The Impact of Cardiovascular Disease Gene Polymorphism and Interaction with Homocysteine on Deep Vein Thrombosis.

Deep vein thrombosis (DVT) affects vascular health and can even threaten life; however, its pathogenesis remains unclear. Cardiovascular disease (CVD) and DVT share common risk factors, such as dyslipidemia, aging, etc. We aimed to investigate the loci of published CVD susceptibility genes and their association with environmental factors that might be related to DVT.

Novel Strategy for Human Deep Vein Thrombosis Diagnosis Based on Metabolomics and Stacking Machine Learning.

Deep vein thrombosis (DVT) is a serious health issue that often leads to considerable morbidity and mortality. Diagnosis of DVT in a clinical setting, however, presents considerable challenges. The fusion of metabolomics techniques and machine learning methods has led to high diagnostic and prognostic accuracy for various pathological conditions.

Pathological Characteristics and Classification of Unstable Coronary Atherosclerotic Plaques.

Important forensic diagnostic indicators of sudden death in coronary atherosclerotic heart disease, such as acute or chronic myocardial ischemic changes, sometimes make it difficult to locate the ischemic site due to the short death process, the lack of tissue reaction time. In some cases, the deceased died of sudden death on the first-episode, resulting in difficulty for medical examiners to make an accurate diagnosis. However, clinical studies on coronary instability plaque revealed the key role of coronary spasm and thrombosis caused by their lesions in sudden coronary death process.

Multi-omics integration strategy in the post-mortem interval of forensic science.

Estimates of post-mortem interval (PMI), which often serve as pivotal evidence in forensic contexts, are fundamentally based on assessments of variability among diverse molecular markers (including proteins and metabolites), their correlations, and their temporal changes in post-mortem organisms. Nevertheless, the present approach to estimating the PMI is not comprehensive and exhibits poor performance. We developed an innovative approach that integrates multi-omics and artificial intelligence, using multimolecular, multimarker, and multidimensional information to accurately describe the intricate biological processes that occur after death, ultimately enabling inference of the PMI.

Novel ratio-expressions of genes enables estimation of wound age in contused skeletal muscle.

Given that combination with multiple biomarkers may well raise the predictive value of wound age, it appears critically essential to identify new features under the limited cost. For this purpose, the present study explored whether the gene expression ratios provide unique time information as an additional indicator for wound age estimation not requiring the detection of new biomarkers and allowing full use of the available data. The expression levels of four wound-healing genes (Arid5a, Ier3, Stom, and Lcp1) were detected by real-time polymerase chain reaction, and a total of six expression ratios were calculated among these four genes.

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.

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.

Postmortem Interval Estimation Using Protein Chip Technology Combined with Multivariate Analysis Methods.

Objectives: To estimate postmortem interval (PMI) by analyzing the protein changes in skeletal muscle tissues with the protein chip technology combined with multivariate analysis methods.

Methods: Rats were sacrificed for cervical dislocation and placed at 16 ℃. Water-soluble proteins in skeletal muscles were extracted at 10 time points (0 d, 1 d, 2 d, 3 d, 4 d, 5 d, 6 d, 7 d, 8 d and 9 d) after death.

Forensic identification of sudden cardiac death: a new approach combining metabolomics and machine learning.

The determination of sudden cardiac death (SCD) is one of the difficult tasks in the forensic practice, especially in the absence of specific morphological changes in the autopsies and histological investigations. In this study, we combined the metabolic characteristics from corpse specimens of cardiac blood and cardiac muscle to predict SCD. Firstly, ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS)-based untargeted metabolomics was applied to obtain the metabolomic profiles of the specimens, and 18 and 16 differential metabolites were identified in the cardiac blood and cardiac muscle from the corpses of those who died of SCD, respectively.

Combining with lab-on-chip technology and multi-organ fusion strategy to estimate post-mortem interval of rat.

Background: The estimation of post-mortem interval (PMI) is one of the most important problems in forensic pathology all the time. Although many classical methods can be used to estimate time since death, accurate and rapid estimation of PMI is still a difficult task in forensic practice, so the estimation of PMI requires a faster, more accurate, and more convenient method.

Materials And Methods: In this study, an experimental method, lab-on-chip, is used to analyze the characterizations of polypeptide fragments of the lung, liver, kidney, and skeletal muscle of rats at defined time points after death (0, 1, 2, 3, 5, 7, 9, 12, 15, 18, 21, 24, 27, and 30 days).

Comparison of Medical Dispute Resolution Mechanisms in China and Abroad.

Medical disputes are one of the common problems concerned by the whole world. All countries and regions have established their own medical dispute resolution mechanisms, in accordance with their own national conditions. Medical dispute identification opinions, as one of the important bases for identifying the responsibilities of both doctors and patients, play a pivotal role in the process of dispute settlement.

Comparison among Four Deep Learning Image Classification Algorithms in AI-based Diatom Test.

Objectives: To select four algorithms with relatively balanced complexity and accuracy among deep learning image classification algorithms for automatic diatom recognition, and to explore the most suitable classification algorithm for diatom recognition to provide data reference for automatic diatom testing research in forensic medicine.

Methods: The "diatom" and "background" small sample size data set (20 000 images) of digestive fluid smear of corpse lung tissue in water were built to train, validate and test four convolutional neural network (CNN) models, including VGG16, ResNet50, InceptionV3 and Inception-ResNet-V2. The receiver operating characteristic curve (ROC) of subjects and confusion matrixes were drawn, recall rate, precision rate, specificity, accuracy rate and 1 score were calculated, and the performance of each model was systematically evaluated.

Research Progress of Automatic Diatom Test by Artificial Intelligence.

Diatom test is the main laboratory test method in the diagnosis of drowning in forensic medicine. It plays an important role in differentiating the antemortem drowning from the postmortem drowning and inferring drowning site. Artificial intelligence (AI) automatic diatom test is a technological innovation in forensic drowning diagnosis which is based on morphological characteristics of diatom, the application of AI algorithm to automatic identification and classification of diatom in tissues and organs.

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.

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.

Estimating Postmortem Interval Using Intestinal Microbiota Diversity Based on 16S rRNA High-throughput Sequencing Technology.

Objectives: To explore the correlation between intestinal microbiota and postmortem interval(PMI) in rats by using 16S rRNA high-throughput sequencing technology.

Methods: Rats were killed by anesthesia and placed at 16 ℃, and DNA was extracted in caecum at 14 time points of 0, 1, 2, 3, 5, 7, 9, 12, 15, 18, 21, 24, 27 and 30 d after death. The 16S rRNA high-throughput sequencing technology was used to detect intestinal microbiota in rat cecal contents, and the results were used to analyze the rat intestinal microbiota diversity and differences.

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.

Estimating the time of skeletal muscle contusion based on the spatial distribution of neutrophils: a practical approach to forensic problems.

The study aimed to explore the neutrophil's spatial distributions used to estimate the histological age of contused skeletal muscle, and assessed the accuracy of various indicators, such as the proportion of neutrophils, "neutrophil mean distance," and distribution of neutrophils in areas of "contiguous contour lines." Fifty-five Sprague-Dawley rats were divided randomly into a control group and contusion groups at 1, 1.5, 2, 3, 4, and 6 h, as well as 1, 3, 5, and 15 days, post-injury (n = 5 per group).

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.

Neuroblast senescence in the aged brain augments natural killer cell cytotoxicity leading to impaired neurogenesis and cognition.

Normal aging is accompanied by escalating systemic inflammation. Yet the potential impact of immune homeostasis on neurogenesis and cognitive decline during brain aging have not been previously addressed. Here we report that natural killer (NK) cells of the innate immune system reside in the dentate gyrus neurogenic niche of aged brains in humans and mice.

Novel insights into wound age estimation: combined with "up, no change, or down" system and cosine similarity in python environment.

Wound age estimation is a complex, multifactorial issue. It is considered to have great practical significance that combining multi-biomarkers and multi-methods for injury time estimation. We optimized our earlier "up, no change, or down" model by adding data on the expression levels of mRNAs encoding ABHD2, MAD2L2, and ARID5A, and we converted the relative quantitative expression levels of seven genes into a vector rather than a color model.

Temporal expression of wound healing-related genes inform wound age estimation in rats after a skeletal muscle contusion: a multivariate statistical model analysis.

Although many time-dependent parameters involved in wound healing have been exhaustively investigated, establishing an objective and reliable means for estimating wound age remains a challenge. In this study, 78 Sprague-Dawley rats were divided randomly into a control group and contusion groups at 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, and 48 h post-injury (n = 6 per group). The expression of 35 wound healing-related genes was explored in contused skeletal muscle by real-time polymerase chain reaction.

Pharmacokinetics of meperidine (pethidine) in rabbit oral fluid: correlation with plasma concentrations after controlled administration.

Oral fluid assays for quantifying drugs are useful in forensic toxicology and drug monitoring. Compared with blood and urine specimens, oral fluid collection is simple, non-invasive, and more difficult to adulterate. Therefore, we investigated whether meperidine and its metabolites could be detected in oral fluid and whether there was a predictable relationship between oral fluid and plasma concentrations.

Comparison of the serum metabolic signatures based on H NMR between patients and a rat model of deep vein thrombosis.

Deep vein thrombosis (DVT) and pulmonary embolism (PE) have high morbidity, reduce quality of life, and can cause death. Biomarkers or genetic risk factors have not been identified in patients with DVT. In present study, serum of 61 patients suffering from DVT and a rat DVT model (n = 10) were assayed by a proton nuclear magnetic resonance (H NMR) metabolomics technique combing with multivariate statistical analysis to identify the metabolites.