Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias.

BACKGROUND: Skeletal dysplasias collectively affect a large number of patients worldwide. Most of these disorders cause growth anomalies. Hence, evaluating skeletal maturity via the determination of bone age (BA) is a useful tool. Moreover, consecutive BA measurements are crucial for monitoring the growth of patients with such disorders, especially for timing hormonal treatment or orthopedic interventions. However, manual BA assessment is time-consuming and suffers from high intra- and inter-rater variability. This is further exacerbated by genetic disorders causing severe skeletal malformations. While numerous approaches to automate BA assessment have been proposed, few are validated for BA assessment on children with skeletal dysplasias. OBJECTIVE: We present Deeplasia, an open-source prior-free deep-learning approach designed for BA assessment specifically validated on patients with skeletal dysplasias. MATERIALS AND METHODS: We trained multiple convolutional neural network models under various conditions and selected three to build a precise model ensemble. We utilized the public BA dataset from the Radiological Society of North America (RSNA) consisting of training, validation, and test subsets containing 12,611, 1,425, and 200 hand and wrist radiographs, respectively. For testing the performance of our model ensemble on dysplastic hands, we retrospectively collected 568 radiographs from 189 patients with molecularly confirmed diagnoses of seven different genetic bone disorders including achondroplasia and hypochondroplasia. A subset of the dysplastic cohort (149 images) was used to estimate the test-retest precision of our model ensemble on longitudinal data. RESULTS: The mean absolute difference of Deeplasia for the RSNA test set (based on the average of six different reference ratings) and dysplastic set (based on the average of two different reference ratings) were 3.87 and 5.84 months, respectively. The test-retest precision of Deeplasia on longitudinal data (2.74 months) is estimated to be similar to a human expert. CONCLUSION: We demonstrated that Deeplasia is competent in assessing the age and monitoring the development of both normal and dysplastic bones.

Sperm capacitation as a predictor of boar fertility.

Prediction of a boar's fertility level has great economic importance for sow herds. After standard sperm morphology and motility metrics are met, approximately 25% of boars have less than 80% conception rates. Due in part to the many factors involved in the fertilization process, a multifactorial model incorporating multiple relevant sperm physiology factors will likely lead to increased understanding of boar fertility. Here we review the current literature on boar sperm capacitation as a predictor of boar fertility. While limited, several studies have provided correlations between the percentage of sperm in an ejaculate that are capable of undergoing sperm capacitation in a chemically defined media and artificial insemination field fertility as well as proteome and other methods. Work summarized here underscores the need for further understanding of boar fertility.

BRD4 degradation blocks expression of MYC and multiple forms of stem cell resistance in Ph+ chronic myeloid leukemia.

In most patients with chronic myeloid leukemia (CML) clonal cells can be kept under control by BCR::ABL1 tyrosine kinase inhibitors (TKI). However, overt resistance or intolerance against these TKI may occur. We identified the epigenetic reader BRD4 and its downstream-effector MYC as growth regulators and therapeutic targets in CML cells. BRD4 and MYC were found to be expressed in primary CML cells, CD34+ /CD38- leukemic stem cells (LSC), and in the CML cell lines KU812, K562, KCL22, and KCL22T315I . The BRD4-targeting drug JQ1 was found to suppress proliferation in KU812 cells and primary leukemic cells in the majority of patients with chronic phase CML. In the blast phase of CML, JQ1 was less effective. However, the BRD4 degrader dBET6 was found to block proliferation and/or survival of primary CML cells in all patients tested, including blast phase CML and CML cells exhibiting the T315I variant of BCR::ABL1. Moreover, dBET6 was found to block MYC expression and to synergize with BCR::ABL1 TKI in inhibiting the proliferation in the JQ1-resistant cell line K562. Furthermore, BRD4 degradation was found to overcome osteoblast-induced TKI resistance of CML LSC in a co-culture system and to block interferon-gamma-induced upregulation of the checkpoint antigen PD-L1 in LSC. Finally, dBET6 was found to suppress the in vitro survival of CML LSC and their engraftment in NSG mice. Together, targeting of BRD4 and MYC through BET degradation sensitizes CML cells against BCR::ABL1 TKI and is a potent approach to overcome multiple forms of drug resistance in CML LSC.

Platinum Nanoparticle Size and Density Impacts Purine Electrochemistry with Fast-Scan Cyclic Voltammetry.

We demonstrate the density and shape of platinum nanoparticles (PtNP) on carbon-fiber microelectrodes with fast-scan cyclic voltammetry (FSCV) directly impacts detection of adenosine. Previously, we showed that metal nanoparticle-modified carbon significantly improves adenine-based purine detection; however, how the size and shape of the particles impact electrochemical detection was not investigated. Electrochemical investigations of how the surface topology and morphology impacts detection is necessary for designing ultrasensitive electrodes and for expanding fundamental knowledge of electrode-analyte interactions. To change the density and shape of the PtNP's on the surface, we varied the concentration of K2PtCl6 and electrodeposition time. We show that increasing the concentration of K2PtCl6 increases the density of PtNP's while increasing the electrodeposition time impacts both the density and size. These changes manipulate the adsorption behavior which impacts sensitivity. Based on these results, an optimal electrodeposition procedure was determined to be 1.0 mg/mL of K2PtCl6 deposited for 45 s and this results in an average increase in adenosine detection by 3.5 ±0.3-fold. Interestingly, increasing the size and density of PtNPs negatively impacts dopamine detection. Overall, this work provides fundamental insights into the differences between adenosine and dopamine interaction at electrode surfaces.

Quality of life of patients following orthognathic surgery.

The aim of this study was to evaluate how patients with dentofacial deficiency who have undergone orthognathic surgery perceive their quality of life (QoL) with respect to functional, esthetic, and psychosocial issues. In an observational, cross-sectional, descriptive, and quantitative study, 10 patients who had undergone orthognathic surgery answered questionnaires used internationally for assessing QoL: the Short Form Health Survey (SF-36), Oral Health Impact Profile 14 (OHIP-14), and Orthognathic Quality of Life Questionnaire (OQLQ). In addition, the patients completed the Self-Perception Questionnaire of Esteem, Appearance, and Interpersonal Relationships (ASR-26), which explored the differences between their current self-esteem, appearance satisfaction, and interpersonal relationships and their memories of their presurgical feelings about those topics. The data were submitted to descriptive and multivariable statistical analyses. There was a statistically significant difference between the preoperative and postoperative periods regarding self-esteem, appearance satisfaction, and professional relationships (P < 0.05). The data collected with the SF-36, OHIP-14, and OQLQ questionnaires showed high internal consistency (Cronbach α coefficient). The index (mean) scores for the SF-36 (81.5), OHIP-14 (0.6), and OQLQ (5.0) were close to the conditions of high QoL. Principal component analysis revealed 3 distinct groups of patients, and 70% of patients composed a group with high QoL scores, showing no complaints of physical pain, functional limitation, psychological discomfort, social disability, or excessive concern about their oral condition. In this small sample of patients, orthognathic surgery resulted in improved health-related QoL with variations among patients regarding physical pain, psychological discomfort, oral function, facial esthetics, physical function, social function, and self-awareness of facial deformity. The results of this study indicate the importance of applying a questionnaire in individuals who have undergone orthognathic surgery to investigate their personal motivations for treatment and which physical, social, and psychological problems are limiting their QoL.

Proposed diagnostic criteria for classical chronic myelomonocytic leukemia (CMML), CMML variants and pre-CMML conditions.

Chronic myelomonocytic leukemia (CMML) is a myeloid neoplasm characterized by dysplasia, abnormal production and accumulation of monocytic cells and an elevated risk of transforming into acute leukemia. Over the past two decades, our knowledge about the pathogenesis and molecular mechanisms in CMML has increased substantially. In parallel, better diagnostic criteria and therapeutic strategies have been developed. However, many questions remain regarding prognostication and optimal therapy. In addition, there is a need to define potential pre-phases of CMML and special CMML variants, and to separate these entities from each other and from conditions mimicking CMML. To address these unmet needs, an international consensus group met in a Working Conference in August 2018 and discussed open questions and issues around CMML, its variants, and pre-CMML conditions. The outcomes of this meeting are summarized herein and include diag nostic criteria and a proposed classification of pre-CMML conditions as well as refined minimal diagnostic criteria for classical CMML and special CMML variants, including oligomonocytic CMML and CMML associated with systemic mastocytosis. Moreover, we propose diagnostic standards and tools to distinguish between 'normal', pre-CMML and CMML entities. These criteria and standards should facilitate diagnostic and prognostic evaluations in daily practice and clinical studies in applied hematology.

Lab-on-a-Drone: Toward Pinpoint Deployment of Smartphone-Enabled Nucleic Acid-Based Diagnostics for Mobile Health Care.

We introduce a portable biochemical analysis platform for rapid field deployment of nucleic acid-based diagnostics using consumer-class quadcopter drones. This approach exploits the ability to isothermally perform the polymerase chain reaction (PCR) with a single heater, enabling the system to be operated using standard 5 V USB sources that power mobile devices (via battery, solar, or hand crank action). Time-resolved fluorescence detection and quantification is achieved using a smartphone camera and integrated image analysis app. Standard sample preparation is enabled by leveraging the drone's motors as centrifuges via 3D printed snap-on attachments. These advancements make it possible to build a complete DNA/RNA analysis system at a cost of ∼$50 ($US). Our instrument is rugged and versatile, enabling pinpoint deployment of sophisticated diagnostics to distributed field sites. This capability is demonstrated by successful in-flight replication of Staphylococcus aureus and λ-phage DNA targets in under 20 min. The ability to perform rapid in-flight assays with smartphone connectivity eliminates delays between sample collection and analysis so that test results can be delivered in minutes, suggesting new possibilities for drone-based systems to function in broader and more sophisticated roles beyond cargo transport and imaging.

Deep learning classification method for boar sperm morphology analysis.

BACKGROUND: Boar semen quality emphasizes three major criteria: sperm concentration, motility, and morphology. Methods to analyze concentration and motility quickly and objectively readily exist, but few exist for analyzing morphology outside of subjective manual counting. Other vital factors for fertilization, like acrosome health, lack efficient detection methods due to limitations in detection by the human eye and costly biomarker analysis, which is rarely used in semen diagnostics. OBJECTIVE: To overcome these challenges, we propose a novel approach integrating deep-learning technology with high-throughput image-based flow cytometry (IBFC) for objective and accurate analysis of both morphology and label-free acrosome health of thousands of individual spermatozoa at once, as opposed to manually counting on a microscope slide. MATERIALS AND METHODS: Images of 10,000 spermatozoa were captured using an IBFC and manually annotated based on the primary morphological defect or acrosome health status for the training of the convolutional neural network (CNN). The CNN used these images to train and then applied that training to unannotated images to predict the model accuracy. RESULTS: Using the CNNs, high F1 scores of 96.73%, 98.55%, and 99.31% for 20x, 40x, and 60x magnifications, respectively, for morphological classification were attained. Additionally, the model demonstrates an F1 score of 99.8% in detecting subtle acrosome health variations at the 60x magnification. DISCUSSION AND CONCLUSIONS: We have established an integrated approach to rapidly collect and classify morphological defects and acrosome health status, without the use of manual counting or biomarker labeling. Our study underscores the potential of artificial intelligence in semen diagnostics, reducing technician variability, streamlining assays, and facilitating the development of additional label-free detection methods. This innovative approach addresses the barriers hindering biomarker adoption in semen analysis, offering a promising avenue for enhancing reproductive health assessments.

Metal Nanoparticle Modified Carbon-Fiber Microelectrodes Enhance Adenosine Triphosphate Surface Interactions with Fast-Scan Cyclic Voltammetry.

Adenosine triphosphate (ATP) is an important rapid signaling molecule involved in a host of pathologies in the body. Historically, ATP is difficult to directly detect electrochemically with fast-scan cyclic voltammetry (FSCV) due to limited interactions at bare carbon-fibers. Systematic investigations of how ATP interacts at electrode surfaces is necessary for developing more sensitive electrochemical detection methods. Here, we have developed gold nanoparticle (AuNP), and platinum nanoparticle (PtNP) modified carbon-fiber microelectrodes coupled to FSCV to measure the extent to which ATP interacts at metal nanoparticle-modified surfaces and to improve the sensitivity of direct electrochemical detection. AuNP and PtNPs were electrodeposited on the carbon-fiber surface by scanning from -1.2 to 1.5 V for 30 s in 0.5 mg/mL HAuCl4 or 0.5 mg/mLK2PtCl6. Overall, we demonstrate an average 4.1 ± 1.0-fold increase in oxidative ATP current at AuNP-modified and a 3.5 ± 0.3-fold increase at PtNP-modified electrodes. Metal nanoparticle-modified surfaces promoted improved electrocatalytic conversion of ATP oxidation products at the surface, facilitated enhanced adsorption strength and surface coverage, and significantly improved sensitivity. ATP was successfully detected within living murine lymph node tissue following exogenous application. Overall, this study demonstrates a detailed characterization of ATP oxidation at metal nanoparticle surfaces and a significantly improved method for direct electrochemical detection of ATP in tissue.

A Novel Method for Adult Height Prediction in Children With Idiopathic Short Stature Derived From a German-Dutch Cohort.

Context: Prediction of adult height (AH) is important in clinical management of short children. The conventional methods of Bayley-Pinneau (BP) or Roche-Wainer-Thissen (RWT) have limitations. Objective: We aimed to develop a set of algorithms for AH prediction in patients with idiopathic short stature (ISS) which are specific for combinations of predicting variables. Methods: Demographic and auxologic data were collected in childhood (1980s) and at AH (1990s). Data were collected by Dutch and German referral centers for pediatric endocrinology. A total of 292 subjects with ISS (219 male, 73 female) were enrolled. The population was randomly split into modeling (n = 235) and validation (n = 57) cohorts. Linear multi-regression analysis was performed with predicted AH (PAH) as response variable and combinations of chronological age (CA), baseline height, parental heights, relative bone age (BA/CA), birth weight, and sex as exploratory variables. Results: Ten models including different exploratory variables were selected with adjusted R² ranging from 0.84 to 0.78 and prediction errors from 3.16 to 3.68 cm. Applied to the validation cohort, mean residuals (PAH minus observed AH) ranged from -0.29 to -0.82 cm, while the conventional methods showed some overprediction (BP: +0.53 cm; RWT: +1.33 cm; projected AH: +3.81 cm). There was no significant trend of residuals with PAH or any exploratory variables, in contrast to BP and projected AH. Conclusion: This set of 10 multi-regression algorithms, developed specifically for children with ISS, provides a flexible tool for AH prediction with better accuracy than the conventional methods.