Ligita Jancoriene

Background: Future health professionals are expected to recommend vaccines, communicate with patients about vaccine safety, and support clinical trial literacy and recruitment during public health emergencies. However, their own willingness to participate in vaccine trials, and the barriers that shape that willingness, remain incompletely characterized across Europe. Objective: This study aimed to assess willingness to participate in future vaccine trials among European students in health-related disciplines and to describe their perceived barriers, motivations, vaccine attitudes, and preferred sources of health information. Methods: We conducted an open, multilingual, cross-sectional web survey within the VACCELERATE network from June to November 2023. National coordinators distributed the survey through institutional mailing lists, student associations, and program channels in participating European countries. Eligible participants were students aged 18 years or older enrolled in medicine, dentistry, nursing, pharmacy, psychology, biomedical sciences, physiotherapy, occupational therapy, or other health-related programs. The questionnaire assessed demographic characteristics, primary sources of health information, vaccine attitudes, willingness to participate in future vaccine trials, barriers among unwilling or unsure respondents, and motivations for participation. Analyses were descriptive, and denominators varied by item because nonmandatory questions could be skipped. Results: Of 1381 eligible respondents, 1192 (86.3%) answered the primary willingness question: 32.9% (n=392) were willing to participate in a future vaccine trial, 38.8% (n=463) were unsure, and 28.3% (n=337) were unwilling. Among the 769 respondents who were unwilling or unsure and selected barriers, the most frequent concerns were potential side effects (n=574, 74.6%) and not wanting to be a “test subject” (n=276, 35.9%). Among 1289 respondents included in the motivation analysis, commonly selected motivators included knowing the risks and side effects involved (n=575, 44.6%), becoming part of the solution to an important health problem (n=584, 45.3%), and knowing that personal safety would be protected (n=500, 38.8%). Conclusions: In this multicountry European sample of health professional students, only one-third reported willingness to participate in future vaccine trials, while the largest group was unsure. The findings suggest that trial-readiness initiatives for future health professionals should prioritize transparent communication about safety monitoring, risks and side effects, participant protections, and the practical conduct of vaccine trials. Because recruitment used an open convenience approach, the results should be interpreted as exploratory and hypothesis generating rather than representative of all European health professional students.

Background: Since its emergence, the COVID-19 infection has led to significant morbidity and mortality worldwide. Early identification of patients at risk for severe disease is essential for more effective triage, timely therapeutic intervention, and optimal resource allocation. Differences in population characteristics may contribute to variability in disease outcomes, which emphasizes the need for regional-level data, especially from underrepresented regions. The main aim of this study was to identify the demographic, clinical, and laboratory predictors of severe COVID-19, defined as the need for oxygen therapy, in Lithuania. Materials and methods: We conducted an ambispective observational cohort study at Vilnius University Hospital Santaros Klinikos in Vilnius, Lithuania, from March 2020 to December 2021. Adult patients with a confirmed diagnosis of COVID-19 and hospitalized longer than 24 hours were included in this study. Data were collected from the electronic medical records and patient interviews. To identify predictors of severe COVID-19 course, a multivariable binary logistic regression model was performed. Results: Among 495 patients, 52.9% were male, the median age was 55 years, and 61.2% had at least one underlying condition. The most common symptoms on admission were malaise (77.1%), subfebrile fever (65.9%), and cough (69.7%). CRP demonstrated the highest predictive value for severe COVID-19 (AUC = 0.84), followed by LDH (AUC = 0.80). Older age (OR 1.04 per year, 95% CI 1.00–1.08), obesity (OR 3.55, 95% CI 1.35–9.30), lymphopenia (OR 3.70, 95% CI 1.37–9.99), higher LDH (OR 1.008, 95% CI 1.00–1.01) and CRP (OR 1.021, 95% CI 1.01–1.04) levels were identified as the strongest predictors for severe COVID-19 disease course. Conclusion: Older age, obesity, lymphopenia, and higher CRP and LDH were associated with developing severe COVID-19 disease, indicating that combining patient history and laboratory parameters can provide a practical risk stratification approach to help clinicians identify high-risk patients early upon hospitalisation.

Background and Objectives: Healthcare workers are at heightened risk of SARS-CoV-2 infection. Understanding the duration and protective value of vaccine-induced immunity is critical to inform booster strategies. This study investigates longitudinal dynamics of anti-SARS-CoV-2 receptor-binding domain IgG (anti-RBD IgG) antibodies and their association with infection risk among vaccinated healthcare workers. Materials and Methods: A prospective cohort study was conducted at Vilnius University Hospital Santaros Klinikos, Lithuania. A total of 1778 healthcare workers who completed a primary COVID-19 vaccination series were followed. Blood samples were collected every three months to measure anti-RBD IgG levels. Participants also received up to three booster doses. COVID-19 was identified by PCR, antigen tests, or positive anti-nucleocapsid IgG. For serologically detected cases, infection timing was assigned to the interval between study visits. Antibody dynamics were analyzed across vaccination stages, time, age groups, and circulating SARS-CoV-2 variants. Results: Anti-RBD IgG titers peaked in the first quarter after primary vaccination (mean 7904 AU/mL), declined sharply by quarters 2–3, and rose substantially after booster doses. Following the first booster, titers increased to ~12,598 AU/mL in quarter 1 and continued rising through quarter 3. The highest levels were observed after the second booster (24,456 AU/mL in Q1), followed by gradual decline. A high-titer plateau persisted from quarters 6 to 9 (~21,000 AU/mL), followed by decline in quarters 10–11 and partial rebound in Q12. Approximately 49.6% of participants experienced COVID-19 during follow-up. Antibody response patterns were similar across age groups, with only minor transient differences. Conclusions: COVID-19 booster doses significantly enhance and prolong humoral immunity in healthcare workers compared with the primary vaccination series. However, antibody waning over time emphasizes the need for timely boosters, particularly during periods of variant circulation. These findings support continued booster vaccination and monitoring of long-term immune protection, although anti-RBD IgG should be interpreted as a surrogate marker of humoral rather than overall immunity.

Introduction: Genetic changes in COVID-19 variants/sublineages (VSLs) can reduce vaccine effectiveness (VE). Timely VSL-specific VE estimates are essential, but study-specific VSL identification by whole genome sequencing (the “gold standard”) is expensive and time-consuming. Alternatively, VSL-specific VE has been estimated from external sequencing data (VSL predominance period by proxy: PP). We propose two novel approaches for use in test-negative design (TND) studies to estimate VSL-specific VE when study-specific VSL identification is not possible. Methods: We demonstrate the variant category model (VCM) and the variant proportion model (VPM) approaches. Using data from a hospital-based TND study among adults ≥65 years, during the period of sequential predominance of XBB and BA.2.86 in 2023/24, we estimated the VE as (1-OR) x 100%. For the VCM, we used a binary variable categorising “most likely underlying sublineage” based on publicly available sequencing data. For the VPM, we used a continuous variable with values from 0 to 1 representing the weekly proportion of BA.2.86. We validated results using study-specific VSL identification from sequenced study data (SD) and the standard PP approach. Results: Overall, at 14–59 days post vaccination, VE point estimates against XBB were within ±3% absolute for the VE estimated using both models, with an equivalent standard PP validation. We could not validate using SD, as there were no vaccinated XBB cases. Against BA.2.86, VE was lower than against XBB, and the VCM and VPM results were within ±7% absolute of each other, with lowest validation results from SD but equivalent results from the PP. Conclusions: Both proposed approaches produced similar VE estimates to those from well-known methods. The VPM could also provide VE estimates when the validation techniques were limited by low sample size.

Background: The link between clinical recovery and immune restoration after severe COVID-19 remains poorly defined. Although most survivors experience symptomatic improvement, persistent symptoms have been hypothesized to reflect ongoing immune dysregulation. Methods: This prospective cohort study followed 93 unvaccinated adults with RT-PCR-confirmed moderate-to-critical COVID-19 at 3, 6, and 12 months post-discharge. Clinical assessments used structured interviews to evaluate the persistent symptoms. Peripheral blood analyses were used to measure lymphocyte subsets, immunoglobulins, and complement components. Results: Clinical recovery was substantial; fatigue prevalence declined from 70.9% to 24.7% and dyspnea prevalence from 81.7% to 25.8% by 12 months (p < 0.001 for both). However, immune recovery exhibited divergent patterns. Activated T cells (CD3⁺HLA-DR⁺) decreased significantly (from 20% to 13%; p < 0.001), complement C3c levels paradoxically increased from 1.23 to 1.35 g/L (p < 0.001), and serum IgA increased by 32% (p = 0.003). NK cells remained stable overall but were persistently reduced in a subset (~25%) of patients, particularly among those with fatigue and dyspnea. Critical illness was associated with slower T-cell resolution, prolonged IgM elevation, and increased complement activity. Conclusions: One year after hospitalization, most patients achieved substantial clinical improvement, but immune reconstitution lagged behind. These findings highlight the dissociation between clinical and immunological recovery and suggest that persistent immune dysregulation may be associated with long COVID manifestations. Incorporating immune monitoring into post-COVID care may help identify patients at risk of prolonged sequelae and guide targeted therapeutic strategies.

We estimated COVID‐19 vaccine effectiveness (VE) against PCR‐confirmed SARS‐CoV‐2 hospitalization in patients ≥ 60 years with severe acute respiratory infection, using a multicenter, test‐negative, case–control study across seven sites in six European countries between September 2024 and May 2025. We included 352 cases (115 vaccinated; 33%) and 9980 controls (5024 vaccinated; 50%). VE was 42% (95% CI: 15; 61) 14–59 days post‐vaccination, 32% (95% CI: −1; 54) at 60–119 days, and 36% (95% CI: 2; 60) at 120–179 days, and no effect thereafter. Among adults aged 60–79 and ≥ 80 years, we observed moderate VE against COVID‐19 hospitalization for up to 2 and 4 months, respectively.

Background and Objectives: As SARS-CoV-2 transitions toward endemic circulation, understanding long-term health impacts on quality of life (HRQoL) is critical for healthcare planning. While most longitudinal HRQoL studies originate from Western settings, data from Central and Eastern Europe remain scarce. This study aimed to track HRQoL changes over 12 months and explore the sociodemographic and clinical predictors of recovery in Lithuania. Materials and Methods: We conducted a prospective cohort study of 93 adults hospitalized with severe or critical COVID-19 at Vilnius University Hospital from October 2021 to October 2022. HRQoL was assessed at 3, 6, and 12 months post-discharge using the Short Form-36 Health Survey (SF-36). Longitudinal changes were analyzed using non-parametric tests, with minimal clinically important differences (MCIDs) applied. Multivariable regression identified predictors of 12-month outcomes. Results: Ninety-three participants (mean age 58.2 years; 53.8% female; 60.2% with critical disease; 95.7% unvaccinated) completed all follow-up visits. Seven of eight SF-36 domains showed clinically meaningful improvement over 12 months, most notably Bodily Pain (+18.8 points, r = 0.41), General Health (+14.6, r = 0.42), and Social Functioning (+10.4, r = 0.38). Role-Emotional improved minimally (+3.6, r = 0.16). Better Physical Functioning at 12 months was independently associated with male sex, employment, and fewer comorbidities. HRQoL scores remained below age-matched population norms. Only 12.9% accessed structured (Stage II) rehabilitation. Conclusions: This is the first comprehensive 12-month SF-36–based HRQoL assessment among hospitalized COVID-19 survivors in Central and Eastern Europe. Most domains improved significantly; however, emotional and social deficits persisted. Interpretation is limited by the single-center design, absence of pre-COVID baseline data, and use of a generic HRQoL measure. Low rehabilitation uptake underscores gaps in post-COVID care, highlighting the need for integrated, equity-focused recovery programs.

Therapeutic interventions for complex diseases depend on the targeted modulation of key pathological pathways. While growing clinical needs continue to drive advancements in the drug discovery space, current strategies primarily rely on searching large volumes of chemical data without addressing the specific contributions of molecular features. Moreover, both clinicians and researchers recognize the need for improved drug discovery methods and characterization that could aid in clinical strategy selection. To address these challenges, we propose a new perspective on targeted therapy development as well as interactome mapping, utilizing molecular fragments. The present study focuses on therapeutic areas that represent emerging targets, namely JAK2 and GLP-1R, both of which have broad clinical potential. We developed a new self-adjusting neural network that enabled us to discover novel therapeutic candidates with improved in silico binding profiles, gain additional insights into drug-target binding that were not previously reported, and identify new metabolic trajectories. Importantly, our work revealed that even a small compound library can effectively generate lead candidates, expediting the search and exploration process. In addition, the fragment-guided bridging of chemical and biological spaces has revealed new opportunities for drug repurposing efforts and a means of improving the prediction of side effects. We concluded our study with insights into the recent high-profile clinical trial failure of danuglipron and how this could have been prevented with our methodology. Thus, building a robust in silico pipeline with integrated screening data can significantly reduce costs and guide therapy adoption. Furthermore, our proposed strategy highlights promising avenues for the discovery of new therapeutics and the development of clinical interventions. TOC Graphic: