Laser-disintegrated copper-activated salicylic acid (Cu-SA) nanoparticles suppressed the seed-borne pathogens of rice with enhanced seed invigoration parameters.

Leading phytopathological research is focused on managing seed-borne pathogens of rice through the utilization of engineered nanomaterials. Herein, blue laser-induced topo-morphologically nano-advanced copper salicylates (Cu-SA) (Cu/SA in 1:1 and 1:2 ratio) were prepared and evaluated for their augmented antifungal potential along seed invigoration effects in contrast to their prepared sonicated formulations. Laser disintegration on the Cu-SA (Cu/SA in 1:1 and 1:2 ratio) was achieved with high degree of success and precision using blue laser, which yielded uniformly distributed spherical nanoparticles with a narrow size distribution and better crystallinity than aqua-dispersed sonicated formulations. In vitro antifungal evaluation against seed-borne fungi of rice viz. Fusarium verticillioides and Fusarium fujikuroi revealed multiple times the augmented potential of laser-disintegrated nanoformulations (l-CuSA) than sonicated (s-CuSA) and bulk samples. Laser-induced nano-sodium bis(2-oxobenzoato)cuprate (II) (l-CuSA2) with Cu/SA in 1:2 ratio was the best to inhibit the in vitro fungal growth. Ultra-micrographs and fungal double-staining assay further rationalized the membrane disruption as the mode of action for the fungitoxicity. Nanopriming of fungal infested rice seeds with l-CuSA2 at 2500 µg/mL for 8 h showed the maximum reduction of seed rot (80.43%) and seedling blight (63.15%) with respect to control (untreated). The seed-invigorating factors of l-CuSA2 nanoprimed seeds were enhanced to maximum extent and showed the highest per cent germination (35.29%), shoot length (11.42%), root length (21.14%), dry weight (75.43%) and vigour index (81.04%) over the control. Inclusively, the seed nanopriming with l-CuSA2 proved as agro-compatible hypo-toxic semi natural nanoplatform for sustainable agriculture.

In vitro assessment of newer colistin-sparing antimicrobial agents for clinical isolates of carbapenem-resistant organisms.

OBJECTIVES: Carbapenem-resistant organisms (CROs) are a significant public health threat globally, particularly in countries like India with high antibiotic resistance rates. The current study investigates the prevalence of CROs, detects resistance mechanisms using phenotypic methods and assesses the efficacy of newer antibiotics against CROs. METHODS: A prospective study conducted at a tertiary care hospital in India during 2021-23. Clinical specimens were processed and bacterial identification was performed using MALDI-TOF mass spectrometry followed by antimicrobial susceptibility testing using CLSI guidelines against a plethora of newer antibiotics for CROs. Carbapenemase production was detected using phenotypic methods, and the presence of the mcr-1 gene was assessed by real-time PCR. RESULTS: During the study period, predominantly (70 %) Gram-negative bacteria were isolated; amongst which 5692 strains were carbapenem-resistant, wherein resistance to different carbapenems ranged from 34.1% to 79 %. Majority of the carbapenemase producers were metallo-ß-lactamases (MBL) producers (75 %). Colistin resistance was noted in 5.4 % of selected carbapenem-resistant isolates. Among newer antibiotics, cefiderocol demonstrated the lowest resistance rates (0-14 %), while resistance to newer ß-lactam/ß-lactamase inhibitor combinations was very high in carbapenem-resistant isolates. Fosfomycin, minocycline and tigecycline, each showing variable efficacy depending on the site of infection. Moreover, newer ß-lactam/ß-lactamase inhibitor combinations offer restricted benefits due to widespread prevalence of MBL in the region. CONCLUSION: The escalating prevalence of CROs in India underscores the urgency for alternative treatment options beyond colistin. Hence, highlighting the critical importance of developing effective strategies to combat carbapenem resistance.

Quality assurance of SARS-CoV-2 testing laboratories during the pandemic period in India - An experience from a designated provider laboratory.

PURPOSE: Indian Council of Medical Research (ICMR) initiated an Inter-Laboratory Quality Control testing (ILQC) program for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) testing. Under this program, SARS-CoV-2 testing laboratories across the country submit specimens to the assigned State Quality Control (SQCs) laboratories for ILQC testing. This study aimed to investigate the performance of public and private SARS-CoV-2 testing laboratories in Delhi and highlights the country's effort in ramping up testing facility with close monitoring of the quality of Covid-19 testing results. METHODS: In the present study, two-years of SARS-CoV-2 testing data is included. During July 2020 through February 2022, a total of 1791 anonymised specimens were received from 56 public and private laboratories. These specimens were processed by reverse transcriptase - polymerase chain reaction (RT-PCR) tests as per National Institute of Virology (NIV) protocol and the results were uploaded on the ICMR quality control/quality assurance (QC/QA) portal without directly conveying the results to respective participating laboratories. This portal generated a final report stating concordance and intimate results to individual laboratories. RESULTS: Among the 1791 specimens, 25 were rejected and the remaining 1766 were tested. Among these specimens 1691 (95.75%) revealed concordance, and 75 (4.24%) were discordant. A total of 29 laboratories had 100% concordance, 21 laboratories had over 90% concordance and six laboratories had over 80% concordance. CONCLUSIONS: The study demonstrates that the establishment of an inter-laboratory comparison program for SARS-CoV-2 testing helped in monitoring quality of SARS-CoV-2 testing in the country.

Vertebrate response to microplastics, nanoplastics and co-exposed contaminants: Assessing accumulation, toxicity, behaviour, physiology, and molecular changes.

Pollution from microplastics (MPs) and nanoplastics (NPs) has gained significant public attention and has become a serious environmental problem worldwide. This review critically investigates MPs/NPs' ability to pass through biological barriers in vertebrate models and accumulate in various organs, including the brain. After accumulation, these particles can alter individuals' behaviour and exhibit toxic effects by inducing oxidative stress or eliciting an inflammatory response. One major concern is the possibility of transgenerational harm, in which toxic consequences are displayed in offspring who are not directly exposed to MPs/NPs. Due to their large and marked surface hydrophobicity, these particles can easily absorb and concentrate various environmental pollutants, which may increase their toxicity to individuals and subsequent generations. This review systematically provides an analysis of recent studies related to the toxic effects of MPs/NPs, highlighting the intricate interplay between co-contaminants in vitro and in vivo. We further delve into mechanisms of MPs/NPs-induced toxicity and provide an overview of potential therapeutic approaches to lessen the negative effects of these MPs/NPs. The review also emphasizes the urgency of future studies to examine the long-term effects of chronic exposure to MPs/NPs and their size- and type-specific hazardous dynamics, and devising approaches to safeguard the affected organisms.

Streptococcus gordonii-associated infective endocarditis: Case series, literature review, and genetic study.

Key Clinical Message: Streptococcus gordonii-associated endocarditis is a rare occurrence, raising diagnostic challenges, and is often associated with considerable morbidity. However, vigilance can prevent devastating consequences. Abstract: Streptococcus gordonii-associated endocarditis is rarely reported but often associated with considerable morbidity. We describe three cases of infective endocarditis caused by S. gordonii during a four-week period in 2023, and the use of whole-genome sequencing to determine whether these isolates were genetically related. The available literature was reviewed.

Deciphering the Genomic Landscape and Virulence Mechanisms of the Wheat Powdery Mildew Pathogen Blumeria graminis f. sp. tritici Wtn1: Insights from Integrated Genome Assembly and Conidial Transcriptomics.

A high-quality genome sequence from an Indian isolate of Blumeria graminis f. sp. tritici Wtn1, a persistent threat in wheat farming, was obtained using a hybrid method. The assembly of over 9.24 million DNA-sequence reads resulted in 93 contigs, totaling a 140.61 Mb genome size, potentially encoding 8480 genes. Notably, more than 73.80% of the genome, spanning approximately 102.14 Mb, comprises retro-elements, LTR elements, and P elements, influencing evolution and adaptation significantly. The phylogenomic analysis placed B. graminis f. sp. tritici Wtn1 in a distinct monocot-infecting clade. A total of 583 tRNA anticodon sequences were identified from the whole genome of the native virulent strain B. graminis f. sp. tritici, which comprises distinct genome features with high counts of tRNA anticodons for leucine (70), cysteine (61), alanine (58), and arginine (45), with only two stop codons (Opal and Ochre) present and the absence of the Amber stop codon. Comparative InterProScan analysis unveiled "shared and unique" proteins in B. graminis f. sp. tritici Wtn1. Identified were 7707 protein-encoding genes, annotated to different categories such as 805 effectors, 156 CAZymes, 6102 orthologous proteins, and 3180 distinct protein families (PFAMs). Among the effectors, genes like Avra10, Avrk1, Bcg-7, BEC1005, CSEP0105, CSEP0162, BEC1016, BEC1040, and HopI1 closely linked to pathogenesis and virulence were recognized. Transcriptome analysis highlighted abundant proteins associated with RNA processing and modification, post-translational modification, protein turnover, chaperones, and signal transduction. Examining the Environmental Information Processing Pathways in B. graminis f. sp. tritici Wtn1 revealed 393 genes across 33 signal transduction pathways. The key pathways included yeast MAPK signaling (53 genes), mTOR signaling (38 genes), PI3K-Akt signaling (23 genes), and AMPK signaling (21 genes). Additionally, pathways like FoxO, Phosphatidylinositol, the two-component system, and Ras signaling showed significant gene representation, each with 15-16 genes, key SNPs, and Indels in specific chromosomes highlighting their relevance to environmental responses and pathotype evolution. The SNP and InDel analysis resulted in about 3.56 million variants, including 3.45 million SNPs, 5050 insertions, and 5651 deletions within the whole genome of B. graminis f. sp. tritici Wtn1. These comprehensive genome and transcriptome datasets serve as crucial resources for understanding the pathogenicity, virulence effectors, retro-elements, and evolutionary origins of B. graminis f. sp. tritici Wtn1, aiding in developing robust strategies for the effective management of wheat powdery mildew.

Nanobiostimulant action of trigolic formulated zinc sulfide nanoparticles (ZnS-T NPs) on rice seeds by triggering antioxidant defense network and plant growth specific transcription factors.

Under a changing climate, nanotechnological interventions for climate resilience in crops are critical to maintaining food security. Prior research has documented the affirmative response of nano zinc sulfide (nZnS) on physiological traits of fungal-infested rice seeds. Here, we propose an application of trigolic formulated zinc sulfide nanoparticles (ZnS-T NPs) on rice seeds as nanobiostimulant to improve physiological parameters by triggering antioxidative defense system, whose mechanism was investigated at transcriptional level by differential expression of genes in germinated seedlings. Nanopriming of healthy rice seeds with ZnS-T NPs (50 µg/ml), considerably intensified the seed vitality factors, including germination percentage, seedling length, dry weight and overall vigor index. Differential activation of antioxidant enzymes, viz. SOD (35.47%), APX (33.80%) and CAT (45.94%), in ZnS-T NPs treated seedlings reduced the probability of redox imbalance and promoted the vitality of rice seedlings. In gene expression profiling by reverse transcription quantitative real time PCR (qRT-PCR), the notable up-regulation of target antioxidant genes (CuZn SOD, APX and CAT) and plant growth specific genes (CKX and GRF) in ZnS-T NPs treated rice seedlings substantiates their molecular role in stimulating both antioxidant defenses and plant growth mechanisms. The improved physiological quality parameters of ZnS-T NPs treated rice seeds under pot house conditions corresponded well with in vitro findings, which validated the beneficial boosted impact of ZnS-T NPs on rice seed development. Inclusively, the study on ZnS-T NPs offers fresh perspectives into biochemical and molecular reactions of rice, potentially positioning them as nanobiostimulant capable of eliciting broad-spectrum immune and growth-enhancing responses.

Combined effect of polystyrene nanoplastic and di-n-butyl phthalate on testicular health of male Swiss albino mice: analysis of sperm-related parameters and potential toxic effects.

Plastics, especially polystyrene nanoplastic particles (PSNPs), are known for their durability and absorption properties, allowing them to interact with environmental pollutants such as di-n-butyl phthalate (DBP). Previous research has highlighted the potential of these particles as carriers for various pollutants, emphasizing the need to understand their environmental impact comprehensively. This study focuses on the subchronic exposure of male Swiss albino mice to PSNP and DBP, aiming to investigate their reproductive toxicity between these pollutants in mammalian models. The primary objective of this study is to examine the reproductive toxicity resulting from simultaneous exposure to PSNP and DBP in male Swiss albino mice. The study aims to analyze sperm parameters, measure antioxidant enzyme activity, and conduct histopathological and morphometric examinations of the testis. By investigating the individual and combined effects of PSNP and DBP, the study seeks to gain insights into their impact on the reproductive profile of male mice, emphasizing potential synergistic interactions between these environmental pollutants. Male Swiss albino mice were subjected to subchronic exposure (60 days) of PSNP (0.2 mg/m, 50 nm size) and DBP (900 mg/kg bw), both individually and in combination. Various parameters, including sperm parameters, antioxidant enzyme activity, histopathological changes, and morphometric characteristics of the testis, were evaluated. The Johnsen scoring system and histomorphometric parameters were employed for a comprehensive assessment of spermatogenesis and testicular structure. The study revealed non-lethal effects within the tested doses of PSNP and DBP alone and in combination, showing reductions in body weight gain and testis weight compared to the control. Individual exposures and the combination group exhibited adverse effects on sperm parameters, with the combination exposure demonstrating more severe outcomes. Structural abnormalities, including vascular congestion, Leydig cell hyperplasia, and the extensive congestion in tunica albuginea along with both ST and Leydig cell damage, were observed in the testis, underscoring the reproductive toxicity potential of PSNP and DBP. The Johnsen scoring system and histomorphometric parameters confirmed these findings, providing interconnected results aligning with observed structural abnormalities. The study concludes that simultaneous exposure to PSNP and DBP induces reproductive toxicity in male Swiss albino mice. The combination of these environmental pollutants leads to more severe disruptions in sperm parameters, testicular structure, and antioxidant defense mechanisms compared to individual exposures. The findings emphasize the importance of understanding the interactive mechanisms between different environmental pollutants and their collective impact on male reproductive health. The use of the Johnsen scoring system and histomorphometric parameters provides a comprehensive evaluation of spermatogenesis and testicular structure, contributing valuable insights to the field of environmental toxicology.

Disease suppression, growth promotion and colonization attributes of resident endophytic bacteria against white root rot (Dematophora necatrix Hartig) of apple.

The inherent potential of apple plants was investigated to explore bacterial endophytes and their role in suppressing Dematophora necatrix, the causative pathogen of white root rot disease. Resultantly 34 endophytic bacteria isolated from healthy apple plants, and subsequently 6 most efficient isolates viz., Bacillus megaterium strain EA3, Enterobacter sp. strain EA7, Bacillus megaterium strain EK2, Stenotrophomonas maltophilia strain EK6, Acinetobacter nosocomialis strain ES2 and Pseudomonas aeruginosa strain ES8 depicting anti-pathogen interactions through preliminary screening were assessed further under in vitro, glasshouse and field conditions against white root rot pathogen/disease. Maximum mycelial growth inhibition (80.37%) was obtained with S. maltophilia strain EK6 encouraging for its seed treatment and soil application thereby providing significant effective control (87.91%) of white root rot under glasshouse conditions to other five bacterial endophytes evaluated simultaneously, followed by field efficacy of 83.70%. In addition, it has significantly enhanced the growth parameters of apple trees under both glasshouse and field conditions. The inoculated healthy plants were assessed for endophytic colonization which revealed maximum endosphere colonialism by S. maltophilia strain EK6. Additionally, confocal microscopic images of transverse sections of root cells colonized by bacterial endophytes as compared to untreated control implied their persistence and establishment in endosphere of apple seedlings. The study provides the first report on interaction between apple associated bacterial root endophytes and D. necatrix. The obtained endophytic strains could be employed as alternative for mitigating white root rot disease in future.

Blood borne infections and Hepatitis B virus immunization levels among medical students in India.

BACKGROUND: Medical students are actively involved in direct patient care during their training and subsequent medical practice, making them susceptible to acquiring blood-borne pathogens, including HBV. This study aimed to assess the occurrence of blood-borne infections and the Hepatitis B immunization status among medical students. Furthermore, it sought to identify gaps in risk assessment for blood-borne pathogens among medical students at a Medical College in New Delhi. METHODS: The study included 108 medical students who participated in a blood donation camp. Blood samples collected from these individuals underwent testing for blood-borne pathogens in accordance with standard screening protocols at the blood bank. The quantitative estimation was performed for anti-HBs IgG using ELISA. RESULTS: All 108 participants were pursuing their undergraduate or postgraduate medical degrees. All students tested negative for HBsAg and HIV markers and showed no reactivity to Syphilis and Malaria. However, one student tested positive for HCV. Two postgraduate students had a history of needlestick injuries. Eighty-one (75%) students had received Hepatitis B vaccination. Among the vaccinated students, 34 (41.97%) were immunized before the age of five years, 22 (27.16%) after the age of five years, while 25 (30.86%) couldn't recall the exact age of their Hepatitis B vaccination. Protective anti-HBs titer of anti-Hepatitis B antibodies (>10 mIU/ml) were detected in 48.15% (52/108) of students. CONCLUSIONS: Over 50% of medical students did not possess sufficient immunity against HBV infection, putting them at a heightened risk of acquiring HBV during their active participation in patient care in the near future. It is imperative to establish a policy for routine anti-HBs titer assessment and ensure easy access to Hepatitis B immunization for medical students, thereby enhancing their protection against this infectious agent.

Evaluation of potential role of R-loop and G-quadruplex DNA in the fragility of c-MYC during chromosomal translocation associated with Burkitt's lymphoma.

t(8;14) translocation is the hallmark of Burkitt's lymphoma and results in c-MYC deregulation. During the translocation, c-MYC gene on chromosome 8 gets juxtaposed to the Ig switch regions on chromosome 14. Although the promoter of c-MYC has been investigated for its mechanism of fragility, little is known about other c-MYC breakpoint regions. We have analyzed the translocation break points at the exon 1/intron 1 of c-MYC locus from patients with Burkitt's lymphoma. Results showed that the breakpoint region, when present on a plasmid, could fold into an R-loop confirmation in a transcription-dependent manner. Sodium bisulfite modification assay revealed significant single-strandedness on chromosomal DNA of Burkitt's lymphoma cell line, Raji, and normal lymphocytes, revealing distinct R-loops covering up to 100 bp region. Besides, ChIP-DRIP analysis reveals that the R-loop antibody can bind to the breakpoint region. Further, we show the formation of stable parallel intramolecular G-quadruplex on non-template strand of the genome. Finally, incubation of purified AID in vitro or overexpression of AID within the cells led to enhanced mutation frequency at the c-MYC breakpoint region. Interestingly, anti-γH2AX can bind to DSBs generated at the c-MYC breakpoint region within the cells. The formation of R-loop and G-quadruplex was found to be mutually exclusive. Therefore, our results suggest that AID can bind to the single-stranded region of the R-loop and G4 DNA, leading to the deamination of cytosines to uracil and induction of DNA breaks in one of the DNA strands, leading to double-strand break, which could culminate in t(8;14) chromosomal translocation.

Molecular diagnostic assay for pre-harvest detection of Tilletia indica infection in wheat plants.

The current study describes a new diagnostic method for the rapid and accurate detection of Tilletia indica, the pathogen accountable for causing Karnal bunt (KB) disease in wheat. This method uses quantitative real-time polymerase chain reaction (qPCR) and a primer set derived from glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene of T. indica to identify the presence of the pathogen. The qPCR assay using this primer set was found highly sensitive, with a limit of detection (LOD) value of 4 pg of T. indica DNA. This level of sensitivity allows for the detection of the pathogen even in cases of different growth stages of wheat, where no visible symptoms of infection on the wheat plants can be seen by naked eyes. The study also validated the qPCR assay on ten different wheat cultivars. Overall, this study presents a valuable molecular tool for rapid, specific and sensitive detection of KB fungus in wheat host. This method has practical applications in disease management, screening of wheat genotypes against KB and can aid in the development of strategies to mitigate the impact of Karnal bunt disease on wheat production.

Nano polystyrene induced changes in anxiety and learning behaviour are mediated through oxidative stress and gene disturbance in mouse brain regions.

It is widely reported now that nanoplastic particles have potential neurotoxic effects and may disturb central nervous system (CNS) function. However, the mechanism behind these toxic effects still needs to be elucidated. In the current study, we investigated the effects of polystyrene nanoplastics (PS-NPs) on changes in learning, memory, and anxiety-related behavior in mice based on some selected biochemical, molecular, and histopathological changes in three important brain regions (Cortex, Hypothalamus, and Hippocampus). Male mice were orally administered daily with two doses of 50 nm PS-NPs (0.2 mg/ml and 1 mg/ml) for 8 weeks. We observed decreased expression of neurotransmitter-related genes (VAChT, GAD, and SYP) in the cortex, hypothalamus, and hippocampus areas of the mouse brain. Other biochemical variables including, antioxidant enzymes, biomarkers for oxidative stress, and acetylcholinesterase activity showed significant alterations in all three brain regions. Molecular and neurochemical data thus suggest significant neurobehavioral changes following sub-chronic exposure to PS-NPs which may lead to enhanced anxiety-related and spatial learning and memory-related impairments by affecting limbic areas of the brain.

DeepADRA2A: predicting adrenergic α2a inhibitors using deep learning.

Adrenergic α2a (ADRA2A) receptors play a crucial role in modulating various physiological actions, thereby influencing the proper functioning of different systems in the body. ADRA2A regulation is associated with a wide range of effects, including alterations in blood pressure, hypertension, heightened heart rate, etc. Inhibition of these receptors results in the release of noradrenaline, leading to heightened physiological activity, improved alertness, reduced blood pressure, and alleviation of hypertension. Conventional approaches for identifying ADRA2A inhibitors are burdened with high costs, labor-intensive procedures, and time-consuming processes. In light of these challenges, leveraging the power of artificial intelligence offers a promising solution for drug discovery and development. This study endeavors to harness the potential of artificial intelligence to develop robust models capable of accurately predicting ADRA2A inhibitors and non-inhibitors. By doing so, we aim to streamline and expedite the identification of potential drug candidates in this domain. In this study, we employed four different machine learning (ML) and deep learning (DL) algorithms to develop prediction models based on various molecular descriptors (1D, 2D, and molecular fingerprints). Among these models, the DL-based prediction model demonstrated superior performance, achieving accuracies of 98.25% and 97.23% on the training and test datasets, respectively. These results underscore the efficacy of DL-based model, as a highly effective tool for predicting ADRA2A inhibitors. The model is made available at https://github.com/PGlab-NIPER/DeepADRA2A.git.Communicated by Ramaswamy H. Sarma.

XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm.

Determining the structure-odor relationship has always been a very challenging task. The main challenge in investigating the correlation between the molecular structure and its associated odor is the ambiguous and obscure nature of verbally defined odor descriptors, particularly when the odorant molecules are from different sources. With the recent developments in machine learning (ML) technology, ML and data analytic techniques are significantly being used for quantitative structure-activity relationship (QSAR) in the chemistry domain toward knowledge discovery where the traditional Edisonian methods have not been useful. The smell perception of odorant molecules is one of the aforementioned tasks, as olfaction is one of the least understood senses as compared to other senses. In this study, the XGBoost odor prediction model was generated to classify smells of odorant molecules from their SMILES strings. We first collected the dataset of 1278 odorant molecules with seven basic odor descriptors, and then 1875 physicochemical properties of odorant molecules were calculated. To obtain relevant physicochemical features, a feature reduction algorithm called PCA was also employed. The ML model developed in this study was able to predict all seven basic smells with high precision (>99%) and high sensitivity (>99%) when tested on an independent test dataset. The results of the proposed study were also compared with three recently conducted studies. The results indicate that the XGBoost-PCA model performed better than the other models for predicting common odor descriptors. The methodology and ML model developed in this study may be helpful in understanding the structure-odor relationship.Communicated by Ramaswamy H. Sarma.

Autoimmune encephalitis: A diagnostic enigma.

Autoimmune encephalitis with anti-NMDA (N-methyl-D-aspartate) receptor encephalitis is the most common type. This condition can be associated with underlying malignancy, making appropriate screening essential. Prompt identification and intervention of autoimmune encephalitis is essential for a successful outcome and full recovery from this serious and potentially fatal condition. Immunotherapy is typically used to suppress the immune response and reduce brain inflammation. The choice of therapy depends on the type and severity of autoimmune encephalitis, as well as the presence of an underlying tumour.

Biochemical probing of phloem sap defensive traits in Brassica juncea-B. fruticulosa introgression lines following Lipaphis erysimi infestation.

The lack of resistance to Lipaphis erysimi in cultivated Brassicas makes caused this pest highly devastating resulting in significant loss of rapeseed-mustard productivity in India. B. fruticulosa, a wild crucifer is known as an excellent source of resistance to L. erysimi. Therefore, we planned to assess defense associated biochemical alterations and molecular components of B. juncea-B. fruticulosa ILs to mustard aphid. Phenotypic assessment of ILs on the basis of aphid population per plant (APP) categorized genotypes into resistant (7.15-18.50 APP), moderately susceptible (42.29-53.33 APP) and susceptible (70.00-77.07 APP) genotypes. Mustard aphid infested minimally B. fruticulosa (0.80 APP) among tested genotypes. The maximum increase in catalase (CAT) activity was determined in B. fruticulosa and resistant ILs after 48 h (2.03 and 1.76-fold, respectively) and one week (2.98 and 1.79-fold, respectively) of mustard aphid infestation. The strong induction of CAT2 transcripts (19.25-fold) and CAT activity (5.88-fold) along with low aphid count in resistant IL, Ad4-64 (13.85 APP) suggested the pivotal role of CAT in resistance to mustard aphid. Guaiacol peroxidase (GPX) was significantly decreased following pest infestation at both infestation stages. The ascorbate content was highest in resistant IL, ADV-6RD (2.14-fold) after one week of aphid infestation. H2O2 content rapidly increased in B. juncea-B. fruticulosa derived lines after 48 h of aphid infestation. The negative and significant association between APP and CAT (- 0.56** and - 0.48*, respectively), glutathione (- 0.43* and - 0.40*, respectively), H2O2 (- 0.57** and - 0.43*, respectively) at both 48 h and one week infestation stages signified their role in deterring mustard aphid infestation. The positive and significant association between total sugars (0.33* at 7 DPI), reducing sugars (0.33* at 7 DPI), sucrose (0.36** at 48 h) and APP indicated that higher the sugars content, higher will be mustard aphid infestation in B. juncea derived ILs. The information being generated and key candidates (CAT2, ascorbate and H2O2) being identified may help in effective deployment of B. fruticulosa resistance in mustard breeding.

Is Omicron really mild? - Comparative analysis of comorbidities and disease outcomes associated with SARS-CoV-2 Omicron (B.1.1.529) and Delta (B.1.617.2) variants.

PURPOSE: Multiple variants of SARS-CoV-2 from Alpha to Omicron have an estimated 6.1 million deaths globally till date. These variants have been found to vary in transmissibility and severity. The present study deals with comparison of morbidity and mortality with SARS-CoV-2 Omicron (B.1.1.529) and Delta (B.1.617.2) variants. MATERIALS AND METHOD: An observational retrospective cohort study was conducted on a cohort of laboratory confirmed patients of SARS-CoV-2 diagnosed by qRT-PCR of nasopharyngeal swabs in periods; April-2021 and January-2022; that were sequenced and variants were recorded. Patients were invited for a telephonic interview after voluntary and informed consent was obtained from each participant wherein, the demographics, co-morbidities, oxygen requirement and mortality outcomes of the patients were enquired about. RESULTS: A total of 200 patients, with 100 from each period were included in the study. Major comorbidities in patients included hypertension, diabetes mellitus and pulmonary disease. Patients who succumbed to the Delta variant (26%) were higher as compared to the Omicron variant (10%); with the elderly (68 â€‹± â€‹9.7 â€‹years) having significant mortality during the Omicron variant. The mortality was increased in patients with comorbidities as with hypertension (53.8%, 70%), diabetes mellitus (26.9%, 40%), chronic pulmonary disease (30.8%, 20%), and smoking (15.4%, 40%) in the patients infected with both Delta and Omicron variants, respectively. CONCLUSION: The study concluded that the newer strains of SARS-CoV-2 have potential of high transmissibility and milder disease for the population by large, however, for patients with comorbidities have a higher proportion of adverse outcomes, irrespective of the variant.

FGFR1Pred: an artificial intelligence-based model for predicting fibroblast growth factor receptor 1 inhibitor.

Fibroblast growth factor receptors (FGFRs) are a family of cell surface receptors that bind to fibroblast growth factor (FGF) and mediate various cellular functions (translocating proteins, tissue repair, cell proliferation, development, and differentiation) through complex signaling pathways. The FGFR1 growth receptor is essential in the pathogenesis of numerous malignancies, including but not limited to breast cancer, bladder cancer, hepatocellular carcinoma (HCC), and cholangiocarcinoma. The higher levels of FGFR1 expression on the surface of cancer cells cause overly active signaling, which leads to rapid cell proliferation, resulting in a high spread of cancer cells. The kinases that FGFR1 activates migrate across the cell nucleus, activating genes and kinase proteins necessary for the growth and survival of cancerous cells. Therefore, FGFR1 targeting shows therapeutic promise in some diseases, including cancer. Inhibitors of FGFR1s are being developed and studied for their potential to block aberrant FGFR1 signaling and inhibit cancer growth. Since the discovery of new FGFR1 inhibitors in the laboratory is difficult, expensive, time-consuming, and labor-intensive, only a small number of FGFR1 inhibitors have been approved by the FDA for use in the treatment of cancer. To accelerate drug discovery by efficiently exploring the vast chemical space, and identifying potential candidates with higher accuracy and reduced cost, we developed artificial intelligence (AI)-based prediction models for FGFR1 inhibitors using a dataset of 2356 chemical compounds. Four machine learning (ML) algorithms (SVM, RF, k-NN, and ANN) were used to train different prediction models based on molecular descriptors (1D and 2D, with and without molecular fingerprints). Among all trained models, the random forest (RF)-based prediction model achieved the highest accuracy on the training (98.9%), test (89.8%), and external test (90.3%) datasets. The developed inhibitor prediction model (FGFR1Pred) provides a valuable tool for identifying potential FGFR1 inhibitors, expediting the drug discovery process and ultimately facilitating the development of new therapeutics. The model is made available at https://github.com/PGlab-NIPER/FGFR1Pred.git.

TNFipred: a classification model to predict TNF-α inhibitors.

Rheumatoid arthritis (RA), characterized by severe inflammation in the joint lining, is a progressive, chronic, autoimmune disorder with high morbidity and mortality rates. There are several mechanisms responsible for joint damage, but overproduction of TNF-α is a significant mechanism that results in excess swelling and pain. Drugs acting on TNF-α are known to significantly reduce the disease progression and improve the quality of life in many RA patients. Hence, inhibiting TNF-α is considered one of the most effective treatments for RA. Currently, there are only a few FDA-approved TNF-α inhibitors, which are mainly monoclonal antibodies, fusion proteins, or biosimilars with disadvantages such as poor stability, difficulty in route of administration (often given as injection or infusion), cost-prohibitive large-scale production, and increased side effects. There are just a handful of small compounds known to have TNF- inhibitory capabilities. Thus, there is a dire need for new drugs, especially small molecules in the market, such as TNF-α inhibitors. The conventional method of identifying TNF-α inhibitors is expensive, labor, and time intensive. Machine learning (ML) can be used to solve existing drug discovery and development problems. In this study, four classification algorithms-naïve Bayes (NB), random forest (RF), k-nearest neighbor (kNN), and support vector machine (SVM)-were used to train ML models for classifying TNF-α inhibitors based on three sets of features. The performance of the RF model was found to be best when using 1D, 2D, and fingerprints as features, with an accuracy of 87.96 and a sensitivity of 86.17. To our knowledge, this is the first ML model for TNF-α inhibitor prediction. The model is available at http://14.139.57.41/tnfipred/.