Prediction of xerostomia in elderly based on clinical characteristics and salivary flow rate with machine learning.

Xerostomia may be accompanied by changes in salivary flow rate and the incidence increases in elderly. We aimed to use machine learning algorithms, to identify significant predictors for the presence of xerostomia. This study is the first to predict xerostomia with salivary flow rate in elderly based on artificial intelligence. In a cross-sectional study, 829 patients with oral discomfort were enrolled, and six features (sex, age, unstimulated and stimulated salivary flow rates (UFR and SFR, respectively), number of systemic diseases, and medication usage) were used in four machine learning algorithms to predict the presence of xerostomia. The incidence of xerostomia increased with age. The SFR was significantly higher than the UFR, and the UFR and SFR were significantly correlated. The UFR, but not SFR, decreased with age significantly. In patients more than 60 years of age, the UFR had a significantly higher predictive accuracy for xerostomia than the SFR. Using machine learning algorithms with tenfold cross-validation, the prediction accuracy increased significantly. In particular, the prediction accuracy of the multilayer perceptron (MLP) algorithm that combined UFR and SFR data was significantly better than either UFR or SFR individually. Moreover, when sex, age, number of systemic diseases, and number of medications were added to the MLP model, the prediction accuracy increased from 56 to 68%.

DeepFold: enhancing protein structure prediction through optimized loss functions, improved template features, and re-optimized energy function.

MOTIVATION: Predicting protein structures with high accuracy is a critical challenge for the broad community of life sciences and industry. Despite progress made by deep neural networks like AlphaFold2, there is a need for further improvements in the quality of detailed structures, such as side-chains, along with protein backbone structures. RESULTS: Building upon the successes of AlphaFold2, the modifications we made include changing the losses of side-chain torsion angles and frame aligned point error, adding loss functions for side chain confidence and secondary structure prediction, and replacing template feature generation with a new alignment method based on conditional random fields. We also performed re-optimization by conformational space annealing using a molecular mechanics energy function which integrates the potential energies obtained from distogram and side-chain prediction. In the CASP15 blind test for single protein and domain modeling (109 domains), DeepFold ranked fourth among 132 groups with improvements in the details of the structure in terms of backbone, side-chain, and Molprobity. In terms of protein backbone accuracy, DeepFold achieved a median GDT-TS score of 88.64 compared with 85.88 of AlphaFold2. For TBM-easy/hard targets, DeepFold ranked at the top based on Z-scores for GDT-TS. This shows its practical value to the structural biology community, which demands highly accurate structures. In addition, a thorough analysis of 55 domains from 39 targets with publicly available structures indicates that DeepFold shows superior side-chain accuracy and Molprobity scores among the top-performing groups. AVAILABILITY AND IMPLEMENTATION: DeepFold tools are open-source software available at https://github.com/newtonjoo/deepfold.

Advantages of deep learning with convolutional neural network in detecting disc displacement of the temporomandibular joint in magnetic resonance imaging.

This study investigated the usefulness of deep learning-based automatic detection of anterior disc displacement (ADD) from magnetic resonance imaging (MRI) of patients with temporomandibular joint disorder (TMD). Sagittal MRI images of 2520 TMJs were collected from 861 men and 399 women (average age 37.33 ± 18.83 years). A deep learning algorithm with a convolutional neural network was developed. Data augmentation and the Adam optimizer were applied to reduce the risk of overfitting the deep-learning model. The prediction performances were compared between the models and human experts based on areas under the curve (AUCs). The fine-tuning model showed excellent prediction performance (AUC = 0.8775) and acceptable accuracy (approximately 77%). Comparing the AUC values of the from-scratch (0.8269) and freeze models (0.5858) showed lower performances of the other models compared to the fine-tuning model. In Grad-CAM visualizations, the fine-tuning scheme focused more on the TMJ disc when judging ADD, and the sparsity was higher than that of the from-scratch scheme (84.69% vs. 55.61%, p < 0.05). The three fine-tuned ensemble models using different data augmentation techniques showed a prediction accuracy of 83%. Moreover, the AUC values of ADD were higher when patients with TMD were divided by age (0.8549-0.9275) and sex (male: 0.8483, female: 0.9276). While the accuracy of the ensemble model was higher than that of human experts, the difference was not significant (p = 0.1987-0.0671). Learning from pre-trained weights allowed the fine-tuning model to outperform the from-scratch model. Another benefit of the fine-tuning model for diagnosing ADD of TMJ in Grad-CAM analysis was the deactivation of unwanted gradient values to provide clearer visualizations compared to the from-scratch model. The Grad-CAM visualizations also agreed with the model learned through important features in the joint disc area. The accuracy was further improved by an ensemble of three fine-tuning models using diversified data. The main benefits of this model were the higher specificity compared to human experts, which may be useful for preventing true negative cases, and the maintenance of its prediction accuracy across sexes and ages, suggesting a generalized prediction.

Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms.

The aim of this study is to investigate the relationship of 18 radiomorphometric parameters of panoramic radiographs based on age, and to estimate the age group of people with permanent dentition in a non-invasive, comprehensive, and accurate manner using five machine learning algorithms. For the study population (209 men and 262 women; mean age, 32.12 ± 18.71 years), 471 digital panoramic radiographs of Korean individuals were applied. The participants were divided into three groups (with a 20-year age gap) and six groups (with a 10-year age gap), and each age group was estimated using the following five machine learning models: a linear discriminant analysis, logistic regression, kernelized support vector machines, multilayer perceptron, and extreme gradient boosting. Finally, a Fisher discriminant analysis was used to visualize the data configuration. In the prediction of the three age-group classification, the areas under the curve (AUCs) obtained for classifying young ages (10-19 years) ranged from 0.85 to 0.88 for five different machine learning models. The AUC values of the older age group (50-69 years) ranged from 0.82 to 0.88, and those of adults (20-49 years) were approximately 0.73. In the six age-group classification, the best scores were also found in age groups 1 (10-19 years) and 6 (60-69 years), with mean AUCs ranging from 0.85 to 0.87 and 80 to 0.90, respectively. A feature analysis based on LDA weights showed that the L-Pulp Area was important for discriminating young ages (10-49 years), and L-Crown, U-Crown, L-Implant, U-Implant, and Periodontitis were used as predictors for discriminating older ages (50-69 years). We established acceptable linear and nonlinear machine learning models for a dental age group estimation using multiple maxillary and mandibular radiomorphometric parameters. Since certain radiomorphological characteristics of young and the elderly were linearly related to age, young and old groups could be easily distinguished from other age groups with automated machine learning models.

Transcriptome alterations in spermatogonial stem cells exposed to bisphenol A.

Owing to their self-renewal and differentiation abilities, spermatogonial stem cells (SSCs) are essential for maintaining male fertility and species preservation through spermatogenesis. With an increase in exposure to plasticizers, the risk of endocrine-disrupting chemicals exerting mimetic effects on estrogen receptors, such as bisphenol A (BPA), has also increased. This has led to concerns regarding the preservation of male fertility. BPA impairs spermatogenesis and the maintenance of SSCs; however, the transcriptome differences caused by BPA in SSCs are poorly understood. Thus, this study aimed to investigate the transcriptome differences in SSCs exposed to BPA, using RNA sequencing (RNA-Seq) analysis. We found that cell proliferation and survival were suppressed by SSC exposure to BPA. Therefore, we investigated transcriptome differences through RNA-Seq, functional annotation, and gene set enrichment analysis. Our results showed repetitive and abundant terms related to two genes of lysosomal acidification and five genes of glycosaminoglycan degradation. Furthermore, we validated the transcriptome analyses by detecting mRNA and protein expression levels. The findings confirmed the discovery of differentially expressed genes (DEGs) and the mechanism of SSCs following exposure to BPA. Taken together, we expect that the identified DEGs and lysosomal mechanisms could provide new insights into the preservation of male fertility and related research.

Effective cryopreservation protocol for preservation of male primate (Macaca fascicularis) prepubertal fertility.

RESEARCH QUESTION: Can specimen types (cells versus tissues) and additive cryoprotectant agents contribute to efficient cryopreservation of primate spermatogonial stem cells (SSC)? DESIGN: Testicular tissues or cells from four prepubertal monkeys were used in this study. The freezing efficacy of testicular tissue was compared with cell suspensions using conventional freezing media (1.4 mol/l dimethyl sulfoxide [DMSO]) and the efficacy of cryoprotectant additives (1.4 mol/l DMSO combined with trehalose 200 mmol/l, hypotaurine 14 mmol/l, necrostatin-1 50 µmol/l or melatonin 100 µmol/l) was evaluated in testicular tissue freezing. RESULTS: The survival rate (46.0 ± 4.8% versus 33.7 ± 6.0%; P = 0.0286) and number of recovered cells (5.0 ± 1.5 × 106 cells/g versus 0.7 ± 0.8 × 106 cells/g; P = 0.0286) were significantly higher in frozen tissues than in frozen cell suspensions. After tissue freezing, a higher number of recovered PGP9.5+ cells were observed with 200 mmol/l trehalose treatment than in DMSO controls (2.4 ± 0.6 × 106 cells/g versus 1.1 ± 0.3 × 106 cells/g; P = 0.0164). Normal establishment of donor-derived colony was observed in SSC after tissue freezing with 200 mmol/l trehalose. CONCLUSIONS: Testicular tissue freezing is more effective than single cell suspension freezing for higher recovery of undifferentiated spermatogonia. Moreover, it was verified that slow freezing using 200 mmol/l trehalose, 1.4 mol/l DMSO and 10% KnockOut™ Serum Replacement in Dulbecco's phosphate-buffered saline is an effective cryopreservation protocol for primate testicular tissue.

Hair growth stimulated by conditioned medium of adipose-derived stem cells is enhanced by hypoxia: evidence of increased growth factor secretion.

Adipose-derived stem cells (ADSCs) and their secretomes mediate diverse skin-regeneration effects, such as wound-healing and antioxidant protection, that are enhanced by hypoxia. We investigated the hair-growth-promoting effect of conditioned medium (CM) of ADSCs to determine if ADSCs and their secretomes regenerate hair and if hypoxia enhances hair regeneration. If so, we wanted to identify the factors responsible for hypoxia-enhanced hair-regeneration. We found that ADSC-CM administrated subcutaneously induced the anagen phase and increased hair regeneration in C(3)H/NeH mice. In addition, ADSC-CM increased the proliferation of human follicle dermal papilla cells (HFDPCs) and human epithelial keratinocytes (HEKs), which are derived from two major cell types present in hair follicles. We investigated the effect of hypoxia on ADSC function using the same animal model in which hypoxia increased hair regrowth. Forty-one growth factors in ADSC-CM from cells cultured under hypoxic or normoxic conditions were analyzed. The secretion of insulin-like growth factor binding protein (IGFBP)-1, IGFBP-2, macrophage colony-stimulating factor (M-CSF), M-CSF receptor, platelet-derived growth factor receptor-beta, and vascular endothelial growth factor was significantly increased by hypoxia, while the secretion of epithelial growth factor production was decreased. It is reasonable to conclude that ADSCs promote hair growth via a paracrine mechanism that is enhanced by hypoxia.

Subungual glomus tumor: clinical manifestations and outcome of surgical treatment.

Surgical excision is the treatment of choice for subungual glomus tumor. However, the anatomical location has inherent difficulties. We report the outcomes of surgical treatments for subungual glomus tumor. Sixteen patients, who were seen over an eight-year period (1995-2003) and confirmed as gloums tumor by histopathologic examination were reviewed. The most common subjective symptom was pain induced by contact in 81%. The tumor presented as a discolorated spot or subungual nodule and 38% of tumors were acccompanied with nail dystrophy. All tumors showed discolorated spots or subungual nodules. As shown in the Table 2, the dystrophic nail change was found in 38% of tumors. Differently oriented incisions were made according to the location of tumor, matrix, or bed. The original nail plate was restored in eight patients. Thirteen patients (81%) had cosmetically excellent nail plates, and three patients (19%) had partial distal splits of nail plates. There was no recurrence. Our series suggests that a transungual approach with nail avulsion and an incision selected according to the tumor location can produce an excellent outcome with minimal postoperative complications. Dressing with a trimmed nail plate may also be beneficial in managing the wound and preventing postoperative nail deformity.