Optimal risk thresholds for prescribing statins as primary prevention of cardiovascular disease in Iranian general population: a benefit-harm modelling study.

PURPOSE: The use of statins for the primary prevention of cardiovascular diseases (CVD) is associated with various beneficial outcomes, alongside certain undesirable effects. This study aims to determine optimal risk thresholds above which statin therapy yields a net benefit, considering both the positive effects and potential adverse effects, as well as their probabilities and patient preferences. METHODS: Quantitative benefit-harm balance modeling was applied to the Iranian general population aged 40 to 75 years with no history of CVD. The analysis utilized data from prior studies, including statin effect estimates for different outcomes from a meta-analysis, patient preferences obtained from an Iranian survey, and baseline incidence rates of adverse outcomes sourced from the Global Burden of Disease study for Iran. Outcomes were defined as angina, myocardial infarction, fatal coronary heart disease, fatal or non-fatal stroke, and heart failure. Benefit-harm balance indices were calculated for various combinations of age, sex, and 10-year CVD risk. RESULTS: Statin therapy was found to be advantageous at a lower 10-year CVD risk threshold in men (18-23%) compared to women (24-28%). Furthermore, individuals aged 40-45 years exhibited a lower risk threshold (18% in men, 24% in women) than those aged 70-75 years (23% in men, 28% in women). CONCLUSION: The desirable 10-year risk thresholds for statin prescription in the primary prevention of CVD vary by age and gender, ranging from 18 to 28%, encompassing a spectrum of outcomes from angina to CVD mortality. These results suggest hard-CVD risk thresholds of 7.5% to 10% for both sexes.

Determining soil conservation strategies: Ecological risk thresholds of arsenic and the influence of soil properties.

The establishment of ecological risk thresholds for arsenic (As) plays a pivotal role in developing soil conservation strategies. However, despite many studies regarding the toxicological profile of As, such thresholds varying by diverse soil properties have rarely been established. This study aims to address this gap by compiling and critically examining an extensive dataset of As toxicity data sourced from existing literature. Furthermore, to augment the existing information, experimental studies on As toxicity focusing on barley-root elongation were carried out across various soil types. The As concentrations varied from 12.01 to 437.25 mg/kg for the effective concentrations that inhibited 10% of barley-root growth (EC10). The present study applied a machine-learning approach to investigate the complex associations between the toxicity thresholds of As and diverse soil properties. The results revealed that Mn-/Fe-ox and clay content emerged as the most influential factors in predicting the EC10 contribution. Additionally, by using a species sensitivity distribution model and toxicity data from 21 different species, the hazardous concentration for x% of species (HCx) was calculated for four representative soil scenarios. The HC5 values for acidic, neutral, alkaline, and alkaline calcareous soils were 80, 47, 40, and 28 mg/kg, respectively. This study establishes an evidence-based methodology for deriving soil-specific guidance concerning As toxicity thresholds.

Development and demonstration of a data visualization platform of short-term guidelines for ambient air levels of benzene during disaster response in Houston, Texas.

Industrial disasters have caused hazardous air pollution and public health impacts. Response officials have developed limited exposure guidelines to direct them during the event; often, guidelines are outdated and may not represent relevant elevated-exposure periods. The 2019 Intercontinental Terminals Company (ITC) fire in Houston, Texas led to large-scale releases of benzene and presented a public health threat. This incident highlights the need for effective response and nimble, rapid public health communication. We developed a data-driven visualization tool to store, display, and interpret ambient benzene concentrations to assist health officials during environmental emergencies. Guidance values to interpret risk from acute exposure to benzene were updated using recent literature that also considers exposure periodicity. The visualization platform can process data from different sampling instruments and air monitors automatically, and displays information publicly in real time, along with the associated risk information and action recommendations. The protocol was validated by applying it retrospectively to the ITC event. The new guidance values are 6-30 times lower than those derived by the Texas regulatory agency. Fixed-site monitoring data, assessed using the protocol and revised thresholds, indicated that eight shelter-in-place and 17 air-quality alerts may have been considered. At least one of these shelter-in-place alerts corresponded to prolonged, elevated benzene concentrations (~1000 ppb). This new tool addresses essential gaps in the timely communication of air pollution measurements, provides context to understand potential health risks from exposure to benzene, and provides a clear protocol for local officials in responding to industrial air releases of benzene. Integr Environ Assess Manag 2024;20:533-546. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Machine learning-based prediction model and visual interpretation for prostate cancer.

BACKGROUND: Most prostate cancers(PCa) rely on serum prostate-specific antigen (PSA) testing for biopsy confirmation, but the accuracy needs to be further improved. We need to continue to develop PCa prediction model with high clinical application value. METHODS: Benign prostatic hyperplasia (BPH) and prostate cancer data were obtained from the Chinese National Clinical Medical Science Data Center for retrospective analysis. The model was constructed using the XGBoost algorithm, and patients' age, body mass index (BMI), PSA-related parameters and serum biochemical parameters were used as model variables. Using decision analysis curve (DCA) to evaluate the clinical utility of the models. The shapley additive explanation (SHAP) framework was used to analyze the importance ranking and risk threshold of the variables. RESULTS: A total of 1915 patients were included in this study, including 823 (43.0%) were BPH patients and 1092 (57.0%) were PCa patients. The XGBoost model provided better performance (AUC 0.82) compared with f/tPSA (AUC 0.75),tPSA (AUC 0.68) and fPSA (AUC 0.61), respectively. Based on SHAP values, f/tPSA was the most important variable, and the top five most important biochemical parameter variables were inorganic phosphorus (P), potassium (K), creatine kinase MB isoenzyme (CKMB), low-density lipoprotein cholesterol (LDL-C), and creatinine (Cre). PCa risk thresholds for these risk markers were f/tPSA (0.13), P (1.29 mmol/L), K (4.29 mmol/L), CKMB ( 11.6U/L), LDL-C (3.05mmol/L) and Cre (74.5-99.1umol/L). CONCLUSION: The present model has advantages of wide-spread availability and high net benefit, especially for underdeveloped countries and regions. Furthermore, these risk thresholds can assist in the diagnosis and screening of prostate cancer in clinical practice.

[Biological Evaluation and Key Stress Factor Diagnosis of Compound Contaminated Soil Based on Environmental DNA].

Healthy biological community composition and diversity are the basis for soil ecosystems to provide complete ecological functions and services. The current technical bottleneck in soil pollution control is the lack of systematic and comprehensive biodiversity indicators. In this study, the compound-polluted soil in an industrial park was taken as the object, and environmental DNA (eDNA)technology was used to monitor and analyze the composition of the biological community in the polluted soil. The environmental DNA-species sensitivity distribution (eDNA-SSD) method was constructed to diagnose the key stress factors and their ecological threshold. The results showed that:① compound pollution did not have a significant impact on the α-diversity of soil organisms in the park but led to changes in the composition of biological communities and dominant species with high relative abundance. The dominant groups in the polluted soil were Proteobacteria (25.54%), Ascomycota (15.65%), Actinobacteria (8.75%), and Chloroflexi (7.64%). ② Environmental factors accounted for 51.27% of the differences in soil biodistribution, and aniline, sulfate, petroleum hydrocarbon, and lead were the key stress factors for soil biomes. Among them, aniline had the most significant effect on the community distribution of bacteria (P=0.030), sulfate on fungi (P=0.025), and metazoans (P=0.032). ③ The ecological risk threshold (hazardous concentration for 5% of species, HC5) of pollutants with 95% species as the protection target showed a gradient decreasing trend of sulfate (470.00 mg·kg-1), petroleum hydrocarbon (17.00 mg·kg-1), lead (13.00 mg·kg-1), arsenic (6.20 mg·kg-1), 4-chloroaniline (0.16 mg·kg-1), and aniline (0.11 mg·kg-1). Nevertheless, the existing soil risk control value was significantly higher than the ecological risk value, indicating that the existing control standards targeting human health were not suitable for protecting soil health. The comprehensive pollution index showed that the soil pollution in the park was mostly mild pollution accompanied by heavy pollution at individual points. The biological evaluation method based on eDNA-SSD could achieve the comprehensive evaluation of composite pollution in the absence of the benchmark value of pollutants and at the same time better distinguish the differences in soil biological community structure and composition under different pollution levels (P=0.053). In conclusion, the environmental DNA soil biomonitoring and assessment method developed in this study has guiding significance and application value for soil ecosystem restoration assessment.

Risk Threshold and Assessment of Chloramphenicol Antibiotics in Sediment in the Fenhe River Basin, China.

Chloramphenicol antibiotics (CAs) are broad-spectrum antibiotics which are widely used in the prevention and treatment of infectious diseases in livestock and poultry breeding. However, overused CAs can enter the watershed and eventually enter the sediment. Antibiotics in sediment can cause secondary pollution through disturbance and suspension. In this study, taking the Fenhe River Basin as the research area, the risk of CAs in sediment were assessed by collecting sediment samples. The results showed that CAs were detected in all sediment samples of the Fenhe River Basin. The mean concentration of CAs was 79.1 µg/kg, and the concentration of thiamphenicol (THI) was dominant, which was up to 58.3 µg/kg. Temporally, there are great differences in different seasons; the concentration of CAs was higher in winter than that in summer, up to 4.79-174 times. Spatially, the mean concentration of CAs in midstream was 83.5 µg/kg, which was higher than that in the upstream and downstream. The concentration of CAs in tributaries were generally higher than that in the main stream, and the mean concentration of tributaries was 1.1 times that of the main stream. CAs in S2 (Lanhe River) was the most prominent among all sample sites; the concentration of CAs was 190.8 µg/kg. The risk threshold of CAs in the sediment was calculated using the Equilibrium Partitioning approach (EqP), based on the distribution coefficient (Kp) and the predicted no-effect concentration (PNEC) in the water, and the values were 0.091-1.44 mg/kg. Based on the risk threshold, the ecological risk of the CAs in sediment was assessed using risk quotients (RQ). The results showed that the Chloramphenicol (CHL) was the most prominent in the Fenhe River Basin, and the proportion of medium-risk areas reached 21.7%, while all the other areas showed low risk. Secondly, the proportion of medium-risk areas was 17.4% for THI, and all the other areas showed low risk. The risk for Florfenicol (FF) was least among all CAs, and the proportion of low-risk areas was only 8.7%, while all the other areas were of insignificant risk.

Atmospheric dryness thresholds of grassland productivity decline in China.

Atmospheric dryness events are bound to have a broad and profound impact on the functions and structures of grassland ecosystems. Current research has confirmed that atmospheric dryness is a key moisture constraint that inhibits grassland productivity, yet the risk threshold for atmospheric dryness to initiate ecosystem productivity loss has not been explored. Based on this, we used four terrestrial ecosystem models to simulate gross primary productivity (GPP) data, analyzed the role of vapor pressure deficit (VPD) in regulating interannual variability in Chinese grasslands by focusing on the dependence structure of VPD and GPP, and then constructed a bivariate linkage function to calculate the conditional probability of ecosystem GPP loss under atmospheric dryness, and further analyzed the risk threshold of ecosystem GPP loss triggered by atmospheric dryness. The main results are as follows: we found that (1) the observed and modeled VPD of Chinese grasslands increases rapidly in both historical and future periods. VPD has a strongly negative regulation on ecosystem GPP, and atmospheric dryness is an important moisture constraint that causes deficit and even death to ecosystem GPP. (2) The probability of the enhanced atmospheric dryness that induced GPP decline in Chinese grasslands in the future period increases significantly. (3) When the VPD is higher than 40.07 and 27.65 percentile of the past and future time series, respectively, the risk threshold of slight ecosystem GPP loss can be easily initiated by atmospheric dryness. (4) When the VPD is higher than 82.57 and 65.09 percentile, respectively, the threshold of moderate ecosystem GPP loss can be exceeded by the benchmark probability. (5) The risk threshold of severe ecosystem GPP loss is not initiated by atmospheric dryness in the historical period, and the threshold of severe ecosystem GPP loss can be initiated when the future VPD is higher than 91.92 percentile. In total, a slight atmospheric dryness event is required to initiate a slight ecosystem GPP loss threshold, and a stronger atmospheric dryness event is required to initiate a severe ecosystem GPP loss. Our study enhances the understandings of past and future atmospheric dryness on grassland ecosystems, and strongly suggests that more attention be invested in improving next-generation models of vegetation dynamics processes with respect to the response of mechanisms of ecosystem to atmospheric dryness.

An optimization for postpartum depression risk assessment and preventive intervention strategy based machine learning approaches.

BACKGROUND: Postpartum depression (PPD) is one of the most common psychiatric disorders for women after delivery. The establishment of an effective PPD prediction model helps to distinguish high-risk groups, and verifying whether such high-risk groups can benefit from drug intervention is very important for clinical guidance. METHODS: We collected data of parturients that underwent a cesarean delivery. The Control group was divided into a training cohort and a testing cohort. Six different ML models were constructed and we compared their prediction performance in the testing cohort. For model interpretation, we introduced SHapley Additive exPlanations (SHAP). Then, training cohort, ketamine group and dexmedetomidine (DEX) group were classified as high or low risk for PPD by the model. A 1:1 propensity score matching (PSM) was performed to compare the incidence of PPD between two groups in different risk cohorts. RESULTS: Extreme gradient enhancement (XGB) had the best recognition effect, with an area under the receiver operating characteristic curve (AUROC) of 0.789 (95 % CI 0.742-0.836) in the training cohort and 0.744 (95 % CI 0.655-0.823) in the testing cohort, respectively. A threshold of 21.5 % PPD risk probability was determined. After PSM, the results showed that the incidence of PPD in the two intervention groups was significantly different from the control group in the high-risk cohort (P < 0.001) but not in the low-risk cohort (P > 0.001). CONCLUSION: Our study demonstrated that the XGB algorithm provided a more accurate in prediction of PPD risk, and it was beneficial to receive early intervention for the high-risk groups distinguished by the model.

LDPE and biodegradable PLA-PBAT plastics differentially affect plant-soil nitrogen partitioning and dynamics in a Hordeum vulgare mesocosm.

Micro and macroplastics are emerging contaminants in agricultural settings, yet their impact on nitrogen (N) cycling and partitioning in plant-soil-microbial systems is poorly understood. In this mesocosm-scale study, spring barley (Hordeum vulgare L.) was exposed to macro or microplastic produced from low density polyethylene (LDPE) or biodegradable plastic at concentrations equivalent to 1, 10 and 20 years of plastic mulch film use. Partitioning of 15N-labelled fertiliser into plant biomass, soil and leachate yielded a partial mass balance. Soil N partitioning was probed via compound-specific 15N-stable isotope analyses of soil microbial protein. Concentration-dependent decreases in plant 15N uptake occurred with increased leached nitrogen for LDPE microplastic. Assimilation into soil microbial protein was higher for biodegradable plastics, which we associate with early-stage biodegradable plastic degradation. Partitioning of 15N into inorganic soil N pools was affected by LDPE size, with lower assimilation into the microbial protein pool. While microplastics and macroplastics altered soil N cycling, the limited impacts on plant health indicated the threshold for negative effects was not reached at agriculturally relevant concentrations. This study highlights the difference between conventional and biodegradable plastics, and emphasises that the interplay of micro and macroplastics on soil N cycling must be considered in future studies.

Establishment and Optimization of Soil Cd Risk Threshold in Typical Karst Area with Potato Production, China.

The threshold is key to risk assessment of soil cadmium (Cd) pollution. However, there is limited research on the soil Cd risk threshold of potatoes. Soil and potato samples (n = 256) were used to establish and optimize the Cd risk threshold by using relative cumulative frequency, bioconcentration factor, and regression model. The results showed that suggested risk screening values (SRSVs) for soil Cd were divided into 2.465 (pH ≤ 5.5), 2.564 (5.5 < pH ≤ 6.5), 2.778 (6.5 < pH ≤ 7.5), and 4.348 mg kg-1 (pH > 7.5). SRSVs were applied to classify soil Cd risk assessment by collecting soil samples (n = 100). Low-risk areas only comprised 0.98% of the total area using risk screening values (RSVs) (GB15618-2018), and risk areas comprised as much as 99.02%. Low-risk area and risk area comprised 97.75% and 2.25% of the total area based on SRSVs. SRSVs are appropriate for potato production in typical karst areas.

Method for Calculating the Simultaneous Maximum Acceptable Risk Threshold (SMART) from Discrete-Choice Experiment Benefit-Risk Studies.

BACKGROUND: Medical decisions require weighing expected benefits of treatment against multiple adverse outcomes under uncertainty (i.e., risks) that must be accepted as a bundle. However, conventional maximum acceptable risk (MAR) estimates derived from discrete-choice experiment benefit-risk studies evaluate the acceptance of individual risks, assuming other risks are fixed, potentially leading decision makers to misinterpret levels of risk acceptance. DESIGN: Using simulations and a published discrete-choice experiment, we demonstrate a method for identifying multidimensional risk-tolerance measures given a treatment level of benefit. RESULTS: Simultaneous Maximum Acceptable Risk Thresholds (SMART) represents combinations of risks that would be jointly accepted in exchange for specific treatment benefits. The framework shows how the expectation of utility associated with treatments that involve multiple risks are related even when preferences for potential adverse events are independent. We find that the form of the marginal effects of adverse-event probabilities on the expected utility of treatment determines the magnitude of differences between SMART and conventional single-outcome MAR estimates. LIMITATIONS: Preferences for potential adverse events not considered in a study or preferences for adverse-event attributes held constant in risk-tolerance calculations may affect estimated risk tolerance. Further research is needed to understand the right balance between realistically reflecting clinical treatments with many potential adverse events and the cognitive burden of evaluating risk-risk tradeoffs in research and in practice. CONCLUSIONS AND IMPLICATIONS: SMART analysis should be considered in preference studies evaluating the joint acceptance of multiple potential adverse events. HIGHLIGHTS: Conventional approaches to calculate maximum-acceptable risk (MAR) using discrete-choice experiment data account for 1 adverse-event risk at a time, requiring that decision makers infer the acceptability of treatments when patients are exposed to multiple risks simultaneously.The Simultaneous Maximum Acceptable Risk Threshold (SMART) maps combinations of adverse-event risks that would be jointly acceptable given a specific treatment benefit and provides a transparent and precise portrayal of acceptance of multiple risks.Risk levels that would be accepted using individual MAR estimates might not be acceptable when simultaneous risks are considered, especially when marginal expected disutility of risk is decreasing nonlinearly with risk probabilities.Preference researchers should calculate SMARTs in any discrete-choice study in which 2 or more adverse-event risks are presented, particularly if risk preferences are nonlinear.

Assessing the longitudinal association between the GGT/HDL-C ratio and NAFLD: a cohort study in a non-obese Chinese population.

BACKGROUND: A cross-sectional association between the combination indicator of high-density lipoprotein cholesterol (HDL-C) and gamma-glutamyl transferase (GGT) and fatty liver has been described in several recent studies, and this study aims to further evaluate the longitudinal relationship between the ratio of GGT to HDL-C (GGT/HDL-C ratio) and nonalcoholic fatty liver disease (NAFLD). METHODS: This cohort study included 12,126 individuals without NAFLD at baseline, followed prospectively for 5 years, and the endpoint of interest was new-onset NAFLD. The relationship of the GGT/HDL-C ratio with new-onset NAFLD and the shape of the association was assessed by Cox regression models and restricted cubic spline (RCS) regression, respectively. Time-dependent receiver operator characteristics (ROC) curves were constructed to evaluate the predictive value of GGT, HDL-C, GGT/HDL-C ratio and BMI for the occurrence of NAFLD at different time points in the future. RESULTS: The prevalence of NAFLD was 72.46/1000 person-years during the 5-year follow-up period. Results of multivariate Cox regression analysis showed a positive association of the GGT/HDL-C ratio with new-onset NAFLD after adequate adjustment of the related confounding factors, and the degree of correlation was slightly higher than that of GGT, and further subgroup analysis found that this association was more significant in the population with elevated systolic blood pressure (SBP). In addition, we also found a nonlinear relationship of the GGT/HDL-C ratio with the risk of new-onset NAFLD using the RCS regression, where the saturation threshold was about 31.79 U/mmol. Time-dependent ROC analysis results showed that the GGT/HDL-C ratio was increasingly valuable in predicting NAFLD over time, and was better than HDL-C in predicting NAFLD in the early stage (1-3 years), but was not superior to BMI and GGT. CONCLUSIONS: In this large longitudinal cohort study based on a Chinese population, our results supported that the GGT/HDL-C ratio was positively and nonlinearly associated with the risk of new-onset NAFLD in a non-obese population. In the assessment of future NAFLD risk, the GGT/HDL-C ratio was slightly better than GGT alone; However, the GGT/HDL-C ratio did not appear to have a significant advantage over GGT and BMI alone in predicting NAFLD.

Exploring the Risk Thresholds of Soil Heavy Metals in Carbonate and Non-carbonate Rock Areas: The Case of Qianjiang District in Chongqing, China.

To determine whether the national soil heavy metal standards (GB 15618-2018) are applicable to some carbonate and non-carbonate zones in Southwest China, rice and rhizosphere soil samples were collected in Chongqing and analyzed for heavy metal contents, pH, and other chemical parameters. In addition, regression analysis was also used to predict the risk threshold of soil heavy metals. The Cd risk screening value in GB 15618-2018 was strict for alkaline soils (pH > 7.5) as compared to those revealed in carbonate and non-carbonate areas, while the calculated pollution threshold for Cd in acidic soils (pH ≤ 5.5) in the non-carbonate area was lower than that in GB 15618-2018. Therefore, to improve the applicability of the evaluation results, a soil-crop system evaluation is recommended.

A feasibility study of risk prediction modelling for vasoocclusive crisis in children with sickle cell disease.

BACKGROUND: The availability of a selection of biomarkers that includes information about disease risk is very important in the treatment of sickle cell disease (SCD). We used the predictiveness curve (PC), which classifies diseased individuals according to low- and high-risk thresholds, for this purpose. Our aim was to define this new statistical method and to determine the biomarkers that predict vaso-occlusive crisis (VOC) in children with SCD to guide preventive treatment. METHODS: Thirty-eight pediatric patients with SCD were included in this feasibility study. Leucocytes (WBC), C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor (TNF-α), and YKL-40 were studied in patients with VOC and without VOC. The patient group with a low or high risk of VOC was assessed using the PC. Risk prediction and classification performance were evaluated using the PC and receiver operating characteristic (ROC) curve. RESULTS: According to the PC, patients with a high risk of VOC could be detected via TNF-α, IL-6, and WBC, and TNF-α was the best risk prediction marker (TPF = 0.67). CONCLUSIONS: The PC provides disease risk information by comparing more than one biomarker and can thereby help clinicians determine appropriate preventive treatments. This is the first study to evaluate biomarkers to predict VOC risk in SCD patients.

Efficacy of Risk Prediction Models and Thresholds to Select Patients for Lung Cancer Screening.

PurposeScreening for lung cancer is recommended to reduce lung cancer mortality, but there is no consensus on patient selection for screening in Canada. Risk prediction models are more efficacious than the screening recommendations of the Canadian Task Force on Preventive Health Care (CTFPHC), but it remains to be determined which model and threshold are optimal. MethodsWe retrospectively applied the PLCOm2012, PLCOall2014 and LLPv2 risk prediction models to 120 lung cancer patients from a Canadian province, at risk thresholds of ≥ 1.51% and ≥ 2.00%, to determine screening eligibility at time of diagnosis. OncoSim modelling was used to compare these risk thresholds. ResultsSensitivities of the risk prediction models at a threshold of ≥ 1.51% were similar with 93 (77.5%), 96 (80.0%), and 97 (80.8%) patients selected for screening, respectively. The PLCOm2012 and PLCOall2014 models selected significantly more patients for screening at a ≥ 1.51% threshold. The OncoSim simulation model estimated that the ≥ 1.51% threshold would detect 4 more cancers per 100 000 people than the ≥ 2.00% threshold. All risk prediction models, at both thresholds, achieved greater sensitivity than CTFPHC recommendations, which selected 56 (46.7%) patients for screening. ConclusionCommonly considered lung cancer screening risk thresholds (≥1.51% and ≥2.00%) are more sensitive than the CTFPHC 30-pack-years criterion to detect lung cancer. A lower risk threshold would achieve a larger population impact of lung cancer screening but would require more resources. Patients with limited or no smoking history, young patients, and patients with no history of COPD may be missed regardless of the model chosen.

Recommended risk screening values for Cd in high geological background area of Guangxi, China.

Because of unusually high cadmium concentrations in the soil, the risk screening values of soil Cd in the existing standard is not applicable to the Cd high geological background areas. The aim of our study is to explore recommended risk screening values applicable for Cd high geological background areas of Guangxi, China, to help locals with land management and guarantee the quality and safety of crops as well as providing the theoretical basis for guiding the production safety. A total of 903 pairs of rice samples and root soil samples were collected. The Cd concentration of soil-rice samples and soil pH were determined. The scatter diagram method was used to gradually increase the screening values, and the value with the most samples in the correct interval was counted as the recommended risk screening value. The soil Cd concentrations ranged from 0.06 to 7.08 mg·kg-1 and the rice Cd ranged from 0.002 to 1.488 mg·kg-1; 64.89% of soil samples exceed the RSVs and 27.8% of rice samples exceed the allowable limit of Cd. The recommended risk screening values of Cd in study area were 0.5, 0.7, 1.5, and 2.4 mg·kg-1 for soil with pH ≤ 5.5, 5.5 < pH ≤ 6.5, 6.5 < pH ≤ 7.5, and pH > 7.5, respectively. Compared with the standard screening value, the accuracy of using the recommended screening value as the reference value to judge whether the Cd concentration in rice exceeds the standard was increased by 12%, 20%, 21%, and 47%, respectively. The recommended screening value can be used as the standard value to better indicate the soil environmental quality in the study area.

The development and application of a prediction model for postpartum depression: optimizing risk assessment and prevention in the clinic.

BACKGROUND: Preventive intervention can significantly reduce the human and economic costs of postpartum depression (PPD) compared with treatment post-diagnosis. However, identifying women with a high PPD risk and making a judgement as to the benefits of preventive intervention is a major challenge. METHODS: This is a retrospective study of parturients that underwent a cesarean delivery. Control group was used as development cohort and validation cohort to construct the risk prediction model of PPD and determine a risk threshold. Ketamine group and development cohort were used to verify the risk classification of parturients by evaluating whether the incidence of PPD decreased significantly after ketamine treatment in high-risk for PPD population. RESULTS: The AUC for the development cohort and validation cohort of the PPD prediction model were 0.751 (95%CI:0.700-0.802) and 0.748 (95%CI:0.680-0.816), respectively. A threshold of 19% PPD risk probability was determined, with a specificity and sensitivity in the validation cohort are 0.766 and 0.604, respectively. After matching the high-risk group and the low-risk group by propensity score, the results demonstrated that PPD incidence significantly reduced in the high-risk group following ketamine, versus non-ketamine, intervention (p < 0.01). In contrast, intervention in the low-risk group showed no significant difference in PPD outcomes (p > 0.01). LIMITATION: Randomized trials are needed to further verify the feasibility of the model and the thresholds proposed. CONCLUSION: This prediction model developed in this study shows utility in predicting PPD risk. Ketamine intervention significantly lowers PPD incidence in parturients with a risk classification threshold greater than 19%.

Should Age-Dependent Absolute Risk Thresholds Be Used for Risk Stratification in Risk-Stratified Breast Cancer Screening?

In risk-stratified cancer screening, multiple risk factors are incorporated into the risk assessment. An individual's estimated absolute cancer risk is linked to risk categories with tailored screening recommendations for each risk category. Absolute risk, expressed as either remaining lifetime risk or shorter-term (five- or ten-year) risk, is estimated from the age at assessment. These risk estimates vary by age; however, some clinical guidelines (e.g., enhanced breast cancer surveillance guidelines) and ongoing personalised breast screening trials, stratify women based on absolute risk thresholds that do not vary by age. We examine an alternative approach in which the risk thresholds used for risk stratification vary by age and consider the implications of using age-independent risk thresholds on risk stratification. We demonstrate that using an age-independent remaining lifetime risk threshold approach could identify high-risk younger women but would miss high-risk older women, whereas an age-independent 5-year or 10-year absolute risk threshold could miss high-risk younger women and classify lower-risk older women as high risk. With risk misclassification, women with an equivalent risk level would be offered a different screening plan. To mitigate these problems, age-dependent absolute risk thresholds should be used to inform risk stratification.

Serum Prostate-Specific Antigen Testing for Early Detection of Prostate Cancer: Managing the Gap between Clinical and Laboratory Practice.

BACKGROUND: Current clinical practice guidelines (CPGs) for early detection of prostate cancer recommend for clinical decision-making a personalized prostate-specific antigen (PSA)-based management to improve the risk-benefit ratio of the screening strategy. Some important critical issues regarding the PSA determination in the clinical framework are, however, still neglected in current guidelines and a major focus of recommendations on those aspects would be needed to improve their effectiveness. CONTENT: Evidence sources in the available literature concerning the interchangeability of total PSA results measured with different commercial methods were critically appraised. We discuss how the heterogeneity of the measurand, the intermethod bias, and the design and selectivity of immunoassays may affect the diagnostic accuracy of selected PSA thresholds, and how knowledge of the analytical characteristics of assays in service, such as the recognized PSA circulating forms and the cross-reactivity with PSA homologs, is basic for improving both clinical decision-making in cancer screening and the reliability of the clinical interpretation of results at the individual level. SUMMARY: Current CPGs ignore the poor interchangeability of PSA results obtained from different assays and the substantial role of laboratory issues in clinical performance of PSA testing. Involved stakeholders should contribute to fill the existing gap by: (a) preparing commutable reference materials for immunoassay calibration; (b) providing analytical characteristics that may explain the different performance of assays; (c) deriving outcome-based analytical performance specifications for PSA measurement; and (d) giving more focus on laboratory items when CPGs are prepared.