Surgical treatment of malignancies in the oral cavity (mandible, tongue, floor of the mouth, alveolus, buccal sulcus) often results in an unfavourable anatomic condition for prosthodontic rehabilitation. Hence, maxillofacial prosthetic rehabilitation becomes a mightier task when resection is accompanied by radiation therapy. In selected cases, implant therapy comes to rescue. The following report throws light on the case of prosthetic rehabilitation of a patient who underwent right marginal mandibulectomy and right partial glossectomy, with the aid of a single implant, semi precision attachment and magnet supported partial denture.
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http://dx.doi.org/10.7860/JCDR/2015/13749.6542 | DOI Listing |
Cureus
December 2024
Department of Microbiology, All India Institute of Medical Sciences, New Delhi, New Delhi, IND.
Background: Tick-borne diseases (TBDs) play a crucial role in human morbidity and mortality, as ticks are highly effective in spreading diseases by transmitting harmful pathogens to humans and animals. The last few decades have seen an increase in the number of recognized tick-borne pathogens and the incidence of TBD worldwide. Several of these diseases are ubiquitous in India.
View Article and Find Full Text PDFBr J Ophthalmol
January 2025
Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
Background/aims: Large language models (LLMs) have substantial potential to enhance the efficiency of academic research. The accuracy and performance of LLMs in a systematic review, a core part of evidence building, has yet to be studied in detail.
Methods: We introduced two LLM-based approaches of systematic review: an LLM-enabled fully automated approach (LLM-FA) utilising three different GPT-4 plugins (Consensus GPT, Scholar GPT and GPT web browsing modes) and an LLM-facilitated semi-automated approach (LLM-SA) using GPT4's Application Programming Interface (API).
Talanta
January 2025
State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai District, Tianjin, 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin, 301617, PR China. Electronic address:
Metabolites identification is the major bottleneck in untargeted LC-MS metabolomics, primarily due to the limited availability of MS information for most detected metabolites in data dependent acquisition (DDA) mode. To solve this problem, we have integrated the iterative, interval, and segmented window acquisition concepts to develop an innovative non-fixed segmented window interval data dependency acquisition (NFSWI-DDA) mode, which achieves comparable MS coverage to data independent acquisition (DIA) mode. This acquisition strategy harnesses the strengths of both DDA and DIA, which could provide extensive coverage and excellent reproducibility of MS spectra.
View Article and Find Full Text PDFPhys Med Biol
January 2025
School of Software Engineering, Xi'an Jiaotong University, Xi 'an Jiaotong University Innovation Port, Xi 'an, Shaanxi Province, Xi'an, Shaanxi, 710049, CHINA.
Deformable registration aims to achieve nonlinear alignment of image space by estimating a dense displacement field. It is commonly used as a preprocessing step in clinical and image analysis applications, such as surgical planning, diagnostic assistance, and surgical navigation. We aim to overcome these challenges: Deep learning-based registration methods often struggle with complex displacements and lack effective interaction between global and local feature information.
View Article and Find Full Text PDFSci Rep
January 2025
College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong, 030800, China.
To address the challenges of unbalanced class labels with varying maturity levels of tomato fruits and low recognition accuracy for both fruits and stems in intelligent harvesting, we propose the YOLOX-SE-GIoU model for identifying tomato fruit maturity and stems. The SE focus module was incorporated into YOLOX to improve the identification accuracy, addressing the imbalance in the number of tomato fruits and stems. Additionally, we optimized the loss function to GIoU loss to minimize discrepancies across different scales of fruits and stems.
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