Ideal Curve Research Articles

Comprehensive analysis of psychological symptoms and quality of life in early patients with IBD: a multicenter study from China.

To investigate the prevalence and risk factors of psychological symptoms and quality of life (QoL) in early patients with inflammatory bowel disease (IBD). From September 2021 to May 2022, a unified questionnaire was developed to collect clinical data from early patients with IBD from 42 tertiary care hospitals. The influencing factors of psychological symptoms and poor QoL are screened by logistic regression analysis for constructing model in predicting poor QoL. The consistency index, receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the model. A total of 939 early patients with IBD were surveyed, Among them, 20.3% exhibited anxiety, 21.7% had depression, 57.3% experienced sleep disturbance, and 41.9% reported poor QoL. The factors influencing psychological symptoms varied between ulcerative colitis (UC) and Crohn's disease (CD) patients. The QoL was primarily affected by disease activity, income level and depression. The AUC value of the model in the training group was 0.781 (95% CI: 0.748-0.814). The calibration diagram of the model closely matched the ideal curve. Compared to other prediction models, our model showed superior predictive capability, with NRI and IDI values of 0.324 (95%CI:0.196-0.4513) and 0.026 (95%CI:0.014-0.038), respectively. DCA indicated that the nomogram model could provide clinical benefits. Early patients with IBD exhibit a high prevalence of psychological symptoms and poor QoL. The nomogram prediction model we constructed demonstrates high accuracy and performance in predicting QoL in early patients with IBD.

Construction of a nomogram for personalized prediction of lower limb lymphedema risk after cervical cancer surgery

ObjectiveThis study aims to construct and evaluate an individualized nomogram for predicting the risk of lower limb lymphedema after cervical cancer surgery. Healthcare professionals can utilize line chart models to predict the probability of postoperative lower limb lymphedema in different patients, allowing for the early identification of high-risk patients and facilitating early prevention and treatment.MethodsA retrospective study was conducted among 411 cervical cancer patients treated at our hospital from May 2021 to December 2023. The patients were randomly divided into a modeling group (313 cases) and a validation group (98 cases) according to an approximate 3:1 ratio. The modeling group was further divided into a lower limb lymphedema group (61 cases) and a non-lower limb lymphedema group (252 cases) based on the presence of postoperative lower limb lymphedema. Multiple factors Logistic regression was used to identify risk factors, and a nomogram was constructed using R software version 4.0.2, with internal and external validation performed.ResultsRisk factors for lower limb lymphedema following cervical cancer surgery include age 60 years or older, a Body Mass Index (BMI) of 24 kg/m² or higher, hypertension, the removal of 30 or more lymph nodes, adjuvant radiotherapy and chemotherapy, and prolonged standing for six hours or more (P < 0.05). Internal and external validation results demonstrated that the calibration curve closely aligned with the ideal curve. The Area Under Curve(AUC) of the Receiver Operating Characteristic(ROC) curve was 0.890 (95% CI: 0.844 ∼ 0.936) and 0.876 (95% CI: 0.821 ∼ 0.930), indicating high model calibration and discrimination. Decision Curve Analysis(DCA) curve revealed that the Logistic model had good net returns and high clinical practicality when the probability range of the high-risk threshold was 0.11 ∼ 0.98.ConclusionThe nomogram, developed using factors such as age, BMI, hypertension, number of lymph nodes dissected, adjuvant radiotherapy and chemotherapy, and duration of standing, has strong predictive value and offers significant clinical benefits, making it a valuable tool for clinical decision-making.

Exploring Asymmetric Lens–Total Internal Reflection (AL–TIR) Optics for Uniform Ceiling Illumination in Interior Lighting

This study presents a significant advancement in LED interior lighting through the development and application of Asymmetric Lens–Total Internal Reflection (AL–TIR) optics, with a focus on enhancing lighting uniformity and indoor comfort by simulating sky-like lighting distribution. AL–TIR technology employs asymmetric lenses combined with total internal reflection to efficiently redirect and spread light, achieving a controlled and even ceiling illumination suitable for various interior applications. This research explored the establishment of ideal luminous intensity curves, devised practical AL–TIR optical designs through numerical calculations, and conducted extensive simulations to assess performance in typical indoor environments. Our findings demonstrated substantial improvements in lighting uniformity, with the AL and AL–TIR systems achieving direct illuminance uniformities of 0.78 and 0.83, respectively, compared to traditional tube LEDs at 0.25. These results, validated in several office rooms, highlight the efficacy of AL–TIR optics in revolutionizing indoor lighting design by balancing optimal lighting distribution with occupant comfort and well-being.

Development and Validation of a Novel Classification System and Prognostic Model for Open Traumatic Brain Injury: A Multicenter Retrospective Study.

Open traumatic brain injury (OTBI) is associated with high mortality and morbidity; however, the classification of these injuries and the determination of patient prognosis remain uncertain, hindering the selection of optimal treatment strategies. This study aimed to develop and validate a novel OTBI classification system and a prognostic model for poor prognosis. This retrospective study included patients with isolated OTBI who received treatment at three large medical centersin China between January 2020 and June 2022 as the training set. Data on patients with OTBI collected at the Fuzong Clinical Medical College of Fujian Medical University between July 2022 and June 2023 were used as the validation set. Clinical parameters, including clinical data at admission, radiological and laboratory findings, details of surgical methods, and prognosis were collected. Prognosis was assessed through a dichotomized Glasgow Outcome Scale (GOS). A novel OTBI classification was proposed, categorizing patients based on a combination of intracranial hematoma and midline shift observed on imaging, and logistic regression analyses were performed to identify risk factors associated with poor prognosis and to investigate the association between the novel OTBI classification and prognosis. Finally, a nomogram suitable for clinical application was established and validated. Multivariable logistic regression analysis identified OTBI classification type C (p<0.001), a Glasgow Coma Scale score (GCS)≤8 (p<0.001), subarachnoid hemorrhage (SAH) (p=0.004), subdural hematoma (SDH) (p=0.011), and coagulopathy (p=0.020) as independent risk factors for poor prognosis. The addition of the OTBI classification to a model containing all the other identified prognostic factors improved the predictive ability of the model (Z=1.983; p=0.047). In the validation set, the model achieved an area under the curve (AUC) of 0.917 [95% confidence interval (CI)=0.864-0.970]. The calibration curve closely approximated the ideal curve, indicating strong predictive performance of the model. The implementation of our proposed OTBI classification system and its use alongside the other prognostic factors identified here may improve the prediction of patient prognosis and aid in the selection of the most suitable treatment strategies.

Construction of a nomogram risk prediction model for depressive symptoms in elderly breast cancer patients

The primary objective of this study was to identify the factors associated with the development of depressive symptoms in elderly breast cancer (BC) patients and to construct a nomogram model for predicting these symptoms. We recruited 409 patients undergoing BC treatment in the breast departments of two tertiary-level hospitals in Jiangsu Province from November 2023 to April 2024 as our study cohort. Participants were categorized into depressed and non-depressed groups based on their clinical outcomes. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors for depression among BC patients. Multivariate analysis revealed that monthly income, pain score, family support score, and physical activity score significantly influenced the onset of depression in older BC patients (P < 0.05).The risk prediction model, constructed using these identified factors, demonstrated excellent discriminatory power, as evidenced by an area under the ROC curve (AUC) of 0.824. The maximum Youden index was 0.627, with a sensitivity of 90.60%, specificity of 72.10%, and a diagnostic threshold value of 1.501. The results of the Hosmer-Lemeshow goodness-of-fit test (χ² = 3.181, P = 0.923) indicated that the model fit the data well. The calibration curve for the model closely followed the ideal curve, suggesting a strong fit and high predictive accuracy. Our nomogram model exhibited superior predictive performance, enabling healthcare professionals to identify high-risk patients early and implement preventative measures to mitigate the development of depressive symptoms. This study is a cross-sectional study that lacks longitudinal data and has a small sample size. Future research could involve larger samples, multicenter studies, and prospective designs to build better clinical predictive models.

Electronic Barriers Behavioral Analysis of a Schottky Diode Structure Featuring Two-Dimensional MoS2

This research presents a comprehensive study of a Schottky diode fabricated using a gold wafer and a bilayer molybdenum disulfide (MoS2) film. Through detailed simulations, we investigated the electric field distribution, potential profile, carrier concentration, and current–voltage characteristics of the device. Our findings confirm the successful formation of a Schottky barrier at the Au/MoS2 interface, characterized by a distinct nonlinear I–V relationship. Comparative analysis revealed that the Au/MoS2 diode significantly outperforms a traditional W/Si structure in terms of rectification performance. The Au/MoS2 diode exhibited a current density of 1.84 × 10−9 A/cm2, substantially lower than the 3.62 × 10−5 A/cm2 in the W/Si diode. Furthermore, the simulated I–V curves of the Au/MoS2 diode closely resembled the ideal diode curve, with a Pearson correlation coefficient of approximately 0.9991, indicating an ideality factor near 1. A key factor contributing to the superior rectification performance of the Au/MoS2 diode is its higher Schottky barrier height of 0.9 eV compared to the 0.67 eV of W/Si. This increased barrier height is evident in the band diagram analysis, which further elucidates the underlying physics of Schottky barrier formation in the Au/MoS2 junction. This research provides insights into the electronic properties of Schottky contacts based on two-dimensional MoS2, particularly the relationship between electronic barriers, system dimensions, and current flow. The demonstration of high-ideality-factor Au/MoS2 diodes contributes to the design and optimization of future electronic and optoelectronic devices based on 2D materials. These findings have implications for advancements in semiconductor technology, potentially enabling the development of smaller, more efficient, and flexible devices.

Development and validation of a dynamic nomogram for high care dependency during the hospital-family transition periods in older stroke patients

BackgroundThis research aimed to develop and validate a dynamic nomogram for predicting the risk of high care dependency during the hospital-family transition periods in older stroke patients.Methods309 older stroke patients in the hospital-family transition periods who were treated in the Department of Neurology outpatient clinics of three general hospitals in Jinzhou, Liaoning Province from June to December 2023 were selected as the training set. The patients were investigated with the General Patient Information Questionnaire, the Care Dependency Scale (CDS), the Tilburg Frailty Inventory (TFI), the Hamilton Anxiety Rating Scale (HAMA), the Hamilton Depression Rating Scale-17 (HAMD-17), and the Mini Nutrition Assessment Short Form (MNA-SF). Lasso-logistic regression analysis was used to screen the risk factors for high care dependency in older stroke patients during the hospital-family transition period, and a dynamic nomogram model was constructed. The model was uploaded in the form of a web page based on Shiny apps. The Bootstrap method was employed to repeat the process 1000 times for internal validation. The model’s predictive efficacy was assessed using the calibration plot, decision curve analysis curve (DCA), and area under the curve (AUC) of the receiver operator characteristic (ROC) curve. A total of 133 older stroke patients during the hospital-family transition periods who visited the outpatient department of Neurology of three general hospitals in Jinzhou from January to March 2024 were selected as the validation set for external validation of the model.ResultsBased on the history of stroke, chronic disease, falls in the past 6 months, depression, malnutrition, and frailty, build a dynamic nomogram. The AUC under the ROC curves of the training set was 0.830 (95% CI: 0.784–0.875), and that of the validation set was 0.833 (95% CI: 0.766-0.900). The calibration curve was close to the ideal curve, and DCA results confirmed that the nomogram performed well in terms of clinical applicability.ConclusionThe online dynamic nomogram constructed in this study has good specificity, sensitivity, and clinical practicability, which can be applied to senior stroke patients as a prediction and assessment tool for high care dependency. It is of great significance to guide the development of early intervention strategies, optimize resource allocation, and reduce the care burden on families and society.

Development of a prediction model for the incidence of type 2 diabetic kidney disease and its application based on a regional health data platform

Objective: To construct a risk prediction model for diabetes kidney disease (DKD). Methods: Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation. Results: The study included 49 706 subjects, with an median (Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions: This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.

Automatic Landing Control for Fixed-Wing UAV in Longitudinal Channel Based on Deep Reinforcement Learning

The objective is to address the control problem associated with the landing process of unmanned aerial vehicles (UAVs), with a particular focus on fixed-wing UAVs. The Proportional–Integral–Derivative (PID) controller is a widely used control method, which requires the tuning of its parameters to account for the specific characteristics of the landing environment and the potential for external disturbances. In contrast, neural networks can be modeled to operate under given inputs, allowing for a more precise control strategy. In light of these considerations, a control system based on reinforcement learning is put forth, which is integrated with the conventional PID guidance law to facilitate the autonomous landing of fixed-wing UAVs and the automated tuning of PID parameters through the use of a Deep Q-learning Network (DQN). A traditional PID control system is constructed based on a fixed-wing UAV dynamics model, with the flight state being discretized. The landing problem is transformed into a Markov Decision Process (MDP), and the reward function is designed in accordance with the landing conditions and the UAV’s attitude, respectively. The state vectors are fed into the neural network framework, and the optimized PID parameters are output by the reinforcement learning algorithm. The optimal policy is obtained through the training of the network, which enables the automatic adjustment of parameters and the optimization of the traditional PID control system. Furthermore, the efficacy of the control algorithms in actual scenarios is validated through the simulation of UAV state vector perturbations and ideal gliding curves. The results demonstrate that the controller modified by the DQN network exhibits a markedly superior convergence effect and maneuverability compared to the unmodified traditional controller.

Predictive model for acute radiation esophagitis in esophageal carcinoma based on prognostic nutritional index and systemic inflammatory index and its application.

Acute radiation esophagitis (ARE) is a common complication in patients with esophageal cancer undergoing radiotherapy. Therefore, it is important to construct an effective ARE risk-prediction model for clinical treatment. The present study performed a retrospective analysis of 225 patients with esophageal cancer who received radiotherapy at the First Affiliated Hospital of Anhui Medical University (Hefei, China) from January 2018 to December 2022. Univariate and logistic regression analyses were performed to screen patients with esophageal cancer after radiotherapy. The results revealed that 147 patients developed radiation esophagitis. Logistic regression analysis results demonstrated that the prognostic nutritional index [odds ratio (OR), 0.864; 95% confidence interval (CI), 0.809-0.924], neutrophil to lymphocyte ratio (OR, 1.795; 95% CI, 1.209-2.667) and platelet to lymphocyte ratio (OR, 1.011; 95% CI, 1.000-1.022) were independent predictors of ARE in patients receiving intensity-modulated conformal radiotherapy for esophagus cancer (P<0.05). A nomogram model for predicting the occurrence of ARE was established based on the three risk factors. The decision curve suggested a high net benefit value when the threshold probability was within 0.25-1.0. External verification confirmed the reproducibility and generalizability of the nomogram model. In general, the calibration curve of this model was close to the ideal curve and had excellent prediction accuracy. Therefore, it may be used as a new tool for early prediction of the ARE risk.

The value evaluation of Nomogram prediction model based on CTA imaging features for selecting treatment methods for isolated superior mesenteric artery dissection

ObjectiveTo evaluate value of Nomogram prediction model based on CTA imaging features for selecting treatment methods for isolated superior mesenteric artery dissection (ISMAD).MethodsSymptomatic ISMAD patients were randomly divided into a training set and a validation set in a 7:3 ratio. In the training set, relevant risk factors for conservative treatment failure in ISMAD patients were analyzed, and a Nomogram prediction model for treatment outcome of ISMAD was constructed with risk factors. The predictive value of the model was evaluated.ResultsLow true lumen residual ratio (TLRR), long dissection length, and large arterial angle (superior mesenteric artery [SMA]/abdominal aorta [AA]) were identified as independent high-risk factors for conservative treatment failure (P < 0.05). The receiver operating characteristic curve (ROC) results showed that the area under curve (AUC) of Nomogram prediction model was 0.826 (95% CI: 0.740–0.912), indicating good discrimination. The Hosmer-Lemeshow goodness-of-fit test showed good consistency between the predicted curve and the ideal curve of the Nomogram prediction model. The decision curve analysis (DCA) analysis results showed that when probability threshold for the occurrence of conservative treatment failure predicted was 0.05–0.98, patients could obtain more net benefits. Similar results were obtained for the predictive value in the validation set.ConclusionLow TLRR, long dissection length, and large arterial angle (SMA/AA) are independent high-risk factors for conservative treatment failure in ISMAD. The Nomogram model constructed with independent high-risk factors has good clinical effectiveness in predicting the failure.

Construction and validation of a nomogram prediction model for the catheter-related thrombosis risk of central venous access devices in patients with cancer: a prospective machine learning study.

Central venous access devices (CVADs) are integral to cancer treatment. However, catheter-related thrombosis (CRT) poses a considerable risk to patient safety. It interrupts treatment; delays therapy; prolongs hospitalisation; and increases the physical, psychological and financial burden of patients. Our study aims to construct and validate a predictive model for CRT risk in patients with cancer. It offers the possibility to identify independent risk factors for CRT and prevent CRT in patients with cancer. We prospectively followed patients with cancer and CVAD at Xiangya Hospital of Central South University from January 2021 to December 2022 until catheter removal. Patients with CRT who met the criteria were taken as the case group. Two patients with cancer but without CRT diagnosed in the same month that a patient with cancer and CRT was diagnosed were selected by using a random number table to form a control group. Data from patients with CVAD placement in Qinghai University Affiliated Hospital and Hainan Provincial People's Hospital (January 2023 to June 2023) were used for the external validation of the optimal model. The incidence rate of CRT in patients with cancer was 5.02% (539/10 736). Amongst different malignant tumour types, head and neck (9.66%), haematological (6.97%) and respiratory (6.58%) tumours had the highest risks. Amongst catheter types, haemodialysis (13.91%), central venous (8.39%) and peripherally inserted central (4.68%) catheters were associated with the highest risks. A total of 500 patients with CRT and 1000 without CRT participated in model construction and were randomly assigned to the training (n = 1050) or testing (n = 450) groups. We identified 11 independent risk factors, including age, catheterisation method, catheter valve, catheter material, infection, insertion history, D-dimer concentration, operation history, anaemia, diabetes and targeted drugs. The logistic regression model had the best discriminative ability amongst the three models. It had an area under the curve (AUC) of 0.868 (0.846-0.890) for the training group. The external validation AUC was 0.708 (0.618-0.797). The calibration curve of the nomogram model was consistent with the ideal curve. Moreover, the Hosmer-Lemeshow test showed a good fit (P > 0.05) and high net benefit value for the clinical decision curve. The nomogram model constructed in this study can predict the risk of CRT in patients with cancer. It can help in the early identification and screening of patients at high risk of cancer CRT.

Machine learning derived model for the prediction of bleeding in dual antiplatelet therapy patients.

This study aimed to develop a predictive model for assessing bleeding risk in dual antiplatelet therapy (DAPT) patients. A total of 18,408 DAPT patients were included. Data on patients' demographics, clinical features, underlying diseases, past history, and laboratory examinations were collected from Affiliated Dongyang Hospital of Wenzhou Medical University. The patients were randomly divided into two groups in a proportion of 7:3, with the most used for model development and the remaining for internal validation. LASSO regression, multivariate logistic regression, and six machine learning models, including random forest (RF), k-nearest neighbor imputing (KNN), decision tree (DT), extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and Support Vector Machine (SVM), were used to develop prediction models. Model prediction performance was evaluated using area under the curve (AUC), calibration curves, decision curve analysis (DCA), clinical impact curve (CIC), and net reduction curve (NRC). The XGBoost model demonstrated the highest AUC. The model features were comprised of seven clinical variables, including: HGB, PLT, previous bleeding, cerebral infarction, sex, Surgical history, and hypertension. A nomogram was developed based on seven variables. The AUC of the model was 0.861 (95% CI 0.847-0.875) in the development cohort and 0.877 (95% CI 0.856-0.898) in the validation cohort, indicating that the model had good differential performance. The results of calibration curve analysis showed that the calibration curve of this nomogram model was close to the ideal curve. The clinical decision curve also showed good clinical net benefit of the nomogram model. This study successfully developed a predictive model for estimating bleeding risk in DAPT patients. It has the potential to optimize treatment planning, improve patient outcomes, and enhance resource utilization.

A nomogram prediction model for individualized prediction of the risk of covert (minimal) hepatic encephalopathy occurrence in patients with liver cirrhosis

Objective: To construct an individualized nomogram prediction model for predicting the risk of the occurrence of covert hepatic encephalopathy (CHE) in patients with liver cirrhosis. Methods: 325 cases of liver cirrhosis admitted from January 2020 to December 2022 were selected as the study subjects. Patients were divided into training (n=213) and validation (n=112) sets using a cluster randomization method. The risk factors for CHE occurrence in patients with cirrhosis in the training set were analyzed by univariate and multivariate logistic regression. A prediction model related to the nomogram was established. Results: Independent risk factors for the occurrence of CHE in patients with cirrhosis were a history of hepatic encephalopathy, co-infection, gastrointestinal bleeding, severe ascites, prothrombin time ≥16 seconds, high total bilirubin, and high blood ammonia levels (P<0.05). Nomogram model validation results: The model had a net benefit for the training and validation sets, with C-indices of 0.830 (95%CI: 0.802-0.858) and 0.807 (95%CI: 0.877-0.837), respectively, within the range of 0-96%. The calibration curves of both sets were evenly close to the ideal curves. The AUCs for the ROC curves in both sets were 0.827 (95%CI: 0.796-0.858) and 0.811 (95%CI: 0.787-0.836), respectively. Conclusion: Patients with cirrhosis have many risk factors for CHE occurrence. The nomogram model constructed based on these risk factors possesses a good predictive value for assessing CHE occurrence in cirrhotic patients.

The potential of a nomogram risk assessment model for the diagnosis of abdominal aortic aneurysm: a multicenter retrospective study

The incidence of abdominal aortic aneurysm (AAA) is very high, but there is no risk assessment model for early identification of AAA in clinic. The aim of this study was to develop a nomogram risk assessment model for predicting AAA. The data of 280 patients diagnosed as AAA and 385 controls in The Affiliated Suzhou Hospital of Nanjing Medical University were retrospectively reviewed. The LASSO regression method was applied to filter variables, and multivariate logistic regression was used to construct a nomogram. The discriminatory ability of the model was determined by calculating the area under the curve (AUC). The calibration capability of the model is evaluated by using bootstrap (resampling = 1000) internal validation and Hosmer–Lemeshow test. The clinical utility and clinical application value were evaluated by decision curve analysis (DCA) and clinical impact curve (CIC). In addition, a retrospective review of 133 AAA patients and 262 controls from The First Affiliated Hospital of Soochow University was performed as an external validation cohort. Eight variables are selected to construct the nomogram of AAA risk assessment model. The nomogram predicted AAA with AUC values of 0.928 (95%CI, 0.907–0.950) in the training cohort, and 0.902 (95%CI, 0.865–0.940) in the external validation cohort, the risk prediction model has excellent discriminative ability. The calibration curve and Hosmer–Lemeshow test proved that the nomogram predicted outcomes were close to the ideal curve, the predicted outcomes were consistent with the real outcomes, the DCA curve and CIC curve showed that patients could benefit. This finding was also confirmed in the external validation cohort. In this study, a nomogram was constructed that incorporated eight demographic and clinical characteristics of AAA patients, which can be used as a practical approach for the personalized early screening and auxiliary diagnosis of the potential risk factors.

Accuracy of goodness-of-fit criteria for nonlinear regression: a study via Monte Carlo simulation

Nonlinear regression models are widespread in the literature, and one of their main applications is in the study of sigmoidal growth curves. There are several models for this purpose, and the most used are the logistic, Gompertz, von Bertalanffy, and Brody models. Each one of them has its own characteristics and is more suitable for a given curve shape. There are several criteria in the literature for selecting the most appropriate model, but there is no consensus on the best criteria. Thus, the present study aims to evaluate the accuracy of the main selection criteria via Monte Carlo simulation, considering the logistic, Gompertz, von Bertalanffy, and Brody nonlinear regression models. Four simulation scenarios are used, each simulated with ideal curves of the logistic, Gompertz, von Bertalanffy and Brody models. Next, the 4 models are adjusted for each of the scenarios, and the main quality criteria found in the literature are calculated to assess the ability of the criteria to identify the most appropriate model for each scenario. The results show that the criteria asymptotic index, mean absolute error and coefficient of determination choose the correct model more often than the other criteria studied. Although the measures of the Battes and Watts curvature and box bias are important for the evaluation of the goodness-of-fit of the models, they are not indicated for the selection of the best model.

The Influence of 3D Printing Methods and Materials on the Response of Printed Symmetric Carbon Supercapacitors

We compare the electrochemical response and intrinsic limitations of symmetric carbon-based supercapacitors using two 3D-printing techniques, vat polymerization (Vat-P) and fused deposition modelling (FDM). Two cell types were made in this study, one with metallized Vat-P-printed current collectors, the other with PLA (polylactic acid) FDM-printed current collectors in a similarly designed printed coin cell. Carbon-based electrode slurry (various combinations of SWCNT, GNP, Super-P, PVDF) and aqueous 6 M KOH electrolyte were used in these cells. We demonstrate the influence of internal resistance of each 3D-printing method by direct comparison of cyclic voltammetry and galvanostatic charge-discharge tests. The metallized conductive Vat-P cells display better conductivity and more ideal rectangular cyclic voltammetry response but suffer from poor cycle life in initial experiments (∼5,000 charge-discharge cycles before losing all specific capacitance). The FDM current collector cells using graphite-containing PLA materials have poorer conductivity, less ideal cyclic voltammetry curves, and are structurally less robust and partially porous, but offer very stable cycle life for supercapacitor cells retaining most of their specific capacitance after 100,000 charge-discharge cycles. The cycle life of the metallized Vat-P cells are improved by reducing the voltage window to 0.2–0.7 V to limit metal delamination and using Super-P and PVDF additives.

Establishment of a risk prediction model for cerebrogenic multiple organ dysfunction syndrome in patients with acute cerebral hemorrhage

To construct and validate a predictive model for the risk of cerebrogenic multiple organ dysfunction syndrome (CMODS) in patients with acute cerebral hemorrhage. Clinical data of 93 patients with acute cerebral hemorrhage admitted to Wannan Rehabilitation Hospital from January 2019 to June 2023 were retrospectively analyzed. Data included baseline information, disease severity score, laboratory examination indicators, cerebral hemorrhage status, treatment status, etc. Patients were divided into CMODS group and non-CMODS group according to whether CMODS occurred during hospitalization. The clinical data of the two groups were compared. Multivariate Logistic regression was used to analyze the risk factors of CMODS in patients with acute cerebral hemorrhage. A nomogram model was constructed to predict the risk of CMODS in patients with acute cerebral hemorrhage, and the model was validated. Receiver operator characteristic curve (ROC curve) was used to evaluate the predictive efficiency of nomogram model for CMODS in patients with acute cerebral hemorrhage. A total of 93 patients with acute cerebral hemorrhage were enrolled, including 26 patients in CMODS group and 67 patients in non-CMODS group. Compared with the non-CMODS group, the ratio of diabetes, acute physiological and chronic health evaluation II (APACHE II)≥35 score, cerebral hemorrhage volume ≥30 mL, endotoxemia, and national institutes of health stroke scale (NIHSS) and intracranial pressure of patients in the CMODS group were significantly higher, while the Glasgow coma score (GCS) was significantly lower and the length of hospital stay was significantly longer, with statistically significant differences (all P < 0.05). Multivariate Logistic regression analysis showed that diabetes mellitus [odds ratio (OR) = 3.615, 95% confidence interval (95%CI) was 1.487-8.785, P = 0.000], APACHE II score (OR = 4.697, 95%CI was 1.933-11.416, P = 0.000), endotoxemia (OR = 4.577, 95%CI was 1.883-11.123, P = 0.000), and cerebral hemorrhage volume (OR = 4.039, 95%CI was 1.662-9.816, P = 0.000) were the risk factors for CMODS in patients with acute cerebral hemorrhage. Taking the above risk factors as predictive variables, a nomogram prediction model was established. The verification results of the nomogram model showed that the C index was 0.804 (95%CI was 0.768-0.841), and the calibration curve was close to the ideal curve with good fit (P > 0.05). ROC curve results showed that the sensitivity and specificity of the nomogram model in predicting CMODS in patients with acute cerebral hemorrhage were 76.92%, 86.57%, respectively, and the area under the ROC curve (AUC) was 0.855 (95%CI was 0.776-0.935). Diabetes mellitus, APACHE II score, endotoxemia and intracerebral hemorrhage are risk factors for CMODS in patients with acute cerebral hemorrhage. The risk prediction model based on these risk factors is effective in evaluating the risk of CMODS in patients with acute cerebral hemorrhage.

Machine learning-based predictive model for the development of thrombolysis resistance in patients with acute ischemic stroke

BackgroundThe objective of this study was to establish a predictive model utilizing machine learning techniques to anticipate the likelihood of thrombolysis resistance (TR) in acute ischaemic stroke (AIS) patients undergoing recombinant tissue plasminogen activator (rt-PA) intravenous thrombolysis, given that nearly half of such patients exhibit poor clinical outcomes.MethodsRetrospective clinical data were collected from AIS patients who underwent intravenous thrombolysis with rt-PA at the First Affiliated Hospital of Bengbu Medical University. Thrombolysis resistance was defined as ([National Institutes of Health Stroke Scale (NIHSS) at admission − 24-hour NIHSS] × 100％/ NIHSS at admission) ≤ 30%. In this study, we developed five machine learning models: logistic regression (LR), extreme gradient boosting (XGBoost), support vector machine (SVM), the least absolute shrinkage and selection operator (LASSO), and random forest (RF). We assessed the model’s performance by using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), and presented the results through a nomogram.ResultsThis study included a total of 218 patients with AIS who were treated with intravenous thrombolysis, 88 patients experienced TR. Among the five machine learning models, the LASSO model performed the best. The area under the curve (AUC) on the testing group was 0.765 (sensitivity: 0.767, specificity: 0.694, accuracy: 0.727). The apparent curve in the calibration curve was similar to the ideal curve, and DCA showed a positive net benefit. Key features associated with TR included NIHSS at admission, blood glucose, white blood cell count, neutrophil count, and blood urea nitrogen.ConclusionMachine learning methods with multiple clinical variables can help in early screening of patients at high risk of thrombolysis resistance, particularly in contexts where healthcare resources are limited.

Single-center external validation and reconstruction of multiple predictive models for skip lateral lymph node metastasis in papillary thyroid carcinoma.

The unique metastatic pattern of skip lateral lymph node metastasis (SLLNM) in PTC patients may lead to missed diagnosis of lateral cervical metastatic lymph nodes. Therefore, many different SLLNM prediction models were constructed. In this study, partially eligible models (Hu 2020, Wang 2020, and Zhao 2023 nomograms) were selected for external validation, and then new variables were incorporated for model reconstruction to extend clinical applicability. 576 PTC patients from our center were selected to evaluate the performance of the three nomograms using the receiver operating characteristic curve (ROC), calibration curves, and decision curve analyses (DCA). Three new variables were added to calibrate the model, including assessment of LN status on ultrasound (US-SLLNM), the distance from the tumor to the capsule (Capsular distance), and the number of central lymph node dissections (CLND number). Univariate and multivariate logistic regression analyses were used to screen independent predictors to reconstruct the model, and 1000 Bootstrap internal validations were performed. SLLNM were present in 69/576 patients (12.0%). In external validation, the area under the ROC curves (AUCs) for Hu 2020, Wang 2020, and Zhao 2023 nomograms were 0.695 (95% CI:0.633-0.766), 0.792 (95% CI=0.73-0.845), and 0.769 (95% CI:0.713-0.824), respectively. The calibration curves for the three models were overall poorly fitted; DCA showed some net clinical benefit. Model differentiation and net clinical benefit improved by adding three new variables. Based on multivariate analysis, female, age, and maximum tumor diameter ≤ 10 mm, located at the upper pole, Capsular distance < 0mm, US-SLLNM, CLND number ≤ 5 were identified as independent predictors of SLLNM and were used to construct the new model. After 1000 Bootstrap internal validations, the mean AUC of the model was 0.870 (95% CI:0.839-0.901), the calibration curve was close to the ideal curve, and the net clinical benefit was significant. Overall, these nomograms were well differentiated and provided some net clinical benefit, but with varying degrees of underestimation or overestimation of the actual risk and high false-negative rates. New dynamic nomogram was constructed based on the addition of new variables and larger samples, showing better performance.