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Introduction: In 2017, a provincial Liberal government launched the Ontario Basic Income Pilot (OBIP) to assess a promising approach to poverty reduction in Ontario, Canada. It was prematurely canceled by a subsequent Conservative government, despite election promises assuring that it would continue. The cancelation affected 4000 OBIP recipients. Objective: This study explored how participants' lives were on the OBIP compared to their lives before and after the pilot project using the social determinants of mental health (SDoMH) as a key lens. Study Design and Data Collection: Semistructured interview data, gathered in 2019, were collected from a sample of 46 OBIP participants from three study sites, viewed through the lenses of structural violence and the SDoMH, and analyzed using reflexive thematic analysis. Findings: We identified eight themes from the data, all of which pointed to the positive impacts of the OBIP on the SDoMH of participants. These included expanded opportunities for education and employment, improved housing stability, enhanced social inclusion (including an escape from the denigrations of receiving charity), improved sense of security and mental health, the wherewithal to eschew "crappy foods" in favor of healthier options, and the freedom to live one's life as one chooses. All these positive impacts were threatened or reversed by the Pilot's cancelation. Conclusions and Implications: The discontinuation of the OBIP, a benefit that appeared to be improving the mental health and well‐being of OBIP participants, can be viewed as an act of structural violence by government leaders. This study contributes to the growing evidence showing that cash transfer programs, such as a basic income, can alleviate psychological distress and improve mental well‐being for people living in poverty. A groundswell of mobilized citizens, and public health and mental health practitioners specifically, must hold governments accountable for acts of structural violence that can readily be addressed through policies that eradicate poverty. [ABSTRACT FROM AUTHOR]

Immunotherapy has ushered in a new era of cancer treatment, yet cancer remains a leading cause of global mortality. Among various therapeutic strategies, cancer vaccines have shown promise by activating the immune system to specifically target cancer cells. While current cancer vaccines are primarily prophylactic, advancements in targeting tumor-associated antigens (TAAs) and neoantigens have paved the way for therapeutic vaccines. The integration of artificial intelligence (AI) into cancer vaccine development is revolutionizing the field by enhancing various aspect of design and delivery. This review explores how AI facilitates precise epitope design, optimizes mRNA and DNA vaccine instructions, and enables personalized vaccine strategies by predicting patient responses. By utilizing AI technologies, researchers can navigate complex biological datasets and uncover novel therapeutic targets, thereby improving the precision and efficacy of cancer vaccines. Despite the promise of AI-powered cancer vaccines, significant challenges remain, such as tumor heterogeneity and genetic variability, which can limit the effectiveness of neoantigen prediction. Moreover, ethical and regulatory concerns surrounding data privacy and algorithmic bias must be addressed to ensure responsible AI deployment. The future of cancer vaccine development lies in the seamless integration of AI to create personalized immunotherapies that offer targeted and effective cancer treatments. This review underscores the importance of interdisciplinary collaboration and innovation in overcoming these challenges and advancing cancer vaccine development. [ABSTRACT FROM AUTHOR]

With the aging population, the financial burdens associated with chronic diseases continue to escalate. Among various management strategies, family physician contracting stands out as a significant initiative aimed at addressing this challenge, officially introduced in China in 2016. However, current research has given relatively little attention to the economic benefits of family physician contracting in developing countries. This paper evaluates the economic effect of family physician contracting by employing microdata of patients with chronic diseases at the Yuyuantan Community Health Service Center, Haidian District, Beijing, from January 2019 to May 2021. This research contributes to the theoretical discourse in healthcare services by introducing a modified Anderson model. The empirical findings demonstrated that family physician contracting effectively reduced healthcare costs for patients with chronic diseases. Notably, the effect of family physician contracting on healthcare costs was more pronounced among patients with chronic diseases aged less than 65 years. At the same time, it is inferred that expediting the reform of the health insurance payment system, coupled with the promotion of family physician contracting services, would enhance the wellbeing of patients with chronic diseases. This research offers valuable insights into the ongoing and future development of family physician contracting in developing countries. [ABSTRACT FROM AUTHOR]

The current prediction model for the system efficiency of pumping units primarily relies on a mechanistic approach. However, this approach incorporates numerous unnecessary factors, thereby, increasing the cost associated with predictions. With the improvement of the oil field database, the available information is increasing. Some scholars propose a prediction model based on a single neural network, however, such models face challenges in effectively capturing complex data, resulting in lower prediction accuracy and limited resistance to interference. To solve the above problems, the study proposes a novel stacking integrated learning prediction model, which incorporates fivefold cross‐validation. First, the magnitude of the correlation coefficient was quantified using the Pearson correlation coefficient. Second, the impact and predictive features were normalized. Final, convolutional neural network (CNN), recurrent neural network (RNN), Long Short‐Term Memory network (LSTM), gated recurrent unit (GRU), and transformer are used as the base models, and fully connected neural network (FNN) is used as the metamodel. Each base model was trained by fivefold cross‐validation, and the predicted values of each fold were stacked by rows. Next, the predicted values of each base model are stacked by columns as input variables to the metamodel and metamodel learning is performed, and the stacking integrated learning prediction model based on fivefold crossover validation is established. To validate the accuracy of the model, we selected 5,000 actual well parameters, including 26 impact features and one predictive feature, for comparative analysis. This analysis presents the maximum percentage reduction in mean square error (MSE), mean absolute error (MAE), and root‐mean‐square error (RMSE) of our proposed integrated learning model concerning a single neural network prediction model as 28.26%, 24.40%, and 15.66%, respectively. The maximum percentage improvement in R2 is 17.74%. It shows that our proposed integrated learning prediction model has high prediction accuracy. [ABSTRACT FROM AUTHOR]

The texture is identifiable in optical and easy ways. Texture classification is an imperative region in texture analysis, where it gives descriptors for classifying the images. The categorization of normal and abnormal matter by magnetic resonance (MR), computed tomography (CT), and texture images has made noteworthy evolution in modern years. Recently, different novel robust classification techniques have been introduced to classify the different kinds of images for prediction. However, the accuracy of classification was not improved with lesser time. To address these issues, the edge‐preserved Tversky indexive Hellinger and deep perceptive Czekanowski classifier (ETIH‐DPCC) technique is introduced to segment and classify the images with more accuracy. The ETIH‐DPCC technique includes diverse processes namely preprocessing, segmentation, feature extraction, as well as classification. At first, different types of images, such as magnetic resonance imaging, CT, and texture, are used as input. With the acquired input, edge‐preserving normalized adaptive bilateral filtering is employed to carry the image preprocessing. In this stage, the noisy pixels are removed and edges are preserved. Then, the Tversky‐indexed quantile regression is applied to segment the images into diverse texture regions. After that, the feature extraction is performed on the segmented region using Hellinger kernel feature extraction, where a more informative feature for image prediction is extracted. During this process, the irrelevant features are avoided to decrease the dimensionality and feature extraction time. These extracted features are finally classified into positive and negative classes for disease prediction using DPCC. DPCC comprises multiple layers to deeply analyze the association between training and testing features. With this, the prediction accuracy is improved. Experimental outcomes show that the ETIH‐DPCC technique efficiently enhances prediction accuracy and less time compared to conventional methods. [ABSTRACT FROM AUTHOR]

Fuel poverty was a significant problem during the cost‐of‐living crisis that started in 2021 as energy prices increased. Local authorities launched warm bank schemes around the United Kingdom to address this problem by providing warm spaces for residents to visit who could not heat their homes. This paper reports on an evaluation of the warm bank scheme launched by South Tyneside Council in winter 2022, locally known as Welcoming Places. Over 70 locations were opened around the borough. The evaluation consisted of two stages. In stage 1, we conducted semistructured interviews (n = 16) with residents who attended Welcoming Places and two focus groups with people running them (n = 9). In stage 2, we discussed the initial findings from stage 1 in a focus group with residents (n = 8). Three major themes are presented: "help and support with the cost‐of‐living crisis," "it's worth its weight in gold," and "capability and strength of Welcoming Places." This is one of the first evaluations of a warm bank scheme in England. We illustrate how the Welcoming Places in South Tyneside were used and experienced by residents. The scheme was valued by residents, provided significant support during the cost‐of‐living crisis, and had a positive impact on self‐reported mental health among participants. Warm bank schemes offer a practical, rapid public health response to the cost‐of‐living crisis for local authorities as well as improving residents' mental health and well‐being; however, holistic approaches must be taken to address the underlying causes of fuel poverty. [ABSTRACT FROM AUTHOR]