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The dire need for the assessment of human and environmental endangerments of nanoparticulate material has motivated the formulation of novel scientific tools and techniques to detect, quantify, and characterize these nanomaterials. Several of these paradigms possess enormous possibilities for applications in many of the realms of nanotoxicology. Furthermore, in a large number of cases, the limited capabilities to assess the environmental and human toxicological outcomes of customized and tailored multifunctional nanoparticles used for drug delivery have hindered their full exploitation in preclinical and clinical settings. With the ever-compounded availability of nanoparticulate materials in commercialized settings, an ever-arising popular debate has been egressing on whether the social, human, and environmental costs associated with the risks of nanomaterials outweigh their profits. Here we briefly review the various health, pharmaceutical, and regulatory aspects of nanotoxicology of engineered multifunctional nanoparticles in vitro and in vivo. Several aspects and issues encountered during the safety and toxicity assessments of these drug-delivery nanocarriers have also been summarized. Furthermore, recent trends implicated in the nanotoxicological evaluations of nanoparticulate matter in vitro and in vivo have also been discussed. Due to the absence of robust and rigid regulatory guidelines, researchers currently frequently encounter a larger number of challenges in the toxicology assessment of nanocarriers, which have also been briefly discussed here. Nanotoxicology has an appreciable and significant part in the clinical translational development as well as commercialization potential of nanocarriers; hence these aspects have also been touched upon. Finally, a brief overview has been provided regarding some of the nanocarrier-based medicines that are currently undergoing clinical trials, and some of those which have recently been commercialized and are available for patients. It is expected that this review will instigate an appreciable interest in the research community working in the arena of pharmaceutical drug development and nanoformulation-based drug delivery.

The UK NHS Women’s National Breast Screening programme aims to detect breast cancer early. The reference standard approach requires mammograms to be independently double-read by qualified radiology staff. If two readers disagree, arbitration by an independent reader is undertaken. Whilst this process maximises accuracy and minimises recall rates, the procedure is labour-intensive, adding pressure to a system currently facing a workforce crisis. Artificial intelligence technology offers an alternative to human readers. While artificial intelligence has been shown to be non-inferior versus human second readers, the minimum requirements needed (effectiveness, set-up costs, maintenance, etc) for such technology to be cost-effective in the NHS have not been evaluated. We developed a simulation model replicating NHS screening services to evaluate the potential value of the technology. Our results indicate that if non-inferiority is maintained, the use of artificial intelligence technology as a second reader is a viable and potentially cost-effective use of NHS resources. AI technology has the potential to substitute a human reader to aid services struggling to recruit staff or meet patient demand. Here, the authors show that the technology is a viable and potentially cost-effective strategy for use in the NHS.

Prolactin (PRL) is an anterior pituitary hormone which has its principle physiological action in initiation and maintenance of lactation. In human reproduction, pathological hyperprolactinemia most commonly presents as an ovulatory disorder and is often associated with secondary amenorrhea or oligomenorrhea. Galactorrhea, a typical symptom of hyperprolactinemia, occurs in less than half the cases. Out of the causes of hyperprolactinemia, pituitary tumors may be responsible for almost 50% of cases and need to be investigated especially in the absence of history of drug induced hyperprolactinemia. In women with hyperprolactinemic amenorrhea one important consequence of estrogen deficiency is osteoporosis, which deserves specific therapeutic consideration. Problem in diagnosing and treating hyperprolactinemia is the occurrence of the ′big big molecule of prolactin′ that is biologically inactive (called macroprolactinemia), but detected by the same radioimmunoassay as the biologically active prolactin. This may explain many cases of very high prolactin levels sometimes found in normally ovulating women and do not require any treatment. Dopamine agonist is the mainstay of treatment. However, presence of a pituitary macroadenoma may require surgical or radiological management.

The present study documented the effect of bio-organics in legume intercropped strawberry cv. Camarosa during the years 2022 and 2023. Bio-organic fertilizer inputs included were Jeevamrit (JV), Ghan-Jeevamrit (GJ) and Azolla . Coriander-Strawberry-Fenugreek as intercropping system was adopted. The treatments comprised were T 1 : GJ at 100 g/m 2  + JV at 10% + Azolla at 200 g/plant, T 2 : GJ at 150 g/m 2  + JV at 10% + Azolla at 200 g/plant, T 3 : GJ at 100 g/m 2  + JV at 20% + Azolla at 200 g/plant, T 4 : GJ at 150 g/m 2  + JV at 20% + Azolla at 200 g/plant, T 5 : GJ at 100 g/m 2  + JV at 10% + Azolla at 250 g/plant, T 6 : GJ at 150 g/m 2  + JV at 10% + Azolla at 250 g/plant, T 7 : GJ at 100 g/m 2  + JV at 20% + Azolla at 250 g/plant, T 8 : GJ at 150 g/m 2  + JV at 20% + Azolla at 250 g/plant, T 9 : GJ at 150 g/m 2  + JV at 20% as per SPNF, T 10 : Farmyard manure (100% N basis) and T 11 : Recommended dose of N: P:K (80:40:40 kg/ha) as control. Application of bio-stimulants at 50 g/plant and AM fungi @ 20 g/ plant was applied uniformly in treatments T 1 –T 8 . One month after transplanting, T 3 showed positive influence on vegetative growth traits of strawberry plantlets. Minimum number of days taken to flower, maximum duration of flowering (142) and number of flowers (51) were also recorded. This treatment application also observed maximum fruit yield (677.93 g/ plant) and yield efficiency (7.91 g/cm 2 of leaf area) compared to all other bio-organic combinations applied. Post harvest soil chemical indicators were also significantly influenced except pH and electrical conductivity compared to FYM (100% N equivalence) and Recommended dose of fertilizers (RDF) of NPK (80:40:40 kg/ha). Microbial biomass in terms of total bacteria, soil fungi, actinobacterial count, phosphorous solubilizing bacteria, AM spore population, Azotobacter count and Soil enzymatic activity of phosphatase and dehydrogenases showed a steady rise after application of GJ @ 100 g/m 2  + JV @ 20% + Azolla @ 200 g/plant. In addition, overall increase of the yield of coriander and fenugreek compared to FYM (100% N equivalence) and RDF of NPK (80:40:40 kg/ha) was recorded. The positive influence both on leaf and fruit NPK contents were also recorded when plantlets were supplemented with GJ @100 g/m 2  + JV@ 20% + Azolla @ 200 g/plant. This study inferred that application of bio-organic inputs sources which can boost up cropping behavior, post harvest soil indicators, native microbial properties and enzymatic activity in rhizosphere, and thus can have the potential to improve crop resilience and soil productivity on sustainable basis.

Image-based prediction models for disease detection are sensitive to changes in data acquisition such as the replacement of scanner hardware or updates to the image processing software. The resulting differences in image characteristics may lead to drifts in clinically relevant performance metrics which could cause harm in clinical decision making, even for models that generalise in terms of area under the receiver-operating characteristic curve. We propose Unsupervised Prediction Alignment, a generic automatic recalibration method that requires no ground truth annotations and only limited amounts of unlabelled example images from the shifted data distribution. We illustrate the effectiveness of the proposed method to detect and correct performance drift in mammography-based breast cancer screening and on publicly available histopathology data. We show that the proposed method can preserve the expected performance in terms of sensitivity/specificity under various realistic scenarios of image acquisition shift, thus offering an important safeguard for clinical deployment. Automatic correction of performance drift caused by changes in image acquisition is key for safe AI deployment. Here, the authors present a solution that restores the expected clinical performance of image classification systems in breast screening and histopathology.

In recent years, cheminformatics has experienced significant advancements through the development of new open-source software tools based on various cheminformatics programming toolkits. However, adopting these toolkits presents challenges, including proper installation, setup, deployment, and compatibility management. In this work, we present the Cheminformatics Microservice. This open-source solution provides a unified interface for accessing commonly used functionalities of multiple cheminformatics toolkits, namely RDKit, Chemistry Development Kit (CDK), and Open Babel. In addition, more advanced functionalities like structure generation and Optical Chemical Structure Recognition (OCSR) are made available through the Cheminformatics Microservice based on pre-existing tools. The software service also enables developers to extend the functionalities easily and to seamlessly integrate them with existing workflows and applications. It is built on FastAPI and containerized using Docker, making it highly scalable. An instance of the microservice is publicly available at https://api.naturalproducts.net . The source code is publicly accessible on GitHub, accompanied by comprehensive documentation, version control, and continuous integration and deployment workflows. All resources can be found at the following link: https://github.com/Steinbeck-Lab/cheminformatics-microservice . Graphical Abstract

Background Double reading (DR) in screening mammography increases cancer detection and lowers recall rates, but has sustainability challenges due to workforce shortages. Artificial intelligence (AI) as an independent reader (IR) in DR may provide a cost-effective solution with the potential to improve screening performance. Evidence for AI to generalise across different patient populations, screening programmes and equipment vendors, however, is still lacking. Methods This retrospective study simulated DR with AI as an IR, using data representative of real-world deployments (275,900 cases, 177,882 participants) from four mammography equipment vendors, seven screening sites, and two countries. Non-inferiority and superiority were assessed for relevant screening metrics. Results DR with AI, compared with human DR, showed at least non-inferior recall rate, cancer detection rate, sensitivity, specificity and positive predictive value (PPV) for each mammography vendor and site, and superior recall rate, specificity, and PPV for some. The simulation indicates that using AI would have increased arbitration rate (3.3% to 12.3%), but could have reduced human workload by 30.0% to 44.8%. Conclusions AI has potential as an IR in the DR workflow across different screening programmes, mammography equipment and geographies, substantially reducing human reader workload while maintaining or improving standard of care. Trial registration ISRCTN18056078 (20/03/2019; retrospectively registered).

Conventional agricultural practices have been associated with detrimental effects such as soil degradation, reduction in biodiversity, environmental contamination due to agrochemical use, and a decrease in the nutritional quality of crops. These challenges necessitate a transition toward sustainable and ecologically sound farming systems. Natural Farming, the regenerative agriculture has shown promising results in restoring soil organic carbon, enhancing microbial biomass and enzymatic activity, improving water retention, and supporting nutrient cycling through natural inputs. This approach emphasizes on-farm biomass recycling while excluding all synthetic inputs, fostering an economic and environment-friendly system. The current study was carried out over two cropping seasons to explore the potential of natural farm inputs on sustainable and high-quality strawberry crop production. Biological modifications namely, Ghan-jeevamrit and Jeevamrit have been used. Ghan-jeevamrit contained 4–5 days air dried indigenous cow dung (100 kg), raw sugar (1 kg), phosphorus solubilizing bacteria rich pulse flour (1 kg), cow urine (3 L) and forest soil (250 g). Liquid microbial culture of Jeevamrit contained cow dung-urine (pH-5.65, EC-0.23 dS/m) and was enriched with Azotobacter chroococcum , Pseudomonas species and actinobacteria. The trial included, Ghan-jeevamrit-2.5 kg/m 2 ; Ghan-jeevamrit-5 kg/m 2 ; Ghan-jeevamrit-2.5 kg/m 2  + Jeevamrit-2.0 L/m 2 ; Jeevamrit-2.0 L/m 2 ; Ghan-jeevamrit-2.5 kg/m 2  + Jeevamrit-1.0 L/m 2 and Farmyard manure (FYM)-100% of nitrogen equivalent basis. The results showed that Ghan-jeevamrit-2.5 kg/m 2  + Jeevamrit-2.0 L/m 2 significantly improved the production parameters, quality metrics and yield of strawberries. Microbial formulations resulted in maximum build-up of bacteria, fungi and arbuscular-mycorrhizal fungi (AMF) in the soils which received Ghan-jeevamrit-2.5 kg/m 2  + Jeevamrit-2.0 L/m 2 . Bio-mobilization and recycling of native nutrients through combined application of Ghan-jeevamrit and Jeevamrit encouraged dehydrogenases and acid phosphatase enzymatic activity to maintain soil health and productivity for long-term and sustainable strawberry production. Principal component analysis (PCA) revealed highest cumulative variation for AMF population, dehydrogenase, soil bacteria and fungi. The study further recognised as a practical and affordable solution to farmers in order to improve soil health, increase crop nutrition and lower production costs. This study highlights that the adoption of natural farm inputs can enhance soil biological health, while, promoting high-quality and sustainable strawberry production.

Agriculture is the backbone of Punjab’s economy, and with much of India’s population dependent on agriculture, the requirement for accurate and timely monitoring of land has become even more crucial. Blending remote sensing with state-of-the-art machine learning algorithms enables the detailed classification of agricultural lands through thematic mapping, which is critical for crop monitoring, land management, and sustainable development. Here, a Hyper-tuned Deep Neural Network (Hy-DNN) model was created and used for land use and land cover (LULC) classification into four classes: agricultural land, vegetation, water bodies, and built-up areas. The technique made use of multispectral data from Sentinel-2 and Landsat-8, processed on the Google Earth Engine (GEE) platform. To measure classification performance, Hy-DNN was contrasted with traditional classifiers—Convolutional Neural Network (CNN), Random Forest (RF), Classification and Regression Tree (CART), Minimum Distance Classifier (MDC), and Naive Bayes (NB)—using performance metrics including producer’s and consumer’s accuracy, Kappa coefficient, and overall accuracy. Hy-DNN performed the best, with overall accuracy being 97.60% using Sentinel-2 and 91.10% using Landsat-8, outperforming all base models. These results further highlight the superiority of the optimised Hy-DNN in agricultural land mapping and its potential use in crop health monitoring, disease diagnosis, and strategic agricultural planning.

A series of 30 non-covalent imidazo[1,2-a]quinoxaline-based inhibitors of epidermal growth factor receptor (EGFR) were designed and synthesized. EGFR inhibitory assessment (against wild type) data of compounds revealed 6b, 7h, 7j, 9a and 9c as potent EGFRWT inhibitors with IC50 values of 211.22, 222.21, 193.18, 223.32 and 221.53 nM, respectively, which were comparable to erlotinib (221.03 nM), a positive control. Furthermore, compounds exhibited excellent antiproliferative activity when tested against cancer cell lines harboring EGFRWT; A549, a non-small cell lung cancer (NSCLC), HCT-116 (colon), MDA-MB-231 (breast) and gefitinib-resistant NSCLC cell line H1975 harboring EGFRL858R/T790M. In particular, compound 6b demonstrated significant inhibitory potential against gefitinib-resistant H1975 cells (IC50 = 3.65 μM) as compared to gefitinib (IC50 > 20 μM). Moreover, molecular docking disclosed the binding mode of the 6b to the domain of EGFR (wild type and mutant type), indicating the basis of inhibition. Furthermore, its effects on redox modulation, mitochondrial membrane potential, cell cycle analysis and cell death mode in A549 lung cancer cells were also reported.