Optimizing EEG monitoring in critically ill children at risk for electroencephalographic seizures.

OBJECTIVE: Strategies are needed to optimally deploy continuous EEG monitoring (CEEG) for electroencephalographic seizure (ES) identification and management due to resource limitations. We aimed to construct an efficient multi-stage prediction model guiding CEEG utilization to identify ES in critically ill children using clinical and EEG covariates. METHODS: The largest prospective single-center cohort of 1399 consecutive children undergoing CEEG was analyzed. A four-stage model was developed and trained to predict whether a subject required additional CEEG at the conclusion of each stage given their risk of ES. Logistic regression, elastic net, random forest, and CatBoost served as candidate methods for each stage and were evaluated using cross validation. An optimal multi-stage model consisting of the top-performing stage-specific models was constructed. RESULTS: When evaluated on a test set, the optimal multi-stage model achieved a cumulative specificity of 0.197 and cumulative F1 score of 0.326 while maintaining a high minimum cumulative sensitivity of 0.938. Overall, 11 % of test subjects with ES were removed from the model due to a predicted low risk of ES (falsely negative subjects). CEEG utilization would be reduced by 32 % and 47 % compared to performing 24 and 48 h of CEEG in all test subjects, respectively. We developed a web application called EEGLE (EEG Length Estimator) that enables straightforward implementation of the model. CONCLUSIONS: Application of the optimal multi-stage ES prediction model could either reduce CEEG utilization for patients at lower risk of ES or promote CEEG resource reallocation to patients at higher risk for ES.

A subpopulation of lipogenic brown adipocytes drives thermogenic memory.

Sustained responses to transient environmental stimuli are important for survival. The mechanisms underlying long-term adaptations to temporary shifts in abiotic factors remain incompletely understood. Here, we find that transient cold exposure leads to sustained transcriptional and metabolic adaptations in brown adipose tissue, which improve thermogenic responses to secondary cold encounter. Primary thermogenic challenge triggers the delayed induction of a lipid biosynthesis programme even after cessation of the original stimulus, which protects from subsequent exposures. Single-nucleus RNA sequencing and spatial transcriptomics reveal that this response is driven by a lipogenic subpopulation of brown adipocytes localized along the perimeter of Ucp1hi adipocytes. This lipogenic programme is associated with the production of acylcarnitines, and supplementation of acylcarnitines is sufficient to recapitulate improved secondary cold responses. Overall, our data highlight the importance of heterogenous brown adipocyte populations for 'thermogenic memory', which may have therapeutic implications for leveraging short-term thermogenesis to counteract obesity.

Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA.

Cell populations in the tumor microenvironment (TME), including their abundance, composition, and spatial location, are critical determinants of patient response to therapy. Recent advances in spatial transcriptomics (ST) have enabled the comprehensive characterization of gene expression in the TME. However, popular ST platforms, such as Visium, only measure expression in low-resolution spots and have large tissue areas that are not covered by any spots, which limits their usefulness in studying the detailed structure of TME. Here, we present TESLA, a machine learning framework for tissue annotation with pixel-level resolution in ST. TESLA integrates histological information with gene expression to annotate heterogeneous immune and tumor cells directly on the histology image. TESLA further detects unique TME features such as tertiary lymphoid structures, which represents a promising avenue for understanding the spatial architecture of the TME. Although we mainly illustrated the applications in cancer, TESLA can also be applied to other diseases.

DeepComBat: A Statistically Motivated, Hyperparameter-Robust, Deep Learning Approach to Harmonization of Neuroimaging Data.

Neuroimaging data from multiple batches (i.e. acquisition sites, scanner manufacturer, datasets, etc.) are increasingly necessary to gain new insights into the human brain. However, multi-batch data, as well as extracted radiomic features, exhibit pronounced technical artifacts across batches. These batch effects introduce confounding into the data and can obscure biological effects of interest, decreasing the generalizability and reproducibility of findings. This is especially true when multi-batch data is used alongside complex downstream analysis models, such as machine learning methods. Image harmonization methods seeking to remove these batch effects are important for mitigating these issues; however, significant multivariate batch effects remain in the data following harmonization by current state-of-the-art statistical and deep learning methods. We present DeepCombat, a deep learning harmonization method based on a conditional variational autoencoder architecture and the ComBat harmonization model. DeepCombat learns and removes subject-level batch effects by accounting for the multivariate relationships between features. Additionally, DeepComBat relaxes a number of strong assumptions commonly made by previous deep learning harmonization methods and is empirically robust across a wide range of hyperparameter choices. We apply this method to neuroimaging data from a large cognitive-aging cohort and find that DeepCombat outperforms existing methods, as assessed by a battery of machine learning methods, in removing scanner effects from cortical thickness measurements while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically-motivated deep learning harmonization methods.

SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning.

Spatially resolved transcriptomics (SRT) has advanced our understanding of the spatial patterns of gene expression, but the lack of single-cell resolution in spatial barcoding-based SRT hinders the inference of specific locations of individual cells. To determine the spatial distribution of cell types in SRT, we present SpaDecon, a semi-supervised learning approach that incorporates gene expression, spatial location, and histology information for cell-type deconvolution. SpaDecon was evaluated through analyses of four real SRT datasets using knowledge of the expected distributions of cell types. Quantitative evaluations were performed for four pseudo-SRT datasets constructed according to benchmark proportions. Using mean squared error and Jensen-Shannon divergence with the benchmark proportions as evaluation criteria, we show that SpaDecon performance surpasses that of published cell-type deconvolution methods. Given the accuracy and computational speed of SpaDecon, we anticipate it will be valuable for SRT data analysis and will facilitate the integration of genomics and digital pathology.

Subject satisfaction and psychological well-being with escalating abobotulinumtoxinA injection dose for the treatment of moderate to severe glabellar lines.

BACKGROUND: Previous studies indicate that the efficacy and durability of a single AbobotulinumtoxinA (ABO) treatment for moderate to severe glabellar lines may be enhanced with increasing dose, while safety outcomes remain consistent with those of the licensed dose (50 U). AIMS: Evaluation of subject-reported indicators of treatment efficacy, satisfaction, and psychological well-being with ABO dose escalation. METHODS: A Phase 2, 36-week, multicenter, randomized, dose-ranging, double-blind, placebo-controlled study was conducted in adults with moderate to severe glabellar lines. Subjects received a single ABO treatment, dosed at 50, 75, 100, or 125 U, or placebo. Efficacy endpoints comprised subject-assessed improvement in line severity of ≥1-grade from baseline at maximum frown, global aesthetic improvement scale (GAIS) grade, FACE-Q™ appraisal of lines, psychological well-being and age, and subject satisfaction. RESULTS: The study included 399 subjects (88.2% were female). Respective responder rates (≥1-grade improvement) with ABO 50-125 U doses ranged between 96.3%-100% at Week 4, 65.0%-67.9% at Week 24, and 33.8%-44.4% at Week 36. GAIS responder rate and FACE-Q appraisal of lines showed a similar pattern of change. Satisfaction was high and psychological well-being was improved from Week 4 through Week 36, with natural, youthful, and refreshed appearance reported for all ABO doses. CONCLUSIONS: A single ABO treatment (dosed at 50-125 U) provided significant and sustained improvements in glabellar line severity over durations up to 36 weeks, versus placebo. Treatment satisfaction was high with all doses. Participants reported natural and youthful appearance, alongside improvements in psychological well-being.

Challenging Cases in Neuroimmunology.

Neuroimmunology is rapidly evolving field extending from well-known, but incompletely understood conditions like multiple sclerosis, to novel antibody-mediated disorders, of which dozens have been described in the past 10 years. The ongoing expansion in knowledge needed to effectively diagnose and treat these patients presents myriad challenges for clinicians. Here, we discuss six informative cases from our institution. By highlighting these challenging cases, we hope to instill fundamental points on the nuances of diagnosis and management for conditions including tumefactive multiple sclerosis, antibody-mediated encephalitis, antiphospholipid antibody syndrome, neuromyelitis optica, and myelin oligodendrocyte glycoprotein IgG-associated disease.

SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network.

Recent advances in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive characterization of gene expression patterns in the context of tissue microenvironment. To elucidate spatial gene expression variation, we present SpaGCN, a graph convolutional network approach that integrates gene expression, spatial location and histology in SRT data analysis. Through graph convolution, SpaGCN aggregates gene expression of each spot from its neighboring spots, which enables the identification of spatial domains with coherent expression and histology. The subsequent domain guided differential expression (DE) analysis then detects genes with enriched expression patterns in the identified domains. Analyzing seven SRT datasets using SpaGCN, we show it can detect genes with much more enriched spatial expression patterns than competing methods. Furthermore, genes detected by SpaGCN are transferrable and can be utilized to study spatial variation of gene expression in other datasets. SpaGCN is computationally fast, platform independent, making it a desirable tool for diverse SRT studies.

AbobotulinumtoxinA for the Treatment of Moderate-to-Severe Glabellar Lines: A Randomized, Dose-Escalating, Double-Blind Study.

OBJECTIVE: To evaluate the efficacy and safety of AbobotulinumtoxinA (ABO) dose escalation in the correction of moderate-to-severe glabellar lines. DESIGN: Phase 2, 36-week, multicenter, randomized, dose-ranging, double-blind, placebo-controlled study. METHODS: Adults with moderate-to-severe glabellar lines received a single ABO treatment, dosed at 50, 75, 100, or 125 U, or placebo. Primary endpoint was week 4 composite ≥2-grade responder rate among those achieving a severity score of 0 (none) or 1 (mild) at maximum frown, evaluated using concurrent investigator and subject assessments. Secondary endpoints included ≥1-grade severity improvement, duration of effect, and reporting of treatment-emergent adverse events (TEAEs). RESULTS: Overall, 399 subjects were included (88.2% were female). Week 4 composite ≥2-grade ABO responder rate was 80.0% (50 U), 88.8% (75 U), 90.0% (100 U) and 95.1% (125 U), versus 2.6% with placebo (P<0.001). Responder rate (≥1-grade) ranged between 53% (50 U) and 69% (125 U) at week 24 and between 18% (50 U) and 31% (125 U) at week 36. Median time (weeks) to return to baseline severity/worse, among those scoring 0 (none) or 1 (mild), was 32.3 (50 U), 34.3 (75 U), 36.0 (100 U) and 36.6 (125 U), versus 23.7 (placebo). ABO-related TEAEs were reported in 4% of subjects (80% were mild). No seroconversion to ABO neutralizing antibodies was seen. CONCLUSION: A single ABO treatment provided rapid and effective improvements in glabellar line severity at all doses. Higher doses tended to demonstrate elevated response rates and longer duration of effect. All ABO doses were well-tolerated with low TEAE incidence. J Drugs Dermatol. 2021;20(9):980-987. doi:10.36849/JDD.6263.

Statistical and machine learning methods for spatially resolved transcriptomics with histology.

Recent developments in spatially resolved transcriptomics (SRT) technologies have enabled scientists to get an integrated understanding of cells in their morphological context. Applications of these technologies in diverse tissues and diseases have transformed our views of transcriptional complexity. Most published studies utilized tools developed for single-cell RNA sequencing (scRNA-seq) for data analysis. However, SRT data exhibit different properties from scRNA-seq. To take full advantage of the added dimension on spatial location information in such data, new methods that are tailored for SRT are needed. Additionally, SRT data often have companion high-resolution histology information available. Incorporating histological features in gene expression analysis is an underexplored area. In this review, we will focus on the statistical and machine learning aspects for SRT data analysis and discuss how spatial location and histology information can be integrated with gene expression to advance our understanding of the transcriptional complexity. We also point out open problems and future research directions in this field.

A Large, Open-Label, Phase 3 Safety Study of DaxibotulinumtoxinA for Injection in Glabellar Lines: A Focus on Safety From the SAKURA 3 Study.

BACKGROUND: SAKURA 3 was a Phase 3, open-label, repeat-dose safety study of DaxibotulinumtoxinA for Injection (DAXI); a component of the largest Phase 3 clinical development program of an aesthetic neuromodulator in glabellar lines. OBJECTIVE: To evaluate the use of DAXI (40U) up to 3 treatments for moderate or severe glabellar lines. METHODS: Eligible subjects rolled over from the placebo-controlled trials (n = 477) or were de novo (n = 2,214) and received 1 to 3 treatments over a maximum of 84 weeks. Safety and efficacy were evaluated at least every 4 weeks up to Week 36 (Treatments 1 and 2) and Week 12 (Treatment 3). Select subjects could be retreated after Week 12 if glabellar lines returned to baseline. RESULTS: Safety results are reported for 2,691 subjects, of which 882 received a second treatment and 568 a third. Treatment-related adverse events (AEs) occurred in 17.8% of subjects, which were generally mild and resolved. No serious AEs were treatment-related. Eyelid ptosis occurred in 0.9% of treatments. Adverse events were consistent across treatments and no new safety signals were observed. CONCLUSION: The safety of DAXI in this large open-label safety study confirms the findings from the pivotal Phase 3 trials, providing reassurance in its overall safety profile.

Update on Nonfacial Fat Transplantation.

BACKGROUND: Fat transplantation is becoming increasingly popular for off-face rejuvenation. OBJECTIVE: To provide an update in the literature of current knowledge and emerging concepts in the use of fat transplantation for nonfacial applications. MATERIALS AND METHODS: This update includes the potential benefits and risks of using fat transfer techniques on the body. RESULTS: The current literature and author experiences are provided to help understand this growing field of aesthetic procedures. CONCLUSIONS: The use of nonfacial fat transplantation is increasing and will become a larger part of aesthetic practices.

Male Body Contouring.

BACKGROUND: Men are increasingly seeking out cosmetic procedures, especially minimally and noninvasive body-contouring procedures. OBJECTIVE: With the relative lack of scientific evidence related specifically to the use of body-contouring procedures in men, there is a need for more education and scientific discussion in this growing group. MATERIALS AND METHODS: Understanding the male anatomy and aesthetics, and how body-contouring techniques and new modalities can be used in men, can lead to better outcomes. CONCLUSIONS: This review of body contouring in men emphasizes currently available literature and author experiences.

Combination Therapy for Rejuvenation of the Outer Thigh and Buttock: A Review and Our Experience.

BACKGROUND: Combination therapies are becoming more popular as a multifaceted approach to treating common aesthetic conditions. OBJECTIVE: Often, the use of multiple techniques and modalities can lead to improved outcomes; however, there is a lack of current evidence on the use of combination therapies in the literature. METHODS AND MATERIALS: With the recent expansion of minimally and noninvasive options for treatment of the outer thigh and buttock, it is important to understand how these techniques can be used together while avoiding increased risk. RESULTS AND CONCLUSIONS: This review of current available therapeutic options for treatment of the outer thigh and buttock emphasizes current available literature and author experiences.