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(1) Background: Distal pancreatectomy is a standard treatment for tumours of the pancreatic body and tail. Minimally invasive techniques for all types of pancreatic tumours (benign and malignant) are being established, while concerns regarding oncological safety, cost effectiveness and learning curves are being explored with prospective studies. This paper presents our unit's data in the context of the above concerns and provides a relevant narrative review of the current literature. (2) Methods: Data were collected retrospectively between 2014 and 2021 for all adult patients who underwent elective distal pancreatectomy in our tertiary care referral HPB Unit. Data on demographics, underlying pathology, perioperative variables and post-operative complications were collected and reported using descriptive statistics. On review of the Miami guidelines, four important but less validated areas regarding distal pancreatectomy are presented in light of the current evidence; these are recent randomised controlled trials, oncological safety, cost effectiveness and learning curves in minimally invasive distal pancreatectomy (MIDP). (3) Results: 207 patients underwent distal pancreatectomy in total from 2014–2021, with 114 and 93 patients undergoing open and minimally invasive techniques, respectively. 44 patients were operated on for PDAC in the open vs. 17 in the minimally invasive group. The operative time was 212 min for the open and 248 min for the minimally invasive group. The incidence of pancreatic fistula was higher in the minimally invasive group vs. the open group (16% vs. 4%). (4) Conclusions: Our unit's data conform with the published literature, including three randomised control trials. These published studies will not only pave the way for establishing minimally invasive techniques for suitable patients, but also define their limitations and indications. Future studies will inform us about the oncological safety, cost effectiveness, overall survival and learning curves regarding patients undergoing minimally invasive distal pancreatectomy. [ABSTRACT FROM AUTHOR]

Colorectal liver metastases (CRLM) afflict a significant proportion of patients with colorectal cancer (CRC), ranging from 25% to 30% of patients throughout the course of the disease. In recent years, there has been a surge of interest in the application of near-infrared fluorescence (NIRF) imaging as an intraoperative imaging technique for liver surgery. The utilisation of NIRF-guided liver surgery, facilitated by the administration of fluorescent dye indocyanine green (ICG), has gained traction in numerous medical institutions worldwide. This innovative approach aims to enhance lesion differentiation and provide valuable guidance for surgical margins. The use of ICG, particularly in minimally invasive surgery, has the potential to improve lesion detection rates, increase the likelihood of achieving R0 resection, and enable anatomically guided resections. However, it is important to acknowledge the limitations of ICG, such as its low specificity. Consequently, there has been a growing demand for the development of tumour-specific fluorescent probes and the advancement of camera systems, which are expected to address these concerns and further refine the accuracy and reliability of intraoperative fluorescence imaging in liver surgery. While NIRF imaging has been extensively studied in patients with CRLM, it is worth noting that a significant proportion of published research has predominantly focused on the detection of hepatocellular carcinoma (HCC). In this study, we present a comprehensive literature review of the existing literature pertaining to intraoperative fluorescence imaging in minimally invasive surgery for CRLM. Moreover, our analysis places specific emphasis on the techniques employed in liver resection using ICG, with a focus on tumour detection in minimal invasive surgery (MIS). Additionally, we delve into recent developments in this field and offer insights into future perspectives for further advancements. [ABSTRACT FROM AUTHOR]

Emerging technologies such as network function virtualization and software-defined networking (SDN) have made a phenomenal breakthrough in network management by introducing softwarization. The provision of assets to each virtualized network functions autonomously as well as efficiently and searching for an optimal pattern for traffic routing challenges are still under consideration. Unfortunately, the traditional methods for estimating the desired performance indicators are insufficient for a self-driven SDN. In the last decade, a combination of machine learning and cognitive techniques construct a knowledge plane (KP) for the Internet which introduces numerous benefits to networking, like automation and recommendation. Furthermore, the inclusion of KP to the conventional three planes SDN architectures recently has added another knowledge defined networking (KDN) architecture to drive an SDN autonomously. In this article, a self-driving system has been proposed based on KDN to achieve the selection of an optimal path for the deployment of service function chaining (SFC) and reactive traffic routing among the edge clouds. Considering the limited resource of edge clouds, the proposed system also maintains a balance among edge cloud resources while orchestrating SFC resources. The graph neural network has been also applied in the proposed system to recognize the composite relationship concerning topology, traffic features, and routing patterns for accurate estimation of key performance indicators. The proposed system improves resource utilization efficiency for SFC deployment by 20%, maximum network throughput by 5%, and CPU load by 13%. [ABSTRACT FROM AUTHOR]

Video cameras are among the most commonly used devices throughout the world which results in imaging technology being one of the most important areas for research and development. Imaging technology requires constant research as it is used in crucial applications such as video conferencing and surveillance. In the field of image processing, motion detection and estimation are fundamental steps in extracting information on objects segmented from their backgrounds. In this paper, a cohesive approach is presented that uses two algorithms for motion estimation and detection. The proposed method is able to detect moving objects using maximum average correlation height (MACH) filter. Upon obtaining the accurate coordinates of an object of interest from the MACH filter, the next part of the algorithm starts tracking the object. For tracking, a particle filter is used to estimate the motion of the object using a Markov chain. To enhance the accuracy of particle filter, an approximate proximal gradient algorithm is employed for unconstrained minimization of the particles which restricts the tracking process to target templates (most essential information) only. Finally, a comparison between the proposed algorithm and recent similar algorithms is made that demonstrates the minimization of tracking errors using the proposed technique. [ABSTRACT FROM AUTHOR]

Spontaneous occurrence of Tumor Lysis Syndrome (TLS) is very rare. Only 3 pediatric case reports were reported in literature. All 3 patients presented with therapy-sensitive disease. Unfortunately, only one patient survived. It is preventable in 100% of patients, but TLS is a hard nut to crack. We present three distinct cases of spontaneous tumor lysis syndrome within the pediatric population, each associated with different types of tumors. A 3-year-old boy with mediastinal germ cell tumor of stage III, had hyperuricemia and hyperphosphatemia on presentation, tumor lysis syndrome improved but later died with sepsis, another patient of 4-year-old girl with precursor B cell acute lymphoblastic lymphoma had hyperkalemia, hyperuricemia and hyperphosphatemia on presentation and third patient was 6-year-old boy with abdominal Burkitt lymphoma stage III presented with hyperkalemia, hyperuricemia, hyperphosphatemia and Acute Kidney Injury (AKI) before initiation of chemotherapy. Later both these patients improved and survived. Spontaneous tumor lysis syndrome has high mortality, so it is of utmost importance to diagnose it timely. Swift and effective responses to spontaneous TLS can significantly reduce the risk of mortality linked to this critical emergency condition. [ABSTRACT FROM AUTHOR]

Objectives: Treatment of cancer pain presents a challenging problem. Up to 90% of cancer patients can be treated with conventional pharmacological therapy. The remaining 10% of patients require additional interventions for management of their pain. Intrathecal chemical neurolysis (ICN) is a beneficial intervention in management of cancer pain. The primary objective of this study was to study the effects of pain relief of ICN on the Visual Analogue Score (VAS) of patients with metastatic cancer disease on palliative therapy. Methodology: This retrospective case series was performed at Shaukat Khanum Memorial Cancer Hospital & Research Centre, Lahore (Pakistan). We reviewed the medical records of thirty nine patients who underwent ICN at our institute. The mean VAS before procedure was 7.8. Results: After ICN, mean pain scores fell to 1.44 at first day, 2.23 at first week and 3.5 at first month. Mean opioid analgesic free period was 23.5 days. Conclusion: We conclude that ICN is a beneficial procedure for selected patients with intractable refractory pain. [ABSTRACT FROM AUTHOR]

Given the long traverse times and severe environmental constraints on a planet like Mars, the only option feasible now is to observe and explore the planet through more sophisticated planetary rovers. To achieve increasingly ambitious mission objectives under such extreme conditions, the rovers must have autonomy. Increased autonomy, obviously, relies on the quality of estimates of rover's state i.e. its position and orientation relative to some starting frame of reference. This research presents a state estimation approach based on Extended Kalman Filter (EKF) to fuse distance from odometry and attitude from an Inertial Measurement Unit (IMU), thus mitigating the errors generated by the use of either system alone. To simulate a Sun-sensor based approach for absolute corrections, a magnetic compass was used to give absolute heading updates. The technique was implemented on MotherBot, a custom-designed skid steered rover. Experimental results validate the application of the presented estimation strategy. It showed an error range within 3% of the distance travelled as compared to about 8% error obtained from direct fusion. [ABSTRACT FROM AUTHOR]

Left sided superior vena cava (SVC) is a rare anatomic variant of the normal anatomy of the heart and the venous system that has clinical implications. The presence of this variant is relevant to central venous catheter placement, cardioverter defibrillator placement, coronary artery bypass grafting, and numerous other medical procedures. In this report, we describe a rare case of left sided SVC, which was diagnosed during central venous catheter insertion. We also discuss the vascular and anesthetic considerations for individuals with this uncommon variant. [ABSTRACT FROM AUTHOR]

Selection of orthotopic liver transplantation (OLT) candidates is increasingly inclusive of patients with high BMI. We aim to characterize the influence of obesity on the surgical outcome measures of prolonged operative time and unplanned reoperation. We reviewed the records of obese and normal weight OLT recipients over a 10-year period from a single institution. Variables that trended (P < 0.1) with endpoints on univariate analysis were put into multivariate logistic regression models to determine independent association (P < 0.05). We included 195 obese and 171 normal weight OLT recipients in our study. On multivariate analysis, obesity was the only preoperative risk factor that trended with unplanned reoperation (odds ratio 2, P = 0.05). Similarly, only obesity remained independently associated with prolonged length of operation (defined as ≥275 minutes) on multivariate analysis (odds ratio 1.7, P = 0.04). In summary, obesity may make OLT more technically challenging and, thus, represents an independent risk factor for unplanned reoperations and prolonged operative time. [ABSTRACT FROM AUTHOR]

An experiment was conducted at the Research Farm of the Muhammad Nawaz Shareef University of Agriculture, Multan during the winter season of 2018-19 with the objective to evaluate weed dynamics, growth, and productivity of wheat crop under stale seedbed. This experiment was held under RCBD with three replications. The study consisted of eight treatments viz: T1: stale seedbed + no herbicide, T2: stale seedbed + glyphosate @ 711 a.i mL ha-1, T3: stale seedbed + paraquat-dichloride @ 494 a.i mL ha-1, T4: stale seedbed + atlantis (mesosulfuron-methyl and iodosulfuron-methyl-sodium) @ 15 a.i g ha-1, T5: stale seedbed + glyphosate @ 356 a.i mL ha-1 + paraquat @ 247 a.i mL ha-1, T6: stale seedbed + glyphosate @ 356 a.i mL ha-1 + atlantis @ 7.5 a.i g ha-1, T7: stale seedbed + paraquat @ 247 a.i mL ha-1 + atlantis @ 7.5 a.i g ha-1, T8: stale seedbed + glyphosate @ 237 a.i mL ha-1 + paraquat @ 165 a.i mL ha-1+ atlantis @ 5 a.i g ha-1. The highest WCE (84%), HEI (13.01), Pn (13.83 µmol CO2 m-2 s-1) and gs (234.33 mmol m-2 s-1) was recorded in plots treated with glyphosate + paraquat + atlantis. The highest NAR (3.60 gm-2day-1), LAI (4.82), GCR (7.87 gm-2day-1), LAD (186.43 days) was recorded in the treatment Stale seedbed + glyphosate + paraquat + atlantis. This treatment depicted the highest productive tillers m-2 (246.33), grains spike-1 (53.67), 100-grains weight (31.88 g), and grain yield 40% (4.65 Tons ha-1) in comparison with the no herbicide treated plots. From this study, it is concluded that using the stale seedbed, pre- and post-emergence herbicides in reduced doses can restrict weeds growth till 40 DAS to maximize wheat productivity. [ABSTRACT FROM AUTHOR]

Several power allocation algorithms for cooperative relay networks are presented in the literature. These contributions assume perfect channel knowledge and capacity achieving codes. However in practice, obtaining the channel state information at a relay or at the destination is an estimation problem and can generally not be error free. The investigation of the power allocation mechanism in a wireless network due to channel imperfections is important because it can severely degrade its performance regarding throughput and bit error rate. In this paper, the impact of imperfect channel state information on the power allocation of an adaptive relay network is investigated. Moreover, a framework including Automatic Repeat reQuest (ARQ) mechanism is provided to make the power allocation mechanism robust against these channel imperfections. For this framework, the end-to-end SNR is calculated considering imperfect channel knowledge using ARQ analytically. The goal is to emphasize the impact of imperfect channel knowledge on the power allocation mechanism. In this paper, the simulation results illustrate the impact of channel uncertainties on the average outage probability, throughput, and consumed sum power for different qualities of channel estimation. It is shown that the presented framework with ARQ is extremely robust against the channel imperfections. [ABSTRACT FROM AUTHOR]

Video visualization (VV) is considered to be an essential part of multimedia visual analytics. Many challenges have arisen from the enormous video content of cameras which can be solved with the help of data analytics and hence gaining importance. However, the rapid advancement of digital technologies has resulted in an explosion of video data, which stimulates the needs for creating computer graphics and visualization from videos. Particularly, in the paradigm of smart cities, video surveillance as a widely applied technology can generate huge amount of videos from 24/7 surveillance. In this paper, a state of the art algorithm has been proposed for 3D conversion from traffic video content to Google Map. Time-stamped glyph-based visualization is used effectively in outdoor surveillance videos and can be used for event-aware detection. This form of traffic visualization can potentially reduce the data complexity, having holistic view from larger collection of videos. The efficacy of the proposed scheme has been shown by acquiring several unprocessed surveillance videos and by testing our algorithm on them without their pertaining field conditions. Experimental results show that the proposed visualization technique produces promising results and found effective in conveying meaningful information while alleviating the need of searching exhaustively colossal amount of video data. [ABSTRACT FROM AUTHOR]

This article, published in the Qatar Medical Journal, discusses the incidence of right ventricular (RV) strain in pulmonary embolism (PE) in patients with Covid-19 pneumonia. The study analyzed 63 cases of PE in Covid-19 patients and found an RV strain incidence of 22.2% and a mortality rate of 25.4%. However, there was no significant association between RV strain and clot burden or mortality. The authors suggest that further research with a larger sample size is needed to fully understand the relationship between RV strain and PE in Covid-19 pneumonia. [Extracted from the article]

Background: Portomesenteric Vein Thrombosis (PMVT) following Laparoscopic Sleeve Gastrectomy (LSG) is an uncommon but potentially debilitating complication. Catheter-Directed Thrombolysis (CDT) has an evolving role in recanalizing the venous flow and preventing thrombus propagation. Therefore, it can be used as an alternative or in combination with systemic anticoagulants in selected patients. We report two trans-hepatic and trans-splenic CDT. The patient's clinical details, radiological findings, safety, and efficacy are reported. Cases presentation: Two patients presented to the Emergency Department (ED) within 14 days of surgery. The presenting complaints were generally nonspecific. The diagnosis of PMVT was established in both patients based on abdominal Contrast-Enhanced Computed Tomography (CECT). The two patients received a combined therapy of subcutaneous (SC) heparinization and CDT using a trans-hepatic approach in case 1 and a trans-splenic approach in case 2. Subsequent post-procedure venograms and CECT were performed and showed significant thrombus resolution. Both patients received oral anticoagulant therapy upon discharge with a successful overall recovery. Conclusion: PMVT is an infrequent and severe post LSG complication. Various approaches for re-establishing the portal venous flow have been described according to the severity of venous thrombosis. This article describes CDT therapy as a safe and effective option for treating PMVT in symptomatic patients. [ABSTRACT FROM AUTHOR]

Background: The learning curve of new surgical procedures has implications for the education, evaluation and subsequent adoption. There is currently no standardised surgical training for those willing to make their first attempts at minimally invasive pancreatic surgery. This study aims to ascertain the learning curve in minimally invasive pancreatic surgery. Methods: A systematic search of PubMed, Embase and Web of Science was performed up to March 2021. Studies investigating the number of cases needed to achieve author-declared competency in minimally invasive pancreatic surgery were included. Results: In total, 31 original studies fulfilled the inclusion criteria with 2682 patient outcomes being analysed. From these studies, the median learning curve for distal pancreatectomy was reported to have been achieved in 17 cases (10–30) and 23.5 cases (7–40) for laparoscopic and robotic approach respectively. The median learning curve for pancreaticoduodenectomy was reported to have been achieved at 30 cases (4–60) and 36.5 cases (20–80) for a laparoscopic and robotic approach respectively. Mean operative times and estimated blood loss improved in all four surgical procedural groups. Heterogeneity was demonstrated when factoring in the level of surgeon's experience and patient's demographic. Conclusions: There is currently no gold standard in the evaluation of a learning curve. As a result, derivations are difficult to utilise clinically. Existing literature can serve as a guide for current trainees. More work needs to be done to standardise learning curve assessment in a patient-centred manner. [ABSTRACT FROM AUTHOR]

In a vehicular ad hoc network (VANET), the vehicles communicate with each other to develop an intelligent transport system (ITS) which provides safety and convenience while driving. The major challenge of VANET is that the topology changes dynamically due to the high speed and unpredictable mobility of vehicles resulting in an inefficient real time message dissemination, especially in emergency scenarios such as in the accident event where it can cause high level of destruction. To the best of our knowledge, there is no such mechanism in existing literature which can handle real time multicast communication in VANET for both urban and highway scenarios. In this paper, we propose a novel real time vehicular communication (RTVC) framework which consists of a VANET cluster scheme (VCS) and VANET multicast routing (VMR) to achieve efficient vehicle communication within both urban and highway scenarios. The RTVC framework develops stable communication links and achieves high throughput with low overhead despite high mobility by combining the multicast routing with a unique cluster based scheme. In VCS, the cluster head (CH) is elected upon cluster threshold value (CTV) to disseminate the messages within the cluster members (CMs) and to other cluster heads by intercluster communication, which reduces the network overhead. In addition, the vehicles cluster head election (VCHE) procedure is proposed to reduce the number of CHs and CMs switches which results in lower overhead of maintaining the clusters. Moreover, another novelty of the framework is that the CTV of VCHE can be adjusted by speed adjustment factor (SAF) to achieve the desired cluster stability depending upon the required VANET application. The simulation results illustrate that the proposed framework has achieved the goal of stable, efficient, and real time communication despite highly dynamic environment of VANET. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) are autonomous and self-configurable wireless ad hoc networks and considered as a subset of Mobile Ad Hoc Networks (MANETs). MANET is composed of self-organizing mobile nodes which communicate through a wireless link without any network infrastructure. A VANET uses vehicles as mobile nodes for creating a network within a range of 100 to 1000 meters. VANET is developed for improving road safety and for providing the latest services of intelligent transport system (ITS). The development and designing of efficient, self-organizing, and reliable VANET are a challenge because the node’s mobility is highly dynamic which results in frequent network disconnections and partitioning. VANET protocols reduce the power consumption, transmission overhead, and network partitioning successfully by using multicast routing schemes. In multicasting, the messages are sent to multiple specified nodes from a single source. The novel aspect of this paper is that it categorizes all VANET multicast routing protocols into geocast and cluster-based routing. Moreover, the performance of all protocols is analyzed by comparing their routing techniques and approaches. [ABSTRACT FROM AUTHOR]

The automatic classification of bone texture into healthy or osteoporotic cases presents a major challenge since there is no visual difference between the two cases. This classification requires an inspection of the fine granularity in the bone radiographs which is usually difficult with a naked eye. We have proposed a novel method in this paper, that can be used for the classification of bone radiographs into healthy or osteoporotic cases. We mimic the observations of the physicians by preprocessing the bone radiographs with Gabor filters bearing a high frequency. Later, we design and utilize a convolutional neural network wherein filtered images are fed as input to the system which classifies the images into their respective classes. The proposed algorithm has been validated on a bone radiograph challenge dataset. Our results depict that the method proposed in this research exhibits very good results in terms of classification. A comparison of the proposed and the contemporary research methods has also been shown in this paper. The experimental results show that by exploiting high frequency Gabor filters and employing the convolutional neural network architecture, good results in performing the classification of bone radiographs are achieved. [ABSTRACT FROM AUTHOR]

Background: Interferon-gamma (IFN-γ) is an important mediator of type I immune response and has antiviral, immunoregulatory and anti-tumor properties, plays a wide range of roles in inflammation and autoimmune diseases. The aim of this study was to obtain monoclonal antibody (mAb) against caprine IFN-γ by immunizing of BALB/c mice with the purified rIFN-γ. Results: Recombinant caprine IFN-γ was expressed in Escherichia coli strain BL21 (DE3) and monoclonal antibodies against caprine IFN-γ were produced by immunizing of BALB/c mice with rIFN-γ. One hybridoma secreting mAb was screened by enzyme-linked immunosorbent assay (ELISA) which was designated as 2C. MAb secreted by this cell line were analyzed through ELISA, western blot and application of the mAb was evaluated by immunofluorescence analysis using goat lip tissues infected with Orf virus. ELISA analysis revealed that mAb 2C can specifically recognize rIFN-γ protein and culture supernatant of goat peripheral blood mononuclear cells (PBMCs) stimulated by concanavalin A (Con A) but cannot recognize the fusion tag protein of pET-32a. Western blot analysis showed that mAb 2C can specifically react with the purified 34.9 kDa rIFN-γ protein but does not react with the fusion tag protein of pET-32a. Immunofluorescence results demonstrated that mAb 2C can detect IFN-γ secreted in histopathological sites of goats infected with Orf virus. Conclusions: A caprine IFN-γ-specific mAb was successfully developed in this study. Further analyses showed that the mAb can be used to detect IFN-γ expression level during contagious ecthyma in goats. [ABSTRACT FROM AUTHOR]

Data and image segmentation plays pivotal role in the application of machine learning. k -means, as a tool for unsupervised clustering, is a widely used algorithm for segmentation due to its inherent simplicity and efficiency. k -means partitions datasets into subsets based on their fitness value. As such k -means is a well suited algorithm for implementation on hardware platform such as Field Programmable Gate Array (FPGA) but requires high computation time. Hardware accelerators can help in reducing the computation complexity of the algorithm. In this paper, we present a simplified multicore based scalable hardware architecture for implementation of k -means. Mean and fitness modules in proposed architecture are further unfolded to further enhance the speed of k -means clustering algorithm. The unfolding factor has to be selected by keeping the area of the target device in check. In the proposed architecture, the cores are further connected through Network on Chip (NoC) interconnect network which allows for higher scalability while elevating the bottleneck of message passing. The performance of our MPSoC architecture has been evaluated with respect to Average Speedup, Average Throughput and Area consumption with and without use of NoC interconnect. Finally, we compare the use of different NoC interconnect models with respect to maximum Operating Frequency, average Throughput and Area overhead. [ABSTRACT FROM AUTHOR]

The early prediction of Alzheimer's disease (AD) can be vital for the endurance of patients and establishes as an accommodating and facilitative factor for specialists. The proposed work presents a robotized predictive structure, dependent on machine learning (ML) methods for the forecast of AD. Neuropsychological measures (NM) and magnetic resonance imaging (MRI) biomarkers are deduced and passed on to a recurrent neural network (RNN). In the RNN, we have used long short-term memory (LSTM), and the proposed model will predict the biomarkers (feature vectors) of patients after 6, 12, 21 18, 24, and 36 months. These predicted biomarkers will go through fully connected neural network layers. The NN layers will then predict whether these RNN-predicted biomarkers belong to an AD patient or a patient with a mild cognitive impairment (MCI). The developed methodology has been tried on an openly available informational dataset (ADNI) and accomplished an accuracy of 88.24%, which is superior to the next-best available algorithms. [ABSTRACT FROM AUTHOR]

Motion pattern analysis can be performed automatically on the basis of object trajectories by means of tracking videos; an effective approach to analyse and to model the traffic behaviour; is important to describe motion by taking the whole trajectory whereas it’s more essential to identify and evaluate object behaviour online. In this paper, pattern detection approach is presented which takes spatio-temporal characteristic of vehicle trajectories. A real time system is built to infer and track the object behaviour quickly by online performing trajectory analysis. Every independent vehicle in the video frame is tracked over time. As the anomaly behaviour occurs, glyph is generated to show it occurrences. Vehicle counting is done by estimating the trajectories and compared with Hungarian tracker. Several surveillance videos are taken into account for the performance checking of system. Experimental results demonstrated that proposed method in comparison with the state of the art algorithms, provides robust vehicle density estimation and event information i.e., lane change information. [ABSTRACT FROM AUTHOR]