Exploring Relations Between Unique Patient Characteristics and Virtual Reality Immersion Level on Anxiety and Pain in Patients Undergoing Venipuncture: Secondary Analysis of a Randomized Control Trial.

BACKGROUND: Virtual reality (VR) is a well-researched digital intervention that has been used for managing acute pain and anxiety in pediatric patients undergoing various medical procedures. This study focuses on investigating the role of unique patient characteristics and VR immersion level on the effectiveness of VR for managing pediatric pain and anxiety during venipuncture. OBJECTIVE: The purpose of this study is to determine how specific patient characteristics and level of immersion during a VR intervention impact anxiety and pain levels for pediatric patients undergoing venipuncture procedures. METHODS: This study is a secondary data analysis of 2 combined, previously published randomized control trials on 252 pediatric patients aged 10-21 years observed at Children's Hospital Los Angeles from April 12, 2017, to July 24, 2019. One randomized clinical trial was conducted in 3 clinical environments examining peripheral intravenous catheter placement (radiology and an infusion center) and blood draw (phlebotomy). Conditional process analysis was used to conduct moderation and mediation analyses to assess the impact of immersion level during the VR intervention. RESULTS: Significant moderation was found between the level of immersion and anxiety sensitivity when predicting postprocedural anxiety (P=.01). Patients exhibiting the highest anxiety sensitivity within the standard of care yielded a 1.9 (95% CI 0.9-2.8; P<.001)-point elevation in postprocedural anxiety relative to individuals with high immersion levels. No other significant factors were found to mediate or moderate the effect of immersion on either postprocedural anxiety or pain. CONCLUSIONS: VR is most effective for patients with higher anxiety sensitivity who report feeling highly immersed. Age, location of the procedure, and gender of the patient were not found to significantly impact VR's success in managing levels of postprocedural pain or anxiety, suggesting that immersive VR may be a beneficial intervention for a broad pediatric population. TRIAL REGISTRATION: ClinicalTrials.gov NCT04268901; https://clinicaltrials.gov/study/NCT04268901.

Psychosocial Experiences of Spanish-Speaking Parents of Children With Craniofacial Microsomia.

Craniofacial microsomia (CFM) and microtia psychosocial research in the US is primarily with English-speaking participants. Given that 19% of the US is Latino, and there is a higher prevalence of CFM in Latino populations, this study aims to describe psychosocial experiences related to CFM among Spanish-speaking Latino caregivers to better inform health care. Narrative interviews (mean 73±17 min) were completed in Spanish with parents of children with CFM aged 3 to 17 (mean age 10.8±4.8 years). Transcripts were analyzed using quantitative linguistic analyses and reflexive thematic analysis. Participants (N=12) were mostly mothers (83%) who had immigrated to the US and had low socioeconomic status. Based upon analysis of grouped word counts, participants spent approximately half of their narratives discussing the first two years of their child's life. Themes selected based on US Latino sociodemographics and cultural values included the Impact of Language, Healthcare Challenges, Supportive Healthcare Experiences, Caregiver Coping with CFM, Family Roles, and Addressing Social Implications of CFM. Results highlighted that the first years of care are of critical importance to parents and suggest this is an optimal time to focus on education and support services for families. Additional treatment suggestions include providing interpretation and informational materials in Spanish, addressing care barriers, supporting familial and child coping, accounting for the role of extended family, and helping address social concerns. Ongoing research with Latino families can further assist in guiding culturally sensitive CFM health care.

Revolutionising food advertising monitoring: a machine learning-based method for automated classification of food videos.

OBJECTIVE: Food advertising is an important determinant of unhealthy eating. However, analysing a large number of advertisements (ads) to distinguish between food and non-food content is a challenging task. This study aims to develop a machine learning-based method to automatically identify and classify food and non-food ad videos. DESIGN: Methodological study to develop an algorithm model that prioritises both accuracy and efficiency in monitoring and classifying advertising videos. SETTING: From a collection of Brazilian television (TV) ads data, we created a database and split it into three sub-databases (i.e. training, validation and test) by extracting frames from ads. Subsequently, the training database was classified using the EfficientNet neural network. The best models and data-balancing strategies were investigated using the validation database. Finally, the test database was used to apply the best model and strategy, and results were verified with field experts. PARTICIPANTS: The study used 2124 recorded Brazilian TV programming hours from 2018 to 2020. It included 703 food ads and over 20 000 non-food ads, following the protocol developed by the INFORMAS network for monitoring food marketing on TV. RESULTS: The results showed that the EfficientNet neural network associated with the balanced batches strategy achieved an overall accuracy of 90·5 % on the test database, which represents a reduction of 99·9 % of the time spent on identifying and classifying ads. CONCLUSIONS: The method studied represents a promising approach for differentiating food and non-food-related video within monitoring food marketing, which has significant practical implications for researchers, public health policymakers, and regulatory bodies.

Effects of dietary palm oil supplementation on ruminal degradation and apparent digestibility of nutrients in sheep.

The aim of this study was to evaluate the effects of the inclusion of palm oil on the ruminal environment and nutrient digestibility of sheep diets. Twenty rumen-cannulated sheep were kept in individual stalls equipped with feeding and drinking troughs The animals were fed five diets based on Elephant grass (Pennisetum purpureum Schum. cv. Roxo) silage and supplemented with 0, 25, 50, 75, or 100 g kg-1 of palm oil (based on total DM). The Elephant grass was harvested at 90 days of regrowth and the concentrate was based on ground corn grain, soybean meal and mineral mix (20 g kg-1 DM), offered to the sheep at a ratio of 1.5 g kg-1d-1 of body weight (restricted intake) to maintain a forage-to-concentrate ratio of 1:1, based on DM. There were no differences (P = 0.324) in ruminal disappearance and degradability parameters with up to 75 g of oil per kg of DM. Organic matter showed a linear reduction in apparent digestibility, while ether extract increased linearly. Palm oil affected the digestibility and nutritional parameters in ruminant diets.

Machine Learning Improves the Identification of Individuals With Higher Morbidity and Avoidable Health Costs After Acute Coronary Syndromes.

OBJECTIVES: Traditional risk scores improved the definition of the initial therapeutic strategy in acute coronary syndrome (ACS), but they were not designed for predicting long-term individual risks and costs. In parallel, attempts to directly predict costs from clinical variables in ACS had limited success. Thus, novel approaches to predict cardiovascular risk and health expenditure are urgently needed. Our objectives were to predict the risk of major/minor adverse cardiovascular events (MACE) and estimate assistance-related costs. METHODS: We used a 2-step approach that: (1) predicted outcomes with a common pathophysiological substrate (MACE) by using machine learning (ML) or logistic regression (LR) and compared with existing risk scores; (2) derived costs associated with noncardiovascular deaths, dialysis, ambulatory-care-sensitive-hospitalizations (ACSH), strokes, and MACE. With consecutive ACS individuals (n = 1089) from 2 cohorts, we trained in 80% of the population and tested in 20% using a 4-fold cross-validation framework. The 29-variable model included socioeconomic, clinical/lab, and coronarography variables. Individual costs were estimated based on cause-specific hospitalization from the Brazilian Health Ministry perspective. RESULTS: After up to 12 years follow-up (mean = 3.3 ± 3.1; MACE = 169), the gradient-boosting machine model was superior to LR and reached an area under the curve (AUROC) of 0.891 [95% CI 0.846-0.921] (test set), outperforming the Syntax Score II (AUROC = 0.635 [95% CI 0.569-0.699]). Individuals classified as high risk (>90th percentile) presented increased HbA1c and LDL-C both at <24 hours post-ACS and 1-year follow-up. High-risk individuals required 33.5% of total costs and showed 4.96-fold (95% CI 3.71-5.48, P < .00001) greater per capita costs compared with low-risk individuals, mostly owing to avoidable costs (ACSH). This 2-step approach was more successful for finding individuals incurring high costs than predicting costs directly from clinical variables. CONCLUSION: ML methods predicted long-term risks and avoidable costs after ACS.

A survey on deep learning for skin lesion segmentation.

Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrinsic factors (e.g., lesion shape and contrast), and variations in image acquisition conditions make skin lesion segmentation a challenging task. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. In this survey, we cross-examine 177 research papers that deal with deep learning-based segmentation of skin lesions. We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions both from the viewpoint of select seminal works, and from a systematic viewpoint, examining how those choices have influenced current trends, and how their limitations should be addressed. To facilitate comparisons, we summarize all examined works in a comprehensive table as well as an interactive table available online3.

The framing of time-dependent machine learning models improves risk estimation among young individuals with acute coronary syndromes.

Acute coronary syndrome (ACS) is a common cause of death in individuals older than 55 years. Although younger individuals are less frequently seen with ACS, this clinical event has increasing incidence trends, shows high recurrence rates and triggers considerable economic burden. Young individuals with ACS (yACS) are usually underrepresented and show idiosyncratic epidemiologic features compared to older subjects. These differences may justify why available risk prediction models usually penalize yACS with higher false positive rates compared to older subjects. We hypothesized that exploring temporal framing structures such as prediction time, observation windows and subgroup-specific prediction, could improve time-dependent prediction metrics. Among individuals who have experienced ACS (nglobal_cohort = 6341 and nyACS = 2242), the predictive accuracy for adverse clinical events was optimized by using specific rules for yACS and splitting short-term and long-term prediction windows, leading to the detection of 80% of events, compared to 69% by using a rule designed for the global cohort.

Tumor antigen-unbiased variable epitope library contains mimotopes with antitumor effect in a mouse model of breast cancer.

Breast cancer is one of the leading causes of death that affects the female population worldwide. Despite advances in treatments and a greater understanding of the disease, there are still difficulties in successfully treating patients. Currently, the main challenge in the field of cancer vaccines is antigenic variability which can reduce antigen-specific T- cell response efficacy. The search for and validation of immunogenic antigen targets increased dramatically over the past few decades and, with the advent of modern sequencing techniques, permitting the fast and accurate identification of the neoantigen landscape of tumor cells, will undoubtedly continue to grow exponentially for years to come. We have previously implemented Variable Epitope Libraries (VEL) as an unconventional vaccine strategy in preclinical models and for identifying and selecting mutant epitope variants. Here, we used an alanine-based sequence to generate a 9-mer VEL-like combinatorial mimotope library G3d as a new class of vaccine immunogen. An in silico analysis of the 16,000 G3d-derived sequences revealed potential MHC-I binders and immunogenic mimotopes. We demonstrated the antitumor effect of treatment with G3d in the 4T1 murine model of breast cancer. Moreover, two different T cell proliferation screening assays against a panel of randomly selected G3d-derived mimotopes allowed the isolation of both stimulatory and inhibitory mimotopes showing differential therapeutic vaccine efficacy. Thus, the mimotope library is a promising vaccine immunogen and a reliable source for isolating molecular cancer vaccine components.

Rationale and design of the Brazilian diabetes study: a prospective cohort of type 2 diabetes.

BACKGROUND: Optimal control of traditional risk factors only partially attenuates the exceeding cardiovascular mortality of individuals with diabetes. Employment of machine learning (ML) techniques aimed at the identification of novel features of risk prediction is a compelling target to tackle residual cardiovascular risk. The objective of this study is to identify clinical phenotypes of T2D which are more prone to developing cardiovascular disease. METHODS: The Brazilian Diabetes Study is a single-center, ongoing, prospective registry of T2D individuals. Eligible patients are 30 years old or older, with a confirmed T2D diagnosis. After an initial visit for the signature of the informed consent form and medical history registration, all volunteers undergo biochemical analysis, echocardiography, carotid ultrasound, ophthalmologist visit, dual x-ray absorptiometry, coronary artery calcium score, polyneuropathy assessment, advanced glycation end-products reader, and ambulatory blood pressure monitoring. A 5-year follow-up will be conducted by yearly phone interviews for endpoints disclosure. The primary endpoint is the difference between ML-based clinical phenotypes in the incidence of a composite of death, myocardial infarction, revascularization, and stroke. Since June/2016, 1030 patients (mean age: 57 years, diabetes duration of 9.7 years, 58% male) were enrolled in our study. The mean follow-up time was 3.7 years in October/2021. CONCLUSION: The BDS will be the first large population-based cohort dedicated to the identification of clinical phenotypes of T2D at higher risk of cardiovascular events. Data derived from this study will provide valuable information on risk estimation and prevention of cardiovascular and other diabetes-related events. CLINICALTRIALS.GOV IDENTIFIER: NCT04949152.

A data-driven approach to referable diabetic retinopathy detection.

Prior art on automated screening of diabetic retinopathy and direct referral decision shows promising performance; yet most methods build upon complex hand-crafted features whose performance often fails to generalize. OBJECTIVE: We investigate data-driven approaches that extract powerful abstract representations directly from retinal images to provide a reliable referable diabetic retinopathy detector. METHODS: We gradually build the solution based on convolutional neural networks, adding data augmentation, multi-resolution training, robust feature-extraction augmentation, and a patient-basis analysis, testing the effectiveness of each improvement. RESULTS: The proposed method achieved an area under the ROC curve of 98.2% (95% CI: 97.4-98.9%) under a strict cross-dataset protocol designed to test the ability to generalize - training on the Kaggle competition dataset and testing using the Messidor-2 dataset. With a 5 × 2-fold cross-validation protocol, similar results are achieved for Messidor-2 and DR2 datasets, reducing the classification error by over 44% when compared to most published studies in existing literature. CONCLUSION: Additional boost strategies can improve performance substantially, but it is important to evaluate whether the additional (computation- and implementation-) complexity of each improvement is worth its benefits. We also corroborate that novel families of data-driven methods are the state of the art for diabetic retinopathy screening. SIGNIFICANCE: By learning powerful discriminative patterns directly from available training retinal images, it is possible to perform referral diagnostics without detecting individual lesions.

A differential sex-specific pattern of IgG2 and IgG4 subclasses of anti-drug antibodies (ADAs) induced by glatiramer acetate in relapsing-remitting multiple sclerosis patients.

BACKGROUND: Glatiramer acetate (GA) is a drug for Multiple Sclerosis (MS) treatment. However, its administration induces anti-drug antibodies (ADA). This research evaluated the sex differences in humoral response against GA in RR-MS patients METHODS: We analyzed 69 RR-MS patients, 43 treated with GA and 26 treated with IFN-ß. In all cases, the serum concentration of IgG antibodies was determined by UPLC, whereas the levels of IgG subclasses (1-4) of anti-GA antibodies and the concentration of IL-6 were detected by Multiplex and IL-10, and IFN-γ were detected by ELISA. RESULTS: The total concentration of IgG antibodies in patients did not differ between treatments, whereas the IgG levels of ADA were higher in male and female patients treated with GA (P ≤ 0.0001). The subclasses of IgG anti-GA antibodies were as follows: IgG4>>IgG3>IgG1>IgG2. Statistical analysis showed differences in the IgG2 (P ≤ 0.01) and IgG4 (P ≤ 0.0001) subclasses by sex in RR-MS patients. Levels of IgG1 subclass in male patients correlated positively with the circulatory levels of IL-6 (rs = 0.587, P ≤ 0.04) and IFN-γ (rs = 0.721, P ≤ 0.001), while IgG2 subclass levels in female patients correlated with serum levels of IFN-γ (rs = 0.628, P ≤ 0.0006). Statistical analysis did not detect correlations between the levels of IgG (1-4) subclasses of anti-GA antibodies and the evaluated clinical parameters. CONCLUSION: This study showed differences in the levels of IgG2 and IgG4 subclasses of ADA between male and female RR-MS patients. Further studies are necessary to take advantage of the clinical potential of this finding.

Development of Functional Antibodies Directed to Human Dialyzable Leukocyte Extract (Transferon®).

Transferon® is an immunomodulator made of a complex mixture of peptides from human dialyzable leucocyte extracts (hDLEs). Development of surrogate antibodies directed to hDLE is an indispensable tool for studies during process control and preclinical trials. These antibodies are fundamental for different analytical approaches, such as identity test and drug quantitation, as well as to characterize its pharmacokinetic and mechanisms of action. A previous murine study showed the inability of the peptides of Transferon® to induce antibody production by themselves; therefore, in this work, two approaches were tested to increase its immunogenicity: chemical conjugation of the peptides of Transferon® to carrier proteins and the use of a rabbit model. Bioconjugates were generated with Keyhole Limpet Hemocyanin (KLH) or Bovine Serum Albumin (BSA) through maleimide-activated carrier proteins. BALB/c mice and New Zealand rabbits were immunized with Transferon® conjugated to KLH or nonconjugated Transferon®. Animals that were immunized with conjugated Transferon® showed significant production of antibodies as evinced by the recognition of Transferon®-BSA conjugate in ELISA assays. Moreover, rabbits showed higher antibody titers when compared with mice. Neither mouse nor rabbits developed antibodies when immunized with nonconjugated Transferon®. Interestingly, rabbit antibodies were able to partially block IL-2 production in Jurkat cells after costimulation with Transferon®. In conclusion, it is feasible to elicit specific and functional antibodies anti-hDLE with different potential uses during the life cycle of the product.

[Correlation analysis of surnames and Y-chromosome genetic heritage in 3 provinces of southwestern Colombia]. / De genotipos e isonimias: análisis de correlación entre el apellido y el patrimonio genético heredado en el cromosoma Y en la población de tres departamentos del suroccidente colombiano.

INTRODUCTION: In Colombia, surnames are characters usually passed to the children by the father, and they have been compared to neutral alleles associated with the Y-chromosome. OBJECTIVE: Population frequencies were determined for 17 short tandem repeats (STR) DNA markers on the Y-chromosome to compare the two identity codes and define the correlation between haplotypes and surnames in each individual. MATERIALS AND METHODS: DNA was extracted from blood samples from 308 male individuals in provinces of Valle del Cauca, Cauca and Nariño, all in southwestern Colombia. Sample DNA was analyzed with the commercial kit AmpFLSTR Yfiler (Applied Biosystems) and examined for the following 17 Y-chromosome STR markers: DYS19, DYS389I, DYS389II, DYS390, DYS391, DYS392, DYS393, DYS385a/b, DYS437, DYS438, DYS439, DYS448, DYS456, DYS458, DYS635 and Y-GATA-H4. The frequencies of molecular haplotypes were associated with the surname reported by each individual, and a correlation table was constructed. Amerindian and European surnames were associated with the presence of allele DYS19/13, a characteristic of Amerindian populations. RESULTS: Allele frequencies were reported for each of the 17 STR markers in the southwestern region of Colombia-high genetic and haplotypic diversities were obtained. Approximately 40% of lineage inconsistencies were found when the molecular genotype was compared with the European or Amerindian surnames. CONCLUSIONS: Surnames must be used as population markers with reservation. The genetic evidence indicates that traditional genealogies based on surnames with or without documental support, may be inconsistant with their biological provenance.

A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus.

Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, nonetheless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometric data that are inputted with the developed decision-making algorithm, we were able to provide a set of features that work as a "fingerprint" for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening-faster and more accurate-with improved cost-effectiveness when compared to existing technologies.

Cross sectional study reveals a high percentage of asymptomatic Plasmodium vivax infection in the Amazon Rio Negro area, Brazil.

A parasitological, clinical, serological and molecular cross-sectional study carried out in a highly endemic malaria area of Rio Negro in the Amazon State, Brazil, revealed a high prevalence of asymptomatic Plasmodium vivax infection. A total of 109 persons from 25 families were studied in five villages. Ninety-nine inhabitants (90.8%) had at least one previous episode of malaria. Serology showed 85.7% and 46.9% of positivity when P. falciparum antigens and P. vivax MSP-1, respectively, were used. Twenty blood samples were PCR positive for P. vivax (20.4%) and no P. falciparum infection was evidenced by this technique. No individual presenting positive PCR reaction had clinical malaria during the survey neither in the six months before nor after, confirming that they were cases of asymptomatic infection. Only one 12 year old girl presented a positive thick blood smear for P. vivax. This is the first description of asymptomatic Plasmodium infection in this area studied.

Beyond Lesion-Based Diabetic Retinopathy: A Direct Approach for Referral.

Diabetic retinopathy (DR) is the leading cause of blindness in adults, but can be managed if detected early. Automated DR screening helps by indicating which patients should be referred to the doctor. However, current techniques of automated screening still depend too much on the detection of individual lesions. In this study, we bypass lesion detection, and directly train a classifier for DR referral. Additional novelties are the use of state-of-the-art mid-level features for the retinal images: BossaNova and Fisher Vector. Those features extend the classical Bags of Visual Words and greatly improve the accuracy of complex classification tasks. The proposed technique for direct referral is promising, achieving an area under the curve of 96.4%, thus, reducing the classification error by almost 40% over the current state of the art, held by lesion-based techniques.

Education and armed conflict: consequences on the population of children and adolescents in Colombia: editorial / Educación y conflicto armado: consecuencias en la población de niños, niñas y adolescentes en Colombia: editorial

Throughout history, the right to education has been a determining and unquestionably necessary factor for the improvement of people; however, as a socio-political process, it has not been free of adversities. In this sense, for more than six decades, Colombia has lived in the context of an internal armed conflict

A lo largo de la historia, el derecho a la educación ha sido un factor determinante e indiscutiblemente necesario para la superación de las personas; sin embargo, como proceso sociopolítico, no ha estado exento de adversidades. En este sentido, desde hace más de seis décadas, Colombia vive en el contexto de un conflicto armado interno

Transferon™, a peptide mixture with immunomodulatory properties is not immunogenic when administered with various adjuvants.

Transferon, a human dialyzable leukocyte extract (hDLE), is a biotherapeutic that comprises a complex mixture of low-molecular-weight peptides (< 10 kDa) and is used to treat diseases with an inflammatory component. Some biotherapeutics, including those composed of peptides, can induce anti-drug antibodies (ADA) that block or diminish their therapeutic effect. Nevertheless, few studies have evaluated peptide-derived drug immunogenicity. In this study, the immunogenicity of Transferon was examined in a murine model during an immunization scheme using the following adjuvants: Al(OH)3, incomplete Freund's adjuvant (IFA), or Titermax Gold. The inoculation scheme entailed three routes of administration (intraperitoneal, Day 1; subcutaneous, Day 7; and intramuscular, Day 14) using 200 µg Transferon/inoculation. Serum samples were collected on Day 21. Total IgG levels were quantitated by affinity chromatography, and specific antibodies against components of Transferon were analyzed by dot-blot and ELISA. Ovalbumin (OVA, 44 kDa) and peptides from hydrolyzed collagen (PFHC, < 17 kDa) were used as positive and negative controls, respectively, in the same inoculation scheme and analyses for Transferon. OVA, PFHC, and Transferon increased total IgG concentrations in mice. However, only IgG antibodies against OVA were detected. Based on the results, it is concluded that Transferon does not induce generation of specific antibodies against its components in this model, regardless of adjuvant and route of administration. These results support the safety of Transferon by confirming its inability to induce ADA in this animal model.

Process signatures in glatiramer acetate synthesis: structural and functional relationships.

Glatiramer Acetate (GA) is an immunomodulatory medicine approved for the treatment of multiple sclerosis, whose mechanisms of action are yet to be fully elucidated. GA is comprised of a complex mixture of polypeptides with different amino acid sequences and structures. The lack of sensible information about physicochemical characteristics of GA has contributed to its comprehensiveness complexity. Consequently, an unambiguous determination of distinctive attributes that define GA is of highest relevance towards dissecting its identity. Herein we conducted a study of characteristic GA heterogeneities throughout its manufacturing process (process signatures), revealing a strong impact of critical process parameters (CPPs) on the reactivity of amino acid precursors; reaction initiation and polymerization velocities; and peptide solubility, susceptibility to hydrolysis, and size-exclusion properties. Further, distinctive GA heterogeneities were correlated to defined immunological and toxicological profiles, revealing that GA possesses a unique repertoire of active constituents (epitopes) responsible of its immunological responses, whose modification lead to altered profiles. This novel approach established CPPs influence on intact GA peptide mixture, whose physicochemical identity cannot longer rely on reduced properties (based on complete or partial GA degradation), providing advanced knowledge on GA structural and functional relationships to ensure a consistent manufacturing of safe and effective products.

Origins of sequence diversity in the malaria vaccine candidate merozoite surface protein-2 (MSP-2) in Amazonian isolates of Plasmodium falciparum.

The recent evolution of Plasmodium falciparum is at odds with the extensive polymorphism found in most genes coding for antigens. Here, we examined the patterns and putative mechanisms of sequence diversification in the merozoite surface protein-2 (MSP-2), a major malarial repetitive surface antigen. We compared the msp-2 gene sequences from closely related clones derived from sympatric parasite isolates from Brazilian Amazonia and used microsatellite typing to examine, in these same clones, the haplotype background of chromosome 2, where msp-2 is located. We found examples of msp-2 sequence rearrangements putatively created by nonreciprocal recombinational events, such as replication slippage and gene conversion, while maintaining the chromosome haplotype. We conclude that these nonreciprocal recombination events may represent a major source of antigenic diversity in MSP-2 in P. falciparum populations with low rates of classical meiotic recombination.