CD3ζ ITAMs enable ligand discrimination and antagonism by inhibiting TCR signaling in response to low-affinity peptides.

The T cell antigen receptor (TCR) contains ten immunoreceptor tyrosine-based activation motif (ITAM) signaling sequences distributed within six CD3 subunits; however, the reason for such structural complexity and multiplicity is unclear. Here we evaluated the effect of inactivating the three CD3ζ chain ITAMs on TCR signaling and T cell effector responses using a conditional 'switch' mouse model. Unexpectedly, we found that T cells expressing TCRs containing inactivated (non-signaling) CD3ζ ITAMs (6F-CD3ζ) exhibited reduced ability to discriminate between low- and high-affinity ligands, resulting in enhanced signaling and cytokine responses to low-affinity ligands because of a previously undetected inhibitory function of CD3ζ ITAMs. Also, 6F-CD3ζ TCRs were refractory to antagonism, as predicted by a new in silico adaptive kinetic proofreading model that revises the role of ITAM multiplicity in TCR signaling. Finally, T cells expressing 6F-CD3ζ displayed enhanced cytolytic activity against solid tumors expressing low-affinity ligands, identifying a new counterintuitive approach to TCR-mediated cancer immunotherapy.

Disparities in sleep duration among American children: effects of race and ethnicity, income, age, and sex.

Children in the United States sleep less than the recommended amount and sleep deficiencies may be worse among disadvantaged children. Prior studies that compared sleep time in children of different race/ethnic groups mostly relied on questionnaires or were limited to small sample sizes. Our study takes advantage of the Adolescent Brain Cognitive Development study to compare total sleep time using a week of actigraphy data among American children (n = 4,207, 9 to 13 y old) of different racial/ethnic and income groups. We also assessed the effects of neighborhood deprivation, experience of discrimination, parent's age at child's birth, body mass index (BMI), and time the child fell asleep on sleep times. Daily total sleep time for the sample was 7.45 h and race/ethnicity, income, sex, age, BMI, were all significant predictors of total sleep time. Black children slept less than White children (∼34 min; Cohen's d = 0.95), children from lower income families slept less than those from higher incomes (∼16 min; Cohen's d = 0.44), boys slept less than girls (∼7 min; Cohen's d = 0.18), and older children slept less than younger ones (∼32 min; Cohen's d = 0.91); mostly due to later sleep times. Children with higher BMI also had shorter sleep times. Neither area deprivation index, experience of discrimination, or parent's age at child's birth significantly contributed to sleep time. Our findings indicate that children in the United States sleep significantly less than the recommended amount for healthy development and identifies significant racial and income disparities. Interventions to improve sleep hygiene in children will help improve health and ameliorate racial disparities in health outcomes.

The effect of alcohol, tobacco, and other drug use on vaccine acceptance, uptake, and adherence: a systematic review.

BACKGROUND: Vaccine hesitancy is increasingly recognized as a health challenge affecting populations worldwide. Given the biological vulnerabilities and structural barriers people who use substances and/or have behavioral addictions face, this systematic review aims to evaluate whether this subpopulation is less prone to adhere to vaccination recommendations. METHODS: Electronic searches of published original research were conducted in PubMed, EMBASE, Scopus, and PsycINFO from database inception to December 2022. Our strategy encompassed retrievals regardless of languages and date of publication. Animal studies, abstracts without a full manuscript, and studies which were considered to have lower robustness of scientific evidence were excluded. Outcomes measured were vaccine acceptance, uptake, and adherence. Results were interpreted through a narrative synthesis. RESULTS: The search yielded 103 retrievals encompassing data collected on 5 576 374 persons who were predominantly residents of Europe (n = 39) and North America (n = 27). Tobacco use, the substance for which many studies were found (n = 91), was significantly associated with poorer vaccine acceptance, uptake and adherence for influenza, COVID-19, human papillomavirus (HPV), and maternal and childhood vaccines. Peri-natal and parental substance use was identified as a risk factor for suboptimal vaccine-related outcomes concerning maternal COVID-19 and childhood vaccines. Finally, people identified as 'using', 'abusing', or 'misusing' drugs or substances may be at decreased odds of all outcomes in various vaccines. CONCLUSIONS: Collectively, the studies identified several groups with statistically significant greater vaccine hesitancy and decreased engagement among whom targeted measures could be beneficial. Timely evidence, especially on behavioral addictions and substances besides tobacco, is lacking, and warrants urgent attention.

Use of gonadotropin-releasing hormone agonists for ovarian preservation in patients receiving cyclophosphamide for systemic lupus erythematosus: A meta-analysis.

BACKGROUND: Cyclophosphamide (CYC) has known cytotoxic effects on ovarian reserve and has been linked to premature ovarian failure (POF) in systemic lupus erythematosus (SLE). The concurrent use of gonadotropin-releasing hormone agonists (GnRHas) is postulated to preserve ovarian function by reducing the number of follicles exposed to CYC, but there is paucity of data to establish its efficacy. We conducted a meta-analysis to summarize the effect of concurrent GnRHa use in persevering ovarian function and pregnancy. METHODS: English language databases of PubMed, Embase, and Cochrane were searched to include studies published between 2000 and 2021. Studies in females with rheumatic diseases receiving concurrent GnRHa and CYC therapy to evaluate ovarian preservation as defined by amenorrhea, follicle stimulating hormone (FSH), anti-mullerian hormone (AMH), or estradiol levels or successful pregnancy were included. We used a fixed effect, exact, Mantel-Haenszel approach to estimate the overall odds ratio (OR) and associated 95% confidence intervals (95% CIs). RESULTS: Seven studies with 218 female patients were included. The ovarian function was preserved in 125/132 (94.6%) of women who received GnRHa concurrently with CYC compared to 50/86 (58%) of women who did not receive GnRHa (OR = 10.3, CI = 4.83-36.29). The OR for pregnancy with GnRHa use = 2.94 (CI = 1.04-9.89). CONCLUSION: Our results based on limited published studies suggest that concurrent GnRHa use preserves ovarian function and increase odds of pregnancy. It can be considered for premenopausal SLE females receiving CYC. Long-term follow-up studies are needed to establish the efficacy and safety of GnRHa use for ovarian preservation.

Daily diary study of loneliness, alcohol, and drug use during the COVID-19 Pandemic.

BACKGROUND: Research conducted during the COVID-19 Pandemic has identified two co-occurring public health concerns: loneliness and substance use. Findings from research conducted prior to the pandemic are inconclusive as to the links between loneliness and substance use. This study aimed to measure associations of loneliness with three different types of substance use during COVID-19: daily number of alcoholic drinks, cannabis use, and non-cannabis drug use. METHOD: Data were obtained between October 2020 and May 2021 from 2,648 US adults (Mage  = 38.76, 65.4% women) diverse with respect to race and ethnicity using online recruitment. Participants completed baseline surveys and daily assessments for 30 days. A daily loneliness measure was recoded into separate within- and between-person predictor variables. Daily outcome measures included the number of alcoholic drinks consumed and dichotomous cannabis and non-cannabis drug use variables. Generalized linear multilevel models (GLMLM) were used to examine within- and between-person associations between loneliness and substance use. RESULTS: The unconditional means model indicated that 59.0% of the variance in the daily number of alcoholic drinks was due to within-person variability. GLMLM analyses revealed that, overall, people drank more on days when they felt a particularly high or particularly low degree of loneliness (positive quadratic effect). There was a negative and significant within-person association between daily loneliness and the likelihood of cannabis use. There was also a positive and significant within-person association between daily loneliness and the likelihood of non-cannabis drug use. CONCLUSIONS: Associations between loneliness and substance use vary with substance type and whether within- or between-person differences are assessed. These findings are relevant to the persistence of substance use disorders and thus of potential clinical importance. Individuals who do not experience severe loneliness at intake but who show daily increases in loneliness above baseline levels are at heightened risk of alcohol and non-cannabis drug use. Future research could profitably examine just-in-time adaptive interventions that assess fluctuations in loneliness to prevent the development or exacerbation of substance use disorders.

Loneliness and Daily Alcohol Consumption During the COVID-19 Pandemic.

AIMS: This pilot study aimed to identify associations of loneliness and daily alcohol consumption among US adults during the Coronavirus Disease-2019 pandemic. METHOD: Participants completed daily assessments for 30 days. RESULTS: Results suggest people who feel lonelier on average drink more alcohol, however, people who feel lonelier than usual drink less. CONCLUSION: Findings highlight the need to disaggregate within- and between-person components of alcohol use.

Severity modeling of propionic acidemia using clinical and laboratory biomarkers.

PURPOSE: To conduct a proof-of-principle study to identify subtypes of propionic acidemia (PA) and associated biomarkers. METHODS: Data from a clinically diverse PA patient population ( https://clinicaltrials.gov/ct2/show/NCT02890342 ) were used to train and test machine learning models, identify PA-relevant biomarkers, and perform validation analysis using data from liver-transplanted participants. k-Means clustering was used to test for the existence of PA subtypes. Expert knowledge was used to define PA subtypes (mild and severe). Given expert classification, supervised machine learning (support vector machine with a polynomial kernel, svmPoly) performed dimensional reduction to define relevant features of each PA subtype. RESULTS: Forty participants enrolled in the study; five underwent liver transplant. Analysis with k-means clustering indicated that several PA subtypes may exist on the biochemical continuum. The conventional PA biomarkers, plasma total 2-methylctirate and propionylcarnitine, were not statistically significantly different between nontransplanted and transplanted participants motivating us to search for other biomarkers. Unbiased dimensional reduction using svmPoly revealed that plasma transthyretin, alanine:serine ratio, GDF15, FGF21, and in vivo 1-13C-propionate oxidation, play roles in defining PA subtypes. CONCLUSION: Support vector machine prioritized biomarkers that helped classify propionic acidemia patients according to severity subtypes, with important ramifications for future clinical trials and management of PA.

All-cause and diabetes-related healthcare costs among US adults with type 2 diabetes initiating exenatide once weekly or insulin glargine.

AIMS: To compare healthcare utilization and costs between patients with type 2 diabetes (T2D) treated with exenatide (Bydureon) once weekly (EQW) and patients treated with insulin glargine (IG). MATERIALS AND METHODS: Using the MarketScan Commercial and Medicare Supplemental databases, we conducted a retrospective cohort study of adult US patients with claim with a diagnosis of T2D, initiating EQW or IG from February 1, 2012 to June 30, 2014 (first claim = index date). All-cause and diabetes-related utilization and costs were measured during the 12 months after the index date. EQW patients were matched 1:1 to IG patients, using propensity scores. Logistic and ordinary least-squares regression models were fit to model differences between the matched cohorts. RESULTS: There were 7749 EQW patients matched to 7749 IG patients. EQW patients had significantly (P < .05) lower odds of all-cause inpatient (IP) admissions (odds ratio = 0.737 [95% confidence interval, 0.661, 0.822]), diabetes-related IP admissions (0.720 [95% confidence interval, 0.635, 0.815]) and diabetes-related IP admissions or emergency room visits (0.778 [95% confidence interval, 0.713, 0.847]). EQW patients had significantly (P < .05) lower all-cause (cost difference = -113 USD [95% confidence interval, -120 USD, -106 USD]) and diabetes-related (-806 USD [95% confidence interval, -871 USD, -746 USD]) medical costs, and had significantly (P < .05) higher all-cause total costs (ie, medical plus pharmacy) (3228 USD [95% confidence interval, 3110 USD, 3367 USD]), diabetes-related total costs (1951 USD [95% confidence interval, 1873 USD, 2036 USD]), all-cause pharmacy costs (2792 USD [95% confidence interval, 2700 USD, 2928 USD]) and diabetes-related pharmacy costs (1923 USD [95% confidence interval, 1890 USD, 1957 USD]) than those of IG patients. CONCLUSIONS: Among adults with T2D, EQW initiators had lower odds of IP admission and lower medical costs in the 12 months after initiation than IG initiators. Higher total costs in EQW patients were driven by greater pharmacy costs.

The economic burden of overactive bladder (OAB) and its effects on the costs associated with other chronic, age-related comorbidities in the United States.

AIMS: The prevalence of OAB increases with age and is associated with several chronic comorbidities. However, the impact of OAB on the healthcare costs of patients with such comorbidities is not well-understood. This study aimed to quantify the impact of OAB on healthcare costs and assess the potential moderating effects of OAB on the costs of patients with chronic comorbidities. METHODS: Adults with evidence of OAB/OAB-related therapy between 1/1/2008-12/31/2013 were identified from two large, administrative claims databases. Per-patient-per-month (PPPM) expenditures for OAB cases were estimated and compared to those of propensity score-matched subjects without OAB. Costs were modeled using ordinary least squares regression including main effects and interactions of chronic depression, dementia, diabetes, hypertension, and osteoporosis with OAB. Values for the comparisons were calculated on the original dollar scale using smearing estimators. RESULTS: A total of 110 059 pairs of OAB cases and matched non-OAB controls were identified. The mean, multivariable-adjusted, PPPM all-cause costs of OAB cases from the model without interactions were $3003, compared to $1123 for matched controls (P < 0.0001). In the model assessing the interactions of chronic comorbidities with OAB, those OAB patients with comorbid depression, dementia, diabetes, hypertension, and osteoporosis incurred significantly higher costs than controls with these comorbidities. The synergistic effect of these interactions was estimated to be $95-$574 PPPM. CONCLUSIONS: Within this US-based population, the healthcare costs of OAB patients were more than 2.5 times those of similar patients without OAB. Additionally, patients with OAB and chronic, age-related comorbidities incurred higher healthcare costs than non-OAB controls with the same comorbidities.

Treatment patterns in patients with type 2 diabetes mellitus treated with glucagon-like peptide-1 receptor agonists: Higher adherence and persistence with dulaglutide compared with once-weekly exenatide and liraglutide.

AIMS: To compare adherence (proportion of days covered [PDC]), persistence, and treatment patterns among patients with type 2 diabetes mellitus (T2DM) newly initiating glucagon-like peptide-1 receptor agonists (GLP-1RAs). More specifically, the main objectives were to compare dulaglutide vs exenatide once weekly and dulaglutide vs liraglutide. METHODS: Patients with T2DM newly initiating dulaglutide, albiglutide, exenatide once weekly, exenatide twice daily and liraglutide between November 2014 and April 2015 were hierarchically selected from Truven Health's MarketScan Research Databases. Propensity score matching was used to account for selection bias. Adherence to and persistence with the index GLP-1RA, and switching and augmentation patterns were assessed during the 6-month post-index period. RESULTS: Mean adherence for the matched cohorts was significantly higher for dulaglutide than for exenatide once weekly (0.72 vs 0.61; P < .0001) and liraglutide (0.71 vs 0.67; P < .0001). The percentage of patients achieving PDC ≥ 0.80 was significantly higher for dulaglutide compared with exenatide once weekly (54.2% vs 37.9%; P < .0001) and liraglutide (53.5% vs 44.3%; P < .0001). The mean (standard deviation) days on treatment for all matched patients was significantly higher for patients in the dulaglutide cohort compared with those in the exenatide once-weekly (148.4 [55.4] vs 123.6 [61.6]; P < .0001) and liraglutide cohorts (146.0 [56.9] vs 137.4 [60.1]; P < .0001). A significantly lower proportion of patients on dulaglutide discontinued treatment compared with those on exenatide once weekly (26.2% vs 48.4%; P < .0001) and those on liraglutide (28.0% vs 35.6%; P < .0001). CONCLUSIONS: Dulaglutide initiators had significantly higher adherence, were more persistent, and had lower discontinuation rates compared with initiators of exenatide once weekly or liraglutide during the 6-month follow-up period.

Costs of Newborn Care Following Complications During Pregnancy and Delivery.

The aim of this study is examine the impact of pregnancy and delivery complications on the healthcare costs of newborns during the first 3 months of life. We conducted a retrospective cohort study of newborns born to women ages 15-49 using de-identified medical and pharmacy claims from the Truven Health MarketScan Commercial Claims and Encounters database incurred between January 1, 2007 and December 31, 2011. Total healthcare costs and resource utilization were examined and compared for the first 3 months of life between cohorts of newborns either with or without evidence of categorized maternal complications. Incremental costs were also determined using multivariable analysis for the conditions found to be the most prevalent in the study population. A total of 137,040 infants were studied, 75.4% of which were born to mothers who had experienced at least one complication during pregnancy or delivery. Fetal abnormalities (26.2%), early or threatened labor (16.6%), and hemorrhage (10.8%) were the most frequently observed complications. Diabetes (8.0%) and hypertension (7.7%) were also common, with the majority of other conditions present in 1% or less of the study population. Adjusted analyses found significant differences for seven conditions where incremental costs ranged from $987 to $10,287. Complications are common during pregnancy and delivery and some complications may lead to increased healthcare costs for newborns immediately following birth.

Health care resource utilization and medical costs of spinal cord injury with neuropathic pain in a commercially insured population in the United States.

OBJECTIVE: To evaluate health care resource use, costs, and cost drivers among patients with neuropathic pain (NeP) after spinal cord injury (SCI) in a commercially insured population. DESIGN: Retrospective longitudinal cohort study comparing SCI patients with and without NeP. SETTING: Truven Health MarketScan commercial claims database from 2005 through 2012. PARTICIPANTS: Commercially insured SCI patients with NeP (n=3524) propensity score matched to SCI patients without NeP (n=3524). INTERVENTIONS: Not applicable. MAIN OUTCOMES MEASURES: Health care resource utilization and expenditures for the 12 months after NeP onset (index event; identified through International Classification of Diseases, 9th Revision, Clinical Modification diagnosis 338.0x or use of NeP-specific antiepileptic drugs or NeP-specific antidepressants) in patients with SCI compared with matched patients without NeP. RESULTS: Utilization over 12 months postindex among patients with SCI-associated NeP was higher than among SCI-only patients for inpatient admissions (27.4% vs 22.1%), emergency department visits (36.7% vs 26.4%), and office visits per patient (mean ± SD: 13.0±9.5 vs 9.5±8.3); all P values were <.001. All-cause expenditures showed adjusted incremental costs of $22,545 (95% confidence interval, $19,010-$26,168) per patient with SCI-associated NeP during the 12-month postindex period. CONCLUSIONS: Patients with evidence of NeP secondary to SCI have significantly higher health care utilization and total costs compared with SCI patients without evidence of NeP. Factors contributing to NeP in patients with SCI need to be clinically assessed to determine the optimal approach for treating these individuals.

Characterization of cancer survivors clustered by subjective and objective cognitive function scores.

BACKGROUND: Cancer-related cognitive impairment (CRCI) is a prevalent condition that significantly impacts the quality of life of individuals who receive cancer treatment. Clinical management of CRCI presents challenges due to the absence of a standardized assessment. This study identified clinically relevant phenotypic clusters of CRCI based on subjective and objective cognitive function scores. METHODS: In this cross-sectional study, participants were clustered using the VARCLUS™ based on subjective cognitive impairment assessed through the PROMIS® version 1.0 short-form subscales of cognitive abilities and cognitive concerns and the CANTAB Cambridge Cognition® scores, which included measures of visuospatial working memory capacity, visual episodic memory, new learning, working memory, executive function, and sustained attention. Each cluster's characteristics were described using demographics, physical and psychosocial factors (physical function, affect, optimism, and social support), and psychoneurological symptoms (anxiety, depression, fatigue, neuropathic pain, and sleep disturbance). RESULTS: We obtained five clusters from a total of 414 participants, where 99% were female, and 93% were self-reported white. Clusters 4 and 5 showed the highest PROMIS® cognitive abilities and the lowest measures of cognitive concern, while Clusters 1 and 2 showed the lowest cognitive abilities and the highest cognitive concerns. Clusters 4 and 5 had higher education, income, employment, and higher scores in physical function, positive affect, optimism, and social support. Additionally, individuals in these clusters were less prone to experience severe cancer-related psychoneurological symptoms. CONCLUSION: Our clustering approach, combining subjective and objective cognitive function information, shows promise in identifying phenotypes that hold clinical relevance for categorizing patient presentation of CRCI and facilitating individualized management strategies.

Defining features of hereditary lobular breast cancer due to CDH1 with magnetic resonance imaging and tumor characteristics.

Women with germline pathogenic variants in CDH1, which encodes E-cadherin protein, are at increased lifetime risk of invasive lobular carcinoma (ILC). The associated tumor characteristics of hereditary lobular breast carcinoma (HLBC) in this high-risk population are not well-known. A single-center prospective cohort study was conducted to determine the imaging and pathologic features of HLBC compared to population-based ILC using Surveillance, Epidemiology, and End Results (SEER) data. One hundred fifty-eight women with CDH1 variants were evaluated, of whom 48 (30%) also had an ILC diagnosis. The median age at CDH1 diagnosis was 45 years [interquartile range, IQR 34-57 years] whereas the median age at diagnosis of CDH1 with concomitant ILC (HLBC) was 53 [IQR 45-62] years. Among women with HLBC, 83% (40/48) were identified with CDH1 mutation after diagnosis of ILC. Among 76 women (48%, 76/158) undergoing surveillance for ILC with breast magnetic resonance imaging (MRI), 29% (22/76) had an abnormal MRI result with available biopsy data for comparison. MRI detected ILC in 7 out of 8 biopsy-confirmed cases, corresponding with high sensitivity (88%), specificity (75%), and negative predictive value (98%); however, false-positive and false-discovery rates were elevated also (25% and 68%, respectively). HLBC was most frequently diagnosed at age 40-49 years (44%, 21/48), significantly younger than the common age of diagnosis of ILC in SEER general population data (most frequent age range 60-69 years, 28%; p < 0.001). HLBC tumors were smaller than SEER-documented ILC tumors (median 1.40 vs. 2.00 cm; p = 0.002) and had a higher incidence of background lobular carcinoma in situ (88% vs. 1%; p < 0.001) as well as progesterone receptor positivity (95% vs. 81%, p = 0.032). These findings suggest that HLBC is often detected via conventional screening methods as an early-stage hormone receptor-positive tumor, thus the clinical benefit of intensive screening with MRI may be limited to a subset of women with germline CDH1 variants.

Frailty and Determinants of Health Among Older Adults in the United States 2011-2016.

OBJECTIVE: To characterize frailty phenotype in a representative cohort of older Americans and examine determinants of health factors. METHODS: Retrospective analysis of data from 5,553 adults ≥60 years old in the 2011-2016 cross-sectional National Health and Nutrition Examination Survey (NHANES). World Health Organization "Determinants of Health" conceptual model was used to prioritize variables for multinomial logistic regression for the outcome of modified Fried frailty phenotype. RESULTS: 482 participants (9%) were frail and 2432 (44%) prefrail. Four factors were highly associated with frailty: difficulty with ≥1 activity of daily living (77%; OR 24.81 p < 0.01), ≥2 hospitalizations in the previous year (17%, OR 3.94 p < 0.01), having >2 comorbidities (27%; OR 3.33 p < 0.01), and polypharmacy (66%; OR 2.38 p < 0.01). DISCUSSION: A modified Fried frailty assessment incorporating five self-reported criteria may be useful as a rapid nursing screen in low-resource settings. These assessments can streamline nursing care coordination and case management activities, thereby facilitating targeted frailty interventions to support healthy aging in vulnerable populations.

The Association of Fractal Dimension with Vascularity and Clinical Outcomes in Glioblastoma.

BACKGROUND: Growing evidence indicates fractal analysis (FA) has potential as a computational tool to assess tumor microvasculature in glioblastoma (GBM). As fractal parameters of microvasculature have shown to be reliable quantitative biomarkers in brain tumors, there has been similar success in measuring the architecture of tumor tissue using FA in other tumor types. However, evaluating fractal parameters of tissue structure in relation to the microvasculature has not yet been implemented in GBM. We aimed to assess the utility of this methodology in quantifying structural characteristics of GBM cytoarchitecture and vascularity by correlating fractal parameters with gene expression. METHODS: Formalin-fixed paraffin-embedded specimens were retrospectively collected from 43 patients following resection of a newly diagnosed GBM; 4 normal brain specimens were obtained from epilepsy surgeries as controls. Tumor samples were processed using FA employing a software-based box-counting method algorithm and custom messenger RNA expression assays. Fractal parameters were then correlated with clinical features, outcomes, and a panel of 92 genes associated with vascularity and angiogenesis. RESULTS: Statistical analysis demonstrated that fractal-based indices were not adequate parameters for distinction of GBM cytoarchitecture compared with normal brain specimens. Correlation analysis of our gene expression findings suggested that hematoxylin and eosin-based FA may have adequate sensitivity to detect associations with vascular gene expression. CONCLUSIONS: The combination of neuropathological assessment and histology does not provide optimized data for FA in GBM. However, an association between FA and gene expression in GBM of genes pertaining to cytoarchitecture and angiogenesis warrants further investigation.

Automatic quadriceps and patellae segmentation of MRI with cascaded U2 -Net and SASSNet deep learning model.

PURPOSE: Automatic muscle segmentation is critical for advancing our understanding of human physiology, biomechanics, and musculoskeletal pathologies, as it allows for timely exploration of large multi-dimensional image sets. Segmentation models are rarely developed/validated for the pediatric model. As such, autosegmentation is not available to explore how muscle architectural changes during development and how disease/pathology affects the developing musculoskeletal system. Thus, we aimed to develop and validate an end-to-end, fully automated, deep learning model for accurate segmentation of the rectus femoris and vastus lateral, medialis, and intermedialis using a pediatric database. METHODS: We developed a two-stage cascaded deep learning model in a coarse-to-fine manner. In the first stage, the U2 -Net roughly detects the muscle subcompartment region. Then, in the second stage, the shape-aware 3D semantic segmentation method SASSNet refines the cropped target regions to generate the more finer and accurate segmentation masks. We utilized multifeature image maps in both stages to stabilize performance and validated their use with an ablation study. The second-stage SASSNet was independently run and evaluated with three different cropped region resolutions: the original image resolution, and images downsampled 2× and 4× (high, mid, and low). The relationship between image resolution and segmentation accuracy was explored. In addition, the patella was included as a comparator to past work. We evaluated segmentation accuracy using leave-one-out testing on a database of 3D MR images (0.43 × 0.43 × 2 mm) from 40 pediatric participants (age 15.3 ± 1.9 years, 55.8 ± 11.8 kg, 164.2 ± 7.9 cm, 38F/2 M). RESULTS: The mid-resolution second stage produced the best results for the vastus medialis, rectus femoris, and patella (Dice similarity coefficient = 95.0%, 95.1%, 93.7%), whereas the low-resolution second stage produced the best results for the vastus lateralis and vastus intermedialis (DSC = 94.5% and 93.7%). In comparing the low- to mid-resolution cases, the vasti intermedialis, vastus medialis, rectus femoris, and patella produced significant differences (p = 0.0015, p = 0.0101, p < 0.0001, p = 0.0003) and the vasti lateralis did not (p = 0.2177). The high-resolution stage 2 had significantly lower accuracy (1.0 to 4.4 dice percentage points) compared to both the mid- and low-resolution routines (p value ranged from < 0.001 to 0.04). The one exception was the rectus femoris, where there was no difference between the low- and high-resolution cases. The ablation study demonstrated that the multifeature is more reliable than the single feature. CONCLUSIONS: Our successful implementation of this two-stage segmentation pipeline provides a critical tool for expanding pediatric muscle physiology and clinical research. With a relatively small and variable dataset, our fully automatic segmentation technique produces accuracies that matched or exceeded the current state of the art. The two-stage segmentation avoids memory issues and excessive run times by using a first stage focused on cropping out unnecessary data. The excellent Dice similarity coefficients improve upon previous template-based automatic and semiautomatic methodologies targeting the leg musculature. More importantly, with a naturally variable dataset (size, shape, etc.), the proposed model demonstrates slightly improved accuracies, compared to previous neural networks methods.

Serum neutralization activity declines but memory B cells persist after cure of chronic hepatitis C.

The increasing incidence of hepatitis C virus (HCV) infections underscores the need for an effective vaccine. Successful vaccines to other viruses generally depend on a long-lasting humoral response. However, data on the half-life of HCV-specific responses are lacking. Here we study archived sera and mononuclear cells that were prospectively collected up to 18 years after cure of chronic HCV infection to determine the role of HCV antigen in maintaining neutralizing antibody and B cell responses. We show that HCV-neutralizing activity decreases rapidly in potency and breadth after curative treatment. In contrast, HCV-specific memory B cells persist, and display a restored resting phenotype, normalized chemokine receptor expression and preserved ability to differentiate into antibody-secreting cells. The short half-life of HCV-neutralizing activity is consistent with a lack of long-lived plasma cells. The persistence of HCV-specific memory B cells and the reduced inflammation after cure provide an opportunity for vaccination to induce protective immunity against re-infection.

Predicting cancer immunotherapy response from gut microbiomes using machine learning models.

Cancer immunotherapy has significantly improved patient survival. Yet, half of patients do not respond to immunotherapy. Gut microbiomes have been linked to clinical responsiveness of melanoma patients on immunotherapies; however, different taxa have been associated with response status with implicated taxa inconsistent between studies. We used a tumor-agnostic approach to find common gut microbiome features of response among immunotherapy patients with different advanced stage cancers. A combined meta-analysis of 16S rRNA gene sequencing data from our mixed tumor cohort and three published immunotherapy gut microbiome datasets from different melanoma patient cohorts found certain gut bacterial taxa correlated with immunotherapy response status regardless of tumor type. Using multivariate selbal analysis, we identified two separate groups of bacterial genera associated with responders versus non-responders. Statistical models of gut microbiome community features showed robust prediction accuracy of immunotherapy response in amplicon sequencing datasets and in cross-sequencing platform validation with shotgun metagenomic datasets. Results suggest baseline gut microbiome features may be predictive of clinical outcomes in oncology patients on immunotherapies, and some of these features may be generalizable across different tumor types, patient cohorts, and sequencing platforms. Findings demonstrate how machine learning models can reveal microbiome-immunotherapy interactions that may ultimately improve cancer patient outcomes.