The diagnosis of the post-COVID condition is usually achieved by excluding other diseases; however, cognitive changes are often found in the post-COVID disorder. Therefore, monitoring and treating the recovery from the post-COVID condition is necessary to establish biomarkers to guide the diagnosis of symptoms, including cognitive impairment. Our study employs a prospected cohort and nested case–control design with mixed methods, including statistical analyses, interviews, and focus groups. Our main aim is to identify biomarkers (functional and structural neural changes, inflammatory and immune status, vascular and vestibular signs and symptoms) easily applied in primary care to detect cognitive changes in post-COVID cases. The results will open up a new line of research to inform diagnostic and therapeutic decisions with special considerations for cognitive impairment in the post-COVID condition.

Background Understanding humoral responses and seroprevalence in SARS-CoV-2 infection is essential for guiding vaccination strategies in both infected and uninfected individuals. Methods We determine the kinetics of IgM against the nucleocapsid (N) and IgG against the spike (S) and N proteins of SARS-CoV-2 in a cohort of 860 health professionals (healthy and infected) in northern Barcelona. We model the kinetics of IgG and IgM at nine time points over 13.5 months from infection, using non-linear mixed models by sex and clinical disease severity. Results Of the 781 participants who were followed up, 478 (61.2%) became infected with SARS-CoV-2. Significant differences were found for the three antibodies by disease severity and sex. At day 270 after diagnosis, median IgM(N) levels were already below the positivity threshold in patients with asymptomatic and mild-moderate disease, while IgG(N, S) levels remained positive to days 360 and 270, respectively. Kinetic modelling showed a general rise in both IgM(N) and IgG(N) levels up to day 30, followed by a decay whose rate depended on disease severity. IgG(S) levels increased at day 15 and remained relatively constant over time. Conclusions We describe kinetic models of IgM(N) and IgG(N, S) SARS-CoV-2 antibodies at 13.5 months from infection and disease spectrum. Our analyses delineate differences in the kinetics of IgM and IgG over a year and differences in the levels of IgM and IgG as early as 15 days from symptoms onset in severe cases. These results can inform public health policies around vaccination criteria. Funded by the regional Ministry of Health of the Generalitat de Catalunya (Call COVID19-PoC SLT16_04; NCT04885478)

Background: SARS-CoV-2 booster vaccination remains essential to prevent severe COVID-19, particularly in vulnerable populations such as older adults. This study evaluated the durability and dynamics of immune responses following booster vaccination(s) in >65-year-old individuals and examined their association with protection against new infections. Methods: Immune responses were evaluated at 3, 9, and 15 months post-booster, measuring SARS-CoV-2-specific IgG antibodies against spike [IgG(S)] and nucleocapsid [IgG(N)] proteins, neutralizing activity against the Omicron BA.2 variant, and cellular immunity. A subset of participants was tested before booster administration. Regression analyses examined the influence of clinical and immunological factors—including a bivalent fourth dose—on infection risk over time. Results: Booster vaccination significantly enhanced IgG(S) and neutralizing capacity, peaking at 3 months. Although a decline was observed by 9 months, responses remained above baseline. Individuals with prior SARS-CoV-2 infection exhibited higher IgG(S) levels and neutralizing titers, and significantly lower reinfection rates (15%), compared to uninfected individuals. A fourth vaccine dose further increased IgG(S) levels. While neutralizing capacity was not consistently enhanced by the fourth dose, recipients experienced a lower rate of new infections. Immune trajectory analyses revealed that breakthrough infections elicited strong humoral responses comparable to those seen in previously infected individuals, highlighting the role of hybrid immunity. Conclusions: In older adults, booster vaccination induces durable immune responses, with hybrid immunity offering enhanced protection. A fourth dose boosts antibody levels and reduces infection risk, supporting its use in this high-risk group. Continued monitoring is needed to determine the long-term effectiveness of boosters, particularly against emerging variants.

Background: As the vaccination campaign against COVID-19 progresses, it becomes crucial to comprehend the lasting effects of vaccination on safeguarding against new infections or reinfections. Objective: This study aimed to assess the risk of new SARS-CoV-2 infections based on the number of vaccine doses, prior infections, and other clinical characteristics. Methods: We defined a cohort of 800 health care workers in a 24-month study (March 2020 to December 2022) in northern Barcelona to determine new infections by SARS-CoV-2. We used extended Cox models, specifically Andersen-Gill (AG) and Prentice-Williams-Peterson, and we examined the risk of new infections. The AG model incorporated variables such as sex, age, job title, number of chronic conditions, vaccine doses, and prior infections. Additionally, 2 Prentice-Williams-Peterson models were adjusted, one for those individuals with no or 1 infection and another for those with 2 or 3 infections, both with the same covariates as the AG model. Results: The 800 participants (n=605, 75.6% women) received 1, 2, 3, and 4 doses of the vaccine. Compared to those who were unvaccinated, the number of vaccine doses significantly reduced (P<.001) the risk of infection by 66%, 81%, 89%, and 99%, respectively. Unit increase in the number of prior infections reduced the risk of infection by 75% (P<.001). When separating individuals by number of previous infections, risk was significantly reduced for those with no or 1 infection by 61% (P=.02), and by 88%, 93%, and 99% (P<.001) with 1, 2, 3, or 4 doses, respectively. In contrast, for those with 2 or 3 previous infections, the reduction was only significant with the fourth dose, at 98% (P<.001). The number of chronic diseases only increased the risk by 28%‐31% (P<.001) for individuals with 0‐1 previous infections. Conclusions: The study suggests that both prior infections and vaccination status significantly contribute to SARS-CoV-2 immunity, supporting vaccine effectiveness in reducing risk of reinfection for up to 24 months after follow-up from the onset of the pandemic. These insights contribute to our understanding of long-term immunity dynamics and inform strategies for mitigating the impact of COVID-19.

Diabetes mellitus (DM) has emerged as one of the leading global health concerns due to its increasing prevalence and significant impact on healthcare systems

Background/Objectives: With rising diabetes rates, early detection of complications such as diabetic retinopathy (DR), a leading cause of visual impairment, is crucial. Incorporating DR screening into primary care has shown positive results, and integrating technological advances and artificial intelligence (AI) into these processes offers promising potential. The overall study aims to evaluate the agreement between primary care physicians, ophthalmologists, and an AI system in DR screening and referral decisions within a real-world primary care setting. Methods: In this brief report, we present the study protocol and provide an initial overview and description of our sample. A total of 1517 retinographies, obtained by a non-mydriatic retinal camera, were retrospectively collected from 301 patients with diabetes. Results: Primary care physicians referred 34.5% of the patients to ophthalmology, primarily due to opacification, suspicion of DR, or other retinal diseases. Overall, 13.62% of the participants were suspected of having DR, with 9.63% having a definitive diagnosis. Conclusions: These initial descriptive findings will be further explored in the next phase of the study through the analysis of concordance between primary care physicians, the AI-based software, and ophthalmology specialists. Future results are expected to provide valuable insights into the reliability of DR screening across different evaluators and support the integration of effective DR screening strategies into real-world clinical practice.