Identification of two novel heterozygous variants of SMC3 with Cornelia de Lange syndrome.

BACKGROUND: Cornelia de Lange syndrome (CdLS) is a multisystem genetic disorder, and cases caused by variants in the structural maintenance of chromosomes protein 3 (SMC3) gene are uncommon. Here, we report two cases of CdLS associated with novel pathogenic variants in SMC3 from two Chinese families. METHODS: Clinical presentations of two patients with CdLS were evaluated, and specimens from the patients and other family members were collected for Trio-based whole-exome sequencing. Pyrosequencing, chip-based digital PCR, minigene splicing assay, and in silico analysis were carried out to elucidate the impact of novel variants. RESULTS: Novel heterozygous variants in SMC3 were identified in each proband. One harbored a novel splicing and mosaic variant (c.2535+1G>A) in SMC3. The mutated allele G>A conversion was approximately 23.1% by digital PCR, which indicated that 46.2% of peripheral blood cells had this variant. Additionally, in vitro minigene splicing analysis validated that the c.2535+1G>A variant led to an exon skipping in messenger RNA splicing. The other carried a heterozygous variant (c.435C>A), which was predicted to be pathogenic as well as significantly altered in local electrical potential. The former showed multiple abnormalities and marked clinical severity, and the latter mainly exhibited a speech developmental disorder and slightly facial anomalies. CONCLUSION: Both patients were clinically diagnosed with Cornelia de Lange syndrome 3 (CdLS3). The newly identified SMC3 gene variants can expand the understanding of CdLS3 and provide reliable evidence for genetic counseling to the affected family.

Applications of tandem mass spectrometry (MS/MS) in antimicrobial peptides field: Current state and new applications.

Antimicrobial peptides (AMPs) constitute a group of small molecular peptides that exhibit a wide range of antimicrobial activity. These peptides are abundantly present in the innate immune system of various organisms. Given the rise of multidrug-resistant bacteria, microbiological studies have identified AMPs as potential natural antibiotics. In the context of antimicrobial resistance across various human pathogens, AMPs hold considerable promise for clinical applications. However, numerous challenges exist in the detection of AMPs, particularly by immunological and molecular biological methods, especially when studying of newly discovered AMPs in proteomics. This review outlines the current status of AMPs research and the strategies employed in their development, considering resent discoveries and methodologies. Subsequently, we focus on the advanced techniques of mass spectrometry for the quantification of AMPs in diverse samples, and analyzes their application, advantages, and limitations. Additionally, we propose suggestions for the future development of tandem mass spectrometry for the detection of AMPs.

Development of a screening system of gene sets for estimating the time of early skeletal muscle injury based on second-generation sequencing technology.

The present study is aimed to address the challenge of wound age estimation in forensic science by identifying reliable genetic markers using low-cost and high-precision second-generation sequencing technology. A total of 54 Sprague-Dawley rats were randomly assigned to a control group or injury groups, with injury groups being further divided into time points (4 h, 8 h, 12 h, 16 h, 20 h, 24 h, 28 h, and 32 h after injury, n = 6) to establish rat skeletal muscle contusion models. Gene expression data were obtained using second-generation sequencing technology, and differential gene expression analysis, weighted gene co-expression network analysis (WGCNA) and time-dependent expression trend analysis were performed. A total of six sets of biomarkers were obtained: differentially expressed genes at adjacent time points (127 genes), co-expressed genes most associated with wound age (213 genes), hub genes exhibiting time-dependent expression (264 genes), and sets of transcription factors (TF) corresponding to the above sets of genes (74, 87, and 99 genes, respectively). Then, random forest (RF), support vector machine (SVM) and multilayer perceptron (MLP), were constructed for wound age estimation from the above gene sets. The results estimated by transcription factors were all superior to the corresponding hub genes, with the transcription factor group of WGCNA performed the best, with average accuracy rates of 96% for three models' internal testing, and 91.7% for the highest external validation. This study demonstrates the advantages of the indicator screening system based on second-generation sequencing technology and transcription factor level for wound age estimation.

Clinical characteristics and risk factors for neonatal bloodstream infection due to carbapenem-resistant Enterobacteriaceae: A single-centre Chinese retrospective study.

OBJECTIVES: To analyse the clinical characteristics and risk factors for bloodstream infections (BSIs) caused by carbapenem-resistant Enterobacteriaceae (CRE) in neonates. METHODS: This single-centre, retrospective study included all patients with BSIs admitted to a neonatal intensive care unit between 1 January 2015 and 30 April 2022. The clinical and microbiological data of patients were collected; predictors of 30-day mortality in patients with CRE BSIs were also identified in this study. RESULTS: Among the 224 neonates with Enterobacteriaceae BSIs, 39.29% (88/224) of the patients developed CRE BSIs. The 30-day mortality rate reached up to 21.59% (19/88). The Quick Sequential Organ Failure Assessment score > 2 (odds ratio [OR] and 95% credibility interval [CI]: 3.852 [1.111-13.356], P < 0.05), prior to more than two kinds of antibiotics use (OR and 95% CI: 9.433 [1.562-56.973], P < 0.05), pneumonia (OR and 95% CI: 3.847 [1.133-13.061], P < 0.05), and caesarean section (OR and 95% CI: 2.678 [1.225-5.857], P < 0.05) were independent risk factors associated with CRE BSIs. Moreover, the risk factors for mortality in neonates with CRE BSIs were significantly associated with neonatal Sequential Organ Failure Assessment score > 6 (OR and 95% CI: 16.335 [1.446-184.517], P < 0.05). CONCLUSION: Prior to more than two kinds of antibiotics use, Quick Sequential Organ Failure Assessment score > 2, pneumonia and caesarean section were independent risk factors for CRE BSIs. The Neonatal Sequential Organ Failure Assessment score > 6 was a risk factor for mortality associated with CRE BSIs.

Unsupervised Domain Adaptation Fundus Image Segmentation via Multi-scale Adaptive Adversarial Learning.

Segmentation of the Optic Disc (OD) and Optic Cup (OC) is crucial for the early detection and treatment of glaucoma. Despite the strides made in deep neural networks, incorporating trained segmentation models for clinical application remains challenging due to domain shifts arising from disparities in fundus images across different healthcare institutions. To tackle this challenge, this study introduces an innovative unsupervised domain adaptation technique called Multi-scale Adaptive Adversarial Learning (MAAL), which consists of three key components. The Multi-scale Wasserstein Patch Discriminator (MWPD) module is designed to extract domain-specific features at multiple scales, enhancing domain classification performance and offering valuable guidance for the segmentation network. To further enhance model generalizability and explore domain-invariant features, we introduce the Adaptive Weighted Domain Constraint (AWDC) module. During training, this module dynamically assigns varying weights to different scales, allowing the model to adaptively focus on informative features. Furthermore, the Pixel-level Feature Enhancement (PFE) module enhances low-level features extracted at shallow network layers by incorporating refined high-level features. This integration ensures the preservation of domain-invariant information, effectively addressing domain variation and mitigating the loss of global features. Two publicly accessible fundus image databases are employed to demonstrate the effectiveness of our MAAL method in mitigating model degradation and improving segmentation performance. The achieved results outperform current state-of-the-art (SOTA) methods in both OD and OC segmentation. Codes are available at https://github.com/M4cheal/MAAL.

Comparison of two novel diagnostic criteria for bronchopulmonary dysplasia in predicting adverse outcomes of preterm infants: a retrospective cohort study.

BACKGROUND: This study aimed to compare the predictive value of two diagnostic criteria for bronchopulmonary dysplasia (BPD) in preterm infants with gestational age (GA) < 32 weeks for death or severe respiratory morbidity at corrected age of 18-24 months. METHODS: In this retrospective cohort study, clinical data from July 2019 to September 2021 were classified by 2018 National Institute of Child Health and Human Development (NICHD) and 2019 Jensen definitions of BPD. Based on the follow-up results, the enrolled population was divided into adverse outcome group and normal outcome group. Logistic regression and receiver operating characteristic (ROC) curve analyses were conducted to explore the risk factors of adverse outcomes and evaluate the predictive value of both diagnostic criteria. RESULTS: Of 451 infants, 141 (31.3%) had adverse outcomes, which increased with increasing severity of BPD. Logistic regression analysis showed only BPD was an independent risk factor for adverse outcomes in preterm infants. ROC analysis revealed that both diagnostic criteria showed similar predictive values (2018 NICHD definition AUC = 0.771 vs. 2019 Jensen definition AUC = 0.770), with specificities of 93.5% and 96.8%, respectively; however, combining them separately with GA or birth weight did not improve their predictive values. CONCLUSIONS: The two novel definitions of BPD demonstrate similar predictive values in predicting death or severe respiratory morbidity at corrected age of 18-24 months, with higher specificity observed in both.

Prevalence of Multidrug-Resistant Pathogens Causing Neonatal Early and Late Onset Sepsis, a Retrospective Study from the Tertiary Referral Children's Hospital.

Introduction: Sepsis is the most severe infectious disease with the highest mortality rate, particularly among neonates admitted to the neonatal intensive care unit (NICU). This study retrospectively analyzed the epidemiology, antibiotic resistance profiles, and prevalence of multidrug-resistant (MDR) bacteria isolated from blood or cerebrospinal fluid (CSF) cultures in order to evaluate the appropriateness of initial empirical therapy for neonatal sepsis. Methods: A retrospective study was conducted in the NICU from January 1, 2015, to December 31, 2022. Microbiological data from patients admitted to the NICU were anonymously extracted from the Laboratory of Microbiology database. Neonatal sepsis was classified into two types: early-onset sepsis (EOS), which occurs within the first 72 hours of life, and late-onset sepsis (LOS) for those begins later. Results: A total of 679 bacterial strains, 543 from blood and 136 from CSF, were detected in 631 neonates. Among these, 378 isolates (55.67%) were Gram-positive bacteria, and 301 isolates (44.33%) were Gram-negative bacteria. The most frequently isolated pathogens were Coagulase-negative staphylococci (CoNS) (36.52%), followed by Klebsiella pneumoniae (20.47%) and Escherichia coli (13.84%). In EOS, 121 strains were found, CoNS represented the majority (33.88%), followed by Klebsiella pneumoniae (23.97%) and Escherichia coli (8.26%). Early-onset septicemia exhibited 67 (55.37%) MDR bacteria. In LOS, 558 strains were isolated, CoNS represented the majority of pathogens (37.10%), followed by Klebsiella pneumoniae (19.71%) and Escherichia coli (15.05%). Late-onset septicemia showed 332 (59.50%) MDR bacteria. High rates of MDR were found in CoNS (76.21%), carbapenem-resistant Klebsiella pneumoniae (66.91%), and MRSA (33.33%). Conclusion: The study revealed an alarming prevalence of MDR strains isolated from neonatal sepsis, emphasizing the necessity of finding effective prevention and treatment measures. Colistin can be used for MDR Gram-negative bacteria, while vancomycin and teicoplanin can be considered treatment therapies for staphylococcal infections.

Combined metabolomics and tandem machine-learning models for wound age estimation: a novel analytical strategy.

Wound age estimation is one of the most challenging and indispensable issues for forensic pathologists. Although many methods based on physical findings and biochemical tests can be used to estimate wound age, an objective and reliable method for inferring the time interval after injury remains difficult. In the present study, endogenous metabolites of contused skeletal muscle were investigated to estimate the time interval after injury. Animal model of skeletal muscle injury was established using Sprague-Dawley rat, and the contused muscles were sampled at 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, and 48 h postcontusion (n = 9). Then, the samples were analysed using ultraperformance liquid chromatography coupled with high-resolution mass spectrometry. A total of 43 differential metabolites in contused muscle were determined by metabolomics method. They were applied to construct a two-level tandem prediction model for wound age estimation based on multilayer perceptron algorithm. As a result, all muscle samples were eventually divided into the following subgroups: 4, 8, 12, 16-20, 24-32, 36-40, and 44-48 h. The tandem model exhibited a robust performance and achieved a prediction accuracy of 92.6%, which was much higher than that of the single model. In summary, the multilayer perceptron-multilayer perceptron tandem machine-learning model based on metabolomics data can be used as a novel strategy for wound age estimation in future forensic casework. Key Points: The changes of metabolite profile were correlated with the time interval after injury in contused skeletal muscle.A panel of 43 endogenous metabolites screened by ultraperformance liquid chromatography coupled with high-resolution mass spectrometry could distinguish the wound ages.The multilayer perceptron (MLP) algorithm exhibited a robust performance in wound age estimation using metabolites.The combination of matabolomics and MLP-MLP tandem model could improve the accuracy of inferring the time interval after injury.

Exploration and Practice of the "One Combination, Two Highlights, Three Combinations, Four in One" Innovative Talents Training Mode in Forensic Medicine.

Talent is one of the basic and strategic supports for building a modern socialist country in all aspects. Since the 1980s, the establishment of forensic medicine major and the cultivation of innovative talents in forensic medicine have become hot topics in higher education in forensic medicine. Over the past 43 years, the forensic medicine team of Shanxi Medical University has adhered to the joint education of public security and colleges, and made collaborative innovation, forming a training mode of "One Combination, Two Highlights, Three Combinations, Four in One" for innovative talents in forensic medicine. It has carried out "5+3/X" integrated reform, and formed a relatively complete talent training innovation mode and management system in teaching, scientific research, identification, major, discipline, team, platform and cultural construction. It has made a historic contribution to China's higher forensic education, accumulated valuable experience for the construction of first-class major and first-class discipline of forensic medicine, and provided strong support for the construction of the national new forensic talent training system. The popularization of this training mode is conducive to the rapid and sustainable development of forensic science, and provides more excellent forensic talents for national building, regional social development and the discipline construction of forensic science.

Exploring postmortem succession of rat intestinal microbiome for PMI based on machine learning algorithms and potential use for humans.

The microbial communities may undergo a meaningful successional change during the progress of decay and decomposition that could aid in determining the post-mortem interval (PMI). However, there are still challenges to applying microbiome-based evidence in law enforcement practice. In this study, we attempted to investigate the principles governing microbial community succession during decomposition of rat and human corpse, and explore their potential use for PMI of human cadavers. A controlled experiment was conducted to characterize temporal changes in microbial communities associated with rat corpses as they decomposed for 30 days. Obvious differences of microbial community structures were observed among different stages of decomposition, especially between decomposition of 0-7d and 9-30d. Thus, a two-layer model for PMI prediction was developed based on the succession of bacteria by combining classification and regression models using machine learning algorithms. Our results achieved 90.48% accuracy for discriminating groups of PMI 0-7d and 9-30d, and yielded a mean absolute error of 0.580d within 7d decomposition and 3.165d within 9-30d decomposition. Furthermore, samples from human cadavers were collected to gain the common succession of microbial community between rats and humans. Based on the 44 shared genera of rats and humans, a two-layer model of PMI was rebuilt to be applied for PMI prediction of human cadavers. Accurate estimates indicated a reproducible succession of gut microbes across rats and humans. Together these results suggest that microbial succession was predictable and can be developed into a forensic tool for estimating PMI.

Cost-effectiveness analysis of pembrolizumab plus chemotherapy versus chemotherapy as the first-line treatment for advanced esophageal cancer.

OBJECTIVE: The KEYNOTE-590 trial showed that individuals with advanced esophageal cancer who received Pembrolizumab in combination with chemotherapy as a first-line regimen achieved a significant extension of survival. However, this treatment option increases the financial burden on patients and the economic benefits remain to be further evaluated. METHODS: A Markov model was used to simulate 10-year survival of patients with esophageal cancer from the perspective of United States (US) Medicare payers. We evaluated the economics of Pembrolizumab plus chemotherapy in the PD-L1 positive score (CPS ≥10) and any PD-L1 expression groups, respectively. We estimated total costs, quality-adjusted life years (QALYs), and calculated incremental cost effectiveness ratios (ICERs). Sensitivity analyses were conducted to explore the impact of uncertainties on the results. Subgroup analysis was also performed. RESULTS: The analysis results showed that the ICER for pembrolizumab plus chemotherapy versus chemotherapy alone was $293,513.17/QALYs in the any PD-L1 expression group. This exceeded the threshold of willingness to pay ($150,000/QALYs). ICERs were most sensitive to the cost of pembrolizumab and the ICERs exceeded $150,000/QALYs in all subgroups. CONCLUSIONS: Evidence suggests that first-line pembrolizumab in combination with chemotherapy is not a cost-effective option for advanced esophageal cancer in the US, regardless of PD-L1 expression status.

A novel method for determining postmortem interval based on the metabolomics of multiple organs combined with ensemble learning techniques.

Determining postmortem interval (PMI) is one of the most challenging and essential endeavors in forensic science. Developments in PMI estimation can take advantage of machine learning techniques. Currently, applying an algorithm to obtain information on multiple organs and conducting joint analysis to accurately estimate PMI are still in the early stages. This study aimed to establish a multi-organ stacking model that estimates PMI by analyzing differential compounds of four organs in rats. In a total of 140 rats, skeletal muscle, liver, lung, and kidney tissue samples were collected at each time point after death. Ultra-performance liquid chromatography coupled with high-resolution mass spectrometry was used to determine the compound profiles of the samples. The original data were preprocessed using multivariate statistical analysis to determine discriminant compounds. In addition, three interrelated and increasingly complex patterns (single organ optimal model, single organ stacking model, multi-organ stacking model) were established to estimate PMI. The accuracy and generalized area under the receiver operating characteristic curve of the multi-organ stacking model were the highest at 93% and 0.96, respectively. Only 1 of the 14 external validation samples was misclassified by the multi-organ stacking model. The results demonstrate that the application of the multi-organ combination to the stacking algorithm is a potential forensic tool for the accurate estimation of PMI.

Combined metabolomics and machine learning algorithms to explore metabolic biomarkers for diagnosis of acute myocardial ischemia.

Acute myocardial ischemia (AMI) remains the leading cause of death worldwide, and the post-mortem diagnosis of AMI represents a current challenge for both clinical and forensic pathologists. In the present study, the untargeted metabolomics based on ultra-performance liquid chromatography combined with high-resolution mass spectrometry was applied to analyze serum metabolic signatures from AMI in a rat model (n = 10 per group). A total of 28 endogenous metabolites in serum were significantly altered in AMI group relative to control and sham groups. A set of machine learning algorithms, namely gradient tree boosting (GTB), support vector machine (SVM), random forest (RF), logistic regression (LR), and multilayer perceptron (MLP) models, was used to screen the more valuable metabolites from 28 metabolites to optimize the biomarker panel. The results showed that classification accuracy and performance of MLP model were better than other algorithms when the metabolites consisting of L-threonic acid, N-acetyl-L-cysteine, CMPF, glycocholic acid, L-tyrosine, cholic acid, and glycoursodeoxycholic acid. Finally, 17 blood samples from autopsy cases were applied to validate the classification model's value in human samples. The MLP model constructed based on rat dataset achieved accuracy of 88.23%, and ROC of 0.89 for predicting AMI type II in autopsy cases of sudden cardiac death. The results demonstrated that MLP model based on 7 molecular biomarkers had a good diagnostic performance for both AMI rats and autopsy-based blood samples. Thus, the combination of metabolomics and machine learning algorithms provides a novel strategy for AMI diagnosis.

Wound age estimation based on next-generation sequencing: Fitting the optimal index system using machine learning.

Accurate estimation of the wound age is critical in investigating intentional injury cases. Establishing objective and reliable biological indicators to estimate wound age is still a significant challenge in forensic medicine. Therefore, exploring an objective, flexible, and reliable index system selection method for wound age estimation based on next-generation sequencing gene expression profiles is necessary. We randomly divided 63 Sprague-Dawley rats into a control group, seven experimental groups (n = 7 per group), and an external validation group. After rats in the experimental and external validation groups suffered contusions, we sacrificed them at 4, 8, 12, 16, 20, 24, and 48 h after contusion, respectively. We selected 54 genes with the most significant changes between adjacent time points after contusion and defined set A. The Hub genes with time-related expression patterns were set B, C, and D through next-generation sequencing and bioinformatics analysis. Four different machine learning classification algorithms, including logistic regression, support vector machine, multi-layer perceptron, and random forest were used to compare and verify the efficiency of four index systems to estimate the wound age. The best combination for wound age estimation is the Genes ascribed to set A combined with the random forest classification algorithm. The accuracy of external verification was 85.71%. Only one rat was incorrectly classified (4 h post-injury incorrectly classified as 8 h). This study demonstrated the potential advantage of the index system selection based on next-generation sequencing and bioinformatics analysis for wound age estimation.

Investigating Transcriptional Dynamics Changes and Time-Dependent Marker Gene Expression in the Early Period After Skeletal Muscle Injury in Rats.

Following skeletal muscle injury (SMI), from post-injury reaction to repair consists of a complex series of dynamic changes. However, there is a paucity of research on detailed transcriptional dynamics and time-dependent marker gene expression in the early stages after SMI. In this study, skeletal muscle tissue in rats was taken at 4 to 48 h after injury for next-generation sequencing. We examined the transcriptional kinetics characteristics during above time periods after injury. STEM and maSigPro were used to screen time-correlated genes. Integrating 188 time-correlated genes with 161 genes in each time-related gene module by WGCNA, we finally identified 18 network-node regulatory genes after SMI. Histological staining analyses confirmed the mechanisms underlying changes in the tissue damage to repair process. Our research linked a variety of dynamic biological processes with specific time periods and provided insight into the characteristics of transcriptional dynamics, as well as screened time-related biological indicators with biological significance in the early stages after SMI.

Application of Full-Spectrum Rapid Clinical Genome Sequencing Improves Diagnostic Rate and Clinical Outcomes in Critically Ill Infants in the China Neonatal Genomes Project.

OBJECTIVES: To determine the diagnostic and clinical utility of trio-rapid genome sequencing in critically ill infants. DESIGN: In this prospective study, samples from critically ill infants were analyzed using both proband-only clinical exome sequencing and trio-rapid genome sequencing (proband and biological parents). The study occurred between April 2019 and December 2019. SETTING: Thirteen member hospitals of the China Neonatal Genomes Project spanning 10 provinces were involved. PARTICIPANTS: Critically ill infants (n = 202), from birth up until 13 months of life were enrolled based on eligibility criteria (e.g., CNS anomaly, complex congenital heart disease, evidence of metabolic disease, recurrent severe infection, suspected immune deficiency, and multiple malformations). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of the 202 participants, neuromuscular (45%), respiratory (22%), and immunologic/infectious (18%) were the most commonly observed phenotypes. The diagnostic yield of trio-rapid genome sequencing was higher than that of proband-only clinical exome sequencing (36.6% [95% CI, 30.1-43.7%] vs 20.3% [95% CI, 15.1-26.6%], respectively; p = 0.0004), and the average turnaround time for trio-rapid genome sequencing (median: 7 d) was faster than that of proband-only clinical exome sequencing (median: 20 d) (p < 2.2 × 10-16). The metagenomic analysis identified pathogenic or likely pathogenic microbes in six infants with symptoms of sepsis, and these results guided the antibiotic treatment strategy. Sixteen infants (21.6%) experienced a change in clinical management following trio-rapid genome sequencing diagnosis, and 24 infants (32.4%) were referred to a new subspecialist. CONCLUSIONS: Trio-rapid genome sequencing provided higher diagnostic yield in a shorter period of time in this cohort of critically ill infants compared with proband-only clinical exome sequencing. Precise and fast molecular diagnosis can alter medical management and positively impact patient outcomes.

Insight into molecular profile changes after skeletal muscle contusion using microarray and bioinformatics analyses.

Muscle trauma frequently occurs in daily life. However, the molecular mechanisms of muscle healing, which partly depend on the extent of the damage, are not well understood. The present study aimed to investigate gene expression profiles following mild and severe muscle contusion, and to provide more information about the molecular mechanisms underlying the repair process. A total of 33 rats were divided randomly into control (n=3), mild contusion (n=15), and severe contusion (n=15) groups; the contusion groups were further divided into five subgroups (1, 3, 24, 48, and 168 h post-injury; n=3 per subgroup). A total of 2844 and 2298 differentially expressed genes (DEGs) were identified using microarray analyses in the mild and severe contusions, respectively. From the analysis of the 1620 coexpressed genes in mildly and severely contused muscle, we discovered that the gene profiles in functional modules and temporal clusters were similar between the mild and severe contusion groups; moreover, the genes showed time-dependent patterns of expression, which allowed us to identify useful markers of wound age. The functional analyses of genes in the functional modules and temporal clusters were performed, and the hub genes in each module-cluster pair were identified. Interestingly, we found that genes down-regulated at 24-48 h were largely associated with metabolic processes, especially of the oxidative phosphorylation (OXPHOS), which has been rarely reported. These results improve our understanding of the molecular mechanisms underlying muscle repair, and provide a basis for further studies of wound age estimation.

Identifying biomarkers for evaluating wound extent and age in the contused muscle of rats using microarray analysis: a pilot study.

Wound age estimation is still one of the most important and significant challenges in forensic practice. The extent of wound damage greatly affects the accuracy and reliability of wound age estimation, so it is important to find effective biomarkers to help diagnose wound degree and wound age. In the present study, the gene expression profiles of both mild and severe injuries in 33 rats were assayed at 0, 1, 3, 24, 48, and 168 hours using the Affymetrix microarray system to provide biomarkers for the evaluation of wound age and the extent of the wound. After obtaining thousands of differentially expressed genes, a principal component analysis, the least absolute shrinkage and selection operator, and a time-series analysis were used to select the most predictive prognostic genes. Finally, 15 genes were screened for evaluating the extent of wound damage, and the top 60 genes were also screened for wound age estimation in mild and severe injury. Selected indicators showed good diagnostic performance for identifying the extent of the wound and wound age in a Fisher discriminant analysis. A function analysis showed that the candidate genes were mainly related to cell proliferation and the inflammatory response, primarily IL-17 and the Hematopoietic cell lineage signalling pathway. The results revealed that these genes play an essential role in wound-healing and yield helpful and valuable potential biomarkers for further targeted studies.

Microstructure and Properties of Self-Assembly Graphene Microcapsules: Effect of the pH Value.

Graphene has attracted attention in the material field of functional microcapsules because of its excellent characteristics. The content and state of graphene in shells are critical for the properties of microcapsules, which are greatly affected by the charge adsorption equilibrium. The aim of this work was to investigate the effect of pH value on the microstructure and properties of self-assembly graphene microcapsules in regard to chemical engineering. Microcapsule samples were prepared containing liquid paraffin by a self-assembly polymerization method with graphene/organic hybrid shells. The morphology, average size and shell thickness parameters were investigated for five microcapsule samples fabricated under pH values of 3, 4, 5, 6 and 7. The existence and state of graphene in dry microcapsule samples were analyzed by using methods of scanning electron microscope (SEM), transmission electron microscope (TEM) and X-ray photoelectron spectroscopy (XPS). Fourier Transform Infrared Spectoscopy (FT-IR) and Energy Dispersive Spectrometer (EDS) were applied to analyze the graphene content in shells. These results proved that graphene had existed in shells and the pH values greatly influenced the graphene deposition on shells. It was found that the microcapsule sample fabricated under pH = 5 experienced the largest graphene deposited on shells with the help of macromolecules entanglement and electrostatic adherence. This microcapsules sample had enhanced thermal stability and larger thermal conductivity because of additional graphene in shells. Nanoindentation tests showed this sample had the capability of deforming resistance under pressure coming from the composite structure of graphene/polymer structure. Moreover, more graphene decreased the penetrability of core material out of microcapsule shells.

Fabrication and Characterization of Novel Electrothermal Self-Healing Microcapsules with Graphene/Polymer Hybrid Shells for Bitumenious Material.

Self-healing bituminous material has been a hot research topic in self-healing materials, and this smart self-healing approach is a promising a revolution in pavement material technology. Bitumen has a self-healing naturality relating to temperature, healing time, and aging degree. To date, heat induction and microencapsulation rejuvenator are two feasible approaches, which have been put into real applications. However, both methods have disadvantages limiting their practical results and efficiency. It will be an ideal method combining the advantages and avoiding the disadvantages of the above two methods at the same time. The aim of this work was to synthesize and characterize electrothermal self-healing microcapsules containing bituminous rejuvenator with graphene/organic nanohybrid structure shells. The microcapsules owned electric conductivity capability because of the advent of graphene, and realized the self-healing through the two approaches of heat induction and rejuvenation. The microcapsule shells were fabricated using a strength hexamethoxymethylmelamine (HMMM) resin and graphene by two-step hybrid polymerization. Experimental tests were carried out to character the morphology, integrity, and shell structure. It was found that the electric charge balance determined the graphene/HMMM microstructure. The graphene content in shells could not be greatly increased under an electrostatic balance in emulsion. X-ray photoelectron spectroscopy (XPS), Energy dispersive spectrometer (EDS), Transmission electron microscope (TEM) and Atomic force microscopy (AFM) results indicated that the graphene had deposited on shells. TGA/DTG tests implied that the thermal decomposition temperature of microcapsules with graphene had increased to about 350 °C. The thermal conductivity of microcapsules had been sharply increased to about 8.0 W/m²·K with 2.0 wt % graphene in shells. At the same time, electrical resistivity of microcapsules/bitumen samples had a decrease with more graphene in bitumen.