A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China

IntroductionPulmonary embolism (PE) is a common thrombotic disease and potentially deadly cardiovascular disorder. The ratio of clinical misdiagnosis and missed diagnosis of PE is very large because patients with PE are asymptomatic or non-specific.MethodsUsing the clinical data from the First Affiliated Hospital of Wenzhou Medical University (Wenzhou, China), we proposed a swarm intelligence algorithm-based kernel extreme learning machine model (SSACS-KELM) to recognize and discriminate the severity of the PE by patient’s basic information and serum biomarkers. First, an enhanced method (SSACS) is presented by combining the salp swarm algorithm (SSA) with the cuckoo search (CS). Then, the SSACS algorithm is introduced into the KELM classifier to propose the SSACS-KELM model to improve the accuracy and stability of the traditional classifier.ResultsIn the experiments, the benchmark optimization performance of SSACS is confirmed by comparing SSACS with five original classical methods and five high-performance improved algorithms through benchmark function experiments. Then, the overall adaptability and accuracy of the SSACS-KELM model are tested using eight public data sets. Further, to highlight the superiority of SSACS-KELM on PE datasets, this paper conducts comparison experiments with other classical classifiers, swarm intelligence algorithms, and feature selection approaches.DiscussionThe experimental results show that high D-dimer concentration, hypoalbuminemia, and other indicators are important for the diagnosis of PE. The classification results showed that the accuracy of the prediction model was 99.33%. It is expected to be a new and accurate method to distinguish the severity of PE

Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques

IntroductionPulmonary embolism (PE) is a cardiopulmonary condition that can be fatal. PE can lead to sudden cardiovascular collapse and is potentially life-threatening, necessitating risk classification to modify therapy following the diagnosis of PE. We collected clinical characteristics, routine blood data, and arterial blood gas analysis data from all 139 patients.MethodsCombining these data, this paper proposes a PE risk stratified prediction framework based on machine learning technology. An improved algorithm is proposed by adding sobol sequence and black hole mechanism to the cuckoo search algorithm (CS), called SBCS. Based on the coupling of the enhanced algorithm and the kernel extreme learning machine (KELM), a prediction framework is also proposed.ResultsTo confirm the overall performance of SBCS, we run benchmark function experiments in this work. The results demonstrate that SBCS has great convergence accuracy and speed. Then, tests based on seven open data sets are carried out in this study to verify the performance of SBCS on the feature selection problem. To further demonstrate the usefulness and applicability of the SBCS-KELM framework, this paper conducts aided diagnosis experiments on PE data collected from the hospital.DiscussionThe experiment findings show that the indicators chosen, such as syncope, systolic blood pressure (SBP), oxygen saturation (SaO2%), white blood cell (WBC), neutrophil percentage (NEUT%), and others, are crucial for the feature selection approach presented in this study to assess the severity of PE. The classification results reveal that the prediction model’s accuracy is 99.26% and its sensitivity is 98.57%. It is expected to become a new and accurate method to distinguish the severity of PE

Octahedral ruthenium (II) polypyridyl complexes as antimicrobial agents against mycobacterium

Tuberculosis is one of the world’s deadliest infectious disease with 1.5 millions deaths annually. It is imperative to discover novel compounds with potent activity against M. tuberculosis. In this study, susceptibilities of M. smegmatis to the octahedral ruthenium(II) polypyridyl complexes, 1 {[(bpy)3Ru] (PF6)2 (bpy = 2,2′-bipyridine)}, 2 {[(phen)2Ru(dppz)](PF6)2 (phen = 1,10-phenanthroline, dppz = dipyridophenazine)} and 3 {[(phen)3Ru](PF6)2} were measured by broth microdilution and reported as the MIC values. Toxicities of complex 3 to LO2 and hepG2 cell lines were also measured. Complex 2 inhibited the growth of M. smegmatis with MIC value of 2 µg/mL, while complex 3 was bactericidal with MIC value of 26 µg/mL. Furthermore, the bactericidal activity of complex 3 was dependent on reactive oxygen species production. Complex 3 showed no cytotoxicity against LO2 and hepG2 cell lines at concentration as high as 64 µg/mL, paving the way for further optimization and development as a novel antibacterial agent for the treatment of M. tuberculosis infection

Exploring the Effects of Contextual Factors on Residential Land Prices Using an Extended Geographically and Temporally Weighted Regression Model

A spatial and temporal heterogeneity analysis of residential land prices, in general, is crucial for maintaining high-quality economic development. Previous studies have attempted to explain the geographical evolution rule by studying spatial-temporal heterogeneity, but they have neglected the contextual information, such as school district, industrial zone, population density, and job density, associated with residential land prices. Therefore, in this study, we consider contextual factors and propose a revised local regression algorithm called the contextualized geographically and temporally weighted regression (CGTWR), to effectively address spatiotemporal heterogeneity, and to creatively extend the feasibility of importing the contextualization into the GTWR model. The quantitative impact of contextual information on residential land prices was identified in Shijiazhuang (SJZ) city from 1974 to 2021. Empirical analyses demonstrated that school district and industrial zone factors played important roles in residential land prices. Notably, the distance from a residential area to an industrial zone was significantly positively correlated with residential land prices. In addition, a positive relationship between school districts and residential land prices was also observed. Finally, the R2 value of the CGTWR model was 92%, which was superior to those of ordinary least squares (OLS, 76%), geographically weighted regression (GWR, 85%), contextualized geographically weighted regression (CGWR, 86%), and GTWR (90%) models. These evaluation results indicate that the CGTWR algorithm, which incorporates contextual information and spatiotemporal variation, could provide policy makers with evidence for understanding the nature of varying relationships within a land price dataset in China

Exploring the Effects of Contextual Factors on Residential Land Prices Using an Extended Geographically and Temporally Weighted Regression Model

A spatial and temporal heterogeneity analysis of residential land prices, in general, is crucial for maintaining high-quality economic development. Previous studies have attempted to explain the geographical evolution rule by studying spatial-temporal heterogeneity, but they have neglected the contextual information, such as school district, industrial zone, population density, and job density, associated with residential land prices. Therefore, in this study, we consider contextual factors and propose a revised local regression algorithm called the contextualized geographically and temporally weighted regression (CGTWR), to effectively address spatiotemporal heterogeneity, and to creatively extend the feasibility of importing the contextualization into the GTWR model. The quantitative impact of contextual information on residential land prices was identified in Shijiazhuang (SJZ) city from 1974 to 2021. Empirical analyses demonstrated that school district and industrial zone factors played important roles in residential land prices. Notably, the distance from a residential area to an industrial zone was significantly positively correlated with residential land prices. In addition, a positive relationship between school districts and residential land prices was also observed. Finally, the R2 value of the CGTWR model was 92%, which was superior to those of ordinary least squares (OLS, 76%), geographically weighted regression (GWR, 85%), contextualized geographically weighted regression (CGWR, 86%), and GTWR (90%) models. These evaluation results indicate that the CGTWR algorithm, which incorporates contextual information and spatiotemporal variation, could provide policy makers with evidence for understanding the nature of varying relationships within a land price dataset in China

The Discovery of an Iridium(III) Dimer Complex as a Potent Antibacterial Agent against Non-Replicating Mycobacterium smegmatis

Novel agents are urgently needed to rapidly kill drug-resistant Mycobacterium tuberculosis. Noble metal complexes, particularly polypyridyl iridium complexes serving as therapeutic agents, have attracted considerable interest recently, due to their significant cytotoxic or antimicrobial activities. Here, we reported an polypyridyl iridium dimer complex [Ir(ppy)2Cl]2 (3), with ppy = phenylpyridine, which was found to be active against both exponential growing and non-replicating M. smegmatis, with minimum inhibitory concentration values of 2 μg/mL, and exhibited rapid bactericidal kinetics, killing pathogens within 30–60 min. Moreover, 3 was demonstrated to generate a large amount of reactive oxygen species and to be effective in drug-resistant strains. Taken together, the selectively active iridium(III) dimer complex showed promise for use as a novel drug candidate for the treatment of M. tuberculosis infection