Emerging Approaches for Mitigating Biofilm-Formation-Associated Infections in Farm, Wild, and Companion Animals.

The importance of addressing the problem of biofilms in farm, wild, and companion animals lies in their pervasive impact on animal health and welfare. Biofilms, as resilient communities of microorganisms, pose a persistent challenge in causing infections and complicating treatment strategies. Recognizing and understanding the importance of mitigating biofilm formation is critical to ensuring the welfare of animals in a variety of settings, from farms to the wild and companion animals. Effectively addressing this issue not only improves the overall health of individual animals, but also contributes to the broader goals of sustainable agriculture, wildlife conservation, and responsible pet ownership. This review examines the current understanding of biofilm formation in animal diseases and elucidates the complex processes involved. Recognizing the limitations of traditional antibiotic treatments, mechanisms of resistance associated with biofilms are explored. The focus is on alternative therapeutic strategies to control biofilm, with illuminating case studies providing valuable context and practical insights. In conclusion, the review highlights the importance of exploring emerging approaches to mitigate biofilm formation in animals. It consolidates existing knowledge, highlights gaps in understanding, and encourages further research to address this critical facet of animal health. The comprehensive perspective provided by this review serves as a foundation for future investigations and interventions to improve the management of biofilm-associated infections in diverse animal populations.

Unveiling Novel ERCC1-XPF Complex Inhibitors: Bridging the Gap from In Silico Exploration to Experimental Design.

Modifications in DNA repair pathways are recognized as prognostic markers and potential therapeutic targets in various cancers, including non-small cell lung cancer (NSCLC). Overexpression of ERCC1 correlates with poorer prognosis and response to platinum-based chemotherapy. As a result, there is a pressing need to discover new inhibitors of the ERCC1-XPF complex that can potentiate the efficacy of cisplatin in NSCLC. In this study, we developed a structure-based virtual screening strategy targeting the inhibition of ERCC1 and XPF interaction. Analysis of crystal structures and a library of small molecules known to act against the complex highlighted the pivotal role of Phe293 (ERCC1) in maintaining complex stability. This residue was chosen as the primary binding site for virtual screening. Using an optimized docking protocol, we screened compounds from various databases, ultimately identifying more than one hundred potential inhibitors. Their capability to amplify cisplatin-induced cytotoxicity was assessed in NSCLC H1299 cells, which exhibited the highest ERCC1 expression of all the cell lines tested. Of these, 22 compounds emerged as promising enhancers of cisplatin efficacy. Our results underscore the value of pinpointing crucial molecular characteristics in the pursuit of novel modulators of the ERCC1-XPF interaction, which could be combined with cisplatin to treat NSCLC more effectively.

Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors.

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20−25% inhibition of RIPK1's kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.

Computational Modulation of the V3 Region of Glycoprotein gp125 of HIV-2.

HIV-2 infection is frequently neglected in HIV/AIDS campaigns. However, a special emphasis must be given to HIV-2 as an untreated infection that also leads to AIDS and death, and for which the efficacy of most available drugs is limited against HIV-2. HIV envelope glycoproteins mediate binding to the receptor CD4 and co-receptors at the surface of the target cell, enabling fusion with the cell membrane and viral entry. Here, we developed and optimized a computer-assisted drug design approach of an important HIV-2 glycoprotein that allows us to explore and gain further insights at the molecular level into protein structures and interactions crucial for the inhibition of HIV-2 cell entry. The 3D structure of a key HIV-2ROD gp125 region was generated by a homology modeling campaign. To disclose the importance of the main structural features and compare them with experimental results, 3D-models of six mutants were also generated. These mutations revealed the selective impact on the behavior of the protein. Furthermore, molecular dynamics simulations were performed to optimize the models, and the dynamic behavior was tackled to account for structure flexibility and interactions network formation. Structurally, the mutations studied lead to a loss of aromatic features, which is very important for the establishment of π-π interactions and could induce a structural preference by a specific coreceptor. These new insights into the structure-function relationship of HIV-2 gp125 V3 and surrounding regions will help in the design of better models and the design of new small molecules capable to inhibit the attachment and binding of HIV with host cells.

Cyclodextrin solubilization and complexation of antiretroviral drug lopinavir: In silico prediction; Effects of derivatization, molar ratio and preparation method.

Lopinavir (LPV) is currently used in combination with ritonavir for the clinical management of HIV infections due to its limited oral bioavailability. Herein, we report the application of an in silico method to study cyclodextrin (CyD) host-guest molecular interaction with LPV for the rational selection of the best CyD for developing a CyD based LPV delivery system. The predicted CyD, a (2-hydroxy)propyl-gamma derivative with high degree of substitution (HP17-γ-CyD) was synthesized and comparatively evaluated with γ-CyD and the commercially available HP-γ-CyD. All complexes were prepared by supercritical assisted spray drying (SASD) and co-evaporation (CoEva) at molar ratios (1:1 and 1:2); and afterwards fully characterized. Results indicate a higher LPV amorphization and solubilization ability of HP17-γ-CyD. The SASD processing technology also enhanced LPV solubilization and release from complexes. The application of in silico methodologies is a feasible approach for the rational and/or deductive development of CyD drug delivery systems.

Gobernanza de la salud digital: el arte de la transformación de los sistemas de salud / Governance for Digital Health The art of health systems transformation

La incorporación de las tecnologías de la información y de la comunicación a la cadena de valor del sistema de salud supone su transformación digital. Esta transformación exige una gobernanza que incluye aspectos de derechos, normas, responsabilidades y riesgos en áreas como Internet y salud; utilización de los datos de salud; y sistemas de información. La gobernanza de la salud digital se dirige a la mejora de la calidad, la eficiencia y la efectividad del sistema de salud. La triple carga de morbilidad, enfermedades no transmisibles, enfermedades infecciosas y factores externos; la sostenibilidad fiscal y financiera; y la necesidad de mejora de la efectividad y calidad de la atención médica exigen profundos cambios del sistema de salud que hacen imprescindible su transformación digital. El presente documento presenta un marco y una discusión acerca de como pensar e implementar esquemas de Gobernanza para la salud digital. El documento esta orientado a tomadores de decisión en diferentes niveles y sectores de Gobierno

The digital transformation of the health system is achieved by incorporating information and communication technologies into its value chain. This transformation requires governance that addresses rights, regulations, responsibilities, and risks in areas such as internet and health; using health data; and information systems. The aim of digital health governance is to improve the quality, efficiency, and effectiveness of the health system. The triple Burden of Disease (non-communicable diseases, infectious diseases, and external factors); fiscal and financial sustainability; and the need to improve the effectiveness and quality of medical care necessitate profound changes in the health system and make it critical to bring about its digital transformation

Sugar-based bactericides targeting phosphatidylethanolamine-enriched membranes.

Anthrax is an infectious disease caused by Bacillus anthracis, a bioterrorism agent that develops resistance to clinically used antibiotics. Therefore, alternative mechanisms of action remain a challenge. Herein, we disclose deoxy glycosides responsible for specific carbohydrate-phospholipid interactions, causing phosphatidylethanolamine lamellar-to-inverted hexagonal phase transition and acting over B. anthracis and Bacillus cereus as potent and selective bactericides. Biological studies of the synthesized compound series differing in the anomeric atom, glycone configuration and deoxygenation pattern show that the latter is indeed a key modulator of efficacy and selectivity. Biomolecular simulations show no tendency to pore formation, whereas differential metabolomics and genomics rule out proteins as targets. Complete bacteria cell death in 10 min and cellular envelope disruption corroborate an effect over lipid polymorphism. Biophysical approaches show monolayer and bilayer reorganization with fast and high permeabilizing activity toward phosphatidylethanolamine membranes. Absence of bacterial resistance further supports this mechanism, triggering innovation on membrane-targeting antimicrobials.

Computational Approaches for the Discovery of Human Proteasome Inhibitors: An Overview.

Proteasome emerged as an important target in recent pharmacological research due to its pivotal role in degrading proteins in the cytoplasm and nucleus of eukaryotic cells, regulating a wide variety of cellular pathways, including cell growth and proliferation, apoptosis, DNA repair, transcription, immune response, and signaling processes. The last two decades witnessed intensive efforts to discover 20S proteasome inhibitors with significant chemical diversity and efficacy. To date, the US FDA approved to market three proteasome inhibitors: bortezomib, carfilzomib, and ixazomib. However new, safer and more efficient drugs are still required. Computer-aided drug discovery has long being used in drug discovery campaigns targeting the human proteasome. The aim of this review is to illustrate selected in silico methods like homology modeling, molecular docking, pharmacophore modeling, virtual screening, and combined methods that have been used in proteasome inhibitors discovery. Applications of these methods to proteasome inhibitors discovery will also be presented and discussed to raise improvements in this particular field.

Efectos del aprendizaje colaborativo en el uso de estrategias de afrontamiento / Effects of collaborative learning on the use of coping strategies

El objetivo de este trabajo es demostrar que el uso de técnicas de aprendizaje colaborativo en el aula permite a los alumnos utilizar estrategias mediante las cuales aprenden a afrontar situaciones problemáticas derivadas de las relaciones interpersonales que se producen en el espacio educativo, especialmente entre iguales. Participaron 50 niños y niñas de primer curso de Educación Secundaria Obligatoria (edad media 12.4 años) distribuidos en dos grupos: experimental y control. En el primer grupo, se utilizó un programa basado en el aprendizaje individual asistido por un equipo; en el segundo, se utilizó una metodología de corte tradicional. Para probar las hipótesis formuladas, utilizamos un diseño de grupo de control pretest-postest con grupo de control no equivalente. Consideramos la inteligencia como covariable para mantener constantes sus efectos sobre los resultados independientemente del efecto del programa. Empleamos un análisis de varianza split-plot univariado como procedimiento estadístico. Los resultados muestran que los alumnos que siguen un programa de aprendizaje colaborativo utilizan más y mejores estrategias de afrontamiento que aquellos que no lo hacen. Los participantes de los grupos experimental y control parten de una situación de igualdad para separarse en la situación postest: el grupo experimental aumenta significativamente sus puntuaciones con respecto al grupo de control en lo que se refiere al uso de las estrategias de afrontamiento, y sus participantes tienden a compartir sus problemas con los demás en mayor medida que los miembros del grupo de control, todo ello independientemente del CI.

The purpose of this work is to show that the use of collaborative learning techniques in the classroom allows students to use strategies through which they learn to cope with problematic situations derived from the interpersonal relations that take place at school, especially among peers. The participants have been 50 boys and girls, in first year of compulsory secondary education (average age, 12.4) distributed into two groups: experimental and control. In the first group Team Assisted Individualization has been used. In the second group a traditional methodology has been adopted. A pretest-posttest control group design with non-equivalent control group has been used to test the hypotheses. Intelligence has been used as covariable to keep its effect on the results constant, independently of the effects of the program. A univariate split-plot analysis has been used as statistic procedure. The results show that those students on a collaborative learning program use more and more effective coping strategies than those who are not on a collaborative program. The participants in both the experimental and the control groups set out from a situation of equality to move farther away from each other in the posttest situation: the experimental group noticeably increases scores over the control group as regards the use of coping strategies, and the participants in the experimental group tend to share their problems with their peers in a greater scale than the participants in the control group, independently of the IQ.

Simple automated system for simultaneous production of 11C-labeled tracers by solid supported methylation.

We herein describe a simple setup for the automated simultaneous synthesis of L-[methyl-11C]methionine and N-[methyl-11C]choline by solid-supported methylation. The setup is extremely simple and easy to adapt to other automated systems and due to its versatility, the method can be utilized for the production of other radiopharmaceuticals requiring a simple [11C]methylation step. Furthermore, it can be used for multiple simultaneous synthesis.

Dendritic cells delivered inside human carcinomas are sequestered by interleukin-8.

In the course of a clinical trial consisting of intratumoral injections of dendritic cells (DCs) transfected to produce interleukin-12, the use of (111)In-labeled tracing doses of DCs showed that most DCs remained inside tumor tissue, instead of migrating out. In search for factors that could explain this retention, it was found that tumors from patients suffering hepatocellular carcinoma, colorectal or pancreatic cancer were producing IL-8 and that this chemokine attracted monocyte-derived dendritic cells that uniformly express both IL-8 receptors CXCR1 and CXCR2. Accordingly, neutralizing antihuman IL-8 monoclonal antibodies blocked the chemotactic attraction of DCs by recombinant IL-8, as well as by the serum of the patients or culture supernatants of human colorectal carcinomas. In addition, tissue culture supernatants of colon carcinoma cells inhibited DC migration induced by MIP-3beta in an IL-8-dependent fashion. IL-8 production in malignant tissue and the responsiveness of DCs to IL-8 are a likely explanation of the clinical images, which suggest retention of DCs inside human malignant lesions. Impairment of DC migration toward lymphoid tissue could be involved in cancer immune evasion.

Early lung cancer detection using spiral computed tomography and positron emission tomography.

RATIONALE: Lung cancer screening using computed tomography (CT) is effective in detecting lung cancer in early stages. Concerns regarding false-positive rates and unnecessary invasive procedures have been raised. OBJECTIVE: To study the efficiency of a lung cancer protocol using spiral CT and F-18-fluorodeoxyglucose positron emission tomography (FDG-PET). METHODS: High-risk individuals underwent screening with annual spiral CTs. Follow-up CTs were done for noncalcified nodules of 5 mm or greater, and FDG-PET was done for nodules 10 mm or larger or smaller (> 7 mm), growing nodules. RESULTS: A total of 911 individuals completed a baseline CT study and 424 had at least one annual follow-up study. Of the former, 14% had noncalcified nodules of 5 mm or larger, and 3.6% had nodules of 10 mm or larger. Eleven non-small cell lung cancers (NSCLC) and one small cell lung cancer (SCLC) were diagnosed in the baseline study (prevalence rate, 1.32%), and two NSCLCs in the annual study (incidence rate, 0.47%). All NSCLCs (92% of prevalence cancers) were diagnosed in stage I (12 stage IA, 1 stage IB). FDG-PET was helpful for the correct diagnosis in 19 of 25 indeterminate nodules. The sensitivity, specificity, positive predictive value, and negative predictive value of FDG-PET for the diagnosis of malignancy were 69, 91, 90, and 71%, respectively. However, the sensitivity and negative predictive value of the screening algorithm, which included a 3-month follow-up CT for nodules with a negative FDG-PET, was 100%. CONCLUSION: A protocol for early lung cancer detection using spiral CT and FDG-PET is useful and may minimize unnecessary invasive procedures for benign lesions.