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This study addresses the limitations of traditional coal calorific value prediction models, which primarily rely on linear regression and single-source proximate analysis data. Based on 465 Chinese coal samples and integrating proximate analysis, ultimate analysis, and constructed derived indicators (combustible content—CC, carbon–hydrogen index—CHI, carbon in combustibles—CIC), a nonlinear modeling method combining mean impact value (MIV) feature selection and support vector regression (SVR) is proposed. The results show that the Pearson correlation coefficients between the derived indicators and net calorific value (NCV) all exceed 0.93, outperforming the original items. Using CC–CHI–CIC–FCad as characteristic variables, the established SVR model achieved a mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) of 1.838%, 0.544 MJ/kg, and 0.962, respectively, with exceptionally high statistical significance (F = 1485.96, p < 0.001). The predictive accuracy of this model is significantly superior to traditional linear models, while the proposed linear model based on the derived indicators (R2 > 0.900) can serve as an alternative for rapid estimation. This method effectively enhances the accuracy and robustness of coal calorific value prediction.

Objective This study aims to screen for measures and lipid-derived indicators associated with insulin resistance (IR) in obese children and adolescents and develop a nomogram model for predicting the risk of insulin resistance. Methods A total of 404 eligible obese children and adolescents aged 10–17 years were recruited for this study from a summer camp between 2019 and 2021. The risk factors were screened using the least absolute shrinkage and selection operator (LASSO)-logistic regression model, and a nomogram model was developed. The diagnostic value of the model was evaluated by plotting the receiver operator characteristic curve and calculating the area under the curve. Internal validation was performed using the Bootstrap method, with 1000 self-samples to evaluate the model stability. The clinical applicability of the model was assessed by plotting the clinical decision curve. Results On the basis of the LASSO regression analysis results, three lipid-related derivatives, TG/HDL-c, TC/HDL-c, and LDL-c/HDL-c, were finally included in the IR risk prediction model. The nomogram model AUC was 0.804 (95% CI: 0.760 to 0.849). Internal validation results show a C-Index of 0.799, and the mean absolute error between the predicted and actual risks of IR was 0.015. The results of the Hosmer–Lemeshow goodness-of-fit test show a good model prediction (χ 2  = 9.523, P = 0.300). Conclusion Three early warning factors, TG/HDL-c, TC/HDL-c, and LDL-c/HDL-c, were screened, which can effectively predict the risk of developing IR in obese children and adolescents, and the nomogram model has an eligible diagnostic value.

A new global dataset of derived indicators has been compiled to clarify whether frequency and/or severity of climatic extremes changed during the second half of the 20th century. This period provides the best spatial coverage of homogenous daily series, which can be used for calculating the proportion of global land area exhibiting a significant change in extreme or severe weather. The authors chose 10 indicators of extreme climatic events, defined from a larger selection, that could be applied to a large variety of climates. It was assumed that data producers were more inclined to release derived data in the form of annual indicator time series than releasing their original daily observations. The indicators are based on daily maximum and minimum temperature series, as well as daily totals of precipitation, and represent changes in all seasons of the year. Only time series which had 40 yr or more of almost complete records were used. A total of about 3000 indicator time series were extracted from national climate archives and collated into the unique dataset described here. Global maps showing significant changes from one multi-decadal period to another during the interval from 1946 to 1999 were produced. Coherent spatial patterns of statistically significant changes emerge, particularly an increase in warm summer nights, a decrease in the number of frost days and a decrease in intra-annual extreme temperature range. All but one of the temperature-based indicators show a significant change. Indicators based on daily precipitation data show more mixed patterns of change but significant increases have been seen in the extreme amount derived from wet spells and number of heavy rainfall events. We can conclude that a significant proportion of the global land area was increasingly affected by a significant change in climatic extremes during the second half of the 20th century. These clear signs of change are very robust; however, large areas are still not represented, especially Africa and South America.

Background It has been proposed that inflammation plays a role in the development of sarcopenia. This study aimed to investigate the links of complete blood cell count (CBC) parameters and CBC-derived inflammatory indicators with sarcopenia and mortality. Methods Data pertaining to sarcopenia were extracted from the 1999–2006 National Health and Nutrition Examination Survey (NHANES), and mortality events were ascertained through the National Death Index up to December 31, 2019. The CBC-derived inflammatory indicators assessed in this study included the neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), monocyte-to-lymphocyte ratio (MLR), neutrophil-monocyte to lymphocyte ratio (NMLR), systemic inflammatory response index (SIRI), and systemic immune-inflammation index (SII). The prognostic significance of these CBC-derived inflammatory indicators was evaluated using the random survival forests (RSF) analysis. Results The study encompassed a cohort of 12,689 individuals, among whom 1,725 were diagnosed with sarcopenia. Among individuals with sarcopenia, 782 experienced all-cause mortality, and 195 succumbed to cardiovascular causes. Following adjustment for confounding variables, it was observed that elevated levels of NLR, dNLR, NMLR, SIRI, and SII were associated with an increased prevalence of sarcopenia. Among participants with sarcopenia, those in the highest quartile of NLR (HR = 1.336 [1.095–1.631]), dNLR (HR = 1.274 [1.046–1.550]), MLR (HR = 1.619 [1.290–2.032]), NMLR (HR = 1.390 [1.132–1.707]), and SIRI (HR = 1.501 [1.210–1.862]) exhibited an elevated risk of all-cause mortality compared to those in the lowest quartile of these inflammation-derived indicators. These associations were similarly observed in cardiovascular mortality (HR = 1.874 [1.169–3.003] for MLR, HR = 1.838 [1.175–2.878] for SIRI). The RSF analysis indicated that MLR exhibited the highest predictive power for both all-cause and cardiovascular mortality among individuals with sarcopenia. Conclusions Our findings underscore the association between CBC-derived inflammatory indicators and mortality in adults with sarcopenia. Of note, MLR emerged as the most robust predictor of all-cause and cardiovascular mortality in this population.

The trend of gallstones occurring in younger populations has become a noteworthy public health issue. This study aims to investigate the association between complete blood cell count (CBC)-derived inflammatory indicators and gallstones in adults under 60 years of age in the United States. This cross-sectional study used data from the National Health and Nutrition Examination Survey (NHANES) from 2017 to 2020. Associations between CBC-derived inflammatory biomarkers and gallstones were assessed using multivariable logistic regression models, with results presented as odds ratio (OR) and 95% confidence interval (CI). Restricted cubic splines (RCS) were employed to examine potential non-linear relationships. Subgroup analyses were also conducted to explore differences across population subgroups. This study comprised 4,977 participants, among whom 398 were diagnosed with gallstones. After adjusting for confounding variables, the highest quartile of systemic inflammation response index (SIRI) [OR (95%CI): 1.65(1.12,2.43)], systemic immune-inflammation index (SII) [OR (95%CI): 1.53(1.05,2.25)], monocyte-to-lymphocyte ratio (MLR) [OR (95%CI): 1.66(1.16,2.37)], and pan immune inflammatory value (PIV) [OR (95%CI): 1.82(1.23,2.71)] were associated with a significantly increased risk of gallstones compared to the lowest quartiles. RCS plots indicated a nonlinear relationship between several inflammatory biomarkers and gallstones. Our study found that SIRI, SII, MLR, and PIV can serve as clinical indicators for predicting the risk of gallstones in adults under 60 years of age in the United States.

Objective To explore the relationships between inflammatory markers obtained from complete blood count (CBC) and the prevalence of myocardial infarction (MI). Methods A cross-sectional study was conducted using data from the National Health and Nutrition Examination Survey (NHANES) spanning 1999 to 2020. A total of 46,697 US adults were enrolled, including 1824 with self-reported physician-diagnosed MI history. Systemic inflammatory response index (SIRI) and five other CBC-derived inflammatory indices were included. Logistic regression models, restricted cubic spline analysis, and subgroup analysis were applied to examine the association between these indices and MI prevalence with adjustments for potential confounding variables. Interaction analysis was used to verify the sex-specific effect modification and mediation analysis was performed to explore the mediating role of metabolic diseases including hypertension, diabetes and dyslipidemia. Results After full adjustment for confounding factors, elevated SIRI, NLR, MLR, and NMLR were significantly positively associated with MI prevalence (all P  < 0.001). Restricted cubic spline analysis revealed nonlinear dose–response relationships between SIRI, MLR, SII, and MI prevalence (all P for non-linearity < 0.05). Significant sex-specific heterogeneity was observed. SIRI, NLR, NMLR, and SII had markedly stronger positive correlations with MI in females than males (all P for interaction < 0.05). Mediation analysis indicated metabolic diseases mediated approximately one-quarter to one-third of the SIRI-MI history association. Conclusion This study revealed a significant link between CBC-derived inflammation markers and MI prevalence, with sex acting as a key modifier. Further longitudinal research is crucial to assess the utility of these accessible, low-cost indicators in routine cardiovascular risk assessment and to clarify causal pathways.

PurposeThe role of admission blood indicators in patients with acute compartment syndrome (ACS) remains debated. Our primary purpose was to observe variations of admission blood indicators in patients with ACS, while our secondary goal was to explore potential biomarkers related to ACS.MethodsWe collected information on patients with tibial fracture between January 2013 and July 2023, and divided them into ACS and non-ACS groups. Propensity score matching (PSM) analysis was performed to lower the impact of potential confounding variables such as demographics and comorbidities. Admission blood indicators were analyzed using univariate, logistic regression, and receiver operating characteristic (ROC) curve analyses. Then, we established a nomogram prediction model by using R language software.ResultsAfter propensity PSM analysis, 127 patients were included in each group. Although numerous blood indicators were found to be relevant to ACS on univariate analysis, logistic regression analysis showed that monocytes (MON, p = 0.015), systemic immune-inflammation index (SII, p = 0.011), and creatine kinase myocardial band (CKMB, p < 0.0001) were risk factors for ACS. Furthermore, ROC curve analysis identified 0.79 × 109/L, 1082.55, and 20.99 U/L as the cut-off values to differentiate ACS patients from patients with tibial fracture. We also found that this combination had the highest diagnostic accuracy. Then, we constructed a nomogram prediction model with AUC of 0.869 for the prediction model, with good consistency in the correction curve and good clinical practicality by decision curve analysis.ConclusionsWe found that the levels of MON, SII, and CKMB were related to ACS and may be potential biomarkers. We also identified their cut-off values to separate patients with ACS from those with tibial fracture, helping orthopedists promptly evaluate and take early measures. We established a nomogram prediction model that can efficiently predict ACS in patients with tibial fracture.

Rheumatoid arthritis (RA) is an autoimmune disease for which better biomarkers are needed, especially in seronegative cases. This study evaluates complete blood count (CBC)-derived inflammatory indices - neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) - for RA diagnosis and disease activity assessment, with comparisons between seropositive and seronegative RA. We conducted a retrospective case-control study of 230 RA patients and 115 age- and sex-matched healthy controls. CBC-derived indices were calculated from routine blood counts. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves (area under the curve, AUC) for RA versus controls overall and stratified by serostatus. Associations with disease activity (DAS28-CRP, SDAI, CDAI) were assessed via correlations and ROC analysis for active (moderate/high) versus inactive (remission/low) RA. All five indices were significantly elevated in RA patients compared to controls (all < 0.001). MLR showed the highest diagnostic accuracy (AUC = 0.771), followed by SIRI (0.72) and PLR (0.70); NLR and SII were more modest (≈0.69-0.68). In seronegative RA, diagnostic discrimination declined (best AUC = 0.707 for MLR; SII and SIRI AUCs ~0.56-0.59). NLR, SII, and SIRI correlated moderately with CRP, ESR, and composite scores (Spearman ρ ~0.3-0.4, < 0.001), and were higher in active RA (DAS28-CRP AUCs 0.668-0.700). SII and SIRI provided the top discrimination of active disease (AUC ~0.70). PLR showed minimal correlation with activity and no significant difference between active and inactive RA. CBC-derived inflammatory indices are elevated in RA and reflect systemic inflammation. MLR is the most robust index for distinguishing RA from healthy individuals, while SII, SIRI, and NLR are useful for gauging disease activity. In seronegative RA, diagnostic performance was attenuated overall, with MLR retaining fair discrimination while SII/SIRI/NLR showed limited utility.

Background: Hypertrophic cardiomyopathy (HCM) is a prevalent condition that often coexists with chronic kidney disease (CKD), significantly impacting patient prognosis. This study aimed to investigate the predictive value of complete blood cell counts derived inflammatory indicators in assessing CKD risk in HCM patients. Methods: This study enrolled HCM patients and categorized them into CKD and non-CKD group based on discharge diagnoses. Analyzed indicators included systemic inflammation response index (SIRI), systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). Least absolute shrinkage and selection operator (LASSO) logistic and multivariable logistic regression were employed to identified independent risk factors for CKD, which were subsequently utilized to develop a nomogram. Results: A total of 1795 HCM patients were included, including 112 (6.24%) individuals assigned to the CKD group. In univariate analyses, NLR (AUC: 0.755; 95%CI: 0.711–0.800) demonstrated superior accuracy compared to others. Eighteen baseline characteristics exhibiting statistical difference were incorporated into LASSO-logistic regression. Six factors were further selected by multivariable logistic regression. The results identified male gender (OR: 2.622; 95% CI: 1.565–4.393, p < 0.001), Hb (OR: 0.972; 95% CI: 0.962–0.981, p < 0.001), Pro-BNP (OR: 1.000; 95% CI: 1.000–1.000, p < 0.001), SIRI (OR: 1.037; 95% CI: 1.026–1.049, p < 0.001), and SII (OR: 1.000; 95% CI: 1.000–1.001, p = 0.003) as risk factors. These five factors were used to construct a nomogram, which exhibited good accuracy and consistency. Conclusions: Male gender, Hb, Pro-BNP, SIRI, and SII were identified as risk factors for CKD risk in HCM patients. A nomogram was developed using these factors, which may facilitate early identification and management of high-risk individuals.

Background Fatty liver disease (FLD), characterized by hepatic lipid accumulation, impairs quality of life and can progress to cirrhosis and hepatocellular carcinoma, imposing a healthcare burden. This study investigates the association between the aggregate index of systemic inflammation (AISI) and FLD prevalence, evaluating AISI’s potential as an early biomarker for risk assessment. Methods Data were obtained from the National Health and Nutrition Examination Survey (NHANES) database, which encompasses the years 2017 through 2020. Participants were chosen based on the availability of controlled attenuation parameter (CAP) scores derived from transient elastography (TE), a technique utilized for assessing liver steatosis. The formula employed to compute the AISI is as follows: AISI = N × P × M / L, where N, P, M, and L refer to neutrophils, platelets, monocytes, and lymphocytes, respectively. Additionally, demographic, socioeconomic, dietary, and health-related information was gathered. Logistic regression models were utilized to pinpoint risk factors associated with FLD, and a nomogram was created to forecast FLD risk. Results Of the 3,961 participants, 2,377 (60.0%) were diagnosed with FLD based on a CAP score ≥ 248 dB/m. Elevated AISI was significantly associated with FLD ( P  = 0.021). Other significant risk factors included sex, age, BMI, race, marital status, hypertension, and diabetes. The nomogram demonstrated excellent discriminatory performance with an AUC of 0.814 (95% CI: 0.800, 0.827) and good calibration. Conclusion This study reveals a significant, independent association between elevated AISI and increased FLD risk in the U.S. population, even after adjusting for confounders. AISI demonstrated good discriminative performance for FLD, but its effect size suggests it should supplement, not replace, existing clinical risk assessment tools. AISI, a cost-effective biomarker, holds potential for enhancing FLD screening, particularly in resource-limited settings.