cAMP/PKA signaling regulates TDP-43 aggregation and mislocalization.

Cytoplasmic mislocalization and aggregation of TDP-43 protein are hallmarks of amyotrophic lateral sclerosis (ALS) and are observed in the vast majority of both familial and sporadic cases. How these two interconnected processes are regulated on a molecular level, however, remains enigmatic. Genome-wide screens for modifiers of the ALS-associated genes TDP-43 and FUS have identified the phospholipase D (Pld) pathway as a key regulator of ALS-related phenotypes in the fruit fly Drosophila melanogaster [M. W. Kankel et al., Genetics 215, 747-766 (2020)]. Here, we report the results of our search for downstream targets of the enzymatic product of Pld, phosphatidic acid. We identify two conserved negative regulators of the cAMP/PKA signaling pathway, the phosphodiesterase dunce and the inhibitory subunit PKA-R2, as modifiers of pathogenic phenotypes resulting from overexpression of the Drosophila TDP-43 ortholog TBPH. We show that knockdown of either of these genes results in a mitigation of both TBPH aggregation and mislocalization in larval motor neuron cell bodies, as well as an amelioration of adult-onset motor defects and shortened lifespan induced by TBPH. We determine that PKA kinase activity is downstream of both TBPH and Pld and that overexpression of the PKA target CrebA can rescue TBPH mislocalization. These findings suggest a model whereby increasing cAMP/PKA signaling can ameliorate the molecular and functional effects of pathological TDP-43.

A guide to artificial intelligence for cancer researchers.

Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to a readily accessible tool for cancer researchers. AI-based tools can boost research productivity in daily workflows, but can also extract hidden information from existing data, thereby enabling new scientific discoveries. Building a basic literacy in these tools is useful for every cancer researcher. Researchers with a traditional biological science focus can use AI-based tools through off-the-shelf software, whereas those who are more computationally inclined can develop their own AI-based software pipelines. In this article, we provide a practical guide for non-computational cancer researchers to understand how AI-based tools can benefit them. We convey general principles of AI for applications in image analysis, natural language processing and drug discovery. In addition, we give examples of how non-computational researchers can get started on the journey to productively use AI in their own work.

Analysis of 3D pathology samples using weakly supervised AI.

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

Adaptive MRI-guided stereotactic body radiation therapy for locally advanced pancreatic cancer - A phase II study.

PURPOSE: Stereotactic body radiotherapy (SBRT) has emerged as a promising new modality for locally advanced pancreatic cancer (LAPC). The current study evaluated the efficacy and toxicity of SBRT in patients with LAPC (NCT03648632). METHODS: This prospective single institution phase II study recruited patients with histologically or cytologically proven adenocarcinoma of the pancreas after more than two months of combination chemotherapy with no sign of progressive disease. Patients were prescribed 50-60 Gy in 5-8 fractions. Patients were initially treated on a standard linac (n = 4). Since 2019, patients were treated using online magnetic resonance (MR) image-guidance on a 1.5 T MRI-linac, where the treatment plan was adapted to the anatomy of the day. The primary endpoint was resection rate. RESULTS: Twenty-eight patients were enrolled between August 2018 and March 2022. All patients had non-resectable disease at time of diagnosis. Median follow-up from inclusion was 28.3 months (95 % CI 24.0-NR). Median progression-free and overall survival from inclusion were 7.8 months (95 % CI 5.0-14.8) and 16.5 months (95 % CI 10.7-22.6), respectively. Six patients experienced grade III treatment-related adverse events (jaundice, nausea, vomiting and/or constipation). One of the initial four patients receiving treatment on a standard linac experienced a grade IV perforation of the duodenum. Six patients (21 %) underwent resection. A further one patient was offered resection but declined. CONCLUSION: This study demonstrates that SBRT in patients with LAPC was associated with promising overall survival and resection rates. Furthermore, SBRT was safe and well tolerated, with limited severe toxicities.

Innovative Solutions for High-Performance Silicon Anodes in Lithium-Ion Batteries: Overcoming Challenges and Real-World Applications.

Silicon (Si) has emerged as a potent anode material for lithium-ion batteries (LIBs), but faces challenges like low electrical conductivity and significant volume changes during lithiation/delithiation, leading to material pulverization and capacity degradation. Recent research on nanostructured Si aims to mitigate volume expansion and enhance electrochemical performance, yet still grapples with issues like pulverization, unstable solid electrolyte interface (SEI) growth, and interparticle resistance. This review delves into innovative strategies for optimizing Si anodes' electrochemical performance via structural engineering, focusing on the synthesis of Si/C composites, engineering multidimensional nanostructures, and applying non-carbonaceous coatings. Forming a stable SEI is vital to prevent electrolyte decomposition and enhance Li+ transport, thereby stabilizing the Si anode interface and boosting cycling Coulombic efficiency. We also examine groundbreaking advancements such as self-healing polymers and advanced prelithiation methods to improve initial Coulombic efficiency and combat capacity loss. Our review uniquely provides a detailed examination of these strategies in real-world applications, moving beyond theoretical discussions. It offers a critical analysis of these approaches in terms of performance enhancement, scalability, and commercial feasibility. In conclusion, this review presents a comprehensive view and a forward-looking perspective on designing robust, high-performance Si-based anodes the next generation of LIBs.

Demographic bias in misdiagnosis by computational pathology models.

Despite increasing numbers of regulatory approvals, deep learning-based computational pathology systems often overlook the impact of demographic factors on performance, potentially leading to biases. This concern is all the more important as computational pathology has leveraged large public datasets that underrepresent certain demographic groups. Using publicly available data from The Cancer Genome Atlas and the EBRAINS brain tumor atlas, as well as internal patient data, we show that whole-slide image classification models display marked performance disparities across different demographic groups when used to subtype breast and lung carcinomas and to predict IDH1 mutations in gliomas. For example, when using common modeling approaches, we observed performance gaps (in area under the receiver operating characteristic curve) between white and Black patients of 3.0% for breast cancer subtyping, 10.9% for lung cancer subtyping and 16.0% for IDH1 mutation prediction in gliomas. We found that richer feature representations obtained from self-supervised vision foundation models reduce performance variations between groups. These representations provide improvements upon weaker models even when those weaker models are combined with state-of-the-art bias mitigation strategies and modeling choices. Nevertheless, self-supervised vision foundation models do not fully eliminate these discrepancies, highlighting the continuing need for bias mitigation efforts in computational pathology. Finally, we demonstrate that our results extend to other demographic factors beyond patient race. Given these findings, we encourage regulatory and policy agencies to integrate demographic-stratified evaluation into their assessment guidelines.

Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment.

Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.

A visual-language foundation model for computational pathology.

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of robust models for various pathology tasks across a diverse array of diseases and patient cohorts. However, model training is often difficult due to label scarcity in the medical domain, and a model's usage is limited by the specific task and disease for which it is trained. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text and, notably, over 1.17 million image-caption pairs through task-agnostic pretraining. Evaluated on a suite of 14 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving histopathology images and/or text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, and text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning.

Towards a general-purpose foundation model for computational pathology.

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology.

Assessing global good agricultural practices standard adoption: insights from fruit and vegetable farmers in Pakistan.

This study examines the challenges Pakistani farmers face in adopting global good agricultural practices (GGAP) and highlights the limitations in infrastructure and cost-based clauses. A questionnaire based on GGAP's fruit and vegetable module version 5.0 was developed and validated by the Department of Environmental Sciences, Government College University, Faisalabad. This was a survey-based study of 15 farmers divided into 5 groups according to their annual farm turnover. The findings of the study indicated that, although the basic paperwork requirements of GGAP were implementable, clauses related to capital investment and technical record-keeping were not. Results showed that 90-100% of farmers considered risk assessments, training, and documentation on their farms. However, 42-56% of clauses related to record-keeping, installation, visual presentation, and infrastructure development, and 24-37% of clauses related to external testing, health, safety, and hygiene were declared not implementable. The study revealed a need for adapting GGAP standards to Pakistan's unique agricultural conditions, suggesting the development of localized standards for more practical implementation. The study's findings highlight crucial insights for policymakers and stakeholders in the agriculture sector and suggest the need for target strategies to overcome implementation barriers and optimize the adaptation of Global GAP in Pakistan that would help to increase exports of agricultural commodities.

Implementation and long-term efficacy of a multifaceted intervention to reduce central line-associated bloodstream infections in intensive care units of a low-middle-income country.

BACKGROUND: Central line-associated bloodstream infections (CLABSIs) pose a significant risk to critically ill patients, particularly in intensive care units (ICU), and are a significant cause of hospital-acquired infections. We investigated whether implementation of a multifaceted intervention was associated with reduced incidence of CLABSIs. METHODS: This was a prospective cohort study over nine years. We implemented a bundled intervention approach to prevent CLABSIs, consisting of a comprehensive unit-based safety program (CUSP). The program was implemented in the Neonatal ICU, Medical ICU, and Surgical ICU departments at the Aga Khan University Hospital in Pakistan. RESULTS: The three intervention ICUs combined were associated with an overall 36% reduction in CLABSI rates and a sustained reduction in CLABSI rates for > a year (5 quarters). The Neonatal ICU experienced a decrease of 77% in CLABSI rates lasting ∼1 year (4 quarters). An attendance rate above 88% across all stakeholder groups in each CUSP meeting correlated with a better and more sustained infection reduction. CONCLUSIONS: Our multifaceted approach using the CUSP model was associated with reduced CLABSI-associated morbidity and mortality in resource-limited settings. Our findings suggest that a higher attendance rate (>85%) at meetings may be necessary to achieve sustained effects post-intervention.

MAPS: pathologist-level cell type annotation from tissue images through machine learning.

Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for typically challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.

The effect of having a physician in the triage area on the rate of patients leaving without being seen: A quality improvement initiative at King Fahad Specialist hospital.

OBJECTIVES: To evaluate the effect of the presence of a physician in the triage area on the number of patients who leave without being seen (LWBS) and some of the factors affecting emergency department (ED) crowding. METHODS: This was a pre-post study carried out at King Fahad Specialist Hospital, Dammam, Saudi Arabia. The 3-month study, consisting of 7826 patients, was split into pre-physician and post-physician periods. Variables compared across these periods were the number of LWBS patients, length of hospital stay, time to physician, and time to disposition decision. Statistical analysis was carried out using R version 4.3.0. RESULTS: Our results showed that the presence of a triage physician significantly decreased the number of LWBS patients (p<0.001) and the time taken to encounter an ED physician (p<0.001). However, it did not have any significant impact on the length of hospital stay (p=0.5) or time to disposition decision (p=0.9). CONCLUSION: The appointment of a triage physician has streamlined patient flow and decreased LWBS rates in the ED, demonstrating the need for more thorough research in this area.

Multiple-instance learning of somatic mutations for the classification of tumour type and the prediction of microsatellite status.

Large-scale genomic data are well suited to analysis by deep learning algorithms. However, for many genomic datasets, labels are at the level of the sample rather than for individual genomic measures. Machine learning models leveraging these datasets generate predictions by using statically encoded measures that are then aggregated at the sample level. Here we show that a single weakly supervised end-to-end multiple-instance-learning model with multi-headed attention can be trained to encode and aggregate the local sequence context or genomic position of somatic mutations, hence allowing for the modelling of the importance of individual measures for sample-level classification and thus providing enhanced explainability. The model solves synthetic tasks that conventional models fail at, and achieves best-in-class performance for the classification of tumour type and for predicting microsatellite status. By improving the performance of tasks that require aggregate information from genomic datasets, multiple-instance deep learning may generate biological insight.

Remediation of wastewater by biosynthesized manganese oxide nanoparticles and its effects on development of wheat seedlings.

Introduction: Nanoparticles play a vital role in environmental remediation on a global scale. In recent years, there has been an increasing demand to utilize nanoparticles in wastewater treatment due to their remarkable physiochemical properties. Methods: In the current study, manganese oxide nanoparticles (MnO-NPs) were synthesized from the Bacillus flexus strain and characterized by UV/Vis spectroscopy, X-ray diffraction, scanning electron microscopy, and Fourier transform infrared spectroscopy. Results: The objective of this study was to evaluate the potential of biosynthesized MnO-NPs to treat wastewater. Results showed the photocatalytic degradation and adsorption potential of MnO-NPs for chemical oxygen demand, sulfate, and phosphate were 79%, 64%, and 64.5%, respectively, depicting the potential of MnO-NPs to effectively reduce pollutants in wastewater. The treated wastewater was further utilized for the cultivation of wheat seedlings through a pot experiment. It was observed that the application of treated wastewater showed a significant increase in growth, physiological, and antioxidant attributes. However, the application of treated wastewater led to a significant decrease in oxidative stress by 40%. Discussion: It can be concluded that the application of MnO-NPs is a promising choice to treat wastewater as it has the potential to enhance the growth, physiological, and antioxidant activities of wheat seedlings.

HBV Vaccines: Advances and Development.

Hepatitis B virus (HBV) infection is a global public health problem that is closely related to liver cirrhosis and hepatocellular carcinoma (HCC). The prevalence of acute and chronic HBV infection, liver cirrhosis, and HCC has significantly decreased as a result of the introduction of universal HBV vaccination programs. The first hepatitis B vaccine approved was developed by purifying the hepatitis B surface antigen (HBsAg) from the plasma of asymptomatic HBsAg carriers. Subsequently, recombinant DNA technology led to the development of the recombinant hepatitis B vaccine. Although there are already several licensed vaccines available for HBV infection, continuous research is essential to develop even more effective vaccines. Prophylactic hepatitis B vaccination has been important in the prevention of hepatitis B because it has effectively produced protective immunity against hepatitis B viral infection. Prophylactic vaccines only need to provoke neutralizing antibodies directed against the HBV envelop proteins, whereas therapeutic vaccines are most likely needed to induce a comprehensive T cell response and thus, should include other HBV antigens, such as HBV core and polymerase. The existing vaccines have proven to be highly effective in preventing HBV infection, but ongoing research aims to improve their efficacy, duration of protection, and accessibility. The routine administration of the HBV vaccine is safe and well-tolerated worldwide. The purpose of this type of immunization is to trigger an immunological response in the host, which will halt HBV replication. The clinical efficacy and safety of the HBV vaccine are affected by a number of immunological and clinical factors. However, this success is now in jeopardy due to the breakthrough infections caused by HBV variants with mutations in the S gene, high viral loads, and virus-induced immunosuppression. In this review, we describe various types of available HBV vaccines, along with the recent progress in the ongoing battle to develop new vaccines against HBV.

An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy.

Background: Adaptive MRI-guided radiotherapy (MRIgRT) requires accurate and efficient segmentation of organs and targets on MRI scans. Manual segmentation is time-consuming and variable, while deformable image registration (DIR)-based contour propagation may not account for large anatomical changes. Therefore, we developed and evaluated an automatic segmentation method using the nnU-net framework. Methods: The network was trained on 38 patients (76 scans) with localized prostate cancer and tested on 30 patients (60 scans) with localized prostate, metastatic prostate, or bladder cancer treated at a 1.5 T MRI-linac at our institution. The performance of the network was compared with the current clinical workflow based on DIR. The segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD) metrics. Results: The trained network successfully segmented all 600 structures in the test set. High similarity was obtained for most structures, with 90% of the contours having a DSC above 0.9 and 86% having an MSD below 1 mm. The largest discrepancies were found in the sigmoid and colon structures. Stratified analysis on cancer type showed that the best performance was seen in the same type of patients that the model was trained on (localized prostate). Especially in patients with bladder cancer, the performance was lower for the bladder and the surrounding organs. A complete automatic delineation workflow took approximately 1 minute. Compared with contour transfer based on the clinically used DIR algorithm, the nnU-net performed statistically better across all organs, with the most significant gain in using the nnU-net seen for organs subject to more considerable volumetric changes due to variation in the filling of the rectum, bladder, bowel, and sigmoid. Conclusion: We successfully trained and tested a network for automatically segmenting organs and targets for MRIgRT in the male pelvis region. Good test results were seen for the trained nnU-net, with test results outperforming the current clinical practice using DIR-based contour propagation at the 1.5 T MRI-linac. The trained network is sufficiently fast and accurate for clinical use in an online setting for MRIgRT. The model is provided as open-source.

Geometric distortions in clinical MRI sequences for radiotherapy: insights gained from a multicenter investigation.

BACKGROUND: As magnetic resonance imaging (MRI) becomes increasingly integrated into radiotherapy (RT) for enhanced treatment planning and adaptation, the inherent geometric distortion in acquired MR images pose a potential challenge to treatment accuracy. This study aimed to evaluate the geometric distortion levels in the clinical MRI protocols used across Danish RT centers and discuss influence of specific sequence parameters. Based on the variety in geometric performance across centers, we assess if harmonization of MRI sequences is a relevant measure. MATERIALS AND METHODS: Nine centers participated with 12 MRI scanners and MRI-Linacs (MRL). Using a travelling phantom approach, a reference MRI sequence was used to assess variation in baseline distortion level between scanners. The phantom was also scanned with local clinical MRI sequences for brain, head/neck (H/N), abdomen, and pelvis. The influence of echo time, receiver bandwidth, image weighting, and 2D/3D acquisition was investigated. RESULTS: We found a large variation in geometric accuracy across 93 clinical sequences examined, exceeding the baseline variation found between MRI scanners (σ = 0.22 mm), except for abdominal sequences where the variation was lower. Brain and abdominal sequences showed lowest distortion levels ([0.22, 2.26] mm), and a large variation in performance was found for H/N and pelvic sequences ([0.19, 4.07] mm). Post hoc analyses revealed that distortion levels decreased with increasing bandwidth and a less clear increase in distortion levels with increasing echo time. 3D MRI sequences had lower distortion levels than 2D (median of 1.10 and 2.10 mm, respectively), and in DWI sequences, the echo-planar imaging read-out resulted in highest distortion levels. CONCLUSION: There is a large variation in the geometric distortion levels of clinical MRI sequences across Danish RT centers, and between anatomical sites. The large variation observed makes harmonization of MRI sequences across institutions and adoption of practices from well-performing anatomical sites, a relevant measure within RT.

Morpho-physiological growth performance and anti-oxidative capabilities of Acacia jacquemontii and Acacia nilotica upon exposure to Co3O4 Nbs in lead-contaminated soil.

Immense crowd of heavy metal in cultivated land is evolving as a global concern as a result of boosted level of soil toxicity. Amongst various metals, Lead (Pb) contamination has become alarming for plant and human heath through ingesting of polluted soils and food crops. To counterfeit this, a nanotechnological neutralizer effective in form of soiling of cobalt oxide Co3O4 Nbs to Acacia jacquemontii and Acacia nilotica with various meditations as 25, 50, 75 and 100 ppm). A Substantial result was observed on growth of plants but premium results were got by applications of cobalt oxide Nbs at 75 ppm. By this means, enhanced root length (39%), fresh weight (32%), shoot length (58%), as well as dry weight (28%) in selected Acacia species compared to control. Chlrophy contents in A. jacquemontii were estimated to be 0.23, 2.73 and 3.19 mg/L with treated with different concentrations of cobalt Nbs while in A. nilotica, the contents were 0.51, 2.93 and 3.12 mg/L respectively on same concentration. The atomic absorption (AAS), antioxidant activity and defendable positive comeback by using Co3O4 Nbs. Hence, the greenly synthesized Co3O4 Nbs counter acts lead toxicity to override and preserving the growth of plant. Such nanotechnological kits can consequently enhance the alternative system to stunned toxicity for distinguish the yield demand end to end with the progress of agronomic management approaches.

Morphoanatomical and Physiological Adaptations of Triticum aestivum L. against Allelopathic Extract of Trianthema portulacastrum L. (Horse purslane).

Weed infestation can be harmful to crop growth and cause severe losses in yield by absorbing nutrients and releasing inhibitory secondary metabolites and thus needs to be controlled for food security. The use of synthetic herbicides is one of the most widely applied methods, but its frequent usage is a serious threat to health and the environment and develops resistance in weeds. Allelopathy is an eco-friendly bio-control method, and Trianthema portulacastrum extracts are known to be effective against various weeds in the crop of Triticum aestivum (wheat), but their effect on the main crop (wheat) is still unknown. The pot experiment was carried out, and various concentrations (30, 60, and 100%) of root and shoot extracts of T. portulacastrum and a synthetic herbicide (Metafin Super) along with control (distilled water) were applied to the wheat plants. Various morphological, physiological, and anatomical parameters were recorded under natural conditions. The objective of this study was to explore the allelopathic impact of T. portulacastrum compared to the synthetic herbicide on the growth of wheat. This study displayed that various growth characteristics of wheat were significantly affected at p ≤ 0.05 by root and shoot water extracts of T. portulacastrum but were less inhibitory as compared to the synthetic herbicide. This inhibition of the growth of wheat was coupled with a significant increase in total free amino acids, K ions, CAT (catalase), proline, epidermal and cortical thickness, and abaxial stomatal density. In addition, a reduction in growth parameters was correlated with a decrease in photosynthetic pigments. This study revealed that the use of T. portulacastrum extracts could be safer than synthetic herbicides for wheat plants and would be beneficial to control weeds in a wheat field.