Tuberculosis (TB) continues to pose a global health challenge, exacerbated by the rise of drug-resistant strains. The development of new TB therapies is an arduous and time-consuming process. To expedite the discovery of effective treatments, computational structure-based drug re-purposing has emerged as a promising strategy. From this perspective, conditionally essential targets present a valuable opportunity, and the mycobactin biosynthesis pathway stands out as a prime example highlighting the intricate response of Mycobacterium tuberculosis (Mtb) to changes in iron availability. This study focuses on the re-purposing and revival of FDA-approved drugs (library) as potential inhibitors of MbtA, a crucial enzyme in mycobactin biosynthesis in Mtb conserved among all species of mycobacteria. Literature suggests this pathway to be associated with drug efflux pumps, which potentially contribute to drug resistance. This makes it a potential target for antitubercular drug discovery. Herein we utilized cheminformatics and structure-based drug repurposing approaches, viz., molecular docking, dynamics, and PCA analysis, to decode the intermolecular interactions and binding affinity of the FDA-reported molecules against MbtA. The virtual screening revealed ten molecules with significant binding affinities and interactions with MbtA. These drugs, originally designed for different therapeutic indications (4: antiviral, 3: anticancer, 1: CYP450 inhibitor, 1: ACE inhibitor, and 1: leukotriene antagonist), are repurposed as potential MbtA inhibitors. Furthermore, our study explores the binding modes and interactions between these drugs and MbtA, shedding light on the structural basis of their inhibitory potential. Principal component analysis highlighted significant motions in MbtA-bound ligands, emphasizing the stability of the top Protein-Ligand Complexes (PLCs). This computational approach provides a swift and cost-effective method for identifying new MbtA inhibitors, which can subsequently undergo validation through experimental assays. This streamlined process is facilitated by the fact that these compounds are already FDA-approved and have established safety and efficacy profiles. This study has the potential to lay the groundwork for addressing the urgent global health challenge at hand, specifically in the context of combating Antimicrobial Resistance (AMR) and Tuberculosis (TB).

Molecularly imprinted polymers (MIPs) were synthesized and applied for the selective extraction of oblongifolin C (OC) from fruit extracts of Garcinia yunnanensis Hu. A series of experiments and computational approaches were employed to improve the efficiency of screening for optimal MIP systems in the study. The molar ratio (1:4) was chosen at last based on the comparison of the binding energy of the complexes between the template (OC) and functional monomers using density functional theory (DFT) at the RI-PBE-D3-gCP/def2-TZVP level of theory. The binding characterization and the molecular recognition mechanism of MIPs were further explained using the molecular modeling method and NMR and IR spectra data. The reusability of this approach was demonstrated in over 20 batch rebinding experiments. 140.5 mg of OC (>95% purity) was obtained from the 5 g extracts with 2 g of MIPs with the best binding properties through gradient elution program from 35% to 70% methanol-water solution. At the same time, another structural analog, 46.5 mg of guttiferone K (GK) (>88% purity), was also obtained by the gradient elution procedure. Our results showed that the structural analogs could be separated from the crude extracts by the molecularly imprinted solid-phase extraction (MISPE) using a gradient elution procedure for the first time.

Covid-19 is one of humanity’s biggest threat in the 21st century with WHO figures reporting 636 million cases and up to 6.6 million deaths globally. SARS-CoV-2, the virus that causes the Covid-19 disease, is characterized by high mutation which contributes to its rapid spread. While several vaccines have been produced to minimize the severity of the coronavirus and diverse treatment regimens have been approved by the US FDA under Emergency Use Authorization (EUA), SARS-CoV-2 viral mutations continue to derail the efforts of scientists as the emerging variants evade the recommended therapies. Nonetheless, diverse computational models exist that offer an opportunity to overcome the barriers involved in developing new drugs. In this review, the focus is on the use of various virtual screening techniques like molecular docking, molecular dynamics simulations, QSAR, pharmacophore modeling, and homology modeling in repurposing SARS-CoV-2 therapeutics. The results have been promising from the computer-aided drug design (CADD) studies in suggesting potential compounds for treatment of Covid-19 and helping in bringing the pandemic under control.

Despite being an abundant marine organism in Indonesia, black sea cucumber is still underutilised due to its slightly bitter taste. Previous studies have hinted at the potential of black sea cucumber as an anti-cancer agent. However, specific identification of bioactive compounds that can interact with cancer proteins is still lacking. In the same place, cancer ranks third as Indonesia's leading cause of death. Therefore, this study aims to identify potential anti-cancer compounds from black sea cucumbers using a comprehensive in silico drug discovery approach. This research uses machine learning, molecular docking, and ADMET analysis to identify bioactive compounds that specifically interact with cancer proteins. A combination of the Cascade Deep Forest algorithm and ECFP-AAIndex1 feature combination proved to be the most effective in predicting these interactions. Through molecular docking validation, four bioactive compounds with strong binding affinity were identified: Afimoxifene, Danazol, Taxifolin, and Terfenadine. ADMET analysis highlighted Taxifolin as the most promising candidate, as it passed most ADMET parameters. Further wet laboratory studies are required to confirm the effects and potential of these compounds as anti-cancer agents. This study builds a foundation for future investigations into alternative cancer treatments using abundant natural resources.

Phytochemicals have a long and successful history in drug discovery. With recent advancements in analytical techniques and methodologies, discovering bioactive leads from natural compounds has become easier. Computational techniques like molecular docking, QSAR modelling and machine learning, and network pharmacology are among the most promising new tools that allow researchers to make predictions concerning natural products' potential targets, thereby guiding experimental validation efforts. Additionally, approaches like LC-MS or LC-NMR speed up compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, thus providing invaluable information to understand how phytochemicals interact with potential targets in the body. Emerging computational approach is machine learning involving QSAR modelling and deep neural networks that interrelate phytochemical properties with diverse physiological activities such as antimicrobial or anticancer effects.

In the domain of drug discovery and design, the acquisition of precise structural and intermolecular Kd data for biological molecules is paramount. However, conventional methods for obtaining such data are often beset by challenges including high costs, time intensiveness, and inherent limitations in scalability and diversity. This article puts forward a high-throughput approach for in silico generation of structural and intermolecular binding affinity (Kd) data, which keeps exploring the uncharted territories of drug discovery & design by harnessing computational methodologies to synthetically generate diverse datasets. Through a meticulously designed workflow encompassing molecular structural modeling, structural biophysics-based calculations of intermolecular binding affinity (Kd), this innovative methodology transcends the constraints of traditional experimentation, offering a cost-effective, scalable, and efficient alternative. By simulating molecular interactions and binding interfaces and predicting binding affinities with reasonable accuracy, this approach not only expedites the drug development process but also enables the exploration of vast molecular space, thereby facilitating the discovery of novel therapeutics beyond conventional drug modality as a form factor. Moreover, the versatility of synthetic data extends beyond virtual screening and lead optimization, encompassing applications such as dataset augmentation, model validation, and benchmarking against experimental data. This article elucidates the conceptual underpinnings, methodological intricacies, validation strategies, and potential ramifications of in silico generated data, heralding a paradigm shift in drug discovery paradigms. By fostering synergy between computational and experimental research domains, this innovative approach promises to accelerate the pace of drug discovery, enhance the robustness of predictive models, and pave the way for transformative advancements in the entire pharmaceutical industry.

BackgroundThe present review summarizes the state-of-the-art of in silico methods and techniques that are the most useful in drug discovery, their relationship with data science, as well as the successful application of data science, machine learning (ML) and artificial intelligence (AI) applications. A meta-analysis of the various technologies available is furthermore proposed as a guideline for the non-expert, reader relative to the several subject areas is also discussed in this article. The scope of this meta-analysis is to rank the enlisted technologies by their field of applications and to depict the latter according to knowledge accessibility, from students to experts.Method The search strategy utilized for this review first produced a general collection of 900 papers without duplications, which were subsequently streamlined and divided into two independent collections: the top 300 most-cited papers of all time (since 2000) and the papers with the highest interest for a systematic review analysis (high-impact exciting papers). Results In Part 1, we discuss the most cited and quality 97 articles in these top 300 papers most relevant to the field of in silico drug discovery. The different disciplines are listed according to their industrial and economic incurred to society, independently from the “metric” results of how many new drug approvals (NDAs) each discipline has generated to date.ConclusionBig data, the ensemble of known items stored in publicly available databases, has improved our understanding of the many fates of a potential drug candidate during its development and even after its commercialization. Moreover, the combination of new screening techniques and “omics” with old drugs has led to a new paradigm in which the unknown knowledge of any biological molecule and its cellular structure, now plays an important role as a target for a series of yet-to-be-developed drugs: the chemical space. Furthermore, leveraging big data, data science, ML, and AI can revolutionize drug discovery by swiftly analyzing massive datasets, predicting efficacy and safety profiles, streamlining development, cutting costs, and boosting success rates for new drugs. AI also speeds up the search for promising drug candidates, advancing innovative therapies.

Epilepsy is one of the most prevalent and serious brain disorders, affecting 70 million people worldwide. Antiseizure medications (ASMs) though relieve symptoms and prevent the occurrence of future seizures in epileptic patients have limited effect on epileptogenesis. Addressing the multifaceted nature of epileptogenesis and its association with Nod-like receptor family pyrin domain containing 3 (NLRP3) inflammasome-mediated neuroinflammation requires a comprehensive understanding of the underlying mechanisms for development of targeted therapeutic strategies beyond conventional antiseizure treatments. Several types of NLRP3 inhibitors have been developed and their effect has been validated both in vitro and in vivo models of epileptogenesis. In this review, we discuss the advances in understanding the regulatory mechanisms of NLRP3 activation as well as progress made and challenges facing in the development of NLRP3 inhibitors for the treatment of epilepsy.

The integration of bioinformatics in drug discovery has revolutionized the field of pharmaceutical research. The use of computational tools and techniques has enabled researchers to analyze vast amounts of data, identify potential drug targets, and design new drugs with greater precision and efficiency. The integration of bioinformatics has also facilitated the development of personalized medicine, where drugs can be tailored to individual patients based on their genetic makeup. However, there are still challenges that need to be addressed, such as the need for more accurate predictive models and the ethical considerations surrounding the use of patient data. Overall, the integration of bioinformatics in drug discovery holds great promise for improving human health and advancing our understanding of disease mechanisms. In this mini-review, we discuss how Bioinformatics plays a crucial role in each step of drug discovery by providing tools and techniques to analyze large amounts of data generated from various sources, as well as the challenges and the opportunities offered by bioinformatics.

Tyrosine kinase fibroblast growth factor receptor (FGFR), which is aberrant in various cancer types, is a promising target for cancer therapy. Here we reported the design, synthesis, and biological evaluation of a new series of 6-(2,6-dichloro-3,5-dimethoxyphenyl)-4-substituted-1H-indazoles derivatives as potent FGFR inhibitors. Compound 10a was first identified as a potent FGFR1 inhibitor, with good enzymatic inhibition. Further structure-based optimization revealed that compound 13a is the most potent FGFR1 inhibitor in this series with the enzyme inhibitory activity about 30.2 nM of IC50 value.

The growing interest in Kv7.2/7.3 agonists originates from the involvement of these channels in several brain hyperexcitability disorders. In particular, Kv7.2/7.3 mutants have been clearly associated with epileptic encephalopathies (DEE) as well as with a spectrum of focal epilepsy disorders, often associated with developmental plateauing or regression. Nevertheless, there is a lack of available therapeutic options, considering that retigabine, the only molecule used in clinic as broad-spectrum Kv7 agonist, has been withdrawn from the market in the late 2016. This is why several efforts have been made both by both academia and industry in the search for suitable chemotypes acting as Kv7.2/7.3 agonists. In this context, in silico methods have played a major role, since the precise structures of different Kv7 homotetramers have been only recently disclosed. In the present review the computational methods used for the design of Kv.7.2/7.3 small molecule agonists and the underlying medicinal chemistry are discussed in the context of their biological and structure-function properties.

In this paper, we introduce a specialized, fully automated experimental arrangement designed for assessing the effectiveness of augmented and virtual reality technologies in healthcare setting and explore its design principles focusing on depth sensor characterization. The main goal of this project is to establish a robust benchtop platform for the quantitative evaluation of extended reality technologies in a controlled environment, emphasizing the practical assessment of augmented reality head-mounted devices. We outline a design concept and considerations for an experimental configuration aimed at creating a realistic testing environment. This apparatus comprises an observation platform mounted on a three-degree-of-freedom motorized system and a test phantom stage. Focusing on real-case scenarios, we utilized a range of sensors, including readily available range-sensing cameras and commercial augmented reality headsets, such as the Intel RealSense L515 LiDAR camera, mounted on the motion control system. The setup enabled automated data collection and facilitates controlled assessments and validation of various facets of augmented reality technologies, including object, hand, and head tracking, as well as static and dynamic image registration. This paper describes the system architecture, the process of automated data collection, and presents several evaluation studies conducted with this setup, providing insights into the quality and temporal noise associated with static or moving objects or observers in augmented reality applications.

Lead optimization in drug discovery is a crucial phase where initial hits are refined into compounds with improved pharmacological properties. While traditional methods rely on manual experimentation and modifications, AI-driven techniques have revolutionized this process by leveraging big data and predictive modeling. This review explores how AI-driven approaches accelerate lead optimization, showcasing examples like deep neural networks and reinforcement learning. Integration of multi-omic data and experimental validation further enhances AI-driven strategies. The future lies in refining these AI methods, democratizing tools, and interdisciplinary collaboration to streamline drug discovery and address medical needs efficiently.

To improve efficacy and safety of sunscreen emulsions, UV‐filter skin permeation could be kept as low as possible by reducing UV‐filter content or changing emulsion composition. In this work, different lipid phase ratio (12, 14 and 16% w/w) were assessed for their ability to affect the technological and sensory properties of O/W emulsions in which bemotrizinol (BMTZ), a broad spectrum sunscreen agent, was incorporated free or loaded into nanostructured lipid nanocarriers (NLC) to reduce its release from the vehicle and, hence, its skin permeation. The following technological properties were evaluated in vitro by suitable methodologies: spreadability, viscosity, pH, occlusion factor, BMTZ release, sun protection factor (SPF). Sensory attributes were assessed by trained panelists in three different phases: before/during pick‐up, rub‐in, after application. Raising the lipid phase ratio led to an increase of viscosity and to a corresponding drop of spreadability while the incorporation of BMTZ‐loaded NLC determined a decrease of occlusion factor and an increase of SPF. No BMTZ release was observed from all emulsions. Sensory attributes were mainly affected by lipid phase ratio. These results suggest that lipid phase ratio and BMTZ incorporation into NLC could contribute to determine technological and sensory properties of O/W emulsions.

In the dynamic landscape of drug discovery, Computer-rAided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADD's historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADD's predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. The paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

Mycobacterium tuberculosis (Mtb) is the etiological agent of tuberculosis (TB), a disease that alt-hough preventable and curable, remains a global epidemic due to the emergence of resistance and a latent form responsible for a long period of treatment. Drug discovery in TB is a challenging task due to the heterogeneity of the disease, the emergence of resistance and an uncomplete knowledge of the pathophysiology of the disease. The limited permeability of the cell wall and the presence of multiple efflux pumps remain a major barrier to achieve effective intracellular drug accumulation. While the complete genome sequence of Mtb has been determined and several potential protein targets have been validated, the lack of adequate models for in vitro and in vivo studies is a limit-ing factor in TB drug discovery programs. In current therapeutic regimens, less than 0.5% of bac-terial proteins are targeted being the biosynthesis of the cell wall and the energetic metabolism two of the most important processes exploited for TB chemotherapeutics. This review provides an overview on the current challenges in TB drug discovery and emerging Mtb druggable proteins, and how chemical probes for protein profiling enabled the identification of new targets and bi-omarkers, paving the way to disruptive therapeutic regimens and diagnostic tools.

Background: COVID-19 is a critical pandemic that has affected human communities worldwide, and there is an urgent need to develop effective drugs. Although there are a large number of candidate drug compounds that may be useful for treating COVID-19, the evaluation of these drugs is time-consuming and costly. Thus, screening to identify potentially effective drugs prior to experimental validation is necessary. Method: In this study, we applied the recently proposed method tensor decomposition (TD)-based unsupervised feature extraction (FE) to gene expression profiles of multiple lung cancer cell lines infected with severe acute respiratory syndrome coronavirus 2. We identified drug candidate compounds that significantly altered the expression of the 163 genes selected by TD-based unsupervised FE. Results: Numerous drugs were successfully screened, including many known antiviral drug compounds such as C646, chelerythrine chloride, canertinib, BX-795, sorafenib, sorafenib, QL-X-138, radicicol, A-443654, CGP-60474, alvocidib, mitoxantrone, QL-XII-47, geldanamycin, fluticasone, atorvastatin, quercetin, motexafin gadolinium, trovafloxacin, doxycycline, meloxicam, gentamicin, and dibromochloromethane. The screen also identified ivermectin, which was first identified as an anti-parasite drug and recently the drug was included in clinical trials for SARS-CoV-2. Conclusions: The drugs screened using our strategy may be effective candidates for treating patients with COVID-19.

Artificial intelligence AI or machine learning has proven to be a potential activity in the health and biomedical sciences. Previous research it has found that AI can learn new data and transform it into the useful knowledge. In the field of pharmacology, the aim is to design more efficient and novel vaccines using this method which are also cost effective. The underlying fact is to predict the molecular mechanism and structure for increased likelihood of developing new drugs. Clinical, electronic and high resolution imaging datasets can be used as inputs to aid the drug development niche. Moreover, the use of comprehensive target activity has been performed for repurposing a drug molecule by extending target profiles of drugs which also include off targets with therapeutic potential providing a new indication.

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) continues to be a global threat due to its ability to evolve and generate new subvariants, leading to new waves of infection. Additionally, other coronaviruses like Middle East respiratory syndrome coronavirus (MERS-CoV, formerly known as hCoV-EMC) which first emerged in 2012, persists and continues to present a threat of severe illness to humans. The continued identification of novel coronaviruses, coupled with the potential for genetic recombination between different strains, raises the possibility of new coronavirus clades of global concern emerging. As a result, there is a pressing need for pan-CoV therapeutic drugs and vaccines. After extensive optimization of an HCV protease inhibitor screening hit, a novel 3CLPro inhibitor (MK-7845) was discovered and subsequently profiled. MK-7845 exhibited nanomolar in vitro potency with broad spectrum activity against a panel of clinical SARS-CoV-2 subvariants and MERS-CoV. Furthermore, when administered orally, MK-7845 demonstrated a notable reduction of viral burdens by >6 log orders in the lungs of transgenic mice infected with SARS-CoV-2 (K18-hACE2 mice) and MERS-CoV (K18-hDDP4 mice).

The massive amount of human biological, imaging and clinical data produced by multiple and diverse sources, necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels as well as their orchestration and links are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof of concept study cases. Personalized simulations over the actual anatomy of a patient have been carried out. The hypermodel has also applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin based clinical decision support system and the core of future in silico trial platforms, although additional retrospective adaptation and validation is necessary.

Mosquitoes conveying Plasmodium store parasites into the skin of the mammalian host. Parasites make a trip through the circulation system to the liver, where they cross a few hepatocytes prior to building up a disease. Inside the last hepatocyte the parasite goes through morphogenesis and afterward abiogenetically partitions to become more than 20,000 blood-infective parasites, called merozoites. On account of P. vivax, P. ovale, and P. cynomolgi, the parasites can stay lethargic in the liver in structures called hypnozoites. The merozoites are delivered once again into the circulation system, where they start the repetitive blood stage. Inside erythrocytes, a little division of parasites separate into male or female gametocytes. These gametocytes are ingested by the mosquito during blood taking care of, where they will duplicate explicitly, in the long run prompting the arrangement of sporozoites

Background. Transferrin receptor 1 (TfR1), glucose transporter 1 (GLUT1), and CD98hc are candidates for targeted therapy at the blood-brain barrier (BBB). Our objective was to challenge the expression of TfR1, GLUT1, and CD98hc in brain capillaries by the histone deacetylase inhibitor (HDACi) valproic acid (VPA). Methods. Primary mouse brain capillary endothelial cells (BCECs) and brain capillaries isolated from mice injected intraperitoneally with VPA were examined using RT-qPCR and ELISA. Targeting to the BBB was performed by injecting monoclonal anti-TfR1 (Ri7217)-conjugated gold nanoparticles measured using ICP-MS. Results. In BCECs co-cultured with glial cells, Tfrc mRNA expression was significantly higher after 6 h VPA returning to baseline after 24 h. In vivo Glut1 mRNA expression was significantly higher in males, but not females', receiving VPA, whereas Cd98hc mRNA expression was unaffected by VPA. TfR1 increased significantly in vivo after VPA, whereas GLUT1 and CD98hc were unchanged. Uptake of anti-TfR1-conjugated nanoparticles was unaltered by VPA in spite upregulated TfR expression. Conclusions. VPA upregulates TfR1 in brain endothelium in vivo and in vitro. VPA does not increase GLUT1 and CD98hc proteins. The increase in TfR1 does not result in higher anti-TfR1 antibody targetability, suggesting targeting sufficiently occurs with available transferrin receptors without further contribution from accessory VPA-induced TfR1.

Dengue virus is a global health problem most prevalent in tropical and subtropical regions. However, after the approval of two vaccines, Dengvaxia, TV003/TV005 of Takeda, for its prevention, problems were reported, like enhanced risks of infection or less efficiency of protection. Since then, more struggles have been in demand for the development of better vaccines. Here, we conducted a new design through an in-silico strategy. Initially, epitopes were chosen based on their antigenicity, immunogenicity, and binding affinity with MHC molecule, while excluded with allergenicity, toxicity and potential risk of antibody dependent enhancement. Subsequently, a core antigen was constructed with selected epitopes and linked with distinct adjuvant proteins to achieve three candidate vaccines, PSDV-1~3. PSDV-2 was chosen for further validation based on their advantage in physicochemical and structural properties. With a panel of simulations, this artificial protein showed tight binding with pattern recognition receptors, good stability, robust immune induction, and ultimately was confirmed as a high quality vaccine candidate. The plasmid for its recombinant expression was then designed accordingly. With our new design, one more choice was obtained for the effective protection from Dengue virus. Further experimental validations are still require to confirm it protection capacity and safety.

The morbidity and mortality caused by invasive fungal infections is increasing across the globe due to developments in transplant surgery, the use of immunosuppressive agents, and the emergence of drug-resistant fungal strains, which has led to a challenge in terms of treatment due to the limitations of three classes of drugs. Hence, it is imperative to establish effective strategies to identify and design new antifungal drugs. Drug repurposing is an effective way of expanding the application of existing drugs. In the last years, various existing drugs have been shown to be useful in the prevention and treatment of the invasive fungi. In this review, we summarize the currently used antifungal agents. In addition, the most up to date information on the effectiveness of existing drugs with antifungal activity is discussed. Moreover, the antifungal mechanisms of existing drugs are highlighted. These data will provide valuable knowledge to stimulate further investigation and clinical application in this field.

Induced pluripotent stem cells (iPSC) are derived from reprogrammed adult somatic cells. These adult cells are manipulated in vitro to express genes and factors essential for acquiring and maintaining embryonic stem cell (ESC) properties. This technology is widely applied in many fields, and there has been much attention to developing iPSC-based disease models to validate drug discovery platforms and study pathophysiological molecular processes underlying disease onset. Especially in neurological diseases, there is a great need for iPSC-based technological research, as these cells can be obtained from each patient and carry the individual’s bulk of genetic mutations and properties. Moreover, iPSC can differentiate into multiple cell types. These are essential characteristics since the study of neurological diseases is affected by limited access to injured sites, in vitro models composed of various cell types, the complexity of reproducing the brain’s anatomy, ethical issues, and the fact that post-mortem cell culture is challenging. Neurodegenerative diseases enormously impact global health due to their high incidence, symptoms severity, and usually lack of effective therapies. Recently, analyses using disease-specific iPSC-based models confirmed their efficacy for testing multiple drugs. This review summarizes the advances in iPSC technology used in disease modeling and drug testing with a primary focus on neurodegenerative diseases, including Parkinson’s and Alzheimer’s diseases.

Alzheimer’s disease (AD) is an age-associated neurodegenerative disorder. A wealth of evidence indicates that the amyloid β (Aβ) aggregates result from dyshomeostasis between Aβ production and clearance, which plays a pivotal role in the pathogenesis of AD. Consequently, therapies targeting Aβ reduction represent a promising strategy of AD intervention. Tetramethylpyrazine nitrone (TBN) is a novel tetramethylpyrazine derivative with the potential for the treatment of AD. Previously, we demonstrated that TBN markedly improved cognitive functions and reduced Aβ, APP, BACE 1 and hyperphosphorylated tau levels in 3 × Tg-AD mice. However, the mechanism by which TBN inhibits Aβ deposition still unclear. In this study, we employed the APP/PS1 mice treated with TBN (60 mg/kg, ig, bid) for six months and N2a/APP695swe cells treated with TBN (300 μM) to explore the mechanism of TBN on the Aβ reduction. Our results indicated that TBN significantly alleviated cognitive impairment and reduced Aβ depositions in APP/PS1 mice. Further investigation of the underlying mechanisms revealed that TBN decreased the expression of APP and BACE1, activated the AMPK/mTOR/ULK1 autophagy pathway and inhibited the PI3K/AKT/mTOR/ULK1 autophagy pathway, as well as decreased the phosphorylation levels of JNK and ERK in APP/PS1 mice. Moreover, TBN was found to significantly reduce the mRNA levels of APP and BACE1, as well as those of SP1, CTCF, TGF-β, and NF-κB, transcription factors involved in regulating gene expression. Additionally, TBN was observed to decrease the level of miR-346 and increase the levels of miR-147 and miR-106a in the N2a/APP695swe cells. These findings indicate that TBN may reduce Aβ levels likely via reducing APP expression by regulating APP gene transcriptional factors and miRNAs, reducing BACE1 expression, and promoting autophagy activities.

Precision metagenomic approaches using Oxford Nanopore Technology (ONT) sequencing has been shown to allow recovery of complete genomes of Escherichia coli O157:H7 from overnight enrichments of agricultural waters. This study tests the applicability of a similar approach for Cronobacter genome recovery from powdered infant formula (PIF) overnight enrichments, where Cronobacter typically dominates the overall microbiome (>90%). In this study, we aimed to test whether using ONT sequencing of overnight PIF enrichments could recover a completely closed Cronobacter genome for further genomic characterization. Ten PIF samples, each inoculated with different Cronobacter strains, covering Cronobacter sakazakii, C. muytjensii, C. dublinensis, C. turicensis, and C. universalis, were processed according to the Bacteriological Analytical Manual (BAM) protocol. qPCR was used for initial screening (detection and quantification) of the overnight enrichments and confirmed that the spiked PIF samples after the overnight enrichment have high levels of Cronobacter (107 to 109 CFU/mL). DNA from overnight PIF enrichments was extracted from the enrichment broth and sequenced using ONT. Results showed that ONT sequencing could accurately identify, characterize, and close the genomes of Cronobacter strains from overnight PIF enrichments in 3 days, much faster than the nearly two weeks required by the current BAM method. Complete genome recovery and species differentiation were achieved. This suggests that combining qPCR with ONT sequencing provides a rapid, cost-effective alternative for detecting and characterizing Cronobacter in PIF, enabling timely corrective actions during outbreaks.

Introduction: The combination of Virtual Screening (VS) techniques with in vivo screening in the zebrafish model is currently being used in tandem for drug development in a faster and more efficient way. Areas covered: We review the different virtual screening techniques, the use of zebrafish as a vertebrate model for drug discovery and the synergy that exists between them. Expert opinion: We highlight the advantages of combining virtual and zebrafish larvae screening for drug discovery. On the one hand, VS is a faster and cheaper tool for searching active compounds and possible candidates for therapy than in vivo screening when processing large compound libraries. On the other hand, zebrafish larvae form a vertebrate model which allows in vivo screening of large amounts of the compounds. Importantly physiology and chemical response are mostly conserved between zebrafish and mammals. The availability of the transgenic and mutant zebrafish lines allows an analysis of a specific phenotype upon treatment along with toxicity, off-target effect, side effects, and dosage. Advantages of VS, in vivo whole animal approach screening, and the screening combinations are also reviewed.

Non-systemic oral drugs, which do not get absorbed through the gastrointestinal membrane, are an important group of compounds for targeting diseases residing within the gastrointestinal lumen. However, they require a set of design principles not sufficiently covered by traditional druglikeness metrics, such as Lipinski’s Rule of 5. These druglikeness metrics fail to accurately identify true negative outcomes; such is the requirement for novel cheminformatic based approaches for the discovery of non-systemic small molecules. One such example of these newer metrics is the fraction of SP3-hybridised carbon atoms (fSP3) which has shown promising application as an identifier of poor oral-bioavailability. Herein, we discuss the application of fSP3 in the drug discovery process in a number of case studies, including our own work discovering non-systemic fructose scavengers for the treatment of fructose malabsorption.

Ebola Virus Disease (EVD) is one of the most lethal transmissible infections characterized by a high fatality rate, and caused by members of the Filoviridae family. The recent large outbreak of EVD in West Africa (2013-2016), highlighted the worldwide danger of this disease and its impact on global public health and economy. The development of highly needed anti-Filoviridae antivirals has been so far hampered by the shortage of tools to study their life cycle in vitro, and therefore screen for potential active compounds outside a biosafety level-4 (BSL-4) containment. Importantly, the development of surrogate models to in vitro study of Filoviridae entry in a BSL-2 setting, such as viral pseudotypes and Ebola virus like particles, tremendously boosted both our knowledge on viral life cycle and the identification of promising anti-Filoviridae compounds interfering with viral entry. In this context, the combination of such surrogate systems with large-scale small molecule compounds and haploid genetic screenings, as well as rational drug design and drug repurposing approaches will prove priceless in our quest for the development of a treatment for EVD.

KLEPTOSE® CRYSMEB is a methylated cyclodextrin derivative displaying less methylated group substitution than randomly methylated cyclodextrin. It has demonstrated an impact on atherosclerosis and neurological diseases. linked in part to cholesterol complexation and immune response, but the impact on inflammatory cascade pathways was not clear. Thus, the impact of KLEPTOSE® CRYSMEB on various pharmacological targets was assessed using human umbilical vein endothelial cells in physiological and inflammatory conditions, followed by screening against twelve human primary cell-based systems designed to model complex human tissue and disease biology of the vasculature, skin, lung, and inflammatory tissues using the BioMAP® Diversity PLUS® Panel. Finally, the anti-inflammatory mechanism was investigated on peripheral blood mononuclear cells to evaluate anti-inflammatory or pro-resolving properties. The results showed that KLEPTOSE® CRYSMEB can modulate the immune system in vitro and potentially manage vascular issues by stimulating the expression of molecules involved in the crosstalk between immune cells and other cell types. It showed anti-inflammatory effects driven by pro-inflammatory cytokine secretion inhibition and could have different impacts on different tissue types. Moreover, this cyclodextrin showed no clear impact on pro-resolving lipid mediators. Finally, it appeared that the mechanism of action of KLEPTOSE® CRYSMEB seems to not be shared by well-known anti-inflammatory molecules.

Biological systems are highly regulated. They are also highly resistant to sudden perturbations enabling them to maintain the dynamic equilibrium essential for sustenance of life. This robustness is conferred by regulatory mechanisms that influence the activity of enzymes/proteins within their cellular context, to adapt to changing environmental conditions. However, the initial rules governing the study of enzyme kinetics were tested and implemented for mostly cytosolic enzyme systems that were easy to isolate and/or recombinantly express. Moreover, these enzymes lacked complex regulatory modalities. Now, with academic labs and pharmaceutical companies turning their attention to more complex systems (for instance, multi-protein complexes, oligomeric assemblies, membrane proteins and post-translationally modified proteins), the initial axioms defined by Michaelis-Menten (MM) kinetics are rendered inadequate and the development of a new kind of kinetic analysis to study these systems is required. The current review strives to present an overview of enzyme kinetic mechanisms that are atypical and, oftentimes, do not conform to the classical MM kinetics. Further, it presents initial ideas on the design and analysis of experiments in early drug-discovery for such systems, to enable effective screening and characterisation of small-molecule inhibitors with desirable physiological outcomes.

Inhibition of amyloid fibril formation could benefit patients with systemic amyloidosis. In this group of diseases, deposition of amyloid fibrils derived from normally soluble proteins leads to progressive tissue damage and organ failure. Although many small molecules have been proposed as inhibitors of amyloid formation, few have been successful in clinical trials. Amyloid formation is complex and several individual steps could be targeted by small molecules. However, the exact mechanism of action for a molecule is often not known, which impedes medicinal chemistry efforts to develop more potent molecules. Furthermore, commonly used assays are prone to artifacts that must be controlled for. Here, potential mechanisms by which small molecules could inhibit aggregation of immunoglobulin light chain dimers, the precursor proteins for AL amyloidosis are studied in assays that recapitulate different aspects of amyloidogenesis in vitro. One molecule reduced unfolding-coupled proteolysis of light chains, but no molecules inhibited aggregation of light chains or disrupted pre-formed amyloid fibrils. This work demonstrates the challenges associated with drug development for amyloidosis, but also highlights the potential to combine therapies that target different aspects of amyloidogenesis.

G protein-coupled receptors (GPCRs) are critical drug targets. GPCRs convey signals from the extracellular to the intracellular environment through G proteins. There is evidence that some ligands that bind to the GPCRs activate different downstream signaling pathways. G protein activation or -arrestin biased signaling involves ligands binding to receptors and stabilizing conformations that trigger a specific pathway. Molecular dynamics (MD) simulations are especially valuable for obtaining detailed mechanistic information, including identification of allosteric sites and understanding modulators' interactions between receptors and ligands. Here, we highlight recent simulation studies and methods used to study biased G protein-coupled receptor signaling and their conformational dynamics. We also highlight applications of MD simulations to drug discovery.

In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry and science related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, rational design of combination therapies and the global response to the Covid19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human focused, systems-based approach to drug discovery and research.

2019-n-CoV is the seventh coronavirus known to infect humans and cause serious illness, and due to its lethality there was a worldwide mobilization for the development of new drugs and new vaccines for Covid-19. Therefore, the present work aims to carry out a virtual screening of terpenes with potential anti SARS-CoV-2 activity. To perform the ligand-based virtual, a model was constructed regarding the compounds which already showed inhibitory activity of the SARS-CoV-2. The test series corresponded to terpenes isolated in the Fabaceae Family. The compounds that were most likely to activity were subjected to Molecular Docking and the best poses obtained were subjected to Molecular Dynamics (MD) simulations carried. The molecules under study were submitted to in vitro evaluation to assess inhibitory activity of compounds against SARS-CoV-2 in a phenotypic screening model of the virus. The compounds selected by in silico screening corresponded to triterpenoids from the lupane series. In the in vitro test, both demonstrated activity against the SARS-CoV-2 virus. The virtual screening performed managed to identify the compounds with the highest probability of activity, as well as the most stable compounds, and the method used was validated by the in vitro study carried out.

The field of biobanking human biospecimens has evolved significantly, transitioning from the basic, often poorly documented collection of clinical leftovers kept privately to well-organized and extensively documented collections overseen by both commercial and non-profit platforms. The increasing demand for high-quality and clinically annotated biospecimens is propelled by unprecedented levels of health research activities. Meeting this growing demand presents both new opportunities and challenges, particularly in developing strategies to establish international biospecimen sourcing (IBS) platforms. These platforms aim to facilitate collaboration among biobanks to address future biospecimen needs. In this manuscript, we delve into the advantages and challenges of establishing IBS platforms to provide high-quality and cost-effective biospecimens to drive drug discovery research, ultimately leading to improved health and quality of life for everyone.

Preventing or treating infections at implantation sites where the risk of bacterial contamination is high requires the development of intelligent drug delivery systems. In this work, honeycomb structures as a potential drug carrier were obtained for the first time in a fast, one-step anodization process of titanium grade 4, lasting only 60 seconds. Fourth generation oxide nanotube layers were obtained from an aqueous solution of 0.5% AgNO3 in the voltage range from 50 to 80 at 15(1) °C. The FE-SEM method showed the length of the oxide nanotubes from 1.02(2) µm at 50 V to 230(8) nm at 80 V. The EDS analysis revealed that during anodizing, silver particles are deposited on the Ti G4 up to 2.2(5) wt.%. Electrochemical measurements by open circuit potential and anodic polarization curves methods proved the improvement of in vitro corrosion resistance as a result of Ti G4 anodizing. Assessment of the anodized Ti G4 as gentamicin sulfate carrier carried out using ATR-FTIR and UV-VIS absorption spectroscopy revealed that the obtained oxide nanotube layers can be used for application of drugs directly to the tissue around the implant. This method of application allows full use of the therapeutic dose of the antibiotic used.

African swine fever virus (ASFV), a highly contagious pathogen characterized by a complex structure and a variety of immunosuppression proteins, causes hemorrhagic, acute, and aggressive infectious disease that severely injure the pork product and industry. However, there is no effective vaccine or treatment. The main reasons are not only the complex mechanisms that lead to immunosuppression, but also the unknown functions of various proteins. This review summarizes the interaction between ASFV and host immune system, along with the involvement of virulence related genes and proteins, as well as the corresponding molecular mechanism of immunosuppression of ASFV, encompassing pathways such as cGAS-STING, NF-κB, JAK/STAT, apoptosis, and other modulation. The aim is to summarize the dynamic process during the ASFV infection and entry into host cell, provide a rational insight into development of vaccine and a better clear knowledge of how ASFV impacts host.

Since the first published genome sequence of Trypanosoma cruzi in 2005, there has been tremendous technological advance in genomics, reverse genetics, and assay development for this elusive pathogen. However, there is still an unmet need for new and better drugs to treat Chagas disease. Here we introduce a T. cruzi assay strain that is useful for drug research as well as basic studies in host-pathogen interaction. Trypanosoma cruzi STIB980 is a strain of discrete typing unit TcI that grows well in culture as axenic epimastigotes or intracellular amastigotes. We have evaluated the optimal parameters for genetic transfection and constructed derivatives of T. cruzi STIB980 that express reporter genes for fluorescence- or bioluminescence-based drug efficacy testing, as well as a Cas9-expressing line for CRISPR/Cas9-mediated gene editing. The genome of T. cruzi STIB980 was sequenced by combining short-read Illumina with long-read Oxford Nanopore technologies. The latter served for the primary assembly, the former to correct mistakes and fill the gaps. This resulted in a high-quality nuclear haplotype assembly of 28 Mb in 400 contigs, containing 10,043 open-reading frames with a median length of 1077 bp. We believe that T. cruzi STIB980 is a useful addition to the antichagasic toolbox, and propose that it can serve as a DTU TcI reference strain for drug efficacy testing.

Background: Circulating rare cells participate in breast cancer evolution as systemic components of the disease and thus, are a source of theranostic information. Exploration of cancer-associated rare cells is in its infancy. We determined the validity of a rare-cell detection platform and demonstrated the usefulness of a certain rare cell subset as a novel approach to characterize the breast cancer system. Methods: Linearity of the Rarmax® platform was established using a spike-in approach. The platform includes red blood cell lysis, leukocyte depletion and high-resolution fluorescence image recording. Rare cell analysis was conducted on 28 samples (before and after surgery) from 14 patients with breast cancer, 20 healthy volunteers and 9 non-cancer control volunteers. In-depth identification of rare cells, including circulating tumor cells, endothelial-like cells, erythroblasts and inflammation-associated cells, was performed using a phenotype and morphology-based classification system. Results: The platform performed linearly over a range of 5-950 spiked cells, with an average recovery of 84.6%. Circulating epithelial and endothelial-like cell subsets have been demonstrated to be associated with or independent of cancer with tumor presence. Furthermore, certain cell patterns may be associated with treatment-related adverse effects. The sensitivity in detecting tumor-presence and cancer-associated abnormality before surgery was 50% and 85.7%, respectively and the specificity was 100% and 96.6%, respectively. Conclusion: The present study supports the idea of a cancer-associated rare cell abnormality to represent tumor entities as well as systemic cancer. The latter is independent of the apparent clinical cancer.

Genome-wide sequencing data play an important role in evaluating the genomic level differences between superior and poor-quality crop plants and improving our understanding of molecular association with desired traits. We analyzed the obtained 92,921,066 raw reads from genome-wide resequencing of Cucumis sativus var. hardwickii through in-silico approaches and mapped to the reference genome of Cucumis sativus to identify the genome-wide single nucleotide polymorphisms (SNPs) and Single nucleotide variations (SNV). Here, we report 19, 74,213 candidate SNPs including 1,33,468 insertions and 1,43,237 deletions and 75 Indels genome-wide. A total of 2228224 identified variants were classified into four classes including 0.01% sequence alteration, 5.94% insertion, 6.37% deletion and 87.66% SNV respectively. These variations can be a major source of phenotypic diversity and sequence variation within the species. During the present study these variants were also utilized to resolve orthologous relationships among the genomes of C. melo and C. sativus var. hardwickii. Overall, the discovery of SNPs and genomic variants may help predict the plant response to certain environmental factors and can be utilized to improve crop plants' economically important traits.

The drug discovery process is a rigorous and time-consuming endeavor, typically requiring several years of extensive research and development. Although classical machine learning (ML) has proven successful in this field, its computational demands in terms of speed and resources are significant. In recent years, researchers have sought to explore the potential benefits of quantum computing (QC) in the context of ML, leading to the emergence of Quantum Machine Learning (QML) as a distinct research field. The objective of the current study is twofold: first, to present a review of the proposed QML algorithms for application in the drug discovery pipeline, and second, to compare QML algorithms with their classical and hybrid counterparts in terms of their efficiency. A query-based search of various databases took place, and five different categories of algorithms were identified in which QML was implemented. The majority of QML applications in drug discovery are primarily focused on the initial stages of the drug discovery pipeline, particularly with regard to the identification of novel drug-like molecules. Comparison results revealed that QML algorithms are strong rivals against the classical ones and a hybrid solution is the recommended approach at present.

Bladder cancer (BC) is one of the most lethal genitourinary malignancies owing to its propensity for recurrence and poor survival. The biochemical pathway, signal transducer and activator of transcription 3 (STAT3), has gained significance as a molecular pathway that promotes proliferation, invasion, and chemoresistance. In this study, we explored the targeting of STAT3 with TTI-101 and SH5-07 in bladder cancer (BCa) and elucidated the mechanisms in three-dimensional (3D) spheroid and tumoroid models. We optimized the growth of spheroids from human, rat, and mouse BCa cell lines (J82, NBT-II, and MB49, respectively), and tumoroids from BBN (N-butyl-N-(4-hydroxybutyl)-nitrosamine) induced rat bladder tumors. Cell viability was assessed using MTT and trypan blue assays. ATP production, ROS production, and calcium AM (CA)/EtBr staining were used to measure the spheroid and organoid inhibition and mitochondrial function. Western blot analysis was performed to evaluate the pharmacodynamic markers involved in cell proliferation, apoptosis, cancer stem cells (CSCs), and STAT3 signaling in BCa. We found that targeting STAT3 (using TTI-101 and SH5-07) significantly reduced the proliferation of BCa spheroids, and tumoroids, which was accompanied by decreased expression of pSTAT3, Cyclin D1, and PCNA. Our data also demonstrated that treatment with STAT3 inhibitors induced ROS production and cell death in BCa spheroids and organoids. STAT3 inhibition induced cell death was associated with the activation of caspase 3/7 and PARP cleavage. Moreover, TTI-101 and SH5-07 targets cancer stem cells by downregulating the expression of CD44 and CD133 in 3D models. This study provides the first evidence for the prevention of bladder cancer with small-molecule inhibitors TTI101 and SH5-07 via suppression of CSCs and STAT3 signaling.

Calcium signaling and lipid metabolism are crucial in the infection processes of Apicomplexans parasites. Thus, enzymes involved in these processes can be drug targets against Apicomplexans. For example, in malaria infection, in-depth research into lipid metabolic pathways is crucial in understanding the parasite's infection cycle, particularly during its erythrocytic infection cycle, which has been demonstrated to be a critical stage during the disease progression. Most enzymes that play critical roles in lipid synthesis and calcium signaling have been extensively studied; nonetheless, a vast knowledge gap still exists, especially on specific enzymes and their roles in the transmission and progression of the Apicomplexan parasites. Many types of infections caused by Apicomplexans are life-threatening and hard to treat. These intracellular parasites proliferate within parasitophorous vacuoles in their host cells. As the parasites multiply, they need to meet their high demand for nutrients such as amino acids and lipids. They can acquire nutrients through scavenging and biosynthesis. This review summarizes a few interesting, unique pathways in selected Apicomplexa and how such unique pathways can be targets for drugs.

Onchocerciasis treatment and control relies mainly on the use of ivermectin which has high activity against the microfilarial stage of Onchocerca volvulus but limited activity against the long lived, tissue dwelling adult nematodes. As this neglected tropical disease has now been targeted for elimination there is an urgent need for new drugs to combat these parasites, ideally with macrofilaricidal activity. In this study we have examined the anti-Onchocerca activity of a range of existing FDA approved drugs with a view to repurposing, which can lead to rapid and relatively inexpensive development. From the Pharmakon-1600 library, 106 drugs were selected and tested against O. gutturosa adult male parasites using a concentration of 1.25 x 10-5 M in an in vitro 5-day standard assay to assess motility and viability (using MTT/formazan colorimetry). The findings revealed that 44 drugs produced marginal/moderate activity (50-99% motility and/or MTT reductions) including cefuroxime sodium, methenamine, primaquine phosphate, rivastigmine tartrate, while 23 drugs produced good activity (100% motility reductions and significant MTT reductions), including atovaquone, isradipine, losartan, rifaximin, cefaclor and pyrantel pamoate. Although this study represents only a first step, some of the identified hits indicate there are potential anti-Onchocerca drug candidates worthy of further investigation.

Delivering nucleic acid therapeutics across cell membranes is a significant challenge. Cell-penetrating peptides (CPPs) containing arginine (R), tryptophan (W), and histidine (H) show promise for siRNA delivery. To improve siRNA delivery and silencing a model STAT3 gene, we hypothesized that oleyl acylation to CPPs, specifically (WRH)n, would enhance STAT3 silencing efficiency in breast and ovarian cancer cells. Using Fmoc/tBu solid-phase peptide chemistry, we synthesized, purified, and characterized the oleyl-conjugated (WRH)n (n = 1-4) peptides. The peptide:siRNA complexes were non-cytotoxic at N/P 40 (~20 μM) against MDA-MB-231, MCF-7, SK-OV-3, and HEK-293 cells after 72 h incubation. All peptide:siRNA complexes showed serum stability at N/P ≥40. The synthesized conjugates, with a diameter of <100 nm, formed nano complexes with siRNA and exhibited stable range of zeta potential values (13-18 mV at N/P 40). Confocal microscopy and flow cytometry analysis provided qualitative and quantitative evidence of successful cellular internalization of siRNA. Peptides oleyl-(WRH)3 and oleyl-(WRH)4 showed ~60% and ~75% cellular uptake of siRNA, respectively, in both MDA-MB-231 and SK-OV-3 cells. Western blot analysis of oleyl-(WRH)4 demonstrated effective silencing of the STAT-3 gene, with ~75% silencing in MDA-MB-231 cells and ~45% in SK-OV-3 cells.

Salmonella sp. is a globally prevalent organism responsible for causing salmonellosis, a foodborne illness. Salmonella Typhimurium represents a type of Salmonella sp. not associated with typhoid fever but capable of causing stomach and intestinal inflammation or severe infection. In the present study, 150 uncharacterized proteins of S. Typhimurium were randomly selected from UniProtKB and analyzed using TMHMM, PROSITE, STRING, DEG, and BLASTp. Results indicated that 32 uncharacterized proteins (21%) were predicted to be transmembrane proteins involved in various biological pathways. Among them, 30 transmembrane proteins were predicted to be essential and non-host homologous. This study's findings suggest their potential as drug targets against salmonellosis.

The PAX8/PPARγ rearrangement, producing the PAX8–PPARγ fusion protein (PPFP), is thought to play an essential role in the oncogenesis of thyroid follicular tumors. To identify PPFP-targeted drug candidates and establish an early standard of care for thyroid tumors, we performed ensemble docking-based compound screening. Specifically, we investigated which pocket structure should be adopted to search for a promising ligand compound for the PPFP; the position of the ligand binding pocket on the PPARγ side of the PPFP is common to that of PPARγ; however, the shape is slightly different between them due to environmental factors. We developed a method for selecting a PPFP structure with a relevant pocket and high prediction accuracy for ligand binding. This method was validated using PPARγ, whose structure and activity values are known for many compounds. Then, we performed docking calculations to PPFP for 97 drug or drug-like compounds with a thiazolidine backbone, which is one of the characteristics of ligands that bind well to PPARγ, registered in the DrugBank database. Furthermore, the binding affinities of promising ligand candidates were estimated more reliably using the molecular mechanics-Poisson–Boltzmann surface area method. Thus, we propose promising drug candidates for the PPFP with a thiazolidine backbone.

Despite available global efforts and funding, Tuberculosis (TB) continues to affect a considerable number of patients worldwide. Policy makers and stakeholders set clear goals to reduce TB incidence and mortality, but the emergence of multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) complicate the reach of these goals. Drug-resistance TB needs to be diagnosed rapidly and accurately to effectively treat patients, prevent the transmission of MDR-TB, minimise mortality, reduce treatment costs and avoid unnecessary hospitalisations. In this narrative review, we provide a comprehensive overview of laboratory methods for detecting drug resistance in MTB, focusing on phenotypic, molecular and other drug susceptibility testing (DST) techniques. We found a large variety of methods used, with the BACTEC MGIT 960 being the most common phenotypic DST and the Xpert MTB/RIF being the most common molecular DST. We emphasise the importance of integrating phenotypic and molecular DST to address issues like resistance to new drugs, heteroresistance, mixed infections and low-level resistance mutations. Notably, most of the analysed studies adhered to the outdated definition of XDR-TB and did not consider the pre-XDR definition, thus posing challenges in aligning diagnostic methods with the current landscape of TB resistance.

Intermolecular interactions are the fabrics underlying almost all processes in living organisms, where two cornerstone concepts, intermolecular binding affinity (K d ) and binding energy (ΔG), have long been established to physically describe the strengths of biomolecular interactions, e.g., drug-target K d and ΔG to describe the strength of drug-target interaction. The past two-three years saw a big step forward in the use of artificial intelligence (AI) in structural biology (e.g., AlphaFold for protein structure prediction) and drug discovery & design. In light of the roles of K d and ΔG in drug discovery & design, the speed of this AI progress raises a question of what’s next for its practical application in the pharmaceutical industry, in addition to a system-wide account of biomolecular structures and motions. Last August, the concept of a general intermolecular binding affinity calculator (GIBAC) was for the first time coined and proposed in an MDPI-published preprint. Here, this article puts forward an updated conceptual and practical framework of GIBAC, including its inception, definition, construction, practical applications, technical challenges and limitations, and future directions. Moreover, this article argues that the time is now ripe for the construction of such an accurate, precise and efficient GIBAC to be on the agenda of the entire drug discovery & design community, to ensure its applicability & reliability, and to enhance its value in drug R&D in future.

Aims: In late December 2019, early reports predicted the onset of a potential Coronavirus outbreak in china, given the estimate of a reproduction number for the 2019 Novel Coronavirus (COVID-19). Because of high ability of transmission and widespread prevalence, the mortality of COVID-19 infection is growing fast worldwide. Absent of an anti-COVID-19 has put scientists on the urge to repurpose already approved therapeutics or to find new active compounds against coronavirus. Here in this study, a set of computational approaches were performed in order to repurpose antivirals for dual inhibition of the frontier proteases involved in virus replication, papain-like protease (PLpro; corresponding to nsp3) and a main protease (Mpro), 3C‑like protease (3CLpro; corresponding to nsp5). Materials and Methods: In this regard, a rational virtual screening procedure including exhaustive docking techniques was performed for a database of 160 antiviral agents over 3CLpro and PLpro active sites of SARS-CoV-2. The compounds binding energies and interaction modes over 3CLpro and PLpro active sites were analyzed and ranked with the aid of free Gibbs binding energy. The most potent compounds, based on our filtering process, are then proposed as dual inhibitors of SARSC-CoV-2 proteases. Key findings: Accordingly, seven antiviral agents including two FDA approved (Nelfinavir, Valaganciclovir) and five investigational compounds (Merimepodib, Inarigivir, Remdesivir, Taribavirine and TAS106-106) are proposed as potential dual inhibitors of the enzymes necessary for RNA replication in which Remdesivir as well as Inagrivir have the highest binding affinity for both of the active sites. Significance: The mentioned drug proposed to inhibit both PLpro and 3CLpro enzymes with the aim of finding dual inhibitors of SARSC-CoV-2 proteases.

As the world population grows, fulfilling the need for biomaterials in the healthcare system becomes increasingly important. Biomaterials, which include metals, polymers, ceramics, and composites, play an important role in the creation of medical devices that improve people's lives. The European Society for Biomaterials described biomaterials as non-biological materials used in medical devices to interact with biological systems. This definition has developed to encompass materials that treat, assess, or replace tissues or bodily functions. Biocompatibility, as defined by the FDA, is a key feature of biomaterials, as is the requirement to fulfil design requirements such as mechanical performance, geometry, and electrical control. Over the last decade, advances in regenerative medicine and tissue engineering have boosted biomaterials' usage in medical applications dramatically. Despite its promise, scientists, engineers, and physicians continue to face significant challenges due to the complexity of biomaterials. Biomaterials have various benefits, including biodegradability, non-toxicity, and biocompatibility, making them extremely useful for replacing, sustaining, or improving biological activities in damaged tissues. They are used in a wide range of applications, including tissue engineering implants, human tissue healing and regeneration, medication delivery systems, and biosensors. This study gives insights into the fundamental characteristics, case studies, and ethical issues associated with the usage and use of biomaterials, further examining their potential and significance in healthcare and medicine.

Proteolysis Targeting Chimeras (PROTACs) describe compounds that bind to and induce degradation of a target by simultaneously binding to a ubiquitin ligase. More generally referred to as bifunctional degraders, PROTACs have led the way in the field of targeted protein degradation (TPD) with several compounds currently undergoing clinical testing. Alongside bifunctional degraders, single-moiety compounds, or molecular glue degraders (MGDs), are increasingly being considered as a viable approach for development of therapeutics, driven by advances in rational discovery approaches. This review focusses on drug discovery with respect to bifunctional and molecular glue degraders within the ubiquitin proteasome system, including analysis of mechanistic concepts and discovery approaches, with an overview of current clinical and pre-clinical degrader status in oncology, neurodegenerative and inflammatory disease.

Malaria parasite harbors an essential prokaryotic-like organelle characterized by multiple mem-branes, akin to a plastid. This organelle, known as the apicoplast, evolved from secondary en-dosymbiosis. In this event, a photosynthetic alga was engulfed by a eukaryotic host and subsequently transformed into a symbiotic entity from its free-living origin. As a result, the apicoplast transformed into a remnant structure housing a circular genome, enveloped by four membranes. Concurrently, the host cell relinquished significant metabolic pathways, and a substantial portion of the apicoplast genome migrated to the host cell, establishing a symbiotic relationship. This unique symbiotic bond designates the apicoplast as a constrained environment for diverse metabolic pathways, vital for parasite survival. Notably, the non-mevalonate biosynthesis (MEP/DOXP) pathway is identified as pivotal for the parasite's viability across parasitic stages. In this study, we conducted a comparative analysis of Plasmodium falciparum MEP pathway enzymes, juxtaposing them with their counterparts in other apicomplexans, plants, and pathogenic microorganisms to uncover the conserved traits of these enzymes. Additionally, predictive modeling was employed to shed light on the protein structures, with specific emphasis on elucidating the active site characteristics and examining their binding affinities with various substrates and inhibitors. This article presents a comprehensive in-silico analysis of MEP pathway enzymes, showcasing their potential as prospective targets for innovative antimalarial drugs. The elucidation of these enzymes' intricate functions provides a foundation for the development of advanced therapeutic strategies in the battle against malaria.

Trypanosomatids are mainly responsible for leishmaniasis, sleeping sickness, and Chagas disease, which are the most challenging among the neglected tropical diseases due to the problem of drug resistance. Although problems of target deconvolution and polypharmacology are encountered, a target-based approach is a rational method for screening drug candidates targeting a biomolecule that causes diseases. The present study aims to summarize the latest information re-garding potential inhibitors of squalene synthase and farnesyl phosphate synthase with anti-Trypanosomatidae activity. The information was obtained by referencing textbooks and major scientific databases from their inception until April 2023. Based on in vitro experiments, more than seventy compounds were reported to inhibit squalene synthase and farnesyl diphosphate synthase. Among these compounds, more than 30 were found to be active in vitro against Trypanosomatidae, inferring that these compounds can be prospected as scaffolds for the development of new drugs against trypanosomatid-related infections. Over-all, natural and synthetic products can inhibit enzymes that are crucial for the survival and virulence of trypanosomatids. Moreover, in vitro experiments have confirmed the activity of more than half of these inhibitors using cell-based as-says. Nevertheless, additional studies on the cytotoxicity, pharmacokinetics, and lead optimization of potent anti-Trypanosomatid compounds should be investi-gated.

Glioblastoma (GBM) is the most common primitive tumor in adult central nervous system (CNS), classified as grade IV according to WHO 2016 classification. GBM shows a poor prognosis with an average survival of approximately 15 months, representing an extreme therapeutic challenge. One of its distinctive and aggressive features is aberrant angiogenesis, which drives tumor neovascularization, representing a promising candidate for molecular target therapy. Although several pre-clinical studies and clinical trials have shown promising results, anti-angiogenic drugs have not led to a significant improvement in overall survival (OS), suggesting the necessity of identifying novel therapeutic strategies. Metformin, an anti-hyperglycemic drug of the Biguanides family, used as first line treatment in Type 2 Diabetes Mellitus (T2DM), demonstrated in vitro and in vivo antitumoral efficacy in many different tumors, including GBM. From this evidence, a process of repurposing of the drug has begun, leading to the demonstration of the inhibition of various oncopromoter mechanisms and, consequently, to the identification of the molecular pathways involved. Here, we review and discuss the potential metformin’s antitumoral effects on GBM, inspecting if it could properly act as an anti-angiogenic compound to be considered as a safely add-on therapy in the treatment and management of GBM patients.

With the first case of human immunodeficiency virus (HIV) infection confirmed in US in 1981, the efforts of discovering anti-HIV therapeutics have been continued ever since. Ten years later, the first HIV drug zidovudine (AZT) was developed to inhibit HIV reverse transcriptase. At meantime, scientists were enlightened to discover new drugs of different targets acting on HIV integrase, protease, and host receptors. The advent of combination antiretroviral therapy (cART) is completely a game changer, with high efficiency in suppressing viremia for people with HIV (PWH) by controlling the viral load below the detectable level. On the bright side, the ART treatment has made HIV a chronic infection rather than a fatal disease. However, it cannot eradicate integrated HIV DNA from the host cells, thus the latent viral reservoir has become a lifelong threaten. In this review, we first discuss the scientific history of conventional HIV drug discovery, with more and more anti-HIV agents have been developed to solve drug resistant issue and relieve the side effect. As a complementary therapy, advanced gene editing technologies have been applied to excise HIV provirus from host genome. Within four decades, novel research conducted on HIV treatment and their contributions to eliminate HIV have been altogether summarized in our review.

Tuberculosis (TB) remains a global healthcare crisis with an estimated 10 million new cases and 1.4 million deaths per year TB is caused by infection with the major human pathogen Mycobacte-rium tuberculosis, which is difficult to rapidly diagnose and treat. There is an urgent need for new methods of diagnosis, sufficient in vitro models which capably mimic all physiological condi-tions of the infection, and high-throughput drug screening platforms. Microfluidic-based tech-niques provide single-cell analysis which reduces experimental time, the cost of reagents, and have been extremely useful for gaining insight into monitoring microorganisms. This review out-lines the field of microfluidics and discusses the use of this novel technique so far in M. tuberculo-sis diagnostics, research methods, and drug discovery platforms. The practices of microfluidics have promising future applications for diagnosing and treating TB.

Background: Currently, limited number of therapeutic options are available to treat Diabetes mellitus, and to find a potential candidate, laboratory work takes time and also needs animal studies. So, the aim of this study was to determine the efficacy of some natural phytocompounds with the aid of molecular docking, bioinformatics and in silico drug design approaches. Method: Two proteins (Human CYP3A4 linked to metformin and Human dipeptidyl peptidase-IV) were selected and molecular docking studies were conducted using Pymol, AutoDock Vina, PyRx, and Discovery Studio. Different important pharmacokinetic parameters like ADME and toxicity data were obtained from online databases SwissADME and pkCSM program. Results: It was found that human dipeptidyl peptidase-IV (PDB ID: 4A5S) has exhibited a maximal affinity of -9.7 Kcal/mol for bryonolic acid and hesperidin, but only -6.7 kcal/mol for metformin hydrochloride. Similarly, the highest affinity of hesperidin for human CYP3A4 bound to metformin (PDB ID: 5G5J) is -10.7 Kcal/mol compared to metformin hydrochlor (-6.3 Kcal/mol). Besides, all the compounds have been documented outstanding ADMET profile, and accepted by drug-likeness or Lipinski rule. Conclusions: The present study suggested that these compounds can be further investigated in vitro and in vivo to establish them as lead compounds against Diabetes mellitus..

This study aimed to evaluate the safety and tolerability of STP1, a combination of ibudilast and bumetanide, tailored for the treatment of a specific subgroup of patients with Autism Spectrum Disorder (ASD), namely ASD Phenotype 1 (ASD-Phen1). We conducted a randomized, double-blind, placebo-controlled, parallel-group phase 1b study with two 14-day treatment phases (registered at clinicaltrials.gov as NCT04644003). Nine ASD-Phen1 patients were administered STP1, while three received a placebo. We assessed safety and tolerability, along with electrophysiological markers, such as EEG, Auditory Habituation, and Auditory Chirp Synchronization, to better understand STP1's mechanism of action. Additionally, we used several clinical scales to measure treatment outcomes. The results showed that STP1 was safe and well-tolerated, with electrophysiological markers indicating a significant and dose-related reduction of gamma power in the whole brain and in brain areas associated with executive function and memory. Treatment with STP1 also increased alpha 2 power in frontal and occipital regions and improved habituation and neural synchronization to auditory chirps. Although numerical improvements were observed in several clinical scales, they did not reach statistical significance. Overall, this study suggests that STP1 is safe and well-tolerated in ASD-Phen1 patients and shows indirect target engagement in ASD brain regions of interest.

Porifera, commonly referred to as marine sponges, have stood out as major producers of marine natural products (MNPs). Sponges of the genus Phorbas have attracted much attention along years. They are widespread in all continents, and several structurally unique compounds have been identified from species of this genus. Terpenes, mainly sesterterpenoids, represent the great majority of secondary metabolites isolated from Phorbas species, even though several alkaloids and steroids have also been reported. Many of these compounds have shown a variety of biological activities. Particularly, Phorbas sponges have been demonstrated to be a source of cytotoxic metabolites. In addition, MNPs exhibiting cytostatic, antimicrobial and anti-inflammatory activities, have been isolated and structurally characterized. This work brings an overview of Phorbas secondary metabolites reported since the first study published in 1993 until 2020, and their biological activities.

Zinc, being a part of many proteins and enzymes, plays an important role in the vital processes of the body. Racemic α-lipoic acid (LA) has a wide range of biological activities: antioxidant, antidiabetic, anticancer, and it is also used in diseases such as Parkinson's and Alzheimer's. Nowadays, the synthesis and structural studies of new zinc complexes with biologically active compounds are of great importance for drug discovery. This work shows the studies carried out on the synthesis, structure, identification, also biological activity using in silico methods of a complex compound based on LA and zinc. A complex compound of zinc with LA (ZnLA) was synthesized in the presence of LA and zinc acetate in ethanol. The identification of ZnLA by (FT-IR, XRD and TGA-DTA), also DFT calculations at the B3LYP/def2-TZVPP level of theory was performed. The molecular docking investigation involved docking ZnLA with two different protein receptors such us Glycogen phosphorylase (2GPA) and Kv potassium channel beta subunit (1ZSX) using CB-Dock 2 server and Auto-Dock Vina program to determine the mechanism of action and biological activity. This study reveals potential ligand-protein interactions at the atomic level and suggests that the treatment of diabetes (non-insulin dependent) with ZnLA.

CD44 is a type I transmembrane glycoprotein, which is associated with poor prognosis in various solid tumors. Since CD44 plays critical roles in tumor development by regulating cell adhesion, survival, proliferation, and stemness, it has been considered a target for tumor therapy. Anti-CD44 monoclonal antibodies (mAbs) have been developed and applied to antibody-drug conjugates and chimeric antigen receptor-T cell therapy. We previously developed anti‐pan-CD44 mAbs, C₄₄Mab-5 and C₄₄Mab-46, which can recognize both CD44 standard (CD44s) and variant isoforms. In the present study, a defucosylated mouse IgG_2a version of the anti-pan-CD44 mAbs (5-mG_2a-f and C₄₄Mab-46-mG_2a-f) was generated to evaluate the antitumor activities against CD44-positive cells. Both 5-mG_2a-f and C₄₄Mab-46-mG_2a-f recognized CD44s-overexpressed CHO-K1 (CHO/CD44s) cells and esophageal tumor cell line (KYSE770) in flow cytometry. Furthermore, both 5-mG_2a-f and C₄₄Mab-46-mG_2a-f could activate effector cells in the presence of CHO/CD44s cells and exhibited the complement‐dependent cytotoxicity against both CHO/CD44s and KYSE770 cells. Furthermore, the administration of 5-mG_2a-f and C₄₄Mab-46-mG_2a-f significantly suppressed CHO/CD44s and KYSE770 xenograft tumor development compared with the control defucosylated mouse IgG_2a. These results indicate that 5-mG_2a-f and C₄₄Mab-46-mG_2a-f could exert antitumor activities against CD44-positive cancers and could be a promising therapeutic regimen for tumors.

The temporary immersion system bioreactor named GreenTray®[2], [3]presents advantages over the existing ones. Firstly, there is no need to use forceps to move the shoots or plantlets in or out of the recipient, neither the use of scalpel to divide them. Secondly, the basis of the shoot can remain in the GreenTray® and can sprout again in several cycles of growth. These two characteristics reduce the costs in the process of micropropagation. Besides, shoots or plantlets development is enhanced by the aeration of the recipient, thus enriching with CO2 the recipient atmosphere. The chapter describes and provides images and values of interest in (1) the micropropagation process of some commercial Prunus or Pyrus rootstocks; (2) the plantlet growth under autotrophic conditions; and (2) the inoculation of plants with pathogens that reproduce in vitro the symptoms observed in vivo, using the GreenTray® bioreactor.

Background: Chronic Hepatitis B Virus (HBV) infection is a global health concern, associated with severe liver diseases, necessitating ongoing research on novel drug candidates. This study aims to identify potential drug candidates targeting HBV core protein (HBcAg) and disrupting capsid assembly, a critical step in the virus's life cycle. Methods: HBcAg in complex with HBV inhibitors were obtained from the Protein Data Bank (PDB). CavityPlus server was used for analysis of druggable cavity. Structure-based pharmacophores were extracted from identified cavities, and potential allosteric ligand binding sites were assessed using CavPharmer, CorrSite, and CovCys. LigandScout was employed for ligand-based pharmacophore screening against an FDA-approved library. The ZINC database was screened with features extracted from CavPharmer. Molecular docking studies were conducted using Autodock Vina. Lead compounds were selected based on docking scores, binding modes, and interactions within the druggable cavity. Results: Strong druggable pockets were found for Ciclopirox, while Compound 24, NVR10-001E2, and others showed medium to weak pockets. Ligand-based pharmacophores varied in size and complexity. Screening revealed potential hits matching these pharmacophores, including Ciclopirox olamine, Voriconazole, Enasidenib, and Statins. A large compound database search yielded additional hits like ZINC86859997 and ZINC63280172. Docking analyses confirmed these hits' potential, highlighting their interactions with critical HBc protein residues, offering promising leads for hepatitis B drug development. Conclusions: Voriconazole, Enasidenib, and Lovastatin have shown promises. These hits displayed favorable interactions with crucial HBc protein residues, indicating their potential as lead compounds The mechanism of action of statins with anti-HBV activities also highlighted. This comprehensive approach offers valuable insights into targeting HBc protein for antiviral drug discovery.

One of the G protein-coupled receptors, C-C chemokine receptor 5 (CCR5), is an important regulator for the activation of T and B lymphocytes, dendritic cells, natural killer cells, and macrophages. Upon binding to the ligands, CCR5 activates downstream signaling, which plays critical roles in the innate and adaptive immune response through the promotion of lymphocyte migration and the secretion of pro-inflammatory cytokines. Anti-CCR5 monoclonal antibodies (mAbs) have been developed and evaluated in clinical trials for tumors and inflammatory diseases. In this study, we developed novel mAbs for mouse CCR5 (mCCR5) using the N-terminal peptide immunization. Among the established anti-mCCR5 mAbs, C₅Mab-4 (rat IgG_2a, kappa) and C₅Mab-8 (rat IgG₁, kappa), recognized mCCR5-overexpressed Chinese hamster ovary-K1 (CHO/mCCR5) and an endogenously mCCR5-expressing cell line (L1210) by flow cytometry. The dissociation constant (K_D) values of C₅Mab-4 and C₅Mab-8 for CHO/mCCR5 were determined as 3.5 × 10⁻⁸ M and 7.3 × 10⁻⁹ M, respectively. Furthermore, both C₅Mab-4 and C₅Mab-8 could detect mCCR5 by western blotting. These results indicated that C₅Mab-4 and C₅Mab-8 are useful for detecting mCCR5 by flow cytometry and western blotting and provide a possibility to obtain the proof of concept in preclinical studies.

Brain aging causes a wide variety of changes at molecular and cellular levels, leading to the decline of cognitive functions and increased vulnerability to neurodegenerative disorders. The research aimed at understanding the aging of the brain has made much progress in recent decades. Technological innovations such as single-cell RNA-sequencing (scRNA-seq), proteomic analyses, and spatial transcriptomic analyses have facilitated the research on the dynamic changes occurring within neurons, glia, and other cells along with their impacts on intercellular communication during aging. In this review, we introduce recent trends of how neurons and glia change during aging and discuss the impact of the brain microenvironment such as the blood-brain barrier (BBB).

The ingestion of ethanol presents a significant global public health challenge. Chronic alcohol consumption is associated with the development of conditions such as cancer, cirrhosis, hepatic diseases, cardiovascular diseases, and diabetes, as well as neurological impairments that may predispose individuals to dementia, schizophrenia, and behavioral disorders such as violence and suicide. Alcohol-related fatalities constitute 5.3% of worldwide deaths annually, totaling approximately 3 million. Notably, alcohol consumption is implicated in 25% of cirrhosis-related deaths and 4.1% of cancer-related deaths. Taurine (2-aminoethanesulfonic acid) is a non-protein β-amino acid essential for cellular homeostasis, possessing antioxidant, anti-inflammatory, and cytoprotective properties crucial for life maintenance. This study aimed to evaluate the effects of taurine administration on hippocampal neurogenesis, neuronal preservation or reverse damage in rats exposed to forced ethanol consumption. Results demonstrated the neuroprotective effects of taurine on ethanol-treated rats, promoting tissue regeneration and suggesting its pivotal role in this process. However, this aminoacid failed to induce neurogenesis in the tissues of healthy rats, implying that its activity may be contingent upon post-injury stimuli.

C-C motif chemokine receptor-8 (CCR8) belongs to class A of G protein-coupled receptors (GPCRs). CCR8 interacts with the specific chemokine ligand CCL1/I-309 in humans, which is produced by various cells, including tumor-associated macrophages and regulatory T cells (Treg). CCR8 is highly expressed on Treg and T-helper 2 (T_H2) cells recruited to the inflammation site and is implicated in allergy, asthma, and cancer progression. The CCR8⁺Treg has been suggested to be an important regulator in the immunosuppressive tumor microenvironment (TME); therefore, it has been desired to develop sensitive monoclonal antibodies (mAbs) for CCR8. This study developed a specific mAb for human CCR8 (hCCR8), which is useful for flow cytometry by employing the Cell-Based Immunization and Screening (CBIS) method. The established anti-hCCR8 mAb, C₈Mab-21, (mouse IgM, kappa) reacted with hCCR8-overexpressed Chinese hamster ovary-K1 (CHO/hCCR8) cells, TALL-1 (acute T lymphoblastic leukemia), CCRF-HSB2 (human T-lymphoblastic leukemia), and natural killer cells, which express endogenous hCCR8 by flow cytometry. Furthermore, C₈Mab-21 demonstrated a moderate binding affinity for CHO/hCCR8 and TALL-1 with a dissociation constant of 6.5×10^-8 M and 2.0×10^-8 M, respectively. C₈Mab-21, which was established by the CBIS method, could be a useful tool for analyzing the hCCR8-related biological response using flow cytometry.

Two new dimacrolide sesquiterpene pyridine alkaloids (DMSPAs), dimacroregelines A (1) and B (2), were isolated from the stems of Tripterygium regelii. The structures of both compounds were characterized by extensive 1D and 2D NMR spectroscopic analyses, as well as HRESIMS data. Compounds 1 and 2 are two rare DMSPAs possessing unique 2-(3′-carboxybutyl)-3-furanoic acid units forming the second macrocyclic ring, representing the first example of DMSPAs bearing an extra furan ring in their second macrocyclic ring system. Compound 2 showed inhibitory effects on the proliferation of human rheumatoid arthritis synovial fibroblast cell (MH7A) at a concentration of 20 μM.

Triple negative breast cancer (TNBC) is associated with a generally poor prognosis due to its highly aggressive and metastatic nature, lack of targetable receptors, as well as the frequent development of resistance to chemotherapy. We previously reported that AU1, a small molecule developed as an inhibitor of BPTF (bromodomain PHD finger containing transcription factor), was capable of sensitizing preclinical models of triple negative breast cancer to chemotherapy, in part via the promotion of autophagy. In studies reported here, we identify an additional property of this compound, specifically that sensitization is associated with inhibition of the P-glycoprotein efflux pump. In silico molecular docking studies indicate that AU1 binds to active regions of the efflux pump, in a manner consistent with inhibition of pump function. This work identifies a novel chemical structure that can influence multidrug efflux, an established mechanism of drug resistance in triple negative breast cancer, that has not yet been successfully addressed by clinical efforts.

The clinical practice implies a research translation that helps the use of scientific data and thera-peutic evidence for patient benefits. This review critically summarizes the potential impact of cannabinoids in concomitance with other drugs in the following chronic diseases as Epilepsy, Au-tism Spectrum Disorders (ASD), Oncology, Multiple Sclerosis, and Chronic Pain. The potential in-teractions can change the predicted clinical outcomes of therapeutic protocols and need to be evaluated. Some of the effects would be additive, synergistic or antagonistic, but can also enroll changes in absorption, distribution, metabolism, particularly via cytochrome P450 (CYP) isoen-zymes (e.g CYP2C9 and CYP3A4) and excretion. For instance, the combination of canna-bis-derived compounds and the antifungal drug ketoconazole, a CYP3A4 inhibitor, increases plasma concentration of Δ-9-tetrahydrocannabinol (THC) and CBD (CBD). Opposable, rifampic-in, a CYP3A4 inducer, stands out for the approximately 20-40% reduction in plasma THC levels and 50% to 60% for CBD. Other CYP3A4 inhibitors and inducers are likely to have a similar effect on plasma concentrations if co-administered. Indeed, pharmacokinetic interactions have been also reported with antiseizure drugs. Moreover, pharmacodynamic interactions between cannabinoids and drugs with sympathomimetic effects (eg tachycardia, hypertension), central nervous system depressants (eg drowsiness, ataxia), and anticholinergics (eg tachycardia and drowsiness) are also expected. Even though pending further studies, there is currently clinical evidence supporting drug interaction with cannabinoids, demanding doctors evaluate the risk of drug combinations with cannabinoids. The tables herein provided were designed to facilitate the identification of bi-orelevant interactions that may compromise therapeutic efficacy and toxicity.

Public perception contrasts scientific findings on the depression-related effects of cannabis. However, earlier studies were performed when cannabis was predominantly illegal, its production was mostly uncontrolled, and the idea of medical cannabis was incipient only. We hypothesized that recent changes in attitudes and legislations may have favorably affected research. In addition, publication bias against cannabis may have also decreased. To investigate this hypothesis, we conducted a systematic review of research studies published over the last three years. We found 156 relevant studies. In most cross-sectional studies, depression was higher in those who consumed cannabis than in those who did not. An increase in cannabis consumption was typically followed by an increase in depression, whereas withdrawal from cannabis ameliorated depression in most cases. Albeit medical cannabis reduced depression in most studies, none of these were placebo controlled. In clinical studies published in the same period, placebo also ameliorated depression and in addition, the average effect size of placebo was larger than the average effect size of medical cannabis. We also investigated the plausibility of the antidepressant effects of cannabis by reviewing molecular and pharmacological studies. Taken together, the reviewed findings do not support the antidepressant effects of herbal cannabis.

Reviewed are recent efforts leading to chemo-enzymatic syntheses of strongly fluorescent ribosides of nucleobase analogs, potentially applicable in analytical biochemistry and cell biology. Described are syntheses and properties of fluorescent purine, 8-azapurine and etheno-purine ribosides, obtained using various types of the purine nucleoside phosphorylase (PNP) as catalysts of ribosylation, and -ribose-1-phosphate (r1P) as a second substrate. In several instances, the ribosylation sites were different than the canonical purine N9. Some of the obtained ribosides show fluorescence yields close to 100%. Their main applications are assays of PNP, nucleoside hydrolases, and other enzyme activities both in vitro and, possibly, within the living cells, using fluorescence microscopy.

The Severe acute respiratory syndrome-Corona virus-2 (SARS-CoV-2) which is responsible for recurring pandemics takes advantage of host-cell processes including the glycosylation pathway. The heavily glycosylated spike protein assists viruses in attachment and penetration. The N-glycans of N-terminal domain N165 and N234 play a significant role in conformational dynamics of the receptor-binding domain (RBD) with angiotensin-converting enzyme 2 (ACE2). In addition, the deletion of N-glycan sites N331 and N334 have been associated with reduced infectivity. This signifies the importance of targeting the N-glycans making them unavailable for interacting with the ACE2 receptor, ultimately leading to reduced infectivity. These glycans can be specifically targeted and can be used for designing SARS specific drugs or neutralizing molecules. In the current study, lectins Griffithsin, Cyanovirin-N, Cyanovirin homolog, and BanLec H84T were used to target the spike protein's N-glycans. Molecular docking programs AutoDock Vina and HADDOCK were used to study lectin-glycan interactions. The interactions look convincible in accordance with the lowest interaction energies best-fit approach but the characteristic feature (scoring functions) of the docking programs are questionable concerning Lectin-Glycan interactions exist in nature. There is a high need of the hour for developing specific algorithms for docking glycans with a preferential selection of terminal residues.

In this article, we describe a simple procedure for evaluating the sterility of nutrient media using a shear-horizontal surface acoustic wave (SH-SAW) biosensor. We used the SH-SAW biosensor platform to monitor the time-dependent phase responses of the material for the media that binds to a monolayer of 3-mercaptopropanyl-N-hydroxy succinimide on the biosensor surface. Increases in the phase shift less than 50% of the starting phase shift recorded 180 minutes after the sterilization indicate a successful sterility test of the media. Our devices were then applied to sterility testing using a viable Escherichia coli (E. coli) K-12 solution to confirm our hypothesis. Filtered LB Broth was the negative control, and a 24-hour culture of E. coli was the positive control. Before testing, the cell solution was pelleted, the media decanted and then replaced with a 1x phosphate-buffered saline solution (1x PBS). Since we know that the N-hydroxysuccinimide residues will bind to amine groups, only in cases where microbial species are present will the surface binding increase greater than 50%. We confirmed our hypothesis using SH-SAW biosensors and overnight E. coli cultures. The sensitivity for our 250 MHz quartz SH-SAW sensors was calculated based on the response from the E. coli K-12 solution over the linear dynamic range, which appears to be 2x104 – 2x107 CFU per ml and has a sensitivity of S = 1.655x105 o m2 Kg-1. The limit of detection (LOD) for these sensors was calculated to be approximately 2x10-9 grams (g) which is estimated to be approximately 2,000 Escherichia coli (E. coli) K-12 cells. However, the sensitivity and insertion loss remained approximately unchanged before and after coating the sensor with 3,3'-Dithiobis(succinimidyl) Propionate (DSP).

As modern systems are becoming more complex their control strategy cannot longer relay only on measurement information that usually comes from probes but also from mathematical models. Those systems models can lead to unbearable computation times due to their own complexity, turning the control process non-viable, which leads to the implementation of surrogate models that enable to achieve estimates within acceptable time to take decisions. A control trained with Deep Reinforcement Learning algorithm, using a Physics-Informed Neural Network to obtain the temperature map on the following time step, replaces the need of running Direct Numerical Simulations. On this work we considered an 1D heat conduction problem, which temperature distribution feeds a control system to activate a heat source aiming to obtain a constant, previously defined, temperature value. With this approach, control training becomes much faster without the need of performing numerical simulations or laboratory measurements, as well the control is taken based on Neural Network enabling its implementation on simple processors to edge computing.

Epithelial ovarian cancer (EOC) has not significantly benefited from advances in immunotherapy, mainly because of the lack of well-defined actionable antigen targets. Using proteogenomic analyses of primary EOC tumors, we previously identified 91 aberrantly expressed tumor-specific antigens (TSAs) originating from unmutated genomic sequences. Most of these TSAs derive from non-exonic regions, and their expression results from cancer-specific epigenetic changes. The present study aimed to evaluate the immunogenicity of 48 TSAs selected according to two criteria: presentation by highly prevalent HLA allotypes and expression in a significant fraction of EOC tumors. Using targeted mass spectrometry analyses, we found that pulsing with synthetic TSA peptides leads to a high-level presentation on dendritic cells. TSA abundance correlated with the predicted binding affinity to the HLA allotype. We stimulated naïve CD8 T cells from healthy blood donors with TSA-pulsed dendritic cells and assessed their expansion with two assays: MHC-peptide tetramer staining and TCR Vβ CDR3 sequencing. We report that these TSAs can expand sizeable populations of CD8 T cells and, therefore, represent attractive targets for EOC immunotherapy.

Water footprint has been widely used to illustrate the consumption of water in many situations; for instance, in products, processes or regions of interest. In this work, we analyzed – using a sensitivity analysis approach – the effect of some variables in the calculation of the water footprint in the viticulture in Brazilian Serra Gaúcha (the major producing region of Brazilian Wine).The classical Penman-Monteith model for evapotransporation was considered, with uncertainties in some parameters (dead mulch covering fraction in the vineyard, maximum temperatures for some months, altitudes and latitudes of the site). The sensitivity analysis was conducted using the SAFE toolbox under Octave framework. The results indicated that the the portion of the water footprint corresponding to the evapotranspiration is more sensitive to the values of mulch covering fraction and the altitude of the site in comparison with the latitude and the maximum temperatures.

COVID-19 pandemic has spurred intense research efforts to identify effective treatments for SARS-CoV-2. In silico studies have emerged as a powerful tool in the drug discovery process, particularly in the search for drug candidates that interact with various SARS-CoV-2 receptors. These studies involve the use of computer simulations and computational algorithms to predict the potential interaction of drug candidates with target receptors. The primary receptors targeted by drug candidates include the RNA polymerase, main protease, spike protein, ACE2 receptor, TMPRSS2, and AP2-associated protein kinase 1. In silico studies have identified several promising drug candidates, including Remdesivir, Favipiravir, Ribavirin, Ivermectin, Lopinavir/Ritonavir, and Camostat mesylate, among others. The use of in silico studies offers several advantages, including the ability to screen a large number of drug candidates in a relatively short amount of time, thereby reducing the time and cost involved in traditional drug discovery methods. Additionally, in silico studies allow for the prediction of the binding affinity of drug candidates to target receptors, providing insight into their potential efficacy. However, it is crucial to consider both the advantages and limitations of these studies and to complement them with experimental validation to ensure the efficacy and safety of identified drug candidates.

Based on the need for real-time sag monitoring of Overhead Power Lines (OPL) for electricity transmission, this article presents the implementation of a hardware and software system for online monitoring of OPL cables and cable sag determination. The mathematical model based on differential equations and the methods of algorithmic calculation of OPL cable sag are presented. Considering that, based on the mathematical model presented, the calculation of OPL cable sag can be done in different ways depending on the sensors used, and the presented application uses a variety of sensors. Therefore, a direct calculation is made using one of the different methods. Subsequently, the verification relations are highlighted directly and, respectively, the calculation by the alternative method, which uses another group of sensors, to generate both a verification of the calculation and the functionality of the sensors, thus obtaining a defect observer of the sensors. The hardware architecture of the OPL cable online monitoring application is presented, together with the main characteristics of the sensors and communication equipment used. The configurations required to transmit data using the ModBUS and ZigBee protocols are also presented. The main software modules of the OPL cable condition monitoring application are described, which ensure the monitoring of the main parameters of the power line and the visualisation of the results both on the electricity provider's intranet using a web server and MySQL database, and on the Internet using an Internet of Things (IoT) server. This categorisation of the data visualisation mode is done in such a way as to ensure a high level of cyber security. Starting from the mathematical model of the OPL cable sag calculation, it goes through the stages of creating such a monitoring system, from the numerical simulations carried out using Matlab to the real-time implementation of this monitoring application. The main modules of the application software are: the software module for reading quantities from the sensor network through a ModBUS Input/Output (I/O) server, the software module for real-time calculation of the OPL cable sag, the software module for read/write operations based on MySQL type data, the software module for transferring data to the user's intranet through a web server, and the software module for viewing data by authorised users based on user and password through an IoT server. The development software environments used are Matlab and Laboratory Virtual Instrument Engineering Workbench (LabVIEW).

Developmental hazard evaluation is an important part of assessing chemical risks during pregnancy. Toxicological outcomes from prenatal testing in pregnant animals result from complex chemical-biological interactions, and while New Approach Methods (NAMs) based on in vitro bioactivity profiles of human cells offer promising alternatives to animal testing, most of these assays lack cellular positional information, physical constraints, and regional organization of the intact embryo. Here, we engineered a fully computable model of the embryonic disc in the compucell3d.org modeling environment to simulate epithelial-mesenchymal transition of epiblast cells and self-organization of mesodermal domains (chordamesoderm, paraxial, lateral plate, posterior/extraembryonic). Cell fate in the model is determined by an autonomous homeobox (HOX) clock driven by morphogenetic signals (e.g., FGF, WNT, ATRA, CDX). Executing the model renders a quantitative cell-level computation of mesodermal subpopulations and consequences of perturbation based on known embryogeny. For example, synthetic perturbation of the control network rendered altered phenotypes (cybermorphs) mirroring experimental mouse embryology, with 50% reductions in FGF4, FGF8 and BMP4 signaling resulting in 86%, 98% and 59% reductions, respectively in the posterior mesodermal population, while ATRA exposure also resulted in a 78% decrease in this population. This model enables integration of in vitro chemical bioactivity data for specific molecular targets with known embryology to test mechanistic veracity and quantitative prediction of altered development.

Fungal infections caused by Candida spp. are responsible for high hospital morbidity and mortality rates. Therefore, there is a continuous search for new antifungal medicines, particularly ones with anti-Candida activity. This review analyzed articles published between 2002 and 2023 considering the anti-Candida activity of chemical compounds identified or isolated in plants of the Anacardiaceae family. In addition, the in-silico prediction of the isolated compounds described was performed. The analysis of 35 studies showed that extracts, essential oils, and compounds from the anti-Candida activity were frequently determined in vitro using the minimum inhibitory concentration (MIC). The most commonly tested species were Candida albicans, C. tropicalis, C. parapsilosis, C. glabrata, C. krusei, and C. guilliermondii, respectively. Essential oils were the most used form (37% of the studies). The isolated compounds with antifungal effects include cardanol, estragole, trans-anethole, β-caryophyllene, myrcene, catechin-3-o-rhamnoside, β-sitosterol-3-O-glucoside, 24Z-isomasticadienolic acid, oleanolic acid, and oleanolic aldehyde. The in-silico evaluation of those isolated compounds revealed the compounds' drug-likeness and possible antifungal activity. However, some of them showed high toxicity. In conclusion, compounds from the Anacardiaceae family show promise for developing new therapeutic antifungal drugs, especially in combination with conventional antifungals.

Cancer-specific monoclonal antibodies (CasMabs) that recognize cancer-specific antigens with in vivo antitumor efficacy are innovative therapeutic strategies for minimizing the adverse effects. We previously established a cancer-specific anti-human epidermal growth factor receptor 2 (HER2)monoclonal antibody (mAb), H₂Mab-250/H₂CasMab-2. In flow cytometry and immunohistochemistry, H₂Mab-250 reacted with HER2-positive breast cancer cells but did not show the reactivity to normal epithelial cells. In contrast, a clinically approved anti-HER2 mAb, trastuzumab, strongly recognizes both breast cancer and normal epithelial cells in flow cytometry. The human IgG₁ version of H₂Mab-250 (H₂Mab-250-hG₁) possesses compatible in vivo antitumor effects against breast cancer xenografts with trastuzumab despite the lower affinity and effector activation than trastuzumab in vitro. This study compared the antibody-dependent cellular cytotoxicity (ADCC) and complement-dependent cellular cytotoxicity (CDC) between H₂Mab-250-hG₁ and trastuzumab. Both H₂Mab-250-hG₁ and trastuzumab showed ADCC activity against CHO/HER2 and breast cancer cell lines (BT-474 and SK-BR-3) in the presence of human natural killer cells. Some tendency was observed that trastuzumab showed a more significant ADCC effect compared to H₂Mab-250-hG₁. Importantly, H₂Mab-250-hG₁ exhibited superior CDC activity in these cells compared to trastuzumab. Similar results were obtained in mouse IgG_2a types of both H₂Mab-250 and trastuzumab. These results suggest the different contributions of ADCC and CDC activities to the antitumor effects of H₂Mab-250-hG₁ and trastuzumab and indicate the future direction for the clinical development of H₂Mab-250-hG₁ against HER2-positive tumors.

Probiotic feed additives have attracted considerable research interest in recent years because the effectiveness of probiotics can differ across microbial strains and the supplemented macroorganisms. The present study was conducted on 16 lambs divided into two groups (C - control and E - experimental). The examined lambs were aged 11 days at the beginning of the experiment and 40 days at the end of the experiment. The diet of group E lambs was supplemented with a multi-strain probiotic formulation (Lactobacillus plantarum AMT14, Lactobacillus plantarum AMT4, Lactobacillus rhamnosus AMT15, and Bifidobacterium animalis AMT30), whereas group C lambs did not receive the probiotic additive. At the beginning of the experiment (day 0) and on experimental days 15 and 30, blood was sampled from the jugular vein to determine and compare: phagocytic activity (PHAGOTEST) and oxidative metabolism (PHAGOBURST) of peripheral blood granulocytes and monocytes by flow cytometry. An analysis of the phagocytic activity of granulocytes and monocytes revealed significantly higher levels of phagocytic activity (expressed as the percentage of phagocytic cells and mean fluorescence intensity) in lambs administered the multi-strain probiotic formulation than in the control group. The probiotic feed additive also exerted a positive effect on the oxidative metabolism of both granulocytes and monocytes (expressed as the percentage of oxidative metabolism and mean fluorescence intensity) after stimulation with Escherichia coli bacteria and with PMA (4-phorbol-12-β-myristate-13-acetate).

Multidrug resistant organisms (MDROs) outbreaks have been steadilyincreasing in Intensive care units (ICUs). Still, healthcare institutions and workers (HCW) reached no unanimity on how and when implementing infection prevention and control (IPC) strategies. We aimed to provide a pragmatic, physician practice-oriented, resume of strategies towards different MRDOs outbreaks in ICU. We performed a narrative review on IPC in ICUs, investigating patient-to-staff ratios, education, isolation, decolonization, screening and hygiene practices, outbreak reporting, cost-effectiveness, reproduction-number(R0), and future perspectives. The most effective IPC strategy remains unknown. Most studies focus on a specific pathogen or disease, making the clinician losing the big picture. IPC strategies proved their cost-effectiveness regardless typology, country, or pathogen. A standardized, universal, pragmatic protocol for HCW education should be elaborated. Likewise, a rapid outbreak recognition tool elaboration (i.e., an easy-to-use mathematical model) would improve early diagnosis and spreading prevention. Further studies are needed to express in favor or against MDROs decolonization. New promising strategies are emerging and need to be tested in the field. The lack of IPC strategies application has made, and still makes ICUs a major MDROs reservoir into the community. In a not-too-distant future genetic engineering and phage therapies could represent a plot-twist in MDROs IPC strategies.

Lynch syndrome (LS) is one of the most common hereditary cancer syndrome in human populations, associated with germline variants in MLH1, MSH2/EPCAM, MSH6 and PMS2 genes. The advent of next generation sequencing has proven a significant impact in germline variant detection in the causative genes; however, a large proportion of patients with clinical criteria still receive uncertain or negative results. PMS2 is the least frequent reported gene, associated with up to 15% of LS cases with late-onset disease and low penetrance phenotype; however, the proportion of PMS2-LS cases is considered to be highly underestimated. In this context, we consulted public available databases (ClinVar, LOVD) for missense and intronic PMS2 variants and performed an in silico analysis of the genomic sequence. Splicing bioinformatics tools proven effective in other genes were used to assess both the wild-type DNA sequence and reported genetic variants. Splicing alterations were predicted in silico and using GTEx short-read RNA expression data. Out of the 2384 missense variants discovered, 90% were classified with uncertain significance (VUS). 4.9% of missense variants were shown to have a potential splicing consequence (DS > 0.2) using SpliceAI. As described in the original publication, SpliceAI-visual was proven effective in annotation of short intronic variants (< 50 bp). Four short intronic variants were identified using SpliceAI-visual as potentially splicing disturbing, in spite of using a lower threshold (DS > 0.1). Moreover, we observed that Splicing Regulatory Elements (SREs) may play a fundamental role in alternative splicing in PMS2 gene, ESE motifs being overrepresented in highly expressed exons 5, 11 and 14. Computational analysis performed in our study serves as a valuable filtering step for guiding further RNA sequencing experiments. Additional functional data is necessary to evaluate the significance of our findings.

The main proteinase (Mpro), or 3CLpro, is a critical enzyme in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lifecycle and is responsible for breaking down and releasing vital functional viral proteins crucial for virus development and transmission. As a catalytically active dimer, its dimerization interface has become an attractive target for antiviral drug development. Recent research has extensively investigated the enzymatic activity of Mpro, focusing on its role in regulating the coronavirus replication complex and its significance in virus maturation and infectivity. Computational investigations have identified four druggable pockets, suggesting potential allosteric sites beyond the substrate-binding region. Empirical validation through site-directed alanine mutagenesis targeted residues in both the active and allosteric regions corroborated these predictions. Structural studies of drug target proteins can inform therapeutic approaches, with metadynamics simulations shedding light on the role of H163 in regulating Mpro function and providing insights into its dynamic equilibrium to the wild-type enzyme. Despite the efficacy of vaccines and drugs in mitigating SARS-CoV-2 spread, ongoing viral evolution, selective pressures, and continued transmission pose challenges, potentially leading to resistance mutations. Phylogenetic analyses indicated the existence of several resistance variations predating drug introduction to the human population, emphasizing the likelihood of drug spread. Hydrogen/deuterium-exchange mass spectrometry reveals the structural influence of the mutation, while clinical trials on 3CLPro inhibitors underscore the clinical significance of reduced enzymatic activity and offer avenues for future therapeutic exploration. Understanding the implications of 3CLPro mutations holds promise for shaping forthcoming therapeutic strategies against COVID-19. This review delves into factors influencing mutation rates and identifies areas warranting further investigation, providing a comprehensive overview of Mpro mutations, their categorization, and terminology. Moreover, we examined their associations with clinical outcomes, illness severity, unresolved issues, and future research prospects, including their impact on vaccine efficacy and potential therapeutic targeting.

Based on screening in computational biology and biological in vitro assays, five natural products isolated from extracts of the herbal medicine toad skin, such as cinobufagin (CBFi), bufalin (BFi), arenobufagin (ABFi), telocinobufagin (TBFi), bufotalin (BFTi), were subjected to molecular docking calculations with the use of SARS-CoV-2 main protease (PDB 6LU7 and 7BTF) and top-scoring ligand-receptor complexes were obtained. The results showed that the binding energy of ABFi to the 3CL protein was -17.044kcal/mol, which was higher than CBFi and TBFi. However, the binding energy of ABFi to the RdRp protease was -23.250 kcal/mol, which was much lower than that of CBFi and TBFi, EVEN lower than that of ABFi to the 3CL protein. ABFi also has polar interactions with amino acids such as Glu811, Ser814, Ser681 and Thr680 of RdRp enzyme. The results revealed that ABFi had a moderate inhibitory effect on the cell proliferation of SARS-CoV-2 in vitro, with an inhibition rate of 61.12%, even weaker than Remdesivir. This new discovery provides us with new ideas for in-depth studies on the development of natural products with this class of structural generalizations as inhibitors of SARS-CoV-2, and provides an experimental basis for the next step of mechanistic studies.

Despite the recognized influence of body mass on combat techniques, the relationship between body segment mass (BSM) and combat moves remains unexplored. This study aimed to examine the relationship between striking arm mass (SAM), kicking leg mass (KLM), and body mass (BM) with the dynamic forces of the direct punches (DP), palm strikes (PS), elbow strikes (ES), front kicks (FK), and countermovement jumps (CMJ) performance. Sixteen male military cadets (22.3 ± 1.8 years, 181.4 ± 7.0 cm, 82.1 ± 8.5 kg) performed combat techniques, with performance measured by using a force plate and body segment mass assessed by Dual-energy X-ray absorptiometry. Spearman's correlation analysis, the Wilcoxon test, and Cohen's d were applied. Results indicated the relationship between KLM or BM and FK impulse (r = 0.64, p = 0.01; r = 0.52, p = 0.04, respectively) and CMJ impact force (r = 0.80, p ≤ 0.01; r = 0.70, p ≤ 0.01, respectively). Moreover, FK peak and impact forces were moderately correlated with CMJ jump height (r = 0.74, p ≤ 0.01; r = 0.77, p ≤ 0.01). The highest relationship was found between KLM and FK impulse; however, the difference in variability explained by KLM versus body mass is only 12%. This suggests that knowledge of BSM did not provide a significantly better estimate of the dynamic forces of the punches and FK than BM.

In recent years, the prevalence of tobacco and electronic cigarette (e-cigarette) use among adolescents has raised significant public health concerns worldwide. This study aims to investigate respiratory symptoms among Polish adolescents. We conducted an online survey among Polish school students from all provinces, collecting data over two months in Spring 2021. Students voluntarily complete the anonymous survey, answering questions about respiratory symptoms, smoking habits (both traditional and electronic cigarettes), and demographic information. Analysis focused on four subgroups: non-tobacco users, traditional cigarette smokers, e-cigarette users, and dual users, totaling 10,388 pupils aged 12-18 years, predominantly attending secondary technical and comprehensive schools. A total of 10388 pupils participated in the study, 55.6 % (5778) of whom were girls and 44.4 % (4610) boys. Adolescents, who admitted using both e-cigarettes and traditional cigarettes experienced more frequent episodes of cough during the day (39.70%) and at night (18.40%) compared to their peers in other groups. Chest discomfort, including pain and pressure, was also reported more often by adolescents, who used e-cigarettes and traditional cigarettes concurrently (27.60%) compared to their peers in other groups. Chest pressure was experienced less commonly by non-smoking adolescents (14.40%) than by smokers (18.90%). Higher severity of cough during the day and at night was observed in the group of adolescents using traditional cigarettes and e-cigarettes concurrently compared to the other groups. The adolescents in the dual user group experienced more severe dyspnoea and wheezing compared to the other groups included in the comparison. The results of this study confirm the correlation between the occurrence of respiratory symptoms in adolescents who smoke cigarettes, use e-cigarettes and in dual users. The respiratory symptoms occur most frequently and are the most severe in the group of adolescents who use e-cigarettes and traditional cigarettes

In Brazil, Brazilian borreliosis (BB), characterized molecularly, exhibits symptoms akin to Lyme disease (LD) but presents unique epidemiological, clinical, and morphological features. This study explores the incidence, diagnostic challenges, and awareness initiatives regarding this disease in Brazil. Employing a narrative review methodology, data were sourced from Pubmed, Ebsco, Google Scholar, and Web of Science without temporal constraints. Results reveal diagnostic complexities in Brazil due to the low sensitivity of conventional tests and the use of North American B. burgdorferi antigens. Despite challenges, epidemiological studies indicate an upswing in cases in humans and animals. Therefore, there is an urgent need to define new biomarkers, molecular strategies, or new antigenic targets to advance the development of vaccines and diagnostics. Early and accurate pathogen identification is crucial for active surveillance to comprehend and manage this zoonosis.

Members of the genus Armillaria are widespread forest pathogens against which effective protection has not yet been developed. Due to their longevity and the creation of large-scale cloning of Armillaria individuals, the use of mycoviruses as biocontrol agents (BCAs) against these pathogens could be an effective alternative. This work describes the screening of double-stranded RNA banding patterns as a first approach for potential virus discovery and the use of stranded total RNA-seq for viral detection and characterization in Armillaria spp. collected in the Czech Republic. A total of five single-stranded RNA viruses were detected in Armillaria ostoyae and A. cepistipes, including the first virus of the family Tymoviridae found infecting a member the genus Armillaria and four viruses belonging to the recently described virus group "ambiviruses" with circular ambisense genome organization. Both hammerhead (HHRz) and hairpin (HpRz) ribozymes were detected in all the ambiviricot sequences. Armillaria viruses were compared by phylogenetic analysis and confirmed their specific host by direct RT-PCR, which showed the occurrence of interspecies transmission.

Novel SARS-CoV-2, a bat based virus originated in Wuhan, China that caused a global pandemic in December, 2019 belongs to the Betacorona virus family and contains single stranded genome of ~29Kbp. The host cell invasion of SARS-CoV-2 is facilitated by interaction of C-Terminal Domain (CTD) of Spike (S) protein of virus and host ACE2 receptor in the presence of TMPRSS seine protease secreted by the host cell. In this study the mutation hotspots of S-protein will be identified and the impact of such mutation in the binding affinity will be studied. Additionally, the lead molecule which can bind to the mutated protein also will be identified. Multiple sequence alignment of the spike protein sequence of SARS-CoV-2 shows the number of single amino acid mutation hotspots such as L5F, R214L, R408I, G476S, V483A, H519Q, A520S, T572I, D614G and H655Y. Among these mutations D614G has 57.5% occurrence and G476S, V483A has 7.5% occurrence. The mutated proteins were modelled based on wild type homolog and docked to ACE2 receptor. When the mutated S protein is docked, the ∆G (binding free energy) value is very minimal in mutated protein showed the stability of variants. By the drug repurposing method, 1000 FDA approved drugs were virtually screened for its binding to RBD of S1 domain. Among these drugs Digitoxin, Gliquidone and Zorubicin Hcl binds to spike proteins with higher docking score (lesser than -8.5 Kcal/mol) to both wild type and mutants.

Populational aging is marked by chronic non-communicable diseases, such as metabolic syndrome (MetS). IL-10 and IL-1β are pleiotropic cytokines with multiple biological effects linked to metabolic disorders. This cross-sectional study assessed 193 participants' IL-10 and IL-1β serum levels regarding their role in developing MetS, clinical characteristics, and their IL1B rs1143627 and IL10 rs1800890 variants' genotype frequencies in a population over 60. IL-10 levels correlated weakly with HDL levels and fat mass and inversely with triglycerides, glucose, glycated hemoglobin, and estimated average blood glucose levels. IL-10 levels were also indirectly influenced by the patient's T2DM duration, lean mass amount, and bone mineral content. Participants with altered HDL, elevated serum glucose, raised HbA1c levels, or those over 80 had reduced serum IL-10 levels compared to those with normal levels or other age groups, respectively. Women also had higher serum IL-10 levels than men. Dissimilarity, IL-1β levels correlated directly only with the number of total leukocytes and segmented neutrophils, showing only significant variations with self-reported alcohol consumption. Our study also found that those with the IL10 AA genotype (lower IL-10 levels) had a significantly higher risk of developing MetS. These findings may help direct future research and more targeted therapeutic approaches in older adults.

In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a highly dynamic manufacturing process. Using causal discovery techniques, our method allows domain experts to incorporate background knowledge into the creation of causal graphs. Further, it enhances the interpretability and increases the ability to identify potential problems via changes in causal relations over time. The case study employs a comprehensive analysis of the viscose fiber production process within a prominent textile industry, emphasizing the advantages of causal discovery for monitoring degradation. The results are compared with state-of-the-art methods, which are not considered to be interpretable, specifically LSTM-based autoencoders, showcasing the alignment and validation of our approach. This paper provides valuable information on degradation monitoring strategies, demonstrating the efficacy of causal discovery in dynamic manufacturing environments. The findings contribute to the evolving landscape of process optimization and quality control.

Consumption of ultra-processed foods is associated with abdominal obesity, but this association has not yet been investigated in individuals on hemodialysis. This cross-sectional study, developed with 1014 individuals, aimed to analyze the prevalence of abdominal obesity and its association with the consumption of ultra-processed foods in individuals on hemodialysis. Consumption of ultra-processed foods was measured using a questionnaire, whose list of foods was based on the Surveillance System of Risk and Protective Factors for Chronic Non-Communicable Diseases through Telephone Enquiry. Waist circumference was used to determine the presence of abdominal obesity. The difference in proportions between the independent variables and the outcome was verified using Pearson's chi-square test. Hierarchical logistic regression was used to determine the association between the outcome and the independent variables. The prevalence of abdominal obesity was 77.22%. In the final model, higher consumption of ultra-processed foods, be male and an adult, have between 8 and 11 years of schooling and practice physical activity below the recommended level increased the chances of having abdominal obesity. The research provides a basis for the creation and improvement of public policies that promote improvements in these health determinants.

Conventional chemotherapy for colorectal cancer (CRC) gives only a small increase in patient survival, since it is often diagnosed in late stages, when tumor has disseminated to other organs. Besides, it is common to observe that malignant cells acquire tumor escape mechanisms which leads to therapy resistance. Considering these facts, the discovery of new molecules with therapeutic potential has become an invaluable tool in chemoprevention. In this context, we previously evaluated two hybrids (SAC-CAFA-MET and SAC-CAFA-PENT) which exhibited selective cytotoxicity against SW480, with better results than the conventional chemotherapeutic agent (5-fluorouracil; 5-FU). Here, we investigated a little deeper in the possible mechanism of these molecules to identify potential therapeutic alternatives for the treatment of CRC. Both compounds induced cell damage and reduced ROS formation. Further evaluations showed that SAC-CAFA-MET induces cell death independent from caspases and p53, but probably mediated by the negative regulation of the proapoptotic Bcl-2. In addition, the lack of activation of caspase 8 and the positive regulation of caspase 3 induced by SAC-CAFA-PENT suggest this compound acts through an apoptotic mechanism, probably initiated by intrinsic pathway. Besides, the down regulation of IL-6 by SAC-CAFA-PENT suggests it also induces a significant anti-inflammatory process. In addition, docking studies would suggest caspase-3 modulation as the primary mechanism by which hybrids elicits apoptosis in human colorectal adenocarcinoma SW480. Meanwhile, DFT calculations suggest that hybrids would produce effects in modulation of ROS in SW480 cells via hydrogen atom transfer pathway (HAT). Finally, both, SAC-CAFA-MET and SAC-CAFA-PENT displayed a favorable pharmacokinetic profile. The current work highlights the potential of the lead compounds SAC-CAFA-MET and SAC-CAFA-PENT as potential agents for colorectal cancer chemoprevention.

Cancer treatment is a significant focus in medicine owing to the increasing global incidence. Patients with advanced cancers that do not respond to conventional therapies have limited options and an unfavorable prognosis. Consequently, researchers are investigating complementary approaches to conventional treatments. One such approach is alkalization therapy, which aims to neutralize the acidic tumor microenvironment (TME) by increasing its pH level. The acidic TME promotes inflammation, tumor progression, and drug resistance. Alkalization therapy has been demonstrated to be effective for various cancers. In addition, natural products, such as triterpenoids, parthenolides, fulvic acid, Taxus yunnanensis, and apple pectin have the potential to alleviate symptoms, maintain physical fitness, and improve treatment outcomes of cancer patients through their anti-inflammatory, antioxidant, and anticancer properties. In this review, we focus on the effects of alkalization therapy and natural products on cancer. Furthermore, we present a case series of advanced cancer patients who received alkalization therapy and natural products alongside standard treatments, resulting in long-term survival. We posit that alkalization therapy together with the supplementation of natural products may confer benefits to cancer patients by mitigating the side effects of chemotherapy and complementing standard treatments. However, further research is warranted to validate these clinical findings.