Innovations and challenges in vaccine development: Lessons from the SARS-CoV-2 pandemic and prospects.

The article presents modern data on non-communicable diseases (NCDs) and their risk factors. The tendency of increasing mortality from NCDs due to increase in the number and aging of the population is noted. The data is presented testifying that, in addition to biological risk factors, influence of environmental factors is increasing, including atmospheric and household air pollution, the levels of which are high in a significant number of countries. It is emphasized that green spaces (e.g., trees, grass, forests and parks) and blue spaces (e.g., lakes, rivers, ponds, etc.) provide beneficial effect on human health, being in fact protective factors for NCDs. The NCD risk factors are found to interact closely with each other: air pollution, depression, tobacco smoking, high blood pressure and obesity have been linked to all NCDs. The evidence is provided that presence of behavioral risk factors for NCDs exacerbates severity of infectious pathology, particularly COVID-19 and other infectious diseases (influenza, HIV, tuberculosis, hepatitis, etc.). Given that NCD risk factors can occur in early childhood, promoting healthy lifestyles among expectant mothers and adolescents should be a priority to reduce NCD risks, especially in low- and middle-income countries.

Introduction: Stunting remains a major public health concern in Indonesia, particularly in remote and highland regions such as the Bintang Mountains of Papua. It is characterized by impaired physical and cognitive development due to chronic malnutrition, repeated infections, and inadequate health services. Understanding the determinants of stunting in these settings is critical to inform effective interventions and policies. Methods: This study employed an analytical survey with a case-control design to examine risk factors associated with stunting among toddlers aged 24–59 months in the Oksibil Health Center working area, Bintang Mountains Regency, Papua. A total of 66 respondents were recruited between April and May 2024, consisting of 33 stunted toddlers (cases) and 33 non-stunted toddlers (controls). Data were collected through structured interviews and analyzed using odds ratios (ORs) with 95% confidence intervals to identify significant risk factors. Results: The findings revealed that several factors were significantly associated with stunting: maternal knowledge (OR = 1.911), history of infectious diseases (OR = 8.090), environmental sanitation (OR = 1.375), socio-cultural practices (OR = 3.801), and health service utilization (OR = 9.339). Among these, inadequate health service utilization posed the highest risk. Socio-cultural norms, poor hygiene practices, and limited maternal awareness were also strongly linked to stunting prevalence. Conclusion: Stunting in the Bintang Mountains is strongly influenced by a combination of health service gaps, infectious diseases, socio-cultural factors, and insufficient maternal knowledge. Strengthening maternal Education, improving sanitation, addressing harmful cultural practices, and expanding access to health services are critical strategies to reduce stunting prevalence in highland and resource-limited areas of Indonesia

Introduction: Immunization is one of the most effective public health interventions to prevent infectious diseases in children. However, achieving complete basic immunization coverage remains a global challenge, particularly in low- and middle-income countries. Maternal factors play a crucial role in determining whether children receive complete immunization. Understanding these factors is essential to inform strategies for improving immunization coverage. Methods: This scoping review followed the PRISMA-ScR framework to map existing evidence on maternal determinants of complete basic immunization. A systematic search was conducted in electronic databases such as PubMed, Scopus, and Google Scholar, covering publications between 2010 and 2025. Inclusion criteria were peer-reviewed studies addressing maternal characteristics related to childhood immunization completion. Data were extracted and categorized thematically. Results: The reviewed studies revealed several maternal factors influencing complete immunization. These included maternal education, knowledge and awareness of immunization benefits, socioeconomic status, access to health facilities, and household decision-making autonomy. Maternal age, parity, and antenatal care attendance also showed significant associations with immunization completion. The identified barriers included limited health literacy, cultural beliefs, and logistical challenges such as distance to health services. Conclusion: Maternal factors are central determinants in achieving complete basic immunization. Strategies to increase coverage should prioritize maternal education, empowerment, and accessibility to health services. Strengthening maternal involvement in child health programs can significantly improve immunization rates and reduce preventable childhood morbidity and mortality.

Climate-Sensitive Infectious Diseases (CSIDs) are diseases whose prevalence and transmission are heavily influenced by climatic factors, posing a significant challenge to public health, particularly in vulnerable regions such as Latin America and the Caribbean (LAC). This study employs a bibliometric analysis to evaluate the evolution and distribution of research on CSID and the analytical methods employed in the field. Using bibliometric and text-mining techniques, the analysis examines publication trends, research hotspots, and methodological developments from 2015 to 2024. The results highlight a regional concentration of research, with Brazil leading in CSID studies, particularly on arboviruses such as dengue, Zika, and chikungunya. The analysis also reveals the predominance of regression models, time-series analysis, and spatial analysis as primary methods used to forecast and analyze disease outbreaks. However, advanced techniques such as neural networks and niche modeling are gaining traction, indicating a shift towards more data-intensive approaches. The findings underscore the importance of enhancing forecasting capabilities and integrating analytical models into public-health systems to anticipate the impact of climate change on disease patterns. This study offers critical insights into methodological trends and identifies gaps for future research, contributing to more effective decision making in public health across Latin America and the Caribbean.

Traditional broad-spectrum antibacterial strategies are known to contribute to the increase in drug-resistant microorganisms and the disruption of microbial ecological balance. To break this stalemate, researchers have begun to explore selective antibacterial strategies that minimize the impact on normal flora and maintain microbial ecological balance. Species-specific antibacterial materials, which can target particular bacterial species or even specific strains, offer innovative perspectives and methodologies for the prevention and treatment of infectious diseases. This review first explores the selective mechanisms that underpin species-specific antibacterial strategies, summarizes the main classifications of species-specific antibacterial strategies, investigates the engineering techniques employed in the development of such materials, and emphasizes the importance of structure-activity relationships in the design of species-specific antibacterial materials. It explores techniques like peptide modification, nanoparticle engineering, and genetic manipulation, highlighting that a thorough understanding of the structure and function of antibacterial materials is essential for improving their efficacy and specificity. Ultimately, it anticipates the potential applications of species-specific antibacterial materials in disease diagnosis and treatment, while addressing the challenges associated with clinical translation. It is expected that this comprehensive review will offer novel perspectives for the development of species-specific antibacterial materials.

The transplant environment requires special considerations when testing for viral infections as immunosuppression results in atypical infection profiles. Microbes otherwise considered commensals or causing mild disease can lead to severe infections in transplant environments. Therefore, guidelines tend to recommend broader microbial testing in these populations. In parallel, advances in molecular diagnostics have led to the availability of a wide selection of tests, including highly multiplexed nucleic acid amplification tests (NAATs) and direct next generation sequencing (NGS) based options. These newer technologies may provide information on many potential pathogens simultaneously, more rapidly, and while avoiding invasive specimen collection procedures. However, they are generally more expensive than conventional methods such as culture, and nucleic acid detection of multiple potential pathogens may be nonspecific and confuse the diagnosis. Navigating the complexity of the available molecular test landscape in immunocompromised patients is an opportunity for diagnostic stewardship. Here we discuss the clinical value of different molecular testing strategies for diagnosis of viral infectious diseases in immunocompromised transplant patients using several common transplant infection syndromes as a framework.

Veterinary laboratories are necessary to maintain animal health and store these zoonotic infections safely, but they can present challenges for biosafety and biosecurity. Historical evidence shows that veterinary laboratory accidents have affected thousands of people. The lack of geolocation data on these laboratories and the pathogens they handle is a bottleneck in zoonotic disease surveillance and detection of veterinary laboratory accidents. Therefore, mapping the locations of veterinary laboratories will enhance the ability to detect outbreaks or potential laboratory leaks of veterinary pathogens.

The onset of COVID-19 and subsequent restrictive measures have impacted various infectious diseases, including hepatitis B virus (HBV). This study explored the epidemiological characteristics of HBV infection in Chinese adults before, during the pandemic, and after the easing of restrictive measures. This population-based cohort study used the data from 23,316 adult patients in the southwest of China who had HBV DNA tests from 1 January 2018 to 31 December 2023. HBV DNA was detected in patient serum using real-time fluorescent quantitative PCR. The positive rate of HBV detection was adjusted by age groups, sex, patient types, and seasons, stratified by the stages of the COVID-19 pandemic. Our analysis revealed significant variations in HBV DNA test positivity rates, primarily influenced by age and the pandemic stages. Positivity rates were highest in the 18-25 age group at 0.50 and decreased with advancing age. Males under 35 were at higher risk. Inpatients had the highest positivity rate at 0.42, with seasonal fluctuations peaking in winter. The pandemic stages significantly affected positivity rates, especially in the 36-45 and 56-65 age groups. The findings highlight a complex interplay between pandemic conditions and observed positivity rates. The increase likely stemmed from multiple factors, including shifted testing focus, altered healthcare-seeking behavior, and potential viral reactivation. The COVID-19 response offers insights for optimizing future viral hepatitis control strategies during public health emergencies. Future research should expand demographic and geographic scope and investigate behavioral/social determinants to elucidate underlying mechanisms and guide targeted interventions.

Purpose: Respiratory infectious diseases often manifest as Ground-Glass Opacity (GGO) or consolidation signs in the lungs. Artificial Intelligence (AI)-assisted systems utilizing data mining algorithms such as Waikato Environment for Knowledge Analysis (Weka) can be used for the detection and segmentation of these signs. In this study, we propose using Weka as a comprehensive data mining and machine learning tool to develop the most accurate models for detecting lung signs in chest CT images of patients with respiratory infectious diseases. Materials and Methods: First, we manually selected specific signs from chest Computed Tomography (CT) images from 600 cases using the Graphical User Interface (GUI) Weka plugin. We then trained the random forest algorithm based on different features and presented the best combined model obtained for the automatic detection of the aforementioned signs. Lastly, the model's performance was evaluated with different metrics. Results: Our findings indicate that the hybrid texture description features, including “Structure”, “Entropy”, “Maximum”, “Anisotropic”, and “Laplacian” available in Weka, demonstrated the lowest Out-of-Bag (OOB) error rate, highest Area Under the ROC Curve (AUC) value of 0.992, and accuracy of 98.1%. Conclusion: By leveraging the combination of Weka features, we have successfully developed models for the detection and segmentation of lung signs associated with infectious diseases, from chest CT images. These findings contribute to the field of medical image analysis and hold promise for improving the diagnosis and treatment outcomes of patients with respiratory infectious disorders.

Health and hygiene are crucial for maintaining overall well-being, especially for students who spend long hours interacting with peers in shared environments. Poor hygiene practices continue to contribute to the spread of various communicable diseases. This review-based study summarizes existing research on the awareness and importance of hygiene among students. The findings show that although many students understand basic hygienic practices, regular and consistent implementation is still limited. Factors such as school support, parental influence, availability of facilities, and digital learning tools significantly shape students’ hygiene behaviours. The study concludes that continuous reinforcement and proper facilities are essential to improve hygiene habits and reduce disease transmission in educational settings.

Dogs are recognized as epidemiologically significant reservoirs for Trypanosoma cruzi, the causative agent of Chagas disease (CD), owing to their close association with humans and their role in sustaining the domestic and peridomestic transmission cycle. Canine seropositivity often correlates with human CD prevalence. However, the lack of commercial, high-performance diagnostic assays for canine infections remains a significant barrier to effective surveillance. Previously, our group demonstrated the diagnostic potential of four chimeric T. cruzi antigens in a phase I study, yielding results comparable to those observed in humans. The present phase II study expands upon these findings by evaluating these antigens in a larger canine cohort using indirect enzyme-linked immunosorbent assay (ELISA). The objective of this study was to assess the diagnostic performance of four chimeric recombinant T. cruzi antigens (IBMP-8.1, IBMP-8.2, IBMP-8.3, and IBMP-8.4) in immunoassays for the detection of anti-T. cruzi IgG in dogs with chronic Chagas disease. Immunoassays were optimized by checkerboard titration. In this phase II study, the diagnostic performance of the IBMP antigens was evaluated using 1260 canine serum samples. Cross-reactivity was assessed in an additional 752 samples from dogs with unrelated infectious diseases. The performance of the chimeric antigens was compared with a commercial human-adapted assay (Gold ELISA Chagas). The Instituto de Biologia Molecular do Paraná (IBMP) antigens demonstrated area under the curve (AUC) values ranging from 89.0% to 97.4%, with diagnostic accuracy between 87.4% and 96%. IBMP-8.2 exhibited the highest sensitivity (90.3%), while IBMP-8.1, IBMP-8.3, and IBMP-8.4 achieved sensitivities of 74.8%, 72.6%, and 79.6%, respectively. The highest specificity was observed for IBMP-8.4 (99.6%), followed by IBMP-8.3 (99.0%), IBMP-8.2 (96.5%), and IBMP-8.1 (90.6%). The Gold ELISA Chagas assay showed a sensitivity of 62.3%, specificity of 98.6%, and accuracy of 89.9%. IBMP-8.2 exhibited the lowest cross-reactivity index (0.9%), closely approximating an ideal diagnostic assay. The IBMP chimeric antigens demonstrated strong diagnostic performance for detecting T. cruzi infection in dogs, significantly enhancing immunoassay accuracy and minimizing diagnostic failures due to cross-reactivity. The combined use of these antigens represents a promising strategy to further improve sensitivity and specificity in future diagnostic applications.

Maternal diet shapes the development and identity of tissue-resident macrophages.

Since the COVID-19 pandemic, a growing number of reports suggest an association between severe acute respiratory syndrome coronavirus 2 and autoimmune diseases, including large-vessel vasculitis (LVV). However, the mechanism remains unclear. This report describes three cases of COVID-19-associated LVV with elevated anti-angiotensin-converting enzyme 2 (ACE2) antibodies. The first case was a 59-year-old man who developed a persistent headache and fever 2weeks after SARS-CoV-2 infection. FDG-PET/CT revealed diffuse vascular inflammation extending from the carotid arteries to the abdominal aorta. The second case was a 71-year-old man who presented with prolonged fever after SARS-CoV-2 infection. Imaging demonstrated vascular wall enhancement and FDG uptake from the thoracic aorta to the iliac aorta. The third case was a 67-year-old man who had persistent fever 10days after COVID-19, with FDG-PET/CT showing uptake from the ascending aorta to the aortic arch. In all cases, workups for immune and infectious diseases were negative. Symptoms and inflammatory markers resolved spontaneously or with nonsteroidal anti-inflammatory drugs. Serum anti-ACE2 IgG was positive during the active phase in all three patients and became negative during remission. We have encountered three cases of COVID-19-associated LVV with elevated anti-ACE2 antibodies that normalized after clinical remission. There have been multiple reports of LVV following SARS-CoV-2 infection, with onset typically within weeks of infection, and of elevated anti-ACE2 antibody levels in patients with COVID-19-related neurological complications. Further studies are warranted to determine if anti-ACE2 antibodies are associated with the pathogenesis of post-COVID-19 vasculitis.

Antibodies have emerged as central components of therapeutic strategies against viral infectious diseases, functioning as key effectors in both prevention and treatment. While traditional antibody discovery has relied heavily on high-throughput screening, the field is now shifting toward rational antibody design, which requires integrative insights into sequence-structure-function relationships. However, existing resources provide a valuable foundation but remain limited in scope, highlighting the need for a standardized and well-annotated antibody database that integrates multidimensional features to further support systematic exploration, cross-pathogen comparison, and rational antibody design. Here, we introduce the Multidimensional Antiviral Antibody Database (MAAD; http://www.raabmd.org/raab/index), a curated platform dedicated to antibody, nanobody and single-chain variable fragment targeting three high-impact RNA virus families, Coronaviridae (SARS-CoV-1, SARS-CoV-2, MERS-CoV), Orthomyxoviridae (influenza virus), and Pneumoviridae (respiratory syncytial virus, human metapneumovirus), which were selected due to the large, high-quality datasets accumulated in recent years. MAAD further incorporates a suite of interactive analysis modules, including CDR and germline annotation, similarity-based sequence analysis, sequence-based clustering and structure-guided identification of antigen-antibody interface residues, complemented by per-site entropy and mutation rate profiling. These features enable in-depth exploration of antibody sequence characteristics, thereby facilitating functional and structural insights for rational antibody design. Together, by bridging antibody sequence, structure and function, MAAD offers an open and standardized platform that advances comparative antiviral research and supports therapeutic antibody discovery.

Infectious Bursal Disease Virus (IBDV) remains a significant threat to global poultry health due to its immunosuppressive effects and high mutation rate, which challenge existing vaccine strategies. This study presents a comprehensive computational analysis of the VP2 protein from Malaysian IBDV isolate UPM1432/2019 to elucidate its structural stability and antigenic potential. Phylogenetic analysis revealed evolutionary divergence among VP2 sequences, while physicochemical profiling demonstrated the protein's high thermostability (aliphatic index: 98.77) and cellular stability (instability index: 17.55). Molecular dynamics simulations at 20°C, 40°C, 60°C, and 80°C showed progressive destabilization at elevated temperatures, indicated by increased RMSD, reduced hydrogen bonding, and higher solvent accessibility. Epitope prediction identified twelve linear and seven conformational B-cell epitopes, with several conserved, surface-exposed regions demonstrating high antigenicity. The most immunodominant conformational epitope (L168-E181) achieved a high ElliPro score of 0.903, indicating strong immune recognition potential. These findings highlight structurally stable and immunoreactive domains within VP2, offering valuable insights for the development of next-generation, thermostable epitope-based vaccines for effective IBDV control.

Nonpharmaceutical interventions (NPIs) have positive effects on curbing the COVID-19 pandemic, but the spread of other infectious diseases remains unknown. This study aimed to explore the incidence of notifiable infectious diseases (NIDs) at different stages of the COVID-19 pandemic in Mainland China. We extracted monthly cases of NIDs from the National Health Commission of the People's Republic of China from January 2020 to December 2023. The infections were classified as blood-borne/sexually transmitted diseases, intestinal diseases, zoonotic/insect-borne diseases and respiratory diseases. The Joinpoint regression model was used to quantify the temporal trends of NIDs by calculating annual percentage changes (APCs). There is a negative correlation between the national ridership of urban rail transit and the national COVID-19 cases. During the two emergency response periods from January 2020 to March 2020 and November 2022 to January 2023, 1,760,990 cases and 804,266 cases of 22 types of NIDs were reported nationwide, which decreased by 25.54% and 74.64% respectively compared with the same period in 2019. For different NIDs, respiratory diseases had the greatest reduction (85.99%), followed by intestinal diseases (48.94%), zoonotic/insect-borne diseases (34.22%), and blood-borne/sexually transmitted diseases (33.30%). It was found that strict containment measures decreased social mobility and reduced the number of most NID cases. However, with the weakening of NPIs, hand, foot, and mouth disease, epidemic hemorrhagic fever, influenza and pertussis cases notably increased. This study offers suggestions for preventing the spread of epidemics in extreme situations.

Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus from the genus Flavivirus, which has four serotypes: DENV-1, DENV-2, DENV-3, and DENV-4. This virus can enter the human body through the Aedes aegypti and Aedes albopictus mosquitoes. The hematocrit value is a laboratory parameter needed to monitor the development of DHF. In DHF cases, there is an increase in hematocrit or hemoconcentration due to plasma leakage. Objective: To determine whether there is a relationship between changes in hematocrit values and the severity of dengue fever in children. Method: This study used an observational analytical study with a cross-sectional design. Result: The chi-square test yielded a p-value of 0.000, indicating a relationship between hematocrit and dengue fever severity in patients at Pandan Tapanuli Tengah Regional Hospital. Conclusion: This study found a significant relationship between hematocrit levels and dengue fever severity.

Strong information delay as a driver of epidemic waves: Mathematical modeling for drug trends and epidemic bio-preparedness.

BackgroundThe evolution of medical students’ population and their choices of medical residency is poorly documented in France. The objective of this study was to perform a retrospective analysis of medical student’s residency choices at their national ranking exam (NRE) once they have completed six years of theoretical and practical medical training.MethodsPublished choices after the French NRE were processed from 2011 to 2023. A longitudinal follow-up of the median rank of affected students for each medical speciality and each training university hospitals is presented. Demographics and gender statistics are also presented. Violin plots, heatmaps and bump charts were used for a better analysis. Mann–Kendall test was used for trend analysis.ResultsWe analyzed choices of more than 100,000 students and show that men were more likely to have a better ranking, though with a non homo-geneous distribution compared to women who were more homogeneously distributed. We also proved that younger students better performed at the exam. We observed an unprecedented increased attractiveness of medical gynecology (+ 14 places, p = 0.007) as well as anesthesiology and resucitation (+ 13 places, p = 0.008).Vascular medicine (+ 4 places, p = 0.014), otorhino-laryngology (+ 2 places, p = 0.041) and dermatology (+ 5 places, p = 0.019) also significantly gained attractivity over the study period contrary to neurosurgery (-23 places, p = 0.023) and infectious diseases (-8 places, p = 0.048). Medical schools of Rennes (+ 7 places, p = 0.005) and Caen (+ 3 places, p = 0.048) have been gaining interest over the whole period while Grenoble (-5 places, p = 0.013), Paris (-5 places, p = 0.0083) and Limoges (-4 places, p = 0.006) have been significantly loosing attractivity.ConclusionThis work provides an overview of choices trends at the French medical NRE and illustrates for the first time the evolution of medical students’ demography and residency choices over a thirteen-year period.