Georgetown University Medical Center

The public health impact of alcohol-associated liver disease (ALD), a serious consequence of problematic alcohol use, and alcohol use disorder (AUD) is growing, with ALD becoming a major cause of alcohol-related death overall and the leading indication for liver transplantation in the United States. Comprehensive care for ALD often requires treatment of AUD. Although there is a growing body of evidence showing that AUD treatment is associated with reductions in liver-related morbidity and mortality, only a minority of patients with ALD and AUD receive this care. Integrated and collaborative models that streamline both ALD and AUD care for patients with ALD and AUD are promising approaches to bridge this treatment gap and rely on multidisciplinary and interprofessional teams and partnerships. Here, we review the role of AUD care in ALD treatment, the effects of AUD treatment on liver-related outcomes, the impact of comorbid conditions such as other substance use disorders, obesity, and metabolic syndrome, the current landscape of integrated and collaborative care for ALD and AUD in various treatment settings. We further review knowledge gaps and unmet needs that remain, including the role of precision medicine, the application of harm reduction approaches, the impact of health disparities, the need for additional AUD treatment options as well as further efforts to support implementation and dissemination.

Speech can be defined as the human ability to communicate through a sequence of vocal sounds. Consequently, speech requires an emitter (the speaker) capable of generating the acoustic signal and a receiver (the listener) able to successfully decode the sounds produced by the emitter (i.e., the acoustic signal). Time plays a central role at both ends of this interaction. On the one hand, speech production requires precise and rapid coordination, typically within the order of milliseconds, of the upper vocal tract articulators (i.e., tongue, jaw, lips, and velum), their composite movements, and the activation of the vocal folds. On the other hand, the generated acoustic signal unfolds in time, carrying information at different timescales. This information must be parsed and integrated by the receiver for the correct transmission of meaning. This chapter describes the temporal patterns that characterize the speech signal and reviews research that explores the neural mechanisms underlying the generation of these patterns and the role they play in speech comprehension.

Spinal arachnoid web (AW) is a rare condition causing spinal cord-related issues. Its cause is often idiopathic but can be linked to past trauma or spine surgery. We describe two cases of AWs that developed after subarachnoid hemorrhage (SAH). Case #1 is a 71-year-old male with nonaneurysmal SAH who developed myelopathy 1 year later. Magnetic resonance imaging revealed upper thoracic cord edema and an AW. Case #2 is a 57-year-old female who underwent coiling of a ruptured basilar artery aneurysm and ventriculoperitoneal shunting for hydrocephalus. Twenty months later, she developed mid-thoracic AW requiring surgical resection. Both patients showed symptom improvement postresection avoiding further reoperation. History of SAH is emerging as a risk factor for AW development, emphasizing the importance of monitoring delayed-onset myelopathy and back pain in recent SAH patients.

Echolocating bats are among the most social and vocal of all mammals. These animals are ideal subjects for functional MRI (fMRI) studies of auditory social communication given their relatively hypertrophic limbic and auditory neural structures and their reduced ability to hear MRI gradient noise. Yet, no resting-state networks relevant to social cognition (e.g., default mode-like networks or DMLNs) have been identified in bats since there are few, if any, fMRI studies in the chiropteran order. Here, we acquired fMRI data at 7 Tesla from nine lightly anesthetized pale spear-nosed bats ( Phyllostomus discolor ). We applied independent components analysis (ICA) to reveal resting-state networks and measured neural activity elicited by noise ripples (on: 10 ms; off: 10 ms) that span this species’ ultrasonic hearing range (20 to 130 kHz). Resting-state networks pervaded auditory, parietal, and occipital cortices, along with the hippocampus, cerebellum, basal ganglia, and auditory brainstem. Two midline networks formed an apparent DMLN. Additionally, we found four predominantly auditory/parietal cortical networks, of which two were left-lateralized and two right-lateralized. Regions within four auditory/parietal cortical networks are known to respond to social calls. Along with the auditory brainstem, regions within these four cortical networks responded to ultrasonic noise ripples. Iterative analyses revealed consistent, significant functional connectivity between the left, but not right, auditory/parietal cortical networks and DMLN nodes, especially the anterior-most cingulate cortex. Thus, a resting-state network implicated in social cognition displays more distributed functional connectivity across left, relative to right, hemispheric cortical substrates of audition and communication in this highly social and vocal species.

RNA-Seq data analysis stands as a vital part of genomics research, turning vast and complex datasets into meaningful biological insights. It is a field marked by rapid evolution and ongoing innovation, necessitating a thorough understanding for anyone seeking to unlock the potential of RNA-Seq data. In this chapter, we describe the intricate landscape of RNA-seq data analysis, elucidating a comprehensive pipeline that navigates through the entirety of this complex process. Beginning with quality control, the chapter underscores the paramount importance of ensuring the integrity of RNA-seq data, as it lays the groundwork for subsequent analyses. Preprocessing is then addressed, where the raw sequence data undergoes necessary modifications and enhancements, setting the stage for the alignment phase. This phase involves mapping the processed sequences to a reference genome, a step pivotal for decoding the origins and functions of these sequences. Venturing into the heart of RNA-seq analysis, the chapter then explores differential expression analysis—the process of identifying genes that exhibit varying expression levels across different conditions or sample groups. Recognizing the biological context of these differentially expressed genes is pivotal; hence, the chapter transitions into functional analysis. Here, methods and tools like Gene Ontology and pathway analyses help contextualize the roles and interactions of the identified genes within broader biological frameworks. However, the chapter does not stop at conventional analysis methods. Embracing the evolving paradigms of data science, it delves into machine learning applications for RNA-seq data, introducing advanced techniques in dimension reduction and both unsupervised and supervised learning. These approaches allow for patterns and relationships to be discerned in the data that might be imperceptible through traditional methods.

RNA sequencing (RNA-Seq) has emerged as a powerful and versatile tool for the comprehensive analysis of transcriptomes and has been widely used to investigate gene expression, copy number variation, alternative splicing, and novel transcript discovery. This chapter outlines the methodology for conducting short-read RNA-Seq, starting from RNA enrichment to library preparation and sequencing. Throughout the chapter, practical tips and best practices are provided to guide researchers in order to optimize each step of the RNA-Seq workflow. Multiple quality control steps throughout the workflow that are critical to obtain high-quality RNA-Seq data are also discussed.

There is a growing need to include Spanish-speaking perinatal individuals from across the globe in research to improve treatments and services for this population. This chapter highlights the importance of Spanish-speaking Latin American research, participant engagement, and reporting of findings. It examines the context and contributing factors affecting perinatal mental health research in Spanish-speaking individuals in Latin America and the USA. The chapter uses the socioecological model to define and review those factors. Finally, the chapter discusses opportunities and considerations for researchers and institutions wanting to increase and advance this field.

Multi-omics sequencing is poised to revolutionize clinical care in the coming decade. However, there is a lack of effective and interpretable genome-wide modeling methods for the rational selection of patients for personalized interventions. To address this, we present iGenSig-Rx, an integral genomic signature-based approach, as a transparent tool for modeling therapeutic response using clinical trial datasets. This method adeptly addresses challenges related to cross-dataset modeling by capitalizing on high-dimensional redundant genomic features, analogous to reinforcing building pillars with redundant steel rods. Moreover, it integrates adaptive penalization of feature redundancy on a per-sample basis to prevent score flattening and mitigate overfitting. We then developed a purpose-built R package to implement this method for modeling clinical trial datasets. When applied to genomic datasets for HER2 targeted therapies, iGenSig-Rx model demonstrates consistent and reliable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We anticipate that iGenSig-Rx, as an interpretable class of multi-omics modeling methods, will find broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-024-05835-1.

Background: There is an increasing demand for body contouring and gender-affirming surgeries, and so is the need to compare outcomes between techniques. Gender dysphoria is a discrepancy between gender identity and the sex assigned at birth. One way to address this is to perform procedures to enable patients to look according to their desired gender identity. Gaps in knowledge regarding the best approaches and which surgical techniques yield the most patient satisfaction remain. This article summarizes up-to-date studies, including upper and lower body contouring procedures. Methods: A systematic review was performed using terms related to body contouring in gender-affirming surgery for transgender patients. All articles included surgical and patient-reported outcomes following either chest or lower body contouring procedures. Results: 15 studies, including trans male chest wall contouring, trans female breast augmentation, and lower body contouring, with 1811 patients, fulfilled the inclusion criteria. The double incision (DI) techniques consistently resected more tissue and had better BODY Q scores than non-overweight patients. Bleeding was increased in periareolar, semicircular, and obese patients with DI techniques. Nipple depigmentation and sensation loss were more common with double-incision-free nipple graft techniques (DIFNG). Lower body contouring patients had average implant sizes bigger than 200 mL and reported 2 gluteal implant displacements, 1 exposure, and one rupture. Eight percent of patients who underwent large-volume fat grafting reported dissatisfaction due to fat reabsorption. Conclusions: The debate between the double incision and periareolar techniques continues. Variations of the DIFNG technique continue to be the most common approach; however, nipple depigmentation and loss of sensation are also more common with it. Regarding increased bleeding with periareolar techniques, there is still no evidence that hormonal therapy may be playing a role in it. For lower-body trans female contouring, implants could help with the longevity of contouring results in patients needing large-volume fat grafting. There is an increasing evaluation of gender-affirming body contouring patient-reported outcomes; however, there is still a need for a validated way to report satisfaction scores in lower body contouring. Validated surveys could help identify surgical candidates based on satisfaction patterns, specifically for transgender and non-binary patients.

This Viewpoint examines watershed moments in improving health care for people with disabilities in the US.

Simple Summary Immune checkpoint inhibitors (ICIs) are a type of immunotherapy used to treat a variety of cancers by helping a patient’s own immune system to kill cancer cells. ICIs received their regulatory approval based on the results of large, randomized clinical trials. However, certain patient groups were excluded from these trials, so their outcomes are unknown. We performed a multicenter, retrospective study of real-world data in the United States in patients who had received at least one cycle of ICI treatment to evaluate the efficacy and safety of ICIs in patient groups underrepresented in clinical trials. Unique patient groups included age > 75 years, non-White race, positive smoking history, poor performance status, obesity, autoimmune diseases, chronic viral infections, multiple previous cancer therapies, or >three metastatic sites. Overall, ICIs were safe and efficacious in these patient groups. We noted that poor performance status and a history of multiple cancer therapies were associated with poor ICI efficacy, and Black patients, compared to White patients, experienced fewer immune-related adverse events. Abstract Regulatory approval of immune checkpoint inhibitors (ICIs) was based on results of large, randomized clinical trials, resulting in limited outcomes data in patient cohorts typically underrepresented in such trials. The objective of this study was to evaluate the efficacy and safety of ICIs in these unique patient cohorts. This is a multicenter, retrospective analysis of real-world data at six academic and community clinics in the United States from 1 January 2011 to 1 April 2018. Patients were included if they had received at least one cycle of ICI treatment. Unique patient cohorts included age > 75 years, non-White race, positive smoking history, ECOG performance status (PS) ≥ 2, BMI ≥ 30 kg/m², autoimmune diseases (AIDs), chronic viral infections (CVI), extensive prior lines of therapy (LOTs), or >three metastatic sites. Immune-related adverse events (irAEs), overall survival (OS), and time to treatment failure were evaluated in the entire cohort and in NSCLC patients treated with PD-(L)1 monotherapy. Outcomes and their association with unique patient cohorts were compared on univariate analysis and multivariate analysis to those without a particular characteristic in the entire NSCLC PD-(L)1 monotherapy cohorts. In total, 1453 patients were included: 56.5%—smokers, 30.4%—non-White, 22.8%—elderly, 20.8%—ECOG PS ≥ 2, 15.7%—history of AIDs, and 4.7%—history of CVI. The common ICIs were nivolumab (37.1%) and pembrolizumab (22.2%). Black patients, compared to White patients, experienced fewer irAEs (OR 0.54, p < 0.001). An ECOG PS of ≥2 (HR = 2.01, p < 0.001) and an increased number of previous LOTs were associated with poor OS (the median OS of 26.2 vs. 16.2 vs. 9.6 months for one vs. two vs. three prior LOTs, p < 0.001). The above results were confirmed in anti-PD-(L)1 monotherapy non-small cell lung cancer patients (n = 384). Overall, ICIs were safe and efficacious in these typically underrepresented patient cohorts. We noted ECOG PS ≥ 2 and an increased prior LOTs were associated with poor ICI efficacy, and Black patients, compared to White patients, experienced fewer irAEs.

Purpose Atypical teratoid rhabdoid tumor (ATRT) is a deadly, fast-growing form of pediatric brain cancer with poor prognosis. Most ATRTs are associated with inactivation of SMARCB1, a subunit of the chromatin remodeling complex, which is involved in developmental processes. The recent identification of SMARCB1 as a tumor suppressor gene suggests that restoration of SMARCB1 could be an effective therapeutic approach. Methods We tested SMARCB1 gene therapy in SMARCB1-deficient rhabdoid tumor cells using a novel tumor-targeted nanomedicine (termed scL-SMARCB1) to deliver wild-type SMARCB1. Our nanomedicine is a systemically administered immuno-lipid nanoparticle that can actively cross the blood-brain barrier via transferrin receptor-mediated transcytosis and selectively target tumor cells via transferrin receptor-mediated endocytosis. We studied the antitumor activity of the scL-SMARCB1 nanocomplex either as a single agent or in combination with traditional treatment modalities in preclinical models of SMARCB1-deficient ATRT. Results Restoration of SMARCB1 expression by the scL-SMARCB1 nanocomplex blocked proliferation, and induced senescence and apoptosis in ATRT cells. Systemic administration of the scL-SMARCB1 nanocomplex demonstrated antitumor efficacy as monotherapy in mice bearing ATRT xenografts, where the expression of exogenous SMARCB1 modulates MYC-target genes. scL-SMARCB1 demonstrated even greater antitumor efficacy when combined with either cisplatin-based chemotherapy or radiation therapy, resulting in significantly improved survival of ATRT-bearing mice. Conclusion Collectively, our data suggest that restoring SMARCB1 function via the scL-SMARCB1 nanocomplex may lead to therapeutic benefits in ATRT patients when combined with traditional chemoradiation therapies.

Objectives Estrogens may protect the gut barrier and reduce microbial translocation and immune activation, which are prevalent in HIV infection. We investigated relationships of the menopausal transition and estrogens with gut barrier, microbial translocation, and immune activation biomarkers in women with and without HIV. Design Longitudinal and cross-sectional studies nested in the Women’s Interagency HIV Study. Methods Intestinal fatty acid binding protein (IFAB), lipopolysaccharide binding protein (LBP), and soluble CD14 (sCD14) levels were measured in serum from 77 women (43 with HIV) before, during, and after the menopausal transition (∼6 measures per woman over ∼13 years). A separate cross-sectional analysis was conducted among 72 post-menopausal women with HIV with these biomarkers and serum estrogens. Results Women in the longitudinal analysis were a median age of 43 years at baseline. In piece-wise linear mixed-effects models with cut-points 2 years before and after the final menstrual period to delineate the menopausal transition, sCD14 levels increased over time during the menopausal transition (Beta [95% CI]=38 [12, 64] ng/mL/year, p=0.004), followed by a decrease post-transition (-46 [-75, -18], p=0.001), with the piece-wise model providing a better fit than a linear model (p=0.0006). In stratified analyses, these results were only apparent in women with HIV. In cross-sectional analyses among women with HIV, free estradiol was inversely correlated with sCD14 levels (r=-0.26, p=0.03). LBP and IFAB levels did not appear related to the menopausal transition and estrogen levels. Conclusion Women with HIV may experience heightened innate immune activation during menopause, possibly related to depletion of estrogens.

The identification and validation of radiation biomarkers is critical for assessing the radiation dose received in exposed individuals and for developing radiation medical countermeasures that can be used to treat acute radiation syndrome (ARS). Additionally, a fundamental understanding of the effects of radiation injury could further aid in the identification and development of therapeutic targets for mitigating radiation damage. In this study, blood samples were collected from fourteen male nonhuman primates (NHPs) that were exposed to 7.2 Gy ionizing radiation at various time points (seven days prior to irradiation; 1, 13, and 25 days post-irradiation; and immediately prior to the euthanasia of moribund (preterminal) animals). Plasma was isolated from these samples and was analyzed using a liquid chromatography tandem mass spectrometry approach in an effort to determine the effects of radiation on plasma proteomic profiles. The primary objective was to determine if the radiation-induced expression of specific proteins could serve as an early predictor for health decline leading to a preterminal phenotype. Our results suggest that radiation induced a complex temporal response in which some features exhibit upregulation while others trend downward. These statistically significantly altered features varied from pre-irradiation levels by as much as tenfold. Specifically, we found the expression of integrin alpha and thrombospondin correlated in peripheral blood with the preterminal stage. The differential expression of these proteins implicates dysregulation of biological processes such as hemostasis, inflammation, and immune response that could be leveraged for mitigating radiation-induced adverse effects.

Ion channels are transmembrane proteins essential for cellular functions and are important drug targets. Surface plasmon resonance (SPR) is a powerful technique for investigating protein–protein and protein–small molecule ligand interactions. SPR has been underutilized for studies of ion channels, even though it could provide a wealth of information on the mechanisms of ion channel regulation and aid in ion channel drug discovery. Here we provide a detailed description of the use of SPR technology for investigating inter-domain interactions in KCNH potassium-selective and voltage-gated ion channels.