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Vehicle-to-everything (V2X) communication is essential in 5G and upcoming networks as it enables seamless interaction between vehicles and infrastructure, ensuring the reliable transmission of critical and time-sensitive data. Challenges like unstable communication in highly mobile vehicular networks, limited channel state information, high transmission overhead, and significant communication costs hinder vehicle-to-vehicle (V2V) communication. To tackle these issues, a unified approach utilizing distributed deep reinforcement learning is proposed to enhance the overall network performance while meeting the quality of service (QoS), latency, and rate requirements. Recognizing the complexity of this NP-hard, non-convex problem, a machine learning framework based on the Markov decision process (MDP) is adopted for a robust strategy. This framework facilitates the formulation of a reward function and the selection of optimal actions with certainty. Furthermore, a spectrum-based allocation framework employing multi-agent deep reinforcement learning (MADRL) is confidently introduced. The deep deterministic policy gradient (DDPG) within this framework enables the exchange of historical data globally during the primary learning phase, effectively removing the need for signal interaction and manual intervention in optimizing system efficiency. The data transmission policy follows an augmented online policy scheme, known as the proximal online policy scheme (POPS), which confidently reduces the computational complexity during the learning process. The complexity is marginally adjusted using the clipping substitute technique with assurance in the learning phase. Simulation results validate that the proposed method outperforms existing decentralized systems in achieving a higher average data transmission rate and ensuring quality of service (QoS) satisfaction confidently. [ABSTRACT FROM AUTHOR]

Devices that are employed in applications related to the Internet of Things (IoT) are constrained by limited energy resources. Consequently, ensuring a continuous supply of energy while also maintaining uninterrupted connectivity within IoT units (IoTUs) is of great importance. In this particular context, we present a scheme that facilitates both, the transfer of wireless power and the transmission of information for IoTUs along with the capability of harvesting solar energy. This scheme is further supported by the utilization of unmanned aerial vehicles (UAV) and the deployment of reconfigurable intelligent surfaces (RIS) for communication purposes. To be more precise, initially, IoTUs obtain energy from the UAV through the process of wireless power transmission (WPT). Subsequently, in the second stage, the UAV retrieves data from the IoTUs using transmitting information. In order to simplify the complexity of the communication issue, we assume that a solar-powered UAV remains stationary at a predetermined altitude. Our objective is to maximize the energy efficiency (EE) of the entire network by coordinating the scheduling of IoTU energy harvesting (EH) and UAV trajectory optimization. We suggest a multi-agent federated reinforcement learning (MFRL) algorithm that maximizes EE through parameter optimization in order to achieve this goal. By utilizing the collective experiences of several agents and reducing energy usage, this algorithm also improves the overall performance of the system. The proposed technique achieves 96.3% and 97.5% accuracy in communication rounds and RIS elements, with a 9 − 33 % increase in EE compared to the best-performing benchmark scheme. The suggested approach outperforms the benchmark algorithms in terms of EE, trajectory optimization, and learning accuracy, according to simulation findings. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) infrastructure is inherently complex, often involving a multitude of sensors and devices. Ensuring the secure operation and maintenance of these systems is increasingly critical, making anomaly detection a vital tool for guaranteeing the success of IIoT deployments. In light of the distinctive features of the IIoT, graph-based anomaly detection emerges as a method with great potential. However, traditional graph neural networks, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), have certain limitations and significant room for improvement. Moreover, previous anomaly detection methods based on graph neural networks have focused only on capturing dependencies in the spatial dimension, lacking the ability to capture dynamics in the temporal dimension. To address these shortcomings, we propose an anomaly detection method based on Spatio-Temporal Gated Attention Networks (STGaAN). STGaAN learns a graph structure representing the dependencies among sensors and then utilizes gated graph attention networks and temporal convolutional networks to grasp the spatio-temporal connections in time series data of sensors. Furthermore, STGaAN optimizes the results jointly based on both reconstruction and prediction loss functions. Experiments on public datasets indicate that STGaAN performs better than other advanced baselines. We also visualize the learned graph structures to provide insights into the effectiveness of graph-level anomaly detection. [ABSTRACT FROM AUTHOR]

A circular Pythagorean fuzzy set is a very dominant and reliable technique for coping with uncertain and vague information in genuine life problems because it contains the membership function, non-membership function, and radius between both grades with a condition that is the sum of the pair will be contained in the unit interval. The objective of this article is to define some improved Dombi operational laws based on circular improved Pythagorean fuzzy values. Moreover, we discuss some drawbacks of the existing Dombi aggregation operators based on circular improved Pythagorean fuzzy numbers. We also defined some improved Dombi averaging and geometric aggregation operators and their properties. Later on, a technique of evaluation based on distance from the average solution is stated by using the proposed operators. Additionally, we resolve the problem of policy analysis and decision support systems based on multi-attribute decision-making problems for initiated techniques to enhance the worth of the derived theory. Finally, we illustrate some examples for showing the supremacy and validity of the proposed theory with the help of comparative analysis between proposed ranking values and some existing ranking values. [ABSTRACT FROM AUTHOR]

In the Vehicle-to-Grid (V2G) scenario, a multitude of coordinated electric vehicles (EVs) equipped with high-capacity batteries actively participate in power grid dispatching as energy carriers, aiming to achieve a tripartite objective encompassing peak load reduction and valley filling, enhanced utilization of renewable energy sources, and added benefits for electric vehicle owners. To address the existing limitations in the charging–discharging decision-making process for electric vehicles based on V2G, such as the lack of consideration for charging pile constraints, EV profitability, EV transportation timeliness, and high costs associated with central servers, we proposed a reinforcement learning-based Multi-vehicle Joint Routing and Charging–Discharging Decision algorithm (MJRCDD). Firstly, the Markov decision process (MDP) was established to describe the problem, and the route selection and charging–discharging behavior of the vehicle were innovatively integrated in the vehicle action space. Secondly, the multi-vehicle joint route planning and charging–discharging decision problem was solved by multi-agent reinforcement learning. Finally, the effectiveness of MJRCDD was verified by simulation and comparison experiments based on PeMS. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Ophthalmology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unicystic ameloblastoma is a distinct entity of ameloblastoma characterized by slow growth and locally aggressive behavior. This retrospective study aimed to assess the efficacy of different treatment modalities of unicystic ameloblastoma, focusing on clinico-radiological and histopathological features. Data from patients diagnosed with unicystic ameloblastoma were retrospectively analyzed. Patients were categorized into luminal and intraluminal (Group A) and mural (Group B) variants based on the Ackermann classification, which has a significant influence on their biological behavior, treatment approaches, and prognosis. Patients in Group A underwent enucleation with chemical cauterization, peripheral ostectomy, and iodoform packing, whereas those in Group B were treated with resection and reconstruction. Post-operatively, the patients were subjected to radiographic assessments via digital orthopantomogram at regular intervals. Because of the rarity of unicystic ameloblastoma, only 17 patients were included in the study (Group A: 9 patients; Group B: 8 patients), with a mean follow-up of 4.9 years (range: 1.4–11.8 years). The primary outcome measure was the absence of recurrence, which indicated treatment success. No patient in either group experienced recurrence within the follow-up period. This study provides evidence supporting the successful treatment of luminal and intraluminal variants of unicystic ameloblastoma in young individuals using a conservative approach. However, the more aggressive mural variant demonstrated favorable outcomes with radical treatment. These findings emphasize the importance of the Ackermann classification in guiding treatment decisions for unicystic ameloblastoma and contribute valuable insights into optimizing therapeutic strategies based on clinico-radiological and histopathological findings. [ABSTRACT FROM AUTHOR]

To comprehensively explore the physio-biochemical and molecular changes of paclobutrazol (PBZ) at the ideal dose under water deficit stress (WDS) conditions, we investigated the effects of 100 mg/kg PBZ applied via drenching on various physio-biochemical and molecular parameters in three rice varieties (N22, IR64, and IR64 DTY1.1) under both mild [75%‒80% relative water content (RWC)] and severe (60%‒65% RWC) WDS conditions. The results showed that PBZ treatment positively influenced the physio-biochemical parameters, significantly increasing dry matter (16.27%‒61.91%), RWC (6.48%‒ 16.34%), membrane stability index (4.37%‒10.35%), and total chlorophyll content (8.97%‒29.09%) in the rice varieties under both mild and severe WDS. Moreover, PBZ treatment reduced drought susceptibility (0.83‒0.95) and enhanced drought tolerance efficiency (60.92%‒86.78%), indicating its potential as a stress-mitigating agent. Global methylation analysis revealed changes in DNA methylation patterns, indicating the regulatory influence of PBZ on gene expression. The expression analysis of genes involved in the diversification of geranylgeranyl pyrophosphate towards the biosynthesis of abscisic acid, gibberellin acid, and chlorophyll showed alterations in their expression levels, suggesting the involvement of PBZ in the isoprenoid pathway. Overall, this study provides valuable insights into the potential mechanisms by which PBZ modulates physiological and molecular responses in rice plants under WDS. The findings highlight the importance of PBZ as a promising agent for enhancing drought tolerance in rice and offer valuable information for future research in crop stress management. [ABSTRACT FROM AUTHOR]

This study aimed to use natural language processing to predict the presence of intra-abdominal injury using unstructured data from electronic medical records. This was a random-sample retrospective observational cohort study leveraging unstructured data from injured patients taken to one of 9 acute care hospitals in an integrated health system between 2015 and 2021. Patients with International Classification of Diseases External Cause of Morbidity codes were identified. History and physical, consult, progress, and radiology report text from the first 8 hours of care were abstracted. Annotator dyads independently annotated encounters' text files to establish ground truth regarding whether intra-abdominal injury occurred. Features were extracted from text using natural language processing techniques, bag of words, and principal component analysis. We tested logistic regression, random forests, and gradient boosting machine to determine accuracy, recall, and precision of natural language processing to predict intra-abdominal injury. A random sample of 7,000 patient encounters of 177,127 was annotated. Only 2,951 had sufficient information to determine whether an intra-abdominal injury was present. Among those, 84 (2.9%) had an intra-abdominal injury. The concordance between annotators was 0.989. Logistic regression of features identified with bag of words and principal component analysis had the best predictive ability, with an area under the receiver operating characteristic curve of 0.9, recall of 0.73, and precision of 0.17. Text features with greatest importance included "abdomen," "pelvis," "spleen," and "hematoma." Natural language processing could be a screening decision support tool, which, if paired with human clinical assessment, can maximize precision of intra-abdominal injury identification. [ABSTRACT FROM AUTHOR]

Identification of patients at risk for atrial fibrillation (AF) after typical atrial flutter (tAFL) ablation is important to guide monitoring and treatment. The purpose of this study was to create and validate a risk score to predict AF after tAFL ablation We identified patients who underwent tAFL ablation with no AF history between 2017 and 2022 and randomly allocated to derivation and validation cohorts. We collected clinical variables and measured conduction parameters in sinus rhythm on an electrophysiology recording system (CardioLab, GE Healthcare). Univariate and multivariate logistic regressions (LogR) were used to evaluate association with AF development. A total of 242 consecutive patients (81% male; mean age 66 ± 11 years) were divided into derivation (n =142) and validation (n = 100) cohorts. Forty-two percent developed AF over median follow-up of 330 days. In multivariate LogR (derivation cohort), proximal to distal coronary sinus time (pCS-dCS) ≥70 ms (odds ratio [OR] 16.7; 95% confidence interval [CI] 5.6–49), pCS time ≥36 ms (OR 4.5; 95% CI 1.5–13), and CHADS 2 -VASc score ≥3 (OR 4.3; 95% CI 1.6–11.8) were independently associated with new AF during follow-up. The Atri-Risk Conduction Index (ARCI) score was created with 0 as minimal and 4 as high-risk using pCS-dCS ≥70 ms = 2 points; pCS ≥36 ms = 1 point; and CHADS 2 -VASc score ≥3 = 1 point. In the validation cohort, 0% of patients with ARCI score = 0 developed AF, whereas 89% of patients with ARCI score = 4 developed AF. We developed and validated a risk score using atrial conduction parameters and clinical risk factors to predict AF after tAFL ablation. It stratifies low-, moderate-, and high-risk patients and may be helpful in individualizing approaches to AF monitoring and anticoagulation. [ABSTRACT FROM AUTHOR]

Poor accessibility of health care facilities is a major barrier for differently abled people when seeking health care. Yet, accessibility is rarely audited. This study reports findings from the first assessment of the accessibility in a health care institution of national importance. This study also assumes importance from various laws and legislation that assure equality and rights for people with disabilities (PWDs). Keeping the objectives in mind, this study was performed with an aim to study the situational analysis of the health institution of national importance for assessing the compliance of hospital premises for being disabled friendly, to find any lacunae, and to suggest remedial measures based on the study finding. The aim was to conduct a content and quality review of research into the hospital experiences of PWD and to identify gaps. This was a hospital-based cross sectional study done in a period of 30 days. The gap analysis was performed with validated checklist provided by Central Public Works Department, Government of India (CPWD). Out of total 126 pointers, 45 pointers were found to be totally compliant, 30 pointers were found to be partially compliant, and remaining 51 pointers were found to be not applicable to the hospitals. This assessment of the accessibility of health care facilities showed that that it is feasible to undertake these audits on a large scale, and these audits should be repeated in other settings. It highlights important gaps in accessibility, increasing the risk of the violation of the right to health of PWDs. [ABSTRACT FROM AUTHOR]

The fifth generation (5G) network enabled communication between devices has emerged as a state-of-the-art technology. In the era of proliferating smart devices and intelligent wireless communication networks, Reflecting Intelligent Surfaces (RIS) and Unpiloted Air Vehicles (UAV) duplet has turn out to be a trustworthy, lucrative and handy solution for various appearing real world communication issues. This article pitches into the downlink UAV communication empowered by RIS, where UAV communicates with Mobile Instruments (MI) via RIS patches installed at a tall tower. Considering the attributes like transmitted power and UAV trajectory, Deep Reinforcement Learning (DRL) based approach is recommended to maximize the overall Sum-rate. In present scenario, DRL technology has popped up as a commanding tool that allows a network to regulate itself in order to deliver optimum solution. In this article, we have proposed a novel viewpoint evolved from Deep Deterministic Policy Gradient (D-DPG) Algorithm specifically Shared Deep Deterministic Policy Gradient (SD-DPG) algorithm for downlink UAV-MI power allocation and trajectory optimization problem. Numerical outcomes manifest that our model, concerned to maximizing sum-rate, outperformed other DRL based method DD-DPG by at least 30% and D-DPG by approximately 3 folds together with optimizing power, phase-shift and UAV trajectory. [ABSTRACT FROM AUTHOR]

Simultaneous wireless information and power transfer (SWIPT) is a method through which users can simultaneously obtain energy and receive data from the base station (BS). This allows them to charge their batteries, which have limited power capacity, and enhance the quality of service (QoS). Nevertheless, this technology encounters the double near-far issue, where users located at the edges of the cell experience insufficient energy due to energy path loss, resulting in a decrease in system performance. In this investigation, we integrate the self-energy recycling technology with a D2D communication system that enables full-duplex operation. By employing this technology, the D2D cell edge user (DCEU) can harvest energy from self-interference and utilize it to transmit data to the nearest D2D cell center user (DCCU). The DCCU combines its data with the DCEU and sends it to the BS using uplink non-orthogonal multiple access (NOMA). Both the DCCU and DCEU use a significant amount of energy when transmitting data, which negatively affects the quality of service (QoS). The goal of this research is to decrease the overall energy consumption of the DCEU and DCCU while maintaining their QoS. The defined problem is complex due to time and power limitations, as well as the presence of self and NOMA interference. To address this issue, we utilize a technique that combines time and power allocation. The numerical results demonstrated that the proposed scheme consumes 28.12% and 40.67% lesser amount of energy as compared to the baseline schemes. [ABSTRACT FROM AUTHOR]

Vibration-controlled transient elastography (VCTE) is used in clinical practice to risk-stratify liver transplant (LT) recipients; however, there are currently little data demonstrating the relationship between VCTE and clinical outcomes. A total of 362 adult LT recipients with successful VCTE examination between 2015 and 2022 were included. Presence of advanced fibrosis was defined as liver stiffness measurement (LSM) ≥10.5 kPa and hepatic steatosis as controlled attenuation parameter (CAP) ≥270 dB/m. The outcomes of interest included all-cause mortality, myocardial infarction (MI), and graft cirrhosis using cumulative incidence analysis that accounted for the competing risks of these outcomes. The LSM was elevated in 64 (18%) and CAP in 163 (45%) LT recipients. The baseline LSM values were similar in patients with elevated vs normal CAP values. After a median follow-up of 65 (interquartile range, 20–140) months from LT to baseline VCTE, 66 (18%) patients died, 12 (3%) developed graft cirrhosis, and 18 (5%) experienced an MI. Baseline high LSM was independently associated with all-cause mortality (hazard ratio [HR], 1.97; 95% confidence interval [CI], 1.11–3.50; P =.02) and new onset cirrhosis (HR, 6.74; 95% CI, 2.08–21.79; P <.01). A higher CAP value was significantly and independently associated with increased risk of experiencing a MI over study follow-up (HR, 4.14; 95% CI, 1.29–13.27; P =.017). The VCTE-based parameters are associated with clinical outcomes and offer the potential to be incorporated into clinical risk-stratification strategies to improve outcomes among LT recipients. [Display omitted] [ABSTRACT FROM AUTHOR]

Access to MRI in low- and middle-income countries (LMICs) remains among the poorest in the world. The lack of skilled MRI personnel exacerbates access gaps, reinforcing long-standing health disparities. The Scan With Me (SWiM) program aims to sustainably create a network of highly skilled MRI technologists in LMICs who will facilitate the transfer of MRI knowledge and skills to their peers and contribute to the implementation of highly valuable imaging protocols for effective clinical and research use. The program introduces a case-based curriculum designed using a novel train-the-trainer approach, integrated with peer-collaborative learning to upskill practicing MRI technologists in LMICs. The 6-week curriculum uses the teach-try-use approach, which combines self-paced didactic lectures covering the basics of MR image acquisition (teach) with hands-on expert-guided scanning experience (try) and the implementation of protocols tailored to provide the best possible images on their infrastructures (use). Each program includes research translation skills training using an established advanced MRI technique relevant to LMICs. A pilot program focused on cardiac MRI (CMR) was conducted to assess the program's curriculum, delivery, and evaluation methods. Forty-three MRI technologists from 16 LMICs participated in the pilot CMR program and, over the course of the training, implemented optimized CMR protocols that reduced acquisition times while improving image quality. The training resources and scanner-specific standardized protocols are published openly for public use in an online repository. In general, at the end of the program, learners reported considerable improvements in CMR knowledge and skills. All respondents to the program evaluation survey agreed to recommend the program to their colleagues, while 87% indicated interest in returning to help train others. The SWiM program is the first master class in MRI acquisition for practicing imaging technologists in LMICs. The program holds the potential to help reduce disparities in MRI expertise and access. The support of the MRI community, imaging societies, and funding agencies will increase its reach and further its impact in democratizing MRI. [ABSTRACT FROM AUTHOR]

Vascular surgeons are often called upon to provide emergent surgical assistance to other specialties for iatrogenic complications, both intraoperatively and in the inpatient setting. The management of iatrogenic vascular injury remains a critical role of the vascular surgeon, especially in the context of the increasing adoption of percutaneous procedures by other specialties. This study aims to characterize consultation timing, management, and outcomes for iatrogenic vascular injuries. This study identified patients for whom vascular surgery was consulted for iatrogenic vascular complications from February 1, 2022, to May 12, 2023. Patient information, including demographic information, injury details, and details of any operative intervention, was retrospectively collected from February 1, 2022, to October 13, 2022, and prospectively collected for the remainder of the study period. Analyses were performed with R (version 2022.02.03). There were 87 patients with consultations related to iatrogenic vascular injury. Of these, 42 (46%) were female and the mean age was 59 years (±18 years). The most common consulting services were cardiology (32%), cardiothoracic surgery (26%), general surgery (8%), and neurointerventional radiology (10%). Reasons for consultation included hemorrhage (36%), limb ischemia (36%), and treatment of pseudoaneurysm (23%). A total of 24% of consults were intraoperative, 20% of consults related to extracorporeal membrane oxygenation cannulation, and 16% of consults related to ventricular assist devices including left ventricular assist device and intra-aortic balloon pump. The majority of these consult requests (60%) occurred during evening and night hours (5 PM to 7 AM). Emergent intervention was required in 62% of cases and consisted of primary open surgical repair of arterial injury (54%), endovascular intervention (21%), and open thromboembolectomy (15%). Overall, in-hospital mortality for the patient cohort was 20% and the reintervention rate was 23%, reflecting the underlying complexity of the illness and nature of the vascular injury in this patient group. Vascular surgeons play an essential role in managing emergent life-threatening hemorrhagic and ischemic iatrogenic vascular complications in the hospitalized setting. The complications require immediate bedside or intraoperative consult and often emergent open surgical or endovascular intervention. Furthermore, many of these require urgent management in the evening or overnight hours, and therefore the high frequency of these events represents a potential significant resource utilization and workforce issue to the vascular surgery workforce. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Ophthalmology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Personalized medicine has witnessed remarkable progress with the emergence of RNA therapy, offering new possibilities for the treatment of various diseases, and in particular in the context of cardiovascular disease (CVD). The ability to target the human genome through RNA manipulation offers great potential not only in the treatment of cardiac pathologies but also in their diagnosis and prevention, notably in cases of hyperlipidemia and myocardial infarctions. While only a few RNA-based treatments have entered clinical trials or obtained approval from the US Food and Drug Administration, the growing body of research on this subject is promising. However, the development of RNA therapies faces several challenges that must be overcome. These include the efficient delivery of drugs into cells, the potential for immunogenic responses, and safety. Resolving these obstacles is crucial to advance the development of RNA therapies. This review explores the newest developments in medical studies, treatment plans, and results related to RNA therapies for heart disease. Furthermore, it discusses the exciting possibilities and difficulties in this innovative area of research. [ABSTRACT FROM AUTHOR]

Radiomics uses advanced mathematical analysis of pixel-level information from radiologic images to extract existing information in traditional imaging algorithms. It is intended to find imaging biomarkers related to the genomics of tumors or disease patterns that improve medical care by advanced detection of tumor response patterns in tumors and to assess prognosis. Radiomics expands the paradigm of medical imaging to help with diagnosis, management of diseases and prognostication, leveraging image features by extracting information that can be used as imaging biomarkers to predict prognosis and response to treatment. Radiogenomics is an emerging area in radiomics that investigates the association between imaging characteristics and gene expression profiles. There are an increasing number of research publications using different radiomics approaches without a clear consensus on which method works best. We aim to describe the workflow of radiomics along with a guide of what to expect when starting a radiomics-based research project. [ABSTRACT FROM AUTHOR]

For psoriatic patients who need to receive nonlive or live vaccines, evidence-based recommendations are needed regarding whether to pause or continue systemic therapies for psoriasis and/or psoriatic arthritis. To evaluate literature regarding vaccine efficacy and safety and to generate consensus-based recommendations for adults receiving systemic therapies for psoriasis and/or psoriatic arthritis receiving nonlive or live vaccines. Using a modified Delphi process, 22 consensus statements were developed by the National Psoriasis Foundation Medical Board and COVID-19 Task Force, and infectious disease experts. Key recommendations include continuing most oral and biologic therapies without modification for patients receiving nonlive vaccines; consider interruption of methotrexate for nonlive vaccines. For patients receiving live vaccines, discontinue most oral and biologic medications before and after administration of live vaccine. Specific recommendations include discontinuing most biologic therapies, except for abatacept, for 2-3 half-lives before live vaccine administration and deferring next dose 2-4 weeks after live vaccination. Studies regarding infection rates after vaccination are lacking. Interruption of antipsoriatic oral and biologic therapies is generally not necessary for patients receiving nonlive vaccines. Temporary interruption of oral and biologic therapies before and after administration of live vaccines is recommended in most cases. [ABSTRACT FROM AUTHOR]

The Medical Record (MR) contains the information which is needed to plan, provide, and evaluate the care given to the individual. It also serves as a pivotal tool for communicating information to all the health personnel who manage the patient, and it contributes to the continuity of patient care. There is an unmet need of identifying and correcting the issues faced with MR and Medical Records Departments (MRDs) so that higher efficiency can be achieved. This study was conducted to study the deficiencies and discrepancies found in MRD files during COVID management and to correlate the deficiencies with the facilities available and the workflow. Later Lean Management (LM) was applied to ensure compliance and efficiency in the system. An observational study was done on the audit of COVID 19 patient files and facilities in the care centres. Process mapping was done. The data for LM were collected by brainstorming, observation, interview, and workflow review of several processes, values, number of wastes, and suggestions were documented the MRD staff. Area available was 400 m2 which is adequate against the norm of 350 m2. The existing staff of 30 was adequate as per norms. Deficiencies were observed in physical examination, history, radiology, and laboratory reports. The findings showed that the MRD units had 13 current processes, 26 wastes, and 10 values were identified. In addition, they were offered a total of 25 comments on eliminating the waste. Staff and equipment were adequate. Recommendations include regular staff training and usage of electronic medical records, focus on deficiency check by specific MRD staff on regular basis monitored by the administration and supported by the medical audit committee. The study also recommends that suggestions applied after LM should be implemented in letter and spirit and a repeat study of LM is advisable after regular intervals to maintain the quality standards and to maintain or further improve the efficiency. [ABSTRACT FROM AUTHOR]

Microvascular resistance reserve (MRR) can characterize coronary microvascular dysfunction (CMD); however, its prognostic impact in ST-segment elevation myocardial infarction (STEMI) patients remains undefined. This study sought to investigate the prevalence of CMD in STEMI patients and to elucidate the prognostic performance of MRR. This prospective cohort study enrolled 210 STEMI patients with multivessel disease who underwent successful revascularization and returned at 3 months for coronary physiology assessments with bolus thermodilution. The prevalence of CMD (MRR <3) and the association between MRR and major adverse cardiovascular and cerebrovascular events (MACCEs) at 12 months were investigated. The median age of patients was 65 years, and 59.5% were men. At the 3-month follow-up, 56 patients (27%) had CMD (MRR <3.0). The number of MACCEs at 12 months was higher in patients with vs without CMD (48.2% vs 11.0%; P < 0.001). MRR was independently associated with 12-month MACCEs (HR: 0.45 per unit increase; 95% CI: 0.31-0.67; P < 0.001) and with stroke, heart failure, and poorer recovery in left ventricular systolic function. The areas under the receiver-operating characteristic curves for predicting MACCEs at 12 months with fractional flow reserve, coronary flow reserve (CFR), the index of microvascular resistance (IMR), and MRR were 0.609, 0.762, 0.781, and 0.743, respectively. The prognostic performance of CFR, IMR, and MRR were all comparable. The novel parameter MRR is a prognostic marker of MACCEs in STEMI patients with a comparable performance to CFR and IMR. (Impact of TMAO Serum Levels on Hyperemic IMR in STEMI Patients [TAMIR]; NCT05406297) [Display omitted] [ABSTRACT FROM AUTHOR]

Population aging is one of the most important demographic transformations of our time. Increasing the "health span"—the proportion of life spent in good health—is a global priority. Biological aging comprises molecular and cellular modifications over many years, which culminate in gradual physiological decline across multiple organ systems and predispose to age-related illnesses. Cardiovascular disease is a major cause of ill health and premature death in older people. The rate at which biological aging occurs varies across individuals of the same age and is influenced by a wide range of genetic and environmental exposures. The authors review the hallmarks of biological cardiovascular aging and their capture using imaging and other noninvasive techniques and examine how this information may be used to understand aging trajectories, with the aim of guiding individual- and population-level interventions to promote healthy aging. [Display omitted] • Biological cardiovascular aging is complex, nonlinear, and has variable relationship to chronological age. • The phenotypic alterations of the aging heart may be captured and tracked using imaging and other noninvasive techniques. • Cardiovascular biomarkers may be used as predictors to estimate biological heart age distinct from chronological age; this information can be used to identify individuals who deviate from predicted healthy aging trends, and to investigate the determinants of cardiovascular aging. • Further research is required to examine the prognostic utility of biological age estimates over chronological age and easily measured demographics. [ABSTRACT FROM AUTHOR]

With expansion of academic cancer center networks across geographically-dispersed sites, ensuring high-quality delivery of care across all network affiliates is essential. We report on the characteristics and efficacy of a radiation oncology peer-review quality assurance (QA) system implemented across a large-scale multinational cancer network. Since 2014, weekly case-based peer-review QA meetings have been standard for network radiation oncologists with radiation oncology faculty at a major academic center. This radiotherapy (RT) QA program involves pre-treatment peer-review of cases by disease site, with disease-site subspecialized main campus faculty members. This virtual QA platform involves direct review of the proposed RT plan as well as supporting data, including relevant pathology and imaging studies for each patient. Network RT plans were scored as being concordant or nonconcordant based on national guidelines, institutional recommendations, and/or expert judgment when considering individual patient-specific factors for a given case. Data from January 1, 2014, through December 31, 2019, were aggregated for analysis. Between 2014 and 2019, across 8 network centers, a total of 16,601 RT plans underwent peer-review. The network-based peer-review case volume increased over the study period, from 958 cases in 2014 to 4,487 in 2019. A combined global nonconcordance rate of 4.5% was noted, with the highest nonconcordance rates among head-and-neck cases (11.0%). For centers that joined the network during the study period, we observed a significant decrease in the nonconcordance rate over time (3.1% average annual decrease in nonconcordance, P = 0.01); among centers that joined the network prior to the study period, nonconcordance rates remained stable over time. Through a standardized QA platform, network-based multinational peer-review of RT plans can be achieved. Improved concordance rates among newly added network affiliates over time are noted, suggesting a positive impact of network membership on the quality of delivered cancer care. [ABSTRACT FROM AUTHOR]

Rhinoplasty enhances facial symmetry and functionality. However, the accurate and reliable quantification of nasal defects pre-surgery remains an ongoing challenge. This study introduces a novel approach for defect quantification using 2D images and artificial intelligence, providing a tool for better preoperative planning and improved surgical outcomes. A pre-trained AI model for facial landmark detection was utilised on a dataset of 250 images of male patients aged 18 to 24 who underwent rhinoplasty for cosmetic nasal deformity correction. The analysis concentrated on 36 different distances between the facial landmarks. These distances were normalised using min-max scaling to counter image size and quality variations. Post-normalisation, statistical parameters, including mean, median, and standard deviation, were calculated to identify and quantify nasal defects. The methodology was tested and validated using images from different ethnicities and regions, showing promising potential as a beneficial surgical aid. The normalised data produced reliable quantifications of nasal defects (average 76.2%), aiding in preoperative planning and improving surgical outcomes and patient satisfaction. The developed method can be extended to other facial plastic surgeries. Furthermore, it can be used to create app-based software, assist medical education, and improve patient-doctor communication. This novel method for defect quantification in rhinoplasty using AI and image processing holds significant potential in improving surgical planning, outcomes, and patient satisfaction, marking an essential step in the fusion of AI and plastic surgery. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the administrative and clinical impacts of prior authorization (PA) processes in the office-based laboratory (OBL) setting. This single-institution, retrospective analysis studied all OBL PAs pursued between January 2018 and March 2022. Case, PA, and coding information was obtained from the practice's scheduling database. Over the study period, 1854 OBL cases were scheduled; 8% (n = 146) required PA. Of these, 75% (n = 110) were for lower extremity arterial interventions, 19% (n = 27) were for deep venous interventions, and 6% (n = 9) were for other interventions. Of 146 PAs, 19% (n = 27) were initially denied but 74.1% (n = 7) of these were overturned on appeal. Deep venous procedures were initially denied, at 43.8% (n = 14), more often than were arterial procedures, at 11.8% (n = 13). Of 146 requested procedures, 4% (n = 6) were delayed due to pending PA determination by a mean 14.2 ± 18.3 working days. An additional 6% (n = 8) of procedures were performed in the interest of time before final determination. Of the seven terminally denied procedures, 57% (n = 4) were performed at cost to the practice based on clinical judgment. Using PA appeals mechanisms, while administratively onerous, resulted in the overturning of most initial denials. [ABSTRACT FROM AUTHOR]

Oxygen consumption (VO 2), carbon dioxide generation (VCO 2), and respiratory quotient (RQ), which is the ratio of VO 2 to VCO 2 , are critical indicators of human metabolism. To seek a link between the patient's metabolism and pathophysiology of critical illness, we investigated the correlation of these values with mortality in critical care patients. This was a prospective, observational study conducted at a suburban, quaternary care teaching hospital. Age 18 years or older healthy volunteers and patients who underwent mechanical ventilation were enrolled. A high-fidelity automation device, which accuracy is equivalent to the gold standard Douglas Bag technique, was used to measure VO 2 , VCO 2 , and RQ at a wide range of fraction of inspired oxygen (F I O 2). We included a total of 21 subjects including 8 post-cardiothoracic surgery patients, 7 intensive care patients, 3 patients from the emergency room, and 3 healthy volunteers. This study included 10 critical care patients, whose metabolic measurements were performed in the ER and ICU, and 6 died. VO 2 , VCO 2 , and RQ of survivors were 282 +/− 95 mL/min, 202 +/− 81 mL/min, and 0.70 +/− 0.10, and those of non-survivors were 240 +/− 87 mL/min, 140 +/− 66 mL/min, and 0.57 +/− 0.08 (p = 0.34, p = 0.10, and p < 0.01), respectively. The difference of RQ was statistically significant (p < 0.01) and it remained significant when the subjects with F I O 2 < 0.5 were excluded (p < 0.05). Low RQ correlated with high mortality, which may potentially indicate a decompensation of the oxygen metabolism in critically ill patients. [ABSTRACT FROM AUTHOR]

Degenerative mitral regurgitation (DMR) has earned great interest because of modern and innovative technologies emerging in its treatment. MR affects roughly one-tenth of those older adults over the age of 75. MR if untreated leads to adverse heart remodeling, resulting in left ventricular dysfunction, pulmonary hypertension, and heart failure syndrome. Despite surgical valve repair/replacement treatment being the standard of care, a significant proportion of severe MR patients face unmet clinical needs because of high or prohibitive surgical risks. This has led to the emergence of transcatheter therapies for high- and prohibitive-risk surgical patients, most notably mitral transcatheter edge-to-edge repair devices. [ABSTRACT FROM AUTHOR]

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Although glycemic status is associated with impaired cardiac structure and function, less is known on left atrial (LA) function across the glycemic spectrum. We evaluated the association of diabetes and glycemic control with LA function in a community-based cohort of older adults. This cross-sectional analysis included 5075 participants from the Atherosclerosis Risk in Communities Study (mean age 75.5 years, 58 % women, and 20 % Black adults) with echocardiographic strain data for LA reservoir, conduit, and contractile function. Multivariable linear regression was used to assess associations of diabetes status and glycemic control with LA function. In participants without diabetes, we used ordinal linear regression to evaluate associations of fasting glucose and HbA1c with LA function. Compared to individuals with a normal fasting glucose, prevalent diabetes was associated with 0.68 % lower LA conduit function (95 % confidence interval (CI): 1.11 to −0.25) and prediabetes a 0.47 % reduction (95 % CI: 0.85 to −0.09) in fully adjusted analyses. Persons with diabetes and high HbA1c (HgbA1c ≥ 7 % vs <7 %) had 1.05 % lower LA conduit function (95 % CI: 1.63, −0.48). Among individuals without diagnosed diabetes, higher fasting glucose, but not HbA1c, was significantly associated with worse LA conduit function. No significant associations were observed for LA reservoir and contractile function. A history of diabetes, prediabetes, and higher fasting glucose levels in persons without diabetes were associated with worse LA conduit function. Corroborative research is needed in prospective cohorts as well as studies that explore underlying mechanisms. • We evaluated differences in LA function according to diabetes status and glycemic control. • Diabetes and prediabetes were associated with worse LA function compared to those with normal fasting glucose levels. • Poorer diabetes control was associated with worse LA function. • Higher fasting glucose amongst those without diabetes was associated with worse LA function. • Findings suggest impaired glucose may adversely affect LA function. [ABSTRACT FROM AUTHOR]

This paper introduces a novel framework that eliminates the often cumbersome "build and install" step when running software. Our framework packages a collection of techniques to automatically infer and generate sandboxes, specifically Linux containers, for running applications directly from their source code. This approach significantly simplifies software execution, improving developer productivity and promoting reuse. We demonstrate the framework's effectiveness through real-world examples and use cases implemented in Python, NodeJS, and Bash. [ABSTRACT FROM AUTHOR]

The nature of lawsuits involving interventional radiologists (IRs) is not well understood. The purposes of this article are to provide an overview of the causes of action underlying medical malpractice lawsuits related to IRs and to characterize the associated factors and outcomes. Two large legal databases were used to search for US legal cases in which there were jury awards and settlements involving IRs in the United States. Cases were screened to include only those cases in which the cause of action involved negligence on the part of IRs. A total of 389 published case summaries were identified, of which 93 were eligible to be included in the analysis. In 46% of the cases (43 of 93), medical malpractice was alleged against an individual IR, whereas in 43% (40 of 93), it was alleged against both an individual IR and a health care institution. Thirty-five percent of IR malpractice cases (33 of 93) involved the performance of a vascular procedure, most commonly embolization procedures (30% [n = 10]), stenting or angioplasty (21% [n = 7]), and diagnostic arteriography and angiography (18% [n = 6]). Twenty-six percent of cases (24 of 93) involved IR performance of a biopsy. Eighteen percent of cases (17 of 93) involved a failure to gain informed consent in addition to an allegation of medical negligence during treatment. Eleven percent of cases (10 of 93) were resolved by settlement, with an average settlement amount of $877,500 (range, $200,000-$2,700,000). Among the 72 cases that went to trial, 74% (53 of 72) resulted in judgments for the defendants, and 26% (19 of 72) resulted in judgements for the plaintiffs, with an average award of $2,012,243 (range, $101,667-$6,400,000). Vascular procedures and biopsies were the most frequent reasons for malpractice lawsuits involving IRs. Failure to gain informed consent in addition to an allegation of medical negligence during treatment was not infrequent. Although the majority of published medical malpractice claims involving IRs resulted in judgments in favor of the defendants, the average amount awarded to plaintiffs was higher compared with previous data reported for all physicians. [ABSTRACT FROM AUTHOR]

• The ILAE 2022 diagnostic criteria for epilepsy syndromes were easily applied among 858 children in a resource-limited setting. • 11.5 % children were newly classified under an epilepsy syndrome who had previously remained unclassified. • 1.3 % children who were previously classified into an epilepsy syndrome could not be classified using the new diagnostic criteria. • 0.9 % were shifted to a new syndromic category. Recently, the ILAE Nosology and Definitions Task Force defined diagnostic criteria for epilepsy syndromes. There is paucity of data on the use of these new diagnostic criteria in children with epilepsy, and how these criteria may lead to changes from previous practice. This was a retrospective chart review of data of children attending the epilepsy clinic in a tertiary care children's hospital from January 2011 to January 2023. The clinical details such as age at onset, types of seizures, co-morbidities, and results of EEG, MRI and genetic testing were reviewed. Epilepsy syndrome diagnosis was made as per the ILAE 2022 criteria, and compared with the previous syndrome diagnosis as per records. Data from 1550 children (63 % boys) with epilepsy were analysed, and 55.4 % children were classified to have epilepsy syndromes as per the new ILAE 2022 diagnostic criteria. Application of the new 2022 ILAE diagnostic criteria was associated with a change in name alone in 676 (77.8 %) children. Hundred (11.5 %) children were newly classified under an epilepsy syndrome who had previously remained unclassified. Eleven (1.3 %) children who were previously classified into an epilepsy syndrome could not be classified using the new diagnostic criteria. Eight (0.9 %) were shifted to a new syndromic category. Overall, change in diagnosis occurred in 13.7 (11.5 + 1.3 + 0.9)%. No change in epilepsy syndrome classification/nomenclature occurred in 74 (8.5 %) children. The new diagnostic criteria led to an overall change in diagnosis in 13.7 % of children with epilepsy. These criteria will hopefully lead to uniformity in diagnosis of epilepsy syndromes across diverse settings. [ABSTRACT FROM AUTHOR]

Introduction-Motorized Two-Wheeler (MTW) is a convenient and affordable mode of transportation despite being highly vulnerable to rollover crashes. The possible fact may be that MTW is a single-track vehicle and thus lack longitudinal stability, particularly on horizontal curves (H C). This stability depends on external factors such as curve geometrics and contact surface properties. Therefore, this research aims to identify the impact of pavement surface conditions and roadway geometrics on MTW run-off road crashes on H C. Method-To consider the temporal effect of pavement surface conditions, this study analyzed different combinations of light and surface moisture conditions for pre- and post-pavement surfacing conditions. Thus, Generalized Estimating Equation (GEE) linked with the log function was used to account for repeated observations. Sample data were collected bi-annually from 76 H C for two years of pre- and post-resurfacing. Pavement surface condition parameter includes skid resistance, rut depth, and international roughness index (IRI). However, to restrain the impact of pavement surface conditions, a separate analysis was carried out to investigate the impact of geometrical design attributes. Results-The analysis concluded that surface moisture condition, light condition, rut depth, and their interactions with AADT were found significant for pre-resurfacing. For post-resurfacing, surface moisture condition, and light condition, IRI and its interaction with AADT were found significant. Similarly, roadway geometrics such as curve radius and their interaction with the presence of sight distance, public activity centre and cross-slope were found significant. Conclusion-Overall, the study insights and confirms how and to what extent pavement surface properties and geometric attributes impact the subject crashes. The results will enhance the understanding of roadway architecture and may help highway designers to implement, either at the intervention or treatment level, to improve safety and comfort. • Safety performance of wet & dry pavement surface condition was evaluated. • Generalized Estimating Equation (GEEs) was used developed the scenarios. • Separate controlled analysis quantified the impact of geometric design attributes. • Pavement resurfacing showed positive impact on relevant crashes. • Consideration at intervention/treatment level improves both safety and comfort. [ABSTRACT FROM AUTHOR]

Building an efficient summarization and search tool for research articles is a complex task that involves interdisciplinary expertise in NLP, database management, web development, and user experience design. With the rapid growth of the scientific content, manually reading and selecting important content of research articles became challenging. Thus, there is a need for a summarization tool to help scholars reading their content fast along with a search tool that will find important contents and keep them in organized way. To summarize the contents of different articles a summarization tool is proposed in this work that generates extractive and abstractive summaries. Along with summarization a search engine also been proposed in this work that save the searched results in a comma-separated value (CSV) format including the search queries and meta information of articles such as keyword, title, author name, URL, year of publication, abstracts and summaries. These CSVs help users to get idea about article contents in offline mode without reading or searching the whole text. The efficiency of the summarizer tool is evaluated in terms of precision(pr), recall(re) and F-measure(F-m) of Rouge-1(R1), Rouge-2(R2), Rouge_sum(R_sum) and Bertscore(BS) measures for ten research articles. The average pr, re and F-m obtained from BS are 42%, 42% and 42% for extractive summarization and 41%, 41% and 41% for abstractive summarization. This tool will be helpful to research scholars in the collection of literature and the preparation of related work for their research. [ABSTRACT FROM AUTHOR]

Brain tumors pose a significant medical challenge, demanding early detection and diagnosis to enhance patient outcomes. However, existing brain tumor detection methods often suffer from inefficiency and inaccuracy. Presently, deep learning models have gained prominence in classifying brain MRI images due to their superior accuracy compared to traditional classifiers. Nonetheless, optimizing hyperparameters manually to achieve precise image classification is a daunting and time-consuming task. To address these challenges, we present a novel approach in this paper—the Manta Ray Foraging Optimized Convolutional Neural Network (MRFO-CNN) model to classify brain MRIs into tumorous and non-tumorous categories. We leverage the Manta Ray Foraging Optimization technique to automatically determine the optimal hyperparameters, specifically batch size and epoch settings. Our model is trained on a dataset comprising 3000 brain MR images and validated on an additional 351 brain MR images. The results unequivocally demonstrate the superiority of our proposed MRFO-CNN model over conventional CNN approaches, achieving an impressive training accuracy of 99.3% and a validation accuracy of 98.7%. This research not only showcases the potential of deep learning in brain tumours classification but also underscores the efficiency and effectiveness of automated hyperparameter optimization in medical image analysis. [ABSTRACT FROM AUTHOR]

The Internet of Underwater Things (IoUT) is an interconnected communication ecosystem for underwater devices in maritime environments. IoUT devices range from seabed sensors to ships and boats in oceans which help in diverse practical applications of underwater information ranging from marine transportation, navigation, and underwater exploration to disaster prevention with intelligent monitoring. There exist significant obstacles within the present architecture of the IoUT. This article proposes three distinct methodologies aimed at enhancing the robustness and reliability of the communication architecture for 5G and future networks. Firstly, the employment of the Deep Reinforcement Learning approach is advocated for the identification of anomalies and faulty sensors. Secondly, energy consumption is identified as a crucial factor for IoUT devices. To address this concern, the integration of renewable energy sources, namely oxygen rechargeable batteries, with nano-generators is employed. This integration allows for the replenishment of the batteries through the harnessing of kinetic energy from water. Thirdly, the process by which information can be efficiently transmitted from IoUT sensors located on the seabed to the final terrestrial network is delineated, with an emphasis on minimizing the number of hops required. Additionally, a case study is presented, focusing on the mitigation of interference within the IoUT. Lastly, the article delves into the various research challenges and unresolved matters that persist within the realm of IoUT. [ABSTRACT FROM AUTHOR]