This study aimed to develop a predictive tool for surgical site infections (SSI) following hysterectomy and propose strategies for their prevention and control. We conducted a retrospective analysis at a tertiary maternity and child specialist hospital in Zhejiang Province, focusing on patients who underwent hysterectomy between January 2018 and December 2023 for gynecological malignancies or benign reproductive system diseases resistant to medical treatment. Risk factors associated with surgical site infections (SSI) following hysterectomy were identified using LASSO regression analysis on data from 2018 to 2022 as the training set. Independent risk factors were then used to develop a nomogram. The model was validated using data from 2023 as the validation set. Model performance was assessed using the area under the receiver operating characteristic curve (ROC), while calibration curves were employed to gauge model accuracy. Furthermore, clinical utility was evaluated through clinical decision curve analysis (DCA) and clinical impact curve analysis (CIC), providing insights into the practical application of the nomogram. Multivariate analysis identified six independent risk factors associated with SSI development after hysterectomy: BMI ≥ 24 kg/m2 (OR: 2.58; 95% CI 1.14-6.19; P < 0.05), hypoproteinaemia diagnosis (OR: 4.99; 95% CI 1.95-13.02; P < 0.05), postoperative antibiotic use for ≥ 3 days (OR: 49.53; 95% CI 9.73-91.01; P < 0.05), history of previous abdominal surgery (OR: 7.46; 95% CI 2.93-20.01; P < 0.05), hospital stay ≥ 10 days (OR: 9.67; 95% CI 2.06-76.46; P < 0.05), and malignant pathological type (OR: 4.62; 95% CI 1.78-12.76; P < 0.05). A nomogram model was constructed using these variables. ROC and calibration curves demonstrated good model calibration and discrimination in both training and validation sets. Analysis with DCA and CIC confirmed the clinical utility of the nomogram. Personalized nomogram mapping for SSI after hysterectomy enables early identification of high-risk patients, facilitating timely interventions to reduce SSI incidence post-surgery.

BACKGROUND: Frailty is a multifactorial syndrome; through this study, we aimed to investigate the physiological, psychological, and social factors associated with frailty and frailty worsening in community-dwelling older adults.
METHODS: We conducted a cross-sectional and longitudinal study using data from the \"Community Empowerment and Well-Being and Healthy Long-term Care: Evidence from a Cohort Study (CEC),\" which focuses on community dwellers aged 65 and above in Japan. The sample of the cross-sectional study was drawn from a CEC study conducted in 2014 with a total of 673 participants. After excluding those who were frail during the baseline assessment (2014) and at the 3-year follow-up (2017), the study included 373 participants. Frailty assessment was extracted from the Kihon Checklist, while social relationships were assessed using the Social Interaction Index (ISI). Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and their predictive abilities were tested. Factors associated with frailty status and worsening were identified through the Maximum-min Hillclimb algorithm applied to Bayesian networks (BNs).
RESULTS: At baseline, 14.1% (95 out of 673) participants were frail, and 24.1% (90 out of 373) participants experienced frailty worsening at the 3-years follow up. LASSO regression identified key variables for frailty. For frailty identification (cross-sectional), the LASSO model\'s AUC was 0.943 (95%CI 0.913-0.974), indicating good discrimination, with Hosmer-Lemeshow (H-L) test p = 0.395. For frailty worsening (longitudinal), the LASSO model\'s AUC was 0.722 (95%CI 0.656-0.788), indicating moderate discrimination, with H-L test p = 0.26. The BNs found that age, multimorbidity, function status, and social relationships were parent nodes directly related to frailty. It revealed an 85% probability of frailty in individuals aged 75 or older with physical dysfunction, polypharmacy, and low ISI scores; however, if their social relationships and polypharmacy status improve, the probability reduces to 50.0%. In the longitudinal-level frailty worsening model, a 75% probability of frailty worsening in individuals aged 75 or older with declined physical function and ISI scores was noted; however, if physical function and ISI improve, the probability decreases to 25.0%.
CONCLUSIONS: Frailty and its progression are prevalent among community-dwelling older adults and are influenced by various factors, including age, physical function, and social relationships. BNs facilitate the identification of interrelationships among these variables, quantify the influence of key factors. However, further research is required to validate the proposed model.

BACKGROUND: Despite evidence showing a connection between inflammation and endometrial cancer (EC) risk, the surveys on genetic correlation and cohort studies investigating the impact on long-term outcomes have yet to be refined. We aimed to address the impact of inflammation factors on the pathogenesis, progression and consequences of EC.
METHODS: For the genetic correlation analyses, a two-sample of Mendelian randomization (MR) study was applied to investigate inflammation-related single-nucleotide polymorphisms involved with endometrial cancer from GWAS databases. The observational retrospective study included consecutive patients diagnosed with EC (stage I to IV) with surgeries between January 2010 and October 2020 at the Cancer Hospital of Shantou University Medical College.
RESULTS: The 2-sample MR surveys indicated no causal relationship between inflammatory cytokines and endometrial cancer. 780 cases (median age, 55.0 years ) diagnosed with EC were included in the cohort and followed up for an average of 6.8 years. Increased inflammatory parameters at baseline were associated with a higher FIGO stage and invasive EC risk (odds ratios [OR] 1.01 to 4.20). Multivariate-cox regression suggested that multiple inflammatory indicators were significantly associated with overall survival (OS) and progression-free survival (PFS) (P < 0.05). Nomogram models based on inflammatory risk and clinical factors were developed for OS and PFS with C-index of 0.811 and 0.789, respectively. LASSO regression for the validation supported the predictive efficacy of inflammatory and clinical factors on the long-term outcomes of EC.
CONCLUSIONS: Despite the fact that the genetic surveys did not show a detrimental impact of inflammatory cytokines on the endometrial cancer risk, our cohort study suggested that inflammatory level was associated with the progression and long-term outcomes of EC. This evidence may contribute to new strategies targeted at decreasing inflammation levels during EC therapy.

It is widely accepted that loneliness is associated with health problems, but less is known about the predictors of loneliness. In this study, we constructed a model to predict individual risk of loneliness during adulthood. Data were from the prospective population-based FinHealth cohort study with 3444 participants (mean age 55.5 years, 53.4% women) who responded to a 81-item self-administered questionnaire and reported not to be lonely at baseline in 2017. The outcome was self-reported loneliness at follow-up in 2020. Predictive models were constructed using bootstrap enhanced LASSO regression (bolasso). The C-index from the final model including 11 predictors from the best bolasso -models varied between 0.65 (95% CI 0.61 to 0.70) and 0.71 (95% CI 0.67 to 0.75) the pooled C -index being 0.68 (95% CI 0.61 to 0.75). Although survey-based individualised prediction models for loneliness achieved a reasonable C-index, their predictive value was limited. High detection rates were associated with high false positive rates, while lower false positive rates were associated with low detection rates. These findings suggest that incident loneliness during adulthood. may be difficult to predict with standard survey data.

BACKGROUND: Early identification of high-risk individuals with cisplatin-induced nephrotoxicity (CIN) is crucial for avoiding CIN and improving prognosis. In this study, we developed and validated a CIN prediction model based on general clinical data, laboratory indications, and genetic features of lung cancer patients before chemotherapy.
METHODS: We retrospectively included 696 lung cancer patients using platinum chemotherapy regimens from June 2019 to June 2021 as the traing set to construct a predictive model using Absolute shrinkage and selection operator (LASSO) regression, cross validation, and Akaike\'s information criterion (AIC) to select important variables. We prospectively selected 283 independent lung cancer patients from July 2021 to December 2022 as the test set to evaluate the model\'s performance.
RESULTS: The prediction model showed good discrimination and calibration, with AUCs of 0.9217 and 0.8288, sensitivity of 79.89% and 45.07%, specificity of 94.48% and 94.81%, in the training and test sets respectively. Clinical decision curve analysis suggested that the model has value for clinical use when the risk threshold ranges between 0.1 and 0.9. Precision-Recall (PR) curve shown in recall interval from 0.5 to 0.75: precision gradually declines with increasing Recall, up to 0.9.
CONCLUSIONS: Predictive models based on laboratory and demographic variables can serve as a beneficial complementary tool for identifying high-risk populations with CIN.

UNASSIGNED: Venous thromboembolism (VTE), encompassing deep vein thrombosis (DVT) and secondary pulmonary embolism (PE), represents a significant complication post-hip fracture in the elderly. It is a prevalent cause of VTE-related complications, prolonged hospitalization, and mortality. This study aimed to investigate the potential of the systemic immune-inflammation index (SII) as a predictive marker for VTE in older patients following hip fracture.
UNASSIGNED: The study was structured as an observational, analytical, retrospective cohort analysis. A total of 346 elderly patients diagnosed with hip fracture were included. We retrospectively collated clinical and laboratory data for these patients. Using the bootstrap method, the patients were divided in a 7:3 ratio into a training cohort (DVT group = 170 patients; no-DVT group = 72 patients) and an internal validation cohort (DVT group = 81 patients; no-DVT group = 23 patients). In the training cohort, relevant indices were initially identified using univariate analysis. Subsequently, least absolute shrinkage and selection operator logistic analysis was employed to determine significant potential independent risk factors (P < 0.05). A dynamic online diagnostic nomogram was developed, with its discriminative ability assessed using the area under the receiver operating characteristic curve (AUC). The nomogram\'s accuracy was further appraised using calibration plots. The clinical utility of the nomogram was evaluated through decision curve analysis (DCA) and corroborated by internal validation within the training set.
UNASSIGNED: SII emerged as the sole independent risk factor identified from the multivariate logistic analysis of the training cohort and was incorporated into the VTE diagnostic nomogram for older patients\' post-hip fracture. The nomogram demonstrated AUC values of 0.648 in the training cohort and 0.545 in the internal testing cohort. Calibration curves corroborated the close alignment of the nomogram\'s predicted outcomes with the ideal curve, indicating consistency between predicted and actual outcomes. The DCA curve suggested that all patients could derive benefit from this model. These findings were also validated in the validation cohort.
UNASSIGNED: The systemic immune-inflammation index is a robust predictor of venous thromboembolism in elderly patients following hip fracture, underscoring its potential as a valuable tool in clinical practice.

UNASSIGNED: Geographic distribution of dietary inflammatory index (DII) in China has not been thoroughly evaluated and evidence on the association between DII and hypertension among Chinese middle-aged and older population was inadequate.
UNASSIGNED: To investigate the geographic disparities of DII and its association with hypertension among Chinese middle-aged and elders.
UNASSIGNED: Data was from the China Adults Chronic Diseases and Nutrition Surveillance (CACDNS 2015) for middle-aged and older participants. The DII for each participant was determined through a combination of 3 days 24 h dietary recall interviews and a food frequency questionnaire. Spatial analysis was employed to investigate the geographic distribution of DII in China. Restricted cubic spline models and binary logistic regression analysis were used to assess the relationship between DII and hypertension. The least absolute shrinkage and selection operator (LASSO) regression was applied for identifying key hypertension-related factors, which was then included in the establishment of a risk prediction nomogram model, with the receiver operating characteristic (ROC) curve and decision curve analysis (DCA) being built to evaluate its discriminatory power for hypertension.
UNASSIGNED: A total of 52,087 middle-aged and older participants were included in the study, among whom 36.6% had hypertension. it revealed that a clear spatial correlation in the national distribution of DII scores (Moran I: 0.252, p = 0.001), with higher DII scores concentrated in the northwest region and lower DII scores concentrated in the southeast region. Hypertensive participants had higher DII scores compared to those without hypertension (OR: 1.507 vs. 1.447, p = 0.003). Restricted cubic spline models and binary logistic regression analysis demonstrated a positive association between DII and hypertension after adjusting for potential confounding factors. There was a significant increasing trend in the proportion of hypertensive individuals as DII scores increase (p for trend = 0.004). The nomogram model, constructed using key factors identified through LASSO regression, demonstrated a robust discriminative capacity, with an area under the curve (AUC) of 73.2% (95% CI, 72.4-74.0%). Decision curve analysis confirmed the reliability and effectiveness of the nomogram model. Sensitivity analysis conducted within the subpopulation aged under 45 years yielded results consistent with the primary analysis.
UNASSIGNED: In Chinese adults middle-aged and older, geographic disparities in dietary inflammatory potential are notable, with lower levels observed in the southeastern coastal regions of China and higher levels in the northwestern regions. Meanwhile, there is a positive association between the inflammatory potential of the diet and hypertension. Additional research is needed to investigate regional disparities in dietary inflammatory potential and pinpoint specific dietary patterns associated with lower inflammation.

The main objective of this study was to analyze dietary patterns using data-driven approaches and to explore preventive or risk dietary factors for kidney stone disease (KSD). A case-control matching study was conducted in adults (n = 6396) from a suburb of Shanghai. A food frequency questionnaire was used to assess the consumption of various types of food, and B-ultrasound was used to identify kidney stones. Principal component analysis and regression were used to generate dietary patterns and further explore the relationship between dietary patterns and KSD. LASSO regression and post-selection inference were used to identify food groups most associated with KSD. Among males, the \"balanced but no-sugary-beverages pattern\" (OR = 0.78, p < 0.05) and the \"nuts and pickles pattern\" (OR = 0.84, p < 0.05) were protective dietary patterns. Among females, \"high vegetables and low-sugary-beverages pattern\" (OR = 0.83, p < 0.05) and \"high-crustaceans and low-vegetables pattern\" (OR = 0.79, p < 0.05) were protective dietary patterns, while the \"comprehensive pattern with a preference for meat\" (OR = 1.06, p < 0.05) and \"sugary beverages pattern\" (OR = 1.16, p < 0.05) were risk dietary patterns. We further inferred that sugary beverages (p < 0.05) were risk factors and pickles (p < 0.05) and crustaceans (p < 0.05) were protective factors.

UNASSIGNED: Developmental dyslexia (DD) has been generally recognized as a multifactorial psychological disorder in recent decades. However, studies on reading and learning environment, social and demographic factors affecting Chinese developmental dyslexia (DD) are still scarce in China. This study aims to explore multidimensional home influencing factors associated with DD before and after birth.
UNASSIGNED: A total of 60 dyslexic and 252 normal elementary school students graded 2-5 were recruited in Shantou, China. The Least Absolute Shrinkage and Selection Operator (LASSO) regression model was used for the social and demographic variables screening. Odds ratios (ORs) with 95 % confidence intervals (CIs) for associations between DD and related factors were estimated by multivariate logistic regression models.
UNASSIGNED: Through LASSO regression, we ultimately identified 13 key variables, including maternal education level and family monthly income, among others. The logistic regression analyses showed that the risk of DD was higher in children with lower maternal education levels. Divergent parenting styles may be a risk factor for developing DD as opposed to consistent parenting styles (OR = 4.93, 95%CI: 1.11-21.91). Children whose mothers suffered from malnutrition during pregnancy were more likely to develop DD (OR = 10.31, 95%CI: 1.84-37.86), as well as exposure to second-hand smoking at home every day (OR = 5.33, 95%CI: 1.52-18.66). Interestingly, children\'s active reading (OR = 0.26, 95%CI: 0.08-0.84; OR = 0.17, 95%CI: 0.04-0.76 for \"sometimes\" and \"often\" compared to none, respectively), children having extracurricular reading fairy tale books (OR = 0.37, 95%CI: 0.15-0.90), and children having extracurricular reading composition books (OR = 0.25, 95%CI: 0.09-0.69) were significant protective factors for DD.
UNASSIGNED: Home reading environment, several educational, sociometric and demographic factors may influence the development of dyslexia. We should pay attention to these factors on the development of dyslexia, so as to provide the well social and familial environment to ensure the healthy development of children.

Patients with pancreatic cancer (PC) have a poor prognosis. Radiotherapy (RT) is a standard palliative treatment in clinical practice, and there is no effective clinical prediction model to predict the prognosis of PC patients receiving radiotherapy. This study aimed to analyze PC\'s clinical characteristics, find the factors affecting PC patients\' prognosis, and construct a visual Nomogram to predict overall survival (OS).
SEER*Stat software was used to collect clinical data from the Surveillance, Epidemiology, and End Results (SEER) database of 3570 patients treated with RT. At the same time, the relevant clinical data of 115 patients were collected from the Affiliated Cancer Hospital of Zhengzhou University. The SEER database data were randomly divided into the training and internal validation cohorts in a 7:3 ratio, with all patients at The Affiliated Cancer Hospital of Zhengzhou University as the external validation cohort. The lasso regression was used to screen the relevant variables. All non-zero variables were included in the multivariate analysis. Multivariate Cox proportional risk regression analysis was used to determine the independent prognostic factors. The Kaplan-Meier(K-M) method was used to plot the survival curves for different treatments (surgery, RT, chemotherapy, and combination therapy) and calculate the median OS. The Nomogram was constructed to predict the survival rates at 1, 3, and 5 years, and the time-dependent receiver operating characteristic curves (ROC) were plotted with the calculated curves. Calculate the area under the curve (AUC), the Bootstrap method was used to plot the calibration curve, and the clinical efficacy of the prediction model was evaluated using decision curve analysis (DCA).
The median OS was 25.0, 18.0, 11.0, and 4.0 months in the surgery combined with chemoradiotherapy (SCRT), surgery combined with radiotherapy, chemoradiotherapy (CRT), and RT alone cohorts, respectively. Multivariate Cox regression analysis showed that age, N stage, M stage, chemotherapy, surgery, lymph node surgery, and Grade were independent prognostic factors for patients. Nomogram models were constructed to predict patients\' OS. 1-, 3-, and 5-year Time-dependent ROC curves were plotted, and AUC values were calculated. The results suggested that the AUCs were 0.77, 0.79, and 0.79 for the training cohort, 0.79, 0.82, and 0.81 for the internal validation cohort, and 0.73, 0.93, and 0.88 for the external validation cohort. The calibration curves Show that the model prediction probability is in high agreement with the actual observation probability, and the DCA curve shows a high net return.
SCRT significantly improves the OS of PC patients. We developed and validated a Nomogram to predict the OS of PC patients receiving RT.