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Abstract Objective Despite the identification of various prognostic factors for anaplastic thyroid carcinoma (ATC) patients over the years, a precise prognostic tool for these patients is still lacking. This study aimed to develop and validate a prognostic model for predicting survival outcomes for ATC patients using random survival forests (RSF), a machine learning algorithm. Methods A total of 1222 ATC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into a training set of 855 patients and a validation set of 367 patients. We developed an RSF model and a traditional Cox model using the training cohort and further compared their performance based on calibration and discrimination. integrated brier score (iBS) was used to estimate the calibration ability. The Brier score, C-index value, the receiver operating characteristic (ROC) curve with the area under the curve (AUC) and Decision Curves Analysis (DCA) were evaluated. Furthermore, we assessed the feature importance within the RSF model and validated its performance using the validation group. Results An RSF model and a traditional Cox model were successfully developed in training set. The Brier score for the RSF model was 0.055, which is lower than the Cox model’s score of 0.063, indicating better performance since a lower Brier score signifies superior model accuracy. The RSF model exceeded the Cox model in performance based on the C-index and AUC. Additionally, the DCA curve indicated that the RSF model provided substantial clinical benefit. And we further ranked the time-dependent features according to their permutation importance and observed that surgery, radiotherapy, and chemotherapy were the most influential predictors initially. Moreover, according to the RSF model predictions, the ATC patients were successfully stratified into 2 prognostic groups displaying significant difference in survival. Conclusions This prognostic study first revealed that RSF offers more precise overall survival predictions and superior prognostic stratification compared to the Cox regression model for ATC patients.

Brain tumors are one of the major health threats to humans, and their complex pathological features and anatomical structures make accurate segmentation and detection crucial. However, existing models based on Transformers and Convolutional Neural Networks (CNNs) still have limitations in medical image processing. While Transformers are proficient in capturing global features, they suffer from high computational complexity and require large amounts of data for training. On the other hand, CNNs perform well in extracting local features but have limited performance when handling global information. To address these issues, this paper proposes a novel network framework, MUNet, which combines the advantages of UNet and Mamba, specifically designed for brain tumor segmentation. MUNet introduces the SD-SSM module, which effectively captures both global and local features of the image through selective scanning and state-space modeling, significantly improving segmentation accuracy. Additionally, we design the SD-Conv structure, which reduces feature redundancy without increasing model parameters, further enhancing computational efficiency. Finally, we propose a new loss function that combines mIoU loss, Dice loss, and Boundary loss, which improves segmentation overlap, similarity, and boundary accuracy from multiple perspectives. Experimental results show that, on the BraTS2020 dataset, MUNet achieves DSC values of 0.835, 0.915, and 0.823 for enhancing tumor (ET), whole tumor (WT), and tumor core (TC), respectively, and Hausdorff95 scores of 2.421, 3.755, and 6.437. On the BraTS2018 dataset, MUNet achieves DSC values of 0.815, 0.901, and 0.815, with Hausdorff95 scores of 4.389, 6.243, and 6.152, all outperforming existing methods and achieving significant performance improvements. Furthermore, when validated on the independent LGG dataset, MUNet demonstrated excellent generalization ability, proving its effectiveness in various medical imaging scenarios. The code is available at https://github.com/Dalin1977331/MUNet.

Abstract Background Widespread neuropathic pain usually affects a wide range of body areas and inflicts huge suffering on patients. However, little is known about how it happens and effective therapeutic interventions are lacking. Methods Widespread neuropathic pain was induced by partial infraorbital nerve transection (p-IONX) and evaluated by measuring nociceptive thresholds. In vivo/vitro electrophysiology were used to evaluate neuronal activity. Virus tracing strategies, combined with optogenetics and chemogenetics, were used to clarify the role of remodeling circuit in widespread neuropathic pain. Results We found that in mice receiving p-IONX, along with pain sensitization spreading from the orofacial area to distal body parts, glutamatergic neurons in the ventral posteromedial nucleus of the thalamus (VPMGlu) were hyperactive and more responsive to stimulations applied to the hind paw or tail. Tracing experiments revealed that a remodeling was induced by p-IONX in the afferent circuitry of VPMGlu, notably evidenced by more projections from glutamatergic neurons in the dorsal column nuclei (DCNGlu). Moreover, VPMGlu receiving afferents from the DCN extended projections further to glutamatergic neurons in the posterior insular cortex (pIC). Selective inhibition of the terminals of DCNGlu in the VPM, the soma of VPMGlu or the terminals of VPMGlu in the pIC all alleviated trigeminal and widespread neuropathic pain. Conclusion These results demonstrate that hyperactive VPMGlu recruit new afferents from the DCN and relay the extra-cephalic input to the pIC after p-IONX, thus hold a key position in trigeminal neuropathic pain and its spreading. This study provides novel insights into the circuit mechanism and preclinical evidence for potential therapeutic targets of widespread neuropathic pain.

Protein O-glycosylation, also known as mucin-type O-glycosylation, is one of the most abundant glycosylation in mammalian cells. It is initially catalyzed by a family of polypeptide GalNAc transferases (ppGalNAc-Ts). The trimeric spike protein (S) of SARS-CoV-2 is highly glycosylated and facilitates the virus’s entry into host cells and membrane fusion of the virus. However, the functions and relationship between host ppGalNAc-Ts and O-glycosylation on the S protein remain unclear. Herein, we identify 15 O-glycosites and 10 distinct O-glycan structures on the S protein using an HCD-product-dependent triggered ETD mass spectrometric analysis. We observe that the isoenzyme T6 of ppGalNAc-Ts (ppGalNAc-T6) exhibits high O-glycosylation activity for the S protein, as demonstrated by an on-chip catalytic assay. Overexpression of ppGalNAc-T6 in HEK293 cells significantly enhances the O-glycosylation level of the S protein, not only by adding new O-glycosites but also by increasing O-glycan heterogeneity. Molecular dynamics simulations reveal that O-glycosylation on the protomer-interface regions, modified by ppGalNAc-T6, potentially stabilizes the trimeric S protein structure by establishing hydrogen bonds and non-polar interactions between adjacent protomers. Furthermore, mutation frequency analysis indicates that most O-glycosites of the S protein are conserved during the evolution of SARS-CoV-2 variants. Taken together, our finding demonstrate that host O-glycosyltransferases dynamically regulate the O-glycosylation of the S protein, which may influence the trimeric structural stability of the protein. This work provides structural insights into the functional role of specific host O-glycosyltransferases in regulating the O-glycosylation of viral envelope proteins.

BackgroundCurrent lines of evidence indicate that cysteine-rich secretory protein 3 (CRISP3) is an immunoregulatory factor. Nevertheless, no study has explored the relationships between the values of CRISP3 and sepsis.MethodsWe conducted a comprehensive literature search and meta-analysis from the Gene Expression Omnibus (GEO) and ArrayExpress to determine the expression of CRISP3 in sepsis patients. Then, we explored whether plasma CRISP3 could serve as a potential biomarker to predict the risk of sepsis via two retrospective trauma cohorts. We evaluated the prediction power using the area under the curve (AUC).ResultsA total of 23 datasets were recruited for the comprehensive meta-analysis, and the combined standardized mean difference (SMD) of CRISP3 was 0.90 (0.50–1.30) (p < 0.001), suggesting that CRISP3 was overexpressed in sepsis patients. Meanwhile, sepsis patients had higher CRISP3 concentrations than non-sepsis patients in 54 trauma patients (p < 0.001). Plasma CRISP3 on admission was significantly associated with the incidence of sepsis [OR = 1.004 (1.002–1.006), p < 0.001]. As a predictive biomarker, CRISP3 obtained a better AUC [0.811 (0.681–0.905)] than C-reactive protein (CRP) [0.605 (0.463–0.735)], procalcitonin (PCT) [0.554 (0.412–0.689)], and Sequential Organ Failure Assessment (SOFA) [0.754 (0.618–0.861)]. Additionally, the clinical relationships between plasma CRISP3 and sepsis were verified in another trauma cohort with 166 patients [OR = 1.002 (1.001–1.003), p < 0.001]. The AUC of CRISP3 was 0.772 (0.701–0.834), which was better than that of CRP [0.521 (0.442–0.599)] and PCT [0.531 (0.452–0.609)], but not SOFA [0.791 (0.717–0.853)].ConclusionOur study indicated and validated that CRISP3 was highly expressed in sepsis. More importantly, CRISP3 may serve as a latent biomarker to predict the risk of sepsis.

Abstract Purpose This study investigated the changes in the fasting blood glucose (FBG), fasting triglyceride (FTG), and fasting total cholesterol (FTC) levels during neoadjuvant therapy (NAT) for human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC) and the association with pathologic complete response (pCR). Methods Relevant data from Sichuan Cancer Hospital from June 2019 to June 2022 were collected and analyzed, and FBG, FTG, and FTC were divided into baseline, change, and process groups, which were grouped to analyze the changes after receiving NAT and the association with pCR. Results In the estrogen receptor (ER)-negative subgroup, patients with low levels of FTG in the process group were more likely to achieve pCR compared to high levels, and in the progesterone receptor (PR)-negative subgroup, patients with lower FTG compared to higher FTG after receiving NAT was more likely to achieve pCR. Conclusions Patients with HER2-positive BC undergoing NAT develop varying degrees of abnormalities (elevated or decreased) in FBG, FTG, and FTC; moreover, the status of FTG levels during NAT may predict pCR in ER-negative or PR-negative HER2-positive BC.Early monitoring and timely intervention for FTG abnormalities may enable this subset of patients to increase the likelihood of obtaining a pCR along with management of abnormal markers.

Abstract. Background:. Adamantinomatous craniopharyngioma (ACP) is the commonest pediatric sellar tumor. No effective drug is available and interpatient heterogeneity is prominent. This study aimed to identify distinct molecular subgroups of ACP based on the multi-omics profiles, imaging findings, and histological features, in order to predict the response to anti-inflammatory treatment and immunotherapies. Methods:. Totally 142 Chinese cases diagnosed with craniopharyngiomas were profiled, including 119 ACPs and 23 papillary craniopharyngiomas. Whole-exome sequencing (151 tumors, including recurrent ones), RNA sequencing (84 tumors), and DNA methylome profiling (95 tumors) were performed. Consensus clustering and non-negative matrix factorization were used for subgrouping, and Cox regression were utilized for prognostic evaluation, respectively. Results:. Three distinct molecular subgroups were identified: WNT, ImA, and ImB. The WNT subgroup showed higher Wnt/β-catenin pathway activity, with a greater number of epithelial cells and more predominantly solid tumors. The ImA and ImB subgroups had activated inflammatory and interferon response pathways, with enhanced immune cell infiltration and more predominantly cystic tumors. Mitogen-activated protein kinases (MEK/MAPK) signaling was activated only in ImA samples, while IL-6 and epithelial–mesenchymal transition biomarkers were highly expressed in the ImB group, mostly consisting of children. The degree of astrogliosis was significantly elevated in the ImA group, with severe finger-like protrusions at the invasive front of the tumor. The molecular subgrouping was an independent prognostic factor, with the WNT group having longer event-free survival than ImB (Cox, P = 0.04). ImA/ImB cases were more likely to respond to immune checkpoint blockade (ICB) therapy than the WNT group (P

Background: Age-related Macular Degeneration (AMD) poses a growing global health concern as the leading cause of central vision loss in elderly people. Objection: This study focuses on unraveling the intricate involvement of Natural Killer (NK) cells in AMD, shedding light on their immune responses and cytokine regulatory roles. Methods: Transcriptomic data from the Gene Expression Omnibus database were utilized, employing single-cell RNA-seq analysis. High-dimensional weighted gene co-expression network analysis (hdWGCNA) and single-cell regulatory network inference and clustering (SCENIC) analysis were applied to reveal the regulatory mechanisms of NK cells in early-stage AMD patients. Machine learning models, such as random forests and decision trees, were employed to screen hub genes and key transcription factors (TFs) associated with AMD. Results: Distinct cell clusters were identified in the present study, especially the T/NK cluster, with a notable increase in NK cell abundance observed in AMD. Cell-cell communication analyses revealed altered interactions, particularly in NK cells, indicating their potential role in AMD pathogenesis. HdWGCNA highlighted the turquoise module, enriched in inflammation-related pathways, as significantly associated with AMD in NK cells. The SCENIC analysis identified key TFs in NK cell regulatory networks. The integration of hub genes and TFs identified CREM, FOXP1, IRF1, NFKB2 , and USF2 as potential predictors for AMD through machine learning. Conclusion: This comprehensive approach enhances our understanding of NK cell dynamics, signaling alterations, and potential predictive models for AMD. The identified TFs provide new avenues for molecular interventions and highlight the intricate relationship between NK cells and AMD pathogenesis. Overall, this study contributes valuable insights for advancing our understanding and management of AMD.

Brain tumors are diseases characterized by abnormal cell growth within or around brain tissues, including various types such as benign and malignant tumors. However, there is currently a lack of early detection and precise localization of brain tumors in MRI images, posing challenges to diagnosis and treatment. In this context, achieving accurate target detection of brain tumors in MRI images becomes particularly important as it can improve the timeliness of diagnosis and the effectiveness of treatment. To address this challenge, we propose a novel approach–the YOLO-NeuroBoost model. This model combines the improved YOLOv8 algorithm with several innovative techniques, including dynamic convolution KernelWarehouse, attention mechanism CBAM (Convolutional Block Attention Module), and Inner-GIoU loss function. Our experimental results demonstrate that our method achieves mAP scores of 99.48 and 97.71 on the Br35H dataset and the open-source Roboflow dataset, respectively, indicating the high accuracy and efficiency of this method in detecting brain tumors in MRI images. This research holds significant importance for improving early diagnosis and treatment of brain tumors and provides new possibilities for the development of the medical image analysis field.

Fermentation broth from fruit and vegetable waste (FFVW) has demonstrated remarkable ability as a soil amendment and in reducing antibiotic resistance genes (ARGs) pollution. However, the potential of FFVW to mitigate other microbial contamination such as human bacterial pathogens (HBPs) and virulence factor genes (VFGs), which are closely associated with human health, remains unknown. In this study, metagenomic analysis revealed that FFVW reduced the HBPs with high-risk of ARGs and VFGs including Klebsiella pneumoniae (reduced by 40.4 %), Mycobacterium tuberculosis (reduced by 21.4 %) and Streptococcus pneumoniae (reduced by 38.7 %). Correspondingly, VFG abundance in soil decreased from 3.40 copies/cell to 2.99 copies/cell. Further analysis illustrated that these was mainly attributed to the inhibition of quorum sensing (QS). FFVW reduced the abundance of QS signals, QS synthesis genes such as rpaI and luxS, as well as receptor genes such as rpfC and fusK, resulting in a decreased in risk of ARGs and VFGs. The pure culture experiment revealed that the expression of genes related to QS, VFGs, ARGs and mobile genetic elements (MGEs) were downregulated in Pseudomonas aeruginosa, Staphylococcus aureus, Escherichia coli and K. pneumoniae treated by FFVW, consistent with the result of metagenomic analysis. This study suggested an environmentally friendly approach for controlling soil VFGs/ARGs-carrying HBPs, which is crucial for both soil and human health under the framework of “One Health”.

DNA glycosylase, as one member of DNA repair machineries, plays an essential role in correcting mismatched/damaged DNA nucleotides by cleaving the N-glycosidic bond between the sugar and target nucleobase through the base excision repair (BER) pathways. Efficient corrections of these DNA lesions are critical for maintaining genome integrity and preventing premature aging and cancers. The target-site searching/recognition mechanisms and the subsequent conformational dynamics of DNA glycosylase, however, remain challenging to be characterized using experimental techniques. In this review, we summarize our recent studies of sequential structural changes of thymine DNA glycosylase (TDG) during the DNA repair process, achieved mostly by molecular dynamics (MD) simulations. Computational simulations allow us to reveal atomic-level structural dynamics of TDG as it approaches the target-site, and pinpoint the key structural elements responsible for regulating the translocation of TDG along DNA. Subsequently, upon locating the lesions, TDG adopts a base-flipping mechanism to extrude the mispaired nucleobase into the enzyme active-site. The constructed kinetic network model elucidates six metastable states during the base-extrusion process and suggests an active role of TDG in flipping the intrahelical nucleobase. Finally, the molecular mechanism of product release dynamics after catalysis is also summarized. Taken together, we highlight to what extent the computational simulations advance our knowledge and understanding of the molecular mechanism underlying the conformational dynamics of TDG, as well as the limitations of current theoretical work.

Lithium-ion batteries (LIBs) have the lead as the most used power source for electric vehicles and grid storage systems, and a battery thermal management system (BTMS) can ensure the efficient and safe operation of lithium-ion batteries. Epoxy resin board (ERB) offers a wide range of applications in LIBs due to its significant advantages such as high dielectric strength, electrical insulation, good mechanical strength, and stiffness. This study proposes an air-cooled battery module comprised of sixteen prismatic batteries incorporating an ERB layer between the batteries. To compare the performance of the ERB-based air-cooling system, two other air-cooling structures are also assessed in this study. Three-dimensional numerical models for the three cases are established in this paper, and the heat dissipation processes of the battery module under varying discharge rates (1C, 2C, and 5C) are simulated and analyzed to comprehensively evaluate the performance of the different cooling systems. Comparative simulations reveal that incorporating ERB into the battery assembly significantly reduces battery surface temperatures and promotes temperature uniformity across individual batteries and the entire pack at various discharge rates. Notably, under 5C discharge conditions, the ERB-based thermal management system achieves a maximum battery surface temperature increase of 16 °C and a maximum temperature difference of 8 °C between batteries. Additionally, this paper also analyzes the impact of battery arrangement on air-cooling system performance. Therefore, further optimization of the structural design or the integration of supplementary cooling media might be necessary for such demanding conditions.

The RecQ family of helicases are important for maintenance of genomic integrity. Although functions of constructive subdomains of this family of helicases have been extensively studied, the helical hairpin (HH) in the RecQ-C-terminal domain (RQC) has been underappreciated and remains poorly understood. Here by using single-molecule fluorescence resonance energy transfer, we found that HH in the human BLM transiently intercepts different numbers of nucleotides when it is unwinding a double-stranded DNA. Single-site mutations in HH that disrupt hydrogen bonds and/or salt bridges between DNA and HH change the DNA binding conformations and the unwinding features significantly. Our results, together with recent clinical tests that correlate single-site mutations in HH of human BLM with the phenotype of cancer-predisposing syndrome or Bloom's syndrome, implicate pivotal roles of HH in BLM's DNA unwinding activity. Similar mechanisms might also apply to other RecQ family helicases, calling for more attention to the RQC helical hairpin.

One-dimensional (1-D) sliding of transcription factor (TF) protein along DNA is essential for facilitated diffusion of the TF to locate target DNA site for genetic regulation. Detecting base-pair (bp) resolution of the TF sliding or stepping on the DNA is still experimentally challenging. We have recently performed all-atom molecular dynamics (MD) simulations capturing spontaneous 1-bp stepping of a small WRKY domain TF protein along DNA. Based on the 10 µs WRKY stepping path obtained from such simulations, the protocol here shows how to conduct more extensive conformational samplings of the TF-DNA systems, by constructing the Markov state model (MSM) for the 1-bp protein stepping, with various numbers of micro- and macro-states tested for the MSM construction. In order to examine processive 1-D diffusional search of the TF protein along DNA with structural basis, the protocol further shows how to conduct coarse-grained (CG) MD simulations to sample long-time scale dynamics of the system. Such CG modeling and simulations are particularly useful to reveal the protein-DNA electrostatic impacts on the processive diffusional motions of the TF protein above tens of microseconds, in comparison with sub-microseconds to microseconds protein stepping motions revealed from the all-atom simulations.

Introduction: This study investigated the differences in efficacy between IHC(2+)/FISH-positive and IHC(3+) in HER2-positive breast cancer (BC) during neoadjuvant chemotherapy (NAC) combined with trastuzumab and pertuzumab. The research also aimed to provide insight into treatment strategies for clinical HER2(2+)/FISH-positive and HER2(3+) BC. Materials and methods: A retrospective analysis was performed on the clinical and pathological data of patients with confirmed diagnoses of invasive BC treated via combined NAC and dual-target therapy who underwent surgery at the Breast Surgery Center of Sichuan Cancer Hospital between June 2019 and June 2022. The correlation between the clinicopathological characteristics and pathological complete response (pCR) was analyzed via the χ2 test, while logistic regression was performed using the SAS 9.4 statistical analysis software. Results: This study examined 224 patients with an overall pCR rate of approximately 59.82%, which included 36 IHC(2+)/FISH-positive and 188 IHC(3+) cases with approximate pCR rates of 41.67% and 63.30%, respectively. Univariate and multifactorial analysis of the clinical and pathological data determined that age, menstrual status, family history, Ki67 expression, number of treatment cycles, and treatment regimen did not influence pCR. No statistical differences were evident between the univariate and multivariate models. However, the clinical stage, hormone receptor, and HER2 expression status significantly impacted pCR, with considerable consistent differences between the univariate and multifactor analyses. Conclusions: HER2 IHC(3+) BC displays a higher pCR rate than HER2 IHC(2+)/FISH-positive BC (p ≤ 0.05), with a positive correlation between the HER2 protein expression levels and the response to anti-HER2 therapy.

Abstract Genomic abnormalities are strongly associated with cancer and infertility. In this study, we develop a simple and efficient method — multiple genetic abnormality sequencing (MGA-Seq) — to simultaneously detect structural variation, copy number variation, single-nucleotide polymorphism, homogeneously staining regions, and extrachromosomal DNA (ecDNA) from a single tube. MGA-Seq directly sequences proximity-ligated genomic fragments, yielding a dataset with concurrent genome three-dimensional and whole-genome sequencing information, enabling approximate localization of genomic structural variations and facilitating breakpoint identification. Additionally, by utilizing MGA-Seq, we map focal amplification and oncogene coamplification, thus facilitating the exploration of ecDNA’s transcriptional regulatory function.

Abstract In this report, the development of a Dynamical Statistical Analog Ensemble Forecast model for landfalling typhoon disasters (LTDs) and some applications over coastal China are described. This model consists of the following four elements: (i) obtaining the forecast track of a target landfalling typhoon, (ii) constructing its generalized initial value (GIV), (iii) identifying its analogs based on the GIV, and (iv) assembling typhoon disasters of the analogs. Typhoon track, intensity, and landfall date are introduced in GIV at this early development stage. The pre-assessment results show that the mean threat scores of two important damage levels of LTDs reach 0.48 and 0.55, respectively. Of significance is that most of the damage occurs near the typhoon centers around the time of landfall. These results indicate the promising performance of the model in capturing the main damage characteristics of typhoon disasters, which would help coastal community mitigate damage from destructive typhoons.

Cas1 and Cas2 are highly conserved proteins across clustered-regularly-interspaced-short-palindromic-repeat-Cas systems and play a significant role in protospacer acquisition. Based on crystal structure of twofold symmetric Cas1-Cas2 in complex with dual-forked protospacer DNA (psDNA), we conducted all-atom molecular dynamics simulations to study the psDNA binding, recognition, and response to cleavage on the protospacer-adjacent-motif complementary sequence, or PAMc, of Cas1-Cas2. In the simulation, we noticed that two active sites of Cas1 and Cas1' bind asymmetrically to two identical PAMc on the psDNA captured from the crystal structure. For the modified psDNA containing only one PAMc, as that to be recognized by Cas1-Cas2 in general, our simulations show that the non-PAMc association site of Cas1-Cas2 remains destabilized until after the stably bound PAMc being cleaved at the corresponding association site. Thus, long-range correlation appears to exist upon the PAMc cleavage between the two active sites (∼10 nm apart) on Cas1-Cas2, which can be allosterically mediated by psDNA and Cas2 and Cas2' in bridging. To substantiate such findings, we conducted repeated runs and further simulated Cas1-Cas2 in complex with synthesized psDNA sequences psL and psH, which have been measured with low and high frequency in acquisition, respectively. Notably, such intersite correlation becomes even more pronounced for the Cas1-Cas2 in complex with psH but remains low for the Cas1-Cas2 in complex with psL. Hence, our studies demonstrate that PAMc recognition and cleavage at one active site of Cas1-Cas2 may allosterically regulate non-PAMc association or even cleavage at the other site, and such regulation can be mediated by noncatalytic Cas2 and DNA protospacer to possibly support the ensued psDNA acquisition.

Abstract Background Pancreatic ductal adenocarcinoma (PDA) is a pernicious disease characterized by an immunosuppressive milieu that is unresponsive to current immunotherapies. Interleukin-1 receptor antagonist (IL-1Ra) is a natural anti-inflammatory cytokine; however, its contribution to cancer pathogenesis and immunosuppression remains elusive. In this research, we investigated the role and mechanism of IL-1Ra in malignant progression of PDA. Results Through analyzing clinical dataset and examining the pathological tumor tissues and serum samples, we have demonstrated that IL-1Ra expression is elevated in human PDA and positively associated with malignant progression of PDA. To study the biological function of IL-1Ra in tumors, we generated a set of mouse pancreatic cancer cell lines with a knockout (KO) of the Il1rn gene, encoding IL-1Ra, and compared the tumor growth rates in immune-competent and immune-deficient mice. We found that the Il1rn KO cells exhibited greater tumor inhibition in immune-competent mice, highlighting the crucial role of a functional immune system in Il1rn KO-mediated anti-tumor response. Consistently, we found an increase in CD8+ T cells and a decrease in CD11b+Ly6G− immunosuppressive mononuclear population in the tumor microenvironment of Il1rn KO-derived tumors. To monitor the inhibitory effects of IL-1Ra on immune cells, we utilized a luciferase-based reporter CD4+ T cell line and splenocytes, which were derived from transgenic mice expressing ovalbumin-specific T cell receptors in CD8+ T cells, and mice immunized with ovalbumin. We showed that IL-1Ra suppressed T cell receptor signaling and inhibited antigen-specific interferon-γ (IFN-γ) secretion and cytolytic activity in splenocytes. Conclusions Our findings illustrate the immunosuppressive properties of the natural anti-inflammatory cytokine IL-1Ra, and provide a rationale for considering IL-1Ra-targeted therapies in the treatment of PDA.

Objective Increasing evidence has shown that calf muscular vein thrombosis (CMVT) can develop into proximal deep vein thrombosis, even causing pulmonary embolism. However, opinions about the prevalence and risk factors are still controversial. This study aimed to investigate the prevalence and risk factors for CMVT in elderly patients with hip fractures to facilitate their preoperative management. Methods We included 419 elderly patients with hip fracture who were treated in the orthopaedic department of our hospital from June 2017 to December 2020. The patients were divided into CMVT and non‐CMVT groups based on color Doppler ultrasound screening of the venous system in the lower extremities. Clinical data, such as age, sex, body mass index, time from injury to admission, and laboratory data were collected. Univariate and multivariate logistic regression analyses were performed to determine independent risk factors for CMVT. A receiver operating characteristic curve was used to analyze the predictive effectiveness of the model. Finally, the clinical utility of the model was analyzed using decision curve analysis and clinical impact curves. Results The prevalence of preoperative CMVT was 30.5% (128/419). Independent predictors of preoperative CMVT identified by univariate and multivariate logistic regression analyses were sex, time from injury to admission, American Society of Anesthesiologists (ASA) classification, C‐reactive protein (CRP) level, and D‐dimer level (p 0.05). The clinical utility of the model was verified using decision curve analysis and clinical impact curves. Conclusion Sex, time from injury to admission, ASA classification, CRP level, and D‐dimer levels are independent preoperative predictors of CMVT in elderly patients with hip fractures. Measures should be taken for patients with these risk factors to prevent the occurrence and deterioration of CMVT.

Despite considerable progress in tropical cyclone (TC) research, our current understanding and prediction capabilities regarding the TC intensity–size relation remain limited. This study systematically analyzes the key characteristics and performance of different types of mathematical models for TC intensity–size relations using the 6-hourly Tropical Cyclone Extended Best Track Dataset spanning 1988 to 2020. The models investigated include statistical, idealized (e.g., Rankine vortex), parametric, and theoretical models. In addition to directly comparing the solutions obtained from individual models to the observed TC records, we assess the models that can produce a unique finite-sized radial profile of surface winds for each TC record—a minimal requirement to ensure that the predicted radial profile of the surface winds would align with the observed profile. The results reveal that a sufficient condition to guarantee a unique radial profile of surface winds is that the associated model can be written as a radial invariant quantity, although it does not guarantee a finite-sized profile. Only the effective absolute angular momentum (eAAM) model, among all the models examined in this study, meets the minimum requirement. Furthermore, the solutions obtained from the eAAM model are well correlated with their observational counterparts (85 to 95%) with little systematic bias and small absolute mean errors that are very close to the observational resolution. The eAAM model’s ability to capture the complex intensity–size relation of observed TCs, in combination with these desirable features, suggests its high potential for gaining a better understanding of the underlying physics governing the observed TC intensity–size relation.

Microplastic (MP) pollution is a rapidly growing global environmental concern that has led to the emergence of a new environmental compartment, the plastisphere, which is a hotspot for the accumulation of antibiotic resistance genes (ARGs) and human bacterial pathogens (HBPs). However, studies on the effects of long-term organic fertilizer application on the dispersal of ARGs and virulence factor genes (VFGs) in the plastisphere of farmland soil have been limited. Here, we performed a field culture experiment by burying nylon bags filled with MPs in paddy soil that had been treated with different fertilizers for over 30 years to explore the changes of ARGs and VFGs in soil plastisphere. Our results show that the soil plastisphere amplified the ARG and VFG pollution caused by organic fertilization by 1.5 and 1.4 times, respectively. And it also led to a 2.7-fold increase in the risk of horizontal gene transfer. Meanwhile, the plastisphere tended to promote deterministic process in the community assembly of HBPs, with an increase of 1.4 times. Network analysis found a significant correlation between ARGs, VFGs, and bacteria in plastisphere. Correlation analysis highlight the important role of mobile genetic elements (MGEs) and bacterial communities in shaping the abundance of ARGs and VFGs, respectively. Our findings provide new insights into the health risk associated with the soil plastisphere due ARGs and VFGs derived from organic fertilizers.