The efficacy and safety of dexmedetomidine in cardiac surgery patients: A systematic review and meta-analysis.

This study aimed to evaluate the efficacy and safety of dexmedetomidine versus any other treatment without dexmedetomidine in patients who have undergone cardiac surgery. Electronic databases including PubMed, Embase, and Cochrane Library were systematically searched without limitations of language and publication time. Randomized controlled trials (RCTs) aiming to evaluate the efficacy and safety of dexmedetomidine versus any other treatment without dexmedetomidine in patients that have undergone cardiac surgery were selected. Endpoints such as hemodynamic indexes and adverse events in eligible studies were extracted by two researchers, independently. The data was analyzed using RevMan 5.3 and Stata 11.0 software. A total of 18 RCTs met the inclusion criteria, involving 1730 patients. Compared to control (any treatment without dexmedetomidine), dexmedetomidine showed a pooled mean difference (MD) of -14.46 [95% confidence interval(CI): -24.69, -4.23; p0.05) for atrial fibrillation, and 0.99 (95%CI: 0.51, 1.90; p>0.05) for hypotension. In addition, dexmedetomidine could reduce time of surgery and stay in intensive care units, improve delirium with good safety. Our study shows clinical application of dexmedetomidine in cardiac surgery patients can reduce risks of abnormal hemodynamics with good safety

Table1_A decision support system for upper limb rehabilitation robot based on hybrid reasoning with RBR and CBR.pdf

The rehabilitation robot can assist hemiplegic patients to complete the training program effectively, but it only focuses on helping the patient’s training process and requires the rehabilitation therapists to manually adjust the training parameters according to the patient’s condition. Therefore, there is an urgent need for intelligent training prescription research of rehabilitation robots to promote the clinical applications. This study proposed a decision support system for the training of upper limb rehabilitation robot based on hybrid reasoning with rule-based reasoning (RBR) and case-based reasoning (CBR). The expert knowledge base of this system is established base on 10 professional rehabilitation therapists from three different rehabilitation departments in Shanghai who are enriched with experiences in using desktop-based upper limb rehabilitation robot. The rule-based reasoning is chosen to construct the cycle plan inference model, which develops a 21-day training plan for the patients. The case base consists of historical case data from 54 stroke patients who underwent rehabilitation training with a desktop-based upper limb rehabilitation robot. The case-based reasoning, combined with a Random Forest optimized algorithm, was constructed to adjust the training parameters for the patients in real-time. The system recommended a rehabilitation training program with an average accuracy of 91.5%, an average AUC value of 0.924, an average recall rate of 88.7%, and an average F1 score of 90.1%. The application of this system in rehabilitation robot would be useful for therapists.</p

Calibration curve and sensitivity of BIE for detecting CMV antibodies.

<p>(A) Grayscale images of different CMV antibody concentration levels detected; (B) 3-D grayscale distribution map of different concentrations of CMV antibodies detected; and (C) standard curve of CMV-3A as ligand to detect five serial dilutions of containing CMV antibodies (0.011, 0.043, 0.170, 0.681, and 2.725 IU/mL). In the first step, CMV-3A was immobilized as the ligand on two columns. In the second step, PBST buffer was added as a blank control to two areas on the first row. Simultaneously, purified CMV antibody was added as a positive control to two areas on the second row. Negative serum was added as a negative control to two areas on the third row. The serial dilutions of CMV antibodies were added as analytical samples on the following rows. The same concentration was measured in two duplicate areas.</p

Detection of Cytomegalovirus Antibodies Using a Biosensor Based on Imaging Ellipsometry

<div><p>Background</p><p>Cytomegalovirus (CMV) is the most common infectious cause of mental disability in newborns in developed countries. There is an urgent need to establish an early detection and high-throughput screening method for CMV infection using portable detection devices.</p><p>Methods</p><p>An antibody analysis method is reported for the detection and identification of CMV antibodies in serum using a biosensor based on high spatial resolution imaging ellipsometry (BIE). CMV antigen (CMV-3A) was immobilized on silicon wafers and used to capture CMV antibodies in serum. An antibody against human immunoglobulin G (anti-IgG) was used to confirm the IgG antibody against CMV captured by the CMV-3A.</p><p>Results</p><p>Our results show that this assay is rapid and specific for the identification of IgG antibody against CMV. Further, patient serum was quantitatively assessed using the standard curve method, and the quantitative results were in agreement with the enzyme-linked immunosorbent assay. The CMV antibody detection sensitivity of BIE reached 0.01 IU/mL.</p><p>Conclusions</p><p>This novel biosensor may be a valuable diagnostic tool for analysis of IgG antibody against CMV during CMV infection screening.</p></div

Detection of CMV antibodies in patient serum using BIE.

<p>In the first step, CMV-3A was immobilized as the ligand on six columns. In the second step, PBST buffer was added as a blank control to six areas on the first row. Simultaneously, purified CMV antibody was added as a positive control to the last two areas on the second row. Patient serum samples were added as analytical samples on the following areas, respectively. The same serum sample was measured in two duplicate areas (No.940, 959,938 no sample, P15-9, and PBST control are underlined).</p

Specificity and qualitative detection of CMV antibodies using BIE.

<p>(A) Grayscale images of different CMV antibodies samples; and (B) 3-D grayscale distribution map of different CMV antibody samples in image A. In the first step, CMV-3A was immobilized as the ligand on two columns. In the second step, PBST buffer was added as a blank control to two areas on the first row. Simultaneously, purified CMV antibody was added as a positive control to two areas on the second row. Normal serum without CMV antibodies was added as a negative control to two areas on the third row. According to ELISA data, No. 956 had a high CMV antibody concentration, and No. 933 and 978 had medium CMV antibody concentrations. To observe qualitative detection of CMV antibodies in serum, samples with higher concentrations were chosen.</p

Comparison of BIE with ELISA.

<p>P15-1 and P15-2 were used as healthy controls. Concentration of CMV antibody (0.5 IU/mL) located in the normal reference range (0.4–0.6 IU/mL). The correlation coefficient (<i>r</i>-value) and <i>P</i>-value were calculated.</p

Identification of IgG antibody against CMV by BIE.

<p>(A) Grayscale images of IgG antibody identification in serum; and (B) grayscale value and <i>P</i>-value of the IgG antibody identification. “*” indicates significant changes in grayscale values. In the first step, IgG was immobilized as ligand on the first and second columns. CMV-3A was immobilized on the third, fourth, fifth, and sixth columns. In the second step, PBST buffer was added as blank control to the corresponding areas in the image. Sample No. 948 was added to the third and fourth columns, and sample No. 940 was added to the fifth and sixth columns. In the third step, PBST buffer was added as blank control to the first areas in every column. Anti-IgG and anti-IgM were added to the third and fourth rows, respectively.</p