Potential Discrimination Research Articles

Cervicovaginal lavages uncover growth factors as key biomarkers for early diagnosis and prognosis of endometrial cancer

Endometrial cancer (EC) rates are continuing to rise and it remains the most common gynecologic cancer in the US. Existing diagnostic methods are invasive and can cause pain and anxiety. Hence, there is a need for less invasive diagnostics for early EC detection. The study objective was to evaluate the utility of growth factors collected through minimally invasive cervicovaginal lavage (CVL) sampling as diagnostic and prognostic biomarkers for EC. CVL samples from 192 individuals undergoing hysterectomy for benign or malignant conditions were collected and used to quantify the concentrations of 19 growth and angiogenic factors using multiplex immunoassays. Patients were categorized based on disease groups: benign conditions (n = 108), endometrial hyperplasia (n = 18), and EC (n = 66). EC group was stratified into grade 1/2 endometrial endometrioid cancer (n = 53) and other EC subtypes (n = 13). Statistical associations were assessed using receiver operating characteristics, Spearman correlations and hierarchical clustering. Growth and angiogenic factors: angiopoietin-2, endoglin, fibroblast activation protein (FAP), melanoma inhibitory activity, and vascular endothelial growth factor-A (VEGF-A) were significantly (p < 0.0001) elevated in EC patients. A multivariate model combining 11 proteins with patient age and body mass index exhibited excellent discriminatory potential (area under curve = 0.918) for EC, with a specificity of 90.7% and a sensitivity of 87.8%. Moreover, angiopoietin-2, FAP and VEGF-A significantly (p < 0.05–0.001) associated with tumor grade, size, myometrial invasion, and mismatch repair status. Our results highlight the innovative use of growth and angiogenic factors collected through CVL sampling for the detecting endometrial cancer, showcasing not only their diagnostic potential but also their prognostic value.

Double edged tech: navigating the public health and legal challenges of digital twin technology

Abstract This research addresses the legal challenges posed by the implementation of digital twin technology in public health. Digital twins are advanced computational models which aim to replicate human organ functions and biological processes. Their goal is to enable the simulation of the entire human body and to personalize the outputs with individual patient’s health data. Digital twin technology promises to bridge gap between personalized medicine and precision public health. This research is based on a comprehensive review of the current European and Swiss legal frameworks for medical devices and data protection that apply to digital twin technology. Within these frameworks, digital twins are in a grey area, as they are only considered as medical device software when applied to individuals. They also incorporate artificial intelligence, machine learning, and data analytics, which are already a source of legal concern in a healthcare setting, but their effective use in public health raises additional issues. Preliminary findings suggest significant shortcomings in the legal frameworks regarding data privacy, and the potential for increased surveillance by health insurance systems, which could lead to human rights violations. The public health application of digital twins would require global data analysis of sensitive personal information, raising further questions about the secure storage, transmission, and processing of this data. The study concludes that current legal frameworks must be future proofed to manage the complexities introduced by digital twins in public health. In particular, it suggests that regulatory oversight of medical devices needs to be significantly improved in order to safely harness the potential of digital twin technologies for public health. Finally, the new European Union health data space and the forthcoming EU AI act will also play important roles in the implementation of digital twin technologies. Key messages • Applying digital twin technology in public health faces considerable challenges, particularly the issues around data privacy, potential discrimination, and increased surveillance. • We need to adapt and strengthen existing legal frameworks to effectively manage the complexities introduced by digital twins, ensuring that these technologies can be safely used for public health.

Potential Tax Discrimination against Italian-Sourced Dividends and Capital Gains Received by Foreign Investment Funds Not Compliant with EU Regulatory Framework

This article delves into the complexities of cross-border taxation of Italian-sourced dividends and capital gains paid to Italian and foreign investment funds. More specifically, the authors scrutinize whether Italy’s current domestic tax laws violate the EU fundamental freedoms, particularly regarding foreign funds that, despite not being subject to the UCITS or AIFM Directives, are subject to a regulatory framework similar to that imposed on Italian funds.

Serum lncRNA ITGB2-AS1 and ICAM-1 as novel biomarkers for rheumatoid arthritis and osteoarthritis diagnosis.

The complete circulating long non-coding RNAs (lncRNAs) signature of rheumatoid arthritis (RA) and osteoarthritis (OA) is still uncovered. The lncRNA integrin subunit beta 2 (ITGB2)-anti-sense RNA 1 (ITGB2-AS1) affects ITGB2 expression; however, there is a gap in knowledge regarding its expression and clinical usefulness in RA and OA. This study investigated the potential of serum ITGB2-AS1 as a novel diagnostic biomarker and its correlation with ITGB2 expression and its ligand intercellular adhesion molecule-1 (ICAM-1), disease activity, and severity in RA and primary knee OA patients. Forty-three RA patients, 35 knee OA patients, and 22 healthy volunteers were included. Compared with healthy controls, serum ITGB2-AS1 expression was upregulated in RA patients but wasn't significantly altered in knee OA patients, whereas serum ICAM-1 protein levels were elevated in both diseases. ITGB2-AS1 showed discriminative potential for RA versus controls (AUC = 0.772), while ICAM-1 displayed diagnostic potential for both RA and knee OA versus controls (AUC = 0.804, 0.914, respectively) in receiver-operating characteristic analysis. In the multivariate analysis, serum ITGB2-AS1 and ICAM-1 were associated with the risk of developing RA, while only ICAM-1 was associated with the risk of developing knee OA. A panel combining ITGB2-AS1 and ICAM-1 showed profound diagnostic power for RA (AUC = 0.9, sensitivity = 86.05%, and specificity = 91.67%). Interestingly, serum ITGB2-AS1 positively correlated with disease activity (DAS28) in RA patients and with ITGB2 mRNA expression in both diseases, while ICAM-1 positively correlated with ITGB2 expression in knee OA patients. Our study portrays serum ITGB2-AS1 as a novel potential diagnostic biomarker of RA that correlates with disease activity. A predictive panel combining ITGB2-AS1 and ICAM-1 could have clinical utility in RA diagnosis. We also spotlight the association of ICAM-1 with knee OA diagnosis. The correlation of serum ITGB2-AS1 with ITGB2 expression in both diseases may be insightful for further mechanistic studies.

The Local Welfare State and Differences in Racialized Poverty

ABSTRACT Researchers concerned with the U.S. welfare state have documented that more localized administration allows for greater geographic variation in public policies as well as the greater potential for racial discrimination to influence policy outcomes. I explore whether key attributes of the local welfare state, capacity and spending, are associated with differences in racialized poverty across localities. I develop a conceptual framework drawing on two sociological traditions: spatial inequality framework and local welfare state theory. Spatial inequality scholarship provides a framework for interrogating why poverty varies across localities and racialized groups. Local welfare scholarship conceptualizes the processes by which local governments impact poverty and how those processes may be highly racialized. Drawing on data from the Census of Governments, I examine all local governments’ capacity and spending across counties in the U.S. and their association with poverty rates among white, Black, and Hispanic populations. I find that local governments’ capacity and spending do matter to understanding differences in racialized poverty. Specifically, racialized groups perceived as more deserving are likely to be the beneficiaries of local government interventions and policies. This study calls for greater attention to the local welfare state as a possible mechanism maintaining racial stratification.

Identification and validation of diagnostic markers related to immunogenic cell death and infiltration of immune cells in diabetic nephropathy

IntroductionImmunogenic cell death (ICD) is a unique cell death triggered by chemotherapy. However, studies elucidating the potential therapeutic role of ICD and the underlying mechanism in diabetic nephropathy (DN) are limited. MethodsWGCNA was conducted on the human kidney biopsy data linked to DN, analyzing gene sets associated with ICD. Gene Set Enrichment Analysis and Gene Set Variation Analysis were utilized to examine the discrepancy in biological function. We used Gene Ontology, the Kyoto Encyclopedia of Genes and Genomes, and the GeneMANIA database to investigate the function of the signature genes. An analysis using the receiver operating characteristic (ROC) was conducted to validate the diagnostic value of hub genes. Additionally, immune infiltration-related analyses were also performed. In conclusion, we examined the association between the glomerular filtration rate, serum creatinine, and hub genes. Hub genes were validated by immunohistochemistry using db/db mice kidneys. ResultsWGCNA revealed that the targets in the turquoise unit (1674 genes) exhibited the highest positive correlation with ICD. Furthermore, 4222 statistically significant DEGs were identified when comparing the DN and healthy control groups. Significantly, the KEGG pathway enrichment analysis indicated a connection between ICD and the nuclear factor-kappa B signaling pathway and the synthesis of cytokines (tumor necrosis factor superfamily). ROC analysis revealed that 16 hub genes exhibited strong discriminatory potential as biomarkers for DN. Therefore, immunohistochemical validation, with the potential involvement of chemokines (CCL11, CCR2, CCR7, CX3CR1, CXCL10, CXCL12, and CXCR5) and immune cells (CD3G, CD5, and CD247) may be crucial for the diagnosis and therapy of DN. ConclusionsDKK3, NR4A1, NR4A2, VEGFA, and DUSP1 may be associated with the development of DN. The pathogenesis of DN may specifically involve chemokines (CCL11, CCR2, CCR7, CX3CR1, CXCL10, CXCL12, and CXCR5) and immune cells (CD3G, CD5, and CD247), with LCP2 playing a significant role.

Disentangled contrastive learning for fair graph representations

Graph Neural Networks (GNNs) play a key role in efficiently learning node representations of graph-structured data through message passing, but their predictions are often correlated with sensitive attributes and thus lead to potential discrimination against some groups. Given the increasingly widespread applications of GNNs, solutions are required urgently to prevent algorithmic discrimination associated with GNNs, to protect the rights of vulnerable groups and to build trustworthy artificial intelligence. To learn the fair node representations of graphs, we propose a novel framework, the Fair Disentangled Graph Neural Network (FDGNN). With the proposed FDGNN framework, we enhance data diversity by generating instances that have identical sensitivity values but different adjacency matrices through data augmentation. Additionally, we design a counterfactual augmentation strategy for constructing instances with varying sensitive values while preserving the same adjacency matrices, thereby balancing the distribution of sensitive values across different groups. Subsequently, we employ a disentangled contrastive learning strategy to acquire disentangled representations of non-sensitive attributes such that sensitive information does not affect the prediction of node information. Finally, the learned fair representations of non-sensitive attributes are employed for building a fair predictive model. Extensive experiments on three real-world datasets demonstrate that FDGNN yields the best fairness predictions compared to the baseline methods. Additionally, the results demonstrate the potential of disentanglement in learning fair representations.

Islamic Values in a Multicultural Society: An Analysis of Parenting and Social Integration of Muslim Minorities

This research examines Islamic education parenting and social integration of Muslim minorities in Tanjung Labu Village, Rantau Pulung Sub-district, East Kutai Regency. Using a qualitative method with a case study approach, this research aims to understand the dynamics of Islamic education and social integration strategies in the context of multicultural communities in transmigration areas. Data were collected through participatory observation, in-depth interviews, focus group discussions, and document analysis. The results show that the minority Muslim community in Tanjung Labu Village applies a variety of parenting patterns, from those that tend to be authoritarian in matters of principle to those that are more democratic in other aspects. Social integration efforts are seen in various aspects of life, including education, economy, and culture. Programs such as ‘Friday Taqwa’ in schools and cooperation in farmer groups show positive steps in realizing social integration. However, the research also identified challenges such as the lack of special programs for converts and potential discrimination. The study concludes that efforts to strengthen the social integration of Muslim minorities require a holistic, inclusive, and sustainable approach, involving the active role of the Muslim community and support from various relevant parties. The findings make an important contribution to understanding the complexities of Islamic education and social integration in minority contexts, as well as highlighting the importance of empowering Muslim converts.

Self-other voice discrimination task: A potential neuropsychological tool for clinical assessment of self-related deficits

BackgroundDeficits in self are commonly described through different neuro-pathologies, based on clinical evaluations and experimental paradigms. However, currently available approaches lack appropriate clinical validation, making objective evaluation and discrimination of self-related deficits challenging. MethodsWe applied a statistical standardized method to assess the clinical discriminatory capacity of a Self-Other Voice Discrimination (SOVD) task. This task, validated experimentally as a marker for self-related deficits, was administered to 17 patients eligible for neurosurgery due to focal hemispheric brain tumors or epileptic lesions. ResultsThe clinical discriminatory capacity of the SOVD task was evident in three patients who exhibited impairments for self-voice perception that could not be predicted by other neuropsychological deficits. Impairments in other-voice perception were linked to inhibitory neuropsychological deficits, suggesting a potential association with executive deficits in voice recognition. ConclusionsThis exploratory study highlights the clinical discriminatory potential of the SOVD task and suggests that it could complement the standard neuropsychological assessment, paving the way for enhanced diagnoses and tailored treatments for self-related deficits.

Integrating HRMAS-NMR Data and Machine Learning-Assisted Profiling of Metabolite Fluxes to Classify Low- and High-Grade Gliomas.

Diagnosing and classifying central nervous system tumors such as gliomas or glioblastomas pose a significant challenge due to their aggressive and infiltrative nature. However, recent advancements in metabolomics and magnetic resonance spectroscopy (MRS) offer promising avenues for differentiating tumor grades both in vivo and ex vivo. This study aimed to explore tissue-based metabolic signatures to classify/distinguish between low- and high-grade gliomas. Forty-six histologically confirmed, intact solid tumor samples from glioma patients were analyzed using high-resolution magic angle spinning nuclear magnetic resonance (HRMAS-NMR) spectroscopy. By integrating machine learning (ML) algorithms, spectral regions with the most discriminative potential were identified. Validation was performed through univariate and multivariate statistical analyses, along with HRMAS-NMR analyses of 46 paired plasma samples. Amongst the various ML models applied, the logistics regression identified 46 spectral regions capable of sub-classifying gliomas with accuracy 87% (F1-measure 0.87, Precision 0.82, Recall 0.93), whereas the extra-tree classifier identified three spectral regions with predictive accuracy of 91% (F1-measure 0.91, Precision 0.85, Recall 0.97). Wilcoxon test presented 51 spectral regions significantly differentiating low- and high-grade glioma groups (p < 0.05). Based on sensitivity and area under the curve values, 40 spectral regions corresponding to 18 metabolites were considered as potential biomarkers for tissue-based glioma classification and amongst these N-acetyl aspartate, glutamate, and glutamine emerged as the most important markers. These markers were validated in paired plasma samples, and their absolute concentrations were computed. Our results demonstrate that the metabolic markers identified through the HRMAS-NMR-ML analysis framework, and their associated metabolic networks, hold promise for targeted treatment planning and clinical interventions in the future.

A comprehensive in silico analysis and experimental validation of miRNAs capable of discriminating between lung adenocarcinoma and squamous cell carcinoma.

Accurate differentiation between lung adenocarcinoma (AC) and lung squamous cell carcinoma (SCC) is crucial owing to their distinct therapeutic approaches. MicroRNAs (miRNAs) exhibit variable expression across subtypes, making them promising biomarkers for discrimination. This study aimed to identify miRNAs with robust discriminatory potential between AC and SCC and elucidate their clinical significance. MiRNA expression profiles for AC and SCC patients were obtained from The Cancer Genome Atlas (TCGA) database. Differential expression analysis and supervised machine learning methods (Support Vector Machine, Decision trees and Naïve Bayes) were employed. Clinical significance was assessed through receiver operating characteristic (ROC) curve analysis, survival analysis, and correlation with clinicopathological features. Validation was conducted using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Furthermore, signaling pathway and gene ontology enrichment analyses were conducted to unveil biological functions. Five miRNAs (miR-205-3p, miR-205-5p, miR-944, miR-375 and miR-326) emerged as potential discriminative markers. The combination of miR-944 and miR-326 yielded an impressive area under the curve of 0.985. RT-qPCR validation confirmed their biomarker potential. miR-326 and miR-375 were identified as prognostic factors in AC, while miR-326 and miR-944 correlated significantly with survival outcomes in SCC. Additionally, exploration of signaling pathways implicated their involvement in key pathways including PI3K-Akt, MAPK, FoxO, and Ras. This study enhances our understanding of miRNAs as discriminative markers between AC and SCC, shedding light on their role as prognostic indicators and their association with clinicopathological characteristics. Moreover, it highlights their potential involvement in signaling pathways crucial in non-small cell lung cancer pathogenesis.

Fair and optimal prediction via post‐processing

AbstractWith the development of machine learning algorithms and the increasing computational resources available, artificial intelligence has achieved great success in many application domains. However, the success of machine learning has also raised concerns about the fairness of the learned models. For instance, the learned models can perpetuate and even exacerbate the potential bias and discrimination in the training data. This issue has become a major obstacle to the deployment of machine learning systems in high‐stakes domains, for example, criminal judgment, medical testing, online advertising, hiring process, and so forth. To mitigate the potential bias exhibited by machine learning models, fairness criteria can be integrated into the training process to ensure fair treatment across all demographics, but it often comes at the expense of model performance. Understanding such tradeoffs, therefore, is crucial to the design of optimal and fair algorithms. My research focuses on characterizing the inherent tradeoff between fairness and accuracy in machine learning, and developing algorithms that can achieve both fairness and optimality. In this article, I will discuss our recent work on designing post‐processing algorithms for fair classification, which can be applied to a wide range of fairness criteria, including statistical parity, equal opportunity, and equalized odds, under both attribute‐aware and attribute‐blind settings, and is particularly suited to large‐scale foundation models where retraining is expensive or even infeasible. I will also discuss the connections between our work and other related research on trustworthy machine learning, including the connections between algorithmic fairness and differential privacy as well as adversarial robustness.

Abstract B092: ‘We’ve got to go to the doctor and we’re scared to death’: Community members perspectives on sexual orientation and gender identity data collection in healthcare settings

Abstract Purpose: Much research is needed to advance our knowledge of LGBTQ cancer disparities, but research is hindered by inconsistency in collection of data on sexual orientation and gender identity (SOGI) across clinical and research settings. Nationally, there have been increased efforts to standardize SOGI data collection. The purpose of this study was to identify potential patient-level barriers and facilitators to SOGI data collection within the catchment area of a midwestern cancer center. Methods: Investigators conducted 4 focus groups in partnership with community-based organizations and stakeholder groups that reflected the catchment area’s diverse populations. Using a semi-structured approach, participants were asked about their experiences with SOGI data collection, barriers and facilitators to this collection, and how they viewed the practice of collecting SOGI data. Focus groups were audio recorded and transcribed. Data was coded and interpreted using applied thematic analysis. Results: Four focus groups were conducted with 32 participants in total. There were 2 LGBTQ groups (one mixed race/ethnicity, one African American), and 2 predominantly non-LGBTQ groups (one African American and one Middle Eastern/North African or MENA). Across all participants, 53% were African American (n=17), 25% identified as MENA (n=8), 28% were White (n=9), 44% of the participants identified as women (n=19), one individual identified as a transgender woman &amp; 2 individuals as non-binary, and 53% reported their sexual orientation as gay/lesbian/same-loving. In all groups, participants emphasized a need to educate both patients and providers about the importance of SOGI data collection, why it is collected, and its relevance to improving medical care. Participants further discussed the quality and depth of the patient-provider relationship as a critical factor in a patient’s comfort and willingness to share SOGI data. Participants also underscored the importance of privacy ownership (i.e., the ownership of one’s own private information) and control in relation to SOGI status, and the desire to minimize potential privacy turbulence of an unexpected and unwelcome disclosure breach. All groups discussed how the potential for harm resulting from SOGI disclosure could deter patients from sharing SOGI information. Those who identified as LGBTQ, recounted both personal experiences and the experiences of others in which disclosure of SOGI information directly led to poor treatment by a healthcare professional. It is this potential for harm and discrimination that led many participants to stress the need for privacy control until a safe environment is established. Conclusions: Findings highlight a number of potential patient barriers to SOGI data collection from community members’ perspectives, including the need for widespread education on this topic and clinical environments in which patients are safe disclosing SOGI status. Future research and practice should consider specific methods to normalize the practice of SOGI data collection in the cancer care context. Citation Format: Ariel Washington, Ali Fakih, Lindsey Toman, Riham Ayoub, Emma Noble, Susan Musto, Brittany Dowe, Scott Ellis, Andrea Sakleh, Hayley S. Thompson, Latonya Riddle-Jones. ‘We’ve got to go to the doctor and we’re scared to death’: Community members perspectives on sexual orientation and gender identity data collection in healthcare settings [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B092.

Discrimination potential of sugarcane cultivars (Saccharum spp) through hyperspectral sensors in different production environments

Discrimination potential of sugarcane cultivars (Saccharum spp) through hyperspectral sensors in different production environments

Clinical variability of equine asthma phenotypes and analysis of diagnostic steps in phenotype differentiation

BackgroundEquine asthma is a common, non-infectious, chronic lung disease that affects up to 80% of the horse population. Strict phenotyping and identification of subclinically asthmatic horses can be challenging. The aim of this study was to describe equine asthma phenotypes (mild, moderate, and severe asthma) defined by BALF cytology and occurrence of clinical signs in a population of privately owned horses and to identify the variables and examination steps with best discriminative potential. The standardised examination protocol included clinical examinations, blood work, airway endoscopy with bronchoalveolar lavage fluid analysis, arterial blood gas analysis and radiography under clinical conditions performed by one veterinarian.ResultsOut of 26 horses, four were diagnosed with mild (subclinical), seven with moderate, and seven with severe asthma based on clinical examination and BALF cytology. Eight horses served as controls. Cough with history of coughing was the strongest variable in phenotype differentiation. Factor analysis revealed an increasing clinical variability with disease severity and an overlapping of clinical presentations between phenotypes. Elevated mast cell (4/4 horses) and neutrophil counts (3/4 horses) in bronchoalveolar lavage cytology differentiated mild asthmatic horses from healthy horses. Moderate and severe asthmatic horses were characterised by clinical signs and neutrophil counts.ConclusionsThe results indicate that medical history, clinical examination and bronchoalveolar lavage cytology are minimum indispensable steps to diagnose equine asthma and that phenotypes are clinically overlapping. A differentiation of three phenotypes without neutrophil and mast cell counts in bronchoalveolar lavage cytology is not sufficient for clinical diagnostics. A comparably exact diagnosis cannot be achieved by relying on alternative examinations used in this study. Screenings of inconspicuous horses with bronchoalveolar lavage can aid in diagnosing subclinically affected animals, however, group size was small, the procedure is invasive and clinical relevance of slightly elevated cells in bronchoalveolar lavage remains unclear. Clinical relevance could not be clarified in this study, since follow-up examinations or lung function testing were not performed.

Ethical Aspects of Human Genome Research in Sports-A Narrative Review.

Human genome research in sports raises complex ethical considerations regarding the intersection of genetics and athletic performance. Pursuing genetic enhancements must uphold fairness, equality, and respect for human dignity. This narrative review explores the ethical dimensions of human genome research in sports, its potential implications on athletes, and the integrity of sports. As a narrative review, this study synthesizes the existing literature and expert insights to examine the ethical aspects of human genome research in sports. This study extensively examined the current literature on genetics, sports performance, ethical concerns, human rights, and legal regulations within the European context. The literature was searched using the SPORTDiscus, Scopus, Google Scholar, and PubMed databases. Exploring human genome research in sports reveals significant ethical implications, including potential genetic discrimination, impacts on human rights, and creating a genetic underclass of athletes. There are also definite benefits surrounding genetic testing. In conclusion, this review contends that integrating ethical considerations into developing and applying genetic technologies in sports is crucial to upholding fundamental principles of fairness, equality, and respect for human dignity. It stresses the importance of open and inclusive dialogue about the potential consequences of genetic advancements on athletic performance, future generations, and the integrity of sports.

Unveiling novel serum biomarkers in intrahepatic cholangiocarcinoma: a pilot proteomic exploration.

Recent advancements in proteomics have shown promise in identifying biomarkers for various cancers. Our study is the first to compare the serum proteomes of intrahepatic cholangiocarcinoma (iCCA) with cirrhosis (CIR), primary sclerosing cholangitis (PSC), and hepatocellular carcinoma (HCC), aiming to identify a proteomic signature that can effectively distinguish among these conditions. Utilizing high-throughput mass spectrometry on serum samples, we identified 845 proteins, of which 646 were suitable for further analysis. Unique clustering patterns were observed among the five groups, with significant proteomic differences. Our key findings include: S100 calcium-binding protein A9 (S100A9) and haptoglobin (HP) were more abundant in iCCA, while intercellular adhesion molecule 2 (ICAM2) was higher in HCC. Serum amyloid A1 (SAA1) and A4 (SAA4) emerged as potential biomarkers, with SAA1 significantly different in the iCCA vs healthy controls (HC) comparison, and SAA4 in the HCC vs HC comparison. Elevated levels of vascular cell adhesion molecule 1 (VCAM-1) in HCC suggested its potential as a differentiation and diagnostic marker. Angiopoietin-1 receptor (TEK) also showed discriminatory and diagnostic potential in HCC. ELISA validation corroborated mass spectrometry findings. Our study underscores the potential of proteomic profiling in distinguishing iCCA from other liver conditions and highlights the need for further validation to establish robust diagnostic biomarkers.

Dual-energy CT Radiomics Combined with Quantitative Parameters for Differentiating Lung Adenocarcinoma From Squamous Cell Carcinoma: A Dual-center Study

Rationale and ObjectivesTo evaluate the ability of dual-energy CT(DECT)-based quantitative parameters and radiomics features to differentiate solid lung adenocarcinoma (ADC) from squamous cell carcinoma (SCC). MethodsThis study included 213 patients diagnosed with ADC and SCC who underwent DECT scans at two centers from November 2022 to December 2023. Patients at center 1 were randomly divided into training(n=114) and internal test set (n=50) in a 7:3 ratio, with center 2 serving as the external test set (n=49). Radiologic and clinical data were combined to establish a clinical-radiologic model. Ten types of DECT energy images including conventional images, iodine density (ID), effective atomic number (Zeff), electron density, and virtual monoenergetic images (VMI) were reconstructed in both arterial phases (AP) and venous phases (VP). Quantitative parameters were measured at the uniform enhanced solid portion of the tumor and normalized to the aorta, used to develop a quantification model and calculate the quantitative score (quantscore). Radiologists manually delineated the tumor ROI at the largest level for extracting radiomics features in these 10 energy images. These features were used to establish ten uni-energy models from which the best-performing features were selected to construct the final radiomics model and calculate a radiomics score (radscore). Then, a combined model was developed using the akaike information criterion(AIC) and compared with the clinical-radiological model to test its diagnostic validity. ResultsThe independent predictors of the clinical-radiological model included age, gender, and central or peripheral location, and the AUCs for the training set, internal test set, and external test set were 0.808, 0.837, and 0.802. The quantification model incorporated 40keV CT values, Zeff, normalized Zeff, and the slope of the spectral attenuation curve (λHU) in the AP and normalized ID, Zeff, and λHU in the VP. Uni-energy models based on AP ID maps, AP Zeff maps, and VP VMI 65keV significantly outperformed AUC = 0.5, and 11 radiomics features were selected from these three models to construct the final radiomics model. The combined model, incorporating age, gender, quantscore, and radscore, significantly outperformed the clinical-radiological model in the training set (AUC = 0.952 vs 0.808, P < 0.001), and demonstrated higher performance in both the internal and external test sets, although these differences did not reach statistical significance (AUC = 0.870 vs 0.837, for the internal test set [P = 0.542], 0.888 vs 0.802 for the external test sets [P = 0.128]). The evaluation of the combined model demonstrated good discriminative ability and potential for generalization. ConclusionsThe combined model, integrating quantitative parameters and radiomics features from DECT multi-energy images with clinical-radiological characteristics, can be used as a non-invasive tool to differentiate ADC from SCC.

Prediction of Microinvasion in Breast Ductal Carcinoma in Situ Using Conventional Ultrasound Combined with Contrast-Enhanced Ultrasound Features: A Two-Center Study

BackgroundTo develop and validate a model based on conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) features to preoperatively predict microinvasion in breast ductal carcinoma in situ (DCIS). Patients and MethodsData from 163 patients with DCIS who underwent CUS and CEUS from the internal hospital was retrospectively collected and randomly apportioned into training and internal validation sets in a ratio of 7:3. External validation set included 56 patients with DCIS from the external hospital. Univariate and multivariate logistic regression analysis were performed to determine the independent risk factors associated with microinvasion. These factors were used to develop predictive models. The performance was evaluated through calibration, discrimination, and clinical utility. ResultsMultivariate analysis indicated that centripetal enhancement direction (odds ratio [OR], 13.268; 95% confidence interval [CI], 3.687-47.746) and enhancement range enlarged on CEUS (OR, 4.876; 95% CI, 1.470-16.181), lesion size of ≥20 mm (OR, 3.265; 95% CI, 1.230-8.669) and calcification detected on CUS (OR, 5.174; 95% CI, 1.903-14.066) were independent risk factors associated with microinvasion. The nomogram incorporated the CUS and CEUS features achieved favorable discrimination (AUCs of 0.850, 0.848, and 0.879 for the training, internal and external validation datasets), with good calibration. The nomogram outperformed the CUS model and CEUS model (all P < .05). Decision curve analysis confirmed that the predictive nomogram was clinically useful. ConclusionThe nomogram based on CUS and CEUS features showed promising predictive value for the preoperative identification of microinvasion in patients with DCIS.

SPEEDING UP JUSTICE: A CRITICAL ANALYSIS OF THE PRINCIPLE OF SPEEDY, SIMPLE, AND LOW-COST TRIALS IN INDONESIAN CORRUPTION CASES

This article investigates the issue of delays in law enforcement's implementation of criminal law in corruption cases, particularly in relation to the principles of speedy, simple, and low-cost trials. Utilizing a normative legal method and statutory approach, alongside case studies, the study explores the persistent delays in the Indonesian criminal justice system despite the emphasis on these principles. Delays in the reading of indictments have raised suspicions and potential discrimination in the enforcement of corruption cases. The right of a defendant to a prompt resolution, as outlined in the Criminal Procedure Code aligning with the shift from an inquisitorial to accusatorial principle and human rights development, is highlighted. However, the findings reveal that these principles are not consistently and effectively applied in Indonesia's criminal justice system. Key issues identified include prolonged, complex, and costly law enforcement processes in corruption cases, as well as obstacles hindering the swift and cost-effective resolution of state losses. Discriminatory practices, though not overt, manifest in various stages of the legal process, impeding the realization of fast, simple, and low-cost justice. To address these challenges and ensure fair trials, law enforcement must prioritize ethical and moral considerations in their practices