Modeling Group Research Articles

The Role of Alexithymia and Moral Disengagement in Childhood Physical Abuse and Depressive Symptoms: A Comparative Study Among Rural and Urban Chinese College Students.

There is robust evidence indicating the adverse association between childhood abuse and depressive symptoms. However, the ways in which childhood physical abuse is indirectly associated with adulthood depressive symptoms by alexithymia and moral disengagement remain poorly understood, particularly in the context of China. The present study aims to investigate how childhood physical abuse may be associated with depressive symptoms via the serial mediation effects of alexithymia and moral disengagement among college students in China. A total of 686 college students (female: 53.2%) aged from 17 to 28 years old (M = 21.33; SD = 2.53) were recruited from mainland China to participate in an online survey assessing the variables of interest. Structural equation modeling and multiple group analysis were performed using Mplus 7.0 to investigate the hypothesized model. College students' experiences with childhood physical abuse was significantly associated with their depressive symptoms. This relationship was partially mediated by alexithymia and then by moral disengagement. Meanwhile, alexithymia and moral disengagement had a chain mediating effect on childhood physical abuse and depressive symptoms. Furthermore, multigroup analysis revealed that the association between alexithymia and depressive symptoms was stronger among rural students compared to urban students. Furthermore, the mediation model involving moral disengagement applies exclusively to rural students. Alexithymia and moral disengagement exerted serial mediating effect on the association between childhood physical abuse and depressive symptoms. This pattern was particularly pronounced among rural students. These findings underscore the importance of addressing both childhood adversity and maladaptive cognitive processes in the prevention and treatment of depression.

Construction and verification of a risk prediction model for cognitive frailty in older patients with chronic obstructive pulmonary disease and diabetes mellitus.

We explored risk factors for cognitive frailty in older patients with chronic obstructive pulmonary disease (COPD) and diabetes mellitus to develop and verify a risk prediction model for cognitive frailty. This was a cross-sectional study. Convenience sampling was used to randomly select 378 patients hospitalized between February 2022 and December 2023. We allocated 265 patients who visited between February 2022 and February 2023 to a modeling group to analyze risk factors for cognitive frailty and create a logistic regression model for risk prediction. Another 113 patients who visited between March 2023 and December 2023 were included in a validation group for model verification. The cognitive frailty incidence in the 265 patients was 35.09% (93/265). Regression analysis showed that age >80 years (odds ratio [OR] = 4.576), regular exercise (OR = 0.390, polypharmacy (OR = 3.074), depression (OR = 2.395) duration of COPD combined with diabetes (OR = 1.902), Family APGAR index score (OR = 0.428), and chronic pain (OR = 2.156) were factors influencing the occurrence of cognitive frailty in older patients with COPD accompanied by diabetes. The constructed risk prediction model for cognitive frailty in older patients with COPD and diabetes showed good predictive value, aiding in the clinical identification of high-risk patients and facilitating timely intervention and guidance.

Using systems modeling to facilitate undergraduate physiology student learning and retention of difficult concepts.

Physiology concepts, such as acid-base balance, may be difficult for students to understand. Systems modeling, a cognitive tool, allows students to visualize their mental model of the problem space to enhance learning and retention. Methods: We performed a within-subjects three-period randomized control comparison of systems modeling versus written discussion activities in an undergraduate asynchronous online Anatomy and Physiology II course. Participants (n=108) were randomized to groups with differing treatment orders across three course units: endocrine, immune, and acid-base balance. Participants demonstrated content understanding either through constructing systems modeling diagrams (M) or written discussion posts (W) in a MWM, MMW, or WMM sequence. Results: For each of three units, student performance was assessed on six standardized multiple-choice questions embedded within a 45-question exam. The same six questions per unit, eighteen questions total, was again assessed on the 75-question final exam. The groups demonstrated no significant difference in performance in the endocrine unit exam (mean difference, MD=-0.036). However, the modeling group outperformed the writing group in the immune unit exam (MD=0.209) and widened the gap in the acid-base balance unit exam (MD=0.243). On the final exam, performance was again higher for the modeling group on acid-base balance content as mean difference increased to 0.306 despite the final exam content for acid-base balance being significantly more difficult compared to other units (modeling: F(2)=29.882, p<.001; writing: F(2)=25.450, p<.001). Conclusions: These results provide initial evidence that participation in systems modeling activities enhance student learning of difficult physiology content as evidenced by improved multiple-choice question performance.

Estimation equation of limb lean soft tissue mass in Asian athletes using bioelectrical impedance analysis.

The lean soft tissue mass (LSTM) of the limbs is approximately 63% of total skeletal muscle mass. For athletes, measurement of limb LSTM is the basis for rapid estimation of skeletal muscle mass. This study aimed to establish the estimation equation of LSTM in Asian athletes using bioelectrical impedance analysis (BIA). A total of 198 athletes (121 males, 77 females; mean age 22.04 ± 5.57 years) from different sports in Taiwan were enrolled. A modeling group (MG) of 2/3 (n = 132) of subjects and a validation group (VG) of 1/3 (n = 68) were randomly assigned. Using the InBody S-10, resistance and reactance were measured at 50 kHz from the right palm to the right sole while the participant was in the supine position. Predictor variables were height (h), weight (W), age, Sex, Xc, resistance index (RI; RI = h2 / R). LSTM of arms and legs measured by dual-energy X-ray absorptiometry (DXA) was the response variable. Multivariate stepwise regression analysis method was used to establish BIA estimation equations as ArmsLSTMBIA-Asian and LegsLSTMBIA-Asian. Estimation equations performance was confirmed by cross-validation. Estimation equation "ArmsLSTMBIA-Asian = 0.096 h2/R- 1.132 Sex + 0.030 Weight + 0.022 Xc- 0.022 h + 0.905, r2 = 0.855, SEE = 0.757 kg, n = 132" and "LegsLSTMBIA Asian = 0.197h2/R" + 0.120 h- 1.242 Sex + 0.055 Weight- 0.052 Age + 0.033 Xc -16.136, r2 = 0.916, SEE = 1.431 kg, n = 132" were obtained from MG. Using DXA measurement results of VG for correlation analysis and Limit of Agreement (LOA) of Bland-Altman Plot, ArmsLST is 0.924, -1.53 to 1.43 kg, and LegsLST is 0.957, -2.68 to 2.90 kg. The established single-frequency BIA hand-to-foot (whole body) estimation equation quickly and accurately estimates LSTM of the arms and legs of Asian athletes.

Establishment of predictive models and determinants of preoperative gastric retention in endoscopic retrograde cholangiopancreatography.

Study on influencing factors of gastric retention before endoscopic retrograde cholangiopancreatography (ERCP) background: With the wide application of ERCP, the risk of preoperative gastric retention affects the smooth progress of the operation. The study found that female, biliary and pancreatic malignant tumor, digestive tract obstruction and other factors are closely related to gastric retention, so the establishment of predictive model is very important to reduce the risk of operation. To analyze the factors influencing preoperative gastric retention in ERCP and establish a predictive model. A retrospective analysis was conducted on 190 patients admitted to our hospital for ERCP preparation between January 2020 and February 2024. Patient baseline clinical data were collected using an electronic medical record system. Patients were randomly matched in a 1:4 ratio with data from 190 patients during the same period to establish a validation group (n = 38) and a modeling group (n = 152). Patients in the modeling group were divided into the gastric retention group (n = 52) and non-gastric retention group (n = 100) based on whether gastric retention occurred preoperatively. General data of patients in the validation group and modeling group were compared. Univariate and multivariate logistic regression analyses were performed to identify factors influencing preoperative gastric retention in ERCP patients. A predictive model for preoperative gastric retention in ERCP patients was constructed, and calibration curves were used for validation. The receiver operating characteristic (ROC) curve was analyzed to evaluate the predictive value of the model. We found no statistically significant difference in general data between the validation group and modeling group (P > 0.05). The comparison of age, body mass index, hypertension, and diabetes between the two groups showed no statistically significant difference (P > 0.05). However, we noted statistically significant differences in gender, primary disease, jaundice, opioid use, and gastrointestinal obstruction between the two groups (P < 0.05). Multivariate logistic regression analysis showed that gender, primary disease, jaundice, opioid use, and gastrointestinal obstruction were independent factors influencing preoperative gastric retention in ERCP patients (P < 0.05). The results of logistic regression analysis revealed that gender, primary disease, jaundice, opioid use, and gastrointestinal obstruction were included in the predictive model for preoperative gastric retention in ERCP patients. The calibration curves in the training set and validation set showed a slope close to 1, indicating good consistency between the predicted risk and actual risk. The ROC analysis results showed that the area under the curve (AUC) of the predictive model for preoperative gastric retention in ERCP patients in the training set was 0.901 with a standard error of 0.023 (95%CI: 0.8264-0.9567), and the optimal cutoff value was 0.71, with a sensitivity of 87.5 and specificity of 84.2. In the validation set, the AUC of the predictive model was 0.842 with a standard error of 0.013 (95%CI: 0.8061-0.9216), and the optimal cutoff value was 0.56, with a sensitivity of 56.2 and specificity of 100.0. Gender, primary disease, jaundice, opioid use, and gastrointestinal obstruction are factors influencing preoperative gastric retention in ERCP patients. A predictive model established based on these factors has high predictive value.

Prediction model establishment and validation for enteral nutrition aspiration during hospitalization in patients with acute pancreatitis.

Acute pancreatitis (AP) is a disease caused by abnormal activation of pancreatic enzymes and can lead to self-digestion of pancreatic tissues and dysfunction of other organs. Enteral nutrition plays a vital role in the treatment of AP because it can meet the nutritional needs of patients, promote the recovery of intestinal function, and maintain the barrier and immune functions of the intestine. However, the risk of aspiration during enteral nutrition is high; once aspiration occurs, it may cause serious complications, such as aspiration pneumonia, and suffocation, posing a threat to the patient's life. This study aims to establish and validate a prediction model for enteral nutrition aspiration during hospitalization in patients with AP. To establish and validate a predictive model for enteral nutrition aspiration during hospitalization in patients with AP. A retrospective review was conducted on 200 patients with AP admitted to Chengdu Shangjin Nanfu Hospital, West China Hospital of Sichuan University from January 2020 to February 2024. Clinical data were collected from the electronic medical record system. Patients were randomly divided into a validation group (n = 40) and a modeling group (n = 160) in a 1:4 ratio, matched with 200 patients from the same time period. The modeling group was further categorized into an aspiration group (n = 25) and a non-aspiration group (n = 175) based on the occurrence of enteral nutrition aspiration during hospitalization. Univariate and multivariate logistic regression analyses were performed to identify factors influencing enteral nutrition aspiration in patients with AP during hospitalization. A prediction model for enteral nutrition aspiration during hospitalization was constructed, and calibration curves were used for validation. Receiver operating characteristic curve analysis was conducted to evaluate the predictive value of the model. There was no statistically significant difference in general data between the validation and modeling groups (P > 0.05). The comparison of age, gender, body mass index, smoking history, hypertension history, and diabetes history showed no statistically significant difference between the two groups (P > 0.05). However, patient position, consciousness status, nutritional risk, Acute Physiology and Chronic Health Evaluation (APACHE-II) score, and length of nasogastric tube placement showed statistically significant differences (P < 0.05) between the two groups. Multivariate logistic regression analysis showed that patient position, consciousness status, nutritional risk, APACHE-II score, and length of nasogastric tube placement were independent factors influencing enteral nutrition aspiration in patients with AP during hospitalization (P < 0.05). These factors were incorporated into the prediction model, which showed good consistency between the predicted and actual risks, as indicated by calibration curves with slopes close to 1 in the training and validation sets. Receiver operating characteristic analysis revealed an area under the curve (AUC) of 0.926 (95%CI: 0.8889-0.9675) in the training set. The optimal cutoff value is 0.73, with a sensitivity of 88.4 and specificity of 85.2. In the validation set, the AUC of the model for predicting enteral nutrition aspiration in patients with AP patients during hospitalization was 0.902, with a standard error of 0.040 (95%CI: 0.8284-0.9858), and the best cutoff value was 0.73, with a sensitivity of 91.9 and specificity of 81.8. A prediction model for enteral nutrition aspiration during hospitalization in patients with AP was established and demonstrated high predictive value. Further clinical application of the model is warranted.

Constructing a predictive model for live birth following fresh embryo transfer in antagonist protocol for polycystic ovary syndrome.

The present research aims to assess the factors that influence live birth outcomes following fresh embryo transfers using antagonist protocols in individuals diagnosed with polycystic ovary syndrome (PCOS). Furthermore, it seeks to develop a predictive nomogram model to facilitate clinical decision-making and provide personalized treatment strategies. This retrospective cohort research analyzed the clinical data of 1242 individuals having PCOS who went through fresh embryo transfers employing antagonist protocols and in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) at Fujian Provincial Maternal and Child Health Hospital between January 2018 and December 2022. Individuals were assigned randomly to a modeling group (869 cases) and a validation group (373 cases) in a 7:3 ratio. The Boruta algorithm and multivariable logistic regression were utilized to identify independent risk factors linked to live births after transfer. A predictive nomogram was subsequently developed. The discriminatory power of the model and its accuracy were monitored by utilizing receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. Multivariable logistic regression analysis identified several independent factors that influence live birth rates in fresh embryo transfer cycles for individuals having PCOS using antagonist protocols, including female age, body mass index (BMI), infertility duration, serum testosterone levels, progesterone levels at the time of human chorionic gonadotropin (hCG) injection, number of high-quality cleavage-stage embryos, type of embryo transferred, and the total number of embryos transferred. Based on these findings, a predictive nomogram was developed. The area under the ROC curve stood at 0.804 (95% confidence interval (CI), 0.775-0.833) for the modeling group and 0.807 (95% CI, 0.762-0.851) for the validation group. Calibration curves confirmed that the predictions of the nomogram closely matched the actual live birth outcomes. Decision curve analysis highlighted that the model provides significant net benefits for predicting live birth rates, with optimal performance across a probability range of 16.5 to 88.6%. Independent factors, including female age, infertility duration, BMI, serum testosterone levels, progesterone levels on the day of hCG injection, and the number and type of high-quality cleavage-stage embryos transferred are pivotal in influencing live birth outcomes in fresh embryo transfer cycles under antagonist protocols in individuals with PCOS undergoing IVF/ICSI treatments. The predictive nomogram developed from these factors offers substantial predictive accuracy and clinical utility, providing a reliable basis for clinical prognosis, targeted interventions, and the development of personalized treatment plans.

Determination of Propeller-Rudder-Hull Interaction Coefficients in Ship Manoeuvring Prediction

Abstract The assessment of ship manoeuvring properties is a crucial part of the process of ship design and is usually first carried out during the model test phase of the project. According to the International Maritime Organisation (IMO), the manoeuvrability of the ship can be assessed on the basis of the standard trial manoeuvres. In order to do this, free running model tests or captive model tests are used, in conjunction with a mathematical model of ship motion; this is considered to be a reliable prediction method. In recent years, numerical-based methods have also been widely used in ship hydrodynamics and constantly improving computing power and more accurate fluid dynamics models have made the simulation of more complex cases possible. The study presented in this paper focuses on the determination of propeller-rudder-hull interaction coefficients based on the Mathematical Modelling Group (MMG) standard method in ship manoeuvring prediction. The identification of the parameters uses both captive model tests and a simplified numerical method, as well as regression formulas. The results of 35° turning and 10°/10° zig-zag manoeuvres, obtained with the use of each prediction method, are then compared. The test case used in the study is the container type cargo ship equipped with a single propeller and rudder. The model scale, for which the referenced model tests were carried out, is equal to 1:25 and a NACA 0020 rudder profile was used. This research highlights the advantages and disadvantages of each presented prediction method and their potential for future improvement.

Real-time simulation of ship overtake maneuvering at different water depths with a ship motion controller

This study aimed to predict ship trajectories in deep and shallow waters in real-time by properly accounting for the effects of hydrodynamic interactions on ship maneuvering. Ship maneuverability and motion control were investigated under hydrodynamic interactions arising from the effects of water depth, ship speed, and lateral distance between interacting ships on parallel courses. The Maneuvering Modeling Group (MMG) model was used, and the hydrodynamic interaction effect was estimated using an efficient three-dimensional potential-flow code that can run in real time. The influence of water depth on ship maneuverability was analyzed. The ship velocity response to the hydrodynamic interactions and maneuvering motion was simulated during the overtaking process of two ships at a range of water depths, lateral distances, and speeds. The findings suggest that, in shallow waters, changing the heading of the ship becomes more difficult, and the course deviation associated with the overtaking maneuver of two ships also increases. A conventional proportional-integral-derivative (PID) controller was employed for course-keeping. When the ratio of the water depth to ship draft was less than 2.0, the PID controller continued to perform effectively for the overtaking ship. For the overtaken ship, the effectiveness of the controller was diminished owing to the relatively low rudder effect at slow speeds.

Predictive model of risk factors for 28-day mortality in patients with sepsis or sepsis-associated delirium based on the MIMIC-IV database

Research on the severity and prognosis of sepsis with or without progressive delirium is relatively insufficient. We constructed a prediction model of the risk factors for 28-day mortality in patients who developed sepsis or sepsis-associated delirium. The modeling group of patients diagnosed with Sepsis-3 and patients with progressive delirium of related indicators were selected from the MIMIC-IV database. Relevant independent risk factors were determined and integrated into the prediction model. Receiver operating characteristic (ROC) curves and the Hosmer–Lemeshow (HL) test were used to evaluate the prediction accuracy and goodness-of-fit of the model. Relevant indicators of patients with sepsis or progressive delirium admitted to the intensive care unit (ICU) of a 3A hospital in Xinjiang were collected and included in the verification group for comparative analysis and clinical validation of the prediction model. The total length of stay in the ICU, hemoglobin levels, albumin levels, activated partial thrombin time, and total bilirubin level were the five independent risk factors in constructing a prediction model. The area under the ROC curve of the predictive model (0.904) and the HL test result (χ2 = 8.518) indicate a good fit. This model is valuable for clinical diagnosis and treatment and auxiliary clinical decision-making.

Relationship between surgical difficulty and postoperative complications of hand-assisted laparoscopic living donor nephrectomy and establishment of prediction model

BackgroundFew studies have systematically explored the factors influencing the difficulty of hand-assisted laparoscopic living donor nephrectomy. To investigate the relationship between the difficulty of hand-assisted laparoscopic living donor nephrectomy and postoperative complications of the donor as well as the recipient, and then build a model for predicting the difficulty of surgery.MethodsIn this study, 60 patients who underwent hand-assisted laparoscopic living donor nephrectomy by the same surgeon from September 2022 to March 2024 were included as the modeling group. 20 patients operated on by another surgeon served as the external validation group. The subjective score (1–3 points) of surgical difficulty was used as the quantitative index of surgical difficulty. Pearson and Spearman correlation tests were used to explore the correlation between preoperative data and surgical difficulty scores of kidney donors, and finally built a prediction model through multiple linear regression analysis.ResultsWith the increase in the difficulty of operation, both donors and recipients’ complications were increased. Linear regression analysis showed that only the number of renal arteries, visceral fat thickness and MAP score were independent risk factors for the difficulty of hand-assisted laparoscopic living donor nephrectomy. The prediction equation is as follows: Difficulty score = 0.584*Number of renal arteries + 0.731*MAP score + 0.110*visceral fat thickness.ConclusionsDonors with higher surgical difficulty are more likely to have serious complications after surgery as well as the recipient. We also established a reliable prediction model for the difficulty of hand-assisted laparoscopic donor nephrectomy.

Prediction model of malnutrition in hospitalized patients with acute stroke

ABSTRACT Objective The prognosis of stroke patients is greatly threatened by malnutrition. However, there is no model to predict the risk of malnutrition in hospitalized stroke patients. This study developed a predictive model for identifying high-risk malnutrition in stroke patients. Methods Stroke patients from two tertiary hospitals were selected as the objects. Binary logistic regression was used to build the model. The model’s performance was evaluated using various metrics including the receiver operating characteristic curve, Hosmer-Lemeshow test, sensitivity, specificity, Youden index, clinical decision curve, and risk stratification. Results A total of 319 stroke patients were included in the study. Among them, 27% experienced malnutrition while in the hospital. The prediction model included all independent variables, including dysphagia, pneumonia, enteral nutrition, Barthel Index, upper arm circumference, and calf circumference (all p < 0.05). The AUC area in the modeling group was 0.885, while in the verification group, it was 0.797. The prediction model produces greater net clinical benefit when the risk threshold probability is between 0% and 80%, as revealed by the clinical decision curve. All p values of the Hosmer test were > 0.05. The optimal cutoff value for the model was 0.269, with a sensitivity of 0.849 and a specificity of 0.804. After risk stratification, the MRS scores and malnutrition incidences increased significantly with escalating risk levels (p < 0.05) in both modeling and validation groups. Conclusions This study developed a prediction model for malnutrition in stroke patients. It has been proven that the model has good differentiation and calibration.

Modeling Group Discourse with Epistemic Network Analysis: Unpacking Connections, Perspectives, and Individual Contributions

AbstractOperationalizing and modeling group talk has proved to be a consistent challenge in educational research. In this paper, we suggest that epistemic network analysis (ENA) could provide unique insights concerning group talk. Specifically, we use ENA to model the talk orientations put forward in the Exploratory Talk framework (Cumulative, Disputational, Exploratory). Participants (n = 60, 67% female, 33% male) were undergraduate students in an Introduction to Psychology course who took part in three 90-min collaborative online tasks. We coded student discourse according to a set of basic communicative acts reflective of the Exploratory Talk framework. Then, using ENA, we identified different groups’ patterns of discourse at the group and individual level. Presenting the epistemic networks of four purposefully chosen groups, this paper offers three key contributions to modeling and conceptualizing group dialogue: (1) illustrating how ENA could offer new ways to analyze group talk by focusing on the frequency of co-occurrence of connections between a basic set of communicate acts rather than the different communicative acts used; (2) refining the theoretical conceptualization of Exploratory Talk by distinguishing two sub-variations—other-oriented vs. self-oriented Exploratory Talk—that differ according to the depth of engagement with other perspectives; (3) examining how ENA allows unpacking diverging dynamics of individual contributions to group discourse, focusing on the role of individuals that function as “instigators” or “connectors.”

Predicting hepatocellular carcinoma: A new non-invasive model based on shear wave elastography.

Integrating conventional ultrasound features with 2D shear wave elastography (2D-SWE) can potentially enhance preoperative hepatocellular carcinoma (HCC) predictions. To develop a 2D-SWE-based predictive model for preoperative identification of HCC. A retrospective analysis of 884 patients who underwent liver resection and pathology evaluation from February 2021 to August 2023 was conducted at the Oriental Hepatobiliary Surgery Hospital. The patients were divided into the modeling group (n = 720) and the control group (n = 164). The study included conventional ultrasound, 2D-SWE, and preoperative laboratory tests. Multiple logistic regression was used to identify independent predictive factors for malignant liver lesions, which were then depicted as nomograms. In the modeling group analysis, maximal elasticity (Emax) of tumors and their peripheries, platelet count, cirrhosis, and blood flow were independent risk indicators for malignancies. These factors yielded an area under the curve of 0.77 (95% confidence interval: 0.73-0.81) with 84% sensitivity and 61% specificity. The model demonstrated good calibration in both the construction and validation cohorts, as shown by the calibration graph and Hosmer-Lemeshow test (P = 0.683 and P = 0.658, respectively). Additionally, the mean elasticity (Emean) of the tumor periphery was identified as a risk factor for microvascular invasion (MVI) in malignant liver tumors (P = 0.003). Patients receiving antiviral treatment differed significantly in platelet count (P = 0.002), Emax of tumors (P = 0.033), Emean of tumors (P = 0.042), Emax at tumor periphery (P < 0.001), and Emean at tumor periphery (P = 0.003). 2D-SWE's hardness value serves as a valuable marker for enhancing the preoperative diagnosis of malignant liver lesions, correlating significantly with MVI and antiviral treatment efficacy.

Development and validation of a predictive model for double-J stent encrustation after upper urinary tract calculi surgery

The study is aimed to establish a predictive model of double-J stent encrustation after upper urinary tract calculi surgery. We collected the clinical data of 561 patients with indwelling double-J tubes admitted to a hospital in Shandong Province from January 2019 to December 2020 as the modeling group and 241 cases of indwelling double-J tubes from January 2021 to January 2022 as the verification group. Univariate and binary logistic regression analyses were used to explore risk factors, the risk prediction equation was established, and the receiver operating characteristic (ROC) curve analysis model was used for prediction. In this study, 104 of the 561 patients developed double-J stent encrustation, with an incidence rate of 18.5%. We finally screened out BMI (body mass index) > 23.9 (OR = 1.648), preoperative urine routine white blood cell quantification (OR = 1.149), double-J tube insertion time (OR = 1.566), postoperative water consumption did not reach 2000 ml/d (OR = 8.514), a total of four factors build a risk prediction model. From the ROC curve analysis, the area under the curve (AUC) was 0.844, and the maximum Oden index was 0.579. At this time, the sensitivity was 0.735 and the specificity was 0.844. The research established in this study has a high predictive value for the occurrence of double-J stent encrustation in the double-J tube after upper urinary tract stone surgery, which provides a basis for the prevention and treatment of double-J stent encrustation.

Effect of Peitu Yifei Granules on autophagy mechanism in rats with idiopathic pulmonary fibrosis based on PI3K/Akt/mTOR signaling pathway

To investigate the mechanism by which Peitu Yifei Granules inhibit idiopathic pulmonary fibrosis(IPF) in rats, fifty specific-pathogen-free(SPF) grade male Wistar rats were randomly divided into blank group and modeling group. IPF was induced in the modeling group rats by tracheal infusion of 5 mg·kg~(-1) bleomycin(BLM) and then randomly divided into model group, pirfenidone group, and high-dose, medium-dose, and low-dose groups treated with Peitu Yifei Granules. After 24 hours of modeling, the treatment groups received intragastric administration of either Peitu Yifei Granules or pirfenidone as a positive control drug; meanwhile, the model group received an equal volume of normal saline. After 21 days of treatment administration, lung tissue samples were collected for analysis. Pathological changes in lung tissues were assessed using hematoxylin-eosin(HE) staining and Masson's trichrome staining. The expression levels of protein kinase B(Akt), mammalian target of rapamycin(mTOR), their phosphorylated forms, and sequestosome 1(p62) were determined through Western blot(WB). Fluorescent quantitative real-time polymerase chain reaction(RT-qPCR) was used to measure messenger ribonucleic acid(mRNA) expression levels of Beclin-1, microtubule-associated proteins 1A/1B light chain 3B(LC3B), and p62. Immunohistochemistry was performed to assess protein expression levels of Beclin-1 and LC3B in lung tissue samples. RESULTS:: demonstrated that lung tissue structure appeared normal without significant collagen deposition in the blank group rats. In contrast, rats from the model group exhibited thickened alveolar septa along with evident inflammatory changes and collagen deposition. Compared to the model group rats, those treated with Peitu Yifei Granules or pirfenidone showed significantly improved lung tissue structure with reduced inflammation and collagen deposition observed histologically. Furthermore, compared with those of the blank group, the expressions of p62 and its mRNA, p-Akt and p-mTOR protein in lung tissues of the model group were significantly increased, while Beclin-1, LC3B and their mRNA levels were significantly decreased. Compared with those of the model group, the expressions of p62 and its mRNA, p-Akt and p-mTOR in lung tissues of the pirfenidone group and Peitu Yifei Granules high-dose and medium-dose groups were significantly decreased, while Beclin-1, LC3B and their mRNA expressions were significantly increased. The above results indicate that Peitu Yifei Granules can improve autophagy levels in lung tissues by inhibiting the phosphoinositide 3-kinase(PI3K)/Akt/mTOR signaling pathway and delay the development of IPF disease.

Comparison of Cox regression and generalized Cox regression models to machine learning in predicting survival of children with diffuse large B-cell lymphoma.

The incidence of diffuse large B-cell lymphoma (DLBCL) in children is increasing globally. Due to the immature immune system in children, the prognosis of DLBCL is quite different from that of adults. We aim to use the multicenter large retrospective analysis for prognosis study of the disease. For our retrospective analysis, we retrieved data from the Surveillance, Epidemiology and End Results (SEER) database that included 836 DLBCL patients under 18 years old who were treated at 22 central institutions between 2000 and 2019. The patients were randomly divided into a modeling group and a validation group based on the ratio of 7:3. Cox stepwise regression, generalized Cox regression and eXtreme Gradient Boosting (XGBoost) were used to screen all variables. The selected prognostic variables were used to construct a nomogram through Cox stepwise regression. The importance of variables was ranked using XGBoost. The predictive performance of the model was assessed by using C-index, area under the curve (AUC) of receiver operating characteristic (ROC) curve, sensitivity and specificity. The consistency of the model was evaluated by using a calibration curve. The clinical practicality of the model was verified through decision curve analysis (DCA). ROC curve demonstrated that all models except the non-proportional hazards and non-log linearity (NPHNLL) model, achieved AUC values above 0.7, indicating high accuracy. The calibration curve and DCA further confirmed strong predictive performance and clinical practicability. In this study, we successfully constructed a machine learning model by combining XGBoost with Cox and generalized Cox regression models. This integrated approach accurately predicts the prognosis of children with DLBCL from multiple dimensions. These findings provide a scientific basis for accurate clinical prognosis prediction.

Establishment of a logistic regression model nomogram for clinicopathological characteristics and risk factors with axillary lymph node metastasis in T1 locally advanced breast cancer: a retrospective study.

Although the research reports on locally advanced breast cancer (LABC) are increasing year by year, there are few reports on T1 LABC axillary lymph node metastasis (ALNM). By establishing a prediction model for T1 LABC ALNM, this study provides a reference value for the probability of ALNM of related patients, which helps clinicians to develop a more effective and individualized treatment plan for LABC. Cases with pathologically confirmed T1 breast cancer (BC) between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were identified. Logistic regression was used to analyze the correlation between LABC lymph node metastasis and every factor, and the odds ratio (OR) and 95% confidence interval (CI) were used to identify any influencing factors. A nomogram was drawn after incorporating meaningful factors identified in multivariate logistic regression into the model. The receiver operating characteristic (ROC) curve of the model was drawn, and the area under the curve (AUC) and its 95% CI were calculated. Hosmer-Lemeshow goodness-of-fit test and clinical decision curve analysis (DCA) were performed. The results were validated in the validation group. A total of 200,933 female T1 BC patients were included in this study. Univariate and multivariate logistic regression analysis of T1 BC showed that progesterone receptor (PR)-negative, race, age, lobular carcinoma, micropapillary ductal carcinoma, axillary tail tumor, poor differentiation, and larger tumor diameter increased the probability of ALNM in T1 LABC. A predictive nomogram was established using the above predictors, the AUC of the modeling group was 0.739 (95% CI: 0.732-0.747), and when the AUC cut-off value was 0.026, the specificity and sensitivity of the model were 65.78% and 69.99%, respectively. Validation of the model showed that the AUC of the validation group (n=60,280) was 0.741. When all the risk factors were met, the predicted probability of N2-N3 was 50.40%. In this study, it was found that PR-negative, Black race, age, lobular carcinoma, micropapillary ductal carcinoma, axillary tail tumor, poor differentiation, and tumor diameter increased the probability of large lymph node metastasis in T1 LABC small tumors.

Prediction and analysis of albumin-bilirubin score combined with liver function index and carcinoembryonic antigen on liver metastasis of colorectal cancer.

Colorectal cancer (CRC) is a common malignant tumor, and liver metastasis is one of the main recurrence and metastasis modes that seriously affect patients' survival rate and quality of life. Indicators such as albumin bilirubin (ALBI) score, liver function index, and carcinoembryonic antigen (CEA) have shown some potential in the prediction of liver metastasis but have not been fully explored. To evaluate its predictive value for liver metastasis of CRC by conducting the combined analysis of ALBI, liver function index, and CEA, and to provide a more accurate liver metastasis risk assessment tool for clinical treatment guidance. This study retrospectively analyzed the clinical data of patients with CRC who received surgical treatment in our hospital from January 2018 to July 2023 and were followed up for 24 months. According to the follow-up results, the enrolled patients were divided into a liver metastasis group and a nonliver metastasis group and randomly divided into a modeling group and a verification group at a ratio of 2:1. The risk factors for liver metastasis in patients with CRC were analyzed, a prediction model was constructed by least absolute shrinkage and selection operator (LASSO) logistic regression, internal validation was performed by the bootstrap method, the reliability of the prediction model was evaluated by subject-work characteristic curves, calibration curves, and clinical decision curves, and a column graph was drawn to show the prediction results. Of 130 patients were enrolled in the modeling group and 65 patients were enrolled in the verification group out of the 195 patients with CRC who fulfilled the inclusion and exclusion criteria. Through LASSO regression variable screening and logistic regression analysis. The ALBI score, alanine aminotransferase (ALT), and CEA were found to be independent predictors of liver metastases in CRC patients [odds ratio (OR) = 8.062, 95% confidence interval (CI): 2.545-25.540], (OR = 1.037, 95%CI: 1.004-1.071) and (OR = 1.025, 95%CI: 1.008-1.043). The area under the receiver operating characteristic curve (AUC) for the combined prediction of CRLM in the modeling group was 0.921, with a sensitivity of 78.0% and a specificity of 95.0%. The H-index was 0.921, and the H-L fit curve had χ2 = 0.851, a P value of 0.654, and a slope of the calibration curve approaching 1. This indicates that the model is extremely accurate, and the clinical decision curve demonstrates that it can be applied effectively in the real world. We conducted internal verification of one thousand resamplings of the modeling group data using the bootstrap method. The AUC was 0.913, while the accuracy was 0.869 and the kappa consistency was 0.709. The combination prediction of liver metastasis in patients with CRC in the verification group had an AUC of 0.918, sensitivity of 85.0%, specificity of 95.6%, C-index of 0.918, and an H-L fitting curve with χ 2 = 0.586, P = 0.746. The ALBI score, ALT level, and CEA level have a certain value in predicting liver metastasis in patients with CRC. These three criteria exhibit a high level of efficacy in forecasting liver metastases in patients diagnosed with CRC. The risk prediction model developed in this work shows great potential for practical application.

Young muslim generation’s preferences for using digital platforms for Zakat payments: A cross-country study of Indonesia and Malaysia

The young Muslim generation’s embracing digital platforms for Zakat payments represents a dynamic fusion of enduring religious values with the modern digital landscape, heralding a new era in Islamic charitable practices. This trend illustrates a profound transformation within the Islamic world, where the pillars of faith are being reimagined and revitalized through the lens of technological advancement. The present study delved into the factors influencing the young Muslim generation’s preference for digital platforms in Zakat transactions across Indonesia and Malaysia. We examined variables such as Performance Expectancy, Effort Expectancy, Social Influence, Trust, Zakat Literacy, and Digital Infrastructure, aiming to discern their impact on the propensity for digital Zakat contributions with the extension of Unified Theory of Acceptance and Use of Technology (UTAUT) model. The research encompassed a diverse sample of 382 participants and utilized advanced methodologies, specifically Partial Least Squares Structural Equation Modeling (PLS-SEM) and PLS Multi Group Analysis (PLS-MGA), for rigorous data analysis. The results indicated that Effort Expectancy, Social Influence, Digital Infrastructure, and Zakat Literacy notably influenced the use of digital platforms for Zakat. Furthermore, PLS-MGA uncovered significant cross-country differences where Digital Infrastructure showed a more pronounced positive impact in Malaysian context, whereas Social Influence had a greater effect in Indonesia. These findings offer critical insights into the young Muslim community’s digital engagement for religious financial obligations, underscoring the need for tailored digital Zakat solutions that cater to the unique preferences of this demographic. This research not only enriches the understanding of digital adoption in religious practices but also challenges the notion of a universal approach, advocating for context-specific strategies in the realm of digital religious financial services. Future researchers are suggested to consider longitudinal investigations as well as examining cross-regional contexts in this realm of research.