Nanoassembly-Mediated Exendin-4 Derivatives to Decrease Renal Retention.

An Exendin-4 analogue that was conjugated with 68Ga exhibited an excellent diagnostic effect on insulinoma in clinical practice. On account of its low molecular weight and short hydration radius, 68Ga-Exendin-4 showed high accumulation in kidney tissues. Nanoparticle-mediated strategies have attracted much attention due to polyvalent properties and the size amplification effect. In this study, Exendin-4 derivatives of radionuclide nanodevices were developed and evaluated. The Exendin-4 derivatives consisting of a ternary block recombinant protein were purified by an inverse transition cycle (ITC) and allowed to self-assemble into a nanodevice under physiological conditions. Our results showed that the nanoassemblies of Exendin-4 derivatives formed homogeneous spherical nanoparticles, exhibited outstanding affinity for insulinoma cells, and could be deposited in insulinoma tissues in vivo. The nanoassembly-mediated Exendin-4 derivatives showed fivefold reduced renal retention and exhibited an outstanding tumor-suppression effect.

Identification of two distinct head and neck squamous cell carcinoma subtypes based on fatty acid metabolism-related signatures: Implications for immunotherapy and chemotherapy.

The dysregulation of lipid metabolism is a critical factor in the initiation and progression of tumors. In this investigation, we aim to characterize the molecular subtypes of head and neck squamous cell carcinoma (HNSCC) based on their association with fatty acid metabolism and develop a prognostic risk model. The transcriptomic and clinical data about HNSCC were obtained from public databases. Clustering analysis was conducted on fatty acid metabolism genes (FAMG) associated with prognosis, utilizing the non-negative matrix factorization algorithm. The immune infiltration, response to immune therapy, and drug sensitivity between molecular subtypes were evaluated. Differential expression genes were identified between subtypes, and a prognostic model was constructed using Cox regression analyses. A nomogram for HNSCC was constructed and evaluated. Thirty FAMGs have been found to exhibit differential expression in HNSCC, out of which three are associated with HNSCC prognosis. By performing clustering analysis on these 3 genes, 2 distinct molecular subtypes of HNSCC were identified that exhibit significant heterogeneity in prognosis, immune landscape, and treatment response. Using a set of 7778 genes that displayed differential expression between the 2 molecular subtypes, a prognostic risk model for HNSCC was constructed comprising 11 genes. This model has the ability to stratify HNSCC patients into high-risk and low-risk groups, which exhibit significant differences in prognosis, immune infiltration, and immune therapy response. Moreover, our data suggest that this risk model is negatively correlated with B cells and most T cells, but positively correlated with macrophages, mast cells, and dendritic cells. Ultimately, we constructed a nomogram incorporating both the risk signature and radiotherapy, which has demonstrated exceptional performance in predicting prognosis for HNSCC patients. A molecular classification system and prognostic risk models were developed for HNSCC based on FAMGs. This study revealed the potential involvement of FAMGs in modulating tumor immune microenvironment and response to treatment.

Ice sheet and precession controlled subarctic Pacific productivity and upwelling over the last 550,000 years.

The polar oceans play a vital role in regulating atmospheric CO2 concentrations (pCO2) during the Pleistocene glacial cycles. However, despite being the largest modern reservoir of respired carbon, the impact of the subarctic Pacific remains poorly understood due to limited records. Here, we present high-resolution, 230Th-normalized export productivity records from the subarctic northwestern Pacific covering the last five glacial cycles. Our records display pronounced, glacial-interglacial cyclicity superimposed with precessional-driven variability, with warm interglacial climate and high boreal summer insolation providing favorable conditions to sustain upwelling of nutrient-rich subsurface waters and hence increased export productivity. Our transient model simulations consistently show that ice sheets and to a lesser degree, precession are the main drivers that control the strength and latitudinal position of the westerlies. Enhanced upwelling of nutrient/carbon-rich water caused by the intensification and poleward migration of the northern westerlies during warmer climate intervals would have led to the release of previously sequestered CO2 from the subarctic Pacific to the atmosphere. Our results also highlight the significant role of the subarctic Pacific in modulating pCO2 changes during the Pleistocene climate cycles, especially on precession timescale ( ~ 20 kyr).

A machine learning stacking model accurately estimating gastric fluid volume in patients undergoing elective sedated gastrointestinal endoscopy.

BACKGROUND: The current point-of-care ultrasound (POCUS) assessment of gastric fluid volume primarily relies on the traditional linear approach, which often suffers from moderate accuracy. This study aimed to develop an advanced machine learning (ML) model to estimate gastric fluid volume more accurately. METHODS: We retrospectively analyzed the clinical data and POCUS data (D1: craniocaudal diameter, D2: anteroposterior diameter) of 1386 patients undergoing elective sedated gastrointestinal endoscopy (GIE) at Nanjing First Hospital to predict gastric fluid volume using ML techniques, including six different ML models and a stacking model. We evaluated the models using the adjusted Coefficient of Determination (R2), mean absolute error (MAE) and root mean square error (RMSE). The SHapley Additive exPlanations (SHAP) method was used to interpret the importance of the variables. Finally, a web calculator was constructed to facilitate its clinical application. RESULTS: The stacking model (Linear regression + Multilayer perceptron) performed best, with the highest adjusted R2 of 0.718 (0.632 to 0.804). The mean prediction bias was 4 ml (MAE: 4.008 (3.68 to 4.336)), which is better than that of the linear model. D1 and D2 ranked high in the SHAP plot and performed better in the right lateral decubitus (RLD) than in the supine position. The web calculator can be accessed at https://cheason.shinyapps.io/Stacking_regressor/. CONCLUSION: The stacking model and its web calculator can serve as practical tools for accurately estimating gastric fluid volume in patients undergoing elective sedated GIE. It is recommended that anesthesiologists measure D1 and D2 in the patient's RLD position.

Machine learning-based risk prediction of hypoxemia for outpatients undergoing sedation colonoscopy: a practical clinical tool.

OBJECTIVES: Hypoxemia as a common complication in colonoscopy under sedation and may result in serious consequences. Unfortunately, a hypoxemia prediction model for outpatient colonoscopy has not been developed. Consequently, the objective of our study was to develop a practical and accurate model to predict the risk of hypoxemia in outpatient colonoscopy under sedation. METHODS: In this study, we included patients who received colonoscopy with anesthesia in Nanjing First Hospital from July to September 2021. Risk factors were selected through the least absolute shrinkage and selection operator (LASSO). Prediction models based on logistic regression (LR), random forest classifier (RFC), extreme gradient boosting (XGBoost), support vector machine (SVM), and stacking classifier (SCLF) model were implemented and assessed by standard metrics such as the area under the receiver operating characteristic curve (AUROC), sensitivity and specificity. Then choose the best model to develop an online tool for clinical use. RESULTS: We ultimately included 839 patients. After LASSO, body mass index (BMI) (coefficient = 0.36), obstructive sleep apnea-hypopnea syndrome (OSAHS) (coefficient = 1.32), basal oxygen saturation (coefficient = -0.14), and remifentanil dosage (coefficient = 0.04) were independent risk factors for hypoxemia. The XGBoost model with an AUROC of 0.913 showed the best performance among the five models. CONCLUSION: Our study selected the XGBoost as the first model especially for colonoscopy, with over 95% accuracy and excellent specificity. The XGBoost includes four variables that can be quickly obtained. Moreover, an online prediction practical tool has been provided, which helps screen high-risk outpatients with hypoxemia swiftly and conveniently.

Colonoscopy under sedation is an effective technique for the inspection and treatment of alimentary canal diseases, but hypoxemia associated with this process cannot be ignored, since prolonged or severe hypoxemia may result in several serious consequences.We wanted to develop a practical and accurate model to predict the risk of hypoxemia for outpatient colonoscopy under sedation, which could help clinicians make more accurate and objective judgments to prevent patients from being harmed.A total of 839 patients were included in our study and we constructed five machine learning models and selected the best one, which demonstrated satisfactory performance. On this basis, a user-friendly data interface has been developed for convenient application. Clinicians can log in to this interface at any time and it will automatically calculate the patient's risk of hypoxemia when entering patient information.This study offers evidence that machine learning algorithms can accurately predict the risk of hypoxemia for outpatient colonoscopy under sedation and the model we developed is a practical and interpretable tool that could be used as a clinical decision-making aid.

Hypoxia-derived molecular subtype and gene signature characterize prognoses and therapeutic responses in head and neck squamous cell carcinoma.

Intratumoral hypoxia is widely associated with the development of malignancy, treatment resistance, and worse prognoses. This study aims to investigate the role of hypoxia-related genes (HRG) in the immune landscape, treatment response, and prognosis of head and neck squamous cell carcinoma (HNSCC). The transcriptome and clinical data of HNSCC were downloaded from TCGA and GEO databases, and HNSCC molecular subtypes were identified using non-negative matrix factorization (NMF) clustering. Prognostic models were constructed using univariate, Lasso, and multivariate Cox regression analyses. The relationship between HRGs and immune cell infiltration, immune therapy response, and drug sensitivity was evaluated, and a nomogram was constructed. 47 HRGs were differentially expressed in HNSCC, among which 10 genes were significantly associated with HNSCC prognosis. Based on these 10 genes, 2 HNSCC molecular subtypes were identified, which showed significant heterogeneity in terms of prognosis, immune infiltration, and treatment response. A total of 3280 differentially expressed genes were identified between the subtypes. After univariate, Lasso, and multivariate Cox regression analysis, 18 genes were selected to construct a novel prognostic model, which showed a significant correlation with B cells, T cells, and macrophages. Using this model, HNSCC was classified into high-risk and low-risk groups, which exhibited significant differences in terms of prognosis, immune cell infiltration, immune therapy response, and drug sensitivity. Finally, a nomogram based on this model and radiotherapy was constructed, which showed good performance in predicting HNSCC prognosis and guiding personalized treatment strategies. The decision curve analysis demonstrated its better clinical applicability compared to other strategies. HRGs can identify 2 HNSCC molecular subtypes with significant heterogeneity, and the HRG-derived risk model has the potential for prognostic prediction and guiding personalized treatment strategies.

Prediction of medial knee contact force using multisource fusion recurrent neural network and transfer learning.

Estimation of knee contact force (KCF) during gait provides essential information to evaluate knee joint function. Machine learning has been employed to estimate KCF because of the advantages of low computational cost and real-time. However, the existing machine learning models do not adequately consider gait-related data's temporal-dependent, multidimensional, and highly heterogeneous nature. This study is aimed at developing a multisource fusion recurrent neural network to predict the medial condyle KCF. First, a multisource fusion long short-term memory (MF-LSTM) model was established. Then, we developed a transfer learning strategy based on the MF-LSTM model for subject-specific medial KCF prediction. Four subjects with instrumented tibial prostheses were obtained from the literature. The results showed that the MF-LSTM model could predict medial KCF to a certain high level of accuracy (the mean of ρ = 0.970). The transfer learning model improved the prediction accuracy (the mean of ρ = 0.987). This study shows that the MF-LSTM model is a powerful and accurate computational tool for medial KCF prediction. Introducing transfer learning techniques could further improve the prediction performance for the target subject. This coupling strategy can help clinicians accurately estimate and track joint contact forces in real time.

Online interpretable dynamic prediction models for postoperative delirium after cardiac surgery under cardiopulmonary bypass developed based on machine learning algorithms: A retrospective cohort study.

OBJECTIVE: Postoperative delirium (POD) is strongly associated with poor early and long-term prognosis in cardiac surgery patients with cardiopulmonary bypass (CPB). This study aimed to develop dynamic prediction models for POD after cardiac surgery under CPB using machine learning (ML) algorithms. METHODS: From July 2021 to June 2022, clinical data were collected from patients undergoing cardiac surgery under CPB at Nanjing First Hospital. A dataset from the same center (October 2022 to November 2022) was also used for temporal external validation. We used ML and deep learning to build models in the training set, optimized parameters in the test set, and finally validated the best model in the validation set. The SHapley Additive exPlanations (SHAP) method was introduced to explain the best models. RESULTS: Of the 885 patients enrolled, 221 (25.0%) developed POD. 22 (22.0%) of 100 validation cohort patients developed POD. The preoperative and postoperative artificial neural network (ANN) models exhibited optimal performance. The validation results demonstrated satisfactory predictive performance of the ANN model, with area under the receiver operator characteristic curve (AUROC) values of 0.776 and 0.684 for the preoperative and postoperative models, respectively. Based on the ANN algorithm, we constructed dynamic, highly accurate, and interpretable web risk calculators for POD. CONCLUSIONS: We successfully developed online interpretable dynamic ANN models as clinical decision aids to identify patients at high risk of POD before and after cardiac surgery to facilitate early intervention or care.

Accurately predicting the risk of unfavorable outcomes after endovascular coil therapy in patients with aneurysmal subarachnoid hemorrhage: an interpretable machine learning model.

BACKGROUND: Despite endovascular coiling as a valid modality in treatment of aneurysmal subarachnoid hemorrhage (aSAH), there is a risk of poor prognosis. However, the clinical utility of previously proposed early prediction tools remains limited. We aimed to develop a clinically generalizable machine learning (ML) models for accurately predicting unfavorable outcomes in aSAH patients after endovascular coiling. METHODS: Functional outcomes at 6 months after endovascular coiling were assessed via the modified Rankin Scale (mRS) and unfavorable outcomes were defined as mRS 3-6. Five ML algorithms (logistic regression, random forest, support vector machine, deep neural network, and extreme gradient boosting) were used for model development. The area under precision-recall curve (AUPRC) and receiver operating characteristic curve (AUROC) was used as main indices of model evaluation. SHapley Additive exPlanations (SHAP) method was applied to interpret the best-performing ML model. RESULTS: A total of 371 patients were eventually included into this study, and 85.4% of them had favorable outcomes. Among the five models, the DNN model had a better performance with AUPRC of 0.645 (AUROC of 0.905). Postoperative GCS score, size of aneurysm, and age were the top three powerful predictors. The further analysis of five random cases presented the good interpretability of the DNN model. CONCLUSION: Interpretable clinical prediction models based on different ML algorithms have been successfully constructed and validated, which would serve as reliable tools in optimizing the treatment decision-making of aSAH. Our DNN model had better performance to predict the unfavorable outcomes at 6 months in aSAH patients compared with Yan's nomogram model.

Development and validation of a novel nomogram model to assess the risk of gastric contents in outpatients undergoing elective sedative gastrointestinal endoscopy procedures.

BACKGROUND: Gastric contents may contribute to patients' aspiration during anesthesia. Ultrasound can accurately assess the risk of gastric contents in patients undergoing sedative gastrointestinal endoscopy (GIE) procedures, but its efficiency is limited. Therefore, developing an accurate and efficient model to predict gastric contents in outpatients undergoing elective sedative GIE procedures is greatly desirable. METHODS: This study retrospectively analyzed 1501 patients undergoing sedative GIE procedures. Gastric contents were observed under direct gastroscopic vision and suctioned through the endoscope. High-risk gastric contents were defined as having solid content or liquid volume > 25 ml and pH < 2.5; otherwise, they were considered low-risk gastric contents. Univariate analysis and multivariate analysis were used to select the independent risk factors to predict high-risk gastric contents. Based on the selected independent risk factors, we assigned values to each independent risk factor and established a novel nomogram. The performance of the nomogram was verified in the testing cohort by the metrics of discrimination, calibration, and clinical usefulness. In addition, an online accessible web calculator was constructed. RESULTS: We found BMI, cerebral infarction, cirrhosis, male, age, diabetes, and gastroesophageal reflux disease were risk factors for gastric contents. The AUROCs were 0.911 and 0.864 in the development and testing cohort, respectively. Moreover, the nomogram showed good calibration ability. Decision curve analysis and Clinical impact curve demonstrated that the predictive nomogram was clinically useful. The website of the nomogram was https://medication.shinyapps.io/dynnomapp/. CONCLUSIONS: This study demonstrates that clinical variables can be combined with algorithmic techniques to predict gastric contents in outpatients. Nomogram was constructed from routine variables, and the web calculator had excellent clinical applicability to assess the risk of gastric contents accurately and efficiently in outpatients, assist anesthesiologists in assessment and identify the most appropriate patients for ultrasound.

A convenient machine learning model to predict full stomach and evaluate the safety and comfort improvements of preoperative oral carbohydrate in patients undergoing elective painless gastrointestinal endoscopy.

BACKGROUND AND AIMS: Assessment of the patient's gastric contents is the key to avoiding aspiration incidents, however, there is no effective method to determine whether elective painless gastrointestinal endoscopy (GIE) patients have a full stomach or an empty stomach. And previous studies have shown that preoperative oral carbohydrates (POCs) can improve the discomfort induced by fasting, but there are different perspectives on their safety. This study aimed to develop a convenient, accurate machine learning (ML) model to predict full stomach. And based on the model outcomes, evaluate the safety and comfort improvements of POCs in empty- and full stomach groups. METHODS: We enrolled 1386 painless GIE patients between October 2022 and January 2023 in Nanjing First Hospital, and 1090 patients without POCs were used to construct five different ML models to identify full stomach. The metrics of discrimination and calibration validated the robustness of the models. For the best-performance model, we further interpreted it through SHapley Additive exPlanations (SHAP) and constructed a web calculator to facilitate clinical use. We evaluated the safety and comfort improvements of POCs by propensity score matching (PSM) in the two groups, respectively. RESULTS: Random Forest (RF) model showed the greatest discrimination with the area under the receiver operating characteristic curve (AUROC) 0.837 [95% confidence interval (CI): 79.1-88.2], F1 71.5%, and best calibration with a Brier score of 15.2%. The web calculator can be visited at https://medication.shinyapps.io/RF_model/. PSM results demonstrated that POCs significantly reduced the full stomach incident in empty stomach group (p < 0.05), but no differences in full stomach group (p > 0.05). Comfort improved in both groups and was more significant in empty stomach group. CONCLUSIONS: The developed convenient RF model predicted full stomach with high accuracy and interpretability. POCs were safe and comfortably improved in both groups, with more benefit in empty stomach group. These findings may guide the patients' gastrointestinal preparation.

This study is the first model utilizing advanced ML techniques based on multiple clinical variables to identify full stomach. The model is suitable for patient-rich outpatient clinics, primary hospitals, remote regions, and specific clinical settings where POCUS is not available.The developed convenient RF model predicted full stomach with high accuracy and interpretability. The test cohort AUROC was 0.837. We further established an online accessible individualized risk calculator and provided waterfall plots to increase the interpretability of each prediction.The propensity score matching (PSM) showed that preoperative oral carbohydrate (POCs) were safe and comfortably improved in both groups, with more benefit in empty stomach group. These findings may provide information for anesthesiologists to guide patients on POCs.

A novel interpretative tool for early prediction of low cardiac output syndrome after valve surgery: online machine learning models.

OBJECTIVE: Low cardiac output syndrome (LCOS) is a severe complication after valve surgery, with no uniform standard for early identification. We developed interpretative machine learning (ML) models for predicting LCOS risk preoperatively and 0.5 h postoperatively for intervention in advance. METHODS: A total of 2218 patients undergoing valve surgery from June 2019 to Dec 2021 were finally enrolled to construct preoperative and postoperative models. Logistic regression, support vector machine (SVM), random forest classifier, extreme gradient boosting, and deep neural network were executed for model construction, and the performance of models was evaluated by area under the curve (AUC) of the receiver operating characteristic and calibration curves. Our models were interpreted through SHapley Additive exPlanations, and presented as an online tool to improve clinical operability. RESULTS: The SVM algorithm was chosen for modeling due to better AUC and calibration capability. The AUCs of the preoperative and postoperative models were 0.786 (95% CI 0.729-0.843) and 0.863 (95% CI 0.824-0.902), and the Brier scores were 0.123 and 0.107. Our models have higher timeliness and interpretability, and wider coverage than the vasoactive-inotropic score, and the AUC of the postoperative model was significantly higher. Our preoperative and postoperative models are available online at http://njfh-yxb.com.cn:2022/lcos. CONCLUSIONS: The first interpretable ML tool with two prediction periods for online early prediction of LCOS risk after valve surgery was successfully built in this study, in which the SVM model has the best performance, reserving enough time for early precise intervention in critical care.

Dynamic and visual nomograms to online predict unfavorable outcome of mechanical thrombectomy for acute basilar artery occlusion.

BACKGROUND: The evidence of mechanical thrombectomy (MT) in basilar artery occlusion (BAO) was limited. This study aimed to develop dynamic and visual nomogram models to predict the unfavorable outcome of MT in BAO online. METHODS: BAO patients treated with MT were screened. Preoperative and postoperative nomogram models were developed based on clinical parameters and imaging features. An independent dataset was collected to perform external validation. Web-based calculators were constructed to provide convenient access. RESULTS: A total of 127 patients were included in the study, and 117 of them were eventually included in the analysis. The nomogram models showed robust discrimination, with an area under the receiver operating characteristic (ROC) of 0.841 (preoperative) and 0.916 (postoperative). The calibration curves showed good agreement. The preoperative predictors of an unfavorable outcome were previous stroke, the National Institutes of Health Stroke Scale (NIHSS) at admission, and the posterior circulation Alberta Stroke Program Early Computed Tomography Score (pc-ASPECTS). The postoperative predictors were previous stroke, NIHSS at 24 h, and pc-ASPECTS. CONCLUSION: Dynamic and visual nomograms were constructed and validated for the first time for BAO patients treated with MT, which provided precise predictions for the risk of an unfavorable outcome. The preoperative model may assist clinicians in selecting eligible patients, and the postoperative model may facilitate individualized poststroke management.

A risk prediction model based on machine learning for postoperative cognitive dysfunction in elderly patients with non-cardiac surgery.

BACKGROUND: Early identification of elderly patients undergoing non-cardiac surgery who may be at high risk for postoperative cognitive dysfunction (POCD) can increase the chances of prevention for them, as extra attention and limited resources can be allocated more to these patients. AIM: We performed this analysis with the aim of developing a simple, clinically useful machine learning (ML) model to predict the probability of POCD at 3 months in elderly patients after non-cardiac surgery. METHODS: We collected information on patients who received surgical treatment at Nanjing First Hospital from May 2020 to May 2021. We used LASSO regression to select key features and built 5 ML models to assess the risk of POCD at 3 months in elderly patients after non-cardiac surgery. The Shapley Additive exPlanations (SHAP) and methods were introduced to interpret the best model. RESULTS: A total of 415 patients with non-cardiac surgery were included. The support vector machine (SVM) was the best-performing model of the five ML models. The model showed excellent performance compared to the other four models. The SHAP results showed that VAS score, age, intraoperative hypotension, and preoperative hemoglobin were the four most important features, indicating that the SVM model had good interpretability and reliability. The website of the web-based calculator was https://modricreagan-non-3-pocd-9w2q78.streamlit.app/ . CONCLUSION: Based on six important perioperative variables, we successfully established a series of ML models for predicting POCD occurrence at 3 months after surgery in elderly non-cardiac patients, with SVM model being the best-performing model. Our models are expected to serve as decision aids for clinicians to monitor screened high-risk patients more closely or to consider further interventions.

Incorporating intraoperative blood pressure time-series variables to assist in prediction of acute kidney injury after type a acute aortic dissection repair: an interpretable machine learning model.

BACKGROUND: Acute kidney injury (AKI) is a common and serious complication after the repair of Type A acute aortic dissection (TA-AAD). However, previous models have failed to account for the impact of blood pressure fluctuations on predictive performance. This study aims to develop machine learning (ML) models combined with intraoperative medicine and blood pressure time-series data to improve the accuracy of early prediction for postoperative AKI risk. METHODS: Indicators reflecting the duration and depth of hypotension were obtained by analyzing continuous mean arterial pressure (MAP) monitored intraoperatively with multiple thresholds (<65, 60, 55, 50) set in the study. The predictive features were selected by logistic regression and the least absolute shrinkage and selection operator (LASSO), and 4 ML models were built based on the above features. The performance of the models was evaluated by area under receiver operating characteristic curve (AUROC), calibration curve and decision curve analysis (DCA). Shapley additive interpretation (SHAP) was used to explain the prediction models. RESULTS: Among the indicators reflecting intraoperative hypotension, 65 mmHg showed a statistically superior difference to other thresholds in patients with or without AKI (p < .001). Among 4 models, the extreme gradient boosting (XGBoost) model demonstrated the highest AUROC: 0.800 (95% 0.683-0.917) and sensitivity: 0.717 in the testing set and was verified the best-performing model. The SHAP summary plot indicated that intraoperative urine output, cumulative time of mean arterial pressure lower than 65 mmHg outside cardiopulmonary bypass (OUT_CPB_MAP_65 time), autologous blood transfusion, and smoking were the top 4 features that contributed to the prediction model. CONCLUSION: With the introduction of intraoperative blood pressure time-series variables, we have developed an interpretable XGBoost model that successfully achieve high accuracy in predicting the risk of AKI after TA-AAD repair, which might aid in the perioperative management of high-risk patients, particularly for intraoperative hemodynamic regulation.

In this study, we combined intraoperative blood pressure time-series data for the first time to build 4 machine learning (ML) models that successfully improve the accuracy of early prediction of postoperative AKI risk, with the XGBoost model displaying the best predictive performance.We explored the impact of multiple intraoperative hypotension thresholds (MAP <65, <60, <55 < 50 mmHg) on the occurrence of postoperative AKI in patients and attempted to provide clinicians with recommendations for hemodynamic management during surgery.Our study found that 65 mmHg showed a statistically superior difference to other thresholds in patients with or without AKI after undergoing TA-AAD repair (p < .001).

Hepatic retinaldehyde deficiency is involved in diabetes deterioration by enhancing PCK1- and G6PC-mediated gluconeogenesis.

Type 2 diabetes (T2D) is often accompanied with an induction of retinaldehyde dehydrogenase 1 (RALDH1 or ALDH1A1) expression and a consequent decrease in hepatic retinaldehyde (Rald) levels. However, the role of hepatic Rald deficiency in T2D progression remains unclear. In this study, we demonstrated that reversing T2D-mediated hepatic Rald deficiency by Rald or citral treatments, or liver-specific Raldh1 silencing substantially lowered fasting glycemia levels, inhibited hepatic glucogenesis, and downregulated phosphoenolpyruvate carboxykinase 1 (PCK1) and glucose-6-phosphatase (G6PC) expression in diabetic db/db mice. Fasting glycemia and Pck1/G6pc mRNA expression levels were strongly negatively correlated with hepatic Rald levels, indicating the involvement of hepatic Rald depletion in T2D deterioration. A similar result that liver-specific Raldh1 silencing improved glucose metabolism was also observed in high-fat diet-fed mice. In primary human hepatocytes and oleic acid-treated HepG2 cells, Rald or Rald + RALDH1 silencing resulted in decreased glucose production and downregulated PCK1/G6PC mRNA and protein expression. Mechanistically, Rald downregulated direct repeat 1-mediated PCK1 and G6PC expression by antagonizing retinoid X receptor α, as confirmed by luciferase reporter assays and molecular docking. These results highlight the link between hepatic Rald deficiency, glucose dyshomeostasis, and the progression of T2D, whilst also suggesting RALDH1 as a potential therapeutic target for T2D.

Pre- and Post-Operative Online Prediction of Outcome in Patients Undergoing Endovascular Coiling after Aneurysmal Subarachnoid Hemorrhage: Visual and Dynamic Nomograms.

BACKGROUND: Aneurysmal subarachnoid hemorrhage (aSAH) causes long-term functional dependence and death. Early prediction of functional outcomes in aSAH patients with appropriate intervention strategies could lower the risk of poor prognosis. Therefore, we aimed to develop pre- and post-operative dynamic visualization nomograms to predict the 1-year functional outcomes of aSAH patients undergoing coil embolization. METHODS: Data were obtained from 400 aSAH patients undergoing endovascular coiling admitted to the People's Hospital of Hunan Province in China (2015-2019). The key indicator was the modified Rankin Score (mRS), with 3-6 representing poor functional outcomes. Multivariate logistic regression (MLR)-based visual nomograms were developed to analyze baseline characteristics and post-operative complications. The evaluation of nomogram performance included discrimination (measured by C statistic), calibration (measured by the Hosmer-Lemeshow test and calibration curves), and clinical usefulness (measured by decision curve analysis). RESULTS: Fifty-nine aSAH patients (14.8%) had poor outcomes. Both nomograms showed good discrimination, and the post-operative nomogram demonstrated superior discrimination to the pre-operative nomogram with a C statistic of 0.895 (95% CI: 0.844-0.945) vs. 0.801 (95% CI: 0.733-0.870). Each was well calibrated with a Hosmer-Lemeshow p-value of 0.498 vs. 0.276. Moreover, decision curve analysis showed that both nomograms were clinically useful, and the post-operative nomogram generated more net benefit than the pre-operative nomogram. Web-based online calculators have been developed to greatly improve the efficiency of clinical applications. CONCLUSIONS: Pre- and post-operative dynamic nomograms could support pre-operative treatment decisions and post-operative management in aSAH patients, respectively. Moreover, this study indicates that integrating post-operative variables into the nomogram enhanced prediction accuracy for the poor outcome of aSAH patients.

Telomere maintenance genes-derived prognosis signature characterizes immune landscape and predicts prognosis of head and neck squamous cell carcinoma.

Telomere dysfunction has been identified as a biological marker of cancer progression in several types of cancer, including Head and Neck Squamous Cell Carcinoma (HNSCC). This study aimed to characterize the telomere maintenance genes (TMG)-related signature in prognosis and treatment response in HNSCC. The transcriptome and clinical data of HNSCC were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases, respectively. Non-negative matrix factorization (NMF) was used to identify molecular subtypes derived from TMG. Gene set enrichment analysis (GSEA) was performed to analyze the differentially expressed pathways between subtypes, and a risk score model derived from TMG was established. Kaplan-Meier survival analysis was used to evaluate inter-group prognostic features, and the correlation between TMG-derived molecular subtypes and risk score model with immune infiltration, immunotherapy, and chemosensitivity was assessed. Two HNSCC subtypes were identified based on 59 TMG-related genes, which exhibit significant heterogeneity in prognosis, immune cell infiltration, and treatment response. Additionally, a TMG-derived risk signature containing 9 genes was developed to assess the prognosis of HNSCC patients. The signature had significant predictive ability for HNSCC prognosis and was significantly correlated with immune cell infiltration and immunotherapy response. A nomogram integrating the risk signature, N stage and radiotherapy was constructed to predict 1-, 3-, and 5-year overall survival (OS) of HNSCC patients, which had better performance than other prognostic models and included TMG-derived risk score, radiotherapy, and N stage. This study identified TMG-derived molecular subtypes in HNSCC and developed a novel prognostic score model, highlighting the potential value of TMG in HNSCC prognosis and immunotherapy.

An adhesion-based method for rapid and low-cost isolation of circulating tumor cells.

BACKGROUND: Noninvasive monitoring of cancer through circulating tumor cells (CTCs) is hampered long by unsatisfactory CTCs testing techniques. Efficient isolation of CTCs in a rapid and price-favorable way from billions of leukocytes is crucial for testing. METHODS: We developed a new method based on the stronger adhesive power of CTCs versus leukocytes to sensitively isolate CTCs. Using a BSA-coated microplate and low-speed centrifuge, this method could easily separate cancer cells within 20 min at a very low cost. RESULT: The capture ratio can reach 70.7-86.6% in various cancer cell lines (breast/lung/liver/cervical/colorectal cancer) covering different epithelial-mesenchymal transformation (EMT) phenotypes and cell sizes, demonstrating the potential for efficient pan-cancer CTCs detection. Moreover, the label-free process can well preserve cell viability (∼99%) to fit downstream DNA/RNA sequencing. CONCLUSIONS: A novel technique for non-destructive and rapid enrichment of CTCs has been devised. It has enabled the successful isolation of rare tumor cells in the patient blood sample and pleural effusion, highlighting a promising future of this method in clinical translation.