Computer Screen Research Articles

CavitOmiX Drug Discovery: Engineering Antivirals with Enhanced Spectrum and Reduced Side Effects for Arboviral Diseases.

Advancing climate change increases the risk of future infectious disease outbreaks, particularly of zoonotic diseases, by affecting the abundance and spread of viral vectors. Concerningly, there are currently no approved drugs for some relevant diseases, such as the arboviral diseases chikungunya, dengue or zika. The development of novel inhibitors takes 10-15 years to reach the market and faces critical challenges in preclinical and clinical trials, with approximately 30% of trials failing due to side effects. As an early response to emerging infectious diseases, CavitOmiX allows for a rapid computational screening of databases containing 3D point-clouds representing binding sites of approved drugs to identify candidates for off-label use. This process, known as drug repurposing, reduces the time and cost of regulatory approval. Here, we present potential approved drug candidates for off-label use, targeting the ADP-ribose binding site of Alphavirus chikungunya non-structural protein 3. Additionally, we demonstrate a novel in silico drug design approach, considering potential side effects at the earliest stages of drug development. We use a genetic algorithm to iteratively refine potential inhibitors for (i) reduced off-target activity and (ii) improved binding to different viral variants or across related viral species, to provide broad-spectrum and safe antivirals for the future.

Exploring Natural Compounds as Promising Matrix Metalloproteinase-2 inhibitors for Cancer Management: A Biocomputational Study

Background: Matrix metalloproteinase-2 (MMP2) plays a role in breaking down the components of the extracellular matrix, which allows cancer cells to advance and invade. Therefore, the inhibition of MMP2 shows potential as a promising strategy for treating cancer.Methods: This study employed computational screening to identify MMP2 inhibitors from a collection of 2,405 natural compounds. GLXC-26716, the co-crystal ligand of MMP2, served as the positive control. Virtual screening was performed using PyRx 8.0 software to find molecules that might inhibit the active site of MMP2.Results: The virtual screening process has identified five potential candidates: ZINC000000001412, ZINC000001612328, ZINC000001614079, ZINC000000119988, and ZINC0000000047553. These candidates were selected based on their strong binding affinities and interactions with MMP2. These compounds, which adhere to Lipinski's Rule of Five and have significant physicochemical properties, show promise as MMP2 inhibitors.Conclusion: The finding of this study indicates a preliminary investigation into an innovative approach for managing cancer that inhibits the invasion and dissemination of cancer cells.Keywords: Matrix Metalloproteinase-2; Natural compounds; Cancer; Virtual screening

First-Principles Computational Screening of Two-Dimensional Polar Materials for Photocatalytic Water Splitting.

The band gap constraint of the photocatalyst for overall water splitting limits the utilization of solar energy. A strategy to broaden the range of light absorption is employing a two-dimensional (2D) polar material as photocatalyst, benefiting from the deflection of the energy level due to their intrinsic internal electric field. Here, by using first-principles computational screening, we search for 2D polar semiconductors for photocatalytic water splitting from both ground- and excited-state perspectives. Applying a unique electronic structure model of polar materials, there are 13 photocatalyst candidates for the hydrogen evolution reaction (HER) and 8 candidates for the oxygen evolution reaction (OER) without barrier energies from the perspective of the ground-state free energy variation calculation. In particular, Cu2As4Cl2S3 and Cu2As4Br2S3 can catalyze HER and OER simultaneously, becoming promising photocatalysts for overall water splitting. Furthermore, by combining ground-state band structure calculations with excited-state charge distribution and transfer calculated by linear-response time-dependent density functional theory (LR-TDDFT) and time-dependent ab initio nonadiabatic molecular dynamics (NAMD), respectively, the rationality of the 2D polar material model has been manifested. The intrinsic built-in electric field promotes the separation of charge carriers while suppressing their recombination. Therefore, our computational work provides a high-throughput method to design high-performance photocatalysts for water splitting.

Reactive Processing of Furan-Based Monomers via Frontal Ring-Opening Metathesis Polymerization for High Performance Materials.

Frontal ring-opening metathesis polymerization (FROMP) presents an energy-efficient approach to produce high-performance polymers, typically utilizing norbornene derivatives from Diels-Alder reactions. This study broadens the monomer repertoire for FROMP, incorporating the cycloaddition product of biosourced furan compounds and benzyne, namely 1,4-dihydro-1,4-epoxynaphthalene (HEN) derivatives. A computational screening of Diels-Alder products is conducted, selecting products with resistance to retro-Diels-Alder but also sufficient ring strain to facilitate FROMP. The experiments reveal that varying substituents both modulate the FROMP kinetics and enable the creation of thermoplastic materials characterized by different thermomechanical properties. Moreover, HEN-based crosslinkers are designed to enhance the resulting thermomechanical properties at high temperatures (>200°C). The versatility of such materials is demonstrated through direct ink writing (DIW) to rapidly produce 3D structures without the need for printed supports. This research significantly extends the range of monomers suitable for FROMP, furthering efficient production of high-performance polymeric materials.

Design of parallel 𝛽-sheet nanofibrils using Monte Carlo search, coarse-grained simulations, and experimental testing.

Peptide self-assembly into amyloid fibrils provides numerous applications in drug delivery and biomedical engineering applications. We augment our previously-established computational screening technique along with experimental biophysical characterization to discover 7-mer peptides that self-assemble into "parallel β-sheets", that is, β-sheets with N-terminus-to-C-terminus 𝛽-strand vectors oriented in parallel. To accomplish the desired β-strand organization, we applied the PepAD amino acid sequence design software to the Class-1 cross-β spine defined by Sawaya et al. This molecular configuration includes two layers of parallel β-sheets stacked such that N-terminus-to-C-terminus vectors are oriented antiparallel for molecules on adjacent β-sheets. The first cohort of PepAD identified peptides were examined for their fibrillation behavior in DMD/PRIME20 simulations, and the top performing sequence was selected as a prototype for a subsequent round of sequence refinement. The two rounds of design resulted in a library of eight 7-mer peptides. In DMD/PRIME20 simulations, five of these peptides spontaneously formed fibril-like structures with a predominantly parallel 𝛽-sheet arrangement, two formed fibril-like structure with <50% in parallel 𝛽-sheet arrangement and one remained a random coil. Among the eight candidate peptides produced by PepAD and DMD/PRIME20, five were synthesized and purified. All five assembled into amyloid fibrils composed of parallel β-sheets based on Fourier transform infrared spectroscopy, circular dichroism, electron microscopy, and thioflavin-T fluorescence spectroscopy measurements.

System Design and Development of a Novel, Unique Neuro Physical Medical Treatment Method for Spinal Muscular Atrophy and Similar Neurological Muscle Diseases.

The project is entirely original to the author. It proposes the theory of a method under development aimed at treating SMA and other related neurological muscle disorders The study will utilize a 14-channel EEG device to analyze brain data. The brain signals recorded by this device will be transmitted to the patient's muscles, potentially restoring functions lost in SMA patients. These functions include coughing, swallowing, breathing, chewing, walking, and movements of the hands, arms, and other muscles. By using this EEG device, brain signals can be visualized as waves, and its specialized software allows users to manipulate objects on a computer screen with thought alone. The system also simulates facial movements and expressions on-screen

African Swine Fever Virus Protein-Protein Interaction Prediction.

The African swine fever virus (ASFV) is an often deadly disease in swine and poses a threat to swine livestock and swine producers. With its complex genome containing more than 150 coding regions, developing effective vaccines for this virus remains a challenge due to a lack of basic knowledge about viral protein function and protein-protein interactions between viral proteins and between viral and host proteins. In this work, we identified ASFV-ASFV protein-protein interactions (PPIs) using artificial intelligence-powered protein structure prediction tools. We benchmarked our PPI identification workflow on the Vaccinia virus, a widely studied nucleocytoplasmic large DNA virus, and found that it could identify gold-standard PPIs that have been validated in vitro in a genome-wide computational screening. We applied this workflow to more than 18,000 pairwise combinations of ASFV proteins and were able to identify seventeen novel PPIs, many of which have corroborating experimental or bioinformatic evidence for their protein-protein interactions, further validating their relevance. Two protein-protein interactions, I267L and I8L, I267L__I8L, and B175L and DP79L, B175L__DP79L, are novel PPIs involving viral proteins known to modulate host immune response.

Including Dynamic Viewing Behavior in Determining Vertical Eye-Level Light: Does it Matter for Office Design?

ABSTRACT When considering non-image-forming (NIF) light effects on people, knowing the light vertically at eye-level is necessary. However, people are dynamic in their behavior and constantly change their posture and viewing direction. This means that light measured vertically toward a constant direction might differ from the actual light that reaches people’s eyes. If the difference is large, occupant behavior might need to be included in lighting design measurements and simulations predicting the potential of NIF light effects. This paper presents an experiment on the difference between the actual dynamic eye-level light of office workers while seated at a desk (dynamic condition) and light measured statically toward a computer screen (static condition). Eye-level light conditions in relation to NIF effects were quantified using the melanopic Equivalent Daylight Illuminance (mEDI) and the relative alerting response (r D ). Measured dynamic mEDI was 4% to 10% larger than the static one. Analysis of r D was not possible due to saturation of the response. The dominant face orientation was toward the computer screen, with a deviation in the horizontal and vertical direction within 15 degrees from the center. The results show that measuring and simulating personal light conditions of office workers statically toward a computer screen might be a small underestimation of actual eye-level light, but the relevance of this underestimation in lighting design practice is not evident.

Identifying plant-derived antiviral alkaloids as dual inhibitors of SARS-CoV-2 main protease and spike glycoprotein through computational screening.

COVID-19 is currently considered the ninth-deadliest pandemic, spreading through direct or indirect contact with infected individuals. It has imposed a consistent strain on both the financial and healthcare resources of many countries. To address this challenge, there is a pressing need for the development of new potential therapeutic agents for the treatment of this disease. To identify potential antiviral agents as novel dual inhibitors of SARS-CoV-2, we retrieved 404 alkaloids from 12 selected medicinal antiviral plants and virtually screened them against the renowned catalytic sites and favorable interacting residues of two essential proteins of SARS-CoV-2, namely, the main protease and spike glycoprotein. Based on docking scores, 12 metabolites with dual inhibitory potential were subjected to drug-likeness, bioactivity scores, and drug-like ability analyses. These analyses included the ligand-receptor stability and interactions at the potential active sites of target proteins, which were analyzed and confirmed through molecular dynamic simulations of the three lead metabolites. We also conducted a detailed binding free energy analysis of pivotal SARS-CoV-2 protein inhibitors using molecular mechanics techniques to reveal their interaction dynamics and stability. Overall, our results demonstrated that 12 alkaloids, namely, adouetine Y, evodiamide C, ergosine, hayatinine, (+)-homoaromoline, isatithioetherin C, N,alpha-L-rhamnopyranosyl vincosamide, pelosine, reserpine, toddalidimerine, toddayanis, and zanthocadinanine, are shortlisted as metabolites based on their interactions with target proteins. All 12 lead metabolites exhibited a higher unbound fraction and therefore greater distribution compared with the standards. Particularly, adouetine Y demonstrated high docking scores but exhibited a nonspontaneous binding profile. In contrast, ergosine and evodiamide C showed favorable binding interactions and superior stability in molecular dynamics simulations. Ergosine demonstrated exceptional performance in several key pharmaceutical metrics. Pharmacokinetic evaluations revealed that ergosine exhibited pronounced bioactivity, good absorption, and optimal bioavailability. Additionally, it was predicted not to cause skin sensitivity and was found to be non-hepatotoxic. Importantly, ergosine and evodiamide C emerged as superior drug candidates for dual inhibition of SARS-CoV-2 due to their strong binding affinity and drug-like ability, comparable to known inhibitors like N3 and molnupiravir. This study is limited by its in silico nature and demands the need for future in vitro and in vivo studies to confirm these findings.

Computational screening of metalloporphyrin catalysts for the activation of carbon dioxide

Electrocatalytic CO2 reduction (eCO2R) to value-added chemicals offers a promising route for carbon capture and utilization. Metalloporphyrin (M-POR) is a class of catalysts for eCO2R that has drawn attention due to its tuneable electronic and structural properties. This work presents a computational screening, based on density functional theory calculations, of one of the key steps in the eCO2R: the adsorption of CO2 on 110 M-PORs with varying peripheral ligands, metal centres, and oxidation states, to understand how these factors can influence CO2 activation. A set of criteria was used to shortlist M-PORs based on their ability to lengthen the C–O bond, bend the O–C–O angle, bind CO2, and donate charge from the metal of the M-POR to the carbon of CO2. 16 systems were selected for their potential to activate CO2. These systems predominantly have the electron configuration of the metal centre in the d[6] and d[7] configurations. Natural bond orbital analysis revealed the impact of electron-withdrawing groups in the system, which increases orbital splitting and, consequently, lowers the ability of the M-POR to activate CO2. Second-order perturbation theory analysis confirms that the presence of electron-donating groups in the ligand structure enhances CO2 activation. This work demonstrates the interconnected effect of peripheral ligands, metal centres, and oxidation states in M-PORs on their ability to adsorb and activate CO2, thereby establishing structure-activity relationships within M-PORs.

Hybrid Virtual Screening Approach to Predict Novel Natural Compounds against HIV-1 CCR5.

Human immunodeficiency virus (HIV) infection continues to pose a major global health challenge. HIV entry into host cells via membrane fusion mediated by the viral envelope glycoprotein gp120/gp41 is a key step in the HIV life cycle. CCR5, expressed on CD4+ T cells and macrophages, acts as a coreceptor facilitating HIV-1 entry. The CCR5 antagonist maraviroc is used to treat HIV infection. However, it can cause adverse effects and has limitations such as only inhibiting CCR5-tropic viruses. There remains a need to develop alternative CCR5 inhibitors with improved safety profiles. Natural products may offer advantages over synthetic inhibitors including higher bioavailability, binding affinity, effectiveness, lower toxicity, and molecular diversity. However, screening the vast chemical space of natural compounds to identify novel CCR5 inhibitors presents challenges. This study aimed to address this gap through a hybrid ligand-based pharmacophore modeling and molecular docking approach to virtually screen large natural product databases. A reliable pharmacophore model was developed based on 311 known CCR5 antagonists and validated against an external data set. Five natural product databases containing over 306,000 compounds were filtered based on drug-likeness rules. The validated pharmacophore model screened the databases to identify 611 hits. Key residues of the CCR5 receptor crystal structure were identified for docking. The top hits were docked, and interactions were analyzed. Molecular dynamics simulations were conducted to examine complex stability. Computational prediction evaluated pharmacokinetic properties. Three compounds exhibited similar interactions and binding energies to maraviroc. MD simulations demonstrated complex stability comparable to maraviroc. One compound showed optimal predicted absorption, minimal metabolism, and a lower likelihood of interactions than maraviroc. This computational screening workflow identified three natural compounds with promising CCR5 inhibition and favorable pharmacokinetic profiles. One compound emerged as a lead based on bioavailability potential and minimal interaction risk. These findings present opportunities for developing alternative CCR5 antagonists and warrant further experimental investigation. Overall, the hybrid virtual screening approach proved effective for mining large natural product spaces to discover novel molecular entities with drug-like properties.

Computational Screening of Repurposed Drugs for HMG-CoA Synthase 2 in Alzheimer's Disease.

HMGCS2 (mitochondrial 3-hydroxy-3-methylglutaryl-COA synthase 2) plays a pivotal role as a control enzyme in ketogenesis, and its association with the amyloid-β protein precursor (AβPP) in mitochondria implicates a potential involvement in Alzheimer's disease (AD) pathophysiology. Our study aimed at identifying repurposed drugs using the DrugBank database capable of inhibiting HMGCS2 activity. Exploiting the power of drug repurposing in conjunction with virtual screening and molecular dynamic (MD) simulations against 'HMGCS2', we present new in-silico insight into structure-based drug repurposing. The initial molecules were screened for their binding affinity to HMGCS2. Subsequent interaction analyses and extensive 300 ns MD simulations were conducted to explore the conformational dynamics and stability of HMGCS2 in complex with the screened molecules, particularly Penfluridol and Lurasidone. The study revealed that HMGCS2 forms stable protein-ligand complexes with Penfluridol and Lurasidone. Our findings indicate that Penfluridol and Lurasidone competitively bind to HMGCS2 and warrant their further exploration as potential repurposed molecules for anti-Alzheimer's drug development.

Development of Novel Bacterial Topoisomerase Inhibitors Assisted by Computational Screening.

Multidrug-resistant bacterial infections pose an ever-evolving threat to public health. Since the outset of the antibacterial age, bacteria have developed a multitude of diverse resistance mechanisms that suppress the effectiveness of current therapies. New drug entities, such as Novel Bacterial Topoisomerase Inhibitors (NBTIs), can circumvent this major issue. A computational docking model was employed to predict the binding to DNA gyrase of atypical NBTIs with novel pharmacophores. Synthesis of NBTIs based on computational docking and subsequent antibacterial evaluation against both Gram-positive and Gram-negative bacteria yielded congeners with outstanding anti-staphylococcal activity and varying activity against select Gram-negative pathogens.

Identification of Anticancer Peptides from the Genome of Candida albicans: in Silico Screening, in Vitro and in Vivo Validations.

Anticancer peptides (ACPs) are promising future therapeutics, but their experimental discovery remains time-consuming and costly. To accelerate the discovery process, we propose a computational screening workflow to identify, filter, and prioritize peptide sequences based on predicted class probability, antitumor activity, and toxicity. The workflow was applied to identify novel ACPs with potent activity against colorectal cancer from the genome sequences of Candida albicans. As a result, four candidates were identified and validated in the HCT116 colon cancer cell line. Among them, PCa1 and PCa2 emerged as the most potent, displaying IC50 values of 3.75 and 56.06 μM, respectively, and demonstrating a 4-fold selectivity for cancer cells over normal cells. In the colon xenograft nude mice model, the administration of both peptides resulted in substantial inhibition of tumor growth without causing significant adverse effects. In conclusion, this work not only contributes a proven computational workflow for ACP discovery but also introduces two peptides, PCa1 and PCa2, as promising candidates poised for further development as targeted therapies for colon cancer. The method as a web service is available at https://app.cbbio.online/acpep/home and the source code at https://github.com/cartercheong/AcPEP_classification.git.

Computational screening combined with well-tempered metadynamics simulations identifies potential TMPRSS2 inhibitors

Type-II transmembrane serine proteases are effective pharmacological targets for host defence against viral entry and in certain cancer cell progressions. These serine proteases cleave viral spike proteins to expose the fusion peptide for cell entry, which is essential to the life cycle of the virus. TMPRSS2 inhibitors can also fight against respiratory viruses that employ them for cell entry. Our study combining virtual screening, all-atom molecular dynamics, and well-tempered metadynamics simulation identifies vicenin-2, neohesperidin, naringin, and rhoifolin as promising TMPRSS2 antagonists. The binding energies obtained are − 16.3, − 15.4, − 13.6, and − 13.8 kcal/mol for vicenin-2, neohesperidin, naringin, and rhoifolin respectively. The RMSD, RMSF, PCA, DCCM, and binding free energy profiles also correlate with the stable binding of these ligands at the active site of TMPRSS2. The study reveals that these molecules could be promising lead molecules for combating future outbreaks of coronavirus and other respiratory viruses.

Glutamate acts on acid-sensing ion channels to worsen ischaemic brain injury.

Glutamate is traditionally viewed as the first messenger to activate NMDAR (N-methyl-D-aspartate receptor)-dependent cell death pathways in stroke1,2, but unsuccessful clinical trials with NMDAR antagonists implicate the engagement of other mechanisms3-7. Here we show that glutamate and its structural analogues, including NMDAR antagonist L-AP5 (also known as APV), robustly potentiate currents mediated by acid-sensing ion channels (ASICs) associated with acidosis-induced neurotoxicity in stroke4. Glutamate increases the affinity of ASICs for protons and their open probability, aggravating ischaemic neurotoxicity in both in vitro and in vivo models. Site-directed mutagenesis, structure-based modelling and functional assays reveal a bona fide glutamate-binding cavity in the extracellular domain of ASIC1a. Computational drug screening identified a small molecule, LK-2, that binds to this cavity and abolishes glutamate-dependent potentiation of ASIC currents but spares NMDARs. LK-2 reduces the infarct volume and improves sensorimotor recovery in a mouse model of ischaemic stroke, reminiscent of that seen in mice with Asic1a knockout or knockout of other cation channels4-7. We conclude that glutamate functions as a positive allosteric modulator for ASICs to exacerbate neurotoxicity, and preferential targeting of the glutamate-binding site on ASICs over that on NMDARs may be strategized for developing stroke therapeutics lacking the psychotic side effects of NMDAR antagonists.

Reverse vaccinology approach for identification of epitopes from E1 protein as peptide vaccine against HCV: A proof of concept

The development of effective vaccines against Hepatitis C Virus (HCV) remains a global health priority and challenge. In this study, we employed an integrative approach combining computational epitope prediction with experimental validation to identify immunogenic peptides targeting the E1 glycoprotein of HCV. In the present report, computational data from various epitope prediction algorithms such as IEDB and SYFPEITHI, followed by molecular dynamics (MD) simulations and immuno-informatics analysis is presented. Through computational screening, we identified potential epitope candidates, with QVRNSSGLY (P3) and QLFTFSPRH (P7) emerging as promising candidates. MD simulations revealed stable interactions between these epitopes and MHC molecule, further validated by free energy estimations using MMPBSA method. Immuno-informatics analysis supported these findings, showing high binding potential and immunogenicity scores for the selected peptides. Subsequent synthesis and characterization of epitope peptides confirmed their structural integrity and purity required for conducting immune activation assays. Experimental immunological assays carried out in this study involved epitope peptide induced activation of CD8 + and CD4 + T cells from healthy human subjects and HCV- recovered patients. Data from experimental validation revealed significant cytokine release upon exposure to epitope peptides, particularly TNF-a, IL-6, and GM-CSF, indicative of robust immune responses. Notably, peptides P3 and P7 exhibited the most pronounced cytokine induction profiles, underscoring their potential as vaccine candidates. Further investigations addressing the mechanism of action of these epitope peptides under preclinical and clinical settings may help in developing effective vaccine against HCV.

Unraveling the separation mechanism of gas mixtures in MOFs by combining the breakthrough curve with machine learning and high-throughput calculation

In the field of metal–organic frameworks (MOFs) screening studies, the batch calculation of the mixed gas breakthrough time difference (ΔTi) in MOFs and its intricate correlation with various descriptors remain underexplored. This research undertook batch calculations of the breakthrough curves (BC) for different gases within a simulated natural gas environment, designating ΔTi as the performance metric for MOFs in gas separation. The separation performance of computation-ready experimental MOFs for CH4/C2H6 and CH4/CO2 mixtures was analyzed in depth utilizing machine learning (ML)-assisted high-throughput computational screening (HTCS) techniques. Then, five ML algorithms were used to quantify the relationship between MOF descriptors and performance, and the effect of the metal center site on the separation performance was further explored. Ultimately, the top ten MOFs were selected for each system. Combining HTCS, ML, and BC, this work provides fresh insights for understanding and designing MOFs with customized adsorption and separation properties.

Artificial Intelligence and High-Throughput Computational Workflows Empowering the Fast Screening of Metal-Organic Frameworks for Hydrogen Storage.

Metal-organic frameworks (MOFs) are one of the most promising hydrogen-storing materials due to their rich specific surface area, adjustable topological and pore structures, and modified functional groups. In this work, we developed automatically parallel computational workflows for high-throughput screening of ∼11,600 MOFs from the CoRE database and discovered 69 top-performing MOF candidates with work capacity greater than 1.00 wt % at 298.5 K and a pressure swing between 100 and 0.1 bar, which is at least twice that of MOF-5. In particular, ZITRUP, OQFAJ01, WANHOL, and VATYIZ showed excellent hydrogen storage performance of 4.48, 3.16, 2.19, and 2.16 wt %. We specifically analyzed the relationship between pore-limiting diameter, largest cavity diameter, void fraction, open metal sites, metal elements or nonmetallic atomic elements, and deliverable capacity and found that not only geometrical and physical features of crystalline but also chemical properties of adsorbate sites determined the H2 storage capacity of MOFs at room temperature. It is highlighted that we first proposed the modified crystal graph convolutional neural networks by incorporating the obtained geometrical and physical features into the convolutional high-dimensional feature vectors of period crystal structures for predicting H2 storage performance, which can improve the prediction accuracy of the neural network from the former mean absolute error (MAE) of 0.064 wt % to the current MAE of 0.047 wt % and shorten the consuming time to about 10-4 times of high-throughput computational screening. This work opens a new avenue toward high-throughput screening of MOFs for H2 adsorption capacity, which can be extended for the screening and discovery of other functional materials.

895 Innovating Automated Progress Tracking System (APS) for Laparoscopic Simulation Training in Low-Middle-Income-Countries Using LaApSi Models: Low-Cost High-Fidelity Performance Assessment System with Microcontroller Connectivity

Abstract Aim To develop a Low-Cost Automated Progress tracking system (APS) embedded in the silicon based Laparoscopic Appendicectomy Simulation (LaApSi) Model which enables real-time assessment of Laparoscopic Appendicectomy (LA). To assess its application and validity in the low-resource setting via Laparoscopic Surgery courses. Method APS architecture includes thin insulated wires embedded in the LaApSi Models at the mesoappendix, appendicular artery, and the base of the appendix which acts as an indicator when cut during the appendicectomy. These wires are further connected to the Printed Circuit Board (PCB) located at the base of the model which connects with the microcontroller and establishes communication with the computer giving real-time data. Results The prototyped APS model was able to establish key features: 1. Total duration of the procedure 2. Identify potential surgical hazard: blood vessel injury 3. Length at which the appendicectomy is performed 4. Injury to surrounding structures. These performance data were recorded in the database for further review, analysis, and comparison for progression in the simulation training. The system is set up on a points basis hence when a user makes a surgical error e.g., blood vessel injury during the surgical simulation in the APS LaApSi Model, -20 points (Blood Vessels Cut) appear on the computer screen (images available). Further assessment and validity of the APS models are planned to be carried out in low-resource settings in the next 8 weeks. Conclusions The availability of a model blueprint of this cost-effective APS system (&amp;lt;20£) to LMICs will address the key barrier of surgical simulation in low-resource settings.