Spectrum of Clinical Research in Juvenile Idiopathic Arthritis: A Cross-Sectional Analysis of Registered Studies in Clinicaltrials.gov and Clinicaltrialsregister.eu

The management of juvenile idiopathic arthritis (JIA) has improved tremendously in recent years due to the introduction of new drug therapies but remains complex in terms of non-pharmaceutical issues. In order to determine the direction of scientific progress by characterizing the current spectrum of ongoing clinical research in JIA, we analyzed all ongoing studies in the field of JIA—registered in clinicaltrials.gov and clinicaltrialsregister.eu—concerning sponsoring, enrollment, duration, localization, and particularly objectives. The close of the database was 7 January 2021. After identifying double-registered studies, n = 72 went into further analysis. Of these, 61.1% were academia-sponsored and 37.5% were sponsored by the pharma industry. The majority of the studies was of the interventional type (77.8%), while others (22.2%) were observational. The median planned enrollments were 100 participants (interventional studies) and 175 participants (observational studies), respectively. The duration differed remarkably from one month to more than 15 years, with a median of 42.5 months. A total of 61.1% of studies were located in a single country, and 38.9% were in several. Europe and North America clearly dominated the study localizations. The study objectives were DMARDs (56.9%), followed by diagnostics and disease activity measurement (18.1%), and medication other than DMARD (12.5%), besides others. Studies on DMARDs were mainly sponsored by industry, predominantly interventional studies on established and novel biologics, with several on specific issues such as systemic JIA and others. The spectrum of registered studies is currently centered on drug therapy and diagnostics, while other issues in JIA play a subordinated role in current research. Drug development was transferred from adult rheumatology into the JIA population with little innovation for children. Future research should take specific pediatric needs better into account.

A Novel Ant Colony Based DBN Framework to Analyze the Drug Reviews

Nowadays, big data is directing the entire advanced world with its function and applications. Moreover, to make better decisions from the ever emerging big data belonging to the respective organizations, deep learning (DL) models are required. DL is also widely used in the sentiment classification tasks considering data from social networks.Furthermore, sentiment classification signifies the best way to analyze the big data and make decisions accordingly. Analyzing the sentiments from big data applications is quite challenging task and also requires more time for the execution process. Therefore, to analyze and classify big data emerging from social networks in a better way, DL models are utilized. DL techniques are being used among the researchers to get high end results. A novel Ant Colonybased Deep Belief Neural Network (AC-DBN) framework is proposed in this research. Drug review tweets are opted to perform sentiment classification by using the proposed framework in python environment. A model fitness function is initiated in the DL framework and is observed that it is attaining high accuracy with low computation time. Additionally, the obtained results attained from the proposed framework are validated with existing methods for evaluating the efficiency of the proposed AC-DBN approach.

A Review on Role of Pharmacist in Current Healthcare Scenrio

During the last few years, the pharmacy profession has expand significantly in terms of professional services delivery and now has been recognized as an important profession in the multidisciplinary provision of health care. The paper highlights the current scenario the Pharmacy profession in health care system. Pharmacist is a backbone that strengthens to health care system. Different roles of Pharmacist in different sectors of pharmacy profession like Industrial, academics, community health, clinical research, drug design and discovery, developing NDDS etc. In nutshell pharmacist play an integral part of health care system. “Physician gives medicine to the patients but life to medicine given by pharmacist”.

With great power comes great responsibility: high-dimensional spectral flow cytometry to support clinical trials

Flow cytometry is a powerful technology used in research, drug development and clinical sample analysis for cell identification and characterization, allowing for the simultaneous interrogation of multiple targets on various cell subsets from limited samples. Recent advancements in instrumentation and fluorochrome availability have resulted in significant increases in the complexity and dimensionality of flow cytometry panels. Though this increase in panel size allows for detection of a broader range of markers and sub-populations, even in restricted biological samples, it also comes with many challenges in panel design, optimization, and downstream data analysis and interpretation. In the current paper we describe the practices we established for development of high-dimensional panels on the Aurora spectral flow cytometer to aid clinical sample analysis.

Artificial Intelligence in Healthcare

Artificial intelligence (AI) is the ability of a computer program or machine to think or learn that possess human-like intelligence. These computing devices use this intelligence to provide services such as speech recognition, natural language processing and identifying disease in healthcare. To work efficiently, AI requires adequate data that is used to train systems. The efficiency of any AI system depends on the availability of this data. This article is mainly focused on recent advents in the technology of Artificial Intelligence. The importance of AI in healthcare is identified and described in this report. The applications of Artificial Intelligence in healthcare such as clinical care, medical research, drug research and public healthcare are briefly discussed here. The purpose of this article is to demonstrate that artificial intelligence is being used in all domains of life and particularly in the field of healthcare. This report presents the role of Artificial Intelligence in healthcare.

Artificial Intelligence and Cancer Drug Development

Background: The development of cancer drugs is among the most focused “bench to bedside activities” to improve human health. Because of the amount of data publicly available to cancer research, drug development for cancers has significantly benefited from big data and AI. In the meantime, challenges, like curating the data of low quality, remain to be resolved. Objective: This review focused on the recent advancements in and challenges of AI in developing cancer drugs. Method: We discussed target validation, drug repositioning, de novo design, and compounds' synthetic strategies. Results and Conclusion: AI can be applied to all stages during drug development, and some excellent reviews detailing the applications of AI in specific stages are available.

Open Innovation in Healthcare Ecosystem – a systematic review

Background: Several countries are facing significant troubles of health services, particularly rising prices. Innovative technologies and services are expected to help boost medical quality and cut costs. In this sense, there is a lack of innovative work in spite of a growing interest in open innovation and approaches that advocate for expanded cooperation among various actors in healthcare. Objective: This paper describes the findings of a study concerning the commitment of the healthcare sector to open innovation. Materials and methods: The search for literature focused on English-language papers to 12 January 2020. Based on the indicated criteria for inclusion, 29 articles were included. Results: Results show that most experiments concentrate on the areas of pharmaceutical research (drug discovery) and health informatics (health information systems and infrastructures) that were brought out as concepts or applied as pilot and prototype. Conclusions: The participation of the healthcare sector limited in open innovation, and more work is required with an emphasis how to get open innovation.

A CLINICAL COMPARATIVE STUDY TO EVALUATE THE EFFECTIVENESS OF RASNA SWADAMSTRADI KSHEERAPAKA WITH SUKUMARAM KASHAYAM IN SPASMODIC DYSMENORRHOEA

Many women are familiar with the experience of spasmodic dysmenorrhoea, one of the commonest gynaecological conditions that affects the quality of life of many in their reproductive years. This condition manifested as painful menstruation, is the most frequently encountered gynaecological complaint and it can be included under Udavartha yonivyapat, caused by Apana vata vaigunya described in Ayurvedic classics. As the condition has significant effect on quality of life, personal health, and working hours and there are several limitations and adverse effects in modern medicine, its Ayurvedic management is of great importance. Randomized clinical study was conducted in Govt. Ayurveda College Hospital for Women and Children, Poojappura to evaluate the effectiveness of Rasna swadamstraadi ksheerapaaka in spasmodic dysmenorrhoea and to compare its result with that of Sukumaram kashayam. Total 30 patients between the age group 15-35 yrs were taken in to the study who had complaints of severe or moderate lower abdominal pain and associated complaints such as low back ache, nausea, vomiting, diarrhoea, and allocated them into two groups. Study group were treated with Rasna swadamstraadi ksheerapaaka and control group with Sukumaram kashayam. Administration of drug started10 days before menstruation and continued till 4th day of menstruation for 3 consecutive cycles for study group and control group. Follow up without medicine was done for next 3 consecutive cycles for both the groups. Results were analyzed and compared statistically. The research drug Rasna swadamstraadi ksheerapaaka had shown effectiveness in controlling pain in spasmodic dysmenorrhoea and associated symptoms like low back ache and nausea, but in the case of vomiting, and diarrhoea it showed less sustained action in follow up period. The control drug Sukumaram kashayam had also shown effectiveness in controlling pain in spasmodic dysmenorrhoea and associated complaints nausea and low back ache. But in the case of vomiting, and diarrhoea this medicine also showed less sustained action in follow up period. On conclusion the study revealed that the research drug Rasna swadamstraadi ksheerapaaka and control drug Sukumaram kashayam are equally effective in treating spasmodic dysmenorrhoea, without any side effects.

Special Issue on Digital Pathology, Tissue Image Analysis, Artificial Intelligence, and Machine Learning: Approximation of the Effect of Novel Technologies on Toxicologic Pathology

For decades, it has been postulated that digital pathology is the future. By now it is safe to say that we are living that future. Digital pathology has expanded into all aspects of pathology, including human diagnostic pathology, veterinary diagnostics, research, drug development, regulatory toxicologic pathology primary reads, and peer review. Digital tissue image analysis has enabled users to extract quantitative and complex data from digitized whole-slide images. The following editorial provides an overview of the content of this special issue of Toxicologic Pathology to highlight the range of key topics that are included in this compilation. In addition, the editors provide a commentary on important current aspects to consider in this space, such as accessibility of publication content to the machine learning-novice pathologist, the importance of adequate test set selection, and allowing for data reproducibility.

The significance of the original characteristics of the active substance of injectable chondroitin sulfate preparations for intramuscular injections in the evidence-based medicine

Given the data from normative control resources and the research and development information system of the Ministry of Science and Education of the Russian Federation, we were unable to review the results and the quality of publications comprising method development and analytical validation of quantitative and qualitative values of injectable chondroitin sulfate preparations for intramuscular injections. Therefore, the results obtained in the research The development of a method for determining the intrinsic viscosity of an injectable chondroitin sodium sulfate preparation for intramuscular injections of 100 mg/ml, 2 ml remain valid, the method is considered reliable and practical. The materials and methods used in the research The development of a method for determining the intrinsic viscosity of an injectable chondroitin sulfate preparation have provided the possibility to determine the intrinsic viscosity for injectable chondroitin sodium sulfate preparations for intramuscular injections and the use of 0.2 M sodium chloride solution as a solvent. Also, the viscosity of the tested solutions in the concentration range of chondroitin sulfate 4.5-20 mg/ml was accurately сalculated, the value of the intrinsic viscosity determined for each of the preparations under test was in the range of 0.03-0.042 m3/kg. This study presents the results of a review of recent information, published in a number of academic journals, concerning modern approaches to the treatment and major clinical problems when applying chondroprotectors for arthrological diseases treatment. Considering the continuing interest in chondroprotectors, stability problems with formulations and modern possibilities in the application of machine learning in drug discovery, additional pharmaceutical design research (Drug design) is expected - from the docking stage to the quantum calculations stage.