Original Index Research Articles

A calibration framework for outdoor thermal comfort index and neutral range: Application in subtropical hot-humid campus

Outdoor thermal comfort affects occupants’ well-being and building energy use. A reliable thermal index tailored to diverse climates is crucial for improving outdoor conditions. Current calibration methods for the neutral thermal range typically set a Thermal Sensation Vote threshold between −0.5 and 0.5. However, this threshold was derived from the indoor-focused Predicted Mean Vote model, which may not be applicable to outdoor conditions. To address this limitation, a revised calibration framework for outdoor scenarios is proposed, taking subtropical hot-humid campus as a case study. The key aspect of this framework is refining the neutral Thermal Sensation Vote interval for outdoor settings. First, an evaluation of the original outdoor thermal comfort index identified the necessity for recalibration. Next, the correlation between Thermal Comfort Vote and Thermal Sensation Vote was established to accurately localize context-specific neutral Thermal Sensation Vote interval. Finally, the proposed framework was applied to UTCI calibration, yielding a modified neutral UTCI range of 4.5–27.2°C through localized regression analysis. This study also highlights the impact of spatial configurations on outdoor thermal comfort, finding higher thermal tolerance within natural landscapes. It provides insights into creating outdoor thermal settings in subtropical hot-humid locations, especially for campus construction or renovation.

Signature of immune-related metabolic genes predicts the prognosis of hepatocellular carcinoma.

The majority of liver cancer cases (90%) are attributed to hepatocellular carcinoma (HCC), which exhibits significant heterogeneity and an unfavorable prognosis. Modulating the immune response and metabolic processes play a crucial role in both the prevention and treatment of HCC. However, there is still a lack of comprehensive understanding regarding the immune-related metabolic genes that can accurately reflect the prognosis of HCC. In order to address this issue, we developed a prognostic prediction model based on immune and metabolic genes. To evaluate the accuracy of our model, we performed survival analyses including Kaplan-Meier (K-M) curve and time-dependent receiver operating characteristic (ROC) curve. Furthermore, we compared the predictive performance of our risk model with existing models. Finally, we validated the accuracy of our risk model using mouse models with in situ transplanted liver cancer. By conducting lasso regression analysis, we identified four independent prognostic genes: fatty acid binding protein 6 (FABP6), phosphoribosyl pyrophosphate amidotransferase (PPAT), spermine synthase (SMS), and dihydrodiol dehydrogenase (DHDH). Based on these findings, we constructed a prognostic model. Survival analysis revealed that the high-risk group had significantly lower overall survival (OS) rates. Besides that, the ROC curve demonstrated the effective prognostic capability of our risk model for hepatocellular carcinoma (HCC) patients. Furthermore, through animal experiments, we validated the accuracy of our model by showing a correlation between high-risk scores and poor prognosis in tumor development. In conclusion, our prognostic model surpasses those solely based on immune genes or metabolic genes in terms of accuracy. We observed variations in prognosis among different risk groups, accompanied by distinct immune and metabolic characteristics. Therefore, our model provides an original evaluation index for personalized clinical treatment strategies targeting HCC patients.

Population pharmacokinetic analyses for sulbactam-durlobactam using Phase 1, 2, and 3 data.

Sulbactam-durlobactam is a β-lactam/β-lactamase inhibitor combination approved in the United States for the treatment of hospital-acquired and ventilator-associated bacterial pneumonia caused by susceptible isolates of Acinetobacter baumannii-calcoaceticus in adults. A population pharmacokinetic (PK) model of sulbactam-durlobactam in plasma was developed using data from eight Phase 1-3 studies. A total of 432 subjects and 8,100 plasma concentrations were available for the population PK data set. The combined model was a four-compartment (two compartments per drug) model with linear kinetics. Both renal clearance and nonrenal clearance were estimated, and total clearance was calculated as the sum of renal and nonrenal clearance. Individual renal clearances were scaled by baseline creatinine clearance. The sampling-importance-resampling analysis indicated that the parameters were estimated reliably with adequate precision. Hemodialysis (HD) and epithelial lining fluid (ELF) sub-models were developed for each analyte separately. Intermittent HD resulted in an approximately 30% decrease in the daily area under the concentration-time curve (AUC0-24) when HD was started 1 hour after the end of the infusion. Assuming protein binding estimates of 10% and 38% for durlobactam and sulbactam, respectively, ELF penetration ratios were found to be 41.3% for durlobactam and 86.0% for sulbactam. Of the statistically significant covariates of PK identified, which included body weight, body mass index, infection type, and region of origin, renal function was the only clinically relevant covariate. Overall, a robust description of the plasma PK of sulbactam and durlobactam was achieved. The resultant population PK model was expected to be appropriate for model-based simulations and assessment of pharmacokinetic-pharmacodynamic relationships.

Analyzing Soil Pollution by Image Processing and Machine Learning at Contaminated Agricultural Field

Due to the fast advancement of big data, applying Machine Learning (ML) techniques to detect Soil Pollution (SP) at Potentially Contaminated Sites (PCS) across many sectors and regional sizes has emerged as a prominent research focus. The challenges in acquiring essential indices of SP sources and routes result in present methodologies exhibiting low predictive accuracy and an inadequate scientific foundation. This study gathered environmental data concerning heavy metal and organic contamination from 200 PCS across six representative sectors. Twenty-one indices derived from fundamental data, potential SP from products and materials, SP efficacy, and the migrating capability of SP were employed to build the SP detection index method. The research integrated the score into the new characteristic group, including 11 indicators using consolidation computation. The newly selected feature subset was utilized for training ML designs, including Random Forests (RF), Support Vector Machines (SVM), and Multilayer Perceptrons (MLP), and evaluated to ascertain its impact on SP recognition methods. The study findings indicated that the four newly developed indices by feature fusion exhibit an association with SP comparable to that of the original index. The component analysis suggests that several indices related to fundamental information, contamination potential from products and raw materials, and SP prevention levels significantly influence SP to varying extents. The index of the migratory capability of soil contaminants has minimal influence on the classification job of SP detection inside PCS. This research introduces a novel technological approach for identifying SP via big data and ML techniques while offering an overview and scientific foundation for PCS's environmental administration and SP mitigation.

Assessment of Inter-observer Reproducibility of the Residual Cancer Burden Index and Neoadjuvant Chemotherapy Response in Breast Carcinoma

Background : Locally advanced breast cancers are nowadays treated with neoadjuvant chemotherapy (NAC) to downstage the tumor. One way to assess the NAC response is to use “Residual cancer burden index (RCB).” The aim of the study was to assess inter-pathologist reproducibility of residual cancer burden index and to evaluate different clinico-pathological parameters that determine the pathological response. Methods: In this retrospective cohort study, surgically excised specimens of 49 NAC treated breast cancer were examined by histopathology and immunohistochemistry over a period of one and half years. Four pathologists with different level of experience reviewed the reports (unaware of original RCB index) and slides and assigned the RCB indices for each case. Clinical, histopathological and immunohistochemical parameters were evaluated.. Fleiss- Kappa statistics was applied to assess interobserver reproducibility of RCB index. Results: No significant relationship was observed between RCB index (of original report) with age, largest tumor dimension and number of chemotherapy cycles . The RCB index of 49 cases as assigned by four pathologists was calculated by Fleiss-kappa statistics and it showed good overall agreement (82.6%). Conclusion: It has been observed that there is no significant relation between pathological response to NAC with age, largest tumor dimension and number of chemotherapy cycles of breast carcinoma. It can also be concluded that RCB can be reliably used to report neoadjuvant chemotherapy treated specimens of breast cancer.

The Internal Socio-Economic Polarization of Urban Neighborhoods: The Case of the Municipality of Nice

In continuity with the research on social segregation and the phenomenon of urban gentrification, this article examines the cohabitation patterns of populations with diametrically opposed incomes within the same neighborhood, typically observed in the city center. This phenomenon is defined here as internal socio-economic polarization. It is measured through the combination of two original indexes (poverty and wealth indexes) constructed based on income deciles per consumption unit for the year of 2017. The analysis focuses on the municipality of Nice, characterized by a low demographic dynamic, a relative concentration of seniors, and a strong tourist attractiveness, particularly in the highly polarized neighborhoods that occupy almost the entire city center. This study is complemented by a principal component analysis summarizing the characteristics of the population and housing stock in the neighborhoods of Nice. The main objective of this research is to identify and locate polarized neighborhoods within the urban context of Nice, to analyze the distinctive traits of their population and housing stock, and, finally, to highlight potential trends in the population’s socio-economic status. Moreover, the economic trajectories of polarized neighborhoods, in connection with their population and housing characteristics (such as the secondary use of a portion of the housing stock, often low-quality old buildings, social housing, and the overrepresentation of retirees), help explain the forms of socio-economic polarization observed in these neighborhoods (such as the indications of gentrification, unfinished gentrification, and sustainable cohabitation).

Genomic epidemiology reveals the variation and transmission properties of SARS-CoV-2 in a single-source community outbreak.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the coronavirus disease 2019 (COVID-19) pandemic, which is still a global public health concern. During March 2022, a rapid and confined single-source outbreak of SARS-CoV-2 was identified in a community in Nanjing municipal city. Overall, 95 individuals had laboratory-confirmed SARS-CoV-2 infection. The whole genomes of 61 viral samples were obtained, which were all members of the BA.2.2 lineage and clearly demonstrated the presence of one large clade, and all the infections could be traced back to the original index case. The most distant sequence from the index case presented a difference of 4 SNPs, and 118 intrahost single-nucleotide variants (iSNVs) at 74 genomic sites were identified. Some minor iSNVs can be transmitted and subsequently rapidly fixed in the viral population. The minor iSNVs transmission resulted in at least two nucleotide substitutions among all seven SNPs identified in the outbreak, generating genetically diverse populations. We estimated the overall transmission bottleneck size to be 3 using 11 convincing donor-recipient transmission pairs. Our study provides new insights into genomic epidemiology and viral transmission, revealing how iSNVs become fixed in local clusters, followed by viral transmission across the community, which contributes to population diversity.

The effect of lipid-lowering treatment on indices of MASLD in familial hypercholesterolemia patients

Background & AimsThe effect of lipid-lowering treatment (LLT) on metabolic dysfunction associated steatotic liver disease (MASLD) is unclear. This is relevant for patients with familial hypercholesterolemia (FH) who are on lifelong LLT. We aimed to evaluate the effect of LLT on MASLD indices in this population. MethodsPatients with at least possible diagnosis of FH were included into the Hellenic FH Registry (HELLAS-FH) registry. We analyzed the effect of statin monotherapy, statin/ezetimibe and statin/ezetimibe/proprotein convertase subtilisin/kexin 9 inhibitors (PCSK9i) on MASLD indices, i.e., original triglyceride-glucose index (TyGO) and triglyceride-glucose index (TyG). We compared changes of TyG and TyGO before any treatment and after at least one year of stable LLT. ResultsWe included 1289 patients: n=569 in the statin monotherapy group (mean age=51±15 years, 52.7% males), n=629 in the statin/ezetimibe group (52±14 years, 51.8%), and n=91 in the statin/ezetimibe/PCSK9i group (54±13 years, 58.2%). Compared with baseline, TyGO and TyG decreased significantly following statin monotherapy (8.61 vs 8.49 and 4.65 vs 4.59, respectively, both p<0.01), statin/ezetimibe (8.59 vs 8.41 and 4.64 vs 4.55, respectively, both p<0.01) and statin/ezetimibe/PCSK9i (8.79 vs 8.55 and 4.74 vs 4.62, respectively, both p<0.01). There was no difference regarding the change of TyGO and TyG between groups after adjusting for baseline levels. A greater percentage of patients in the statin/ezetimibe and statin/ezetimibe/PCSK9i groups exhibited TyGO-defined MASLD resolution compared with statin monotherapy (p<0.05). After adjustment for possible confounders, LLT was significantly associated with MASLD resolution. ConclusionsMASLD indices were significantly improved in all LLT groups in FH patients. Statin/ezetimibe and statin/ezetimibe/PCSK9i were associated with greater TyGO-defined MASLD resolution compared with statin monotherapy.

FlowerMate: Multidimensional reciprocity and inaccuracy indices for style-polymorphic plant populations.

Heterostyly in plants promotes pollen transfer between floral morphs, because female and male sex organs are located at roughly reciprocal heights within the flowers of each morph. Reciprocity indices, which assess the one-dimensional variation in the height of sex organs, are used to define the phenotypic structure of heterostyly in plant populations and to make inferences about selection. Other reciprocal stylar polymorphisms (e.g., enantiostyly) may function in a similar manner to heterostyly. In-depth assessment of their potential fit with pollinators requires accounting for the multidimensional variation in the location of sex organs. We have adapted the existing reciprocity indices used for heterostylous plant populations to incorporate multidimensional data. We illustrate the computation of the adapted and original indices in the freely available R package FlowerMate. FlowerMate provides fast computation of reliable indices to facilitate understanding of the evolution and function of the full diversity of reciprocal polymorphisms.

A visual guide for the Brabant index to score dental macrowear quantity and direction

AbstractThis study aimed to reduce subjectivity bias in scoring dental macrowear quantity and direction using the Brabant index, which previously relied solely on written descriptions. To achieve this, we present a new, optimized visual guide incorporating buccal and lingual scores. The optimization process involved conceptualizing and illustrating a visual guide using Holocene southern African hunter‐gatherer and herder teeth, featuring both buccal and lingual scores for multicuspid teeth. The guide was hand‐drawn using a stippling technique and digitized to depict surface details for each wear stage and tooth type. We conducted intra‐ and inter‐observer assessments to evaluate the optimized method using both the original and optimized Brabant indices. Statistical analysis was performed in R using Cohen's kappa for direction and Cohen's weighted kappa for quantity. Intra‐observer results for the original method yielded kappa values of 0.84 for direction and 0.94 for quantity, while the optimized version both resulted in improved values of 0.99. Inter‐observer results revealed some differences between an inexperienced and an experienced observer. The inexperienced observer achieved kappa scores of 0.20 for direction and 0.86 for quantity with the original method, and 0.17 and 0.80, respectively, with the optimized version. The experienced observer's results using the original index were 0.66 for direction and 0.89 for quantity, and 0.75 and 0.96, respectively, with the optimized version. These findings demonstrate that the optimized method enhances data reliability for experienced observers, highlighting the value of a published visual guide and multicuspid scoring adjustments. However, reduced or unappreciable changes in accuracies for the inexperienced observer illustrate the need for dental expertise when scoring for dental wear, even with a modified method.

Research and Modification on the Park-Ang Damage Index for Railway Rectangular Piers

ABSTRACT This study addressed the issue of distortion in evaluating the seismic damage state of railway bridge piers using the original Park-Ang damage index. By constructing a parameterized co-simulation program with two finite element models, OpenSees and Abaqus, five sets of flexural failure and five sets of flexural-shear failure rectangular pier quasi-static test results were selected to validate the rationality of the calculation program. Four hundred and eighty parameterized calculations were conducted using this program. The combination coefficient β and critical damage index DI within the Park-Ang damage index were modified using nonlinear regression algorithm and convolutional neural network algorithm. The adaptability of the damage index calculation was ultimately verified through an engineering case study of a simply supported beam bridge. The research findings are as follows: 1) The main factor influencing β is the longitudinal reinforcement ratio, while the axial compression ratio, shear span ratio, volumetric stirrup ratio, aspect ratio, concrete strength, and steel strength are secondary parameters influencing β. 2) Compared to the nonlinear regression algorithm, the convolutional neural network algorithm can better establish a calculation model between pier structural parameters and β, clarifying their relationship. 3) It is recommended to use critical values of 0.11, 0.29, 0.49, and 1.00 for assessing pier damage as no, slight, moderate, severe, and collapse damage when using the damage index calculation model. 4) The Park-Ang index corrected by convolutional neural networks demonstrates improved accuracy and computational stability, making it suitable for practical assessment of seismic damage in bridge structures.

Spatial distribution of PAHs, nickel, and vanadium in sediments from a large coastal lagoon near a petroleum extraction area in the southern Gulf of Mexico

One of the world's crucial areas for crude oil exploration and extraction is the southern Gulf of Mexico, where Terminos Lagoon (TL) is located. Sediments from the TL region were used to assess the spatial patterns, origins, and ecotoxicological risks associated with 16 priority polycyclic aromatic hydrocarbons (PAHs; 3.1–248.9 ng⸳g−1 dry weight basis, dw) and trace metals (Ni = 11.0–104.0 mg⸳kg−1; V = 2.0–35.0 mg⸳kg−1 dw) linked to anthropogenic activities. Although origin indices based on PAHs and metals concentrations indicate no crude oil pollution in the region, sources of pyrogenic PAHs were identified. A chemometric approach demonstrated associations between organic matter and PAHs, and that metal accumulation depends mostly by the input of lithogenic materials. Ecotoxicological risk estimations showed a higher risk of possible adverse effects in sites near swamps and mangrove zones, highlighting the need of future monitoring. This study provides a reference for policymakers to conserve Mexico's largest coastal lagoon and other oil-impacted coastal areas worldwide.

An Innovative Recompression Scheme for VQ Index Tables

As we move into the digital era, the pace of technological advancement is accelerating rapidly. Network traffic often becomes congested during the transmission of large data volumes. To mitigate this, data compression plays a crucial role in minimizing transmitted data. Vector quantization (VQ) stands out as a potent compression technique where each image block is encoded independently as an index linked to a codebook, effectively reducing the bit rate. In this paper, we introduce a novel scheme for recompressing VQ indices, enabling lossless restoration of the original indices during decoding without compromising visual quality. Our method not only considers pixel correlations within each image block but also leverages correlations between neighboring blocks, further optimizing the bit rate. The experimental results demonstrated the superior performance of our approach over existing methods.

The Role of Field Measurements of Fine Dead Fuel Moisture Content in the Canadian Fire Weather Index System—A Study Case in the Central Region of Portugal

The Canadian Fire Weather Index System (CFWIS), empirically developed for forests in Canada, estimates the fuel moisture content (mf) at different depths and loads through meteorological parameters. While it is often suggested that adapting an existing fire danger rating system like CFWIS for a new environment requires developing new relationships or modifying existing ones, it is worth considering if such adaptations are always necessary. Based on a dataset of field measurements for surface litter (Pinus pinaster) carried out in the central region of Portugal (2014–2023), we propose a correction of mf based on the Fine Fuel Moisture Code (FFMC) of the CFWIS. This moisture correction was used to determine the Initial Spread Index (ISI) directly and, subsequently, the Fire Weather Index (FWI). Fire records from the study region were used to analyze the performance of the corrected indices. We found that the moisture correction led to higher values and potentially more accurate indices under dry conditions but did not provide a significant improvement in predicting the number of fires and burned areas compared to the original indices. The results suggest that, in relation to fire activity, the CFWIS is sufficiently robust to variations in the fuel moisture content in the study region.

Responsiveness of the Oswestry Disability Index and Zurich Claudication Questionnaire in patients with lumbar spinal stenosis: evaluation of surgically treated patients from the NORDSTEN study

PurposeTo evaluate the responsiveness of the original low back pain specific Oswestry Disability Index (ODI) and the spinal stenosis specific Zürich Claudication Questionnaire (ZCQ), and to investigate cut-off values for clinical “success” for ODI and ZCQ in surgically treated patients with lumbar spinal stenosis (LSS).MethodsWe included 601 LSS patients (218 with, 383 without degenerative spondylolisthesis) from the NORDSTEN trials. Outcome measures included ODI and ZCQ (symptom severity and physical function scales) with three alternative response parameters: scores at follow-up, absolute and relative changes from baseline to two-year follow-up. Effect size and standardised response mean evaluated internal responsiveness. External responsiveness was assessed by the Spearman rank correlation between patient-reported global perceived effect scale (GPE) and ODI and ZCQ, and receiver operating characteristics (ROC). We evaluated which cut-off values could maximise the percentage of correctly classified patients according to the GPE-anchor “completely recovered” / “much improved” for each parameter.ResultsInternal and external responsiveness were high for all three indices with effect sizes, standardized response means, ROC and corresponding area under the curve > 0.8. Correlations with GPE responses were moderate (> 0.50) for absolute change and strong (> 0.67) for relative change and follow-up scores. The 30% ODI relative change cut-off correctly classified 81% of patients to “success”, within a range of accurate cut-offs according to the GPE-anchor.ConclusionODI and ZCQ demonstrate comparable responsiveness in evaluating outcomes for surgically treated LSS patients. The 30% ODI threshold was consistent with treatment “success” in NORDSTEN trials.Trial registrationClinicalTrials.gov; NCT02007083 10/12/2013, NCT02051374 31/01/2014 and NCT03562936 20/06/2018.

Tree Resilience Indices of Norway Spruce Provenances Tested in Long-Term Common Garden Experiments in the Romanian Carpathians.

Provenance trials provide a valuable opportunity to evaluate the impact of extreme events on growth and wood properties. In this study, we have evaluated 81 Norway spruce provenances, tested in three provenance trials established in the Romanian Carpathians in 1972. The response to drought of the Norway spruce provenances has been examined using the following tree resilience indices: resistance, recovery, resilience, and relative resilience. The relationship between climate and growth, the correlations between wood traits, and the coordinates of the origin and tree resilience indices were also analysed. In each provenance trial, there were significant differences between provenances and years regarding wood widths and latewood percentage (LWP). Regarding drought extreme events, the years when they occurred in all three provenance trials were 2000 and 2003. Significant differences between provenances for at least one tree resilience index have been found in all provenance trials, for the year 2000. By using subperiods of 25 years, changes in the relationship between climate and growth have been observed. Several provenances with high radial growth and good resistance and/or recovery have been identified. Provenances that performed better in common garden experiments could be used in assisted migration, even in the proximity of the current natural range.

Can hybrid model improve the forecasting performance of stock price index amid COVID-19? Contextual evidence from the MEEMD-LSTM-MLP approach

The sudden eruption of COVID-19 has inflicted tremendous damage to the worldwide economy, and stock markets have become violently volatile due to its negative impact. Therefore, accurate forecasting of stock price index has been playing an essential role in maintaining national economic security and formulating related policies. In this paper, a novel decomposition-ensemble model is proposed to predict the highly fluctuating stock price index. To begin with, the modified ensemble empirical mode decomposition (MEEMD) method is adopted to decompose the original stock price index into subsequences with different frequencies. Then, the last high-frequency subsequence and other subsequences are predicted through multilayer perceptron (MLP) and long short-term memory (LSTM), respectively. Finally, the prediction outcomes of different model subsequences are reconstructed into the ultimate prediction results by utilizing the integration method. Compared with the contrast models, the MEEMD-LSTM-MLP model proposed in our paper not only demonstrates significant advantages in multi-step forecasting for both emerging and developed markets, but also achieves excellent prediction performance amidst the severe market fluctuations triggered by COVID-19. Furthermore, the application of the MEEMD-LSTM-MLP model is extended to financial time series with different data characteristics and market types, which further proves its high applicability and reliability. Therefore, the conducted hybrid MEEMD-LSTM-MLP model is an effective and stable multi-step forecasting tool to provide valuable intelligent technical support for governments and enterprises in complex economic conditions.

Reliability and Validity of the Turkish Version of the Screening Index for Voice Disorder (SIVD-TR)

ObjectivesWe investigated the reliability and validity of the Turkish version of the Screening Index for Voice Disorder (SIVD-TR). MethodsThe original Screening Index for Voice Disorder (SIVD)1 translated into Turkish, followed by a translation back into English by a linguist. The text was finalized by an evaluation committee. This translated version was then administered to a cohort of 223 teachers in Ankara, Turkey, encompassing both those with and without voice disorders (WVD and WOVD). After a duration of 7-14 days, a random selection of 53 teachers underwent the questionnaire once more. Statistical analyses were conducted to evaluate the reliability and validity of the index. Cronbach's alpha and test-retest methods were used to scale the reliability. A cutoff point was determined to decide the risk of a voice disorder, by using a receiver operating characteristic (ROC) curve. The validation process is concluded by computing sensitivity and specificity values, comparing mean scores between with and without voice disorders (WVD and WOVD) subjects, finally examining correlations between SIVD-TR and the Turkish version of the Voice Handicap Index (VHI-10). ResultsThe internal consistency reliability exhibited high significance, with Cronbach's alpha measuring at 0.872. The test-retest correlation coefficient for the total scores was 0.80. The SIVD-TR consists of 12 symptoms, each accounting for 1 point on the scale. The identified cutoff for identifying the risk of a voice disorder is 4 (four) symptoms, with a sensitivity of 55.2%. A correlation of 69% was observed between SIVD-TR and VHI-TR. A significant association was noted between the risk of having a voice disorder and the actual presence of a voice disorder. Subjects with a voice disorder exhibited higher mean SIVD scores, providing further evidence of the questionnaire's discriminative validity. ConclusionThe Turkish adaptation of SIVD demonstrated both reliability and validity, establishing itself as a robust tool for identifying voice disorders.

Assessment of perioperative cardiac risk using preoperative quantitative flow ratio in patients with coronary artery disease undergoing noncardiac surgery: a retrospective cohort study.

Quantitative flow ratio (QFR) is a novel diagnostic modality for the functional testing of coronary artery stenosis, but evidence concerning the postoperative prognostic implication of QFR in noncardiac surgery (NCS) of patients with coronary artery disease (CAD) is limited. The purpose of this study was to examine the role of QFR in perioperative risk prediction in patients with coronary heart disease. This retrospective cohort study was conducted in The First Affiliated Hospital of Wenzhou Medical University between 2013 and 2022, and consecutively included patients with CAD who had undergone NCS <1 year after coronary angiography. The primary endpoint was major adverse cardiovascular events (MACEs), which were defined as a composite of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, cardiopulmonary arrest, malignant ventricular arrhythmia (MVA), congestive heart failure, and revascularization. Univariate and multifactorial Cox regression was used to identify the independent risk factors for perioperative cardiovascular events and to construct new models. The area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to compare the newly constructed model with existing traditional models. Among the 929 participants enrolled (median age 68 years; 72.0% male), the primary endpoint was met in 67 (7.2%) patients within 30 days of follow-up. There was no significant difference in the incidence of the primary endpoint between patients with QFR <0.75 and those with "gray zone" lesions (0.75≤ QFR ≤0.8) (log-rank P=0.325). Patients with QFR <0.75 and those with "gray zone" lesions (0.75≤ QFR ≤0.8) had a higher incidence of primary endpoint events compared to patients with QFR >0.8. [QFR <0.75 vs. QFR >0.8: adjusted hazard ratio (HR) =20.70, P<0.001; 0.75≤ QFR ≤0.8 vs. QFR >0.8: HR =15.99, P<0.001]. The independent predictors of MACEs events within 30 days after NCS were albumin level [HR =0.92, 95% confidence interval (CI): 0.87-0.98; P=0.008], emergency surgery (HR =4.12, 95% CI: 1.66-10.23; P=0.002), and QFR ≤0.8 (HR =15.92, 95% CI: 5.96-42.51; P<0.001). In addition, adjusting the original Revised Cardiac Risk Index (RCRI) with QFR ≤0.8 as a risk factor significantly improved the risk stratification of postoperative adverse events, with the adjusted AUC rising from 0.574 to 0.740 (P<0.001). QFR ≤0.8 could independently predict perioperative cardiovascular adverse events in patients with CAD undergoing NCS and improve the predictive value of original predictive index. Gray-zone lesions (0.75≤ QFR ≤0.8) should be actively treated.

A meta-ensemble machine learning strategy to assess groundwater holistic vulnerability in coastal aquifers

Assessing the vulnerability of groundwater in coastal aquifers is crucial for mitigating risks associated with seawater intrusion and anthropogenic impacts. This study introduces an innovative machine learning (ML)-enhanced methodology that synergizes the strengths of two established vulnerability assessment frameworks, DRASTIC and GALDIT. The hybrid approach overcomes the limitations of each framework—DRASTIC's inadequacies in coastal settings and GALDIT's limited consideration of agricultural and industrial impacts. Utilizing advanced decision tree-based ML algorithms—Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and Random Forest (RF)—this research was conducted in the Azarshahr Plain, NW Iran. Model efficacy was validated using Pearson's correlation coefficient (r) and distance correlation (DC), with nitrate (NO3−) and total dissolved solids (TDS) serving as proxies for evaluating the DRASTIC and GALDIT models, respectively. The original DRASTIC indices exhibited weak correlations with NO3− (r = 0.24, DC = 0.25), but ML-enhanced models, particularly AdaBoost, showed significant improvements (r = 0.78, DC = 0.79). Similar enhancements were observed with GALDIT, where correlations improved markedly with AdaBoost integration. A sophisticated second-level AdaBoost meta-ensemble was developed to integrate enhanced DRASTIC and GALDIT assessments, achieving superior correlation metrics (r = 0.80, DC = 0.84 for NO3−; r = 0.82, DC = 0.83 for TDS). These results underscore the effectiveness of an integrated ML-based approach in advancing beyond traditional vulnerability assessment methods, providing a more comprehensive, accurate, and robust evaluation of coastal aquifer vulnerability.