The Application of Artificial-Intelligence-Assisted Dental Age Assessment in Children with Growth Delay.

BACKGROUND: This study aimed to reveal the efficacy of the artificial intelligence (AI)-assisted dental age (DA) assessment in identifying the characteristics of growth delay (GD) in children. METHODS: The panoramic films matching the inclusion criteria were collected for the AI model training to establish the population-based DA standard. Subsequently, the DA of the validation dataset of the healthy children and the images of the GD children were assessed by both the conventional methods and the AI-assisted standards. The efficacy of all the studied modalities was compared by the paired sample t-test. RESULTS: The AI-assisted standards can provide much more accurate chronological age (CA) predictions with mean errors of less than 0.05 years, while the traditional methods presented overestimated results in both genders. For the GD children, the convolutional neural network (CNN) revealed the delayed DA in GD children of both genders, while the machine learning models presented so only in the GD boys. CONCLUSION: The AI-assisted DA assessments help overcome the long-standing populational limitation observed in traditional methods. The image feature extraction of the CNN models provided the best efficacy to reveal the nature of delayed DA in GD children of both genders.

A Multimodal Imaging-Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study.

BACKGROUND: Retinal vascular diseases, including diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), myopic choroidal neovascularization (mCNV), and branch and central retinal vein occlusion (BRVO/CRVO), are considered vision-threatening eye diseases. However, accurate diagnosis depends on multimodal imaging and the expertise of retinal ophthalmologists. OBJECTIVE: The aim of this study was to develop a deep learning model to detect treatment-requiring retinal vascular diseases using multimodal imaging. METHODS: This retrospective study enrolled participants with multimodal ophthalmic imaging data from 3 hospitals in Taiwan from 2013 to 2019. Eye-related images were used, including those obtained through retinal fundus photography, optical coherence tomography (OCT), and fluorescein angiography with or without indocyanine green angiography (FA/ICGA). A deep learning model was constructed for detecting DME, nAMD, mCNV, BRVO, and CRVO and identifying treatment-requiring diseases. Model performance was evaluated and is presented as the area under the curve (AUC) for each receiver operating characteristic curve. RESULTS: A total of 2992 eyes of 2185 patients were studied, with 239, 1209, 1008, 211, 189, and 136 eyes in the control, DME, nAMD, mCNV, BRVO, and CRVO groups, respectively. Among them, 1898 eyes required treatment. The eyes were divided into training, validation, and testing groups in a 5:1:1 ratio. In total, 5117 retinal fundus photos, 9316 OCT images, and 20,922 FA/ICGA images were used. The AUCs for detecting mCNV, DME, nAMD, BRVO, and CRVO were 0.996, 0.995, 0.990, 0.959, and 0.988, respectively. The AUC for detecting treatment-requiring diseases was 0.969. From the heat maps, we observed that the model could identify retinal vascular diseases. CONCLUSIONS: Our study developed a deep learning model to detect retinal diseases using multimodal ophthalmic imaging. Furthermore, the model demonstrated good performance in detecting treatment-requiring retinal diseases.

Tumor cells require thymidylate kinase to prevent dUTP incorporation during DNA repair.

The synthesis of dTDP is unique because there is a requirement for thymidylate kinase (TMPK). All other dNDPs including dUDP are directly produced by ribonucleotide reductase (RNR). We report the binding of TMPK and RNR at sites of DNA damage. In tumor cells, when TMPK function is blocked, dUTP is incorporated during DNA double-strand break (DSB) repair. Disrupting RNR recruitment to damage sites or reducing the expression of the R2 subunit of RNR prevents the impairment of DNA repair by TMPK intervention, indicating that RNR contributes to dUTP incorporation during DSB repair. We identified a cell-permeable nontoxic inhibitor of TMPK that sensitizes tumor cells to doxorubicin in vitro and in vivo, suggesting its potential as a therapeutic option.

Structural insights into the catalytic active site and activity of human Nit2/ω-amidase: kinetic assay and molecular dynamics simulation.

Human nitrilase-like protein 2 (hNit2) is a putative tumor suppressor, recently identified as ω-amidase. hNit2/ω-amidase plays a crucial metabolic role by catalyzing the hydrolysis of α-ketoglutaramate (the α-keto analog of glutamine) and α-ketosuccinamate (the α-keto analog of asparagine), yielding α-ketoglutarate and oxaloacetate, respectively. Transamination between glutamine and α-keto-γ-methiolbutyrate closes the methionine salvage pathway. Thus, hNit2/ω-amidase links sulfur metabolism to the tricarboxylic acid cycle. To elucidate the catalytic specificity of hNit2/ω-amidase, we performed molecular dynamics simulations on the wild type enzyme and its mutants to investigate enzyme-substrate interactions. Binding free energies were computed to characterize factors contributing to the substrate specificity. The predictions resulting from these computations were verified by kinetic analyses and mutational studies. The activity of hNit2/ω-amidase was determined with α-ketoglutaramate and succinamate as substrates. We constructed three catalytic triad mutants (E43A, K112A, and C153A) and a mutant with a loop 116-128 deletion to validate the role of key residues and the 116-128 loop region in substrate binding and turnover. The molecular dynamics simulations successfully verified the experimental trends in the binding specificity of hNit2/ω-amidase toward various substrates. Our findings have revealed novel structural insights into the binding of substrates to hNit2/ω-amidase. A catalytic triad and the loop residues 116-128 of hNit2 play an essential role in supporting the stability of the enzyme-substrate complex, resulting in the generation of the catalytic products. These observations are predicted to be of benefit in the design of new inhibitors or activators for research involving cancer and hyperammonemic diseases.