Single Variable Model Research Articles

Impact of medication nonadherence and drug-drug interaction testing on the management of primary care patients with polypharmacy: a randomized controlled trial

BackgroundClinical management of patients with chronic cardiometabolic disease is complicated by polypharmacy. Consequently, when patients clinically deteriorate, physicians are challenged to distinguish both medication nonadherence and drug-drug interactions (DDI) from chronic disease progression.MethodsIn this randomized controlled trial, we enrolled U.S. board-certified Primary Care Physicians (PCPs) serving patients with cardiometabolic disease. PCPs were randomized and managed their patients with (intervention), or without (control), a novel chronic disease management test (CDMT) that can detect medication nonadherence and DDIs. Patients’ medical records were abstracted at baseline and 3-month follow-up. Primary outcomes were the CDMT’s impact on both the PCPs’ detection of medication nonadherence and DDI, and the frequency of performing medication nonadherence- and DDI-related clinical actions. Secondary outcomes examined the types of clinical actions performed. Primary and secondary outcomes were analyzed by logistic regression using single variable and clustered multivariable modeling to adjust for similarities in patient characteristics, by PCP practice.ResultsSixteen intervention and 20 control PCPs shared de-identified records for 126 and 207 patients, respectively. There were no significant demographic differences between the two study arms, among PCPs or patients. At baseline, there was no significant difference between the intervention and control PCPs in the percentage of clinical actions performed for medication nonadherence (P = 0.98) and DDI (P = 0.41). At 3-month follow-up (after CDMT), 69.1% of intervention compared to 20.3% of control patients with medication nonadherence had a related clinical action performed (P < 0.001). Regarding DDI, 37.3% of intervention compared to 0.5% of control patients had a relevant clinical action performed in follow-up (P < 0.001). Across the range of medication nonadherence- and DDI-related actions, the intervention compared to the control PCPs were more likely to adjust the medication regimen (24.1% vs. 9.5%) and document medication nonadherence in the patient chart (31.0% vs. 14.3%) at follow-up (P = 0.04).ConclusionsAlthough intervention and control PCPs similarly detected and acted upon medication nonadherence and DDI at baseline, intervention PCPs’ detection increased significantly after using the CDMT. Also, the clinical actions performed with CDMT support were more clinically rigorous. These outcomes support the clinical utility of CDMT in the management of symptomatic patients with cardiometabolic disease and polypharmacy.Trial registrationhttps://clinicaltrials.gov/study/NCT05910684.

Assessment of physicochemical attributes and variation in mass-volume of Himalayan pears: Computer vision-based modeling

The current study attempts to examine the physicochemical properties of Himalayan pears and envision the relationship between mass and volume with various physical properties. These properties are measured using image processing techniques at different storage days (1st day, 4th day, 7th day, 10th day, and 13th day). The study employs both single and multivariable regression models, including linear, quadratic, rational, and exponential models to establish predictive relationships. Among the single variable models, the length-based linear and rational models demonstrated exceptional suitability for envisioning the mass and volume of pears, achieving higher R2 values of 0.92 and 0.90, respectively. For mass and volume prediction considering combined physical properties, the rational and exponential models exhibited the best fit with higher R2 values of 0.94 and 0.91, accompanied by low RMSE values of 0.217 and 0.141. Consequently, the established relationship between the mass and volume of Himalayan pears with its physical attributes contributes to the development of a faster, more authentic, and accurate grading system.

Longevity of glass ionomer restorations in an older adult population

ObjectivesThis study investigates longevity of glass ionomer restorations in an older adult population. MethodsThis was a retrospective study based on clinical records. Patient records for 3,665 restorations in 1,777 adults 65 or older who had received a restoration between 12th July 2016 and 20th October 2022 were extracted from the electronic dental records system of the University of Iowa College of Dentistry and Dental Clinics. The data were analyzed to determine the influence of patient factors and restorative material on restoration longevity. Single variable and multiple variable survival models were created. ResultsMany variables that showed a statistically significant influence on restoration longevity in the single variable survival models and did not show such an influence in the multivariable model, indicating the effect of these variables is explained when other factors are controlled for. In particular, patient age at the time of restoration showed no significant influence in the multivariable model, and no medical factor had a statistically significant influence. The restorative material did show a significant influence in the multivariable model; using the resin-modified glass ionomer restorative (Fuji II LC) as a reference, the conventional glass ionomer restorative (Fuji IX) had a hazard ratio of 2.0 (p = 0.011), and the coated-conventional glass ionomer restorative (Equia) had a hazard ratio of 2.8 (p < 0.001). SignificanceResin-modified glass ionomer restorations may have superior longevity for older adults than conventional and coated-conventional glass ionomer restorations.

Analysis of tourist flow prediction model of rural tourism on the edge of big cities in China

Introduction: With the adoption of the rural rehabilitation strategy in recent years, China’s rural tourist industry has entered a golden age of growth. Due to the lack of management and decision-support systems, many rural tourist attractions in China experience a “tourist overload” problem during minor holidays or Golden Week, an extended vacation of seven or more consecutive days in mainland China formed by transferring holidays during a specific holiday period. This poses a severe challenge to tourist attractions and relevant management departments. Objective: This study aims to summarize the elements influencing passenger flow by examining the features of rural tourist attractions outside China’s largest cities. Additionally, the study will investigate the variations in the flow of tourists. Method: Grey Model (1,1) is a first-order, single-variable differential equation model used for forecasting trends in data with exponential growth or decline, particularly when dealing with small and incomplete datasets. Four prediction algorithms—the conventional GM(1,1) model, residual time series GM(1,1) model, single-element input BP neural network model, and multi-element input BP network model—were used to anticipate and assess the passenger flow of scenic sites. Result: The multi-input BP neural network model and residual time series GM(1,1) model have significantly higher prediction accuracy than the conventional GM(1,1) model and unit-input BP neural network model. A multi-input BP neural network model and the residual time series GM(1,1) model were used in tandem to develop a short-term passenger flow warning model for rural tourism in China’s outskirts. Conclusion: This model can guide tourists to staggered trips and alleviate the problem of uneven allocation of tourism resources.

Permeability Upscaling Conversion Based on Reservoir Classification

Deep and ultra-deep reservoirs are characterized by low porosity and permeability, pronounced heterogeneity, and complex pore structures, complicating permeability evaluations. Permeability, directly influencing the fluid production capacity of reservoirs, is a key parameter in comprehensive reservoir assessments. In the X Depression, low-porosity and low-permeability formations present highly discrete and variable core data points for porosity and permeability, rendering single-variable regression models ineffective. Consequently, accurately representing permeability in heterogeneous reservoirs proves challenging. In the following study, lithological and physical property data are integrated with mercury injection data to analyze pore structure types. The formation flow zone index (FZI) is utilized to differentiate reservoir types, and permeability is calculated based on core porosity–permeability relationships from logging data for each flow unit. Subsequently, the average permeability for each flow unit is computed according to reservoir classification, followed by a weighted average according to effective thickness. This approach transforms logging permeability into drill stem test permeability. Unlike traditional point-by-point averaging methods, this approach incorporates reservoir thickness and heterogeneity, making it more suitable for complex reservoir environments and resulting in more reasonable conversion outcomes.

Significance of Multi-Variable Model Calibration in Hydrological Simulations within Data-Scarce River Basins: A Case Study in the Dry-Zone of Sri Lanka

Traditional hydrological model calibration using limitedly available streamflow data often becomes inadequate, particularly in dry climates, as the flow regimes may abruptly vary from arid conditions to devastating floods. Newly available remote-sensing-based datasets can be supplemented to overcome such inadequacies in hydrological simulations. To address this shortcoming, we use multi-variable-based calibration by setting up and calibrating a lumped-hydrological model using observed streamflow and remote-sensing-based soil moisture data from Soil Moisture Active Passive Level 4. The proposed method was piloted at the Maduru Oya River Basin, Sri Lanka, as a proof of concept. The relative contributions from streamflow and soil moisture were assessed and optimised via the Kling–Gupta Efficiency (KGE). The Generalized Reduced Gradient non-linear solver function was used to optimise the Tank Model parameters. The findings revealed satisfactory performance in streamflow simulations under single-variable model validation (KGE of 0.85). Model performances were enhanced by incorporating soil moisture data (KGE of 0.89), highlighting the capability of the proposed multi-variable calibration technique for improving the overall model performance. Further, the findings of this study highlighted the instrumental role of remote sensing data in representing the soil moisture dynamics of the study area and the importance of using multi-variable calibration to ensure robust hydrological simulations of river basins in dry climates.

Sex estimation using post-mortem computed tomographic images of the clavicle in a Malaysian population

Forensic practitioners need contemporary anthropological data for the identification of human remains. The clavicle possesses a high degree of variability in its anatomical, biomechanical, and morphological features that are sex-dependent albeit population specific. The aim of this study was to develop sex estimation models for Malaysian individuals using post-mortem computed tomographic images of the clavicle. Sample comprised scans of 2.0 mm resolution of 405 individuals (209 male; 196 female) aged between 19 to 88 years. These scans were reconstructed and visualized using Infinitt. Six clavicular measurements (i.e. maximum length, C1; midshaft circumference, C2; midshaft maximum diameter, C3; midshaft minimum diameter, C4; maximum breadth of the sternal end, C5; and maximum breadth of the acromial articular surface, C6) were obtained from these images. Data were analyzed using descriptive statistics and discriminant function analysis. Measurements taken from the images were highly precise (ICC = 0.770–0.999). There is a significant difference between all parameters and sex (p < 0.001), however none for age and ethnic group. A multivariate sex estimation model was developed: Sex = (C1*0.86) + (C2*0.236) + (C3*-0.145) + (C5*- 0.074) − 17.618; with an accuracy rate of 89.1 % and sex bias of −3.2 %. Lower accuracy rates were obtained for single variable models (61.5–83.2 %). The resultant sex discriminant models can be used for estimating sex based on the clavicle in our local forensic practice.

Agricultural Drought Model Based on Machine Learning Cubist Algorithm and its Evaluation

Soil moisture is the most direct evaluation index for agricultural drought. It is not only directly affected by meteorological conditions such as precipitation and temperature but is also indirectly influenced by environmental factors such as climate zone, surface vegetation type, soil type, elevation, and irrigation conditions. These influencing factors have a complex, nonlinear relationship with soil moisture. It is difficult to accurately describe this non-linear relationship using a single indicator constructed from meteorological data, remote sensing data, and other data. It is also difficult to fully consider environmental factors using a single drought index on a large scale. Machine learning (ML) models provide new technology for nonlinear problems such as soil moisture retrieval. Based on the multi-source drought indexes calculated by meteorological, remote sensing, and land surface model data, and environmental factors, and using the Cubist algorithm based on a classification decision tree (CART), a comprehensive agricultural drought monitoring model at 10 cm, 20 cm, and 50 cm depth in Gansu Province is established. The influence of environmental factors and meteorological factors on the accuracy of the comprehensive model is discussed, and the accuracy of the comprehensive model is evaluated. The results show that the comprehensive model has a significant improvement in accuracy compared to the single variable model, which is a decrease of about 26% and 28% in RMSE and MAPE, respectively, compared to the best MCI model. Environmental factors such as season, DEM, and climate zone, especially the DEM, play a crucial role in improving the accuracy of the integrated model. These three environmental factors can comprehensively reduce the average RMSE of the comprehensive model by about 25%. Compared to environmental factors, meteorological factors have a slightly weaker effect on improving the accuracy of comprehensive models, which is a decrease of about 6.5% in RMSE. The fitting accuracy of the comprehensive model in humid and semi-humid areas, as well as semi-arid and semi-humid areas, is significantly higher than that in arid and semi-arid areas. These research results have important guiding significance for improving the accuracy of agricultural drought monitoring in Gansu Province.

Fluvial habitat associations of riverine dragonflies (Odonata, Gomphidae) in the Huron Mountains (Michigan, USA)

AbstractThe presence of sediment‐burrowing clubtail dragonflies (Gomphidae) may be directly related to riverine geomorphic properties. Their synchronous emergence behavior, marked by persistent exuviae, allows for the examination of emerging adult‐stage habitats. Here we explore how emerging adult and benthic larvae are related to hydrogeomorphic factors through regression modeling. Nine sites in two subwatersheds of the Huron Mountain region (Michigan, USA) were surveyed during two periods: May/June 2021 and August 2021. Temperature, pH, dissolved oxygen, conductivity, and discharge were measured concurrent with invertebrate surveys, and field samples of total suspended solids, nutrients, and substrate particle size distribution were also taken. In spring, 317 exuviae were collected, including 153 Gomphidae. In August, 45 larvae were collected including 22 Gomphidae. Spearman's rank correlations preceded robust regression analysis to aid variable selection. Although nonsignificant, Gomphidae exuviae were negatively correlated with conductivity, average water depth, and percent sand while non‐Gomphidae exuviae were positively correlated with the same variables. The model selection found the top models for Gomphidae and non‐Gomphidae exuviae abundance to relate to depth and conductivity, while the top model for Gomphidae larvae was with discharge. All single variable models (discharge, width, and percent sand) had similar AICc criterion values when examining their relationship with non‐Gomphidae larval abundance. Our study demonstrates that larvae of several riverine dragonfly taxa in the Huron Mountains co‐occur despite hydrogeomorphic variation, yet, at emergence, specific taxa may be responding to different stream properties. Understanding the habitat requirements of riverine dragonflies and how these potentially shift throughout development can support conservation efforts.

Major Psychiatric Disorders, Substance Use Behaviors, and Longevity

Observational studies suggest that major psychiatric disorders and substance use behaviors reduce longevity, making it difficult to disentangle their relationships with aging-related outcomes. To evaluate the associations between the genetic liabilities for major psychiatric disorders, substance use behaviors (smoking and alcohol consumption), and longevity. This 2-sample mendelian randomization (MR) study assessed associations between psychiatric disorders, substance use behaviors, and longevity using single-variable and multivariable models. Multiomics analyses were performed elucidating transcriptomic underpinnings of the MR associations and identifying potential proteomic therapeutic targets. This study sourced summary-level genome-wide association study (GWAS) data, gene expression, and proteomic data from cohorts of European ancestry. Analyses were performed from May 2022 to November 2023. Genetic susceptibility for major depression (n = 500 199), bipolar disorder (n = 413 466), schizophrenia (n = 127 906), problematic alcohol use (n = 435 563), weekly alcohol consumption (n = 666 978), and lifetime smoking index (n = 462 690). The main outcome encompassed aspects of health span, lifespan, and exceptional longevity. Additional outcomes were epigenetic age acceleration (EAA) clocks. Findings from multivariable MR models simultaneously assessing psychiatric disorders and substance use behaviorsm suggest a negative association between smoking and longevity in cohorts of European ancestry (n = 709 709; 431 503 [60.8%] female; β, -0.33; 95% CI, -0.38 to -0.28; P = 4.59 × 10-34) and with increased EAA (n = 34 449; 18 017 [52.3%] female; eg, PhenoAge: β, 1.76; 95% CI, 0.72 to 2.79; P = 8.83 × 10-4). Transcriptomic imputation and colocalization identified 249 genes associated with smoking, including 36 novel genes not captured by the original smoking GWAS. Enriched pathways included chromatin remodeling and telomere assembly and maintenance. The transcriptome-wide signature of smoking was inversely associated with longevity, and estimates of individual smoking-associated genes, eg, XRCC3 and PRMT6, aligned with the smoking-longevity MR analyses, suggesting underlying transcriptomic mediators. Cis-instrument MR prioritized brain proteins associated with smoking behavior, including LY6H (β, 0.02; 95% CI, 0.01 to 0.03; P = 2.37 × 10-6) and RIT2 (β, 0.02; 95% CI, 0.01 to 0.03; P = 1.05 × 10-5), which had favorable adverse-effect profiles across 367 traits evaluated in phenome-wide MR. The findings suggest that the genetic liability of smoking, but not of psychiatric disorders, is associated with longevity. Transcriptomic associations offer insights into smoking-related pathways, and identified proteomic targets may inform therapeutic development for smoking cessation strategies.

The effect of fasting plasma glucose on in-hospital mortality after acute myocardial infarction in patients with and without diabetes: findings from a prospective, nationwide, and multicenter registry.

To evaluate the predictive value of fasting plasma glucose (FPG) for in-hospital mortality in patients with acute myocardial infarction (AMI) with different glucose metabolism status. We selected 5,308 participants with AMI from the prospective, nationwide, multicenter CAMI registry, of which 2,081 were diabetic and 3,227 were nondiabetic. Patients were divided into high FPG and low FPG groups according to the optimal cutoff values of FPG to predict in-hospital mortality for diabetic and nondiabetic cohorts, respectively. The primary endpoint was in-hospital mortality. Overall, 94 diabetic patients (4.5%) and 131 nondiabetic patients (4.1%) died during hospitalization, and the optimal FPG thresholds for predicting in-hospital death of the two cohorts were 13.2 mmol/L and 6.4 mmol/L, respectively. Compared with individuals who had low FPG, those with high FPG were significantly associated with higher in-hospital mortality in diabetic cohort (10.1% vs. 2.8%; odds ratio [OR] = 3.862, 95% confidence interval [CI]: 2.542-5.869) and nondiabetic cohort (7.4% vs. 1.7%; HR = 4.542, 95%CI: 3.041-6.782). After adjusting the potential confounders, this significant association was not changed. Furthermore, FPG as a continuous variable was positively associated with in-hospital mortality in single-variable and multivariable models regardless of diabetic status. Adding FPG to the original model showed a significant improvement in C-statistic and net reclassification in diabetic and nondiabetic cohorts. This large-scale registry indicated that there is a strong positive association between FPG and in-hospital mortality in AMI patients with and without diabetes. FPG might be useful to stratify patients with AMI.

PSAT1 enhances the efficacy of the prognosis estimation nomogram model in stage-based clear cell renal cell carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is associated with a high prevalence of cancer-related deaths. The survival rates of patients are significantly lower in late-stage ccRCC than in early-stage ccRCC, due to the spread and metastasis of late-stage ccRCC, surgery has not reached the goal of radical cure, and the effect of traditional radiotherapy and chemotherapy is poor. Thus, it is crucial to accurately assess the prognosis and provide personalized treatment at an early stage in ccRCC. This study aims to develop an efficient nomogram model for stratifying and predicting the survival of ccRCC patients based on tumor stage.MethodsWe first analyzed the microarray expression data of ccRCC patients from the Gene Expression Omnibus (GEO) database and categorized them into two groups based on the disease stage (early and late stage). Subsequently, the GEO2R tool was applied to screen out the genes that were highly expressed in all GEO datasets. Finally, the clinicopathological data of the two patient groups were obtained from The Cancer Genome Atlas (TCGA) database, and the differences were compared between groups. Survival analysis was performed to evaluate the prognostic value of candidate genes (PSAT1, PRAME, and KDELR3) in ccRCC patients. Based on the screened gene PSAT1 and clinical parameters that were significantly associated with patient prognosis, we established a new nomogram model, which was further optimized to a single clinical variable-based model. The expression level of PSAT1 in ccRCC tissues was further verified by qRT-PCR, Western blotting, and immunohistochemical analysis.ResultsThe datasets GSE73731, GSE89563, and GSE150404 identified a total of 22, 89, and 120 over-expressed differentially expressed genes (DEGs), respectively. Among these profiles, there were three genes that appeared in all three datasets based on different stage groups. The overall survival (OS) of late-stage patients was significantly shorter than that of early-stage patients. Among the three candidate genes (PSAT1, PRAME, and KDELR3), PSAT1 was shown to be associated with the OS of patients with late-stage ccRCC. Multivariate Cox regression analysis showed that age, tumor grade, neoadjuvant therapy, and PSAT1 level were significantly associated with patient prognosis. The concordance indices were 0.758 and 0.725 for the 3-year and 5-year OS, respectively. The new model demonstrated superior discrimination and calibration compared with the single clinical variable model. The enhancer PSAT1 used in the new model was shown to be significantly overexpressed in tissues from patients with late-stage ccRCC, as demonstrated by the mRNA level, protein level, and pathological evaluation.ConclusionThe new prognostic prediction nomogram model of PSAT1 and clinicopathological variables combined was thus established, which may provide a new direction for individualized treatment for different-stage ccRCC patients.

Objectively measured the impact of ambient air pollution on physical activity for older adults

BackgroundAir pollution poses a significant health risk to the human population, especially for vulnerable groups such as the elderly, potentially discouraging their engagement in physical activity. However, there is a lack of sufficient objective and longitudinal data in current research on how air pollution affects physical activity among older adults. With these gaps, we aimed to explore the relationship between air pollution and objective measurement-based physical activity among older adults by engaging in a longitudinal study design.MethodsA total of 184 older adults were recruited from three cities with varying levels of air quality. Mean daily minutes of physical activity were measured with 7 consecutive days of accelerometer monitoring (ActiGraph GT3X-BT). Corresponding air pollution data including daily PM2.5 (µg/m3), PM10 (µg/m3) and air quality index (AQI) were sourced from the China National Environmental Monitoring Centre at monitor locations close to older adults’ addresses. Associations between air quality and physical activity were estimated using a fixed effect model, adjusting for average daytime temperature, rain, age and weight.ResultsAQI and PM2.5 were observed to exhibit significant, inverse, and linear associations with mean daily walk steps, minutes of light physical activity (LPA), moderate physical activity (MPA) and moderate-to-vigorous physical activity (MVPA) in the single variable models. A one-level increase in AQI corresponded to a decline in 550.04 steps (95% [CI] = -858.97, -241.10; p < 0.001), 10.43 min (95% [CI] = -17.07, -3.79; p < 0.001), 4.03 min (95% [CI] = -7.48, -0.59; p < 0.001) and 4.16 min (95% [CI] = -7.77, -0.56; p < 0.001) in daily walking steps, LPA, MPA, and MVPA, respectively. A one-level increase in PM2.5 correlated with a decline in daily walk steps, LPA, MPA and MVPA by 361.85 steps (95% [CI] = -516.53, -207.16; p < 0.001), 8.97 min (95% [CI] = -12.28, -5.66; p < 0.001), 3.73 min (95% [CI] = -5.46, -2.01; p < 0.001,) and 3.79 min (95% [CI] = -5.59, -1.98; p < 0.001), respectively. However, PM10 displayed a significant negative association exclusively with LPA, with one-level increase in PM10 resulting in a 3.7-minute reduction in LPA (95% [CI] = -6.81, -0.59, p < 0.05).ConclusionAir pollution demonstrates an inverse association with physical activity levels among older adults, potentially discouraging their engagement in physical activity. Different air quality indicators may exert varying impacts on physical activity. Future studies are warranted to enhance policy interventions aimed at reducing air pollution and promoting physical activity.

Relationship between thyroid-stimulating hormone, BDNF levels, and hippocampal volume in antipsychotic-naïve first-episode psychosis patients.

Thyroid hormones play an essential role in hippocampal development, a key structure in psychosis. However, the role of these hormones in first-episode psychosis (FEP) has received limited attention. It has been hypothesized that thyroid hormones could cause morphological modifications in the hippocampal structure through the upregulation of brain-derived neurotrophic factor (BDNF). In this study, we primarily aimed to determine the relationship between thyroid-stimulating hormone (TSH) levels, peripheral BDNF levels, and hippocampal volume in antipsychotic-naïve FEP patients. We also aimed to determine whether TSH levels were associated with clinical symptomatology. A total of 50 antipsychotic-naïve FEP patients were included in the study. At baseline, we collected fasting blood samples and registered sociodemographic and clinical variables (substance use, DUP, PANSS, GAF, and CDSS). Structural T1 MRI was performed at baseline to quantify brain volumes. No control group was used for this study. Of the 50 patients, more than one-third (36%) presented alterations in TSH levels, mainly elevated levels (32% of patients). The TSH levels were inversely correlated with both peripheral BDNF and hippocampal volume. On the multivariate analysis, the model that best predicted the relative hippocampal volume was a single variable model (TSH levels). No significant association was observed between TSH levels and clinical symptomatology. These results suggest that thyroid hormones could have a neuroprotective effect on the hippocampus in FEP patients, possibly through their effect by increasing BDNF concentrations, which could attenuate brain injury and neuroinflammation. Nevertheless, thyroid hormones could also affect hippocampal volume through other pathways.

Which Factors Are Associated with Distal Pancreatectomy Outcomes' Optimization with the Application of an Enhanced Recovery After Surgery Program?

Background: Distal pancreatectomy (DP) represents the best therapeutic option for patients with body-tail pancreatic neoplasms (PNs). The enhanced recovery after surgery protocol is widely used for treating patients with PN to speed up postoperative recovery. This study aims to describe our institute's experience in the application of fast recovery protocol in a cohort of patients treated with DP, identifying predictors facilitating a decrease in the length of hospital stay. Patient and Methods: Were retrospectively enrolled 60 consecutive cases of DP performed from January 2016 to June 2022 in patients treated with enhanced recovery protocol, 25% of them were treated with spleen preserving procedure. Single-variable logistic regression models were used to evaluate the potential association between patient characteristics and the probability of postoperative complications. Standard linear regression models were used for length of stay, number of postoperative days (PODs) from surgery to full bowel function recovery, and PODs to the interruption of intravenous analgesia administration. Results: Thirty-four (57%) patients underwent open surgery and 26 (43%) laparoscopic surgery. Patients who underwent laparoscopic surgery and spleen-preserving procedures experienced a lower complication rate (P = .037), shorter length of stay, and time of analgesic requirements. With single-variable logistic regression models patients treated with laparoscopic surgery had statistically significant higher recovery times in terms of nasogastric tube removal (P = .004) and early enteral nutrition (P = .001). Conclusion: Continual refinement with enhanced recovery protocol for treating PN patients based on perioperative counseling and surgical decision-making is crucial to reduce patient morbidity and time for recovery.

Nearly Half of Extreme Preterm Infants Are Iron Deficient at 4-6 Months of Corrected Age Despite Prophylactic Iron Supplementation

Background: Iron deficiency (ID) is the most prevalent micronutrient deficiency globally and has been associated with negative neurodevelopmental and behavioral outcomes in infants and young children. Susceptibility to ID is heightened in preterm infants due to lower iron reserves at birth, rapid growth during the initial weeks of life, immature erythropoiesis, and frequent blood samplings during hospitalization. To mitigate this problem, international bodies recommend prophylactic iron therapy from 2-6 weeks of age until at least 6-12 months in preterm infants. Currently, the literature regarding iron status in extremely preterm infants (EPI, infants born less than 28 weeks gestational age) who receive prophylactic iron supplementation after birth is limited. Considering the potential long-term detrimental effects of ID on the developing brain of these high-risk preterm infants, examining the prevalence and risk factors for ID are crucial to implement prevention strategies. Objective: To investigate the prevalence and risk factors associated with ID in EPI Methods: A retrospective cohort study was conducted using a population-based Provincial Perinatal Follow-Up database on all live EPIs born in Nova Scotia between 2005 and 2018. Infants with congenital malformations, chromosomal anomalies, blood disorders or those who died before outcome assessment were excluded. Prophylactic iron supplementation (2-3 mg/kg/day) starting at 2-4 weeks of chronological age was a standard of care for all included infants. Further, the dosage of iron was adjusted regularly during the hospital stay based on serum ferritin (SF) levels. Discharge instructions recommended continuing iron prophylaxis until 9-12 months corrected age (CA). At 4-6 months CA, all infants underwent blood tests to evaluate hemoglobin, and SF levels. ID was defined as SF levels below 20g/L or 12g/L at 4 and 6 months CA, respectively. A univariate analysis was performed by using a series of single-variable logistic regression models to identify the factors associated with presence of ID. Factors with a p-value &amp;lt; 0.20 in the univariate analysis were entered into a multivariable risk model using a backward selection procedure. Variables with a p-value &amp;lt; 0.05 were retained. Results: Among 146 infants, 67 (45.9%) had ID. Ferritin (16.4 µg/L vs 50.0 µg/L, p &amp;lt; .001) and reticulocyte hemoglobin equivalent (28.4pg vs 31.6pg, p &amp;lt; .001) were significantly lower in the ID group than non-ID group. The prevalence of ID decreased over time, from 59.7% in 2005-2011 to 40.3% in 2012-2018 (p = 0.52). Table 1 compares the antenatal and neonatal characteristics of the ID and non-ID groups. Table 2 compares sociodemographic variables and clinical variables at 4-6 months CA between two groups. Breastfeeding (exclusive or partial) versus formula alone at a 4-6 months CA was identified as an independent factor protective for ID (Odds: 0.2 (0.1 - 0.6), p = 0.003). Iron therapy at follow-up was also protective for ID but did not remain significant in the final model. Of note, iron therapy was stopped in 40 (27.4%) EPI before 4-6 months CA. Supplemental iron intake at 4-6 months CA was significantly lower in the infants receiving exclusive formula feeding (66.1%) compared with those breastfed (99.1%, p = 0.006). Conclusion(s): Despite prophylactic iron supplementation, nearly half of the EPI had ID at 4-6 months CA, and iron therapy was discontinued in roughly a quarter of all EPI before 4-6 months CA. Breastfeeding at 4-6 months follow-up was found to be protective for ID. High prevalence of ID in EPI highlights the need for further research in this area to avert the negative neurodevelopmental consequences of ID on the developing brain of these high-risk infants. Closer monitoring of iron status and early identification of children at high risk for ID is imperative to maximize the benefits of prophylactic iron supplementation in EPI. Further, there is lack of sufficient data for iron requirements in EPI leading to a variability in iron therapy recommendations by different professional organizations. Future prospective studies to investigate specific iron requirements of EPI as well as risk factors for ID in this high-risk vulnerable population are essential to optimize their neurodevelopmental outcomes.

LSTM time series NDVI prediction method incorporating climate elements: A case study of Yellow River Basin, China

Accurate prediction of the trend of Normalized Difference Vegetation Index (NDVI) time series in the Yellow River Basin (YRB) is crucial for the assessment of the hydrological and ecological environment in this region. Currently, the NDVI time series prediction model is primarily based on traditional models and single-variable neural network models. Nevertheless, these models present challenges in considering the limitations of multiple factors, causing the NDVI time series prediction results to lack reliability. To predict NDVI time-series in the YRB of China, this study constructed a multilayer multivariate Long-Short Term Memory (LSTM) neural network model including climatic components. The initial important climatic elements in this region were identified using GeoDetector. Then, the relationship between NDVI and climatic factors in the YRB of China is established. Finally, numerical scale data are used to train and predict a multilayer multivariate LSTM model with climatic components. According to the results, the three-layer multivariate LSTM neural network NDVI time series prediction model developed in this study has the best performance among the evaluated indices. When compared to existing time series prediction models, the proposed model in this study takes into account the common constraint effect of various climate factors on NDVI. This leads to a significantly improved prediction accuracy, presenting new opportunities for enhancing the prediction model. By analyzing the NDVI time series prediction outcomes for the YRB, it has been determined that the ecological environment of the area will continuously improve in the future. This study offers significant technological and theoretical backing for assessing the hydrological and ecological environment of the YRB and comparable ecologically vulnerable regions in China.

Modelling flood frequency and magnitude in a glacially conditioned, heterogeneous landscape: testing the importance of land cover and land use

Abstract. A reliable flood frequency analysis (FFA) requires selection of an appropriate statistical distribution to model historical streamflow data and, where streamflow data are not available (ungauged sites), a regression-based regional flood frequency analysis (RFFA) often correlates well with downstream channel discharge to drainage area relations. However, the predictive strength of the accepted RFFA relies on an assumption of homogeneous watershed conditions. For glacially conditioned fluvial systems, inherited glacial landforms, sediments, and variable land use can alter flow paths and modify flow regimes. This study compares a multivariate RFFA that considers 28 explanatory variables to characterize variable watershed conditions (i.e., surficial geology, climate, topography, and land use) to an accepted power-law relationship between discharge and drainage area. Archived gauge data from southern Ontario, Canada, are used to test these ideas. Mathematical goodness-of-fit criteria best estimate flood discharge for a broad range of flood recurrence intervals, i.e., 1.25, 2, 5, 10, 25, 50, and 100 years. The log-normal, Gumbel, log-Pearson type III, and generalized extreme value distributions are found most appropriate in 42.5 %, 31.9 %, 21.7 %, and 3.9 % of cases, respectively, suggesting that systematic model selection criteria are required for FFA in heterogeneous landscapes. Multivariate regression of estimated flood quantiles with backward elimination of explanatory variables using principal component and discriminant analyses reveal that precipitation provides a greater predictive relationship for more frequent flood events, whereas surficial geology demonstrates more predictive ability for high-magnitude, less-frequent flood events. In this study, all seven flood quantiles identify a statistically significant two-predictor model that incorporates upstream drainage area and the percentage of naturalized landscape with 5 % improvement in predictive power over the commonly used single-variable drainage area model (p&lt;2.2×10-16). Leave-one-out model testing and an analysis of variance (ANOVA) further support the parsimonious two-predictor model when estimating flood discharge in this low-relief landscape with pronounced glacial legacy effects and heterogeneous land use.

Dry Rot Caused by the Complex Colletotrichum falcatum and Thielaviopsis paradoxa Emerges as a Key Stalk Disorder in Newly Expanded Sugarcane Plantations from Northwestern São Paulo, Brazil

Sugarcane dry rot emerged as an important stalk disorder in newly expanded plantations in northwestern São Paulo, Brazil, under the current no-burning fully mechanical harvest policy gradually implemented in the past 20 years. This emergence was probably due to a considerable increase in both pathogen inocula and insect pest populations in sugarcane crop residues kept in the field. In this study, we surveyed the incidence of three stalk-related disorders in commercial sugarcane fields in six municipalities in northwestern São Paulo and the corresponding yield losses. The three stalk-related disorders surveyed were as follows: the red rot disease caused by the fungal pathogen Colletotricum falcatum, the spittlebug-induced shoot stunting, and the stem dry rot, which is associated with the simultaneous infection of C. falcatum and Thielaviopsis paradoxa, the pineapple set rot pathogen. Red rot disease was detected in 88.2% of the fields surveyed, while the spittlebug-induced shoot stunting disorder and the internal stem dry rot were found in 97.1% of the fields. Stem dry rot had the highest incidence and resulted in the highest yield losses. Total sugarcane yield losses were estimated at 20.1%, with an average of 14.2 (±3.8) t·ha−1 per field. The multiple regression model constructed to determine which of the three stem-related disorders contributed the most to total yield losses was not significant. Subsequently, the performance analyses of single-variable polynomial regression models indicated that the simple linear model was the best fit in terms of independently predicting sugarcane yield losses based on each stem-related disorder. Positive and significant correlations were only detected between sugarcane yield losses in t·ha−1 and the incidence of red rot disease or leafhopper-induced shoot stunting. We concluded that the stalk’s internal dry rot, as a disease complex associated with both C. falcatum and T. paradoxa, was the most important disorder in sugarcane fields in the northwest region of São Paulo state. A sustainable pest management program is needed to reduce the impact of all three stalk-associated disorders on regional sugarcane production.

Effects of planning variables on urban traffic noise at different scales

This study examines the control of traffic noise in urban planning, considering the differences in noise impacts at various scales. Acoustic simulations and spatial statistics were employed to compare traditional planning variables and planning big data for noise analysis. The study investigates noise impact in urban centres and fringes and analyses varying effects of a given variable on traffic noise at scales of 300, 600, and 1200 m. Additionally, sound environment optimisation strategies are proposed and validated for different scales and areas. The major findings are: (1) planning big data had more impact in single-variable models, while traditional variables were more significant in multivariable models; (2) the noise impact of most variables varied with the area and scale, for example, at 1200 m, the total building perimeter has opposing effects in urban centres and fringes, and the greening rate changes from positive to negative with increasing scale; (3) the proposed strategies reduced traffic noise by an average of 4.2, 3.2, and 2.3 dB at scales of 300, 600, and 1200 m, respectively. These findings provide valuable insights for the optimisation of urban sound environments.