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Alveoli are the functional units of blood-gas exchange in the lung and thus are constantly exposed to outside environments and frequently encounter pathogens, particles and other harmful substances. For example, the alveolar epithelium is one of the primary targets of the SARS-CoV-2 virus that causes COVID-19 lung disease. Therefore, it is essential to understand the cellular and molecular mechanisms by which the integrity of alveoli epithelial barrier is maintained. Alveolar epithelium comprises two cell types: alveolar type I cells (AT1) and alveolar type II cells (AT2). AT2s have been shown to function as tissue stem cells that repair the injured alveoli epithelium. Recent studies indicate that AT1s and subgroups of proximal airway epithelial cells can also participate alveolar repair process through their intrinsic plasticity. This review discussed the potential mechanisms that drive the reparative behaviors of AT2, AT1 and some proximal cells in responses to injury and how an abnormal repair contributes to some pathological conditions.

Ferroptosis, a recently identified and iron-dependent cell death, differs from other cell death such as apoptosis, necroptosis, pyroptosis, and autophagy-dependent cell death. This form of cell death does not exhibit typical morphological and biochemical characteristics, including cell shrinkage, mitochondrial fragmentation, nuclear condensation. The dysfunction of lipid peroxide clearance, the presence of redox-active iron as well as oxidation of polyunsaturated fatty acid (PUFA)-containing phospholipids are three essential features of ferroptosis. Iron metabolism and lipid peroxidation signaling are increasingly recognized as central mediators of ferroptosis. Ferroptosis plays an important role in the regulation of oxidative stress and inflammatory responses. Accumulating evidence suggests that ferroptosis is implicated in a variety of cardiovascular diseases such as atherosclerosis, stroke, ischemia-reperfusion injury, and heart failure, indicating that targeting ferroptosis will present a novel therapeutic approach against cardiovascular diseases. Here, we provide an overview of the features, process, function, and mechanisms of ferroptosis, and its increasingly connected relevance to oxidative stress, inflammation, and cardiovascular diseases.

Background Drug-Drug interactions (DDIs) are a challenging problem in drug research. Drug combination therapy is an effective solution to treat diseases, but it can also cause serious side effects. Therefore, DDIs prediction is critical in pharmacology. Recently, researchers have been using deep learning techniques to predict DDIs. However, these methods only consider single information of the drug and have shortcomings in robustness and scalability. Results In this paper, we propose a multi-type feature fusion based on graph neural network model (MFFGNN) for DDI prediction, which can effectively fuse the topological information in molecular graphs, the interaction information between drugs and the local chemical context in SMILES sequences. In MFFGNN, to fully learn the topological information of drugs, we propose a novel feature extraction module to capture the global features for the molecular graph and the local features for each atom of the molecular graph. In addition, in the multi-type feature fusion module, we use the gating mechanism in each graph convolution layer to solve the over-smoothing problem during information delivery. We perform extensive experiments on multiple real datasets. The results show that MFFGNN outperforms some state-of-the-art models for DDI prediction. Moreover, the cross-dataset experiment results further show that MFFGNN has good generalization performance. Conclusions Our proposed model can efficiently integrate the information from SMILES sequences, molecular graphs and drug-drug interaction networks. We find that a multi-type feature fusion model can accurately predict DDIs. It may contribute to discovering novel DDIs.

There is an urgent need to create novel models using human disease-relevant cells to study severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) biology and to facilitate drug screening. Here, as SARS-CoV-2 primarily infects the respiratory tract, we developed a lung organoid model using human pluripotent stem cells (hPSC-LOs). The hPSC-LOs (particularly alveolar type-II-like cells) are permissive to SARS-CoV-2 infection, and showed robust induction of chemokines following SARS-CoV-2 infection, similar to what is seen in patients with COVID-19. Nearly 25% of these patients also have gastrointestinal manifestations, which are associated with worse COVID-19 outcomes 1 . We therefore also generated complementary hPSC-derived colonic organoids (hPSC-COs) to explore the response of colonic cells to SARS-CoV-2 infection. We found that multiple colonic cell types, especially enterocytes, express ACE2 and are permissive to SARS-CoV-2 infection. Using hPSC-LOs, we performed a high-throughput screen of drugs approved by the FDA (US Food and Drug Administration) and identified entry inhibitors of SARS-CoV-2, including imatinib, mycophenolic acid and quinacrine dihydrochloride. Treatment at physiologically relevant levels of these drugs significantly inhibited SARS-CoV-2 infection of both hPSC-LOs and hPSC-COs. Together, these data demonstrate that hPSC-LOs and hPSC-COs infected by SARS-CoV-2 can serve as disease models to study SARS-CoV-2 infection and provide a valuable resource for drug screening to identify candidate COVID-19 therapeutics. The use of lung and colonic organoid systems to assess the susceptibility of lung and gut cells to SARS-CoV-2 and to screen FDA-approved drugs that have antiviral activity against SARS-CoV-2 is demonstrated.

Background Protein–protein interactions are widespread in biological systems and play an important role in cell biology. Since traditional laboratory-based methods have some drawbacks, such as time-consuming, money-consuming, etc., a large number of methods based on deep learning have emerged. However, these methods do not take into account the long-distance dependency information between each two amino acids in sequence. In addition, most existing models based on graph neural networks only aggregate the first-order neighbors in protein–protein interaction (PPI) network. Although multi-order neighbor information can be aggregated by increasing the number of layers of neural network, it is easy to cause over-fitting. So, it is necessary to design a network that can capture long distance dependency information between amino acids in the sequence and can directly capture multi-order neighbor information in protein–protein interaction network. Results In this study, we propose a multi-hop neural network (LDMGNN) model combining long distance dependency information to predict the multi-label protein–protein interactions. In the LDMGNN model, we design the protein amino acid sequence encoding (PAASE) module with the multi-head self-attention Transformer block to extract the features of amino acid sequences by calculating the interdependence between every two amino acids. And expand the receptive field in space by constructing a two-hop protein–protein interaction (THPPI) network. We combine PPI network and THPPI network with amino acid sequence features respectively, then input them into two identical GIN blocks at the same time to obtain two embeddings. Next, the two embeddings are fused and input to the classifier for predict multi-label protein–protein interactions. Compared with other state-of-the-art methods, LDMGNN shows the best performance on both the SHS27K and SHS148k datasets. Ablation experiments show that the PAASE module and the construction of THPPI network are feasible and effective. Conclusions In general terms, our proposed LDMGNN model has achieved satisfactory results in the prediction of multi-label protein–protein interactions.

Iodomethane is a commonly used methylation reagent in organic synthesis. However, reports of acute iodomethane poisoning are rare. Herein, we report the successful treatment of a patient with acute iodomethane poisoning. During processing and production, owing to inadequate personal protection, the patient’s eyes were directly exposed to iodomethane gas, leading to blurred vision and magnetic resonance imaging findings consistent with toxic encephalopathy. Following a comprehensive diagnosis and treatment at our hospital, outpatient follow-ups and clinical assessments confirmed the patient’s recovery.

Long non-coding RNAs (lncRNAs) were confirmed to play important roles in human cancers. In this study, we explored the functional role of lncRNA double homeobox A pseudogene 8 (DUXAP8) in non-small-cell lung cancer (NSCLC). Real-time quantitative PCR (RT-qPCR) was used to detect DUXAP8 and microRNA-409-3p (miR-409-3p) expression. CCK-8, cell colony formation assay, and Transwell migration assay were performed to measure cell growth and migration, respectively. The expression of the relative proteins was detected by Western blot. Cell glycolysis was determined by glucose uptake, adenosine triphosphate (ATP) concentration, lactate generation, extracellular acidification rate and oxygen consumption rate assays. Bioinformatics analysis and dual-luciferase reporter assay were used to measure the interaction among DUXAP8, miR-409-3p, hexokinase 2 (HK2) and lactate dehydrogenase A (LDHA). In vivo, subcutaneous tumor formation assay was performed in the nude mice. DUXAP8 was highly expressed in NSCLC, while miR-409-3p was downregulated. High expression of DUXAP8 was positively related to the grade division and negatively associated with the 5-year survival rate of NSCLC patients. Downregulated DUXAP8 significantly suppressed cell growth, metastasis and glycolysis. Besides, DUXAP8 sponged miR-409-3p to promote HK2 and LDHA expression. DUXAP8 promoted cell viability, migration and glycolysis by regulating miR-409-3p/HK2/LDHA axis. Moreover, DUXAP8 downregulation markedly inhibited tumor growth in vivo. Our findings demonstrated that DUXAP8 served as an oncogene in the progression of NSCLC.

Predicting organic reaction feasibility and robustness against environmental factors is challenging. We address this issue by integrating high throughput experimentation (HTE) and Bayesian deep learning. Diverging from existing HTE studies focused on niche chemical spaces, in this work, our in-house HTE platform conducted 11,669 distinct acid amine coupling reactions in 156 working hours, yielding the most extensive single HTE dataset at a volumetric scale for industrial delivery. Our Bayesian neural network model achieved a benchmark for prediction accuracy of 89.48% for reaction feasibility. Furthermore, our fine-grained uncertainty disentanglement enables efficient active learning, reducing 80% of data requirements. Additionally, our uncertainty analysis effectively identifies out-of-domain reactions and evaluates reaction robustness or reproducibility against environmental factors for scaling up, offering a practical framework for navigating chemical spaces and designing highly robust industrial processes. Although theoretical advances in the reactivity of organic compounds have progressed rapidly, a complete understanding of the causal relationships between molecular structures and reaction outcomes based solely on first principles remains elusive. Here, the authors use an in-house HTE platform to conduct over 11,000 distinct acid amine couplings, with an accompanying Bayesian neural network model that gives a prediction accuracy of 89.48% for reaction feasibility.

Personalized federated learning (pFL) aims to address data heterogeneity by training client-specific models. However, it faces two critical challenges under few-shot conditions. First, existing methods often overlook the hierarchical structure of neural representations, limiting their ability to balance generalization and personalization. Second, recent approaches incorporate representation-level inductive biases that typically rely on rigid assumptions, such as fixed perturbation patterns or compact class clusters, making them vulnerable to distribution shifts in federated environments. To overcome these limitations, we propose pFedH2A, a novel hierarchical framework incorporating brain-inspired mechanisms, tailored for personalized federated learning in few-shot scenarios. First, we design a dual-branch hypernetwork (DHN) that employs two structurally distinct branches to generate aggregation weights. Each branch is biased toward capturing either low-level shared features or high-level personalized representations, enabling fine-grained personalization by mimicking the brain’s division of perceptual and representational processing. Second, we introduce a relation-aware module that learns an adaptive similarity function for each client, supporting few-shot classification by measuring whether a pair of samples belongs to the same class without relying on rigid prototype assumptions. Extensive experiments on public image classification datasets demonstrate that pFedH2A outperforms existing pFL baselines under few-shot scenarios, validating its effectiveness.

Compound schizonepeta fumigation lotion is a type of Chinese patent medicine for external use. It has the effect of dispelling wind, eliminating dampness, reducing swelling, and relieving pain. Clinically, it is used for anal fumigation and treatment of external hemorrhoids, anal fissures, and other diseases. Aconitum species are widely used in the field of traditional Chinese medicine. However, improper use and overdose can easily cause acute poisoning, leading to malignant arrhythmias, cardiogenic shock, and even death. This study retrospectively analyzed the clinical data of eight patients who were treated in our hospital from June 2023 to February 2024 after taking compound schizonepeta fumigation lotion by mistake. We report 8 cases of patients who took compound schizonepeta fumigation lotion by mistake. Most patients were illiterate and older, and poisoning was attributed to the misuse of an external drug. The affected patients exhibited different degrees of arrhythmia, with 2 receiving tracheal intubation, assisted ventilation, and hemoperfusion. Finally, all patients were clinically cured and subsequently discharged. A considerable number of accidental aconitine poisoning cases were reported over a short period, alerting clinicians to their growing incidence. Meanwhile, when prescribing medications, clinicians must provide clear instructions on proper usage and ensure that patients strictly adhere to the dosage and administration guidelines outlined in the drug instructions. Additionally, patients should be informed that any errors in medication administration require urgent medical attention. Health professionals should aim to improve public understanding of safe medication practices, promote health literacy.