Accuracy and clinical validity of automated cephalometric analysis using convolutional neural networks

BACKGROUND

This study aimed to assess the error range of cephalometric measurements based on the landmarks detected using cascaded CNNs and determine how horizontal and vertical positional errors of individual landmarks affect lateral cephalometric measurements.

METHODS

In total, 120 lateral cephalograms were obtained consecutively from patients (mean age, 32.5 ± 11.6) who visited the Asan Medical Center, Seoul, Korea, for orthodontic treatment between 2019 and 2021. An automated lateral cephalometric analysis model previously developed from a nationwide multi-centre database was used to digitize the lateral cephalograms. The horizontal and vertical landmark position error attributable to the AI model was defined as the distance between the landmark identified by the human and that identified by the AI model on the x- and y-axes. The differences between the cephalometric measurements based on the landmarks identified by the AI model vs those identified by the human examiner were assessed. The association between the lateral cephalometric measurements and the positioning errors in the landmarks comprising the cephalometric measurement was assessed.

RESULTS

The mean difference in the angular and linear measurements based on AI vs human landmark localization was .99 ± 1.05°, and .80 ± .82 mm, respectively. Significant differences between the measurements derived from AI-based and human localization were observed for all cephalometric variables except SNA, pog-Nperp, facial angle, SN-GoGn, FMA, Bjork sum, U1-SN, U1-FH, IMPA, L1-NB (angular) and interincisal angle.

CONCLUSIONS

The errors in landmark positions, especially those that define reference planes, may significantly affect cephalometric measurements. The possibility of errors generated by automated lateral cephalometric analysis systems should be considered when using such systems for orthodontic diagnoses.

Exotic Dark Matter Search with the Majorana Demonstrator

With excellent energy resolution and ultralow-level radiogenic backgrounds, the high-purity germanium detectors in the Majorana Demonstrator enable searches for several classes of exotic dark matter (DM) models. In this work, we report new experimental limits on keV-scale sterile neutrino DM via the transition magnetic moment from conversion to active neutrinos ν_{s}→ν_{a}. We report new limits on fermionic dark matter absorption (χ+A→ν+A) and sub-GeV DM-nucleus 3→2 scattering (χ+χ+A→ϕ+A), and new exclusion limits for bosonic dark matter (axionlike particles and dark photons). These searches utilize the (1-100)-keV low-energy region of a 37.5-kg y exposure collected by the Demonstrator between May 2016 and November 2019 using a set of ^{76}Ge-enriched detectors whose surface exposure time was carefully controlled, resulting in extremely low levels of cosmogenic activation.

Constraints on the Decay of ^{180m}Ta

^{180m}Ta is a rare nuclear isomer whose decay has never been observed. Its remarkably long lifetime surpasses the half-lives of all other known β and electron capture decays due to the large K-spin differences and small energy differences between the isomeric and lower-energy states. Detecting its decay presents a significant experimental challenge but could shed light on neutrino-induced nucleosynthesis mechanisms, the nature of dark matter, and K-spin violation. For this study, we repurposed the Majorana Demonstrator, an experimental search for the neutrinoless double-beta decay of ^{76}Ge using an array of high-purity germanium detectors, to search for the decay of ^{180m}Ta. More than 17 kg, the largest amount of tantalum metal ever used for such a search, was installed within the ultralow-background detector array. In this Letter, we present results from the first year of Ta data taking and provide an updated limit for the ^{180m}Ta half-life on the different decay channels. With new limits up to 1.5×10^{19}  yr, we improved existing limits by 1-2 orders of magnitude which are the most sensitive searches for a single β and electron capture decay ever achieved. Over all channels, the decay can be excluded for T_{1/2}<0.29×10^{18}  yr.

The prediction of sagittal chin point relapse following two-jaw surgery using machine learning

The study aimed to identify critical factors associated with the surgical stability of pogonion (Pog) by applying machine learning (ML) to predict relapse following two-jaw orthognathic surgery (2 J-OGJ). The sample set comprised 227 patients (110 males and 117 females, 207 training and 20 test sets). Using lateral cephalograms taken at the initial evaluation (T0), pretreatment (T1), after (T2) 2 J-OGS, and post treatment (T3), 55 linear and angular skeletal and dental surgical movements (T2-T1) were measured. Six ML modes were utilized, including classification and regression trees (CART), conditional inference tree (CTREE), and random forest (RF). The training samples were classified into three groups; highly significant (HS) (≥ 4), significant (S) (≥ 2 and < 4), and insignificant (N), depending on Pog relapse. RF indicated that the most important variable that affected relapse rank prediction was ramus inclination (RI), CTREE and CART revealed that a clockwise rotation of more than 3.7 and 1.8 degrees of RI was a risk factor for HS and S groups, respectively. RF, CTREE, and CART were practical tools for predicting surgical stability. More than 1.8 degrees of CW rotation of the ramus during surgery would lead to significant Pog relapse.

Erratum: Search for Spontaneous Radiation from Wave Function Collapse in the Majorana Demonstrator [Phys. Rev. Lett. 129, 080401 (2022)]

This corrects the article DOI: 10.1103/PhysRevLett.129.080401.

Long-Term Influenza Outbreak Forecast Using Time-Precedence Correlation of Web Data

Influenza leads to many deaths every year and is a threat to human health. For effective prevention, traditional national-scale statistical surveillance systems have been developed, and numerous studies have been conducted to predict influenza outbreaks using web data. Most studies have captured the short-term signs of influenza outbreaks, such as one-week prediction using the characteristics of web data uploaded in real time; however, long-term predictions of more than 2-10 weeks are required to effectively cope with influenza outbreaks. In this study, we determined that web data uploaded in real time have a time-precedence relationship with influenza outbreaks. For example, a few weeks before an influenza pandemic, the word "colds" appears frequently in web data. The web data after the appearance of the word "colds" can be used as information for forecasting future influenza outbreaks, which can improve long-term influenza prediction accuracy. In this study, we propose a novel long-term influenza outbreak forecast model utilizing the time precedence between the emergence of web data and an influenza outbreak. Based on the proposed model, we conducted experiments on: 1) selecting suitable web data for long-term influenza prediction; 2) determining whether the proposed model is regionally dependent; and 3) evaluating the accuracy according to the prediction timeframe. The proposed model showed a correlation of 0.87 in the long-term prediction of ten weeks while significantly outperforming other state-of-the-art methods.

Final Result of the Majorana Demonstrator's Search for Neutrinoless Double-β Decay in ^{76}Ge

The Majorana Demonstrator searched for neutrinoless double-β decay (0νββ) of ^{76}Ge using modular arrays of high-purity Ge detectors operated in vacuum cryostats in a low-background shield. The arrays operated with up to 40.4 kg of detectors (27.2 kg enriched to ∼88% in ^{76}Ge). From these measurements, the Demonstrator has accumulated 64.5 kg yr of enriched active exposure. With a world-leading energy resolution of 2.52 keV FWHM at the 2039 keV Q_{ββ} (0.12%), we set a half-life limit of 0νββ in ^{76}Ge at T_{1/2}>8.3×10^{25}  yr (90% C.L.). This provides a range of upper limits on m_{ββ} of (113-269) meV (90% C.L.), depending on the choice of nuclear matrix elements.

When patients teach students empathy: a systematic review of interventions for promoting medical student empathy

BACKGROUND

Despite growing emphasis on empathic care, numerous studies demonstrate diminishing empathy in medical students. Involving patient educators in medical curricula may be a solution. Therefore, we conducted a systematic review to evaluate patient-involved interventions that promote empathy among medical students.

METHOD

A literature search of MEDLINE, Embase, PsycINFO, and ERIC databases was performed using the keywords "empathy," "medical student," and their synonyms. Results were independently screened in duplicate. Conflicts were resolved by group consensus. All English studies describing interventions that promote empathy in medical students engaging patient educators were included. Relevant data was extracted and summarized.

RESULTS

1467 studies were screened. 14 studies were included, of which 10 were pilot studies. Studies included patient involved interventions such as storytelling (5/14), shadowing patients (3/14), recorded videos (3/14), or combinations of methods (3/14). Qualitative measurements of empathy included written feedback and group discussions. Quantitative measurements included validated scales measuring empathy. All studies demonstrated increase in empathy among medical students. Participants reported satisfaction with training and patients reported being proud of giving back by training future physicians.

CONCLUSION

Interventions engaging patient educators were shown to have a positive impact on medical student empathy. Furthermore, patient-led education was shown to increase medical student understanding of subject and knowledge retention while empowering patients. Further implementation of patient-involved education is an important step forward in patient-partnered care and may identify additional advantages of patient engagement in medical education.

Changes in Volatile Compounds in Short-Term High CO2-Treated 'Seolhyang' Strawberry (Fragaria × ananassa) Fruit during Cold Storage

'Seolhyang' strawberry is harvested before it is fully ripened and treated with CO₂ to extend the shelf-life. However, the volatile changes in the 'Seolhyang' strawberry after short-term CO₂ treatment have not been investigated, although the volatile profile is an important quality attribute. Herein, we investigated the effect of short-term high CO₂ treatment on the changes in the composition of volatile compounds in 'Seolhyang' strawberries at two ripening stages (i.e., half-red and bright-red) during cold storage using headspace solid-phase microextraction and gas chromatography-mass spectrometry. Furthermore, the effect of CO₂ treatment on fruit quality with respect to the aroma was investigated. A total of 30 volatile compounds were identified. Storage increased the volatile compound concentrations, and the total concentration of volatiles in the CO₂-treated strawberries was lower than that of the untreated strawberries during storage. The production of some characteristic strawberry volatiles (e.g., 4-methoxy-2,5-dimethyl-3(2H)-furanone) was inhibited in CO₂-treated strawberries. However, CO₂ treatment helped maintain the concentrations of hexanal and 2-hexenal, which are responsible for the fresh odor in strawberries. Interestingly, CO₂ treatment suppressed the production of off-odor volatiles, acetaldehyde, and hexanoic acid during strawberry storage. Thus, short-term CO₂ treatment may help maintain the fresh aroma of strawberries during cold storage.

Search for Spontaneous Radiation from Wave Function Collapse in the Majorana Demonstrator

The Majorana Demonstrator neutrinoless double-beta decay experiment comprises a 44 kg (30 kg enriched in ^{76}Ge) array of p-type, point-contact germanium detectors. With its unprecedented energy resolution and ultralow backgrounds, Majorana also searches for rare event signatures from beyond standard model physics in the low energy region below 100 keV. In this Letter, we test the continuous spontaneous localization (CSL) model, one of the mathematically well-motivated wave function collapse models aimed at solving the long-standing unresolved quantum mechanical measurement problem. While the CSL predicts the existence of a detectable radiation signature in the x-ray domain, we find no evidence of such radiation in the 19-100 keV range in a 37.5 kg-y enriched germanium exposure collected between December 31, 2015, and November 27, 2019, with the Demonstrator. We explored both the non-mass-proportional (n-m-p) and the mass-proportional (m-p) versions of the CSL with two different assumptions: that only the quasifree electrons can emit the x-ray radiation and that the nucleus can coherently emit an amplified radiation. In all cases, we set the most stringent upper limit to date for the white CSL model on the collapse rate, λ, providing a factor of 40-100 improvement in sensitivity over comparable searches. Our limit is the most stringent for large parts of the allowed parameter space. If the result is interpreted in terms of the Diòsi-Penrose gravitational wave function collapse model, the lower bound with a 95% confidence level is almost an order of magnitude improvement over the previous best limit.

Search for Solar Axions via Axion-Photon Coupling with the Majorana Demonstrator

Axions were originally proposed to explain the strong-CP problem in QCD. Through axion-photon coupling, the Sun could be a major source of axions, which could be measured in solid state detection experiments with enhancements due to coherent Primakoff-Bragg scattering. The Majorana Demonstrator experiment has searched for solar axions with a set of ^{76}Ge-enriched high purity germanium detectors using a 33 kg-yr exposure collected between January, 2017 and November, 2019. A temporal-energy analysis gives a new limit on the axion-photon coupling as g_{aγ}<1.45×10^{-9}  GeV^{-1} (95% confidence level) for axions with mass up to 100  eV/c^{2}. This improves laboratory-based limits between about 1  eV/c^{2} and 100  eV/c^{2}.

Accuracy of artificial intelligence-assisted landmark identification in serial lateral cephalograms of Class III patients who underwent orthodontic treatment and two-jaw orthognathic surgery

Objective

To investigate the pattern of accuracy change in artificial intelligence-assisted landmark identification (LI) using a convolutional neural network (CNN) algorithm in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent two-jaw orthognathic surgery.

Methods

A total of 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n = 23 per group) for LI. Each C-III patient in the test set had four Lat-cephs initial (T0), pre-surgery (T1, presence of orthodontic brackets [OBs]), post-surgery (T2, presence of OBs and surgical plates and screws [S-PS]), and debonding (T3, presence of S-PS and fixed retainers [FR]). After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed.

Results

The total mean error was 1.17 mm without significant difference among the four time-points (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points ([T0, T1] vs. [T2, T3]), ANS, A point, and B point showed an increase in error (p < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showeda decrease in error (all p < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups.

Conclusions

The CNN model can be used for LI in serial Lat-cephs despite the presence of OB, S-PS, FR, genioplasty, and bone remodeling.

Results from the Baksan Experiment on Sterile Transitions (BEST)

The Baksan Experiment on Sterile Transitions (BEST) was designed to investigate the deficit of electron neutrinos ν_{e} observed in previous gallium-based radiochemical measurements with high-intensity neutrino sources, commonly referred to as the "gallium anomaly," which could be interpreted as evidence for oscillations between ν_{e} and sterile neutrino (ν_{s}) states. A 3.414-MCi ^{51}Cr ν_{e} source was placed at the center of two nested Ga volumes and measurements were made of the production of ^{71}Ge through the charged current reaction, ^{71}Ga(ν_{e},e^{-})^{71}Ge, at two average distances. The measured production rates for the inner and the outer targets, respectively, are [54.9_{-2.4}^{+2.5}(stat)±1.4(syst)] and [55.6_{-2.6}^{+2.7}(stat)±1.4(syst)] atoms of ^{71}Ge/d. The ratio (R) of the measured rate of ^{71}Ge production at each distance to the expected rate from the known cross section and experimental efficiencies are R_{in}=0.79±0.05 and R_{out}=0.77±0.05. The ratio of the outer to the inner result is 0.97±0.07, which is consistent with unity within uncertainty. The rates at each distance were found to be similar, but 20%-24% lower than expected, thus reaffirming the anomaly. These results are consistent with ν_{e}→ν_{s} oscillations with a relatively large Δm^{2} (>0.5  eV^{2}) and mixing sin^{2}2θ (≈0.4).

α -event characterization and rejection in point-contact HPGe detectors

P-type point contact (PPC) HPGe detectors are a leading technology for rare event searches due to their excellent energy resolution, low thresholds, and multi-site event rejection capabilities. We have characterized a PPC detector's response to α particles incident on the sensitive passivated and p + surfaces, a previously poorly-understood source of background. The detector studied is identical to those in the Majorana Demonstrator experiment, a search for neutrinoless double-beta decay ( 0 ν β β ) in 76 Ge. α decays on most of the passivated surface exhibit significant energy loss due to charge trapping, with waveforms exhibiting a delayed charge recovery (DCR) signature caused by the slow collection of a fraction of the trapped charge. The DCR is found to be complementary to existing methods of α identification, reliably identifying α background events on the passivated surface of the detector. We demonstrate effective rejection of all surface α events (to within statistical uncertainty) with a loss of only 0.2% of bulk events by combining the DCR discriminator with previously-used methods. The DCR discriminator has been used to reduce the background rate in the 0 ν β β region of interest window by an order of magnitude in the Majorana Demonstrator  and will be used in the upcoming LEGEND-200 experiment.

Accuracy of one-step automated orthodontic diagnosis model using a convolutional neural network and lateral cephalogram images with different qualities obtained from nationwide multi-hospitals

Objective

The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals.

Methods

Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradient-weighted class activation mapping (Grad-CAM).

Results

In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis.

Conclusions

Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.

Tissue-specific distribution of primary and secondary metabolites of Baemoochae (×Brassicoraphanus) and its changes as a function of developmental stages

The distribution and changes in the primary and secondary metabolite profiles of Baemoochae, an inter-generic hybrid of Chinese cabbage and radish, during the plant's developmental stages were investigated. Metabolites were analyzed using gas chromatography-mass spectrometry (GC-MS) and ultra-high-performance liquid chromatography-electrospray ionization-quadrupole time-of-flight (UHPLC-ESI-qTOF MS). Free sugar, organic acid, and amino acid composition depended on the tissue type and developmental stage of Baemoochae. For example, glucose and alanine levels were higher in mature leaves than in young leaves; citric acid content in mature roots was lower than that in young roots. Several glucosinolates were identified for the first time in Baemoochae. Glucoraphasatin was predominant in both leaves and roots, regardless of plant maturity. Total glucosinolate content was significantly higher in roots than in leaves and in mature than in young plants. The roots of mature Baemoochae could be used as a rich source of glucosinolates, with several potential health-promoting effects.

Inkjet Printed Textile Force Sensitive Resistors for Wearable and Healthcare Devices

Pressure sensors for wearable healthcare devices, particularly force sensitive resistors (FSRs) are widely used to monitor physiological signals and human motions. However, current FSRs are not suitable for integration into wearable platforms. This work presents a novel technique for developing textile FSRs (TFSRs) using a combination of inkjet printing of metal-organic decomposition silver inks and heat pressing for facile integration into textiles. The insulating void by a thermoplastic polyurethane (TPU) membrane between the top and bottom textile electrodes creates an architectured piezoresistive structure. The structure functions as a simple logic switch where under a threshold pressure the electrodes make contact to create conductive paths (on-state) and without pressure return to the prior insulated condition (off-state). The TFSR can be controlled by arranging the number of layers and hole diameters of the TPU spacer to specify a wide range of activation pressures from 4.9 kPa to 7.1 MPa. For a use-case scenario in wearable healthcare technologies, the TFSR connected with a readout circuit and a mobile app shows highly stable signal acquisition from finger movement. According to the on/off state of the TFSR with LED bulbs by different weights, it can be utilized as a textile switch showing tactile feedback.

Ethyl carbamate in retail market condiments and risk assessment of its dietary exposure for the Korean population

Ethyl carbamate (EC), a potential human dietary carcinogen, is found in fermented foods including the fermented soybean-based condiments, the major part of the Korean diet. Therefore, it is expected that their EC contents might pose health risks. Herein, we collected 111 condiments and estimated their EC contents via gas chromatography-mass spectrometry. Further, dietary intake of EC was evaluated, and the risk levels were assessed via the margin of exposure (MOE) approach and excess cancer risk assessment. EC contents of the condiments ranged from not detectable to 39.47 μg/kg, and the daily EC exposure ranged from 1.4 to 2.0 ng/kg BW per day, depending on gender and age groups in Korea. Of the condiments, soy sauce was the largest contributor to EC exposure. MOE and excess cancer risks for the average consumer were 166,300 and 9.0 × 10^-8, respectively, and those for the consumers in the 95^th percentiles (P95) were 53,504 and 2.8 × 10^-7, respectively, indicating that the risk of exposure to EC is of lower concern in average consumers than heavy consumers. However, the EC exposure from condiments was higher than that in other Asian countries.Abbreviations: EC: ethyl carbamate; GC-MS: gas chromatography-mass spectrometry; MOE: margin of exposure; MRL: maximum residue level; IDL: instrumental detection level; IQL: instrumental quantification level; MDL: method detection level; MQL: method quantification level; EDI: estimated daily intakes; BMDL₁₀: benchmark dose lower confidence limit.

Microstructures in All-Inkjet-Printed Textile Capacitors with Bilayer Interfaces of Polymer Dielectrics and Metal-Organic Decomposition Silver Electrodes

Soft printed electronics exhibit unique structures and flexibilities suited for a plethora of wearable applications. However, forming scalable, reliable multilayered electronic devices with heterogeneous material interfaces on soft substrates, especially on porous and anisotropic structures, is highly challenging. In this study, we demonstrate an all-inkjet-printed textile capacitor using a multilayered structure of bilayer polymer dielectrics and particle-free metal-organic decomposition (MOD) silver electrodes. Understanding the inherent porous/anisotropic microstructure of textiles and their surface energy relationship was an important process step for successful planarization. The MOD silver ink formed a foundational conductive layer through the uniform encapsulation of individual fibers without blocking fiber interstices. Urethane-acrylate and poly(4-vinylphenol)-based bilayers were able to form a planarized dielectric layer on polyethylene terephthalate textiles. A unique chemical interaction at the interfaces of bilayer dielectrics performed a significant role in insulating porous textile substrates resulting in high chemical and mechanical durability. In this work, we demonstrate how textiles' unique microstructures and bilayer dielectric layer designs benefit reliability and scalability in the inkjet process as well as the use in wearable electronics with electromechanical performance.

Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study

Background

Each year, influenza affects 3 to 5 million people and causes 290,000 to 650,000 fatalities worldwide. To reduce the fatalities caused by influenza, several countries have established influenza surveillance systems to collect early warning data. However, proper and timely warnings are hindered by a 1- to 2-week delay between the actual disease outbreaks and the publication of surveillance data. To address the issue, novel methods for influenza surveillance and prediction using real-time internet data (such as search queries, microblogging, and news) have been proposed. Some of the currently popular approaches extract online data and use machine learning to predict influenza occurrences in a classification mode. However, many of these methods extract training data subjectively, and it is difficult to capture the latent characteristics of the data correctly. There is a critical need to devise new approaches that focus on extracting training data by reflecting the latent characteristics of the data.

Objective

In this paper, we propose an effective method to extract training data in a manner that reflects the hidden features and improves the performance by filtering and selecting only the keywords related to influenza before the prediction.

Methods

Although word embedding provides a distributed representation of words by encoding the hidden relationships between various tokens, we enhanced the word embeddings by selecting keywords related to the influenza outbreak and sorting the extracted keywords using the Pearson correlation coefficient in order to solely keep the tokens with high correlation with the actual influenza outbreak. The keyword extraction process was followed by a predictive model based on long short-term memory that predicts the influenza outbreak. To assess the performance of the proposed predictive model, we used and compared a variety of word embedding techniques.

Results

Word embedding without our proposed sorting process showed 0.8705 prediction accuracy when 50.2 keywords were selected on average. Conversely, word embedding using our proposed sorting process showed 0.8868 prediction accuracy and an improvement in prediction accuracy of 12.6%, although smaller amounts of training data were selected, with only 20.6 keywords on average.

Conclusions

The sorting stage empowers the embedding process, which improves the feature extraction process because it acts as a knowledge base for the prediction component. The model outperformed other current approaches that use flat extraction before prediction.

Clinical and electrophysiological characteristics of extra-pulmonary vein triggers in patients who underwent catheter ablation for atrial fibrillation

Funding Acknowledgements

Type of funding sources: None.

Background

Extra-pulmonary vein (PV) triggers play significant roles in atrial fibrillation (AF) recurrence after AF catheter ablation (AFCA).

Purpose

We explore the clinical and electrophysiological characteristics of extra-PV triggers in de novo and repeat-AFCA procedure.

Methods

We included 2,151 patients who had de novo AFCA and 319 repeat AFCA (female 28.0%, 59.1 ± 10.8 years old, paroxysmal AF 65.1%) those underwent post-procedural isoproterenol (ISO) provocation tests. We analysed the clinical, electrophysiological, and procedure-related factors associated with extra-PV triggers.

Results

Extra-PV triggers were documented in 11.9% (1.19 ± 0.42 foci) after de novo-AFCA and 27.0% (1.37 ± 0.65 foci) after repeat-AFCA (p = 0.004). LA volume index (OR 1.02 [1.01-1.03], p = 0.004), history of vascular disease (OR 0.55[0.31-0.91], p = 0.028) and Lead I amplitude of electrocardiogram (OR &lt;0.01 [&lt;0.01-0.62], p = 0.032) were independently associated with the existence of extra-PV triggers in de-novo procedure. Women (OR 1.84 [1.03-3.25], p = 0.037) and LA appendage volume (OR 1.04 [1.01-1.07] p = 0.027) were independently associated with extra-PV triggers during the redo-mapping procedure. Septum (28.4%), coronary sinus (24.0%), and superior vena cava (19.6%) were common extra-PV foci, and septal foci were more commonly found in repeat mapping (38.4% vs. 25.0%, p = 0.025). Among 65 patients who showed extra-PV at the repeat procedures, 19 (29.2%) matched with previous focal or empirical extra-PV ablation sites and 9 (13.8%) were multiple or unmappable sites. AF recurrence rates were significantly higher in both patients with extra-PV triggers after de novo procedures (Log-rank P &lt;0.001; HR 1.93 [1.58-2.36], p= &lt;0.001) and repeat procedures (Log-rank P &lt;0.001, HR 1.87 [1.29-2.70], p= &lt;0.001).

Conclusion

ISO provoked extra-PV triggers commonly found in AF patients with significant remodelling and previous empirical or focal extra-PV ablations. Existence of extra-PV triggers were independently associated with poorer rhythm outcome after both de novo and repeat AFCA. Denovo AF ablation outcome OverallExtra-PV triggers (-)Extra-PV triggers (+)p-value(n = 2151)(n = 1895)(n = 256)Age, yrs58.98 ± 10.9558.73 ± 11.0360.76 ± 10.200.006Male, (%)1550 ( 72.1)1389 ( 73.3)161 ( 62.9)0.001Follow up duration, month50.30 (37.71)51.65 (37.95)40.71 (34.58)&lt;0.001Early recurrence (%)579 ( 27.8)455 ( 24.9)124 ( 48.4)&lt;0.001Clinical recurrence (%)699 ( 33.6)584 ( 32.0)115 ( 44.9)&lt;0.001Abstract Figure. AF free survival according to Extra PVT

Sympathetic predominance before tourniquet deflation is associated with a reduction in arterial blood pressure after tourniquet deflation during total knee arthroplasty

High dependency of arterial blood pressure (ABP) on enhanced sympathetic activity, which maintains vascular tone, leads to hypotension after hemodynamic insults that blunt the sympathetic activity. Therefore, we hypothesized that sympathovagal balance before tourniquet deflation (TD) determines the extent of a reduction in ABP after TD during total knee arthroplasty (TKA). Fifty-four hypertensive female patients undergoing TKA under spinal anesthesia were analyzed. The sympathovagal balance [low-to-high frequency ratio of heart rate variability (LF/HF)] before TD was defined as (LF/HF during 5 min before TD-preanesthetic LF/HF)/preanesthetic LF/HF (%). An increase in its value represents a shift in sympathovagal balance toward sympathetic predominance. The percent change in the mean ABP (MAP) after TD was defined as (minimum MAP during 10 min after TD-averaged MAP during 5 min before TD)/averaged MAP during 5 min before TD (%). Simple linear regression was performed to assess the correlation between the sympathovagal balance before TD and change in MAP after TD. The correlation was also assessed by multiple linear regression controlling for age, duration of tourniquet inflation, and spinal anesthesia-induced hypotension. Thirty-two minutes (on average) after tourniquet inflation, the MAP was decreased by 12.1 (-3.0 to 47.9) % [mean (range)] upon TD (P<0.001). The sympathovagal balance before TD was negatively proportional to the change in MAP after TD in both simple and multiple linear regression models (R2=0.323 and 0.340, P<0.001). A shift in sympathovagal balance toward sympathetic predominance before TD is associated with a decrease in ABP after TD.

Accuracy of automated identification of lateral cephalometric landmarks using cascade convolutional neural networks on lateral cephalograms from nationwide multi-centres

OBJECTIVE

To investigate the accuracy of automated identification of cephalometric landmarks using the cascade convolutional neural networks (CNN) on lateral cephalograms acquired from nationwide multi-centres.

SETTINGS AND SAMPLE POPULATION

A total of 3150 lateral cephalograms were acquired from 10 university hospitals in South Korea for training.

MATERIALS AND METHODS

We evaluated the accuracy of the developed model with independent 100 lateral cephalograms as an external validation. Two orthodontists independently identified the anatomic landmarks of the test data set using the V-ceph software (version 8.0, Osstem, Seoul, Korea). The mean positions of the landmarks identified by two orthodontists were regarded as the gold standard. The performance of the CNN model was evaluated by calculating the mean absolute distance between the gold standard and the automatically detected positions. Factors associated with the detection accuracy for landmarks were analysed using the linear regression models.

RESULTS

The mean inter-examiner difference was 1.31 ± 1.13 mm. The overall automated detection error was 1.36 ± 0.98 mm. The mean detection error for each landmark ranged between 0.46 ± 0.37 mm (maxillary incisor crown tip) and 2.09 ± 1.91 mm (distal root tip of the mandibular first molar). A significant difference in the detection accuracy among cephalograms was noted according to hospital (P = .011), sensor type (P < .01), and cephalography machine model (P < .01).

CONCLUSION

The automated cephalometric landmark detection model may aid in preliminary screening for patient diagnosis and mid-treatment assessment, independent of the type of the radiography machines tested.

317 Why Are We Cancelling Theatre Cases at Ealing Hospital? What Can Be Done?

Introduction

Following a recent spate of on the day cancellation of elective operations being cancelled on the day of surgery, a need for a wider review was felt. Our aim was to identify the causes behind these cancellations.

Method

Retrospective data collection was performed to categorise patients according to the reason for cancellation. Analysis consisted of case presentations being prepared and presented to the surgical consultants.

Results

A total of 130 operations were cancelled on the day of surgery. 22 patients were cancelled due to the procedure not being required and 10 patients were cancelled due to insufficient pre-operative investigations.

Analysis of the 32 cases demonstrated that the majority of cancelled patients were seen and listed for surgery after clinical review by the registrar without involvement of the consultant.

Some cancellations occurred when the consultant responsible for listing the patient was different of the operating consultant.

Both factors lead to differences in opinion arising on the day of surgery.

Conclusions

Ensuring patients are only listed after discussion with the consultant would reduce cancellations arising from lack of experience and clinical misjudgment of the registrar. It would also ensure that cases added to the pooled operating lists are less subjective in their indication and readiness.

Automatic tip detection of surgical instruments in biportal endoscopic spine surgery

BACKGROUND

Recent advances in robotics and deep learning can be used in endoscopic surgeries and can provide numerous advantages by freeing one of the surgeon's hands. This study aims to automatically detect the tip of the instrument, localize a point, and evaluate the detection accuracy in biportal endoscopic spine surgery (BESS). The tip detection could serve as a preliminary study for the development of vision intelligence in robotic endoscopy.

METHODS

The dataset contains 2310 frames from 9 BESS videos with x and y coordinates of the tip annotated by an expert. We trained two state-of-the-art detectors, RetinaNet and YOLOv2, with bounding boxes centered around the tip annotations with specific margin sizes to determine the optimal margin size for detecting the tip of the instrument and localizing the point. We calculated the recall, precision, and F1-score with a fixed box size for both ground truth tip coordinates and predicted midpoints to compare the performance of the models trained with different margin size bounding boxes.

RESULTS

For RetinaNet, a margin size of 150 pixels was optimal with a recall of 1.000, precision of 0.733, and F1-score of 0.846. For YOLOv2, a margin size of 150 pixels was optimal with a recall of 0.864, precision of 0.808, F1-score of 0.835. Also, the optimal margin size of 150 pixels of RetinaNet was used to cross-validate its overall robustness. The resulting mean recall, precision, and F1-score were 1.000 ± 0.000, 0.767 ± 0.033, and 0.868 ± 0.022, respectively.

CONCLUSIONS

In this study, we evaluated an automatic tip detection method for surgical instruments in endoscopic surgery, compared two state-of-the-art detection algorithms, RetinaNet and YOLOv2, and validated the robustness with cross-validation. This method can be applied in different types of endoscopy tip detection.

Phenolic Composition and Antioxidant Activity of Purple Sweet Potato (Ipomoea batatas (L.) Lam.): Varietal Comparisons and Physical Distribution

The outer layer of purple sweet potato is removed during processing; however, this layer serves as a potential source of phenolics, especially anthocyanins. Herein, the phenolic composition and antioxidant activity were determined for the inner and outer layers of five purple sweet potato cultivars (‘Sinjami’, ‘Jami’, ‘Danjami’, ‘Yeonjami’, and ‘Borami’) harvested in Korea. Anthocyanins were identified using ultra-high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometer (UHPLC-(ESI)-qTOF-MS) and ultra-high-performance liquid chromatography-linear ion trap mass spectrometer (UHPLC-Ion trap-MS), and their composition was quantified using HPLC-coupled with diode array detector (DAD). Non-anthocyanin phenolic compounds (phenolic acids and flavonols) were quantified using UHPLC-(ESI)-triple quadrupole (QqQ). A total of 20 anthocyanins, including non-acylated or acylated peonidin, cyanidin, and pelargonidin glycosides, were identified. Peonidin 3-caffeoyl-p-hydroxybenzoyl sophoroside-5-glucoside was the major anthocyanin, with the highest level in the ‘Sinjami’ cultivar (outer; 12,366 mg/kg DW, inner; 14,832 mg/kg DW). Additionally, 12 phenolic acids and 6 flavonols (quercetin derivatives) were identified, with the outer layers of all cultivars displaying higher total levels than the inner layers. ‘Sinjami’ and ‘Jami’ had higher phenolic acid and quercetin derivative content and antioxidant activities than the other three cultivars (p < 0.05). Thus, the outer layers of ‘Sinjami’ and ‘Jami’ cultivars could be potential sources of anthocyanins and other phenolics.

Liposomal irinotecan plus fluorouracil/leucovorin versus FOLFIRINOX as the second-line chemotherapy for patients with metastatic pancreatic cancer: a multicenter retrospective study of the Korean Cancer Study Group (KCSG)

Background

There is no clear consensus on the recommended second-line treatment for patients with metastatic pancreatic cancer who have disease progression following gemcitabine-based therapy. We retrospectively evaluated the clinical outcomes of liposomal irinotecan (nal-IRI) plus fluorouracil/leucovorin (FL) and FOLFIRINOX (fluorouracil, leucovorin, irinotecan, and oxaliplatin) in patients who had failed on the first-line gemcitabine-based therapy.

Patients and methods

From January 2015 to August 2019, 378 patients with MPC who had received nal-IRI/FL (n = 104) or FOLFIRINOX (n = 274) as second-line treatment across 11 institutions were included in this retrospective study.

Results

There were no significant differences in baseline characteristics between groups, except age and first-line regimens. With a median follow-up of 6 months, the median progression-free survival (PFS) was 3.7 months with nal-IRI/FL versus 4.6 months with FOLFIRINOX (P = 0.44). Median overall survival (OS) was 7.7 months with nal-IRI/FL versus 9.7 months with FOLFRINOX (P = 0.13). There was no significant difference in PFS and OS between the two regimens in the univariate and multivariate analyses. The subgroup analysis revealed that younger age (<70 years) was associated with better OS with FOLFIRINOX. In contrast, older age (≥70 years) was associated with better survival outcomes with nal-IRI/FL. Adverse events were manageable with both regimens; however, the incidence of grade 3 or higher neutropenia and peripheral neuropathy was higher in patients treated with FOLFIRINOX than with nal-IRI/FL.

Conclusions

Second-line nal-IRI/FL and FOLFIRINOX showed similar effectiveness outcomes after progression following first-line gemcitabine-based therapy. Age could be the determining factor for choosing the appropriate second-line therapy.

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This multicenter retrospective study investigated nal-IRI/FL and FOLFIRINOX outcomes after gemcitabine-based therapy.

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We found no significant differences in outcome between nal-IRI/FL and FOLFIRINOX treatment.

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Both regimens were well tolerated; however, neutropenia and peripheral neuropathy were more frequent with FOLFIRINOX.

•

Age (cut-off, 70 years) showed differential efficacy between chemotherapy regimens.

Direct-Write Spray Coating of a Full-Duplex Antenna for E-Textile Applications

Recent advancements in printing technologies have greatly improved the fabrication efficiency of flexible and wearable electronics. Electronic textiles (E-textiles) garner particular interest because of their innate and desirable properties (i.e., conformability, breathability, fabric hand), which make them the ideal platform for creating wireless body area networks (WBANs) for wearable healthcare applications. However, current WBANs are limited in use due to a lack of flexible antennas that can provide effective wireless communication and data transfer. In this work, we detail a novel fabrication process for flexible textile-based multifunctional antennas with enhanced dielectric properties. Our fabrication process relies on direct-write printing of a dielectric ink consisting of ultraviolet (UV)-curable acrylates and urethane as well as 4 wt.% 200 nm barium titanate (BT) nanoparticles to enhance the dielectric properties of the naturally porous textile architecture. By controlling the spray-coating process parameters of BT dielectric ink on knit fabrics, the dielectric constant is enhanced from 1.43 to 1.61, while preserving the flexibility and air permeability of the fabric. The novel combination textile substrate shows great flexibility, as only 2 N is required for a 30 mm deformation. The final textile antenna is multifunctional in the sense that it is capable of operating in a full-duplex mode while presenting a relatively high gain of 9.12 dB at 2.3 GHz and a bandwidth of 79 MHz (2.260-2.339 GHz) for each port. Our proposed manufacturing process shows the potential to simplify the assembly of traditionally complex E-textile systems.

Low-temperature large-area fabrication of ZnO nanowires on flexible plastic substrates by solution-processible metal-seeded hydrothermal growth

We have developed the low-temperature conformal ZnO nanowire fabrication on flexible plastic substrates by utilizing the solution-processible metal seed-assisted hydrothermal ZnO crystallization. Structural evolution of ZnO nanowires controlled by major parameters involving growth temperature, growth time, and seed coating condition, has been systematically investigated towards uniform and large-area growth of conformal ZnO nanowires. Direct ZnO nanowire growth on flexible plastics without undergoing the high-temperature seed sintering has been realized by developing the low-temperature Ag-seeded hydrothermal ZnO nanowire growth. The nanoporous Ag layer favorable for ZnO crystal nucleation and continued nanowire growth can be reduced from the Ag ion solution coating at the temperature as low as 130 °C. This tactfully enables the selective hydrothermal growth of ZnO nanowires on the Ag patterns on flexible plastics. Such an all-solution-processible low-temperature fabrication protocol may provide an essential and practical solution to develop many diverse applications including wearable and transparent electronics, sensors, and photocatalytic devices. As one example, we demonstrate that a transparent UV sensor can be devised based on the ZNW growth on the Ag micromesh electrode.

Changes of Neurotransmitters in Youth with Internet and Smartphone Addiction: A Comparison with Healthy Controls and Changes after Cognitive Behavioral Therapy

BACKGROUND AND PURPOSE

Neurotransmitter changes in youth addicted to the Internet and smartphone were compared with normal controls and in subjects after cognitive behavioral therapy. In addition, the correlations between neurotransmitters and affective factors were investigated.

MATERIALS AND METHODS

Nineteen young people with Internet and smartphone addiction and 19 sex- and age-matched healthy controls (male/female ratio, 9:10; mean age, 15.47 ± 3.06 years) were included. Twelve teenagers with Internet and smartphone addiction (male/female ratio, 8:4; mean age, 14.99 ± 1.95 years) participated in 9 weeks of cognitive behavioral therapy. Meshcher-Garwood point-resolved spectroscopy was used to measure γ-aminobutyric acid and Glx levels in the anterior cingulate cortex. The γ-aminobutyric acid and Glx levels in the addicted group were compared with those in controls and after cognitive behavioral therapy. The γ-aminobutyric acid and Glx levels correlated with clinical scales of Internet and smartphone addiction, impulsiveness, depression, anxiety, insomnia, and sleep quality.

RESULTS

Brain parenchymal and gray matter volume-adjusted γ-aminobutyric acid-to-creatine ratios were higher in subjects with Internet and smartphone addiction (P = .028 and .016). After therapy, brain parenchymal- and gray matter volume-adjusted γ-aminobutyric acid-to-creatine ratios were decreased (P = .034 and .026). The Glx level was not statistically significant in subjects with Internet and smartphone addiction compared with controls and posttherapy status. Brain parenchymal- and gray matter volume-adjusted γ-aminobutyric acid-to-creatine ratios correlated with clinical scales of Internet and smartphone addictions, depression, and anxiety. Glx/Cr was negatively correlated with insomnia and sleep quality scales.

CONCLUSIONS

The high γ-aminobutyric acid levels and disrupted balance of γ-aminobutyric acid-to-Glx including glutamate in the anterior cingulate cortex may contribute to understanding the pathophysiology and treatment of Internet and smartphone addiction and associated comorbidities.