Coordinated Multiple Cadaver Use for Minimally Invasive Surgical Training

BackgroundThe human cadaver remains the gold standard for anatomic training and is highly useful when incorporated into minimally invasive surgical training programs. However, this valuable resource is often not used to its full potential due to a lack of multidisciplinary cooperation. Herein, we propose the coordinated multiple use of individual cadavers to better utilize anatomical resources and potentiate the availability of cadaver training.MethodsTwenty-two postgraduate surgeons participated in a robot-assisted surgical training course that utilized shared cadavers. All participants completed a Likert 4-scale satisfaction questionnaire after their training session. Cadaveric tissue quality and the quality of the training session related to this material were assessed.ResultsNine participants rated the quality of the cadaveric tissue as excellent, 7 as good, 5 as unsatisfactory, and 1 as poor. Overall, 72% of participants who operated on a previously used cadaver were satisfied with their training experience and did not perceive the previous use deleterious to their training.ConclusionThe coordinated use of cadavers, which allows for multiple cadaver use for different teaching sessions, is an excellent training method that increases availability of human anatomical material for minimally invasive surgical training

Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union

Semantic segmentation datasets often exhibit two types of imbalance: \textit{class imbalance}, where some classes appear more frequently than others and \textit{size imbalance}, where some objects occupy more pixels than others. This causes traditional evaluation metrics to be biased towards \textit{majority classes} (e.g. overall pixel-wise accuracy) and \textit{large objects} (e.g. mean pixel-wise accuracy and per-dataset mean intersection over union). To address these shortcomings, we propose the use of fine-grained mIoUs along with corresponding worst-case metrics, thereby offering a more holistic evaluation of segmentation techniques. These fine-grained metrics offer less bias towards large objects, richer statistical information, and valuable insights into model and dataset auditing. Furthermore, we undertake an extensive benchmark study, where we train and evaluate 15 modern neural networks with the proposed metrics on 12 diverse natural and aerial segmentation datasets. Our benchmark study highlights the necessity of not basing evaluations on a single metric and confirms that fine-grained mIoUs reduce the bias towards large objects. Moreover, we identify the crucial role played by architecture designs and loss functions, which lead to best practices in optimizing fine-grained metrics. The code is available at \href{https://github.com/zifuwanggg/JDTLosses}{https://github.com/zifuwanggg/JDTLosses}.Comment: NeurIPS 202

Immunological characterization of chromogranins A and B and secretogranin II in the bovine pancreatic islet

Antisera against chromogranin A and B and secretogranin II were used for analysing the bovine pancreas by immunoblotting and immunohistochemistry. All three antigens were found in extracts of fetal pancreas by one dimensional immunoblotting. A comparison with the soluble proteins of chromaffin granules revealed that in adrenal medulla and in pancreas antigens which migrated identically in electrophoresis were present. In immunohistochemistry, chromogranin A was found in all pancreatic endocrine cell types with the exception of most pancreatic polypeptide-(PP-) producing cells. For chromogranin B, only a faint immunostaining was obtained. For secretorgranin II, A-and B-cells were faintly positive, whereas the majority of PP-cells exhibited a strong immunostaining for this antigen. These results establish that chromogranins A and B and secretogranin II are present in the endocrine pancreas, but that they exhibit a distinct cellular localization

Evaluation of neuroendocrine markers in renal cell carcinoma

<p>Abstract</p> <p>Background</p> <p>The purpose of the study was to examine serotonin, CD56, neurone-specific enolase (NSE), chromogranin A and synaptophysin by immunohistochemistry in renal cell carcinomas (RCCs) with special emphasis on patient outcome.</p> <p>Methods</p> <p>We studied 152 patients with primary RCCs who underwent surgery for the removal of kidney tumours between 1990 and 1999. The mean follow-up was 90 months. The expression of neuroendocrine (NE) markers was determined by immunohistochemical staining using commercially available monoclonal antibodies. Results were correlated with patient age, clinical stage, Fuhrman grade and patient outcome.</p> <p>Results</p> <p>Eight percent of tumours were positive for serotonin, 18% for CD56 and 48% for NSE. Chromogranin A immunostaining was negative and only 1% of the tumours were synaptophysin immunopositive. The NSE immunopositivity was more common in clear cell RCCs than in other subtypes (<it>p </it>= 0.01). The other NE markers did not show any association with the histological subtype. Tumours with an immunopositivity for serotonin had a longer RCC-specific survival and tumours with an immunopositivity for CD56 and NSE had a shorter RCC-specific survival but the difference was not significant. There was no relationship between stage or Fuhrman grade and immunoreactivity for serotonin, CD56 and NSE.</p> <p>Conclusions</p> <p>Serotonin, CD56 and NSE but not synaptophysin and chromogranin A are expressed in RCCs. However, the prognostic potential of these markers remains obscure.</p

Chromogranin A, a significant prognostic factor in small cell lung cancer

Chromogranin A (CgA) is a protein present in neuroendocrine vesicles. Small cell lung cancer (SCLC) is considered a neuroendocrine tumour. It is possible to demonstrate CgA expression in SCLC by immunohistochemical methods. Since CgA is released to the circulation it might also work as a clinical tumour marker. We used a newly developed two-site enzyme-linked immunosorbent assay for CgA in plasma from 150 newly diagnosed patients with SCLC. Follow-up was for a minimum of 5 years. Thirty-seven per cent of the patients had elevated pretreatment values and the values were significantly related to stage of disease. Multivariable analysis by Cox's proportional hazard model including nine known prognostic factors disclosed performance status as the most influential prognostic factor followed by stage of disease, CgA and LDH. A simple prognostic index (PI) could be established based on these four pretreatment features. In this way the patients could be separated into three groups with significant different prognosis. The median survival and 95% confidence intervals for the three groups were as follows: 424 days (311–537), 360 days (261–459) and 174 days (105–243). © 1999 Cancer Research Campaig

Cellular therapies for treating pain associated with spinal cord injury

Spinal cord injury leads to immense disability and loss of quality of life in human with no satisfactory clinical cure. Cell-based or cell-related therapies have emerged as promising therapeutic potentials both in regeneration of spinal cord and mitigation of neuropathic pain due to spinal cord injury. This article reviews the various options and their latest developments with an update on their therapeutic potentials and clinical trialing

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.Comment: Shared first authors: Annika Reinke, Minu D. Tizabi; shared senior authors: Paul F. J\"ager, Lena Maier-Hei