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

UAV-Assisted Wireless Charging Incentive Mechanism Design Based on Contract Theory

In wireless sensor networks, terminal devices with restricted cost and size have limited battery life. Meanwhile, these energy-constrained devices are not easy to access, especially when the terminal devices are located in severe environments. To recharge the energy-constrained devices and extend their network service time, unmanned aerial vehicles (UAVs) equipped with wireless power chargers are leased by the third-party control center. To incent the participation of UAVs with different charging capabilities and ensure the strategy-proofness of the incentive mechanism, a hidden information based contract theory model, specifically adverse selection, is introduced. By leveraging individual rationality and incentive compatibility, a contract theory based optimization problem is then formulated. After reducing redundant constraints, the optimal contract items are derived by Lagrangian multiplier. Finally, numerical simulation results are implemented to compare the prepared algorithm with three other baselines, which validates the effectiveness of our proposed incentive mechanism

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

Network pharmacology combined with molecular docking and experimental validation to explore the potential mechanism of Cinnamomi ramulus against ankylosing spondylitis

AbstractBackground Cinnamomi ramulus (C. ramulus) is frequently employed in the treatment of ankylosing spondylitis (AS). However, the primary constituents, drug targets, and mechanisms of action remain unidentified.Methods In this study, various public databases and online tools were employed to gather information on the compounds of C. ramulus, drug targets, and disease targets associated with ankylosing spondylitis. The intersection of drug targets and disease targets was then determined to identify the common targets, which were subsequently used to construct a protein-protein interaction (PPI) network using the STRING database. Network analysis and the analysis of hub genes and major compounds were conducted using Cytoscape software. Furthermore, the Metascape platform was utilized for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Molecular docking studies and immunohistochemical experiments were performed to validate the core targets.Results The network analysis identified 2-Methoxycinnamaldehyde, cinnamaldehyde, and 2-Hydroxycinnamaldehyde as the major effective compounds present in C. ramulus. The PPI network analysis revealed PTGS2, MMP9, and TLR4 as the most highly correlated targets. GO and KEGG analyses indicated that C. ramulus exerts its therapeutic effects in ankylosing spondylitis through various biological processes, including the response to hormones and peptides, oxidative stress response, and inflammatory response. The main signaling pathways involved were IL-17, TNF, NF-kappa B, and Toll-like receptor pathways. Molecular docking analysis confirmed the strong affinity between the key compounds and the core targets. Additionally, immunohistochemical analysis demonstrated an up-regulation of PTGS2, MMP9, and TLR4 levels in ankylosing spondylitis.Conclusions This study provides insights into the effective compounds, core targets, and potential mechanisms of action of C. ramulus in the treatment of ankylosing spondylitis. These findings establish a solid groundwork for future fundamental research in this field

The Roles of <i>transformer-2</i> (<i>tra-2</i>) in the Sex Determination and Fertility of <i>Riptortus pedestris</i>, a Hemimetabolous Agricultural Pest

In most holometabolous insects, transformer-2 (tra-2) is an auxiliary gene required for sex determination, exerting a crucial role in regulating sexual differentiation; however, the study of tra-2 in hemimetabolous insects remains very sparse and limited to just a few species. In this study, we investigated the sequence and expression profile of the tra-2 gene in the bean bug, Riptortus pedestris, an agricultural pest belonging to the Heteroptera order. Three non-sex-specific splicing isoforms of Rptra-2 were found, Rptra-2293, Rptra-2284, and Rptra-2299, which shared most exons and exhibited similar expression throughout all stages of development, with particularly elevated levels in the embryo, ovary, and testis. RNAi knockdown experiments revealed that the suppression of Rptra-2 in nymphs led to abnormal females, characterized the formation of male-specific external genital, and also caused longer nymph duration. Knockdown of the expression of the Rptra-2 gene in newly emergent virgin females would cause ovarian arrest, and injecting the 8th-day virgin females with dsRptra-2 also caused a noticeable decline in the offspring numbers. Conversely, in dsRptra-2-treated males, the testes maintained normal morphology but experienced impaired reproductive capacity, attributed to diminished sperm viability. These findings highlight the crucial role of Rptra-2 in the sex determination and fertility of R. pedestris, providing valuable insights into the sex determination mechanisms of hemimetabolous insects

Radiation shielding optimization design research based on bare-bones particle swarm optimization algorithm

In order to further meet the requirements of weight, volume, and dose minimization for new nuclear energy devices, the bare-bones multi-objective particle swarm optimization algorithm is used to automatically and iteratively optimize the design parameters of radiation shielding system material, thickness, and structure. The radiation shielding optimization program based on the bare-bones particle swarm optimization algorithm is developed and coupled into the reactor radiation shielding multi-objective intelligent optimization platform, and the code is verified by using the Savannah benchmark model. The material type and thickness of Savannah model were optimized by using the BBMOPSO algorithm to call the dose calculation code, the integrated optimized data showed that the weight decreased by 78.77%, the volume decreased by 23.10% and the dose rate decreased by 72.41% compared with the initial solution. The results show that the method can get the best radiation shielding solution that meets a lot of different goals. This shows that the method is both effective and feasible, and it makes up for the lack of manual optimization

Identification and Functional Analysis of the <i>fruitless</i> Gene in a Hemimetabolous Insect, <i>Nilaparvata lugens</i>

The fruitless (fru) gene functions as a crucial “tuner” in male insect courtship behavior through distinct expression patterns. In Nilaparvata lugens, our previous research showed doublesex (dsx) influencing male courtship songs, causing mating failures with virgin females. However, the impact of fru on N. lugens mating remains unexplored. In this study, the fru homolog (Nlfru) in N. lugens yielded four spliceosomes: Nlfru-374-a/b, Nlfru-377, and Nlfru-433, encoding proteins of 374aa, 377aa, and 433aa, respectively. Notably, only Nlfru-374b exhibited male bias, while the others were non-sex-specific. All NlFRU proteins featured the BTB conserved domain, with NlFRU-374 and NlFRU-377 possessing the ZnF domain with different sequences. RNAi-mediated Nlfru or its isoforms’ knockdown in nymph stages blocked wing-flapping behavior in mating males, while embryonic knockdown via maternal RNAi resulted in over 80% of males losing wing-flapping ability, and female receptivity was reduced. Nlfru expression was Nldsx-regulated, and yet courtship signals and mating success were unaffected. Remarkably, RNAi-mediated Nlfru knockdown up-regulated the expression of flightin in macropterous males, which regulated muscle stiffness and delayed force response, suggesting Nlfru’s involvement in muscle development regulation. Collectively, our results indicate that Nlfru functions in N. lugens exhibit a combination of conservation and species specificity, contributing insights into fru evolution, particularly in Hemiptera species

Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study

Abstract Spinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients