Browse Articles

Risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers

Abstract This study aims to develop a multidimensional risk prediction model, identify characteristic inflammation-nutrition biomarkers, and optimize clinical decision-making. The study included 500 lung cancer patients diagnosed between October 2019 and October 2024 at a tertiary medical institution in Guiyang, China. The exposure variables included eight inflammation-nutrition biomarkers: neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), hemoglobin-albumin-lymphocyte-platelet score (HALP), prognostic nutritional index (PNI), hemoglobin-to-red cell distribution width ratio (HRR), and albumin-to-globulin ratio (ALB/GLB). The outcome variable was overall survival (OS). This study aimed to predict 1-year mortality rather than conduct traditional time-to-event survival analysis. All patients were followed until death or a uniform administrative censoring point.LASSO logistic regression was employed to model the outcome as a binary classification (death within 1 year: yes/no).This study employed a small-sample modeling approach, initially using LASSO regression for feature selection and dimensionality reduction, followed by variance inflation factor and collinearity screening for secondary feature selection. Finally, the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm was used to optimize feature variables. The results showed that age, clinical stage, poor differentiation, ECOG PS 0–1, serum albumin level, LMR, HRR, and ALB/GLB were independent prognostic factors. Based on these factors, a lung cancer mortality risk prediction model was developed, and a corresponding web-based calculator was created, providing a practical tool to support clinical decision-making and personalized treatment strategies.

Correction: Enhancing capacitive performance of magnetite-reduced graphene oxide nanocomposites through magnetic field-assisted ion migration

Key frame extraction based abnormal vehicle identification technique using statistical distribution analysis

The association between Albumin-Corrected Anion Gap (ACAG) and the risk of acute kidney injury in patients with acute pancreatitis: A retrospective analysis based on the MIMIC-IV database

Purpose Acute kidney injury (AKI), a common and severe complication of acute pancreatitis (AP), is significantly linked to patient prognosis. Albumin-corrected anion gap (ACAG) is a modified acid-base balance assessment metric with potential clinical significance in various critical illnesses. However, the role of ACAG in forecasting the risk of AKI in AP patients remains unclear. This study sheds light on the relationship between ACAG levels and AKI risk in the AP population. Methods This retrospective study utilized data from the MIMIC-IV database, including 1,552 adult patients diagnosed with AP during their stay in the intensive care unit (ICU). ACAG was calculated using a standard formula, and patients were grouped according to their ACAG levels. Cox proportional hazards and restricted cubic spline (RCS) models were employed to assess the correlation of ACAG levels with AKI risk in AP patients. The incidence of AKI was the primary outcome, and in-hospital mortality was the secondary outcome. Differences in primary and secondary outcomes between ACAG groups were evaluated through Kaplan-Meier (KM) survival analysis. Subgroup analyses were performed for examining the influence of confounding factors. Results Higher ACAG levels were significantly related to an elevated risk of AKI. The RCS model demonstrated a nonlinear correlation between higher ACAG levels and increased AKI risk in the AP cohort, and a linear association of ACAG with in-hospital death. KM survival analysis showed that patients exhibiting higher ACAG levels had poorer renal function outcomes and higher ICU mortality. Subgroup analyses further proved this correlation across varied patient characteristics. Conclusions Elevated ACAG is an independent predictor of AKI risk in the AP cohort. ACAG may be useful for early AKI risk stratification and clinical decision-making in critically ill AP sufferers.

Recreation comforting degree identification and assessment in national parks by integrating text mining technology and fuzzy DEMATEL-based ANP method

Barriers to agricultural employment among African American youth: Evidence from the American Community Survey

This paper investigates the occupational choices of African American youth in U.S. agricultural and food sectors. Using nationally representative data from the American Community Survey, we estimate a multinomial logit model to assess how socioeconomic conditions influence employment in three occupational categories: farming, farm-related work, and food preparation. Results reveal that agricultural employment among African American youth remains rare, and that gender is a strong predictor of occupational choice. Young African American women are significantly less likely than men to work in farming or related sectors, and more likely to be employed in food preparation. Educational attainment and student status are positively associated with food service employment but do not predict participation in farming occupations. These findings have important implications for agricultural policy, particularly as policymakers seek to address demographic disparities and revitalize the rural workforce. The results highlight the need for youth-specific policies, including targeted outreach, farm incubator programs, and access to capital, that address the compounded barriers facing youth in agricultural employment.

Handling multiple time-varying exposures in survival analysis using real-world pediatric data from the pedianet database

Operational thresholds of urease-mediated microbial cementation: Multivariate optimization and field validation in ambient groundwater environments

Microbially induced calcium carbonate precipitation (MICP) provides a sustainable method for soil stabilization; however, its practical application is limited by rapid reaction kinetics that cause localized clogging and the reliance on energy-intensive environmental controls. This study develops a multivariate optimization framework for urease-inhibited MICP using N-(n-butyl)-thiophosphoric triamide (NBPT), with an emphasis on practical thresholds under ambient groundwater conditions. Five operational parameters—NBPT concentration, cementing solution concentration, bacteria-to-cement solution ratio, temperature, and pH—were systematically investigated through sand column tests and continuous urease activity monitoring. The results demonstrate that a low NBPT concentration of 0.1%, in conjunction with a 1:1 volumetric ratio of bacterial suspension to cementing solution, achieves effective biocementation while maintaining 93% of the unconfined compressive strength observed in inhibitor-free controls. Optimal performance was achieved at a cementing solution concentration of 1 mol/L, temperatures exceeding 20°C, and a neutral pH range of 7–8. The proposed thresholds bridge the critical gap between laboratory-optimized MICP and real-world environmental variability, providing directly implementable guidelines for sustainable geotechnical applications. This study advances NBPT-MICP as a cost-effective and environmentally compatible solution for sand stabilization in natural hydrochemical systems.

Non-canonical Gq activation by orexin receptor type 2 and lemborexant observed in microsecond molecular dynamics simulations

Anemia prevalence and severity among pregnant refugee women settled in the West Nile region, Uganda

Background Anemia during pregnancy is a significant global public health issue, associated with adverse outcomes for both the mother and the newborn. While the prevalence and impact of anemia in pregnancy have been well-documented in general populations, the burden and severity of anemia among pregnant refugee women particularly those residing in the West Nile region of Uganda, remain unknown. Our study aimed to determine the prevalence and severity of anemia in this specific population to address this critical knowledge gap. Methods Between 1st April and 30th June 2023, we conducted a cross-sectional study among pregnant women attending the antenatal care clinic at 3 health centers serving the refugee communities in Adjumani district, Uganda. Anemia in pregnancy was defined as a hemoglobin (Hb) level of < 11.0 g/dl and microcytosis as a mean corpuscular volume of < 76 fL. Multivariable logistic regression was done to assess for the independent predictors of anemia in pregnancy. Data were analyzed in Stata version 15.0. Results We enrolled 304 participants. The mean age of the participants was 25.4 years with a standard deviation of 4.9 years. The prevalence of anemia was 52.6% (95% CI 46.9–58.4). Of these, 85 (28.0%) were classified as mild, 73 (24.1%) moderate, and 2 (0.7%) had severe anemia. The median Hb was 10.8g/dL (10.1–11.7). Forty-one (25.6%) of those with anemia had microcytosis. Having at least a secondary/tertiary education level (aOR: 1.51, 95%CI: 0.98–1.78, p < 0.001), being single (aOR: 1.77, 95%CI: 1.53–1.93, p < 0.001) and parity >4 (aOR: 1.51, 95%CI: 0.98–1.78, p < 0.001) were associated with higher odds of having anemia. Conclusions Anemia in pregnancy is prevalent among pregnant refugee women in the West Nile Region of Uganda. Our findings suggest the need for targeted interventions such as health education about the consequences of anemia, promotion of early antenatal care (ANC) attendance, and provision of iron and folate supplementation during pregnancy, to potentially reduce the burden of anemia in this vulnerable population.

Variable sample size based EWMA control chart with an exponential scaling mechanism for production process monitoring

Efficiency analysis of listed pharmaceutical companies in China: A method combining three-stage DEA with undesirable output, PCA, and tobit regression

The pharmaceutical industry plays a vital role in safeguarding public health and enhancing industrial competitiveness. However, pharmaceutical industry in China faces persistent challenges, including high pollution, insufficient innovation, and limited profit margins, which constrain its global competitiveness. The current researches on the operational efficiency were either from financial or innovation aspects-lacking a comprehensive assessment framework. Moreover, the impact of environments on efficiency relied on limited indicators and lacked in multicollinearity research. Finally, there was an absence of research on internal resource allocation affects operation efficiency. To fill this, this study aims to evaluate the current operation efficiency of the listed pharmaceutical enterprises in China from finance, innovation, and sustainability, and to reveal the influence environments on efficiency and the impact of the internal resource allocation on efficiency. To achieve these objectives, the study adopts a Three-stage Data Envelopment Analysis with undesirable outputs, integrated with Principal Component Analysis and Tobit regression, to comprehensively evaluate the operational efficiency of Chinese listed pharmaceutical enterprises over the ten-year period from 2013 to 2022 across the aforementioned three dimensions. The findings reveal generally low efficiency with significant regional disparities. North and Northwest China benefit from favorable environmental conditions, while Northeast China suffers from negative impacts. Improvements in external factors, such as innovation, living standards, labor supply, and openness level reduce costs, whereas internal uncontrollable factors, such as state-owned enterprise attributes, increase costs and suppress research and development. In terms of resource allocation, higher management expenses, and personnel allocations decrease efficiency, while increased sales expenditures and improved staff quality enhance efficiency. This study constructs a comprehensive framework for evaluating the operational efficiency and reveals the impact of external environments and internal resource allocation on efficiency. It provides empirical support for policymakers and operation managers seeking to improve the efficiency of the pharmaceutical companies.

Deep learning time-series modeling for assessing land subsidence under reduced groundwater use

Alkaloid production of Solanum elaeagnifolium Cav from callus for anticancer potential using gene expression of cancer-related genes

Cancer is one of the major diseases that threaten human life and causes death for many people worldwide. Some alkaloids derived from plants show promising potential for cancer treatment. Solanaceae family is of these plants that have promising alkaloid. In this study alkaloid production from Solanum elaeagnifolium callus cultures was investigated and their anticancer properties were evaluated. Optimal callus growth and alkaloid production were achieved in media enriched with a combination of BAP and 2,4-D at a ratio of 1.0:1.0 mg/L. LC-MS/MS analysis showed that β-Solamargine, tomatidenol, Solasonine, solanidine and solasodine are the most important alkaloids of Solanum elaeagnifolium. Moreover, the analysis revealed that β-Solamargine is the predominant alkaloid (78.7%) in callus extracts. The results of MTT assay, demonstrated that the most effective response were obtained from callus extracts medium containing a balanced concentration of BAP and 2,4-D, and it yielding an IC50 of 6.25 µl/ml. In contrast, lower efficacy was observed with IC50 values of 25 and 50 µl/ml, when callus medium were supplemented with NaCl and yeast extract, respectively. Gene expression analysis shows an increase in the Bax/Bcl-2 ratio following 24 h of the extracts treatment. Along with a down regulation of CDK1 gene expression in comparison to untreated MCF7 cells, the CDK1 levels were elevated. Alkaloids derived from S. elaeagnifolium may be a promising candidate for anticancer therapy; further investigation is needed in vivo.

Machine learning models for accurately predicting properties of CsPbCl3 Perovskite quantum dots

Extracting rubber tree parameters and estimating carbon storage using airborne LiDAR

Facing the dual challenges of global warming and carbon neutrality, forestry carbon sinks play a vital role in achieving carbon neutrality. Rubber plantations, in particular, offer significant ecological and economic co-benefits. However, the efficient and rapid acquisition of data on rubber plantations and the calculation of carbon stock remain key challenges in forestry carbon sink studies. Airborne LiDAR is a powerful tool for forest surveys, yet its inability to directly measure DBH remains a major limitation. This study seeks to address this issue. High-resolution point cloud data were collected, followed by noise removal and ground point classification. Four individual tree segmentation methods were compared, and a linear regression model based on crown diameter parameters was proposed to estimate DBH. The results indicate that the direct point cloud segmentation method achieved the highest accuracy in tree identification. The proposed linear regression model for DBH estimation effectively predicts DBH, enabling precise biomass estimation. The total biomass estimated in the study area was 592,770.57 kg (aboveground biomass: 550,336.17 kg, belowground biomass: 42,434.39 kg), with the corresponding total carbon stock estimated at 278,602.17 kg.

Integrated intrusion detection design with discretion of leading agent using machine learning for efficient MANET system

Longitudinal associations between socioeconomic status and cardiovascular disease in a Chinese population: Evidence from CHARLS

Importance The relationship between socioeconomic status and cardiovascular disease among the general Chinese population is inconclusive. Objective This study aims to investigate the relationship between socioeconomic status and cardiovascular disease in the Chinese general population through a large sample. Design We retrospectively analyzed data from the 2015 and 2018 waves of the China Health and Retirement Longitudinal Study (CHARLS). Participants were required to be at least 45 years old and have cardiovascular disease (CVD) data. The study was divided into two parts: a cross-sectional analysis using 2015 data, and a longitudinal analysis of participants without CVD in 2015 but with complete follow-up data in 2018. Socioeconomic status (SES) was assessed using self-reported household income, occupation, education level, and health insurance. CVD events were identified through participant-reported diagnoses of heart disease or stroke. Logistic and Cox proportional hazards regression models were used to estimate the association between SES and CVD. Participants 16,560 participants were included in the cross-sectional analysis and 11,587 in the longitudinal analysis. Exposures Socioeconomic status. Main outcomes and measures Cardiovascular disease, heart disease, and stroke. Results In the cross-sectional analysis, 16% of the 16,560 participants had CVD, with higher prevalence in lower SES groups. After adjustments, middle SES was significantly associated with increased CVD risk (OR 1.77, 95% CI 1.21–2.58). In the longitudinal analysis, during a 3-year follow-up, 12.1% of 11,587 participants developed CVD. Middle SES was associated with a 67% higher risk of new-onset CVD (HR 1.67, 95% CI 1.02–2.74). Sensitivity analysis confirmed these findings, with middle SES showing a significant association with CVD risk (HR 1.32, 95% CI 1.04–1.67). Conclusions and relevance In the general population of China, middle socioeconomic status is positively associated with cardiovascular disease and is more likely to be associated with new-onset cardiovascular disease. Our findings support the need for trade-offs between socioeconomic status groups to benefit different populations, especially considering the middle socioeconomic status group, which is an easily overlooked group. However, more long-term prospective studies are needed to further elucidate the relationship between changes in socioeconomic status and cardiovascular disease in China.

Functional characteristics of fresh antitumor immune interferer GDF-15 in multiple cancers

Unveiling the hidden burden of COVID-19 in Brazil’s obstetric population with severe acute respiratory syndrome: A machine learning model

Objective To predict the actual number of COVID-19 cases in Brazilian pregnant and postpartum women diagnosed with Severe Acute Respiratory Syndrome using a predictive model created based on data from Brazilian database. Methods This is a cross-sectional study with pregnant and postpartum women diagnosed with Severe Acute Respiratory Syndrome (SARS) between January 2016 and November 2021 in Brazil. Patients were divided into two groups (COVID-19 and non-COVID-19) for comparative analysis, and a predictive model was constructed to classify cases without a defined causative agent. Main outcome measures Estimated number of COVID cases in obstetric patients with SARS and no defined agent. Results 38,774 pregnant and postpartum women diagnosed with SARS were identified and categorized by date and causative agent. Women in the COVID-19 group (19.138) were older (29.86 ± 7.20 years), self-reported more frequently as non-white race (50.9%), and more often had educational status marked as blank or ignored (29.6% and 26.9%, respectively) compared to the Other confirmed agents’ group (2.233). The groups differed in all presented variables, and patients in the COVID-19 group were diagnosed more often in the third trimester of pregnancy or in the postpartum period. Using the XGBoost model, 13,978 cases of SARS with undefined etiology from 2020 and 2021 were reclassified: 13,799 (98.7%) as predicted COVID-19 and 179 (1.3%) as predicted non-COVID-19. Conclusions The number of COVID-19 cases and deaths in the obstetric population were even higher than reported by authorities, indicating a significant impact on the maternal mortality ratio during this period.