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Background Neuropsychiatric and cognitive symptoms account for substantial morbidity in Huntington's disease (HD), but their impact on functional status may not be captured using the Total Functional Capacity (TFC) scale. The objective of this study was to assess the impact of motor, cognitive, and neuropsychiatric symptoms on functional status in persons with HD, comparing two instruments. Methods Multiple regression analyses assessed the relationship between neuropsychiatric, cognitive, and motor symptoms and functional status as measured using TFC and Adult Functional Adaptive Behavior (AFAB) scales. Results Greater burden of neuropsychiatric (P = 0.017), cognitive (P = 0.001), and motor (P = 0.001) symptoms was associated with greater impairments to functional status as measured by the AFAB scale. Only motor symptoms were associated with TFC scores (P = 0.002). The 3 symptom domains explained more of the variance in AFAB than TFC scores (P = 0.016). Conclusions TFC may have limited applicability, particularly in early-stage HD patients, as a measure of functional status. The AFAB scale can be used in HD studies as a more holistic measure of functional status. © 2020 International Parkinson and Movement Disorder Society.

Objective Currently available treatments for neuropathic pain are only modestly efficacious when assessed in randomized clinical trials and work for only some patients in the clinic. Induced-pain or gain-of-function phenotypes have been shown to predict response to analgesics (vs placebos) in patients with neuropathic pain. However, the predictive value of these phenotypes has never been studied in post-traumatic neuropathic pain. Methods Mixed-effects models for repeated measures were used to evaluate the efficacy of pregabalin vs placebo in subgroups with induced-pain phenotypes (i.e., hyperalgesia or allodynia) in data from a recent, multinational randomized clinical trial (N = 539) that identified phenotypic subgroups through the use of a structured clinical exam. Results The difference in mean pain score between the active and placebo groups (i.e., delta) after 15 weeks of treatment for the subgroup with hyperalgesia was –0.76 (P = 0.001), compared with 0.19 (P = 0.47) for the subgroup that did not have hyperalgesia. The treatment-by-phenotype interaction, which tests whether subgroups have statistically different treatment responses, was significant (P = 0.0067). The delta for the subgroup with allodynia was –0.31 (P = 0.22), compared with –0.30 (P = 0.22) for the subgroup that did not have allodynia (treatment-by-phenotype interaction P = 0.98). Conclusions These data suggest that hyperalgesia, but not allodynia, predicts response to pregabalin in patients with chronic post-traumatic neuropathic pain. This study extends the growing data supporting the utility of induced-pain phenotypes to predict response to analgesics in post-traumatic neuropathic pain. Sensory phenotyping in large, multisite trials through the use of a structured clinical exam has the potential to accelerate the development of new analgesics and improve the generalizability of clinical trial results.

Background Inflammation may contribute to cognitive difficulties in patients with breast cancer. We tested 2 hypotheses: inflammation is elevated in patients with breast cancer vs noncancer control participants and inflammation in patients is associated with worse attention and processing speed over the course of chemotherapy. Methods Serum cytokines (interleukin [IL]–4, 6, 8, 10; tumor necrosis factor [TNF]–α) and soluble receptors [sTNFRI, II]) were measured in 519 females with breast cancer before and after chemotherapy and 338 females without cancer serving as control participants. Attention and processing speed were measured by Rapid Visual Processing (RVP), Backward Counting (BCT), and Trail Making-A (TMT-A) tests. Linear regression models examined patient vs control cytokines and receptor levels, adjusting for covariates. Linear regression models also examined relationships between patient cytokines and receptor levels and test performance, adjusting for age, body mass index, anxiety, depression, cognitive reserve, and chemotherapy duration. Statistical tests were 2-sided (α = .05). Results sTNFRI and sTNFRII increased over time in patients relative to controls, whereas IL-4, IL-6, and IL-10 decreased. Prechemotherapy, higher IL-8 associated with worse BCT (β = 0.610, SE = 0.241, P = .01); higher IL-4 (β = −1.098, SE = 0.516, P = .03) and IL-10 (β = −0.835, SE = 0.414, P = .04) associated with better TMT-A. Postchemotherapy, higher IL-8 (β = 0.841, SE = 0.260, P = .001), sTNFRI (β = 6.638, SE = 2.208, P = .003), and sTNFRII (β = 0.913, SE = 0.455, P = .045) associated with worse BCT; higher sTNFRII also associated with worse RVP (β = −1.316, SE = 0.587, P = .03). At prechemotherapy, higher IL-4 predicted RVP improvement over time (β = 0.820, SE = 0.336, P = .02); higher sTNFRI predicted worse BCT over time (β = 5.566, SE = 2.367, P = .02). Longitudinally, increases in IL-4 associated with BCT improvement (β = −0.564, SE = 0.253, P = .03). Conclusions Generally, worse attention and processing speed were associated with higher inflammatory cytokines and receptors and lower anti-inflammatory cytokines in patients; future confirmatory studies are needed.

INTRODUCTION: We have shown the Exercise for Cancer Patients (EXCAP(©®)) exercise program improved physical function and symptoms and reduced inflammatory markers in patients with cancer. However, adherence to exercise was lower in older adults compared to their younger counterparts. We then leveraged a mobile app to deliver EXCAP(©®) and adapted the intervention [Geriatric-Oncology (GO)-EXCAP] for older patients with myeloid neoplasms. In this pilot randomized trial, the primary goal is to determine effect sizes. We propose to assess the preliminary efficacy of GO-EXCAP compared to a behavioral placebo control on physical function, patient-reported outcomes (fatigue, mood, and quality of life), and inflammatory markers in 100 patients aged ≥60 years with myeloid neoplasms receiving outpatient chemotherapy. METHODS: GO-EXCAP consists of the EXCAP(©®) exercise prescription (daily home-based progressive aerobic walking and resistance exercises with rated perceived exercise of 5–8), EXCAP(©®) kit (i.e., activity tracker, resistance bands, print manual, bag), a mobile app, and an in-person or virtual session with the exercise physiologist to deliver exercise prescription. The intervention will last for three cycles of chemotherapy (approximately 12 weeks). The primary outcome measure will be physical function (Short Physical Performance Battery). Secondary outcome measures include fatigue (Brief Fatigue Inventory), mood (Center for Epidemiologic Studies Depression Scale), and quality of life (Functional Assessment of Cancer Therapy-Leukemia). Exploratory outcome measures include inflammatory markers. DISCUSSION: Older adults with myeloid neoplasms receiving outpatient chemotherapy serve as an ideal model for studying an individually tailored mobile health exercise intervention in vulnerable older patients receiving cancer treatments to prevent physical function decline and improve symptoms.

Untreated tooth decays affect nearly one third of the world and is the most prevalent disease burden among children. The disease progression of tooth decay is multifactorial and involves a prolonged decrease in pH, resulting in the demineralization of tooth surfaces. Bacterial species that are capable of fermenting carbohydrates contribute to the demineralization process by the production of organic acids. The combined use of machine learning and 16s rRNA sequencing offers the potential to predict tooth decay by identifying the bacterial community that is present in an individual’s oral cavity. A few recent studies have demonstrated machine learning predictive modeling using 16s rRNA sequencing of oral samples, but they lack consideration of the multifactorial nature of tooth decay, as well as the role of fungal species within their models. Here, the oral microbiome of mother–child dyads (both healthy and caries-active) was used in combination with demographic–environmental factors and relevant fungal information to create a multifactorial machine learning model based on the LASSO-penalized logistic regression. For the children, not only were several bacterial species found to be caries-associated (Prevotella histicola, Streptococcus mutans, and Rothia muciloginosa) but also Candida detection and lower toothbrushing frequency were also caries-associated. Mothers enrolled in this study had a higher detection of S. mutans and Candida and a higher plaque index. This proof-of-concept study demonstrates the significant impact machine learning could have in prevention and diagnostic advancements for tooth decay, as well as the importance of considering fungal and demographic–environmental factors.

BACKGROUND: The intestinal microbiome is an appealing target for interventions in ESKD because of its likely contribution to uremic toxicity. Before conducting clinical trials of microbiome-altering treatments, it is necessary to understand the within-person and between-person variability in the composition and function of the gut microbiome in patients with ESKD. METHODS: We conducted a multicenter, nonrandomized, crossover feasibility study of patients on maintenance hemodialysis consisting of three phases: pretreatment (8 weeks); treatment, during which the prebiotic, p-inulin, was administered at a dosage of 8 g twice daily (12 weeks); and post-treatment (8 weeks). Stool samples were collected 1–2 times per week and blood was collected weekly for 28 weeks. The gut microbiome was characterized using 16S ribosomal-RNA sequencing and metabolomic profiling. RESULTS: A total of 11 of the 13 participants completed the 28-week study. Interparticipant variability was greater than intraparticipant variability for microbiome composition (P

Staphylococcus aureus is a major cause of ocular infections, often resulting in devastating vision loss. Despite the significant morbidity associated with these infections, little is yet known regarding the specific strain types that may have a predilection for ocular tissues nor the set of virulence factors that drive its pathogenicity in this specific biological niche. Whole genome sequencing (WGS) can provide valuable insight in this regard by providing a prospective, comprehensive assessment of the strain types and virulence factors driving disease among specific subsets of clinical isolates. As such, a set of 163-member S. aureus ocular clinical strains were sequenced and assessed for both common strain types (multilocus sequence type (MLST), spa, agr) associated with ocular infections as well as the presence/absence of 235 known virulence factors in a high throughput manner. This ocular strain set was then directly compared to a fully sequenced 116-member non-ocular S. aureus strain set curated from NCBI in order to identify key differences between ocular and non-ocular S. aureus isolates. The most common sequence types found among ocular S. aureus isolates were ST5, ST8 and ST30, generally reflecting circulating non-ocular pathogenic S. aureus strains. However, importantly, ocular isolates were found to be significantly enriched for a set of enterotoxins, suggesting a potential role for this class of virulence factors in promoting ocular disease. Further genomic analysis revealed that these enterotoxins are located on mobile pathogenicity islands, thus horizontal gene transfer may promote the acquisition of enterotoxins, potentially amplifying S. aureus virulence in ocular tissues.

PURPOSE: Staphylococcus aureus is a leading cause of keratitis requiring urgent antimicrobial treatment. However, rising antibiotic resistance has rendered current ophthalmic antibiotics increasingly ineffective. First, a diverse, ocular S. aureus strain set was evaluated for resistance to six commonly used ophthalmic antibiotics. Next, a recently discovered antimicrobial drug combination containing polymyxin B/trimethoprim + rifampin that displayed impressive efficacy towards S. aureus in both in vitro and in vivo studies was evaluated as a potential novel keratitis therapeutic through testing this combination’s efficacy against the clinical strain set. METHODS: 163 S. aureus isolates were collected commercially or from the University of Rochester, Flaum Eye Institute. The minimum inhibitory concentrations (MICs) of moxifloxacin, levofloxacin, vancomycin, erythromycin, tobramycin, rifampin and polymyxin B/trimethoprim (PT) were determined for the entire strain set to establish the incidence of resistance to current treatment options among a contemporary clinical isolate set and compared to the performance of PT + rifampin. RESULTS: Among all 163 isolates tested, high rates of antibiotic resistance were found toward erythromycin (69% resistance), moxifloxacin (33%), levofloxacin (40%) and tobramycin (17%). Conversely, the entire strain set, including multi-drug resistant (MDR) isolates, was sensitive to PT + rifampin, demonstrating the potency of this combination. CONCLUSIONS: We establish that antibiotic resistance is pervasive among clinical S. aureus isolates, underscoring the concern for the effectiveness of current ophthalmic antibiotics. The drug combination of PT + rifampin, however, eradicated 100% of isolates tested, demonstrating the ability to overcome existing circulating resistance factors, and as such, may represent a promising therapeutic for S. aureus keratitis

Distance-based ordination methods, such as principal coordinates analysis (PCoA), are widely used in the analysis of microbiome data. However, these methods are prone to pose a potential risk of misinterpretation about the compositional difference in samples across different populations if there is a difference in dispersion effects. Accounting for high sparsity and overdispersion of microbiome data, we propose a GLM-based Ordination Method for Microbiome Samples (GOMMS) in this article. This method uses a zero-inflated quasi-Poisson (ZIQP) latent factor model. An EM algorithm based on the quasi-likelihood is developed to estimate parameters. It performs comparatively to the distance-based approach when dispersion effects are negligible and consistently better when dispersion effects are strong, where the distance-based approach sometimes yields undesirable results. The estimated latent factors from GOMMS can be used to associate the microbiome community with covariates or outcomes using the standard multivariate tests, which can be investigated in future confirmatory experiments. We illustrate the method in simulations and an analysis of microbiome samples from nasopharynx and oropharynx.

Rationale: The oropharyngeal microbiome is a primary source of lung microbiota, contributes to lower respiratory infection, and is also a driver of oral health. Objectives: We sought to understand oropharyngeal microbial communities in advanced lung disease, community dynamics after lung transplantation, and ecological features of dysbiosis. Methods: Oropharyngeal wash samples were obtained from individuals with end-stage disease awaiting transplantation (n = 22) and longitudinally from individuals at 6 weeks, 3 months, and 6 months after transplantation (n = 33), along with healthy control subjects (n = 14). Bacterial 16S and fungal internal transcribed spacer rRNA regions were deep-sequenced, and bacterial community respiratory patterns were imputed from taxonomic composition. Results: Healthy subjects’ oropharyngeal microbiomes showed a gradient of community types reflecting relative enrichment of strictly anaerobic, aerobic, or facultative anaerobic bacteria. Patients with end-stage lung disease showed severe dysbiosis by both taxonomic composition and respiration phenotypes, with reduced richness and diversity, increased facultative and decreased aerobic bacteria, and absence of communities characterized by obligate aerobes. In patients at 6 weeks and 3 months post-transplant, richness and diversity were intermediate between healthy and pretransplant subjects, with near-normal distribution of community types. However, by 6 months post-transplant, oropharyngeal wash resembled the low-diversity facultative-dominated profile of pretransplant subjects. Community ecotype correlated with Candida abundance. Conclusions: End-stage lung disease is associated with marked upper respiratory tract dysbiosis involving both community structure and respiratory metabolism profiles of constituent bacteria. Dynamic changes occur after lung transplantation, with partial normalization early but later appearance of severe dysbiosis similar to pretransplant patients. Aberrant oropharyngeal communities may predispose to abnormal lung microbiota and infection risk both in advanced lung disease and after transplantation.

Motivated by advances in the causal inference in mediation analysis and problems arising in the analysis of metagenomic data, we consider the effect of treatment on an outcome transmitted through compositional mediators. Compositional and high dimensional features of such mediators make the standard mediation analysis not directly applicable. A sparse mediation model for high-dimensional compositional data is proposed in this paper utilizing the algebraic structure of a composition under the simplex space and a constraint linear regression model to achieve sub-compositional coherence. Under this model, we develop estimation method for estimating direct and microbial mediation effects of a randomly assigned treatment on an outcome. Tests for the total mediation effect of all bacterial taxa and individual mediation effects are also proposed. We conduct extensive simulation studies to assess the performance of the proposed method and apply the method to a real metagenomic dataset to investigate the effect of fat intake on body mass index (BMI) mediated through the gut microbiome composition.