Determining the physicochemical properties of a protein can reveal important insights in their structure, biological functions, stability, and interactions with other molecules. Although tools for computing properties of proteins already existed, we could not find a comprehensive tool that enables the calculations of multiple properties for multiple input proteins on the proteome level at once. Facing this limitation, we developed Multiple Protein Profiler (MPP) 1.0 as an integrated tool that allows the profiling of 12 individual properties of multiple proteins in a significant manner. MPP provides a tabular and graphic visualization of properties of multiple proteins. The tool is freely accessible at https://mproteinprofiler.microbiologyandimmunology.dal.ca/.

Background In August 2023, an outbreak of mpox was reported in the eastern part, South Kivu Province, of Democratic Republic of the Congo. In this study, we aimed to investigate the origin of this outbreak and to assess how monkeypox virus spread among humans in the city of Kamituga. Methods We performed an observational cohort study by recruiting hospitalized patients with mpox-like symptoms. Furthermore, we compared structured, de-identified case report forms and interviews were conducted to determine the possible origins and modes of transmission of the mpox outbreak. We describe the clinical characteristics and epidemiology observed in reported infections. Findings During the study period (24 September 2023 to 29 January 2024), 164 patients were admitted to the Kamituga hospital, 51 individuals were enrolled in the study and interviewed, and 37 (73%) of 51 individuals received a molecularly confirmed mpox diagnosis. The median age for males was 24 years (IQR 18-30; range 14-36) and 19 years for females (IQR 17-21; range 1-59). The cohort was comprised of 47 (92%) of 51 individuals who identified as heterosexual, and two (4%) of 51 as bisexual, with 31 (61%) of 51 individuals sexually active with more than one partner within the last six months. The direct transmission routes are unknown; however, it is expected that the majority of infections were transmitted via occupational exposures. Out of the 51 individuals, 24 (47%) were professional sex workers (PSWs), while five (10%) were gold miners, 6 (12%) were students, and four (8%) were farmers; the remaining individual occupations were unknown. The most common symptoms associated with clinical mpox diagnosis were fever, which was described in 38 (75%) of 51 individuals, and rash, which was described in 45 (88%) of 51 individuals. Among those with a rash, 21 (41%) of 51 individuals experienced oral lesions, and 32 (63%) of 51 presented anogenital lesions. Mpox viral DNA was detected by qPCR from vaginal, penile, and oral swabs in 37 (73%) of 51 enrolled individuals. Two deaths were reported. Interpretation In this observational cohort study, mpox virus infection caused symptoms in a wide age range of participants with most cases presenting in sexually active individuals. Symptoms included fever, cough, lymphadenopathy, sore throat, chills, headache, back pain, muscle pain, vomiting, nausea, conjunctivitis, and rash (oral and anogenital). Heterosexual partners dominated human-to-human contact transmission suggesting that heterosexual close contact is the main form of transmission in this outbreak. Furthermore, Professional Sex Workers (PSWs) were the dominant occupation among infected individuals, indicating that PSWs and clients may be at higher risk for developing mpox virus infections.

Introduction: Millions of deaths worldwide are a result of sepsis (viral and bacterial) and septic shock syndromes which originate from microbial infections and cause a dysregulated host immune response. These diseases share both clinical and immunological patterns that involve a plethora of biomarkers that can be quantified and used to explain the severity level of the disease. Therefore, we hypothesize that the severity of sepsis and septic shock in patients is a function of the concentration of biomarkers of patients.Methods: In our work, we quantified data from 30 biomarkers with direct immune function. We used distinct Feature Selection algorithms to isolate biomarkers to be fed into machine learning algorithms, whose mapping of the decision process would allow us to propose an early diagnostic tool.Results: We isolated two biomarkers, i.e., Programmed Death Ligand-1 and Myeloperoxidase, that were flagged by the interpretation of an Artificial Neural Network. The upregulation of both biomarkers was indicated as contributing to increase the severity level in sepsis (viral and bacterial induced) and septic shock patients.Discussion: In conclusion, we built a function considering biomarker concentrations to explain severity among sepsis, sepsis COVID, and septic shock patients. The rules of this function include biomarkers with known medical, biological, and immunological activity, favoring the development of an early diagnosis system based in knowledge extracted from artificial intelligence.

Poxviridae is a family of large, complex, enveloped, and double-stranded DNA viruses. The members of this family are ubiquitous and well known to cause contagious diseases in humans and other types of animals as well. Taxonomically, the poxviridae family is classified into two subfamilies, namely Chordopoxvirinae (affecting vertebrates) and Entomopoxvirinae (affecting insects). The members of the Chordopoxvirinae subfamily are further divided into 18 genera based on the genome architecture and evolutionary relationship. Of these 18 genera, four genera, namely Molluscipoxvirus, Orthopoxvirus, Parapoxvirus, and Yatapoxvirus, are known for infecting humans. Some of the popular members of poxviridae are variola virus, vaccine virus, Mpox (formerly known as monkeypox), cowpox, etc. There is still a pressing demand for the development of effective vaccines against poxviruses. Integrated immunoinformatics and artificial-intelligence (AI)-based methods have emerged as important approaches to design multi-epitope vaccines against contagious emerging infectious diseases. Despite significant progress in immunoinformatics and AI-based techniques, limited methods are available to predict the epitopes. In this study, we have proposed a unique method to predict the potential antigens and T-cell epitopes for multiple poxviruses. With PoxiPred, we developed an AI-based tool that was trained and tested with the antigens and epitopes of poxviruses. Our tool was able to locate 3191 antigen proteins from 25 distinct poxviruses. From these antigenic proteins, PoxiPred redundantly located up to five epitopes per protein, resulting in 16,817 potential T-cell epitopes which were mostly (i.e., 92%) predicted as being reactive to CD8+ T-cells. PoxiPred is able to, on a single run, identify antigens and T-cell epitopes for poxviruses with one single input, i.e., the proteome file of any poxvirus.

Background: Archaea are a vast and unexplored domain. Bioinformatic techniques might enlighten the path to a higher quality genome annotation in varied organisms. Promoter sequences of archaea have the action of a plethora of proteins upon it. The conservation found in a structural level of the binding site of proteins such as TBP, TFB, and TFE aids RNAP-DNA stabilization and makes the archaeal promoter prone to be explored by statistical and machine learning techniques. Results and discussions: In this study, experimentally verified promoter sequences of the organisms Haloferax volcanii, Sulfolobus solfataricus, and Thermococcus kodakarensis were converted into DNA duplex stability attributes (i.e. numerical variables) and were classified through Artificial Neural Networks and an in-house statistical method of classification, being tested with three forms of controls. The recognition of these promoters enabled its use to validate unannotated promoter sequences in other organisms. As a result, the binding site of basal transcription factors was located through a DNA duplex stability codification. Additionally, the classification presented satisfactory results (above 90%) among varied levels of control. Concluding remarks: The classification models were employed to perform genomic annotation into the archaea Aciduliprofundum boonei and Thermofilum pendens, from which potential promoters have been identified and uploaded into public repositories.