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Antimicrobial Resistance (AMR) is a critical global health challenge, and in this review article, we examine the limitations of traditional therapeutic methods and the emerging role of alternative therapies. By examining the reasons behind the failure of conventional treatments, including the inadequacy of one-drug-one-enzyme approaches, the complex evolution of AMR, and the impact of drug biotransformation, we better understand why conventional treatments failed. Moreover, the review discusses several alternative therapies, including RNA-based treatments, aptamers, peptide-based therapies, phage therapy, and probiotics, discussing their applications, advantages, and limitations. Additionally, we discuss the obstacles to develop these therapies, including funding shortages, regulatory barriers, and public perception. This comprehensive analysis aims to provide insight into the future of AMR, emphasizing the need for innovative strategies and practical approaches.

Pneumothorax is a critical medical condition among human-beings. A severe pneumothorax causes collapsed lungs. It is a life-threatening disease. Therefore, pneumothorax detection is an important step for the prevention and curing of a patient. Pneumothorax can be classified into three major categories: primary, secondary, and injury. Magnetic resonance imaging (MRI)-based digital imaging and communications in medicine (DICOM) files of Chest X-ray (CXR) images provide insight and help the doctor to make an appropriate decision. An early decision can prevent the mortality rate among patients. Since the outbreak of the COVID-19 pandemic, the medical systems and staff have gone under massive pressure. Classification from a CXR image by an expert requires huge manpower and a longer time to determine. Deep learning-based automatic classification of Pneumothorax (CXR) images can assist the medical community in a fast diagnosis and reduce the burden of work overload. Doctors can focus on better treatment and cure of Pneumothorax. In this paper, we have proposed seven scratch Convolutional Neural Networks (CNN) architectures and compared them with another seven transfer learning models. The best-performing CNN model (PneuNetV1) is determined based on various standard performance metrics. It has gained the highest test accuracy, efficacy ratio, and F1-score of 0.9123, 5.2370, and 0.9220, respectively with a minimum training time. The obtained results are achieved through rigorous experimentation and yet provide satisfactory performance.

Abstract Environmental RNAi has been developed as a tool for reverse genetics studies and is an emerging pest control strategy. The ability of environmental RNAi to efficiently down-regulate the expression of endogenous gene targets assumes efficient uptake of dsRNA and its processing. In addition, its efficiency can be augmented by the systemic spread of RNAi signals. Environmental RNAi is now a well-established tool for the manipulation of gene expression in the chelicerate acari, including the two-spotted spider mite, Tetranychus urticae. Here, we focused on eight single and ubiquitously-expressed genes encoding proteins with essential cellular functions. Application of dsRNAs that specifically target these genes led to whole mite body phenotypes—dark or spotless. These phenotypes were associated with a significant reduction of target gene expression, ranging from 20 to 50%, when assessed at the whole mite level. Histological analysis of mites treated with orally-delivered dsRNAs was used to investigate the spatial range of the effectiveness of environmental RNAi. Although macroscopic changes led to two groups of body phenotypes, silencing of target genes was associated with the distinct cellular phenotypes. We show that regardless of the target gene tested, cells that displayed histological changes were those that are in direct contact with the dsRNA-containing gut lumen, suggesting that the greatest efficiency of the orally-delivered dsRNAs is localized to gut tissues in T. urticae.

During bacterial infections, one or more virulence factors are required to support the survival, growth, and colonization of the pathogen within the host, leading to the symptomatic characteristic of the disease. The outcome of bacterial infections is determined by several factors from both host as well as pathogen origin. Proteins and enzymes involved in cellular signaling are important players in determining the outcome of host–pathogen interactions. phospholipase C (PLCs) participate in cellular signaling and regulation by virtue of their ability to hydrolyze membrane phospholipids into di-acyl-glycerol (DAG) and inositol triphosphate (IP3), which further causes the activation of other signaling pathways involved in various processes, including immune response. A total of 13 PLC isoforms are known so far, differing in their structure, regulation, and tissue-specific distribution. Different PLC isoforms have been implicated in various diseases, including cancer and infectious diseases; however, their roles in infectious diseases are not clearly understood. Many studies have suggested the prominent roles of both host and pathogen-derived PLCs during infections. PLCs have also been shown to contribute towards disease pathogenesis and the onset of disease symptoms. In this review, we have discussed the contribution of PLCs as a determinant of the outcome of host-pathogen interaction and pathogenesis during bacterial infections of human importance.

Winged bean (Psophocarpus tetragonolobus (L.) DC.) is an underexploited tropical legume whose tuberous-roots have 12–19% proteins. The species has excellent drought tolerance ability thus making it a prospective climate-smart future crop. However, the underlying defense mechanism(s) of the species against the drought stress are not yet understood. Therefore, we studied the role of root- tuber proteins and metabolites in providing drought stress resistance to winged bean. We identified and characterized the relevant proteins and their corresponding peptides, and validated the responsible transcripts in root-tubers of P. tetragonolobus through qRT-PCR. Total proteome-analysis of root-tubers resulted in identification of 2678 proteins belonging to 154 families. Of these, 96% of proteins are associated with known biological activities. Heat-shock proteins (Hsps) of 12–42 kDa were the most abundant protein families and constituted 13% of the total identified proteins in the root-tuber. Metabolite analysis of the root-tuber revealed the prevalence of abiotic stress tolerant amino acids and compounds such as dimethyl amide, hydroxylamine hydrochloride, trimethylamine, cyclopropylamine, α-aminopropionitrile and cyclohexylamine. The other related metabolites present were naphthalene, benzyl alcohol, L- rhamnose, propanol and aromatic aldehydes. The presence of several Hsps, aminoacids, and specific metabolites with known function of stress-amelioration/anti-oxidant activities present in the root-tubers explains the adaptation success of winged bean against drought stress.

The continuous, worldwide spread of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis (TB) endanger the World Health Organization’s (WHO) goal to end the global TB pandemic by the year 2035. During the past 50 years, very few new drugs have been approved by medical agencies to treat drug-resistant TB. Therefore, the development of novel antimycobacterial drug candidates to combat the threat of drug-resistant TB is urgent. In this work, we developed and optimized a total synthesis of the antimycobacterial natural flavonoid chlorflavonin by selective ruthenium(II)-catalyzed ortho-C(sp2)-H-hydroxylation of a substituted 3′-methoxyflavonoid skeleton. We extended our methodology to synthesize a small compound library of 14 structural analogs. The new analogs were tested for their antimycobacterial in vitro activity against Mycobacterium tuberculosis (Mtb) and their cytotoxicity against various human cell lines. The most promising new analog bromflavonin exhibited improved antimycobacterial in vitro activity against the virulent H37Rv strain of Mtb (Minimal Inhibitory Concentrations (MIC90) = 0.78 μm). In addition, we determined the chemical and metabolic stability as well as the pKa values of chlorflavonin and bromflavonin. Furthermore, we established a quantitative structure–activity relationship model using a thermodynamic integration approach. Our computations may be used for suggesting further structural changes to develop improved derivatives.

With the current outbreak caused by SARS-CoV-2, vaccination is acclaimed as a public health care priority. Rapid genetic sequencing of SARS-CoV-2 has triggered the scientific community to search for effective vaccines. Collaborative approaches from research institutes and biotech companies have acknowledged the use of viral proteins as potential vaccine candidates against COVID-19. Nucleic acid (DNA or RNA) vaccines are considered the next generation vaccines as they can be rapidly designed to encode any desirable viral sequence including the highly conserved antigen sequences. RNA vaccines being less prone to host genome integration (cons of DNA vaccines) and anti-vector immunity (a compromising factor of viral vectors) offer great potential as front-runners for universal COVID-19 vaccine. The proof of concept for RNA-based vaccines has already been proven in humans, and the prospects for commercialization are very encouraging as well. With the emergence of COVID-19, mRNA-1273, an mRNA vaccine developed by Moderna, Inc. was the first to enter human trials, with the first volunteer receiving the dose within 10 weeks after SARS-CoV-2 genetic sequencing. The recent interest in mRNA vaccines has been fueled by the state of the art technologies that enhance mRNA stability and improve vaccine delivery. Interestingly, as per the “Draft landscape of COVID-19 candidate vaccines” published by the World Health Organization (WHO) on December 29, 2020, seven potential RNA based COVID-19 vaccines are in different stages of clinical trials; of them, two candidates already received emergency use authorization, and another 22 potential candidates are undergoing pre-clinical investigations. This review will shed light on the rationality of RNA as a platform for vaccine development against COVID-19, highlighting the possible pros and cons, lessons learned from the past, and the future prospects.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds to the angiotensin-converting enzyme 2 (ACE2) receptor and invade the human cells to cause COVID-19-related pneumonia. Despite an emphasis on respiratory complications, the evidence of neurological manifestations of SARS-CoV-2 infection is rapidly growing, which is substantially contributing to morbidity and mortality. The neurological disorders associated with COVID-19 may have several pathophysiological underpinnings, which are yet to be explored. Hypothetically, SARS-CoV-2 may affect the central nervous system (CNS) either by direct mechanisms like neuronal retrograde dissemination and hematogenous dissemination, or via indirect pathways. CNS complications associated with COVID-19 include encephalitis, acute necrotizing encephalopathy, diffuse leukoencephalopathy, stroke (both ischemic and hemorrhagic), venous sinus thrombosis, meningitis, and neuroleptic malignant syndrome. These may result from different mechanisms, including direct virus infection of the CNS, virus-induced hyper-inflammatory states, and post-infection immune responses. On the other hand, the Guillain-Barre syndrome, hyposmia, hypogeusia, and myopathy are the outcomes of peripheral nervous system injury. Although the therapeutic potential of certain repurposed drugs has led to their off-label use against COVID-19, such as anti-retroviral drugs (remdesivir, favipiravir, and lopinavir-ritonavir combination), biologics (tocilizumab), antibiotics (azithromycin), antiparasitics (chloroquine and hydroxychloroquine), and corticosteroids (dexamethasone), unfortunately, the associated clinical neuropsychiatric adverse events remains a critical issue. Therefore, COVID-19 represents a major threat to the field of neuropsychiatry, as both the virus and the potential therapies may induce neurologic as well as psychiatric disorders. Notably, potential COVID-19 medications may also interact with the medications of pre-existing neuropsychiatric diseases, thereby further complicating the condition. From this perspective, this review will discuss the possible neurological manifestations and sequelae of SARS-CoV-2 infection with emphasis on the probable underlying neurotropic mechanisms. Additionally, we will highlight the concurrence of COVID-19 treatment-associated neuropsychiatric events and possible clinically relevant drug interactions, to provide a useful framework and help researchers, especially the neurologists in understanding the neurologic facets of the ongoing pandemic to control the morbidity and mortality.

A brain tumor occurs in humans when a normal cell turns into an aberrant cell inside the brain. Primarily, there are two types of brain tumors in Homo sapiens: benign tumors and malignant tumors. In brain tumor diagnosis, magnetic resonance imaging (MRI) plays a vital role that requires high precision and accuracy for diagnosis, otherwise, a minor error can result in severe consequences. In this study, we implemented various configured convolutional neural network (CNN) paradigms on brain tumor MRI scans that depict whether a person is a brain tumor patient or not. This paper emphasizes objective function values (OFV) achieved by various CNN paradigms with the least validation cross-entropy loss (LVCEL), maximum validation accuracy (MVA), and training time (TT) in seconds, which can be used as a feasible tool for clinicians and the medical community to recognize tumor patients precisely. Experimentation and evaluation were based on a total of 2189 brain MRI scans, and the best architecture shows the highest accuracy of 0.8275, maximum objective function value of 1.84, and an area under the ROC (AUC-ROC) curve of 0.737 to accurately recognize and classify whether or not a person has a brain tumor.

Background: Tuberculosis (TB) is predominantly an airborne disease. However, quantitative and qualitative analysis of bio-aerosols containing the aetiological agent, Mycobacterium tuberculosis (Mtb), has proven very challenging. Our objective is to sample bio-aerosols from newly diagnosed TB patients for detection and enumeration of Mtb bacilli. Methods: We monitored each of 35 newly diagnosed, GeneXpert sputum-positive, TB patients during 1 hour confinement in a custom-built Respiratory Aerosol Sampling Chamber (RASC). The RASC (a small clean-room of 1.4m 3) incorporates aerodynamic particle size detection, viable and non-viable sampling devices, real-time CO 2 monitoring, and cough sound-recording. Microbiological culture and droplet digital polymerase chain reaction (ddPCR) were used to detect Mtb in each of the bio-aerosol collection devices. Results: Mtb was detected in 27/35 (77.1%) of aerosol samples; 15/35 (42.8%) samples were positive by mycobacterial culture and 25/27 (92.96%) were positive by ddPCR. Culturability of collected bacilli was not predicted by radiographic evidence of pulmonary cavitation, sputum smear positivity. A correlation was found between cough rate and culturable bioaerosol. Mtb was detected on all viable cascade impactor stages with a peak at aerosol sizes 2.0-3.5μm. This suggests a median of 0.09 CFU/litre of exhaled air (IQR: 0.07 to 0.3 CFU/l) for the aerosol culture positives and an estimated median concentration of 4.5x10 7 CFU/ml (IQR: 2.9x10 7-5.6x10 7) of exhaled particulate bio-aerosol. Conclusions: Mtb was identified in bio-aerosols exhaled by the majority of untreated TB patients using the RASC. Molecular detection was more sensitive than mycobacterial culture on solid media, suggesting that further studies are required to determine whether this reflects a significant proportion of differentially detectable bacilli in these samples.

Background: Tuberculosis (TB) is predominantly an airborne disease. However, quantitative and qualitative analysis of bio-aerosols containing the aetiological agent, Mycobacterium tuberculosis (Mtb), has proven very challenging. Our objective is to sample bio-aerosols from newly diagnosed TB patients for detection and enumeration of Mtb bacilli. Methods: We monitored each of 35 newly diagnosed, GeneXpert sputum-positive, TB patients during 1 hour confinement in a custom-built Respiratory Aerosol Sampling Chamber (RASC). The RASC (a small clean-room of 1.4m3) incorporates aerodynamic particle size detection, viable and non-viable sampling devices, real-time CO2 monitoring, and cough sound-recording. Microbiological culture and droplet digital polymerase chain reaction (ddPCR) were used to detect Mtb in each of the bio-aerosol collection devices. Results: Mtb was detected in 27/35 (77.1%) of aerosol samples; 15/35 (42.8%) samples were positive by mycobacterial culture and 25/27 (92.96%) were positive by ddPCR. Culturability of collected bacilli was not predicted by radiographic evidence of pulmonary cavitation, sputum smear positivity, or cough rate. Mtb was detected on all viable cascade impactor stages with a peak at aerosol sizes 2.0-3.5μm. This suggests a median of 0.09 CFU/litre of exhaled air (IQR: 0.07 to 0.3 CFU/l) for the aerosol culture positives and an estimated median concentration of 4.5x107 CFU/ml (IQR: 2.9x107-5.6x107) of exhaled particulate bio-aerosol. Conclusions: Mtb was identified in bio-aerosols exhaled by the majority of untreated TB patients using the RASC. Molecular detection was more sensitive than mycobacterial culture on solid media, suggesting that further studies are required to determine whether this reflects a significant proportion of differentially detectable bacilli in these samples.

Knowledge of the airborne nature of respiratory disease transmission owes much to the pioneering experiments of Wells and Riley over half a century ago. However, the mechanical, physiological, and immunopathological processes which drive the production of infectious aerosols by a diseased host remain poorly understood. Similarly, very little is known about the specific physiological, metabolic and morphological adaptations which enable pathogens such as Mycobacterium tuberculosis (Mtb) to exit the infected host, survive exposure to the external environment during airborne carriage, and adopt a form that is able to enter the respiratory tract of a new host, avoiding innate immune and physical defenses to establish a nascent infection. As a first step towards addressing these fundamental knowledge gaps which are central to any efforts to interrupt disease transmission, we developed and characterized a small personal clean room comprising an array of sampling devices which enable isolation and representative sampling of airborne particles and organic matter from tuberculosis (TB) patients. The complete unit, termed the Respiratory Aerosol Sampling Chamber (RASC), is instrumented to provide real-time information about the particulate output of a single patient, and to capture samples via a suite of particulate impingers, impactors and filters. Applying the RASC in a clinical setting, we demonstrate that a combination of molecular and microbiological assays, as well as imaging by fluorescence and scanning electron microscopy, can be applied to investigate the identity, viability, and morphology of isolated aerosolized particles. Importantly, from a preliminary panel of active TB patients, we observed the real-time production of large numbers of airborne particles including Mtb, as confirmed by microbiological culture and polymerase chain reaction (PCR) genotyping. Moreover, direct imaging of captured samples revealed the presence of multiple rod-like Mtb organisms whose physical dimensions suggested the capacity for travel deep into the alveolar spaces of the human lung.