Machine intelligence accelerated design of conductive MXene aerogels with programmable properties.

Designing ultralight conductive aerogels with tailored electrical and mechanical properties is critical for various applications. Conventional approaches rely on iterative, time-consuming experiments across a vast parameter space. Herein, an integrated workflow is developed to combine collaborative robotics with machine learning to accelerate the design of conductive aerogels with programmable properties. An automated pipetting robot is operated to prepare 264 mixtures of Ti3C2Tx MXene, cellulose, gelatin, and glutaraldehyde at different ratios/loadings. After freeze-drying, the aerogels' structural integrity is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated/characterized via robotics-automated platforms, enabling the construction of an artificial neural network prediction model. The prediction model conducts two-way design tasks: (1) predicting the aerogels' physicochemical properties from fabrication parameters and (2) automating the inverse design of aerogels for specific property requirements. The combined use of model interpretation and finite element simulations validates a pronounced correlation between aerogel density and compressive strength. The model-suggested aerogels with high conductivity, customized strength, and pressure insensitivity allow for compression-stable Joule heating for wearable thermal management.

Machine intelligence-accelerated discovery of all-natural plastic substitutes.

One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model's prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database.

Comparison between dynamic versus static models and real-time monitoring of neuronal dysfunction in an amyloid-ß induced neuronal toxic model on a chip platform.

Microfluidics-based organs-on-a-chip offer a promising method for dynamic and 3-dimensional (3D) cell culture to evaluate the cell behaviors within the biomimetic environment. The purpose of this study was to establish neural network connections in a 3D neural stem cell (NSC)-based system with an interstitial level of flow for simulating the brain microenvironment toward a dynamic amyloid-ß (Aß) induced neuronal toxic model on a chip and to compare the biological effects and neurite dysfunction between static and dynamic systems. The brain-on-a-chip system consisted of an impedance analyzing layer, a structured well with a connected channel, and an interface coating with polypeptide films fabricated with modification based on our previous study. The cytotoxicity and percentage of neuron/astrocyte differentiation were all compared in both static and dynamic brain-on-a-chip systems. Reactive oxygen species production, neuron marker expression and neurotransmitter-acetylcholine release were all compared to evaluate functional neurite losses in both static and dynamic systems with/without Aß addition. Moreover, real-time impedance recording was used to consecutively monitor the neurite connection/disconnection in both static and dynamic brain-on-a-chip systems. The NSC-based dynamic brain-on-a-chip may enable the application of different neurodegenerative disease in vitro models for pathogenesis studies, drug discovery and novel therapeutic method development.

Graphene oxide nanosheets augment silk fibroin aerogels for enhanced water stability and oil adsorption.

Nanocomposite aerogels exhibit high porosity and large interfacial surface areas, enabling enhanced chemical transport and reactivity. Such mesoporous architectures can be prepared by freeze-casting naturally-derived biopolymers such as silk fibroin, but often form mechanically weak structures that degrade in water, which limits their performance under ambient conditions. Adding 2D material fillers such as graphene oxide (GO) or transition metal carbides (e.g. MXene) could potentially reinforce these aerogels via stronger intermolecular interactions with the polymeric binder. Here, we show that freeze-casting of GO nanosheets with silk fibroin results in a highly water-stable, mechanically robust aerogel, with considerably enhanced properties relative to silk-only or silk-MXene aerogels. These silk-GO aerogels exhibit high contact angles with water and are highly water stable. Moreover, aerogels can adsorb up 25-35 times their mass in oil, and can be used robustly for selective oil separation from water. This increased stability may occur due to strengthened intermolecular interactions such as hydrogen bonding, despite the random coil and α-helix conformation of silk fibroin, which is typically more soluble in water. Finally, we show these aerogels can be prepared at scale by freeze-casting on a copper mesh. Ultimately, we envision that these multicomponent aerogels could be widely utilized for molecular separations and environmental sensing, as well as for thermal insulation and electrical conductivity.

Surgical Outcomes and Quality of Care Among Adult Appendicitis Patients: A Comparative Study of Tertiary Care Hospitals and Regional Hospitals.

BACKGROUND: Acute appendicitis is one of the most common abdominal emergencies, with management approaches that vary depending on the available resources and setting. However, there is a lack of studies on the differences of surgical outcomes and quality of care between tertiary care hospitals and regional hospitals. METHODS: This multicenter retrospective study included 2158 consecutive adult patients between January 2014 and June 2018 at three hospitals. The patient cohort was divided into regional hospital group (N = 1223) and tertiary care hospital group (N = 935). Baseline characteristics and perioperative outcomes were compared, and factors associated with surgical delay and postoperative complication were investigated. RESULTS: Patients in tertiary care hospital group had longer surgical waiting time (17.3 vs. 12.0 h, p < 0.001), higher risks of surgical delay exceeding 24 h (OR = 2.94, 95% CI 2.17-4.01, p < 0.001), longer operation time (64 vs. 50 min, p < 0.001), more appendix perforation (22.4 vs. 13.3%, p < 0.001), and higher hospital cost compared with regional hospital group. In multivariate analysis, factors associated with surgical delay were tertiary care hospital (OR = 2.94, 95% CI 2.18-4.01, p < 0.001) and delay diagnosis (OR = 18.7, 95% CI 11.7-30.1, p < 0.001), while those associated with postoperative complications were older age (OR = 1.02, 95% CI 1.00-1.04, p = 0.013), male sex (OR = 2.38, 95% CI 1.11-5.52, p = 0.031), surgical delay (OR = 2.99, 95% CI 1.30-6.47, p = 0.007), and appendix perforation (OR = 5.61, 95% CI 2.72-11.85, p < 0.001). CONCLUSIONS: Patients at tertiary care hospitals had longer waiting time, more surgical delays, and appendix perforations, and these were risk factors of postoperative complications. Establishing an effective referral system to redirect appendicitis patients with less complex medical histories from tertiary care hospitals to regional hospitals may enhance the quality of patient care and outcomes, while also reducing medical costs.

Machine Learning Integrated Workflow for Predicting Schwann Cell Viability on Conductive MXene Biointerfaces.

Severe injuries to the peripheral nervous system (PNS) require Schwann cells to aid in neuronal regeneration. Low-frequency electrical stimulation is known to induce the cogrowth of neurons and Schwann cells in an injured PNS. However, the correlations between electrical stimulation and Schwann cell viability are complex and not well understood. In this work, we develop a machine learning (ML)-integrated workflow that uses conductive hydrogel biointerfaces to evaluate the impacts of fabrication parameters and electrical stimulation on the Schwann cell viability. First, a hydrogel array with varying MXene and peptide loadings is fabricated, which serves as conductive biointerfaces to incubate Schwann cells and introduce various electrical stimulation (at different voltages and frequencies). Upon specific fabrication parameters and stimulation, the cell viability is evaluated and input into an artificial neural network model to train the model. Additionally, a data augmentation method is applied to synthesize 1000-fold virtual data points, enabling the construction of a high-accuracy prediction model (with a testing mean absolute error ≤11%). By harnessing the model's predictive power, we can accurately predict Schwann cell viability based on a given set of fabrication/stimulation parameters. Finally, the SHapley Additive exPlanations model interpretation provides several data-scientific insights that are validated by microscopic cellular observations. Our hybrid approach, involving conductive biointerface fabrication, ML algorithms, and data analysis, offers an unconventional platform to construct a preclinical prediction model at the cellular level.

Case report: VA-ECMO for fulminant myocarditis in an infant with acute COVID-19.

Fulminant myocarditis in children was rare during the coronavirus disease 2019 pandemic, but it had the potential for high morbidity and mortality. We describe the clinical course of a previously healthy 9-month-old young male infant who rapidly deteriorated into cardiogenic shock due to coronavirus disease 2019-related fulminant myocarditis. He developed severe heart failure and multiple organ dysfunction syndrome that were treated promptly with central venoarterial extracorporeal membrane oxygenation and continuous venovenous hemofiltration. He made a good recovery without significant morbidity.

CherryML: scalable maximum likelihood estimation of phylogenetic models.

Phylogenetic models of molecular evolution are central to numerous biological applications spanning diverse timescales, from hundreds of millions of years involving orthologous proteins to just tens of days relating to single cells within an organism. A fundamental problem in these applications is estimating model parameters, for which maximum likelihood estimation is typically employed. Unfortunately, maximum likelihood estimation is a computationally expensive task, in some cases prohibitively so. To address this challenge, we here introduce CherryML, a broadly applicable method that achieves several orders of magnitude speedup by using a quantized composite likelihood over cherries in the trees. The massive speedup offered by our method should enable researchers to consider more complex and biologically realistic models than previously possible. Here we demonstrate CherryML's utility by applying it to estimate a general 400 × 400 rate matrix for residue-residue coevolution at contact sites in three-dimensional protein structures; we estimate that using current state-of-the-art methods such as the expectation-maximization algorithm for the same task would take >100,000 times longer.

Prediction of the Ground-State Electronic Structure from Core-Loss Spectra of Organic Molecules by Machine Learning.

The core-loss spectrum reflects the partial density of states (PDOS) of the unoccupied states at the excited state and is a powerful analytical technique to investigate local atomic and electronic structures of materials. However, various molecular properties governed by the ground-state electronic structure of the occupied orbital cannot be directly obtained from the core-loss spectra. Here, we constructed a machine learning model to predict the ground-state carbon s- and p-orbital PDOS in both occupied and unoccupied states from the C K-edge spectra. We also attempted an extrapolation prediction of the PDOS of larger molecules using a model trained by smaller molecules and found that the extrapolation prediction performance can be improved by excluding tiny molecules. Besides, we found that using smoothing preprocess and training by specific noise data can improve the PDOS prediction for noise-contained spectra, which pave a way for the application of the prediction model to the experimental data.

Heterologous Expression of Toxic White Spot Syndrome Virus (WSSV) Protein in Eengineered Escherichia coli Strains.

Aquacultural shrimps suffer economic lost due to the white spot syndrome virus (WSSV) that is the most notorious virus for its fatality and contagion, leading to a 100% death rate on infected shrimps within 7 days. However, the infection of mechanism remains a mystery and crucial problem. To elucidate the pathogenesis of WSSV, a high abundance of protein is required to identify and characterize its functions. Therefore, the optimal WSSV355 overexpression was explored in engineered Escherichia coli strains, in particular C43(DE3) as a toxic tolerance strain remedied 40% of cell growth from BL21(DE3). Meanwhile, a trace amount of WSSV355 was observed in both strains. To optimize the codon of WSSV355 using codon adaption index (CAI), an overexpression was observed with 1.32 mg/mL in C43(DE3), while the biomass was decreased by 35%. Subsequently, the co-expression with pRARE boosted the target protein up to 1.93 mg/mL. Finally, by scaling up production of WSSV355 in the fermenter with sufficient oxygen supplied, the biomass and total and soluble protein were enhanced 67.6%, 44.9%, and 7.8% compared with that in flask condition. Herein, the current approach provides efficacious solutions to produce toxic proteins via codon usage, strain selection, and processing optimization by alleviating the burden and boosting protein production in E. coli.

Cation-Induced Assembly of Conductive MXene Fibers for Wearable Heater, Wireless Communication, and Stem Cell Differentiation.

Emerging wearable electronics, wireless communication, and tissue engineering require the development of conductive fiber-shaped electrodes and biointerfaces. Ti3C2Tx MXene nanosheets serve as promising building block units for the construction of highly conductive fibers with integrated functionalities, yet a facile and scalable fabrication scheme is highly required. Herein, a cation-induced assembly process is developed for the scalable fabrication of conductive fibers with MXene sheaths and alginate cores (abbreviated as MXene@A). The fabrication scheme of MXene@A fibers includes the fast extrusion of alginate fibers followed by electrostatic assembly of MXene nanosheets, enabling high-speed fiber production. When multiple fabrication parameters are optimized, the MXene@A fibers exhibit a superior electrical conductivity of 1083 S cm-1, which can be integrated as Joule heaters into textiles for wearable thermal management. By triggering reversible de/hydration of alginate cores upon heating, the MXene@A fibers can be repeatedly contracted and generate large contraction stress that is >40 times higher than the ones of mammalian skeletal muscle. Furthermore, the MXene@A springs demonstrate large contraction strains up to 65.5% and are then fabricated into a reconfigurable dipole antenna to wirelessly monitor the surrounding heat sources. In the end, with the biocompatibility of MXene nanosheets, the MXene@A fibers enable the guidance of neural stem/progenitor cells differentiation and the promotion of neurite outgrowth. With a cation-induced assembly process, our multifunctional MXene@A fibers exhibit high scalability for future manufacturing and hold the prospect to inspire other applications.

New metal-free one-pot synthesis of α-2-deoxy-Ulosides by microwave-assisted double Michael Addition of ß-enamino ketones.

New metal-free one-pot synthesis of α-2-deoxy-ulosides in moderate to good yields by microwave-assisted double Michael addition of various O-nucleophiles to ß-enamino ketones in the presence of 12 N HCl. These glycosyl additions occurred with high α-stereoselectivity and were complete in 10-25 min in 51-93% yield. In addition, high α-stereoselectivity was also observed when S-nucleophiles were examined.

Thermal Percolation of Antiperovskite Superionic Conductor into Porous MXene Scaffold for High-Capacity and Stable Lithium Metal Battery.

Lithium metal battery is considered an emerging energy storage technology due to its high theoretical capacity and low electrochemical potential. However, the practical exploitations of lithium metal batteries are not realized because of uncontrollable lithium deposition and severe dendrite formation. Herein, a thermal percolation strategy is developed to fabricate a dual-conductive framework using electronically conductive Ti3 C2 Tx MXene aerogels (MXAs) and Li2 OHCl antiperovskite superionic conductor. By melting Li2 OHCl at a low temperature, the molten antiperovskite phase can penetrate the MXA scaffold, resulting in percolative electron/ion pathways. Through density functional theory calculations and electrochemical characterizations, the hybridized lithiophilic (MXA)-lithiophobic (antiperovskite) interfaces can spatially guide the deposition of lithium metals and suppress the growth of lithium dendrites. The symmetric cell with MXA-antiperovskite electrodes exhibits superior cycling stability at high areal capacities of 4 mAh cm-2 over 1000 h. Moreover, the full cell with MXA-antiperovskite anode and high-loading LiFePO4 cathode demonstrates high energy and power densities (415.7 Wh kgcell -1 and 231.0 W kgcell -1 ) with ultralong lifespans. The thermal percolation of lithium superionic conductor into electronically conductive scaffolds promises an efficient strategy to fabricate dual-conductive electrodes, which benefits the development of dendrite-free lithium metal anodes with high energy/power densities.

Topographic design in wearable MXene sensors with in-sensor machine learning for full-body avatar reconstruction.

Wearable strain sensors that detect joint/muscle strain changes become prevalent at human-machine interfaces for full-body motion monitoring. However, most wearable devices cannot offer customizable opportunities to match the sensor characteristics with specific deformation ranges of joints/muscles, resulting in suboptimal performance. Adequate wearable strain sensor design is highly required to achieve user-designated working windows without sacrificing high sensitivity, accompanied with real-time data processing. Herein, wearable Ti3C2Tx MXene sensor modules are fabricated with in-sensor machine learning (ML) models, either functioning via wireless streaming or edge computing, for full-body motion classifications and avatar reconstruction. Through topographic design on piezoresistive nanolayers, the wearable strain sensor modules exhibited ultrahigh sensitivities within the working windows that meet all joint deformation ranges. By integrating the wearable sensors with a ML chip, an edge sensor module is fabricated, enabling in-sensor reconstruction of high-precision avatar animations that mimic continuous full-body motions with an average avatar determination error of 3.5 cm, without additional computing devices.

Metabolic Syndrome and Overactive Bladder Syndrome May Share Common Pathophysiologies.

Metabolic syndrome (MetS) is defined by a group of cardiovascular risk factors, including impaired glucose tolerance, central obesity, hypertension, and dyslipidemia. Overactive bladder (OAB) syndrome consists of symptoms such as urinary urgency, frequency, and nocturia with or without urge incontinence. The high prevalences of metabolic syndrome (MetS) and overactive bladder (OAB) worldwide affect quality of life and cause profound negative impacts on the social economy. Accumulated evidence suggests that MetS might contribute to the underlying mechanisms for developing OAB, and MetS-associated OAB could be a subtype of OAB. However, how could these two syndromes interact with each other? Based on results of animal studies and observations in epidemiological studies, we summarized the common pathophysiologies existing between MetS and OAB, including autonomic and peripheral neuropathies, chronic ischemia, proinflammatory status, dysregulation of nutrient-sensing pathways (e.g., insulin resistance at the bladder mucosa and excessive succinate intake), and the probable role of dysbiosis. Since the MetS-associated OAB is a subtype of OAB with distinctive pathophysiologies, the regular and non-specific medications, such as antimuscarinics, beta-3 agonist, and botulinum toxin injection, might lead to unsatisfying results. Understanding the pathophysiologies of MetS-associated OAB might benefit future studies exploring novel biomarkers for diagnosis and therapeutic targets on both MetS and OAB.

Efficacy, safety, and immunogenicity of an inactivated, adjuvanted enterovirus 71 vaccine in infants and children: a multiregion, double-blind, randomised, placebo-controlled, phase 3 trial.

BACKGROUND: Children are susceptible to severe or fatal enterovirus 71 (EV71) infections. We aimed to evaluate the efficacy, safety, and immunogenicity of EV71vac, an aluminium phosphate-adjuvanted inactivated EV71 vaccine in children aged 2-71 months. METHODS: We did a randomised, double-blinded, placebo-controlled, phase 3 trial at five hospitals in Taiwan and two in Vietnam. Children aged 2-71 months were stratified by country and age, and randomly assigned (1:1) to receive two doses of EV71vac or placebo via intramuscular injection 56 days apart. Children aged 2-23 months received a third booster dose on day 366. The primary endpoint was the clinical efficacy of the total vaccinated cohort against EV71-associated diseases during the follow-up period, from 14 days after the second dose to when 15 cases of EV71 infections were confirmed in the per-protocol population. Our safety analysis included all participants who received at least one dose of EV71vac. This trial is registered with ClinicalTrials.gov, NCT03865238, and is complete. FINDINGS: Between April 23 and Dec 25, 2019, of 3663 children assessed, 3061 were randomly assigned, of whom 3049 were vaccinated: 1521 children in the EV71vac group and 1528 in the placebo group. By May 20, 2021, our primary efficacy analysis included 2959 children, with 1476 children in the EV71vac group and 1483 children in the placebo group. The vaccine efficacy of EV71vac was 96·8% (95% CI 85·5-100) against EV71 associated diseases (p<0·0001). The percentage of participants who reported solicited adverse events were similar in both groups: 865 (56·9%) in the EV71vac group and 852 (55·8%) in the placebo group. Almost all reported solicited adverse events were mild and self-limited. INTERPRETATION: EV71vac is safe, well-tolerated, and highly effective in preventing EV71 associated diseases in children aged 2-71 months. FUNDING: Medigen Vaccine Biologics and A+ Industrial Innovative R&D Program of the Ministry of Economic Affairs, Taiwan.

New Frontiers of Extracorporeal Shock Wave Medicine in Urology from Bench to Clinical Studies.

A shock wave (SW), which carries energy and propagates through a medium, is a type of continuous transmitted sonic wave that can achieve rapid energy transformations. SWs have been applied for many fields of medical science in various treatment settings. In urology, high-energy extracorporeal SWs have been used to disintegrate urolithiasis for 30 years. However, at lower energy levels, SWs enhance the expression of vascular endothelial growth factor (VEGF), endothelial nitric oxide synthase (eNOS), proliferating cell nuclear antigen (PCNA), chemoattractant factors, and the recruitment of progenitor cells, and inhibit inflammatory molecules. Low energy extracorporeal shock wave (LESW) therapy has been used in urology for treating chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS), interstitial cystitis/bladder pain syndrome (IC/BPS), overactive bladder, stress urinary incontinence, and erectile dysfunction through the mechanisms of anti-inflammation, neovascularization, and tissue regeneration. Additionally, LESW have been proven to temporarily increase tissue permeability and facilitate intravesical botulinum toxin delivery for treating overactive bladders in animal studies and in a human clinical trial. LESW assisted drug delivery was also suggested to have a synergistic effect in combination with cisplatin to improve the anti-cancer effect for treating urothelial cancer in an in vitro and in vivo study. LESW assisted drug delivery in uro-oncology is an interesting suggestion, but no comprehensive clinical trials have been conducted as of yet. Taken together, LESW is a promising method for the treatment of various diseases in urology. However, further investigation with a large scale of clinical studies is necessary to confirm the real role of LESW in clinical use. This article provides information on the basics of SW physics, mechanisms of action on biological systems, and new frontiers of SW medicine in urology.

Ge/Si multilayer epitaxy and removal of dislocations from Ge-nanosheet-channel MOSFETs.

Horizontally stacked pure-Ge-nanosheet gate-all-around field-effect transistors (GAA FETs) were developed in this study. Large lattice mismatch Ge/Si multilayers were intentionally grown as the starting material rather than Ge/GeSi multilayers to acquire the benefits of the considerable difference in material properties of Ge and Si for realising selective etching. Flat Ge/Si multilayers were grown at a low temperature to preclude island growth. The shape of Ge nanosheets was almost retained after etching owing to the excellent selectivity. Additionally, dislocations were observed in suspended Ge nanosheets because of the absence of a Ge/Si interface and the disappearance of the dislocation-line tension force owing to the elongation of misfit dislocation at the interface. Forming gas annealing of the suspended Ge nanosheets resulted in a significant increase in the glide force compared to the dislocation-line tension force; the dislocations were easily removed because of this condition and the small size of the nanosheets. Based on this structure, a new mechanism of dislocation removal from suspended Ge nanosheet structures by annealing was described, which resulted in the structures exhibiting excellent gate control and electrical properties.

Highly Stretchable Flame-Retardant Skin for Soft Robotics with Hydrogel-Montmorillonite-Based Translucent Matrix.

Flame-retardant coatings are crucial for intelligent systems operating in high-temperature (300-800°C) scenarios, which typically involve multi-joint discrete or continuous kinematic systems. These multi-segment motion generation systems call for conformable yet resilient skin for dexterous work, including firefighting, packaging inflammable substances, encapsulating energy storage devices, and preventing from burning. In fire scenes, a flame-retardant soft robot shall protect integrated electronic components safely and work for navigation and surveillance effectively. Here, we establish fire-resistant robotic mechanisms with montmorillonite (MMT)-biocompatible hydrogel skin, offering effective flame retardancy (∼78°C surface temperature after 3 min in fire) and high post-fire stretchability (∼360% uniaxial tensile strain). Fatigue test results in the MMT-hydrogel polymer matrix to portray a change in post-fire energy consumption of ∼21% (between the first cycle and the 200th cycle), further indicating robustness. MMT-hydrogel synthetic skin medium is then applied to everyday household items and electronics, offering appealing protections in fire scenes (≤10% capacitance loss after 3 min and ≤14% diode light-intensity loss after 1 min in fire). We deploy shape memory alloy (SMA) actuated inchworm-, starfish-, and snail-like locomotion (average velocity ∼12 mm·min-1) for translating inside fire applications. With the stretchable and flame-retardant translucent barriers, the MMT-hydrogel skinned soft robots demonstrate stable compression/relaxation cycles (25 cycles) within flames (4 min 10 s) while protecting the electronic components inside in fire scene. We solve the agility vs. endurance conundrum in this article with SMA actuation independently via Joule heating without a cross-talk from the surrounding high-temperature arena.

The epidemiological features of pediatric viral respiratory infection during the COVID-19 pandemic in Taiwan.

BACKGROUND: Enhanced nonpharmaceutical interventions (NPIs) to prevent the Coronavirus Disease 2019 (COVID-19) have shown various levels of impact on common respiratory pathogens. We aimed to analyze the epidemiological changes seen in certain common respiratory viruses found in Taiwanese children (e.g., influenza virus, enterovirus, parainfluenza virus, adenovirus and respiratory syncytial virus (RSV)) after the implementation of public health measures, as well as interpret the possible meaning of these changes. METHODS: This retrospective observational study examined the viral isolation from children younger than 18 years at a medical center in central Taiwan during the period January 2015-December 2020, a time frame of six years. Viral isolations prior to the COVID-19 pandemic (January 2015-December 2019), along with those during the post-COVID-19 period (January-December 2020) were analyzed and compared. RESULTS: A total of 6899 throat swab samples were collected during the pre-pandemic period of 2015-2019, with 2681 of them having a positive result (38.86%). There were a total of 713 samples collected in 2020, with 142 of them showing positive results (19.92%). The overall positive rate of viral isolates significantly decreased in 2020 (p < 0.001). Declines in the isolation of the influenza virus, parainfluenza virus, adenovirus and enterovirus were observed. The RSV surprisingly became the leading isolate, with up to 47 (6.59%) instances in 2020, and showing an unusual peak in the winter of 2020. The rise began in September of 2020 and reached its plateau in November of that year. CONCLUSIONS: Most respiratory viruses decreased under NPIs regarding SARS-CoV-2. However, the RSV outbreak in the winter of 2020 had shown the limitation of current NPIs. Possible explanations have been discussed in details and public preventive measures should be reinforced for RSV, particularly amongst people having young children both at home and in care centers.