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.

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.

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.

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.

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.

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.

A new nomogram for predicting lung metastasis in newly diagnosed endometrial carcinoma patients: A study based on SEER.

Background: Lung metastasis (LM) is an independent risk factor for survival in patients with endometrial cancer (EC). Methods: We reviewed data on patients diagnosed with EC between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. The independent predictors of LM in patients with EC were identified using univariate and multivariate logistic regression analyses. A nomogram for predicting LM in patients with EC was developed, and the predictive model was evaluated using calibration and receiver operating characteristic (ROC) curves. Results: Univariate and multivariate logistic regression analyses showed that high grade; specific histological type; high tumor and node stages; larger tumor size; and liver, brain, and bone metastases were positively associated with LM risk. A new nomogram was developed by combining these factors to predict LM in patients newly diagnosed with EC. Internal and external verification of the calibration charts showed that the nomogram was well calibrated. The areas under the ROC curves for the training and validation cohorts were 0.924 and 0.913, respectively. Conclusion: We performed a retrospective analysis of 42,073 patients with EC using the SEER database, established a new nomogram for predicting LM based on eight independent risk factors, and visualized the model using a nomogram for the first time.

Hemilaminectomy for the removal of the spinal tumors: An analysis of 901 patients.

Objective: We report our experience with the use of hemilaminectomy approach for the removal of benign intraspinal tumors. Method: A retrospective review of 1,067 patients who underwent hemilaminectomy in our hospital between 2013 and 2019 was analyzed. Baseline medical data were collected. One hundred sixteen patients were excluded due to degenerative diseases, spinal bone tumors, and malignant tumors. The remaining 901 patients (916 tumors) were enrolled. The Dennis Pain Scale (DPS) was used to assess improvement in pain before surgery and during long-term follow-up. Neurological status was assessed using the American Spinal Injury Association (ASIA) impairment scale. Results: The age of the patients was 48.7 ± 15.3 years, the duration of symptoms was 16.5 ± 32.0 months, and the tumor size was 2.6 ± 1.4 cm. Three hundred two tumors were located in the cervical region, 42 in the cervicothoracic region, 234 in the thoracic region, 57 in the thoracolumbar region, and 281 in the lumbar and lumbosacral region. Twenty-three tumors were ventrally located, 677 were dorsal or dorsolateral, 63 were intramedullary, 87 were epidural, and the rest were dumbbell-shaped. The most common pathologies were schwannomas (601, 66.7%) and meningiomas (172, 19.1%). Total excision was achieved at 97.8%. The operative time was 94.3 ± 32.6 min and the blood loss during surgery was 96.9 ± 116.5 ml. The symptom of pain improved in 87.0% of patients during long-term follow-up, neurological function improved in 68.3% and remained unchanged at 30.5%. Conclusion: The hemilaminectomy approach was a rapid and safe procedure to remove intradural and extradural tumors. This approach has offered several advantages. It could be used for the resection of most extradural or intradural extramedullary lesions, even some intramedullary tumors.

Surgical outcomes of spinal cavernous malformations: A retrospective study of 98 patients.

Objective: Spinal cord cavernous malformation (SCCM) is a rare vascular lesion, and the treatment strategy remains controversial at present. The goal of this retrospective study was to analyze the surgical outcomes of the SCCM and to find more appropriate treatment strategies for a better prognosis. Method: A retrospective review of 98 patients with SCCM from 2009 to 2018 was conducted at the neurosurgical center of our hospital. Neurological function was assessed using the American Spinal Injury Association (ASIA) impairment scale. Clinical features were analyzed using the multivariable logistic regression. Results: Ninety-eight patients with SCCM were included, of whom 36% were female and 64% male. The mean age was 41.6 years; and family history was reported in 8% of patients. Definite hemorrhage was found in 6%. Before surgery, the neurological status was Grade A in 2%, Grade B in 2%, Grade C in 12%, Grade D in 54%, and Grade E in 30% of the patients. 83% (81/98) patients had long-term follow-up, of whom, 42% had improved, 51% were stable and 7% had deteriorated. Patients with dorsal or superficial lesions showed better improvement than those with ventral or lateral deep lesions. Those with symptoms lasting less than three months had higher rates of improvement than those with symptoms lasting more than three months. However, there was no significant difference in prognosis between hemilaminectomy and laminectomy. Conclusion: These results suggest that surgical strategies should be preferred for severe symptomatic SCCMs if total resection can be achieved, thereby avoiding the risk of severe complications with subsequent lesion hemorrhage. Earlier (usually within 3 months of symptom duration) surgical resection generally may lead to a better prognosis. For ventral or lateral deep SCCMs, the surgical strategy should be considered more carefully.

Serum Lactate Level in Early Stage Is Associated With Acute Kidney Injury in Traumatic Brain Injury Patients.

BACKGROUND: Acute kidney injury (AKI) is a common complication in the clinical practice of managing patients with traumatic brain injury (TBI). Avoiding the development of AKI is beneficial for the prognosis of patients with TBI. We designed this study to testify whether serum lactate could be used as a predictive marker of AKI in patients with TBI. MATERIALS AND METHODS: In total, 243 patients with TBI admitted to our hospital were included in this study. Univariate and multivariate logistic regression analyses were utilized to analyze the association between lactate and AKI. The receiver operating characteristic (ROC) curves were drawn to verify the predictive value of lactate and the logistic model. RESULTS: Acute kidney injury group had higher age (p = 0.016), serum creatinine (p < 0.001), lactate (p < 0.001), and lower Glasgow Coma Scale (GCS; p = 0.021) than non-AKI group. Multivariate logistic regression showed that age [odds ratio (OR) = 1.026, p = 0.022], serum creatinine (OR = 1.020, p = 0.010), lactate (OR = 1.227, p = 0.031), fresh frozen plasma (FFP) transfusion (OR = 2.421, p = 0.045), and platelet transfusion (OR = 5.502, p = 0.044) were risk factors of AKI in patients with TBI. The area under the ROC curve (AUC) values of single lactate and predictive model were 0.740 and 0.807, respectively. CONCLUSION: Serum lactate level in the early phase is associated with AKI in patients with TBI. Lactate is valuable for clinicians to evaluate the probability of AKI in patients with TBI.

Development and validation of a nomogram to predict synchronous lung metastases in patients with ovarian cancer: a large cohort study.

PURPOSE: Lung metastasis is an independent risk factor affecting the prognosis of ovarian cancer patients. We developed and validated a nomogram to predict the risk of synchronous lung metastases in newly diagnosed ovarian cancer patients. METHODS: Data of ovarian cancer patients from the Surveillance, Epidemiology, and Final Results (SEER) database between 2010 and 2015 were retrospectively collected. The model nomogram was built on the basis of logistic regression. The consistency index (C-index) was used to evaluate the discernment of the synchronous lung metastasis nomogram. Calibration plots were drawn to analyze the consistency between the observed probability and predicted probability of synchronous lung metastases. The Kaplan-Meier method was used to estimate overall survival rate, and influencing factors were included in multivariate Cox regression analysis (P<0.05) to determine the independent prognostic factors of synchronous lung metastases. RESULTS: Overall, 16059 eligible patients were randomly divided into training (n=11242) and validation cohorts (n=4817). AJCC T, N stage, bone metastases, brain metastases, and liver metastases were evaluated as predictors of synchronous lung metastases. Finally, a nomogram was constructed. The nomogram based on independent predictors was calibrated and showed good discriminative ability. Mixed histological types, chemotherapy, and primary site surgery were factors affecting the overall survival of patients with synchronous lung metastases. CONCLUSION: The clinical prediction model has high accuracy and can be used to predict lung metastasis risk in newly diagnosed ovarian cancer patients, which can guide the treatment of patients with synchronous lung metastases.

Serum Procalcitonin Level Predicts Acute Kidney Injury After Traumatic Brain Injury.

BACKGROUND: A common non-neurologic complication after traumatic brain injury (TBI), acute kidney injury (AKI) is a risk factor of mortality. Some studies confirmed the predictive value of procalcitonin (PCT) on AKI in several clinical settings. We designed this study to explore the predictive value of PCT on AKI after TBI. METHODS: We retrospectively enrolled patients with TBI admitted to our hospital from February 2015 to June 2019. Multivariate logistic regression analysis was performed to find the risk factors of AKI and construct a predictive model for AKI. Receiver operating characteristics curves were drawn to compare the predictive value of PCT and the constructed model. RESULTS: A total of 214 patients were included in this study. The incidence of AKI after TBI was 25.70% in this study. Compared with the non-AKI group, the AKI group had higher age (P = 0.031), lower Glasgow Coma Scale (P < 0.001), and higher incidence of coagulopathy (P < 0.001) and shock (P < 0.001). Moreover, patients complicated with AKI had higher in-hospital mortality (P < 0.001) and worse 90-day outcome (P < 0.001). Multivariate logistic regression analysis indicated that age (P = 0.033), PCT (P = 0.002), serum chlorine (P = 0.011), and creatinine (P < 0.001) were independent risk factors of AKI. We constructed a predictive model using these 4 risk factors. The area under receiver operating characteristics curves of the predictive model was 0.928, which was significantly higher than that of a single PCT value (area under receiver operating characteristics curves = 0.833) (Z = 2.395, P < 0.05). CONCLUSIONS: PCT is valuable in predicting AKI after TBI. To avoid AKI after TBI, physicians can adjust treatment strategies according to the level of PCT.

A clinical model to predict the risk of liver metastases in newly diagnosed ovarian cancer: a population-based study.

BACKGROUND: Liver metastases are important in determining the prognosis of ovarian cancer. We aimed to develop and validate nomograms to predict the risk of liver metastases in patients with early-stage ovarian cancer. METHODS: A total of 13,487 patients were enrolled in the study based on their records in the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors of liver metastases were assessed based on univariable and multivariable logistic regression. A nomogram was also formulated based on the results of multivariable logistic analysis. The area under the receiver-operating characteristic curve was calculated to evaluate the discrimination abilities of the metastasis-related factors and liver metastases nomogram. A calibration plot was generated to analyze the consistency between the observed probability and predicted probability of liver metastases in patients with ovarian cancer. RESULTS: Four related factors were determined based on univariable and multivariable logistic regression, including the T1 stage, N1 stage, and presence of lung and bone metastases. The liver metastases nomogram composed of four features could be used to determine the prediction effect. The calibration plot showed good consistency between the nomogram prediction and actual observation. The receiver-operating characteristic curve showed that the forecast nomogram exhibited a good forecast value. CONCLUSIONS: This clinical prediction model has high accuracy to identify patients with newly diagnosed ovarian cancer who carry a high risk of liver metastases and provide a personalized treatment plan for these patients.

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.