Methamphetamine (METH) addiction is a severe public health issue driven by maladaptive neuroplasticity in reward circuits. Cholecystokinin (CCK), a neuropeptide abundant in the ventral tegmental area (VTA), modulates addiction-related behaviors through its receptors (CCK1R/CCK2R), yet its role in METH-induced conditioned place preference (CPP) remains unclear. Here, we investigated the involvement of CCK2R in the VTA→basolateral amygdala (BLA)→bed nucleus of the stria terminalis (BNST) circuit during METH CPP acquisition. Using CCKflox/flox male mice, we selectively knocked out CCK in VTA dopaminergic neurons, which attenuated METH CPP and reduced neuronal excitability. Optogenetic and chemogenetic manipulations revealed that METH enhanced CCK release from VTA → BLA projections, while CCK2R knockout in BLA glutamatergic neurons abolished CPP and normalized synaptic plasticity. Further, chemogenetic inhibition of the VTA → BLA → BNST pathway disrupted METH reward encoding, and CCK2R deletion in this circuit reversed METH-induced increases in AMPA/NMDA ratios, paired-pulse facilitation, and dendritic spine density in BNST. Electrophysiological and Golgi staining analyses confirmed CCK2R's critical role in regulating neuronal excitability and structural plasticity. Our findings demonstrate that CCK2R in the VTADA→BLAGlu → BNST circuit is essential for METH CPP acquisition, highlighting its potential as a therapeutic target for addiction.

Alterations in mitochondrial fusion and fission dynamics are critical determinants of cellular fate. However, how stress-induced mitochondrial fusion and fission affect the physiological and pathological processes in cardiomyocytes remains poorly understood. Based on an established in vitro model of stress-induced cardiomyocyte injury using isoproterenol-treated H9c2 cells, this study aimed to investigate whether the dysregulation of mitochondrial dynamics-specifically, an imbalance between fusion and fission-activates the IRE1α-ASK1-JNK endoplasmic reticulum stress signaling pathway, thereby contributing to cardiomyocyte damage. Under this experimental paradigm, cell viability was evaluated using the CCK-8 assay. Concurrently, immunofluorescence staining was employed to assess reactive oxygen species accumulation, the expression of key mitochondrial fusion/fission proteins, and components of the ER stress pathway (IRE1α, ASK1, and JNK). Results demonstrated that isoproterenol treatment elevated intracellular ROS levels and induced significant changes in both mitochondrial dynamics-related proteins and the IRE1α-ASK1-JNK signaling axis. In contrast, administration of the mitochondrial fission inhibitor Mdivi-1 attenuated ROS accumulation, restored the expression of the affected proteins toward normal levels, and alleviated cardiomyocyte injury. Collectively, these findings indicate that the disruption of mitochondrial fusion/fission dynamics triggers endoplasmic reticulum stress via the IRE1α-ASK1-JNK cascade, which participates in the pathological progression of cardiomyocyte injury.

BACKGROUND:Methamphetamine (METH) abuse often results in persistent depressive-like behaviors, while current treatments show limited efficacy. Parthenolide, a natural compound with neuroprotective and anti-inflammatory properties, has shown benefits in several CNS disorders, but its role in METH-induced depression remains unknown. PURPOSE:This study aimed to evaluate whether parthenolide alleviates METH-induced depressive-like behaviors and to identify key brain regions and molecular targets involved. METHODS:Mice were administered METH using a 15-day escalating regimen and treated with parthenolide. Behavioral tests, histopathology, Nissl staining, and c-Fos mapping were conducted to assess neural alterations. Metabolomics and network pharmacology were used to predict targets, followed by molecular docking, dynamics simulations, cellular thermal shift assay, and pharmacological modulation for validation. RESULTS:Parthenolide significantly improved METH-induced depressive-like behaviors. The medial prefrontal cortex (mPFC) emerged as the most affected region, where parthenolide reduced neuronal damage. Integrative analyses identified ADORA2A as a key target, further supported by pharmacological inhibition and activation experiments. CONCLUSION:Parthenolide mitigates METH-induced depressive-like behaviors by modulating ADORA2A signaling in the mPFC, providing mechanistic insight into its antidepressant-like effects and supporting its potential as a therapeutic candidate for substance-induced mood disorders.

The objective of this study was to understand the effect of long-term aconitine (AC) oral administration on the digestive tract and serum metabolism. Subjects consumed either 0.9% NaCl (n = 8) or AC (n = 17) gavage designed to represent human chronic AC administrations for 13 days. Organ pathology was determined using hematoxylin-eosin staining and immunohistochemistry. Fecal and proximal intestinal content samples were collected to perform shotgun metagenomic sequencing. Serum samples were collected, and untargeted metabolomics was performed. In this study, AC administration induced proximal intestine, liver, and kidney injury. Microbiome composition remained stable after AC exposure, while several microbes presented dynamic alteration. Moreover, AC affected the abundance of the fatty acid biosynthesis rate-limiting gene accA at day 7. AC induces 30 serum metabolites to significantly change at day 14, including several short-chain acylcarnitines. WGCNA revealed 2 sub-modules associated with the level of several short-chain acylcarnitines. In summary, AC affects the digestive tract and serum metabolism after chronic administration. AC may affect the enrichment of microbial-derived accA gene. The abundance of serum acylcarnitines detected in the AC group may associate with its anti-heart failure effects.

BACKGROUND:Diabetic kidney disease (DKD) is a major microvascular complication of diabetes, with microcirculatory dysfunction and immune injury as its core pathological features. Extracellular vesicles (EVs) act as key mediators of intercellular communication, but it remains unclear whether EV-encapsulated microRNAs (miRNAs) are involved in the crosstalk between endothelial cells (ECs) and monocytes in DKD. METHODS:EVs were isolated from monocytes and ECs under normal-glucose and high-glucose conditions. After characterization of EVs, differentially expressed miRNAs were screened. RNase/Triton functional assays, dual-luciferase reporter assays, gain- and loss-of-function experiments, and rescue experiments were further performed to validate the regulatory mechanisms associated with EV-miRNAs in vivo and in vitro. RESULTS:Under high-glucose conditions, monocyte-derived EVs aggravated EC injury by upregulating miR-191-3p, which targets CYLD and thereby activates the NF-κB pathway. Meanwhile, EC-derived EVs enhanced monocyte inflammation and adhesion by downregulating miR-615-3p, upregulating IFNGR2, and activating the STAT3 pathway. Targeted intervention of these key molecules effectively ameliorated cellular injury in vitro and partially relieved renal damage in vivo. CONCLUSIONS:This study confirms that EV-miRNA-mediated EC-monocyte crosstalk is involved in the pathogenesis of DKD, and establishes two core regulatory axes: miR-191-3p/CYLD/NF-κB and miR-615-3p/IFNGR2/STAT3. It also reveals an intrinsic link between microcirculatory dysfunction and immune injury in DKD, while the potential of these miRNAs as diagnostic biomarkers and therapeutic targets still remains to be further validated.

Over the last two decades, advancements in sequencing technology and data science have significantly deepened the study of transcriptomics, especially non-coding transcriptomics, leading to substantial developments in forensic applications. During the 2000s, forensic transcriptomics analysis technology evolved from targeted messenger ribonucleic acid (mRNA) typing to massive parallel sequencing and deoxyribonucleic acid (DNA) microarray. This progression facilitated the source tracing and degradation dynamics of biomaterials from crime scenes, as well as transcriptomic changes associated with cadavers, injuries and toxicology, thereby providing additional clues for solving forensic cases. In the next decade, the development of high-throughput sequencing technology further expanded the research frontiers of forensic transcriptomics from mRNA to non-coding RNAs (ncRNAs). These molecules have been demonstrated to exhibit unique functions in expression regulation and epigenetic modifications, showing great potential in forensic practices such as forensic polymorphism studies, tissue and body fluid tracing, forensic RNA molecular clock, death & wound analyses, as well as forensic toxicology. Modern transcriptomics combined with deep learning and multimodal analysis through multidisciplinary integration can potentially characterize the dynamic spatiotemporal panoramic features of forensic biological samples. However, these technologies will face bottlenecks such as standardization, sample collection and processing, ethics, and evidence interpretation in forensic practice. Breaking through these obstacles will be the core task of forensic transcriptomics in the next ten years. This integrative review, building on bibliometric analysis, details the new paradigms and latest advances in forensic transcriptomics across multiple forensic fields, demonstrating its wide-ranging prospects in practical applications.

Accurately estimating biological sex is a fundamental step in forensic odontology, anthropology and archaeology. Tooth volume has been recognized as a valuable feature for sex estimation. However, dental morphology exhibits population-specific variations, which remains underexplored in the northern Han Chinese population. Furthermore, manual segmentation of cone-beam computed tomography (CBCT) images is prone to subjectivity and error, and conventional modeling approaches may offer limited predictive accuracy. This study aimed to address these limitations by integrating automated artificial intelligence (AI) segmentation with multiple machine learning algorithms to enhance objectivity and predictive performance in sex prediction. A cohort of 398 CBCT images was analyzed using a fully automated deep learning system for tooth segmentation, and a final cohort of 357 samples (185 females, 172 males) was retained. Volumetric data for teeth were automatically calculated, with missing values imputed via multiple imputation by chained equations (MICE). Four machine learning algorithms were applied to build binary classification models for sex estimation. Hyperparameter optimization was achieved through nested cross-validation, and model performance was evaluated on a test set using a comprehensive set of metrics. All 16 measured teeth showed significantly differences in volume between sexes, with males consistently showing larger mean volumes across all tooth types compared to females. Nested cross-validation showed that all models exhibited promising performance for sex estimation with mean ACC above 0.77 and mean AUC values at or above 0.85. On the test set, the models achieved accuracies ranging from 0.803 to 0.859 and AUCs from 0.893 to 0.903. SHAP interpretability analysis highlighted canines as having the greatest impact on predictions. This study is the first to demonstrate the significant sexual dimorphism of tooth volumes in a north Chinese population. We utilized an AI-based automated segmentation accompanied with machine learning modeling pipeline to accurately estimate sex using tooth volume features, offering a valuable approach for forensic and anthropological applications.

Multi-allelic Single Nucleotide Polymorphisms (SNPs) are a class of genetic markers in forensic genetics, potentially offering higher discriminatory power than their bi-allelic counterparts. However, well-characterized panels specifically designed and evaluated for distant kinship identification and complex mixture analysis remain limited. Here, we developed a marker set of tri-allelic SNPs by applying a rigorous, multi-stage filtering process to the 1000 Genomes Project Phase IV high-coverage data. The process included stringent quality and polymorphism criteria followed by Hardy-Weinberg equilibrium (HWE) testing and linkage disequilibrium (LD) pruning, yielding a final set of 2495 markers. Extensive in silico simulations under idealized conditions showed high discriminatory power of the set for distinguishing up to 3rd-degree relatives from unrelated individuals, with potential for investigating 4th-degree relationships. For mixture analysis, the set estimated the number of contributors (NOC) in 2-to 5-person mixtures with high accuracy under the simulation framework. Furthermore, we established a quantitative framework to estimate the Minimum Necessary Number (MNN) of markers required for these analyses, providing guidance for future panel design. Overall, this study presented a rigorously vetted set of 2495 tri-allelic SNPs and established performance benchmarks within an in silico framework, highlighting the potential utility of multi-allelic markers for forensic applications.

The hypothalamus integrates autonomic, endocrine, and behavioral responses to stress, and stress-induced hypothalamic neuronal injury is implicated in various diseases. However, the underlying molecular mechanisms remain unclear. Mitochondria, as stress-sensitive organelles, play a critical role in cellular injury through structural and functional alterations. Here, we investigate how stress triggers mitochondrial quality control (MQC) dysfunction via glucocorticoid receptor (NR3C1) signaling, contributing to hypothalamic neuronal injury. Using acute and chronic stress rat models, we demonstrate that stress induces hypothalamic neuronal damage. Transmission electron microscopy and WB analysis reveal that stress promotes excessive mitochondrial fission while suppressing fusion, disrupting mitochondrial dynamics. At the cellular level, ChIP-Seq and siRNA experiments confirm that glucocorticoids (GCs) downregulate PRKACG (protein kinase A catalytic subunit gamma) expression via NR3C1-mediated transcriptional repression, reducing DRP1 (dynamin-related protein 1) phosphorylation at Ser637 and leading to aberrant mitochondrial fission. Furthermore, acute and chronic stress differentially activate mitophagy pathways, resulting in mitochondrial depletion. Intriguingly, neuronal death shifts from apoptosis to necroptosis under prolonged stress. In conclusion, our findings establish that NR3C1/PRKACG-mediated MQC dysfunction is a key mechanism in stress-induced hypothalamic neuronal injury. This study not only elucidates how GCs disrupt MQC but also advances our understanding of mitochondrial dysregulation in stress-related neuronal damage, providing a foundation for future mechanistic and therapeutic investigations.

Fentanyl abuse has been associated with neurological and psychological harm. However, the metabolic changes within the peripheral circulation and central nervous system involved in fentanyl addiction have not been well explored. In the present study, metabolic changes in plasma, caudate putamen (CPu), hippocampus (Hip), and prefrontal cortex (PFC) were investigated in mouse models of drug addiction based on fentanyl-induced conditioned place preference (CPP). Metabolic profiles were measured using untargeted UHPLC-Q-Exactive HFX mass spectrometry. A total of 131, 196, 104, and 52 altered metabolites were identified in plasma, CPu, Hip, and PFC, respectively. The identified metabolites mainly included lipid mediators, carbohydrate metabolites, fatty acids, amino acids (AAs) and their derivatives, and nucleotide metabolites. The disturbed metabolic pathways were primarily involved in lipid metabolism, carbohydrate metabolism, AA metabolism, nucleotide metabolism, and the metabolism of cofactors and vitamins. These findings indicate disturbances in cell membrane metabolism, energy metabolism, AA metabolism, and neurotransmitter systems caused by fentanyl addiction. Our study provides a valuable resource for future investigations aimed at defining the role of metabolites in fentanyl addiction, which may help develop new pharmacotherapies.

Transcriptome sequencing data offer a valuable resource for inferring genetic variants, yet their application in forensic individual identification and kinship analysis remains insufficiently explored. This study analyzed an open-access transcriptome sequencing dataset comprising 731 individuals from five continental populations. We obtained a total of 5,863,540 transcript SNPs (tSNPs) across these individuals. By comparing these with SNP genotypes obtained from whole-genome sequencing data, we observed that transcriptome-derived genotypes exhibited high reliability, achieving up to 99

IntroductionRestraint stress is known to induce damage to vital organs. Previous work indicates that acute restraint stress (ARS) causes significant hepatic injury in mice. Interestingly, while the liver gradually recovered following removal of the stressor, stress-induced behavioral abnormalities persisted for up to 7 days after stress cessation. Soluble epoxide hydrolase (sEH) plays a key regulatory role in neuroinflammation and mood disorders. However, whether and how hepatic sEH upregulation contributes to ARS-induced neurological damage remains unknown.MethodsIn a mouse model of ARS, behavioral tests were used to assess neurological impairment. Hepatic function was evaluated by blood flow measurement, metabolomics, and serum ALT/AST levels. Hepatic sEH expression was examined by IHC and western blot, and EETs and IL‑6 were quantified with biochemical kits. Microglial activation and neuronal injury in the hypothalamus were assessed by IHC, and IL‑6 expression in hypothalamic neurons was visualized by immunofluorescence double staining.ResultsRestraint stress-induced behavioral abnormalities in mice. These abnormalities persisted for at least 7 days and were accompanied by reduced hepatic blood flow, liver injury, and perturbation of arachidonic acid metabolism. Stress markedly upregulated hepatic sEH expression, decreased epoxyeicosatrienoic acids (EETs), and increased interleukin-6 (IL-6). In the hypothalamus, sEH expression was elevated, microglia exhibited M1-type activation (CD86+), and neuronal loss occurred, while IL-6 levels within hypothalamic neurons was increased. Treatment with the sEH inhibitor TPPU significantly ameliorated stress-induced behavioral abnormalities, liver injury, hypothalamic neuroinflammation, and the abnormal intraneuronal increase of IL-6.DiscussionThese findings reveal that ARS elicits behavioral abnormalities via the liver–brain axis mediated by hepatic sEH upregulation, which reduces EETs and promotes IL-6 release, leading to hypothalamic microglial activation and subsequent neuronal injury. These results identify hepatic sEH as a potential therapeutic target and highlight the liver–brain axis as a critical mediator in ARS-induced behavioral abnormalities and neurological damage.

To address problems such as fragmented research training, a disconnect between theory and practice, weak value guidance, and a limited international perspective in the traditional training of high-level forensic medicine professionals, the forensic medicine program at Hebei Medical University constructed a new graduate training model titled “Leading Goose Navigation and Four-Dimensional Synergy”. This model leverages the “Leading Goose” effect of high-level academic leaders, such as academicians, to establish strategic focus and a spiritual highland. It reshapes the educational ecosystem by taking research-based education to integrate the innovation chain, practical training to forge professional competence, ideological education to shape professional conviction, and international education to broaden international horizons as specific paths for “Four-Dimensional Synergy”. Using case study and comparative analysis methods, postgraduate cohorts from 2020 to 2023 （N=109） were selected as the post-reform samples and retrospectively compared with data from cohorts between 2015 and 2019. Empirical results showed a 98.6% increase in per capita SCI paper publications and a 16.58% increase in the professional match rate of employment, along with significantly enhanced professional commitment and international perspective. Furthermore, this model demonstrates potential for extension to undergraduate education and evolution through “Forensic Medicine + AI” interdisciplinary integration, providing a valuable reference for cultivating top innovative talents in applied disciplines in the new era.

RNA-based markers hold considerable potential for forensic transcriptomics applications, yet their practical use is constrained by heterogeneous RNA degradation under environmental stress conditions. The thermal stability profiles of different RNA classes in degraded biological samples remain incompletely characterized. Here, we established an in vitro thermal degradation model using commercially purified total RNA extracts derived from human brain, liver, and kidney tissues, and performed whole-transcriptome sequencing to systematically compare the degradation patterns of mRNAs, lncRNAs, and miRNAs under extreme thermal stress. RNA Integrity Number (RIN) values decreased progressively with increasing treatment duration. Notably, high-quality sequencing data were still obtainable from RNA extracts with RIN values as low as approximately 3.5. Degradation kinetics exhibited significant differences across both RNA sources and RNA classes. In brain-derived RNA extracts, lower decay rate constants (indicating greater stability) were associated with shorter transcript lengths and shorter 5' untranslated regions (UTRs) for mRNAs, while stable miRNAs showed higher GC content and lower minimum free energy. In liver-derived RNA extracts, lower decay rate constants were correlated with longer transcript lengths and longer 3' UTRs, whereas no consistent structural associations with stability were identified in kidney-derived RNA extracts. These observations suggest that thermal RNA degradation may differ according to RNA source and RNA class, a possibility that warrants further validation in larger studies using degraded forensic specimens.

Stress is a known contributor to fatal injury; however, the absence of reliable biomarkers complicates determining the cause of death in forensic cases where stress is implicated. To address this, we conducted a retrospective analysis of post-stress fatalities, focusing on key endoplasmic reticulum (ER) stress proteins in the cardiac and brain tissues of individuals who died from sudden coronary heart disease (CHD) or apparent stress-induced causes. Significant differences were identified between these groups in age distribution, time of death, heart weight, left ventricular wall thickness, and interventricular septal thickness. Although both groups showed similar ischemic, hypoxic, and electrophysiological alterations in the myocardium, the expression profiles of key ER stress markers in cardiac and brain tissues were markedly distinct. Therefore, this study aims to compare the expression differences of ER stress markers in cardiac and brain tissues, thereby providing clues for effectively distinguishing between sudden coronary heart disease death and stress-induced death.

In forensic cases, the accurate prediction of time since deposition (TsD) for body fluids plays a critical role in evaluating the relevance of biological evidence to criminal cases and reconstructing the timelines of criminal events. While transcriptomics offers avenues for TsD analysis, the environmental sensitivity of mRNA limits its practical utility. In contrast, miRNAs demonstrate superior potential as biomarkers due to their short sequences, high stability, and environmental resistance; however, their forensic application for TsD estimation remains underexplored. This study applied small RNA sequencing to analyze miRNA expression in semen samples from 10 donors across seven TsD intervals (0-48 h). Time-dependent miRNA expression modules were identified through Mfuzz clustering and weighted gene co-expression network analysis. We implemented a multi-stage feature selection pipeline, commencing with least absolute shrinkage and selection operator regression and random forest (RF) that selected 261 candidate miRNAs for model development, followed by recursive feature elimination with ElasticNet to refine the set to 12 miRNAs, and concluding with XGBoost-based multicollinearity reduction and exhaustive optimization to yield a minimal set of 7 miRNAs. The selected miRNA candidates were subsequently validated using reverse transcription-quantitative polymerase chain reaction on an independent sample set. Machine learning models constructed with the initial 261 miRNAs demonstrated that RF achieved optimal performance in the binary classification of early (0-12 h) versus late (24-48 h) TsD, with an accuracy of 0.76, F1-score of 0.75, and area under the curve of 0.82. In regression analysis, an ensemble model integrating partial least squares, ElasticNet, support vector machine, and Ridge attained a test mean absolute error of 6.76 h and an R 2 of 0.72. This research establishes a novel miRNA-based prediction framework for TsD estimation of semen, integrating dynamic expression patterns with machine learning for the advancement of forensic body fluid analysis.

Genotyping of DNA polymorphic markers, particularly short tandem repeats (STRs), remains the primary approach for individual identification and paternity testing in forensic genetics. Recently, RNA polymorphisms have attracted increasing interest, and mRNA single nucleotide polymorphism (mRNA-SNP) markers have been proposed for body fluid identification and contributor assignment. However, it is unclear whether common forensic DNA-STR/SNP markers can be genotyped at RNA level. In this study, RNA derived from peripheral blood and semen was genotyped using a massively parallel sequencing (MPS)-based panel comprising 204 STRs, 142 SNPs, and mtDNA hypervariable regions (HVRs), with genomic DNA (gDNA) analyzed in parallel for validation. The results revealed that several CODIS loci (CSF1PO, D18S51, D19S433, D22S1045, and D3S1358) were genotyped at a frequency of more than 50% in blood and/or semen RNA samples, and their genotypes were consistent with those obtained from the gDNA. Sex chromosomal STR loci, including DXS6800, DYS438, DYS453, DYS460, DYS504, DYS557, and DYS645 were genotyped in all blood RNA samples. The DYS455, DYS460, and DYS481 were genotyped in over 90% of the semen RNA samples and showed concordance with gDNA profiles. Interestingly, genotyping of certain markers demonstrated sample-type dependency; for example, D10S2325 and DYS557 loci were genotyped in all blood but not in any of the semen RNA samples. Meanwhile, some autosomal SNPs and a fragment of mtDNA (nt 16223-16365) were genotyped in both the blood and semen. In summary, our findings demonstrate that RNA can yield partial STR/SNP genotypes consistent with gDNA, with evidence of body fluid-specific expression. This study sheds new light on the transcription products of forensic DNA-STR/SNP markers, emphasizing the potential forensic value of RNA-STR/SNP markers for compromised samples and signifying the beginning of a new era in forensic RNA genotyping.

Background: Accurate estimation of the postmortem interval (PMI), the time elapsed between death and body discovery, is a critical challenge in forensic science due to the complex interplay of factors affecting decomposition. Traditional methods based on macroscopic changes often lack precision, especially in later postmortem stages. Methods: This study aimed to develop a novel PMI estimation framework by integrating the dynamics of endogenous small non-coding RNAs (sncRNAs) and exogenous bacterial-derived small RNAs (sRNAs) using sRNA transcriptomics and machine learning. Results: Cardiac RNA degradation strongly correlated with PMI, with a random forest (RF) model achieving high accuracy (coefficient of determination (R2) = 0.939, mean absolute error (MAE) = 2.987 h). Employing PANDORA-seq, we profiled temporal changes in sncRNAs (miRNAs, tsRNAs and piRNAs) in postmortem cardiac tissue within 30 h in a mouse model, while simultaneously assessing RNA integrity (RIN) across eight organs. PANDORA-seq revealed stable sncRNA landscapes with specific dynamic shifts, leading to the identification of seven novel biomarkers (four tsRNAs, three piRNAs) for PMI prediction (R2 = 0.760, MAE = 158.990 min). Bacterial-derived sRNAs, predominantly from Staphylococcus aureus, were upregulated at 30 h postmortem, suggesting complementary biomarker potential. Bioinformatics analysis indicated that host miRNAs may target bacterial mRNAs, hinting at cross-kingdom interactions. Conclusion: These findings highlight the potential of integrated endogenous and exogenous sRNA analysis in PMI estimation, providing a high-precision, rapid diagnostic tool and revealing complex postmortem molecular processes.