Unique Clinical Profiles Research Articles

Characterizing Unique Clinical and Virological Profiles in Concurrent Chronic Hepatitis B and Metabolic Dysfunction-Associated Liver Disease: Insights from a Population-Based Cohort Study.

Background: The concurrent presence of chronic hepatitis B virus (CHB) infection and metabolic dysfunction-associated steatotic liver disease (MASLD) presents a unique clinical scenario with implications that are not yet fully understood. This study aims to characterize the distinct clinical and virological features of CHB in the context of MASLD and evaluate its impact on disease progression and outcomes. Methods: Utilizing a comprehensive health maintenance organization database, this study included 1186 patients with CHB from 2000-2020. Patients were categorized into two groups: CHB-MASLD (n = 188) and CHB alone (n = 998). CHB diagnosis was confirmed by serological markers, while MASLD was diagnosed based on imaging and cardiometabolic risk factors. Comparative analysis and multiple regression models were applied to assess variables related to viral parameters and clinical outcomes. Results: The CHB-MASLD group was older (mean age of 45.2 vs. 39.1, p < 0.001) with higher rates of obesity (46.8% vs. 23.8%, p < 0.001), diabetes (36.2% vs. 17.3%, p < 0.001), and dyslipidemia. Distinct viral profiles included higher HBeAg negativity (96.2%), a higher rate of HBeAg-negative infection (70.4% vs. 63.8%; p < 0.001), and increased HBeAg seroconversion under treatment. Cirrhosis was more prevalent in the CHB-MASLD group (9.6% vs. 4.4%, p = 0.007), while HCC rates were comparable. Multivariate analysis identified age, male gender, chronic active hepatitis, and diabetes as predictors of cirrhosis. Conclusions: CHB-MASLD patients were distinguished by a higher prevalence of metabolic features, along with a distinct viral profile marked by increased chronic HBeAg infection, higher rates of HBeAg seroconversion, and a potential association with worse disease outcomes.

Cognitive phenotypes: Unraveling the heterogeneity in cognitive dysfunction among patients with primary brain tumors receiving radiotherapy.

Patients with primary brain tumors demonstrate heterogeneous patterns of cognitive dysfunction, which we explore using latent profile analysis (LPA) to identify cognitive phenotypes and their trajectories in patients receiving radiotherapy (RT). Ninety-six patients completed neuropsychological testing before and post-RT (3, 6, 12-months) on a prospective longitudinal trial, including measures of processing speed, executive function, language, and verbal and visual memory. Models with 2-4 classes were examined. Demographic and clinical data were examined across phenotypes and post-RT cognitive change was evaluated. The optimal model identified three unique cognitive phenotypes including a group of patients with generalized impairments (11.5%), a group with isolated verbal memory impairments (21.9%), and a group with minimal impairments (66.7%). The Verbal Memory phenotype had fewer years of education (p=.007) and a greater proportion of males (p<.001); the Generalized group had a greater proportion of patients with IDH-wild type gliomas and showed greater symptoms of anxiety and poorer quality of life (p-values<.05); and the Minimal Impairment phenotype had higher rates of IDH-Mutant gliomas. Approximately 50% of patients declined on at least one cognitive domain with memory the most vulnerable. Patients that declined reported greater symptoms of depression (p=.007) and poorer quality of life (p=.025). We identified three distinct cognitive phenotypes in patients with primary brain tumors receiving RT, each associated with unique demographic and clinical (e.g., IDH mutational status) profiles, with mood symptoms associated with late cognitive decline. This patient-centered approach enhances our understanding of clinical profiles associated with cognitive dysfunction and treatment-related neurotoxicity.

Unique genetic and risk-factor profiles in clusters of major depressive disorder-related multimorbidity trajectories

The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients.

Clinical profiles of people enrolling in alcohol and other drug treatment in Australia: Do youth differ from young adults and adults?

To examine and compare age groups on socio-demographic, substance use, mental health, social and risk behaviour profiles of people entering alcohol and other drug (AOD) treatment in a large non-government organisation (NGO) in Queensland and New South Wales, Australia. Design-Cross-sectional study; analysis of baseline routine outcome measures (ROM) and AOD minimum datasets for drug and alcohol treatment services. Setting and participants-People enrolling in Lives Lived Well, a large NGO AOD service between November 2020 and October 2022. Main outcome measures-Socio-demographic, substance use, mental health, social factors and risk behaviours, by youth (≤24 years), young adults (25-35 years) and adults (>35 years). Between November 2020 and October 2022, 9413 clients enrolled in Lives Lived Well (LLW) AOD services and completed baseline ROMs. Over one-fifth (21.9%) were youth (n = 2066), one-third (32.4%) were young adults (n = 3052) and just under half (45.6%) were adults >35 years (n = 4295). The most common primary drug of concern was cannabis for youth, methamphetamine for young adults and alcohol for adults >35 years. Nearly two-thirds (61.3%) reported moderate to severe symptoms of depression and 55.0% reported moderate to severe anxiety. Just under half (47.2%) screened positive for PTSD and two-in-five (40.4%) reported recent suicidal ideation. Co-occurring mental health symptoms were more common in youth compared with young adults and adults >35 years. Co-occurring mental health symptoms were high in the sample, particularly among youth. All age groups present with unique and complex socio-demographic and clinical profiles that are important to understand to provide the most appropriate and effective treatment.

Clinical phenotypes and outcomes of patients with left ventricular thrombus: an unsupervised cluster analysis

BackgroundLeft ventricular thrombus (LVT) can develop in a diverse group of patients with various underlying causes, resulting in divergent natural histories and trajectories with treatment. Our aim was to use cluster analysis to identify unique clinical profiles among patients with LVT and then compare their clinical characteristics, treatment strategies, and outcomes. MethodsWe conducted a retrospective study involving 472 patients with LVT whose data were extracted from a tertiary center's echocardiography database, from March 2011 to January 2021. We used the TwoStep cluster analysis method, examining 19 variables. ResultsOur analysis of the 472 patients with LVT revealed two distinct patient clusters. Cluster 1, comprising 247 individuals (52.3%), was characterized by younger patients with a lower incidence of traditional cardiovascular risk factors and relatively fewer comorbidities compared with Cluster 2. Most patients had LVT attributed to an underlying ischemic condition, with a larger proportion being due to post-acute myocardial infarction in Cluster 1 (68.8%), and due to ischemic cardiomyopathy in Cluster 2 (57.8%). Notably, patients in Cluster 2 exhibited a reduced likelihood of LVT resolution (hazard ratio [HR] 0.58, 95% confidence interval [CI] 0.44–0.77, p < 0.001) and a higher risk of all-cause mortality (HR 2.27, 95% CI 1.43–3.60, p = 0.001). These associations persisted even after adjusting for variables such as anticoagulation treatment, the presence of left ventricular aneurysms, and specific LVT characteristics such as mobility, protrusion, and size. ConclusionThrough TwoStep cluster analysis, we identified two distinct clinical phenotypes among patients with LVT, each distinguished by unique baseline clinical attributes and varying prognoses.

Profiling people with Parkinson's disease at risk of cognitive decline: Insights from PPMI and ICICLE-PD data.

A subset of people with Parkinson's disease (PD) develop dementia faster than others. We aimed to profile PD cognitive subtypes at risk of dementia based on their rate of cognitive decline. Latent class mixed models stratified subtypes in Parkinson's Progression Markers Initiative (PPMI) (N=770) and ICICLE-PD (N=212) datasets based on their decline in the Montreal Cognitive Assessment over at least 4 years. Baseline demographic and cognitive data at diagnosis were compared between subtypes to determine their clinical profile. Four subtypes were identified: two with stable cognition, one with steady decline, and one with rapid decline. Performance on Judgement of Line Orientation, but not category fluency, was associated with a steady decline in the PPMI dataset, and deficits in category fluency, but not visuospatial function, were associated with a steady decline in the ICICLE-PD dataset. People with PD susceptible to cognitive decline demonstrate unique clinical profiles at diagnosis, although this differed between cohorts. Four cognitive subtypes were revealed in two Parkinson's disease samples.Unique profiles of cognitive impairment were related to cognitive decline.Judgement of Line Orientation/category fluency predictive of steady decline.Global deficits related to rapid cognitive decline and increased dementia risk.

Epidemiological investigation of neuroendocrine differentiation in carcinomas: Focus on pancreatic and cholangiocarcinoma cohorts.

e16375 Background: Neuroendocrine differentiated (NED) occurs in various non-neuroendocrine organs and is associated with unique clinical profiles, disease progression, and poorer prognosis. However, global research on NED is scarce. To address this, our study utilized standardized detection methods to explore NED's prevalence in several solid tumors. This research aims to clarify NED pathological characteristics and prognostic significance, enhancing diagnostic and treatment approaches in clinical practice. Methods: Patients (18-75 years) with cancers including pancreatic, cholangiocarcinoma, gastric, colorectal, lung, breast, and prostate, were analyzed post-surgery at our hospital (January 2017- June 2018). Criteria for inclusion were normal organ function and no pre-surgery neoadjuvant therapy. Tumors were staged postoperatively using AJCC 8th criteria (stages I-III), followed by standard adjuvant chemotherapy. NED markers like Chromogranin A, Synaptophysin, and CD56 were detected in tumors via immunohistochemical staining. Results: Between January 2017 and June 2018, over 450 cases were enrolled. Initial analysis included 46 pancreatic cancer and 49 cholangiocarcinoma samples. NED markers were found in 11 (23.9%) of the pancreatic cancer patients, comprising 5 Syno (+), 10 ChrA (+), and 1 CD56 (+) cases. In cholangiocarcinoma patients, 12 (24.5%) expressed NED markers, including 4 Syno (+) and 11 ChrA (+) cases. Conclusions: This study reveals a significant occurrence of neuroendocrine differentiation in pancreatic cancer and cholangiocarcinoma. The notable prevalence of NED underscores its potential as a reference for tailoring clinical treatments. The association between NED and patient prognosis warrants further investigation.

A pilot acceptability evaluation of MomMind: A digital health intervention for Peripartum Depression prevention and management focused on health disparities.

Health disparities cause significant strain on the wellbeing of individuals and society. In this study, we focus on the health disparities present in the condition of Peripartum Depression (PPD), a significant public health issue. While PPD can be managed through therapy and medication, many women do not receive adequate PPD treatment due to issues of social stigma and limited access to healthcare resources. Digital health technologies can offer practical tools for PPD management. However, current solutions do not integrate behavior theory and are rarely responsive to the transient information needs stemming from women's unique sociodemographic, clinical and psychosocial profiles. We describe a pilot acceptability evaluation of MomMind, a health-disparities focused digital health intervention for the prevention and management of PPD. A crucial MomMind advantage is its basis on behavior change theory and patient engagement as enabled by the Digilego digital health framework. Following an internal usability evaluation, MomMind was evaluated by patients through cross-sectional acceptability surveys, pre-and-post PPD health literacy surveys, and interviews. Survey respondents included n = 30 peripartum women, of whom n = 16 (53.3%) were Hispanic and n = 17 (56.7%) of low-income. Survey results show that 96.6% of participants (n = 29) approved and welcomed MomMind, and 90% (n = 27) found MomMind to be an appealing intervention. Additionally, significant improvements (p< = 0.05) were observed in participants' PPD health literacy, specifically their ability to recognize PPD symptoms and knowledge of how to seek PPD information. Interview main themes include MomMind's straightforward design and influence of others (family members, providers) on use of technology. Results suggest that enhancement of a digital health framework with health literacy theory can support production of digital health solutions acceptable to vulnerable populations. This study incorporates existing theories from different disciplines into a unified approach for mitigating health disparities, and produced a novel solution for promotion of health in a vulnerable population.

Using machine learning to discover traumatic brain injury patient phenotypes: national concussion surveillance system Pilot

ABSTRACT Objective The objective is to determine whether unsupervised machine learning identifies traumatic brain injury (TBI) phenotypes with unique clinical profiles. Methods Pilot self-reported survey data of over 10,000 adults were collected from the Centers for Disease Control and Prevention (CDC)’s National Concussion Surveillance System (NCSS). Respondents who self-reported a head injury in the past 12 months (n = 1,364) were retained and queried for injury, outcome, and clinical characteristics. An unsupervised machine learning algorithm, partitioning around medoids (PAM), that employed Gower’s dissimilarity matrix, was used to conduct a cluster analysis. Results PAM grouped respondents into five TBI clusters (phenotypes A-E). Phenotype C represented more clinically severe TBIs with a higher prevalence of symptoms and association with worse outcomes. When compared to individuals in Phenotype A, a group with few TBI-related symptoms, individuals in Phenotype C were more likely to undergo medical evaluation (odds ratio [OR] = 9.8, 95% confidence interval[CI] = 5.8–16.6), have symptoms that were not currently resolved or resolved in 8+ days (OR = 10.6, 95%CI = 6.2–18.1), and more likely to report at least moderate impact on social (OR = 54.7, 95%CI = 22.4–133.4) and work (OR = 25.4, 95%CI = 11.2–57.2) functioning. Conclusion Machine learning can be used to classify patients into unique TBI phenotypes. Further research might examine the utility of such classifications in supporting clinical diagnosis and patient recovery for this complex health condition.

Clinical profiles of adolescent personality pathology: a latent structure examination of the Semi-Structured Interview for Personality Functioning DSM-5 (STiP-5.1) in a help-seeking sample

BackgroundDespite the introduction of dimensional conceptualisations of personality functioning in the latest classification systems, such as Criterion A of the Alternative Model of Personality Disorders in the DSM-5, heterogeneous clinical presentation of personality pathology remains a challenge. Relatedly, the latent structure of personality pathology as assessed by the Semi-Structured Interview for Personality Functioning DSM-5 (STiP-5.1) has not yet been comprehensively examined in adolescents. Therefore, this study aimed to examine the latent structure of the STiP-5.1, and, based on those findings, to describe any unique clinical profiles that might emerge.MethodsThe final sample comprised 502 participants aged 11–18 years consecutively recruited from a specialised personality disorder outpatient service, as well as general day clinic and inpatient wards at the University Hospital University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University Hospital Bern, Switzerland. Participants were assessed using the STiP-5.1, as well as a battery of other psychological measures by clinical psychologists or trained doctoral students. Variations of Factor Analysis, Latent Class Analysis and Factor Mixture Models (FMM) were applied to the STiP-5.1 to determine the most appropriate structure.ResultsThe best fitting model was an FMM comprising four-classes and two factors (corresponding to self- and interpersonal-functioning). The classes differed in both overall severity of personality functioning impairment, and in their scores and clinical relevance on each element of the STiP-5.1. When compared to the overall sample, classes differed in their unique clinical presentation: class 1 had low impairment, class 2 had impairments primarily in self-functioning with high depressivity, class 3 had mixed levels of impairment with emerging problems in identity and empathy, and class 4 had severe overall personality functioning impairment.ConclusionsA complex model incorporating both dimensional and categorical components most adequately describes the latent structure of the STiP-5.1 in our adolescent sample. We conclude that Criterion A provides clinically useful information beyond severity (as a dimensional continuum) alone, and that the hybrid model found for personality functioning in our sample warrants further attention. Findings can help to parse out clinical heterogeneity in personality pathology in adolescents, and help to inform early identification and intervention efforts.

Blood pressure response to extended-release naltrexone in heroin and prescription opioid users and its implications for cardiovascular morbidity

Background Consuming opioid agonists is a risk factor for cardiovascular disease particularly in intravenous heroin users. The monthly injectable extended-release opioid antagonist, naltrexone (XR-NTX) is an effective treatment for opioid use disorder. The impact of opioid receptor blockade through XR-NTX on blood pressure, a critical risk factor for cardiovascular morbidity, has not yet been characterized. Methods The study evaluated the change in blood pressure during XR-NTX treatment among 14 patients who predominately used intravenous heroin and 24 patients who used prescription oral opioids, all with opioid use disorder. Blood pressure was measured in each patient immediately before the first XR-NTX injection and ∼two weeks after the first injection. The change in diastolic and systolic pressure was compared between the heroin users and the prescription opioids users using analysis of variance. Results XR-NTX treatment was associated with significant decreases in diastolic blood pressure in the heroin group, but not in the prescription opioids group. Systolic blood pressure values in the heroin users showed a decline at trend level only. Conclusions Further research is warranted to replicate our findings and to determine whether XR-NTX effect is relatively specific to blood pressure or generalizes to other components of metabolic syndrome. Distinguishing between heroin and prescription opioid users could shed light on the unique clinical and pharmacological profiles of opioid drugs, particularly regarding their cardiovascular safety. This information can be useful in developing personalized therapeutic strategies based on the route of opioid administration.

Novel molecular subtypes of intracranial germ cell tumors expand therapeutic opportunities.

Intracranial germ cell tumors (IGCTs) are a rare group of malignancies that are clinically classified as germinomas and nongerminomatous germ cell tumors (NGGCTs). Previous studies have found that somatic mutations involving the mitogen-activated protein kinase/mTOR signaling pathway are common early events. However, a comprehensive genomic understanding of IGCTs is still lacking. We established a cohort including over 100 IGCTs and conducted genomic and transcriptomic sequencing. We identified novel recurrent driver genomic aberrations, including USP28 truncation mutations and high-level copy number amplification of KRAS and CRKL caused by replication of extrachromosomal DNA. Three distinct subtypes associated with unique genomic and clinical profiles were identified with transcriptome analysis: Immune-hot, MYC/E2F, and SHH. Both immune-hot and MYC/E2F were predominantly identified in germinomas and shared similar mutations involving the RAS/MAPK signaling pathway. However, the immune-hot group showed an older disease onset age and a significant immune response. MYC/E2F was characterized by a younger disease onset age and increased genomic instability, with a higher proportion of tumors showing whole-genome doubling. Additionally, the SHH subtype was mostly identified in NGGCTs. Novel genomic aberrations and molecular subtypes were identified in IGCTs. These findings provide molecular basis for the potential introduction of new treatment strategies in this setting.

Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine.

Personalized medicine transforms healthcare by adapting interventions to individuals' unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine.

Effect of dapagliflozin in patients in Asia with heart failure with mildly reduced or preserved ejection fraction: insights from DELIVER

Abstract Background A large and growing fraction of the global heart failure (HF) population lives in Asia. One-fifth of participants in the DELIVER (Dapagliflozin Evaluation to Improve the Lives of Patients with Preserved Ejection Fraction Heart Failure) trial were enrolled from Asia, providing a rich source of contemporary data to understand their unique clinical profiles and responses to sodium-glucose cotransporter 2 (SGLT2) inhibition. Purpose To assess the differences in clinical characteristics, outcomes, and response to dapagliflozin in patients from Asia vs outside Asia among patients with HF with mildly reduced or preserved ejection fraction in the DELIVER trial. Methods DELIVER was a global randomized clinical trial enrolling across 353 sites in 20 countries. Baseline clinical characteristics from patients enrolled in DELIVER trial were compared between patients in Asia vs outside Asia. The primary outcome was a composite of worsening HF or cardiovascular (CV) death. The effect of dapagliflozin on primary and secondary outcomes were compared in patients in Asia vs outside Asia. Results Among 6,263 participants in the DELIVER trial, 1,226 (19.6%) were enrolled in Asia (310 in China, 422 in Japan, and 318 in Taiwan, and 176 in Vietnam). Compared to patients outside Asia, participants from Asia were similar in age, more likely to be men with previous HF hospitalization, less likely to have obesity, diabetes, hypertension, or history of myocardial infarction, and had lower symptom burden despite similar natriuretic peptide levels (Figure 1). After adjusting for age, sex, and baseline LVEF, those in Asia faced similar risks of HF events but lower risk of mortality compared with those enrolled outside Asia (all-cause mortality Asia vs. outside Asia: HR 0.60, 95% confidence interval [CI] 0.49 – 0.72, Table 1). Within Asia, all-cause mortality was highest in Vietnam (6.8 per 100 person-year [py]), followed by Taiwan (6.7 per 100 py), Japan (3.5 per 100 py) and China (3.3 per 100 py). Dapagliflozin reduced the primary outcome in participants enrolled in Asia (hazard ratio [HR] 0.89, 95% CI 0.67-1.18) and outside Asia (HR 0.80, 95% CI 0.71 – 0.92) to a similar extend (P-interaction = 0.54, Figure 1). Similarly, region did not modify the treatment effect of dapagliflozin on CV death, HF hospitalization, all-cause death, CV death and recurrent HF events, or changes in Kansas City Cardiomyopathy Questionnaire scores (P-interaction &amp;gt; 0.30 for all). Serious adverse events and rates of drug discontinuation were also balanced in both treatment arms, irrespective of enrollment in Asia vs. outside Asia. Conclusions In the global DELIVER trial, dapagliflozin reduced the risk of CV death or HF events and was well tolerated both among participants enrolled in Asia and other geographic regions.Figure 1Table 1

1 Significant Psychiatric Burden Exists in Women Veterans with Drug-Resistant Epilepsy

Objective:Epilepsy, and specifically drug-resistant epilepsy (DRE), is associated with an increased risk of psychiatric dysfunction, likely due to a combination of physiological mechanisms, emotional reactions to disease burden, and bi-directional influences. Women with epilepsy warrant special consideration due to many factors, including hormonal influences on seizure susceptibility, reproductive health considerations, and unique psychiatric and clinical profiles. However, there is yet to be large-scale research characterizing women with DRE. The present study characterized psychiatric conditions, treatment, and hospitalization data in a Veterans Health Administration (VHA)-wide sample of women Veterans and then compared results to a male Veteran sample to explore sex differences.Participants and Methods:Data from 52,579 Veterans enrolled in VHA care between FY2014 and 2nd Quarter FY2020 were gathered from the VHA Corporate Data Warehouse administrative data. The sample was comprised of 5,983 women (11.4%) and 46,596 men (88.6%). Demographics, psychiatric diagnoses, psychiatric medications, ER visits, and hospitalizations were characterized. Chi-square analyses were used to examine group differences between men and women.Results:The vast majority of the women Veteran sample had at least one psychiatric diagnosis (86.1%), with over half of the sample diagnosed with depression (68.3%), PTSD (54.1%), and/or anxiety disorders (57.7%). When compared to men, women Veterans were more likely to have a psychiatric diagnosis (86.1% vs. 68.1%), evidenced a higher number of co-morbid psychiatric conditions (2.4 vs. 1.6), and were prescribed more psychiatric medications (3.4 vs. 2.3; all significant at p&lt;0.001). All individual psychiatric diagnoses were more prevalent in women than men and, notably, suicidality was also higher in women (13.5% vs. 10.0%; p &lt; 0.001). Women Veterans also had a higher number of ER visits (6.9 vs. 5.5; p &lt; 0.001) and psychiatric hospitalizations than men (.4 vs. .3, p &lt; 0.001).Conclusions:The present study represents the largest known investigation to date of women with DRE and is also the largest study of women Veterans with any form of epilepsy. It highlights a vast psychiatric burden in this subset of women Veterans, with high rates of psychiatric comorbidity, lending to downstream effects on psychiatric medication burden and risk for emergency care usage and psychiatric hospitalization. Comparisons to men emphasize that women are differentially impacted by the psychiatric toll of DRE and warrant special consideration. The markedly higher rates of depressive disorders and suicidality in women Veterans with DRE is especially notable when considering risk of harm and mortality. Overall, the present work adds to the paucity of literature of women Veterans with seizures and gaps in the broader DRE research base, with implications for specialized screening and maximizing treatment interventions in this population.

Dual positivity for anti-MOG and oligoclonal bands: Unveiling unique clinical profiles and implications

BackgroundDistinguishing between MOG-associated disease (MOGAD) and multiple sclerosis (MS) presents a considerable challenge, as there are instances of overlapping clinical presentations. This complexity is further magnified in cases where patients concurrently exhibit both anti-myelin oligodendrocyte glycoprotein (anti-MOG) positivity and detectable oligoclonal bands (OCBs) This retrospective study investigates the clinical and imaging attributes of dual-positive patients, those with both anti-MOG positivity and OCBs, The study aims to show potential areas of overlap between multiple sclerosis (MS) and MOGAD. MethodsUtilizing data gathered from three medical centers, we evaluated a cohort of 45 patients, stratifying them into two groups: those exclusively positive for anti-MOG antibodies and those displaying dual positivity. Our analysis encompassed a wide range of clinical and imaging parameters. The statistical techniques employed comprised Fisher's Exact Test along with Benjamini-Hochberg correction to ensure robustness of the findings. ResultsThe study involved 45 patients with anti-MOG antibodies; 30 exhibited isolated anti-MOG positivity without OCBs, while 15 were dual-positive. The first group's average age was 10±7 years, compared to 28±17 years in the double-positive group (p = 0.001). CSF analysis showed no significant differences in pleocytosis, protein levels, or opening pressure between the groups. In the exclusive anti-MOG positivity cohort, 9 out of 15 patients received IVIG treatment; a larger subgroup with dual positivity chose anti-CD20 treatment. Notably, papilledema incidence was higher in the single-positive group (p = 0.014). Optic nerve enhancement (p = 0.0038) and nerve thickening (p = 0.0017) were markedly elevated in the single-positive population, with a trend towards pre-chiasmatic lesions (p = 0.06). Double-positive cases exhibited more polyfocal presentation (p = 0.013) and higher attacks per case (p = 0.002, HR=10.2, 95 % CI: 2.19 to 49.23). The double-positive group had more brain lesions (p = 0.0063) but no significant distinctions in other aspects. ConclusionThe results emphasize the challenges inherent in differentiating between MS and a more MOGAD. While the data suggest two plausible scenarios—either falling within the spectrum of MS or representing an intensified MOGAD—we recognize the need for stronger evidence to definitively classify these instances. This study underscores the imperative for thorough investigations to ascertain whether these cases align with the MS spectrum or denote an inflammatory variant of MOGAD.

The Role of Deep Learning in Pharma: Revolutionizing Drug Discovery and Development.

- In recent years, the pharmaceutical industry has witnessed a transformative shift in the way drugs are discovered and developed, thanks to the advent of deep learning. This paper explores the profound impact of deep learning techniques on various stages of drug discovery and development, from target identification and lead optimization to clinical trials and personalized medicine. Deep learning, a subset of artificial intelligence, has demonstrated exceptional capabilities in handling complex biological data, including genomics, proteomics, and chemical informatics. It enables the integration of vast and diverse datasets, facilitating the identification of potential drug targets with unprecedented accuracy. Moreover, deep learning models can predict the binding affinity of drug candidates to specific target proteins, expediting the lead optimization process and reducing the need for costly experimental iterations. Deep learning algorithms enhance patient stratification and biomarker discovery, ultimately leading to more successful trials with higher patient response rates. Additionally, the ability to analyze real-world patient data aids in the identification of adverse events and the development of safer drugs.&#x0D; Perrsonalized medicine is another area greatly influenced by deep learning, as it allows for tailoring treatments to individual patients based on their unique genetic and clinical profiles. This promises to revolutionize patient care, optimizing therapeutic outcomes while minimizing adverse effects. Despite the remarkable advancements facilitated by deep learning, there are challenges to address, such as data privacy, interpretability of models, and regulatory considerations. This paper discusses these challenges and potential solutions. Deep learning has emerged as a powerful tool in the pharmaceutical industry, driving innovation, efficiency, and precision in drug discovery and development. Its integration into the drug development pipeline holds the promise of accelerating the delivery of safer and more effective therapies to patients worldwide, marking a significant milestone in the evolution of pharmaceutical science.

Clinical Profiles of Children and Adolescents With Induced Laryngeal Obstruction (ILO) and Exercise Induced Laryngeal Obstruction (EILO).

To compare clinical profiles of pediatric patients with Induced Laryngeal Obstruction (ILO), Exercise Induced Laryngeal Obstruction (EILO), and EILO with non-exertion related secondary triggers (EILO+). A retrospective observational cohort design was employed. Four-hundred and twenty-three patients <18 years of age were identified from the electronic medical record of a large children's hospital. All patients underwent evaluations with a laryngologist and speech-language pathologist and were diagnosed with EILO/ILO. Patients were divided into 3 groups based on dyspnea triggers reported in initial evaluations. Groups consisted of patients with EILO (N = 281), ILO (N = 30), and EILO+ (N = 112). Patient demographics, EILO/ILO symptoms, endoscopy findings, medical comorbidities, medical history, and EILO/ILO treatment information were extracted and compared across EILO/ILO subtypes. Patients with EILO experienced higher rates of hyperventilation (P < .001), sore throat (P = .023), and chest pain (P = .003). Patients with ILO were significantly younger in age (P = .017) and presented with increased rates of nighttime symptoms (P < .001), globus sensation (P = .008), self-reported reflux symptoms (P = .023), and history of gastrointestinal conditions (P = .034). Patients with EILO+ were more likely to be female (P = .037) and presented with higher prevalence of anxiety (P = .003), ADHD (P = .004), chest tightness (P = .030), and cough (P < .001). Patients with EILO, ILO, and EILO+ present with overlapping but unique clinical profiles. A prospective study is warranted to determine the etiology of these differences and clarify how the efficacy of EILO, ILO, and EILO+ treatment can be maximized. 4.

Diagnostic profiles associated with long-term opioid therapy in active duty servicemembers.

Over-prescription of opioids has diminished in recent years; however, certain populations remain at high risk. There is a dearth of research evaluating prescription rates using specific multimorbidity patterns. To identify distinct clinical profiles associated with opioid prescription and evaluate their relative odds of receiving long-term opioid therapy. Retrospective analysis of the complete military electronic health record. We assessed demographics and 26 physiological, psychological, and pain conditions present during initial opioid prescription. Latent class analysis (LCA) identified unique clinical profiles using diagnostic data. Logistic regression measured the odds of these classes receiving long-term opioid therapy. All electronic health data under the TRICARE network. All servicemembers on active duty during fiscal years 2016 through 2019 who filled at least one opioid prescription. Number and qualitative characteristics of LCA classes; odds ratios (ORs) from logistic regression. We hypothesized that LCA classes characterized by high-risk contraindications would have significantly higher odds of long-term opioid therapy. A total of N = 714,446 active duty servicemembers were prescribed an opioid during the study window, with 12,940 (1.8%) receiving long-term opioid therapy. LCA identified five classes: Relatively Healthy (82%); Musculoskeletal Acute Pain and Substance Use Disorders (6%); High Pain, Low Mental Health Burden (9%); Low Pain, High Mental Health Burden (2%), and Multisystem Multimorbid (1%). Logistic regression found that, compared to the Relatively Healthy reference, the Multisystem Multimorbid class, characterized by multiple opioid contraindications, had the highest odds of receiving long-term opioid therapy (OR = 9.24; p < .001; 95% confidence interval [CI]: 8.56, 9.98). Analyses demonstrated that classes with greater multimorbidity at the time of prescription, particularly co-occurring psychiatric and pain disorders, had higher likelihood of long-term opioid therapy. Overall, this study helps identify patients most at risk for long-term opioid therapy and has implications for health care policy and patient care.

Real-World Clinical Utilization Differences of onabotulinumtoxinA and abobotulinumtoxinA in Upper Limb Spasticity (P12-8.013)

<h3>Objective:</h3> To compare real-world clinical utilization of onabotulinumtoxinA (onabotA) and abobotulinumtoxinA (abobotA) in the treatment of adult upper limb spasticity (ULS). <h3>Background:</h3> 3 botulinum toxin type A (BoNT/A) products are approved by the US Food and Drug Administration (FDA) to treat ULS. Each BoNT/A product is manufactured and tested via proprietary processes, resulting in unique clinical profiles. FDA-mandated boxed warnings state that potency units of one product are not interchangeable and cannot be converted into units of another BoNT/A product. We compared real-world clinical utilization of onabotA and abobotA in the treatment of adult ULS. <h3>Design/Methods:</h3> De-identified medical records were obtained via survey of 101 unique healthcare professionals on patients diagnosed with ULS per International Classification of Diseases, 10th revision, (ICD-10) and treated via botulinum toxin using current procedural terminology (CPT) in the US. 215 patient charts were reviewed: 107 treated with onabotA and 108 with abobotA. Patients received at least 3 treatments of onabotA or abobotA in 1 spastic upper limb prior to March 2020. Number and type of muscles injected were compared between BoNT/A products. <h3>Results:</h3> 75% of patients were classified as having moderate-to-severe ULS by the injector. On average, patients were diagnosed with ULS for &gt;3 years: onabotA 44.2 months (10–200) or abobotA 40.0 months (11–212). A significantly greater average number of muscles were injected with onabotA (4.3; range, 1 to 16), compared with abobotA (3.1; range, 1 to 8), <i>P</i>=0.0001. Significantly more patients received onabotA injections in forearm muscles (87/107, 81.3%) compared with abobotA (70/108, 64.8%), <i>P</i>=0.0064. Similarly, a significant difference was observed in the utilization of onabotA in hand muscles (23/107, 21.5%) compared with abobotA (4/108, 3.7%), <i>P</i>=0.0001. <h3>Conclusions:</h3> These results are consistent with published evidence across other indications, further supporting that each BoNT/A product is unique and not interchangeable as indicated by the FDA. <b>Disclosure:</b> Dr. Nelson has received personal compensation for serving as an employee of Allergan, an AbbVie company. Dr. Nelson has received stock or an ownership interest from Allergan, an AbbVie company. Dr. Zuzek has received personal compensation for serving as an employee of AbbVie Inc.. Dr. Zuzek has stock in AbbVie Inc.. Marc Schwartz has nothing to disclose. Dr. Singh has received personal compensation for serving as an employee of AbbVie. Dr. Singh has stock in AbbVie.