Citations

Introduction: During allogeneic hematopoietic cell transplantation (allo-HCT), conditioning regimens disrupt the epithelial barrier of the gastrointestinal (GI) tract and broad-spectrum antibiotics cause microbiome disruption characterized by low microbial diversity and high abundance of pathogens that can dominate gut communities. Microbiome disruption is associated with increased translocation of pathogenic bacteria leading to risk of bloodstream infections (BSIs) and acute graft-vs-host disease (aGvHD). Nonclinical studies have demonstrated that domination with pathogens leads to immune activation and subsequent GvHD following exposure to bacterial antigens in the gut-associated lymphoid tissue [ Jenq 2015 Biol Blood Marrow Transplant, Stein-Thoeringer 2019 Science, Peled 2020 NEJM]. SER-155, an oral investigational cultivated microbiome therapeutic comprised of 16 bacterial Firmicutes strains, was designed to engraft in the GI tract, improve GI barrier integrity, and restructure the GI microbiome by introducing bacteria with immunomodulatory and anti-inflammatory properties and driving a reduction in abundance of proinflammatory bacteria [ Halvorsen 2021 OFID, 2022 Bone Marrow Transplant]. Herein, we present preliminary clinical data through Day 100 from an open-label cohort of a Phase 1b study in patients undergoing allo-HCT (NCT04995653) and compare the incidence of GI pathogen domination in this cohort to a historical cohort. Methods: Study patients received two treatment courses of SER-155 (before conditioning and after neutrophil engraftment), each comprised of microbiome conditioning with 4 days oral vancomycin (to facilitate engraftment of SER-155 strains into the GI microbiome) followed by 10 days of oral SER-155. The primary endpoint was safety. Stool samples collected pre- and post-HCT were used to evaluate secondary endpoints, including engraftment of SER-155 strains (following each course of SER-155) and GI pathogen domination, defined as a relative abundance of a specific taxa at ≥30% ( Peled, 2020). Engraftment of SER-155 strains was assessed via strain-specific molecular probes (NanoString). Relative abundance of Enterobacteriaceae, Enterococcaceae, Streptococcaceae and Staphylococcaceae was assessed with whole metagenomic shotgun sequencing (WMS). A post-hoc comparison of GI domination was made with a historical cohort of 459 HCT patients with similar patient characteristics from one of the study sites (MSKCC) using stool samples profiled by 16S-amplicon sequencing. Results: Fifteen adults (median age 67 years; 54% male) were enrolled and 13 received study drug. SER-155 was generally well-tolerated. Following each course of SER-155, the majority of the 16 strains in SER-155 were detected in evaluable patients (median = 11 strains in 8 patients before conditioning; median = 12 strains in 7 patients after neutrophil engraftment).Between the day of transplant (Day 0) and Day 30, GI domination with potentially pathogenic bacteria occurred in only 1 of 9 (11%) evaluable patients (Figure 1). In contrast, in the historical comparator cohort, 64% of patients (294 of 459) had pathogen domination during the same timeframe, with domination by Enterococcus being most common. Conclusions: The observation of SER-155 strain engraftment before transplant and low incidence of GI pathogen domination after transplant through Day 30 are encouraging, although these preliminary data are limited by the small size of this open-label cohort of HCT patients. These observations are consistent with the design and preclinical evaluation of SER-155, supporting further development of this investigational cultivated microbiome therapeutic. Enrollment in the second double-blind, randomized, placebo-controlled cohort (approximately 60 participants) has initiated following a preplanned review by the Data Safety Monitoring Committee.

Introduction Artificial Intelligence (AI) is an emerging technology that promises advancements across blood cancer discovery and care delivery. There are already FDA-approved uses of AI in hematology and oncology but few data available about its use or utility. Concerns have been raised over AI's potential biases, “explainability,” and who is responsible for its use. Oncologists' familiarity with AI and perspectives on these issues are not well known and are critical to understanding the ethical deployment of AI in blood cancer care. Methods From January to July 2023, we performed a national cross-sectional survey of practicing oncologists. A draft survey instrument was developed by a team with content and methodological expertise. Iterative refinement occurred through cognitive debriefing with 5 practicing hematologist/oncologists. The final instrument included 24 questions covering domains of AI familiarity, predictions, explainability, and responsibilities. A random sample of US oncologists was identified using the National Plan & Provider Enumeration System. Paper surveys were mailed with $25 incentivizing gift cards. Reminder letters and phone calls were used for non-responders with an electronic survey option provided. Responses were analyzed descriptively. X2 testing assessed bivariate associations between responses and participant practice setting, with odds ratios (OR) reported. Results Of 399 mailed surveys, 12 were undeliverable and 201 were completed for a response rate of 51.9%. Question missingness was <1%. Participants were 63.2% White, 16.9% Indian Asian, 10.0% Eastern Asian or Pacific Islander, and 4.5% Black; 6.0% were Hispanic. They represented 37 states. The majority were male (63.7%), 40-59 years old (54.2%), and had no specific training in AI (52.2%); 68.2% had at least 11 years in practice and 29.9% practiced at a primary academic hospital. Questions assessing overall familiarity with and predictions related to AI are shown in Table 1. Those at primary academic hospitals were more likely to be familiar with radiology models (OR 2.82, p<0.001) but not pathology, prognostic, or clinical decision models (all p>0.05). They were more likely to predict that AI would improve side effect management (OR 2.01, p=0.03) and end-of-life decision making (OR 2.07, p=0.02). High proportions of respondents thought that AI prognostic (67.1%) and clinical decision models (62.1%) needed to be explainable by clinicians and researchers to be used in clinic. When posed with a scenario where an FDA-approved AI model selects a different regimen than the oncologist planned to propose (Figure 1), the largest proportion reported they would present both options and let the patient decide (37.3%). Of those recommending a regimen, respondents from primary academic hospitals more frequently recommend the AI-generated regimen (56.1%), while respondents from elsewhere recommended their original regimen (69.0%; OR 2.85, p=0.007). Acceptability of some direct-to-patient AI model applications is shown in Table 1. A majority of respondents (90.5%) thought that AI developers should be responsible for medico-legal problems caused by AI use; 47.8% thought that physicians using the tool should be responsible. Though most (76.1%) agreed that it was oncologists' responsibility to protect patients from biased AI tools, few (27.3%) were confident in their ability to identify how representative the data used in an AI model was relative to the population with that cancer (including 66.7% of those who agreed it was oncologists' responsibility). More respondents thought that patients should need to consent to the use of AI tools in cancer treatment decisions (81.1%) than cancer diagnosis (55.7%); those from primary academic hospitals were less likely to respond that patients should consent for treatment or diagnosis (OR 0.43, p=0.02 and OR 0.44, p=0.009). Conclusions In this national survey of oncologists' views on AI, there was optimism about its impact on care with notable caveats related to end-of-life care, patient trust, and care disparities. Despite many approving use of AI models too complex for patients to explain, a substantial proportion deferred to patient choice when AI and physician recommendations differed. There was also dissonance between accepting responsibility to protect patients from bias and respondents' self-perceived ability to do so.

ABSTRACT The rapid pace of name changes of medically important fungi is creating challenges for clinical laboratories and clinicians involved in patient care. We describe two sources of name change which have different drivers, at the species versus the genus level. Some suggestions are made here to reduce the number of name changes. We urge taxonomists to provide diagnostic markers of taxonomic novelties. Given the instability of phylogenetic trees due to variable taxon sampling, we advocate to maintain genera at the largest possible size. Reporting of identified species in complexes or series should where possible comprise both the name of the overarching species and that of the molecular sibling, often cryptic species. Because the use of different names for the same species will be unavoidable for many years to come, an open access online database of the names of all medically important fungi, with proper nomenclatural designation and synonymy, is essential. We further recommend that while taxonomic discovery continues, the adaptation of new name changes by clinical laboratories and clinicians be reviewed routinely by a standing committee for validation and stability over time, with reference to an open access database, wherein reasons for changes are listed in a transparent way.

Abstract Introduction In this pilot study, we explored sleep biomarker risk probabilities for different neurodegenerative disorder (NDD) phenotypes across a spectrum of NDD patients, compared with controls. Methods We analyzed a cohort of patients with different NDD phenotypes who underwent in-home recordings with Sleep Profiler, including Lewy body disease (LBD=20), Alzheimer’s disease dementia (AD=29), and isolated REM sleep behavior disorder (iRBD=19). Controls with an MMSE>28 (CG=61) and patients with Parkinson disease (PD=16) and mild-cognitive-impairment (MCI=41) also participated. We developed a machine-learning classifier that assigned NDD probabilities for LBD, AD, iRBD, and CG. The input variables included: time-REM, non-REM hypertonia, autonomic-activation index, spindle-duration, atypical-N3, time-supine, sleep-efficiency, relative-theta, and theta/alpha. Probabilities >50% were assigned “likely”, and for CG>=50% and a NDD group probability 20-50%, the assignment was normal “plus”. Probability assignments were then made for the NDD and CG groups, then further applied to the PD and MCI patient groups. Results The CG group participants were assigned Normal-Likely=74%, Normal+AD=11%, Normal+iRBD=5%, iRBD-Likely=5%, and AD-Likely=5%. LBD patient distributions were LBD-Likely=70%, iRBD-Likely=5%, AD-Likely=5%, Normal+LBD=5%, Normal+iRBD=5%, Normal+AD=5%, and Normal-Likely=5%. AD group distributions were AD-Likely=71%, LBD-Likely=4%, Normal+AD=14%, Normal+iRBD=4%, Normal-Likely=7%. iRBD patients were characterized with iRBD-Likely=37%, LBD-Likely=5%, AD-Likely=5%, Normal+iRBD=27%, Normal-Likely=26%. PD patients were assigned iRBD-Likely=29%, LBD-likely=14%, AD-Likely=14%, Normal+iRBD=21%, Normal+AD=7%, Normal-Likely=14%. MCI distributions were AD-Likely=45%, LBD-Likely=8%, iRBD-Likely=5%, Normal+AD=21%, Normal-Likely=13%. Conclusions For LBD, AD and CG groups, correct risk assignments were >70% while gross misclassifications were <10%. Classification patterns for PD, MCI and iRBD were disbursed in a manner consistent with the range of severities expected in each group.

Abstract Introduction Atypical N3 sleep (AN3=delta waves with limited theta and sigma) has been associated with ICU delirium and sepsis and averaged 25% of sleep time in Japanese ICU patients. We were interested in exploring whether AN3 might be a marker of cerebral dysfunction in ambulatory patients across a range of neurodegenerative disorders, including those with a dementia diagnosis. Methods After ethics review and with informed consent, patients with Lewy body disease (DLB/PDD: n=20,male=90%,age=70 + 6.2), Alzheimers disease dementia (AD: n=29,male=79%,age=75 + 6.7), Parkinson disease (PD: n=16,male=69%,age=67 + 8.7), mild cognitive impairment (MCI: n=41,male=63%,age=70 + 8.5), isolated REM sleep behavior disorder (iRBD: n=19,male=74%,age=64 + 9.6) and a control group (CG: n=61,male=47%,age=65 + 8.3) were studied with the Sleep Profiler and auto-detected AN3 computed. Between-group comparisons were assessed with Mann-Whitney U and Chi-square tests. Results The mean percentages of sleep time with AN3 were significantly greater in DLB/PDD (8 + 12.3) vs. PD (4 + 10.8), AD (2 + 3.7), MCI (2 + 2.3), iRBD (1 + 1.6), and CG (1 + 2.4)(all p<0.02). The proportions of records with abnormal AN3 (>5% of sleep time) were significantly greater in those with DLB/PDD=35% vs. MCI=10%, iRBD=5% and CG=5% (all p<0.05), but not AD=17% and PD=13%. Conclusions Further investigations are needed to determine if abnormal AN3 may be a biomarker that distinguishes patients with Lewy body spectrum diseases from alternative pathologies and/or could be helpful in monitoring the side effects of CNS-acting medications

Abstract Introduction Symptoms associated with autonomic nervous system dysfunction are common to patients with neurodegenerative disorders (NDD) resulting from the accumulation of α-synuclein in the neurons and glial cells. In this study we investigate whether a novel biomarker, the autonomic activation index, can discriminate these potentially affected NDD groups. Methods After ethics review and with informed consent, patients with Lewy body disease (DLB/PDD: n=20%), Alzheimer’s disease dementia (AD: n=27), Parkinson disease (PD: n=14), mild cognitive impairment (MCI: n=37), isolated REM sleep behavior disorder (iRBD: n=15) and a control group (CG: n=58) were studied with the Sleep Profiler. AAI was based on a 6 BPM increase in pulse rate compared to the previous/subsequent 10th second. Between-group comparisons were assessed with Mann-Whitney U and Chi-square tests. Results The AAI values for the DLB/PDD patients were decreased compared to the AD, MCI (both p<0.03), and CG (p<0.0005), while the PD was less than AD and MCI (both < 0.05) and CG (p<0.002). The proportions of cases with abnormal AAI values (<10 events/h) was greatest in DLB/PDD patients, and greater than in ADem (p<0.02), MCI (p<0.02) and CG (p<0.0001), while AAI in PD and iRBD was greater than in CG (p<0.007 and 0.05 respectively). Conclusions These findings suggest that the AAI can be used to objectively confirm subjective complaints of autonomic dysfunction symptoms in patients with synucleinopathies. Longitudinal studies are needed determine factors that influence changes in the AAI.

Abstract Introduction In 2015, Lee et al. observed sleeping position influenced the efficiency of glymphatic clearance in rats. In 2018, Levendowski et al. reported supine sleep was independently associated with neurodegenerative disorders (NDD)(odds ratio=3.7). This update further evaluates the comparative frequency of the supine sleep position across a spectrum of cognitive impairment, including patients diagnosed with a Parkinsonian spectrum disorder (PSD), Alzheimer’s disease dementia (AD) and mild cognitive impairment (MCI), compared with a control group (CG) of patients without a known cognitive disorder. Methods After ethics review and with informed consent, a control group (CG: n=170), PSD (n=36), AD (n=29) and MCI group (n=41) were studied in-home with the Sleep Profiler; 89% across two-nights. Between-group comparisons were assessed with Mann-Whitney U and Chi-square tests. Results The hours of supine sleep were greater in PSD=3.0 + 1.9, AD=3.2 + 2.0 and MCI=3.0 + 2.0 vs. CG=1.9 + 1.8 (all p<0.002). The proportion of patients with >2 hours of supine sleep were PSD=69%, AD=69% and MCI=59% vs. CG=39%, with odds ratios of PSD=3.6, AD=3.5 and MCI=2.2 (all p<0.001). Conclusions Here we provide further evidence for a relatively strong association between supine sleep and neurodegenerative disease including PSD, AD and MCI cohorts. The nature of this association remains unclear from cross sectional study designs. Future prospective studies should test if there is a causal link between supine sleep and subsequent development of neurodegeneration.

AbstractLimited guidance exists regarding the assessment and management of psychogenic non‐epileptic seizures (PNES) in children. Our aim was to develop consensus‐based recommendations to fill this gap. The members of the International League Against Epilepsy (ILAE) Task Force on Pediatric Psychiatric Issues conducted a scoping review adhering to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses extension for Scoping Reviews (PRISMA‐SR) standards. This was supplemented with a Delphi process sent to pediatric PNES experts. Consensus was defined as ≥80% agreement. The systematic search identified 77 studies, the majority (55%) of which were retrospective (only one randomized clinical trial). The primary means of PNES identification was video electroencephalography (vEEG) in 84% of studies. Better outcome was associated with access to counseling/psychological intervention. Children with PNES have more frequent psychiatric disorders than controls. The Delphi resulted in 22 recommendations: Assessment—There was consensus on the importance of (1) taking a comprehensive developmental history; (2) obtaining a description of the events; (3) asking about potential stressors; (4) the need to use vEEG if available parent, self, and school reports and video recordings can contribute to a “probable” diagnosis; and (5) that invasive provocation techniques or deceit should not be employed. Management—There was consensus about the (1) need for a professional with expertise in epilepsy to remain involved for a period after PNES diagnosis; (2) provision of appropriate educational materials to the child and caregivers; and (3) that the decision on treatment modality for PNES in children should consider the child's age, cognitive ability, and family factors. Comorbidities—There was consensus that all children with PNES should be screened for mental health and neurodevelopmental difficulties. Recommendations to facilitate the assessment and management of PNES in children were developed. Future directions to fill knowledge gaps were proposed.