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Background: Cardiovascular research heavily relies on mouse (Mus musculus) models to study disease mechanisms and to test novel biomarkers and medications. Yet, applying these results to patients remains a major challenge and often results in noneffective drugs. Therefore, it is an open challenge of translational science to develop models with high similarities and predictive value. This requires a comparison of disease models in mice with diseased tissue derived from humans., Results: To compare the transcriptional signatures at single-cell resolution, we implemented an integration pipeline called OrthoIntegrate, which uniquely assigns orthologs and therewith merges single-cell RNA sequencing (scRNA-seq) RNA of different species. The pipeline has been designed to be as easy to use and is fully integrable in the standard Seurat workflow.We applied OrthoIntegrate on scRNA-seq from cardiac tissue of heart failure patients with reduced ejection fraction (HFrEF) and scRNA-seq from the mice after chronic infarction, which is a commonly used mouse model to mimic HFrEF. We discovered shared and distinct regulatory pathways between human HFrEF patients and the corresponding mouse model. Overall, 54% of genes were commonly regulated, including major changes in cardiomyocyte energy metabolism. However, several regulatory pathways (e.g., angiogenesis) were specifically regulated in humans., Conclusions: The demonstration of unique pathways occurring in humans indicates limitations on the comparability between mice models and human HFrEF and shows that results from the mice model should be validated carefully. OrthoIntegrate is publicly accessible (https://github.com/MarianoRuzJurado/OrthoIntegrate) and can be used to integrate other large datasets to provide a general comparison of models with patient data., (© The Author(s) 2024. Published by Oxford University Press GigaScience.)

Aims: Cardiac fibrosis drives the progression of heart failure in ischaemic and hypertrophic cardiomyopathy. Therefore, the development of specific anti-fibrotic treatment regimens to counteract cardiac fibrosis is of high clinical relevance. Hence, this study examined the presence of persistent fibroblast activation during longstanding human heart disease at a single-cell resolution to identify putative therapeutic targets to counteract pathological cardiac fibrosis in patients., Methods and Results: We used single-nuclei RNA sequencing with human tissues from two samples of one healthy donor, and five hypertrophic and two failing hearts. Unsupervised sub-clustering of 7110 nuclei led to the identification of 7 distinct fibroblast clusters. De-convolution of cardiac fibroblast heterogeneity revealed a distinct population of human cardiac fibroblasts with a molecular signature of persistent fibroblast activation and a transcriptional switch towards a pro-fibrotic extra-cellular matrix composition in patients with established cardiac hypertrophy and heart failure. This sub-cluster was characterized by high expression of POSTN, RUNX1, CILP, and a target gene adipocyte enhancer-binding protein 1 (AEBP1) (all P < 0.001). Strikingly, elevated circulating AEBP1 blood level were also detected in a validation cohort of patients with confirmed cardiac fibrosis and hypertrophic cardiomyopathy by cardiac magnetic resonance imaging (P < 0.01). Since endogenous AEBP1 expression was increased in patients with established cardiac hypertrophy and heart failure, we assessed the functional consequence of siRNA-mediated AEBP1 silencing in human cardiac fibroblasts. Indeed, AEBP1 silencing reduced proliferation, migration, and fibroblast contractile capacity and α-SMA gene expression, which is a hallmark of fibroblast activation (all P < 0.05). Mechanistically, the anti-fibrotic effects of AEBP1 silencing were linked to transforming growth factor-beta pathway modulation., Conclusion: Together, this study identifies persistent fibroblast activation in patients with longstanding heart disease, which might be detected by circulating AEBP1 and therapeutically modulated by its targeted silencing in human cardiac fibroblasts., Competing Interests: Conflict of interest: E.N. has received speaker fees educational grants from Bayer AG., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Fibroblast growth factor 23 (FGF-23) has been associated with left ventricular hypertrophy (LVH) and heart failure. However, its role in right ventricular (RV) remodeling and RV failure is unknown. This study analyzed the utility of FGF-23 as a biomarker of RV function in patients with pulmonary hypertension (PH)., Methods: In this observational study, FGF-23 was measured in the plasma of patients with PH (n = 627), dilated cardiomyopathy (DCM, n = 59), or LVH with severe aortic stenosis (n = 35). Participants without LV or RV abnormalities served as controls (n = 36)., Results: Median FGF-23 plasma levels were higher in PH patients than in healthy controls (p < 0.001). There were no significant differences between PH, DCM, and LVH patients. Analysis across tertiles of FGF-23 levels in PH patients revealed an association between higher FGF-23 levels and higher levels of NT-proBNP and worse renal function. Furthermore, patients in the high-FGF-23 tertile had a higher pulmonary vascular resistance (PVR), mean pulmonary artery pressure, and right atrial pressure and a lower cardiac index (CI) than patients in the low tertile (p < 0.001 for all comparisons). Higher FGF-23 levels were associated with higher RV end-diastolic diameter and lower tricuspid annular plane systolic excursions (TAPSE) and TAPSE/PASP. Receiver operating characteristic analysis revealed FGF-23 as a good predictor of RV maladaptation, defined as TAPSE < 17 mm and CI < 2.5 L/min/m 2 . Association of FGF-23 with parameters of RV function was independent of the glomerular filtration rate in regression analysis., Conclusion: FGF-23 may serve as a biomarker for maladaptive RV remodeling in patients with PH., (© 2023. The Author(s).)

Two-dimensional atomically thick materials such as graphene and layered molybdenum disulfide (MoS 2 ) have been studied as potential energy storage materials because of their high specific surface area, potential redox activity, and mechanical flexibility. However, because of the layered structure restacking and poor electrical conductivity, these materials are unable to attain their full potential. Composite electrodes made of a mixture of graphene and MoS 2 have been shown to partially resolve these issues in the past, although their performance is still limited by inadequate mixing at the nanoscale. Herein, we report three composites via a simple ball-milling method and analyze supercapacitor electrodes. Compared with pristine graphene and MoS 2 , the composites showed high capacitance. The as-obtained MoS 2 @Graphene composite (1:9) possesses a high surface area and uniform dispersion of MoS 2 on the graphene sheet. The MoS 2 @Graphene (1:9) composite electrode has a high specific capacitance of 248 F g -1 at 5 A g -1 in an electrochemical supercapacitor compared with the other two composites. Simultaneously, the flexible symmetric supercapacitor device prepared demonstrated superior flexibility and a long lifespan (93% capacitance retention after 8000 cycles) with no obvious changes in performance under different angles. In portable and wearable energy storage devices, the current experimental results will result in scalable, freestanding hybrid electrodes with improved, flexible, supercapacitive performance., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

Multiphase flowmeters (MPFMs) measure the flow rates of oil, gas, and brine in a pipeline. MPFMs provide remote access to real-time well production data that are essential for efficient oil field operations. Most MPFMs are complex systems requiring frequent maintenance. An MPFM that is operationally simple and accurate is highly sought after in the energy industry. This paper describes an MPFM that uses only pressure sensors to measure gas and liquid flow rates. The design is an integration of a previously developed densitometer with an innovative Venturi-type flowmeter. New computing models with strong analytical foundations were developed, aided by empirical correlations and machine-learning-based flow-regime identification. A prototype was experimentally validated in a multiphase flow loop over a wide range of field-like conditions. The accuracy of the MPFM was compared to that of other multiphase metering techniques from similar studies. The results point to a robust, practical MPFM.

Background A significant portion of the Pakistani population is affected by diabetes, which has emerged as a global healthcare concern. Objective This study aimed to assess the correlation between glycemic control in diabetes patients and their engagement in diabetes self-care activities (DSCA). Methodology Cross-sectional research was conducted at Hayatabad Medical Complex in Peshawar between June 2019 and May 2020. A total of 280 carefully selected patients with type 2 diabetes mellitus (T2DM) were included. Data collection involved an interviewer-administered questionnaire encompassing sociodemographic information, diabetes-related data, and the summary of the Diabetes Self-Care Activities (SDSCA) scale. Descriptive statistics and Pearson's chi-square test were employed for data analysis. Results The study observed that the majority of participants (40.36%) were females, and the age range of the participants was between 42 and 53 years (68.22%). According to the study, 55.00% of participants had a normal body mass index (BMI), and 71.08% of participants had diabetes in their family. Regarding glycemic control, 55.71% of individuals exhibited good control based on fasting blood sugar (FBS) levels while 74.64% showed poor control according to hemoglobin A1C (HbA1c) values. HbA1c was substantially linked with a general diet (healthy eating plan), physical activities, and adherence to medication ((odds ratios (OR): 3.12), (95% confidence interval (CI): 1.02 - 8.78), (P value: 0.031)); ((OR: 2.19, 95%), (CI:1.18 - 3.79), (P value: 0.003)); ((OR: 2.85), (95% CI: 1.22 - 6.59), P value: 0.021)). Conclusion The findings indicated that health professionals need to create health education programs on DSCA in order to increase DSCA adherence in people with T2DM while maintaining glycemic control., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Khan et al.)

Background: Early graft failure (EGF) after coronary artery bypass grafting (CABG) occurs in up to 12% of grafts, but is often clinically unapparent. EGF may result in perioperative myocardial infarction with consequently increased mortality. The aim of the present study was to analyze the incidence of clinically apparent EGF in patients undergoing CABG and the influence on mortality., Methods: We analyzed outcomes of consecutive patients undergoing CABG from January 2015 to December 2018 with respect to postoperative emergency coronary angiography (CAG) due to suspected EGF and 30-day mortality. Patients with CAG-documented EGF were matched to patients without EGF to examine predictors of mortality., Results: The analysis included 5638 patients undergoing CABG. Eighty-six patients (1.5%) underwent emergency CAG due to suspected EGF. Clinically apparent EGF was observed in 61 of these patients (70.9%), whereas 14 (16.3%) had a culprit lesion in a native coronary artery. The majority of patients (n = 45; 52.3%) were treated with percutaneous coronary intervention and 31 (36%) underwent re-do CABG. The remaining patients were treated conservatively. The 30-day mortality rate of suspected EGF patients undergoing CAG was 22.4% (n = 19), which was higher than the mortality rate of 2.8% overall (P<.001); this remained higher after matching the EGF patients with the control group (11 [20.4%] vs 2 [4.0%]; P=.02)., Conclusion: Emergency CAG after CABG is rare and is primarily carried out in patients with EGF. The 30-day mortality rate of these patients is high, and EGF is an independent predictor of mortality. Perioperative CAG with subsequent treatment is mandatory in these patients.

Living donor liver transplant in addition to its lifesaving therapy is a cost-effective alternate to long-term disease management in patients with chronic liver disease. Financial constraint is the biggest hurdle faced by patients in developing countries in availing the liver transplantation. So, we conducted this study to report a government-funded financial support system for liver transplant services. A total of 198 patients who underwent living donor liver transplant with at least 90 days follow-up were included in the study. According to proxy means test score, 52.2% patients were from low and middle socioeconomic groups and 64.6% of patients underwent liver transplantation through government support. Out of 198 patients who underwent liver transplantation 29.6% had monthly income below 25,000 Pakistani rupees ($114). In recipients, 90-day mortality was 7.1% and morbidity was 67.1%. Donor morbidity was 23.2% without any mortality. This financial model can serve as a valuable source for middle and low income group countries to overcome the financial challenge and make liver transplant an accessible, affordable, and economically viable option., Competing Interests: DISCLOSURE All the authors declare no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Achieving accurate retinal vessel segmentation is critical in the progression and diagnosis of vision-threatening diseases such as diabetic retinopathy and age-related macular degeneration. Existing vessel segmentation methods are based on encoder-decoder architectures, which frequently fail to take into account the retinal vessel structure's context in their analysis. As a result, such methods have difficulty bridging the semantic gap between encoder and decoder characteristics. This paper proposes a Prompt Deep Light-weight Vessel Segmentation Network (PLVS-Net) to address these issues by using prompt blocks. Each prompt block use combination of asymmetric kernel convolutions, depth-wise separable convolutions, and ordinary convolutions to extract useful features. This novel strategy improves the performance of the segmentation network while simultaneously decreasing the number of trainable parameters. Our method outperformed competing approaches in the literature on three benchmark datasets, including DRIVE, STARE, and CHASE.

Objective: Rapid diagnostic tests (RDTs) are increasingly being implemented as antimicrobial stewardship tools to facilitate antibiotic modification and reduce complications related to their overutilization. We measured the clinical impact of a phenotypic RDT with antimicrobial stewardship (AMS) in the setting of gram-negative bacteremia., Setting and Participants: In this single-center retrospective cohort study, we evaluated adult patients with gram-negative bacteremia who received at least 72 hours of an antibiotic., Methods: The primary outcome was the duration of empiric antibiotic therapy for gram-negative bacteremia. Secondary outcomes included time-to-directed therapy, proportion of modifications, hospital length of stay (LOS), and subsequent infection with a multidrug-resistant organism (MDRO) or C. difficile infection (CDI)., Results: The duration of empiric antibiotics decreased in the RDT+AMS group (4 days vs 2 days; P < .01). Time to directed therapy decreased from 75.0 to 27.9 hours ( P < .01)., Conclusions: The clinical outcomes of LOS, MDRO, and CDI were reduced. The phenotypic RDT demonstrated an improvement in stewardship measures and clinical outcomes., (© The Author(s) 2023.)

Aim: Presepsin is a sensitive biomarker for the diagnosis and estimation of prognosis in septic patients. The prognostic role of presepsin in patients undergoing transcatheter aortic valve implantation (TAVI) has never been investigated. Patients, materials & methods: In 343 patients, presepsin and N-terminal pro-B-type natriuretic peptide were measured before TAVI. One-year all-cause mortality was used as outcome measure. Results: Patients with high presepsin levels were more likely to succumb than patients with low presepsin values (16.9% vs 12.3%; p = 0.015). Elevated presepsin remained a significant predictor of 1-year all-cause mortality (odds ratio: 2.2 [95% CI: 1.12-4.29]; p = 0.022) after adjustment. N-terminal pro-B-type natriuretic peptide did not predict 1-year all-cause mortality. Conclusion: Elevated baseline presepsin levels are an independent predictor of 1-year mortality in TAVI patients.

In skin disorders such as microbial and fungal infections, plants and their parts are used. However, there have been very few scientific reports of herbal extracts of the plant Pinus gerardiana to be administered transdermally. The antifungal activity was assessed using poisoned food method against the strains of three pathogenic fungi, namely Alternaria alternata, Curvularia lunata and Bipolaris specifera. Ointment was prepared according to British pharmacopeia and physiochemical evaluation tests were performed. The GCMS was used to determine the chemical composition of the essential oil of Pinus gerardiana. 27 components were obtained. Monoterpenes= 89.97%, Oxygenated monoterpenes = 8.75%, Sesquiterpenes = 2.21% out of 100% of the total composition. The extract of pinus gerardiana showed a zone of inhibition on organism Bipolaris specifera 2.98±0.1µg/ml, Alternaria alternate 3.48±0.21µ/ml and Curvularia lunata 5.04±0.24µg/ml. Ointment was prepared with pH 5.9, conductivity 0.1, viscosity 22.24 and tested for stability. Franz cells were used in vitro and release was determined from 30 minutes to 12 hours.

Metal complexes of drug are used to inhibit growth of pathogenic microorganisms and reduces drug resistance. Moxifloxacin is a dihydroquinoline-3-carboxylic acid 4 th generation fluoroquinolone antibiotic that has tendency to bind with metal ions. In current study four moxifloxacin-metal complexes i.e. Moxifloxacin-sliver (Moxi-Ag), Moxifloxacin-rhodium (Moxi-Rh), Moxifloxacin-titanium (Moxi-Ti) and Moxifloxacin-rubidium (Moxi-Rb) have been synthesized and evaluated for antibacterial activities against resistant microorganisms along with antioxidant effects. The structure elucidation was carried out using FTIR, 1 H- NMR and UV-Vis spectroscopy. Agar well diffusion method and DPPH (1, 1- dipheny1-2-picrylhydrazyl) methods were used to study the antibacterial and antioxidant activity respectively. Both 1 H NMR and FTIR spectra clearly showed that Moxi-metal complexes are formed due to change in their carboxyl stretching band in IR, H-2 and H-5 peak position in 1 H NMR. All the Moxi-metal complexes showed distinguished antibacterial effects against both Gram-negative and Gram-positive bacteria as compared to drug which was found resistant against many microorganisms. Moxi-Rb and Moxi-Ag metal complexes showed higher antioxidant activity (IC 50 values range from 8.26 - 9.19 µg/ml) than Moxi-Ti and Moxi-Rh metal complexes (IC 50 range from 11.23 - 14.65 µg/ml).

White blood cells (WBCs), also known as leukocytes, are one of the valuable parts of the blood and immune system. Typically, pathologists use microscope for the manual inspection of blood smears which is a time-consuming, error-prone, and labor-intensive procedure. To address these issues, we present two novel shallow networks: a leukocyte deep segmentation network (LDS-Net) and leukocyte deep aggregation segmentation network (LDAS-Net) for the joint segmentation of cytoplasm and nuclei in WBC images. LDS-Net is a shallow architecture with three downsampling stages and seven convolution layers. LDAS-Net is an extended version of LDS-Net that utilizes a novel pool-less low-level information transfer bridge to transfer low-level information to the deep layers of the network. This information is aggregated with deep features in a dense feature concatenation block to achieve accurate cytoplasm and nuclei joint segmentation. We evaluated our developed architectures on four WBC publicly available datasets. For cytoplasmic segmentation in WBCs, the proposed method achieved the dice coefficients of 98.97%, 99.0%, 96.05%, and 98.79% on Datasets 1, 2, 3, and 4, respectively. For nuclei segmentation, the dice coefficients of 96.35% and 98.09% are achieved for Datasets 1 and 2, respectively. Proposed method outperforms state-of-the-art methods with superior computational efficiency and requires only 6.5 million trainable parameters.

Infertility is one of the most important health concerns worldwide. It is characterized by not being successful of pregnancy after some periods of periodic unprotected sexual intercourse. In vitro fertilization (IVF) is an assisted reproduction technique that efficiently addresses infertility. IVF replaces the actual mode of reproduction through a manual procedure wherein embryos are cultivated in a controlled laboratory environment until they reach the blastocyst stage. The standard IVF procedure includes the transfer of one or two blastocysts from several blastocysts that are grown in a controlled environment. The morphometric properties of blastocysts with their compartments such as trophectoderm (TE), zona pellucida (ZP), inner cell mass (ICM), and blastocoel (BL), are analyzed through manual microscopic analysis to predict viability. Deep learning has been extensively used for medical diagnosis and analysis and can be a powerful tool to automate the morphological analysis of human blastocysts. However, the existing approaches are inaccurate and require extensive preprocessing and expensive architectures. Thus, to cope with the automatic detection of blastocyst components, this study proposed a novel multiscale aggregation semantic segmentation network (MASS-Net) that combined four different scales via depth-wise concatenation. The extensive use of depthwise separable convolutions resulted in a decrease in the number of trainable parameters. Further, the innovative multiscale design provided rich spatial information of different resolutions, thereby achieving good segmentation performance without a very deep architecture. MASS-Net utilized 2.06 million trainable parameters and accurately detects TE, ZP, ICM, and BL without using preprocessing stages. Moreover, it can provide a separate binary mask for each blastocyst component simultaneously, and these masks provide the structure of each component for embryonic analysis. Further, the proposed MASS-Net was evaluated using publicly available human blastocyst (microscopic) imaging data. The experimental results revealed that it can effectively detect TE, ZP, ICM, and BL with mean Jaccard indices of 79.08, 84.69, 85.88%, and 89.28%, respectively, for embryological analysis, which was higher than those of the state-of-the-art methods.

We aimed to synthesize sensitive electrochemical sensors for hydrogen peroxide sensing by using zinc oxide nanorods grown on a fluorine-doped tin oxide electrode by using the facial hydrothermal method. It was essential to keep the surface morphology of the material (nanorods structure); due to its large surface area, the concerned material has enhanced detection ability toward the analyte. The work presents a non-enzymatic H 2 O 2 sensor using vertically grown zinc oxide nanorods on the electrode (FTO) surfaces with Cu nanoparticles deposited on zinc oxide nanorods to enhance the activity. Scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), energy-dispersive X-Ray (EDX), X-ray diffraction (XRD), and electrochemical methods were used to characterize copper-zinc oxide nanorods. In addition to the high surface area, the hexagonal Cu-ZnO nanorods exhibited enhanced electrochemical features of H 2 O 2 oxidation. Nanorods made from Cu-ZnO exhibit highly efficient sensitivity of 3415 μAmM -1 cm -2 low detection limits (LODs) of 0.16 μM and extremely wide linear ranges (0.001-11 mM). In addition, copper-zinc oxide nanorods demonstrated decent reproducibility, repeatability, stability, and selectivity after being used for H 2 O 2 sensing in water samples with an RSD value of 3.83%. Cu nanoparticles decorated on ZnO nanorods demonstrate excellent potential for the detection of hydrogen peroxide, providing a new way to prepare hydrogen peroxide detecting devices., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer DK declared a past co-authorship with the author IK to the handling editor., (Copyright © 2022 Arsalan, Saddique, Baoji, Awais, Khan, Shamseldin and Mehrez.)

Frequency-modulated continuous wave radar sensors play an essential role for assisted and autonomous driving as they are robust under all weather and light conditions. However, the rising number of transmitters and receivers for obtaining a higher angular resolution increases the cost for digital signal processing. One promising approach for energy-efficient signal processing is the usage of brain-inspired spiking neural networks (SNNs) implemented on neuromorphic hardware. In this article we perform a step-by-step analysis of automotive radar processing and argue how spiking neural networks could replace or complement the conventional processing. We provide SNN examples for two processing steps and evaluate their accuracy and computational efficiency. For radar target detection, an SNN with temporal coding is competitive to the conventional approach at a low compute overhead. Instead, our SNN for target classification achieves an accuracy close to a reference artificial neural network while requiring 200 times less operations. Finally, we discuss the specific requirements and challenges for SNN-based radar processing on neuromorphic hardware. This study proves the general applicability of SNNs for automotive radar processing and sustains the prospect of energy-efficient realizations in automated vehicles., Competing Interests: FK and DS were employed by Infineon Technologies Dresden GmbH & Co., KG. DA, JH, MA, and CG were employed by Infineon Technologies AG. FM was employed by BMW Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Vogginger, Kreutz, López-Randulfe, Liu, Dietrich, Gonzalez, Scholz, Reeb, Auge, Hille, Arsalan, Mirus, Grassmann, Knoll and Mayr.)

Pathological cardiac hypertrophy is a leading cause of heart failure, but knowledge of the full repertoire of cardiac cells and their gene expression profiles in the human hypertrophic heart is missing. Here, by using large-scale single-nucleus transcriptomics, we present the transcriptional response of human cardiomyocytes to pressure overload caused by aortic valve stenosis and describe major alterations in cardiac cellular crosstalk. Hypertrophied cardiomyocytes had reduced input from endothelial cells and fibroblasts. Genes encoding Eph receptor tyrosine kinases, particularly EPHB1, were significantly downregulated in cardiomyocytes of the hypertrophied heart. Consequently, EPHB1 activation by its ligand ephrin (EFN)B2, which is mainly expressed by endothelial cells, was reduced. EFNB2 inhibited cardiomyocyte hypertrophy in vitro, while silencing its expression in endothelial cells induced hypertrophy in co-cultured cardiomyocytes. Our human cell atlas of the hypertrophied heart highlights the importance of intercellular crosstalk in disease pathogenesis and provides a valuable resource., (© 2022. The Author(s).)

Morphological attributes of human blastocyst components and their characteristics are highly correlated with the success rate of in vitro fertilization (IVF). Blastocyst component analysis aims to choose the most viable embryos to improve the success rate of IVF. The embryologist evaluates blastocyst viability by manual microscopic assessment of its components, such as zona pellucida (ZP), trophectoderm (TE), blastocoel (BL), and inner cell mass (ICM). With the success of deep learning in the medical diagnosis domain, semantic segmentation has the potential to detect crucial components of human blastocysts for computerized analysis. In this study, a sprint semantic segmentation network (SSS-Net) is proposed to accurately detect blastocyst components for embryological analysis. The proposed method is based on a fully convolutional semantic segmentation scheme that provides the pixel-wise classification of important blastocyst components that help to automatically check the morphologies of these elements. The proposed SSS-Net uses the sprint convolutional block (SCB), which uses asymmetric kernel convolutions in combination with depth-wise separable convolutions to reduce the overall cost of the network. SSS-Net is a shallow architecture with dense feature aggregation, which helps in better segmentation. The proposed SSS-Net consumes a smaller number of trainable parameters (4.04 million) compared to state-of-the-art methods. The SSS-Net was evaluated using a publicly available human blastocyst image dataset for component segmentation. The experimental results confirm that our proposal provides promising segmentation performance with a Jaccard Index of 82.88%, 77.40%, 88.39%, 84.94%, and 96.03% for ZP, TE, BL, ICM, and background, with residual connectivity, respectively. It is also provides a Jaccard Index of 84.51%, 78.15%, 88.68%, 84.50%, and 95.82% for ZP, TE, BL, ICM, and background, with dense connectivity, respectively. The proposed SSS-Net is providing a mean Jaccard Index (Mean JI) of 85.93% and 86.34% with residual and dense connectivity, respectively; this shows effective segmentation of blastocyst components for embryological analysis.

Retinal blood vessels are considered valuable biomarkers for the detection of diabetic retinopathy, hypertensive retinopathy, and other retinal disorders. Ophthalmologists analyze retinal vasculature by manual segmentation, which is a tedious task. Numerous studies have focused on automatic retinal vasculature segmentation using different methods for ophthalmic disease analysis. However, most of these methods are computationally expensive and lack robustness. This paper proposes two new shallow deep learning architectures: dual-stream fusion network (DSF-Net) and dual-stream aggregation network (DSA-Net) to accurately detect retinal vasculature. The proposed method uses semantic segmentation in raw color fundus images for the screening of diabetic and hypertensive retinopathies. The proposed method's performance is assessed using three publicly available fundus image datasets: Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of Retina (STARE), and Children Heart Health Study in England Database (CHASE-DB1). The experimental results revealed that the proposed method provided superior segmentation performance with accuracy (Acc), sensitivity (SE), specificity (SP), and area under the curve (AUC) of 96.93%, 82.68%, 98.30%, and 98.42% for DRIVE, 97.25%, 82.22%, 98.38%, and 98.15% for CHASE-DB1, and 97.00%, 86.07%, 98.00%, and 98.65% for STARE datasets, respectively. The experimental results also show that the proposed DSA-Net provides higher SE compared to the existing approaches. It means that the proposed method detected the minor vessels and provided the least false negatives, which is extremely important for diagnosis. The proposed method provides an automatic and accurate segmentation mask that can be used to highlight the vessel pixels. This detected vasculature can be utilized to compute the ratio between the vessel and the non-vessel pixels and distinguish between diabetic and hypertensive retinopathies, and morphology can be analyzed for related retinal disorders.

The non-enzymatic electrochemical sensing platform for hydrogen peroxide by using Pt-based nanoparticle was investigated. The characterization of PtNiCo-NPs was done by XRD, TEM, HRTEM, EDS, and XPS. A simple drop-casting technique was used to fabricate the nanomaterial on FTO electrode. The amperometric and cyclic voltammetric results illustrated that PtNiCo-NPs on FTO had excellent electrochemical performance over other mono or bimetallic materials. The catalytic performance for H 2 O 2 sensing based on PtNiCo-NPs possessed a wide linear range from 5 μM to 16.5 mM with a low detection limit of 0.37 μM and a good sensitivity of 1374.4 μA mM -1 cm -2 at a scan rate of 20 mV s -1 (vs. Ag/AgCl). This work presents a new way to produce a ternary nanomaterial for H 2 O 2 sensing with excellent electrochemical performance. In addition, the fabricated nanomaterial showed no interferences for common interfering agents, which indicates the high specificity of the sensor. The PtNiCo-NPs have excellent stability and good reproducibility in real samples.

COVID-19 as a global pandemic has had an unprecedented impact on the entire world. Projecting the future spread of the virus in relation to its characteristics for a specific suite of countries against a temporal trend can provide public health guidance to governments and organizations. Therefore, this paper presented an epidemiological comparison of the traditional SEIR model with an extended and modified version of the same model by splitting the infected compartment into asymptomatic mild and symptomatic severe. We then exposed our derived layered model into two distinct case studies with variations in mitigation strategies and non-pharmaceutical interventions (NPIs) as a matter of benchmarking and comparison. We focused on exploring the United Arab Emirates (a small yet urban centre (where clear sequential stages NPIs were implemented). Further, we concentrated on extending the models by utilizing the effective reproductive number ( R t ) estimated against time, a more realistic than the static R 0 , to assess the potential impact of NPIs within each case study. Compared to the traditional SEIR model, the results supported the modified model as being more sensitive in terms of peaks of simulated cases and flattening determinations., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021.)

Background: A transapical (TA) approach to transcatheter aortic valve replacement (TAVR) may be used when a transfemoral (TF) approach is not feasible. The CHANGE neo TA study evaluated patients treated in routine clinical practice via TA-TAVR with the ACURATE neo bioprosthetic aortic valve., Methods and Results: This single-arm post-market study had a planned enrolment of 200 subjects; enrolment was terminated early due to declining TA-TAVR procedures at participating centers. Final enrolment was 107 patients (mean age: 79.3 years; 54.2% female; mean STS score at baseline: 6.2%). The mortality rate in the intent-to-treat population was 11.2% at 30 days (primary endpoint) and 25.6% at 12 months. The VARC-2 composite endpoint for 30-day safety occurred in 24.3% of patients. Six patients (5.6%) received a permanent pacemaker within 30 days. Site-reported echocardiographic data showed early improvements in mean aortic valve gradient (baseline: 38.8 [SD 13.1] mmHg, discharge: 6.7 [SD 3.7] mmHg) and effective orifice area (baseline: 0.7 [SD 0.2] cm 2 , discharge: 1.9 [SD 0.6] cm 2 ), and the discharge rate of paravalvular regurgitation was low (74.7% none/trace, 24.2% mild, 1.1% severe)., Conclusions: TA-TAVR with the ACURATE neo valve system yields acceptable clinical outcomes, providing an alternative for patients with aortic stenosis who are not candidates for TF-TAVR., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: ‘AH and MH report proctoring for Boston Scientific and consulting on the CRC board for the ongoing ACURATE IDE study; MD and DH report proctoring for Boston Scientific; LC reports being an advisory board member for Boston Scientific; DJA is an employee and shareholder of Boston Scientific; all other authors report no conflicts of interest.’., (© 2021 The Authors. Published by Elsevier B.V.)

Background: Cerebral O 2 saturation (ScO 2 ) reflects cerebral perfusion and can be measured noninvasively by near-infrared spectroscopy (NIRS)., Objectives: In this pilot study, we describe the dynamics of ScO 2 during TAVI in nonventilated patients and its impact on procedural outcome., Methods and Results: We measured ScO 2 of both frontal lobes continuously by NIRS in 50 consecutive analgo-sedated patients undergoing transfemoral TAVI (female 58%, mean age 80.8 years). Compared to baseline ScO 2 dropped significantly during RVP (59.3% vs. 53.9%, p < .01). Five minutes after RVP ScO 2 values normalized (post RVP 62.6% vs. 53.9% during RVP, p < .01; pre 61.6% vs. post RVP 62.6%, p = .53). Patients with an intraprocedural pathological ScO 2 decline of >20% (n = 13) had higher EuroSCORE II (3.42% vs. 5.7%, p = .020) and experienced more often delirium (24% vs. 62%, p = .015) and stroke (0% vs. 23%, p < .01) after TAVI. Multivariable logistic regression revealed higher age and large ScO 2 drops as independent risk factors for delirium., Conclusions: During RVP ScO 2 significantly declined compared to baseline. A ScO 2 decline of >20% is associated with a higher incidence of delirium and stroke and a valid cut-off value to screen for these complications. NIRS measurement during TAVI procedure may be an easy to implement diagnostic tool to detect patients at high risks for cerebrovascular complications and delirium., (© 2020 The Authors. Catheterization and Cardiovascular Interventions published by Wiley Periodicals LLC.)

Here, we design and synthesize a novel 2D Cu-tetrakis(4-carboxyphenyl)porphyrin (TCPP) metal-organic framework (MOF) sheet and ultrasmall Cu 5.4 O nanoparticle (Cu 5.4 O USNP) hybrid (Cu-TCPP MOF/Cu 5.4 O nanocomposite). The graphene-like ultrathin Cu-TCPP MOF sheets offer high surface-to-volume atom ratios and many active sites, which is beneficial for loading more Cu 5.4 O USNPs. The Cu 5.4 O USNPs with ultrasmall size (<5 nm) have promising conductivity and excellent enzymatic ability for H 2 O 2 . The successfully prepared nanocomposites are characterized by transmission electron microscopy (TEM), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and Fourier transform infrared (FT-IR) techniques. The 2D graphene-like ultrathin Cu-TCPP MOF sheets show no H 2 O 2 -sensing signals, whereas Cu 5.4 O USNPs exhibit a clear reduction peak for detection of H 2 O 2 . Interestingly, the combination of two kinds of nanomaterials improved the H 2 O 2 sensing ability due to their synergistic effect. The properties of the unmodified electrodes and the Cu-TCPP MOF/Cu 5.4 O nanocomposite-modified electrodes were systemically studied by cyclic voltammetry (CV), current-time (i-t) response, and square-wave voltammetry (SWV) techniques. The electrochemical sensor for the detection of H 2 O 2 based on the Cu-TCPP MOF/Cu 5.4 O nanocomposite has a lower detection limit of 0.13 μmol·L -1 and wider linear range of 0.1 × 10 -6  ~ 0.59 × 10 -3  mol·L -1 and 1.59 × 10 -3  ~ 20.59 × 10 -3  mol·L -1 when compared with the Cu 5.4 O USNPs-modified electrode. The electrochemical sensor can be further used to detect H 2 O 2 produced by cells. Graphical abstract The mechanism for sensing H 2 O 2 produced from cells based on a Cu-TCPP MOF/Cu 5.4 O USNPs nanocomposite-modified electrode.

Background: Tuberculosis (TB) is one of the most infectious diseases that can be fatal. Its early diagnosis and treatment can significantly reduce the mortality rate. In the literature, several computer-aided diagnosis (CAD) tools have been proposed for the efficient diagnosis of TB from chest radiograph (CXR) images. However, the majority of previous studies adopted conventional handcrafted feature-based algorithms. In addition, some recent CAD tools utilized the strength of deep learning methods to further enhance diagnostic performance. Nevertheless, all these existing methods can only classify a given CXR image into binary class (either TB positive or TB negative) without providing further descriptive information., Objective: The main objective of this study is to propose a comprehensive CAD framework for the effective diagnosis of TB by providing visual as well as descriptive information from the previous patients' database., Methods: To accomplish our objective, first we propose a fusion-based deep classification network for the CAD decision that exhibits promising performance over the various state-of-the-art methods. Furthermore, a multilevel similarity measure algorithm is devised based on multiscale information fusion to retrieve the best-matched cases from the previous database., Results: The performance of the framework was evaluated based on 2 well-known CXR data sets made available by the US National Library of Medicine and the National Institutes of Health. Our classification model exhibited the best diagnostic performance (0.929, 0.937, 0.921, 0.928, and 0.965 for F1 score, average precision, average recall, accuracy, and area under the curve, respectively) and outperforms the performance of various state-of-the-art methods., Conclusions: This paper presents a comprehensive CAD framework to diagnose TB from CXR images by retrieving the relevant cases and their clinical observations from the previous patients' database. These retrieval results assist the radiologist in making an effective diagnostic decision related to the current medical condition of a patient. Moreover, the retrieval results can facilitate the radiologists in subjectively validating the CAD decision., (©Muhammad Owais, Muhammad Arsalan, Tahir Mahmood, Yu Hwan Kim, Kang Ryoung Park. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 07.12.2020.)

Background: The early diagnosis of various gastrointestinal diseases can lead to effective treatment and reduce the risk of many life-threatening conditions. Unfortunately, various small gastrointestinal lesions are undetectable during early-stage examination by medical experts. In previous studies, various deep learning-based computer-aided diagnosis tools have been used to make a significant contribution to the effective diagnosis and treatment of gastrointestinal diseases. However, most of these methods were designed to detect a limited number of gastrointestinal diseases, such as polyps, tumors, or cancers, in a specific part of the human gastrointestinal tract., Objective: This study aimed to develop a comprehensive computer-aided diagnosis tool to assist medical experts in diagnosing various types of gastrointestinal diseases., Methods: Our proposed framework comprises a deep learning-based classification network followed by a retrieval method. In the first step, the classification network predicts the disease type for the current medical condition. Then, the retrieval part of the framework shows the relevant cases (endoscopic images) from the previous database. These past cases help the medical expert validate the current computer prediction subjectively, which ultimately results in better diagnosis and treatment., Results: All the experiments were performed using 2 endoscopic data sets with a total of 52,471 frames and 37 different classes. The optimal performances obtained by our proposed method in accuracy, F1 score, mean average precision, and mean average recall were 96.19%, 96.99%, 98.18%, and 95.86%, respectively. The overall performance of our proposed diagnostic framework substantially outperformed state-of-the-art methods., Conclusions: This study provides a comprehensive computer-aided diagnosis framework for identifying various types of gastrointestinal diseases. The results show the superiority of our proposed method over various other recent methods and illustrate its potential for clinical diagnosis and treatment. Our proposed network can be applicable to other classification domains in medical imaging, such as computed tomography scans, magnetic resonance imaging, and ultrasound sequences., (©Muhammad Owais, Muhammad Arsalan, Tahir Mahmood, Jin Kyu Kang, Kang Ryoung Park. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 26.11.2020.)

In vivo diseases such as colorectal cancer and gastric cancer are increasingly occurring in humans. These are two of the most common types of cancer that cause death worldwide. Therefore, the early detection and treatment of these types of cancer are crucial for saving lives. With the advances in technology and image processing techniques, computer-aided diagnosis (CAD) systems have been developed and applied in several medical systems to assist doctors in diagnosing diseases using imaging technology. In this study, we propose a CAD method to preclassify the in vivo endoscopic images into negative (images without evidence of a disease) and positive (images that possibly include pathological sites such as a polyp or suspected regions including complex vascular information) cases. The goal of our study is to assist doctors to focus on the positive frames of endoscopic sequence rather than the negative frames. Consequently, we can help in enhancing the performance and mitigating the efforts of doctors in the diagnosis procedure. Although previous studies were conducted to solve this problem, they were mostly based on a single classification model, thus limiting the classification performance. Thus, we propose the use of multiple classification models based on ensemble learning techniques to enhance the performance of pathological site classification. Through experiments with an open database, we confirmed that the ensemble of multiple deep learning-based models with different network architectures is more efficient for enhancing the performance of pathological site classification using a CAD system as compared to the state-of-the-art methods.

Background: Various devices are available for endoscopic radial artery harvesting during coronary artery bypass grafting. Thermal spread and graft damage, however, are common concerns. The aim of this study was to compare the MiFusion TLS2 system (Endotrust, Nettetal Germany) with direct heat technique and the LigaSure Maryland system (Medtronic, Dublin, Ireland) using advanced bipolar technique in a prospective randomized study., Methods: One hundred consecutive patients undergoing coronary artery bypass grafting with endoscopic radial artery harvesting were prospectively included and randomized 1:1. The proximal (brachial) ends of the grafts were analyzed using fluorescence microscopy with focus on graft integrity. In addition, harvesting time, graft sealing, residual bleeding, and incidence of neurological disorders were compared., Results: Patient age was 67 ± 8 years. Mean harvesting time was 26.5 ± 9 minutes for the TLS2 and 23.2 ± 8 minutes for the LigaSure (P = .049). Overall graft integrity was good in both groups. A significantly better graft integrity was observed in the LigaSure group (scale 0 to 3; 3 = best) with 2.5 ± 0.6 for TLS2 and 2.8 ± 0.4 for LigaSure (P = .031). LigaSure provided significantly better graft sealing (scale 0 to 2; 0 = best) with 0.6 ± 0.7 vs 1.0 ± 0.6 (TLS2) (P = .006) and less residual bleeding (scale 1 to 5; 1 = best) with 1.4 ± 0.6 vs 2.0 ± 0.9 (TLS2) (P < .001). Transient sensibility disorders were less frequently observed with LigaSure (2% vs 16%, P = .015)., Conclusions: Endoscopic radial artery harvesting can be performed with excellent results and good graft integrity using both devices. Compared with the MiFusion TLS2, the LigaSure device led to faster procedural times and resulted in better graft integrity. Along with providing a better seal, LigaSure was associated with less sensibility disorders., (Copyright © 2020 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

The COVID-19 pandemic has caused unprecedented crisis across the world, with many countries struggling with the pandemic. In order to understand how each country is impacted by the virus and assess the risk on a global scale we present a regression based analysis using two pre-existing indexes, namely the Inform and Infectious Disease Vulnerability Index, in conjunction with the number of elderly living in the population. Further we introduce a temporal layer in our modeling by incorporating the stringency level employed by each country over a period of 6 time intervals. Our results show that the indexes and level of stringency are not ideally suited for explaining variation in COVID-19 risk, however the ratio of elderly in the population is a stand out indicator in terms of its predictive power for mortality risk. In conclusion, we discuss how such modeling approaches can assist public health policy., (Copyright © 2020 Arsalan, Mubin, Alnajjar, Alsinglawi and Zaki.)

Background and Objective : COVID-19 has engulfed the entire world, with many countries struggling to contain the pandemic. In order to understand how each country is impacted by the virus compared with what would have been expected prior to the pandemic and the mortality risk on a global scale, a multi-factor weighted spatial analysis is presented. Method : A number of key developmental indicators across three main categories of demographics, economy, and health infrastructure were used, supplemented with a range of dynamic indicators associated with COVID-19 as independent variables. Using normalised COVID-19 mortality on 13 May 2020 as a dependent variable, a linear regression (N = 153 countries) was performed to assess the predictive power of the various indicators. Results : The results of the assessment show that when in combination, dynamic and static indicators have higher predictive power to explain risk variation in COVID-19 mortality compared with static indicators alone. Furthermore, as of 13 May 2020 most countries were at a similar or lower risk level than what would have been expected pre-COVID, with only 44/153 countries experiencing a more than 20% increase in mortality risk. The ratio of elderly emerges as a strong predictor but it would be worthwhile to consider it in light of the family makeup of individual countries. Conclusion : In conclusion, future avenues of data acquisition related to COVID-19 are suggested. The paper concludes by discussing the ability of various factors to explain COVID-19 mortality risk. The ratio of elderly in combination with the dynamic variables associated with COVID-19 emerge as more significant risk predictors in comparison to socio-economic and demographic indicators.

Ophthalmological analysis plays a vital role in the diagnosis of various eye diseases, such as glaucoma, retinitis pigmentosa (RP), and diabetic and hypertensive retinopathy. RP is a genetic retinal disorder that leads to progressive vision degeneration and initially causes night blindness. Currently, the most commonly applied method for diagnosing retinal diseases is optical coherence tomography (OCT)-based disease analysis. In contrast, fundus imaging-based disease diagnosis is considered a low-cost diagnostic solution for retinal diseases. This study focuses on the detection of RP from the fundus image, which is a crucial task because of the low quality of fundus images and non-cooperative image acquisition conditions. Automatic detection of pigment signs in fundus images can help ophthalmologists and medical practitioners in diagnosing and analyzing RP disorders. To accurately segment pigment signs for diagnostic purposes, we present an automatic RP segmentation network (RPS-Net), which is a specifically designed deep learning-based semantic segmentation network to accurately detect and segment the pigment signs with fewer trainable parameters. Compared with the conventional deep learning methods, the proposed method applies a feature enhancement policy through multiple dense connections between the convolutional layers, which enables the network to discriminate between normal and diseased eyes, and accurately segment the diseased area from the background. Because pigment spots can be very small and consist of very few pixels, the RPS-Net provides fine segmentation, even in the case of degraded images, by importing high-frequency information from the preceding layers through concatenation inside and outside the encoder-decoder. To evaluate the proposed RPS-Net, experiments were performed based on 4-fold cross-validation using the publicly available Retinal Images for Pigment Signs (RIPS) dataset for detection and segmentation of retinal pigments. Experimental results show that RPS-Net achieved superior segmentation performance for RP diagnosis, compared with the state-of-the-art methods.

Deep sternal wound infection (DSWI) is a life threatening complication after cardiac surgery. In severe cases, flaps are needed to cover the wound. However, it is controversial if an aseptic environment is necessary at the time of wound closure. This is a retrospective study of 73 patients with DSWI treated by debridement and local or free flap from June 2008 until December 2017. The influence of positive microbiological findings at the time of plastic reconstructive surgery on reoperation rate and length of in-hospital stay was analyzed. Microbiological exams revealed positive results in 47 (64.4%) and no results in 26 patients. Reoperation had to be performed in 21.3% (positive cultures) versus 15.4% ( p  = .54), mean in hospital stay was 24.1 days (positive cultures) versus 21.8 days ( p  = .39) and in-hospital mortality was 6.4% (positive cultures) versus 7.7% ( p  = .83). Positive microbial findings at the time of plastic reconstructive surgery in patients with DSWI are not associated with a higher reoperation or mortality rate or a longer in-hospital stay. Repeated debridement and vacuum-assisted therapy to achieve negative microbial results might not be necessary in the treatment of these patients.Key messagesPositive microbial findings at the time of plastic reconstructive surgery in patients with deep sternal wound infection seems not to be associated with a higher reoperation or mortality rate or a longer in-hospital stay.The influence of positive microbiological findings at the time of plastic reconstructive surgery on reoperation rate and length of in-hospital stay was analyzed in 73 patients with deep sternal wound infection.Microbiological exams revealed positive results in 47 (64.4%) and no results in 26 patients. Reoperation had to be performed in 21.3% (positive cultures) versus 15.4% ( p  = .54), mean in hospital stay was 24.1 days (positive cultures) versus 21.8 days ( p  = .39) and in-hospital mortality was 6.4% (positive cultures) versus 7.7% ( p  = .83).

Mitral valve regurgitation (MR) belongs to one of the most common acquired valve diseases in western countries with increasing prevalence in older age. For patients with high perioperative risk and older age prohibitive for valve surgery, the development of transcatheter mitral valve therapies offers new options. Assessment of the severity and etiology of MR and thorough imaging of the mitral valve anatomy and pathology are necessary prerequisites for appropriate decision making in the field of transcatheter mitral valve therapies. Different transcatheter repair and replacement techniques are on the market, most of them mimicking surgical techniques. With some techniques (e.g., the MitraClip device), there is good clinical experience (>80,000 devices implanted worldwide), and evidence (three randomized studies), whereas for newer procedures, safety and efficacy data are still very limited. Transcatheter mitral repair and replacement techniques have to be considered as complementary treatment options for high-risk patients indicated by the Heart Teams. The different techniques and devices will be introduced and discussed in the following paper., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/jtd.2019.12.137). The series “Interventional Cardiology” was commissioned by the editorial office without any funding or sponsorship. CW received honoraria from Abbot Vascular. TW received honoraria from Abbot Vascular and Edwards Lifesciences. SF received honoraria from Abbot Vascular and Edwards Lifesciences. Mariuca Vasa-Nicotera received honoraria from Abbot Vascular and Edwards Lifesciences. TH and MA have no other conflicts of interest., (2020 Journal of Thoracic Disease. All rights reserved.)

Automatic chest anatomy segmentation plays a key role in computer-aided disease diagnosis, such as for cardiomegaly, pleural effusion, emphysema, and pneumothorax. Among these diseases, cardiomegaly is considered a perilous disease, involving a high risk of sudden cardiac death. It can be diagnosed early by an expert medical practitioner using a chest X-Ray (CXR) analysis. The cardiothoracic ratio (CTR) and transverse cardiac diameter (TCD) are the clinical criteria used to estimate the heart size for diagnosing cardiomegaly. Manual estimation of CTR and other diseases is a time-consuming process and requires significant work by the medical expert. Cardiomegaly and related diseases can be automatically estimated by accurate anatomical semantic segmentation of CXRs using artificial intelligence. Automatic segmentation of the lungs and heart from the CXRs is considered an intensive task owing to inferior quality images and intensity variations using nonideal imaging conditions. Although there are a few deep learning-based techniques for chest anatomy segmentation, most of them only consider single class lung segmentation with deep complex architectures that require a lot of trainable parameters. To address these issues, this study presents two multiclass residual mesh-based CXR segmentation networks, X-RayNet-1 and X-RayNet-2, which are specifically designed to provide fine segmentation performance with a few trainable parameters compared to conventional deep learning schemes. The proposed methods utilize semantic segmentation to support the diagnostic procedure of related diseases. To evaluate X-RayNet-1 and X-RayNet-2, experiments were performed with a publicly available Japanese Society of Radiological Technology (JSRT) dataset for multiclass segmentation of the lungs, heart, and clavicle bones; two other publicly available datasets, Montgomery County (MC) and Shenzhen X-Ray sets (SC), were evaluated for lung segmentation. The experimental results showed that X-RayNet-1 achieved fine performance for all datasets and X-RayNet-2 achieved competitive performance with a 75% parameter reduction., Competing Interests: The authors declare no conflict of interest.

Breast cancer is the leading cause of mortality in women. Early diagnosis of breast cancer can reduce the mortality rate. In the diagnosis, the mitotic cell count is an important biomarker for predicting the aggressiveness, prognosis, and grade of breast cancer. In general, pathologists manually examine histopathology images under high-resolution microscopes for the detection of mitotic cells. However, because of the minute differences between the mitotic and normal cells, this process is tiresome, time-consuming, and subjective. To overcome these challenges, artificial-intelligence-based (AI-based) techniques have been developed which automatically detect mitotic cells in the histopathology images. Such AI techniques accelerate the diagnosis and can be used as a second-opinion system for a medical doctor. Previously, conventional image-processing techniques were used for the detection of mitotic cells, which have low accuracy and high computational cost. Therefore, a number of deep-learning techniques that demonstrate outstanding performance and low computational cost were recently developed; however, they still require improvement in terms of accuracy and reliability. Therefore, we present a multistage mitotic-cell-detection method based on Faster region convolutional neural network (Faster R-CNN) and deep CNNs. Two open datasets (international conference on pattern recognition (ICPR) 2012 and ICPR 2014 (MITOS-ATYPIA-14)) of breast cancer histopathology were used in our experiments. The experimental results showed that our method achieves the state-of-the-art results of 0.876 precision, 0.841 recall, and 0.858 F1-measure for the ICPR 2012 dataset, and 0.848 precision, 0.583 recall, and 0.691 F1-measure for the ICPR 2014 dataset, which were higher than those obtained using previous methods. Moreover, we tested the generalization capability of our technique by testing on the tumor proliferation assessment challenge 2016 (TUPAC16) dataset and found that our technique also performs well in a cross-dataset experiment which proved the generalization capability of our proposed technique.

With lithium-ion (li-ion) batteries as energy storage devices, operational safety from thermal runaway remains a major obstacle especially for applications in harsh environments such as in the oil industry. In this approach, a facile method via microwave irradiation technique (MWI) was followed to prepare Co 3 O 4 /reduced graphene oxide (RGO)/hexagonal boron nitride (h-BN) nanocomposites as anodes for high temperature li-ion batteries. Results showed that the addition of h-BN not only enhanced the thermal stability of Co 3 O 4 /RGO nanocomposites but also enhanced the specific surface area. Co 3 O 4 /RGO/h-BN nanocomposites displayed the highest specific surface area of 191 m 2 /g evidencing the synergistic effects between RGO and h-BN. Moreover, Co 3 O 4 /RGO/h-BN also displayed the highest specific capacity with stable reversibility on the high performance after 100 cycles and lower internal resistance. Interestingly, this novel nanocomposite exhibits outstanding high temperature performances with excellent cycling stability (100% capacity retention) and a decreased internal resistance at 150 °C.

Current risk scores used for patients undergoing transcatheter aortic valve implantation (TAVI) do not reliably predict adverse events after TAVI. Procalcitonin (PCT) is associated with increased atherosclerotic burden and adverse outcomes in patients with cardiovascular disease. The aim of our study is to assess the predictive value of preprocedural serum PCT levels in comparison with established risk scores in TAVI patients. A total of 243 patients undergoing transfemoral TAVI at our institution were included prospectively in the study and 230 of these patients participated in the follow-up 1 year after TAVI. The primary endpoints were mortality at 30 days and 1 year. Multivariable analysis revealed that preprocedural PCT was the only independent predictor of 30-day mortality (HR 2.84; 95% CI 1.59-5.06; p < 0.001) and 1-year mortality (HR 1.90; 95% CI 1.17-3.11; p = 0.01), whereas high-sensitivity C-reactive protein showed no association with procedural outcomes. The results of ROC analysis showed good predictive power of PCT for both outcomes (AUC 0.75; p = 0.0003 for 30-day mortality and AUC 0.71; p < 0.0001 for 1-year mortality). An optimal cut-off value for PCT of 0.06 ng/ml for short- and long-term mortality was determined with the Youden index. A significantly higher mortality rate was observed in the high-PCT group (≥ 0.06 ng/ml) based on Kaplan-Meier analysis (log rank = 12.1; p = 0.001 at 30 days and log rank = 14.2; p = 0.0002 at 1 year). Patients in the high-PCT group also had a considerably worse clinical pro6file. In conclusion, preprocedural PCT is an independent predictor of 30-day and 1-year mortality after TAVI. In particular, a cut-off value of 0.06 ng/ml discriminates patients at higher risk of mortality within 30 days and 1 year of TAVI.

Background: Thoracic chest drains are placed after cardiac surgery allowing for the clearance of blood, fluid, and air to prevent post-operative complications. Despite its importance, there is little data on the application of digital chest drainage systems in cardiac surgery. Therefore, the differences between an analog and a digital chest drainage system in cardiac surgery patients were investigated in a randomized controlled trial., Methods: A total of 354 elective cardiac surgery patients were preoperatively randomized 1:1 between September 2016 and September 2017 to either an analog (Ocean) or a digital (Thopaz + ) chest drainage system aiming to compare drainage-associated postoperative outcome parameters., Results: A total of 340 patients were included in the analysis (analog: 188; digital: 152) with no significant differences in preoperative baseline parameters. Incidence of X-rays to detect air leaks was significantly lower in the digital group (analog: 20.2%; digital: 8.6%; P<0.01). Patients treated with the digital system showed a 3.3% reduction of re-thoracotomies, however, not statistically significant (analog: 5.3%; digital: 2.0%; P=0.19). Median total fluid amount did not significantly differ between study groups [median (P25; P75); analog: 705 (400; 1,333) mL; digital: 686 (404; 1,229) mL; P=0.83]; however, the use of the digital drainage system resulted in a quicker removal with a reduced median drainage duration of 16 hours (analog: 65 hours; digital: 49 hours; P≤0.01)., Conclusions: The study provides evidence that digital drainage systems can be safely applied in cardiac surgery patients. The use of the digital management system led to a decreased incidence of drainage-associated complications as well as to shortened chest tube duration. Findings require confirmation by additional studies., Competing Interests: Conflicts of Interest: Arnaud Van Linden is consultant for Medela. Christina Brei is an employee of Medela Medizintechnik GmbH & Co. Handels KG and gave scientific input during manuscript preparation. The other authors have no conflicts of interest to declare., (2019 Journal of Thoracic Disease. All rights reserved.)

Automatic segmentation of retinal images is an important task in computer-assisted medical image analysis for the diagnosis of diseases such as hypertension, diabetic and hypertensive retinopathy, and arteriosclerosis. Among the diseases, diabetic retinopathy, which is the leading cause of vision detachment, can be diagnosed early through the detection of retinal vessels. The manual detection of these retinal vessels is a time-consuming process that can be automated with the help of artificial intelligence with deep learning. The detection of vessels is difficult due to intensity variation and noise from non-ideal imaging. Although there are deep learning approaches for vessel segmentation, these methods require many trainable parameters, which increase the network complexity. To address these issues, this paper presents a dual-residual-stream-based vessel segmentation network (Vess-Net), which is not as deep as conventional semantic segmentation networks, but provides good segmentation with few trainable parameters and layers. The method takes advantage of artificial intelligence for semantic segmentation to aid the diagnosis of retinopathy. To evaluate the proposed Vess-Net method, experiments were conducted with three publicly available datasets for vessel segmentation: digital retinal images for vessel extraction (DRIVE), the Child Heart Health Study in England (CHASE-DB1), and structured analysis of retina (STARE). Experimental results show that Vess-Net achieved superior performance for all datasets with sensitivity (Se), specificity (Sp), area under the curve (AUC), and accuracy (Acc) of 80.22%, 98.1%, 98.2%, and 96.55% for DRVIE; 82.06%, 98.41%, 98.0%, and 97.26% for CHASE-DB1; and 85.26%, 97.91%, 98.83%, and 96.97% for STARE dataset., Competing Interests: The authors declare no conflict of interest.

Background Various guidelines exist for female preventative screening tests and medical resident physician adherence to the United States Preventive Services Task Force (USPSTF) guidelines varies. National screening rates for breast cancer and osteoporosis have improved but they are still below the expected target. Material and methods Ambulatory medical clinic records of female patients from the period July 2015 to December 2017 were reviewed for breast cancer and osteoporosis screening. Resident performance and commitment with regards to ordering the aforementioned screening tests according to the USPSTF guidelines were compared to the most recent national screening rates for mammograms and dual-energy X-ray absorptiometry (DXA) scans. Results Of the 1327 charts reviewed, 1025 was included in the study. Of the 545 mammograms performed, 93% of them were indicated according to the USPSTF guidelines (P < 0.0001, 95% CI: 125.9-342.0). A total of 480 mammograms were not ordered, of which 6% were indicated and 93.9% were not indicated. Out of a total of 107 DXA scans performed, 88.7% were correctly indicated (P < 0.0001, 95% CI: 37.11-132.9). Conclusion Resident physician adherence to the USPSTF screening guidelines for breast cancer and DXA scans were higher than the national and state screening rates. Our well-structured educational project (strong faculty mentorship, resident to patient continuity of care and the reasonable resident-clinic load) resulted in higher screening rates., Competing Interests: The authors have declared that no competing interests exist.

To overcome the risk of exothermic lithium-ion battery overheating reactions, we fabricated a novel, high-temperature-stable anode material composed of holey reduced graphene oxide/polystyrene (HRGO/PS) nanocomposites synthesized through in situ bulk polymerization in the presence of HRGO via microwave irradiation. The HRGO/PS nanocomposites were characterized by Fourier transform infrared spectroscopy, X-ray diffraction, Raman spectroscopy, and electron microscopy analyses including field-emission scanning electron microscopy and transmission electron microscopy. All characterization studies demonstrated homogenous dispersion of HRGO in the PS matrix, which enhanced the thermal and electrical properties of the overall nanocomposites. These novel HRGO/PS nanocomposites exhibited excellent electrochemical responses, with reversible charge/discharge capacities of 92.1/92.78 mA·h/g at a current density of 500 mA/g with ~100% capacity retention and ~100% coulombic efficiency at room temperature. Furthermore, an examination of the electrochemical properties of these nanocomposites at 110 °C showed that HRGO/PS nanocomposites still displayed good charge/discharge capacities with stable cycle performances for 150 cycles.

Various techniques using artificial intelligence (AI) have resulted in a significant contribution to field of medical image and video-based diagnoses, such as radiology, pathology, and endoscopy, including the classification of gastrointestinal (GI) diseases. Most previous studies on the classification of GI diseases use only spatial features, which demonstrate low performance in the classification of multiple GI diseases. Although there are a few previous studies using temporal features based on a three-dimensional convolutional neural network, only a specific part of the GI tract was involved with the limited number of classes. To overcome these problems, we propose a comprehensive AI-based framework for the classification of multiple GI diseases by using endoscopic videos, which can simultaneously extract both spatial and temporal features to achieve better classification performance. Two different residual networks and a long short-term memory model are integrated in a cascaded mode to extract spatial and temporal features, respectively. Experiments were conducted on a combined dataset consisting of one of the largest endoscopic videos with 52,471 frames. The results demonstrate the effectiveness of the proposed classification framework for multi-GI diseases. The experimental results of the proposed model (97.057% area under the curve) demonstrate superior performance over the state-of-the-art methods and indicate its potential for clinical applications., Competing Interests: The authors declare no conflict of interest.