8,857 research outputs found

    Linear brain measurement: a new screening method for cognitive impairment in elderly patients with cerebral small vessel disease

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    BackgroundThe old adults have high incidence of cognitive impairment, especially in patients with cerebral small vessel disease (CSVD). Cognitive impairment is not easy to be detected in such populations. We aimed to develop clinical prediction models for different degrees of cognitive impairments in elderly CSVD patients based on conventional imaging and clinical data to determine the better indicators for assessing cognitive function in the CSVD elderly.Methods210 CSVD patients were screened out by the evaluation of Magnetic Resonance Imaging (MRI). Then, participants were divided into the following three groups according to the cognitive assessment results: control, mild cognitive impairment (MCI), and dementia groups. Clinical data were collected from all patients, including demographic data, biochemical indicators, carotid ultrasound, transcranial Doppler (TCD) indicators, and linear measurement parameters based on MRI.ResultsOur results showed that the brain atrophy and vascular lesions developed progressive worsening with increased degree of cognitive impairment. Crouse score and Interuncal distance/Bitemporal distance (IUD/BTD) were independent risk factors for MCI in CSVD patients, and independent risk factors for dementia in CSVD were Crouse Score, the pulsatility index of the middle cerebral artery (MCAPI), IUD/BTD, and Sylvian fissure ratio (SFR). Overall, the parameters with high performance were the IUD/BTD (OR 2.28; 95% CI 1.26–4.10) and SFR (OR 3.28; 95% CI 1.54–6.91), and the AUC (area under the curve) in distinguishing between CSVD older adults with MCI and with dementia was 0.675 and 0.724, respectively. Linear brain measurement parameters had larger observed effect than other indexes to identify cognitive impairments in CSVD patients.ConclusionThis study shows that IUD/BTD and SFR are good predictors of cognitive impairments in CSVD elderly. Linear brain measurement showed a good predictive power for identifying MCI and dementia in elderly subjects with CSVD. Linear brain measurement could be a more suitable and novel method for screening cognitive impairment in aged CSVD patients in primary healthcare facilities, and worth further promotion among the rural population

    Accuracy of radiomics in the diagnosis and preoperative high-risk assessment of endometrial cancer: a systematic review and meta-analysis

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    BackgroundWith the increasing use of radiomics in cancer diagnosis and treatment, it has been applied by some researchers to the preoperative risk assessment of endometrial cancer (EC) patients. However, comprehensive and systematic evidence is needed to assess its clinical value. Therefore, this study aims to investigate the application value of radiomics in the diagnosis and treatment of EC.MethodsPubmed, Cochrane, Embase, and Web of Science databases were retrieved up to March 2023. Preoperative risk assessment of EC included high-grade EC, lymph node metastasis, deep myometrial invasion status, and lymphovascular space invasion status. The quality of the included studies was appraised utilizing the RQS scale.ResultsA total of 33 primary studies were included in our systematic review, with an average RQS score of 7 (range: 5–12). ML models based on radiomics for the diagnosis of malignant lesions predominantly employed logistic regression. In the validation set, the pooled c-index of the ML models based on radiomics and clinical features for the preoperative diagnosis of endometrial malignancy, high-grade tumors, lymph node metastasis, lymphovascular space invasion, and deep myometrial invasion was 0.900 (95%CI: 0.871–0.929), 0.901 (95%CI: 0.877–0.926), 0.906 (95%CI: 0.882–0.929), 0.795 (95%CI: 0.693–0.897), and 0.819 (95%CI: 0.705–0.933), respectively.ConclusionsRadiomics shows excellent accuracy in detecting endometrial malignancies and in identifying preoperative risk. However, the methodological diversity of radiomics results in significant heterogeneity among studies. Therefore, future research should establish guidelines for radiomics studies based on different imaging sources.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=364320 identifier CRD42022364320

    European guidelines for the diagnosis, treatment and follow-up of breast lesions with uncertain malignant potential (B3 lesions) developed jointly by EUSOMA, EUSOBI, ESP (BWG) and ESSO

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    Introduction: Breast lesions of uncertain malignant potential (B3) include atypical ductal and lobular hyperplasias, lobular carcinoma in situ, flat epithelial atypia, papillary lesions, radial scars and fibroepithelial lesions as well as other rare miscellaneous lesions. They are challenging to categorise histologically, requiring specialist training and multidisciplinary input. They may coexist with in situ or invasive breast cancer (BC) and increase the risk of subsequent BC development. Management should focus on adequate classification and management whilst avoiding overtreatment. The aim of these guidelines is to provide updated information regarding the diagnosis and management of B3 lesions, according to updated literature review evidence. Methods: These guidelines provide practical recommendations which can be applied in clinical practice which include recommendation grade and level of evidence. All sections were written according to an updated literature review and discussed at a consensus meeting. Critical appraisal by the expert writing committee adhered to the 23 items in the international Appraisal of Guidelines, Research and Evaluation (AGREE) tool. Results: Recommendations for further management after core-needle biopsy (CNB) or vacuum-assisted biopsy (VAB) diagnosis of a B3 lesion reported in this guideline, vary depending on the presence of atypia, size of lesion, sampling size, and patient preferences. After CNB or VAB, the option of vacuum-assisted excision or surgical excision should be evaluated by a multidisciplinary team and shared decision-making with the patient is crucial for personalizing further treatment. De-escalation of surgical intervention for B3 breast lesions is ongoing, and the inclusion of vacuum-assisted excision (VAE) will decrease the need for surgical intervention in further approaches. Communication with patients may be different according to histological diagnosis, presence or absence of atypia, or risk of upgrade due to discordant imaging. Written information resources to help patients understand these issues alongside with verbal communication is recommended. Lifestyle interventions have a significant impact on BC incidence so lifestyle interventions need to be suggested to women at increased BC risk as a result of a diagnosis of a B3 lesion. Conclusions: These guidelines provide a state-of-the-art overview of the diagnosis, management and prognosis of B3 lesions in modern multidisciplinary breast practice

    Nomograms predicting the 3-year and 5-year overall survival of NECC patients.

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    Adding the scores of each independent prognostic factor, the overall survival was estimated by the total number of points for each factor on the bottom scale. A, five factors model; B, four factors model.</p

    Image3_Machine learning-based prediction of composite risk of cardiovascular events in patients with stable angina pectoris combined with coronary heart disease: development and validation of a clinical prediction model for Chinese patients.TIF

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    Objective: To develop a risk score model for the occurrence of composite cardiovascular events (CVE) in patients with stable angina pectoris (SA) combined with coronary heart disease (CHD) by comparing the modeling effects of various machine learning (ML) algorithms.Methods: In this prospective study, 690 patients with SA combined with CHD attending the Department of Integrative Cardiology, China-Japan Friendship Hospital, from October 2020 to October 2021 were included. The data set was randomly divided into a training group and a testing group in a 7:3 ratio in the per-protocol set (PPS). Model variables were screened using the least absolute shrinkage selection operator (LASSO) regression, univariate analysis, and multifactor logistic regression. Then, nine ML algorithms are integrated to build the model and compare the model effects. Individualized risk assessment was performed using the SHapley Additive exPlanation (SHAP) and nomograms, respectively. The model discrimination was evaluated by receiver operating characteristic curve (ROC), the calibration ability of the model was evaluated by calibration plot, and the clinical applicability of the model was evaluated by decision curve analysis (DCA). This study was approved by the Clinical Research Ethics Committee of China-Japan Friendship Hospital (2020-114-K73).Results: 690 patients were eligible to finish the complete follow-up in the PPS. After LASSO screening and multifactorial logistic regression analysis, physical activity level, taking antiplatelets, Traditional Chinese medicine treatment, Gensini score, Seattle Angina Questionnaire (SAQ)-exercise capacity score, and SAQ-anginal stability score were found to be predictors of the occurrence of CVE. The above predictors are modeled, and a comprehensive comparison of the modeling effectiveness of multiple ML algorithms is performed. The results show that the Light Gradient Boosting Machine (LightGBM) model is the best model, with an area under the curve (AUC) of 0.95 (95% CI = 0.91–1.00) for the test set, Accuracy: 0.90, Sensitivity: 0.87, and Specificity: 0.96. Interpretation of the model using SHAP highlighted the Gensini score as the most important predictor. Based on the multifactorial logistic regression modeling, a nomogram, and online calculators have been developed for clinical applications.Conclusion: We developed the LightGBM optimization model and the multifactor logistic regression model, respectively. The model is interpreted using SHAP and nomogram. This provides an option for early prediction of CVE in patients with SA combined with CHD.</p

    Methods of power consumption in conditions of high-productive areas of coal mines

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    The publication analyses the existing methods of estimated power consumption in relation to the conditions of high-productivity coal mine sites. In addition, a mathematical description of the model of predictive power consumption using the method of correlation and regression analysis is proposed and an algorithm for determining the parameters of specific power consumption in relation to the conditions of high-productivity coal mine sites is proposed. Key words: coal mine; high-performance section, technological equipment, specific power consumption; algorithm; mathematical forecast model of power consumptio

    Nomograms for Predicting Postoperative Sperm Improvements in Varicocele Patients

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    Background: Varicocele is a condition that seriously affects male fertility. It can cause pathological changes in the testicles and affect their spermatogenesis and endocrine function. Objective: To formulate nomograms to predict sperm improvements after microscopic varicocelectomy. Design, setting, and participants: A retrospective analysis was conducted on varicocele patients who met the research criteria and were enrolled from March 2020 to June 2022. They were divided into a development and a validation cohort in a 2:1 ratio. Outcome measurements and statistical analysis: Data on preoperative testicular atrophy index, bilateral testicular elastic modulus, testosterone, pre- and postoperative 6-mo total sperm count, sperm concentration, and sperm vitality were collected. An increase of ≥25% is considered a postoperative improvement in sperm parameters. Predictive nomograms were constructed through forward stepwise LR regression, based on independent risk factors filtered by univariate and multivariate logistic regression analyses. Receiver operating characteristic curve analysis, calibration curve, and decision curve analysis were employed to assess the performance of the models. Results and limitations: The areas under the curve of nomograms for predicting the postoperative improvement of total sperm count, sperm concentration, and sperm vitality were 0.915, 0.986, and 0.924 respectively. The nomogram models demonstrated good predictive performance. The single-center sample size was a limitation of this study. Conclusions: In this study, we developed effective predictive nomogram models for anticipating postoperative improvements in sperm quality among varicocele patients. These models offer a significant value in providing accurate predictions of surgical outcomes. However, it is crucial to conduct further external validation. Patient summary: In this study, a predictive nomogram model was constructed for assessing the improvement of sperm quality in varicocele patients after surgery. The model offered satisfactory results

    A population pharmacokinetic model to guide clozapine dose selection, based on age, sex, ethnicity, body weight and smoking status

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    Aims: Guidance on clozapine dosing in treatment-resistant schizophrenia is based largely on data from White young adult males. This study aimed to investigate the pharmacokinetic profiles of clozapine and Ndesmethylclozapine (norclozapine) across the age range, accounting for sex, ethnicity, smoking status, and body weight. Methods: A population pharmacokinetic model, implemented in Monolix, that linked plasma clozapine and norclozapine via a metabolic rate constant, was used to analyse data from a clozapine therapeutic drug monitoring service, 1993–2017. Results: There were 17,787 measurements from 5960 patients (4315 male) aged 18 to 86 years. The estimated clozapine plasma clearance was reduced from 20.2 to 12.0 L h-1 between 20 and 80 years. Model based dose predictions to attain a pre-dose plasma clozapine concentration of 0.35 mg L-1 was 275 (90% prediction interval 125, 625) mg day-1 in a nonsmoking White male weighing 70 kg and aged 40 years. The corresponding predicted dose was increased by 30% in smokers, decreased by 18% in females and was 10% higher and 14% lower in otherwise analogous Afro-Caribbean and Asian patients, respectively. Overall, the predicted dose decreased by 56% between age 20 and 80 years. Conclusion: The large sample size and wide age range of the patients studied allowed precise estimation of dose requirements to attain a pre-dose plasma clozapine concentration of 0.35 mg L-1. The analysis was however limited by the absence of data on clinical outcome and further studies are required to determine optimal pre-dose concentrations specifically in those aged over 65 years

    Table2_Machine learning-based prediction of composite risk of cardiovascular events in patients with stable angina pectoris combined with coronary heart disease: development and validation of a clinical prediction model for Chinese patients.DOC

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    Objective: To develop a risk score model for the occurrence of composite cardiovascular events (CVE) in patients with stable angina pectoris (SA) combined with coronary heart disease (CHD) by comparing the modeling effects of various machine learning (ML) algorithms.Methods: In this prospective study, 690 patients with SA combined with CHD attending the Department of Integrative Cardiology, China-Japan Friendship Hospital, from October 2020 to October 2021 were included. The data set was randomly divided into a training group and a testing group in a 7:3 ratio in the per-protocol set (PPS). Model variables were screened using the least absolute shrinkage selection operator (LASSO) regression, univariate analysis, and multifactor logistic regression. Then, nine ML algorithms are integrated to build the model and compare the model effects. Individualized risk assessment was performed using the SHapley Additive exPlanation (SHAP) and nomograms, respectively. The model discrimination was evaluated by receiver operating characteristic curve (ROC), the calibration ability of the model was evaluated by calibration plot, and the clinical applicability of the model was evaluated by decision curve analysis (DCA). This study was approved by the Clinical Research Ethics Committee of China-Japan Friendship Hospital (2020-114-K73).Results: 690 patients were eligible to finish the complete follow-up in the PPS. After LASSO screening and multifactorial logistic regression analysis, physical activity level, taking antiplatelets, Traditional Chinese medicine treatment, Gensini score, Seattle Angina Questionnaire (SAQ)-exercise capacity score, and SAQ-anginal stability score were found to be predictors of the occurrence of CVE. The above predictors are modeled, and a comprehensive comparison of the modeling effectiveness of multiple ML algorithms is performed. The results show that the Light Gradient Boosting Machine (LightGBM) model is the best model, with an area under the curve (AUC) of 0.95 (95% CI = 0.91–1.00) for the test set, Accuracy: 0.90, Sensitivity: 0.87, and Specificity: 0.96. Interpretation of the model using SHAP highlighted the Gensini score as the most important predictor. Based on the multifactorial logistic regression modeling, a nomogram, and online calculators have been developed for clinical applications.Conclusion: We developed the LightGBM optimization model and the multifactor logistic regression model, respectively. The model is interpreted using SHAP and nomogram. This provides an option for early prediction of CVE in patients with SA combined with CHD.</p

    DataSheet_1_Prognostic stratification of sepsis through DNA damage response based RiskScore system: insights from single-cell RNA-sequencing and transcriptomic profiling.docx

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    BackgroundA novel risk scoring system, predicated on DNA damage response (DDR), was developed to enhance prognostic predictions and potentially inform the creation of more effective therapeutic protocols for sepsis.MethodsTo thoroughly delineate the expression profiles of DDR markers within the context of sepsis, an analytical approach utilizing single-cell RNA-sequencing (scRNA-seq) was implemented. Our study utilized single-cell analysis techniques alongside weighted gene co-expression network analysis (WGCNA) to pinpoint the genes that exhibit the most substantial associations with DNA damage response (DDR). Through Cox proportional hazards LASSO regression, we distinguished DDR-associated genes and established a risk model, enabling the stratification of patients into high- and low-risk groups. Subsequently, we carried out an analysis to determine our model’s predictive accuracy regarding patient survival. Moreover, we examined the distinct biological characteristics, various signal transduction routes, and immune system responses in sepsis patients, considering different risk categories and outcomes related to survival. Lastly, we conducted experimental validation of the identified genes through in vivo and in vitro assays, employing RT-PCR, ELISA, and flow cytometry.ResultsBoth single-cell RNA sequencing (scRNA-seq) and bulk transcriptomic analyses have demonstrated a strong correlation between DNA damage response (DDR) levels and sepsis prognosis. Specific cell subtypes, including monocytes, megakaryocytes, CD4+ T cells, and neutrophils, have shown elevated DDR activity. Cells with increased DDR scores exhibited more robust and numerous interactions with other cell populations. The weighted gene co-expression network analysis (WGCNA) and single-cell analyses revealed 71 DDR-associated genes. We developed a four-gene risk scoring system using ARL4C, CD247, RPL7, and RPL31, identified through univariate COX, LASSO COX regression, and log-rank (Mantel-Cox) tests. Nomograms, calibration plots, and decision curve analyses (DCA) regarding these specific genes have provided significant clinical benefits for individuals diagnosed with sepsis. The study suggested that individuals categorized as lower-risk demonstrated enhanced infiltration of immune cells, upregulated expression of immune regulators, and a more prolific presence of immune-associated functionalities and pathways. RT-qPCR analyses on a sepsis rat model revealed differential gene expression predominantly in the four targeted genes. Furthermore, ARL4C knockdown in sepsis model in vivo and vitro caused increased inflammatory response and a worse prognosis.ConclusionThe delineated DDR expression landscape offers insights into sepsis pathogenesis, whilst our riskScore model, based on a robust four-gene signature, could underpin personalized sepsis treatment strategies.</p
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