Citations

Chimeric antigen receptor (CAR) T cell immunotherapy for the treatment of neurological autoimmune diseases is promising, but CAR T cell kinetics and immune alterations after treatment are poorly understood. Here, we performed single-cell multi-omics sequencing of paired cerebrospinal fluid (CSF) and blood samples from patients with neuromyelitis optica spectrum disorder (NMOSD) treated with anti–B cell maturation antigen (BCMA) CAR T cells. Proliferating cytotoxic-like CD8 + CAR T cell clones were identified as the main effectors in autoimmunity. Anti-BCMA CAR T cells with enhanced features of chemotaxis efficiently crossed the blood-CSF barrier, eliminated plasmablasts and plasma cells in the CSF, and suppressed neuroinflammation. The CD44-expressing early memory phenotype in infusion products was potentially associated with CAR T cell persistence in autoimmunity. Moreover, CAR T cells from patients with NMOSD displayed distinctive features of suppressed cytotoxicity compared with those from hematological malignancies. Thus, we provide mechanistic insights into CAR T cell function in patients with neurological autoimmune disease.

Context: Elbow medial ulnar collateral ligament (UCL) injuries have become increasingly common in athletes. Despite this, rehabilitation protocols appear to vary drastically, which may explain the clinical equipoise regarding optimal management. Objective: This systematic review reports rehabilitation characteristics reported after UCL injuries and compares reported outcomes based on early versus delayed rehabilitation. Data Sources: Our search utilized PubMed/MEDLINE, Embase, Web of Science, and Cochrane to identify all articles on UCL rehabilitation published between January 1, 2002 and October 1, 2022. Study Selection: Studies in English with ≥5 patients that reported rehabilitation protocols for UCL injuries were evaluated. Study Design: Systematic review. Level of Evidence: Level 4. Data Extraction: Data included sample characteristics, time to achieve physical therapy milestones, outcome scores, and return-to-play (RTP) rate and timing. Results: Our review included 105 articles with a total of 15,928 elbows (98% male; weighted mean age, 23 years; follow-up, 47 months), with 15,077 treated operatively and 851 treated nonoperatively. The weighted mean time patients spent adhering to nonweightbearing status was 42 days. The mean time until patients were given clearance for active range of motion (ROM) 15 days, full ROM 40 days, and elbow strengthening exercises 32 days. The mean time until all restrictions were lifted was 309 days. The mean time to begin a throwing program was 120 days. Across all rehabilitation characteristics, protocols for patients undergoing nonoperative management started patients on rehabilitation earlier. After UCL reconstruction, earlier active ROM (≤14 days), elbow strengthening (≤30 days), no restrictions (≤180 days), and throwing (≤120 days) postoperatively led to earlier RTP without a negative effect on functional outcome scores. Conclusion: Current literature provides a spectrum of protocols for elbow UCL rehabilitation, regardless of management. Nonoperative patients began ROM activities, strengthening, and throwing programs sooner than operative patients, and earlier milestones led to earlier RTP.

Racial, ethnic, and socioeconomic disparities exist in the prevalence and natural history of chronic liver disease, access to care, and clinical outcomes. Solutions to improve health equity range widely, from digital health tools to policy changes. The current review outlines the disparities along the chronic liver disease health care continuum from screening and diagnosis to the management of cirrhosis and considerations of pre-liver and post-liver transplantation. Using a health equity research and implementation science framework, we offer pragmatic strategies to address barriers to implementing high-quality equitable care for patients with chronic liver disease.

Background Although cardiovascular mortality continued declining from 2000 to 2019, the rate of this decrease decelerated. We aimed to assess the trends and disparities in risk factor control and treatment among US adults with atherosclerotic cardiovascular disease to find potential causes of the deceleration. Methods and Results A total of 55 ,021 participants, aged ≥20 years, from the 1999 to 2018 National Health and Nutrition Examination Survey were included, of which 5717 were with atherosclerotic cardiovascular disease. Risk factor control was defined as hemoglobin A1c <7%, blood pressure <140/90 mm Hg, and non–high‐density lipoprotein cholesterol <100 mg/dL. The prevalence of atherosclerotic cardiovascular disease oscillated between 7.3% and 8.9% from 1999 to 2018. A significant increasing trend was observed in the prevalence of diabetes, obesity, heavy alcohol consumption, and self‐reported hypertension within the population with atherosclerotic cardiovascular disease ( P trend ≤0.001). Non–high‐density lipoprotein cholesterol <100 mg/dL increased from 7.1% in 1999 to 2002 to 15.7% in 2003 to 2006, before plateauing. Blood pressure control (<140/90 mm Hg) increased until 2011 to 2014, but declined to 70.1% in 2015 to 2018 ( P trend <0.001, P joinpoint =0.14). Similarly, the proportion of participants achieving hemoglobin A1c control began to decrease after 2006 ( P joinpoint =0.05, P trend =0.001). The percentage of participants achieving all 3 targets increased significantly from 4.5% to 18.6% across 1999 to 2018 ( P trend =0.02), but the increasing trend decelerated after 2005 to 2006 ( P joinpoint <0.001). Striking disparities in risk factor control and medication use persisted between sexes, and between different racial and ethnic populations. Conclusions Worsened control of glycemia, blood pressure, obesity, and alcohol consumption, leveled lipid control, and persistent socioeconomic disparities may be contributing factors to the observed deceleration in decreasing cardiovascular mortality trends.

Background Percutaneous heart valve procedures have been increasingly performed over the past decade, yet real‐world mortality data on valvular heart disease (VHD) in the United States remain limited. Methods and Results We queried the Centers for Disease Control and Prevention's Wide‐Ranging Online Data for Epidemiologic Research database among patients ≥15 years old from 1999 to 2020. VHD and its subtypes were listed as the underlying cause of death. We calculated age‐adjusted mortality rate (AAMR) per 100 000 individuals and determined overall trends by estimating the average annual percent change using the Joinpoint regression program. Subgroup analyses were performed based on demographic and geographic factors. In the 22‐year study, there were 446 096 VHD deaths, accounting for 0.80% of all‐cause mortality (56 014 102 people) and 2.38% of the total cardiovascular mortality (18 759 451 people). Aortic stenosis recorded the highest mortality of VHD‐related death in both male (109 529, 61.74%) and female (166 930, 62.13%) populations. The AAMR of VHD has declined from 8.4 (95% CI, 8.2–8.5) to 6.6 (95% CI, 6.5–6.7) per 100 000 population. Similar decreasing AAMR trends were also seen for the VHD subtypes. Men recorded higher AAMR for aortic stenosis and aortic regurgitation, whereas women had higher AAMR for mitral stenosis and mitral regurgitation. Mitral regurgitation had the highest change in average annual percent change in AAMR. Conclusions The mortality rate of VHD among the US population has declined over the past 2 decades. This highlights the likely efficacy of increasing surveillance and advancement in the management of VHD, resulting in improved outcomes.

AbstractBackgroundDeep‐learning‐based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images.PurposeIn this work, we aim to develop a patient‐data‐based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method.MethodsThe patient‐data‐based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR‐processed images from projection or image data; (4) creation of ensembles of DLIR‐processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (−500, −100, −50, −20, −10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered‐backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN‐weak, DCNN‐medium, and DCNN‐strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion‐present images from the lesion absent images. Modulation transfer functions (MTFs) both in‐plane and along the z‐axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t‐test was used to compare the z‐axis and in‐plane spatial resolution of five algorithms for five different contrasts and three dose levels.ResultsThe in‐plane and z‐axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in‐plane and z‐axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (−10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in‐plane and z‐axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of −100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in‐plane and cross‐plane spatial resolution: FBP > DCNN‐Weak > IR > DCNN‐Medium > DCNN‐Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels.ConclusionsA patient‐data‐based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non‐linear image reconstruction and post‐processing techniques, the evaluated DCNN method degraded the in‐plane and z‐axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.

Abstract Many cancers, including glioblastoma (GBM), have a male-biased sex difference in incidence and outcome. The underlying reasons for this sex bias are unclear but likely involve differences in tumor cell state and immune response. This effect is further amplified by sex hormones, including androgens, which have been shown to inhibit anti-tumor T cell immunity. Here, we show that androgens drive anti-tumor immunity in brain tumors, in contrast to its effect in other tumor types. Upon castration, tumor growth was accelerated with attenuated T cell function in GBM and brain tumor models, but the opposite was observed when tumors were located outside the brain. Activity of the hypothalamus-pituitary-adrenal gland (HPA) axis was increased in castrated mice, particularly in those with brain tumors. Blockade of glucocorticoid receptors reversed the accelerated tumor growth in castrated mice, indicating that the effect of castration was mediated by elevated glucocorticoid signaling. Furthermore, this mechanism was not GBM specific, but brain specific, as hyperactivation of the HPA axis was observed with intracranial implantation of non-GBM tumors in the brain. Together, our findings establish that brain tumors drive distinct endocrine-mediated mechanisms in the androgen-deprived setting and highlight the importance of organ-specific effects on anti-tumor immunity.

AbstractObjectiveTo identify, critically appraise, and quantitatively synthesize current randomized controlled trials to compare the effects of virtual reality and dose‐matched conventional exercises in patients with chronic neck pain.Literature SearchPubMed, Embase, CINAHL, PsycINFO, and Scopus were searched for studies published prior to April 15, 2023. The search strategies combined controlled vocabularies and title/abstract keywords on search themes of virtual reality and neck pain.MethodsA systematic review and meta‐analysis of randomized controlled trials was conducted. Two independent reviewers conducted study selection, data extraction, and quality assessment. Methodological quality was assessed using the Physiotherapy Evidence Database scale. Meta‐analyses were performed using random‐effects models.SynthesisSix randomized controlled trials including 243 participants with chronic neck pain met the inclusion criteria. Four studies were evaluated as good quality and two as fair. Pooled analysis revealed that virtual reality demonstrated significantly better improvements in the Neck Disability Index in both the short term (mean difference [MD] = −2.16; 95% confidence interval [CI]: −3.50 to −0.82) and long term (MD = −2.95; 95% CI: −4.93 to −0.97), and pain intensity in the short term (MD = −0.94, 95% CI: −1.31 to −0.58). No significant between‐group differences were found in pain in the long term and kinesiophobia.ConclusionsVirtual reality is a promising intervention to address disability and pain in patients with chronic neck pain. This review supports the clinical significance of augmenting conventional exercise with virtual reality as part of conservative management of chronic neck pain.