The diagnostic validity and reliability of an internet-based clinical assessment program for mental disorders

BACKGROUND: Internet-based assessment has the potential to assist with the diagnosis of mental health disorders and overcome the barriers associated with traditional services (eg, cost, stigma, distance). Further to existing online screening programs available, there is an opportunity to deliver more comprehensive and accurate diagnostic tools to supplement the assessment and treatment of mental health disorders. OBJECTIVE: The aim was to evaluate the diagnostic criterion validity and test-retest reliability of the electronic Psychological Assessment System (e-PASS), an online, self-report, multidisorder, clinical assessment and referral system. METHODS: Participants were 616 adults residing in Australia, recruited online, and representing prospective e-PASS users. Following e-PASS completion, 158 participants underwent a telephone-administered structured clinical interview and 39 participants repeated the e-PASS within 25 days of initial completion. RESULTS: With structured clinical interview results serving as the gold standard, diagnostic agreement with the e-PASS varied considerably from fair (eg, generalized anxiety disorder: &kappa;=.37) to strong (eg, panic disorder: &kappa;=.62). Although the e-PASS\u27 sensitivity also varied (0.43-0.86) the specificity was generally high (0.68-1.00). The e-PASS sensitivity generally improved when reducing the e-PASS threshold to a subclinical result. Test-retest reliability ranged from moderate (eg, specific phobia: &kappa;=.54) to substantial (eg, bulimia nervosa: &kappa;=.87). CONCLUSIONS: The e-PASS produces reliable diagnostic results and performs generally well in excluding mental disorders, although at the expense of sensitivity. For screening purposes, the e-PASS subclinical result generally appears better than a clinical result as a diagnostic indicator. Further development and evaluation is needed to support the use of online diagnostic assessment programs for mental disorders. <br /

Process development and scale-up for gene circuit engineered CAR-NK cell manufacturing

Allogeneic Natural Killer (NK) cell therapy has shown promise in recent years for treating cancer in patients without inducing graft versus host disease and with potential for off-the-shelf administration. Senti Bio is using gene circuits to introduce logic-gating and regulated expression of payloads into next-generation CAR-NK cell therapies to broaden the therapeutic indications and improved efficacy in liquid and solid tumors. Key process development objectives for gene circuits include the ability to efficiently and stably transduce multi-gene constructs into primary NK cells while retaining cell expandability and anti-cancer function. Here, we describe a scalable GMP-ready manufacturing process for generating clinically relevant numbers of CAR-NK cells, and we demonstrate its potential applicability to our product pipeline. To achieve a batch size target of \u3e10^11 NK cells, we aimed to develop a process to start with ~25*10^6 isolated NK cells, achieve \u3e40% CAR+ transduction, and obtain \u3e5,600-fold expansion over 21 days. Enrichment of adult apheresis material from 12 healthy donors via CD3 depletion and CD56 selection yielded an average of ~3*10^8 NK cells, which were cryopreserved for later use. Upon thaw, NK cells were activated using proprietary irradiated gene-modified feeder cells and expanded in a closed system 1L G-Rex chamber. Seven days later, NK cells were transduced with retroviral vectors using closed system procedures, resulting in up to 80% CAR+ population. Gene circuits were tested across multiple retroviral vector delivery systems, and successful constructs were developed into producer cell lines (HEK293) using various single cell cloning techniques with the goal of generating stable, high titer vector producer clones. Primary NK cell transduction efficiency was optimized by testing a range of MOI, comparing different vector addition and spinoculation vessels, and the effect of GMP-compatible transduction enhancers. Transduced NK cells were expanded further in multiple closed system G-Rex culture vessels for a total process time (initial NK thaw to CAR-NK harvest) of approximately 21 days. Different expansion methods were assessed including different irradiated modified cell lines and feeder-free NK expansion technologies achieving ~10,000-fold expansion in the 1L vessels. At cell harvest, the cell suspension was volume-reduced, harvested and formulated into cryopreservation medium using an automated cell processing system, yielding ~4*10^9 cells per liter of culture. Formulated cells were filled in vials and stored in liquid nitrogen vapor phase. Functional assessment was performed via both in vitro and in vivo studies, demonstrating significant CAR-specific cancer cell killing compared to non-transduced NK cells. We also evaluated multiple donors for transduction efficiency, growth characteristics, cancer cell killing specificity, scalability, immunomodulatory function, single cell transcriptomics and distribution and kinetics in vivo to determine desirable attributes for manufacturing. This CAR-NK manufacturing process is expected to be suitable for translation to GMP clinical manufacturing in support of Senti Bio’s internal allogeneic CAR-NK cell pipeline

Supporting User-Defined Functions on Uncertain Data

Uncertain data management has become crucial in many sensing and scientific applications. As user-defined functions (UDFs) become widely used in these applications, an important task is to capture result uncertainty for queries that evaluate UDFs on uncertain data. In this work, we provide a general framework for supporting UDFs on uncertain data. Specifically, we propose a learning approach based on Gaussian processes (GPs) to compute approximate output distributions of a UDF when evaluated on uncertain input, with guaranteed error bounds. We also devise an online algorithm to compute such output distributions, which employs a suite of optimizations to improve accuracy and performance. Our evaluation using both real-world and synthetic functions shows that our proposed GP approach can outperform the state-of-the-art sampling approach with up to two orders of magnitude improvement for a variety of UDFs. 1

Microstructural Consequences of Blast Lung Injury Characterized with Digital Volume Correlation

This study focuses on microstructural changes that occur within the mammalian lung when subject to blast and how these changes influence strain distributions within the tissue. Shock tube experiments were performed to generate the blast injured specimens (cadaveric Sprague-Dawley rats). Blast overpressures of 100 and 180 kPa were studied. Synchrotron tomography imaging was used to capture volumetric image data of lungs. Specimens were ventilated using a custom-built system to study multiple inflation pressures during each tomography scan. These data enabled the first digital volume correlation (DVC) measurements in lung tissue to be performed. Quantitative analysis was performed to describe the damaged architecture of the lung. No clear changes in the microstructure of the tissue morphology were observed due to controlled low- to moderate-level blast exposure. However, significant focal sites of injury were observed using DVC, which allowed the detection of bias and concentration in the patterns of strain level. Morphological analysis corroborated the findings, illustrating that the focal damage caused by a blast can give rise to diffuse influence across the tissue. It is important to characterize the non-instantly fatal doses of blast, given the transient nature of blast lung in the clinical setting. This research has highlighted the need for better understanding of focal injury and its zone of influence (alveolar interdependency and neighboring tissue burden as a result of focal injury). DVC techniques show great promise as a tool to advance this endeavor, providing a new perspective on lung mechanics after blast

Identification of FHL1 as a regulator of skeletal muscle mass: implications for human myopathy

Regulators of skeletal muscle mass are of interest, given the morbidity and mortality of muscle atrophy and myopathy. Four-and-a-half LIM protein 1 (FHL1) is mutated in several human myopathies, including reducing-body myopathy (RBM). The normal function of FHL1 in muscle and how it causes myopathy remains unknown. We find that FHL1 transgenic expression in mouse skeletal muscle promotes hypertrophy and an oxidative fiber-type switch, leading to increased whole-body strength and fatigue resistance. Additionally, FHL1 overexpression enhances myoblast fusion, resulting in hypertrophic myotubes in C2C12 cells, (a phenotype rescued by calcineurin inhibition). In FHL1-RBM C2C12 cells, there are no hypertrophic myotubes. FHL1 binds with the calcineurin-regulated transcription factor NFATc1 (nuclear factor of activated T cells, cytoplasmic, calcineurin-dependent 1), enhancing NFATc1 transcriptional activity. Mutant RBM-FHL1 forms aggregate bodies in C2C12 cells, sequestering NFATc1 and resulting in reduced NFAT nuclear translocation and transcriptional activity. NFATc1 also colocalizes with mutant FHL1 to reducing bodies in RBM-afflicted skeletal muscle. Therefore, via NFATc1 signaling regulation, FHL1 appears to modulate muscle mass and strength enhancement

An active learning-enabled annotation system for clinical named entity recognition

Abstract Background Active learning (AL) has shown the promising potential to minimize the annotation cost while maximizing the performance in building statistical natural language processing (NLP) models. However, very few studies have investigated AL in a real-life setting in medical domain. Methods In this study, we developed the first AL-enabled annotation system for clinical named entity recognition (NER) with a novel AL algorithm. Besides the simulation study to evaluate the novel AL algorithm, we further conducted user studies with two nurses using this system to assess the performance of AL in real world annotation processes for building clinical NER models. Results The simulation results show that the novel AL algorithm outperformed traditional AL algorithm and random sampling. However, the user study tells a different story that AL methods did not always perform better than random sampling for different users. Conclusions We found that the increased information content of actively selected sentences is strongly offset by the increased time required to annotate them. Moreover, the annotation time was not considered in the querying algorithms. Our future work includes developing better AL algorithms with the estimation of annotation time and evaluating the system with larger number of users.https://deepblue.lib.umich.edu/bitstream/2027.42/137676/1/12911_2017_Article_466.pd

The prevalence of anemia and its association with 90-day mortality in hospitalized community-acquired pneumonia

<p>Abstract</p> <p>Background</p> <p>The prevalence of anemia in the intensive care unit is well-described. Less is known, however, of the prevalence of anemia in hospitalized patients with lesser illness severity or without organ dysfunction. Community-acquired pneumonia (CAP) is one of the most frequent reasons for hospitalization in the United States (US), affecting both healthy patients and those with comorbid illness, and is typically not associated with acute blood loss. Our objective was to examine the development and progression of anemia and its association with 90d mortality in 1893 subjects with CAP presenting to the emergency departments of 28 US academic and community hospitals.</p> <p>Methods</p> <p>We utilized hemoglobin values obtained for clinical purposes, classifying subjects into categories consisting of no anemia (hemoglobin >13 g/dL), at least borderline (≤ 13 g/dL), at least mild (≤ 12 g/dL), at least moderate (≤ 10 g/dL), and severe (≤ 8 g/dL) anemia. We stratified our results by gender, comorbidity, ICU admission, and development of severe sepsis. We used multivariable logistic regression to determine factors independently associated with the development of moderate to severe anemia and to examine the relationship between anemia and 90d mortality.</p> <p>Results</p> <p>A total of 8240 daily hemoglobin values were measured in 1893 subjects. Mean (SD) number of hemoglobin values per patient was 4.4 (4.0). One in three subjects (33.9%) had at least mild anemia at presentation, 3 in 5 (62.1%) were anemic at some point during their hospital stay, and 1 in 2 (54.5%) survivors were discharged from the hospital anemic. Anemia increased with illness severity and was more common in those with comorbid illnesses, female gender, and poor outcomes. Yet, even among men and in those with no comorbidity or only mild illness, anemia during hospitalization was common (~55% of subjects). When anemia was moderate to severe (≤ 10 g/dL), its development was independently associated with increased 90d mortality, even among hospital survivors.</p> <p>Conclusions</p> <p>Anemia was common in hospitalized CAP and independently associated with 90d mortality when hemoglobin values were 10 g/dL or less. Whether prevention or treatment of CAP-associated anemia would improve clinical outcomes remains to be seen.</p