The avian influenza A virus (AIV) is naturally prevalent in aquatic birds, infecting different avian species and transmitting from birds to humans. Both AIVs, the H5N1 and H7N9 viruses, have the potential to infect humans, causing an acute influenza disease syndrome in humans, and are a possible pandemic threat. AIV H5N1 is highly pathogenic, whereas AIV H7N9 has comparatively low pathogenicity. A clear insight into the disease pathogenesis is significant to understand the host’s immunological response, which in turn facilitates the design of the control and prevention strategies. In this review, we aim to provide comprehensive details on the pathogenesis and clinical features of the disease. Moreover, the innate and adaptive immunological responses to AIV and the recent studies conducted on the CD8⁺ T cell immunity against AIVs are detailed upon. Further, the current status and advancement in the development of AIV vaccines, along with the challenges, are also discussed. The information provided will be helpful in combating the transmission of AIV from birds to humans and, thus, preventing severe outbreaks leading to pandemics worldwide.

The COVID-19 pandemic has caused huge socio-economic losses and continues to threat humans worldwide. With more than 4.5 million deaths and more than 221 million confirmed COVID-19 cases, the impact on physical, mental, social and economic resources is immeasurable. During any novel disease outbreak, one of the primary requirements for effective mitigation is the knowledge of clinical manifestations of the disease. However, in absence of any unique identifying characteristics, diagnosis/prognosis becomes difficult. It intensifies misperception and leads to delay in containment of disease spread. Numerous clinical research studies, systematic reviews and meta-analyses have generated considerable data on the same. However, identification of some of the distinct clinical signs and symptoms, disease progression biomarkers and the risk factors leading to adverse COVID-19 outcomes warrant in-depth understanding. In view of this, we assessed 20 systematic reviews and meta-analyses with an intent to understand some of the potential independent predictors/biomarkers/risk factors of COVID-19 severity and mortality.

Human Immunodeficiency Virus-1 (HIV-1) is known to induce the expression of SOCS3 which is a negative feed-back regulator of inflammatory responses. Here, we demonstrate that reactivation of latent HIV-1 leads to degradation of SOCS3 at early time points. Interestingly, SOCS3 degradation following transfection of HIV-1 RNA as well as polyIC in THP-1 cells further confirmed the role of viral RNA signaling in SOCS3 biology. Degradation of SOCS3 contributes toward viral RNA induced inflammatory responses. NF-κB signaling is also induced upon HIV-1 infection which leads to the production of pro-inflammatory cytokines to control the viral spread. Further investigations revealed that SOCS3 inhibits the expression and activity of p65 by interacting with it and inducing its ubiquitin-dependent proteasomal degradation. SH2 domain was critical for SOCS3-p65 interaction and p65 degradation. We also found that expression of SOCS3 promotes HIV-1 replication. Thus, HIV-1 downregulates SOCS3 in early phase of infection to promote inflammatory responses for large production of activated cells which are suitable for viral spread and induces SOCS3 later on to limit inflammatory responses and ensure viral survival.

Protozoal pathogens pose a considerable threat, leading to notable mortality rates and the ongoing challenge of developing resistance to drugs. This situation underscores the urgent need for alternative therapeutic approaches. Antimicrobial peptides stand out as promising candidates for drug development. However, there is a lack of published research focusing on predicting antimicrobial peptides specifically targeting protozoal pathogens. In this study, we introduce a successful machine learning-based framework designed to predict potential antiprotozoal peptides effective against protozoal pathogens. The primary objective of this study is to classify and predict antiprotozoal peptides using diverse negative datasets. A comprehensive literature review was conducted to gather experimentally validated antiprotozoal peptides, forming the positive dataset for our study. To construct a robust machine learning classifier, multiple negative datasets were incorporated, including (i) non-antimicrobial, (ii) antiviral, (iii) antibacterial, (iv) antifungal, and (v) antimicrobial peptides excluding those targeting protozoal pathogens. Various compositional features of the peptides were extracted using the pfeature algorithm. Two feature selection methods, SVC-L1 and mRMR, were employed to identify highly relevant features crucial for distinguishing between the positive and negative datasets. Additionally, five popular classifiers i.e. Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and XGBoost were used to build efficient decision models. XGBoost was the most effective in classifying antiprotozoal peptides from each negative dataset based on the features selected by the mRMR feature selection method. The proposed machine learning framework efficiently differentiate the antiprotozoal peptides from (i) non-antimicrobial (ii) antiviral (iii) antibacterial (iv) antifungal and (v) antimicrobial with accuracy of 97.27 %, 93.64 %, 86.36 %, 90.91 %, and 89.09 % respectively on the validation dataset. The models are incorporated in a user-friendly web server (www.soodlab.com/appred) to predict the antiprotozoal activity of given peptides.