Novel and Advanced Ultrasound Techniques for Thyroid Thermal Ablation.

Thyroid radiofrequency ablation and microwave ablation are widely adopted minimally invasive treatments for diverse thyroid conditions worldwide. Fundamental skills such as the trans-isthmic approach and the moving shot technique are crucial for performing thyroid ablation, and advanced techniques, including hydrodissection and vascular ablation, improve safety and efficacy and reduce complications. Given the learning curve associated with ultrasound-guided therapeutic procedures, operators need training and experience. While training models exist, limited attention has been given to ultrasound maneuvers in ablation needle manipulation. This article introduces two essential maneuvers, the zigzag moving technique and the alienate maneuver, while also reviewing the latest ultrasound techniques in thyroid ablation, contributing valuable insights into this evolving field.

A Pineal Germinoma with Rapid Enlargement following Tumor Resection.

The natural course of pineal germ cell tumors (GCTs), particularly their post-operative progression, is not well understood. We report a rare case of pineal region GCT showing rapid enlargement within 2 weeks following surgical resection. A young adult male presented with progressive headache and diplopia for several weeks. Although elevation of ß-human chorionic gonadotropin (ß-HCG) and α-fetoprotein (AFP) levels suggested that a large pineal mass lesion observed on magnetic resonance imaging (MRI) might be a ß-HCG/AFP-producing tumor, whether the mass was truly a GCT remained unclear. We performed an endoscopy-assisted suboccipital infratentorial approach with removal of the tumor that was diagnosed as germinoma via histopathological investigation. During the week preceding chemotherapy, the patient's consciousness rapidly worsened. MRI showed that the residual pineal germinoma had enlarged and even compressed the tectum and thalamus. Emergency chemotherapy and radiotherapy were prescribed, and the patient received invasive ventilation for respiratory failure. Unexpectedly, the patient recovered within a short period. Importantly, total regression of the pineal germinoma, accompanied by ß-HCG and AFP levels returning to normal range, was observed 4 months after chemotherapy. These phenomena suggest that the rapid enlargement of the pineal germinoma, which might be induced by aggressive surgical cytoreduction, responds well to chemoradiotherapy.

Using a Knowledge-Based Clinical Decision Support System to Reduce the Time to Appropriate Antimicrobial Therapy in Hospitalized Patients With Bloodstream Infections: A Single-Center Observational Study.

Background: Inappropriate antimicrobial use is a crucial determinant of mortality in hospitalized patients with bloodstream infections. Current literature reporting on the impact of clinical decision support systems on optimizing antimicrobial prescription and reducing the time to appropriate antimicrobial therapy is limited. Methods: Kaohsiung Veterans General Hospital implemented a hospital-wide, knowledge-based, active-delivery clinical decision support system, named RAPID (Real-time Alert for antimicrobial Prescription from virtual Infectious Diseases experts), to detect whether there was an antimicrobial agent-pathogen mismatch when a blood culture result was positive. Once RAPID determines the current antimicrobials as inappropriate, an alert text message is immediately sent to the clinicians in charge. This study evaluated how RAPID impacted the time to appropriate antimicrobial therapy among patients with bloodstream infections. Results: During the study period, 633 of 11 297 recorded observations (5.6%) were determined as inappropriate antimicrobial prescriptions. The time to appropriate antimicrobial therapy was significantly shortened after the implementation of RAPID (1.65 vs 2.45 hours, P < .001), especially outside working hours (1.24 vs 6.43 hours, P < .001), in the medical wards (1.40 vs 2.14 hours, P < .001), in participants with candidemia (0.74 vs 5.36 hours, P < .001), and for bacteremia due to non-multidrug-resistant organisms (1.66 vs 2.49 hours, P < .001). Conclusions: Using a knowledge-based clinical decision support system to reduce the time to appropriate antimicrobial therapy in a real-world scenario is feasible and effective. Our results support the continued use of RAPID.

Outcomes of Patients With Metastatic Differentiated Thyroid Cancer After Excellent Response to Treatment.

Background: To evaluate the outcomes in differentiated thyroid cancer (DTC) patients who achieved excellent response to initial treatment and developed distant metastasis during follow-up. Methods: Thyroid cancer patients registered in Chang Gung Memorial Hospital thyroid cancer database between January 1979 and December 2019 were assessed. Results: Among 1053 DTC patients with excellent response to initial therapy, 14 (1.3%) patients developed metastatic disease during follow-up, including 6 males and 8 females with median age of 50.2 years [interquartile range (IQR), 39.9-53.7]. Nine (64.3%) patients had papillary cancer, four (28.6%) had follicular cancer, and one (7.1%) had Hürthle cell cancer. Most patients (92.9%) had stage I disease at diagnosis. The sites of metastasis were lung (71.4%), bone (7.1%), mediastinum (7.1%) and multiple sites (14.3%). With a median follow-up of 18.3 years (IQR, 14.8-23.8), 2 patients had disease-specific mortality. The 5- and 10-year disease-specific survival after the diagnosis of distant metastasis was 92% and 74%, respectively. Multiple sites of metastasis was associated with increased risk of mortality (P = 0.022). Conclusions: A small proportion of DTC patients with an excellence response to initial therapy developed distant metastasis during follow-up. Multiple organ distant metastases conferred a worse disease-specific survival.

Correlation between anxiety and depression risk and atopic dermatitis severity in Taiwan: A cross-sectional study.

BACKGROUND: Limited studies on atopic dermatitis (AD) have investigated the possible covariance of sociodemographic factors with the Hospital Anxiety and Depression Scale (HADS). OBJECTIVE: This study aimed to examine the possible covariance between AD severity and HADS scores of patients in Taiwan. METHODS: Patients with AD from a medical center and 2 regional hospitals in Taiwan were enrolled in this cross-sectional study from April 2018 to April 2019. AD severity was measured using the "scoring atopic dermatitis" index, and anxiety and depression were screened based on HADS. RESULTS: A total of 200 patients were included. After correcting for sociodemographic variables, significantly more borderline (≥8) and abnormal (≥11) cases of anxiety/depression (P < .05) were noted in patients with moderate-to-severe AD. LIMITATIONS: First, the cross-sectional study design cannot show causality. Second, baseline data, including a history of underlying cancer or previous psychiatric disorder, were not obtained in the questionnaire and may confound the HADS scores. Finally, a standardized psychiatric clinical interviews study design should be used for higher accuracy in the assessment of psycho-comorbidities. CONCLUSION: Higher anxiety and depression risks were noted in patients with moderate-to-severe AD. Except for psychosomatic symptoms, all kinds of anxiety and depression symptoms occurred more frequently in patients with moderate-to-severe AD.

The impact of atopic dermatitis on health-related quality of life in Taiwan.

BACKGROUND/PURPOSE: Atopic dermatitis (AD) is a common skin disease. At present, there is little evidence regarding its impact on patients' health-related quality of life (HRQoL) in Taiwan. Therefore, this study investigated the relationship between AD severity and patients' HRQoL in Taiwan. METHODS: Patients with AD were recruited from three hospitals in Taiwan from April 2018 to April 2019. AD severity was measured using the Scoring of AD (SCORAD) scale, and HRQoL was assessed using the Dermatology Life Quality Index (DLQI) and the five-level version of EuroQol five-dimension questionnaire (EQ-5D-5L). RESULTS: A total of 200 patients (mean age: 34.4 years) were recruited, including 103 males and 97 females. They were further classified as 79 mild, 72 moderate, and 58 severe AD patients according to their SCORAD scores. There was a positive correlation between their SCORAD and DLQI scores (Spearman's r = 0.77, p < 0.001). Patients with severe AD had higher scores in all the DLQI questions, particularly the symptoms, feelings, and work/school. In addition, both the EQ-5D visual analogue scale (VAS) scores and utility index values were negatively correlated with the SCORAD scores (Spearman's r = -0.46 and -0.60, respectively, both p < 0.001). Patients with higher AD severity had more problems with mobility, usual activity, pain/discomfort, and anxiety/depression, while demographic characteristics did not significantly affect HRQoL. CONCLUSION: Higher AD severity is associated with poorer HRQoL in Taiwanese AD patients, with AD's effects on symptoms, feelings, and work/school being the most troublesome. Meanwhile, demographic factors did not affect HRQoL significantly.

The Application of Projection Word Embeddings on Medical Records Scoring System.

Medical records scoring is important in a health care system. Artificial intelligence (AI) with projection word embeddings has been validated in its performance disease coding tasks, which maintain the vocabulary diversity of open internet databases and the medical terminology understanding of electronic health records (EHRs). We considered that an AI-enhanced system might be also applied to automatically score medical records. This study aimed to develop a series of deep learning models (DLMs) and validated their performance in medical records scoring task. We also analyzed the practical value of the best model. We used the admission medical records from the Tri-Services General Hospital during January 2016 to May 2020, which were scored by our visiting staffs with different levels from different departments. The medical records were scored ranged 0 to 10. All samples were divided into a training set (n = 74,959) and testing set (n = 152,730) based on time, which were used to train and validate the DLMs, respectively. The mean absolute error (MAE) was used to evaluate each DLM performance. In original AI medical record scoring, the predicted score by BERT architecture is closer to the actual reviewer score than the projection word embedding and LSTM architecture. The original MAE is 0.84 ± 0.27 using the BERT model, and the MAE is 1.00 ± 0.32 using the LSTM model. Linear mixed model can be used to improve the model performance, and the adjusted predicted score was closer compared to the original score. However, the project word embedding with the LSTM model (0.66 ± 0.39) provided better performance compared to BERT (0.70 ± 0.33) after linear mixed model enhancement (p < 0.001). In addition to comparing different architectures to score the medical records, this study further uses a mixed linear model to successfully adjust the AI medical record score to make it closer to the actual physician's score.

A Deep-Learning Algorithm (ECG12Net) for Detecting Hypokalemia and Hyperkalemia by Electrocardiography: Algorithm Development.

BACKGROUND: The detection of dyskalemias-hypokalemia and hyperkalemia-currently depends on laboratory tests. Since cardiac tissue is very sensitive to dyskalemia, electrocardiography (ECG) may be able to uncover clinically important dyskalemias before laboratory results. OBJECTIVE: Our study aimed to develop a deep-learning model, ECG12Net, to detect dyskalemias based on ECG presentations and to evaluate the logic and performance of this model. METHODS: Spanning from May 2011 to December 2016, 66,321 ECG records with corresponding serum potassium (K+) concentrations were obtained from 40,180 patients admitted to the emergency department. ECG12Net is an 82-layer convolutional neural network that estimates serum K+ concentration. Six clinicians-three emergency physicians and three cardiologists-participated in human-machine competition. Sensitivity, specificity, and balance accuracy were used to evaluate the performance of ECG12Net with that of these physicians. RESULTS: In a human-machine competition including 300 ECGs of different serum K+ concentrations, the area under the curve for detecting hypokalemia and hyperkalemia with ECG12Net was 0.926 and 0.958, respectively, which was significantly better than that of our best clinicians. Moreover, in detecting hypokalemia and hyperkalemia, the sensitivities were 96.7% and 83.3%, respectively, and the specificities were 93.3% and 97.8%, respectively. In a test set including 13,222 ECGs, ECG12Net had a similar performance in terms of sensitivity for severe hypokalemia (95.6%) and severe hyperkalemia (84.5%), with a mean absolute error of 0.531. The specificities for detecting hypokalemia and hyperkalemia were 81.6% and 96.0%, respectively. CONCLUSIONS: A deep-learning model based on a 12-lead ECG may help physicians promptly recognize severe dyskalemias and thereby potentially reduce cardiac events.

Projection Word Embedding Model With Hybrid Sampling Training for Classifying ICD-10-CM Codes: Longitudinal Observational Study.

BACKGROUND: Most current state-of-the-art models for searching the International Classification of Diseases, Tenth Revision Clinical Modification (ICD-10-CM) codes use word embedding technology to capture useful semantic properties. However, they are limited by the quality of initial word embeddings. Word embedding trained by electronic health records (EHRs) is considered the best, but the vocabulary diversity is limited by previous medical records. Thus, we require a word embedding model that maintains the vocabulary diversity of open internet databases and the medical terminology understanding of EHRs. Moreover, we need to consider the particularity of the disease classification, wherein discharge notes present only positive disease descriptions. OBJECTIVE: We aimed to propose a projection word2vec model and a hybrid sampling method. In addition, we aimed to conduct a series of experiments to validate the effectiveness of these methods. METHODS: We compared the projection word2vec model and traditional word2vec model using two corpora sources: English Wikipedia and PubMed journal abstracts. We used seven published datasets to measure the medical semantic understanding of the word2vec models and used these embeddings to identify the three-character-level ICD-10-CM diagnostic codes in a set of discharge notes. On the basis of embedding technology improvement, we also tried to apply the hybrid sampling method to improve accuracy. The 94,483 labeled discharge notes from the Tri-Service General Hospital of Taipei, Taiwan, from June 1, 2015, to June 30, 2017, were used. To evaluate the model performance, 24,762 discharge notes from July 1, 2017, to December 31, 2017, from the same hospital were used. Moreover, 74,324 additional discharge notes collected from seven other hospitals were tested. The F-measure, which is the major global measure of effectiveness, was adopted. RESULTS: In medical semantic understanding, the original EHR embeddings and PubMed embeddings exhibited superior performance to the original Wikipedia embeddings. After projection training technology was applied, the projection Wikipedia embeddings exhibited an obvious improvement but did not reach the level of original EHR embeddings or PubMed embeddings. In the subsequent ICD-10-CM coding experiment, the model that used both projection PubMed and Wikipedia embeddings had the highest testing mean F-measure (0.7362 and 0.6693 in Tri-Service General Hospital and the seven other hospitals, respectively). Moreover, the hybrid sampling method was found to improve the model performance (F-measure=0.7371/0.6698). CONCLUSIONS: The word embeddings trained using EHR and PubMed could understand medical semantics better, and the proposed projection word2vec model improved the ability of medical semantics extraction in Wikipedia embeddings. Although the improvement from the projection word2vec model in the real ICD-10-CM coding task was not substantial, the models could effectively handle emerging diseases. The proposed hybrid sampling method enables the model to behave like a human expert.

Artificial Intelligence Learning Semantics via External Resources for Classifying Diagnosis Codes in Discharge Notes.

BACKGROUND: Automated disease code classification using free-text medical information is important for public health surveillance. However, traditional natural language processing (NLP) pipelines are limited, so we propose a method combining word embedding with a convolutional neural network (CNN). OBJECTIVE: Our objective was to compare the performance of traditional pipelines (NLP plus supervised machine learning models) with that of word embedding combined with a CNN in conducting a classification task identifying International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes in discharge notes. METHODS: We used 2 classification methods: (1) extracting from discharge notes some features (terms, n-gram phrases, and SNOMED CT categories) that we used to train a set of supervised machine learning models (support vector machine, random forests, and gradient boosting machine), and (2) building a feature matrix, by a pretrained word embedding model, that we used to train a CNN. We used these methods to identify the chapter-level ICD-10-CM diagnosis codes in a set of discharge notes. We conducted the evaluation using 103,390 discharge notes covering patients hospitalized from June 1, 2015 to January 31, 2017 in the Tri-Service General Hospital in Taipei, Taiwan. We used the receiver operating characteristic curve as an evaluation measure, and calculated the area under the curve (AUC) and F-measure as the global measure of effectiveness. RESULTS: In 5-fold cross-validation tests, our method had a higher testing accuracy (mean AUC 0.9696; mean F-measure 0.9086) than traditional NLP-based approaches (mean AUC range 0.8183-0.9571; mean F-measure range 0.5050-0.8739). A real-world simulation that split the training sample and the testing sample by date verified this result (mean AUC 0.9645; mean F-measure 0.9003 using the proposed method). Further analysis showed that the convolutional layers of the CNN effectively identified a large number of keywords and automatically extracted enough concepts to predict the diagnosis codes. CONCLUSIONS: Word embedding combined with a CNN showed outstanding performance compared with traditional methods, needing very little data preprocessing. This shows that future studies will not be limited by incomplete dictionaries. A large amount of unstructured information from free-text medical writing will be extracted by automated approaches in the future, and we believe that the health care field is about to enter the age of big data.

Lapatinib inhibits CIP2A/PP2A/p-Akt signaling and induces apoptosis in triple negative breast cancer cells.

We tested the efficacy of lapatinib, a dual tyrosine kinase inhibitor which interrupts the HER2 and epidermal growth factor receptor (EGFR) pathways, in a panel of triple-negative breast cancer (TNBC) cells, and examined the drug mechanism. Lapatinib showed an anti-proliferative effect in HCC 1937, MDA-MB-468, and MDA-MB-231 cell lines. Lapatinib induced significant apoptosis and inhibited CIP2A and p-Akt in a dose and time-dependent manner in the three TNBC cell lines. Overexpression of CIP2A reduced lapatinib-induced apoptosis in MDA-MB-468 cells. In addition, lapatinib increased PP2A activity (in relation to CIP2A inhibition). Moreover, lapatinib-induced apoptosis and p-Akt downregulation was attenuated by PP2A antagonist okadaic acid. Furthermore, lapatinib indirectly decreased CIP2A transcription by disturbing the binding of Elk1 to the CIP2A promoter. Importantly, lapatinib showed anti-tumor activity in mice bearing MDA-MB-468 xenograft tumors, and suppressed CIP2A as well as p-Akt in these xenografted tumors. In summary, inhibition of CIP2A determines the effects of lapatinib-induced apoptosis in TNBC cells. In addition to being a dual tyrosine kinase inhibitor of HER2 and EGFR, lapatinib also inhibits CIP2A/PP2A/p-Akt signaling in TNBC cells.

Spatial element distribution control in a fully solution-processed nanocrystals-based 8.6% Cu2ZnSn(S,Se)4 device.

A fully solution-processed high performance Cu2ZnSn(S,Se)4 (CZTSSe, kesterite) device has been demonstrated. It is based on the rational engineering of elemental spatial distributions in the bulk and particularly near the surface of the film from nanocrystal precursors. The nanocrystals are synthesized through a modified colloidal approach, with excellent solubility over a large compositional window, followed by a selenization process to form the absorber. The X-ray photoluminescence (XPS) depth profiling indicates an undesirable Sn-rich surface of the selenized film. An excessive Zn species was quantitatively introduced through nanocrystals precursor to correct the element distribution, and accordingly a positive correlation between the spatial composition in the bulk/surface film and the resulting device parameter is established. The enhanced device performance is associated with the reduced interfacial recombination. With a Zn content 1.6 times more than the stoichiometry; the optimized device, which is fabricated by employing a full solution process from the absorber to the transparent top electrode, demonstrates a performance of 8.6%. This composition-control approach through stoichiometric adjustments of nanocrystal precursors, and the developed correlation between the spatial composition and device performance may also benefit other multielement-based photovoltaics.

Rational defect passivation of Cu2ZnSn(S,Se)4 photovoltaics with solution-processed Cu2ZnSnS4:Na nanocrystals.

An effective defect passivation route has been demonstrated in the rapidly growing Cu2ZnSn(S,Se)4 (CZTSSe) solar cell device system by using Cu2ZnSnS4:Na (CZTS:Na) nanocrystals precursors. CZTS:Na nanocrystals are obtained by sequentially preparing CZTS nanocrystals and surface decorating of Na species, while retaining the kesterite CZTS phase. The exclusive surface presence of amorphous Na species is proved by X-ray photoluminescence spectrum and transmission electron microscopy. With Na-free glasses as the substrate, CZTS:Na nanocrystal-based solar cell device shows 50% enhancement of device performance (∼6%) than that of unpassivated CZTS nanocrystal-based device (∼4%). The enhanced electrical performance is closely related to the increased carrier concentration and elongated minority carrier lifetime, induced by defect passivation. Solution incorporation of extrinsic additives into the nanocrystals and the corresponding film enables a facile, quantitative, and versatile approach to tune the defect property of materials for future optoelectronic applications.

Molecular solution approach to synthesize electronic quality Cu2ZnSnS4 thin films.

Successful implementation of molecular solution processing from a homogeneous and stable precursor would provide an alternative, robust approach to process multinary compounds compared with physical vapor deposition. Targeting deposition of chemically clear, high quality crystalline films requires specific molecular structure design and solvent selection. Hydrazine (N2H4) serves as a unique and powerful medium, particularly to incorporate selected metallic elements and chalcogens into a stable solution as metal chalcogenide complexes (MCC). However, not all the elements and compounds can be easily dissolved. In this manuscript, we demonstrate a paradigm to incorporate previously insoluble transitional-metal elements into molecular solution as metal-atom hydrazine/hydrazine derivative complexes (MHHD), as exemplified by dissolving of the zinc constituent as Zn(NH2NHCOO)2(N2H4)2. Investigation into the evolution of molecular structure reveals the hidden roadmap to significantly enrich the variety of building blocks for soluble molecule design. The new category of molecular structures not only set up a prototype to incorporate other elements of interest but also points the direction for other compatible solvent selection. As demonstrated from the molecular precursor combining Sn-/Cu-MCC and Zn-MHHD, an ultrathin film of copper zinc tin sulfide (CZTS) was deposited. Characterization of a transistor based on the CZTS channel layer shows electronic properties comparable to CuInSe2, confirming the robustness of this molecular solution processing and the prospect of earth abundant CZTS for next generation photovoltaic materials. This paradigm potentially outlines a universal pathway, from individual molecular design using selected chelated ligands and combination of building blocks in a simple and stable solution to fundamentally change the way multinary compounds are processed.

Excited state luminescence of multi-(5-phenyl-1,3,4-oxadiazo-2-yl)benzenes in an electron-donating matrix: exciplex or electroplex?

Multi-(5-phenyl-1,3,4-oxadiazo-2-yl)benzenes show emission in organic solvents from ultraviolet to blue (339-447 nm). The reduction potentials E(1/2)(red) cover a large range of -2.11 V for 2,5-diphenyl-1,3,4-oxadiazole to -0.76 V for 1,2,3,4,5,6-hexa(5-phenyl-1,3,4-oxadiazo-2-yl)benzene. An unexpectedly wide spectral range of the oxadiazole (OXD) exciplex emissions in PVK is observed, ranging from 406 to 603 nm. The OXDs also exhibit similar electroluminescence (EL) when blended into polyvinylcarbazole (PVK). A linear correlation between the lambda(max) of the electroluminescence and photoluminescence is observed, implying that the emission mechanisms in both processes are similar. In addition, the linear correlation between the E(1/2)(red) versus lambda(max) of EL (eV) reflected that the term of the charge-transfer configuration of the contact electron-hole pair plays a major role in the exciplex emission. The exciplex EL of 1,2,5-tri(5-phenyl-1,3,4-oxadiazo-2-yl)benzene (5) could be as high as 1.0 cd/A. Since the exciplex emission usually has a large Stokes shift, this provides a window for us to generate duo emissions for near white light EL with high efficiency. Among the devices we tried, the device of PVK/2-tert-butylphenyl-5-biphenyl-1,3,4-oxadiazole/5/2,5,8,11-tetra-tert-butylperylene (100:40:40:4) gave EL with good current efficiency of 1.63 cd/A.

Spectroscopy and femtosecond dynamics of excited-state proton transfer induced charge transfer reaction.

Fluorescence spectroscopy and femtosecond relaxation dynamics of 2-{[2-(2-hydroxyphenyl)benzo[d]oxazol-6-yl]methylene}malononitrile (diCN-HBO) and 2-{[2-(2-hydroxyphenyl)benzo[d]thiazol-6-yl]methylene}malononitrile (diCN-HBT) are studied to probe the excited-state proton transfer (ESPT) coupled charge transfer (ESCT) reaction. Unlike most of the ESPT/ESCT systems previously designed, in which ESCT takes place prior to ESPT, both diCN-HBO and diCN-HBT undergo ESPT, concomitantly accompanied with the charge transfer process, such that the ESPT reaction dynamics are directly coupled with solvent polarization effects. The long-range solvent polarization interactions result in a solvent-induced barrier that affects the overall proton transfer reaction rate. In cyclohexane, the rate constant of ESPT of diCN-HBO is measured to be 1.1 ps (9.1 x 10(11) s(-1)), which is apparently slower than that of 150 fs for the parent molecule 2-(2'-hydroxyphenyl)benzoxazole (HBO). Upon increasing solvent polarity to, for example, CH 3CN, the rate of ESPT is increased to 300 fs (3.3 x 10(12) s(-1)). The results are rationalized by the stabilization of proton transfer tautomer, which possesses a large degree of charge transfer character via an increase of the solvent polarity, such that the corresponding solvent-induced barrier is reduced. We thus demonstrate a prototypical system in which the photon-induced nuclear motion (proton transfer) is directly coupled with solvent polarization and the corresponding mechanism is reminiscent of that applied in an electron transfer process.