To analyze the impact of the COVID-19 pandemic on food insecurity (FI) among families living in rural and urban areas of Brazil.

Cross-sectional and descriptive study based on the analysis of two nationally representative surveys conducted using probabilistic sampling by clusters in urban and rural areas of Brazil (2020 and 2022). FI was measured using the Brazilian Food Insecurity Scale. The households were classified as food secure, mild FI, or moderate/severe FI. Prevalence and 95% confidence intervals (95%CI) and analyses were performed in Stata 16 considering the respective sample weights (svy). Variations between the two surveys were analyzed by urban and rural area, and associations with gender and race/skin color.

The majority of households were located in urban areas (2020: 85.6% [n = 1,662]; 2022: 85.5% [n = 10,365]) compared to rural areas (2020: 14.5% [n = 518]; 2022: 85.5% [n = 2,382]). with regard to the characteristics of the household reference person, schooling level, being a formal worker and the per capita family income were higher among families from urban areas. Between 2020 and 2022, the proportion of severe levels of FI increased significantly more in households from rural areas. Despite the higher FI in rural areas, a variation of +54% was noted in urban areas, where the prevalence of moderate/severe FI increased from 19.4% (2020) to approximately 30% (2022). There were greater proportions of FI in households headed by men in urban areas (+75.1%) and mixed race/black people (+55.9%), while households headed by white people saw an improvement in FS.

The FI increased unequally between the rural and urban areas of Brazil during the COVID-19 pandemic. The results of this study reinforce the need to plan equitable public policies that debate the different vulnerability profiles aggravated by disparities as a way of guaranteeing food and nutritional security in post pandemic in Brazil.

High-frequency ventilation (HFV) is often used when conventional methods fail. Some studies suggest early HFV intervention might benefit infants with severe lung issues. This study compares early HFV at initial signs of respiratory distress to its later use when conventional ventilation fails.

We conducted a retrospective cohort study on infants born weighing less than 1500 grams and with a gestational age under 28 weeks from January 2017 to December 2020. A guideline for early HFV was introduced in 2019. We analyzed two periods: late HFV (2017-2018), where HFV was applied after conventional ventilation failure (respiratory rate >60 rpm and driving pressure >20 cmH2O) to maintain pH >7.2 and PCO2 <60 mmHg; and early HFV (2019-2020), initiated when mean airway pressure exceeded 10 cmH2O and driving pressure >14 cmH2O.

Of the 139 infants studied, 98 received early HFV, while 41 had late. Early and late HFV groups had similar gestational ages (26.1±2.2 vs. 26.4±2.4 weeks, p=0.47) and birth weights (777±255 vs. 797±260 grams, p=0.66). Early HFV reduced mechanical ventilation duration with a hazard ratio of 0.66 (0.45-0.97) and was not linked to increased risks of hypoxemia, hypercapnia, or neurological issues. Mortality rates increased with late HFV, AdjRR [1.64 (1.05; 2.60)].

Early HFV is effective for preterm infants with respiratory issues, reducing ventilation time and mortality. While results are promising, further randomized studies are essential to validate these findings and guide clinical practice.

The common cold is a frequent disease in humans and can be caused by a multitude of different viruses. Despite its typically mild nature, the high prevalence of the common cold causes significant human suffering and economic costs. Oftentimes, strategies to reduce contacts are used in order to prevent infection. To better understand the dynamics of this ubiquitous ailment, we develop two novel mathematical models: the common cold ordinary differential equation (CC-ODE) model at the population level, and the common cold individual-based (CC-IB) model at the individual level. Our study aims to investigate whether the frequency of population / individual exposure to an exemplary common cold pathogen influences the average disease burden associated with such a virus. Results derived for this situation can also be applied to other common cold pathogens. On the one hand, the CC-ODE model captures the dynamics of the common cold within a population, considering factors such as infectivity and contact rates, as well as development of specific immunity in the population. On the other hand, the CC-IB model provides a granular perspective by simulating individual-level interactions and infection dynamics, incorporating heterogeneity in contact rates. In both modeling approaches, we show that under specific parameter configurations (i.e., characteristics of the virus and the population), increased exposure can result in a lower average disease burden. While increasing contact rates may be ethically justifiable for low-mortality common cold pathogens, we explicitly do not advocate for such measures in severe illnesses. The mathematical approaches we introduce are simple yet powerful and can be taken as a starting point for the investigation of specific common cold pathogens and scenarios.

This study aimed to investigate the association between the weight-adjusted waist index (WWI), a novel obesity metric, and the prevalence of chronic obstructive pulmonary disease (COPD) in a nationally representative sample of U.S. adults, and to compare its predictive utility for COPD against conventional obesity indices.

This cross-sectional study utilized data from the 2017-2020 National Health and Nutrition Examination Survey (NHANES). COPD diagnosis was based on self-report. The association between WWI and COPD was investigated using multivariable logistic regression models, adjusting for key covariates including age, gender, race/ethnicity, smoking status, hypertension, and diabetes. Restricted cubic splines (RCS) were used to explore potential non-linear relationships. Receiver operating characteristic (ROC) curves were used to assess WWI's predictive performance. All statistical analyses were conducted using R software, accounting for the complex survey design and weighting.

This study comprised 3,111 participants, among whom the prevalence of COPD was 8.5%. The findings indicated a significant positive association between WWI and the prevalence of COPD (OR = 1.30, 95% CI: 1.02-1.66). When analyzed by quartiles, a significant positive dose-response relationship was observed (P for trend = 0.031). Furthermore, receiver operating characteristic (ROC) analysis revealed that WWI had significantly better predictive performance for COPD (Area Under the Curve [AUC] = 0.662) than conventional obesity indices.

Our findings suggest a significant positive association between WWI and the self-reported prevalence of COPD. WWI shows promise as a simple, non-invasive anthropometric tool that may aid in identifying individuals with higher odds of having COPD in clinical and public health settings.

Pneumonia is a major cause of mortality among children under five and adults over 65, especially in low-resource settings where access to skilled radiologists is limited. Accurate and early diagnosis is essential, but is often hindered by subjective interpretation and variability in its symptoms.

This study aims to develop a hybrid Artificial Intelligence (AI) based pneumonia diagnosis system that integrates Deep Learning (DL) confidence scores, DenseNet201 with Capsule Network (CapsNet), Mamdani-style fuzzy inference, and a dynamic symptom adjustment mechanism to enhance diagnostic accuracy, transparency, and clinical usability.

The system was evaluated using 17,229 labelled chest X-ray images across multiple cross-validation techniques: Stratified, k-fold, Bootstrap, and Monte Carlo methods, each with five dataset iterations or folds. DenseNet was used to extract spatial features, while CapsNet preserved spatial orientation and hierarchical relationships. A DL based confidence score was generated and used as a fuzzy membership input to support classification in borderline cases, where severity scores were nearly tied, and the confidence score guided the final decision. A dynamic adjustment algorithm further refined symptom severity by incorporating recent trends in patient data.

The DenseNet201 + CapsNet architecture achieved the highest performance in the 5th fold of stratified cross-validation, with a test accuracy of 99.01%. The model also demonstrated strong generalization, with a weighted precision, recall and F1-score of 0.9878, 0.9874, and 0.9876, respectively, across all classes. The paired t-test confirmed that the CapsNet-based approach outperformed traditional fully connected layers, and the fuzzy logic system effectively handled ambiguous cases using DL confidence. The dynamic membership mechanism showed strong adaptability for real-time symptom tracking.

This hybrid model offers a robust, interpretable, and clinically relevant decision-support tool for pneumonia diagnosis. It bridges high-performance AI with real-world medical decision-making, especially in settings with limited radiological expertise.

Despite national recommendations, antiviral prescribing in emergency departments (EDs) for children at higher risk of severe influenza, such as those younger than 5 years and those with specific underlying conditions, remains low.

To assess whether there were changes in antiviral prescribing for children at higher risk of severe influenza in academic pediatric EDs before the COVID-19 pandemic (2016-2020) vs the late pandemic period (2021-2023).

This multicenter, cross-sectional study included influenza-positive children younger than 18 years presenting to the ED at 1 of 7 US pediatric academic hospitals participating in the Centers for Disease Control and Prevention's New Vaccine Surveillance Network. The analysis focused on children at higher risk of severe influenza seen in the ED from December 1, 2016, to June 30, 2023.

High risk of severe influenza.

The primary outcome was antiviral prescribing. Children with influenza who met the criteria for higher risk of severe influenza were included. Antiviral prescribing practices were compared across the prepandemic and late pandemic periods. Mixed-effects logistic regression was used to identify factors associated with prescribing during the late pandemic period.

Of 3378 influenza-positive children (median [IQR] age, 3.9 [1.8-7.2] years), 2514 (74.4%; 1363 male [40.3%]) were classified as having higher risk of severe influenza during the prepandemic and late pandemic periods. Antiviral prescriptions decreased from 32.2% (622 of 1931 children) before the pandemic to 15.6% (91 of 583 children) in the late pandemic period, representing a 53% relative decrease. In the late pandemic period, symptom duration less than 2 days (adjusted odds ratio, 4.08; 95% CI, 2.49-6.71) and clinical influenza testing (adjusted odds ratio, 17.20; 95% CI, 4.08-72.37) were significantly associated with antiviral prescribing.

This multicenter, cross-sectional study of children with influenza in EDs found that, for children at higher risk of severe influenza illness, influenza antiviral prescribing decreased during the COVID-19 pandemic compared with prepandemic levels, despite unchanged treatment guidelines. Interventions are needed to support guideline-concordant prescribing in this population.

Infectious diseases continue to pose a significant global health challenge, necessitating innovative approaches for predicting outbreaks, detecting variants, conducting contact tracing, discovering new drugs and managing misinformation. Artificial intelligence (AI) has significantly supported work in these areas, particularly during the COVID-19 pandemic. However, the benefits of AI must be equitably distributed, and its use must be responsible and inclusive. As various nations implement AI regulations, the global nature of AI necessitates international collaboration to establish ethical guidelines and governance frameworks. In response to these needs, the Global South AI for Pandemic & Epidemic Preparedness & Response Network (AI4PEP) is leading a multinational effort across 16 countries to strengthen public health systems through responsible, Southern-led AI solutions. This opinion piece highlights AI4PEP's initiatives in Latin America and the Caribbean (LAC), examining the region's AI governance models and the challenges they present. By lowering barriers to AI adoption and fostering equitable access to AI-driven public health innovations, our network empowers researchers, healthcare professionals and policymakers in LAC to harness AI for infectious disease preparedness and response, ultimately improving health outcomes in low- and middle-income countries.

The study aim is to explore the experiences of patients who were admitted to intensive care units due to severe SARS-CoV2 infection and their perceptions regarding the health care they received. This is important to provide appropriate support to the patients and prepare organisations for future pandemics.

Qualitative investigation with phenomenological approach.

A semi-structured interview with 27 patients discharged from intensive care units was performed. The research was carried out from May to December 2021 in Italy. Participation was voluntary, and informed verbal consent was obtained from all participants after a full explanation of the study objectives.

Thematic analysis of participants' interviews revealed five core themes related to their ICU hospitalisation experiences and perceptions of care: (1) Quality of received care, (2) Critical care issues, (3) Personal protective equipment and patient-healthcare professional interaction, (4) Relationship with nurses and (5) Strategies to ensure communication. The results show that despite barriers due to safety devices, patients with COVID-19 felt supported and cared for by healthcare professionals in several aspects during hospitalisation. However, post-discharge care programs are needed to reduce the long-term effects of the disease and provide more patient-centered care in the intensive care units and during future outbreaks. The study results offer interesting insights for improving practice in intensive care units and patient care in the event of future pandemics.

Tetanus-diphtheria-acellular pertussis (Tdap) vaccination during pregnancy protects newborns from pertussis in the early months of life. Previous research indicated that Tdap vaccination during pregnancy may blunt Bordetella pertussis (B. pertussis)-specific antibody responses in infants following acellular (aP) and whole cell pertussis (wP) vaccination. However, the effect of preexisting antibodies on infants' responses to diphtheria toxoid (DT), tetanus toxoid (TT) and B. pertussis antigens is less well-understood. This study aims to quantify the effect of preexisting anti-DT, anti-TT, and B. pertussis-specific antibody levels from Tdap vaccination during pregnancy on infants' post-primary and post-booster responses to aP- and wP-containing vaccines. This retrospective analysis utilized data collected from a randomized controlled trial (NCT02408926) between 2015 and 2018. Pregnant women received Tdap vaccination between 27 and 36 weeks gestation. Their term infants were randomized to receive either a pentavalent DTwP-HB-Hib (wP) or a hexavalent DTaP-HB-Hib-IPV (aP) vaccine. Preexisting immunity was defined as the levels of anti-DT, anti-TT, anti-pertussis toxin (PT), anti-filamentous hemagglutinin (FHA), and anti-pertactin (PRN) IgG at month 2 (pre-vaccination). Blood samples were collected at birth, month 2, month 7 (following primary series vaccination at 2, 4, 6 months), month 18 (pre-booster), and month 19 (post booster). A total of 132 aP-vaccinated and 123 wP-vaccinated children completed this study. High levels of pre-vaccination antibody levels correlated with lower geometric mean ratios (GMRs) at post-primary and post-booster following wP- and aP-containing vaccination. This effect was observed consistently across all vaccine antigens following primary and booster doses. The clinical significance of this observation requires further investigation.

In the field of respiratory system diseases, the utilization of respiratory sounds in auscultation plays a crucial role in the specific disease diagnosis. However, during the process of auscultation, the personal experiences and environmental factors may affect the decision making, leading to diagnostic errors. Therefore, to accurately and effectively obtain and analyze respiratory sounds can be positively contribute to the diagnosis and treatment of respiratory system diseases.

Our aim was to develop an analytical method for the visualization and digitization of respiratory audio data, and to validate its capability to differentiate between various background diseases.

This study collected the respiratory sounds of patients admitted to the Department of General Medicine of Shanghai Changhai Hospital from June to December 2023. After strict screening according to the inclusion and exclusion criteria, a total of 84 patients were included. The research process includes using an electronic stethoscope to collect lung sounds from patients in a quiet environment. The patients expose their chests and lie flat. Sound data are collected at six landmark positions on the chest. The collected audio files are imported into an analysis tool for segmentation and feature extraction. Specific analysis methods include distinguishing heart sounds and respiratory sounds, segmenting respiratory sounds, determining the inspiratory and expiratory phases, and using a tool developed by the team for automatic segmentation encoding.

We standardized the respiratory sounds of 84 patients and segmented multiple respiratory cycles. Following the localization and segmentation of the respiratory cycles based on label information, we calculated the average and standard deviation of the amplitude features for each segment of the respiratory cycle. The results indicated differences among various diseases.

The robust algorithm platform is capable of segmenting the respiratory sounds into inhale and exhale phases accordingly, then comparing the difference between different background diseases. This method provides objective evidence for the auscultation of respiratory sounds and visual display of breath sounds.