Generalized Additive Models Research Articles

Association between ambient air pollution and respiratory health in Kampala, Uganda: Implications for policy and practice

Ambient air pollution and weather have an impact on respiratory diseases. This study analysed effects of fine particulate matter (PM2.5) on monthly respiratory-related healthcare facility visits and admissions (herein referred to as events) in Kampala, whilst considering meteorological factors.Monthly healthcare facility events for neonatal pneumonia, asthma, pneumonia and COPD between 2019 and 2020 were retrospectively extracted from the District Health Information Software 2. PM2.5 and meteorological data were obtained from 45 monitoring stations and 5 weather stations in Kampala. Associations between air pollution and healthcare facility events were examined using generalised additive models (GAM) adjusted for rainfall, temperature and humidity.Multivariate analysis showed positive association between monthly PM2.5 with healthcare facility visits due to asthma (incident rate ratio-IRR = 1.007, 95 % CI: 1.002–1.013; p < 0.05) and admissions due to pneumonia (IRR = 1.007, 95 % CI: 1.001–1.013; p < 05). Healthcare facility events were associated with PM2.5 (IRR = 1.005; 95 % CI: 1.002–1.009; p < 0.05) and relative humidity (EDF = 1.000; p < 0.05).PM2.5 impacted on healthcare facility events in Kampala and association was influenced by meteorological factors. However, lack of data for daily healthcare facility events could have affected accuracy of the impact therefore further analyses are needed. Our characterisation could support facilities in development of early warning tools for air pollution events.

Association of PM2.5 exposure in early pregnancy and maternal liver function: A retrospective cohort study in Shenzhen, China

ObjectiveStudies have shown that fine particulate matter (PM2.5) has adverse effects on the liver function, but epidemiological evidence is limited, especially regarding pregnant women. This study aims to investigate the association between PM2.5 exposure in early pregnancy and maternal liver function during pregnancy. MethodsThis retrospective cohort study included 13,342 pregnant participants. PM2.5 and Ozone (O3) exposure level, mean temperature, and relative humidity for each participant were assessed according to their residential address. The levels of serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), and total bilirubin (TBIL) were measured during the second and third trimesters. Data on PM2.5 and O3 exposure level were sourced from Tracking Air Pollution in China (TAP), while the mean temperature and relative humidity were obtained from the ERA5 dataset. The Generalized Additive Model (GAM) was used to analyze the associations between PM2.5 exposure and maternal liver function during pregnancy, adjusting for potential confounding factors. ResultsAccording to the results, each 10 μg/m3 increase in PM2.5 was associated with an increase of 3.57% (95% CI: 0.29%, 6.96%) in ALT and 4.25% (95% CI: 2.33%, 6.21%) in TBIL during the second trimester and 4.51% (95% CI: 2.59%, 6.47%) in TBIL during the third trimester, respectively. After adjusting for O3, these associations remained significant, and the effect of PM2.5 on ALT during the second trimester was further strengthened. No significant association observed between PM2.5 and AST. ConclusionsPM2.5 exposure in early pregnancy is associated with increasement of maternal ALT and TBIL, suggesting that PM2.5 exposure may have an adverse effect on maternal liver function. Although this finding indicates an association between PM2.5 exposure and maternal liver function, more research is needed to confirm our findings and explore the underlying biological mechanisms.

Subject-level spinal osteoporotic fracture prediction combining deep learning vertebral outputs and limited demographic data.

Osteoporotic vertebral fractures are common and morbid. Automated opportunistic screening for incidental vertebral fractures from radiographs, the highest volume imaging modality, could improve osteoporosis detection and management. We consider how to form patient-level fracture predictions and summarization to guide management, using our previously developed vertebral fracture classifier on segmented radiographs from a prospective cohort study of US men (MrOS). We compare the performance of logistic regression (LR) and generalized additive models (GAM) with combinations of individual vertebral scores and basic demographic covariates. Subject-level LR and GAM models were created retrospectively using all fracture predictions or summary variables such as order statistics, adjacent vertebral interactions, and demographic covariates (age, race/ethnicity). The classifier outputs for 8663 vertebrae from 1176 thoracic and lumbar radiographs in 669 subjects were divided by subject to perform stratified fivefold cross-validation. Models were assessed using multiple metrics, including receiver operating characteristic (ROC) and precision-recall (PR) curves. The best model (AUC-ROC = 0.968) was a GAM using the top three maximum vertebral fracture scores and age. Using top-ranked scores only, rather than all vertebral scores, improved performance for both model classes. Adding age, but not ethnicity, to the GAMs improved performance slightly. Maximal vertebral fracture scores resulted in the highest-performing models. While combining multiple vertebral body predictions risks decreasing specificity, our results demonstrate that subject-level models maintain good predictive performance. Thresholding strategies can be used to control sensitivity and specificity as clinically appropriate.

GAM4water: An R-based method for extracting wetted areas from remotely-sensed images

We present ‘GAM4water,’ a R-based method to classify wetted and non-wetted (dry) areas using remotely sensed image indices derived from such images. The GAM4water classification algorithm is built around a Generalized Additive Model (GAM) capable of accounting for non-linear responses. GAM4water can use any type of radiometric data, whether from drones, satellites or other platforms, and can be used with data of different spatial resolutions, geographic extents and spatial reference systems. It is a supervised tool that uses pixel information to distinguish between wetted and dry areas within an image set, extract them and produce a rich output that includes a binary raster, polygons of wetted areas, and a classification performance report. We tested the method in two case-studies, one using high resolution drone images and another using satellite images. The tests show that GAM4water can produce highly accurate classifications of wetted and non-wetted areas, and has the additional benefit of being easily customizable and not requiring complex implementation procedures.•This paper introduces the first R based method of wetted area extraction for remotely-sensed images.•The method is based on Generalized Additive Models and is applicable to any remotely-sensed data.

Pollutants-mediated viral hepatitis in different types: assessment of different algorithms and time series models

The escalating frequency of environmental pollution incidents has raised significant concerns regarding the potential health impacts of pollutant fluctuations. Consequently, a comprehensive study on the role of pollutants in the prevalence of viral hepatitis is indispensable for the advancement of innovative prevention strategies. Monthly incidence rates of viral hepatitis from 2005 to 2020 were sourced from the Chinese Center for Disease Control and Prevention Infectious Disease Surveillance Information System. Pollution data spanning 2014–2020 were obtained from the National Oceanic and Atmospheric Administration (NOAA), encompassing pollutants such as CO, NO2, and O3. Time series analysis models, including seasonal auto-regressive integrated moving average (SARIMA), Holt-Winters model, and Generalized Additive Model (GAM), were employed to explore prediction and synergistic effects related to viral hepatitis. Spearman correlation analysis was utilized to identify pollutants suitable for inclusion in these models. Concurrently, machine learning (ML) algorithms were leveraged to refine the prediction of environmental pollutant levels. Finally, a weighted quantile sum (WQS) regression framework was developed to evaluate the singular and combined impacts of pollutants on viral hepatitis cases across different demographics, age groups, and environmental strata. The incidence of viral hepatitis in Beijing exhibited a declining trend, primarily characterized by HBV and HCV types. In predicting hepatitis prevalence trends, the Holt-Winters additive seasonal model outperformed the SARIMA multiplicative model ((1,1,0) (2,1,0) [12]). In the prediction of environmental pollutants, the SVM model demonstrated superior performance over the GPR model, particularly with Polynomial and Besseldot kernel functions. The combined pollutant risk effect on viral hepatitis was quantified as βWQS (95% CI) = 0.066 (0.018, 0.114). Among different groups, PM2.5 emerged as the most sensitive risk factor, notably impacting patients with HCV and HEV, as well as individuals aged 35–64. CO predominantly affected HAV patients, showing a risk effect of βWQS (95% CI) = − 0.0355 (− 0.0695, − 0.0016). Lower levels of PM2.5 and PM10 were associated with heightened risk of viral hepatitis incidence with a lag of five months, whereas elevated levels of PM2.5 (100–120 μg/m3) and CO correlated with increased hepatitis incidence risk with a lag of six months. The Holt-Winters model outperformed the SARIMA model in predicting the incidence of viral hepatitis. Among machine learning algorithms, SVM and GPR models demonstrated superior performance for analyzing pollutant data. Patients infected with HAV and HEV were primarily influenced by PM10 and CO, whereas SO2 and PM2.5 significantly impacted others. Individuals aged 35–64 years appeared particularly susceptible to these pollutants. Mixed pollutant exposures were found to affect the development of viral hepatitis with a notable lag of 5–6 months. These findings underscore the importance of long-term monitoring of pollutants in relation to viral hepatitis incidence.

Predictive GAM seabed maps can account for defined and fuzzy boundaries to improve accuracy in a Scottish sea loch seascape

Marine seabed mapping is an important element in marine spatial and conservation planning. Recent large scale mapping programmes have greatly increased our knowledge of the seafloor, yet at finer resolutions, large gaps remain. Loch Eriboll, Scotland, is an area of conservation interest with a diverse marine environment supporting habitats and species of conservation importance. Here we test and present strategies for a predictive seabed substrata map for Loch Eriboll using drop down Stereo Baited Remote Underwater Video (SBRUV) imagery collected as part of systematic underwater survey of the Loch. A total of 216 SBRUV deployments were made across the study site in depths of 3 m–117 m, with six seabed classes identified using an adaptation of the EUNIS (European Nature Information System) hierarchical habitat classification scheme. Four statistical learning approaches were tested, we found, Generalised Additive Models (GAMs) provided the optimal balance between over- and underfitted predictions. We demonstrate the creation of a predictive substratum habitat map covering 63 km2 of seabed which predicts the probability of presence and relative proportion of substratum types. Our method enables naturally occurring edges between habitat patches to be described well, increasing the accuracy of mapping habitat boundaries when compared to categorical approaches. The predictions allow for both defined boundaries such as those between sand and rock and fuzzy boundaries seen among fine mixed sediments to exist in the same model structure. We demonstrate that SBRUV imagery can be used to generate cost effective, fine scale predictive substrata maps that can inform marine planning. The modelling procedure presented has the potential for a wide adoption by marine stakeholders and could be used to establish baselines for long term monitoring of benthic habitats and further research such as animal distribution and movement work which require detailed benthic maps.

Differences in Musculoskeletal Injury and Disability Rates in U.S. Navy Sailors Aboard Aircraft Carrier and Amphibious Assault Ships.

Musculoskeletal injuries (MSKI) are the most common clinical condition in the military that affect medical readiness. Evaluation of MSKI burden and the effects of these injuries on readiness in large deck Navy ships is warranted. A retrospective cohort study assessing population-level MSKI rates, short-term disability (restricted duty), and long-term disability episode counts of all Sailors assigned to U.S. Navy Aircraft Carriers (CVNs) and Amphibious Assault Ships (LHA/LHD) from November 2016 to February 2023 was extracted from the Musculoskeletal Naval Epidemiological Surveillance Tool. A negative binomial regression and general additive (Gaussian) models evaluate the association of ship platform, deployment status, days underway, and sex with MSKI rates and the proportion of cases that resulted in short-term disability, returned-to-duty following restricted duty, or progressed to long-term disability. Sailors attached to CVNs contributed a mean 17,893.8 ± 23,280.6 person-months, with those attached to LHA/LHDs contributing an average 5,981.8 ± 8,432.7 person-months. Aboard CVNs, MSKI occurred at a rate of 0.30 ± 0.16/1,000 person-months while deployed and 0.64 ± 0.31/1,000 person-months in homeport. Aboard LHA/LHDs, Sailors incurred MSKI at a rate of 0.59 ± 0.58/1,000 person-months while on deployment and 1.24 ± 0.68/1,000 person-months in homeport. Among Sailors aboard CVNs, short-term disability occurred in 7.95 ± 7.75% of MSKI cases while deployed and 5.13 ± 5.26% while in homeport. Aboard LHA/LHDs, 8.57 ± 13.42% of MSKI cases were placed on short-term disability while deployed and 4.95 ± 5.27% while in homeport. In the multivariable assessment of short-term disability, being deployed underway was a significant factor (B = 3.62 P = .03, variance explained = 3.86%). Sailors that were female and served aboard LHA/LHDs returned to full duty at a significantly greater frequency compared to their male counterparts and Sailors serving aboard CVNs. None of the independent variables evaluated were associated with long-term disability. The findings in the current study demonstrate the substantial burden of MSKI aboard large deck ships, both in homeport and while deployed. Inclusion of a physical therapist aboard LHA/LHDs, like the CVN, may help to prevent and mitigate the effects of MSKI through early access to specialized care and integral injury prevention and performance optimization methods.

Evaluating the importance of vertical environmental variables for albacore fishing grounds in tropical Atlantic Ocean using machine learning and Shapley additive explanations (SHAP) approach

AbstractThis study aims to find reliable vertical environmental variables for modeling the fishing grounds of albacore (Thunnus alalunga) in the tropical waters of the Atlantic Ocean. Logbook data of 13 Chinese longliners operating in the high seas of the Atlantic Ocean from 2016 to 2019 were collected and matched with vertical environmental variables including dissolved oxygen, temperature, and salinity from 0 to 500 m at 50‐m depth intervals. Then four machine learning (ML) models: decision tree (DT), random forest (RF), light gradient boosting (LGB) and categorical boosting (CGB) were constructed and compared with generalized additive models (GAMs) within spatial resolutions of .5° × .5°, 1° × 1°, and 2° × 2° grids to find the significant features. The importance of each variable was ranked and compared based on Shapley additive explanations (SHAP) approach across five ML models at three resolutions. Results showed that (1) the vertical environmental variables—temperature at the depth of 100 m and dissolved oxygen concentration at the depth of 100 and 150 m—were the significant features that contributed most to all the ML models at three spatial resolutions; (2) the models with a spatial resolution of 2° × 2° grid exhibited higher accuracy compared to the models with .5° × .5° and 1° × 1° grids; (3) the RF model had the best prediction performance among all the models tested. Our results suggested that significant vertical environmental variables showed similar importance across different ML models at different resolutions, and these specific variables can be relied upon for accurately predicting the fishing grounds of albacore in the tropical waters of the Atlantic Ocean.

The association between the weight-adjusted-waist index and frailty in US older adults: a cross-sectional study of NHANES 2007-2018.

This study aimed to evaluate the relationship between the weight-adjusted waist circumference index (WWI) and the frailty in American adults aged over 60 years. We utilized data from the National Health and Nutrition Examination Surveys (NHANES) spanning from 2007 to 2018. WWI was calculated using the square root of waist circumference (cm) divided by body weight (kg). The frailty index ≥ 0.25 was employed to assess frailty. Weighted multivariate logistic regression was conducted to explore the association between WWI and frailty. Generalized Additive Modeling (GAM) was used to explore potential non-linear relationships. Receiver operating characteristic curve (ROC) analysis was used to assess the predictive ability of WWI for frailty. The study encompassed 7765 participants. Higher WWI was significantly associated with higher odds of frailty. In the fully adjusted model, each unit increase of WWI was associated with an 82% increased odds of frailty (OR: 1.82, 95% CI: 1.61 - 2.06; P < 0.001). GAM found significant nonlinear relationships and threshold effects. The study presented a robust correlation between elevated WWI and increased odds of frailty among American older adults. However, these findings require further validation in large-scale, prospective studies.

Metal contamination of the surface water and its effect on the higher gastric cancer incidence of Lujiang County, China

Metal contamination in surface water is one of the major environmental risk factors contributing to the incidence and mortality of gastric cancer (GC) in rural China. In this study, the generalized additive model (GAM) was used to explore the single and interactive effects of metal elements in surface water on GC incidence on the watershed scale in Lujiang County, east of China. Spatially, the areas with a high incidence of GC were concentrated in the northwest watershed where the drainage system was relatively developed. Apart from Sr, the average concentrations of Al, As, Be, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Sn, and Zn in the northern watershed were higher than those in the southern watershed. Among them, the average concentrations of Be (0.036 mg/L), Fe (1.539 mg/L), Mn (0.355 mg/L), and Ni (0.040 mg/L) well exceeded China’s Grade III water quality standards. On the sub-watershed scale Be (F(5.130)), Pb (F(4.963)), As (F(3.508)), Cu (F(3.023)), Zn (F(2.922)), Cr (F(2.725)), Sn (F(2.588)), and Co (F(2.140)) in surface water were the main elements contributing to the high incidence of GC in Lujiang County, and the interaction effect mostly showed nonlinear enhancement. Especially, the nonlinear enhancement effect of Be with Cu (F(7.051)), Co (F(5.798)), Sn (F(5.328)), and Pb (F(5.193)) was more significant and had a greater effect on the incidence of GC. This study provides compelling direct evidence on the effects of metal contamination in surface water on the higher GC incidence in Lujiang County, China. It is recommended that there be routine monitoring of metal concentrations in surface water resources.

Fine particulate matter and ozone variability with regional and local meteorology in Beijing, China

Fine particulate matter (PM2.5) and surface ozone air pollution have severe health consequences. Since China implemented the nationwide Air Pollution Prevention and Control Action Plan (APPCAP) in 2013, annual average PM2.5 concentrations have decreased while O3 concentration have increased in Beijing. It is unclear, however, to the extent that short-term meteorological variability has influenced these trends. To separate the influence of meteorology from long-term emissions-induced changes, we quantify the portion of the long-term PM2.5 and O3 concentration signals associated with short-term meteorological variability. Building off recent literature highlighting the regional nature of pollutant variability, we incorporate both locally observed meteorology and average regional reanalysis. We isolated sub-seasonal variability using the Kolmogorov-Zurbenko filter. Next, we trained and compared the utility of three statistical models of varying complexity (a linear model, a general additive model with splines, and a random forest) in their ability to predict out-of-sample daily concentrations. The random forest model yields the best predictive capability in holdout tests and predicts changing meteorological contributions to PM2.5 and ozone concurrent with changing concentrations across the study period. Results show an overall decrease in meteorology induced PM2.5 concentration variability at daily scales since 2013, but variability from meteorology has increased relative to mean PM2.5 concentrations. In contrast, O3 shows the opposite trend, with increasing meteorology-induced variability (meteorology-induced variability normalized by mean observed O3 has decreased). Our results show the importance of including regional meteorology in explaining local PM2.5 and O3 variability and quantify links between long-term policy implementation and short-term meteorological contributions to pollutant concentrations.

Present and future distribution of the deep-sea habitat-forming sponge - Pheronema carpenteri ( ) in a changing ocean

Sponges play vital roles in the ecosystem function of the deep sea. Some species, such as the birds' nest sponge Pheronema carpenteri, can form highly structured and dense habitats (i.e., aggregations), which contribute to the increase of nearby biodiversity. Climate change is expected to have a pronounced impact on the deep sea, particularly on Vulnerable Marine Ecosystems such as those formed by the glass sponge Pheronema carpenteri. These ecosystems are especially vulnerable to climate change and other anthropogenic activities since they are formed by sensitive species with slow growth rates and limited dispersal capability, which can hinder their adaptive capability and recovery after disturbance. The impact that climate change will have on Pheronema carpenteri remains unclear, although it is expected to influence the species' available suitable habitat and distribution range. The aim of this study was to predict the distribution of the glass sponge Pheronema carpenteri both for present day and under several future climate scenarios in the North Atlantic. An ensemble modelling approach was employed, combining Maximum Entropy, Generalized Additive Models and Random Forest techniques. Changes in available suitable habitat were projected to present day and to three future climatic scenarios (RCP 2.6, RCP 4.5 and RCP 8.5). Depth, temperature and dissolved oxygen were identified as the key predictor variables of habitat suitability, which patterns suggest a strong influence of the Mediterranean Outflow Water in shaping the present day distribution of the species, particularly in the eastern North Atlantic. Our results indicate a potential expansion of available suitable habitat in the northernmost region of the study area, with a contraction at lower latitudes, more prominent in the Portuguese archipelago of the Azores. Under the worst-case scenario (RCP 8.5), the area of suitable habitat will likely double compared to present, occupying approximately 6% of the total study area. The management and conservation of areas where Pheronema aggregations can occur should be articulated between different countries, particularly in the Northeast Atlantic since, cumulatively, most of Pheronema's climate refugia occurs within their EEZs. Nonetheless, a significant proportion of the species' climate refugia is located in areas within the High Seas (i.e., Rockall plateau).

Characterizations of air pollutants at roadside monitoring stations and traffic emission effects: A case study in Tianjin, China

To investigate the response of Roadside Monitoring Stations (RSs) to traffic-related air pollution, traffic and pollutant characteristics, influencing factors, and potential source characterization in Tianjin, China were determined based on roadside monitoring of real-world data conducted at RSs in 2022. The diurnal variation trend of pollutants at RSs was consistent with that at the National Monitoring Station (NM), with notably higher pollutant fluctuations during the morning and evening peak traffic times at RSs, where the average diurnal concentration was 41.46 % higher than that at the NM. The generalized additive model (GAM) for nitrogen oxides (NOx) and carbon monoxide (CO), responding to the multiple influencing factors, performed well at RSs, with deviance explained by 86.6 and 61.4 %, respectively. The synergistic effects of wind direction and speed contributed to most of the variations in NOx and CO, which were 14.74 and 12.87 %, respectively. Pollutant concentrations were highest under windless conditions, with pollutants originating primarily from local vehicle emissions. The model results indicated that medium-duty truck (MDT) traffic flow predominantly contributed to the variability in NOx emissions, whereas passenger car (PC) traffic flow was the primary source of CO emissions from traffic variables. MDTs should be the focus of urban NOx traffic emissions control. Potential-source analysis validated the results obtained from the GAM, and both analyses showed that RSs can better characterize traffic-related air pollutants. Furthermore, more stringent emission standards have effectively mitigated the release of pollutants from motor vehicles and contributed to the modernization of vehicle fleet composition, effectively decreasing CO concentrations.

Analysis of multi-scale effects and spatial heterogeneity of environmental factors influencing purse seine tuna fishing activities in the Western and Central Pacific Ocean

Understanding the spatial fishing activity distribution characteristics is important for the sustainable development of fisheries. Spatial nonstationarity is always present, especially in marine ecosystems. To explore how marine environmental factors affect the fishing effort of tuna purse seine vessels, data from 2015 to 2020 on the fishing activities of these fleets and environmental variables in the Western and Central Pacific Ocean (WCPO) were analyzed. A Generalized Additive Model (GAM), Geographically Weighted Regression model (GWR), and Multi-Scale Geographically Weighted Regression (MGWR) model were applied to explore the drivers of fishing activity and the impacts of environmental factors on spatial heterogeneity. The results indicate that: (1) The MGWR models has the highest prediction accuracy and effectively reflects the spatial heterogeneity and multi-scale effects of environmental factors in a year. (2) Environmental factors exhibit significant multi-scale effects and spatial heterogeneity on the fishing activities of purse seine tuna vessels. Sea floor depth, salinity at 200 m depth and sea surface temperature show the greatest spatial heterogeneity in their impact on fishing activities. (3) Sea surface temperature, distance to port, and primary productivity and salinity at 200 m depth are key variables influencing the fishing activities of purse seine tuna vessels. These findings are expected to provide scientific and effective guidance for fishery management and sustainable development by assessing the spatial variations in fishing activities at multiple scales.

Rescue ICSI Improved Cumulative Live Birth Rate for Cycles with Second Polar Body Extrusion Rate

ObjectiveTo determine the indications for early rescue intracytoplasmic sperm injection (ICSI) application. DesignA retrospective cohort study SubjectsThe study included 19,808 patients who underwent conventional in vitro fertilization (IVF) or rescue ICSI for their first cycles between February 2017 and December 2021. ExposureRescue ICSI cycles constituted the study group, where oocytes that had not extruded the second polar body 4-6 hours after insemination were rescued by ICSI. The control group consisted of conventional IVF cycles with no interventions to rescue oocytes without the second polar body. Generalized additive models (GAMs) were constructed to describe the relationship between the second polar body extrusion rate and cumulative live birth rate in conventional IVF and rescue ICSI cycles, respectively. The cutoff value of the second polar body extrusion rate guiding rescue ICSI application was determined from the intersection point of GAMs. Maternal age range applicable to rescue ICSI was further analyzed using the same method. Clinical outcomes were compared between conventional IVF and rescue ICSI cycles across different second polar body extrusion rate and maternal age subgroups. Main outcome measuresThe second polar body extrusion rate and maternal age range for rescue ICSI application, normal fertilization rate, and cumulative live birth rate. ResultsGAMs showed that the cutoff value for the second polar body extrusion rate about rescue ICSI application was 50%. When the rate <50%, normal fertilization rate and cumulative live birth rate (63.7% vs. 46.1%, OR: 1.609, 95% CI: 1.276-2.030) were significantly higher in rescue ICSI cycles than conventional IVF cycles. When the rate ≥50%, rescue ICSI cycles had similar normal fertilization rate and cumulative live birth rate compared to conventional IVF cycles. Further analysis on maternal age in cycles with second polar body extrusion rate <50% released that rescue ICSI cycles showed a higher cumulative live birth rate (67.7% vs. 48.3%, OR: 1.732, 95% CI: 1.361-2.202) than conventional IVF cycles for women aged < 38 years. ConclusionIVF cycles with second polar body extrusion rate <50% in women aged < 38 years was applicable to early rescue ICSI.

The Relationship Between Age and 30-Day Outcomes Following Unicompartmental Versus Total Knee Arthroplasty

BackgroundAn enhanced understanding of the short-term complication profile following unicompartmental knee arthroplasty (UKA) versus total knee arthroplasty (TKA) can improve surgical decision-making and subsequent patient outcomes. This study aimed to determine if the difference in risk of 30-day morbidity and mortality between UKA and TKA varied as a function of patient age. MethodsThis retrospective study of a national quality improvement database using data from 2014 to 2020 included 403,342 patients undergoing UKA (n = 12,324) or TKA (n = 391,018). A generalized additive model (GAM) evaluated non-linear relationships between primary outcome and predictors (age, procedure type, and procedure type x age interaction) using a 1:5 UKA to TKA matched sample. Probabilities and odds ratios (95% CI [confidence interval]) estimated the relative risk of complications across the age spectrum. ResultsIn the GAM, TKA patients relative to UKA had 1.30 higher odds (95% CI 1.19 to 1.43, P < 0.001) of 30-day morbidity and mortality. There was a significant, non-linear relationship between age and the primary outcome (P = 0.02), such that the odds were lowest at younger ages. They increased slowly until about age 65 years, at which point the slope became steeper. The interaction terms for age and procedure type were not significant (P = 0.30). The 30-day probability for short-term complications of a 65-, 75-, and 85-year-old undergoing UKA was 2.1% (95% CI, 1.8 to 2.3), 2.4% (95% CI, 2.0 to 2.8), and 3.2% (95% CI, 2.3 to 4.1), respectively. Conversely, the probability of a 65-, 75-, and 85-year-old undergoing TKA was 2.9% (95% CI, 2.7 to 3.0), 3.6% (95% CI, 3.3 to 3.8), and 5.5% (95% CI, 4.7 to 6.3). ConclusionsPatients undergoing UKA had a quantifiable lower likelihood of morbidity or mortality than TKA at all ages. These data can provide individualized risk for UKA and TKA across the age spectrum and could be helpful in counseling patients regarding their risk for perioperative complications.

A novel method for the evaluation of right ventricular dysfunction in acute pulmonary embolism: Myocardial work indices.

The presence of right ventricular dysfunction indicates a higher risk status in patients with pulmonary embolism (PE). The RV strain evaluated by speckle-tracking echocardiography seems to be more reliable method in the evaluation of RV dysfunction as compared to standard echocardiographic measures. In this study, we aimed to determine the value of myocardial-work indices in evaluating serial changes of RV function in acute PE. Our study comprised 83 consecutive acute PE patients who admitted to our tertiary cardiovascular hospital. Echocardiography was performed within the first 24-hours of hospitalization, and RV and LV myocardial-work parameters were obtained along with standard echocardiographic parameters. The change in the RV/LVr detected on tomography was selected as the primary outcome measure, and its' predictors were analyzed with classical linear regression and a generalized additive model (GAM). Among the LV-RV strain and myocardial work parameters, the RV global longitudinal strain (GLS) has borderline statistical significance in predicting the RV/LVr change whereas the RV global work efficiency (RV-GWE) strongly predicted RV/LVr change (p: 0.049 and <0.001, respectively). In this study, classical linear regression and GAM analyses showed that RV-GWE seems to offer a better prediction of RV/LVr change in patients with acute PE.

Mapping soil organic carbon density via geographically weighted regression with smooth terms: A case study in Shanxi Province

Soil, as the largest organic carbon reservoir in terrestrial ecosystems, plays a crucial role in the global carbon cycle. Accurately predicting the spatial distribution of soil organic carbon density (SOCD) is highly important for estimating organic carbon stocks and guiding agricultural production. Due to the complex relationship between the spatial distribution of SOCD and environmental factors, its relationship with a specific region is not exclusively linear or nonlinear; both types of relationships may coexist simultaneously. In this study, a new model, geographically weighted regression with smooth terms (SGWR), was proposed. This model can simultaneously consider both global spatial nonlinear relationships and local spatial nonstationary relationships. It was utilized to predict SOCD in Shanxi Province and was compared to multiple linear regression (MLR), generalized additive models (GAM) and geographically weighted regression (GWR) in terms of spatial prediction performance. The results showed that SGWR was highly competitive in predicting the spatial distribution of SOCD in Shanxi Province. Of factors influencing SOCD in Shanxi Province, elevation and slope were considered local linear variables, while the normalized difference vegetation index (NDVI), gross primary productivity (GPP), annual precipitation (AP), and soil moisture (SM) were considered global nonlinear variables. Among these factors, the NDVI and GPP had the most significant impacts on the distribution of SOCD, while elevation and SM had the least significant impacts on it. The results imply that integrating global linear, local linear, and global nonlinear relationships concurrently, as implemented in SGWR, improves predictive accuracy compared to approaches that solely address either local or global linear relationships based on environmental variable changes in SGWR. The results of this study indicate that the SGWR model is potentially a competitive method for digital soil mapping.

Generalized models for estimating cerebral lateralisation of young children using functional transcranial Doppler ultrasound.

Thompson et al., 2023 (Generalized models for quantifying laterality using functional transcranial Doppler ultrasound. Human Brain Mapping, 44(1), 35-48) introduced generalised model-based analysis methods for determining cerebral lateralisation from functional transcranial Doppler ultrasound (fTCD) data which substantially decreased the uncertainty of individual lateralisation estimates across several large adult samples. We aimed to assess the suitability of these methods for increasing precision in lateralisation estimates for child fTCD data. We applied these methods to adult fTCD data to establish the validity of two child-friendly language and visuospatial tasks. We also applied the methods to fTCD data from 4- to 7-year-old children. For both samples, the laterality estimates from the complex generalised additive model (GAM) approach correlated strongly with the traditional methods while also decreasing individual standard errors compared to the popular period-of-interest averaging method. We recommend future research using fTCD with young children consider using GAMs to reduce the noise in their LI estimates.

Blood urea nitrogen-to-albumin ratio independently predicts 30-day mortality in acute respiratory failure patients: a retrospective cohort study.

It is crucial to identify patients at high risk for acute respiratory failure (ARF) to provide appropriate and optimal clinical treatment. While previous studies have explored the use of prognostic biomarkers based on a combination of blood urea nitrogen (BUN) and albumin levels, no reports to date have evaluated its utility across a wide range of ARF etiologies in a large and diverse critical care population. Therefore, we aimed to ascertain the association between the BUN-to-albumin ratio (BAR) and mortality in these patients. Data recorded in the first 24 h following intensive care unit (ICU) admission, including demographics, vital signs, laboratory test results, comorbidities, and score systems were retrieved from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. A general additive model was used to determine whether there was a non-linear relationship between BAR and 30-day mortality. A multivariate Cox analysis was performed to measure the association between them. The study enrolled 9,734 patients with ARF. In comparison to survivors, non-survivors exhibited higher BAR [10.79 (6.25-18.81) vs. 7.35 (4.48-13.62), P<0.001]. The correlation between baseline BAR and 30-day all-cause mortality in patients with ARF was non-linear, with a significant inflection point (11.76 mg/g). The Kaplan-Meier curve demonstrated that ARF patients had higher 30-day all-cause mortality rates when they had higher BAR levels (>11.76 mg/g) with hazard ratio (HR) 1.54 [95% confidence interval (CI): 1.39-1.70]. A high BAR was linked to a higher risk of mortality in ARF patients. BAR is a straightforward and possibly useful prognostic biomarker for ARF.