Phenotypic and transcriptomics characterization uncovers genes underlying tuber yield traits and gene expression marker development in potato under aeroponics.

MAIN CONCLUSION: Transcriptome analysis in potato varieties revealed genes associated with tuber yield-related traits and developed gene expression markers. This study aimed to identify genes involved in high tuber yield and its component traits in test potato varieties (Kufri Frysona, Kufri Khyati, and Kufri Mohan) compared to control (Kufri Sutlej). The aeroponic evaluation showed significant differences in yield-related traits in the varieties. Total RNA sequencing was performed using tuber and leaf tissues on the Illumina platform. The high-quality reads (QV > 25) mapping with the reference potato genomes revealed statistically significant (P < 0.05) differentially expressed genes (DEGs) into two categories: up-regulated (> 2 Log2 fold change) and down-regulated (< -2 Log2 fold change). DEGs were characterized by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Collectively, we identified genes participating in sugar metabolism, stress response, transcription factors, phytohormones, kinase proteins, and other genes greatly affecting tuber yield and its related traits. A few selected genes were UDP-glucose glucosyltransferase, glutathion S-transferase, GDSL esterase/lipase, transcription factors (MYB, WRKY, bHLH63, and BURP), phytohormones (auxin-induced protein X10A, and GA20 oxidase), kinase proteins (Kunitz-type tuber invertase inhibitor, BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1) and laccase. Based on the selected 17 peptide sequences representing 13 genes, a phylogeny tree and motifs were analyzed. Real time-quantitative polymerase chain reaction (RT-qPCR) analysis was used to validate the RNA-seq results. RT-qPCR based gene expression markers were developed for the genes such as 101 kDa heat shock protein, catechol oxidase B chloroplastic, cysteine protease inhibitor 1, Kunitz-type tuber invertase inhibitor, and laccase to identify high yielding potato genotypes. Thus, our study paved the path for potential genes associated with tuber yield traits in potato under aeroponics.

miRNA-Targeted Vaccines: A Promising Approach for Viral Attenuation and Immunogenicity Enhancement.

MicroRNAs (miRNAs) have emerged as a significant tool in the realm of vaccinology, offering novel approaches to vaccine development. This study investigates the potential of miRNAs in the development of advanced vaccines, with an emphasis on how they regulate immune response and control viral replication. We go over the molecular features of miRNAs, such as their capacity to direct post-transcriptional regulation toward mRNAs, hence regulating the expression of genes in diverse tissues and cells. This property is harnessed to develop live attenuated vaccines that are tissue-specific, enhancing safety and immunogenicity. The review highlights recent advancements in using miRNA-targeted vaccines against viruses like influenza, poliovirus, and tick-borne encephalitis virus, demonstrating their attenuated replication in specific tissues while retaining immunogenicity. We also explored the function of miRNAs in the biology of cancer, highlighting their potential to develop cancer vaccines through targeting miRNAs that are overexpressed in tumor cells. The difficulties in developing miRNA vaccines are also covered in this work, including delivery, stability, off-target effects, and the requirement for individualized cancer treatment plans. We wrap off by discussing the potential of miRNA vaccines and highlighting how they will influence the development of vaccination techniques for cancer and infectious diseases in the future.

Graphene and its hybrid nanocomposite: A Metamorphoses elevation in the field of tissue engineering.

In this discourse, we delve into the manifold applications of graphene-based nanomaterials (GBNs) in the realm of biomedicine. Graphene, characterized by its two-dimensional planar structure, superconductivity, mechanical robustness, chemical inertness, extensive surface area, and propitious biocompatibility, stands as an exemplary candidate for diverse biomedical utility. Graphene include various distinctive characteristics of its two-dimensional planar structure, enormous surface area, mechanical and chemical stability, high conductivity, and exceptional biocompatibility. We investigate graphene and its diverse derivatives, which include reduced graphene oxides (rGOs), graphene oxides (GOs), and graphene composites, with a focus on elucidating the unique attributes relevant to their biomedical utility. In this review article it highlighted the unique properties of graphene, synthesis methods of graphene and functionalization methods of graphene. In the quest for novel materials to advance regenerative medicine, researchers have increasingly turned their attention to graphene-based materials, which have emerged as a prominent innovation in recent years. Notably, it highlights their applications in the regeneration of various tissues, including nerves, skeletal muscle, bones, skin, cardiac tissue, cartilage, and adipose tissue, as well as their influence on induced pluripotent stem cells, marking significant breakthroughs in the field of regenerative medicine. Additionally, this review article explores future prospects in this evolving area of study.

Combination of dual JAK/HDAC inhibitor with regorafenib synergistically reduces tumor growth, metastasis, and regorafenib-induced toxicity in colorectal cancer.

BACKGROUND: Treatment with regorafenib, a multiple-kinase inhibitor, to manage metastatic colorectal cancers (mCRCs) shows a modest improvement in overall survival but is associated with severe toxicities. Thus, to reduce regorafenib-induced toxicity, we used regorafenib at low concentration along with a dual JAK/HDAC small-molecule inhibitor (JAK/HDACi) to leverage the advantages of both JAK and HDAC inhibition to enhance antitumor activity. The therapeutic efficacy and safety of the combination treatment was evaluated with CRC models. METHODS: The cytotoxicity of JAK/HDACi, regorafenib, and their combination were tested with normal colonic and CRC cells exhibiting various genetic backgrounds. Kinomic, ATAC-seq, RNA-seq, cell cycle, and apoptosis analyses were performed to evaluate the cellular functions/molecular alterations affected by the combination. Efficacy of the combination was assessed using patient-derived xenograft (PDX) and experimental metastasis models of CRC. To evaluate the interplay between tumor, its microenvironment, and modulation of immune response, MC38 syngeneic mice were utilized. RESULTS: The combination therapy decreased cell viability; phosphorylation of JAKs, STAT3, EGFR, and other key kinases; and inhibited deacetylation of histone H3K9, H4K8, and alpha tubulin proteins. It induced cell cycle arrest at G0-G1 phase and apoptosis of CRC cells. Whole transcriptomic analysis showed that combination treatment modulated molecules involved in apoptosis, extracellular matrix-receptor interaction, and focal adhesion pathways. It synergistically reduces PDX tumor growth and experimental metastasis, and, in a syngeneic mouse model, the treatment enhances the antitumor immune response as evidenced by higher infiltration of CD45 and cytotoxic cells. Pharmacokinetic studies showed that combination increased the bioavailability of regorafenib. CONCLUSIONS: The combination treatment was more effective than with regorafenib or JAK/HDACi alone, and had minimal toxicity. A clinical trial to evaluate this combination for treatment of mCRCs is warranted.

COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography.

Background and novelty: When RT-PCR is ineffective in early diagnosis and understanding of COVID-19 severity, Computed Tomography (CT) scans are needed for COVID diagnosis, especially in patients having high ground-glass opacities, consolidations, and crazy paving. Radiologists find the manual method for lesion detection in CT very challenging and tedious. Previously solo deep learning (SDL) was tried but they had low to moderate-level performance. This study presents two new cloud-based quantized deep learning UNet3+ hybrid (HDL) models, which incorporated full-scale skip connections to enhance and improve the detections. Methodology: Annotations from expert radiologists were used to train one SDL (UNet3+), and two HDL models, namely, VGG-UNet3+ and ResNet-UNet3+. For accuracy, 5-fold cross-validation protocols, training on 3,500 CT scans, and testing on unseen 500 CT scans were adopted in the cloud framework. Two kinds of loss functions were used: Dice Similarity (DS) and binary cross-entropy (BCE). Performance was evaluated using (i) Area error, (ii) DS, (iii) Jaccard Index, (iii) Bland-Altman, and (iv) Correlation plots. Results: Among the two HDL models, ResNet-UNet3+ was superior to UNet3+ by 17 and 10% for Dice and BCE loss. The models were further compressed using quantization showing a percentage size reduction of 66.76, 36.64, and 46.23%, respectively, for UNet3+, VGG-UNet3+, and ResNet-UNet3+. Its stability and reliability were proved by statistical tests such as the Mann-Whitney, Paired t-Test, Wilcoxon test, and Friedman test all of which had a p < 0.001. Conclusion: Full-scale skip connections of UNet3+ with VGG and ResNet in HDL framework proved the hypothesis showing powerful results improving the detection accuracy of COVID-19.

Advancements of Glucose Monitoring Biosensor: Current State, Generations of Technological Progress, and Innovation Dynamics.

Glucose monitoring is essential for managing diabetes, and continuous glucose monitoring biosensors can offer real-time monitoring with little invasiveness. However, challenges remain in improving sensor accuracy, selectivity, and overall performance. This article aims to review current trends and recent advancements in glucose-monitoring biosensors while evaluating their benefits and limitations for diabetes monitoring. An analysis of current literature on transdermal glucose sensors was conducted, focusing on detection techniques, novel nanomaterials, and integrated sensor systems. Recent research has led to advancements in electrochemical, optical, electromagnetic, and sonochemical sensors for transdermal glucose detection. The use of novel nanomaterials and integrated sensor designs has improved sensitivity, selectivity, and accuracy. However, issues like calibration requirements, motion artifacts, and skin irritation persist. Transdermal glucose sensors show promise for non-invasive, convenient diabetes monitoring but require further enhancements to address limitations in accuracy, reliability, and biocompatibility. Continued research and innovation focusing on sensor materials, designs, and surface chemistry is needed to optimize biosensor performance and utility. The study offers a comprehensive analysis of the present status of technological advancement and highlights areas that need more research.

Detailed-based dictionary learning for low-light image enhancement using camera response model for industrial applications.

Images captured in low-light environments are severely degraded due to insufficient light, which causes the performance decline of both commercial and consumer devices. One of the major challenges lies in how to balance the image enhancement properties of light intensity, detail presentation, and colour integrity in low-light enhancement tasks. This study presents a novel image enhancement framework using a detailed-based dictionary learning and camera response model (CRM). It combines dictionary learning with edge-aware filter-based detail enhancement. It assumes each small detail patch could be sparsely characterised in the over-complete detail dictionary that was learned from many training detail patches using iterative ℓ 1 -norm minimization. Dictionary learning will effectively address several enhancement concerns in the progression of detail enhancement if we remove the visibility limit of training detail patches in the enhanced detail patches. We apply illumination estimation schemes to the selected CRM and the subsequent exposure ratio maps, which recover a novel enhanced detail layer and generate a high-quality output with detailed visibility when there is a training set of higher-quality images. We estimate the exposure ratio of each pixel using illumination estimation techniques. The selected camera response model adjusts each pixel to the desired exposure based on the computed exposure ratio map. Extensive experimental analysis shows an advantage of the proposed method that it can obtain enhanced results with acceptable distortions. The proposed research article can be generalised to address numerous other similar problems, such as image enhancement for remote sensing or underwater applications, medical imaging, and foggy or dusty conditions.

Four Transformer-Based Deep Learning Classifiers Embedded with an Attention U-Net-Based Lung Segmenter and Layer-Wise Relevance Propagation-Based Heatmaps for COVID-19 X-ray Scans.

Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention and Vision Transformers (ViTs) to enhance lung disease segmentation and classification. We hypothesize that Attention U-Net will enhance segmentation accuracy and that ViTs will improve classification performance. The explainability methodologies will shed light on model decision-making processes, aiding in clinical acceptance. Methodology: A comparative approach was used to evaluate deep learning models for segmenting and classifying lung illnesses using chest X-rays. The Attention U-Net model is used for segmentation, and architectures consisting of four CNNs and four ViTs were investigated for classification. Methods like Gradient-weighted Class Activation Mapping plus plus (Grad-CAM++) and Layer-wise Relevance Propagation (LRP) provide explainability by identifying crucial areas influencing model decisions. Results: The results support the conclusion that ViTs are outstanding in identifying lung disorders. Attention U-Net obtained a Dice Coefficient of 98.54% and a Jaccard Index of 97.12%. ViTs outperformed CNNs in classification tasks by 9.26%, reaching an accuracy of 98.52% with MobileViT. An 8.3% increase in accuracy was seen while moving from raw data classification to segmented image classification. Techniques like Grad-CAM++ and LRP provided insights into the decision-making processes of the models. Conclusions: This study highlights the benefits of integrating Attention U-Net and ViTs for analyzing lung diseases, demonstrating their importance in clinical settings. Emphasizing explainability clarifies deep learning processes, enhancing confidence in AI solutions and perhaps enhancing clinical acceptance for improved healthcare results.

Transcriptome analysis reveals genes associated with late blight resistance in potato.

Late blight is a serious disease of potato worldwide. Our study aimed to unveil genes involved in late blight resistance in potato by RNA-seq analysis after artificial inoculation under controlled conditions. In this study, two potato somatic hybrids (P7 and Crd6) and three varieties such as Kufri Girdhari, Kufri Jyoti and Kufri Bahar (control) were used. Transcriptiome analysis revealed statistically significant (p < 0.05) differentially expressed genes (DEGs), which were analysed into up-regulated and down-regulated genes. Further, DEGs were functionally characterized by the Gene Ontology annotations and the Kyoto Encyclopedia of Genes and Genomes pathways. Overall, some of the up-regulated genes in resistant genotypes were disease resistance proteins such as CC-NBS-LRR resistance protein, ankyrin repeat family protein, cytochrome P450, leucine-rich repeat family protein/protein kinase family, and MYB transcription factor. Sequence diversity analysis based on 38 peptide sequences representing 18 genes showed distinct variation and the presence of three motifs in 15 amino acid sequences. Selected genes were also validated by real-time quantitative polymerase chain reaction analysis. Interestingly, gene expression markers were developed for late blight resistant genotypes. Our study elucidates genes involved in imparting late blight resistance in potato, which will be beneficial for its management strategies in the future.

Synthesis and Biological Evaluation of Amino Acid and Peptide Conjugates of 5-Bromovaleric Acid.

BACKGROUND: Among various carboxylic acid derivatives, valeric acid or pentanoic acid is found to be widely distributed in nature. It is a straight-chain alkyl carboxylic acid containing five carbon atoms. Due to the therapeutic value of valeric acid, it is used as a versatile nucleus in the pharmaceutical field. Valeric acid derivatives are associated with a broad spectrum of biological activities, like anticonvulsant, antiplatelet, antidiabetic, and plant growth activities. AIM: It has previously been revealed that peptide derivatives of carboxylic acids are accountable for enhanced antimicrobial activity. Therefore, it was hypothesized that coupling peptides with valeric acid would increase the antimicrobial properties of the target compounds. So, the objective of the present study was to synthesize peptide derivatives of 5-bromovaleric acid and evaluate their antibacterial and antifungal activities. METHODS: 5-bromovaleric acid was synthesized by the reaction of cyclopentanone and hydrogen peroxide in the presence of copper bromide and sodium bromide. Additionally, 5-bromovaleric acid was coupled with amino acid methyl esters, dipeptides, tripeptides, and tetrapeptides in the presence of dicyclohexylcarbodimide (DCC) and N-methylmorpholine (NMM) as a base under continuous stirring for 36 hours to produce its peptide derivatives. RESULTS: The results obtained showed that 5-bromovaleric acid possesses more potent antibacterial activity than N-terminal 5-bromovaleric acid conjugates of selected di-, tri, and tetra peptide Cterminal methyl esters against ciprofloxacin as a standard. The selected dipeptide and tripeptide Nterminal 5-bromovaleric acid-conjugated C-terminal methyl ester derivatives were more active than the selected tetrapeptide methyl ester analogue. Using fluconazole as a reference, the antifungal efficacy of 5-bromovaleric acid against C. albicans and A. niger declined as it was combined with C-terminal methyl esters of selected dipeptides, tripeptides, and tetrapeptides. CONCLUSION: The novel selected peptide derivatives had less antibacterial and antifungal action than the parent 5-bromovaleric acid. Antibacterial and antifungal investigations showed that 5- bromopentanoic acid peptide derivatives might impair antimicrobial efficacy. Further, attaching 5- bromopentanoic acid to di, tri, and tetra peptides did not boost their antibacterial potential.

Development and evaluation of a training program on non-communicable diseases to empower community health workers in rural India.

Objective: We developed and evaluated a training program for Accredited Social Health Activists (ASHAs), female community health workers (CHWs) in India, on non-communicable diseases (NCDs). Methods: A 5-day training program, developed using government-approved manuals, was tested in a randomised controlled trial in the Tehri-Garhwal district. Quantitative comparisons were undertaken using Student's t-test and two-way ANOVA. ASHAs in the intervention group were asked questions about new skills learnt. Results: Thirty-six ASHAs (20 intervention, 16 controls) participated (response rate 75.0%). Mean pre-test knowledge score was 43.3/100 points (95% CI 36.7-49.9) for the intervention group and 44.4 (38.9-49.9) for controls. The mean post-test knowledge score increased more in the intervention group (48.5-point increase; P < 0.0001), than in controls (9.8-point increase, P = 0.016; ANOVA interaction term (time*allocation) P < 0.0001). ASHAs in the intervention group reported learning new skills for detecting NCDs. Conclusion: The training program increased knowledge of ASHAs on NCDs and improved their skills to detect NCDs. Our development and testing process for this training program, coupled with open-source resources, fosters innovation and collaboration in managing NCDs in LMICs. Innovation: Our novel and adaptable training program incorporates interactive elements, case studies, and real-world scenarios to augment routine communication between ASHAs and community members for preventing NCDs.

Epidemiological trends of colorectal cancer cases in young population of Eastern India: A retrospective observational study.

BACKGROUND: Colorectal cancer (CRC) is a disease of the older population in developed countries where the incidence among the young is rising despite the decline in the overall incidence. Contrary to this, in India, which is a low-incidence country for CRCs, the incidence among all age groups including the young is rising. This study aimed at describing the clinico-demographic profile of young CRC cases and the epidemiological trend of the proportion of young cases from 2014 to 2021 in a tertiary cancer center in Eastern India. METHODS: This retrospective observational study was conducted at Department of Radiation Oncology, State Cancer Institute, IGIMS Patna, India a prominent tertiary cancer care center of Bihar. All histopathologically confirmed CRC cases in the 0-39 years age group were considered young and evaluated for the clinical, demographic profile as well as yearly trends in proportion out of total CRC cases. Microsoft Excel (2021) was used for statistical analysis. A P value of 0.05 was considered significant. RESULTS: Young colorectal (less than 40 years) patients constituted a third (n = 344, 33.4%) of total colorectal (n = 1028) cases. The median age among the young CRC cases was 30 years (range: 12 to 39 years). Rectum was the most common subsite noted (n = 255,74.1%) among this group of young patients. The most commonly encountered stage of the disease was III (n = 107, 31.1%) and chemotherapy was the most common treatment offered (n = 153, 44.5%). The proportion of young (0-39 years) CRC cases ranged between 29.4 and 37.4 (mean 33.5 ± 2.77, P value = 0.725) over the calendar years of the study period. CONCLUSION: The proportion of young (<40 years of age) cases out of total CRC cases in our study is higher than that in developed countries. However, the trends of this proportion have been consistent over the study period, i.e., from 2014 to 2021 without any significant change in our hospital-based cancer registry. Rectal cancer affected nearly three out of every four CRC patients in this age group. More advanced disease at presentation emphasizes the need for measures of screening, early diagnosis, and adequate infrastructure for treatment.

Antagonistic potential and biological control mechanisms of Pseudomonas strains against banded leaf and sheath blight disease of maize.

Rhizoctonia solani, the causal agent of banded leaf and sheath blight (BL&SB), poses a significant threat to maize and various crops globally. The increasing concerns surrounding the environmental and health impacts of chemical fungicides have encouraged intensified concern in the development of biological control agents (BCAs) as eco-friendly alternatives. In this study, we explored the potential of 22 rhizobacteria strains (AS1-AS22) isolates, recovered from the grasslands of the Pithoragarh region in the Central Himalayas, as effective BCAs against BL&SB disease. Among these strains, two Pseudomonas isolates, AS19 and AS21, exhibited pronounced inhibition of fungal mycelium growth in vitro, with respective inhibition rates of 57.04% and 54.15% in cell cultures and 66.56% and 65.60% in cell-free culture filtrates. Additionally, both strains demonstrated effective suppression of sclerotium growth. The strains AS19 and AS21 were identified as Pseudomonas sp. by 16S rDNA phylogeny and deposited under accession numbers NAIMCC-B-02303 and NAIMCC-B-02304, respectively. Further investigations revealed the mechanisms of action of AS19 and AS21, demonstrating their ability to induce systemic resistance (ISR) and exhibit broad-spectrum antifungal activity against Alternaria triticina, Bipolaris sorokiniana, Rhizoctonia maydis, and Fusarium oxysporum f. sp. lentis. Pot trials demonstrated significant reductions in BL&SB disease incidence (DI) following foliar applications of AS19 and AS21, with reductions ranging from 25 to 38.33% compared to control treatments. Scanning electron microscopy revealed substantial degradation of fungal mycelium by the strains, accompanied by the production of hydrolytic enzymes. These findings suggest the potential of Pseudomonas strains AS19 and AS21 as promising BCAs against BL&SB and other fungal pathogens. However, further field trials are warranted to validate their efficacy under natural conditions and elucidate the specific bacterial metabolites responsible for inducing systemic resistance. This study contributes to the advancement of sustainable disease management strategies and emphasizes the potential of Pseudomonas strains AS19 and AS21 in combating BL&SB and other fungal diseases affecting agricultural crops.

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.

Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding: No funding received.

Tobacco rattle virus-based virus-induced gene silencing (VIGS) as an aid for functional genomics in Saffron (Crocus sativus L.).

Several limitations in genetic engineering interventions in saffron exist, hindering the development of genetically modified varieties and the widespread application of genetic engineering in this crop. Lack of genome sequence information, the complexity of genetic makeup, and lack of well-established genetic transformation protocols limit its in planta functional validation of genes that would eventually lead toward crop optimization. In this study, we demonstrate agro infiltration in leaves of adult plants and whole corm before sprouting are suitable for transient gene silencing in saffron using Tobacco Rattle Virus (TRV) based virus-induced gene silencing (VIGS) targeting phytoene desaturase (PDS). Silencing of PDS resulted in bleached phenotype in leaves in both methods. TRV-mediated VIGS could be attained in saffron leaves and corms, providing an opportunity for functional genomics studies in this expensive spice crop. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-024-01459-0.

Emergent dynamics due to chemo-hydrodynamic self-interactions in active polymers.

The field of synthetic active matter has, thus far, been led by efforts to create point-like, isolated (yet interacting) self-propelled objects (e.g. colloids, droplets, microrobots) and understanding their collective dynamics. The design of flexible, freely jointed active assemblies from autonomously powered sub-components remains a challenge. Here, we report freely-jointed active polymers created using self-propelled droplets as monomeric units. Our experiments reveal that the self-shaping chemo-hydrodynamic interactions between the monomeric droplets give rise to an emergent rigidity (the acquisition of a stereotypical asymmetric C-shape) and associated ballistic propulsion of the active polymers. The rigidity and propulsion of the chains vary systematically with their lengths. Using simulations of a minimal model, we establish that the emergent polymer dynamics are a generic consequence of quasi two-dimensional confinement and auto-repulsive trail-mediated chemical interactions between the freely jointed active droplets. Finally, we tune the interplay between the chemical and hydrodynamic fields to experimentally demonstrate oscillatory dynamics of the rigid polymer propulsion. Altogether, our work highlights the possible first steps towards synthetic self-morphic active matter.

Manufacturing and preclinical toxicity of GLP grade gene deleted attenuated Leishmania donovani parasite vaccine.

Centrin1 gene deleted Leishmania donovani parasite (LdCen1-/-) was developed and extensively tested experimentally as an intracellular stage-specific attenuated and immunoprotective live parasite vaccine candidate ex vivo using human PBMCs and in vivo in animals. Here we report manufacturing and pre-clinical evaluation of current Good-Laboratory Practice (cGLP) grade LdCen1-/- parasites, as a prerequisite before proceeding with clinical trials. We screened three batches of LdCen1-/- parasites manufactured in bioreactors under cGLP conditions, for their consistency in genetic stability, attenuation, and safety. One such batch was preclinically tested using human PBMCs and animals (hamsters and dogs) for its safety and protective immunogenicity. The immunogenicity of the CGLP grade LdCen1-/- parasites was similar to one grown under laboratory conditions. The cGLP grade LdCen1-/- parasites were found to be safe and non-toxic in hamsters and dogs even at 3 times the anticipated vaccine dose. When PBMCs from healed visceral leishmaniasis (VL) cases were infected with cGLP LdCen1-/-, there was a significant increase in the stimulation of cytokines that contribute to protective responses against VL. This effect, measured by multiplex ELISA, was greater than that observed in PBMCs from healthy individuals. These results suggest that cGLP grade LdCen1-/- manufactured under cGMP complaint conditions can be suitable for future clinical trials.

Promising Combinatorial Therapeutic Strategies against Non-Small Cell Lung Cancer.

Non-small cell lung cancer (NSCLC) presents a complex and diverse disease, exhibiting variations at individuals' cellular and histological levels. This complexity gives rise to different subtypes and genetic mutations, posing challenges for accurate diagnosis and effective treatment. Nevertheless, continuous progress in medical research and therapies is continually shaping the landscape of NSCLC diagnosis and management. The treatment of NSCLC has undergone significant advancements in recent years, especially with the emergence of targeted therapies that have shown remarkable efficacy in patients with actionable mutations. This has ushered in the era of personalized medicine in NSCLC treatment, with improvements in molecular and immunohistochemical techniques contributing to enhanced progression-free survival. This review focuses on the latest progress, challenges, and future directions in developing targeted therapies for NSCLC, including tyrosine kinase inhibitors (TKIs), DNA-damaging agents, immunotherapy regimens, natural drug therapy, and nanobodies. Furthermore, recent randomized studies have demonstrated enhanced overall survival in patients receiving different targeted and natural drug therapies.