BACKGROUND AND OBJECTIVE

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the causative agent of COVID-19 has significantly challenged the public health landscape in late 2019. After almost 3 years of the first ever SARS-CoV-2 case, the World Health Organization (WHO) declared the end of this global health emergency in May 2023. Although, despite the subsequent drop of COVID-19 cases, the SARS-CoV-2 infection still exhibited multiple waves of infection, primarily attributed to the appearance of new variants. Five of these variants have been classified as Variants of Concern (VOC): Alpha, Beta, Gamma, Delta, and the most recent, Omicron. Therefore, the development of methods for the timely and accurate detection of viral variants remains fundamental, ensuring an ongoing and effective response to the disease. This study aims to evaluate the feasibility of the application of an in-house approach in genomic surveillance for the detection of SARS-CoV-2 variants using in silico designed primers.

METHODS

The primers used for the study were particularly designed based on conserved regions of certain genes in the virus, targeting distinct mutations found in known variants of SARS-CoV-2. Viral RNA extracts from nasopharyngeal samples (n=14) were subjected to quantitative and qualitative tests (Nanodrop and AGE). Selected samples were then analyzed by RT-PCR and amplicons were submitted for sequencing. Sequence alignment analysis was carried out to identify the prevailing COVID-19 variant present in the sample population.

RESULTS

The study findings demonstrated that the in-house method was able to successfully amplify conserved sequences (spike, envelope, membrane, ORF1ab) and enabled identification of the circulating SARS-CoV-2 variant among the samples. Majority of the samples were identified as Omicron variant. Three out of four designed primers effectively bound into the conserved sequence of target genes present in the sample, revealing the specific SARSCoV-2 variant. The detected mutations characterized for Omicron found in the identified lineages included K417N, S477N, and P681H which were also identified as mutations of interest. Furthermore, identification of the B.1.448 lineage which was not classified in any known variant also provided the potential of the developed in-house method in detecting unknown variants of COVID-19.

CONCLUSION

Among the five VOCs, Omicron is the most prevalent and dominant variant. The in-house direct PCR product sequencing surveillance (DPPSS) method provided an alternative platform for SAR-CoV-2 variant analysis which is accessible and affordable than the conventional diagnostic surveillance methods and the whole genome sequencing. Further evaluation and improvements on the oligonucleotide primers may offer significant contribution to the development of a specific and direct PCRbased detection of new emerging COVID-19 variants.

Sars-cov-2 ; Polymerase Chain Reaction ; Dna Primers ; Oligonucleotide Primers ; Computer Simulation ; Conserved Sequence ; Coronavirus ; Covid-19 ; Disease ; Emergencies ; Evaluation Studies As Topic ; Genes ; Genome ; Global Health ; Health ; Identification (psychology) ; Infection ; Infections ; Membranes ; Methods ; Mutation ; Oligonucleotides ; Organizations ; Population ; Public Health ; Rna ; Rna, Viral ; Sars Virus ; Sequence Alignment ; Severe Acute Respiratory Syndrome ; Syndrome ; Viruses ; Whole Genome Sequencing ; World Health Organization

Computational modeling is an invaluable tool for mechanism analysis, directed engineering, and rational design of biological parts, metabolic networks, and even cellular systems. It can provide new technological solutions to address biological challenges at different levels and has become a central focus of research in biomanufacturing. In the computational modeling of proteins, which are the key parts in biological systems, the traditional physics-based methods (computer software and mathematical model) have been widely used to study the physical and chemical processes in the functioning of proteins, and have thus been recognized as a powerful tool for understanding complex biological systems and guiding experimental designs. As the scale of computational modeling continues to expand, traditional modeling techniques face difficulties in balancing computational accuracy and speed. In recent years, the explosive growth of biological data has made it possible to construct high-performance artificial intelligence (AI) models, which brings new opportunities to the computational modeling of proteins, and the AI-enhanced physics-based computational modeling technologies have emerged. This combined strategy not only incorporates the chemical knowledge and established physical principles but also is powerful in data processing and pattern recognition, which greatly improves the computational efficiency and prediction accuracy, as well as possesses stronger interpretation ability, transferability, and robustness. The AI-enhanced physics-based computational modeling technologies have already shown great potential and value in biocatalysis, paving a new way for the future development of biomanufacturing.
Artificial Intelligence ; Proteins/chemistry* ; Computer Simulation ; Software ; Computational Biology/methods*

Transcription factor (TF)-based biosensors have been widely applied in metabolic engineering, synthetic biology, metabolites monitoring, etc. These biosensors are praised for the high orthogonality, modularity, and operability. However, most natural TFs with weak responses and low specificity still demand optimization for desired performance in applications. Herein, we comprehensively summarize the recent advances in the engineering and optimization of TF-based biosensors with the assistance of computational simulation and artificial intelligence. This review includes the regulatory protein engineering aided by protein structure prediction and ligand binding simulation and the regulatory protein responses predicted by a mathematical model obtained from machine learning of mutagenesis data. In comparison with conventional tools, computational simulation and artificial intelligence enable more accurate and rapid design and construction of biosensors. Thus, these technologies will greatly promote the development of novel biosensors for applications.
Biosensing Techniques/methods* ; Transcription Factors/metabolism* ; Artificial Intelligence ; Protein Engineering/methods* ; Computer Simulation ; Synthetic Biology ; Machine Learning

As green, sustainable, and environmentally friendly material processing processes using biological cells or enzymes to achieve substance conversion, bioprocesses play an increasingly important role in biomanufacturing. It is difficult to optimize bioprocesses because of the complex relationship at multiple levels and multiple scales. The knowledge of mesoscale behaviors is the key to understanding the dynamics of bioprocesses and to sort out the complex relationships of parameter variations in the spatial-temporal domain. Mesoscale numerical simulation paves a way for understanding these phenomena, and the integration of artificial intelligence (AI) and mesoscale simulation offers new vitality into the optimization of bioprocesses. This article reviews the progress in mesoscale simulation and AI optimization of bioprocesses and discusses the possible development directions, aiming to promote the development of this field.
Artificial Intelligence ; Biotechnology/trends* ; Computer Simulation

This study aims to evaluate the application of flower petal-structured electrodes in pulsed field ablation (PFA) technology, with a particular focus on their performance in myocardial tissue ablation. Through a combination of simulation techniques and in vitro experiments, the study investigates the effects of different voltage levels, electrode-to-tissue contact distances, and their impact on ablation depth, continuity, and transmurality. The research methods include the construction of a myocardial tissue simulation model, electric field distribution simulation using COMSOL Multiphysics, and in vitro ablation experiments on potato tissue. The results indicate that as voltage increases, the ablation depth significantly increases. At a voltage of 2500 V, a transmural ablation depth of 4 mm can be achieved, and the ablation area remains relatively continuous. The in vitro experiments confirm the consistency of the simulation results, and pulsed field ablation does not induce significant temperature rise, confirming its non-thermal characteristic. The conclusion suggests that PFA technology requires less electrode contact and offers higher ablation efficiency, providing a new technological pathway for the clinical treatment of atrial fibrillation and effectively reducing the risk of complications associated with traditional ablation techniques.
Electrodes ; Catheter Ablation/instrumentation* ; Computer Simulation ; Flowers ; Atrial Fibrillation/surgery* ; Myocardium

In the YY 0989.3-2023 standard, clause 27.106 specifies the protection test against electromagnetic interference, but it only briefly describes the test level for electromagnetic exposure, and does not detail the parameters of the torso. This study aims to explore the internal field distribution for different torso parameters under electromagnetic exposure, and explore the patterns of field distribution through modeling and simulation. The results indicate that the parameters of the torso significantly affect the internal field distribution. The findings of this study provide a basis and reference for the electromagnetic compatibility test for implantable neurostimulator products.
Electromagnetic Fields ; Implantable Neurostimulators ; Computer Simulation

OBJECTIVES: To investigate the impact of incorrect specification of the working correlation structure matrix on estimated sample size in a 2×2 crossover design based on the generalized estimating equation (GEE). METHODS: Based on Monte Carlo simulation, the influence of incorrect specification of the work-related structure matrix on the sample size estimation under different conditions was evaluated after controlling the total sample size n, the proportion of subjects assigned to AB sequence (s=1) θ, the correlation coefficient ρ, and the placebo effect OR. Bias and mean square error (MSE) were used to assess the difference between the sample size estimates and the theoretical values. RESULTS: When the correctly specified working correlation structure matrix is independent, the sample size estimation effect of correctly specifying the working correlation structure matrix is better than that of incorrect specification. But when the correctly specified working correlation structure matrix is equal and the correlation coefficient is closer to 0, with other factors being smaller (n≤50, θ≤0.5, OR=2 in this article), there is a situation where the bias of the sample size estimation value for the correctly specified working correlation structure matrix is greater than the bias for the incorrectly specified working correlation structure matrix. CONCLUSIONS Under most conditions, incorrectly specifying the working correlation structure matrix can cause the estimated sample size to deviate significantly from the theoretical value, but under certain conditions, the impact of incorrectly specifying the working correlation structure matrix can be small on the estimated sample size.
Sample Size ; Monte Carlo Method ; Humans ; Cross-Over Studies ; Computer Simulation ; Research Design ; Bias

Within the prefrontal-cingulate cortex, abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions, contributing to the development of mental disorders such as depression. Despite this understanding, the neural circuit mechanisms underlying this phenomenon remain elusive. In this study, we present a biophysical computational model encompassing three crucial regions, including the dorsolateral prefrontal cortex, subgenual anterior cingulate cortex, and ventromedial prefrontal cortex. The objective is to investigate the role of coupling relationships within the prefrontal-cingulate cortex networks in balancing emotions and cognitive processes. The numerical results confirm that coupled weights play a crucial role in the balance of emotional cognitive networks. Furthermore, our model predicts the pathogenic mechanism of depression resulting from abnormalities in the subgenual cortex, and network functionality was restored through intervention in the dorsolateral prefrontal cortex. This study utilizes computational modeling techniques to provide an insight explanation for the diagnosis and treatment of depression.
Prefrontal Cortex/physiology* ; Humans ; Emotions/physiology* ; Cognition/physiology* ; Gyrus Cinguli/physiology* ; Computer Simulation ; Models, Neurological ; Neural Pathways/physiology* ; Nerve Net/physiology*

Transcranial temporal interference stimulation (tTIS) is a novel non-invasive transcranial electrical stimulation technique that achieves deep brain stimulation through multiple electrodes applying electric fields of different frequencies. Current studies on the mechanism of tTIS effects are primarily based on rodents, but experimental outcomes are often significantly influenced by electrode configurations. To enhance the performance of tTIS within the limited cranial space of rodents, we proposed various electrode configurations for tTIS and conducted finite element simulations using a realistic mouse model. Results demonstrated that ventral-dorsal, four-channel bipolar, and two-channel configurations performed best in terms of focality, diffusion of activated brain regions, and scalp impact, respectively. Compared to traditional transcranial direct current stimulation (tDCS), these configurations improved by 94.83%, 50.59%, and 3 514.58% in the respective evaluation metrics. This study provides a reference for selecting electrode configurations in future tTIS research on rodents.
Animals ; Transcranial Direct Current Stimulation/instrumentation* ; Electrodes ; Mice ; Computer Simulation ; Finite Element Analysis ; Brain/physiology*

Clinical trials have demonstrated that kilohertz-frequency transcutaneous spinal cord stimulation (TSCS) can be used to facilitate the recovery of sensory-motor function for patients with spinal cord injury, whereas the neural mechanism of TSCS is still undetermined so that the choice of stimulation parameters is largely dependent on the clinical experience. In this paper, a finite element model of transcutaneous spinal cord stimulation was used to calculate the electric field distribution of human spinal cord segments T ₁₂ to L ₂, whereas the activation thresholds of spinal fibers were determined by using a double-cable neuron model. Then the variation of activation thresholds was obtained by varying the carrier waveform, the interphase delay, the modulating frequency, and the modulating pulse width. Compared with the sinusoidal carrier, the usage of square carrier could significantly reduce the activation threshold of dorsal root (DR) fibers. Moreover, the variation of activation thresholds was no more than 1 V due to the varied modulating frequency and decreases with the increased modulating pulse width. For a square carrier at 10 kHz modulated by rectangular pulse with the frequency of 50 Hz and the pulse width of 1 ms, the lowest activation thresholds of DR fibers and dorsal column fibers were 27.6 V and 55.8 V, respectively. An interphase delay of 5 μs was able to reduce the activation thresholds of the DR fibers to 20.1 V. The simulation results can lay a theoretical foundation on the selection of TSCS parameters in clinical trials.
Humans ; Spinal Cord Stimulation/methods* ; Nerve Fibers/physiology* ; Finite Element Analysis ; Spinal Cord/physiology* ; Computer Simulation ; Spinal Cord Injuries/physiopathology* ; Lumbosacral Region ; Lumbar Vertebrae ; Transcutaneous Electric Nerve Stimulation/methods* ; Models, Neurological