Phage display-derived alpaca nanobodies as potential therapeutics for Naja atra snake envenomation.

Naja atra, the Chinese cobra, is a major cause of snake envenomation in Asia, causing hundreds of thousands of clinical incidents annually. The current treatment, horse serum-derived antivenom, has unpredictable side effects and presents manufacturing challenges. This study focused on developing new-generation snake venom antidotes by using microbial phage display technology to derive nanobodies from an alpaca immunized with attenuated N. atra venom. Following confirmation of the immune response in the alpaca, we amplified VHH genes from isolated peripheral blood mononuclear cells and constructed a phage display VHH library of 1.0 × 107 transformants. After four rounds of biopanning, the enriched phages exhibited increased binding activity to N. atra venom. Four nanobody clones with high binding affinities were selected: aNAH1, aNAH6, aNAH7, and aNAH9. Specificity testing against venom from various snake species, including two Southeast Asian cobra species, revealed nanobodies specific to the genus Naja. An in vivo mouse venom neutralization assay demonstrated that all nanobodies prolonged mouse survival and aNAH6 protected 66.6% of the mice from the lethal dosage. These findings highlight the potential of phage display-derived nanobodies as valuable antidotes for N. atra venom, laying the groundwork for future applications in snakebite treatment.IMPORTANCEChinese cobra venom bites present a formidable medical challenge, and current serum treatments face unresolved issues. Our research applied microbial phage display technology to obtain a new, effective, and cost-efficient treatment approach. Despite interest among scientists in utilizing this technology to screen alpaca antibodies against toxins, the available literature is limited. This study makes a significant contribution by introducing neutralizing antibodies that are specifically tailored to Chinese cobra venom. We provide a comprehensive and unbiased account of the antibody construction process, accompanied by thorough testing of various nanobodies and an assessment of cross-reactivity with diverse snake venoms. These nanobodies represent a promising avenue for targeted antivenom development that bridges microbiology and biotechnology to address critical health needs.

Translating Risk Ratios, Baseline Incidence, and Proportions Diseased to Correlations and Chi-Squared Statistics: Simulation Epidemiology.

Background In a population, when a disease is causing a symptom, the overall symptom incidence can be determined by proportions diseased, baseline symptom incidence, and risk ratios of developing the symptom due to the disease. There are various measures of association, including risk ratios. How risk ratios are linked to other measures of association, such as correlation coefficients and chi-squared statistics, has not been explicitly discussed. This study aims to demonstrate their connection via equations and simulations, assuming one disease causes symptoms. Methods The equations for correlation coefficients and chi-square statistics were rewritten using epidemiological measures: proportions diseased, baseline symptom incidence, and risk ratios. Simulations were conducted to test the accuracy of the equations. The baseline symptom incidence and the proportions diseased were assumed to be 0.05, 0.1, 0.2, 0.4, or 0.8. The risk ratios were assumed to be 0.5, 1, 2, 5, 10, and 25. Another disease that correlates with this disease was created (correlation = 0, 0.3, or 0.7). For each combination of symptom incidence, proportions diseased, risk ratios, and between-disease correlations, 10,000 subjects were simulated. The correlation coefficients and chi-squared statistics were approximated with epidemiologic measures and their interaction terms. R-squared was used to assess the importance of the epidemiologic measures. Results In the simulations, the overall symptom incidence, correlation coefficients, and chi-squared statistics between the disease and symptoms could be fully explained by the epidemiologic measures in the equations (R-squared = 1). When approximating correlation coefficients and chi-squared statistics with individual measures or their interaction terms, the importance of these measures depended on whether the at-risk incidence reached 1 or not. The numbers in the four cells in the contingency table predicted correlation coefficients, or chi-squared statistics, with different R-squared. Conclusion To our knowledge, this is the first study to translate the three epidemiologic measures (risk ratios, baseline symptom incidence, and proportions diseased) into correlation coefficients and chi-squared statistics. However, chi-squared statistics also depend on sample sizes. This study also provides a platform for developing teaching cases for students to investigate the causal relationship between diseases and symptoms or exposure and outcomes.

Characterization of chicken-derived antibody against Alpha-Enolase of Streptococcus pneumoniae.

Streptococcus pneumoniae is a clinically relevant pathogen notorious for causing pneumonia, meningitis, and otitis media in immunocompromised patients. Currently, antibiotic therapy is the most efficient treatment for fighting pneumococcal infections. However, an arise in antimicrobial resistance in S. pneumoniae has become a serious health issue globally. To resolve the problem, alternative and cost-effective strategies, such as monoclonal antibody-based targeted therapy, are needed for combating bacterial infection. S. pneumoniae alpha-enolase (spEno1), which is thought to be a great target, is a surface protein that binds and converts human plasminogen to plasmin, leading to accelerated bacterial infections. We first purified recombinant spEno1 protein for chicken immunization to generate specific IgY antibodies. We next constructed two single-chain variable fragments (scFv) antibody libraries by phage display technology, containing 7.2 × 107 and 4.8 × 107 transformants. After bio-panning, ten scFv antibodies were obtained, and their binding activities to spEno1 were evaluated on ELISA, Western blot and IFA. The epitopes of spEno1 were identified by these scFv antibodies, which binding affinities were determined by competitive ELISA. Moreover, inhibition assay displayed that the scFv antibodies effectively inhibit the binding between spEno1 and human plasminogen. Overall, the results suggested that these scFv antibodies have the potential to serve as an immunotherapeutic drug against S. pneumoniae infections.

Mental Illness Diagnoses May Not Cause All Mental Symptoms: A Simulation Study for Major Depressive Episodes, Dysthymic Disorder, and Manic Episodes.

Objectives This study aims to understand the statistical significance of the associations between diagnoses and symptoms based on simulations that have been used to understand the interpretability of mental illness diagnoses. Methods The symptoms for the diagnosis of major depressive episodes, dysthymic disorder, and manic episodes were extracted from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR, American Psychiatric Association, Philadelphia, Pennsylvania). Without real-world symptom data, we simulated populations using various combinations of symptom prevalence and correlations. Assuming symptoms occurred with similar prevalence and correlations, for each combination of symptom prevalence (0.05, 0.1, 0.3, 0.5, and 0.7) and correlation (0, 0.1, 0.4, 0.7, and 0.9), 100 cohorts with 10,000 individuals were randomly created. Diagnoses were made according to the DSM-IV-TR criteria. The associations between the diagnoses and their input symptoms were quantified with odds ratios and correlation coefficients. P-values from 100 cohorts for each combination of symptom prevalence and correlation were summarized. Results Three mental illness diagnoses were not significantly correlated with their own symptoms in all simulations, particularly when symptoms were not correlated, except for the symptom in the major criteria of major depressive episodes or dysthymic disorder. The symptoms for the diagnosis of major depressive episodes and dysthymic disorder were significantly correlated with these two diagnoses in some simulations, assuming 0.1, 0.4, 0.7, or 0.9 symptom correlations, except for one symptom. The overlap in the input symptoms for the diagnosis of major depressive episodes and dysthymic disorder also leads to significant correlations between these two diagnoses, assuming 0.1, 0.4, 0.7, and 0.9 correlations between input symptoms. Manic episodes are not significantly associated with the input symptoms of major depressive episodes and dysthymic disorder. Conclusion There are challenges to establish the causation between psychiatric symptoms and mental illness diagnoses. There is insufficient prevalence and incidence data to show all psychiatric symptoms exist or can be observed in patients. The diagnostic accuracy of symptoms to detect a disease cause is far from perfect. Assuming the symptoms of three mood disorders may present in patients, three diagnoses are not significantly associated with all psychiatric symptoms used to diagnose them. The diagnostic criteria of the three diagnoses have not been designed to guarantee significant associations between symptoms and diagnoses. Because statistical associations are important for making causal inferences, there may be a lack of causation between diagnoses and symptoms. Previous research has identified factors that lead to insignificant associations between diagnoses and symptoms, including biases due to data processing and a lack of epidemiological evidence to support the design of mental illness diagnostic criteria.