Hasil untuk "Therapeutics. Pharmacology"

Gaëlle Audéoud, Louis Moine, Laura Bonnecaze et al.

The aggregation of amyloid-forming peptides is a dynamic, complex process that underlies their diverse biological activities, from physiological functions to disease-associated dysfunctions. While the structure of fibrillar end-products is well-characterized for most amyloids, the heterogeneous and often transient oligomers, likely key in cytotoxicity, remain poorly investigated, especially for peptides with low-yield aromatic residues. Here, by exploiting and developing flow induced dispersion analysis in both peak and front modes, we demonstrate that intrinsic phenylalanine fluorescence can be harnessed to quantify the conversion of diffusing monomers into non-diffusing oligomers and fibrils. We further characterize low-molecular-weight oligomers, and their size evolution from 2 to 10 nm over time. Importantly, we validate the robustness of our approach using two tryptophan-free and fast-fibrillating amyloid peptides, PSM$α$3 and hIAPP, known for their key roles in S. aureus virulence and type 2 diabetes respectively. Our results overcome the limitations of traditional biochemical and biophysical amyloid assays by extending analysis from large oligomers and fibrils to small heterogeneous oligomers, under near-physiological conditions. This study thus offers a new analytical framework, thereby filling a critical gap in amyloid research, to probe the early stages of aggregation, key in the design of alternative therapeutics for amyloid-diseases.

Haomei Fan, Haomei Fan, Haomei Fan et al.

BackgroundClindamycin hydrochloride, a first-line antibiotic for acne treatment, faces challenges with poor skin penetration due to its hydrophilicity and the barrier posed by the stratum corneum. To address this limitation, we developed gelatin-methacryloyl (GelMA) hydrogel-based biodegradable microneedles (GM-Clin-MN) for sustained intradermal drug delivery, thereby enhancing therapeutic efficacy.MethodsThe microneedle patches loaded with 1 wt% clindamycin hydrochloride were fabricated using PDMS molds and characterized through scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and fluorescence microscopy. Drug loading and release were assessed using UV-Vis spectroscopy at 520 nm, while mechanical strength was evaluated with a universal testing machine. Skin penetration was tested on ex vivo rat abdominal skin. Biosafety was determined through human skin fibroblast (HSF) cytotoxicity and hen’s egg test-chorioallantoic membrane (HET-CAM) irritation tests. Antibacterial efficacy against Cutibacterium acnes (C. acnes) was measured via colony counting. In vivo acne treatment of the microneedles was evaluated in a rat acne model. Gross morphological changes, histological sections, and immunohistochemical staining were used to evaluate the efficacy and potential mechanisms of acne treatment.ResultsClindamycin hydrochloride-loaded GelMA microneedles (GM-Clin-MN) achieved a drug loading of 0.49 ± 0.025 μg/needle, exhibiting rapid release on Day 1 (54.8% ± 2.1%) and sustained release by Day 10 (72.1% ± 1.5%). The microneedles penetrated the skin to a depth of 658 ± 66 μm, swelled by 185.4% ± 12.1%, and completely dissolved within 10 min. GM-Clin-MN displayed no cytotoxicity or skin irritation and effectively inhibited the growth of C. acnes (bacterial inhibition rate of 100%). In vivo studies revealed that acne-related inflammation was effectively suppressed with potential anti-scarring properties, characterized by reduced pro-inflammatory IL-1β levels, increased anti-inflammatory IL-10 expression, and diminished MMP-2 activity — a key enzyme in collagen overproduction during scarring.ConclusionGM-Clin-MN enables sustained, minimally invasive clindamycin delivery through the stratum corneum, offering a dual-action therapeutic strategy that combines potent antibacterial activity with anti-inflammatory modulation for acne management.

Yan-Ping Yu, Caroline Obert, Bao-Guo Ren et al.

Hepatocellular carcinoma (HCC) is one of the most lethal cancers for humans. HCC is highly heterogeneous. In this study, we performed ultra-depth (∼1 million reads per spot) sequencing of 6,320 spatial transcriptomes on a case of HCC. Sixteen distinct spatial expression clusters were identified. Each of these clusters was spatially contiguous and had distinct gene expression patterns. In contrast, benign liver tissues showed minimal heterogeneity in terms of gene expression. Numerous immune cell-enriched spots were identified in both HCC and benign liver regions. Cells adjacent to these immune cell-enriched spots showed significant alterations in their gene expression patterns. Interestingly, the responses of HCC cells to the nearby immune cells were significantly more intense and broader, while the responses of benign liver cells to immune cells were somewhat narrow and muted, suggesting an innate difference in immune cell activities towards HCC cells in comparison with benign liver cells. However, cell-cell interaction analyses showed significant immune evasion by HCC cancer cells. When standard-depth sequencing was performed, significant numbers of genes and pathways that were associated with these changes disappeared. Qualitative differences in some pathways were also found. These results suggest that deep spatial sequencing may help to uncover previously unidentified mechanisms of liver cancer development.

Basma M. Qandeel, Samar Mowafy, Mohamed F. El-Badawy et al.

The escalating threat of antimicrobial resistance calls for novel therapeutic agents. This study employed a ligand-based design approach to develop three series of N-arylpyrrole derivatives (Va–e, VIa–e, and VIIa–e), refined through molecular modeling. Synthesized compounds were evaluated against ESKAPE pathogens, MRSA, and Mycobacterium phlei. Series Va–e showed the most promise, with compounds Vb, Vc, and Ve outperforming levofloxacin against MRSA (MIC = 4 μg/mL vs. 8 μg/mL). Vc also exhibited activity against E. coli, K. pneumoniae, and A. baumannii, and showed significant inhibition against M. phlei (MIC = 8 μg/mL). Compounds were evaluated for antibiofilm and antivirulence properties, targeting resistance mechanisms linked to infection persistence and dissemination. Most exhibited broad-spectrum biofilm inhibition and antivirulence activity. Cytotoxicity studies revealed selectivity for bacterial cells. ADMET studies supported drug-like properties. Docking studies suggested UPPP inhibition as the potential mechanism. SAR analysis was conducted to support future optimizations.

Kai Wu, Min Liao, Juan Deng et al.

ObjectiveThe potential of ciprofol in endoscopic anesthesia is receiving increasing attention. Compared to propofol, ciprofol exhibits stronger sedative effects and requires a lower dosage. This study aimed to compare the safety of ciprofol and propofol in Chinese patients undergoing endoscopic retrograde cholangio-pancreatography (ERCP) anesthesia.MethodsA comprehensive literature search was conducted across eight common databases before 1 January 2025, including PubMed, Embase, the Cochrane Library, and Web of Science, China National Knowledge Infrastructure, China Science and Technology Journal Database, WanFang, and SinoMed. After screening the literature according to established standards, the meta-analysis and trial sequential analysis (TSA) were conducted using Review Manager 5.3 and TSA 0.9.5.10 beta, respectively. Finally, publication bias for each outcome was assessed using Harbord regression analysis.ResultsSeven randomized controlled trials (RCTs) with 1,264 participants undergoing ERCP were included, and all included studies were conducted in China, with participants representing the Chinese population. The meta-analysis showed that compared to propofol, ciprofol reduced bradycardia (risk ratio [RR] 0.44, 95% confidence interval [CI] 0.26–0.76, P = 0.003, n = 4), hypotension (RR 0.72, 95% CI 0.55–0.95, P = 0.02, n = 4), respiratory depression (RR 0.25, 95% CI 0.14–0.44, P < 0.00001, n = 5), hypoxemia (RR 0.35, 95% CI 0.21–0.58, P < 0.0001, n = 5), and injection pain (RR 0.17, 95% CI 0.11–0.26, P < 0.00001, n = 7), but had no significant effect on choking cough, involuntary movements, or nausea and vomiting. TSA showed a conclusive benefit for bradycardia, respiratory depression, hypoxemia, and injection pain, whereas the benefit for hypotension needs further validation. Harbord regression analysis showed no publication bias for any of the outcomes, except for hypotension.ConclusionCompared with propofol, ciprofol has been shown to reduce the incidence of bradycardia, respiratory depression, hypoxemia, and injection pain in patients undergoing ERCP; however, its effect on the occurrence of hypotension still requires further investigation. Future studies are warranted to clarify the safety, efficacy, and optimal dosing of ciprofol across various patient populations, particularly those with complex comorbidities. These efforts would facilitate the broader application of ciprofol in ERCP and other surgical procedures, such as gastrointestinal and ophthalmic surgeries.Systematic review registrationwww.crd.york.ac.uk/PROSPERO/view/CRD420251090047, identifer, CRD420251090047

Mozhgan Mozaffarilegha, Sajjad Gharaghani

BackgroundCombination therapies play a crucial role in the treatment of complex diseases, such as cancer. They enhance efficacy, minimize resistance, and reduce toxicity by leveraging synergistic effects. However, identifying effective combinations is challenging due to the vast number of possible pairings and the high-priced costs of experimental validation. Machine learning (ML) and deep learning (DL) models have advanced drug synergy prediction by integrating diverse datasets and modeling the interactions between drugs and cell lines. Despite these advancements, most algorithms primarily rely on drug-specific features, such as chemical structures, with limited incorporation of functional drug information and cellular content features.Methods:We propose a novel approach that integrates Drug Resistance Signatures (DRS) as a biologically informed representation of drug information. This approach provides a more comprehensive framework for identifying effective combination therapies. We evaluated the predictive power of DRS features across various machine learning models (LASSO, Random Forest, AdaBoost, and XGBoost) and the deep learning model SynergyX. We compared their performance with that of conventional drug signatures and chemical structure-based descriptors.Results:Our results demonstrate that models incorporating DRS features consistently outperform traditional approaches across all evaluated algorithms. Validation on independent datasets, including ALMANAC, O’Neil, OncologyScreen, and DrugCombDB, confirms the robustness and generalizability of the proposed framework.DiscussionThese findings emphasize the importance of integrating resistance-informed transcriptomic features into computational models. By capturing drug functionality in a biologically relevant context, DRS improves both the accuracy and interpretability of drug synergy prediction, offering a powerful strategy for guiding the discovery of effective combination therapies.

Wei Lai, Hu Geliang, Xu Bin et al.

Abstract As an emerging technology, organoids are promising new tools for basic and translational research in disease. Currently, the culture of organoids relies mainly on a type of unknown composition scaffold, namely Matrigel, which may pose problems in studying the effect of mechanical properties on organoids. Hydrogels, a new material with adjustable mechanical properties, can adapt to current studies. In this review, we summarized the synthesis of recent advance in developing definite hydrogel scaffolds for organoid culture and identified the critical parameters for regulating mechanical properties. In addition, classified by different mechanical properties like stiffness and viscoelasticity, we concluded the effect of mechanical properties on the development of organoids and tumor organoids. We hope this review enhances the understanding of the development of organoids by hydrogels and provides more practical approaches to investigating them.

Yi He, Ailun Wang, Zhi Wang et al.

Recent advances in generative models, particularly diffusion and auto-regressive models, have revolutionized fields like computer vision and natural language processing. However, their application to structure-based drug design (SBDD) remains limited due to critical data constraints. To address the limitation of training data for models targeting SBDD tasks, we propose an evolutionary framework named MEVO, which bridges the gap between billion-scale small molecule dataset and the scarce protein-ligand complex dataset, and effectively increase the abundance of training data for generative SBDD models. MEVO is composed of three key components: a high-fidelity VQ-VAE for molecule representation in latent space, a diffusion model for pharmacophore-guided molecule generation, and a pocket-aware evolutionary strategy for molecule optimization with physics-based scoring function. This framework efficiently generate high-affinity binders for various protein targets, validated with predicted binding affinities using free energy perturbation (FEP) methods. In addition, we showcase the capability of MEVO in designing potent inhibitors to KRAS$^{\textrm{G12D}}$, a challenging target in cancer therapeutics, with similar affinity to the known highly active inhibitor evaluated by FEP calculations. With high versatility and generalizability, MEVO offers an effective and data-efficient model for various tasks in structure-based ligand design.

Garima Chib, Parisa Mollaei, Amir Barati Farimani

G-Protein Coupled Receptors (GPCRs) are integral to numerous physiological processes and are the target of approximately one-third of FDA-approved therapeutics. Despite their significance, only a limited subset of GPCRs has been successfully targeted, primarily due to challenges in accurately modeling their structures. AlphaFold, a state-of-the-art deep learning model, has demonstrated remarkable capability in predicting protein structures with high accuracy. This study conducts an evaluation of AlphaFold performance in predicting GPCR structures and their conformational states by comparing its predictions to experimentally determined structures using metrics such as average deformation between alpha carbon atoms and the Helix 3 - Helix 6 (H3-H6) distance. Our analysis reveals that both AlphaFold 2 (AF2) and AlphaFold 3 (AF3) produce more accurate predictions for GPCRs in inactive conformations, with lower activity levels correlating with smaller deformations. Conversely, higher activity levels are associated with increased variability in AlphaFold performance due to difficulties with accurately predicting conformational changes upon GPCR activation and ligand binding. Additionally, AlphaFold performance varies across different GPCR classes, influenced by the availability and quality of training data as well as the structural complexity and diversity of the receptors. These findings demonstrate the potential of AlphaFold in advancing drug discovery efforts, while also highlighting the necessity for continued refinement to enhance predictive accuracy for active conformations.

Omar Costilla-Reyes, Morgan Talbot

Body Dysmorphic Disorder (BDD) is a highly prevalent and frequently underdiagnosed condition characterized by persistent, intrusive preoccupations with perceived defects in physical appearance. In this extended analysis, we employ multiple machine learning approaches to predict treatment outcomes -- specifically treatment response and remission -- with an emphasis on interpretability to ensure clinical relevance and utility. Across the various models investigated, treatment credibility emerged as the most potent predictor, surpassing traditional markers such as baseline symptom severity or comorbid conditions. Notably, while simpler models (e.g., logistic regression and support vector machines) achieved competitive predictive performance, decision tree analyses provided unique insights by revealing clinically interpretable threshold values in credibility scores. These thresholds can serve as practical guideposts for clinicians when tailoring interventions or allocating treatment resources. We further contextualize our findings within the broader literature on BDD, addressing technology-based therapeutics, digital interventions, and the psychosocial determinants of treatment engagement. An extensive array of references situates our results within current research on BDD prevalence, suicidality risks, and digital innovation. Our work underscores the potential of integrating rigorous statistical methodologies with transparent machine learning models. By systematically identifying modifiable predictors -- such as treatment credibility -- we propose a pathway toward more targeted, personalized, and ultimately efficacious interventions for individuals with BDD.

Binglin Zeng, Jialin Lai, Jingyuan Chen et al.

Self-propelled micromotors can efficiently convert ambient energy into mechanical motion, which is of great interest for its potential biomedical applications in delivering therapeutics noninvasively. However, navigating these micromotors through biological barriers remains a significant challenge as most micromotors do not provide sufficient disruption forces in in-vivo conditions. In this study, we employed focused scanning laser from conventional confocal microscope to manipulate carbon microbottle based microswimmers. With the increasing of the laser power, the microswimmers' motions translates from autonomous to directional, and finally the high power laser induced the microswimmer explosions, which effectively deliveres microbottle fragments through the cell membrane. It is revealed that photothermally-induced cavitation bubbles enable the propulsion of microbottles in liquids, where the motion direction can be precisely regulated by the scanning orientation of the laser. Furthermore, the membrane penetration ability of the microbottles promised potential applications in drug delivery and cellular injections. As microbottles navigate toward cells, we strategically increase the laser power to trigger their explosion. By loading microswimmers with transfection genes, cytoplasmic transfection can be realized, which is demonstrated by successful gene transfection of GPF in cells. Our findings open new possibilities for cell injection and gene transfection using micromotors.

Ritam Ghosh, Nibraas Khan, Miroslava Migovich et al.

Apathy impairs the quality of life for older adults and their care providers. While few pharmacological remedies exist, current non-pharmacologic approaches are resource intensive. To address these concerns, this study utilizes a user-centered design (UCD) process to develop and test a set of dyadic activities that provide physical, cognitive, and social stimuli to older adults residing in long-term care (LTC) communities. Within the design, a novel framework that combines socially assistive robots and non-immersive virtual reality (SAR-VR) emphasizing human-robot interaction (HRI) and human-computer interaction (HCI) is utilized with the roles of the robots being coach and entertainer. An interdisciplinary team of engineers, nurses, and physicians collaborated with an advisory panel comprising LTC activity coordinators, staff, and residents to prototype the activities. The study resulted in four virtual activities: three with the humanoid robot, Nao, and one with the animal robot, Aibo. Fourteen participants tested the acceptability of the different components of the system and provided feedback at different stages of development. Participant approval increased significantly over successive iterations of the system highlighting the importance of stakeholder feedback. Five LTC staff members successfully set up the system with minimal help from the researchers, demonstrating the usability of the system for caregivers. Rationale for activity selection, design changes, and both quantitative and qualitative results on the acceptability and usability of the system have been presented. The paper discusses the challenges encountered in developing activities for older adults in LTCs and underscores the necessity of the UCD process to address them.

Ashwin Rao, Nazanin Sabri, Siyi Guo et al.

Effective communication during health crises is critical, with social media serving as a key platform for public health experts (PHEs) to engage with the public. However, it also amplifies pseudo-experts promoting contrarian views. Despite its importance, the role of emotional and moral language in PHEs' communication during COVID-19 remains under explored. This study examines how PHEs and pseudo-experts communicated on Twitter during the pandemic, focusing on emotional and moral language and their engagement with political elites. Analyzing tweets from 489 PHEs and 356 pseudo-experts from January 2020 to January 2021, alongside public responses, we identified key priorities and differences in messaging strategy. PHEs prioritize masking, healthcare, education, and vaccines, using positive emotional language like optimism. In contrast, pseudo-experts discuss therapeutics and lockdowns more frequently, employing negative emotions like pessimism and disgust. Negative emotional and moral language tends to drive engagement, but positive language from PHEs fosters positivity in public responses. PHEs exhibit liberal partisanship, expressing more positivity towards liberals and negativity towards conservative elites, while pseudo-experts show conservative partisanship. These findings shed light on the polarization of COVID-19 discourse and underscore the importance of strategic use of emotional and moral language by experts to mitigate polarization and enhance public trust.

Timm Michel, Jürgen Behr, Hamed Sabzalipoor et al.

Large-scale synthesis of DNA and RNA is a crucial technology for modern biological research ranging from genomics to nucleic acid therapeutics and for technological research ranging from nanofabrication of materials to molecular-level writing of digital data. Maskless Array Synthesis (MAS) is a versatile and efficient approach for creating the required complex microarrays and libraries of DNA and other nucleic acids for these applications and, more generally, for the synthesis of sequence-defined engineered and biological oligomers. MAS uses digital photomasks displayed by a digital micromirror device (DMD) illuminated by an appropriate light source and imaged into a photochemical reaction chamber with an optical relay system. Previously, Offner relay systems were used for imaging, but modern DMD formats with more and smaller micromirrors favor a different solution. We present a desktop MAS optical system with the larger numerical aperture and larger field of view required by 1080p and other large-format DMDs. The resulting catadioptric relay is well suited to modern DMDs in this application, and is corrected for first order axial and lateral color, enabling the use of high-power LEDs as inexpensive and long-lasting light sources spanning the ultraviolet-to-violet to perform the required photochemistry. Additional characteristics of the system, including high contrast and low scatter, make it ideal for reducing the error rates in photochemical synthesis of biomolecules.

Shailaja P. Desai, S.K. Mohite, Saad Alobid et al.

Background and objectives: Researchers have recently focused on the biological and synthetic effects of 1, 2, and 4-triazole fused heterocyclic molecules because they have tremendous medicinal value. The objective of the present study was to carry out the 3D QSAR evaluation on the substituted 1,2, and 4 triazole derivatives for anticancer potential using k-Nearest Neighbor-Molecular Field Analysis (kNN-MFA) method. Methods: Using the molecular design suite, a three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis was undertaken on a series of 4-amino-5-(pyridin3yl)-4H-1, 2, and 4-triazole-3-thiol anticancer drugs (Vlife MDS). This study used a genetic algorithm and a manual selection approach on 20 substituted 1, 2, and 4-triazole derivatives. Based on the genetic algorithm (GA), the 3D-QSAR model was generated. Statistical significance and predictive capacity were evaluated using internal and external validation. Results: The most significant model has a correlation coefficient of 0.9334 (squared correlation coefficient r2 = 0.8713), showing that biological activity and descriptors have a strong relationship. The model exhibited internal predictivity of 74.45 percent (q2 = 0.2129), external predictivity of 81.09 percent (pred r2 = 0.8417), and the smallest error term for the predictive correlation coefficient (pred r2se = 0.1255). The model revealed steric (S 1047––0.0780––0.0451S 927) and electrostatic (E 1002) data points that contribute remarkably to anticancer activity. A molecular field study demonstrates a link between the structural features of substituted triazole derivatives and their activities. Conclusion: The good-to-moderate anticancer potential of compounds confirms the significant pharmacological role of 1,2,4-triazole derivatives. These results could lead to the identification of potential chemical compounds with optimal anticancer activity and minimal side effects.

Wei‐Chih Lien, Xin‐Ran Zhou, Ya‐Jyun Liang et al.

Abstract Urological chronic pelvic pain syndrome (UCPPS) manifests as pelvic pain with frequent urination and has a 10% prevalence rate without effective therapy. Nanoceria (cerium oxide nanoparticles [CNPs]) were synthesized in this study to achieve potential long‐term pain relief, using a commonly used UCPPS mouse model with cyclophosphamide‐induced cystitis. Transcriptome sequencing analysis revealed that serpin family B member 2 (SerpinB2) was the most upregulated marker in mouse bladder, and SerpinB2 was downregulated with CNP pretreatment. The transcriptome sequencing analysis results agreed with quantitative polymerase chain reaction and western blot analysis results for the expression of related mRNAs and proteins. Analysis of Gene Expression Omnibus (GEO) datasets revealed that SerpinB2 was a differentially upregulated gene in human UCPPS. In vitro SerpinB2 knockdown downregulated proinflammatory chemokine expression (chemokine receptor CXCR3 and C‐X‐C motif chemokine ligand 10) upon treatment with 4‐hydroperoxycyclophosphamide. In conclusion, CNP pretreatment may prevent the development of UCPPS, and reactive oxygen species (ROS) scavenging and SerpinB2 downregulation may modulate the immune response in UCPPS.

Yeqing Lin, Mohammed AlQuraishi

Proteins power a vast array of functional processes in living cells. The capability to create new proteins with designed structures and functions would thus enable the engineering of cellular behavior and development of protein-based therapeutics and materials. Structure-based protein design aims to find structures that are designable (can be realized by a protein sequence), novel (have dissimilar geometry from natural proteins), and diverse (span a wide range of geometries). While advances in protein structure prediction have made it possible to predict structures of novel protein sequences, the combinatorially large space of sequences and structures limits the practicality of search-based methods. Generative models provide a compelling alternative, by implicitly learning the low-dimensional structure of complex data distributions. Here, we leverage recent advances in denoising diffusion probabilistic models and equivariant neural networks to develop Genie, a generative model of protein structures that performs discrete-time diffusion using a cloud of oriented reference frames in 3D space. Through in silico evaluations, we demonstrate that Genie generates protein backbones that are more designable, novel, and diverse than existing models. This indicates that Genie is capturing key aspects of the distribution of protein structure space and facilitates protein design with high success rates. Code for generating new proteins and training new versions of Genie is available at https://github.com/aqlaboratory/genie.

Yifei Wang, Yunrui Li, Lin Liu et al.

The versatility of multimodal deep learning holds tremendous promise for advancing scientific research and practical applications. As this field continues to evolve, the collective power of cross-modal analysis promises to drive transformative innovations, opening new frontiers in chemical understanding and drug discovery. Hence, we introduce Asymmetric Contrastive Multimodal Learning (ACML), a specifically designed approach to enhance molecular understanding and accelerate advancements in drug discovery. ACML harnesses the power of effective asymmetric contrastive learning to seamlessly transfer information from various chemical modalities to molecular graph representations. By combining pre-trained chemical unimodal encoders and a shallow-designed graph encoder with 5 layers, ACML facilitates the assimilation of coordinated chemical semantics from different modalities, leading to comprehensive representation learning with efficient training. We demonstrate the effectiveness of this framework through large-scale cross-modality retrieval and isomer discrimination tasks. Additionally, ACML enhances interpretability by revealing chemical semantics in graph presentations and bolsters the expressive power of graph neural networks, as evidenced by improved performance in molecular property prediction tasks from MoleculeNet and Therapeutics Data Commons (TDC). Ultimately, ACML exemplifies its potential to revolutionize molecular representational learning, offering deeper insights into the chemical semantics of diverse modalities and paving the way for groundbreaking advancements in chemical research and drug discovery.

Hristo Inouzhe, María Xosé Rodríguez-Álvarez, Lorenzo Nagar et al.

A study of changes in the transmission of a disease, in particular, a new disease like COVID-19, requires very flexible models which can capture, among others, the effects of non-pharmacological and pharmacological measures, changes in population behaviour and random events. In this work, we give priority to data-driven approaches and choose to avoid a priori and ad-hoc methods. We introduce a generalised family of epidemiologically informed mechanistic models, guided by Ordinary Differential Equations and embedded in a probabilistic model. The mechanistic models SIKR and SEMIKR with K Infectious and M Exposed sub-compartments (resulting in non-exponential infectious and exposed periods) are enriched with a time-dependent transmission rate, parametrized using Bayesian P-splines. This enables an extensive flexibility in the transmission dynamics, with no ad-hoc intervention, while maintaining good differentiability properties. Our probabilistic model relies on the solutions of the mechanistic model and benefits from access to the information about under-reporting of new infected cases, a crucial property when studying diseases with a large fraction of asymptomatic infections. Such a model can be efficiently differentiated, which facilitates the use of Hamiltonian Monte Carlo for sampling from the posterior distribution of the model parameters. The features and advantages of the proposed approach are demonstrated through comparison with state-of-the-art methods using a synthetic dataset. Furthermore, we successfully apply our methodology to the study of the transmission dynamics of COVID-19 in the Basque Country (Spain) for almost a year, from mid February 2020 to the end of January 2021.

Andreia Martins, Eva Maia, Isabel Praça

Complementary and alternative medicine are commonly used concomitantly with conventional medications leading to adverse drug reactions and even fatality in some cases. Furthermore, the vast possibility of herb-drug interactions prevents health professionals from remembering or manually searching them in a database. Decision support systems are a powerful tool that can be used to assist clinicians in making diagnostic and therapeutic decisions in patient care. Therefore, an original and hybrid decision support system was designed to identify herb-drug interactions, applying artificial intelligence techniques to identify new possible interactions. Different machine learning models will be used to strengthen the typical rules engine used in these cases. Thus, using the proposed system, the pharmacy community, people's first line of contact within the Healthcare System, will be able to make better and more accurate therapeutic decisions and mitigate possible adverse events.