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The primary goal during cancer removal surgery is to completely excise the malignant tumor. Because the color of the tumor and surrounding tissues is very similar, it is difficult to observe with the naked eye, posing a risk of damaging surrounding blood vessels during the tumor removal process. Therefore, fluorescence emission is induced using a fluorescent contrast agent, and color classification is monitored through camera imaging. LEDs must be irradiated to generate the fluorescent emission electromotive force. However, the power and beam width of the LED are insufficient to generate this force effectively, so the beam width and intensity must be increased to irradiate the entire lesion. Additionally, there should be no shaded areas in the beam irradiation range. This paper proposes a method to enhance the beam width and intensity while eliminating shadow areas. A total reflection beam mirror was used to increase beam width and intensity. However, when the beam width increased, a shadow area appeared at the edge, limiting irradiation of the entire lesion. To compensate for this shadow area, a concave lens was combined with the beam mirror, resulting in an increase in beam width and intensity by more than 1.42 times and 18.6 times, respectively. Consequently, the beam width reached 111.8°, and the beam power was 13.6 mW. The proposed method is expected to be useful for observing tumors through the induction of fluorescence emission during cancer removal surgery or for pathological examination in the pathology department.

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Cancer surgery is aimed at complete tumor resection and accurate lymph node detection. However, numerous blood vessels are distributed within the tumor, and the colors of the tumor, blood vessels, and lymph nodes are similar, making observations with the naked eye difficult. Therefore, tumors, blood vessels, and lymph nodes can be monitored via color classification using an operating microscope to induce fluorescence emission. However, as the beam width of the LED required to induce fluorescence emission is narrow and the power loss of the beam is significant at a certain working distance, there are limitations to inducing fluorescence emission, and light reflection occurs in the observation image, obstructing the view of the observation area. Therefore, the removal of reflected light is essential to avoid missing the diagnosis of the lesion under observation. This paper proposes the use of a beam mirror and polarizing filter to increase the beam width and beam intensity. The refraction and reflection effects of the beam were utilized using the beam mirror, and the rotation angle of the polarizing filter was adjusted to remove light reflection. Consequently, the minimum beam power using the beam mirror was 10.9 mW, the beam width was doubled to 40.2°, and more than 98% of light reflection was removed at 90° and 270°. With light reflection effectively eliminated, clear observation of lesions is possible. This method is expected to be used effectively in surgical, procedural, and diagnostic departments.

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This study aimed to investigate whether metabolic dysfunction-associated fatty liver disease (MAFLD) defined by the fatty liver index (FLI) affects the decline in kidney function and whether this relationship is still observed in MAFLD defined by ultrasonography (USG). A retrospective cohort study was conducted using de-identified data from participants who received health checkups at Samsung Changwon Hospital between 2002 and 2018. The primary and secondary exposures were the presence of FLI- and USG-defined MAFLD, respectively. The primary outcome was 5-years slope of eGFR. The secondary outcome was a rapid decline in kidney function, defined as a 5-years slope of estimated glomerular filtration rate (eGFR) of less than - 3 mL/min/1.73 m2 per year. A total of 37,500 participants were included in the analysis. Participants with FLI-defined MAFLD had a larger decline in 5-year eGFR slope than those without FLI-defined MAFLD (beta coefficients - 0.11; 95% CI - 0.14 to - 0.08). Participants with FLI-defined MAFLD had a higher risk of rapid kidney function decline than those without FLI-defined MAFLD (odds ratio 1.33; 95% confidence intervals (CIs) 1.05-1.69). However, USG-defined MAFLD was less related to kidney function decline. In conclusion, the presence of FLI-defined MAFLD was associated with larger and faster kidney function decline.

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Garnet-type Li7La3Zr2O12 (LLZO) Li-ion solid electrolytes are promising candidates for safe, next-generation solid-state batteries. In this study, we synthesize Ga-doped LLZO (Ga-LLZO) electrolytes using a microwave-assisted solvothermal method followed by low-temperature heat treatment. The nanostructured precursor (<50 nm) produced by the microwave-assisted solvothermal process has a high surface energy, facilitating the reaction for preparing garnet-type Ga-LLZO powders (<800 nm) within a short time (<5 h) at a low calcination temperature (<700 °C). Additionally, the calcined nanostructured Ga-LLZO powder can be sintered to produce a high-density pellet with minimized grain boundaries under moderate sintering conditions (temperature: 1150 °C, duration: 10 h). The optimal doping concentration was determined to be 0.4 mol% Ga, which resulted significantly increased the ionic conductivity (1.04 × 10-3 S cm-1 at 25 °C) and stabilized the cycling performance over 1700 h at 0.4 mA cm-2. This approach demonstrates the potential to synthesize oxide-type solid electrolyte materials with improved properties for solid-state batteries.

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High-energy impacts, like vehicle crashes or falls, can lead to pelvic ring injuries. Rapid diagnosis and treatment are crucial due to the risks of severe bleeding and organ damage. Pelvic radiography promptly assesses fracture extent and location, but struggles to diagnose bleeding. The AO/OTA classification system grades pelvic instability, but its complexity limits its use in emergency settings. This study develops and evaluates a deep learning algorithm to classify pelvic fractures on radiographs per the AO/OTA system. Pelvic radiographs of 773 patients with pelvic fractures and 167 patients without pelvic fractures were retrospectively analyzed at a single center. Pelvic fractures were classified into types A, B, and C using medical records categorized by an orthopedic surgeon according to the AO/OTA classification system. Accuracy, Dice Similarity Coefficient (DSC), and F1 score were measured to evaluate the diagnostic performance of the deep learning algorithms. The segmentation model showed high performance with 0.98 accuracy and 0.96-0.97 DSC. The AO/OTA classification model demonstrated effective performance with a 0.47-0.80 F1 score and 0.69-0.88 accuracy. Additionally, the classification model had a macro average of 0.77-0.94. Performance evaluation of the models showed relatively favorable results, which can aid in early classification of pelvic fractures.

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In pathological diagnostics, histological images highlight the oncological features of excised specimens, but they require laborious and costly staining procedures. Despite recent innovations in label-free microscopy that simplify complex staining procedures, technical limitations and inadequate histological visualization are still problems in clinical settings. Here, we demonstrate an interconnected deep learning (DL)-based framework for performing automated virtual staining, segmentation, and classification in label-free photoacoustic histology (PAH) of human specimens. The framework comprises three components: (1) an explainable contrastive unpaired translation (E-CUT) method for virtual H&E (VHE) staining, (2) an U-net architecture for feature segmentation, and (3) a DL-based stepwise feature fusion method (StepFF) for classification. The framework demonstrates promising performance at each step of its application to human liver cancers. In virtual staining, the E-CUT preserves the morphological aspects of the cell nucleus and cytoplasm, making VHE images highly similar to real H&E ones. In segmentation, various features (e.g., the cell area, number of cells, and the distance between cell nuclei) have been successfully segmented in VHE images. Finally, by using deep feature vectors from PAH, VHE, and segmented images, StepFF has achieved a 98.00% classification accuracy, compared to the 94.80% accuracy of conventional PAH classification. In particular, StepFF's classification reached a sensitivity of 100% based on the evaluation of three pathologists, demonstrating its applicability in real clinical settings. This series of DL methods for label-free PAH has great potential as a practical clinical strategy for digital pathology.

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Past studies have explored formant centering, a corrective behavior of convergence over the duration of an utterance toward the formants of a putative target vowel. In this study, we establish the existence of a similar centering phenomenon for pitch in healthy elderly controls and examine how such corrective behavior is altered in Alzheimer's Disease (AD). We found the pitch centering response in healthy elderly was similar when correcting pitch errors below and above the target (median) pitch. In contrast, patients with AD showed an asymmetry with a larger correction for the pitch errors below the target phonation than above the target phonation. These findings indicate that pitch centering is a robust compensation behavior in human speech. Our findings also explore the potential impacts on pitch centering from neurodegenerative processes impacting speech in AD.

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Obstructive sleep apnea is characterized by a decrease or cessation of breathing due to repetitive closure of the upper airway during sleep, leading to a decrease in blood oxygen saturation. In this study, employing a U-Net model, we utilized drug-induced sleep endoscopy images to segment the major causes of airway obstruction, including the epiglottis, oropharynx lateral walls, and tongue base. The evaluation metrics included sensitivity, specificity, accuracy, and Dice score, with airway sensitivity at 0.93 (± 0.06), specificity at 0.96 (± 0.01), accuracy at 0.95 (± 0.01), and Dice score at 0.84 (± 0.03), indicating overall high performance. The results indicate the potential for artificial intelligence (AI)-driven automatic interpretation of sleep disorder diagnosis, with implications for standardizing medical procedures and improving healthcare services. The study suggests that advancements in AI technology hold promise for enhancing diagnostic accuracy and treatment efficacy in sleep and respiratory disorders, fostering competitiveness in the medical AI market.

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The development of greener and more sustainable synthesis processes for manufacturing commodity chemicals is of great importance. The majority of current phenol production methods involve harsh reaction conditions with high energy consumption, causing severe environmental pollution. In this study, we present a novel approach for the decarboxylation of hydroxybenzoic acids (HBAs) to phenol using a choline chloride-urea (ChCl-urea) deep eutectic solvent (DES). Our study reveals the remarkable dual performance of ChCl-urea both as a catalyst and solvent for the decarboxylation of HBA, resulting in a high phenol yield (94 mol%) under mild reaction conditions. The proposed reaction pathway, established through a combination of experiments and computational simulations, enhances our understanding of this process. The recyclability of the DES system during decarboxylation was also assessed. Our findings demonstrate that the integration of DES into conventional chemical processes can pave the way for sustainable manufacturing, exemplifying a novel approach for producing phenol from abundant natural resources using designer solvents.

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The skin prick test (SPT) is a key tool for identifying sensitized allergens associated with immunoglobulin E-mediated allergic diseases such as asthma, allergic rhinitis, atopic dermatitis, urticaria, angioedema, and anaphylaxis. However, the SPT is labor-intensive and time-consuming due to the necessity of measuring the sizes of the erythema and wheals induced by allergens on the skin. In this study, we used an image preprocessing method and a deep learning model to segment wheals and erythema in SPT images captured by a smartphone camera. Subsequently, we assessed the deep learning model's performance by comparing the results with ground-truth data. Using contrast-limited adaptive histogram equalization (CLAHE), an image preprocessing technique designed to enhance image contrast, we augmented the chromatic contrast in 46 SPT images from 33 participants. We established a deep learning model for wheal and erythema segmentation using 144 and 150 training datasets, respectively. The wheal segmentation model achieved an accuracy of 0.9985, a sensitivity of 0.5621, a specificity of 0.9995, and a Dice similarity coefficient of 0.7079, whereas the erythema segmentation model achieved an accuracy of 0.9660, a sensitivity of 0.5787, a specificity of 0.97977, and a Dice similarity coefficient of 0.6636. The use of image preprocessing and deep learning technology in SPT is expected to have a significant positive impact on medical practice by ensuring the accurate segmentation of wheals and erythema, producing consistent evaluation results, and simplifying diagnostic processes.

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Cerium oxide nanoparticles are known for their antibacterial effects resulting from Ce3+ to Ce4+ conversion. Application of such cerium oxide nanoparticles in dentistry has been previously considered but limited due to deterioration of mechanical properties. Hence, this study aimed to examine mesoporous silica (MCM-41) coated with cerium oxide nanoparticles and evaluate the antibacterial effects and mechanical properties when applied to dental composite resin. Cerium oxide nanoparticles were coated on the MCM-41 surface using the sol-gel method by adding cerium oxide nanoparticle precursor to the MCM-41 dispersion. The samples were tested for antibacterial activity against Streptococcus mutans via CFU and MTT assays. The mechanical properties were assessed by flexural strength and depth of cure according to ISO 4049. Data were analyzed using a t-test, one-way ANOVA, and Tukey's post-hoc test (p = 0.05). The experimental group showed significantly increased antibacterial properties compared to the control groups (p < 0.005). The flexural strength exhibited a decreasing trend as the amount of cerium oxide nanoparticle-coated MCM-41 increased. However, the flexural strength and depth of cure values of the silane group met the ISO 4049 standard. Antibacterial properties increased with increasing amounts of cerium oxide nanoparticles. Although the mechanical properties decreased, silane treatment overcame this drawback. Hence, the cerium oxide nanoparticles coated on MCM-41 may be used for dental resin composite.

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Malakoplakia is a rare chronic inflammatory disease that has been rarely reported in the genitourinary tract, gastrointestinal tract, adrenal glands, skin, lungs, bone, and endometrium. Central nervous system malakoplakia is extremely rare, and even then, it has only been reported in the cerebrum and cerebellum. A definite diagnosis of malakoplakia depends on microscopic detection of Michaelis-Gutmann bodies. We would like to present the case of a 61-year-old male who, after undergoing a liver transplant and receiving prolonged antibiotic treatment for Escherichia coli bacteremia, presented with quadriparesis and gait disturbance. The clinical and radiologic appearance of malakoplakia mimics that of malignant tumor. This is a condition with no established appropriate treatment and presents challenges due to its spinal cord location. However, this case presents a case of spinal cord malakoplakia and may provide newly differential diagnosis of an intramedullary mass in the spinal cord.

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Background: Affective recognition and sensory processing are impaired in people with autism. However, no mouse model of autism comanifesting these symptoms is available, thereby limiting the exploration of the relationship between affective recognition and sensory processing in autism and the molecular mechanisms involved. Methods: With Negr1 -/- mice, we conducted the affective state discrimination test and an odor habituation/dishabituation test. Data were analyzed using the k-means clustering method. We also employed a whole-cell patch clamp and bromodeoxyuridine incorporation assay to investigate underlying mechanisms. Results: When encountering mice exposed to restraint stress or chronic pain, wild-type mice discriminated between them by either approaching the stressed mouse or avoiding the painful mouse, whereas Negr1 -/- mice showed unbiased social interactions with them. Next, we demonstrated that both wild-type and Negr1 -/- mice used their olfaction for social interaction in the experimental context, but Negr1 -/- mice showed aberrant olfactory habituation and dishabituation against social odors. In electrophysiological studies, inhibitory inputs to the mitral cells in the olfactory bulb were increased in Negr1 -/- mice compared with wild-type mice, and subsequently their excitability was decreased. As a potential underlying mechanism, we found that adult neurogenesis in the subventricular zone was diminished in Negr1 -/- mice, which resulted in decreased integration of newly generated inhibitory neurons in the olfactory bulb. Conclusions: NEGR1 contributes to mouse affective recognition, possibly by regulating olfactory neurogenesis and subsequent olfactory sensory processing. We propose a novel neurobiological mechanism of autism-related behaviors based on disrupted adult olfactory neurogenesis.

A deficit in affective discrimination is one of the major symptoms of autism spectrum disorder, the molecular/cellular mechanisms of which have yet to be explored. Here, we demonstrated that Negr1-deficient autism-relevant mice did not show preferential social interaction with affectively provoked mice (i.e., stress and pain) and showed its association with aberrant olfactory processing for other mice. As a potential underlying cellular mechanism, we found a decrease in adult-born neurons and excitatory/inhibitory imbalance in the olfactory bulb region. These results suggest that further investigation into the role of Negr1 and olfactory processing could provide valuable insights into molecular and cellular mechanisms of autism.

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BACKGROUND: Postoperative hypoparathyroidism is a major complication of thyroidectomy, occurring when the parathyroid glands are inadvertently damaged during surgery. Although intraoperative images are rarely used to train artificial intelligence (AI) because of its complex nature, AI may be trained to intraoperatively detect parathyroid glands using various augmentation methods. The purpose of this study was to train an effective AI model to detect parathyroid glands during thyroidectomy. METHODS: Video clips of the parathyroid gland were collected during thyroid lobectomy procedures. Confirmed parathyroid images were used to train three types of datasets according to augmentation status: baseline, geometric transformation, and generative adversarial network-based image inpainting. The primary outcome was the average precision of the performance of AI in detecting parathyroid glands. RESULTS: 152 Fine-needle aspiration-confirmed parathyroid gland images were acquired from 150 patients who underwent unilateral lobectomy. The average precision of the AI model in detecting parathyroid glands based on baseline data was 77%. This performance was enhanced by applying both geometric transformation and image inpainting augmentation methods, with the geometric transformation data augmentation dataset showing a higher average precision (79%) than the image inpainting model (78.6%). When this model was subjected to external validation using a completely different thyroidectomy approach, the image inpainting method was more effective (46%) than both the geometric transformation (37%) and baseline (33%) methods. CONCLUSION: This AI model was found to be an effective and generalizable tool in the intraoperative identification of parathyroid glands during thyroidectomy, especially when aided by appropriate augmentation methods. Additional studies comparing model performance and surgeon identification, however, are needed to assess the true clinical relevance of this AI model.

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BACKGROUND: We aimed to determine the feasibility of utilizing deep learning-based predictions of the indications for cracked tooth extraction using panoramic radiography. METHODS: Panoramic radiographs of 418 teeth (group 1: 209 normal teeth; group 2: 209 cracked teeth) were evaluated for the training and testing of a deep learning model. We evaluated the performance of the cracked diagnosis model for individual teeth using InceptionV3, ResNet50, and EfficientNetB0. The cracked tooth diagnosis model underwent fivefold cross-validation with 418 data instances divided into training, validation, and test sets at a ratio of 3:1:1. RESULTS: To evaluate the feasibility, the sensitivity, specificity, accuracy, and F1 score of the deep learning models were calculated, with values of 90.43-94.26%, 52.63-60.77%, 72.01-75.84%, and 76.36-79.00%, respectively. CONCLUSION: We found that the indications for cracked tooth extraction can be predicted to a certain extent through a deep learning model using panoramic radiography.

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Recent advancements in machine learning potentials (MLPs) have significantly impacted the fields of chemistry, physics, and biology by enabling large-scale first-principles simulations. Among different machine learning approaches, kernel-based MLPs distinguish themselves through their ability to handle small datasets, quantify uncertainties, and minimize over-fitting. Nevertheless, their extensive computational requirements present considerable challenges. To alleviate these, sparsification methods have been developed, aiming to reduce computational scaling without compromising accuracy. In the context of isothermal and isobaric ML molecular dynamics (MD) simulations, achieving precise pressure estimation is crucial for reproducing reliable system behavior under constant pressure. Despite progress, sparse kernel MLPs struggle with precise pressure prediction. Here, we introduce a virial kernel function that significantly enhances the pressure estimation accuracy of MLPs. Additionally, we propose the active sparse Bayesian committee machine (BCM) potential, an on-the-fly MLP architecture that aggregates local sparse Gaussian process regression (SGPR) MLPs. The sparse BCM potential overcomes the steep computational scaling with the kernel size, and a predefined restriction on the size of kernel allows for fast and efficient on-the-fly training. Our advancements facilitate accurate and computationally efficient machine learning-enhanced MD (MLMD) simulations across diverse systems, including ice-liquid coexisting phases, Li10Ge(PS6)2 lithium solid electrolyte, and high-pressure liquid boron nitride.

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This study investigates the impact of intravenous (IV) infusion protocols on the stability of Intravenous Immunoglobulin G (IVIG) and Rituximab, with a particular focus on subvisible particle generation. Infusion set based on peristaltic movement (Medifusion DI-2000 pump) was compared to a gravity-based infusion system (Accu-Drip) at different flow rates. The impacts of different diluents (0.9 % saline and 5.0 % dextrose) and plastic syringes with or without silicone oil (SO) were also investigated. The results from the aforementioned particular case demonstrated that peristaltic pumps generated high levels of subvisible particles (prominently < 25 µm), exacerbated by increasing flow rates, specifically in formulations lacking surfactants. Other factors, such as diluent type and syringe composition, also increased the number of subvisible particles. Strategies that can help overcome these complications include surfactant addition as well as the use of SO-free syringes and a gravity infusion system, which aid in reducing particle formation and preserving antibody monomer during administration. Altogether, these findings highlight the importance of the careful selection of formulations and infusion protocols to minimize particle generation during IV infusion both for patients' safety and treatment efficacy.

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