Search Results (222)

The transgenic animals have been yielding invaluable insights into amyloid pathology by replicating the key features of Alzheimer’s disease (AD). However, there is no clear relationship between senile plaques and memory deficits. Instead, cognitive impairment and synaptic dysfunction are particularly linked to a rise in Aβ_1-42 oligomer level. Thus, injection of Aβ_1-42 oligomers into a specific brain region is considered an alternative approach to investigate the effects of increased soluble Aβ species without any plaques, offering higher controllability, credibility and validity compared to the transgenic model. The hippocampal CA1 (cornu ammonis 1) region is selectively affected in the early stage of AD and specific targeting of CA1 region directly links Aβ oligomer-related pathology with memory impairment in early AD. Next, the inhibitory avoidance (IA) task, a learning paradigm to assess the synaptic basis of CA1-dependent contextual learning, triggers training-dependent synaptic plasticity similar to in vitro HFS (high-frequency stimulation). Given its reliability in assessing contextual memory and synaptic plasticity, this task provides an effective framework for studying early stage AD-related memory deficit. Therefore, in this review, we will focus on why Aβ_1-42 oligomer injection is a valid in vivo model to investigate the early stage of AD and why dorsal CA1 region serves as a target area to understand the adverse effects of Aβ_1-42 oligomers on contextual learning through the IA task. Full article

Antimicrobial peptides (AMPs) provide a robust alternative to conventional antibiotics, combating escalating microbial resistance through their diverse functions and broad pathogen-targeting abilities. While current deep learning technologies enhance AMP generation, they face challenges in developing multifunctional AMPs due to intricate amino acid interdependencies and limited consideration of diverse functional activities. To overcome this challenge, we introduce a novel de novo multifunctional AMP design framework that enhances a Feedback Generative Adversarial Network (FBGAN) by integrating a global quantitative AMP activity regression module and a multifunctional-attribute integrated prediction module. This integrated approach not only facilitates the automated generation of potential AMP candidates, but also optimizes the network’s ability to assess their multifunctionality. Initially, by integrating an effective pre-trained regression and classification model with feedback-loop mechanisms, our model can not only identify potential valid AMP candidates, but also optimizes computational predictions of Minimum Inhibitory Concentration (MIC) values. Subsequently, we employ a combinatorial predictor to simultaneously identify and predict five multifunctional AMP bioactivities, enabling the generation of multifunctional AMPs. The experimental results demonstrate the efficiency of generating AMPs with multiple enhanced antimicrobial properties, indicating that our work can provide a valuable reference for combating multi-drug-resistant infections. Full article

Background: Antimicrobial resistance (AMR) is increasingly acknowledged as a critical global challenge, posing serious risks to human and animal health and potentially disrupting poultry production systems. Commensal bacteria such as Staphylococcus spp., Enterococcus spp., and Escherichia coli may serve as important reservoirs and vectors of resistance genes. Objectives: This study aimed to assess the AMR profiles of bacterial strains isolated from industrial chicken farms in the Dél-Alföld region of Hungary, providing region-specific insights into resistance dynamics. Methods: A total of 145 isolates, including Staphylococcus spp., Enterococcus spp., and E. coli isolates, were subjected to minimum inhibitory concentration (MIC) testing against 15 antimicrobial agents, following Clinical and Laboratory Standards Institute (CLSI) guidelines. Advanced multivariate statistics, machine learning algorithms, and network-based approaches were employed to analyze resistance patterns and co-resistance associations. Results Multidrug resistance (MDR) was identified in 43.9% of Staphylococcus spp. isolates, 28.8% of Enterococcus spp. isolates, and 75.6% of E. coli isolates. High levels of resistance to florfenicol, enrofloxacin, and potentiated sulfonamides were observed, whereas susceptibility to critical antimicrobials such as imipenem and vancomycin remained largely preserved. Discussion: Our findings underscore the necessity of implementing region-specific AMR monitoring programs and strengthening multidisciplinary collaboration within the “One Health” framework with proper animal hygiene and biosecurity measures to limit the spread of antimicrobial resistance and protect both animal and human health. Full article

Background/Objectives: The resistance mutations EGFR^{L858R/T790M/C797S} in epidermal growth factor receptor (EGFR) are key factors in the reduced efficacy of Osimertinib. Predicting the inhibitory effects of Osimertinib derivatives against these mutations is crucial for the development of more effective inhibitors. This study aims to predict the inhibitory effects of Osimertinib derivatives against EGFR^{L858R/T790M/C797S} mutations. Methods: Six models were established using heuristic method (HM), random forest (RF), gene expression programming (GEP), gradient boosting decision tree (GBDT), polynomial kernel function support vector machine (SVM), and mixed kernel function SVM (MIX-SVM). The descriptors for these models were selected by the heuristic method or XGBoost. Comprehensive learning particle swarm optimizer was adopted to optimize hyperparameters. Additionally, the internal and external validation were performed by leave-one-out cross-validation ($Q_{LOO}^2$), 5-fold cross validation ($Q_{5-fold}^2$) and concordance correlation coefficient (CCC), $Q_{F1}^2$, and $Q_{F2}^2$. The properties of novel EGFR inhibitors were explored through molecular docking analysis. Results: The model established by MIX-SVM whose kernel function is a convex combination of three regular kernel functions is best: $R^2$ and RMSE for training set and test set are 0.9445, 0.1659 and 0.9490, 0.1814, respectively; $Q_{LOO}^2$, $Q_{5-fold}^2$, CCC, $Q_{F1}^2$, and $Q_{F2}^2$ are 0.9107, 0.8621, 0.9835, 0.9689, and 0.9680. Based on these results, the IC₅₀ values of 162 newly designed compounds were predicted using the HM model, and the top four candidates with the most favorable physicochemical properties were subsequently validated through PEA. Conclusions: The MIX-SVM method will provide useful guidance for the design and screening of novel EGFR^{L858R/T790M/C797S} inhibitors. Full article

Background/Objectives: In recent work, we have demonstrated that principal component analysis (PCA) and Fourier Transformation Infrared (FTIR) spectra are powerful tools for analyzing the changes in microorganisms at the biomolecular level to detect changes in bacteria with resistance to antibiotics. Here biochemical structural changes in Staphylococcus aureus were analyzed over exposure time with the goal of identifying trends inside the samples that have been exposed to antibiotics for increasing amounts of time and developed resistance. Methods: All studied data was obtained from FTIR spectra of samples with induced antibiotic resistance to either Azithromycin, Oxacillin, or Trimethoprim/Sulfamethoxazole following the evolution of this development over four increasing antibiotic exposure periods. Results: The processing and data analysis with machine learning algorithms performed on this FTIR spectral database allowed for the identification of patterns across minimum inhibitory concentration (MIC) values associated with different exposure times and both clusters from hierarchical classification and PCA. Conclusions: The results enable the observation of resistance development pathways for the sake of knowing the present stage of resistance of a bacterial sample. This is carried out via machine learning methods for the purpose of faster and more effective infection treatment in healthcare settings. Full article

(1) Background: The highly pathogenic avian influenza virus H5N1 clade 2.3.4.4b is an emerging threat that poses a great risk to the poultry industry. A few human cases have been linked to the infection with this clade in many parts of the world, including the USA. Unfortunately, there are no specific vaccines or antiviral drugs that could help prevent and treat the infection caused by this virus in birds. Our major objective is to identify/repurpose some (novel/known) antiviral compounds that may inhibit viral replication by targeting some key viral proteins. (2) Methods: We used state-of-the-art machine learning tools such as molecular docking and MD-simulation methods from Biovia Discovery Studio (v24.1.0.321712). The key target proteins such as hemagglutinin (HA), neuraminidase (NA), Matrix-2 protein (M2), and the cap-binding domain of PB2 (PB2/CBD) homology models were validated through structural assessment via DOPE scores, Ramachandran plots, and Verify-3D metrics, ensuring reliable structural representations, confirming their reliability for subsequent in silico approaches. These approaches include molecular docking followed by molecular dynamics simulation for 50 nanoseconds (ns), highlighting the structural stability and compactness of the docked complexes. (3) Results: Molecular docking revealed strong binding affinities for both sofosbuvir and GS441524, particularly with the NA and PB2/CBD protein targets. Among them, GS441524 exhibited superior interaction scores and a greater number of hydrogen bonds with key functional residues of NA and PB2/CBD. The MM-GBSA binding free energy calculations further supported these findings, as GS441524 displayed more favorable binding energies compared to several known standard inhibitors, including F0045S for HA, Zanamivir for NA, Rimantadine and Amantadine for M2, and PB2-39 for PB2/CBD. Additionally, 50 ns molecular dynamics simulations highlighted the structural stability and compactness of the GS441524-PB2/CBD complex, further supporting its potential as a promising antiviral candidate. Furthermore, hydrogen bond monitor analysis over the 50 ns simulation confirmed persistent and specific interactions between the ligand and proteins, suggesting that GS441524 may effectively inhibit the NA, and PB2/CBD might potentially disrupt PB2-mediated RNA synthesis. (4) Conclusions: Our findings are consistent with previous evidence supporting the antiviral activity of certain nucleoside analog inhibitors, including GS441524, against various coronaviruses. These results further support the potential repurposing of GS441524 as a promising therapeutic candidate against H5N1 avian influenza clade 2.3.4.4b. However, further functional studies are required to validate these in silico predictions and support the inhibitory action of GS441524 against the targeted proteins of H5N1, specifically clade 2.3.4.4b. Full article

Background/Objectives: Human carbonic anhydrases (hCAs) are metalloenzymes involved in essential physiological processes, and their selective inhibition holds therapeutic potential across a wide range of disorders. However, the high degree of structural similarity among isoforms poses a significant challenge for the design of selective inhibitors. In this work, we present a machine learning (ML)-based platform for the isoform-specific prediction and profiling of small molecules targeting hCA I, II, IX, and XII. Methods: By integrating four molecular representations with four ML algorithms, we built 64 classification models, each extensively optimized and validated. The best-performing models for each isoform were applied in a virtual screening campaign for ~2 million compounds. Results: Following a multi-step refinement process, 12 candidates were identified, purchased, and experimentally tested. Several compounds showed potent inhibitory activity in the nanomolar to submicromolar range, with selectivity profiles across the isoforms. To gain mechanistic insights, SHAP-based feature importance analysis and molecular docking supported by molecular dynamics simulations were employed, highlighting the structural determinants of the predicted activity. Conclusions: This study demonstrates the effectiveness of integrating ML, cheminformatics, and experimental validation to accelerate the discovery of selective carbonic anhydrase inhibitors and provides a generalizable framework for activity profiling across enzyme isoforms. Full article

Generative artificial intelligence (GAI) is emerging as a disruptive force, both economically and socially, with its use spanning from the provision of goods and services to everyday activities such as healthcare and household management. This study analyzes the enabling and inhibiting factors of GAI use in Spain based on a large-scale survey conducted by the Spanish Center for Sociological Research on the use and perception of artificial intelligence. The proposed model is based on the Theory of Planned Behavior and is fitted using machine learning techniques, specifically decision trees, Random Forest extensions, and extreme gradient boosting. While decision trees allow for detailed visualization of how variables interact to explain usage, Random Forest provides an excellent model fit (R² close to 95%) and predictive performance. The use of Shapley Additive Explanations reveals that knowledge about artificial intelligence, followed by innovation orientation, is the main explanatory variable of GAI use. Among sociodemographic variables, Generation X and Z stood out as the most relevant. It is also noteworthy that the perceived privacy risk does not show a clear inhibitory influence on usage. Factors representing the positive consequences of GAI, such as performance expectancy and social utility, exert a stronger influence than the negative impact of hindering factors such as perceived privacy or social risks. Full article

There is no consensus in existing research on whether the application of digital technology diversification strengthens or weakens export resilience. This study fills this gap by exploring the influence mechanism of digital technology diversity on enterprise export resilience. Based on organizational inertia theory, dynamic capabilities perspective, and organizational learning theory, this study uses data from Chinese listed export manufacturing companies from 2019 to 2023 in order to empirically examine the relationship between digital technology diversity and enterprise export resilience. The results show that digital technology diversity significantly inhibits enterprise export resilience, supply chain finance plays a partially mediating role in this relationship, and digital interlock alleviates the inhibitory effect of digital technology diversity on supply chain finance. The findings provide guidance for the digital technology application strategy of export enterprises in adversity. Full article

The real estate market significantly influences individual lives, corporate decisions, and national economic sustainability. Therefore, constructing a data-driven, interpretable real estate market prediction model is essential. It can clarify each factor’s role in housing prices and transactions, offering a scientific basis for market regulation and enterprise investment decisions. This study comprehensively measures the evolution trends of the real estate markets in Beijing, Shanghai, Guangzhou, and Shenzhen, China, from 2003 to 2022 through three dimensions. Then, various machine learning methods and interpretability methods like SHAP values are used to explore the impact of supply, demand, policies, and expectations on the real estate market of China’s first-tier cities. The results reveal the following: (1) In terms of commercial housing sales area, adequate housing supply, robust medical services, and high population density boost the sales area, while demand for small units reflects buyers’ balance between affordability and education. (2) In terms of commercial housing average sales price, growth is driven by education investment, population density, and income, with loan interest rates serving as a stabilizing tool. (3) In terms of commercial housing sales amount, educational expenditure, general public budget expenditure, and real estate development investment amount drive revenue, while the five-year loan benchmark interest rate is the primary inhibitory factor. These findings highlight the divergent impacts of supply, demand, policy, and expectation factors across different market dimensions, offering critical insights for enterprise investment strategies. Full article

Hemophilia, an X-linked bleeding disorder, is characterized by a deficiency in coagulation factors. It manifests as spontaneous bleeding, leading to severe complications if not properly managed. In contrast, acquired hemophilia is an autoimmune condition marked by the development of inhibitory antibodies against coagulation factors. Both forms present significant diagnostic and therapeutic challenges, highlighting the need for advanced genetic, molecular, laboratory, and clinical assessments. Recent advances in artificial intelligence have opened new avenues for the management of hemophilia. Machine learning and deep learning technologies enhance the ability to predict bleeding risks, optimize treatment regimens, and monitor disease progression with greater precision. Artificial intelligence-driven applications in medical imaging have also improved the detection of joint damage and hemarthrosis, ensuring timely interventions and better clinical outcomes. Moreover, the integration of artificial intelligence into clinical practice holds the potential to transform hemophilia care through predictive analytics and personalized medicine, promising not only faster and more accurate diagnoses but also a reduction in long-term complications. However, ethical considerations and the need for data standardization remain critical for its widespread adoption. The application of artificial intelligence in hemophilia represents a paradigm shift towards precision medicine, with the promise of significantly improving patient outcomes and quality of life. Full article

Synaptic plasticity is the key mechanism underlying learning and memory. Neurexins are pre-synaptic molecules that play a pivotal role in synaptic plasticity, interacting with many different post-synaptic molecules in the formation of neural circuits. Neurexins are alternatively spliced at different splice sites, yielding thousands of isoforms with different properties of interaction with post-synaptic molecules for a quick adaptation to internal and external inputs. The endocannabinoid system also plays a central role in synaptic plasticity, regulating key retrograde signaling at both excitatory and inhibitory synapses. This study aims at elucidating the crosstalk between alternative splicing of neurexin and the endocannabinoid system in the hippocampus. By employing an ex vivo hippocampal system, we found that pharmacological activation of cannabinoid receptor 1 (CB1) with the specific agonist ACEA led to reduced neurotransmission, associated with increased expression of the Nrxn1–3 spliced isoforms excluding the exon at splice site 4 (SS4−). In contrast, treatment with the CB1 antagonist AM251 increased glutamatergic activity and promoted the expression of the Nrxn variants including the exon (SS4+) Knockout of the involved splicing factor SLM2 determined the suppression of the exon splicing at SS4 and the expression only of the SS4+ variants of Nrxns1–3 transcripts. Interestingly, in SLM2 ko hippocampus, modulation of neurotransmission by AM251 or ACEA was abolished. These findings suggest a direct crosstalk between CB1-dependent signaling, neurotransmission and expression of specific Nrxns splice variants in the hippocampus. We propose that the fine-tuned regulation of Nrxn1–3 genes alternative splicing may play an important role in the feedback control of neurotransmission by the endocannabinoid system. Full article

Plant-derived materials from Salvia officinalis L. (sage) have demonstrated significant antimicrobial potential when applied during fresh cheese production. In this study, the mechanism of action of sage components against Listeria monocytogenes, Escherichia coli, and Staphylococcus aureus was investigated through the development of predictive models that describe the influence of key parameters on antimicrobial efficacy. Molecular modeling techniques were employed to identify the major constituents responsible for the observed inhibitory activity. Epirosmanol, carvacrol, limonene, and thymol were identified as the primary compounds contributing to the antimicrobial effects during cheese production. The highest weighted predicted binding energy was observed for thymol against the KdpD histidine kinase from Staphylococcus aureus, with a value of −33.93 kcal/mol. To predict the binding affinity per unit mass of these sage-derived compounds against the target pathogens, machine learning models—including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Boosted Trees Regression (BTR)—were developed and evaluated. Among these, the ANN model demonstrated the highest predictive accuracy and robustness, showing minimal bias and a strong coefficient of determination (R² = 0.934). These findings underscore the value of integrating molecular modeling and machine learning approaches for the identification of bioactive compounds in functional food systems. Full article

Background/Objectives: Thyroid hormone (TH) deficiency during the pregnancy and lactation periods leads to enduring memory impairments in offspring. However, the mechanisms underlying the cognitive and memory deficits induced by developmental hypothyroidism remain largely unexplored. Methods: Mice were exposed to propylthiouracil (PTU) or purified water to detect changes in hippocampal neurogenesis and differentiation of their offspring to explain the pathogenesis of impaired learning and memory. In addition, HT22 cell line were used to investigate the regulation between Maf and Mef2c. Results: Our findings indicate that developmental exposure to PTU results in abnormalities of the preferential differentiation of GABAergic interneurons and a subsequent reduction in PV⁺ inhibitory interneurons in the hippocampus of mouse pups. More significantly, we also indicate that the downregulation of Maf and the consequent alteration of Mef2c are likely responsible for the mechanisms through which developmental hypothyroidism influences the differentiation and development of PV⁺ inhibitory interneurons in offspring. Conclusions: Consequently, the aberrant development of PV⁺ interneuron in the hippocampus of mice subjected to developmental hypothyroidism potentially contributes to memory deficits during adolescence and adulthood. Full article

Type 2 diabetes mellitus (T2DM) is a complex and prevalent metabolic disorder, and dipeptidyl peptidase 4 (DPP4) inhibitors have proven effective, yet the identification of novel inhibitors remains challenging due to the vastness of chemical space. In this study, we developed DPPPRED-IV, a web-based ensembled system integrating both binary classification and continuous regression Quantitative Structure Activity Relationships (QSAR) models to predict human DPP4 inhibitory activity. A curated dataset of 4 676 ChEMBL compounds was subjected to genetic algorithm descriptor selection and multiple machine learning algorithms; classification models were combined via a soft voting ensemble, while regression models estimated IC₅₀ values. All models underwent external 10-fold cross-validation and applicability domain analysis. The final models were integrated into a user-friendly web server, allowing predictions from SMILES inputs. Experimental testing of 29 MolPort compounds at 1.5 µM confirmed that 14 predicted actives exhibited significant inhibition, supporting the tool’s performance in early-stage screening. DPPPRED IV is freely available within the ChemoPredictionSuite and offers a resource to accelerate decision making, reduce costs and minimize animal use in T2DM drug discovery. Full article