University of Sargodha

Silica nanoparticles-embedded smart-gels are efficient drug carrier systems due to their structural flexibility, high porosity, and ease of formulation development. Herein, the extent of interaction of minoxidil (MXD), a potent vasodilator prodrug, with silica nanoparticles (SiNPs) and alginate (ALG) was investigated. The SiNPs were prepared by extracted silica from rice husk ash and these SiNPs were further used to prepare MXD-loaded-SiNPs (MXD-SiNPs) by loading with an appropriate amount of MXD. The as-prepared MXD-SiNPs were encapsulated in ALG polymer by freeze-gelation method and evaluated by various characterization techniques. The amorphous nature of the SiNPs was confirmed by XRD examination, while the nature of physical interaction and encapsulation of the drug in the SiNPs and ALG gel was examined by FTIR analysis. TEM analysis revealed that the MXD-SiNPs had a monodisperse collection of spherical nanoparticles, while the particle size (~150 nm) of as-prepared formulation was determined from DLS studies. The drug entrapment efficiency was 86% and the loading efficiency was 22%. The as-developed MXD-SiNPs@ALG gel formulation exhibited sustained release over 12 h compared to pure MXD and MXD-SiNPs. These results suggest that the newly developed formulation has several advantageous properties that make it suitable for cutaneous administration of the drug.

Labetalol is an anti-hypertensive medication available in both tablet and liquid injectable forms. However, a transdermal delivery system may offer a more convenient option for patients requiring this medication. Due to its solubility in organic solvents and high molecular weight, a transdermal patch could face challenges in effectively delivering the drug through the stratum corneum, the outermost layer of the skin. To overcome this challenge, labetalol-loaded nanoparticles were prepared using a solvent evaporation method and incorporated into dissolvable microneedle patches. The nanoparticles ensured controlled drug release, while microneedles facilitated drug penetration through the stratum corneum. The patches were formulated with hydroxypropyl methylcellulose and carbopol polymers and evaluated for their mechanical properties, penetration efficacy, drug loading, in vitro drug release, and biological safety. Scanning electron microscopy confirmed uniform nanoparticle distribution, and drug loading efficiency reached 95.25 ± 1.68%. The optimized formulation achieved a sustained in vitro drug release of 89.27 ± 2.34% over 24 h, significantly improving release efficiency compared to conventional oral and injectable labetalol formulations. In contrast to the burst release observed with oral and injectable formulations, the microneedle patch offered controlled and sustained release, enhancing therapeutic outcomes and reducing side effects. Penetration studies demonstrated successful nanoparticle delivery into deeper skin layers, while irritation studies confirmed the safety of the patches. These findings suggest that nanoparticle-loaded dissolvable microneedle patches provide a promising strategy for the transdermal delivery of labetalol, offering controlled drug release and enhanced patient compliance.

In applied research, fractional calculus plays an important role for comprehending a wide range of intricate physical phenomena. One of the Klein-Gordon model’s peculiar case yields the Phi-four equation. Additionally, throughout the past few decades it has been utilized to explain the kink and anti-kink solitary waveform contacts that occur in biological systems and in the field of nuclear mechanics. In this current work, the key objective is to analyze the consequences of fractional variables on the soliton wave dynamic behavior in a nonlinear time-fractional Phi-four equation. Using the formulation of the conformable fractional derivative it illustrates some of the recovered solutions and analyze their dynamic behavior. The analytical solutions are drawn by using the extended direct algebraic and the Bernoulli Sub-ODE scheme. Various types of soliton solutions are proficiently expressed. Adjusting the specific values of fractional parameters allows to produce the periodic, kink, bell shape, anti-bell shape and W-shaped solitons. The impact of the conformable derivative on the precise solutions of the fractional Phi-four equation is demonstrated with a series of 2D, 3D and contour graphical representations.

Purpose The solid lipid nanoparticles of transitional metal complexes (POMs) were prepared with natural lipids with the aim of developing a safer therapeutic approach for cancer treatment. Methods Natural lipids were used to create solid lipid nanoparticles containing transitional metal complexes (POMs). Results The nanoparticles had displayed appreciable entrapment and loading percentage of P5W30. The zeta capacitance was measured to be −32.57±6.44 mV with average particle dimension of 160.5±8.61 nm and polydispersity index (PDI) of around 0.3814±0.096. The effectiveness of P5W30-BW-SLNs in inhibiting the growth of HeLa cells was found to be higher (IC50 = 3.02±2.14 µg/mL) compared to pure P5W30 (IC50 = 7.93±5.08 µg/mL). Further examinations of DNA damage were made through comet test and flow cytometry techniques. The assessment of tumor regression and survival was conducted, and comparison was recorded. The P5W30-BW-SLNs resulted in a 72.91% increase in survival rates and a reduction in tumor burden by 2.967±0.543%. Moreover, the computational findings demonstrate a strong connection with the actual data, providing a plausible explanation for the notable chemopreventive efficacy of POM against HeLa cell lines. Conclusion The study’s findings might pave the way for a more efficient delivery system in cancer treatment.

Green and low-cost sources for remediation of environmental pollution are the focus of research nowadays. In this regard, nanoparticles derived from different waste materials are a sizzling area of research particularly graphene-based carbon dots are employed by many researchers for waste management. Graphene has a single layer of carbon atoms arranged in a hexagonal pattern which gives it a large surface area, significant conduction power, and surface properties suitable for the attachment of different waste materials from aqueous media. Agricultural waste materials are mostly employed to extract graphene and then different nanoparticles are synthesized by employing new and eco-friendly techniques to boost the potential of graphene for Environmental applications. Heavy metals are persistent pollutants as they cannot be degraded or destroyed. The only remedy to eliminate them is to remove them from aqueous media. Graphene can effectively remove heavy metals like lead, cadmium, mercury, and chromium from contaminated water. The adsorption capacity of graphene for heavy metals is high, making it an efficient remediation tool. Functionalized graphene can enhance its heavy metal adsorption capabilities. In this chapter, waste waste-derived graphene and its applications for the removal of heavy metals are thoroughly described in focus of current research.

Colorectal cancer is considered one of the deadliest diseases, contributing to an alarming increase in annual deaths worldwide, with colorectal polyps recognized as precursors to this malignancy. Early and accurate detection of these polyps is crucial for reducing the mortality rate of colorectal cancer. However, the manual detection of polyps is a time-consuming process and requires the expertise of trained medical professionals. Moreover, it often misses polyps due to their varied size, color, and texture. Computer-aided diagnosis systems offer potential improvements, but they often struggle with precision in complex visual environments. This study presents an enhanced deep learning approach using encoder-decoder architecture for colorectal polyp segmentation to capture and utilize complex feature representations. Our approach introduces an enhanced dual attention mechanism, combining spatial and channel-wise attention to focus precisely on critical features. Channel-wise attention, implemented via an optimized Squeeze-and-Excitation (S&E) block, allows the network to capture comprehensive contextual information and interrelationships among different channels, ensuring a more refined feature selection process. The experimental results showed that the proposed model achieved a mean Intersection over Union (IoU) of 0.9054 and 0.9277, a dice coefficient of 0.9006 and 0.9128, a precision of 0.8985 and 0.9517, a recall of 0.9190 and 0.9094, and an accuracy of 0.9806 and 0.9907 on the Kvasir-SEG and CVC-ClinicDB datasets, respectively. Moreover, the proposed model outperforms the existing state-of-the-art resulting in improved patient outcomes with the potential to enhance the early detection of colorectal polyps.

Surprisingly innovation process based on deliberate practice has rarely been unearthed that might explore the boundary conditions of the eco-friendly deliberate practice and eco-innovation performance relationship. Anchored on the organizational support theory and the social cognitive, the current study seeks to investigate the impacts of perceived organizational support (POS) and developmental leadership (DL) on eco-innovation performance (EP) through the mediating role of eco-friendly deliberate practice (EDP). In addition, the study explores the boundary effects of employee resilience (ER) on the relationship between EDP and EP. The study collects time-lagged (i.e., “three-wave”) and multisource (i.e., “self-rated and supervisor-rated”) data from 383 respondents working in the service sector organizations in Pakistan. The authors processed data in SmartPLS (v 4.0) to assess the measurement model and the structural model. The study finds that POS and DL have significant positive relationships with EDP. Further, EDP partially mediates the links between POS, DL, and EP. Moreover, ER intervenes the association between EDP and EP such that at high levels of ER, the relationship is stronger and vice versa. Despite growing interest in deliberate practice, the boundary conditions of EDP in the work context are rarely investigated. This is the first study that explores the contextual and individual factors that can underpin the influence of EDP on EP.

The negative binomial regression model (NBRM) is popular for modeling count data and addressing overdispersion issues. Generally, the maximum likelihood estimator (MLE) is used to estimate the NBRM coefficients. However, when the explanatory variables in the NBRM are correlated, the MLE yields inaccurate estimates. To tackle this challenge, we propose a James–Stein estimator for the NBRM. The matrix mean squared error (MSE) and the scalar MSE properties are derived and compared with other estimators, including the ridge estimator (RE), Liu estimator (LE), and the MLE. We assess the performance of the suggested estimator using two real applications and a simulation study, with MSE serving as the assessment criterion. Results from both simulations and real applications demonstrate the superior performance of the proposed estimator over the RE, LE, and MLE.

As demonstrated in the section above, the stock market place is a dynamic factor, which makes it possible for traders and investors to make good decisions based on the information acquired through accurate prediction. This research aims at improving the prediction of stock market by applying a new method to Multi-attribute Group Decision making (MAGDM). MAGDM goes through a cycle of evaluating and ranking several criteria hence enhancing the decision-making aspects further. To overcome the shortcomings of prior models, some EU and FU is incorporated by combining Zadeh’s ${\hat{Z}}$-numbers with Picture Fuzzy Sets (PFSs). This integration is to enhance the ability of the model to address completely unclear decisions utilizing the peculiarities of ${\hat{Z}}$-numbers. To compare decisions between decision-makers, we proposed picture fuzzy ${\hat{Z}}$-numbers (PF${\hat{Z}}$N) and for their aggregation, introduced picture fuzzy weighted averaging, picture fuzzy ordered weighted averaging, picture fuzzy hybrid averaging, picture fuzzy weighted geometric, picture fuzzy ordered weighted geometric and picture fuzzy hybrid geometric operators based algebraic ${\mathfrak {T}}$-norm (${\mathfrak {T}}-N$) and ${\mathfrak {T}}$-conorm (${\mathfrak {T}}-CNs$) To verify the efficiency of our suggested technique, we compare these operators with the Combined Compromised Solution (CoCoSo) model focusing on the stock market analysis. Our results, therefore, show how these operators are important in improving decision making accuracy and precision in conditions of risk. This research laid down the basis for enhancing decision-making and dealing with uncertainty in different fields especially in the application of stock market prediction. The proposed methodology can be attributed to providing a systematic and a more efficient way of dealing with uncertainty which in one way or the other has an outcome of enhancing the credibility of the decision making process in the financial sector.

The continuous contamination of heavy metals (HMs) in our ecosystem due to industrialization, urbanization and other anthropogenic activities has become a serious environmental constraint to successful crop production. Lead (Pb) toxicity causes ionic, oxidative and osmotic injuries which induce various morphological, physiological, metabolic and molecular abnormalities in plants. Polyethylene glycol (PEG) is widely used to elucidate drought stress induction and alleviation mechanisms in treated plants. Some recent studies have unveiled the potential of PEG in regulating plant growth and developmental procedures including seed germination, root and shoot growth and alleviating the detrimental impacts of abiotic stresses in plants. Therefore, the current study aimed to assess the effects of seed priming with various concentrations (10%, 20%, 30% and 40%) of PEG on the growth and development of radish plants growing under Pb stress (75 mg/kg soil). Lead toxicity reduced root growth (32.89%), shoot growth (32.81%), total chlorophyll (56.25%) and protein content (58.66%) in treated plants. Similarly, plants showed reduced biomass production of root (35.48%) and shoot (31.25%) under Pb stress, while 30% PEG seed priming enhanced biomass production of root (28.57%) and shoot (35.29%) under Pb contaminated regimes. On the other hand, seedlings obtained from 30% PEG priming demonstrated a notable augmentation in the concentrations of photosynthetic pigments, antioxidative activity and biomass accumulation of the plants. PEG-treated plants showed modulations in the enzymatic activities of peroxidase (PO), catalase (CAT) and superoxide dismutase (SOD). These changes collectively played a role in mitigating the adverse effects of Pb on plant physiology. Our data revealed that PEG interceded stress extenuation encompasses numerous regulatory mechanisms including scavenging of ROS through antioxidant and non-antioxidants, improved photosynthetic activity and appropriate nutrition. Hence, it becomes necessary to elucidate the beneficial role of PEG in developing approaches for improving plant growth and stress tolerance.

In this study, sonication was employed using a Box-Behnkin design (BBD) to measure total phenolic content, total flavonoid content, total flavonol content, anthocyanin content, and radical scavenging activity. The process was optimized by varying three key factors: time (35–55 min), temperature (45–65 °C), and amplitude (70–90%). The maximum total phenolic content (TPC) was recorded at 35 min, 65 °C, and 90% amplitude. Similarly, the maximum total flavonoid content (TFC) and total flavonol content (TFoC) values were achieved under the same conditions. For anthocyanins, the optimal extraction occurred at 45 min, 55 °C, and 80% amplitude. The greatest radical scavenging activity was recorded at 55 min, 65 °C, and 90% amplitude. These three variables were identified as the most significant in enhancing the extraction of phenolic compounds and the antioxidant properties of the extracts produced. The method showed high precision, short extraction times, and good recovery rates. This study explored sonication as an efficacious phytochemical extraction technique, which achieved an extraction efficiency of 83.75%. Overall, ultrasound-assisted extraction proved to be a highly effective and straightforward approach for recovering biologically active substances from oats. Graphical abstract

This study investigates the effectiveness of a two-stage process aimed at achieving sustainable aromatic production through the co-pyrolysis of biomass and plastic, followed by catalytic reforming utilizing an Fe-modified ZSM-5 catalyst. To characterize the thermal decomposition behavior and monitor the progression of volatile compounds during the co-pyrolysis process, we employed thermogravimetric analysis (TGA) in conjunction with Fourier transform infrared (FTIR) spectroscopy and gas chromatography-mass spectrometry (GC–MS). Additionally, we conducted a compositional analysis of the resultant bio-oil. This study demonstrates that the co-pyrolysis process, employing a catalyst-to-feedstock ratio of 1:1 with the 10Fe/ZSM-5 catalyst (10 wt.% Fe loading), significantly increases the yield of aromatic hydrocarbons to 67.38%. This yield is markedly higher compared to the aromatic hydrocarbon yield of 55.37% obtained from catalytic pyrolysis of biomass alone. Moreover, these findings highlight the potential of this integrated methodology as a promising pathway for transforming waste materials into value-added chemicals, thereby improving bio-oil quality through the enrichment of aromatic compounds.

Ablative laser propulsion earned a potential significance for its ability to propel any distant object. It can be used in defense and security applications, such as disabling unauthorized drones or neutralizing explosives from a distance. It also has applications to micro- and nano-satellites for micro-orbiting and attitude control along with debris removal. To investigate the propulsive parameters of barium ferrite (BaFe12O19) by ablative laser propulsion, the material was subjected to Nd:YAG laser at 1064 nm wavelength and 5 ns pulse duration at laboratory conditions. BaFe12O19 was synthesized using chemical methods and subjected to different sonication time to check its effectiveness as propellants. The specific impulse and coupling coefficient were calculated at laser fluence 1e5–5e5J/m2. A maximum of specific impulse (ISP) 2500 s along with enhanced coupling coefficient (Cm) in the range of 3.1119e−4 to 3.2190e−4 N s/J has been achieved. The achieved results showed that it can be used as promising material in the manufacturing of laser thrusters to amplify the propulsion applications.

Identifying influential nodes within social and weblog networks plays a pivotal role in gaining insights into information dissemination processes and optimizing the structure of social networks to control and accelerate information diffusion. Existing literature on ranking the influence of network nodes encompasses various centrality measures; however, many of these measures perform effectively only under certain conditions, primarily because of lack of a comprehensive ranking mechanism that covers various aspects of a social network. In this study, we have introduced a novel hybrid node ranking model that is tailored for a specific type of social network known as a community weblog. The proposed model integrates classical network centralities with textual and temporal features. One of the significant aspects of the proposed model is the incorporation of temporal features, which assign higher weight to more recent time intervals during social interactions between network nodes. Additionally, the model employed an effective weighting mechanism based on information entropy. The weighting mechanism calibrates appropriate weights for each feature extracted from textual, temporal, and centrality aspects of a weblog network. These features are then objectively weighted and integrated into a hybrid multi-criterion ranking model named CTUserRank. To assign a unified importance rank to nodes in the weblog network, we leveraged the popular and effective aggregation technique known as TOPSIS. Finally, an empirical analysis is performed based on multiple community weblogs to measure the effectiveness of CSTUserRank against classical and recent centrality models. Our findings demonstrate that CSTUserRank outperformed classical and recent centrality models in terms of effectively ranking influential users.

Purpose This study explored how exogenous silicon (Si) affects growth and salt resistance in maize. Methods The maize was cultivated in sand-filled pots, incorporating varied silicon and salt stress (NaCl) treatments. Silicon was applied at 0, 2, 4, 6, and 8 mM, and salt stress was induced using 0, 60 and120 mM concentrations. Soil salinity triggers a range of physiochemical abnormalities, often leading to growth arrest and, eventually, the demise of susceptible plants. Results The salt stress significantly reduced the total chlorophyll content (12.58–33.14%), antioxidant enzymes, notably SOD (32–46%), POD (10.33–18.48%), and CAT (10.05–13.19%). In contrast, salt stress increased secondary metabolites, including total phenols (49.11–66.35%.), flavonoids (220.99–280.36%), and anthocyanin (50.04–58.6%). Adding silicon under salt stress reduced the absorption of Na⁺ by 6.69%, 20.7%, 41.12%, and 34.28%, respectively, compared to their respective controls. Additionally, applying Si at 8 mM significantly enhanced antioxidant enzymes such as SOD (50.57%), POD (15.58%), CAT (10.06%) and chlorophyll ratio (21.32%). Conclusion Silicon application positively impacted nearly all growth and physiological features, indicating it helps mitigate against salinity. This was achieved by regulating various salinity indicators, where secondary metabolites, including anthocyanin, ascorbic acid, total phenols, and flavonoids, increased. Graphical Abstract

This study examined the effect of triple-frequency ultrasound treatment (TFUT)-assisted lactic acid bacteria (LAB-L. plantarum and L. helveticus fermentation for 24-h and 48-h) on the chemical, structural, morphological, metabolic, and sensory properties of rice lees (RL). Ultrasonicated-assisted RL fermented with L. helveticus (URLH-48) had the greatest total phenolic contents (TPC) (112.1 mg GAE/m), total flavonoid contents (TFC) (163.62 mg RE/mL), and proanthocyanidin contents (PAC) (728.34 mg/mL) compared to RL (control) and other treatments. Furthermore, URLH-48 demonstrated an increase in the concentrations of quinic acid (486.96 mg/L) and gallic acid (201.42 mg/L), as determined by HPLC-UV analysis. Additionally, FTIR spectral analyses demonstrated that TFUT-assisted fermented RL exhibited a greater degree of flexibility and mobility in its secondary structures compared to RL (control). The amino acid’s profile of RL was significantly increased as LAB degraded the RL proteins, and the function of TFUT facilitates bacterial activity. Moreover, SEM observation provides convincing evidence that TFUT improves and speeds up the breakdown of proteins’ structures, resulting in irregular and dense structures. Correlation and molecular docking research suggest that TFUT has different impacts on specific RL and fermented RL characteristics. The analyses conducted using GC–MS and E-nose indicated the generation of highly volatile flavor compounds through fermentation. The sensory evaluation results show an increase in liking following fermentation and TFUT-assisted fermentation, which is attributed to the production of flavor compounds. Consequently, the combined use of TFUT-assisted fermentation markedly improves the polyphenolic composition, antioxidant capacity, flavor profile, micromorphology, and overall quality of RL, which may enhance their functionality and broaden their applications in the food industry.

Unanticipated and rapid change in facial expression are micro-expression (ME) that are hard to hide after an emotionally charged event. Facial microexpressions are transient and subtle, making identification challenging. Recognition of MEs are very crucial in the light of personal intention phase identification. Previous studies had challenges recognizing ME due to complicated spatiotemporal linkage in video data. Using the ConvMixer architecture, we Proposed a novel technique for facial microexpression identification based on convolutional attention mechanism. The research uses SAMM, SMIC, and CASME-II are benchmark datasets used to perform experiments. ConvMixer deployed to analyze the SAMM dataset where ConvMixer achieved an amazing 99.73% accuracy, 97.3% precision, 96.5% recall, and 99% F1-Score while 10-fold cross-validation. In addition, we extended our analysis to the CASME-II dataset, where ConvMixer attained an F1-Score of 99.4%, an accuracy of 99.12%, a precision of 98.3%, and a recall of 98.7%. These findings indicate that ConvMixer regularly outperforms other MER architectures, while capturing video specific and dynamic characteristics. ConvMixer architecture are good in capturing both spatial and temporal correlations and extracts spatial information using depthwise convolutions and channel mixing processes. High F1-Score, recall, precision, and accuracy across several datasets demonstrate the robustness and adaptability of the ConvMixer architecture. Finally, our findings show that the Convolutional Attention-Based Mechanism for facial microexpression recognition (CABM-FMER) works effectively for identifying facial MEs.

Inspite of so much development in medical technology, Tuberculosis (TB) is still the problem for humans. Fewstudies, in Pakistan highlighted the factors that affect patients health related quality of life (HRQOL) with activeTB. The aim of this study is to measure short form six dimension (Sf-6D) utility scores of patients with activeTB of Sargodha district. 120 active TB patients were interviewed and short form-36 questionnaire was followed.District TB hospital of Sargodha district was visited. Results show that Utility scores of female patients werebetter than male, while patients belong to urban areas have better utility scores as compared to rural patients ofTB. Indoor patient’s utility scores were better than outdoor patients. Disease severity, use of drugs, depression,pain and death threat were the factors that negatively affect the patients health related quality of life, whileopportunity of leisure and income level increase patients HRQOL.