FPGA IMPLEMENTATION OF LOW COMPLEXITY LINEAR PERIODICALLY TIME VARYING FILTER

ABSTRACT This paper presents a low complexity architecture for a linear periodically time varying (LPTV) filter. This architecture is based on multi-input multi-output(MIMO) representation of LPTV filters. The input signal is divided into blocks and parallel processing is incorporated, there by considerably reducing the effective input sampling rate. A single multiplier can be shared for each linear time invariant (LTI) filter in the representation. Each LTI filter is realized in the transposed direct form filter using multiplier less multiplication structures based on Binary common bit patterns (BCS). The proposed structure is simulated, synthesized and implemented on Virtex v50efg256-7 Field Programmable Gate Array (FPGA). LPTV systems can be expressed as generalization of Linear time invariant (LTI) systems. If the input for a M-period LPTV system is delayed by M samples, output is also delayed by the same number of samples. An LPTV system with a period of '1' is nothing but an LTI syste

BrainCDNet: a concatenated deep neural network for the detection of brain tumors from MRI images

IntroductionBrain cancer is a frequently occurring disease around the globe and mostly developed due to the presence of tumors in/around the brain. Generally, the prevalence and incidence of brain cancer are much lower than that of other cancer types (breast, skin, lung, etc.). However, brain cancers are associated with high mortality rates, especially in adults, due to the false identification of tumor types, and delay in the diagnosis. Therefore, the minimization of false detection of brain tumor types and early diagnosis plays a crucial role in the improvement of patient survival rate. To achieve this, many researchers have recently developed deep learning (DL)-based approaches since they showed a remarkable performance, particularly in the classification task.MethodsThis article proposes a novel DL architecture named BrainCDNet. This model was made by concatenating the pooling layers and dealing with the overfitting issues by initializing the weights into layers using ‘He Normal’ initialization along with the batch norm and global average pooling (GAP). Initially, we sharpen the input images using a Nimble filter, which results in maintaining the edges and fine details. After that, we employed the suggested BrainCDNet for the extraction of relevant features and classification. In this work, two different forms of magnetic resonance imaging (MRI) databases such as binary (healthy vs. pathological) and multiclass (glioma vs. meningioma vs. pituitary) are utilized to perform all these experiments.Results and discussionEmpirical evidence suggests that the presented model attained a significant accuracy on both datasets compared to the state-of-the-art approaches, with 99.45% (binary) and 96.78% (multiclass), respectively. Hence, the proposed model can be used as a decision-supportive tool for radiologists during the diagnosis of brain cancer patients

X-linked genodermatoses from diagnosis to tailored therapy

Background: Genodermatoses are rare heterogeneous genetic skin diseases with multiorgan involvement. They severely impair an individual's well-being and can also lead to early death. Methods: During the progress of this review, we have implemented a targeted research approach, diligently choosing the most relevant and exemplary articles within the subject matter. Our method entailed a systematic exploration of the scientific literature to ensure a compre-hensive and accurate compilation of the available sources. Results: Among genodermatoses, X-linked ones are of particular importance and should always be considered when pediatric males are affected. Regardless of other syndromic forms without prevalence of skin symptoms, X-linked genodermatoses can be classified in three main groups: keratinization defects, pigmentation defects, and inflammatory skin diseases. Typical examples are dyskeratosis congenita, keratosis follicularis spinulosa decalvans, hypohidrotic ectodermal dysplasia, chondrodysplasia punctata, hypohidrotic ectodermal dysplasia, incontinentia pigmenti, chronic granulomatous disease, CHILD syndrome and ichthyosis. In this field, genetic diagnosis of the specific disease is important, also considering that numerous clinical trials of orphan drugs and genetic therapies are being proposed for these rare genetic diseases. Conclusions: Thus, this chapter starts from clinical to molecular testing and ends with a review of all clinical trials on orphan drugs and gene therapy for genodermatoses

In vitro cell culture of amniotic fluid keratinocytes on amniotic membrane: the ideal tissue for repairing skin ulcers

OBJECTIVE: The amniotic fluid contains a large population of stem keratinocytes demonstrating minimal immunological rejection. Recent evidence suggests that stem cells from the amniotic fluid can be employed in the field of tissue engineering. In this work we identified precursors of the epithelial cells and expanded them in vitro.MATERIALS AND METHODS: After collecting samples of amniotic fluid and separating the cells via centrifugation, we seeded a portion of these cells in selection media to analyze the proliferation of epithelial cells. The stem cells precursors of keratinocytes were identified through specific markers. The expression of these markers was evaluated by immunofluorescence and reverse transcription polymerase chain reaction (PCR).RESULTS: The stem cells demonstrated 90% confluence, after undergoing proliferation in the selection medium for 15 days. Most of these cells tested positive for the keratinocyte-specific markers, but negative for stem cell specific markers. Of note, the identity of the keratinocytes was well established even after several subcultures.CONCLUSIONS: These results suggested that it is feasible to isolate and expand differentiated cell populations in the amniotic fluid from precursor cells. Furthermore, amniotic membranes can be utilized as scaffolds to grow keratinocytes, which can be potentially exploited in areas of skin ulcer transplantation and tissue engineering interventions

Omics sciences and precision medicine in melanoma

Background: This article provides an overview of the application of omics sciences in melanoma research. The name omics sciences refers to the large-scale analysis of biological molecules like DNA, RNA, proteins, and metabolites. Methods: In the course of this review, we have adopted a focu-sed research strategy, meticulously selecting the most pertinent and emblematic articles related to the topic. Our methodology included a systematic examination of the scientific literature to guarantee a thorough and precise synthesis of the existing sources. Results: With the advent of high-throughput technologies, omics have become an essential tool for understanding the complexity of melanoma. In this article, we discuss the different omics approaches used in melanoma research, including genomics, transcriptomics, proteomics, and metabolomics. We also highlight the major findings and insights gained from these studies, including the identification of new therapeutic targets and the development of biomarkers for diagnosis and prognosis. Finally, we discuss the challenges and future directions in omics-based melanoma research, including the integration of multiple omics data and the development of personalized medicine approaches. Conclusions: Overall, this article emphasizes the importance of omics science in advancing our understanding of melanoma and its potential for improving patient outcomes

Autoantibodies detection in patients affected by autoimmune retinopathies

Objective: Autoimmune retinopathies (ARs) encompass a spectrum of immune diseases that are characterized by the presence of autoantibodies against retinal proteins in the bloodstream. These autoantibodies (AAbs) lead to a progressive and sometimes rapid loss of vision. ARs commonly affect subjects over 50 years of age, but also rare cases of kids under 3 years of age have been reported. Patients and methods: In this study, 47 unrelated Caucasian patients were enrolled. All subjects showed negative cancer diagnoses and negative results in their genetic screenings. We studied 8 confirmed retinal antigens using Western blotting analysis, with α-enolase followed by carbonic anhydrase II being the two most frequently found in the patients' sera. Results: Nineteen patients were positive (40.4%), thirteen uncertain (27.7%), and fifteen were negative (31.9%). Their gender did not correlate with the presence of AAbs (p=0.409). Conclusions: AAbs are responsible for retinal degeneration in some cases, while in others, they contribute to exacerbating the progression of the disease; however, their detection is crucial to reaching a better diagnosis and developing more effective treatments for these conditions. Moreover, finding good biomarkers is important not only for AR monitoring and prognosis, but also for helping with early cancer diagnosis

Study of the effects of Lemna minor extracts on human immune cell populations

OBJECTIVE: Lemna minor is a plant with a huge repertoire of secondary metabolites. The literature indicates that extracts of Lemna minor have antioxidant, antiradical, immunomodulatory and anti-inflammatory properties. The objective of the present study was to find a suitable technique to extract active compounds from this plant and verify whether these extracts have immunomodulatory activity. MATERIALS AND METHODS: We grew L. minor on a standard medium with Gamborg B5 and vitamins. We extracted compounds from the plant by maceration and decoction. The phytochemical profile of the extracts was characterized by chromatography, spectrophotometry, and spectroscopy. The extracts were tested on cultures of mononuclear cells from four human subjects. These cells were pulsed with carboxyfluorescein succinimidyl ester, grown in triplicate in standard culture medium without (control) and with increasing concentrations of Lemna extracts. Flow cytometry was used to evaluate cell death and proliferation of the total mononuclear cell population and of CD4+, CD8+, B cell and monocyte populations. RESULTS: The Lemna extracts were not cytotoxic and did not cause cell necrosis or apoptosis in immune cells. At low concentrations, they induced very limited proliferation of CD4+ cells within 48 hours. At high concentrations, they induced proliferation of CD8+ cells and B lymphocytes within 48 hours. CONCLUSIONS: Unfortunately, we failed to confirm any immunomodulatory activity of Lemna extracts. Growth and death rates of human immune cells were not significantly affected by adding Lemna extracts to the culture medium

Not Available

Not AvailableThe microbial diversity in the rhizosphere of different biotypes is influenced by different factors like plant species, root exudates and soil environment. Culturable microbial diversity in the rhizosphere of six biotypes (Chenopodium murale (CM), Spergula arvensis (SA), Launaea nudicaulis (LN), Brassica juncea (BJ), Phalaris minor (PM) and Triticum aestivum (TA)) growing in variable saline environment (ECe 8.0 dS m−1) was assessed and compared with the diversity of bulk soils (BS) of same environments. The significantly (P < 0.0001) highest bacterial and actinomycetes population were found in the rhizosphere of BJ whereas SA possessed higher fungal population. Phosphorus and zinc solubilizing bacteria was also found highest in BJ and TA rhizosphere, respectively. High saline soils had greater endospore forming bacterial population. The TA (0.88) and LN (0.87) rhizospheres showed significantly greater Shannon–Weiner diversity index compared to bulk soils (0.45–0.61). Pielou’s index of evenness of different samples ranged from 0.13 to 0.43. Discriminant function analysis revealed that rhizospheres of SA, CM and TA were clearly distinct. The rhizospheric soil of PM and BJ were similar to each other but clearly distinct from others. The observed separation of different biotypes was regulated by dimorphic fungi, nitrogen fixing bacteria, Pseudomonas, and fungi. Thus, our study clearly suggests that culturable microbial populations are influenced by different biotypes and salinity levels.Not Availabl

Towards real-time heartbeat classification : evaluation of nonlinear morphological features and voting method

Abnormal heart rhythms are one of the significant health concerns worldwide. The current state-of-the-art to recognize and classify abnormal heartbeats is manually performed by visual inspection by an expert practitioner. This is not just a tedious task; it is also error prone and, because it is performed, post-recordings may add unnecessary delay to the care. The real key to the fight to cardiac diseases is real-time detection that triggers prompt action. The biggest hurdle to real-time detection is represented by the rare occurrences of abnormal heartbeats and even more are some rare typologies that are not fully represented in signal datasets; the latter is what makes it difficult for doctors and algorithms to recognize them. This work presents an automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies. Although the algorithm is designed and tested on a computer, it is intended ultimately to run on a portable i.e., field-programmable gate array (FPGA) devices. Our algorithm tested on Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH) database as per Association for the Advancement of Medical Instrumentation(AAMI) recommendations. The simulation results show the superiority of the proposed method, especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100%

The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition

In medical image processing, magnetic resonance imaging (MRI) and computed tomography (CT) modalities are widely used to extract soft and hard tissue information, respectively. However, with the help of a single modality, it is very challenging to extract the required pathological features to identify suspicious tissue details. Several medical image fusion methods have attempted to combine complementary information from MRI and CT to address the issue mentioned earlier over the past few decades. However, existing methods have their advantages and drawbacks. In this work, we propose a new multimodal medical image fusion approach based on variational mode decomposition (VMD) and local energy maxima (LEM). With the help of VMD, we decompose source images into several intrinsic mode functions (IMFs) to effectively extract edge details by avoiding boundary distortions. LEM is employed to carefully combine the IMFs based on the local information, which plays a crucial role in the fused image quality by preserving the appropriate spatial information. The proposed method’s performance is evaluated using various subjective and objective measures. The experimental analysis shows that the proposed method gives promising results compared to other existing and well-received fusion methods