Thresholding Computing with Heterogeneous Integration of Memristive Kernel with Metal-Oxide-Semiconductor Capacitor for Temporal Data Analysis.

Precise event detection within time-series data is increasingly critical, particularly in noisy environments. Reservoir computing, a robust computing method widely utilized with memristive devices, is efficient in processing temporal signals. However, it typically lacks intrinsic thresholding mechanisms essential for precise event detection. This study introduces a new approach by integrating two Pt/HfO2/TiN (PHT) memristors and one Ni/HfO2/n-Si (NHS) metal-oxide-semiconductor capacitor (2M1MOS) to implement a tunable thresholding function. The current-voltage nonlinearity of memristors combined with the capacitance-voltage nonlinearity of the capacitor forms the basis of the 2M1MOS kernel system. The proposed kernel hardware effectively records feature-specified information of the input signal onto the memristors through capacitive thresholding. In electrocardiogram analysis, the memristive response exhibited a more than ten-fold difference between arrhythmia and normal beats. In isolated spoken digit classification, the kernel achieved an error rate of only 0.7% by tuning thresholds for various time-specific conditions. The kernel is also applied to biometric authentication by extracting personal features using various threshold times, presenting more complex and multifaceted uses of heartbeats and voice data as bio-indicators. These demonstrations highlight the potential of thresholding computing in a memristive framework with heterogeneous integration.

Hyperplane tree-based data mining with a multi-functional memristive crossbar array.

This study explores the stochastic and binary switching behaviors of a Ta/HfO2/RuO2 memristor to implement a combined data mining approach for outlier detection and data clustering algorithms in a multi-functional memristive crossbar array. The memristor switches stochastically with high state dispersion in the stochastic mode and deterministically between two states with low dispersion in the binary mode, while they can be controlled by varying operating voltages. The stochastic mode facilitates the parallel generation of random hyperplanes in a tree structure, used to compress spatial information of the dataset in the Euclidian space into binary format, still retaining sufficient spatial features. The ensemble effect from multiple trees improved the classification performance. The binary mode facilitates parallel Hamming distance calculation of the binary codes containing spatial information, which measures similarity. These two modes enable efficient implementation of the newly proposed minority-based outlier detection method and modified K-means method on the same hardware. Array measurements and hardware simulations investigate various hyperparameters' impact and validate the proposed methods with practical datasets. The proposed methods show linear O(n) time complexity and high energy efficiency, consuming <1% of the energy compared to digital computing with conventional algorithms while demonstrating software-comparable performance in both tasks.

Effect of Fluorescence Lymph Node Mapping on Improving Diagnostic Values of CT D3 Lymph Node Staging for Right-Sided Colon Cancer.

BACKGROUND/OBJECTIVES: This study evaluated the impact of fluorescence lymph node mapping (FLNM) using indocyanine green (ICG) on the diagnostic accuracy of preoperative computed tomography (CT) in right-sided colon cancer. METHODS: A total of 218 patients who underwent laparoscopic right hemicolectomy with D3 lymph node dissection (LND) were analyzed: 86 patients in the FLNM group and 132 in the conventional surgery group. The FLNM technique allowed for enhanced intraoperative visualization of lymph node (LN) and more precise dissection, improving the identification of metastatic LNs. The diagnostic value of preoperative CT staging was assessed in both the FLNM and control groups by calculating the apparent prevalence, true prevalence, sensitivity, specificity, positive predictive value (PPV), negative predictive value, positive likelihood ratio (PLR), negative likelihood ratio, false positive and false negative proportions, and accuracy. RESULTS: FLNM increased the accuracy of CT staging for detecting D3 LN metastasis in advanced cancer cases, with a higher PPV, PLR, and accuracy. In the FLNM group, the false-positive rate was significantly reduced, and the specificity was higher compared to the control group. Multivariate analysis identified FLNM as an independent factor associated with improved D3 LN metastasis detection. These findings suggest that incorporating FLNM into surgical procedures enhances the diagnostic value of preoperative CT by improving the precision of LND, particularly in patients with advanced colon cancer. CONCLUSIONS: The use of FLNM for D3 LND enhances the diagnostic accuracy of cN staging in right-sided colon cancer by improving surgical precision.

A stochastic photo-responsive memristive neuron for an in-sensor visual system based on a restricted Boltzmann machine.

In-sensor computing has gained attention as a solution to overcome the von Neumann computing bottlenecks inherent in conventional sensory systems. This attention is due to the ability of sensor elements to directly extract meaningful information from external signals, thereby simplifying complex data. The advantage of in-sensor computing can be maximized with the sampling principle of a restricted Boltzmann machine (RBM) to extract significant features. In this study, a stochastic photo-responsive neuron is developed using a TiN/In-Ga-Zn-O/TiN optoelectronic memristor and an Ag/HfO2/Pt threshold-switching memristor, which can be configured as an input neuron in an in-sensor RBM. It demonstrates a sigmoidal switching probability depending on light intensity. The stochastic properties allow for the simultaneous exploration of various neuron states within the network, making identifying optimal features in complex images easier. Based on semi-empirical simulations, high recognition accuracies of 90.9% and 95.5% are achieved using handwritten digit and face image datasets, respectively. In addition, the in-sensor RBM effectively reconstructs abnormal face images, indicating that integrating in-sensor computing with probabilistic neural networks can lead to reliable and efficient image recognition under unpredictable real-world conditions.

Immunoglobulin G4-Related Disease in the Diaphragm: A Case Report.

Immunoglobulin G4-related disease (IgG4-RD) is an immune-mediated condition characterized by mass-forming inflammation with a sclerosing pattern that can affect nearly any organ. However, involvement of the diaphragm in IgG4-RD is exceptionally rare. We present the case of a 62-year-old male patient with chest radiographic abnormalities. Further investigation with CT revealed an infiltrative mass in the right hemidiaphragm. This mass, composed of engorged feeding vessels, an atypical manifestation of IgG4-RD, was also associated with lymphadenopathy. Surgical excision confirmed the presence of IgG4-positive cell infiltration, solidifying the diagnosis of IgG4-RD. Notably, the patient remained asymptomatic and did not require any treatment postoperatively. This case highlights the uncommon presentation of IgG4-RD as an infiltrative diaphragmatic mass.

In-Materia Annealing and Combinatorial Optimization Based on Vertical Memristive Array.

Due to its area and energy efficiency, a memristive crossbar array (CBA) has been extensively studied for various combinatorial optimization applications, from network problems to circuit design. However, conventional approaches include heavily burdening software fine-tuning for the annealing process. Instead, this study introduces the "in-materia annealing" method, where the inter-layer interference of vertically stacked memristive CBA is utilized as an annealing method. When mapping combinatorial optimization problems into the configuration layer of the CBA, exponentially decaying annealing profiles are generated in nearby noise layers. Moreover, in-materia annealing profiles can be controlled by changing compliance current, read voltage, and read pulse width. Therefore, the annealing profiles can be arbitrarily controlled and generated individually for each cell, providing rich noise sources to solve the problem efficiently. Consequently, the experimental and simulation of Max-Cut and weighted Max-Cut problems achieve notable results with the minimum software burden.

A human brain atlas of χ-separation for normative iron and myelin distributions.

Iron and myelin are primary susceptibility sources in the human brain. These substances are essential for a healthy brain, and their abnormalities are often related to various neurological disorders. Recently, an advanced susceptibility mapping technique, which is referred to as χ-separation (pronounced as "chi"-separation), has been proposed, successfully disentangling paramagnetic iron from diamagnetic myelin. This method provided a new opportunity for generating high-resolution iron and myelin maps of the brain. Utilizing this technique, this study constructs a normative χ-separation atlas from 106 healthy human brains. The resulting atlas provides detailed anatomical structures associated with the distributions of iron and myelin, clearly delineating subcortical nuclei, thalamic nuclei, and white matter fiber bundles. Additionally, susceptibility values in a number of regions of interest are reported along with age-dependent changes. This atlas may have direct applications such as localization of subcortical structures for deep brain stimulation or high-intensity focused ultrasound and also serve as a valuable resource for future research.

Heterogeneous density-based clustering with a dual-functional memristive array.

In the big data era, the requirement for data clustering methods that can handle massive and heterogeneous datasets with varying distributions increases. This study proposes a clustering algorithm for data sets with heterogeneous density using a dual-mode memristor crossbar array for data clustering. The array consists of a Ta/HfO2/RuO2 memristor operating in analog or digital modes, controlled by the reset voltage. The digital mode shows low dispersion and a high resistance ratio, and the analog mode enables precise conductance tuning. The local outlier factor is introduced to handle a heterogeneous density, and the required Euclidean and K-distances within the given dataset are calculated in the analog mode in parallel. In the digital mode, clustering is performed based on the connectivity among data points after excluding the detected outliers. The proposed algorithm boasts linear time complexity for the entire process. Extensive evaluations of synthetic datasets demonstrate significant improvement over representative density-based algorithms, and the datasets with heterogeneous density are clustered feasibly. Finally, the proposed algorithm is used to cluster the single-molecule localization microscopy data, demonstrating the feasibility of the suggested method for real-world problems.

Effectiveness and satisfaction with virtual and donor dissections: A randomized controlled trial.

In recent years, human anatomy education has faced challenges with traditional donor dissection, leading to the emergence of virtual dissection as an alternative. This study aims to investigate the academic performance and satisfaction of medical students by comparing the virtual and donor dissections. An open-labeled crossover randomized controlled trial was conducted with 154 first-year medical students in Human Anatomy and Neuroanatomy laboratories, which were divided into three classes. Students were randomly assigned to either the virtual (virtual dissection followed by donor dissection) or donor (donor dissection followed by virtual dissection) groups in each class. A curriculum, incorporating head-mounted displays (HMDs), a life-sized touchscreen, and tablets, was developed. Data was evaluated through quizzes and surveys. In the Human Anatomy laboratory, each class of the donor group conducted heart extraction, dissection and observation. In observation class, the virtual group had a significantly higher mean quiz score than the donor group (p < 0.05). Compared to the donor, satisfaction was significantly higher for the HMD (understanding of concept and immersion), life-size touchscreen (esthetics, understanding of the concept, and spatial ability), and tablet (esthetics, understanding of the concept, spatial ability, and continuous use intention). In the Neuroanatomy laboratory, the virtual group showed significantly higher mean quiz scores than the donor group (p < 0.05), and tablet showed a significantly higher satisfaction than donor in terms of esthetics, understanding of the concept, and spatial ability. These results suggest that virtual dissection has the potential to supplement or replace donor dissection in anatomy education. This study is innovative in that it successfully delivered scenario-based virtual content and validated the efficacy in academic performance and satisfaction when using virtual devices compared to donor.Trial registration: This research has been registered in the Clinical Research Information Service (CRIS, https://cris.nih.go.kr/cris/search/detailSearch.do?search_lang=E&focus=reset_12&search_page=L&pageSize=10&page=undefined&seq=26002&status=5&seq_group=26002 ) with registration number "KCT0009075" and registration date "27/12/2023".

Impact of intensive blood pressure lowering after multiple-attempt endovascular thrombectomy: A secondary analysis of the OPTIMAL-BP trial.

BACKGROUND: Multiple attempts of thrombectomy have been linked to a higher risk of intracerebral hemorrhage and worsened functional outcomes, potentially influenced by blood pressure (BP) management strategies. Nonetheless, the impact of intensive BP management following successful recanalization through multiple attempts remains uncertain. AIMS: This study aimed to investigate whether conventional and intensive BP managements differentially affect outcomes according to multiple-attempt recanalization (MAR) and first-attempt recanalization (FAR) groups. METHODS: In this secondary analysis of the OPTIMAL-BP trial, which was a comparison of intensive (systolic BP target: <140 mm Hg) and conventional (systolic BP target = 140-180 mm Hg) BP managements during the 24 h after successful recanalization, we included intention-to-treat population of the trial. Patients were divided into the MAR and the FAR groups. We examined a potential interaction between the number of thrombectomy attempts (MAR and FAR groups) and the effect of BP managements on clinical and safety outcomes. The primary outcome was functional independence at 3 months. Safety outcomes were symptomatic intracerebral hemorrhage within 36 h and mortality within 3 months. RESULTS: Of the 305 patients (median = 75 years), 102 (33.4%) were in the MAR group and 203 (66.6%) were in the FAR group. The intensive BP management was significantly associated with a lower rate of functional independence in the MAR group (intensive, 32.7% vs conventional, 54.9%, adjusted odds ratio (OR) = 0.33, 95% confidence interval (CI) = 0.12-0.90, p = 0.03). In the FAR group, the proportion of patients with functional independence was not significantly different between the BP managements (intensive, 42.5% vs conventional, 54.2%, adjusted OR = 0.73, 95% CI = 0.38-1.40). Incidences of symptomatic intracerebral hemorrhage and mortality rates were not significantly different according to the BP managements in both MAR and FAR groups. CONCLUSIONS: Among stroke patients who received multiple attempts of thrombectomy, intensive BP management for 24 h resulted in a reduced chance of functional independence at 3 months and did not reduce symptomatic intracerebral hemorrhage following successful reperfusion.

Implementation of Bayesian networks and Bayesian inference using a Cu0.1Te0.9/HfO2/Pt threshold switching memristor.

Bayesian networks and Bayesian inference, which forecast uncertain causal relationships within a stochastic framework, are used in various artificial intelligence applications. However, implementing hardware circuits for the Bayesian inference has shortcomings regarding device performance and circuit complexity. This work proposed a Bayesian network and inference circuit using a Cu0.1Te0.9/HfO2/Pt volatile memristor, a probabilistic bit neuron that can control the probability of being 'true' or 'false.' Nodal probabilities within the network are feasibly sampled with low errors, even with the device's cycle-to-cycle variations. Furthermore, Bayesian inference of all conditional probabilities within the network is implemented with low power (<186 nW) and energy consumption (441.4 fJ), and a normalized mean squared error of ∼7.5 × 10-4 through division feedback logic with a variational learning rate to suppress the inherent variation of the memristor. The suggested memristor-based Bayesian network shows the potential to replace the conventional complementary metal oxide semiconductor-based Bayesian estimation method with power efficiency using a stochastic computing method.

Impact of Neoadjuvant Chemotherapy (NAC) on Biomarker Expression in Breast Cancer.

Background and Objectives: This study aimed to explore biomarker change after NAC (neoadjuvant chemotherapy) and to investigate biomarker expression as a prognostic factor in patients with residual disease (RD) after NAC. Materials and Methods: We retrospectively evaluated 104 patients with invasive breast cancer, who underwent NAC and surgery at Pusan National University Hospital from 2015 to July 2022. The expression of the biomarker was assessed, and the overall survival (OS) and disease-free survival (DFS) were investigated. Results: After NAC, 24 patients (23.1%) out of 104 total patients had a pathological complete response (pCR). We found that changes in at least one biomarker were observed in 41 patients (51.2%), among 80 patients with RD. In patients with RD after NAC (n = 80), a subtype change was identified in 20 patients (25.0%). Any kind of change in the HER2 status was present 19 (23.7%) patients. The hormone receptor (HR)+/HER2+ subtype was significantly associated with better disease-free survival (DFS) (HR, 0.13; 95% CI, 0.02-0.99; p = 0.049). No change in p53 was associated with better DFS, and negative-to-positive change in p53 expression after NAC was correlated with worse DFS (p < 0.001). Negative-to-positive change in p53 was an independent, worse DFS factor in the multivariate analysis (HR,18.44; 95% CI, 1.86-182.97; p = 0.013). Conclusions: Biomarker change and subtype change after NAC were not infrequent, which can affect the further treatment strategy after surgery. The expression change of p53 might have a prognostic role. Overall, we suggest that the re-evaluation of biomarkers after NAC can provide a prognostic role and is needed for the best decision to be made on further treatment.

TFE3/PI3K/Akt/mTOR Axis in Renal Cell Carcinoma Affects Tumor Microenvironment.

The role of the phosphatidylinositol 3-kinase (PI3K)/Akt pathway in renal cell carcinoma (RCC) progression, metastasis, and resistance to therapies has not been investigated thoroughly. Transcription factor E3 (TFE3) expression is related to a poorer prognosis and tumor microenvironment in patients with RCC. This study aimed to determine the relationship between TFE3 and the PI3K/Akt pathway. TFE3 down-regulation was achieved by transient transfection of siRNA and shRNA in UOK146 cells. TFE3 overexpression was induced by transient transfection with pcDNA3.1 encoding the constitutively active form of TFE3. The cells were treated with mammalian target of rapamycin (mTOR) and PI3K inhibitors. Western blot was performed to detect TFE3, programmed death-ligand 1, phospho-Akt, and Akt. Phospho-Akt expression increased significantly upon TFE3 down-regulation, and decreased significantly upon up-regulation. When RCC cells were treated with a PI3K inhibitor (LY294002), TFE3 expression increased and phospho-Akt expression decreased. Data from this study indicate that TFE3 plays a role in the PI3K/Akt pathway in RCC. The results of this study suggest that PI3K/Akt inhibitors may aid in the treatment of patients with RCC by affecting the tumor microenvironment.

Functional Outcomes Associated With Blood Pressure Decrease After Endovascular Thrombectomy.

Importance: The associations between blood pressure (BP) decreases induced by medication and functional outcomes in patients with successful endovascular thrombectomy remain uncertain. Objective: To evaluate whether BP reductions induced by intravenous BP medications are associated with poor functional outcomes at 3 months. Design, Setting, and Participants: This cohort study was a post hoc analysis of the Outcome in Patients Treated With Intra-Arterial Thrombectomy-Optimal Blood Pressure Control trial, a comparison of intensive and conventional BP management during the 24 hours after successful recanalization from June 18, 2020, to November 28, 2022. This study included 302 patients who underwent endovascular thrombectomy, achieved successful recanalization, and exhibited elevated BP within 2 hours of successful recanalization at 19 stroke centers in South Korea. Exposure: A BP decrease was defined as at least 1 event of systolic BP less than 100 mm Hg. Patients were divided into medication-induced BP decrease (MIBD), spontaneous BP decrease (SpBD), and no BP decrease (NoBD) groups. Main Outcomes and Measures: The primary outcome was a modified Rankin scale score of 0 to 2 at 3 months, indicating functional independence. Primary safety outcomes were symptomatic intracerebral hemorrhage within 36 hours and mortality due to index stroke within 3 months. Results: Of the 302 patients (median [IQR] age, 75 [66-82] years; 180 [59.6%] men), 47 (15.6%)were in the MIBD group, 39 (12.9%) were in the SpBD group, and 216 (71.5%) were in the NoBD group. After adjustment for confounders, the MIBD group exhibited a significantly smaller proportion of patients with functional independence at 3 months compared with the NoBD group (adjusted odds ratio [AOR], 0.45; 95% CI, 0.20-0.98). There was no significant difference in functional independence between the SpBD and NoBD groups (AOR, 1.41; 95% CI, 0.58-3.49). Compared with the NoBD group, the MIBD group demonstrated higher odds of mortality within 3 months (AOR, 5.15; 95% CI, 1.42-19.4). The incidence of symptomatic intracerebral hemorrhage was not significantly different among the groups (MIBD vs NoBD: AOR, 1.89; 95% CI, 0.54-5.88; SpBD vs NoBD: AOR, 2.75; 95% CI, 0.76-9.46). Conclusions and Relevance: In this cohort study of patients with successful endovascular thrombectomy after stroke, MIBD within 24 hours after successful recanalization was associated with poor outcomes at 3 months. These findings suggested lowering systolic BP to below 100 mm Hg using BP medication might be harmful.

Multiphase Reset Induced Reliable Dual-Mode Resistance Switching of the Ta/HfO2/RuO2 Memristor.

Higher functionality should be achieved within the device-level switching characteristics to secure the operational possibility of mixed-signal data processing within a memristive crossbar array. This work investigated electroforming-free Ta/HfO2/RuO2 resistive switching devices for digital- and analog-type applications through various structural and electrical analyses. The multiphase reset behavior, induced by the conducting filament modulation and oxygen vacancy generation (annihilation) in the HfO2 layer by interacting with the Ta (RuO2) electrode, was utilized for the switching mode change. Therefore, a single device can manifest stable binary switching between low and high resistance states for the digital mode and the precise 8-bit conductance modulation (256 resistance values) via an optimized pulse application for the analog mode. An in-depth analysis of the operation in different modes and comparing memristors with different electrode structures validate the proposed mechanism. The Ta/HfO2/RuO2 resistive switching device is feasible for a mixed-signal processable memristive array.

Effectiveness of endovascular treatment for in-hospital stroke vs. community-onset stroke: a propensity score-matched analysis.

BACKGROUND: The effectiveness of endovascular treatment for in-hospital stroke remains debatable. We aimed to compare the outcomes between patients with in-hospital stroke and community-onset stroke who received endovascular treatment. METHODS: This prospective registry-based cohort study included consecutive patients who underwent endovascular treatment from January 2013 to December 2022 and were registered in the Selection Criteria in Endovascular Thrombectomy and Thrombolytic Therapy study and Yonsei Stroke Cohort. Functional outcomes at day 90, radiological outcomes, and safety outcomes were compared between the in-hospital and community-onset groups using logistic regression and propensity score-matched analysis. RESULTS: Of 1,219 patients who underwent endovascular treatment, 117 (9.6%) had in-hospital stroke. Patients with in-hospital onset were more likely to have a pre-stroke disability and active cancer than those with community-onset. The interval from the last known well to puncture was shorter in the in-hospital group than in the community-onset group (155 vs. 355 min, p<0.001). No significant differences in successful recanalization or safety outcomes were observed between the groups; however, the in-hospital group exhibited worse functional outcomes and higher mortality at day 90 than the community-onset group (all p<0.05). After propensity score matching including baseline characteristics, functional outcomes after endovascular treatment did not differ between the groups (OR: 1.19, 95% CI 0.78-1.83, p=0.4). Safety outcomes did not significantly differ between the groups. CONCLUSION: Endovascular treatment is a safe and effective treatment for eligible patients with in-hospital stroke. Our results will help physicians in making decisions when planning treatment and counseling caregivers or patients.

2Memristor-1Capacitor Integrated Temporal Kernel for High-Dimensional Data Mapping.

Compact but precise feature-extracting ability is core to processing complex computational tasks in neuromorphic hardware. Physical reservoir computing (RC) offers a robust framework to map temporal data into a high-dimensional space using the time dynamics of a material system, such as a volatile memristor. However, conventional physical RC systems have limited dynamics for the given material properties, restricting the methods to increase their dimensionality. This study proposes an integrated temporal kernel composed of a 2-memristor and 1-capacitor (2M1C) using a W/HfO2/TiN memristor and TiN/ZrO2/Al2O3/ZrO2/TiN capacitor to achieve higher dimensionality and tunable dynamics. The kernel elements are carefully designed and fabricated into an integrated array, of which performances are evaluated under diverse conditions. By optimizing the time dynamics of the 2M1C kernel, each memristor simultaneously extracts complementary information from input signals. The MNIST benchmark digit classification task achieves a high accuracy of 94.3% with a (196×10) single-layer network. Analog input mapping ability is tested with a Mackey-Glass time series prediction, and the system records a normalized root mean square error of 0.04 with a 20×1 readout network, the smallest readout network ever used for Mackey-Glass prediction in RC. These performances demonstrate its high potential for efficient temporal data analysis.

Predicting Safe Liver Resection Volume for Major Hepatectomy Using Artificial Intelligence.

(1) Background: Advancements in the field of liver surgery have led to a critical need for precise estimations of preoperative liver function to prevent post-hepatectomy liver failure (PHLF), a significant cause of morbidity and mortality. This study introduces a novel application of artificial intelligence (AI) in determining safe resection volumes according to a patient's liver function in major hepatectomies. (2) Methods: We incorporated a deep learning approach, incorporating a unique liver-specific loss function, to analyze patient characteristics, laboratory data, and liver volumetry from computed tomography scans of 52 patients. Our approach was evaluated against existing machine and deep learning techniques. (3) Results: Our approach achieved 68.8% accuracy in predicting safe resection volumes, demonstrating superior performance over traditional models. Furthermore, it significantly reduced the mean absolute error in under-predicted volumes to 23.72, indicating a more precise estimation of safe resection limits. These findings highlight the potential of integrating AI into surgical planning for liver resections. (4) Conclusion: By providing more accurate predictions of safe resection volumes, our method aims to minimize the risk of PHLF, thereby improving clinical outcomes for patients undergoing hepatectomy.

Development of a Novel Biomarker for the Progression of Idiopathic Pulmonary Fibrosis.

The progression of idiopathic pulmonary fibrosis (IPF) is diverse and unpredictable. We identified and validated a new biomarker for IPF progression. To identify a candidate gene to predict progression, we assessed differentially expressed genes in patients with advanced IPF compared with early IPF and controls in three lung sample cohorts. Candidate gene expression was confirmed using immunohistochemistry and Western blotting of lung tissue samples from an independent IPF clinical cohort. Biomarker potential was assessed using an enzyme-linked immunosorbent assay of serum samples from the retrospective validation cohort. We verified that the final candidate gene reflected the progression of IPF in a prospective validation cohort. In the RNA-seq comparative analysis of lung tissues, CD276, COL7A1, CTSB, GLI2, PIK3R2, PRAF2, IGF2BP3, and NUPR1 were up-regulated, and ADAMTS8 was down-regulated in the samples of advanced IPF. Only CTSB showed significant differences in expression based on Western blotting (n = 12; p < 0.001) and immunohistochemistry between the three groups of the independent IPF cohort. In the retrospective validation cohort (n = 78), serum CTSB levels were higher in the progressive group (n = 25) than in the control (n = 29, mean 7.37 ng/mL vs. 2.70 ng/mL, p < 0.001) and nonprogressive groups (n = 24, mean 7.37 ng/mL vs. 2.56 ng/mL, p < 0.001). In the prospective validation cohort (n = 129), serum CTSB levels were higher in the progressive group than in the nonprogressive group (mean 8.30 ng/mL vs. 3.00 ng/mL, p < 0.001). After adjusting for baseline FVC, we found that CTSB was independently associated with IPF progression (adjusted OR = 2.61, p < 0.001). Serum CTSB levels significantly predicted IPF progression (AUC = 0.944, p < 0.001). Serum CTSB level significantly distinguished the progression of IPF from the non-progression of IPF or healthy control.

Methotrexate-Induced Accelerated Nodulosis in a Patient with Systemic Lupus Erythematosus.

Methotrexate (MTX)-induced accelerated nodulosis (MIAN) reportedly occurs in patients with rheumatic arthritis receiving MTX therapy. However, it has also been reported in patients with other autoinflammatory conditions, such as systemic lupus erythematosus (SLE). A 38-year-old woman diagnosed with SLE presented with multiple movable, firm, flesh-colored nodules on both hands that had developed 3 years ago. She was taking oral medications, specifically hydroxychloroquine, azathioprine, and MTX. Histopathological examination revealed palisaded granulomatous inflammation, surrounded by histiocytes and lymphocytes, along the dermis to the subcutaneous fat layer. Fibrinoid degeneration was observed at the center of the granulomatous inflammation, and dermal mucin deposition was not observed. The patient was diagnosed with MIAN, and therefore discontinuation of MTX was recommended. Subsequently, the lesions almost completely disappeared with no signs of recurrence. MIAN exhibits clinicopathological features similar to those of rheumatoid nodules; therefore, it can be easily misdiagnosed. Herein, we report a case of MIAN in a patient with SLE to contribute to the accurate diagnosis and appropriate management.