A machine learning model for orthodontic extraction/non-extraction decision in a racially and ethnically diverse patient population.

INTRODUCTION: The purpose of the present study was to create a machine learning (ML) algorithm with the ability to predict the extraction/non-extraction decision in a racially and ethnically diverse sample. METHODS: Data was gathered from the records of 393 patients (200 non-extraction and 193 extraction) from a racially and ethnically diverse population. Four ML models (logistic regression [LR], random forest [RF], support vector machine [SVM], and neural network [NN]) were trained on a training set (70% of samples) and then tested on the remaining samples (30%). The accuracy and precision of the ML model predictions were calculated using the area under the curve (AUC) of the receiver operating characteristics (ROC) curve. The proportion of correct extraction/non-extraction decisions was also calculated. RESULTS: The LR, SVM, and NN models performed best, with an AUC of the ROC of 91.0%, 92.5%, and 92.3%, respectively. The overall proportion of correct decisions was 82%, 76%, 83%, and 81% for the LR, RF, SVM, and NN models, respectively. The features found to be most helpful to the ML algorithms in making their decisions were maxillary crowding/spacing, L1-NB (mm), U1-NA (mm), PFH:AFH, and SN-MP(̊), although many other features contributed significantly. CONCLUSIONS: ML models can predict the extraction decision in a racially and ethnically diverse patient population with a high degree of accuracy and precision. Crowding, sagittal, and vertical characteristics all featured prominently in the hierarchy of components most influential to the ML decision-making process.

Can we predict orthodontic extraction patterns by using machine learning?

OBJECTIVE: To investigate the utility of machine learning (ML) in accurately predicting orthodontic extraction patterns in a heterogeneous population. MATERIALS AND METHODS: The material of this retrospective study consisted of records of 366 patients treated with orthodontic extractions. The dataset was randomly split into training (70%) and test sets (30%) and was stratified according to race/ethnicity and gender. Fifty-five cephalometric and demographic input data were used to train and test multiple ML algorithms. The extraction patterns were labelled according to the previous treatment plan. Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) algorithms were used to predict the patient's extraction patterns. RESULTS: The highest class accuracy percentages were obtained for the upper and lower 1st premolars (U/L4s) (RF: 81.63%, LR: 63.27%, SVM: 63.27%) and upper 1st premolars only (U4s) extraction patterns (RF: 61.11%, LR: 72.22%, SVM: 72.22%). However, all methods revealed low class accuracy rates (<50%) for the upper 1st and lower 2nd premolars (U4/L5s), upper 2nd and lower 1st premolars (U5/L4s), and upper and lower 2nd premolars (U/L5s) extraction patterns. For the overall accuracy, RF yielded the highest percentage with 54.55%, followed by SVM with 52.73% and LR with 49.09%. CONCLUSION: All tested supervised ML techniques yielded good accuracy in predicting U/L4s and U4s extraction patterns. However, they predicted poorly for the U4/L5s, U5/L4s, and U/L5s extraction patterns. Molar relationship, mandibular crowding, and overjet were found to be the most predictive indicators for determining extraction patterns.

Insurance claim data trends in pulpal therapy for pediatric patients.

BACKGROUND: The purpose of this study was to identify differences in usage trends for 2 specific pulpal therapy treatments in pediatric patients during an 11-year period from January 1, 2010, through December 31, 2020. METHODS: Insurance data claims for children aged 2 through 12 years undergoing a pulpotomy or a pulpectomy performed by a general dentist (GD) or pediatric dentist (PD) from 2010 through 2020 were extracted from a dental data warehouse. The state where the provider was located was included in the extracted claim. RESULTS: Rates of undergoing a pulpotomy or pulpectomy declined from 2010 through 2020 (odds ratio [OR], 0.978 or 0.946, respectively; P < .001). PDs were more likely to perform pulpotomies than GDs (OR, 1.393; P < .001), but PDs were less likely to perform pulpectomies than GDs (OR, 0.225; P < .001). Younger patient age was a significant predictor for undergoing pulpotomy treatment for both GDs and PDs (ORs, 0.850 and 0.892, respectively; P < .001). With increasing patient age, PDs had increased odds of performing a pulpectomy (OR, 1.030; P < .001) and GDs had decreased odds of performing a pulpectomy (OR, 0.995; P = .04). When examining effects according to American Academy of Pediatric Dentistry national membership districts, the trends remained consistent with those above. CONCLUSIONS: The percentage of children undergoing pulpotomy and pulpectomy therapy declined from 2010 through 2020 among both GDs and PDs. PRACTICAL IMPLICATIONS: These changes in pulpal therapy practice might indicate a teaching change in pulpal therapy guidelines, suggesting that less invasive pulpal therapy can be used rather than pulpotomies or pulpectomies.

Transcriptome analysis of the 2,4-dichlorophenoxyacetic acid (2,4-D)-tolerant cotton chromosome substitution line CS-B15sh and its susceptible parental lines G. hirsutum L. cv. Texas Marker-1 and G. barbadense L. cv. Pima 379.

The cotton chromosome substitution line, CS-B15sh, exhibits 41% lower injury from 2,4-D when applied at the field recommended rate of 1.12 kg ae ha-1 (1×) than does Texas Marker-1 (TM-1). CS-B15sh was developed in the genetic background of Gossypium hirsutum L. cv TM-1 and has chromosome introgression on the short arm of chromosome 15 from Gossypium barbadense L. cv. Pima 379. In a previous experiment, we observed reduced translocation of [14C]2,4-D outside the treated leaf tissue in CS-B15sh, which contrasted with an increased translocation of the herbicide in the tissues above and below the treated leaf in TM-1. Our results indicate a potential 2,4-D tolerance mechanism in CS-B15sh involving altered movement of 2,4-D. Here, we used RNA sequencing (RNA-seq) to determine the differential expression of genes between 2,4-D-challenged and control plants of the tolerant (CS-B15sh) and susceptible lines (TM-1 and Pima 379). Several components of the 2,4-D/auxin-response pathway-including ubiquitin E3 ligase, PB1|AUX/IAA, ARF transcription factors, and F-box proteins of the SCFTIR1/AFB complex-were upregulated with at least threefold higher expression in TM-1 compared with CS-B15sh, while both Pima 379 and TM-1 showed the same fold change expression for PB1|AUX/IAA mRNA. Some genes associated with herbicide metabolism, including flavin monooxygenase (Gohir.A01G174100) and FAD-linked oxidase (Gohir.D06G002600), exhibited at least a twofold increase in CS-B15sh than in TM-1 (the gene was not expressed in Pima 379), suggesting a potential relationship between the gene's expression and 2,4-D tolerance. It is interesting to note that glutathione S-transferase was differentially expressed in both CS-B15sh and Pima 379 but not in TM-1, while cytochrome P450 and other genes involved in the oxidation-reduction process were significantly expressed only in CS-B15sh in response to 2,4-D. Gene set enrichment analysis on the union DEGs of the three cotton genotypes revealed the depletion of transcripts involved in photosynthesis and enrichment of transcripts involved in ABA response and signaling.

Demarcated Primary Second Molar Hypomineralization: Prevalence Data and Associated Sociodemographic Determinants from Indiana.

Purpose: Demarcated primary second molar hypomineralization (DMH-Es) is a common developmental defect of enamel, with prevalence estimates between five percent and 20 percent. From the Americas, studies exploring the problem of DMH-Es and explicitly using the European Academy of Pediatric Dentistry diagnostic criteria were limited to some South American countries, but no similar studies were available from any of the North American countries including the United States. The purpose of this study was to investigate the prevalence and sociodemographic determinants of DMH-Es among schoolchildren in Indiana, USA. Methods: Four hundred twenty-three schoolchildren (average age equals 7.6 [±2.2 standard deviation] years) were examined by a calibrated pediatric dentist. Sociodemographic data were collected from patients' questionnaires and electronic dental records. Results: DMH-Es had a prevalence estimate of six percent versus 40 percent overall of any enamel defect (AED) of the primary second molars (PSMs) and/or the permanent first molars (PFMs). Race/ethnicity was significantly associated with a higher overall prevalence of AED of PSMs but not with the prevalence estimate of DMH-Es. Older age group (10 years or older), living in central Indiana, and water fluoridation were significantly associated with a higher overall prevalence of AEDs (P<0.01) but not with the prevalence of DMH-Es. Caries experience was significantly higher in children with demarcated molar hypomineralization (DMH) of PFMs and/or PSMs than in the group without. Conclusions: DMH-Es prevalence estimate was similar to the global figures. Certain demographic characteristics were significantly associated with the overall prevalence of the enamel defects of the examined teeth.

Accuracy of Model Estimation versus Tanaka and Johnston Arch Length Analysis.

OBJECTIVES: This study examines how accurate pediatric dentists are at estimating dental arch lengths by comparing their model estimations (guesstimating the arch length without measuring) to the Tanaka and Johnston mixed dentition arch length analysis. STUDY DESIGN: This study consisted of two parts, a survey of practitioners and a model estimating and measuring component. The survey was designed and given to 44 pediatric dentists to determine how many were practicing orthodontics and using arch length analyses routinely. Then 18 pediatric dentists and 13 pediatric dental residents examined 20 sets of mixed dentition models and estimated how much space was available. These estimations were compared to the calculated gold standard, the Tanaka and Johnston arch length analysis of the same models. RESULTS AND CONCLUSIONS: More than half of the dentists surveyed that practice comprehensive orthodontics use arch length estimates. Pediatric dentists and pediatric dental residents are just as good as each other at estimating arch length. Pediatric dentists and pediatric dental residents underestimated arch length by -3.6 and -3.1 mm, respectively. More research needs to be done to determine if model estimation is a clinically acceptable way to judge arch length.

Prevalence of molar-incisor hypomineralization and other enamel defects and associated sociodemographic determinants in Indiana.

BACKGROUND: The aim of this study was to determine the prevalence and severity of molar-incisor hypomineralization (MIH) in a cohort of school-aged children in Indiana. METHODS: A calibrated examiner screened eligible school-aged children for MIH and other enamel defects. The authors used the integrated Modified Developmental Defects of Enamel Index and the European Academy of Pediatric Dentistry criteria to examine the permanent first molars, permanent incisors, and primary second molars. The authors used descriptive statistics, exact 95% confidence intervals, and χ2 tests for analysis (α = 5%). RESULTS: A total of 337 participants (mean [standard deviation] age, 9.1 (1.7) years; 52% 6 through 8 years; 66% non-Hispanic white) were examined. The prevalence estimate for MIH was 13% as opposed to a 52% prevalence estimate for any enamel defect (AED) of any of the index teeth. Living in an area with water fluoridation levels greater than 0.7 parts per million or being non-Hispanic black was significantly associated with higher prevalence of AED (P < .05) but not with the prevalence of MIH. Demarcated opacities were the most prevalent defects (43%), followed by atypical restorations (32%). Higher age and higher number of MIH-affected surfaces were associated with larger MIH defect extension (P < .05). CONCLUSIONS: Nearly 1 in 6 children in Indiana had at least 1 permanent first molar with MIH. Water fluoridation levels and race or ethnicity were associated with the prevalence of AED but not with MIH prevalence. PRACTICAL IMPLICATIONS: US dental practitioners should be cognizant that MIH is a common finding. Children with a high number of MIH-affected surfaces would benefit the most from early identification and management as the extension of the defects tends to worsen with age.

An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis.

The microscopic assessment of tissue samples is instrumental for the diagnosis and staging of cancer, and thus guides therapy. However, these assessments demonstrate considerable variability and many regions of the world lack access to trained pathologists. Though artificial intelligence (AI) promises to improve the access and quality of healthcare, the costs of image digitization in pathology and difficulties in deploying AI solutions remain as barriers to real-world use. Here we propose a cost-effective solution: the augmented reality microscope (ARM). The ARM overlays AI-based information onto the current view of the sample in real time, enabling seamless integration of AI into routine workflows. We demonstrate the utility of ARM in the detection of metastatic breast cancer and the identification of prostate cancer, with latency compatible with real-time use. We anticipate that the ARM will remove barriers towards the use of AI designed to improve the accuracy and efficiency of cancer diagnosis.

U.S. Pediatric Dentists' Perception of Molar Incisor Hypomineralization.

Purpose: The purpose of this survey-based study was to target U.S. pediatric dentists in the Midwest region to determine their knowledge, perceptions, and clinical management strategies of molar incisor hypomineralization (MIH). Methods: After obtaining appropriate authorizations, all pediatric dentists identified by the American Academy of Pediatric Dentistry's 2016 to 2017 membership directory in the 12 Midwest states were invited to take part in the study. The questionnaire, adopted from previous studies, incorporated information of the participants' demographics and educational/clinical backgrounds and MIH-focused questions. Descriptive statistics and chi-square tests were used for analysis. An alpha level less than 0.05 was considered statistically significant. Results: A total of 251 out of 975 surveys were completed (26 percent). Nearly all participants were familiar with MIH. The majority reported the MIH prevalence to be less than 10 percent in their clinical practice (62 percent). Most respondents were either very confident (65 percent) or confident (34 percent) when diagnosing teeth with MIH. The most cited clinical challenge in managing MIH teeth was "long-term success of restorations" (79 percent). When analyzed individually, responses differed significantly for different demographics and educational characteristics of the respondents (P<0.05). Conclusion: MIH is generally well acknowledged by U.S. Midwest pediatric dentists, with differences related to their perceptions of the condition's prevalence as well as clinical and restorative management challenges.

Exploring the loblolly pine (Pinus taeda L.) genome by BAC sequencing and Cot analysis.

Loblolly pine (LP; Pinus taeda L.) is an economically and ecologically important tree in the southeastern U.S. To advance understanding of the loblolly pine (LP; Pinus taeda L.) genome, we sequenced and analyzed 100 BAC clones and performed a Cot analysis. The Cot analysis indicates that the genome is composed of 57, 24, and 10% highly-repetitive, moderately-repetitive, and single/low-copy sequences, respectively (the remaining 9% of the genome is a combination of fold back and damaged DNA). Although single/low-copy DNA only accounts for 10% of the LP genome, the amount of single/low-copy DNA in LP is still 14 times the size of the Arabidopsis genome. Since gene numbers in LP are similar to those in Arabidopsis, much of the single/low-copy DNA of LP would appear to be composed of DNA that is both gene- and repeat-poor. Macroarrays prepared from a LP bacterial artificial chromosome (BAC) library were hybridized with probes designed from cell wall synthesis/wood development cDNAs, and 50 of the "targeted" clones were selected for further analysis. An additional 25 clones were selected because they contained few repeats, while 25 more clones were selected at random. The 100 BAC clones were Sanger sequenced and assembled. Of the targeted BACs, 80% contained all or part of the cDNA used to target them. One targeted BAC was found to contain fungal DNA and was eliminated from further analysis. Combinations of similarity-based and ab initio gene prediction approaches were utilized to identify and characterize potential coding regions in the 99 BACs containing LP DNA. From this analysis, we identified 154 gene models (GMs) representing both putative protein-coding genes and likely pseudogenes. Ten of the GMs (all of which were specifically targeted) had enough support to be classified as intact genes. Interestingly, the 154 GMs had statistically indistinguishable (α = 0.05) distributions in the targeted and random BAC clones (15.18 and 12.61 GM/Mb, respectively), whereas the low-repeat BACs contained significantly fewer GMs (7.08 GM/Mb). However, when GM length was considered, the targeted BACs had a significantly greater percentage of their length in GMs (3.26%) when compared to random (1.63%) and low-repeat (0.62%) BACs. The results of our study provide insight into LP evolution and inform ongoing efforts to produce a reference genome sequence for LP, while characterization of genes involved in cell wall production highlights carbon metabolism pathways that can be leveraged for increasing wood production.

Scalable and accurate deep learning with electronic health records.

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient's chart.

Proteomic analysis of the venom and venom sac of the woodwasp, Sirex noctilio - Towards understanding its biological impact.

UNLABELLED: The European horntail woodwasp, Sirex noctilio, is an invasive insect that attacks conifer hosts, particularly Pinus species. Venom injected by female S. noctilio, together with its symbiotic fungus, damages the normal physiology of Pinus, leading to death of the tree. To identify the proteinaceous components in the venom and uncover the interplay between venom proteins and tree proteins, clarification of the overall profile of proteins produced in the venom gland apparatus was carried out in this work. The venom sac proteome utilised in-solution digested in either a natural or deglycosylated state, prior to nanoHPLC LTQ-Orbitrap under CID/ETD mode. Here, we report the identification of 1454 and 1225 proteins in venom and sac, respectively, with 410 mutual proteins. Approximately 90 proteins were predicted to be secretory, of which 8 have features characteristic of toxins. Chemosensory binding proteins were also identified. Gene ontology and KEGG pathway analysis were employed to predict the protein functions and biological pathways in venom and sac. Protein-protein interaction (PPI) analysis suggested that one-step responses represent the majority of the Sirex-Pinus PPIs, and the proteins representing network hub nodes could be of importance for the development of pest management strategies. SIGNIFICANCE: The woodwasp Sirex noctilio is an invasive species in many parts of the world, including Australia and North America, where it is considered within the top 10 most serious forest insects. Where they have been introduced, the female woodwasps attack living pine trees, causing significant economic losses. Central to this destruction is the woodwasp's life cycle requirement to bore a hole to deposit eggs and a toxic mucus that disables the tree's network for transporting water and nutrients, yet aids in larval survival. Here we specifically examine the mucus gland apparatus and its contents, revealing the protein components that together with 'noctilisin' facilitate this complex association. The identification of chemosensory binding proteins further supports a role for the woodwasp ovipositor as an instrument for early stages of host tree selection. These findings could provide important clues towards the development of novel control tools against this pest.

Eleventh-century shift in timber procurement areas for the great houses of Chaco Canyon.

An enduring mystery from the great houses of Chaco Canyon is the origin of more than 240,000 construction timbers. We evaluate probable timber procurement areas for seven great houses by applying tree-ring width-based sourcing to a set of 170 timbers. To our knowledge, this is the first use of tree rings to assess timber origins in the southwestern United States. We found that the Chuska and Zuni Mountains (>75 km distant) were the most likely sources, accounting for 70% of timbers. Most notably, procurement areas changed through time. Before 1020 Common Era (CE) nearly all timbers originated from the Zunis (a previously unrecognized source), but by 1060 CE the Chuskas eclipsed the Zuni area in total wood imports. This shift occurred at the onset of Chaco florescence in the 11th century, a time with substantial expansion of existing great houses and the addition of seven new great houses in the Chaco Core area. It also coincides with the proliferation of Chuskan stone tools and pottery in the archaeological record of Chaco Canyon, further underscoring the link between land use and occupation in the Chuska area and the peak of great house construction. Our findings, based on the most temporally specific and replicated evidence of Chacoan resource procurement obtained to date, corroborate the long-standing but recently challenged interpretation that large numbers of timbers were harvested and transported from distant mountain ranges to build the great houses at Chaco Canyon.