Search Results (4,554)

Background/Objectives: Cervical cancer, primarily driven by persistent high-risk HPV infection, remains a major global health issue. Xiao-ai-fei honey ointment, a traditional Uyghur multi-ingredient preparation, has shown clinical promise in cancer treatment, but its mechanisms against cervical cancer are not fully understood. This study aimed to investigate the potential molecular mechanisms of ethanolic extract of Xiao-ai-fei honey ointment (XAFHO) in cervical cancer using network pharmacology, single-cell RNA sequencing, and experimental validation. Methods: Differentially expressed genes (DEGs) in cervical cancer were identified from TCGA database. Active components and corresponding targets of XAFHO were retrieved from the TCMSP database, and disease targets were obtained from GeneCard, OMIM, and the TTD. Intersection targets were subjected to multivariate Cox and LASSO regression to construct a prognostic model. Immune infiltration, TMB, and MSI were compared between risk groups. Single-cell RNA-seq data were analyzed to determine cellular origins and inter-cellular communication. In vitro assays were performed on HeLa and SiHa cells to assess the anti-cancer activity of XAFHO. Molecular docking evaluated binding affinities between active compounds and core targets. The expression and functional roles of FASN and SPP1 were further validated by RT-qPCR, Western blotting, and siRNA transfection. Results: Sixty-three potential XAFHO targets were identified, and an 11-gene prognostic model was established, effectively stratifying patients into high- and low-risk groups with significantly different overall survival (AUC > 0.7). The high-risk group exhibited an immunosuppressive microenvironment and higher TMB. Single-cell analysis revealed that FASN and ACACA were predominantly expressed in tumor cells, while SPP1 was enriched in macrophages/monocytes. Tumor cells communicated with immune cells via the TGFB1–TGFβR1/R2 axis, promoting immune evasion. In vitro, XAFHO significantly inhibited proliferation, colony formation, migration, and invasion of cervical cancer cells. Molecular docking confirmed the strong binding of quercetin, kaempferol, and isorhamnetin to FASN and SPP1 (binding energy < –6.0 kcal/mol). Functional validation indicated that upregulated FASN and SPP1 contribute to malignant behaviors in cervical cancer cells. Conclusions: This study integrates network pharmacology with single-cell and experimental approaches to demonstrate that XAFHO exerts multi-target and multi-cell anti-cervical cancer effects, potentially by modulating lipid metabolism and immune-related pathways via FASN and SPP1. These findings provide a scientific basis for the therapeutic application of XAFHO in cervical cancer. Full article

As social infrastructure intensively developed during the high economic growth period of the 1970s faces simultaneous aging, there is an urgent need to transition from conventional reactive maintenance to preventive maintenance utilizing various data (data-driven asset management. However, the greatest barrier in practice is that inspection data is unevenly distributed in analog formats such as paper and unstructured files, and heavily relies on the subjective visual evaluation of expert engineers (e.g., discrete graded evaluations from A to D). The intervention of this “Assessor Bias” makes it difficult to ensure the robustness required for direct statistical analysis. This paper serves as a bridge between this analog expert knowledge and quantitative data science. It formulates human cognitive conflicts (true state, peer pressure, avoidance of cognitive load) using the distance-decay model of the Analytic Hierarchy Process (AHP) and the Softmax function, constructing a micro-macro link model accompanied by stochastic variations. Through large-scale multi-agent simulations ($N={10}^7$) validating the model’s convergence, it was demonstrated that in long-tail distributions formed under peer pressure, macroscopic statistical distance metrics such as the Kullback-Leibler (KL) divergence ignore the fact that a small number of true signals are non-linearly suppressed, causing a statistical misinterpretation that “the error is within an acceptable range”. This implies that as long as macroscopic statistical indicators are over-trusted, signs of critical deterioration (minorities) will be structurally marginalized. Returning to the debate on “Homogeneity (Homogenität)” in German social statistics, this paper advocates that in order to realize objective “Micro-segmentation of Homogeneous Statistical Populations,” a paradigm shift from qualitative methods relying on human intuition to quantitative methods incorporating multi-criteria decision making is essential, rather than simply expanding the sample size. Full article

This paper presents a modular system architecture and an automated signal processing pipeline designed to quantify neurophysiological relaxation responses to fragrance using consumer-grade wearable electroencephalography (EEG). By integrating real-time data streaming via Open Sound Control (OSC) with a high-performance backend, the platform enables objective assessment of olfactory stimuli through a reproducible Sleep Readiness Index (SRI) derived from spectral power shifts. To mitigate the signal quality constraints inherent in portable hardware, the framework utilizes a robust suite of engineering controls, including zero-phase filtering and automated artifact rejection, ensuring data integrity across short-window trials. Validation through construct-level analysis of public sleep datasets and synthetic sensitivity testing confirms the index’s directional reliability, while runtime benchmarking demonstrates sub-millisecond compute times suitable for interactive wellness applications. Ultimately, this framework provides a transparent, auditable engineering scaffold that replaces subjective self-reports with a standardized, within-session proxy metric for comparative fragrance evaluation. Full article

Objective: To develop machine learning models by integrating transvaginal ultrasound (TVUS) with clinical indicators, conduct visual analysis of the models, and systematically assess their diagnostic efficacy in differentiating early cervical neoplastic lesions. Methods: A total of 144 eligible patients (84 cases of early cervical cancer and 60 cases of cervical myoma) admitted to the First Affiliated Hospital of Chongqing Medical University from January 2018 to August 2025 were retrospectively enrolled in this study. Their clinical data, human papillomavirus (HPV) test results, Thinprep Cytologic Test (TCT) findings, TVUS images and magnetic resonance (MR) imaging data were collected and subjected to comprehensive statistical analysis. Univariate and multivariate Logistic Regression analyses were performed to identify independent differentiating factors for lesion classification. Eleven machine learning models were subsequently constructed, and their diagnostic performance was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and the DeLong test. Finally, a nomogram was developed based on the optimal-performing model for clinical visualization. Results: The TVUS–clinical indicator integration model identified five independent differentiating factors: HPV status, TCT findings, menopausal status, ultrasonic tumor blood supply, and ultrasonic tumor morphology. In contrast, the MR–clinical indicator integration model screened out three independent factors: HPV status, TCT findings, and intratumoral signal intensity on MR T2-weighted imaging (T2WI). The TVUS integration model demonstrated marginally superior diagnostic performance, with a sensitivity of 0.988, specificity of 0.983, and an area under the ROC curve (AUC) of 0.991, compared with the MR integration model (sensitivity: 0.952, specificity: 0.950, AUC: 0.975); however, this difference in AUC values was not statistically significant (p = 0.911). Among the 11 machine learning models, the Logistic Regression model exhibited optimal classification performance and stability. DCA curves confirmed that all constructed models outperformed single-index diagnostic strategies in clinical decision-making for lesion differentiation. A nomogram was further established based on the Logistic Regression model for intuitive clinical application. Conclusions: Multiple machine learning models integrating TVUS with clinical indicators are successfully developed, and a corresponding nomogram is constructed in this study. Full article

Introduction: Distress during pediatric magnetic resonance imaging (MRI) neuroimaging can compromise scan quality and negatively impact children’s experiences. This review aimed to systematically synthesize biomarkers and psychological factors associated with distress in children, adolescents, and young adults undergoing neuroimaging. Methods: This systematic review was conducted according to PRISMA and AMSTAR-2 guidelines and preregistered in OSF. A systematic search was performed in six electronic databases, including observational articles published between 2000 and 2025 that assessed distress during MRI and functional MRI (fMRI). Data extraction and risk of bias assessment (QUIPS tool) were performed independently by two reviewers. Results: Ten studies (n = 558) examining distress during neuroimaging were included in this review. Distress was assessed through subjective self- and parent-reports, objective physiological measures, and qualitative interviews. Overall, distress levels were low to moderate; most participants tolerated scans well, though younger age, male sex, parental anxiety, procedure length, and chronic illness were associated with greater discomfort. Noise, immobility, and boredom emerged as the most frequent triggers, while strategies such as distraction, age-appropriate information, and reducing waiting times were perceived as helpful. Among participants with cancer, scan-related anxiety was closely linked to fear of recurrence and perceived stress. Risk of bias across studies was moderate to high, particularly in domains of attrition and statistical reporting. Conclusions: Distress during scanning is driven by anticipatory and parental anxiety, procedure length, and chronic illness. Biomarkers (e.g., cortisol, blood pressure) showed inconsistent links with subjective distress, highlighting the need for integrated measures. Full article

As cleaner production gains prominence in wooden product manufacturing, green evaluation of process schemes during early design is crucial. However, dust concentration, a key environmental indicator in wood product manufacturing, is often evaluated in a subjective and fragmented manner, which greatly hinders the selection of green process schemes in early design. To address this gap, an adaptability evaluation model for green process schemes was proposed based on process knowledge graphs (PKG) and fuzzy Bayesian network (FBN), with the objective of minimizing dust concentration. First, a PKG for wooden products was constructed based on the requirement-function-structure-characteristic-process-equipment (RFSCPE) ontology using patents and process manuals. Second, candidate process schemes were generated via the PKG, and dust-related causal relationships encoded in the PKG were mapped onto a Bayesian network structure. Third, conditional probabilities were obtained by combining probabilistic hesitant fuzzy sets and experimental dust data. The FBN was then updated to perform probabilistic reasoning on dust concentration. Finally, a case study on a wooden toy car validated the proposed approach, and sensitivity analysis identified the key dust-influencing factors, thereby providing quantitative support for greener process decisions. Full article

Background and Objectives: The pathogenesis of liver steatosis is associated with obesity and systemic inflammation, particularly in subjects with body mass index (BMI) above 40 kg/m² and altered serum levels of tumor necrosis factor alpha (TNF-α) and interleukin-10 (IL-10). Recent evidence suggests that disruption of the blood–brain barrier (BBB) may be associated with the development of steatosis, although limited data are available in humans. Thus, we assessed serum levels of neuron-specific enolase (NSE), transglutaminase 2 (TGM2), and glial fibrillary acidic protein (GFAP) as indirect markers of BBB dysfunction and examined their associations with steatosis severity, TNF-α and IL-10 in patients with morbid obesity. Materials and Methods: We biopsied the liver during bariatric surgery to assess steatosis by histology and serum markers by ELISA. Results: Most study subjects were women aged 38.7 ± 9.9 years with an average BMI of 42.3 ± 7.9 kg/m² and a steatosis prevalence of 78.9%. After grading steatosis as none (n = 8), mild (n = 17), moderate (n = 8), or severe (n = 5), we found no differences in sex, age, BMI, comorbidities, or laboratory variables, including liver enzymes. One-way ANOVA showed that serum IL-10 was 4-fold less in severe steatosis than in mild steatosis (p = 0.038), whereas TNF-α levels increased twice in severe steatosis compared to no steatosis (p = 0.029). NSE and GFAP serum levels, but not TGM2, increased proportionally to steatosis stage, showing differences between severe steatosis and no steatosis (p = 0.012 and p = 0.0002, respectively). Pearson correlation coefficients showed that NSE and GFAP were significantly associated with TNF-α (r = 0.600 and r = 0.402, respectively), but not with IL-10. Conclusions: Steatosis severity is significantly associated with markers of BBB disruption and systemic inflammation in patients with morbid obesity, suggesting a link between the BBB and liver steatosis. Full article

Human lifespan is increasing in parallel with the development levels of societies. Consequently, the number of elderly individuals worldwide is also rising day by day. One of the most significant risks these individuals face is falling. In this study, fall and daily activity data were collected from different home environments using a continuous-wave (CW) radar. Micro-Doppler signatures were generated from 700 data samples obtained from 10 individuals. Furthermore, the dataset was expanded by doubling the number of spectrogram images through data augmentation. The YOLO architecture, generally used in vision-based studies for object detection and tracking, was preferred for radar-based fall and activity detection. Classifications were performed with different YOLO structures, and comparative results are presented. At this stage, binary (fall/non-fall) and multi-class (seven different classes) classifications were carried out, achieving 100% accuracy for binary classification and 88.02% for multi-class classification. Additionally, the generalizability of the proposed architecture is demonstrated using the Leave-One-Subject-Out (LOSO) approach on the collected data and through the analysis of a public dataset. These results demonstrate the applicability of YOLO architectures in radar-based fall detection studies. Full article

Children’s scooters, as products integrating mobility, safety, and developmental functions, require systematic and reliable design decision-making approaches. However, existing processes often suffer from unsystematic user demand extraction, strong subjectivity in weight determination, and insufficient quantitative support for evaluating alternative schemes. To address these issues, this study proposes an integrated AHP–CRITIC–VIKOR framework for engineering-oriented design optimization. User requirements are identified through field investigation, questionnaires, and affinity diagram analysis, and a multi-level evaluation indicator system is constructed. AHP is applied to determine subjective weights, while CRITIC incorporates objective data characteristics, enabling balanced weighting. VIKOR is then used to evaluate design schemes and obtain compromise solutions under multi-criteria conflicts. The results show that safety-related factors, including material safety, braking performance, and load-bearing capacity, dominate the decision process. The optimal scheme demonstrates the closest proximity to the ideal solution. Sensitivity analysis confirms the robustness of the model, and comparison with TOPSIS shows consistent results and improved compromise decision capability. The proposed framework enhances decision reliability and provides an effective quantitative tool for multi-criteria product design optimization. Full article

Background: Effective sick-day management, including ketosis home management aimed at preventing diabetic ketoacidosis (DKA), is essential for families living with a child/adolescent with type 1 diabetes (T1D). Methods: Adolescents living with T1D and caregivers of younger children living with T1D were invited to participate in an interview consisting of five parts: (I) demographic data, (II) subjective self-ratings on competence in ketosis home management, (III) objective assessment of competence in ketosis home management using a standardized clinical case scenario consisting of 10 management steps, in which participants were asked to describe the actions they would take to prevent DKA, and (IV) practical demonstrations to objectively assess skills in (IVa) urine dipstick self-testing and (IVb) insulin administration, (V) household availability of (Va) urine dipsticks and (Vb) insulin cartridges. Results: (I) We enrolled 61 adolescents and 79 caregivers. (II) Competence in ketosis home management was subjectively self-rated as good to very good. (III) Adolescents reported 4 (median; Q25/Q75 3/5) and caregivers 5 (4/5) of 10 management steps. Never self-testing ketone levels was reported by 33% of adolescents and 11% of caregivers. (IVa) At least one handling error occurred in 100% of adolescents’ and in 98% of caregivers’ practical demonstrations of urine dipstick self-testing and in (IVb) 98% of adolescents’ and 98% of caregivers’ insulin administrations. (Va) Altogether urine dipsticks were available in 43% of households, whereas (Vb) insulin cartridges were available in 78% of households. Conclusions: Our results demonstrate a mismatch between challenges in ketosis home management and high subjective self-ratings. Full article

Background/Objectives: Helicobacter pylori (HP) antibiotic eradication treatment in Germany is, at present, empirical according to the national guidelines. The aim of our prospective multicentre study was to implement routine susceptibility testing in daily clinical practice to facilitate resistance-oriented first-line antibiotic therapy and to collect local resistance data. Methods: From 1 January 2024 to 30 April 2025, in two German hospitals (in Bielefeld and Datteln), the patients who underwent gastroscopy and those who had biopsies for histopathology also underwent biopsies for the Helicobacter urease test (HUT). Positive HUT samples were sent for susceptibility testing: they were checked for phenotypic/cultural resistance to amoxicillin, clarithromycin, metronidazole, levofloxacin, rifampicin and tetracycline and genotypic/molecular resistance to clarithromycin and fluoroquinolones. Results: In total, in 1503 cases, both HUT and histology were performed, in which 256 (17.0%) histologies were HP-positive. We sent 311/1503 (20.7%) positive HUTs for susceptibility testing. In 120 (38.6%) of them, it was possible to culture HP, and for 118 cases, phenotypic resistance testing was performed. In 200/311 cases, PCR was also executed, with 111/200 cases being subjected to subsequent molecular resistance testing. Resistance patterns varied regionally, with metronidazole resistance observed in 3–33%, clarithromycin resistance in 16–20% and levofloxacin resistance in 13–16% cases. Conclusions: it is technically and logically feasible to perform HP antibiotic susceptibility testing via the same biopsy used for the HUT. The proposed procedures might be applied both in hospital and outpatient settings in endoscopic offices. Routine susceptibility testing is useful not only for the individual patient but also for monitoring the development of regional resistance patterns as a basis for better-targeted empiric therapy. Additionally, this approach might help to reduce the resistance dynamics at large in terms of antimicrobial stewardship. Full article

Background/Objectives: The optimal management of anterior cruciate ligament (ACL) rupture remains debated, especially regarding long-term outcomes after early ACL reconstruction (ACLR) versus rehabilitation-first with optional delayed ACLR. The interpretation of randomized evidence is complicated by frequent treatment crossover. This review synthesized evidence from randomized controlled trial (RCT) cohorts comparing surgical versus rehabilitation-first management strategies across available follow-up durations. Methods: A structured review based on a systematic literature search and narrative synthesis was conducted, with study identification and reporting guided by PRISMA 2020. MEDLINE (via PubMed) and Google Scholar were searched in February 2026 for English-language human RCTs (2000–2026) comparing early ACLR plus rehabilitation with rehabilitation-first management allowing delayed ACLR for persistent instability. A linked-report PubMed search using the KANON trial registration number (ISRCTN84752559) was additionally performed to identify cohort-derived follow-up publications. Reports were grouped by underlying RCT cohort. Data were extracted on crossover, follow-up, and clinical outcomes. Risk of bias for primary RCT reports was assessed with Cochrane RoB 2. Results: Twenty-seven reports representing three RCT cohorts (KANON, COMPARE, ACL SNNAP) were included; six index reports were prioritized for synthesis. In acute ACL rupture (KANON, COMPARE), early ACLR did not show a consistent long-term superiority in patient-reported outcomes versus rehabilitation-first with optional delayed ACLR, although COMPARE reported a statistically significant 2-year subjective functional difference favoring early ACLR; early ACLR more consistently improved mechanical stability and reduced instability episodes. Crossover from rehabilitation to delayed ACLR was common. In non-acute ACL injury with persistent symptomatic instability (ACL SNNAP), surgery-first improved 18-month patient-reported outcomes. Meniscal procedure rates and osteoarthritis-related outcomes did not consistently favor early ACLR. Conclusions: In acute ACL rupture, rehabilitation-first with timely access to delayed ACLR appears to provide long-term patient-reported outcomes comparable to an early ACLR strategy in many patients, while early ACLR more consistently improves knee stability. In non-acute symptomatic ACL deficiency, a surgery-first strategy appears more effective in the mid-term. These randomized trials should be interpreted as comparisons of management strategies rather than of “pure” operative versus nonoperative treatment approaches. Full article

Researchers have long speculated that sleep quality is tied to academic performance. This paper examines this relationship through a meta-analysis using the PRISMA 2020 guidelines. To clarify the relationship between sleep quality and academic performance, this study examined data from 72 independent effect sizes extracted from 59 articles involving 163,357 participants. The results indicate a modest positive correlation between sleep quality and academic performance (r = 0.17). Factors such as social jetlag significantly but negatively moderated the relationship between sleep quality and academic performance (r = −0.104), while sleep duration showed a significant positive correlation (r = 0.132). The school subject (Q = 14.986), age of participants (Q = 8.606), and culture (Q = 4.585) were significant moderators. Furthermore, while the research method did not significantly moderate the relationship between sleep quality and academic performance, the positive and significant association is robust across self-reported, objectively measured, and other-reported sleep quality, despite descriptive differences in effect-size magnitude. Given that sleep is an important physiological process associated with learning and development, students’ sleep quality warrants careful attention. Full article

Background/Objectives: Dietary patterns may influence depression, yet findings remain inconsistent, partly due to methodological variation in dietary pattern identification. As data-driven approaches may help reduce subjectivity and improve reproducibility in dietary pattern identification, this study aimed to identify dietary patterns using a machine learning approach and examine their associations with depression among Korean adults. Methods: Using data from 21,321 Korean adults aged 19–64 years from the Korea National Health and Nutrition Examination Survey (2016–2021), we applied K-means clustering to identify dietary patterns based on both food group and nutrient intake. Dietary intake was assessed using a 24 h dietary recall, and depression status was based on physician diagnosis. Results: Three distinct patterns were identified in both food group-based and nutrient-based analyses. In the food group-based analysis, a balanced and diverse dietary pattern (Cluster 3) was associated with lower odds of depression compared with a pattern characterized by overall low food intake (Cluster 1) (OR 0.64; 95% CI, 0.47–0.88; p = 0.007) after full adjustment, whereas no significant association was observed for the high processed food pattern (Cluster 2 vs. Cluster 1) (OR 0.73; 95% CI, 0.53–1.01). No significant associations were observed for nutrient-based clusters after full adjustment. Conclusions: Our findings suggest that adherence to balanced and diverse dietary patterns based on whole foods is associated with lower odds of depression. Food group-based clustering approaches may offer more reproducible and interpretable insights than nutrient-based approaches, supporting their potential utility in epidemiological research and public health strategies. Full article

The integration of artificial intelligence with unobtrusive sensing technologies is transforming occupational health monitoring by enabling continuous, objective assessment of worker activities in real industrial environments. This study focuses on the accurate recognition of subtle motion actions within logistics workflows using multichannel optical motion-capture data. We investigate several deep learning architectures commonly employed for temporal motion analysis, including tCNN, Transformer, CNN–LSTM, and ConvLSTM. To enhance robustness and fairness across workers with varying movement styles, a subject-independent evaluation protocol is adopted, and a multiscale temporal learning strategy is explored to better capture fine-grained and low-saliency actions. Experimental results show that the proposed multiscale tCNN achieves the highest accuracy, obtaining per-class recall range between 73% and 83% and an overall accuracy of approximately 79%, consistently outperforming recurrent and attention-based architectures. These findings demonstrate the effectiveness of multiscale convolution-based temporal modeling for recognizing subtle motion actions and highlight the potential of combining optical motion capture with AI analytics to support unobtrusive, reliable occupational health monitoring in smart industry environments. Full article