Utilizing ultrasound findings of a single indicator joint to assess non-systemic juvenile idiopathic arthritis

Background Musculoskeletal ultrasound (MSUS) has been used worldwide in adult patients with rheumatoid arthritis (RA) but is beginning to play an increasing role in patients with juvenile idiopathic arthritis (JIA). The aim of this study was to investigate the application of MSUS findings of a single indicator joint in JIA to assess the disease activity and classify disease subtype. Methods Thirty-five non-systemic JIA patients with a total of 62 visits were retrospectively recruited in this study. Among the involved joints, the joint with highest value of grey-scale (GS) plus power Doppler (PD) (=GSPD) was selected as the indicator joint at each visit. The correlations between each MSUS parameter (GS, PD, GSPD) of indicator joints and the Physician Global Assessment (PGA) score, the Childhood Health Assessment Questionnaire-disability index (CHAQ-DI), and laboratory data were analyzed. The ultrasound features in different subtypes of JIA were also compared. Results PD was weakly correlated with the PGA score (rho = 0.323, p = 0.010), while both GS and GSPD were moderately correlated with the PGA score (rho = 0.405, p = 0.001; rho = 0.434, p = 0.000). On the other hand, GS, PD, and GSPD were weakly correlated with CHAQ-DI. Although erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) had a weak correlation with PGA, they were not statistically correlated with GS, PD, or GSPD. The proportions of effusion, synovial hypertrophy, and enthesopathy in three different subtypes, showed significant differences (Fisher’s exact test, p = 0.037; p = 0.004; p = 0.019). Enthesopathy was only seen in joints of enthesitis-related arthritis (ERA), but not in joints of polyarthritis and oligoarthritis. Conclusions MSUS is an acceptable non-invasive tool for the patients with JIA, particularly for those with non-systemic JIA, that might assist disease classification, and whose parameters of the indicator joints may potentially contribute to the evaluation of disease activity.

Combining Variables to Improve Subadult Age Estimation

Anthropologists have reported that the combination of multiple variables and indicators generally increases precision and reduces bias in age estimates. However, endeavors specific to subadult age estimation have primarily focused on estimating age of the living and therefore on variables and indicators that are active later in ontogeny and easy to image. The current study aimed to determine if multivariable, single-indicator age-estimation models outperform single-variable age-estimation models throughout ontogeny using the three most common subadult age indicators: diaphyseal dimensions, epiphyseal fusion, and dental development. Data were collected from individuals from South Africa between birth and 12 years (N = 601) using Lodox Statscan radiographic images and from the United States between the ages of birth and 20 years (N = 1,277) using computed tomography images. Multivariate adaptive regression splines were used to build the multivariable, single-indicator, and single-variable models. Each subset used for model development had a unique training sample to build the model and testing sample to ensure that the results were generalizable. The multivariable models presented with increased precision and accuracy, reduced bias, and greater consistency across ontogeny compared to the single-variable models for both samples. Eighty percent of the independent test models (20/24) had ≥ 93% coverage, and 75% (18/24) of the independent tests models had ≥ 95% coverage. Besides providing more information to the resulting age estimate, multivariable models remove any a priori beliefs regarding variable importance and eliminate the requirement to contrive a final age estimate from multiple single-variable age-estimation models.

A Review of the Research on the Indicator Evaluation of the New Development Philosophy

The philosophy of innovative, coordinated, green, open and inclusive development, collectively referred to as the new development philosophy, is an important component of Xi Jinping Thought on Socialist Economy with Chinese Characteristics for a New Era. It plays a vital role in China’s efforts to resolve problems in economic development and cultivate new advantages. Based on a keen understanding of the new development philosophy, we review the literature on the evaluation of a single indicator of the five dimensions of philosophy and that on a comprehensive evaluation of all the indicators of philosophy. The results show varied depths of research in single-indicator evaluation, and a lack of systematic study on comprehensive evaluation. We propose a multi-tier (macro–micro)-indicator evaluation system of new development philosophy, and recommend to establish a smart evaluation platform by integrating big data with traditional data, which will serve as a foundation and provide reference for the development of a scientific, well-conceived indicator evaluation system of new development philosophy that can guide the practice.

Features for making decisions in the interests of organizing transport processes

The practice of management activities suggests that the experience and knowledge of managers alone cannot always provide an optimal or even rational solution without additional scientifically grounded methods for assessing the effectiveness of possible options for organizing transport processes. All this generated a need for the development of scientific methods for making (developing, supporting, justifying) decisions that would make it possible to develop specific recommendations for the head who manages the facility in difficult situations. These methods included the formation of effectiveness measures in the form of maximum or minimum of a single indicator — the target function with or without constraints and various algorithms for finding the values of arguments that provide the required value of this function. Subsequently, mathematical methods for solving multi-criteria problems involving different goals of the operation and using, respectively, a lot of alternative effectiveness measures began to develop.

Method for assessing single indicators of quality of buildings and structures at hazardous production facilities using S-shaped curves

The article considers the issue of establishing a mathematical dependence in the form of an S-shaped curve for quantifying the quality of buildings and structures at hazardous production facilities with subsequent automation of the quality assessment calculation mechanism based on the obtained mathematical model. It is proposed to take a piecewise nonlinear dependence described by a polynomial of odd degree as the basis of an S-shaped curve. Using the least squares method, the equation of the S-shaped curve of the quality assessment M from the value of a single quality indicator p_(i ) is obtained. The software «Qualimetric Unit Quality Estimation» has been developed, which allows to carry out a quantitative assessment of the quality of a single indicator by the method of qualimetry, as well as to give a direct visual representation in the form of a graph. The process of calculating the quality assessment of the selected single indicator reduces the time for calculating and analyzing the results, increases the efficiency of the procedure for assessing the quality of materials, products, structures of buildings and structures at hazardous production facilities.

Developing a Single Indicator or Multiple Indicator Market Timing System

In this chapter, the authors use genetic algorithms (GAs) to optimize the parameters of the trading system, which is made by various technical indicators. These trading systems or rules will give buy or sell signals when applied on past prices of a particular stock. Genetic algorithms (GAs) have an ability to find optimal trading indicators that will predict the market direction or trend with greater accuracy. Use of genetic algorithms (GAs) in conjunction to a trading rule refutes efficient market hypothesis (EMH) in a weak form.

A study of the prevalence of metabolic syndrome in newly detected Diabetes Mellitus Type 2

The metabolic disorder is depicted as bunches of irregularities, including stomach corpulence, insulin opposition, hypertension, hyperglycaemia, expanded fatty oils, and diminished high-thickness lipoprotein cholesterol. The study was aimed to assess the Pervasiveness of Metabolic disorder in recently identified sort 2 Diabetes mellitus Among 100 newly detected type 2 DM patients, 44.18% of male and 55.82% of female had metabolic syndrome. The maximum number of patients were in the age of 30-49 years. Most of the patients had a history of DM of 4-6 months (54%). Majority 60 patients (69.76%) and IDF criteria predicting MS in 40(46.51%) patients which is least compared to others. Modified WHO and NCEP ATP III criteria's were predicting MS 55(63.95%), 48(55.81%) respectively. Raised serum TG for men (45.3%) and diminished HDL for ladies (45.3%) were most grounded single indicator successfully showing the presence of MS in recently identified type 2 DM patients. Significant WC was seen in 51.2% of patients, and 46.5% of patients had significant waist/hip ratio. CONCLUSION- Raised serum TG for men and lessened HDL for women were most grounded single pointers, satisfactorily exhibiting the presence of MS in as of late distinguished sort 2 DM patients.

Automated Cluster Detection of Health Care–Associated Infection Based on the Multisource Surveillance of Process Data in the Area Network: Retrospective Study of Algorithm Development and Validation

Background The cluster detection of health care–associated infections (HAIs) is crucial for identifying HAI outbreaks in the early stages. Objective We aimed to verify whether multisource surveillance based on the process data in an area network can be effective in detecting HAI clusters. Methods We retrospectively analyzed the incidence of HAIs and 3 indicators of process data relative to infection, namely, antibiotic utilization rate in combination, inspection rate of bacterial specimens, and positive rate of bacterial specimens, from 4 independent high-risk units in a tertiary hospital in China. We utilized the Shewhart warning model to detect the peaks of the time-series data. Subsequently, we designed 5 surveillance strategies based on the process data for the HAI cluster detection: (1) antibiotic utilization rate in combination only, (2) inspection rate of bacterial specimens only, (3) positive rate of bacterial specimens only, (4) antibiotic utilization rate in combination + inspection rate of bacterial specimens + positive rate of bacterial specimens in parallel, and (5) antibiotic utilization rate in combination + inspection rate of bacterial specimens + positive rate of bacterial specimens in series. We used the receiver operating characteristic (ROC) curve and Youden index to evaluate the warning performance of these surveillance strategies for the detection of HAI clusters. Results The ROC curves of the 5 surveillance strategies were located above the standard line, and the area under the curve of the ROC was larger in the parallel strategy than in the series strategy and the single-indicator strategies. The optimal Youden indexes were 0.48 (95% CI 0.29-0.67) at a threshold of 1.5 in the antibiotic utilization rate in combination–only strategy, 0.49 (95% CI 0.45-0.53) at a threshold of 0.5 in the inspection rate of bacterial specimens–only strategy, 0.50 (95% CI 0.28-0.71) at a threshold of 1.1 in the positive rate of bacterial specimens–only strategy, 0.63 (95% CI 0.49-0.77) at a threshold of 2.6 in the parallel strategy, and 0.32 (95% CI 0.00-0.65) at a threshold of 0.0 in the series strategy. The warning performance of the parallel strategy was greater than that of the single-indicator strategies when the threshold exceeded 1.5. Conclusions The multisource surveillance of process data in the area network is an effective method for the early detection of HAI clusters. The combination of multisource data and the threshold of the warning model are 2 important factors that influence the performance of the model.

Utilizing ultrasound findings of a single indicator joint to assess non-systemic juvenile idiopathic arthritis

Background: Musculoskeletal ultrasound (MSUS) has been used worldwide in adult patients with rheumatoid arthritis (RA) but is beginning to play an increasing role in patients with juvenile idiopathic arthritis (JIA). The aim of this study was to investigate the application of MSUS findings of a single indicator joint in JIA to assess the disease activity and classify disease subtype.Methods: Thirty-five non-systemic JIA patients with a total of 62 visits were retrospectively recruited in this study. Among the involved joints, the joint with highest value of grey-scale (GS) plus power Doppler (PD) (=GSPD) was selected as the indicator joint at each visit. The correlations between each MSUS parameter (GS, PD, GSPD) of indicator joints and the Physician Global Assessment (PGA) score, the Childhood Health Assessment Questionnaire‐disability index (CHAQ-DI), and laboratory data were analyzed. The ultrasound features in different subtypes of JIA were also compared.Results: PD was weakly correlated with the PGA score (rho=0.323, p=0.010), while both GS and GSPD were moderately correlated with the PGA score (rho=0.405, p=0.001; rho=0.434, p=0.000). On the other hand, GS, PD, and GSPD were weakly correlated with CHAQ-DI. Although erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) had a weak correlation with PGA, they were not statistically correlated with GS, PD, or GSPD. The proportions of effusion, synovial hypertrophy, and enthesopathy in three different subtypes, showed significant differences (Fisher’s exact test, p=0.037; p=0.004; p=0.019). Enthesopathy was only seen in joints of enthesitis-related arthritis (ERA), but not in joints of polyarthritis and oligoarthritis.Conclusions: MSUS is an acceptable non-invasive tool for the patients with JIA, particularly for those with non-systemic JIA, that might assist disease classification, and whose parameters of the indicator joints may potentially contribute to the evaluation of disease activity.