Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study.

BACKGROUND: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. METHODS: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO2/FIO2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine). The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley's additive explanations (SHAP). RESULTS: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756-0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626-0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. CONCLUSION: Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.

Investigating the different transformations of tetracycline using birnessite under different reaction conditions and various humic acids.

Prior studies have successfully used manganese oxides to facilitate the transformation of tetracycline in aqueous solution. To further understand the kinetic and the transformation pathway of tetracycline via birnessite (δ-MnO2) under different conditions, experiments were conducted at pH levels of 3, 6, and 9 in the presence or absence of Aldrich humic acid (ADHA). Tetracycline removal followed the pseudo-second-order reaction model in all investigated cases, and the removal efficiency (g mg-1 h -1) followed the following trend: pH 3 (0.45/0.27) > pH 6 (0.036/0.087) > pH 9 (0.036/0.103) in the absence/presence of ADHA. Liquid chromatography-mass spectrometry/mass spectrometry results identified five main transformation products at m/z 495, 477, 493, 459, and 415, produced by the transformation reactions, including hydration, oxidation, desaturation, and oxy reduction. Notably, in the presence of ADHA at pH 3, products with higher toxicity secondary (m/z 477 and 495) were reduced, while less toxicity products (m/z 459 and 415) were enhanced. The experiments utilizing tetracycline and δ-MnO2 with varied humic acids (HA) revealed that HA with high polar organic carbon groups, such as O-alkyl, exhibited higher removal efficiency at pH 6. This research offers the first comprehensive insights into the pathway transformations of tetracycline via δ-MnO2 under different pH conditions and HA types. For further understanding, future work should investigate the binding of HA, TTC, and/or Mn2+ and the oxidation capacity of MnO2 after the reaction to clarify Mn2+ elution mechanisms.

Combination of UHPLC-MS/MS with context-specific network and cheminformatic approaches for identifying bioactivities and active components of propolis.

Discovering new bioactivities and identifying active compounds of food materials are major fields of study in food science. However, the process commonly requires extensive experiments and can be technically challenging. In the current study, we employed network biology and cheminformatic approaches to predict new target diseases, active components, and related molecular mechanisms of propolis. Applying UHPLC-MS/MS analysis results of propolis to Context-Oriented Directed Associations (CODA) and Combination-Oriented Natural Product Database with Unified Terminology (COCONUT) systems indicated atopic dermatitis as a novel target disease. Experimental validation using cell- and human tissue-based models confirmed the therapeutic potential of propolis against atopic dermatitis. Moreover, we were able to find the major contributing compounds as well as their combinatorial effects responsible for the bioactivity of propolis. The CODA/COCONUT system also provided compound-associated genes explaining the underlying molecular mechanism of propolis. These results highlight the potential use of big data-driven network biological approaches to aid in analyzing the impact of food constituents at a systematic level.

The Impact of Union Membership on Nursing Turnover and Job Satisfaction.

OBJECTIVE: The present study examines the scope of nursing unions and the relationship with turnover and job satisfaction among RNs. BACKGROUND: There is no recent empirical literature documenting evidence on workplace performance measures (turnover and job satisfaction) among unionized nurses at the national level. METHODS: This cross-sectional study analyzed secondary data (n = 43 960), the 2018 National Sample Survey of Registered Nurses. RESULTS: Approximately 16% of the sample reported being represented by labor unions. The overall nursing turnover rate for the sample was 12.8%. Unionized nurses were less likely than their counterparts to report turnover (mean, 10.9% vs 13.16%; P = 0.02) and less job satisfaction (mean, 3.20 vs 3.28). Union nurses were more likely than nonunion nurses to be male (12.72% vs 9.46%; P = 0.0004), minorities (37.65% vs 25.67%, P < 0.001), be employed in hospital settings (70.1% vs 57.9%, P = 0.001), but report fewer hours of work per week (mean, 36.73 vs 37.66; P = 0.003). Regression results revealed a positive association between union status and nursing turnover (odds ratio, 0.83; P < 0.05), whereas union status was inversely associated with job satisfaction (B = -0.13, P < 0.001) after adjusting for age, gender, race/ethnicity, care coordination time per week, weekly hours of work, and employment settings. CONCLUSION: Overall, job satisfaction was high among all nurses, regardless of union membership. However, when comparing specifically union and nonunion members, unionized nurses were less likely to report turnover, but were more likely to experience job dissatisfaction.

Molecularly Imprinted Chemiresistive Sensor for Specific Recognition of Furaneol as a Biomarker of Strawberry Flavor Conditions.

This work introduces the concept of a molecularly imprinted gas sensor to monitor the condition of naturally ripened strawberries. Furaneol, 2,5-dimethyl-4-hydroxy-3(2H)-furanone, is considered as an important biomarker related to the strawberry flavor. Identification of furaneol concentration is still a challenge because of its weak adsorption, nonpolar, and unreactive properties. Therefore, no study has been reported yet to measure furaneol gases via a simple chemiresistive mechanism. Herein, we demonstrate the sensor based on molecularly imprinted polymer (MIP)-based polyaniline (PANI). The sensitive and selective detection of furaneol gas with a MIP-PANI gas sensor was observed at room temperature and under different humidity conditions. The comparison between MIP and the nonimprinted (NIP)-based PANI shows a strong interaction between furaneol and the molecularly imprinted polymer. The furaneol gas sensing mechanism is explained based on the interaction between the gas molecules and the charge carriers of MIP-PANI, which results in the functional group change in the carboxylic group. Furthermore, the developed MIP-chemiresistive sensor for real strawberries was compared with a commercial e-nose system. The results show the potential to offer a rapid and cost-effective platform for specific recognition of furaneol.

Simultaneous determination of L-tryptophan impurities in meat products.

L-tryptophan has been used as a feed additive for swine and poultry and as a nutrient supplement for humans. However, some impurities in L-tryptophan have been reported as causative components of eosinophilia-myalgia syndrome. Therefore, from a safety perspective, it is important to analyze meat samples for these impurities. This study aims to develop an analytical method for the simultaneous detection of L-tryptophan impurities in meat products using LC-MS/MS. Among the various impurities, detection methods for (S)-2-amino-3-(5-hydroxy-1H-indol-3-yl)propanoic acid (5-hydroxytryptophan) (HTP), 1-methyl-1,2,3,4-tetrahydro-ß-carboline-3-carboxylic acid (MTCA), 3a-hydroxy-1,2,3,3a,8,8a-hexahydropyrrolo-[2,3-b]-indole-2-carboxylic acid (PIC), and 1,1'-ethylidenebistryptophan (EBT) and 2-(3-indoylmethyl)-L-tryptophan (IMT) were developed. The developed method allowed simultaneous determination of these four impurities in 5 min. No interferences from the matrix were observed, and the method showed good sensitivity to each analyte. The method detection limit and limit of quantification in meat matrices were below 11.2 and 35.7 µg/kg, respectively.

Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning.

BACKGROUND: Accurate and rapid measurement of the MRI volume of meningiomas is essential in clinical practice to determine the growth rate of the tumor. Imperfect automation and disappointing performance for small meningiomas of previous automated volumetric tools limit their use in routine clinical practice. PURPOSE: To develop and validate a computational model for fully automated meningioma segmentation and volume measurement on contrast-enhanced MRI scans using deep learning. STUDY TYPE: Retrospective. POPULATION: A total of 659 intracranial meningioma patients (median age, 59.0 years; interquartile range: 53.0-66.0 years) including 554 women and 105 men. FIELD STRENGTH/SEQUENCE: The 1.0 T, 1.5 T, and 3.0 T; three-dimensional, T1 -weighted gradient-echo imaging with contrast enhancement. ASSESSMENT: The tumors were manually segmented by two neurosurgeons, H.K. and C.-K.P., with 10 and 26 years of clinical experience, respectively, for use as the ground truth. Deep learning models based on U-Net and nnU-Net were trained using 459 subjects and tested for 100 patients from a single institution (internal validation set [IVS]) and 100 patients from other 24 institutions (external validation set [EVS]), respectively. The performance of each model was evaluated with the Sørensen-Dice similarity coefficient (DSC) compared with the ground truth. STATISTICAL TESTS: According to the normality of the data distribution verified by the Shapiro-Wilk test, variables with three or more categories were compared by the Kruskal-Wallis test with Dunn's post hoc analysis. RESULTS: A two-dimensional (2D) nnU-Net showed the highest median DSCs of 0.922 and 0.893 for the IVS and EVS, respectively. The nnU-Nets achieved superior performance in meningioma segmentation than the U-Nets. The DSCs of the 2D nnU-Net for small meningiomas less than 1 cm3 were 0.769 and 0.780 with the IVS and EVS, respectively. DATA CONCLUSION: A fully automated and accurate volumetric measurement tool for meningioma with clinically applicable performance for small meningioma using nnU-Net was developed. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Development of a deep learning-based auto-segmentation algorithm for hepatocellular carcinoma (HCC) and application to predict microvascular invasion of HCC using CT texture analysis: preliminary results.

BACKGROUND: Automatic segmentation has recently been developed to yield objective data. Prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) using radiomics has been reported. PURPOSE: To develop a deep learning-based auto-segmentation algorithm (DL-AS) for the detection of HCC and to predict MVI using computed tomography (CT) texture analysis. MATERIAL AND METHODS: We retrospectively collected training data from 249 patients with HCC and validation set from 35 patients. Lesions of the training set were manually drawn by radiologist, in the delayed phase. 2D U-Net was selected as the DL architecture. Using the validation set, one radiologist manually drew 2D and 3D regions of interest twice, and the developed DL-AS was performed twice with a one-month time interval. The reproducibility was calculated using intraclass correlation coefficients (ICC). Logistic regression was performed to predict MVI. RESULTS: ICC was in the range of 0.190-0.998/0.341-0.997 in the manual 3D/2D segmentation. In contrast, it was perfect in 3D/2D using DL-AS, with a success rate of 88.6% for the detection of HCC. For predicting MVI, sphericity was a significant parameter (odds ratio <0.001; 95% confidence interval <0.001-0.206; P = 0.020) for predicting MVI using 2D DL-AS. However, 3D DL-AS segmentation did not yield a predictive parameter. CONCLUSION: The auto-segmentation of HCC using DL-AS provides perfect reproducibility, although it failed to detect 11.4% (4/35). However, the extracted parameters yielded different important predictors of MVI in HCC. Sphericity was a significant predictor in 2D DL-AS and 3D manual segmentation, while discrete compactness was a significant predictor in 2D manual segmentation.

Polyaniline/Graphene-Functionalized Flexible Waste Mask Sensors for Ammonia and Volatile Sulfur Compound Monitoring.

A flexible resistive-type polyaniline-based gas sensor was fabricated by simple dip-coating of graphene combined with in situ polymerization of aniline on a flexible waste mask substrate. The prepared polypropylene/graphene/polyaniline (PP/G/PANI) hybrid sensor demonstrated a fast response (114 s) and recovery time (23 s), ppb-level detection limit (100 ppb), high response value (250% toward 50 ppm NH3, which is over four times greater than that of the pristine PANI sensor), acceptable flexibility, excellent selectivity, and long-term stability at room temperature. The morphological and structural properties of the composite sensor materials were characterized by scanning electron microscopy and energy-dispersive spectroscopy characterization, and the surface chemistry of the hybrid sensors was analyzed by Fourier transform infrared spectroscopy. The excellent sensing performance was mainly ascribed to the larger specific surface area and efficient conducting paths of the porous PP/G/PANI network. Moreover, the PP/G/PANI hybrid gas sensor exhibited excellent sensing capability on volatile sulfur compounds contained in human breath, indicating that the hybrid sensor can be applied to breath analysis and kidney disease diagnosis.

Nutritional composition and phytochemical screening in different parts of Hibiscus syriacus L.

As the national flower of Korea, the Hibiscus syriacus L. (Rose of Sharon) is symbolic in its abundance and is a prominent feature of Korean culture. H. syriacus has played an important role in Korea, not only as an ornamental plant but also as an essential ingredient in folk remedies through its various parts. This study aimed to characterize the nutritional and biochemical composition of each plant unit of H. syriacus "Wonhwa." The units are namely: the petals, leaves, roots, and sprouts from its seeds. According to the results each unit produced, the sprouts had the highest content of amino acids and fatty acids which adhere to the requirements of nutritionally excellent food ingredients. The petals produced high quantities of glucose, sucrose, and fumaric acid, with the highest antioxidant activity among the four units. The main bioactive compounds detected in H. syriacus extracts in the four units were o-coumaric acid, p-coumaric acid, schaftoside, isoschaftoside, apigenin-6-C-glucoside-7-o-glucoside, and kaempferol-3-O-galactoside-7-O-rhamnoside. Overall, the highest number of bioactive compounds, 2 phenolic acids and 22 flavonoids, were identified in the petals. These results suggest the possibility of excellent pharmacological activity in the petals.

Structural characteristics of sediment humins from South Korean lakes and their phenanthrene binding compared to other carbon sources.

Sediment humins are extremely important for binding hydrophobic organic contaminants in rivers and lakes. Nonetheless, little is known about their structure and binding. We, therefore, examined the structure and phenanthrene sorption affinity of sediment humin samples upstream, midstream, and downstream from two artificial lakes in South Korea by using the elemental 13C-NMR analysis, Freundlich model, and Langmuir model. The characteristics and phenanthrene sorption affinity of sediment humins were also compared with those of sediment humic acids from similar origins as well as soil humins/humic acids in South Korea from previous studies by using principal component analysis. In both lakes, downstream sediment humins exhibited lower N/C, O/C, and (N + O)/C ratios, lower internal oxidation, and higher aliphaticity due to the presence of long-chain aliphatic compounds generated during anaerobic decomposition. The principal component analysis results also showed that C,H-alkyl, O-alkyl, and polar organic carbon contents were significantly different when comparing the up-mid stream and downstream sediment samples in Daecheong Lake. In addition, midstream sediment humin in Andong Lake presented higher C,H-alkyl and lower polar organic carbon contents compared to those of up-downstream samples. In both lakes, the sorption coefficient and adsorption isotherm linearity were positively correlated with the C,H-alkyl content and negatively correlated with the O-aryl content. Similar to C,H-alkyl and POC, C,H-alkyl, and (N + O)/C had an extremely high correlation coefficient when predicting the sorption coefficient (Freundlich model) and the maximum adsorption capacity (Langmuir model) of sediment humins. Sediment humins had higher C,H-alkyl contents and lower sorption coefficients than those of sediment humic acids and soil humins/humic acids. These findings provide key information for monitoring water quality and polycyclic aromatic hydrocarbon contamination in South Korean lake sediments.

Comparison of the Incidence of Postoperative Hypothyroidism in Patients Undergoing Conventional Thyroid Lobectomy and Pyramid- and Isthmus-Preserving Lobectomy.

Hypothyroidism is a recognized sequela of conventional thyroid lobectomy. However, there have been no studies on the incidence of hypothyroidism following the preservation of the isthmus and pyramid during lobectomy. Therefore, in the present study, we compared the incidence of hypothyroidism following conventional lobectomy and lobectomy during which the isthmus and pyramidal lobe were preserved. Data for a total of 65 patients collected between September 2018 and April 2019 were reviewed retrospectively. Circulating thyroid-stimulating hormone (TSH) concentration was measured before and after surgery in a group who underwent conventional thyroid lobectomy (n = 29) and in a group in which the isthmus and pyramid were preserved (n = 36). We found no significant difference in TSH concentration between the two groups before surgery, or 3 months or 1 year after surgery. Thus, there might be no difference in the incidence of postoperative hypothyroidism between patients who undergo conventional thyroid lobectomy and those in which the isthmus and pyramid are preserved.

A self-degradable hydrogel sensor for a nerve agent tabun surrogate through a self-propagating cascade.

Nerve agents that irreversibly deactivate the enzyme acetylcholinesterase are extremely toxic weapons of mass destruction. Thus, developing methods to detect these lethal agents is important. To create an optical sensor for a surrogate of the nerve agent tabun, as well as a physical barrier that dissolves in response to this analyte, we devise a network hydrogel that decomposes via a self-propagating cascade. A Meldrums acid-derived linker is incorporated into a hydrogel that undergoes a declick reaction in response to thiols, thereby breaking network connections, which releases more thiols, propagating the response throughout the gel. A combination of chemical reactions triggered by the addition of the tabun mimic initiates the cascade. The dissolving barrier is used to release dyes, as well as nanocrystals that undergo a spontaneous aggregation. Thus, this sensing system for tabun generates a physical response and the delivery of chemical agents in response to an initial trigger.

Clinical application of 3D virtual and printed models for cerebrovascular diseases.

OBJECTIVE: Three-dimensional (3D) printing techniques are rapidly advancing in the medical industry and in clinical practice. We aimed to evaluate the usefulness of 3D virtual and printed models of 6 representative cerebrovascular diseases using the software we developed. METHODS: Six cases consisted of 4 intracranial aneurysms (IAs) including complex ones with intrasaccular thrombosis, large size and a skull base location; 1 cavernous malformation in the pons; and 1 arteriovenous malformation in the parietal lobe. The 3D modeling process was performed retrospectively in 3 cases and prospectively in 1 IA. Segmentation of raw data and rendering and modification for 3D virtual models were processed mostly automatically. RESULTS: Most intracranial structures were satisfactorily made, including the skull, brain, vessels, thrombus, tentorium and major cranial nerves. Based on 3D modeling, surgical plan was changed in 1 prospective IA case. However, it was still difficult to discriminate small vessels and cranial nerves, to feel a realistic tactile sense and to directly perform presurgical simulations, such as dissection, removal, clipping and microanastomosis. CONCLUSIONS: The 3D modeling was thought to be very helpful in experiencing the operative views from various directions in advance, in selecting an appropriate surgical approach, and in educating physicians and patients. With advancements in radiological resolution, processing techniques and material properties, 3D modeling is expected to simulate real brain tissues more closely.

Clinical application of patient-specific 3D printing brain tumor model production system for neurosurgery.

The usefulness of 3-dimensional (3D)-printed disease models has been recognized in various medical fields. This study aims to introduce a production platform for patient-specific 3D-printed brain tumor model in clinical practice and evaluate its effectiveness. A full-cycle platform was created for the clinical application of a 3D-printed brain tumor model (3D-printed model) production system. Essential elements included automated segmentation software, cloud-based interactive communication tools, customized brain models with exquisite expression of brain anatomy in transparent material, adjunctive devices for surgical simulation, and swift process cycles to meet practical needs. A simulated clinical usefulness validation was conducted in which neurosurgeons assessed the usefulness of the 3D-printed models in 10 cases. We successfully produced clinically applicable patient-specific models within 4 days using the established platform. The simulated clinical usefulness validation results revealed the significant superiority of the 3D-printed models in surgical planning regarding surgical posture (p = 0.0147) and craniotomy design (p = 0.0072) compared to conventional magnetic resonance images. The benefit was more noticeable for neurosurgeons with less experience. We established a 3D-printed brain tumor model production system that is ready to use in daily clinical practice for neurosurgery.

Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse Using Root-Flipping: DeepRFSLR.

A novel approach of applying deep reinforcement learning to an RF pulse design is introduced. This method, which is referred to as DeepRFSLR, is designed to minimize the peak amplitude or, equivalently, minimize the pulse duration of a multiband refocusing pulse generated by the Shinar Le-Roux (SLR) algorithm. In the method, the root pattern of SLR polynomial, which determines the RF pulse shape, is optimized by iterative applications of deep reinforcement learning and greedy tree search. When tested for the designs of the multiband pulses with three and seven slices, DeepRFSLR demonstrated improved performance compared to conventional methods, generating shorter duration RF pulses in shorter computational time. In the experiments, the RF pulse from DeepRFSLR produced a slice profile similar to the minimum-phase SLR RF pulse and the profiles matched to that of the computer simulation. Our approach suggests a new way of designing an RF by applying a machine learning algorithm, demonstrating a "machine-designed" MRI sequence.

Removal of 1,4-Naphthoquinone by Birnessite-Catalyzed Oxidation: Effect of Phenolic Mediators and the Reaction Pathway.

This study investigated the birnessite (δ-MnO2) catalyzed oxidative removal of 1,4-naphthoquinone (1,4-NPQ) in the presence of phenolic mediators; specifically, the kinetics of 1,4-NPQ removal under various conditions was examined, and the reaction pathway of 1,4-NPQ was verified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The removal rate of 1,4-NPQ by birnessite-catalyzed oxidation (pH = 5) was faster in the presence of phenolic mediators with electron-donating substituents (pseudo-first-order initial stage rate constant (k1) = 0.380-0.733 h-1) than with electron-withdrawing substituents (k1 = 0.071-0.244 h-1), and the effect on the substituents showed a positive correlation with the Hammett constant (Σσ) (r2 = 0.85, p < 0.001). The rate constants obtained using variable birnessite loadings (0.1-1.0 g L-1), catechol concentrations (0.1-1.0 mM), and reaction sequences indicate that phenolic mediators are the major limiting factor for the cross-coupling reaction of 1,4-NPQ in the initial reaction stages, whereas the birnessite-catalyzed surface reaction acts as the major limiting factor in the later reaction stages. This was explained by the operation of two different reaction mechanisms and reaction products identified by LC-MS/MS.

Chemically Triggered Synthesis, Remodeling, and Degradation of Soft Materials.

Polymer topology dictates dynamic and mechanical properties of materials. For most polymers, topology is a static characteristic. In this article, we present a strategy to chemically trigger dynamic topology changes in polymers in response to a specific chemical stimulus. Starting with a dimerized PEG and hydrophobic linear materials, a lightly cross-linked polymer, and a cross-linked hydrogel, transformations into an amphiphilic linear polymer, lightly cross-linked and linear random copolymers, a cross-linked polymer, and three different hydrogel matrices were achieved via two controllable cross-linking reactions: reversible conjugate additions and thiol-disulfide exchange. Significantly, all the polymers, before or after topological changes, can be triggered to degrade into thiol- or amine-terminated small molecules. The controllable transformations of polymeric morphologies and their degradation herald a new generation of smart materials.

Artificial neural network for myelin water imaging.

PURPOSE: To demonstrate the application of artificial neural network (ANN) for real-time processing of myelin water imaging (MWI). METHODS: Three neural networks, ANN-IMWF , ANN-IGMT2 , and ANN-II, were developed to generate MWI. ANN-IMWF and ANN-IGMT2 were designed to output myelin water fraction (MWF) and geometric mean T2 of intra- and extra-cellular water signal (GMT2,IEW ), respectively, whereas ANN-II generates a T2 distribution. For the networks, gradient and spin echo data from 18 healthy controls (HC) and 26 multiple sclerosis patients (MS) were utilized. Among them, 10 HC and 12 MS had the same scan parameters and were used for training (6 HC and 6 MS), validation (1 HC and 1 MS), and test sets (3 HC and 5 MS). The remaining data had different scan parameters and were applied to exam effects of the scan parameters. The network results were compared with those of conventional MWI in the white matter mask and regions of interest. RESULTS: The networks produced highly accurate results, showing averaged normalized root-mean-squared error under 3% for MWF and 0.4% for GMT2,IEW in the white matter mask of the test set. In the region of interest analysis, the differences between ANNs and conventional MWI were less than 0.1% in MWF and 0.1 ms in GMT2,IEW (no statistical difference and R2 > 0.97). Datasets with different scan parameters showed increased errors. The average processing time was 0.68 s in ANNs, gaining 11,702 times acceleration in the computational speed (conventional MWI: 7,958 s). CONCLUSION: The proposed neural networks demonstrate the feasibility of real-time processing for MWI with high accuracy.

Inhalation toxicity of benzalkonium chloride and triethylene glycol mixture in rats.

Benzalkonium chloride (BAC), a disinfectant, and triethylene glycol (TEG), an organic solvent/sanitizer, are frequently combined in commercially available household sprays. To assess the respiratory effect of this combination, Sprague-Dawley rats were exposed to an aerosol containing BAC (0.5%, w/v) and TEG (10%, w/v) for up to 2 weeks in a whole-body inhalation chamber. BAC (4.1-4.5 mg/m3, sprayed from 0.5% solution) promoted pulmonary cell damage and inflammation as depicted by the increase in total protein, lactate dehydrogenase, polymorphonuclear leukocytes, and macrophage inflammatory protein-2 in the bronchoalveolar lavage fluid, whereas TEG (85.3-94.5 mg/m3, sprayed from 10% solution) did not affect the lung. Rats exposed to the BAC/TEG mixture for 2 weeks showed severe respiratory symptoms (sneezing, wheezing, breath shortness, and chest tightness), but no lung damage or inflammation was observed. However, significant ulceration and degenerative necrosis were observed in the nasal cavities of rats repeatedly exposed to the BAC/TEG mixture. The mass median aerodynamic diameters of the aqueous, BAC, TEG and BAC/TEG aerosols were 1.24, 1.27, 3.11 and 3.24 µm, respectively, indicating that TEG-containing aerosols have larger particles than those of the aqueous and BAC alone aerosols. These results suggest that the toxic effects of BAC and BAC/TEG aerosols on the different respiratory organs may be associated with the difference in particle diameter, since particle size is important in determining the deposition site of inhaled materials.