Acupuncture ameliorates atopic dermatitis by modulating gut barrier function in a gut microbiota-dependent manner in mice.

OBJECTIVE: Atopic dermatitis (AD) is a chronic inflammatory skin disease that may be linked to changes in the gut microbiome. Acupuncture has been proven to be effective in reducing AD symptoms without serious adverse events, but its underlying mechanism is not completely understood. The purpose of this study was to investigate whether the potential effect of acupuncture on AD is gut microbiota-dependent. METHODS: AD-like skin lesions were induced by applying MC903 topically to the cheek of the mouse. Acupuncture was done at the Gok-Ji (LI11) acupoints. AD-like symptoms were assessed by lesion scores, scratching behavior, and histopathological changes; intestinal barrier function was measured by fecal output, serum lipopolysaccharide levels, histopathological changes, and mRNA expression of markers involved in intestinal permeability and inflammation. Gut microbiota was profiled using 16S rRNA gene sequencing from fecal samples. RESULTS: Acupuncture effectively improved chronic itch as well as the AD-like skin lesions with epidermal thickening, and also significantly altered gut microbiota structure as revealed by ß-diversity indices and analysis of similarities. These beneficial effects were eliminated by antibiotic depletion of gut microbiota, but were reproduced in gut microbiota-depleted mice that received a fecal microbiota transplant from acupuncture-treated mice. Interestingly, AD mice had intestinal barrier dysfunction as indicated by increased intestinal permeability, atrophy of the mucosal structure (reduced villus height and crypt depth), decreased expression of tight junctions and mucus synthesis genes, and increased expression of inflammatory mediators in the ileum. Acupuncture attenuated these abnormalities, which was gut microbiota-dependent. CONCLUSION: Acupuncture ameliorates AD-like phenotypes in a gut microbiota-dependent manner and some of these positive benefits are explained by modulation of the intestinal barrier, providing new perspective for non-pharmacological strategies for modulating gut microbiota to prevent and treat AD. Please cite this article as: Yeom M, Ahn S, Hahm DH, Jang SY, Jang SH, Park SY, Jang JH, Park J, Oh JY, Lee IS, Kim K, Kwon SK, Park HJ. Acupuncture ameliorates atopic dermatitis by modulating gut barrier function in a gut microbiota-dependent manner in mice. J Integr Med. 2024; Epub ahead of print.

Effects of L-Ornithine-L-Aspartate on Angiogenesis and Perfusion in Subacute Hind Limb Ischemia: Preliminary Study.

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.

Optimization of Fast Non-Local Means Noise Reduction Algorithm Parameter in Computed Tomographic Phantom Images Using 3D Printing Technology.

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.

How to determine the focus of emergent medical care in healthcare policy: Proposal of treatable death.

INTRODUCTION: The global provision of essential healthcare stands as a critical concern. Consequently, healthcare policies play a pivotal role in determining the allocation of resources. However, the optimal indicators for prioritizing such policies remain uncertain. This study proposes that employing the concept of treatable mortality in a stepwise manner could serve as a viable approach to setting healthcare policy priorities. Furthermore, it aims to demonstrate this concept through the application of real-world data. METHODS: A model was developed to assess treatable mortality at a national level focusing on severe emergency conditions. We established stepwise targets, encompassing short-term, mid-term, and long-term goals to reduce mortality rates and enhance healthcare efficiency. The short-term target consists of directing attention to regions exhibiting in-hospital mortality rates surpassing the national average within a specific disease category and reducing them to the national average. The mid-term objective entails decreasing the in-hospital mortality rate of the specific disease group to match that of the region with the lowest rate nationwide. As for long-term target, it aligns the in-hospital mortality rate with that of OECD countries possessing average or lowest rates. The model was applied using data from South Korea's National Emergency Department Information System, specifically analyzing acute myocardial infarction (AMI), stroke, and sepsis. RESULTS: Reaching the short-term target resulted in the treatable deaths for AMI numbered 191, for stroke 249, and for sepsis 546. Meeting the mid-term target led to treatable deaths for AMI at 749, for stroke at 958, and for sepsis at 1552. Finally, achieving the long-term target yielded the treatable deaths for AMI at 2606, for stroke at 1642, and for sepsis at 2619. Consequently, a reallocation of more healthcare resources to sepsis over AMI or stroke is recommended. CONCLUSIONS: This study proposes the utilization of treatable mortality as a metric for establishing healthcare policies. The stepwise approach provides valuable insights for policymaking at various levels. Despite limitations, the model offers a foundation for resource allocation and international mortality rate comparisons, aiming for achievable rates worldwide.

Effects of NF-κB Inhibitor on Sepsis Depend on the Severity and Phase of the Animal Sepsis Model.

Hyperinflammation occurs in sepsis, especially in the early phase, and it could have both positive and negative effects on sepsis. Previously, we showed that a new concept of NF-κB inhibitor, exosome-based super-repressor IκBα (Exo-srIκB) delivery, has a beneficial effect on sepsis. Here, we further investigate the therapeutic effects of Exo-srIκB at different severities and phases of sepsis using an animal polymicrobial intra-abdominal infection model. We used a rat model of fecal slurry polymicrobial sepsis. First, we determined the survival effects of Exo-srIκB on sepsis according to the severity. We used two different severities of the animal sepsis model. The severe model had a mortality rate of over 50%. The mild/moderate model had a less than 30% mortality rate. Second, we administered the Exo-srIκB at various time points (1 h, 6 h, and 24 h after fecal slurry administration) to determine the therapeutic effect of Exo-srIκB at different phases of sepsis. Lastly, we determined the effects of the Exo-srIκB on cytokine production, arterial blood gas, electrolyte, and lactate. The survival gain was statistically significant in the severe sepsis model when Exo-srIκB was administered 6 h after sepsis. Interleukin 6 and interleukin-10 were significantly decreased in the kidney when administered with Exo-srIκB. The laboratory data showed that lactate, glucose, and potassium levels were significantly lowered in the NF-κB inhibitor group. In conclusion, Exo-srIκB exhibited a beneficial therapeutic effect when administered 6 h post fecal slurry administration in a severe sepsis model.

Investigation of Deconvolution Method with Adaptive Point Spread Function Based on Scintillator Thickness in Wavelet Domain.

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.

Chronic Gut Inflammation and Dysbiosis in IBS: Unraveling Their Contribution to Atopic Dermatitis Progression.

Emerging evidence suggests a link between atopic dermatitis (AD) and gastrointestinal disorders, particularly in relation to gut microbial dysbiosis. This study explored the potential exacerbation of AD by gut inflammation and microbial imbalances using an irritable bowel syndrome (IBS) mouse model. Chronic gut inflammation was induced in the model by intrarectal injection of 2,4,6-trinitrobenzene sulfonic acid (TNBS), followed by a 4-week development period. We noted significant upregulation of proinflammatory cytokines in the colon and evident gut microbial dysbiosis in the IBS mice. Additionally, these mice exhibited impaired gut barrier function, increased permeability, and elevated systemic inflammation markers such as IL-6 and LPS. A subsequent MC903 challenge on the right cheek lasting for 7 days revealed more severe AD symptoms in IBS mice compared to controls. Further, fecal microbial transplantation (FMT) from IBS mice resulted in aggravated AD symptoms, a result similarly observed with FMT from an IBS patient. Notably, an increased abundance of Alistipes in the feces of IBS mice correlated with heightened systemic and localized inflammation in both the gut and skin. These findings collectively indicate that chronic gut inflammation and microbial dysbiosis in IBS are critical factors exacerbating AD, highlighting the integral relationship between gut and skin health.

Role of gut-derived bacterial lipopolysaccharide and peripheral TLR4 in immobilization stress-induced itch aggravation in a mouse model of atopic dermatitis.

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.

Performance and usefulness evaluation of a software-based scatter correction technique for mammographic images.

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.

Sex differences in in-hospital management in patients with sepsis and septic shock: a prospective multicenter observational study.

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 < 0.001). Among the bundle therapy components, broad-spectrum antibiotics were administered less frequently in women than in men (25.4% vs. 31.6%, p < 0.001), whereas adequate fluid resuscitation was performed more frequently in women than in men (96.8% vs. 95.0%, p = 0.029). In multivariable logistic regression analysis, 1-h bundle therapy was performed less frequently in women than in men [adjusted odds ratio (aOR) 1.559; 95% confidence interval (CI) 1.245-1.951; p < 0.001] after adjustment. Among the bundle therapy components, broad-spectrum antibiotics were administered less frequently to women than men (aOR 1.339, 95% CI 1.118-1.605; p = 0.002), whereas adequate fluid resuscitation was performed more frequently for women than for men (aOR 0.629, 95% CI 0.413-0.959; p = 0.031). Invasive arterial blood pressure monitoring was performed less frequently in women than in men. Resuscitation fluid, vasopressor, steroid, central-line insertion, ICU admission, length of stay in the emergency department, mechanical ventilator use, and renal replacement therapy use were comparable for both the sexes. Among patients with sepsis and septic shock, 1-h bundle therapy was performed less frequently in women than in men. Continuous efforts are required to increase adherence to the 1-h bundle therapy and to decrease sex differences in the in-hospital management of patients with sepsis and septic shock.

Framework for dual-energy-like chest radiography image synthesis from single-energy computed tomography based on cycle-consistent generative adversarial network.

BACKGROUND: Dual-energy (DE) chest radiography (CXR) enables the selective imaging of two relevant materials, namely, soft tissue and bone structures, to better characterize various chest pathologies (i.e., lung nodule, bony lesions, etc.) and potentially improve CXR-based diagnosis. Recently, deep-learning-based image synthesis techniques have attracted considerable attention as alternatives to existing DE methods (i.e., dual-exposure-based and sandwich-detector-based methods) because software-based bone-only and bone-suppression images in CXR could be useful. PURPOSE: The objective of this study was to develop a new framework for DE-like CXR image synthesis from single-energy computed tomography (CT) based on a cycle-consistent generative adversarial network. METHODS: The core techniques of the proposed framework are divided into three categories: (1) data configuration from the generation of pseudo CXR from single energy CT, (2) learning of the developed network architecture using pseudo CXR and pseudo-DE imaging using a single-energy CT, and (3) inference of the trained network on real single-energy CXR. We performed a visual inspection and comparative evaluation using various metrics and introduced a figure of image quality (FIQ) to consider the effects of our framework on the spatial resolution and noise in terms of a single index through various test cases. RESULTS: Our results indicate that the proposed framework is effective and exhibits potential synthetic imaging ability for two relevant materials: soft tissue and bone structures. Its effectiveness was validated, and its ability to overcome the limitations associated with DE imaging techniques (e.g., increase in exposure dose owing to the requirement of two acquisitions, and emphasis on noise characteristics) via an artificial intelligence technique was presented. CONCLUSIONS: The developed framework addresses X-ray dose issues in the field of radiation imaging and enables pseudo-DE imaging with single exposure.

Correction: Prediction of disease severity using serum biomarkers in patients with mild-moderate Atopic Dermatitis: A pilot study.

[This corrects the article DOI: 10.1371/journal.pone.0293332.].

Noise Reduction for a Virtual Grid Using a Generative Adversarial Network in Breast X-ray 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.

Optimization Method to Predict Optimal Noise Reduction Parameters for the Non-Local Means Algorithm Based on the Scintillator Thickness in Radiography.

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.

Modified Cardiovascular Sequential Organ Failure Assessment Score in Sepsis: External Validation in Intensive Care Unit Patients.

BACKGROUND: There is a need to update the cardiovascular (CV) Sequential Organ Failure Assessment (SOFA) score to reflect the current practice in sepsis. We previously proposed the modified CV SOFA score from data on blood pressure, norepinephrine equivalent dose, and lactate as gathered from emergency departments. In this study, we externally validated the modified CV SOFA score in multicenter intensive care unit (ICU) patients. METHODS: A multicenter retrospective observational study was conducted on ICU patients at six hospitals in Korea. We included adult patients with sepsis who were admitted to ICUs. We compared the prognostic performance of the modified CV/total SOFA score and the original CV/total SOFA score in predicting 28-day mortality. Discrimination and calibration were evaluated using the area under the receiver operating characteristic curve (AUROC) and the calibration curve, respectively. RESULTS: We analyzed 1,015 ICU patients with sepsis. In overall patients, the 28-day mortality rate was 31.2%. The predictive validity of the modified CV SOFA (AUROC, 0.712; 95% confidence interval [CI], 0.677-0.746; P < 0.001) was significantly higher than that of the original CV SOFA (AUROC, 0.644; 95% CI, 0.611-0.677). The predictive validity of modified total SOFA score for 28-day mortality was significantly higher than that of the original total SOFA (AUROC, 0.747 vs. 0.730; 95% CI, 0.715-0.779; P = 0.002). The calibration curve of the original CV SOFA for 28-day mortality showed poor calibration. In contrast, the calibration curve of the modified CV SOFA for 28-day mortality showed good calibration. CONCLUSION: In patients with sepsis in the ICU, the modified SOFA score performed better than the original SOFA score in predicting 28-day mortality.

Prediction of disease severity using serum biomarkers in patients with mild-moderate Atopic Dermatitis: A pilot study.

Atopic dermatitis (AD) is an inflammatory skin condition that relies largely on subjective evaluation of clinical signs and symptoms for diagnosis and severity assessment. Using multivariate data, we attempted to construct prediction models that can diagnose the disease and assess its severity. We combined data from 28 mild-moderate AD patients and 20 healthy controls (HC) to create random forest models for classification (AD vs. HC) and regression analysis to predict symptom severities. The classification model outperformed the random permutation model significantly (area under the curve: 0.85 ± 0.10 vs. 0.50 ± 0.15; balanced accuracy: 0.81 ± 0.15 vs. 0.50 ± 0.15). Correlation analysis revealed a significant positive correlation between measured and predicted total SCORing Atopic Dermatitis score (SCORAD; r = 0.43), objective SCORAD (r = 0.53), eczema area and severity index scores (r = 0.58, each p < 0.001), but not between measured and predicted itch ratings (r = 0.21, p = 0.18). We developed and tested multivariate prediction models and identified important features using a variety of serum biomarkers, implying that discovering the deep-branching relationships between clinical measurements and serum measurements in mild-moderate AD patients may be possible using a multivariate machine learning method. We also suggest future methods for utilizing machine learning algorithms to enhance drug target selection, diagnosis, prognosis, and customized treatment in AD.

ROMIAE (Rule-Out Acute Myocardial Infarction Using Artificial Intelligence Electrocardiogram Analysis) trial study protocol: a prospective multicenter observational study for validation of a deep learning-based 12-lead electrocardiogram analysis model for detecting acute myocardial infarction in patients visiting the emergency department.

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.

Effects of Mitochondrial Transplantation on Transcriptomics in a Polymicrobial Sepsis Model.

Previously, we demonstrated that mitochondrial transplantation has beneficial effects in a polymicrobial sepsis model. However, the mechanism has not been fully investigated. Mitochondria have their own genes, and genomic changes in sepsis are an important issue in terms of pathophysiology, biomarkers, and therapeutic targets. To investigate the changes in transcriptomic features after mitochondrial transplantation in a polymicrobial sepsis model, we used a rat model of fecal slurry polymicrobial sepsis. Total RNA from splenocytes of sham-operated (SHAM, n = 10), sepsis-induced (SEPSIS, n = 7), and sepsis receiving mitochondrial transplantation (SEPSIS + MT, n = 8) samples was extracted and we conducted a comparative transcriptome-wide analysis between three groups. We also confirmed these results with qPCR. In terms of percentage of mitochondrial mapped reads, the SEPSIS + MT group had a significantly higher mapping ratio than the others. RT1-M2 and Cbln2 were identified as highly expressed in SEPSIS + MT compared with SEPSIS. Using SHAM expression levels as another control variable, we further identified six genes (Fxyd4, Apex2l1, Kctd4, 7SK, SNORD94, and SNORA53) that were highly expressed after sepsis induction and observed that their expression levels were attenuated by mitochondrial transplantation. Changes in transcriptomic features were identified after mitochondrial transplantation in sepsis. This might provide a hint for exploring the mechanism of mitochondrial transplantation in sepsis.

Prognostic Performance of Sequential Organ Failure Assessment, Acute Physiology and Chronic Health Evaluation III, and Simplified Acute Physiology Score II Scores in Patients with Suspected Infection According to Intensive Care Unit Type.

We investigated the prognostic performance of scoring systems by the intensive care unit (ICU) type. This was a retrospective observational study using data from the Marketplace for Medical Information in the Intensive Care IV database. The primary outcome was in-hospital mortality. We obtained Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic Health Evaluation (APACHE) III, and Simplified Acute Physiology Score (SAPS) II scores in each ICU type. Prognostic performance was evaluated with the area under the receiver operating characteristic curve (AUROC) and was compared among ICU types. A total of 29,618 patients were analyzed, and the in-hospital mortality was 12.4%. The overall prognostic performance of APACHE III was significantly higher than those of SOFA and SAPS II (0.807, [95% confidence interval, 0.799-0.814], 0.785 [0.773-0.797], and 0.795 [0.787-0.811], respectively). The prognostic performance of SOFA, APACHE III, and SAPS II scores was significantly different between ICU types. The AUROC ranges of SOFA, APACHE III, and SAPS II were 0.723-0.826, 0.728-0.860, and 0.759-0.819, respectively. The neurosurgical and surgical ICUs had lower prognostic performance than other ICU types. The prognostic performance of scoring systems in patients with suspected infection is significantly different according to ICU type. APACHE III systems have the highest prediction performance. ICU type may be a significant factor in the prognostication.