Nanobodies derived from camelids and sharks offer unique advantages in therapeutic applications due to their ability to bind to epitopes that were previously inaccessible. Traditional methods of nanobody development face challenges such as ethical concerns and antigen toxicity. Our study presents a synthetic, phagedisplayed nanobody library using trinucleotide-directed mutagenesis technology, which allows precise amino acid composition in complementarity-determining regions (CDRs), with a focus on CDR3 diversity. This approach avoids common problems such as frameshift mutations and stop codon insertions associated with other synthetic antibody library construction methods. By analyzing FDA-approved nanobodies and Protein Data Bank sequences, we designed sub-libraries with different CDR3 lengths and introduced amino acid substitutions to improve solubility. The validation of our library through the successful isolation of nanobodies against targets such as PD-1, ATXN1 and STAT3 demonstrates a versatile and ethical platform for the development of high specificity and affinity nanobodies and represents a significant advance in biotechnology.

Nanobodies derived from camelids and sharks offer unique advantages in therapeutic applications due to their ability to bind to epitopes that were previously inaccessible. Traditional methods of nanobody development face challenges such as ethical concerns and antigen toxicity. Our study presents a synthetic, phagedisplayed nanobody library using trinucleotide-directed mutagenesis technology, which allows precise amino acid composition in complementarity-determining regions (CDRs), with a focus on CDR3 diversity. This approach avoids common problems such as frameshift mutations and stop codon insertions associated with other synthetic antibody library construction methods. By analyzing FDA-approved nanobodies and Protein Data Bank sequences, we designed sub-libraries with different CDR3 lengths and introduced amino acid substitutions to improve solubility. The validation of our library through the successful isolation of nanobodies against targets such as PD-1, ATXN1 and STAT3 demonstrates a versatile and ethical platform for the development of high specificity and affinity nanobodies and represents a significant advance in biotechnology.

Nanobodies derived from camelids and sharks offer unique advantages in therapeutic applications due to their ability to bind to epitopes that were previously inaccessible. Traditional methods of nanobody development face challenges such as ethical concerns and antigen toxicity. Our study presents a synthetic, phagedisplayed nanobody library using trinucleotide-directed mutagenesis technology, which allows precise amino acid composition in complementarity-determining regions (CDRs), with a focus on CDR3 diversity. This approach avoids common problems such as frameshift mutations and stop codon insertions associated with other synthetic antibody library construction methods. By analyzing FDA-approved nanobodies and Protein Data Bank sequences, we designed sub-libraries with different CDR3 lengths and introduced amino acid substitutions to improve solubility. The validation of our library through the successful isolation of nanobodies against targets such as PD-1, ATXN1 and STAT3 demonstrates a versatile and ethical platform for the development of high specificity and affinity nanobodies and represents a significant advance in biotechnology.

Nanobodies derived from camelids and sharks offer unique advantages in therapeutic applications due to their ability to bind to epitopes that were previously inaccessible. Traditional methods of nanobody development face challenges such as ethical concerns and antigen toxicity. Our study presents a synthetic, phagedisplayed nanobody library using trinucleotide-directed mutagenesis technology, which allows precise amino acid composition in complementarity-determining regions (CDRs), with a focus on CDR3 diversity. This approach avoids common problems such as frameshift mutations and stop codon insertions associated with other synthetic antibody library construction methods. By analyzing FDA-approved nanobodies and Protein Data Bank sequences, we designed sub-libraries with different CDR3 lengths and introduced amino acid substitutions to improve solubility. The validation of our library through the successful isolation of nanobodies against targets such as PD-1, ATXN1 and STAT3 demonstrates a versatile and ethical platform for the development of high specificity and affinity nanobodies and represents a significant advance in biotechnology.

Nanobodies derived from camelids and sharks offer unique advantages in therapeutic applications due to their ability to bind to epitopes that were previously inaccessible. Traditional methods of nanobody development face challenges such as ethical concerns and antigen toxicity. Our study presents a synthetic, phagedisplayed nanobody library using trinucleotide-directed mutagenesis technology, which allows precise amino acid composition in complementarity-determining regions (CDRs), with a focus on CDR3 diversity. This approach avoids common problems such as frameshift mutations and stop codon insertions associated with other synthetic antibody library construction methods. By analyzing FDA-approved nanobodies and Protein Data Bank sequences, we designed sub-libraries with different CDR3 lengths and introduced amino acid substitutions to improve solubility. The validation of our library through the successful isolation of nanobodies against targets such as PD-1, ATXN1 and STAT3 demonstrates a versatile and ethical platform for the development of high specificity and affinity nanobodies and represents a significant advance in biotechnology.

Purpose: Renal tumors account for approximately 7% of all childhood cancers. These include Wilms tumor (WT), clear cell sarcoma of the kidney (CCSK), malignant rhabdoid tumor of the kidney (MRTK), renal cell carcinoma (RCC), congenital mesoblastic nephroma (CMN) and other rare tumors. We investigated the epidemiology of pediatric renal tumors in Korea. Materials and Methods: From January 2001 to December 2015, data of pediatric patients (0–18 years) newly-diagnosed with renal tumors at 26 hospitals were retrospectively analyzed. Results: Among 439 patients (male, 240), the most common tumor was WT (n=342, 77.9%), followed by RCC (n=36, 8.2%), CCSK (n=24, 5.5%), MRTK (n=16, 3.6%), CMN (n=12, 2.7%), and others (n=9, 2.1%). Median age at diagnosis was 27.1 months (range 0-225.5) and median follow-up duration was 88.5 months (range 0-211.6). Overall, 32 patients died, of whom 17, 11, 1, and 3 died of relapse, progressive disease, second malignant neoplasm, and treatment-related mortality. Five-year overall survival and event free survival were 97.2% and 84.8% in WT, 90.6% and 82.1% in RCC, 81.1% and 63.6% in CCSK, 60.3% and 56.2% in MRTK, and 100% and 91.7% in CMN, respectively (p < 0.001). Conclusion The pediatric renal tumor types in Korea are similar to those previously reported in other countries. WT accounted for a large proportion and survival was excellent. Non-Wilms renal tumors included a variety of tumors and showed inferior outcome, especially MRTK. Further efforts are necessary to optimize the treatment and analyze the genetic characteristics of pediatric renal tumors in Korea.

This study aims to analyze the color distribution of maxillary permanent incisors in Korean pediatric patients and determine the effects of age and root developmental stage on tooth color. The L*a*b* values of 404 sound and fully erupted maxillary incisors without dental caries, restorations, trauma history or discoloration from 101 Korean patients between ages 7 and 15, with a mean age 10.0 ± 1.5, were analyzed with a spectrophotometer. CIE L*a*b* values were 84.01, 0.17, and 24.07 in maxillary central incisors, and 82.33, 0.31, and 25.99 in maxillary lateral incisors. L* values of maxillary central incisors were higher, and b* values of maxillary central incisors were lower than those of maxillary lateral incisors (p < 0.001). The color differences among the subregions exceeded the clinical perceptibility threshold in both of the maxillary central and lateral incisors. L* value for children at age 10 and younger was 84.13 in maxillary central incisors and 84.04 in maxillary lateral incisors, and those of older patients were 80.62 and 80.56, respectively. L* value of maxillary incisors of children at age 10 and younger was significantly higher than that of older patients. The root developmental stage did not affect tooth color. This study suggests that the color differences between maxillary central and lateral incisors and among the subregions of a tooth and the effects of age should be considered for aesthetic restorations of permanent incisors in pediatric patients.

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988–0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618– 0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988–0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618– 0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.