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Objective Pneumocystis jirovecii pneumonia (PJP) can be a life-threatening opportunistic infection. We aimed to evaluate the diagnostic accuracy of metagenomic next-generation sequencing (mNGS) for PJP. Methods A comprehensive electronic literature search of Web of Knowledge, PubMed, Cochrane Library, CNKI and Wanfang data was performed. Bivariate analysis was conducted to calculate the pooled sensitivity, specificity, diagnostic odds ratio (DOR), the area under the summary receiver operator characteristic (SROC) curve and the Q-point value (Q*). Results The literature search resulted in 9 studies with a total of 1343 patients, including 418 cases diagnosed with PJP and 925 controls. The pooled sensitivity of mNGS for diagnosis of PJP was 0.974 [95% confidence interval (CI), 0.953–0.987]. The pooled specificity was 0.943 (95% CI, 0.926–0.957), the DOR was 431.58 (95% CI, 186.77-997.27), the area under the SROC curve was 0.987, and the Q* was 0.951. The I 2 test indicated no heterogeneity between studies. The Deek funnel test suggested no potential publication bias. Subgroup analyses showed that the area under the SROC curve of mNGS for diagnosis of PJP in immunocompromised and non-HIV patients was 0.9852 and 0.979, respectively. Conclusions Current evidence indicates that mNGS exhibits excellent accuracy for the diagnosis of PJP. The mNGS is a promising tool for assessment of PJP in both immunocompromised and non-HIV patients.

Urban floods induced by rainstorms can lead to severe losses of lives and property, making rapid flood prediction essential for effective disaster prevention and mitigation. However, traditional deep learning (DL) models often overlook the spatial heterogeneity of rainstorms and lack interpretability. Here, we propose an end‐to‐end rapid prediction method for urban flood inundation incorporating spatiotemporal varying rainstorms using a Convolutional Long Short‐Term Memory Network (ConvLSTM) DL model. We compare the performance of the proposed method with that of a 3D Convolutional Neural Network (3D CNN) model and introduce the spatial visualization technique Grad‐CAM to interpret the rainstorms contributions to flood predictions. Results demonstrate that: (a) Compared to the physics‐based model, the proposed ConvLSTM model achieves satisfactory accuracy in predicting flood inundation evolution under spatio‐temporal varying rainstorms, with an average Pearson correlation coefficient (PCC) of 0.958 and a mean absolute error (MAE) of 0.021 m, successfully capturing the locations of observed inundation points under actual rainstorm conditions. (b) The ConvLSTM model can rapidly predict urban rainstorm inundation process in just 2 s for a study area of 74 km2, which is 170 times more efficient than a physics‐based model. (c) The interpretability of the ConvLSTM model for urban flood prediction can be enhanced through Grad‐CAM, revealing the model naturally focuses on local or upstream rainfall concentration areas most responsible for inundation, aligning well with hydrological understanding. Overall, the ConvLSTM model serves as a powerful surrogate for rapid urban flood simulation, providing an important reference for real‐time flood early warning and mitigation.

The yield of photovoltaic hydrogen production systems is influenced by a number of factors, including weather conditions, the cleanliness of photovoltaic modules, and operational efficiency. Temporal variations in weather conditions have been shown to significantly impact the output of photovoltaic systems, thereby influencing hydrogen production. To address the inaccuracies in hydrogen production capacity predictions due to weather-related temporal variations in different regions, this study develops a method for predicting photovoltaic hydrogen production capacity using the long short-term memory (LSTM) neural network model. The proposed method integrates meteorological parameters, including temperature, wind speed, precipitation, and humidity into a neural network model to estimate the daily solar radiation intensity. This approach is then integrated with a photovoltaic hydrogen production prediction model to estimate the region’s hydrogen production capacity. To validate the accuracy and feasibility of this method, meteorological data from Lanzhou, China, from 2013 to 2022 were used to train the model and test its performance. The results show that the predicted hydrogen production agrees well with the actual values, with a low mean absolute percentage error (MAPE) and a high coefficient of determination (R2). The predicted hydrogen production in winter has a MAPE of 0.55% and an R2 of 0.985, while the predicted hydrogen production in summer has a slightly higher MAPE of 0.61% and a lower R2 of 0.968, due to higher irradiance levels and weather fluctuations. The present model captures long-term dependencies in the time series data, significantly improving prediction accuracy compared to conventional methods. This approach offers a cost-effective and practical solution for predicting photovoltaic hydrogen production, demonstrating significant potential for the optimization of the operation of photovoltaic hydrogen production systems in diverse environments.

Obesity is a major risk factor for chronic diseases, underscoring the need for early diagnosis and effective management. This study presents a novel expert system designed to accurately classify obesity levels and provide personalised treatment recommendations. Five machine learning algorithms—decision tree, random forest, multinomial logistic regression (MLR), Naive Bayes, and support vector machine (SVM)—were evaluated using the SEMMA data mining methodology and the tidymodels framework. MLR demonstrated the highest accuracy (97.48%) and was selected as the final model. The system features a user‐friendly interface built with R Shiny, facilitating real‐time interaction and a seamless user experience. Treatment recommendations are generated through if‐then rule‐based logic, ensuring tailored guidance for each obesity category. Comparative analysis highlights the system's superior diagnostic accuracy and practical application in treatment guidance. Its accessibility, particularly in underserved rural populations, enhances public health outcomes by enabling early diagnosis, targeted interventions, and proactive obesity management.

Objective Chronic bronchitis and emphysema (CBE) are two main types of chronic obstructive pulmonary disease (COPD). We aimed to investigate the relationship between dietary calcium intake and the risk of CBE. Methods Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 2007–2012. The ratio of forced expiratory volume in 1 s (FEV1) to forced vital capacity (FVC) < 0.7 was used to define airflow obstruction. Multivariate logistic regression was performed to assess the effects of dietary calcium intake on CBE and airflow obstruction. Dietary calcium intake was divided into quartiles, with the lowest quartile set as the reference group. Linear regression models were applied to explore the association between dietary calcium intake and lung function. Results A total of 10,143 participants were enrolled in the study, including 594 CBE and 9549 non-CBE individuals. The average dietary calcium intake was 908.5 ± 636.1 mg/day in the CBE group and 951.9 ± 599.7 mg/day in the non-CBE group. When using the lowest quartile of dietary calcium intake as a reference, the second, third, and fourth quartiles reduced the risk of CBE by 0.803 [95% confidence interval (CI): 0.802–0.804; P  < 0.001], 0.659 (95% CI: 0.659–0.660; P  < 0.001) and 0.644 (95% CI: 0.643–0.644; P  < 0.001) times, respectively. Increased dietary calcium intake was correlated with reduced risk of airflow obstruction. Dietary calcium intake positively predicts FEV1 ( β  = 0.225, P  < 0.001) and FVC ( β  = 0.232, P  < 0.001). Conclusion Increased intake of dietary calcium may contribute to higher lung function, a lower risk of CBE and airflow obstruction. Since the cross-sectional design makes it difficult to determine a causal relationship, further research is needed to confirm these findings and explore the underlying mechanisms.

Despite the increase in the global prevalence of metabolic dysfunction-associated fatty liver disease (MAFLD), no approved drug currently exists for the disease. Poria cocos (Schw.) Wolf ( P. cocos ) is a medicinal mushroom belonging to a family of polyporaceae widely used in TCM clinics to protect the liver and treat obesity. However, its efficacy, practical components, and underlying mechanism against MAFLD are yet to be determined. In this study, we evaluated the effects of Poria cocos (P. cocos) ethanol extract (EPC) on hepatic dyslipidemia, steatosis, and inflammation by both bioinformatics analysis and MAFLD rats induced by HFD feeding. We found EPC treatment dramatically reduced lipid accumulation, inflammatory cell infiltration, and liver injury. EPC reduced serum TC, TG levels, and hepatic TG, TBA, and NEFA contents. UHPLC Q-Trap/MS examination of BA profiles in serum and feces showed that EPC increased fecal conjugated BAs, decreased free BAs, and improved BA metabolism in HFD-fed rats. Western blot and RT-qPCR analysis showed that EPC could activate hepatic FXR and PPARα expression and reduce CYP7A1 and SREBP-1c expression. Systemic pharmacology combined with molecular docking suggested that poricoic acid B and polyporenic acid C, the major active compounds in EPC, could ameliorate lipid homeostasis by activating the nuclear receptor PPARα. We further confirmed their inhibition effects of lipid droplet deposition in steatized L-02 hepatocytes. In summary, EPC alleviated HFD-induced MAFLD by regulating lipid homeostasis and BA metabolism via the FXR/PPARα-SREBPs signaling pathway. P. cocos triterpenes, such as poricoic acid B and polyporenic acid C, were the characteristic substances of P. cocos for the treatment of MAFLD.

Understanding the strengths and limitations of the modeling capacity of surface flooding in urbanized floodplains is of utmost importance as such events are becoming increasingly frequent and extreme. In this study, we assess two computational models against laboratory observations of surface urban flooding in a reduced‐scale physical model of idealized urban districts. Four urban layouts were considered, involving each three inlets and three outlets as well as a combination of three‐ and four‐branch crossroads together with open spaces. The first model (2D) solves the shallow‐water equations while the second one (3D) solves the Reynolds‐averaged Navier‐Stokes equations. Both models accurately predict the flow depths in the inlet branches. For the discharge partition between the outlets, deviations between the computations and laboratory observations remain close to the experimental uncertainties (maximum 2.5 percent‐points). The velocity fields computed in 3D generally match the measured surface velocity fields. In urban layouts involving mostly a network of streets, the depth‐averaged velocity fields computed by the 2D model agree remarkably well with those of the 3D model, with differences not exceeding 10%, despite the presence of helicoidal flow (revealed by the 3D computations). In configurations with large open areas, the 3D model captures generally well the trajectory and velocity distribution of main surface flow jet and recirculations; but the 2D model does not perform as well as it does in relatively channelized flow regions. Visual inspection of the jet trajectories computed by the 2D model in large open areas reveals that they substantially deviate from the observations. Plain Language Summary Advancing our modeling capacity of urban flooding is of utmost importance for improving the design of risk reduction measures. During extreme urban flooding, complex flow patterns develop in urban environments, involving three‐dimensional flow structures. Though, urban floods are commonly simulated with two‐dimensional computational models. So far, no detailed comparison between flow fields predicted by two‐ and three‐dimensional computational models were conducted and assessed against reference data such as experimental observations for representative configurations of urban flooding. In this study, we assess two computational models against laboratory observations of urban flooding in a reduced‐scale physical model of an idealized district. Key Points Predictions of 2D and 3D computational models were compared against laboratory experiments representing urban flooding in a steady‐state Both models perform equally well to predict upstream flow depth, outlet discharge partition, and velocity field in street networks In urban layouts with large open spaces, only the 3D model accurately predicts the velocity field

Background Hereditary tyrosinemia type 1 (HT1; OMIM# 276700) is a genetic metabolism disorder caused by disease-causing variants in the fumarylacetoacetate hydrolase ( FAH ) gene encoding the last enzyme of the tyrosine catabolic pathway. Herein, we describe the clinical features and genetic characteristics of HT1 in a five years and seven months old Chinese patient. Methods After clinical diagnosis of the proband with HT1, genetic testing was performed by Sanger sequencing of the FAH gene in all family members. Functional analysis of the disease-causing variant was performed by cDNA sequencing to understand the effect of the variant on FAH transcript. To further predict the variant effect, we used Human Splicing Finder (HSF) and PyMol in silico analysis. Results We identified a novel previously undescribed intronic variant in the FAH gene (c.914-1G>A). It was detected in a child who was homozygous for the variant and had the clinical presentation of HT1. cDNA sequencing showed that this splice-junction variant affected the transcription of FAH by formation of two different transcripts. Our observations and laboratory experiments were in line with in silico methods. Conclusions Our study provides new insight into the HT1 variant spectrum and a better understanding of this disease in the Chinese population. This will be useful for molecular diagnosis in our country in cases where premarital screening, prenatal diagnosis and preimplantation genetic diagnosis are planned.

Hydrogen safety is a critical issue during the construction and development of the hydrogen energy industry. Hydrogen refueling stations play a pivotal role in the hydrogen energy chain. In the event of an accidental hydrogen leak at a hydrogen refueling station, the ability to quickly predict the leakage location is crucial for taking immediate and effective measures to prevent disastrous consequences. Therefore, the development of precise and efficient technologies to predict leakage locations is vital for the safe and stable operation of hydrogen refueling stations. This paper studied the localization technology of high-risk leakage locations in the fuel cell system of a skid-mounted hydrogen refueling station. The hydrogen leakage and diffusion processes in the fuel cell system were predicted using CFD simulations, and the hydrogen concentration data at various monitoring points were obtained. Then, a multilayer feedforward neural network was developed to predict leakage locations using simulated concentration data as training samples. After multiple adjustments to the network structure and hyperparameters, a final model with two hidden layers was selected. Each hidden layer consisted of 10 neurons. The hyperparameters included a learning rate of 0.0001, a batch size of 32, and 10-fold cross-validation. The Softmax classifier and Adam optimizer were used, with a training set for 1500 epochs. The results show that the algorithm can predict leakage locations not included in the training set. The accuracy achieved by the model was 95%. This approach addresses the limitations of sensor detection in accurately locating leaks and mitigates the risks associated with manual inspections. This paper provides a feasible method for locating hydrogen leakage in hydrogen energy application scenarios.

Background Epidemiological studies have found an association between air pollution and cardiovascular and cerebrovascular diseases (CACD) and its subtypes. However, there is a lack of individual-level data to explore the associations of air pollutants on CACD and its subtypes, the interaction among them, and the potential mechanism. Methods This study employed a two-stage design, combining a time-stratified case-crossover study with a cohort study, analyzing data from one million individuals from China and half million from the UK. The study assessed the impact of air pollutants on CACD and its subtypes, while also examining the mediating effects of inflammation. Distributed lag non-linear models were used to analyze the lagged effects of pollutants, and mediation analysis was conducted to evaluate the role of inflammatory markers (SII, SIRI, AISI) in the relationship between air pollution and CACD. Results A total of 829,135 CSDs patients were recorded in this study. An interquartile range (IQR) increase in concentrations of PM 2.5 , PM 10 , NO 2 , SO 2 , CO, and O 3 was associated with increases of 11.3% [95% confidence interval (CI) 9.5%-13.2%], 10.5% (95% CI 8.6%-12.3%), 3% (95% CI 1%-5%), 15.2% (95% CI 13.3%-17.1%), 15.5% (95% CI 11.6%-19.5%), and 2.8% (95% CI 2.2%-3.4%) in CSDs, respectively. A similar positive association was also observed for cardiovascular and ischemic heart diseases. A significant synergistic interaction between PM 2.5 and NO 2 and CO for CSDs. Approximately 64.75%, 21.13%, 32.2%, 2.31%, 43.7% and 43.7% of the effects of PM 2.5 , PM 10 , NO 2 , SO 2 , CO, and O 3 on CSDs were significantly mediated by SII. Conclusions This study provides robust evidence that short-term exposure to common air pollutants significantly increases the risk of CACD and its subtypes, with inflammation playing a crucial mediating role. The findings underscore the importance of coordinated air pollution control strategies and public health interventions to mitigate the cardiovascular risks associated with air pollution.