Prevention of Vaccine-Preventable Diseases through Information and Education at the California Department of Public Health

Immunizations are one of the most important developments in the twentieth century towards the prevention of infectious diseases. Vaccinations against communicable diseases have helped reduce morbidity and mortality all over the world. Despite having numerous vaccines that protect individuals from serious and deadly diseases, vaccination rates in the United States remain at an all-time low. The Information and Education section of the Immunization Branch within the Division of Communicable Disease Control at the California Department of Public Health (CDPH) focuses on working with local health departments, primary care providers, immunization coalitions, and other statewide organizations to increase the rates of vaccinations in the United States. The Information and Education section aims to address vaccination disparities in the state of California. The main reasons parents and guardians opt out of fully vaccinating their children are due to personal belief exemptions (PBEs), concerns regarding vaccine-related side effects, and low provider recommendation. The Information and Education section is in charge of creating statewide campaigns and educational materials to help increase immunization rates. Four goals were developed for this fieldwork experience: general familiarization with the Immunization Branch; participate in the planning of Preteen Vaccine Week Campaign 2015, an immunization informing campaign; conduct impact evaluation and education material; and participate in maintenance of other immunization informing campaigns. The following paper is a summary of a 300-hour fieldwork experience at the CDPH to help increase awareness of vaccine-preventable diseases and the importance of being fully immunized through the development of informational and educational materials

Effect of hydroxybenzoic acids antioxidants on the oxidative stability of sardine oil

The antioxidant capacities of three derivatives of hydroxybenzoic acids (Gentisic acid, protochatechuic acid and vanillic acid) in sardine oil were compared. Peroxide value, conjugated diene value, p-anisidine value and thiobarbituric acid reactive substances (TBARS) value were assessed to determine the oxidative stability provided by these substances to the sardine oil. Results showed that gentisic acid (2,5 dihydroxy benzoic acid) was the most effective of the chosen hydroxybenzoic acids in imparting oxidative stability to the sardine oil. Protochatechuic acid (3,4 dihydroxy benzoic acid) provided relatively less oxidative stability, while vanillic acid had no effect. Results from this work showed that the position of hydroxylation and methyl substitution influences the antioxidant capacity of the molecules in sardine oil. Furthermore, it was found that the extent of oxidative stability conferred by the antioxidants in lipid systems is influenced by several other physical and chemical factors as well

Screening of polymeric membranes for membrane assisted deacidification of sardine oil

The diversification in fish oil use and the need for softer processing demand new oil refining processes. In considering the advantages of membrane technology, three different membranes (polyamide (PA), polytetrafluoroethylene (PTFE) and polyethersulfone (PES)) were used in this particular study. Preliminary results in the separation of free fatty acids (FFA) from glycerides of sardine oil/ethanol mixtures using a single dead end microfiltration mode have been reported here. The influence of experimental factors like pressure and oil/ethanol ratios (w/v) on the permeate flux and the reduction of FFA (%) in the permeate was studied. PTFE membrane showed promising results in terms of residual FFA in permeate (%), % oil loss (15.12%, 5.45%) as compared to PA (20.50%, 6.66%) and PES membranes (20.60%, 8.92%). PA membrane showed a higher flux of 4.4 L/m2 /h, followed by PTFE 3.34 L/m2 /h and PES, 1.19 L/m2 /h at 3.5 bar trans-membrane pressure. These results showed that using PTFE membrane could be an ideal strategy for the membrane assisted deacidification of sardine oil using solvents

Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data Balancing

Computational drug repurposing is an efficient method to utilize existing knowledge for understanding and predicting their effect on neurological diseases. The ability of a molecule to cross the blood-brain barrier is a primary criteria for effective therapy. Thus, accurate predictions by employing Machine learning models can effectively identify the drug candidates that could be repurposed for neurological conditions. This study comprehensively analyzes the performance of the well-known machine learning models on two different datasets to overcome dataset-related biases. We found that random forest and extratrees (i.e., tree-based ensembled models) have the highest accuracy with mol2vec fingerprint for BBB permeability prediction, attaining AUC_ROC of 0.9453 and 0.9601 on BBB and B3DB dataset, respectively. Additionally, we have analyzed the impact of the data balancing technique (i.e., SMOTE) to improve the specificity of the models. Finally, we have explored the impact of different fingerprint combinations on accuracy. By employing SMOTE and fingerprint combination, SVC attains the highest AUC_ROC of 0.9511 on BBB dataset. Finally, we used the best-performing models of the B3DB dataset to evaluate the BBB permeability for drugs intended to be used for repurposing. Model validation for repurposing predicted the non-passage for most antihypertensive drugs and passage for CYP17A1 cancer drugs

In Silico Modeling as a Perspective in Developing Potential Vaccine Candidates and Therapeutics for COVID-19

The potential of computational models to identify new therapeutics and repurpose existing drugs has gained significance in recent times. The current ‘COVID-19’ pandemic caused by the new SARS CoV2 virus has affected over 200 million people and caused over 4 million deaths. The enormity and the consequences of this viral infection have fueled the research community to identify drugs or vaccines through a relatively expeditious process. The availability of high-throughput datasets has cultivated new strategies for drug development and can provide the foundation towards effective therapy options. Molecular modeling methods using structure-based or computer-aided virtual screening can potentially be employed as research guides to identify novel antiviral agents. This review focuses on in-silico modeling of the potential therapeutic candidates against SARS CoVs, in addition to strategies for vaccine design. Here, we particularly focus on the recently published SARS CoV main protease (Mpro) active site, the RNA-dependent RNA polymerase (RdRp) of SARS CoV2, and the spike S-protein as potential targets for vaccine development. This review can offer future perspectives for further research and the development of COVID-19 therapies via the design of new drug candidates and multi-epitopic vaccines and through the repurposing of either approved drugs or drugs under clinical trial

Effect of hydroxybenzoic acids antioxidants on the oxidative stability of sardine oil

The antioxidant capacities of three derivatives of hydroxybenzoic acids (Gentisic acid, protochatechuic acid and vanillic acid) in sardine oil were compared. Peroxide value, conjugated diene value, p-anisidine value and thiobarbituric acid reactive substances (TBARS) value were assessed to determine the oxidative stability provided by these substances to the sardine oil. Results showed that gentisic acid (2,5 dihydroxy benzoic acid) was the most effective of the chosen hydroxybenzoic acids in imparting oxidative stability to the sardine oil. Protochatechuic acid (3,4 dihydroxy benzoic acid) provided relatively less oxidative stability, while vanillic acid had no effect. Results from this work showed that the position of hydroxylation and methyl substitution influences the antioxidant capacity of the molecules in sardine oil. Furthermore, it was found that the extent of oxidative stability conferred by the antioxidants in lipid systems is influenced by several other physical and chemical factors as well

Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology

More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential