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Objective: To examine the medical students' awareness of laparoscopic surgery as well as assess the perceived importance of laparoscopic simulation training, and its impact on students' confidence, career aspirations, proficiency, spatial skills, and physical tolerance.

Design: Descriptive and comparative study using pre- and post-training assessments.

Setting: Simulation training sessions centred on laparoscopic surgery techniques.

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MicroRNAs (miRNAs) and transfer RNA-derived stress-induced RNAs (tiRNAs) have emerged as crucial players in the post-transcriptional regulation of gene expression in various cellular processes, including immunity and host defense against infections. In recent years, increasing evidence has highlighted their complex role in influencing the host response during viral and bacterial infections. miRNAs have been shown to play multiple roles in host-pathogen interaction like TLR activation and altered disease virulence during bacterial infections.

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Article Synopsis
  • The negative symptoms of schizophrenia, like lack of emotion and motivation, are hard to treat and significantly impact daily functioning.
  • This review highlights current research on treatment options for these symptoms, categorizing them into different types and evaluating various assessment scales.
  • Although no treatments are conclusively proven as the best for these symptoms, some off-label and investigational medications show promise, including cariprazine and memantine, and further research is needed to explore new therapeutic possibilities.
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A real-time approach for surgical activity recognition and prediction based on transformer models in robot-assisted surgery.

Int J Comput Assist Radiol Surg

January 2025

Advanced Medical Devices Laboratory, Kyushu University, Nishi-ku, Fukuoka, 819-0382, Japan.

Purpose: This paper presents a deep learning approach to recognize and predict surgical activity in robot-assisted minimally invasive surgery (RAMIS). Our primary objective is to deploy the developed model for implementing a real-time surgical risk monitoring system within the realm of RAMIS.

Methods: We propose a modified Transformer model with the architecture comprising no positional encoding, 5 fully connected layers, 1 encoder, and 3 decoders.

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Article Synopsis
  • Understanding the associations between traits and microbial composition is critical for microbiome research, but machine learning models often struggle due to the data's high-dimensional, compositional, and imbalanced nature.
  • To tackle these challenges, a new data augmentation method called PhyloMix has been developed, which uses phylogenetic relationships to generate synthetic microbial samples that enhance model performance.
  • PhyloMix significantly outperforms other data augmentation techniques and is effective in both supervised learning and contrastive representation learning, demonstrating its broad applicability in microbiome studies.
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