Search Results (7,260)

Background: Colorectal cancer (CRC), with high incidence but low rates of early diagnosis, poses significant challenges to public health worldwide. Lipid metabolic reprogramming has been closely associated with CRC occurrence and development. This study aimed to identify key fatty acid metabolism-related molecules involved in the development of CRC and to explore potential prognostic biomarkers and therapeutic targets. Methods: Based on The Cancer Genome Atlas (TCGA) data from colon adenocarcinoma (COAD) patients, we applied weighted gene co-expression network analysis (WGCNA), Cox regression, and least absolute shrinkage and selection operator (LASSO) to identify fatty acid metabolism-related signature genes in CRC. Expression validation and prognostic analysis were conducted. Summary-data-based Mendelian randomization (SMR) was used to infer causal relationships between target genes and CRC. Single-cell transcriptomics and immune infiltration analysis elucidated underlying pathogenic mechanisms. Cellular and animal experiments validated tumor-suppressive effects and lipid metabolic regulatory mechanisms. Results: RPS6KA1 and CHGA were identified as fatty acid metabolism-related signature genes in COAD. Only RPS6KA1 was significantly downregulated in COAD and negatively correlated with poor prognosis (p = 0.0069). SMR confirmed its tumor-suppressive role, potentially associated with enhanced antitumor functions of CD8⁺T cells and follicular helper T cells. In vitro and in vivo experiments demonstrated that RPS6KA1 inhibits malignant progression of colon cancer and modulates fatty acid metabolism. Conclusions: Integrated multi-dimensional bioinformatic and experimental analyses reveal that RPS6KA1 remodels fatty acid metabolism and suppresses malignant progression, indicating its value as a prognostic biomarker in CRC and providing new insights for therapeutic strategies. Full article

The cancer care pathway comprises several stages encompassing diagnosis, treatment, and follow-up. Studies show that delays in treatment initiation are associated with worse outcomes, including increased mortality, reduced progression-free survival, and diminished post-treatment quality of life. To address this, patient navigation tools have emerged as a strategy to identify bottlenecks and mitigate delays. In this context, process mining offers a promising approach to discover, model, and optimize workflows using real data from hospital information systems. This paper presents a case study on the application of process mining to analyze care pathways in an oncology clinic. The focus was on identifying critical pathways and delays in the treatment journey to support the patient navigation program. Based on the insights gained, targeted improvement actions were proposed to enhance the patient journey. Using the PM² methodology, event data were extracted and processed from the clinic’s information systems to model and analyze two key processes: (i) departmental workflows related to ambulatory care and (ii) longitudinal treatment pathways from initial evaluation to discharge. The results confirm the value of process mining for improving oncology patient journey and highlight its potential as a decision-support tool for healthcare administrators and clinical leaders. Full article

The cement industry will be officially regulated by China’s national carbon market. Authenticity and accuracy of emission data are prerequisites and the foundation for ensuring the healthy and stable operation of the market. At present, China’s carbon market mainly adopts the Calculation-Based Method (CBM) for data accounting. However, in the cement sector, this method faces challenges due to the inherent complexity of both raw materials and fuels, making it difficult to obtain accurate emission data through CBM alone. Therefore, regulatory authorities are promoting the installation and application of the Continuous Emission Monitoring System (CEMS) by enterprises. Pilot studies, however, have revealed considerable discrepancies between the data from the two methods. In this study, a combined data monitoring and accounting method was proposed, in which CBM and CEMS were combined to improve emission data quality. The actual operational and emission data from a case enterprise was taken as an example, and this study conducted systematic analysis and research on data collection and preprocessing, operating condition classification, correlation model construction, and abnormal data diagnosis. The results revealed that this combined method can effectively improve the degree of correlation between CBM and CEMS carbon emissions. Moreover, higher accuracy of abnormal data identification can be achieved through statistical testing. This combined monitoring method not only strengthens data tamper-resistance at the enterprise level but also has the potential to reduce regulatory oversight costs, thereby providing reliable technical support for emission data quality control. Full article

Background: Tobacco smoke exposure in the home remains common among U.S. families and has been increasingly associated with adverse mental health outcomes, including anxiety and depression, among children and adolescents. Rising rates of youth anxiety and depression, coupled with evidence that secondhand smoke and related psychosocial stressors may disrupt emotional development, underscore the importance of examining household smoking exposures as a modifiable risk factor for youth mental health. This study examines associations between exposure to smoke in households and the likelihood of caregiver-reported anxiety and depression in US children and adolescents aged 6–17 years, using data from the 2022–2023 National Survey of Children’s Health (NSCH). Methods: A retrospective analysis of NSCH data for two age cohorts, children (6–11 years) and adolescents (12–17 years), for the years 2022–2023 was conducted. Descriptive statistics were generated for the selected sample by frequencies and counts for each of the dependent and independent variables, followed by binary logistic regressions for each measured mental health variable based on current diagnosis, severity levels (not severe, mild, moderate, severe) and household tobacco use. Results: This study found significant associations between parental smoking and increased odds of caregiver-reported anxiety and depression in both children and adolescents. Specifically, children living with parents who smoke had 1.55 times the odds of severe anxiety, while adolescents had 1.38 times the odds of currently experiencing anxiety and 1.31 times the odds of currently experiencing depression. Smoking inside the household was not significantly associated with caregiver-reported anxiety or depression. These findings suggest that parental smoking serves as a marker for broader psychosocial and environmental stressors that contribute to youth mental health outcomes. Conclusions: Parental smoking is a significant, modifiable risk factor for anxiety and depression among US children and adolescents. These results emphasize the need for targeted, evidence-based interventions to reduce parental smoking, improve awareness of associated mental health risks, and address social determinants of health. Policies promoting smoke-free households, integrated cessation support, and culturally tailored education programs are essential to mitigate the impact of parental smoking on child and adolescent mental health. Full article

Sustainability is increasingly viewed as a crucial element shaping contemporary transport policies and operational strategies. This article presents a comprehensive economic evaluation of Polish international road freight carriers in 2024 compared with the results from previous years. It introduces an original and innovative method for assessing the economic condition of transport companies, based on real-time operational data and an integrated demand–supply diagnosis of the road freight market, which also supports macroeconomic forecasting. The study covers carriers operating in Eastern and European Union (EU) markets and spans an exceptionally long period (2009–2024), enabling the identification of long-term trends across four business cycles. Unlike existing research, which typically analyses isolated profitability or efficiency indicators, the proposed method offers a universal and contextual framework linking economic outcomes with detailed company characteristics. It provides a structured assessment of cost components across eight categories and reveals relationships between economic performance and factors such as transport directions, fleet utilisation, company size, diversification strategies, and region of origin. The analysis includes a comparison of two carrier groups, statistical profiling of companies, and average vehicle kilometre costs by company size and transport direction. This contextual analysis, including a comparison between the Polish and Lithuanian markets, strengthens the credibility of the results by situating them within a broader comparative framework and supporting a more accurate interpretation of the observed patterns. The pilot nature of this cross contextual approach constitutes an additional contribution of the study, providing a basis for future comparative research on the functioning of transport enterprises across the EU and the Eastern markets. In addition, the assessment incorporates a pilot comparative study of external factors influencing the transport market, conducted among Polish and Lithuanian companies. This multifaceted and internationally unprecedented approach strengthens the interpretability of the results and offers a robust foundation for strategic decision-making and organisational adaptation in an increasingly competitive and uncertain transport market. Full article

Migraine and epilepsy are common neurological disorders that share overlapping symptoms, such as visual disturbances and altered consciousness, making accurate diagnosis challenging. Although their underlying mechanisms differ, both conditions involve recurrent irregular brain activity, and traditional EEG-based diagnosis relies heavily on clinical interpretation, which may be subjective and insufficient for clear differentiation. To address this challenge, this study introduces an automated EEG classification framework combining Dual Tree Complex Wavelet Transform (DTCWT) for feature extraction with a Random Subspace Ensemble Classifier for multi-class discrimination. EEG data recorded under photic and nonphotic stimulation were analyzed to capture both temporal and frequency characteristics. DTCWT proved effective in modeling the non-stationary nature of EEG signals and extracting condition-specific features, while the ensemble classifier improved generalization by training multiple models on diverse feature subsets. The proposed system achieved an average accuracy of 99.50%, along with strong F-measure, AUC, and Kappa scores. Notably, although previous studies suggest heightened EEG activity in migraine patients during flash stimulation, findings here indicate that flash stimulation alone does not reliably distinguish migraine from epilepsy. Overall, this research highlights the promise of advanced signal processing and machine learning techniques in enhancing diagnostic precision for complex neurological disorders. Full article

Timely identification of infectious disease threats is essential for public health readiness. Conventional indicator-based surveillance systems, while dependable for tracking established pathogens, frequently lack the agility and sensitivity to detect new infections promptly. Syndromic surveillance, which examines pre-diagnostic and non-specific health indicators from many data sources in near real time, serves as a significant complementary method that improves early warning and situational awareness. This narrative study analysed selected experiences with syndromic surveillance, utilising peer-reviewed literature and institutional records. Four primary data streams were examined: emergency department and hospital records, pharmacy and over the counter (OTC) sales, participative citizen-generated data, and hybrid multi-source systems. Syndromic indicators were reported to identify outbreaks two to fourteen days before standard laboratory reporting across many trials. Data from the emergency department exhibited the highest sensitivity and specificity (47.34% and 91.95%, respectively), whereas pharmacy and participative data offered early indicators at the community level. Integrated systems like ESSENCE (Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA) and SurSaUD^® (Saint-Maurice cedex, Paris, France) attained enhanced accuracy yet necessitated significant data integration and governance capabilities. Syndromic surveillance enhances epidemic preparation by detecting atypical health-seeking behaviours and variations from baseline patterns prior to formal diagnosis. Nonetheless, its efficacy is contingent upon data quality, interoperability, and contextual adaptation. Countries like Bulgaria could improve national early-warning capabilities and overall health security through the gradual adoption of pilot projects and integration with existing surveillance networks. Full article

Early prediction of diabetes remains a major healthcare challenge, as timely diagnosis can significantly reduce the risk of severe complications. Most existing studies emphasize complex predictive models or extensive preprocessing pipelines, while comparatively limited attention has been given to improving feature representations. In this study, we propose a statistical framework for enriching the feature space of diabetes data using Gaussian Mixture Models (GMMs) and Kernel Density Estimation (KDE) to capture latent distributional characteristics and local density patterns. The resulting enriched features enhance class discrimination while preserving model simplicity, allowing standard classifiers to operate more effectively. The proposed framework is evaluated on two diabetes datasets with different sample sizes and class distributions. Models trained on the enriched feature set are compared with baseline models trained solely on the original features, using multiple strategies, including direct feature concatenation and ensemble-based approaches. Experimental results show that the enriched feature space consistently outperforms the original feature set, leading to notable improvements in overall predictive performance. In particular, the framework achieves a substantial increase in sensitivity for the minority class (diabetic cases), which is especially important for clinical applications where positive samples are typically scarce relative to healthy cases. Full article

Background/Objectives: Non-atherosclerotic pathological findings on coronary arteries involve various disorders that might lead to myocardial ischemia, independent of plaque complications and consequent lumen narrowing and obstruction. These patients often present with non-specific symptoms such as shortness of breath, rapid fatigue, and exertional chest tightness. When the underlying causes are non-atherosclerotic, these findings are frequently overlooked in radiology reports as a possible differential diagnosis. Therefore, the objective of this paper is to present the role of multidetector computed tomography (MD CT) coronary angiography in the diagnostic work-up of patients with rare but clinically valuable non-atherosclerotic pathological conditions of coronary arteries. Methods: We performed a literature search on Medline (via PubMed) for works presenting data on rare, non-occlusive, pathological findings on coronary arteries. Results: The review of the collected literature was performed in a narrative manner, intended to summarize mainly findings of imaging characteristics of non-occlusive pathologies: myocardial bridge, coronary aneurysm, ectasia, fistula, stenosis, and dissection. MD CT images of selected cases that were examined at our department, showing non-occlusive pathological changes in the coronary arteries, are displayed in planar and/or volume-rendered formats. Conclusions: Non-atherosclerotic abnormalities of the coronary vessel wall should be considered in the differential diagnosis of coronary causes of chest pain, dyspnea, and arrhythmias, as they may lead to both acute and chronic myocardial ischemia. Based on the presented literature and specific cases from our clinical practice, MD CT is shown to be an important tool for the rapid, non-invasive evaluation of non-atherosclerotic pathologies. Full article

Background: Traumatic brain injury (TBI) imposes a substantial public health burden through long-term physical, cognitive, and psychiatric effects. This includes substance use disorders (SUDs) for which TBI is a demonstrated risk factor; however, prior studies have not comprehensively compared relative incidences of SUD subtypes post-TBI or differences between intracranial hemorrhage (ICH) and non-ICH TBI in patients without prior SUD history. This global retrospective analysis using the TriNetX database aims to quantify new-onset SUD incidence post-TBI in the largest cohort of patients evaluated to date, with cohorts stratified by SUD subtype and ICH versus non-ICH TBI, to highlight opportunities for post-injury care models to mitigate SUD risk. Methods: De-identified data from the TriNetX Research Network were used to select patients with TBI (n = 1,889,112) and define distinct cohorts based upon the presence (n = 420,868) or absence (n = 1,471,592) of ICH. Patients with previously diagnosed SUD before the date of TBI were excluded. Patient demographics and medical comorbidities were calculated for each group. The incidence of new SUD diagnosis over the lifetime and at 1-, 3-, and 5-years post-TBI were calculated and compared. Subtypes of SUD were defined and calculated based on the specific substance used. Propensity scores were calculated to create balanced matched ICH and non-ICH cohorts (n = 331,812 each) were used for comparisons of 5-year SUD incidence. Results: In the full TBI cohort, 5-year new SUD incidence was 4.2% overall, with nicotine (2.4%) and alcohol (1.1%) predominating, followed by cannabis (0.9%) and opioids (0.4%). Rates of SUDs increased over time, but attenuated beyond 5 years, with approximately 50% of those who would ultimately be diagnosed with SUD manifesting (lifetime) by 3 years post-TBI. After propensity matching, non-ICH TBI showed higher 5-year risk for any SUD (4.2% vs. 3.6%; risk difference −0.65%, p < 0.0001) and all subtypes (p < 0.05) except inhalants (p = 0.53). Conclusions: This largest-to-date analysis of new-onset SUD post-TBI demonstrates significantly higher rates of SUD in TBI patients; rates of nicotine, alcohol, cannabis, and opioid use disorders were most common. Non-ICH TBI patients demonstrated greater rates of SUD after injury than patients with ICH-associated TBI. Of patients suffering from TBI without ICH who would eventually be diagnosed with SUD, approximately 50% had obtained that diagnosis within 3 years of the injury. Taken together, these findings demonstrate the clinical need for routine SUD screening in post-TBI care, especially for 3 years post-injury. Such an intervention has the potential to significantly alleviate the public health burden and associated cost of care for TBI-associated substance use disorder patients. Full article

Triple-negative breast cancer (TNBC) represents the most aggressive breast cancer subtype, with its highly heterogeneous tumor microenvironment posing substantial challenges for precision diagnosis and therapy. To address this, we aim to construct a novel prognostic framework based on tumor-immune interactions. Through integrative analysis of single-cell RNA sequencing data from 30 TNBC samples (106,132 cells), we identify key tumor expression metaprograms and uncover their interaction with an immunosuppressive dendritic-cell subset, a process associated with the NECTIN1–NECTIN4 axis. Leveraging these interactions, we developed and validated two immunological prognostic models using multi-cohort transcriptomic data, including the stress response tumor cell and pDC_CLEC4C prognostic model (SPSM) and the immune response tumor cell and pDC_CLEC4C prognostic model (IPSM). These models effectively stratified TNBC patients into distinct risk groups, with the low-risk group characterized by an immunologically active microenvironment and elevated expression of immune checkpoint genes, suggesting a potential responsiveness to immunotherapy. Furthermore, we identified several potential therapeutic agents, including imatinib and bortezomib. Collectively, our dual-model framework provides a tool for risk stratification, offers translational insights for precision treatment, and presents new directions for understanding TNBC heterogeneity and therapeutic development. Full article

Background: The precise segmentation and classification of dermoscopic images remain prominent obstacles in automated skin cancer evaluation due, in part, to variability in lesions, low-contrast borders, and additional artifacts in the background. There have been recent developments in foundation models, with a particular emphasis on the Segment Anything Model (SAM)—these models exhibit strong generalization potential but require domain-specific adaptation to function effectively in medical imaging. The advent of new architectures, particularly Vision Transformers (ViTs), expands the means of implementing robust lesion identification; however, their strengths are limited without spatial priors. Methods: The proposed study lays out an integrated foundation-model-based framework that utilizes SAM-Adapter-fine-tuning for lesion segmentation and a ViT-based classifier that incorporates lesion-specific cropping derived from segmentation and cross-attention fusion. The SAM encoder is kept frozen while lightweight adapters are fine-tuned only, to introduce skin surface-specific capacity. Segmentation priors are incorporated during the classification stage through fusion with patch-embeddings from the images, creating lesion-centric reasoning. The entire pipeline is trained using a joint multi-task approach using data from the ISIC 2018, HAM10000, and PH2 datasets. Results: From extensive experimentation, the proposed method outperforms the state-of-the-art segmentation and classification across the dataset. On the ISIC 2018 dataset, it achieves a Dice score of 94.27% for segmentation and an accuracy of 95.88% for classification performance. On PH2, a Dice score of 95.62% is achieved, and for HAM10000, an accuracy of 96.37% is achieved. Several ablation analyses confirm that both the SAM-Adapters and lesion-specific cropping and cross-attention fusion contribute substantially to performance. Paired t-tests are used to confirm statistical significance for all the previously stated measures where improvements over strong baselines indicate a $p<0.01$ for most comparisons and with large effect sizes. Conclusions: The results indicate that the combination of prior segmentation from foundation models, plus transformer-based classification, consistently and reliably improves the quality of lesion boundaries and diagnosis accuracy. Thus, the proposed SAM-ViT framework demonstrates a robust, generalizable, and lesion-centric automated dermoscopic analysis, and represents a promising initial step towards clinically deployable skin cancer decision-support system. Next steps will include model compression, improved pseudo-mask refinement and evaluation on real-world multi-center clinical cohorts. Full article

Background: Both borderline personality disorder (BPD) and schizoaffective disorder (SAD), as well as their potential connection, remain controversial diagnoses. To explore whether BPD may be part of the spectrum of SAD, we conducted a longitudinal study of a large clinical cohort of patients with BPD. Methods: We assessed the diagnostic trajectories of 402 patients with BPD in a 20-year retrospective study based on electronic clinical records from a psychiatric hospital using ICD-9 diagnoses. Data were descriptively examined on concurrent and sequential diagnoses in patients with BPD. For the classification of SAD, a proxy diagnosis was used. Results: The study population showed a high prevalence of affective disorders and a high frequency of concurrent diagnoses of affective–BPD. Together, stable BPD, stable affective disorder sequences and transitions from affective disorders to BPD represented 79% of all longitudinal trajectories. Conclusion: These findings should be considered exploratory and do not allow confirmation or refutation of the hypothesis that BPD serves as a precursor, prodrome, or component within the spectrum of SAD. Full article

Background/Objectives: Orthodontically induced root resorption (OIRR) is a well-documented but undesired consequence of orthodontic treatment. This systematic review and meta-analysis aimed to assess the diagnostic performance of artificial intelligence (AI) models applied to cone-beam computed tomography (CBCT) for detecting and quantifying OIRR while evaluating their agreement with manual reference standards and the impact of model architecture, validation design, and quantification strategy. Methods: Comprehensive searches were conducted across PubMed/MEDLINE, Scopus, Web of Science, and EMBASE up to November 2025. Studies were included if they employed AI for OIRR diagnosis using CBCT and reported relevant performance metrics. Following PRISMA guidelines, data were extracted and a random-effect meta-analysis was performed. Subgroup analyses explored the influence of model design and validation. Results: Seven studies were included. Pooled sensitivity from three eligible studies was 0.903 (95% CI: 0.818–0.989), suggesting excellent true positive rates. Specificity ranged from 82% to 98%, and area under the receiver operating characteristic curve values reached up to 0.96 across studies using EfficientNet, U-Net, and other convolutional neural network (CNN)-based architectures. The pooled intraclass correlation coefficient for agreement with manual quantification was 1.000, reflecting near-perfect concordance. Subgroup analyzes showed slightly superior performance in CNN-only models compared to hybrid approaches, and better diagnostic metrics with internal validation. Linear assessments appeared more sensitive to early apical shortening than volumetric methods. Conclusions: AI models applied to CBCT demonstrate excellent diagnostic accuracy and high concordance with expert assessments for OIRR detection. These findings support their potential integration into clinical orthodontic workflows. Full article

Background/Objectives: The study primarily examined whether an IoT-based medication assistance system enhances ART adherence relative to standard care, and secondarily evaluated device feasibility and error patterns over time. Methods: This prospective study was conducted between June 2022 and October 2023 at a tertiary hospital in South Korea. Adults (≥19 years) living with HIV and prescribed ART were included; those with comorbid hepatitis B or C were excluded. People living with HIV who agreed to use the IoT-based InPHRPILL system (Sofnet Inc., Seoul, Republic of Korea) were assigned to the intervention group, whereas those who declined were assigned to the control group. Viral suppression, CD4⁺ cell counts, and adherence rates were measured. Additional analyses evaluated 12-month longitudinal adherence using pill-count data in both groups, and device-measured adherence and device-associated error rates in the intervention group. Results: Thirty-five participants (12 in the intervention group and 23 in the control group) were included. The intervention group demonstrated marginally shorter durations since HIV diagnosis and ART initiation at study enrollment, as well as slightly higher baseline HIV-RNA levels; however, these differences did not reach statistical significance. The median pill-counting and IoT device adherence rates were 100% and 87.4%, respectively (median deviation error rate = 4.4%). Poisson regression revealed significantly reduced error rates over time (β = −0.06493, p < 0.01), suggesting improved device use proficiency. Conclusions: IoT-based medication assistance systems may provide objective, real-time monitoring of ART adherence and facilitate identification of discrepancies between clinical evaluations and actual adherence patterns. Larger studies targeting individuals with suboptimal adherence are warranted to determine whether such systems can enhance adherence outcomes. Full article