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Abstract This study addresses the challenge of predicting mortality in sepsis among burn patients. Given the heterogeneity of sepsis, especially in the context of burn injuries, this study aims to identify reliable biomarkers for mortality prediction. The study is a retrospective review, focusing on the evaluation of various biomarkers and their changes over time in a burn patient cohort. Conducted in the Burn Intensive Care Unit of Hangang Sacred Heart Hospital, the study involved a retrospective review of 1,659 adult burn patients from January 2010 to December 2022. Key biomarkers analyzed include lactate levels, pH, platelets, procalcitonin, and others. Advanced clustering methodologies, such as dynamic time warping and hierarchical clustering, were utilized to classify patients into distinct groups based on their biomarker profiles and clinical outcomes. The study identified four patient clusters with unique lactate level trajectories. Significant findings include the identification of procalcitonin, pH, and platelets as key predictors of mortality, with varying degrees of efficacy across different clusters. For instance, in the “Persistent Rise” cluster, pH and platelet count showed Area Under the Curve (AUC) values of 0.756 and 0.753, respectively, indicating their strong predictive power. The study concludes that a combination of biomarkers, especially lactate dynamics, can effectively predict mortality in burn-induced sepsis. The results advocate for a more personalized approach in managing sepsis in burn patients, considering the specific biomarker trajectories. These findings are crucial for enhancing treatment strategies and improving patient outcomes in burn care.

Abstract Acute Respiratory Distress Syndrome (ARDS) is a critical form of Acute Lung Injury (ALI), challenging clinical diagnosis and severity assessment. This study evaluates the potential utility of various hematological markers in burn-mediated ARDS, including Neutrophil-to-Lymphocyte Ratio (NLR), Mean Platelet Volume (MPV), MPV-to-Lymphocyte Ratio (MPVLR), Platelet count, and Platelet Distribution Width (PDW). Employing a retrospective analysis of data collected over 12 years, this study focuses on the relationship between these hematological markers and ARDS diagnosis and severity in hospitalized patients. The study establishes NLR as a reliable systemic inflammation marker associated with ARDS severity. Elevated MPV and MPVLR also emerged as significant markers correlating with adverse outcomes. These findings suggest these economical, routinely measured markers can enhance traditional clinical criteria, offering a more objective approach to ARDS diagnosis and severity assessment. Hematological markers such as NLR, MPV, MPVLR, Platelet count, and PDW could be invaluable in clinical settings for diagnosing and assessing ARDS severity. They offer a cost-effective, accessible means to improve diagnostic accuracy and patient stratification in ARDS. However, further prospective studies are necessary to confirm these findings and investigate their integration with other diagnostic tools in diverse clinical settings.

Abstract This study aims to evaluate the utility of complete blood count (CBC) markers, in conjunction with the acute kidney injury network (AKIN) criteria, for the early detection, severity assessment, and prediction of mortality outcomes of acute kidney injury (AKI) in burn patients. The research seeks to fill existing gaps in knowledge and validate the cost-effectiveness of using CBC as a routine diagnostic tool for better management of AKI. The study was conducted at Hangang Sacred Heart Hospital. We performed a large-scale retrospective analysis of 2758 adult patients admitted to the burn intensive care unit over a 12-year period. Among these patients, AKI occurred in 1554 patients (56.3%). Based on the AKIN stage classification, 794 patients (28.8%) were categorized as AKIN 1, 494 patients (17.9%) as AKIN 2, and 266 patients (9.6%) as AKIN 3. We defined several ratio markers, including the Neutrophil-to-lymphocyte ratio (NLR), Platelet-to-lymphocyte ratio (PLR), Monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), and various mean platelet volume (MPV) ratios. Our statistical analyses, conducted using the R programming language, revealed significant correlations between these markers and AKI severity. The AUC values for neutrophil count and WBC count were 0.790 and 0.793, respectively, followed by immature granulocyte count with an AUC of 0.727. For red blood cell (RBC)-related parameters, the AUC values for hematocrit (Hct), hemoglobin (Hb), and RBC count were 0.725, 0.713, and 0.713, respectively. Among the platelet-related parameters, only platelet distribution width (PDW) had an AUC of 0.677. Among the ratio markers, the NLR had the highest AUC at 0.772, followed by MPVNR and SII with AUC values of 0.700 and 0.680, respectively. The findings underscore the potential of CBC as an economical, routine test for AKI, thereby paving the way for enhanced patient outcomes. Our study suggests the utility of routine CBC tests, specifically WBC count and PLR, for predicting AKI and platelet, MPV, and NLR for mortality assessment in burn patients. These findings underscore the potential of easily accessible CBC tests in enhancing AKI management. However, further multicenter studies are needed for validation.

Abstract Sepsis and septic shock are prevalent and life-threatening complications in burn patients. Despite their severity, existing diagnostic methods are limited. This study aims to evaluate the efficacy of Complete Blood Count (CBC) and CBC ratio markers in diagnosing sepsis and septic shock, and in predicting mortality among burn patients. A cohort of 2757 burn patients was examined to ascertain the correlation between various CBC parameters, their ratios, and the incidence of sepsis and related mortality. Key markers analyzed included Red Cell Distribution Width (RDW), Mean Platelet Volume (MPV), Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), and Mean Platelet Volume-to-Platelet Ratio (MPVPR). Our findings indicate that 65.5% of the patients developed sepsis, and 24.3% succumbed to their conditions. The CBC parameters RDW, MPV, NLR, MPVPR, and MPV-to-Lymphocyte Ratio (MPVLR) were significantly associated with sepsis and mortality. These markers showed considerable temporal variation and yielded an Area Under the Curve (AUC) of over 0.65 in an unadjusted Generalized Estimating Equations (GEE) model. This study underscores the potential of RDW, MPV, NLR, MPVPR, and MPVLR as vital prognostic tools for diagnosing sepsis, septic shock, and predicting mortality in burn patients. Although based on a single-center dataset, our results contribute to the enhancement of sepsis management by facilitating earlier, more precise diagnosis and treatment strategies. Further multi-center research is necessary to confirm these findings and broaden their applicability, establishing a solid base for future explorations in this crucial field.

Abstract Background Sepsis is a grave medical disorder characterized by a systemic inflammatory response to infection. Furthermore, it is a leading cause of morbidity and mortality, especially in hospitalized patients. Acute kidney injury (AKI) is a common complication of sepsis and is associated with increased morbidity and mortality. Patients with burns are particularly vulnerable to developing sepsis and AKI due to the extensive tissue damage and immune suppression resulting from burn injury. In this study, unsupervised clustering algorithms were used to track longitudinal biomarkers in patients with burns and assess their impact on mortality. Methods This retrospective study included adult patients with burns aged ≥ 18 years, who were admitted to the burn intensive care unit of Hallym University and Hangang Sacred Heart Hospital between July 2010 and December 2021. The patients were divided into two subgroups: those with sepsis (538 patients) and those without sepsis (826 patients). The longitudinal biomarkers were grouped into three clusters using the k-means clustering algorithm. Each cluster was assigned a letter from A to C according to its mortality rate. Results The odds ratio (OR) of pH was 9.992 in the positive group and 31.745 in the negative group in cluster C. The OR for lactate dehydrogenase (LD) was 3.704 in the positive group and 6.631 in the negative group in cluster C. The OR for creatinine was 2.784 in the positive group and 8.796 in the negative group in cluster C. The OR for blood urea nitrogen (BUN) in the negative group was 0.348, indicating a negative predictor of mortality. Regarding the application of Continuous Renal Replacement Therapy (CRRT) and ventilation, ventilation was significant in both groups. In contrast, CRRT application was not significant in the sepsis-positive group. Furthermore, it was not selected as a variable in the negative group. Conclusions The pH, LD, and creatinine were significant in both groups, while lactate and platelets were significant in the sepsis-positive group. In addition, albumin, glucose, and BUN were significant in the sepsis-negative group. Continuous renal replacement therapy was not significant in either group. However, the use of a ventilator was associated with poor prognosis.

Abstract Burn injuries often result in a high level of clinical heterogeneity and poor prognosis in patients with severe burns. Clustering algorithms, which are unsupervised methods that can identify groups with similar trajectories in patients with heterogeneous diseases, can provide insights into the mechanisms of the disease pathogenesis. This study aimed to analyze routinely collected biomarkers to understand their mortality prediction power, identify the clinical meanings or subtypes, and inform treatment decisions to improve the outcomes of patients with burns. This retrospective cohort study included patients aged ≥ 18 years who were admitted between January 2010 and December 2021. The patients were divided into four subgroups based on the time period of their admission: week 1, 2, 3, and 4. The study revealed that 22 biomarkers were evaluated, and the red blood cell distribution width, bicarbonate level, pH, platelets, and lymphocytes were significantly associated with the mortality risk. Latent class analysis further demonstrated that the pH, platelets, lymphocytes, lactate, and albumin demonstrated the lowest levels in the cluster with the highest risk of mortality, with the lowest levels of pH and lactate being particularly noteworthy in week 1 of the study. During the week 2, the pH and lymphocyte levels were demonstrated to be significant predictors of the mortality risk, whereas the lymphocyte and platelet counts were meaningful predictors in week 3. During week 4, pH, platelet count, and albumin level were important predictors of mortality risk. Analysis of routinely collected biomarkers using clustering algorithms and latent class analysis can provide valuable insights into the heterogeneity of burn injuries and improve the ability to predict disease progression and mortality. Our findings suggest that lactate levels are a better indicator of cellular hypoxia in the early stages of burn shock, whereas platelet and lymphocyte levels are more indicative of infections such as sepsis. Albumin levels are considered a better indicator of reduced nutritional loss with decrease in unhealed burn wounds; however, the pH levels reflect the overall condition of the patient throughout the study period. These findings can be used to inform treatment decisions and improve the outcomes of burn patients.

Acute kidney injury (AKI) is a significant complication in burn patients, impacting outcomes substantially. This study explores the heterogeneity of AKI in burn patients by analyzing creatinine time-series data to identify distinct AKI clusters and evaluating routine biomarkers’ predictive values. A retrospective cohort analysis was performed on 2608 adult burn patients admitted to Hangang Sacred Heart Hospital’s Burn Intensive Care Unit (BICU) from July 2010 to December 2022. Patients were divided into four clusters based on creatinine trajectories, ranging from high-risk, severe cases to lower-risk, short-term care cases. Cluster A, characterized by high-risk, severe cases, showed the highest mortality and severity, with significant predictors being PT and TB. Cluster B, representing intermediate recovery cases, highlighted PT and albumin as useful predictors. Cluster C, a low-risk, high-resilience group, demonstrated predictive values for cystatin C and eGFR cys. Cluster D, comprising lower-risk, short-term care patients, indicated the importance of PT and lactate. Key biomarkers, including albumin, prothrombin time (PT), cystatin C, eGFR cys, and total bilirubin (TB), were identified as significant predictors of AKI development, varying across clusters. Diagnostic accuracy was assessed using area under the curve (AUC) metrics, reclassification metrics (NRI and IDI), and decision curve analysis. Cystatin C and eGFR cys consistently provided significant predictive value over creatinine, with AUC values significantly higher (p < 0.05) in each cluster. This study highlights the need for a tailored, biomarker-driven approach to AKI management in burn patients, advocating for the integration of diverse biomarkers in clinical practice to facilitate personalized treatment strategies. Future research should validate these biomarkers prospectively to confirm their clinical utility.

Recent studies have reported that oncoprotein YAP can function as tumour suppressor in certain contexts. Here the authors show that inhibition of Hippo signalling or YAP activation blocks ERα transcriptional program and ER + breast cancer growth and mechanistically this is through YAP interfering with TEAD-ERα signalling axis.

Significance: Early assessment of local tissue oxygen saturation is essential for clinicians to determine the burn wound severity.Background: We assessed the burn extent and depth in the skin of the extremities using a custom-built 36-channel functional near-infrared spectroscopy system in patients with burns.Methods: A total of nine patients with burns were analyzed in this study. All second-degree burns were categorized as superficial, intermediate, and deep burns; non-burned skin on the burned side; and healthy skin on the contralateral non-burned side. Hemodynamic tissue signals from functional near-infrared spectroscopy attached to the burn site were measured during fNIRS using a blood pressure cuff. A nerve conduction study was conducted to check for nerve damage.Results: All second-degree burns were categorized into superficial, intermediate, and deep burns; non-burned skin on the burned side and healthy skin on the contralateral non-burned side showed a significant difference distinguishable using functional near-infrared spectroscopy. Hemodynamic measurements using functional near-infrared spectroscopy were more consistent with the diagnosis of burns 1 week later than that of the degree of burns diagnosed visually at the time of admission.Conclusion: Functional near-infrared spectroscopy may help with the early judgment of burn extent and depth by reflecting differences in the oxygen saturation levels in the skin.

Background/Aims This study aimed to compare the efficacy of a recently developed clear-cut knife with that of widely used knives for endoscopic submucosal dissection (ESD) in early gastric cancer (EGC). Materials and Methods Data were collected retrospectively from the National Health Insurance Service Ilsan Hospital between May 2017 and April 2019. The study included 105 patients (108 lesions) with EGC who underwent ESD. Fifty-six EGCs were dissected using an insulation-tipped diathermic knife in combination with a needle knife, while 52 EGCs were dissected using a clear-cut knife, a single device with an integrated needle and an insulated-tipped knife. Oncologic outcomes and technical success were compared between the two groups. Results The distribution of the tumor locations, composition of the undifferentiated types of EGCs, and the size of EGCs (14.3±8.0 vs. 16.6±7.3 mm; P=0.130) were not significantly different between the groups. The complete (98.2% vs. 94.2%; P=0.273) and curative (87.5% vs. 88.5%; P=0.878) resection rates were not statistically different. Furthermore, no significant difference between the total procedure times (39±34.1 vs. 35.7±20.1 minutes; P=0.554), areas of the dissected specimen (858.6±406.5 vs. 980.3±393.8 mm2; P=0.119) and dissected areas per minute (32.4±17.9 vs. 35.5±16.2 mm2/min; P=0.345) were noted. Regarding complications, the overall perforation rates (3.6% vs. 4.0%) and bleeding rates (3.6% vs. 8.0%) were not statistically different. Conclusions The newly developed clear-cut knife is as useful as the combination of classical knives for ESD in EGC.

PurposeSepsis-3 is a life-threatening organ dysfunction caused by dysregulated host responses to infection; and defined using the Sepsis-3 criteria, introduced in 2016, however, the criteria need to be validated in specific clinical fields. We investigated mortality prediction and compared the diagnostic performance of quick Sequential Organ Failure Assessment (qSOFA), systemic inflammatory response syndrome (SIRS), and burn-specific SIRS (bSIRS) in burn patients.MethodsThis single-center retrospective cohort study examined burn patients in Seoul, Korea during January 2010-December 2020. Overall, 1,391 patients with suspected infection were divided into four sepsis groups using SOFA, qSOFA, SIRS, and burn-specific SIRS.ResultsHazard ratios (HRs) of all unadjusted models were statistically significant; however, the HR (0.726, p = 0.0080.001) in the SIRS ≥2 group is below 1. In the adjusted model, HRs of the SOFA ≥2 (2.426, ConclusionSepsis-3 is a good alternative diagnostic tool because it reflects sepsis severity without delaying diagnosis. SIRS showed higher sensitivity than qSOFA and bSIRS and may therefore more adequately diagnose sepsis.

SignificanceElectrical burns can cause severe damage to the nervous system, resulting in autonomic dysfunction with reduced cerebral perfusion. However, few studies have investigated these consequences.AimTo elucidate changes in prefrontal cerebral hemodynamics using functional near-infrared spectroscopy (fNIRS) during the head-up tilt table test (HUT) for patients with electrical burns.ApproachWe recruited 17 patients with acute electrical burns within 1 week after their accidents and 10 healthy volunteers. The NIRS parameters acquired using an fNIRS device attached to the forehead were analyzed in five distinct HUT phases.ResultsBased on their HUT response patterns, patients with electrical burns were classified into the group with abnormal HUT results (APG, n = 4) or normal HUT results (NPG, n = 13) and compared with the healthy control (HC, n = 10) participants. We found trends in hemodynamic changes during the HUT that distinguished HC, NPG, and APG. Reduced cerebral perfusion and decreased blood oxygenation during the HUT were found in both the NPG and APG groups. Patients with electrical burns had autonomic dysfunction compared to the HC participants.ConclusionsUsing fNIRS, we observed that acute-stage electrical burn injuries could affect cerebral perfusion.

Abstract Background The progression of biomarkers over time is considered an indicator of disease progression and helps in the early detection of disease, thereby reducing disease-related mortality. Their ability to predict outcomes has been evaluated using conventional cross-sectional methods. This study investigated the prognostic performance of biomarkers over time. Methods Patients aged > 18 years admitted to the burn intensive care unit within 24 h of a burn incident were enrolled. Information regarding longitudinal biomarkers, including white blood cells; platelet count; lactate, creatinine, and total bilirubin levels; and prothrombin time (PT), were retrieved from a clinical database. Time-dependent receiver operating characteristic curves using cumulative/dynamic and incident/dynamic (ID) approaches were used to evaluate prognostic performance. Results Overall, 2259 patients were included and divided into survival and non-survival groups. By determining the area under the curve using the ID approach, platelets showed the highest c-index [0.930 (0.919–0.941)] across all time points. Conversely, the c-index of PT and creatinine levels were 0.862 (0.843–0.881) and 0.828 (0.809–0.848), respectively. Conclusions Platelet count was the best prognostic marker, followed by PT. Total bilirubin and creatinine levels also showed good prognostic ability. Although lactate was a strong predictor, it showed relatively poor prognostic performance in burns patients.

Initially identified in Drosophila, the Hippo signaling pathway has emerged as an evolutionarily conserved tumor suppressor pathway that controls tissue growth and organ size by simultaneously inhibiting cell proliferation and promoting cell death. Deregulation of Hippo pathway activity has been implicated in a wide range of human cancers. The core Hippo pathway consists of a kinase cascade: an upstream kinase Hippo (Hpo)/MST1/2 phosphorylates and activates a downstream kinase Warts (Wts)/Lats1/2, leading to phosphorylation and inactivation of a transcriptional coactivator Yki/YAP/Taz. Many upstream signals, including cell adhesion, polarity, mechanical stress, and soluble factors, regulate Hippo signaling through the kinase cascade, leading to change in the cytoplasmic/nuclear localization of Yki/YAP/Taz. However, recent studies have uncovered other mechanisms that regulate Yki/YAP/Taz subcellular localization, stability, and activity independent of the Hpo kinase cascade. These mechanisms provide additional layers of pathway regulation, nodes for pathway crosstalk, and opportunities for pathway intervention in cancer treatment and regenerative medicine.

Pulmonary extraintestinal manifestation is rare in Crohn’s disease and has been reported in only a few cases. Despite the presence of pulmonary abnormalities in a significant proportion of patients with inflammatory bowel disease, there are only few case reports, due to complicated diagnosis and low recognition by clinicians. Currently, treatment guidelines for pulmonary Crohn’s disease have not been established. There are some case reports of pulmonary Crohn’s disease that achieved remission after infliximab treatment. Clinical and radiological remission of pulmonary extraintestinal involvement in Crohn’s disease after adalimumab therapy has not been reported yet. Here, we report one case of lung involvement of Crohn’s disease, which shows radiological and clinical remission after adalimumab treatment.

The Hippo signaling pathway controls tissue growth by regulating the subcellular localization of Yorkie /Yap. Here the authors show that PRP4K, a kinase involved in mRNA splicing, phosphorylates Yki/Yap in the nucleus, which prevents its nuclear accumulation and inhibits Hippo signaling.

PURPOSE:The purpose of this study was to develop a new prediction model to reflect the risk of mortality and severity of disease and to evaluate the ability of the developed model to predict mortality among adult burn patients. METHODS:This study included 2009 patients aged more than 18 years who were admitted to the intensive care unit (ICU) within 24 hours after a burn. We divided the patients into two groups; those admitted from January 2007 to December 2013 were included in the derivation group and those admitted from January 2014 to September 2017 were included in the validation group. Shrinkage methods with 10-folds cross-validation were performed to identify variables and limit overfitting of the model. The discrimination was analyzed using the area under the curve (AUC) of the receiver operating characteristic curve. The Brier score, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were also calculated. The calibration was analyzed using the Hosmer-Lemeshow goodness-of-fit test (HL test). The clinical usefulness was evaluated using a decision-curve analysis. RESULTS:The Hangang model showed good calibration with the HL test (χ2 = 8.785, p = 0.361); the highest AUC and the lowest Brier score were 0.943 and 0.068, respectively. The NRI and IDI were 0.124 (p-value = 0.003) and 0.079 (p-value

BACKGROUND:Diagnosing acute kidney injury quickly is imperative since it is known as an independent risk factor for mortality in burn patients. We evaluated the diagnostic power of creatinine, cystatin, serum and urine neutrophil gelatinase-associated lipocalin at different time periods and observed the changes from baseline for each biomarker. METHODS:This was a prospective observation study from January 2015 to February 2016. A total of 84 patients were enrolled consecutively. Serum creatinine, serum cystatin C, and serum and urine neutrophil gelatinase-associated lipocalin were measured at admission, 7th, 14th, 21st, and 28th days after admission. All samples were collected until acute kidney injury developed. RESULTS:Acute kidney injury developed in 35 patients. The mean age was 49.6 years with a male predominance. The median urine neutrophil gelatinase-associated lipocalin was the lowest (11.6 ng/dL) at admission, and the highest at 85.5 ng/dL on day 7. Mean creatinine level was the highest (0.88 mg/dL) at admission and the median creatinine level was the lowest (0.56 mg/dL) on the 14th day. The area under the curve of creatinine levels was the highest with 0.857 during the 1st week. The area under the curve of urine neutrophil gelatinase-associated lipocalin was the highest with 0.803 during the 5th week. CONCLUSIONS:Within 1 week of acute kidney injury, creatinine level was the optimal biomarker for diagnosis while urine neutrophil gelatinase-associated lipocalin showed better diagnostic performance following the 4- week period.

Inhalation injury is known to be an important factor in predicting mortality in burns patients. However, the diagnosis is complicated by the heterogeneous presentation and inability to determine the severity of inhalation injury. The purpose of this study was to identify clinical features of inhalation injury that affect mortality and the values that could predict the outcome more precisely in burns patients with inhalation injury. This retrospective observational study included 676 burns patients who were over 18 years of age and hospitalized in the Burns Intensive Care Unit between January 2012 and December 2015. We analyzed variables that are already known to be prognostic factors (age, percentage of total body surface area (%TBSA) burned, and inhalation injury) and factors associated with inhalation injury (carboxyhemoglobin and PaO2/FiO2 [PF] ratio) by univariate and multivariate logistic regression. Age group (odds ratio [OR] 1.069, p

Background and Aims. Endoscopic submucosal dissection (ESD) is routinely performed in treating gastric neoplasia and requires long-term higher levels of sedation. Endoscopist-directed nurse-administered sedation (EDNAS) has not been well studied in ESD. This study aimed to evaluate the safety and effectiveness of EDNAS for ESD. Methods. Patients treated with ESD for gastric tumors between 2013 and 2015 were retrospectively collected. Patients were divided into a midazolam-treated group (M group) and a midazolam plus propofol-treated group (MP group). Clinical outcome, safety, effectiveness, adverse events of ESD, and adverse events of sedation were analyzed. Results. Of 209 collected patients, 83 were in the M group and 126 were in the MP group. Of all patients, 67 patients had the circulatory adverse event during the ESD procedure. Sedation method was the only significant risk factor (M versus MP: 2.17 (1.14–4.15), p=0.019). In analysis of MP subgroups, 47 patients suffered an adverse event from sedation, and current smoking was the only significant association factor for adverse event (0.15 (0.03–0.68), p=0.014). Conclusions. In performing ESD, the effect of sedation is reduced in smoking patients. EDNAS may be acceptable for ESD under careful monitoring of vital sign and oxygen saturation.

Contrastive learning of sentence representations has achieved great improvements in several natural language processing tasks. However, the supervised contrastive learning model trained on the natural language inference (NLI) dataset is insufficient to elucidate the semantics of sentences since it is prone to make a prediction based on heuristics. Herein, by using the ParsEVAL and the word overlap metric, it is shown that sentence pairs in the NLI dataset have strong syntactic similarity and propose a framework to compensate for this problem in two aspects. 1) Apply simple syntactic transformations to the hypothesis and 2) expand the objective to SupCon Loss to leverage variants of sentences. The method is evaluated on semantic textual similarity (STS) tasks and transfer tasks. The proposed methods improve the performance of the BERT‐based baseline in STS Benchmark and SICK Relatedness by 1.48% and 2.2%. Furthermore, the model achieves 82.65% on the HANS benchmark dataset, to the best of our knowledge, which is a state‐of‐the‐art performance demonstrating that our approach is effective in grasping semantics without heuristics in the NLI dataset at supervised contrastive learning. The code is available at https://github.com/whnhch/Break-the-Similarity.

Recent studies on image super-resolution (SR) have focused on just expanding the receptive field. However, the insight from local attribution maps has enlightened the need for an improved exploitation of information within the receptive field. To address this, we propose a novel image super-resolution model, named Uniwin, that balances local and global interactions through unifying two types of window-based local attention mechanisms: shifted-window attention and sliding-window attention. Uniwin combines swift global context access with comprehensive local context capture. Our approach involves the initial sliding-window attention to collect comprehensive local pattern information, followed by the non-overlapping shifted-window attention to expand the receptive field for global interactions. Empirical evaluations demonstrate Uniwin’s superiority over state-of-the-art models across five benchmark datasets. Specifically, in SR $\times 2$ task, our model achieved 0.25dB higher PSNR on the Set14 dataset, and 0.14dB higher PSNR on the Urban100 dataset than existing state-of-the-art model using a similar number of parameters. Additionally, our model achieved comparable performance to existing large-scale models with only 58% of the parameters. Ablation studies on sliding-window and shifted-window attention mechanisms reveal the critical importance of the context harmonization for image super-resolution. In conclusion, Uniwin represents a reasonable solution that effectively integrates global and local attention mechanisms, enhancing image super-resolution performance.

Anomaly detection has gained significant attention with the advancements in deep neural networks. Effective training requires both normal and anomalous data, but this often leads to a class imbalance, as anomalous data is scarce. Traditional augmentation methods struggle to maintain the correlation between anomalous patterns and their surroundings. To address this, we propose an adjacent augmentation technique that generates synthetic anomaly images, preserving object shapes while distorting contours to enhance correlation. Experimental results show that adjacent augmentation captures high-quality anomaly features, achieving superior AU-ROC and AU-PR scores compared to existing methods. Additionally, our technique produces synthetic normal images, aiding in learning detailed normal data features and reducing sensitivity to minor variations. Our framework considers all training images within a batch as positive pairs, pairing them with synthetic normal images as positive pairs and with synthetic anomaly images as negative pairs. This compensates for the lack of anomalous features and effectively distinguishes between normal and anomalous features, mitigating class imbalance. Using the ResNet50 network, our model achieved perfect AU-ROC and AU-PR scores of 100% in the bottle category of the MVTec-AD dataset. We are also investigating the relationship between anomalous pattern size and detection performance.

Background The aging population in Korea has driven a surge in demand for elderly care services, leading to significant growth in elderly welfare facilities, particularly Adult Daycare Centers (ADCs). However, despite advancements in care facilities, caregivers continue to face challenges in providing suitable elderly care due to difficulties arising from gaps in the latest information on the elderly and their coping abilities. Objective The objective of this study is to develop and evaluate the effectiveness of the elderly care assistant system, which facilitates the sharing of information and knowledge necessary for elderly care among caregivers. Methods The ECA system was designed to support knowledge sharing through a knowledge management system based on an ontological knowledge model, with a web-based user interface for improved accessibility. A field trial was conducted at ADC in Seoul from August 17 to September 21, with eight caregivers participating. A mixed-methods approach, involving both surveys and interviews, was employed to gauge the ECA system's effectiveness. Results The study found that the use of the ECA was beneficial in promoting knowledge sharing among caregivers. Additionally, caregivers noted the potential benefits of using the ECA in conjunction with family caregivers, who can offer additional information and perspectives on elderly care. Conclusions This study presents preliminary evidence of the potential benefits of a care knowledge sharing system among various caregivers in elderly care. Although the elderly care assistant effectively promotes knowledge sharing, more research is needed to fully understand its impact on elderly care outcomes.

As the performance of computing devices such as graphics processing units (GPUs) has improved dramatically, many deep neural network models, especially convolutional neural networks (CNNs), have been widely applied to various applications such as image classification, semantic segmentation, and object recognition. However, effective first-order optimization methods for CNNs have rarely been studied, although many CNN models have been successfully developed. Accordingly, this paper investigates various advanced adaptive solution search methods and proposes a new first-order optimization algorithm for CNNs called Adam-ASC. Our approach uses four sophisticated adaptive solution search methods to adjust its search strength in the complicated large-dimensional weight solution space spanned by a loss function. At the same time, we explain how they can be combined compensatively to form a complete optimizer with a detailed implementation. From the experiments, we found that our Adam-ASC can significantly improve the image recognition performance of practical CNNs in both the image classification and segmentation tasks. These experimental results show that the four fundamental methods of Adam-ASC and their compensative combination strategy play a crucial role in training CNNs by effectively finding their optimal weights.

Transformer-based networks using attention mechanisms have shown promising results in low-level vision tasks, such as image super-resolution (SR). Specifically, recent studies that utilize window-based self-attention mechanisms have exhibited notable advancements in image SR. However, window-based self-attention, results in a slower expansion of the receptive field, thereby restricting the modeling of long-range dependencies. To address this issue, we introduce a novel dilated window transformer, namely DWT, which utilizes a dilation strategy. We employ a simple yet efficient dilation strategy that enlarges the window by inserting intervals between the tokens of each window to enable rapid and effective expansion of the receptive field. In particular, we adjust the interval between the tokens to become wider as the layers go deeper. This strategy enables the extraction of local features by allowing interaction between neighboring tokens in the shallow layers while also facilitating efficient extraction of global features by enabling interaction between not only adjacent tokens but also distant tokens in the deep layers. We conduct extensive experiments on five benchmark datasets to demonstrate the superior performance of our proposed method. Our DWT surpasses the state-of-the-art network of similar sizes by a PSNR margin of 0.11dB to 0.27dB on the Urban100 dataset. Moreover, even when compared to state-of-the-art network with about 1.4 times more parameters, DWT achieves competitive results for both quantitative and visual comparisons.