Pulmonary fibrosis (PF) is a progressive lung disorder characterized by excessive deposition of extracellular matrix (ECM) in the alveoli, with irreversible fibrotic remodeling and destruction of alveolar structures. Clinically, PF is manifested by progressive dyspnea and a decline in lung function. These manifestations contribute to a poor prognosis and significantly diminish the quality of life of affected individuals. Current therapeutic options for PF remain limited, highlighting the urgent need to elucidate its molecular mechanisms in order to develop novel treatment strategies. Recent studies have revealed that autophagy, a lysosome-dependent cellular degradation process, and ferroptosis, an iron-dependent form of regulated cell death, engage in intricate crosstalk that may regulate PF pathogenesis. Autophagy maintains cellular homeostasis by mediating lysosome-dependent degradation, whereas ferroptosis is characterized by iron-dependent lipid peroxidation. The interplay between autophagy and ferroptosis plays a pivotal role in modulating fibrosis progression. However, the mechanistic interactions between autophagy and ferroptosis in PF remain poorly understood, particularly regarding their bidirectional crosstalk and their unique role in PF progression. This review aims to systematically synthesize the current understanding of autophagy-ferroptosis interactions in PF, thereby identifying potential therapeutic strategies and drug targets for PF treatment.

Abdominal aortic aneurysm (AAA) is a life-threatening vascular disorder characterized by localized dilation of the abdominal aorta and a high mortality rate once rupture occurs. At present, therapeutic approaches for AAA are primarily confined to operative repair. Thus, a comprehensive understanding of its pathogenesis is crucial for the development of novel pharmacological interventions. Although the underlying mechanisms remain incompletely defined, several critical pathological features have been identified, including inflammation, extracellular matrix degradation, and loss of vascular smooth muscle cells. In recent years, perivascular adipose tissue (PVAT), an active endocrine and paracrine organ surrounding blood vessels, has emerged as an essential regulator of various cardiovascular diseases. PVAT has been shown to remodel the extracellular matrix and influence vascular smooth muscle cell behavior by establishing a proinflammatory milieu, thereby affecting the structural integrity and reactivity of the arterial wall. This review summarizes recent research advances in PVAT-driven inflammation in AAA and highlights the translational potential for clinical applications, aiming to provide new insights for future research.

Postoperative acute kidney injury (AKI) and postoperative delirium (POD) are two of the most common complications after surgery. It is essential to understand their common mechanisms not only to prevent and predict the occurrence of these complications, but also to guide perioperative management and improve surgical outcomes. This review summarizes the recent literature focused on these postoperative complications and explores the relationship between AKI and POD. Clinical studies suggest that AKI and POD may coexist or occur sequentially during the perioperative period, particularly in susceptible patients undergoing major surgery. Some research findings indicate that the brain and the kidney exhibit bidirectional communication that is affected by systemic inflammation, drug accumulation, uremic toxins, vascular endothelial injury, and fluid volume redistribution during the perioperative period. AKI and POD may share pathophysiological mechanisms involving multiple interacting pathways, leading to impairment of renal and cerebral functions.

Pigs are important hosts in the ecology and cross-species transmission of influenza A viruses because their respiratory tract can contain both human-type α2,6-linked and avian-type α2,3-linked sialylated glycans. These glycan structures may allow pigs to support infection by influenza viruses of different host origins and can facilitate viral reassortment when coinfection occurs. Here, we used CRISPR/Cas9-mediated gene editing to disrupt the porcine ST6GAL1 and ST3GAL4 genes, which encode sialyltransferases involved in the biosynthesis of α2,6-linked and α2,3-linked sialylated glycans, respectively. We generated ST6GAL1/ST3GAL4 double-knockout Landrace pigs and observed markedly reduced Sambucus nigra agglutinin (SNA)- and Maackia amurensis lectin Ⅱ (MAL Ⅱ)-reactive sialylated glycans in the respiratory tract. Fibroblasts derived from the double-knockout pigs showed reduced susceptibility to H1N1, H3N2, and H9N2 influenza A virus infection in vitro. Transcriptomic analysis further revealed distinct gene-expression profiles between knockout and wild-type fibroblasts before and after H1N1 infection. Complementation of ST6GAL1 and ST3GAL4 restored sialylated glycan expression and increased viral susceptibility in cultured cells, supporting a causal contribution of these sialyltransferases to influenza A virus infection. Further in vivo challenge, transmission, safety, and independent-founder studies are required to determine whether this strategy can reduce influenza A virus infection or transmission in live pigs.

This study aimed to characterize frequency- and latency-dependent network alterations in migraine using magnetoencephalography (MEG)-based static and dynamic functional connectivity analyses during auditory stimulation. Thirty interictal patients with migraine and 30 healthy controls underwent whole-head MEG recordings during a lateralized auditory task. Connectivity was estimated using corrected amplitude-envelope correlation across seven predefined frequency bands spanning 2-120 Hz. Group differences were assessed using two-sided independent-samples t-tests, with Bonferroni correction for static analyses and false discovery rate correction for dynamic analyses. Static differences were confined to low-gamma (30-59 Hz), high-gamma (60-89 Hz), and ripple (90-120 Hz) bands. Static functional connectivity was significantly higher in patients with migraine (Cohen's d = 1.40-1.48 for the illustrated connections). No static connections survived correction in the delta, theta, alpha, or beta bands. Dynamic analyses revealed early gamma/ripple increases and additional low-frequency changes across time windows. These findings suggest altered auditory network organization in interictal migraine. Their potential relevance to sensory processing requires further testing in independent cohorts, including direct assessment of auditory symptoms.

The study assessed the taxonomic diversity and composition of gut microbiota in Arkhangelsk residents, Northwestern Russia, with varying metabolic statuses. A population-based cross-sectional "Know Your Heart" study (2015-2017, participants aged 35-69 years) included a health examination and gut microbiota analysis (n = 685). Participants were divided into four metabolic phenotypes: metabolically healthy non-obese (MHN), metabolically unhealthy non-obese (MUN), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO). Analyses were performed using RStudio software (version 4.2.0) with the vegan and phyloseq packages (version 1.42.0) for microbiota analysis. The sample was distributed across phenotypes as follows: MHN (47.7%), MUN (22.6%), MHO (10.4%), and MUO (19.3%). Beta-diversity analysis revealed significant differences in overall microbiome composition between MUO and MHN participants, while alpha-diversity did not differ significantly across phenotypes. The MHN group was characterized by a higher abundance of beneficial commensals such as Christensenellaceae R-7 group, Ruminococcaceae UCG-005, and Eubacterium xylanophilum group, which are taxa previously associated with metabolic health and longevity. In contrast, the MUO group showed an increased abundance of Streptococcus salivarius and Negativibacillus, taxa whose potential roles in metabolic health warrant further investigation. Blautia spp. emerged as a major hub in the microbiota of obese participants, consistent with its reported association with visceral fat. In conclusion, gut microbial composition differed across metabolic phenotypes, with the most notable taxonomic differences observed between MHN and MUO participants. Several taxa associated with metabolic health were enriched in the MHN group, whereas other taxa were more abundant in the MUO group. These findings suggest that distinct gut microbial signatures are associated with obesity and metabolic dysfunction, although the cross-sectional design precludes causal inference.

Glycated albumin (GA) is a promising glycemic biomarker that offers advantages over glycated hemoglobin (HbA1c), especially under conditions that affect red blood cell turnover. In type 2 diabetes (T2DM), disturbances in glucose and lipid metabolism often coexist. This study aimed to evaluate the discriminatory performance of GA, HbA1c, and their composite indices incorporating high-density lipoprotein cholesterol (HDL-C) in a Serbian cohort of participants with established T2DM and controls. A total of 124 adults (including both T2DM patients and healthy controls) were analyzed for GA, HbA1c, glucose, and lipid profiles. Composite indices, namely, GA/HDL-C and HbA1c/HDL-C, were calculated. Logistic regression and receiver operating characteristic (ROC) analyses were used to assess discriminatory performance. GA and HbA1c levels were significantly higher in T2DM patients, while HDL-C levels were lower. Both GA/HDL-C and HbA1c/HDL-C showed strong associations with T2DM. HbA1c exhibited the highest area under the ROC curve (0.966), followed by HbA1c/HDL-C (0.883), GA/HDL-C (0.859), and GA (0.855). The GA/HDL-C index exhibited particularly high specificity (92.06%). GA levels in the control group were consistent with reference values reported in other populations. To our knowledge, this is the first study to report GA data in a Serbian cohort. The findings highlight GA as a useful complementary marker for distinguishing individuals with established T2DM from controls. Composite indices, particularly GA/HDL-C and HbA1c/HDL-C, may provide complementary information for integrated glycemic-lipid assessment.