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Objective This study aimed to investigate the correlation between ossification of the posterior longitudinal ligament (OPLL) size and multifidus fatty degeneration (MFD), hypothesizing that larger OPLL sizes are associated with worse MFD. Methods One hundred four patients with cervical OPLL who underwent surgery were screened. OPLL occupying diameter and area ratios, the severity of MFD using the Goutallier classification, and range of motion (ROM) of cervical flexion-extension (ΔCobb) were measured. Correlation analyses between OPLL size, MFD severity, and ΔCobb were conducted. MFD severity was compared for each OPLL type using one-way analysis of variance. Results The final clinical data from 100 patients were analyzed. The average Goutallier grade of C2–7 significantly correlated with the average OPLL diameter and area occupying ratios, and OPLL involved vertebral level (r = 0.58, p < 0.01; r = 0.40, p < 0.01; r = 0.47, p < 0.01, respectively). The OPLL size at each cervical level significantly correlated with MFD of the same or 1–3 adjacent levels. ΔCobb angle was negatively correlated with the average Goutallier grade (r = -0.31, p < 0.01) and average OPLL occupying diameter and area ratios (r = -0.31, p < 0.01; r = -0.35, p < 0.01, respectively). Patients with continuous OPLL exhibited worse MFD than those with segmental OPLL (p < 0.01). Conclusion OPLL size is clinically correlated with MFD and cervical ROM. OPLL at one spinal level affects MFD at the same and 1–3 adjacent spinal levels. The worsening severity of MFD is associated with the longitudinal continuity of OPLL.

Background: ST-T segment elevation myocardial infarction (STEMI) is a critical condition that requires rapid diagnosis and treatment. Recently, various ECG recording devices have been developed. In this study, we aim to determine the utility of a 6-lead handheld ECG recording device to shorten the time taken for the diagnosis of inferior wall STEMI. Methods and Design: HINT-MI is an investigator-derived, observational, prospective study that will evaluate the ability of a handheld 6-lead ECG device to diagnose acute inferior wall STEMI. Patients who have undergone coronary angiography for STEMI or for other reasons will be enrolled in the study. This study aims to evaluate sensitivity and specificity of a handheld 6-lead ECG device by the level of agreement with a standard 12-lead ECG for diagnosing inferior wall STEMI. Further, we will determine whether the use of the handheld device can reduce the time needed for reperfusion treatment through faster diagnosis. Conclusions: This study aims to investigate the feasibility of a handheld 6-lead ECG device for diagnosing inferior wall STEMI to reduce the time required to diagnose inferior wall STEMI and to allow timely treatment.

Abstract Background The baseline coronary plaque burden is the most important factor for rapid plaque progression (RPP) in the coronary artery. However, data on the independent predictors of RPP in the absence of a baseline coronary plaque burden are limited. Thus, this study aimed to investigate the predictors for RPP in patients without coronary plaques on baseline coronary computed tomography angiography (CCTA) images. Methods A total of 402 patients (mean age: 57.6 ± 10.0 years, 49.3% men) without coronary plaques at baseline who underwent serial coronary CCTA were identified from the Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging (PARADIGM) registry and included in this retrospective study. RPP was defined as an annual change of ≥ 1.0%/year in the percentage atheroma volume (PAV). Results During a median inter-scan period of 3.6 years (interquartile range: 2.7–5.0 years), newly developed coronary plaques and RPP were observed in 35.6% and 4.2% of the patients, respectively. The baseline traditional risk factors, i.e., advanced age (≥ 60 years), male sex, hypertension, diabetes mellitus, hyperlipidemia, obesity, and current smoking status, were not significantly associated with the risk of RPP. Multivariate linear regression analysis showed that the serum hemoglobin A1c level (per 1% increase) measured at follow-up CCTA was independently associated with the annual change in the PAV (β: 0.098, 95% confidence interval [CI]: 0.048–0.149; P

Accurate and efficient classification and quantification of CD34+ cells are essential for the diagnosis and monitoring of leukemia. Current methods, such as flow cytometry, are complex, time-consuming, and require specialized expertise and equipment. This study proposes a novel approach for the label-free identification of CD34+ cells using a deep learning model and lens-free shadow imaging technology (LSIT). LSIT is a portable and user-friendly technique that eliminates the need for cell staining, enhances accessibility to nonexperts, and reduces the risk of sample degradation. The study involved three phases: sample preparation, dataset generation, and data analysis. Bone marrow and peripheral blood samples were collected from leukemia patients, and mononuclear cells were isolated using Ficoll density gradient centrifugation. The samples were then injected into a cell chip and analyzed using a proprietary LSIT-based device (Cellytics). A robust dataset was generated, and a custom AlexNet deep learning model was meticulously trained to distinguish CD34+ from non-CD34+ cells using the dataset. The model achieved a high accuracy in identifying CD34+ cells from 1929 bone marrow cell images, with training and validation accuracies of 97.3% and 96.2%, respectively. The customized AlexNet model outperformed the Vgg16 and ResNet50 models. It also demonstrated a strong correlation with the standard fluorescence-activated cell sorting (FACS) technique for quantifying CD34+ cells across 13 patient samples, yielding a coefficient of determination of 0.81. Bland–Altman analysis confirmed the model’s reliability, with a mean bias of −2.29 and 95% limits of agreement between 18.49 and −23.07. This deep-learning-powered LSIT offers a groundbreaking approach to detecting CD34+ cells without the need for cell staining, facilitating rapid CD34+ cell classification, even by individuals without prior expertise.

Accurate assessment of the response to the antiarrhythmic drug (AAD) in atrial fibrillation (AF) is crucial to achieve adequate rhythm control. We evaluated the effectiveness of extended cardiac monitoring using an adhesive ECG patch in the detection of drug-refractory paroxysmal AF. Patients diagnosed with paroxysmal AF and receiving AAD therapy were enrolled. The subjects simultaneously underwent 11-day adhesive ECG patch monitoring and a 24-h Holter test. The primary study outcome was a detection rate of drug-refractory AF or atrial tachycardia (AT) lasting ≥30 s. A total of 59 patients were enrolled and completed the study examinations. AF or AT was detected in 28 (47.5%) patients by an 11-day ECG patch monitor and in 8 (13.6%) patients by a 24-h Holter test (p < 0.001). The 11-day ECG patch monitor identified an additional 20 patients (33.8%) with drug-refractory AF not detected by the 24-h Holter, and as a result, the treatment plan was changed in 11 patients (10 catheter ablations, one medication change). In conclusion, extended cardiac rhythm monitoring using an adhesive ECG patch in patients with paroxysmal AF under AAD therapy led to over a threefold higher detection of drug-refractory AF episodes, compared to the 24-h Holter test.

Abstract Patient-specific phenotyping of coronary atherosclerosis would facilitate personalized risk assessment and preventive treatment. We explored whether unsupervised cluster analysis can categorize patients with coronary atherosclerosis according to their plaque composition, and determined how these differing plaque composition profiles impact plaque progression. Patients with coronary atherosclerotic plaque (n = 947; median age, 62 years; 59% male) were enrolled from a prospective multi-national registry of consecutive patients who underwent serial coronary computed tomography angiography (median inter-scan duration, 3.3 years). K-means clustering applied to the percent volume of each plaque component and identified 4 clusters of patients with distinct plaque composition. Cluster 1 (n = 52), which comprised mainly fibro-fatty plaque with a significant necrotic core (median, 55.7% and 16.0% of the total plaque volume, respectively), showed the least total plaque volume (PV) progression (+ 23.3 mm3), with necrotic core and fibro-fatty PV regression (− 5.7 mm3 and − 5.6 mm3, respectively). Cluster 2 (n = 219), which contained largely fibro-fatty (39.2%) and fibrous plaque (46.8%), showed fibro-fatty PV regression (− 2.4 mm3). Cluster 3 (n = 376), which comprised mostly fibrous (62.7%) and calcified plaque (23.6%), showed increasingly prominent calcified PV progression (+ 21.4 mm3). Cluster 4 (n = 300), which comprised mostly calcified plaque (58.7%), demonstrated the greatest total PV increase (+ 50.7mm3), predominantly increasing in calcified PV (+ 35.9 mm3). Multivariable analysis showed higher risk for plaque progression in Clusters 3 and 4, and higher risk for adverse cardiac events in Clusters 2, 3, and 4 compared to that in Cluster 1. Unsupervised clustering algorithms may uniquely characterize patient phenotypes with varied atherosclerotic plaque profiles, yielding distinct patterns of progressive disease and outcome.

Micro/nano patterns based on quantum dots (QDs) are of great interest for applications ranging from electronics to photonics to sensing devices for biomedical purposes. Several patterning methods have been developed, but all lack the precision and reproducibility required to fabricate precise, complex patterns of less than one micrometer in size, or require specialized crosslinking ligands, limiting their application. In this study, we present a novel approach to directly pattern QD nanopatterns by electron beam lithography using commercially available colloidal QDs without additional modifications. We have successfully generated reliable dot and line QD patterns with dimensions as small as 140 nm. In addition, we have shown that using a 10 nm SiO2 spacer layer on a 50 nm Au layer substrate can double the fluorescence intensity compared to QDs on the Au layer without SiO2. This method takes advantage of traditional nanolithography without the need for a resist layer.

Smartphone-based point-of-care testing (POCT) is rapidly emerging as an alternative to traditional screening and laboratory testing, particularly in resource-limited settings. In this proof-of-concept study, we present a smartphone- and cloud-based artificial intelligence quantitative analysis system (SCAISY) for relative quantification of SARS-CoV-2-specific IgG antibody lateral flow assays that enables rapid evaluation (p = 0.008; Pearson correlation coefficient: 0.56, p = 0.012) between the OD450 values of the enzyme-linked immunosorbent assay and the antibody levels obtained by SCAISY. This study suggests that SCAISY is a simple and powerful tool for real-time public health surveillance, enabling the acceleration of quantifying SARS-CoV-2-specific antibodies generated by either vaccination or infection and tracking of personal immunity levels.

Abstract Background The association between triglyceride glucose (TyG) index and coronary atherosclerotic change remains unclear. We aimed to evaluate the association between TyG index and coronary plaque progression (PP) using serial coronary computed tomography angiography (CCTA). Methods A total of 1143 subjects (aged 60.7 ± 9.3 years, 54.6% male) who underwent serial CCTA with available data on TyG index and diabetic status were analyzed from The Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography IMaging (PARADIGM) registry. PP was defined as plaque volume (PV) (mm3) at follow-up minus PV at index > 0. Annual change of PV (mm3/year) was defined as PV change divided by inter-scan period. Rapid PP was defined as the progression of percent atheroma volume (PV divided by vessel volume multiplied by 100) ≥ 1.0%/year. Results The median inter-scan period was 3.2 (range 2.6–4.4) years. All participants were stratified into three groups based on TyG index tertiles. The overall incidence of PP was 77.3%. Baseline total PV (group I [lowest]: 30.8 (0.0–117.7), group II: 47.2 (6.2–160.4), and group III [highest]: 57.5 (8.4–154.3); P

A key difficulty to overcome in pyridinium salts functionalization is the regioselectivity at the two competing C2 and C4 sites. Here, the authors show bifunctional N-aminopyridinium salts reagents delivering both the aminyl radical and the pyridyl group to an olefin with C4-pyridyl regioselectivity.

Self-heating has emerged as an important performance/reliability challenge for modern MOSFETs. The challenge is further acerbated for III-V transistors, especially when integrated monolithically in a 3-D platform for applications in ultrafast logic, imagers, etc. A key challenge is the difficulty of heat-dissipation through the ultra-thin channels needed to ensure electrostatic integrity of scaled transistors. In this paper, we demonstrate an innovative use of a heat-dissipating shunt of Ni-InGaAs on InGaAs(111) in the S/D extension region, as well as the use of high-conductivity Mo contact to simultaneously improve electrical and thermal stability and heat dissipation in III-V transistors, such that the peak channel temperature is reduced by as much as 25%-30%. Given the exponential temperature sensitivity of transistor reliability, heat shunts will improve transistor lifetime significantly.

Background Although antiplatelet therapy (APT) has been recommended to balance ischemic‐bleeding risks, it has been left to an individualized decision‐making based on physicians' perspectives before non‐cardiac surgery. The study aimed to assess the advantages of a consensus among physicians, surgeons, and anesthesiologists on continuation and regimen of preoperative APT in patients with coronary drug‐eluting stents. Methods and Results A total of 3582 adult patients undergoing non‐cardiac surgery after percutaneous coronary intervention with second‐generation stents was retrospectively included from a multicenter cohort. Physicians determined whether APT should be continued or discontinued for a recommended period before non‐cardiac surgery. There were 3103 patients who complied with a consensus decision. Arbitrary APT, not based on a consensus decision, was associated with urgent surgery, high bleeding risk of surgery, female sex, and dual APT at the time of preoperative evaluation. Arbitrary APT independently increased the net clinical adverse event (adjusted odds ratio [ORadj], 1.98; 95% CI, 1.98–3.11), major adverse cardiac event (ORadj, 3.11; 95% CI, 1.31–7.34), and major bleeding (ORadj, 2.34; 95% CI, 1.45–3.76) risks. The association was consistently noted, irrespective of the surgical risks, recommendations, and practice on discontinuation of APT. Conclusions Most patients were treated in agreement with a consensus decision about preoperative APT based on a referral system among physicians, surgeons, and anesthesiologists. The risk of perioperative adverse events increased if complying with a consensus decision was failed. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT03908463.

The lens-free shadow imaging technique (LSIT) is a well-established technique for the characterization of microparticles and biological cells. Due to its simplicity and cost-effectiveness, various low-cost solutions have been developed, such as automatic analysis of complete blood count (CBC), cell viability, 2D cell morphology, 3D cell tomography, etc. The developed auto characterization algorithm so far for this custom-developed LSIT cytometer was based on the handcrafted features of the cell diffraction patterns from the LSIT cytometer, that were determined from our empirical findings on thousands of samples of individual cell types, which limit the system in terms of induction of a new cell type for auto classification or characterization. Further, its performance suffers from poor image (cell diffraction pattern) signatures due to their small signal or background noise. In this work, we address these issues by leveraging the artificial intelligence-powered auto signal enhancing scheme such as denoising autoencoder and adaptive cell characterization technique based on the transfer of learning in deep neural networks. The performance of our proposed method shows an increase in accuracy >98% along with the signal enhancement of >5 dB for most of the cell types, such as red blood cell (RBC) and white blood cell (WBC). Furthermore, the model is adaptive to learn new type of samples within a few learning iterations and able to successfully classify the newly introduced sample along with the existing other sample types.

The refractive index (RI), an important optical property of a material, is measured by commercial refractometers in the food, agricultural, chemical, and manufacturing industries. Most of these refractometers must be equipped with a prism for light dispersion, which drastically limits the design and size of the refractometer. Recently, there have been several reports on the development of a surface plasmon resonance (SPR)-based RI detector, which is characterized by its high sensitivity and simplicity. However, regardless of the prism, an expensive spectrometer is required to analyze the resonance wavelength or angle of incidence. This paper proposes a method that eliminates the need for the prism and other conventional spectrometer components. For this purpose, total internal reflection SPR technology was used on an Ag thin film, and RI analysis was combined with a lens-free CMOS image sensor or a smartphone camera. A finite-difference time-domain (FDTD) numerical simulation was performed to evaluate the relationship between the output power intensity and Ag film thickness for different RIs at three wavelengths of commercial light-emitting diodes (LEDs). The maximum sensitivity of −824.54 RIU−1 was achieved with AG20 at an incident wavelength of 559 nm. Due to its simple design and cost effectiveness, this prism-less, SPR-based refractometer combined with a lens-free CMOS image sensor or a smartphone could be a superior candidate for a point-of-care device that can determine the RIs of various analytes in the field of biological or chemical sensing.

Background Continuing antiplatelet therapy (APT) has been generally recommended during noncardiac surgery, but it is uncertain if preoperative discontinuation of APT has been avoided or harmful in patients with second‐generation drug‐eluting coronary stents. Methods and Results Patients undergoing noncardiac surgery after second‐generation drug‐eluting coronary stent implantation were assessed in a multicenter cohort in Korea. Net adverse clinical events within 30 days postoperatively, defined as all‐cause death, major adverse cardiac events, and major bleeding, were evaluated. Of 3582 eligible patients, 49% patients discontinued APT during noncardiac surgery. The incidence of net adverse clinical events was comparable between patients with continuation versus discontinuation (4.1% versus 3.4%; P=0.257) of APT during noncardiac surgery. Perioperative discontinuation of APT did not impact on net adverse clinical events (adjusted hazard ratio [HR], 1.00; 95% CI, 0.69–1.44; P=0.995). In subgroup analysis, patients undergoing intra‐abdominal surgery were exposed to less risk of major bleeding by discontinuing APT (adjusted HR, 0.26; 95% CI, 0.08–0.91; P=0.035). Prolonged discontinuation of APT for ≥9 days was associated with higher risk of a major adverse cardiac event compared with continuing APT (adjusted HR, 3.38; 95% CI, 1.36–8.38; P=0.009). Conclusions APT was discontinued preoperatively in almost half of patients with second‐generation drug‐eluting coronary stents. Our explorative analysis showed that there was no significant impact of discontinuing APT on the risk of perioperative adverse events except that discontinuing APT may be associated with decreased hemorrhagic risk in patients undergoing intra‐abdominal surgery. Registration URL: https://www.clini​caltr​ials.gov; Unique identifier: NCT03908463.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level.

Abstract Rapidly expanding human activities have profoundly affected various biophysical and biogeochemical processes of the Earth system over a broad range of scales, and freshwater systems are now amongst the most extensively altered ecosystems. In this study, we examine the human-induced changes in land surface water and energy balances and the associated climate impacts using a coupled hydrological–climate model framework which also simulates the impacts of human activities on the water cycle. We present three sets of analyses using the results from two model versions—one with and the other without considering human activities; both versions are run in offline and coupled mode resulting in a series of four experiments in total. First, we examine climate and human-induced changes in regional water balance focusing on the widely debated issue of the desiccation of the Aral Sea in central Asia. Then, we discuss the changes in surface temperature as a result of changes in land surface energy balance due to irrigation over global and regional scales. Finally, we examine the global and regional climate impacts of increased atmospheric water vapor content due to irrigation. Results indicate that the direct anthropogenic alteration of river flow in the Aral Sea basin resulted in the loss of ~510 km3 of water during the latter half of the twentieth century which explains about half of the total loss of water from the sea. Results of irrigation-induced changes in surface energy balance suggest a significant surface cooling of up to 3.3 K over 1° grids in highly irrigated areas but a negligible change in land surface temperature when averaged over sufficiently large global regions. Results from the coupled model indicate a substantial change in 2 m air temperature and outgoing longwave radiation due to irrigation, highlighting the non-local (regional and global) implications of irrigation. These results provide important insights on the direct human alteration of land surface water and energy balances, highlighting the need to incorporate human activities such as irrigation into the framework of global climate models and Earth system models for better prediction of future changes under increasing human influence and continuing global climate change.

Field-portable observation and analysis of particulate matter (PM) is required to enhance healthy lives. Various types of the PM measurement methods are in use; however, each of these methods has significant limitations in that real time measurement is impossible, the detection system is bulky, or the measurement accuracy is insufficient. In this work, we introduce an optical method to perform a fast and accurate PM analysis with a higher-contrast microscopic image enabled by a side-illuminated total internal reflection (TIR) technique to be implemented in a compact device. The superiority of the proposed method was quantitatively demonstrated by comparing the signal-to-noise ratio of the proposed side-illuminated TIR method with a traditional halogen lamp-based transmission microscope. With the proposed device, signal-to-noise ratios (SNRs) for microbeads (5~20 µm) and ultrafine dust particles (>5 µm) increased 4.5~17 and 4~10 dB, respectively, compared to the conventional transmission microscope. As a proof of concept, the proposed method was also applied to a low-cost commercial smartphone toy microscope enabling field-portable detection of PMs. We believe that the proposed side-illuminated TIR PM detection device holds significant advantages over other commonly used systems due to its sufficient detection capability along with simple and compact configuration as well as low cost.