Single Index Research Articles

Pollution risk assessment and spatial distribution of potentially hazardous elements in selenium-rich soils in the Menyuan Basin of the northeastern Tibetan Plateau

The scientific utilization of selenium (Se)-rich soils necessitates the evaluation of the contents of potentially hazardous elements (PHEs). The Menyuan Basin, a representative Se-rich region located on the northern periphery of the Qinghai–Tibet Plateau, was chosen as the research area. Pollution risk and spatial source analysis was conducted for eight PHEs (As, Cr, Cu, Pb, Zn, Cd, Ni and Hg) using 5297 topsoil samples and 480 deep-soil samples. The results show that more than 50% of the total area has a Se content >0.24 mg kg –1 . According to both single and comprehensive pollution indices, there are extremely few sampling sites with PHEs at risk of contamination. Low ecological risk was seen in 93.34% of the topsoil samples. The results show that although the overall level of surface soil pollution in the study area may be deemed negligible, on the surface or in deeper layers, the concentration centres of As, Cr, Ni, Zn, Pb and other elements show consistency, indicating that underlying parent rocks are the primary source of these elements. The findings presented in this study establish a scientific foundation for the secure development and utilization of Se-rich soils.

Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device

Background/Objectives: There have been attempts to detect depression using medical-grade electroencephalograph (EEG) data based on a machine learning approach. EEG has garnered interest as a method for assessing brainwaves by attaching electrodes to the scalp to obtain electrical activity in the brain. Recently, machine learning has been applied to the EEG data to detect depression, with encouraging results. Specifically, studies using medical-grade EEG data have shown that depression can be accurately detected. However, there is a need to expand the range of applications by achieving a score with machine learning using simpler consumer-grade brain wave sensors. At present, a sufficient score has not been achieved.; Methods: To improve the score of depression detection, we quantified various EEG indices to train models such as power spectrum, asymmetry, complexity, and functional connectivity. In addition, feature selection was performed to ensure that the model learns only promising EEG indices for depression detection. The feature selection methods were Light Gradient Boosting Machine (LightGBM) feature importance, mutual information, ReliefF and ElasticNet coefficients. The selected EEG indices were learned by the LightGBM model, which is reported to be as accurate as the latest deep learning models. In cross-validation, the independence of test and training data was ensured to avoid excessively calculated score; Results: The results showed that the Macro F1 score was 91.59%, suggesting that a consumer-grade EEG can detect depression. In addition, analysis of the EEG indices selected by feature selection indicated that the Macro F1 score was about 80% for single EEG indices such as differential entropy in the frequency band β and functional connectivity in the left frontal region in the frequency band 1–128 Hz; Conclusions: Although the data were obtained from a consumer-grade EEG, the results suggest that these EEG indices are promising for detection depression.

Monitoring the level of health and inequality over time using the Population Health Performance Index

Abstract Background The Population Health Performance Index (PHPI) is a composite measure that combines average health outcomes and their distribution within a population, thereby allowing for a comprehensive assessment of population health and inequality. The aim of this study is to examine the extent to which the PHPI adds value to monitoring of health and inequality over time in comparison to absolute and relative inequality. Methods Data from the WHO Equity Monitor database on under-5 mortality disaggregated by wealth quintiles for Nepal, Ethiopia, Peru, Rwanda, and Zimbabwe were analysed using different years between 1994 and 2020. Descriptive comparative statistics were employed to assess trends in the PHPI and conventional pairwise measures of absolute and relative inequalities, with consideration of different weights for inequality aversion. Results PHPI trends correspond to the trend of absolute inequality in all five countries, although they exhibit less volatility over time. Relative inequality follows the trend of the PHPI in only two of the five countries (Ethiopia and Zimbabwe), reflecting the methodology utilised for the PHPI. PHPI scores vary based on different inequality aversion weights, highlighting the sensitivity of the index to methodological adjustments. Conclusions The PHPI demonstrates notable convergence of trends with conventional methods, thereby substantiating its utility as a comprehensive tool for the simultaneous measurement of health and inequality. As it is able to consider both absolute and relative inequality within its own calculations, the PHPI allows for independent analysis of its two key components. The PHPI facilitates a more nuanced understanding of population health and may be more easily understood by policymakers and other stakeholders. As it does not address the underlying causes of changes in health inequalities, further research is necessary to supplement monitoring activities. Key messages • PHPI is a valuable tool for health and inequality monitoring by integrating both average health and disparity in a single index, facilitating communication of trends over time. • The dual focus complements conventional methods with its multidimensionality and may enable stakeholders to assess health and inequalities in alignment with broader health policy objectives.

Road Surface Texture Evaluation and Relation to Low-Speed Skid Resistance for Different Types of Mixtures

Pavement skid resistance is significant for driving safety. British Pendulum Number (BPN) is commonly used as a low-speed skid resistance indicator, whereas sometimes it is impractical for data collection on roads in service. Since skid resistance is greatly affected by pavement surface texture, this research aims to evaluate pavement surface texture comprehensively and estimate the low-speed friction BPN from road surface texture on macro- and micro- scale. Asphalt Concrete (AC) and Stone Mastic Asphalt (SMA) were included. Road surface texture was evaluated from four aspects, texture depth, amplitude-related Root Means Square (RMS), elevation variances corresponding to different wavebands and texture spectral analysis. Texture depth indicators include Mean Texture Depth (MTD) and Mean Profile Depth (MPD). Elevation variances with three wavebands, from 5 mm to 50 mm, from 0.5 mm to 5 mm and from 0.024 mm to 0.5 mm respectively, were obtained. The results show that MPD is well correlated with MTD. Elevation variances with different wavebands demonstrates that the elevation variance of macro-texture with long wavelengths from 5 mm to 50 mm dominates the total variance. Spectral analysis shows that texture level is larger when the wavelength is beyond 4 mm, which is consistent with elevation variances. A linear regression between BPN and single texture index, as well as multiple linear regression analysis were conducted. The former regression result indicates that it is not feasible to estimate BPN using single index due to low correlation coefficient R2. The latter shows that the BPN can be estimated from texture levels corresponding to 64 mm and 2 mm, and the micro-texture. The R2 can be up to 0.684. This research will contribute to fast acquisition of BPN from pavement surface texture, thus improving skid resistance.

Respiratory rate-oxygenation index on the 3rd day is the best predictor of treatment failure in COVID-19 patients.

Predictors of outcomes are essential to identifying severe COVID-19 cases and optimizing treatment and care settings. The respiratory rate-oxygenation (ROX) index, originally introduced for predicting the failure of non-invasive support in acute hypoxemic respiratory failure (AHRF), has not been extensively studied over time during hospitalization. This multicenter prospective observational study analyzed COVID-19-related AHRF patients admitted to eight Italian hospitals during the second pandemic wave. The study assessed the ROX index using receiver operator characteristic curves and areas under the curve with 95% confidence intervals to predict treatment failure, defined as endotracheal intubation (ETI) or death. A total of 227 patients (69.2% males) were enrolled, with a median arterial partial pressure of oxygen (PaO2)/fraction of inspired oxygen (FiO2) ratio at admission of 248 (interquartile range: 170-295). Nearly one-third (29.5%) required ETI or died during hospitalization. Those who experienced treatment failure were older (median age 70 versus 61 years, p<0.001), more likely to be current or former smokers (8.5% versus 6.4% and 42.4% versus 25.5%, p=0.039), had a higher prevalence of cardiovascular diseases (74.6% versus 46.3%, p<0.001), and had a lower PaO2/FiO2 ratio at presentation (median 229 versus 254, p=0.014). Gender, body mass index, and other comorbidities showed no significant differences. In patients who failed treatment, the ROX index was higher at presentation and worsened sharply by days 3 and 4. Conversely, in patients who survived without requiring ETI, the ROX index remained stable and reduced after 5-6 days. The ROX index's predictive ability improved notably by the third day of hospitalization, with the best cut-off value identified at 8.53 (sensitivity 75%, specificity 68%). Kaplan-Meier curves indicated that a ROX index of 8.53 or lower on days 1, 2, or 3 was associated with a higher risk of treatment failure. Thus, a single ROX index assessment on day 3 is more informative than its variability over time, with values of 8.53 or lower predicting non-invasive respiratory support failure in hospitalized COVID-19 patients.

TreeCompR: Tree competition indices for inventory data and 3D point clouds

Abstract In times of more frequent global change‐type droughts and associated tree mortality events, competition release is one silvicultural measure discussed to have an impact on the resilience of managed forest stands. Understanding how trees compete with each other is therefore crucial, but different measurement options and competition indices (CI) leave users with a difficult choice, as no single competition index has proven universally superior. To help users with the choice and computation of appropriate indices, we present the open‐source TreeCompR package, which handles 3D point clouds and classical forest inventory data, enabling the calculation of both innovative point cloud‐based indices and traditional distance‐dependent indices. It serves as a centralized platform for exploring and comparing different CIs, allowing users to test and select the most suitable CI for their specific research questions within a common interface. To evaluate the package, we used TreeCompR to quantify the competition situation of 307 European beech trees from 13 sites in Central Europe. Based on this dataset, we discuss the interpretation, comparability and sensitivity of the different indices to their parameterization and identify possible sources of uncertainty and ways to minimize them. The compatibility of TreeCompR with different data formats and different data collection methods makes it accessible and useful for a wide range of users, specifically ecologists and foresters. Due to the flexibility in the choice of input formats as well as the emphasis on tidy, well‐structured output, our package can easily be integrated into existing data‐analysis workflows both for 3D point cloud and classical forest inventory data.

Exploring Genetic Variability and Diversity in Exotic Germplasm Collection of Cocoa (Theobroma cacao L.)

The current research investigates the genetic variation and diversity in exotic accessions of cocoa to identify materials with desired characteristics as well as important traits contributing to yield and variability. The experiment was conducted at Cocoa Research Centre, Kerala Agricultural University, from 2021 to 2023. The study comprises 23 exotic genotypes in which observations were recorded on quantitative traits on flowers, pods, and beans. Statistical analysis was carried out based on the observations recorded. The morphological evaluation revealed ample variability among the genotypes for all the quantitative characters studied. The average dry bean weight recorded was 1.08 grams, which is a desirable trait in selecting individuals with high processing efficiency for the chocolate industry. Characters such as single bean dry weight, pod value, pod index, and efficiency index have recorded high values of GCV, heritability and genetic advance as percent of the mean. Principal component (PC) analysis showed that the first three PCs with more than one Eigen-value contributed to 92.18 percent of total variability among the germplasm. Yield components viz. pod value, total bean weight pod-1 and single bean dry weight significantly contribute to maximum variability among the exotic germplasm. Moreover, genotypes were grouped into five clusters based on hierarchical agglomerative clustering technique. Hybridization program involving superior genotypes identified in diverse clusters as parents can produce high-yielding hybrids. The high values recorded for the genetic parameters by yield components suggest the selection of genotypes based on which can result in better-performing progeny.

Ecological and Human Health Risk Assessment Based on Stream Sediments from Coastal Oecusse (Timor)

Timor Island is located in a geologically complex region strongly affected by the collision of the Australian margin with the Banda volcanic arc. In Oecusse, an enclave of East Timor in the western part of Timor, crop out several lithological units of the Banda Terrane that are associated with the obduction of oceanic crust and upper mantle on the Australian continental crust. This study reports the geochemistry of stream sediments from the coastal region of the Oecusse enclave, where the Banda Terrane is best represented, employing statistical analyses to discern the sources of metal(oid)s and assessing ecological and health risks. Arsenic, Cr, and Ni are the elements with higher potential ecological risk factors. The potential ecological risk index (PERI), which combines single indexes of ecological risk factors for multiple elements, is very high in a stream sourced by the Lolotoi-Mutis Complex. Significant risks for human health were found for As (sourced by the Lolotoi-Multis Metamorphic Complex and basalts of the Barique Formation), Mn (sourced by the Maubisse and Barique formations), and V (sourced by the Manamas Formation). The highest values of hazard index (HI), however, were determined with Cr, in particular for children (HI higher than 10 in 12 sediments sourced by ultramafic units and their covering sedimentary units). This investigation shows that high geogenic concentrations of several elements, particularly those derived from the oceanic crust and the upper mantle, raise significant cancer and non-carcinogenic risks.

Evaluation of Urbanization Influences on Beach Sediment Contamination with Heavy Metals Along the Littoral Zone of Alexandria City, Egypt

The western coastal area of Alexandria City, Egypt, faces significant environmental challenges due to heavy metal (HM) contamination in beach sediments, driven by intensive urbanization, tourism, commercial harbors, and industrial activities. Therefore, this study focuses on geochemically assessing HM levels in sediment samples from 28 sites and employed various descriptive and multivariate statistical approaches, pollution indices, and sediment quality guidelines (SQGs) to identify pollution hotspots, define contamination grades, and assess the quality of beach sediments. The average concentrations of Cd, Co, Cr, Cu, Pb, and Zn were 25.7, 30, 2.2, 7.5, 2.9, and 8.0 times, respectively, compared to the geochemical background (carbonate sedimentary rocks). et al.-Hanuvil Beach, relatively elevated contents of these metals were recorded. In addition, samples showed Zn levels higher than the corresponding probable effect concentration (PEC) and effect range medium (ERM) values, suggesting adverse impacts on biota. The principal component (PC) analysis revealed the anthropogenic origin of Cd, Co, and Pb in PC1, the mixed origin of Cr, Cu, Mn, and Zn in PC2, and the natural source of Fe in PC3. According to the applied single pollution indices, HM contamination grades were ranked in ascending order of Mn < Fe < Cr < Cd < Cu < Pb < Zn < Co. In conclusion, the HMs (Cu, Zn, Cd, Co, Pb, and Cr) in the study area are anthropogenic, attributed mainly to untreated discharge of municipal and industrial wastewater and solid wastes, atmospheric deposition of air pollutants, and tourism activities.

Long-Term Stable and Multifeature Microfluidic Impedance Flow Cytometry Based on a Constricted Channel for Single-Cell Mechanical Phenotyping.

The microfluidic impedance flow cytometer (m-IFC) using constricted microchannels is an appealing choice for the high-throughput measurement of single-cell mechanical properties. However, channels smaller than the cells are susceptible to irreversible blockage, extremely affecting the stability of the system and the throughput. Meanwhile, the common practice of extracting a single quantitative index, i.e., total cell passage time, through the constricted part is inadequate to decipher the complex mechanical properties of individual cells. Herein, this study presents a long-term stable and multifeature m-IFC based on a constricted channel for single-cell mechanical phenotyping. The blockage problem is effectively overcome by adding tiny xanthan gum (XG) polymers. The cells can pass through the constricted channel at a flow rate of 500 μL/h without clogging, exhibiting high throughput (∼240 samples per second) and long-term stability (∼2 h). Moreover, six detection regions were implemented to capture the multiple features related to the whole process of a single cell passing through the long-constricted channel, e.g., creep, friction, and relaxation stages. To verify the performance of the multifeature m-IFC, cells treated with perturbations of microtubules and microfilaments within the cytoskeleton were detected, respectively. It suggests that the extracted features provide more comprehensive clues for single-cell analysis in structural and mechanical transformation. Overall, our proposed multifeature m-IFC exhibits the advantages of nonclogging and high throughput, which can be extended to other cell types for nondestructive and real-time mechanical phenotyping in cost-effective applications.

Quality of life after stereotactic radiosurgery for brain metastasis: an assessment from a prospective national registry.

Stereotactic radiosurgery (SRS) is frequently used in the management of brain metastasis patients. However, there is an urgent need to evaluate post-treatment outcomes and quality of life metrics for patients undergoing SRS for brain metastases. The NeuroPoint Alliance (NPA) SRS Quality Registry conducted prospective enrollment of patients undergoing SRS from 2017 to 2024. Patients with brain metastases from lung cancer, breast cancer, and melanoma were included in the analysis. Outcomes of interest included quality of life metrics, as captured by the five-dimension Euro-QOL (EQ-5D) at 6-12 months and last record follow-up, overall survival, local progression, out-of-field progression, and overall intracranial progression. 522 patients comprised our analytic cohort, and 315 patients had available EQ-5D data at the time of SRS and final follow-up. 264 (47.8%), 197 (35.7%), and 91 (16.5%) patients had 1, 2-4, and 5-14 lesions pre-SRS, respectively. The median overall survival time from diagnosis was 27.3 months. The median time-to-local progression was not reached. At final follow-up, 107 (34.0%) patients had improvement, 51 (16.2%) patients had stable, and 113 patients (35.9%) had worsening EQ-5D scores when compared to baseline. For 44 (13.9%) patients mixed responses across the EQ-5D indices were reported. Linear regression analysis showed that male sex, smoking status, primary tumor type, time-to-overall progression, cumulative intracranial tumor volume (CITV), and baseline EQ-5D were statistically significantly associated with EQ-5D single index at the final follow-up. Real-world data from the SRS NPA Registry demonstrated that most patients with brain metastasis had no change or improvement in quality of life after SRS. Baseline EQ-5D was predictive of EQ-5D single index at final follow-up, and, as such, EQ-5D at baseline would be a valuable assessment measure for brain metastasis patients undergoing SRS.

Rolling bearing fault diagnosis method based on SSA-IWT-EMD

Aiming at the problem of insufficient wavelet threshold noise reduction and unclear extraction of characteristic frequency of EMD decomposition, a rolling bearing fault diagnosis method based on SSA-IWT-EMD is proposed. Two adjustment factors are introduced to propose an improved threshold function (IWT), which overcomes the shortcomings of traditional soft and hard thresholds, and the sparrow search optimization algorithm (SSA) is used to globally optimize its parameters to achieve rolling bearing signal noise reduction. A comprehensive index P is proposed to select and reconstruct the components generated by empirical mode decomposition (EMD) to highlight the fault characteristic information of the signal. Envelope spectrum analysis is used to realize bearing fault diagnosis. Simulation and measurement results verify the effectiveness of the proposed method; at the same time, compared with the method of selecting components with a single index and the literature method, it is shown that the comprehensive index P and the proposed method have stronger noise reduction and feature extraction capabilities, and the envelope spectrum amplitude and frequency doubling components are more obvious, which can better realize the fault diagnosis of rolling bearings.

Combined detection of serum adiponectin and pregnancy-associated plasma protein A for early prediction of gestational diabetes mellitus.

Early diagnosis of gestational diabetes mellitus (GDM) reduces the risk of adverse perinatal and maternal outcomes. At present, the value of serum adiponectin (ADP) and pregnancy-associated plasma protein A (PAPP-A) in clinical practice for the diagnosis of GDM in early pregnancy is unclear. To investigate the predictive value of serum ADP and PAPP-A in GDM. The electronic medical record data of all pregnant women from Zhongshan People's Hospital from 2018 to 2021 were retrospectively collected and divided into GDM group and control group according to whether GDM occurred. ADP and PAPP-A levels of the 2 groups were detected in early pregnancy, and the related factors of GDM were analyzed by binary logistic regression analysis. Receiver operating characteristic (ROC) curves of ADP and PAPP-A in predicting GDM in the early pregnancy were plotted and their clinical predictive value was analyzed. The significance level for all statistical tests is 0.05. Compared with the non-GDM group, the ADP of the GDM group was significantly lower than that of the non-GDM group [(8.19 ± 2.24) vs. (10.04 ± 2.73)]mg/L, the difference between groups was statistically significant (P < .05), and the multiple of median (MoM) of PAPP-A was significantly lower than that of the non-GDM group (1.13 ± 0.52) versus (1.45 ± 0.61) (P < .05). Binary logistic regression analysis showed that elevated serum ADP and PAPP-A levels were negatively correlated with the subsequent development of GDM [odds ratio (OR) 95% confidence interval (95% CI)] was 0.626 (0.536, 0.816), 0.934 (0.908, 0.961), respectively, P < .05.ROC curve analysis showed that the sensitivity and specificity of ADP and PAPP-A in predicting gestational diabetes were79.1% and 58.6%, respectively, 92.7% and 73.1%, and respectively. The area under curve (AUC) is 0.755 for ADP and 0.770 for PAPP-A. The AUC of the combined detection was 0.867, both of which were higher than that of single index diagnosis, and the sensitivity and specificity of the combined detection were 0.958 and 0.853, respectively. In summary, PAPP-A and ADP levels are independent related factors affecting the occurrence of GDM. The combined detection of PAPP-A and ADP should be utilized in diagnosing GDM to improve pregnancy outcomes for pregnant women.

Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems.

ANALYSIS OF PORTFOLIO FORMATION ON THE LQ45 STOCKS INDEX, USING THE MARKOWITZ AND SINGLE INDEX MODELS

In an investment, there will always be a return and risk, especially in the capital market in the form of stocks. The risk in an investment can be minimized by diversifying assets into several stocks to form a portfolio formation. Several models, such as the Single Index and Markowitz, can evaluate optimal portfolio formation. In this study we provide additional information and discourse on capital market studies and as the input for investors in making investment decisions in the form of stocks. The study shows that based on 25 companies, the Markowitz model gives 12 companies as the optimal portfolio with the largest proportion of funds owned by PT Bank Central Asia Tbk (BBCA), 82.22%. The portfolio of those 12 stocks can provide an expected return of 44.8% where its risk is about 13.77%. The Single Index model provides a formation based on 9 companies as the optimal portfolio with the largest proportion of funds owned by -again- PT Bank Central Asia Tbk (BBCA) which is 66.23%. The portfolio of these nine stocks can provide the expected return of 1.68% and its risk is 0.43%. The ratio of risk and return from each model justifies that the Single Index model gives better portfolio formation. This result should be further compared with other stock indexes, nationally and globally, and also needs to be compared with the period after the pandemic.

Measurement and evaluation of participant response consistency in Case 1 Best-Worst-Scaling (BWS) in food consumer science

Best-Worst Scaling (BWS) is well suited to survey research, and since this way of collecting data is gaining popularity, it is likely that applications of BWS, especially online, will increase. Despite the many advantages of online surveys, there is a growing awareness that particular attention needs to be given to data quality including the identification and elimination of ’bad respondents’. Post hoc data cleaning is the focus of the present research, which in the context of Case 1 BWS (object case) was directed to two indices of participant response consistency – root likelihood (RLH) and normalised error variance (ErrVarNorm), where the latter was newly developed. Across 18 studies in food consumer science, the two indices are applied, compared and evaluated. It is possible to apply both indices to the data, but if only a single index is to be used, the ErrVarNorm index is recommended because it is easy to calculate, directly measures response consistency and is logically coherent. ErrVarNorm ranges from 0 to 1, where higher values indicate greater response consistency. When excluding participants based on ErrVarNorm < 0.3, between 0 % and 12.4 % of participants were excluded (mean = 5.75 %), while ErrVarNorm < 0.5 led to between 0 % and 23.8 % of participants being excluded (mean = 13.1 %). Excluded participants were more likely to be men and below the age of 45 years old. The impact on study conclusions when excluding participants based on ErrVarNorm < 0.5 were illustrated for three studies in support of the importance of data cleaning.

Cost-effectiveness of a patient-reported outcome-based remote monitoring and alert intervention for early detection of critical recovery after joint replacement: A randomised controlled trial.

While the effectiveness of patient-reported outcome measures (PROMs) as an intervention to impact patient pathways has been established for cancer care, it is unknown for other indications. We assessed the cost-effectiveness of a PROM-based monitoring and alert intervention for early detection of critical recovery paths following hip and knee replacement. The cost-effectiveness analysis (CEA) is based on a multicentre randomised controlled trial encompassing 3,697 patients with hip replacement and 3,110 patients with knee replacement enrolled from 2019 to 2020 in 9 German hospitals. The analysis was conducted with a subset of 546 hip and 492 knee replacement cases with longitudinal cost data from 24 statutory health insurances. Patients were randomised 1:1 to a PROM-based remote monitoring and alert intervention or to a standard care group. All patients were assessed at 12-months post-surgery via digitally collected PROMs. Patients within the intervention group were additionally assessed at 1-, 3-, and 6-months post-surgery to be contacted in case of critical recovery paths. For the effect evaluation, a PROM-based composite measure (PRO-CM) was developed, combining changes across various PROMs in a single index ranging from 0 to 100. The PRO-CM included 6 PROMs focused on quality of life and various aspects of physical and mental health. The primary outcome was the incremental cost-effectiveness ratio (ICER). The intervention group showed incremental outcomes of 2.54 units PRO-CM (95% confidence interval (CI) [0.93, 4.14]; p = 0.002) for patients with hip and 0.87 (95% CI [-0.94, 2.67]; p = 0.347) for patients with knee replacement. Within the 12-months post-surgery period the intervention group had less costs of 376.43€ (95% CI [-639.74, -113.12]; p = 0.005) in patients with hip, and 375.50€ (95% CI [-767.40, 16.39]; p = 0.060) in patients with knee replacement, revealing a dominant ICER for both procedures. However, it remains unclear which step of the multistage intervention contributes most to the positive effect. The intervention significantly improved patient outcomes at lower costs in patients with hip replacements when compared with standard care. Further it showed a nonsignificant cost reduction in knee replacement patients. This reinforces the notion that PROMs can be utilised as a cost-effective instrument for remote monitoring in standard care settings. Registration: German Register for Clinical Studies (DRKS) under DRKS00019916.

Pollution Characteristics, Risk Assessment, and Source Analysis of Heavy Metals in Soil from a Typical Abandoned Antimony Smelting Factory

Considering the surface soil （0-20 cm） from a typical abandoned antimony smelting factory area in Dachang Town, Qinglong County, Guizhou Province, as a case study, a total of 14 soil samples were systematically collected from both within and outside the smelting factory area. The analysis focused on the pollution status, distribution characteristics, and potential ecological risks of heavy metals such as Sb, As, Cd, Cr, Pb, Cu, Zn, Ni, and V in the soil. Additionally, an evaluation and analysis of pollution sources were conducted. The results showed that the mean concentrations of heavy metals including ω（Sb）, ω（As）, ω（Cd）, ω（Cr）, ω（Pb）, ω（Cu）, ω（Zn）, ω（Ni）, and ω（V） in the surface soil of the abandoned antimony smelting factory ranged from 4.58 to 15 049.33 mg·kg-1. With the exception of Cr and Ni, all values exceeded the background values of soils in Guizhou province. The single factor pollution indices of Sb and As were 83.61 and 7.01, respectively, indicating severe contamination. In contrast, Pb fell within the non-polluted to slightly polluted range. The comprehensive potential ecological risk of soil heavy metals was characterized by severe potential ecological risk levels for Sb, As, and Cd, while the remaining heavy metals fell within a range of moderate to substantial potential ecological risk levels. The assessment of the geoaccumulation index revealed that the soil in the study area was primarily contaminated by Sb and As, predominantly exhibiting contamination levels ranging from moderate to severe. The results from the RAC method suggested that Sb was the dominant focus for remediation in this abandoned smelting factory. The two primary pollutants, Sb and As, exhibited elevated levels in leachate toxicity, acid-soluble fraction, available fraction, gastric phase, and intestinal phase in terms of bioavailable content, indicating a certain potential hazard. Further, correlation analysis indicated a certain correlation between the total amount of heavy metals and leachate toxicity, available fraction, acid-soluble fraction, reducible fraction, oxidizable fraction, gastric phase extractable fraction, and intestinal phase extractable fraction. The APCS-MLR model indicated that the sources of Sb, As, Zn, Cu, and Cd were primarily industrial, while the sources of Cr and V were mainly natural, and Pb originated mainly from mixed sources.

Correlation analysis of FT3/FT4 and frailty in elderly patients with coronary heart disease.

In previous studies on thyroid hormones and frailty, most of the target population were elderly, and there were relatively few studies on elderly patients with coronary heart disease (CHD). Inflammation, oxidative stress, and haemodynamic instability in cardiovascular disease (CVD) can influence fluctuations in thyroid hormone (TH) levels and increase the incidence of frailty. The purpose of the present study was to explore the effect of TH on the risk of frailty in elderly patients with CHD. The Fried scale was used to assess the frailty of participants. The predictive value of TH for frailty was determined using the patient's operating characteristic curve. Multivariate logistic regression model was utilised to analyse the relationship between TH and frailty. A total of 277 elderly patients with CHD were included in the study, of whom 29.96% were in a state of frailty. Free triiodothyronine (FT3)/free thyroxine (FT4) predicted frailty with the largest area under the curve of 0.634. Unordered multinomial logistic regression analysis showed that a lower T3 level was a risk factor for pre-frailty (p < 0.05). Lower levels of T3, FT3, and FT3/FT4 were risk factors for frailty (p < 0.05) after adjusting for demographic variables and blood indexes. The predictive value of FT3/FT4 for frailty was more accurate than that of a single index. Moreover, T3 ≤ 1.095 nmol/L, FT3 ≤ 4.085 pmol/L, and FT3/FT4 ≤ 0.336 were shown to be the influencing factors of frailty, while T3 ≤ 1.095 nmol/L is an independent risk factor pre-frailty. Clinicians should focus on timely identification of the risk of frailty in order to improve patient quality of life and to reduce the risk of complications.

Phenotypic and Genomic Characterization of a Sulfate-Reducing Bacterium Pseudodesulfovibrio methanolicus sp. nov. Isolated from a Petroleum Reservoir in Russia.

The search for the microorganisms responsible for sulfide formation and corrosion of steel equipment in the oil fields of Tatarstan (Russia) resulted in the isolation of a new halotolerant strictly anaerobic sulfate-reducing bacterium, strain 5S69T. The cells were motile curved Gram-negative rods. Optimal growth was observed in the presence of 2.0-4.0% (w/v) NaCl, at pH 6.5, and at 23-28 °C under sulfate-reducing conditions. The isolate was capable of chemoorganotrophic growth with sulfate and other sulfoxides as electron acceptors, resulting in sulfide formation; and of pyruvate fermentation resulting in formation of H2 and acetate. The strain utilized lactate, pyruvate, ethanol, methanol, fumarate, and fructose, as well as H2/CO2/acetate for sulfate reduction. The genome size of the type strain 5S69T was 4.16 Mb with a G + C content of 63.0 mol%. On the basis of unique physiological properties and results of the 16S rRNA gene-based phylogenetic analysis, phylogenomic analysis of the 120 conserved single copy proteins and genomic indexes (ANI, AAI, and dDDH), assigning the type strain 5S69T ((VKM B-3653T = KCTC 25499T) to a new species within the genus Pseudodesulfovibrio, is suggested, with the proposed name Pseudodesulfovibrio methanolicus sp. nov. Genome analysis of the new isolate showed several genes involved in sulfate reduction and its sulfide-producing potential in oil fields with high saline formation water.