Cystine-cored diphenylalanine appended peptide-based self-assembled fluorescent nanostructures direct redox-responsive drug delivery

Fabrication of stimuli-responsive superstructure capable of delivering chemotherapeutics directly to the cancer cell by sparing healthy cells is crucial. Herein, we developed redox-responsive hollow spherical assemblies through self-assembly of disulfide-linked cysteine-diphenylalanine (SN). These fluorescent hollow spheres display intrinsic green fluorescence, are proteolytically stable and biocompatible, and allow for real-time monitoring of their intracellular entry. The disulfide bond facilitates selective degradation in the presence of high glutathione (GSH) concentrations, prevalent in cancer cells. We achieved efficient encapsulation (68.72%) of the anticancer drug doxorubicin (Dox) and demonstrated GSH-dependent, redox-responsive drug release within cancerous cells. SN-Dox exhibited a 20-fold lower effective concentration (2.5 μM) for compromising breast cancer cell viability compared to non-malignant cells (50 μM). The ability of SN-Dox to initiate DNA damage signaling and trigger apoptosis was comparable to that of the unencapsulated drug. Our findings highlight the potential of SN for creating site-specific drug delivery vehicles for sustained therapeutic release.

IoT-enabled smart healthcare data and health monitoring based machine learning algorithms

A smart healthcare network can use sensors and the Internet of Things (IoT) to enhance patient care while decreasing healthcare expenditures. It has become more difficult for healthcare providers to keep track and analyze the massive amounts of data it generates. Health care data created by IoT devices and e-health systems must be handled more efficiently. A wide range of healthcare industries can benefit from machine learning (ML) algorithms in the digital world. However, each of these algorithms has to be taught to anticipate or solve a certain problem. IoT-enabled healthcare data and health monitoring-based machine learning algorithms (IoT-HDHM-MLA) have been proposed to solve the difficulties faced by healthcare providers. Sensors and IoT devices are vital for monitoring an individual’s health. The proposed IoT-HDHM-MLA aims to deliver healthcare services via remote monitoring with experts and machine learning algorithms. In this system, patients are monitored in real-time for various key characteristics using a collection of small wireless wearable nodes. The health care business benefits from systematic data collection and efficient data mining. Thus, the experimental findings demonstrate that IoT-HDHM-MLA enhances efficiency in patient health surveillance.

Fuzzy Machine Learning Model in Real-World Physical Domains; A State-of-the-Art Approach

Day after day, individuals all across the world come up with fresh ideas for improving the future. Several intriguing discoveries and ideas paved the way for a new age of electronics, telecommunications, business, and medicinal innovation. Using less resources, greater changes in these domains can be achieved. As improving efficiency and productivity allow exponential development in some areas of the global economy, artificial intelligence (AI) and machine learning (ML) is being adopted by a growing number of individuals, corporations, and governments. Since real-world scenarios influence imprecise and unpredictable situations, fuzzy systems have become an inescapable machine learning aspect. Thus, this research presents a qualitative analysis of the significance of fuzzy machine learning systems like fuzzy support vector machines (FSVM) in various physical domains. Based on this analysis, this research extends with the proposal of a fuzzy machine learning-based framework for two different physical domains: (1) intelligent transportation and (2) ecological risk handling. Thereby, this state-of-the-art approach presents fault detection using FSVM (FD-FSVM) model in the intelligent transportation domain. In ecological risk handling, this study proposes an improved FSVM for risk level classification (FSVM-RLC) approach, which uses the persistent organic pollutants data for training and validation. These two domains are chosen randomly to evaluate the classification performance of the fuzzy machine learning algorithms based on their mean absolute error, accuracy, precision, recall, and F1 score. Apart from this, the mean square error and mean absolute error are measured. Compared to existing machine learning models, the individual results of these two approaches show the highest performance. Furthermore, this fuzzy integrated machine learning technique kept consistency in both domains by giving 98.2% and 97.89% accuracy levels for FD-FSVM and FSVM-RLC, respectively.

Concurrent service access and management framework for user-centric future internet of things in smart cities

Future Internet of Things (FIoT) is a service concentric distributed architecture that is used by the smart city users for information sharing and access. The design of FIoT focuses in achieving reliable service and response to the growing user demands through different interoperability features. In this manuscript, concurrent service access and management framework is introduced to improve the swiftness in user concentric request processing. Based on the availability of the services and the density of the users, the concurrency in information access is provided to the users in a reliable manner. The framework incorporates convolution neural learning process in linear and differential manner for improving the access and service usage rates of the requesting users. The access sessions are differentiated for the accessible and offloaded requests to the available service providers based on the learning instances. The proposed framework is assessed using the metrics access rate, service usage rate, access delay, time lag, and failure ratio.

Automatic Human Emotion Classification in Web Document Using Fuzzy Inference System (FIS)

Textual information mining deals with various information extraction methods that can be evolved from the rapid growth of textual information through human machine interface for analyzing emotions which are taken by a facial expression. The problem of emotions in text is concerned with the fast development of web 2.0 documents that are assigned by users with emotion labels, namely: sadness, surprise, happiness, empathy, anger, warmness, boredom, and amusement. Such emotions can give a new characteristic for document categorization. Textual information mining deals with various information extraction methods that can evolved from the rapid growth of textual information through a human machine interface for analyzing emotions, which are taken by a facial expression. The problem of emotions from text is concerned with the fast development of web 2.0 documents that are assigned by users with emotion labels. Such emotions can give a new characteristic for document categorization.

DIC-DOC-K-means: Dissimilarity-based Initial Centroid selection for DOCument clustering using K-means for improving the effectiveness of text document clustering

In this article, a new initial centroid selection for a K-means document clustering algorithm, namely, Dissimilarity-based Initial Centroid selection for DOCument clustering using K-means (DIC-DOC- K-means), to improve the performance of text document clustering is proposed. The first centroid is the document having the minimum standard deviation of its term frequency. Each of the other subsequent centroids is selected based on the dissimilarities of the previously selected centroids. For comparing the performance of the proposed DIC-DOC- K-means algorithm, the results of the K-means, K-means++ and weighted average of terms-based initial centroid selection +  K-means (Weight_Avg_Initials +  K-means) clustering algorithms are considered. The results show that the proposed DIC-DOC- K-means algorithm performs significantly better than the K-means, K-means++ and Weight_Avg_Initials+  K-means clustering algorithms for Reuters-21578 and WebKB with respect to purity, entropy and F-measure for most of the cluster sizes. The cluster sizes used for Reuters-8 are 8, 16, 24 and 32 and those for WebKB are 4, 8, 12 and 16. The results of the proposed DIC-DOC- K-means give a better performance for the number of clusters that are equal to the number of classes in the data set.

Cloud based framework for diagnosis of diabetes mellitus using K-means clustering

Diabetes mellitus is a serious health problem affecting the entire population all over the world for many decades. It is a group of metabolic disorder characterized by chronic disease which occurs due to high blood sugar, unhealthy foods, lack of physical activity and also hereditary. The sorts of diabetes mellitus are type1, type2 and gestational diabetes. The type1 appears during childhood and type2 diabetes develop at any age, mostly affects older than 40. The gestational diabetes occurs for pregnant women. According to the statistical report of WHO 79% of deaths occurred in people under the age of 60, due to diabetes. With a specific end goal to deal with the vast volume, speed, assortment, veracity and estimation of information a scalable environment is needed. Cloud computing is an interesting computing model suitable for accommodating huge volume of dynamic data. To overcome the data handling problems this work focused on Hadoop framework along with clustering technique. This work also predicts the occurrence of diabetes under various circumstances which is more useful for the human. This paper also compares the efficiency of two different clustering techniques suitable for the environment. The predicted result is used to diagnose which age group and gender are mostly affected by diabetes. Further some of the attributes such as hyper tension and work nature are also taken into consideration for analysis.

Maintaining Security and Privacy in Health Care System Using Learning Based Deep-Q-Networks

In the recent past, Internet of Things (IoT) plays a significant role in different applications such as health care, industrial sector, defense and research etc.… It provides effective framework in maintaining the security, privacy and reliability of the information in internet environment. Among various applications as mentioned health care place a major role, because security, privacy and reliability of the medical information is maintained in an effective way. Even though, IoT provides the effective protocols for maintaining the information, several intermediate attacks and intruders trying to access the health information which in turn reduce the privacy, security and reliability of the entire health care system in internet environment. As a result and to solve the issues, in this research Learning based Deep-Q-Networks has been introduced for reducing the malware attacks while managing the health information. This method examines the medical information in different layers according to the Q-learning concept which helps to minimize the intermediate attacks with less complexity. The efficiency of the system has been evaluated with the help of experimental results and discussions.

Synthesis of Evolvulus alsinoides derived gold nanoparticles for medical applications

Plant mediated synthesis of metallic nanoparticles is an increasing commercial demand due to the wide applicability in various areas such as electronics, catalysis, chemistry, energy, cosmetics and medicine. This is greatly due to their special features, which include unusual optical and electronic properties, high stability and biological compatibility, controllable morphology and size dispersion, and easy surface functionalization. In the present investigation, synthesis of gold nanoparticle is done by using leaf extracts of Evolvulus alsinoides. Gold nanoparticles (AuNPs) were characterized by using UV visible absorption spectra. Their morphology, elemental composition and crystalline phase were determined by scanning electron microscopy, energy dispersive X-ray spectroscopy and FT-IR analysis was used to confirm the presence of gold nanoparticles in the extracts. The plant derived gold nanoparticles were also showing more inhibition activity in both bacterial and fungus strains. In bacteria, gram negative strains are highly affected by the test samples than gram positive. In fungal strains, the highest effect was noticed in Trichophyton rubrum while less effect was observed in Candida albicans.

Tumor antigen ROR1 targeted drug delivery mediated selective leukemic but not normal B-cell cytotoxicity in chronic lymphocytic leukemia

Selective cytotoxicity to cancer cells without compromising their normal counterparts pose a huge challenge for traditional drug design. Here we developed a tumor antigen targeted delivery of immunonanoparticle carrying a novel non-immunosuppressive FTY720 derivative OSU-2S with potent cytotoxicity against leukemic B cells. OSU-2S induces activation of protein phosphatase 2A, phosphorylation and nuclear translocation of SHP1^S591 and deregulation of multiple cellular processes in chronic lymphocytic leukemia (CLL) resulting in potent cytotoxicity. To preclude OSU-2S mediated effects on these ubiquitous phosphatases in unintended cells and avoid potential adverse effects we developed a OSU-2S targeted delivery immunonanoparticles (2A2-OSU-2S-ILP), that mediated selective cytotoxicity of CLL but not normal B cells through targeting receptor tyrosine kinase ROR1 expressed in leukemic but not normal B cells. Developing a novel spontaneous CLL mouse model expressing human ROR1 (hROR1) in all leukemic B cells, we demonstrate the therapeutic benefit of enhanced survival with 2A2-OSU-2S-ILP in-vivo. The newly developed non-immunosuppressive OSU-2S, its delivery using human CLL directed immunonanoparticles and the novel transgenic mouse model of CLL that expresses hROR1 exclusively in leukemic B cell surface are highly innovative and can be applied to CLL and other ROR1+ malignancies including mantle cell lymphoma and acute lymphoblastic leukemia.

Our experience of application of Autologous Bone Marrow Stem Cells in critical limb ischemia in six diabetic patients - A five-year follow-up

BACKGROUND

Numerous Clinical studies have reported the safety and efficacy of injection of one Marrow and Peripheral Blood Mononuclear cells in patients with lower limb ischemia. Earlier we have reported the six months follow-up of successful application of autologous bone marrow mononuclear cells in patients with Fontaine Stage IV critical limb ischemia due to diabetes. As a continuation of the previous study, herein we report the long term results of the six patients after a follow-up for five years.

MATERIALS & METHODS

Six Diabetic patients with Fontaine Stage IV critical limb ischemia with ulcers were given intra-lesional injections of their autologous bone marrow mononuclear cells (BMMNC), isolated following the cGMP protocols. The patients have been followed up at regular intervals for five years after the treatment with all relevant clinical investigations.

RESULTS

Six months follow-up results revealed that all the patients showed improvements with appearance of healthy granulation tissue and uniform revascularization. Complete healing was reported at a mean duration of nine months in five patients and one patient died due to a complication of renal failure, peritoneal dialysis and cardiac failure, which were unrelated to the BMMNC injection. Five year continuous follow-up revealed that the healed tissue with or without skin grafting remained healthy in all the five patients and two of the patients are able to walk without support with a pain free walking distance of greater than 100m.There were no adverse effects in any of the patients..

CONCLUSION

Autologous bone marrow stem cell therapy has been found to be salvaging the affected limb in patients with Fontaine Stage IV Critical Limb ischemia patients where revascularization was not feasible. Hence with our experience of six patients we recommend that the same should be considered in patients of similar clinical parameters before considering an amputation.

Successful transplantation of in vitro expanded human corneal endothelial precursors to corneal endothelial surface using a nanocomposite sheets

BACKGROUND

Though the transplantation of in vitro expanded human corneal endothelial precursors in animal models of endothelial damage by injecting into the anterior chamber has been reported, the practical difficulties of accomplishing such procedure in human patients have been a hurdle to clinical translation. Here we report the successful transplantation of in vitro expanded human corneal precursor cells to an animal eye using a transparent Nano-composite sheet and their engraftment.

MATERIALS & METHODS

Human Corneal endothelial cells (HCEC) were isolated from human cadaver eyes with informed consent and expanded in the lab using a sphere forming assay in a novel Thermoreversible Gelation Polymer (TGP) for 26 days. HCEC obtained by sphere forming assay were seeded in a novel Nano-composite sheet, which was made of PNIPA-NC gels by in-situ, free-radical polymerization of NIPA monomer in the presence of exfoliated clay (synthetic hectorite "Laponite XLG") uniformly dispersed in aqueous media. After a further seven days in vitro culture of HCEC in the Nano-composite sheet, cells were harvested and transplanted on cadaver-bovine eyes (n=3). The cells were injected between the corneal endothelial layer and the Nano-composite sheet that had been placed prior to the injection in close proximity to the endothelial layer. After three hours, the transplanted Nano-composite sheets were removed from the bovine eyes and subjected to microscopic examination. The corneas were subjected to Histo-pathological studies along with controls.

RESULTS

HCEC formed sphere like colonies in TGP which expressed relevant markers as confirmed by RT-PCR. Microscopic studies of the Nanosheets and histopathological studies of the cornea of the Bull's eye revealed that the HCEC got engrafted to the corneal endothelial layer of the bovine eyes with no remnant cells in the Nanosheet.

CONCLUSION

Transplantation of in vitro expanded donor human corneal endothelial cells using a transparent Nano-composite sheet was feasible in bovine eyes and the HCEC an engrafted within three hours of transplantation. Pilot human studies could be planned for utilization of this material and strategy.

Evaluation of Anti-inflammatory activity and toxicity studies of Chloroxylon sweitenia in Rats

The extract of Chloroxylon sweitenia (Family: Rutaceae) leaves were investigated for its anti-inflammatory activity at the different doses in the standard animal models. The experimental paradigms used were carrageenan induced rat paw oedema (acute), and cotton pellet induced granuloma (chronic) models in rats for anti-inflammatory activity. In rats the toxicity was also performed for the extract by oral administration. The chloroform extract of Chloroxylon sweitenia (CECS) exhibited significant anti-inflammatory effect at the dose 50, 100 and 200 mg/kg. Maximum inhibition (55.32 %) was noted at the dose of 200 mg/kg after 3 h of drug treatment in carrageenan induced paw oedema, whereas the Diclofenac (standard drug) produced 61.33 % of inhibition. In the chronic model (cotton pellet induced granuloma) the CECS (200 mg/kg) and standard drug showed decreased formation of granuloma tissue by 52.32 % and 56.32 % (p < 0.001) respectively. The CECS further evaluated for their toxicity effect at the doses of 100 mg/kg administered for 14 days to orally in rats. At the end of experiments the blood, liver function and kidney metabolism was observed. The effect of CECS was assessed by the change in the body weight, lipid peroxidation and glutathione content (GSH) activities were measured from hepatic tissues. The hematological profile and different biochemical parameters such as SGOT, SGPT, and ALP were also estimated. Thus, the present study revealed that the chloroform extract of Chloroxylon sweitenia exhibited significant anti-inflammatory activity in the tested models Toxicity study indicates that the extract is non-toxic at the tested doses.