Image Smart Segmentation Analysis Against Diabetic Foot Ulcer Using Internet of Things with Virtual Sensing.

Diabetic foot ulcer (DFU) is a problem worldwide, and prevention is crucial. The image segmentation analysis of DFU identification plays a significant role. This will produce different segmentation of the same idea, incomplete, imprecise, and other problems. To address these issues, a method of image segmentation analysis of DFU through internet of things with the technique of virtual sensing for semantically similar objects, the analysis of four levels of range segmentation (region-based, edge-based, image-based, and computer-aided design-based range segmentation) for deeper segmentation of images is implemented. In this study, the multimodal is compressed with the object co-segmentation for semantical segmentation. The result is predicting the better validity and reliability assessment. The experimental results demonstrate that the proposed model can efficiently perform segmentation analysis, with a lower error rate, than the existing methodologies. The findings on the multiple-image dataset show that DFU obtains an average segmentation score of 90.85% and 89.03% correspondingly in two types of labeled ratios before DFU with virtual sensing and after DFU without virtual sensing (i.e., 25% and 30%), which is an increase of 10.91% and 12.22% over the previous best results. In live DFU studies, our proposed system improved by 59.1% compared with existing deep segmentation-based techniques and its average image smart segmentation improvements over its contemporaries are 15.06%, 23.94%, and 45.41%, respectively. Proposed range-based segmentation achieves interobserver reliability by 73.9% on the positive test namely likelihood ratio test set with only a 0.25 million parameters at the pace of labeled data.

Predictors of Inappropriately Rapid Coronary Lesion Progression in Patients Undergoing Percutaneous Coronary Interventions.

Background: Patients undergoing percutaneous coronary intervention (PCI) may experience rapid atherosclerotic plaque progression in nontreated vessels that is unlikely to result from natural de novo atherosclerosis. We hypothesize that intra-lesion bleeding plays a central role in this process. The aim of this study is to investigate the factors that may contribute to accelerated narrowing in coronary diameter. Methods: We reviewed 65 interventional procedures and their consequent staged PCIs and mapped the coronary tree into 16 segments (as divided by the American Heart Association), grading the percentage of stenosis in each segment and spotting the rapidly progressing lesions. Demographic, procedural, and laboratory data were recorded and analyzed. Results: For the lesions that progressed rapidly in the time period between angiographies, the administration of eptifibatide intra-procedurally was associated with rapid progression of coronary lesions. Moreover, an increased white blood cell count prior to the index procedure was also associated with a trend toward rapid plaque progression. Conclusions: In this hypothesis-generating study, treatment with a IIb/IIIa inhibitor in the index PCI was associated with an accelerated short-term progression of some of the nontreated lesions, suggesting that this mode of anti-aggregation therapy could facilitate plaque hemorrhage and consequent acceleration of coronary atherosclerosis in eroded plaques.

Contexte: Les patients qui subissent une intervention coronarienne percutanée (ICP) peuvent présenter une progression rapide de plaques d'athérosclérose dans des vaisseaux non traités, phénomène qui n'est probablement pas le résultat d'une athérosclérose de novo naturelle. Nous formulons l'hypothèse qu'un saignement intralésionnel jouerait un rôle central dans ce processus. Cette étude vise à explorer les facteurs qui pourraient contribuer à l'accélération de la réduction du diamètre coronarien. Méthodologie: Nous avons étudié 65 interventions et les ICP en plusieurs étapes qui s'en étaient suivies, ainsi que divisé l'arbre coronarien en 16 segments (conformément à la segmentation de l'American Heart Association), afin d'évaluer le pourcentage de sténose dans chaque segment et de repérer les lésions qui progressaient rapidement. Les données démographiques et celles relatives aux interventions et aux résultats de laboratoire ont été consignées et analysées. Résultats: En ce qui concerne les lésions qui avaient progressé rapidement durant l'intervalle entre les angiographies, l'administration d'éptifibatide lors de l'intervention semblait être un facteur contributif. De plus, un nombre accru de leucocytes avant l'intervention initiale a également été associé à une évolution rapide des plaques. Conclusions: Dans le cadre de cette étude servant à émettre une hypothèse, le traitement par un inhibiteur de la glycoprotéine IIb-IIIa lors de l'ICP initiale a été associé à une accélération de la progression à court terme de certaines lésions non traitées, ce qui laisse croire que ce mode de traitement antiagrégant pourrait favoriser les hémorragies intraplaques et l'accélération de l'athérosclérose coronarienne dans les plaques érodées.

Separation of Amino Acids, Dyes, and Pigments Using Novel Pressurized Circular TLC Assembly for Secure Medical Imaging Applications.

A novel pressurized flow system for circular thin-layer chromatography (PC-TLC) has been successfully established and employed for the separation of amino acids, dyes, and pigments for safe medical imaging applications. In this system, the mobile phase is applied to a regular TLC plate through the tube and needle of an intravenous infusion set. The needle was fused in a hole underneath the center of the plate, while the second side end of the tube was connected to a microburette containing the solvent. This new assembly proved itself better in terms of separation time (within 5 minutes) and controlled flow of the solvent and horizontal movement of analyte components over chromatograms with better separation and R f values (glutamine: 0.26, valine: 0.44, phenylalanine: 0.60, chlorophyll a: 0.52, chlorophyll b: 0.43, xanthophyll: 0.18, carotenoid: 0.97, and pheophytin: 0.60) when a number of samples of amino acids, dyes, and pigments were separated by the developed apparatus and the conventional TLC procedure. The developed method was found distinctly rapid, precise, and eco-friendly (less solvent consuming) as compared to traditional ascending TLC.

Analysis of modern circulation industry development level using industrial structure mechanism.

This study focuses on China's industrial transformation and urban income inequality. It is shown that between 2011 and 2020, improvements in China's industrial structure have a significant positive influence on lowering income gaps between urban and rural areas when used in conjunction with the empirical research approach. The mechanical study shows that the urban population impacts this causation. Rural-to-urban economic gaps have been reduced through modernisation in different parts of the country. The result remains the same even if the urban-rural consumption gap is used as a proxy for income discrepancy. The mechanism for the industrial structure upgrading model (MISUM) is proposed in this article for the modern circulation industry. Key contributions include: (1) environmental rules in these components have no impact on each other, but the updating of industrial buildings indicates a substantial location-specific dependence; (2) environmental standards have impacts on industrial structures throughout provinces; and (3) environmental standards have a long-term qualifying impact on the industrial structures. This essay focuses on combining environmental regulation with industrial expansion in different regions. In this study, government environmental requirements for industrial structural improvements are shown to be in operation. The test results show the MISUM has been described with high accuracy of 94.2%, carbon emission level of 18%, soil emission level of 11% and efficiency ratio of 97.8% compared to other methods.

Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks.

The field of medical image processing plays a significant role in brain tumor classification. The survival rate of patients can be increased by diagnosing the tumor at an early stage. Several automatic systems have been developed to perform the tumor recognition process. However, the existing systems could be more efficient in identifying the exact tumor region and hidden edge details with minimum computation complexity. The Harris Hawks optimized convolution network (HHOCNN) is used in this work to resolve these issues. The brain magnetic resonance (MR) images are pre-processed, and the noisy pixels are eliminated to minimize the false tumor recognition rate. Then, the candidate region process is applied to identify the tumor region. The candidate region method investigates the boundary regions with the help of the line segments concept, which reduces the loss of hidden edge details. Various features are extracted from the segmented region, which is classified by applying a convolutional neural network (CNN). The CNN computes the exact region of the tumor with fault tolerance. The proposed HHOCNN system was implemented using MATLAB, and performance was evaluated using pixel accuracy, error rate, accuracy, specificity, and sensitivity metrics. The nature-inspired Harris Hawks optimization algorithm minimizes the misclassification error rate and improves the overall tumor recognition accuracy to 98% achieved on the Kaggle dataset.

Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing.

Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study. We use three distinct methods to produce different HNSs for identification of the driver genes/nodes, core modules, and core genes/nodes. Following the creation of the optimized HNS (OHNS) by the integration of DN (driver nodes), CM (core module), and CN (core nodes), the effectiveness of various HNSs was assessed based on the significance of the network topology, control potential, and clinical value. Immunohistochemical (IHC), qRT-PCR, and Western blotting were adopted to measure the expression of hub genes and proteins involved in epithelial ovarian cancer (EOC). We discovered that the OHNS has two key advantages: the network's central location and controllability. It also plays a significant role in the illness network due to its wide range of capabilities. The OHNS and clinical samples revealed the endometrial cancer signaling, and the PI3K/AKT, NER, and BMP pathways. MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN hub genes were predicted and may serve as potential candidates for new treatments and biomarkers for EOC. This research can aid in better capturing the disease progression, the creation of potent multi-target medications, and the direction of the therapeutic community in the optimization of effective treatment regimens by various research objectives in cancer treatment.

Systemic inflammatory markers in elderly patients undergoing transcatheter aortic valve replacement.

BACKGROUND: Transcatheter aortic valve replacement (TAVR) provokes an early injury response, partially represented by dynamic changes in inflammatory markers. TAVR greatly benefits the elderly and we aimed to determine whether increased inflammatory markers post-TAVR in octagenerians were different than their younger counterparts and whether it was associated with adverse clinical outcomes. METHODS: Patients with severe symptomatic aortic stenosis who underwent transfemoral TAVR from January 2010 to December 2021 were enrolled. Total white blood cells (WBC) count and subpopulation dynamics were evaluated. RESULTS: Five-hundred and seven patients were finally included in the study, 65% of these patients were 80 or more years old (54% female, median age 84 [82-87]) years, with severe symptomatic aortic stenosis. In patients aged above 80 years (patients ≥ 80), we noticed significant kinetic changes in the WBC and their differential cellular subpopulations (P < 0.0001) between admission and early days post-procedure. This was evident by a significant increase in total WBC (median 7.1 to 9.4) and absolute neutrophil count (median 4.7 to 7.4), neutrophil-lymphocyte (NL) ratio (median 2.82 to 7.21), and a meaningful decrease in absolute lymphocyte count (median 1.5 to 1.0). Implantation of self-expandable valves (SEVs) was associated with a more pronounced inflammatory response than balloon-expandable valves (BEVs). Higher WBC and neutrophil counts were associated with higher mortality and major vascular complications at 30 days, in addition, higher neutrophil counts and NL ratios were found to be correlated to arrhythmia at 30 days with P values of 0.04 and 0.028, respectively. CONCLUSION: This is the first description of a differential age-related inflammatory response in patients after TAVR, which shows an association between inflammatory markers post procedure and clinical outcome. Nevertheless, survival rates were similar in the elderly population and in younger patients, despite the presence of comorbid conditions.

An Adaptive ECG Noise Removal Process Based on Empirical Mode Decomposition (EMD).

The electrocardiogram (ECG) is a generally used instrument for examining cardiac disorders. For proper interpretation of cardiac illnesses, a noise-free ECG is often preferred. ECG signals, on the other hand, are suffering from numerous noises throughout gathering and programme. This article suggests an empirical mode decomposition-based adaptive ECG noise removal technique (EMD). The benefits of the proposed methods are used to dip noise in ECG signals with the least amount of distortion. For decreasing high-frequency noises, traditional EMD-based approaches either cast off the preliminary fundamental functions or use a window-based methodology. The signal quality is then improved via an adaptive process. The simulation study uses ECG data from the universal MIT-BIH database as well as the Brno University of Technology ECG Quality Database (BUT QDB). The proposed method's efficiency is measured using three typical evaluation metrics: mean square error, output SNR change, and ratio root mean square alteration at various SNR levels (signal to noise ratio). The suggested noise removal approach is compatible with other commonly used ECG noise removal techniques. A detailed examination reveals that the proposed method could be served as an effective means of noise removal ECG signals, resulting in enhanced diagnostic functions in automated medical systems.

Intrapartum cardiotocography trace pattern pre-processing, features extraction and fetal health condition diagnoses based on RCOG guideline.

Context: The computerization of both fetal heart rate (FHR) and intelligent classification modeling of the cardiotocograph (CTG) is one of the approaches that are utilized in assisting obstetricians in conducting initial interpretation based on (CTG) analysis. CTG tracing interpretation is crucial for the monitoring of the fetal status during weeks into the pregnancy and childbirth. Most contemporary studies rely on computer-assisted fetal heart rate (FHR) feature extraction and CTG categorization to determine the best precise diagnosis for tracking fetal health during pregnancy. Furthermore, through the utilization of a computer-assisted fetal monitoring system, the FHR patterns can be precisely detected and categorized. Objective: The goal of this project is to create a reliable feature extraction algorithm for the FHR as well as a systematic and viable classifier for the CTG through the utilization of the MATLAB platform, all the while adhering to the recognized Royal College of Obstetricians and Gynecologists (RCOG) recommendations. Method: The compiled CTG data from spiky artifacts were cleaned by a specifically created application and compensated for missing data using the guidelines provided by RCOG and the MATLAB toolbox after the implemented data has been processed and the FHR fundamental features have been extracted, for example, the baseline, acceleration, deceleration, and baseline variability. This is followed by the classification phase based on the MATLAB environment. Next, using the guideline provided by the RCOG, the signals patterns of CTG were classified into three categories specifically as normal, abnormal (suspicious), or pathological. Furthermore, to ensure the effectiveness of the created computerized procedure and confirm the robustness of the method, the visual interpretation performed by five obstetricians is compared with the results utilizing the computerized version for the 150 CTG signals. Results: The attained CTG signal categorization results revealed that there is variability, particularly a trivial dissimilarity of approximately (+/-4 and 6) beats per minute (b.p.m.). It was demonstrated that obstetricians' observations coincide with algorithms based on deceleration type and number, except for acceleration values that differ by up to (+/-4). Discussion: The results obtained based on CTG interpretation showed that the utilization of the computerized approach employed in infirmaries and home care services for pregnant women is indeed suitable. Conclusions: The classification based on CTG that was used for the interpretation of the FHR attribute as discussed in this study is based on the RCOG guidelines. The system is evaluated and validated by experts based on their expert opinions and was compared with the CTG feature extraction and classification algorithms developed using MATLAB.

Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models.

Chronic diseases are the most severe health concern today, and heart disease is one of them. Coronary artery disease (CAD) affects blood flow to the heart, and it is the most common type of heart disease which causes a heart attack. High blood pressure, high cholesterol, and smoking significantly increase the risk of heart disease. To estimate the risk of heart disease is a complex process because it depends on various input parameters. The linear and analytical models failed due to their assumptions and limited dataset. The existing studies have used medical data for classification purposes, which help to identify the exact condition of the patient, but no one has developed any correlation equation which can be directly used to identify the patients. In this paper, mathematical models have been developed using the medical database of patients suffering from heart disease. Curve fitting and artificial neural network (ANN) have been applied to model the condition of patients to find out whether the patient is suffering from heart disease or not. The developed curve fitting model can identify the cardiac patient with accuracy, having a coefficient of determination (R 2-value) of 0.6337 and mean absolute error (MAE) of 0.293 at a root mean square error (RMSE) of 0.3688, and the ANN-based model can identify the cardiac patient with accuracy having a coefficient of determination (R 2-value) of 0.8491 and MAE of 0.20 at RMSE of 0.267, it has been found that ANN provides superior mathematical modeling than curve fitting method in identifying the heart disease patients. Medical professionals can utilize this model to identify heart patients without any angiography or computed tomography angiography test.

Delay Optimal Schemes for Internet of Things Applications in Heterogeneous Edge Cloud Computing Networks.

Over the last decade, the usage of Internet of Things (IoT) enabled applications, such as healthcare, intelligent vehicles, and smart homes, has increased progressively. These IoT applications generate delayed- sensitive data and requires quick resources for execution. Recently, software-defined networks (SDN) offer an edge computing paradigm (e.g., fog computing) to run these applications with minimum end-to-end delays. Offloading and scheduling are promising schemes of edge computing to run delay-sensitive IoT applications while satisfying their requirements. However, in the dynamic environment, existing offloading and scheduling techniques are not ideal and decrease the performance of such applications. This article formulates joint and scheduling problems into combinatorial integer linear programming (CILP). We propose a joint task offloading and scheduling (JTOS) framework based on the problem. JTOS consists of task offloading, sequencing, scheduling, searching, and failure components. The study's goal is to minimize the hybrid delay of all applications. The performance evaluation shows that JTOS outperforms all existing baseline methods in hybrid delay for all applications in the dynamic environment. The performance evaluation shows that JTOS reduces the processing delay by 39% and the communication delay by 35% for IoT applications compared to existing schemes.

A Novel Fuzzy Parameterized Fuzzy Hypersoft Set and Riesz Summability Approach Based Decision Support System for Diagnosis of Heart Diseases.

Fuzzy parameterized fuzzy hypersoft set (Δ-set) is more flexible and reliable model as it is capable of tackling features such as the assortment of attributes into their relevant subattributes and the determination of vague nature of parameters and their subparametric-valued tuples by employing the concept of fuzzy parameterization and multiargument approximations, respectively. The existing literature on medical diagnosis paid no attention to such features. Riesz Summability (a classical concept of mathematical analysis) is meant to cope with the sequential nature of data. This study aims to integrate these features collectively by using the concepts of fuzzy parameterized fuzzy hypersoft set (Δ-set) and Riesz Summability. After investigating some properties and aggregations of Δ-set, two novel decision-support algorithms are proposed for medical diagnostic decision-making by using the aggregations of Δ-set and Riesz mean technique. These algorithms are then validated using a case study based on real attributes and subattributes of the Cleveland dataset for heart-ailments-based diagnosis. The real values of attributes and subattributes are transformed into fuzzy values by using appropriate transformation criteria. It is proved that both algorithms yield the same and reliable results while considering hypersoft settings. In order to judge flexibility and reliability, the preferential aspects of the proposed study are assessed by its structural comparison with some related pre-developed structures. The proposed approach ensures that reliable results can be obtained by taking a smaller number of evaluating traits and their related subvalues-based tuples for the diagnosis of heart-related ailments.

Effects of short-term exposure to the heavy metal, nickel chloride (Nicl2) on gill histology and osmoregulation components of the gray mullet, Mugil cephalus.

The gray mullet, Mugil cephalus is an inshore and bottom-feeding fish species of Oman sea. Therefore, the gray mullet may be more exposed to heavy metal contamination, as the toxic impacts of heavy metals mullet has been reported in various studies. This study was conducted to evaluate the toxic effects of the heavy metal, nickel (as NiCl2) on osmoregulation of the gray mullet by measuring blood biochemicals, hormones, minerals and gill histology. Fish (10 fish/tank) were experimentally exposed to NiCl2 at three environmentally relevant concentrations of 5, 10 and 15 µg/l for 96 h. Then, fish were challenged with seawater (35 mg/l) for a period of 120 min. The samples (blood and gill tissue) were collected After 96 exposure to NiCl2 and during salinity challenge (30, 60 and 120 min post challenge). The plasma levels of cortisol and glucose significantly increased in NiCl2-exposed fish. In addition, cortisol increased in all experimental groups 30 min after salinity challenge and then returned gradually to the same levels as the control at 120 min post salinity challenge (PSC). The triiodothyronine (T3) and thyroxine (T4) levels significantly decreased in response to 10 and 15 µg/l NiCl2. In all groups, the thyroid hormones significantly elevated at 30 min PSC. After 30 min PSC, T3 levels in all NiCl2-exposed fish and T4 in the treatment, 10 µg/l NiCl2 remained unchanged throughout the salinity challenge. In the treatment, 5 µg/l NiCl2, T4 levels were recovered at 120 min PSC and reached the same levels as the control. Exposure of fish to high concentrations of NiCl2 and salinity stress increased the lactate levels. However, lactate levels in 5 and 10 µg/l NiCl2 groups were recovered at 120 min PSC and reached the same levels as the control. Furthermore, plasma protein increased in response to 10 and 15 µg/l NiCl2. At 30 PSC, the protein levels decreased in control and 5 µg/l NiCl2 group, while it remained unchanged in fish exposed to 10 and 15 µg/l NiCl2 throughout the salinity challenge. Exposure of fish to NiCl2 disrupted the electrolyte (Na+, Cl-) balance both before and after salinity challenge, which may be due to gill lesions induced by the heavy metal and following alternations in gill permeability. However, fish in 5 µg/l NiCl2 re-established the ionic balance in the blood at the end of salinity challenge period. The malondialdehyde (MDA) levels significantly increased in response to 10 and 15 µg/l NiCl2. The MDA levels returned to the same levels as the control group at 120 min PSC. The results of the present study showed that nickel-induced toxicity (especially at high concentrations) can reduce the osmoregulation capabilities of mullet. However, fish are able to recover from the toxic effects over time, if contamination be eliminated.

Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams.

Schistosoma mansoni is one of the tropical diseases with the greatest epidemic reach in the world. One of the WHO guidelines is the prior and efficient diagnosis for mapping foci and applying the appropriate treatment of infected people. The current process for diagnosis still depends on an analysis of parasitological exams performed by a human being under a laboratory microscope. The area of pattern recognition in images presents itself as a promising alternative to support and automate image-based exams, and deep learning techniques have been successfully applied for this purpose. In order to automate this process, it is proposed in this work the application of deep learning methods for the detection of schistosomiasis eggs, and a comparison is made between two deep learning techniques, convolutional neural network (CNN) and structured pyramidal neural network (SPNN). The results obtained in a real database indicate that the techniques are effective in the recognition of schistosomiasis eggs, in which both obtained AUC (area under the curve) above 0.90, with the CNN showing superiority in this aspect. . However, the SPNN proved to be faster than the CNN.

Remotely Monitoring COVID-19 Patient Health Condition Using Metaheuristics Convolute Networks from IoT-Based Wearable Device Health Data.

Today, COVID-19-patient health monitoring and management are major public health challenges for technologies. This research monitored COVID-19 patients by using the Internet of Things. IoT-based collected real-time GPS helps alert the patient automatically to reduce risk factors. Wearable IoT devices are attached to the human body, interconnected with edge nodes, to investigate data for making health-condition decisions. This system uses the wearable IoT sensor, cloud, and web layers to explore the patient's health condition remotely. Every layer has specific functionality in the COVID-19 symptoms' monitoring process. The first layer collects the patient health information, which is transferred to the second layer that stores that data in the cloud. The network examines health data and alerts the patients, thus helping users take immediate actions. Finally, the web layer notifies family members to take appropriate steps. This optimized deep-learning model allows for the management and monitoring for further analysis.

Study of Membranes with Nanotubes to Enhance Osmosis Desalination Efficiency by Using Machine Learning towards Sustainable Water Management.

Water resources management is one of the most important issues nowadays. The necessity of sustainable management of water resources, as well as finding a solution to the water shortage crisis, is a question of our survival on our planet. One of the most important ways to solve this problem is to use water purification systems for wastewater resources, and one of the most necessary reasons for the research of water desalination systems and their development is the problem related to water scarcity and the crisis in the world that has arisen because of it. The present study employs a carbon nanotube-containing nanocomposite to enhance membrane performance. Additionally, the rise in flow brought on by a reduction in the membrane's clogging surface was investigated. The filtration of brackish water using synthetic polyamide reverse osmosis nanocomposite membrane, which has an electroconductivity of 4000 Ds/cm, helped the study achieve its goal. In order to improve porosity and hydrophilicity, the modified raw, multi-walled carbon nanotube membrane was implanted using the polymerization process. Every 30 min, the rates of water flow and rejection were evaluated. The study's findings demonstrated that the membranes have soft hydrophilic surfaces, and by varying concentrations of nanocomposite materials in a prescribed way, the water flux increased up to 30.8 L/m2h, which was notable when compared to the water flux of the straightforward polyamide membranes. Our findings revealed that nanocomposite membranes significantly decreased fouling and clogging, and that the rejection rate was greater than 97 percent for all pyrrole-based membranes. Finally, an artificial neural network is utilized to propose a predictive model for predicting flux through membranes. The model benefits hyperparameter tuning, so it has the best performance among all the studied models. The model has a mean absolute error of 1.36% and an R2 of 0.98.

Differential systemic inflammatory responses after TAVI: The role of self versus balloon expandable devices.

OBJECTIVE: Transcatheter aortic valve implantation (TAVI) provokes early injury response, represented in part by dynamic changes in the inflammatory markers. The association of self-expanding valves (SEVs) and balloon-expandable valves (BEVs) with the consequent inflammatory response remains uncertain. MATERIALS AND METHODS: Patients with severe symptomatic aortic stenosis who underwent transfemoral TAVI: SEVs or BEVs, from January 2010 to December 2019 were enrolled. Whole white blood cells (WBC) and subpopulation dynamics as well the neutrophil to lymphocyte ratio (NLR) were evaluated. RESULTS: Three-hundred seventy consecutive patients (mean age 81.75 ± 6.8 years, 199 women's) were enrolled. In the entire population, significant kinetic changes in the WBC response (p <0.0001) between admission and first 24 hours post procedure, with a significant increase in total WBC (7.46 ± 2.26 to 10.08 ± 3.55) and absolute neutrophil count (4.97 ± 2.06 to 8.19 ± 3.43), NL ratio (3.72 ± 2.8 to 9.76 ± 7.29), and a meaningful decrease in absolute lymphocytes count (1.67 ± 1.1 to 1.1 ± 0.76). When compared between the types of valves, SEVs were associated with a more pronounced inflammatory response than BEVs, with total WBC (10.44 ± 3.86 vs. 9.45 ± 3.19) neutrophils (8.56 ± 3.75 vs. 7.55 ± 3.06) with p 0.016 and 0.012 respectively. CONCLUSION: This is the first description of a differential inflammatory response between the two leading delivery systems. SEV appears to trigger a more robust inflammatory response as compared to BEV. Clinical studies are warranted to assess the long term effect of our findings.

A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival.

BACKGROUND: Breast cancer intrinsic molecular subtype (IMS) as classified by the expression-based PAM50 assay is considered a strong prognostic feature, even when controlled for by standard clinicopathological features such as age, grade, and nodal status, yet the molecular testing required to elucidate these subtypes is not routinely performed. Furthermore, when such bulk assays as RNA sequencing are performed, intratumoral heterogeneity that may affect prognosis and therapeutic decision-making can be missed. METHODS: As a more facile and readily available method for determining IMS in breast cancer, we developed a deep learning approach for approximating PAM50 intrinsic subtyping using only whole-slide images of H&E-stained breast biopsy tissue sections. This algorithm was trained on images from 443 tumors that had previously undergone PAM50 subtyping to classify small patches of the images into four major molecular subtypes-Basal-like, HER2-enriched, Luminal A, and Luminal B-as well as Basal vs. non-Basal. The algorithm was subsequently used for subtype classification of a held-out set of 222 tumors. RESULTS: This deep learning image-based classifier correctly subtyped the majority of samples in the held-out set of tumors. However, in many cases, significant heterogeneity was observed in assigned subtypes across patches from within a single whole-slide image. We performed further analysis of heterogeneity, focusing on contrasting Luminal A and Basal-like subtypes because classifications from our deep learning algorithm-similar to PAM50-are associated with significant differences in survival between these two subtypes. Patients with tumors classified as heterogeneous were found to have survival intermediate between Luminal A and Basal patients, as well as more varied levels of hormone receptor expression patterns. CONCLUSIONS: Here, we present a method for minimizing manual work required to identify cancer-rich patches among all multiscale patches in H&E-stained WSIs that can be generalized to any indication. These results suggest that advanced deep machine learning methods that use only routinely collected whole-slide images can approximate RNA-seq-based molecular tests such as PAM50 and, importantly, may increase detection of heterogeneous tumors that may require more detailed subtype analysis.