Project description:Genes encoding a novel multidrug efflux pump, AadT, from the Drug:H+ antiporter 2 family, were discovered in Acinetobacter multidrug resistance plasmids. Here, we profiled the antimicrobial resistance potential, and examined the distribution of these genes. aadT homologs were found in many Acinetobacter and other Gram-negative species and were typically adjacent to novel variants of adeAB(C), which encodes a major tripartite efflux pump in Acinetobacter. The AadT pump decreased bacterial susceptibility to at least eight diverse antimicrobials, including antibiotics (erythromycin and tetracycline), biocides (chlorhexidine), and dyes (ethidium bromide and DAPI) and was able to mediate ethidium transport. These results show that AadT is a multidrug efflux pump in the Acinetobacter resistance arsenal and may cooperate with variants of AdeAB(C).
Project description:Advances in understanding the genetic basis of cancer have driven alternative treatment approaches. Recent findings have demonstrated the potential of bacteria and it's components to serve as robust theranostic agents for cancer eradication. Compared to traditional cancer therapies like surgery, chemotherapy, radiotherapy, bacteria mediated tumor therapy has exhibited superior cancer suppressing property which is attributed a lot to it's tumor proliferating and accumulating characteristics. Genetically modified bacteria has reduced inherent toxicity and enhanced specificity towards tumor microenvironment. This anti- tumor activity of bacteria is attributed to its toxins and other active components from the cell membrane, cell wall and spores. Furthermore, bacterial genes can be regulated to express and deliver cytokines, antibodies and cancer therapeutics. Although there is less clinical data available, the pre- clinical research clearly indicates the feasibility and potential of bacteria- mediated cancer therapy.
Project description:The Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) has been recently found responsible for the pandemic outbreak of a novel coronavirus disease (COVID-19). In this work, a novel approach based on deep learning is proposed for identifying precursors of small active RNA molecules named microRNA (miRNA) in the genome of the novel coronavirus. Viral miRNA-like molecules have shown to modulate the host transcriptome during the infection progression, thus their identification is crucial for helping the diagnosis or medical treatment of the disease. The existence of the mature miRNAs derived from computationally predicted miRNA precursors (pre-miRNAs) in the novel coronavirus was validated with small RNA-seq data from SARS-CoV-2-infected human cells. The results demonstrate that computational models can provide accurate and useful predictions of pre-miRNAs in the SARS-CoV-2 genome, underscoring the relevance of machine learning in the response to a global sanitary emergency. Moreover, the interpretability of our model shed light on the molecular mechanisms underlying the viral infection, thus contributing to the fight against the COVID-19 pandemic and the fast development of new treatments. Our study shows how recent advances in machine learning can be used, effectively, in response to public health emergencies. The approach developed in this work could be of great help in future similar emergencies to accelerate the understanding of the singularities of any viral agent and for the development of novel therapies. Data and source code available at: https://sourceforge.net/projects/sourcesinc/files/aicovid/.
Project description:With the alarming surge in COVID-19 cases globally, vaccination must be prioritised to achieve herd immunity. Immune dysfunction is detected in the majority of patients with COVID-19; however, it remains unclear whether the immune responses elicited by COVID-19 vaccination function against the Omicron subvariant BA.2. Of the 508 enrolled patients infected with Omicron BA.2, 102 were unvaccinated controls, and 406 were vaccinated. Despite the presence of clinical symptoms in both groups, vaccination led to a significant decline in nausea or vomiting, abdominal pain, headache, pulmonary infection, and overall clinical symptoms and a moderate rise in body temperature. The individuals infected with Omicron BA.2 were also characterised by a mild increase in both serum pro- and anti-inflammatory cytokine levels after vaccination. There were no significant differences or trend changes between T- and B-lymphocyte subsets; however, a significant expansion of NK lymphocytes in COVID-19-vaccinated patients was observed. Moreover, the most effective CD16brightCD56dim subsets of NK cells showed increased functional capacities, as evidenced by a significantly greater IFN-γ secretion and a stronger cytotoxic potential in the patients infected with Omicron BA.2 after vaccination. Collectively, these results suggest that COVID-19 vaccination interventions promote the redistribution and activation of CD16brightCD56dim NK cell subsets against viral infections and that they could facilitate the clinical management of patients infected with Omicron BA.2.
Project description:Cancer is a complex disease that, despite advances in treatment and the greater understanding of the tumor biology until today, continues to be a prevalent and lethal disease. Chemotherapy, radiotherapy, and surgery are the conventional treatments, which have increased the survival for cancer patients. However, the complexity of this disease together with the persistent problems due to tumor progression and recurrence, drug resistance, or side effects of therapy make it necessary to explore new strategies that address the challenges to obtain a positive response. One important point is that tumor cells can interact with the microenvironment, promoting proliferation, dissemination, and immune evasion. Therefore, immunotherapy has emerged as a novel therapy based on the modulation of the immune system for combating cancer, as reflected in the promising results both in preclinical studies and clinical trials obtained. In order to enhance the immune response, the combination of immunotherapy with nanoparticles has been conducted, improving the access of immune cells to the tumor, antigen presentation, as well as the induction of persistent immune responses. Therefore, nanomedicine holds an enormous potential to enhance the efficacy of cancer immunotherapy. Here, we review the most recent advances in specific molecular and cellular immunotherapy and in nano-immunotherapy against cancer in the light of the latest published preclinical studies and clinical trials.
Project description:The SARS-CoV-2 coronavirus pandemic outbreak in 2019 resulted in the need to search for an effective and safe strategy for treating infected patients, relieving symptoms, and preventing severe disease. SARS-CoV-2 is an RNA virus that can cause acute respiratory failure and thrombosis, as well as impair circulatory system function. Permanent damage to the heart muscle or other cardiovascular disorders may occur during or after the infection. The severe course of the disease is associated with the release of large amounts of pro-inflammatory cytokines. Due to their documented anti-inflammatory, antioxidant, and antiviral effects, reactive sulfur compounds, including hydrogen sulfide (H2S), lipoic acid (LA), N-acetylcysteine (NAC), glutathione (GSH), and some other lesser-known sulfur compounds, have attracted the interest of scientists for the treatment and prevention of the adverse effects of diseases caused by SARS-CoV-2. This article reviews current knowledge about various endogenous or exogenous reactive sulfur compounds and discusses the possibility, or in some cases the results, of their use in the treatment or prophylaxis of COVID-19.
Project description:We report our experience of COVID-19 disease burden among patients aged 0-21 years at two tertiary care institutions in the Northeast and Midwest from New Jersey and Iowa. Our results showed that during the initial surge (March to August 2020) at both geographic locations, majority of COVID-19 disease burden occurred in adolescents and that they were more likely to be hospitalized for COVID-related illnesses, as well as develop severe disease needing intensive care. The study results emphasize the need for providing more targeted interventions toward this group to help prevent disease acquisition and transmission.
Project description:BackgroundIn December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts.ObjectiveThis scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation.MethodsA scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data.ResultsWe considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome-related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine.ConclusionsThe included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.