Evolution and Comparative Genomics of the Transforming Growth Factor-ß-Related Proteins in Nile Tilapia.

The members of the transforming growth factor ß (TGF-ß) family of cell signaling polypeptides have garnered a great deal of interest due to its capacity from nematodes to mammals to regulate cell-based activities which control the growth of embryos and sustain tissue homeostasis. The current study designed a computational analysis of the TGF-ß protein family for understanding these proteins at the molecular level. This study determined the genomic structure of TGF-ß gene family in Nile tilapia for the first time. We chose 33 TGF-ß genes for identification and divided them into two subgroups, TGF-like and BMP-like. Moreover, the subcellular localization of the Nile tilapia TGF-ß proteins have showed that majority of the members of TGF-ß proteins family are present into extracellular matrix and plasma except BMP6, BMP7, and INHAC. All TGF-ß proteins were thermostable excluding BMP1. Each protein exhibited basic nature, excluding of BMP1, BMP2, BMP7, BMP10, GDF2, GDF8, GDF11, AMH, INHA, INHBB, and NODAL M. All proteins gave impression of being unstable depending on the instability index, having values exceeding 40 excluding BMP1 and BMP2. Each TGF-ß protein was found to be hydrophobic with lowered values of GRAVY. Moreover, every single one of the discovered TGF-ß genes had a consistent evolutionary pattern. The TGF-ß gene family had eight segmental duplications, and the Ka/Ks ratio demonstrated that purifying selection had an impact on the duplicated gene pairs which have experienced selection pressure. This study highlights important functionality of TGF-ß and depicts the demand for further investigation to better understand the role and mechanism of transforming growth factor ß in fishes and other species.

Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing.

Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils.

Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning.

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient's data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

Fluorescent and Phosphorescent Nitrogen-Containing Heterocycles and Crown Ethers: Biological and Pharmaceutical Applications.

Fluorescent molecules absorb photons of specific wavelengths and emit a longer wavelength photon within nanoseconds. Recently, fluorescent materials have been widely used in the life and material sciences. Fluorescently labelled heterocyclic compounds are useful in bioanalytical applications, including in vivo imaging, high throughput screening, diagnostics, and light-emitting diodes. These compounds have various therapeutic properties, including antifungal, antitumor, antimalarial, anti-inflammatory, and analgesic activities. Different neutral fluorescent markers containing nitrogen heterocycles (quinolones, azafluoranthenes, pyrazoloquinolines, etc.) have several electrochemical, biological, and nonlinear optic applications. Photodynamic therapy (PDT), which destroys tumors and keeps normal tissues safe, works in the presence of molecular oxygen with light and a photosensitizing drugs (dye) to obtain a therapeutic effect. These compounds can potentially be effective templates for producing devices used in biological research. Blending crown compounds with fluorescent residues to create sensors has been frequently investigated. Florescent heterocyclic compounds (crown ether) increase metal solubility in non-aqueous fluids, broadening the application window. Fluorescent supramolecular polymers have widespread use in fluorescent materials, fluorescence probing, data storage, bio-imaging, drug administration, reproduction, biocatalysis, and cancer treatment. The employment of fluorophores, including organic chromophores and crown ethers, which have high selectivity, sensitivity, and stability constants, opens up new avenues for research. Fluorescent organic compounds are gaining importance in the biological world daily because of their diverse functionality with remarkable structural features and positive properties in the fields of medicine, photochemistry, and spectroscopy.

Breast Cancer Detection and Classification Empowered With Transfer Learning.

Cancer is a major public health issue in the modern world. Breast cancer is a type of cancer that starts in the breast and spreads to other parts of the body. One of the most common types of cancer that kill women is breast cancer. When cells become uncontrollably large, cancer develops. There are various types of breast cancer. The proposed model discussed benign and malignant breast cancer. In computer-aided diagnosis systems, the identification and classification of breast cancer using histopathology and ultrasound images are critical steps. Investigators have demonstrated the ability to automate the initial level identification and classification of the tumor throughout the last few decades. Breast cancer can be detected early, allowing patients to obtain proper therapy and thereby increase their chances of survival. Deep learning (DL), machine learning (ML), and transfer learning (TL) techniques are used to solve many medical issues. There are several scientific studies in the previous literature on the categorization and identification of cancer tumors using various types of models but with some limitations. However, research is hampered by the lack of a dataset. The proposed methodology is created to help with the automatic identification and diagnosis of breast cancer. Our main contribution is that the proposed model used the transfer learning technique on three datasets, A, B, C, and A2, A2 is the dataset A with two classes. In this study, ultrasound images and histopathology images are used. The model used in this work is a customized CNN-AlexNet, which was trained according to the requirements of the datasets. This is also one of the contributions of this work. The results have shown that the proposed system empowered with transfer learning achieved the highest accuracy than the existing models on datasets A, B, C, and A2.

Design and Evaluation of pH Sensitive PEG-Protamine Nanocomplex of Doxorubicin for Treatment of Breast Cancer.

Cancer is the most common cause of mortality worldwide. There is dire need of modern strategies-such as surface modification of nanocarriers-to combat this global illness. Incorporation of active targeting ligands has arisen as a novel platform for specific tumor targeting. The aim of the current study was to formulate PEG-protamine complex (PPC) of doxorubicin (DOX) for treatment of breast cancer (BC). DOX coupling with PEG can enhance cell-penetrating ability: combating resistance in MDA-MB 231 breast cancer cells. Ionic gelation method was adopted to fabricate a pH sensitive nanocomplex. The optimized nanoformulation was characterized for its particle diameter, zeta potential, surface morphology, entrapment efficiency, crystallinity, and molecular interaction. In vitro assay was executed to gauge the release potential of nanoformulation. The mean particle size, zeta potential, and polydispersity index (PDI) of the optimized nanoparticles were observed to be 212 nm, 15.2 mV, and 0.264, respectively. Crystallinity studies and Fourier transform infrared (FTIR) analysis revealed no molecular interaction and confirmed the amorphous nature of drug within nanoparticles. The in vitro release data indicate sustained drug release at pH 4.8, which is intracellular pH of breast cancer cells, as compared to the drug solution. PPC loaded with doxorubicin can be utilized as an alternative and effective approach for specific targeting of breast cancer.

Hydrogel Containing Solid Lipid Nanoparticles Loaded with Argan Oil and Simvastatin: Preparation, In Vitro and Ex Vivo Assessment.

Transdermal hydrogels have the potential to improve therapeutic outcomes via enhancing bioavailability and reducing toxicity associated with oral delivery. The goal of the present study was to formulate and optimise argan oil loaded transdermal hydrogel containing lipid nanoparticles. The high pressure homogenization (HPH) method was utilised to fabricate Simvastatin loaded solid lipid nanoparticles (SIM-SLNs) with precirol ATO 5 as a lipid core and Poloxamer 407 (P407) to stabilise the core. The optimised nanoformulation was characterised for its particle diameter, zeta potential, surface morphology, entrapment efficiency, crystallinity and molecular interaction. Furthermore, transdermal hydrogel was characterised for physical appearance, rheology, pH, bio adhesion, extrudability, spreadability and safety profile. In vitro and ex vivo assays were executed to gauge the potential of SLNs and argan oil for transdermal delivery. The mean particle size, zeta potential and polydispersity index (PDI) of the optimised nanoparticles were 205 nm, -16.6 mV and 0.127, respectively. Crystallinity studies and Fourier transform infrared (FTIR) analysis revealed no molecular interaction. The in vitro release model explains anomalous non-Fickian release of drug from matrix system. Ex vivo skin penetration studies conducted through a fluorescence microscope confirmed penetration of the formulation across the stratum corneum. Hydrogel plays a crucial role in controlling the burst release and imparting the effect of argan oil as hypolipidemic agent and permeation enhancer.

Oral maxillofacial surgical residency and its impact on the academic and research productivity at different levels in Pakistan.

Oral and maxillofacial (OMF) surgery is a unique speciality. In many countries, OMFS is a dental speciality but the scope of its practice significantly overlaps with other specialities, including otolaryngology, head and neck surgery, plastic surgery, and orthopaedics. Thus, OMF surgery represents a true amalgamation of medical and dental specialities. There are different requirements of OMF residency training, which include a dental undergraduate training, medical training, or both. The training pathways for this speciality have evolved much in the last three decades and there is still no consensus over a single uniform path of becoming an OMF surgeon. An OMF surgeon deals with trauma, cysts, tumours and other pathologies of the maxilla, mandible, and zygomatic complex that need surgical correction. In addition to being a diverse speciality, the academic and research domains of residency have also changed. In Pakistan, residency training in OMF surgery started in 1994 and since then a lot of growth has taken place. This paper summarises the evolution and scope of OMF surgery and the contribution of this speciality in the overall academia and research in Pakistan's national dental scenario.

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning.

Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.

Detection of Benign and Malignant Tumors in Skin Empowered with Transfer Learning.

Skin cancer is a major type of cancer with rapidly increasing victims all over the world. It is very much important to detect skin cancer in the early stages. Computer-developed diagnosis systems helped the physicians to diagnose disease, which allows appropriate treatment and increases the survival ratio of patients. In the proposed system, the classification problem of skin disease is tackled. An automated and reliable system for the classification of malignant and benign tumors is developed. In this system, a customized pretrained Deep Convolutional Neural Network (DCNN) is implemented. The pretrained AlexNet model is customized by replacing the last layers according to the proposed system problem. The softmax layer is modified according to binary classification detection. The proposed system model is well trained on malignant and benign tumors skin cancer dataset of 1920 images, where each class contains 960 images. After good training, the proposed system model is validated on 480 images, where the size of images of each class is 240. The proposed system model is analyzed using the following parameters: accuracy, sensitivity, specificity, Positive Predicted Values (PPV), Negative Predicted Value (NPV), False Positive Ratio (FPR), False Negative Ratio (FNR), Likelihood Ratio Positive (LRP), and Likelihood Ratio Negative (LRN). The accuracy achieved through the proposed system model is 87.1%, which is higher than traditional methods of classification.

Skin cancer biology and barriers to treatment: Recent applications of polymeric micro/nanostructures.

Background: Skin cancer has been the leading type of cancer worldwide. Melanoma and non-melanoma skin cancers are now the most common types of skin cancer that have been reached to epidemic proportion. Based on the rapid prevalence of skin cancers, and lack of efficient drug delivery systems, it is essential to surge the possible ways to prevent or cure the disease. Aim of review: Although surgical modalities and therapies have been made great progress in recent years, however, there is still an urgent need to alleviate its increased burden. Hence, understanding the precise pathophysiological signaling mechanisms and all other factors of such skin insults will be beneficial for the development of more efficient therapies. Key scientific concepts of review: In this review, we explained new understandings about onset and development of skin cancer and described its management via polymeric micro/nano carriers-based therapies, highlighting the current key bottlenecks and future prospective in this field. In therapeutic drug/gene delivery approaches, polymeric carriers-based system is the most promising strategy. This review discusses that how polymers have successfully been exploited for development of micro/nanosized systems for efficient delivery of anticancer genes and drugs overcoming all the barriers and limitations associated with available conventional therapies. In addition to drug/gene delivery, intelligent polymeric nanocarriers platforms have also been established for combination anticancer therapies including photodynamic and photothermal, and for theranostic applications. This portfolio of latest approaches could promote the blooming growth of research and their clinical availability.

Recognizing Movement Disorder Emergencies - A Practical Review For Non-Neurologist.

Neurology still remains one of the most underserved specialties of medicine in Pakistan with roughly one neurologist per million people. Movement disorders (MD) are neurological problems that interfere with patient's motor abilities and diagnosis is typically clinical. In this review, we describe a practical approach to common MD emergencies that may be encountered by a non-neurologist physician, emphasizing on formulating a working diagnosis and their immediate management. Movement disorder emergencies can be classified based on MD phenomenology and we will provide a brief overview of dystonia including acute dystonic reaction, PAID syndrome and dystonic storm; chorea, myoclonus including serotonin syndrome and startle disease; and rigidity including neuroleptic malignant syndrome and malignant hyperthermia.

Discord Between the Facial and Maxillary Midline and Intermaxillary Midline Among the Population of Asir, Saudi Arabia

Abstract Objective: To determine the proportion of Saudi population in the Asir region that displayed discordance between the facial and maxillary midline and intermaxillary midline, to form an informative guideline for esthetic rehabilitation of patients. Material and Methods: We evaluated 2418 Saudi citizens using positioning guides of the orthopantomography machine to record the relationship of the dental midline to the facial midline. The relationship of the maxillary midline to that of the mandible was observed clinically, and diagnostic mounting of particular cases was performed for confirmation. The examination was carried out by four trained observers (two dentists and two radiology technicians) to overcome the parallax effect. The cases with disagreements were repeated. The record was grouped into (1) coincidence, (2) deviation of the mandible to the right, and (3) deviation to the left. The relationship between facial-dental midline concordance and intermaxillary concordance was assessed using the Chi-squared test Results: Facial and maxillary midline did not coincide in 42.5% participants, whereas intermaxillary midline discordance was observed in 51.5%. Among those exhibiting discordance, 57% had right discordance and 43% had left discordance Conclusion: A significant proportion of the population displayed discordance between the facial-maxillary midline as well as the intermaxillary midline. The female population showed not only a higher number of intermaxillary discordance than males but also a significant number of intermaxillary discordance towards the right.

Predictors of Mortality in Patients Undergoing Mitral Valve Replacement.

OBJECTIVE: Although mitral valve replacement is frequently performed in patients of all age groups, there are few studies available which determine the causes of operative mortality in mitral valve replacement especially in our region. Therefore, the objective of this study was to identify factors that are significantly associated with operative mortality in mitral valve replacement. METHODS: From August 2012 to March 2013, 80 consecutive patients undergoing mitral valve replacement in a single tertiary hospital were included. Patients with a history of previous coronary artery bypass graft surgery or congenital heart problems were excluded from the sample. The included patients were observed for a period of 30 days. Pre and post-operative variables were used to identify significant predictors of mortality. RESULTS: The overall hospital mortality (30 days) was 15%. High post-perative creatinine (P =0.05), high ASO titre (P=0.03), young age (P=0.011), low cardiac output (P=0.0001), small mitral valve size (P=0.002) and new onset of atrial fibrillation (P=0.007) were the significant independent predictors of operative morality. CONCLUSION: Mitral valve replacement can be performed in third world countries with limited resources with low mortality. However, optimal selection of mitral valve size can help to improve operative mortality.