Genotypic insights to Panton-Valentine leukocidin positive methicillin-resistant Staphylococcus aureus isolated from cattle mastitis.

In this research communication we investigate the prevalence and antimicrobial susceptibility of S. aureus harboring virulent genes responsible for mastitis in cattle of Punjab, Pakistan. A total of 690 milk samples were collected from commercial dairy farms for analysis of the prevalence of subclinical and clinical mastitis and isolation of S. aureus. Virulence ability and methicillin resistance in S. aureus (MRSA) was determined by targeting the pvl (the gene for Panton-Valentine leukocidin) and mecA genes, respectively. A total of 175 S. aureus isolates exhibiting prevalence of pvl gene (6.28%) and mecA gene (22.28%) were determined. Antimicrobial susceptibility testing of pvl positive and negative MRSA against different classes of antibiotics revealed 100% resistance against ß-lactams while 100% sensitivity towards tylosin and linezolid.

Determining the cost of acute stroke care for a single stay at a tertiary care centre in Pakistan.

OBJECTIVE: To assess the economic burden of acute stroke, and to determine the average cost of acute stroke care for a single hospital stay in a public tertiary care hospital. METHODS: The cross-sectional study was conducted at the Medical Teaching Institute, Bacha Khan Medical Complex, Swabi, Pakistan, from May 16 to September 19, 2022, and comprised patients of either gender who were hospitalised with an acute stroke for the first time. All costs incurred during the care of the patients were measured using the micro-costing methodology, and the association of the cost with other variables was evaluated. Data was analysed using SPSS 24. RESULTS: Of the 34 patients, 24(70.6%) were males and 10(29.4%) were females. The overall mean age was 66+/-13.00 years. The mean length of hospital stay was 4+/-3.00 days. The mean total cost was 18,156+/-9,068 Pakistani rupees, which was the equivalent of 76.89+/-38.4 United States dollars. The cost of the first day of admission was the highest, declining per day as the stay progressed, and imaging/laboratory investigations formed the highest component of the overall cost (p<0.001). CONCLUSIONS: The cost of acute stroke care was found to be high even in a public hospital. The length of hospital stay was the most important determinant of the overall cost.

The therapeutic potential of isosakuranetin against perfluorooctane sulfonate instigated cardiac toxicity via modulating Nrf-2/Keap-1 pathway, inflammatory, apoptotic, and histological profile.

Perfluorooctane sulfonate (PFOS) is a pervasive organic toxicant that damages body organs, including heart. Isosakuranetin (ISN) is a plant-based flavonoid that exhibits a broad range of pharmacological potentials. The current investigation was conducted to evaluate the potential role of ISN to counteract PFOS-induced cardiac damage in rats. Twenty-four albino rats (Rattus norvegicus) were distributed into four groups, including control, PFOS (10 mg/kg) intoxicated, PFOS + ISN (10 mg/kg + 20 mg/kg) treated, and ISN (20 mg/kg) alone supplemented group. It was revealed that PFOS intoxication reduced the expressions of Nrf-2 and its antioxidant genes while escalating the expression of Keap-1. Furthermore, PFOS exposure reduced the activities of glutathione reductase (GSR), superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), glutathione S-transferase (GST), Heme oxygenase-1 (HO-1) and glutathione (GSH) contents while upregulating the levels of reactive oxygen species (ROS) and malondialdehyde (MDA). Besides, PFOS administration upregulated the levels of creatine kinase-MB (CK-MB), troponin I, creatine phosphokinase (CPK), and lactate dehydrogenase (LDH). Moreover, the levels of tumor necrosis factor-alpha (TNF-α), nuclear factor kappa-B (NF-κB), interleukin-6 (IL-6), and interleukin-1ß (IL-1ß) were increased after PFOS intoxication. Additionally, PFOS exposure downregulated the expression of Bcl-2 while upregulating the expressions of Bax and Caspase-3. Furthermore, PFOS administration disrupted the normal architecture of cardiac tissues. Nonetheless, ISN treatment remarkably protected the cardiac tissues via regulating aforementioned dysregulations owing to its antioxidative, anti-inflammatory, and antiapoptotic properties.

The HABD: Home of All Biological Databases Empowering Biological Research With Cutting-Edge Database Systems.

The emergence of computer technologies and computing power has led to the development of several database systems that provide standardized access to vast quantities of data, making it possible to collect, search, index, evaluate, and extract useful knowledge across various fields. The Home of All Biological Databases (HABD) has been established as a continually expanding platform that aims to store, organize, and distribute biological data in a searchable manner, removing all dead and non-accessible data. The platform meticulously categorizes data into various categories, such as COVID-19 Pandemic Database (CO-19PDB), Database relevant to Human Research (DBHR), Cancer Research Database (CRDB), Latest Database of Protein Research (LDBPR), Fungi Databases Collection (FDBC), and many other databases that are categorized based on biological phenomena. It currently provides a total of 22 databases, including 6 published, 5 submitted, and the remaining in various stages of development. These databases encompass a range of areas, including phytochemical-specific and plastic biodegradation databases. HABD is equipped with search engine optimization (SEO) analyzer and Neil Patel tools, which ensure excellent SEO and high-speed value. With timely updates, HABD aims to facilitate the processing and visualization of data for scientists, providing a one-stop-shop for all biological databases. Computer platforms, such as PhP, html, CSS, Java script and Biopython, are used to build all the databases. © 2024 Wiley Periodicals LLC.

Implementing vision transformer for classifying 2D biomedical images.

In recent years, the growth spurt of medical imaging data has led to the development of various machine learning algorithms for various healthcare applications. The MedMNISTv2 dataset, a comprehensive benchmark for 2D biomedical image classification, encompasses diverse medical imaging modalities such as Fundus Camera, Breast Ultrasound, Colon Pathology, Blood Cell Microscope etc. Highly accurate classifications performed on these datasets is crucial for identification of various diseases and determining the course of treatment. This research paper presents a comprehensive analysis of four subsets within the MedMNISTv2 dataset: BloodMNIST, BreastMNIST, PathMNIST and RetinaMNIST. Each of these selected datasets is of diverse data modalities and comes with various sample sizes, and have been selected to analyze the efficiency of the model against diverse data modalities. The study explores the idea of assessing the Vision Transformer Model's ability to capture intricate patterns and features crucial for these medical image classification and thereby transcend the benchmark metrics substantially. The methodology includes pre-processing the input images which is followed by training the ViT-base-patch16-224 model on the mentioned datasets. The performance of the model is assessed using key metrices and by comparing the classification accuracies achieved with the benchmark accuracies. With the assistance of ViT, the new benchmarks achieved for BloodMNIST, BreastMNIST, PathMNIST and RetinaMNIST are 97.90%, 90.38%, 94.62% and 57%, respectively. The study highlights the promise of Vision transformer models in medical image analysis, preparing the way for their adoption and further exploration in healthcare applications, aiming to enhance diagnostic accuracy and assist medical professionals in clinical decision-making.

Accurate prediction of hyaluronic acid concentration under temperature perturbations using near-infrared spectroscopy and deep learning.

Accurate prediction of the concentration of a large number of hyaluronic acid (HA) samples under temperature perturbations can facilitate the rapid determination of HA's appropriate applications. Near-infrared (NIR) spectroscopy analysis combined with deep learning presents an effective solution to this challenge, with current research in this area being scarce. Initially, we introduced a novel feature fusion method based on an intersection strategy and used two-dimensional correlation spectroscopy (2DCOS) and Aquaphotomics to interpret the interaction information in HA solutions reflected by the fused features. Subsequently, we created an innovative, multi-strategy improved Walrus Optimization Algorithm (MIWaOA) for parameter optimization of the deep extreme learning machine (DELM). The final constructed MIWaOA-DELM model demonstrated superior performance compared to partial least squares (PLS), extreme learning machine (ELM), DELM, and WaOA-DELM models. The results of this study can provide a reference for the quantitative analysis of biomacromolecules in complex systems.

Molecular insights into expression and silencing of resistance determinants in Staphylococcus aureus.

OBJECTIVE: This study aimed to investigate the status of antimicrobial-resistant strains of Staphylococcus aureus in Pakistan, their association in terms of co-occurrence with the biofilm-forming genes, resistance profiling and associated discrepancies in diagnostic methods. METHODOLOGY: A total of 384 milk samples from bovine was collected by using convenient sampling technique and were initially screened for subclinical mastitis, further preceded by isolation and confirmation of S. aureus. The S. aureus isolates were subjected to evaluation of antimicrobial resistance by phenotypic identification using Kirby-Bauer disc diffusion method, while the genotypic estimation was done by polymerase chain reaction to declare isolates as methicillin, beta-lactam, vancomycin, tetracycline, and aminoglycoside resistant S. aureus (MRSA, BRSA, VRSA, TRSA, and ARSA), respectively. RESULTS: The current study revealed an overall prevalence of subclinical mastitis and S. aureus to be 59.11% and 46.69%, respectively. On a phenotypic basis, the prevalence of MRSA, BRSA, VRSA, TRSA, and ARSA was found to be 44.33%, 58.49%, 20.75%, 35.84%, and 30.18%, respectively. The results of PCR analysis showed that 46.80% of the tested isolates were declared as MRSA, 37.09% as BRSA, and 36.36% as VRSA, while the occurrence of TRSA and ARSA was observed in 26.31% and 18.75%, respectively. The current study also reported the existence of biofilm-producing genes (icaA and icaD) in 49.06% and 40.57% isolates, respectively. Lastly, this study also reported a high incidence of discrepancies for both genotypic and phenotypic identification methods of resistance evaluation, with the highest discrepancy ratio for the accA-aphD gene, followed by tetK, vanB, blaZ, and mecA genes. CONCLUSION: The study concluded that different antibiotic resistance strains of S. aureus are prevalent in study districts with high potential to transmit between human populations. The study also determined that there are multiple resistance determinants and mechanisms that are responsible for the silencing and expression of antibiotic resistance genes.

A review on mycogenic metallic nanoparticles and their potential role as antioxidant, antibiofilm and quorum quenching agents.

The emergence of antimicrobial resistance among biofilm forming pathogens aimed to search for the efficient and novel alternative strategies. Metallic nanoparticles have drawn a considerable attention because of their significant applications in various fields. Numerous methods are developed for the generation of these nanoparticles however, mycogenic (fungal-mediated) synthesis is attractive due to high yields, easier handling, eco-friendly and being energy efficient when compared with conventional physico-chemical methods. Moreover, mycogenic synthesis provides fungal derived biomolecules that coat the nanoparticles thus improving their stability. The process of mycogenic synthesis can be extracellular or intracellular depending on the fungal genera used and various factors such as temperature, pH, biomass concentration and cultivation time may influence the synthesis process. This review focuses on the synthesis of metallic nanoparticles by using fungal mycelium, mechanism of synthesis, factors affecting the mycosynthesis and also describes their potential applications as antioxidants and antibiofilm agents. Moreover, the utilization of mycogenic nanoparticles as quorum quenching agent in hampering the bacterial cell-cell communication (quorum sensing) has also been discussed.

Improvement in variance estimation using transformed auxiliary variable under simple random sampling.

This paper offers a novel approach to formulate efficient ratio estimator of the population variance using a transformed auxiliary variable. The impact of transformation on auxiliary information has also been discussed. It is observed that incorporating a transformed auxiliary variable result in a high gain in efficiency. Theoretical properties of the newly developed estimators have been derived. The empirical and simulation studies show that the suggested estimators outperformed the existing estimators.

Ameliorative potential of eriocitrin against cadmium instigated hepatotoxicity in rats via regulating Nrf2/keap1 pathway.

BACKGROUND: Cadmium (Cd) is a hazardous heavy metal that adversely affects the vital body organs particularly liver. Eriocitrin (ERCN) is a plant-based flavonoid that is well-known for its wide range of pharmacological potential. This research trial was aimed to determine the ameliorative potential of ERCN against Cd provoked hepatotoxicity in rats. METHODOLOGY: Twenty-four rats (Rattus norvegicus) were apportioned into control, Cd treated (5 mg/kg), Cd (5 mg/kg) + ERCN (25 mg/kg) and only ERCN (25 mg/kg) administrated group. Expressions of Nrf2/Keap1 pathway and apoptotic markers were assessed through qRT-PCR. The levels of inflammatory and liver function markers were evaluated by using standard ELISA kits. KEY FINDINGS: Cd exposure reduced the expression of Nrf2 and anti-oxidant genes as well as the activity of catalase (CAT), glutathione reductase (GSR), superoxide dismutase (SOD), glutathione peroxidase (GPx), glutathione S-transferase (GST) and glutathione (GSH) contents while escalating the expression of Keap1. Furthermore, Cd intoxication augmented malondialdehyde (MDA) and reactive oxygen species (ROS) levels in hepatic tissues. Exposure to Cd resulted in a notable elevation in the levels of alanine transaminase (ALT), alkaline phosphatase (ALP) and aspartate aminotransferase (AST). Cd administration upregulated nuclear factor-kappa B (NF-κB), interleukin-1 beta (IL-1ß), tumor necrosis factor-alpha (TNF-α), and interleukin-6 (IL-6) levels as well as cyclooxygenase-2 (COX-2) activity. Furthermore, Cd administration upsurged Bax and Caspase-3 expression while reducing the expression of Bcl-2. Moreover, Cd intoxication disrupted the normal architecture of hepatic tissues. However, supplementation of ERCN significantly (p < 0.05) ameliorated the aforementioned disruptions induced by Cd intoxication. CONCLUSION: ERCN treatment remarkably ameliorated the hepatic tissues owing to its antioxidant, anti-inflammatory, and anti-apoptotic potentials. These findings underscore the therapeutic potential of ERCN to counteract the adverse effects of environmental pollutants on hepatic tissues.

Breaking barriers in cancer treatment: nanobiohybrids empowered by modified bacteria and vesicles.

Cancer, the leading global cause of mortality, poses a formidable challenge for treatment. The effectiveness of cancer therapies, ranging from chemotherapy to immunotherapy, relies on the precise delivery of therapeutic agents to tumor tissues. Nanobiohybrids, resulting from the fusion of bacteria with nanomaterials, constitute a promising delivery system. Nanobiohybrids offer several advantages, including the ability to target tumors, genetic engineering capabilities, programmed product creation, and the potential for multimodal treatment. Recent advances in targeted tumor treatments have leveraged bacteria-based nanobiohybrids. Here, we outline the progress in cancer treatment using nanobiohybrids. Our focus is particularly on various therapeutic approaches within the context of nanobiohybrid systems, where bacteria are integrated with nanomaterials to combat cancer. It has been demonstrated that bacteria-based nanobiohybrids present a robust and effective method for tumor theranostics.

Apigetrin ameliorates doxorubicin prompted testicular damage: biochemical, spermatological and histological based study.

Doxorubicin (DOX) is a highly effective, commonly prescribed, potent anti-neoplastic drug that damages the testicular tissues and leads to infertility. Apigetrin (APG) is an important flavonoid that shows diverse biological activities. The present research was designed to evaluate the alleviative role of APG against DOX-induced testicular damages in rats. Forty-eight adult male albino rats were randomly distributed into 4 groups, control, DOX administered (3 mgkg-1), DOX + APG co-administered (3 mgkg-1 of DOX; 15 mgkg-1 of APG), and APG administered group (15 mgkg-1). Results of the current study indicated that DOX treatment significantly reduced the activities of superoxide dismutase (SOD), glutathione reductase (GSR), catalase (CAT) and glutathione peroxidase (GPx), while increasing the levels of malondialdehyde (MDA) and reactive oxygen species (ROS). DOX treatment also reduced the sperm count, viability, and motility. Moreover, DOX significantly increased the sperm morphological anomalies and reduced the levels of plasma testosterone, luteinizing hormone (LH) and follicle-stimulating hormone (FSH). The administration of DOX significantly increased the expressions of Bax and Caspase-3, as well as the levels of inflammatory markers. Additionally, DOX treatment significantly downregulated the expressions of steroidogenic enzymes (StAR, 3ß-HSD and 17ß-HSD) and Bcl-2. Furthermore, DOX administration provoked significant histopathological abnormalities in the testicular tissues. However, APG supplementation significantly reversed all the testicular damages due to its androgenic, anti-apoptotic, anti-oxidant and anti-inflammatory nature. Therefore, it is concluded that APG may prove a promising therapeutic agent to treat DOX-induced testicular damages.

Automated detection and recognition system for chewable food items using advanced deep learning models.

Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods, providing dietary preferences to people who are restricted to a particular diet, showcasing its cultural significance, etc. In this research paper, the aim is to design a novel methodology that helps to identify food items by analyzing their eating sounds using various deep learning models. To achieve this objective, a system has been proposed that extracts meaningful features from food-eating sounds with the help of signal processing techniques and deep learning models for classifying them into their respective food classes. Initially, 1200 audio files for 20 food items labeled have been collected and visualized to find relationships between the sound files of different food items. Later, to extract meaningful features, various techniques such as spectrograms, spectral rolloff, spectral bandwidth, and mel-frequency cepstral coefficients are used for the cleaning of audio files as well as to capture the unique characteristics of different food items. In the next phase, various deep learning models like GRU, LSTM, InceptionResNetV2, and the customized CNN model have been trained to learn spectral and temporal patterns in audio signals. Besides this, the models have also been hybridized i.e. Bidirectional LSTM + GRU and RNN + Bidirectional LSTM, and RNN + Bidirectional GRU to analyze their performance for the same labeled data in order to associate particular patterns of sound with their corresponding class of food item. During evaluation, the highest accuracy, precision,F1 score, and recall have been obtained by GRU with 99.28%, Bidirectional LSTM + GRU with 97.7% as well as 97.3%, and RNN + Bidirectional LSTM with 97.45%, respectively. The results of this study demonstrate that deep learning models have the potential to precisely identify foods on the basis of their sound by computing the best outcomes.

Zeta potential changing self-nanoemulsifying drug delivery systems: A newfangled approach for enhancing oral bioavailability of poorly soluble drugs.

The mucus is a defensive barrier for different drug-loaded systems. To overcome this obstacle, the crucial factor is the surface charge. Due to mucus negative charge behavior; it was revealed that negatively charged formulations can move across mucus, whereas positively charged nanoformulations could not diffuse via mucus due to interactions. However, cellular intake of negatively charged nanoformulations to the epithelium by endocytosis is less prominent as compared to positively charged carriers. Self-emulsifying drug delivery systems (SEDDS) improve the drug permeability of drugs, especially which have poor oral drug solubility. Moreover, SEDDS have the ability to reduce the degradation of drugs in the GI tract. Currently, drug carrier systems that can shift zeta potential from negative to positive were developed. The benefits of inducing zeta potential changing approach are that negatively charged nanoformulations permeate quickly across the mucus and surface charges reversed to positive at epithelium surface to increase cellular uptake. Among various systems of drug delivery, zeta potential changing SEDDS seem to signify a promising approach as they can promptly diffuse over mucus due to their smaller size and shape distortion ability. Due to such findings, mucus permeation and drug diffusion may improve by the mixture of the zeta potential changing approach and SEDDS.

Enhancing parasitic organism detection in microscopy images through deep learning and fine-tuned optimizer.

Parasitic organisms pose a major global health threat, mainly in regions that lack advanced medical facilities. Early and accurate detection of parasitic organisms is vital to saving lives. Deep learning models have uplifted the medical sector by providing promising results in diagnosing, detecting, and classifying diseases. This paper explores the role of deep learning techniques in detecting and classifying various parasitic organisms. The research works on a dataset consisting of 34,298 samples of parasites such as Toxoplasma Gondii, Trypanosome, Plasmodium, Leishmania, Babesia, and Trichomonad along with host cells like red blood cells and white blood cells. These images are initially converted from RGB to grayscale followed by the computation of morphological features such as perimeter, height, area, and width. Later, Otsu thresholding and watershed techniques are applied to differentiate foreground from background and create markers on the images for the identification of regions of interest. Deep transfer learning models such as VGG19, InceptionV3, ResNet50V2, ResNet152V2, EfficientNetB3, EfficientNetB0, MobileNetV2, Xception, DenseNet169, and a hybrid model, InceptionResNetV2, are employed. The parameters of these models are fine-tuned using three optimizers: SGD, RMSprop, and Adam. Experimental results reveal that when RMSprop is applied, VGG19, InceptionV3, and EfficientNetB0 achieve the highest accuracy of 99.1% with a loss of 0.09. Similarly, using the SGD optimizer, InceptionV3 performs exceptionally well, achieving the highest accuracy of 99.91% with a loss of 0.98. Finally, applying the Adam optimizer, InceptionResNetV2 excels, achieving the highest accuracy of 99.96% with a loss of 0.13, outperforming other optimizers. The findings of this research signify that using deep learning models coupled with image processing methods generates a highly accurate and efficient way to detect and classify parasitic organisms.

FedHealthFog: A federated learning-enabled approach towards healthcare analytics over fog computing platform.

The emergence of federated learning (FL) technique in fog-enabled healthcare system has leveraged enhanced privacy towards safeguarding sensitive patient information over heterogeneous computing platforms. In this paper, we introduce the FedHealthFog framework, which was meticulously developed to overcome the difficulties of distributed learning in resource-constrained IoT-enabled healthcare systems, particularly those sensitive to delays and energy efficiency. Conventional federated learning approaches face challenges stemming from substantial compute requirements and significant communication costs. This is primarily due to their reliance on a singular server for the aggregation of global data, which results in inefficient training models. We present a transformational approach to address these problems by elevating strategically placed fog nodes to the position of local aggregators within the federated learning architecture. A sophisticated greedy heuristic technique is used to optimize the choice of a fog node as the global aggregator in each communication cycle between edge devices and the cloud. The FedHealthFog system notably accounts for drop in communication latency of 87.01%, 26.90%, and 71.74%, and energy consumption of 57.98%, 34.36%, and 35.37% respectively, for three benchmark algorithms analyzed in this study. The effectiveness of FedHealthFog is strongly supported by outcomes of our experiments compared to cutting-edge alternatives while simultaneously reducing number of global aggregation cycles. These findings highlight FedHealthFog's potential to transform federated learning in resource-constrained IoT environments for delay-sensitive applications.

PrecisionLymphoNet: Advancing Malignant Lymphoma Diagnosis via Ensemble Transfer Learning with CNNs.

Malignant lymphoma, which impacts the lymphatic system, presents diverse challenges in accurate diagnosis due to its varied subtypes-chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). Lymphoma is a form of cancer that begins in the lymphatic system, impacting lymphocytes, which are a specific type of white blood cell. This research addresses these challenges by proposing ensemble and non-ensemble transfer learning models employing pre-trained weights from VGG16, VGG19, DenseNet201, InceptionV3, and Xception. For the ensemble technique, this paper adopts a stack-based ensemble approach. It is a two-level classification approach and best suited for accuracy improvement. Testing on a multiclass dataset of CLL, FL, and MCL reveals exceptional diagnostic accuracy, with DenseNet201, InceptionV3, and Xception exceeding 90% accuracy. The proposed ensemble model, leveraging InceptionV3 and Xception, achieves an outstanding 99% accuracy over 300 epochs, surpassing previous prediction methods. This study demonstrates the feasibility and efficiency of the proposed approach, showcasing its potential in real-world medical applications for precise lymphoma diagnosis.

THEILERIA EQUI INFECTION IN WORKING HORSES OF PAKISTAN: EPIDEMIOLOGY, MOLECULAR CHARACTERIZATION, AND HEMATOBIOCHEMICAL ANALYSIS.

Theileria equi is 1 of the emerging and prevailing tick-borne hemoprotozoans adversely affecting the equids worldwide, including Pakistan. The current study aimed to investigate the prevalence and molecular characterization of T. equi in working horses (n = 194), the comparative efficacy of different diagnostic tests, associated risk factors, and hematobiochemical analysis. The blood samples of horses were subjected to microscopic examination, cELISA, and polymerase chain reaction (PCR) and the results revealed a prevalence of 9.79, 21.13, and 13.40%, respectively, for T. equi in working horses. The comparison of microscopy and cELISA results with PCR showed that cELISA had higher sensitivity (84.62%), but lower specificity (88.69%) and accuracy (88.14%) in comparison to microscopy (57.69, 97.62, and 92.27%). Molecular characterization of T. equi by phylogenetic analysis revealed a 61% resemblance of study isolates with each other OL662926, OL662925, and 82% similarity with isolate OL662924 while also showing homology with T. equi isolates of South Africa, South Korea, India, Pakistan, and Brazil. The risk factor analysis revealed a significant association (P < 0.05) of tick control status, previous tick history, tick infestation, house hygiene, deworming/vaccination, and the presence of other livestock species with T. equi infection in horses. The hematobiochemical profile revealed a significant (P < 0.05) decrease in red blood cells (RBCs), hemoglobin (Hb), packed cell volume (PCV), white blood cells (WBCs), platelet (PLT), phosphorus, and an increase in lymphocytes, granulocytes, aspartate aminotransferase (AST), glucose, bilirubin, blood urea nitrogen (BUN), and creatinine in T. equi-infected horses. The current study is the first comprehensive report for comparative evaluation of microscopy, cELISA, and PCR, assessment of epidemiological risk factors as well as hematobiochemical variations due to T. equi infection in Pakistan.

pH-sensitive docetaxel-loaded chitosan/thiolated hyaluronic acid polymeric nanoparticles for colorectal cancer.

Aim: This study aimed to develop and evaluate pH-sensitive docetaxel-loaded thiolated hyaluronic acid (HA-SH) nanoparticles (NPs) for targeted treatment of colon cancer. Materials & methods: HA-SH, synthesized via oxidation and subsequent covalent linkage to cysteamine, served as the precursor for developing HA-SH NPs through polyelectrolyte complexation involving chitosan and thiol-bearing HA. Results & conclusion: HA-SH NPs displayed favorable characteristics, with small particle sizes (184-270 nm), positive zeta potential (15.4-18.6 mV) and high entrapment efficiency (91.66-95.02%). In vitro, NPs demonstrated potent mucoadhesion and enhanced cytotoxicity compared with free docetaxel. In vivo assessments confirmed safety and biocompatibility, suggesting HA-SH NPs as promising pH-sensitive drug carriers with enhanced antitumor activity for colorectal cancer treatments.