Browse Articles
Discover research articles across all indexed journals
Palbociclib and endocrine therapy diminish adaptive anti-tumor immunity in early breast cancer: The NeoRHEA phase 2 study
The impact of spinal manipulation on lumbar proprioception and its link to pain relief: a randomized controlled trial
Abstract Manual therapy, such as spinal manipulation (SM), is commonly used to treat non-specific chronic low back pain (CLBP), although its mechanisms remain poorly understood. It has been hypothesized that the mechanical forces applied during spinal manipulation (SM) influence proprioceptive function, which is often impaired in patients with CLBP. This study aimed to investigate the effect of a single SM intervention on lumbar proprioceptive function and its potential relationship with analgesic effects in patients with CLBP. In a single-blind randomized controlled trial, data from 142 adults with or without CLBP were analyzed after random assignment to receive lumbar spinal manipulation (LMANIP), lumbar mobilization (LMOB), or no intervention (NI). The primary outcome was the proprioceptive weighting (PW) ratio, which reflects the central nervous system’s preferred source of proprioceptive input for balance control, specifically from the lumbar and ankle muscles. PW ratios were assessed immediately before and after intervention by analyzing postural sway changes during vibrotactile stimulation (60 Hz). PW changed in both healthy participants and patients after the intervention, with a significantly stronger lumbar-steered PW following LMANIP compared to NI (β = − 0.047, t(422) = − 2.71, p = 0.007) and LMOB (β = − 0.039, t(422) = − 2.17, p = 0.030). Moreover, LMANIP was particularly effective in reducing pain in patients with stronger lumbar-steered PW before intervention ( p < 0.017). These findings suggest that a single SM session enhances proprioceptive input from the lumbar muscles and that the strength of the analgesic effect is associated with the baseline PW status.
In silico design of novel multi-epitope peptide vaccine against Neospora caninum induced cattle abortion targeting extracellular GRA2 and Nc-p43 protein
Abstract Neospora caninum (NC) is a protozoan infection causing neosporosis, a disease that leads to substantial economic loss in livestock, especially in cattle, sheep, and goats. Unfortunately, there is presently no viable vaccination or authorized therapy available. Our research applied a bioinformatics approach to design a multi-epitope peptide (MEP) vaccine aimed at the pathogenic proteins GRA2 and Nc-p43 of NC, which are critical in parasite-mediated antigenicity and host interactions. Consequently, our research employs an in-silico methodology, including protein sequence retrieval, epitope prediction, vaccine design, structural analysis, molecular docking, molecular dynamics simulations, immunological simulation, and codon optimization using in silico cloning. We conducted analyses of antigenicity, allergenicity, toxicity, topology, and immunogenicity using multiple bioinformatics methods and identified 4 CTL, 4 HTL, and 2 B-cell epitopes. The vaccine design was created by integrating an adjuvant and a PADRE sequence to enhance immunogenicity, along with linkers (AAY, GPGPG, KK) to facilitate appropriate structural assembly, which was then analyzed for optimal complete profiles. Molecular docking with the Bos taurus (cattle) TLR9 receptor demonstrated a robust binding affinity, scoring − 1183.4, while molecular dynamics (MD) simulations over 50 ns validated persistent interactions between the vaccine and immune receptors. Moreover, immunological models forecasted a robust adaptive immune response, marked by an increase in the production of cytokines and the establishment of memory T-lymphocytes. Ultimately, codon optimization and in silico cloning validated the capacity for effective expression in E. coli . The results demonstrate that the developed vaccine has considerable immunogenic potential against NC. Nonetheless, more in vitro and in vivo studies are necessary to confirm its efficiency.
This is what lightning on Mars sounds like
Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization
Abstract The rapid and economical synthesis of novel bioactive compounds remains a hurdle in drug discovery efforts. This study demonstrates an integrated medicinal chemistry workflow that effectively diversifies hit and lead structures, enabling an acceleration of the critical hit-to-lead optimization phase. Employing high-throughput experimentation (HTE), we generated a comprehensive data set encompassing 13,490 novel Minisci-type C-H alkylation reactions. These data served as the foundation for training deep graph neural networks to accurately predict reaction outcomes. Scaffold-based enumeration of potential Minisci reaction products, starting from moderate inhibitors of monoacylglycerol lipase (MAGL), yielded a virtual library containing 26,375 molecules. This virtual chemical library was evaluated using reaction prediction, physicochemical property assessment, and structure-based scoring, identifying 212 MAGL inhibitor candidates. Of these, 14 compounds were synthesized and exhibited subnanomolar activity, representing a potency improvement of up to 4500 times over the original hit compound. These ligands also showed favorable pharmacological profiles. Co-crystallization of three computationally designed ligands with the MAGL protein provided structural insights into their binding modes. This study demonstrates the potential of combining miniaturized HTE with deep learning and optimization of molecular properties to reduce cycle times in hit-to-lead progression.
Effect of nanocellulose content in a polymer-based soil stabilizer on the wind erosion resistance of sandy soil under laboratory conditions
Knockdown of GCNT2 promoted osteoblast differentiation by activating PI3K/AKT/mTOR pathway in osteoblasts
Uncovering the immune mechanisms underlying the emergence of immunotherapy-induced pneumonitis in lung cancer patients
IM-MetaLAB: the first digital laboratory for teaching the fundamental concepts of instrumentation and measurement in metaverse
Abstract This paper presents the Instrumentation and Measurements Metaverse Laboratory (IM-MetaLAB), an immersive virtual laboratory developed within the metaverse to support hands-on training in instrumentation and measurement. Unlike previous Augmented Reality (AR)/Virtual Reality (VR)-based solutions or simulated environments, IM-MetaLAB integrates real laboratory instruments through digital twins that communicate in real time with their physical counterparts using Standard Commands for Programmable Instruments (SCPI) commands over Message Queuing Telemetry Transport (MQTT). The platform supports synchronous and asynchronous learning modes, promotes collaborative activities among students, and replicates real-world laboratory procedures in a safe and accessible 3D environment. A representative case study, focused on root means square (RMS) voltage measurement, demonstrates the system’s capabilities in executing full experimental workflows. Comparative analysis with traditional, online, and remote lab-based teaching methods was conducted using key performance indicators such as student engagement, completion rate, and examination scores. The results indicate that IM-MetaLAB significantly improves learning outcomes and practical proficiency. Ongoing work addresses scalability and cost-reduction through the implementation of a custom hardware interface.
Post marketing safety surveillance of atezolizumab based on a retrospective analysis of the FDA adverse events reporting system (FAERS)
Cyclin CLB2 mRNA localization and protein synthesis link cell cycle progression to bud growth
Abstract Clb2 is a conserved B-type cyclin essential for mitotic progression in Saccharomyces cerevisiae , with expression tightly regulated at transcriptional and proteolytic levels. However, it remains unclear whether Clb2 protein synthesis is regulated and responsive to cell growth. Here, we show that CLB2 mRNA localizes to the bud via the She2/She3 complex, while Clb2 protein accumulates in the mother nucleus. This mRNA localization enhances translation without affecting protein localization. A structured RNA element, a ZIP-code, is located within the coding sequence and is required, but not sufficient, for both mRNA transport and protein expression. Mutation of this ZIP code disrupts mRNA localization, reduces Clb2 synthesis, increases budded phase duration and daughter cell size. In wild-type cells, Clb2 protein levels scale with bud growth, a coupling lost in ZIP code mutants. These findings reveal a mechanism by which mRNA localization and translation are coordinated to link cell growth with cell cycle progression.
Hybrid quantum/classical docking of covalent and non-covalent ligands with Attracting Cavities
Abstract Ligand–protein docking aims to predict how a ligand binds to a biological macromolecule and is a fundamental technique in structure-based computer-aided drug design. However, accurately modeling covalent binding, metal coordination, and polarization effects remains challenging for classical docking algorithms. Here, we present an extension of our Attracting Cavities docking algorithm that enables hybrid quantum mechanical/molecular mechanical (QM/MM) calculations at various levels of theory. To evaluate its performance, we benchmarked the method on three diverse datasets covering non-covalent drug–target complexes, covalent complexes, and hemoprotein complexes, using both semi-empirical and density functional theory approaches. The results demonstrate that QM/MM docking is especially advantageous for metal-binding complexes, where the fast semi-empirical PM7 method yields a significant improvement over classical docking. When describing the active site residues at the density functional theory level, dispersion corrections are crucial for meaningful energies. Overall, the QM/MM method outperforms the classical approach for metalloproteins, performs comparably for covalent complexes, and shows slightly lower success rates for non-covalent complexes.