Development of a nomogram to predict the incidence of acute kidney injury among ischemic stroke individuals during ICU hospitalization.

Background: Limited clinical prediction models exist to assess the likelihood of acute kidney injury (AKI) occurrence in ischemic stroke individuals. In this retrospective study, our aim was to construct a nomogram that utilizes commonly available clinical features to predict the occurrence of AKI during intensive care unit hospitalization among this patient population. Methods: In this study, the MIMIC-IV database was utilized to investigate potential risk factors associated with the incidence of AKI among ischemic stroke individuals. A predictive nomogram was developed based on these identified risk factors. The discriminative performance of the constructed nomogram was assessed. Calibration analysis was utilized to evaluate the calibration performance of the constructed model, assessing the agreement between predicted probabilities and actual outcomes. Furthermore, decision curve analysis (DCA) was employed to assess the clinical net benefit, taking into account the potential risks and benefits associated with different decision thresholds. Results: A total of 2089 ischemic stroke individuals were included and randomly allocated into developing (n = 1452) and verification cohorts (n = 637). Risk factors for AKI incidence in ischemic stroke individuals, determined through LASSO and logistic regression. The constructed nomogram had good performance in predicting the occurrence of AKI among ischemic stroke patients and provided significant improvement compared to existing scoring systems. DCA demonstrated satisfactory clinical net benefit of the constructed nomogram in both the validation and development cohorts. Conclusions: The developed nomogram exhibits robust predictive performance in forecasting AKI occurrence in ischemic stroke individuals.

Unveiling poly(rC)-binding protein 2 as the target protein for curcusone C against prostate cancer: mechanism validation through click chemistry-activity based proteomics profiling approach.

BACKGROUND: Prostate cancer is a disease that seriously troubles men. However, there are some inevitable limitations in interventional therapy for prostate cancer patients at present, most of which are caused by low selectivity and high toxic side effects due to unclear drug targets. In this study, we identified the target protein of Curcusone C with anti-prostate cancer potential activity and verified its target and mechanism of action. METHODS: Click chemistry-activity based proteomics profiling (CC-ABPP) method was used to find target protein of Curcusone C against prostate cancer. Competitive CC-ABPP, drug affinity responsive target stability (DARTS) and surface plasmon resonance (SPR) methods were used to verifying the target protein. Moreover, potential mechanism was validated by western blot in vitro and by hematoxylin-eosin (HE) staining, detection of apoptosis in tumor tissue (TUNEL), and immunohistochemical (IHC) in vivo. RESULTS: We found that poly(rC)-binding protein 2 (PCBP2) was the target protein of Curcusone C. In addition, Curcusone C might disrupt the Bax/Bcl-2 balance in PC-3 cells by inhibiting the expression of the target protein PCBP2, thereby inducing mitochondrial damage and activation of the mitochondrial apoptosis pathway, and ultimately inducing apoptosis of prostate cancer cells. CONCLUSIONS: Curcusone C is a potential compound with anti-prostate cancer activity, and this effect occurs by targeting the PCBP2 protein, which in turn may affect the TGF/Smad signaling pathway and Bax/Bcl-2 balance. Our results laid a material and theoretical foundation for Curcusone C, to be widely used in anti-prostate cancer.

Development and verification of a nomogram for predicting short-term mortality in elderly ischemic stroke populations.

Stroke is a major healthcare problem worldwide, particularly in the elderly population. Despite limited research on the development of prediction models for mortality in elderly individuals with ischemic stroke, our study aimed to address this knowledge gap. By leveraging data from the Medical Information Mart for Intensive Care IV database, we collected comprehensive raw data pertaining to elderly patients diagnosed with ischemic stroke. Through meticulous screening of clinical variables associated with 28-day mortality, we successfully established a robust nomogram. To assess the performance and clinical utility of our nomogram, various statistical analyses were conducted, including the concordance index, integrated discrimination improvement (IDI), net reclassification index (NRI), calibration curves and decision curve analysis (DCA). Our study comprised a total of 1259 individuals, who were further divided into training (n = 894) and validation (n = 365) cohorts. By identifying several common clinical features, we developed a nomogram that exhibited a concordance index of 0.809 in the training dataset. Notably, our findings demonstrated positive improvements in predictive performance through the IDI and NRI analyses in both cohorts. Furthermore, calibration curves indicated favorable agreement between the predicted and actual incidence of mortality (P > 0.05). DCA curves highlighted the substantial net clinical benefit of our nomogram compared to existing scoring systems used in routine clinical practice. In conclusion, our study successfully constructed and validated a prognostic nomogram, which enables accurate short-term mortality prediction in elderly individuals with ischemic stroke.

Prediction of long-term mortality in patients with ischemic stroke based on clinical characteristics on the first day of ICU admission: An easy-to-use nomogram.

Background: This study aimed to establish and validate an easy-to-use nomogram for predicting long-term mortality among ischemic stroke patients. Methods: All raw data were obtained from the Medical Information Mart for Intensive Care IV database. Clinical features associated with long-term mortality (1-year mortality) among ischemic stroke patients were identified using least absolute shrinkage and selection operator regression. Then, binary logistic regression was used to construct a nomogram, the discrimination of which was evaluated by the concordance index (C-index), integrated discrimination improvement (IDI), and net reclassification index (NRI). Finally, a calibration curve and decision curve analysis (DCA) were employed to study calibration and net clinical benefit, compared to the Glasgow Coma Scale (GCS) and the commonly used disease severity scoring system. Results: Patients who were identified with ischemic stroke were randomly assigned into developing (n = 1,443) and verification (n = 646) cohorts. The following factors were associated with 1-year mortality among ischemic stroke patients, including age on ICU admission, marital status, underlying dementia, underlying malignant cancer, underlying metastatic solid tumor, heart rate, respiratory rate, oxygen saturation, white blood cells, anion gap, mannitol injection, invasive mechanical ventilation, and GCS. The construction of the nomogram was based on the abovementioned features. The C-index of the nomogram in the developing and verification cohorts was 0.820 and 0.816, respectively. Compared with GCS and the commonly used disease severity scoring system, the IDI and NRI of the constructed nomogram had a statistically positive improvement in predicting long-term mortality in both developing and verification cohorts (all with p < 0.001). The actual mortality was consistent with the predicted mortality in the developing (p = 0.862) and verification (p = 0.568) cohorts. Our nomogram exhibited greater net clinical benefit than GCS and the commonly used disease severity scoring system. Conclusion: This proposed nomogram has good performance in predicting long-term mortality among ischemic stroke patients.

Internet of things and Big Data as potential solutions to the problems in waste electrical and electronic equipment management: An exploratory study.

Management of Waste Electrical and Electronic Equipment (WEEE) is a vital part in solid waste management, there are still some difficult issues require attentionss. This paper investigates the potential of applying Internet of Things (IoT) and Big Data as the solutions to the WEEE management problems. The massive data generated during the production, consumption and disposal of Electrical and Electronic Equipment (EEE) fits the characteristics of Big Data. Through using the state-of-the-art communication technologies, the IoT derives the WEEE "Big Data" from the life cycle of EEE, and the Big Data technologies process the WEEE "Big Data" for supporting decision making in WEEE management. The framework of implementing the IoT and the Big Data technologies is proposed, with its multiple layers are illustrated. Case studies with the potential application scenarios of the framework are presented and discussed. As an unprecedented exploration, the combined application of the IoT and the Big Data technologies in WEEE management brings a series of opportunities as well as new challenges. This study provides insights and visions for stakeholders in solving the WEEE management problems under the context of IoT and Big Data.

Comparsion between Intravenous Delivered Human Fetal Bone Marrow Mesenchymal Stromal Cells and Mononuclear Cells in the Treatment of Rat Cerebral Infarct.

Objective To compare the effecacy of human mesenchymal stromal cell (hMSC) with human mononuclear cell (hMNC) in treating rat cerebral infarct.Methods The SD rat models of cerebral infarct were established by distal middle cerebral artery occlusion (dMCAO). Rats were divided into four groups: sham,ischemia vehicle,MSC,and MNC transplantation groups. For the transplantation group,1×106 hMSCs or hMNCs were intravascularly transplanted into the tail vein 1 hour after the ischemia onset. The ischemia vehicle group received dMCAO surgery and intravascular saline injection 1,3,5,and 7 days after the ischemia onset,and then behavioral tests were performed. At 48 h after the ischemia onset,the abundance of Iba- 1,the symbol of activated microglia,was evaluated in the peri-ischemia striatum area; meanwhile,the neurotrophic factors such as glial cell line-derived neurotrophic factor (GDNF) and brain-derived neurotrophic factor (BDNF) in ipsilateral peri-ischemia striatum area were also measured. Results The relative infarct volume in ischemia vehicle group,hMSC group,and hMNC transplantation group were (37.85±4.40)%,(33.41±3.82)%,and (30.23±3.63)%,respectively. The infarct volumes of MSC group (t=2.100,P=0.034) and MNC group (t=2.109,P=0.0009) were significantly smaller than that of ischemia vehicle group,and that of MNC group was significantly smaller than that of MSC group (t=1.743,P=0.043). One day after transplantation,the score of ischemia vehicle group in limb placing test was (4.32±0.71)%,which was significantly lower than that in sham group (9.73±0.36)% (t=2.178,P=8.61×10-11). The scores of MSC and MNC group,which were (5.09±0.62)% (t=2.1009,P=0.024) and (5.90±0.68)% (t=2.1008,P=0.0001),respectively,were significantly higher than that of ischemia vehicle group; also,the score of MNC group was significantly higher than that of MSC group(t=2.1009,P=0.0165). The contralateral forelimb scores of MSC and MNC groups in beam walking test were (5.56±0.86)% (t=2.120,P=0.020) and (5.13±0.95)% (t=2.131,P=0.003),were both significantly lower than that of ischemia vehicle group [(6.47±0.61)%]. Three days after the transplantation,the limb placing test score of MNC group [(6.91±1.10)%] was significantly higher than that of ischemia vehicle group (5.80±0.82)% (t=2.110,P=0.027). The score of MSC group [(6.30±0.77)%] showed no statistic difference with that of ischemia vehicle group(t=2.101,P=0.199).The contralateral forelimb scores of MNC group in beam walking test [(4.34±0.58)%] was significantly lower than that of ischemia vehicle group [(5.31±0.65)%] (t=2.100,P=0.006) and MSC group [(4.92±0.53)%] (t=2.100,P=0.041); there was no statistic difference between MSC group and ischemia vehicle group (t=2.109,P=0.139). The relative abundance of Iba- 1 in sham,ischemia vehicle,MSC,and MNC groups was 1.00+0.00,1.72±0.21,1.23±0.08,and 1.48±0.06,respectively. The Iba-1 relative abundance of ischemia vehicle group was significantly higher than that of sham group (t=2.262,P=2.9×10-6). The Iba-1 relative abundances of both MSC (t=2.178,P=3.91×10-5)and MNC (t=2.200,P=0.007)groups were significantly lower than that of ischemia vehicle group. It was also significantly lower in MNC group than in MSC group also (t=2.120,P=7.09×10-6). Three days after transplantation,the BDNF and GDNF levels of MSC group,which were (531.127±73.176)pg/mg (t=2.109,P=0.003)and(127.780±16.733)pg/mg(t=2.100,P=2.76×10-5),respectively,were significantly higher than those of ischemia vehicle group,which were (401.988±89.006)pg/mg and (86.278±14.832) pg/mg,respectively. The BDNF and GDNF levels of MNC group,which were (627.429±65.646)pg/mg (t=2.144,P=0.017) and (153.117±20.443)pg/mg (t=2.109,P=0.010),respectively,were all significantly higher than that of MSC group. At day 7,the BDNF and GDNF levels of MSC group,which were (504.776±83.282)pg/mg (t=2.101,P=0.005) and (81.641±11.019)pg/mg (t=2.100,P=0.002),respectively,were significantly higher than those of ischemia vehicle group,which were (389.257±70.440)pg/mg and (64.322±9.855) pg/mg,respectively. The BDNF and GDNF levels of MNC group,which were (589.068±63.323)pg/mg (t=2.100,P=0.027) and (102.161±19.932)pg/mg (t=2.144,P=0.017),respectively,were all significantly higher than that of MSC group. Conclusions Both hMSC and hMNC are beneficial to the ischemia-damaged brain when they are intravascularly transplanted within 1 h after the onset of ischemia. The anti-inflammation ability and secretion of neurotrophic factors are the underlying mechanisms of the therapeutic effects. MNC is more effective than MSC in reducing infarct area and improving behaviors,which might be explained by the fact that MNC induces more GDNF and BDNF in brain than MSC.

Plasma pituitary adenylate cyclase-activating polypeptide concentrations and mortality after acute spontaneous basal ganglia hemorrhage.

BACKGROUND: Plasma pituitary adenylate cyclase activating polypeptide (PACAP) concentrations are elevated after traumatic brain injury. We assessed the prognostic value of PACAP for short-term and long-term mortality of acute intracerebral hemorrhage (ICH) patients. METHODS: A total of 150 patients and 150 age- and gender- matched healthy controls were recruited. The plasma PACAP concentrations were measured using sandwich immunoassays. ICH severity was assessed using hematoma volume and National Institutes of Health Stroke Scale (NIHSS) score. The end points included 1-week mortality and 6-month mortality. The relationships between plasma PACAP concentrations and ICH severity and the end points were analyzed statistically. RESULTS: Plasma PACAP concentrations were statistically significantly higher in the ICH patients than in the healthy controls and were correlated positively with hematoma volumes and NIHSS scores using a multivariate linear regression. Multivariate analysis results indicated that plasma PACAP concentration was an independent predictor of 1-week mortality, 6-month mortality and 6-month overall survival. It also had high predictive value based on receiver operating characteristic curve. CONCLUSIONS: Plasma PACAP concentrations are increased and are highly associated with the severity of ICH; PACAP may be a good predictor of short-term and long-term mortality of ICH.