Development of predictive model for the neurological deterioration among mild traumatic brain injury patients using machine learning algorithms.

BACKGROUND: Mild traumatic brain injury (mTBI) comprises a majority of traumatic brain injury (TBI) cases. While some mTBI would suffer neurological deterioration (ND) and therefore have poorer prognosis. This study was designed to develop the predictive model for the ND among mTBI using machine learning algorithms. METHODS: mTBI patients recorded in the Medical Information Mart for Intensive Care-III were selected for the study. The ND was defined as a drop of Glasgow Coma Scale ≥ 2 within the first 7 day after admission. Eight machine learning algorithms were trained and validated with 5-fold cross validation including extreme gradient boosting, logistic regression, light gradient boosting machine, random forest, adaptive boosting, decision tree, complement naïve Bayes, and support vector machine. The value of eight machine learning algorithms was compared by the area under the receiver operating characteristic curve (AUC). RESULTS: 361 mTBI patients suffered the ND with the incidence of 30.7%. The ND group had higher 30-day mortality (p = 0.001). In the training cohort of mTBI patients, the random forest performed the best on predicting the ND with the AUC of 1.000. The XGBoost and AdaBoost had an AUC of 0.827 and 0.815, respectively. The logistic regression performed the best on predicting the ND in the validation cohort with the AUC of 0.741. The XGBoost, random forest and AdaBoost had an AUC of 0.729, 0.735, 0.736 in the validation cohort, respectively. After adjusting confounding effects, the multivariate logistic regression found only two independent risk factors for the ND including Sequential Organ Failure Assessment (SOFA) (p < 0.001) and hypertension (p = 0.001). The logistic regression predictive model composed of SOFA and hypertension had an AUC of 0.741. CONCLUSIONS: SOFA score and complicated hypertension are two independent risk factors for the neurological deterioration among mTBI patients. The logistic regression predictive model incorporating SOFA and hypertension is helpful to identify mTBI patients with the high risk of ND.

Nomogram for predicting cardiovascular disease mortality in patients with meningioma: a competing risk analysis.

Cancer patients may have increased risk of cardiovascular mortality than general population. We designed this study to investigate the incidence and risk factors of cardiovascular mortality in meningioma patients. Meningioma patients recorded in Surveillance Epidemiology and End Results (SEER) database between 2004 and 2016 were eligible for this study. The standardized mortality ratio (SMR) was calculated to present the relative risk of cardiovascular mortality (ICD-10 codes I00-I99) in meningioma patients compared with general population. Fine-Gray subdistribution proportional hazards regression was performed to identify risk factors of cardiovascular mortality and construct nomogram for predicting cardiovascular-specific survival in meningioma patients. Among 94,067 meningioma patients included in this study, 6145 (6.5%) and 16549 (17.6%) patients died due to cardiovascular diseases and other causes, respectively. The cardiovascular disease-related SMR of included meningioma patients was 25.31 compared with the general population. Results of multivariate competing risk analysis showed that age, male gender, race, marital status, insurance status, tumor size, tumor location, histologic type, and surgery options were risk factors of cardiovascular mortality. The C-index of our constructed nomogram for predicting cardiovascular specific survival was 0.730 (0.712-0.748) and 0.726 (0.696-0.756) in training cohort and validation cohort, respectively. Incorporating demographic and clinical variables, the nomogram we constructed is effective in predicting cardiovascular mortality in meningioma patients and could guide physicians to reasonably control clinical risk factors of cardiovascular mortality in meningioma patients.

Liver fibrosis score is associated with the mortality of traumatic brain injury patients.

The fibrosis-4 score is a marker of liver fibrosis and has been confirmed to be associated with the prognosis of various diseases. There is no study exploring the prognostic value of the fibrosis-4 score in traumatic brain injury (TBI) patients. We design this study to explore the association between the fibrosis-4 score and mortality from TBI. TBI patients from the Medical Information Mart for Intensive Care-III database were extracted for the study. Univariate and multivariate logistic regressions were sequentially performed to analyze the association between fibrosis-4 and mortality in TBI. The area under the receiver operating characteristic curve (AUC) was drawn to evaluate the prognostic value of fibrosis-4 and other scores. A total of 1018 TBI patients were included, with a 30-day mortality of 24.2%. Non-survivors had older age, lower Glasgow Coma Scale (GCS), and higher injury severity score (ISS) than survivors. The aspartate aminotransferase platelet ratio index (APRI) and fibrosis-4 score were significantly higher in non-survivors. Univariate logistic regression showed that age, GCS, ISS, white blood cell, hemoglobin, fibrosis-4 score, subarachnoid hemorrhage, and anticoagulants were associated with the mortality of TBI patients. Multivariate logistic regression presented that age, GCS, ISS, fibrosis-4 score, subarachnoid hemorrhage, and anticoagulants were independent risk factors of mortality in TBI patients after adjusting for confounding effects. The AUC of the GCS, ISS, APRI, and fibrosis-4 score for predicting mortality was 0.711, 0.625, 0.592, and 0.627, respectively. Composed of age, GCS, ISS, fibrosis-4 score, subarachnoid hemorrhage, and anticoagulants, the predictive model had the highest AUC value of 0.790. The fibrosis-4 score is an independent risk factor for mortality in TBI. The model incorporating fibrosis-4 performs well in predicting the prognosis of TBI patients.

Prediction of Acute Respiratory Distress Syndrome in Traumatic Brain Injury Patients Based on Machine Learning Algorithms.

Background: Acute respiratory distress syndrome (ARDS) commonly develops in traumatic brain injury (TBI) patients and is a risk factor for poor prognosis. We designed this study to evaluate the performance of several machine learning algorithms for predicting ARDS in TBI patients. Methods: TBI patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were eligible for this study. ARDS was identified according to the Berlin definition. Included TBI patients were divided into the training cohort and the validation cohort with a ratio of 7:3. Several machine learning algorithms were utilized to develop predictive models with five-fold cross validation for ARDS including extreme gradient boosting, light gradient boosting machine, Random Forest, adaptive boosting, complement naïve Bayes, and support vector machine. The performance of machine learning algorithms were evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy and F score. Results: 649 TBI patients from the MIMIC-III database were included with an ARDS incidence of 49.5%. The random forest performed the best in predicting ARDS in the training cohort with an AUC of 1.000. The XGBoost and AdaBoost ranked the second and the third with an AUC of 0.989 and 0.815 in the training cohort. The random forest still performed the best in predicting ARDS in the validation cohort with an AUC of 0.652. AdaBoost and XGBoost ranked the second and the third with an AUC of 0.631 and 0.620 in the validation cohort. Several mutual top features in the random forest and AdaBoost were discovered including age, initial systolic blood pressure and heart rate, Abbreviated Injury Score chest, white blood cells, platelets, and international normalized ratio. Conclusions: The random forest and AdaBoost based models have stable and good performance for predicting ARDS in TBI patients. These models could help clinicians to evaluate the risk of ARDS in early stages after TBI and consequently adjust treatment decisions.

Association between early elevated phosphate and mortality among critically ill elderly patients: a retrospective cohort study.

BACKGROUND: Phosphate disturbances are relatively common in hospitalized patients, especially in critically ill patients. The abnormal phosphate levels may indicate an abnormal body condition. However, little is known about the association between elevated serum phosphate and outcome in critically ill elderly patients. Therefore, the purpose of the present study was to investigate the association between early elevated phosphate and mortality in critically ill elderly patients. METHODS: The present study was a retrospective cohort study based on the medical information mart for intensive care IV (MIMIC-IV) database. Patients with age ≥60 years old were enrolled in the present study. The primary outcome in the present study was ICU mortality. Univariate and multivariate Cox proportional hazard regression analyses were used to evaluate the association between early elevated phosphate and ICU mortality in critically ill elderly patients. RESULTS: Twenty-four thousand two hundred eighty-nine patients were involved in this analysis and 2,417 patients died in ICU. The median age of involved patients was 78.4 (67.5, 82.9) years old. The median level of serum phosphate in the survivor group was 3.6 (3.0, 4.3) mg/dL, and the median level of serum phosphate in the non-survivor group was 4.4 (3.4, 5.8) mg/dL. The level of serum phosphate in the non-survivor group was significantly higher than the survivor group (4.4 vs. 3.6, P<0.001). The multivariate Cox proportional hazard regression demonstrated that elevated phosphate was an independent risk factor for ICU mortality, after adjustment for other covariates (HR=1.056, 95%CI: 1.028-1.085, P<0.001). CONCLUSIONS: In critically ill elderly patients, early elevated phosphate was significantly associated with increased ICU mortality.

Molecular mechanism of islet ß-cell functional alternations during type 2 diabetes.

In recent years, the incidence rate of type 2 diabetes (T2D) has risen rapidly and has become a global health crisis. Recent experimental and clinical studies have shown that islet ß-cell dysfunction is an important cause of T2D and its related complications. ß-cells undergo dynamic compensation and decompensation in the course of T2D. In this process, metabolic stress responses, such as ER stress, oxidative stress and inflammation, are key regulators of ß-cell functional alternations. In this review, we summarize the research progress on the ß-cell functional dynamics in the course of T2D, in order to deepen the understanding of the molecular mechanism of T2D, and provide reference for its precise diagnosis and clinical intervention.

The Prognostic Nutritional Index is associated with mortality of COVID-19 patients in Wuhan, China.

BACKGROUND: Declared as pandemic by WHO, the coronavirus disease 2019 (COVID-19) pneumonia has brought great damage to human health. The uncontrollable spread and poor progression of COVID-19 have attracted much attention from all over the world. We designed this study to develop a prognostic nomogram incorporating Prognostic nutritional index (PNI) in COVID-19 patients. METHODS: Patients confirmed with COVID-19 and treated in Renmin Hospital of Wuhan University from January to February 2020 were included in this study. We used logistic regression analysis to find risk factors of mortality in these patients. A prognostic nomogram was constructed and receiver operating characteristics (ROC) curve was drawn to evaluate the predictive value of PNI and this prognostic model. RESULTS: Comparison of baseline characteristics showed non-survivors had higher age (P < .001), male ratio (P = .038), neutrophil-to-lymphocyte ratio (NLR) (P < .001), platelet-to-lymphocyte ratio (PLR) (P < .001), and PNI (P < .001) than survivors. In the multivariate logistic regression analysis, independent risk factors of mortality in COVID-19 patients included white blood cell (WBC) (OR 1.285, P = .039), PNI (OR 0.790, P = .029), LDH (OR 1.011, P < .015). These three factors were combined to build the prognostic model. Area under the ROC curve (AUC) of only PNI and the prognostic model was 0.849 (95%Cl 0.811-0.888) and 0.950 (95%Cl 0.922-0.978), respectively. And calibration plot showed good stability of the prognostic model. CONCLUSION: This research indicates PNI is independently associated with the mortality of COVID-19 patients. Prognostic model incorporating PNI is beneficial for clinicians to evaluate progression and strengthen monitoring for COVID-19 patients.

Direct interaction between selenoprotein P and tubulin.

Selenium (Se), an essential trace element for human health, mainly exerts its biological function via selenoproteins. Among the 25 selenoproteins identified in human, selenoprotein P (SelP) is the only one that contains multiple selenocysteines (Sec) in the sequence, and has been suggested to function as a Se transporter. Upon feeding a selenium-deficient diet, mice lacking SelP develop severe neurological dysfunction and exhibit widespread brainstem neurodegeneration, indicating an important role of SelP in normal brain function. To further elucidate the function of SelP in the brain, SelP was screened by the yeast two-hybrid system from a human fetal brain cDNA library for interactive proteins. Our results demonstrated that SelP interacts with tubulin, alpha 1a (TUBA1A). The interaction between SelP and tubulin was verified by fluorescence resonance energy transfer (FRET) and co-immunoprecipitation (co-IP) assays. We further found that SelP interacts with the C-terminus of tubulin by its His-rich domain, as demonstrated by FRET and Isothermal Titration Calorimetry (ITC) assays. The implications of the interaction between SelP and tubulin in the brain and in Alzheimer's disease are discussed.

Abnormal serum Magnesium Level is Associated with the Coagulopathy in Traumatic Brain Injury Patients.

BACKGROUND: Coagulopathy is associated with poor prognosis of traumatic brain injury (TBI) patients. This study is performed to explore the association between serum magnesium level and the risk of coagulopathy in TBI. METHODS: TBI patients from the Medical Information Mart for Intensive Care-III database were included for this study. Logistic regression analysis was performed to explore risk factors and develop a predictive model for coagulopathy in TBI. The restricted cubic spline (RCS) was utilized to analyze the association between serum magnesium level and the development of coagulopathy. Receiver operating characteristic curve was drawn to evaluate the performance of the predictive model for coagulopathy. RESULTS: The incidence of coagulopathy in TBI was 32.6%. The RCS indicated the association between magnesium and coagulopathy was U-shaped. Multivariate logistic regression confirmed age, coronary heart disease, cerebral vascular disease, chronic liver disease, GCS, ISS, epidural hematoma, hemoglobin, shock index and magnesium level were independently associated with the coagulopathy in TBI. Compared with patients of magnesium level between 1.7 and 2.3 mg/dL, those with magnesium level below 1.7 mg/dL or above 2.2 mg/dL had a higher risk of coagulopathy. CONCLUSION: Both hypermagnesemia and hypomagnesemia are associated with higher risk of coagulopathy in TBI patients. Physicians should pay more attention on preventing coagulopathy in TBI patients with hypomagnesemia or hypermagnesemia.

The association between ondansetron use and mortality risk of traumatic brain injury patients: a population-based study.

Background: Traumatic brain injury (TBI) patients suffer high risks of mortality. Ondansetron has been verified to be effective in improving the prognosis of some kinds of critically ill patients. We design this study to explore whether ondansetron use is associated with lower risks of mortality among TBI patients. Methods: TBI patients from the Medical Information Mart for Intensive Care-III were collected. The usage of ondansetron, including intravenous injection and oral tablet, since admission to the Beth Israel Deaconess Medical Center between 2001 and 2012 was identified. Univariate and multivariate logistic regression were performed to analyze the relationship between the ondansetron use and mortality of TBI patients. Propensity score matching (PSM) was utilized to generate balanced cohorts of the non-ondansetron use group and ondansetron use group. Sub-group analysis was performed to verify the association between the ondansetron use and mortality of TBI patients in different TBI severity levels after PSM. Results: In TBI cohorts before PSM, the usage incidence of ondansetron was 37.2%. The 30-day mortality was significantly lower in the ondansetron group (p < 0.001). The multivariate logistic regression showed that ondansetron was associated with the lower mortality of TBI patients (p = 0.008). In TBI cohorts after PSM, the 30-day mortality of the ondansetron group was lower than that of the non-ondansetron group, although without statistical significance (p = 0.079). Logistic regression indicated ondansetron use was significantly associated with the lower mortality of moderate-to-severe TBI (p < 0.001) but not mild TBI (p = 0.051). In addition, Cox regression also presented that ondansetron use was significantly associated with the lower mortality of moderate-to-severe TBI (p < 0.001) but not mild TBI (p = 0.052). Conclusion: Ondansetron usage is associated with a lower mortality risk of moderate-to-severe TBI but not mild TBI patients. Ondansetron may be a novel adjunctive therapeutic strategy to improve the prognosis of moderate-to-severe TBI patients.

Classification and Regression Tree Predictive Model for Acute Kidney Injury in Traumatic Brain Injury Patients.

Background: Acute kidney injury (AKI) is prevalent in hospitalized patients with traumatic brain injury (TBI), and increases the risk of poor outcomes. We designed this study to develop a visual and convenient decision-tree-based model for predicting AKI in TBI patients. Methods: A total of 376 patients admitted to the emergency department of the West China Hospital for TBI between January 2015 and June 2019 were included. Demographic information, vital signs on admission, laboratory test results, radiological signs, surgical options, and medications were recorded as variables. AKI was confirmed since the second day after admission, based on the Kidney Disease Improving Global Outcomes criteria. We constructed two predictive models for AKI using least absolute shrinkage and selection operator (LASSO) regression and classification and regression tree (CART), respectively. Receiver operating characteristic (ROC) curves of these two predictive models were drawn, and the area under the ROC curve (AUC) was calculated to compare their predictive accuracy. Results: The incidence of AKI on the second day after admission was 10.4% among patients with TBI. Lasso regression identified five potent predictive factors for AKI: glucose, serum creatinine, cystatin C, serum uric acid, and fresh frozen plasma transfusions. The CART analysis showed that glucose, serum uric acid, and cystatin C ranked among the top three in terms of the feature importance of the decision tree model. The AUC value of the decision-tree predictive model was 0.892, which was higher than the 0.854 of the LASSO regression model, although the difference was not statistically significant. Conclusion: The decision tree model is valuable for predicting AKI among patients with TBI. This tree-based flowchart is convenient for physicians to identify patients with TBI who are at high risk of AKI and prompts them to develop suitable therapeutic strategies.

A prognostic model incorporating the albumin-corrected anion gap in patients with aneurysmal subarachnoid hemorrhage.

Background: Aneurysmal subarachnoid hemorrhage (aSAH) patients typically have poor prognoses. The anion gap (AG) has been proven to correlate with mortality in various critically ill patients. However, hypoalbuminemia can lead to underestimations of the true anion gap levels. This study was conducted to verify the prognostic value of single AG and albumin-corrected anion gap (ACAG) among aSAH patients. Methods: Significant factors in the univariate logistic regression analysis were included in the multivariate logistic regression analysis to explore the risk factors for mortality in aSAH patients and to confirm the independent relationship between ACAG and mortality. The restricted cubic spline (RCS) was used to visually show the relationship between ACAG level and mortality risk of aSAH patients. The predictive model for mortality was developed by incorporating significant factors into the multivariate logistic regression analysis. The prognostic value of ACAG and the developed model was evaluated by calculating the area under the receiver operating characteristics curve (AUC). Results: Among 710 aSAH patients, a 30-day mortality was observed in 20.3% of the cases. A positive relationship was demonstrated between the ACAG level and mortality in aSAH patients using the RCS curve. The multivariate logistic regression analysis helped discover that only six factors were finally and independently related to mortality of aSAH patients after adjusting for confounding effects, including the Hunt-Hess scale score (p = 0.006), surgical options (p < 0.001), white blood cell count (p < 0.001), serum chloride levels (p = 0.023), ACAG (p = 0.039), and delayed cerebral ischemia (p < 0.001). The AUC values for the AG, albumin, and ACAG in predicting mortality among aSAH patients were 0.606, 0.536, and 0.617, respectively. A logistic regression model, which includes the Hunt-Hess scale score, surgical options, white blood cell count, serum chloride levels, ACAG, and delayed cerebral ischemia, achieved an AUC of 0.911 for predicting mortality. Conclusion: The ACAG is an effective prognostic marker for aSAH patients. A prognostic model incorporating ACAG could help clinicians evaluate the risk of poor outcomes among aSAH patients, thereby facilitating the development of personalized therapeutic strategies.

EASIX is an effective marker in predicting mortality of traumatic brain injury patients.

BACKGROUND: The Endothelial Activation and Stress Index (EASIX) is a novel marker of endothelial injury and correlates with survival of various patients. The endothelial dysfunction plays an important role on the pathophysiological process of traumatic brain injury (TBI). This study was designed to explore the prognostic value of EASIX on TBI patients. METHODS: 358 TBI patients hospitalized in the West China hospital between October 2018 and October 2022 were enrolled for this study. The EASIX was calculated based on the formula: lactate dehydrogenase (U/L) × creatinine (mg/dL)/platelets (109 cells/L). The univariate and multivariate logistic regression with forward method was performed to explore the association between EASIX and mortality. A prognostic model was developed combining significant risk factors in the multivariate logistic regression. The receiver operating characteristic (ROC) curve was used to compare the predictive accuracy of the EASIX and the developed model. RESULTS: The 30-day mortality of enrolled 358 TBI patients was 51.1%. Non-survivors had higher EASIX than survivors (p < 0.001). The multivariate logistic regression confirmed seven risk factors for mortality of TBI including injury mechanism (p = 0.010), GCS (p < 0.001), glucose (p < 0.001), EASIX (p = 0.017), subdural hematoma (p = 0.012), coagulopathy (p = 0.001). The AUC of EASIX, SOFA, GCS was 0.747, 0.748 and 0.774, respectively. The AUC of developed predictive model was 0.874 with the sensitivity of 0.913 and specificity of 0.686. CONCLUSIONS: The EASIX is a reliable marker for predicting mortality of TBI patients. The predictive model incorporating EASIX is helpful for clinicians to evaluate the mortality risk of TBI patients.

Thiamine use is associated with better outcomes for traumatic brain injury patients.

Background: Traumatic brain injury (TBI) is a global health concern that often leads to poor prognosis. We designed this study to explore whether thiamine use is associated with a better prognosis of TBI. Methods: TBI patients selected from the Medical Information Mart for Intensive Care-III database were included in the study. Univariate and multivariate Cox regression analyses were performed to examine the relationship between thiamine use and mortality in TBI patients. Propensity score matching (PSM) was utilized to generate balanced cohorts of the non-thiamine use group and the thiamine use group. Subgroup analysis was performed in the cohort after PSM to verify the association between thiamine use and mortality in TBI patients across different stratifications. Results: The incidence of thiamine use in TBI was 18.3%. The thiamine use group had a lower 30-day mortality rate (p < 0.001), a longer length of ICU stay (p < 0.001), and a longer length of hospital stay (p < 0.001) than the non-thiamine use group, both in the primary cohort before PSM and the cohort after PSM. A multivariate Cox regression analysis confirmed that thiamine use was independently associated with mortality (OR = 0.454, p < 0.001) after adjusting for confounding effects. In the cohort after PSM, the subgroup analysis showed that thiamine use is associated with lower mortality in TBI patients with a Glasgow Coma Scale (GCS) score of < 13, but it is not associated with mortality in TBI patients whose GCS score is ≥13. Conclusion: Thiamine supplementation is effective in improving the outcome of TBI, except in cases of mild TBI. The optimal thiamine supplementation strategy for TBI is worthwhile to be explored in future studies.

MEAN ARTERIAL PRESSURE/NOREPINEPHRINE EQUIVALENT DOSE INDEX AS AN EARLY MEASURE FOR MORTALITY RISK IN PATIENTS WITH SHOCK ON VASOPRESSORS.

ABSTRACT: Purpose: We aimed to investigate the association between the early mean arterial pressure (MAP)/norepinephrine equivalent dose (NEQ) index and mortality risk in patients with shock on vasopressors and further identify the breakpoint value of the MAP/NEQ index for high mortality risk. Methods: Based on the Medical Information Mart for Intensive Care IV database, we conducted a retrospective cohort study involving 19,539 eligible intensive care unit records assigned to three groups (first tertile, second tertile, and third tertile) by different MAP/NEQ indexes within 24 h of intensive care unit admission. The study outcomes were 7-, 14-, 21-, and 28-day mortality. A Cox model was used to examine the risk of mortality following different MAP/NEQ indexes. The receiving operating characteristic curve was used to evaluate the predictive ability of the MAP/NEQ index. The restricted cubic spline was applied to fit the flexible correlation between the MAP/NEQ index and risk of mortality, and segmented regression was further used to identify the breakpoint value of the MAP/NEQ index for high mortality risk. Results: Multivariate Cox analysis showed that a high MAP/NEQ index was independently associated with decreased mortality risks. The areas under the receiving operating characteristic curve of the MAP/NEQ index for different mortality outcomes were nearly 0.7. The MAP/NEQ index showed an L-shaped association with mortality outcomes or mortality risks. Exploration of the breakpoint value of the MAP/NEQ index suggested that a MAP/NEQ index less than 183 might be associated with a significantly increased mortality risk. Conclusions: An early low MAP/NEQ index was indicative of poor prognosis in patients with shock on vasopressors.

Manganese@Albumin Nanocomplex and Its Assembled Nanowire Activate TLR4-Dependent Signaling Cascades of Macrophages.

The immunomodulatory effect of divalent manganese cations (Mn2+ ), such as activation of the cGAS-STING pathway or NLRP3 inflammasomes, positions them as adjuvants for cancer immunotherapy. In this study, it is found that trace Mn2+ ions, bound to bovine serum albumin (BSA) to form Mn@BSA nanocomplexes, stimulate pro-inflammatory responses in human- or murine-derived macrophages through TLR4-mediated signaling cascades. Building on this, the assembly of Mn@BSA nanocomplexes to obtain nanowire structures enables stronger and longer-lasting immunostimulation of macrophages by regulating phagocytosis. Furthermore, Mn@BSA nanocomplexes and their nanowires efficiently activate peritoneal macrophages, reprogramme tumor-associated macrophages, and inhibit the growth of melanoma tumors in vivo. They also show better biosafety for potential clinical applications compared to typical TLR4 agonists such as lipopolysaccharides. Accordingly, the findings provide insights into the mechanism of metalloalbumin complexes as potential TLR agonists that activate macrophage polarization and highlight the importance of their nanostructures in regulating macrophage-mediated innate immunity.

Galectin-3 impairs calcium transients and ß-cell function.

In diabetes, macrophages and inflammation are increased in the islets, along with ß-cell dysfunction. Here, we demonstrate that galectin-3 (Gal3), mainly produced and secreted by macrophages, is elevated in islets from both high-fat diet (HFD)-fed and diabetic db/db mice. Gal3 acutely reduces glucose-stimulated insulin secretion (GSIS) in ß-cell lines and primary islets in mice and humans. Importantly, Gal3 binds to calcium voltage-gated channel auxiliary subunit gamma 1 (CACNG1) and inhibits calcium influx via the cytomembrane and subsequent GSIS. ß-Cell CACNG1 deficiency phenocopies Gal3 treatment. Inhibition of Gal3 through either genetic or pharmacologic loss of function improves GSIS and glucose homeostasis in both HFD-fed and db/db mice. All animal findings are applicable to male mice. Here we show a role of Gal3 in pancreatic ß-cell dysfunction, and Gal3 could be a therapeutic target for the treatment of type 2 diabetes.

DRAK2 suppresses autophagy by phosphorylating ULK1 at Ser56 to diminish pancreatic ß cell function upon overnutrition.

Impeded autophagy can impair pancreatic ß cell function by causing apoptosis, of which DAP-related apoptosis-inducing kinase-2 (DRAK2) is a critical regulator. Here, we identified a marked up-regulation of DRAK2 in pancreatic tissue across humans, macaques, and mice with type 2 diabetes (T2D). Further studies in mice showed that conditional knockout (cKO) of DRAK2 in pancreatic ß cells protected ß cell function against high-fat diet feeding along with sustained autophagy and mitochondrial function. Phosphoproteome analysis in isolated mouse primary islets revealed that DRAK2 directly phosphorylated unc-51-like autophagy activating kinase 1 (ULK1) at Ser56, which was subsequently found to induce ULK1 ubiquitylation and suppress autophagy. ULK1-S56A mutation or pharmacological inhibition of DRAK2 preserved mitochondrial function and insulin secretion against lipotoxicity in mouse primary islets, Min6 cells, or INS-1E cells. In conclusion, these findings together indicate an indispensable role of the DRAK2-ULK1 axis in pancreatic ß cells upon metabolic challenge, which offers a potential target to protect ß cell function in T2D.

Different Forms of Selenoprotein M Differentially Affect Aß Aggregation and ROS Generation.

Selenoprotein M (SelM), one of the executants of selenium in vivo, is highly expressed in human brain and most probably involved in antioxidation, neuroprotection, and intracellular calcium regulation, which are the key factors for preventing the onset and progression of Alzheimer's disease (AD). In this paper, human SelM was successfully overexpressed in human embryonic kidney cells HEK293T. Sodium selenite (Na(2)SeO(3) 0.5 µmol/L) increased the expression of full-length SelM and inhibited the expression of truncated SelM. The full-length SelM exhibited higher antioxidant activity than its selenocysteine-to-cysteine mutation form SelM', whereas the truncated SelM had an adverse effect that increased the oxidative stress level of cells. When ß-amyloid (Aß(42), an AD relevant peptide) was cotransfected with the empty expression vector, SelM, or SelM' under the induction of 0.5 µmol/L Na(2)SeO(3), the intracellular Aß(42) aggregation rates were detected to be 57.9% ± 5.5%, or 22.3% ± 2.6%, or 26.3% ± 2.1%, respectively, showing the inhibitory effects on Aß aggregation by the full-length SelM and SelM'. Meanwhile, the intumescentia of mitochondria caused by Aß(42) transfection was significantly mitigated by the cotransfection of SelM or SelM' with Aß(42) under the induction of 0.5 µmol/L Na(2)SeO(3). On the contrary, cotransfection of SelM and Aß(42) without the induction of Na(2)SeO(3) increased Aß(42) aggregation rate to 65.1% ± 3.2%, and it could not inhibit the Aß-induced intumescent mitochondria. In conclusion, full-length SelM and SelM¢ might prevent Aß aggregation by resisting oxidative stress generated during the formation of Aß oligomers in cells.