Uloga pacijenta, liječnika i zdravstvenog sustava u iskustvima oboljelih od raka tijekom ambulantnog onkološkog liječenja

Background: Today, cancer is one of the most important medical problems. Successful management of cancer patients requires an understanding of their experiences. The study aimed to explore cancer patients’ experiences under outpatient oncology treatments. Methods: The phenomenological approach was used to access the depths of experiences felt by patients. The study was done in specialized cancer hospitals and private hospitals. Data was gathered through in-depth and unstructured interviews with participants with previous arrangements. Data precision and robustness were determined based on Guba and Lincoln criteria. Data analysis was carried out based on Colaizzi’s seven-step method. Results: A total of 9 people participated in this study. The experiences of cancer patients were categorized into five main concepts, including 1) Individual problems, 2) Expectations from the physician and health care system, 3) the quality of the health care system, 4) Coping with cancer, and 5) Support in the fight against cancer. Conclusions: These patients have a wide range of medical, psychological, support, and emotional needs, which are only known to themselves. Cancer itself is not the source of all these problems; instead, they have various sources, including family, society, policy-making, and treatment teams, many of which can be solved with proper education. Therefore, it is necessary to offer comprehensive planning, starting with medical teams, and create the required infrastructures in society.Dosadašnje spoznaje: Danas je rak jedan od najvažnijih medicinskih problema. Uspješna sveobuhvatna skrb o pacijentima s rakom zahtijeva razumijevanje njihovih iskustava. Cilj studije bio je istražiti iskustva pacijenata s rakom tijekom ambulantnog onkološkog liječenja. Metode: Fenomenološki pristup korišten je za pristup analizi iskustava koje proživljavaju pacijenti. Istraživanje je provedeno u specijaliziranim bolnicama za rak i privatnoj bolnici. Podaci su prikupljeni kroz dubinski i nestrukturirani razgovor sa sudionicima. Preciznost i robusnost podataka određeni su na temelju Gubinih i Lincolnovih kriterija. Analiza podataka provedena je na temelju Colaizzijeve metode u sedam koraka. Rezultati: U istraživanju je sudjelovalo ukupno 9 osoba. Iskustva pacijenata s rakom kategorizirana su u pet glavnih koncepata, uključujući 1) Individualni problemi, 2) Očekivanja liječnika i zdravstvenog sustava, 3) kvaliteta zdravstvenog sustava, 4) Suočavanje s rakom i 5) Podrška u borbi protiv raka. Zaključci: Ovi pacijenti imaju širok raspon medicinskih, psiholoških, potpornih i emocionalnih potreba, koje su poznate samo njima. Sam rak nije izvor svih tih problema; Umjesto toga, imaju različite izvore, uključujući obitelj, društvo, timove za donošenje politika i liječenje, od kojih se mnogi mogu riješiti odgovarajućim obrazovanjem. Stoga je potrebno ponuditi sveobuhvatno planiranje i edukaciju, počevši od medicinskih timova, i na taj način stvoriti potrebnu infrastrukturu u društvu

Antidepressant effects of Aloe vera hydroalcoholic extract

The antidepressant effects of aloe vera hydro alcoholic extract at different concentrations were compared with the fluoxetine-treated and the control groups of mice using forced-swimming, FST and open box, OFT tests. The mice were evaluated in five groups (control, taking aloe vera at the dosage levels of 150 mg/kg, 300 mg/kg, and 450 mg/kg, and finally fluoxetine at a dose of 10 mg/kg) by the FST and OFT tests on 1st, 7th, and 14th days. The results of the OFT test showed no significant differences between these five groups. The results of FST test indicate the antidepressant effects of aloe vera even at low doses and it was found that the effect of fluoxetine at a dose of 10 mg/kg was equivalent to the effect of aloe vera at a dose of 150 mg/kg for the reduction in immobility time in mice in FST test. According to the results obtained from FST test, the antidepressant effects on mice treated with the 450 mg/kg dose of aloe vera showed better recovery as compared with other groups on 1st, 7th, and 14th days. With regard to the experiments performed at different times, all the evidence pointed to the conclusion that the antidepressant effect of aloe vera was more than the control group.  Based on the results of the OFT and FST tests, aloe vera extract at different doses, has favorable antidepressant effects on mice as compared to the fluoxetine-treated and the control groups  and the better effects were seen by increasing the dose and duration of drug use.

Open-Set Graph Anomaly Detection via Normal Structure Regularisation

This paper considers an under-explored Graph Anomaly Detection (GAD) task, namely open-set GAD, which aims to detect anomalous nodes using a small number of labelled training normal and anomaly nodes (known as seen anomalies) that cannot illustrate all possible inference-time abnormalities. The task has attracted growing attention due to the availability of anomaly prior knowledge from the label information that can help to substantially reduce detection errors. However, current methods tend to over-emphasise fitting the seen anomalies, leading to a weak generalisation ability to detect unseen anomalies, i.e., those that are not illustrated by the labelled anomaly nodes. Further, they were introduced to handle Euclidean data, failing to effectively capture important non-Euclidean features for GAD. In this work, we propose a novel open-set GAD approach, namely normal structure regularisation (NSReg), to leverage the rich normal graph structure embedded in the labelled nodes to tackle the aforementioned two issues. In particular, NSReg trains an anomaly-discriminative supervised graph anomaly detector, with a plug-and-play regularisation term to enforce compact, semantically-rich representations of normal nodes. To this end, the regularisation is designed to differentiate various types of normal nodes, including labelled normal nodes that are connected in their local neighbourhood, and those that are not connected. By doing so, it helps incorporate strong normality into the supervised anomaly detector learning, mitigating their overfitting to the seen anomalies. Extensive empirical results on real-world datasets demonstrate the superiority of our proposed NSReg for open-set GAD

CARLA: A Self-supervised Contrastive Representation Learning Approach for Time Series Anomaly Detection

We introduce a Self-supervised Contrastive Representation Learning Approach for Time Series Anomaly Detection (CARLA), an innovative end-to-end self-supervised framework carefully developed to identify anomalous patterns in both univariate and multivariate time series data. By taking advantage of contrastive representation learning, We introduce an innovative end-to-end self-supervised deep learning framework carefully developed to identify anomalous patterns in both univariate and multivariate time series data. By taking advantage of contrastive representation learning, CARLA effectively generates robust representations for time series windows. It achieves this by 1) learning similar representations for temporally close windows and dissimilar representations for windows and their equivalent anomalous windows and 2) employing a self-supervised approach to classify normal/anomalous representations of windows based on their nearest/furthest neighbours in the representation space. Most of the existing models focus on learning normal behaviour. The normal boundary is often tightly defined, which can result in slight deviations being classified as anomalies, resulting in a high false positive rate and limited ability to generalise normal patterns. CARLA's contrastive learning methodology promotes the production of highly consistent and discriminative predictions, thereby empowering us to adeptly address the inherent challenges associated with anomaly detection in time series data. Through extensive experimentation on 7 standard real-world time series anomaly detection benchmark datasets, CARLA demonstrates F1 and AU-PR superior to existing state-of-the-art results. Our research highlights the immense potential of contrastive representation learning in advancing the field of time series anomaly detection, thus paving the way for novel applications and in-depth exploration in this domain.Comment: 33 pages, 9 figures, 10 table

Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series

Time series classification (TSC) is a challenging task due to the diversity of types of feature that may be relevant for different classification tasks, including trends, variance, frequency, magnitude, and various patterns. To address this challenge, several alternative classes of approach have been developed, including similarity-based, features and intervals, shapelets, dictionary, kernel, neural network, and hybrid approaches. While kernel, neural network, and hybrid approaches perform well overall, some specialized approaches are better suited for specific tasks. In this paper, we propose a new similarity-based classifier, Proximity Forest version 2.0 (PF 2.0), which outperforms previous state-of-the-art similarity-based classifiers across the UCR benchmark and outperforms state-of-the-art kernel, neural network, and hybrid methods on specific datasets in the benchmark that are best addressed by similarity-base methods. PF 2.0 incorporates three recent advances in time series similarity measures -- (1) computationally efficient early abandoning and pruning to speedup elastic similarity computations; (2) a new elastic similarity measure, Amerced Dynamic Time Warping (ADTW); and (3) cost function tuning. It rationalizes the set of similarity measures employed, reducing the eight base measures of the original PF to three and using the first derivative transform with all similarity measures, rather than a limited subset. We have implemented both PF 1.0 and PF 2.0 in a single C++ framework, making the PF framework more efficient

Reproductive Health Need Assessment of Adolescent Boys and Girls during Puberty: A Qualitative Study

Background Adolescence is the transient period from childhood to adulthood and the beginning of physical, mental and social changes. Understanding and meeting adolescents’ needs may have important effects on reducing theirs dangerous behaviors and therefore, will result in a more health society. This study aimed to assess the reproductive health needs of Iranian adolescent. Materials and Methods This qualitative design study explored the reproductive health needs among Iranian adolescent boys and girls. Purposeful sampling method was used until data saturation was reached. Data was gathered using focus group for girls and face to face interviews for boys. The interviews used semi-structured questions. Data analysis was carried out using the method proposed by Altschuld for need assessment. Trustworthiness of the data was assessed using Guba and Lincoln Indicators. Results Participants were 10 adolescent girls (age range from 13-15) and ten 14-17 year age male adolescences. The reproductive health needs of girls included 4 categories:  Menarche and puberty health, Discrimination in family and society, Sexual orientation, education and Consultation demands. The needs extracted from interviews of adolescent boys included adaptation with changes in puberty, sexual orientation, educational and consultation demands. Conclusion Despite some differences between males and females needs, their reproductive health needs are greatly similar. Adolescents need more Education and consultatio