Mechanical activation of TWIK-related potassium channel by nanoscopic movement and rapid second messenger signaling.

Rapid conversion of force into a biological signal enables living cells to respond to mechanical forces in their environment. The force is believed to initially affect the plasma membrane and then alter the behavior of membrane proteins. Phospholipase D2 (PLD2) is a mechanosensitive enzyme that is regulated by a structured membrane-lipid site comprised of cholesterol and saturated ganglioside (GM1). Here we show stretch activation of TWIK-related K+ channel (TREK-1) is mechanically evoked by PLD2 and spatial patterning involving ordered GM1 and 4,5-bisphosphate (PIP2) clusters in mammalian cells. First, mechanical force deforms the ordered lipids, which disrupts the interaction of PLD2 with the GM1 lipids and allows a complex of TREK-1 and PLD2 to associate with PIP2 clusters. The association with PIP2 activates the enzyme, which produces the second messenger phosphatidic acid (PA) that gates the channel. Co-expression of catalytically inactive PLD2 inhibits TREK-1 stretch currents in a biological membrane. Cellular uptake of cholesterol inhibits TREK-1 currents in culture and depletion of cholesterol from astrocytes releases TREK-1 from GM1 lipids in mouse brain. Depletion of the PLD2 ortholog in flies results in hypersensitivity to mechanical force. We conclude PLD2 mechanosensitivity combines with TREK-1 ion permeability to elicit a mechanically evoked response.

"Ouch!": you have just stabbed your little toe on the sharp corner of a coffee table. That painful sensation stems from nerve cells converting information about external forces into electric signals the brain can interpret. Increasingly, new evidence is suggesting that this process may be starting at fat-based structures within the membrane of these cells. The cell membrane is formed of two interconnected, flexible sheets of lipids in which embedded structures or molecules are free to move. This organisation allows the membrane to physically respond to external forces and, in turn, to set in motion chains of molecular events that help fine-tune how cells relay such information to the brain. For instance, an enzyme known as PLD2 is bound to lipid rafts ­ precisely arranged, rigid fatty 'clumps' in the membrane that are partly formed of cholesterol. PLD2 has also been shown to physically interact with and then activate the ion channel TREK-1, a membrane-based protein that helps to prevent nerve cells from relaying pain signals. However, the exact mechanism underpinning these interactions is difficult to study due to the nature and size of the molecules involved. To address this question, Petersen et al. combined a technology called super-resolution imaging with a new approach that allowed them to observe how membrane lipids respond to pressure and fluid shear. The experiments showed that mechanical forces disrupt the careful arrangement of lipid rafts, causing PLD2 and TREK-1 to be released. They can then move through the surrounding membrane where they reach a switch that turns on TREK-1. Further work revealed that the levels of cholesterol available to mouse cells directly influenced how the clumps could form and bind to PLD2, and in turn, dialled up and down the protective signal mediated by TREK-1. Overall, the study by Petersen et al. shows that the membrane of nerve cells can contain cholesterol-based 'fat sensors' that help to detect external forces and participate in pain regulation. By dissecting these processes, it may be possible to better understand and treat conditions such as diabetes and lupus, which are associated with both pain sensitivity and elevated levels of cholesterol in tissues.

Role of Hepatic Stellate and Liver Sinusoidal Endothelial Cells in a Human Primary Cell Three-Dimensional Model of Nonalcoholic Steatohepatitis.

Nonalcoholic steatohepatitis (NASH) is an inflammatory and fibrotic liver disease that has reached epidemic proportions and has no approved pharmacologic therapies. Research and drug development efforts are hampered by inadequate preclinical models. This research describes a three-dimensional bioprinted liver tissue model of NASH built using primary human hepatocytes and nonparenchymal liver cells (hepatic stellate cells, liver sinusoidal endothelial cells, and Kupffer cells) from either healthy or NASH donors. Three-dimensional tissues bioprinted with cells sourced from diseased patients showed a NASH phenotype, including fibrosis. More importantly, this NASH phenotype occurred without the addition of disease-inducing agents. Bioprinted tissues composed entirely of healthy cells exhibited significantly less evidence of disease. The role of individual cell types in driving the NASH phenotype was examined by producing chimeric bioprinted tissues composed of healthy cells together with the addition of one or more diseased nonparenchymal cell types. These experiments reveal a role for both hepatic stellate and liver sinusoidal endothelial cells in the disease process. This model represents a fully human system with potential to detect clinically active targets and eventually therapies.

High-throughput ultrasound neuromodulation in awake and freely behaving rats.

Transcranial ultrasound neuromodulation is a promising potential therapeutic tool for the noninvasive treatment of neuropsychiatric disorders. However, the expansive parameter space and difficulties in controlling for peripheral auditory effects make it challenging to identify ultrasound sequences and brain targets that may provide therapeutic efficacy. Careful preclinical investigations in clinically relevant behavioral models are critically needed to identify suitable brain targets and acoustic parameters. However, there is a lack of ultrasound devices allowing for multi-target experimental investigations in awake and unrestrained rodents. We developed a miniaturized 64-element ultrasound array that enables neurointerventional investigations with within-trial active control targets in freely behaving rats. We first characterized the acoustic field with measurements in free water and with transcranial propagation. We then confirmed in vivo that the array can target multiple brain regions via electronic steering, and verified that wearing the device does not cause significant impairments to animal motility. Finally, we demonstrated the performance of our system in a high-throughput neuromodulation experiment, where we found that ultrasound stimulation of the rat central medial thalamus, but not an active control target, promotes arousal and increases locomotor activity.

A cluster of neuropeptide S neurons regulates breathing and arousal.

Neuropeptide S (NPS) is a highly conserved peptide found in all tetrapods that functions in the brain to promote heightened arousal; however, the subpopulations mediating these phenomena remain unknown. We generated mice expressing Cre recombinase from the Nps gene locus (NpsCre) and examined populations of NPS+ neurons in the lateral parabrachial area (LPBA), the peri-locus coeruleus (peri-LC) region of the pons, and the dorsomedial thalamus (DMT). We performed brain-wide mapping of input and output regions of NPS+ clusters and characterized expression patterns of the NPS receptor 1 (NPSR1). While the activity of all three NPS+ subpopulations tracked with vigilance state, only NPS+ neurons of the LPBA exhibited both increased activity prior to wakefulness and decreased activity during REM sleep, similar to the behavioral phenotype observed upon NPSR1 activation. Accordingly, we found that activation of the LPBA but not the peri-LC NPS+ neurons increased wake and reduced REM sleep. Furthermore, given the extended role of the LPBA in respiration and the link between behavioral arousal and breathing rate, we demonstrated that the LPBA but not the peri-LC NPS+ neuronal activation increased respiratory rate. Together, our data suggest that NPS+ neurons of the LPBA represent an unexplored subpopulation regulating breathing, and they are sufficient to recapitulate the sleep/wake phenotypes observed with broad NPS system activation.

Dorsomedial and preoptic hypothalamic circuits control torpor.

Endotherms can survive low temperatures and food shortage by actively entering a hypometabolic state known as torpor. Although the decrease in metabolic rate and body temperature (Tb) during torpor is controlled by the brain, the specific neural circuits underlying these processes have not been comprehensively elucidated. In this study, we identify the neural circuits involved in torpor regulation by combining whole-brain mapping of torpor-activated neurons, cell-type-specific manipulation of neural activity, and viral tracing-based circuit mapping. We find that Trpm2-positive neurons in the preoptic area and Vgat-positive neurons in the dorsal medial hypothalamus are activated during torpor. Genetic silencing shows that the activity of either cell type is necessary to enter the torpor state. Finally, we show that these cells receive projections from the arcuate and suprachiasmatic nucleus and send projections to brain regions involved in thermoregulation. Our results demonstrate an essential role of hypothalamic neurons in the regulation of Tb and metabolic rate during torpor and identify critical nodes of the torpor regulatory network.

Variation in salivary cortisol responses in yearling Thoroughbred racehorses during their first year of training.

Thoroughbred horses are bred for competitive racing and undergo intense training regimes. The maintenance of physical soundness and desirable behavioural characteristics are critical to the longevity of a racing career. Horses intended for Flat racing generally enter training as yearlings and undergo introductory training prior to exercise conditioning for racing. This period requires rapid adjustment to a novel environment. As a prey animal, a horse's 'fight-or-flight' response is highly adapted, in which a well-understood component of this response, the hypothalamic-pituitary-axis, is activated in response to a stress stimulus, releasing cortisol. In the Thoroughbred, a significant difference in salivary cortisol concentrations between pre- and post-first time ridden (i.e., first backing) by a jockey have previously been identified. Here, to test the hypothesis that salivary cortisol concentrations may be used to objectively detect individual variations in the acute physiological stress response we investigate individual variation in cortisol response to training milestones. Saliva samples were collected from a cohort of n = 96 yearling Flat racehorses, at the same training yard, across three timepoints at rest: before entering the training yard (n = 66), within three days of entry to the training yard (n = 67) and following 2-3 weeks in the training yard (n = 50). Salivary cortisol concentration was measured using an ELISA. There was no significant difference in cortisol concentration (ANOVA, P > 0.05) across the samples collected at timepoints at rest. Samples were also collected before and 30 minutes after exposure to three novel training events: first time long-reined (n = 6), first time backed by a jockey (n = 34), and first time ridden on the gallops (n = 10). Mean salivary cortisol concentration after all three novel training events was significantly higher than prior to the training event (Paired t-test, P <0.005). The ranges of post-event salivary cortisol concentration across all timepoints suggest individual variation in the measured stress response, reflecting individual differences in stress response to the early training environment. This measure may be used as an objective assessment of the stress response of Thoroughbred racehorses during training.

Integrative genomics analysis highlights functionally relevant genes for equine behaviour.

Behavioural plasticity enables horses entering an exercise training programme to adapt with reduced stress. We characterised SNPs associated with behaviour in yearling Thoroughbred horses using genomics analyses for two phenotypes: (1) handler-assessed coping with early training events [coping] (n = 96); and (2) variation in salivary cortisol concentration at the first backing event [cortisol] (n = 34). Using RNA-seq derived gene expression data for amygdala and hippocampus tissues from n = 2 Thoroughbred stallions, we refined the SNPs to those with functional relevance to behaviour by cross-referencing to the 500 most highly expressed genes in each tissue. The SNPs of high significance (q < 0.01) were in proximity to genes (coping - GABARAP, NDM, OAZ1, RPS15A, SPARCL1, VAMP2; cortisol - CEBPA, COA3, DUSP1, HNRNPH1, RACK1) with biological functions in social behaviour, autism spectrum disorder, suicide, stress-induced anxiety and depression, Alzheimer's disease, neurodevelopmental disorders, neuroinflammatory disease, fear-induced behaviours and alcohol and cocaine addiction. The strongest association (q = 0.0002) was with NDN, a gene previously associated with temperament in cattle. This approach highlights functionally relevant genes in the behavioural adaptation of Thoroughbred horses that will contribute to the development of genetic markers to improve racehorse welfare.

Robotic Resection of Gallbladder Cancer: A Single-Center Retrospective Comparative Study to Open Resection.

BACKGROUND: Minimally invasive surgery is gaining support for resection of gallbladder cancer (GBC). This study aims to compare operative and early outcomes of robotic resection (RR) to open resection (OR) from a single institution performing a high volume of robotic HPB surgery. METHODS: Twenty patients with GBC underwent RR from January 2013 to August 2019. Outcomes were compared to a historical control of 23 patients with OR. Radical cholecystectomy for suspected GBC and completion operations for incidental GBC after routine cholecystectomy were both included. RESULTS: Robotic resection had lower blood loss compared to OR (150 vs 350 mL, P = .002) and shorter postoperative length of stay (2.5 vs 6 days, P < .001), while median operative time was similar (193 vs 208 min, P = .604). There were no statistical differences in 30-day major complications or readmissions. No 30-day mortalities occurred. There was no statistical difference in survival trend (P = .438) or median lymph node harvest (5 vs 3, P = .189) for RR compared to OR. CONCLUSION: Robotic resection of GBC is safe and efficient, with lower length of hospital stay and blood loss compared to OR. Technical benefits of robotic-assisted surgery may prove advantageous though larger studies are still needed.

A tool for monitoring cell type-specific focused ultrasound neuromodulation and control of chronic epilepsy.

Focused ultrasound (FUS) is a powerful tool for noninvasive modulation of deep brain activity with promising therapeutic potential for refractory epilepsy; however, tools for examining FUS effects on specific cell types within the deep brain do not yet exist. Consequently, how cell types within heterogeneous networks can be modulated and whether parameters can be identified to bias these networks in the context of complex behaviors remains unknown. To address this, we developed a fiber Photometry Coupled focused Ultrasound System (PhoCUS) for simultaneously monitoring FUS effects on neural activity of subcortical genetically targeted cell types in freely behaving animals. We identified a parameter set that selectively increases activity of parvalbumin interneurons while suppressing excitatory neurons in the hippocampus. A net inhibitory effect localized to the hippocampus was further confirmed through whole brain metabolic imaging. Finally, these inhibitory selective parameters achieved significant spike suppression in the kainate model of chronic temporal lobe epilepsy, opening the door for future noninvasive therapies.

Prevalence and determinants of oral and cervicogenital HPV infection: Baseline analysis of the Michigan HPV and Oropharyngeal Cancer (MHOC) cohort study.

We determined baseline oral and cervicogenital human papillomavirus (HPV) prevalence and determinants of infection in the Michigan HPV and Oropharyngeal Cancer (MHOC) study. We enrolled 394 college-age and older participants of both sexes in Ann Arbor, Michigan and the surrounding area. All participants provided an oral sample at baseline, and 130 females provided a cervicogenital sample. Samples were tested for 18 HPV genotypes using polymerase chain reaction (PCR) MassArray. Participants filled out sociodemographic and behavioral questionnaires. Prevalence ratios for HPV oral or cervicogenital prevalence by predictor variables were estimated in univariable log-binomial models. Analysis was conducted 2018-20. In the full cohort, baseline oral HPV prevalence was 10.0% for any detected genotype (among the 338 valid oral tests at baseline) and 6.5% for high-risk types, and cervicogenital prevalence was 20.0% and 10.8%, respectively (among the 130 first valid cervicogenital tests). Oral HPV prevalence did not vary by sex, with 10.5% of women and 9.0% of men having an infection. We found a high prevalence of oral and cervicogenital HPV infection in college-age participants reporting no lifetime sexual partners. Reporting a single recent partner was associated with a lower oral HPV prevalence (PR 0.39, 95% CI: 0.16, 0.96) than reporting no recent (but at least one ever) partner. No similar protective effect was seen for cervicogenital HPV. Both oral and cervicogenital prevalence increased with the number of recent partners for most sexual behaviors. We observed an ecological fallacy masking the direction of impact of vaccination on HPV prevalence in the full cohort compared to the college-aged and the age 23+ populations considered separately. Substance use was not significantly associated with oral or cervicogenital HPV infection. Many studies report substantially higher oral HPV infection prevalence in men than in women. That difference may not be uniform across populations in the US.

Development and Validation of Image-Based Deep Learning Models to Predict Surgical Complexity and Complications in Abdominal Wall Reconstruction.

Importance: Image-based deep learning models (DLMs) have been used in other disciplines, but this method has yet to be used to predict surgical outcomes. Objective: To apply image-based deep learning to predict complexity, defined as need for component separation, and pulmonary and wound complications after abdominal wall reconstruction (AWR). Design, Setting, and Participants: This quality improvement study was performed at an 874-bed hospital and tertiary hernia referral center from September 2019 to January 2020. A prospective database was queried for patients with ventral hernias who underwent open AWR by experienced surgeons and had preoperative computed tomography images containing the entire hernia defect. An 8-layer convolutional neural network was generated to analyze image characteristics. Images were batched into training (approximately 80%) or test sets (approximately 20%) to analyze model output. Test sets were blinded from the convolutional neural network until training was completed. For the surgical complexity model, a separate validation set of computed tomography images was evaluated by a blinded panel of 6 expert AWR surgeons and the surgical complexity DLM. Analysis started February 2020. Exposures: Image-based DLM. Main Outcomes and Measures: The primary outcome was model performance as measured by area under the curve in the receiver operating curve (ROC) calculated for each model; accuracy with accompanying sensitivity and specificity were also calculated. Measures were DLM prediction of surgical complexity using need for component separation techniques as a surrogate and prediction of postoperative surgical site infection and pulmonary failure. The DLM for predicting surgical complexity was compared against the prediction of 6 expert AWR surgeons. Results: A total of 369 patients and 9303 computed tomography images were used. The mean (SD) age of patients was 57.9 (12.6) years, 232 (62.9%) were female, and 323 (87.5%) were White. The surgical complexity DLM performed well (ROC = 0.744; P < .001) and, when compared with surgeon prediction on the validation set, performed better with an accuracy of 81.3% compared with 65.0% (P < .001). Surgical site infection was predicted successfully with an ROC of 0.898 (P < .001). However, the DLM for predicting pulmonary failure was less effective with an ROC of 0.545 (P = .03). Conclusions and Relevance: Image-based DLM using routine, preoperative computed tomography images was successful in predicting surgical complexity and more accurate than expert surgeon judgment. An additional DLM accurately predicted the development of surgical site infection.

Exploring Feasibility of Multivariate Deep Learning Models in Predicting COVID-19 Epidemic.

Background: Mathematical models are powerful tools to study COVID-19. However, one fundamental challenge in current modeling approaches is the lack of accurate and comprehensive data. Complex epidemiological systems such as COVID-19 are especially challenging to the commonly used mechanistic model when our understanding of this pandemic rapidly refreshes. Objective: We aim to develop a data-driven workflow to extract, process, and develop deep learning (DL) methods to model the COVID-19 epidemic. We provide an alternative modeling approach to complement the current mechanistic modeling paradigm. Method: We extensively searched, extracted, and annotated relevant datasets from over 60 official press releases in Hubei, China, in 2020. Multivariate long short-term memory (LSTM) models were developed with different architectures to track and predict multivariate COVID-19 time series for 1, 2, and 3 days ahead. As a comparison, univariate LSTMs were also developed to track new cases, total cases, and new deaths. Results: A comprehensive dataset with 10 variables was retrieved and processed for 125 days in Hubei. Multivariate LSTM had reasonably good predictability on new deaths, hospitalization of both severe and critical patients, total discharges, and total monitored in hospital. Multivariate LSTM showed better results for new and total cases, and new deaths for 1-day-ahead prediction than univariate counterparts, but not for 2-day and 3-day-ahead predictions. Besides, more complex LSTM architecture seemed not to increase overall predictability in this study. Conclusion: This study demonstrates the feasibility of DL models to complement current mechanistic approaches when the exact epidemiological mechanisms are still under investigation.

Neurofibromin regulates metabolic rate via neuronal mechanisms in Drosophila.

Neurofibromatosis type 1 is a chronic multisystemic genetic disorder that results from loss of function in the neurofibromin protein. Neurofibromin may regulate metabolism, though the underlying mechanisms remain largely unknown. Here we show that neurofibromin regulates metabolic homeostasis in Drosophila via a discrete neuronal circuit. Loss of neurofibromin increases metabolic rate via a Ras GAP-related domain-dependent mechanism, increases feeding homeostatically, and alters lipid stores and turnover kinetics. The increase in metabolic rate is independent of locomotor activity, and maps to a sparse subset of neurons. Stimulating these neurons increases metabolic rate, linking their dynamic activity state to metabolism over short time scales. Our results indicate that neurofibromin regulates metabolic rate via neuronal mechanisms, suggest that cellular and systemic metabolic alterations may represent a pathophysiological mechanism in neurofibromatosis type 1, and provide a platform for investigating the cellular role of neurofibromin in metabolic homeostasis.

Pure and Hybrid Deep Learning Models can Predict Pathologic Tumor Response to Neoadjuvant Therapy in Pancreatic Adenocarcinoma: A Pilot Study.

BACKGROUND: Neoadjuvant therapy may improve survival of patients with pancreatic adenocarcinoma; however, determining response to therapy is difficult. Artificial intelligence allows for novel analysis of images. We hypothesized that a deep learning model can predict tumor response to NAC. METHODS: Patients with pancreatic cancer receiving neoadjuvant therapy prior to pancreatoduodenectomy were identified between November 2009 and January 2018. The College of American Pathologists Tumor Regression Grades 0-2 were defined as pathologic response (PR) and grade 3 as no response (NR). Axial images from preoperative computed tomography scans were used to create a 5-layer convolutional neural network and LeNet deep learning model to predict PRs. The hybrid model incorporated decrease in carbohydrate antigen 19-9 (CA19-9) of 10%. Accuracy was determined by area under the curve. RESULTS: A total of 81 patients were included in the study. Patients were divided between PR (333 images) and NR (443 images). The pure model had an area under the curve (AUC) of .738 (P < .001), whereas the hybrid model had an AUC of .785 (P < .001). CA19-9 decrease alone was a poor predictor of response with an AUC of .564 (P = .096). CONCLUSIONS: A deep learning model can predict pathologic tumor response to neoadjuvant therapy for patients with pancreatic adenocarcinoma and the model is improved with the incorporation of decreases in serum CA19-9. Further model development is needed before clinical application.

An objective approach to evaluate novice robotic surgeons using a combination of kinematics and stepwise cumulative sum (CUSUM) analyses.

BACKGROUND: Current evaluation methods for robotic-assisted surgery (ARCS or GEARS) are limited to 5-point Likert scales which are inherently time-consuming and require a degree of subjective scoring. In this study, we demonstrate a method to break down complex robotic surgical procedures using a combination of an objective cumulative sum (CUSUM) analysis and kinematics data obtained from the da Vinci® Surgical System to evaluate the performance of novice robotic surgeons. METHODS: Two HPB fellows performed 40 robotic-assisted hepaticojejunostomy reconstructions to model a portion of a Whipple procedure. Kinematics data from the da Vinci® system was recorded using the dV Logger® while CUSUM analyses were performed for each procedural step. Each kinematic variable was modeled using machine learning to reflect the fellows' learning curves for each task. Statistically significant kinematics variables were then combined into a single formula to create the operative robotic index (ORI). RESULTS: The inflection points of our overall CUSUM analysis showed improvement in technical performance beginning at trial 16. The derived ORI model showed a strong fit to our observed kinematics data (R2 = 0.796) with an ability to distinguish between novice and intermediate robotic performance with 89.3% overall accuracy. CONCLUSIONS: In this study, we demonstrate a novel approach to objectively break down novice performance on the da Vinci® Surgical System. We identified kinematics variables associated with improved overall technical performance to create an objective ORI. This approach to robotic operative evaluation demonstrates a valuable method to break down complex surgical procedures in an objective, stepwise fashion. Continued research into objective methods of evaluation for robotic surgery will be invaluable for future training and clinical implementation of the robotic platform.

Oncologic Outcomes of Robotic Left Pancreatectomy for Pancreatic Adenocarcinoma: A Single-Center Comparison to Laparoscopic Resection.

BACKGROUND: Feasibility and safety of robotic surgery for pancreatic disease has been well demonstrated; however, there is scarce literature on long-term oncologic outcomes. We compared perioperative and oncologic outcomes between robotic left pancreatectomy (RLP) and laparoscopic left pancreatectomy (LLP) for pancreatic adenocarcinoma. METHODS: A retrospective review evaluated left pancreatectomies performed for pancreatic adenocarcinoma from 2009 to 2019 in a tertiary institution. Baseline characteristics, operative and oncologic outcomes were compared between RLP and LLP. RESULTS: There were 75 minimally invasive left pancreatectomy cases for pancreatic adenocarcinoma identified of which 33 cases were done robotically and 42 laparoscopically. Baseline characteristics demonstrated no difference in gender, age, BMI, T stage, N stage, neoadjuvant, or adjuvant chemotherapy. An analysis of operative variables demonstrated no difference in blood loss, increased duration, and higher lymph node yield with RLP (20 vs 12; P = .0029). Postoperatively, both cohorts had 30% pancreatic fistulas and no difference in complications. There were no differences in length of stay (LOS), 30- or 90-day readmission rates, or 90-day mortality. The analysis of oncologic outcomes demonstrated similar R0 resections (RLP: 72% vs OLP: 67%), recurrence rates (RLP: 36% vs OLP: 41%), and time to recurrence (RLP: 324 vs OLP 218 days). There was increased survival in the RLP cohort that was not significant (32 vs 19 months). CONCLUSION: This analysis demonstrates RLP is at least equivalent to LLP in perioperative and oncologic outcomes. The significantly higher lymph node yield and trend toward an improved survival suggests oncologic advantage. Randomized controlled studies are needed to clarify benefit.

Faster Return to Intended Oncologic Treatment (RIOT) After Trisectionectomy Does Not Translate to Better Outcomes.

BACKGROUND: Resection with trisectionectomy may necessitate liver molding for adequate future liver remnant (FLR), and subsequent complications can impact return to intended oncologic therapy (RIOT). This study evaluated whether a difference in RIOT exists with the use of molding and between liver molding techniques (associating liver partition and portal vein ligation for staged hepatectomy [ALPPS] and portal vein embolization [PVE]) with trisectionectomy. METHODS: A retrospective review evaluated trisectionectomies for malignancy. Outcomes were compared with and without molding, and RIOT was determined. RESULTS: Fifty-one patients underwent trisectionectomy: 11 ALPPS, 14 PVE, 26 without molding. 73% of ALPPS, 64% of PVE, and 58% without molding achieved RIOT (P = .971). There were no differences found in baseline characteristics, R0 rate, length of stay, readmission, complications, or mortality. Time to RIOT was significantly different (ALPPS: 3.3 months; PVE: 5.2 months; none: 2.4 months, P = .0203). There were no differences in recurrence or survival. CONCLUSIONS: Liver molding should not cause apprehension as there are no differences in achieving RIOT. Although technique alters time to RIOT, this does not translate into improved outcomes, implicating disease biology, and regeneration stimulus.

Operative Microwave Ablation for the Multimodal Treatment of Neuroendocrine Liver Metastases.

Background and Purpose: Operative microwave ablation (MWA) is a safe modality for treating hepatic tumors. The aim of this study is to present our 10-year, single-center experience of operative MWA for neuroendocrine liver metastases (NLM). Methods: A single-institution retrospective review of patients who underwent operative MWA for NLM was performed (2008-2018). Demographics, primary tumor site, operative approach, combined surgical operations, and carcinoid symptoms were recorded. Clinical outcomes for major complications, readmission, and mortality were analyzed 30 days postoperatively. Postablation imaging was evaluated for incomplete ablation/missed lesions, and surveillance imaging reviewed for local, regional, and metastatic recurrence. Results: Of the 50 patients (166 targeted lesions) who received MWA for NLM, 41 (82%) were treated with a minimally invasive approach, and 22 (44%) underwent MWA concomitant with hepatectomy and/or primary tumor resection. Within the study cohort 70% of patients were treated with curative intent with a 77% (27/35) success rate. Carcinoid symptoms were reported in 40% (20/50) of patients preoperatively, and MWA treatment improved symptoms in 19/20 patients. Incomplete ablation occurred in 1/166 treated lesions. Recurrence-free survival at 1 and 5 years was 86% and 28%, respectively. Overall survival at 1 and 5 years was 94% and 70%, respectively (median follow-up 32 months, range 0-116 months). Conclusion: Operative MWA is a versatile modality, which can be safe and effectively performed alone or combined with hepatectomy for NLM, preferably using a minimally invasive approach, to achieve symptom control and possibly improve survival.

Treatment of spontaneously ruptured hepatocellular carcinoma: use of laparoscopic microwave ablation and washout.

BACKGROUND: Ruptured, or bleeding, hepatocellular carcinoma (rHCC) is a relatively rare disease presentation associated with high acute mortality rates. This study sought to evaluate outcomes following laparoscopic microwave ablation (MWA) and washout in rHCC. METHODS: A retrospective single-center review was performed to identify patients with rHCC (2008-2018). The treatment algorithm consisted of transarterial embolization (TAE) or trans-arterial chemoembolization (TACE) followed by laparoscopic MWA and washout. RESULTS: Fifteen patients with rHCC were identified (n = 5 single lesion, n = 5 multifocal disease, n = 5 extrahepatic metastatic disease). Median tumor size was 83 mm (range 5-228 mm), and 10 of 15 underwent TAE or TACE followed by laparoscopic MWA/washout. One patient required additional treatment for bleeding after MWA with repeat TAE. Thirty-day mortality was 6/15. For those patients discharged (n = 9), additional treatments included chemotherapy (n = 5), TACE (n = 3), and/or partial lobectomy (n = 2). Median follow-up was 18.2 months and median survival was 431 days (range 103-832) (one-year survival n = 7; two-year survival n = 4; three-year survival n = 3). Six patients had post-operative imaging from which one patient demonstrated recurrence. CONCLUSION: Using laparoscopic MWA with washout may offer advantage in the treatment of ruptured HCC. It not only achieves hemostasis but also could have oncologic benefit by targeting local tumor and decreasing peritoneal carcinomatosis risk.