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.

Incorporating preoperative frailty to assist in early prediction of postoperative pneumonia in elderly patients with hip fractures: an externally validated online interpretable machine learning model.

BACKGROUND: This study aims to implement a validated prediction model and application medium for postoperative pneumonia (POP) in elderly patients with hip fractures in order to facilitate individualized intervention by clinicians. METHODS: Employing clinical data from elderly patients with hip fractures, we derived and externally validated machine learning models for predicting POP. Model derivation utilized a registry from Nanjing First Hospital, and external validation was performed using data from patients at the Fourth Affiliated Hospital of Nanjing Medical University. The derivation cohort was divided into the training set and the testing set. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used for feature screening. We compared the performance of models to select the optimized model and introduced SHapley Additive exPlanations (SHAP) to interpret the model. RESULTS: The derivation and validation cohorts comprised 498 and 124 patients, with 14.3% and 10.5% POP rates, respectively. Among these models, Categorical boosting (Catboost) demonstrated superior discrimination ability. AUROC was 0.895 (95%CI: 0.841-0.949) and 0.835 (95%CI: 0.740-0.930) on the training and testing sets, respectively. At external validation, the AUROC amounted to 0.894 (95% CI: 0.821-0.966). The SHAP method showed that CRP, the modified five-item frailty index (mFI-5), and ASA body status were among the top three important predicators of POP. CONCLUSION: Our model's good early prediction ability, combined with the implementation of a network risk calculator based on the Catboost model, was anticipated to effectively distinguish high-risk POP groups, facilitating timely intervention.

SABRE Hyperpolarization of Exchangeable Protons in Methanol-d4 through Dynamic Covalent Bonds.

Utilizing para-hydrogen (p-H2)-induced hyperpolarization to increase the sensitivity of nuclear magnetic resonance, especially signal amplification by reversible exchange (SABRE), has been widely studied. Here, we achieved hyperpolarization of exchangeable protons in methanol-d4 by introducing dynamic covalent bonds as reversible exchange following the SABRE process. To release the hyperpolarized CD3OH, the pyridine-based ligands with aldehyde groups underwent acetal exchange between the aldehyde and hydroxyl groups of CD3OH after being first hyperpolarized by SABRE. Our mechanistic study highlights the importance of the reversible exchange of functional groups and chemical kinetics in realizing hyperpolarization of exchangeable protons in methanol-d4. Our work broadens SABRE's chemical system compatibility and possible applications.

Transcription factors in chimeric antigen receptor T-cell development.

Chimeric antigen receptor (CAR) T-cell therapy is a new and innovative approach to treating cancers that has shown promising results in the treatment of lymphoma. However, it has been found to be less effective in the treatment of solid tumors. To overcome the limitation, researchers have explored the use of combined CAR-T therapy with other complementary regimens that target specific genes or biomarkers, which would enhance the synergistic therapeutic effects. Transcription factors (TFs) have been identified as potential markers that can regulate gene expression in CAR-T cells to enhance their cytotoxicity and safety. TFs are known to bind DNA specifically and recruit cofactor proteins to regulate the expression of target genes. By targeting TFs, it is possible to improve the anti-tumor response of CAR-T cells by altering their phenotype and transcriptional map, thereby increasing their effector function, such as reducing the exhaustion, enhancing the survival, and cytotoxicity of CAR-T cells. This review summarizes the application of transcription factors in CART therapy to enhance the synergistic therapeutic effect of CAR-T cells in the treatment of solid tumors and improve their anti-tumor responses.

Overexpressing Bcl-2 enhances murine chimeric antigen receptor T cell therapy against solid tumor.

Chimeric antigen receptor T (CART) cell therapy has demonstrated promising potential in the treatment of hematologic malignancies. However, its application to solid tumors is limited due to the restrictive nature of the tumor microenvironment, resulting in functional failure and poor persistence of CART cells. Overexpression of Bcl-2 in human CART cells (hCART) has been found to significantly enhance their anti-apoptotic effects both in vitro and in vivo. Nevertheless, the evaluation of hCART cells in preclinical studies has predominantly relied on immunodeficient mice xenograft tumor models, making it challenging to assess the impact of hCART cells on normal tissues and the immune system. We established a murine CART (mCART) that overexpresses Bcl-2 and targets the epidermal growth factor receptor variant III (EGFRvIII), named EGFRvIII·mCART-Bcl2. It demonstrated superior proliferation, cytotoxicity, and anti-apoptotic capabilities in vitro. In an immunocompetent mouse model of abdominal metastasis of colorectal cancer, EGFRvIII·mCART-Bcl2 exhibited improved survival of CART in the abdomen, increased tumor clearance, and significantly prolonged overall mouse survival. In summary, our study provides evidence that the introduction of Bcl-2 into mCART cells can enhance their therapeutic efficacy against solid tumors while ensuring safety.

Synergistic effect of chimeric antigen receptor modified with Bcl-2 on enhanced solid tumour targeting.

Engineered T cells expressing chimeric antigen receptors (CARs) have shown remarkable therapeutic effects on haematological malignancies. However, CART cells are less effective on solid tumours mainly due to their weak persistence, which might be caused by activation-induced cell death (AICD). To overcome this limitation, CART cell with the antigen, Epidermal growth factor receptor variant III (EGFRvIII), targeting was modified to carry the anti-apoptotic molecule B cell lymphoma 2 (Bcl-2), and the final construct was named as EGFRvIII·CART-Bcl2 cells. Compared with the EGFRvIII·CART cells, EGFRvIII·CART-Bcl2 cells revealed higher capacities of proliferation, anti-apoptosis and tumour cell killing in vitro. Moreover, EGFRvIII·CART-Bcl2 cells had a longer persistence rate and exerted better anti-tumour effects than EGFRvIII·CART cells in cervical carcinoma xenograft model. Taken together, our findings suggest that incorporating anti-apoptotic molecules into CART cells may enhance its therapeutic effects against solid tumours.

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).

Assessing hypertension and diabetes knowledge, attitudes and practices among residents in Akatsi South District, Ghana using the KAP questionnaire.

Objective: Low awareness of hypertension and diabetes is a public health concern in Ghana. Assessing the general population's behaviour via knowledge, attitude, and practice (KAP) will be invaluable in these diseases, where prevention and control need a lifelong commitment to a healthy lifestyle. Hence, our goal was to assess the behaviour of Akatsi South residents towards the diseases to assist health providers in implementing tailored intervention programs. Methods: This was a population-based cross-sectional study with 150 adults (18-70 years) from November to December 2021. A semi-structured questionnaire with face-to-face interviews was used to obtain data. All variables in the model had descriptive statistics. The Chi-square (χ2) test was used to examine correlations between variables, and a value of p < 0.05 was considered statistically significant. The factors associated with checking blood sugar levels and blood pressure were determined using binary logistic regression. Results: The respondents' mean age and BMI were 32.40 years (± 12.07) and 24.98 kg/m2 (± 2.36), respectively. Only 46.67% of the respondents frequently monitor their blood pressure and 17.33% their blood glucose (at least once a year). Less than half of those surveyed had a good knowledge of hypertension (42.7%) and diabetes (32.0%), whereas nearly 3/4 had poor attitudes regarding both conditions. A binary logistic regression analysis revealed that having a good attitude toward hypertension (exp B = 2.479, p = 0.036) and diabetes (exp B = 4.547, p = 0.009) were the participants' strongest predictor of blood pressure and sugar level checks. However, being overweight (exp B = 0.046, p = 0.002,) or obese (exp B = 0.144, p = 0.034) negatively influenced the frequency with which our respondents checked their blood glucose levels. Conclusion: In the study, we found that the population generally has poor knowledge, which affects their behaviour (attitudes and practices) towards the diseases. To enable healthcare practitioners to reduce disease-associated mortality and morbidity in the future, frequent public health education and promotion about the conditions is critical to closing the knowledge gap.

An optimization for postpartum depression risk assessment and preventive intervention strategy based machine learning approaches.

BACKGROUND: Postpartum depression (PPD) is one of the most common psychiatric disorders for women after delivery. The establishment of an effective PPD prediction model helps to distinguish high-risk groups, and verifying whether such high-risk groups can benefit from drug intervention is very important for clinical guidance. METHODS: We collected data of parturients that underwent a cesarean delivery. The Control group was divided into a training cohort and a testing cohort. Six different ML models were constructed and we compared their prediction performance in the testing cohort. For model interpretation, we introduced SHapley Additive exPlanations (SHAP). Then, training cohort, ketamine group and dexmedetomidine (DEX) group were classified as high or low risk for PPD by the model. A 1:1 propensity score matching (PSM) was performed to compare the incidence of PPD between two groups in different risk cohorts. RESULTS: Extreme gradient enhancement (XGB) had the best recognition effect, with an area under the receiver operating characteristic curve (AUROC) of 0.789 (95 % CI 0.742-0.836) in the training cohort and 0.744 (95 % CI 0.655-0.823) in the testing cohort, respectively. A threshold of 21.5 % PPD risk probability was determined. After PSM, the results showed that the incidence of PPD in the two intervention groups was significantly different from the control group in the high-risk cohort (P < 0.001) but not in the low-risk cohort (P > 0.001). CONCLUSION: Our study demonstrated that the XGB algorithm provided a more accurate in prediction of PPD risk, and it was beneficial to receive early intervention for the high-risk groups distinguished by the model.

[Research progress in B10 cells and autoimmune diseases].

Regulatory B cells (Bregs) are a group of B cells with negative immune regulation, which produce negative regulatory cytokines, such as interleukin-10 (IL-10), transform growth factor ß (TGF- ß), or participate in immune regulation and inhibit inflammatory response by intercellular activities. B10 cells are a kind of Bregs that generates IL-10. In recent years, a large number of studies have reported that B10 cells are involved in the development of a variety of autoimmune diseases. Here, we make a systematic review of the origin and immune mechanism of B10 cells, their roles in autoimmune diseases (rheumatoid arthritis, inflammatory bowel disease and Sjogren's syndrome, etc.) and regulations, coupled with its applications in the treatment of autoimmune diseases.We also discussed the B10 cells as a potential method feasible for treating autoimmune diseases.

Combination of a Novel Fusion Protein CD3εζ28 and Bispecific T Cell Engager Enhances the Persistance and Anti-Cancer Effects of T Cells.

Bi-specific T cell engager (BiTE), an artificial bi-functional fusion protein, has shown promising therapeutic potential in preclinical and clinical studies. However, T cells cannot be sufficiently activated by BiTE, most likely due to lacking co-stimulatory signal. We reasoned that incorporating co-stimulatory signal might have the potential to enhance the T cell activation mediated by BiTE. We, therefore, designed a chimeric fusion protein, named as CD3εζ28, which consists of the CD3ε extracellular region, the CD28 costimulatory signal and the intracellular region of CD3ζ in tandem. T cells genetically modified to express both CD3εζ28 and GFP (T-CD3εζ28-GFP) were generated by retroviral transduction. The results from in vitro experiments showed that T-CD3εζCD28-GFP cells had superior cytotoxic effects on tumor cells in presence of BiTE compared with control T cells, as evidenced by IL-2 and IFN-γ production, T cell proliferation and sequential killing assay. In vivo, T-CD3εζCD28-GFP cells showed superior anti-tumor effects in Hela-BiTE. EGFRvIII xenograft tumor model, as evaluated by tumor growth rate and T cell persistence in comparison with control T cells. In order to further confirm these findings, we generated T cells modified to express both CD3εζCD28 on cell surface and BiTE.CD19 by autocrine manner (T-CD3εζCD28-BiTE.19). The superior anti-tumor effects of T-CD3εζCD28-BiTE.19 cells could also be evidenced by the similar in vitro and in vivo experiments; thus, incorporating co-stimulatory signal may be an effective approach to improve the effector function of T cells mediated by BiTE.

Proteomic profiling of saliva reveals association of complement system with primary Sjögren's syndrome.

INTRODUCTION: To compare the saliva proteomes of experimental Sjögren's syndrome (ESS) model mice and healthy controls to identify potential diagnostic biomarkers for primary Sjögren's syndrome (pSS). METHODS: Proteins were extracted from the saliva of three ESS and three normal control mice using the data-independent acquisition technique. R language was used to identify the differentially expressed proteins (DEPs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed to functionally annotate the DEPs. The protein-protein interaction (PPI) network was constructed and the core proteins were identified with the STRING website and Cytoscape software. The concentrations of Serpin family G member 1 (SERPING1), C3, complement factor H (CFH), fibrinogen alpha (FGA), and fibrinogen gamma (FGG) in saliva were determined by ELISA. RESULTS: A total of 1722 DEPs were identified in the saliva of the ESS mice relative to the controls, of which 50 showed significantly different expression levels between the two groups. SERPING1, C3, CFH, FGA, and FGG were significantly downregulated, and keratin 4 (Krt4) and transglutaminase 3 (TGM3) were upregulated in the saliva of ESS mice. The PPI network showed that SERPING1, C3, FGG, FGA, TGM3, and hemopexin (HPX) were the core proteins. ELISA results showed that the expression of C3, CFH, FGA, and SERPING1 were significantly downregulated in the saliva of ESS mice. However, the expression of FGG was a little downregulated but with no significant difference. SERPING1, FGG, and FGA may downregulate the complement C3 by inhibiting immune complement system, thereby promoting pSS progression. CONCLUSIONS: The salivary proteome of ESS mice was markedly different from that of healthy controls, suggesting that salivary proteomics is a promising noninvasive diagnostic tool for pSS. SERPING1, C3, CFH, FGA, and FGG are potential biomarkers of pSS.

Quinones as preventive agents in Alzheimer's diseases: focus on NLRP3 inflammasomes.

OBJECTIVES: Alzheimer's disease (AD) is a hidden neurological degenerative disease, which main clinical manifestations are cognitive dysfunction, memory impairment and mental disorders. Neuroinflammation is considered as a basic response of the central nervous system. NLRP3 (Nucleotide-binding domain leucine-rich repeat (NLR) and pyrin domain containing receptor 3) inflammasome is closely related to the occurrence of neuroinflammation. Activation of the NLRP3 inflammasome results in the release of cytokines, pore formation and ultimately pyroptosis, which has demonstrated one of the critical roles in AD pathogenesis. Inhibition of the activity of NLRP3 is one of the focuses of the research. Therefore, NLRP3 represents an attractive pharmacological target, and discovery compounds with good NLRP3 inhibitory activity are particularly important. KEY FINDINGS: Quinones have good neuroprotective effects and prevent AD, which may be related to their regulation of inflammatory response. The molecular docking was used to explore 12 quinones with AD prevention and treatment and NLRP3. Docking results showed that the combination of anthraquinones and NLRP3 were the best, and the top two chemical compounds were Purpurin and Rhein, which are the most promising NLRP3 inhibitors. SUMMARY: These quinones may provide the theoretical basis for finding lead compounds for novel neuroprotective agents.