Search Results (1,853)

Breast cancer remains one of the most prevalent and lethal malignancies in women, particularly the estrogen receptor-positive (ER+) subtype, which accounts for approximately 70% of cases. Traditional endocrine therapies, including aromatase inhibitors, selective estrogen receptor degraders/antagonists (SERDs), and selective estrogen receptor modulators (SERMs), have improved outcomes for metastatic ER+ breast cancer. However, resistance to these agents presents a significant challenge. This study explores a novel therapeutic strategy involving the simultaneous inhibition of the estrogen receptor (ER) and the chaperone protein Hsp90, which is crucial for the stabilization of various oncoproteins, including ER itself. We employed a hybrid, hierarchical in silico virtual screening approach to identify new dual ER/Hsp90 inhibitors, utilizing the Biotarget Predictor Tool (BPT) for efficient multitarget screening of a large compound library. Subsequent structure-based studies, including molecular docking analyses, were conducted to further evaluate the interaction of the top candidates with both ER and Hsp90. Supporting this, molecular dynamics simulations demonstrate the high stability of the multitarget inhibitor 755435 in complex with ER and Hsp90. Our findings suggest that several small molecules, particularly compound 755435, exhibit promising potential as dual inhibitors, representing a new avenue to overcome resistance in ER+ breast cancer. Full article

Public touchscreens, such as those used in automated teller machines or ticket payment systems, which are accessed by different people in a short period of time, could transmit pathogens and thus spread infections. Therefore, the aim of this study was to develop and test a prototype of a touchscreen system for the public sector that disinfects itself quickly and automatically between two users without harming any humans. A quartz pane was installed in front of a commercial 19” monitor, into which 120 UVC LEDs emitted laterally. The quartz plate acted as a light guide and irradiated microorganisms on its surface, but—due to total reflection—not the user in front of the screen. A near-infrared commercial touch frame was installed to recognize touch. The antibacterial effect was tested through intentional staphylococcus contamination. The prototype, composed of a Raspberry Pi microcomputer with a display, a touchscreen, and a touch frame, was developed, and a simple game was programmed that briefly switched on the UVC LEDs between two users. The antimicrobial effect was so strong that 1% of the maximum UVC LED current was sufficient for a 99.9% staphylococcus reduction within 25 s. At 17.5% of the maximum current, no bacteria were observed after 5 s. The residual UVC irradiance at a distance of 100 mm in front of the screen was only 0.18 and 2.8 µW/cm² for the two currents, respectively. This would allow users to stay in front of the system for 287 or 18 min, even if the LEDs were to emit UVC continuously and not be turned off after a few seconds as in the presented device. Therefore, fast, automatic touchscreen disinfection with UVC LEDs is already possible today, and with higher currents, disinfection durations below 1 s seems to be feasible, while the light guide approach virtually prevents the direct irradiation of the human user. Full article

Background/Objectives: Glucagon-like peptide-1 (GLP-1) receptor is currently one of the most explored targets exploited for the management of diabetes and obesity, with many aspects of its mechanisms behind cardiovascular protection yet to be fully elucidated. Research dedicated towards the development of oral GLP-1 therapy and non-peptide ligands with broader clinical applications is crucial towards unveiling the full therapeutic capacity of this potent class of medicines. Methods: This study describes the virtual screening of a natural product database consisting of 695,133 compounds for positive GLP-1 allosteric modulation. The database, obtained from the Coconut website, was filtered according to a set of physicochemical descriptors, then was shape screened against the crystal ligand conformation. This filtered database consisting of 26,325 compounds was used for virtual screening against the GLP-1 allosteric site. Results: The results identified ten best hits with the XP score ranging from −9.6 to −7.6 and MM-GBSA scores ranging from −50.8 to −32.4 and another 58 hits from docked pose filter and a second round of XP docking and MM-GBSA calculation followed by molecular dynamics. The analysis of results identified hits from various natural products (NPs) classes, to whom attributed antidiabetic and anti-obesity effects have been previously reported. The results also pointed to β-lactam antibiotics that may be evaluated in drug repurposing studies for off-target effects. The calculated ADMET properties for those hits revealed suitable profiles for further development in terms of bioavailability and toxicity. Conclusions: The current study identified several NPs as potential GLP-1 positive allosteric modulators and revealed common structural scaffolds including peptidomimetics, lactams, coumarins, and sulfonamides with peptidomimetics being the most prominent especially in indole and coumarin cores. Full article

Congenital heart defects (CHDs) are the most common congenital defect, occurring in approximately 1 in 100 live births and being a leading cause of perinatal morbidity and mortality. Of note, approximately 25% of these defects are classified as critical, requiring immediate postnatal care by pediatric cardiology and neonatal cardiac surgery teams. Consequently, early and accurate diagnosis of CHD is key to proper prenatal and postnatal monitoring in a tertiary care setting. In this scenario, fetal echocardiography is considered the gold standard imaging ultrasound method for the diagnosis of CHD. However, the availability of this examination in clinical practice remains limited due to the need for a qualified specialist in pediatric cardiology. Moreover, in light of the relatively low prevalence of CHD among at-risk populations (approximately 10%), ultrasound cardiac screening for potential cardiac anomalies during routine second-trimester obstetric ultrasound scans represents a pivotal aspect of diagnosing CHD. In order to maximize the accuracy of CHD diagnoses, the views of the ventricular outflow tract and the superior mediastinum were added to the four-chamber view of the fetal heart for routine ultrasound screening according to international guidelines. In this context, four-dimensional spatio-temporal image correlation software (STIC) was developed in the early 2000s. Some of the advantages of STIC in fetal cardiac evaluation include the enrichment of anatomical details of fetal cardiac images in the absence of the pregnant woman and the ability to send volumes for analysis by an expert in fetal cardiology by an internet link. Sequentially, new technologies have been developed, such as fetal intelligent navigation echocardiography (FINE), also known as “5D heart”, in which the nine fetal cardiac views recommended during a fetal echocardiogram are automatically generated from the acquisition of a cardiac volume. Furthermore, artificial intelligence (AI) has recently emerged as a promising technological innovation, offering the potential to warn of possible cardiac anomalies and thus increase the ability of non-cardiology specialists to diagnose CHD. In the early 2010s, the advent of 3D reconstruction software combined with high-definition printers enabled the virtual and 3D physical reconstruction of the fetal heart. The 3D physical models may improve parental counseling of fetal CHD, maternal–fetal interaction in cases of blind pregnant women, and interactive discussions among multidisciplinary health teams. In addition, the 3D physical and virtual models can be an useful tool for teaching cardiovascular anatomy and to optimize surgical planning, enabling simulation rooms for surgical procedures. Therefore, in this review, the authors discuss advanced image technologies that may optimize prenatal diagnoses of CHDs. Full article

As online shopping has increased, the business models of online stores have diversified. When consumers cannot experience an actual product, merchants will promote products through a display to attract customers. Virtual reality (VR) provides an immersive platform for consumers to interact with virtual scenarios. Unfortunately, cybersickness remains a problem in VR. The uncomfortable effects of VR hinder its commercial expansion and the broader adoption of 3D virtual stores. Cybersickness has many causes, including personal characteristics, hardware interfaces, and operation behavior. This study develops a fuzzy-control anti-cybersickness intelligent system (FCACIS) with these factors dynamically and actively. The system retrieves the operation value and inferences the cybersickness symptom value (CSSV). When the CSSV exceeds the alarm value, a dialog mode is introduced to remind users to be aware of possible cybersickness. If the CSSV continues to increase, a cybersickness defense mechanism is activated, such as decreasing the field of view and freezing the screen. The experimental results revealed a significant difference in SSQ scores between subjects who navigated a 3D virtual store with and without the FCACIS. The SSQ scores of subjects with the FCACIS (SSQ = 20.570) were significantly lower than those of subjects without the FCACIS (SSQ = 32.880). The FCACIS effectively alleviated cybersickness for subjects over 40 years old. Additionally, the FCACIS effectively slowed the onset of cybersickness in men and women. The anti-cybersickness effect of the FCACIS on flat-panel displays was greater than that on HMDs. The symptoms of cybersickness for a 3DOF controller were also reduced. Full article

The progressive development of resistance in Neisseria gonorrhoeae to almost all available antibiotics has made it crucial to develop novel approaches to tackling multi-drug resistance (MDR). One of the primary causes of antibiotic resistance is the over-expression of the MtrCDE efflux pump protein, making this protein a vital target for fighting against antimicrobial resistance (AMR) in N. gonorrhoeae. This study was aimed at evaluating the potential MtrCDE efflux pump inhibitors (EPIs) and their stability in treating gonorrhoea infection. This is significant because finding novel EPIs would allow for the longer maintenance of antibiotics at therapeutic levels, thereby prolonging the susceptibility of currently available antibiotics. A virtual screening of the selected Helichrysum populifolium compounds (4,5-dicaffeoylquinic acid, apigeninin-7-glucoside, and carvacrol) was conducted to evaluate their potential EPI activity. An integrated computational framework consisting of molecular docking (MD), molecular mechanics generalized born, and surface area solvation (MMGBSA) analysis, molecular dynamics simulations (MDS), and absorption, distribution, metabolism, and excretion (ADME) properties calculations were conducted. Of the tested compounds, 4,5-dicaffeoylquinic acid revealed the highest molecular docking binding energies (−8.8 kcal/mol), equivalent MMGBSA binding free energy (−54.82 kcal/mol), indicative of consistent binding affinity with the MtrD protein, reduced deviations and flexibility (root mean square deviation (RMSD) of 5.65 Å) and, given by root mean square fluctuation (RMSF) of 1.877 Å. Carvacrol revealed a docking score of −6.0 kcal/mol and a MMGBSA computed BFE of −16.69 kcal/mol, demonstrating the lowest binding affinity to the MtrD efflux pump compared to the remaining test compounds. However, the average RMSD (4.45 Å) and RMSF (1.638 Å) of carvacrol-bound MtrD protein showed no significant difference from the unbound MtrD protein, except for the reference compounds, implying consistent MtrD conformation throughout simulations and indicates a desirable feature during drug design. Additionally, carvacrol obeyed the Lipinski rule of five which confirmed the compound’s drug-likeness properties making it the most promising EPI candidate based on its combined attributes of a reasonable binding affinity, sustained stability during MDS, its obedience to the Lipinski rule of five and compliance with drug-likeness criteria. An in vitro validation of the potential EPI activities of H. populifolium compounds confirmed that 4,5-dicaffeoylquinic acid reduced the expulsion of the bis-benzimide dye by MtrCDE pump, while carvacrol showed low accumulation compared to other compounds. While 4,5-dicaffeoylquinic acid demonstrated the highest binding affinity in computational analysis and an EPI activity in vitro, it showed lower stability compared to the other compounds, as indicated in MDS. This leaves carvacrol, as a better EPI candidate for the management of gonorrhoea infection. Full article

RAB11, a pivotal RabGTPase, regulates essential cellular processes such as endocytic recycling, exocytosis, and autophagy. The protein was implicated in various human diseases, including cancer, neurodegenerative disorders, viral infections, and podocytopathies. However, a small-molecular inhibitor is lacking. The complexity and workload associated with potential assays make conducting large-scale screening for RAB11 challenging. We employed a tiered approach for drug discovery, utilizing deep learning-based computational screening to preselect compounds targeting a specific pocket of RAB11 protein with experimental validation by an in vitro platform reflecting RAB11 activity through the exocytosis of GFP. Further validation included the exposure of Drosophila by drug feeding. In silico pre-screening identified 94 candidates, of which 9 were confirmed using our in vitro platform for Rab11 activity. Focusing on compounds with high potency, we assessed autophagy, which independently requires RAB11, and validated three of these compounds. We further analyzed the dose–response relationship, observing a biphasic, potentially hormetic effect. Two candidate compounds specifically caused a shift in Rab11 vesicles to the cell periphery, without significant impact on Rab5 or Rab7. Drosophila larvae exposed to another candidate compound with predicted oral bioavailability exhibited minimal toxicity, subcellular dispersal of endogenous Rab11, and a decrease in RAB11-dependent nephrocyte function, further supporting an inhibitory role. Taken together, the combination of computational screening and experimental validation allowed the identification of small molecules that modify the function of Rab11. This discovery may further open avenues for treating RAB11-associated disorders. Full article

The rise in antibiotic-resistant Staphylococcal infections necessitates innovative approaches to identify new therapeutic agents. This study investigates the application of machine learning models to identify potential phytochemical inhibitors against BacA, a target related to Staphylococcal infections. Active compounds were retrieved from BindingDB while the decoy was generated from DUDE. The RDKit was utilized for feature engineering. Machine learning models such as k-nearest neighbors (KNN), the support vector machine (SVM), random forest (RF), and naive Bayes (NB) were trained on an initial dataset consisting of 226 active chemicals and 2550 inert compounds. Accompanied by an MCC of 0.93 and an accuracy of 96%, the RF performed better. Utilizing the RF model, a library of 9000 phytochemicals was screened, identifying 300 potentially active compounds, of which 192 exhibited drug-like properties and were further analyzed through molecular docking studies. Molecular docking results identified Ergotamine, Withanolide E, and DOPPA as top inhibitors of the BacA protein, accompanied by interaction affinities of −8.8, −8.1, and −7.9 kcal/mol, respectively. Molecular dynamics (MD) was applied for 100 ns to these top hits to evaluate their stability and dynamic behavior. RMSD, RMSF, SASA, and Rg analyses showed that all complexes remained stable throughout the simulation period. Binding energy calculations using MMGBSA analysis revealed that the BacA_Withanolide E complex exhibited the most favorable binding energy profile with significant van der Waals interactions and a substantial reduction in gas-phase energy. It also revealed that van der Waals interactions contributed significantly to the binding stability of Withanolide E, while electrostatic interactions played a secondary role. The integration of machine learning models with molecular docking and MD simulations proved effective in identifying promising phytochemical inhibitors, with Withanolide E emerging as a potent candidate. These findings provide a pathway for developing new antibacterial agents against Staphylococcal infections, pending further experimental validation and optimization. Full article

Background/Objectives: Breast cancer is the second most common malignancy worldwide and poses a significant threat to women’s health. However, the prognostic biomarkers and therapeutic targets of breast cancer are unclear. A prognostic model can help in identifying biomarkers and targets for breast cancer. In this study, a novel prognostic model was developed to optimize treatment, improve clinical prognosis, and screen potential phosphoglycerate kinase 1 (PGK1) inhibitors for breast cancer treatment. Methods: Using data from the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) were identified in normal individuals and breast cancer patients. The biological functions of the DEGs were examined using bioinformatics analysis. A novel prognostic model was then constructed using the DEGs through LASSO and multivariate Cox regression analyses. The relationship between the prognostic model, survival, and immunity was also evaluated. In addition, virtual screening was conducted based on the risk genes to identify novel small molecule inhibitors of PGK1 from Chemdiv and Targetmol libraries. The effects of the potential inhibitors were confirmed through cell experiments. Results: A total of 230 up- and 325 down-regulated DEGs were identified in HER2, LumA, LumB, and TN breast cancer subtypes. A new prognostic model was constructed using ten risk genes. The analysis from The Cancer Genome Atlas (TCGA) indicated that the prognosis was poorer in the high-risk group compared to the low-risk group. The accuracy of the model was confirmed using the ROC curve. Furthermore, functional enrichment analyses indicated that the DEGs between low- and high-risk groups were linked to the immune response. The risk score was also correlated with tumor immune infiltrates. Moreover, four compounds with the highest score and the lowest affinity energy were identified. Notably, D231-0058 showed better inhibitory activity against breast cancer cells. Conclusions: Ten genes (ACSS2, C2CD2, CXCL9, KRT15, MRPL13, NR3C2, PGK1, PIGR, RBP4, and SORBS1) were identified as prognostic signatures for breast cancer. Additionally, results showed that D231-0058 (2-((((4-(2-methyl-1H-indol-3-yl)-1,3-thiazol-2-yl)carbamoyl)methyl)sulfanyl)acetic acid) may be a novel candidate for treating breast cancer. Full article

Writing is first-order instrumental learning that develops throughout the life cycle, a complex process evolving from early childhood education. The identification of risk predictors of dysgraphia at age 5 has the potential to significantly reduce the impact of graphomotor difficulties in early primary school, which affects handwriting performance to such an extent that it can become illegible. Building on established scientific literature, this study focuses on screening processes, with particular attention to writing requirements. This paper proposes a novel prevention and intervention system based on new technologies for teachers and educators or therapists. Specifically, it presents a pilot study testing an innovative tactile device to analyze graphomotor performance and motor coordination in real time. The research explores whether this haptic device can be used as an effective pedagogical aid for preventing graphomotor issues in children aged 5 to 6 years. The results showed a high level of engagement and usability among young participants. Furthermore, the quality of graphomotor traces, respectively executed by children after virtual and physical training, were comparable, supporting the use of the tool as a complementary training resource for the observation and enhancement of graphomotor processes. Full article

Background/Objectives: Cognition plays a major role in prosthetic rehabilitation success. The ability to identify patients who may have difficulty understanding and adapting to the rehabilitation process is beneficial for clinicians and patients to allow for targeted and appropriate therapy. The research aim was to codesign a process that facilitates routine cognitive screening into the amputee inpatient journey. Methods: A convenience sample of sixteen medical and allied health practitioners from one local health district undertook a codesign process over 10 months from March to November 2023. A combination of virtual and face-to-face data collection occurred. Each of the codesign meetings was audio recorded, following which transcription occurred. Transcripts were reviewed using thematic analysis-based techniques to capture themes and consensus within the group. Results: Two pathways were established for use within one local health district, allowing clinicians to measure the cognition of patients in both inpatient and outpatient settings either before or after they underwent amputation. The newly established pathways provide step-by-step guidance for clinicians, such as how to address contraindicators for testing and providing guidance for subsequent neuropsychological testing. The Montreal Cognitive Assessment (MoCA), both paper based and electronic based, was selected as the cognitive screening tool for implementation. Conclusions: Utilizing codesign as a method for generating a cognitive screening pathway for amputees was successful. The pathways generated should be reviewed for suitability for application in other health settings. Full article

Background/Objectives: Monkeypox is a re-emerging viral disease with features of infectiously transmitted zoonoses. It is now considered a public health priority because of its rising incidence and transmission from person to person. Monkeypox virus (MPXV) VP39 protein is identified as an essential protein for replication of the virus, and therefore, it is a potential target for antiviral drugs. Methods: This work analyzes the binding affinities and the differential conformational stability of three target compounds and one control compound with the VP39 protein through multiple computational methods. Results: The re-docking analysis revealed that the compounds had high binding affinities towards the target protein; among these compounds, compounds 1 and 2 showed the highest binding energies in the virtual screening, and thus, these were considered as the most active inhibitor candidates. Intermolecular interaction analysis revealed distinct binding mechanisms. While compound 1 had very strong hydrogen bonds and hydrophobic interactions, compound 2 had numerous water-mediated interactions, and compound 3 had only ionic and hydrophobic contacts. In molecular dynamic simulations, compounds 1 and 2 showed that the protein–ligand complexes had a stable conformation, with protein RMSD values around 2 Å for both compounds. In contrast, compound 3 was slightly flexible, and the control compound was more flexible. MM/GBSA analysis again supported these results, which gave the binding free energies that were also supportive for these compounds. Conclusions: Notably, all the selected compounds, especially compounds 1 and 2, demonstrate high binding affinity. Therefore, these compounds can be further tested as antiviral agents against monkeypox treatment. Full article

Objective: To examine patient-user symptom reporting to an AI-based online virtual triage (VT) and care-referral engine to assess patterns of mental health symptoms (MHS) reporting prior to and during the COVID-19 pandemic. Methods: The frequencies of 11 MHS reported through VT were analyzed over three time intervals: one year prior to the WHO declaring a global COVID-19 emergency; from pandemic declaration to a mid-point in US vaccine distribution/uptake; and one year thereafter. Results: A total of 4,346,987 VT encounters/interviews presenting somatic and MHS occurred, increasing over time and peaking in the COVID-19 post-vaccine interval with 2,257,553 encounters (51.9%). In 866,218 encounters (19.9%), at least one MHS was reported. MHS reporting declined across subsequent time intervals, was lowest in the COVID-19 post-vaccine period (19.1%), and slightly higher in the pre-pandemic and COVID-19 pre-vaccine intervals (p = 0.05). The most frequently reported symptoms were anxiety, sleep disorder, general anxiety, irritability, and nervousness. Women reported anxiety less often and nervousness and irritability more often. Individuals aged 60+ years reported anxiety and nervousness less frequently, insomnia and sleep disorder more often than individuals 18–39 and 40–59 years old, and sleep disorder more often than those aged 40–59 years in all periods (all p = 0.05). Conclusions: Overall VT usage for somatic and mental health symptom reporting and care referral increased dramatically during the pandemic. VT effectively screened and provided care referral for patient-users presenting with MHS. Virtual triage offers a valuable additional vehicle to detect mental health symptoms and potentially accelerate care referral for patients needing care. Full article

Atopic dermatitis (AD) is a chronic inflammatory skin disease with rising prevalence, marked by eczematous lesions, itching, and a weakened skin barrier often tied to filaggrin gene mutations. This breakdown allows allergen and microbe entry, with thymic stromal lymphopoietin (TSLP) playing a crucial role by activating immune pathways that amplify the allergic response. TSLP’s central role in AD pathogenesis makes it a promising therapeutic target. Consequently, in this study, we used the virtual drug screening, molecular dynamics simulation, and binding free energies calculation approaches to explore the African Natural Product Database against the TSLP protein. The molecular screening identified four compounds with high docking scores, namely SA_0090 (−7.37), EA_0131 (−7.10), NA_0018 (−7.03), and WA_0006 (−6.99 kcal/mol). Furthermore, the KD analysis showed a strong binding affinity of these compounds with TSLP, with values of −5.36, −5.36, −5.34, and −5.32 kcal/mol, respectively. Moreover, the strong binding affinity of these compounds was further validated by molecular dynamic simulation analysis, which revealed that the WA_0006-TSLP is the most stable complex with the lowest average RMSD. However, the total binding free energies were −40.5602, −41.0967, −27.3293, and −51.3496 kcal/mol, respectively, showing the strong interaction between the selected compounds and TSLP. Likewise, these compounds showed excellent pharmacokinetics characteristics. In conclusion, this integrative approach provides a foundation for the development of safe and effective treatments for AD, potentially offering relief to millions of patients worldwide. Full article

Leishmaniasis, caused by the protozoan Leishmania spp. and transmitted by sandflies, affects 2 million people worldwide yearly and is recognized as a global problem by the WHO. Current treatments, including amphotericin B, Pentamidine, and Glucantime, show limited efficacy and serious side effects. Trypanothione reductase is a promising protein target for developing new promising drugs against Leishmaniasis. This study explores Schiff base compounds as potential alternatives to current treatments by inhibiting trypanothione reductase. Thirty-nine structures from the PubChem database were selected and analyzed using AutoDock Vina 1.1, an in silico molecular docking tool. Promising Schiff base candidates, indicated as compound 21 (3-Quinolinamine, N-(2-quinolinylmethylene)-, compound 24 (1,3-Bis[(E)-(2-Amino-4-Ethyl-5-Hydroxy-Phenyl)Methyleneamino]Urea, and compound 39 (Naphtaldehyde disulfide Schiff base), exhibited significant inhibitory binding affinity against trypanothione reductase, outperforming commercial inhibitors. Therefore, the present study proposes alternative Schiff base compounds for treating Leishmaniasis. Full article