Preclinical Development of a Novel Epitope-based DNA Vaccine Candidate against SARS-CoV-2 and Evaluation of Immunogenicity in BALB/c Mice

The protective efficacies of current licensed vaccines against COVID-19 have significantly reduced as a result of SARS-CoV-2 variants of concern (VOCs) which carried multiple mutations in the Spike (S) protein. Considering that these vaccines were developed based on the S protein of the original SARS-CoV-2 Wuhan strain, we designed a recombinant plasmid DNA vaccine based on highly conserved and immunogenic B and T cell epitopes against SARS-CoV-2 Wuhan strain and the Omicron VOC. Literature mining and bioinformatics were used to identify 6 immunogenic peptides from conserved regions of the SARS-CoV-2 S and membrane (M) proteins. Nucleotide sequences encoding these peptides representing highly conserved B and T cell epitopes were cloned into a pVAX1 vector to form the pVAX1/S2-6EHGFP recombinant DNA plasmid vaccine. The DNA vaccine was intranasally or intramuscularly administered to BALB/c mice and evaluations of humoral and cellular immune responses were performed. The intramuscular administration of pVAX1/S2-6EHGFP was associated with a significantly higher percentage of CD8+ T cells expressing IFN-γ when compared with the empty vector and PBS controls. Intramuscular or intranasal administrations of pVAX1/S2-6EHGFP resulted in robust IgG antibody responses. Sera from mice intramuscularly immunized with pVAX1/S2-6EHGFP were found to elicit neutralizing antibodies capable of SARS-CoV-2 Omicron variant with the ACE2 cell surface receptor. This study demonstrated that the DNA vaccine construct encoding highly conserved immunogenic B and T cell epitopes was capable of eliciting potent humoral and cellular immune responses in mice.

Development of next-generation reference interval models to establish reference intervals based on medical data: current status, algorithms and future consideration

Evidence derived from laboratory medicine plays a pivotal role in the diagnosis, treatment monitoring, and prognosis of various diseases. Reference intervals (RIs) are indispensable tools for assessing test results. The accuracy of clinical decision-making relies directly on the appropriateness of RIs. With the increase in real-world studies and advances in computational power, there has been increased interest in establishing RIs using big data. This approach has demonstrated cost-effectiveness and applicability across diverse scenarios, thereby enhancing the overall suitability of the RI to a certain extent. However, challenges persist when tests results are influenced by age and sex. Reliance on a single RI or a grouping of RIs based on age and sex can lead to erroneous interpretation of results with significant implications for clinical decision-making. To address this issue, the development of next generation of reference interval models has arisen at an historic moment. Such models establish a curve relationship to derive continuously changing reference intervals for test results across different age and sex categories. By automatically selecting appropriate RIs based on the age and sex of patients during result interpretation, this approach facilitates clinical decision-making and enhances disease diagnosis/treatment as well as health management practices. Development of next-generation reference interval models use direct or indirect sampling techniques to select reference individuals and then employed curve fitting methods such as splines, polynomial regression and others to establish continuous models. In light of these studies, several observations can be made: Firstly, to date, limited interest has been shown in developing next-generation reference interval models, with only a few models currently available. Secondly, there are a wide range of methods and algorithms for constructing such models, and their diversity may lead to confusion. Thirdly, the process of constructing next-generation reference interval models can be complex, particularly when employing indirect sampling techniques. At present, normative documents pertaining to the development of next-generation reference interval models are lacking. In summary, this review aims to provide an overview of the current state of development of next-generation reference interval models by defining them, highlighting inherent advantages, and addressing existing challenges. It also describes the process, advanced algorithms for model building, the tools required and the diagnosis and validation of models. Additionally, a discussion on the prospects of utilizing big data for developing next-generation reference interval models is presented. The ultimate objective is to equip clinical laboratories with the theoretical framework and practical tools necessary for developing and optimizing next-generation reference interval models to establish next-generation reference intervals while enhancing the use of medical data resources to facilitate precision medicine.

Next-generation of melanoma researchers: Trainee perspectives from around the world

The Society for Melanoma Research (SMR) was created 20 years ago and has unequivocally contributed to the vast progress of the field, particularly for the treatment of melanoma patients with metastatic disease by facilitating synergistic collaborations between clinicians, researchers at the bench, and industry. In commemoration of the 20th anniversary of the first SMR International Congress (held in 2003 in Philadelphia), we look to the future by highlighting the perspectives of the next generation of rising stars, medical, and graduate students across six continents.

Perspectives of Next-Generation Live-Attenuated Rift Valley Fever Vaccines for Animal and Human Use

Live-attenuated Rift Valley fever (RVF) vaccines transiently replicate in the vaccinated host, thereby effectively initiating an innate and adaptive immune response. Rift Valley fever virus (RVFV)-specific neutralizing antibodies are considered the main correlate of protection. Vaccination with classical live-attenuated RVF vaccines during gestation in livestock has been associated with fetal malformations, stillbirths, and fetal demise. Facilitated by an increased understanding of the RVFV infection and replication cycle and availability of reverse genetics systems, novel rationally-designed live-attenuated candidate RVF vaccines with improved safety profiles have been developed. Several of these experimental vaccines are currently advancing beyond the proof-of-concept phase and are being evaluated for application in both animals and humans. We here provide perspectives on some of these next-generation live-attenuated RVF vaccines and highlight the opportunities and challenges of these approaches to improve global health.

Next-generation liquid biopsy instruments: Challenges and opportunities

Conventional cancer diagnosis needs to excise diseased tissue from the patient's body for biopsy, causing severe injury to patients. Liquid biopsy (LB), with the superior advantage of minimal invasiveness, has shown its ability to cancer diagnosis in real-time and has been developing promising diagnostic instruments. However, until today, the developed instrument still cannot be an alternative to tissue biopsy in the majority of research and clinical settings. In this paper, we first summarize the challenges and limitations suffered by the existing LB instrument. Then, the opportunities and future progression of the next-generation instrument are discussed in detail. In all, we hope that the future LB instrument can be eventually integrated into the clinical workflow and serve as a validated and reliable tool for cancer diagnosis.

Intra-Exon Motif Correlations as a Proxy Measure for Mean Per-Tile Sequence Quality Data in RNA-Seq

Given the wide variability in the quality of next-generation sequencing data submitted to public repositories, it is essential to identify methods that can perform quality control on these data sets when additional quality control data, such as mean tile data, are missing from public repositories. In this study, we present evidence that correlating counts of reads corresponding to pairs of motifs separated over specific distances on individual exons can be used as a proxy mean tile data in the data sets we analyzed and hence could be used when mean tile data are not available. As test data sets we use the Homo sapiens in vitro transcribed (IVT) data set, and a Drosophila melanogaster data set comprising wild and mutant types. We find that a FastQC analysis of the available parts of these data sets demonstrates that the per-tile sequencing quality is good for all the data sets apart from the mutant-type data where the mutant-r3 data are worse than the mutant-r2 data. Correspondingly, intra-exon motif correlations are reasonably large for all data sets except this latter case where the mutant-r2 correlations are low and the mutant-r3 correlations close to zero. We propose that these extremely low correlations are indicative of bias of technical origin, such as flowcell errors. In addition to this, the intra-exon motif correlations as a function of both guanosine-cytosine (GC) content parameters are somewhat higher and less dependent on the GC content parameters in the IVT-Plasmids messenger RNA (mRNA) selection free RNA-Seq sample (control) than in the other RNA-Seq samples that did undergo mRNA selection: both ribosomal depletion (IVT-Only) and PolyA selection (IVT-PolyA, wild type, and mutant).

Technological Transformation of Telco Operators towards Seamless IoT Edge-Cloud Continuum

This article investigates and discusses challenges in the telecommunication field from multiple perspectives, both academic and industry sides are catered for, surveying the main points of technological transformation toward edge-cloud continuum from the view of a telco operator to show the complete picture, including the evolution of cloud-native computing, Software-Defined Networking (SDN), and network automation platforms. The cultural shift in software development and management with DevOps enabled the development of significant technologies in the telecommunication world, including network equipment, application development, and system orchestration. The effect of the aforementioned cultural shift to the application area, especially from the IoT point of view, is investigated. The enormous change in service diversity and delivery capabilities to mass devices are also discussed. During the last two decades, desktop and server virtualization has played an active role in the Information Technology (IT) world. With the use of OpenFlow, SDN, and Network Functions Virtualization (NFV), the network revolution has got underway. The shift from monolithic application development and deployment to micro-services changed the whole picture. On the other hand, the data centers evolved in several generations where the control plane cannot cope with all the networks without an intelligent decision-making process, benefiting from the AI/ML techniques. AI also enables operators to forecast demand more accurately, anticipate network load, and adjust capacity and throughput automatically. Going one step further, zero-touch networking and service management (ZSM) is proposed to get high-level human intents to generate a low-level configuration for network elements with validated results, minimizing the ratio of faults caused by human intervention. Harmonizing all signs of progress in different communication technologies enabled the use of edge computing successfully. Low-powered (from both energy and processing perspectives) IoT networks have disrupted the customer and end-point demands within the sector, as such paved the path towards devising the edge computing concept, which finalized the whole picture of the edge-cloud continuum.

Detecting and classifying neurotransmitter signals from ultra-high sensitivity PET data: the future of molecular brain imaging

Efforts to build the next generation of brain PET scanners are underway. It is expected that a new scanner (NS) will offer anorder-of-magnitude improvementin sensitivity to counts compared to the current state-of-the-art, Siemens HRRT. Our goal was to explore the use of the anticipated increased sensitivity in combination with the linear-parametric neurotransmitter PET (lp-ntPET) model to improve detection and classification of transient dopamine (DA) signals. We simulated striatal [¹¹C]raclopride PET data to be acquired on a future NS which will offer ten times the sensitivity of the HRRT. The simulated PET curves included the effects of DA signals that varied in start-times, peak-times, and amplitudes. We assessed the detection sensitivity of lp-ntPET to various shapes of DA signal. We evaluated classification thresholds for their ability to separate 'early'- versus 'late'-peaking, and 'low'- versus 'high'-amplitude events in a 4D phantom. To further refine the characterization of DA signals, we developed a weighted k-nearest neighbors (wkNN) algorithm to incorporate information from the neighborhood around each voxel to reclassify it, with a level of certainty. Our findings indicate that the NS would expand the range of detectable neurotransmitter events to 72%, compared to the HRRT (31%). Application of wkNN augmented the detection sensitivity to DA signals in simulated NS data to 92%. This work demonstrates that the ultra-high sensitivity expected from a new generation of brain PET scanner, combined with a novel classification algorithm, will make it possible to accurately detect and classify short-lived DA signals in the brain based on their amplitude and timing.

The composition and variation of the BCR CDR3s in gastric cancer

Gastric cancer (GC) is the fourth most common type of cancer and the second most common cause of cancer-associated mortality worldwide. B cell-associated autoantibodies against tumor-associated antigens are attractive biomarkers for the development of noninvasive serological tests for the early detection of cancer. This is due to their specificity and stability in the sera. In the present study multiplex polymerase chain reaction and Illumina high-throughput sequencing (HTS) was used to study the composition and variation of the B cell receptor (BCR) complimentary-determining region 3 (CDR3) in GC. The peripheral blood, cancer tissues and peri-cancer tissues were included from 7 patients with GC. On average there was a total of 403,959 CDR3 sequences, with 72,367 unique CDR3 nt sequences and 61,709 unique CDR3 aa sequences per sample identified, which are critical for further understanding the BCR repertoire in GC. The details of GC CDR3s may accelerate the screening process for possible new autoantigens and may provide additional information necessary for generating effective B cell targeted diagnosis and therapeutic strategies.

Development and Regulation of Novel Influenza Virus Vaccines: A United States Young Scientist Perspective

Vaccination against influenza is the most effective approach for reducing influenza morbidity and mortality. However, influenza vaccines are unique among all licensed vaccines as they are updated and administered annually to antigenically match the vaccine strains and currently circulating influenza strains. Vaccine efficacy of each selected influenza virus vaccine varies depending on the antigenic match between circulating strains and vaccine strains, as well as the age and health status of the vaccine recipient. Low vaccine effectiveness of seasonal influenza vaccines in recent years provides an impetus to improve current seasonal influenza vaccines, and for development of next-generation influenza vaccines that can provide broader, long-lasting protection against both matching and antigenically diverse influenza strains. This review discusses a perspective on some of the issues and formidable challenges facing the development and regulation of the next-generation influenza vaccines.

Neuropsychiatric genomics in precision medicine: diagnostics, gene discovery, and translation

Only a few years after its development, next-generation sequencing is rapidly becoming an essential part of clinical care for patients with serious neurological conditions, especially in the diagnosis of early-onset and severe presentations. Beyond this diagnostic role, there has been an explosion in definitive gene discovery in a range of neuropsychiatric diseases. This is providing new pointers to underlying disease biology and is beginning to outline a new framework for genetic stratification of neuropsychiatric disease, with clear relevance to both individual treatment optimization and clinical trial design. Here, we outline these developments and chart the expected impact on the treatment of neurological, neurodevelopmental, and psychiatric disease.

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

Complex changes in gene expression typically mediate a large portion of a cellular response. Each gene may change expression with unique kinetics as the gene is regulated by the particular timing of one of many stimuli, signaling pathways or secondary effects. In order to capture the entire gene expression response to hypoxia in the yeast S. cerevisiae, RNA-seq analysis was used to monitor the mRNA levels of all genes at specific times after exposure to hypoxia. Hypoxia was established by growing cells in ~100% N2 gas. Importantly, unlike other hypoxic studies, ergosterol and unsaturated fatty acids were not added to the media because these metabolites affect gene expression. Time points were chosen in the range of 0 - 4 h after hypoxia because that period captures the major changes in gene expression. At each time point, mid-log hypoxic cells were quickly filtered and frozen, limiting exposure to O2 and concomitant changes in gene expression. Total RNA was extracted from cells and used to enrich for mRNA, which was then converted to cDNA. From this cDNA, multiplex libraries were created and eight or more samples were sequenced in one lane of a next-generation sequencer. A post-sequencing pipeline is described, which includes quality base trimming, read mapping and determining the number of reads per gene. DESeq2 within the R statistical environment was used to identify genes that change significantly at any one of the hypoxic time points. Analysis of three biological replicates revealed high reproducibility, genes of differing kinetics and a large number of expected O2-regulated genes. These methods can be used to study how the cells of various organisms respond to hypoxia over time and adapted to study gene expression during other cellular responses.

Blood transcriptomes reveal novel parasitic zoonoses circulating in Madagascar's lemurs

Zoonotic diseases are a looming threat to global populations, and nearly 75% of emerging infectious diseases can spread among wildlife, domestic animals and humans. A 'One World, One Health' perspective offers us an ideal framework for understanding and potentially mitigating the spread of zoonoses, and the island of Madagascar serves as a natural laboratory for conducting these studies. Rapid habitat degradation and climate change on the island are contributing to more frequent contact among humans, livestock and wildlife, increasing the potential for pathogen spillover events. Given Madagascar's long geographical isolation, coupled with recent and repeated introduction of agricultural and invasive species, it is likely that a number of circulating pathogens remain uncharacterized in lemur populations. Thus, it is imperative that new approaches be implemented for de novo pathogen discovery. To this end, we used non-targeted deep sequencing of blood transcriptomes from two species of critically endangered wild lemurs (Indri indri and Propithecus diadema) to characterize blood-borne pathogens. Our results show several undescribed vector-borne parasites circulating within lemurs, some of which may cause disease in wildlife, livestock and humans. We anticipate that advanced methods for de novo identification of unknown pathogens will have broad utility for characterizing other complex disease transmission systems.

Neuropsychiatric genomics in precision medicine: diagnostics, gene discovery, and translation

Only a few years after its development, next-generation sequencing is rapidly becoming an essential part of clinical care for patients with serious neurological conditions, especially in the diagnosis of early-onset and severe presentations. Beyond this diagnostic role, there has been an explosion in definitive gene discovery in a range of neuropsychiatric diseases. This is providing new pointers to underlying disease biology and is beginning to outline a new framework for genetic stratification of neuropsychiatric disease, with clear relevance to both individual treatment optimization and clinical trial design. Here, we outline these developments and chart the expected impact on the treatment of neurological, neurodevelopmental, and psychiatric disease.

Fighting an old disease with modern tools: characteristics and molecular detection methods of drug-resistant Mycobacterium tuberculosis

Tuberculosis (TB) is an ancient disease, but not a disease of the past. The increasing prevalence of drug-resistant strains of Mycobacterium tuberculosis, the causative agent of TB, demands new measures to combat the situation. Rapid and accurate detection of the pathogen, and its drug susceptibility pattern, is essential for timely initiation of treatment, and ultimately, control of the disease. Molecular-based methods offer a great chance to improve detection of drug-resistant TB; however, their development and usage should be accompanied with a profound understanding of drug resistance mechanisms and circulating M. tuberculosis strains in specific settings, as otherwise, the usefulness of such tests may be limited. This review gives an overview of the history of TB treatment and drug resistance, drug resistance mechanisms for the most commonly used drugs and molecular methods designed to detect drug-resistant strains.

Improved Protocols for Illumina Sequencing

In this unit, we describe a set of improvements that have been made to the standard Illumina protocols to make the sequencing process more reliable in a high-throughput environment, reduce amplification bias, narrow the distribution of insert sizes, and reliably obtain high yields of data.

Pyrosequencing for microbial identification and characterization

Pyrosequencing is a versatile technique that facilitates microbial genome sequencing that can be used to identify bacterial species, discriminate bacterial strains and detect genetic mutations that confer resistance to anti-microbial agents. The advantages of pyrosequencing for microbiology applications include rapid and reliable high-throughput screening and accurate identification of microbes and microbial genome mutations. Pyrosequencing involves sequencing of DNA by synthesizing the complementary strand a single base at a time, while determining the specific nucleotide being incorporated during the synthesis reaction. The reaction occurs on immobilized single stranded template DNA where the four deoxyribonucleotides (dNTP) are added sequentially and the unincorporated dNTPs are enzymatically degraded before addition of the next dNTP to the synthesis reaction. Detection of the specific base incorporated into the template is monitored by generation of chemiluminescent signals. The order of dNTPs that produce the chemiluminescent signals determines the DNA sequence of the template. The real-time sequencing capability of pyrosequencing technology enables rapid microbial identification in a single assay. In addition, the pyrosequencing instrument, can analyze the full genetic diversity of anti-microbial drug resistance, including typing of SNPs, point mutations, insertions, and deletions, as well as quantification of multiple gene copies that may occur in some anti-microbial resistance patterns.