Browse Articles

Clinical Efficacy and Safety of Ibutilide in Cardioversion of Atrial Fibrillation or Flutter in Indian Patients: A Multicenter Study

Effectiveness of Balanced Electrolyte Solution vs Normal Saline in the Resuscitation of Adult Patients with Diabetic Ketoacidosis: An Updated Systematic Review and Meta-analysis

Author Response: Continuous Infusion of Propofol or Dexmedetomidine should not be the First Choice to Prevent Postoperative Delirium in Patients after Hip Fracture

Difficult Airway: Is this the Time to Focus on Point-of-care Ultrasonography?

Evaluation of Point-of-care Ultrasound of Airway to Predict Difficult Laryngoscopy and Intubation in Intensive Care Unit Patients

In Light of the LANDI-SEP Trial: New Evidence or Double Jeopardy?

Risk Factors for Mortality after Out-of-hospital Resuscitation are More Diverse than Assumed

Epidemiology of Neurotrauma in Pediatric Intensive Care Unit: A Single-center Experience of 10 Years

Pediatric Neurotrauma: Closing the Gaps

Author Response: Insights into Immunomodulatory Therapy for Sepsis

Enhanced Surgical Recovery Nursing Program: A Multidisciplinary Approach to Optimize Postoperative Patient Recovery

Continuous Infusion of Propofol or Dexmedetomidine should not be the First Choice to Prevent Postoperative Delirium after Hip Fracture

ARDS Ventilation, The Man Behind the Evolution

Intravenous Fluid Prescription in Diabetic Ketoacidosis: Where is the Evidence?

Critical Insights into Novel Immunomodulatory Therapy for Sepsis: Evaluating Promise Amidst Limitations

NEUROPROTECTIVE ACTIVITY OF ACALYPHA WILKESIANA IN MANAGEMENT OF NEURODEGENERATIVE DISEASES

To evaluate the neuroprotective activity of Ethanolic extract of leaves of Acalypha wilkesiana (EELAW) using Elevated Plus Maze in mice model. Neuroprotective activity in Alzheimer’s disease was assessed in mice by using Elevated Plus Maze. The present study was undertaken to establish the potential of EELAW as a memory enhancer. The elevated plus maze was employed to evaluate learning and memory parameters. Two doses (250 and 500 mg/kg, po.) of EELAW were administered for 8 successive days to mice. The 200 mg/kg dose of EELAW has significantly improved learning and memory in mice and also reversed the amnesia induced by diazepam (1 mg/kg ip) and scopolamine (0.4 mg/kg, ip). It is observed that scopolamine-induced amnesia was reversed, i.e. an enhancement/ improvement in memory was observed may be due to the facilitation of cholinergic transmission in the brain. Hence EELAW might prove to be a useful memory restorative agent in the treatment of dementia conditions seen in elderly persons. The underlying mechanism of action can be attributed to its antioxidant properties of the phytoconstituents present in the extract.

ACECLOFENAC AND DICLOFENAC: A REVIEW ON LIQUID CHROMATOGRAPHY TECHNIQUES

The literature entitles the various analytical techniques like, High Performance Liquid Chromatography, Liquid Chromatography-Mass spectrometry. In this literature we reviewed the various analytical, stability studies, impurity profiling and bio-analytical methods used for the estimation of selected drugs. This review gives the concise and collective information about the analytical validative parameters like Limit of detection (LOD), Limit of Quantification (LOQ), Standard Curve, Accuracy & Precision for the analysis of Aceclofenac and diclofenac alone or combination with other drugs. This review helps to carry out further analytical and Bioanalytical studies on the mentioned drugs.

Soft Continuum Robots Design for Supervision and Investigation

Researchers are developing a new class of continuum robots characterized by tip extension, significant length change, and directional control. In this article, we call these vine robots because of their similarity to plants in their growth trailing behavior. Due to their growth based movement, vine robots are well suited for navigation and exploration in cluttered environments. Until now, however, they have not been deployed outside the lab. There are three features that are key for successful deployment in the field. First is portability. Second is the ability to be guided over long enough distances to be useful for navigation. Third is intuitive human in the loop teleoportation, which enables movement in unknown and dynamic environments. We present a vine robot system that is teleoperated using a custom designed flexible joystick and camera system, can extend long enough for use in navigation tasks, and is portable for use in the field. We report on the deployment of this system in two scenarios: completion of a soft robot navigation competition and exploration of an archaeological site. The competition course required movement over uneven terrain, past unstable obstacles, and through a small aperture. The archeological site required movement over rocks and through horizontal and vertical turns. The robot tip successfully moved past the obstacles and through the tunnels, demonstrating the capability of vine robots to achieve navigation and exploration tasks in the field.

Sustainable Solution in Solid Waste Management

Managing solid waste (SWM) has become an increasingly urgent issue in both urban and rural areas around the globe. The rapid growth of cities, coupled with population expansion and industrialization, has led to a dramatic increase in waste generation. This, in turn, results in environmental damage, health risks, and in efficient resource management. This paper explores sustainable methods for addressing solid waste management, with a focus on minimizing waste generation, enhancing recycling efforts, recovering resources, and integrating circular economy practices. Through the examination of case studies, innovative waste treatment technologies, and community-oriented strategies, the paper discusses solutions such as source separation, composting, waste-to-energy processes, and the role of policy in supporting lasting change. Special attention is given to the potential for renewable energy production from waste, the use of eco-friendly materials, and the promotion of public awareness to encourage responsible consumption. By evaluating the economic, environmental, and social impacts of both current and emerging waste management techniques, the paper underscores the importance of a holistic and integrated approach. Recommendations include reinforcing regulations, improving waste management infrastructure, and empowering communities to take active roles in waste reduction and recycling. Achieving effective SWM solutions is vital for Long term environmental preservation, resource efficiency, and enhanced societal well-being.

Handwritten Digit Classification Using CNN with MNIST Dataset

Classifying handwritten digits is a crucial issue in computer vision and machine learning, and it serves as the foundation for many real-world applications like automated form processing, digitalizing postal addresses, and financial systems. A standard for assessing machine learning algorithms is the MNIST dataset, which consists of 70,000 grayscale pictures of handwritten numbers (0–9), with 60,000 for training and 10,000 for testing. A key problem in computer vision and machine learning is the classification of handwritten numbers, which forms the basis of numerous practical applications such as automated form processing, digitalizing postal addresses, and financial systems. The MNIST dataset, which comprises 70,000 grayscale images of handwritten numbers (0–9)—60,000 for training and 10,000 for testing—is a benchmark for evaluating machine learning methods. The model is trained using an adaptive optimizer like Adam, and strategies like dropout are used to avoid overfitting in order to increase the model's robustness and generalization. The categorical cross-entropy loss function is minimized during the training phase, and performance is assessed using measures like accuracy. To improve the model's ability to adapt to a variety of handwriting styles, data preprocessing techniques such as normalization and data augmentation are used. Tests show that the CNN-based model outperforms conventional machine learning techniques by achieving high classification accuracy. This achievement demonstrates how effective deep learning is at picture identification tasks. This study could be expanded by using transfer learning for more complicated datasets, streamlining the architecture for quicker inference, or incorporating the model into real-time handwritten digit recognition systems. This study advances automation in handwriting recognition and lays the groundwork for tackling increasingly difficult problems in computer vision by offering a scalable and effective solution.