Progress in human ovarian rejuvenation: Current platelet-rich plasma and condensed cytokine research activity by scope and international origin

Objective: As clinicians and patients await consensus on intraovarian platelet-rich plasma (PRP) treatment, this project evaluated contemporary research trends in the literature. Methods: A PubMed/NLM search aggregated all ovarian PRP-related publications (n=54) to evaluate their scope, abstract utility, submission-to-publication interval, journal selected, article processing charge (APC), free reader access to full-text manuscripts, number and nationality of authors, and inclusion of international collaborators. The NIH Clinical Trials database was also audited. Results: Published output on intraovarian PRP has increased consistently since 2016, especially among investigators in Greece, Iran, USA, and Turkey. Between 2013 and 2021, 42 articles met the relevancy criteria, of which 40.5% reported clinical studies, small series, or case reports, 33% described experimental animal models, and 23.8% were opinion/review papers. Only two works included a placebo control group. The submission-to-publication interval (mean±standard deviation) was 130±96 days, there were 5.9±3.2 authors per project, and journals invoiced US $1,642±1,466 (range, $0–$3,860) for APCs. Conclusion: There was no correlation between APC and time to publish (Pearson’s r=–0.01). Abstract content was inconsistent; sample size and patient age were often missing, yet free full-text “open access” was available for most publications (59.5%). The NIH Clinical Trials portal lists eight registered studies on “ovarian rejuvenation,” of which two are actively recruiting patients, while four have been terminated or have an uncertain status. Two studies have concluded, with results from one posted to the NIH website. PRP and its derivatives for ovarian treatment show early promise, but require further investigation. Research is accelerating and should be encouraged, particularly placebo-controlled randomized clinical trials.

Pengaruh Budaya Organisasi, Disiplin Kerja dan Tunjangan Kinerja terhadap Kinerja Pegawai pada Sekretariat Kepaniteraan Mahkamah Agung RI

More about background, purpose, up to, the results of research, and manai research This research aims to find out the influence between Organizational Culture, Work Discipline and Performance Allowance on Employee Performance. This research makes previous research as one of the sources of literature as well as reference. But this study has differences with previous studies that generally lie in populations or samples, variables, places and data processing methods used. The research data was obtained from questionnaires distributed directly to Supreme Court clerks. Data processing is done using the SPSS version 20.0 program which includes validity tests, reliability tests, correlation tests, multiple linear regression tests, and classical assumption tests. The results showed that partially Work Discipline had a significant effect on Employee Performance by t calculated (4,036) > t table (1,996). Performance Allowance has a significant effect on Employee Performance by t calculated (4,894) > t calculated. Simultaneously showing a significant influence between Organizational Culture, Work Discipline on Performance by 11,334 > F tables of 2.70 and the magnitude of influence is 32.4%. Abstract contains up to 250 words, single write spaces with italics (Italics) for English abstracts. Below the abstract are listed keywords consisting of six words, where the first word is again the forward. Abstract in Indonesian can be a translation of an English translation. Tif editor for abstract syning for reasons of abstract content.

Optimasi Hyperparameter TensorFlow dengan Menggunakan Optuna di Python: Study Kasus Klasifikasi Dokumen Abstrak Skripsi

In today's rapidly growing digital era, the role of computing in artificial intelligence is needed to be able to help business people. Both in the fields of economy, health, and education. The use of machine learning will help related parties in viewing, analyzing, and making decisions. With machine learning, all problems related to data can be solved quickly and precisely. The problem is that the thesis document will increase every year, it will become a useless document if the data processing is not carried out. Past thesis data can be used for analysis and decision-making in the next thesis era. Python is one of the most popular programming languages used for machine learning. One reason is that there are many python-based libraries. Keras is a python-based machine learning library. TensorFlow can be used when dealing with large amounts of data processing, including thesis abstract data. Thus, this study classified 140 thesis abstract documents using hard-TensorFlow with the aim that based on the abstract content it would be classified into 6 classes, namely Android Applications, Data Mining, RPL, SPK, Digital Image Processing, and Expert Systems. The results of the classification with training data as many as 82 documents with model setting batch size = 12 and epoch = 2 with an Accuracy value of 89.04%. While the test loss test data has a higher value than the Accuracy value obtained by 66.66%. By utilizing maximizing TensorFlow performance by adding a parameter that Scikit Learn has, namely Optuna. The test data was optimized with a trial value of 500, the Accuracy increased to 76.19%

Translational Modelling of the Burden of Disease for Acute Aortic Dissection

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A Methodology for Semantic Enrichment of Cultural Heritage Images Using Artificial Intelligence Technologies

Cultural heritage images are among the primary media for communicating and preserving the cultural values of a society. The images represent concrete and abstract content and symbolise the social, economic, political, and cultural values of the society. However, an enormous amount of such values embedded in the images is left unexploited partly due to the absence of methodological and technical solutions to capture, represent, and exploit the latent information. With the emergence of new technologies and availability of cultural heritage images in digital formats, the methodology followed to semantically enrich and utilise such resources become a vital factor in supporting users need. This paper presents a methodology proposed to unearth the cultural information communicated via cultural digital images by applying Artificial Intelligence (AI) technologies (such as Computer Vision (CV) and semantic web technologies). To this end, the paper presents a methodology that enables efficient analysis and enrichment of a large collection of cultural images covering all the major phases and tasks. The proposed method is applied and tested using a case study on cultural image collections from the Europeana platform. The paper further presents the analysis of the case study, the challenges, the lessons learned, and promising future research areas on the topic.

Emotional engagement in expressive writing

HaCohen et al. (2018) identified three types of narratives that emerge in the context of integrating a difficult event into one’s life story. We use their identification while focusing on the quality of emotional involvement evidenced in texts, and combining it with an abstract-content text analysis. This allows us to quantify emotional engagement in Expressive Writing (EW) texts. We analyze personal-experience narratives produced in EW, and examine whether good EW outcome cases (in terms of well-being improvement) would be characterized with different types of narratives than poor outcome cases. Results show that texts produced by good outcome cases presented more emotional involvement than poor cases. Furthermore, good cases presented with a more complex and well-integrated narrative of their story than poor cases. It is suggested that good outcome participants’ writings are more emotionally involved, integrated and personal. Our findings emphasize the importance of context-sensitive and function-oriented accounts of EW texts.

H63D Syndrome: data and facts

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Trustworthy computational evidence through transparency and reproducibility

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