Forensic entomotoxicology is a subdiscipline that utilizes necrophagous insects as bioindicators for detecting drugs and toxicants in decomposed remains, particularly in cases where conventional biological matrices are no longer available. Toxic substances can profoundly alter insect development, physiology, and community succession, potentially impacting the accuracy of postmortem interval (PMI) estimation. This review systematically summarizes the effects of various xenobiotics, including pesticides, illicit drugs, sedatives, heavy metals, and antibiotics on larval growth, physiological traits, and gut microbial composition in forensically relevant flies. However, most studies to date have relied primarily on phenotypic observations, with limited insight into underlying molecular mechanisms. Significant interspecies and dose-dependent variability also exists in the absorption, metabolism, and physiological responses to xenobiotics. We highlight recent advances in multi-omics technologies that facilitate the identification of molecular biomarkers associated with xenobiotic exposure, particularly within the insect detoxification system. Key components such as cytochrome P450 monooxygenases (P450s), glutathione S-transferases (GSTs), and ATP-binding cassette (ABC) transporters play essential roles in xenobiotic metabolism and insecticide resistance. Additionally, the insect fat body serves as a central hub for detoxification, hormonal regulation, and energy metabolism. It integrates signals related to xenobiotic exposure and modulates larval development, making it a promising model for future mechanistic studies in insect toxicology. Altogether, this review offers a comprehensive and reliable framework for understanding the complex interactions between toxic substance exposure, insect ecology, and decomposition in forensic investigations.

Estimating the postmortem interval (PMI) is critical in the field of forensic science, and necrophagous insects play a significant role in this process. Chrysomya megacephala (Fabricius) (Diptera: Calliphoridae) is a common necrophagous insect species, making its rapid and accurate identification essential. However, commonly used molecular biology methods, such as DNA barcode, still have some limitations in identifying necrophagous insects as they are often complex, time-consuming, and reliant on laboratory instruments. Therefore, in this study, we have developed an innovative detection system for the rapid and accurate identification of C. megacephala based on the Cytochrome b gene using recombinase polymerase amplification (RPA) and lateral flow dipstick (LFD) in combination. The developed RPA-LFD detection system achieved complete amplification in just 15 min at 37 °C with good sensitivity and specificity. Only 7.8 × 10⁻⁴ ng or more of target DNA fragments were required, and a positive detection rate of 100% was achieved in 18 C. megacephala samples from actual cases. In addition, the ability of the developed RPA-LFD detection system in combination with rapid DNA extraction methods to enable on-site detection was preliminarily explored. The results suggested that when the RPA-LFD detection system was combined with the grinding ddH₂O extraction method (a rapid DNA extraction method), the process from species acquisition to visualization of detection results could be completed in less than 20 min. In conclusion, this innovative RPA-LFD detection system outperforms commonly used molecular biology methods for C. megacephala identification in terms of speed, sensitivity and convenience, making it suitable for direct application at crime scenes, promising to provide important assistance in estimating PMI and expanding the impact of forensic entomological evidence.

The pupal stage in necrophagous flies represents the longest and least morphologically distinct phase of development, posing a persistent challenge for accurately estimating postmortem intervals (PMI) in forensic investigations. Here, we present a novel molecular approach to pupal age estimation in Sarcophaga peregrina, a forensically important species, by profiling microRNA (miRNA) expression dynamics. High-throughput sequencing across early, mid, and late pupal stages identified 191 known miRNAs, of which nine exhibited distinct monotonic temporal trends. Six miRNAs (miR-210-3p, miR-285, miR-927-5p, miR-956-3p, miR-92b, and miR-275-5p) were validated by qRT-PCR and demonstrated consistent time-dependent expression patterns. Polynomial regression models revealed a strong correlation between miRNA abundance and developmental age (R2 = 0.88–0.99). Functional enrichment analyses of predicted miRNA targets highlighted their roles in key regulatory pathways, including ecdysteroid signaling, hypoxia response, autophagy, and energy metabolism. This study establishes, for the first time, a robust miRNA-based framework for estimating pupal age in forensic entomology, underscoring the potential of miRNAs as temporally precise biomarkers for PMI estimation.

Hyperspectral imaging (HSI) has potential for forensic entomology, but its practical use is limited by manual region-of-interest (ROI) delineation before spectral extraction. This step is time-consuming, operator-dependent, and difficult to standardize across insect species and developmental stages. Here, we developed an automated individual-level ROI-to-spectrum workflow for HSI analysis of forensically important insects. The dataset included 63 hyperspectral images and 1868 manually annotated insect individuals, covering larvae, pupae, and adults. The proposed Hyperspectral Imaging Fully Convolutional Network (HSI-FCN) segmented insect body regions from three-band pseudo-RGB images, back-projected the predicted masks to the original HSI data cubes, generated individual-level ROIs, and extracted full-band mean spectra. On an independent test set containing 204 insect individuals, HSI-FCN achieved mean Dice and intersection over union (IoU) values of 0.9079 and 0.8328, respectively, and showed the best overall performance among representative segmentation models. All test individuals were successfully matched with their corresponding manual ROIs. Spectra extracted from automated ROIs were highly consistent with manual ROI spectra, with a mean spectral angle mapper of 3.06° and a Pearson correlation coefficient of 0.9956. These results show that the proposed workflow can replace manual ROI delineation with a reproducible preprocessing step for insect HSI analysis, supporting standardized spectral extraction and future applications in forensic entomology.

Accurate species identification of necrophagous flies is fundamental to forensic entomology, particularly for postmortem interval (PMI) estimation in decomposed remains. Here, we conducted a targeted carrion-baited survey along the Shandong Peninsula and documented 15 Sarcophaga species, including the first regional records of S. cinerea, S. pingi, and S. pterygota. We established an expert-validated image dataset for automated identification. We then developed a parameter-efficient identification framework by fine-tuning a pretrained Vision Transformer with Low-Rank Adaptation (ViT-LoRA) on this custom dataset. Compared with conventional CNN-based models, ViT-LoRA achieved 98.50% species-level accuracy while updating only ~0.16 M trainable parameters, and it converged rapidly and stably within ~10 epochs, demonstrating efficient adaptation under limited training data. This study provides faunistic and distributional data on carrion-associated Sarcophaga species in the coastal Shandong Peninsula, characterizes their regional distribution patterns, and offers a scalable image-based identification approach for forensically important sarcophagid flies.