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Objective: Interferon (IFN)‐1 signatures are a hallmark of patients with systemic sclerosis (SSc). However, its significance in clinical stratification and contribution to deterioration still need to be better understood. Methods: For hypothesis generation, we performed single‐cell RNA sequencing (scRNA‐seq) on skin biopsies (four patients with SSc and two controls) using the BD Rhapsody platform. Two publicly available data sets of skin scRNA‐seq were used for validation (GSE138669: 12 patients with diffuse cutaneous SSc [dcSSc] and 10 controls; GSE195452: 52 patients with dcSSc and 41 patients with limited cutaneous SSc [lcSSc] and 54 controls). The IFN‐1 signature was mapped, functionally investigated in a bleomycin plus IFNα‐2 adenovirus‐associated virus (AAV)–induced model and verified in an SSc cohort (n = 61). Results: The discovery and validation data sets showed similar findings. Endothelial cells (ECs) had the most prominent IFN‐1 signature among dermal nonimmune cells. The EC IFN‐1 signature was increased both in patients with SSc versus controls and in patients with dcSSc versus those with lcSSc. Among EC subclusters, the IFN‐1 signature was statistically higher in the capillary ECs of patients with dcSSc, which was higher than those in patients with lcSSc, which in turn was higher than those in healthy controls (HCs). Endothelial‐to‐mesenchymal transition (EndoMT) scores increased in parallel. Deteriorated bleomycin‐induced dermal fibrosis, EndoMT, and perivascular fibrosis and caused blood vessel loss with EC apoptosis. Vascular myxovirus resistance (MX) 1, an IFN‐1 response protein, was significantly increased both in total SSc versus HC skin and in dcSSc versus lcSSc skin. Baseline vascular MX1 performed similarly to skin score in predicting disease progression over 6 to 34 months in total SSc and was superior in the dcSSc subpopulation. Conclusion: The EC IFN‐1 signature distinguished SSc skin subtypes and disease progression and may contribute to vasculopathy and fibrosis. [ABSTRACT FROM AUTHOR]

CD73 plays a critical role in the pathogenesis and immune escape in pancreatic ductal adenocarcinoma (PDAC). AB680, an exceptionally potent and selective inhibitor of CD73, is administered in an early clinical trial, in conjunction with gemcitabine and anti‐PD‐1 therapy, for the treatment of PDAC. Nevertheless, the specific therapeutic efficacy and immunoregulation within the microenvironment of AB680 monotherapy in PDAC have yet to be fully elucidated. In this study, AB680 exhibits a significant effect in augmenting the infiltration of responsive CD8+ T cells and prolongs the survival in both subcutaneous and orthotopic murine PDAC models. In parallel, it also facilitates chemotaxis of myeloid‐derived suppressor cells (MDSCs) by tumor‐derived CXCL5 in an AMP‐dependent manner, which may potentially contribute to enhanced immunosuppression. The concurrent administration of AB680 and PD‐1 blockade, rather than gemcitabine, synergistically restrain tumor growth. Notably, gemcitabine weakened the efficacy of AB680, which is dependent on CD8+ T cells. Finally, the supplementation of a CXCR2 inhibitor is validated to further enhance the therapeutic efficacy when combined with AB680 plus PD‐1 inhibitor. These findings systematically demonstrate the efficacy and immunoregulatory mechanism of AB680, providing a novel, efficient, and promising immunotherapeutic combination strategy for PDAC. [ABSTRACT FROM AUTHOR]

Multicentric reticulohistiocytosis (MRH) is a rare systemic disorder characterized by inflammatory histiocytic infiltrates, mainly affecting the synovium, skin, and subcutaneous tissues. More patients are needed to confirm the effectiveness of JAKi treatment; but we suggest JAKi as a promising treatment option for MRH, especially when conventional drugs have failed. Additionally, CD1a and S100 stains are negative, indicating that the histiocytes are not Langerhans cells.[2] As the etiology and pathogenesis of MRH remain unknown, treatment of MRH remains largely empirical. It is more commonly observed in women (female:male ratio of 3:1) with mean presentation age between 40 and 50 years old.[2] Arthropathy is often rapidly progressive and can lead to destructive arthritis in 50% of MRH cases and flexion deformities in 14%.[[2]] Distal interphalangeal joints are the most frequently involved, and are one of the defining characteristics of MRH. [Extracted from the article]

This article discusses a case of Bier spots, which are transient anemic macules that develop on the extremities. The patient, an Asian woman in her 30s, presented with multiple white spots on her palms and soles, which appeared after she had been diagnosed with COVID-19 three months earlier. The article suggests that Bier spots may be a result of post-COVID endothelial dysfunction. The patient was prescribed vasodilators, but no improvement was observed, and the symptoms disappeared gradually after six months. The article emphasizes the importance of early recognition of Bier spots as a dermatologic manifestation associated with COVID-19 to avoid overtreatment. [Extracted from the article]

This article discusses a case of Adie's tonic pupil in a 37-year-old Asian woman with primary Sjögren syndrome. The patient presented with symptoms of dry eyes, dry mouth, bilateral dilated pupils, and blurred vision. Further examination revealed irregular shaped pupils and a sluggish response to light. The presence of Adie's tonic pupil was confirmed through pharmacological testing. The article emphasizes the importance of recognizing this rare manifestation of Sjögren syndrome for early diagnosis and proper management. [Extracted from the article]

To extend existing distance metrics in the $L^{p}$ space, we define a novel distance, named relative Euclidean distance (RED), and prove that it is positive definite, symmetric, and satisfies the triangle inequality. This distance has a value range of [0, 1]. We prove a uniqueness of it in the $L^{p}$ space. The technique for order preference by similarity to ideal solution (TOPSIS) is widely used for multiple-attribute decision problems. The commonly used relative closeness measure in the TOPSIS is not a mathematical distance and thus does not have all the nice properties of such a distance. To improve the TOPSIS, particularly its relative closeness measure, we propose two measures, named optimistic distance (OD) and pessimistic distance (PD), based on our RED, and use them to measure the relative closeness in the TOPSIS. Both measures are positive definite, symmetric, and satisfy the triangle inequality. The best choice in OD (or PD) is on the Pareto frontier. Three examples of performance ranking are given to illustrate the usefulness of the TOPSIS with our proposed measures. [ABSTRACT FROM AUTHOR]

In conclusion, we present the first case of JAK inhibitor treatment for a patient with sarcoidosis who also had glaucoma, a relative contraindication to corticosteroids. This is the first case to provide significant evidence that JAK inhibitor is an effective treatment for both subcutaneous and pulmonary sarcoidosis, and it is also the first case in which tofacitinib was used in a patient who has a contraindication for corticosteroid therapy. [Extracted from the article]

Objective: The present study was to investigate whether dihydroartemisinin (DHA), which is a highly effective and safe drug in the treatment of malaria, could be repurposed for the treatment of skin fibrosis and vascular dysfunction in systemic sclerosis (SSc). Methods: The value of DHA was determined using a bleomycin-induced model of skin fibrosis. mRNA transcriptome analysis was performed, and the targets of DHA on fibroblasts were identified. Immunofluorescence staining was used to identify dermal vessels undergoing endothelial-to-mesenchymal transition (EndoMT). Autophagic flux was detected by western blot and mRFP-GFP-LC3 adenovirus vector transfection. Results: Both systemic and topical administration of DHA decreased dermal thickness and collagen deposition and alleviated EndoMT in bleomycin-induced skin fibrosis mice model. Treatment of human umbilical vein endothelial cells (HUVECs) with TGF-β1 resulted in the acquisition of the activation marker (α-SMA) and loss of endothelial markers (CD31 and VE-cadherin), a process that was restored by DHA. DHA significantly suppressed skin fibroblast activation and collagen-1 production mainly through regulating PI3K-Akt pathway. DHA also induced fibroblast autophagic flux and that autophagy dependently suppressed collagen-1 production. Conclusion: The results of the present study revealed that oral and topical DHA administration ameliorated tissue fibrosis and protected dermal blood vessels from bleomycin-induced EndoMT. Our study has elucidated the value of repurposing DHA for the treatment of SSc. Key Points • Oral or topical usage of DHA alleviated dermal fibrosis and EndoMT in bleomycin-induced skin fibrosis mice models. • DHA autophagy dependently inhibited fibroblast activation and collagen deposition via PI3K-ATK pathway. • DHA inhibited EndoMT of HUVECs induced by TGF-β1 by the downregulation of α-SMA and the upregulation of CD31 and VE-cadherin. [ABSTRACT FROM AUTHOR]

On examination, we saw puffy fingers, periungual erythema, dilated capillaries and hemorrhages of the proximal nail folds in all fingers except her thumbs. To observe nail fold capillaries more clearly, placing a dermatoscope on the proximal nail fold can show capillaries appearing as small red dots or loops. [2] Nail fold capillary abnormalities, especially hemorrhages and giant capillaries, are sometimes visible to the unaided eye. [Extracted from the article]

In hydrology, runoff predictions are challenging when the data is lacking (e.g., predictions in un-gauged basins (PUB) and predictions with limited data (PLD)). Here, PLD refers to the case that the data is not enough for training or fine-tuning a data-driven model well (e.g., a new-gauged basin). We also name PLD as PNB (predictions in new-gauged basins). The difference between PNB and PUB is that the new-gauged basins can provide some data (e.g., runoff observations) while the un-gauged basins cannot. The long short-term memory (LSTM)-based models have shown good performance in runoff predictions due to their advanced structures. However, those structures have low level of flexibility and two nonadjacent positions cannot communicate directly. For high level of flexibility and better performance, we propose a simple Transformer-based rainfall-runoff model named RRS-Former, and want to show its power and also the power of the previously proposed RR-Former in PNB compared with LSTM-based models. The main part of RRS-Former is attention modules in which two arbitrary positions can be connected directly. Four hydrological units including 241 basins in the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset are used to compare the performance of our Transformer-based models and that of LSTM-based models. Besides using the k-fold validation to test performance for PNB, we propose a new way named unit-fold validation, in which we train the models by using the basins in three hydrological units and then test the performance using basins in the rest one hydrological unit. The results based on both k-fold and unit-fold show that our RRS-Former and RR-Former have better performance and are more reliable compared with the LSTM-based models. • A data-driven rainfall-runoff model based on Transformer is proposed and named RRS-Former. • A Transformer-based model is more flexible than LSTM and has better transfer ability. • RRS-Former has less parameters and less training time compared with RR-Former. • A Transformer-based model is more reliable than LSTM in the case of data lacking. • Unit-fold has more advantages for testing the transfer ability compared with k-fold. [ABSTRACT FROM AUTHOR]

Given multiple statistical estimators, how to rank their performance is a worthwhile problem. We propose an approach to estimation performance ranking based on pairwise comparison. Because ranking is about two or more estimators and is relative, pairwise comparison (e.g., Pitman’s measure of closeness) suits the needs. However, pairwise comparison cannot guarantee transitivity, which is needed for ranking. To get around this problem, we propose a ranking vector (RV) approach based on pairwise comparison. Here, an RV is obtained by using pairwise comparison results without using pairwise ranks directly. An RV provides ordinal information determining the rank and also supplementary cardinal information exhibiting how much one estimator is better than another. Ordinal information is more important and thus is guaranteed by using an order-preserving mapping in obtaining an RV. Our RV approach based on pairwise comparison is also applied to multiple-attribute decision problems. The approach is easily applicable and it does not need data normalization. [ABSTRACT FROM AUTHOR]

The article presents the case of a man in his 50s with a year-long history of retinitis pigmentosa (RP) who was rushed to a clinic due to severe digital ulcerations and puffy hands to discuss nail involvement in systemic sclerosis (SSc).

Breast cancer is the most prevalent cancer in the world. Currently, the main treatments for breast cancer are radiotherapy, chemotherapy, targeted therapy and surgery. The treatment measures for breast cancer depend on the molecular subtype. Thus, the exploration of the underlying molecular mechanisms and therapeutic targets for breast cancer remains a hotspot in research. In breast cancer, a high level of expression of DNMTs is highly correlated with poor prognosis, that is, the abnormal methylation of tumor suppressor genes usually promotes tumorigenesis and progression. MiRNAs, as non-coding RNAs, have been identified to play key roles in breast cancer. The aberrant methylation of miRNAs could lead to drug resistance during the aforementioned treatment. Therefore, the regulation of miRNA methylation might serve as a therapeutic target in breast cancer. In this paper, we reviewed studies on the regulatory mechanisms of miRNA and DNA methylation in breast cancer from the last decade, focusing on the promoter region of tumor suppressor miRNAs methylated by DNMTs and the highly expressed oncogenic miRNAs inhibited by DNMTs or activating TETs. [ABSTRACT FROM AUTHOR]

• A new framework named LSTM-SS is proposed to deal with rainfall-runoff modeling. • LSTM-SS improves performance significantly on the CAMELS data set. • LSTM-SS achieves good performance for multi-day-ahead runoff predictions. • LSTM-SS has good performance with short input sequence length. Rainfall-runoff modeling, a nonlinear time series process, is challenging and important in hydrological sciences. Among the data-driven approaches, those ones based on the long short-term memory (LSTM) network show their promising performance. In this paper, for rainfall-runoff modeling, we propose a novel data-driven framework named long short-term memory based step-sequence (LSTM-SS) framework, which contains m specific models for m -step-ahead runoff predictions. This model uses the sequential information of runoff series and follows the causality in practice: the current runoff is not affected by the later meteorological data. To show its performance and advantages, we test it on 241 basins of the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) data set and predict the 7-day-ahead runoff. The results show that our rainfall-runoff models outperform the benchmark (physically-based or data-driven) models significantly employing for the CAMELS data set, including the Sacramento Soil Moisture Accounting Model (SAC-SMA) coupled with the Snow-17 snow routine, a two-layer LSTM network, and a LSTM-based sequence-to-sequence network. For 1-day-ahead runoff predictions, the median of Nash–Sutcliffe model efficiency for the 241 basins provided by our model is 0.85 , while that provided by the two-layer LSTM network is 0.65. Furthermore, the results also show that our proposed LSTM-SS framework not only can significantly improve the performance of a single daily runoff prediction, but also has good performance for multiple-step-ahead runoff predictions. [ABSTRACT FROM AUTHOR]

For estimation performance ranking, the ranking vector (RV) approach using pairwise comparison information is efficient and processes advanced properties (e,g, homogeneity, invariance, monotonicity, decisiveness, and getting around intransitivity problem). However, the existing RV approach does not use quantity information of comparison and thus may be unreliable for inadequate number of error measures. To address this drawback, we develop two extended versions of the previously proposed RV approach in this paper. The first one focusing on using quantity information is based on a novel data normalization approach named pairwise data normalization. It quantifies the relative distance (goodness) of two elements with respect to a perfect point as the reference. The second version accounts for attribute difference and determines the weights of attributes accordingly. If the data of an error measure are highly different from those of others, this error measure should be weighted more in the performance ranking because it definitely reflects a different performance aspect. We compare different RV approaches, analyze their pros and cons, and discuss how to apply them for different cases. Examples are also provided to illustrate the application of these approaches to estimation performance ranking. • A new data normalization method named pairwise data normalization is proposed. • We propose an extended RV approach by considering the quantity information. • We propose another extended version by considering attributes differences. • We fully compare different RV approaches and analyze their pros and cons. [ABSTRACT FROM AUTHOR]

• A Transformer based rainfall-runoff model is proposed for the first time. • RR-Former can connect arbitrary positions directly and is more flexible than LSTM. • RR-Former achieves excellent performance on individual rainfall-runoff modeling. • RR-Former achieves excellent performance on regional rainfall-runoff modeling. • RR-Former suits for big datasets and provides multi-step-ahead runoff predictions. Recently, the long short-term memory (LSTM) based rainfall-runoff models have achieved good performance and thus have received many attentions. In this paper, we propose a novel rainfall-runoff model named RR-Former based on the Transformer, which is entirely composed of attention mechanisms. Compared with a LSTM-based model, the architecture of RR-Former can connect two arbitrary positions in a time series process directly by using attention modules. It can strengthen or weaken the connection of two arbitrary positions and thus is more flexible than a LSTM-based model. Therefore, the RR-Former has potential to achieve better performance. By employing the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset, we test the performance of RR-Former in two tasks: individual rainfall-runoff modeling and regional rainfall-runoff modeling. In the first task, our RR-Former outperforms two LSTM-based sequence-to-sequence models significantly for 7-day-ahead runoff predictions. For example, the median and the mean of Nash–Sutcliffe efficiency for the 673 basins provided by our RR-Former achieve 0.8265 and 0.7904 , respectively, while those provided by the benchmark model (the better one between two benchmark models) are 0.7448 and 0.6952 , respectively. In the second task, our RR-Former also shows its power and suits for a big dataset better. [ABSTRACT FROM AUTHOR]

[Display omitted] • Four S2S models show their power to runoff predictions in ungauged basins (PUB). • For 1-day-ahead PUB, our model performs best and much better than benchmark models. • For multi-day-ahead PUB, S2S models can provide promising performance. • Attribute-weighting module is introduced to PUB for the first time • Multi-head-attention module is beneficial for improving performance of PUB. How to improve the performance of runoff predictions in ungauged basins (PUB) is challenging. Recently, the long short-term memory (LSTM) based models have excellent performance and receive many attentions. In this paper, to improve the performance for 1-day-ahead runoff PUB and provide good performance for multi-day-ahead runoff PUB, we propose four sequence-to-sequence (S2S) models to deal with PUB. Furthermore, we introduce two modules named attribute-weighting module and multi-head-attention module for improving the performance. To show the power of these S2S models and the advantages of those two modules, we test our four S2S models and also three benchmark models including a PUB-LSTM model and two process-driven models by using k-fold validation on 531 basins of the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset. For 1-day-ahead runoff PUB, the median and the mean of Nash–Sutcliffe efficiency for the 531 basins provided by our best model achieve 0.78 and 0.70 , respectively, while those provided by the PUB-LSTM model (the best one among those three benchmark models) are 0.69 and 0.54 , respectively. Besides, our S2S models have promising results for multi-day-ahead runoff PUB. For 7 th-day-ahead runoff PUB, the median and the mean of Nash–Sutcliffe efficiency for the 531 basins provided by our best model are 0.68 and 0.57 , respectively. Furthermore, the results also show that the two modules are beneficial for improving the performance. [ABSTRACT FROM AUTHOR]

• A new seq2seq rainfall-runoff model named LSTM-MSV-S2S is proposed. • LSTM-MSV-S2S has promising performance on the CAMELS data set. • LSTM-MSV-S2S is more appropriate for multi-day-ahead runoff predictions. Rainfall-runoff modeling is a challenging and important nonlinear time series problem in hydrological sciences. Recently, among the data-driven rainfall-runoff models, those ones based on the long short-term memory (LSTM) network show good performance. Furthermore, LSTM-based sequence-to-sequence (LSTM-S2S) models achieve promising performance for multi-step-ahead runoff predictions. In this paper, for multi-day-ahead runoff predictions, we propose a novel data-driven model named LSTM-based multi-state-vector sequence-to-sequence (LSTM-MSV-S2S) rainfall-runoff model, which contains m multiple state vectors for m -step-ahead runoff predictions. It differs from the existing LSTM-S2S rainfall-runoff models using only one state vector and is more appropriate for multi-day-ahead runoff predictions. To show its performance and advantages, we compare it with two LSTM-S2S models by testing them on 673 basins of the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) data set. The results show that our LSTM-MSV-S2S model has better performance in general and thus using multiple state vectors is more appropriate for multi-day-ahead runoff predictions. [ABSTRACT FROM AUTHOR]

Background: Adjuvant chemotherapy (ACT) is widely accepted in patients with pancreatic ductal adenocarcinoma (PDAC) after surgery; however, effective models for predicting ACT response are scarce. Thus, the objective of this study was to develop a novel signature for predicting its response and overall survival (OS) in resected PDAC patients. Methods: A total of 50 PDAC patients with the transcriptome expression profiles, information about chemotherapy, and relevant clinical data were retrieved from the Cancer Genome Atlas (TCGA), and twenty-nine patients with tissue specimens and clinical data from our hospital were included as a validation. A novel gene signature was developed using bioinformatic differentially expressed genes (DEGs) analysis, Lasso-penalized Cox regression, and multivariate Cox regression studies. Results: Between chemotherapy-resistant and chemotherapy-sensitive cohorts, 569 DEGs were identified, with 490 upregulated and 79 downregulated genes mainly specialized in the regulation of peptide/protein/hormone secretion, calcium ion homeostasis, and T cell activation regulation in biological processes. After Lasso-penalized Cox and multivariate Cox regression analysis, BAT (BCHE, ADH1A, and TNS4) signature was established to predict ACT response and OS. Moreover, BAT signature was verified as an independent risk factor for ACT response (p = 0.042) and OS (median OS: 17.5 months vs. 34.8 months, p = 0.040) and significantly associated with immune infiltrations (p < 0.05). Then, this signature was further validated as the independent risk factor for recurrence-free survival (RFS) in PDAC patients receiving postoperative ACT (median RFS: 9.0 months vs. not reached, p = 0.014), and tumor-infiltrating CD4+ and CD8+ T cells were further validated to be significantly decreased in tissues with higher BAT signature scores (p = 0.015 and 0.021, respectively). Conclusion: The BAT signature is a novel formulated and independent risk factor for predicting ACT response and long-term survival in patients with resected PDAC. This signature could comprehensively reflect local immune-related response, tumor purity, potential biological behavior, and chemo drug susceptibility. [ABSTRACT FROM AUTHOR]

Therapeutic approaches for cancer have become increasingly diverse in recent times. A comprehensive understanding of the tumor microenvironment (TME) holds great potential for enhancing the precision of tumor therapies. Neoadjuvant therapy offers the possibility of alleviating patient symptoms and improving overall quality of life. Additionally, it may facilitate the reduction of inoperable tumors and prevent potential preoperative micrometastases. Within the TME, cancer-associated fibroblasts (CAFs) play a prominent role as they generate various elements that contribute to tumor progression. Particularly, extracellular matrix (ECM) produced by CAFs prevents immune cell infiltration into the TME, hampers drug penetration, and diminishes therapeutic efficacy. Therefore, this review provides a summary of the heterogeneity and interactions of CAFs within the TME, with a specific focus on the influence of neoadjuvant therapy on the microenvironment, particularly CAFs. Finally, we propose several potential and promising therapeutic strategies targeting CAFs, which may efficiently eliminate CAFs to decrease stroma density and impair their functions. [Display omitted] • This paper summarizes the results of the latest research advances related to CAFs. • A summary of the changes in CAFs after different types of neoadjuvant therapy has been made. • A detailed accountof the impact altered CAFs after neoadjuvant therapy may have on clinical therapeutic interventions. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) remains a refractory disease. Programmed cell death protein-1 (PD-1) monotherapy has shown strong performance in targeting several malignancies. However, the effect and mechanism of intrinsic PD-1 in pancreatic cancer cells is still unknown. In this study, associations between clinicopathological characteristics and stained tissue microarrays of PDAC specimens were analyzed along with profiling and functional analyses. The results showed that cell-intrinsic PD-1 was significantly correlated with overall survival (OS). Independently of adaptive immunity, intrinsic PD-1 promoted tumor growth in PDAC. Concomitantly, the overexpression of intrinsic PD-1 enhanced cancer proliferation and inhibited cell apoptosis in vitro and in vivo. Mechanistically, PD-1 binds to the downstream MOB1, thereby inhibiting its phosphorylation. Moreover, greater synergistic tumor suppression in vitro resulted from combining Hippo inhibitors with anti-PD-1 treatment compared with the suppression achieved by either single agent alone. Additionally, Hippo downstream targets, CYR61 (CCN1) and CTGF (CCN2), were directly affected by PD-1 mediated Hippo signaling activation in concert with survival outcomes. Finally, the formulated nomogram showed superior predictive accuracy for OS in comparison with the TNM stage alone. Therefore, PD-1 immunotherapy in combination with Hippo pathway inhibitors may optimize the anti-tumor efficacy in PDAC patients via targeting cell-intrinsic PD-1. [ABSTRACT FROM AUTHOR]

Neutrophils, the most prevalent innate immune cells in humans, have garnered significant attention in recent years due to their involvement in cancer progression. This comprehensive review aimed to elucidate the important roles and underlying mechanisms of neutrophils in cancer from the perspective of their whole life cycle, tracking them from development in the bone marrow to circulation and finally to the tumor microenvironment (TME). Based on an understanding of their heterogeneity, we described the relationship between abnormal neutrophils and clinical manifestations in cancer. Specifically, we explored the function, origin, and polarization of neutrophils within the TME. Furthermore, we also undertook an extensive analysis of the intricate relationship between neutrophils and clinical management, including neutrophil-based clinical treatment strategies. In conclusion, we firmly assert that directing future research endeavors towards comprehending the remarkable heterogeneity exhibited by neutrophils is of paramount importance. [ABSTRACT FROM AUTHOR]

Background: Intraductal papillary mucinous neoplasms (IPMNs) are the precursor lesions of pancreatic cancers, requiring active surgical intervention during cancer development. However, the current criteria for predicting malignant IPMNs remain challenging and limited. Hence, this study aimed to assess the discriminatory performance of circulating cytokines, including TNF‐α, IL‐2R, IL‐6, and IL‐8, then build a novel predictive model to improve the diagnostic accuracy. Method: A total of 131 retrospective (from March 2016 to December 2019) and 53 prospective (from March 2020 to January 2021) patients who were histologically confirmed as IPMNs were consecutively collected and analyzed. Result: The circulating levels of TNF‐α, IL‐2R, IL‐6, and IL‐8 were significantly elevated in malignant IPMNs, and were verified as independent factors for malignant IPMNs (p < 0.05). Then, a novel score, the circulating cytokine score (CCS), was calculated and demonstrated as an independent predictive indicator with a higher area under the curve (AUC) than each cytokine alone (p < 0.001). Besides the CCS, two high‐risk stigmata features, the presence of solid component (PSC), and main pancreatic duct (MPD) dilation ≥10 mm were also demonstrated as independent indicators for predicting malignant IPMNs. Finally, a novel nomogram incorporating the CCS and these two high‐risk stigmata features presented a remarkable diagnostic performance, both in the training and validation cohorts with AUCs of 0.928 and 0.873, respectively. Conclusion: The CCS can be considered a novel independent predictive indicator for malignant IPMNs. Additionally, the formulated nomogram model integrating the CCS, PSC, and MPD ≥10 mm can be a valuable and promising tool for predicting the malignant transformation of IPMNs during long‐term follow‐ups to assist in timely and accurate surgical decisions. [ABSTRACT FROM AUTHOR]

In the present work, the momentum fluxes of gravity wave (GW) around the mesopause are estimated, using the decadal continuous observations by meteor radar at Mohe (53.5°N, 122.3°E). Applying the Hocking's (2005) approach with the modified-composite-day (MCD) analysis, the GW momentum fluxes of short-periods (less than 2 h) are estimated month by month. As the first step, several experiments are designed to evaluate the accuracy and uncertainty in the estimation. The results show that Mohe meteor radar has the ability to give reasonable estimations on the GW momentum fluxes at a height of 82–94 km, in which errors are generally less than 5 m2/s2. The uncertainty induced by different angular information of the detected meteor in each month achieves ~2 m2/s2. It is inferred that the variability of the GW momentum fluxes over 2 m2/s2 can be distinguished in the observation. The interannual variation of the estimated GW momentum fluxes show a significant enhancement in 2012, and a depression in 2013, with a fluctuation over ±10 m2/s2 at 82 km. However, no obvious quasi-biennial oscillation (QBO) -like signal has been found in the Lomb–Scargle periodogram. [ABSTRACT FROM AUTHOR]

Acute respiratory distress syndrome (ARDS) is a lethal clinical entity that has become an emergency event with the outbreak of COVID-19. However, to date, there are no well-proven pharmacotherapies except dexamethasone. This study is aimed to evaluate IRAK4 inhibitors as a potential treatment for ARDS-cytokine release syndrome (CRS). We applied two IRAK4 inhibitors, BAY-1834845 and PF-06650833 to an inhaled lipopolysaccharide (LPS)-induced ARDS mouse model with control of high dose dexamethasone (10 mg/kg). Unexpectedly, although both compounds had excellent IC 50 on IRAK4 kinase activity, only BAY-1834845 but not PF-06650833 or high dose dexamethasone could significantly prevent lung injury according to a blinded pathology scoring. Further, only BAY-1834845 and BAY-1834845 combined with dexamethasone could effectively improve the injury score of pre-existed ARDS. Compared with PF-06650833 and high dose dexamethasone, BAY-1834845 remarkably decreased inflammatory cells infiltrating lung tissue and neutrophil count in BALF. BAY-1834845, DEX, and the combination of the two agents could decrease BALF total T cells, monocyte, and macrophages. In further cell type enrichment analysis based on lung tissue RNA-seq, both BAY-1834845 and dexamethasone decreased signatures of inflammatory cells and effector lymphocytes. Interestingly, unlike the dexamethasone group, BAY-1834845 largely preserved the signatures of naïve lymphocytes and stromal cells such as endothelial cells, chondrocytes, and smooth muscle cells. Differential gene enrichment suggested that BAY-1834845 downregulated genes more efficiently than dexamethasone, especially TNF, IL-17, interferon, and Toll-like receptor signaling. • IRAK4 inhibition is a potentially feasible treatment for ARDS. • BAY-1834845 shows unexpectedly discrepant efficacy in vivo with PF-06650833. • BAY-1834845 inhibits natural immunity signaling stronger than dexamethasone. [ABSTRACT FROM AUTHOR]

Space objects around the Earth are a potential pollution source for ground-based radio observations. The Sanya incoherent scatter radar (SYISR) is a newly built active digital phased array, all solid-state transmitting and digital receiving incoherent scatter radar in Sanya (18.3°N, 109.6°E), with the main purpose of ionospheric monitoring and investigations. In this study, we presented the effect of the greatly increased number of space objects on ionospheric observations through SYISR. Firstly, we showed the space object pollution on the range-time-intensity (RTI), autocorrelation function (ACF)/power spectra, and ionosphere parameter of SYISR measurements. An altitude of around 600 km is the region where space objects occur most frequently. Then, we eliminated the space object pollution using the traditional smallest of constant-false-alarm-rate (SO-CFAR) algorithm. However, pollution from smaller space objects remains, whose reflected echo is comparable to or lower than the background ionosphere, which results in unrealistic retrieved ionospheric electron density. Furthermore, we quantitatively assessed the space object effect based on the current space object orbit database and simulation. The pollution should linearly increase with the increase in the number of space objects in the future. Among the space objects, whose radar cross section (RCS) and orbit information are now published, there still exist ~9000 (~37% of the total number) space objects, whose effect is difficult to eliminate. This study is beneficial to the data process of SYISR and has implications for similar types of ionospheric observations by radar. [ABSTRACT FROM AUTHOR]

• A two-branch structure and a learnable upsampling strategy DUpsampling is adopted to construct an accurate ice semantic segmentation network. • A new Downsampling module and a dense connection block in the context branch is designed to construct a real-time ice semantic segmentation network. • Two river ice segmentation datasets NWPU_YRCC_EX and NWPU_YRCC2 are enlarged and relabeled. • The proposed method can obtain better results in practical application. River ice semantic segmentation is a crucial task, which can provide us with information for river monitoring, disaster forecasting, and transportation management. Previous works mainly focus on higher accuracy acquirement, while efficiency is also important for reality usage. In this paper, a real-time and accurate river ice semantic segmentation network is proposed, named FastICENet. The general architecture consists of two branches, i.e., a shallow high-resolution spatial branch and a deep context semantic branch, which are carefully designed for the scale diversity and irregular shape of river ice in remote sensing images. Then, a novel Downsampling module and a dense connection block based on a lightweight Ghost module are adopted in the context branch to reduce the computation cost. Furthermore, a learnable upsampling strategy DUpsampling is utilized to replace the commonly used bilinear interpolation to improve the segmentation accuracy. We deploy detailed experiments on three publicly available datasets, named NWPU_YRCC_EX, NWPU_YRCC2, and Alberta River Ice Segmentation Dataset. The experimental results demonstrate that our method achieves state-of-the-art performance with competing methods, on the NWPU_YRCC_EX dataset, we can achieve the segmentation speed as 90.84FPS and the segmentation accuracy as 90.770 % mIoU, which also illustrates the good leverage between accuracy and speed. Our code is available at https://github.com/nwpulab113/FastICENet [ABSTRACT FROM AUTHOR]

Background: The prognosis of pancreatic ductal adenocarcinoma (PDAC) remains poor due to the difficulty of disease diagnosis and therapy. Immunotherapy has had robust performance against several malignancies, including PDAC. In this study, we aim to analyze the expression of CD8 and FoxP3 on T lymphocytes and TGF-β expression in tumor tissues, and then analyze the possible clinical significance of these finding in order to find a novel effective immunotherapy target in PDAC using a murine model.Methods: A tissue microarray using patient PDAC samples was stained and analyzed for associations with clinicopathological characteristics. A preclinical murine model administrated with various immunotherapies were analyzed by growth inhibitor, flow cytometry, enzyme-linked immuno sorbent assay and immunohistochemistry.Results: The infiltrating FoxP3+ regulatory T cells (Tregs) in tumor tissues were associated with survival, while CD8+ tumor infiltrating lymphocytes (TILs) were not. Considering the drawbacks of these measure alone, the number of CD8+ and FoxP3+ T cells were combined to create a new estimated value-integrated immune ratio (IIR), which showed excellent validity in survival risk stratification. IIR was further verified as an independent prognostic factor according to multivariate analysis as well as TGF-β expression. Association between TGF-β expression and infiltrating Tregs was also verified. Then, in our preclinical murine model, CD25 and TGF-β combination blockade had a higher tumor growth inhibitor value. This combination therapy significantly depleted periphery and intra-tumor FoxP3+ Tregs while increasing intra-tumor CD8+ TILs levels compared to controls or anti-TGF-β monotherapy (p < 0.05). Anti-CD25 monotherapy alone also had the ability to deplete periphery and intra-tumor Tregs (p < 0.05). The excretion of intra-tumor IL-10, TGF-β was notably lower but higher IFN-γ excretion in this combination immunotherapy. Such combination immunotherapy was further confirmed to synergize with anti-PD-1 monotherapy to improve tumor growth inhibition and cure rates.Conclusions: The combination of CD25, TGF-β and PD-1 blockade plays a potentially effective role in inhibiting tumor formation and progression. Our results also provide a strong rational strategy for use of IIR in future immunotherapy clinical trials. [ABSTRACT FROM AUTHOR]

• A two-parallel-path super-network is carefully designed from three levels: operation-level, channel-level and training-level. • Instead of using two paths to train the super-network, we adopt the binary gate to randomly eliminate at most one path to reduce the memory cost. • We adopt the partially-connected strategy to further reduce the memory cost, where each sub-network only adopts partial channels. • Sigmoid function is introduced to select the best input for each node across two operator sub-sets. • The proposed method can obtain better results in speed and effect. Neural Architecture Search (NAS) has made remarkable progress in automatic machine learning. However, it still suffers massive computing overheads limiting its wide applications. In this paper, we present an efficient search method, Fast Parallel Differential Neural Architecture Search (FP-DARTS). The proposed method is carefully designed from three levels to construct and train the super-network. Firstly, at the operation-level, to reduce the computational burden, different from the standard DARTS search space (8 operations), we decompose the operation set into two non-overlapping operator sub-sets (4 operations for each). Adopting these two reduced search spaces, two over-parameterized sub-networks are constructed. Secondly, at the channel-level, the partially-connected strategy is adopted, where each sub-network only adopts partial channels. Then these two sub-networks construct a two-parallel-path super-network by addition. Thirdly, at the training-level, the binary gate is introduced to control whether a path participates in the super-network training. It may suffer an unfair issue when using softmax to select the best input for intermediate nodes across two operator sub-sets. To tackle this problem, the sigmoid function is introduced, which measures the performance of operations without compression. Extensive experiments demonstrate the effectiveness of the proposed algorithm. Specifically, FP-DARTS achieves 2.50% test error with only 0.08 GPU-days on CIFAR10, and a state-of-the-art top-1 error rate of 23.7% on ImageNet using only 2.44 GPU-days for search. [ABSTRACT FROM AUTHOR]

• A Robust Training Sample Selection strategy is proposed to effectively solve the problem of hyperparameter dependence. • Mitigate the impact of centerness loss without decreasing by designing a dynamic optimization loss. • A mixed attention module is proposed to enhance the multi-scale representation ability of feature pyramids. [Display omitted] As a pioneering work of introducing the idea of full convolutional network into the field of object detection, the fully convolutional one-stage object detection network (FCOS) has the advantage of excellent performance with low memory overhead. However, there are certain problems with FCOS that merit more research: the centerness quality assessment loss does not decrease during the late training stage, and its adaptive training sample selection (ATSS) relies heavily on the hyperparameter. To solve the aforementioned problems, we propose a novel object detection network, named augmented fully convolutional one-stage object detection network (AugFCOS). First of all, we propose an improved dynamic optimization loss (DOL) to mitigate the impact of the original centerness loss not decreasing. Then, a Robust Training Sample Selection (RTSS) is proposed to get rid of the dependence of hyper-parameter in ATSS of FCOS. Finally, a novel mixed attention feature pyramid network (MAFPN) is presented to enhance the multi-scale representation ability of feature pyramid network (FPN) and further improve the ability of multi-scale detection. The experimental results on MS COCO demonstrate the effectiveness of our proposed AugFCOS, where AugFCOS achieves approximate 2.0% to 2.9% increase when compared with ATSS and FCOS. [ABSTRACT FROM AUTHOR]

• Adsorption was well fitted by the pseudo-second-order model and Langmuir model. • An efficient method for the enrichment of total flavonoids was developed. • Enrichment improved antioxidant and antiproliferative properties. The aim of this work is to develop an efficient and economical method for the enrichment of total flavonoids from Pteris ensiformis Burm. extracts. Resin screening, adsorption kinetics, adsorption isotherms and thermodynamics were successively researched prior to the dynamic adsorption and desorption tests. NKA-II resin was chosen as the best adsorbent, and the adsorption data were best described by the pseudo-second-order kinetics model and Langmuir isotherm model. The optimum enrichment conditions were as follows: for adsorption the total flavonoids concentration, flow rate and volume of sample were 1.84 mg/mL, 2 BV/h and 5 BV, respectively, and for desorption the flavonoids-loaded NKA-II resin column was desorbed by 7 BV of 50% ethanol at a rate of 2 BV/h. The product had a 6.63-fold higher total flavonoids content than crude extracts, and the recovery yield of total flavonoids was 80.65%. Furthermore, flavonoids-enriched extracts exhibited higher in vitro scavenging activity against superoxide anion radical and hydroxyl radical than crude extracts. In addition, higher antiproliferative activity of flavonoids-enriched extracts against MCF-7 and HepG-2 cell lines was also found as compared to the crude extracts. The developed method is appropriate for large-scale enrichment of total flavonoids from Pteris ensiformis Burm. extracts in the food and pharmaceutical industries. [ABSTRACT FROM AUTHOR]