Many patients elect to have repeat treatments with hyaluronic acid dermal fillers to maintain wrinkle correction, but the clinical performance of these products after repeat treatments has not been formally assessed. The primary objective of this study was to evaluate the effectiveness of Juvéderm injectable gel (Juvéderm Ultra, Juvéderm Ultra Plus, and Juvéderm 30) through 1 year after repeat treatment of nasolabial folds (NLFs) that were previously treated with Juvéderm or Zyplast 6–9 months prior to the repeat treatment. Upon completion of the pivotal IDE clinical trial for Juvéderm, five of the original 11 study sites were selected to participate in an extended follow-up evaluation, and a total of 80 subjects were enrolled. For the Juvéderm-treated NLFs in each treatment group, the median injection volume was 1.5–1.6 mL for initial treatment but only 0.5–0.6 mL for the repeat treatment (p < 0.0001). Mean Investigator-assigned NLF severity scores on a scale of 0–4 for the Juvéderm-treated NLFs improved from 2.5–2.7 (moderate to severe) at baseline to 1.2–1.5 (mild) just prior to repeat treatment (>24 weeks) and 0.7–0.9 (mild) at 4 weeks after repeat treatment. At 48 weeks post-repeat treatment, the mean NLF scores were 1.1–1.3 (mild), and 78–90% of subjects were considered responders (≥1 point improvement). Thus, subjects sustained a total of 18–21 months of wrinkle correction with a repeat treatment at 6–9 months and needed substantially less filler (60% less) for repeat treatment than for initial treatment, indicating that retreatment at this timepoint may be beneficial to patients.

Atomistic Molecular Dynamics (MD) simulations provide researchers the ability to model biomolecular structures such as proteins and their interactions with drug-like small molecules with greater spatiotemporal resolution than is otherwise possible using experimental methods. MD simulations are notoriously expensive computational endeavors that have traditionally required massive investment in specialized hardware to access biologically relevant spatiotemporal scales. Our goal is to summarize the fundamental algorithms that are employed in the literature to then highlight the challenges that have affected accelerator implementations in practice. We consider three broad categories of accelerators: Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs). These categories are comparatively studied to facilitate discussion of their relative trade-offs and to gain context for the current state of the art. We conclude by providing insights into the potential of emerging hardware platforms and algorithms for MD.

Background: The International Conference on Applications of Neuroimaging to Alcoholism was held at Yale University in New Haven, CT, in January 2004. The following is a brief summary of the contributions of five speakers who presented their work during the magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) session. Methods: This session addressed how MRI and DTI are used to assess macro- and microstructural brain alterations in alcoholism. Structural MRI methods can address regional gray and white matter volumetric/ morphometric abnormalities, and DTI methods can address microstructural disruptions of white matter tracts. These methods can be applied across the spectrum of alcoholism to elucidate distinct brain abnormalities underlying clinical subtypes, to disentangle brain volume deficits that precede, from those that follow, the onset of alcoholic drinking in chronic alcoholics, and to examine effects of prenatal alcohol exposures on brain development in children. The presentations highlighted recent scientific findings and methodological advances in these areas. Results: Disease-specific probabilistic atlases, designed to reflect the unique anatomy and physiology of particular clinical subpopulations, can be developed for alcoholism. Such an atlas can be used to identify efficiently patterns of altered structure or function in alcoholism and can guide algorithms for knowledge-based image analysis. DTI is sensitive to constraints on the random diffusion of water molecules in axons, allowing assessment of white matter tract integrity in neuropsychiatric diseases, including alcoholism. Recent MRI and DTI data were presented showing region-specific brain abnormalities at both macro- and microstructural levels that varied differentially according to sex, time of alcohol exposure in life, and alcoholism subtype. Conclusion: The International Conference on Applications of Neuroimaging to Alcoholism brought together leading experts in MRI and DTI techniques to discuss their applications to the study of alcoholism. The extant and new imaging technologies provide us with multiple modalities to study the brain in vivo. These noninvasive tools enable us to monitor the time course of alcohol effects on the brain and to characterize macro- and microstructural brain abnormalities across the full spectrum of alcoholism, including its precursors and its sequelae.

Protein-ligand interactions are essential to drug discovery and drug development efforts. Desirable on-target or multitarget interactions are the first step in finding an effective therapeutic, while undesirable off-target interactions are the first step in assessing safety. In this work, we introduce a novel ligand-based featurization and mapping of human protein pockets to identify closely related protein targets and to project novel drugs into a hybrid protein-ligand feature space to identify their likely protein interactions. Using structure-based template matches from PDB, protein pockets are featured by the ligands that bind to their best co-complex template matches. The simplicity and interpretability of this approach provide a granular characterization of the human proteome at the protein-pocket level instead of the traditional protein-level characterization by family, function, or pathway. We demonstrate the power of this featurization method by clustering a subset of the human proteome and evaluating the predicted cluster associations of over 7000 compounds.

Chemotherapy combined with immunotherapy has improved the treatment of certain solid tumors, but effective regimens remain elusive for pancreatic ductal adenocarcinoma (PDAC). We conducted a randomized phase 2 trial evaluating the efficacy of nivolumab (nivo; anti-PD-1) and/or sotigalimab (sotiga; CD40 agonistic antibody) with gemcitabine/nab-paclitaxel (chemotherapy) in patients with first-line metastatic PDAC ( NCT03214250 ). In 105 patients analyzed for efficacy, the primary endpoint of 1-year overall survival (OS) was met for nivo/chemo (57.7%, P = 0.006 compared to historical 1-year OS of 35%, n = 34) but was not met for sotiga/chemo (48.1%, P = 0.062, n = 36) or sotiga/nivo/chemo (41.3%, P = 0.223, n = 35). Secondary endpoints were progression-free survival, objective response rate, disease control rate, duration of response and safety. Treatment-related adverse event rates were similar across arms. Multi-omic circulating and tumor biomarker analyses identified distinct immune signatures associated with survival for nivo/chemo and sotiga/chemo. Survival after nivo/chemo correlated with a less suppressive tumor microenvironment and higher numbers of activated, antigen-experienced circulating T cells at baseline. Survival after sotiga/chemo correlated with greater intratumoral CD4 T cell infiltration and circulating differentiated CD4 T cells and antigen-presenting cells. A patient subset benefitting from sotiga/nivo/chemo was not identified. Collectively, these analyses suggest potential treatment-specific correlates of efficacy and may enable biomarker-selected patient populations in subsequent PDAC chemoimmunotherapy trials.