Quinlangravgaard0231

The results showed that the copper and zinc could introduce better multi-biofunctions to the TiO2 nanotubes arrays for the application in cardiovascular biomaterials.The male reproductive system is exposed to a great number of chemical substances which can interfere with the normal hormonal milieu and reproductive function; these are called endocrine disruptors (EDs). Despite a growing number of studies evaluating the negative effects of EDs, their production is continuously growing although some of which have been prohibited. The prevalence of poor semen quality, hypospadias, cryptorchidism, and testicular cancer have increased in the last decades, and recently, it has been postulated that these could all be part of a unique syndrome called testicular dysgenesis syndrome. This syndrome could be related to exposure to a number of EDs which cause imbalances in the hormonal milieu and oestrogenic over-exposure during the foetal stage. The same EDs can also impair spermatogenesis in offspring and have epigenetic effects. Although studies on animal and in vitro models have raised concerns, data are conflicting. However, these studies must be considered as the basis for future research to promote male reproductive health.

Febrile seizures (FS) are the most common seizures in children younger than 5 years. In the last decade, various coding and noncoding sequence variations of voltage-gated sodium channels SCN2A have been identified in patients with seizures, implying their genetic base. We aimed to evaluate the association between SCN2A c. G/A genetic polymorphism among Egyptian children with febrile seizure plus. <P> Methods The present cross-sectional study was carried out on 100 epileptic infants and children, attendants of the Neurology Unit, pediatric department, Menoufia University Hospitals (Group Ι). The patients were sub-classified into two groups, according to response to anti-epileptic treatment; Group Ι a (drug responder) and Group Ι b (drug-resistant). Evenly divided number of apparently healthy, age and gender-matched children were selected as controls (Group II). A complete history, throughout the systemic examination and radiological & metabolic assessment, whenever needed was provided, all participth developing febrile seizure plus and could modulate the patient's response to anti-epileptic medications.

Drawbacks and side effects of currently available therapies to colorectal cancer (CRC) devoted the researchers to search for new therapeutic strategies.

This study was designed to investigate the effects of zinc nanoparticles biosynthesized with berberine (ZnNPs-BER) on Caco-2 cells compared to 5-Fluorouracil (5-FU) and explore the possible underlying pathways.

Caco-2 and Vero cells were treated with 5-FU, BER, or ZnNPs-BER for 24 h. Cell viability was measured by MTT assay. Oxidative stress and apoptotic markers and cell cycle were determined. Additionally, Cox-2 and NF-kB levels were also measured.

The IC50 of 5-FU, BER, and ZnNPs-BER on Caco-2 cells were 34.65 µM, 19.86 µg/ml and 10.49 µg/ml, respectively by MTT assay. The IC50 value for 5-FU in Vero cells was 21.7 μg/ml, however, BER and BER-ZnNPs treatment showed non-toxic effects to the Vero cells. Further, ZnNPs-BER exerted significant induction of ROS besides exhaustion of the antioxidant capacity of tumor cells indicated by declined GSH and eed for induction of oxidative stress, inflammation, and apoptotic changes in tumor cells. Our study documents the new therapeutic potential of Zn nanoparticles conjugated with BER, as a new option for combined chemotherapy.

Metaplastic breast carcinoma is an uncommon malignancy that constitutes < 5% of all breast cancers. There are 5 subtypes which are spindle cell, squamous cell, carcinosarcoma, matrix-producing and metaplastic with osteoclastic giant cells. Spindle cell carcinoma represents approximately <0.3% of invasive breast carcinomas. It is typically a triple-negative cancer with distinct pathological characteristics, but relatively a non-conclusive imaging findings.

An elderly lady presented with an enlarging painful left breast lump for 1 year. Palpable left breast lump noted on clinical examination. Mammography demonstrated a high density, oval lesion with a partially indistinct margin. Corresponding ultrasound showed a large irregular heterogeneous lesion with solid-cystic areas. Histopathology showed atypical spindle-shaped cells which stained positive for cytokeratins and negative for hormone and human epidermal growth factor receptors, which favours spindle cell metaplastic carcinoma. Left mastectomy and axillary dissection were performed, and the final diagnosis was consistent with metaplastic spindle cell carcinoma.

Spindle cell carcinoma of the breast is a rare aggressive histological type of carcinoma which may present with benign features on imaging. Tissue diagnosis is essential for prompt diagnosis with multidisciplinary team discussion to guide management and improve patient's outcome.

Spindle cell carcinoma of the breast is a rare aggressive histological type of carcinoma which may present with benign features on imaging. Tissue diagnosis is essential for prompt diagnosis with multidisciplinary team discussion to guide management and improve patient's outcome.COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infective disease, yet there are no well-defined absolute treatments available at present. selleck kinase inhibitor Several vaccination drives with emergency use authorisation vaccines are being done across many countries, however, their long term efficacy and side-effects study are yet to be done. The research community is analysing the situation by collecting the datasets from various sources. Healthcare professionals must thoroughly analyse the situation, devise preventive measures for this pandemic, and even develop possible drug combinations. Various analytical and statistical models have been developed, however, their outcome rate is prolonged. Thus, modern science stresses on the application of state-of-the-art methods in this combat against COVID-19. The application of Artificial intelligence (AI), and AI-driven tools are emerging as effective tools, especially with X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions etc. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e. >95%, as reported in various studies. AI-driven tools are being used in COVID diagnostic, therapeutics, trend prediction, drug design and prevention to help fight against this pandemic. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in this battle against the COVID-19 pandemic. The extensive literature is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 Prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing.

The objective of any multimodal medical image fusion algorithm is to assist a radiologist for better decision-making during the diagnosis and therapy by integrating the anatomical (magnetic resonance imaging) and functional (positron emission tomography/single-photon emission computed tomography) information.

We proposed a new medical image fusion method based on content-based decomposition, principal component analysis (PCA), and sigmoid function. We considered empirical wavelet transform (EWT) for content-based decomposition purposes since it can preserve crucial medical image information such as edges and corners. PCA is used to obtain initial weights corresponding to each detail layer.

In our experiments, we found that direct usage of PCA for detail layer fusion introduces severe artifacts into the fused image due to weight scaling issues. In order to tackle this, we considered using the sigmoid function for better weight scaling. We considered 24 pairs of MRI-PET and 24 pairs of MRI-SPECT images for fusion and the results are measured using four significant quantitative metrics.

Finally, we compared our proposed method with other state-of-the-art transform-based fusion approaches, using traditional and recent performance measures. An appreciable improvement is observed in both qualitative and quantitative results compared to other fusion methods.

Finally, we compared our proposed method with other state-of-the-art transform-based fusion approaches, using traditional and recent performance measures. An appreciable improvement is observed in both qualitative and quantitative results compared to other fusion methods.There is an increasing amount of data arising from neurobehavioral sciences and medical records that cannot be adequately analyzed by traditional research methods. New drugs develop at a slow rate and seem unsatisfactory for the majority of neurobehavioral disorders. Machine learning (ML) techniques, instead, can incorporate psychopathological, computational, cognitive, and neurobiological underpinning knowledge leading to a refinement of detection, diagnosis, prognosis, treatment, research, and support. Machine and deep learning methods are currently used to accelerate the process of discovering new pharmacological targets and drugs.

The present work reviews current evidence regarding the contribution of machine learning to the discovery of new drug targets.

Scientific articles from PubMed, SCOPUS, EMBASE, and Web of Science Core Collection published until May 2021 were included in this review.

The most significant areas of research are schizophrenia, depression and anxiety, Alzheimer´s disease, and substance use disorders. ML techniques have pinpointed target gene candidates and pathways, new molecular substances, and several biomarkers regarding psychiatric disorders. Drug repositioning studies using ML have identified multiple drug candidates as promising therapeutic agents.

Next-generation ML techniques and subsequent deep learning may power new findings regarding the discovery of new pharmacological agents by bridging the gap between biological data and chemical drug information.

Next-generation ML techniques and subsequent deep learning may power new findings regarding the discovery of new pharmacological agents by bridging the gap between biological data and chemical drug information.Gynura procumbens (Lour.) Merr. is a well-known plant used in the folkloric medicine in tropical Asian countries. The plant is prevalently employed by traditional healers in the treatment of diabetes, cancer, hypertension, inflammation, fever and skin disorders. Several scientific studies reported that, Gynura procumbens possesses considerable therapeutic value for the development of emerging treatment options. The diverse pharmacological effects of this plant are attributed to its vast phytoconstituent content. Different chemical classes including alkaloids, flavonoids, phenolics, steroids, proteins and polysaccharides have been isolated from this plant. In this review, we tried to explore the different aspects of Gynura procumbens as an established medicinal plant. The data gathered here give an indication that the plant Gynura procumbens is a good natural source of chemical compounds with different types of pharmacological actions and these chemical compounds can be used as model for the development of de novo therapeutic agents.