This study assessed the factors associated with the use of alcohol among Brazilian graduate students. A cross-sectional survey was conducted with 5,112 graduate students across Brazil between May and July 2022, using an electronic form on the Research Electronic Data Capture (REDCap) platform. The ordinal logistic regression model with cumulative logits was used for the analysis in the R software. There were higher odds of alcohol use among graduate students facing significant difficulties in accessing the internet (OR = 2.25; CI = 1.51-3.38; p = 0.001), those spending more than three hours daily on social media (OR = 1.39; CI = 1.15-1.66; p = 0.005), using various medications (OR = 1.28; CI = 1.07-1.53; p = 0.008) including those for COVID-19 protection (OR = 1.37; CI = 1.00-1.88; p = 0.05) and psychotropic drugs without prescription (OR = 1.91; CI = 1.26-2.89; p = 0.0023), as well as tobacco (OR = 2.93; CI = 2.28-3.77; p < 0,001), cocaine (OR = 4.29; CI = 2.46-7.48; p < 0.001), and cannabis (OR = 2.19; CI = 1.78-2.71; p < 0,001). Being female (OR = 0.66; CI = 0.55-0.79; p < 0.001) and attending online classes equivalent to in-person ones (OR = 0.66; CI = 0.54-0.81; p < 0.001) were protective factors.

To investigate food consumption according to the degree of processing and associated factors in the state of Piauí, a population-based cross-sectional study with multi-stage cluster sampling was carried out. Sociodemographic and economic variables, as well as food consumption, were investigated using Pearson's chi-square test and prevalence ratio. Among the 2,104 participants (≥2 years of age), the highest consumption of natural and minimally processed foods was observed in people living in the capital (98.6%; 95%CI: 96.5-99.4), aged 20 to 59 years (94.1%; 95%CI: 91.9-95.7) and ≥60 years (92.6%; 95%CI: 88.1-95.5), black (95.0%; 95%CI: 90.7-97.4), with medium (92.9%; 95%CI: 89.0-95.5) and higher (95%; 95%CI: 91.2-97.2) education, family income >2 minimum wages (95.8%; 95%CI: 92.3-97.2) and who did not receive government benefits (95.2%; 95%CI: 83.4-91.0). In contrast, individuals aged 2 to 19 years (41.7%; 95% CI: 32.3-51.7) showed significantly higher consumption of ultra-processed foods (UPF). These results highlight the need for public policies that promote healthy eating from childhood and expand access to quality foods, especially among the most economically vulnerable groups.

Dengue remains a major mosquito-borne disease in China. The COVID-19 pandemic and associated non-pharmaceutical interventions (NPIs) profoundly altered human mobility and public health responses, potentially reshaping dengue transmission dynamics. However, comprehensive nationwide evidence on their long-term epidemiological impact is limited.

This study aimed to evaluate changes in dengue epidemiology in China before, during, and after the COVID-19 pandemic, with a focus on temporal periodicity, importation and indigenous transmission, counterfactual deviations from pre-pandemic trends, and spatial risk redistribution.

We conducted a nationwide retrospective spatiotemporal study using reported dengue cases in China from 2015 to 2024. Epidemiological characteristics were compared across pre-pandemic, pandemic, and post-pandemic phases. Morlet wavelet analysis was used to assess temporal periodicity. A SARIMA model fitted to pre-pandemic data generated counterfactual forecasts for 2020-2024. Spatial patterns were evaluated using provincial incidence mapping, Global Moran's I, and Local Indicators of Spatial Association.

A total of 87 182 dengue cases were reported, with major peaks in 2019 and 2024. Imported cases declined sharply during 2020-2022 and rebounded after 2023, whereas indigenous transmission became increasingly dominant. Annual periodicity weakened during the pandemic but re-emerged after 2023. Observed incidence during 2020-2022 was frequently below counterfactual expectations, while substantial positive deviations appeared from mid-2023 and intensified in 2024. Post-pandemic dengue risk re-emerged in South and Southwest China and expanded inland, with weaker provincial clustering.

COVID-19 and related NPIs substantially reshaped dengue epidemiology in China, suppressing transmission during 2020-2022 but followed by a marked post-pandemic resurgence, increasing indigenous transmission, disrupted but re-emerging seasonality, and broader inland spread.

Influenza infection is associated with an increase of cardiovascular events and hospitalizations due to cardiovascular disease. Influenza vaccination is in turn associated with a reduction or mitigation of cardiovascular complications of influenza. For this reason, influenza vaccination represents an additional pillar for cardiovascular prevention and protection. Recent randomized studies conducted in a very large European population have shown that high-dose influenza vaccination provides significant advantages when compared to standard-dose vaccination. These new data derived from the FLUNITY-HD pooled analysis strongly suggest that high-dose vaccination may be appropriate in elderly patients as well as in patients with cardiovascular disease in whom strengthening the immune response may provide better protection. The present consensus document has been promoted by five Italian cardiovascular or internal medicine scientific societies committed to promote cardiovascular prevention with the aim to update current knowledge on influenza vaccination and promote call-to-action initiatives in Italy to enhance the use of influenza vaccination and overcome current hesitancy and relatively low uptake in the population at risk.

BACKGROUNDRespiratory viruses are a major cause of morbidity and mortality worldwide. Their circulation patterns were markedly altered by the COVID-19 pandemic. The Information System for Infection Surveillance in Catalonia (SIVIC), established in 2022, provides continuous, population-representative sentinel surveillance integrating syndromic and microbiological data.AIMOur aim was to describe the timing, regularity and overlap of epidemics across 18 respiratory viruses using microbiologically confirmed data from SIVIC.METHODSWe conducted an observational, retrospective study using weekly data from the SIVIC sentinel network in Catalonia between October 2022 and September 2025. We calculated weekly positivity for each virus and defined epidemic start, peak and end based on week-to-week positivity growth and decline. Mean timing and variability across epidemiological years were estimated after applying a 3-week moving average to compensate for fluctuations.RESULTSAmong 32,782 analysed samples, 21,278 (64.9%) tested positive for at least one virus. Distinct and recurrent epidemic patterns were observed. Respiratory syncytial virus and influenza viruses exhibited consistent sequential peaks in late autumn and early winter, while rhinovirus and enterovirus showed bimodal peaks in autumn and spring. Some viruses (human adenovirus, human parainfluenza 1 and 4, human coronavirus NL63) circulated year-round at low levels. In contrast, SARS-CoV-2 showed a persistent summer epidemic pattern.CONCLUSIONMost respiratory viruses in Catalonia have re-established regular seasonal dynamics, with SARS-CoV-2 displaying a distinct summer peak. Year-round sentinel surveillance for timely monitoring of viral circulation can help anticipate the timing and sequence of epidemics peaks.

Silicosis is an irreversible, fibrotic occupational lung disease caused by the chronic inhalation of respirable crystalline silica dust, and persists as a significant source of morbidity among sandstone quarry workers in Rajasthan, India. Despite the prevalence of silicosis, there has been no comprehensive assessment of the symptom load and occupational risk factors of Chronic Respiratory Symptoms among certified silicosis patients in the Balesar sandstone area.

A community-based cross-sectional study was conducted in 355 certified silicosis patients (mean age 50.4 ± 10.6 years; all were exclusively male) who had resided for a minimum of 2 years in Balesar Block, Jodhpur, Rajasthan. Respiratory symptoms were evaluated using the ATS-DLD questionnaire. Predictors with a univariate chi-square p < 0.25 were included in a multivariable binary logistic regression model (IBM SPSS v25) to determine the independent predictors of composite Chronic Respiratory Symptoms status.

The findings showed that 286/355 patients, 80.6%, had composite Chronic Respiratory Symptoms. The most prevalent symptoms were morning cough (80.0%), morning phlegm production (79.7%), and dyspnoea attributable to cardiopulmonary impairment (78.0%); wheeze was noted by 51.3% and nocturnal breathlessness by 30.4%. The average age of symptom start was 40.4 ± 8.6 years, compared to 50.4 ± 10.6 years at certification, indicating a delay of about 10 years. In logistic regression, a conventional 8-h daily continuous work shift emerged as the most potent independent predictor of chronic respiratory disease symptoms (AOR 6.53, 95% CI 1.74-24.51, p = 0.005), while residing 6-10 km from the quarry also shown a significant correlation (AOR 2.74, 95% CI 1.07-6.99, p = 0.035). The associations between age at diagnosis (31-50 years) and illiteracy were statistically significant in the model (p = 0.021 and p = 0.035, respectively).

This is the first study conducted on certified silicosis patients, illustrating that 80.6% of Balesar Block's certified silicosis patients had active Chronic Respiratory Symptoms. The independent predictors were continuous standard-shift exposure and intermediate residential distance from quarries, strengthening the rationale for stricter daily exposure limits, mandatory dust suppression, and initial active case diagnosis in this occupational group.

Severe pneumonia remains a major cause of mortality in critically ill patients, with severe community-acquired pneumonia representing a clinically important and representative scenario. This review focuses primarily on severe community-acquired pneumonia as the clinical context, in which outcomes are determined by the interplay between pathogen burden and heterogeneity of host responses. Beyond pathogen-directed therapies, severe pneumonia is frequently characterized by dynamic and partially overlapping states of hyperinflammation, immune dysfunction, and subsequent immunosuppression. Different host conditions, including immunocompromised states, advanced age, post-sepsis critical illness, and pediatric populations, further increase the complexity and heterogeneity of immune responses. These immune states can be evaluated through conventional inflammatory biomarkers, cellular immune parameters, cytokine profiling, and emerging high-dimensional profiling technologies. This review summarizes the immunopathological basis of severe pneumonia and presents an integrated translational framework encompassing immune monitoring, immune phenotype characterization, risk stratification, evidence-based intervention selection, and serial reassessment. Importantly, immune phenotypic classification should be distinguished from prognostic prediction: models capable of predicting mortality risk do not necessarily identify biologically meaningful immune phenotypes that are amenable to therapeutic intervention. Current evidence supports the use of immune biomarkers primarily as complementary tools for risk stratification and hypothesis-driven patient selection, whereas most immune-targeted interventions remain investigational. Future clinical implementation requires standardized assays, phenotype definitions that incorporate both pathogen characteristics and disease trajectory, external validation, and prospective clinical trials demonstrating improvements in clinically meaningful patient outcomes.

The One Health approach recognizes the interconnectedness of human, animal, and environmental health in addressing the growing threat of zoonotic diseases. This study aims to establish an integrated One Health surveillance system for the early detection and monitoring of zoonotic respiratory viral infections at the human-animal-environment interface in Tiruvallur District, Tamil Nadu, India.

The study aims to establish a longitudinal surveillance system for the early detection of emerging and re-emerging zoonotic respiratory viral infections across livestock animals, animal handlers, and the farm environment interface; and ii) determine the occurrence and distribution of selected zoonotic respiratory viruses, and assess the influence of seasonal and climatic factors on their occurrence and transmission.

Following sensitization of farm owners and animal handlers through the Animal Husbandry Department, 40 livestock farms from four selected blocks of Tiruvallur District, rearing cattle, buffalo, goats, sheep, swine, and poultry, will be enrolled in each surveillance round. Animal nasal swabs, respiratory samples from animal handlers, and environmental samples (air and wastewater) will be collected. Field surveillance will be carried out by a study team comprising a veterinarian and field investigators. Sixteen surveillance rounds (eight rounds annually) will be conducted over 2 years, covering two complete climatic cycles. Approximately 7,360 animal swab samples, 960 samples from animal handlers, and 960 environmental samples will be collected in each study phase. Animal samples will be screened using a pooled testing strategy followed by real-time RT-PCR for the detection of selected zoonotic respiratory viruses, while human samples will be tested individually.

The study is expected to establish a robust One Health surveillance framework and generate baseline epidemiological data on zoonotic respiratory viruses circulating among livestock, animal handlers, and the farm environment in Tamil Nadu. The findings will strengthen integrated surveillance, support early warning systems, facilitate timely veterinary and public health responses, and provide evidence to inform future One Health policies and preparedness strategies for emerging zoonotic respiratory viral infections.

Non-invasive ventilation (NIV) failure is associated with increased mortality, underscoring the need for early predictors to guide timely intervention.

To explore the predictive value of ICU nurse-measured tidal-breathing maximal inspiratory pressure (MIP), maximal expiratory pressure (MEP), peak inspiratory flow (PIF) and peak expiratory flow (PEF) for NIV failure.

This prospective observational study enrolled patients receiving NIV as first-line therapy in a Chinese ICU. ICU nurses measured tidal-breathing MIP, MEP, PIF and PEF before NIV initiation. NIV failure was defined as the requirement of intubation.

Among 102 enrolled patients, 17 (17%) experienced NIV failure. The failure group showed significantly higher respiratory parameters: MIP (16.5 ± 5.4 vs. 10.2 ± 3.7 cmH₂O), MEP (17.6 ± 7.1 vs. 11.0 ± 4.6 cmH₂O), PIF (59 ± 21 vs. 38 ± 14 L/Min) and PEF (57 ± 23 vs. 37 ± 15 L/Min) (All p < 0.001). AUCs for predicting NIV failure were 0.83 (95% CI: 0.74-0.89) for MIP, 0.81 (95% CI: 0.72-0.88) for MEP, 0.79 (95% CI: 0.70-0.87) for PIF and 0.77 (95% CI: 0.68-0.85) for PEF. The adjusted odds ratio for NIV failure was 1.42 (95% CI: 1.10-1.82, p < 0.001) per one-unit increase in MIP, 1.30 (95% CI: 1.03-1.65, p = 0.027) per one-unit increase in MEP, 1.05 (95% CI: 0.99-1.11, p = 0.076) per one-unit increase in PIF and 1.03 (95% CI: 0.99-1.09, p = 0.180) per one-unit increase in PEF.

Nurse-performed bedside tidal-breathing MIP, MEP, PIF and PEF may predict NIV failure. Elevated baseline MIP and MEP may increase the risk of NIV failure in critically ill patients.

Nurse-conducted bedside tidal-breathing MIP and MEP measurement at NIV initiation supports rapid risk stratification. Identifying patients with elevated parameters enables intensified monitoring, timely ventilator adjustment and early intubation preparation, avoiding delayed interventions and optimising patient outcomes.

To evaluate changes in falls and medication-related quality indicators (QIs) including high sedative load, antipsychotic use and anti-anxiety or hypnotic medications in long-term care facilities (LTCFs), and to examine their alignment with major national reforms and COVID-19 in New South Wales, Australia.

A repeated cross-sectional study was conducted using electronic health data from 27 LTCFs (8298 residents). From Jul 2014 to Jun 2022, quarterly prevalence estimates of four QIs were evaluated adjusting for potential confounders. Joinpoint regression analysis was used to identify QI trend changes. Average quarterly percentage changes for the study period were estimated.

The quarterly prevalence of residents who had any falls (28.5%-33.3%) and injurious falls (14.5%-18.6%) remained stable during the study period. The prevalence of residents who had falls requiring hospitalisation varied (3.8%-9.1%) and average quarterly percentage increase was 1.01% (95% confidence interval (CI) 0.30, 2.08). There were changes in quarterly prevalence of residents experiencing high sedative load (28.1%-32.1%), using antipsychotic(s) (8.9%-19.3%), and anti-anxiety and hypnotic medication(s) (11.4%-19.0%). Average quarterly percentage decreases were at 0.17% (95% CI 0.01, 0.35) in residents experiencing a high sedative load, 2.14% (95% CI 1.90, 2.41) for antipsychotics use and 1.63% (95% CI 1.45, 1.82) for anti-anxiety or hypnotic medication use. Regarding trend changes, medication-related QIs decreased from Q3 or Q4 2018, or Q1 2019, around when the Royal Commission was established. Medication-related QIs continued to decline when restrictions on risperidone were applied and COVID-19 started.

Medication-related QIs decreased during the study period, especially after national reforms.