Significance of platelets in the early warning of new-onset AKI in the ICU by using supervise learning: a retrospective analysis.

OBJECTIVE: To explore a machine learning model for the early prediction of acute kidney injury (AKI) and to screen the related factors affecting new-onset AKI in the ICU. METHODS: A retrospective analysis was performed used the MIMIC-III data source. New onset of AKI defined based on the serum creatinine changed. We included 19 variables for AKI assessment using four machine learning models: support vector machines, logistic regression, and random forest. and XGBoost, using accuracy, specificity, precision, recall, F1 score, and AUROC (area under the ROC curve) to evaluate model performance. The four models predicted new-onset AKI 3-6-9-12 h ahead. The SHapley Additive exPlanation (SHAP) value is used to evaluate the feature importance of the model. RESULTS: We finally respectively extracted 1130 AKI patients and non-AKI patients from the MIMIC-III database. With the extension of the early warning time, the prediction performance of each model showed a downward trend, but the relative performance was consistent. The prediction performance comparison of the four models showed that the XGBoost model performed the best in all evaluation indicators in all the time point at new-onset AKI 3-6-9-12 h ahead (accuracy 0.809 vs 0.78 vs 0.744 vs 0.741, specificity 0.856 vs 0.826 vs 0.797 vs 0.787, precision 0.842 vs 0.81 vs 0.775 vs 0.766, recall 0.759 vs 0.734 vs 0.692 vs 0.694, Fl score 0.799 vs 0.769 vs 0.731 vs 0.729, AUROC 0.892 vs 0.857 vs 0.827 vs 0.818). In the prediction of AKI 6, 9 and 12 h ahead, the importance of creatinine, platelets, and height was the most important based on SHapley. CONCLUSIONS: The machine learning model described in this study can predict AKI 3-6-9-12 h before the new-onset of AKI in ICU. In particular, platelet plays an important role.

The new-onset of AKI in ICU is a common and important problem, which early be identified the risk of AKI can improve patients' outcomes.We explored MIMIC-III and determined the exact time point of occurrence of AKI as the basis for the new-onset of AKI in ICU.XGBoost model performed the best prediction in all the time point at new-onset AKI 3­6­9­12 h ahead.For patients with the new-onset of AKI in ICU, platelets become an important factor associated with AKI.

Circulating Small Extracellular Vesicle-Derived miR-342-5p Ameliorates Beta-Amyloid Formation via Targeting Beta-site APP Cleaving Enzyme 1 in Alzheimer's Disease.

Alzheimer's disease (AD) is a common neurodegenerative disorder with progressive cognitive impairment in the elderly. Beta-amyloid (Aß) formation and its accumulation in the brain constitute one of the pathological hallmarks of AD. Until now, how to modulate Aß formation in hippocampal neurons remains a big challenge. Herein, we investigated whether the exosomal transfer of microRNA (miR) relates to amyloid pathology in the recipient neuron cells. We isolated circulating small extracellular vesicles (sEVs) from AD patients and healthy controls, determined the miR-342-5p level in the sEVs by RT-PCR, and evaluated its diagnostic performance in AD. Then, we took advantage of biomolecular assays to estimate the role of miR-342-5p in modulating the amyloid pathway, including amyloid precursor protein (APP), beta-site APP cleaving enzyme 1 (BACE1), and Aß42. Furthermore, we subjected HT22 cells to the sEVs from the hippocampal tissues of transgenic APP mice (Exo-APP) or C57BL/6 littermates (Exo-CTL), and the Exo-APP enriched with miR-342-5p mimics or the control to assess the effect of the sEVs' delivery of miR-342-5p on Aß formation. We observed a lower level of miR-342-5p in the circulating sEVs from AD patients compared with healthy controls. MiR-342-5p participated in Aß formation by modulating BACE1 expression, specifically binding its 3'-untranslated region (UTR) sequence. Exo-APP distinctly promoted Aß42 formation in the recipient cells compared to Exo-CTL. Intriguingly, miR-342-5p enrichment in Exo-APP ameliorated amyloid pathology in the recipient cells. Our study indicated that miR-342-5p was dysregulated in human circulating sEVs from AD patients; sEV transfer of miR-342-5p ameliorates Aß formation by modulating BACE1 expression. These findings highlight the promising potential of exosomal miRNAs in AD clinical therapy.

Random Forest-Based Prediction of Acute Respiratory Distress Syndrome in Patients Undergoing Cardiac Surgery.

BACKGROUND: To develop a machine learning-based model for predicting the risk of acute respiratory distress syndrome (ARDS) after cardiac surgery. METHODS: Data were collected from 1011 patients, who underwent cardiac surgery between February 2018 and September 2019. We developed a predictive model on ARDS by using the random forest algorithm of machine learning. The discrimination of the model was then shown by the area under the curve (AUC) of the receiver operating characteristic curve. Internal validation was performed by using a 5-fold cross-validation technique, so as to evaluate and optimize the predictive model. Model visualization was performed to reveal the most influential features during the model output. RESULTS: Of the 1011 patients included in the study, 53 (5.24%) suffered ARDS episodes during the first postoperative week. This random forest distinguished ARDS patients from non-ARDS patients with an AUC of 0.932 (95% CI=0.896-0.968) in the training set and 0.864 (95% CI=0.718-0.997) in the final test set. The top 10 variables in the random forest were cardiopulmonary bypass time, transfusion red blood cell, age, EuroSCORE II score, albumin, hemoglobin, operation time, serum creatinine, diabetes, and type of surgery. CONCLUSION: Our findings suggest that machine learning algorithm is highly effective in predicting ARDS in patients undergoing cardiac surgery. The successful application of the generated random forest may guide clinical decision-making and aid in improving the long-term prognosis of patients.

Scoring System to Evaluate the Performance of ICU Ventilators in the Pandemic of COVID-19: A Lung Model Study.

Ventilators in the intensive care units (ICU) are life-support devices that help physicians to gain additional time to cure the patients. The aim of the study was to establish a scoring system to evaluate the ventilator performance in the context of COVID-19. The scoring system was established by weighting the ventilator performance on five different aspects: the stability of pressurization, response to leaks alteration, performance of reaction, volume delivery, and accuracy in oxygen delivery. The weighting factors were determined with analytic hierarchy process (AHP). Survey was sent out to 66 clinical and mechanical experts. The scoring system was built based on 54 valid replies. A total of 12 commercially available ICU ventilators providing non-invasive ventilation were evaluated using the novel scoring system. A total of eight ICU ventilators with non-invasive ventilation mode and four dedicated non-invasive ventilators were tested according to the scoring system. Four COVID-19 phenotypes were simulated using the ASL5000 lung simulator, namely (1) increased airway resistance (IR) (10 cm H2O/L/s), (2) low compliance (LC) (compliance of 20 ml/cmH2O), (3) low compliance plus increased respiratory effort (LCIE) (respiratory rate of 40 and inspiratory effort of 10 cmH2O), (4) high compliance (HC) (compliance of 50 ml/cmH2O). All of the ventilators were set to three combinations of pressure support and positive end-expiratory pressure levels. The data were collected at baseline and at three customized leak levels. Significant inaccuracies and variations in performance between different non-invasive ventilators were observed, especially in the aspect of leaks alteration, oxygen and volume delivery. Some ventilators have stable performance in different simulated phenotypes whereas the others have over 10% scoring differences. It is feasible to use the proposed scoring system to evaluate the ventilator performance. In the COVID-19 pandemic, clinicians should be aware of possible strengths and weaknesses of ventilators.

Profiling of Serum Exosome MiRNA Reveals the Potential of a MiRNA Panel as Diagnostic Biomarker for Alzheimer's Disease.

Alzheimer's disease (AD) is the most common neurodegenerative disease in the older adults. Although much effort has been made in the analyses of diagnostic biomarkers, such as amyloid-ß, tau, and neurofilament light chain, identifying peripheral blood-based biomarkers is in extremely urgent need for their minimal invasiveness and more convenience. Here we characterized the miRNA profile by RNA sequencing in human serum exosomes from AD patients and healthy controls (HC) to investigate its potential for AD diagnosis. Subsequently, Gene Ontology analysis and pathway analysis were performed for the targeted genes from the differentially expressed miRNAs. These basic functions were differentially enriched, including cell adhesion, regulation of transcription, and the ubiquitin system. Functional network analysis highlighted the pathways of proteoglycans in cancer, viral carcinogenesis, signaling pathways regulating pluripotency of stem cells, and cellular senescence in AD. A total of 24 miRNAs showed significantly differential expression between AD and HC with more than ± 2.0-fold change at p value < 0.05 and at least 50 reads for each sample. Logistic regression analysis established a model for AD prediction by serum exosomal miR-30b-5p, miR-22-3p, and miR-378a-3p. Sequencing results were validated using quantitative reverse transcription PCR. The data showed that miR-30b-5p, miR-22-3p, and miR-378a-3p were significantly deregulated in AD, with area under the curve (AUC) of 0.668, 0.637, and 0.718, respectively. The combination of the three miRs gained a better diagnostic capability with AUC of 0.880. This finding revealed a miR panel as potential biomarker in the peripheral blood to distinguish AD from HC.

Flexible and wearable strain sensor based on electrospun carbon sponge/polydimethylsiloxane composite for human motion detection.

Flexible and wearable strain sensors have attracted considerable attention due to their potential applications in human motion detection. In this work, the as-fabricated strain sensor was obtained by encapsulation of electrospun carbon sponge (CS) with polydimethylsiloxane (PDMS). The formation mechanism of the self-assembled sponge has been explored. Meanwhile, the piezoresistive properties and the strain sensing mechanism of the CS/PDMS sensor were investigated. The results showed that the as-fabricated CS/PDMS sensor had high piezoresistive sensibility with a maximum gauge factor up to 130.49, superior stability and fast response to various cyclic loading with a tensile strain from 0% up to 40% and a tensile speed range of 2-18 mm min-1. Finally, all the superior performances endow the sensor with abilities to precisely detect pronunciation, human palm motion, wrist joint motion, elbow joint motion, and finger motion in real-time. These results indicate that the strain sensor based on the CS/PDMS could have promising applications in flexible and wearable devices for human motion detection.

Compact Inner-Wall Grating Slot Microring Resonator for Label-Free Sensing.

In this paper, we present and analyze a compact inner-wall grating slot microring resonator (IG-SMRR) with the footprint of less than 13 µm × 13 µm on the silicon-on-insulator (SOI) platform for label-free sensing, which comprises a slot microring resonator (SMRR) and inner-wall grating (IG). Its detection range is significantly enhanced without the limitation of the free spectral region (FSR) owing to the combination of SMRR and IG. The IG-SMRR has an ultra-large quasi-FSR of 84.5 nm as the detection range, and enlarged factor is up to over 3 compared with the conventional SMRR. The concentration sensitivities of sodium chloride solutions and D-glucose solutions are 996.91 pm/% and 968.05 pm/%, respectively, and the corresponding refractive index (RI) sensitivities are 559.5 nm/RIU (refractive index unit) and 558.3 nm/RIU, respectively. The investigation on the combination of SMRR and IG is a valuable exploration of label-free sensing application for ultra-large detection range and ultra-high sensitivity in future.

Nonblocking 3 × 3 polymer thermo-optic switch array based on total-internal-reflection effect.

A nonblocking 3×3 polymer thermo-optic (TO) switch array based on the total-internal-reflection (TIR) effect is demonstrated. The switch array consists of three 2×2 TIR TO switch elements with a total device length of 15.5 mm, and achieves nine switching states with no more than one switch element working. The fabricated 3×3 TO switch array shows excellent switching performance with low crosstalk for all switching states of less than -20 dB. The power consumption of each switch element is below 53 mW, and the switching rise time and fall time are ∼450 and 550 µs at 1550 nm wavelength, respectively.

A 12-Words-for-Life-Nurturing Exercise Program as an Alternative Therapy for Cervical Spondylosis: A Randomized Controlled Trial.

In this paper, we carried out a randomized controlled clinical trial to explore the effect of 12-words-for-life-nurturing exercise on patients presenting with cervical spondylosis. After exercise intervention, the mean VAS and NDI scores of the patients decreased significantly and the scores of BP, VT, and MH in SF-36 Health Questionnaire were significantly higher. Exercise therapy showed significant effect on relieving pain and improving vitality and mental health. The 12-words-for-life-nurturing exercise may be a potential effective therapy for patients with cervical spondylosis.

Effects of antidiabetic drug metformin on the migration and invasion abilities of human pulmonary adenocarcinoma A549 cell line in vitro.

BACKGROUND AND PURPOSE: There is growing evidence that metformin, a clinically widely used drug in the treatment of type II diabetes, may impede the growth of human tumors. However in a recent study it was found that metformin treatment might result in promotion of the angiogenic phenotype and promote early tumorigenic progression. In order to evaluate the relevance between metformin and tumor metastases, we investigated the effects of metformin on the migration and invasion abilities of human pulmonary adenocarcinoma cell line A549 in vitro and explored the possible underlying mechanisms. METHODS: A549 cells were treated with 0.5 mmol/L, 2 mmol/L and 8 mmol/L metformin for 72h. The laterad-migration and invasion abilities of the cells in vitro were evaluated by scratch assay and Boyden-Chamber assay, respectively. Expressions of MMP2 and MMP9 mRNA of the cells before and after metformin treatment were measured by Real-Time PCR. RESULTS: The migration rate of A549 cells was increased after metformin treatment at the concentration of 8mmol/L. The invasion ability was also significantly increased from 37.4±4.6 to 59.8±7.2(P<0.05) by 8mmol/L metformin treatment. No significant difference of the migration and invasion abilities was observed between the Group 0.5mmol/L, 2mmol/L and the Control. The expressions of MMP2 and MMP9 mRNA were both up-regulated after metformin treatment, while in the 8mmol/L Group the genes changes were the most significant. CONCLUSIONS: Metformin can increase the migration speed and enhance invasion abilities of A549 cells in vitro, which may be attributed to the up-regulation of MMP2 and MMP9.