Search Results (58)

Detecting small targets in infrared imagery remains highly challenging due to sub-pixel target sizes, low signal-to-noise ratios, and complex background clutter. This paper proposes PSHNet, a hybrid deep-learning framework that combines dense spatial heatmap supervision with geometry-aware regression for accurate infrared small-target detection. The network generates position–scale heatmaps to guide coarse localization, which are further refined through sub-pixel offset and size regression. A Complete IoU (CIoU) loss is introduced as a geometric regularization term to improve alignment between predicted and ground-truth bounding boxes. To better preserve fine spatial details essential for identifying small thermal signatures, an Enhanced Low-level Feature Module (ELFM) is incorporated using multi-scale dilated convolutions and channel attention. Experiments on the NUDT-SIRST and IRSTD-1k datasets demonstrate that PSHNet outperforms existing methods in IoU, detection probability, and false alarm rate, achieving IoU improvement and robust performance under low-SNR conditions. Full article

Infrared small target detection (IRSTD) aims to locate and separate targets from complex backgrounds. The challenges in IRSTD primarily come from extremely sparse target features and strong background clutter interference. However, existing methods typically perform discrimination directly on the features extracted by deep networks, neglecting the distinct characteristics of weak and small targets in the frequency domain, thereby limiting the improvement of detection capability. In this paper, we propose a frequency-aware masked-attention network (FM-Net) that leverages multi-scale frequency clues to assist in representing global context and suppressing noise interference. Specifically, we design the wavelet residual block (WRB) to extract multi-scale spatial and frequency features, which introduces a wavelet pyramid as the intermediate layer of the residual block. Then, to perceive global information on the long-range skip connections, a frequency-modulation masked-attention module (FMM) is used to interact with multi-layer features from the encoder. FMM contains two crucial elements: (a) a mask attention (MA) mechanism for injecting broad contextual feature efficiently to promote full-level semantic correlation and focus on salient regions, and (b) a channel-wise frequency modulation module (CFM) for enhancing the most informative frequency components and suppressing useless ones. Extensive experiments on three benchmark datasets (e.g., SIRST, NUDT-SIRST, IRSTD-1k) demonstrate that FM-Net achieves superior detection performance. Full article

Lung adenocarcinoma (LUAD) is characterized by substantial genetic heterogeneity, making it challenging to identify reliable biomarkers for diagnosis and treatment. Tumor mutational burden (TMB) is widely recognized as a predictive biomarker due to its association with immune response and treatment efficacy. In this study, we take a different approach by treating TMB as a response variable to uncover its genetic drivers using multiomics data. We conducted a thorough evaluation of recent feature selection methods through extensive simulations and identified three top-performing approaches: projection correlation screening (PC-Screen), distance correlation sure independence screening (DC-SIS), and Wasserstein distance-based screening (WD-Screen). Unlike traditional approaches that rely on simple statistical tests or dataset splitting for validation, we adopt a method-based validation strategy, selecting top-ranked features from each method and identifying consistently selected genes across all three. Using The Cancer Genome Atlas (TCGA) dataset, we integrated copy number alteration (CNA), mRNA expression, and DNA methylation data as predictors and applied our selected methods. In the two-platform analysis (mRNA + CNA), we identified 13 key genes, including both previously reported LUAD-associated genes (CCNG1, CKAP2L, HSD17B4, SHROOM1, TIGD6, and TMEM173) and novel candidates (DTWD2, FLJ33630, NME5, NUDT12, PCBD2, REEP5, and SLC22A5). Expanding to a three-platform analysis (mRNA + CNA + methylation) further refined our findings, with PCBD2 and TMEM173 emerging as the robust candidates. These results highlight the complexity of multiomics integration and the need for advanced feature selection techniques to uncover biologically meaningful patterns. Our multiomics strategy and robust selection approach provide insights into the genetic determinants of TMB, offering potential biomarkers for targeted LUAD therapies and demonstrating the power of Wasserstein distance-based feature selection in complex genomic analysis. Full article

Background: Myelotoxicity, usually manifested by moderate leukopenia (particularly neutropenia), is a well-known adverse drug reaction to azathioprine (AZA) therapy. Thiopurine methyltransferase (TMPT) and nucleoside diphosphate-linked moiety X-type motif 15 (NUDT15) genotyping are not routinely performed in patients starting AZA therapy due to their low cost-effectiveness. Additionally, the concomitant use of xanthine oxidase inhibitors and 5-aminosalicylates may slow the metabolism of 6-mercaptopurine. Case Description: We describe a case of a 26-year-old Caucasian man with Crohn’s disease and psoriatic arthritis treated with mesalazine and AZA (100 mg daily) who developed prolonged bone marrow aplasia and neutropenic fever after increasing the daily dose of AZA from 100 to 150 mg (from 44 to 66 mg/m²), without frequent total blood count monitoring. Discontinuation of AZA, multiple transfusions of red blood cells and platelet concentrate, filgrastim, empirical antibiotic therapy, and antiviral and antifungal prophylaxis were obtained after 11 days complete recovery of bone marrow aplasia. Methods: Genomic DNA genotyping of coding regions of TPMT (exons 2–9) and NUDT15 (exons 1–3). Results: Heterozygous alleles in the untranslated region (c.460G>A and c.719A>G) associated with TPMT deficiency and a benign variant (c.*7G>A) in the 3′-UTR of NUDT15 with no effect on enzyme activity were found. Conclusions: This case highlights the importance of monitoring the total blood count frequently during the first weeks of treatment with moderate-to-high doses of AZA. Furthermore, the interaction between AZA and mesalazine may play a significant role in the development of prolonged bone marrow aplasia. Full article

With the flourish of deep learning, transformer models have achieved remarkable performance in dealing with many computer vision tasks. However, their applications in infrared small target detection is limited due to two factors: (1) the high computational complexity of the conventional transformer models reduces the efficiency of detection; (2) the small target is easily left out in the visual environment with complex backgrounds. To deal with the issues, we propose a lightweight infrared small target detection method based on a linear transformer named IstdVit, which achieves high accuracy and low delay in infrared small target detection. The model consists of two parts: a multi-scale linear transformer and a lightweight dual feature pyramid network. It combines the strengths of a lightweight feature extraction module and the multi-head attention mechanism, effectively representing the small targets in the complex background at an economical computational cost. Additionally, it incorporates rotational position encoding to improve understanding of spatial context. The experiments conducted on the NUDT-SIRST and IRSTD-1K datasets indicate that IstdVit achieves a good balance between speed and accuracy, outperforming other state-of-the-art methods while maintaining a low number of parameters. Full article

MTH1 (MutT Homolog 1) protein is one of the enzymes that protect cells from mutagenetic actions of reactive oxygen species. It sanitizes the pool of free nucleotides, making sure that oxidized dNTPs are not incorporated into the DNA. Any misfunction of it would lead to mutations. As such, it has attracted interest of cancer researchers, and multiple studies have been conducted over the years to determine its role in tumor cells. It has been found that MTH1 is not downregulated in most tumor tissues but, to the contrary, often overexpressed. This suggests that MTH1 is used by cancer as an adaptation to increased oxidative stress caused by metabolic reprogramming to support excessive proliferation. Based on this premise, many recent studies have evaluated MTH1 as either prognostic factor, general biomarker or therapeutic target in cancer. Here, we summarize all available research on MTH1 mRNA, protein and its enzymatic activity in clinical samples across various cancer types, identifying a subset of cancers where MTH1 plays an important role. This is particularly evident in cancers characterized by high metabolic activity and oxygen-rich environments, such as hepatocellular carcinoma, renal cell carcinoma, or non-small cell lung adenocarcinoma. Full article

Infrared small target detection in complex environments remains a significant challenge due to low signal-to-noise ratios (SNRs), background clutter, and target scale variations. To address these issues, we propose an Anisotropic Dynamic-aware Multi-scale Network for Infrared Small Target Detection (IR-ADMDet). The core of IR-ADMDet is a Dual-Path Hybrid Feature Extractor Network (DPHFENet). This network effectively synergizes local residual learning with global context modeling. It enhances faint target signatures while suppressing interference. Additionally, a Hierarchical Adaptive Fusion Framework (HAFF) is utilized. HAFF integrates bidirectional gating, recursive graph enhancement, and interlink fusion. This framework optimally refines features across multiple scales. The entire architecture is optimized for efficiency using dynamic feature recalibration. Extensive experiments were conducted on benchmark datasets including SIRSTv2, IRSTD-1k, and NUDT-SIRST. These experiments demonstrate the superiority of IR-ADMDet. It achieves state-of-the-art (SOTA) results, such as 0.96 AP50 and 0.95 F1-score on SIRSTv2. This performance is achieved with significantly fewer parameters, only 5.77 M, compared to existing methods. This shows remarkable robustness in low-contrast, high-noise scenarios. IR-ADMDet also outperforms contemporary segmentation-based approaches. Full article

Background: Endometrial carcinoma (EC) is the most common gynecological malignancy, with increasing incidence contributing to a significant global health burden. Despite recent advancements, the molecular mechanisms underlying EC progression remain insufficiently understood, limiting the development of targeted therapies. This study aims to investigate the role of nucleoside diphosphate-linked moiety X motif 5 (NUDT5) in EC and evaluate its potential as a biomarker and therapeutic target. Methods: This study analyzed gene expression data from The Cancer Genome Atlas and performed tissue microarray validation to assess NUDT5 expression in EC samples. Immunohistochemistry was used to evaluate NUDT5 protein levels and their correlation with clinicopathological features. Functional assays, including cell proliferation, migration, invasion, and apoptosis analysis, were conducted to determine the oncogenic effects of NUDT5 in vitro. Weighted gene co-expression network analysis (WGCNA) and experimental validation were performed to explore the impact of NUDT5 on the PI3K-AKT signaling pathway, while tumor growth assays in xenograft models assessed the therapeutic potential of NUDT5 inhibition in vivo. Results: NUDT5 was significantly overexpressed in EC tissues and correlated with advanced histological grade and poor prognosis. Functional experiments demonstrated that NUDT5 promotes cell proliferation, migration, and invasion while inhibiting apoptosis. Mechanistically, NUDT5 activated the PI3K-AKT pathway, contributing to tumor progression. In vivo, NUDT5 knockdown suppressed tumor growth. Conclusions: These findings suggest that NUDT5 functions as an oncogene in EC, serving as a potential diagnostic and prognostic biomarker. Targeting NUDT5 may provide a novel therapeutic strategy for EC management. Full article

This study aims to address a series of challenges in infrared small target detection, particularly in complex backgrounds and high-noise environments. In response to these issues, we propose a deep learning model called the Feature Multi-Scale Enhancement and Adaptive Dynamic Fusion Network (FMADNet). This model is based on a U-Net architecture and incorporates a Residual Multi-Scale Feature Enhancement (RMFE) module and an Adaptive Feature Dynamic Fusion (AFDF) module. The RMFE module not only achieves efficient feature extraction but also adaptively adjusts feature responses across multiple scales, further enhancing the detection capabilities for small targets. Additionally, the AFDF module effectively integrates features from the encoder and decoder during the upsampling phase, enabling dynamic learning of upsampling and focusing on spatially important features, significantly improving detection accuracy. Evaluated on the NUDT-SIRST and IRSTD-1k datasets, our model exhibits strong performance, showcasing its effectiveness and precision in identifying infrared small targets in diverse complex environments, along with its remarkable robustness. Full article

Sjögren’s syndrome (SS) is a chronic autoimmune disorder characterised by lymphocytic infiltration of the exocrine glands, which leads to dryness of the eyes and mouth; systemic manifestations such as arthritis, vasculitis, and interstitial lung disease; and increased risks of lymphoma and cardiovascular diseases. SS predominantly affects women, with a strong genetic component linked to sex chromosomes. Genome-wide association studies (GWASs) have identified numerous single-nucleotide polymorphisms (SNPs) associated with primary SS (pSS), revealing insights into its pathogenesis. The adaptive and innate immune systems are crucial to SS’s development, with viral infections implicated as environmental triggers that exacerbate autoimmune responses in genetically susceptible individuals. Moreover, recent research has highlighted the role of vitamin D in modulating immune responses in pSS patients, suggesting its potential therapeutic implications. In this review, we focus on the recently identified SNPs in genes like OAS1, NUDT15, LINC00243, TNXB, and THBS1, which have been associated with increased risks of developing more severe symptoms and other diseases such as fatigue, lymphoma, neuromyelitis optica spectrum disorder (NMOSD), dry eye syndrome (DES), and adverse drug reactions. Future studies should focus on larger, multi-ethnic cohorts with standardised protocols to validate findings and identify new associations. Integrating genetic testing into clinical practise holds promise for improving SS management and treatment strategies, enabling personalised interventions based on comprehensive genetic profiles. By focusing on specific SNPs, vitamin D, and their implications, future research can lead to more effective and personalised approaches for managing pSS and its complications. Full article

Following the success of mRNA vaccines against COVID-19, mRNA-based therapeutics have now become a great interest and potential. The development of this approach has been preceded by studies of modifications found on mRNA ribonucleotides that influence the stability, translation and immunogenicity of this molecule. The 5′ cap of eukaryotic mRNA plays a critical role in these cellular functions and is thus the focus of intensive chemical modifications to affect the biological properties of in vitro-prepared mRNA. Enzymatic removal of the 5′ cap affects the stability of mRNA in vivo. The NUDIX hydrolase Dcp2 was identified as the first eukaryotic decapping enzyme and is routinely used to analyse the synthetic cap at the 5′ end of RNA. Here we highlight three additional NUDIX enzymes with known decapping activity, namely Nudt2, Nudt12 and Nudt16. These enzymes possess a different and some overlapping activity towards numerous 5′ RNA cap structures, including non-canonical and chemically modified ones. Therefore, they appear as potent tools for comprehensive in vitro characterisation of capped RNA transcripts, with special focus on synthetic RNAs with therapeutic activity. Full article

Intratumoral hypoxia is associated with tumor progression, aggressiveness, and therapeutic resistance in several cancers. Hypoxia causes cancer cells to experience replication stress, thereby activating DNA damage and repair pathways. MutT homologue-1 (MTH1, also known as NUDT1), a member of the Nudix family, maintains the genomic integrity and viability of tumor cells in the hypoxic tumor microenvironment. Although hypoxia is associated with poor prognosis and can cause therapeutic resistance by regulating the microenvironment, it has not been considered a treatable target in cancer. This study aimed to investigate whether hypoxia-induced MTH1 is a useful target for immunotherapy and whether hypoxic conditions influence the antitumor activity of immune cells. Our results showed that MTH1 expression was elevated under hypoxic conditions in head and neck cancer cell lines. Furthermore, we identified a novel MTH1-targeting epitope peptide that can activate peptide-specific CD4+ helper T cells with cytotoxic activity. The proliferation and cytotoxic activity of T cells were maintained under hypoxic conditions, and PD-1 blockade further augmented the cytotoxicity. These results indicate that MTH1-targeted immunotherapy combined with checkpoint blockade can be an effective strategy for the treatment of hypoxic tumors. Full article

Camera arrays can enhance the signal-to-noise ratio (SNR) between dim targets and backgrounds through multi-view synthesis. This is crucial for the detection of dim targets. To this end, we design and develop an infrared camera array system with a large baseline. The multi-view synthesis of camera arrays relies heavily on the calibration accuracy of relative poses in the sub-cameras. However, the sub-cameras within a camera array lack strict geometric constraints. Therefore, most current calibration methods still consider the camera array as multiple pinhole cameras for calibration. Moreover, when detecting distant targets, the camera array usually needs to adjust the focal length to maintain a larger depth of field (DoF), so that the distant targets are located on the camera’s focal plane. This means that the calibration scene should be selected within this DoF range to obtain clear images. Nevertheless, the small parallax between the distant sub-aperture views limits the calibration. To address these issues, we propose a calibration model for camera arrays in distant scenes. In this model, we first extend the parallax by employing dual-array frames (i.e., recording a scene at two spatial locations). Secondly, we investigate the linear constraints between the dual-array frames, to maintain the minimum degrees of freedom of the model. We develop a real-world light field dataset called NUDT-Dual-Array using an infrared camera array to evaluate our method. Experimental results on our self-developed datasets demonstrate the effectiveness of our method. Using the calibrated model, we improve the SNR of distant dim targets, which ultimately enhances the detection and perception of dim targets. Full article

An electrochemical sensor sensitive to coenzyme A (CoA) was designed using a CoA-responsive polyallylamine–manganese oxide–polymer dot nanogel coated on the electrode surface to detect various genetic models of osteoarthritis (OA). The CoA-responsive nanogel sensor responded to the abundance of CoA in OA, causing the breakage of MnO₂ in the nanogel, thereby changing the electroconductivity and fluorescence of the sensor. The CoA-responsive nanogel sensor was capable of detecting CoA depending on the treatment time and distinguishing the response towards different OA genetic models that contained different levels of CoA (wild type/WT, NudT7 knockout/N7KO, and Acot12 knockout/A12KO). The WT, N7KO, and A12KO had distinct resistances, which further increased as the incubation time were changed from 12 h (R_12h = 2.11, 2.40, and 2.68 MΩ, respectively) to 24 h (R_24h = 2.27, 2.59, and 2.92 MΩ, respectively) compared to the sensor without treatment (R_control = 1.63 MΩ). To simplify its application, the nanogel sensor was combined with a wireless monitoring device to allow the sensing data to be directly transmitted to a smartphone. Furthermore, OA-indicated anabolic (Acan) and catabolic (Adamts5) factor transcription levels in chondrocytes provided evidence regarding CoA and nanogel interactions. Thus, this sensor offers potential usage in simple and sensitive OA diagnostics. Full article

Infrared small target detection technology plays a crucial role in various fields such as military reconnaissance, power patrol, medical diagnosis, and security. The advancement of deep learning has led to the success of convolutional neural networks in target segmentation. However, due to challenges like small target scales, weak signals, and strong background interference in infrared images, convolutional neural networks often face issues like leakage and misdetection in small target segmentation tasks. To address this, an enhanced U-Net method called MST-UNet is proposed, the method combines multi-scale feature decomposition and fusion and attention mechanisms. The method involves using Haar wavelet transform instead of maximum pooling for downsampling in the encoder to minimize feature loss and enhance feature utilization. Additionally, a multi-scale residual unit is introduced to extract contextual information at different scales, improving sensory field and feature expression. The inclusion of a triple attention mechanism in the encoder structure further enhances multidimensional information utilization and feature recovery by the decoder. Experimental analysis on the NUDT-SIRST dataset demonstrates that the proposed method significantly improves target contour accuracy and segmentation precision, achieving IoU and nIoU values of 80.09% and 80.19%, respectively. Full article