Aim:The aim of this study is to evaluate the effect of honey on clinically scoring grades of oral mucositis.Materials and Methods:This interventional study was carried out in Radiation Oncology Department of Mayo Hospital, Lahore. In this study, 82 patients of both genders, of head and neck cancer, planned for radiotherapy, were divided into two groups by random sampling numbers. Patients in both groups were treated with a total dose of 60–78 Grays in 4–6 weeks. In treatment group, patients were instructed to take 20 mL of honey. In control group, they were advised to rinse with 0.9% of saline. Patients were evaluated every week to assess the grades of oral mucositis up to 6 weeks. The assessment tool was Radiation Therapy Oncology Group Grading System. The statistical analysis was done by Chi-square test.Results:In honey-treated group, the proportion of mucositis (Grades 3 and 4) was lower and statistically significant as compared to control group at the end of 6 weeks of radiation.Conclusion:This study showed that oral intake of honey during radiotherapy is valuable in the reduction of severity of oral mucositis.
Depression is a prevalent sickness, spreading worldwide with potentially serious implications. Timely recognition of emotional responses plays a pivotal function at present, with the profound expansion of social media and users of the internet. Mental illnesses are highly hazardous, stirring more than three hundred million people. Moreover, that is why research is focused on this subject. With the advancements of machine learning and the availability of sample data relevant to depression, there is the possibility of developing an early depression diagnostic system, which is key to lessening the number of afflicted individuals. This paper proposes a productive model by implementing the Long-Short Term Memory (LSTM) model, consisting of two hidden layers and large bias with Recurrent Neural Network (RNN) with two dense layers, to predict depression from text, which can be beneficial in protecting individuals from mental disorders and suicidal affairs. We train RNN on textual data to identify depression from text, semantics, and written content. The proposed framework achieves 99.0% accuracy, higher than its counterpart, frequency-based deep learning models, whereas the false positive rate is reduced. We also compare the proposed model with other models regarding its mean accuracy. The proposed approach indicates the feasibility of RNN and LSTM by achieving exceptional results for early recognition of depression in the emotions of numerous social media subscribers.
Healthcare information is essential for both service providers and patients. Further secure sharing and maintenance of Electronic Healthcare Records (EHR) are imperative. EHR systems in healthcare have traditionally relied on a centralized system (e.g., cloud) to exchange health data across healthcare stakeholders, which may expose private and sensitive patient information. EHR has struggled to meet the demands of several stakeholders and systems in terms of safety, isolation, and other regulatory constraints. Blockchain is a distributed, decentralized ledger technology that can provide secured, validated, and immutable data sharing facilities. Blockchain creates a distributed ledger system using techniques of cryptography (hashes) that are consistent and permit actions to be carried out in a distributed manner without needing a centralized authority. Data exploitation is difficult and evident in a blockchain network due to its immutability. We propose an architecture based on blockchain technology that authenticates the user identity using a Proof of Stake (POS) cryptography consensus mechanism and Secure Hash Algorithm (SHA256) to secure EHR sharing among different electronic healthcare systems. An Elliptic Curve Digital Signature Algorithm (ECDSA) is used to verify EHR sensors to assemble and transmit data to cloud infrastructure. Results indicate that the proposed solution performs exceptionally well when compared with existing solutions, which include Proof-Of-Work (POW), Secure Hash Algorithm (SHA-1), and Message Digest (MD5) in terms of power consumption, authenticity, and security of healthcare records.
Body weight loss is a negative consequence of radiotherapy in head and neck cancer. The aim of this study is to determine the efficacy of honey on body weight of the patients. Materials and Methods: This interventional study was carried out in Radiation Oncology department of Mayo hospital, Lahore. This study involved 82 patients, divided into two groups by random sampling, who received 60-70 Grays of radiation in 22-30 fractions with curative intent. In treatment group, patients were instructed to take 20 mL of honey. In control group, they were advised to rinse with 0.9% of saline. The weight loss during radiotherapy was calculated as the difference between the weight at the start and the end of radiotherapy. The statistical analysis was done by t-test. Results: In honey-treated group, patients showed static and positive change in body weight when compared to control group and it is statistically significant. Conclusion: This study showed that oral intake of honey during radiotherapy is valuable for maintaining body weight during and after radiotherapy.
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