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Abstract The Advent of Artificial Intelligence (AI) has led to the use of auditory data for detecting various diseases, including COVID-19. SARS-CoV-2 infection has claimed more than six million lives to date and therefore, needs a robust screening technique to control the disease spread. In the present study we created and validated the Swaasa AI platform, which uses the signature cough sound and symptoms presented by patients to screen and prioritize COVID-19 patients. We collected cough data from 234 COVID-19 suspects to validate our Convolutional Neural Network (CNN) architecture and Feedforward Artificial Neural Network (FFANN) (tabular features) based algorithm. The final output from both models was combined to predict the likelihood of having the disease. During the clinical validation phase, our model showed a 75.54% accuracy rate in detecting the likely presence of COVID-19, with 95.45% sensitivity and 73.46% specificity. We conducted pilot testing on 183 presumptive COVID subjects, of which 58 were truly COVID-19 positive, resulting in a Positive Predictive Value of 70.73%. Due to the high cost and technical expertise required for currently available rapid screening methods, there is a need for a cost-effective and remote monitoring tool that can serve as a preliminary screening method for potential COVID-19 subjects. Therefore, Swaasa would be highly beneficial in detecting the disease and could have a significant impact in reducing its spread.

Abstract Acoustic signal analysis has been employed in various medical devices. However, studies involving cough sound analysis to screen the potential pulmonary tuberculosis (PTB) suspects are very few. The main objective of this cross-sectional validation study was to develop and validate the Swaasa AI platform to screen and prioritize at risk patients for PTB based on the signature cough sound as well as symptomatic information provided by the subjects. The voluntary cough sound data was collected at Andhra Medical College-India. An Algorithm based on multimodal convolutional neural network architecture and feedforward artificial neural network (tabular features) was built and validated on a total of 567 subjects, comprising 278 positive and 289 negative PTB cases. The output from these two models was combined to detect the likely presence (positive cases) of PTB. In the clinical validation phase, the AI-model was found to be 86.82% accurate in detecting the likely presence of PTB with 90.36% sensitivity and 84.67% specificity. The pilot testing of model was conducted at a peripheral health care centre, RHC Simhachalam-India on 65 presumptive PTB cases. Out of which, 15 subjects truly turned out to be PTB positive with a positive predictive value of 75%. The validation results obtained from the model are quite encouraging. This platform has the potential to fulfil the unmet need of a cost-effective PTB screening method. It works remotely, presents instantaneous results, and does not require a highly trained operator. Therefore, it could be implemented in various inaccessible, resource-poor parts of the world.

TOP1‐binding arginine/serine‐rich protein (TOPORS), a really interesting new gene finger protein, has the ability to bind to a palindromic consensus DNA sequence that enables it to function as a potential transcriptional regulator. However, its role in regulating the transcription of cancer‐associated genes is yet to be explored. As Toll‐like receptor 4 (TLR4) agonists are known to regress solid tumors, we observed that lipopolysaccharide (LPS) induces TOPORS via a TLR4‐TIR domain‐containing adapter‐inducing interferon‐β‐dependent pathway, which in turn modulates the transcription of tumor suppressor scaffold/matrix attachment region‐binding protein 1 (SMAR1, also known as BANP). ChIP analysis showed that TOPORS binds on the SMAR1 promoter and its occupancy increases upon LPS treatment. A previous study from our laboratory revealed that SMAR1 acts as a repressor of signal transducer and activator of transcription 3 (STAT3) transcription. Tumor growth, as well as tumor‐associated macrophage polarization, depends on the status of the STAT1:STAT3 ratio. LPS‐induced SMAR1 expression decreases STAT3 expression and also skews the macrophage polarization toward M1 phenotype. In contrast, LPS failed to polarize tumor‐associated macrophages to M1 phenotype in a SMAR1‐silenced condition, which shows the involvement of SMAR1 in dictating the fate of colorectal cancer progression. Identification of the molecular mechanism behind LPS‐mediated tumor regression would be crucial for designing cancer treatment strategies involving bacterial components.

Abstract Background Highly proliferating cancer cells exhibit the Warburg effect by regulation of PKM alternative splicing and promoting the expression of PKM2. Majority of the alternative splicing events are known to occur in the nuclear matrix where various MARBPs actively participate in the alternative splicing events. SMAR1, being a MARBP and an important tumor suppressor, is known to regulate the splicing of various cancer-associated genes. This study focuses on the regulation of PKM alternative splicing and inhibition of the Warburg effect by SMAR1. Methods Immunohistochemistry was performed in breast cancer patient samples to establish the correlation between SMAR1 and PKM isoform expression. Further, expression of PKM isoforms upon modulation in SMAR1 expression in breast cancer cell lines was quantified by qRT-PCR and western blot. The acetylation status of PTBP1 was estimated by immunoprecipitation along with its enrichment on PKM pre-mRNA by CLIP in SMAR1 knockdown conditions. The role of SMAR1 in tumor metabolism and tumorigenesis was explored by in vitro enzymatic assays and functional assays upon SMAR1 knockdown. Besides, in vivo tumor formation by injecting adeno-SMAR1-transduced MDA-MB-231 cells in NOD/SCID mice was performed. Results The expression profile of SMAR1 and PKM isoforms in breast cancer patients revealed that SMAR1 has an inverse correlation with PKM2 and a positive correlation with PKM1. Further quantitative PKM isoform expression upon modulation in SMAR1 expression also reflects that SMAR1 promotes the expression of PKM1 over tumorigenic isoform PKM2. SMAR1 deacetylates PTBP1 via recruitment of HDAC6 resulting in reduced enrichment of PTBP1 on PKM pre-mRNA. SMAR1 inhibits the Warburg effect, tumorigenic potential of cancer cells, and in vivo tumor generation in a PKM2-dependent manner. Conclusions SMAR1 regulates PKM alternative splicing by causing HDAC6-dependent deacetylation of PTBP1, resulting in reduced enrichment of PTBP1 on PKM pre-mRNA. Additionally, SMAR1 suppresses glucose utilization and lactate production via repression of PKM2 expression. This suggests that tumor suppressor SMAR1 inhibits tumor cell metabolism and tumorigenic properties of cancer cells via regulation of PKM alternative splicing.

Obesity is one of the biggest public health concerns identified by an increase in adipose tissue mass as a result of adipocyte hypertrophy and hyperplasia. Pertaining to the importance of adipose tissue in various biological processes, any alteration in its function results in impaired metabolic health. In this review, we discuss how adipose tissue maintains the metabolic health through secretion of various adipokines and inflammatory mediators and how its dysfunction leads to the development of severe metabolic disorders and influences cancer progression. Impairment in the adipocyte function occurs due to individuals’ genetics and/or environmental factor(s) that largely affect the epigenetic profile leading to altered gene expression and onset of obesity in adults. Moreover, several crucial aspects of adipose biology, including the regulation of different transcription factors, are controlled by epigenetic events. Therefore, understanding the intricacies of adipogenesis is crucial for recognizing its relevance in underlying disease conditions and identifying the therapeutic interventions for obesity and metabolic syndrome.

After the 1918 flu pandemic, the world is again facing a similar situation. However, the advancement in medical science has made it possible to identify that the novel infectious agent is from the coronavirus family. Rapid genome sequencing by various groups helped in identifying the structure and function of the virus, its immunogenicity in diverse populations, and potential preventive measures. Coronavirus attacks the respiratory system, causing pneumonia and lymphopenia in infected individuals. Viral components like spike and nucleocapsid proteins trigger an immune response in the host to eliminate the virus. These viral antigens can be either recognized by the B cells or presented by MHC complexes to the T cells, resulting in antibody production, increased cytokine secretion, and cytolytic activity in the acute phase of infection. Genetic polymorphism in MHC enables it to present some of the T cell epitopes very well over the other MHC alleles. The association of MHC alleles and its downregulated expression has been correlated with disease severity against influenza and coronaviruses. Studies have reported that infected individuals can, after recovery, induce strong protective responses by generating a memory T-cell pool against SARS-CoV and MERS-CoV. These memory T cells were not persistent in the long term and, upon reactivation, caused local damage due to cross-reactivity. So far, the reports suggest that SARS-CoV-2, which is highly contagious, shows related symptoms in three different stages and develops an exhaustive T-cell pool at higher loads of viral infection. As there are no specific treatments available for this novel coronavirus, numerous small molecular drugs that are being used for the treatment of diseases like SARS, MERS, HIV, ebola, malaria, and tuberculosis are being given to COVID-19 patients, and clinical trials for many such drugs have already begun. A classical immunotherapy of convalescent plasma transfusion from recovered patients has also been initiated for the neutralization of viremia in terminally ill COVID-19 patients. Due to the limitations of plasma transfusion, researchers are now focusing on developing neutralizing antibodies against virus particles along with immuno-modulation of cytokines like IL-6, Type I interferons (IFNs), and TNF-α that could help in combating the infection. This review highlights the similarities of the coronaviruses that caused SARS and MERS to the novel SARS-CoV-2 in relation to their pathogenicity and immunogenicity and also focuses on various treatment strategies that could be employed for curing COVID-19.

Unlike organ transplants where an immunosuppressive environment is required, a successful pregnancy involves an extremely robust, dynamic, and responsive maternal immune system to maintain the development of the fetus. A specific set of hormones and cytokines are associated with a particular stage of pregnancy. Any disturbance that alters this fine balance could compromise the development and function of the placenta. Although there are numerous underlying causes of pregnancy-related complications, untimely activation of Toll-like receptors (TLR), primarily TLR4, by intrauterine microbes poses the greatest risk. TLR4 is an important Pattern Recognition Receptor (PRR), which activates both innate and adaptive immune cells. TLR4 activation by LPS or DAMPs leads to the production of pro-inflammatory cytokines via the MyD88 dependent or independent pathway. Immune cells modulate the materno–fetal interface by TLR4-mediated cytokine production, which changes at different stages of pregnancy. In most pregnancy disorders, such as PTB, PE, or placental malaria, the TLR4 expression is upregulated in immune cells or in maternal derived cells, leading to the aberrant production of pro-inflammatory cytokines at the materno–fetal interface. Lack of functional TLR4 in mice has reduced the pro-inflammatory responses, leading to an improved pregnancy, which further strengthens the fact that abnormal TLR4 activation creates a hostile environment for the developing fetus. A recent study proposed that endothelial and perivascular stromal cells should interact with each other in order to maintain a homeostatic balance during TLR4-mediated inflammation. It has been reported that depleting immune cells or supplying anti-inflammatory cytokines can prevent PTB, PE, or fetal death. Blocking TLR4 signaling or its downstream molecule by inhibitors or antagonists has proven to improve pregnancy-related complications to some extent in clinical and animal models. To date, there has been a lack of knowledge regarding whether TLR4 accessories such as CD14 and MD-2 are important in pregnancy and whether these accessory molecules could be promising drug targets for combinatorial treatment of various pregnancy disorders. This review mainly focuses on the activation of TLR4 during pregnancy, its immunomodulatory functions, and the upcoming advancement in this field regarding the improvement of pregnancy-related issues by various therapeutic approaches.

TopicalPdb (http://crdd.osdd.net/raghava/topicalpdb/) is a repository of experimentally verified topically delivered peptides. Data was manually collected from research articles. The current release of TopicalPdb consists of 657 entries, which includes peptides delivered through the skin (462 entries), eye (173 entries), and nose (22 entries). Each entry provides comprehensive information related to these peptides like the source of origin, nature of peptide, length, N- and C-terminal modifications, mechanism of penetration, type of assays, cargo and biological properties of peptides, etc. In addition to natural peptides, TopicalPdb contains information of peptides having non-natural, chemically modified residues and D-amino acids. Besides this primary information, TopicalPdb stores predicted tertiary structures as well as peptide sequences in SMILE format. Tertiary structures of peptides were predicted using state-of-art method PEPstrMod. In order to assist users, a number of web-based tools have been integrated that includes keyword search, data browsing, similarity search and structural similarity. We believe that TopicalPdb is a unique database of its kind and it will be very useful in designing peptides for non-invasive topical delivery.