Bilateral Subdiaphragmatic Vagal Nerve Stimulation Using a Novel Waveform Decreases Body Weight, Food Consumption, Adiposity, and Activity in Obesity-Prone Rats.

INTRODUCTION: Obesity affects millions of Americans. The vagal nerves convey the degree of stomach fullness to the brain via afferent visceral fibers. Studies have found that vagal nerve stimulation (VNS) promotes reduced food intake, causes weight loss, and reduces cravings and appetite. METHODS: Here, we evaluate the efficacy of a novel stimulus waveform applied bilaterally to the subdiaphragmatic vagal nerve stimulation (sVNS) for almost 13 weeks. A stimulating cuff electrode was implanted in obesity-prone Sprague Dawley rats maintained on a high-fat diet. Body weight, food consumption, and daily movement were tracked over time and compared against three control groups: sham rats on a high-fat diet that were implanted with non-operational cuffs, rats on a high-fat diet that were not implanted, and rats on a standard diet that were not implanted. RESULTS: Results showed that rats on a high-fat diet that received sVNS attained a similar weight to rats on a standard diet due primarily to a reduction in daily caloric intake. Rats on a high-fat diet that received sVNS had significantly less body fat than other high-fat controls. Rats receiving sVNS also began moving a similar amount to rats on the standard diet. CONCLUSION: Results from this study suggest that bilateral subdiaphragmatic vagal nerve stimulation can alter the rate of growth of rats maintained on a high-fat diet through a reduction in daily caloric intake, returning their body weight to that which is similar to rats on a standard diet over approximately 13 weeks.

Enabling AI in synthetic biology through Construction File specification.

The Construction File (CF) specification establishes a standardized interface for molecular biology operations, laying a foundation for automation and enhanced efficiency in experiment design. It is implemented across three distinct software projects: PyDNA_CF_Simulator, a Python project featuring a ChatGPT plugin for interactive parsing and simulating experiments; ConstructionFileSimulator, a field-tested Java project that showcases 'Experiment' objects expressed as flat files; and C6-Tools, a JavaScript project integrated with Google Sheets via Apps Script, providing a user-friendly interface for authoring and simulation of CF. The CF specification not only standardizes and modularizes molecular biology operations but also promotes collaboration, automation, and reuse, significantly reducing potential errors. The potential integration of CF with artificial intelligence, particularly GPT-4, suggests innovative automation strategies for synthetic biology. While challenges such as token limits, data storage, and biosecurity remain, proposed solutions promise a way forward in harnessing AI for experiment design. This shift from human-driven design to AI-assisted workflows, steered by high-level objectives, charts a potential future path in synthetic biology, envisioning an environment where complexities are managed more effectively.

Human Neural Progenitors Expressing GDNF Enhance Retinal Protection in a Rodent Model of Retinal Degeneration.

Stem cell therapy for retinal degenerative diseases has been extensively tested in preclinical and clinical studies. However, preclinical studies performed in animal models at the early stage of disease do not optimally translate to patients that present to the clinic at a later stage of disease. As the retina degenerates, inflammation and oxidative stress increase and trophic factor support declines. Testing stem cell therapies in animal models at a clinically relevant stage is critical for translation to the clinic. Human neural progenitor cells (hNPC) and hNPC engineered to stably express GDNF (hNPCGDNF) were subretinally injected into the Royal College of Surgeon (RCS) rats, a well-established model for retinal degeneration, at early and later stages of the disease. hNPCGDNF treatment at the early stage of retinal degeneration provided enhanced visual function compared to hNPC alone. Treatment with both cell types resulted in preserved retinal morphology compared to controls. hNPCGDNF treatment led to significantly broader photoreceptor protection than hNPC treatment at both early and later times of intervention. The phagocytic role of hNPC appears to support RPE cell functions and the secreted GDNF offers neuroprotection and enables the extended survival of photoreceptor cells in transplanted animal eyes. Donor cells in the RCS rat retina survived with only limited proliferation, and hNPCGDNF produced GDNF in vivo. Cell treatment led to significant changes in various pathways related to cell survival, antioxidative stress, phagocytosis, and autophagy. A combined stem cell and trophic factor therapy holds great promise for treating retinal degenerative diseases including retinitis pigmentosa and age-related macular degeneration.

GMP-grade human neural progenitors delivered subretinally protect vision in rat model of retinal degeneration and survive in minipigs.

BACKGROUND: Stem cell products are increasingly entering early stage clinical trials for treating retinal degeneration. The field is learning from experience about comparability of cells proposed for preclinical and clinical use. Without this, preclinical data supporting translation to a clinical study might not adequately reflect the performance of subsequent clinical-grade cells in patients. METHODS: Research-grade human neural progenitor cells (hNPC) and clinical-grade hNPC (termed CNS10-NPC) were injected into the subretinal space of the Royal College of Surgeons (RCS) rat, a rodent model of retinal degeneration such as retinitis pigmentosa. An investigational new drug (IND)-enabling study with CNS10-NPC was performed in the same rodent model. Finally, surgical methodology for subretinal cell delivery in the clinic was optimized in a large animal model with Yucatan minipigs. RESULTS: Both research-grade hNPC and clinical-grade hNPC can survive and provide functional and morphological protection in a dose-dependent fashion in RCS rats and the optimal cell dose was defined and used in IND-enabling studies. Grafted CNS10-NPC migrated from the injection site without differentiation into retinal cell phenotypes. Additionally, CNS10-NPC showed long-term survival, safety and efficacy in a good laboratory practice (GLP) toxicity and tumorigenicity study, with no observed cell overgrowth even at the maximum deliverable dose. Finally, using a large animal model with the Yucatan minipig, which has an eye size comparable to the human, we optimized the surgical methodology for subretinal cell delivery in the clinic. CONCLUSIONS: These extensive studies supported an approved IND and the translation of CNS10-NPC to an ongoing Phase 1/2a clinical trial (NCT04284293) for the treatment of retinitis pigmentosa.

Weight loss after endoscopic sleeve gastroplasty is independent of suture pattern: results from a randomized controlled trial.

Background and study aims This was a single-blind, single-center, prospective randomized controlled trial aimed at comparing the efficacy of three different suture patterns for endoscopic sleeve gastroplasty using Endomina (E-ESG). Patients and methods The suture patterns aimed to modify gastric accommodation by increasing the fundus distention ability (Group A), to reduce gastric volume (Group B) or to interrupt gastric emptying (Group C). Patients were randomized 1:1:1 and underwent clinical follow-up, gastric emptying scintigraphy, and satiety tests at baseline and 6 and 12 months post-procedure. The primary outcome was total body weight loss (TBWL) and excess weight loss (EWL) at 12 months post-procedure. Secondary outcomes included the impact of the suture patterns on gastric emptying and satiety. Results Overall, 48 patients (40 [83.3 %] female, aged 41.9 ±â€Š9.5 years, body mass indexI 33.8 ±â€Š2.7 kg/m 2 ) were randomized (16 in each group). In the entire cohort, mean (95 % confidence interval [CI]) TBWL and EWL at the end of the follow-up were 10.11 % (7.1-13.12) and 42.56 (28.23-56.9), respectively. There was no difference among the three study groups in terms of TBWL (95 %CI) (9.13 % [2.16-16.11] vs. 11.29 % [5.79-16.80] vs. 9.96 % [4.58-15.35]; P  = 0.589) and EWL (95 %CI) (34.54 % [6.09-62.99] vs. 44.75 % [23.63-65.88] vs. 46.94 % [16.72-77.15]; P  = 0.888) at 12 months post-procedure. The three groups did not differ in terms of mean gastric emptying time or in terms of satiety tests at the end of the follow-up. No serious adverse events occurred. Conclusions Three different suture patterns during E-ESG demonstrated comparable efficacy in terms of weight loss, with an overall EWL of > 25 % and TBWL of > 10 % at 12 months.

Targeting SARS-CoV-2 papain-like protease in the postvaccine era.

While vaccines remain at the forefront of global healthcare responses, pioneering therapeutics against SARS-CoV-2 are expected to fill the gaps for waning immunity. Rapid development and approval of orally available direct-acting antivirals targeting crucial SARS-CoV-2 proteins marked the beginning of the era of small-molecule drugs for COVID-19. In that regard, the papain-like protease (PLpro) can be considered a major SARS-CoV-2 therapeutic target due to its dual biological role in suppressing host innate immune responses and in ensuring viral replication. Here, we summarize the challenges of targeting PLpro and innovative early-stage PLpro-specific small molecules. We propose that state-of-the-art computer-aided drug design (CADD) methodologies will play a critical role in the discovery of PLpro compounds as a novel class of COVID-19 drugs.

Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks.

Computational prediction of ligand-target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein-ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)-the hallmark target of SARS-CoV-2 coronavirus.

Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking.

With the recent explosion of chemical libraries beyond a billion molecules, more efficient virtual screening approaches are needed. The Deep Docking (DD) platform enables up to 100-fold acceleration of structure-based virtual screening by docking only a subset of a chemical library, iteratively synchronized with a ligand-based prediction of the remaining docking scores. This method results in hundreds- to thousands-fold virtual hit enrichment (without significant loss of potential drug candidates) and hence enables the screening of billion molecule-sized chemical libraries without using extraordinary computational resources. Herein, we present and discuss the generalized DD protocol that has been proven successful in various computer-aided drug discovery (CADD) campaigns and can be applied in conjunction with any conventional docking program. The protocol encompasses eight consecutive stages: molecular library preparation, receptor preparation, random sampling of a library, ligand preparation, molecular docking, model training, model inference and the residual docking. The standard DD workflow enables iterative application of stages 3-7 with continuous augmentation of the training set, and the number of such iterations can be adjusted by the user. A predefined recall value allows for control of the percentage of top-scoring molecules that are retained by DD and can be adjusted to control the library size reduction. The procedure takes 1-2 weeks (depending on the available resources) and can be completely automated on computing clusters managed by job schedulers. This open-source protocol, at https://github.com/jamesgleave/DD_protocol , can be readily deployed by CADD researchers and can significantly accelerate the effective exploration of ultra-large portions of a chemical space.

Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules.

Recent explosive growth of 'make-on-demand' chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise.

Effects of Isometric Exercises versus Static Stretching in Warm-up Regimens for Running Sport Athletes: A Systematic Review.

The objective of this review was to identify studies that report the pre-exercise effects of isometric exercise versus static stretching on performance and injury rates of running athletes in comparison to their outcomes. Seven electronic databases were searched: Cochrane, PEDro, CINAHL, PubMed, MEDLINE, SportDiscus, and GoogleScholar. Data was collected using an established PICO question, and assembled logic grid. The included articles were required to (1) assess running performance or injury prevention and (2) include isometric exercises/muscle activation and/or static stretching. Articles published prior to the year 2000, non-English, and non-human studies were excluded. Quality was assessed using the PEDro quality appraisal tool for RCTs, and NIH-NHLBI appraisal tool for others. The Cochrane collaboration tool for risk of bias as well as the PRISMA 2020 statement were also used in this review. In the nine articles appraised in the study, variables assessed included running economy, injury rate, soreness levels, sprint times, and countermovement and drop jump height. Static stretching demonstrated a significant negative effect on sprint performance and countermovement/drop jump height. It also demonstrated a decrease in variables associated with injury over extended periods and no impact on running economy. Isometric holds demonstrated no significant effect on sprint performance or countermovement/drop jump height. It also demonstrated decreases in soreness levels and no impact on running economy. Isometric holds have positive effects/fewer negative results on running athletes when compared to static stretching for pre-exercise performance. Research with decreased risk of bias is needed to determine maximal benefits from timing/dosage of isometric hold in warm-up.

The value of antimicrobial peptides in the age of resistance.

Accelerating growth and global expansion of antimicrobial resistance has deepened the need for discovery of novel antimicrobial agents. Antimicrobial peptides have clear advantages over conventional antibiotics which include slower emergence of resistance, broad-spectrum antibiofilm activity, and the ability to favourably modulate the host immune response. Broad bacterial susceptibility to antimicrobial peptides offers an additional tool to expand knowledge about the evolution of antimicrobial resistance. Structural and functional limitations, combined with a stricter regulatory environment, have hampered the clinical translation of antimicrobial peptides as potential therapeutic agents. Existing computational and experimental tools attempt to ease the preclinical and clinical development of antimicrobial peptides as novel therapeutics. This Review identifies the benefits, challenges, and opportunities of using antimicrobial peptides against multidrug-resistant pathogens, highlights advances in the deployment of novel promising antimicrobial peptides, and underlines the needs and priorities in designing focused development strategies taking into account the most advanced tools available.

Determining the dielectric constant of injection-molded polymer-matrix nanocomposites filled with barium titanate.

Barium titanate (BTO) is a ferroelectric perovskite with potential in energy storage applications. Previous research suggests that BTO dielectric constant increases as nanoparticle diameter decreases. This report recounts an investigation of this relationship. Injection-molded nanocomposites of 5 vol% BTO nanoparticles incorporated in a low-density polyethylene matrix were fabricated and measured. Finite-element analysis was used to model nanocomposites of all BTO sizes and the results were compared with experimental data. Both indicated a negligible relationship between BTO diameter and dielectric constant at 5 vol%. However, a path for fabricating and testing composites of 30 vol% and higher is presented here. SUPPLEMENTARY MATERIAL: The supplementary material for this article can be found at 10.1557/mrc.2020.69.

DeepCOP: deep learning-based approach to predict gene regulating effects of small molecules.

MOTIVATION: Recent advances in the areas of bioinformatics and chemogenomics are poised to accelerate the discovery of small molecule regulators of cell development. Combining large genomics and molecular data sources with powerful deep learning techniques has the potential to revolutionize predictive biology. In this study, we present Deep gene COmpound Profiler (DeepCOP), a deep learning based model that can predict gene regulating effects of low-molecular weight compounds. This model can be used for direct identification of a drug candidate causing a desired gene expression response, without utilizing any information on its interactions with protein target(s). RESULTS: In this study, we successfully combined molecular fingerprint descriptors and gene descriptors (derived from gene ontology terms) to train deep neural networks that predict differential gene regulation endpoints collected in LINCS database. We achieved 10-fold cross-validation RAUC scores of and above 0.80, as well as enrichment factors of >5. We validated our models using an external RNA-Seq dataset generated in-house that described the effect of three potent antiandrogens (with different modes of action) on gene expression in LNCaP prostate cancer cell line. The results of this pilot study demonstrate that deep learning models can effectively synergize molecular and genomic descriptors and can be used to screen for novel drug candidates with the desired effect on gene expression. We anticipate that such models can find a broad use in developing novel cancer therapeutics and can facilitate precision oncology efforts. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quantitative Structure-Price Relationship (QS$R) Modeling and the Development of Economically Feasible Drug Discovery Projects.

In recent years, the field of quantitative structure-activity/property relationship (QSAR/QSPR) modeling has developed into a stable technology capable of reliably predicting new bioactive molecules. With the availability of inexpensive commercial sources of both synthetic chemicals and bioactivity assays, a cheminformatics-savvy scientist can readily establish a virtual drug discovery enterprise. A skilled computational chemist can not only develop a computer-aided drug discovery pipeline but also acquire or have the drug candidates made inexpensively for economical screening of desired on-target activity, critical off-target effects, and essential drug-likeness properties. As part of our drug discovery pipeline, a novel machine-learning model was built to relate chemical structures of synthetically accessible molecules to their prices. The model was trained from our "in stock" and "made on demand" diverse chemical entities, ranging in price from $20 to >$10,000. This novel model is encoded here as the quantitative structure-price relationship (QS$R) model.

Small molecule-induced degradation of the full length and V7 truncated variant forms of human androgen receptor.

The androgen receptor (AR) is a hormone-activated transcription factor that regulates the development and progression of prostate cancer (PCa) and represents one of the most well-established drug targets. Currently clinically approved small molecule inhibitors of AR, such as enzalutamide, are built upon a common chemical scaffold that interacts with the AR by the same mechanism of action. These inhibitors eventually fail due to the emergence of drug-resistance in the form of AR mutations and expression of truncated AR splice variants (e.g. AR-V7) that are constitutively active, signalling the progression of the castration-resistant state of the disease. The urgent need therefore continues for novel classes of AR inhibitors that can overcome drug resistance, especially since AR signalling remains important even in late-stage advanced PCa. Previously, we identified a collection of 10-benzylidene-10H-anthracen-9-ones that effectively inhibit AR transcriptional activity, induce AR degradation and display some ability to block recruitment of hormones to the receptor. In the current work, we extended the analysis of the lead compounds, and used methods of both ligand- and structure-based drug design to develop a panel of novel 10-benzylidene-10H-anthracen-9-one derivatives capable of suppressing transcriptional activity and protein expression levels of both full length- and AR-V7 truncated forms of human androgen receptor. Importantly, the developed compounds efficiently inhibited the growth of AR-V7 dependent prostate cancer cell-lines which are completely resistant to all current anti-androgens.

Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images.

The majority of computational methods for predicting toxicity of chemicals are typically based on "nonmechanistic" cheminformatics solutions, relying on an arsenal of QSAR descriptors, often vaguely associated with chemical structures, and typically employing "black-box" mathematical algorithms. Nonetheless, such machine learning models, while having lower generalization capacity and interpretability, typically achieve a very high accuracy in predicting various toxicity endpoints, as unambiguously reflected by the results of the recent Tox21 competition. In the current study, we capitalize on the power of modern AI to predict Tox21 benchmark data using merely simple 2D drawings of chemicals, without employing any chemical descriptors. In particular, we have processed rather trivial 2D sketches of molecules with a supervised 2D convolutional neural network (2DConvNet) and demonstrated that the modern image recognition technology results in prediction accuracies comparable to the state-of-the-art cheminformatics tools. Furthermore, the performance of the image-based 2DConvNet model was comparatively evaluated on an external set of compounds from the Prestwick chemical library and resulted in experimental identification of significant and previously unreported antiandrogen potentials for several well-established generic drugs.

Non-invasive diagnosis of liver fibrosis in patients with alcohol-related liver disease by transient elastography: an individual patient data meta-analysis.

BACKGROUND: The value of transient elastography for the non-invasive diagnosis of alcohol-related liver fibrosis is subject to debate. We did an individual patient data (IPD) meta-analysis to determine specific diagnostic cutoff values for liver stiffness in alcohol-related fibrosis, and to assess the effect of aminotransferase concentrations, bilirubin concentrations, and presence of asymptomatic and non-severe alcoholic hepatitis on liver stiffness. METHODS: We searched for studies that included patients with alcohol-related liver disease, liver biopsy, and transient elastography, and with a statistical method for determining the diagnostic cutoffs for alcohol-induced liver fibrosis on the basis of the FibroScan results, in PubMed between Jan 1, 2000, and Sept 30, 2017. Native data bases were obtained from corresponding authors in an Excel form. Pooled diagnostic cutoffs for the various fibrosis stages were determined in a two-stage, random-effects meta-analysis. The effects of aspartate aminotransferase (AST) concentrations, bilirubin concentrations, and histological features of asymptomatic and non-severe alcoholic hepatitis on liver stiffness cutoff were assessed in one-stage, random-effects meta-analysis. FINDINGS: Of 188 studies assessed, ten studies comprising 1026 patients were included in the meta-analysis, yielded liver stiffness cutoffs of 7·0 kPa (area under the receiver operating characteristic curve 0·83 [SE 0·02; 95% CI 0·79-0·87]) for F≥1 fibrosis, 9·0 kPa (0·86 [0·02; 0·82-0·90]) for F≥2, 12·1 kPa (0·90 [0·02; 0·86-0·94]) for F≥3, and 18·6 kPa (0·91 [0·04; 0·83-0·99]) for F=4. AST and bilirubin concentrations had a significant effect on liver stiffness, with higher concentrations associated with higher liver stiffness values (p<0·0001), and with significantly higher cutoff values for diagnosis of all fibrosis stages but F≥1. The presence of histological features of asymptomatic and non-severe alcoholic hepatitis was associated with increased liver stiffness (p<0·0001). In a multivariate analysis, AST (p<0·0001) and bilirubin (p=0·0002) concentrations, and prothrombin activity (p=0·01), were independently associated with the presence of histological features of asymptomatic and non-severe alcoholic hepatitis. Lastly, specific liver stiffness cutoffs were determined on the basis of concentrations of AST and bilirubin. Liver stiffness cutoff values increased in patients with increased AST concentrations, bilirubin concentrations, or both. INTERPRETATION: This IPD meta-analysis highlights the link between liver stiffness and the histological features of asymptomatic and non-severe alcoholic hepatitis, reflected by AST and bilirubin concentrations. In alcohol-related liver disease, FibroScan assessments of liver fibrosis should take into account AST and bilirubin concentrations through the use of specifically adjusted liver stiffness cutoffs. FUNDING: None.

Machine Learning for Silver Nanoparticle Electron Transfer Property Prediction.

Nanoparticles exhibit diverse structural and morphological features that are often interconnected, making the correlation of structure/property relationships challenging. In this study a multi-structure/single-property relationship of silver nanoparticles is developed for the energy of Fermi level, which can be tuned to improve the transfer of electrons in a variety of applications. By combining different machine learning analytical algorithms, including k-mean, logistic regression, and random forest with electronic structure simulations, we find that the degree of twinning (characterized by the fraction of hexagonal closed packed atoms) and the population of the {111} facet (characterized by a surface coordination number of nine) are strongly correlated to the Fermi energy of silver nanoparticles. A concise three layer artificial neural network together with principal component analysis is built to predict this property, with reduced geometrical, structural, and topological features, making the method ideal for efficient and accurate high-throughput screening of large-scale virtual nanoparticle libraries and the creation of single-structure/single-property, multi-structure/single-property, and single-structure/multi-property relationships in the near future.

Machine learning and genetic algorithm prediction of energy differences between electronic calculations of graphene nanoflakes.

Computational screening is key to understanding structure-function relationships at the nanoscale but the high computational cost of accurate electronic structure calculations remains a bottleneck for the screening of large nanomaterial libraries. In this work we propose a data-driven strategy to predict accuracy differences between different levels of theory. Machine learning (ML) models are trained with structural features of graphene nanoflakes to predict the differences between electronic properties at two levels of approximation. The ML models yield an overall accuracy of 94% and 88%, for energy of the Fermi level and the band gap, respectively. This strategy represents a successful application of established ML methods to the selection of optimum level of theory, enabling more rapid and efficient screening of nanomaterials, and is extensible to other materials and computational methods.