Investigation of the Role of Chemical Analysis in Causality Assessment of Herbal and Dietary Supplement-Induced Liver Injury.

BACKGROUND:  The attribution of drug-induced liver injury (DILI) to specific herbal and dietary supplements (HDS) is confounded by inaccurate labels and undisclosed ingredients. The US Drug-Induced Liver Injury Network (DILIN) determines the attribution of injury to an agent through its structured expert opinion causality assessment process, but without the use of chemical analysis data of HDS. We aimed to determine the impact of chemical analysis of HDS products on prior causality assessment scores. METHODS: Obtained samples of HDS consumed by DILIN-enrolled patients were analyzed by high-performance liquid chromatography-mass spectrometry (HPLC-MS). Chemical analysis data were compared to label accuracy and detect whether the product contained botanical and non-botanical compounds. A comparison of the causality scores reassessed with chemical analysis was compared with the original scores. RESULTS: A total of 54 previously adjudicated cases with chemical analysis available were reassessed for causality with chemical analysis data; reviewers were blinded to original causality scores. Using the chemical analysis data, 37% (n = 20) of the 54 cases were scored with a higher likelihood of DILI compared with the original causality scores; 14 of the 20 (70%) moved from probable to highly likely; 52% had no change in causality score; and 11% of cases were scored as a lower likelihood of DILI. CONCLUSIONS:  Our study demonstrates that there is value in using HDS chemical analysis data in the causality assessment process for DILI. In more than a third of cases, chemical analysis of products led to an increased confidence in DILI attribution to HDS. These findings suggest that chemical analysis is an important tool in causality assessment for HDS agents, specifically in challenging situations, and further studies are needed to confirm its applicability in clinical practice.

Transparent RFID tag wall enabled by artificial intelligence for assisted living.

Current approaches to activity-assisted living (AAL) are complex, expensive, and intrusive, which reduces their practicality and end user acceptance. However, emerging technologies such as artificial intelligence and wireless communications offer new opportunities to enhance AAL systems. These improvements could potentially lower healthcare costs and reduce hospitalisations by enabling more effective identification, monitoring, and localisation of hazardous activities, ensuring rapid response to emergencies. In response to these challenges, this paper introduces the Transparent RFID Tag Wall (TRT-Wall), a novel system taht utilises a passive ultra-high frequency (UHF) radio-frequency identification (RFID) tag array combined with deep learning for contactless human activity monitoring. The TRT-Wall is tested on five distinct activities: sitting, standing, walking (in both directions), and no-activity. Experimental results demonstrate that the TRT-Wall distinguishes these activities with an impressive average accuracy of 95.6 % under four distinct distances (2, 2.5, 3.5 and 4.5 m) by capturing the RSSI and phase information. This suggests that our proposed contactless AAL system possesses significant potential to enhance elderly patient-assisted living.

Is prolonged operative time associated with postoperative complications in liver surgery? An international multicentre cohort study of 5424 patients.

BACKGROUND: The relation between operative time and postoperative complications in liver surgery is unclear. The aim of this study is to assess the impact of operative time on the development of postoperative complications in patients who underwent minimally invasive or open liver resections of various anatomical extent and technical difficulty levels. METHODS: In this retrospective cohort study, patients that underwent a right hemihepatectomy (RH), technically major resection (anatomically minor resection in segment 1, 4a, 7 or 8; TMR) or left lateral sectionectomy (LLS) between 2000 and 2022 were extracted from a multicenter database comprising the prospectively maintained databases of 31 centers in 13 countries. Minimally invasive procedures performed during the learning curve were omitted. Logistic regression models, performed separately for 9 different groups based on stratification by procedure type and allocated surgical approach, were used to assess the association between the fourth quartile of operative time (25% of patients with the longest operative time) and postoperative complications. RESULTS: Overall, 5424 patients were included: 1351 underwent RH (865 open, 373 laparoscopic and 113 robotic), 2821 TMR (1398 open, 1225 laparoscopic and 198 robotic), and 1252 LLS (241 open, 822 laparoscopic and 189 robotic). After adjusting for potential confounders (age, BMI, gender, ASA grade, previous abdominal surgery, disease type and extent, blood loss, Pringle, intraoperative transfusions and incidents), the fourth quartile of operative time, compared to the first three quartiles, was associated with an increased risk of postoperative complications after open, laparoscopic and robotic TMR (aOR 1.35, p = 0.031; aOR 1.74, p = 0.001 and aOR 3.11, p = 0.014, respectively), laparoscopic and robotic RH (aOR 1.98, p = 0.018 and aOR 3.28, p = 0.055, respectively) and solely laparoscopic LLS (aOR 1.69, p = 0.019). CONCLUSIONS: A prolonged operative time is associated with an increased risk of postoperative complications, although it remains to be defined if this is a causal relationship.

Endogenous ZAP affects Zika virus RNA interactome.

One of the most recent advances in the analysis of viral RNA-cellular protein interactions is the Comprehensive Identification of RNA-binding Proteins by Mass Spectrometry (ChIRP-MS). Here, we used ChIRP-MS in mock-infected and Zika-infected wild-type cells and cells knockout for the zinc finger CCCH-type antiviral protein 1 (ZAP). We characterized 'ZAP-independent' and 'ZAP-dependent' cellular protein interactomes associated with flavivirus RNA and found that ZAP affects cellular proteins associated with Zika virus RNA. The ZAP-dependent interactome identified with ChIRP-MS provides potential ZAP co-factors for antiviral activity against Zika virus and possibly other viruses. Identifying the full spectrum of ZAP co-factors and mechanisms of how they act will be critical to understanding the ZAP antiviral system and may contribute to the development of antivirals.

Development and evaluation of a societal core robotic surgery accreditation curriculum for the UK.

Standardised proficiency-based progression is the cornerstone of safe robotic skills acquisition, however, is currently lacking within surgical training curricula. Expert consensuses have defined a modular pathway to accredit surgeons. This study aimed to address the lack of a formal, pre-clinical core robotic skills, proficiency-based accreditation curriculum in the UK. Novice robotic participants underwent a four-day pre-clinical core robotic skills curriculum incorporating multimodal assessment. Modifiable-Global Evaluative Assessment of Robotic Skills (M-GEARS), VR-automated performance metrics (APMs) and Objective Clinical Human Reliability Analysis (OCHRA) error methodology assessed performance at the beginning and end of training. Messick's validity concept and a curriculum evaluation model were utilised. Feedback was collated. Proficiency-based progression, benchmarking, tool validity and reliability was assessed through comparative and correlational statistical methods. Forty-seven participants were recruited. Objective assessment of VR and dry models across M-GEARS, APMs and OCHRA demonstrated significant improvements in technical skill (p < 0.001). Concurrent validity between assessment tools demonstrated strong correlation in dry and VR tasks (r = 0.64-0.92, p < 0.001). OCHRA Inter-rater reliability was excellent (r = 0.93, p < 0.001 and 81% matched error events). A benchmark was established with M-GEARS and for the curriculum at 80%. Thirty (63.82%) participants passed. Feedback was 5/5 stars on average, with 100% recommendation. Curriculum evaluation fulfilled all five domains of Messick's validity. Core robotic surgical skills training can be objectively evaluated and benchmarked to provide accreditation in basic robotic skills. A strategy is necessary to enrol standardised curricula into national surgical training at an early stage to ensure patient safety.

Safety of robotic cholecystectomy as index training procedure: the UK experience.

AIMS: To evaluate the safety profile of robotic cholecystectomy performed within the United Kingdom (UK) Robotic Hepatopancreatobiliary (HPB) training programme. METHODS: A retrospective evaluation of prospectively collected data from eleven centres participating in the UK Robotic HPB training programme was conducted. All adult patients undergoing robotic cholecystectomy for symptomatic gallstone disease or gallbladder polyp were considered. Bile duct injury, conversion to open procedure, conversion to subtotal cholecystectomy, length of hospital stay, 30-day re-admission, and post-operative complications were the evaluated outcome parameters. RESULTS: A total of 600 patients were included. The median age was 53 (IQR 65-41) years and the majority (72.7%; 436/600) were female. The main indications for robotic cholecystectomy were biliary colic (55.5%, 333/600), cholecystitis (18.8%, 113/600), gallbladder polyps (7.7%, 46/600), and pancreatitis (6.2%, 37/600). The median length of stay was 0 (IQR 0-1) days. Of the included patients, 88.5% (531/600) were discharged on the day of procedure with 30-day re-admission rate of 5.5% (33/600). There were no bile duct injuries and the rate of conversion to open was 0.8% (5/600) with subtotal cholecystectomy rate of 0.8% (5/600). CONCLUSION: The current study confirms that robotic cholecystectomy can be safely implemented to routine practice with a low risk of bile duct injury, low bile leak rate, low conversion to open surgery, and low need for subtotal cholecystectomy.

Clinical characteristics and HLA associations of azithromycin-induced liver injury.

BACKGROUND: Azithromycin (AZ) is a widely used antibiotic. The aim of this study was to characterise the clinical features, outcomes, and HLA association in patients with drug-induced liver injury (DILI) due to AZ. METHODS: The clinical characteristics of individuals with definite, highly likely, or probable AZ-DILI enrolled in the US Drug-Induced Liver Injury Network (DILIN) were reviewed. HLA typing was performed using an Illumina MiSeq platform. The allele frequency (AF) of AZ-DILI cases was compared to population controls, other DILI cases, and other antibiotic-associated DILI cases. RESULTS: Thirty cases (4 definite, 14 highly likely, 12 probable) of AZ-DILI were enrolled between 2004 and 2022 with a median age of 46 years, 83% white, and 60% female. Median duration of AZ treatment was 5 days. Latency was 18.5 days. 73% were jaundiced at presentation. The injury pattern was hepatocellular in 60%, cholestatic in 27%, and mixed in 3%. Ten cases (33%) were severe or fatal; 90% of these were hepatocellular. Two patients required liver transplantation. One patient with chronic liver disease died of hepatic failure. Chronic liver injury developed in 17%, of which 80% had hepatocellular injury at onset. HLA-DQA1*03:01 was significantly more common in AZ-DILI versus population controls and amoxicillin-clavulanate DILI cases (AF: 0.29 vs. 0.11, p = 0.001 and 0.002, respectively). CONCLUSION: Azithromycin therapy can lead to rapid onset of severe hepatic morbidity and mortality in adult and paediatric populations. Hepatocellular injury and younger age were associated with worse outcomes. HLA-DQA1*03:01 was significantly more common in AZ cases compared to controls.

Deep learning empowered breast cancer diagnosis: Advancements in detection and classification.

Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science has given rise to Computer-Aided Diagnosis (CAD) systems, offering vital tools to aid medical professionals in tumor detection, classification, recurrence tracking, and prognosis prediction. Breast cancer, a significant global health concern, is particularly prevalent in Asia due to diverse factors like lifestyle, genetics, environmental exposures, and healthcare accessibility. Early detection through mammography screening is critical, but the accuracy of mammograms can vary due to factors like breast composition and tumor characteristics, leading to potential misdiagnoses. To address this, an innovative CAD system leveraging deep learning and computer vision techniques was introduced. This system enhances breast cancer diagnosis by independently identifying and categorizing breast lesions, segmenting mass lesions, and classifying them based on pathology. Thorough validation using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) demonstrated the CAD system's exceptional performance, with a 99% success rate in detecting and classifying breast masses. While the accuracy of detection is 98.5%, when segmenting breast masses into separate groups for examination, the method's performance was approximately 95.39%. Upon completing all the analysis, the system's classification phase yielded an overall accuracy of 99.16% for classification. The potential for this integrated framework to outperform current deep learning techniques is proposed, despite potential challenges related to the high number of trainable parameters. Ultimately, this recommended framework offers valuable support to researchers and physicians in breast cancer diagnosis by harnessing cutting-edge AI and image processing technologies, extending recent advances in deep learning to the medical domain.

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.

This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment. The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising. The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future research and advancements in this area.

Tag-free indoor fall detection using transformer network encoder and data fusion.

This work presents a radio frequency identification (RFID)-based technique to detect falls in the elderly. The proposed RFID-based approach offers a practical and efficient alternative to wearables, which can be uncomfortable to wear and may negatively impact user experience. The system utilises strategically positioned passive ultra-high frequency (UHF) tag array, enabling unobtrusive monitoring of elderly individuals. This contactless solution queries battery-less tag and processes the received signal strength indicator (RSSI) and phase data. Leveraging the powerful data-fitting capabilities of a transformer model to take raw RSSI and phase data as input with minimal preprocessing, combined with data fusion, it significantly improves activity recognition and fall detection accuracy, achieving an average rate exceeding 96.5 % . This performance surpasses existing methods such as convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM), demonstrating its reliability and potential for practical implementation. Additionally, the system maintains good accuracy beyond a 3-m range using minimal battery-less UHF tags and a single antenna, enhancing its practicality and cost-effectiveness.

Characterisation of an outbreak of acute liver injury after ingestion of plant-based food supplement.

BACKGROUND: In April 2022, French Lentil and Leek Crumble (FLLC), a new frozen food preparation manufactured by Daily Harvest™ (containing Tara flour) was offered as a natural high-protein meal product. Soon thereafter, widespread anecdotal reports of acute gastrointestinal symptoms with liver injury were reported, leading to its voluntary withdrawal in June 2022, after shipment of 28,000 preparations. AIMS: To summarise the clinical and laboratory features of 17 patients with FLLC associated liver injury from the Drug Induced Liver Injury Network (DILIN). METHODS: Patients with FLLC-associated liver injury were enrolled into a prospective protocol and followed for 6 months. Cases were adjudicated by expert opinion causality assessment with summary statistics for data analysis. RESULTS: Enrolled subjects had a mean age of 41 years, 82% were female with mean BMI of 24 kg/m2. All were Caucasian without underlying liver disease. In most cases, abdominal pain and nausea arose within hours of FLLC ingestion. Mean days from ingestion to identification of liver injury was 3.1 days (±2.8). On enrolment, 53% had jaundice, 47% nausea, 24% fever, 59% abdominal pain, 41% itching and 12% rash. The mean initial serum ALT was 475 U/L (±302), AST 315 U/L (±315), alkaline phosphatase 190 U/L (±76), with a total bilirubin of 2.6 mg/dL (±2). In this study, 63% presented with a hepatocellular pattern of liver injury, 6% cholestatic and 31% mixed as determined by the R value. In addition, 24% of patients were hospitalised, and there were no fatalities or liver transplants. Liver biopsy in one subject revealed acute hepatitis with mild ductular reaction, mild lymphocytic and eosinophilic portal inflammation, mild lobular inflammation, preserved bile ducts and absence of interface hepatitis, steatosis, granulomatous reaction or cholestasis. Phylogenetic analysis confirmed the presence of Tara spinosa, the source of Tara flour. CONCLUSIONS: Natural food products are increasingly ubiquitous and may unexpectedly cause significant illness. All clinicians should inquire whether patients are consuming natural food products or herbal supplements and consider them as a potential cause of liver injury.

A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing.

Semantic segmentation of Remote Sensing (RS) images involves the classification of each pixel in a satellite image into distinct and non-overlapping regions or segments. This task is crucial in various domains, including land cover classification, autonomous driving, and scene understanding. While deep learning has shown promising results, there is limited research that specifically addresses the challenge of processing fine details in RS images while also considering the high computational demands. To tackle this issue, we propose a novel approach that combines convolutional and transformer architectures. Our design incorporates convolutional layers with a low receptive field to generate fine-grained feature maps for small objects in very high-resolution images. On the other hand, transformer blocks are utilized to capture contextual information from the input. By leveraging convolution and self-attention in this manner, we reduce the need for extensive downsampling and enable the network to work with full-resolution features, which is particularly beneficial for handling small objects. Additionally, our approach eliminates the requirement for vast datasets, which is often necessary for purely transformer-based networks. In our experimental results, we demonstrate the effectiveness of our method in generating local and contextual features using convolutional and transformer layers, respectively. Our approach achieves a mean dice score of 80.41%, outperforming other well-known techniques such as UNet, Fully-Connected Network (FCN), Pyramid Scene Parsing Network (PSP Net), and the recent Convolutional vision Transformer (CvT) model, which achieved mean dice scores of 78.57%, 74.57%, 73.45%, and 62.97% respectively, under the same training conditions and using the same training dataset.

Meta-analysis and trial sequential analysis of pancreatic stump closure using a hand-sewn or stapler technique in distal pancreatectomy.

This study aimed to compare outcomes of hand-sewn and stapler closure techniques of pancreatic stump in patients undergoing distal pancreatectomy (DP). Impact of stapler closure reinforcement using mesh on outcomes was also evaluated. Literature search was carried out using multiple data sources to identify studies that compared hand-sewn and stapler closure techniques in management of pancreatic stump following DP. Odds ratio (OR) was determined for clinically relevant postoperative pancreatic fistula (POPF) via random-effects modelling. Subsequently, trial sequential analysis was performed. Thirty-two studies with a total of 4,022 patients undergoing DP with hand-sewn (n = 1,184) or stapler (n = 2,838) closure technique of pancreatic stump were analyzed. Hand-sewn closure significantly increased the risk of clinically relevant POPF compared to stapler closure (OR: 1.56, p = 0.02). When stapler closure was considered, staple line reinforcement significantly reduced formation of such POPF (OR: 0.54, p = 0.002). When only randomized controlled trials were considered, there was no significant difference in clinically relevant POPF between hand-sewn and stapler closure techniques (OR: 1.20, p = 0.64) or between reinforced and standard stapler closure techniques (OR: 0.50, p = 0.08). When observational studies were considered, hand-sewn closure was associated with a significantly higher rate of clinically relevant POPF compared to stapler closure (OR: 1.59, p = 0.03). Moreover, when stapler closure was considered, staple line reinforcement significantly reduced formation of such POPF (OR: 0.55, p = 0.02). Trial sequential analysis detected risk of type 2 error. In conclusion, reinforced stapler closure in DP may reduce risk of clinically relevant POPF compared to hand-sewn closure or stapler closure without reinforcement. Future randomized research is needed to provide stronger evidence.

Perspectives on robotic HPB training in the UK: a survey analysis.

BACKGROUND: We Published a step-up approach for robotic training in hepato-pancreato-biliary (HPB) surgery has been previously. The approach was mostly based on personal experience and communications between experts and needed appraisal and validation by the HPB surgical community. At the Great Britain and Ireland HPB Association (GBIHPBA) robotic HPB meeting held in Coventry, UK in October 2022, the authors sought consensus from the live audience, with an open forum for answering key questions. The aim of this exercise was to appraise the step-up approach, and in turn, lay the foundation for a more substantial UK robotic HPB surgical curriculum. METHODS: The study was conducted using VEVOX online polling platform at the October 2022 GBIHPBA robotic HPB meeting in Coventry, UK. The questionnaire was designed based on a literature search and was externally validated. The data were collated and analysed to assess patterns of response. RESULTS: A median (IQR) of 104 (96-117) responses were generated for each question. 93 consultants and 61 trainees were present Over 90% were in favour of a standardised training pathway. 93.6% were in favour of the proposed step-up approach, with a significant number (67.3%; p < 0.001) considering three levels of case complexity. CONCLUSION: The survey shows a favourable outlook on adopting step-up training in robotic HPB surgery. Ongoing monitoring of progress, clinical outcomes, and collaboration among surgeons and units will bolster this evidence, potentially leading to an official UK robotic HPB curriculum.

Recent advances in the diagnosis of drug-induced liver injury.

Drug-induced liver injury (DILI) is a major problem in the United States, commonly leading to hospital admission. Diagnosing DILI is difficult as it is a diagnosis of exclusion requiring a temporal relationship between drug exposure and liver injury and a thorough work up for other causes. In addition, DILI has a very variable clinical and histologic presentation that can mimic many different etiologies of liver disease. Objective scoring systems can assess the probability that a drug caused the liver injury but liver biopsy findings are not part of the criteria used in these systems. This review will address some of the recent updates to the scoring systems and the role of liver biopsy in the diagnosis of DILI.

Robotic Versus Laparoscopic Liver Resection in Various Settings: An International Multicenter Propensity Score Matched Study of 10.075 Patients.

OBJECTIVE: To compare the perioperative outcomes of robotic liver surgery (RLS) and laparoscopic liver surgery (LLS) in various settings. BACKGROUND: Clear advantages of RLS over LLS have rarely been demonstrated, and the associated costs of robotic surgery are generally higher than those of laparoscopic surgery. Therefore, the exact role of the robotic approach in minimally invasive liver surgery remains to be defined. METHODS: In this international retrospective cohort study, the outcomes of patients who underwent RLS and LLS for all indications between 2009 and 2021 in 34 hepatobiliary referral centers were compared. Subgroup analyses were performed to compare both approaches across several types of procedures: (1) minor resections in the anterolateral (2, 3, 4b, 5, and 6) or (2) posterosuperior segments (1, 4a, 7, 8), and (3) major resections (≥3 contiguous segments). Propensity score matching was used to mitigate the influence of selection bias. The primary outcome was textbook outcome in liver surgery (TOLS), previously defined as the absence of intraoperative incidents ≥grade 2, postoperative bile leak ≥grade B, severe morbidity, readmission, and 90-day or in-hospital mortality with the presence of an R0 resection margin in case of malignancy. The absence of a prolonged length of stay was added to define TOLS+. RESULTS: Among the 10.075 included patients, 1.507 underwent RLS and 8.568 LLS. After propensity score matching, both groups constituted 1.505 patients. RLS was associated with higher rates of TOLS (78.3% vs 71.8%, P < 0.001) and TOLS+ (55% vs 50.4%, P = 0.026), less Pringle usage (39.1% vs 47.1%, P < 0.001), blood loss (100 vs 200 milliliters, P < 0.001), transfusions (4.9% vs 7.9%, P = 0.003), conversions (2.7% vs 8.8%, P < 0.001), overall morbidity (19.3% vs 25.7%, P < 0.001), and microscopically irradical resection margins (10.1% vs. 13.8%, P = 0.015), and shorter operative times (190 vs 210 minutes, P = 0.015). In the subgroups, RLS tended to have higher TOLS rates, compared with LLS, for minor resections in the posterosuperior segments (n = 431 per group, 75.9% vs 71.2%, P = 0.184) and major resections (n = 321 per group, 72.9% vs 67.5%, P = 0.086), although these differences did not reach statistical significance. CONCLUSIONS: While both produce excellent outcomes, RLS might facilitate slightly higher TOLS rates than LLS.

Amiodarone and Dronedarone Causes Liver Injury with Distinctly Different Clinical Presentations.

OBJECTIVE: To describe hepatotoxicity due to amiodarone and dronedarone from the DILIN and the US FDA's surveillance database. METHODS: Hepatotoxicity due to amiodarone and dronedarone enrolled in the U.S. Drug Induced Liver Injury Network (DILIN) from 2004 to 2020 are described. Dronedarone hepatotoxicity cases associated with liver biopsy results were obtained from the FDA Adverse Event Reporting System (FAERS) from 2009 to 2020. RESULTS: Among DILIN's 10 amiodarone and 3 dronedarone DILIN cases, the latency for amiodarone was longer than with dronedarone (388 vs 119 days, p = 0.50) and the median ALT at DILI onset was significantly lower with amiodarone (118 vs 1191 U/L, p = 0.05). Liver biopsies in five amiodarone cases showed fibrosis, steatosis, and numerous Mallory-Denk bodies. Five patients died although only one from liver failure. One patient with dronedarone induced liver injury died of a non-liver related cause. Nine additional cases of DILI due to dronedarone requiring hospitalization were identified in the FAERS database. Three patients developed liver injury within a month of starting the medication. Two developed acute liver failure and underwent urgent liver transplant, one was evaluated for liver transplant but then recovered spontaneously, while one patient with cirrhosis died of liver related causes. CONCLUSION: Amiodarone hepatotoxicity resembles that seen in alcohol related liver injury, with fatty infiltration and inflammation. Dronedarone is less predictable, typically without fat and with a shorter latency of use before presentation. These differences may be explained, in part, by the differing pharmacokinetics of the two drugs leading to different mechanisms of hepatotoxicity.

Comparative study of operative expenses: robotic vs. laparoscopic vs. open liver resections at a university hospital in the UK.

Robotic liver resections (RLR) are increasingly being performed and has previously been considered more costly. The aim is to explore the cost of RLR compared with laparoscopic and open liver resection in a single National Health Service (NHS) hospital. A retrospective review of patients who underwent RLR, LLR, and OLR from April 2014 to December 2022 was conducted. The primary outcomes were the cost of consumables and median income, and the secondary outcomes were the overall length of stay and mortality at 90 days. Overall, 332 patients underwent liver resections. There were 204 males (61.4%) and 128 females (38.6%), with a median age of 62 years (IQR: 51-77 years). Of these, 60 patients (18.1%) underwent RLR, 21 patients (6.3%) underwent LLR, and 251 patients (75.6%) underwent OLR. Median consumables cost per case was £3863 (IQR: £3458-£5061) for RLR, £4326 (IQR: £4273-£4473) for LLR, and £4,084 (IQR: £3799-£5549) for the OLR cohort (p = 0.140). Median income per case was £7999 (IQR: £4509-£10,777) for RLR, £7497 (IQR: £2407-£14,576) for LLR, and £7493 (IQR: £2542-£14,121) for OLR. The median length of stay (LOS) for RLR was 3 days (IQR: 2-4.7 days) compared to 5 days for LLR (IQR: 4.5-7 days) and 6 days for OLR (IQR: 5-8 days, p < 0.001). Within the NHS, RLR has consumable costs comparable to OLR and LLR. It is also linked with a shorter LOS and generates similar income for patients undergoing OLR and LLR.

The Brescia Internationally Validated European Guidelines on Minimally Invasive Pancreatic Surgery (EGUMIPS).

OBJECTIVE: To develop and update evidence-based and consensus-based guidelines on laparoscopic and robotic pancreatic surgery. SUMMARY BACKGROUND DATA: Minimally invasive pancreatic surgery (MIPS), including laparoscopic and robotic surgery, is complex and technically demanding. Minimizing the risk for patients requires stringent, evidence-based guidelines. Since the International Miami Guidelines on MIPS in 2019, new developments and key publications have been reported, necessitating an update. METHODS: Evidence-based guidelines on 22 topics in 8 domains were proposed: terminology, indications, patients, procedures, surgical techniques and instrumentation, assessment tools, implementation and training, and artificial intelligence. The Brescia Internationally Validated European Guidelines on Minimally Invasive Pancreatic Surgery (EGUMIPS, September 2022) used the Scottish Intercollegiate Guidelines Network (SIGN) methodology to assess the evidence and develop guideline recommendations, the Delphi method to establish consensus on the recommendations among the Expert Committee, and the AGREE II-GRS tool for guideline quality assessment and external validation by a Validation Committee. RESULTS: Overall, 27 European experts, 6 international experts, 22 international Validation Committee members, 11 Jury Committee members, 18 Research Committee members, and 121 registered attendees of the 2-day meeting were involved in the development and validation of the guidelines. In total, 98 recommendations were developed, including 33 on laparoscopic, 34 on robotic, and 31 on general MIPS, covering 22 topics in 8 domains. Out of 98 recommendations, 97 reached at least 80% consensus among the experts and congress attendees, and all recommendations were externally validated by the Validation Committee. CONCLUSIONS: The EGUMIPS evidence-based guidelines on laparoscopic and robotic MIPS can be applied in current clinical practice to provide guidance to patients, surgeons, policy-makers, and medical societies.