Integrating Experimental and Computational Analyses for Mechanical Characterization of Titanium Carbide/Aluminum Metal Matrix Composites.

This study investigates the mechanical properties of titanium carbide/aluminum metal matrix composites (AMMCs) using both experimental and computational methods. Through accumulative roll bonding (ARB) and cryorolling (CR) processes, AA1050 alloy surfaces were reinforced with TiCp particles to create the Al-TiCp composite. The experimental analysis shows significant improvements in tensile strength, yield strength, elastic modulus, and hardness. The finite element analysis (FEA) simulations, particularly the microstructural modeling of RVE-1 (the experimental case model), align closely with the experimental results observed through scanning electron microscopy (SEM). This validation underscores the accuracy of the computational models in predicting the mechanical behavior under identical experimental conditions. The simulated elastic modulus deviates by 5.49% from the experimental value, while the tensile strength shows a 6.81% difference. Additionally, the simulated yield strength indicates a 2.85% deviation. The simulation data provide insights into the microstructural behavior, stress distribution, and particle-matrix interactions, facilitating the design optimization for enhanced performance. The study also explores the influence of particle shapes and sizes through Representative Volume Element (RVE) models, highlighting nuanced effects on stress-strain behavior. The microstructural evolution is examined via transmission electron microscopy (TEM), revealing insights regarding grain refinement. These findings demonstrate the potential of Al-TiCp composites for lightweight applications.

Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma.

BACKGROUND: The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma (lrNPC) is limited due to their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in lrNPC. METHODS: This multicenter, retrospective study included 921 patients with lrNPC. A machine learning signature and nomogram based on pretreatment MRI features were developed for predicting overall survival (OS) in a training cohort and validated in two independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index (C-index) and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA sequencing (RNA-seq) analysis. RESULTS: The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving C-indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables significantly improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with significantly distinct OS rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-seq analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. CONCLUSIONS: An MRI-based radiomic signature predicted OS more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for lrNPC patients.

Prodigiosin as an Antibiofilm Agent against the Bacterial Biofilm-Associated Infection of Pseudomonas aeruginosa.

Pseudomonas aeruginosa is known to generate bacterial biofilms that increase antibiotic resistance. With the increase of multi-drug resistance in recent years, the formulation of a new therapeutic strategy has seemed urgent. Preliminary findings show that Prodigiosin (PG), derived from chromium-resistant Serratia marcescens, exhibited efficient anti-biofilm activity against Staphylococcus aureus. However, its anti-biofilm activity against P. aeruginosa remains largely unexplored. The anti-biofilm activity of PG against three clinical single drug-resistant P. aeruginosa was evaluated using crystal violet staining, and the viability of biofilms and planktonic cells were also assessed. A model of chronic lung infection was constructed to test the in vivo antibiofilm activity of PG. The results showed that PG inhibited biofilm formation and effectively inhibited the production of pyocyanin and extracellular polysaccharides in vitro, as well as moderated the expression of interleukins (IL-1ß, IL-6, IL-10) and tumor necrosis factor (TNF-α) in vivo, which might be attributed to the downregulation of biofilm-related genes such as algA, pelA, and pslM. These findings suggest that PG could be a potential treatment for drug-resistant P aeruginosa and chronic biofilm infections.

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction.

Objective.In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of 'signal-image-knowledge' has remained unchanged. However, the process of 'signal to image' inevitably introduces information distortion, ultimately leading to irrecoverable biases in the 'image to knowledge' process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal).Approach. This study focuses on computed tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of 'human-signal-image' using the workflow 'CT-simulated data- reconstructed CT,' and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1994 patients with retrospective cases of solid lung nodules and modeled different types of data.Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an area under the curve (AUC) of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866).Significance. The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of 'signal-to-image' can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of 'signal-image-knowledge', opening up new avenues for more accurate medical diagnostics.

Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer.

BACKGROUND: The potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and there are no preoperative methods for assessment of ESTM yet. PURPOSE: This multicenter study aimed to develop a deep learning-based radiomics model to preoperatively identify ESTM and evaluate its prognostic value. METHODS: A total of 959 GC patients were enrolled from two centers and split into a training cohort (N = 551) and a test cohort (N = 236) for ESTM evaluation. Additionally, an external survival cohort (N = 172) was included for prognostic analysis. Four models were established based on clinical characteristics and multiphase computed tomography (CT) images for preoperative identification of ESTM, including a deep learning model, a hand-crafted radiomic model, a clinical model, and a combined model. C-index, decision curve, and calibration curve were utilized to assess the model performances. Survival analysis was conducted to explore the ability of stratifying overall survival (OS). RESULTS: The combined model showed good discrimination of the ESTM [C-indices (95% confidence interval, CI): 0.770 (0.729-0.812) and 0.761 (0.718-0.805) in training and test cohorts respectively], which outperformed deep learning model, radiomics model, and clinical model. The stratified analysis showed this model was not affected by patient's tumor size, the presence of lymphovascular invasion, and Lauren classification (p < 0.05). Moreover, the model score showed strong consistency with the OS [C-indices (95%CI): 0.723 (0.658-0.789, p < 0.0001) in the internal survival cohort and 0.715 (0.650-0.779, p < 0.0001) in the external survival cohort]. More interestingly, univariate analysis showed the model score was significantly associated with occult distant metastasis (p < 0.05) that was missed by preoperative diagnosis. CONCLUSIONS: The model combining CT images and clinical characteristics had an impressive predictive ability of both ESTM and prognosis, which has the potential to serve as an effective complement to the preoperative TNM staging system.

Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review.

Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and basic workflow of radiomics and deep learning in medical image analysis. We then conduct a detailed review of their applications to seven typical tasks in the clinical diagnosis and treatment of nasopharyngeal carcinoma, covering various aspects of image synthesis, lesion segmentation, diagnosis, and prognosis. The innovation and application effects of cutting-edge research are summarized. Recognizing the heterogeneity of the research field and the existing gap between research and clinical translation, potential avenues for improvement are discussed. We propose that these issues can be gradually addressed by establishing standardized large datasets, exploring the biological characteristics of features, and technological upgrades.

Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study.

BACKGROUND: Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC. METHODS: This multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes. RESULTS: The radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713-0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2-62.5% vs. 16.3-18.8%, P < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07-6.75, P < 0.001) and all causes of deaths (HR 1.53-2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity. CONCLUSIONS: We present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC.

Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma.

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model's ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.

Elevated first-trimester hepcidin level is associated with reduced risk of iron deficiency anemia in late pregnancy: a prospective cohort study.

Background: Iron deficiency (ID) and iron deficiency anemia (IDA) during pregnancy are highly prevalent worldwide. Hepcidin is considered an important biomarker of iron status. Currently, few longitudinal cohort studies have assessed the potential causal relationship between hepcidin and ID/IDA. Therefore, we aimed to investigate the association of first-trimester maternal serum hepcidin with third-trimester ID/IDA risk in a prospective cohort. Methods: Total of 353 non-ID/IDA pregnant women at 11-13 weeks' gestation were enrolled in Southern China and followed up to 38 weeks of gestation. Data on demography and anthropometry were obtained from a structured questionnaire at enrollment. Iron biomarkers including hepcidin were measured at enrollment and follow-up. Regression models were used to evaluate the association of first-trimester hepcidin with third-trimester ID/IDA risk. Results: Serum hepcidin levels substantially decreased from 19.39 ng/mL in the first trimester to 1.32 ng/mL in the third trimester. Incidences of third-trimester ID and IDA were 46.2 and 11.4%, respectively. Moreover, moderate and high levels of first-trimester hepcidin were positively related to third-trimester hepcidin (log-transformed ß = 0.51; 95% CI = 0.01, 1.00 and log-transformed ß = 0.66; 95% CI = 0.15, 1.17). Importantly, elevated first-trimester hepcidin was significantly associated with reduced risk of third-trimester IDA (OR = 0.38; 95% CI = 0.15, 0.99), but not with ID after adjustment with potential confounders. Conclusion: First-trimester hepcidin was negatively associated with IDA risk in late pregnancy, indicating higher first-trimester hepcidin level may predict reduced risk for developing IDA. Nonetheless, given the limited sample size, larger studies are still needed.

Benign-malignant classification of pulmonary nodules by low-dose spiral computerized tomography and clinical data with machine learning in opportunistic screening.

BACKGROUND: Many people were found with pulmonary nodules during physical examinations. It is of great practical significance to discriminate benign and malignant nodules by using data mining technology. METHODS: The subjects' demographic data, baseline examination results, and annual follow-up low-dose spiral computerized tomography (LDCT) results were recorded. The findings from annual physical examinations of positive nodules, including highly suspicious nodules and clinically tentative benign nodules, was analyzed. The extreme gradient boosting (XGBoost) model was constructed and the Grid Search CV method was used to select the super parameters. External unit data were used as an external validation set to evaluate the generalization performance of the model. RESULTS: A total of 135,503 physical examinees were enrolled. Baseline testing found that 27,636 (20.40%) participants had clinically tentative benign nodules and 611 (0.45%) participants had highly suspicious nodules. The proportion of highly suspicious nodules in participants with negative baseline was about 0.12%-0.46%, which was lower than the baseline level except the follow-up of >5 years. In the 27,636 participants with clinically tentative benign nodules, only in the first year of LDCT re-examination was the proportion of highly suspicious nodules (1.40%) significantly greater than that of baseline screening (0.45%) (p < 0.001), and the proportion of highly suspicious nodules was not different between the baseline screening and other follow-up years (p > 0.05). Furthermore, 322 cases with benign nodules and 196 patients with malignant nodules confirmed by surgery and pathology were compared. A model and the top 15 most important clinical variables were determined by XGBoost algorithm. The area under the curve (AUC) of the model was 0.76 [95% CI: 0.67-0.84], and the accuracy was 0.75. The sensitivity and specificity of the model under this threshold were 0.78 and 0.73, respectively. In the validation of model using external data, the AUC was 0.87 and the accuracy was 0.80. The sensitivity and specificity were 0.83 and 0.77, respectively. CONCLUSIONS: It is important that pulmonary nodules could be more accurately identified at the first LDCT examination. A model with 15 variables which are routinely measured in the clinic could be helpful to distinguish benign and malignant nodules. It could help the radiological team issue a more accurate report; and it may guide the clinical team regarding LDCT follow-up.

In situ subtotal spleen resection combined with selective pericardial devascularization for the treatment of portal hypertension.

BACKGROUND: Hypersplenism and esophageal varices bleeding are the major complications of portal hypertension (PHT). In recent years, increasing attention has been given to spleen preservation operations. The mode and long-term effects of subtotal splenectomy and selective pericardial devascularization for PHT remain controversial. AIM: To investigate the clinical efficacy and safety of subtotal splenectomy combined with selective pericardial devascularization for the treatment of PHT. METHODS: This was a retrospective study of 15 patients with PHT who underwent subtotal splenectomy not preserving the splenic artery or vein combined with selective pericardial devascularization in the Department of Hepatobiliary Surgery, Qilu Hospital of Shandong University from February 2011 to April 2022. Fifteen propensity score-matched patients with PHT who underwent total splenectomy at the same time served as the control group. The patients were followed for up to 11 years after surgery. We compared the postoperative platelet levels, perioperative splenic vein thrombosis, and serum immunoglobulin levels between the two groups. Abdominal enhanced computed tomography was used to evaluate the blood supply and function of the residual spleen. The operation time, intraoperative blood loss, evacuation time, and hospital stay were compared between the two groups. RESULTS: The postoperative platelet level of patients in the subtotal splenectomy group was significantly lower than that in the total splenectomy group (P < 0.05), and the postoperative portal system thrombosis rate in the subtotal splenectomy group was also much lower than that in the total splenectomy group. The levels of serum immunoglobulins (IgG, IgA, and IgM) showed no significant differences after surgery compared with before surgery in the subtotal splenectomy group (P > 0.05), but serum immunoglobulin IgG and IgM levels decreased dramatically after total splenectomy (P < 0.05). The operation time in the subtotal splenectomy group was longer than that in the total splenectomy group (P < 0.05), but there were no significant differences in the amount of intraoperative blood loss, evacuation time, or hospital stay between the two groups. CONCLUSION: Subtotal splenectomy not preserving the splenic artery or vein combined with selective pericardial devascularization is a safe and effective surgical treatment for patients with PHT, not only correcting hypersplenism but also preserving splenic function, especially immunological function.

CT-based identification of pediatric non-Wilms tumors using convolutional neural networks at a single center.

BACKGROUND: Deep learning (DL) is more and more widely used in children's medical treatment. In this study, we have developed a computed tomography (CT)-based DL model for identifying undiagnosed non-Wilms tumors (nWTs) from pediatric renal tumors. METHODS: This study collected and analyzed the preoperative clinical data and CT images of pediatric renal tumor patients diagnosed by our center from 2008 to 2020, and established a DL model to identify nWTs noninvasively. RESULTS: A total of 364 children who had been confirmed by histopathology with renal tumors from our center were enrolled, including 269 Wilms tumors (WTs) and 95 nWTs. For DL model development, all cases were randomly allocated to training set (218 cases), validation set (73 cases), and test set (73 cases). In the test set, the DL model achieved area under the curve of 0.831 (95% CI: 0.712-0.951) in discriminating WTs from nWTs, with the accuracy, sensitivity, and specificity of 0.781, 0.563, and 0.842, respectively. The sensitivity of our model was higher than a radiologist with 15 years of experience. CONCLUSIONS: We presented a DL model for identifying undiagnosed nWTs from pediatric renal tumors, with the potential to improve the image-based diagnosis. IMPACT: Deep learning model was used for the first time to identify pediatric renal tumors in this study. Deep learning model can identify non-Wilms tumors from pediatric renal tumors. Deep learning model based on computed tomography images can improve tumor diagnosis rate.

Probabilistic risk assessment of dietary exposure to pentachlorophenol in Guangzhou, China.

Pentachlorophenol (PCP) is a ubiquitous environmental contaminant commonly existing as its sodium salt (NaPCP), which enters the human body primarily through long term but low-level dietary exposure. PCP contributes to chemical carcinogenesis and teratogenesis. In this study, the probabilistic risk of dietary exposure to PCP in Guangzhou citizens was investigated. In total, 923 food samples in the categories of pork, livestock (beef and lamb), poultry, offal, eggs, and freshwater fish (considered to be relatively susceptible to PCP contamination) were collected from various markets in Guangzhou and tested for PCP. Probabilistic risk assessment model calculations for PCP dietary exposure and margin of exposure (MOE) values were performed using @RISK software, based on a Monte Carlo simulation with 10,000 iterations. The overall detection rate of PCP (above 1 µg kg-1, the detection limit) was 19.9% (184/923), with an average of 7.9 µg kg-1. The highest rate of PCP detection, 28.2%, was in livestock (beef and lamb). The MOE value for dietary PCP exposure in general Guangzhou residents averaged 400, which was far below 5,000 (the borderline for judging a health risk). The lowest MOE value, 190, was observed in the 3- to-6-year old population and indicates a significant risk. In conclusion, this study suggests that PCP exposure in Guangzhou residents is of considerable health risk, especially for the pre-school young children.

[A cross-sectional study of early-onset epilepsy of intracerebral hemorrhage and construction of a risk prediction model].

OBJECTIVE: To study the early-onset epilepsy of intracerebral hemorrhage and build a prediction model to evaluate its prediction efficiency. METHODS: A cross-sectional investigation was conducted to construct a specialized optimized prediction model. The prediction model was converted into a visual optimized scoring scale, so as to quantify the probability of secondary epilepsy after intracerebral hemorrhage. Based on the current prediction model of acute cerebral infraction and post-stroke seizure (AIS-PSS), the evaluation efficacy of optimized score for secondary epilepsy after hemorrhagic stroke was explored. RESULTS: (1) After sample size calculation and sufficient inclusion and exclusion, 159 patients with cerebral hemorrhage were continuously selected as the model group of this cross-sectional study. A total of 29 patients with early-onset epilepsy and 130 patients without secondary epilepsy were enrolled. The time span was from January 2021 to August 2021. In addition, 77 patients with acute cerebral hemorrhage from August 2021 to February 2022 were selected as the verification group, among which 12 patients had early-onset epilepsy and 65 patients had not any secondary epilepsy. (2) There were significant differences in demographic characteristics such as diabetes history, cerebral infarction history, smoking history, National Institutes of Health Stroke Scale (NIHSS) score, intracerebral hemorrhage hematoma volume, serum creatinine (SCr), neuron-specific enolase (NSE), S-100 protein and intracerebral hemorrhage site between the two model groups with different prognosis (all P < 0.05). (3) The above indexes were included in univariate and multivariate Poisson regression analysis, and the results showed that the duration of diabetes [relative risk (RR) = 1.229, 95% confidence interval (95%CI) was 1.065-1.896, P = 0.036], smoking history (RR = 1.419, 95%CI was 1.133-2.160, P = 0.030), history of cerebral infarction (RR = 1.634, 95%CI was 1.128-2.548, P = 0.041), hematoma volume of cerebral hemorrhage (RR = 1.222, 95%CI was 1.024-2.052, P = 0.041), NES content (RR = 1.146, 95%CI was 1.041-1.704, P = 0.032), were independent influencing factors to constitute the prediction model. The prediction model was converted into a visual optimized scoring scale in the form of a line diagram to obtain the prediction probability corresponding to the corresponding score. (4) Receiver operator characteristic curve (ROC curve) was used to test the evaluation efficiency of optimized score and AIS-PSS score for early-onset cerebral hemorrhage epilepsy. Relevant data of patients in the verification group were extracted according to the information of two scores, and the final score of each patient in the verification group was obtained. The score and prognosis were put into the ROC curve to evaluate the predictive ability of different prediction models. The results showed that the cut-off value of the optimized score and the AIS-PSS score were 144 points and 7 points, respectively, and the area under the ROC curve (AUC) and the Yoden index of the optimized score were slightly lower than the AIS-PSS score. However, compared with AIS-PSS score, there was no significant difference in the evaluation efficiency of optimized score for early-onset epilepsy (Z = 1.874, P > 0.05). CONCLUSIONS: This study constructed a specific early-onset epilepsy prediction model for patients with hemorrhagic stroke, and transformed it into an optimized score that is easy for clinical use, and its evaluation efficiency is reliable.

Iron Supplementation Is Associated with Improvement of Motor Development, Hemoglobin Level, and Weight in Preterm Infants during the First Year of Life in China.

Iron supplementation is recommended for preterm infants due to impaired iron endowment. However, the health outcomes of this recommendation remain controversial. Thus, this study aimed to determine the association of iron supplementation with neurobehavioral development, hemoglobin (Hb), and anthropometric characteristics in preterm infants. A retrospective cohort design was applied to collect data from 1568 preterm infants at 0-3 months of corrected age (mo CA) from a hospital in South China. Infants were categorized into a 3-month iron supplementation group (IG, n = 697) or a control group (CG, n = 871) according to medical records, and then followed through to 12 mo CA. Data on neurobehavioral development, anthropometry, Hb level, history of diseases, and nutrition were collected at 3, 6, and 12 mo CA. The results showed that, compared with the CG, iron supplementation was positively related to improved gross motor skills and weight at 6 mo CA (ß = 1.894, ß = 5.322) and 12 mo CA (ß = 4.019, ß = 6.830) and fine motor skills at 12 mo CA (ß = 1.980), after adjustment for confounding factors including illness, nutritional supplements, and diet. Iron supplementation was also related to elevated Hb levels and its increase at 3 mo CA (ß = 2.196, ß = 3.920) and 6 mo CA (ß = 3.011, ß = 7.259). In conclusion, iron supplementation for 3 months in Chinese preterm infants is positively associated with improved motor development, elevated Hb levels, and higher body weight during the first year of life.

Impact of occupational exposure on job satisfaction and overall happiness among Chinese physicians and nurses: A cross-sectional study.

AIMS: This study aims to investigate the impact of occupational exposure on job satisfaction and overall happiness and to identify related factors of job satisfaction and overall happiness among physicians and nurses. BACKGROUND: Occupational exposure against physicians and nurses has become one of the most serious public health issues worldwide. METHODS: A cross-sectional study was conducted among physicians and nurses from 14 public tertiary hospitals using purposive sampling. Propensity score matching was used to compare job satisfaction and overall happiness among physicians and nurses with and without occupational exposure. Furthermore, binary logistic regression analysis was used to identify and analyse the influencing factors of job satisfaction and overall happiness. RESULTS: A total of 2139 physicians and nurses (55.59%) from 3791 participants had experienced occupational exposure hazards. Before matching, the job satisfaction and overall happiness among the physicians and nurses were 38.54% and 42.14%, respectively. Participants who experienced occupational exposure were more likely to develop job dissatisfaction (OR = 1.08, 95% confidence interval [CI]: 0.90-1.28) and overall unhappiness (OR = 1.24, 95% CI: 1.05-1.46) than those who did not. Participants' work experience, self-evaluated health status, satisfaction with the work environment, evaluation of doctor-patient relationship and stress were common factors affecting job satisfaction and overall happiness. CONCLUSIONS: Our findings suggest that physicians and nurses who experience occupational exposure are more likely to develop job dissatisfaction and overall unhappiness, especially if they have shorter work experience and a tense or neutral relationship with patients. IMPLICATIONS FOR NURSING MANAGEMENT: It is necessary to pay attention to the occupational exposure. When physicians and nurses experience occupational exposure, managers could provide support to prevent job dissatisfaction and unhappiness.

The potential of prostate gland radiomic features in identifying the Gleason score.

BACKGROUND: Gleason score (GS) is one of the most critical predictors of diagnosing prostate cancer (PCa). The prostate gland, including both lesions and their microenvironment, may contain more comprehensive information about the PCa. We aimed to investigate the potential of prostate gland radiomic features in identifying Gleason scores (GS) < 7, = 7, and >7. METHODS: We retrospectively examined preoperative magnetic resonance imaging (MRI) results, clinical data, and postoperative pathological findings from 489 PCa patients. The three-dimensional (3D) and two-dimensional (2D) radiomic features were extracted from the manually segmented 3D prostate gland and its maximum 2D layer on MRI, respectively. Significant features were selected, and sequence signatures were then developed via multi-class linear regression (MLR) accordingly. Subsequently, 2D and 3D radiomic models were constructed by applying MLR to the combination of the sequence signatures, respectively. The stability of the significant features was discussed by their average ranking in the other 30 random cohorts. Based on our distance matrix algorithm, we generated different regions of interest to simulate the manual segmentation biases and discuss the model's tolerance to them. RESULTS: Our 2D model reached a C-index of 0.728 and an average area under the receiver operating characteristic curve of 0.794 in the validation cohort. The corresponding key features were stable, with an average ranking of the top 8.352% in 30 random cohorts, and the model could tolerate a segmentation boundary deviation of 2 mm without significant performance degradation. CONCLUSION: 2D prostate-gland-MRI-based radiomic features showed stable potential in identifying GS.

FOSL1 promotes proneural-to-mesenchymal transition of glioblastoma stem cells via UBC9/CYLD/NF-κB axis.

Proneural (PN) to mesenchymal (MES) transition (PMT) is a crucial phenotypic shift in glioblastoma stem cells (GSCs). However, the mechanisms driving this process remain poorly understood. Here, we report that Fos-like antigen 1 (FOSL1), a component of AP1 transcription factor complexes, is a key player in regulating PMT. FOSL1 is predominantly expressed in the MES subtype, but not PN subtype, of GSCs. Knocking down FOSL1 expression in MES GSCs leads to the loss of MES features and tumor-initiating ability, whereas ectopic expression of FOSL1 in PN GSCs is able to induce PMT and maintain MES features. Moreover, FOSL1 facilitates ionizing radiation (IR)-induced PMT and radioresistance of PN GSCs. Inhibition of FOSL1 enhances the anti-tumor effects of IR by preventing IR-induced PMT. Mechanistically, we find that FOSL1 promotes UBC9-dependent CYLD SUMOylation, thereby inducing K63-linked polyubiquitination of major nuclear factor κB (NF-κB) intermediaries and subsequent NF-κB activation, which results in PMT induction in GSCs. Our study underscores the importance of FOSL1 in the regulation of PMT and suggests that therapeutic targeting of FOSL1 holds promise to attenuate molecular subtype switching in patients with glioblastomas.

Cross-Phase Adversarial Domain Adaptation for Deep Disease-free Survival Prediction with Gastric Cancer CT Images.

Predicting gastric cancer disease-free survival (DFS) and identifying patients probably with high risk are imperative for more appropriate clinical treatment plans. Compared with CT-based radiomics researches adopting linear Cox proportional hazards models, deep neural networks can perform nonlinear transformations and investigate complex associations of image features with prognosis. Exploring shared information between post-contrast CT (with better visual enhancement) and pre-contrast CT (with few side effects and contraindications) is another challenge. In this work, a cross-phase adversarial domain adaptation (CPADA) framework is proposed to adapt a deep DFS prediction network (DDFS-Net) from arterial phase to pre-contrast phase. The DDFS-Net is designed for feature learning and trained by optimizing the average negative log function of Cox partial likelihood. The CPADA maps the feature space of pre-contrast phase (target) to arterial phase (source) in an adversarial manner by measuring Wasserstein distance. The proposed methods are evaluated on a dataset of 249 gastric cancer patients by concordance index, receiver operating characteristic curves, and Kaplan-Meier survival curves. The results demonstrate that our DDFS-Net outperforms linear survival analysis methods, and the CPADA works better than supervised learning and direct transfer schemes.Clinical Relevance-This work enables preoperative DFS prediction and risk stratification in gastric cancer. It is feasible and effective to infer a patient's risk of failure given a pre-contrast CT image by DDFS-Net adapted by CPADA.

Multi-physics coupling simulation of electrode induction melting gas atomization for advanced titanium alloys powder preparation.

A numerical modeling method is proposed for the melting process of Titanium metals of Titanium alloys powder preparation used for 3D printing. The melting process simulation, which involves the tight coupling between electromagnetic field, thermal field and fluid flow as well as deformation associated during the melting process, is conducted by adopting the finite element method. A two-way coupling strategy is used to include the interactions between these fields by incorporating the material properties dependent on temperature and the coupling terms. In addition, heat radiation and phase change are also considered in this paper. The arbitrary Lagrangian-Eulerian formulation is exploited to model the deformation of Titanium metal during the melting process. The distribution of electromagnetic flux density, eddy current density, temperature, and fluid flow velocity at different time can be determined by utilizing this numerical method. In a word, the method proposed in this paper provides a general way to predict the melting process of electrode induction melting gas atomization (EIGA) dynamically, and it also could be used as a reference for the design and optimization of EIGA.