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Xanthelasma palpebrarum is one of the most common cutaneous xanthomas in humans. Currently, there are various methods available for treating xanthelasma palpebrarum, but the high treatment frequency and recurrence rate remain significant challenges for patients. Therefore, it is necessary to establish a reasonable and effective clinical grading system to guide the diagnosis and treatment of xanthelasma palpebrarum. We developed a clinical scoring system related to local injection of pingyangmycin for the treatment of xanthelasma palpebrarum, which can be used to predict early prognosis and treatment outcomes in patients. We collected and retrospectively studied 246 outpatient cases of xanthelasma palpebrarum treated with local injection of pingyangmycin in the Department of Plastic Surgery at Shanghai East Hospital from February 2020 to August 2022. Potential independent risk factors for adverse outcomes (recurrence or non-recurrence) were considered in univariate and multivariate logistic regression models. Predictive factors were determined based on the multivariate logistic regression model and Cox model, and a scoring grading system was established. External validation was conducted on an independent cohort of 110 patients. Based on logistic regression analysis, the number, area, and color of lesions were identified as significant predictive indicators (P < 0.05), with respective AUCs of 0.710, 0.799, and 0.755. The Cox model established hazard ratios for four new severity indicators of xanthelasma palpebrarum: hyperlipidemia, number of lesions, lesion area, and lesion grayscale value. Based on these findings, a new clinical grading model was developed, which was validated to be effective in the external cohort. The new scoring-based clinical predictive model can effectively predict the number of pingyangmycin injection treatments and prognosis in patients with xanthelasma palpebrarum. It holds promise for broader application in clinical practice. [ABSTRACT FROM AUTHOR]

Purpose: Invasive candidiasis poses a life-threatening risk, and early prognosis assessment is vital for timely interventions to reduce mortality. Serum C5a levels have recently been linked to prognosis, but confirmation in cancer patients is pending. Methods: We detected the concentrations of serum C5a in hospitalized cancer patients with invasive candidiasis from 2020 to 2023, and retrospectively analyzed the clinical data. Results: 372 cases were included in this study, with a 90-day mortality rate of 21.8%. Candida albicans (48.7%) remained the predominant pathogen, followed by Candida glabrata (25.5%), Candida tropicalis (12.4%), and Candida parapsilosis (8.3%). Gastrointestinal cancer was the most diagnosed pathology type (37.6%). Serum C5a demonstrated a noteworthy correlation with 90-day mortality, and employing a cutoff value of 36.7 ng/ml revealed significantly higher 90-day mortality in low-C5a patients (41.2%) compared to high-C5a patients (6.3%) (p < 0.001). We also identified no source control, no surgery, metastasis, or chronic renal failure independently correlated with the 90-day mortality. Based on this, a prognostic model combining C5a and clinical parameters was constructed, which performed better than models built solely on C5a or clinical parameters. Furthermore, we weighted scores to each parameter in the model and presented diagnostic sensitivity and specificity corresponding to different score points calculated by the model. Conclusion: We constructed a prognostic scoring model including serum C5a and clinical parameters, which would contribute to precise prognosis assessment and benefit the outcome among cancer patients. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a decentralized and privacy-preserving machine learning technique that protects data privacy by learning models locally and not sharing datasets. However, due to limited computing resources on devices and highly heterogeneous data in practical situations, the training efficiency and resource utilization of federated learning is low. In order to resolve these challenges, we introduce a blockchain-assisted dynamic adaptive and personalized federated learning framework (TV-FedAvg) in the presence of restricted computing power resources and data heterogeneity. After each round of local training, we utilize an improved scoring model based on VIKOR and TOPSIS to comprehensively score the devices. The scores are then utilized to choose devices for participation in global aggregation and to carry out model aggregation through blockchain consensus. Furthermore, resources are reallocated for the next round to enhance resource efficiency, model fairness, and performance. Finally, we demonstrate through experimentation that TV-FedAvg outperforms other models such as pFedMe, FedAvg, Per-FedAvg, and TOPSIS in terms of both efficiency and performance. [ABSTRACT FROM AUTHOR]

The purpose of this paper ist to develop a scoring model that takes into account the specific criteria of the recycling industry. First of all the specific criteria have to be identified and afterwards weighted. In order for being able to identify the specific criteria, different methodology first have to be applied. For this the method of descriptive statistics, inference statistics and the systematic literature research are used. The identified criteria are summarized in a survey. Various companies of the recycling and disposal industry receive the survey and weight the criteria according to their subjective assessment. Through the individual subjective evaluations of the criteria, a weighted average and thus a scoring model can be created. The contribution of this paper is to offer the recycling companies the possibility to identifiy and select the optimal strategic partner based on objective criteria. As a result, the quality of the choice of a partner and the quality of service for the customer can be improved, which means that the paper also has a practical benefit.

Abstract Background The aim of this study was to find early predictors of Intravenous Immunoglobulin (IVIG)-Resistant Kawasaki Disease. Methods Patients diagnosed with Kawasaki disease were enrolled in this study. Univariate analysis and multiple logistic regression were used to analyze the clinical characteristics and laboratory findings of patients in both groups before IVIG treatment. Independent predictors of Intravenous Immunoglobulin-Resistant Kawasaki Disease were analyzed, and a prediction model for children with Intravenous Immunoglobulin-Resistant Kawasaki Disease was constructed. Results A total of 108 children (67 males and 41 females) with IVIG-sensitive Kawasaki disease and 31 children (20 males and 11 females) with IVIG-resistant Kawasaki disease participated in this study. Compared with the IVIG-sensitive group, the duration of hospitalization, ALT, AST, GLB, r-GT, IgG, PCT, and ESR was elevated in the IVIG-resistant KD group, and ATG16L1, LC3II, BECN1, RBC, HGB, ALB, A/G, and CK were significantly lower (P

The income and expenses of a commercial organization are one of the most important objects of economic analysis and are in the constant field of view of theoretical economists and practitioners. Recently, many economists have been paying attention to the need not only for a structural and dynamic analysis of the income and expenses of a commercial organization, but also for assessment of their quality. Appropriate criteria and indicators were developed. The application of the developed methods in the conditions of real economic entities does not always allow us to draw an unambiguous conclusion about the quality of their income and expenses incurred due to the divergence of the obtained values of indicators. The problem of assessing the quality of income in integration forms of associations is particularly relevant, which would allow for the ranking of individual organizations that are part of them, not only by size, but also by the quality of income received. This study attempts to develop a tool to solve this problem. The purpose of the study is to develop a methodology for the integral assessment of the quality of income and expenses of a commercial organization and its validation in the conditions of one of the integration forms of associations. The object of the study is the State Association "Vitebsk concern "Meat and Dairy Products". The scientific novelty of the research is an attempt to develop a methodology for the integral assessment of the quality of income and expenses based on the synthesis of a scoring model, taking into account the weighting coefficients of individual criteria and their point evaluation. The validation of the developed model in the conditions of the selected object of research has shown that it can be used not only to differentiate the structural units of the association, but also to identify the most problematic areas in the management of income and expenses of a particular commercial organization.

Introduction: We aimed to identify objective factors associated with failure of nonoperative management (NOM) of gastroduodenal peptic ulcer perforation (GDUP) and establish a scoring model for early identification of patients in whom NOM of GDUP may fail. Methods: A total of 71 patients with GDUP were divided into NOM (cases of NOM success) and operation groups (cases requiring emergency operation or conversion from NOM to operation). Using logistic regression analysis, a scoring model was established based on the independent factors. The patients were stratified into low-risk and high-risk groups according to the scores. Results: Of the 71 patients, 18 and 53 were in the NOM and operation groups, respectively. Ascites in the pelvic cavity on computed tomography (CT) and sequential organ failure assessment (SOFA) score at admission were identified as independent factors for NOM failure. The scoring model was established based on the presence of ascites in the pelvic cavity on CT and SOFA score ≥2 at admission. The operation rates for GDUP were 28.6% and 86.0% in the low-risk (score, 0) and high-risk groups (scores, 2 and 4), respectively. Conclusion: Our scoring model may help determine NOM failure or success in patients with GDUP and make decisions regarding initial treatment. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to find early predictors of Intravenous Immunoglobulin (IVIG)-Resistant Kawasaki Disease. Methods: Patients diagnosed with Kawasaki disease were enrolled in this study. Univariate analysis and multiple logistic regression were used to analyze the clinical characteristics and laboratory findings of patients in both groups before IVIG treatment. Independent predictors of Intravenous Immunoglobulin-Resistant Kawasaki Disease were analyzed, and a prediction model for children with Intravenous Immunoglobulin-Resistant Kawasaki Disease was constructed. Results: A total of 108 children (67 males and 41 females) with IVIG-sensitive Kawasaki disease and 31 children (20 males and 11 females) with IVIG-resistant Kawasaki disease participated in this study. Compared with the IVIG-sensitive group, the duration of hospitalization, ALT, AST, GLB, r-GT, IgG, PCT, and ESR was elevated in the IVIG-resistant KD group, and ATG16L1, LC3II, BECN1, RBC, HGB, ALB, A/G, and CK were significantly lower (P < 0.05). mRNA expression of ESR, BECN1, and LC3II were independent risk factors for IVIG-resistant Kawasaki disease. A logistic regression model and scoring system were established, and the cut-off values of independent risk factors were derived from ROC curves: ESR ≥ 79.5 mm/h, BECN1 ≤ 0.645, LC3II ≤ 0.481. A new scoring system was established according to the respective regression coefficients as follows: ESR ≥ 79.5 mm/h (1 point), BECN1 ≤ 0.645 (1 point). LC3II ≤ 0.481 (2 points), 0–1 as low risk for IVIG non-response, and ≥ 2 as high risk. Applied to this group of study subjects, the sensitivity was 87.10%, specificity 83.33%, Youden index 0.70, AUC 0.9. Conclusions: Autophagy markers ATG16L1, BECN1, and LC3II are down-regulated in the expression of IVIG -resistant KD. ESR, BECN1, and LC3II mRNAs are independent risk factors for IVIG-resistant KD and may be involved in the development of IVIG-resistant KD. This study established a new model that can be used to predict IVIG-resistant KD, and future validation in a larger population is needed. [ABSTRACT FROM AUTHOR]

The aim of the study is to select adequate econometric instruments for building a scoring model on a specific array of initial data, which contains the vast majority of fictitious variables. Despite a significant number of developments devoted to the construction of scoring models, a universal method allowing to obtain a highly efficient classifier for any data has not been identified. Therefore, the task of selection of the best method for building a scoring model remains relevant, depending on the characteristics of the available data. The most successful approach when selecting a model for solving the problem of binary classification is the use of several types of econometric models and the choice of the best of them according to the results of classification. In the presented study, the following types of models were applied: discriminant model, logit and probit regressions, and polynomial logistic regression. Training samples with different structure were used. Comparison of all obtained models allows us to conclude that polynomial logistic regression is preferable in this case. This model demonstrates high classification rates for all introduced object classes and has an important advantage compared to models that make a binary selection. The advantage of polynomial logistic regression is also the possibility of selecting in each case a convenient scale for dividing borrowers into more than two classes and determining the level of probability of reliability of the borrower acceptable for its own conditions, at which it should be assigned to one of the selected classes. Prospects for further research in this direction are the use of machine learning methods that will be able to use ensembles of the best of the considered models. In addition, the proposed models can be used in solving similar problems in other spheres of economic activity.

Abstract Background The mortality rate of gangrenous/perforated appendicitis is higher than that of uncomplicated appendicitis. However, non-operative management of such patients is ineffective. This necessitates their careful exam at presentation to identify gangrenous/perforated appendicitis and aid surgical decision-making. Therefore, this study aimed to develop a new scoring model based on objective findings to predict gangrenous/perforated appendicitis in adults. Methods We retrospectively analyzed 151 patients with acute appendicitis who underwent emergency surgery between January 2014 and June 2021. We performed univariate and multivariate analyses to identify independent objective predictors of gangrenous/perforated appendicitis, and a new scoring model was developed based on logistic regression coefficients for independent predictors. Receiver operating characteristic (ROC) curve analysis and the Hosmer–Lemeshow test were performed to assess the discrimination and calibration of the model. Finally, the scores were classified into three categories based on the probability of gangrenous/perforated appendicitis. Results Among the 151 patients, 85 and 66 patients were diagnosed with gangrenous/perforated appendicitis and uncomplicated appendicitis, respectively. Using the multivariate analysis, C-reactive protein level, maximal outer diameter of the appendix, and presence of appendiceal fecalith were identified as independent predictors for developing gangrenous/perforated appendicitis. Our novel scoring model was developed based on three independent predictors and ranged from 0 to 3. The area under the ROC curve was 0.792 (95% confidence interval, 0.721—0.863), and the Hosmer–Lemeshow test showed a good calibration of the novel scoring model (P = 0.716). Three risk categories were classified: low, moderate, and high risk with probabilities of 30.9%, 63.8%, and 94.4%, respectively. Conclusions Our scoring model can objectively and reproducibly identify gangrenous/perforated appendicitis with good diagnostic accuracy and help in determining the degree of urgency and in making decisions about appendicitis management.

Copyright of Journal of Modern Laboratory Medicine is the property of Journal of Modern Laboratory Medicine Editorial Department 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.)

Phishing detection is a critical component of cyber security, aiming to safeguard users from malicious attempts to deceive and exploit. In this research, proposed an innovative approach that combines an adaptive threshold optimization technique with a learning-based graph convolutional network (GCN) and a scoring model to enhance phishing detection accuracy. The learning-based GCN is designed to analyze the intricate relationships within a graph structure, capturing nuanced dependencies between various entities such as email senders, recipients, and URLs. Leveraging this graph analysis, a scoring model assigns likelihood scores to instances being potential phishing attempts. Adaptive threshold optimization is a technique commonly used in feature selection to determine which features dynamically. In the context of phishing website detection, adaptive threshold optimization is used to select and prioritize the most informative features extracted from the websites. As a result of GCN, the model is able to differentiate between legitimate websites and phishing sites both locally and globally. In an effort to improve online security and safeguard users from phishing attacks, the proposed model is providing 99.3% accuracy in detecting phishing attacks. This enables a flexible and fine-tuned decision-making process, optimizing the trade-off between false positives and false negatives. [ABSTRACT FROM AUTHOR]

The purpose of this paper ist to develop a scoring model that takes into account the specific criteria of the recycling industry. First of all the specific criteria have to be identified and afterwards weighted. In order for being able to identify the specific criteria, different methodology first have to be applied. For this the method of descriptive statistics, inference statistics and the systematic literature research are used. The identified criteria are summarized in a survey. Various companies of the recycling and disposal industry receive the survey and weight the criteria according to their subjective assessment. Through the individual subjective evaluations of the criteria, a weighted average and thus a scoring model can be created. The contribution of this paper is to offer the recycling companies the possibility to identifiy and select the optimal strategic partner based on objective criteria. As a result, the quality of the choice of a partner and the quality of service for the customer can be improved, which means that the paper also has a practical benefit. [ABSTRACT FROM AUTHOR]

Theoretical background: Value investing is one of the most popular investing approaches. In their frame, there could be a high B/M investing strategy identified. F-Score, developed by Piotroski, is a scoring model applied to the sample of high B/M entities. Its purpose is to select companies with strong financial foundations and buy their shares for the investment portfolio to generate positive market-adjusted returns in the following periods. The effectiveness of this model was mostly empirically confirmed, especially regarding developed markets. Purpose of the article: The main aim of the paper was to build F-Score-like models based on the data from the Polish stock market. The main hypothesis concerned the higher effectiveness of such models than F-Score, as the specificity of a given market should result in a better fit to the data. Research methods: Building of the models based on the discriminant analysis and formation of the investment portfolios based on the indications of these models as well as F-Score. Finally, backtesting of the portfolios built to assess their effectiveness. The sample covered most of the Polish-listed companies. The period taken into account was 2012–2022. Main findings: Models built (X-Score and Y-Score) were less efficient than F-Score. Moreover, they led to generating negative rates of return (both raw and market-adjusted). On the other hand, using of F-Score for the analyzed period seems to be purposeful due to the 1.35% mean annual market-adjusted return generated. Apart from the scoring models analyzed, the research partially confirmed the advisability of using a high B/M investing strategy. Generally, the results obtained are in line with the findings of most of other authors –regarding the F-Score effectiveness. However, an approach based on Mohanram’s idea – using the differences between absolute values of a given variable and median from the sample – proved to be inadequate in the Polish stock market.

Abstract Stroke has become a significant threat to global public health, the ideal solution to which is primary prevention. Identification and management of determinants of stroke among various variables in different datasets are essential steps for its primary prevention. This study aimed to develop a flexible scoring model, which can easily modify different datasets. The public dataset containing 41,931 cases with 643 occurrences of stroke was randomly divided into training, validation, and test datasets comprising 25,158 (60%), 8,386 (20%), and 8,387 (20%) cases, respectively. Three continuous variables (age, body mass index, and average glucose level) and seven categorical variables (heart disease, hypertension, sex, married/smoking/work/residence status) in the dataset were converted using the weight of evidence method. The significant variables among 10 transformed variables were selected using multivariable logistic regression analyses. The scoring model for stroke occurrence was developed in the training and validation datasets, and performance was evaluated in the test dataset. Age, average glucose level, heart disease, and hypertension were significant variables of stroke occurrence. The scoring model was easily calculated using four determinants and indicates that the stroke occurrence ranged from 0.04 to 12.50%. The performance of the scoring model on the test dataset was similar to that on the validation dataset. This novel point scoring model is flexible enough to modify various datasets and can be used for determinant identification. Furthermore, its simplicity allows individuals to manage determinants by self-calculating stroke occurrence. Our model contributes to primary prevention using determinant identification and management.

Privacy policies, intended to provide information to individuals regarding how their personal data is processed, are often complex and challenging for users to understand. Businesses often demonstrate non-compliance with personal data protection laws, ranging from the absence of privacy policies to the existence of policies that do not adhere to legal requirements. This paper aims to (1) develop a quantitative and systematic tool for evaluating privacy policies' compliance with the Personal Data Protection Act (PDPA), (2) assess compliance among Small and Medium Enterprises (SMEs) in Thailand, and (3) provide recommendations for enhancing compliance practices. To achieve this, we proposed a multi-criteria privacy policy scoring model integrated with comprehensive statistical data analyses. The privacy policy scoring model consists of ten privacy principles and 31 privacy criteria, providing a structured framework for evaluating privacy policies. During a two-year postponement period for enforcing the PDPA law, we conducted a stratified random-sampling survey of 384 SMEs to evaluate their privacy policies using the proposed scoring model. The accomplished results revealed significantly lower scores than anticipated, with the nationwide average score of SMEs reaching only 6.1909 out of 100 points. More than half of the SMEs collected personal data without announcing privacy policies, and those with privacy policies adhered to an average of only 12.15 out of 31 privacy criteria. These findings highlight the pressing need to improve compliance practices among SMEs in Thailand. The proposed methodology can be customized and applied to align with the requirements of personal data protection laws in other countries. Additionally, our findings indicate that compliance with the PDPA is influenced by the Thailand Standard Industrial Classification (TSIC) sections, suggesting the adoption of tailored approaches by policymakers to address the specific needs of different TSIC sections.

Background: Patients with metastatic pancreatic cancer refractory to first-line chemotherapy (CTx) have few treatment options. It is unclear what kind of patients could be brought about survival benefit by 2nd-line CTx after refractory to gemcitabine + nab-PTX (GnP) or FOLFIRINOX. Methods: This analysis was conducted as part of a multicenter retrospective study of GnP or FOLFIRINOX in patients with metastatic pancreatic cancer. Excluding censored cases, 156 and 77 patients, respectively, received second-line chemotherapy (CTx) and best supportive care (BSC). Using prognostic factors for post-discontinuation survivals (PDSs) at the first-line determination in multivariate analysis, we developed a scoring system to demonstrate the benefit of second-line CTx. Results: The second-line CTx group had a median PDS of 5.2 months, whereas the BSC group had a median PDS of 2.7 months (hazard ratio 0.42; 95% confidence interval [CI] 0.31–0.57; p < 0.01). According to the Cox regression model, serum albumin levels below 3.5 g/dL, and CA19-9 levels above 1000 U/mL were independent prognostic factors (p < 0.01). Serum albumin (≥ and < 3.5 g/dL allotted to scores 0 and 1) and CA19-9 (< and ≥ 1000 U/mL allotted to scores 0 and 1) at first-line determination were used to develop the scoring system. The PDSs of patients with scores of 0 and 1 were significantly better than those of the BSC group; however, there was no significant difference between the PDSs of patients with score 2 and the BSC group. Conclusion: The survival advantage of second-line CTx, was observed in patients with scores of 0 and 1 but not in those with score 2. [ABSTRACT FROM AUTHOR]

The scoring models used for protein structure modeling and ranking are mainly divided into unified field and protein-specific scoring functions. Although protein structure prediction has made tremendous progress since CASP14, the modeling accuracy still cannot meet the requirements to a certain extent. Especially, accurate modeling of multi-domain and orphan proteins remains a challenge. Therefore, an accurate and efficient protein scoring model should be developed urgently to guide the protein structure folding or ranking through deep learning. In this work, we propose a protein structure global scoring model based on equivariant graph neural network (EGNN), named GraphGPSM, to guide protein structure modeling and ranking. We construct an EGNN architecture, and a message passing mechanism is designed to update and transmit information between nodes and edges of the graph. Finally, the global score of the protein model is output through a multilayer perceptron. Residue-level ultrafast shape recognition is used to describe the relationship between residues and the overall structure topology, and distance and direction encoded by Gaussian radial basis functions are designed to represent the overall topology of the protein backbone. These two features are combined with Rosetta energy terms, backbone dihedral angles and inter-residue distance and orientations to represent the protein model and embedded into the nodes and edges of the graph neural network. The experimental results on the CASP13, CASP14 and CAMEO test sets show that the scores of our developed GraphGPSM have a strong correlation with the TM-score of the models, which are significantly better than those of the unified field score function REF2015 and the state-of-the-art local lDDT-based scoring models ModFOLD8, ProQ3D and DeepAccNet, etc. The modeling experimental results on 484 test proteins demonstrate that GraphGPSM can greatly improve the modeling accuracy. GraphGPSM is further used to model 35 orphan proteins and 57 multi-domain proteins. The results show that the average TM-score of the models predicted by GraphGPSM is 13.2 and 7.1% higher than that of the models predicted by AlphaFold2. GraphGPSM also participates in CASP15 and achieves competitive performance in global accuracy estimation. [ABSTRACT FROM AUTHOR]

Background: The mortality rate of gangrenous/perforated appendicitis is higher than that of uncomplicated appendicitis. However, non-operative management of such patients is ineffective. This necessitates their careful exam at presentation to identify gangrenous/perforated appendicitis and aid surgical decision-making. Therefore, this study aimed to develop a new scoring model based on objective findings to predict gangrenous/perforated appendicitis in adults. Methods: We retrospectively analyzed 151 patients with acute appendicitis who underwent emergency surgery between January 2014 and June 2021. We performed univariate and multivariate analyses to identify independent objective predictors of gangrenous/perforated appendicitis, and a new scoring model was developed based on logistic regression coefficients for independent predictors. Receiver operating characteristic (ROC) curve analysis and the Hosmer–Lemeshow test were performed to assess the discrimination and calibration of the model. Finally, the scores were classified into three categories based on the probability of gangrenous/perforated appendicitis. Results: Among the 151 patients, 85 and 66 patients were diagnosed with gangrenous/perforated appendicitis and uncomplicated appendicitis, respectively. Using the multivariate analysis, C-reactive protein level, maximal outer diameter of the appendix, and presence of appendiceal fecalith were identified as independent predictors for developing gangrenous/perforated appendicitis. Our novel scoring model was developed based on three independent predictors and ranged from 0 to 3. The area under the ROC curve was 0.792 (95% confidence interval, 0.721—0.863), and the Hosmer–Lemeshow test showed a good calibration of the novel scoring model (P = 0.716). Three risk categories were classified: low, moderate, and high risk with probabilities of 30.9%, 63.8%, and 94.4%, respectively. Conclusions: Our scoring model can objectively and reproducibly identify gangrenous/perforated appendicitis with good diagnostic accuracy and help in determining the degree of urgency and in making decisions about appendicitis management. [ABSTRACT FROM AUTHOR]

Abstract Background Due to the low efficiency of a single clinical feature or laboratory variable in the diagnosis of tuberculous pleural effusion (TBPE), the diagnosis of TBPE is still challenging. This study aimed to build a scoring diagnostic model based on laboratory variables and clinical features to differentiate TBPE from non-tuberculous pleural effusion (non-TBPE). Methods A retrospective study of 125 patients (63 with TBPE; 62 with non-TBPE) was undertaken. Univariate analysis was used to select the laboratory and clinical variables relevant to the model composition. The statistically different variables were selected to undergo binary logistic regression. Variables B coefficients were used to define a numerical score to calculate a scoring model. A receiver operating characteristic (ROC) curve was used to calculate the best cut-off value and evaluate the performance of the model. Finally, we add a validation cohort to verify the model. Results Six variables were selected in the scoring model: Age ≤ 46 years old (4.96 points), Male (2.44 points), No cancer (3.19 points), Positive T-cell Spot (T-SPOT) results (4.69 points), Adenosine Deaminase (ADA) ≥ 24.5U/L (2.48 point), C-reactive Protein (CRP) ≥ 52.8 mg/L (1.84 points). With a cut-off value of a total score of 11.038 points, the scoring model’s sensitivity, specificity, and accuracy were 93.7%, 96.8%, and 99.2%, respectively. And the validation cohort confirms the model with the sensitivity, specificity, and accuracy of 92.9%, 93.3%, and 93.1%, respectively. Conclusion The scoring model can be used in differentiating TBPE from non-TBPE.

Stroke has become a significant threat to global public health, the ideal solution to which is primary prevention. Identification and management of determinants of stroke among various variables in different datasets are essential steps for its primary prevention. This study aimed to develop a flexible scoring model, which can easily modify different datasets. The public dataset containing 41,931 cases with 643 occurrences of stroke was randomly divided into training, validation, and test datasets comprising 25,158 (60%), 8,386 (20%), and 8,387 (20%) cases, respectively. Three continuous variables (age, body mass index, and average glucose level) and seven categorical variables (heart disease, hypertension, sex, married/smoking/work/residence status) in the dataset were converted using the weight of evidence method. The significant variables among 10 transformed variables were selected using multivariable logistic regression analyses. The scoring model for stroke occurrence was developed in the training and validation datasets, and performance was evaluated in the test dataset. Age, average glucose level, heart disease, and hypertension were significant variables of stroke occurrence. The scoring model was easily calculated using four determinants and indicates that the stroke occurrence ranged from 0.04 to 12.50%. The performance of the scoring model on the test dataset was similar to that on the validation dataset. This novel point scoring model is flexible enough to modify various datasets and can be used for determinant identification. Furthermore, its simplicity allows individuals to manage determinants by self-calculating stroke occurrence. Our model contributes to primary prevention using determinant identification and management. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

Background: Spinal schwannomas (SSs) are benign tumors affecting the nerve sheath, accounting for 25% of spinal nerve root tumors. Surgery represents the mainstay of treatment for SS patients. Following surgery, approximately 30% of patients experienced developed new or worsening neurological deterioration, which probably represented an inevitable complication of nerve sheath tumor surgery. The objective of this study was to identify the rates of new or worsening neurological deterioration in our center and accurately predict the neurological outcomes of patients with SS by developing a new scoring model. Methods: A total of 203 patients were retrospectively enrolled at our center. Risk factors associated with postoperative neurological deterioration were identified by multivariate logistic regression analysis. b-coefficients for independent risk factors were used to define a numerical score to generate a scoring model. The validation cohort at our center was used to verify the accuracy and reliability of the scoring model. Receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of the scoring model. Results: In this study, five measured variables were selected for the scoring model: duration of preoperative symptoms (1 point), radiating pain (2 points), tumor size (2 points), tumor site (1 point), and dumbbell tumor (1 point). The scoring model divided the spinal schwannoma patients into three categories: low risk (0-2 points), intermediate risk (3-5 points), and high risk (6-7 points), with predicted risks of neurological deterioration of 8.7%, 36%, and 87.5%, respectively. And the validation cohort confirmed the model with the predicted risks of 8.6%, 46.4%, and 66.6%, respectively. Conclusion: The new scoring model might intuitively and individually predict the risk of neurological deterioration and may aid individualized treatment decisionmaking for SS patients. [ABSTRACT FROM AUTHOR]

With the increasingly serious burden of osteoarthritis (OA) on modern society, it is urgent to propose novel diagnostic biomarkers and differentiation models for OA. 7-methylguanosine (m7G), as one of the most common base modification forms in post transcriptional regulation, through which the seventh position N of guanine (G) of messenger RNA is modified by methyl under the action of methyltransferase; it has been found that it plays a crucial role in different diseases. Therefore, we explored the relationship between OA and m7G. Based on the expression level of 18 m7G-related regulators, we identified nine significant regulators. Then, via a series of methods of machine learning, such as support vector machine recursive feature elimination, random forest and lasso-cox regression analysis, a total of four significant regulators were further identified (DCP2, EIF4E2, LARP1 and SNUPN). Additionally, according to the expression level of the above four regulators, two different m7G-related clusters were divided via consensus cluster analysis. Furthermore, via immune infiltration, differential expression analysis and enrichment analysis, we explored the characteristic of the above two different clusters. An m7G-related scoring model was constructed via the PCA algorithm. Meanwhile, there was a different immune status and correlation for immune checkpoint inhibitors between the above two clusters. The expression difference of the above four regulators was verified via real-time quantitative polymerase chain reaction. Overall, a total of four biomarkers were identified and two different m7G-related subsets of OA with different immune microenvironment were obtained. Meanwhile, the construction of m7G-related Scoring model may provide some new strategies and insights for the therapy and diagnosis of OA patients. [ABSTRACT FROM AUTHOR]

Ischemic heart disease (IHD) is the leading cause of death and emergency department (ED) admission. We aimed to develop more accurate and straightforward scoring models to optimize the triaging of IHD patients in ED. This was a retrospective study based on the MIMIC-IV database. Scoring models were established by AutoScore formwork based on machine learning algorithm. The predictive power was measured by the area under the curve in the receiver operating characteristic analysis, with the prediction of intensive care unit (ICU) stay, 3d-death, 7d-death, and 30d-death after emergency admission. A total of 8381 IHD patients were included (median patient age, 71 years, 95% CI 62–81; 3035 [36%] female), in which 5867 episodes were randomly assigned to the training set, 838 to validation set, and 1676 to testing set. In total cohort, there were 2551 (30%) patients transferred into ICU; the mortality rates were 1% at 3 days, 3% at 7 days, and 7% at 30 days. In the testing cohort, the areas under the curve of scoring models for shorter and longer term outcomes prediction were 0.7551 (95% CI 0.7297–0.7805) for ICU stay, 0.7856 (95% CI 0.7166–0.8545) for 3d-death, 0.7371 (95% CI 0.6665–0.8077) for 7d-death, and 0.7407 (95% CI 0.6972–0.7842) for 30d-death. This newly accurate and parsimonious scoring models present good discriminative performance for predicting the possibility of transferring to ICU, 3d-death, 7d-death, and 30d-death in IHD patients visiting ED. [ABSTRACT FROM AUTHOR]

Theoretical background: Value investing is one of the most popular investing approaches. In their frame, there could be a high B/M investing strategy identified. F-Score, developed by Piotroski, is a scoring model applied to the sample of high B/M entities. Its purpose is to select companies with strong financial foundations and buy their shares for the investment portfolio to generate positive market-adjusted returns in the following periods. The effectiveness of this model was mostly empirically confirmed, especially regarding developed markets. Purpose of the article: The main aim of the paper was to build F-Score-like models based on the data from the Polish stock market. The main hypothesis concerned the higher effectiveness of such models than F-Score, as the specificity of a given market should result in a better fit to the data. Research methods: Building of the models based on the discriminant analysis and formation of the investment portfolios based on the indications of these models as well as F-Score. Finally, backtesting of the portfolios built to assess their effectiveness. The sample covered most of the Polish-listed companies. The period taken into account was 2012–2022. Main findings: Models built (X-Score and Y-Score) were less efficient than F-Score. Moreover, they led to generating negative rates of return (both raw and market-adjusted). On the other hand, using of F-Score for the analyzed period seems to be purposeful due to the 1.35% mean annual market-adjusted return generated. Apart from the scoring models analyzed, the research partially confirmed the advisability of using a high B/M investing strategy. Generally, the results obtained are in line with the findings of most of other authors – regarding the F-Score effectiveness. However, an approach based on Mohanram’s idea – using the differences between absolute values of a given variable and median from the sample – proved to be inadequate in the Polish stock market. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to derive a new scoring model from estimating the severity grade of mesenteric artery stenosis. We sought to analyze the relationship between the new scoring model and the development, treatment, and mortality of chronic mesenteric ischemia (CMI). Methods: This retrospective study included 242 patients (128 (53%) women and 114 (47%) men) with suspected CMI from January 2011 to December 2020. A weighted sum six-point score (CSI-score; the celiac artery is abbreviated by "C", superior mesenteric artery by "S", and inferior mesenteric artery by "I") based on the number of affected vessels and the extent and grade of the stenosis or occlusion of the involved visceral arteries was derived by maximizing the area under the ROC curve. The calculated CSI-score ranged from 0 to 22. The patients were divided according to the best cut-off point into low-score (CSI-score < 8) and high-score (CSI-score ≥ 8) groups. Results: The area under the receiver operating characteristic curve (AUC) of the CSI-score was 0.86 (95% CI, 0.82–0.91). The best cut-off point of "8" represented the highest value of Youden's index (0.58) with a sensitivity of 87% and specificity of 72%. The cohort was divided according to the cut-off point into a low-score group (n = 100 patients, 41%) and high-score group (n = 142 patients, 59%) and according to the clinical presentation into a CMI group (n = 109 patients, 45%) and non-CMI group (n = 133 patients, 55%). The median CSI-score for all patients was 10 (range: 0 -22). High-scoring patients showed statistically significant higher rates of coronary artery disease (54% vs. 36%, p = 0.007), chronic renal insufficiency (50% vs. 30%, p = 0.002), and peripheral arterial disease (57% vs. 16%, p < 0.001). A total of 109 (45%) patients underwent invasive treatment of the visceral arteries and were more often in the high-score group (69% vs. 11%, p < 0.001). Of those, 79 (72%) patients underwent primary endovascular treatment, and 44 (40%) patients underwent primary open surgery or open conversion after endovascular treatment. Sixteen (7%) patients died during the follow-up, with a statistically significant difference between high- and low-scoring patients (9% vs. 0%, p = 0.008). The score stratification showed that the percentage of patients treated with endovascular and open surgical methods, the recurrence of the stenosis or failure of the endovascular treatment, the need for a bypass procedure, and the mortality rates significantly increased in the subgroups. The CSI-score demonstrated an excellent ability to discriminate between patients who needed treatment and those who did not, with an AUC of 0.87 (95% CI, 0.82–0.91). Additionally, the CSI-score's ability to predict the patients' mortality was moderate, with an AUC of 0.73 (95% CI, 0.62–0.83). Conclusions: The new scoring model can estimate the severity grade of the stenosis of the mesenteric arteries. Our study showed a strong association of the score with the presence of chronic mesenteric ischemia, the need for treatment, the need for open surgery, and mortality. [ABSTRACT FROM AUTHOR]

The issue of searching for tender proposals satisfying the conditions of selection on the basis of the relevance assessment algorithm is considered. The algorithm is based on a mathematical scoring model. The approbation of the model based on the historical data of the software company is presented. The possibility of using such a method to determine relevance is proved. The assumption is made about the possibility of using scoring to evaluate tenders not only for the development of software products but also in other market segments. [ABSTRACT FROM AUTHOR]

Cell death, including apoptosis and necrosis, collectively known as widespread apoptosis. The present study aims to investigate the mechanism of action in widespread apoptosis-related modification patterns in bladder cancer. Using a clinical genomics database, we obtained transcriptomic data and related clinical information of bladder cancer patients. By employing the least absolute shrinkage analysis, we were able to construct a risk model and single-cell sequencing analysis of differential genes in bladder cancer. Five differentially expressed genes (TMPRSS4, TPST1, FOXD1, ELOVL4, EMP1) associated with widespread apoptosis were identified as features for predicting the prognosis of bladder cancer patients. Survival curve analysis revealed significant differences in prognosis (P<0.05). Additionally, multivariate Cox regression analysis determined the independent risk factor for bladder cancer prognosis as the risk score (P<0.001), with high confidence in the scoring model validated internally (P<0.001). Single-cell sequencing reveals high expression of CDKN2A, ERBB2, and TMPRSS4 in B cells, while HRAS is significantly expressed in fibroblasts. PANscore, as a potential prognostic and immunotherapeutic biomarker, will provide more precise and rational basis for personalized treatment strategies [ABSTRACT FROM AUTHOR]

Small and medium-sized enterprises (SMEs) play a key role in the Russian economy. However, banks and investors are reluctant to provide debt financing to these firms. This is underpinned by SMEs’ speculative credit quality and information asymmetry between borrowers and lenders. In this study, we aim to identify the insolvency drivers of Russian SMEs and compare them with those in other markets. The relevance of the study is underpinned by the scarcity of research in this field and the high demand for an accurate rating system for domestic SMEs. Logistic regression was selected as the modeling method. The sample contained 177 non-financial domestic SMEs over the period 2015–2019. The set of explanatory variables consisted of firm-specific financial, categorical, and macroeconomic factors. An accuracy ratio of >80% was achieved. We found that, unlike those in Asian emerging markets, financial factors explained around 70% of domestic SMEs’ credit health. Significant financial factors included profitability, debt leverage, and coverage ratios and the term structure of debt. Non-financial drivers included ownership of the firm by large businesses (or group of companies), firm size, and territory of operation within Russia. Among macroeconomic drivers, the unemployment level was the most significant driver of SMEs’ credit quality. In addition, we developed a rating system for domestic SMEs and determined the relative benchmarks from Expert RA and Moody’s agencies. We found that the existing scales of rating agencies did not provide the granular assessment of SMEs’ creditworthiness. This confirmed our hypothesis that distinct rating frameworks and methodologies for domestic SMEs in the Russian market are imperative. As shown in the literature, the greater the rating granularity and transparency, the more enhanced the debt market’s appropriate risk-return tradeoff analysis.

Objectives: The differential diagnosis of ACTH-dependent Cushing's disease (CS) is challenging. The gold standard approach bilateral inferior petrosal sinus sampling (BIPSS) is expensive and invasive, while other noninvasive tests, like the high-dose dexamethasone suppression test (HDDST), provide unsatisfactory diagnostic accuracy. This study aimed to find a new noninvasive practical approach with higher diagnostic accuracy to differently diagnose ACTH-dependent CS, which can be used in centers where BIPSS cannot be applied. Methods: 264 Cushing's disease (CD) patients and 47 ectopic ACTH secretion syndrome (EAS) patients were analyzed in this single-center retrospective study (2011–2021). The multivariate logistic model was used to construct the scoring model. Results: Female (adjusted OR 3.030, 95%CI 1.229–7.471), hypokalemia (0.209, 0.076–0.576), ACTH (0.988, 0.982–0.994), MRI pituitary lesion positive (8.671, 3.521–21.352), and HDDST positive (2.768, 1.139–6.726) have a strong association with the differential diagnosis of ACTH-dependent CS and were included in the final multivariable logistic regression model. A -14-to-14-point noninvasive scoring model was built on the model. The AUC of the noninvasive scoring model was 0.915 (95% CI 0.869–0.960), significantly higher than the AUC of HDDST (0.756, 95% CI 0.685–0.825, P = 0.004). The optimal cutoff of the model was ≥0 to diagnose CD. The sensitivity of the noninvasive scoring model was 91.3% (95% CI 87.3%–94.1%), and the specificity was 80.9% (95% CI 67.5%–89.6%). When the model's sensitivity was 100.0%, the cutoff was ≥ −10 with a specificity of 19.2%; when the model's specificity was 100.0%, the cutoff was ≥ 13 with a sensitivity of 22.7%. Conclusions: We developed a noninvasive scoring model to distinguish CD and EAS in ACTH-dependent CS patients with higher diagnostic utility than HDDST in the same cohort. The noninvasive scoring model might be applied in areas where BIPSS is unavailable, the CRH is hard to obtain, or the desmopressin stimulation is not widely applied. It also provided a triage tool for selecting patients that might benefit the most from a further BIPSS test. [ABSTRACT FROM AUTHOR]

Background: Due to the low efficiency of a single clinical feature or laboratory variable in the diagnosis of tuberculous pleural effusion (TBPE), the diagnosis of TBPE is still challenging. This study aimed to build a scoring diagnostic model based on laboratory variables and clinical features to differentiate TBPE from non-tuberculous pleural effusion (non-TBPE).Methods: A retrospective study of 125 patients (63 with TBPE; 62 with non-TBPE) was undertaken. Univariate analysis was used to select the laboratory and clinical variables relevant to the model composition. The statistically different variables were selected to undergo binary logistic regression. Variables B coefficients were used to define a numerical score to calculate a scoring model. A receiver operating characteristic (ROC) curve was used to calculate the best cut-off value and evaluate the performance of the model. Finally, we add a validation cohort to verify the model.Results: Six variables were selected in the scoring model: Age ≤ 46 years old (4.96 points), Male (2.44 points), No cancer (3.19 points), Positive T-cell Spot (T-SPOT) results (4.69 points), Adenosine Deaminase (ADA) ≥ 24.5U/L (2.48 point), C-reactive Protein (CRP) ≥ 52.8 mg/L (1.84 points). With a cut-off value of a total score of 11.038 points, the scoring model's sensitivity, specificity, and accuracy were 93.7%, 96.8%, and 99.2%, respectively. And the validation cohort confirms the model with the sensitivity, specificity, and accuracy of 92.9%, 93.3%, and 93.1%, respectively.Conclusion: The scoring model can be used in differentiating TBPE from non-TBPE. [ABSTRACT FROM AUTHOR]

Background: Anomalous systemic arterial supply to the normal basal segments of the lower lobe (ASALL) is a rare anomaly with a common complication of hemoptysis. To estimate the risk of hemoptysis, this study aims to investigate the value of contrast-enhanced computed tomography (CT) and construct a risk-scoring model based on radiological features and clinical materials of patients with ASALL., Methods: Forty-three eligible individuals (17 women and 26 males), who underwent multiphase contrast-enhanced CT, were included in this study. Hemoptysis was predicted by combined systemic arterial features (C D-A ) and combined demographic and radiological features (C D-R ). Potential hemoptysis predictors were identified using multivariate regression analysis. A receiver operating characteristic (ROC) curve analysis was used to assess the prediction efficiency. The coefficient of regression model was used to build a combined risk scoring (C RS ) model for hemoptysis. The decision curve analysis (DCA) was performed to evaluate the clinical usefulness of the risk-scoring model., Results: Hemoptysis was present in 17 (39.5%) ASALL patients. The areas under the curve (AUCs) for the predicted performance of C D-A and C D-R were 0.869 and 0.890, respectively. Independent predictors generated a scoring model using the formula C RS = 3 × age + 3 × sex + 4 × [ground glass opacity (GGO)] + 3 × (C D-A >0.522). The prediction performance of this model was displayed with an AUC of 0.939. This scoring model was demonstrated to be significantly preferable to C D-A (P=0.046) and C D-R (P=0.02) by the Hanley and McNeil test. The DCA showed that the C RS model was more beneficial when the threshold probability was between 5% and 92%., Conclusions: The scoring model offers a viable method for evaluating the risk of hemoptysis in patients with ASALL by combining radiological and clinical data., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-738/coif). The authors have no conflicts of interest to declare., (2024 AME Publishing Company. All rights reserved.)

The interaction between cancer-associated fibroblasts (CAFs) and the tumor microenvironment (TME) is a key factor for promoting tumor progression. In lung cancer, the crosstalk between CAFs and malignant and immune cells is expected to provide new directions for the development of immunotherapy. In this study, we have systematically analyzed a single-cell dataset and identified interacting genes between CAFs and other cells. Subsequently, a robust fibroblast-related score (FRS) was developed. Kaplan-Meier (KM) and ROC analyses showed its good predictive power for patient prognoses in the training set comprising of specimens from the cancer genome atlas (TCGA) and in three external validation sets from the Gene Expression Omnibus (GEO). Univariate and multivariate Cox regression analyses suggested that FRS was a significant prognostic factor independent of multiple clinical characteristics. Functional enrichment and ssGSEA analyses indicated that patients with a high FRS developed "cold" tumors with active tumor proliferation and immunosuppression capacities. In contrast, those with a low FRS developed "hot" tumors with active immune function and cell killing abilities. Genomic variation analysis showed that the patients with a high FRS possessed a higher somatic mutation burden and copy number alterations and were more sensitive to chemotherapy; patients with a low FRS were more sensitive to immunotherapy, particularly anti-PD1 therapy. Overall, these findings advance the understanding of CAFs in tumor progression and we generated a reliable FRS-based model to assess patient prognoses and guide clinical decision-making. [ABSTRACT FROM AUTHOR]

Background: Balance rehabilitation programs represent the most common treatments for balance disorders. Nonetheless, lack of resources and lack of highly expert physiotherapists are barriers for patients to undergo individualized rehabilitation sessions. Therefore, balance rehabilitation programs are often transferred to the home environment, with a considerable risk of the patient misperforming the exercises or failing to follow the program at all. Holobalance is a persuasive coaching system with the capacity to offer full-scale rehabilitation services at home. Holobalance involves several modules, from rehabilitation program management to augmented reality coach presentation. Objective: The aim of this study was to design, implement, test, and evaluate a scoring model for the accurate assessment of balance rehabilitation exercises, based on data-driven techniques. Methods: The data-driven scoring module is based on an extensive data set (approximately 1300 rehabilitation exercise sessions) collected during the Holobalance pilot study. It can be used as a training and testing data set for training machine learning (ML) models, which can infer the scoring components of all physical rehabilitation exercises. In that direction, for creating the data set, 2 independent experts monitored (in the clinic) 19 patients performing 1313 balance rehabilitation exercises and scored their performance based on a predefined scoring rubric. On the collected data, preprocessing, data cleansing, and normalization techniques were applied before deploying feature selection techniques. Finally, a wide set of ML algorithms, like random forests and neural networks, were used to identify the most suitable model for each scoring component. Results: The results of the trained model improved the performance of the scoring module in terms of more accurate assessment of a performed exercise, when compared with a rule-based scoring model deployed at an early phase of the system (k-statistic value of 15.9% for sitting exercises, 20.8% for standing exercises, and 26.8% for walking exercises). Finally, the resulting performance of the model resembled the threshold of the interobserver variability, enabling trustworthy usage of the scoring module in the closed-loop chain of the Holobalance coaching system. Conclusions: The proposed set of ML models can effectively score the balance rehabilitation exercises of the Holobalance system. The models had similar accuracy in terms of Cohen kappa analysis, with interobserver variability, enabling the scoring module to infer the score of an exercise based on the collected signals from sensing devices. More specifically, for sitting exercises, the scoring model had high classification accuracy, ranging from 0.86 to 0.90. Similarly, for standing exercises, the classification accuracy ranged from 0.85 to 0.92, while for walking exercises, it ranged from 0.81 to 0.90. [ABSTRACT FROM AUTHOR]

A voting rule that permits some voters to favor a candidate by revealing only the initial segment of their sincere rankings is said to be vulnerable to the truncation paradox. In this paper, we consider four models for counting truncated ballots, optimistic, pessimistic (the most common), averaged, and round-down. Under the impartial anonymous culture assumption, the choice of model generally has a real impact on truncation-paradox vulnerability, but exceptions exist. When the election is decided by a one-shot scoring rule, the optimistic model is invulnerable to the truncation paradox, but all other models are vulnerable. We identify new voting rules immune to the truncation paradox, such as the Modified Borda Count. To obtain a more complete picture of the impact of processing model, we assess the likelihood of the truncation paradox in three-candidate elections with large electorates, focusing not only on one-shot scoring rules but also scoring rules with one-by-one or below-average elimination. Our assessment confirms that the processing model for truncated ballots may really matter. [ABSTRACT FROM AUTHOR]

Abstract Background Cryptococcal meningitis (CM) remains a leading cause of death in HIV-infected patients, despite advances in CM diagnostic and therapeutic strategies. This study was performed with the aim to develop and validate a novel scoring model to predict mortality risk in HIV-infected patients with CM (HIV/CM). Methods Data on HIV/CM inpatients were obtained from a Multicenter Cohort study in China. Independent risk factors associated with mortality were identified based on data from 2013 to 2017, and a novel scoring model for mortality risk prediction was established. The bootstrapping statistical method was used for internal validation. External validation was performed using data from 2018 to 2020. Results We found that six predictors, including age, stiff neck, impaired consciousness, intracranial pressure, CD4+ T-cell count, and urea levels, were associated with poor prognosis in HIV/CM patients. The novel scoring model could effectively identify HIV/CM patients at high risk of death on admission (area under curve 0.876; p

BackgroundDistinguishing Crohn's disease (CD) and intestinal Behçet's disease (BD) is difficult in clinical practice.AimTo evaluate the ability of CT enterography (CTE) to enhance the diagnostic value of endoscopy in differentiating CD from intestinal BD and to establish differential diagnosis models.MethodsA total of 113 patients with CD and 70 patients with intestinal BD from seven tertiary inflammatory bowel disease centers were enrolled. The univariate and multivariate analyses were used by SAS software version 9.2. Three differential scoring models based on the multivariate analysis of endoscopic features alone (model 1), endoscopic features combined with clinical symptoms (model 2), and endoscopic features combined with clinical symptoms and CTE (model 3) were established.ResultsThe results showed that model 2 increased the efficacy of model 1 in differential diagnosis and model 3 had the highest accuracy of 84.15% at a cutoff value of two points. The scoring of model 3 was as follows: genital ulcer (−3 points), skin lesions (−3 points), oval ulcer (-2 points), longitudinal ulcer (1 point), number of ulcers > 5 (3 points), inflammatory polyps (2 points), mucosal severe enhancement (2 points), and fibrofatty proliferation (1 point).ConclusionClinical symptoms and CTE increased the ability of endoscopy to differentiate CD from intestinal BD.

With the increasingly serious burden of osteoarthritis (OA) on modern society, it is urgent to propose novel diagnostic biomarkers and differentiation models for OA. 7-methylguanosine (m7G), as one of the most common base modification forms in post transcriptional regulation, through which the seventh position N of guanine (G) of messenger RNA is modified by methyl under the action of methyltransferase; it has been found that it plays a crucial role in different diseases. Therefore, we explored the relationship between OA and m7G. Based on the expression level of 18 m7G-related regulators, we identified nine significant regulators. Then, via a series of methods of machine learning, such as support vector machine recursive feature elimination, random forest and lasso-cox regression analysis, a total of four significant regulators were further identified (DCP2, EIF4E2, LARP1 and SNUPN). Additionally, according to the expression level of the above four regulators, two different m7G-related clusters were divided via consensus cluster analysis. Furthermore, via immune infiltration, differential expression analysis and enrichment analysis, we explored the characteristic of the above two different clusters. An m7G-related scoring model was constructed via the PCA algorithm. Meanwhile, there was a different immune status and correlation for immune checkpoint inhibitors between the above two clusters. The expression difference of the above four regulators was verified via real-time quantitative polymerase chain reaction. Overall, a total of four biomarkers were identified and two different m7G-related subsets of OA with different immune microenvironment were obtained. Meanwhile, the construction of m7G-related Scoring model may provide some new strategies and insights for the therapy and diagnosis of OA patients.

Background: Differential diagnosis of Crohn's disease (CD) and ulcerative primary intestinal lymphoma (UPIL) is a tough problem in clinical practice. Aims: Our study identified key differences between CD and UPIL patients and aimed to further establish a scoring model for differential diagnosis. Methods: A total of 91 CD and 50 UPIL patients from 9 tertiary inflammatory bowel disease centers were included. Univariate and multivariate analyses were used to determine significant markers for differentiating CD and UPIL. A differential scoring model was established by logistic regression analysis. Results: The differential model was based on clinical symptoms, endoscopic and imaging features that were assigned different scores: intestinal bleeding (−2 points), extraintestinal manifestation (2 points), segmental lesions (1 point), cobblestone sign (2 points), homogeneous enhancement (−1 point), mild enhancement (−1 point), engorged vasa recta (1 point). A total score of ≥1 point indicates CD, otherwise UPIL was indicated. This model produced an accuracy of 83.66% and an area under the ROC curve of 0.947. The area under the ROC curve for validation using the 10-fold validation method was 0.901. Conclusion: This study provided a convenient and useful model to differentiate CD from UPIL. [ABSTRACT FROM AUTHOR]

Abstract Background Acute aortic dissection type A is a life-threatening disease required emergency surgery during acute phase. Different clinical manifestations, laboratory tests, and imaging features of patients with acute aortic dissection type A are the risk factors of preoperative mortality. This study aims to establish a simple and effective preoperative mortality risk assessment model for patients with acute aortic dissection type A. Methods A total of 673 Chinese patients with acute aortic dissection type A who were admitted to our hospital were retrospectively included. All patients were unable to receive surgically treatment within 3 days from the onset of disease. The patients included were divided into the survivor and deceased groups, and the endpoint event was preoperative death. Multivariable analysis was used to investigate predictors of preoperative mortality and to develop a prediction model. Results Among the 673 patients, 527 patients survived (78.31%) and 146 patients died (21.69%). The developmental dataset had 505 patients, calibration by Hosmer Lemeshow was significant (χ2 = 3.260, df = 8, P = 0.917) and discrimination by area under ROC curve was 0.8448 (95% CI 0.8007–0.8888). The validation dataset had 168 patients, calibration was significant (χ2 = 5.500, df = 8, P = 0.703) and the area under the ROC curve was 0.8086 (95% CI 0.7291–0.8881). The following independent variables increased preoperative mortality: age (OR = 1.008, P = 0.510), abrupt chest pain (OR = 3.534, P