Determining the cost of acute stroke care for a single stay at a tertiary care centre in Pakistan.

OBJECTIVE: To assess the economic burden of acute stroke, and to determine the average cost of acute stroke care for a single hospital stay in a public tertiary care hospital. METHODS: The cross-sectional study was conducted at the Medical Teaching Institute, Bacha Khan Medical Complex, Swabi, Pakistan, from May 16 to September 19, 2022, and comprised patients of either gender who were hospitalised with an acute stroke for the first time. All costs incurred during the care of the patients were measured using the micro-costing methodology, and the association of the cost with other variables was evaluated. Data was analysed using SPSS 24. RESULTS: Of the 34 patients, 24(70.6%) were males and 10(29.4%) were females. The overall mean age was 66+/-13.00 years. The mean length of hospital stay was 4+/-3.00 days. The mean total cost was 18,156+/-9,068 Pakistani rupees, which was the equivalent of 76.89+/-38.4 United States dollars. The cost of the first day of admission was the highest, declining per day as the stay progressed, and imaging/laboratory investigations formed the highest component of the overall cost (p<0.001). CONCLUSIONS: The cost of acute stroke care was found to be high even in a public hospital. The length of hospital stay was the most important determinant of the overall cost.

An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein Biometrics.

This research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiments are performed for three different databases, i.e., the Near-Infra-Red Hand Images (NIRHI), Hong Kong Polytechnic University (HKPU) and University of Twente Finger Vein Pattern (UTFVP) databases. First, the Finger Texture biometric employs an efficient texture feature extracting algorithm, i.e., Linear Binary Pattern. Then, the classification is performed using Support Vector Machine, a proven machine learning classification algorithm. Second, the transfer learning of pre-trained convolutional neural networks (CNNs) is performed for the Finger Vein biometric, employing two approaches. The three selected CNNs are AlexNet, VGG16 and VGG19. In Approach 1, before feeding the images for the training of the CNN, the necessary preprocessing of NIR images is performed. In Approach 2, before the pre-processing step, image intensity optimization is also employed to regularize the image intensity. NIRHI outperforms HKPU and UTFVP for both of the modalities of focus, in a unimodal setup as well as in a multimodal one. The proposed multimodal biometric system demonstrates a better overall identification accuracy of 99.62% in comparison with 99.51% and 99.50% reported in the recent state-of-the-art systems.

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.

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.

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.

Evaluation of 13 rapidly mutating Y-STRs in endogamous Punjabi and Sindhi ethnic groups from Pakistan.

Y-chromosomal short tandem repeats (Y-STRs) are commonly used to study population histories, discover ancestral relationships, and identify males for criminal justice purposes. Y-STRs being largely in forensic use have low haplotype diversity in some populations and cannot discriminate between paternal male relatives. Rapidly mutating Y-STRs (RM Y-STRs) were breakthrough and have been paid much attention. A set of 13 rapidly mutating (RM) Y-STRs (DYF387S1, DYF399S1, DYF403S1a/b1/b2, DYF404S1, DYS449, DYS518, DYS526I/II, DYS547, DYS570, DYS576, DYS612, DYS626, and DYS627) typically reveals higher haplotype diversities than the commercially available Y-STR sets and allows differentiating male relatives for which commercial Y-STR sets are usually not informative. Here, we amplified the 13 RM Y-STRs in 168 (37 Sindhi and 131 Punjabi) individuals from Pakistani population, which is characterized by high rates of endogamy. The haplotype diversity and discrimination capacity were 1. Allelic frequencies ranged from 0.0060 to 0.5060, while gene diversity ranged from 0.6759 (DYS526a) to 0.9937 (DYF399S1). A total 319 different alleles were observed. Results of our study showed that RM Y-STRs provided substantially stronger discriminatory power in Pakistani populations.

Network challenges for cyber physical systems with tiny wireless devices: a case study on reliable pipeline condition monitoring.

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

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.

In Silico Analysis: Molecular Characterization and Evolutionary Study of CLCN Gene Family in Buffalo.

Chloride channels (ClCs) have received global interest due to their significant role in the regulation of ion homeostasis, fluid transport, and electrical excitability of tissues and organs in different mammals and contributing to various functions, such as neuronal signaling, muscle contraction, and regulating the electrolytes' balance in kidneys and other organs. In order to define the chloride voltage-gated channel (CLCN) gene family in buffalo, this study used in silico analyses to examine physicochemical properties, evolutionary patterns, and genome-wide identification. We identified eight CLCN genes in buffalo. The ProtParam tool analysis identified a number of important physicochemical properties of these proteins, including hydrophilicity, thermostability, in vitro instability, and basic nature. Based on their evolutionary relationships, a phylogenetic analysis divided the eight discovered genes into three subfamilies. Furthermore, a gene structure analysis, motif patterns, and conserved domains using TBtool demonstrated the significant conservation of this gene family among selected species over the course of evolution. A comparative amino acid analysis using ClustalW revealed similarities and differences between buffalo and cattle CLCN proteins. Three duplicated gene pairs were identified, all of which were segmental duplications except for CLCN4-CLCN5, which was a tandem duplication in buffalo. For each gene pair, the Ka/Ks test ratio findings showed that none of the ratios was more than one, indicating that these proteins were likely subject to positive selection. A synteny analysis confirmed a conserved pattern of genomic blocks between buffalo and cattle. Transcriptional control in cells relies on the binding of transcription factors to specific sites in the genome. The number of transcription factor binding sites (TFBSs) was higher in cattle compared to buffalo. Five main recombination breakpoints were identified at various places in the recombination analysis. The outcomes of our study provide new knowledge about the CLCN gene family in buffalo and open the door for further research on candidate genes in vertebrates through genome-wide studies.

Preliminary study on mitochondrial DNA analysis from different sports items.

Criminals often attempt to conceal blood-stained weapons used in violent crimes, making forensic evidence crucial in solving cases. This study explores the recovery and extraction of trace DNA from sports equipment, including cricket bats, table tennis racquets, and hockey sticks, which are frequently implicated in such incidents. Our research evaluates various double swab collection methods for retrieving trace DNA from these sports items, emphasizing those associated with blunt force trauma. We also compare presumptive and confirmatory tests to establish a direct correlation. This research consistently demonstrated robust DNA recovery, surpassing a 50 % threshold across all tests. Specifically, DNA recovery from buried samples reached an impressive 87 %, while washed samples still yielded a substantial 80 % efficiency. We conducted a comparative analysis between presumptive and confirmatory testing methods, establishing a direct correlation between the two. Variability in DNA recovery efficiency was observed and attributed to factors like the type of surface the items contacted, and ambient humidity levels. In addition to presenting robust DNA recovery rates, statistical analyses were employed to compare methods, establishing correlations and highlighting the influence of environmental factors on DNA recovery efficiency. These findings have significant implications for forensic investigations involving silent weapons crafted from sports equipment, emphasizing the need for standardized protocols and consideration of environmental factors in DNA analysis.

Meta-Genomic Analysis of Different Bacteria and Their Genomes Found in Raw Buffalo Milk Obtained in Various Farms Using Different Milking Methods.

Milking methods have significant impacts on the microbiological composition, which could affect the quality of raw buffalo milk. Hence, the current study was conducted on the impact of milking methods on microorganisms in buffalo tank raw milk from 15 farms in Guangxi, China. The farms were divided into two groups based on the milking method: mechanical milking (MM, n = 6) and hand milking (HM, n = 9). Somatic cell counts, bacterial cell counts and nutrients of the raw buffalo milk samples were analyzed. The comparison of raw buffalo milk samples was analyzed using metagenomic sequencing to detect any differences between the two groups. There was no significant difference in the basic nutritional compositions and somatic cell count of raw buffalo milk between the two milking methods. However, the HM samples had significantly higher bacterial counts and diversity compared to the MM samples. The results showed that Staphylococcus spp., Klebsiella spp., Streptococcus spp., and Pseudomonas spp. were the major microbes present in canned raw buffalo milk. However, the differences between the two milking methods were the relative abundance of core microorganisms and their potential mastitis-causing genera, including the content of antibiotic-resistance genes and virulence genes. Our study revealed that Staphylococcus spp. and Streptococcus spp. were significantly more abundant in the MM group, while Klebsiella spp. was more abundant in the HM group. Regardless of the milking method used, Pseudomonas spp. was identified as the primary genus contributing to antibiotic resistance and virulence genes in canned raw buffalo milk. These findings affirm that there are differences in the microbial and genomic levels in canned raw milk. To prove the functional roles of the discovered genes and how these genes affect milk quality, further research and experimental validation are necessary.

Association between chronic low back pain, anxiety and depression in patients at a tertiary care centre.

OBJECTIVE: To observe the prevalence of anxiety and depression in chronic low back pain population at a tertiary care centre. METHODS: The prospective cross-sectional study was conducted using convenience sampling at the Department of Neurosurgery, at Liaquat National Hospital, Karachi, Pakistan, from January to June 2010. The prevalence of anxiety and depression in chronic low back pain patients was studied according to specified age and gender groups using Hospital Anxiety and Depression Scale. RESULTS: Of the 140 patients in the study, 66 (47.14%) were females and 74 (52.85%) were males.The average age of the patients was 43.02+/-13.34 years. The average duration of symptoms was 4.29+/-3.3 years. Abnormal level of anxiety and depression were found in 77 (55%) and 68 (48.57%) patients respectively. Out of them 54 (38.5%) and 51 (36.4%) were borderline abnormal for anxiety and depression respectively, while 23 (16.4%) and 17 (12.1%) were abnormal for anxiety and depression respectively. Among the males, there were 20 (14.28%) and 23 (16.42%) patients with abnormal levels of the corresponding numbers among the females were 57 (40.71%) and 45 (32.14%). There was a significant association in anxiety (p<0.01) and depression (p<0.01) levels with respect to gender and no significant association with respect to age (p>0.05). CONCLUSION: Individuals with chronic low back pain were at high risk to experience anxiety and depression.This risk was higher for females.

Bioaccumulation Pattern and Health Risk Assessment of Heavy Metals in Cirrhinus mrigala at Panjnad Headworks, Bahawalpur, Pakistan.

Heavy metal accumulation in freshwater ecosystem has become one of the major aquatic environmental concerns for freshwater flora and fauna due to their higher stability and bioaccumulation as well as bio-magnification properties. Furthermore, passing through the food web, these heavy metals affect human populations ultimately. This study assessed the heavy metal accumulation in Cirrhinus mrigala in spring, autumn, and winter at different locations (I, II, and III) of Panjnad headwork. Furthermore, the human health risk assessment for the consumption of C. mrigala from the sampling locations was also carried out. Fish were collected from upper (I), middle (II), and lower (III) stream of Panjnad on a monthly basis. The current study evaluated the accumulation of Aluminum (Al), Arsenic (As), Barium (Ba), and Lead (Pb) in various fish organs (liver, kidney, gills, fins, skin, muscles and bones) and assessed their potential hazard to human health through health risk assessment indicators. The results demonstrated a significant difference (p < 0.05) in heavy metal accumulation in different fish organs, seasons, and locations. The accumulation of Al, As, Ba, and Pb were considerably higher in liver and kidney as compared to the other body organs and followed a trend of liver > kidney > gills > fins > skin > bones > muscle and the overall mean concentrations of metals in different body tissues of C. mrigala were in the order of Al > As > Ba > Pb. The results also concluded that C. mrigala caught from the Panjnad headwork is not safe for human consumption due to higher values of TTHQIng (3.76), THQIng for Ba (3.27) and CRIng for As (6.4742).

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.

Fabrication and in vitro evaluation of chitosan-gelatin based aceclofenac loaded scaffold.

Scaffold development is a nascent field in drug development. The scaffolds mimic the innate microenvironment of the body. The goal of this study was to formulate a biocompatible and biodegradable scaffold, loaded with an analgesic drug, aceclofenac (Ace). The bioscaffold is aimed to have optimum mechanical strength and rheology, with drug released in a sustained manner. It was prepared via chemical cross-linking method: a chitosan (CS) solution was prepared and loaded with Ace; gelatin (GEL) was added and the mixture was cross-linked to get a hydrogel. 20 formulations were prepared to optimize different parameters including the stirring speed, drug injection rate and crosslinker volume. The optimal formulation was selected based on the viscosity, drug solubility, homogeneity, porosity and swelling index. A very high porosity and swelling index were attained. In vitro release data showed sustained drug delivery, with effective release at physiological and slightly acidic pH. SEM analysis revealed a homogeneous microstructure with highly interconnected pores within an extended polymer matrix. FT-IR spectra confirmed the absence of polymer-drug interactions, XRD provided evidences for efficient drug entrapment within the scaffold. Rheological analysis corroborated the scaffold injectability. Mathematical models were applied to in-vitro data, and the best fit was attained with Korsmeyer-Peppas.

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.

Simvastatin-Loaded Lipid Emulsion Nanoparticles: Characterizations and Applications.

Simvastatin (SIM) is a diet drug to treat high lipid levels in the blood. It has the drawback of being metabolized in humans' gastrointestinal tract (GIT) when taken in an oral dosage form. To enhance the role of SIM in treating hyperlipidemias and bypassing its metabolism in GIT, a biodegradable nanocarrier as a SIM-loaded lipid emulsion nanoparticle via the solvent injection method was designed. Cholesterol acts as a lipid core, and Tween 80 was utilized to stabilize the core. The optimized nanoformulation was characterized for its particle diameter, zeta potential, surface morphology, entrapment efficiency, crystallinity, and molecular interaction. Furthermore, the transdermal hydrogel was characterized by physical appearance, rheology, pH, and spreadability. In vitro assays were executed to gauge the potential of LENPs and olive oil for transdermal delivery. The mean particle size and zeta potential of the optimized nanoparticles were 174 nm and -22.5 mV 0.127, respectively. Crystallinity studies and Fourier transform infrared analyses revealed no molecular interactions. Hydrogels showed a sustained release compared to SIM-loaded LENPs that can be proposed as a better delivery system for SIM. We encourage further investigations to explore the effect of reported formulations for transdermal delivery by in vivo experiments.

Optimize PLA/EVA Polymers Blend Compositional Coating for Next Generation Biodegradable Drug-Eluting Stents.

In this research work, polymer blends of poly-lactic acid (PLA)/ethylene vinyl acetate (EVA) were prepared as the drug carrier materials for a bi-layer drug-loaded coating film for coronary stents. Different optimum compositions of blends were prepared by using an intense mixer. Then, the blends were hot-pressed and later cold-pressed to prepare for films of different thickness. The changes in weight, surface analysis and biodegradability with increasing time were studied using Scanning electron microscopy (SEM), weight loss and biodegradability tests. The mechanical and thermal properties of drug-loaded films were studied through universal testing machine (UTM) and thermo-gravimetric analysis (TGA). The effects of PLA, EVA and drug contents on in-vitro drug contents were investigated through the Ultraviolet-Visible Spectroscopy (UV-VIS) chemical analysis technique. The results obtained clearly showed that the addition of PLA promoted the unleashing of the drug whereas the addition of EVA nearly did not have the same affect. The mechanical properties of these various films can be tuned by adjusting the contents of blend parts. The factors affecting the unleashing of the drug became a serious matter of concern in evaluating the performance of bio-resorbable drug eluting stents. As a result, today's chemical blends may be useful drug carrier materials for drug-loaded tube coatings capable delivering purgative drug in an incredibly tunable and regulated manner.

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.