Detection Method Research Articles

Establishment of recombinase polymerase amplification detection method for Dactylobotrys graminicola

Hulless barley sheath rot is a spike disease caused by Dactylobotrys graminicola. In recent years, it has generally occurred in hulless barley growing areas in China, resulting in reduced hulless barley yields. In this study, primers and probes were designed based on conserved genome sequences, and a method was established using recombinant enzyme polymerase amplification-lateral flow burette (RPA-LFD) technology for the rapid diagnosis of sheath rot in hulless barley. The method can be successfully established in five minutes at a constant temperature of 39℃, and the results are consistent with those of normal PCR analysis. The method demonstrated high sensitivity, with a detection limit of 10 fg/µL. Furthermore, the rapid method was able to successfully detect D. graminicola in hulless barley during field incubation, which highlighted the significant advantage of the method in practical applications. In conclusion, the RPA method established in this study exhibited several advantageous characteristics, including high efficiency, simplicity, rapidity and practicality, which provide a theoretical basis for the early detection and prevention of D. graminicola.

AI-Augmented Fault Detection and Diagnosis in VLSI Circuits: A Step toward Intelligent Chip Design

Fault detection and diagnosis are critical challenges in Very Large Scale Integration (VLSI) circuits, where even minor defects can cause significant performance degradation or system failure. Traditional fault detection methods often struggle with scalability and real-time analysis as circuits grow increasingly complex. This paper proposes an AI-augmented framework that leverages machine learning algorithms and neural networks to enhance the fault detection process in VLSI circuits. The proposed model not only identifies and classifies faults with high accuracy but also provides root-cause diagnostics, significantly reducing testing time and maintenance costs. Simulation results demonstrate the effectiveness of the AI-based approach in comparison with conventional techniques, showcasing improved fault coverage, faster detection, and scalability for modern chip designs. This study serves as a step toward the integration of intelligent diagnostics into chip manufacturing, paving the way for more robust and self-healing electronic systems.

Sampling random graphs with specified degree sequences

The configuration model is a standard tool for uniformly generating random graphs with a specified degree sequence, and is often used as a null model to evaluate how much of an observed network’s structure can be explained by its degree structure alone. A Markov chain Monte Carlo (MCMC) algorithm, based on a degree-preserving double-edge swap, provides an asymptotic solution to sample from the configuration model. However, accurately and efficiently detecting when this Markov chain is sufficiently close to its stationary distribution remains an unsolved problem. Here, we provide a solution to sample from the configuration model using this standard MCMC algorithm. We develop an algorithm, based on the assortativity of the sampled graphs, for estimating the gap between effectively independent MCMC states, and a computationally efficient gap-estimation heuristic derived from analyzing a corpus of 509 empirical networks. We provide a convergence detection method based on the Dickey-Fuller Generalized Least Squares test, which we show is more accurate and efficient than three alternative Markov chain convergence tests. Supplementary materials for the proposed methods can be found here.

RE-Trace : Re-identification of Modified GPS Trajectories

GPS trajectories are a critical asset for building spatio-temporal predictive models in urban regions in the context of road safety monitoring, traffic management, and mobility services. Currently, reliable and efficient data misuse detection methods for such personal, spatio-temporal data, particularly in data breach cases, are missing. This article addresses an essential aspect of data misuse detection, namely, the re-identification of leaked and potentially modified GPS trajectories. We present RE-Trace —a contrastive learning-based model that facilitates reliable and efficient re-identification of GPS trajectories and resists specific trajectory transformation attacks aimed to obscure a trajectory’s origin. RE-Trace utilizes contrastive learning with a transformer-based trajectory encoder to create trajectory representations, robust to various trajectory modifications. We present a comprehensive threat model for GPS trajectory modifications and demonstrate the effectiveness and efficiency of the RE-Trace re-identification approach on three real-world datasets. Our evaluation results demonstrate that RE-Trace significantly outperforms state-of-the-art baselines on all datasets and identifies modified GPS trajectories effectively and efficiently.

EGMA: Ensemble Learning-Based Hybrid Model Approach for Spam Detection

Spam messages have emerged as a significant issue in digital communication, adversely affecting users’ mental health, personal safety, and network resources. Traditional spam detection methods often suffer from low detection rates and high false positives, underscoring the need for more effective solutions. This paper proposes the EGMA model, an ensemble learning-based hybrid approach for spam detection in SMS messages, which integrates gated recurrent unit (GRU), multilayer perceptron (MLP), and hybrid autoencoder models utilizing a majority voting algorithm. The EGMA model enhances performance by incorporating additional statistical features extracted from message content and employing text vectorization techniques, such as Term Frequency–Inverse Document Frequency (TF-IDF) and CountVectorizer. The proposed model achieved impressive classification accuracies of 99.28% on the SMS Spam Collection dataset, 99.24% on the Email Spam dataset, 99.00% on the Enron-Spam dataset, 98.71% on the Super SMS dataset, and 95.09% on UtkMl’s Twitter Spam dataset. These results demonstrate that the EGMA model outperforms individual models and existing methods in the literature, providing a robust solution for enhancing spam detection performance and effectively mitigating the threats that spam messages pose in digital communication.

A highly sensitive electrochemical aptasensor for detecting broad-spectrum estrogen molecules in clinical samples

The lack of sensitive and accurate monitoring methods for in vivo estrogen levels presents challenges for better prevention of estrogen-induced diseases. We have developed a label-free electrochemical aptasensor that demonstrates high sensitivity and selectivity for the broad-spectrum detection of estrogen molecules. This biosensor uses gold nanoparticles for electrochemical signal amplification and aptamers for broad-spectrum target recognition, enabling precise detection of trace amounts of estrogen in serum samples. The aptasensor demonstrates high sensitivity in estrogen detection, with a linear detection range of 0.01–1 nM and a minimum detection limit of 3 pM. It also exhibits excellent selectivity and interference resistance, with a detection error of less than 19 %, even in the presence of high concentrations of other biological substances. Additionally, molecular dynamics simulations were performed to provide insights into the molecular mechanism of aptamer broad-spectrum recognition and construction principles underlying the sensor. We anticipate that this aptasensor will serve as a robust, convenient, and cost-effective detection method, offering a valuable solution for the prevention of estrogen-induced diseases.

Automating cancer diagnosis using advanced deep learning techniques for multi-cancer image classification

Cancer detection poses a significant challenge for researchers and clinical experts due to its status as the leading cause of global mortality. Early detection is crucial, but traditional cancer detection methods often rely on invasive procedures and time-consuming analyses, creating a demand for more efficient and accurate solutions. This paper addresses these challenges by utilizing automated cancer detection through AI-based techniques, specifically focusing on deep learning models. Convolutional Neural Networks (CNNs), including DenseNet121, DenseNet201, Xception, InceptionV3, MobileNetV2, NASNetLarge, NASNetMobile, InceptionResNetV2, VGG19, and ResNet152V2, are evaluated on image datasets for seven types of cancer: brain, oral, breast, kidney, Acute Lymphocytic Leukemia, lung and colon, and cervical cancer. Initially, images undergo segmentation techniques, proceeded by contour feature extraction where parameters such as perimeter, area, and epsilon are computed. The models are rigorously evaluated, with DenseNet121 achieving the highest validation accuracy as 99.94%, 0.0017 as loss, and the lowest Root Mean Square Error (RMSE) values as 0.036056 for training and 0.045826 for validation. These results revealed the capability of AI-based techniques in improving cancer detection accuracy, with DenseNet121 emerging as the most effective model in this study.

An innovative homogeneous electrochemistry coupled with colorimetry dual-model sensing strategy for perfluorooctane sulfonate based on Cu@CuO aerogel nanozyme.

Bypreparing Cu@CuO aerogel as a nanozyme which exhibits prominent peroxidase-like (POD) activity, an innovative homogeneous electrochemistry (HEC) coupled with the colorimetry dual-model sensing strategy is proposed to detect perfluorooctane sulfonate(PFOS) for the first time. Cu@CuO aerogel accelerates the oxidation process of colorless o-phenylenediamine to form yellow 2,3-diaminophenazinc (DAP), and meanwhile, DAP as an electroactive substance creates a reduction peak current upon the electrochemical measurements. Interestingly, in the presence of PFOS, the POD activity of Cu@CuO aerogel isinhibited since the specific coordination between PFOS and Cu(II) can cover the active sites, resulting in the color of the sensing system becoming light and the peak current of DAP decreasing. This innovative dual-mode detection method showed excellent electrochemical detection of PFOS in the concentration range 10.0 ~ 1125.0nM with a limit of detection (LOD) as low as 3.3nM and a LOD of 20.8nM in the colorimetric detection in the range 62.3 ~ 875nM. Furthermore, the sensor was successfully used for the analysis of real samples with an RSD value ≤ 6.5%. The successful application of this two-mode sensing method for the determination of PFOS holds promise for the detection of other contaminants in the future.

Preparation and Pharmacokinetics of Brain-Targeted Nanoliposome Loaded with Rutin

Rutin is a flavonoid compound with potential for treating Alzheimer’s disease, preventing brain damage, mitigating cerebral ischemia–reperfusion injury, and exhibiting anti-glioblastoma activity. However, its efficacy is limited by its low solubility, poor bioavailability, and limited permeability across the blood–brain barrier (BBB). To enhance the bioavailability and brain-targeting ability of Rutin, transferrin-modified Rutin liposome (Tf-Rutin-Lip) was developed using liposomes as a delivery system. Rutin liposomes were prepared using the thin-film dispersion method, and the preparation conditions were optimized using the response surface methodology. Then, transferrin (Tf) was incorporated into the liposomes through covalent modification, yielding Tf-Rutin liposomes. The toxicity of these liposomes on bEnd.3 cells, as well as their impact on the tight junctions of these cells, was rigorously evaluated. Additionally, in vitro and in vivo experiments were conducted to validate the brain-targeting efficacy of the Tf-Rutin liposomes. A susceptible detection method was developed to characterize the pharmacokinetics of Tf-Rutin-Lip further. The optimized conditions for the preparation of Tf-Rutin-Lip were determined as follows: a lipid-to-cholesterol ratio of 4.63:1, a drug-to-lipid ratio of 1:45.84, a preparation temperature of 42.7 °C, a hydration volume of 20 mL, a sonication time of 10 min, a surfactant concentration of 80 mg/mL, a DSPE-MPEG-2000 concentration of 5%, and a DSPE-PEG2000-COOH to DSPE-MPEG-2000 molar ratio of 10%. The liposomes did not affect the cell activity of bEnd.3 cells at 24 h and did not disrupt the tight junction of the blood–brain barrier. Tf-modified liposomes were taken up by bEnd.3 cells, which, in turn, passed through the BBB, thus improving liposomal brain targeting. Furthermore, the results of pharmacokinetic experiments showed that the Cmax, AUC0-∞, AUC0-t, MRT0-∞, and t1/2 of Tf-Rutin-Lip increased 1.99-fold, 2.77-fold, 2.58-fold, 1.26-fold, and 1.19-fold compared to those of free Rutin solution, respectively. These findings suggest that Tf-Rutin-Lip is brain-targeted and may enhance the efficacy of Rutin in the treatment of brain disorders.

Research on power battery anomaly detection method based on improved TimesNet

Health monitoring and abnormality detection of power batteries for new energy vehicles has been one of the hot topics in recent years. Accurate and efficient power battery anomaly detection is crucial to ensure stable operation of the battery system and energy saving. However, power battery data are often non-linear and unstable due to external factors, such as temperature conditions, which pose challenges for anomaly detection. Existing methods for collecting battery data’s temporal and spatial features are available, but many of them yield suboptimal detection accuracy and feature extraction efficiency due to their disregard for the multi-periodicity of the data. This paper proposes a novel network structure for power battery anomaly detection based on an improved TimesNet. Firstly, the original battery data undergo preprocessing, and the feature correlation coefficient matrix is established using the MIC algorithm. Secondly, the improved TimesNet network is employed to convert the one-dimensional time feature sequence into a two-dimensional tensor, enabling the extraction of temporal features and dependencies at various cycle scales. Finally, the two-dimensional tensor is transformed into a one-dimensional time series, which undergoes information-weighted aggregation to obtain the final anomaly detection results. To assess the effectiveness and generalization of the proposed model, experiments are conducted using Battery and four public datasets for anomaly detection. The experimental results demonstrate that the proposed model outperforms the transformer, autoformer, TimesNet, and Dlinear models, achieving an improvement of 1%–19% in the F1 value and 1%–3% in the ACC compared to the other models.

Automated Cloud Shadow Detection from Satellite Orthoimages with Uncorrected Cloud Relief Displacements

Clouds and their shadows significantly affect satellite imagery, resulting in a loss of radiometric information in the shadowed areas. This loss reduces the accuracy of land cover classification and object detection. Among various cloud shadow detection methods, the geometric-based method relies on the geometry of the sun and sensor to provide consistent results across diverse environments, ensuring better interpretability and reliability. It is well known that the direction of shadows in raw satellite images depends on the sun’s illumination and sensor viewing direction. Orthoimages are typically corrected for relief displacements caused by oblique sensor viewing, aligning the shadow direction with the sun. However, previous studies lacked an explicit experimental verification of this alignment, particularly for cloud shadows. We observed that this implication may not be realized for cloud shadows, primarily due to the unknown height of clouds. To verify this, we used Rapideye orthoimages acquired in various viewing azimuth and zenith angles and conducted experiments under two different cases: the first where the cloud shadow direction was estimated based only on the sun’s illumination, and the second where both the sun’s illumination and the sensor’s viewing direction were considered. Building on this, we propose an automated approach for cloud shadow detection. Our experiments demonstrated that the second case, which incorporates the sensor’s geometry, calculates a more accurate cloud shadow direction compared to the true angle. Although the angles in nadir images were similar, the second case in high-oblique images showed a difference of less than 4.0° from the true angle, whereas the first case exhibited a much larger difference, up to 21.3°. The accuracy results revealed that shadow detection using the angle from the second case improved the average F1 score by 0.17 and increased the average detection rate by 7.7% compared to the first case. This result confirms that, even if the relief displacement of clouds is not corrected in the orthoimages, the proposed method allows for more accurate cloud shadow detection. Our main contributions are in providing quantitative evidence through experiments for the application of sensor geometry and establishing a solid foundation for handling complex scenarios. This approach has the potential to extend to the detection of shadows in high-resolution satellite imagery or UAV images, as well as objects like high-rise buildings. Future research will focus on this.

Aluminum dust concentration detection based on LSTM-Kalman filter

Industrial dust emissions present serious hazards, including respiratory issues and explosion risks. Traditional dust concentration detection methods are often compromised by environmental factors. This study introduces a novel FFT-KF-LSTM-NET model to predict high-concentration aluminum powder levels, utilizing 128 sets of electrostatic induction measurement data. By applying the Fast Fourier Transform (FFT) to eliminate low-frequency interference, integrating Long Short-Term Memory (LSTM) networks for advanced time series analysis, and using the Kalman Filter (KF) for rapid model convergence, this approach significantly enhances prediction accuracy. The model achieves an 82% improvement in Mean Squared Error (MSE), reducing it to 0.1336, outperforming traditional methods. Furthermore, by modeling the voltage signal generated by charged dust particles in the electrostatic induction sensor's sensing area and using the first derivative of the voltage signal as a learning feature, the model's prediction speed is increased from 1.5 s-2 s–0.5 s, with improved anti-interference capabilities. These advancements position the FFT-KF-LSTM-NET model as a crucial tool for real-time, reliable dust concentration detection in industrial environments.

Identification of novel 3D-genome altering and complex structural variants underlying retinitis pigmentosa type 17 through a multistep and high-throughput approach

IntroductionAutosomal dominant retinitis pigmentosa type 17 (adRP, type RP17) is caused by complex structural variants (SVs) affecting a locus on chromosome 17 (chr17q22). The SVs disrupt the 3D regulatory landscape by altering the topologically associating domain (TAD) structure of the locus, creating novel TAD structures (neo-TADs) and ectopic enhancer-gene contacts. Currently, screening for RP17-associated SVs is not included in routine diagnostics given the complexity of the variants and a lack of cost-effective detection methods. The aim of this study was to accurately detect novel RP17-SVs by establishing a systematic and efficient workflow.MethodsGenetically unexplained probands diagnosed with adRP (n = 509) from an international cohort were screened using a smMIPs or genomic qPCR-based approach tailored for the RP17 locus. Suspected copy number changes were validated using high-density SNP-array genotyping, and SV breakpoint characterization was performed by mutation-specific breakpoint PCR, genome sequencing and, if required, optical genome mapping. In silico modeling of novel SVs was performed to predict the formation of neo-TADs and whether ectopic contacts between the retinal enhancers and the GDPD1-promoter could be formed.ResultsUsing this workflow, potential RP17-SVs were detected in eight probands of which seven were confirmed. Two novel SVs were identified that are predicted to cause TAD rearrangement and retinal enhancer-GDPD1 contact, one from Germany (DE-SV9) and three with the same SV from the United States (US-SV10). Previously reported RP17-SVs were also identified in three Australian probands, one with UK-SV2 and two with SA-SV3.DiscussionIn summary, we describe a validated multi-step pipeline for reliable and efficient RP17-SV discovery and expand the range of disease-associated SVs. Based on these data, RP17-SVs can be considered a frequent cause of adRP which warrants the inclusion of RP17-screening as a standard diagnostic test for this disease.

Comparison of progression risk of monoclonal gammopathy of undetermined significance by method of detection

Monoclonal gammopathy of undetermined significance (MGUS) is an asymptomatic pre-malignant disorder. The current standard of care is not to screen for MGUS, so it is often incidentally diagnosed in the clinic. It is unknown whether the outcomes of screened versus clinically detected MGUS differ. We compared the progression risk between screened versus clinical MGUS cohorts and assessed whether the MGUS detection method impacted risk prediction of established clinical factors (score). We included 379 screened MGUS from the Olmsted County population based study and 1384 MGUS patients diagnosed during routine clinical evaluation at Mayo Clinic. Median follow-up time for the screened versus clinical cohort was 26.6 and 40.1 years, respectively. Accounting for death as a competing risk, the cumulative incidence of progression at 25 years was similar in the screened (11.1% [95% CI 8.3-14.8]) versus clinical (10.1% [95% CI 8.6-11.8%]) MGUS cohorts, even when stratified by sex, age, or the baseline MGUS risk score. Overall, 0.9 (95% CI 0.6-1.2) screened versus 1.0 (95% CI 0.9-1.2) clinically detected MGUS patients experienced disease progression for every 100 person years of follow-up. MGUS detection method did not modify the association between MGUS risk score and progression risk (pinteraction=0.217) and did not add to known risk factors for progression (likelihood ratio test, p=0.839). Here we show that progression risk among patients with screened versus clinically detected heavy-chain MGUS was similar. Future studies are needed to assess if tailored follow-up of screened MGUS patients affects clinical outcomes.

A novel dual probe-based method for mutation detection using isothermal amplification.

Cost efficient and rapid detection tools to detect mutations especially those linked to drug-resistance are important to address concerns of the rising multi-drug resistance infections. Here we integrated dual probes, namely a calibrator probe and an indicator probe, into isothermal amplification detection system. These two probes are designed to bind distinct regions on the same amplicon to determine the presence or absence of mutation. The calibrator probe signal is used as an internal signal calibrator for indicator probe which detects the presence or absence of the mutation. As an illustrative example, we evaluated the applicability of this dual probe method for detecting mutations associated with rifampicin (RIF) drug resistance at codons 516, 526 and 531 of the rpoB gene in Mycobacterium tuberculosis. In this assessment, we examined 127 artificial samples comprising wild types and mutants with single or multiple mutations. Our results demonstrated 100% accuracy for both wild types and mutants for mutations at codons 526 and 531. As regards to mutations at codon 516, the wild type was identified with 100% accuracy, while the mutants were identified with 95% accuracy. Moreover, when we extended our evaluation to include clinical MTB strains and the Zeptometrix MTB Verification panel, our method achieved 100% accuracy (5 out of 5) in identifying wild-type strains. Additionally, we successfully detected a RIF-resistant strain with mutations at codon 531 of the rpoB gene in Zeptometrix verification panel. Our isothermal mutation detection system, relying on dual probes exhibits a versatile approach. With the capability to identify mutations without prior knowledge of their specific mutation direction, our dual-probe method shows significant promise for applications in drug resistance nucleic acid testing, particularly in resource-limited settings.

The Chinese Ear Shenmen Point and the French Cosmonaut Point: Same or Different?

The Shenmen Point is an auricular acupuncture point that is also known in France as the Cosmonaut Point. It is widely utilized therapeutically for its property of reducing stress. In some ways it is a controversial point. Differences of opinion exist regarding the most appropriate name of the point, who discovered it first, its location within the triangular fossa, its best method of detection, its best method of stimulation, its actions, its indications, its contraindications, its therapeutic effects, and even its very existence. Furthermore, given the extent of controversy concerning the Shenmen and Cosmonaut points, one may reasonably question if they represent the same point, or two separate points. The aim of this report will be to review the similarities and differences of opinion regarding the Shenmen and Cosmonaut points, drawing mainly from French, German, Chinese, and American sources. The clinical implications of these comparisons will be summarized.

A Review of Thermal Detectors of THz Radiation Operated at Room Temperature

This article concerns optical detection issues in the terahertz (THz) range. This is a kind of guide to various types of uncooled thermal detectors in the most often applications. Particular attention is paid to the principle of their operation, technology, and practical features. In addition, some detection methods were also characterized by comparing their performances. The article ends with a performance summary of the selected THz thermal detectors.

Optimization and Clinical Application Potential of Single Nucleotide Polymorphism Detection Method Based on CRISPR/Cas12a and Recombinase Polymerase Amplification.

Conventional methods for detecting single nucleotide polymorphisms (SNPs) in clinical practice often require substantial time, labor, and specialized equipment, limiting their widespread application. To address this limitation, we refined our previous SNP detection method, IMAS-RPA [introducing an extra mismatched base adjacent to the single-base mutant site by recombinase polymerase amplification (RPA)], resulting in an updated version termed IMAS-RPAv2. We began by introducing a suboptimal protospacer adjacent motif (PAM) sequence, GTTG, into the double-stranded DNA (dsDNA) products using either RPA or reverse transcription RPA. This modification decreased the efficiency with which CRISPR RNA (crRNA) recognizes the PAM and locally unwinds the dsDNA to form an R loop. After a delay, the R loop forms. However, due to the intentional incorporation of a mismatched base on the crRNA relative to the wild-type double-stranded DNA (WT-dsDNA), a continuous two-base mismatch is established between the crRNA and WT-dsDNA. Consequently, WT-dsDNA does not activate CRISPR/Cas12a's cleavage activity within a short time, while variant-type dsDNA continues to activate CRISPR/Cas12a and produce a robust fluorescence signal. This improvement significantly enhances the SNP discrimination sensitivity, allowing for detection at the single-copy level. Results were observed using both a conventional microplate reader and a specially designed portable device created through 3D printing. This device allows a direct fluorescence observation without the need for additional equipment. Consequently, the entire detection process becomes independent of large-scale equipment. This greatly expands its range of applications and offers promising prospects for clinical use.

Study on AI Generated Fake-Media Detection

The rapid growth of AI-generated images, especially with techniques such as Generative Adversarial Networks (GANs), has complicated the ability to tell apart genuine content from artificial creations. This issue is vital for preserving the authenticity of visual media, where conventional detection methods often struggle. Current detection approaches concentrate on machine learning and deep learning techniques, including neural networks (CNNs). These methods aim to reveal subtle flaws and irregularities in images, like inconsistencies in pixel distribution and lighting, which serve as critical signs of AI involvement. The research emphasizes the necessity for ongoing development in detection technologies to keep up with the quick progress of AI advancements. Ensuring that these detection methods are accurate and dependable is crucial for protecting against misinformation and maintaining confidence in digital content. This paper reviewed Deepfake detection system.

Circulation and Codetections of Influenza Virus, SARS-CoV-2, Respiratory Syncytial Virus, Rhinovirus, Adenovirus, Bocavirus, and Other Respiratory Viruses During 2022–2023 Season in Latvia

This retrospective study analysed the routine data obtained by multiplex real-time RT-qPCR methods for respiratory virus detection. A total of 4814 respiratory specimens collected during 1 September 2022–31 August 2023 were included in the study. A total of 38% of the specimens were positive for at least one target, with the incidence maximum (82%) for the small children (age group 0–4 years). The five dominant virus groups were rhinovirus (RV, 12%), influenza virus A (IAV, 7%), adenovirus (AdV, 6%), respiratory syncytial virus (RSV, 5%), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, 5%). The specimens with multi-detections represented 19% of the positives, unevenly distributed (n = 225, 56, 43, 24) among the age groups 0–4, 5–14, 15–64, and 65< years, respectively. The dominant virus groups in multi-positive specimens were RV (53%), AdV (43%), and bocavirus (BoV, 35%)—in mutual pairs as well as all three together—followed by RSV (21%), and IAV (15%). Our study focused on the specimens with codetections and provides an insight into the variety of the respiratory virus interactions in Latvia during the first year since pandemic-related social restriction measures were eased. The observations also emphasise the need to consider the differentiation between rhinoviruses and enteroviruses, especially for the youngest patients in the age group 0–4.