Various plant species from the Litsea genus have been claimed to be beneficial for pain relief. The PRISMA approach was adopted to identify studies that reported analgesic properties of plants from the Litsea genus. Out of 450 records returned, 19 primary studies revealed the analgesic potential of nine Litsea species including (1) Litsea cubeba, (2) Litsea elliptibacea, (3) Litsea japonica, (4) Litsea glutinosa, (5) Litsea glaucescens, (6) Litsea guatemalensis, (7) Litsea lancifolia, (8) Litsea liyuyingi and (9) Litsea monopetala. Six of the species, 1, 3, 4, 7, 8 and 9, demonstrated peripheral antinociceptive properties as they inhibited acetic-acid-induced writhing in animal models. Species 1, 3, 4, 8 and 9 further showed effects via the central analgesic route at the spinal level by increasing the latencies of heat stimulated-nocifensive responses in the tail flick assay. The hot plate assay also revealed the efficacies of 4 and 9 at the supraspinal level. Species 6 was reported to ameliorate hyperalgesia induced via partial sciatic nerve ligation (PSNL). The antinociceptive effects of 1 and 3 were attributed to the regulatory effects of their bioactive compounds on inflammatory mediators. As for 2 and 5, their analgesic effect may be a result of their activity with the 5-hydroxytryptamine 1A receptor (5-HT_1AR) which disrupted the pain-stimulating actions of 5-HT. Antinociceptive activities were documented for various major compounds of the Litsea plants. Overall, the findings suggested Litsea species as good sources of antinociceptive compounds that can be further developed to complement or substitute prescription drugs for pain management.

Heterotrigona itama, a widely reared stingless bee species, produces highly valued honey. These bees naturally secure their colonies within logs, accessed via a single entrance tube, but remain vulnerable to intruders and predators. Guard bees play a critical role in colony defense, exhibiting the ability to discriminate between nestmates and non-nestmates and employing strategies such as pheromone release, buzzing, hissing, and vibrations to alert and recruit hive mates during intrusions. This study investigated the acoustic signals produced by H. itama guard bees during intrusions to determine their potential for intrusion detection. Using a Jetson Nano equipped with a microphone and camera, guard bee sounds were recorded and labeled. After preprocessing the sound data, Mel Frequency Cepstral Coefficients (MFCCs) were extracted as features, and various dimensionality reduction techniques were explored. Among them, Linear Discriminant Analysis (LDA) demonstrated the best performance in improving class separability. The reduced feature set was used to train both Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. KNN outperformed SVM, achieving a Precision of 0.9527, a Recall of 0.9586, and an F1 Score of 0.9556. Additionally, KNN attained an Overall Cross-Validation Accuracy of 95.54% (±0.67%), demonstrating its superior classification performance. These findings confirm that H. itama produces distinct alarm sounds during intrusions, which can be effectively classified using machine learning; thus, demonstrating the feasibility of sound-based intrusion detection as a cost-effective alternative to image-based approaches. Future research should explore real-world implementation under varying environmental conditions and extend the study to other stingless bee species.