Purpose: This study employed a convolutional neural network (CNN) algorithm to develop an automated dental age estimation method based on the London Atlas of Tooth Development and Eruption. The primary objectives were to create and validate CNN models trained on panoramic radiographs to achieve accurate dental age predictions using a standardized approach. Materials and Methods: A dataset of 801 panoramic radiographs from outpatients aged 5 to 15 years was used. A CNN model for dental age estimation was developed using a 16-layer CNN architecture implemented in Python with TensorFlow and Scikit-learn, guided by the London Atlas of Tooth Development. The model included 6 convolutional layers for feature extraction, each followed by a pooling layer to reduce the spatial dimensions of the feature maps. A confusion matrix was used to evaluate key performance metrics, including accuracy, precision, recall, and F1 score. Results: The proposed model achieved an overall accuracy, precision, recall, and F1 score of 74% on the validation set. The highest F1 scores were observed in the 10-year and 12-year age groups, indicating superior performancein these categories. In contrast, the 6-year age group demonstrated the highest misclassification rate, highlightingpotential challenges in accurately estimating age in younger individuals. Conclusion: Integrating a CNN algorithm for dental age estimation represents a significant advancement in forensic odontology. The application of AI improves both the precision and efficiency of age estimation processes, providing results that are more reliable and objective than those obtained via traditional methods.

Purpose: This study employed a convolutional neural network (CNN) algorithm to develop an automated dental age estimation method based on the London Atlas of Tooth Development and Eruption. The primary objectives were to create and validate CNN models trained on panoramic radiographs to achieve accurate dental age predictions using a standardized approach. Materials and Methods: A dataset of 801 panoramic radiographs from outpatients aged 5 to 15 years was used. A CNN model for dental age estimation was developed using a 16-layer CNN architecture implemented in Python with TensorFlow and Scikit-learn, guided by the London Atlas of Tooth Development. The model included 6 convolutional layers for feature extraction, each followed by a pooling layer to reduce the spatial dimensions of the feature maps. A confusion matrix was used to evaluate key performance metrics, including accuracy, precision, recall, and F1 score. Results: The proposed model achieved an overall accuracy, precision, recall, and F1 score of 74% on the validation set. The highest F1 scores were observed in the 10-year and 12-year age groups, indicating superior performancein these categories. In contrast, the 6-year age group demonstrated the highest misclassification rate, highlightingpotential challenges in accurately estimating age in younger individuals. Conclusion: Integrating a CNN algorithm for dental age estimation represents a significant advancement in forensic odontology. The application of AI improves both the precision and efficiency of age estimation processes, providing results that are more reliable and objective than those obtained via traditional methods.

Purpose: This study employed a convolutional neural network (CNN) algorithm to develop an automated dental age estimation method based on the London Atlas of Tooth Development and Eruption. The primary objectives were to create and validate CNN models trained on panoramic radiographs to achieve accurate dental age predictions using a standardized approach. Materials and Methods: A dataset of 801 panoramic radiographs from outpatients aged 5 to 15 years was used. A CNN model for dental age estimation was developed using a 16-layer CNN architecture implemented in Python with TensorFlow and Scikit-learn, guided by the London Atlas of Tooth Development. The model included 6 convolutional layers for feature extraction, each followed by a pooling layer to reduce the spatial dimensions of the feature maps. A confusion matrix was used to evaluate key performance metrics, including accuracy, precision, recall, and F1 score. Results: The proposed model achieved an overall accuracy, precision, recall, and F1 score of 74% on the validation set. The highest F1 scores were observed in the 10-year and 12-year age groups, indicating superior performancein these categories. In contrast, the 6-year age group demonstrated the highest misclassification rate, highlightingpotential challenges in accurately estimating age in younger individuals. Conclusion: Integrating a CNN algorithm for dental age estimation represents a significant advancement in forensic odontology. The application of AI improves both the precision and efficiency of age estimation processes, providing results that are more reliable and objective than those obtained via traditional methods.

Introduction: Throughout the school day, teachers often endure extended periods of standing, resulting in frequent experiences of pain and muscle fatigue by day's end. The purpose of this study was to identify the effectiveness of a prototype of vibrating insole on muscular activity on teachers. Materials and methods: A total of 124 female school teachers participated in this study. During a one-hour classroom teaching session, school teachers were randomly assigned to either the experimental group or the control group. Throughout the hour-long session, wireless surface electromyography (sEMG) sensors were used to continually monitor the muscles in the right and left legs. Results: During the one-hour prototype testing, compared to control group, participants in the experimental group showed a reduction in muscle activity exertion ranging from 13% to 16% in both the tibialis anterior and peroneus longus muscles of the right and left legs. Moreover, there were significant changes of muscle activity exertion among school teachers, X2 (15) = 289.94, p<0.001 within testing period. Conclusion: The study revealed a significant decrease in muscle activities, especially the feet, demonstrating a gradual adaptation to the vibration effects from the insole prototype. This contributed significantly to lower leg comfort during teaching sessions. As a result, the vibrating insole prototype was well-received by school teachers and had a positive impact on their feet comfort throughout the experimental testing session.

Kemunculan teknologi penyuntingan genom, terutamanya CRISPR/Cas9, telah mengubah dan merevolusikan landskap bidang genetik dan biologi molekul dengan begitu drastik. Sistem CRISPR/Cas9 diadaptasi daripada sistem adaptasi imuniti bakteria, menggunakan enzim Cas9 yang dipandu oleh sgRNA untuk penyingkiran gen atau penyuntingan genom secara jitu. Walau bagaimanapun, cabaran seperti kesan luar sasaran (off-target effects) telah mendorong pembangunan beberapa varian Cas9 seperti dCas9. dCas9 diubah suai untuk tidak memiliki sebarang aktiviti endonuklease dan kini dCas9 telah digunakan untuk pelbagai aplikasi yang melangkaui fungsi tradisional CRISPR/Cas9 sebagai kaedah pengeditan genom. Selain itu, teknologi terkini penyuntingan perdana (prime editing) telah menggabungkan enzim Cas9 yang diubahsuai bersama enzim transkriptase berbalik (reverse transcriptase) untuk meningkatkan lagi tahap keberkesanan penyuntingan genom secara jitu. Walaupun wujud kerisauan terhadap pertimbangan etika dan kebimbangan terkait keselamatan, namun teknologi ini menjanjikan dampak yang besar di dalam menangani penyakit genetik serta aplikasi dalam bidang perubatan jitu (precision medicine). Memahami dan mengoptimumkan potensi CRISPR/Cas9 dan teknologi penyuntingan perdana menandakan bermula era baharu dalam bidang penyelidikan berkaitan biologi dan perubatan, dan seterusnya menyediakan satu platform untuk penyuntingan genom yang tepat dan pengawalseliaan proses transkripsi gen.

Introduction: Race estimation of unknown individual is essential in forensic investigation. The resiliency of frontal sinus makes it a potential tool for biological profiling, particularly in cases where fragmented skeleton persists. Geometric morphometrics is an efficient way to characterise shape. However, the use of frontal sinus to identify race of Malaysians is yet to be investigated. This research employed a two-dimensional (2D) geometric morphometric to examine the morphological differences of the frontal sinus among the major races in Malaysia. Methods: Lateral skull radiographs which comprising of 453 adult Malaysian (151 Malays, Chinese and Indian respectively) were used. The 2D landmarks of eight were placed on the digitalized radiographs and 2D geometric morphometric analysis was performed using MorphoJ software. Results: Procrustes ANOVA revealed a significantly different frontal sinus shape (p-value < 0.05) between races. Canonical variate analysis showed significantly different frontal sinus morphology (p-value < 0.05) between Malay and Indian as well as Chinese and Indian. Discriminant function analysis with cross-validation demonstrated a 57.4% accuracy rate. Conclusion: This population-specific study based on frontal sinus of Malaysians using the 2D geometric morphometric, though less reliable, sheds new light on the potential applicability of this method for race estimation purpose.

Purpose: The objective of this study was to determine the minimum number of teeth in the anterior dental arch that would yield accurate results for individual identification in forensic contexts. Materials and Methods: The study involved the analysis of 28 sets of 3-dimensional (3D) point cloud data, focused on the labial surface of the anterior teeth. These datasets were superimposed within each group in both genuine and imposter pairs. Group A incorporated data from the right to the left central incisor, group B from the right to the left lateral incisor, and group C from the right to the left canine. A comprehensive analysis was conducted, including the evaluation of root mean square error (RMSE) values and the distances resulting from the superimposition of dental arch segments. All analyses were conducted using CloudCompare version 2.12.4 (Telecom ParisTech and R&D, Kyiv, Ukraine). Results: The distances between genuine pairs in groups A, B, and C displayed an average range of 0.153 to 0.184 mm. In contrast, distances for imposter pairs ranged from 0.338 to 0.522 mm. RMSE values for genuine pairs showed an average range of 0.166 to 0.177, whereas those for imposter pairs ranged from 0.424 to 0.638. A statistically significant difference was observed between the distances of genuine and imposter pairs (P<0.05). Conclusion The exceptional performance observed for the labial surfaces of anterior teeth underscores their potential as a dependable criterion for accurate 3D dental identification. This was achieved by assessing a minimum of 4 teeth.

Background: Poor disease management and irregular vector control could predispose sheltered animals to disease such as feline Bartonella infection, a vector-borne zoonotic disease primarily caused by Bartonella henselae. Objectives: This study investigated the status of Bartonella infection in cats from eight (n = 8) shelters by molecular and serological approaches, profiling the CD4:CD8 ratio and the risk factors associated with Bartonella infection in shelter cats. Methods: Bartonella deoxyribonucleic acid (DNA) was detected through polymerase chain reaction (PCR) targeting 16S-23S rRNA internal transcribed spacer gene, followed by DNA sequencing. Bartonella IgM and IgG antibody titre, CD4 and CD8 profiles were detected using indirect immunofluorescence assay and flow cytometric analysis, respectively. Results: B. henselae was detected through PCR and sequencing in 1.0% (1/101) oral swab and 2.0% (1/50) cat fleas, while another 3/50 cat fleas carried B. clarridgeiae. Only 18/101 cats were seronegative against B. henselae, whereas 30.7% (31/101) cats were positive for both IgM and IgG, 8% (18/101) cats had IgM, and 33.7% (34/101) cats had IgG antibody only. None of the eight shelters sampled had Bartonella antibody-free cats. Although abnormal CD4:CD8 ratio was observed in 48/83 seropositive cats, flea infestation was the only significant risk factor observed in this study. Conclusions The present study provides the first comparison on the Bartonella spp. antigen, antibody status and CD4:CD8 ratio among shelter cats. The high B. henselae seropositivity among shelter cats presumably due to significant flea infestation triggers an alarm of whether the infection could go undetectable and its potential transmission to humans.

The development of metal complexes has inspired researchers to progress in this domain due to their extensive applications in the biological field. Regarding the application, binuclear metal complexes are less explored than their mononuclear counterparts. Recent development in transition metal Schiff base complexes was outlined and presented in detail with their respective vast applications, especially antibacterial. The relationship of their structure, functions, properties, and key elements that affected antibacterial activities was demonstrated. This review was aimed to present the latest advancement of numerous lower and higher transition metal complexes, especially mononuclear ones. Moreover, their various properties are highlighted for future work related to binuclear Schiff base metal complexes and to persuade future research in this exciting field.