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Internet of Things (IoT) technology has been used in medical care as the Internet of Medical Things (IoMT) to gather sensor data for diagnosing and predicting cardiac disease. IoMT allows users to access real-time tracking information and manually estimate the person's health using Machine Learning (ML) algorithms. The primary goal of the study proposal is to categorize data and forecast heart illness using health information and medical imagery. The suggested IoMT-based Heart Health Prediction and Classification (IoMT-HHPC) model is a medical data categorization and forecasting framework in two phases. If the first stage's outcome effectively predicts heart disease, the second step is image classification. Data collected from medical equipment attached to the person's body were initially categorized. Echocardiography (ECG) images were analyzed to forecast cardiac problems. This article used many ML techniques to forecast cardiac disease. An IoMT-HHPC model with ANN achieved an accuracy of 99.02%, surpassing the performance of other ML algorithms.

Effective detection and diagnostic procedures are necessary to enhance patient results for the common and life-threatening illness of breast cancer. Current approaches have limits in scalability and efficiency, highlighting the need for more study. This work introduces a hybrid Breast Cancer (BC) detecting approach that merges Deep Learning (DL) with pre-trained modeling of Histopathology Images (HPI) and an ensemble-based Machine Learning (ML) approach. DL integration allows learning and identifying hidden trends in intricate BC pictures, while ML techniques provide interpretability and generalization skills. Contrast Limited Adaptive Histogram Equalization (CLAHE) was used on HPI as a pre-processing technique to improve picture quality. The ResNet50V2 model was used for deep feature extraction. The Ensemble Learning (EL) model combines predictions from four basic ML approaches using soft voting. The research attained a superior accuracy, precision, recall, and F1 score compared to the most advanced models. This study provides substantial advancements in breast cancer diagnosis, thorough performance evaluation, and reliable assessment. Furthermore, it helps medical personnel make well-informed choices, enhance patient care, and improve results for BC sufferers.

The mesothelium supports homeostasis and regeneration, yet its development origins remain unclear. Here, the authors uncovered the earliest mesothelium progenitor cells in zebrafish, linking Hand2 gene function to mesothelium formation and its re-activation to mesothelioma tumors.

Commercially available performance-enhancing supplements can contain banned performance-enhancing drugs (PEDs) and undisclosed steroid hormones that can induce hormonal abnormalities with associated clinical signs. We present a case of a 40-year-old male who developed bilateral gynecomastia and biochemical hypogonadotropic hypogonadism with a corresponding 6-month history of consuming commercially available performance-enhancing supplements for gym workouts. These performance-enhancing supplements were found to contain amounts of RAD-140, a selective androgen receptor modulator, MK-677, a GH secretagogue and cardarine, all of which are banned PEDs. In vitro analysis also detected undisclosed hormones testosterone, estradiol, and GH in all 3 supplements, with further steroid analysis using liquid chromatography mass spectrometry identifying an unidentified compound coeluting close to the testosterone peak. Cessation of these supplements led to full resolution of symptoms including normalization of hypogonadotropic hypogonadism. This case highlights the need for clinicians to consider commercially available performance-enhancing supplements as potential sources of PEDs and exogenous steroid hormones that can have adverse clinical consequences. [ABSTRACT FROM AUTHOR]

© 2014 IEEE. Seeking fundamental insights into multi-hop multi-flow networks we study the simplest non-trivial setting, a 2 × 2 × 2 MIMO interference network comprised of two sources, two relays and two destinations, wherein all nodes have M antennas, all first-hop channels are of rank D1, and all second hop channels are of rank D2. For this setting, we show that the optimal sum DoF is min(4D1, 4D2, 2M - |D1 - D2|). While 4D1, 4D2 are the obvious min-cut bottlenecks that are active when either hop is severely rank-deficient, what is remarkable is that under moderate rank-deficiencies the DoF are limited not by the higher or the lower of the two ranks D1, D2, but only by the difference of the two ranks |D1 - D2|. This suggests an interesting 'rank-matching' design principle for multi-hop networks, reminiscent of 'impedance matching', wherein the goal is not necessarily to increase or decrease the rank of each hop, but rather to use linear processing at intermediate hops to create effectively a two-hop setting with matching ranks.

This paper explores how degrees of freedom (DoF) results from wireless networks can be translated into capacity or linear capacity results for their finite field counterparts that arise in network coding applications. The main insight is that scalar (SISO) finite field channels over \(\mathbb pn are analogous to n × n vector (MIMO) channels in the wireless setting, but with an important distinction - there is additional structure due to finite field arithmetic, which enforces commutativity of matrix multiplication and limits the channel diversity to n, making these channels similar to diagonal channels in the wireless setting. Within the limits imposed by the channel structure, the DoF optimal precoding solutions for wireless networks can be translated into capacity or linear capacity optimal solutions for their finite field counterparts. This is shown through the study of capacity of the 2-user X channel and linear capacity of the 3-user interference channel. Besides bringing the insights from wireless networks into network coding applications, the study of finite field networks over pn also touches upon important open problems in wireless networks (finite SNR, finite diversity scenarios) through interesting parallels between p and SNR, and n and diversity. © 2014 IEEE.

Introduction: Breast cancer is a heterogeneous disease, comprising numerous distinct entities that have different biological features and clinical behaviour. There are many biomarkers that are related to the progression and therapeutic response of invasive breast cancer. One emerging biomarker is Cyclin D1 which is a key cell cycle regulatory protein, encoded by the gene, CCND1 or PRAD1, positioned on chromosome 11q13. Thus, the present study is an attempt to analyse immunohistochemical expression of Cyclin D1 in breast carcinoma and correlate it with other known histopathological prognostic markers. Materials and Methods: A study of forty breast carcinoma patients carried out in the Dept. of Pathology during 2019-2022. Immunohistochemical staining of Cyclin D1 was performed. The intensity of Cyclin D1 staining will be scored on a scale from 0 to 3 and results were tabulated. Data was entered in MS Excel and analyzed using Pearson's Chi-square test and fisher exact test. A P value <0.05 was considered as statistically significant. Results: Statistically significant results were obtained with Cyclin D1 and grade of the tumor (p value 0.04). However, correlation of Cyclin D1 with size, lymphnode status and lymphovascular invasion was not statistically significant. Conclusion: Our study confirms the strong association between Cyclin D1 and grade of the tumour which is one of the oldest and the strongest prognostic marker known in breast carcinoma. Cyclin D1 expression appears to be a good independent marker in breast cancer and can be included in the pool of prognostic markers like tumor size, nodal status, histopathological grade. [ABSTRACT FROM AUTHOR]

Rhythmic and sequential segmentation of the growing vertebrate body relies on the segmentation clock, a multi-cellular oscillating genetic network. The clock is visible as tissue-level kinematic waves of gene expression that travel through the presomitic mesoderm (PSM) and arrest at the position of each forming segment. Here, we test how this hallmark wave pattern is driven by culturing single maturing PSM cells. We compare their cell-autonomous oscillatory and arrest dynamics to those we observe in the embryo at cellular resolution, finding similarity in the relative slowing of oscillations and arrest in concert with differentiation. This shows that cell-extrinsic signals are not required by the cells to instruct the developmental program underlying the wave pattern. We show that a cell-autonomous timing activity initiates during cell exit from the tailbud, then runs down in the anterior-ward cell flow in the PSM, thereby using elapsed time to provide positional information to the clock. Exogenous FGF lengthens the duration of the cell-intrinsic timer, indicating extrinsic factors in the embryo may regulate the segmentation clock via the timer. In sum, our work suggests that a noisy cell-autonomous, intrinsic timer drives the slowing and arrest of oscillations underlying the wave pattern, while extrinsic factors in the embryo tune this timer’s duration and precision. This is a new insight into the balance of cell-intrinsic and -extrinsic mechanisms driving tissue patterning in development.

The adoption of supplementary cementitious materials (SCMs) as a partial replacement of Ordinary Portland Cement (OPC) is increasing in anticipation of reducing the carbon footprint of cement and concrete industry. The resistance of concretes with SCMs against natural carbonation (a reactive-diffusion mechanism) is still a topic of research. Most literature adopt accelerated carbonation tests (under constant humidity and temperature conditions) to estimate the natural carbonation depth (dCO2) in concrete. This does not always represent the actual scenario in the field structures. Moreover, accelerated carbonation conditions could result in significantly different pore structure than that are formed in the case of natural carbonation; hence, not always reliable to model the service life. Hence, there is a need for data on long-term natural dCO2 to help design reinforced concrete for a target service life. The data presented in this paper contains long-term natural dCO2 data of 45 different concretes prepared using various SCMs like volcanic ash (from Greece), fly ash, slag, and limestone (from France). The concrete specimens were prepared in France, shipped to India by air (in moist burlap and plastic cover), and then kept in sheltered and unsheltered exposure conditions on the terrace of a building in Chennai, India, experiencing tropical climate (warm and humid conditions). The data includes mixture proportions (type of binder, water-to-binder ratio, binder content, % SCMs, fine and coarse aggregate (river sand and gravel) content, and air content). In addition, curing time and compressive strength of concrete mixes are provided in Set A. Sets B and C consist of the natural dCO2 of concrete measured at various time instances between 1 and 11 years of natural exposure. Arguably, this is the first of its kind natural dCO2 database from a tropical climate zone. The natural dCO2 values were measured using a phenolphthalein indicator prepared according to RILEM CPC 18. This data can be used by researchers and practitioners to calibrate the existing carbonation models or to develop new models to estimate natural dCO2 for concretes exposed to tropical climates. Such models can help design concretes to achieve the target corrosion-free service life for reinforced concrete systems.