Training in minimal access surgery has always been difficult in developing countries with limited resources, non availability of formal animal labs, inaffordability of conventional endotrainers and lack of trained endosurgeons to help the amateurs. It is always difficult to start a new procedure in such places where not only the patients but the orthodox surgical fraternity are reluctant to accept new ideas and newer trends in surgery. After thorough discussions with senior surgeons, the author (who was the only trained endosurgeon to begin with) developed a training policy to train the surgeons over a period of time through various exercises before allowing them to assist him in the actual surgeries. A homemade, inexpensive endotrainer was designed for these exercises. Audio-visual seminars were held in between the training sessions. This training module can be employed by other rural hospitals to improve the skills of surgeons who are new to the art of endosurgery.
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http://dx.doi.org/10.1258/td.2008.070359 | DOI Listing |
Alzheimers Dement
January 2025
Virginia Center on Aging, College of Health Professions, Virginia Commonwealth University, Richmond, Virginia, USA.
Introduction: The Virginia Memory Project (VMP) is a statewide epidemiological registry for Alzheimer's disease and related disorders (ADRD) and other neurodegenerative conditions. It aims to support dementia research, policy, and care by leveraging the Centers for Disease Control (CDC) Healthy Brain Initiative (HBI) Roadmap.
Methods: To capture comprehensive data, the VMP integrates self-enrollment and automatic enrollment using Virginia's All-Payer Claims Database (APCD).
Indian J Med Ethics
January 2025
Director Professor, Department of Physiology, University College of Medical Sciences, Delhi University, Delhi, INDIA.
Background: It is challenging to teach the complexity of the doctor-patient relationship through attitude, ethics, and communication (AETCOM) modules, particularly without being formally trained and especially to first-year medical students who do not interact directly with patients. The present study was undertaken to assess the effectiveness of trigger films (TFs) or short movie clips as a teaching-learning tool to train undergraduate medical students on various aspects of doctor-patient relationships.
Methods: Two modules on various aspects of the doctor-patient relationship were developed using TFs and written case studies and implemented on Phase Ⅰ medical students.
Cogn Neurodyn
December 2025
School of Mechatronical Engineering, Beijing Institute of Technology, No. 5 Zhongguancun South Street, Haidian District, Beijing, 100081 China.
Enhancing the accuracy of emotion recognition models through multimodal learning is a common approach. However, challenges such as insufficient modal feature learning in multimodal inference and scarcity of sample data continue to pose obstacles that need to be overcome. Therefore, we propose a novel adaptive lightweight multimodal efficient feature inference network (ALME-FIN).
View Article and Find Full Text PDFALTEX
January 2025
National Institutes of Health, National Institute for Environmental Health Sciences, DTT/NICEATM, Durham, NC, USA.
The integration of artificial intelligence (AI) into new approach methods (NAMs) for toxicology rep-resents a paradigm shift in chemical safety assessment. Harnessing AI appropriately has enormous potential to streamline validation efforts. This review explores the challenges, opportunities, and future directions for validating AI-based NAMs, highlighting their transformative potential while acknowledging the complexities involved in their implementation and acceptance.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, PR China; Shanghai Yangpu Mental Health Center, Shanghai, 200093, PR China. Electronic address:
Background And Objective: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models.
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