Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study.

BACKGROUND: Consumer sleep trackers (CSTs) have gained significant popularity because they enable individuals to conveniently monitor and analyze their sleep. However, limited studies have comprehensively validated the performance of widely used CSTs. Our study therefore investigated popular CSTs based on various biosignals and algorithms by assessing the agreement with polysomnography. OBJECTIVE: This study aimed to validate the accuracy of various types of CSTs through a comparison with in-lab polysomnography. Additionally, by including widely used CSTs and conducting a multicenter study with a large sample size, this study seeks to provide comprehensive insights into the performance and applicability of these CSTs for sleep monitoring in a hospital environment. METHODS: The study analyzed 11 commercially available CSTs, including 5 wearables (Google Pixel Watch, Galaxy Watch 5, Fitbit Sense 2, Apple Watch 8, and Oura Ring 3), 3 nearables (Withings Sleep Tracking Mat, Google Nest Hub 2, and Amazon Halo Rise), and 3 airables (SleepRoutine, SleepScore, and Pillow). The 11 CSTs were divided into 2 groups, ensuring maximum inclusion while avoiding interference between the CSTs within each group. Each group (comprising 8 CSTs) was also compared via polysomnography. RESULTS: The study enrolled 75 participants from a tertiary hospital and a primary sleep-specialized clinic in Korea. Across the 2 centers, we collected a total of 3890 hours of sleep sessions based on 11 CSTs, along with 543 hours of polysomnography recordings. Each CST sleep recording covered an average of 353 hours. We analyzed a total of 349,114 epochs from the 11 CSTs compared with polysomnography, where epoch-by-epoch agreement in sleep stage classification showed substantial performance variation. More specifically, the highest macro F1 score was 0.69, while the lowest macro F1 score was 0.26. Various sleep trackers exhibited diverse performances across sleep stages, with SleepRoutine excelling in the wake and rapid eye movement stages, and wearables like Google Pixel Watch and Fitbit Sense 2 showing superiority in the deep stage. There was a distinct trend in sleep measure estimation according to the type of device. Wearables showed high proportional bias in sleep efficiency, while nearables exhibited high proportional bias in sleep latency. Subgroup analyses of sleep trackers revealed variations in macro F1 scores based on factors, such as BMI, sleep efficiency, and apnea-hypopnea index, while the differences between male and female subgroups were minimal. CONCLUSIONS: Our study showed that among the 11 CSTs examined, specific CSTs showed substantial agreement with polysomnography, indicating their potential application in sleep monitoring, while other CSTs were partially consistent with polysomnography. This study offers insights into the strengths of CSTs within the 3 different classes for individuals interested in wellness who wish to understand and proactively manage their own sleep.

Association between Obesity and Anemia in a Nationally Representative Sample of South Korean Adolescents: A Cross-Sectional Study.

Anemia is associated with physical, cognitive, and developmental problems. Given that there are limited studies on anemia prevalence among obese Asian adolescents and that past evidence is controversial, this study investigated the relationship between obesity and anemia in a nationally representative sample of South Korean adolescents. Data were obtained from the 2007-2019 Korea National Health and Nutrition Examination Survey. Overall, 10,231 subjects were included in the analysis. Multiple logistic regression was performed to determine the relationship between obesity and anemia. Compared with the non-obesity group, the adjusted odds ratio (OR; 95% confidence interval [CI]) of anemia was 1.00 (0.66-1.50) in the obesity group. However, in the early adolescent subgroup of 10-13 years (adjusted OR, 2.88; 95% CI, 1.20-6.95), the risk of anemia was significantly higher in the obese group than in the non-obese group. Obesity was associated with an increased risk of anemia in early adolescents. Obese adolescents aged 10-13 require special care, especially through regular examination and management for anemia.

A Randomized Study on PLLA Using Higher Dilution Volume and Immediate Use Following Reconstitution.

BACKGROUND: The reconstitution volume of a PLLA-containing injectable device has gradually increased in clinical practice, often in combination with adding lidocaine to the solution. OBJECTIVE: This study, SCRIPT (Sculptra Contemporary Reconstitution & Injection Procedure Trial) evaluated PLLA for correction of nasolabial folds after changes in reconstitution and injection procedures. Primary endpoint for effectiveness was change from baseline of nasolabial folds, assessed by blinded evaluation using a validated wrinkle assessment scale (WAS), at week 48. METHODS: Subjects were treated to optimal correction of nasolabial folds at a single treatment regimen consisting of ≤4 injection sessions, with PLLA reconstituted with 8 mL or 5 mL sterile water for injection (SWFI), randomized 2:1. The 8 mL product included an additional 1 mL 2%-lidocaine and was injected immediately following reconstitution. Assessments included wrinkle severity, aesthetic improvement and safety. RESULTS: A total of 80 subjects were included in the study. Most subjects were female (95%), mean age was 51.5 years. Primary endpoint was met and subjects from both study groups demonstrated high WAS responder rates (≥1-grade improvement from baseline) at week 24 (≥75%) and week 48 (≥67%). Aesthetic improvement was high (≥86%) throughout the study. Adverse events related to study product or injection procedure were mostly mild and transient. CONCLUSION: PLLA reconstituted with 8 mL SWFI demonstrated a comparable treatment effect to that of the reference group in reducing wrinkle severity of nasolabial folds. Safety was not compromised using a higher reconstitution volume including lidocaine, injected immediately after reconstitution.

Metal-Contact Improvement in a Multilayer WSe2 Transistor through Strong Hot Carrier Injection.

Hot carrier injection (HCI), occurring when the horizontal electric field is strongly applied, usually affects the degradation of nanoelectronic devices. In addition, metal contacts play a significant role in nanoelectronic devices. In this study, Schottky contacts in multilayer tungsten diselenide (WSe2) field-effect transistors (FETs) by hot carrier injection (HCI), occurring when a high drain voltage is applied, is investigated. A small number of hot carriers with high energy reduces the Schottky barrier height and improves the performance of FETs effectively rather than damaging the channel. Thermal annealing at the end of the fabrication process increases device performance by causing interfacial reactions of the source/drain electrodes. HCI causes a significant enhancement in the local asymmetry, especially in the subthreshold region. The subthreshold swing (SS) of the thermally annealed FETs is significantly improved from 9.66 to 0.562 V dec-1 through the energy of HCI generated by a strong horizontal electric field. In addition, the contact resistances (RSD), also called series resistances, extracted by a four-probe measurement and a Y-function method were also improved by decreasing to a 10th through the energy of HCI. To understand the asymmetrical characteristics of the channel after the stress, we performed electrical analysis, electrostatic force microscopy (EFM), and Raman spectroscopy.

Artificial intelligence assessment for early detection of heart failure with preserved ejection fraction based on electrocardiographic features.

Aims: Although heart failure with preserved ejection fraction (HFpEF) is a rapidly emerging global health problem, an adequate tool to screen it reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance. Methods and results: This retrospective cohort study included two hospitals. 34 103 patients who underwent echocardiography and ECG within 1 week and indicated normal left ventricular systolic function were included in this study. A DLM based on an ensemble neural network was developed using 32 671 ECGs of 20 169 patients. The internal validation included 1979 ECGs of 1979 patients. Furthermore, we conducted an external validation with 11 955 ECGs of 11 955 patients from another hospital. The endpoint was to detect HFpEF. During the internal and external validation, the area under the receiver operating characteristic curves of a DLM using 12-lead ECG for detecting HFpEF were 0.866 (95% confidence interval 0.850-0.883) and 0.869 (0.860-0.877), respectively. In the 1412 individuals without HFpEF at initial echocardiography, patients whose DLM was defined as having a higher risk had a significantly higher chance of developing HFpEF than those in the low-risk group (33.6% vs. 8.4%, P < 0.001). Sensitivity map showed that the DLM focused on the QRS complex and T-wave. Conclusion: The DLM demonstrated high performance for HFpEF detection using not only a 12-lead ECG but also 6- single-lead ECG. These results suggest that HFpEF can be screened using conventional ECG devices and diverse life-type ECG machines employing the DLM, thereby preventing disease progression.

Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram.

INTRODUCTION: Early detection and intervention of atrial fibrillation (AF) is a cornerstone for effective treatment and prevention of mortality. Diverse deep learning models (DLMs) have been developed, but they could not be applied in clinical practice owing to their lack of interpretability. We developed an explainable DLM to detect AF using ECG and validated its performance using diverse formats of ECG. METHODS: We conducted a retrospective study. The Sejong ECG dataset comprising 128,399 ECGs was used to develop and internally validated the explainable DLM. DLM was developed with two feature modules, which could describe the reason for DLM decisions. DLM was external validated using data from 21,837, 10,605, and 8528 ECGs from PTB-XL, Chapman, and PhysioNet non-restricted datasets, respectively. The predictor variables were digitally stored ECGs, and the endpoints were AFs. RESULTS: During internal and external validation of the DLM, the area under the receiver operating characteristic curves (AUCs) of the DLM using a 12­lead ECG in detecting AF were 0.997-0.999. The AUCs of the DLM with VAE using a 6­lead and single­lead ECG were 0.990-0.999. The AUCs of explainability about features such as rhythm irregularity and absence of P-wave were 0.961-0.993 and 0.983-0.993, respectively. CONCLUSIONS: Our DLM successfully detected AF using diverse ECGs and described the reason for this decision. The results indicated that an explainable artificial intelligence methodology could be adopted to the DLM using ECG and enhance the transparency of the DLM for its application in clinical practice.

Defect spectroscopy of sidewall interfaces in gate-all-around silicon nanosheet FET.

Through time-dependent defect spectroscopy and low-frequency noise measurements, we investigate and characterize the differences of carrier trapping processes occurred by different interfaces (top/sidewall) of the gate-all-around silicon nanosheet field-effect transistor (GAA SiNS FET). In a GAA SiNS FET fabricated by the top-down process, the traps at the sidewall interface significantly affect the device performance as the width decreases. Compare to expectations, as the width of the device decreases, the subthreshold swing (SS) increases from 120 to 230 mV/dec, resulting in less gate controllability. In narrow-width devices, the effect of traps located at the sidewall interface is significantly dominant, and the 1/f 2 noise, also known as generation-recombination (G-R) noise, is clearly appeared with an increased time constant (τ i ). In addition, the probability density distributions for the normalized current fluctuations (ΔI D) show only one Gaussian in wide-width devices, whereas they are separated into four Gaussians with increased in narrow-width devices. Therefore, fitting is performed through the carrier number fluctuation-correlated with mobility fluctuations model that separately considered the effects of sidewall. In narrow-width GAA SiNS FETs, consequently, the extracted interface trap densities (N T ) distribution becomes more dominant, and the scattering parameter ([Formula: see text]) distribution increases by more than double.

A deep learning algorithm to detect anaemia with ECGs: a retrospective, multicentre study.

BACKGROUND: Anaemia is an important health-care burden globally, and screening for anaemia is crucial to prevent multi-organ injury, irreversible complications, and life-threatening adverse events. We aimed to establish whether a deep learning algorithm (DLA) that enables non-invasive anaemia screening from electrocardiograms (ECGs) might improve the detection of anaemia. METHODS: We did a retrospective, multicentre, diagnostic study in which a DLA was developed using ECGs and then internally and externally validated. We used data from two hospitals, Sejong General Hospital (hospital A) and Mediplex Sejong Hospital (hospital B), in South Korea. Data from hospital A was for DLA development and internal validation, and data from hospital B was for external validation. We included individuals who had at least one ECG with a haemoglobin measurement within 1 h of the index ECG and excluded individuals with missing demographic, electrocardiographic, or haemoglobin information. Three types of DLA were developed with 12-lead, 6-lead (limb lead), and single-lead (lead I) ECGs to detect haemoglobin concentrations of 10 g/dL or less. The DLA was built by a convolutional neural network and used 500-Hz raw ECG, age, and sex as input data. FINDINGS: The study period ran from Oct 1, 2016, to Sept 30, 2019, in hospital A and March 1, 2017, to Sept 30, 2019, in hospital B. 40 513 patients at hospital A and 4737 patients at hospital B were eligible for inclusion. We excluded 281 patients at hospital A and 72 patients at hospital B because of missing values for clinical information and ECG data. The development dataset comprised 57 435 ECGs from 31 898 patients, and the algorithm was internally validated with 7974 ECGs from 7974 patients. The external validation dataset included 4665 ECGs from 4665 patients. 586 (internal) and 194 (external) patients within the combined dataset were found to be anaemic. During internal and external validation, the area under the receiver operating characteristics curve (AUROC) of the DLA using a 12-lead ECG for detecting anaemia was 0·923 for internal validation and 0·901 for external validation. Using a 90% sensitivity operating point for the development data, the sensitivity, specificity, negative predictive value, and positive predictive value of internal validation were 89·8%, 81·5%, 99·4%, and 20·0%, respectively, and those of external validation were 86·1%, 76·2%, 99·2%, and 13·5%, respectively. The DLA focused on the QRS complex for deciding the presence of anaemia in a sensitivity map. The AUROCs of DLAs using 6 leads and a single lead were in the range of 0·841-0·890. INTERPRETATION: In this study, using raw ECG data, a DLA accurately detected anaemia. The application of artificial intelligence to ECGs could enable screening for anaemia. FUNDING: None.

Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography.

Rapid diagnosis of myocardial infarction (MI) using electrocardiography (ECG) is the cornerstone of effective treatment and prevention of mortality; however, conventional interpretation methods has low reliability for detecting MI and is difficulty to apply to limb 6-lead ECG based life type or wearable devices. We developed and validated a deep learning-based artificial intelligence algorithm (DLA) for detecting MI using 6-lead ECG. A total of 412,461 ECGs were used to develop a variational autoencoder (VAE) that reconstructed precordial 6-lead ECG using limb 6-lead ECG. Data from 9536, 1301, and 1768 ECGs of adult patients who underwent coronary angiography within 24 h from each ECG were used for development, internal and external validation, respectively. During internal and external validation, the area under the receiver operating characteristic curves of the DLA with VAE using a 6-lead ECG were 0.880 and 0.854, respectively, and the performances were preserved by the territory of the coronary lesion. Our DLA successfully detected MI using a 12-lead ECG or a 6-lead ECG. The results indicate that MI could be detected not only with a conventional 12 lead ECG but also with a life type 6-lead ECG device that employs our DLA.

Real-time effect of electron beam on MoS2 field-effect transistors.

Irradiation of MoS2 field-effect transistors (FETs) fabricated on Si/SiO2 substrates with electron beams (e-beams) below 30 keV creates electron-hole pairs (EHP) in the SiO2, which increase the interface trap density (Nit ) and change the current path in the channel, resulting in performance changes. In situ measurements of the electrical characteristics of the FET performed using a nano-probe system mounted inside a scanning electron microscope show that e-beam irradiation enables both multilayer and monolayer MoS2 channels act as conductors. The e-beams mostly penetrate the channel owing to their large kinetic energy, while the EHPs formed in the SiO2 layer can contribute to the conductance by flowing into the MoS2 channel or inducing the gate bias effect. The analysis of the device parameters in the initial state and the vent-evacuation state after e-beam irradiation can clarify the effect of the interplay between the e-beam-induced EHPs and ambient adsorbates on the carrier behavior, which depends on the thickness of the MoS2 layer. DC and low frequency noise analysis reveals that the e-beam-induced EHPs increase Nit from 109-1010 to 1011 cm-2 eV-1 in both monolayer and multilayer devices, while the interfacial Coulomb scattering parameter αSC increases by three times in the monolayer and decreases to one-tenth of its original value in the multilayer. In other words, an MoS2 layer with a thickness of ∼30 nm is less sensitive to adsorbates by surface screening. Thus, the carrier mobility in the monolayer device decreases from 45.7 to 40 cm2 V-1 s-1, while in the 30 nm-thick multilayer device, it increases from 4.9 to 5.6 cm2 V-1 s-1. This is further evidenced by simulations of the distribution of interface traps and channel carriers in the MoS2 FET before and after e-beam irradiation, demonstrating that Coulomb scattering decreases as the effective channel moves away from the interface.

A Curriculum Learning Strategy to Enhance the Accuracy of Classification of Various Lesions in Chest-PA X-ray Screening for Pulmonary Abnormalities.

We evaluated the efficacy of a curriculum learning strategy using two steps to detect pulmonary abnormalities including nodule[s], consolidation, interstitial opacity, pleural effusion, and pneumothorax with chest-PA X-ray (CXR) images from two centres. CXR images of 6069 healthy subjects and 3417 patients at AMC and 1035 healthy subjects and 4404 patients at SNUBH were obtained. Our approach involved two steps. First, the regional patterns of thoracic abnormalities were identified by initial learning of patch images around abnormal lesions. Second, Resnet-50 was fine-tuned using the entire images. The network was weakly trained and modified to detect various disease patterns. Finally, class activation maps (CAM) were extracted to localise and visualise the abnormal patterns. For average disease, the sensitivity, specificity, and area under the curve (AUC) were 85.4%, 99.8%, and 0.947, respectively, in the AMC dataset and 97.9%, 100.0%, and 0.983, respectively, in the SNUBH dataset. This curriculum learning and weak labelling with high-scale CXR images requires less preparation to train the system and could be easily extended to include various diseases in actual clinical environments. This algorithm performed well for the detection and classification of five disease patterns in CXR images and could be helpful in image interpretation.

Ultrasonographic and Three-Dimensional Analyses at the Glabella and Radix of the Nose for Botulinum Neurotoxin Injection Procedures into the Procerus Muscle.

Botulinum neurotoxin (BoNT) injections are widely used for facial rejuvenation procedures, and the procerus muscle is a major target in cases of glabellar transverse lines or rhytids. Although there have been many cadaveric studies of the procerus, its depth and thickness have not been investigated thoroughly. The aim of this study was to measure the depth and thickness of the procerus and identify the location of the intercanthal vein using ultrasonographic (US) imaging and the three-dimensional scanning method, which is needed to know to avoid side effects during BoNT injections. The morphology of the procerus was classified into two types based on the US images obtained at the glabella. The procerus was located deeper below the skin surface at the glabella than the sellion (3.8 ± 0.7 mm versus 2.7 ± 0.6 mm). The width of the procerus in US images increased from the sellion (10.9 ± 0.2 mm) to the glabella (14.5 ± 4.6 mm), whereas its thickness decreased (from 1.6 ± 0.6 mm to 1.1 ± 0.5 mm). The intercanthal vein was located 5.1 ± 4.0 mm superior to the sellion and 3.0 ± 0.6 mm below the skin's surface. The present findings provide anatomical knowledge as well as the reference location information for use when injecting BoNT into the procerus.

A Technology-Computer-Aided-Design-Based Reliability Prediction Model for DRAM Storage Capacitors.

A full three-dimensional technology-computer-aided-design-based reliability prediction model was proposed for dynamic random-access memory (DRAM) storage capacitors. The model can be used to predict the time-dependent dielectric breakdown as well as leakage current of a state-of-the-art DRAM storage capacitor with a complex three-dimensional structure.

Deep Learning Applications in Chest Radiography and Computed Tomography: Current State of the Art.

Deep learning is a genre of machine learning that allows computational models to learn representations of data with multiple levels of abstraction using numerous processing layers. A distinctive feature of deep learning, compared with conventional machine learning methods, is that it can generate appropriate models for tasks directly from the raw data, removing the need for human-led feature extraction. Medical images are particularly suited for deep learning applications. Deep learning techniques have already demonstrated high performance in the detection of diabetic retinopathy on fundoscopic images and metastatic breast cancer cells on pathologic images. In radiology, deep learning has the opportunity to provide improved accuracy of image interpretation and diagnosis. Many groups are exploring the possibility of using deep learning-based applications to solve unmet clinical needs. In chest imaging, there has been a large effort to develop and apply computer-aided detection systems for the detection of lung nodules on chest radiographs and chest computed tomography. The essential limitation to computer-aided detection is an inability to learn from new information. To overcome these deficiencies, many groups have turned to deep learning approaches with promising results. In addition to nodule detection, interstitial lung disease recognition, lesion segmentation, diagnosis and patient outcomes have been addressed by deep learning approaches. The purpose of this review article was to cover the current state of the art for deep learning approaches and its limitations, and some of the potential impact on the field of radiology, with specific reference to chest imaging.

Soft-type trap-induced degradation of MoS2 field effect transistors.

The practical applicability of electronic devices is largely determined by the reliability of field effect transistors (FETs), necessitating constant searches for new and better-performing semiconductors. We investigated the stress-induced degradation of MoS2 multilayer FETs, revealing a steady decrease of drain current by 56% from the initial value after 30 min. The drain current recovers to the initial state when the transistor is completely turned off, indicating the roles of soft-traps in the apparent degradation. The noise current power spectrum follows the model of carrier number fluctuation-correlated mobility fluctuation (CNF-CMF) regardless of stress time. However, the reduction of the drain current was well fitted to the increase of the trap density based on the CNF-CMF model, attributing the presence of the soft-type traps of dielectric oxides to the degradation of the MoS2 FETs.

A Case of Nuclear Protein in Testis Midline Carcinoma Arising From the Submandibular Gland Duct in a Pregnant Patient.

This report describes the first reported case of a nuclear protein in testis midline carcinoma (NMC) arising from the submandibular gland (SMG) duct in a pregnant woman. A 29-year-old pregnant woman presented with a left-side mass in the floor of the mouth. An NMC arising from the SMG duct was confirmed by excisional biopsy examination. Intensive treatment, including surgery and chemotherapy, was provided without termination of the pregnancy. Additional chemotherapy and radiotherapy were provided after delivery. The treatment was successful. Neither the patient nor her infant had any complications and the patient remained disease free 20 months after her initial surgery. This report describes the successful diagnosis and treatment of a rare presentation of an NMC of the SMG duct in a pregnant woman.

Characterization of Lactobacillus fermentum PL9988 Isolated from Healthy Elderly Korean in a Longevity Village.

In this work, we wanted to develop a probiotic from famous longevity villages in Korea. We visited eight longevity villages in Korea to collect fecal samples from healthy adults who were aged above 80 years and had regular bowel movements, and isolated lactic-acid-producing bacteria from the samples. Isolated colonies that appeared on MRS agar containing bromophenol blue were identified by means of 16S rRNA sequencing, and 102 of the isolates were identified as lactic-acid-producing bacteria (18 species). Lactobacillus fermentum was the most frequently found species. Eight isolates were selected on the basis of their ability to inhibit the growth of six intestinal pathogens (Escherichia coli O157:H7, Salmonella enterica subsp. enterica Typhimurium, Salmonella enterica subsp. enterica Enteritidis, Enterococcus faecalis, Staphylococcus aureus, and Listeria monocytogenes) and their susceptibility to 15 antimicrobial agents. Among these eight isolates, four Lactobacillus fermentum isolates were found not to produce any harmful enzymes or metabolites. Among them, Lactobacillus fermentum isolate no. 24 showed the strongest binding to intestinal epithelial cells, the highest immune-enhancing activity, anti-inflammation activity, and anti-oxidation activity as well as the highest survival rates in the presence of artificial gastric juice and bile solution. This isolate, designated Lactobacillus fermentum PL9988, has all the characteristics for a good probiotic.

Intramuscular innervation patterns of the brachialis muscle.

The aim of this study was to provide accurate anatomical descriptions of the patterns of innervation of the brachialis muscle by the musculocutaneous, radial, and median nerves. Sihler's staining method was applied to 20 brachialis muscles from 10 cadavers to reveal the intramuscular distribution patterns of the musculocutaneous, radial, and median nerves. Three patterns of innervation of the brachialis muscle by the three studied nerves were found: single, double, and triple. These innervation patterns were categorized into four types: Type I--only the musculocutaneous nerve; Type II--double innervation by the musculocutaneous and radial nerves; Type III--double innervation by the musculocutaneous and median nerves; and Type IV--triple innervation by all three nerves. Single, double, and triple innervation patterns occurred 25%, 70% (Type II, 55%; Type III, 15%), and 5% of the samples, respectively. The brachialis muscle is not solely innervated by the musculocutaneous nerve but also by the radial and median nerves, thus making it a potentially triply innervated muscle. Double innervation of this muscle with either the musculocutaneous and median nerve or the musculocutaneous and radial nerves was also observed.

Covered stent placement for the treatment of malignant superior vena cava syndrome: is unilateral covered stenting safe and effective?

OBJECTIVE: To evaluate the safety and efficacy of unilateral covered stent placement in patients with malignant superior vena cava (SVC) syndrome. MATERIALS AND METHODS: Between October 2008 and November 2012, expanded polytetrafluoroethylene-covered stent placement for malignant SVC syndrome was performed in 40 consecutive patients (35 men and five women; mean age, 61.4 years; range, 35-81 years). All covered stents were unilaterally placed within the SVC or across the venous confluence when needed to relieve venous obstruction and prevent tumor overgrowth, regardless of patency of contralateral brachiocephalic veins. RESULTS: Stent placement was technically successful in all patients. There were no major complications. Of the 37 patients symptomatic prior to stent placement, 34 (92%) experienced complete symptomatic relief 1-8 days after stent placement. Of the 29 patients who underwent covered stent placement across the venous confluence, nine patients had patent contralateral brachiocephalic veins prior to stent placement. However, no sign of SVC obstruction or contralateral upper extremity venous thrombosis was observed during the follow-up period. Kaplan-Meier analysis revealed median patient survival of 163 days. Stent occlusion occurred in four (10%) of 40 patents. Cumulative stent patency rates at 1, 3, 6, and 12 months were 95%, 92%, 86%, and 86%, respectively. CONCLUSION: Unilateral covered stent placement appears to be a safe and effective method for treating malignant SVC syndrome, despite the location of SVC occlusion.

Long-term vascular access ports as a means of sedative administration in a rodent fMRI survival model.

The purpose of this study is to develop a rodent functional magnetic resonance imaging (fMRI) survival model with the use of heparin-coated vascular access devices. Such a model would ease the administration of sedative agents, reduce the number of animals required in survival experiments and eliminate animal-to-animal variability seen in previous designs. Seven male Sprague-Dawley rats underwent surgical placement of an MRI-compatible vascular access port, followed by implantable electrode placement on the right median nerve. Functional MRI during nerve stimulation and resting-state functional connectivity MRI (fcMRI) were performed at times 0, 2, 4, 8 and 12 weeks postoperatively using a 9.4T scanner. Anesthesia was maintained using intravenous dexmedetomidine and reversed using atipamezole. There were no fatalities or infectious complications during this study. All vascular access ports remained patent. Blood oxygen level dependent (BOLD) activation by electrical stimulation of the median nerve using implanted electrodes was seen within the forelimb sensory region (S1FL) for all animals at all time points. The number of activated voxels decreased at time points 4 and 8 weeks, returning to a normal level at 12 weeks, which is attributed to scar tissue formation and resolution around the embedded electrode. The applications of this experiment extend far beyond the scope of peripheral nerve experimentation. These vascular access ports can be applied to any survival MRI study requiring repeated medication administration, intravenous contrast, or blood sampling.