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Ultrasonography (USG) has become an invaluable tool in the assessment of neurocritical patients in the operating theaters and critical care units. Due to its easy availability, reliability, safety, and repeatability, neuro-intensivists and neuro-anesthesiologists utilize USG to make a diagnosis, assess prognosis, and decide upon treatment. In neurocritical care units, USG has myriad indications for use, both systemic and neurologic. The neurological indications include the assessment of stroke, vasospasm, traumatic brain injury, brain death, acute brain damage, optic nerve sheath diameter, and pupillary reflexes to name a few. The systemic indications range from assessment of cardio-pulmonary function and intravascular volume status to detection of deep venous thromboses, vocal cord assessment in intubated patients, placement of central venous catheters, and percutaneous tracheostomy. In this narrative review, we iterate the clinical applications of USG in neuroanesthesia and neurocritical care, which we penned after searching relevant databases in PubMed, Medline, Ovid, and Google Scholar by using terms such as 'applications of transcranial Doppler', 'optic nerve sheath diameter', 'USG applications in the critical care unit', and so on. Our search database includes several research papers, neurocritical care books, review articles, and scientific databases. This article reviews various applications of USG in neuroanesthesia, neurosurgery, and neurocritical care. [ABSTRACT FROM AUTHOR]

The aim of this paper is to discuss the significance of the intersectionality framework for addressing prejudices, racism and inequalities in nursing education and clinical learning environments. Discrimination and racism against nursing students and educators based on their gender, ethnicity, race and social identities is well-documented in the nursing literature. Despite documented discrimination and incivility based on intersectional factors, it is reported that often nurse educators show limited interest in the culture, diverse experiences and values of nursing students with culturally and linguistically diverse backgrounds. Discussion paper The discussion was based on contemporary literature about intersectionality, discrimination and racism in nursing. We completed a cursory search of literature in nursing education journal and selected nursing and health science databases. This was not a formal literature review. Using a fictional example, the application of intersectionality to address inequalities in educational settings is illustrated. Intersectionality is an invaluable tool for examining interwoven power relations and power struggles arising from racial, gender, ethnic, religious and sexuality and disability-related differences. Nurse educators, students and leaders should be more cognizant of their preconceived views, sociocultural stereotypes and varied forms of sociocultural oppression affecting their interactions with each other in clinical learning environments. Incorporating intersectionality can address prejudices, racism and inequalities arising due to sociocultural, ethnic, power-related and intergenerational issues among educators, students and other personnel involved in creating clinical learning environments. [ABSTRACT FROM AUTHOR]

Accurate 3D tracking of hand and fingers movements poses significant challenges in computer vision. The potential applications span across multiple domains, including human–computer interaction, virtual reality, industry, and medicine. While gesture recognition has achieved remarkable accuracy, quantifying fine movements remains a hurdle, particularly in clinical applications where the assessment of hand dysfunctions and rehabilitation training outcomes necessitate precise measurements. Several novel and lightweight frameworks based on Deep Learning have emerged to address this issue; however, their performance in accurately and reliably measuring finger movements requires validation against well-established gold standard systems. In this paper, the aim is to validate the hand-tracking framework implemented by Google MediaPipe Hand (GMH) and an innovative enhanced version, GMH-D, that exploits the depth estimation of an RGB-Depth camera to achieve more accurate tracking of 3D movements. Three dynamic exercises commonly administered by clinicians to assess hand dysfunctions, namely hand opening–closing, single finger tapping and multiple finger tapping are considered. Results demonstrate high temporal and spectral consistency of both frameworks with the gold standard. However, the enhanced GMH-D framework exhibits superior accuracy in spatial measurements compared to the baseline GMH, for both slow and fast movements. Overall, our study contributes to the advancement of hand tracking technology, and the establishment of a validation procedure as a good-practice to prove efficacy of deep-learning-based hand-tracking. Moreover, it proves that GMH-D is a reliable framework for assessing 3D hand movements in clinical applications. • Hands are vital for carrying out daily-life tasks without external aid. • Measuring objectively hand dexterity can support new digital health treatments. • Deep Learning allows for video-based hand tracking of fine motor tasks. • We validate accuracy of Google Mediapipe Hand and GMH-D against motion capture. • Both frameworks achieve good-to-high spatial and temporal hand tracking performance. [ABSTRACT FROM AUTHOR]

Recent trends and current market analysis suggest demand for herbal formulations, including herbal creams, is increasing over synthetic creams due to their lower toxicity and fewer side effects, followed by more skin nourishment capability, according to several published studies. Moreover, published literature lacks any updated, compiled explanatory review on herbal creams. Therefore, we designed the present communication to accomplish the current requirement. The major objective of the present communication is to provide the clinical data-based evidence and state of the art adopted for the formulation and development of herbal cream, including its composition and manufacturing processes, to establish therapeutic effectiveness. Both online and offline literature searches were performed and screened. The publications accounted for in vitro and clinical studies, followed by various preparation methods, classification and manufacturing process were included. There were a total of 150 papers finally selected and reviewed in the present communications addressing concerns related to herbal cream from 1995 to July 2023. It was established that herbal creams can be produced using a variety of techniques and methods. Majorly anti-inflammatory, dermatitis, anti-trauma, anti-aging, anti-acne, and vulvovaginal herbal creams were enlisted in terms of different clinical and therapeutic application. The evidence from the present systematic review suggests that more human clinical trials with larger sample sizes are needed in order to achieve higher accuracy in terms of the safety and efficacy of herbal creams. [Display omitted] • Composition, preparation and classification of herbal cream. • Manufacturing of herbal cream. • Clinical and therapeutic application of herbal cream from 1995 to 2023. • Regulations and the future perspective of herbal creams. [ABSTRACT FROM AUTHOR]

The ability to automatically segment anatomical targets on medical images is crucial for clinical diagnosis and interventional therapy. However, supervised learning methods often require a large number of pixel-wise labels that are difficult to obtain. This paper proposes a weakly supervised glottis segmentation (WSGS) method for training end-to-end neural networks using only point annotations as training labels. This method functions by iteratively generating pseudo-labels and training the segmentation network. An automatic seeded region growing (ASRG) algorithm is introduced to generate quality pseudo labels to diffuse point annotations based on network prediction and image features. Additionally, a novel loss function based on the structural similarity index measure (SSIM) is designed to enhance boundary segmentation. Using the trained network as its core, a glottis state monitor is developed to detect the motion behavior of the glottis and assist the anesthesiologist. Finally, the performance of the proposed approach was evaluated on two datasets, achieving an average mIoU and accuracy of 82.7% and 91.3%. The proposed monitor was demonstrated to be effective, which holds significance in clinical applications. • Weakly supervised glottis segmentation method only with point annotations. • Geometry-constrained diffusion strategy for quality pseudo-label generation. • Development of a glottis state monitor to detect the motion behavior of the glottis. [ABSTRACT FROM AUTHOR]