Search Results (50)

Fever of unknown origin (FUO) remains a persistent diagnostic challenge in clinical medicine despite significant advances in laboratory testing and imaging techniques. The definition of FUO has evolved since the original criteria proposed in 1961 and currently refers to persistent fever exceeding approximately 38.2–38.3 °C without a definitive diagnosis after an adequate diagnostic evaluation. Gastrointestinal diseases represent an important but often underrecognized group of conditions associated with FUO. The aim of this review is to synthesize current evidence on the gastroenterological causes of FUO, with particular emphasis on pathophysiological mechanisms, diagnostic strategies, and therapeutic management. The analysis highlights the role of inflammatory, infectious, and neoplastic gastrointestinal disorders in the etiology of prolonged fever. Key mechanisms involve systemic inflammatory responses mediated by cytokines such as interleukin-1, interleukin-6, and tumor necrosis factor, as well as immune processes associated with the gut-associated lymphoid tissue (GALT) and interactions between intestinal microbiota and host immunity. Among the most frequently reported gastroenterological causes of FUO are inflammatory bowel diseases, intra-abdominal infections and abscesses, hepatobiliary disorders, pancreatitis, and gastrointestinal malignancies. Diagnostic evaluation requires a stepwise approach integrating laboratory testing, microbiological studies, imaging modalities, and endoscopic procedures, with advanced techniques such as computed tomography and fluorodeoxyglucose positron emission tomography improving detection of occult inflammatory or neoplastic processes. Therapeutic management is primarily guided by the identification of the underlying cause, while empirical treatment should be carefully considered to avoid masking diagnostic clues. A better understanding of the gastrointestinal mechanisms underlying FUO and the development of more efficient diagnostic algorithms may improve clinical outcomes and reduce the number of undiagnosed cases. Full article

Rates of suicide among children and adolescents have increased significantly in the past two decades, especially among minoritized youth. Identification of modifiable factors associated with suicidality in diverse samples is important for informing targeted prevention and intervention efforts. Toward this end, this study utilized a multi-informant approach to examine the association between aggression and suicide risk in an urban pediatric sample. Children and adolescents (N = 136; 69% Black or African American) between the ages of 6 and 17 (M_age = 11.4 ± 3.0) were recruited while attending a well-child visit at a Baltimore City pediatric primary care clinic. Pediatric participants and their caregivers completed measures of aggressive behavior and depressive problems. Suicide risk was derived from parent-, youth-, and clinician-reports of pediatric participants experiencing suicidal thoughts and behaviors. After controlling for demographic variables, results of stepwise logistic regressions revealed that parent- and youth-reported aggressive behavior were significantly associated with suicide risk (OR = 1.18, p = 0.005 and OR = 1.23, p = 0.006, respectively). When depressive problems were added to the model, depressive problems were significantly associated with suicide risk (parent-report OR = 1.34, p = 0.015 and youth-report OR = 1.28, p = 0.025), but aggressive behavior was no longer significantly associated. Findings from this study suggest that aggression could be an important indicator of suicide risk, but not above and beyond the influence of depressive symptoms. In this sample, aggressive behavior may be at least partially explained by depressive symptoms and a manifestation of internal distress. Full article

Background: Fatigued driving is a key contributing factor to major traffic accidents. Existing detection technologies suffer from issues such as delayed identification, high error rates, and a lack of quantified causal relationships between physiological and behavioral indicators. This study aims to clarify the intrinsic relationship between electrophysiological and driving behavior data during the progression of driving fatigue. Methods: Four categories of driving behavior data and electrocardiographic (ECG) heart rate variability (HRV) indicators were selected as the study subjects. Based on a four-stage standardized simulated driving experiment ranging from wakefulness to severe fatigue, the correlations between indicators were quantified using Pearson correlation analysis, and a four-layer physiological–behavioral fusion fatigue assessment model was constructed. Results: Autonomic dysregulation is the intrinsic cause of abnormal driving behavior. The two exhibit a highly synchronized, stepwise progressive evolution pattern, with |r| ≥ 0.75 among core indicators. The accuracy of the constructed model exceeded 90% for all fatigue stages, reaching 97.8% for severe fatigue detection, with a response time of ≤0.5 s. Conclusions: This model effectively addresses the limitations of single-monitoring technologies and provides theoretical support and technical guidance for multimodal identification and graded early warning of driving fatigue. Full article

Time-series InSAR is an important means for early identification and monitoring of landslides. However, in complex mountainous areas, it still faces challenges such as significant geometric distortions and complicated deformation mechanisms. To address these issues, this paper proposes a landslide identification and prediction framework that integrates ascending and descending orbits InSAR observations with physics-guided deep learning. Taking Yangbi County, Yunnan Province, as a case study, we combined ascending and descending Sentinel-1A data and employed the SBAS-InSAR method to identify potential landslides, detecting a total of 41 hazardous sites. The cumulative displacement time series of typical landslides were further extracted along the slope aspect to more realistically reflect landslide movement characteristics. On this basis, wavelet decomposition was introduced to separate the displacement series into trend and periodic components. Gray relational analysis was then used to select influencing factors such as precipitation and temperature, and a stepwise prediction model based on LSTM (WT-LSTM) was constructed. The results indicate that the model achieves significantly higher prediction accuracy at characteristic points of the representative landslide (RMSE = 1.16–2.19 mm) compared to standalone LSTM and SVR models. These findings demonstrate its effectiveness and potential applicability in landslide deformation monitoring and prediction in complex mountainous areas, while also providing a useful reference for landslide risk early warning. Full article

Background: Recurrence remains a major challenge in the management of Clostridioides difficile infection (CDI), with reported rates of 20–30% after an index episode. Identification of factors associated with recurrence is essential for improved risk stratification. Methods: This retrospective cohort study included 100 adult patients diagnosed with CDI. Factors associated with recurrent CDI were evaluated using univariable analyses, receiver operating characteristic analysis and backward stepwise logistic regression. Results: Eighteen patients (18%) developed recurrent CDI. Baseline demographic characteristics, comorbidity burden, clinical presentation and admission laboratory parameters were not significantly associated with recurrence. Previous hospitalization within the preceding 12 months, longer duration of antibiotic therapy and poor or partial response to initial treatment were independently associated with recurrence. Duration of antibiotic treatment showed the strongest discriminatory performance (AUC 0.712). Predictive models combining treatment response, antibiotic duration and prior hospitalization demonstrated incremental improvement in discrimination, achieving an AUC of 0.775. Associations with specific antibiotic classes did not persist after adjustment for healthcare exposure and treatment duration. Conclusions: Recurrent CDI was associated primarily with healthcare exposure and post-diagnosis treatment characteristics rather than baseline clinical or laboratory features. These findings support the relevance of integrating antibiotic burden and early treatment response into recurrence risk assessment. However, the relatively small number of recurrent cases warrants cautious interpretation of these findings. Full article

Mining-resource-based cities, as distinctive human–environment systems, face urgent challenges from intensified urbanization and mining, leading to land imbalance and ecosystem service degradation. To enhance resilience, it is essential to identify the evolution and drivers of ecosystem services and construct targeted ecological compensation models. This study focuses on Xinzhou, a representative mining city in China, and systematically analyzes three aspects: (1) spatiotemporal dynamics of land use and ecosystem service value (ESV) from 2000 to 2023 using Markov chains, equivalent factor method, hotspot and sensitivity analyses; (2) identification of ESV driving mechanisms through an integrated “stepwise regression + geographical detector” framework; and (3) formulation of ecological compensation models via quantification of priority indices, demand intensity coefficients, and compensation standards. Key findings indicate that land conversion was concentrated in coalfield zones and surrounding built-up areas, involving 2,518,341.75 hm² (35.76% of total area), primarily characterized by a reduction in farmland and expansion of forest, grassland, and construction land. ESV showed a striped spatial pattern, with higher values in mountainous zones and lower values in valleys and basins with frequent human activity. The northwest coalfield region experienced an initial decline followed by a recovery in ESV. Annual mean temperature emerged as the dominant driver, while DEM influence increased annually. All factor interactions exhibited synergistic effects, with natural variables exerting greater influence than socio-economic ones. Ecological compensation demand was high overall, especially in Wutai, Kelan, and Pianguan counties, with high-value compensation areas mainly distributed in the eastern and central parts of Xinzhou. Looking ahead, a compensation framework prioritizing ecological–economic optimization should be developed, guided by zoned, typological, and dynamic configurations. By analyzing ecosystem governance from the perspective of a mining-resource-based city, this study enhances global ecosystem service evaluation frameworks and offers a replicable model to advance transnational ecological cooperation and green urban transformation. Full article

The identification of supplementary cultivated land as a reserve resource is of great significance for ensuring implementation of the new mechanism of land occupation and compensation balance in China. Using Jiangsu Province as a case study, here, we use a “multi-period land use change patterns–multi-scenario land use simulation–cultivation suitability evaluation–identification of supplementary cultivated land” framework to explore identification of supplementary cultivated land. A single land use dynamic index and a land use transfer matrix were used to analyze land use pattern changes in Jiangsu Province and showed that the area of cultivated land in Jiangsu Province decreased significantly, mainly by being converted into land used for buildings, and waters and conservancy facilities. A Markov-FLUS model was used to simulate and predict land use quantity and spatial distribution under four scenarios: an inertial development scenario, a cultivated land protection scenario, an economic development priority scenario, and an ecological protection priority scenario. Sixteen factor indicators were selected from the four dimensions of natural land quality, social economy, management, and the ecological condition of the land, and the degree of suitability of cultivated land in Jiangsu was evaluated by multi-factor stepwise correction. The southern and central parts of Jiangsu had higher suitability, while the northern part had lower suitability. By superimposing these data on current land use data from 2023, the plots of land that were converted to or from cultivated land were identified. Combined with the suitability degree, the potential three major categories and eight types of sources for supplementary cultivated land, totaling 29,015.92 km², were identified, along with their distribution. A time sequence arrangement for these sources was initially set up. Corresponding management suggestions were proposed based on the adaptability of different supplementary cultivated land sources, with the aim of providing scientific references for the acquisition of supplementary cultivated land sources in the implementation of the national and local government’s farmland balance management. Full article

Mining above confined aquifers fundamentally depends on understanding the evolution of floor permeability for water hazard control and water conservation mining. A mechanical model was developed to characterize the coordinated deformation of floor aquiclude strata, accounting for non-uniform distributions of stress and water pressure. The competing mechanisms whereby neutral plane strain and flexural deflection dominantly control permeability at different dip angles were elucidated, and the influence of dip angle on the stability of the water-resistant key strata was quantified. On this basis, a quantitative method for assessing the feasibility of in situ water conservation mining above confined aquifers was developed and its effectiveness was verified through field application. The main findings are as follows: The deflection of the floor aquiclude increases with water pressure, advance distance, and panel length. Larger coal seam dip angles correspond to smaller aquiclude deflection, with a strong dependence on the water pressure treatment method. The equivalent permeability of the floor increases with water pressure, panel length, and advance distance, and its variation is most pronounced with water pressure. As the dip angle increases, the equivalent permeability exhibits a trend of first rising and then decreasing; the transition between deflection-dominated and neutral plane strain-dominated control occurs at a dip angle of 35°. Lithological assemblage is found to govern the position of the neutral plane and the bending stiffness matrix, while a soft–hard interbedded floor is effective in suppressing deformation and mitigating the increase in the equivalent permeability. For inclined aquiclude key strata, the ranking of zones most prone to failure and water inrush is as follows: lower end > upper end > coal wall position > behind the goaf. A quadratic multi-parameter response model for the mining-induced equivalent permeability at the Fenyuan Coal Mine is established, yielding the sensitivity ranking under single factor and interaction effects as follows: water pressure > panel length > advance distance > water pressure (quadratic) > water pressure × panel length interaction. The higher the water pressure, the stronger the influence of dip angle on the equivalent permeability. Groundwater ion evolution is dominated by dissolution/leaching, with sulfate (SO₄²⁻) serving as a diagnostic ion for source identification. The stepwise criteria and grouting-reinforcement parameters for in situ protection of confined aquifers are proposed. Using water quality and quantity as evaluation metrics, Working Face 5-103 at the Fenyuan Coal Mine, which is a large-inclination-angle and high-pressure working face, has achieved in situ protection of the floor water. Full article

The adoption of robots in the construction phase can improve safety by replacing hazardous tasks and enhance productivity by automating repetitive work. Despite these advantages, adoption remains slow, constrained by economic, industrial, institutional, socio-cultural, and technological barriers. Wider acceptance is particularly urgent in construction, where fragmented processes, low profit margins, and safety risks make innovation both necessary and challenging. This study identified 22 critical barriers through a systematic literature review and categorized them into five dimensions. Beyond identification, the study prioritized these barriers using ISM and MICMAC analysis, clarifying which factors are fundamental drivers and which are outcome-related. The results showed that economic drivers occupy the base of the hierarchy and exert the greatest systemic influence, socio-cultural barriers emerge as highly dependent outcomes, and software usability acts as a linkage factor connecting technological immaturity with social acceptance. These findings reveal that barriers are interdependent rather than isolated and underscore the need for a structured prioritization framework. By applying ISM and MICMAC, this study presents a stepwise roadmap that differentiates fundamental drivers from outcome-related constraints, offering academic insights and practical guidance for policymakers to design strategies such as investment incentives, standardization, legal frameworks, and R&D expansion to accelerate adoption. Full article

Objective: Accurate risk stratification at an early stage may reduce the incidence of infection and improve the survival rate of recipients by adopting targeted interventions. This study aimed to develop a nomogram to predict the risk of multidrug-resistant organism (MDRO) infections in liver transplant (LT) recipients. Methods: We retrospectively collected clinical data from 301 LT recipients and randomly divided them into a training set (210 cases) and validation set (91 cases) using a 7:3 split ratio. Factors related to the risk of MDRO infection after LT were determined using univariate and multivariate bidirectional stepwise logistic regression. The model’s predictive performance and discrimination ability were evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: 56 (18.60%) patients developed a MDRO infection, including 37 (17.62%) in the training cohort and 19 (20.88%) in the validation cohort. Ultimately, five factors related to MDRO infection after LT surgery were established: ascites (OR = 3.48, 95% CI [1.33–9.14], p = 0.011), total bilirubin (OR = 1.01, 95% CI [1.01–1.01], p < 0.001), albumin (OR = 0.85, 95% CI [0.75–0.96], p = 0.010), history of preoperative ICU stay (OR = 1.09, 95% CI [1.01–1.17], p = 0.009), and length of ICU stay (OR = 3.70, 95% CI [1.39–9.84], p = 0.019). The model demonstrated strong discrimination, and the area under the curve (AUC), sensitivity, and specificity of the training set were 0.88 (95% CI [0.81–0.94]), 0.82 (95% CI [0.76–0.87]), and 0.86 (95% CI [0.75–0.98]), respectively, while for the validation set, they were 0.77 (95% CI [0.65–0.90]), 0.76 (95% CI [0.67–0.86]), and 0.68 (95% CI [0.48–0.89]). The mean absolute error (MAE) in the validation cohort was 0.029, indicating a high accuracy. DCA showed a clinical benefit within a threshold probability range of 0.1 to 0.7. Conclusions: This study developed a clinically accessible nomogram to predict the risk of MDRO infection in LT recipients, enabling early risk stratification and the real-time assessment of infection risk based on the length of postoperative ICU stay. The model incorporates five easily obtainable clinical parameters (ascites, total bilirubin, albumin, preoperative ICU stay history, and length of ICU stay) and demonstrates strong predictive performance, facilitating the early identification of high-risk patients. Future research should focus on refining the model by incorporating additional clinical factors (e.g., immunosuppressive therapy adherence) and validating its generalizability in multicenter, large-sample cohorts to enhance its clinical utility. Full article

Background/Objectives: Food matching aligns food consumption and food composition data to quantify intakes of a food component or category. A systematic approach to food matching is required to obtain the highest quality match and, therefore, most accurately quantify the intake of the food component under investigation. This study aims to provide a tool to assess adherence with the Australian Dietary Guideline food group recommendations by the development of a food matching method that links dietary intake data from a food frequency questionnaire to food group data in the Australian Dietary Guideline database. Methods: Two researchers trained in food composition independently applied a stepwise approach to link the Dietary Questionnaire for Epidemiological Studies Version 2 food frequency questionnaire and the Australian Dietary Guideline database. Food preparation methods, mixed dishes and Australian Dietary Guideline database representative foods were considered to ensure the highest quality result. Average values were calculated for foods for which multiple items were matched. Results: The Australian Dietary Guideline Adherence Tool (ADG-AT) was produced, providing the number of servings of the five Australian Dietary Guideline food groups and discretionary foods per 100 g of food for 5742 food items. Conclusions: The ADG-AT produced in this study allows convenient evaluation of Australian Dietary Guideline adherence in studies using the Dietary Questionnaire for Epidemiological Studies food frequency questionnaire to collect dietary intake data. This informs the identification of dietary risk factors for nutritional inadequacy and chronic disease. The systematic methods used in this study can be reapplied to different dietary intake collection tools and food composition databases. Full article

With the development of urbanization and industrialization worldwide, soil heavy metal pollution has become a critical and pressing environmental problem in urban areas. Soil heavy metals exhibit complex and varying spatial aggregation and diffusion processes within diverse urban landscapes, especially in different urban areas with varying urbanization levels. However, many existing experimental methods and conventional models overlook the crucial aspects of spatial autocorrelation and heterogeneity between soil heavy metals and influencing factors. This neglect poses significant environmental concerns, as rapid monitoring of soil heavy metals and accurate identification of their determinants become imperative. This study investigated four environmentally sensitive and potentially harmful soil heavy metals, arsenic (As), cadmium (Cd), copper (Cu), and lead (Pb), in two urban areas in China with varying urbanization levels. Enshi (a prefecture-level city) and Wuhan (a provincial capital city) were selected for comparison of the spatially variable relationships between soil heavy metals and their influencing factors. We employed a global stepwise linear regression (STR) model and a local spatial model-geographically weighted regression (GWR) to map the spatial distribution of soil heavy metals based on 121 auxiliary variables, including terrain, geophysical, socioeconomic factors, and remote sensing data. Our results showed that: (1) soil heavy metals exhibited strong spatial aggregation in the prefecture-level city (Enshi) but, nonetheless, have strong spatial heterogeneity in the provincial capital city (Wuhan) due to elevated anthropogenic disturbances; (2) GWR accurately mapped the spatial distributions of As (r = 0.47 and 0.66), Cd (r = 0.74 and 0.53), Cu (r = 0.60 and 0.54), and Pb (r = 0.44 and 0.50) based on auxiliary variables in different cities and also can clearly reveal the spatially variable relationships with main influence factors; (3) human activities were the primary driving factors influencing As and Pb, while natural environment variables were identified as the main potential sources of Cd and Cu. This study demonstrates a methodology to explore spatially variable characteristics of soil heavy metals and their spatial varying relationships with influence factors. The comparative analysis between two cities provides insights that can greatly enhance quantitative source apportionment and support sustainable management strategies for controlling soil heavy metal pollution across varied urban environments. Full article

Background: Acute kidney disease (AKD) is a recent definition reflecting ongoing physiopathological processes of an acute renal injury (AKI). Information on AKD in hematopoietic stem cell transplant (HSCT) is scarce and there is no available data on long-term outcomes. We aimed to determine the cumulative incidence of AKD in the first 100 days after HSCT; to identify risk factors for AKD in HSCT; and to determine the impact of AKD in 3-year overall survival and relapse-free survival in HSCT. Methods: A retrospective cohort study was conducted, considering AKD when AKI was present and the patient continued to meet the KDIGO criteria (creatinine and/or urinary output criteria) for 7 days or more. Survival analysis methods considering competing events were used for risk factors and disease-free survival, Cox proportional regression for overall survival, and stepwise regression methods for multivariable models. Results: We enrolled 422 patients. AKD incidence was 22.9% (95% CI: 19.2–27.4%). Higher body mass index (HR: 1.05, 95% CI 1.01–1.10; p = 0.034), HCT-CI score ≥ 2 (HR: 1.83, 95% CI 1.11–3.13; p = 0.027), allogeneic transplantation (HR:2.03, 95% CI 1.26–3.33; p = 0.004), higher C-reactive protein (HR:1.01, 95% CI 1.01–1.02; p < 0.001), and exposure to nephrotoxic drugs (HR: 4.81, 95% CI 1.54–4.95; p = 0.038) were independently associated with AKD. AKD had a significant impact on overall survival (HR: 1.75; 95% CI 1.27–2.39; p = 0.001). Conclusion: An awareness of the risk factors for AKD allows the identification of high-risk patients, enabling the timely implementation of preventive measures to alleviate the progression and impact of the disease. Full article

Background/Objectives: The integration of microbiome and metabolome data could unveil profound insights into biological processes. However, widely used multi-omic data analyses often employ a stepwise mining approach, failing to harness the full potential of multi-omic datasets and leading to reduced detection accuracy. Synergistic analysis incorporating microbiome/metabolome data are essential for deeper understanding. Method: This study introduces a Coupled Matrix and Tensor Factorization (CMTF) framework for the joint analysis of microbiome and metabolome data, overcoming these limitations. Two CMTF frameworks were developed to factorize microbial taxa, functional pathways, and metabolites into latent factors, facilitating dimension reduction and biomarker identification. Validation was conducted using three diverse microbiome/metabolome datasets, including built environments and human gut samples from inflammatory bowel disease (IBD) and COVID-19 studies. Results: Our results revealed biologically meaningful biomarkers, such as Bacteroides vulgatus and acylcarnitines associated with IBD and pyroglutamic acid and p-cresol associated with COVID-19 outcomes, which provide new avenues for research. The CMTF framework consistently outperformed traditional methods in both dimension reduction and biomarker detection, offering a robust tool for uncovering biologically relevant insights. Conclusions: Despite its stringent data requirements, including the reliance on stratified microbial-based pathway abundances and taxa-level contributions, this approach provides a significant step forward in multi-omics integration and analysis, with potential applications across biomedical, environmental, and agricultural research. Full article

Mustard is an important cash crop of the genus Brassica in the family Cruciferae. Low temperature is an important environmental factor limiting the growth of mustard. In this study, 65 leafy mustard cultivars were used as experimental materials, 25 °C was set as the control temperature, and 5 °C was set as chilling stress temperature to investigated the physiological response of chlorophyll (Chl) content, soluble sugar (SS) content, proline (Pro) content, antioxidant enzyme activity, malondialdehyde (MDA) content, and chlorophyll fluorescence to chilling injury. The chilling tolerance coefficients of each individual index were measured and correlation analysis, principal component analysis (PCA), the membership function method, and cluster analysis were applied to evaluate chilling tolerance. In a comprehensive analysis, the most chilling-tolerant cultivar was SJTKJ, the least chilling-tolerant cultivar was DX. Stepwise regression was used to establish a mathematical model for evaluating the chilling tolerance of mustard, and four chilling tolerance identification indices, including Fv/Fm, ΦPSII, POD activity, and Rfd were screened. This study provides a reference for the evaluation of the chilling tolerance of mustard and the breeding of new chilling-tolerant cultivars. Full article