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"Skinner, Joseph"
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Risk Factors for Late-Onset Psychosis: A Case-Control Study
Abstract
Background and Hypothesis
The onset of schizophrenia occurs after the age of 40 in up to 20% of cases. We aim to depict risk factors for first-episode psychosis after the age of 40 by comparing late-onset psychosis (LOP) patients to healthy age-matched controls.
Study Design
In this case-control study using electronic health records, 142 individuals aged 40–65 years with an encounter for a first episode of psychosis between 2013 and 2021 were included. Four controls (N = 568) were matched to each case on age, sex, race, and year of encounter. Potential risk factors for the primary analysis were captured via structured data and text-mining of medical notes. Conditional logistic regression models were used to assess the odds of LOP with potential risk factors.
Study Results
After adjusting for all variables in the main analysis, odds for LOP were increased by immigration (OR 3.30, 95% CI, 1.56–6.98), depression (OR 3.58, 95% CI, 2.01–6.38), anxiety (OR 2.12, 95% CI, 1.20–3.75), cannabis use (OR 3.00, 95% CI, 1.36–6.61), alcohol use disorder (OR 5.46, 95% CI, 2.41–12.36), polysubstance use (OR 4.22, 95% CI, 1.30–13.7), severe trauma (OR 2.29, 95% CI, 1.08–4.48), and caregiver burden (OR 15.26, 95% CI, 3.85–60.48).
Conclusions
Life stressors along with the effects of substance use and other psychiatric conditions may confer some risk to the development of LOP. Replication is required in independent prospective studies. Further research is necessary to truly parse out which of these factors belong on the causal pathway.
Journal Article
Ancient ethnography : new approaches
\"By providing a platform for scholars working in a variety of fields, this volume presents cutting-edge research dealing with various aspects of ancient ethnographic thought: its formation and devlopment, its intellectual and cultural milieux, the later reception of ethnographic traditons, and the extent to which these represent major constitutive elements of shifting notions of culture, power and identity\"-- Provided by publisher.
Book
Identifying psychosis episodes in psychiatric admission notes via rule-based methods, machine learning, and pre-trained language models
Early and accurate diagnosis is crucial for effective treatment and improved outcomes, yet identifying psychotic episodes presents significant challenges due to its complex nature and the varied presentation of symptoms among individuals. One of the primary difficulties lies in the underreporting and underdiagnosis of psychosis, compounded by the stigma surrounding mental health and the individuals' often diminished insight into their condition. Existing efforts leveraging Electronic Health Records (EHRs) to retrospectively identify psychosis typically rely on structured data, such as medical codes and patient demographics, which frequently lack essential information. Addressing these challenges, our study leverages Natural Language Processing (NLP) algorithms to analyze psychiatric admission notes for the diagnosis of psychosis, providing a detailed evaluation of rule-based algorithms, machine learning models, and pre-trained language models. Additionally, the study investigates the effectiveness of employing keywords to streamline extensive note data before training and evaluating the models. Analyzing 4629 initial psychiatric admission notes (1196 cases of psychosis versus 3433 controls) from 2005 to 2019, including patients aged 16-35 years, selected based on the 75th percentile for age at onset of schizophrenia, we discovered that the XGBoost classifier employing Term Frequency-Inverse Document Frequency (TF-IDF) features derived from notes pre-selected by expert-curated keywords, attained the highest performance with an F1 score of 0.8881 (AUROC [95% CI]: 0.9725 [0.9717, 0.9733]). BlueBERT demonstrated comparable efficacy with an F1 score of 0.8841 (AUROC [95% CI]: 0.97 [0.9580, 0.9820]) on the same set of notes. Both models markedly outperformed traditional International Classification of Diseases (ICD) code-based detection methods from discharge summaries, which had an F1 score of 0.7608, thus improving the margin by 0.12. Furthermore, our findings indicate that keyword pre-selection markedly enhances the performance of both machine learning and pre-trained language models. This study illustrates the potential of NLP techniques to improve psychosis detection within admission notes and aims to serve as a foundational reference for future research on applying NLP for psychosis identification in EHR notes.
Journal Article
Position-Sensitive Scanning Fluorescence Correlation Spectroscopy
Fluorescence correlation spectroscopy (FCS) uses a stationary laser beam to illuminate a small sample volume and analyze the temporal behavior of the fluorescence fluctuations within the stationary observation volume. In contrast, scanning FCS (SFCS) collects the fluorescence signal from a moving observation volume by scanning the laser beam. The fluctuations now contain both temporal and spatial information about the sample. To access the spatial information we synchronize scanning and data acquisition. Synchronization allows us to evaluate correlations for every position along the scanned trajectory. We use a circular scan trajectory in this study. Because the scan radius is constant, the phase angle is sufficient to characterize the position of the beam. We introduce position-sensitive SFCS (PSFCS), where correlations are calculated as a function of lag time and phase. We present the theory of PSFCS and derive expressions for diffusion, diffusion in the presence of flow, and for immobilization. To test PSFCS we compare experimental data with theory. We determine the direction and speed of a flowing dye solution and the position of an immobilized particle. To demonstrate the feasibility of the technique for applications in living cells we present data of enhanced green fluorescent protein measured in the nucleus of COS cells.
Journal Article
Fluorescence Fluctuation Spectroscopy in the Presence of Immobile Fluorophores
Fluorescence contributions from immobile sources present a challenge for fluorescence fluctuation spectroscopy (FFS) because the absence of signal fluctuations from stationary fluorophores leads to a biased analysis. This is especially of concern for cellular FFS studies on proteins that interact with immobile structures. Here we present a method that correctly analyzes FFS experiments in the presence of immobile sources by exploiting selective photobleaching of immobile fluorophores. The fluorescence decay due to photobleaching of the immobile species is modeled taking into account the nonuniform illumination volume. The experimentally observed decay curve serves to separate the mobile and immobile fluorescence contribution, which is used to calculate the molecular brightness from the FFS data. We experimentally verify this approach in vitro using the fluorescent protein EGFP as our immobilized species and a diffusing dye of a different color as the mobile one. For this special case, we also use an alternative method of determining the brightness by spectrally resolving the two species. By conducting a dilution study, we show that the correct parameters are obtained using either technique for a wide range of mobile fractions. To demonstrate the application of our technique in living cells, we perform experiments using the histone core protein H2B fused with EGFP expressed in COS-1 cells. We successfully recovered the brightness of the mobile fraction of H2B-EGFP.
Journal Article
All or Nothing
I consider myself a betting man, and while my work is inspired by traditional religious and artistic backgrounds, I implement unconventional materials and methods of image creation to reflect an underlying, experimental, risk-and-reward nature. All or Nothing contextualizes the lottery through the narrative lens of Christianity and portrays traditional iconographies through contemporary printmaking methodologies, to reflect personal, spiritual, social, and commercialized contexts. Citing Jonathan Cohen’s For a Dollar and a Dream, I critique the lottery as a tax upon the poor and investigate connections of gambling and religion from antiquity to contemporary Christianity. For this body of work, I use discarded lottery tickets as a medium for collage and assemblage. In layering tickets one upon another, they are amalgamated and reactivated as a metaphorical geologic record of a community seeking hope and escape from drudgery. I also examine the value assigned to printed ephemera and explore themes of faith, fate, chance, and probability, equating each individual ticket to a prayer from its player. In reclaiming and repurposing tickets as votive tokens, I also seek to transform the gallery into a space akin to a chapel, in which viewers may reflect upon their own wishes, wants and priorities, while also considering the unrealized hopes and dreams of countless everyday workers essential to this exhibition.
Dissertation
The Communicating and Marketing of Radicalism: A Case Study of ISIS and Cyber Recruitment
This article considers social media as a radicalization venue within the context of terrorism. The 2016 extremist/terrorist attack in Orlando, Florida showed the potential of an ISIS type extremist organization to leverage social media toward a lethal outcome within American society. While the ISIS organization originated overseas in a culture of which mainstream American society is unfamiliar, it is in many ways remarkable the level of success they quickly achieved connecting globally. Their efforts reflect the potential of social media to market a message of radicalism worldwide toward generating murderous converts who are willing to travel to join the fight or attack at home. Given these notions, this article considers the use of social media as an extremist cyber-recruitment tool.
Journal Article
Dual-Color Photon-Counting Histogram
We report on the development of dual-color photon-counting histogram (PCH) analysis. Dual-color PCH is an extension of regular PCH and considers the photon counts received in two detection channels instead of one. Because each detection channel records a different color, dual-color PCH distinguishes fluorescent species not only by differences in their brightness, but also according to their color. The additional discrimination by color increases the sensitivity of PCH in resolving a mixture of species considerably. Most dual-color fluorescence fluctuation experiments are performed on fluorophores with overlapping emission spectra. This overlap results in spectral cross talk between the detector channels, which reduces resolvability. Here, we demonstrate that dual-color PCH is able to resolve binary dye mixtures in the presence of cross talk from a single measurement without any additional information about the sample. We discuss the effect of sampling time on the fit parameters of dual-color PCH. Differences between dual-color fluorescence correlation spectroscopy and dual-color PCH will also be addressed. We quantitatively resolve a mixture of the two fluorescent proteins CFP and YFP, which is challenging because of the strong spectral overlap of their emission spectra. Dichroic mirrors are needed to direct the light into the two detection channels. We quantify the influence of these filters on dual-color PCH analysis and determine the optimal transition wavelength of the dichroic mirror for the CFP-YFP pair.
Journal Article