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.

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

OBJECTIVE: The aim of this study was to compare the perioperative outcomes of robotic liver surgery (RLS) and laparoscopic liver surgery (LLS) in various settings. SUMMARY BACKGROUND DATA: 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: minor resections in the anterolateral (2, 3, 4b, 5, and 6) or posterosuperior segments (1, 4a, 7, 8), and major resections (≥3 contiguous segments). Propensity score matching (PSM) 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 PSM, 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 R0 resection margins (89.8% vs. 86%, P=0.015), but longer operative times (190 vs. 210 min, P=0.015). In the subgroups, RLS tended to have higher TOLS rates, compared to 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 producing excellent outcomes, RLS might facilitate slightly higher TOLS rates than LLS.

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.

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.

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.

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.

Bouveret's syndrome: An old diagnosis. A modern multimodality approach (endoscopic and robotic surgical) of gastric outlet obstruction: Report of two cases.

INTRODUCTION AND IMPORTANCE: Bouveret's syndrome is an uncommon condition characterized by the impaction of a gallstone in the pylorus or duodenum via a cholecysto-enteric fistula causing gastric outlet obstruction. We report two unusual cases of Bouveret's syndrome causing gastric outlet obstruction in two elderly patients. CASE PRESENTATION: Two elderly female patients presented to the surgical assessment unit with features of gastric outlet obstruction. In both cases, an urgent computed tomography (CT) of the abdomen showed pneumobilia, gastric distension, and gallstones impaction at the duodenal bulb. In Patient 1, endoscopic removal of the impacted gallstones was done successfully. She was discharged three days following an uneventful recovery. In Patient 2, an endoscopic removal of a single large gallstone was attempted, which was unsuccessful. She underwent robotic gastrotomy with extraction of the large gallstone with primary repair. She was discharged on 8th postoperative day. CLINICAL DISCUSSION: Treatment options for Bouveret's syndrome include endoscopic management and surgery. The selection of treatment options depends upon factors like the degree of obstruction, the impaction site, number, type or size of gallstones, patient co-morbidities and clinical parameters at presentation, as well as expertise available, both endoscopic and surgical. CONCLUSIONS: Bouveret's syndrome is one of the rare complications of gallstone. Endoscopic management can be effective at removing the impacted gallstones, which is particularly helpful for those elderly patients who have multiple medical co-morbidities, as in our first patient. Surgical management like minimal invasive surgery (robotic) can be beneficial in failed endoscopic attempt of removal of stone like in the second patient.

Automatic neonatal sleep stage classification: A comparative study.

Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study.

Comparative Analysis of Machine Learning Models for Image Detection of Colonic Polyps vs. Resected Polyps.

(1) Background: Colon polyps are common protrusions in the colon's lumen, with potential risks of developing colorectal cancer. Early detection and intervention of these polyps are vital for reducing colorectal cancer incidence and mortality rates. This research aims to evaluate and compare the performance of three machine learning image classification models' performance in detecting and classifying colon polyps. (2) Methods: The performance of three machine learning image classification models, Google Teachable Machine (GTM), Roboflow3 (RF3), and You Only Look Once version 8 (YOLOv8n), in the detection and classification of colon polyps was evaluated using the testing split for each model. The external validity of the test was analyzed using 90 images that were not used to test, train, or validate the model. The study used a dataset of colonoscopy images of normal colon, polyps, and resected polyps. The study assessed the models' ability to correctly classify the images into their respective classes using precision, recall, and F1 score generated from confusion matrix analysis and performance graphs. (3) Results: All three models successfully distinguished between normal colon, polyps, and resected polyps in colonoscopy images. GTM achieved the highest accuracies: 0.99, with consistent precision, recall, and F1 scores of 1.00 for the 'normal' class, 0.97-1.00 for 'polyps', and 0.97-1.00 for 'resected polyps'. While GTM exclusively classified images into these three categories, both YOLOv8n and RF3 were able to detect and specify the location of normal colonic tissue, polyps, and resected polyps, with YOLOv8n and RF3 achieving overall accuracies of 0.84 and 0.87, respectively. (4) Conclusions: Machine learning, particularly models like GTM, shows promising results in ensuring comprehensive detection of polyps during colonoscopies.

AI-Enabled Traffic Control Prioritization in Software-Defined IoT Networks for Smart Agriculture.

Smart agricultural systems have received a great deal of interest in recent years because of their potential for improving the efficiency and productivity of farming practices. These systems gather and analyze environmental data such as temperature, soil moisture, humidity, etc., using sensor networks and Internet of Things (IoT) devices. This information can then be utilized to improve crop growth, identify plant illnesses, and minimize water usage. However, dealing with data complexity and dynamism can be difficult when using traditional processing methods. As a solution to this, we offer a novel framework that combines Machine Learning (ML) with a Reinforcement Learning (RL) algorithm to optimize traffic routing inside Software-Defined Networks (SDN) through traffic classifications. ML models such as Logistic Regression (LR), Random Forest (RF), k-nearest Neighbours (KNN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Trees (DT) are used to categorize data traffic into emergency, normal, and on-demand. The basic version of RL, i.e., the Q-learning (QL) algorithm, is utilized alongside the SDN paradigm to optimize routing based on traffic classes. It is worth mentioning that RF and DT outperform the other ML models in terms of accuracy. Our results illustrate the importance of the suggested technique in optimizing traffic routing in SDN environments. Integrating ML-based data classification with the QL method improves resource allocation, reduces latency, and improves the delivery of emergency traffic. The versatility of SDN facilitates the adaption of routing algorithms depending on real-time changes in network circumstances and traffic characteristics.

A hybrid deep learning-based intrusion detection system for IoT networks.

The Internet of Things (IoT) is a rapidly evolving technology with a wide range of potential applications, but the security of IoT networks remains a major concern. The existing system needs improvement in detecting intrusions in IoT networks. Several researchers have focused on intrusion detection systems (IDS) that address only one layer of the three-layered IoT architecture, which limits their effectiveness in detecting attacks across the entire network. To address these limitations, this paper proposes an intelligent IDS for IoT networks based on deep learning algorithms. The proposed model consists of a recurrent neural network and gated recurrent units (RNN-GRU), which can classify attacks across the physical, network, and application layers. The proposed model is trained and tested using the ToN-IoT dataset, specifically collected for a three-layered IoT system, and includes new types of attacks compared to other publicly available datasets. The performance analysis of the proposed model was carried out by a number of evaluation metrics such as accuracy, precision, recall, and F1-measure. Two optimization techniques, Adam and Adamax, were applied in the evaluation process of the model, and the Adam performance was found to be optimal. Moreover, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. The results show that the proposed system achieves an accuracy of 99% for network flow datasets and 98% for application layer datasets, demonstrating its superiority over previous IDS models.

Multi-step attack detection in industrial networks using a hybrid deep learning architecture.

In recent years, the industrial network has seen a number of high-impact attacks. To counter these threats, several security systems have been implemented to detect attacks on industrial networks. However, these systems solely address issues once they have already transpired and do not proactively prevent them from occurring in the first place. The identification of malicious attacks is crucial for industrial networks, as these attacks can lead to system malfunctions, network disruptions, data corruption, and the theft of sensitive information. To ensure the effectiveness of detection in industrial networks, which necessitate continuous operation and undergo changes over time, intrusion detection algorithms should possess the capability to automatically adapt to these changes. Several researchers have focused on the automatic detection of these attacks, in which deep learning (DL) and machine learning algorithms play a prominent role. This study proposes a hybrid model that combines two DL algorithms, namely convolutional neural networks (CNN) and deep belief networks (DBN), for intrusion detection in industrial networks. To evaluate the effectiveness of the proposed model, we utilized the Multi-Step Cyber Attack (MSCAD) dataset and employed various evaluation metrics.

Enhancing Patient Management and Financial Efficiency: Adherence to National Institute for Health and Care Excellence (NICE) Guidelines for Pediatric Distal Radius Buckle Fractures.

INTRODUCTION: Pediatric distal radius buckle fractures are commonly encountered in the emergency department (ED) and are considered non-complex and stable injuries. The National Institute for Health and Care Excellence (NICE) guidelines recommend managing these fractures with a soft cast and discharging patients directly from the ED. However, prevailing practices often involve rigid casts and follow-up clinic visits, leading to unnecessary congestion, prolonged waiting times, excessive radiographic examinations, and frequent cast changes, resulting in additional financial burdens on hospitals. METHODS AND MATERIALS: We conducted an initial audit over a 6-month period at Hull University and Teaching Hospitals, reviewing 184 pediatric distal radius fractures, of which 84 were buckle fractures in children under 12 years old. Data on demographics, subsequent clinic visits, treating doctor's grade, additional radiographs, initial and final treatment approaches, and cast change frequency were collected. After the initial audit, NICE guideline compliance was promoted through the education of parents and healthcare providers. A second audit was performed on patients within the following 6-month period. RESULTS: This study assessed the management of pediatric distal radius buckle fractures in a cohort of 84 patients. 39/84 (46.4%) of patients sought medical attention within one week of sustaining the injury, with 33/84 individuals being discharged during their first visit, either by consultants or registrars. Most patients (69/84) required only a single X-ray examination in the ED, while some needed two or three X-rays during their evaluation. However, after implementing NICE guidelines, in the second audit cycle, 62 out of 64 were discharged directly from the ED, with 42 receiving focal rigidity casts (FRCs) removed at home and 10 discharged with simple crepe bandages.  Conclusions: This closed-loop audit effectively showcased that adherence to NICE guidelines yielded better patient management by avoiding unnecessary visits, radiographs, and platers. The adoption of the guidelines leads to the conservation of time and resources.

Asian Zika virus can acquire generic African-lineage mutations during in utero infection.

The Zika virus 2015 epidemic showed an unusual phenotype for human flaviviruses, specifically fetal infection. We previously showed that in utero inoculation with the Asian Zika virus isolated from the human sample causes persistent infection in porcine fetuses. Here, we characterized the evolution of the Asian Zika virus in the fetal brain and placenta. Interestingly, the Asian Zika virus acquired generic African lineage K101R (A408G) and R1609 K (G4932A) mutations during in utero infection. Both African mutations were nonsynonymous and had a high frequency of nearly 100% in the fetal brain. Then, we synthetically generated the wild-type Asian variant and fetal brain-specific variant with generic African-lineage K101R and R1609 K mutations. In mosquito C6/36 cells, but not in human and pig cells, the fetal brain-specific variant showed higher virus loads compared to the Asian wild-type prototype. While in utero infection with both variants caused comparable virus loads in the placenta and amniotic fluids, fetuses injected with the fetal brain-specific variant had the trend to higher virus loads in lymph nodes. Also, introduced K101R and R1609 K mutations were stable and had high nearly 100% frequency at 28 days after in utero inoculation in both directly injected and trans-infected fetuses. These findings evoke concerns because Zika persists in pig herds and mosquitoes on farms in Mexico. It will be essential to identify how persistent in utero infection affects virus evolution and whether in utero-emerged Zika variants have the potential for shedding into the environment, more efficient transmission, and more aggressive infection phenotypes.

Basalt Fiber Reinforced Concrete: A Compressive Review on Durability Aspects.

The creation of sustainable composites reinforced with natural fibers has recently drawn the interest of both industrial and academics. Basalt fiber (BF) stands out as the most intriguing among the natural fibers that may be utilized as reinforcement due to their characteristics. Numerous academics have conducted many tests on the strength, durability, temperature, and microstructure characteristics of concrete reinforced with BF and have found promising results. However, because the information is dispersed, readers find it problematic to assess the advantages of BF reinforced concrete, which limits its applications. Therefore, a condensed study that provides the reader with an easy route and summarizes all pertinent information is needed. The purpose of this paper (Part II) is to undertake a compressive assessment of basalt fiber reinforced concrete's durability features. The results show that adding BF significantly increased concrete durability. The review also identifies a research deficiency that must be addressed before BF is used in practice.