Chronic rhinosinusitis with nasal polyps (CRSwNP) is characterized by Th2-skewed inflammation and increased colonization by Staphylococcus aureus. CRSwNP can be distinguished as eosinophilic (ECRSwNP) and non-eosinophilic (NECRSwNP) by the infiltration of eosinophils. The local microbiota plays an important role in the persistent inflammation of CRSwNP. To evaluate the bacterial community composition on the distinct types of CRSwNP patients, we collected nasal swabs from 16 ECRSwNP patients, 18 NECRSwNP patients, and 39 healthy control subjects. The microbiome structure for all the samples were analyzed by high-throughput 16S rRNA gene sequencing. Concentration of S. aureus was determined using TaqMan quantitative polymerase chain reaction (qPCR) targeting the nuclease (nuc) gene. The result showed significant differences in the sinus microbiome among healthy control subjects and CRSwNP patients. Microbiota community diversity was significantly lower in NECRSwNP samples compared to that of healthy control subjects. Interestingly, the abundance of several pathogenic bacteria was diverse between ECRSwNP and NECRSwNP patients. Although Staphylococcus prevailed in all groups, the abundance of Staphylococcus was significantly higher in the healthy control group than the ECRSwNP group. More importantly, the abundance of S. aureus was much higher in NECRSwNP patients. This study highlights that microbiota composition may contribute to the different clinical types of CRSwNP, inspiring new therapeutic strategies to resolve this chronic inflammation process.
Time-interleaved analog to digital convertor (TIADC) is widely used in engineering to increase the sample rate of acquisition system. However, the mismatches between sub-ADCs in TIADC system result in the distortion of sample output and decrease the sample performance. This paper focuses on the calibration of offset, gain and timing skew mismatches. A novel method based on statistical theory is proposed to estimate offset mismatch of each channel. This method can reduce the impact of noise on offset mismatch calibration. The amplitude of main spectrum is utilized to calibrate gain mismatch, and the average value of each sub-ADC is employed to calibrate timing skew mismatch. Meanwhile, gain mismatch and timing skew mismatch are calibrated by STPNM (Simplified Three Point Newton's method). The proposed calibration method is implemented in a four-channel TIADC based digital storage oscilloscope whose sample rate is 5GSPS. The experiment results show that the impact of mismatches can be reduced effectively and the calibration can be finished in a short time because the convergence is very fast.
Time-interleaved technique is widely used to increase the sampling rate of analog-to-digital converter (ADC). However, the channel mismatches degrade the performance of time-interleaved ADC (TIADC). When input signal frequency is very high, timing skews have significant effect on distortion. Therefore, a new timing skew calibration method is proposed in this paper. This method is based on the truth that timing skews are related to the product of the outputs of sub-ADCs. After timing skews are estimated, the digital controlled delay elements (DCDE) in ADC and phase locked loop (PLL) are utilized to calibrate timing skews. No auxiliary circuit and digital filter are needed for this calibration method. Simulation results show that the proposed method can estimate timing skew accurately. It is also proved that an accurate estimation can be obtained even the signal to noise ratio (SNR) of input signal is 20[Formula: see text]dB. The proposed method is employed to calibrate timing skews in a 16-channel TIADC-based 20[Formula: see text]GSPS digital storage oscilloscope (DSO). The experiment results demonstrate the usefulness of the proposed method. We can see that after timing skews are calibrated, the spectrum spurs have been effectively eliminated.
Currently, the choice of medical treatment for major depressive disorder (MDD) is primarily based on a trial-and-error process. Thus, identification of individual factors capable of predicting treatment response is of great clinical relevance. Recent work points towards beclin-1 and inflammatory factors as potential biomarkers of antidepressant treatment response. The primary aim of the study was to investigate whether pre-treatment serum levels of beclin-1 and inflammatory factors could predict antidepressant treatment response in Chinese Han patients with MDD. Forty patients with MDD were treated with either a selective serotonin reuptake inhibitor (SSRI) (paroxetine in 20 cases) or a serotonin–norepinephrine reuptake inhibitor (SNRI) (duloxetine in 13 cases and venlafaxine in 7 cases). Depression scores and serum levels of beclin-1 were measured at the baseline and after 8 weeks of antidepressant treatment. Serum C-reactive protein (CRP), interleukin (IL)-1B, and IL-6 levels were determined using enzyme-linked immunosorbent assay kits at the baseline. Twenty-seven patients were identified as treatment responders, whereas 13 were identified as non-responders after 8 weeks of antidepressant treatment. Baseline serum beclin-1 levels were significantly higher in non-responders than in responders (p = 0.001), whereas no differences were found in baseline serum CRP, IL-1B, or IL-6 levels between responders and non-responders. There were no significant correlations between baseline levels of beclin-1 and baseline IL-1β, IL-6, and CRP levels—neither in the total sample nor in responder and non-responder groups. Moreover, logistic regression models and a random forest model showed that baseline serum beclin-1, but not inflammatory factors, was an independent and the most important predictor for antidepressant treatment response. Furthermore, serum beclin-1 levels were significantly increased in responders (p = 0.027) but not in non-responders after 8 weeks of treatment (p = 0.221). Baseline serum beclin-1 levels may be a predictive biomarker of antidepressant response in patients with MDD. Moreover, beclin-1 may be involved in the therapeutic effect of antidepressant drugs.
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