Search Results (17,197)

Background and Objectives: Abortion on request, contraceptive access barriers, and mental health may jointly shape women’s quality of life (QoL). We examined how abortion history, structural barriers, and psychosocial factors relate to modern contraceptive use, depressive and anxiety symptoms, and QoL among women attending a Romanian tertiary center. Methods: We conducted a single-center observational study combining retrospective chart review with an online survey of 200 women aged 18–45 years. Validated instruments (Patient Health Questionnaire-9 [PHQ-9], Generalized Anxiety Disorder-7 [GAD-7], World Health Organization Five-Item Well-Being Index [WHO-5], and World Health Organization Quality of Life–BREF [WHOQOL-BREF]) and indices of access barriers, perceived stigma, and social support were used. Analyses included multivariable regression, structural equation modelling, latent class analysis, and moderation analysis. Results: Overall, 55.0% of women reported ≥1 abortion on request. Compared with those without abortion history, they were older (31.2 ± 4.9 vs. 26.8 ± 4.8 years, p < 0.001), more often had lower levels of education (51.8% vs. 33.3%, p = 0.013), and were less likely to use modern contraception at last intercourse (52.7% vs. 71.1%, p = 0.012). PHQ-9 (8.8 ± 4.0 vs. 7.3 ± 4.3) and GAD-7 (7.0 ± 3.2 vs. 5.7 ± 3.4) scores were higher (both p = 0.010), while QoL was lower (55.4 ± 8.1 vs. 59.5 ± 7.8, p < 0.001). In adjusted models, access barriers (OR per point = 1.3, 95% CI 1.1–1.6), but not abortion history, predicted non-use of modern contraception. QoL correlated strongly with PHQ-9 (r = −0.6) and WHO-5 (r = 0.5; both p < 0.001). Latent class analysis identified a “high-barrier, distressed, abortion-experienced” profile with the poorest mental health and QoL. Conclusions: Structural access barriers and current depressive and anxiety symptoms, rather than abortion history alone, were key correlates of contraceptive gaps and reduced QoL, underscoring the need for integrated reproductive and mental health care. Full article

Contemporary medical practitioners increasingly recognize the critical impact of healing-environment design on patients’ recovery, positioning it as a pivotal consideration in healthcare facility planning. While existing research has predominantly focused on enhancing the functionality and efficiency of healthcare environments, it has often overlooked the significance of individual patient needs and their distinct experiences. This paper aims to utilize the principles of epidemiology and empirical analysis to explore the application and research trends of the patient-centered care (PCC) concept in healthcare facility design, to promote interdisciplinary collaboration and achieve customized healthcare environments. Based on bibliometric analysis and key literature review methods, this paper systematically examines and interprets the research development trends of PCC in healing environment design, integrating both macro and micro perspectives, and reveals how design factors in therapeutic environments support the realization of PCC principles, thereby improving patients’ rehabilitation experiences and health outcomes. The results indicate that current research on PCC is trending towards increasingly diversified integration via high-frequency keywords such as recovery, healing environment, and evidence-based design, highlighting the shift from functional optimization to emotional care, technological integration, and nature-based interactions in design. Notably, patient-centered care has become a consensus and core integrating concept in this field. This paper not only reveals the key role of healing environments in constructing PCC practice pathways but also provides theoretical support and strategic reference for the planning of healthcare spaces and the collaborative design of nursing processes, and demonstrates that healing environments have evolved from passive spaces into active rehabilitation mediums through interdisciplinary collaboration, thereby facilitating the implementation of the patient-centered healthcare philosophy. Full article

Background: This study introduces a new qualitative enhancement descriptor for contrast-enhanced mammography (CEM), termed Ground-Glass Enhancement (GGE). The objective was to categorize breast lesions using this descriptor and evaluate its association with malignancy and markers of tumor aggressiveness. Methods: In this single-center retrospective study, 249 patients with a single enhancing lesion on CEM were included. Lesions were classified into pure Ground-Glass Enhancement (PGGE), Heterogeneous Ground-Glass Enhancement (HGGE), or Opaque Enhancement (OE) based on the degree of obscuration of the underlying parenchyma. Clinical, imaging, and pathological features were compared across groups. Multivariable logistic regression was used to identify independent predictors of malignancy. Results: Significant differences across enhancement patterns were found in lesion conspicuity, enhancement type, size, background enhancement, and patient age. OE lesions more frequently showed high conspicuity (83% vs. 62% in HGGE and 20% in PGGE) and a mass-like appearance (94% vs. 73% in HGGE and 81% in PGGE). HGGE lesions had the largest median size (25 mm, vs. 17 mm in OE and 13 mm in PGGE), and OE lesions most often exhibited minimal background enhancement (77%, vs. 50% in HGGE). In multivariable analysis, mass-like enhancement (OR = 4.59), larger size (OR = 1.27 per +5 mm), and high conspicuity (OR = 3.43) were independently associated with malignancy. Although GGE categories correlated with malignancy in univariable analysis, this was not confirmed in the adjusted model. OE lesions were significantly associated with higher Ki-67 expression (73% with Ki-67 >20%), indicating increased proliferative activity compared with PGGE (43%) and HGGE (57%). Conclusions: The GGE descriptor captures clinically relevant imaging features and may support visual stratification of breast lesions on CEM. While not an independent predictor of malignancy, it appears more closely related to markers of tumor aggressiveness. Full article

The accelerating energy demands from artificial intelligence (AI) deployment introduce systemic challenges for achieving carbon neutrality. Large language models (LLMs) represent a dominant driver of AI energy consumption, with inference operations constituting 80–90% of total energy usage. Current energy benchmarks report aggregate metrics without domain-level breakdowns, preventing accurate carbon footprint estimation for workloadspecific operations. This study addresses this critical gap by introducing a carbon-aware framework centered on the carbon cost of intelligence (CCI), a novel metric enabling workload-specific energy and carbon calculation that balances accuracy and efficiency across heterogeneous domains. This paper presents a comprehensive cross-domain energy benchmark using the massive multitask language understanding (MMLU) dataset, measuring accuracy and energy consumption in five representative domains: clinical knowledge (medicine), professional accounting (finance), professional law (legal), college computer science (technology), and general knowledge. Empirical analysis of GPT-4 across 100 MMLU questions, 20 per domain, reveals substantive variations: legal queries consume 4.3× more energy than general knowledge queries (222 J vs. 52 J per query), while energy consumption varies by domain due to input length differences. Our analysis demonstrates the evolution from simple ratio-based approaches (weighted accuracy divided by weighted energy) to harmonic mean aggregation, showing that the harmonic mean, by preventing bias from extreme values, provides more accurate carbon usage estimates. The CCI metric, calculated using weighted harmonic mean (analogous to P/E ratios in finance, where A/E represents accuracy-to-energy ratio), enables practitioners to accurately estimate energy and carbon emissions for specific workload mixes (e.g., 80% medicine + 15% general + 5% law). Results demonstrate that the domain workload mix significantly impacts carbon footprint: a law firm workload (60% law) consumes 96% more energy per query than a hospital workload (80% medicine), representing 49% potential savings through workload optimization. Carbon footprint analysis using US Northeast grid intensity (320 gCO₂e/kWh) shows domain-specific emissions ranging from 0.0046–0.0197 gCO₂ per query. CCI is validated through comparison with simple weighted average, demonstrating differences up to 12.1%, confirming that the harmonic mean provides more accurate and conservative carbon estimates essential for carbon reporting and neutrality planning. Our findings provide a novel cross-domain energy benchmark for GPT-4 and establish a practical carbon calculator framework for sustainable AI deployment aligned with carbon neutrality goals. Full article

The complex topography and harsh natural environment of the Loess Plateau in Longzhong have been suffering from an undefined living circle structure, which has hindered rural planning and development. A rural community living circle is a spatial unit centered on meeting the needs of villagers, within which various service facilities are rationally allocated within a specific spatial scope. To refine its spatial patterns, the concept of living circles was introduced to address travel challenges. The extent of these living circles is affected by the accessibility of public service facilities and barriers to travel. Using land use data, DEM, population density, and road networks, this study employed the MCR model, gravity model, and ArcGIS spatial analysis to examine the patterns of rural community living circles. The focus was on analyzing the living circle structure of rural communities on the Loess Plateau in Longzhong, considering both natural and artificial environmental constraints. The results show: (1) Rural community living circles present multi-scale spatial features. The basic living circle covers a 15 min slow-travel area. The central living circle corresponds to village-level needs, accessible within 35 min by both slow and motorized travel. The town living circle covers a 10 km radius, reachable within 60 min by a mix of transport modes. The county living circle, dominated by motorized travel, represents the top tier of public service configuration. (2) Quantitatively, the delineation identified 2753 basic, 444 central, 19 township, and 1 county-level living circles in the Anding District of Dingxi City. The Northern, Eastern, and Southwest Zones suffer from fragmented mountainous landscapes, limiting mobility and accessibility. The Central Zone, however, benefits from a combination of mountainous terrain and river valley plains, offering superior service accessibility. (3) The analysis results based on the MCR model and gravity model aligned more closely with reality, reflecting the scale patterns of rural community living circles. The results of this study can provide theoretical guidance for rural planning, construction, and management in the hilly and gully areas of the Loess Plateau. Full article

This systematic review examines 205 studies on the use of Large Language Models (LLMs) in psychiatry, psychology, psychotherapy, and clinical workflows. Furthermore, studies that directly evaluated at least one LLM in a mental health context were included in the extended detailed analysis. GPT-4 and GPT-3.5 were the most commonly assessed models. Although LLMs showed promising short-term performance across domains, most evaluations relied on small, non-longitudinal datasets and single-session testing, limiting generalizability. The evidence indicates rapid growth but significant methodological inconsistency, emphasizing the need for more diverse datasets, standardized evaluation, and long-term validation before clinical integration. This review also examines how LLMs are being incorporated into mental health practice, outlining key challenges, limitations, and emerging opportunities. Ethical, clinical, and technological considerations are proposed to guide responsible adoption. Given the complexity of mental health care, a multidisciplinary, human-centered approach remains essential to ensure that future LLM applications augment—rather than replace—professional expertise. Full article

The Iberá Wetlands in northeastern Argentina constitute the second largest wetland system in South America, yet the fern and lycophyte flora of this region remains poorly documented. The aims of this work were to update the species richness of these plant groups, evaluate the intensity of collecting efforts, identify conservation priorities, estimate the potential true species richness, and make biogeographical inferences. We compiled a database of species from multiple sources, and the study area (21,853 km²) was divided into 19 grid cells for analysis. Sampling effort and species richness were quantified, and non-parametric estimators (Chao2, ICE, Jack2) were used to evaluate inventory completeness. Several similarity analyses were performed using the Jaccard index, incorporating reference areas from the Chaco and Paranaense phytogeographic provinces. The Ituzaingó–La Paz geological fracture and the geological formations present in the area were also considered. We recorded 76 taxa, whereas estimators suggested a potential richness of 130–140 species. The center of the Iberá Wetlands showed the lowest sampling effort, while the eastern sector exhibited the highest species richness. The distribution of species appears to be correlated with geological formations. These findings emphasize the importance of continuing sampling in the area. Full article

Background: Pain management in patients with severe burns remains one of the most complex challenges in perioperative care. Burn-related pain is multifactorial, resulting from tissue destruction, intense inflammation, surgical procedures, and repeated dressing changes. Opioids remain the cornerstone of analgesia; however, prolonged use is associated with tolerance, dependence, adverse effects, and prolonged hospitalization. Multimodal and opioid-sparing strategies, including regional anesthesia, may improve postoperative outcomes by enhancing analgesia while reducing systemic drug exposure. This study aimed to evaluate the effectiveness of a standardized regional anesthesia protocol in reducing postoperative pain and opioid requirements in burn patients undergoing lower-limb escharectomy and autologous skin grafting. Methods: We conducted a retrospective, single-center analysis of 25 adult patients with deep thermal burns of the lower limbs who underwent escharectomy and split-thickness skin grafting. All patients received a combined ultrasound-guided sciatic popliteal block and adductor canal block on both the burned limb and the donor site. Ropivacaine 0.375% with clonidine was administered without exceeding a total dose of 3.0 mg/kg. Postoperative pain was assessed using the Numerical Rating Scale (NRS), and opioid consumption was recorded as rescue doses in intravenous morphine equivalents. Secondary outcomes included perioperative complications and 30-day hospital readmission. Results: Regional anesthesia provided effective postoperative pain control. Thirty-two percent of patients reported no pain (NRS 0), 52% reported mild pain (NRS 1–3), and 16% reported moderate pain (NRS 4–6). No patient reported severe pain (NRS 7–10). Only four patients (16%) required rescue opioids. No perioperative complications or block-related adverse events occurred, and no patient required hospital readmission within 30 days. Conclusions: In this cohort, regional anesthesia was associated with satisfactory postoperative analgesia and minimal opioid requirements. By reducing opioid exposure, this approach may help improve patient comfort and potentially limit opioid-related adverse effects. Larger prospective studies are needed to confirm these findings and to assess long-term outcomes. Full article

Cultural ecosystem services (CESs) are the non-material benefits people derive from ecosystems and are important for human well-being. Most research has focused on individual CES supply–demand relationships, with little systematic study of the overall CES structure, interactions, and mechanisms in metropolitan areas. This study takes the Nanjing Metropolitan Area as a case study, integrating multi-source geospatial data and employing the MaxEnt model, self-organizing maps (SOMs), Spearman correlation analysis, and the Optimal Parameters-based Geographical Detector (OPGD). It analyzes supply–demand matching, trade-offs, synergies, and drivers for four CES categories: aesthetic (AE), recreational entertainment (RE), knowledge education (KE), and cultural diversity (CD). The main findings are as follows: (1) CES supply and demand are spatially zoned: the core area has surplus supply, secondary centers are balanced, and the periphery has both weak supply and demand. (2) Three supply–demand bundles have distinct synergy and trade-off patterns: Bundle 1 primarily exhibits strong synergy between AE and CD; Bundle 2 shows a weak trade-off relationship; and Bundle 3 forms a synergy centered on AE. (3) The explanatory power of driving factors exhibits pronounced spatial heterogeneity: Bundle 1 is dominated by non-quantifiable social factors; Bundle 2 features dual synergistic drivers of population and transportation; and Bundle 3 demonstrates synergistic effects driven by facilities and economic factors. Overall, this study contributes an integrated metropolitan-scale framework that connects CES supply–demand mismatch patterns with bundle typologies, interaction structures, and bundle-specific drivers. The results provide an operational basis for targeted planning and coordinated ecological–cultural governance in the Nanjing Metropolitan Area and offer a transferable reference for other metropolitan regions. Full article

Background: Depression remains a major global mental health concern, significantly intensified during the COVID-19 pandemic. As social media usage surged during this period, it emerged as a valuable source for identifying early signs of depression. Artificial intelligence (AI) offers powerful tools to analyze large volumes of user-generated content, enabling timely and effective detection of depressive symptoms. This review aims to preliminarily explore and compare evidence on the use of AI models for detecting depression in social content across the pre-, during, and post-pandemic phases, assessing their effectiveness and limitations. Methods: A narrative literature review was conducted using PubMed and Scopus, following the SANRA guidelines to ensure methodological quality and reproducibility. The study was pre-registered in the OSF database and employed the PICOS framework for the strategy. Inclusion criteria comprised studies in English from the past 10 years that analyzed depression detection via AI, machine learning (ML), and deep learning (DL) applied to textual data, images, and social metadata. This review addresses the following four research questions: (1) whether AI models improved effectiveness in detecting depression during/after the pandemic vs. pre-pandemic; (2) whether textual, visual, or multimodal data approaches became more effective during the pandemic; (3) whether AI models better addressed technical challenges (data quality/diversity) post-pandemic; and (4) whether strategies for responsible AI implementation improved during/after the pandemic. Results: Out of 349 identified records, nine primary studies were included, as most excluded articles had a predominantly technical focus and did not meet the clinical relevance criteria. AI models demonstrated strong potential in detecting depression, particularly through text-based classification and social content analysis. Several studies reported high predictive performance, with notable improvements in accuracy and sensitivity during and after the pandemic, although evidence remains limited. Conclusions: Our preliminary analysis suggests that AI-based depression detection on social media shows potential for clinical use, highlighting interdisciplinary collaboration, ethical considerations, and patient-centered approaches. These findings require confirmation and validation through larger, well-designed systematic reviews. Full article

Background/Objectives: This study aimed to identify risk factors for postoperative wound infections and healing disorders in patients with brain tumors, based on a large, single-center analysis, and to establish an evidence-based foundation for prevention. Methods: A retrospective analysis was conducted on 1480 patients who underwent intracranial tumor resection in our department over a ten-year period, without the influence of anticoagulant or antiplatelet medication. Potential predictors of wound healing disorders were evaluated, focusing on demographic variables and pre-existing conditions. Results: Among the 1480 patients, postoperative wound infections occurred in 47 cases, corresponding to a cumulative incidence of 3.17%. Platelet count (p = 0.018) and partial thromboplastin time (p = 0.011) emerged as potential risk factors for postoperative wound infections. Length of hospital stay appeared as a distinct outcome-associated marker in cases of postoperative wound infection (p = 0.018). In contrast, demographic characteristics (age, sex, blood type), comorbidities (hypertension, diabetes mellitus, cardiovascular disease, kidney disease, chronic inflammatory conditions), and other surgical or laboratory parameters showed no significant association with wound healing disorders. Conclusions: In patients with brain tumors undergoing surgery without the influence of anticoagulant or antiplatelet therapy, most demographic factors, common comorbidities, and selected laboratory parameters were not associated with an increased risk of postoperative wound infections. Awareness of the identified risk factors may help guide preventive strategies and nursing care. Full article

Objectives: This study aimed to evaluate adherence to therapy in hypoparathyroidism and related endocrine–metabolic disorders and to assess its association with biochemical outcomes, hypocalcemia episodes, and health-related quality of life (HRQoL). Methods: In accordance with PRISMA 2020 guidelines, PubMed, Scopus, Google Scholar, and the Cochrane Library were searched until September 2025. The eligible studies were randomized controlled trials, cohort, case–control studies, cross-sectional, and observational studies that reported adherence to calcium/vitamin D or recombinant parathyroid hormone therapy. Results: twenty-three studies were included in the qualitative synthesis, and 11 studies were included in the quantitative meta-analysis. Pooled medication adherence compliance was 70–82% and improved with simplified regimens and the use of recombinant PTH. Additionally, this was also associated with an improvement in HRQoL (p < 0.0001) and a lower risk of hypocalcemia (p < 0.0001). Conversely, multifactorial regulation was observed as the level of adherence had no significant effect on serum calcium levels (p = 0.7116). Sensitivity analyses demonstrate the strength of findings and indicate no significant publication bias. Conclusions: Medication adherence is a key factor in determining patient-centered outcomes in hypoparathyroidism. Better adherence is linked to a higher quality of life and fewer episodes of hypocalcemia, while its effect on biochemical parameters seems minimal. Educational programs, simple treatment regimens, and wider access to rhPTH therapy can be used to improve patient management of the disease over time. Full article

Background: Ongoing healthcare and medical education reforms in Kazakhstan have been accompanied by persistent workforce shortages and reduced inpatient capacity in pediatric care. Therefore, this study aimed to assess and forecast selected healthcare system indicators using acute mastoiditis (AM) as a sentinel condition while also describing its clinical and epidemiological characteristics. Materials and Methods: This study combined an analysis of national healthcare and demographic statistics in Kazakhstan from 1998 to 2024 with a retrospective review of pediatric AM patients treated at a tertiary referral center. Long-term trends in healthcare resources were assessed, and future needs were projected via average annual percentage change (AAPC) and time series forecasting methods. Clinical, laboratory, and radiological data were extracted from medical records. Statistical analyses were performed via SPSS version 24.0 (IBM Corp., Armonk, NY, USA). Results: From 1998 to 2024, the number of pediatricians and ENT hospital beds declined, whereas the density of ENT physicians remained relatively stable, and the proportion of ENT surgical procedures increased. Projections to 2030 suggest continued constraints in pediatric and ENT workforce capacity and further reductions in inpatient beds despite sustained growth in surgical demand. Among 95 pediatric AM cases, complications, most commonly subperiosteal abscess and zygomatic abscess, were identified in 40% of patients. Conclusions: AM may be considered a contextual indicator of pressures within specialized pediatric ENT services rather than a direct measure of healthcare system performance. These findings highlight the need for further studies to validate these observations and better inform healthcare planning. Full article

Functional ankle instability (FAI) is a common consequence of lateral ankle sprains, characterized by impaired sensorimotor control. While orthoses and localized vibration have shown individual benefits for FAI, their combined application in a wearable device has not been previously investigated. This pilot randomized trial compared the effects of a vibrotactile foot orthosis (VFO) and a vibrotactile ankle orthosis (VAO) on joint position sense (JPS) and postural control in individuals with FAI. Sixteen participants were randomized to receive either a VFO or a VAO, both delivering 30–50 Hz pulsed vibration in 20 min sessions, three times a week, for two weeks. Outcome measures included joint position sense (JPS) error (°), center of pressure (COP) velocity (mm/s), the Star Excursion Balance Test (SEBT), and the Six-Meter Hop Test (SMHT), which were assessed pre-intervention, immediately post-intervention, and after two weeks of use. The analysis showed a statistically significant interaction between time and intervention group for JPS error (p = 0.02, η² = 0.42). Specifically, the VFO group improved JPS significantly more than VAO at two weeks follow-up (MD = −1.75°, p = 0.005, d = −1.68). Both groups significantly reduced in anteroposterior COP velocity after two weeks (VFO: MD = 1, p = 0.003, d = 1.47; VAO: MD = 1.39, p ˂ 0.001, d = 2.05) with no between-group differences. No changes were observed in the SEBT or SMHT. Plantar-based vibrotactile stimulation was more effective than ankle-based stimulation in enhancing proprioceptive acuity in individuals with FAI. Both interventions improved static postural stability, supporting the potential of integrated vibrotactile orthoses in FAI rehabilitation. No major practical issues were reported during the intervention. Two participants experienced minor discomfort related to the electronic housing bulk in the first week, which was resolved by week two. No further complaints regarding device weight or usability were observed. Full article

While circuit parameter optimization has matured significantly, the systematic discovery of novel circuit topologies remains a bottleneck in analog design automation. This work presents SymXplorer, an open-source Python framework designed for automated topology exploration through symbolic modeling of analog components. The framework enables a component-agnostic approach to architecture-level synthesis, integrating stability analysis and higher-order filter exploration within a streamlined API. By modeling non-idealities as lumped parameters, the framework accounts for physical constraints directly within the symbolic analysis. To facilitate circuit sizing, SymXplorer incorporates a multi-objective optimization toolbox featuring Bayesian optimization and evolutionary algorithms for simulation-in-the-loop evaluation. Using this framework, we conduct a systematic search for differential Common-Gate (CG) Bandpass Transimpedance Amplifier (TIA) topologies tailored for 5G New Radio (NR) Radio-over-Fiber applications. We propose a novel, orthogonally tunable Bandpass TIA architecture identified by the tool. Implementation in 65 nm CMOS technology demonstrates the efficacy of the framework. Post-layout results exhibit a tunable gain of 30–50 dB$\Omega$, a center frequency of 3.5 GHz, and a tuning range of 500 MHz. The design maintains a power consumption of less than 400 $\mathsf{\mu }$W and an input-referred noise density of less than 50 pA/$\sqrt{\mathrm{Hz}}$ across the passband. Finally, we discuss how this symbolic framework can be integrated into future agentic EDA workflows to further automate the analog design cycle. SymXplorer is open-sourced to encourage innovation in symbolic-driven analog design automation. Full article