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Noise reduction in low-dose positron emission tomography (PET) is a well-researched topic aimed at reducing patient radiation doses and improving diagnosis. Software-based noise reduction mainly improves the contrast between regions by reducing the variation of the acquired image. However, it should be performed under appropriate parameters to reduce discrimination. We propose a method that derives optimal noise-reduction parameters using the multi-scale structural similarity index measure and visual information fidelity, which are metrics for image quality assessment. Simulation and experimental studies demonstrated the viability of the proposed algorithm. The contrast-to-noise ratio value of the denoised reconstruction slice, which was used as the optimal parameter, increased approximately three times compared to that of the low-dose slice while preserving the resolution. The results indicate that the proposed method successfully predicted the parameters according to the noise-reduction algorithm and PET system conditions in the sinogram domain. The proposed algorithm should help prevent misdiagnosis and provide standardized medical images for clinical application by performing appropriate noise reduction.

Among photon counting detector (PCD)-based technologies, the K-edge subtraction (KES) method has a very high material decomposition efficiency. Yet, since the increase in noise in the X-ray image to which the KES method is applied is inevitable, research on image quality improvement is essential. Here, we modeled a block-matching and 3D filtering (BM3D) algorithm and applied it to PCD-based X-ray images with the improved KES (IKES) method. For PCD modeling, Monte Carlo simulation was used, and a phantom composed of iodine substances with different concentrations was designed. The IKES method was modeled by adding a log term to KES, and the X-ray image used for subtraction was obtained by applying the 3.0 keV range based on the K-edge region of iodine. As a result, the IKES image using the BM3D algorithm showed the lowest normalized noise power spectrum value. In addition, we confirmed that the contrast-to-noise ratio and no-reference-based evaluation results when the BM3D algorithm was applied to the IKES image were improved by 29.36 % and 20.56 %, respectively, compared to the noisy image. In conclusion, we demonstrated that the IKES imaging technique using a PCD-based detector and the BM3D algorithm fusion technique were very efficient for X-ray imaging.

IntroductionEnd-stage lung disease causes cardiac remodeling and induces electrocardiogram (ECG) changes. On the other way, whether lung transplantation (LTx) in end-stage lung disease patients are associated with ECG change is unknown. The object of this study was to investigate ECG changes before and after LTx in end-stage lung disease patients and whether these changes had clinical significance.MethodThis was a single-center retrospective cohort study of 280 end-stage lung disease patients who consecutively underwent LTx at a tertiary referral hospital. ECG findings before LTx and within 1 week and 1, 3, and 6 months after LTx were obtained and analyzed. To find clinical meaning, the ECG at 1 month after LTx was analyzed according to 1-year survival (survivor vs non-survivor groups). Survival data were estimated using the Kaplan–Meier method.ResultsSignificant differences were observed in the PR interval, QRS duration, QT interval, QTc interval, and heart rate before LTx and 1 month after LTx; the PR interval, QRS duration, QTc interval, and heart rate were decreased. Particularly, the QTc interval was significantly decreased 1 month after LTx, whereas there was no significant change in the QTc interval from 1 to 6 months thereafter. The PR interval, QT interval, QTc interval, and heart rate were significantly different between the survivor and non-survivor groups. The serial changes in QTc interval before LTx and 1 and 3 months after LTx were also significantly different between the survivor and non-survivor groups (p = 0.040 after adjusting for age and body mass index). Upon dividing the patients based on the range of QTc interval change ≤ -8 ms, >-8–10 ms, >10–35 ms, >35 ms), the survival rate was significantly lower in the group whose QTc interval at 1 month after LTx decreased by > 35 m (p = 0.019).ConclusionLTx in patients with end-stage lung disease may induce ECG changes. Patients whose QTc interval at 1 month after LTx decreased by > 35 ms have a significantly higher 1-year mortality rate. Hence, these ECG changes may have clinical and prognostic significance.

Abstract Psychological stress and intestinal leakage are key factors in atopic dermatitis (AD) recurrence and exacerbation. Here, we demonstrate the mechanism underlying bacterial translocation across intestinal epithelial barrier damaged due to stress and further aggravation of trimellitic anhydride (TMA)–induced itch, which remain unclear, in AD mice. Immobilization (IMO) stress exacerbated scratching bouts and colon histological damage, and increased serum corticosterone and lipopolysaccharide (LPS). Orally administered fluorescein isothiocyanate (FITC)-dextran and surgically injected (into the colon) Cy5.5-conjugated LPS were detected in the serum and skin after IMO stress, respectively. The relative abundance of aerobic or facultative anaerobic bacteria was increased in the colon mucus layer, and Lactobacillus murinus, E. coli, Staphylococcus nepalensis, and several strains of Bacillus sp. were isolated from the spleens and mesenteric lymph nodes. Oral antibiotics or intestinal permeability blockers, such as lubiprostone (Lu), 2,4,6-triaminopyrimidine (TAP) and ML-7, inhibited IMO stress-associated itch; however, it was reinduced through intradermal or i.p. injection of LPS without IMO stress. I.p. injection of TAK-242 (resatorvid), a TLR4 inhibitor, abrogated IMO stress-associated itch, which was also confirmed in TLR4-KO mice. IMO stress alone did not cause itch in naïve mice. IMO stress-induced itch aggravation in TMA-treated AD mice might be attributed to the translocation of gut-derived bacterial cells and LPS, which activates peripheral TLR4 signaling.

Background: Particulate matter (PM) has been extensively observed due to its negative association with human health. Previous research revealed the possible negative effect of air pollutant exposure on oral health. However, the predictive model between air pollutant exposure and the prevalence of periodontitis has not been observed yet. Therefore, this study aims to propose a predictive model for the number of patients with periodontitis exposed to PM and atmospheric factors in South Korea using deep learning.Methods: This study is a retrospective cohort study utilizing secondary data from the Korean Statistical Information Service and the Health Insurance Review and Assessment database for air pollution and the number of patients with periodontitis, respectively. Data from 2015 to 2022 were collected and consolidated every month, organized by region. Following data matching and management, the deep neural networks (DNN) model was applied, and the mean absolute percentage error (MAPE) value was calculated to ensure the accuracy of the model. Results: As we evaluated the DNN model with MAPE, the multivariate model of air pollution including exposure to PM2.5, PM10, and other atmospheric factors predict approximately 85% of the number of patients with periodontitis. The MAPE value ranged from 12.85 to 17.10 (mean±standard deviation=14.12±1.30), indicating a commendable level of accuracy. Conclusion: In this study, the predictive model for the number of patients with periodontitis is developed based on air pollution, including exposure to PM2.5, PM10, and other atmospheric factors. Additionally, various relevant factors are incorporated into the developed predictive model to elucidate specific causal relationships. It is anticipated that future research will lead to the development of a more accurate model for predicting the number of patients with periodontitis.

High uniformity of positron emission tomography (PET) images in the field of nuclear medicine is necessary to obtain excellent and stable data from the system. In this study, we aimed to apply and optimize a PET/magnetic resonance (MR) imaging system by approaching the gray-level co-occurrence matrix (GLCM), which is known to be efficient in the uniformity correction of images. CAIPIRINHA Dixon-VIBE was used as an MR image acquisition pulse sequence for the fast and accurate attenuation correction of PET images, and the phantom was constructed by injecting NaCl and NaCl + NiSO4 solutions. The lambda value of the GLCM algorithm for uniformity correction of the acquired PET images was optimized in terms of energy and contrast. By applying the GLCM algorithm optimized in terms of energy and contrast to the PET images of phantoms using NaCl and NaCl + NiSO4 solutions, average percent image uniformity (PIU) values of 26.01 and 83.76 were derived, respectively. Compared to the original PET image, an improved PIU value of more than 30% was derived from the PET image to which the proposed optimized GLCM algorithm was applied. In conclusion, we demonstrated that an algorithm optimized in terms of the GLCM energy and contrast can improve the uniformity of PET images.

Abstract Sex differences in the in-hospital management of sepsis exist. Previous studies either included patients with sepsis that was defined using previous definitions of sepsis or evaluated the 3-h bundle therapy. Therefore, this study sought to assess sex differences in 1-h bundle therapy and in-hospital management among patients with sepsis and septic shock, defined according to the Sepsis-3 definitions. This observational study used data from Korean Shock Society (KoSS) registry, a prospective multicenter sepsis registry. Adult patients with sepsis between June 2018 and December 2021 were included in this study. The primary outcome was adherence to 1-h bundle therapy. Propensity score matching (PSM) and multivariable logistic regression analyses were performed. Among 3264 patients with sepsis, 3129 were analyzed. PSM yielded 2380 matched patients (1190 men and 1190 women). After PSM, 1-h bundle therapy was performed less frequently in women than in men (13.0% vs. 19.2%; p

Single-photon emission computed tomography SPECT image reconstruction methods have a significant influence on image quality, with filtered back projection (FBP) and ordered subset expectation maximization (OSEM) being the most commonly used methods. In this study, we proposed newly-designed adaptive non-blind deconvolution with a structural similarity (SSIM) index that can take advantage of the FBP and OSEM image reconstruction methods. After acquiring brain SPECT images, the proposed image was obtained using an algorithm that applied the SSIM metric, defined by predicting the distribution and amount of blurring. As a result of the contrast to noise ratio (CNR) and coefficient of variation evaluation (COV), the resulting image of the proposed algorithm showed a similar trend in spatial resolution to that of FBP, while obtaining values similar to those of OSEM. In addition, we confirmed that the CNR and COV values of the proposed algorithm improved by approximately 1.69 and 1.59 times, respectively, compared with those of the algorithm involving an inappropriate deblurring process. To summarize, we proposed a new type of algorithm that combines the advantages of SPECT image reconstruction techniques and is expected to be applicable in various fields.

Objective Based on the development of artificial intelligence (AI), an emerging number of methods have achieved outstanding performances in the diagnosis of acute myocardial infarction (AMI) using an electrocardiogram (ECG). However, AI-ECG analysis using a multicenter prospective design for detecting AMI has yet to be conducted. This prospective multicenter observational study aims to validate an AI-ECG model for detecting AMI in patients visiting the emergency department. Methods Approximately 9,000 adult patients with chest pain and/or equivalent symptoms of AMI will be enrolled in 18 emergency medical centers in Korea. The AI-ECG analysis algorithm we developed and validated will be used in this study. The primary endpoint is the diagnosis of AMI on the day of visiting the emergency center, and the secondary endpoint is a 30-day major adverse cardiac event. From March 2022, patient registration has begun at centers approved by the institutional review board. Discussion This is the first prospective study designed to identify the efficacy of an AI-based 12-lead ECG analysis algorithm for diagnosing AMI in emergency departments across multiple centers. This study may provide insights into the utility of deep learning in detecting AMI on electrocardiograms in emergency departments. Trial registration ClinicalTrials.gov identifier: NCT05435391. Registered on June 28, 2022.

Computed tomography (CT) imaging using low-dose radiation effectively reduces radiation exposure; however, it introduces noise amplification in the resulting image. This study models an adaptive nonlocal total variation (NL-TV) algorithm that efficiently reduces noise in X-ray-based images and applies it to low-dose CT images. In this study, an AAPM CT performance phantom is used, and the resulting image is obtained by applying an annotation filter and a high-pitch protocol. The adaptive NL-TV filter was designed by applying the optimal window value calculated by confirming the difference between Gaussian filtering and the basic NL-TV approach. For quantitative image quality evaluation parameters, contrast-to-noise ratio (CNR), coefficient of variation (COV), and sigma value were used to confirm the noise reduction effectiveness and spatial resolution value. The CNR and COV values in low-dose CT images using the adaptive NL-TV filter, which performed an optimization process, improved by approximately 1.29 and 1.45 times, respectively, compared with conventional NL-TV. In addition, the adaptive NL-TV filter was able to acquire spatial resolution data that were similar to a CT image without applying noise reduction. In conclusion, the proposed NL-TV filter is feasible and effective in improving the quality of low-dose CT images.

This study aimed to verify whether increased body mass index (BMI) increases the noise in computed tomography (CT) images due to heightened effective thickness, impacting calcium scores. Calcium scores were measured in 30 sets of images from normal weight patients. Calcium scores were also measured in 30 sets of images from hypothetical overweight and obese patients, generated by extracting the noise from overweight and obese patients, respectively, and inserting it into the images of normal weight patients. In addition, a phantom study was performed using three calcium phantoms with intensities below the threshold of 130 Hounsfield units and three calcium phantoms with intensities above this threshold. Calcium scores were measured in the absence and presence of a bolus at the heart level to simulate an obese patient. All calcium scores were measured by three radiologists. In the patient study, the total calcium scores of the hypothetical overweight and hypothetical obese groups were 14.93% (p = 0.014) and 22.19% (p = 0.012) higher than those of the normal weight group. In the phantom study, the total calcium score of the six calcium phantoms without a bolus was 1.61% higher at a tube voltage of 120 kV than at 100 kV, and 12.06% higher at a slice thickness of 1 mm than at 3 mm. The total calcium score of the six calcium phantoms with a bolus was 0.13% higher at a tube voltage of 120 kV than at 100 kV, and 14.76% higher at a slice thickness of 1 mm than at 3 mm. These results can be used as a reference to train automated calcium scoring programs on effective thickness through deep learning to reduce calcium score errors caused by increased BMI.

Background: Early risk stratification is crucial due to septic patients’ heterogeneity. Serum albumin level may reflect the severity of sepsis and host status. This study aimed to evaluate the prognostic ability of the initial sequential organ failure assessment (SOFA) score alone and combined with serum albumin levels for predicting 28-day mortality in patients with septic shock. Methods: We conducted an observational study using a prospective, multicenter registry of septic shock patients between October 2015 and May 2022 from 12 emergency departments in the Korean Shock Society and the results were validated by examining those from the septic shock cohort in Asan Medical Center. The primary outcome was 28-day mortality. The area under the receiver operating characteristic (ROC) curve was used to compare the predictive values of SOFA score alone and SOFA score combined with serum albumin level. Results: Among 5805 septic shock patients, 1529 (26.3%) died within 28 days. Mortality increased stepwise with decreasing serum albumin levels (13.6% in albumin ≥3.5, 20.7% in 3.5–3.0, 29.7% in 3.0–2.5, 44.0% in 2.5–2.0, 56.4% in Conclusions: The combination of the initial SOFA score with albumin can improve prognostic accuracy for patients with septic shock, suggesting the albumin SOFA score may be used as an early mortality stratification tool.

Although the Surviving Sepsis Campaign guidelines provide standardized and generalized guidance, they are less individualized. This review focuses on recent updates in the hemodynamic management of septic shock. Monitoring and intervention for septic shock should be personalized according to the phase of shock. In the salvage phase, fluid resuscitation and vasopressors should be given to provide life-saving tissue perfusion. During the optimization phase, tissue perfusion should be optimized. In the stabilization and de-escalation phases, minimal fluid infusion and safe fluid removal should be performed, respectively, while preserving organ perfusion. There is controversy surrounding the use of restrictive versus liberal fluid strategies after initial resuscitation. Fluid administration after initial resuscitation should depend upon the patient’s fluid responsiveness and requires individualized management. A number of dynamic tests have been proposed to monitor fluid responsiveness, which can help clinicians decide whether to give fluid or not. The optimal timing for the initiation of vasopressor agents is unknown. Recent data suggest that early vasopressor initiation should be considered. Inotropes can be considered in patients with decreased cardiac contractility associated with impaired tissue perfusion despite adequate volume status and arterial blood pressure. Venoarterial extracorporeal membrane oxygenation should be considered for refractory septic shock with severe cardiac systolic dysfunction.

Scintillator materials are widely used in the medical and industrial fields for imaging systems using gamma cameras. In this study, image evaluation is performed by modeling a gamma camera system based on a lutetium-yttrium oxyorthosilicate (LYSO) scintillation detector using a pinhole collimator that can improve the spatial resolution. A LYSO detector-based gamma camera system is modeled using a Monte Carlo simulation tool. The geometric concept of the pinhole collimator is designed using various magnification factors, and the spatial resolution is measured using the acquired source image. To evaluate the resolution, the full width at half maximum (FWHM) and natural image quality assessment (NIQE), a no-reference-based parameter, are used. We confirm that the FWHM and NIQE values decrease simultaneously when the diameter of the pinhole collimator increases. Additionally, we confirm that the spatial resolution improves as the magnification factor increases under the same pinhole diameter condition. Particularly, a 0.57 mm FWHM value is obtained using the modeled gamma camera system with a LYSO scintillation detector. In conclusion, our results demonstrate that a pinhole collimator with a LYSO scintillation detector is a promising gamma camera imaging system.

A ventilator central monitoring system (VCMS) that can efficiently respond to and treat patients’ respiratory issues in intensive care units (ICUs) is critical. Using Internet of Things (IoT) technology without loss or delay in patient monitoring data, clinical staff can overcome spatial constraints in patient respiratory management by integrated monitoring of multiple ventilators and providing real-time information through remote mobile applications. This study aimed to establish a VCMS and assess its effectiveness in an ICU setting. A VCMS comprises central monitoring and mobile applications, with significant real-time information from multiple patient monitors and ventilator devices stored and managed through the VCMS server, establishing an integrated monitoring environment on a web-based platform. The developed VCMS was analyzed in terms of real-time display and data transmission. Twenty-one respiratory physicians and staff members participated in usability and satisfaction surveys on the developed VCMS. The data transfer capacity derived an error of approximately 10−7, and the difference in data transmission capacity was approximately 1.99×10−7±9.97×10−6 with a 95% confidence interval of −1.16×10−7 to 5.13×10−7 among 18 ventilators and patient monitors. The proposed VCMS could transmit data from various devices without loss of information within the ICU. The medical software validation, consisting of 37 tasks and 9 scenarios, showed a task completion rate of approximately 92%, with a 95% confidence interval of 88.81–90.43. The satisfaction survey consisted of 23 items and showed results of approximately 4.66 points out of 5. These results demonstrated that the VCMS can be readily used by clinical ICU staff, confirming its clinical utility and applicability. The proposed VCMS can help clinical staff quickly respond to the alarm of abnormal events and diagnose and treat based on longitudinal patient data. The mobile applications overcame space constraints, such as isolation to prevent respiratory infection transmission of clinical staff for continuous monitoring of respiratory patients and enabled rapid consultation, ensuring consistent care.

The current treatment options for peripheral arterial disease (PAD) are limited due to a lack of significant high-level evidence to inform clinical decisions and unfavorable outcomes in terms of cost-effectiveness and amputation rates. In order to suggest the use of the commercially available L-Ornithine-L-Aspartate (LOLA) for treating PAD, we induced hind limb ischemia (HLI) by unilaterally ligating the femoral artery in a rat model. The rats were randomly divided into three groups, with seven rats assigned to each group: group 1 (control), group 2 (sorbitol), and group 3 (LOLA). Intraperitoneal injections were administered five times on post-operative days (PODs) 3, 5, 7, 10, and 12. Perfusion imaging was conducted on PODs 7 and 14 and compared to pre-operative perfusion imaging. Immunohistochemistry staining and Western blotting were performed after the final perfusion imaging. Group 3 showed a significant increase in perfusion, high CD31-positive capillary lumen density, and substantial overexpression of VEGF in the ischemic limb during the subacute phase of HLI. In conclusion, this study provides the first documented evidence of angiogenesis and perfusion recovery in the subacute phase of the HLI model following the administration of LOLA. With LOLA readily available on the commercial market, the implementation of LOLA treatment for PAD in humans can be expedited compared to other therapies still in the developmental stage.

Noise in computed tomography (CT) is inevitably generated, which lowers the accuracy of disease diagnosis. The non-local means approach, a software technique for reducing noise, is widely used in medical imaging. In this study, we propose a noise reduction algorithm based on fast non-local means (FNLMs) and apply it to CT images of a phantom created using 3D printing technology. The self-produced phantom was manufactured using filaments with similar density to human brain tissues. To quantitatively evaluate image quality, the contrast-to-noise ratio (CNR), coefficient of variation (COV), and normalized noise power spectrum (NNPS) were calculated. The results demonstrate that the optimized smoothing factors of FNLMs are 0.08, 0.16, 0.22, 0.25, and 0.32 at 0.001, 0.005, 0.01, 0.05, and 0.1 of noise intensities, respectively. In addition, we compared the optimized FNLMs with noisy, local filters and total variation algorithms. As a result, FNLMs showed superior performance compared to various denoising techniques. Particularly, comparing the optimized FNLMs to the noisy images, the CNR improved by 6.53 to 16.34 times, COV improved by 6.55 to 18.28 times, and the NNPS improved by 10−2 mm2 on average. In conclusion, our approach shows significant potential in enhancing CT image quality with anthropomorphic phantoms, thus addressing the noise issue and improving diagnostic accuracy.

Keratinocytes can be activated by Cutibacterium acnes, leading to the production of proinflammatory cytokines via toll-like receptors (TLRs) 2 and 4. Although several studies have investigated keratinocytes, the mechanism of calcium-mediated activation remains unclear. Herein, we investigated whether calcium influx via TLR2 and TLR4 stimulation was involved in cytokine secretion by keratinocytes in HaCaT cells. Although TLR2 stimulation by peptidoglycan (PGN) increased intracellular calcium influx, TLR4 stimulation by lipopolysaccharide (LPS) did not increase it, as analyzed using flow cytometry with the calcium indicator Fluo-3. However, activation by either TLR2 or TLR4 ligands upregulated the intracellular calcium influx in THP-1 monocytes. Additionally, the expression of major proinflammatory cytokines and chemokines, such as interleukin (IL)-6, IL-8, IL-1α, granulocyte-macrophage colony-stimulating factor (GM-CSF), and monocyte chemoattractant protein-1 (MCP-1), was significantly increased by TLR2 in HaCaT cells. Moreover, treatment with the intracellular calcium chelator, BAPTA-AM, disrupted PGN-mediated induction of IL-6, IL-8, and MCP-1 production. Real-time quantitative polymerase chain reaction (PCR) and western blotting revealed that TLR2 stimulation induced expression of the epidermal differentiation marker keratin 1. In conclusion, TLR2-induced intracellular calcium influx plays a pivotal role in the secretion of proinflammatory cytokines, such as IL-6 and MCP-1, in keratinocytes. Moreover, the continuous influx of calcium via TLR2 activation leads to keratinization. In vitro studies using HaCaT cells provide basic research on the effect of TLR2-induced calcium on C. acnes-mediated inflammation in keratinocytes. These studies are limited in their ability to clinically predict what happens in human keratinocytes. Clinical studies on patients with acne, including three-dimensional (3D) cultures of primary keratinocytes, are required to develop new diagnostic markers for determining the severity of acne vulgaris.

We developed a modified cardiovascular (CV) Sequential Organ Failure Assessment (SOFA) score using an emergency department-based cohort data, incorporating norepinephrine equivalent dose and lactate to represent current clinical practice patterns for vasopressor utilization and the diagnostic significance of lactate, respectively. In this study, we sought to validate this modified CV-SOFA score in intensive care unit patients with suspected infection using the Marketplace for Medical Information in Intensive Care (MIMIC)-IV database. This was a retrospective study that utilized data from the MIMIC-IV database. Modified CV/total SOFA score and original CV/total SOFA score were compared for predicting in-hospital mortality. Area under the receiver operating characteristic curve (AUROC) and the calibration curve were employed to evaluate discrimination and calibration, respectively. A total of 29,618 ICU patients with suspected infections was analyzed. The in-hospital mortality rate was 12.4% (n = 3,675). Modified CV-SOFA score (AUROC 0.667; 95% confidence interval [CI] 0.657-0.677 vs. 0.663; 95% CI 0.654-0.673; p = 0.283) and modified total SOFA score (0.784 [95% CI 0.776-0.793] vs. 0.785 [95% CI 0.777-0.793], p = 0.490) did not differ significantly from the original CV-SOFA score and original total SOFA score, respectively. The calibration curve of the original CV-SOFA score was inferior to that of the modified CV-SOFA score. The modified CV- and total SOFA scores were better calibrated than the original CV- and total SOFA scores, but their discriminative performance was not significantly different. Further studies of the modified CV-SOFA score in different settings and populations are required to assess the generalizability of this score.

Although physical grids improve contrast in radiographic images by reducing scattered radiation, various artifacts such as grid shadow, moire, and cutoff result in increased patient doses. To overcome these problems, this study evaluates the applicability and usefulness of a material thickness-based scatter-correction technique for mammography. Specifically, this study aims to compare and evaluate the performance of mammography using the proposed software-based scatter correction framework and a physical grid. The proposed technique enables scatter correction based on pre-calculated parameters of a thickness-based scatter kernel at a water slab phantom and an empirical quantity of scatter components in a mammographic system. In the Monte Carlo simulation and experiment, the proposed framework displayed an intensity profile and full width at half maximum that closely approximated those seen in the physical grid. In addition, by applying the proposed framework to the ACR phantom, it was verified that all structures, including specks, were distinctly distinguished. The results demonstrate that the X-ray scatter-correction method with a software-based framework for mammography is applicable to the field of diagnostic imaging, as this approach yields image quality equivalent to that achieved with physical grids while also enabling a reduction in radiation doses for patients.

In radiography, an antiscatter grid is a well-known device for eliminating unexpected x-ray scatter. We investigate a new stationary grid artifact suppression method based on a nonsubsampled contourlet transform (NSCT) incorporated with Gaussian band-pass filtering. The proposed method has an advantage that extracts the Moiré components while minimizing the loss of image information and apply the prior information of Moiré component positions in multi-decomposition sub-band images. We implemented the proposed algorithm and performed a simulation and an experiment to demonstrate its viability. We did this experiment using an x-ray tube (M-113T, Varian, focal spot size: 0.1 mm), a flat-panel detector (ROSE-M Sensor, Aspenstate, pixel dimension: 3032 × 3800 pixels, pixel size: 0.076 mm), and carbon graphite-interspaced grids (JPI Healthcare, 18 cm × 24 cm, line density: 103 LP/inch and 150 LP/inch, ratio: 5:1, focal distance: 65 cm). Our results indicate that the proposed method successfully suppressed grid artifacts by reducing them without either reducing the spatial resolution or causing negative side effects. Consequently, we anticipate that the proposed method can improve image acquisition in a stationary grid x-ray system as well as in extended x-ray imaging.

In recent years, indirect digital radiography detectors have been actively studied to improve radiographic image performance with low radiation exposure. This study aimed to achieve low-dose radiation imaging with a thick scintillation detector while simultaneously obtaining the resolution of a thin scintillation detector. The proposed method was used to predict the optimal point spread function (PSF) between thin and thick scintillation detectors by considering image quality assessment (IQA). The process of identifying the optimal PSF was performed on each sub-band in the wavelet domain to improve restoration accuracy. In the experiments, the edge preservation index (EPI) values of the non-blind deblurred image with a blurring sigma of σ = 5.13 pixels and the image obtained with optimal parameters from the thick scintillator using the proposed method were approximately 0.62 and 0.76, respectively. The coefficient of variation (COV) values for the two images were approximately 1.02 and 0.63, respectively. The proposed method was validated through simulations and experimental results, and its viability is expected to be verified on various radiological imaging systems.

Objective Steroids are used in cases of sepsis, especially in patients experiencing septic shock. However, clinical trials to date have reported contradictory results. Different patient endotypes and variations in the type and dose of steroid may be at fault for this discrepancy, and further investigation is warranted. In this paper, we propose a new DEXA-SEPSIS study design. Methods We plan to conduct a multicenter, double-blinded randomized pilot study (DEXA-SEPSIS) investigating the feasibility and safety of early use of dexamethasone in sepsis. Participants will be high-risk septic patients presenting to the emergency department with a systolic blood pressure of 2 mmol/L. Participants will be randomized to the following three groups: control, 0.1 mg/kg of dexamethasone, or 0.2 mg/kg of dexamethasone per day for 1 to 2 days. The primary outcome will be 28-day mortality. Secondary outcomes will include time to septic shock, shock reversal, additional steroid administration, number of ventilator-free days, use of continuous renal-replacement therapy, length of stay in the intensive care unit and/or hospital, delta Sequential Organ Failure Assessment score on days 3 and 7, superinfection, gastrointestinal bleeding, hypernatremia, and hyperglycemia. Discussion The DEXA-SEPSIS study will provide insight regarding the feasibility and safety of early use of dexamethasone in high-risk sepsis. The results could provide data to design a future phase III study. Trial registration ClinicalTrials.gov Identifier: NCT05136560

Abstract Background The Sepsis-3 criteria introduced the system that uses the Sequential Organ-Failure Assessment (SOFA) score to define sepsis. The cardiovascular SOFA (CV SOFA) scoring system needs modification due to the change in guideline-recommended vasopressors. In this study, we aimed to develop and to validate the modified CV SOFA score. Methods We developed, internally validated, and externally validated the modified CV SOFA score using the suspected infection cohort, sepsis cohort, and septic shock cohort. The primary outcome was 28-day mortality. The modified CV SOFA score system was constructed with consideration of the recently recommended use of the vasopressor norepinephrine with or without lactate level. The predictive validity of the modified SOFA score was evaluated by the discrimination for the primary outcome. Discrimination was assessed using the area under the receiver operating characteristics curve (AUC). Calibration was assessed using the calibration curve. We compared the prognostic performance of the original CV/total SOFA score and the modified CV/total SOFA score to detect mortality in patients with suspected infection, sepsis, or septic shock. Results We identified 7,393 patients in the suspected cohort, 4038 patients in the sepsis cohort, and 3,107 patients in the septic shock cohort in seven Korean emergency departments (EDs). The 28-day mortality rates were 7.9%, 21.4%, and 20.5%, respectively, in the suspected infection, sepsis, and septic shock cohorts. The model performance is higher when vasopressor and lactate were used in combination than the vasopressor only used model. The modified CV/total SOFA score was well-developed and internally and externally validated in terms of discrimination and calibration. Predictive validity of the modified CV SOFA was significantly higher than that of the original CV SOFA in the development set (0.682 vs 0.624, p

Label-free rapid deep-learning-based identification of bacterial species that classifies 3D refractive index tomograms into the species.

Noise and blurring in light microscope images are representative factors that affect accurate identification of cellular and subcellular structures in biological research. In this study, a method for l1-norm-based blind deconvolution after noise reduction with non-subsampled contourlet transform (NSCT) was designed and applied to a light microscope image to analyze its feasibility. The designed NSCT-based algorithm first separated the low- and high-frequency components. Then, the restored microscope image and the deblurred and denoised images were compared and evaluated. In both the simulations and experiments, the average coefficient of variation (COV) value in the image using the proposed NSCT-based algorithm showed similar values compared to the denoised image; moreover, it significantly improved the results compared with that of the degraded image. In particular, we confirmed that the restored image in the experiment improved the COV by approximately 2.52 times compared with the deblurred image, and the NSCT-based proposed algorithm showed the best performance in both the peak signal-to-noise ratio and edge preservation index in the simulation. In conclusion, the proposed algorithm was successfully modeled, and the applicability of the proposed method in light microscope images was proved based on various quantitative evaluation indices.

Ultrasound imaging is widely used as a noninvasive lesion detection method in diagnostic medicine. Improving the quality of these ultrasound images is very important for accurate diagnosis, and deep learning-based algorithms have gained significant attention. This study proposes a multiscale deep encoder–decoder with phase congruency (MSDEPC) algorithm based on deep learning to improve the quality of diagnostic ultrasound images. The MSDEPC algorithm included low-resolution (LR) images and edges as inputs and constructed a multiscale convolution and deconvolution network. Simulations were conducted using the Field 2 program, and data from real experimental research were obtained using five clinical datasets containing images of the carotid artery, liver hemangiomas, breast malignancy, thyroid carcinomas, and obstetric nuchal translucency. LR images, bicubic interpolation, and super-resolution convolutional neural networks (SRCNNs) were modeled as comparison groups. Through visual assessment, the image processed using the MSDEPC was the clearest, and the lesions were clearly distinguished. The structural similarity index metric (SSIM) value of the simulated ultrasound image using the MSDEPC algorithm improved by approximately 38.84% compared to LR. In addition, the peak signal-to-noise ratio (PSNR) and SSIM values of clinical ultrasound images using the MSDEPC algorithm improved by approximately 2.33 times and 88.58%, respectively, compared to LR. In conclusion, the MSDEPC algorithm is expected to significantly improve the spatial resolution of ultrasound images.

In this study, we aimed to address the issue of noise amplification after scatter correction when using a virtual grid in breast X-ray images. To achieve this, we suggested an algorithm for estimating noise level and developed a noise reduction algorithm based on generative adversarial networks (GANs). Synthetic scatter in breast X-ray images were collected using Sizgraphy equipment and scatter correction was performed using dedicated software. After scatter correction, we determined the level of noise using noise-level function plots and trained a GAN using 42 noise combinations. Subsequently, we obtained the resulting images and quantitatively evaluated their quality by measuring the contrast-to-noise ratio (CNR), coefficient of variance (COV), and normalized noise–power spectrum (NNPS). The evaluation revealed an improvement in the CNR by approximately 2.80%, an enhancement in the COV by 12.50%, and an overall improvement in the NNPS across all frequency ranges. In conclusion, the application of our GAN-based noise reduction algorithm effectively reduced noise and demonstrated the acquisition of improved-quality breast X-ray images.

The resulting image obtained from an X-ray imaging system depends significantly on the characteristics of the detector. In particular, when an X-ray image is acquired by thinning the detector, a relatively large amount of noise inevitably occurs. In addition, when a thick detector is used to reduce noise in X-ray images, blurring increases and the ability to distinguish target areas deteriorates. In this study, we aimed to derive the optimal X-ray image quality by deriving the optimal noise reduction parameters based on the non-local means (NLM) algorithm. The detectors used were of two thicknesses (96 and 140 μm), and images were acquired based on the IEC 62220-1-1:2015 RQA-5 protocol. The optimal parameters were derived by calculating the edge preservation index and signal-to-noise ratio according to the sigma value of the NLM algorithm. As a result, a sigma value of the optimized NLM algorithm (0.01) was derived, and this algorithm was applied to a relatively thin X-ray detector system to obtain appropriate noise level and spatial resolution data. The no-reference-based blind/referenceless image spatial quality evaluator value, which analyzes the overall image quality, was best when using the proposed method. In conclusion, we propose an optimized NLM algorithm based on a new method that can overcome the noise amplification problem in thin X-ray detector systems and is expected to be applied in various photon imaging fields in the future.

Abstract Hair loss is one of the most common skin problems experienced by more than half of the world's population. In East Asia, medicinal herbs have been used widely in clinical practice to treat hair loss. Recent studies, including systematic literature reviews, indicate that medicinal herbs may demonstrate potential effects for hair loss treatment. In a previous study, we identified medical herbs used frequently for alopecia treatment. Herein, we explored the potential novel therapeutic mechanisms of 20 vital medicinal herbs for alopecia treatment that could distinguish them from known mechanisms of conventional drugs using network pharmacology analysis methods. We determined the herb-ingredient–target protein networks and ingredient-associated protein (gene)-associated pathway networks and calculated the weighted degree centrality to define the strength of the connections. Data showed that 20 vital medicinal herbs could exert therapeutic effects on alopecia mainly mediated via regulation of various target genes and proteins, including acetylcholinesterase (AChE), phospholipase A2 (PLA2) subtypes, ecto-5-nucleotidase (NTE5), folate receptor (FR), nicotinamide N-methyltransferase (NNMT), and quinolinate phosphoribosyltransferase (QPRT). Findings regarding target genes/proteins and pathways of medicinal herbs associated with alopecia treatment offer insights for further research to better understand the pathogenesis and therapeutic mechanism of medicinal herbs for alopecia treatment with traditional herbal medicine.

In low-dose computed tomography (LDCT), lung segmentation effectively improves the accuracy of lung cancer diagnosis. However, excessive noise is inevitable in LDCT, which can decrease lung segmentation accuracy. To address this problem, it is necessary to derive an optimized kernel size when using the median modified Wiener filter (MMWF) for noise reduction. Incorrect application of the kernel size can result in inadequate noise removal or blurring, degrading segmentation accuracy. Therefore, various kernel sizes of the MMWF were applied in this study, followed by region-growing-based segmentation and quantitative evaluation. In addition to evaluating the segmentation performance, we conducted a similarity assessment. Our results indicate that the greatest improvement in segmentation performance and similarity was at a kernel size 5 × 5. Compared with the noisy image, the accuracy, F1-score, intersection over union, root mean square error, and peak signal-to-noise ratio using the optimized MMWF were improved by factors of 1.38, 33.20, 64.86, 7.82, and 1.30 times, respectively. In conclusion, we have demonstrated that by applying the MMWF with an appropriate kernel size, the optimization of noise and blur reduction can enhance segmentation performance.

An indirect conversion X-ray detector uses a scintillator that utilizes the proportionality of the intensity of incident radiation to the amount of visible light emitted. A thicker scintillator reduces the patient’s dose while decreasing the sharpness. A thin scintillator has an advantage in terms of sharpness; however, its noise component increases. Thus, the proposed method converts the spatial resolution of radiographic images acquired from a normal-thickness scintillation detector into a thin-thickness scintillation detector. Note that noise amplification and artifacts were minimized as much as possible after non-blind deconvolution. To accomplish this, the proposed algorithm estimates the optimal point-spread function (PSF) when the structural similarity index (SSIM) and feature similarity index (FSIM) are the most similar between thick and thin scintillator images. Simulation and experimental results demonstrate the viability of the proposed method. Moreover, the deconvolution images obtained using the proposed scheme show an effective image restoration method in terms of the human visible system compared to that of the traditional PSF measurement technique. Consequently, the proposed method is useful for restoring degraded images using the adaptive PSF while preventing noise amplification and artifacts and is effective in improving the image quality in the present X-ray imaging system.

Because single-photon emission computed tomography (SPECT) is one of the widely used nuclear medicine imaging systems, it is extremely important to acquire high-quality images for diagnosis. In this study, we designed a super-resolution (SR) technique using dense block-based deep convolutional neural network (CNN) and evaluated the algorithm on real SPECT phantom images. To acquire the phantom images, a real SPECT system using a99mTc source and two physical phantoms was used. To confirm the image quality, the noise properties and visual quality metric evaluation parameters were calculated. The results demonstrate that our proposed method delivers a more valid SR improvement by using dense block-based deep CNNs as compared to conventional reconstruction techniques. In particular, when the proposed method was used, the quantitative performance was improved from 1.2 to 5.0 times compared to the result of using the conventional iterative reconstruction. Here, we confirmed the effects on the image quality of the resulting SR image, and our proposed technique was shown to be effective for nuclear medicine imaging.

Abstract We developed a method to measure the relative blood flow speed using optical coherence tomography angiography (OCTA) in retina and choroid, and investigated the feasibility of this method for assessing microcirculatory function in rat models of sepsis and hemorrhagic shock. Two sepsis models, 6-h severe sepsis without treatment and 30-h moderate sepsis maintaining mean arterial pressure, and volume controlled hemorrhagic shock and fluid resuscitation model were used to see the change of microcirculation. The blood flow index (BFI), which was calculated from the OCTA images to represent the average relative blood flow, was decreasing during the 6-h severe sepsis model. Its change is in parallel with the mean arterial blood pressure (MAP) and blood lactate levels. In the 30-h moderate sepsis model, the BFI was decreased while maintaining MAP, and lactate was increased. In the hemorrhagic shock model, the change of BFI is in line with MAP and lactate levels. In all models, BFI change is more sensitive in choroid than in retina. This study presents the OCTA-based retinal and choroidal microcirculatory blood flow monitoring method and shows its utility for assessment of critical illness.

In this study, we optimized the σ-values of a block matching and 3D filtering (BM3D) algorithm to reduce noise in magnetic resonance images. Brain T2-weighted images (T2WIs) were obtained using the BrainWeb simulation program and Rician noise with intensities of 0.05, 0.10, and 0.15. The BM3D algorithm was applied to the optimized BM3D algorithm and compared with conventional noise reduction algorithms using Gaussian, median, and Wiener filters. The clinical feasibility was assessed using real brain T2WIs from the Alzheimer’s Disease Neuroimaging Initiative. Quantitative evaluation was performed using the contrast-to-noise ratio, coefficient of variation, structural similarity index measurement, and root mean square error. The simulation results showed optimal image characteristics and similarity at a σ-value of 0.12, demonstrating superior noise reduction performance. The optimized BM3D algorithm showed the greatest improvement in the clinical study. In conclusion, applying the optimized BM3D algorithm with a σ-value of 0.12 achieved efficient noise reduction.

Abstract Background Atopic dermatitis (AD) is a chronic recurrent inflammatory skin disease that affects 1–3% of adults worldwide. Currently, it is not possible to completely cure AD; therefore, alternative treatments need to be developed to meet the patients’ needs. Here, based on our previous pilot study, we conducted the first confirmatory randomized controlled trial to evaluate the effect of acupuncture in patients with mild to moderate AD. Methods A randomized, participant- and assessor-blinded, sham-controlled trial was designed with an intervention period twice-weekly for 4 weeks and a 4-week follow-up. We equally allocated 36 participants to the verum acupuncture (VA) and sham acupuncture (SA) groups. The main outcome measure was the change in SCORing Atopic Dermatitis index (SCORAD) score before and after treatment. Results A total of 36 participants, aged 19 to 38 years, were enrolled, and 35 were included in the intention-to-treat analyses. The mean change in total SCORAD score differed significantly among the two groups at 4 weeks after randomization (P

Abstract Background Sepsis has a high mortality rate, but no specific drug has been proven effective, prompting the development of new drugs. Immunologically, sepsis can involve hyperinflammation, immune paralysis, or both, which might pose challenges during drug development. Recently, mitochondrial transplantation has emerged as a treatment modality for various diseases involving mitochondrial dysfunction, but it has never been tested for sepsis. Methods We isolated mitochondria from L6 muscle cells and umbilical cord mesenchymal stem cells and tested the quality of the isolated mitochondria. We conducted both in vivo and in vitro sepsis studies. We investigated the effects of intravenous mitochondrial transplantation on cecal slurry model in rats in terms of survival rate, bacterial clearance rate, and the immune response. Furthermore, we observed the effects of mitochondrial transplantation on the immune reaction regarding both hyperinflammation and immune paralysis. To do this, we studied early- and late-phase cytokine production in spleens from cecal slurry model in rats. We also used a lipopolysaccharide (LPS)-stimulated human PBMC monocyte model to confirm the immunological effects of mitochondrial transplantation. Apoptosis and the intrinsic apoptotic pathway were investigated in septic spleens. Results Mitochondrial transplantation improved survival and bacterial clearance. It also mitigated mitochondrial dysfunction and apoptosis in septic spleens and attenuated both hyperinflammation and immune paralysis in the spleens of cecal slurry model in rats. This effect was confirmed with an LPS-stimulated human PBMC study. Conclusions In rat polymicrobial cecal slurry model, the outcome is improved by mitochondrial transplantation, which might have an immunomodulatory effect.

Abstract Background The human placenta (HP) is a complex organ used to alleviate tiredness and promote wound healing. Previous research showed the hair growth-promoting effect of HP. However, no reports have addressed the effects of HP on hair regrowth in chemotherapy-induced alopecia. In this study, we investigated the effects of HP on the apoptosis and proliferation of hair follicles in chemotherapy-induced alopecia. Methods Male C57BL/6 mice in telogen were depilated to enter anagen. After 9 days, dystrophic catagen was induced by the intraperitoneal injection of 150 mg/kg cyclophosphamide. During 9 to 16 days, 0.1 and 1 mg/mL HP were topically applied to depilated dorsal skin. Results Dystrophic hair follicles by cyclophosphamide were recovered by HP treatment. New hair shafts containing hair fibers appeared to be straight after HP treatment. Immunohistological staining revealed a significant increase of Ki67-positive cells in hair follicles treated with 1 mg/mL HP. Topical HP treatment increased the ratio of Bcl-2/Bax, while it attenuated the expression of pro-apoptotic Bax, p53, and cytochrome c with caspase-9 and -3. In addition, the expression of KGF and the phosphorylation of AKT were upregulated by HP treatment. Conclusion These results suggest that HP treatment induced hair growth by inhibiting apoptosis and promoting the proliferation of hair follicles. HP may be useful for treating chemotherapy-induced alopecia.

Objective To test the hypothesis that the quick Sepsis-related Organ Failure Assessment (qSOFA) score, derived from vital signs taken during triage and recommended by current sepsis guidelines for screening patients with infections for organ dysfunction, is not sensitive enough to predict the risk of mortality in emergency department (ED) sepsis patients. Methods Patients diagnosed with severe sepsis and septic shock using the old definition between May 2014 and April 2015 were retrospectively reviewed in three urban tertiary hospital EDs. The sensitivities of systemic inflammatory response syndrome (SIRS) criteria, qSOFA, and Sequential Organ Failure Assessment (SOFA) scores ≥2 were compared using McNemar’s test. Diagnostic performances were evaluated using specificity, positive predictive value, and negative predictive value. Results Among the 928 patients diagnosed with severe sepsis or septic shock using the old definition, 231 (24.9%) died within 28 days. More than half of the sepsis patients (493/928, 53.1%) and more than one-third of the mortality cases (88/231, 38.1%) had a qSOFA score

Objective To investigate the epidemiology of sepsis in Korea and identify risk factors for death in sepsis. Methods We conducted a longitudinal, population-based epidemiological study of sepsis in Korea from 2005 to 2012 using the National Health Insurance Service-National Sample Cohort, a population-based cohort representing 2.2% of the Korean population. The primary objective was to assess the incidence, mortality and cost of sepsis. The secondary objective was to identify the risk factors for death in sepsis. Claim records of admitted adult patients (aged ≥15 years) were analyzed. Sepsis was defined as 1) bacterial or fungal infection or the conditions they often complicate, 2) prescription of intravenous antibiotics, and 3) presence of any organ dysfunction. Comorbidities were defined using the Charlson/Deyo method. Risk factors for 6-month mortality were assessed using multivariable logistic regression. Results A total of 22,882 cases were identified. Both incidence and 6-month mortality increased from 265.7 (95% confidence interval [CI], 254.7 to 277.1) to 453.1 (95% CI, 439.0 to 467.5) per 100,000 person-years (P-trend

Low-dose exposure and work convenience are required for mobile X-ray systems during the COVID-19 pandemic. We investigated a novel X-ray detector (FXRD-4343FAW, VIEWORKS, Anyang, Korea) composed of a thin-film transistor based on amorphous silicon with a flexible plastic substrate. This detector is composed of a thallium-doped cesium iodide scintillator with a pixel size of 99 μm, pixel matrix of 4316 × 4316, and weight of 2.95 kg. The proposed detector has the advantages of high-noise characteristics and low weight, which provide patients and workers with an advantage in terms of the dose and work efficiency, respectively. We performed a quantitative evaluation and an experiment to demonstrate its viability. The modulation transfer function, noise power spectrum, and detective quantum efficiency were identified using the proposed and comparative detectors, according to the International Electrotechnical Commission protocol. Additionally, the contrast-to-noise ratio and coefficient of variation were investigated using a human-like phantom. Our results indicate that the proposed detector efficiently increases the image performance in terms of noise characteristics. The detailed performance evaluation demonstrated that the outcomes of the use of the proposed detector confirmed the viability of mobile X-ray devices that require low doses. Consequently, the novel FXRD-4343FAW X-ray detector is expected to improve the image quality and work convenience in extended radiography.

Steroids are currently being used in sepsis, particularly in septic shock. However, clinical trials to date have shown contradictory results. This could be attributed to the different patient endotypes and steroid doses, which have also contributed to the inconclusive results. We investigated the effects of glucocorticoid therapy on sepsis in a polymicrobial sepsis model in a variety of settings, such as steroid dose, severity, and sepsis phase. We used a rat model of fecal slurry polymicrobial sepsis. First, we investigated the optimum dose of steroids in a sepsis model. We administered different doses of dexamethasone after sepsis induction (0.1DEX; 0.1 mg/kg, 0.2DEX; 0.2 mg/kg, 5DEX; 5 mg/kg). Second, we used two different severities of the fecal slurry polymicrobial sepsis rat model to examine the effects of the steroids. A moderate or severe model was defined as a survival rate of approximately 70% and 30%, respectively. Third, we administered steroids in an early (1 h after sepsis induction) or late phase (25 h after sepsis). In all the experiments, we investigated the survival rates. In the determined optimal model and settings, we measured serum lactate, alanine transferase (ALT), creatinine, tumor necrosis factor-α (TNF-α), interleukin (IL)-6, IL-10, and arterial blood gas. We evaluated the bacterial burden in the blood and spleen. Endotoxin tolerance of peripheral blood mononuclear cells (PBMCs) and splenocytes was also investigated to determine the level of immune suppression 24 h after sepsis by measuring TNF-α production after stimulation with lipopolysaccharide (LPS) in an ex vivo model. Early treatment of 0.2 mg/kg dexamethasone in a severe sepsis model showed the best beneficial effects. In moderate- or late-phase sepsis, there was no survival gain with steroid treatment. DEX0.2 group showed less acute kidney injury manifested by serum creatinine and blood urea nitrogen. DEX decreased the levels of cytokines, including IL-6, IL-10, and TNF-α. Colony-forming units were significantly decreased in the blood when administered with dexamethasone. Endotoxin tolerance was not significantly different between the DEX0.2 and control groups. In conclusion, early treatment of 0.2 mg/kg dexamethasone improved the outcomes of rats in a severe sepsis model.

Alzheimer’s disease (AD) is the most common progressive neurodegenerative disease. 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely used to predict AD using a deep learning model. However, the effects of noise and blurring on 18F-FDG PET images were not considered. The performance of a classification model trained using raw, deblurred (by the fast total variation deblurring method), or denoised (by the median modified Wiener filter) 18F-FDG PET images without or with cropping around the limbic system area using a 3D deep convolutional neural network was investigated. The classification model trained using denoised whole-brain 18F-FDG PET images achieved classification performance (0.75/0.65/0.79/0.39 for sensitivity/specificity/F1-score/Matthews correlation coefficient (MCC), respectively) higher than that with raw and deblurred 18F-FDG PET images. The classification model trained using cropped raw 18F-FDG PET images achieved higher performance (0.78/0.63/0.81/0.40 for sensitivity/specificity/F1-score/MCC) than the whole-brain 18F-FDG PET images (0.72/0.32/0.71/0.10 for sensitivity/specificity/F1-score/MCC, respectively). The 18F-FDG PET image deblurring and cropping (0.89/0.67/0.88/0.57 for sensitivity/specificity/F1-score/MCC) procedures were the most helpful for improving performance. For this model, the right middle frontal, middle temporal, insula, and hippocampus areas were the most predictive of AD using the class activation map. Our findings demonstrate that 18F-FDG PET image preprocessing and cropping improves the explainability and potential clinical applicability of deep learning models.

Objective This study aimed to describe the conceptualization, development, and implementation processes of the newly established Korean Cardiac Arrest Resuscitation Consortium (KoCARC) to improve out-of-hospital cardiac arrest (OHCA) outcomes. Methods The KoCARC was established in 2014 by recruiting hospitals willing to participate voluntarily. To enhance professionalism in research, seven research committees, the Epidemiology and Preventive Research Committee, Community Resuscitation Research Committee, Emergency Medical System Resuscitation Research Committee, Hospital Resuscitation Research Committee, Hypothermia and Postresuscitation Care Research Committee, Cardiac Care Resuscitation Committee, and Pediatric Resuscitation Research Committee, were organized under a steering committee. The KoCARC registry was developed with variables incorporated in the currently existing regional OHCA registries and Utstein templates and were collected via a web-based electronic database system. The KoCARC study population comprises patients visiting the participating hospitals who had been treated by the emergency medical system for OHCA presumed to have a cardiac etiology. Results A total of 62 hospitals volunteered to participate in the KoCARC, which captures 33.0% of the study population in Korea. Web-based data collection started in October 2015, and to date (December 2016), there were 3,187 cases compiled in the registry collected from 32 hospitals. Conclusion The KoCARC is a self-funded, voluntary, hospital-based collaborative research network providing high level evidence in the field of OHCA and resuscitation. This paper will serve as a reference for subsequent KoCARC manuscripts and for data elements collected in the study.

Abstract Background Although alopecia affects the quality of life, its pathogenesis is unknown, because cellular interactions in the hair follicle are complex. Several authors have suggested using herbal medicine to treat alopecia, and bioinformatics and network pharmacology may constitute a new research strategy in this regard because herbal medicines contain various chemical components. This study used association rule mining (ARM) and network analysis to analyze the combinations of medicinal herbs used to treat alopecia. Methods We searched Chinese, Korean, and English databases for literature about alopecia treatment, extracting the names of each herbal prescription and herb. The meridian tropism and classification category of each herb were also investigated. Using ARM, we identified frequently combined two-herb and three-herb sets. Using network analysis, we divided the herbs into several modules according to prescription pattern. Results Fifty-six articles and 489 herbal medicines were included—312 internal and 177 external medicines. Among the 312 medicinal herbs used in internal medicine group, the most frequently combined two-herb set was Polygonum multiflorum Thunb. (何首烏) and Angelica sinensis (Oliv.) Dlels (當歸). The most frequently used three-herb combination was Polygonum multiflorum Thunb., Angelica sinensis (Oliv.) Dlels, and Ligusticum chuanxiong Hort. (川芎). In network analysis, three modules were identified. The herbs of Module 1 were related to the liver and kidney meridians, and those of Module 3 were related to the Stomach meridian. Conclusions We identified the frequency, characteristics, and functional modules of herb combinations frequently used in alopecia treatment. We confirmed the value of classical medicinal herb theory. This finding will prompt further bioinformatics and network pharmacology research on alopecia.

The purpose of this study was to investigate the viability of the proposed method in preventing the loss of metallic components including the clip and coil in cerebral computed tomography angiography (CTA). Forty patients undergoing surgery for aneurysms carried metallic materials. The proposed method is based on conventional bone subtraction CTA (BS-CTA) system. Briefly, the position of metal components was determined using the threshold value and a region of interest (ROI). An appropriate threshold was used to separate the background from the target materials based on the Otsu method. A three-dimensional (3D) rendering was performed from the proposed BS-CTA data carrying the extracted target information. The accuracy of clip and coil region measured using the dice similarity coefficient (DSC) and bidirectional Hausdorff distance (HD) is reported. The metallic components of the proposed BS-CTA were significantly visualized in various patient cases. Quantitative evaluation using the proposed method is based on the mean DSC of 0.93 with a standard deviation (SD) of ±0.05 (e.g., maximum value = 0.99, minimum value = 0.75, 95% confidence interval (CI) = 0.91 to 0.95, and all p < 0.05). The mean HD was 1.50 voxels with an SD of ± 0.58 (e.g., maximum value = 5.95, minimum value = 0.12, 95% CI = 1.10 to 1.90, and all p < 0.05). The proposed method demonstrates effective segmentation of the metallic component and application to the existing conventional BS-CTA system.

Abstract Background Septic shock can be defined both by the presence of hyperlactatemia and need of vasopressors. Lactate levels should be measured after volume resuscitation (as per the Sepsis-3 definition). However, currently, no studies have evaluated patients who have been excluded by the new criteria for septic shock. The aim of this study was to determine the clinical characteristics and prognosis of these patients, based on their lactate levels after initial fluid resuscitation. Methods This observational study was performed using a prospective, multi-center registry of septic shock, with the participation of 10 hospitals in the Korean Shock Society, between October 2015 and February 2017. We compared the 28-day mortality between patients who were excluded from the new definition (defined as lactate level