Innovations in detecting skull fractures: A review of computer-aided techniques in CT imaging.

BACKGROUND/INTRODUCTION: Traumatic brain injury (TBI) remains a leading cause of disability and mortality, with skull fractures being a frequent and serious consequence. Accurate and rapid diagnosis of these fractures is crucial, yet current manual methods via cranial CT scans are time-consuming and prone to error. METHODS: This review paper focuses on the evolution of computer-aided diagnosis (CAD) systems for detecting skull fractures in TBI patients. It critically assesses advancements from feature-based algorithms to modern machine learning and deep learning techniques. We examine current approaches to data acquisition, the use of public datasets, algorithmic strategies, and performance metrics RESULTS: The review highlights the potential of CAD systems to provide quick and reliable diagnostics, particularly outside regular clinical hours and in under-resourced settings. Our discussion encapsulates the challenges inherent in automated skull fracture assessment and suggests directions for future research to enhance diagnostic accuracy and patient care. CONCLUSION: With CAD systems, we stand on the cusp of significantly improving TBI management, underscoring the need for continued innovation in this field.

Implementation of Sound Direction Detection and Mixed Source Separation in Embedded Systems.

In recent years, embedded system technologies and products for sensor networks and wearable devices used for monitoring people's activities and health have become the focus of the global IT industry. In order to enhance the speech recognition capabilities of wearable devices, this article discusses the implementation of audio positioning and enhancement in embedded systems using embedded algorithms for direction detection and mixed source separation. The two algorithms are implemented using different embedded systems: direction detection developed using TI TMS320C6713 DSK and mixed source separation developed using Raspberry Pi 2. For mixed source separation, in the first experiment, the average signal-to-interference ratio (SIR) at 1 m and 2 m distances was 16.72 and 15.76, respectively. In the second experiment, when evaluated using speech recognition, the algorithm improved speech recognition accuracy to 95%.

N-Containing Porous Carbon-Based MnO Composites as Anode with High Capacity and Stability for Lithium-Ion Batteries.

MnO has attracted much attention as the anode for Li-ion batteries (LIBs) owing to its high specific capacity. However, the low conductivity limited its large application. An effective solution to solve this problem is carbon coating. Biomass carbon materials have aroused much interest for being low-cost and rich in functional groups and hetero atoms. This work designs porous N-containing MnO composites based on the chemical-activated tremella using a self-templated method. The tremella, after activation, could offer more active sites for carbon to coordinate with the Mn ions. And the as-prepared composites could also inherit the special porous nanostructures of the tremella, which is beneficial for Li+ transfer. Moreover, the pyrrolic/pyridinic N from the tremella can further improve the conductivity and the electrolyte wettability of the composites. Finally, the composites show a high reversible specific capacity of 1000 mAh g-1 with 98% capacity retention after 200 cycles at 100 mA g-1. They also displayed excellent long-cycle performance with 99% capacity retention (relative to the capacity second cycle) after long 1000 cycles under high current density, which is higher than in most reported transition metal oxide anodes. Above all, this study put forward an efficient and convenient strategy based on the low-cost biomass to construct N-containing porous composite anodes with a fast Li+ diffusion rate, high electronic conductivity, and outstanding structure stability.

CT-based quantification of intratumoral heterogeneity for predicting pathologic complete response to neoadjuvant immunochemotherapy in non-small cell lung cancer.

Objectives: To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image. Methods: This retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). Results: In the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686. Conclusion: The quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.

Allochthonous Trichoderma Isolates Boost Atractylodes lancea Herb Quality at the Cost of Rhizome Growth.

Atractylodes lancea is a perennial herb whose rhizome (AR) is a valuable traditional Chinese medicine with immense market demand. The cultivation of Atractylodes lancea faces outbreaks of root rot and deterioration in herb quality due to complex causes. Here, we investigated the effects of Trichoderma spp., well-known biocontrol agents and plant-growth-promoters, on ARs. We isolated Trichoderma strains from healthy ARs collected in different habitats and selected three T. harzianum strains (Th2, Th3 and Th4) with the strongest antagonizing effects on root rot pathogens (Fusarium spp.). We inoculated geo-authentic A. lancea plantlets with Th2, Th3 and Th4 and measured the biomass and quality of 70-day-old ARs. Th2 and Th3 promoted root rot resistance of A. lancea. Th2, Th3 and Th4 all boosted AR quality: the concentration of the four major medicinal compounds in ARs (atractylon, atractylodin, hinesol and ß-eudesmol) each increased 1.6- to 18.2-fold. Meanwhile, however, the yield of ARs decreased by 0.58- to 0.27-fold. Overall, Th3 dramatically increased the quality of ARs at a relatively low cost, namely lower yield, showing great potential for practical application. Our results showed selectivity between A. lancea and allochthonous Trichoderma isolates, indicating the importance of selecting specific microbial patches for herb cultivation.

An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System.

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle's onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle's onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems.

Exploring the causal association between genetically determined circulating metabolome and hemorrhagic stroke.

Background: Hemorrhagic stroke (HS), a leading cause of death and disability worldwide, has not been clarified in terms of the underlying biomolecular mechanisms of its development. Circulating metabolites have been closely associated with HS in recent years. Therefore, we explored the causal association between circulating metabolomes and HS using Mendelian randomization (MR) analysis and identified the molecular mechanisms of effects. Methods: We assessed the causal relationship between circulating serum metabolites (CSMs) and HS using a bidirectional two-sample MR method supplemented with five ways: weighted median, MR Egger, simple mode, weighted mode, and MR-PRESSO. The Cochran Q-test, MR-Egger intercept test, and MR-PRESSO served for the sensitivity analyses. The Steiger test and reverse MR were used to estimate reverse causality. Metabolic pathway analyses were performed using MetaboAnalyst 5.0, and genetic effects were assessed by linkage disequilibrium score regression. Significant metabolites were further synthesized using meta-analysis, and we used multivariate MR to correct for common confounders. Results: We finally recognized four metabolites, biliverdin (OR 0.62, 95% CI 0.40-0.96, PMVMR = 0.030), linoleate (18. 2n6) (OR 0.20, 95% CI 0.08-0.54, PMVMR = 0.001),1-eicosadienoylglycerophosphocholine* (OR 2.21, 95% CI 1.02-4.76, PMVMR = 0.044),7-alpha-hydroxy-3 -oxo-4-cholestenoate (7-Hoca) (OR 0.27, 95% CI 0.09-0.77, PMVMR = 0.015) with significant causal relation to HS. Conclusion: We demonstrated significant causal associations between circulating serum metabolites and hemorrhagic stroke. Monitoring, diagnosis, and treatment of hemorrhagic stroke by serum metabolites might be a valuable approach.

Comparison of commercial atlas-based automatic segmentation software for prostate radiotherapy treatment planning.

This study aims to assess the accuracy of automatic atlas-based contours for various key anatomical structures in prostate radiotherapy treatment planning. The evaluated structures include the bladder, rectum, prostate, seminal vesicles, femoral heads and penile bulb. CT images from 20 patients who underwent intensity-modulated radiotherapy were randomly chosen to create an atlas library. Atlas contours of the seven anatomical structures were generated using four software packages: ABAS, Eclipse, MIM, and RayStation. These contours were then compared to manual delineations performed by oncologists, which served as the ground truth. Evaluation metrics such as dice similarity coefficient (DSC), mean distance to agreement (MDA), and volume ratio (VR) were calculated to assess the accuracy of the contours. Additionally, the time taken by each software to generate the atlas contour was recorded. The mean DSC values for the bladder exhibited strong agreement (>0.8) with manual delineations for all software except for Eclipse and RayStation. Similarly, the femoral heads showed significant similarity between the atlas contours and ground truth across all software, with mean DSC values exceeding 0.9 and MDA values close to zero. On the other hand, the penile bulb displayed only moderate agreement with the ground truth, with mean DSC values ranging from 0.5 to 0.7 for all software. A similar trend was observed in the prostate atlas contours, except for MIM, which achieved a mean DSC of over 0.8. For the rectum, both ABAS and MIM atlases demonstrated strong agreement with the ground truth, resulting in mean DSC values of more than 0.8. Overall, MIM and ABAS outperformed Eclipse and RayStation in both DSC and MDA. These results indicate that the atlas-based segmentation employed in this study produces acceptable contours for the anatomical structures of interest in prostate radiotherapy treatment planning.

Harvesting Multicolor Photoluminescence in Nonaromatic Interpenetrated Metal-Organic Framework Nanocrystals via Pressure-Modulated Carbonyls Aggregation.

Interpenetrated metal-organic frameworks (MOFs) with nonaromatic ligands provide a unique platform for adsorption, catalysis, and sensing applications. However, nonemission and the lack of optical property tailoring make it challenging to fabricate smart responsive devices with nonaromatic interpenetrated MOFs based on ligand-centered emission. In this paper, the pressure-induced aggregation effect is introduced in nonaromatic interpenetrated Zn4O(ADC)4(Et3N)6 (IRMOF-0) nanocrystals (NCs), where carbonyl groups aggregation results in O─O distances smaller than the sum of the van der Waals radii (3.04 Å), triggering the photoluminescence turn-on behavior. It is noteworthy that the IRMOF-0 NCs display an ultrabroad emission tunability of 130 nm from deep blue (440 nm) to yellow (570 nm) upon release to ambient conditions at different pressures. The eventual retention of through-space n-π* interactions in different degrees via pressure treatment is primarily responsible for achieving a controllable multicolor emission behavior in initially nonemissive IRMOF-0 NCs. The fabricated multicolor phosphor-converted light-emitting diodes based on the pressure-treated IRMOF-0 NCs exhibit excellent thermal, chromaticity, and fatigue stability. The proposed strategy not only imparts new vitality to nonaromatic interpenetrated MOFs but also offers new perspectives for advancements in the field of multicolor displays and daylight illumination.

An advanced nomogram model using deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma.

PURPOSE: To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing the occult lymph node metastasis (OLNM) status in clinical stage IA lung adenocarcinoma. METHODS: A cohort of 473 cases of lung adenocarcinomas from two hospitals was included, with 404 cases allocated to the training cohort and 69 cases to the testing cohort. Clinical characteristics and semantic features were collected, and radiomics features were extracted from the computed tomography (CT) images. Additionally, deep transfer learning (DTL) features were generated using RseNet50. Predictive models were developed using the logistic regression (LR) machine learning algorithm. Moreover, gene analysis was conducted on RNA sequencing data from 14 patients to explore the underlying biological basis of deep learning radiomics scores. RESULT: The training and testing cohorts achieved AUC values of 0.826 and 0.775 for the clinical model, 0.865 and 0.801 for the radiomics model, 0.927 and 0.885 for the DTL-radiomics model, and 0.928 and 0.898 for the nomogram model. The nomogram model demonstrated superiority over the clinical model. The decision curve analysis (DCA) revealed a net benefit in predicting OLNM for all models. The investigation into the biological basis of deep learning radiomics scores identified an association between high scores and pathways related to tumor proliferation and immune cell infiltration in the microenvironment. CONCLUSIONS: The nomogram model, incorporating clinical-semantic features, radiomics, and DTL features, exhibited promising performance in predicting OLNM. It has the potential to provide valuable information for non-invasive lymph node staging and individualized therapeutic approaches.

COVID-19 hospitalization risk after outpatient nirmatrelvir/ritonavir use, January to August 2022, North Carolina.

BACKGROUND: In the USA, nirmatrelvir/ritonavir is authorized for the treatment of mild-to-moderate COVID-19 in patients at least 12 years of age, at high risk for progression to severe COVID-19. OBJECTIVES: To estimate the impact of outpatient nirmatrelvir/ritonavir on COVID-19 hospitalization risk in a US healthcare system. METHODS: We conducted a cohort study using electronic health records among outpatients with a positive SARS-CoV-2 PCR test between January and August 2022. We evaluated the association of nirmatrelvir/ritonavir therapy with time to hospitalization by estimating adjusted HRs and assessed the impact of nirmatrelvir/ritonavir on predicted COVID-19 hospitalizations using machine-learning methods. RESULTS: Among 44 671 patients, 4948 (11%) received nirmatrelvir/ritonavir, and 201 (0.4%) were hospitalized within 28 days of COVID-19 diagnosis. Nirmatrelvir/ritonavir recipients were more likely to be older, white, vaccinated, have comorbidities and reside in areas with higher average socioeconomic status. The 28 day cumulative incidence of hospitalization was 0.06% (95% CI: 0.02%-0.17%) among nirmatrelvir/ritonavir recipients and 0.52% (95% CI: 0.46%-0.60%) among non-recipients. For nirmatrelvir/ritonavir versus no therapy, the age-adjusted HR was 0.08 (95% CI: 0.03-0.26); the fully adjusted HR was 0.16 (95% CI: 0.05-0.50). In the machine-learning model, the primary features reducing predicted hospitalization risk were nirmatrelvir/ritonavir, younger age, vaccination, female gender and residence in a higher socioeconomic status area. CONCLUSIONS: COVID-19 hospitalization risk was reduced by 84% among nirmatrelvir/ritonavir recipients in a large, diverse healthcare system during the Omicron wave. These results suggest that nirmatrelvir/ritonavir remained highly effective in a setting substantially different than the original clinical trials.

Association of remnant cholesterol with decreased kidney function or albuminuria: a population-based study in the U.S.

BACKGROUND: Dyslipidemia is frequently exhibited in individuals with chronic kidney disease (CKD). Remnant cholesterol (RC), an emerging novel lipid marker, plays an elusive role in CKD progression. This study sought to investigate the association of RC with decreased kidney function or albuminuria in the general population of U.S. METHOD: Data were retrieved from the continuous 2001 to 2018 cycle of the National Health and Nutrition Examination Survey (NHANES). Individuals aged between 18 and 70 years were included. RC was divided into quartiles. Albuminuria was defined by albumin-to-creatinine ratio (ACR) ≥30 mg/g, while reduced kidney function was described as an estimated glomerular filtration rate (eGFR) below 60 ml/min/1.73 m2. Using a multivariable regression model, the association of RC with decreased eGFR or albuminuria was examined. The dose‒response relationship between RC and eGFR or ACR was also investigated using a restricted cubic spline (RCS) model. RESULTS: A total of 1551 (10.98%) participants with impaired renal function or albuminuria were identified. After multivariate adjustment, RC was not significantly associated with kidney function decline or albuminuria (odds ratio (OR) 1.24, 95% confidence interval (95% CI): 0.95, 1.61). However, a significantly inverse correlation was observed between RC and eGFR in a dose‒response manner (ß -2.12, 95% CI: -3.04, -1.21). This association remained consistent when stratifying data by gender, age, race, hypertension, diabetes and body mass index (BMI). CONCLUSION: A higher RC was significantly correlated with a lower eGFR in the general population. The role of RC in predicting kidney outcomes needed further investigation in prospective studies.

Integral Compensation Function Observer and Its Application to Disturbance-Rejection Control of QUAV Attitude.

Observer-based disturbance rejection holds substantial theoretical and practical relevance in the field of control engineering, with numerous variants of disturbance observers already schemed. Nevertheless, the criteria for accuracy and avenues for enhancement remain areas warranting further investigation. This article introduces an integral compensation function observer (CFO) featuring a novel structure and efficient utilization of information for estimating disturbances in n th-order uncertain systems. This approach enhances estimation accuracy by addressing the inherent limitations of the linear extended state observer (LESO), such as low order, lacking usage of information, nonconvergence, and limited bandwidth. Through the derivation and quantification of the disturbance sensitivity transfer function (DSTF), this study examines the disturbance sensitivities of the CFO, LESO, and an improved ESO (IESO). The findings indicate that the CFO elevates the estimable order of disturbance and surpasses both LESO and IESO in bandwidth and disturbance estimation accuracy. In evaluating both the estimation accuracy of disturbance of the CFO and the disturbance-rejection performance (DRP) of CFO-based control, nonlinear pole assignment controls (NPACs) employing 2nd/3rd-order CFO, IESO, LESO, and 4th-order CFO are implemented in the context of attitude control for a quadrotor unmanned aerial vehicle (QUAV) that is exposed to prearranged disturbance torques. The results illustrate that the CFO outperforms the IESO and LESO in terms of accurately estimating the prearranged disturbing torques. Furthermore, the recorded magnitudes of attitude in response to disturbances underscore the superior DRP of CFO-NPAC relative to IESO-NPAC and LESO-NPAC.

Remodeling in Aortic Stenosis With Reduced and Preserved Ejection Fraction: Insight on Motion Abnormality Via 3D + Time Personalized LV Modeling in Cardiac MRI.

BACKGROUND: Increased afterload in aortic stenosis (AS) induces left ventricle (LV) remodeling to preserve a normal ejection fraction. This compensatory response can become maladaptive and manifest with motion abnormality. It is a clinical challenge to identify contractile and relaxation dysfunction during early subclinical stage to prevent irreversible deterioration. PURPOSE: To evaluate the changes of regional wall dynamics in 3D + time domain as remodeling progresses in AS. STUDY TYPE: Retrospective. POPULATION: A total of 31 AS patients with reduced and preserved ejection fraction (14 AS_rEF: 7 male, 66.5 [7.8] years old; 17 AS_pEF: 12 male, 67.0 [6.0] years old) and 15 healthy (6 male, 61.0 [7.0] years old). FIELD STRENGTH/SEQUENCE: 1.5 T Magnetic resonance imaging/steady state free precession and late-gadolinium enhancement sequences. ASSESSMENT: Individual LV models were reconstructed in 3D + time domain and motion metrics including wall thickening (TI), dyssynchrony index (DI), contraction rate (CR), and relaxation rate (RR) were automatically extracted and associated with the presence of scarring and remodeling. STATISTICAL TESTS: Shapiro-Wilk: data normality; Kruskal-Wallis: significant difference (P < 0.05); ICC and CV: variability; Mann-Whitney: effect size. RESULTS: AS_rEF group shows distinct deterioration of cardiac motions compared to AS_pEF and healthy groups (TIAS_rEF : 0.92 [0.85] mm, TIAS_pEF : 5.13 [1.99] mm, TIhealthy : 3.61 [1.09] mm, ES: 0.48-0.83; DIAS_rEF : 17.11 [7.89]%, DIAS_pEF : 6.39 [4.04]%, DIhealthy : 5.71 [1.87]%, ES: 0.32-0.85; CRAS_rEF : 8.69 [6.11] mm/second, CRAS_pEF : 16.48 [6.70] mm/second, CRhealthy : 10.82 [4.57] mm/second, ES: 0.29-0.60; RRAS_rEF : 8.45 [4.84] mm/second; RRAS_pEF : 13.49 [8.56] mm/second, RRhealthy : 9.31 [2.48] mm/second, ES: 0.14-0.43). The difference in the motion metrics between healthy and AS_pEF groups were insignificant (P-value = 0.16-0.72). AS_rEF group was dominated by eccentric hypertrophy (47.1%) with concomitant scarring. Conversely, AS_pEF group was dominated by concentric remodeling and hypertrophy (71.4%), which could demonstrate hyperkinesia with slight wall dyssynchrony than healthy. Dysfunction of LV mechanics corresponded to the presence of myocardial scarring (54.9% in AS), which reverted the compensatory mechanisms initiated and performed by LV remodeling. DATA CONCLUSION: The proposed 3D + time modeling technique may distinguish regional motion abnormalities between AS_pEF, AS_rEF, and healthy cohorts, aiding clinical diagnosis and monitoring of AS progression. Subclinical myocardial dysfunction is evident in early AS despite of normal EF. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 1.

Distributed Leader-Following Consensus of Feedforward Nonlinear Delayed Multiagent Systems via General Switched Compensation Control.

This work examines the distributed leader-following consensus problem of feedforward nonlinear delayed multiagent systems involving directed switching topologies. In contrast to the existing studies, we focus on time delays acting on the outputs of feedforward nonlinear systems, and we permit that the partial topology dissatisfy the directed spanning tree condition. In the cases, we present a novel output feedback-based general switched cascade compensation control method that addresses the above-mentioned problem. First, we put forward a distributed switched cascade compensator by introducing multiple equations, and we design the delay-dependent distributed output feedback controller with the compensator. Subsequently, when the control parameters-dependent linear matrix inequality is met and the switching signal of the topologies obeys a general switching law, we prove that the established controller can render that the follower's state asymptotically tracks the leader's state by employing an appropriate Lyapunov-Krasovskii functional. The given algorithm allows output delays to be arbitrarily large and increases the switching frequency of the topologies. A numerical simulation is presented to demonstrate the practicability of our proposed strategy.

Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography.

RATIONALE AND OBJECTIVES: To accurately identify the high-risk pathological factors of pulmonary nodules, our study constructed a model combined with clinical features, radiomics features, and deep transfer learning features to predict high-risk pathological pulmonary nodules. MATERIALS AND METHODS: The study cohort consisted of 469 cases of lung adenocarcinoma patients, divided into a training cohort (n = 400) and an external validation cohort (n = 69). We obtained computed tomography (CT) semantic features and clinical characteristics, as well as extracted radiomics and deep transfer learning (DTL) features from the CT images. Selected features were used for constructing prediction models using the logistic regression (LR) algorithm. The performance of the models was evaluated through metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis. RESULTS: The clinical model achieved an AUC of 0.774 (95% CI: 0.728-0.821) in the training cohort and 0.762 (95% confidence interval [CI]: 0.650-0.873) in the external validation cohort. The radiomics model demonstrated an AUC of 0.847 (95% CI: 0.810-0.884) in the training cohort and 0.800 (95% CI: 0.693-0.907) in the external validation cohort. The radiomics-DTL (Rad-DTL) model showed an AUC of 0.871 (95% CI: 0.838-0.905) in the training cohort and 0.806 (95% CI: 0.698-0.914) in the external validation cohort. The proposed combined model yielded AUC values of 0.872 and 0.814 in the training and external validation cohorts, respectively. The combined model demonstrated superiority over both the clinical model and the Rad-DTL model. There were no statistically significant differences observed in the comparison between the combined model incorporating clinical features and the Rad-DTL model. Decision curve analysis (DCA) indicated that the models provided a net benefit in predicting high-risk pathologic pulmonary nodules. CONCLUSION: Rad-DTL signature is a potential biomarker for predicting high-risk pathologic pulmonary nodules using preoperative CT, determining the appropriate surgical strategy, and guiding the extent of resection.

Effects of hydroxy methionine zinc on growth performance, immune response, antioxidant capacity, and intestinal microbiota of red claw crayfish (Procambarus clarkii).

This study aimed to evaluate the effects of varying zinc (Zn) levels on the growth performance, non-specific immune response, antioxidant capacity, and intestinal microbiota of red claw crayfish (Procambarus clarkii (P. clarkii)). Adopting hydroxy methionine zinc (Zn-MHA) as the Zn source, 180 healthy crayfish with an initial body mass of 6.50 ± 0.05 g were randomly divided into the following five groups: X1 (control group) and groups X2, X3, X4, and X5, which were fed the basal feed supplemented with Zn-MHA with 0, 15, 30, 60, and 90 mg kg-1, respectively. The results indicated that following the addition of various concentrations of Zn-MHA to the diet, the following was observed: Specific growth rate (SGR), weight gain rate (WGR), total protein (TP), total cholesterol (TC), the activities of alkaline phosphatase (AKP), phenoloxidase (PO), total antioxidant capacity (T-AOC), total superoxide dismutase (T-SOD) and catalase (CAT), the expression of CTL, GPX, and CuZn-SOD genes demonstrated a trend of rising and then declining-with a maximum value in group X4-which was significantly higher than that in group X1 (P < 0.05). Zn deposition in the intestine and hepatopancreas, the activity of GSH-PX, and the expression of GSH-PX were increased, exhibiting the highest value in group X5. The malonaldehyde (MDA) content was significantly reduced, with the lowest value in group X4, and the MDA content of the Zn-MHA addition groups were significantly lower than the control group (P < 0.05). In the analysis of the intestinal microbiota of P. clarkii, the number of operational taxonomic units in group X4 was the highest, and the richness and diversity indexes of groups X3 and X4 were significantly higher than those in group X1 (P < 0.05). Meanwhile, the dietary addition of Zn-MHA decreased and increased the relative abundance of Proteobacteria and Tenericutes, respectively. These findings indicate that supplementation of dietary Zn-MHA at an optimum dose of 60 mg kg-1 may effectively improve growth performance, immune response, antioxidant capacity, and intestinal microbiota richness and species diversity in crayfish.

First Report of Erysiphe astragali Causing Powdery Mildew on Astragalus mongholicus in China.

Astragalus mongholicus Bge. [A. membranaceus Bge. var. mongholicus (Bge.) Hsiao] is a highly valuable perennial medicinal plant mainly distributed in China, whose dry roots are known as Huangqi in traditional Chinese medicine for reinforcing vital energy, strengthening superficial resistance, and promoting tissue regeneration (Lin et al. 2000). A. mongholicus roots of high quality are produced in Northwest and North China. Since July 2021, powdery mildew outbreaks happened annually on the leaves of A. mongholicus in a plantation (123° 56' 40'' E, 47° 22' 20'' N) in Qiqihar city, Heilongjiang Province, China. Disease incidence reached 100% by October (Fig. 1A-C), causing severe impairment of growth. Powdery mildew spots of circular or irregular shapes emerged on upper surface of leaf, resulting in plentiful lesion specks. Dense white hyphae appeared chaotically intertwined. Hyphae were hyaline and highly flexuous, 5.3 - 10.7 µm in diameter (n = 20). Chasmothecia were globose or slightly ovoid-shaped and turned dark brown when matured. Chasmothecia (diameter: 135.2 - 222.9 µm, n = 20) existed abundantly on the diseased leaves in the fields. Conidiophores were 89.0 - 129.9 µm in length (n = 20) and composed of one cylindrical, straight foot cell, followed by two cells and one to three conidia. Conidia were slim ellipsoid-shaped, occasionally ovoid-shaped, measuring 14.6 - 24.7 µm by 6.4 to10.4 µm, length/width ratio was 1.8 - 3.0 (n = 30). Hyphal appressoria were nipple-shaped and appeared in singular, occasionally in pairs. Unbranched germ tube emerged reaching out of the germinating conidia while forming an acute angle with the long axis. Comprehensively, the pathogen exhibited micro-morphology of the genus Erysiphe. For molecular identification, pathogen was carefully scraped off diseased leaves for DNA extraction. We used the DNA samples of three biological replicates for the sequencing of the ITS rDNA fragment (primers by (White et al. 1990). All the samples resulted in an identical ITS sequence (deposited in GenBank as OQ390098.1). It displayed 99.83% identity with OP806835.1 of an E. astragali voucher collected in Iran (Fig. 1D-M, O). Hence, our pathogen was identified as an E. astragali stain. Additionally, we amplified the Mcm7 sequence (using primers by (Ellingham et al. 2019), deposited as OQ397582.1). We propagated 40-day-old A. mongholicus plants via germinating seeds in pot soil and performed pathogenicity tests. Firstly, we incubated detached healthy leaves of propagated plants with severely symptomatic leaves collected from the fields in petri dishes under saturated moisture content and room temperature. Powdery mildew symptoms emerged on each healthy leaf (n = 5) after two weeks. Further, we infected healthy plants (n = 5) by gently pressing and rubbing symptomatic leaves on each healthy leaf, and kept them in a greenhouse (24 ℃, 80% humidity, 16/8-hour light/dark cycle). After a month, symptoms emerged on a number of leaves of each infected plant. We performed micromorphology observation (Fig. 1N-P) and ITS sequencing to confirm that the results fulfilled Koch's postulates. Powdery mildew caused by E. astragali on A. strictus in Tibet (Wang and Jiang 2023) and on A. scaberrimus in Inner Mongolia (Sun et al. 2023) have been reported. Here we report powdery mildew caused by E. astragali on Astragalus mongholicus for the first time. These Astragalus spp. are all acknowledged to have medicinal values in China but their usages are quite different.

Federated machine learning for predicting acute kidney injury in critically ill patients: a multicenter study in Taiwan.

Purpose: To address the contentious data sharing across hospitals, this study adopted a novel approach, federated learning (FL), to establish an aggregate model for acute kidney injury (AKI) prediction in critically ill patients in Taiwan. Methods: This study used data from the Critical Care Database of Taichung Veterans General Hospital (TCVGH) from 2015 to 2020 and electrical medical records of the intensive care units (ICUs) between 2018 and 2020 of four referral centers in different areas across Taiwan. AKI prediction models were trained and validated thereupon. An FL-based prediction model across hospitals was then established. Results: The study included 16,732 ICU admissions from the TCVGH and 38,424 ICU admissions from the other four hospitals. The complete model with 60 features and the parsimonious model with 21 features demonstrated comparable accuracies using extreme gradient boosting, neural network (NN), and random forest, with an area under the receiver-operating characteristic (AUROC) curve of approximately 0.90. The Shapley Additive Explanations plot demonstrated that the selected features were the key clinical components of AKI for critically ill patients. The AUROC curve of the established parsimonious model for external validation at the four hospitals ranged from 0.760 to 0.865. NN-based FL slightly improved the model performance at the four centers. Conclusion: A reliable prediction model for AKI in ICU patients was developed with a lead time of 24 h, and it performed better when the novel FL platform across hospitals was implemented. Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-023-00248-5.

[Advances in research and application of Trichoderma for inducing resistance against root rot diseases in root and rhizome of Chinese medicinal materials].

Root rot is a microbial disease that is difficult to control and can result in serious losses in the planting of most Chinese medicinal materials. As high as 87.6% of roots or rhizomes of Chinese medicinal materials are susceptible to root rot, which seriously affects the cultivation development of Chinese medicinal materials. Trichoderma fungi, possessing biological control functions, can induce plants to improve their resistance to microbial diseases, promote plant growth, and effectively reduce the losses caused by various microbial diseases on cultivation. At present, Trichoderma is rarely used in the cultivation of Chinese medicinal materials, so it has great application potential for the prevention and control of root rot diseases in farmed Chinese medicinal materials. Based on the above situation, after comparison and discussion, it is believed that compared with chemical control and physical control, biological control of root rot diseases of Chinese medicinal materials is more efficient and meets the development needs of Chinese medicinal materials ecological planting in China. This paper reviewed the progress in the research and application of Trichoderma in the control of root rot diseases in the root and rhizome of farmed Chinese medicinal materials in the past 10 years and found that most of the current research on the biological control of root rot diseases in Chinese medicinal materials was mostly limited to the verification of the inhibitory effect of Trichoderma strains on the growth of the pathogenic microbes. Studies on the induction effect of Trichoderma on Chinese medicinal materials are not in depth. Studies on the responding mechanisms of most Chinese medicinal materials to Trichoderma are highly absent. Moreover, there are few reports on field experiments, which indicates that there is a long way to go before Trichoderma is widely applied in the farming practice of Chinese medicinal materials. To sum up, this paper aimed to link the present and the future and advocated further relevant research and more experiments on the application of Trichoderma in the farming of Chinese medicinal materials.