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Introduction Cancer is a major public health problem and a global leading cause of death where the screening, diagnosis, prediction, survival estimation, and treatment of cancer and control measures are still a major challenge. The rise of Artificial Intelligence (AI) and Machine Learning (ML) techniques and their applications in various fields have brought immense value in providing insights into advancement in support of cancer control. Methods A systematic and thematic analysis was performed on the Scopus database to identify the top 100 cited articles in cancer research. Data were analyzed using RStudio and VOSviewer.Var1.6.6. Results The top 100 articles in AI and ML in cancer received a 33 920 citation score with a range of 108 to 5758 times. Doi Kunio from the USA was the most cited author with total number of citations (TNC = 663). Out of 43 contributed countries, 30% of the top 100 cited articles originated from the USA, and 10% originated from China. Among the 57 peer-reviewed journals, the “Expert Systems with Application” published 8% of the total articles. The results were presented in highlight technological advancement through AI and ML via the widespread use of Artificial Neural Network (ANNs), Deep Learning or machine learning techniques, Mammography-based Model, Convolutional Neural Networks (SC-CNN), and text mining techniques in the prediction, diagnosis, and prevention of various types of cancers towards cancer control. Conclusions This bibliometric study provides detailed overview of the most cited empirical evidence in AI and ML adoption in cancer research that could efficiently help in designing future research. The innovations guarantee greater speed by using AI and ML in the detection and control of cancer to improve patient experience.

Sickle cell disease (SCD) remains a global public health problem in sub‐Saharan Africa and other countries. Limited information is available on the scientific research productivity on SCD. The study aims to assess the growth of global scientific research output on SCD and provide historical insights for a scientific research community. A bibliometric method was applied using the Web of Science (WoS) database from 1900 to 2020. Keywords terms: Title ‘sickle cell*’ OR ‘sickle cell anemia*’ were used. Fundamental bibliometric indicators were analyzed using HistCite, VOS-viewer, and “Bibliometrix app.” using R-studio cloud and SPSS version. 0.19.Search strategy retrieved 11917 documents, written by 31072 authors and published in 1813 prestigious journals, with an average of 27.34 citations per document, 2.8 collaboration index, and 2.61 authors per document. Blood, American and British Journals of Hematology are the most productive journals, and the most active author is Serjeant GR. The fund comes from the United States Department of Health Human Services, and the USA was the most productive country. ‘Anemia,’ ‘children,’ ‘disease,’ and management were the most credence keywords.‘Hematology’ and ‘General internal medicine’ were top WoS research categories. There is a significant (P

Background: Research indicates that the psychological well-being of orthopedic patients can benefit from a robust social support system that offers emotional assistance both before and after surgery. However, the impact of social networks on mental health outcomes remains inconclusive due to variations in availability. Objectives: This study investigated the influence of support from family and friends in the relationship between post-traumatic stress disorder (PTSD) and emotional distress among orthopedic patients in Nigeria. Materials and Methods: The study employed a hospital-based research design, using the 17-item PTSD self-report measure (PCL-C) and depression anxiety stress scales (DASS-21) to assess psychological distress and dimensions of social support from friends and family, involving 542 orthopedic patients from the National Orthopedic Hospital in Enugu, Nigeria. Results: The findings revealed a significant association between PTSD and emotional distress (β = 0.866; t = 9.245; P < 0.001). Family support was found to exacerbate emotional distress among orthopedic patients (β = 0.448; t = 4.176; P < 0.001), while support from friends showed an inverse effect on emotional distress (β = −0.310; t = −2.880; P < 0.01). Moreover, moderation analysis indicated that high family support reduces the effect of PTSD on emotional distress for orthopedic patients (β = −0.035; t = −3.539; [CI: −0.055, −0.016], P < 0.01). Conclusion: The study recommends prompt intervention for orthopedic patients to enhance their well-being by leveraging the available support from family and friends to alleviate the emotional distress associated with PTSD. Additionally, it underscores the importance of educating families and friends of orthopedic patients on their role in supporting the patient’s recovery journey.