The soluble guanylate cyclase (sGC)/GMPc pathway plays an important in pulmonary arterial hypertension (PAH).

We investigated whether trans-4-methoxy-β-nitrostyrene (T4MN), a novel sGC stimulator, prevents development of monocrotaline (MCT)-induced HAP in rats.

At Day 0 (D0), rats were injected with MCT (60 mg/kg, sc). Control (CNT) rats received an equal volume of MCT vehicle only. MCT-injected rats were divided into four groups, which were treated orally once a day from D1 to D28 with one of the following: T4MN vehicle (MCT-V group), T4MN at 18.75 mg/kg (MCT-T4MN1 group), T4MN at 37.50 mg/kg (MCT-T4MN2 group), or sildenafil (SIL; 10 mg/kg) (MCT-SIL group).

Compared to the CNT group, MCT treatment induced a significant increase in heart weight to body weight, lung weight to body weight, and right ventricular systolic pressure but significantly reduced the maximum vasorelaxation (Rmax) induced by acetylcholine in aortic ring preparations. Indeed, MCT treatment increased the wall thickness of small pulmonary arterioles and reduced the mRNA levels of BMPR2, eNOS, and VEGF-A while it increased that of IL-6 in pulmonary tissue. All these effects of MCT, except those on mRNA expression levels in the lungs, were significantly reduced by preventive treatment with T4MN or SIL.

Taken together, the present study provided the first evidence that T4MN prevents the development of MCT-induced HAP in rats. Further mechanistic studies of these protective effects of T4MN are warranted.

Influenza imposes a heavy global public health burden, with current therapies limited by drug resistance and side effects. Natural products have antiviral potential, and intranasal delivery targets the respiratory tract. However, preclinical evidence lacks systematic evaluation, necessitating this review.

This study is a systematic review and meta-analysis conducted per the PRISMA guidelines. Studies meeting the inclusion criteria were retrieved and screened from the PubMed, Embase, Web of Science, China National Knowledge Internet, VIP Information Chinese Periodical Service Platform, China Biology Medicine Disc, and Wanfang Data Knowledge Service Platform databases. A meta-analysis was performed using R Studio software. The mean difference (MD) and relative risk (RR) were calculated using fixed effects or random effects models. Sources of heterogeneity, sensitivity, and publication bias were also explored.

A total of 23 studies were included. The results of the meta-analysis revealed that compared with the control treatment, intranasal natural products significantly increased the survival rate (RR = 3.47, 95% confidence interval [CI]: 2.44 to 4.93, p < 0.001) and reduced the viral titer (MD = -1.30, 95% CI: -2.27--0.32, p=0.0092 < 0.01) and lung index (MD =-0.10, 95% CI: -0.19--0.02, p = 0.015 < 0.05). In addition, intranasal natural products exerted regulatory effects on the body weight and inflammatory cytokines of influenza-infected mice.

The results show that intranasal natural products significantly increase survival and reduce the lung viral load in influenza models, with preliminary exploration of the underlying mechanisms and therapeutic potential. Owing to methodological limitations and heterogeneity, high-quality preclinical studies and standardized animal experiments are needed.

https://inplasy.com/, identifier INPLASY INPLASY2025120006.

Obstructive Sleep Apnea (OSA) is a prevalent syndrome characterized by intermittent hypoxemia and elevated risk of comorbidities, including cancer. In this context, the immune response may contribute to tumor evasion though immune checkpoints. Herein, we investigate the TIGIT immune checkpoint in OSA patients and its association with hypoxemia.

We recruited 94 severe OSA patients without cancer evidence and 92 control subjects to study the TIGIT receptors and their ligands in T cells and monocytes, respectively. Furthermore, we examined the role of hypoxemia - particularly the involvement of HIF-1α (hypoxia inducible factor-1α) - using a combination of in vitro models. Moreover, we evaluated the effect of one year of standard therapy with CPAP (continuous positive airway pressure) in OSA patients.

Our data suggests that the TIGIT expression increase on T cells from OSA patients and is associated with clinical indicators of hypoxemia. In vitro hypoxemia models confirm the role of HIF-1α in the upregulation of TIGIT expression. However, within the OSA cohort without evidence of cancer, we did not detect significant differences in TIGIT ligands, either in their membrane-bound or soluble forms. Importantly, one year of CPAP treatment reduce the TIGIT expression.

Hypoxemia in OSA patients increases TIGIT expression, contributing to a T-cell exhaustion phenotype. CPAP treatment reduces TIGIT expression on T lymphocytes. Altogether, these findings highlight the impact of hypoxemia effect on immune response, which may help explain the high cancer incidence in OSA patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is known as the etiological agent of coronavirus disease 2019 (COVID-19). Extrapulmonary manifestations of COVID-19 have gained increasing recognition as significant contributors to disease severity and long-term complications. The aim of this study is to investigate the neuroprotective properties of vaccines based on modified Vaccinia Virus Ankara (MVA) against SARS-CoV-2 infection in K18-hACE2 mice using different immunization protocols. Animals received PBS, vector, recombinant MVA expressing native (S) or stabilized (ST) SARS-CoV-2 spike protein, nucleocapsid protein (N) or both ST and N protein twice, followed by infection with SARS-CoV-2 four weeks later. In further experiments, mice were immunized only once and infected two days (Emergency experiment) or four weeks (Prime experiment) later. Both the control groups and the animals immunized with vaccines expressing only N-protein showed mild to moderate, lymphohistiocytic meningoencephalitis, microgliosis and numerous virus antigen-positive neurons in the brains and to a lesser extent in the retinas. Groups immunized four weeks prior to infection with vaccines containing viral spike protein showed no or minimal inflammatory changes and no neuroinvasion. Animals infected two days after immunization showed milder lesions than unvaccinated control groups.

This study aimed to investigate the effects of different concentrations of tobacco smoke on airway remodeling and airway inflammation in mouse models of asthma with distinct inflammatory phenotypes.

Asthmatic mouse models were established, including an eosinophilic asthma (EA) model via ovalbumin (OVA) sensitization/challenge, a neutrophilic asthma (NA) model via OVA combined with lipopolysaccharide (LPS, 10μg) sensitization/challenge, and a mixed granulocytic asthma (MGA) model via OVA combined with LPS (0.1μg) sensitization/challenge. These mice were then exposed to tobacco environments at concentrations of 100 mg/m³ and 800 mg/m³. Body weight changes and clinical symptoms were observed and recorded. Levels of inflammatory factors (TNF-α, IFN-γ, IL-6, IL-12, MCP-1, and IL-10) in bronchoalveolar lavage fluid (BALF) were measured by ELISA. Lung histopathological changes were assessed via hematoxylin and eosin (HE) staining, while bronchial goblet cell hyperplasia and mucus secretion were observed using periodic acid-Schiff (PAS) staining. Pulmonary fibrosis was assessed using Masson's trichrome staining.

Compared with the blank control group, mice in the EA, NA, and MGA model groups showed significantly reduced body weight growth rate (P < 0.001) and aggravated respiratory symptoms. Tobacco smoke exposure further exacerbated these symptoms in a concentration-dependent manner. Regarding inflammatory factors, the levels of pro-inflammatory factors (TNF-α, IFN-γ, IL-6, MCP-1, IL-12) in BALF were significantly elevated, while the level of the anti-inflammatory factor IL-10 was significantly decreased (P < 0.001) in the EA, NA, and MGA model groups. Tobacco smoke exposure significantly aggravated the airway inflammatory response in these different asthmatic models in a concentration-dependent manner. HE staining results demonstrated that tobacco smoke exposure worsened pathological injuries in the lung tissues of the different asthmatic models, including alveolar collapse, inflammatory cell infiltration, and epithelial cell necrosis, with the most severe damage observed in the 800 mg/m³ exposure group. PAS staining results revealed a significant increase in the percentage of goblet cells in the lung tissues of the different asthmatic models exposed to 800 mg/m³ tobacco smoke (P < 0.01, P < 0.05, and P < 0.01, respectively). Masson staining results showed that the degree of pulmonary fibrosis in mice from the EA model group, NA model group, and MGA model group exposed to an 800 mg/m³ tobacco environment was significantly increased (P < 0.001, P < 0.001, and P < 0.01, respectively).

Tobacco smoke exposure exacerbates clinical symptoms, airway inflammation (by up-regulating pro-inflammatory factors and down-regulating the anti-inflammatory factor IL-10), and lung pathological damage in asthmatic mice in a concentration-dependent manner.

Recurrent wheezing following acute lower respiratory tract infection (ALRTI) in early childhood is a common clinical problem and may be an early indicator of chronic respiratory diseases. Early identification of associated risk factors is essential for early intervention and prevention.

To investigate the risk factors and timing of recurrent wheezing episodes following ALRTI in children.

A retrospective cohort study was conducted among pediatric patients ≤ 5 years old admitted with ALRTI and wheezing at Naresuan University Hospital between July 1, 2020 and June 30, 2023. Participants were followed for 12 months. Data from electronic and paper records were analyzed using STATA 18.0. Multivariable logistic regression identified independent risk factors. Kaplan-Meier analysis and log-rank tests compared recurrence and non-recurrence groups through survival curves.

Significant predictors of recurrent wheezing included age 12-24 months [odds ratio (OR): 2.38; 95%CI: 1.50-3.78, P < 0.001], prematurity (OR: 1.66; 95%CI: 1.07-2.58, P = 0.024), allergic rhinitis (OR: 1.50; 95%CI: 1.04-2.17, P = 0.031), urban residency (OR: 1.68; 95%CI: 1.19-2.38, P = 0.003), eosinophilia (absolute eosinophil count > 500 cells/μL) (OR: 3.29; 95%CI: 1.57-6.91, P = 0.002), and prior lower respiratory tract infection (OR: 1.82; 95%CI: 1.20-2.76, P = 0.005). The median time to recurrence was 100 days (interquartile range: 43-125), varying across clinical and environmental subgroups.

Children with certain demographic and clinical features have higher recurrent wheezing risk after ALRTI, highlighting the need for closer monitoring and early preventive care.

Computational methods for therapeutic target discovery face challenges in integrating multi-scale biological data and predicting system-wide therapeutic effects. We present a computational framework that integrates single-cell transcriptomics, weighted gene co-expression network analysis (WGCNA), and computational perturbation simulation to systematically discover novel therapeutic targets. The framework constructs a knowledge graph comprising 29 896 nodes (23 530 genes and 6366 pathways) with 322 136 edges, integrating gene-gene, gene-pathway, and module relationships. Using perturbation simulation algorithms, we systematically explored 265 candidate targets, scoring each based on perturbation magnitude, module response, therapeutic effect, and statistical significance. Applied to single-cell RNA sequencing data from 38 patients (51 415 cells), the framework identified 66 novel therapeutic targets, including nine very high novelty targets. Computational validation demonstrates efficient scalability (knowledge graph construction: <5 min; 265 perturbation simulations: <2 min) and robust performance across different module sizes. This approach represents a novel computational method for systems-level therapeutic target discovery, with generalizable applications to other complex diseases.

Pregnant individuals were prioritised for COVID-19 research due to concerns about increased susceptibility and limited clinical trial data. This narrative review synthesises evidence on maternal infection, immunological adaptations, placental susceptibility, and antibody transfer following maternal SARS-CoV-2 vaccination. Symptomatic COVID-19 during pregnancy increases risks of severe outcomes, whereas vertical transmission remains rare. Placental pathology is characterised mainly by maternal vascular malperfusion and inflammation, with limited evidence of direct viral infection. Maternal vaccination-particularly with mRNA vaccines-induces robust IgG responses with efficient transplacental and lactational transfer, conferring passive neonatal protection. Key uncertainties include optimal vaccine timing, durability of neonatal immunity, and variant-specific responses. Strengthening standardised research and ensuring inclusion of pregnant individuals is essential for global maternal health policy.

The COVID-19 pandemic significantly disrupted global mobility patterns, with widespread population movement playing a key role in the transmission of the virus. In such a situation, Google introduced the Community Mobility Reports, which use anonymized and aggregated location data to monitor changes in movement across various location categories. These mobility trends provide important insights that help inform timely public health interventions and support data-driven decisions during and after the pandemic. This study aims to forecast human mobility trends in Thailand during the COVID-19 pandemic using data from Google's reports. Three forecasting models were applied: Facebook Prophet, ARIMA, and Feature Engineered XGBoost. The Granger Causality Test was used to examine the relationship between mobility patterns and COVID-19 case numbers across different phases of lockdown. The results indicated that Feature Engineered XGBoost demonstrated the highest overall accuracy in forecasting mobility trends across all six location categories. In conclusion, this study demonstrates the effectiveness of machine learning models in forecasting mobility movement across various location types while public health restrictions have been implemented. This underscores the importance of understanding mobility patterns as a key factor in disease transmission. The insights gained from this analysis can help formulate strategic and targeted mobility management policies and public health responses for future outbreaks, ultimately helping to contain the spread of disease more effectively.

Infectious disease spread is a multiscale process composed of within-host (biological) and between-host (social) drivers and disentangling them from each other is a central challenge in epidemiology. Here, we introduce VIBES, a multiscale modeling framework that explicitly integrates viral dynamics based on patient-level data with population-level transmission on a data-driven network of social contacts. Using SARS-CoV-2 as a case study, we analyze three emergent epidemic properties, namely the generation time, serial interval, and presymptomatic transmission. First, we established a purely biological baseline, thus independent of the reproduction number (R), from the within-host model, estimating a generation time of 6.3 d for symptomatic individuals and 43.1% presymptomatic transmission. Then, using the full model incorporating social contacts, we found a shorter generation time (5.4 d at R = 3.0) and an increase in presymptomatic transmission (52.8% at R = 3.0), disentangling the impact of social drivers from a purely biological baseline. We further show that as pathogen transmissibility increases (R from 1.3 to 6), competition among infectious individuals shortens the generation time and serial interval by up to 21% and 13%, respectively. Conversely, a social intervention, like isolation, increases the proportion of presymptomatic transmission by about 30%. Our framework also estimates metrics that are challenging to obtain empirically, such as the generation time for asymptomatic individuals (5.6 d; 95%CI: 5.1 to 6.0 at R = 1.3). Our findings establish multiscale modeling as a powerful tool for mechanistically quantifying how pathogen biology and human social behavior shape epidemic dynamics as well as for assessing public health interventions.