Search Results (53,402)

Grammatical error correction (GEC) for Chinese learner English is still dominated by sentence-level modeling, which limits discourse-level consistency and weakens adaptation to learner-specific error profiles. From an instructional perspective, these limitations also reduce the value of automated feedback as a basis for spaced-repetition instructional structures sensitive to individual differences. This study proposes a symmetry-aware dual-encoder architecture for context-aware GEC in Chinese learner English. A context encoder captures preceding-sentence information, while a source encoder integrates BERT-based semantic representations with Bi-GRU-based syntactic features for the current sentence. A gated decoder performs asymmetric fusion of local and contextual evidence. To better reflect corpus-level tendencies in Chinese learner English, a CLEC-informed augmentation strategy generates synthetic errors using empirical category frequencies as a coarse sampling prior. Experiments on CoNLL-2014, JFLEG, and CLEC show consistent improvements over strong neural baselines in F_0.5 and GLEU under the current desktop-oriented implementation setting. Nevertheless, the integration of BERT, dual encoders, and gated decoding introduces non-negligible computational overhead, and the present system is therefore better suited to desktop writing-support scenarios than to strict real-time or large-scale online deployment. The proposed framework thus provides a practical technical basis for personalized grammar feedback and for future spaced-repetition instructional designs in ESL writing support. Full article

Gait analysis study comparing unicompartmental vs. total knee arthroplasty, differences in knee kinematics: a retrospective cohort study. Background and Objectives: Total knee arthroplasty (TKA) is an effective treatment for advanced knee osteoarthritis, although functional outcomes may remain suboptimal in many patients. Unicompartmental knee arthroplasty (UKA) often provides better functional recovery but shows lower long-term implant survival. Recently, personalized TKA approaches have been developed to improve kinematic restoration and patient satisfaction. This study aimed to compare knee kinematics among patients who underwent personalized TKA, medial UKA, and healthy controls. Materials and Methods: This retrospective cohort study included 9 patients treated with robotic-assisted personalized TKA, 9 patients treated with medial UKA, and 9 healthy controls matched for age, sex, and BMI. Inclusion criteria were age 60–80 years, Kellgren–Lawrence grade III–IV, a minimum follow-up of 12 months, deviation from neutral HKA < 15°, healthy contralateral knee, and high postoperative functional scores. Exclusion criteria included valgus knees (HKA > 180°), postoperative complications, and neuromotor disorders. In the TKA group, a Medial Congruent implant was implanted with ROSA robotic assistance using a restricted kinematic alignment (±5° HKA) and asymmetric intercompartmental balancing. In the UKA group, a fixed-bearing medial implant (Physica ZUK) was used. Gait analysis was performed on a markerless instrumented treadmill (WalkerView™; Dalmine, Italy). Differences between groups were analyzed using one-way ANOVA and Tukey’s post-hoc test (p < 0.05). Results: UKA patients walked with a stiffer knee during stance. Knee range of motion during stance increased from UKA (6.3° ± 7.2°) to TKA (13.6° ± 8.8°, p = 0.045) and to controls (16.6° ± 4.5°, p = 0.02). During loading response, UKA patients showed lower flexion (10.2° ± 6.1°) than TKA (19.4° ± 7.9°, p = 0.049) and controls (19.6° ± 2.8°, p = 0.004). Knee flexion during swing was comparable between UKA and TKA. Conclusions: UKA patients demonstrated reduced knee flexion during early stance compared with robotic-assisted TKA and healthy controls. The observed differences may reflect multiple factors, including surgical technique, implant design, and patient-related characteristics. Because preoperative functional data were not available, potential selection bias cannot be excluded. These findings should be interpreted cautiously and warrant confirmation in larger prospective studies. Full article

Background: Adjuvant RT remains a standard therapy for breast cancer, reducing recurrence risk and improving survival; however, it can also induce side effects, including pain. Inflammasome-related biomarkers, such as interleukin-18 (IL-18), play a role in inflammation-mediated pain, and we hypothesize that IL-18 may serve as a potential biomarker for breast cancer RT-induced pain. Methods: The association between IL-18 and pain was assessed among breast cancer patients receiving adjuvant RT. Plasma IL-18 protein concentration was quantified before and after RT using Ella SimplePlex technology (Biotechne). Clinically relevant pain outcomes included pre-RT pain (pain score ≥ 4), post-RT pain (pain score ≥ 4), and RT-related pain (increase in pain from <4 pre-RT to ≥4 post-RT). Multivariable logistic regression assessed the association between IL-18 and pain outcomes, adjusting for demographic and treatment-related factors. The joint effect of IL-18 and obesity on pain were also explored. Results: Patients in the highest pre-RT IL-18 quartile experienced higher odds of both post-RT pain (OR = 2.36, 95% CI: 1.15–4.87) and RT-related pain (OR = 2.73, 95% CI: 1.20–6.26). IL-18 levels increased from pre-RT to post-RT with a mean change of 0.07 (SD = 0.35). In addition, patients with elevated pre-RT IL-18 levels and obesity experienced the highest odds of post-RT pain (OR = 3.97, 95% CI: 1.98–7.98) and RT-related pain (OR = 2.84, 95% CI: 1.32–6.09), suggesting a potential combined effect. Conclusions: Elevated pre-RT IL-18 levels were associated with an increased risk of pain following adjuvant RT, particularly in breast cancer patients with obesity. Thus, IL-18 may serve as a potential biomarker for identifying patients at increased risk for RT-related pain and informing treatment decision. Full article

Background/Objectives: Patients with liver cirrhosis experience a high symptom burden and low Health-Related Quality of Life (HR-QoL). Hepatic encephalopathy (HE) occurs in 75% of patients with cirrhosis but is sparsely described from the patient’s perspective. Due to recurrent cognitive impairment, a marginalized diagnosis, and a healthcare discourse emphasizing involvement and self-responsibility, these patients appear vulnerable when navigating a complex healthcare system. This study aims to explore how patients with chronic liver disease experience living with HE as a recurring condition, and how these patients are met by healthcare professionals (HCPs). Methods: Eight semi-structured interviews were conducted with four patients and four HCPs. Data were analyzed thematically following Braun and Clarke’s six-step analysis within the framework of Interpretive Description. The study was reported according to COREQ Guidelines. Results: The overarching theme “Becoming a different person” captured the profound identity changes experienced by patients. Three main themes emerged: 1. change and loss—in identity and self-understanding, in relationships, in relation to losing control, and in relation to experiencing isolation; 2. new paths—mental and practical alternative strategies; 3. HE in clinical encounters—requiring empathy, flexibility, and continuity. Stigma related to cirrhosis and its association with alcohol further intensified patients’ vulnerability. Conclusions: HE is experienced as a transformative and isolating condition, deeply affecting patients’ autonomy and social roles through vulnerability. The clinical encounter is shaped by the cognitive impairment due to HE, requiring tailored and sensitive care. Full article

Station-based car-sharing has been shown to reduce resource-intensive private car ownership. However, only a small proportion of the population uses station-based car-sharing, which could be improved by redesigning the service to reduce walking distances and increase availability. We developed a method for designing an efficient and cost-effective station-based car-sharing network for smart cities that emphasizes user comfort and convenience, while reducing the number of needed cars. To quantify the placements, we created a high-resolution synthetic population for Munich, Germany as a case study. The population was based on census and OpenStreetMap data, and each person was assigned to a suitable mobility plan derived from two mobility surveys. Since car ownership and station-based car-sharing are particularly associated with trips for vacations, we supplemented the mobility plans with long-distance travel data from a one-year tracking dataset. This allowed us to perform a spatial and temporal analysis of the theoretical potential of various station placements for station-based car-sharing. The tested station networks varied in user comfort, especially in the distance to the nearest station and the group size of car-sharing users. Our findings indicate that the best trade-off between convenience and efficiency is a station design with a group size of 217–949 people. We further found that the car-sharing fleet size is strongly influenced by long-distance trips, and that a substitution rate of 1:1.25 to 3.3 with private cars is possible. Full article

Objectives: The study aimed to examine the association between a dietary index for gut microbiota (DI-GM) and the risk of incident colorectal cancer (CRC). Clarifying the role of diet-induced alterations in the composition and function of gut microbiota on the development of CRC can contribute to prevention efforts. Methods: Participants from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening trial enrolled in the intervention arm and who completed baseline assessments were included in the analysis (n = 55,685). The DI-GM is a literature-derived index used to score diet quality in terms of maintaining healthy gut microbiota. A time-dependent Cox model stratified by follow-up years (<5 and ≥5 person-years) was used to evaluate the relationships between the dietary patterns and risk of incident CRC. Results: A total of 735 incident CRC were identified over 650,470 person-years of follow-up. During < 5 years of follow-up, those with higher diet quality (DI-GM scores above 67th percentile) had an 18% lower risk of incident CRC (HR_adjusted = 0.82, 95% CI: 0.63, 1.07) compared with those with lower diet quality (DI-GM scores below the 67th percentile), though effect estimates were imprecise. During ≥ 5 years of follow-up, there was no association between incident CRC and DI-GM (HR_adjusted = 1.01, 95% CI: 0.80, 1.26). Conclusions: Diet quality measured using the DI-GM was associated with the risk of CRC in the first five years of follow-up in a large prospective cohort study. A diet that enhances the composition and function of gut microbiota may contribute to reduction in CRC risk. Full article

Background: Polycystic ovary syndrome (PCOS) is characterized by substantial inter-individual variability in ovarian sensitivity to recombinant follicle-stimulating hormone (rFSH) during controlled ovarian stimulation (COS). Clinically applicable tools for personalized dosing in this population remain limited. Methods: This retrospective single-center study (2013–2024) analyzed 369 PCOS patients undergoing GnRH antagonist protocols who achieved optimal ovarian responses (10–20 oocytes with at least 40% of follicles ≥ 16 mm in diameter on trigger day). The final retrospective dataset was randomly split into modeling (n = 258) and validation (n = 111) groups. A multivariate linear regression model incorporating age, BMI, basal FSH, basal LH, AMH, and AFC was developed to estimate the average daily rFSH dose. Model performance was evaluated using correlation analysis, prediction error metrics, and calibration assessment. Results: Age, BMI, and basal FSH were positively associated with average daily rFSH dose, whereas basal LH, AMH, and AFC were negatively associated. The model explained 40.4% of the variability in average daily rFSH dose. In the modeling cohort, 77.9% of estimated doses fell within ±20% of the observed values, with a moderate correlation between predicted and observed doses (ρ = 0.646). In the validation cohort, 67.6% of estimates met the predefined accuracy threshold (ρ = 0.676). Calibration analyses demonstrated robust agreement between predicted and observed doses. Conclusions: By integrating endocrine markers, ovarian reserve indicators, and clinical characteristics, this study provides a practical example of personalized medicine in COS in women with PCOS. The internally validated approach may support individualized rFSH dosing during COS and serve as a basis for future development of decision support tools in this specific population. Full article

Hematocrit (HCT) fluctuations and ambient temperature variations are two critical interference factors limiting the accuracy of electrochemical glucose test strips in self-monitoring of blood glucose (SMBG). In this study, a high-precision screen-printed glucose sensor incorporating in situ impedance-based HCT correction and temperature compensation was developed. The system employs a time-division multiplexing strategy, integrating a normalized thermodynamic model and an in situ impedance-based HCT correction algorithm, to achieve synergistic decoupling and precise compensation of temperature and HCT interferences. Experimental results demonstrate that after multi-parameter synergistic correction, the system exhibits excellent stability across a wide temperature range (10–35 °C) and a broad HCT range (10–70%). The accuracy indicators significantly surpass ISO 15197:2013 standards. In contrast, uncorrected measurements showed deviations ranging from approximately −80% to +30% due to HCT fluctuations. This multiple correction strategy effectively resolves systematic errors in whole blood testing without increasing electrode complexity or requiring pretreatment steps, providing a robust technical solution for high-precision, low-cost personal glucose monitoring. Full article

The rapid increase in companion animal ownership has intensified the demand for innovative tools that support animal health and overall welfare. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a promising approach in veterinary medicine. However, its application beyond clinical diagnostics, especially in behaviour and personality assessment, remains fragmented and insufficiently integrated into routine practice. This systematic review aims to synthesise current knowledge on AI-based applications in companion animal care, with a focus on behavioural monitoring, personality prediction, and welfare-related challenges. Following PRISMA guidelines, a structured literature search was conducted in the Scopus and PubMed databases from 2020 to 2025. In addition, grey literature sources were searched to capture relevant non-peer-reviewed data. A total of 115 studies met the inclusion criteria and were included in the analysis. Eligibility criteria included studies applying AI methods (machine learning or deep learning) to companion animals (dogs, cats, and exotic pets), while studies on humans, farm animals, or without AI methods were excluded. Due to the heterogeneity of included studies, no formal risk of bias assessment was performed, and results were synthesised narratively. The findings indicate that AI applications are most advanced in diagnostic imaging and clinical decision support, where data availability and methodological maturity are highest. In contrast, AI-based approaches for behaviour and personality prediction remain limited, particularly in cats and exotic companion animals, largely due to small, heterogeneous datasets, potential bias, and a lack of external validation. Emerging technologies such as wearable sensors, computer vision, and multimodal data integration demonstrate substantial potential for continuous behavioural monitoring and early detection of welfare-related issues in real household environments. Nevertheless, significant challenges persist, including data heterogeneity, limited model explainability, ethical considerations, and the absence of regulatory frameworks specifically addressing AI-based veterinary applications. Overall, this review highlights a substantial gap between the technical potential of AI and its current readiness for widespread application in companion animal behaviour and welfare assessment. Future research should prioritise large-scale and standardised data collection, cross-species validation, and interdisciplinary collaboration to ensure that AI-driven tools effectively support veterinary decision-making, animal welfare, and the well-being of owners. Full article

Objective: This study aimed to examine the effects of personality features and attitudes toward the nursing profession on job motivation and intention to quit among male nurses within the framework of the Job Demands–Resources (JD-R) model. In this framework, personality traits and perceptions of the profession were conceptualized as personal resources, while job motivation represents a motivational process that may influence turnover intention. Methods: A cross-sectional design was employed with 303 male nurses actively working in different regions of Turkey. Data were collected via an online survey using non-probability sampling methods. The measurement tools included the Attitude Scale Toward the Nursing Profession, Job Motivation Scale, Personality Features Scale, and the Nurse Turnover Intention Scale. Data were analyzed using descriptive statistics, confirmatory factor analysis, and structural equation modeling. Results: Structural equation modeling revealed that attitudes toward the nursing profession (β = −0.90, p < 0.001), personality features (β = −0.10, p < 0.001), and job motivation (β = −0.14, p < 0.001) had significant and negative effects on intention to quit. Attitudes toward the profession emerged as the strongest predictor, explaining 49% of the variance in intention to quit. Attitudes toward the nursing profession, personality features, and job motivation were found to have significant and negative effects on intention to quit among male nurses. Consistent with the JD-R model, the findings suggest that personal resources (personality and professional perception) and motivational processes (job motivation) may play an important role in shaping turnover intentions among male nurses. Accordingly, professional identity-strengthening initiatives, role model-based mentoring, and motivation-enhancing training programs may help support the retention of male nurses in the profession. Full article

Background: Since sedentary habits have become a growing global public health concern, the promotion of physical activity (PA) from early childhood could help children live healthy lifestyles. The aim of this systematic review was to analyze the level of compliance with PA in preschoolers in relation to the reference guidelines. Method: A systematic review of relevant articles was carried out using four databases (PubMed, ProQuest, SCOPUS, and FECYT (Web of Sciences, CCC, CIDW, KJD, MEDLINE, RSCI, and SCIELO)) until 14 May 2025. The methodological assessment process was performed by using an adapted version of the MINORS assessment criteria. Results: A total of 623 studies were initially found and 23 were included in the qualitative synthesis. Conclusions: The results revealed that the average in most contexts usually ranges between 30% and 65% of the child population. Due to different operational criteria, compliance was generally higher when PA was assessed separately using single-behavior guidelines as opposed to when integrated 24 h movement frameworks were used. However, these results should be considered with caution because establishing the level of adherence to PA guidelines is difficult due to the different outcomes and guidelines used to compare the level of children’s PA. In future research, it is important to establish common baseline criteria (specifying more specific ages, common questionnaires, and criteria for calculating PA quantity and intensity) to facilitate more objective and reliable comparisons between studies. This systematic review is important because it highlights the need for healthy educational habits from the first years of a person’s life. Full article

The ketogenic diet (KD) is a critical, evidence-based intervention within medical nutrition therapy for managing neurological disorders. In this article, we reviewed the published research on the efficacy of the ketogenic diet and its variations in treating epilepsy, particularly for patients unresponsive to anti-epileptic drugs. The literature review was performed on PubMed between 2022 and 2025. The review of clinical studies across various age groups reveals that, while the KD is effective for both focal and generalized seizures, infants often achieve higher rates of seizure freedom compared to adults, potentially due to better dietary compliance. Despite its success, the restrictive nature of the diet presents significant challenges for individuals suffering from epilepsy. The key challenges that reduce compliance over time include side effects, such as gastrointestinal issues, potential for malnutrition, and a high risk of micronutrient deficiencies. The role of the registered dietitian is paramount in this interdisciplinary approach, ensuring personalized education by monitoring growth and adjusting nutritional plans to optimize health outcomes for children unresponsive to anti-epileptic drugs. Ultimately, integrating MNT with traditional pharmacological or surgical treatments offers the most promising path for significant seizure reduction and improved quality of life for epileptic patients. Full article

This study investigates how structural, relational, and psychosocial factors influence antiretroviral therapy (ART) engagement among women living with Human Immunodeficiency Virus [HIV] in Kenya. Using nationally representative data from the 2022 Kenya Demographic and Health Survey, we analyzed 332 HIV-positive women aged 15–49 years, applying a multidimensional outcome that combines ART use with measures of internalized stigma and exposure to harassment. Multivariable logistic regression and person-centered cluster analysis were used to identify determinants of enhanced engagement and to characterize distinct vulnerability profiles. The results show that women living in poverty and those with a history of anxiety had significantly lower odds of achieving enhanced ART engagement, despite ART being widely available and free. Cluster analysis revealed co-occurring vulnerabilities across structural, psychosocial, and reproductive domains, indicating that women can be “on ART” while remaining socially and psychologically vulnerable. These findings highlight that biomedical access alone is insufficient for meaningful engagement in care. Interventions that address socioeconomic constraints, mental health, stigma, and intimate partner violence are essential to support sustained ART engagement among women in Kenya. Full article

In her fiction, Joan Didion crafted female protagonists who embodied the strange stirrings documented by Betty Friedan in The Feminine Mystique, as common among mid-century White, educated women. Didion’s protagonists are all daughters, wives, and mothers who come to realize their lives are built on empty compromises. However, in her late 20th-century novels, their awareness leads to actual changes: the Didion Women who confront the void in Democracy and The Last Thing He Wanted find their lives impacted by the machinations of U.S. Cold War policies. These novels specifically trace the impact of American imperialism on wives and daughters at home—those that the policies claimed to protect. These protagonists, and their witnesses, refuse to be passive casualties. Their narration by an embedded professional female journalist adds weight to the journeys of these overlooked women. Through her protagonists of privilege, Didion unflinchingly documents the physical and psychological damages of patriarchy—both personal and political—presenting female models of awareness and resistance. This essay will examine Didion’s Democracy and The Last Thing He Wanted as the capstones of her woman-centered fiction, presenting detailed portraits of matrons who deliberately disentangle themselves from history. Full article

This research examined the component structure of two child measures, the Trait Emotional Intelligence Questionnaire–Child Form (TEIQue-CF; 75 items) and its short form (TEIQue-CSF; 36 items), developed specifically for children aged 8 to 12 years. Study 1 analysed TEIQue-CF data using the nine facet scores from 720 UK primary school pupils in Years 3 through 6 using principal component analysis with parallel analysis for factor retention. Results supported a unifactorial solution in the total sample, with a single factor explaining 43.48% of the variance. Exploratory subgroup factor analyses (in boys and older children in Years 5 to 6) in Study 1 suggested a potentially interpretable bifactorial pattern, though parallel analysis did not support retaining the second factor. Study 2 examined the TEIQue-CSF in 1582 Year 6 pupils using parcel-level analysis. A clearer two-factor structure emerged, with Socioemotionality (Adaptability, Peer relations, Self-esteem, Emotion expression, Affective disposition, Emotion perception) and Emotion control (Impulse control, Emotion regulation, Self-motivation) explaining 53.7% of the variance. This structure replicated across gender subgroups. Taken together, the findings suggest a developmental trend in which trait EI shifts from a largely undifferentiated structure in middle childhood to a more differentiated two-factor organisation by the end of primary school. They support the use of global trait EI scores in younger children while indicating that differentiated assessment becomes appropriate as children approach adolescence. Full article