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Background: Cerebral small vessel disease (CSVD) is a significant contributor to stroke, intracerebral hemorrhages, and vascular dementia, particularly in the elderly. Early diagnosis remains challenging. This study aimed to develop and validate a novel nomogram for the early diagnosis of cerebral small vessel disease (CSVD). We focused on integrating cerebrovascular risk factors and blood biochemical markers to identify individuals at high risk of CSVD, thus enabling early intervention. Methods: In a retrospective study conducted at the neurology department of the Affiliated Hospital of Hebei University from January 2020 to June 2022, 587 patients were enrolled. The patients were randomly divided into a training set (70%, n = 412) and a validation set (30%, n = 175). The nomogram was developed usingmultivariable logistic regression analysis, with variables selected through the Least Absolute Shrinkage and SelectionOperator (LASSO) technique. The performance of the nomogram was evaluated based on the area under the receiver operating characteristic curve (AUC-ROC), calibration plots, and decision curve analysis (DCA). Results: Out of 88 analyzed biomarkers, 32 showed significant differences between the CSVD and non-CSVD groups. The LASSO regression identified 12 significant indicators, with nine being independent clinical predictors of CSVD. The AUC-ROC values of the nomogram were 0.849 (95% CI: 0.821-0.894) in the training set and 0.863 (95% CI: 0.810-0.917) in the validation set, indicating excellent discriminative ability. Calibration plots demonstrated good agreement between predicted and observed probabilities in both sets. DCA showed that the nomogram had significant clinical utility. Conclusions: The study successfully developed a nomogram predictive model for CSVD, incorporating nine clinical predictive factors. This model offers a valuable tool for early identification and risk assessment of CSVD, potentially enhancing clinical decision-making and patient outcomes. [ABSTRACT FROM AUTHOR]

Vitiligo is an acquired depigmenting disorder. To date, there is no predictive model for its response rate to narrowband ultraviolet B (NBUVB) phototherapy. The aim of this study was to investigate the different types of response of patients with non-segmental vitiligo undergoing NBUVB 3 times a week. Many patients who were previously considered non-responders were given the opportunity to continue the treatment. Long-term maintenance of treatment and follow-up of a cohort of 579 patients enabled different subtypes of response (very rapid, rapid, average, slow and “non-responders”) to be described for the first time, and a predictive model of response to be constructed based on repigmentation rate in the first 48 sessions of NBUVB. Among those patients who did not respond during the first 48 sessions, a new subgroup of patients was found, termed “very-slow” responders, who achieved a low, but significant, level of repigmentation after 96 sessions of NBUVB.

The field of Big data is a trending research area in computer domain and several of the trades all round the world. It has gained immense affluence in extensive and varied application divisions. This comprises social media, education, stock markets, healthcare, weather forecasting, etc. Various sharp machine learning techniques were proposed and used to impart predictionbased on analytics solutions. So this paper deals with study of commonly used machine learning methods for big data analytics. [ABSTRACT FROM AUTHOR]