PANoptosis-mediated mechanisms underlying AST elevation in Talaromyces marneffei infection.

Background: Talaromyces marneffei (T. marneffei), a life-threatening opportunistic fungal pathogen, is endemic to Southeast Asia. Although elevated aspartate aminotransferase (AST) levels are commonly observed in infected individuals, the origin and mechanism of this phenomenon remain unclear.

High-efficient refolding and purification of recombinant human interleukin-2 from inclusion bodies.

Human interleukin-2 (hIL-2) serves as a crucial cytokine in the treatment of cancer and autoimmune disorders. Nevertheless, the advancement of research and clinical applications involving this cytokine has been hindered by the constraints associated with the production of recombinant human interleukin-2 (rhIL-2). This study presents a scalable and robust purification protocol for rhIL-2 derived from inclusion bodies (IBs) in Escherichia coli.

A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends.

Image restoration (IR) seeks to recover high-quality images from degraded observations caused by a wide range of factors, including noise, blur, compression, and adverse weather. While traditional IR methods have made notable progress by targeting individual degradation types, their specialization often comes at the cost of generalization, leaving them ill-equipped to handle the multifaceted distortions encountered in real-world applications. In response to this challenge, the all-in-one image restoration (AiOIR) paradigm has recently emerged, offering a unified framework that adeptly addresses multiple degradation types.

FusionINV: A Diffusion-Based Approach for Multimodal Image Fusion.

Infrared images exhibit a significantly different appearance compared to visible counterparts. Existing infrared and visible image fusion (IVF) methods fuse features from both infrared and visible images, producing a new "image" appearance not inherently captured by any existing device. From an appearance perspective, infrared, visible, and fused images belong to different data domains.

DAWN+: Wavelet-Based Image Deraining Meets Direction-Aware Attention and Mutual Representation.

The single-image deraining aims to restore clean scenes from rainy inputs by eliminating precipitation artifacts. Current methods often neglect the directional nature of rain streaks-a critical oversight that causes heterogeneous degradation, particularly in texture regions aligned with rain orientations. To address this issue and advance image deraining, we propose a novel direction-aware attention wavelet network (DAWN) for rain streaks removal.

Global, regional, and national trends in dengue incidence, mortality, and disability burden among adults aged 70 years and above: An analysis from the Global Burden of Disease Study 2021.

Background: Dengue remains a significant public health threat, yet the disease burden among the elderly has remained poorly quantified. This study aims to analyse the spatiotemporal trends in the dengue burden among the elderly at global, regional, and national levels from 1990 to 2021.

Methods: Data on the dengue burden were obtained from the Global Burden of Disease (GBD) 2021 study.

Lactylation and viral infections: A novel link between metabolic reprogramming and immune regulation.

Post-translational modifications (PTMs) regulate protein structure, function, and interactions, playing pivotal roles in cellular processes and disease progression. Lactate, a byproduct of the Warburg effect, accumulates excessively during viral infections and functions as a signaling molecule, disrupting mitochondrial antiviral-signaling protein activity and facilitating viral immune evasion. Lactylation, a recently identified PTM derived from lactate metabolism, links cellular metabolism and immune regulation by modulating gene expression and metabolic reprogramming.

Impact of first-line antiretroviral therapy choice on adverse drug reactions in children with HIV infection: a retrospective cohort study in southwest China.

Antiretroviral therapy (ART) is essential in managing children with human immunodeficiency virus (HIV) infection, but is often complicated by adverse drug reactions (ADRs), which can significantly impact therapeutic outcomes. This study investigates ADRs in 375 children with HIV receiving zidovudine (AZT)- or abacavir (ABC)-based ART regimens, using Kaplan-Meier, Cox regression analyses to evaluate ADR incidences, and propensity score matching (PSM) to address confounders. We found ADRs occurred in 21.

SARS-CoV-2-Induced PAPOLG Contributes to Inflammation Response by Stabilizing NF-κB mRNA.

One of the unknowns related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is the mechanism underlying the inflammatory response induced by the virus. Poly(A) polymerase gamma (PAPOLG) was previously shown to be upregulated during SARS-CoV-2 infection. The present study explored how PAPOLG affects the inflammatory reaction triggered by SARS-CoV-2.

ChatGPT-Assisted Deep Learning Models for Influenza-Like Illness Prediction in Mainland China: Time Series Analysis.

Background: Influenza in mainland China results in a large number of outpatient and emergency visits related to influenza-like illness (ILI) annually. While deep learning models show promise for improving influenza forecasting, their technical complexity remains a barrier to practical implementation. Large language models, such as ChatGPT, offer the potential to reduce these barriers by supporting automated code generation, debugging, and model optimization.

Dual-Level Cross-Modality Neural Architecture Search for Guided Image Super-Resolution.

Guided image super-resolution (GISR) aims to reconstruct a high-resolution (HR) target image from its low-resolution (LR) counterpart with the guidance of a HR image from another modality. Existing learning-based methods typically employ symmetric two-stream networks to extract features from both the guidance and target images, and then fuse these features at either an early or late stage through manually designed modules to facilitate joint inference. Despite significant performance, these methods still face several issues: i) the symmetric architectures treat images from different modalities equally, which may overlook the inherent differences between them; ii) lower-level features contain detailed information while higher-level features capture semantic structures.

Disentangled Pseudo-bag Augmentation for Whole Slide Image Multiple Instance Learning.

As the predominant approach for pathological whole slide image (WSI) classification, multiple instance learning (MIL) methods struggle with limited labeled WSIs. Although MIL has achieved notable progress with pseudo-bag-oriented augmentation methods, their effectiveness is often constrained by noisy pseudo-labels and low-quality pseudo-bags. To overcome these problems, we revisit the use of pseudo-bags for WSI data augmentation and propose a new pseudo-bag generation paradigm, dubbed DPBAug.

Learning Dynamic Prompts for All-in-One Image Restoration.

All-in-one image restoration, which seeks to handle multiple types of degradation within a unified model, has become a prominent research topic in computer vision. While existing deep learning models have achieved remarkable success in specific restoration tasks, extending these models to heterogenous degradations presents significant challenges. Current all-in-one methods predominantly concentrate on extracting degradation priors, often employing learned and fixed task prompts to guide the restoration process.

Integration of Transcriptomics and Metabolomics Reveals Metabolism Dysregulation in HIV-1-Infected Macrophages.

HIV-1 infection leads to metabolic changes in macrophages, yet a comprehensive understanding of its pathogenesis remains limited. To address this, we integrated transcriptomic and metabolomic analyses to uncover intracellular metabolic alterations in HIV-1-infected macrophages. We identified differentially expressed genes (DEGs) using RNA-sequencing, while metabolomic profiling was performed with UHPLC-QE-MS.

Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare.

The widespread integration of AI algorithms in healthcare has sparked ethical concerns, particularly regarding privacy and fairness. Federated Learning (FL) offers a promising solution to learn from a broad spectrum of patient data without directly accessing individual records, enhancing privacy while facilitating knowledge sharing across distributed data sources. However, healthcare institutions face significant variations in access to crucial computing resources, with resource budgets often linked to demographic and socio-economic factors, exacerbating unfairness in participation.

Pixel2Pixel: A Pixelwise Approach for Zero-Shot Single Image Denoising.

We propose Pixel2Pixel, a novel zero-shot image denoising framework that leverages the non-local self-similarity of images to generate a large number of training samples using only the input noisy image. This framework employs a compact convolutional neural network architecture to achieve high-quality image denoising. Given a single observed noisy image, we first aim to obtain multiple images with different noise versions.

MaeFuse: Transferring Omni Features With Pretrained Masked Autoencoders for Infrared and Visible Image Fusion via Guided Training.

In this paper, we introduce MaeFuse, a novel autoencoder model designed for Infrared and Visible Image Fusion (IVIF). The existing approaches for image fusion often rely on training combined with downstream tasks to obtain high-level visual information, which is effective in emphasizing target objects and delivering impressive results in visual quality and task-specific applications. Instead of being driven by downstream tasks, our model called MaeFuse utilizes a pretrained encoder from Masked Autoencoders (MAE), which facilities the omni features extraction for low-level reconstruction and high-level vision tasks, to obtain perception friendly features with a low cost.

Learning Lossless Compression for High Bit-Depth Volumetric Medical Image.

Recent advances in learning-based methods have markedly enhanced the capabilities of image compression. However, these methods struggle with high bit-depth volumetric medical images, facing issues such as degraded performance, increased memory demand, and reduced processing speed. To address these challenges, this paper presents the Bit-Division based Lossless Volumetric Image Compression (BD-LVIC) framework, which is tailored for high bit-depth medical volume compression.

Prognostic Significance of a 9-Gene Signature Related to Tertiary Lymphoid Structures in Male Stomach Adenocarcinoma Patients.

Background: The role of tertiary lymphoid structures (TLSs) in stomach adenocarcinoma (STAD) remains unclear despite their known potential effects on tumor progression and prognosis.

Methods: Data were collected from 362 patients with STAD from The Cancer Genome Atlas (TCGA) database. Using single-sample genomic enrichment analysis, TLSs were quantified based on a 9-gene signature, and the patients were categorized into TLS-signature high (TLS-high) and TLS-signature low (TLS-low) groups.

Optimization on cell lysis and capture process of human adenovirus type 5 produced in suspension HEK293 cells.

Adenovirus (Adv) is increasingly recognized for its significance in the fields of gene therapy and viral vector vaccines. The diverse applications in clinical trials and fundamental research necessitate the development of environmentally and economically sustainable purification processes that are straightforward and scalable for both academic and industrial contexts. In the initial segment of this study, we evaluated the lysis efficiency of polysorbate 20 (PS20) in comparison to polysorbate 80 (PS80).

Prognostic value of inflammatory markers for all-cause mortality in patients with acute myocardial infarction in the coronary care unit: a retrospective study based on MIMIC-IV database.

Background: Acute myocardial infarction (AMI) is prevalent and perilous, leading to mortality and disability in the coronary care unit (CCU). This paper was to verify the correlation of neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR), and systemic inflammation response index (SIRI) with all-cause mortality for AMI patients in the CCU.

Methods: Adult patients diagnosed with AMI and admitted to CCU were selected from the MIMIC-IV database.

Forsythoside B activates Siglec-14 to inhibit HIV-1 replication via the JAK1/STAT1 pathway.

The HIV/AIDS epidemic poses a severe global health challenge. While antiretroviral therapy is crucial, it has limitations, including high costs and resistance, and requires long-term use. Consequently, novel antiviral agents with unique structures and innovative mechanisms are needed for better management of HIV/AIDS.

Enhancing consistency and mitigating bias: A data replay approach for incremental learning.

Deep learning systems are prone to catastrophic forgetting when learning from a sequence of tasks, as old data from previous tasks is unavailable when learning a new task. To address this, some methods propose replaying data from previous tasks during new task learning, typically using extra memory to store replay data. However, it is not expected in practice due to memory constraints and data privacy issues.

Two Disaccharide-Bearing Polyethers, K-41B and K-41Bm, Potently Inhibit HIV-1 via Mechanisms Different from That of Their Precursor Polyether, K-41A.

The screening of novel antiviral agents from marine microorganisms is an important strategy for new drug development. Our previous study found that polyether K-41A and its analog K-41Am, derived from a marine Streptomyces strain, exhibit anti-HIV activity by suppressing the activities of HIV-1 reverse transcriptase (RT) and its integrase (IN). Among the K-41A derivatives, two disaccharide-bearing polyethers-K-41B and K-41Bm-were found to have potent anti-HIV-1 activity in vitro.