Potential Discrimination Research Articles

The Impact of Mobile Data Cost on Consumer Price Sensitivity: A Study of a Hotel Booking App

The Impact of Mobile Data Cost on Consumer Price Sensitivity: A Study of a Hotel Booking App Mobile data cost arises from the perception that mobile data are more expensive than Wi-Fi because of additional charges incurred when exceeding fixed data quotas. Understanding mobile data cost is a global and industry-wide concern as the perception of this cost is common in both developed and developing countries, and even subtle behavioral shifts because of this cost can have significant impacts given the prevalence of mobile shopping. Using hotel search data, we find that consumers are approximately twice as price sensitive when using mobile data compared with Wi-Fi, leading to an average revenue reduction of $17 per hotel booking. Additionally, consumer search effort decreases across decision stages when using mobile data rather than Wi-Fi, showing that mobile data users pursue satisfactory options without extensive search effort, obtain less product information, and therefore rely more on price information, becoming more price sensitive. This study provides implications for mobile shopping environments in which limited mobile data plans are common, suggesting that firms should consider consumers’ connectivity types in marketing strategies, offer data subsidies to encourage more thorough searches, and develop customized recommendations and app versions for mobile data users and that policymakers should monitor potential price discrimination to protect consumer interests.

Emerging role of circulating piRNAs in the diagnosis of heart transplant rejection.

Liquid biopsy offers a potential alternative to decrease or eliminate endomyocardial biopsy (EMB) for diagnosing allograft rejection. piRNAs are novel and promising disease biomarkers for their intrinsic characteristics such as stability in body fluids; however, their role in allograft rejection remains unexplored. A training set based on small RNA sequencing technology was performed to identify piRNAs in endomyocardial tissue (n=8) and serum samples (n=40) from patients following heart transplantation. A validation set of the potential piRNAs identified in the training study was conducted in an independent larger cohort for the detection of acute cellular rejection (ACR, n=105) and antibody-mediated rejection (AMR, n=61). We identified 20292 piRNAs in endomyocardial tissue and 24602 piRNAs in serum samples from patients following heart transplantation. We identified seven piRNAs with a coincident expression profile in both types of samples and potential capacity for the non-invasive detection of cardiac rejection. Validation in a large independent cohort demonstrated that a panel of these piRNAs showed excellent performance for detecting grade ≥2R ACR (AUC=0.819; p<0.0001) and grade 1R ACR (AUC=0.721; p=0.001). Furthermore, our piRNA panel showed a potential discrimination ability of pAMR2 (AUC=0.967; p<0.0001). To the best of knowledge, this study is the first to report the presence of piRNAs in both endomyocardial tissue and serum samples of patients after heart transplant, including their association with allograft rejection events. We propose a novel piRNA panel for the detection of cardiac allograft rejection.

Nothing really matters: evaluating demand-side moderators of age discrimination in hiring

Abstract As age discrimination hampers the OECD’s ambition to extend the working population, an efficient anti-discrimination policy targeted at the right employers is critical. Therefore, the context in which age discrimination is most prevalent must be identified. In this study, we thoroughly review the current theoretical arguments and empirical findings regarding moderators of age discrimination in different demand-side domains (i.e. decision-maker, vacancy, occupation, organization and sector). Our review demonstrates that the current literature is highly fragmented and often lacks field-experimental evidence, raising concerns about its internal and external validity. To address this gap, we conducted a correspondence experiment and systematically linked the resulting data to external data sources. In so doing, we were able to study the priorly determined demand-side moderators within a single multi-level analysis and simultaneously control multiple correlations between potential moderators and discrimination estimates. Having done so, we found no empirical support for any of these moderators.

ЕКОНОМІКО-ПРАВОВЕ ОБҐРУНТУВАННЯ СУЧАСНОГО СТАНУ ГЕНДЕРНОГО ЗАМІЩЕННЯ ТРУДОВОГО ПОТЕНЦІАЛУ

Modern society erases traditional gender boundaries in the professional activities of employees of different levels, statuses, and fields of employment. Professions that were previously considered purely male are now effectively performed by women. This testifies not only to their high qualification and professionalism, but also to the ability to reincarnate for their own benefit and social necessity. In the conditions of Russia’s war against Ukraine, the demand for so-called «male» professions among women has increased significantly. This is due to the need to fill the workforce in strategically important sectors – in defense, industry, transport, construction, etc. The relevance of the study lies in the analysis of the socio-economic consequences of this process and its impact on the gender replacement of labor resources. It is important to assess how the involvement of women in traditionally male professions affects labor productivity, the level of employment and social integration of women in today’s conditions. The study will also contribute to the development of recommendations to support gender equality in the labor market in times of crisis. In general, the study of this topic is a necessary factor influencing the formation of an effective economic and legal policy in Ukraine. The increasing number of cases of women taking up «male» professions has both positive and negative factors. The positives include expanding economic opportunities for women, reducing gender stereotypes, and increasing labor efficiency due to the diversity of the free labor sphere. Negative factors include potential discrimination, physical exertion that is difficult for some women, and social conflicts due to the violation of traditional gender roles. The study of these factors is important for the development of policies aimed at promoting equality in the labor market and ensuring the harmonious coexistence of all workers. This will help create an effective work environment that maximizes the labor resources of all employees, regardless of gender. Key words: profession, gender substitution, labor resources, labor potential, social integration, «male» professions.

Human identification via digital palatal scans: a machine learning validation pilot study

BackgroundThis study aims to validate a machine learning algorithm previously developed in a training population on a different randomly chosen population (i.e., test set). The discrimination potential of the palatal intraoral scan-based geometric and superimposition methods was evaluated.MethodsA total of 23 participants (16 females and seven males) from different countries underwent palatal scans using the Emerald intraoral scanner. Geometric-based identification involved measuring the height, width, and depth of the palatal vault in each scan. These parameters were then input into Fisher’s linear discriminant equations with coefficients determined previously on a training set. Sensitivity and specificity were calculated. For the superimposition method, scan repeatability was compared to between-subjects differences, calculating mean absolute differences (MAD) between aligned scans. Multiple linear regression analysis determined the effects of sex, longitude, and latitude of country of origin on concordance.ResultsThe geometric-based method achieved 91.2% sensitivity and 97.1% specificity, consistent with the results from the training set, showing no significant difference. Latitude and longitude did not significantly affect geometric-based matches. In the superimposition method, the between-subjects MAD range (1.068–0.214 mm) and the repeatability range (0.011–0.093 mm) did not overlap. MAD was minimally affected by longitude and not influenced by latitude. The sex determination function recognized females over males with 69.0% sensitivity, similar to the training set. However, the specificity (62.5%) decreased.ConclusionsThe assessment of geometric and superimposition discrimination has unequivocally demonstrated its robust reliability, remaining impervious to population. In contrast, the distinction between sexes carries only moderate reliability. The significant correlation observed among longitude, latitude, and palatal height suggests the feasibility of a comprehensive large-scale study to determine one’s country of origin.Clinical significancePortable intraoral scanners can aid forensic investigations as adjunct identification methods by applying the proposed discriminant function to palatal geometry without population restrictions.Trial registrationThe Clinicatrial.gov registration number is NCT05349942 (27/04/2022).

Implementing Facial Recognition Technology in a University Setting: An Organizational Justice Perspective

Facial recognition technology (FRT) has become widespread in society, and its adoption in organizational settings is viewed as a natural continuation of established surveillance practices. However, concerns and resistance to FRT often arise due to controversial applications and improper use. This study contributes to the understanding of FRT in organizational contexts by conducting semi-structured interviews in a university that illustrates the larger themes of organizational justice and the ethical complexities surrounding the deployment of this technology. Drawing on organizational justice theory, we analyze users’ attitudes towards FRT deployment, their perceptions of organizational justice, and if and how the deployment adhered to or violated organizational justice principles. Our study revealed that this implementation case violated all aspects of organizational justice. Users expressed concerns regarding face capture, potential discrimination, authoritarian practices, and harm to community norms. We also provide recommendations to address organizational justice issues associated with implementing new, controversial technologies, considering technology acceptability perspectives.

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.

Diagnostic Utility of Diffusion-Weighted Imaging in Distinguishing Common Pediatric Posterior Fossa Tumors: A Single Center Retrospective Study.

Pediatric posterior fossa tumors pose diagnostic challenges due to their diverse histopathological features and variable clinical presentations. Conventional magnetic resonance imaging (MRI) serves as the initial diagnostic tool; however, additional modalities, such as diffusion-weighted imaging (DWI), are essential for refining tumor classification. This retrospective single-center study aimed to evaluate the diagnostic utility of apparent diffusion coefficient (ADC) parameters in distinguishing between the most common pediatric posterior fossa tumors. Fifty-nine patients under the age of 18 (27 females and 32 males) with histopathologically diagnosed primary posterior fossa tumors underwent pre-treatment conventional and diffusion MRI. Apparent diffusion coefficient values were measured from solid tumor regions and normal cerebellar parenchyma, with subsequent calculation of tumor/normal cerebellar ADC ratios. The median ADC values for pilocytic astrocytomas (PAs) were 1786.2 × 10-6 mm2 /s, ependymomas 1144.9 × 10-6 mm2 /s, and for medulloblastomas 666.1 × 10-6 mm2 /s were significantly different (P < .001 for all three). Similarly, the median ADC ratios demonstrated discriminatory potential, with PAs showing the highest ratio (2.46), followed by ependymomas (1.55) and medulloblastomas (0.89) (P < .001 for all three). Receiver operating characteristic analysis revealed distinct ADC cutoffs and ratios for differentiating all tumor types from each other. Despite limitations, such as a small cohort size and different MRI protocols, our results show that ADC metrics are especially useful for distinguishing between the most common pediatric posterior fossa tumors. We recommend that future studies integrate advanced imaging techniques and larger cohorts to improve diagnostic accuracy.

Impact of anti-human IgG hinge peptide antibodies on identification of patients with early seronegative rheumatoid arthritis.

The early diagnosis of seronegative rheumatoid arthritis (SNRA), characterised by the absence of rheumatoid factor and anti-citrullinated antibody, involves a greater challenge compared to seropositive RA (SPRA). This study aimed to assess the discriminatory potential of anti-human IgG hinge antibodies (AHAs) for patients with early SNRA. DMARDs-naive patients with SPRA (n=43), SNRA (n=21), and non-RA (n=49), with disease duration < 2 years, were included. Antigens comprised IgG1 or IgG4 F(ab')2 cleaved by pepsin or MMP-3 and their hinge peptide analogues. Eight IgG anti-hinge antibodies (AHAs) against these antigens were measured in sera from the patients and 58 healthy controls (HCs) using ELISA. Serum CRP and MMP-3 levels, and clinical disease activity index (CDAI), were obtained from medical records. The area under the curve (AUC) obtained from logistic regression and receiver operating characteristic curve analyses were used as a discriminant indicator. The levels of the IgG AHAs were as follows: SPRA≥SNRA≈non-RA>HC. None of the AHAs were effective in discriminating SNRA from non-RA. However, the combination of MMP-3 and AHAs against IgG4 hinge peptide analogues demonstrated the utility (AUC=0.94). Furthermore, combination of MMP-3, AHAs against IgG1 hinge peptide analogues and CDAI maximally exerted discriminatory power (AUC=0.997). Specific AHAs in combination with MMP-3 and CDAI are potentially useful to discriminate SNRA from non-RA.

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.

Double Discrimination: Attitudes toward Syrian and Ukrainian Immigrant Entrepreneurs' Job Postings in Türkiye

This study uses a vignette experiment to examine the discriminatory attitudes towards immigrant entrepreneurs. The aim is to assess how biases based on ethnicity affect the job application intentions directed towards immigrant entrepreneurs, specifically comparing those from Syria and Ukraine with their Turkish counterparts. The research addresses several objectives: to determine whether discriminatory practices against immigrant entrepreneurs differ from those faced by immigrant workers; to explore the presence and nature of discrimination against immigrant entrepreneurs in Türkiye, including taste-based and statistical forms of discrimination; to investigate potential double discrimination between entrepreneurs of Middle Eastern (Syria) and European (Ukraine) origins; and to analyze whether ethnic cues in job advertisements influence job application intentions. Findings reveal that immigrant entrepreneurs experience significant discrimination, with Syrian entrepreneurs facing more bias compared to Ukrainians, influenced by geopolitical and cultural factors. This study offers a new perspective by examining how potential applicants respond to job advertisements from immigrant entrepreneurs, differing from previous research on discrimination faced by immigrant workers. The results highlight the need for inclusive policies and practices to support immigrant entrepreneurs and enhance their participation in the Turkish economy, contributing to a more equitable and diverse entrepreneurial landscape.

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.