Multifunctional bioactive biomaterials with integrated bone and soft tissue regenerability hold great promise for the regeneration of trauma-affected skin and bone defects. The aim of this research was to fabricate aerogel scaffolds (GD-BF) by blending the appropriate proportions of short bioactive glass fiber (BGF), gelatin (Gel), and dopamine (DA). Electrospun polyvinyl pyrrolidone (PVP)-BGF fibers were converted into short BGF through calcination and homogenization. Microporous GD-BF scaffolds displayed good elastic deformation recovery and promoted neo-tissue formation. The DA could enable thermal crosslinking and enhance the mechanical properties and structural stability of the GD-BF scaffolds. The BGF-mediated release of therapeutic ions shorten hemostatic time (<30 s) in a rat tail amputation model and a rabbit artery injury model alongside inducing the regeneration of skin appendages (e.g., blood vessels, glands, etc.) in a full-thickness excisional defect model in rats (percentage wound closure: GD-BF2, 98 % vs. control group, 83 %) at day 14 in vitro. Taken together, these aerogel scaffolds may have significant promise for soft and hard tissue repair, which may also be worthy for the other related disciplines.
OBJECTIVE: To explore the influencing factors of urinary tract infection (UTI) in hospitalized patients with spinal cord injury and to construct and verify the nomogram prediction model. METHODS: This study is a retrospective cohort study. From January 2017 to March 2022, 558 patients with spinal cord injury admitted to the Department of Rehabilitation Medicine of a tertiary hospital in Anhui Province, China, were selected as the research objects, and they were randomly divided into training group (n = 390) and verification group (n = 168) according to the ratio of 7:3, and clinical data including socio-demographic characteristics, disease-related data, and laboratory examination data were collected. Univariate analysis and multivariate logistic regression were used to analyze the influencing factors of UTI in hospitalized patients with spinal cord injuries. Based on this, a nomogram prediction model was constructed with the use of R software, and the risk prediction efficiency of the nomogram model was verified by the receiver operating characteristic curve and calibration curve. RESULTS: Logistic regression analysis showed that the American Spinal Cord Injury Association (ASIA)-E grade (compared with ASIA-A grade) was an independent protective factor for UTI in hospitalized patients with spinal cord injury (odds ratio < 1, P < 0.05), while white blood cell count and indwelling catheter were independent risk factors for UTI in hospitalized patients with spinal cord injury (odds ratio > 1, P < 0.05). Based on this, a nomogram risk predictive model for predicting UTI in hospitalized rehabilitation patients with spinal cord injury was constructed, which proved to have good predictive efficiency. In the training group and the verification group, the area under the receiver operating characteristic curve of the nomogram model is 0.808 and 0.767, and the 95% confidence interval of the area under the receiver operating characteristic curve of the nomogram in the training group and the verification group is 0.760â¼0.856 and 0.688â¼0.845, respectively, indicating the nomogram model has good discrimination. According to the calibration curve, the prediction probability of the nomogram model and the actual frequency of UTI in the training group and the verification group are in good consistency, and the results of the Hosmer-Lemeshow bias test also suggest that the nomogram model has a good calibration degree in both the training group and the verification group (P = 0.329, 0.067). CONCLUSIONS: ASIA classification level, white blood cell count, and indwelling catheter are independent influencing factors of UTI in hospitalized patients with spinal cord injury. The nomogram prediction model based on the above factors can simply and effectively predict the risk of UTI in hospitalized patients with spinal cord injury, which is helpful for clinical medical staff to identify high-risk groups early and implement prevention, treatment, and nursing strategies in time.
OBJECTIVE: To determine the risk factors affecting the severity of coronary artery disease (CAD) in older postmenopausal women with coronary heart disease (CHD) and to construct a personalized risk predictive model. METHODS: In this cohort study, clinical records of 527 female patients aged ≥60 with CHD who were hospitalized in the First Affiliated Hospital of the University of Science and Technology of China from March 2018 to February 2019 were analyzed retrospectively. The severity of CAD was determined using the Gensini scores that are based on coronary angiography findings. Patients with Gensini scores ≥40 and <40 were divided into high-risk (n=277) and non-high-risk groups (n=250), respectively. Logistic regression analysis was used to assess independent predictors of CAD severity. The nomogram prediction model of CAD severity was plotted by the R software. The area under the receiver operating characteristic (ROC) and calibration curves were used to evaluate the predictive efficiency of the nomogram model, and the decision curve analysis (DCA) was used to assess the clinical applicability of the nomogram model. RESULTS: Multivariate analysis showed that high-sensitivity C-reactive protein, RBC count, WBC count, BMI, and diabetes mellitus were independent risk factors associated with CAD severity in older menopausal women (P<0.05); the area under the ROC curve of the nomogram constructed based on the independent risk factors was 0.846 (95% CI: 0.756-0.937). The area under the ROC curve after internal validation of the nomogram by the Bootstrap method after resampling 1000 times was 0.840 (95% CI: 0.741-0.923). The calibration curve suggested that the nomogram had an excellent predictive agreement, and the DCA curve indicated that the net benefit of applying the nomogram was significantly higher than that of the "no intervention" and "all intervention" methods when the risk probability of patients with high-risk CAD severity was 0.30-0.81. CONCLUSION: A personalized risk assessment model was constructed based on the risk factors of severe CAD in older menopausal women with CHD, which had good prediction efficiency based on discrimination, calibration, and clinical applicability evaluation indicators. This model could assist cardiology medical staff in screening older menopausal women with CHD who are at a high risk of severe CAD to implement targeted interventions.
BACKGROUND: Acute myocardial infarction (AMI) is a severe cardiovascular disease caused by the blockage of coronary arteries that leads to ischemic necrosis of the myocardium. Timely medical contact is critical for successful AMI treatment, and delays increase the risk of death for patients. Pre-hospital delay time (PDT) is a significant challenge for reducing treatment times, as identifying high-risk patients with AMI remains difficult. This study aims to construct a risk prediction model to identify high-risk patients and develop targeted strategies for effective and prompt care, ultimately reducing PDT and improving treatment outcomes. AIM: To construct a nomogram model for forecasting pre-hospital delay (PHD) likelihood in patients with AMI and to assess the precision of the nomogram model in predicting PHD risk. METHODS: A retrospective cohort design was employed to investigate predictive factors for PHD in patients with AMI diagnosed between January 2022 and September 2022. The study included 252 patients, with 180 randomly assigned to the development group and the remaining 72 to the validation group in a 7:3 ratio. Independent risk factors influencing PHD were identified in the development group, leading to the establishment of a nomogram model for predicting PHD in patients with AMI. The model's predictive performance was evaluated using the receiver operating characteristic curve in both the development and validation groups. RESULTS: Independent risk factors for PHD in patients with AMI included living alone, hyperlipidemia, age, diabetes mellitus, and digestive system diseases (P < 0.05). A nomogram model incorporating these five predictors accurately predicted PHD occurrence. The receiver operating characteristic curve analysis indicated area under the receiver operating characteristic curve values of 0.787 (95% confidence interval: 0.716-0.858) and 0.770 (95% confidence interval: 0.660-0.879) in the development and validation groups, respectively, demonstrating the model's good discriminatory ability. The Hosmer-Lemeshow goodness-of-fit test revealed no statistically significant disparity between the anticipated and observed incidence of PHD in both development and validation cohorts (P > 0.05), indicating satisfactory model calibration. CONCLUSION: The nomogram model, developed with independent risk factors, accurately forecasts PHD likelihood in AMI individuals, enabling efficient identification of PHD risk in these patients.
Purpose: To investigate the predictive factors of pathologic complete response (pCR) in locally advanced rectal cancer (LARC) patients who had been treated with neoadjuvant chemoradiation (nCRT). Methods and materials: For this retrospective study, 53 LARC patients (37 males and 16 females; age range 25 to 79 years) were selected. Clinical characteristics, baseline mrTNM staging, MR gross tumor volumes (GTV), and pCR were evaluated. The diagnostic accuracy of GTV for predicting pCR was calculated. Results: Among 53 LARC patients, 15 patients achieved pCR (28.3%), while 38 patients achieved non-pCR. Only three (5.7%) out of 53 patients did not downstage after nCRT. GTV and tumor differentiation were the significant prognostic parameters for predicting pCR. A tumor volume threshold of 21.1 cm3 was determined as a predictor for pCR, with a sensitivity of 84% and specificity of 47%. In addition, GTV was associated with mrN stage, circumferential resection margin (CRM) status, extramural vascular invasion (EMVI) status, and pretreatment serum CEA level. Conclusion: Tumor volume and tumor differentiation have significant predictive values in preoperative assessment of pCR among LARC patients. These findings aid clinicians to discriminate those patients who may likely benefit from preoperative regimens and to make optimal treatment plans.
The management of critical-sized bone defects presents a formidable clinical challenge, especially given the increasing incidence of bone diseases in the aging population. Consequently, there is an increased demand for minimally invasive bone repair materials that can effectively address this challenge, particularly in outpatient settings. In this study, the goal is to develop an injectable and biodegradable biomaterial that adheres to and fills bone-defect sites to support bone regeneration. The osteogenic and angiogenic activities of animal horn peptides are investigated by incorporating them into biologically active moieties, in combination with a novel thermosensitive hydrogel. The resulting thermosensitive hydrogel exhibited essential biological functionalities, allowing precise modulation of its physical and chemical properties. Notably, the hydrogel incorporating the horn peptide rapidly filled the bone defect site, promoting both angiogenesis and bone induction. Consequently, this approach significantly accelerates new bone regeneration. In summary, the findings of this study present a promising, minimally invasive solution for addressing critical-sized bone defects.
OBJECTIVE: This study aimed to create a predictive model for hyperuricemia (HUA) in patients diagnosed with hypertension and evaluate its predictive accuracy. METHODS: Employing a retrospective cohort design, this study investigated HUA incidence and clinical data among 228 patients with essential hypertension selected from the Department of Cardiology at a tertiary A-level hospital in Anhui Province, China, between January 2018 and June 2021. The patients were divided randomly into a training group (168 cases) and a validation group (60 cases) at a 7:3 ratio. The training group underwent univariate and multivariate logistic regression analyses to identify risk factors for HUA. Additionally, an R software-generated nomogram model estimated HUA risk in hypertensive patients. The validation group assessed the nomogram model's discriminatory power and calibration using receiver operating characteristic curve analysis and the Hosmer-Lemeshow goodness-of-fit test. RESULTS: The study found a 29.39% prevalence of HUA among the 228 participants. Logistic regression analyses identified age, body mass index, and concomitant coronary heart disease as independent HUA risk factors (odds ratio [OR] > 1 and P < 0.05). Conversely, high-density lipoprotein cholesterol emerged as an independent protective factor against HUA in hypertensive patients (OR < 1 and P < 0.05). Using these factors, a nomogram model was constructed to assess HUA risk, with an AUC of 0.873 (95% confidence interval [CI]: 0.818-0.928) in the training group and 0.841 (95% CI: 0.735-0.946) in the validation group, indicating a strong discriminatory ability. The Hosmer-Lemeshow goodness-of-fit test showed no significant deviation between predicted and actual HUA frequency in both groups (χ2 = 5.980, 9.780, P = 0.649, 0.281), supporting the nomogram's reliability. CONCLUSION: The developed nomogram model, utilizing independent risk factors for HUA in hypertensive patients, exhibits strong discrimination and calibration. It holds promise as a valuable tool for cardiovascular professionals in clinical decision-making.
Objective: To develop and validate a heart failure risk prediction model for elderly patients after coronary rotational atherectomy based on machine learning methods. Methods: A retrospective cohort study was conducted to select 303 elderly patients with severe coronary calcification as the study subjects. According to the occurrence of postoperative heart failure, the study subjects were divided into the heart failure group (n = 53) and the non-heart failure group (n = 250). Retrospective collection of clinical data from the study subjects during hospitalization. After processing the missing values in the original data and addressing sample imbalance using Adaptive Synthetic Sampling (ADASYN) method, the final dataset consists of 502 samples: 250 negative samples (i.e., patients not suffering from heart failure) and 252 positive samples (i.e., patients with heart failure). According to a 7:3 ratio, the datasets of 502 patients were randomly divided into a training set (n = 351) and a validation set (n = 151). On the training set, logistic regression (LR), extreme gradient boosting (XGBoost), support vector machine (SVM), and lightweight gradient boosting machine (LightGBM) algorithms were used to construct heart failure risk prediction models; Evaluate model performance on the validation set by calculating the area under the receiver operating characteristic curve (ROC) curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, F1-score, and prediction accuracy. Result: A total of 17.49% of 303 patients occured postoperative heart failure. The AUC of LR, XGBoost, SVM, and LightGBM models in the training set were 0.872, 1.000, 0.699, and 1.000, respectively. After 10 fold cross validation, the AUC was 0.863, 0.972, 0.696, and 0.963 in the training set, respectively. Among them, XGBoost had the highest AUC and better predictive performance, while SVM models had the worst performance. The XGBoost model also showed good predictive performance in the validation set (AUC = 0.972, 95% CI [0.951-0.994]). The Shapley additive explanation (SHAP) method suggested that the six characteristic variables of blood cholesterol, serum creatinine, fasting blood glucose, age, triglyceride and NT-proBNP were important positive factors for the occurrence of heart failure, and LVEF was important negative factors for the occurrence of heart failure. Conclusion: The seven characteristic variables of blood cholesterol, blood creatinine, fasting blood glucose, NT-proBNP, age, triglyceride and LVEF are all important factors affecting the occurrence of heart failure. The prediction model of heart failure risk for elderly patients after CRA based on the XGBoost algorithm is superior to SVM, LightGBM and the traditional LR model. This model could be used to assist clinical decision-making and improve the adverse outcomes of patients after CRA.
Atherectomy, Coronary , Heart Failure , Aged , Humans , Retrospective Studies , Atherectomy, Coronary/adverse effects , Blood Glucose , Heart Failure/etiology , Machine Learning , Triglycerides , Cholesterol
Loss of ARID1A, a subunit of the SWI/SNF chromatin remodeling complex, contributes to malignant progression in multiple cancers including non-small cell lung cancer (NSCLC). In the search for key genes mediating the aggressive phenotype caused by ARID1A loss, we analyzed 3 Gene Expression Omnibus (GEO) datasets that contain RNA sequencing data from ARID1A-depleted cancer cells. PLAU was identified as a common gene that was induced in different cancer cells upon ARID1A depletion. Overexpression of PLAU positively modulated NSCLC cell growth, colony formation, cisplatin resistance, and survival under serum deprivation. Moreover, enforced expression of PLAU enhanced tumorigenesis of NSCLC cells in nude mice. Mechanistically, PLAU interacted with TM4SF1 to promote the activation of Akt signaling. TM4SF1-overexpressing NSCLC cells resembled those with PLAU overepxression. Knockdown of TM4SF1 inhibited the growth and survival and increased cisplatin sensitivity in NSCLC cells. The interaction between PLAU and TM4SF1 led to the activation of Akt signaling that endowed ARID1A-depleted NSCLC cells with aggressive properties. In addition, treatment with anti-TM4SF1 neutralizing antibody reduced the growth, cisplatin resistance, and tumorigenesis of ARID1A-depleted NSCLC cells. Taken together, PLAU serves as a target gene of ARID1A and promotes NSCLC growth, survival, and cisplatin resistance by stabilizing TM4SF1. Targeting TM4SF1 may be a promising therapeutic strategy for ARID1A-mutated NSCLC.
Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Animals , Mice , Carcinoma, Non-Small-Cell Lung/drug therapy , Carcinoma, Non-Small-Cell Lung/genetics , Carcinoma, Non-Small-Cell Lung/pathology , Cisplatin/pharmacology , Cisplatin/metabolism , Cisplatin/therapeutic use , Lung Neoplasms/drug therapy , Lung Neoplasms/genetics , Lung Neoplasms/pathology , Proto-Oncogene Proteins c-akt/genetics , Mice, Nude , Cell Proliferation , Carcinogenesis/genetics , Cell Line, Tumor , Gene Expression Regulation, Neoplastic
Objective: To identify factors related to poor prognosis in patients with cerebral infarction (CI) and to construct and validate a personalized prediction model based on these factors. Methods: A retrospective analysis was conducted on the clinical and follow-up data of 857 patients with CI who were diagnosed in the neurology department of a tertiary A hospital in Anhui Province, China from April 2020 to March 2022. Based on follow-up data and the Modified Rankin Scale (mRS) score one year after discharge, patients were divided into a good prognosis group (793 cases, mRS ≤2) and a poor prognosis group (64 cases, mRS >2). Multivariate logistic regression analysis was used to identify independent risk factors, which were then used to establish a nomogram model. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (ROC, AUC), and the calibration curve was used to evaluate the calibration of the nomogram. Results: There was a statistically significant difference in the distribution of eight variables between the groups, including post-discharge use of biguanide hypoglycemic drugs, insulin, systolic blood pressure, exercise status, alcohol consumption, smoking status, age, and gender (P < 0.05). Multivariate logistic regression analysis suggested that gender, smoking after discharge, alcohol consumption, lack of exercise, and oral administration of biguanide hypoglycemic drugs are independent risk factors for poor prognosis in patients with CI (P < 0.05). The personalized poor prognosis nomogram constructed based on these five predictive factors showed good discriminative ability and predictive stability, with AUCs of 0.768 (95 % CI: 0.712-0.825) and 0.775 (95 % CI: 0.725-0.836) before and after internal validation, respectively. The calibration curve confirmed the accuracy and consistency of the nomogram (P = 0.956). Conclusion: Female gender, smoking, alcohol consumption, lack of exercise, and post-discharge use of biguanide hypoglycemic drugs are independent risk factors for poor prognosis in patients with CI. The constructed nomogram shows good predictive efficiency for post-discharge prognosis and can help in clinical decision-making.
OBJECTIVE: Artificial hip arthroplasty (AHA) is widely accepted in elderly patients with femoral neck fractures, but it is associated with high risk of death and various postoperative complications due to old age and accompanying chronic diseases. Therefore, this study aimed to explore the risk factors for death in elderly patients with femoral neck fractures after AHA and to establish a nomogram risk prediction model, which is expected to reveal high-risk patients and improve the postoperative quality of life and survival rate of patients. METHODS: Elderly patients who underwent AHA for femoral neck fractures in our hospital from September 2014 to May 2021were retrospectively analyzed. These patients were divided into a survival group and a death group according to their clinical outcomes. The following clinical data were recorded for the patients in the two groups: sex, age, underlying diseases, smoking and drinking history, preoperative nutritional risk score (NRS) and American Society of Anesthesiologists (ASA) score, as well as relevant indicators about the operation. These data were subject to univariate analysis and then logistic analysis to determine the risk factors of death. Subsequently, a nomogram risk prediction model was established and further validated with the receiver operating characteristic curve (ROC) and the Hosmer-Lemeshow test. Finally, the effects of predictive risk factors were analyzed using the Kaplan-Meier survival curve. RESULTS: Follow-up was completed by 260 patients, including 206 patients in the survival group and 54 patients in the death group; the overall death rate was 20.77%, and the follow-up time, age, postoperative 1, 3 and 5-year death rates were 3.47 ± 1.93 years, 75.32 ± 9.12 years, 5.77%, 12.51%, and 25.61%, respectively. The top three causes of death in 54 patients were respiratory disease, cerebrocardiovascular disease, and digestive disease, respectively. The logistic analysis indicated that elderly patients with femoral neck fractures, the risk factors for death after AHA were age ≥ 80 years, preoperative NRS ≥ 4, HB ≤ 90 g/L, CR ≥ 110 umol/L, and ASA score ≥ 3, as well as postoperative albumin ≤ 35 g/L, the nomogram was established, and then its predictive performance was successfully validated using the ROC curve (AUC = 0.814, 95% confidence interval = 0.749-0.879) and the Hosmer-Lemeshow test (p = 0.840). Furthermore, Kaplan-Meier survival curve analysis revealed that the abovementioned six indicators were correlated with the post-AHA survival time of elderly patients with femoral neck fractures (pLog Rank < 0.05). CONCLUSION: Old age, preoperatively high NRS and ASA score, anemia, poor renal function, and postoperative hypoproteinemia are the major risk factors for death in elderly patients with femoral neck fractures after AHA; they are also associated with postoperative survival. Early identification and effective interventions for optimization of modifiable risk factors are recommended to improve the postoperative quality of life and survival rates.
Arthroplasty, Replacement, Hip , Femoral Neck Fractures , Humans , Aged , Aged, 80 and over , Arthroplasty, Replacement, Hip/adverse effects , Nomograms , Retrospective Studies , Quality of Life , Femoral Neck Fractures/surgery , Femoral Neck Fractures/etiology , Risk Factors
Nod-like receptor protein 3 (NLRP3) inflammasome, autophagy, and the aggregation of ß-amyloid (Aß) are key factors in Alzheimer's disease (AD) development. Ghrelin has shown promise in providing neuroprotection for AD. However, the mechanism underlying ghrelin's ability to improve AD by modulating autophagy and the NLRP3 inflammasome requires further clarification. Primary hippocampus neurons and mice were stimulated with Aß1-42 to create an in vitro and in vivo AD model, followed by ghrelin administration for intervention. Additionally, we subjected the cells to 3-methyladenine (3-MA) treatment. Neuron morphology, microtubule-associated protein 2 (MAP-2) expression, apoptosis, cytokine levels, and protein expression were measured using various techniques. The escape latency of mice was assessed using the Morris water maze (MWM) test, and histopathology of the hippocampus was determined using hematoxylin-eosin staining. At 1-100 nM, ghrelin increased neuron/synapse numbers and MAP-2 expression dose-dependently while blocking apoptosis in Aß1-42-treated cells. Moreover, ghrelin reduced the expression of Aß1-42, p-Tau/Tau, p62, NLRP3, ASC, and cleaved Caspase-1, while increasing the expression of LC3II/LC3I and Beclin1 in AD cells. Furthermore, ghrelin treatment also decreased the levels of Aß1-42, IL-1ß, and IL-18 in the cells. The effects of ghrelin were reversed by 3-MA. Our in vivo experiments provided further confirmation of the above effect of ghrelin on AD. Additionally, the injection of Aß1-42 induced increased escape latency in mice and histopathological changes in hippocampal neurons. All of these abnormalities were significantly improved following administration of ghrelin. Ghrelin mitigated Aß1-42-induced neurotoxicity and relieved neuronal damage by upregulating autophagy to inactivate NLRP3, thus showing promising potential in treating AD.
Alzheimer Disease , Animals , Mice , Alzheimer Disease/drug therapy , Alzheimer Disease/metabolism , Alzheimer Disease/pathology , Autophagy , Ghrelin/pharmacology , Inflammasomes/metabolism , NLR Family, Pyrin Domain-Containing 3 Protein/metabolism , NLR Proteins
Abnormal ship behavior detection is essential for maritime navigation safety. Most existing abnormal ship behavior detection methods only build A ship trajectory position outlier detection model; however, the construction of a ship speed outlier detection model is also significant for maritime navigation safety. In addition, in most existing methods for detecting a ship's abnormal behavior based on abnormal thresholds, one unsuitable threshold leads to the risk of the ship not being minimized as much as possible. In this paper, we proposed an abnormal ship behavior detection method based on distance measurement and an isolation mechanism. First, to address the problem of traditional trajectory compression methods and density clustering methods only using ship position information, the minimum description length principle based on acceleration (AMDL) algorithm and Multi-Dimensional Density Clustering (MDDBSCAN) algorithm is used in this study. These algorithms not only considered the position information of the ship, but also the speed information. Second, regarding the issue of the difficulty in determining the anomaly threshold, one method for determining the anomaly threshold based on the relationship between the velocity weights and noise points of the MDDBSCAN algorithm has been introduced. Finally, due to the randomness issue of the selected segmentation value in iForest, a strategy of selectively constructing isolated trees was proposed, thus further improving the efficiency of abnormal ship behavior detection. The experimental results on the historical automatic identification system data set of Xiamen port prove the practicality and effectiveness of our proposed method. Our experiment results show that the proposed method achieves an improvement of about 10% over the trajectory outlier detection based on the local outlier fraction method, about 14% over the isolation-based online anomalous trajectory method in terms of the accuracy of ship position information anomaly detection, and about 3% over the feature fusion method in terms of the accuracy of ship speed anomaly detection. This method improves algorithm efficiency by about 5% compared to the traditional isolation forest anomaly detection algorithm.
BACKGROUND: During anesthesia administration for cataract surgery, low pH of proparacaine may induce pain or complications such as corneal damage and poor wound healing, with the use of additional drops intraoperatively increasing the risk of complications. Accordingly, there is a clinical need for adjuncts to local anesthesia needs to improve the efficiency of anesthesia and reduce the required amount of intraoperative proparacaine. AIM: To identify a method of anesthesia for geriatric cataract phacoemulsification that provides more efficient analgesia and improves clinical efficacy. METHODS: A total of 130 geriatric patients with cataracts who attended Hebei Eye Hospital from December 2020 to December 2022 were included in the present study. Patients were divided into the proparacaine surface anesthesia (SA) group (65 cases) and the compound acupuncture-medicine anesthesia group (CAMA group, 65 cases). Patients in the CAMA group were provided acupuncture analgesia in addition to SA. Preoperative anxiety [Self-Rating Anxiety Scale (SAS) score and state anxiety inventory (SAI) score], intraoperative stress, vital signs, analgesia, and cooperation, as well as postoperative adverse events, were compared between groups. RESULTS: More marked reductions in anxiety were observed among patients in the CAMA group, with corresponding reductions in SAS and SAI scores. During the operation, no change in the secretion of E, NE, or Cor group compared to the preoperative period was observed in the CAMA, which was markedly lower than that in the SA group. Heart rate, blood pressure, and respiratory rate were more stable intraoperatively in the CAMA group. In addition, the incidence of intraoperative pain and the number of additional doses of anesthesia required in the CAMA group were markedly lower than in the SA group. Accordingly, patients in the CAMA group were able to avoid eye movements and eyelid closing leading to greater cooperation with surgeons during surgery. Furthermore, marked reductions in intraoperative adverse effects were observed in the CAMA group, indicating greater overall safety. CONCLUSION: Proparacaine SA combined with acupuncture as an analgesic provides improved analgesia with greater safety compared to surface anesthesia with proparacaine during geriatric cataract phacoemulsification.
Objective: To investigate the incidence and influencing factors affecting the non-adherence behavior of patients with coronary heart disease (CHD) to antiplatelet therapy after discharge and to construct a personalized predictive tool. Methods: In this retrospective cohort study, 289 patients with CHD who were admitted to the Department of Cardiology of The First Affiliated Hospital of the University of Science and Technology of China between June 2021 and September 2021 were enrolled. The clinical data of all patients were retrospectively collected from the hospital information system, and patients were followed up for 1 year after discharge to evaluate their adherence level to antiplatelet therapy, analyze their present situation and influencing factors for post-discharge adherence to antiplatelet therapy, and construct a nomogram model to predict the risk of non-adherence. Results: Based on the adherence level to antiplatelet therapy within 1 year after discharge, the patients were divided into the adherence (n = 216) and non-adherence (n = 73) groups. Univariate analysis revealed statistically significant differences between the two groups in terms of variable distribution, including age, education level, medical payment method, number of combined risk factors, percutaneous coronary intervention, duration of antiplatelet medication, types of drugs taken at discharge, and CHD type (P < 0.05). Furthermore, multivariate logistic regression analysis revealed that, except for the medical payment method, all the seven abovementioned variables were independent risk factors for non-adherence to antiplatelet therapy (P < 0.05). The areas under the receiver operating characteristic curve before and after the internal validation of the predictive tool based on the seven independent risk factors and the nomogram were 0.899 (95% confidence interval [CI]: 0.858-0.941) and 0.89 (95% CI: 0.847-0.933), respectively; this indicates that the tool has good discrimination ability. The calibration curve and Hosmer-Lemeshow goodness of fit test revealed that the tool exhibited good calibration and prediction consistency (χ2 = 5.17, P = 0.739). Conclusion: In this retrospective cohort study, we investigated the incidence and influencing factors affecting the non-adherence behavior of patients with CHD after discharge to antiplatelet therapy. For this, we constructed a personalized predictive tool based on seven independent risk factors affecting non-adherence behavior. The predictive tool exhibited good discrimination ability, calibration, and clinical applicability. Overall, our constructed tool is useful for predicting the risk of non-adherence behavior to antiplatelet therapy in discharged patients with CHD and can be used in personalized intervention strategies to improve patient outcomes.
Coronary Disease , Platelet Aggregation Inhibitors , Humans , Prognosis , Retrospective Studies , Platelet Aggregation Inhibitors/therapeutic use , Patient Discharge , Aftercare , Risk Factors , Coronary Disease/drug therapy
Prediction of prognosis after radical resection of gastric cancer has not been well established. Therefore, we aimed to establish a prognostic model based on a new score system of patients with gastric cancer. A total of 1235 patients who underwent curative gastrectomy at our hospital from October 2015 to April 2017 were included in this study. Univariate and multivariate analyses were used to screen for prognostic risk factors. Construction of the nomogram was based on Cox proportional hazard regression models. The construction of the new score models was analyzed by the receiver operating characteristic curve (ROC curve), calibration curve, and decision curve. Multivariate analysis showed that tumor size, T, N, carcinoembryonic antigen, CA125, and CA19-9 were independent prognostic factors. The new score model had a greater AUC (The area under the ROC curve) than other systems, and the C-index of the nomogram was highly reliable for evaluating the survival of patients with gastric cancer. Based on the tumor markers and other clinical indicators, we developed a precise model to predict the prognosis of patients with gastric cancer after radical surgery. This score system can be helpful to both surgeons and patients.
PURPOSE: Carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) are the most common tumor markers detected before and after gastric cancer (GC) surgery. However, the impact of post-preoperative CEA/CA19-9 increments on prognosis of GC remains unclear. In addition, there is no research incorporating post-preoperative CEA/CA19-9 increments into the prognostic model. METHODS: Patients who underwent radical gastrectomy for GC at the First Affiliated Hospital of Anhui Medical University and Anhui Provincial Hospital from January 2013 to December 2017 were enrolled and divided into the discovery and validation cohort. Prognostic value of post-preoperative CEA/CA19-9 increments and preoperative CEA/CA199 levels were assessed by Kaplan-Meier log-rank analysis and compared by time-dependent receiver operating characteristic (t-ROC) curves. Multivariate Cox regression analysis was applied to establish the nomogram. The performance of the prognostic model was validated by the concordance index (C-index), calibration curve, and ROC curve analysis. RESULTS: A total of 562 GC patients were included in this study. Overall survival (OS) rates decreased with an increasing number of incremental tumor markers after surgery. The t-ROC curves implied that the prognostic ability of the number of incremental post-preoperative tumor markers was superior to that of the number of positive preoperative tumor markers. Cox regression analysis suggested that the number of incremental post-preoperative tumor markers was an independent prognostic factor. The nomogram incorporated with the post-preoperative CEA/CA19-9 increments showed reliable accuracy. CONCLUSIONS: Incremental post-preoperative CEA/CA19-9 were indicator of poor prognosis of GC. The prognostic value of post-preoperative CEA/CA19-9 increments exceed that of preoperative CEA/CA19-9 levels.
Biomarkers, Tumor , Stomach Neoplasms , Humans , Carcinoembryonic Antigen , Prognosis , CA-19-9 Antigen , Stomach Neoplasms/pathology , Retrospective Studies
BACKGROUND: The incidence of gastric cancer (GC) ranks fourth among all malignant tumors worldwide, and the fatality rate ranks second among all malignant tumors. Several Chinese traditional medicines have been used in the treatment of advanced gastric cancer. This study aims to investigate the effect of combinational use of natural product cryptotanshinone (CTS) with anti-cancer drug trifluorothymidine (FTD) in GC. METHODS: Cell Counting Kit-8 assay was used to detect the inhibitory effect of the combinational or separate use of FTD and CTS on the growth of HGC-27 and AGS GC cells. The combined index of FTD and CTS was calculated using CompuSyn software. To understand the mechanism, we applied flow cytometry to study the cell cycle and cell apoptosis after treatment. We also investigated the amount of FTD incorporated into the DNA by immunofluorescence assay. The expression of relevant proteins was monitored using western blot. Furthermore, the effect of using TAS-102 in combination with CTS was studied in xenograft tumor nude mice model. RESULTS: FTD and CTS inhibited the growth of GC cells in a dose-dependent manner, respectively. They both exhibited low to sub-micromolar potency in HGC-27 and AGS cells. The combination of FTD and CTS showed synergistic anticancer effect in HGC-27 cells and AGS cells. Our mechanism studies indicate that FTD could block HGC-27 cells at G2/M phase, while CTS could block HGC-27 cells at G1/G0 phase, while FTD combined with CTS could mainly block HGC-27 cells at G2 phase. FTD in combination with CTS significantly increased the apoptosis of HGC-27 cells. We observed that CTS treatment increased the incorporation of FTD into the DNA HGC-27 cell. FTD treatment activated STAT3 phosphorylation in HGC-27 cells, while CTS treatment down-regulated the concentration of p-STAT3. Interestingly, the combination of CTS and FTD reduced STAT3 phosphorylation induced by FTD. In the in vivo experiments, we observed that the combination of TAS-102 with CTS was significantly more potent than TAS-102 on tumor growth inhibition. CONCLUSIONS: FTD combined with CTS has a synergistic anti-gastric cancer effect as shown by in vitro and in vivo experiments, and the combined treatment of FTD and CTS will be a promising treatment option for advanced gastric cancer.
Phenanthrenes , Stomach Neoplasms , Trifluridine , Humans , Cell Line, Tumor , Animals , Mice , Heterografts , Neoplasm Transplantation , Trifluridine/administration & dosage , Trifluridine/pharmacology , Phenanthrenes/administration & dosage , Phenanthrenes/pharmacology , Cell Proliferation/drug effects , Mice, Nude , Drug Synergism , Apoptosis/drug effects , STAT3 Transcription Factor/metabolism , Stomach Neoplasms/drug therapy
BACKGROUND: For the prognosis of patients with early gastric cancer (EGC), lymph node metastasis (LNM) plays a crucial role. A thorough and precise evaluation of the patient for LNM is now required. AIM: To determine the factors influencing LNM and to construct a prediction model of LNM for EGC patients. METHODS: Clinical information and pathology data of 2217 EGC patients downloaded from the Surveillance, Epidemiology, and End Results database were collected and analyzed. Based on a 7:3 ratio, 1550 people were categorized into training sets and 667 people were assigned to testing sets, randomly. Based on the factors influencing LNM determined by the training sets, the nomogram was drawn and verified. RESULTS: Based on multivariate analysis, age at diagnosis, histology type, grade, T-stage, and size were risk factors of LNM for EGC. Besides, nomogram was drawn to predict the risk of LNM for EGC patients. Among the categorical variables, the effect of grade (well, moderate, and poor) was the most significant prognosis factor. For training sets and testing sets, respectively, area under the receiver-operating characteristic curve of nomograms were 0.751 [95% confidence interval (CI): 0.721-0.782] and 0.786 (95%CI: 0.742-0.830). In addition, the calibration curves showed that the prediction model of LNM had good consistency. CONCLUSION: Age at diagnosis, histology type, grade, T-stage, and tumor size were independent variables for LNM in EGC. Based on the above risk factors, prediction model may offer some guiding implications for the choice of subsequent therapeutic approaches for EGC.
