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Abstract Background The management of internal derangement (ID) of the TMJ is challenging because of multiple etiologic factors and varying degrees of severity. The aim of this study was to evaluate the clinical outcomes of patients with unilateral ID treated with arthrocentesis and stabilization splint therapy during a 6-month period. Methods A total of 105 patients (87 females, 18 males) with unilateral ID were included in this study. Patients were divided into unilateral anterior disc displacement with reduction (ADDwR) and unilateral anterior disc displacement without reduction (ADDwoR). Patients with ADDwoR were subdivided according to the erosive bone changes. Objective parameters on mandibular movement and subjective parameters on pain were obtained and assessed. Their clinical outcomes before and after arthrocentesis and stabilization splint therapy were compared with the chi-square, Fisher’s exact test, paired t-test, or Wilcoxon singed-rank test. Results All objective parameters of unilateral ID patients significantly increased at the 6-month follow-up. The differences in mean visual analog scale (VAS) pain scores were statistically significant in all subjective variables (p

Abstract Choroid plexus tumors (CPTs) are intraventricular tumors derived from the choroid plexus epithelium and occur frequently in children. The aim of this study was to investigate the genomic and epigenomic characteristics of CPT and identify the differences between choroid plexus papilloma (CPP) and choroid plexus carcinoma (CPC). We conducted multiomics analyses of 20 CPT patients including CPP and CPC. Multiomics analysis included whole-genome sequencing, whole-transcriptome sequencing, and methylation sequencing. Mutually exclusive TP53 and EPHA7 point mutations, coupled with the amplification of chromosome 1, were exclusively identified in CPC. In contrast, amplification of chromosome 9 was specific to CPP. Differential gene expression analysis uncovered a significant overexpression of genes related to cell cycle regulation and epithelial-mesenchymal transition pathways in CPC compared to CPP. Overexpression of genes associated with tumor metastasis and progression was observed in the CPC subgroup with leptomeningeal dissemination. Furthermore, methylation profiling unveiled hypomethylation in major repeat regions, including long interspersed nuclear elements, short interspersed nuclear elements, long terminal repeats, and retrotransposons in CPC compared to CPP, implying that the loss of epigenetic silencing of transposable elements may play a role in tumorigenesis of CPC. Finally, the differential expression of AK1, regulated by both genomic and epigenomic factors, emerged as a potential contributing factor to the histological difference of CPP against CPC. Our results suggest pronounced genomic and epigenomic disparities between CPP and CPC, providing insights into the pathogenesis of CPT at the molecular level.

Alcohol dehydrogenases (ADHs) are critical enzymes involved in the oxidation of alcohols, contributing to various metabolic pathways across organisms. This study investigates type I functional divergence within three ADH1 families: Saccharomyces cerevisiae (PDB ID: 4W6Z), Gadus morhua (PDB ID: 1CDO), and Homo sapiens (PDB ID: 1HDX). Understanding the molecular evolution and mechanisms underlying functional divergence of ADHs is essential for comprehending their adaptive significance. For this purpose, we performed a computational analysis that included structural characterization of ADHs through three-dimensional modeling, site-specific analysis to evaluate selective pressures and evolutionary constraints, and network analysis to elucidate relationships between structural features and functional divergence. Our findings indicate substantial variations in evolutionary and structural adaptations among the ADH families.

Background: Electronic health record (EHR) nursing summaries have the potential to support nurses in locating and synthesizing patient information. However, nurses’ role-specific perspectives are often excluded in the design of the EHR system. The purpose of this study was to describe nurses’ current use of nursing summaries and vital sign information within them and glean their ideas for design improvements. Methods: This research followed Nielsen and Norman's 4-phased design cycle and focused on the discovery and exploration phases to identify the context of nurses' use of the nursing summary (discovery) and to create prototype design layout using physical materials (exploration). Results: Ten clinical inpatient nurses participated in interviews and co-design activities. Nurses hardly use the nursing summary to overview a comprehensive patient's health status. The current design of a nursing summary lacks comprehensive patient information and contains much irrelevant data. Nurses prefer vital signs to be prominently displayed on the summary screen for easy visibility. Conclusion: Involving nurses in the design process can lead to a nursing summary that better meets their needs.

Fecal calprotectin (FC) is commonly used to assess Crohn’s disease (CD) activity. However, standardized cut-off values accounting for bowel resection history and disease location are lacking. In this study, we analyzed data from patients with CD who underwent magnetic resonance enterography, ileocolonoscopy, and FC measurements from January 2017 to December 2018. In 267 cases from 254 patients, the FC levels in the ‘operated’ patients were higher when the disease was active compared with those who were in the remission group (178 vs. 54.7 μg/g; p < 0.001), and similar findings were obtained for the ‘non-operated’ patients (449.5 vs. 40.95 μg/g; p < 0.001). The FC levels differed significantly according to the location of inflammation, with lower levels in the small bowel compared to those in the colon. The FC cut-off levels of 70.8 μg/g and 142.0 μg/g were considered optimal for predicting active disease for operated and non-operated patients, respectively. The corresponding FC cut-off levels of 70.8 μg/g and 65.0 μg/g were observed for patients with disease only in the small bowel. In conclusion, different FC cut-off values would be applicable to patients with CD based on their bowel resection history and disease location. Tight control with a lower FC target may benefit those with a history of bowel resection or small-bowel-only disease.

Abstract Chronic myeloid leukemia is generally required bone marrow biopsy for diagnosis. Although examining peripheral blood is less invasive, it has not been fully validated as a routine diagnostic test due to suboptimal sensitivity. To overcome this limitation, a number of methodologies based on microfluidics have been developed for sorting circulating tumor cells from peripheral blood of patients with leukemia. In order to develop a more convenient method, we designed an analysis protocol using motion microscopy that amplifies cellular micro motions in a captured video by re-rendering pixels to generate extreme magnified visuals. Intriguingly, no fluctuations around leukemic myeloblasts were observed with a motion microscope at any wavelength of 0–10 Hz. However, use of 0.05% hyaluronic acid, one type of non-newtonian fluid, demonstrated fluctuations around leukemic myeloblasts under conditions of 25 μm/s and 0.5–1.5 Hz with a motion microscope. Thus, the non-invasive detection of leukemic myeloblasts can offer a valuable supplementary diagnostic tool for assessment of drug efficacy for monitoring patients with chronic myeloid leukemia.

As the size of the IoT solutions and services market proliferates, industrial fields utilizing IoT devices are also diversifying. However, the proliferation of IoT devices, often intertwined with users’ personal information and privacy, has led to a continuous surge in attacks targeting these devices. However, conventional network-level intrusion detection systems with pre-defined rulesets are gradually losing their efficacy due to the heterogeneous environments of IoT ecosystems. To address such security concerns, researchers have utilized ML-based network-level intrusion detection techniques. Specifically, transfer learning has been dedicated to identifying unforeseen malicious traffic in IoT environments based on knowledge distillation from the rich source domain data sets. Nevertheless, since most IoT devices operate in heterogeneous but small-scale environments, such as home networks, selecting adequate source domains for learning proves challenging. This paper introduces a framework designed to tackle this issue. In instances where assessing an adequate data set through pre-learning using transfer learning is non-trivial, our proposed framework advocates the selection of a data set as the source domain for transfer learning. This selection process aims to determine the appropriateness of implementing transfer learning, offering the best practice in such scenarios. Our evaluation demonstrates that the proposed framework successfully chooses a fitting source domain data set, delivering the highest accuracy.

Dye-sensitized solar cells (DSSCs) can be used as greenhouse glazing materials in agrivoltaic systems because they are translucent, have different colors, and can produce electricity. However, the light quality of DSSCs differs from that of sunlight, and the visible light transmittance is low. Therefore, we compared the plant shape, growth, and leaf color of coleus, a highly photosensitive plant, under transparent glass and red-colored DSSCs. Coleus ‘Highway Rose’ was grown in transparent (T, the control), shaded (S), and DSSC (D) chambers maintained at 23 ± 2 °C. The DSSC chambers were additionally illuminated with blue (B), green (G), white (W), B+G, and R+B+W light-emitting diodes (LEDs) (D+L) at 60 μmol·m−2·s−1 photosynthetic photon flux density for 15 h from 05:00 to 20:00. The coleus generally exhibited good growth under the T treatment. However, the light quality of DSSCs differed from that of sunlight, and the visible light transmittance decreased. Coleus exhibited increased growth and leaf color characteristics under the supplemental B lighting treatments (D+L(RBW), D+L(B), D+L(BG), and D+L(W)). Supplemental lighting with B LEDs using DSSCs improved plant morphology growth and leaf color. On the other hand, supplemental G lighting reinforced the shade avoidance syndrome. Moreover, DSSCs could aid in reducing the energy required to control the environment.

Histological examination of collagen fiber organization is essential for pathologists to observe the wound healing process. A convolutional neural network (CNN) can be utilized to visually analyze collagen fibers during tissue remodeling in histology images. In this study, a universal CNN (UCNN) independent of the histological staining process is proposed to classify the histology images of burn-induced scar tissues and characterize collagen fiber organization. Normal and scar tissues obtained from an in vivo rodent model are stained using Masson’s Trichrome (MT) and Hematoxylin & Eosin (H&E). The proposed universal model is trained using both MT- and H&E-stained histological image datasets over multiple scales with color augmentation, and classification accuracies of up to 98% and 97% are achieved for the MT- and H&E-stained image datasets, respectively. Regardless of the histological staining process used, the collagen characteristics are visualized by determining the density and directional variance of the normal and scar tissues by using the features extracted with the proposed universal model. Statistical analysis results demonstrated clear differences between scar and normal tissues in terms of collagen fiber organization. The proposed UCNN model can contribute to the development of an intelligent and efficient method that pathologists can use to rapidly evaluate wound healing and tissue remodeling.

The evaluation of the morphology and organization of collagen fibers is critical in understanding wound healing and tissue remodeling after a thermal injury of the skin. However, histological analysis conducted by pathologists is often labor-intensive and limited to qualitative evaluations and scoring within a narrow field of view. In this study, we propose a convolutional neural network (CNN) model to classify Masson’s trichrome (MT)-stained histology images of burn-induced scar tissue and to characterize the microstructures of normal tissue and scar tissue in a quantitative manner. The scar tissue is created on in vivo rodent models and prepared for MT-stained histology slides after wound healing. A CNN model is developed, trained, and tested with various sizes of the histology images for classification and characterization. The proposed model classifies both normal tissue (i.e., without burn, as the control) and scar tissue at various scales with over 97% accuracy. The features acquired from the proposed CNN model visually characterizes the density and directional variance of the collagen fibers distributed in the dermal layers from whole histology images. The proposed deep learning technique can provide an objective and reliable method to rapidly assess and quantify wound repair and remodeling.

BackgroundThe COVID-19 pandemic has prompted an interest in whole-person health and emotional well-being. Informatics solutions through user-friendly tools such as mobile health apps offer immense value. Prior research developed a consumer-facing app MyStrengths + MyHealth using Simplified Omaha System Terms (SOST) to assess whole-person health. The MyStrengths + MyHealth app assesses strengths, challenges, and needs (SCN) for 42 concepts across four domains (My Living, My Mind and Networks, My Body, My Self-care; eg, Income, Emotions, Pain, and Nutrition, respectively). Given that emotional well-being was a predominant concern during the COVID-19 pandemic, we sought to understand whole-person health for participants with/without Emotions challenges. ObjectiveThis study aims to use visualization techniques and data from attendees at a Midwest state fair to examine SCN overall and by groups with/without Emotions challenges, and to explore the resilience of participants. MethodsThis cross-sectional and descriptive correlational study surveyed adult attendees at a 2021 Midwest state fair. Data were visualized using Excel and analyzed using descriptive and inferential statistics using SPSS. ResultsThe study participants (N=182) were primarily female (n=123, 67.6%), aged ≥45 years (n=112, 61.5%), White (n=154, 84.6%), and non-Hispanic (n=177, 97.3%). Compared to those without Emotions challenges, those with Emotions challenges were aged 18-44 (P

Abstract We report the extraction of silicon via a carbothermal reduction process using a CO2 laser beam as a heat source. The surface of a mixture of silica and carbon black powder became brown after laser beam irradiation for a few tens of seconds, and clear peaks of crystalline silicon were observed by Raman shift measurements, confirming the successful carbothermal reduction of silica. The influence of process parameters, including the laser beam intensity, radiation time, nitrogen gas flow in a reaction chamber, and the molar ratios of silica/carbon black of the mixture, on the carbothermal reduction process is explained in detail.

Background/Aims A link between oral cavity infections and chemotherapy-induced oral mucositis (CIOM) in patients with hematological malignancies (HMs) undergoing intensive chemotherapy (IC) or hematopoietic stem cell transplantation (HSCT) has been suggested. However, conclusive data are lacking, and there are no current guidelines for the prophylactic use of antimicrobials to prevent CIOM in these populations. Methods The relationships between herpes simplex virus (HSV) reactivation and Candida colonization in the oral cavity and CIOM in patients with HMs undergoing IC or HSCT were evaluated. Patients aged ≥ 19 years with HMs undergoing IC or HSCT were enrolled. Each patient was evaluated for HSV and Candida in the oral cavity along with CIOM at baseline and during the 2nd, 3rd, and 4th weeks. Results Seventy presentations among 56 patients were analyzed. CIOM was observed in 23 presentations (32.9%), with a higher incidence associated with HSCT (17 of 35 presentations, 48.6%) than with IC (six of 35 presentations, 8.6%). The reactivation of HSV-1 was significantly associated with an increased incidence of CIOM after adjusting for age, sex, type of disease, and treatment stage. A higher HSV-1 viral load was associated with an increased incidence of CIOM. The presence of Candida was not associated with CIOM. Conclusions HSV-1 reactivation in the oral cavity was highly associated with CIOM in patients with HMs undergoing high-dose chemotherapy.

Abstract One of the major obstacles to successful chemotherapy is multi-drug resistance (MDR). A multi-drug resistant cancerous cell abnormally overexpresses membrane transporters that pump anticancer drugs out of the cell, resulting in low anticancer drug delivery efficiency. To overcome the limitation, many attempts have been performed to inhibit the abilities of efflux receptors chemically or genetically or to increase the delivery efficiency of anticancer drugs. However, the results have not yet been satisfactory. In this study, we developed nanoparticle-microbubble complexes (DOX-NPs/Ce6-MBs) by conjugating doxorubicin loaded human serum albumin nanoparticles (DOX-NPs) onto the surface of Chlorin e6 encapsulated microbubbles (Ce6-MBs) in order to maximize anticancer efficiency by overcoming MDR. Under the ultrasound irradiation, DOX-NPs and Ce6 encapsulating self-assembled liposomes or micelles were effectively delivered into the cells due to the sonoporation effect caused by the microbubble cavitation. At the same time, reactive oxygen (ROS) generated from intracellularly delivered Ce6 by laser irradiation arrested the activity of ABCG2 efflux receptor overexpressed in doxorubicin-resistant breast cancer cells (MCF-7/ADR), resulting in increased the chemotherapy efficacy. In addition, the total number of side population cells that exhibit the properties of cancer stem-like cells were also reduced by the combination of photodynamic therapy and chemotherapy. In conclusion, DOX-NPs/Ce6-MBs will provide a platform for simultaneously overcoming MDR and increasing drug delivery and therefore, treatment efficiency, under ultrasound irradiation.

To realize high-quality transit-time ultrasonic flow measurements, accurate and precise estimates of the transit-time difference are essential. In this study, we propose deep learning-based neural network (NN) models to measure the transit-time difference in an ultrasonic flowmeter using a linear array transducer. Three approaches to compute the transit-time difference are presented: the cross-correlation with phase zero-crossing (XCorr), fully connected NN, and recurrent neural network (RNN) with long short-term memory (LSTM). The training data for the NN were generated by simulating target time differences by utilizing the experimental data acquired in the pipe system. To evaluate the performance of the proposed methods, linear regression, the Bland-Altman plot, and the root mean squared error (RMSE) were analyzed using testing data from the experiment. The results of this study show that the RNN-based approach yielded improved performance with an accuracy of up to 94% and a 33.48% reduction in the RMSE, compared to the XCorr method. In addition to the time difference estimation, our proposed RNN-based model can replace the entire flow rate estimation process, including interpolation, velocity correction, and zero-flow calibration. This study demonstrates the feasibility of an intelligent ultrasonic flowmeter employing the RNN-based model with potential in industrial applications.

Defect inspection is essential in the semiconductor industry to fabricate printed circuit boards (PCBs) with minimum defect rates. However, conventional inspection systems are labor-intensive and time-consuming. In this study, a semi-supervised learning (SSL)-based model called PCB_SS was developed. It was trained using labeled and unlabeled images under two different augmentations. Training and test PCB images were acquired using automatic final vision inspection systems. The PCB_SS model outperformed a completely supervised model trained using only labeled images (PCB_FS). The performance of the PCB_SS model was more robust than that of the PCB_FS model when the number of labeled data is limited or comprises incorrectly labeled data. In an error-resilience test, the proposed PCB_SS model maintained stable accuracy (error increment of less than 0.5%, compared with 4% for PCB_FS) for noisy training data (with as much as 9.0% of the data labeled incorrectly). The proposed model also showed superior performance when comparing machine-learning and deep-learning classifiers. The unlabeled data utilized in the PCB_SS model helped with the generalization of the deep-learning model and improved its performance for PCB defect detection. Thus, the proposed method alleviates the burden of the manual labeling process and provides a rapid and accurate automatic classifier for PCB inspections.

Background/Aims: Advanced biliary tract cancer (BTC) is associated with poor survival. A recent phase II study of triplet combination chemotherapy, including gemcitabine, cisplatin, and nanoparticle albumin-bound (nab)-paclitaxel, has shown promising results. This study aimed to compare the efficacy of triplet and standard doublet chemotherapy in a real-world setting. Methods: Patients with advanced BTC treated with triplet and doublet chemotherapy regimens were recruited. The propensity-score nearest neighbor matching method with a ratio of one-to-one was used to create a matched cohort for comparison. Progression-free survival (PFS), overall survival (OS), and safety profiles were examined in both groups. Results: A total of 68 patients (n=34 per group) were included in the matched cohort, and their baseline characteristics were well balanced. Survival outcomes in the triplet chemotherapy group were not better than those in the doublet chemotherapy group, with a median PFS of 7.5 months (95% confidence interval [CI], 4.1 to 10.9) versus 7.2 months (95% CI, 5.6 to 8.9) (hazard ratio [HR], 0.93; 95% CI, 0.53 to 1.62; p=0.793) and a median OS of 13.7 months (95% CI, 8.8 to 18.7) versus 12.2 months (95% CI, 8.4 to 16.0) (HR 0.73; 95% CI, 0.38 to 1.41; p=0.354), respectively. In addition, the treatment-related severe adverse events, such as neutropenia, were more common in the triplet chemotherapy group. Conclusions: Gemcitabine, cisplatin, and nab-paclitaxel did not improve the PFS or OS compared to that achieved by standard chemotherapy in patients with advanced BTC. The benefits of triplet chemotherapy in advanced BTC require examination in large randomized controlled trials.

Abstract Background Temperature monitoring during high-intensity focused ultrasound (HIFU) therapy on tissue is essential to regulate the degree of thermal coagulation and to achieve the desired treatment outcomes eventually. The aim of the current study was to design and investigate the feasibility of a proportional–integral–derivative (PID) temperature controller-integrated portable HIFU driver for thermal coagulation. Methods A portable HIFU driver was designed and operated at a maximum output voltage of 50 V with pulse-width modulation signals at 2 MHz. The temperature of ex vivo bovine liver tissue was monitored using a K-type thermocouple during the 2-MHz HIFU exposure. Results The tissue temperature was maintained at 60 °C using a PID controller-integrated HIFU driver that modulated the output voltage during the 300-s HIFU exposure. The ex vivo testing demonstrated that the tissue temperature at the focal point approached the chosen temperature, i.e., 60 °C, within 70 s. The temperature was maintained with a deviation of less than 4 °C until the HIFU driver voltage was turned off at 300 s. Conclusions The designed PID controller-integrated HIFU driver can be used as a small portable tool to regulate the tissue temperature in real time and achieve thermal coagulation via HIFU sonication.

Abstract Background Until now, there is no effective anti-fibrotic therapy available for liver cirrhosis. Stem cell therapies have been studied for the treatment of hepatic fibrosis. However, the use of embryonic stem cells or induced pluripotent stem cells (iPSC) has limitations such as ethical concern or malignancy potential. Induced hepatocyte-like cells (iHEPs) generated by direct reprogramming technology may overcome these limitations. Methods In this study, we generated iHEPs by direct reprogramming from mouse embryonic fibroblast (MEF) either using specific transcription factors such as c-Myc and Klf-4 (type A), or adding small molecules to HNF1α (type B). Results We investigated the effect of iHEPs on acute liver injury and chronic hepatic fibrosis animal models induced by CCl4 intra-peritoneal injection in BALB/C nude mice. In acute liver injury model, serum AST/ALT levels peaked at 24 h after CCl4 injection. Intra-splenic transplantation of iHEPs significantly attenuated CCl4-induced acute liver injury. GFP-labeled iHEPs (type A) migrated to the liver after intra-splenic transplantation that was confirmed by Western blotting and immunofluorescence staining. We found that GFP and albumin were co-localized in migrated iHEPs in the liver suggesting migrated iHEPs were functional. In chronic hepatic fibrosis mice experiment, transplantation of either type A or type B iHEPs significantly attenuated liver fibrosis induced by CCl4 injection for 10 weeks. Conclusions Our study suggests that iHEPs may be used as a novel therapeutic strategy for the treatment of hepatic fibrosis.

This study was to determine the effect of oleogel on lipid metabolism in rats fed high-fat diet. Male SD rats (n = 32; 4-wk-old) and obese Zucker rats (n = 6) fed either beef tallow containing high fat diet (HFD) or beeswax oleogel containing canola oil (BOG) for 2 months. Substitution of 50% BT by BOG significantly reduced body weight. Increased serum triglyceride by HFD was reduced (P

Dental caries are one of the chronic diseases caused by organic acids made from oral microbes. However, there was a lack of knowledge about the oral microbiome of Korean children. The aim of this study was to analyze the metagenome data of the oral microbiome obtained from Korean children and to discover bacteria highly related to dental caries with machine learning models. Saliva and plaque samples from 120 Korean children aged below 12 years were collected. Bacterial composition was identified using Illumina HiSeq sequencing based on the V3–V4 hypervariable region of the 16S rRNA gene. Ten major genera accounted for approximately 70% of the samples on average, including Streptococcus, Neisseria, Corynebacterium, and Fusobacterium. Differential abundant analyses revealed that Scardovia wiggsiae and Leptotrichia wadei were enriched in the caries samples, while Neisseria oralis was abundant in the non-caries samples of children aged below 6 years. The caries and non-caries samples of children aged 6–12 years were enriched in Streptococcus mutans and Corynebacterium durum, respectively. The machine learning models based on these differentially enriched taxa showed accuracies of up to 83%. These results confirmed significant alterations in the oral microbiome according to dental caries and age, and these differences can be used as diagnostic biomarkers.

Magnolia ofeliae A. Vázquez & Cuevas, a plant species endemic to south Jalisco, Mexico, is a critically endangered (CR) species based on the IUCN Red List. In this study, we assembled its complete chloroplast (cp) genome. The total genome size of M. ofeliae was 159,839 bp including four subregions: a large single-copy (LSC) region of 88,027 bp and a small single-copy (SSC) region of 18,752 bp separated by a pair of identical inverted repeat regions (IRs) of 26,530 bp each. The GC content of the cp genome of M. ofeliae is 39.3%. The cp genome encoded a set of 113 genes, containing 79 protein-coding genes, 30 tRNA genes, and four rRNA genes. Phylogenetic analysis results that M. ofeliae is a sister to all other magnolias in the subfamily Magnolioideae.

Infrared thermal imaging has been widely used to show the correlation between thermal characteristics of the body and muscle activation. This study aims to investigate a method using thermal imaging to visualize and differentiate target muscles during resistance training. Thermal images were acquired to monitor three target muscles (i.e., biceps brachii, triceps brachii, and deltoid muscle) in the brachium while varying the training weight, duration, and order of training. The acquired thermal images were segmented and converted to heat maps. By generating difference heat maps from pairs of heat maps during training, the target muscles were clearly visualized, with an average temperature difference of 0.86 °C. It was observed that training order had no significant effect on skin surface temperature. The difference heat maps were also used to train a convolutional neural network (CNN) to show the feasibility of target muscle classification, with an accuracy of 92.3%. This study demonstrated that infrared thermal imaging could be effectively utilized to locate and differentiate target muscle activation during resistance training.

Mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN) account for a large portion of solitary pancreatic cystic neoplasms (PCN). In this study we implemented a convolutional neural network (CNN) model using ResNet50 to differentiate between MCN and SCN. The training data were collected retrospectively from 59 MCN and 49 SCN patients from two different hospitals. Data augmentation was used to enhance the size and quality of training datasets. Fine-tuning training approaches were utilized by adopting the pre-trained model from transfer learning while training selected layers. Testing of the network was conducted by varying the endoscopic ultrasonography (EUS) image sizes and positions to evaluate the network performance for differentiation. The proposed network model achieved up to 82.75% accuracy and a 0.88 (95% CI: 0.817–0.930) area under curve (AUC) score. The performance of the implemented deep learning networks in decision-making using only EUS images is comparable to that of traditional manual decision-making using EUS images along with supporting clinical information. Gradient-weighted class activation mapping (Grad-CAM) confirmed that the network model learned the features from the cyst region accurately. This study proves the feasibility of diagnosing MCN and SCN using a deep learning network model. Further improvement using more datasets is needed.

Polyhexamethylene guanidine (PHMG), a major component of humidifier disinfectants (HDs), is responsible for the outbreak of pulmonary diseases in pregnant women and children in South Korea. This study aims to characterize the developmental toxicity and thyroid endocrine disruption of Vegetable Home Cleanup HD and its main component, PHMG-hydrochloride (PHMG-H), in zebrafish embryo/larvae after a 7-d exposure. Acute lethality, development, whole-body thyroid hormones, and transcription of genes related to the hypothalamus-pituitary-thyroid axis were investigated. Zebrafish embryos exposed to the actual-use concentration (0.6%) of HD exhibited significant embryo coagulation and larval mortality. The concentration of triiodothyronine (T3) was significantly high in fish exposed to 0.4 mg/L PHMG-H, accompanied by the downregulation of the tshβ gene. These results suggest a feedback mechanism for the regulation of increased T3 levels. Significant decrease of thyroxine (T4) concentration, increase of T3/T4 ratio, and upregulation of the deio2 gene in fish exposed to PHMG-H suggested that there was an increase in the active T3 due to the catalysis of outer ring deiodination. Future research is required to determine the factors that contribute to the differences in toxicity between the two counter ions of PHMG.

Vascular damage is one of the therapeutic mechanisms of photodynamic therapy (PDT). In particular, short-term PDT treatments can effectively destroy malignant lesions while minimizing damage to nonmalignant tissue. In this study, we investigate the feasibility of label-free quantitative photoacoustic microscopy (PAM) for monitoring the vasculature changes under the effect of PDT in mouse ear melanoma tumors. In particular, quantitative vasculature evaluation was conducted based on Hessian filter segmentation. Three-dimensional morphological PAM and depth-resolved images before and after PDT treatment were acquired. In addition, five quantitative vasculature parameters, including the PA signal, vessel diameter, vessel density, perfused vessel density, and vessel complexity, were analyzed to evaluate the influence of PDT on four different areas: Two melanoma tumors, and control and normal vessel areas. The quantitative and qualitative results successfully demonstrated the potential of the proposed PAM-based quantitative approach to evaluate the effectiveness of the PDT method.

Laser lights have been used by dermatologists for tattoo removal through photothermal interactions. However, most clinical studies used a visual scoring method to evaluate the tattoo removal process less objectively, leading to unnecessary treatments. This study aimed to develop a simple and quantitative imaging method to monitor the degree of tattoo removal in in vivo skin models. Sprague Dawley rat models were tattooed with four different concentrations of black inks. Laser treatment was performed weekly on the tattoos using a wavelength of 755 nm over six weeks. Images of non-treated and treated samples were captured using the same method after each treatment. The intensities of the tattoos were measured to estimate the contrast for quantitative comparison. The results demonstrated that the proposed monitoring method quantified the variations in tattoo contrast after the laser treatment. Histological analysis validated the significant removal of tattoo inks, no thermal injury to adjacent tissue, and uniform remodeling of epidermal and dermal layers after multiple treatments. This study demonstrated the potential of the quantitative monitoring technique in assessing the degree of clearance level objectively during laser treatments in clinics.

Most ultrasonic flowmeters utilize several wedge sensors for transmission and reception. Thus, the location and alignment of the sensors are critical factors that determine the performance of the ultrasonic flowmeter. In this study, we proposed an ultrasound liquid flowmeter utilizing a 128-element linear array transducer with a transmit delay control for varying the incidence angles of ultrasound wave transmission. The performance of the flowmeter was evaluated at flow rates of 0−50 L/min in a specially designed pipe system. Flow estimation was performed with the transit-time method using cross-correlation with phase zero-crossing for sub-sample estimation. While a single plane wave approach performed invasive electromagnetic measurements with only 74% accuracy as a reference, a multiple angular compensation method with 24 angles was proposed to increase the accuracy of measurements up to 93%. This study demonstrated the capability of the non-invasive single-sided ultrasonic flowmeter with a linear array transducer for liquid flow measurements in the metal pipe system.

The G protein-coupled receptor (GPCR) family of proteins comprises signaling proteins that mediate cellular responses to various hormones and neurotransmitters, and serves as a prime target for drug discovery. Towards our goal of discovering secondary metabolites from natural sources that can function as neuronal drugs, we evaluated the modulatory effect of eckol on various GPCRs via cell-based functional assays. In addition, we conducted in silico predictions to obtain molecular insights into the functional effects of eckol. Functional assays revealed that eckol had a concentration-dependent agonist effect on dopamine D3 and D4 receptors. The half maximal effective concentration (EC50) of eckol for the dopamine D3 and D4 receptors was 48.62 ± 3.21 and 42.55 ± 2.54 µM, respectively, while the EC50 values of dopamine as a reference agonist for these two receptors were 2.9 and 3.3 nM, respectively. In silico studies revealed that a low binding energy in addition to hydrophilic, hydrophobic, π⁻alkyl, and π⁻π T-shaped interactions are potential mechanisms by which eckol binds to the dopamine receptors to exert its agonist effects. Molecular dynamics (MD) simulation revealed that Phe346 of the dopamine receptors is important for binding of eckol, similar to eticlopride and dopamine. Our results collectively suggest that eckol is a potential D3/D4 agonist for the management of neurodegenerative diseases, such as Parkinson’s disease.

Understanding the residual tattoo ink in skin after laser treatment is often critical for achieving good clinical outcomes. The current study aims to investigate the feasibility of a light-emitting diode (LED)-assisted CMOS camera to estimate the relative variations in tattoo contrast after the laser treatment. Asian mice were tattooed using two color inks (black and red). The LED illumination was a separate process from the laser tattoo treatment. Images of the ink tattoos in skin were acquired under the irradiation of three different LED colors (red, green, and blue) for pre- and post-treatment. The degree of contrast variation due to the treatment was calculated and compared with the residual tattoo distribution in the skin. The black tattoo demonstrated that the contrast consistently decreased after the laser treatment for all LED colors. However, the red tattoo showed that the red LED yielded an insignificant contrast whereas the green and blue LEDs induced a 30% (p < 0.001) and 26% (p < 0.01) contrast reduction between the treatment conditions, respectively. The proposed LED-assisted CMOS camera can estimate the relative variations in the image contrast before and after the laser tattoo treatment.

A number of laser parameters are often regulated to enhance ablation efficiency during laser surgery. As one of clinical treatments, laser removal of benign prostate hyperplasia has been well accepted by surgical urologists. However, due to complex interactions of the surgical parameters, the procedure is still lengthy and dependent upon the surgeon’s skill and experience. The aim of the current study is to evaluate the feasibility of response surface method (RSM) to comprehend ablative interactions of multi-parameters and to identify the optimal ablation rate (AR). As a surrogate model in the feasibility study, bovine liver tissue was utilized for ex vivo ablation testing. Three laser parameters pertinent to laser prostatectomy were selected: power (P), treatment speed (TS), and beam spot (BS). As a three-level fractional factorial RSM, Box Behnken design (BBD) was employed to identify the range of each parameter for achieving the optimal AR. The results showed that regardless of TS, AR was linearly contingent on both P and BS due to high irradiance. TS of 6~7 mm/s induced the maximal AR when P of 180 W and BS of 0.4 mm2. The corresponding volumetric density energy yielded an ablation volume of 80 mm2, which was close to a transition to volumetric saturation. The BBD-based model showed a good agreement with the experimental data in terms of ablation volume. The proposed multivariate parametric analysis can be an efficient design method to identify the optimal conditions for laser therapeutics. Further investigations will be performed on prostatic tissue to validate the proposed approach and to explore various optimization processes.

C-phycocyanin, a natural blue-colored pigment-protein complex was explored as a novel photosensitizer for use in low-level laser therapy under 625-nm laser illumination. C-phycocyanin produced singlet oxygen radicals and the level of reactive oxygen species (ROS) were raised in extended time of treatment. It did not exhibit any visible toxic effect in the absence of light. Under 625-nm laser irradiation, c-phycocyanin generated cytotoxic stress through ROS induction, which killed MDA-MB-231 breast cancer cells depending on concentrations. Different fluorescent staining of laser-treated cells explored apoptotic cell death characteristics like the shrinking of cells, cytoplasmic condensation, nuclei cleavage, and the formation of apoptotic bodies. In conclusion, phycocyanin is a non-toxic fluorescent pigment that can be used in low-level light therapy.

Volumetric ultrasound imaging has been primarily used as a clinical tool to diagnose cardiac dysfunction. However, the modality still suffers from system complexity due to a large number of receive channels for volumetric reconstruction. The aim of the study was to numerically assess a cross-shaped sparse array with a dynamic transmit focusing method to reduce the system complexity and to compensate for the image quality of unfocused volumetric imaging. Analytical simulations with two-way point spread functions demonstrated that the cross-shaped sparse array entailed up to an 84% increase in side-lobes (up to 32 dB), thus leading to significant degradation of the point spread function in the diagonal plane. A dynamic transmit focusing method was able to vividly overcome the image degradation by transmitting more beams with tilting angles. This also yielded a comparable image sensitivity (65 dB) to that of the full array when 31 × 31 tilted waves were used. The proposed cross-shaped sparse array with the dynamic transit focusing method can be used to enhance the frame rate as well as the image quality for unfocused ultrasound imaging.

Astaxanthin, a kind of photosynthetic pigment, was employed for gold nanoparticle formation. Nanoparticles were characterized using Ulteraviolet-Visible (UV-Vis) spectroscopy, transmission electron microscopy, and X-ray diffraction, and the possible presence of astaxanthin functional groups were analyzed by Fourier transform infrared spectroscopy (FTIR). The cytotoxic effect of synthesized nanoparticles was evaluated against MDA-MB-231 (human breast cancer cells) using a tetrazolium-based assay, and synthesized nanoparticles exhibited dose-dependent toxicity. The morphology upon cell death was differentiated through fluorescent microscopy using different stains that predicted apoptosis. The synthesized nanoparticles were applied in ultrasound-coupled photoacoustic imaging to obtain good images of treated cells. Astaxanthin-reduced gold nanoparticle has the potential to act as a promising agent in the field of photo-based diagnosis and therapy.

In the rapidly evolving landscape of modern healthcare, nurses must proficiently navigate data utilization and grasp the principles of data science. Despite this urgency, nursing stakeholders currently do not fully understand the extent of data literacy or data science literacy they need to acquire. This paper aims to elucidate the distinctions between data literacy and data science literacy, offering insights into strategies for nurturing these competencies within nursing education, research, and practice. Through a state-of-the-art review of 22 articles and six healthcare industry resources, we identified a notable absence of comprehensive frameworks and assessment tools, highlighting key areas for future development. [ABSTRACT FROM AUTHOR]