Objective To investigate the risk factors for early neurological deterioration in mild acute ischemic stroke，to construct a clinical predictive model，and to perform internal validation of this model. Methods A retrospective analysis was performed for 739 patients with mild acute ischemic stroke who were admitted to Department of Neurology，Kuntong Hospital of Zunhua，from October 2020 to December 2023，and they were randomly divided into a training set with 534 patients （72.3%） and a validation set with 205 patients （27.7%） at a ratio of 7∶3. Univariate and multivariate logistic regression analyses were performed for the training set to determine the risk factors for early neurological deterioration in mild acute ischemic stroke. A clinical predictive model was constructed，and internal validation was performed in terms of discriminatory ability，calibration，and clinical decision making. A nomogram was plotted. Results The multivariate logistic regression analysis showed that female sex （OR=1.87，95% CI 1.14~3.09，P=0.014），time window ≤6 hours （OR=3.10，95%CI 1.56~6.19，P=0.001），a baseline NIHSS score of 2 points （OR=3.72，95%CI 1.30~10.61，P=0.014），a baseline NIHSS score of 3 points （OR=4.24，95%CI 1.45~12.35，P=0.008），a TOAST classification of large artery atherosclerosis （OR=3.88，95%CI 2.20~6.83，P<0.001），and the responsible arteries of the basilar artery，the middle cerebral artery，and the internal carotid artery （OR=8.39，95%CI 2.28~30.85，P=0.001； OR=6.22，95%CI 1.78~21.71，P=0.004； OR=5.38，95%CI 1.15~25.13，P=0.032） were independent risk factors for early neurological deterioration in mild acute ischemic stroke. The clinical predictive model constructed showed a moderate discriminatory ability （AUC>0.7），good calibration （P>0.05） in the Hosmer-Lemeshow goodness-of-fit test），and good clinical benefits in both the training set and the validation set. Conclusion This clinical predictive model can effectively predict the onset of early neurological deterioration in mild acute ischemic stroke and guide clinicians to make decisions，and therefore，it holds promise for clinical application.
Nomograms

INTRODUCTION

Neonatal sepsis is a significant cause of morbidity and mortality, particularly among preterm infants, and remains a pressing global health concern. Early-onset neonatal sepsis is particularly challenging to diagnose due to its nonspecific clinical presentation, necessitating effective and timely diagnostic tools to reduce adverse outcomes. Traditional methods, such as microbial cultures, are slow and often unavailable in resource-limited settings. This study aimed to develop a robust multivariable risk prediction model tailored to improve early detection of Early Onset Sepsis (EOS) among preterm infants in the Philippines.

We conducted a retrospective analysis at a tertiary hospital in the Philippines using data from 1,354 preterm infants admitted between January 2019 and June 2024. Logistic regression models were employed, and predictors were selected through reverse stepwise elimination. Two scoring methods were developed: one based on beta coefficients divided by standard errors and another standardized to a total score of 100. The models were validated using Receiver Operator Characteristic curve analysis.

Version 1 of the scoring model demonstrated an Area Under the Curve (AUC) of 0.991, with a sensitivity of 90.91% and a specificity of 98.10%. Version 2 achieved an AUC of 0.999, with a sensitivity of 96.4% and a specificity of 92.44%.

The developed models provide a reliable, region specific tool for early detection of neonatal sepsis. Further validation across diverse populations and the integration of emerging diagnostic technologies, such as biomarkers and artificial intelligence, are warranted to enhance their applicability and accuracy.

Computer Simulation ; Models, Statistical

BACKGROUND AND AIM: Remnant cholesterol (remnant-C) mediates the progression of major adverse cardiovascular events. It is unclear whether remnant-C, and particularly cumulative exposure to remnant-C, is associated with nonalcoholic fatty liver disease (NAFLD). This study aimed to explore whether remnant-C, not only baseline but cumulative exposure, can be used to independently evaluate the risk of NAFLD. METHODS: This study included 1 cohort totaling 21,958 subjects without NAFLD at baseline who underwent at least 2 repeated health checkups and 1 sub-cohort totaling 2,649 subjects restricted to those individuals with at least 4 examinations and no history of NAFLD until Exam 3. Cumulative remnant-C was calculated as a timeweighted model for each examination multiplied by the time between the 2 examinations divided the whole duration. Cox regression models were performed to estimate the association between baseline and cumulative exposure to remnant-C and incident NAFLD. RESULTS: After multivariable adjustment, compared with the quintile 1 of baseline remnant-C, individuals with higher quintiles demonstrated significantly higher risks for NAFLD (hazard ratio [HR] 1.48, 95%CI 1.31-1.67 for quintile 2; HR 2.07, 95%CI 1.85-2.33 for quintile 3; HR 2.55, 95%CI 2.27-2.88 for quintile 4). Similarly, high cumulative remnant-C quintiles were significantly associated with higher risks for NAFLD (HR 3.43, 95%CI 1.95-6.05 for quintile 2; HR 4.25, 95%CI 2.44-7.40 for quintile 3; HR 6.29, 95%CI 3.59-10.99 for quintile 4), compared with the quintile 1. CONCLUSION Elevated levels of baseline and cumulative remnant-C were independently associated with incident NAFLD. Monitoring immediate levels and longitudinal trends of remnant-C may need to be emphasized in adults as part of NAFLD prevention strategy.
Adult ; Humans ; Cohort Studies ; Non-alcoholic Fatty Liver Disease/etiology* ; Cholesterol ; Proportional Hazards Models ; Risk Factors

Objective To investigate the influencing factors for white matter hyperintensity （WMH） in middle-aged and elderly patients with hypertension， and to establish and verity a nomogram prediction model. Methods A total of 198 middle-aged and elderly patients with hypertension and WMH who were hospitalized in our hospital from January 2022 to April 2023 were enrolled. Related clinical data were analyzed， and related data were recorded. A binary logistic regression analysis was used to investigate the independent risk factors for WMH and establish a nomogram， and the receiver operating characteristic （ROC） curve and the calibration curve were used to evaluate the diagnostic efficacy of the nomogram. Results Age， course of hypertension， cystatin C， homocysteine，red blood cell distribution width， and cognitive impairment were the independent influencing factors for WMH in the middle-aged and elderly patients with hypertension. The nomogram established showed good diagnostic efficacy （AUC=0.815， 95% CI 0.756~0.874，P<0.001） and calibration ability （C index=0.794）. Conclusion The nomogram established in this study has a good predictive ability for moderate-to-severe WMH in middle-aged and elderly patients with hypertension and can provide certain help for clinical workers.
Nomograms

OBJECTIVES: To construct a clinical prediction model for the impact of acupuncture on pregnancy outcomes in poor ovarian response (POR) patients, providing insights and methods for predicting pregnancy outcomes in POR patients undergoing acupuncture treatment. METHODS: Clinical data of 268 POR patients (2 cases were eliminated) primarily treated with "thirteen needle acupuncture for Tiaojing Cuyun (regulating menstruation and promoting pregnancy)" was collected from the international patient registry platform of acupuncture moxibustion (IPRPAM) from September 19, 2017 to April 30, 2023, involving 24 clinical centers including Acupuncture-Moxibustion Hospital of China Academy of Chinese Medical Sciences. LASSO and univariate Cox regression were used to screen factors influencing pregnancy outcomes, and a multivariate Cox regression model was established based on the screening results. The best model was selected using the Akaike information criterion (AIC), and a nomogram for clinical pregnancy prediction was constructed. The prediction model was evaluated using receiver operating characteristic (ROC) curves and calibration curves, and internal validation was performed using the Bootstrap method. RESULTS: (1) Age, level of anti-Müllerian hormone (AMH), and total treatment numbers of acupuncture were independent predictors of pregnancy outcomes in POR patients receiving acupuncture (P<0.05). (2) The AIC value of the best subset-Cox multivariate model (560.6) was the smallest, indicating it as the optimal model. (3) The areas under curve (AUCs) of the clinical prediction model after 6, 12, 24, and 36 months treatment were 0.627, 0.719, 0.770, and 0.766, respectively, and in the validation group, they were 0.620, 0.704, 0.759, and 0.765, indicating good discrimination and repeatability of the prediction model. (4) The calibration curve showed that the prediction curve of the clinical prediction model was close to the ideal model's prediction curve, indicating good calibration of the prediction model. CONCLUSIONS The clinical prediction model for the impact of acupuncture on pregnancy outcomes in POR patients based on the IPRPAM platform has good clinical application value and provides insights into predicting pregnancy outcomes in POR patients undergoing acupuncture treatment.
Pregnancy ; Female ; Humans ; Pregnancy Outcome ; Models, Statistical ; Prognosis ; Acupuncture Therapy ; Registries

Objective To study the expression of selenoprotein genes in human immunodeficiency virus(HIV)infection and its mother-to-child transmission,so as to provide a theoretical basis for the prevention,diagnosis,and treatment of acquired immunodeficiency syndrome.Methods The dataset GSE4124 was downloaded from the Gene Expression Omnibus(GEO).Two groups of HIV-positive mothers(n=25)and HIV-negative mothers(n=20)were designed.HIV-positive mothers included a subset of transmitter(TR)mothers(n=11)and non-transmitter(NTR)mothers(n=14).Then,t-test was carried out to compare the expression levels of selenoprotein genes between the four groups(HIV-positive vs. HIV-negative,NTR vs. HIV-negative,TR vs. HIV-negative,TR vs. NTR).Univariate and multivariate Logistic regression were adopted to analyze the effects of differentially expressed genes on HIV infection and mother-to-child transmission.R software was used to establish a nomogram prediction model and evaluate the model performance.Results Compared with the HIV-negative group,HIV-positive,NTR,and TR groups had 8,5 and 8 down-regulated selenoprotein genes,respectively.Compared with the NTR group,the TR group had 4 down-regulated selenoprotein genes.Univariate Logistic regression analysis showed that abnormally high expression of GPX1,GPX3,GPX4,TXNRD1,TXNRD3,and SEPHS2 affected HIV infection and had no effect on mother-to-child transmission.The multivariate Logistic regression analysis showed that the abnormally high expression of TXNRD3(OR=0.032,95%CI=0.002-0.607,P=0.022)was positively correlated with HIV infection.As for the nomogram prediction model,the area under the receiver-operating characteristic curve for 1-year survival of HIV-infected patients was 0.840(95%CI=0.690-1.000),and that for 3-year survival of HIV-infected patients was 0.870(95%CI=0.730-1.000).Conclusions Multiple selenoprotein genes with down-regulated expression levels were involved in the regulation of HIV infection and mother-to-child transmission.The abnormal high expression of TXNRD3 was positively correlated with HIV infection.The findings provide new ideas for the prevention,diagnosis,and treatment of acquired immunodeficiency syndrome.
Humans ; Female ; HIV Infections ; Acquired Immunodeficiency Syndrome ; Infectious Disease Transmission, Vertical ; Nomograms ; Selenoproteins/genetics*

Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.
Humans ; Keloid/genetics* ; Nomograms ; Algorithms ; Calibration ; Machine Learning

Reconstructing three-dimensional (3D) models from two-dimensional (2D) images is necessary for preoperative planning and the customization of joint prostheses. However, the traditional statistical modeling reconstruction shows a low accuracy due to limited 3D characteristics and information loss. In this study, we proposed a new method to reconstruct the 3D models of femoral images by combining a statistical shape model with Laplacian surface deformation, which greatly improved the accuracy of the reconstruction. In this method, a Laplace operator was introduced to represent the 3D model derived from the statistical shape model. By coordinate transformations in the Laplacian system, novel skeletal features were established and the model was accurately aligned with its 2D image. Finally, 50 femoral models were utilized to verify the effectiveness of this method. The results indicated that the precision of the method was improved by 16.8%-25.9% compared with the traditional statistical shape model reconstruction. Therefore, the method we proposed allows a more accurate 3D bone reconstruction, which facilitates the development of personalized prosthesis design, precise positioning, and quick biomechanical analysis.
Imaging, Three-Dimensional/methods* ; Tomography, X-Ray Computed/methods* ; Femur/surgery* ; Models, Statistical ; Lower Extremity

OBJECTIVE: To construct and evaluate nomogram prediction model for periprosthetic fractures in patients undergoing total hip arthroplasty (THA). METHODS: A total of 538 patients who underwent THA from April 2013 to February 2019 were selected as the research subjects, including 318 males and 220 females, aged 40 to 60 years old with an average age of (50.79±6.37) years old. All patients with THA were divided into non-fracture group (506 patients) and fracture group (32 pathents) according to the 3-year follow-up results. Univariate and multivariate Logistic regression analyses were performed to analyze the influencing factors of postoperative periprosthetic fractures in patients with THA. A nomogram prediction model for periprosthetic fractures in patients undergoing THA was constructed, and the validity and discrimination of the prediction model were evaluated. RESULTS: The proportion of patients with osteoporosis, trauma history, and hip revision in the fracture group were higher than those in the non-fracture group(P<0.05), and the proportion of bone cement prosthesis was lower than that in the non-fracture group(P<0.05). The osteoporosis status[OR=4.177, 95%CI(1.815, 9.617), P<0.05], trauma history[OR=7.481, 95%CI(3.104, 18.031), P<0.05], and hip revision[OR=11.371, 95%CI(3.220, 40.153, P<0.05] were independent risk factors for postoperative periprosthetic fractures in patients undergoing THA, cemented prosthesis [OR=0.067, 95%CI(0.019, 0.236), P<0.05] was an independent protective factor for postoperative periprosthetic fractures in patients undergoing THA(P<0.05). Hosmer-Lemeshow goodness of fit test showed that χ²=7.864, P=0.325;the area under the curve (AUC) for periprosthetic fractures in patients undergoing THA was 0.892 with a sensitivity of 87.5% and a specificity of 77.7% by receiver operating characteristic(ROC) curve. CONCLUSION The nomogram prediction model for periprosthetic fractures after THA constructed in this study has good discrimination, which is beneficial to clinical prediction of periprosthetic fractures in patients undergoing THA, and facilitates individualized fracture prevention.
Male ; Female ; Humans ; Adult ; Middle Aged ; Arthroplasty, Replacement, Hip/adverse effects* ; Periprosthetic Fractures/surgery* ; Nomograms ; Reoperation/adverse effects* ; Risk Factors ; Osteoporosis/surgery* ; Retrospective Studies ; Hip Prosthesis