DistancePPG: Robust non-contact vital signs monitoring using a camera

Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ health tracking (e.g. on mobile phone and computers with webcams). Recently, camera-based non-contact vital sign monitoring have been shown to be feasible. However, camera-based vital sign monitoring is challenging for people with darker skin tone, under low lighting conditions, and/or during movement of an individual in front of the camera. In this paper, we propose distancePPG, a new camera-based vital sign estimation algorithm which addresses these challenges. DistancePPG proposes a new method of combining skin-color change signals from different tracked regions of the face using a weighted average, where the weights depend on the blood perfusion and incident light intensity in the region, to improve the signal-to-noise ratio (SNR) of camera-based estimate. One of our key contributions is a new automatic method for determining the weights based only on the video recording of the subject. The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios. Further, a dataset will be released, comprising of synchronized video recordings of face and pulse oximeter based ground truth recordings from the earlobe for people with different skin tones, under different lighting conditions and for various motion scenarios.OCIS codes: (170.0110) Imaging systems, (170.1470) Blood or tissue constituent monitoring, (280.0280) Remote sensing and sensors, (170.3660) Light propagation in tissues     

A form of protease nexin I is expressed on the platelet surface during platelet activation

A protein that has several similarities to protease nexin I, a fibroblast thrombin and urokinase inhibitor, has been detected on platelets (Gronke RS, Bergman BL, and Baker JB: J Biol Chem 262:3030, 1987). On incubation of platelets with 125I-thrombin, this platelet protein forms complexes with 125I-thrombin that are found both in the incubation medium and, as demonstrated here, associated with purified platelet plasma membranes. The present results indicate that interaction with the platelet surface may modulate the conformation and function of this platelet form of protease nexin I (PNIp) because: (a) an antibody against protease nexin I inhibited released PNIp, but not platelet-bound PNIp from complexing 125I-thrombin, and (b) whereas PNIp extracted from platelets bound both thrombin and urokinase, platelet- bound PNIp bound only thrombin. In experiments using several different platelet isolation methods, PNIp accounted for a large fraction of the rapid high affinity binding of 125I-thrombin to platelets. However, platelets isolated and maintained in the presence of metabolic inhibitors failed to take added thrombin into 125I-thrombin-PNIp complexes. This finding suggests that PNIp is released from inside platelets during activation, and thus does not function to transmit the primary activating signal that is generated by thrombin binding to platelets.     

Fibrotic idiopathic interstitial pneumonia: the prognostic value of longitudinal functional trends

Survival is linked to the histopathologic distinction between usual interstitial pneumonia (UIP) and nonspecific interstitial pneumonia (NSIP), the most commonly encountered fibrotic idiopathic interstitial pneumonia. We retrospectively compared the prognostic significance of histopathologic diagnoses, baseline pulmonary function indices, and serial trends in pulmonary function indices (diffusing capacity, FVC, FEV1, the recently defined composite physiologic index) at 6 and 12 months in 104 patients (UIP, n = 63; fibrotic NSIP, n = 41). Survival was lower in UIP than in fibrotic NSIP (p = 0.001) but not in patients with severe functional impairment; mortality during the first 2 years was linked solely to the severity of functional impairment at presentation. The composite physiologic index was the strongest determinant of outcome (p < 0.001). At 6 months, serial diffusing capacity levels (p = 0.003) and histopathologic diagnosis (p = 0.002) were prognostically equivalent. At 12 months, serial pulmonary function trends were the only major prognostic determinant (p < 0.0005 for all variables), with no independent significance associated with the distinction between UIP and fibrotic NSIP. We conclude that at 12 months, serial pulmonary function trends have considerable prognostic value in UIP and NSIP. Their histologic distinction provides no additional prognostic information when pulmonary function trends are clear cut or when functional impairment is severe.     

Elevated urinary IL-1β levels in multidrug resistant Escherichia coli and Klebsiella infections

Inflammation Research - Multidrug resistant (MDR) E. coli and Klebsiella infections are rising. IL-1β has been implicated in the differentiation of symptomatic and asymptomatic urinary tract...     

A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples

Unbiased next-generation sequencing (NGS) approaches enable comprehensive pathogen detection in the clinical microbiology laboratory and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. Here we describe SURPI (“sequence-based ultrarapid pathogen identification”), a computational pipeline for pathogen identification from complex metagenomic NGS data generated from clinical samples, and demonstrate use of the pipeline in the analysis of 237 clinical samples comprising more than 1.1 billion sequences. Deployable on both cloud-based and standalone servers, SURPI leverages two state-of-the-art aligners for accelerated analyses, SNAP and RAPSearch, which are as accurate as existing bioinformatics tools but orders of magnitude faster in performance. In fast mode, SURPI detects viruses and bacteria by scanning data sets of 7–500 million reads in 11 min to 5 h, while in comprehensive mode, all known microorganisms are identified, followed by de novo assembly and protein homology searches for divergent viruses in 50 min to 16 h. SURPI has also directly contributed to real-time microbial diagnosis in acutely ill patients, underscoring its potential key role in the development of unbiased NGS-based clinical assays in infectious diseases that demand rapid turnaround times.There is great interest in the use of unbiased next-generation sequencing (NGS) technology for comprehensive detection of pathogens from clinical samples (Dunne et al. 2012; Wylie et al. 2012; Chiu 2013; Firth and Lipkin 2013). Conventional diagnostic testing for pathogens is narrow in scope and fails to detect the etiologic agent in a significant percentage of cases (Barnes et al. 1998; Louie et al. 2005; van Gageldonk-Lafeber et al. 2005; Bloch and Glaser 2007; Denno et al. 2012). Failure to accurately diagnose and treat infection in a timely fashion contributes to continued transmission and increased mortality in hospitalized patients (Kollef et al. 2008). Ongoing discovery of novel pathogens, such as Bas-Congo rhabdovirus (Grard et al. 2012) and MERS (Middle East Respiratory Syndrome) coronavirus (Zaki et al. 2012), also underscores the need for rapid, broad-spectrum diagnostic assays that are able to recognize these emerging agents.Unbiased NGS holds the promise of identifying all potential pathogens in a single assay without a priori knowledge of the target. Given sufficiently long read lengths, multiple hits to the microbial genome, and a well-annotated reference database, nearly all microorganisms can be uniquely identified on the basis of their specific nucleic acid sequence. Thus, NGS has widespread microbiological applications, including infectious disease diagnosis in clinical laboratories (Dunne et al. 2012), pathogen discovery in acute and chronic illnesses of unknown origin (Chiu 2013), and outbreak investigation on a global level (Firth and Lipkin 2013). However, the latest NGS laboratory workflows incur minimum turnaround times exceeding 8 h from clinical sample to sequence (Quail et al. 2012). Thus, it is critical that subsequent computational analyses of NGS data be performed within a timeframe suitable for actionable responses in clinical medicine and public health (i.e., minutes to hours). Such pipelines must also retain sensitivity, accuracy, and throughput in detecting a broad range of clinically relevant pathogenic microorganisms.Computational analysis of metagenomic NGS data for pathogen identification remains challenging for several reasons. First, alignment/classification algorithms must contend with massive amounts of sequence data. Recent advances in NGS technologies have resulted in instruments that are capable of producing >100 gigabases (Gb) of reads in a day (Loman et al. 2012). Reference databases of host and pathogen sequences range in size from 2 Gb for viruses to 3.1 Gb for the human genome to 42 Gb for all nucleotide sequences in the National Center for Biotechnology Information (NCBI) nucleotide (nt) collection (NCBI nt DB) as of January 2013. Second, only a small fraction of short NGS reads in clinical metagenomic data typically correspond to pathogens (a “needle-in-a-haystack” problem) (Kostic et al. 2012; Wylie et al. 2012; Yu et al. 2012), and such sparse reads often do not overlap sufficiently to permit de novo assembly into longer contiguous sequences (contigs) (Kostic et al. 2011). Thus, individual reads, typically only 100–300 nucleotides (nt) in length, must be classified to a high degree of accuracy. Finally, novel microorganisms with divergent genomes, particularly viruses, are not adequately represented in existing reference databases and often can only be identified on the basis of remote amino acid homology (Xu et al. 2011; Grard et al. 2012).To address these challenges, the most widely used approach is computational subtraction of reads corresponding to the host (e.g., human), followed by alignment to reference databases that contain sequences from candidate pathogens (MacConaill and Meyerson 2008; Greninger et al. 2010; Kostic et al. 2011; Zhao et al. 2013). Traditionally, the BLAST algorithm (Altschul et al. 1990) is used for classification of human and nonhuman reads at the nucleotide level (BLASTn), followed by low-stringency protein alignments using a translated nucleotide query (BLASTx) for detection of divergent sequences from novel pathogens (Delwart 2007; Briese et al. 2009; Xu et al. 2011; Grard et al. 2012; Chiu 2013). However, BLAST is too slow for routine analysis of NGS metagenomics data (Niu et al. 2011), and end-to-end processing times, even on multicore computational servers, can take several days to weeks. Analysis pipelines that use faster, albeit less sensitive, algorithms upfront for host computational subtraction, such as PathSeq (Kostic et al. 2011), still rely on traditional BLAST approaches for final pathogen determination. In addition, whereas PathSeq works well for tissue samples in which the vast majority of reads are host-derived and thus subject to subtraction, the pipeline becomes computationally prohibitive when analyzing complex clinical metagenomic samples open to the environment, such as respiratory secretions or stool (Fig. 1B; Supplemental Table S1). Other published pipelines are focused solely on limited detection of specific types of microorganisms, are unable to identify highly divergent novel pathogens, and/or utilize computationally taxing algorithms such as BLAST (Bhaduri et al. 2012; Borozan et al. 2012; Dimon et al. 2013; Naeem et al. 2013; Wang et al. 2013; Zhao et al. 2013). Furthermore, there is hitherto scarce reported data on the real-life performance of these pipelines for pathogen identification in clinical samples.Open in a separate windowFigure 1.The SURPI pipeline for pathogen detection. (A) A schematic overview of the SURPI pipeline. Raw NGS reads are preprocessed by removal of adapter, low-quality, and low-complexity sequences, followed by computational subtraction of human reads using SNAP. In fast mode, viruses and bacteria are identified by SNAP alignment to viral and bacterial nucleotide databases. In comprehensive mode, reads are aligned using SNAP to all nucleotide sequences in the NCBI nt collection, enabling identification of bacteria, fungi, parasites, and viruses. For pathogen discovery of divergent microorganisms, unmatched reads and contigs generated from de novo assembly are then aligned to a viral protein database or all protein sequences in the NCBI nr collection using RAPSearch. SURPI reports include a list of all classified reads with taxonomic assignments, a summary table of read counts, and both viral and bacterial genomic coverage maps. (B) Relative proportion of NGS reads classified as human, bacterial, viral, or other in different clinical sample types. (C) The SNAP nucleotide aligner (Zaharia et al. 2011). SNAP aligns reads by generating a hash table of sequences of length “s” from the reference database and then comparing the hash index with “n” seeds of length “s” generated from the query sequence, producing a match based on the edit distance “d.” (D) The RAPSearch protein similarity search tool (Zhao et al. 2012). RAPSearch aligns translated nucleotide queries to a protein database using a compressed amino acid alphabet at the level of chemical similarity for greatly increased processing speed.Here we describe SURPI (“sequence-based ultrarapid pathogen identification”), a cloud-compatible bioinformatics analysis pipeline that provides extensive classification of reads against viral and bacterial databases in fast mode and against the entire NCBI nt DB in comprehensive mode (Fig. 1A). Novel pathogens are also identified in comprehensive mode by amino acid alignment to viral and/or NCBI nr protein databases. Notably, SURPI generates results in a clinically actionable timeframe of minutes to hours by leveraging two alignment tools, SNAP (Fig. 1C; Zaharia et al. 2011) and RAPSearch (Fig. 1D; Zhao et al. 2012), which have computational times that are orders of magnitude faster than other available algorithms. Here we evaluate the performance of these tools for pathogen detection using both in silico-generated and clinical data and describe use of the SURPI pipeline in the analysis of 15 independent NGS data sets consisting of 157 clinical samples multiplexed across 47 barcodes and including over 1.1 billion reads. These data sets encompass a variety of clinical infections, detected pathogens, sample types, and depths of coverage. We also demonstrate use of the pipeline for detection of emerging novel outbreak viruses and for clinical diagnosis of a case of unknown fever in a returning traveler.     

Volumetric analysis of IDH-mutant lower-grade glioma: a natural history study of tumor growth rates before and after treatment

BackgroundLower-grade gliomas (LGGs) with isocitrate dehydrogenase 1 and/or 2 (IDH1/2) mutations have long survival times, making evaluation of treatment efficacy difficult. We investigated the volumetric growth rate of IDH mutant gliomas before and after treatment with established glioma therapies to determine whether a significant change in growth rate could be documented and perhaps be used in the future to evaluate treatment response to investigational agents in LGG trials.MethodsIn this multicenter retrospective study, 230 adult patients with IDH1/2 mutated LGGs (World Health Organization grade II or III) undergoing surgery, radiation, or chemotherapy for progressive non-enhancing tumor were identified. Subjects were required to have 3 MRI scans containing T2/fluid attenuated inversion recovery imaging spanning a minimum of 6 months prior to treatment. A mixed-effect model was used to estimate tumor growth prior to treatment. A subset of 95 patients who received chemotherapy, radiotherapy, or chemoradiotherapy and had 2 posttreatment imaging time points available were evaluated for change in pre- and posttreatment volumetric growth rates using a piecewise mixed model.ResultsThe pretreatment volumetric growth rate across all 230 patients was 27.37%/180 days (95% CI: [23.36%, 31.51%]). In the 95 patients with both pre- and posttreatment scans available, there was a significant difference in volumetric growth rates before (26.63%/180 days, 95% CI: [19.31%, 34.40%]) and after treatment (−15.24% /180 days, 95% CI: [−21.37%, −8.62%]) (P < 0.0001). The growth rates for patient subgroup with 1p/19q codeletion (N = 118) was significantly slower than the rate of the 1p/19q non-codeleted group (N = 68) (22.84% vs 35.49%, P = 0.0108).ConclusionIn this study, we evaluated the growth rates of IDH mutant gliomas before and after standard therapy. Further study is needed to establish whether a change in growth rate is associated with patient survival and its use as a surrogate endpoint in clinical trials for IDH mutant LGGs.     

Biological characteristics of influenza virus strains isolated at the Government of India Influenza Centre, Coonoor, during 1950-60

The isolation of influenza virus strains of different types from the same or closely adjacent localities in the Nilgiris district of India in 1959-60 led the authors to investigate the biological characteristics of those strains and to compare them with strains isolated in previous years at the Government of India Influenza Centre at Coonoor. The haemagglutination with erythrocytes of different animal species, the sensitivity to inhibitors in normal sera, the effect of heat and the effect of ether are reported on in this paper.     

Antigenic value of lyophilized phenolized antirabies vaccine

The authors present the results of experiments, carried out at the Pasteur Institute of Southern India, Coonoor, in which various preparations of lyophilized and liquid phenolized antirabies vaccines were assessed for antigenicity in relation to the NIH (United States National Institutes of Health) Reference Vaccine 164 (the proposed International Reference Preparation of Rabies Vaccine). The claim that phenolized antirabies vaccines can be lyophilized without loss of antigenicity was fully substantiated: the lyophilized vaccines were found to possess high antigenic values and to retain their antigenicity better than the liquid vaccines during storage under the same conditions.     

Influenza virus isolations at the Government of India Influenza Centre, Coonoor, during 1950-60

In 1950, responding to an invitation by the World Health Organization to all its Member States to establish regional laboratories for the study, in collaboration with the World Influenza Centre, of the distribution and antigenic pattern of influenza viruses, the Government of India set up an Influenza Centre at the Pasteur Institute of Southern India, Coonoor. The author presents a study of the antigenic pattern and variation of the influenza virus strains isolated at the Government of India Influenza Centre during 1950-60. Of the 152 strains isolated, 135 were type A viruses (23 belonging to the A1/Liverpool/50 subtype, 5 to A1/Eire/55, 10 to A1/Ned/56 and 97 to A2/Asia/57), 15 were type B viruses, and 2 were type C viruses. Two striking facts that emerged from this study were the absence of the Scandinavian strains from the area in which the viruses were isolated and the total disappearance of old strains after a new one had appeared.