Machine Learning-Aided Microdroplets Breakup Characteristic Prediction in Flow-Focusing Microdevices by Incorporating Variations of Cross-Flow Tilt Angles.

Controlling droplet breakup characteristics such as size, frequency, regime, and droplet quality within flow-focusing microfluidic devices is critical for different biomedical applications of droplet microfluidics such as drug delivery, biosensing, and nanomaterial preparation. The development of a prediction platform capable of forecasting droplet breakup characteristics can significantly improve the iterative design and fabrication processes required for achieving desired performance. The present study aims to develop a multipurpose platform capable of predicting the working conditions of user-specific droplet size and frequency and reporting the quality of the generated droplets, regime, and hydrodynamical breakup characteristics in flow-focusing microdevices with different cross-junction tilt angles. Four different neural network-based prediction platforms were compared to accurately estimate capsule size, generation rate, uniformity, and circle metric. The trained capsule size and frequency networks were optimized using the heuristic optimization approach for establishing the Pareto optimal solution plot. To investigate the transition of the droplet generation regime (i.e., squeezing, dripping, and jetting), two different classification models (LDA and MLP) were developed and compared in terms of their prediction accuracy. The MLP model outperformed the LDA model with a cross-validation measure evaluated as 97.85%, demonstrating that the droplet quality and regime prediction models can provide an engineering judgment for the decision maker to choose between the suggested solutions on the Pareto front. The study followed a comprehensive hydrodynamical analysis of the junction angle effect on the dispersed thread formation, pressure, and velocity domains in the orifice.

Investigation of a Sparse Autoencoder-Based Feature Transfer Learning Framework for Hydrogen Monitoring Using Microfluidic Olfaction Detectors.

Alternative fuel sources, such as hydrogen-enriched natural gas (HENG), are highly sought after by governments globally for lowering carbon emissions. Consequently, the recognition of hydrogen as a valuable zero-emission energy carrier has increased, resulting in many countries attempting to enrich natural gas with hydrogen; however, there are rising concerns over the safe use, storage, and transport of H2 due to its characteristics such as flammability, combustion, and explosivity at low concentrations (4 vol%), requiring highly sensitive and selective sensors for safety monitoring. Microfluidic-based metal-oxide-semiconducting (MOS) gas sensors are strong tools for detecting lower levels of natural gas elements; however, their working mechanism results in a lack of real-time analysis techniques to identify the exact concentration of the present gases. Current advanced machine learning models, such as deep learning, require large datasets for training. Moreover, such models perform poorly in data distribution shifts such as instrumental variation. To address this problem, we proposed a Sparse Autoencoder-based Transfer Learning (SAE-TL) framework for estimating the hydrogen gas concentration in HENG mixtures using limited datasets from a 3D printed microfluidic detector coupled with two commercial MOS sensors. Our framework detects concentrations of simulated HENG based on time-series data collected from a cost-effective microfluidic-based detector. This modular gas detector houses metal-oxide-semiconducting (MOS) gas sensors in a microchannel with coated walls, which provides selectivity based on the diffusion pace of different gases. We achieve a dominant performance with the SAE-TL framework compared to typical ML models (94% R-squared). The framework is implementable in real-world applications for fast adaptation of the predictive models to new types of MOS sensor responses.

Advances in Implantable Microelectrode Array Insertion and Positioning.

OBJECTIVES: Microelectrode arrays offer a means to probe the functional circuitry of the brain and the promise of cortical neuroprosthesis for individuals suffering from paralysis or limb loss. These devices are typically comprised of one or more shanks incorporating microelectrode sites, where the shanks are positioned by inserting the devices along a straight path that is normal to the brain surface. The lack of consistent long-term chronic recording technology has driven interest in novel probe design and approaches that go beyond the standard insertion approach that is limited to a single velocity or axis. This review offers a description of typical approaches and associated limitations and surveys emergent methods for implantation of microelectrode arrays, in particular those new approaches that leverage embedded microactuators and extend the insertion direction beyond a single axis. MATERIALS AND METHODS: This review paper surveys the current technologies that enable probe implantation, repositioning, and the capability to record/stimulate from a tissue volume. A comprehensive literature search was performed using PubMed, Web of Science, and Google Scholar. RESULTS: There has been substantial innovation in the development of microscale and embedded technology that enables probe repositioning to maintain quality recordings in the brain. Innovations in material science have resulted in novel strategies for deployable structures that can record from or stimulate a tissue volume. Moreover, new developments involving magnetically steerable catheters and needles offer an alternative approach to "pull" rather than "push" a probe into the tissue. CONCLUSION: We envision the emergence of a new generation of probes and insertion methodologies for neuromodulation applications that enable reliable chronic performance from devices that can be positioned virtually anywhere in the brain.

Liquid Crystalline Polymers: Opportunities to Shape Neural Interfaces.

OBJECTIVES: Polymers have emerged as constituent materials for the creation of microscale neural interfaces; however, limitations regarding water permeability, delamination, and material degradation impact polymeric device robustness. Liquid crystal polymers (LCPs) have molecular order like a solid but with the fluidity of a liquid, resulting in a unique material, with properties including low water permeability, chemical inertness, and mechanical toughness. The objective of this article is to review the state-of-the-art regarding the use of LCPs in neural interface applications and discuss challenges and opportunities where this class of materials can advance the field of neural interfaces. MATERIALS AND METHODS: This review article focuses on studies that leverage LCP materials to interface with the nervous system in vivo. A comprehensive literature search was performed using PubMed, Web of Science (Clarivate Analytics), and Google Scholar. RESULTS: There have been recent efforts to create neural interfaces that leverage the material advantages of LCPs. The literature offers examples of LCP as a basis for implantable medical devices and neural interfaces in the form of planar electrode arrays for retinal prosthetic, electrocorticography applications, and cuff-like structures for interfacing the peripheral nerve. In addition, there have been efforts to create penetrating intracortical devices capable of microstimulation and resolution of biopotentials. Recent work with a subclass of LCPs, namely liquid crystal elastomers, demonstrates that it is possible to create devices with features that deploy away from a central implantation site to interface with a volume of tissue while offering the possibility of minimizing tissue damage. CONCLUSION: We envision the creation of novel microscale neural interfaces that leverage the physical properties of LCPs and have the capability of deploying within neural tissue for enhanced integration and performance.

Sheath-assisted focusing of microparticles on lab-on-a-chip platforms.

Lab-on-a-chip (LOC) technologies can take advantage of sheath flows for particle/cell focusing before sensing or sorting. The integration of focusing with other microscale manipulation techniques (e.g., sorting) creates a trade-off between the throughput of the device and its performance. Therefore, exploring the effective parameters for cells/particles focusing enables us to improve the desired output of LOC devices. A common configuration for sheath-assisted focusing is Y junctions, which are parametrically studied in this paper. First, a computational model was developed and validated by comparing it with our experimental results. Using COMSOL Multiphysics modeling, the effects of multiple parameters were studied. These parameters include the sheath flow ratio (sheath flow over total flow), width ratio (width of the sheath inlet over the total width), junction angles, and particle size on the focusing width and the distribution of the particles within the focusing region. Then, the numerical data were used to develop two generalized linear models to predict the focusing width of the particles and the standard deviation of the position of the particles. The results showed that the focusing width is greatly impacted by the sheath flow rate ratio. Further, the standard deviation of the position of the particles, which represents the concentration of the particles, is mostly dependent on the flow rate ratio, width ratio, and particle size. Our results provide a better understanding of how the device geometrical and operational factors affect the position of the particles in the development of high-performance on-chip sensing and sorting of both cells and particles.

Synthesis and characterization of a photocleavable collagen-like peptide.

A 34-amino acid long collagen-like peptide rich in proline, hydroxyproline, and glycine, and with four photoreactive N-acyl-7-nitroindoline units incorporated into the peptide backbone was synthesized by on-resin fragment condensation. Its circular dichroism supports a stable triple helix structure. The built-in photochemical function enables the decomposition of the peptide into small peptide fragments by illumination with UV light of 350 nm in aqueous solution. Illumination of a thin film of the peptide, or a thin film of a photoreactive amino acid model compound containing a 5-bromo-7-nitroindoline moiety, with femtosecond laser light at 710 nm allows for the creation of well-resolved micropatterns. The cytocompatibility of the peptide was demonstrated using human mesenchymal stem cells and mouse embryonic fibroblasts. Our data show that the full-length peptide is cytocompatible as it can support cell growth and maintain cell viability. In contrast, the small peptide fragments created by photolysis are somewhat cytotoxic and therefore less cytocompatible. These data suggest that biomimetic collagen-like photoreactive peptides could potentially be used for growing cells in 2D micropatterns based on patterns generated by photolysis prior to cell growth.

Dietary Lipid Type, Rather Than Total Number of Calories, Alters Outcomes of Enteric Infection in Mice.

Dietary lipids modulate immunity, yet the means by which specific fatty acids affect infectious disease susceptibility remains unclear. Deciphering lipid-induced immunity is critical to understanding the balance required for protecting against pathogens while avoiding chronic inflammatory diseases. To understand how specific lipids alter susceptibility to enteric infection, we fed mice isocaloric, high-fat diets composed of corn oil (rich in n-6 polyunsaturated fatty acids [n-6 PUFAs]), olive oil (rich in monounsaturated fatty acids), or milk fat (rich in saturated fatty acids) with or without fish oil (rich in n-3 PUFAs). After 5 weeks of dietary intervention, mice were challenged with Citrobacter rodentium, and pathological responses were assessed. Olive oil diets resulted in little colonic pathology associated with intestinal alkaline phosphatase, a mucosal defense factor that detoxifies lipopolysaccharide. In contrast, while both corn oil and milk fat diets resulted in inflammation-induced colonic damage, only milk fat induced compensatory protective responses, including short chain fatty acid production. Fish oil combined with milk fat, unlike unsaturated lipid diets, had a protective effect associated with intestinal alkaline phosphatase activity. Overall, these results reveal that dietary lipid type, independent of the total number of calories associated with the dietary lipid, influences the susceptibility to enteric damage and the benefits of fish oil during infection.

Corruption and hierarchy: a replication of studies 1c and 6 of Fath & Kay 2018.

Corruption represents a complex problem firmly embedded within our societal structures, governments, and organizations. The current study aimed to build a clearer consensus on the extent to which perceptions of organizational corruption are associated with organizational hierarchy. Two high-powered close replications of studies 1c and 6 by Fath and Kay provide further evidence for the claim that taller organizational structures are associated with greater perceived potential for corruption, and that these perceptions may compromise subsequent trust-related outcomes. Our results reinforce the importance of organizational design and aim to inspire future works to consider the ways in which researchers and organizations can minimize corruption. Preregistration, data and materials can be found on the OSF: https://osf.io/zb5j2.

Optimal growth conditions to enhance Chlorella vulgaris biomass production in indoor phyto tank and quality assessment of feed and culture stock.

Commercial microalgae cultivation is a dynamic field with ongoing efforts to improve efficiency, reduce costs, and explore new applications. We conducted a study to examine how different light exposure periods affect Chlorella vulgaris's growth. We employed a Phyto tank batch system of approximately 3.5 L with LED light control, controlled airflow, and sterilized bags, maintained at 22.0 ± 2.0 °C indoors. Various methods, including spectrophotometry, and cell counter were employed to monitor Chlorella vulgaris growth under different light exposure cycles. Additionally, quality analysis as feed source was employed by proximate, amino acid, beta-glucan, and microbial content analysis. The results revealed significant variations in C. vulgaris biomass production based on light exposure duration. Notably, the 16:8-h light-dark photoperiod exhibited the highest biomass concentration, reaching 6.48 × 107 ± 0.50 cells/mL with an optical density (OD) of 1.165 absorbance at 682 nm. The 12:12-h light-dark photoperiod produced the second-highest biomass concentration, with 2.305 × 106 ± 0.60 cells/mL at an OD of 0.489. Proximate analysis of dry algae powder revealed low lipid content (0.48 %), high protein content (37.61 %), variable ash concentration (average 10.75 %), and a significant carbohydrate fraction (51.16 %) during extended daylight and shorter dark periods. Amino acid analysis identified nine essential amino acids, with glutamic acid being the most abundant (17.7 %) and methionine the least (0.4 %). Furthermore, quality analysis and microbiological assays demonstrated that the C. vulgaris biomass is well-suited for fish and livestock use as a feed source and possibility as human nutraceuticals. These findings can be considered more environmentally friendly and ethically sound due to the absence of genetic modification.

Biological features, distribution, and conservation of the near-threatened Gangetic leaf fish Nandus nandus (Hamilton, 1822): A review.

Nandus nandus (Hamilton, 1822) is a small indigenous species (SIS) and commercially important food fish found in Bangladesh, India, Nepal, Pakistan, Myanmar, and Thailand. Concerns have arisen due to the gradual decline in its abundance, reflected by poor catches and limited availability of this species in local markets. Habitat loss and overexploitation pose significant threats to this species, leading to its classification as near threatened by the IUCN Bangladesh. Despite the absence of dedicated conservation efforts for N. nandus in Indian subcontinent, this study aims to investigate the species' biology, ecology, distribution within the country and globally, as well as its threats, conservation strategies, and research needs. Additionally, a proposed conservation framework for this near-threatened species is presented, which can be adapted for implementation in other nations facing similar challenges.

Highly selective molecularly imprinted polymer nanoparticles (MIP NPs)-based microfluidic gas sensor for tetrahydrocannabinol (THC) detection.

A highly selective microfluidic integrated metal oxide gas sensor for THC detection is reported based on MIP nanoparticles (MIP NPs). We synthesized MIP NPs with THC recognition sites and coated them on a 3D-printed microfluidic channel surface. The sensitivity and selectivity of coated microfluidic integrated gas sensors were evaluated by exposure to THC, cannabidiol (CBD), methanol, and ethanol analytes in 300-700 ppm at 300 °C. For comparison, reference signals were obtained from a microfluidic channel coated with nonimprinted polymers (NIP NPs). The MIP and NIP NPs were characterized using scanning electron microscopy (SEM) and Raman spectroscopy. MIP and NIP NPs channels response data were combined and classified with 96.3% accuracy using the Fine KNN classification model in MATLAB R2021b Classification Learner App. Compared to the MIP NPs coated channel, the NIP NPs channel had poor selectivity towards THC, demonstrating that the THC recognition sites in the MIP structure enabled selective detection of THC. The findings demonstrated that the recognition sites of MIP NPs properly captured THC molecules, enabling the selective detection of THC compared to CBD, methanol, and ethanol.

Microplastic in situ detection based on a portable triboelectric microfluidic sensor.

Microplastics (MPs) are a size-based category of plastic pollutants between 1 µm to 5 mm in particle size that are ubiquitous in land and water resources due to anthropogenic activity. Current methodologies for MPs identification and characterization require laboratory instruments and specialized training. In the present study, a miniaturized microfluidic triboelectric sensor (M-TES) is proposed for the rapid detection of MPs from water samples. The viability and versatility of this device is illustrated for in situ measurement of the size and concentration of polystyrene (PS) micro-particles in water. The M-TES sensor relies on inducing and measuring electrical charges generated by microplastic flow within water droplets passing through a microfluidic channel. The experimental samples encompass pure polystyrene (PS) microparticles ranging from 500 nm to 10 µm, as well as actual samples collected from a coffee machine. The results reveal that the sensor's response exhibits a linear correlation with the increase in both microplastic size and concentration. The proposed sensing system could distinguish between microplastics of different sizes and concentrations. The results demonstrate the applicability of the M-TES in the field of sensors for environmental monitoring.

Flexible, stretchable, and single-molecule-sensitive SERS-active sensor for wearable biosensing applications.

The development of wearable sensors for remote patient monitoring and personalized medicine has led to a revolution in biomedical technology. Plasmonic metasurfaces that enhance Raman scattering signals have recently gained attention as wearable sensors. However, finding a flexible, sensitive, and easy-to-fabricate metasurface has been a challenge for decades. In this paper, a novel wearable device, the flexible, stretchable, and single-molecule-sensetive SERS-active sensor, is proposed. This device offers an unprecedented SERS enhancement factor in the order of 1011, along with other long-desired characteristics for SERS applications such as a high scattering to absorption ratio (∼2.5) and a large hotspot volume (40 nm × 40 nm × 5 nm). To achieve flexibility, we use polydimethylsiloxane (PDMS) as the substrate, which is stable, transparent, and biologically compatible. Our numerical calculations show that the proposed sensor offers reliable SERS performance even under bending (up to 100° angles) or stretching (up to 50% stretch). The easy-to-fabricate and flexible nature of our sensor offers a promising avenue for developing highly sensitive wearable sensors for a range of applications, particularly in the field of personalized medicine and remote patient monitoring.

Microfluidic integrated gas sensors for smart analyte detection: a comprehensive review.

The utilization of gas sensors has the potential to enhance worker safety, mitigate environmental issues, and enable early diagnosis of chronic diseases. However, traditional sensors designed for such applications are often bulky, expensive, difficult to operate, and require large sample volumes. By employing microfluidic technology to miniaturize gas sensors, we can address these challenges and usher in a new era of gas sensors suitable for point-of-care and point-of-use applications. In this review paper, we systematically categorize microfluidic gas sensors according to their applications in safety, biomedical, and environmental contexts. Furthermore, we delve into the integration of various types of gas sensors, such as optical, chemical, and physical sensors, within microfluidic platforms, highlighting the resultant enhancements in performance within these domains.

Exploring the effects of adiponectin and leptin in correlating obesity with cognitive decline: a systematic review.

Obesity and cognitive decline including dementia and Alzheimer's Disease (AD) affect millions worldwide. Several studies have shown that obese individuals suffer from cognitive decline. Here, we suggest that adiponectin and leptin, protein hormones secreted by white adipose tissue explain the relationship between obesity and cognitive decline. We systematically searched PubMed and World Health Organization (WHO) websites with the keywords obesity and dementia and compiled literature that explains how adiponectin and leptin impact obesity and cognitive decline. Full-text, free-access articles on PubMed published after 2009 have been included. Whereas articles published before 2009, books, and reports were excluded. We concentrated on mechanisms via which adiponectin and leptin affect energy expenditure, fatty acid catabolism, satiety, hunger, Body Mass Index (BMI), neurogenesis, and brain structures that lead to the development of cognitive dysfunction. Moreover, we hypothesized that adiponectin and leptin hormones explain how obesity and dementia are connected. After compiling the research studies, we summarized that adiponectin and leptin negatively correlate to BMI. Adiponectin arbitrates energy expenditure and fatty acid catabolism to prevent obesity. In the presence of adiponectin, hippocampal cells proliferate, whereas neurogenesis is reduced in its absence. However, leptin prevents obesity by promoting satiety, reducing hunger, and increasing insulin sensitivity. It also has neuroprotective effects thus reducing the risk of developing cognitive decline. So, physical exercise, diet alteration, weight reduction, adiponectin, and leptin supplementation should be carried out to protect against obesity-induced cognitive decline. Therefore, further research studies should be done in this area.

The Advancement in Membrane Bioreactor (MBR) Technology toward Sustainable Industrial Wastewater Management.

The advancement in water treatment technology has revolutionized the progress of membrane bioreactor (MBR) technology in the modern era. The large space requirement, low efficiency, and high cost of the traditional activated sludge process have given the necessary space for the MBR system to come into action. The conventional activated sludge (CAS) process and tertiary filtration can be replaced by immersed and side-stream MBR. This article outlines the historical advancement of the MBR process in the treatment of industrial and municipal wastewaters. The structural features and design parameters of MBR, e.g., membrane surface properties, permeate flux, retention time, pH, alkalinity, temperature, cleaning frequency, etc., highly influence the efficiency of the MBR process. The submerged MBR can handle lower permeate flux (requires less power), whereas the side-stream MBR can handle higher permeate flux (requires more power). However, MBR has some operational issues with conventional water treatment technologies. The quality of sludge, equipment requirements, and fouling are major drawbacks of the MBR process. This review paper also deals with the approach to address these constraints. However, given the energy limitations, climatic changes, and resource depletion, conventional wastewater treatment systems face significant obstacles. When compared with CAS, MBR has better permeate quality, simpler operational management, and a reduced footprint requirement. Thus, for sustainable water treatment, MBR can be an efficient tool.

Microbial Fuel Cell Construction Features and Application for Sustainable Wastewater Treatment.

A microbial fuel cell (MFC) is a system that can generate electricity by harnessing microorganisms' metabolic activity. MFCs can be used in wastewater treatment plants since they can convert the organic matter in wastewater into electricity while also removing pollutants. The microorganisms in the anode electrode oxidize the organic matter, breaking down pollutants and generating electrons that flow through an electrical circuit to the cathode compartment. This process also generates clean water as a byproduct, which can be reused or released back into the environment. MFCs offer a more energy-efficient alternative to traditional wastewater treatment plants, as they can generate electricity from the organic matter in wastewater, offsetting the energy needs of the treatment plants. The energy requirements of conventional wastewater treatment plants can add to the overall cost of the treatment process and contribute to greenhouse gas emissions. MFCs in wastewater treatment plants can increase sustainability in wastewater treatment processes by increasing energy efficiency and reducing operational cost and greenhouse gas emissions. However, the build-up to the commercial-scale still needs a lot of study, as MFC research is still in its early stages. This study thoroughly describes the principles underlying MFCs, including their fundamental structure and types, construction materials and membrane, working mechanism, and significant process elements influencing their effectiveness in the workplace. The application of this technology in sustainable wastewater treatment, as well as the challenges involved in its widespread adoption, are discussed in this study.

Nutritional characterization and antioxidant properties of various edible portions of Cucurbita maxima: A potential source of nutraceuticals.

Pumpkin (Cucurbita maxima) is a widely grown vegetable in Bangladesh and is known as the sole supplier of various nutrients. Many studies evidence the nutritional value of flesh and seed while peel, flower, and leaves were reported scarcely with limited information. Therefore, the study aimed to investigate the nutritional composition and antioxidant properties of flesh, peel, seed, leaves, and flowers of Cucurbita maxima. The seed had a remarkable composition of nutrients and amino acids. Flowers and leaves possessed higher content of minerals, phenols, flavonoids, carotenes, and total antioxidant activity. The order of IC50 value (peel > seed > leaves > flesh > flower) indicates higher DPPH radicals scavenging activity of the flower. Moreover, a significant positive relationship was observed among these phytochemical constituents (TPC, TFC, TCC, TAA) and DPPH radicals scavenging activity. It could be concluded that these five parts of the pumpkin plant have an intense potency to be an exigent component of functional food or medicinal herbs.

Selective monitoring of natural gas sulphur-based odorant mixture of t-butyl mercaptan and methyl ethyl sulphide using an array of microfluidic gas sensors.

A natural gas (NG) odorization system requires continuous monitoring as well as an optimal injection to satisfy the odorization guidelines, minimize over-odorization, and prevent hazardous gas leaks. NG consists of hydrocarbons such as methane, odorants such as tert-butyl mercaptan, and other sulphur-based VOCs such as hydrogen sulphide; therefore, selectivity is paramount for the reliable and accurate monitoring of odorants. In this study, we developed a portable device integrated with an array of five different sensors to detect a mixture of tert-butyl mercaptan and methyl ethyl sulphide for a concentration range of 1 ppm to 10 ppm. A machine learning model was developed to predict the presence and concentration of NG odorants from the sensor data. The best-performing sensors in the array achieved high sensitivity and selectivity indicators (measured using the Davies-Bouldin index) of 0.3667 (1/ppm) and 0.125, respectively. The sensor system achieved a classification accuracy of 98.75% between NG odorants and hydrogen sulphide, with an overall Mean Squared Error (MSE) and R2 error (for the regression model) of 0.50 and 95.16%. These results indicate that the developed portable device and the machine learning model have promising applications for the selective monitoring of NG odorants.

Vulnerability for Respiratory Infections in Asthma Patients: A Systematic Review.

Asthma is a non-communicable and long-term condition affecting children and adults. The air passages in the lungs become narrow due to inflammation and tightening of the muscles around the small airways. Symptoms of asthma are intermittent and include cough, wheeze, shortness of breath, and chest tightness. Asthma is very often underdiagnosed and under-treated in many regions, especially in developing countries. While many studies show that viral infections can precipitate asthmatic attacks, very few studies have been conducted to see if history or current asthmatic attack increases the risk of viral infections. Our study aims to determine the predisposition of asthmatics to develop various viral infections and susceptibility toward certain viruses that cause upper respiratory tract infections. We performed a literature review of both published and unpublished articles. We included case reports, case series, reviews, clinical trials, cohort, and case-control studies, written only in English. Commentaries, letters to editors, and book chapters were excluded. Our initial search yielded 948 articles, of which 826 were rejected either because they were irrelevant or because they did not meet our inclusion criteria. We finally screened 122 abstracts and identified 24 relevant articles. People with a history of asthma have an abnormal innate immune response, making them potentially slower in clearing the infection and susceptible to both infections and virus-induced cell cytotoxicity. Also, in these studies, deficiencies in the interferon alpha response of peripheral blood mononuclear cells and plasmacytoid dendritic cells have been observed in asthmatics, both adults and children. Asthmatics with a viral infection usually present with an acute exacerbation of asthma, represented by dyspnea and cough, with other prodromal symptoms including vomiting and general malaise. The review includes an update on the relevance of dysregulated immune pathways in causing viral infections in asthmatic populations. It focuses on the evidence to suggest that people with asthma are at increased risk of viral infection, and viral infections in turn are known to precipitate and worsen the asthmatic status, making this a vicious cycle. The authors also suggest that further studies be undertaken to elucidate the pathophysiology and identify the critical therapeutic steps to break this vicious cycle and improve the quality of life for people with asthma.