Lineage-specific transcriptional regulation of DICER by MITF in melanocytes.

DICER is a central regulator of microRNA maturation. However, little is known about mechanisms regulating its expression in development or disease. While profiling miRNA expression in differentiating melanocytes, two populations were observed: some upregulated at the pre-miRNA stage, and others upregulated as mature miRNAs (with stable pre-miRNA levels). Conversion of pre-miRNAs to fully processed miRNAs appeared to be dependent upon stimulation of DICER expression--an event found to occur via direct transcriptional targeting of DICER by the melanocyte master transcriptional regulator MITF. MITF binds and activates a conserved regulatory element upstream of DICER's transcriptional start site upon melanocyte differentiation. Targeted KO of DICER is lethal to melanocytes, at least partly via DICER-dependent processing of the pre-miRNA-17 approximately 92 cluster thus targeting BIM, a known proapoptotic regulator of melanocyte survival. These observations highlight a central mechanism underlying lineage-specific miRNA regulation which could exist for other cell types during development.

Natural Language Processing for Imaging Protocol Assignment: Machine Learning for Multiclass Classification of Abdominal CT Protocols Using Indication Text Data.

A correct protocol assignment is critical to high-quality imaging examinations, and its automation can be amenable to natural language processing (NLP). Assigning protocols for abdominal imaging CT scans is particularly challenging given the multiple organ specific indications and parameters. We compared conventional machine learning, deep learning, and automated machine learning builder workflows for this multiclass text classification task. A total of 94,501 CT studies performed over 4 years and their assigned protocols were obtained. Text data associated with each study including the ordering provider generated free text study indication and ICD codes were used for NLP analysis and protocol class prediction. The data was classified into one of 11 abdominal CT protocol classes before and after augmentations used to account for imbalances in the class sample sizes. Four machine learning (ML) algorithms, one deep learning algorithm, and an automated machine learning (AutoML) builder were used for the multilabel classification task: Random Forest (RF), Tree Ensemble (TE), Gradient Boosted Tree (GBT), multi-layer perceptron (MLP), Universal Language Model Fine-tuning (ULMFiT), and Google's AutoML builder (Alphabet, Inc., Mountain View, CA), respectively. On the unbalanced dataset, the manually coded algorithms all performed similarly with F1 scores of 0.811 for RF, 0.813 for TE, 0.813 for GBT, 0.828 for MLP, and 0.847 for ULMFiT. The AutoML builder performed better with a F1 score of 0.854. On the balanced dataset, the tree ensemble machine learning algorithm performed the best with an F1 score of 0.803 and a Cohen's kappa of 0.612. AutoML methods took a longer time for completion of NLP model training and evaluation, 4 h and 45 min compared to an average of 51 min for manual methods. Machine learning and natural language processing can be used for the complex multiclass classification task of abdominal imaging CT scan protocol assignment.

Ransomware Recovery and Imaging Operations: Lessons Learned and Planning Considerations.

In this era, almost all healthcare workflows are digital and rely on robust institutional networks; a ransomware attack in a healthcare system can have catastrophic patient care consequences. The usual downtime processes in an institution might not address the breadth of this disruption and timelines for recovery. This article shares our lessons learned from ransomware recovery. From this experience, a four-phase recovery planning framework has been developed. The primary focus is on acute patient care, incident communication, and emergency imaging operations in the initial phase. In the next phase, continued digital asset unavailability necessitates a transition to long-term analog workflows. In the infrastructure recovery and reconciliation phases, each taking weeks or months, the emphasis is on rebuilding a ransomware-free environment and reconciling the data accrued during extended downtime. In preparation for future events, we have initiated a continuous readiness process. A response task force has been formed to guide physicians, technologists, nurses, and informatics units on recovery workflows appropriate for extended downtime and keeping these procedures updated. Incident command structure has been discussed for communications and resource allocation during a ransomware attack, possibly in the context of a multi-incident scenario such as that involving concurrent staffing shortage amidst a pandemic. Finally, we discuss considerations for tabletop simulation, which may be valuable to the planning process.

Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance.

Automated quantitative and probabilistic medical image analysis has the potential to improve the accuracy and efficiency of the radiology workflow. We sought to determine whether AI systems for brain MRI diagnosis could be used as a clinical decision support tool to augment radiologist performance. We utilized previously developed AI systems that combine convolutional neural networks and expert-derived Bayesian networks to distinguish among 50 diagnostic entities on multimodal brain MRIs. We tested whether these systems could augment radiologist performance through an interactive clinical decision support tool known as Adaptive Radiology Interpretation and Education System (ARIES) in 194 test cases. Four radiology residents and three academic neuroradiologists viewed half of the cases unassisted and half with the results of the AI system displayed on ARIES. Diagnostic accuracy of radiologists for top diagnosis (TDx) and top three differential diagnosis (T3DDx) was compared with and without ARIES. Radiology resident performance was significantly better with ARIES for both TDx (55% vs 30%; P < .001) and T3DDx (79% vs 52%; P = 0.002), with the largest improvement for rare diseases (39% increase for T3DDx; P < 0.001). There was no significant difference between attending performance with and without ARIES for TDx (72% vs 69%; P = 0.48) or T3DDx (86% vs 89%; P = 0.39). These findings suggest that a hybrid deep learning and Bayesian inference clinical decision support system has the potential to augment diagnostic accuracy of non-specialists to approach the level of subspecialists for a large array of diseases on brain MRI.

Reduced expression of ribosomal proteins relieves microRNA-mediated repression.

MicroRNAs (miRNAs) regulate physiological and pathological processes by inducing posttranscriptional repression of target messenger RNAs (mRNAs) via incompletely understood mechanisms. To discover factors required for human miRNA activity, we performed an RNAi screen using a reporter cell line of miRNA-mediated repression of translation initiation. We report that reduced expression of ribosomal protein genes (RPGs) dissociated miRNA complexes from target mRNAs, leading to increased polysome association, translation, and stability of miRNA-targeted mRNAs relative to untargeted mRNAs. RNA sequencing of polysomes indicated substantial overlap in sets of genes exhibiting increased or decreased polysomal association after Argonaute or RPG knockdowns, suggesting similarity in affected pathways. miRNA profiling of monosomes and polysomes demonstrated that miRNAs cosediment with ribosomes. RPG knockdowns decreased miRNAs in monosomes and increased their target mRNAs in polysomes. Our data show that most miRNAs repress translation and that the levels of RPGs modulate miRNA-mediated repression of translation initiation.

From Data to Value: How Artificial Intelligence Augments the Radiology Business to Create Value.

The radiology practice has access to a wealth of data in the radiologist information system, dictation reports, and electronic health records. Although many artificial intelligence applications in radiology have focused on computer vision and the interpretive use cases, many opportunities exist to enhance the radiologist's value proposition through business analytics. This article explores how AI lends an analytical lens to the radiology practice to create value.

Correlation between attention-deficit/hyperactivity disorder, its pharmacotherapy and thyroid dysfunction: A nationwide population-based study in Taiwan.

OBJECTIVE: The aim of this study was to examine the comorbid rates of thyroid dysfunction among patients with attention-deficit/hyperactivity disorder (ADHD) and the general population. We further examined whether pharmacotherapy affects ADHD patients' risk of developing thyroid dysfunction. DESIGN AND MEASUREMENT: We recruited 75 247 newly diagnosed ADHD patient and 75 247 healthy controls between January 1999 and December 2011 from the National Health Insurance database in Taiwan. We compared hyperthyroidism, hypothyroidism and other common paediatric psychiatric diseases between ADHD patients and controls. We carried out logistic regression analysis to identify an independent factor for predicting thyroid dysfunction. Furthermore, we analysed the time sequence of the diagnosis and the risk of developing a thyroid disorder after receiving pharmacotherapy. RESULTS: Compared to the control group, the ADHD group had higher comorbidity rates of both hyperthyroidism (1.1% of ADHD vs 0.7% of controls, aOR: 1.72, P < 0.001) and hypothyroidism (0.6% of ADHD vs 0.2% of controls, aOR: 2.23, P < 0.001). Of the ADHD patients with comorbid thyroid dysfunction, about two-thirds and half of patients were diagnosed with ADHD prior to their diagnosis of hyperthyroidism and hypothyroidism, respectively. Furthermore, pharmacotherapy had no significant influence on the risk of developing hyperthyroidism (aHR: 1.09, P = 0.363) or hypothyroidism (aHR: 0.95, P = 0.719) among ADHD patients. CONCLUSION: Patients with ADHD had greater comorbid rates with thyroid dysfunction than the control subjects, but pharmacotherapy for treating ADHD did not affect thyroid dysfunction later in life. However, these findings should be further verified using a clinical cohort with comprehensive laboratory assessment in future.

Intronic miR-211 assumes the tumor suppressive function of its host gene in melanoma.

When it escapes early detection, malignant melanoma becomes a highly lethal and treatment-refractory cancer. Melastatin is greatly downregulated in metastatic melanomas and is widely believed to function as a melanoma tumor suppressor. Here we report that tumor suppressive activity is not mediated by melastatin but instead by a microRNA (miR-211) hosted within an intron of melastatin. Increasing expression of miR-211 but not melastatin reduced migration and invasion of malignant and highly invasive human melanomas characterized by low levels of melastatin and miR-211. An unbiased network analysis of melanoma-expressed genes filtered for their roles in metastasis identified three central node genes: IGF2R, TGFBR2, and NFAT5. Expression of these genes was reduced by miR-211, and knockdown of each gene phenocopied the effects of increased miR-211 on melanoma invasiveness. These data implicate miR-211 as a suppressor of melanoma invasion whose expression is silenced or selected against via suppression of the entire melastatin locus during human melanoma progression.

IoT in Radiology: Using Raspberry Pi to Automatically Log Telephone Calls in the Reading Room.

The work environment for medical imaging such as distractions, ergonomics, distance, temperature, humidity, and lighting conditions generates a paucity of data and is difficult to analyze. The emergence of Internet of Things (IoT) with decreasing cost of single-board computers like Raspberry Pi makes creating customized hardware to collect data from the clinical environment within the reach of a clinical imaging informaticist. This article will walk the reader through a series of basic project using a variety sensors and devices in conjunction with a Pi to gather data, culminating in a complex example designed to automatically detect and log telephone calls.

Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports.

A significant volume of medical data remains unstructured. Natural language processing (NLP) and machine learning (ML) techniques have shown to successfully extract insights from radiology reports. However, the codependent effects of NLP and ML in this context have not been well-studied. Between April 1, 2015 and November 1, 2016, 9418 cross-sectional abdomen/pelvis CT and MR examinations containing our internal structured reporting element for cancer were separated into four categories: Progression, Stable Disease, Improvement, or No Cancer. We combined each of three NLP techniques with five ML algorithms to predict the assigned label using the unstructured report text and compared the performance of each combination. The three NLP algorithms included term frequency-inverse document frequency (TF-IDF), term frequency weighting (TF), and 16-bit feature hashing. The ML algorithms included logistic regression (LR), random decision forest (RDF), one-vs-all support vector machine (SVM), one-vs-all Bayes point machine (BPM), and fully connected neural network (NN). The best-performing NLP model consisted of tokenized unigrams and bigrams with TF-IDF. Increasing N-gram length yielded little to no added benefit for most ML algorithms. With all parameters optimized, SVM had the best performance on the test dataset, with 90.6 average accuracy and F score of 0.813. The interplay between ML and NLP algorithms and their effect on interpretation accuracy is complex. The best accuracy is achieved when both algorithms are optimized concurrently.

Toward Data-Driven Radiology Education-Early Experience Building Multi-Institutional Academic Trainee Interpretation Log Database (MATILDA).

The residency review committee of the Accreditation Council of Graduate Medical Education (ACGME) collects data on resident exam volume and sets minimum requirements. However, this data is not made readily available, and the ACGME does not share their tools or methodology. It is therefore difficult to assess the integrity of the data and determine if it truly reflects relevant aspects of the resident experience. This manuscript describes our experience creating a multi-institutional case log, incorporating data from three American diagnostic radiology residency programs. Each of the three sites independently established automated query pipelines from the various radiology information systems in their respective hospital groups, thereby creating a resident-specific database. Then, the three institutional resident case log databases were aggregated into a single centralized database schema. Three hundred thirty residents and 2,905,923 radiologic examinations over a 4-year span were catalogued using 11 ACGME categories. Our experience highlights big data challenges including internal data heterogeneity and external data discrepancies faced by informatics researchers.

Unique patterns of transcript and miRNA expression in the South American strong voltage electric eel (Electrophorus electricus).

BACKGROUND: With its unique ability to produce high-voltage electric discharges in excess of 600 volts, the South American strong voltage electric eel (Electrophorus electricus) has played an important role in the history of science. Remarkably little is understood about the molecular nature of its electric organs. RESULTS: We present an in-depth analysis of the genome of E. electricus, including the transcriptomes of eight mature tissues: brain, spinal cord, kidney, heart, skeletal muscle, Sachs' electric organ, main electric organ, and Hunter's electric organ. A gene set enrichment analysis based on gene ontology reveals enriched functions in all three electric organs related to transmembrane transport, androgen binding, and signaling. This study also represents the first analysis of miRNA in electric fish. It identified a number of miRNAs displaying electric organ-specific expression patterns, including one novel miRNA highly over-expressed in all three electric organs of E. electricus. All three electric organ tissues also express three conserved miRNAs that have been reported to inhibit muscle development in mammals, suggesting that miRNA-dependent regulation of gene expression might play an important role in specifying an electric organ identity from its muscle precursor. These miRNA data were supported using another complete miRNA profile from muscle and electric organ tissues of a second gymnotiform species. CONCLUSIONS: Our work on the E. electricus genome and eight tissue-specific gene expression profiles will greatly facilitate future research on determining the coding and regulatory sequences that specify the function, development, and evolution of electric organs. Moreover, these data and future studies will be informed by the first comprehensive analysis of miRNA expression in an electric fish presented here.

Rubuspuyumaensis, a new species of Rosaceae from Taiwan.

Background: The genus Rubus L. (Rosaceae), comprising approximately 750 species and classified into 12 subgenera, is distributed worldwide and is one of the largest plant genera. In Taiwan, Rubus comprises 41 taxa, including 35 species, three varieties and three hybrids. Amongst the genus Rubus, the species, previously recorded as R.howii in Taiwan, was misidentified and this study recognised it as a new species. New information: Due to its distribution mainly in south-eastern Taiwan, we named this new species as Rubuspuyumaensis, after the local aborigine tribe Puyuma. Taxonomic descriptions and colour photographs of the new species are provided to assist in identification. R.puyumaensis is most similar to R.howii and R.refractus. They can be distinguished by the colour of young leaves, leaf shape, arrangement of florets, trichomes of inflorescences, size of sepal lobes, petal colour, types and trichomes of filaments and the length of stamens and pistils.

Skeletal muscle quantity and quality evaluation in heart failure: comparing thoracic versus abdominopelvic CT approaches.

Given the critical role of skeletal muscle in healthy aging, low muscle mass (myopenia) and quality (myosteatosis) can be used as predictors of poor functional and cardiometabolic outcomes. Myopenia is also a part of sarcopenia and malnutrition diagnostic criteria. However, there is limited evidence for using chest computed tomography (CT) to evaluate muscle health. We aimed to compare chest CT landmarks to the widely used L3 vertebra for single-slice skeletal muscle evaluation in patients with heart failure (HF). Patients admitted for acute decompensated HF between January 2017 and December 2018 were retrospectively analyzed. Body composition measurements were made on CT of the chest and abdomen/pelvis with or without contrast one month before discharge. Skeletal muscle index (SMI) and intermuscular adipose tissue percentage (IMAT%) were calculated at several thoracic levels (above the aortic arch, T8, and T12) and correlated to the widely used L3 level. A total of 200 patients were included, 89 (44.5%) female. The strongest correlation of thoracic SMI (for muscle quantity) and IMAT% (for muscle quality) with L3 was at the T12 level (r = 0.834, p < 0.001 and r = 0.757, p < 0.001, respectively). Cutoffs to identify low muscle mass for T12 SMI (derived from the lowest sex-stratified L3 SMI tertile) were 31.1 cm²/m² in men and 26.3 cm²/m² in women. SMI and IMAT% at T12 had excellent correlations with the widely used L3 level for muscle quantity and quality evaluation in patients with HF.

Combined Prognostic Impact of Low Muscle Mass and Hypoalbuminemia in Patients Hospitalized for Heart Failure: A Retrospective Cohort Study.

BACKGROUND: Sarcopenia and hypoalbuminemia have been identified as independent predictors of increased adverse outcomes, including mortality and readmissions, in hospitalized older adults with acute decompensated heart failure (ADHF). However, the impact of coexisting sarcopenia and hypoalbuminemia on morbidity and death in adults with ADHF has not yet been investigated. We aimed to investigate the combined effects of lower muscle mass (LMM) as a surrogate for sarcopenia and hypoalbuminemia on in-hospital and postdischarge outcomes of patients hospitalized for ADHF. METHODS AND RESULTS: A total of 385 patients admitted for ADHF between 2017 and 2020 at a single institution were retrospectively identified. Demographic and clinical data were collected, including serum albumin levels at admission and discharge. Skeletal muscle indices were derived from semi-automated segmentation software analysis on axial chest computed tomography at the twelfth vertebral level. Our analysis revealed that patients who had LMM with admission hypoalbuminemia experienced increased diagnoses of infection and delirium with longer hospital length of stay and more frequent discharge to a facility. Upon discharge, 27.9% of patients had higher muscle mass without discharge hypoalbuminemia (reference group), 9.7% had LMM without discharge hypoalbuminemia, 38.4% had higher muscle mass with discharge hypoalbuminemia, and 24.0% had LMM with discharge hypoalbuminemia; mortality rates were 37.6%, 51.4%, 48.9%, and 63.2%, respectively. 1- and 3-year mortality risks were highest in those with LMM and discharge hypoalbuminemia; this relationship remained significant over a median 23.6 (3.1-33.8) months follow-up time despite multivariable adjustments (hazard ratio, 2.03 [95% CI, 1.31-3.16]; P=0.002). CONCLUSIONS: Hospitalization with ADHF, LMM, and hypoalbuminemia portend heightened mortality risk.

Pre-test Prediction of Non-ischemic Cardiomyopathies using Time-Series EHR Data.

Clinical imaging is an important diagnostic test to diagnose non-ischemic cardiomyopathies (NICM). However, accurate interpretation of imaging studies often requires readers to review patient histories, a time consuming and tedious task. We propose to use time-series analysis to predict the most likely NICMs using longitudinal electronic health records (EHR) as a pseudo-summary of EHR records. Time-series formatted EHR data can provide temporality information important towards accurate prediction of disease. Specifically, we leverage ICD-10 codes and various recurrent neural network architectures for predictive modeling. We trained our models on a large cohort of NICM patients who underwent cardiac magnetic resonance imaging (CMR) and a smaller cohort undergoing echocardiogram. The performance of the proposed technique achieved good micro-area under the curve (0.8357), F1 score (0.5708) and precision at 3 (0.8078) across all models for cardiac magnetic resonance imaging (CMR) but only moderate performance for transthoracic echocardiogram (TTE) of 0.6938, 0.4399 and 0.5864 respectively. We show that our model has the potential to provide accurate pre-test differential diagnosis, thereby potentially reducing clerical burden on physicians.

Low Muscle Mass by Preprocedural Computed Tomography Is Associated With Worse Short-Term Outcomes in Transcatheter Aortic Valve Replacement Recipients.

Low muscle mass (LMM) is associated with worse outcomes in various clinical situations. Traditional frailty markers have been used for preoperative risk stratification in patients who underwent transcatheter aortic valve replacement (TAVR). However, preoperative imaging provides an opportunity to directly quantify skeletal muscle mass to identify patients at higher risk of procedural complications. We reviewed all TAVR recipients from January to December 2018 and included subjects with preprocedural chest computed tomography. Multi-slice automated measurements of skeletal muscle mass were made from the ninth to twelfth thoracic vertebrae and normalized by height squared to obtain skeletal muscle index (cm2/m2). LMM was defined as the lowest gender-stratified skeletal muscle index tertile. Strength testing was collected during pre-TAVR evaluation. Primary outcome was a composite of perioperative complications, 1-year rehospitalization, or 1-year mortality. In our cohort, 238 patients met inclusion criteria, and 80 (33.6%) were identified to have LMM. Patients with LMM were older with lower body mass index, decreased grip strength, lower hemoglobin A1c, and higher N-terminal pro-brain natriuretic peptide. They had greater rates of the composite outcome and 2-year all-cause mortality, which remained significant on multivariable adjustment (hazard ratio 1.71, 95% confidence interval 1.05 to 2.78, p = 0.030 and hazard ratio 2.31, 95% confidence interval 1.02 to 5.24, p = 0.045, respectively) compared with patients without LMM; there was no significant difference in 5-year all-cause mortality. In conclusion, LMM was associated with an increase in the primary composite outcome and 2-year all-cause mortality in TAVR recipients. Using automatic muscle processing software on pre-TAVR computed tomography scans may serve as an additional preoperative risk stratification tool.

Keeping Patient Data Secure in the Age of Radiology Artificial Intelligence: Cybersecurity Considerations and Future Directions.

Artificial intelligence (AI)-based solutions are increasingly being incorporated into radiology workflows. Implementation of AI comes along with cybersecurity risks and challenges that practices should be aware of and mitigate for a successful and secure deployment. In this article, these cybersecurity issues are examined through the lens of the "CIA" triad framework-confidentiality, integrity, and availability. We discuss the implications of implementation configurations and development approaches on data security and confidentiality and the potential impact that the insertion of AI can have on the truthfulness of data, access to data, and the cybersecurity attack surface. Finally, we provide a checklist to address important security considerations before deployment of an AI application, and discuss future advances in AI addressing some of these security concerns.

Climbing strategies of Taiwan climbers.

BACKGROUND: The climbing strategies of lianas and herbaceous vines influence climber competition abilities and survival. The aim of this study was to investigate the climbing strategies of each plant species and observe their organs of origin. RESULTS: The results showed that all Taiwan climbers were approximately 555 species, accounting for 11% of the native flora. Among the 555 climbers, the twining stem type was the most common, with a total of 255 species (46%), the remaining climbing methods accounted for 300 species. Approximately twenty one climbing methods, including nine combination types, were exhibited, of which the most common type was the twining stem, followed by simple scrambling and twining tendrils. Most species of Fabaceae and Apocynaceae were twining stems in dextrorse, excluding Wisteriopsis reticulata and Alyxia taiwanensis, which were in sinistrorse. The prehensile branch of Fissistigma genus, Ventilago genus, and Dalbergia benthamii, originated from second-order or modified stems. In the simple scrambling type, some climbers were covered spines and prickles to attach the host, and the others were clinging to the supports or creeping on the ground without speculation. The hooks or grapnels of the genus Uncaria are derived from the branches, and a pair of curved hooks or a spine of Artabotrys hexapetalus are originated from the inflorescence to tightly attach to a host. The Piper genus use adhesive roots to climb their hosts. Among the genus Trichosanthes, only Trichosanthes homophylla exhibits a combination of twining modified shoots and adhesive roots. Gentianales includes four families with seven climbing mechanisms, while Fabales includes only Fabaceae, which presents six climbing methods. CONCLUSIONS: The twining tendrils had nine organs of origin in Taiwan climber, that these opinions of originated organs might be available to the studies of convergent evolution. The data presented herein provide crucial basic information of the climber habits types and origin structures, which are available for terms standardization to improve field investigation. The terminologies would aid in the establishment of climber habits as commonly taxon-specific and the combination of two climber habits could be a characteristic of taxonomic value.