Combined deep learning and radiomics in pretreatment radiation esophagitis prediction for patients with esophageal cancer underwent volumetric modulated arc therapy.

PURPOSE: To develop a combined radiomics and deep learning (DL) model in predicting radiation esophagitis (RE) of a grade ≥ 2 for patients with esophageal cancer (EC) underwent volumetric modulated arc therapy (VMAT) based on computed tomography (CT) and radiation dose (RD) distribution images. MATERIALS AND METHODS: A total of 273 EC patients underwent VMAT were retrospectively reviewed and enrolled from two centers and divided into training (n = 152), internal validation (n = 66), and external validation (n = 55) cohorts, respectively. Radiomic and dosiomic features along with DL features using convolutional neural networks were extracted and screened from CT and RD images to predict RE. The performance of these models was evaluated and compared using the area under curve (AUC) of the receiver operating characteristic curves (ROC). RESULTS: There were 5 and 10 radiomic and dosiomic features were screened, respectively. XGBoost achieved a best AUC of 0.703, 0.694 and 0.801, 0.729 with radiomic and dosiomic features in the internal and external validation cohorts, respectively. ResNet34 achieved a best prediction AUC of 0.642, 0.657 and 0.762, 0.737 for radiomics based DL model (DLR) and RD based DL model (DLD) in the internal and external validation cohorts, respectively. Combined model of DLD + Dosiomics + clinical factors achieved a best AUC of 0.913, 0.821 and 0.805 in the training, internal, and external validation cohorts, respectively. CONCLUSION: Although the dose was not responsible for the prediction accuracy, the combination of various feature extraction methods was a factor in improving the RE prediction accuracy. Combining DLD with dosiomic features was promising in the pretreatment prediction of RE for EC patients underwent VMAT.

Diversity and inclusion: A hidden additional benefit of Open Data.

The recent imperative by the National Institutes of Health to share scientific data publicly underscores a significant shift in academic research. Effective as of January 2023, it emphasizes that transparency in data collection and dedicated efforts towards data sharing are prerequisites for translational research, from the lab to the bedside. Given the role of data access in mitigating potential bias in clinical models, we hypothesize that researchers who leverage open-access datasets rather than privately-owned ones are more diverse. In this brief report, we proposed to test this hypothesis in the transdisciplinary and expanding field of artificial intelligence (AI) for critical care. Specifically, we compared the diversity among authors of publications leveraging open datasets, such as the commonly used MIMIC and eICU databases, with that among authors of publications relying exclusively on private datasets, unavailable to other research investigators (e.g., electronic health records from ICU patients accessible only to Mayo Clinic analysts). To measure the extent of author diversity, we characterized gender balance as well as the presence of researchers from low- and middle-income countries (LMIC) and minority-serving institutions (MSI) located in the United States (US). Our comparative analysis revealed a greater contribution of authors from LMICs and MSIs among researchers leveraging open critical care datasets (treatment group) than among those relying exclusively on private data resources (control group). The participation of women was similar between the two groups, albeit slightly larger in the former. Notably, although over 70% of all articles included at least one author inferred to be a woman, less than 25% had a woman as a first or last author. Importantly, we found that the proportion of authors from LMICs was substantially higher in the treatment than in the control group (10.1% vs. 6.2%, p<0.001), including as first and last authors. Moreover, we found that the proportion of US-based authors affiliated with a MSI was 1.5 times higher among articles in the treatment than in the control group, suggesting that open data resources attract a larger pool of participants from minority groups (8.6% vs. 5.6%, p<0.001). Thus, our study highlights the valuable contribution of the Open Data strategy to underrepresented groups, while also quantifying persisting gender gaps in academic and clinical research at the intersection of computer science and healthcare. In doing so, we hope our work points to the importance of extending open data practices in deliberate and systematic ways.

Identification of potential therapeutic target SPP1 and related RNA regulatory pathway in keloid based on bioinformatics analysis.

OBJECTIVE: To explore the complex mechanisms of keloid, new approaches have been developed by different strategies. However, conventional treatment did not significantly reduce the recurrence rate. This study aimed to identify new biomarkers and mechanisms for keloid progression through bioinformatics analyses. METHODS: In our study, microarray datasets for keloid were downloaded from the GEO database. Differentially expressed genes (DEGs) were identified by R software. Multiple bioinformatics tools were used to identify hub genes, and reverse predict upstream miRNAs and lncRNA molecules of target hub genes. Finally, the total RNA-sequencing technique and miRNA microarray were combined to validate the identified genes. RESULTS: Thirty-one DEGs were screened out and the upregulated hub gene SPP1 was finally identified, which was consistent with our RNA-sequencing analysis results and validation dataset. In addition, a ceRNA network of mRNA (SPP1)-miRNA (miR-181a-5p)-lncRNA (NEAT1, MALAT1, LINC00667, NORAD, XIST and MIR4458HG) was identified by the bioinformatics databases. The results of our miRNA microarray showed that miR-181a-5p was upregulated in keloid, also we found that the lncRNA NEAT1 could affect keloid progression by retrieving the relevant literature. CONCLUSIONS: We speculate that SPP1 is a potential candidate biomarker and therapeutic target for patients with keloid, and NEAT1/miR-181a-5p/SPP1 might be the RNA regulatory pathway that regulates keloid formation.

Identify new biomarkers in keloid, potentially improve disease diagnosis and treatment.Through a variety of bioinformatics analysis tools, we found that the miRNA pathway NEAT1/miR-181a-5p/SPP1 may participant in controlling disease progression in the keloid.Providing insight into the mechanisms of disease development in the keloid at the transcriptome level.

Emergence of plasmid-borne tet(X4) resistance gene in clinical isolate of eravacycline- and omadacycline-resistant Klebsiella pneumoniae ST485.

Omadacycline and eravacycline are gradually being used as new tetracycline antibiotics for the clinical treatment of Gram-negative pathogens. Affected by various tetracycline-inactivating enzymes, there have been reports of resistance to eravacycline and omadacycline in recent years. We isolated a strain carrying the mobile tigecycline resistance gene tet(X4) from the feces of a patient in Zhejiang Province, China. The strain belongs to the rare ST485 sequence type. The isolate was identified as Klebsiella pneumoniae by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The MICs of antimicrobial agents were determined using either the agar dilution method or the micro broth dilution method. The result showed that the isolate was resistant to eravacycline (MIC = 32 mg/L), omadacycline (MIC > 64 mg/L), and tigecycline (MIC > 32 mg/L). Whole-genome sequencing revealed that the tet(X4) resistance gene is located on the IncFII(pCRY) conjugative plasmid. tet(X4) is flanked by ISVsa3, and we hypothesize that this association contributes to the spread of the resistance gene. Plasmids were analyzed by S1-nuclease pulsed-field gel electrophoresis (S1-PFGE), Southern blotting, and electrotransformation experiment. We successfully transferred the plasmid carrying tet(X4) to the recipient bacteria by electrotransformation experiment. Compared with the DH-5α, the MICs of the transformant L3995-DH5α were increased by eight-fold for eravacycline and two-fold higher for omadacycline. Overall, the emergence of plasmid-borne tet(X4) resistance gene in a clinical isolate of K. pneumoniae ST485 underscores the essential requirement for the ongoing monitoring of tet(X4) to prevent and control its further dissemination in China.IMPORTANCEThere are still limited reports on Klebsiella pneumoniae strains harboring tetracycline-resistant genes in China, and K. pneumoniae L3995hy adds a new example to those positive for the tet(X4) gene. Importantly, our study raises concerns that plasmid-mediated resistance to omadacycline and eravacycline may spread further to a variety of ecological and clinical pathogens, limiting the choice of medication for extensively drug-resistant bacterial infections. Therefore, it is important to continue to monitor the prevalence and spread of tet(X4) and other tetracyclines resistance genes in K. pneumoniae and diverse bacterial populations.

Photovoltaic Rotation and Transportation of a Fragile Fluorescent Microrod Toward Assembling a Tunable Light-Source System.

Continuous rotation of a fragile, photosensitive microrod in a safe, flexible way remains challenging in spite of its importance to microelectro-mechanical systems. We propose a photovoltaic strategy to continuously rotate a fragile, fluorescent microrod on a LiNbO3/Fe (LN/Fe) substrate using a continuous wave visible (473 nm) laser beam with an ultralow power (few tens of µW) and a simple structure (Gaussian profile). This strategy does not require the laser spot to cover the entire microrod nor does it result in a sharp temperature rise on the microrod. Both experiments and simulation reveal that the strongest photovoltaic field generated beside the laser spot firmly traps one corner of the microrod and the axisymmetric photovoltaic field exerts an electrostatic torque on the microrod driving it to rotate continuously around the laser spot. The dependence of the rotation rate on the laser power indicates contributions from both deep and shallow photovoltaic centers. This rotation mode, combined with the transportation mode, enables the controllable movement of an individual microrod along any complex trajectory with any specific orientation. The tuning of the end-emitting spectrum and the photothermal cutting of the fluorescent microrod are also realized by properly configuring the laser illumination. By taking a microrod as the emitter and a polystyrene microsphere as the focusing lens, we demonstrate the photovoltaic assembly of a microscale light-source system with both spectrum and divergence-angle tunabilities, which are realized by adjusting the photoexcitation position along the microrod and the geometry relationship in the system, respectively.

A cell-free fluorescence biosensor based on allosteric transcription factor NalC for detection of pentachlorophenol.

Pentachlorophenol (PCP) was once used as a pesticide, germicide, and preservative due to its stable properties and resistance to degradation. This study aimed to design a biosensor for the quantitative and prompt detection of capable of PCP. A cell-free fluorescence biosensor was developed while employing NalC, an allosteric Transcription Factor responsive to PCP and In Vitro Transcription. By adding a DNA template and PCP and employing Electrophoretic Mobility Shift Assay while monitoring the dynamic fluorescence changes in RNA, this study offers evidence of NalC's potential applicability in sensor systems developed for the specific detection of PCP. The biosensor showed the capability for the quantitative detection of PCP, with a Limit of Detection (LOD) of 0.21 µM. Following the addition of Nucleic Acid Sequence-Based Amplification, the fluorescence intensity of RNA revealed an excellent linear relationship with the concentration of PCP, showing a correlation coefficient (R2) of 0.9595. The final LOD was determined to be 0.002 µM. This study has successfully translated the determination of PCP into a fluorescent RNA output, thereby presenting a novel approach for detecting PCP within environmental settings.

Epidemiological investigation of Senecavirus A infection in pig herds in China from 2018 to 2021.

Senecaviurs A (SVA) infection, an emerging infectious disease in pig populations, is characterized by vesicular lesions predominantly affecting the mouth, snout, and hooves of infected pigs, similar to the symptoms of Foot and Mouth Disease Virus (FMDV). This disease first spread into China in 2015, causing great panic in the pig breeding industry. To determine the prevalence of SVA in pig herds in China from 2018 to 2021, a total of 4,901 pig tissue samples were collected from 18 provinces, autonomous regions and municipalities (P.A.M.s) for epidemiological investigation, virus isolation and genetic analysis. In 2021, the individual positive rates (IPRs) from the perspective of spatial distribution in East China, South China, Central China, North China, Southwest China, Northwest China, and Northeast China were 0, 0, 1.69, 0.94, 11.70, 3.31 and 2.21%, respectively. The herd positive rates (HPRs) were 0, 0, 9.52, 9.09, 50.00, 7.69 and 23.08%. From the perspective of temporal distribution, the IPR showed an overall downwards trend from 2018 to 2021, with only a slight increase in 2020. Moreover, the HPR decreased from 36.63 to 10.07%. From the perspective of population distribution in 2021, the IPR (2.62%) and HPR (12.00%) in apparently healthy pig herds (slaughterhouses) were greater than those in non-healthy pig herds (2.10 and 5.13%, respectively), consistent with the results in 2019. To characterize the prevalent strains, 10 SVA strains isolated from positive samples in 2019 were clustered in Clades I and VII; SVA-FJ039-2019, SVA-HuN032-2019, SVA-GX011-2019, SVA-FJ036-2019, SVA-GXF011-2019 and SVA-GXF053-2019 were clustered in Clade I; and SVA-FJ018-2019, SVA-SD069-2019, SVA-SD072-2019, and SVA-SD074-2019 were clustered in Clade VII. In conclusion, until 2021, the prevalence of SVA in pig herds in China was still relatively high, the contaminated area was still large, and there were a number of hidden infections. In the future, the epidemic status of SVA in pig herds in China must be closely monitored and the prevention and control measures must be adjusted in a timely manner.

Extreme condition-tolerant stretchable flexible supercapacitor and triboelectric nanogenerator based on carrageenan-enhanced gel for energy storage, energy collection and self-powered sensing.

As flexible electronics devices for energy storage, mechanical energy collection and self-powered sensing, stretchable flexible supercapacitor and triboelectric nanogenerator (TENG) have attracted extensive attention. However, it is difficult to satisfy the requirements of high safety and resistance to extreme conditions. Dual roles of mechanical and electrical enhancement of inorganic salt are put forward, and a carrageenan (CG) enhanced poly (N-hydroxyethyl acrylamide)/CG/lithium chloride/glycerol (PCLG) conductive gel is prepared by designing hydrogen bonding self-crosslinking and chain entanglement. A high concentration and rapid deposition strategy is proposed to prepare a PCLG gel-based stretchable flexible all-in-one supercapacitor for energy storage, and a single electrode PCLG gel-based TENG is designed for mechanical energy collection, self-powered strain and tactile sensing. The supercapacitor has high capacitance, excellent cycling stability. The TENG possesses efficient energy harvesting with high and stable output voltage and power density, and sensitive and stable self-powered strain and tactile sensing without external power supply. Even under extreme conditions such as low temperatures, self-healing after damage, prolonged placement, deformation, post-deformation, multiple continuous work, pinprick and burning, the supercapacitor and TENG still have excellent properties. Therefore, we provide novel ideas to design flexible supercapacitor and TENG used under extreme conditions for future wearable electronics.

Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence.

BACKGROUND: Research into the acute kidney disease (AKD) after acute ischemic stroke (AIS) is rare, and how clinical features influence its prognosis remain unknown. We aim to employ interpretable machine learning (ML) models to study AIS and clarify its decision-making process in identifying the risk of mortality. METHODS: We conducted a retrospective cohort study involving AIS patients from January 2020 to June 2021. Patient data were randomly divided into training and test sets. Eight ML algorithms were employed to construct predictive models for mortality. The performance of the best model was evaluated using various metrics. Furthermore, we created an artificial intelligence (AI)-driven web application that leveraged the top ten most crucial features for mortality prediction. RESULTS: The study cohort consisted of 1633 AIS patients, among whom 257 (15.74%) developed subacute AKD, 173 (10.59%) experienced AKI recovery, and 65 (3.98%) met criteria for both AKI and AKD. The mortality rate stood at 4.84%. The LightGBM model displayed superior performance, boasting an AUROC of 0.96 for mortality prediction. The top five features linked to mortality were ACEI/ARE, renal function trajectories, neutrophil count, diuretics, and serum creatinine. Moreover, we designed a web application using the LightGBM model to estimate mortality risk. CONCLUSIONS: Complete renal function trajectories, including AKI and AKD, are vital for fitting mortality in AIS patients. An interpretable ML model effectively clarified its decision-making process for identifying AIS patients at risk of mortality. The AI-driven web application has the potential to contribute to the development of personalized early mortality prevention.

Multi-omics deep learning for radiation pneumonitis prediction in lung cancer patients underwent volumetric modulated arc therapy.

BACKGROUND AND OBJECTIVE: To evaluate the feasibility and accuracy of radiomics, dosiomics, and deep learning (DL) in predicting Radiation Pneumonitis (RP) in lung cancer patients underwent volumetric modulated arc therapy (VMAT) to improve radiotherapy safety and management. METHODS: Total of 318 and 31 lung cancer patients underwent VMAT from First Affiliated Hospital of Wenzhou Medical University (WMU) and Quzhou Affiliated Hospital of WMU were enrolled for training and external validation, respectively. Models based on radiomics (R), dosiomics (D), and combined radiomics and dosiomics features (R+D) were constructed and validated using three machine learning (ML) methods. DL models trained with CT (DLR), dose distribution (DLD), and combined CT and dose distribution (DL(R+D)) images were constructed. DL features were then extracted from the fully connected layers of the best-performing DL model to combine with features of the ML model with the best performance to construct models of R+DLR, D+DLD, R+D+DL(R+D)) for RP prediction. RESULTS: The R+D model achieved a best area under curve (AUC) of 0.84, 0.73, and 0.73 in the internal validation cohorts with Support Vector Machine (SVM), XGBoost, and Logistic Regression (LR), respectively. The DL(R+D) model achieved a best AUC of 0.89 and 0.86 using ResNet-34 in training and internal validation cohorts, respectively. The R+D+DL(R+D) model achieved a best performance in the external validation cohorts with an AUC, accuracy, sensitivity, and specificity of 0.81(0.62-0.99), 0.81, 0.84, and 0.67, respectively. CONCLUSIONS: The integration of radiomics, dosiomics, and DL features is feasible and accurate for the RP prediction to improve the management of lung cancer patients underwent VMAT.

Facile synthesis of MoS2@red phosphorus heterojunction for synergistically photodynamic and photothermal therapy of renal cell carcinoma.

The therapy of the clear cell renal cell carcinoma (ccRCC) is crucial for the human healthcare due to its easy metastasis and recurrence, as well as resistance to radiotherapy and chemotherapy. In this work, we propose the synthesis of MoS2@red phosphorus (MoS2@RP) heterojunction to induce synergistic photodynamic and photothermal therapy (PDT/PTT) of ccRCC. The MoS2@RP heterojunction exhibits enhanced spectra absorption in the NIR range and produce local heat-increasing under the NIR laser irradiation compared with pure MoS2 and RP. The high photocatalytic activity of the MoS2@RP heterojunction contributes to effective transferring of the photo-excited electrons from the RP to MoS2, which promotes the production of various types of radical oxygen species (ROS) to kill the ccRCC cells. After the NIR irradiation, the MoS2@RP can effectively induce the apoptosis in the ccRCC cells through localized hyperthermia and the generation of ROS, while exhibiting low cytotoxicity towards normal kidney cells. In comparison to MoS2, the MoS2@RP heterojunction shows an approximate increase of 22 % in the lethality rate of the ccRCC cells and no significant change in toxicity towards normal cells. Furthermore, the PDT/PTT treatment using the MoS2@RP heterojunction effectively eradicates a substantial number of deep-tissue ccRCC cells in vivo without causing significant damage to major organs. This study presents promising effect of the MoS2@RP heterojunction-based photo-responsive therapy for effective ccRCC treatment.

Learning Disentangled Priors for Hyperspectral Anomaly Detection: A Coupling Model-Driven and Data-Driven Paradigm.

Accurately distinguishing between background and anomalous objects within hyperspectral images poses a significant challenge. The primary obstacle lies in the inadequate modeling of prior knowledge, leading to a performance bottleneck in hyperspectral anomaly detection (HAD). In response to this challenge, we put forth a groundbreaking coupling paradigm that combines model-driven low-rank representation (LRR) methods with data-driven deep learning techniques by learning disentangled priors (LDP). LDP seeks to capture complete priors for effectively modeling the background, thereby extracting anomalies from hyperspectral images more accurately. LDP follows a model-driven deep unfolding architecture, where the prior knowledge is separated into the explicit low-rank prior formulated by expert knowledge and implicit learnable priors by means of deep networks. The internal relationships between explicit and implicit priors within LDP are elegantly modeled through a skip residual connection. Furthermore, we provide a mathematical proof of the convergence of our proposed model. Our experiments, conducted on multiple widely recognized datasets, demonstrate that LDP surpasses most of the current advanced HAD techniques, exceling in both detection performance and generalization capability.

Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy.

BACKGROUND: To integrate radiomics and dosiomics features from multiple regions in the radiation pneumonia (RP grade ≥ 2) prediction for esophageal cancer (EC) patients underwent radiotherapy (RT). METHODS: Total of 143 EC patients in the authors' hospital (training and internal validation: 70%:30%) and 32 EC patients from another hospital (external validation) underwent RT from 2015 to 2022 were retrospectively reviewed and analyzed. Patients were dichotomized as positive (RP+) or negative (RP-) according to CTCAE V5.0. Models with radiomics and dosiomics features extracted from single region of interest (ROI), multiple ROIs and combined models were constructed and evaluated. A nomogram integrating radiomics score (Rad_score), dosiomics score (Dos_score), clinical factors, dose-volume histogram (DVH) factors, and mean lung dose (MLD) was also constructed and validated. RESULTS: Models with Rad_score_Lung&Overlap and Dos_score_Lung&Overlap achieved a better area under curve (AUC) of 0.818 and 0.844 in the external validation in comparison with radiomics and dosiomics models with features extracted from single ROI. Combining four radiomics and dosiomics models using support vector machine (SVM) improved the AUC to 0.854 in the external validation. Nomogram integrating Rad_score, and Dos_score with clinical factors, DVH factors, and MLD further improved the RP prediction AUC to 0.937 and 0.912 in the internal and external validation, respectively. CONCLUSION: CT-based RP prediction model integrating radiomics and dosiomics features from multiple ROIs outperformed those with features from a single ROI with increased reliability for EC patients who underwent RT.

Distribution and molecular characterization of carbapenemase-producing gram-negative bacteria in Henan, China.

This study aimed to investigate the epidemiological characteristics and trends over time of carbapenemase-producing (e.g., KPC, NDM, VIM, IMP, and OXA-48) Gram-negative bacteria (CPGNB). Non-duplicated multi-drug resistant Gram-negative bacteria (MDRGNB) were collected from the First Affiliated Hospital of Zhengzhou University from April 2019 to February 2023. Species identification of each isolate was performed using the Vitek2 system and confirmed by matrix-assisted laser desorption ionization-time of flight mass spectrometry according to the manufacturer's instructions. PCR detected carbapenem resistance genes in the strains, strains carrying carbapenem resistance genes were categorized as CPGNB strains after validation by carbapenem inactivation assay. A total of 5705 non-repetitive MDRGNB isolates belonging to 78 different species were collected during the study period, of which 1918 CPGNB were validated, with the respiratory tract being the primary source of specimens. Epidemiologic statistics showed a significant predominance of ICU-sourced strains compared to other departments. Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii, and Pseudomonas aeruginosa were the significant CPGNB in Henan, and KPC and NDM were the predominant carbapenemases. Carbapenem-resistant infections in Henan Province showed an overall increasing trend, and the carriage of carbapenemase genes by CPGNB has become increasingly prevalent and complicated. The growing prevalence of CPGNB in the post-pandemic era poses a significant challenge to public safety.

A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction.

BACKGROUND: Error analysis plays a crucial role in clinical concept extraction, a fundamental subtask within clinical natural language processing (NLP). The process typically involves a manual review of error types, such as contextual and linguistic factors contributing to their occurrence, and the identification of underlying causes to refine the NLP model and improve its performance. Conducting error analysis can be complex, requiring a combination of NLP expertise and domain-specific knowledge. Due to the high heterogeneity of electronic health record (EHR) settings across different institutions, challenges may arise when attempting to standardize and reproduce the error analysis process. OBJECTIVES: This study aims to facilitate a collaborative effort to establish common definitions and taxonomies for capturing diverse error types, fostering community consensus on error analysis for clinical concept extraction tasks. MATERIALS AND METHODS: We iteratively developed and evaluated an error taxonomy based on existing literature, standards, real-world data, multisite case evaluations, and community feedback. The finalized taxonomy was released in both .dtd and .owl formats at the Open Health Natural Language Processing Consortium. The taxonomy is compatible with several different open-source annotation tools, including MAE, Brat, and MedTator. RESULTS: The resulting error taxonomy comprises 43 distinct error classes, organized into 6 error dimensions and 4 properties, including model type (symbolic and statistical machine learning), evaluation subject (model and human), evaluation level (patient, document, sentence, and concept), and annotation examples. Internal and external evaluations revealed strong variations in error types across methodological approaches, tasks, and EHR settings. Key points emerged from community feedback, including the need to enhancing clarity, generalizability, and usability of the taxonomy, along with dissemination strategies. CONCLUSION: The proposed taxonomy can facilitate the acceleration and standardization of the error analysis process in multi-site settings, thus improving the provenance, interpretability, and portability of NLP models. Future researchers could explore the potential direction of developing automated or semi-automated methods to assist in the classification and standardization of error analysis.

Intelligent double-layer film based on gellan gum/modified anthocyanin/curcumin/sodium alginate/zinc oxide for monitoring shrimp freshness.

In this study, intelligent double-layer films were prepared using modified black rice anthocyanin (MBRA)-curcumin (CUR)-gellan gum (GG) as the inner indicator layer and sodium alginate (ALG)­zinc oxide (ZnO) as the outer antimicrobial layer. The bilayer films were successfully prepared, as revealed by scanning electron microscopy, Fourier-transform infrared spectroscopy, and X-ray diffraction measurements. The mechanical characteristics, moisture content, and water vapor resistance of GG-MBRA/CUR1@ALG-ZnO, GG-MBRA/CUR2@ALG-ZnO, and GG-MBRA/CUR3@ALG-ZnO films showed significant enhancement compared to GG-MBRA/CUR3 and ALG-ZnO films. The bilayer films exhibited excellent pH responsiveness and reacted effectively to ammonia. The outer layer significantly improved the antioxidant and antibacterial properties of the inner layer. When the films were applied to shrimp, it was found that the double-layer films not only monitored the freshness of the shrimp in real-time but also were influential in extending the shelf life of the shrimp by about 1 d. Therefore, the double-layer film demonstrated potential as a smart packaging material for real-time monitoring of meat product freshness.

MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer.

BACKGROUND AND PURPOSE: Adjuvant treatments are valuable to decrease the recurrence rate and improve survival for early-stage cervical cancer patients (ESCC), Therefore, recurrence risk evaluation is critical for the choice of postoperative treatment. A magnetic resonance imaging (MRI) based radiomics nomogram integrating postoperative adjuvant treatments was constructed and validated externally to improve the recurrence risk prediction for ESCC. MATERIAL AND METHODS: 212 ESCC patients underwent surgery and adjuvant treatments from three centers were enrolled and divided into the training, internal validation, and external validation cohorts. Their clinical data, pretreatment T2-weighted images (T2WI) were retrieved and analyzed. Radiomics models were constructed using machine learning methods with features extracted and screen from sagittal and axial T2WI. A nomogram for recurrence prediction was build and evaluated using multivariable logistic regression analysis integrating radiomic signature and adjuvant treatments. RESULTS: A total of 8 radiomic features were screened out of 1020 extracted features. The extreme gradient boosting (XGboost) model based on MRI radiomic features performed best in recurrence prediction with an area under curve (AUC) of 0.833, 0.822 in the internal and external validation cohorts, respectively. The nomogram integrating radiomic signature and clinical factors achieved an AUC of 0.806, 0.718 in the internal and external validation cohorts, respectively, for recurrence risk prediction for ESCC. CONCLUSION: In this study, the nomogram integrating T2WI radiomic signature and clinical factors is valuable to predict the recurrence risk, thereby allowing timely planning for effective treatments for ESCC with high risk of recurrence.

Identifying the Pattern Characteristics of Anoikis-Related Genes in Keloid.

Objective: Anoikis is a kind of programmed cell death that is triggered when cells lose contact with each other or with the matrix. However, the potential value of anoikis-related genes (ARGs) in keloid (KD) has not been investigated. Approach: We downloaded three keloid fibroblast (KF) RNA sequencing (RNA-seq) datasets from the Gene Expression Omnibus (GEO) and obtained 338 ARGs from a search of the GeneCards database and PubMed articles. Weighted correlation network analysis was used to construct the coexpression network and obtain the KF-related ARGs. The LASSO-Cox method was used to screen the hub ARGs and construct the best prediction model. Then, GEO single-cell sequencing datasets were used to verify the expression of hub genes. We used whole RNA-seq for gene-level validation and the correlation between KD immune infiltration and anoikis. Results: Our study comprehensively analyzed the role of ARGs in KD for the first time. The least absolute shrinkage and selection operator (LASSO) regression analysis identified six hub ARGs (HIF1A, SEMA7A, SESN1, CASP3, LAMA3, and SIK2). A large number of miRNAs participate in the regulation of hub ARGs. In addition, correlation analysis revealed that ARGs were significantly correlated with the infiltration levels of multiple immune cells in patients with KD. Innovation: We explored the expression characteristics of ARGs in KD, which is extremely important for determining the molecular pathways and mechanisms underlying KD. Conclusions: This study provides a useful reference for revealing the characteristics of ARGs in the pathogenesis of KD. The identified hub genes may provide potential therapeutic targets for patients. This study provides new ideas for individualized therapy and immunotherapy.

Disruption of TIGAR-TAK1 alleviates immunopathology in a murine model of sepsis.

Macrophage-orchestrated inflammation contributes to multiple diseases including sepsis. However, the underlying mechanisms remain to be defined clearly. Here, we show that macrophage TP53-induced glycolysis and apoptosis regulator (TIGAR) is up-regulated in murine sepsis models. When myeloid Tigar is ablated, sepsis induced by either lipopolysaccharide treatment or cecal ligation puncture in male mice is attenuated via inflammation inhibition. Mechanistic characterizations indicate that TIGAR directly binds to transforming growth factor ß-activated kinase (TAK1) and promotes tumor necrosis factor receptor-associated factor 6-mediated ubiquitination and auto-phosphorylation of TAK1, in which residues 152-161 of TIGAR constitute crucial motif independent of its phosphatase activity. Interference with the binding of TIGAR to TAK1 by 5Z-7-oxozeaenol exhibits therapeutic effects in male murine model of sepsis. These findings demonstrate a non-canonical function of macrophage TIGAR in promoting inflammation, and confer a potential therapeutic target for sepsis by disruption of TIGAR-TAK1 interaction.