Aerosol-Synthesized Surfactant-Free Single-Walled Carbon Nanotube-Based NO2 Sensors: Unprecedentedly High Sensitivity and Fast Recovery.

This study pioneers a chemical sensor based on surfactant-free aerosol-synthesized single-walled carbon nanotube (SWCNT) films for detecting nitrogen dioxide (NO2). Unlike conventional CNTs, the SWCNTs used in this study exhibit one of the highest surface-to-volume ratios. They show minimal bundling without the need for surfactants and have the lowest number of defects among reported CNTs. Furthermore, the dry-transferrable and facile one-step lamination results in promising industrial viability. When applied to devices, the sensor shows excellent sensitivity (41.6% at 500 ppb), rapid response/recovery time (14.2/120.8 s), a remarkably low limit of detection (below ≈0.161 ppb), minimal noise, repeatability for more than 50 cycles without fluctuation, and long-term stability for longer than 6 months. This is the best performance reported for a pure CNT-based sensor. In addition, the aerosol SWCNTs demonstrate consistent gas-sensing performance even after 5000 bending cycles, indicating their suitability for wearable applications. Based on experimental and theoretical analyses, the proposed aerosol CNTs are expected to overcome the limitations associated with conventional CNT-based sensors, thereby offering a promising avenue for various sensor applications.

Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study.

BACKGROUND: Predicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction for the entire hospital is crucial, predicting occupancy at a detailed level, such as specific wards and rooms, is more practical and useful for hospital scheduling. OBJECTIVE: The aim of this study was to develop a web-based support tool that allows hospital administrators to grasp the BOR for each ward and room according to different time periods. METHODS: We trained time-series models based on long short-term memory (LSTM) using individual bed data aggregated hourly each day to predict the BOR for each ward and room in the hospital. Ward training involved 2 models with 7- and 30-day time windows, and room training involved models with 3- and 7-day time windows for shorter-term planning. To further improve prediction performance, we added 2 models trained by concatenating dynamic data with static data representing room-specific details. RESULTS: We confirmed the results of a total of 12 models using bidirectional long short-term memory (Bi-LSTM) and LSTM, and the model based on Bi-LSTM showed better performance. The ward-level prediction model had a mean absolute error (MAE) of 0.067, mean square error (MSE) of 0.009, root mean square error (RMSE) of 0.094, and R2 score of 0.544. Among the room-level prediction models, the model that combined static data exhibited superior performance, with a MAE of 0.129, MSE of 0.050, RMSE of 0.227, and R2 score of 0.600. Model results can be displayed on an electronic dashboard for easy access via the web. CONCLUSIONS: We have proposed predictive BOR models for individual wards and rooms that demonstrate high performance. The results can be visualized through a web-based dashboard, aiding hospital administrators in bed operation planning. This contributes to resource optimization and the reduction of hospital resource use.

Are polypharmacy side effects predicted by public data still valid in real-world data?

Background and Objective: Although interest in predicting drug-drug interactions is growing, many predictions are not verified by real-world data. This study aimed to confirm whether predicted polypharmacy side effects using public data also occur in data from actual patients. Methods: We utilized a deep learning-based polypharmacy side effects prediction model to identify cefpodoxime-chlorpheniramine-lung edema combination with a high prediction score and a significant patient population. The retrospective study analyzed patients over 18 years old who were admitted to the Asan medical center between January 2000 and December 2020 and took cefpodoxime or chlorpheniramine orally. The three groups, cefpodoxime-treated, chlorpheniramine-treated, and cefpodoxime & chlorpheniramine-treated were compared using inverse probability of treatment weighting (IPTW) to balance them. Differences between the three groups were analyzed using the Kaplan-Meier method and Cox proportional hazards model. Results: The study population comprised 54,043 patients with a history of taking cefpodoxime, 203,897 patients with a history of taking chlorpheniramine, and 1,628 patients with a history of taking cefpodoxime and chlorpheniramine simultaneously. After adjustment, the 1-year cumulative incidence of lung edema in the patient group that took cefpodoxime and chlorpheniramine simultaneously was significantly higher than in the patient groups that took cefpodoxime or chlorpheniramine only (p=0.001). Patients taking cefpodoxime and chlorpheniramine together had an increased risk of lung edema compared to those taking cefpodoxime alone [hazard ratio (HR) 2.10, 95% CI 1.26-3.52, p<0.005] and those taking chlorpheniramine alone, which also increased the risk of lung edema (HR 1.64, 95% CI 0.99-2.69, p=0.05). Conclusions: Validation of polypharmacy side effect predictions with real-world data can aid patient and clinician decision-making before conducting randomized controlled trials. Simultaneous use of cefpodoxime and chlorpheniramine was associated with a higher long-term risk of lung edema compared to the use of cefpodoxime or chlorpheniramine alone.

LDP-GAN : Generative adversarial networks with local differential privacy for patient medical records synthesis.

Electronic medical records(EMR) have considerable potential to advance healthcare technologies, including medical AI. Nevertheless, due to the privacy issues associated with the sharing of patient's personal information, it is difficult to sufficiently utilize them. Generative models based on deep learning can solve this problem by creating synthetic data similar to real patient data. However, the data used for training these deep learning models run into the risk of getting leaked because of malicious attacks. This means that traditional deep learning-based generative models cannot completely solve the privacy issues. Therefore, we suggested a method to prevent the leakage of training data by protecting the model from malicious attacks using local differential privacy(LDP). Our method was evaluated in terms of utility and privacy. Experimental results demonstrated that the proposed method can generate medical data with reasonable performance while protecting training data from malicious attacks.

Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data.

Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be easily utilized by emergency department physicians. We presented machine learning models that predicted the likelihood of hospitalizations within 24 hours and estimated waiting times. Moreover, we revealed the enhanced performance of these machine learning models compared to existing models by incorporating unstructured text data. Among several evaluated models, the extreme gradient boosting model that incorporated text data yielded the best performance. This model achieved an area under the receiver operating characteristic curve score of 0.922 and an area under the precision-recall curve score of 0.687. The mean absolute error revealed a difference of approximately 3 hours. Using this model, we classified the probability of patients not being admitted within 24 hours as Low, Medium, or High and identified important variables influencing this classification through explainable artificial intelligence. The model results are readily displayed on an electronic dashboard to support the decision-making of emergency department physicians and alleviate overcrowding, thereby resulting in socioeconomic benefits for medical facilities.

Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea.

As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized warfarin dosage. Adverse drug events(ADE) resulting from warfarin overdose can be critical, so that typically physicians adjust the warfarin dosage through the INR monitoring twice a week when starting warfarin. Our study aimed to develop machine learning (ML) models that predicts the discharge dosage of warfarin as the initial warfarin dosage using clinical data derived from electronic medical records within 2 days of hospitalization. During this retrospective study, adult patients who were prescribed warfarin at Asan Medical Center (AMC) between January 1, 2018, and October 31, 2020, were recruited as a model development cohort (n = 3168). Additionally, we created an external validation dataset (n = 891) from a Medical Information Mart for Intensive Care III (MIMIC-III). Variables for a model prediction were selected based on the clinical rationale that turned out to be associated with warfarin dosage, such as bleeding. The discharge dosage of warfarin was used the study outcome, because we assumed that patients achieved target INR at discharge. In this study, four ML models that predicted the warfarin discharge dosage were developed. We evaluated the model performance using the mean absolute error (MAE) and prediction accuracy. Finally, we compared the accuracy of the predictions of our models and the predictions of physicians for 40 data point to verify a clinical relevance of the models. The MAEs obtained using the internal validation set were as follows: XGBoost, 0.9; artificial neural network, 0.9; random forest, 1.0; linear regression, 1.0; and physicians, 1.3. As a result, our models had better prediction accuracy than the physicians, who have difficulty determining the warfarin discharge dosage using clinical information obtained within 2 days of hospitalization. We not only conducted the internal validation but also external validation. In conclusion, our ML model could help physicians predict the warfarin discharge dosage as the initial warfarin dosage from Korean population. However, conducting a successfully external validation in a further work is required for the application of the models.

Heterogeneous graph construction and HinSAGE learning from electronic medical records.

Graph representation learning is a method for introducing how to effectively construct and learn patient embeddings using electronic medical records. Adapting the integration will support and advance the previous methods to predict the prognosis of patients in network models. This study aims to address the challenge of implementing a complex and highly heterogeneous dataset, including the following: (1) demonstrating how to build a multi-attributed and multi-relational graph model (2) and applying a downstream disease prediction task of a patient's prognosis using the HinSAGE algorithm. We present a bipartite graph schema and a graph database construction in detail. The first constructed graph database illustrates a query of a predictive network that provides analytical insights using a graph representation of a patient's journey. Moreover, we demonstrate an alternative bipartite model where we apply the model to the HinSAGE to perform the link prediction task for predicting the event occurrence. Consequently, the performance evaluation indicated that our heterogeneous graph model was successfully predicted as a baseline model. Overall, our graph database successfully demonstrated efficient real-time query performance and showed HinSAGE implementation to predict cardiovascular disease event outcomes on supervised link prediction learning.

Controlled Removal of Surfactants from Double-Walled Carbon Nanotubes for Stronger p-Doping Effect and Its Demonstration in Perovskite Solar Cells.

Double-walled carbon nanotubes (DWNTs) have shown potential as promising alternatives to conventional transparent electrodes owing to their solution processability as well as high conductivity and transparency. However, their DC to optical conductivity ratio is limited by the surrounding surfactants that prevent the p-doping of the DWNTs. To maximize the doping effectiveness, the surfactants are removed from the DWNTs, with negligible damage to the nanotubes, by calcination in an Ar atmosphere. The effective removal of the surfactants is characterized by various analyses, and the results show that the optimal calcination temperature is 400 °C. The conductivity of the DWNTs films improves when doped by triflic acid. While the conductivity increase of the surfactants-wrapped DWNT films is 31.9%, the conductivity increase of the surfactants-removed DWNT is found to be 59.7%. Using the surfactants-removed, p-doped, solution-processed transparent electrodes, inverted-type perovskite solar cells are fabricated, resulting in a power conversion efficiency of 17.7% without hysteresis. This work advances the application of DWNTs in transparent conductors, as the efficiency obtained is the highest value achieved to date for carbon nanotube electrode-based perovskite solar cells and solution-processable transparent electrode-based solar cells.

A Facile and Effective Ozone Exposure Method for Wettability and Energy-Level Tuning of Hole-Transporting Layers in Lead-Free Tin Perovskite Solar Cells.

Lead-free perovskite solar cells (PSCs) have attracted interest among scientists searching for eco-friendly energy harvesting devices. Herein, the effects of ozone exposure on poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) in lead-free tin halide PSCs as a facile and low-cost process for improving device performance are analyzed. Two types of tin-based PSCs and one typical lead-based PSC were fabricated. The ozone exposure on PEDOT:PSS increases the short-circuit current density (JSC) and the fill factor (FF) of PSCs in all cases with perovskite grain enlargement and hole-mobility enhancement of the devices, respectively. For open-circuit voltage (VOC), the outcome depends on the band gap and the energy levels of the perovskite films. While ozone exposure treatment is favorable for PEA0.15FA0.85SnI3-based tin PSCs, VOC decreases with ozone exposure in the case of Ge:EDA0.01FA0.98SnI3-based tin PSCs because of a misalignment of the energy levels. Regardless, the efficiency of PEA0.15FA0.85SnI3-based tin PSCs increases from 8.7 to 10.1% when measured inside a glovebox upon ozone exposure of PEDOT:PSS. The efficiency of Ge:EDA0.01FA0.98SnI3-based tin PSCs increases from 6.8 to 8.1%, and the devices retain an efficiency of 5.0% even after 50 days in air.

Multi-Walled Carbon Nanotube-Assisted Encapsulation Approach for Stable Perovskite Solar Cells.

Perovskite solar cells (PSCs) are regarded as the next-generation thin-film energy harvester, owing to their high performance. However, there is a lack of studies on their encapsulation technology, which is critical for resolving their shortcomings, such as their degradation by oxygen and moisture. It is determined that the moisture intrusion and the heat trapped within the encapsulating cover glass of PSCs influenced the operating stability of the devices. Therefore, we improved the moisture and oxygen barrier ability and heat releasing capability in the passivation of PSCs by adding multi-walled carbon nanotubes to the epoxy resin used for encapsulation. The 0.5 wt% of carbon nanotube-added resin-based encapsulated PSCs exhibited a more stable operation with a ca. 30% efficiency decrease compared to the ca. 63% decrease in the reference devices over one week under continuous operation. Specifically, the short-circuit current density and the fill factor, which are affected by moisture and oxygen-driven degradation, as well as the open-circuit voltage, which is affected by thermal damage, were higher for the multi-walled carbon nanotube-added encapsulated devices than the control devices, after the stability test.

Carbon Nanotube Mask Filters and Their Hydrophobic Barrier and Hyperthermic Antiviral Effects on SARS-CoV-2.

Carbon nanotube face mask filters have strong and uniform hydrophobicity, high durability, and high thermal conductivity and exhibit excellent barrier and antiviral effects against SARS-CoV-2. The nanocarbon filter functions as a superior barrier compared to those in conventional masks owing to the stronger, more uniform, and more durable hydrophobic nature of the carbon nanotubes. A tightly knit carbon nanotube network has a pore size smaller than that of the average coronavirus; nevertheless, the breathability is equal to that of the conventional polypropylene filter. The exceptional thermal conductivity of carbon nanotubes transpires hyperthermic antiviral effects, which offers stronger protection against the virus, as well as reusability. The facile processability, low cost, and light weight of the aerosol-synthesized carbon nanotube filter warrants its viability, reinforcing the fight against the COVID-19 pandemic.

Denatured M13 Bacteriophage-Templated Perovskite Solar Cells Exhibiting High Efficiency.

The M13 bacteriophage, a nature-inspired environmentally friendly biomaterial, is used as a perovskite crystal growth template and a grain boundary passivator in perovskite solar cells. The amino groups and carboxyl groups of amino acids on the M13 bacteriophage surface function as Lewis bases, interacting with the perovskite materials. The M13 bacteriophage-added perovskite films show a larger grain size and reduced trap-sites compared with the reference perovskite films. In addition, the existence of the M13 bacteriophage induces light scattering effect, which enhances the light absorption particularly in the long-wavelength region around 825 nm. Both the passivation effect of the M13 bacteriophage coordinating to the perovskite defect sites and the light scattering effect intensify when the M13 virus-added perovskite precursor solution is heated at 90 °C prior to the film formation. Heating the solution denatures the M13 bacteriophage by breaking their inter- and intra-molecular bondings. The denatured M13 bacteriophage-added perovskite solar cells exhibit an efficiency of 20.1% while the reference devices give an efficiency of 17.8%. The great improvement in efficiency comes from all of the three photovoltaic parameters, namely short-circuit current, open-circuit voltage, and fill factor, which correspond to the perovskite grain size, trap-site passivation, and charge transport, respectively.

Improvement of High Affinity and Selectivity on Biosensors Using Genetically Engineered Phage by Binding Isotherm Screening.

The genetically engineered M13 bacteriophage (M13 phage), developed via directed evolutionary screening process, can improve the sensitivity of sensors because of its selective binding to a target material. Herein, we propose a screening method to develop a selective and sensitive bioreporter for toxic material based on genetically engineered M13 phage. The paraquat (PQ)-binding M13 phage, developed by directed evolution, was used. The binding affinities of the PQ-binding M13 phage to PQ and similar molecules were analyzed using isothermal titration calorimetry (ITC). Based on the isotherms measured by ITC, binding affinities were calculated using the one-site binding model. The binding affinity was 5.161 × 10-7 for PQ, and 3.043 × 10-7 for diquat (DQ). The isotherm and raw ITC data show that the PQ-binding M13 phage does not selectively bind to difenzoquat (DIF). The phage biofilter experiment confirmed the ability of PQ-binding M13 bacteriophage to bind PQ. The surface-enhanced Raman scattering (SERS) platform based on the bioreporter, PQ-binding M13 phage, exhibited 3.7 times the signal intensity as compared with the wild-type-M13-phage-coated platform.

Self-Assembled Nanoporous Biofilms from Functionalized Nanofibrous M13 Bacteriophage.

Highly periodic and uniform nanostructures, based on a genetically engineered M13 bacteriophage, displayed unique properties at the nanoscale that have the potential for a variety of applications. In this work, we report a multilayer biofilm with self-assembled nanoporous surfaces involving a nanofiber-like genetically engineered 4E-type M13 bacteriophage, which was fabricated using a simple pulling method. The nanoporous surfaces were effectively formed by using the networking-like structural layers of the M13 bacteriophage during self-assembly. Therefore, an external template was not required. The actual M13 bacteriophage-based fabricated multilayered biofilm with porous nanostructures agreed well with experimental and simulation results. Pores formed in the final layer had a diameter of about 150⁻500 nm and a depth of about 15⁻30 nm. We outline a filter application for this multilayered biofilm that enables selected ions to be extracted from a sodium chloride solution. Here, we describe a simple, environmentally friendly, and inexpensive fabrication approach with large-scale production potential. The technique and the multi-layered biofilms produced may be applied to sensor, filter, plasmonics, and bio-mimetic fields.

Feasibility of using a bacteriophage-based structural color sensor for screening the geographical origins of agricultural products.

An M13 bacteriophage-based color sensor, which can change its structural color upon interaction with a gaseous molecule, was evaluated as a screening tool for the discrimination of the geographical origins of three different agricultural products (garlic, onion, and perilla). Exposure of the color sensor to sample odors induced the self-assembled M13 bacteriophage bundles to swell by the interaction of amino acid residues (repeating units of four glutamates) on the bacteriophage with the odor components, resulting in a change in the structural color of the sensor. When the sensor was exposed to the odors of garlic and onion samples, the RGB color changes were considerable because of the strong interactions of the odor components such as disulfides with the glutamate residues on the sensor. Although the patterns of the color variations were generally similar between the domestic and imported samples, some degrees of dissimilarities in their intensities were also observed. Although the magnitude of color change decreased for perilla, the color change patterns between the two groups were somewhat different. With the acquired RGB data, a support vector machine was employed to distinguish the domestic and imported samples, and the resulting accuracies in the measurements of garlic, onion, and perilla samples were 94.1, 88.7, and 91.6%, respectively. The differences in the concentrations of the odor components between both groups and/or the presence of specific components exclusively in the odor of one group allowed the color sensor-based discrimination. The demonstrated color sensor was thus shown to be a potentially versatile and simple as an on-site screening tool. Strategies able to further improve the sensor performance were also discussed.

Altered TNF-α response by Aconibal® and methotrexate in a lipopolysaccharide-induced setting of inflammatory conditions: Potential on a synergistic combination.

ETHNOPHARMACOLOGICAL RELEVANCE: Aconitum carmichaelii (AC) is a common herbal medicine used as anti-inflammatory and analgesic agent in Eastern Asia. In Korea, a commercial processed AC (Aconibal®) is traditionally used to treat the symptoms of spondylosis deformans and rheumatic pain. AIM OF STUDY: Rheumatoid arthritis (RA) is systemic and autoimmune disease characterized by chronic inflammation. Methotrexate (MTX) is often the first-line therapy for RA. If MTX monotherapy is ineffective or RA is initially severe, adding a tumor necrosis factor alpha (TNF-α) inhibitor to the treatment can be beneficial. However, its inhibitory effects on RA when combined with MTX are unknown. Therefore, we investigated the stable modulation of and synergistic to additive effect on TNF-α using AC combined with MTX (AMC). MATERIALS AND METHODS: An inflammatory response mimicking RA was induced in the mouse macrophage cell line Raw 264.7 using interferon-γ or lipopolysaccharide (LPS). We predicted that AC and MTX at a 3:1 ratio would have synergistic therapeutic effects and this was determined using the Chou-Talalay method of median effect analysis and CalcuSyn software. We analyzed the profiles of various inflammatory cytokine-related proteins using Search tool for retrieval of interacting genes and Kyoto Encyclopedia of Genes and Genomes. RESULTS: The expression levels of selected inflammatory immune mediators such as interleukin (IL)-6, IL-1α, chemokine ligand 5, granulocyte-colony stimulating factor, nitric oxide synthase, and cyclooxygenase were reduced via regulation of the mitogen-activated protein kinase signaling pathway. AMC inhibited the levels of matrix metalloproteinases-1 and -3 in the human synovial cell line SW982. CONCLUSIONS: Our data show for the first time the potential beneficial effects of AMC in RA management.

Bioinspired M-13 bacteriophage-based photonic nose for differential cell recognition.

A bioinspired M-13 bacteriophage-based photonic nose was developed for differential cell recognition. The M-13 bacteriophage-based photonic nose exhibits characteristic color patterns when phage bundle nanostructures, which were genetically modified to selectively capture vapor phase molecules, are structurally deformed. We characterized the color patterns of the phage bundle nanostructure in response to cell proliferation via several biomarkers differentially produced by cells, including hydrazine, o-xylene, ethylbenzene, ethanol and toluene. A specific color enables the successful identification of different types of molecular and cellular species. Our sensing technique utilized the versatile M-13 bacteriophage as a building block for fabricating bioinspired photonic crystals, which enables ease of fabrication and tunable selectivity through genetic engineering. Our simple and versatile bioinspired photonic nose could have possible applications in sensors for human health and national security, food discrimination, environmental monitoring, and portable and wearable sensors.

Correction: Bioinspired M-13 bacteriophage-based photonic nose for differential cell recognition.

[This corrects the article DOI: 10.1039/C6SC02021F.].

Roles of endoplasmic reticulum stress-mediated apoptosis in M1-polarized macrophages during mycobacterial infections.

Alteration of macrophage function has an important regulatory impact on the survival of intracellular mycobacteria. We found that macrophages infected with attenuated Mycobacterium tuberculosis (Mtb) strain H37Ra had elevated expression of M1-related molecules, whereas the M2 phenotype was dominant in macrophages infected with virulent Mtb H37Rv. Further, the TLR signalling pathway played an important role in modulating macrophage polarization against Mtb infection. Interestingly, endoplasmic reticulum (ER) stress was significantly increased in M1 polarized macrophages and these macrophages effectively removed intracellular Mtb, indicating that ER stress may be an important component of the host immune response to Mtb in M1 macrophages. This improved understanding of the mechanisms that regulate macrophage polarization could provide new therapeutic strategies for tuberculosis.