Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms

This study focused on the development and validation of a diagnostic model to differentiate between spinal tuberculosis (STB) and pyogenic spondylitis (PS). We analyzed a total of 387 confirmed cases, out of which 241 were diagnosed with STB and 146 were diagnosed with PS. These cases were randomly divided into a training group (n = 271) and a validation group (n = 116). Within the training group, four machine learning (ML) algorithms (least absolute shrinkage and selection operator [LASSO], logistic regression analysis, random forest, and support vector machine recursive feature elimination [SVM-RFE]) were employed to identify distinctive variables. These specific variables were then utilized to construct a diagnostic model. The model’s performance was subsequently assessed using the receiver operating characteristic (ROC) curves and the calibration curves. Finally, internal validation of the model was undertaken in the validation group. Our findings indicate that PS patients had an average platelet-to-neutrophil ratio (PNR) of 277.86, which was significantly higher than the STB patients’ average of 69.88. The average age of PS patients was 54.71 years, older than the 48 years recorded for STB patients. Notably, the neutrophil-to-lymphocyte ratio (NLR) was higher in PS patients at 6.15, compared to the 3.46 NLR in STB patients. Additionally, the platelet volume distribution width (PDW) in PS patients was 0.2, compared to 0.15 in STB patients. Conversely, the mean platelet volume (MPV) was lower in PS patients at an average of 4.41, whereas STB patients averaged 8.31. Hemoglobin (HGB) levels were lower in PS patients at an average of 113.31 compared to STB patients' average of 121.64. Furthermore, the average red blood cell (RBC) count was 4.26 in PS patients, which was less than the 4.58 average observed in STB patients. After evaluation, seven key factors were identified using the four ML algorithms, forming the basis of our diagnostic model. The training and validation groups yielded area under the curve (AUC) values of 0.841 and 0.83, respectively. The calibration curves demonstrated a high alignment between the nomogram-predicted values and the actual measurements. The decision curve indicated optimal model performance with a threshold set between 2% and 88%. In conclusion, our model offers healthcare practitioners a reliable tool to efficiently and precisely differentiate between STB and PS, thereby facilitating swift and accurate diagnoses

Prenatal, Perinatal and Neonatal Risk Factors for Intellectual Disability: A Systemic Review and Meta-Analysis

<div><p>Background</p><p>The etiology of non-genetic intellectual disability (ID) is not fully known, and we aimed to identify the prenatal, perinatal and neonatal risk factors for ID.</p><p>Method</p><p>PubMed and Embase databases were searched for studies that examined the association between pre-, peri- and neonatal factors and ID risk (keywords “intellectual disability” or “mental retardation” or “ID” or “MR” in combination with “prenatal” or “pregnancy” or “obstetric” or “perinatal” or “neonatal”. The last search was updated on September 15, 2015. Summary effect estimates (pooled odds ratios) were calculated for each risk factor using random effects models, with tests for heterogeneity and publication bias.</p><p>Results</p><p>Seventeen studies with 55,344 patients and 5,723,749 control individuals were eligible for inclusion in our analysis, and 16 potential risk factors were analyzed. Ten prenatal factors (advanced maternal age, maternal black race, low maternal education, third or more parity, maternal alcohol use, maternal tobacco use, maternal diabetes, maternal hypertension, maternal epilepsy and maternal asthma), one perinatal factor (preterm birth) and two neonatal factors (male sex and low birth weight) were significantly associated with increased risk of ID.</p><p>Conclusion</p><p>This systemic review and meta-analysis provides a comprehensive evidence-based assessment of the risk factors for ID. Future studies are encouraged to focus on perinatal and neonatal risk factors and the combined effects of multiple factors.</p></div

Study on Fuel Selection for a Long-Life Small Lead-Based Reactor

The choice of an appropriate fuel can effectively prolong the refueling cycle of a reactor core. The Th-U cycle and U-Pu cycle are commonly used fuel breeding cycles. Oxide fuels, nitride fuels, and metal fuels are the primary candidate fuels for lead-based reactors. For fuel selection, a core model of a 60 MWt reactor was established. The results show that the breeding performance of the breeding fuel Th-232 is better than that of U-238, and the driving performance of the driving fuel Pu-239 is better than that of U-235. Therefore, PuO2-ThO2, PuN-ThN, and Pu-Th-Zr fuels may have good performance. By comparing the reactivity loss of three types of fuel, it was found that the reactivity loss of PuN-ThN fuel is the smallest. Hence, using PuN-ThN fuel as a core fuel can result in a longer refueling cycle. On this basis, PuN-ThN fuel was used in the preliminary design of the 120 MWt core physical model. It can be seen that when PuN-ThN fuel is used as the core fuel, a smaller reactivity swing (1408 pcm), smaller power peak factor, and super long refueling cycle (more than 30 years) can be obtained

Meta-analysis of prenatal risk factors for ID.

<p>Meta-analysis of prenatal risk factors for ID.</p

Funnel plot for publication bias test.

<p>The two oblique lines indicate the pseudo 95% confidence limits.</p

Meta-analysis of prenatal risk factors for ID (Continued).

<p>Meta-analysis of prenatal risk factors for ID (Continued).</p

Characteristics of the included studies in meta-analysis.

<p>Characteristics of the included studies in meta-analysis.</p

Flow gram of study selection process.

<p>Flow gram of study selection process.</p

Funnel plot for publication bias test (Continued).

<p>The two oblique lines indicate the pseudo 95% confidence limits.</p