Confocal Laser Microscopy for in vivo Intraoperative Application: Diagnostic Accuracy of Investigator and Machine Learning Strategies

<b><i>Background:</i></b> Confocal laser microscopy (CLM) is one of the optical techniques that are promising methods of intraoperative in vivo real-time tissue examination based on tissue fluorescence. However, surgeons might struggle interpreting CLM images intraoperatively due to different tissue characteristics of different tissue pathologies in clinical reality. Deep learning techniques enable fast and consistent image analysis and might support intraoperative image interpretation. The objective of this study was to analyze the diagnostic accuracy of newly trained observers in the evaluation of normal colon and peritoneal tissue and colon cancer and metastasis, respectively, and to compare it with that of convolutional neural networks (CNNs). <b><i>Methods:</i></b> Two hundred representative CLM images of the normal and malignant colon and peritoneal tissue were evaluated by newly trained observers (surgeons and pathologists) and CNNs (VGG-16 and Densenet121), respectively, based on tissue dignity. The primary endpoint was the correct detection of the normal and cancer/metastasis tissue measured by sensitivity and specificity of both groups. Additionally, positive predictive values (PPVs) and negative predictive values (NPVs) were calculated for the newly trained observer group. The interobserver variability of dignity evaluation was calculated using kappa statistic. The F1-score and area under the curve (AUC) were used to evaluate the performance of image recognition of the CNNs’ training scenarios. <b><i>Results:</i></b> Sensitivity and specificity ranged between 0.55 and 1.0 (pathologists: 0.66–0.97; surgeons: 0.55–1.0) and between 0.65 and 0.96 (pathologists: 0.68–0.93; surgeons: 0.65–0.96), respectively. PPVs were 0.75 and 0.90 in the pathologists’ group and 0.73–0.96 in the surgeons’ group, respectively. NPVs were 0.73 and 0.96 for pathologists’ and between 0.66 and 1.00 for surgeons’ tissue analysis. The overall interobserver variability was 0.54. Depending on the training scenario, cancer/metastasis tissue was classified with an AUC of 0.77–0.88 by VGG-16 and 0.85–0.89 by Densenet121. Transfer learning improved performance over training from scratch. <b><i>Conclusions:</i></b> Newly trained investigators are able to learn CLM images features and interpretation rapidly, regardless of their clinical experience. Heterogeneity in tissue diagnosis and a moderate interobserver variability reflect the clinical reality more realistic. CNNs provide comparable diagnostic results as clinical observers and could improve surgeons’ intraoperative tissue assessment.

Exploring the Complementarity of Pancreatic Ductal Adenocarcinoma Preclinical Models

Purpose: Compare pancreatic ductal adenocarcinoma (PDAC), preclinical models, by their transcriptome and drug response landscapes to evaluate their complementarity. Experimental Design: Three paired PDAC preclinical models—patient-derived xenografts (PDX), xenograft-derived pancreatic organoids (XDPO) and xenograft-derived primary cell cultures (XDPCC)—were derived from 20 patients and analyzed at the transcriptomic and chemosensitivity level. Transcriptomic characterization was performed using the basal-like/classical subtyping and the PDAC molecular gradient (PAMG). Chemosensitivity for gemcitabine, irinotecan, 5-fluorouracil and oxaliplatin was established and the associated biological pathways were determined using independent component analysis (ICA) on the transcriptome of each model. The selection criteria used to identify the different components was the chemosensitivity score (CSS) found for each drug in each model. Results: PDX was the most dispersed model whereas XDPO and XDPCC were mainly classical and basal-like, respectively. Chemosensitivity scoring determines that PDX and XDPO display a positive correlation for three out of four drugs tested, whereas PDX and XDPCC did not correlate. No match was observed for each tumor chemosensitivity in the different models. Finally, pathway analysis shows a significant association between PDX and XDPO for the chemosensitivity-associated pathways and PDX and XDPCC for the chemoresistance-associated pathways. Conclusions: Each PDAC preclinical model possesses a unique basal-like/classical transcriptomic phenotype that strongly influences their global chemosensitivity. Each preclinical model is imperfect but complementary, suggesting that a more representative approach of the clinical reality could be obtained by combining them. Translational Relevance: The identification of molecular signatures that underpin drug sensitivity to chemotherapy in PDAC remains clinically challenging. Importantly, the vast majority of studies using preclinical in vivo and in vitro models fail when transferred to patients in a clinical setting despite initially promising results. This study presents for the first time a comparison between three preclinical models directly derived from the same patients. We show that their applicability to preclinical studies should be considered with a complementary focus, avoiding tumor-based direct extrapolations, which might generate misleading conclusions and consequently the overlook of clinically relevant features.

Locomotive syndrome: from paradigms to clinical reality

Locomotive syndrome is an unsatisfactory condition of patients over 60 years of age who need or may require outside help in the near future due to functional deterioration of the musculoskeletal system, including pathology of bone tissue, joints, muscles and nervous tissue. In real clinical practice, one often has to deal with the following manifestations of locomotive syndrome: osteoarthritis, sarcopenia, balance disorders, chronic musculoskeletal pain. Today, there is a clear understanding that drug therapy should be long-term, include comprehensive support for muscle tissue, balance training, and mandatory cognitive-behavioral therapy. Maximum safety of long-term drug therapy can be ensured by the use of vital micronutrients, which include highly purified forms of chondroitin sulfate and glucosamine sulfate, which have a wide range of anti-inflammatory and regenerative effects.

Christian N Barnard: A Man Who Dared the First Human to Human Heart Transplant

Heart transplant had already developed by a group of US surgeons in the early 1950s, and it was the American pioneer Norman Shumway who validated the technical feasibility in a dog model at Stanford University in 1958. This milestone in medicine was the beginning of a huge race for numerous physicians and researchers to make this operation a clinical reality. Barnard was intrigued by the idea to perform heart transplantation at Groote Schuur Hospital and therefore made it a major focus in his department in the early 1960s. While Shumway and co-workers were further refining the surgical technique in these year, based on his extensive cardio-surgical experience, Barnard was already convinced about the technical feasibility and wanted to enter this new field of cardiac surgery. To accomplish his goal, he recognized that he had to learn more about immunosuppressive therapy and therefore he spent a few months in Richmond, VA, USA, to obtain this important knowledge for postoperative care. In 1966, when Shumway and colleagues announced that they would be ready for a first human patient, Barnard moved ahead of them on 3 December 1967, and performed the world’s first human-to-human heart transplantation.

Ethics simulation in nursing education: Nursing students' experiences

Background: Ethics stimulation in nursing education focuses on human, non-technical factors in a clinical reality. Simulation as a teaching method began in the 1930s with flight simulators. In the beginning of the 1990s, simulations developed further in tandem with other technological and digital inventions, including touchscreen and three-dimensional anatomical models. Medical science first used simulation as a pedagogical teaching tool. In nursing education, simulation has been used for approximately a hundred years. Teaching has mainly focused on medical-technical, patient-specific interventions and their management. Objective: The objective of this study was, from a caring science didactic perspective, to deepen the understanding of ethics simulation in nursing education. Design: Qualitative design and explorative, descriptive and hermeneutic approach of an inductive character. Methods: Semi-structured face-to-face interviews in 2016–2017 with six Norwegian nursing students who were encouraged to narrate about their experiences of ethics simulation in nursing education. Ethical considerations: Informed consent was obtained from the participants. Anonymity and confidentiality regarding data material were guaranteed. Results: Interpretation of the nursing students’ narratives resulted in the following meaning units: ethical being and ethos, nursing students’ formation process, bridge-building between theory and clinical practice, and teacher and ethics simulation. Conclusion: Through ethics simulation, nursing students can obtain an increased knowledge and a sense of being able to handle difficult ethical situations. Nursing students’ values, moral actions and ethical value base offer a positive point of departure, for both theoretical and practical ethics teaching, and an awareness of the unique human being, the patient, in clinical reality. The implementation of ethics simulation needs more attention in nursing education.

PharmaKU: A Web-Based Tool Aimed at Improving Outreach and Clinical Utility of Pharmacogenomics

With the tremendous advancements in genome sequencing technology in the field of pharmacogenomics, data have to be made accessible to be more efficiently utilized by broader clinical disciplines. Physicians who require the drug–genome interactome information, have been challenged by the complicated pharmacogenomic star-based classification system. We present here an end-to-end web-based pharmacogenomics tool, PharmaKU, which has a comprehensive interface easy-to-use interface. PharmaKU can help to overcome several hurdles posed by previous pharmacogenomics tools, including input in hg38 format only, while hg19/GRCh37 is now the most popular reference genome assembly among clinicians and geneticists, as well as the lack of clinical recommendations and other pertinent dosage-related information. This tool extracts genetic variants from nine well-annotated pharmacogenes (for which diplotype to phenotype information is available) from whole genome variant files and uses Stargazer software to assign diplotypes and apply prescribing recommendations from pharmacogenomic resources. The tool is wrapped with a user-friendly web interface, which allows for choosing hg19 or hg38 as the reference genome version and reports results as a comprehensive PDF document. PharmaKU is anticipated to enable bench to bedside implementation of pharmacogenomics knowledge by bringing precision medicine closer to a clinical reality.