HIV-1 Vpr drives a tissue residency-like phenotype during selective infection of resting memory T cells

HIV-1 replicates in CD4+ T cells, leading to AIDS. Determining how HIV-1 shapes its niche to create a permissive environment is central to informing efforts to limit pathogenesis, disturb reservoirs, and achieve a cure. A key roadblock in understanding HIV-T cell interactions is the requirement to activate T cells in vitro to make them permissive to infection. This dramatically alters T cell biology and virus-host interactions. Here we show that HIV-1 cell-to-cell spread permits efficient, productive infection of resting memory T cells without prior activation. Strikingly, we find that HIV-1 infection primes resting T cells to gain characteristics of tissue-resident memory T cells (TRM), including upregulating key surface markers and the transcription factor Blimp-1 and inducing a transcriptional program overlapping the core TRM transcriptional signature. This reprogramming is driven by Vpr and requires Vpr packaging into virions and manipulation of STAT5. Thus, HIV-1 reprograms resting T cells, with implications for viral replication and persistence

Regression analysis for peak designation in pulsatile pressure signals

Following recent studies, the automatic analysis of intracranial pressure (ICP) pulses appears to be a promising tool for forecasting critical intracranial and cerebrovascular pathophysiological variations during the management of many disorders. A pulse analysis framework has been recently developed to automatically extract morphological features of ICP pulses. The algorithm is able to enhance the quality of ICP signals, to segment ICP pulses, and to designate the locations of the three ICP sub-peaks in a pulse. This paper extends this algorithm by utilizing machine learning techniques to replace Gaussian priors used in the peak designation process with more versatile regression models. The experimental evaluations are conducted on a database of ICP signals built from 700 h of recordings from 64 neurosurgical patients. A comparative analysis of different state-of-the-art regression analysis methods is conducted and the best approach is then compared to the original pulse analysis algorithm. The results demonstrate a significant improvement in terms of accuracy in favor of our regression-based recognition framework. It reaches an average peak designation accuracy of 99% using a kernel spectral regression against 93% for the original algorithm

Ventricular beat detection in single channel electrocardiograms

BACKGROUND: Detection of QRS complexes and other types of ventricular beats is a basic component of ECG analysis. Many algorithms have been proposed and used because of the waves' shape diversity. Detection in a single channel ECG is important for several applications, such as in defibrillators and specialized monitors. METHODS: The developed heuristic algorithm for ventricular beat detection includes two main criteria. The first of them is based on steep edges and sharp peaks evaluation and classifies normal QRS complexes in real time. The second criterion identifies ectopic beats by occurrence of biphasic wave. It is modified to work with a delay of one RR interval in case of long RR intervals. Other algorithm branches classify already detected QRS complexes as ectopic beats if a set of wave parameters is encountered or the ratio of latest two RR intervals RR(i-1)/RR(i )is less than 1:2.5. RESULTS: The algorithm was tested with the AHA and MIT-BIH databases. A sensitivity of 99.04% and a specificity of 99.62% were obtained in detection of 542014 beats. CONCLUSION: The algorithm copes successfully with different complicated cases of single channel ventricular beat detection. It is aimed to simulate to some extent the experience of the cardiologist, rather than to rely on mathematical approaches adopted from the theory of signal analysis. The algorithm is open to improvement, especially in the part concerning the discrimination between normal QRS complexes and ectopic beats

Cycle length evaluation in persistent atrial fibrillation using kernel density estimation to identify transient and stable rapid atrial activity

Purpose Left atrial (LA) rapid AF activity has been shown to co-localise with areas of successful atrial fibrillation termination by catheter ablation. We describe a technique that identifies rapid and regular activity. Methods Eight-second AF electrograms were recorded from LA regions during ablation for psAF. Local activation was annotated manually on bipolar signals and where these were of poor quality, we inspected unipolar signals. Dominant cycle length (DCL) was calculated from annotation pairs representing a single activation interval, using a probability density function (PDF) with kernel density estimation. Cumulative annotation duration compared to total segment length defined electrogram quality. DCL results were compared to dominant frequency (DF) and averaging. Results In total 507 8 s AF segments were analysed from 7 patients. Spearman’s correlation coefficient was 0.758 between independent annotators (P < 0.001), 0.837–0.94 between 8 s and ≥ 4 s segments (P < 0.001), 0.541 between DCL and DF (P < 0.001), and 0.79 between DCL and averaging (P < 0.001). Poorer segment organization gave greater errors between DCL and DF. Conclusion DCL identifies rapid atrial activity that may represent psAF drivers. This study uses DCL as a tool to evaluate the dynamic, patient specific properties of psAF by identifying rapid and regular activity. If automated, this technique could rapidly identify areas for ablation in psAF

QRS detection based on morphological filter and energy envelope for applications in body sensor networks

10.1007/s11265-009-0430-8Journal of Signal Processing Systems642187-19