Primary Metrics Research Articles

Repetitive transcranial magnetic stimulation impacts the executive function of patients with vascular cognitive impairment: a systematic review and meta-analysis.

Executive dysfunction is a core symptom of vascular cognitive impairment (VCI), which seriously affects patients' prognosis. This paper aims to investigate the effectiveness of rTMS on executive function in VCI. The databases selected for this study included Pubmed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang, China Science and Technology Journal Database (VIP), and China Biology Medicine Disc (CBM). The screening times were conducted from the time of library construction until August 23, 2023. The inclusion criteria for this meta-analysis were randomized controlled trials (RCTs) on rTMS for VCI, which include executive function scores. The primary metrics were executive subscale scores of the Cognitive Comprehensive Scale and total scores of the Executive Specificity Scale. The secondary metrics were subscale scores of the Executive Specificity Scale. The quality of each eligible study was assessed using the Cochrane Risk of Bias tool. Meta-analysis and bias analysis were performed using Stata (version 16.0) and RevMan (version 5.3). A total of 20 high-quality clinical RCTs with 1,049 samples were included in this paper. The findings from the primary outcomes revealed that within the rTMS group, there were significantly higher scores observed for the executive sub-item on the cognitive composite scale (SMD = 0.93, 95% CI = 0.77-1.08, p < 0.00001, I 2 = 14%) and the total score on the executive specific scale (SMD = 0.69, 95% CI = 0.44-0.94, p < 0.00001, I 2 = 0%) compared to the control group. As for the secondary outcome measures, as shown by the Trail Making Test-A (time) (MD = -35.75, 95% CI = -68.37 to -3.12, p = 0.03, I 2 = 55%), the Stroop-C card (time) (SMD = -0.46, 95% CI = -0.86 to -0.06, p = 0.02, I 2 = 0%) and the Stroop-C card (correct number) (SMD = 0.49, 95% CI = 0.04-0.94, p = 0.03, I 2 = 0%), the experimental group shorts time and enhances accuracy of executive task in comparison to the control group. Subgroup analysis of the main outcome demonstrated that intermittent theta burst stimulation (iTBS), higher frequency, lower intensity, longer duration, and combined comprehensive therapy exhibited superior efficacy. rTMS is effective in the treatment of the executive function of VCI. The present study has some limitations, so multi-center, large-sample, objective indicators and parameters are needed to further explore in the future.Systematic review registration:https://www.crd.york.ac.uk/prospero/, CRD42023459669.

The Identification and Evaluation of Interleukin-7 as a Myokine Biomarker for Peripheral Artery Disease Prognosis.

Background/Objectives: Myokines have been demonstrated to be associated with cardiovascular diseases; however, they have not been studied as biomarkers for peripheral artery disease (PAD). We identified interleukin-7 (IL-7) as a prognostic biomarker for PAD from a panel of myokines and developed predictive models for 2-year major adverse limb events (MALEs) using clinical features and plasma IL-7 levels. Methods: A prognostic study was conducted with a cohort of 476 patients (312 with PAD and 164 without PAD) that were recruited prospectively. Their plasma concentrations of five circulating myokines were measured at recruitment, and the patients were followed for two years. The outcome of interest was two-year MALEs (composite of major amputation, vascular intervention, or acute limb ischemia). Cox proportional hazards analysis was performed to identify IL-7 as the only myokine that was associated with 2-year MALEs. The data were randomly divided into training (70%) and test sets (30%). A random forest model was trained using clinical characteristics (demographics, comorbidities, and medications) and plasma IL-7 levels with 10-fold cross-validation. The primary model evaluation metric was the F1 score. The prognostic model was used to classify patients into low vs. high risk of developing adverse limb events based on the Youden Index. Freedom from MALEs over 2 years was compared between the risk-stratified groups using Cox proportional hazards analysis. Results: Two-year MALEs occurred in 28 (9%) of patients with PAD. IL-7 was the only myokine that was statistically significantly correlated with two-year MALE (HR 1.56 [95% CI 1.12-1.88], p = 0.007). For the prognosis of 2-year MALEs, our model achieved an F1 score of 0.829 using plasma IL-7 levels in combination with clinical features. Patients classified as high-risk by the predictive model were significantly more likely to develop MALEs over a 2-year period (HR 1.66 [95% CI 1.22-1.98], p = 0.006). Conclusions: From a panel of myokines, IL-7 was identified as a prognostic biomarker for PAD. Using a combination of clinical characteristics and plasma IL-7 levels, we propose an accurate predictive model for 2-year MALEs in patients with PAD. Our model may support PAD risk stratification, guiding clinical decisions on additional vascular evaluation, specialist referrals, and medical/surgical management, thereby improving outcomes.

SIMULATION STUDY ON PARAMETRIC MODELLING OF MAGNETORHEOLOGICAL DAMPER SYSTEM

The Magnetorheological (MR) damper system serves as a semi-active suspension system designed to absorb road disturbances. This research aims to replicate the MR damper system through a parametric modeling approach, specifically employing the Simple Bouc-Wen Model, Spencer Model, and Hyperbolic-Tangent Model. The objective of this paper is to assess the effectiveness of these modeled MR damper systems with the utilization of a Fuzzy-PID controller. The simulation process is conducted within MATLAB Simulink, whereby a block diagram is devised based on equations related to the suspension system and the parametric models. The inputs for the system encompass sinusoidal and bump road profiles. The outcomes from the modeled MR damper systems reveal that the semi-active suspension system outperforms the passive suspension system. The primary simulation metrics under consideration include sprung displacement, sprung acceleration, and unsprung acceleration. Among the three MR damper models, the Hyperbolic-tangent model displays the most favorable performance, with a mean squared error of 51.44%, while the Bouc-Wen model exhibits the least favorable performance, registering a mean squared error of 25.96%. The integration of the Fuzzy-PID controller demonstrates an enhancement in the sprung suspension system, though the sprung acceleration remains relatively unchanged.

Comparative techno-economic and environmental analysis of a relocatable solar power tower for low to medium temperature industrial process heat supply

The primary objective of this study is to promote the adoption of solar power tower (SPT) technology in low to medium-temperature industrial process heat (IPH) applications and compare it with other heat generation alternatives. Through comprehensive techno-economic and environmental analyses, a proposed relocatable SPT-based IPH plant (PR-SPT-IPH), with a capacity of 1 MWth, is thoroughly evaluated against traditional concentrating solar-thermal power (CSP) technologies like parabolic trough collectors (PTC) and linear Fresnel reflectors (LFR), as well as natural gas (NG) and photovoltaic (PV)-based IPH systems. The techno-economic assessment of the PR-SPT-IPH is performed using an in-house developed MATLAB code, while the system advisor model (SAM) is used to simulate equivalent PTC-, LFR-, and PV-based IPH plants. The primary techno-economic assessment metric is the levelized cost of heat (LCOH), while the environmental analysis quantifies the avoided greenhouse gas (GHG) emissions and other pollutants resulting from NG combustion. The results show that the PR-SPT-IPH plant achieves the lowest LCOH of 2.42 cents/kWhth, generating annual thermal energy of 5.62 GWhth with a capacity factor of 48.27 %. With certain assumptions, this cost can be further reduced to 1.5 cents/kWhth, meeting the U.S. Department of Energy targets. The PR-SPT-IPH plant could annually avoid emitting 1112 kg of CO2, 54 kg of particulate matter, 1201.76 kg of NOx, and 5.08 kg of SO2. It could also save annual external costs between $43,100.28 and $153,194.67, while the annually saved fuel cost could reach $151,632. The study indicates that the PR-SPT-IPH plant surpasses PTC-, LFR-, NG-, and PV-based IPH plants both technically and financially. With reduced capital costs and appropriate incentives, the PR-SPT-IPH plant emerges as an economically and environmentally viable choice for low to medium-temperature IPH applications.

Perioperative Administration of Tranexamic Acid and Low Molecular Weight Heparin for Enhanced Blood Management in Intertrochanteric Fractures: A Randomized Controlled Study.

BACKGROUND This prospective study from a single center aimed to compare the perioperative blood loss (PBL) in 79 patients with intertrochanteric fractures (IF) treated with intramedullary nailing (IMN) using 3 regimens of combined tranexamic acid (TXA) and low molecular weight heparin (LMWH), proposing a novel therapy of 4-dose TXA. MATERIAL AND METHODS We recruited 79 patients and randomly divided them into 3 groups. The 4-dose TXA group (22 patients) received 1.0 g intravenous TXA 30 min before surgery and 1.0 g at intervals of 3, 6, and 9 h before surgery. The 1-dose TXA group (25 patients) received 1.0 g intravenous TXA 30 min before surgery, while the control group (32 patients) did not receive TXA. LMWH was applied 12 h after surgery in each group. The primary metrics evaluated included hidden blood loss (HBL), total blood loss (TBL), and the number and incidence rate of deep vein thrombosis (DVT). RESULTS Analysis of the HBL revealed that the 4-dose TXA group had the lowest average (583.13±318.08 ml), followed by the 1-dose TXA group (902.94±509.99 ml), and the control group showed the highest (1154.39±452.06 ml) (P<0.05). A similar result was observed for TBL (4-dose group: 640.86±337.22 ml, 1-dose group: 971.74±511.14 ml, control group: 1226.27±458.22 ml, P<0.05). Regarding DVT, the 4-dose TXA group had 5 cases (incidence rate 22.73%), the 1-dose TXA group had 6 cases (incidence rate 24.00%), and the control group had 8 cases (incidence rate 25.00%), with no significant difference among groups (P>0.05). CONCLUSIONS Treatment using 4-dose TXA and LMWH can effectively reduce PBL without increasing the DVT risk in IF patients with IMN.

The trade-off between grain weight and grain number in wheat is explained by the overlapping of the key phases determining these major yield components.

Enhancing grain yield is a primary goal in the cultivation of major staple crops, including wheat. Recent research has focused on identifying the physiological and molecular factors that influence grain weight, a critical determinant of crop yield. However, a bottleneck has arisen due to the trade-off between grain weight and grain number, whose underlying causes remain elusive. In a novel approach, a wheat expansin gene, TaExpA6, known for its expression in root tissues, was engineered to express in the grains of the spring wheat cultivar Fielder. This modification led to increases in both grain weight and yield without adversely affecting grain number. Conversely, a triple mutant line targeting the gene TaGW2, a known negative regulator of grain weight, resulted in increased grain weight but decreased grain number, potentially offsetting yield gains. This study aimed to evaluate the two aforementioned modified wheat genotypes (TaExpA6 and TaGW2) alongside their respective wild-type counterparts. Conducted in southern Chile, the study employed a Complete Randomized Block Design with four replications, under well-managed field conditions. The primary metrics assessed were grain yield, grain number, and average grain weight per spike, along with detailed measurements of grain weight and dimensions across the spike, ovary weight at pollination (Waddington's scale 10), and post-anthesis expression levels of TaExpA6 and TaGW2. Results indicated that both the TaExpA6 and the triple mutant lines achieved significantly higher average grain weights compared to their respective wild types. Notably, the TaExpA6 line did not exhibit a reduction in grain number, thereby enhancing grain yield per spike. By contrast, the triple mutant line showed a reduced grain number per spike, with no significant change in overall yield. TaExpA6 expression peaked at 10 days after anthesis (DAA), and its effect on grain weight over the WT became apparent after 15 DAA. In contrast, TaGW2 gene disruption in the triple mutant line increased ovary size at anthesis, leading to improved grain weight above the WT from the onset of grain filling. These findings suggest that the trade-off between grain weight and number could be attributed to the overlapping of the critical periods for the determination of these traits.

Using machine learning algorithms to identify farms on the 2022 Census of Agriculture

As is the case for many National Statistics Institutes, the United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) has observed dwindling survey response rates, and the requests for more information at finer temporal and spatial scales have led to increased response burdens. Non-survey data are becoming increasingly abundant and accessible. Consequently, NASS is exploring the potential to complete some or all of a survey record using non-survey data, which would reduce respondent burden and potentially lead to increased response rates. In this paper, the focus is on a large set of records associated with potential farms, which are operations with undetermined farm status (farm/non-farm) and are referred to here as operations with unknown status (OUS). Although they usually have some agriculture, most OUS records are eventually classified as non-farms. Those OUS that are classified as farms tend to have higher proportions of producers from under-represented groups compared to other records. Determining the probability that an OUS record is a farm is an important step in the imputation process. The OUS records that responded to the 2017 U.S. Census of Agriculture were used to develop models to predict farm status using multiple data sources. Evaluated models include bootstrap random forest (RF), logistic regression (LR), neural network (NN), and support vector machine (SVM). Although the SVM had the best outcomes for three of the five metrics, the sensitivity for identifying farms was the lowest (13.8%). The NN model had a sensitivity of 80.5%, which was substantially higher than the other models, and its specificity of 45.3% was the lowest of all models. Because sensitivity was the primary metric of interest and the NN performed reasonably well on the other metrics, the NN was selected as the preferred model.

Wireless federated learning for PR identification and analysis based on generalized information

This paper introduces a novel approach to personal risk (PR) identification using federated learning (FL) in wireless communication scenarios, leveraging generalized information. The primary focus is on harnessing the power of distributed data across various wireless devices while ensuring data privacy and security, a critical concern in PR assessment. To this end, we propose an FL-based model that effectively aggregates learning from diverse, decentralized data sources to analyze the PR factors. The proposed method involves training local models on individual devices, which are then aggregated to form a comprehensive global model. This process not only preserves data privacy by keeping sensitive information on the device but also utilizes the widespread availability and connectivity of wireless devices to enhance data richness and model robustness. To address the challenges posed by the wireless environment, such as data heterogeneity and communication constraints, we further implement advanced aggregation algorithms and optimization techniques tailored to these unique conditions. We finally evaluate the performance of our proposed method based on two primary metrics of identification accuracy and convergence rate of the federated learning process. Through extensive simulations and real-world experiments, we demonstrate that our approach not only achieves high accuracy in PR identification but also ensures rapid convergence, making it a viable solution for real-time risk assessment in wireless networks.

Development of a predictive model for 1-year postoperative recovery in patients with lumbar disk herniation based on deep learning and machine learning.

The aim of this study is to develop a predictive model utilizing deep learning and machine learning techniques that will inform clinical decision-making by predicting the 1-year postoperative recovery of patients with lumbar disk herniation. The clinical data of 470 inpatients who underwent tubular microdiscectomy (TMD) between January 2018 and January 2021 were retrospectively analyzed as variables. The dataset was randomly divided into a training set (n = 329) and a test set (n = 141) using a 10-fold cross-validation technique. Various deep learning and machine learning algorithms including Random Forests, Extreme Gradient Boosting, Support Vector Machines, Extra Trees, K-Nearest Neighbors, Logistic Regression, Light Gradient Boosting Machine, and MLP (Artificial Neural Networks) were employed to develop predictive models for the recovery of patients with lumbar disk herniation 1 year after surgery. The cure rate score of lumbar JOA score 1 year after TMD was used as an outcome indicator. The primary evaluation metric was the area under the receiver operating characteristic curve (AUC), with additional measures including decision curve analysis (DCA), accuracy, sensitivity, specificity, and others. The heat map of the correlation matrix revealed low inter-feature correlation. The predictive model employing both machine learning and deep learning algorithms was constructed using 15 variables after feature engineering. Among the eight algorithms utilized, the MLP algorithm demonstrated the best performance. Our study findings demonstrate that the MLP algorithm provides superior predictive performance for the recovery of patients with lumbar disk herniation 1 year after surgery.

Enhancing movie recommendations through comparative analysis of UCB algorithm variants

In the digital realm, recommendation systems are pivotal in shaping user experiences on online platforms, tailoring content based on user feedback. A notable algorithm in this domain is the multi-armed bandit algorithm, with the Upper Confidence Bound (UCB) emerging as a classic and effective variant. This paper delves into an array of Upper Confidence Bound algorithm variations, encompassing UCB1, Asymptotically Optimal UCB, UCB-V, and UCB1Tuned. The research harnesses the MovieLens dataset to assess the performance of these algorithms, employing cumulative regret as the primary metric. For l in UCB1 and c in UCB-V, both oversized and undersized parameters will result in negative outcomes. And UCB1Tuned outperforms the other three algorithms in this experiment, since it considers variance and adjusts parameters dynamically. The study demonstrates that setting a appropriate UCB index is crucial for enhancing the performance of the UCB algorithm in recommendation system. It holds significance for both improve recommendation system algorithms and enhance user experience.

What does effective population size tell us about loss of allelic variation?

There are two primary measures of the amount of genetic variation in a population at a locus: heterozygosity and the number of alleles. Effective population size (N e) provides both an expectation of the amount of heterozygosity in a population at drift-mutation equilibrium and the rate of loss of heterozygosity because of genetic drift. In contrast, the number of alleles in a population at drift-mutation equilibrium is a function of both N e and census size (N C). In addition, populations with the same N e can lose allelic variation at very different rates. Allelic variation is generally much more sensitive to bottlenecks than heterozygosity. Expressions used to adjust for the effects of violations of the ideal population on N e do not provide good predictions of the loss of allelic variation. These effects are much greater for loci with many alleles, which are often important for adaptation. We show that there is a linear relationship between the reduction of N C and the corresponding reduction of the expected number of alleles at drift-mutation equilibrium. This makes it possible to predict the expected effect of a bottleneck on allelic variation. Heterozygosity provides good estimates of the rate of adaptive change in the short-term, but allelic variation provides important information about long-term adaptive change. The guideline of long-term N e being greater than 500 is often used as a primary genetic metric for evaluating conservation status. We recommend that this guideline be expanded to take into account allelic variation as well as heterozygosity.

Evaluating the efficacy and suggesting technical optimizations for endoscopic lumbar interbody fusion across different lumbar spondylolisthesis types

PurposeTo evaluate the efficacy of the endoscopic lumbar interbody fusion technique across different types of lumbar spondylolisthesis, specifically Grade I and Grade II, and suggest technical optimizations based on therapeutic outcomes, complications, and patient satisfaction for both grades. MethodsWe analyzed data from 57 L4 to 5 spondylolisthesis patients, all categorized as either Grade I or Grade II, comprising 31 males and 26 females. Of these, 36 were diagnosed with Grade I and 21 with Grade II. All subjects underwent the endoscopic lumbar interbody fusion procedure. Primary evaluation metrics included pre and post-operative Vasual Analogue Scale（VAS） pain scores, Osewewtry Disability Index（ODI） functional scores, surgical duration, intraoperative blood loss, degree of spondylolisthesis correction, complications, and patient satisfaction levels. ResultsAt a minimum of 6 months post-operation, the VAS score for the Grade I cohort reduced from an initial 7.30 ± 0.69 to 2.97 ± 0.47, while the Grade II cohort saw a decrease from 7.53 ± 0.56 to 3.37 ± 0.62 (P = 0.0194). The ODI score in the Grade I group declined from 66.88 ± 5.15 % pre-operation to 29.88 ± 6.36 % post-operation, and in the Grade II group, it decreased from 69.33 ± 5.27 % to 34.66 ± 6.01 % (P = 0.0092). The average surgical duration for the Grade I group stood at 155.72 ± 17.75 min, compared to 180.38 ± 14.72 min for the Grade II group (P < 0.001). The mean intraoperative blood loss for the Grade I group was 144.58 ± 28.61 ml, whereas the Grade II group registered 188.23 ± 9.41 ml (P < 0.001). Post-surgery, 83 % of the Grade I patients achieved a correction degree exceeding 80 %, and 61 % of the Grade II patients surpassed 50 % (P = 0.0055). Complication rates were recorded at 8 % for Grade I and 16 % for Grade II. Patient satisfaction reached 94 % in the Grade I cohort and 90 % in the Grade II cohort. ConclusionEndoscopic lumbar interbody fusion showcases promising therapeutic outcomes for both Grade I and Grade II lumbar spondylolisthesis. However, surgeries for Grade II spondylolisthesis tend to be lengthier, more challenging, involve greater blood loss, and have a heightened complication risk. Tailored technical adjustments and enhancements are essential for addressing the distinct spondylolisthesis types.

Compact and cost-effective laser-powered speckle contrast optical spectroscopy fiber-free device for measuring cerebral blood flow.

In the realm of cerebrovascular monitoring, primary metrics typically include blood pressure, which influences cerebral blood flow (CBF) and is contingent upon vessel radius. Measuring CBF noninvasively poses a persistent challenge, primarily attributed to the difficulty of accessing and obtaining signal from the brain. Our study aims to introduce a compact speckle contrast optical spectroscopy device for noninvasive CBF measurements at long source-to-detector distances, offering cost-effectiveness, and scalability while tracking blood flow (BF) with remarkable sensitivity and temporal resolution. The wearable sensor module consists solely of a laser diode and a board camera. It can be easily placed on a subject's head to measure BF at a sampling rate of 80Hz. Compared to the single-fiber-based version, the proposed device achieved a signal gain of about 70 times, showed superior stability, reproducibility, and signal-to-noise ratio for measuring BF at long source-to-detector distances. The device can be distributed in multiple configurations around the head. Given its cost-effectiveness, scalability, and simplicity, this laser-centric tool offers significant potential in advancing noninvasive cerebral monitoring technologies.

Resident-led improvement project to screen for primary hyperaldosteronism in patients with resistant hypertension in an outpatient clinic

Clinical practice guidelines recommend screening for primary hyperaldosteronism (PH) in patients with resistant hypertension. However, screening rates are low in the outpatient setting. We sought to increase screening rates for...

Real time conditioning monitoring of MOSFET using artificial neural network regression

The metal–oxide–semiconductor field-effect transistor (MOSFET) is a critical semiconductor device widely used in electrical and electronic systems. The performance of systems depends on the component’s reliability. More than 30% of failures of electronic and electrical systems are due to MOSFET failure. Industries are paying more apprehension towards the improvement of MOSFET reliability. Temperature stress is one of the key parameters to examine the reliability performance of semiconductor devices and to predict the lifetime of the components. This article provides a quick analysis of the MOSFET degradation models. The primary failure precursor metric that reflects the level of degradation is the MOSFET on-resistance R DS(on). In this paper, a linear, non-linear, and artificial neural network (ANN) deterioration model of MOSFETs is presented with the experimental data. The coefficient of determination (R-square), and root mean square error (RMSE) are used to compare the real data. Finally, it is demonstrated that the proposed model works well with experimental data. More than 500 data were trained for the proposed model. It can therefore predict the real-time conditioning monitoring of MOSFETs to increase the reliability of electrical systems.

Navigating the Cyber Threat Landscape: An In-Depth Analysis of Attack Detection within IoT Ecosystems

The Internet of Things (IoT) is seeing significant growth, as the quantity of interconnected devices in communication networks is on the rise. The increased connectivity of devices has heightened their susceptibility to hackers, underscoring the need to safeguard IoT devices. This research investigates cybersecurity in the context of the Internet of Medical Things (IoMT), which encompasses the cybersecurity mechanisms used for various healthcare devices connected to the system. This study seeks to provide a concise overview of several artificial intelligence (AI)-based methodologies and techniques, as well as examining the associated solution approaches used in cybersecurity for healthcare systems. The analyzed methodologies are further categorized into four groups: machine learning (ML) techniques, deep learning (DL) techniques, a combination of ML and DL techniques, Transformer-based techniques, and other state-of-the-art techniques, including graph-based methods and blockchain methods. In addition, this article presents a detailed description of the benchmark datasets that are recommended for use in intrusion detection systems (IDS) for both IoT and IoMT networks. Moreover, a detailed description of the primary evaluation metrics used in the analysis of the discussed models is provided. Ultimately, this study thoroughly examines and analyzes the features and practicality of several cybersecurity models, while also emphasizing recent research directions.

Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning.

Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predict 30-day outcomes following lower extremity endovascular revascularization. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity endovascular revascularization (angioplasty, stent, or atherectomy) for peripheral artery disease between 2011 and 2021. Input features included 38 preoperative demographic/clinical variables. The primary outcome was 30-day postprocedural major adverse limb event (composite of major reintervention, untreated loss of patency, or major amputation) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Overall, 21 886 patients were included, and 30-day major adverse limb event/death occurred in 1964 (9.0%) individuals. The best performing model for predicting 30-day major adverse limb event/death was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92-0.94). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.70-0.74). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.09. The top 3 predictive features in our algorithm were (1) chronic limb-threatening ischemia, (2) tibial intervention, and (3) congestive heart failure. Our machine learning models accurately predict 30-day outcomes following lower extremity endovascular revascularization using preoperative data with good discrimination and calibration. Prospective validation is warranted to assess for generalizability and external validity.

First and second-generation commercial banks in Bangladesh: A comparative financial analysis

Purpose: This study assesses the overall performance of commercial banks in Bangladesh by classifying them into first- and second-generation groups. Research Methodology: The investigation encompassed secondary data from 2016 to 2020, along with a meticulous examination of the ratios of ten banks during that timeframe. Standard software such as the Microsoft Office package was utilized for conducting horizontal, vertical, and rational analyses. Return on assets (ROA), return on equity (ROE), and net profit margins are the primary metrics used to assess overall performance. Results: Research indicates that the first generation excels in certain instances, while the second generation performs at its peak capacity in other cases. Therefore, a comprehensive outcome exists. Implications: These indicators offer a concise and influential message to all stakeholders that may shape their decision-making process. Limitations: It may be beneficial to consider a longer timeframe and gather insights from interviews with industry professionals for subsequent studies. Other factors to consider include customer insights, industry conditions, the nation's economy, monetary policy, and the global trade situation. Novelty: This study focuses on the influence of employee retention on organizational performance in a service-oriented government institution.

Bibliometric and visual analysis of ACE2/Ang 1–7/MasR axis in diabetes and its microvascular complications from 2000 to 2023

BackgroundThe pathogenesis of diabetes and its microvascular complications are intimately associated with renin angiotensin system dysregulation. Evidence suggests the angiotensin converting enzyme 2 (ACE2)/angiotensin 1-7 (Ang 1–7)/Mas receptor (MasR) axis regulates metabolic imbalances, inflammatory responses, reduces oxidative stress, and sustains microvascular integrity, thereby strengthening defences against diabetic conditions. This study aims to conduct a comprehensive analysis of the ACE2/Ang 1–7/MasR axis in diabetes and its microvascular complications over the past two decades, focusing on key contributors, research hotspots, and thematic trends. MethodsThis cross-sectional bibliometric analysis of 349 English-language publications was performed using HistCite, VOSviewer, CiteSpace, and Bibliometrix R for visualization and metric analysis. Primary analytical metrics included publication count and keyword trend dynamics. ResultsThe United States, contributing 105 articles, emerged as the most productive country, with the University of Florida leading institutions with 18 publications. Benter IF was the most prolific author with 14 publications, and Clinical Science was the leading journal with 13 articles. A total of 151 of the 527 author's keywords with two or more occurrences clustered into four major clusters: diabetic microvascular pathogenesis, metabolic systems, type 2 diabetes, and coronavirus infections. Keywords such as “SARS”, “ACE2”, “coronavirus”, “receptor” and “infection” displayed the strongest citation bursts. The thematic evolution in this field expanded from focusing on the renin angiotensin system (2002–2009) to incorporating ACE2 and diabetes metabolism (2010–2016). The latter period (2017–2023) witnessed a significant surge in diabetes research, reflecting the impact of COVID-19 and associated conditions such as diabetic retinopathy and cardiomyopathy. ConclusionsThis scientometric study offers a detailed analysis of the ACE2/Ang 1–7/MasR axis in diabetes and its microvascular complications, providing valuable insights for future research directions.

Leveraging Novel Clinical Decision Support to Improve Preferred Language Documentation in a Neonatal Intensive Care Unit.

Recognition of the patient and family's diverse backgrounds and language preference is critical for communicating effectively. In our hospital's electronic health record, a patient or family's identified language for communication is documented in a discrete field known as "preferred language." This field serves as an interdepartmental method to identify patients with a non-English preferred language, creating a bolded banner for non-English speakers easily identifiable by health care professionals. Despite the importance of documenting preferred language to facilitate equitable care, this field is often blank. Using the Institute for Healthcare Improvement's Model for Improvement, our team sought to increase preferred language documentation within the neonatal intensive care unit (NICU) from a baseline of 74% in September 2021 to above 90% within 6 months. A multidisciplinary team was assembled to address preferred language documentation. Our team incorporated guidance regarding preferred language documentation into a novel clinical decision support (CDS) tool aimed at addressing various safety and quality measures within the NICU. Our primary outcome metric was documentation of family's preferred language. Process measures included overall compliance with the CDS tool. A secondary outcome was the assessment of preferred language documentation accuracy. The average rate of preferred language documentation increased from a baseline of 74 to 92% within 6 months and is currently sustained at 96%. Moreover, our follow-up assessments found that 100% of a random sample of contacted patients (n = 88) had their language preferences documented correctly. Overall compliance with the CDS tool remained at 85% throughout the project. Using a quality improvement framework coupled with a CDS initiative, our team was able to successfully and accurately improve preferred language documentation in our NICU. Future projects include strategies for more equitable care for patients with non-English preferences such as improved interpreter usage and discharge instructions in their preferred language.