Search Results (15,113)

The widespread production and poor management of plastic waste have led to the pervasive presence of microplastics (MPs) in environmental and biological systems. Among various polymers, polyethylene (PE) is the most widely produced plastic globally, primarily due to its use in single-use packaging. Its persistence in ecosystems and resistance to degradation processes result in the continuous formation of PE-derived MPs. These particles have been detected in human biological matrices, including blood, lungs, placenta, and even the brain, raising increasing concerns about their bioavailability and potential health effects. Once internalized, PE MPs can interact with cellular membranes, induce oxidative stress, inflammation, and apoptosis, and interfere with epigenetic regulatory pathways. In vitro studies on epithelial, immune, and neuronal cells reveal concentration-dependent cytotoxicity, mitochondrial dysfunction, membrane disruption, and activation of pro-inflammatory cytokines. Moreover, recent findings suggest that PE MPs can induce epithelial-to-mesenchymal transition (EMT), senescence, and epigenetic dysregulation, including altered expression of miRNAs and DNA methyltransferases. These cellular changes highlight the potential role of MPs in disease development, especially in cardiovascular, metabolic, and possibly cancer-related conditions. Despite growing evidence, no standardized method currently exists for quantifying MPs in human samples, complicating comparisons across studies. Further, MPs can carry harmful additives and environmental contaminants such as bisphenols, phthalates, dioxins, and heavy metals, which enhance their toxicity. Global estimates indicate that humans ingest and inhale tens of thousands of MPs particles each year, yet long-term human research remains limited. Given these findings, it is crucial to expand research on PE MP toxicodynamics and to establish regulatory policies to reduce their release. Promoting alternative biodegradable materials and improved waste management practices will be vital in decreasing human exposure to MPs and minimizing potential health risks. Full article

Background/Objective: The overnight 1-mg dexamethasone suppression test (DST) represents the conventional/standard tool for endogenous hypercortisolemia screening, typically in relationship with adrenal and pituitary masses. Nevertheless, an associated spectrum of challenges and pitfalls is found in daily practice. This analysis aimed to evaluate: (I.) the diagnosis relevance of 1-mg DST in patients with adrenal incidentalomas (AIs) with/without mild autonomous cortisol secretion (MACS) exploring different cutoffs of the second-day plasma cortisol after dexamethasone administration (cs-DST) with respect to cardio-metabolic outcomes; (II.) the potential utility of adding other biomarkers to DST [plasma morning adrenocorticotropic hormone (ACTH), 24-h urinary free cortisol (UFC), late-night salivary cortisol (LNSC), dehydroepiandrosterone sulfate (DHEAS)]; and (III.) DST variability in time. Methods: This narrative analysis was based on searching full-text, English articles in PubMed (between January 2023 and April 2025) via using different term combinations: “dexamethasone suppression test” (n = 239), “diagnosis test for autonomous cortisol secretion” (n = 22), “diagnosis test for mild autonomous cortisol secretion” (n = 13) and “diagnosis test for Cushing Syndrome” (n = 61). We manually checked the title and abstract and finally included only the studies that provided hormonal testing results in adults with non-functional adenomas (NFAs) ± MACS. We excluded: reviews, meta-analyses, editorials, conference abstracts, case reports, and case series; non-human research; studies that did not provide clear criteria for distinguishing between Cushing syndrome and MACS; primary aldosteronism. Results: The sample-focused analysis (n = 13 studies) involved various designs: cross-sectional (n = 4), prospective (n = 1), retrospective (n = 7), and cohort (n = 1); a total of 4203 patients (female-to-male ratio = 1.45), mean age of 59.92 years. I. Cs-DST cutoffs varied among the studies (n = 6), specifically, 0.87, 0.9, 1.2, and 1.4 µg/dL in relationship with the cardio-metabolic outcomes. After adjusting for age (n = 1), only the prevalence of cardiovascular disease remained significantly higher in >0.9 µg/dL vs. ≤0.9 group (OR = 2.23). Multivariate analysis (n = 1) found cs-DST between 1.2 and 1.79 µg/dL was independently associated with hypertension (OR = 1.55, 95%CI: 1.08–2.23, p = 0.018), diabetes (OR = 1.60, 95%CI: 1.01–2.57, p = 0.045), and their combination (OR = 1.96, 95%CI:1.12–3.41, p = 0.018) after adjusting for age, gender, obesity, and dyslipidemia. A higher cs-DST was associated with a lower estimated glomerular filtration rate (eGFR), independently of traditional cardiovascular risk factors. Post-adrenalectomy eGFR improvement was more pronounced in younger individuals, those with lower eGFR before surgery, and with a longer post-operative follow-up. Cs-DST (n = 1) was strongly associated with AIs size and weakly associated with age, body mass index and eGFR. Cortisol level increased by 9% (95% CI: 6–11%) for each 10 mL/min/1.73 m² decrease in eGFR. A lower cs-DST was associated with a faster post-adrenalectomy function recovery; the co-diagnosis of diabetes reduced the likelihood of this recovery (OR = 24.55, p = 0.036). II. Additional biomarkers assays (n = 5) showed effectiveness only for lower DHEAS to pinpoint MACS amid AIs (n = 2, cutoffs of <49.31 µg/dL, respectively, <75 µg/dL), and lower ACTH (n = 1, <12.6 pmol/L). III. Longitudinal analysis of DST’s results (n = 3): 22% of NFAS switch to MACS after a median of 35.7 months (n = 1), respectively, 29% (n = 1) after 48.6 ± 12.5 months, 11.8% (n = 1) after 40.4 ± 51.17 months. A multifactorial model of prediction showed the lowest risk of switch (2.4%) in individuals < 50 years with unilateral tumor and cs-DST < 0.45 µg/dL. In the subgroup of subjects without cardio-metabolic comorbidities at presentation, 25.6% developed ≥1 comorbidities during surveillance. Conclusions: The importance of exploring the domain of AIs/NFAs/MACS relates to an increasing detection in aging population, hence, the importance of their optimum hormonal characterization and identifying/forestalling cardio-metabolic consequences. The spectrum of additional biomarkers in MACS (other than DST) remains heterogeneous and still controversial, noting the importance of their cost-effectiveness, and availability in daily practice. Cs-DST serves as an independent predictor of cardio-metabolic outcomes, kidney dysfunction, while adrenalectomy may correct them in both MACS and NFAs, especially in younger population. Moreover, it serves as a predictor of switching the NFA into MACS category during surveillance. Changing the hormonal behavior over time implies awareness, since it increases the overall disease burden. Full article

Long COVID, characterized by persistent symptoms following acute SARS-CoV-2 infection, has emerged as a significant public health challenge with wide-ranging clinical and socioeconomic implications. Developing an effective risk assessment strategy is essential for the early identification and management of individuals susceptible to prolonged symptoms. This study uses a quantitative approach to characterize the dose–response relationships between spike protein concentrations and effects, including Long COVID symptom numbers and the release of proinflammatory mediators. A mathematical model is also developed to describe the time-dependent change in spike protein concentrations post diagnosis in twelve Long COVID patients with a cluster analysis. Based on the spike protein concentration–Long COVID symptom numbers relationship, we estimated a maximum symptom number (~20) that can be used to reflect a persistent predictor. We found that among the crucial biomarkers associated with Long COVID proinflammatory mediator, CXCL8 has the lowest 50% effective dose (0.01 μg mL⁻¹), followed by IL-6 (0.39), IL-1β (0.46), and TNF-α (0.56). This work provides a comprehensive risk assessment strategy with dose–response tools and mathematical modeling developed to estimate potential spike protein concentration. Our study suggests persistent Long COVID guidelines for personalized care strategies and could inform public health policies to support early interventions that reduce long-term disability and healthcare burdens with possible other post-infection syndromes. Full article

As a high-frequency and essential type of special electromechanical equipment, a vertical elevator has a significant societal implication for their safe operation. The load-weighing module, serving as the core component for overload warning, is susceptible to precision degradation due to the nonlinear deformation of rubber buffers installed at the base of the elevator car. This deformation arises from the coupled effects of environmental factors such as temperature, humidity, and material aging, leading to potential safety risks including missed overload alarms and false empty status detections. To address the issue of accuracy deterioration in elevator load-weighing systems, this study proposes an online self-calibration method based on multimodal information fusion. A reference detection model is first constructed to map the relationship between applied load and the corresponding relative compression of the rubber buffers. Subsequently, displacement data from a draw-wire sensor are integrated with target detection model outputs, enabling real-time extraction of dynamic rubber buffers’ deformation characteristics under empty conditions. Based on the above, a displacement-based compensation term is derived to enhance the accuracy of load estimation. This is further supported by a dynamic error compensation mechanism and an online computation framework, allowing the system to self-calibrate without manual intervention. The proposed approach eliminates the dependency on manual tuning inherent in traditional methods and forms a highly robust solution for load monitoring. Field experiments demonstrate the effectiveness of the proposed method and the stability of the prototype system. The results confirm that the synergistic integration of multimodal perception and adaptive calibration technologies effectively resolves the challenge of load-weighing precision degradation under complex operating conditions, offering a novel technical paradigm for elevator safety monitoring. Full article

Background: Female infertility represents a significant global public health concern, yet its evolving trends and data-driven risk prediction remain under examined in nationally representative cohorts. This study investigates temporal changes in infertility prevalence and evaluates Machine Learning (ML) models for infertility risk prediction using harmonized clinical features from NHANES cycles (2015, 2016, 2017, 2018, 2021, 2022, and 2023). Methods: Women aged 19 to 45 with complete data on infertility-related variables (including reproductive history, menstrual irregularity, Pelvic Infection Disease (PID), hysterectomy, and bilateral oophorectomy) were analyzed. Descriptive statistics and cohort comparisons employed ANOVA and Chi-square tests, while multivariate Logistic Regression (LR) estimated Adjusted Odds Ratios (OR) and informed feature importance. Predictive models (LR, Random Forest, XGBoost, Naive Bayes, SVM, and a Stacking Classifier ensemble) were trained and tuned via GridSearchCV with five-fold cross-validation. Model performance was evaluated using accuracy, precision, recall, F1-score, specificity, and AUC-ROC. Results: We observed a notable increase in infertility prevalence from 14.8% in 2017–2018 to 27.8% in 2021–2023, suggesting potential post-pandemic impacts on reproductive health. In multivariate analysis, prior childbirth emerged as the strongest protective factor (Adjusted OR $\approx 0.00$), while menstrual irregularity showed a significant positive association with infertility (OR $=0.55$, 95% CI $0.40$ to $0.77$, $p<0.001$). Unexpectedly, PID, hysterectomy, and bilateral oophorectomy were not significantly associated with infertility after adjustment ($p>0.05$), which may partly reflect the inherent definition of self-reported infertility used in this study. All six ML models demonstrated excellent and comparable predictive ability (AUC $>0.96$), reinforcing the effectiveness of even a minimal common predictor set for infertility risk stratification. Conclusions: The rising prevalence of self-reported infertility among U.S. women underscores emerging public health challenges. Despite relying on a streamlined feature set, interpretable and ensemble ML models successfully predicted infertility risk, showcasing their potential applicability in broader surveillance and personalized care strategies. Future models should integrate additional sociodemographic and behavioral factors to enhance precision and support tailored interventions. Full article

Background: Fetal size is often dichotomized as normal or abnormal using the 10th percentile of estimated fetal weight (EFW) or abdominal circumference as a cutoff. While the risk of adverse perinatal outcomes decreases with increasing fetal weight percentile, no percentile completely eliminates that risk. Objective: The aim of this study was to compare perinatal outcomes between fetuses with an EFW between the 10th and 20th percentiles and those with an EFW between the 20th and 90th percentiles (i.e., >20 and <90) at the beginning of the accelerated growth stage (28.0–30.0 weeks’ gestation). Methods: We conducted a retrospective cohort study of singleton pregnancies managed at a quaternary center in Mexico City (2017–2024). Outcomes were compared based on EFW percentiles at 28.0–30.0 weeks. The primary outcome was adverse neonatal outcome (ANeO), defined as the presence of at least one of the following: umbilical artery pH ≤ 7.1, 5 min Apgar ≤ 7, NICU admission, early neonatal hypoglycemia, non-reassuring fetal status, respiratory distress syndrome, intraventricular hemorrhage, hypoxic–ischemic encephalopathy, or perinatal death. Secondary outcomes included progression to fetal growth restriction (FGR) and low birth weight. Modified Poisson regression was used to estimate adjusted risk ratios (aRRs) with 95% confidence intervals (CIs). Results: Among 650 cases, ANeO occurred in 45.8% of fetuses in the 10th–20th percentile group vs. 29.4% in the 20th–90th percentile group (aRR: 1.51, 95% CI: 1.22–1.86; p < 0.001). FGR and low birth weight were also more frequent in the 10th–20th percentile group (21.1% and 27.6% vs. 6.4% and 5.8%, respectively; p < 0.001). Conclusions: Fetuses between the 10th and 20th percentiles at 28–30 weeks have increased risks of neonatal morbidity, FGR, and low birth weight. Full article

Diabetes type 2 (DT2) entails significant health, economic, and productivity repercussions around the world. Poor glycaemic control, defined as an HbA1c >7.0%, has been associated with a number of complications. In spite of the large share of healthcare resources allocated to DT2 treatment, the proportion of controlled Mexican patients is among the lowest in the world (34.4%). Certain protein-encoding genetic polymorphisms involved in metformin transport may affect glycaemic control. We focused on determining the frequency of rs2289669, rs2252281, rs12943590, and rs34834489 polymorphisms in Mexican-Mestizo patients from the Tertiary Care Regional Hospital of Ixtapaluca, State of Mexico, Mexico, as well as assessing their possible association with therapeutic efficacy, as estimated through glycated haemoglobin. The individual polymorphism analysis did not reveal an association with glycaemic control; however, when combined with rs72552763 and rs622342, we found a significant positive correlation between HbA1c levels and metformin dose, which prevailed among patients carrying allelic variants of rs2289669 or rs12943590 who were also simultaneously carrying allelic variants of rs72552763 or rs622342. Patients carrying the reference allele of rs34834489 reported a significant positive correlation between HbA1c levels and metformin dose as well, regardless of their rs72552763 or rs622342 genotype. Thus, we identified alleles and allelic combinations of SLC47A1, SLC47A2, and SLC22A1 polymorphisms posing a potential glycaemic control risk in Mexican-Mestizo patients. Full article

Background: Patients after a transient ischemic attack (TIA) are at high risk of subsequent stroke. There are various scores that aim to accurately identify patients at the highest risk of stroke. However, without comparisons between these scores, it is still unknown which is the score with the best predictive utility. Our study aims to identify the risk stratification score with the highest utility to identify patients at high risk for stroke within 90 days after a TIA. Methods: The MEDLINE and Scopus databases were systematically searched on 1 December 2023 for observational cohort studies assessing the ability of a score to predict a stroke within the first 90 days from the index TIA event. Only studies that had a direct comparison of at least two scores were included. A random-effects network meta-analysis was performed. Sensitivity and specificity, along with relevant 95% credible intervals, and between-score and between-study heterogeneity were estimated. We also estimated relative sensitivities and relative specificities compared with the ABCD2 score. We ranked each score according to its predictive accuracy based on both sensitivity and specificity estimates, using the diagnostic odds ratio (DOR) and the summary receiver operating characteristic (SROC) curve. Results: Our systematic review highlighted 9 studies including 14 discrete cohorts. The performance of all scores to identify patients at high risk for stroke recurrence within 90 days following a TIA was low (pooled sensitivity range 48–64%, pooled specificity range 59–72%). In the network meta-analysis, we analyzed 6 studies with 11 discrete cohorts, including data from 8217 patients. The ABCD3-I score demonstrated the highest DOR, followed by the ESRS, ABCD, California, and ABCD2. The SROC curves demonstrate no significant differences in the performance of the scores, using the ABCD score as the common comparator. Conclusions: In this systematic review and network meta-analysis of observational cohort studies of patients who experienced TIA and were followed for the occurrence of subsequent stroke, we failed to identify a score performing significantly better for the prediction of stroke at 90 days. New models are needed for the prediction and stroke risk stratification following a TIA. Full article

Fuel Moisture Content (FMC) is a critical indicator for assessing forest fire risk and formulating early warning strategies, as its spatiotemporal dynamics directly influence the accuracy of fire danger rating. To improve the accuracy of forest FMC estimation, this study proposes an innovative deep learning method integrating multi-source remote sensing data. By combining the global feature extraction capability of the Transformer architecture with the local temporal modeling advantages of Gated Recurrent Units (GRU) (referred to as the Transformer-GRU model), a high-precision FMC estimation framework is established. The study focuses on forested areas in California, USA, utilizing ground-measured FMC data alongside multi-source remote sensing datasets from MODIS, Sentinel-1, and Sentinel-2. A systematic comparison was conducted among Transformer-GRU model, standalone Transformer models, single GRU models, and two classical machine learning models (Random Forest, RF, and Support Vector Regression, SVR). Additionally, forward feature selection was employed to evaluate the performance of different models and feature combinations. The results demonstrate that (1) All models effectively utilize the derived features from multi-source remote sensing data, confirming the significant enhancement of multi-source data fusion for forest FMC estimation; (2) The Transformer-GRU model outperforms other models in capturing the nonlinear relationship between FMC and remote sensing data, achieving superior estimation accuracy (R² = 0.79, MAE = 8.70%, RMSE = 11.44%, rRMSE = 12.60%); (3) The spatiotemporal distribution patterns of forest FMC in California generated by the Transformer-GRU model align well with regional geographic characteristics and climatic variability, while exhibiting a strong relationship with historical wildfire occurrences. The proposed Transformer-GRU model provides a novel approach for high-precision FMC estimation, offering reliable technical support for dynamic forest fire risk early warning and resource management. Full article

In this paper, using the leaching models, we quantified the leaching content of Cd, Pb, Cu, and Zn, and estimated the ecological risk changes in farmland soil caused by leaching and the ecological risk in leachate in China. Jiangxi, Guangxi, Guizhou, Hainan, Hunan, Zhejiang, Guangdong, and Chongqing are hotspot areas. The leaching of Cd in these regions exceed reported mean values in Europe (2.56 g ha⁻¹ year⁻¹). Although the total ecological risk of heavy metals in the soil of various provinces (ranged from 20 to 130) was generally low, Cd was the most important contributor to ecological risks, while 9 provinces exhibited considerable ecological risk from Cd. The calculated Cd, Pb, and Zn in leachate exceed drinking water standards (GB 5749-2022) in five provinces. Overall, the leaching of heavy metals in Chinese agricultural soils, particularly in the southern regions, is a critical issue that warrants attention. Soil pH is the most prominent factor influencing heavy metal leaching. A 5% increase in pH reduces leaching by 31.2% for Cd, 25.42% for Pb, 22.07% for Cu, and 38.37% for Zn. Adjusting the pH to 6 can effectively solve the problem of excessive heavy metal content in leachate in most areas. The study recommends prioritizing groundwater monitoring in critical provinces such as Jiangxi and adjusting the soil pH of farmland in key regions. Full article

Background/objectives: Falls are the most common adverse events in hospitals. This study aimed to estimate excess hospitalisation costs attributable to inpatient falls, using Diagnosis-Related Group (DRG) relative weights as a proxy for resource consumption. Methods: Case–control study. Cases included patients who had sustained a fall during hospitalisation between 2020 and 2022 in 19 inpatient units. Controls were selected with matching technique based on age and admission period. Diagnosis-Related Groups and their resource consumption and cost estimators (relative weights) were provided by the Hospital’s Coding Unit. Results: A total of 613 falls were analysed against 623 controls. The Diagnosis-Related Group ‘Lower limb amputation except toes’ was associated with a fourfold higher risk of falling compared to others. Five more were identified in which the case group incurred significantly higher costs than the control group. These included three surgical Diagnosis-Related Group, ‘Urethral and transurethral procedures’, ‘Heart valve procedures without acute myocardial infarction or complex diagnosis’, and ‘Arterial procedures on the lower limb’, and two medical, ‘Heart failure’ and ‘Major pulmonary infections and inflammations’. Conclusions/Implications for practice: Identifying Diagnosis-Related Groups in which falls are associated with increased hospitalisation costs allows for a comprehensive assessment of the process, taking into account resource consumption and the clinical characteristics of hospitalised patients. These findings will enable nurses to develop targeted strategies to enhance the safety of hospitalised patients that contribute to the sustainability of the healthcare system. Full article

This is the first systematic review and meta-analysis on the prevalence and risk factors of M. hyopneumoniae infection in swine farms across mainland China from 2003 to 2024. A total of 54 eligible cross-sectional studies were analyzed by stratifying farms as subclinically or clinically infected. The overall pooled prevalence of M. hyopneumoniae was estimated as 33.4%, with clinical infection farms showing a significantly higher prevalence (52.9%) than subclinical farms (11.5%). Subgroup analyses revealed significant variations in infection rates based on age, sampling year, geographic region, farming scale, season, sampling type, and diagnostic method. Small-scale farms, farms with breeding swine, and farms in the Northwest region showed the highest infection rates. Diagnostic methods and sampling types also significantly influenced detection rates. Sensitivity analyses confirmed the robustness of the results, while publication bias was addressed using the Trim-and-Fill method. To effectively reduce the burden of M. hyopneumoniae in the swine industry in mainland China, future efforts should prioritize enhanced biosecurity, improved diagnostic accuracy, and region-specific vaccination and management strategies. Full article

The growing importance of social networks has led to increased research into trust estimation and interpretation among network entities. It is important to predict the trust score between users in order to minimize the risks in user interactions. This article enables the identification of the most reliable and least reliable entities in a network by expressing trust scores numerically. In this paper, the social network is modeled as a graph, and trust scores are calculated by taking the powers of the ratio matrix between entities and summing them. Taking the power of the proportion matrix based on the number of entities in the network requires a lot of arithmetic load. After taking the powers of the eigenvalues of the ratio matrix, these are multiplied by the eigenvector matrix to obtain the power of the ratio matrix. In this way, the arithmetic cost required for calculating trust between entities is reduced. This paper calculates the trust score between entities using linear algebra techniques to reduce the arithmetic load. Trust detection algorithms use shortest paths and similar methods to eliminate paths that are deemed unimportant, which makes the result questionable because of the loss of data. The novelty of this method is that it calculates the trust score without the need for explicit path numbering and without any data loss. Full article

Background: Liver cancer is a significant disease burden, with metabolic factors potentially influencing its risk. Diabetics, due to metabolic abnormalities, may be more sensitive to environmental exposures. Beverages like tea and alcohol could impact liver cancer risk and may influence prevention in diabetics. Methods: This study included 30,289 diabetics and 482,292 non-diabetics aged 30–79 years from the China Kadoorie Biobank. Baseline alcohol and tea consumption during the past year was collected through questionnaires, including frequency, amount, duration, and types. Incident liver cancer cases were identified from the national health insurance system and local disease registries. Cox proportional hazards regression models estimated hazard ratios (HRs) and 95% confidence intervals (CIs). Results: During a median follow-up of 9.6 years for diabetics and 10.1 years for non-diabetics, 193 (0.69 cases/1000 person-years) and 398 (0.45 cases/1000 person-years) incident liver cancer cases were documented, respectively. Weekly alcohol consumption was associated with higher liver cancer risk in both groups, stronger in diabetics (HR = 1.62; 95% CI: 1.12, 2.34) than in non-diabetics (HR = 1.20, 95% CI: 1.07, 1.35). Among diabetics, the risk was higher in some weekly alcohol consumption subgroups: high-level intake (HR = 2.21; 95% CI: 1.28, 3.80), ≥30 years (HR = 1.70; 95% CI: 1.06, 2.71), or spirit (≥50% alcohol) alcohol-specific consumption (HR = 1.91; 95% CI: 1.20, 3.04), and these associations were stronger than those in non-diabetics. For weekly tea consumption, low-level intake (HR = 0.82; 95% CI: 0.68, 0.99), <10 years (HR = 0.74; 95% CI: 0.58, 0.93), 10–29 years (HR = 0.84; 95% CI: 0.71, 0.99), and green tea-specific consumption (HR = 0.86; 95% CI: 0.75, 0.98) were associated with reduced liver cancer risk in non-diabetics. However, these associations were not significant in those with diabetes. Conclusions: Weekly alcohol consumption is significantly associated with an increased risk of liver cancer, especially in diabetics, while tea consumption appears to lower risk only in non-diabetics, highlighting the need for alcohol reduction in diabetics. Full article

Alexithymia is regarded as one of the risk factors for several prevalent mental disorders, and there is a growing need for convenient and objective methods to assess alexithymia. Therefore, this study proposes a method for constructing models to assess alexithymia using machine learning and electroencephalogram (EEG) signals. The explanatory variables for the models were functional connectivity calculated from resting-state EEG data, reflecting the default mode network (DMN). The functional connectivity was computed for each frequency band in brain regions estimated by source localization. The objective variable was defined as either low or high alexithymia severity. Explainable artificial intelligence (XAI) was used to analyze which features the models relied on for their assessments. The results indicated that the classification model suggested effective assessment depending on the threshold used to define low and high alexithymia. The maximum receiver operating characteristic area under the curve (ROC-AUC) score was 0.70. Furthermore, analysis of the classification model indicated that functional connectivity in the theta and gamma frequency bands, and specifically in the Left Hippocampus, was effective for alexithymia assessment. This study demonstrates the potential applicability of EEG signals and machine learning in alexithymia assessment. Full article