Advances in Understanding Signet Ring Cell Gastric Cancer: Prognostic Insights Following Surgery. A machine learning approach.

BACKGROUND: Signet-ring cell (SRC) gastric carcinoma has traditionally been associated with a poor prognosis, though the literature presents contradictory results. Linear models are the standard statistical tools typically used to study these conditions. However, machine learning models have the potential to replace or even outperform linear models in predictive performance. STUDY DESIGN: We analyzed 608 patients diagnosed with gastric cancer at our institution. The analysis compared traditional linear models with machine learning models. Variables examined included demographic data, presence of an SRC component, lymph nodes resected and affected (ratio), stage of the disease, body mass index, pathological features, type of surgery, tumor location, and CEA levels to evaluate their influence on 5-year mortality and 2-year recurrence rates. RESULTS: SRC carcinoma was associated with poorer prognosis in terms of 5-year overall survival compared to non-signet ring cell carcinoma (NSRC). Additionally, SRC showed higher rates of lymph node metastasis, a higher lymph node ratio (resected/affected), and was more prevalent in younger patients (<65 years old). However, SRC was not an independent factor in the multivariate analysis. Linear models showed worse predictions for 5-year mortality and 2-year recurrence compared to machine learning models. Notably, the machine learning models did not consider the presence of the SRC component as an important variable. CONCLUSIONS: SRC gastric carcinoma continues to present an uncertain prognosis. Machine learning models can evaluate prognosis more accurately than traditional linear models. Large-scale studies employing machine learning algorithms are necessary to fully elucidate the predictive potential of these models.

The effect of air pollutants on COPD-hospitalized patients in Lanzhou, China (2015-2019).

Background: Lanzhou is the largest heavy industrial city in northwest China and it is a typical geographical valley-like city. However, there are few studies on the relationship between air pollutants and COPD, and their respective sample sizes are small, resulting in inconsistent results. The aim of this study is to analyze the effects of air pollutants on COPD hospitalizations in Lanzhou, China. Methods: An ecological time series study with distributed lag non-linear model (DLNM) was used for analysis. Daily COPD hospitalization data in Lanzhou from 1 January 2015 to 31 December 2019 were collected from 25 hospitals, as well as air pollutant data and meteorological data. Results: A total of 18,275 COPD hospitalizations were enrolled. For 10 µg/m3 increase in PM2.5, PM10, SO2, NO2, and 1 mg/m3 increase in CO at lag 07 day, the RR95%CI of COPD hospitalizations were 1.048 (1.030, 1.067), 1.008 (1.004, 1.013), 1.091 (1.048, 1.135), 1.043 (1.018, 1.068), and 1.160 (1.084, 1.242), respectively. The exposure-response curves between air pollutants (except O3-8h) and COPD hospitalizations were approximately linear with no thresholds. Female, and the harmful effect of PM on aged <65 years, the effect of gaseous pollutant on those aged ≥65 years, were stronger, particularly in the cold season. Exposure to air pollutants (except O3-8h) might increase the risk of COPD hospitalizations. O3-8h has a weak and unstable effect on COPD. Conclusion: Exposure to air pollutants (except O3-8h) increases the risk of COPD hospitalizations. O3-8h has a weak and unstable effect on COPD hospital admissions. The harmful effect of gaseous pollutants (except O3-8h) on COPD-hospitalized patients was stronger than that of PM.

Driving Sustainability in the United States: Efficacy of State-Level Biodiesel Policy Approaches.

The United States has significant greenhouse gas and criteria pollutant emissions that lead to global warming, human health, ozone, and smog issues, partially attributed to its diesel-consuming transport fleet. Until fleet electrification reaches cost parity with internal combustion engines, biodiesel use can reduce these negative impacts. In this study, we analyzed and categorized the biodiesel-supporting policies of each U.S. state using manual inductive coding to compare them against state-level biodiesel consumption and production. Through statistical modeling, we determined the efficacy of these policy approaches. The policy analysis identified that biodiesel policies that support infrastructure development and biodiesel production correlate significantly with increased biodiesel consumption at the state level. We also show that a combination of these policy categories correlates significantly with overall higher biodiesel consumption. Our methodological approach and policy analysis findings reveal valuable insight into the efficacy and outcomes from existing biofuel policies in the United States.

Lagged effects of climate factors on bacillary dysentery in western China.

BACKGROUND: Evidence has shown that the incidence of bacillary dysentery (BD) is associated with climatic factors. However, the lagged effects of climatic factors on BD are still unclear, especially lacking research evidence from arid and semi-arid regions. Therefore, this study aims to add new insights into this research field. METHODS: Spatial autocorrelation, time series analysis and spatiotemporal scans were used to perform descriptive analyses of BD cases from 2009 to 2019. On the basis of monthly data from 2015 to 2019, multivariable distributed lag non-linear models were used to investigate the lagged effects of climatic factors on BD. RESULTS: The hot spots for BD incidence are gradually decreasing and becoming increasingly concentrated in the southern part of Gansu Province. The maximum cumulative relative risks for monthly average temperature, sunshine duration, average relative humidity and precipitation were 3.21, 1.64, 1.55 and 1.41, respectively. The lagged effects peaked either in the current month or with a 1-month lag, and the shape of the exposure-response curve changed with the increase in maximum lag time. After stratification by per capita gross domestic product, there were differences in the effects. CONCLUSIONS: Climatic factors can influence the incidence of BD, with effects varying across different lag times. It is imperative to vigilantly track the disparities in the incidence of BD attributable to economic factors.

Assessing abundance-suitability models to prioritize conservation areas for the dwarf caimans in South America.

Species-environment relationships have been extensively explored through species distribution models (SDM) and species abundance models (SAM), which have become key components to understand the spatial ecology and population dynamics directed at biodiversity conservation. Nonetheless, within the internal structure of species' ranges, habitat suitability and species abundance do not always show similar patterns, and using information derived from either SDM or SAM could be incomplete and mislead conservation efforts. We gauged support for the abundance-suitability relationship and used the combined information to prioritize the conservation of South American dwarf caimans (Paleosuchus palpebrosus and P. trigonatus). We used 7 environmental predictor sets (surface water, human impact, topography, precipitation, temperature, dynamic habitat indices, soil temperature), 2 regressions methods (Generalized Linear Models-GLM, Generalized Additive Models-GAM), and 4 parametric distributions (Binomial, Poisson, Negative binomial, Gamma) to develop distribution and abundance models. We used the best predictive models to define four categories (low, medium, high, very high) to plan species conservation. The best distribution and abundance models for both Paleosuchus species included a combination of all predictor sets, except for the best abundance model for P. trigonatus which incorporated only temperature, precipitation, surface water, human impact, and topography. We found non-consistent and low explanatory power of environmental suitability to predict abundance which aligns with previous studies relating SDM-SAM. We extracted the most relevant information from each optimal SDM and SAM and created a consensus model (2,790,583 km2) that we categorized as low (39.6%), medium (42.7%), high (14.9%), and very high (2.8%) conservation priorities. We identified 279,338 km2 where conservation must be critically prioritized and only 29% of these areas are under protection. We concluded that optimal models from correlative methods can be used to provide a systematic prioritization scheme to promote conservation and as surrogates to generate insights for quantifying ecological patterns.

An Extended Application of the Fast Multi-Locus Ridge Regression Algorithm in Genome-Wide Association Studies of Categorical Phenotypes.

Categorical (either binary or ordinal) quantitative traits are widely observed to measure count and resistance in plants. Unlike continuous traits, categorical traits often provide less detailed insights into genetic variation and possess a more complex underlying genetic architecture, which presents additional challenges for their genome-wide association studies. Meanwhile, methods designed for binary or continuous phenotypes are commonly used to inappropriately analyze ordinal traits, which leads to the loss of original phenotype information and the detection power of quantitative trait nucleotides (QTN). To address these issues, fast multi-locus ridge regression (FastRR), which was originally designed for continuous traits, is used to directly analyze binary or ordinal traits in this study. FastRR includes three stages of continuous transformation, variable reduction, and parameter estimation, and it can computationally handle categorical phenotype data instead of link functions introduced or methods inappropriately used. A series of simulation studies demonstrate that, compared with four other continuous or binary or ordinal approaches, including logistic regression, FarmCPU, FaST-LMM, and POLMM, the FastRR method outperforms in the detection of small-effect QTN, accuracy of estimated effect, and computation speed. We applied FastRR to 14 binary or ordinal phenotypes in the Arabidopsis real dataset and identified 479 significant loci and 76 known genes, at least seven times as many as detected by other algorithms. These findings underscore the potential of FastRR as a very useful tool for genome-wide association studies and novel gene mining of binary and ordinal traits.

Efficiency-Oriented Model Predictive Control: A Novel MPC Strategy to Optimize the Global Process Performance.

Existing control strategies, such as Real-time Optimization (RTO), Dynamic Real-time Optimization (DRTO), and Economic Model Predictive Control (EMPC) cannot enable optimal operation and control behavior in an optimal fashion. This work proposes a novel control strategy, named the efficiency-oriented model predictive control (MPC), which can fully realize the potential of the optimization margin to improve the global process performance of the whole system. The ideas of optimization margin and optimization efficiency are first proposed to measure the superiority of the control strategy. Our new efficiency-oriented MPC innovatively uses a nested optimization structure to optimize the optimization margin directly online. To realize the computation, a Periodic Approximation technique is proposed, and an Efficiency-Oriented MPC Type I is constructed based on the Periodic Approximation. In order to alleviate the strict constraint of Efficiency-Oriented MPC Type I, the zone-control-based optimization concept is used to construct an Efficiency-Oriented MPC Type II. These two well-designed efficiency-oriented controllers were compared with other control strategies over a Continuous Stirred Tank Reactor (CSTR) application. The simulation results show that the proposed control strategy can generate superior closed-loop process performance, for example, and the Efficiency-Oriented MPC Type I can obtain 7.11% higher profits than those of other control strategies; the effectiveness of the efficiency-oriented MPC was, thereby, demonstrated.

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.

Regression-Based Proximal Causal Inference.

Negative controls are increasingly used to evaluate the presence of potential unmeasured confounding in observational studies. Beyond the use of negative controls to detect the presence of residual confounding, proximal causal inference (PCI) was recently proposed to de-bias confounded causal effect estimates, by leveraging a pair of treatment and outcome negative control or confounding proxy variables. While formal methods for statistical inference have been developed for PCI, these methods can be challenging to implement as they involve solving complex integral equations that are typically ill-posed. We develop a regression-based PCI approach, employing two-stage generalized linear regression models (GLMs) to implement PCI, which obviates the need to solve difficult integral equations. The proposed approach has merit in that (i) it is applicable to continuous, count, and binary outcomes cases, making it relevant to a wide range of real-world applications, and (ii) it is easy to implement using off-the-shelf software for GLMs. We establish the statistical properties of regression-based PCI and illustrate their performance in both synthetic and real-world empirical applications.

Association between compound extreme weather event types and the spectrum of emergency ambulance calls: A metropolitan study in Shenzhen.

BACKGROUND: Compound extreme weather events, a combination of weather and climate drivers that lead to potentially high-impact events, are becoming more frequent with climate change. The number of emergency ambulance calls (EACs) is expected to increase during compound extreme weather events. However, the extent of these increases and the trends over time have not been fully assessed. METHODS: We obtained 242,165 EAC records for Shenzhen from January 1, 2020, to June 30, 2023. A compound extreme weather event was defined as the occurrence of at least two extreme weather events on the same day. A distributed lag non-linear model was used to explore the exposure-response and lag-response relationships between various compound extreme weather events and all-cause and specific-cause EACs. FINDING: Compound Cold & Strong Monsoon events had more significant impacts on EACs for all causes and endocrine diseases, with the cumulative relative risk (CRR) of 1.401 (95% confidence interval (CI):1.290-1.522) and 1.641 (95% CI:1.279-2.105). Compound Heat Wave & Lightning events had more obvious impacts on digestive disease and endocrine disease EACs, with the CRRs of 1.185 (95% CI:1.041-1.348) and 1.278 (95% CI:0.954-1.711), respectively. Compound Rainstorm & Lightning & Heat Wave events also led to increased RRs of EACs for all causes (CRR: 1.168, 95% CI:1.012-1.348), cardiovascular diseases (CRR: 1.221, 95% CI:0.917-1.624), digestive diseases (CRR: 1.395, 95% CI:1.130-1.721), and endocrine diseases (CRR: 1.972, 95% CI:1.235-3.149). There was no increased RR in the compound Rainstorm & Lightning events for all types of EACs. INTERPRETATION: Our study explored the relationship between EACs and compound extreme weather events, suggesting that compound extreme weather events are associated with the acute onset of cardiovascular diseases, digestive diseases, and endocrine diseases, increasing the burden on emergency ambulance resources for both all causes and specific diseases mentioned above.

Empirical modelling of 2205 DSS flow curves using strain-compensated Arrhenius rate-type constitutive model.

This work predicts, hot flow curves of 2205 DSS using strain-compensated Arrhenius rate-type constitutive model. Twenty-five (25) × Ø10 diameter × 15 mm height cylindrical samples were hot compressed at a temperature between 850 and 1050 °C at an interval of 50 °C and strain rates between 0.001 and 5 s-1, using Gleeble 1500D. After the tests, corrected flow curves were plotted followed by computation of deformations constants at various deformation conditions using steady state stress. The values of the constants were (α = 0.009708, Q = 445 kJ/mol and n = 3.7) and seemed comparable to the previous studies of DSS. Steady state predictive model was then constructed using the calculated constants and showed a reasonably good accuracy with low value of MARE = 7.78%. Furthermore, calculated strain compensated Arrhenius rate type model was used to predict flow curves at various deformation. The model had a good estimation of flow curves of flow curves at 900-1050 °C across all strain rates as reflected by MARE = 5.47%. A notable discrepancy between predicted and experimental flow stress was observed at 850 °C and across all the strain rates. A model refinement using generalised reduced gradient improved the accuracy of the model by 34.7% despite deformation conditions at 850 °C and low strain rates (0.01/ 0.1) s-1 showing minimum improvement. Further modification of Z-parameter by compensating for the strain rate improved the accuracy of the model at 850 °C/0.01 s-1/0.1 s-1. Lastly, a comparison of the current model with the other non-linear model showed that the latter was more accurate in estimation of flow curves since it relied on characteristics flow stress points controlled by underlying active deformation mechanisms.

An improved semi-supervised segmentation of the retinal vasculature using curvelet-based contrast adjustment and generalized linear model.

Diagnosis of most ophthalmic conditions, such as diabetic retinopathy, generally relies on an effective analysis of retinal blood vessels. Techniques that depend solely on the visual observation of clinicians can be tedious and prone to numerous errors. In this article, we propose a semi-supervised automated approach for segmenting blood vessels in retinal color images. Our method effectively combines some classical filters with a Generalized Linear Model (GLM). We first apply the Curvelet Transform along with the Contrast-Limited Histogram Adaptive Equalization (CLAHE) technique to significantly enhance the contrast of vessels in the retinal image during the preprocessing phase. We then use Gabor transform to extract features from the enhanced image. For retinal vasculature identification, we use a GLM learning model with a simple link identity function. Binarization is then performed using an automatic optimal threshold based on the maximum Youden index. A morphological cleaning operation is applied to remove isolated or unwanted segments from the final segmented image. The proposed model is evaluated using statistical parameters on images from three publicly available databases. We achieve average accuracies of 0.9593, 0.9553 and 0.9643, with Receiver Operating Characteristic (ROC) analysis yielding Area Under Curve (AUC) values of 0.9722, 0.9682 and 0.9767 for the CHASE_DB1, STARE and DRIVE databases, respectively. Compared to some of the best results from similar approaches published recently, our results exceed their performance on several datasets.

Verifying the feasibility of wastewater-based epidemiological monitoring for the small catchment and sewage networks with significant pretreatment.

Wastewater-based epidemiology (WBE) has emerged as a valuable tool for COVID-19 monitoring, especially as the frequency of clinical testing diminishes. Beyond COronaVIrus Disease 19 (COVID-19), the tool's versatility extends to addressing various public health concerns, including antibiotic resistance and drug consumption. However, the complexity of sewage systems introduces noise when measuring chemical tracer concentrations, potentially compromising their applicability for modeling. In our study, we detail the approach adopted to determine the concentration of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) ribonucleiec acid (RNA) in wastewater from the Ponte a Niccheri wastewater treatment plant in Tuscany (Italy), with a sample size of N = 13,935 inhabitants. The unique characteristics of this wastewater system, including mandatory pretreatment in septic tanks with extended retention times, the presence of a hospital for COVID-19 patients, and mixed sewage networks, posed additional challenges. Nevertheless, our results highlight a robust and significant correlation between our measurements and the number of infections within the wastewater treatment plant's catchment area at the time of sampling. A simple linear model also shows promising results in estimating the number of infected people within the area.

Level-Dependent Subcortical Electroencephalography Responses to Continuous Speech.

The auditory brainstem response (ABR) is a measure of subcortical activity in response to auditory stimuli. The wave V peak of the ABR depends on the stimulus intensity level, and has been widely used for clinical hearing assessment. Conventional methods estimate the ABR average electroencephalography (EEG) responses to short unnatural stimuli such as clicks. Recent work has moved toward more ecologically relevant continuous speech stimuli using linear deconvolution models called temporal response functions (TRFs). Investigating whether the TRF waveform changes with stimulus intensity is a crucial step toward the use of natural speech stimuli for hearing assessments involving subcortical responses. Here, we develop methods to estimate level-dependent subcortical TRFs using EEG data collected from 21 participants listening to continuous speech presented at 4 different intensity levels. We find that level-dependent changes can be detected in the wave V peak of the subcortical TRF for almost all participants, and are consistent with level-dependent changes in click-ABR wave V. We also investigate the most suitable peripheral auditory model to generate predictors for level-dependent subcortical TRFs and find that simple gammatone filterbanks perform the best. Additionally, around 6 min of data may be sufficient for detecting level-dependent effects and wave V peaks above the noise floor for speech segments with higher intensity. Finally, we show a proof-of-concept that level-dependent subcortical TRFs can be detected even for the inherent intensity fluctuations in natural continuous speech.

Spatial Pattern of Host Tree Size, Rather than of Host Tree Itself, Affects the Infection Likelihood of a Fungal Stem Disease.

The spatial pattern of diseased forest trees is a product of the spatial pattern of host trees and the disease itself. Previous studies have focused on describing the spatial pattern of diseased host trees, and it remains largely unknown whether an antecedent spatial pattern of host/nonhost trees affects the infection pattern of a disease and how large the effect sizes of the spatial pattern of host/nonhost trees and host size are. The results from trivariate random labeling showed that the antecedent pattern of the host ash tree, Fraxinus mandshurica, but not of nonhost tree species, impacted the infection pattern of a stem fungal disease caused by Inonotus hispidus. To investigate the effect size of the spatial pattern of ash trees, we employed the SADIE (Spatial Analysis by Distance IndicEs) aggregation index and clustering index as predictors in the GLMs. Globally, the spatial pattern (vi index) of ash trees did not affect the infection likelihood of the focal tree; however, the spatial pattern of DBH (diameter at breast height) of ash trees significantly affected the infection likelihood of the focal tree. We sampled a series of circular plots with different radii to investigate the spatial pattern effect of host size on the infection likelihood of the focal tree locally. The results showed that the location (patch/gap) of the DBH of the focal tree, rather than that of the focal tree itself, significantly affected its infection likelihood in most plots of the investigated sizes. A meta-analysis was employed to settle the discrepancy between plots of different sizes, which led to results consistent with those of global studies. The results from meta-regression showed that plot size had no significant effects.

Inference of monosynaptic connections from parallel spike trains: A review.

This article presents a mini-review about the progress in inferring monosynaptic connections from spike trains of multiple neurons over the past twenty years. First, we explain a variety of meanings of "neuronal connectivity" in different research areas of neuroscience, such as structural connectivity, monosynaptic connectivity, and functional connectivity. Among these, we focus on the methods used to infer the monosynaptic connectivity from spike data. We then summarize the inference methods based on two main approaches, i.e., correlation-based and model-based approaches. Finally, we describe available source codes for connectivity inference and future challenges. Although inference will never be perfect, the accuracy of identifying the monosynaptic connections has improved dramatically in recent years due to continuous efforts.

Analyzing the heterogenous effects of factors on high-range speeding likelihood of taxi speeders: Does explainable deep learning provides more insights than random parameter approach?

The random parameters Generalized Linear Model (GLM) is frequently used to model speeding characteristics and capture the heterogenous effects of factors. However, this statistical approach is seldom employed for prediction and generalization due to the challenge of transferring its predefined errors. Recently, the emergence of explainable AI techniques has illuminated a new path for analyzing factors associated with risky driving behaviors. Despite this, there remains a gap that comparing results from machine and deep learning (ML/DL) approaches with those from random parameters GLM. This study aims to apply the random parameter GLM and explainable deep learning to evaluate the heterogenous effects of factors on the taxis' high-range speeding likelihood. Initially, a Beta GLM with random parameters (BGLM-RP) is developed to model the high-range speeding likelihood among taxi drivers. Additionally, XGBoost, a simple convolutional neural network (Simple-CNN), a deeper CNN (DCNN), and a deeper CNN with self-attention (DCNN-SA) are developed. The quantified explanations and illustrations of the factors' heterogenous effects from ML/DL models are derived from pseudo coefficients by decomposing factors' SHapley Additive exPlanations (SHAP) values. All the developed statistical, ML, and DL models are compared in terms of mean absolute errors and mean square errors on testing and full data. Results show that DCNN-SA excels in prediction on testing data, indicating its superior generalization capabilities, while BGLM-RP outperforms other models on full data. The DCNN-SA can reveal the heterogenous effects of factors for both in-sample and out-of-sample data, which is not possible for the random parameter GLM. However, BGLM-RP can reveal larger magnitudes of the factors' heterogenous effects for in-sample data. The signs and significances are identical between the varying coefficients from BGLM-RP and the pseudo coefficients from the ML/DL models, demonstrating the validity and rationale of using the proposed explanation framework to quantify the factors' effects in ML/DL models. The study also discusses the contributions of various factors to the high-range speeding likelihood of taxi drivers.

Assessing the complexation of dissolved organic matter with heavy metals (Cu2+, Pb2+) in leachate from an old Japanese landfill site using fluorescence quenching.

Dissolved organic matter (DOM) in landfill leachate impacts the toxicity, bioavailability, and migration of heavy metals. The present study investigated the complexation of heavy metals (Cu2+ and Pb2+) with DOM from two landfill leachate samples, representing an old landfill site containing incineration residues and incombustible waste. The logarithms of the stability constant (log KM) and percentage of complexed fluorophores were calculated using both the Ryan-Weber non-linear model and the modified Stern-Volmer model, yielding good agreement. The log KM values (at pH = 6.0 ± 0.1) calculated using both methods for the two sampling points were 5.02-5.13 and 4.85-5.11 for Cu2+-DOM complexation, and 5.01-5.13 and 4.46-4.87 for Pb2+-DOM complexation, respectively. Log KM was slightly higher for binding of DOM with Cu2+ than Pb2+, and the quenching degree was stronger for complexation with Cu2+ (28.5-30.6% and 38.0-45.9%) than Pb2+ (6.5-7.1% and 10.0-15.4%) in both leachate samples. While log KM values were similar, differences in the contributions of functional groups and molecular composition led to varying degrees of quenching. This study reveals the potential for heavy metal binding by DOM in landfill leachate with a unique solid waste composition and emphasizes variations in fluorescence quenching between Cu2+ and Pb2+ despite similar log KM levels. These findings may be useful for assessing heavy metal behavior in landfill leachate and its impacts on the surrounding environment.

Evaluating perioperative stresses in children by noninvasive modalities using salivary cortisol and autonomic reactivity.

PURPOSE: Salivary cortisol (SalC) and low to high pulse ratio (LHR) were used for evaluating perioperative stresses in children. METHODS: Children aged 6 months-16 years having elective general (thoracic/abdominal) or minor (open/minimally invasive: MI) procedures underwent pulse monitoring during AM (08:00-12:00) and PM (17:00-21:00) saliva collections from the day before surgery (S-1) to 3 days after surgery (S + 3). SalC/LHR were correlated with age, sex, caregiver attendance, operative time, and surgical site/approach using mixed model analysis and face/numeric pain rating scales (FRS/NRS). RESULTS: Mean ages (years): minor-open (n = 31) 4.7 ± 2.0, thoracic-open (n = 2) 8.7 ± 4.9, thoracic-MI (n = 6) 9.6 ± 6.1, abdominal-open (n = 14) 4.3 ± 4.1, and abdominal-MI (n = 32) 8.0 ± 5.0. Postoperative SalC increased rapidly and decreased to preoperative levels by S + 3 (p < 0.001). LHR increased slightly without decreasing (p = 0.038). SalC correlated positively with operative time (p = 0.036) and open surgery (p = 0.0057), and negatively with age (p < 0.0001) and caregiver attendance (p < 0.001). SalC correlated positively with FRS (n = 51) at S + 2(PM) (p = 0.023), S + 3(AM) (p < 0.001), S + 3(PM) (p = 0.012) and NRS (n = 34) at S + 1(AM) (p = 0.031), S + 3(AM) (p < 0.044). LHR positively correlated with age (p = 0.0072), female sex (p = 0.0047), and caregiver attendance (p = 0.0026). Postoperative SalC after robotic-assisted MI was significantly lower than after open surgery at S + 2(AM) (p = 0.020). CONCLUSIONS: SalC correlated with pain. Caregiver attendance effectively alleviated stress.

Identifying risk clusters for African swine fever in Korea by developing statistical models.

Introduction: African swine fever (ASF) is a disease with a high mortality rate and high transmissibility. Identifying high-risk clusters and understanding the transmission characteristics of ASF in advance are essential for preventing its spread in a short period of time. This study investigated the spatial and temporal heterogeneity of ASF in the Republic of Korea by analyzing surveillance data on wild boar carcasses. Methods: We observed a distinct annual propagation pattern, with the occurrence of ASF-infected carcasses trending southward over time. We developed a rank-based statistical model to evaluate risk by estimating the average weekly number of carcasses per district over time, allowing us to analyze and identify risk clusters of ASF. We conducted an analysis to identify risk clusters for two distinct periods, Late 2022 and Early 2023, utilizing data from ASF-infected carcasses. To address the underestimation of risk and observation error due to incomplete surveillance data, we estimated the number of ASF-infected individuals and accounted for observation error via different surveillance intensities. Results: As a result, in Late 2022, the risk clusters identified by observed and estimated number of ASF-infected carcasses were almost identical, particularly in the northwestern Gyeongbuk region, north Chungbuk region, and southwestern Gangwon region. In Early 2023, we observed a similar pattern with numerous risk clusters identified in the same regions as in Late 2022. Discussion: This approach enhances our understanding of ASF spatial dynamics. Additionally, it contributes to the epidemiology and study of animal infectious diseases by highlighting areas requiring urgent and focused intervention. By providing crucial data for the targeted allocation of resources for disease management and preventive measures, our findings lay vital groundwork for improving ASF management strategies, ultimately aiding in the containment and control of this devastating disease.