaging of Flap Endonuclease 1 in Cancer Cells and Tissues.

Flap endonuclease 1 (FEN1) is a structure-specific DNA repair enzyme that has emerged as a potential target for cancer diagnosis and treatment. However, existing FEN1 assays often suffer from complicated reaction schemes and laborious procedures, and only a few methods are available for the detection and imaging of FEN1 in living cells. Especially, FEN1 is not exclusive to cancer cells, but it is also shared by normal cells. Consequently, the specific detection of FEN1 in cancer cells remains a challenge. Herein, we develop a simple and selective fluorescent biosensor for the specific imaging of FEN1 in cancer cells and tissues by engineering a FEN1 detection probe with a telomerase-responsive unit. In the presence of telomerase, it induces an extension reaction and subsequent intramolecular reconfiguration of the detection probe, generating a suitable branched DNA structure for FEN1 recognition and facilitating the cleavage of the flap by FEN1 for the recovery of fluorescence signal. Because telomerase is undetectable in normal cells but highly upregulated in cancer cells, the detection probe can only be activated in cancer cells to generate a high signal. This assay is quite simple, with the requirement of merely a single probe for dual enzyme recognition and signal output. With the integration of the single-molecule counting technology, this biosensor can achieve a detection limit of 1.2 × 10-5 U/μL, and it can accurately detect FEN1 in living cells and clinical tissues, providing a new avenue for FEN1-associated fundamental research and clinical diagnosis.

PH-Activatable Molecular Probe for COX-2 Imaging in Human Oral Squamous Carcinoma Cells and Patient-Derived Tissues.

For developing a successful cancer therapeutic modality, the early precise detection of cancer cells in patient biopsies in oral squamous cell carcinoma (OSCC) is crucial. This could help researchers create new diagnostic and therapeutic tools and assist clinicians in recommending more effective treatment plans and improving patient survival. We have developed an SMPD, cyclooxygenase-2 (COX-2) targeting pH-activable fluorophore named CNP, combining a potent COX-2 inhibitor, celecoxib, linked to a naphthalimide fluorophore with an acidic microenvironment-responsive piperazine moiety for specific optical imaging of OSCC in cells and patient tissues. Compared to reference probe RNP lacking celecoxib, CNP selectively enters the COX-2 overexpressing oral cancer cells. Its acidity-responsive imaging response enhances selectivity over cancers with lower COX-2 expression levels and normal cells. Further, CNP is demonstrated in imaging OSCC cells in patient-derived biopsies. Thus, multifunctional CNP shows potential in exploring more reagents for fluorescence-based detection of OSCC cells in patient tissues with translational applications.

Dynamic Phase Behavior of Amorphous Solid Dispersions Revealed with In Situ Stimulated Raman Scattering Microscopy.

This study reports the application of in situ stimulated Raman scattering (SRS) microscopy for real-time chemically specific imaging of dynamic phase phenomena in amorphous solid dispersions (ASDs). Using binary ritonavir and poly(vinylpyrrolidone-vinyl acetate) films with different drug loadings (0-100% w/w) as model systems, we employed SRS microscopy with fast spectral focusing to analyze ASD behavior upon contact with a dissolution medium. Multivariate unmixing of the SRS spectra allowed changes in the distributions of the drug, polymer, and water to be (semi)quantitatively imaged in real time, both in the film and the adjacent dissolution medium. The SRS analyses were further augmented with complementary correlative sum frequency generation and confocal reflection for additional crystallinity and phase sensitivity. In the ASDs with drug loadings of 20, 40, and 60% w/w, the water penetration front within the film, followed by both surface-directed and bulk phase separation in the film, was apparent but differed quantitatively. Additionally, drug-loading and phase-dependent polymer and drug release behavior was imaged, and liquid-liquid phase separation was observed for the 20% drug loading ASD. Overall, SRS microscopy with fast spectral focusing provides quantitative insights into water-induced ASD phase phenomena, with chemical, solid-state, temporal, and spatial resolution. These insights are important for optimal ASD formulation development.

Screening for Fabry disease in patients with hypertrophic cardiomyopathy using cardiac magnetic resonance imaging.

Fabry disease (FD) usually mimics hypertrophic cardiomyopathy (HCM). Decreased native T1 mapping and a unique late gadolinium enhancement (LGE) pattern by cardiac magnetic resonance (CMR) imaging are specific imaging markers for FD. Explore the performance of multiparametric CMR imaging in screening for FD in patients with a HCM phenotype. A prospective cohort of 602 patients with a HCM phenotype was assessed from April 2012 to December 2022. Participants underwent CMR imaging and genetic testing. FD diagnosis was according to genetic testing and enzyme-activity test of α-galactosidase A. Multiparameter CMR imaging included cardiac function, native T1 mapping, extracellular volume (ECV), T2 mapping, LGE, and myocardial strains. Diagnostic performance of CMR parameters in identifying FD from HCM was done by analysis of receiver operating characteristic (ROC) curves. FD prevalence was 1.8% (11 cases) in this cohort with HCM. Native T1 mapping was significantly lower in FD compared with HCM (FD vs. HCM: native T1 mapping: 1174.08 ± 60.60 vs. 1293.94 ± 55.86, p < 0.001). Ventricular function, mass, ventricular wall thickness, and strains did not show significant differences between the two groups. Binary logistic regression and analysis of ROC curves demonstrated myocardial native T1 mapping of the left ventricular basal slice had the best performance in screening for FD in patients with a HCM phenotype (cutoff: 1216 ms; AUC: 0.947; sensitivity: 91%; specificity: 90%). Native T1 mapping is the best parameter for screening FD in a Chinese population with a HCM phenotype. Question The prevalence of Fabry Disease (FD) in the study population is unknown and the efficacy of cardiac MRI (CMR) parameter screening for FD needs validating. Findings We report the prevalence of FD among a Chinese hypertrophic cardiomyopathy (HCM) cohort and found T1 mapping is the best CMR parameter for screening FD. Clinical relevance Native T1 mapping is the best CMR parameter for screening FD in the HCM cohort, providing an effective method for rapid screening of FD in clinic, which may help identify patients for early treatment of FD.

Grafting a chromophore on AMD070 analogues for CXCR4 bioimaging: Chemical synthesis and in vitro assessment of the inhibition properties of the CXCR4 receptor

Thank to their particular pharmacokinetics, the use of small organic molecules can be a very promising alternative to macromolecular targeting biomolecules (i.e. antibodies, peptides…) for specific imaging of tumours. Herein, the potential of two AMD070-like inhibitors as CXCR4-targeting units for specific imaging of cancer cells, and the influence of chromophore-grafting on their recognition properties was investigated.

Using Compressed JPEG and JPEG2000 Medical Images in Deep Learning: A Review

Machine Learning (ML), particularly Deep Learning (DL), has become increasingly integral to medical imaging, significantly enhancing diagnostic processes and treatment planning. By leveraging extensive datasets and advanced algorithms, ML models can analyze medical images with exceptional precision. However, their effectiveness depends on large datasets, which require extended training times for accurate predictions. With the rapid increase in data volume due to advancements in medical imaging technology, managing the data has become increasingly challenging. Consequently, irreversible compression of medical images has become essential for efficiently handling the substantial volume of data. Extensive research has established recommended compression ratios tailored to specific anatomies and imaging modalities, and these guidelines have been widely endorsed by government bodies and professional organizations globally. This work investigates the effects of irreversible compression on DL models by reviewing the relevant literature. It is crucial to understand how DL models respond to image compression degradations, particularly those introduced by JPEG and JPEG2000—both of which are the only permissible irreversible compression techniques in the most commonly used medical image format—the Digital Imaging and Communications in Medicine (DICOM) standard. This study provides insights into how DL models react to such degradations, focusing on the loss of high-frequency content and its implications for diagnostic interpretation. The findings suggest that while existing studies offer valuable insights, future research should systematically explore varying compression levels based on modality and anatomy, and consider developing strategies for integrating compressed images into DL model training for medical image analysis.

Narodziny „Lucifera”: poetycki "Kaukaz" Tadeusza Micińskiego

The author of this study analyzes the poetic cycle of the distinguished Polish lyric poet Tadeusz Miciński (1873–1918). The cycle, consisting of 19 poems titled Caucasus, was published in 1903 in the magazine Ateneum and later, after years of fragmentation, incorporated into the novel Nietota. The Secret Book of the Tatras (Kraków 1910). The interpreter questions what Miciński’s Caucasus represents against the backdrop of the Polish tradition of Caucasus portrayals. Exile to the Caucasus, forced enlistment into the Tsarist army, and Polish literature emerging from and upon return from the Caucasus have created a specific image of it: mountains boiling with history, exotic mountains. The Caucasus also appears in Polish ethnogenetic myths and in Messianism. Meanwhile, for Miciński, it serves as a symbol, metaphor, and metonymy of an internal striving for transformation. As a result, an internally dialectical “I” is formed, composed of human, luciferian, and divine elements. One of the few Caucasian accents here is the invocation of Shota Rustaveli, the Georgian poet, author of The Knight in the Tiger’s Skin, who is depicted as the “melancholic” singer of Queen Tamar. In the context of this image, an unsorrowful, vital trinitarian subject emerges in the lyrics, who, within the cycle-constellation code, addresses Lucifer with “I will go there.” The evolution of Miciński’s subject leads him, in 1913, to active engagement in history-making once again.

Group information enhances recognition of both learned and unlearned face appearances.

Are people better at recognizing individuals of more relevant groups, such as ingroup compared to outgroup members or high-status compared to low-status individuals? Previous studies that associated faces with group information found a robust effect of group on face recognition but only tested it using the same images presented during the learning phase. They therefore cannot tell whether group information enhances encoding of the specific image presented during learning or encoding of the person who appears in it, which should generalize to other images of that person. In addition, the measures used in these studies do not sufficiently distinguish between sensitivity and response bias. In this article, we addressed these limitations and examined in three experiments the effect of group membership (Experiments 1 and 2) and social status (Experiment 3) on face recognition. In all experiments, we assessed recognition of both learned and unlearned views of the learned faces. Our results show improved recognition of ingroup members compared to outgroup members and of individuals of high-status groups compared to low-status groups for both learned and unlearned views. These effects emerged also when we used measures of memory accuracy that adequately control for response bias. These findings highlight the importance of group and status information in person recognition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Management of critically ill children with acute necrotizing encephalitis during an H1N1 outbreak in a tertiary pediatric hospital: a series of three cases and literature review.

Acute necrotizing encephalopathy (ANE) is a severe neurologic condition that is diagnosed clinically and upon specific radiologic imaging. Medical treatment of this condition is uncertain, and timing is likely very important to minimize brain damage and systemic inflammation. The present case series describes three patients suffering from ANE secondary to influenza A/H1N1 infection during a recent outbreak, treated with increasingly aggressive anti-inflammatory approach, and with significantly different outcomes.

Equitable Skin Disease Prediction Using Transfer Learning and Domain Adaptation

In the realm of dermatology, the complexity of diagnosing skin conditions manually necessitates the expertise of dermatologists. Accurate identification of various skin ailments, ranging from cancer to inflammatory diseases, is paramount. However, existing artificial intelligence (AI) models in dermatology face challenges, particularly in accurately diagnosing diseases across diverse skin tones, with a notable performance gap in darker skin. Additionally, the scarcity of publicly available, unbiased datasets hampers the development of inclusive AI diagnostic tools. To tackle the challenges in accurately predicting skin conditions across diverse skin tones, we employ a transfer-learning approach that capitalizes on the rich, transferable knowledge from various image domains. Our method integrates multiple pre-trained models from a wide range of sources, including general and specific medical images, to improve the robustness and inclusiveness of the skin condition predictions. We rigorously evaluated the effectiveness of these models using the Diverse Dermatology Images (DDI) dataset, which uniquely encompasses both underrepresented and common skin tones, making it an ideal benchmark for assessing our approach. Among all methods, Med-ViT emerged as the top performer due to its comprehensive feature representation learned from diverse image sources. To further enhance performance, we conducted domain adaptation using additional skin image datasets such as HAM10000. This adaptation significantly improved model performance across all models.