Comparatively analysing the postoperative optical performance of different intraocular lenses: a prospective observational study.

BACKGROUND: Postoperative performance, including best corrected distance visual acuity (BCDVA) and optical metrics (from the OQAS and iTrace devices), was compared among 4 different intraocular lenses (IOLs). METHODS: This prospective observational study included 104 eyes from 104 subjects who underwent cataract surgery combined with implantation of 4 different IOLs: monofocal (Mon) IOLs, segmental refractive (SegRef) IOLs, diffractive (Dif) IOLs and extended depth of focus (EDoF) IOLs. Postoperative BCDVA and optical metrics were collected at the 6th month. The OQAS optical metrics included the objective scattering index (OSI), Strehl ratio (SR), modulation transfer function (MTF) cut-off frequency, and predicted visual acuity (PVA); the iTrace optical metrics included blur/double vision, glare/halo, starburst, mixed focus, night myopia, and night hyperopia. RESULTS: There was no significant difference in BCDVA among the 4 groups (P = 0.059; power = 70.3%). Differences were observed in all OQAS optical metrics among the groups (all P < 0.001). Overall, Mon IOLs and EDoF IOLs exhibited better performance than Dif IOLs and SegRef IOLs. Starburst was the only iTrace optical metric that differed among the groups (P < 0.001): SegRef IOLs caused more starbursts than Mon IOLs (P = 0.001), Dif IOLs (P = 0.006) and EDoF IOLs (P < 0.001). Spearman rank correlation analysis was used to determine the relationships among the iTrace optical metrics, OQAS optical metrics and BCDVA: starburst was negatively correlated with BCDVA, PVA at contrasts of 100% and 20%, OSI, and MTF cut-off frequency (all P ≤ 0.001); mixed focus was positively correlated with BCDVA, PVA at contrasts of 100% and 20%, OSI, and MTF cut-off frequency (all P ≤ 0.001). CONCLUSIONS: Postoperative BCDVA and optical metrics varied among the different IOLs, which should be taken into account in the selection and management of IOLs for cataract patients. TRIAL REGISTRATION: This study was approved by the First Affiliated Hospital of Guangzhou Medical University Ethical Review Board (No. 50 2022).

Correlation between Charlson comorbidity index and surgical prognosis in elderly patients with femoral neck fractures: a retrospective study.

OBJECTIVE: Femoral neck fractures (FNF) are known to have significant morbidity and mortality rates. Multiple chronic conditions (MCC) are defined as the presence of two or more chronic diseases that greatly affect the quality of life in older adults. The aim of this study is to explore the impact of MCC and Charlson comorbidity index (CCI) on surgical outcomes in patients with FNF. METHODS: Patients with FNF who underwent joint replacement surgery were selected for this study. Patients who had two or more diseases simultaneously were divided into two groups: the MCC group and the non-MCC (NMCC) group. The CCI was calculated to assess the severity of patients' comorbidities in the MCC group. Baseline data, surgical details, and prognosis-related indicators were analyzed and compared between the two patient groups. Spearman correlation analysis was performed to assess the relationship between CCI and length of hospital stay, Harris score, skeletal muscle index (SMI), and age. Univariate and multivariate logistic regression analysis was conducted to identify the risk factors for mortality in FNF patients at 1 and 5 years after surgery. RESULTS: A total of 103 patients were included in the MCC group, while the NMCC group consisted of 40 patients. However, the patients in the MCC group were found to be older, had a higher incidence of sarcopenia, and lower SMI values (p < 0.001). Patients in the MCC group had longer hospitalization times, lower Harris scores, higher intensive care unit (ICU) admission rates, and higher complication rates (p = 0.045, p = 0.035, p = 0.019, p = 0.010). Spearman correlation analysis revealed that CCI was positively correlated with hospitalization and age (p < 0.001, p < 0.001), while it was negatively correlated with Harris score and SMI value (p < 0.001, p < 0.001). Univariate and multivariate logistic regression analysis demonstrated that MCC patients had higher 1-year and 5-year mortality rates. Hospitalization time was identified as a risk factor for death in FNF patients 1 year after joint replacement (p < 0.001), whereas CCI and age were identified as risk factors for death 5 years after surgery (p < 0.001, p < 0.001). Kaplan-Meier survival analysis results showed that the difference in death time between the two groups of patients with MCC and NMCC was statistically significant (p < 0.001). Cox proportional hazard model analysis showed that CCI, age and SMI were risk factors affecting patient death. CONCLUSION: The surgical prognosis of patients with MCC, CCI and FNF is related. The higher the CCI, the worse the patient's function and the higher the long-term risk of death.

Quality Assessment of TikTok as a Source of Information About Mitral Valve Regurgitation in China: Cross-Sectional Study.

BACKGROUND: In China, mitral valve regurgitation (MR) is the most common cardiovascular valve disease. However, patients in China typically experience a high incidence of this condition, coupled with a low level of health knowledge and a relatively low rate of surgical treatment. TikTok hosts a vast amount of content related to diseases and health knowledge, providing viewers with access to relevant information. However, there has been no investigation or evaluation of the quality of videos specifically addressing MR. OBJECTIVE: This study aims to assess the quality of videos about MR on TikTok in China. METHODS: A cross-sectional study was conducted on the Chinese version of TikTok on September 9, 2023. The top 100 videos on MR were included and evaluated using quantitative scoring tools such as the modified DISCERN (mDISCERN), the Journal of the American Medical Association (JAMA) benchmark criteria, the Global Quality Score (GQS), and the Patient Education Materials Assessment Tool for Audio-Visual Content (PEMAT-A/V). Correlation and stepwise regression analyses were performed to examine the relationships between video quality and various characteristics. RESULTS: We obtained 88 valid video files, of which most (n=81, 92%) were uploaded by certified physicians, primarily cardiac surgeons, and cardiologists. News agencies/organizations and physicians had higher GQS scores compared with individuals (news agencies/organizations vs individuals, P=.001; physicians vs individuals, P=.03). Additionally, news agencies/organizations had higher PEMAT understandability scores than individuals (P=.01). Videos focused on disease knowledge scored higher in GQS (P<.001), PEMAT understandability (P<.001), and PEMAT actionability (P<.001) compared with videos covering surgical cases. PEMAT actionability scores were higher for outpatient cases compared with surgical cases (P<.001). Additionally, videos focused on surgical techniques had lower PEMAT actionability scores than those about disease knowledge (P=.04). The strongest correlations observed were between thumbs up and comments (r=0.92, P<.001), thumbs up and favorites (r=0.89, P<.001), thumbs up and shares (r=0.87, P<.001), comments and favorites (r=0.81, P<.001), comments and shares (r=0.87, P<.001), and favorites and shares (r=0.83, P<.001). Stepwise regression analysis identified "length (P<.001)," "content (P<.001)," and "physicians (P=.004)" as significant predictors of GQS. The final model (model 3) explained 50.1% of the variance in GQSs. The predictive equation for GQS is as follows: GQS = 3.230 - 0.294 × content - 0.274 × physicians + 0.005 × length. This model was statistically significant (P=.004) and showed no issues with multicollinearity or autocorrelation. CONCLUSIONS: Our study reveals that while most MR-related videos on TikTok were uploaded by certified physicians, ensuring professional and scientific content, the overall quality scores were suboptimal. Despite the educational value of these videos, the guidance provided was often insufficient. The predictive equation for GQS developed from our analysis offers valuable insights but should be applied with caution beyond the study context. It suggests that creators should focus on improving both the content and presentation of their videos to enhance the quality of health information shared on social media.

GIS-based spatiotemporal mapping of malaria prevalence and exploration of environmental inequalities.

Malaria poses a significant threat to global health, with particular severity in Nigeria. Understanding key factors influencing health outcomes is crucial for addressing health disparities. Disease mapping plays a vital role in assessing the geographical distribution of diseases and has been instrumental in epidemiological research. By delving into the spatiotemporal dynamics of malaria trends, valuable insights can be gained into population dynamics, leading to more informed spatial management decisions. This study focused on examining the evolution of malaria in Nigeria over twenty years (2000-2020) and exploring the impact of environmental factors on this variation. A 5-year-period raster map was developed using malaria indicator survey data for Nigeria's six geopolitical zones. Various spatial analysis techniques, such as point density, spatial autocorrelation, and hotspot analysis, were employed to analyze spatial patterns. Additionally, statistical methods, including Principal Component Analysis, Spearman correlation, and Ordinary Least Squares (OLS) regression, were used to investigate relationships between indicators and develop a predictive model. The study revealed regional variations in malaria prevalence over time, with the highest number of cases concentrated in northern Nigeria. The raster map illustrated a shift in the distribution of malaria cases over the five years. Environmental factors such as the Enhanced Vegetation Index, annual land surface temperature, and precipitation exhibited a strong positive association with malaria cases in the OLS model. Conversely, insecticide-treated bed net coverage and mean temperature negatively correlated with malaria cases in the same model. The findings from this research provide valuable insights into the spatiotemporal patterns of malaria in Nigeria and highlight the significant role of environmental drivers in influencing disease transmission. This scientific knowledge can inform policymakers and aid in developing targeted interventions to combat malaria effectively.

Estimation of direct and maternal covariance along with genetic and phenotypic trends of reproduction traits in Murrah buffalo.

The research utilized data from 662 Murrah buffaloes meticulously recorded over 24 years (1996-2019) from historical pedigree sheets maintained at the buffalo farm of the Department of Livestock Production and Management (LPM) at Lala Lajpat Rai University of Veterinary and Animal Sciences (LUVAS), Hisar. A series of six univariate animal models were employed to calculate estimates of (co)variance components and heritability for first lactation reproduction traits. Among these models, Model 2 was identified as the best fit for age at first calving (AFC) and calving interval (CI), while Model 1 proved optimal for service period (SP) and conception rate (CR). The heritability estimates for AFC, SP, CR and CI across the models were ranged between 0.11 and 0.32, 0.01 and 0.03, 0.05 and 0.06, and 0.01 and 0.06, respectively. Maternal effects (m2 ) were observed in AFC and CI, ranging from 0.10 to 0.20 and 0.01 to 0.03, respectively. Across all three traits, there was a consistent negative genetic correlation (-0.75 to -0.92) between direct additive and maternal effects. The breeding values for AFC, SP, CR and CI varied within specific ranges from -32.85 to 44.33 days, -15.61 to 28.42 days, -7.41 to 6.48% and -20.64 to 35.79 days, respectively. Significantly, Spearman's rank correlation analysis revealed highly significant relationships (p < .01) between the breeding values of different models, indicating strong and consistent associations within these traits. These findings underscore the stable and reliable connections observed within the breeding values for these essential reproductive traits across the various models used in the study. The majority of reproductive traits showed favourable negative trends, indicating a positive outcome. A decrease in AFC, SP and CI suggests an extended economic life for the animals. Additionally, the upward trends in CR reflect positive indications of effective management practices and skilled operational procedures.

Estimation of additive and maternal covariance of production traits in Murrah buffalo.

The study was done to determine additive, maternal and common permanent environmental effects and best-suited model for some production traits using six univariate animal models that differed in the (co)variance components fitted to assess the importance of maternal effect using likelihood ratio test in Murrah buffaloes. Data from 614 Murrah buffaloes related to production traits were collected from history pedigree sheets maintained at the buffalo farm, Department of Livestock Production and Management (LPM), LUVAS, Hisar. The production traits under this study were 305 days milk yield (305DMY), peak yield (PY), lactation length (LL), dry period (DP), lactation milk yield (LMY) and wet average (WA). The heritability estimates were in the range of 0.33-0.44 for 305DMY, 0.25-0.51 for PY, 0.05-0.13 for LL, 0.03-0.23 for DP, 0.17-0.40 for LMY and 0.37-0.66 for WA. Model 1 was considered best for most of the traits, viz., 305DMY, PY, LL, LMY and WA followed by model 2 for DP. Covariance and correlated values within the traits caused inflation of heritability in model 3 and model 6. The maximum covariance between the additive and maternal effect was found in trait LMY, which was 14,183.90 in model 3 and the minimum value was also reported in the same trait for model 6, valued at -3522.37. Multivariate analysis showed that all production traits were moderate to high and positively correlated with each other except for DP, which was low and negative genetic and phenotypic correlated. Spearman's rank correlation coefficients of breeding value among all six models were high and significant, ranged from 0.78 to 1.00 for all the traits except DP, therefore any of the models could be taken into account depending upon the availability of data.

LiDAR Dynamic Target Detection Based on Multidimensional Features.

To address the limitations of LiDAR dynamic target detection methods, which often require heuristic thresholding, indirect computational assistance, supplementary sensor data, or postdetection, we propose an innovative method based on multidimensional features. Using the differences between the positions and geometric structures of point cloud clusters scanned by the same target in adjacent frame point clouds, the motion states of the point cloud clusters are comprehensively evaluated. To enable the automatic precision pairing of point cloud clusters from adjacent frames of the same target, a double registration algorithm is proposed for point cloud cluster centroids. The iterative closest point (ICP) algorithm is employed for approximate interframe pose estimation during coarse registration. The random sample consensus (RANSAC) and four-parameter transformation algorithms are employed to obtain precise interframe pose relations during fine registration. These processes standardize the coordinate systems of adjacent point clouds and facilitate the association of point cloud clusters from the same target. Based on the paired point cloud cluster, a classification feature system is used to construct the XGBoost decision tree. To enhance the XGBoost training efficiency, a Spearman's rank correlation coefficient-bidirectional search for a dimensionality reduction algorithm is proposed to expedite the optimal classification feature subset construction. After preliminary outcomes are generated by XGBoost, a double Boyer-Moore voting-sliding window algorithm is proposed to refine the final LiDAR dynamic target detection accuracy. To validate the efficacy and efficiency of our method in LiDAR dynamic target detection, an experimental platform is established. Real-world data are collected and pertinent experiments are designed. The experimental results illustrate the soundness of our method. The LiDAR dynamic target correct detection rate is 92.41%, the static target error detection rate is 1.43%, and the detection efficiency is 0.0299 s. Our method exhibits notable advantages over open-source comparative methods, achieving highly efficient and precise LiDAR dynamic target detection.

Correlations between sewage sludge composting physicochemical parameters and emissions of greenhouse gases and ammonia: A statistical analysis.

Mitigating the environmental impact of composting by the reduction of greenhouse gases (N2O, CH4) and ammonia (NH3) emissions is a major challenge. To meet this challenge, the understanding of the relationships between composted substrates initial physicochemical parameters and gas emissions is essential. From a long-term perspective, it will allow to guide the recipe formulation of the initial mixture to be composted, with a view to reducing gas emissions during composting. This study gathered literature data targeting sewage sludge composting and performed statistical correlation analyses between cumulative gas emissions and the following parameters: sewage sludge, bulking agent and composted mixture initial physicochemical parameters (pH, dry matter, total carbon, total nitrogen, C/N), the dry mass ratio of sewage sludge to bulking agent and the duration of composting. The average values of cumulative emissions show a large variability: 1.37 ± 2.71 gC.kg initial mix DM-1, 0.13 ± 0.17 gN.kg initial mix DM-1 and 2.23 ± 2.79 gN.kg initial mix DM-1 for CH4, N2O and NH3 emissions respectively. Although the correlation analysis highlighted some significant interesting correlations between initial physicochemical parameters and gas emissions (p.value < 0.05), reliable multiparametric model could not fit the data, meaning that the actual literature data are not sufficient to explain most part of gas emissions. Among the most interesting relationships, the study showed that the dry matter of the composted mixture is negatively correlated to N2O emissions, while the ratio of sewage sludge to bulking agent and the duration of composting are positively correlated to N2O emissions. It was also shown that the pH of the bulking agent is positively correlated to NH3 emissions. Considering the large part of the emission variability that is not explained and the difficulty to link the correlation with their causality, it will be interesting to improve composting gas emissions knowledge in future research by analyzing free air space, bulking agent adsorption capacity and available and biodegradable organic matter. These parameters are of particular interest in solving the main problems associated with sewage sludge composting, namely porosity and nitrogen retention. This study also highlighted the necessity to extend the duration of the composting studies over 40 days in order to measure possible N2O late release and better identify parameters influencing N2O emissions.

The effect of organic pollution on the seasonal dynamics of water quality in a semi-arid zone: case of the Hammam Boughrara Dam, Tlemcen (Algeria).

The present study aims to assess the impact of human activities on the water quality of the Hammam Boughrara dam. It also highlights the crucial importance of sustainable management of water resources in the face of persistent challenges related to various forms of pollution. The study is based on an exhaustive database covering a period spread over 16 years, with monthly measurements of organic pollution indicator parameters, namely BOD5, COD, [Formula: see text],[Formula: see text], [Formula: see text], [Formula: see text], Organic Matter (OM), TDS, Dissolved Oxygen (DO) and pH. The box plots showed an asymmetric distribution of almost all the parameters, with significant seasonal variations in the interquartile (IQR) range. The IQR ranges for [Formula: see text] extends from 0.575 mg/l (summer) to 4.445 mg/l (spring), and for [Formula: see text] from 1.3075 mg/l (autumn) to 1.8625 mg/l (spring). This led to the use of the Spearman method for the analysis of correlations between different parameters. The seasonal study of the five categories of water quality, according to the Organic Pollution Index (OPI), revealed considerable organic pollution. At the 1% significance level, the seasonal correlation between OPI and [Formula: see text] varies between -0.71 and -0.85, while that with [Formula: see text] fluctuates between -0.69 and -0.86. During the period analyzed, the COD/BOD Ratio (CBR) reveals two dominant categories with seasonal variations, i.e. the Moderately Biodegradable Effluents (MBE), with 96 cases, reaching 29 in autumn and 20 in spring. The Difficult to Biodegrade or Non-Biodegradable Effluents (DBE or NBE) category records 94 cases, with a maximum frequency of 26 in winter and minimum of 21 in autumn. These results therefore show the persistence of organic pollution, which had an impact on water quality over the four seasons and throughout the period studied. The results indicate persistent organic pollution affecting water quality. Therefore, prompt actions and sustainable strategies are deemed necessary to mitigate these harmful impacts and to ensure the sustainability of the water resource.

Application of machine learning and deep learning techniques in modeling the associations between air pollution and meteorological parameters in urban areas of tehran metropolis.

Tehran, the most crowded city in Iran, suffers from severe air pollution, particularly during the cold months. This research endeavored to examine the statistical relationships between criteria air pollutants (CO, NO2, SO2, O3, PM10, and PM2.5) and meteorological elements (temperature, rainfall, wind speed, relative humidity, air pressure, sunshine hours, solar radiation, and cloudiness), as well as assess and compare the efficacy of six different algorithms (multiple linear regression (MLR), generalized additive model (GAM), classification and regression trees (CART), random forest (RF), gradient boosting machine (GBM), and deep learning (DL)) in modeling pollutants and climatic factors responsible for variations in Tehran's air pollution levels from 2001 to 2021 using R 4.3.2 software. The results of this study showed that O3 was strongly affected by weather conditions, while other pollutants were mainly influenced by each other than by meteorological parameters and more extensive research is required to pinpoint the precise impact of human activity on these pollutant levels in Tehran. Also based on the predictive model performance evaluation and concerning the principle of parsimony, in half of the cases, the MLR outperformed other models, despite its seeming simplicity and principal assumptions dependence. In other situations, the GAM was a good substitute.

Identifying potential significant factors impacting zero-inflated proportion data.

Classical supervised methods like linear regression and decision trees are not completely adapted for identifying impacting factors on a response variable corresponding to zero-inflated proportion data (ZIPD) that are dependent, continuous and bounded. In this article we propose a within-block permutation-based methodology to identify factors (discrete or continuous) that are significantly correlated with ZIPD, we propose a performance indicator quantifying the percentage of correlation explained by the subset of significant factors, and we show how to predict the ranks of the response variables conditionally on the observation of these factors. The methodology is illustrated on simulated data and on two real data sets dealing with epidemiology. In the first data set, ZIPD correspond to probabilities of transmission of Influenza between horses. In the second data set, ZIPD correspond to probabilities that geographic entities (eg, states and countries) have the same COVID-19 mortality dynamics.

Impact of Region-of-Interest Size on Immune Profiling Using Multiplex Immunofluorescence Tyramide Signal Amplification for Paraffin-Embedded Tumor Tissues.

INTRODUCTION: Representative regions of interest (ROIs) analysis from the whole slide images (WSI) are currently being used to study immune markers by multiplex immunofluorescence (mIF) and single immunohistochemistry (IHC). However, the amount of area needed to be analyzed to be representative of the entire tumor in a WSI has not been defined. METHODS: We labeled tumor-associated immune cells by mIF and single IHC in separate cohorts of non-small cell lung cancer (NSCLC) samples and we analyzed them as whole tumor area as well as using different number of ROIs to know how much area will be need to represent the entire tumor area. RESULTS: For mIF using the InForm software and ROI of 0.33 mm2 each, we observed that the cell density data from five randomly selected ROIs is enough to achieve, in 90% of our samples, more than 0.9 of Spearman correlation coefficient and for single IHC using ScanScope tool box from Aperio and ROIs of 1 mm2 each, we found that the correlation value of more than 0.9 was achieved using 5 ROIs in a similar cohort. Additionally, we also observed that each cell phenotype in mIF influence differently the correlation between the areas analyzed by the ROIs and the WSI. Tumor tissue with high intratumor epithelial and immune cells phenotype, quality, and spatial distribution heterogeneity need more area analyzed to represent better the whole tumor area. CONCLUSION: We found that at minimum 1.65 mm2 area is enough to represent the entire tumor areas in most of our NSCLC samples using mIF.

Long-term spatiotemporal analysis of the climate related impact on the transmission rate of COVID-19.

BACKGROUND: The association between weather conditions and the spread of COVID-19 was demonstrated by previous studies but focused on specific countries or investigated shorter periods of duration limiting the interpretation of the results. AIM: To make an international comprehensive insight into the association between the weather conditions and the spread of COVID-19 by spanning many regions in the Northern and Southern hemispheres over a period of two years for the COVID-19 Outbreak. METHODS: The data were analyzed by using statistical description, linear and multiple regressions, and the Spearman rank correlation test. Daily and weekly COVID-19 cases, the average temperatures, Wind Speed, the amount of precipitation as well as the relative humidity rates were collected from Irbid, Jordan as the main location of analyses, as well as comparison cities and countries in both hemispheres. RESULTS: we found that certain climate variables are significant factors in determining the transmission rate of COVID-19 worldwide. Where, The temperature in the northern hemisphere regions was the most important climate factor that affects the increase in the transmission rate of COVID-19 (Northern Hemisphere rs = -0.65; Irbid rs = -0.74995; P < 0.001), While in southern hemisphere, the climate factor that affects the increase in the transmission rate of COVID-19 was the humidity (rs = 0.55; P < 0.01), In addition, we found the negligible and oscillated effect of wind speed on the transmission rate of COVID-19 worldwide. Moreover, we found that in Irbid 82% of COVID-19 cases were in the fall and winter seasons, while in summer the percentage of COVID-19 cases didn't exceed 3% during the total study period. CONCLUSION: This study can help develop international strategies and policies against COVID-19-related pandemic peaks, especially during the colder seasons in the Northern Hemisphere regions from the first month of fall to the last month of winter.

Statistical heart rate variability analysis for healthy person: Influence of gender and body posture.

Short-term ECG-derived heart rate variability can assess autonomic function non-invasively. The purpose of this study is to investigate the influence of body posture and gender on parasympathetic-sympathetic balance by utilising electrocardiogram (ECG). A total of sixty participants including thirty males (95% CI: 23.34-26.32 years old) and thirty females (95% CI: 23.33-26.07 years old) voluntarily executed three sets of 5-min ECG recordings in supine, sitting and standing posture. A nonparametric Friedman test followed by Bonferroni post-hoc test was carried out to find the statistical differences between the group. A significant difference was observed for RR mean, low frequency (LF), high frequency (HF), ratio LF/HF and the ratio long term variability to short term variability (SD2/SD1) for p < 0.01 while respiration rate (Resp Rate), standard deviation of heart rate (STD_HR), long term variability (SD2), approximate entropy (ApEn), correlation dimension (CD) are non-significant (p > 0.01) for supine, sitting and standing. HRV indices such as standard deviation of NN (SDNN), HRV triangular index (HRVi), and triangular interpolation of NN interval (TINN) are statistically not significant for males but there are significant differences for females at a significance level 1%. Relative reliability and relatedness were evaluated through the interclass coefficient (ICC) and spearman correlation coefficient. The experimental results advocate that there is a posture-specific difference in HRV indices while the correlational studies suggest no such significant differences.

Saussurea involucrata oral liquid regulates gut microbiota and serum metabolism during alleviation of collagen-induced arthritis in rats.

Saussurea involucrata oral liquid (SIOL) can clinically relieve symptoms, such as joint pain and swelling, and morning stiffness, in patients with rheumatoid arthritis (RA). However, the mechanism of action remains unclear. This study used a combination of gut microbiota and serum metabolomics analysis to investigate the effects and potential mechanisms of SIOL intervention on rats with RA induced by type II bovine collagen and Freund's complete adjuvant. Results showed that SIOL treatment consequently improved the degree of ankle joint swelling, joint histopathological changes, joint pathological score, and expression of serum-related inflammatory cytokines (interleukin (IL)-1ß, IL-4, IL-6, IL-10, and tumor necrosis factor-α) in RA model rats. 16 S rRNA sequencing results showed that SIOL increased the relative richness of the Lactobacillus and Bacteroides genus and decreased the relative richness of Romboutsia, Alloprevotella, Blautia, and Helicobacter genus. Serum nontargeted metabolomic results indicated that SIOL could regulate metabolites related to metabolic pathways, such as glycine, serine, threonine, galactose, cysteine, and methionine metabolism. Spearman correlation analysis showed that the regulatory effects of SIOL on the tricarboxylic acid (TCA) cycle, phenylalanine metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, and glyoxylate and dicarboxylate metabolism pathways were correlated with changes in the richness of the Lactobacillus, Romboutsia, Bacteroides, and Alloprevotella genus in the gut microbiome. In conclusion, this study revealed the ameliorative effects of SIOL on RA and suggested that the therapeutic effects of SIOL on RA may be related to the regulation of the community richness of the Lactobacillus, Romboutsia, Bacteroides, and Alloprevotella genus, thereby improving the TCA cycle; phenylalanine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis, and glyoxylate and dicarboxylate metabolism-related pathways.

Fluorescent Photoelectric Detection of Peroxide Explosives Based on a Time Series Similarity Measurement Method.

Due to the characteristics of peroxide explosives, which are difficult to detect via conventional detection methods and have high explosive power, a fluorescent photoelectric detection system based on fluorescence detection technology was designed in this study to achieve the high-sensitivity detection of trace peroxide explosives in practical applications. Through actual measurement experiments and numerical simulation methods, the derivative dynamic time warping (DDTW) algorithm and the Spearman correlation coefficient were used to calculate the DDTW-Spearman distance to achieve time series correlation measurements. The detection sensitivity of triacetone triperoxide (TATP) and H2O2 was studied, and the detection of organic substances of acetone, acetylene, ethanol, ethyl acetate, and petroleum ether was carried out. The stability and specific detection ability of the fluorescent photoelectric detection system were determined. The research results showed that the fluorescence photoelectric detection system can effectively identify the detection data of TATP, H2O2, acetone, acetonitrile, ethanol, ethyl acetate, and petroleum ether. The detection limit of 0.01 mg/mL of TATP and 0.0046 mg/mL of H2O2 was less than 10 ppb. The time series similarity measurement method improves the analytical capabilities of fluorescence photoelectric detection technology.

Spearman-like correlation measure adjusting for covariates in bivariate survival data.

We propose an extension of Spearman's correlation for censored continuous and discrete data that permits covariate adjustment. Previously proposed nonparametric and semiparametric Spearman's correlation estimators require either nonparametric estimation of the bivariate survival surface or parametric assumptions about the dependence structure. In practice, nonparametric estimation of the bivariate survival surface is difficult, and parametric assumptions about the correlation structure may not be satisfied. Therefore, we propose a method that requires neither and uses only the marginal survival distributions. Our method estimates the correlation of probability-scale residuals, which has been shown to equal Spearman's correlation when there is no censoring. Because this method relies only on marginal distributions, it tends to be less variable than the previously suggested nonparametric estimators, and the confidence intervals are easily constructed. Although under censoring, it is biased for Spearman's correlation as our simulations show, it performs well under moderate censoring, with a smaller mean squared error than nonparametric approaches. We also extend it to partial (adjusted), conditional, and partial-conditional correlation, which makes it particularly relevant for practical applications. We apply our method to estimate the correlation between time to viral failure and time to regimen change in a multisite cohort of persons living with HIV in Latin America.

Correlation of heavy metals' exposure with the prevalence of coronary heart disease among US adults: findings of the US NHANES from 2003 to 2018.

We sought to explore the association between heavy metal exposure and coronary heart disease (CHD) based on data from the US National Health and Nutrition Examination Survey (NHANES, 2003-2018). In the analyses, participants were all aged > 20 and had participated in heavy metal sub-tests with valid CHD status. The Mann-Kendall test was employed to assess the trends in heavy metals' exposure and the trends in CHD prevalence over 16 years. Spearman's rank correlation coefficient and a logistics regression (LR) model were used to estimate the association between heavy metals and CHD prevalence. 42,749 participants were included in our analyses, 1802 of whom had a CHD diagnosis. Total arsenic, dimethylarsonic acid, monomethylarsonic acid, barium, cadmium, lead, and antimony in urine, and cadmium, lead, and total mercury in blood all showed a substantial decreasing exposure level tendency over the 16 years (all Pfor trend < 0.05). CHD prevalence varied from 3.53 to 5.23% between 2003 and 2018. The correlation between 15 heavy metals and CHD ranges from - 0.238 to 0.910. There was also a significant positive correlation between total arsenic, monomethylarsonic acid, and thallium in urine and CHD by data release cycles (all P < 0.05). The cesium in urine showed a negative correlation with CHD (P < 0.05). We found that exposure trends of total arsenic, dimethylarsonic acid, monomethylarsonic acid, barium, cadmium, lead, and antimony in urine and blood decreased. CHD prevalence fluctuated, however. Moreover, total arsenic, monomethylarsonic acid, and thallium in urine all showed positive relationships with CHD, while cesium in urine showed a negative relationship with CHD.

Data quality evaluation and calibration of on-road remote sensing systems based on exhaust plumes.

In recent years, with rapid increases in the number of vehicles in China, the contribution of vehicle exhaust emissions to air pollution has become increasingly prominent. To achieve the precise control of emissions, on-road remote sensing (RS) technology has been developed and applied for law enforcement and supervision. However, data quality is still an existing issue affecting the development and application of RS. In this study, the RS data from a cross-road RS system used at a single site (from 2012 to 2015) were collected, the data screening process was reviewed, the issues with data quality were summarized, a new method of data screening and calibration was proposed, and the effectiveness of the improved data quality control methods was finally evaluated. The results showed that this method reduces the skewness and kurtosis of the data distribution by up to nearly 67%, which restores the actual characteristics of exhaust diffusion and is conducive to the identification of actual clean and high-emission vehicles. The annual variability of emission factors of nitric oxide decreases by 60% - on average - eliminating the annual drift of fleet emissions and improving data reliability.

Use of Pearson and Spearman correlation testing in Indian anesthesia journals: An audit.

Background and Aims: Correct usage and interpretation of biostatistical tests is imperative. Aim of the present article was to evaluate the use of "correlation test" for biostatistical analysis in two leading Indian journals of anesthesia and sensitize the readers regarding its correct usage. Material and Methods: A prospective analysis was done for all original articles using the correlation test (Pearson or Spearman) that were published in "Indian Journal of Anaesthesia" (IJA) or "Journal of Anaesthesiology and Clinical Pharmacology" (JOACP) in the years 2019 and 2020. Results: Amongst all included original studies, correlation test were used in 6% (JOACP) and 6.5% (IJA) respectively (averaged for the years 2019 and 2020). Correlation test was usedinappropriately) for evaluating an aim of prediction/agreement/comparison, rather than association, in 25% and 10% instances each (JOACP and IJA). In both JOACP and IJA, there were high rates of using and interpreting results without citing 95% confidence intervals (CIs) of correlation coefficient (88% and 90%, respectively), P value for significance of the association (50% and 90%, respectively), or coefficient of discrimination (88% and 70%, respectively). In majority of the instances, test to ascertain presence of mandatory prerequisites such as normal distribution of data could not be found (62% and 90%, respectively). Conclusion: The complete potential of correlation test in exploring research questions is probably underappreciated. Further, even when used, its application and interpretation are prone to errors. We hope that the present analysis and narrative is a well-timed appropriate step in bridging the gaps in existing knowledge regarding use of correlation test in national anesthesia literature.