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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Editorial: Recent Advances in Deep Learning and Medical Imaging for Cancer Treatment.

In the evolving landscape of medical imaging, the escalating need for deep-learningmethods takes center stage, offering the capability to autonomously acquire abstract datarepresentations crucial for early detection and classification for cancer treatment. Thecomplexities in handling diverse inputs, high-dimensional features, and subtle patternswithin imaging data are acknowledged as significant challenges in this technologicalpursuit. This Special Issue, "Recent Advances in Deep Learning and Medical Imagingfor Cancer Treatment", has attracted 19 high-quality articles that cover state-of-the-artapplications and technical developments of deep learning, medical imaging, automaticdetection, and classification, explainable artificial intelligence-enabled diagnosis for cancertreatment. In the ever-evolving landscape of cancer treatment, five pivotal themes haveemerged as beacons of transformative change. This editorial delves into the realms ofinnovation that are shaping the future of cancer treatment, focusing on five interconnectedthemes: use of artificial intelligence in medical imaging, applications of AI in cancerdiagnosis and treatment, addressing challenges in medical image analysis, advancementsin cancer detection techniques, and innovations in skin cancer classification.

Bibliometric analysis of antimalarial drug resistance.

Background: Malaria has always been a serious infectious disease prevalent in the world. Antimalarial drugs such as chloroquine and artemisinin have been the main compounds used to treat malaria. However, the massive use of this type of drugs accelerates the evolution and spread of malaria parasites, leading to the development of resistance. A large number of related data have been published by researchers in recent years. CiteSpace software has gained popularity among us researchers in recent years, because of its ability to help us obtain the core information we want in a mass of articles. In order to analyze the hotspots and develop trends in this field through visual analysis, this study used CiteSpace software to summarize the available data in the literature to provide insights. Method: Relevant literature was collected from the Web of Science Core Collection (WOSCC) from 1 January 2015 to 29 March 2023. CiteSpace software and Microsoft Excel were used to analyze and present the data, respectively. Results: A total of 2,561 literatures were retrieved and 2,559 literatures were included in the analysis after the removal of duplicates. An irrefutable witness of the ever-growing interest in the topic of antimalarial drug resistance could be expressed by the exponentially increased number of publications and related citations from 2015 to 2022, and its sustained growth trend by 2023. During the past 7 years, USA, Oxford University, and David A Fidock are the country, institution, and author with the most publications in this field of research, respectively. We focused on the references and keywords from literature and found that the research and development of new drugs is the newest hotspot in this field. A growing number of scientists are devoted to finding new antimalarial drugs. Conclusion: This study is the first visual metrological analysis of antimalarial drug resistance, using bibliometric methods. As a baseline information, it is important to analyze research output published globally on antimalarial drug resistance. In order to better understand the current research situation and future research plan agenda, such baseline data are needed accordingly.

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.

Repurposing of non-steroidal anti-inflammatory drugs for combination therapies to combat multidrug-resistant S. aureus of bovine reproductive tract origin.

Multidrug-resistant bacteria have become the predominant etiology in bovine female reproductive tract infections and thus require effective treatment approaches. The main goal of this study was the molecular detection of mecA, blaZ, tetK, and aacA-aphD genes in Staphylococcus aureus (S. aureus) responsible for methicillin, beta-lactam, tetracycline, and aminoglycoside resistance respectively. Phylogenetic analysis was conducted to check the homology of staphylococcal genes with NCBI sequences. The in-vitro efficacy of non-steroidal anti-inflammatory drugs (NSAIDs) in combination therapies against MDR S. aureus was evaluated using well diffusion assay and checkerboard method. Vaginal swab samples (n = 384) collected from bovines suffering from endometritis, pyometra, and retained placenta were tested for S. aureus. Results showed a 17.96% overall prevalence. Both phenotypic and genotypic resistance was observed among S. aureus isolates with 50.72% and 37.68% isolates being confirmed as methicillin-resistant (MRSA), 36.23% and 18.84% isolates exhibiting beta-lactam, 40.58%, and 27.54% isolates showing tetracycline, and 33.33% and 36.23% isolates showing aminoglycosides resistance based on disc diffusion and gene confirmation, respectively. Phylogenetic analysis indicated homology with previously reported Pakistani isolates suggesting the possibility of MDR S. aureus transmission within and between animals. Synergy testing indicated that combinations of ceftriaxone-ketoprofen (153.77%), ceftriaxone-meloxicam (149.55%), amoxiclav-flunixin meglumine (106.06%), and oxytetracycline-flunixin meglumine (104.47%) showed synergy on well diffusion assay. Based on the fractional inhibitory concentration index by checkerboard method, oxytetracycline-meloxicam and gentamicin-ketoprofen combinations exhibited synergistic interaction. In conclusion, MDR S. aureus resistance was mitigated in-vitro through the combination of antibiotics (oxytetracycline, gentamicin) with NSAIDs (meloxicam, ketoprofen) that could be used to create therapeutic strategies for bovine reproductive issues.

Curative effects of tectochrysin on paraquat-instigated testicular toxicity in rats: A biochemical and histopathological based study.

Background: Paraquat (PQ) is a herbicide that is used globally in the agriculture sector to eradicate unwanted weeds, however it also induces significant damages in various organs of the body such as testes. Tectochrysin (TEC) is an important flavonoid that shows versatile therapeutic potentials. Currently, there is no established antidote to cure PQ-induced testicular toxicity. Objective: The present study was conducted to evaluate the ameliorative effects of TEC against PQ prompted testicular damage. Methods: Sprague-Dawley rats (n = 48) were used to conduct the trial. Rats were allocated in to 4 groups i.e., Control, PQ administrated group (5 mgkg-1), PQ + TEC co-administrated group (5 mgkg-1 + 2.5 mgkg-1) and TEC only administrated group (2.5 mgkg-1). The trial was conducted for 8 weeks. The activity of anti-oxidants and the levels of MDA and ROS were determined by spectrophotometric method. Steroidogenic enzymes as well as apoptotic markers expressions were evaluated by qRT-PCR. The level of hormones and inflammatory indices was quantified by enzyme-linked immunosorbent assay. Results: PQ exposure markedly (P < 0.05) disturbed the biochemical, spermatogenic and histological profile in the rats. Nevertheless, TEC treatment considerably (P < 0.05) increased CAT, GPx GSR and SOD activity, besides decreasing MDA and ROS contents. TEC administration also increased sperm viability, count and motility. 17ß-HSD, 3ß-HSD, StAR and Bcl-2 expressions were also increased following TEC administration. The supplementation of TEC substantially (P < 0.05) decreased Bax, Caspase-3 expression and the levels of inflammatory markers i.e., interleukin-1ß (IL-1ß), interleukin-6 (IL-6), nuclear factor kappa-B (NF-κB), tumor necrosis factor-α (TNF-α) and cyclooxygenase-2 (COX-2) activity. Additionally, the levels of plasma testosterone, follicle-stimulating hormone (FSH) and luteinizing hormone (LH) were increased following TEC supplementation. Furthermore, TEC supplementation considerably decreased sperm structural abnormalities and histomorphological damages of the testes. The mitigative role of TEC might be due to its anti-inflammatory, anti-apoptotic, androgenic and anti-oxidant potentials. Conclusion: Taken together, it is concluded that TEC can be used as a potential candidate to treat testicular toxicity.

The in vivo study on antioxidant activity of wendan decoction in treating hyperlipidemia: a pharmacokinetic-pharmacodynamic (PK-PD) model.

Background: Wendan Decoction (WDD) is a six-herb Chinese medicine recipe that was first mentioned in about 652 AD. It is frequently used to treat hyperlipidemic patients' clinical complaints. According to reports, oxidative stress has a significant role in hyperlipidemia. Purpose: There has not yet been a thorough pharmacokinetic-pharmacodynamic (PK-PD) examination of the clinical efficacy of WDD in the context of hyperlipemia-related oxidative stress. Therefore, the goal of this research is to explore the antioxidant essence of WDD by developing a PK-PD model, ordering to assure its implication in treating hyperlipidemia in medical practice. Methods: The model rats of foodborne hyperlipidemia were established by feeding with high-fat feed, and the lipid-lowering effect of WDD was explored. The plasma drug concentration of rats at different doses were measured by UPL-MS/MS technology, and PK parameters were calculated using Phoenix WinNonlin 8.1 software. The level of lipid peroxide (LPO) in plasma at different time points was measured by enzyme labeling instrument. Finally, the PK-PD model was established by using Phoenix WinNonlin 8.1 software, to explore the lipid-lowering effect of WDD and the relation between the dynamic changes of chemical components and antioxidant effect. Results: The findings suggested that, WDD can reduce the levels of triglyceride (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) in plasma, and high-density lipoprotein cholesterol (HDL-C) was related to the dosage. Between the peak drug levels and the WDD's maximal therapeutic response, there existed a hysteresis. WDD's effect-concentration curves displayed a counterclockwise delaying loop. Alternatively, among the ten components of WDD, hesperetin, quercetin, naringenin and tangeretin might exert more significant effects in regulating the LPO levels in hyperlipidemic rats. Conclusion: This study can be helpful for other investigators to study the lipid-lowering effect of WDD.

Factors affecting injury severity in motorcycle crashes: Different age groups analysis using Catboost and SHAP techniques.

OBJECTIVE: Motorcycle crashes often result in severe injuries on roads that affect people's lives physically, financially, and psychologically. These injuries could be notably harmful to drivers of all age groups. The main objective of this study is to investigate the risk factors contributing to the severity of crash injuries in different age groups. METHODS: This Objective is achieved by developing accurate machine learning (ML) based prediction models. This research examines the relationship between potential risk factors of motorcycle-associated crashes using (ML) and Shapley Additive explanations (SHAP) technique. The SHAP technique further helped interpreting ML methods for traffic injury severity prediction. It indicates the significant non-linear interactions between dependent and independent variables. The data for this study was collected from the Provincial Emergency Response Service RESCUE 1122 for the Rawalpindi region (Pakistan) over three years (from 2017 to 2020). The Synthetic Minority Oversampling Technique (SMOTE) is employed to balance injury severity classes in the pre-processing phase. RESULTS: The results demonstrate that age, gender, posted speed limit, the number of lanes, and month of the year are positively associated with severe and fatal injuries. This research also assesses how the modeling framework varies between the ML and classical statistical methods. The predictive performance of proposed ML models was assessed using several evaluation metrics, and it is found that Catboost outperformed the XGBoost, Random Forest (RF) and Multinomial Logit (MNL) model. CONCLUSION: The findings of this study will assist road users, road safety authorities, stakeholders, policymakers, and decision-makers in obtaining substantial and essential guidance for reducing the severity of crash injuries in Pakistan and other countries with prevailing conditions.

Cell membrane-coated biomimetic nanomedicines: productive cancer theranostic tools.

As the second-leading cause of human death, cancer has drawn attention in the area of biomedical research and therapy from all around the world. Certainly, the development of nanotechnology has made it possible for nanoparticles (NPs) to be used as a carrier for delivery systems in the treatment of tumors. This is a biomimetic approach established to craft remedial strategies comprising NPs cloaked with membrane obtained from various natural cells like blood cells, bacterial cells, cancer cells, etc. Here we conduct an in-depth exploration of cell membrane-coated NPs (CMNPs) and their extensive array of applications including drug delivery, vaccination, phototherapy, immunotherapy, MRI imaging, PET imaging, multimodal imaging, gene therapy and a combination of photothermal and chemotherapy. This review article provides a thorough summary of the most recent developments in the use of CMNPs for the diagnosis and treatment of cancer. It critically assesses the state of research while recognizing significant accomplishments and innovations. Additionally, it indicates ongoing problems in clinical translation and associated queries that warrant deeper research. By doing so, this study encourages creative thinking for future projects in the field of tumor therapy using CMNPs while also educating academics on the present status of CMNP research.