Candidate Designs Research Articles

Quinoline and quinolone carboxamides: A review of anticancer activity with detailed structure-activity relationship analysis.

Quinoline is a highly privileged scaffold with significant pharmacological potential. Introducing a carbonyl group into the quinoline ring generates a quinolone ring, which exhibits promising biological properties. Incorporating a carboxamide linkage at different positions within the quinoline and quinolone frameworks has proven an effective strategy for enhancing pharmacological properties, particularly anticancer potency. Consequently, various scientific communities have explored quinoline and quinolone carboxamides for their anticancer activities, introducing modifications at key positions. This review article aims to compile the anticancer activity of various quinoline and quinolone carboxamide derivatives, accompanied by a detailed structure-activity relationship (SAR) analysis. It also categorizes the data into activities of isolated/fused quinoline and quinolone carboxamide derivatives, which were further subclassified based on the mechanisms of anticancer action. Among the numerous derivatives studied, compounds 8, 19, 31, 34, 40, 68, 108, 116, and 132 have emerged as the most potent anticancer agents, making them strong candidates for further drug design and development. The mechanisms underlying the anticancer activity of these potent compounds have been identified as inhibitors of topoisomerase (8, 19, 31, and 34), protein kinase (40, 108, and 116), human dihydroorotate dehydrogenase (68), and as a cannabinoid receptor 2 agonist (132). We anticipate this review will be valuable to researchers engaged in the structural design and development of quinoline and quinolone carboxamide-based anticancer drugs with high efficacy.

Ulva Seaweed-Derived Ulvan: A Promising Marine Polysaccharide as a Sustainable Resource for Biomaterial Design

Green seaweed is currently underused compared with other major seaweed types. Many scientists have reported applications of the green seaweed Ulva in various fields in recent years, which makes it a candidate for biomass production in industrial biorefineries. Ulva contains a unique polysaccharide called ulvan, which is being considered for medicinal and pharmacological applications. Ulvan is a sulfated polysaccharide including rhamnose and glucuronic acid residues, which has a range of bioactivities, including immunomodulatory, antimicrobial, and anticoagulant properties. The biocompatibility of ulvan makes it a versatile candidate for biomaterial design. This review presents an in-depth analysis of the potential applications of ulvan, starting with extraction methods and structural/biological characterization and moving on to biomaterial design. We also highlight the advantages of ulvan over traditional seaweed polysaccharides such as agar, carrageenan, and alginate.

Exploring Inhibition Mechanisms in Wildtype and T315I BCR-ABL1: An In Silico Approach Integrating Virtual Screening, MD Simulations, and MM-GBSA Analysis.

The BCR-ABL tyrosine kinase which is responsible for the pathogenesis of chronic myeloid leukemia (CML), has emerged as a promising therapeutic target. To address this issue, we employed a comprehensive computational approach integrating virtual screening, molecular dynamics (MD) simulations, and MM-GBSA (Molecular Mechanics/Generalized Born Surface Area) analysis to identify potential inhibitors and elucidate their binding mechanisms. Initially, virtual screening was conducted on 994 compounds from the ZINC database and, these compounds were docked against wildtype and T315I mutant ABL1 for the Type I and Type II ABL1 kinase inhibition mechanisms. In our molecular docking analysis for Type I inhibition, compound 911 demonstrated notable affinity towards the wildtype ABL1, with a binding energy of -14.91 kcal/mol, while compound 972 showed significant binding affinity towards the mutant ABL1, with a binding energy of -14.27 kcal/mol. In the Type II inhibition mechanism, the compounds with the highest binding affinity were compound 261 in wildtype ABL1 with -17.05 kcal/mol binding energy and compound 966 to the mutant ABL1 with a binding energy of -16.29 kcal/mol. Furthermore, analyses of MD simulations and MM/GBSA binding free energy (ΔG) were performed for target proteins with compounds, that exhibited the most favorable binding affinities with target proteins. The selected hit compounds showed ΔG scores ranging from -118.09 to -74.85 kJ/mol in both wildtype and mutant ABL1. Considering all in silico studies performed, it can be inferred that the identified molecules hold promise as potential candidates for drug design aimed at targeting CML.

Optimization of Flexible Rotor for Ultrasonic Motor Based on Response Surface and Genetic Algorithm.

The flexible rotor, as a crucial component of the traveling wave rotary ultrasonic motor, effectively reduces radial friction. However, issues such as uneven contact between the stator and rotor, as well as rotor-deformation-induced stress, still persist. This paper presents an optimization method that combines the Kriging response surface model with a multi-objective genetic algorithm (MOGA). Drawing on the existing rotor structure, a novel rotor design is proposed to match the improved TRUM60 stator. During the optimization process, the contact surface between the stator and rotor is taken as the optimization target, and an objective function is established. The Kriging response surface model is constructed using Latin hypercube sampling, and an MOGA is employed to optimize this model, allowing the selection of the optimal balanced solution from multiple candidate designs. Following stator optimization, the objective function value decreased from 0.631 to 0.036, and the maximum contact stress on the rotor inner ring was reduced from 32.77 MPa to 9.96 MPa. Experimental validation confirmed the reliability of this design, significantly improving the overall performance and durability of the motor.

Evaluation of tick salivary and midgut extracellular vesicles as anti-tick vaccines in White-tailed deer (Odocoileus virginianus).

Current tick control measures are focused on the use of synthetic acaricides and personal protective measures. However, the emergence of acaricide resistance and the maintenance of tick populations in wildlife has precluded the efficient management of ticks. Thus, host-targeted, non-chemical control measures are needed to reliably reduce ticks parasitizing sylvatic reservoirs. This project aimed to evaluate extracellular vesicles (EVs) from Amblyomma americanum as vaccine candidates for white-tailed deer (Odocoileus virginianus; WTD). Salivary gland (SG) and midgut (MG) EVs were isolated by ultracentrifugation. Three deer were vaccinated with SG and MG EVs and received two boosters at days 28 and 50. Two control deer were injected with adjuvant and PBS only. On day 58, WTD were infested with 100 A. americanum nymphs, 50 females, and 50 males that were allowed to feed to repletion. On-host and off-host mortality, tick engorgement weight, nymph molting, time to oviposition, and egg hatchability were evaluated. Serum samples were recovered every seven days until the last day of tick drop off, and then at one year (Y1) and 1-year and 1-month (Y1M1). Vaccination resulted in seroconversion and significant increases in total IgG levels that remained significantly higher than controls and pre-vaccination levels at Y1 and Y1M1. No negative effects were observed in nymphs, but on-host mortality of female A. americanum was significantly higher in vaccinated animals. No effects were observed on reproductive parameters. These results indicate that proteins within female tick SG and MG vesicles are not good candidates for vaccine design against nymphs; however, the on-host adult mortality suggests that tick EVs harbor protective antigens against A. americanum females.

Quinoa Proteins Isolate, a Candidate for Functional Ingredients Design.

The objective of the present contribution was to design and characterize resveratrol (RSV) and tocopherol (TOC) loaded quinoa protein (QP) nanocomplexes, their antioxidant and antiradical functionality were also evaluated. QP intrinsic fluorescence spectra showed a quenching effect exerted by RSV and TOC, demonstrating protein-bioactive compounds interactions. Stern-Volmer and Scatchard models application confirmed the static quenching effect and allowed to obtain parameters that described the QP-RSV or QP-TOC complexation process. Isothermal titration calorimetry allowed obtaining thermodynamic parameters to describe the interaction between RSV or TOC with QP. Protein aggregates up to 40nm in diameter were induced by bioactive compounds as detected by atomic force microscopy. QP-RSV and QP-TOC nanocomplexes showed additive effect in their antioxidant capacity. These nanocomplexes could constitute a real platform for the design of functional ingredients.

Parametric Analysis and Design Optimization of a Loudspeaker-Driven Synthetic Jet Actuator Using Response Surface Model in Quiescent Conditions

Synthetic jet actuators (SJAs) are devices that control fluid flow by synthesizing surrounding fluid without altering the flow from the cavity. Before optimization, the selected diaphragm frequencies are subjected to a CFD solution using a dynamic mesh. Output parameters of selected loudspeaker-driven SJA are optimized under quiescent conditions. Comparisons are made between the best optimization candidate design point, experimental reference, and baseline CFD study before optimization. Re increases by 17% at 50[Formula: see text]Hz and 8.7% at other frequencies. St decreases by almost 30% across all frequencies. [Formula: see text] is maximized and held over 4. Optimized design, based on response surface model, demonstrated a consistent ability to produce higher, more centralized velocity profiles.

Identification of potential antigenic proteins and epitopes for the development of a monkeypox virus vaccine: an in silico approach.

Virus assembly, budding, or surface proteins play important roles such as viral attachment to cells, fusion, and entry into cells. The present study aimed to identify potential antigenic proteins and epitopes that could be used to develop a vaccine or diagnostic assay against the Monkeypox virus (MPXV) which may cause a potential epidemic. To do this, 39 MPXV proteins (including assembly, budding, and surface proteins) were analyzed using an in silico approach. Of these 39 proteins, the F5L virus protein was found to be the best vaccine candidate due to its signal peptide properties, negative GRAVY value, low transmembrane helix content, moderate aliphatic index, large molecular weight, long-estimated half-life, beta wrap motifs, and being stable, soluble, and containing non-allergic features. Moreover, the F5L protein exhibited alpha-helical secondary structures, making it a potential "structural antigen" recognized by antibodies. The other viral protein candidates were A9 and A43, but A9 lacked beta wrap motifs, while A43 had a positive GRAVY value and was insoluble. These two proteins were not as suitable candidates as the F5L protein. The KRVNISLTCL epitope from the F5L protein demonstrated the highest antigen score (2.4684) for MHC-I, while the GRFGYVPYVGYKCI epitope from the A9 protein exhibited the highest antigenicity (1.754) for MHC-II. Both epitopes met the criteria for high antigenicity, non-toxicity, solubility, non-allergenicity, and the presence of cleavage sites. Molecular docking and dynamics (MD) simulations further validated their potential, revealing stable and energetically favorable interactions with MHC molecules. The immunogenicity assessment showed that GRFGYVPYVGYKCI could strongly induce immune responses through both IFN-γ and IL-4 pathways, suggesting its capacity to provoke a balanced Th1 and Th2 response. In contrast, KRVNISLTCL exhibited limited immunostimulatory potential. Overall, these findings lay the groundwork for future vaccine development, indicating that F5L, particularly the GRFGYVPYVGYKCI epitope, may serve as an effective candidate for peptide-based vaccine design against MPXV.

Surrogate-assisted optimization under uncertainty for design for remanufacturing considering material price volatility

Remanufacturing is a well-established end-of-life (EOL) strategy that promises significant savings in energy and carbon emissions. However, the current design practices are not remanufacturing-inclusive, i.e., the majority of products are designed for a single life cycle. As a result, potential products that can sustain multiple life cycles are deprived of additional benefits of being designed for remanufacturing, such as reduced material usage, lower cost, and improved environmental impact. Moreover, the uncertainty in design, material selection, and economics are not considered to produce remanufacturable designs. Accordingly, this research proposes a design for remanufacturing (DfRem) framework that accounts for design uncertainty and material price volatility. The framework systematically explores the design space, performs design optimization under uncertainty, followed by topology optimization to provide additional mass savings, and finally, a price volatility analysis for plausible design material choices. The candidate designs are evaluated based on their design mass, material price volatility, failure mode characteristics, carbon footprint, and embodied energy impacts. The proposed framework's utility is demonstrated via the use of an engine cylinder head case study subjected to thermo-mechanical loads along with fatigue and wear failure. Considering grey cast iron and aluminum alloy as the design material choices, it was found that the cast iron design reduced the initial design mass by 6% as opposed to a 5% decrease for aluminum. On the other hand, about 8% area of the cast iron design failed due to fatigue, compared to 3% for aluminum. We further observed that although the aluminum design provided better mechanical performance than the cast iron design, this material was more expensive and volatile in price.

Genomic-Wide Association Studies For Resistance to Anthracnose in a Pseudo-F2 Mango Population

Goal: This study aimed to apply genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with resistance to mango anthracnose. Theoretical Framework: The statistical methods of GWAS aim to identify associations among genotypes with phenotypes for marker-assisted selection (MAS) or genome dissection of important traits. Method: Ninety-four plants of a ‘Haden’ × ‘Tommy Atkins’ pseudo-F2 population were genotyped with 705 SNPs and phenotyped for resistance to Colletotrichum siamense, anthracnose causal agent, by spraying young leaves, three times at different periods, with 103 conidia/mL. Qualitative GWAS, with Plink Fisher’s exact test and Cochran-Armitage trend genotypic test, and quantitative GWAS, with Tassel general (GLM) and mixed linear models (MLM) were applied. F2 plants with symptoms mean absence >75% were considered moderately resistant to the fungus. Results and discussion: Qualitative and quantitative GWAS methods combined enable the identification of two consensus SNP loci controlling 24.76% of total variance to mango anthracnose resistance. Other four SNPs, identified, exclusively in quantitative or qualitative analysis, explained 28.55% of total variance to mango anthracnose. These SNPs loci are promising candidate for further PCR primers design in order to apply MAS in mango. Research implications: This study facilitates the identification of consensus chromosomal regions associated with mango anthracnose resistance. The application of MAS in mango breeding will accelerate the development of new cultivars. Originality/Value: To our knowledge, this a pioneering GWAS study applied to anthracnose, which is the most significant and widespread fungal disease affecting mango fruit globally.

Genomic Sequence of the Threespine Stickleback Iridovirus (TSIV) from Wild Gasterosteus aculeatus in Stormy Lake, Alaska.

The threespine stickleback iridovirus (TSIV), a double-stranded DNA virus, was the first megalocytivirus detected in wild North American fishes. We report a second occurrence of TSIV in threespine stickleback (Gasterosteus aculeatus) from Stormy Lake, Alaska, and assemble a nearly complete genome of TSIV. The 115-kilobase TSIV genome contains 94 open reading frames (ORFs), with 91 that share homology with other known iridoviruses. We identify three ORFs that likely originate from recent lateral gene transfers from a eukaryotic host and one ORF with homology to B22 poxvirus proteins that likely originated from a lateral gene transfer between viruses. Phylogenetic analysis of 24 iridovirus core genes and pairwise sequence identity analysis support TSIV as a divergent sister taxon to other megalocytiviruses and a candidate for a novel species designation. Screening of stickleback collected from Stormy Lake before and after a 2012 rotenone treatment to eliminate invasive fish shows 100% positivity for TSIV in the two years before treatment (95% confidence interval: 89-100% prevalence) and 0% positivity for TSIV in 2024 after treatment (95% confidence interval: 0 to 3.7% prevalence), suggesting that the rotenone treatment and subsequent crash and reestablishment of the stickleback population is associated with loss of TSIV.

Optimization of a Hydrogen-Fueled Parametric Strut Injector Using an Automated Workflow Computational Fluid Dynamics Method

Abstract A strut injector for burning hydrogen has been optimized using an automated workflow system. In order to choose the optimal set of injectors to cover the design space, an optimized design of experiments (DOE) method was used to automatically choose the parameters that best spanned the design space. One hundred candidate designs were chosen, and a script was used to generate a series of stereolithography (STL) files for each design. The STL files were then uploaded to a supercomputer for computational fluid dynamics analysis. For each of the 100 designs, a four step process was followed to generate the required data, and this included an automated mesh generation step, a field initialization step, a mesh adaptation step, and finally an large eddy simulation all within the yales2 numerical framework. In order to reduce the time required for postprocessing and the amount of data required, the simulations relied heavily on an on-the-fly postprocessing methodology, which reduced the complex time-unsteady flow fields to a small number of quantified outputs of interest that measured the suitability of each design such as the pressure drop across the injector and the efficiency of the mixing process. At the conclusion of these simulations, automated scripts translated these outputs into a smaller set of parameters that could be used to compare each design and allow subsequent optimization and surrogate modeling. Several surrogate modeling methods were attempted with mixed results; however, a simple classification methodology quickly identified the parameters of interest.

Comparison of the Three Types of Central Composite Designs Over Subsets of Reduced Models by Design Optimality Criteria

The purpose of this article is to compare the 3 important types of central composite designs (CCDs) consisting of central composite circumscribed design (CCCD), central composite inscribed design (CCID), and central composite face-centroid design (CCFD) in response surface methodology (RSM). The difference among these designs is the distance from the center design to the axial points. The comparison was performed across the full second-order response surface model and across a set of reduced models for 3, 4, and 5 design factors ( 3, 4, and 5) including 1, 3, and 5 center runs ( 1, 3, and 5). This study used D-, A-, and G- optimality criteria to evaluate the performance of CCDs by presenting the design optimality criteria comparison ranking throughout the reduced-model subsets of 43, 224, and 839 models for 3, 4, and 5 design variables, respectively. The results showed that CCCD was superior to CCID and CCFD according to A- and D- optimality criteria, while CCCD and CCID performed better than CCFD based on G- optimality criterion over a set of reduced models for 3, 4, and 5 design factors. It was observed that D-, A-, and G- optimality efficiencies were robust to changes in the linear and cross-product terms and sensitive to deviation in the square terms. The study will provide recommendations to assist the experimenters in the choice of the best design among the candidate designs for practice applications when some model effects may be insignificant. HIGHLIGHTS Comparison of 3 classifications of CCD. Assessment by using D-, A-, and G- optimality criteria over the reduced-model subsets for the full second-order response surface model. Generated the reduced models by using weak heredity. CCCD was superior to CCID and CCFD according to A- and D- optimality criteria, while CCCD and CCID performed better than CCFD based on G- optimality criterion over a set of reduced models for 3, 4, and 5 design factors. GRAPHICAL ABSTRACT

The phonon-modulated Jahn–Teller distortion of the nitrogen vacancy center in diamond

The negatively charged nitrogen vacancy (NV) center in diamond is an optically accessible material defect with a unique combination of spin and optical properties that has attracted interest in quantum-information sciences and as a design candidate for nanoscale quantum sensors. Here, we present time-resolved nonlinear optical spectroscopy measurements, conducted with ultrabroadband laser pulses, that reveal strong modulation of the excited-state by the longitudinal optical (LO) phonon of the diamond lattice. The LO phonon and its overtones geometrically distort neighboring NV centers, driving long lived (3.5 ps) excited state relaxation of coupled NV centers after the initial excitation and ultrafast (<150 fs) decay of the Jahn–Teller distortion. These observations elevate the LO phonon to an important tuning mode of the Jahn–Teller conical intersection and help resolve previous spectroscopy experiments that noted longer-lived excited-state dynamics.

Designing of a Novel Aerolysin-based Multiepitope Vaccine against Aeromonas hydrophila Isolated from Osphronemus goramy Using Reverse Vaccinology: an in Silico Approaches

Graphical Abstract Highlight Research The study aims to develop a multi-epitope vaccine (MEV) against A. hydrophila by targeting the aerolysin toxin, a key virulence factor responsible for infections in fish and humans. Computational methods identified and optimized B-cell and T-cell epitopes, focusing on their ability to trigger immune responses without causing toxicity or allergenicity. In silico simulations demonstrated that the MEV has a strong binding affinity to immune receptors like TLR-4, MHC-I, and MHC-II, indicating its potential to induce robust cellular and humoral immunity. Structural analysis of the MEV showed a stable 3D conformation, with most residues in favorable regions, ensuring stability during immune activation. The MEV could enhance disease control in aquaculture and reduce human infection risks, offering a promising solution to address antibiotic resistance and the absence of effective vaccines. Abstract Aeromonas hydrophila, gram-negative, is a major pathogen responsible for various diseases in mammals, reptiles, amphibia, and vertebrates, including fish and humans. Targeting the specific toxin aerolysin in A. hydrophila is crucial to address antibiotic resistance and the lack of adequate and protective vaccines against this intracellular pathogen. This study aimed to identify a multi-epitope vaccination (MEV) candidate targeting A. hydrophila aerolysin toxin to combat the disease effectively. Standard biochemical characterization methods and sequencing of the 16S rRNA, rpoB, and aerA genes identified the isolate AHSA1 as A. hydrophila. Subsequently, we identified B and T cell epitopes on the aerolysin protein and separately predicted MHC-I and MHC-II epitopes. The epitopes are then evaluated for toxicity, antigenicity, allergenicity, and solubility. The vaccine design integrated multi-epitope-based constructs, utilizing specialized linkers (GPGPG) and EAAAK linkers to connect epitope peptides with adjuvants in the cholera toxin B component, thereby enhancing immunogenicity. Ramachandran plots showed that 85.25% of the residues were located in the most favorable regions, which was followed by the generously allowed zone (1.30%), the additional allowed regions (10.80%), and the forbidden regions (2.65%), thus confirming the feasibility of the modeled vaccine design. Based on docking simulations, MEV had the highest binding and interaction energies with TLR-4, TLR-9, MHC-I, and MHC-II (-1081.4, -723.2, 866.2, -9043.3 kcal/mol). Based on computational modelling, we expect the Aerolysin MEV candidate design to activate diverse immune mechanisms, stimulate robust responses against A. hydrophila, and maintain safety. The significant solubility, absence of toxicity or allergic response, and minimal side effects in animal testing all contribute to the potential clinical utility of this vaccine candidate.

Thermal Metamaterials with Configurable Mechanical Properties.

Thermal metamaterials are typically achieved by mixing different natural materials to realize effective thermal conductivities (ETCs) that conventional materials do not possess. However, the necessity for multifunctional design of metamaterials, encompassing both thermal and mechanical functionalities, is somewhat overlooked, resulting in the fixation of mechanical properties in thermal metamaterials designed within current research endeavors. Thus far, conventional methods have faced challenges in designing thermal metamaterials with configurable mechanical properties because of intricate inherent relationships among the structural configuration, thermal and mechanical properties in metamaterials. Here, a data-driven approach is proposed to design a thermal metamaterial capable of seamlessly achieving thermal functionalities and harnessing the advantages of microstructural diversity to configure its mechanical properties. The designed metamaterial possesses thermal cloaking functionality while exhibiting exceptional mechanical properties, such as load-bearing capacity, shearing strength, and tensile resistance, thereby affording mechanical protection for the thermal metadevice. The proposed approach can generate numerous distinct inverse design candidate topological functional cells (TFCs), designing thermal metamaterials with dramatic improvements in mechanical properties compared to traditional ones, which sets up a novel paradigm for discovering thermal metamaterials with extraordinary mechanical structures. Furthermore, this approach also paves the way for investigating thermal metamaterials with additional physical properties.

In silico design of Mycobacterium tuberculosis multi-epitope adhesin protein vaccines

Mycobacterium tuberculosis (Mtb) adhesin proteins are promising candidates for subunit vaccine design. Multi-epitope Mtb vaccine and diagnostic candidates were designed using immunoinformatic tools. The antigenic potential of 26 adhesin proteins were determined using VaxiJen 2.0. The truncated heat shock protein 70 (tnHSP70), 19 kDa antigen lipoprotein (lpqH), Mtb curli pili (MTP), and Phosphate transport protein S1 (PstS1) were selected based on the number of known epitopes on the Immune Epitope Database (IEDB). B- and T-cell epitopes were identified using BepiPred2.0, ABCpred, SVMTriP, and IEDB, respectively. Population coverage was analysed using prominent South African specific alleles on the IEDB. The allergenicity, physicochemical characteristics and tertiary structure of the tri-fusion proteins were determined. The in silico immune simulation was performed using C-ImmSim. Three truncated sequences, with predicted B and T cell epitopes, and without allergenicity or signal peptides were linked by three glycine-serine residues, resulting in the stable, hydrophilic molecules, tnlpqH-tnPstS1-tnHSP70 (64,86 kDa) and tnMTP-tnPstS1-tnHSP70 (63,96 kDa). Restriction endonuclease recognition sequences incorporated at the N- and C-terminal ends of each construct, facilitated virtual cloning using Snapgene, into pGEX6P-1, resulting in novel, highly immunogenic vaccine candidates (0,912–0,985). Future studies will involve the cloning, recombinant protein expression and purification of these constructs for downstream applications.

Deep generative model-based synthesis framework of four-bar linkage mechanisms with target conditions

Abstract This paper proposes a deep generative model-based framework for synthesizing four-bar linkage mechanisms that satisfy specified kinematic and quasi-static conditions. We define two objective functions for crank-rocker mechanisms using kinematic workspaces and geometric configurations. Our approach utilizes a conditional generative adversarial network (cGAN) modified for mechanism synthesis, which learns the relationship between mechanism requirements and linkage lengths. The results demonstrate that the proposed model successfully generates multiple distinct mechanisms meeting specific kinematic and quasi-static requirements. We compare our cGAN approach to traditional optimization methods and other deep learning-based generative models. Our method offers several advantages over traditional design approaches, enabling efficient generation of diverse yet feasible design candidates while exploring a large design space. By considering both kinematic and quasi-static requirements, the proposed model can produce more effective mechanisms for real-world applications. This makes it a promising tool for linkage mechanism design, offering designers a way to efficiently generate multiple viable design options that satisfy key performance criteria.

A Foundation Model Identifies Broad-Spectrum Antimicrobial Peptides against Drug-Resistant Bacterial Infection

Development of potent and broad-spectrum antimicrobial peptides (AMPs) could help overcome the antimicrobial resistance crisis. We develop a peptide language-based deep generative framework (deepAMP) for identifying potent, broad-spectrum AMPs. Using deepAMP to reduce antimicrobial resistance and enhance the membrane-disrupting abilities of AMPs, we identify, synthesize, and experimentally test 18 T1-AMP (Tier 1) and 11 T2-AMP (Tier 2) candidates in a two-round design and by employing cross-optimization-validation. More than 90% of the designed AMPs show a better inhibition than penetratin in both Gram-positive (i.e., S. aureus) and Gram-negative bacteria (i.e., K. pneumoniae and P. aeruginosa). T2-9 shows the strongest antibacterial activity, comparable to FDA-approved antibiotics. We show that three AMPs (T1-2, T1-5 and T2-10) significantly reduce resistance to S. aureus compared to ciprofloxacin and are effective against skin wound infection in a female wound mouse model infected with P. aeruginosa. In summary, deepAMP expedites discovery of effective, broad-spectrum AMPs against drug-resistant bacteria.

Novel indole Schiff base β-diiminato compound as an anti-cancer agent against triple-negative breast cancer: In vitro anticancer activity evaluation and in vivo acute toxicity study

Breast cancer is the most prevalent cancer among women globally, with triple-negative breast cancer (TNBC) associated with poor prognosis and low five-year survival rates. Schiff base compounds, known for their extensive pharmacological activities, have garnered significant attention in cancer drug research. This study aimed to evaluate the anticancer potential of a novel β-diiminato compound and elucidate its mechanism of action. The compound’s effect on cell viability was assessed using MTT assays in breast cancer cell lines including MCF-7 and MDA-MB-231. Cytotoxic effects were further analyzed using trypan blue exclusion and lactate dehydrogenase (LDH) release assays. In order to assess the mechanism of inhibitory activity and mode of cell death induced by this compound, flow cytometry of cell cycle distribution and apoptosis analysis were carried out. Apoptosis incidence was initially assessed through cell and nuclear morphological changes (Hoechst 33342/Propidium iodide (PI) staining) and further confirmed by Annexin V/PI staining and flow cytometry analysis. In addition, the effect of this compound on the disruption of mitochondrial membrane potential (MMP) and generation of the reactive oxygen species (ROS) was determined using the JC-1 indicator and DCFDA dye, respectively. The results demonstrated that the 24 h treatment with β-diiminato compound significantly suppressed the viability of MDA-MB-231 and MCF-7 cancer cells in a dose-dependent manner with the IC50 value of 2.41 ± 0.29 and 3.51 ± 0.14, respectively. The cytotoxic effect of the compound was further confirmed with a dose-dependent increase in the number of dead cells and enhanced LDH level in the culture medium. This compound exerted its anti-proliferative effect by G2/M phase cell growth arrest in MDA-MB-231 breast cancer cells and induced apoptosis-mediated cell death, which involved characteristic changes in cell and nuclear morphology, phosphatidylserine externalization, mitochondrial membrane depolarization, and increased ROS level. Neither hepatotoxicity nor nephrotoxicity was detected in the biochemical and histopathological analysis confirming the safety characterization of this compound usage. Therefore, the results significantly confirmed the potential anticancer activity of a novel β-diiminato compound, as evidenced by the induction of cell cycle arrest and apoptosis, which might be driven by the ROS‑mediated mitochondrial death pathway. This compound can be a promising candidate for future anticancer drug design and TNBC treatment, and further preclinical and clinical studies are warranted.