Remediation of phenanthrene contaminated soil by persulphate coupled with Pseudomonas aeruginosa GZ7 based on oxidation prediction model.

Chemical oxidation coupled with microbial remediation has attracted widespread attention for the removal of polycyclic aromatic hydrocarbons (PAHs). Among them, the precise evaluation of the feasible oxidant concentration of PAH-contaminated soil is the key to achieving the goal of soil functional ecological remediation. In this study, phenanthrene (PHE) was used as the target pollutant, and Fe2+-activated persulphate (PS) was used to remediate four types of soils. Linear regression analysis identified the following important factors influencing remediation: PS dosage and soil PHE content for PHE degradation, Fe2+ dosage, hydrolysable nitrogen (HN), and available phosphorus for PS decomposition. A comprehensive model of "soil characteristics-oxidation conditions-remediation effect" with a high predictive accuracy was constructed. Based on model identification, Pseudomonas aeruginosa GZ7, which had high PAHs degrading ability after domestication, was further applied to coupling repair remediation. The results showed that the optimal PS dose was 0.75% (w/w). The response relationship between soil physical, chemical, and biological indicators at the intermediate interface and oxidation conditions was analysed. Coupled remediation effects were clarified using microbial diversity sequencing. The introduction of Pseudomonas aeruginosa GZ7 stimulated the relative abundance of Cohnella, Enterobacter, Paenibacillus, and Bacillus, which can promote material metabolism and energy transformation during remediation.

On QSAR modeling with novel degree-based indices and thermodynamics properties of eye infection therapeutics.

Topological descriptors are numerical results generated from the structure of a chemical graph that are useful in identifying the physicochemical characteristics of a wide range of drugs. The introduction of molecular descriptors advances quantitative structure-property relationship research. This article focuses on the nine degree-based topological indices and the linear regression model of the eye infection drugs. We introduced two new indices, namely, the "first revised Randic index" and the "second revised Randic index, for the analysis of eye infection drugs. Topological indices are calculated by using edge partitioning, vertex degree counting, and vertex degree labeling. This analysis is done with a scientific calculator and then authenticated with Matlab, a potent tool for examining data. The experimental data and results of the topological indices serve as inputs for the statistical computations and provide the values of intercepts, slopes, and correlation coefficients. All the correlations for the eye-infection drugs are positive, indicating a direct relationship between the experimental and estimated results of the drugs. There are significant results of the p-test for all of the characteristics of eye infection, such as molecular weight, boiling point, enthalpy, flash point, molar refraction, and molar volume, that validate the accuracy of the computations. A significant link was determined in this study between the defined indices with two properties: molar weight and molar refraction. The molar weight and molar refraction have a correlation coefficient ranging from 0.9. These results demonstrate a strong association between the indices and the properties under investigation. The linear regression approach is a valuable tool for chemists and pharmacists to obtain data about different medicines quickly and cost-effectively.

Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador.

The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite's effectiveness in anomaly detection. Our research's value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.

Learners' continuance intention in multimodal language learning education: An innovative multiple linear regression model.

Confronted with the unprecedented COVID-19 pandemic, millions of learners have received, are receiving, or will receive multimodal language learning education. This study aims to explore the relationships between various factors influencing learners' continuance intention by proposing an innovative multiple linear regression model in multimodal language learning education. Participants were randomly recruited (N = 334) in China who had received multimodal language learning education by combining Massive Open Online Courses, Rain Classroom, and WeChat. The research instrument, a comprehensive questionnaire, was sent through the online system named Questionnaire Star developed by technical experts. A multiple linear regression analysis was adopted to test the proposed hypotheses and fit the research model. This study confirms the relationships between the Technology Acceptance Model-inclusive constructs such as perceived ease of use, perceived usefulness, attitudes toward multimodal language learning education, and continuance intention of participating in multimodal language learning education. The Technology Acceptance Model is also associated with other constructs, e.g. Task-technology fit, Individual-technology fit, Openness, and Reputation of multimodal language learning educational institutes, and personal investment in multimodal language learning education. However, personal investment neither directly nor indirectly predicts continuance intention. Educators and designers could make every effort to improve multimodal language learning education to enhance personal investment and foster its association with continuance intention of learners.

A comprehensive evaluation of influencing factors of neonicotinoid insecticides (NEOs) in farmland soils across China: First focus on film mulching.

Neonicotinoid insecticide (NEO) residues in agricultural soils have concerning and adverse effects on agroecosystems. Previous studies on the effects of farmland type on NEOs are limited to comparing greenhouses with open fields. On the other hand, both NEOs and microplastics (MPs) are commonly found in agricultural fields, but their co-occurrence characteristics under realistic fields have not been reported. This study grouped farmlands into three types according to the covering degree of the film, collected 391 soil samples in mainland China, and found significant differences in NEO residues in the soils of the three different farmlands, with greenhouse having the highest NEO residue, followed by farmland with film mulching and farmland without film mulching (both open fields). Furthermore, this study found that MPs were significantly and positively correlated with NEOs. As far as we know this is the first report to disclose the association of film mulching and MPs with NEOs under realistic fields. Moreover, multiple linear regression and random forest models were used to comprehensively evaluate the factors influencing NEOs (including climatic, soil, and agricultural indicators). The results indicated that the random forest model was more reliable, with MPs, farmland type, and total nitrogen having higher relative contributions.

Cross-Sectional but Not Prospective Association of Accelerometry-Derived Physical Activity With Quality of Life in Children and Adolescents.

Objectives: This study aims to quantify the cross-sectional and prospective associations between quality of life (QoL) and moderate-to-vigorous physical activity (MVPA). Methods: This study was based on the Swiss children's Objectively measured PHYsical Activity cohort. The primary endpoint is the overall QoL score and its six dimensions. The main predictor is the average time spent in MVPA per day. Linear mixed effects and linear regression models respectively were used to investigate the cross-sectional and prospective associations between MVPA and QoL. Results: There were 352 participants in the study with complete data from baseline (2013-2015) and follow-up (2019). MVPA was positively associated with overall QoL and physical wellbeing (p = 0.023 and 0.002 respectively). The between-subject MVPA was positively associated with the overall QoL, physical wellbeing, and social wellbeing (p = 0.030, 0.017, and 0.028 respectively). Within-subject MVPA was positively associated with physical wellbeing and functioning at school (p = 0.039 and 0.013 respectively). Baseline MVPA was not associated with QoL 5 years later. Conclusion: Future longitudinal studies should employ shorter follow-up times and repeat measurements to assess the PA and QoL association.

Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact.

BACKGROUND: Cancer, a disease with high morbidity and mortality rates, poses a significant threat to human health. Driver genes, which harbor mutations accountable for the initiation and progression of tumors, play a crucial role in cancer development. Identifying driver genes stands as a paramount objective in cancer research and precision medicine. RESULTS: In the present work, we propose a method for identifying driver genes using a Generalized Linear Regression Model (GLM) with Shrinkage and double-Weighted strategies based on Functional Impact, which is named GSW-FI. Firstly, an estimating model is proposed for assessing the background functional impacts of genes based on GLM, utilizing gene features as predictors. Secondly, the shrinkage and double-weighted strategies as two revising approaches are integrated to ensure the rationality of the identified driver genes. Lastly, a statistical method of hypothesis testing is designed to identify driver genes by leveraging the estimated background function impacts. Experimental results conducted on 31 The Cancer Genome Altas datasets demonstrate that GSW-FI outperforms ten other prediction methods in terms of the overlap fraction with well-known databases and consensus predictions among different methods. CONCLUSIONS: GSW-FI presents a novel approach that efficiently identifies driver genes with functional impact mutations using computational methods, thereby advancing the development of precision medicine for cancer.

Optical Properties Prediction for Red and Near-Infrared Emitting Carbon Dots Using Machine Learning.

Functional nanostructures build up a basis for the future materials and devices, providing a wide variety of functionalities, a possibility of designing bio-compatible nanoprobes, etc. However, development of new nanostructured materials via trial-and-error approach is obviously limited by laborious efforts on their syntheses, and the cost of materials and manpower. This is one of the reasons for an increasing interest in design and development of novel materials with required properties assisted by machine learning approaches. Here, the dataset on synthetic parameters and optical properties of one important class of light-emitting nanomaterials - carbon dots are collected, processed, and analyzed with optical transitions in the red and near-infrared spectral ranges. A model for prediction of spectral characteristics of these carbon dots based on multiple linear regression is established and verified by comparison of the predicted and experimentally observed optical properties of carbon dots synthesized in three different laboratories. Based on the analysis, the open-source code is provided to be used by researchers for the prediction of optical properties of carbon dots and their synthetic procedures.

How to make calibration less painful-A proposition for an automatic, reliable and time-efficient procedure.

In behavioral and neurophysiological pain studies, multiple types of calibration methods are used to quantify the individual pain sensation stimuli. Often, studies lack a detailed calibration procedure description, data linearity, and quality quantification and omit required control for sex pain differences. This hampers study repetition and interexperimental comparisons. Moreover, typical calibration procedures require a high number of stimulations, which may cause discomfort and stimuli habituation among participants. To overcome those shortcomings, we present an automatic calibration procedure with a novel stimuli estimation method for intraepidermal stimulation. We provide an in-depth data analysis of the collected self-reports from 70 healthy volunteers (37 males) and propose a method based on a dynamic truncated linear regression model (tLRM). We compare its estimates for the sensation (t) and pain (T) thresholds and mid-pain stimulation (MP), with those calculated using traditional estimation methods and standard linear regression models. Compared to the other methods, tLRM exhibits higher R2 and requires 36% fewer stimuli applications and has significantly higher t intensity and lower T and MP intensities. Regarding sex differences, t and T were found to be lower for females compared to males, regardless of the estimation method. The proposed tLRM method quantifies the calibration procedure quality, minimizes its duration and invasiveness, and provides validation of linearity between stimuli intensity and subjective scores, making it an enabling technique for further studies. Moreover, our results highlight the importance of control for sex in pain studies.

Thallium content in vegetables and derivation of threshold for safe food production in soil: A meta-analysis.

Soil thallium (Tl) pollution is a serious environmental problem, and vegetables are the primary pathway for human exposure to Tl. Therefore, it is important to investigate the characteristics of soil Tl uptake by vegetables. In this study, the meta-analysis approach was first applied to explore the relationship between Tl content in vegetables and soil environment, as well as key factors influencing soil physical-chemical properties, and to derive soil thresholds for Tl. The results indicated that various types of vegetables have different capabilities for Tl accumulation. Vegetables from contaminated areas showed high Tl accumulation, and the geomean Tl content in different types of vegetables was in the following order: leafy > root-stalk > solanaceous vegetables. Taro and kale had significantly higher capability to accumulate soil Tl among the 35 species studied, with Tl bioconcentration factor values of 0.060 and 0.133, respectively. Pearson correlation analysis and meta-analysis revealed that the Tl content in vegetables was significantly correlated with soil pH and Tl content in soil. The linear predictive model for Tl accumulation in vegetables based on soil Tl content described the data well, and the fitting coefficient R2 increased with soil pH value. According to potential dietary toxicity, the derived soil Tl thresholds for all, leafy and root-stalk vegetables increased with an increase in soil pH, and were in the range of 1.46-6.72, 1.74-5.26 and 0.92-6.06 mg/kg, respectively. The soil Tl thresholds for kale, lettuce and carrot were in the range of 0.24-4.89, 2.94-3.32 and 3.77-14.43 mg/kg, respectively. Ingestion of kale, beet, sweet potato, potato, taro, pepper, turnip, Chinese cabbage, eggplant and carrot poses potential health risks. The study provides scientific guidance for vegetable production in Tl-contaminated areas and can help with the selection of vegetable species suitable for avoiding the absorption of Tl from contaminated soil.

Segregation of formulated powders in direct compression process and evaluations by small bench-scale testers.

Powder segregation can cause severe issues in processes of pharmaceutical drugs for control of content uniformity if the powder is likely to be free or easy flowing. Assessing segregation intensity of formulated powders in a process is challenging at the formulation stage because of the limited availability of samples. An advanced segregation evaluation using small bench-scale testers can be useful for formulation decisions and suggestions of operation conditions in the process, which has not been practically investigated before. In this study, eight formulations (two co-processed excipients blended with one active pharmaceutical ingredient at different ratios) were used for the segregation study on two types of bench-scale testers (air-induced and surface rolling segregation tester), and a pilot simulation process rig as a comparative study. The results show that segregation measured on the bench-scale testers can give a good indication of the segregation intensity of a blend if the segregation intensity is not more than 20%. The comparison also shows that both the bench-scale testers have a good correlation to the process rig, respectively, which means either segregation tester can be used independently for the evaluation. A linear regression model was explored for prediction of segregation in the process.

Association of intestinal bacteria with immune activation in a cohort of healthy adults.

IMPORTANCE: Chronic inflammation may develop over time in healthy adults as a result of a variety of factors, such as poor diet directly affecting the composition of the intestinal microbiome, or by causing obesity, which may also affect the intestinal microbiome. These effects may trigger the activation of an immune response that could eventually lead to an inflammation-related disease, such as colon cancer. Before disease develops it may be possible to identify subclinical inflammation or immune activation attributable to specific intestinal bacteria normally found in the gut that could result in future adverse health impacts. In the present study, we examined a group of healthy men and women across a wide age range with and without obesity to determine which bacteria were associated with particular types of immune activation to identify potential preclinical markers of inflammatory disease risk. Several associations were found that may help develop dietary interventions to lower disease risk.

The consumer price index prediction using machine learning approaches: Evidence from the United States.

The consumer price index (CPI) is one of the most important macroeconomic indicators for determining inflation, and accurate predictions of CPI changes are important for a country's economic development. This study uses multivariate linear regression (MLR), support vector regression (SVR), autoregressive distributed lag (ARDL), and multivariate adaptive regression splines (MARS) to predict the CPI of the United States. Data from January 2017 to February 2022 were randomly selected and divided into two stages: 80 % for training and 20% for testing. The US CPI was modeled for the observed period and relied on a mix of elements, including crude oil price, world gold price, and federal fund effective rate. Evaluation metrics-mean absolute percentage value, mean absolute error, root mean square error, R-squared, and correlation of determination-were employed to estimate forecasted values. The MLR, SVR, ARDL, and MARS models attained high accuracy parameters, while the MARS algorithm generated higher accuracy in US CPI forecasts than the others in the testing phase. These outputs could support the US government in overseeing economic policies, sectors, and social security, thereby boosting national economic development.

Establishment and comparison of piecewise linear regression models to measure thyroid volume by 2D and 3D ultrasound.

OBJECTIVE: Compared thyroid volumes measured by 2-D and 3-D US with those of resected specimens and proposed new models to improve measurement accuracy. METHODS: This study included 80 patients who underwent total thyroidectomy. One 2D_model and one 3D_model were developed using piecewise linear regression analysis. The accuracy of these models was compared using an ellipsoid model (2-D_US value × 0.5), 3-D_US value, and Ying's model [1.76 + (2-D_US value × 0.38)]. RESULTS: The new 2D_model was: V=2.66 + (0.71 * X1) - (1.51 * X2). In this model, if 2-D_US value <= 228.39, X1 = 2-D_US value and X2 = 0; otherwise, X1 = 2-D_US value and X2 = 2-D_US value - 228.39. The 3D_model was: V= 2.90 + (1.08 * X1) + (2.43 * X2). In this model, if 3-D_US value <= 102.06, X1 = 3-D_US value and X2 = 0; otherwise, X1 = 3-D_US value and X2 = 3-D_US value - 102.06. The accuracy of the new models was higher than that of the 3-D_US value, the ellipsoid model, and Ying's model (P<0.05). CONCLUSION: The models established are more accurate than the traditional ones and can accurately measure thyroid volume.

Trends in sperm quality by computer-assisted sperm analysis of 49,189 men during 2015-2021 in a fertility center from China.

Background: Sperm quality, including semen volume, sperm count, concentration, and total and progressive motility (collectively, "semen parameters"), has declined in the recent decades. Computer-assisted sperm analysis (CASA) provides sperm kinematic parameters, and the temporal trends of which remain unclear. Our objective is to examine the temporal trend of both semen parameters and kinematic parameters in Shanghai, China, in the recent years. Methods: This retrospective study analyzed semen parameters and kinematic parameters of 49,819 men attending our reproductive center by using CASA during 2015-2021. The total sample was divided into two groups: samples that surpassed the WHO guideline (2010) low reference limits ("above reference limit" group, ARL; n = 24,575) and samples that did not ("below reference limit" group, BRL; n = 24,614). One-way analysis of variance, Kruskal-Wallis test, independent samples t-test, and covariance analysis were used to assess the differences among groups. Year, age, and abstinence time were included in the multiple linear regression model of the ARL group to adjust the confounders and depict the trends in sperm quality. Results: Among all the total sample and the ARL and BRL groups, the age of subjects increased in recent years. Semen volume and sperm count showed declined tendency with years in the total sample, the ARL and BRL groups, and the subgroup of age or abstinence time, whereas sperm velocities showed increased tendency with years on the contrary. The multiple linear regression model of the ARL group, adjusting for age and abstinence time, confirmed these trends. Semen volume (ß1= -0.162; CI: -0.172, -0.152), sperm count (ß1= -9.97; CI: -10.813, -9.128), sperm concentration (ß1 = -0.535; CI: -0.772, -0.299), motility (ß1 = -1.751; CI: -1.830, -1.672), and progressive motility (ß1 = -1.12; CI: -0.201, -0.145) decreased with year, whereas curvilinear line velocity (VCL) (ß1 = 3.058; CI: 2.912, 3.203), straight line velocity (VSL) (ß1 = 2.075; CI: 1.990, 2.161), and average path velocity (VAP) (ß1 = 2.305; CI: 2.224, 2.386) increased over time (all p < 0.001). In addition, VCL, VSL, and VAP significantly declined with age and abstinence time. Conclusion: The semen parameters declined, whereas the kinematic parameters increased over the recent years. We propose that, although sperm count and motility declined over time, sperm motion velocity increased, suggesting a possible compensatory mechanism of male fertility.

Inferring Cell-Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie Model.

Cellular communication through biochemical signaling is fundamental to every biological activity. Investigating cell signaling diffusions across cell types can further help understand biological mechanisms. In recent years, this has become an important research topic as single-cell sequencing technologies have matured. However, cell signaling activities are spatially constrained, and single-cell data cannot provide spatial information for each cell. This issue may cause a high false discovery rate, and using spatially resolved transcriptomics data is necessary. On the other hand, as far as we know, most existing methods focus on providing an ad hoc measurement to estimate intercellular communication instead of relying on a statistical model. It is undeniable that descriptive statistics are straightforward and accessible, but a suitable statistical model can provide more accurate and reliable inference. In this way, we propose a generalized linear regression model to infer cellular communications from spatially resolved transcriptomics data, especially spot-based data. Our BAyesian Tweedie modeling of COMmunications (BATCOM) method estimates the communication scores between cell types with the consideration of their corresponding distances. Due to the properties of the regression model, BATCOM naturally provides the direction of the communication between cell types and the interaction of ligands and receptors that other approaches cannot offer. We conduct simulation studies to assess the performance under different scenarios. We also employ BATCOM in a real-data application and compare it with other existing algorithms. In summary, our innovative model can fill gaps in the inference of cell-cell communication and provide a robust and straightforward result.

On-Site Sensor Calibration Procedure for Quality Assurance of Barometric Process Separation (BaPS) Measurements.

Barometric process separation (BaPS) is an automated laboratory system for the simultaneous measurement of microbial respiration and gross nitrification rates in soil samples. To ensure optimal functioning, the sensor system, consisting of a pressure sensor, an O2 sensor, a CO2 concentration sensor, and two temperature probes, must be accurately calibrated. For the regular on-site quality control of the sensors, we developed easy, inexpensive, and flexible calibration procedures. The pressure sensor was calibrated by means of a differential manometer. The O2 and CO2 sensors were simultaneously calibrated through their exposure to a sequence of O2 and CO2 concentrations obtained by sequentially exchanging O2/N2 and CO2/N2 calibration gases. Linear regression models were best suited for describing the recorded calibration data. The accuracy of O2 and CO2 calibration was mainly affected by the accuracy of the utilized gas mixtures. Because the applied measuring method is based on the O2 conductivity of ZrO2, the O2 sensor is particularly susceptible to aging and to consequent signal shifts. Sensor signals were characterized by high temporal stability over the years. Deviations in the calibration parameters affected the measured gross nitrification rate by up to 12.5% and affected the respiration rate by up to 5%. Overall, the proposed calibration procedures are valuable tools for ensuring the quality of BaPS measurements and for promptly identifying sensor malfunctions.

Quantification of pharmaceuticals in hospital effluent: Weighted ranking of environmental risk using a fuzzy hybrid multicriteria method.

An analytical method for quantification of seventeen pharmaceuticals and one metabolite was validated and applied in the analysis of hospital effluent samples. Two different sampling strategies were used: seasonal sampling, with 7 samples collected bimonthly; and hourly sampling, with 12 samples collected during 12 h. Thus, the variability was both seasonal and within the same day. High variability was observed in the measured concentrations of the pharmaceuticals and the metabolite. The quantification method, performed using weighted linear regression model, demonstrated results of average concentrations in seasonal samples ranged between 0.19 µgL-1 (carbamazepine) and higher than 61.56 µgL-1 (acetaminophen), while the hourly samples showed average concentrations between 0.07 µgL-1 (diazepam) and higher than 54.91 µgL-1 (acetaminophen). It is described as higher because the maximum concentration of the calibration curve took into account the dilution factor provided by DLLME. The diurnal results showed a trend towards higher concentrations in the first and last hours of sampling. The risk quotient (RQ) was calculated using organisms from three different trophic levels, for all the analytes quantified in the samples. Additionally, in order to understand the level of importance of each RQ, an expert panel was established, with contributions from 23 specialists in the area. The results were analyzed using a hybrid decision-making approach based on a Fuzzy Analytic Hierarchy Process (FAHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, in order to rank the compounds by environmental risk priority. The compounds of greatest concern were losartan, acetaminophen, 4-aminoantipyrine, sulfamethoxazole, and metoclopramide. Comparison of the environmental risk priority ranking with the potential human health risk was performed by applying the same multicriteria approach, with the prediction of endpoints using in silico (Q)SAR models. The results obtained suggested that sulfamethoxazole and acetaminophen were the most important analytes to be considered for monitoring.

Association Between Exposure to Ozone (O3) and the Short-Term Effect on Tuberculosis Outpatient Visits: A Time-Series Study in 16 Cities of Anhui Province, China.

Introduction: Evidence has shown that air pollutant exposure plays a vital role in the progression of tuberculosis (TB). The aim of this research was to assess the short-term effects of ozone (O3) exposure and TB outpatient visits in 16 prefecture-level cities of Anhui, China, 2015-2020. Methods: Distributed lag nonlinear model (DLNM), Poisson generalized linear regression model and random effects model were applied in this study. The effects of different age and gender on TB were investigated by stratified analysis, and then we performed sensitivity analyses to verify the stability of the results. Results: A total of 186,623 active TB cases were registered from January 1, 2015 to December 31,2020 in Anhui. The average concentration of ozone is 92.77 ± 42.95 µg/m3. The maximum lag-specific and cumulative relative risk (RR) of TB outpatient visits was 1.0240 (95% CI: 1.0170-1.0310, lag 28 days) for each 10 µg/m³ increase in O3 in the single-pollutant model. Estimation for 16 prefecture-level cities indicated that the strong association between O3 and the risk of TB outpatient visits was in tongling (RR = 1.0555, 95% CI: 1.0089-1.1042), Suzhou (RR = 1.0475, 95% CI: 1.0268-1.0687), wuhu (RR = 1.0358, 95% CI: 1.0023-1.0704). Stratified analysis showed that the health effects of ozone exposure remained significant in male and older adults, and there was no significant association between exposure to ozone in children and adolescents and the risk of tuberculosis. Discussion: We found that ozone exposure increases the risk of TB infection in outpatient patients, with males and the elderly being more susceptible, and it is necessary for government departments to develop targeted publicity and prevention measures in response to the local air quality conditions.

A Regressive Model to Study the Hospitalization for Laparoscopic Appendectomy: A Multicenter Study.

Appendicitis is a most common abdominal condition worldwide, and appendectomy especially laparoscopic appendectomy is among the most commonly performed general surgeries. In this study, data were collected from patients who underwent laparoscopic appendectomy surgery at the Evangelical Hospital "Betania" in Naples, Italy. Linear multiple regression was used to obtain a simple predictor that can also assess which of the independent variables considered to be a risk factor. The model with R2 of 0.699 shows that comorbidities and complications during surgery are the main risk factors for prolonged LOS. This result is validated by other studies conducted in the same area.