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Blood flow in narrow channels such as veins and arteries is the major topic of interest here. The Casson fluid with its shear-thinning attribute serves as the blood model. Owing to the arterial walls, the channel is configured curved in shape. The activation energy and nonlinear thermal radiation aspects are highlighted. The channel boundaries are flexible with peristaltic wave travelling along the channel. The mathematical description of the problem is developed under physical laws and then simplified using the lubrication technique. The obtained system is then sketched in graphs directly using the numerical scheme NDSolve in Mathematica software. The physical interpretation of parameters on axial velocity, temperature profile, concentration, and streamline pattern is discussed in the last section.

Melatonin and celecoxib are antioxidants and anti-inflammatory agents that exert protective effects in different experimental models. In this study, the neuroprotective effects of melatonin and celecoxib were demonstrated against ethanol-induced neuronal injury by in silico, morphological, and biochemical approaches. For the in silico study, 3-D structures were constructed and docking analysis performed. For in vivo studies, rats were treated with ethanol, melatonin, and celecoxib. Brain samples were collected for biochemical and morphological analysis. Homology modeling was performed to build 3-D structures for IL1β), TNFα, TLR4, and inducible nitric oxide synthase. Structural refinement was achieved via molecular dynamic simulation and processed for docking and postdocking analysis. Further in vivo experiments showed that ethanol induced marked neuronal injury characterized by downregulated glutathione, glutathione -transferase, and upregulated inducible nitric oxide synthase. Additionally, ethanol increased the expression of TNFα and IL1β. Finally, neuronal apoptosis was demonstrated in ethanol-intoxicated animals using caspase 3 and activated JNK staining. On the other hand, melatonin and celecoxib treatment ameliorated the biochemical and immunohistochemical alterations induced by ethanol. These results demonstrated that ethanol induced neurodegeneration by activating inflammatory and apoptotic proteins in rat brain, while melatonin and celecoxib may protect rat brain by downregulating inflammatory and apoptotic markers.

Radiologists encounter significant challenges when segmenting and determining brain tumors in patients because this information assists in treatment planning. The utilization of artificial intelligence (AI), especially deep learning (DL), has emerged as a useful tool in healthcare, aiding radiologists in their diagnostic processes. This empowers radiologists to understand the biology of tumors better and provide personalized care to patients with brain tumors. The segmentation of brain tumors using multi-modal magnetic resonance imaging (MRI) images has received considerable attention. In this survey, we first discuss multi-modal and available magnetic resonance imaging modalities and their properties. Subsequently, we discuss the most recent DL-based models for brain tumor segmentation using multi-modal MRI. We divide this section into three parts based on the architecture: the first is for models that use the backbone of convolutional neural networks (CNN), the second is for vision transformer-based models, and the third is for hybrid models that use both convolutional neural networks and transformer in the architecture. In addition, in-depth statistical analysis is performed of the recent publication, frequently used datasets, and evaluation metrics for segmentation tasks. Finally, open research challenges are identified and suggested promising future directions for brain tumor segmentation to improve diagnostic accuracy and treatment outcomes for patients with brain tumors. This aligns with public health goals to use health technologies for better healthcare delivery and population health management.

This study aims to analyze the impact of ICT, renewable energy consumption, and financial development on CO 2 emissions in selected developing countries of East and South Asia. Using panel data spanning 1985–2020, Pooled Mean Group (PMG) estimator is used to analyze the short-run and long-run effects. Results suggest that ICT and financial development positively contribute to the degradation of the environment in the long run, while their impact on CO 2 emissions is insignificant in the short run. On the other hand, renewable energy consumption affects environmental quality positively in both the long run and short run. It is also examined that economic growth affects CO 2 emissions positively but the squared economic growth reduces CO 2 emissions which validates inverted U-shaped EKC hypothesis. The empirical findings of the Granger Causality test suggest unidirectional causality from ICT and financial development to CO 2 emissions, while a bi-directional relationship is found among renewable energy and CO 2 emissions. Results imply that governments in these countries need to invest in renewable energy to control environmental degradation.

This study aims to analyze the asymmetric relation between renewable energy consumption and CO2 emissions in China using the STIRPAT-Kaya-EKC framework. To delve into the asymmetric effect of renewable energy consumption on the environment, the non-linear ARDL model is used. The results of this study confirm the asymmetric impact of renewable energy on the environment in the long run as well as in the short run. However, the negative shocks to renewable energy have a greater detrimental influence on the environment than the benign effect due to the positive shock to renewable energy. Population growth affects the environment in the short run, whereas technology only affects environment quality in the long run. Moreover, the study supports the EKC theory in China. This research emphasizes that the administration can improve the economy’s lifespan by allocating substantial funds to establish legislation to maintain a clean environment by subsidizing renewable energy infrastructure and research and innovations for low-carbon projects.

Background and Objective: Prolonged stays in the Post-Anesthesia Care Unit (PACU) can negatively impact patient safety, hospital length of stay, and surgical workflow. While clinical reasons for delayed discharge are often studied, non-clinical factors such as bed unavailability and staff busyness are less frequently addressed. Our objective was to identify non-clinical factors contributing to delayed PACU discharge and implement quality improvement strategies to reduce PACU length of stay. Methodology: A new departmental Key Performance Indicator (KPI) was introduced in July 2023 to monitor PACU length of stay, with “delayed discharge” defined as exceeding one-hour post-fitness for discharge. An audit was conducted using retrospective data from 69 patients in September 2023. Two Plan-Do-Study-Act (PDSA) cycles were conducted. Interventions included regular educational sessions, expanding the audit team, initiating monthly departmental quality improvement meetings, creating a new data collection form to assess staff busyness, and adding two beds to the High Dependency Unit (HDU). Results: Baseline data showed that only 62% of patients were discharged within the target timeframe. After the first PDSA cycle, compliance improved to 74%. Following the second PDSA cycle, supported by multidisciplinary collaboration and infrastructure improvements, the department achieved its target (≥95%) by February 2024. Contributing non-clinical factors leading to delayed discharges included staff busyness (e.g., shift changes, porter delays) and lack of available beds. Conclusion: Addressing non-clinical factors such as staffing logistics and bed availability significantly improved PACU discharge efficiency. Structured quality improvement initiatives and interdepartmental collaboration were key to achieving sustained improvements in patient flow and departmental performance.

Micronutrient application has a crucial role in mitigating salinity stress in crop plants. This study was carried out to investigate the effect of zinc (Zn) and boron (B) as foliar applications on fenugreek growth and physiology under salt stress (0 and 120 mM). After 35 days of salt treatments, three levels of zinc (0, 50, and 100 ppm) and two levels of boron (0 and 2 ppm) were applied as a foliar application. Salinity significantly reduced root length (72.7%) and shoot length (33.9%), plant height (36%), leaf area (37%), root fresh weight (48%) and shoot fresh weight (75%), root dry weight (80%) and shoot dry weight (67%), photosynthetic pigments (78%), number of branches (50%), and seeds per pod (56%). Fenugreek’s growth and physiology were improved by foliar spray of zinc and boron, which increased the length of the shoot (6%) and root length (2%), fresh root weight (18%), and dry root weight (8%), and chlorophyll a (1%), chlorophyll b (25%), total soluble protein content (3%), shoot calcium (9%) and potassium (5%) contents by significantly decreasing sodium ion (11%) content. Moreover, 100 ppm of Zn and 2 ppm of B enhanced the growth and physiology of fenugreek by reducing the effect of salt stress. Overall, boron and zinc foliar spray is suggested for improvement in fenugreek growth under salinity stress.

The present study describes the peristaltic flow of Jeffrey fluid with nanomaterial bounded under peristaltic waves in a curved channel. Silver (Ag) is the nanomaterial used for this purpose, and base fluid is water. The diversity of peristaltic waves is captured under four different wave profiles traveling along the curved channel. The consequences of heat generation and mass concentration are also taken. The problem is modeled under physical laws and then simplified using the lubrication technique. The obtained system is sketched in graphs directly using a numerical scheme. The physical outcomes of involved parameters on axial velocity, temperature variation, concentration profile, and streamline patterns are discussed in the last section.

This article details the development of new anesthesia services at Shaukat Khanum Memorial Cancer Hospital and Research Center in Peshawar, Pakistan. This involved recruiting skilled staff, who were supported by intensive training. Pre-anesthesia evaluation clinics were established to ensure patient safety and reduce surgical cancelations. Continuous professional development and innovative pain management strategies were introduced to enhance service quality, positioning SKMCH & RC as a leader in cancer-related anesthesia services in the region. doi: https://doi.org/10.12669/pjms.41.4.11139 How to cite this: Mansoor F, Jan S, Zain Ul Abidin, Khan HRA. Establishing comprehensive anesthesia services for cancer surgery at SKMCH Peshawar: Challenges, achievements, and innovations. Pak J Med Sci. 2025;41(4):944-946. doi: https://doi.org/10.12669/pjms.41.4.11139 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Steam-assisted gravity drainage (SAGD) is a widely used thermal enhanced oil recovery (EOR) technique in North America, particularly in high-permeability oil sand reservoirs. While effective, its economic viability has declined due to low oil prices and high greenhouse gas (GHG) emissions from the steam generation. To improve cost-effectiveness and reduce emissions, solvent-assisted SAGD techniques have been explored. Expanding Solvent-SAGD (ES-SAGD) involves co-injecting light hydrocarbons like propane or butane with steam to enhance oil viscosity reduction. This approach lowers the steam–oil ratio by combining solvent dissolution effects with thermal effects. However, the high cost of solvents, particularly butane, challenges its commercial feasibility. Propane is cheaper but less effective, while butane improves performance but remains expensive. This research aims to optimize ES-SAGD by using a propane–butane mixture to achieve efficient performance at a lower cost than pure butane. A linear sand pack is used to evaluate different propane/butane compositions, maintaining constant operational conditions and a solvent concentration of 15 vol.%. Temperature monitoring provides insights into steam chamber growth. Results show that solvent injection significantly enhances ES-SAGD performance compared to conventional SAGD. Performance improves with increasing butane concentration, up to 80% butane in the C3–C4 mixture at the test pressure and ambient temperature. Propane alone results in the lowest system temperature, while conventional SAGD reaches the highest temperature. These findings highlight the potential of optimized solvent mixtures to improve ES-SAGD efficiency while reducing costs and GHG emissions.