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.

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.

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.

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.

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.

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.

Polyurethane scaffold-based 3D lung cancer model recapitulates in vivo tumor biological behavior for nanoparticulate drug screening.

Lung cancer is the leading cause of cancer mortality worldwide. Preclinical studies in lung cancer hold the promise of screening for effective antitumor agents, but mechanistic studies and drug discovery based on 2D cell models have a high failure rate in getting to the clinic. Thus, there is an urgent need to explore more reliable and effective in vitro lung cancer models. Here, we prepared a series of three-dimensional (3D) waterborne biodegradable polyurethane (WBPU) scaffolds as substrates to establish biomimetic tumor models in vitro. These 3D WBPU scaffolds were porous and could absorb large amounts of free water, facilitating the exchange of substances (nutrients and metabolic waste) and cell growth. The scaffolds at wet state could simulate the mechanics (elastic modulus ∼1.9 kPa) and morphology (porous structures) of lung tissue and exhibit good biocompatibility. A549 lung cancer cells showed adherent growth pattern and rapidly formed 3D spheroids on WBPU scaffolds. Our results showed that the scaffold-based 3D lung cancer model promoted the expression of anti-apoptotic and epithelial-mesenchymal transition-related genes, giving it a more moderate growth and adhesion pattern compared to 2D cells. In addition, WBPU scaffold-established 3D lung cancer model revealed a closer expression of proteins to in vivo tumor, including tumor stem cell markers, cell proliferation, apoptosis, invasion and tumor resistance proteins. Based on these features, we further demonstrated that the 3D lung cancer model established by the WBPU scaffold was very similar to the in vivo tumor in terms of both resistance and tolerance to nanoparticulate drugs. Taken together, WBPU scaffold-based lung cancer model could better mimic the growth, microenvironment and drug response of tumor in vivo. This emerging 3D culture system holds promise to shorten the formulation cycle of individualized treatments and reduce the use of animals while providing valid research data for clinical trials.

The prognostic value of an age-adjusted BIG score in adult patients with traumatic brain injury.

Background: The base deficit, international normalized ratio, and Glasgow Coma Scale (BIG) score was previously developed to predict the outcomes of pediatric trauma patients. We designed this study to explore and improve the prognostic value of the BIG score in adult patients with traumatic brain injury (TBI). Methods: Adult patients diagnosed with TBI in a public critical care database were included in this observational study. The BIG score was calculated based on the Glasgow Coma Scale (GCS), the international normalized ratio (INR), and the base deficit. Logistic regression analysis was performed to confirm the association between the BIG score and the outcome of included patients. Receiver operating characteristic (ROC) curves were drawn to evaluate the prognostic value of the BIG score and novel constructed models. Results: In total, 1,034 TBI patients were included in this study with a mortality of 22.8%. Non-survivors had higher BIG scores than survivors (p < 0.001). The results of multivariable logistic regression analysis showed that age (p < 0.001), pulse oxygen saturation (SpO2) (p = 0.032), glucose (p = 0.015), hemoglobin (p = 0.047), BIG score (p < 0.001), subarachnoid hemorrhage (p = 0.013), and intracerebral hematoma (p = 0.001) were associated with in-hospital mortality of included patients. The AUC (area under the ROC curves) of the BIG score was 0.669, which was not as high as in previous pediatric trauma cohorts. However, combining the BIG score with age increased the AUC to 0.764. The prognostic model composed of significant factors including BIG had the highest AUC of 0.786. Conclusion: The age-adjusted BIG score is superior to the original BIG score in predicting mortality of adult TBI patients. The prognostic model incorporating the BIG score is beneficial for clinicians, aiding them in making early triage and treatment decisions in adult TBI patients.

Platelet count predicts mortality in patients with sepsis: A retrospective observational study.

Platelet count is a key component of sepsis severity score. However, the predictive value of the platelet count at admission for mortality in sepsis remains unclear. We designed a retrospective observational study of patients with sepsis admitted to our hospital from January 2017 to September 2021 to explore the predictive value of platelet count at admission for mortality. A total of 290 patients with sepsis were included in this study. Multivariate logistic regression analysis was used to evaluate the risk factors for mortality and construct a predictive model with statistically significant factors. Compared with survivors, nonsurvivors tended to be much older and had significantly higher acute physiology and chronic health evaluation II and sequential organ failure assessment scores (P < .001). The platelet count was significantly lower in the nonsurvivor group than in the survivor group (P < .001). Multivariate logistic regression analysis indicated that age (P = .003), platelet count (P < .001) and lactate level (P = .018) were independent risk factors for mortality in patients with sepsis. Finally, the area under the receiver operating characteristic curve of platelet count predicting mortality in sepsis was 0.763 (95% confidence interval, 0.709-0.817, P < .001), with a sensitivity of 55.6% and a specificity of 91.8%. In our study, platelet count at admission as a single biomarker showed good predictability for mortality in patients with sepsis.

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.

Machine learning algorithms for prediction of ventilator associated pneumonia in traumatic brain injury patients from the MIMIC-III database.

BACKGROUND: Ventilator associated pneumonia (VAP) is a common complication and associated with poor prognosis of traumatic brain injury (TBI) patients. OBJECTIVES: This study was conducted to explore the predictive performance of different machine-learning algorithms for VAP in TBI patients. METHODS: TBI patients receiving mechanical ventilation more than 48 hours from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were eligible for the study. The VAP was confirmed based on the ICD-9 code. Included patients were separated to the training cohort and the validation cohort with a ratio of 7:3. Predictive models based on different machine learning algorithms were developed using 5-fold cross validation in the training cohort and then verified in the validation cohort by evaluating the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy and F score. RESULTS: 786 TBI patients from the MIMIC-III were finally included with the VAP incidence of 44.0%. The random forest performed the best on predicting VAP in the training cohort with a AUC of 1.000. The XGBoost and AdaBoost were ranked the second and the third with a AUC of 0.915 and 0.789 in the training cohort. While the AdaBoost performed the best on predicting VAP in the validation cohort with a AUC of 0.706. The XGBoost and random forest were ranked the second and the third with the AUC of 0.685 and 0.683 in the validation cohort. Generally, the random forest and XGBoost were likely to be over-fitting while the AdaBoost was relatively stable in predicting the VAP. CONCLUSIONS: The AdaBoost performed well and stably on predicting the VAP in TBI patients. Developing programs using AdaBoost in portable electronic devices may effectively assist physicians in assessing the risk of VAP in TBI.

Procalcitonin as a biomarker of nosocomial pneumonia in aneurysmal subarachnoid hemorrhage patients treated in neuro-ICU.

BACKGROUND: Nosocomial pneumonia commonly develops in aneurysmal subarachnoid hemorrhage (aSAH) patients and is associated with poor prognosis of these patients. This study is designed to verify the predictive value of procalcitonin (PCT) on nosocomial pneumonia in aSAH patients. METHODS: 298 aSAH patients received treatments in the neuro-intensive care unit (NICU) of West China hospital were included. Logistic regression was conducted to verify the association between PCT level and nosocomial pneumonia and to construct a model for predicting pneumonia. Area under the receiver operating characteristic curve (AUC) were calculated to evaluate the accuracy of the single PCT and the constructed model. RESULTS: 90 (30.2%) patients developed pneumonia during hospitalizations among included aSAH patients. Pneumonia group had higher procalcitonin level (p < 0.001) than non-pneumonia group. The mortality (p < 0.001), mRS (p < 0.001), length of ICU stay (p < 0.001), length of hospital stay (p < 0.001) were both higher or longer in pneumonia group. Multivariate logistic regression indicated WFNS (p = 0.001), acute hydrocephalus (p = 0.007), WBC (p = 0.021), PCT (p = 0.046) and C-reactive protein (CRP) (p = 0.031) were independently associated with the development of pneumonia in included patients. The AUC value of procalcitonin for predicting nosocomial pneumonia was 0.764. Composed of WFNS, acute hydrocephalus, WBC, PCT and CRP, the predictive model for pneumonia has higher AUC of 0.811. CONCLUSIONS: PCT is an available and effective predictive marker of nosocomial pneumonia in aSAH patients. Composed of WFNS, acute hydrocephalus, WBC, PCT and CRP, our constructed predictive model is helpful for clinicians to evaluate the risk of nosocomial pneumonia and guide therapeutics in aSAH patients.

Hypomagnesemia Is Associated with the Acute Kidney Injury in Traumatic Brain Injury Patients: A Pilot Study.

BACKGROUND: Acute kidney injury (AKI) commonly develops among traumatic brain injury (TBI) patients and causes poorer outcomes. We perform this study to explore the relationship between serum magnesium and the risk of AKI among TBI. METHODS: TBI patients recorded in the Medical Information Mart for Intensive Care-III database were eligible for this research. The restricted cubic spline (RCS) was utilized to fit the correlation between serum magnesium level and the AKI. Univariate and subsequent multivariate logistic regression analysis were utilized to explore risk factors of AKI and confirmed the correlation between serum magnesium and AKI. RESULTS: The incidence of AKI in included TBI was 21.0%. The RCS showed that the correlation between magnesium level and risk of AKI was U-shaped. Compared with patients whose magnesium level was between 1.5 and 2.0 mg/dL, those with a magnesium level of <1.5 mg/dL or >2.0 mg/dL had a higher incidence of AKI. Multivariate logistic regression confirmed age, chronic renal disease, ISS, serum creatinine, vasopressor, mechanical ventilation, and serum magnesium <1.5 mg/dL were independently related with the AKI in TBI. CONCLUSION: Abnormal low serum magnesium level is correlated with AKI development in TBI patients. Physicians should pay attention on renal function of TBI patients especially those with hypomagnesemia.

Serum cystatin C is correlated with mortality of traumatic brain injury patients partially mediated by acute kidney injury.

BACKGROUND: Evaluating risk of poor outcome for Traumatic Brain Injury (TBI) in early stage is necessary to make treatment strategies and decide the need for intensive care. This study is designed to verify the prognostic value of serum cystatin C in TBI patients. METHODS: 415 TBI patients admitted to West China hospital were included. Logistic regression was performed to explore risk factors of mortality and testify the correlation between cystatin C and mortality. Mediation analysis was conducted to test whether Acute Kidney Injury (AKI) and brain injury severity mediate the relationship between cystatin C level and mortality. Area under the receiver operating characteristic curve (AUC) was used to evaluate the prognostic value of cystatin C and the constructed model incorporating cystatin C. RESULTS: The mortality rate of 415 TBI patients was 48.9%. Non-survivors had lower GCS (5 vs 8, p < 0.001) and higher cystatin C (0.92 vs 0.71, p < 0.001) than survivors. After adjusting confounding effects, multivariate logistic regression indicated GCS (p < 0.001), glucose (p < 0.001), albumin (p = 0.009), cystatin C (p < 0.001) and subdural hematoma (p = 0.042) were independent risk factors of mortality. Mediation analysis showed both AKI and brain injury severity exerted mediating effects on relationship between cystatin C and mortality of included TBI patients. The AUC of combining GCS with cystatin C was 0.862, which was higher than that of GCS alone (Z = 1.7354, p < 0.05). CONCLUSION: Both AKI and brain injury severity are mediating variables influencing the relationship between cystatin C and mortality of TBI patients. Serum cystatin C is an effective prognostic marker for TBI patients.

Prediction of Mortality in Geriatric Traumatic Brain Injury Patients Using Machine Learning Algorithms.

Background: The number of geriatric traumatic brain injury (TBI) patients is increasing every year due to the population's aging in most of the developed countries. Unfortunately, there is no widely recognized tool for specifically evaluating the prognosis of geriatric TBI patients. We designed this study to compare the prognostic value of different machine learning algorithm-based predictive models for geriatric TBI. Methods: TBI patients aged ≥65 from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were eligible for this study. To develop and validate machine learning algorithm-based prognostic models, included patients were divided into a training set and a testing set, with a ratio of 7:3. The predictive value of different machine learning based models was evaluated by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy and F score. Results: A total of 1123 geriatric TBI patients were included, with a mortality of 24.8%. Non-survivors had higher age (82.2 vs. 80.7, p = 0.010) and lower Glasgow Coma Scale (14 vs. 7, p < 0.001) than survivors. The rate of mechanical ventilation was significantly higher (67.6% vs. 25.9%, p < 0.001) in non-survivors while the rate of neurosurgical operation did not differ between survivors and non-survivors (24.3% vs. 23.0%, p = 0.735). Among different machine learning algorithms, Adaboost (AUC: 0.799) and Random Forest (AUC: 0.795) performed slightly better than the logistic regression (AUC: 0.792) on predicting mortality in geriatric TBI patients in the testing set. Conclusion: Adaboost, Random Forest and logistic regression all performed well in predicting mortality of geriatric TBI patients. Prognostication tools utilizing these algorithms are helpful for physicians to evaluate the risk of poor outcomes in geriatric TBI patients and adopt personalized therapeutic options for them.

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.

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.

Initial Serum Magnesium Level Is Associated with Mortality Risk in Traumatic Brain Injury Patients.

BACKGROUND: Electrolyte disorder is prevalent in traumatic brain injury (TBI) patients. This study is designed to explore the association between initial serum magnesium levels and mortality of TBI patients. METHODS: TBI patients recorded in the Medical Information Mart for Intensive Care-III database were screened for this study. Logistic regression analysis was used to explore risk factors for mortality of included TBI patients. The restricted cubic spline (RCS) was applied to fit the correlation between initial serum magnesium level and mortality of TBI. RESULTS: The 30-day mortality of included TBI patients was 17.0%. Patients with first-tertile and third-tertile serum magnesium levels had higher mortality than those of the second tertile. Univariate regression analysis showed that the serum magnesium level was not associated with mortality. Unadjusted RCS indicated the relationship between serum magnesium level mortality was U-shaped. After adjusting confounding effects, multivariate regression analysis presented that serum magnesium level was positively associated with mortality. CONCLUSION: TBI patients with abnormally low or high levels of serum magnesium both have a higher incidence of mortality. At the same time, a higher initial serum magnesium level is independently associated with mortality in TBI patients. Physicians should pay attention to the clinical management of TBI patients, especially those with higher serum magnesium levels.

A Machine Learning Approach Using XGBoost Predicts Lung Metastasis in Patients with Ovarian Cancer.

Background: Liver metastasis (LM) is an independent risk factor that affects the prognosis of patients with ovarian cancer; however, there is still a lack of prediction. This study developed a limit gradient enhancement (XGBoost) to predict the risk of lung metastasis in newly diagnosed patients with ovarian cancer, thereby improving prediction efficiency. Patients and Methods. Data of patients diagnosed with ovarian cancer in the Surveillance, Epidemiology, and Final Results (SEER) database from 2010 to 2015 were retrospectively collected. The XGBoost algorithm was used to establish a lung metastasis model for patients with ovarian cancer. The performance of the predictive model was tested by the area under the curve (AUC) of the receiver operating characteristic curve (ROC). Results: The results of the XGBoost algorithm showed that the top five important factors were age, laterality, histological type, grade, and marital status. XGBoost showed good discriminative ability, with an AUC of 0.843. Accuracy, sensitivity, and specificity were 0.982, 1.000, and 0.686, respectively. Conclusion: This study is the first to develop a machine-learning-based prediction model for lung metastasis in patients with ovarian cancer. The prediction model based on the XGBoost algorithm has a higher accuracy rate than traditional logistic regression and can be used to predict the risk of lung metastasis in newly diagnosed patients with ovarian cancer.

Risk factors and prognosis of liver metastasis in gallbladder cancer patients: A SEER-based study.

Background: Liver metastasis is a common complication in gallbladder cancer (GBC). We design this study to develop models for predicting the development of liver metastasis in GBC patients and evaluate the risk of mortality in these patients with liver metastasis. Methods: GBC patients from Surveillance Epidemiology and End Results (SEER) between 2010 and 2016 were included in this study. Logistic regression was performed to discover risk factors and construct predictive models for liver metastasis in GBC patients. Cox regression was utilized to find risk factors of mortality in GBC patients with liver metastasis. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the performance of the constructed predictive models. Results: Multivariate logistic regression confirmed that T stage, N stage, and tumor grade were risk factors for liver metastasis in GBC patients. Composed of these factors, the model for predicting the development of liver metastasis had AUCs of 0.707 and 0.657 in the training cohort and testing cohort, respectively. Multivariate Cox regression showed that surgery of the primary site and chemotherapy were independently associated with the mortality of GBC patients with liver metastasis. Composed of these two factors, the predictive model for 1-year mortality of GBC patients with liver metastasis had AUCs of 0.734 and 0.776 in the training cohort and testing cohort, respectively. Conclusion: The predictive models that we constructed are helpful for surgeons to evaluate the risk of liver metastasis in GBC patients and the survival condition of those with liver metastasis. Surgery of the primary site and chemotherapy should be provided for GBC with liver metastasis.