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Introduction: The purpose of this study was to evaluate the role of body mass index (BMI) in predicting postoperative complications following myocutaneous free flap transfer. In addition, we sought to identify certain body composition variables that may be used to stratify patients into low- versus high-risk for gracilis myocutaneous free flap with skin paddle failure., Methods: Using the National Surgical Quality Improvement Program database, we collected data for all patients who underwent myocutaneous free flap transfer from 2015 to 2021. Demographic data, medical history, surgical characteristics, and postoperative outcomes, including complications, reoperations, and readmissions, were collected. Body mass index was correlated with outcome measures to determine its role in predicting myocutaneous free flap reliability. Subsequently, we retrospectively obtained measurements of perigracilis anatomy in patients who underwent computed tomography angiography bilateral lower extremity scans with intravenous contrast at our institution. We compared body composition data with mathematical equations calculating the potential area along the skin of the thigh within which the gracilis perforator may be found., Results: Across the United States, 1549 patients underwent myocutaneous free flap transfer over the 7-year study period. Being in obesity class III (BMI ≥40 kg/m2) was associated with a 4-times greater risk of flap complications necessitating a return to the operating room compared with being within the normal BMI range. In our computed tomography angiography analysis, average perigracilis adipose thickness was 18.3 ± 8.0 mm. Adipose thickness had a strong, positive exponential relationship with the area of skin within which the perforator may be found., Conclusions: In our study, higher BMI was associated with decreased myocutaneous free flap reliability. Specifically, inner thigh adipose thickness can be used to estimate the area along the skin within which the gracilis perforator may be found. This variable, along with BMI, can be used to identify patients who are considered high-risk for flap failure and who may benefit from additional postoperative monitoring, such as the use of a color flow Doppler probe and more frequent and prolonged skin paddle monitoring., Competing Interests: Conflicts of interest and sources of funding: The American College of Surgeons National Surgical Quality Improvement Program and the hospitals participating in the American College of Surgeons National Surgical Quality Improvement Program are the sources of the data used herein; they have not verified and are not responsible for the statistical validity of the data analysis or the conclusions derived by the authors., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

» Most isolated ulnar shaft fractures are stable and heal without complication regardless of treatment protocol.» Casting above the elbow may create unwanted stiffness without enhancing union rates.» Defining unstable fractures and determining which injuries would benefit from operative management remain a challenge.» Recent development of intramedullary devices has shown promise in preliminary studies, but more vigorous investigation is needed to determine their role., Competing Interests: Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJSREV/A952)., (Copyright © 2023 by The Journal of Bone and Joint Surgery, Incorporated.)

Single-atom nanozymes (SAzymes) are promising in next-generation nanozymes, nevertheless, how to rationally modulate the microenvironment of SAzymes with controllable multi-enzyme properties is still challenging. Herein, we systematically investigate the relationship between atomic configuration and multi-enzymatic performances. The constructed Mn SA -N 3 -coordinated SAzymes (Mn SA -N 3 -C) exhibits much more remarkable oxidase-, peroxidase-, and glutathione oxidase-like activities than that of Mn SA -N 4 -C. Based on experimental and theoretical results, these multi-enzyme-like behaviors are highly dependent on the coordination number of single atomic Mn sites by local charge polarization. As a consequence, a series of colorimetric biosensing platforms based on Mn SA -N 3 -C SAzymes is successfully built for specific recognition of biological molecules. These findings provide atomic-level insight into the microenvironment of nanozymes, promoting rational design of other demanding biocatalysts., (© 2023 Wiley-VCH GmbH.)

Background: The risk of outbreaks escalating into pandemics has soared with globalization. Therefore, understanding transmission mechanisms of infectious diseases has become critical to formulating global public health policy. This systematic review assessed evidence in the medical and public health literature for the military as a disease vector., Methods: We searched 3 electronic databases without temporal restrictions. Two researchers independently extracted study data using a standardized form. Through team discussions, studies were grouped according to their type of transmission mechanism and direct quotes were extracted to generate themes and sub-themes. A content analysis was later performed and frequency distributions for each theme were generated., Results: Of 6477 studies, 210 met our inclusion criteria and provided evidence, spanning over two centuries (1810-2020), for the military as a pathogen transmitter, within itself or between it and civilians. Biological mechanisms driving transmission included person-to-person transmission, contaminated food and water, vector-borne, and airborne routes. Contaminated food and/or water were the most common biological transmission route. Social mechanisms facilitating transmission included crowded living spaces, unhygienic conditions, strenuous working, training conditions, absent or inadequate vaccination programs, pressure from military leadership, poor compliance with public health advice, contractor mismanagement, high-risk behaviours, and occupation-specific freedom of movement. Living conditions were the most common social transmission mechanism, with young, low ranking military personnel repeatedly reported as the most affected group. Selected social mechanisms, such as employment-related freedom of movement, were unique to the military as a social institution. While few studies explicitly studied civilian populations, considerably more contained information that implied that civilians were likely impacted by outbreaks described in the military., Conclusions: This study identified features of the military that pose a significant threat to global health, especially to civilian health in countries with substantial military presence or underdeveloped health systems. While biological transmission mechanisms are shared by other social groups, selected social transmission mechanisms are unique to the military. As an increasingly interconnected world faces the challenges of COVID-19 and future infectious diseases, the identified features of the military may exacerbate current and similar challenges and impair attempts to implement successful and equitable global public health policies., (© 2021. The Author(s).)

During a survey of fungal diversity associated with insects, mud, soil, and freshwater niches in different areas in Korea, nine interesting fungal strains were isolated. Based on their morphological characteristics and molecular phylogeny analyses, using a combined data set of β-tubulin ( BenA ), calmodulin ( CaM ), and second largest subunit of RNA polymerase ( RPB2 ) sequences, the strains CNUFC AM-44, CNUFC JCW3-4, CNUFC S708, CNUFC WT202, CNUFC AS1-29, CNUFC JCW3-5, CNUFC JDP37, and CNUFC JDP62 were identified as Aspergillus alabamensis , A. floridensis , A. subversicolor , Penicillium flavigenum , P . laevigatum , P. lenticrescens , Talaromyces adpressus , and T . beijingensis , respectively. The strain CNUFC JT1301 belongs to series Westlingiorum in section Citrina and is phylogenetically related to P . manginii . However, slow growth when cultivated on CYA, MEA, CREA is observed and the property can be used to easily distinguish the new species from these species. Additionally, P. manginii is known to produce sclerotia, while CNUFC JT1301 strain does not. Herein, the new fungal species is proposed as P . aquadulcis sp. nov. Eight species, A . alabamensis , A. floridensis , A. subversicolor , P . flavigenum , P . laevigatum , P. lenticrescens , T . adpressus , and T . beijingensis , have not been previously reported in Korea. The present study expands the known distribution of fungal species belonging to the families Aspergillaceae and Trichocomaceae in Korea., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Korean Society of Mycology.)

To identify high-efficiency metal-nitrogen-doped (M-N-C) electrocatalysts for the electrochemical CO 2 -to-CO reduction reaction (CO 2 RR), a method that uses density functional theory calculation is presented to evaluate their selectivity, activity, and structural stability. Twenty-three M-N 4 -C catalysts are evaluated, and three of them (M = Fe, Co, or Ni) are identified as promising candidates. They are synthesized and tested as proof-of-concept catalysts for CO 2 -to-CO conversion. Different key descriptors, including the maximum reaction energy, differences of the *H and *CO binding energy (ΔG *H -ΔG *CO ), and *CO desorption energy (ΔG *CO→CO( g ) ), are used to clarify the reaction mechanism. These computational descriptors effectively predict the experimental observations in the entire range of electrochemical potential. The findings provide a guideline for rational design of heterogeneous CO 2 RR electrocatalysts., (© 2021 Wiley-VCH GmbH.)

Accurate nuclear segmentation in histopathology images plays a key role in digital pathology. It is considered a prerequisite for the determination of cell phenotype, nuclear morphometrics, cell classification, and the grading and prognosis of cancer. However, it is a very challenging task because of the different types of nuclei, large intraclass variations, and diverse cell morphologies. Consequently, the manual inspection of such images under high-resolution microscopes is tedious and time-consuming. Alternatively, artificial intelligence (AI)-based automated techniques, which are fast and robust, and require less human effort, can be used. Recently, several AI-based nuclear segmentation techniques have been proposed. They have shown a significant performance improvement for this task, but there is room for further improvement. Thus, we propose an AI-based nuclear segmentation technique in which we adopt a new nuclear segmentation network empowered by residual skip connections to address this issue. Experiments were performed on two publicly available datasets: (1) The Cancer Genome Atlas (TCGA), and (2) Triple-Negative Breast Cancer (TNBC). The results show that our proposed technique achieves an aggregated Jaccard index (AJI) of 0.6794, Dice coefficient of 0.8084, and F1-measure of 0.8547 on TCGA dataset, and an AJI of 0.7332, Dice coefficient of 0.8441, precision of 0.8352, recall of 0.8306, and F1-measure of 0.8329 on the TNBC dataset. These values are higher than those of the state-of-the-art methods.

Objective: This study aimed to evaluate the following null hypothesis: there are no differences in the morphology of the temporomandibular joint (TMJ) structures in relation to vertical and sagittal cephalometric patterns., Methods: This retrospective study was performed with 131 participants showing no TMJ symptoms. The participants were divided into Class I, II, and III groups on the basis of their sagittal cephalometric relationships and into hyperdivergent, normodivergent, and hypodivergent groups on the basis of their vertical cephalometric relationships. The following measurements were performed using cone-beam computed tomography images and compared among the groups: condylar volume, condylar size (width, length, and height), fossa size (length and height), and condyle-to-fossa joint spaces at the anterior, superior, and posterior condylar poles., Results: The null hypothesis was rejected. The Class III group showed larger values for condylar width, condylar height, and fossa height than the Class II group ( p < 0.05). Condylar volume and superior joint space in the hyperdivergent group were significantly smaller than those in the other two vertical groups ( p < 0.001), whereas fossa length and height were significantly larger in the hyperdivergent group than in the other groups ( p < 0.01). The hypodivergent group showed a greater condylar width than the hyperdivergent group ( p < 0.01). The sagittal and vertical cephalometric patterns showed statistically significant interactions for fossa length and height., Conclusions: TMJ morphology differed across diverse skeletal cephalometric patterns. The fossa length and height were affected by the interactions of the vertical and sagittal skeletal patterns.

Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labels and the weak generalization of the SER model for an unseen target domain. This study proposes a multi-path and group-loss-based network (MPGLN) for SER to support multi-domain adaptation. The proposed model includes a bidirectional long short-term memory-based temporal feature generator and a transferred feature extractor from the pre-trained VGG-like audio classification model (VGGish), and it learns simultaneously based on multiple losses according to the association of emotion labels in the discrete and dimensional models. For the evaluation of the MPGLN SER as applied to multi-cultural domain datasets, the Korean Emotional Speech Database (KESD), including KESDy18 and KESDy19, is constructed, and the English-speaking Interactive Emotional Dyadic Motion Capture database (IEMOCAP) is used. The evaluation of multi-domain adaptation and domain generalization showed 3.7% and 3.5% improvements, respectively, of the F1 score when comparing the performance of MPGLN SER with a baseline SER model that uses a temporal feature generator. We show that the MPGLN SER efficiently supports multi-domain adaptation and reinforces model generalization.

The conventional finger-vein recognition system is trained using one type of database and entails the serious problem of performance degradation when tested with different types of databases. This degradation is caused by changes in image characteristics due to variable factors such as position of camera, finger, and lighting. Therefore, each database has varying characteristics despite the same finger-vein modality. However, previous researches on improving the recognition accuracy of unobserved or heterogeneous databases is lacking. To overcome this problem, we propose a method to improve the finger-vein recognition accuracy using domain adaptation between heterogeneous databases using cycle-consistent adversarial networks (CycleGAN), which enhances the recognition accuracy of unobserved data. The experiments were performed with two open databases-Shandong University homologous multi-modal traits finger-vein database (SDUMLA-HMT-DB) and Hong Kong Polytech University finger-image database (HKPolyU-DB). They showed that the equal error rate (EER) of finger-vein recognition was 0.85% in case of training with SDUMLA-HMT-DB and testing with HKPolyU-DB, which had an improvement of 33.1% compared to the second best method. The EER was 3.4% in case of training with HKPolyU-DB and testing with SDUMLA-HMT-DB, which also had an improvement of 4.8% compared to the second best method.

Background: Although ophthalmic devices have made remarkable progress and are widely used, most lack standardization of both image review and results reporting systems, making interoperability unachievable. We developed and validated new software for extracting, transforming, and storing information from report images produced by ophthalmic examination devices to generate standardized, structured, and interoperable information to assist ophthalmologists in eye clinics., Results: We selected report images derived from optical coherence tomography (OCT). The new software consists of three parts: (1) The Area Explorer, which determines whether the designated area in the configuration file contains numeric values or tomographic images; (2) The Value Reader, which converts images to text according to ophthalmic measurements; and (3) The Finding Classifier, which classifies pathologic findings from tomographic images included in the report. After assessment of Value Reader accuracy by human experts, all report images were converted and stored in a database. We applied the Value Reader, which achieved 99.67% accuracy, to a total of 433,175 OCT report images acquired in a single tertiary hospital from 07/04/2006 to 08/31/2019. The Finding Classifier provided pathologic findings (e.g., macular edema and subretinal fluid) and disease activity. Patient longitudinal data could be easily reviewed to document changes in measurements over time. The final results were loaded into a common data model (CDM), and the cropped tomographic images were loaded into the Picture Archive Communication System., Conclusions: The newly developed software extracts valuable information from OCT images and may be extended to other types of report image files produced by medical devices. Furthermore, powerful databases such as the CDM may be implemented or augmented by adding the information captured through our program.

Although face-based biometric recognition systems have been widely used in many applications, this type of recognition method is still vulnerable to presentation attacks, which use fake samples to deceive the recognition system. To overcome this problem, presentation attack detection (PAD) methods for face recognition systems (face-PAD), which aim to classify real and presentation attack face images before performing a recognition task, have been developed. However, the performance of PAD systems is limited and biased due to the lack of presentation attack images for training PAD systems. In this paper, we propose a method for artificially generating presentation attack face images by learning the characteristics of real and presentation attack images using a few captured images. As a result, our proposed method helps save time in collecting presentation attack samples for training PAD systems and possibly enhance the performance of PAD systems. Our study is the first attempt to generate PA face images for PAD system based on CycleGAN network, a deep-learning-based framework for image generation. In addition, we propose a new measurement method to evaluate the quality of generated PA images based on a face-PAD system. Through experiments with two public datasets (CASIA and Replay-mobile), we show that the generated face images can capture the characteristics of presentation attack images, making them usable as captured presentation attack samples for PAD system training., Competing Interests: The authors declare no conflict of interest.

Retinal fundus images are used to detect organ damage from vascular diseases (e.g. diabetes mellitus and hypertension) and screen ocular diseases. We aimed to assess convolutional neural network (CNN) models that predict age and sex from retinal fundus images in normal participants and in participants with underlying systemic vascular-altered status. In addition, we also tried to investigate clues regarding differences between normal ageing and vascular pathologic changes using the CNN models. In this study, we developed CNN age and sex prediction models using 219,302 fundus images from normal participants without hypertension, diabetes mellitus (DM), and any smoking history. The trained models were assessed in four test-sets with 24,366 images from normal participants, 40,659 images from hypertension participants, 14,189 images from DM participants, and 113,510 images from smokers. The CNN model accurately predicted age in normal participants; the correlation between predicted age and chronologic age was R 2  = 0.92, and the mean absolute error (MAE) was 3.06 years. MAEs in test-sets with hypertension (3.46 years), DM (3.55 years), and smoking (2.65 years) were similar to that of normal participants; however, R 2 values were relatively low (hypertension, R 2  = 0.74; DM, R 2  = 0.75; smoking, R 2  = 0.86). In subgroups with participants over 60 years, the MAEs increased to above 4.0 years and the accuracies declined for all test-sets. Fundus-predicted sex demonstrated acceptable accuracy (area under curve > 0.96) in all test-sets. Retinal fundus images from participants with underlying vascular-altered conditions (hypertension, DM, or smoking) indicated similar MAEs and low coefficients of determination (R 2 ) between the predicted age and chronologic age, thus suggesting that the ageing process and pathologic vascular changes exhibit different features. Our models demonstrate the most improved performance yet and provided clues to the relationship and difference between ageing and pathologic changes from underlying systemic vascular conditions. In the process of fundus change, systemic vascular diseases are thought to have a different effect from ageing. Research in context. Evidence before this study. The human retina and optic disc continuously change with ageing, and they share physiologic or pathologic characteristics with brain and systemic vascular status. As retinal fundus images provide high-resolution in-vivo images of retinal vessels and parenchyma without any invasive procedure, it has been used to screen ocular diseases and has attracted significant attention as a predictive biomarker for cerebral and systemic vascular diseases. Recently, deep neural networks have revolutionised the field of medical image analysis including retinal fundus images and shown reliable results in predicting age, sex, and presence of cardiovascular diseases. Added value of this study. This is the first study demonstrating how a convolutional neural network (CNN) trained using retinal fundus images from normal participants measures the age of participants with underlying vascular conditions such as hypertension, diabetes mellitus (DM), or history of smoking using a large database, SBRIA, which contains 412,026 retinal fundus images from 155,449 participants. Our results indicated that the model accurately predicted age in normal participants, while correlations (coefficient of determination, R 2 ) in test-sets with hypertension, DM, and smoking were relatively low. Additionally, a subgroup analysis indicated that mean absolute errors (MAEs) increased and accuracies declined significantly in subgroups with participants over 60 years of age in both normal participants and participants with vascular-altered conditions. These results suggest that pathologic retinal vascular changes occurring in systemic vascular diseases are different form the changes in spontaneous ageing process, and the ageing process observed in retinal fundus images may saturate at age about 60 years. Implications of all available evidence. Based on this study and previous reports, the CNN could accurately and reliably predict age and sex using retinal fundus images. The fact that retinal changes caused by ageing and systemic vascular diseases occur differently motivates one to understand the retina deeper. Deep learning-based fundus image reading may be a more useful and beneficial tool for screening and diagnosing systemic and ocular diseases after further development.

Background and Objective: Retinal fundus images are widely used to diagnose retinal diseases and can potentially be used for early diagnosis and prevention of chronic vascular diseases and diabetes. While various automatic retinal vessel segmentation methods using deep learning have been proposed, they are mostly based on common CNN structures developed for other tasks such as classification., Methods: We present a novel and simple multi-scale convolutional neural network (CNN) structure for retinal vessel segmentation. We first provide a theoretical analysis of existing multi-scale structures based on signal processing. In previous structures, multi-scale representations are achieved through downsampling by subsampling and decimation. By incorporating scale-space theory, we propose a simple yet effective multi-scale structure for CNNs using upsampling, which we term scale-space approximated CNN (SSANet). Based on further analysis of the effects of the SSA structure within a CNN, we also incorporate residual blocks, resulting in a multi-scale CNN that outperforms current state-of-the-art methods., Results: Quantitative evaluations are presented as the area-under-curve (AUC) of the receiver operating characteristic (ROC) curve and the precision-recall curve, as well as accuracy, for four publicly available datasets, namely DRIVE, STARE, CHASE&#95;DB1, and HRF. For the CHASE&#95;DB1 set, the SSANet achieves state-of-the-art AUC value of 0.9916 for the ROC curve. An ablative analysis is presented to analyze the contribution of different components of the SSANet to the performance improvement., Conclusions: The proposed retinal SSANet achieves state-of-the-art or comparable accuracy across publicly available datasets, especially improving segmentation for thin vessels, vessel junctions, and central vessel reflexes., (Copyright © 2019 Elsevier B.V. All rights reserved.)

In this paper, we perform a systematic study about the on-body sensor positioning and data acquisition details for Human Activity Recognition (HAR) systems. We build a testbed that consists of eight body-worn Inertial Measurement Units (IMU) sensors and an Android mobile device for activity data collection. We develop a Long Short-Term Memory (LSTM) network framework to support training of a deep learning model on human activity data, which is acquired in both real-world and controlled environments. From the experiment results, we identify that activity data with sampling rate as low as 10 Hz from four sensors at both sides of wrists, right ankle, and waist is sufficient in recognizing Activities of Daily Living (ADLs) including eating and driving activity. We adopt a two-level ensemble model to combine class-probabilities of multiple sensor modalities, and demonstrate that a classifier-level sensor fusion technique can improve the classification performance. By analyzing the accuracy of each sensor on different types of activity, we elaborate custom weights for multimodal sensor fusion that reflect the characteristic of individual activities.

Purpose: We developed a semi-automated volumetric software, NPerfusion, to segment brain tumors and quantify perfusion parameters on whole-brain CT perfusion (WBCTP) images. The purpose of this study was to assess the feasibility of the software and to validate its performance compared with manual segmentation., Methods: Twenty-nine patients with pathologically proven brain tumors who underwent preoperative WBCTP between August 2012 and February 2015 were included. Three perfusion parameters, arterial flow (AF), equivalent blood volume (EBV), and Patlak flow (PF, which is a measure of permeability of capillaries), of brain tumors were generated by a commercial software and then quantified volumetrically by NPerfusion, which also semi-automatically segmented tumor boundaries. The quantification was validated by comparison with that of manual segmentation in terms of the concordance correlation coefficient and Bland-Altman analysis., Results: With NPerfusion, we successfully performed segmentation and quantified whole volumetric perfusion parameters of all 29 brain tumors that showed consistent perfusion trends with previous studies. The validation of the perfusion parameter quantification exhibited almost perfect agreement with manual segmentation, with Lin concordance correlation coefficients (ρ c ) for AF, EBV, and PF of 0.9988, 0.9994, and 0.9976, respectively. On Bland-Altman analysis, most differences between this software and manual segmentation on the commercial software were within the limit of agreement., Conclusions: NPerfusion successfully performs segmentation of brain tumors and calculates perfusion parameters of brain tumors. We validated this semi-automated segmentation software by comparing it with manual segmentation. NPerfusion can be used to calculate volumetric perfusion parameters of brain tumors from WBCTP.

The effect of TiO2 layer applied to the conventional Fe2O3/FTO photoanode to improve the photoelectrochemical performance was assessed from the viewpoint of the microstructure and energy band structure. Regardless of the location of the TiO2 layer in the photoanodes, that is, Fe2O3/TiO2/FTO or TiO2/Fe2O3/FTO, high performance was obtained when α-Fe2O3 and H-TiNT/anatase-TiO2 phases existed in the constituent Fe2O3 and TiO2 layers after optimized heat treatments. The presence of the Fe2O3 nanoparticles with high uniformity in the each layer of the Fe2O3/TiO2/FTO photoanode achieved by a simple dipping process seemed to positively affect the performance improvement by modifying the energy band structure to a more favorable one for efficient electrons transfer. Our current study suggests that the application of the TiO2 interlayer, together with α -Fe2O3 nanoparticles present in the each constituent layers, could significantly contribute to the performance improvement of the conventional Fe2O3 photoanode.

A layer of titanate nanotubular (H-TiNT) particles between Fe2O3 film and FTO substrate was inserted and heat-treated to improve electrochemical property of Fe2O3 for water splitting. Heat-treatment of the synthesized F2O3/H-TiNT/FTO thin film was found to significantly affect photoelectrochemical properties of the film through electrochemical impedance analysis. The film showed larger oxidation-reduction peaks of I-V characteristics and capacitive properties under UV-Vis light illumination with heat treatment temperatures during transformation of H-TiNT into anatase TiO2 phase. The overall results showed an appropriate heat treatment was a promising way to improve the electrochemical property of the photoanode film.

Hydrogen titanate nanotube (H-TiNT) particles were coated porously on a fluorine-doped tin oxide glass using the layer-by-layer self assembling method and then heat-treated at temperatures below 600 degrees C for 10 min in air. The microstructure, crystallinity, and optical absorbance of the heat-treated H-TiNT thin film were investigated using scanning electron microscopy, X-ray diffraction, Raman spectroscopy, and UV-vis spectroscopy, respectively. Also, I-V characteristics of the fibrous H-TiNT particles in the thin film using linear cycle voltammetry under ultraviolet-A irradiation were analyzed to have a maximum current value at applied voltages with the increase in heating temperature for economic water splitting.

To prepare one-dimensional nanostructured Na2Ti6O13 powder, the starting materials of TiO2, NaCl and Na2CO3 were mixed and then heat-treated at 1000 degrees C for 2 hrs in air under molten state of NaCl. Changes in shape and phase, photo absorbance and photocatalytic ability of TiO2 particle were observed controlling added amount of Na2CO3 under constant weight ratio of TiO2 to NaCl using SEM, X-ray diffractometer, Raman spectroscopy, and UV-Vis spectroscopy. The TiO2 particle was changed into rod-shape Na2Ti6O13 with the addition of Na2CO3, showing increase in optical energy band-gap of the powder as well as gradual decrease of the photo-decomposition ability.

Severe acute respiratory syndrome (SARS) is a life-threatening disease for which accurate diagnosis is essential. Although many tools have been developed for the diagnosis of SARS, false-positive reactions in negative sera may occur because of cross-reactivity with other coronaviruses. We have raised polyclonal and monoclonal antibodies (Abs) using a recombinant form of the SARS virus nucleocapsid protein. Cross-reactivity of these anti-SARS Abs against human coronavirus (HCoV) 229E and HCoV OC43 were determined by Western blotting. The Abs produced reacted with recombinant SARS virus nucleocapsid protein, but not with HCoV 229E or HCoV OC43.

Although rodents have previously been used in ecotoxicological studies, they are expensive, time-consuming, and are limited by strict legal restrictions. The present study used a zebrafish (Danio rerio) model and generated data that was useful for extrapolating toxicant effects in this system to that of humans. Here we treated embryos of the naive-type as well as a transiently transfected zebrafish liver cell line carrying a plasmid (phAhREEGFP), for comparing toxicity levels with the well-known aryl hydrocarbon receptor (AhR)-binding toxicants: 3,3',4,4',5-pentachlorobiphenyl (PCB126), 2,3,7,8-tetrachlorodibenzo-p-dioxin, and 3-methylcholanthrene. These toxicants induced a concentration-dependent increase in morphological disruption, indicating toxicity at early life-stages. The transient transgenic zebrafish liver cell line was sensitive enough to these toxicants to express the CYP1A1 regulated enhanced green fluorescent protein. The findings of this study demonstrated that the zebrafish in vivo model might allow for extremely rapid and reproducible toxicological profiling of early life-stage embryo development. We have also shown that the transient transgenic zebrafish liver cell line can be used for research on AhR mechanism studies.

Severe acute respiratory syndrome (SARS) is a lifethreatening emerging respiratory disease caused by the coronavirus, SARS-CoV. The nucleocapsid (N) protein of SARS-CoV is highly antigenic and may be a suitable candidate for diagnostic applications. We constructed truncated recombinant N proteins (N1 [1-422 aa], N2 [1- 109 aa], and N3 [110-422 aa]) and determined their antigenicity by Western blotting using convalescent SARS serum. The recombinants containing N1 and N3 reacted with convalescent SARS serum in Western blotting. However, the recombinant with N2 did not. In ELISA using N1 or N3 as the antigens, positive results were observed in 10 of 10 (100%) SARS-CoV-positive human sera. None of 50 healthy sera gave positive results in either assay. These data indicate that the ELISA using N1 or N3 has high sensitivity and specificity. These results suggest that the middle or C-terminal region of the SARS N protein is important for eliciting antibodies against SARS-CoV during the immune response, and ELISA reactions using N1 or N3 may be a valuable tool for SARS diagnosis.

In transgenic zebrafish (Danio rerio), green fluorescent protein (GFP) is a promising marker for environmental pollutants. In using GFP, one of the obstacles which we faced was how to compare toxicity among different toxicants or among a specific toxicant in different model species with the intensity of GFP expression. Using a fluorescence detection method, we first validated our method for estimating the amount of GFP fluorescence present in transgenic fish, which we used as an indicator of developmental toxicity caused by the well-known toxicant, arsenite. To this end, we developed mosaic transgenic zebrafish with the human heat shock response element (HSE) fused to the enhanced GFP (EGFP) reporter gene to indicate exposure to arsenite. We confirmed that EGFP expression sites correlate with gross morphological disruption caused by arsenite exposure. Arsenite (300.0 microM) caused stronger EGFP fluorescence intensity and quantity than 50.0 microM and 10.0 microM arsenite in our transgenic zebrafish. Furthermore, arsenite-induced apoptosis was demonstrated by TUNEL assay. Apoptosis was inhibited by the antioxidant, N-acetyl-cystein (NAC) in this transgenic zebrafish. The distribution of TUNEL-positive cells in embryonic tissues was correlated with the sites of arsenite toxicity and EGFP expression. The EGFP values quantified using the standard curve equation from the known GFP quantity were consistent with the arsenite-induced EGFP expression pattern and arsenite concentration, indicating that this technique can be a reliable and applicable measurement. In conclusion, we propose that fluorescence-based EGFP quantification in transgenic fish containing the hsp70 promoter-EGFP reporter-gene construct is a useful indicator of development toxicity caused by arsenite.

This study evaluated oxidative stress-induced apoptosis as a possible mechanism of arsenite toxicity in zebrafish liver cell line (ZFL cells). The heat shock protein 70 (HSP70), a chaperone protein, appears to provide protection against oxidative stress and apoptosis. Using the MTT assay, we demonstrated that survival of ZFL cells treated with arsenite for 24h decreased in a dose-dependent manner. The possible mechanisms that promote the cytotoxicity of arsenite were addressed. Cell viability assays revealed that arsenite caused a dose-dependent increase in cell death, and pretreatment of the ZFL cells with antioxidants blunted these effects. Antioxidants such as N-acetyl-cysteine (NAC, 5 mM) and dithiothreitol (DTT, 80 microM) significantly prevented ZFL cells from arsenite-induced death. Nuclear staining was performed using 1 microg/ml Hoechst, and cells were analyzed with a fluorescent microscope. Arsenite (30 microM) induced massive apoptosis that was identified by morphology and condensation and fragmentation of the nuclei of the ZFL cells. Pretreatment with NAC or DTT before arsenite insult effectively protected the cells against oxidative stress-induced apoptosis from the arsenite. Using a transfected human hsp 70 promoter-enhanced green fluorescent protein (EGFP) reporter, pHhsp70-EGFP, the induction of HSP70 against oxidative stress-induced apoptosis by arsenite was observed. The induction of HSP70 by arsenite increased in a dose-dependent manner, and pretreatment of transfected ZFL cells with NAC or DTT before arsenite insult reduced EGFP expression. Taken together, our results provide evidence that stimulation of the heat shock response is a sensitive biomarker of arsenic exposure and that arsenite causes oxidative stress-induced apoptosis in ZFL cells.