Evaluating the impact of mesenchymal stem cell therapy on visual acuity and retinal nerve fiber layer thickness in optic neuropathy patients: a comprehensive systematic review and meta-analysis.

BACKGROUND: Stem cell therapy has emerged as a potential therapeutic avenue for optic neuropathy patients. To assess its safety and efficacy, we conducted a systematic review and meta-analysis, focusing on the latest evidence pertaining to the improvement of visual acuity (VA) through stem cell therapy. METHODS: We analyzed Each database from its inception until June 2024. PubMed, Scopus, and Google Scholar were systematically searched to identify the included studies. Data were extracted regarding the year of publication, the name of the first author, sample size, VA (Log Mar), and Retinal Nerve Fiber Layer (RNFL) thickness. PRISMA protocol was used as a guide to perform this meta-analysis. STATA 16 was used for statistical analysis. RESULTS: A total of 66 eyes were examined in seven papers. Based on the meta-analysis, the mean VA (Log MAR) of patients with optic neuropathy improved from 0.90 to 0.65 following stem cell therapy intervention (p-value = 0.001). The thickness of the RNFLs did not demonstrate a significant change (p-value was 0.174). CONCLUSION: According to this systematic review and meta-analysis, stem cell therapy may improve the visual acuity of patients with optic neuropathy. Aside from the traditional therapy that can be provided to patients with optic neuropathy, stem cell therapy may also be beneficial.

Adaptive Context Caching for IoT-Based Applications: A Reinforcement Learning Approach.

Making internet-of-things (IoT)-based applications context-aware demands large amounts of raw data to be collected, interpreted, stored, and reused or repurposed if needed from many domains and applications. Context is transient but interpreted data can be distinguished from IoT data in many aspects. Managing context in cache is a novel area of research that has been given very little attention. Performance metric-driven adaptive context caching (ACOCA) can have a profound impact on the performance and cost efficiency of context-management platforms (CMPs) when responding to context queries in realtime. Our paper proposes an ACOCA mechanism to maximize both the cost and performance efficiency of a CMP in near realtime. Our novel mechanism encompasses the entire context-management life cycle. This, in turn, distinctively addresses the problems of efficiently selecting context for caching and managing the additional costs of context management in the cache. We demonstrate that our mechanism results in long-term efficiencies for the CMP that have not been observed in any previous study. The mechanism employs a novel, scalable, and selective context-caching agent implemented using the twin delayed deep deterministic policy gradient method. It further incorporates an adaptive context-refresh switching policy, a time-aware eviction policy, and a latent caching decision management policy. We point out in our findings that the additional complexity of adaptation introduced to the CMP through ACOCA is significantly justified, considering the cost and performance gains achieved. Our algorithm is evaluated using a real-world inspired heterogeneous context-query load and a data set based on parking-related traffic in Melbourne, Australia. This paper presents and benchmarks the proposed scheme against traditional and context-aware caching policies. We demonstrate that ACOCA outperforms the benchmarks in both cost and performance efficiency, i.e., up to 68.6%, 84.7%, and 67% more cost efficient compared to traditional data caching policies to cache context, redirector mode, and context-aware adaptive data caching under real-world-like circumstances.

Automated Adjustment of PPE Masks Using IoT Sensor Fusion.

The COVID-19 pandemic has led to a dramatic increase in the use of PPE by the general public as well as health professionals. Scientists and health organizations have developed measures to protect people and minimize the catastrophic outcomes of COVID, including social distancing, frequent and periodic sanitizing, vaccinations, protective coverings, and face masks. During this time, the usage of protective face masks has increased dramatically. A mask only provides full safety to the user if it is a proper fit on their face. The aim of this paper is to automatically analyze and improve the fit of a face mask using IoT sensors. This paper describes the creation of a 3D-printed smart face mask that uses sensors to determine the current mask fit and then automatically tightens mask straps. This is evaluated using adjustment response time and the quality of fit achieved using the automatic adjustment approach with a range of sensor types.

Refresh Rate-Based Caching and Prefetching Strategies for Internet of Things Middleware.

One of the research directions in Internet of Things (IoT) is the field of Context Management Platforms (CMPs) which is a specific type of IoT middleware. CMPs provide horizontal connectivity between vertically oriented IoT silos resulting in a noticeable difference in how IoT data streams are processed. As these context data exchanges can be monetised, there is a need to model and predict the context metrics and operational costs of this exchange to provide relevant and timely context in a large-scale IoT ecosystem. In this paper, we argue that caching all transient context information to satisfy this necessity requires large amounts of computational and network resources, resulting in tremendous operational costs. Using Service Level Agreements (SLAs) between the context providers, CMP, and context consumers, where the level of service imperfection is quantified and linked to the associated costs, we show that it is possible to find efficient caching and prefetching strategies to minimize the context management cost. So, this paper proposes a novel method to find the optimal rate of IoT data prefetching and caching. We show the main context caching strategies and the proposed mathematical models, then discuss how a correctly chosen proactive caching strategy and configurations can help to maximise the profit of CMP operation when multiple SLAs are defined. Our model is accurate up to 0.0016 in Root Mean Square Percentage Error against our simulation results when estimating the profits to the system. We also show our model is valid using the t-test value tending to 0 for all the experimental scenarios.

Adoptive NK-cell transfer as a potential treatment paradigm for Wilms tumor: A preclinical study.

BACKGROUND: Natural killer (NK) cell therapy has been shown to be effective in the treatment of some cancers. However, the effects of this adoptive immunotherapy have not been investigated for Wilms tumor (WT). In this study, the effects of adoptive NK-cell transfer on a patient-derived xenograft (PDX) model of anaplastic WT were evaluated, and the impacts of cell source and ex vivo activation strategy on the therapeutic efficacy of NK-cell product were appraised. METHODS: NK cells were isolated from human peripheral blood mononuclear cells (NKPB ) and human cord blood (NKCB ), and were expanded and activated using a cytokine cocktail. Another group of NK cells (NKET ) was produced through activation with the exosomes extracted from previously challenged NKPB cells with WT. PDX-bearing mice were treated with clinically relevant doses of NKPB , NKCB , NKET , standard chemotherapy, and placebo (phosphate-buffered saline). RESULTS: PDX models treated with NKCB showed a better survival rate, though the difference among the study groups was not significant. Compared with the placebo control group, NKCB significantly improved the histopathologic response, NKPB significantly inhibited the proliferation of neoplastic cells, and NKET led to a significant decrease in the metastasis score (all p-values <.05). Standard chemotherapy provided the greatest tumor growth inhibition and the lowest mitotic count, though it did not show any significant advantage over NK-cell therapies in any of the outcome parameters in two-by-two comparisons. CONCLUSIONS: This study spotlights the efficacy of adoptive NK-cell transfer as a potential treatment candidate for high-risk WT.

An open-label phase 1 clinical trial of the allogeneic side population adipose-derived mesenchymal stem cells in SMA type 1 patients.

INTRODUCTION: Spinal muscular atrophy (SMA), an autosomal recessive neurodegenerative disorder of alpha motor neurons of spinal cord associated with progressive muscle weakness and hypotonia, is the most common genetic cause of infant mortality. Although there is few promising treatment for SMA, but the field of translational research is active in it, and stem cell-based therapy clinical trials or case studies are ongoing. Combination of different therapeutic approaches for noncurative treatments may increase their effectiveness and compliance of patients. We present a phase 1 clinical trial in patients with SMA1 who received side population adipose-derived mesenchymal stem cells (SPADMSCs). METHODS: The intervention group received three intrathecal administrations of escalating doses of SPADMSCs and followed until 24 months or the survival time. The safety analysis was assessed by controlling the side effects and efficacy evaluations performed by the Hammersmith Infant Neurological Examination (HINE), Ballard score, and electrodiagnostic (EDX) evaluation. These evaluations were performed before intervention and at the end of the follow-up. RESULTS: The treatment was safe and well tolerated, without any adverse event related to the stem cell administration. One of the patients in the intervention group was alive after 24 months of study follow-up. He is a non-sitter 62-month-old boy with appropriate weight gain and need for noninvasive ventilation (NIV) for about 8 h per day. Clinical scores, need for supportive ventilation, and number of hospitalizations were not meaningful parameters in the response of patients in the intervention and control groups. All five patients in the intervention group showed significant improvement in the motor amplitude response of the tibial nerve (0.56mV; p: 0.029). CONCLUSION: This study showed that SPADMSCs therapy is tolerable and safe with promising efficacy in SMA I. Probably same as other treatment strategies, early intervention will increase its efficacy and prepare time for more injections. We suggest EDX evaluation for the follow-up of treatment efficacy.

Integration of IoT Sensors to Determine Life Expectancy of Face Masks.

Personal protective equipment (PPE) is widely used around the world to protect against environmental hazards. With the emergence of the COVID-19 virus, the use of PPE domestically has increased dramatically. People use preventive and protective mechanisms now more than ever, leading to the important question of how protective is the PPE that is being used. Face masks are highly recommended or mandatory during the time of the COVID-19 pandemic due to their protective features against aerosol droplets. However, an issue faced by many users of face masks is that they are entirely manual, with users having to decide for themselves whether their mask is still protective or if they should replace their mask. Due to the difficulty in determining this, people tend to overuse masks beyond their optimal usage. The research presented in this paper is an investigation of the viability of integrating IoT sensors into masks that are capable of collecting data to determine its usage. This paper demonstrates the usage of humidity and temperature sensors for the purpose of determining a mask's usage status based on changes in these variables when a mask is put on and taken off. An evaluation was made on the usage of the two sensors, with the conclusion that a humidity sensor provides more accurate results. From this, we present a framework that takes into consideration the factors that affect a mask's performance, such as time, humidity and temperature, to calculate the life expectancy of a mask.

Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms.

With the increasing growth of IoT applications in various sectors (e.g., manufacturing, healthcare, etc.), we are witnessing a rising demand of IoT middleware platform that host such IoT applications. Hence, there arises a need for new methods to assess the performance of IoT middleware platforms hosting IoT applications. While there are well established methods for performance analysis and testing of databases, and some for the Big data domain, such methods are still lacking support for IoT due to the complexity, heterogeneity of IoT application and their data. To overcome these limitations, in this paper, we present a novel situation-aware IoT data generation framework, namely, SA-IoTDG. Given a majority of IoT applications are event or situation driven, we leverage a situation-based approach in SA-IoTDG for generating situation-specific data relevant to the requirements of the IoT applications. SA-IoTDG includes a situation description system, a SySML model to capture IoT application requirements and a novel Markov chain-based approach that supports transition of IoT data generation based on the corresponding situations. The proposed framework will be beneficial for both researchers and IoT application developers to generate IoT data for their application and enable them to perform initial testing before the actual deployment. We demonstrate the proposed framework using a real-world example from IoT traffic monitoring. We conduct experimental evaluations to validate the ability of SA-IoTDG to generate IoT data similar to real-world data as well as enable conducting performance evaluations of IoT applications deployed on different IoT middleware platforms using the generated data. Experimental results present some promising outcomes that validate the efficacy of SA-IoTDG. Learning and lessons learnt from the results of experiments conclude the paper.

Requirements, Limitations and Recommendations for Enabling End-to-End Quality of Context-Awareness in IoT Middleware.

Satisfying a context consumer's quality of context (QoC) requirements is important to context management platforms (CMPs) in order to have credibility. QoC indicates the contextual information's quality metrics (e.g., accuracy, timeliness, completeness). The outcomes of these metrics depend on the functional and quality characteristics associated with all actors (context consumers (or) context-aware applications, CMPs, and context providers (or) IoT-data providers) in context-aware IoT environments. This survey identifies and studies such characteristics and highlights the limitations in actors' current functionalities and QoC modelling approaches to obtain adequate QoC and improve context consumers' quality of experience (QoE). We propose a novel concept system based on our critical analysis; this system addresses the functional limitations in existing QoC modelling approaches. Moreover, we highlight those QoC metrics affected by quality of service (QoS) metrics in CMPs. These recommendations provide CMP developers with a reference system they could incorporate, functionalities and QoS metrics to maintain in order to deliver an adequate QoC.

Context Definition and Query Language: Conceptual Specification, Implementation, and Evaluation.

As IoT grows at a staggering pace, the need for contextual intelligence is a fundamental and critical factor for IoT intelligence, efficiency, effectiveness, performance, and sustainability. As the standardisation efforts for IoT are fast progressing, efforts in standardising context management platforms led by the European Telecommunications Standards Institute (ETSI) are gaining more attention from both academic and industrial research organizations. These standardisation endeavours will enable intelligent interactions between 'things', where things could be devices, software components, web-services, or sensing/actuating systems. Therefore, having a generic platform to describe and query context is crucial for the future of IoT applications. In this paper, we propose Context Definition and Query Language (CDQL), an advanced approach that enables things to exchange, reuse and share context between each other. CDQL consists of two main parts, namely: context definition model, which is designed to describe situations and high-level context; and Context Query Language (CQL), which is a powerful and flexible query language to express contextual information requirements without considering details of the underlying data structures. An important feature of the proposed query language is its ability to query entities in IoT environments based on their situation in a fully dynamic manner where users can define situations and context entities as part of the query. We exemplify the usage of CDQL on three different smart city use cases to highlight how CDQL can be utilised to deliver contextual information to IoT applications. Performance evaluation has demonstrated scalability and efficiency of CDQL in handling a fairly large number of concurrent context queries.

Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications.

As the Internet of Things (IoT) is evolving at a fast pace, the need for contextual intelligence has become more crucial for delivering IoT intelligence, efficiency, effectiveness, performance, and sustainability. Contextual intelligence enables interactions between IoT devices such as sensors/actuators, smartphones and connected vehicles, to name but a few. Context management platforms (CMP) are emerging as a promising solution to deliver contextual intelligence for IoT. However, the development of a generic solution that allows IoT devices and services to publish, consume, monitor, and share context is still in its infancy. In this paper, we propose, validate and explain the details of a novel mechanism called Context Query Engine (CQE), which is an integral part of a pioneering CMP called Context-as-a-Service (CoaaS). CQE is responsible for efficient execution of context queries in near real-time. We present the architecture of CQE and illuminate its workflows. We also conduct extensive experimental performance and scalability evaluation of the proposed CQE. Results of experimental evaluation convincingly demonstrate that CoaaS outperforms its competitors in executing complex context queries. Moreover, the advanced functionality of the embedded query language makes CoaaS a decent candidate for real-life deployments.

Smart textile plasmonic fiber dew sensors.

We propose a novel Surface Plasmon Resonance (SPR)-based sensor that detects dew formation in optical fiber-based smart textiles. The proposed SPR sensor facilitates the observation of two phenomena: condensation of moisture and evaporation of water molecules in air. This sensor detects dew formation in less than 0.25 s, and determines dew point temperature with an accuracy of 4%. It can be used to monitor water layer depth changes during dew formation and evaporation in the range of a plasmon depth probe, i.e., 250 nm, with a resolution of 7 nm. Further, it facilitates estimation of the relative humidity of a medium over a dynamic range of 30% to 70% by measuring the evaporation time via the plasmon depth probe.

Reprogramming of endometrial adult stromal cells in the presence of a ROCK inhibitor, thiazovivin, could obtain more efficient iPSCs.

Today, there is a need for a platform to efficiently generate and maintain a feeder free culture of pluripotent stem cells by small molecules or pharmacological agents. Induced pluripotent stem cell (iPSC) is considered a promising resource for restorative cell therapy in clinical areas. While fully reprogrammed iPSCs are similar to embryonic stem cells, iPSCs could be derived from the patient's own cells (autologous), which avoids the immune rejection activities. Recent advances have demonstrated that iPSCs could be generated from human fibroblasts using only four transcription factors: OCT4, SOX2, CMYC, and KLF4. However, the limitations of reprogramming technologies include low efficiency, slow kinetics, transgene integration and residual expression. Surprisingly, adult stem cells from human endometrium (endometrial stem cells; EnSCs) express OCT4 and KLF4 pluripotency factors. On the other hand, small molecule inhibitors of specific signaling pathways such as thiazovivin have been used in various aspects of iPSC generation and maintenance. Thiazovivin is a selective small molecule that directly targets Rho-associated kinase (ROCK) and increases expression of pluripotency factors. The process using thiazovivin could be easier, faster and more cost effective than transgene integration into somatic cells. So reprogramming of OCT4 and KLF4 expressing EnSCs by a ROCK inhibitor, thiazovivin, could result in producing more efficient iPSCs compared with fibroblasts or conventional somatic cells without integration any transgene and retroviral vector.

Sertraline increases the survival of retinoic acid induced neuronal cells but not glial cells from human mesenchymal stem cells.

An increase in the number of viable in vitro differentiated neuronal cells is important for their use in clinics. A proportion of differentiated cells lose their viability before being used, and therefore we decided to use a pharmacological agent, sertraline, to increase neural cell differentiation and their survival. Purified endometrial stem cells (EnSCs) were examined for neuronal and glial cell specific markers after retinoic acid (RA) and sertraline treatment via RT-PCR, immunocytochemistry and Western blot analysis. The survival of differentiated cells was measured by MTT assay and the frequency of apoptosis, demonstrated by caspase-3-like activity. EnSCs were differentiated into neuronal cells after RA induction. Sertraline increased neuronal cell differentiation by 1.2-fold and their survival by 1.4-fold, and decreased from glial cell differentiation significantly. The findings indicate that sertraline could be used to improve the in vitro differentiation process of stem cells into neuronal cells, and may be involved in regenerative pharmacology in future.

Phase I study of safety and efficacy of allogeneic natural killer cell therapy in relapsed/refractory neuroblastomas post autologous hematopoietic stem cell transplantation.

Despite low incidence, neuroblastoma, an immunologically cold tumor, is the most common extracranial solid neoplasm in pediatrics. In relapsed/refractory cases, the benefits of autologous hematopoietic stem cell transplantation (auto-HSCT) and other therapies are limited. Natural killer (NK) cells apply cytotoxicity against tumor cells independently of antigen-presenting cells and the adaptive immune system. The primary endpoint of this trial was to assess the safety of the injection of allogenic, ex vivo-expanded and primed NK cells in relapsed/refractory neuroblastoma patients after auto-HSCT. The secondary endpoint included the efficacy of this intervention in controlling tumors. NK cells were isolated and primed ex vivo (by adding interleukin [IL]-2, IL-15, and IL-21) in a GMP-compliant CliniMACS system and administered to four patients with relapsed/refractory MYCN-positive neuroblastoma. NK cell injections (1 and 5 × 107 cells/kg in the first and second injections, respectively) were safe, and no acute or sub-acute adverse events were observed. During the follow-up period, one complete response (CR) and one partial response (PR) were observed, while two cases exhibited progressive disease (PD). In follow-up evaluations, two died due to disease progression, including the case with a PR. The patient with CR had regular growth at the 31-month follow-up, and another patient with PD is still alive and receiving chemotherapies 20 months after therapy. This therapy is an appealing and feasible approach for managing refractory neuroblastomas post-HSCT. Further studies are needed to explore its efficacy with higher doses and more frequent administrations for high-risk neuroblastomas and other immunologically cold tumors.Trial registration number: irct.behdasht.gov.ir (Iranian Registry of Clinical Trials, No. IRCT20201202049568N1).

Endometrial stem cell differentiation into smooth muscle cell: a novel approach for bladder tissue engineering in women.

OBJECTIVE: To investigate manufacturing smooth muscle cells (SMCs) for regenerative bladder reconstruction from differentiation of endometrial stem cells (EnSCs), as the recent discovery of EnSCs from the lining of women's uteri, opens up the possibility of using these cells for tissue engineering applications, such as building up natural tissue to repair prolapsed pelvic floors as well as building urinary bladder wall. MATERIALS AND METHODS: Human EnSCs that were positive for cluster of differentiation 146 (CD146), CD105 and CD90 were isolated and cultured in Dulbecco's modified Eagle/F12 medium supplemented with myogenic growth factors. The myogenic factors included: transforming growth factor ß, platelet-derived growth factor, hepatocyte growth factor and vascular endothelial growth factor. Differentiated SMCs on bioabsorbable polyethylene-glycol and collagen hydrogels were checked for SMC markers by real-time reverse-transcriptase polymerase chain reaction (RT-PCR), western blot (WB) and immunocytochemistry (ICC) analyses. RESULTS: Histology confirmed the growth of SMCs in the hydrogel matrices. The myogenic growth factors decreased the proliferation rate of EnSCs, but they differentiated the human EnSCs into SMCs more efficiently on hydrogel matrices and expressed specific SMC markers including α-smooth muscle actin, desmin, vinculin and calponin in RT-PCR, WB and ICC experiments. The survival rate of cultures on the hydrogel-coated matrices was significantly higher than uncoated cultures. CONCLUSIONS: Human EnSCs were successfully differentiated into SMCs, using hydrogels as scaffold. EnSCs may be used for autologous bladder wall regeneration without any immunological complications in women. Currently work is in progress using bioabsorbable nanocomposite materials as EnSC scaffolds for developing urinary bladder wall tissue.

5α-reductase 1 regulates spinal cord testosterone after morphine administration.

The enzyme 5alpha-reductase 1 (5α-R(1)) that converts testosterone (T) to dihydrotestosterone (DHT) is present in many mammalian tissues including the spinal cord. It is established that morphine administration decreases spinal cord T levels, but the mechanism is still undetermined. Here, we investigated the link between T and the enzyme 5α-R(1) in the spinal cord after morphine administration. For spinal cord steroid extraction, all the animals were killed 30 min, 2 h (acute) and 14 days (chronic) after first drug injection by decapitation. The whole spinal cord was removed and kept frozen at -20°C until T and DHT extraction. The effects of acute and chronic morphine administration on 5α-R(1) expression in the adult male rat spinal cord were evaluated using RT-PCR. Spinal cord T and DHT levels were measured using radioimmunoassay before and after the morphine exposure. Morphine significantly reduced the T concentration after acute and chronic exposure in the spinal cord. In contrast, the 5α-R(1) expression and of course DHT levels increased the following chronic morphine administration. One important reason for the decreasing effect of morphine exposure on the spinal cord T level is due to an increase in the 5α-R(1) levels. We suggest that morphine plays a regulatory role in metabolism of neurosteroids, especially T in the spinal cord via 5α-R(1).

Effectiveness of plant-mediated synthesis of hydroxyapatite nano-particles impregnated in Pistachio oleogum resin on mineral contents of human teeth. An in-situ single-blind controlled study.

OBJECTIVES: This study aimed to synthesize and characterize an environmentally friendly nanohydroxyapatite (n-HA) and evaluate its impact on enamel mineral content when incorporated into a Pistachio oleo gum resin (Saqqez) bio-chewing gum for in-situ models. We compared the effects of this green nano-hydroxyapatite (G n-HA) with those of a commercially available synthetic nano-hydroxyapatite (S n-HA). METHODS: Various analytical techniques were employed including XRD, FESEM, FT-IR, EDX/SEM and TGA/DTA to characterize the crystallinity, size and composition of the G n-HA powder. Three chewing gum groups were formulated: (1) Saqqez gum containing 10% wt G n-HA, (2) Saqqez gum containing 10% wt S n-HA, and (3) pure Saqqez gum. In order to evaluate the impact of these chewing gums on enamel, intraoral appliances were fabricated, each containing six enamel specimens. Participants were instructed to chew the gums while wearing these appliances. The calcium (Ca+2) and phosphorus (P) levels in enamel specimens, both with and without exposure to an acid challenge, were quantified using EDX/SEM. FE-SEM was employed to capture the microstructure of the enamel surface. In terms of the statistical analysis, one-way ANOVA and Tukey's post hoc tests were utilized to compare the data, where the significance level (α) was set at 0.05. RESULTS: The characterization tests confirmed the successful synthesis of G n-HA. Furthermore, EDX/SEM analysis of the enamel specimens from the intraoral appliance revealed significant variations in calcium (Ca+2) content among the enamel specimens (P = 0.000). The S n-HA group, in particular, exhibited the highest Ca+2 content, while the pure Saqqez group displayed the lowest. Nonetheless, there was no statistically significant differences in phosphorus (P) content observed among the three groups (P = 0.27). CONCLUSIONS: Saqqez gum can be considered a wholesome natural chewing gum that serves, as a carrier for delivering remineralization agents to the tooth surfaces. This was evident in the groups containing n-HA, exhibiting elevated Ca+2 levels. It's noteworthy that G n-HA demonstrated less efficacy in enamel remineralization compared to S n-HA.

Classification of skin cancer stages using a AHP fuzzy technique within the context of big data healthcare.

BACKGROUND AND OBJECTIVES: Skin conditions in humans can be challenging to diagnose. Skin cancer manifests itself without warning. In the future, these illnesses, which have been an issue for many, will be identified and treated. With the rapid expansion of big data healthcare framework summarization and precise prediction in early stage skin cancer diagnosis, the fuzzy AHP technique produces the best results in both of these fields. Big data is a potent technology that enhances the standard of research and generates better results more rapidly. This essay gives a way to group the stages of skin cancer treatment based on this information. The combination of support vector machine multi-class classification and fuzzy selector with radial basis function-based binary migration classification of virtual machines is put through a number of experiments. The connections have been categorized. ANALYSIS METHOD: These examinations have determined whether the tumors are malignant or benign and how malignant they are. The images of spots on the skin acquired from laboratory images make up the data set used for processing. We have talked about how to handle and process large datasets in the area of classification using MATLAB, like skin spot images. FINDINGS: Our technique outperforms competing approaches by maintaining stability even as the size of the data set grows rapidly and with little error. In comparison to other methods, the suggested approach meets the accuracy criterion for correct classifications with a score of 90.86%. As a result, the proposed solution is viewed as a potentially useful tool for identifying mass stages and categorizing skin cancer severity.

Human endometrial stem cell neurogenesis in response to NGF and bFGF.

The potential of cell therapy is promising in nerve regeneration, but is limited by ethical considerations about the proper and technically safe source of stem cells. We report the successful differentiation of human EnSCs (endometrial stem cells) as a rich source of renewable and safe progenitors into high-efficiency cholinergic neurons. The extracellular signals of NGF (nerve growth factor) and bFGF (basic fibroblast growth factor) could induce cholinergic neuron differentiation. ChAT (choline acetyltransferase), MAP2 (microtubule associated protein 2) and NF-l (neurofilament L) increased after administration of bFGF and NGF to the EnSC cultures. trkC and FGFR2 (fibroblast growth factor receptor 2), which belong to the NGF and bFGF receptors respectively, were determined in populations of EnSCs. NGF, bFGF and their combination differentially influenced human EnSCs high efficiency differentiation. By inducing cholinergic neurons from EnSCs in a chemically defined medium, we could produce human neural cells without resorting to primary culture of neurons. This in vitro method provides an unlimited source of human neural cells and facilitates clinical applications of EnSCs for neurological diseases.