Immobilized Candida antarctica lipase B as an sn-1,3 regiospecific biocatalyst for the interesterification of triacylglycerols with fatty acid ethyl esters.

Candida antarctica lipase B (CALB) is regarded as non-regiospecific. This study aimed to investigate the regiospecificity of CALB in the solvent-free interesterification of high-oleic sunflower oil with stearic acid ethyl ester for 1,3-distearoyl-2-oleoylglycerol (SOS)-rich fat preparation using a packed bed reactor. The content ratio of 1,2-distearoyl-3-oleoylglycerol (SSO) to SOS (denoted by SSO/SOS content) obtained using Lipozyme 435 (a commercially immobilized CALB; 0-4.1%), at residence times (1-32 min) was similar to that obtained using Lipozyme RM IM (0-3.0%), but lower than that obtained using Lipozyme TL IM (6.0-39.4%). When immobilized on Lewatit VP OC 1600, Lipozyme CALB had an SSO/SOS content of 0-10.4%, which was greater than that of Palatase 20,000 L (0-1.1%) but was lower than that of Lipozyme TL 100 L (8.8-97.7%). Our findings suggest that immobilized CALB shows distinct sn-1,3 regiospecificity in the interesterification of triacylglycerol with fatty acid ethyl esters.

7,10-Dihydroxy-8(E)-octadecenoic Acid Displays a Fungicidal Activity against Malassezia furfur.

Microbial conversion of some natural unsaturated fatty acids can produce polyhydroxy fatty acids, giving them new properties, such as higher viscosity and reactivity. Pseudomonas aeruginosa has been intensively studied to produce a novel 7,10-dihydroxy-8(E)-octadecenoic acid (DOD) from oleic acid and natural vegetable oils containing oleic acid. Recently, the antibacterial activities of DOD against food-borne pathogenic bacteria were reported; however, the action of such antibacterial properties against eucaryotic cells remains poorly known. In this study, we determined the antifungal activities of DOD against Malassezia furfur KCCM 12679 quantitatively and qualitatively. The antifungal activity of DOD against M. furfur KCCM 12679 was approximately five times higher than that of ketoconazole, a commercial antifungal agent. The MIC 90 value of DOD against M. furfur KCCM 12679 was 50 µg/mL. In addition, we confirmed that the antifungal property of DOD was exerted through fungicidal activity.

Accuracy and clinical validity of automated cephalometric analysis using convolutional neural networks.

BACKGROUND: This study aimed to assess the error range of cephalometric measurements based on the landmarks detected using cascaded CNNs and determine how horizontal and vertical positional errors of individual landmarks affect lateral cephalometric measurements. METHODS: In total, 120 lateral cephalograms were obtained consecutively from patients (mean age, 32.5 ± 11.6) who visited the Asan Medical Center, Seoul, Korea, for orthodontic treatment between 2019 and 2021. An automated lateral cephalometric analysis model previously developed from a nationwide multi-centre database was used to digitize the lateral cephalograms. The horizontal and vertical landmark position error attributable to the AI model was defined as the distance between the landmark identified by the human and that identified by the AI model on the x- and y-axes. The differences between the cephalometric measurements based on the landmarks identified by the AI model vs those identified by the human examiner were assessed. The association between the lateral cephalometric measurements and the positioning errors in the landmarks comprising the cephalometric measurement was assessed. RESULTS: The mean difference in the angular and linear measurements based on AI vs human landmark localization was .99 ± 1.05°, and .80 ± .82 mm, respectively. Significant differences between the measurements derived from AI-based and human localization were observed for all cephalometric variables except SNA, pog-Nperp, facial angle, SN-GoGn, FMA, Bjork sum, U1-SN, U1-FH, IMPA, L1-NB (angular) and interincisal angle. CONCLUSIONS: The errors in landmark positions, especially those that define reference planes, may significantly affect cephalometric measurements. The possibility of errors generated by automated lateral cephalometric analysis systems should be considered when using such systems for orthodontic diagnoses.

The prediction of sagittal chin point relapse following two-jaw surgery using machine learning.

The study aimed to identify critical factors associated with the surgical stability of pogonion (Pog) by applying machine learning (ML) to predict relapse following two-jaw orthognathic surgery (2 J-OGJ). The sample set comprised 227 patients (110 males and 117 females, 207 training and 20 test sets). Using lateral cephalograms taken at the initial evaluation (T0), pretreatment (T1), after (T2) 2 J-OGS, and post treatment (T3), 55 linear and angular skeletal and dental surgical movements (T2-T1) were measured. Six ML modes were utilized, including classification and regression trees (CART), conditional inference tree (CTREE), and random forest (RF). The training samples were classified into three groups; highly significant (HS) (≥ 4), significant (S) (≥ 2 and < 4), and insignificant (N), depending on Pog relapse. RF indicated that the most important variable that affected relapse rank prediction was ramus inclination (RI), CTREE and CART revealed that a clockwise rotation of more than 3.7 and 1.8 degrees of RI was a risk factor for HS and S groups, respectively. RF, CTREE, and CART were practical tools for predicting surgical stability. More than 1.8 degrees of CW rotation of the ramus during surgery would lead to significant Pog relapse.

Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram.

The lateral cephalogram in orthodontics is a valuable screening tool on undetected obstructive sleep apnea (OSA), which can lead to consequences of severe systematic disease. We hypothesized that a deep learning-based classifier might be able to differentiate OSA as anatomical features in lateral cephalogram. Moreover, since the imaging devices used by each hospital could be different, there is a need to overcome modality difference of radiography. Therefore, we proposed a deep learning model with knowledge distillation to classify patients into OSA and non-OSA groups using the lateral cephalogram and to overcome modality differences simultaneously. Lateral cephalograms of 500 OSA patients and 498 non-OSA patients from two different devices were included. ResNet-50 and ResNet-50 with a feature-based knowledge distillation models were trained and their performances of classification were compared. Through the knowledge distillation, area under receiver operating characteristic curve analysis and gradient-weighted class activation mapping of knowledge distillation model exhibits high performance without being deceived by features caused by modality differences. By checking the probability values predicting OSA, an improvement in overcoming the modality differences was observed, which could be applied in the actual clinical situation.

Orthognathic surgical planning using graph CNN with dual embedding module: External validations with multi-hospital datasets.

BACKGROUND AND OBJECTIVE: Despite recent development of AI, prediction of the surgical movement in the maxilla and mandible by OGS might be more difficult than that of tooth movement by orthodontic treatment. To evaluate the prediction accuracy of the surgical movement using pairs of pre-(T0) and post-surgical (T1) lateral cephalograms (lat-ceph) of orthognathic surgery (OGS) patients and dual embedding module-graph convolution neural network (DEM-GCNN) model. METHODS: 599 pairs from 3 institutions were used as training, internal validation, and internal test sets and 201 pairs from other 6 institutions were used as external test set. DEM-GCNN model (IEM, learning the lat-ceph images; LTEM, learning the landmarks) was developed to predict the amount and direction of surgical movement of ANS and PNS in the maxilla and B-point and Md1crown in the mandible. The distance between T1 landmark coordinates actually moved by OGS (ground truth) and predicted by DEM-GCNN model and pre-existed CNN-based Model-C (learning the lat-ceph images) was compared. RESULTS: In both internal and external tests, DEM-GCNN did not exhibit significant difference from ground truth in all landmarks (ANS, PNS, B-point, Md1crown, all P > 0.05). When the accumulated successful detection rate for each landmark was compared, DEM-GCNN showed higher values than Model-C in both the internal and external tests. In violin plots exhibiting the error distribution of the prediction results, both internal and external tests showed that DEM-GCNN had significant performance improvement in PNS, ANS, B-point, Md1crown than Model-C. DEM-GCNN showed significantly lower prediction error values than Model-C (one-jaw surgery, B-point, Md1crown, all P < 0.005; two-jaw surgery, PNS, ANS, all P < 0.05; B point, Md1crown, all P < 0.005). CONCLUSION: We developed a robust OGS planning model with maximized generalizability despite diverse qualities of lat-cephs from 9 institutions.

Immune-Stimulating Potential of Lacticaseibacillus rhamnosus LM1019 in RAW 264.7 Cells and Immunosuppressed Mice Induced by Cyclophosphamide.

Probiotics, including Lacticaseibacillus rhamnosus (L. rhamnosus), have gained recognition for their potential health benefits, such as enhancing immune function, maintaining gut health, and improving nutrient absorption. This study investigated the effectiveness of L. rhamnosus LM1019 (LM1019) in enhancing immune function. In RAW 264.7 cells, LM1019 demonstrated dose-dependent immune stimulation by increasing nitric oxide production, gene expression of proinflammatory cytokines, and the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2). These effects were mediated through the activation of mitogen-activated protein kinases (MAPKs) and nuclear factor-kappa B (NF-κB) translocation without inducing cytotoxicity. Furthermore, orally administered LM1019 was evaluated in immunosuppressed mice induced by cyclophosphamide (CTX). High-dose administration of LM1019 significantly increased the subpopulations of lymphocytes, specifically helper T cells (CD4+), as well as two subtypes of natural killer (NK) cells, namely, IFN-γ+ and granzyme B+ NK cells. Additionally, LM1019 at a high dose led to elevated levels of proinflammatory cytokines, including IFN-γ and IL-12, compared to CTX-treated mice. These findings highlight the potential of LM1019 in enhancing the immune system. The study contributes to the growing body of research on the beneficial effects of probiotics on immune function.

Lipase-catalyzed preparation of deacidified Buah Merah (Pandanus conoideus) oil.

Buah Merah oil (BMO) is unrefined edible oil containing a high level of free fatty acids (FFA; ∼30% w/w). This study was aimed at preparing deacidified BMO from BMO via lipase-catalyzed esterification of FFA in BMO with added glycerol, using Duolite A568-immobilized Eversa Transform 2.0 (Thermomyces lanuginosus lipase) as biocatalyst. BMO containing 2.4% w/w FFA and 94.6% w/w triacylglycerol was obtained under optimal reaction conditions (temperature, 70°C; FFA-to-glycerol molar ratio, 3:1; enzyme loading based on the protein quantity, 3.75 mg/g BMO, and reaction time, 48 h). No significant difference was found in the contents of ß-carotene, tocopherols, and phytosterols between raw and deacidified BMO. The induction period of oxidation was significantly longer in deacidified BMO (16.37 h) than in raw BMO (0.03 h). These results suggest that deacidified BMO could be enzymatically prepared without the loss of health-beneficial minor components while enhancing the oxidative stability. PRACTICAL APPLICATION: Although BMO has recently received much attention for its potential biological activities, the commercial use of BMO as a healthy oil has been limited due to its high FFA content. Unlike conventional alkali and steam refining, enzymatic deacidification of BMO employed in this study might help the commercialization of BMO, because this procedure enables the improvement of oil yield and the retaining of health-beneficial minor components.

Green Tea and Java Pepper Mixture Prevents Obesity by Increasing Energy Expenditure and Modulating Hepatic AMPK/MicroRNA-34a/370 Pathway in High-Fat Diet-Fed Rats.

This study was performed to evaluate the anti-obesity effects of green tea and java pepper mixture (GJ) on energy expenditure and understand the regulatory mechanisms of AMP-activated protein kinase (AMPK), microRNA (miR)-34a, and miR-370 pathways in the liver. Sprague-Dawley rats were divided into four groups depending on the following diets given for 14 weeks: normal chow diet (NR), 45% high-fat diet (HF), HF + 0.1% GJ (GJL), and HF + 0.2% GJ (GJH). The results revealed that GJ supplementation reduced body weight and hepatic fat accumulation, improved serum lipids, and increased energy expenditure. In the GJ-supplemented groups, the mRNA levels of genes related to fatty acid syntheses, such as a cluster of differentiation 36 (CD36), sterol regulatory element binding protein-1c (SREBP-1c), fatty acid synthase (FAS), and stearoyl-CoA desaturase 1 (SCD1) were downregulated, and mRNA levels of peroxisome proliferator-activated receptor alpha (PPARα), carnitine/palmitoyl-transferase 1 (CPT1), and uncoupling protein 2 (UCP2), which participate in fatty acid oxidation, were upregulated in the liver. GJ increased the AMPK activity and decreased the miR-34a and miR-370 expression. Therefore, GJ prevented obesity by increasing energy expenditure and regulating hepatic fatty acid synthesis and oxidation, suggesting that GJ is partially regulated through AMPK, miR-34a, and miR-370 pathways in the liver.

Long-Term Influenza Outbreak Forecast Using Time-Precedence Correlation of Web Data.

Influenza leads to many deaths every year and is a threat to human health. For effective prevention, traditional national-scale statistical surveillance systems have been developed, and numerous studies have been conducted to predict influenza outbreaks using web data. Most studies have captured the short-term signs of influenza outbreaks, such as one-week prediction using the characteristics of web data uploaded in real time; however, long-term predictions of more than 2-10 weeks are required to effectively cope with influenza outbreaks. In this study, we determined that web data uploaded in real time have a time-precedence relationship with influenza outbreaks. For example, a few weeks before an influenza pandemic, the word "colds" appears frequently in web data. The web data after the appearance of the word "colds" can be used as information for forecasting future influenza outbreaks, which can improve long-term influenza prediction accuracy. In this study, we propose a novel long-term influenza outbreak forecast model utilizing the time precedence between the emergence of web data and an influenza outbreak. Based on the proposed model, we conducted experiments on: 1) selecting suitable web data for long-term influenza prediction; 2) determining whether the proposed model is regionally dependent; and 3) evaluating the accuracy according to the prediction timeframe. The proposed model showed a correlation of 0.87 in the long-term prediction of ten weeks while significantly outperforming other state-of-the-art methods.

Changes in Volatile Compounds in Short-Term High CO2-Treated 'Seolhyang' Strawberry (Fragaria × ananassa) Fruit during Cold Storage.

'Seolhyang' strawberry is harvested before it is fully ripened and treated with CO2 to extend the shelf-life. However, the volatile changes in the 'Seolhyang' strawberry after short-term CO2 treatment have not been investigated, although the volatile profile is an important quality attribute. Herein, we investigated the effect of short-term high CO2 treatment on the changes in the composition of volatile compounds in 'Seolhyang' strawberries at two ripening stages (i.e., half-red and bright-red) during cold storage using headspace solid-phase microextraction and gas chromatography-mass spectrometry. Furthermore, the effect of CO2 treatment on fruit quality with respect to the aroma was investigated. A total of 30 volatile compounds were identified. Storage increased the volatile compound concentrations, and the total concentration of volatiles in the CO2-treated strawberries was lower than that of the untreated strawberries during storage. The production of some characteristic strawberry volatiles (e.g., 4-methoxy-2,5-dimethyl-3(2H)-furanone) was inhibited in CO2-treated strawberries. However, CO2 treatment helped maintain the concentrations of hexanal and 2-hexenal, which are responsible for the fresh odor in strawberries. Interestingly, CO2 treatment suppressed the production of off-odor volatiles, acetaldehyde, and hexanoic acid during strawberry storage. Thus, short-term CO2 treatment may help maintain the fresh aroma of strawberries during cold storage.

Efficient Synthesis of Stearidonic Acid Enriched Triacylglycerol from Ahiflower Seed Oil via a Two-Step Enzyme Reaction.

Stearidonic acid (SDA) is a plant-based n-3 polyunsaturated fatty acid with multiple biological activities. The enrichment of SDA and synthesis of triacylglycerol (TAG) were carried out consecutively via two lipase-catalyzed reactions, hydrolysis, and esterification. First, SDA was enriched into a glyceride fraction from ahiflower seed oil by Candida rugosa lipase-catalyzed hydrolysis. Under the optimum conditions of 35°C, 0.1% lipase powder of Lipase OF, and 50% buffer solution (based on the weight of total substrate), SDA was enriched from 21.6 to 40.7 wt% in glyceride fraction. SDA-enriched TAG was then synthesized from the SDA-enriched glyceride and the SDA-enriched fatty acid via esterification using an in-house immobilized lipase as a biocatalyst. The SDA-enriched fatty acid was obtained from part of the SDA-enriched glyceride by saponification and the in-house immobilized lipase was prepared from Eversa® Transform 2.0 using Lewatit VP OC 1600 as a carrier. The optimum reaction conditions for the synthesis of TAG were a temperature of 50°C, an enzyme loading of 10%, and a vacuum of 10 mmHg. A maximum conversion to TAG of ca. 94% was achieved after 12 h under the optimum conditions.

Accuracy of artificial intelligence-assisted landmark identification in serial lateral cephalograms of Class III patients who underwent orthodontic treatment and two-jaw orthognathic surgery.

Objective: To investigate the pattern of accuracy change in artificial intelligence-assisted landmark identification (LI) using a convolutional neural network (CNN) algorithm in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent two-jaw orthognathic surgery. Methods: A total of 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n = 23 per group) for LI. Each C-III patient in the test set had four Lat-cephs: initial (T0), pre-surgery (T1, presence of orthodontic brackets [OBs]), post-surgery (T2, presence of OBs and surgical plates and screws [S-PS]), and debonding (T3, presence of S-PS and fixed retainers [FR]). After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed. Results: The total mean error was 1.17 mm without significant difference among the four time-points (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points ([T0, T1] vs. [T2, T3]), ANS, A point, and B point showed an increase in error (p < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showeda decrease in error (all p < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups. Conclusions: The CNN model can be used for LI in serial Lat-cephs despite the presence of OB, S-PS, FR, genioplasty, and bone remodeling.

Enzymatic structural modification of monogalactosyldiacylglycerols for potential modulation of hydrophile-lipophile balance.

This study aimed to enzymatically prepare structured monogalactosyldiacylglycerols (MGDGs) with different hydrophile-lipophile balance (HLB) values for use as emulsifiers. Acidolysis of Perilla frutescens-derived MGDGs with capric acid (10:0) was conducted to obtain structured MGDGs containing 10:0. Lewatit VP OC 1600-immobilized Rhizomucor miehei lipase was used as the biocatalyst. Structured MGDGs (HLB value = 2.95-7.17) containing 13.0-70.6 mol% 10:0 were obtained from P. frutescens MGDGs (HLB value = 1.93). A quadratic regression equation (R2 = 0.920) to predict the 10:0 content of the structured MGDGs under the given conditions was established using response surface methodology. Using a linear regression equation (R2 = 0.999) to predict the HLB value by 10:0 content, structured MGDGs containing 27.1-54.6 mol% 10:0 were predicted to have an HLB value of 4-6, indicating their potential applicability as hydrophobic emulsifiers. Structured MGDGs with a purity of âˆ¼ 43% w/w were obtained from the reaction products using silica column chromatography.

Accuracy of one-step automated orthodontic diagnosis model using a convolutional neural network and lateral cephalogram images with different qualities obtained from nationwide multi-hospitals.

OBJECTIVE: The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals. METHODS: Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradientweighted class activation mapping (Grad-CAM). RESULTS: In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis. CONCLUSIONS: Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.

Accuracy of auto-identification of the posteroanterior cephalometric landmarks using cascade convolution neural network algorithm and cephalometric images of different quality from nationwide multiple centers.

INTRODUCTION: The purpose of this study was to evaluate the accuracy of auto-identification of the posteroanterior (PA) cephalometric landmarks using the cascade convolution neural network (CNN) algorithm and PA cephalogram images of a different quality from nationwide multiple centers nationwide. METHODS: Of the 2798 PA cephalograms from 9 university hospitals, 2418 images (2075 training set and 343 validation set) were used to train the CNN algorithm for auto-identification of 16 PA cephalometric landmarks. Subsequently, 99 pretreatment images from the remaining 380 test set images were used to evaluate the accuracy of auto-identification of the CNN algorithm by comparing with the identification by a human examiner (gold standard) using V-Ceph 8.0 (Ostem, Seoul, South Korea). Pretreatment images were used to eliminate the effects of orthodontic bracket, tube and wire, surgical plate, and surgical screws. Paired t test was performed to compare the x- and y-coordinates of each landmark. The point-to-point error and the successful detection rate (range, within 2.0 mm) were calculated. RESULTS: The number of landmarks without a significant difference between the location identified by the human examiner and by auto-identification by the CNN algorithm were 8 on the x-coordinate and 5 on the y-coordinate, respectively. The mean point-to-point error was 1.52 mm. The low point-to-point error (<1.0 mm) was observed at the left and right antegonion (0.96 mm and 0.99 mm, respectively) and the high point-to-point error (>2.0 mm) was observed at the maxillary right first molar root apex (2.18 mm). The mean successful detection rate of auto-identification was 83.3%. CONCLUSIONS: Cascade CNN algorithm for auto-identification of PA cephalometric landmarks showed a possibility of an effective alternative to manual identification.

Tissue-specific distribution of primary and secondary metabolites of Baemoochae (×Brassicoraphanus) and its changes as a function of developmental stages.

The distribution and changes in the primary and secondary metabolite profiles of Baemoochae, an inter-generic hybrid of Chinese cabbage and radish, during the plant's developmental stages were investigated. Metabolites were analyzed using gas chromatography-mass spectrometry (GC-MS) and ultra-high-performance liquid chromatography-electrospray ionization-quadrupole time-of-flight (UHPLC-ESI-qTOF MS). Free sugar, organic acid, and amino acid composition depended on the tissue type and developmental stage of Baemoochae. For example, glucose and alanine levels were higher in mature leaves than in young leaves; citric acid content in mature roots was lower than that in young roots. Several glucosinolates were identified for the first time in Baemoochae. Glucoraphasatin was predominant in both leaves and roots, regardless of plant maturity. Total glucosinolate content was significantly higher in roots than in leaves and in mature than in young plants. The roots of mature Baemoochae could be used as a rich source of glucosinolates, with several potential health-promoting effects.

Ethyl carbamate in retail market condiments and risk assessment of its dietary exposure for the Korean population.

Ethyl carbamate (EC), a potential human dietary carcinogen, is found in fermented foods including the fermented soybean-based condiments, the major part of the Korean diet. Therefore, it is expected that their EC contents might pose health risks. Herein, we collected 111 condiments and estimated their EC contents via gas chromatography-mass spectrometry. Further, dietary intake of EC was evaluated, and the risk levels were assessed via the margin of exposure (MOE) approach and excess cancer risk assessment. EC contents of the condiments ranged from not detectable to 39.47 µg/kg, and the daily EC exposure ranged from 1.4 to 2.0 ng/kg BW per day, depending on gender and age groups in Korea. Of the condiments, soy sauce was the largest contributor to EC exposure. MOE and excess cancer risks for the average consumer were 166,300 and 9.0 × 10-8, respectively, and those for the consumers in the 95th percentiles (P95) were 53,504 and 2.8 × 10-7, respectively, indicating that the risk of exposure to EC is of lower concern in average consumers than heavy consumers. However, the EC exposure from condiments was higher than that in other Asian countries.Abbreviations: EC: ethyl carbamate; GC-MS: gas chromatography-mass spectrometry; MOE: margin of exposure; MRL: maximum residue level; IDL: instrumental detection level; IQL: instrumental quantification level; MDL: method detection level; MQL: method quantification level; EDI: estimated daily intakes; BMDL10: benchmark dose lower confidence limit.

Inkjet Printed Textile Force Sensitive Resistors for Wearable and Healthcare Devices.

Pressure sensors for wearable healthcare devices, particularly force sensitive resistors (FSRs) are widely used to monitor physiological signals and human motions. However, current FSRs are not suitable for integration into wearable platforms. This work presents a novel technique for developing textile FSRs (TFSRs) using a combination of inkjet printing of metal-organic decomposition silver inks and heat pressing for facile integration into textiles. The insulating void by a thermoplastic polyurethane (TPU) membrane between the top and bottom textile electrodes creates an architectured piezoresistive structure. The structure functions as a simple logic switch where under a threshold pressure the electrodes make contact to create conductive paths (on-state) and without pressure return to the prior insulated condition (off-state). The TFSR can be controlled by arranging the number of layers and hole diameters of the TPU spacer to specify a wide range of activation pressures from 4.9 kPa to 7.1 MPa. For a use-case scenario in wearable healthcare technologies, the TFSR connected with a readout circuit and a mobile app shows highly stable signal acquisition from finger movement. According to the on/off state of the TFSR with LED bulbs by different weights, it can be utilized as a textile switch showing tactile feedback.