Multimodal chromatography combining steric exclusion and cation exchange as an intermediate downstream step to purify yellow fever virus-like particles

Yellow fever (YF) is an hemorrhagic viral disease transmitted by infected mosquitoes, which is endemic in many African and Central/South American countries. The severe symptoms and the high mortality rate of the disease can have devastating effects in case an outbreak occurs in an area where the population is non-vaccinated. Before the current YF vaccine became available, outbreaks in cities like Barcelona (Spain) and Philadelphia (USA) led to the death of approximately 10% of the population. Recent outbreaks have shown that YF continues to be a major public health threat due to production capability issues and shortage of vaccine stockpiles, which even led to the use of an emergency fractional (1/5) dose in Africa in 2016 and in Brazil in 2018. Yellow fever virus-like particles (VLPs) represent an interesting alternative to develop a new YF vaccine. With the aim of developing an efficient and affordable process to purifiy yellow fever VLPs, in this work we developed a multimodal strategy combining cation exchange (CEX) and steric exclusion chromatography (SXC) under conditions where the product of interest does not bind to the CEX adsorber, whereas many contaminants do. In this way, the product of interest is retained just due to steric exclusion by the polyethylene glycol (PEG) added to the mobile phase. Product desorption can be achieved by decreasing PEG concentration, while contaminants remain bound to the adsorber and are eluted in the regeneration step. To the best of our knowledge, the application of such a multimodal strategy has not been published before. Please click Download on the upper right corner to see the full abstract

Perfusion process for the production of a new, VLP-based yellow fever vaccine candidate

Yellow fever (YF) is an acute viral hemorrhagic disease endemic in tropical areas of Africa, Central and South America, which is transmitted by the bite of infected mosquitoes. It is a “historically devastating disease” (Paules and Fauci, 2017) that killed during outbreaks in past centuries, before the introduction of the current vaccine, approximately 10% of the population of cities like Philadelphia (USA) and Barcelona (Spain). According to Garske et al. (2014), YF caused in 2013 78,000 deaths worldwide, which is a disease burden comparable to influenza. In the past few years, outbreaks in Angola (2016) and in Brazil (2017-2018) led to the depletion of the WHO vaccine stockpile and to the introduction of the emergency use of a fractional dose (1/5). Furthermore, the Angola outbreak in 2016 caused the first cases of YF ever to occur in Asia (11 imported cases to China), rising the concern about approximately 2 billion immunologically naïve people who would be at high risk in Asia in case local transmission of the virus starts to occur (Wilder-Smith et al., 2019). The urgent need for a new YF vaccine becomes evident from two major issues concerning the current vaccine, which consists of a live-attenuated virus propagated in chicken embryos: (i) vaccine shortage due to limitations in the manufacturing technology; (ii) rare, but fatal adverse effects. Therefore, this work focuses on the development of a safe, non-replicating YF vaccine, produced by a high-productivity perfusion process. Stable recombinant HEK293 cell lines constitutively expressing the structural proteins prM (pre-membrane) and E (envelope) of YFV were generated, enabling long-term production and secretion of recombinant virus-like particles (VLPs). FACS (fluorescence activated cell sorting) was used to sort the transfected population for high producer cells and allowed obtaining an enriched cell pool producing significantly higher amounts of VLPs. Small scale kinetic studies under intermittent perfusion (pseudoperfusion) were performed in order to investigate possible feeding strategies and to evaluate the use of short-chain fatty acids as productivity enhancers. Subsequently, perfusion runs were carried out in stirred-tank bioreactors in order to investigate optimal conditions for VLP production, as well as to evaluate different cell retention devices (e.g. inclined lamella settler and ATF-2). Partial retention of the VLPs in the perfusion bioreactor system occurred when the ATF-2 was used. VLPs produced by perfusion were purified by a two-step chromatographic process, and transmission electron microscopy (TEM) images confirmed the expected size and morphology of the VLPs, enabling their use in mouse immunogenicity studies. References: Garske T, Van Kerkhove MD, Yactayo S, Ronveaux O, Lewis RF, Staples JE, Perea W, Ferguson NM, Yellow Fever Expert Committee (2014). Yellow fever in Africa: estimating the burden of disease and impact of mass vaccination from outbreak and serological data. PLoS Medicine 11:e1001638. Paules CI, Fauci AS (2017), Yellow fever - once again on the radar screen in the Americas, N Engl J Med 376: 1397-1399. Wilder-Smith A, Lee V, Gubler DJ (2019), Yellow fever: is Asia prepared for an epidemic? The Lancet 19:241-242

Classification of schizophrenic traits in transcriptions of audio spectra from patient literature: artificial intelligence models enhanced by geometric properties

Schizophrenia is a severe mental illness that affects approximately 1% of the global population and presents significant challenges for patients, families, and healthcare professionals. Characterized by symptoms such as delusions, hallucinations, disorganized speech or behavior, and cognitive impairment, this condition has an early onset and chronic trajectory, making it a debilitating challenge. Schizophrenia also imposes a substantial burden on society, exacerbated by the stigma associated with mental disorders. Technological advancements, such as computerized semantic, linguistic, and acoustic analyses, are revolutionizing the understanding and assessment of communication alterations, a significant aspect in various severe mental illnesses. Early and accurate diagnosis is crucial for improving prognosis and implementing appropriate treatments. In this context, the advancement of Artificial Intelligence (AI) has provided new perspectives for the treatment of schizophrenia, with machine learning techniques and natural language processing allowing a more detailed analysis of clinical, neurological, and behavioral data sets. The present article aims to present a proposal for computational models for the identification of schizophrenic traits in texts.  The database used in this article was created with 139 excerpts of patients' speeches reported in the book “Memories of My Nervous Disease” by German judge Daniel Paul Schreber, classifying them into three categories: 1 - schizophrenic, 2 - with schizophrenic traits and 3 - without any relation to the disorder. Of these speeches, 104 were used for training the models and the others 35 for validation.Three classification models were implemented using features based on geometric properties of graphs (number of vertices, number of cycles, girth, vertex of maximum degree, maximum clique size) and text entropy. Promising results were observed in the classification, with the Decision Tree-based model [1] achieving 100% accuracy, the KNN- k-Nearest Neighbor model observed with 62.8% accuracy, and the 'centrality-based' model with 59% precision. The high precision rates, observed when geometric properties are incorporated into Artificial Intelligence Models, suggest that the models can be improved to the point of capturing the language deviation traits that are indicative of schizophrenic disorders. In summary, this study paves the way for significant advances in the use of geometric properties in the field of psychiatry, offering a new data-based approach to the understanding and therapy of schizophrenia