Iron oxide nanoparticles (Fe2O3 NPs) were successfully Synthesized in a green manner using Cucurbita pepo pulp extract as a natural capping and reducing agent. UV-vis spectroscopy verified the synthesis with a distinct absorption peak at 285 nm, while FTIR analysis revealed functional groups responsible for reduction and stabilization. X-ray diffraction (XRD) analysis confirmed the crystalline nature of the nanoparticles, with an average size of 21.5 nm. SEM and EDX analyses further validated the nanoparticles’ spherical morphology and elemental composition. Biosynthesized IONPs exhibited notable antibacterial activity against multidrug-resistant bacterial strains such as Klebsiella pneumoniae and Pseudomonas aeruginosa. The inhibition zones ranged between 5–22 mm for Klebsiella pneumoniae and from 4 to 12 mm for Pseudomonas aeruginosa, depending on the concentration of the nanoparticles. Hematological evaluations demonstrated strong anticoagulant and thrombolytic properties. Iron oxide nanoparticles effectively inhibited blood coagulation at $40~\mu $ g/mL and showed significant thrombolytic activity by dissolving preformed clots at $50~\mu $ g/mL. The biosynthesized IONPs showed remarkable antioxidant activity that was comparable to standard. This study underscores the potential of Cucurbita pepo as a sustainable and eco-friendly resource for synthesizing multifunctional IONPs. The results suggest promising applications to address antibiotic resistance and manage blood-related disorders. Furthermore, the findings highlight the critical role of green nanotechnology in the advancement of environmentally sustainable and biocompatible nanomaterials for diverse biomedical applications.

Orthophragminids from the Bartonian Fulra Limestone in Kutch, India and the coeval units in Sulaiman Range in Pakistan suggest the establishment of a significant number of endemic species in the Indian subcontinent (Eastern Tethys). Among a total of fifteen species of Discocyclina, Orbitoclypeus and Asterocyclina, six of them appear to be confined to Indian subcontinent while seven species are common both to the peri-Mediterranean/Europe region (Western Tethys) and Indian subcontinent. Two species, Asterocyclina sireli, a four-ribbed species of possibly Indo-Pacific origin, and Orbitoclypeus haynesi that form large populations in Fulra Limestone, appear to have spread into North Africa and Turkey but not into European platforms as a response to Middle Eocene Climatic Optimum (MECO). The lack of Lutetian and Priabonian fauna in the studied sections, either due to a hiatus or unsuitable depositional environments, hampers the establishment of the actual stratigraphic ranges of the identified taxa. Our record provides us to characterize the orthophragminids in shallow benthic zone (SBZ) 17 for Eastern Tethys in detail by comparing the data from the above localities with those from the North Africa, Europe and Turkey, showing the change in diversity.

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.