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Screening and follow-up of interstitial lung disease associated with rheumatoid arthritis (RA-ILD) is a challenge in clinical practice. In fact, the majority of RA-ILD patients are asymptomatic and optimal tools for early screening and regular follow-up are lacking. Furthermore, some patients may remain oligosymptomatic despite significant radiological abnormalities. In RA-ILD, usual interstitial pneumonia (UIP) is the most frequent radiological and pathological pattern, associated with a poor prognosis and a high risk to develop acute exacerbations and infections. If RA-ILD can be identified early, there may be an opportunity for an early treatment and close follow-up that might delay ILD progression and improve the long-term outcome. In connective tissue disease–associated interstitial lung disease (CTD-ILD), lung ultrasound (LUS) with the assessment of B-lines and serum Krebs von den Lungen-6 antigen (KL-6) has been recognized as sensitive biomarkers for the early detection of ILD. B-line number and serum KL-6 level were found to correlate with high-resolution computed tomography (HRCT), pulmonary function tests (PFTs), and other clinical parameters in systemic sclerosis–associated ILD (SSc-ILD). Recently, the significant correlation between B-lines and KL-6, two non-ionizing and non-invasive biomarkers, was demonstrated. Hence, the combined use of LUS and KL-6 to screen and follow up ILD in RA patients might be useful in clinical practice in addition to existing tools. Herein, we review relevant literature to support this concept, propose a preliminary screening algorithm, and present 2 cases where the algorithm was used.

Background To investigate the diagnostic accuracy of lung ultrasound (LUS) for interstitial lung disease (ILD) in patients with rheumatoid arthritis (RA). Methods This retrospective study included patients over 18 years with RA evaluated at the Department of Rheumatology and Immunology of Shantou Central Hospital. All patients underwent chest high-resolution computed tomography (HRCT) and LUS within one month. The LUS was performed in a total of 50 scanning sites (ScS), and the number of B-lines present in each ScS was counted and summed up as B-lines score. A positive judgement was given on LUS when the B-lines score exceeded 10. The presence and patterns of ILD were defined by HRCT findings. ROC curve analysis was used to calculate the accuracy of LUS to detect ILD. Results A total of 120 RA patients (86 women, with a median age of 56.0 [50.0–64.0] years) were enrolled. Based on the HRCT, 76 patients were found to have radiographic ILD, with 63 exhibiting nonspecific interstitial pneumonia (NSIP) and 13 showing usual interstitial pneumonia (UIP). Sonographic ILD was detected in 76 patients who underwent LUS examination. The concordance rate between two modalities was 83.33% (Kappa value = 0.64, 95% CI 0.50–0.78). The diagnostic sensitivity and specificity of LUS were 86.84% and 77.27%, respectively. The positive predictive value, negative predictive value, a positive likelihood ratio and a negative likelihood ratio were 86.84%, 77.27%, 3.82, and 0.17, respectively. The number of B-lines in RA with ILD and without ILD on HRCT showed a significant difference (34.0 [15.0–96.5] vs. 6.5 [2.5–10.0], P  < 0.001). The presence of 12 B-lines on 50 ScS was the optimal cutoff value for detecting RA-ILD (AUC = 0.89, 95% CI 0.82–0.94, sensitivity of 85.53%, specificity of 81.82%, P  < 0.001). Conclusions Lung ultrasound is a valuable diagnostic tool for RA-ILD and can be used as a screening method to identify patients who require further evaluation with chest HRCT.

Partitioning a complex power network into a number of sub-zones can help realize a “divide-and-conquer” management structure for the whole system, such as voltage and reactive power control, coherency identification, power system restoration, etc. Extensive partitioning methods have been proposed by defining various distances, applying different clustering methods, or formulating varying optimization models for one specific objective. However, a power network partition may serve two or more objectives, where a trade-off among these objectives is required. This paper proposes a novel weighted consensus clustering-based approach for bi-objective power network partition. By varying the weights of different partitions for different objectives, Pareto improvement can be explored based on the node-based and subset-based consensus clustering methods. Case studies on the IEEE 300-bus test system are conducted to verify the effectiveness and superiority of our proposed method.

Antisynthetase syndrome-associated interstitial lung disease (ASS-ILD) exhibits clinical heterogeneity and progression, with unclear immunopathogenic mechanisms. This study aimed to define the cell type-specific interferon immune signatures and transcriptional networks underlying ASS-ILD. Single-cell RNA sequencing (scRNA-seq) was performed on peripheral blood mononuclear cells (PBMCs) from three treatment-naive ASS-ILD patients and three healthy controls (67,421 cells). A comprehensive analysis was conducted in conjunction with an external cohort, encompassing 126,026 cells. The analytical pipelines included the following: AUCell for interferon-stimulated gene (ISG) activity scoring, Seurat for clustering, Monocle for trajectory inference, and CellChat for cell-cell communication. The inference of transcription factor activity was facilitated using decoupleR software. Monocyte-specific ISG activity was identified and validated in an integrated cohort of 126,026 cells. Among the six monocyte subsets, mono2 exhibited elevated expressions and a preferential inflammatory trajectory, marked by upregulated innate and adaptive immune pathways. Cell-cell interaction modeling revealed dysregulated type II interferon (IFN-II) and tumor necrosis factor (TNF) signaling, with mono2, NK, and CD8 T cells as key signal transmitters. Regulatory network analysis revealed that the transcription factors , , , , , and drive inflammatory and profibrotic signatures via the IL-17, JAK-STAT, and TGF-β pathways. This study identifies monocytes as central orchestrators of immune dysregulation in ASS-ILD, highlighting IFN/TNF signaling and associated transcriptional regulators as therapeutic targets.

With the increasing penetration of renewables, power systems have to operate with greater flexibility to address the uncertainties of renewable output. This paper develops an uncertainty locational marginal price (ULMP) mechanism to price these uncertainties. They are denoted as box deviation intervals as suggested by the market participants. The ULMP model solves a robust optimal power flow (OPF) problem to clear market bids, aiming to minimize the system cost as a prerequisite that the reserve margin can address all the relevant uncertainties. The ULMP can be obtained as a by-product of the optimization problem from the Lagrange multipliers. Under the ULMP mechanism, renewables and consumers with uncertainty will make extra payments, and the thermals and financial transmission right (FTR) holders will be compensated. It is further shown that the proposed mechanism has preferable properties, such as social efficiency, budget balance and individual rationality. Numerical tests are conducted on the modified IEEE 5-bus and 118-bus systems to demonstrate the merits and applicability of the proposed mechanism.

Acute fibrinous and organizing pneumonia (AFOP) is a new histopathological pattern of acute lung injury first described by Beasley et al. in 2002. Hallmarks of pathological findings are characterized by the presence of intra-alveolar fibrin in the form of fibrin “balls” within the alveolar spaces and organizing pneumonia with a patchy distribution. Patients with AFOP may have an acute or subacute clinical presentation. Although the pathogenesis of AFOP is not fully elucidated, it may be associated with autoimmune diseases. Reported herein is a patient diagnosed of acute AFOP associated with primary Sjögren’s syndrome. The patient’s condition promptly improved after treatment with corticosteroid.

To investigate the role of serum B-cell activating factor (BAFF) and lung ultrasound (LUS) B-lines in connective tissue disease related interstitial lung disease (CTD-ILD), and their association with different ILD patterns on high resolution computed tomography (HRCT) of chest. We measured the levels of BAFF and KL-6 by ELISA in the sera of 63 CTD-ILD patients [26 with fibrotic ILD (F-ILD), 37 with non-fibrotic ILD (NF-ILD)], 30 CTD patients without ILD, and 26 healthy controls. All patients underwent chest HRCT and LUS examination. Serum BAFF levels were significantly higher in CTD patients compared to healthy subjects (617.6 ± 288.1 pg/ml vs. 269.0 ± 60.4 pg/ml, < 0.01). BAFF concentrations were significantly different between ILD group and non-ILD group (698.3 ± 627.4 pg/ml vs. 448.3 ± 188.6 pg/ml, < 0.01). In patients with ILD, BAFF concentrations were significantly correlated with B-lines number ( = 0.37, 95% CI 0.13-0.56, < 0.01), KL-6 level ( = 0.26, 95% CI 0.01-0.48, < 0.05), and Warrick score ( = 0.33, 95% CI 0.09-0.53, < 0.01), although all correlations were only low to moderate. B-lines number correlated with Warrick score ( = 0.65, 95% CI 0.48-0.78, < 0.01), and KL-6 levels ( = 0.43, 95% CI 0.21-0.61, < 0.01). Patients with F-ILD had higher serum BAFF concentrations (957.5 ± 811.0 pg/ml vs. 516.1 ± 357.5 pg/ml, < 0.05), KL-6 levels (750.7 ± 759.0 U/ml vs. 432.5 ± 277.5 U/ml, < 0.05), B-lines numbers (174.1 ± 82 vs. 52.3 ± 57.5, < 0.01), and Warrick score (19.9 ± 4.6 vs. 13.6 ± 3.4, < 0.01) vs. NF-ILD patients. The best cut-off values to separate F-ILD from NF-ILD using ROC curves were 408 pg/ml for BAFF (AUC = 0.73, < 0.01), 367 U/ml for KL-6 (AUC = 0.72, < 0.05), 122 for B-lines number (AUC = 0.89, < 0.01), and 14 for Warrick score (AUC = 0.87, < 0.01) respectively. Serum BAFF levels and LUS B-lines number could be useful supportive biomarkers for detecting and evaluating the severity and/or subsets of CTD-ILD. If corroborated, combining imaging, serological, and sonographic biomarkers might be beneficial and comprehensive in management of CTD-ILD.

This paper studies the pool strategy for price-makers under imperfect information. In this occasion, market participants cannot obtain essential transmission parameters of the power system. Thus, price-makers should estimate the market results with respect to their offer curves using available historical information. The linear programming model of economic dispatch is analyzed with the theory of rim multi-parametric linear programming (rim-MPLP). The characteristics of system patterns (combinations of status flags for generating units and transmission lines) are revealed. A multi-class classification model based on support vector machine (SVM) is trained to map the offer curves to system patterns, which is then integrated into the decision framework of the price-maker. The performance of the proposed method is validated on the IEEE 30-bus system, Illinois synthetic 200-bus system, and South Carolina synthetic 500-bus system.

The batch and online workload of Internet data centers (IDCs) offer temporal and spatial scheduling flexibility. Given that power generation costs vary over time and location, harnessing the flexibility of IDCs' energy consumption through workload regulation can optimize the power flow within the system. This paper focuses on multi-geographically distributed IDCs managed by an Internet service company (ISC), which are aggregated as a controllable load. The load flexibility resulting from spatial load regulation of online workload is taken into account. A two-step workload scheduling mechanism is adopted, and a computation-power coupling model of ISC is established to facilitate collaborative optimization in active distribution networks (ADNs). To address the model-solving problem based on the assumption of scheduling homogeneity, a model reconstruction method is proposed. An efficient iterative algorithm is designed to solve the reconstructed model. Furthermore, the Nash bargaining solution is employed to coordinate the different optimization objectives of ISC and power system operators, thereby avoiding subjective arbitrariness. Experimental cases based on a 33-node distribution system are designed to verify the effectiveness of the model and algorithm in optimizing ISC's energy consumption and power flow within the system.

The grid-connected electric vehicles (EVs) serve as a promising regulating resource in the distribution grid with Vehicle-to-Grid (V2G) facilities. In the day-ahead stage, electric vehicle batteries (EVBs) need to be precisely dispatched and controlled to ensure high efficiency and prevent degradation. This article focuses on considering a refined battery model, i.e. the electrochemical model (EM), in the optimal dispatch of the local energy system with high penetration of EVs which replenish energy through V2G-equipped charge station and battery swapping station (BSS). In this paper, to utilize the EM efficiently, recursive EVB constraints and a corresponding matrix-based state update method are proposed based on EM power characterization. The charging EV state distribution is profiled and a multi-layer BSS model along with binary aggregation is proposed, in order to overcome the computation complexity of combining the refined battery constraints with the mixed integer optimization. Finally, a local energy system scenario is investigated for evaluation. The efficiency and effectiveness of EM consideration are assessed from the perspective of both the system and battery.