Laparoscopic versus robotic adrenalectomy in severely obese patients.

BACKGROUND: Over the last 20 years, the prevalence of severe obesity (body mass index ≥ 35 kg/m2) has almost doubled. This condition increases the challenge of laparoscopic adrenalectomy (LA) by creating problems with instrument reach, adequate exposure, and visualization. The aim was to compare perioperative outcomes of laparoscopic versus robotic adrenalectomy (RA) in severely obese patients. METHODS: This was an institutional review board-approved retrospective study. Prospectively collected clinical parameters of patients who underwent LA versus RA between 2000 and 2021 at a single center were compared using Mann-Whitney U, ANOVA, Chi-square, and multivariate regression analysis. Continuous data are expressed as median (interquartile range). RESULTS: For lateral transabdominal (LT) adrenalectomies, skin-to-skin operative time (OT) [164.5 (71.0) vs 198.8 (117.0) minutes, p = 0.006] and estimated blood loss [26.2 (15.0) vs 72.6 (50.0) ml, p = 0.010] were less in RA versus LA group, respectively. Positive margin rate, hospital stay and 90-day morbidity were similar between the groups (p = NS). For posterior retroperitoneal (PR) approach, operative time and perioperative outcomes were similar between LA and RA groups. Multivariate analysis demonstrated robotic versus laparoscopic technique (p = 0.006) to be an independent predictor of a shorter OT. CONCLUSION: There was a benefit of robotic over the laparoscopic LT adrenalectomy regarding OT and estimated blood loss. Although limited by the small sample size, there was no difference regarding perioperative outcomes between RA and LA performed through a PR approach.

A Visual Deep Learning Model to Localize Parathyroid-Specific Autofluorescence on Near-Infrared Imaging : Localization of Parathyroid Autofluorescence with Deep Learning.

BACKGROUND AND PURPOSE: Parathyroid glands may be detected by their autofluorescence on near-infrared imaging. Nevertheless, recognition of parathyroid-specific autofluorescence requires a learning curve, with other unrelated bright signals causing confusion. The aim of this study was to find out whether machine learning could be used to facilitate identification of parathyroid-specific autofluorescence signals on intraoperative near-infrared images in patients undergoing thyroidectomy and parathyroidectomy procedures. METHODS: In an institutional review board-approved study, intraoperative near-infrared images of patients who underwent thyroidectomy and/or parathyroidectomy procedures within a year were used to develop an artificial intelligence model. Parathyroid-specific autofluorescence signals were marked with rectangles on intraoperative near-infrared still images and used for training a deep learning model. A randomly chosen 80% of the data were used for training, 10% for testing, and 10% for validation. Precision and recall of the model were calculated. RESULTS: A total of 466 intraoperative near-infrared images of 197 patients who underwent thyroidectomy and/or parathyroidectomy procedures were analyzed. Procedures included total thyroidectomy in 54 patients, thyroid lobectomy in 24 patients, parathyroidectomy in 108 patients, and combined thyroidectomy and parathyroidectomy procedures in 11 patients. The overall recall and precision of the model were 90.5 and 95.7%, respectively. CONCLUSIONS: To our knowledge, this is the first study that describes the use of artificial intelligence tools to assist in recognition of parathyroid-specific autofluorescence signals on near-infrared imaging. The model developed may have utility in facilitating training and decreasing the learning curve associated with the use of this technology.

MicroRNA-135a-3p regulates angiogenesis and tissue repair by targeting p38 signaling in endothelial cells.

Angiogenesis is a critical process in repair of tissue injury that is regulated by a delicate balance between pro- and antiangiogenic factors. In disease states associated with impaired angiogenesis, we identified that miR-135a-3p is rapidly induced and serves as an antiangiogenic microRNA (miRNA) by targeting endothelial cell (EC) p38 signaling in vitro and in vivo. MiR-135a-3p overexpression significantly inhibited EC proliferation, migration, and network tube formation in matrigel, whereas miR-135-3p neutralization had the opposite effects. Mechanistic studies using transcriptomic profiling, bioinformatics, 3'-UTR reporter and miRNA ribonucleoprotein complex -immunoprecipitation assays, and small interfering RNA dependency studies revealed that miR-135a-3p inhibits the p38 signaling pathway in ECs by targeting huntingtin-interacting protein 1 (HIP1). Local delivery of miR-135a-3p inhibitors to wounds of diabetic db/db mice markedly increased angiogenesis, granulation tissue thickness, and wound closure rates, whereas local delivery of miR-135a-3p mimics impaired these effects. Finally, through gain- and loss-of-function studies in human skin organoids as a model of tissue injury, we demonstrated that miR-135a-3p potently modulated p38 signaling and angiogenesis in response to VEGF stimulation by targeting HIP1. These findings establish miR-135a-3p as a pivotal regulator of pathophysiological angiogenesis and tissue repair by targeting a VEGF-HIP1-p38K signaling axis, providing new targets for angiogenic therapy to promote tissue repair.-Icli, B., Wu, W., Ozdemir, D., Li, H., Haemmig, S., Liu, X., Giatsidis, G., Cheng, H. S., Avci, S. N., Kurt, M., Lee, N., Guimaraes, R. B., Manica, A., Marchini, J. F., Rynning, S. E., Risnes, I., Hollan, I., Croce, K., Orgill, D. P., Feinberg, M. W. MicroRNA-135a-3p regulates angiogenesis and tissue repair by targeting p38 signaling in endothelial cells.

MicroRNA-615-5p Regulates Angiogenesis and Tissue Repair by Targeting AKT/eNOS (Protein Kinase B/Endothelial Nitric Oxide Synthase) Signaling in Endothelial Cells.

Objective- In response to tissue injury, the appropriate progression of events in angiogenesis is controlled by a careful balance between pro and antiangiogenic factors. We aimed to identify and characterize microRNAs that regulate angiogenesis in response to tissue injury. Approach and Results- We show that in response to tissue injury, microRNA-615-5p (miR-615-5p) is rapidly induced and serves as an antiangiogenic microRNA by targeting endothelial cell VEGF (vascular endothelial growth factor)-AKT (protein kinase B)/eNOS (endothelial nitric oxide synthase) signaling in vitro and in vivo. MiR-615-5p expression is increased in wounds of diabetic db/db mice, in plasma of human subjects with acute coronary syndromes, and in plasma and skin of human subjects with diabetes mellitus. Ectopic expression of miR-615-5p markedly inhibited endothelial cell proliferation, migration, network tube formation in Matrigel, and the release of nitric oxide, whereas miR-615-5p neutralization had the opposite effects. Mechanistic studies using transcriptomic profiling, bioinformatics, 3' untranslated region reporter and microribonucleoprotein immunoprecipitation assays, and small interfering RNA dependency studies demonstrate that miR-615-5p inhibits the VEGF-AKT/eNOS signaling pathway in endothelial cells by targeting IGF2 (insulin-like growth factor 2) and RASSF2 (Ras-associating domain family member 2). Local delivery of miR-615-5p inhibitors, markedly increased angiogenesis, granulation tissue thickness, and wound closure rates in db/db mice, whereas miR-615-5p mimics impaired these effects. Systemic miR-615-5p neutralization improved skeletal muscle perfusion and angiogenesis after hindlimb ischemia in db/db mice. Finally, modulation of miR-615-5p expression dynamically regulated VEGF-induced AKT signaling and angiogenesis in human skin organoids as a model of tissue injury. Conclusions- These findings establish miR-615-5p as an inhibitor of VEGF-AKT/eNOS-mediated endothelial cell angiogenic responses and that manipulating miR-615-5p expression could provide a new target for angiogenic therapy in response to tissue injury. Visual Overview- An online visual overview is available for this article.

The use of artificial intelligence to detect parathyroid tissue on ex vivo specimens during thyroidectomy and parathyroidectomy procedures using near-infrared autofluorescence signals.

BACKGROUND: In thyroidectomy and parathyroidectomy procedures, diagnostic dilemmas related to whether an index tissue is of parathyroid or nonparathyroid origin frequently arise. Current options of frozen section and parathyroid aspiration are time-consuming. Parathyroid glands appear brighter than surrounding tissues on near-infrared autofluorescence imaging. The aim of this study was to develop an artificial intelligence model differentiating parathyroid tissue on surgical specimens based on near-infrared autofluorescence. METHODS: With institutional review board approval, an image library of ex vivo specimens obtained in thyroidectomy and parathyroidectomy procedures was created between November 2019 and April 2023 at a single academic center. Ex vivo autofluorescence images of surgically removed parathyroid glands, thyroid glands, lymph nodes, and thymic tissue were uploaded into an artificial intelligence platform. Two different models were trained, with the first model using autofluorescence images from all specimens, including thyroid, and the second model excluding thyroid, to prevent the effect of specimen size on the results. Deep-learning models were trained to detect autofluorescence signals specific to parathyroid glands. Randomly chosen 80% of data were used for training, 10% for validation, and 10% for testing. Recall, precision, and area under the curve of models were calculated. RESULTS: Surgical procedures included 377 parathyroidectomies, 239 total thyroidectomies, 97 thyroid lobectomies, and 32 central neck dissections. For the development of the model, 1151 images from a total of 678 procedures were used. The dataset comprised 648 parathyroid, 379 thyroid, 104 lymph node, and 20 thymic tissue images. The overall precision, recall, and area under the curve of the model to detect parathyroid tissue were 96.5%, 96.5%, and 0.985, respectively. False negatives were related to dark and large parathyroid glands. CONCLUSION: The visual deep-learning model developed to identify parathyroid tissue in ex vivo specimens during thyroidectomy and parathyroidectomy demonstrated a high sensitivity and positive predictive value. This suggests potential utility of near-infrared autofluorescence imaging to improve intraoperative efficiency by reducing the need for frozen sections and parathyroid hormone aspirations to confirm parathyroid tissue.

Comparison of incidental parathyroid tissue detection rates on pathology after total thyroidectomy performed with or without near-infrared autofluorescence imaging.

BACKGROUND: Near-infrared autofluorescence imaging is an adjunct to parathyroid identification. As it does not show perfusion, it is important to study its impact during thyroidectomy by measuring quantifiable data on parathyroid detection rather than function. The aim of this study was to compare incidental parathyroidectomy rates in patients undergoing total thyroidectomy with or without near-infrared autofluorescence. METHODS: Retrospective study of patients who underwent total thyroidectomy between 2014 and 2022 at one center. Clinical parameters, including rates of incidental parathyroid tissue on pathology reports, were compared between near-infrared autofluorescence and non-near-infrared autofluorescence groups. Near-infrared autofluorescence was used to guide dissection (identification) and/or to confirm tissue as parathyroid (confirmation). Statistical analysis was done with Wilcoxon rank sum test and χ2 analysis. RESULTS: There were 300 patients in the near-infrared autofluorescence and 750 patients in the non-near-infrared autofluorescence group. The rate of incidental parathyroid tissue detection on final pathology was 13.3% (n = 40) in the near-infrared autofluorescence and 23.2% (n = 174) in the non-near-infrared autofluorescence group (P < .001). The rate of incidental parathyroid tissue detected on pathology with near-infrared autofluorescence decreased when used for identification and confirmation of parathyroid tissue (30.0% to 13.4%, P < .001), but not when used for confirmation only (19.6% to 18.5%, P = .89). Impact of near-infra red autofluorescence in decreasing the rate of incidental parathyroid tissue was more profound for early (38.5% to 17.1%) versus mid-late career surgeons (20% to 13%). CONCLUSION: Our results suggest that the use of near-infrared autofluorescence may help decrease the rate of incidental parathyroid tissue detected on final pathology if used for both identification and confirmation of parathyroid glands during thyroidectomy.