Insight into the sorption and desorption pattern of pyrrolizidine alkaloids and their N-oxides in acidic tea (Camellia sinensis) plantation soils.

Pyrrolizidine alkaloids (PAs) and their N-oxides (PANOs) are phytotoxins produced by various plant species and have been emerged as environmental pollutants. The sorption/desorption behaviors of PAs/PANOs in soil are crucial due to the horizontal transfer of these natural products from PA-producing plants to soil and subsequently absorbed by plant roots. This study firstly investigated the sorption/desorption behaviors of PAs/PANOs in tea plantation soils with distinct characteristics. Sorption amounts for seneciphylline (Sp) and seneciphylline-N-oxide (SpNO) in three acidic soils ranged from 2.9 to 5.9 µg/g and 1.7 to 2.8 µg/g, respectively. Desorption percentages for Sp and SpNO were from 22.2% to 30.5% and 36.1% to 43.9%. In the mixed PAs/PANOs systems, stronger sorption of PAs over PANOs was occurred in tested soils. Additionally, the Freundlich models more precisely described the sorption/desorption isotherms. Cation exchange capacity, sand content and total nitrogen were identified as major influencing factors by linear regression models. Overall, the soils exhibiting higher sorption capacities for compounds with greater hydrophobicity. PANOs were more likely to migrate within soils and be absorbed by tea plants. It contributes to the understanding of environmental fate of PAs/PANOs in tea plantations and provides basic data and clues for the development of PAs/PANOs reduction technology.

Development of an equation to predict delta bilirubin levels using machine learning.

OBJECTIVE: Delta bilirubin (albumin-covalently bound bilirubin) may provide important clinical utility in identifying impaired hepatic excretion of conjugated bilirubin, but it cannot be measured in real-time for diagnostic purposes in clinical laboratories. METHODS: A total of 210 samples were collected, and their delta bilirubin levels were measured four times using high-performance liquid chromatography. Data collected included age, sex, diagnosis code, delta bilirubin, total bilirubin, direct bilirubin, total protein, albumin, globulin, aspartate aminotransferase, alanine transaminase, alkaline phosphatase, gamma-glutamyl transferase, lactate dehydrogenase, hemoglobin, serum hemolysis value, hemolysis index, icterus value (Iv), icterus index (Ii), lipemia value (Lv), and lipemia index. To conduct feature selection and identify the optimal combination of variables, linear regression machine learning was performed 1,000 times. RESULTS: The selected variables were total bilirubin, direct bilirubin, total protein, albumin, hemoglobin, Iv, Ii, and Lv. The best predictive performance for high delta bilirubin concentrations was achieved with the combination of albumin-direct bilirubin-hemoglobin-Iv-Lv. The final equation composed of these variables was as follows: delta bilirubin = 0.35 × Iv + 0.05 × Lv - 0.23 × direct bilirubin - 0.05 × hemoglobin - 0.04 × albumin + 0.10. CONCLUSION: The equation established in this study is practical and can be easily applied in real-time in clinical laboratories.

Diagnostic accuracy of endoscopic ultrasonographic shear wave elastography for assessing early chronic pancreatitis using the Japanese diagnostic criteria 2019.

Background and Aim: Endoscopic ultrasound shear wave elastography (EUS-SWE) can facilitate an objective evaluation of pancreatic fibrosis. Although it is primarily applied in evaluating chronic pancreatitis, its efficacy in assessing early chronic pancreatitis (ECP) remains underinvestigated. This study evaluated the diagnostic accuracy of EUS-SWE for assessing ECP diagnosed using the Japanese diagnostic criteria 2019. Methods: In total, 657 patients underwent EUS-SWE. Propensity score matching was used, and the participants were classified into the ECP and normal groups. ECP was diagnosed using the Japanese diagnostic criteria 2019. Pancreatic stiffness was assessed based on velocity (Vs) on EUS-SWE, and the optimal Vs cutoff value for ECP diagnosis was determined. A practical shear wave Vs value of ≥50% was considered significant. Results: Each group included 22 patients. The ECP group had higher pancreatic stiffness than the normal group (2.31 ± 0.67 m/s vs. 1.59 ± 0.40 m/s, p < 0.001). The Vs cutoff value for the diagnostic accuracy of ECP, as determined using the receiver operating characteristic curve, was 2.24m/s, with an area under the curve of 0.82 (95% confidence interval: 0.69-0.94). A high Vs was strongly correlated with the number of EUS findings (rs = 0.626, p < 0.001). Multiple regression analysis revealed that a history of acute pancreatitis and ≥2 EUS findings were independent predictors of a high Vs. Conclusions: There is a strong correlation between EUS-SWE findings and the Japanese diagnostic criteria 2019 for ECP. Hence, EUS-SWE can be an objective and invaluable diagnostic tool for ECP diagnosis.

Multi-site phantomless bone mineral density from clinical quantitative computed tomography in males.

Volumetric bone mineral density (vBMD) is commonly assessed using QCT. Although standard vBMD calculation methods require phantom rods that may not be available, internal-reference phantomless (IPL) and direct measurements of Hounsfield units (HU) can be used to calculate vBMD in their absence. Yet, neither approach has been systemically assessed across skeletal sites, and HU need further validation as a vBMD proxy. This study evaluated the accuracy of phantomless methods, including IPL and regression-based phantomless (RPL) calibration using HU to calculate vBMD, compared to phantom-based (PB) methods. vBMD from QCT scans of 100 male post-mortem human subjects (PMHS) was calculated using site-specific PB calibration at multiple skeletal sites throughout the body. A development sample of 50/100 PMHS was used to determine site-specific reference material density for IPL calibration and RPL equations. Reference densities and equations from the development sample were used to calculate IPL and RPL vBMD on the remaining 50/100 PMHS for method validation. PB and IPL/RPL vBMD were not significantly different (p > .05). Univariate regressions between PB and IPL/RPL vBMD were universally significant (p < 0.05), except for IPL Rad-30 (p = 0.078), with a percent difference across all sites of 6.97% ± 5.95% and 5.22% ± 4.59% between PB and IPL/RPL vBMD, respectively. As vBMD increased, there were weaker relationships and larger differences between PB vBMD and IPL/RPL vBMD. IPL and RPL vBMD had strong relationships with PB vBMD across sites (R2 = 97.99, R2 = 99.17%, respectively), but larger residual differences were found for IPL vBMD. As the accuracy of IPL/RPL vBMD varied between sites, phantomless methods should be site-specific to provide values more comparable to PB vBMD. Overall, this study suggests that RPL calibration may better represent PB vBMD compared to IPL calibration, increases the utility of opportunistic QCT, and provides insight into bone quality and fracture risk.

Trends and multi-model prediction of hepatitis B incidence in Xiamen.

Background: This study aims to analyze the trend of Hepatitis B incidence in Xiamen City from 2004 to 2022, and to select the best-performing model for predicting the number of Hepatitis B cases from 2023 to 2027. Methods: Data were obtained from the China Information System for Disease Control and Prevention (CISDCP). The Joinpoint Regression Model analyzed temporal trends, while the Age-Period-Cohort (APC) model assessed the effects of age, period, and cohort on hepatitis B incidence rates. We also compared the predictive performance of the Neural Network Autoregressive (NNAR) Model, Bayesian Structural Time Series (BSTS) Model, Prophet, Exponential Smoothing (ETS) Model, Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Hybrid Model, selecting the model with the highest performance to forecast the number of hepatitis B cases for the next five years. Results: Hepatitis B incidence rates in Xiamen from 2004 to 2022 showed an overall declining trend, with rates higher in men than in women. Higher incidence rates were observed in adults, particularly in the 30-39 age group. Moreover, the period and cohort effects on incidence showed a declining trend. Furthermore, in the best-performing NNAR(10, 1, 6)[12] model, the number of new cases is predicted to be 4271 in 2023, increasing to 5314 by 2027. Conclusions: Hepatitis B remains a significant issue in Xiamen, necessitating further optimization of hepatitis B prevention and control measures. Moreover, targeted interventions are essential for adults with higher incidence rates.

Evaluating food security and nutritional pathways of rural farm families: Empirical evidence from northeast India.

The study aimed to ascertain a relationship between agricultural status, socioeconomic factors, and nutrition of farm families. The study was conducted in selected villages in the West Garo Hills district of Meghalaya, using Stratified Random Sampling (St. RS). Using pretested interview schedules, we collected primary data from respondents in 2020 and 2021, focusing on socioeconomic variables, body mass index, and income from agriculture and related sectors. The data was analysed using correlation analyses and separate combined regression estimates for each year and month were obtained. Results from the study indicate that agricultural income significantly influenced nutritional status (p < 0.05) and household income growth was also found significant. The region's agricultural production of cereals, pulses, and vegetables was insufficient, as was the production of meat and meat products, milk, and milk products. Hence, expenditure towards purchasing the above food groups from the market was found to be significant (p < 0.05). Therefore, the markets near the mainland especially in the hilly region play a crucial role in the nutritional pathway of rural farm families.

Evidence that intergenerational income mobility is the strongest predictor of drug overdose deaths in U. S. Midwest counties.

BACKGROUND: Our goal in this report was to quantify the degree to which opioid prescription rates and socioeconomic correlates of income inequality predicted overdose deaths in the 1055 U.S. Midwest counties. The study follows up a state-level analysis which reported that opioid prescription rates, social capital and unemployment explained much of the variance in opioid overdose death rates (Heyman, McVicar, & Brownell, 2019). METHODS: We created a data set that included drug overdose death rates, opioid prescription rates, and correlates of income inequality. Given that the variables of interest varied at the state and county level, multilevel regression was our statistical approach. RESULTS: From 2006 to 2021, Midwest overdose drug deaths increased according to an exponential equation that closely approximated the equation that describes the increases in overdose deaths for the entire U.S. from 1978 to 2016 (e.g., Jalal et al., 2018). Retail opioid prescription sales increased from 2006 to 2012, but then declined so that by 2017 they were lower than in 2006. The regression analyses revealed that intergenerational income mobility was the strongest predictor of overdose deaths. The other consistently statistically significant predictors were opioid prescription rates, social capital, and unemployment rates. Together these predictors, plus pupil teacher ratios, single parent families, and attending college accounted for approximately 47 % of the variance in overdose death rates each year. In keeping with the decline in opioid prescription rates, the explanatory power of opioid prescription rates weakened over the course of the study. CONCLUSIONS: Overdose deaths increased at a constant exponential rate for the years that it was possible to apply our regression model. This occurred even though access to legal opioids decreased. What remained invariant was the predictive strength of intergenerational income mobility; each year it was the predictor that explained the most variance in overdose deaths.

Multilevel Longitudinal Functional Principal Component Model.

Sensor devices, such as accelerometers, are widely used for measuring physical activity (PA). These devices provide outputs at fine granularity (e.g., 10-100 Hz or minute-level), which while providing rich data on activity patterns, also pose computational challenges with multilevel densely sampled data, resulting in PA records that are measured continuously across multiple days and visits. On the other hand, a scalar health outcome (e.g., BMI) is usually observed only at the individual or visit level. This leads to a discrepancy in numbers of nested levels between the predictors (PA) and outcomes, raising analytic challenges. To address this issue, we proposed a multilevel longitudinal functional principal component analysis (mLFPCA) model to directly model multilevel functional PA inputs in a longitudinal study, and then implemented a longitudinal functional principal component regression (FPCR) to explore the association between PA and obesity-related health outcomes. Additionally, we conducted a comprehensive simulation study to examine the impact of imbalanced multilevel data on both mLFPCA and FPCR performance and offer guidelines for selecting optimal methods.

[Current Situation and Influencing Factors of Caregivers' Pressuring Feeding Style in Infants of 6-11 Months Old in Rural Areas of Sichuan Province].

Objective To investigate the status and influencing factors of pressuring feeding style among caregivers in remote rural areas of Sichuan province. Methods Multistage sampling was conducted to select infants of 6-11 months old who had received complementary food and their caregivers in remote rural areas of Sichuan province.A questionnaire was used to collect sociodemographic characteristics of infants and their caregivers,pressuring feeding behaviors,feeding environment,and caregivers' negative emotions.Quantile regression was employed to analyze the factors influencing pressuring feeding among caregivers of infants. Results A total of 1358 pairs of infants and their caregivers were included,with the pressuring feeding behavior score of 11 (8,14).Parity was the protective factor for caregivers' pressuring feeding (ß25=-1.17,P<0.001;ß50=-1.40,P=0.002;ß75=-2.18,P<0.001).Whether infants played with toys while eating (ß25=1.00,P<0.001;ß50=1.20,P=0.003;ß75=1.42,P<0.001) and whether infants watched TV/mobile phones (ß25=0.50,P=0.048;ß50=1.07,P=0.004) were the risk factors.At the 75th percentile,caregivers' negative emotions were the risk factor for pressuring feeding (ß75=0.94,P=0.015).Caregivers' education background (ß25=0.83,P=0.034;ß50=0.87,P=0.021) and family income (ß75=1.09,P=0.012) were also significantly associated with pressuring feeding scores at different quartile points. Conclusion Pressuring feeding behaviors of caregivers in remote rural areas of Sichuan province need to be improved.Based on the characteristics of infants and their families,guidance should be carried out to improve the feeding environment and the mental health of caregivers,thereby promoting reasonable feeding behaviors among caregivers of infants in rural areas.

[Application of ARHGAP8 in Predicting the Efficacy of Neoadjuvant Chemotherapy for Locally Advanced Mid-Low Rectal Cancer].

Objective To analyze the sensitivity of ARHGAP8 in predicting the efficacy of neoadjuvant chemotherapy in the patients with locally advanced mid-low colorectal cancer and provide accurate evidence for the treatment of advanced colorectal cancer. Methods The differentially expressed gene ARHGAP8 was screened out by bioinformatics analysis.Cancer tissue and rectal tissue of 68 patients with primary rectal cancer were selected.The rectal cancer tissue samples and the rectal tissue samples were collected for clinical validation of ARHGAP8 expression by quantitative real-time PCR,Western blotting,and immunohistochemistry.The clinical and pathological features such as gender,age,tumor stage,differentiation degree,and pathological type of the patients were collected for functional validation.Forty-four patients with locally advanced mid-low rectal cancer who received neoadjuvant chemotherapy were selected for immunohistochemical examination of ARHGAP8 expression.The expression level of ARHGAP8 was compared between before and after chemotherapy and among different efficacy groups. Results The bioinformatics analysis revealed differences in the expression level of ARHGAP8 between the cancer tissue and rectal tissue (P<0.001).The expression level of ARHGAP8 was correlated with tumor stage (P=0.024),lymph node metastasis (P=0.007),and age (P=0.005).Quantitative real-time PCR results showed that the mRNA level of ARHGAP8 in the cancer tissue was higher than that in the rectal tissue (P<0.001).Western blotting and immunohistochemistry results demonstrated that the protein level of ARHGAP8 in the cancer tissue was higher than that in the rectal tissue (P=0.011).The expression of ARHGAP8 was correlated with tumor size (P=0.010) and pathological stage (P=0.005),while it showed no significant association with tumor differentiation degree,lymph node metastasis,liver metastasis,Ki-67,or microsatellite instability expression level.The 44 patients receiving neoadjuvant chemotherapy included 13,8,8,and 15 patients of tumor regression grades 0,1,2,and 3,respectively.Among them,65.91% (29/44) patients showed responses to the treatment.After neoadjuvant chemotherapy,the expression of ARHGAP8 in the cancer tissue was down-regulated in the patients who responded to the chemotherapy (P<0.001).The response rate in the patients with low protein level of ARHGAP8 was 92.86%,which was higher than that (53.33%) in the patients with high protein level of ARHGAP8 (P=0.033). Conclusion ARHGAP8 is highly expressed in the rectal cancer tissue.The patients with locally advanced mid-low rectal cancer and low ARHGAP8 expression are more sensitive to neoadjuvant chemotherapy with the XELOX protocol.ARHGAP8 can serve as a potential biomarker for the occurrence and development of rectal cancer and an important index for evaluating the efficacy of neoadjuvant chemotherapy with the XELOX protocol in the patients with locally advanced mid-low rectal cancer.

Discontinuity of psychiatric care among patients with bipolar disorder in the Netherlands.

BACKGROUND: Patients with bipolar disorder benefit from guidelines recommended continuous community-oriented psychiatric and somatic healthcare, but often discontinue psychiatric care. AIMS: The first objective was to identify predictive factors of discontinuity of psychiatric care among patients who had received psychiatric care. The second objective was to examine if practice variation in discontinuity of psychiatric care existed between providers of psychiatric care. METHOD: Registry healthcare data were used in a retrospective cohort study design using logistic regression models to examine potential predictive factors of discontinuity of care. Patient-related predictive factors were: age, sex, urbanization, and previous treatment (type and amount of psychiatric care, alcohol, and opioid treatment). Patients already diagnosed with bipolar disorder were selected if they received psychiatric care in December 2014 to January 2015. Discontinuity of psychiatric care was measured over 2016. RESULTS: A total of 2,355 patients with bipolar disorder were included. In 12.1% discontinuity of care occurred in 2016. Discontinuity was associated with younger age and less outpatient care over 2013 to 2014. Discontinuity of patients who received all eight quarters outpatient care including BD medication was very low at 4%. The final model contained: age, type of psychiatric care, and amount of outpatient care in 2013 to 2014. Practice variation among providers appeared negligible. CONCLUSIONS: The (mental) health service in the Netherlands has few financial or other barriers toward continuity of care for patients with severe mental disorders, such as bipolar disorder. An active network of providers, aim to standardize care. This seems successful. However, 12% discontinuity per year remains problematic and more detailed data on those most at risk to drop out of treatment are necessary.

Unveiling the potential of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights.

Although low-cost air quality sensors facilitate the implementation of denser air quality monitoring networks, enabling a more realistic assessment of individual exposure to airborne pollutants, their sensitivity to multifaceted field conditions is often overlooked in laboratory testing. This gap was addressed by introducing an in-field calibration and validation of three PAQMON 1.0 mobile sensing low-cost platforms developed at the Mining and Metallurgy Institute in Bor, Republic of Serbia. A configuration tailored for monitoring PM2.5 and PM10 mass concentrations along with meteorological parameters was employed for outdoor measurement campaigns in Bor, spanning heating (HS) and non-heating (NHS) seasons. A statistically significant positive linear correlation between raw PM2.5 and PM10 measurements during both campaigns (R > 0.90, p ≤ 0.001) was observed. Measurements obtained from the uncalibrated NOVA SDS011 sensors integrated into the PAQMON 1.0 platforms exhibited a substantial and statistically significant correlation with the GRIMM EDM180 monitor (R > 0.60, p ≤ 0.001). The calibration models based on linear and Random Forest (RF) regression were compared. RF models provided more accurate descriptions of air quality, with average adjR2 values for air quality variables in the range of 0.70 to 0.80 and average NRMSE values between 0.35 and 0.77. RF-calibrated PAQMON 1.0 platforms displayed divergent levels of accuracy across different pollutant concentration ranges, achieving a data quality objective of 50% during both measurement campaigns. For PM2.5, uncertainty ( U r ) was below 50% for concentrations between 9.06 and 34.99 µg/m3 in HS and 5.75 and 17.58 µg/m3 in NHS, while for PM10, it stayed below 50% from 19.11 to 51.13 µg/m3 in HS and 11.72 to 38.86 µg/m3 in NHS.

Dominant role of soil iron and organic matters in arsenic transfer from soil to plant in a mine area in Hunan Province, Central China.

The transfer of arsenic (As) from soil to plant could be significantly influenced by soil parameters through regulating soil As bioavailability. To distinguish the bioavailable As provided by soil and the As uptaken by plants, herein two different soil bioavailable were defined, namely potential soil bioavailable As (evaluated through the bioavailable fraction of As) and actual soil bioavailable As (assessed through plant bioaccumulation factor, BF, and BFavailable). To identify the dominant soil parameters for the two soil bioavailable As forms, soil and plant samples were collected from a former As mine site. The results showed that the potential bioavailable As only accounted for 1.77 to 11.43% in the sampled soils, while the BF and BFavailable in the sampled vegetables ranged from 0.00 to 1.01 and 0.01 to 17.87, respectively. Despite a similar proportion of As in the residual fraction, soil with higher pH and organic matter (OM) content and lower iron (Fe) content showed a higher potential soil bioavailable As. Correlation analysis indicated a relationship between the soil pH and potential soil bioavailable As (r = 0.543, p < 0.01) and between the soil Fe and actual soil bioavailable As (r = - 0.644, p < 0.05, r = - 0.594, p < 0.05). Stepwise multiple linear regression (SMLR) analysis was employed to identify the dominant soil parameters and showed that soil pH and phosphorus (P) content could be used to predict the potential soil bioavailable As (R2 = 0.69, p < 0.001). On the other hand, soil Fe and OM could be used to predict the actual soil bioavailable As (R2 = 0.18-0.86, p < 0.001-0.015, in different vegetables). These results suggest that different soil parameters affect potential and actual soil bioavailable As. Hence, soil Fe and OM are the most important parameters controlling As transfer from soil to plant in the investigated area.

A nomogram for predicting the risk of temporomandibular disorders in university students.

OBJECTIVES: Temporomandibular disorders (TMDs) have a relatively high prevalence among university students. This study aimed to identify independent risk factors for TMD in university students and develop an effective risk prediction model. METHODS: This study included 1,122 university students from four universities in Changchun City, Jilin Province, as subjects. Predictive factors were screened by using the least absolute shrinkage and selection operator (LASSO) regression and the machine learning Boruta algorithm in the training cohort. A multifactorial logistic regression analysis was used to construct a TMD risk prediction model. Internal validation of the model was conducted via bootstrap resampling, and an external validation cohort comprised 205 university students undergoing oral examinations at the Stomatological Hospital of Jilin University. RESULTS: The prevalence of TMD among university students was 44.30%. Ten predictive factors were included in the model, comprising gender, facial cold stimulation, unilateral chewing, biting hard or resilient foods, clenching teeth, grinding teeth, excessive mouth opening, malocclusion, stress, and anxiety. The model demonstrated good predictive ability with area under the receiver operating characteristic curve (AUC) values of 0.853, 0.838, and 0.821 in the training cohort, internal validation cohort, and external validation cohort, respectively. The calibration curves demonstrated that the predicted results were consistent with the actual results, and the decision curve analysis (DCA) indicated the model's high clinical utility. CONCLUSIONS: An online nomogram of TMD in university students with good predictive performance was constructed, which can effectively predict the risk of TMD in university students. The model provides a useful tool for the early identification and treatment of TMDs in university students, helping clinicians to predict the probability of TMDs in each patient, thus providing more personalized and accurate treatment decisions for patients.

A knowledge-transfer-based approach for combining ordinal regression and medical scoring system in the early prediction of sepsis with electronic health records.

OBJECTIVE: The prediction of sepsis, especially early diagnosis, has received a significant attention in biomedical research. In order to improve current medical scoring system and overcome the limitations of class imbalance and sample size of local EHR (electronic health records), we propose a novel knowledge-transfer-based approach, which combines a medical scoring system and an ordinal logistic regression model. MATERIALS AND METHODS: Medical scoring systems (i.e. NEWS, SIRS and QSOFA) are generally robust and useful for sepsis diagnosis. With local EHR, machine-learning-based methods have been widely used for building prediction models/methods, but they are often impacted by class imbalance and sample size. Knowledge distillation and knowledge transfer have recently been proposed as a combination approach for improving the prediction performance and model generalization. In this study, we developed a novel knowledge-transfer-based method for combining a medical scoring system (after a proposed score transformation) and an ordinal logistic regression model. We mathematically confirmed that it was equivalent to a specific form of the weighted regression. Furthermore, we theoretically explored its effectiveness in the scenario of class imbalance. RESULTS: For the local dataset and the MIMIC-IV dataset, the VUS (the volume under the multi-dimensional ROC surface, a generalization measure of AUC-ROC for ordinal categories) of the knowledge-transfer-based model (ORNEWS) based on the NEWS scoring system were 0.384 and 0.339, respectively, while the VUS of the traditional ordinal regression model (OR) were 0.352 and 0.322, respectively. Consistent analysis results were also observed for the knowledge-transfer-based models based on the SIRS/QSOFA scoring systems in the ordinal scenarios. Additionally, the predicted probabilities and the binary classification ROC curves of the knowledge-transfer-based models indicated that this approach enhanced the predicted probabilities for the minority classes while reducing the predicted probabilities for the majority classes, which improved AUCs/VUSs on imbalanced data. DISCUSSION: Knowledge transfer, which combines a medical scoring system and a machine-learning-based model, improves the prediction performance for early diagnosis of sepsis, especially in the scenarios of class imbalance and limited sample size.

Analysis of canopy interception characteristics and influencing factors in typical artificial forest in the Loess Plateau semi-arid region.

Interception loss (IL) is an important process in the hydrological cycle within semi-arid forest ecosystems, directly affecting the amount of effective rainfall. However, the factors influencing IL during individual rainfall events remain to be quantified. This study collected rainfall, vegetation, and interception data during the 2022 and 2023 growing seasons in a typical black locust forest within the Zhifanggou watershed. It employed the Random Forest Regression (RFR) and back-propagation neural network (BPNN) methods to quantitatively evaluate the contribution rates of various factors to the IL and interception loss percentage (ILP). The IL among the 48 effective rainfall events was 172.05 mm, accounting for 19.54% of the rainfall amount. IL and ILP increased as the distance from the trunk decreased. During all rainfall events, both IL and ILP were significantly negatively correlated with the leaf area index (LAI) and canopy cover (CC); IL is significantly positively correlated with total rainfall (TR) and rainfall intensity (RI), while ILP is significantly negatively correlated with TR, RI, and rainfall duration (RD). The BPNN and RFR results indicated that rainfall, canopy, and tree characteristics contributed 43.06%, 44.79%, and 12.15% to IL, respectively, and 57.27%, 34.09%, and 8.63% to ILP, respectively. TR, CC, and LAI represented the primary influencing factors. Rainfall and canopy characteristics were the main factors affecting IL (ILP). As rainfall event magnitude increases, canopy contributions to IL and ILP decrease. In semi-arid areas, managing forest canopies to control IL helps address water imbalances in ecosystems.

Glutamine Synthetase Staining Patterns in Cirrhosis.

Advanced liver fibrosis can regress following the elimination of causative injuries. Glutamine synthetase (GS) immunohistochemical expression is normally in centrizonal perivenular hepatocytes but can be present in periportal hepatocytes in cases of regressed cirrhosis. This study identified periportal staining and investigated the spectrum of GS staining patterns seen in a range of cirrhotic livers with varying disease processes. The hematoxylin and eosin and GS-stained slides of 88 liver resection/explant specimens with advanced fibrosis cases by different causes were reviewed, and trichrome and orcein stains were used to classify cases as progressive, indeterminate, or regressive. Periportal GS staining was seen in 97% of regressive cases and 84% progressive or indeterminate cases. Liver resection specimens with periportal GS staining showed a variety of patterns, including predominantly perivenular, predominantly periseptal, and perinodular staining. The GS periseptal pattern is more common in regressed cirrhosis compared to progressive cases. The perinodular staining was seen in 16 cases resulting from various etiologies, including biliary atresia, steatotic liver disease, primary biliary cholangitis, and viral hepatitis, 75% of which demonstrated cholestasis. This study further subclassified GS staining patterns of "periportal" pattern in cirrhotic liver. Compared to orcein/trichrome staining, GS immunohistochemical staining is not as useful in distinguishing regressed cases from non-regressed cases.

Nine-year survival after intertrochanteric fracture in elderly with chronic kidney disease.

The outcome of this study was to identify 9-year survivors of intertrochanteric fracture at each stage of chronic kidney disease (CKD) and to investigate the risk factors associated with mortality following surgery with proximal femoral nail anti-rotation (PFNA). 443 elderly intertrochanteric fractures underwent PFNA fixation were recruited. Mortality rate was identified until 9 years. We compared the survival time of hip fracture in each stage of CKD. A regression analysis was used to determine the association between risk factors and one-year mortality. The overall median survival time was 7.1 years. The Kaplan-Meier curve was significantly different in each CKD stage especially in CKD5. In addition, the incidence rate of mortality was highest in CKD 5 (17.4%) and the median survival time in CKD 5 was 3.3 years. The multivariate analysis demonstrated that heart disease, operative time > 60 min, presence of pulmonary embolism, and poor to fair Harris hip score were significantly increased mortality. CKD stage 5 is associated with the highest mortality rate and the shortest median time of survival during the 9-year follow up. Patients who have high risk should focus on long-term care planning, including the counseling for their healthcare providers and families.

Comparison of Quantile Regression and Gaussian (Z-scores) Percentiles to BSA in Growth Charts With a Pakistani Population.

Introduction: In the current study, we construct growth charts of body surface area (BSA) for adults using the quantile regression (QR) approach and growth charts of different Gaussian Percentiles (Z-scores) against age. Methods: A cross-sectional data consisting of 3,473 individuals aged 5 or more, both males and females were taken from Multan city. Quantile regression (QR) was used to construct BSA growth charts. Growth charts for different Z-scores were also constructed. Results: For our data set, the mean BSA is 0.48750. The BSA percentiles show a trending higher after the age of 5 until the age of 22, then decrease between age 22 and 35, and then finally increase after age 35. The Z-score curve increases slightly after age 5 and then proceeds higher until age 22. After age 22 and before 35 it plateaus and then increases slightly after age 35. Conclusion: Since the use of empirical BSA percentiles and Z-scores with grouped age provides a discrete approximation for the population percentiles and Z-scores, it is more accurate to use continuous BSA percentile and Z-score, curves against given ages while using quantile regression and Z-score approach. Furthermore, this approach can also be adopted to construct many other growth charts for physiological and medical sciences.

Predicting Non-Mass Breast Cancer Utilizing Ultrasound and Molybdenum Target X-Ray Characteristics.

Objective: The aim of this study is to investigate the influence of ultrasound and molybdenum target X-ray characteristics in predicting non-mass breast cancer. Methods: A retrospective analysis was conducted on the clinical data of 185 patients presenting with non-mass breast lesions between September 2019 and 2021. The non-mass lesions were categorized into benign and malignant types based on ultrasonographic findings, which included lamellar hypoechoic, ductal alteration, microcalcification, and structural disorder types. Furthermore, an examination was undertaken to discern variances in molybdenum target X-ray parameters, ultrasonographic manifestations, and characteristics among individuals diagnosed with non-mass breast lesions. Results: The ultrasonographic depiction of microcalcified lesions and the identification of suspicious malignancy through molybdenum target X-ray evaluation exhibited independent associations with non-mass breast cancer, yielding statistically significant differences (p < 0.05). Subsequently, the logistic regression model was formulated as follows: Logit (P) =-1.757+2.194* microcalcification type on ultrasound + 1.520* suspicious malignancy on molybdenum target X-ray evaluation. The respective areas under the receiver operating characteristic curves for microcalcification type on ultrasound, suspicious malignancy on molybdenum target X-ray, and the integrated diagnostic model were 0.733, 0.667, and 0.827, respectively, demonstrating discriminative capacities. Conclusion: Using both ultrasound and molybdenum target X-ray diagnostics can increase the accuracy of non-mass breast cancer detection. The findings of this study have the potential to augment the detection rate of non-lumpy breast cancer and provide an imaging basis for enhancing the prognosis of individuals with breast cancer.