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Abstract Brain-derived neurotrophic factor (BDNF) improves cognitive function by stimulating neurogenesis and neuroplasticity. We hypothesize that higher plasma BDNF levels are protective against cognitive toxicity among adolescent and young adult cancer patients (15–39 years old). In a prospective, longitudinal study, we recruited 74 newly diagnosed cancer and 118 age-matched non-cancer controls who completed the Cambridge Neuropsychological Test Automated Battery (CANTAB), Functional Assessment of Cancer Therapy-Cognitive Function questionnaire (FACT-Cog) and blood draws. Plasma BDNF was quantified using an enzyme-linked immunosorbent assay. Genomic DNA from buffy coat was genotyped for BDNF Val66Met. Most cancer participants were diagnosed with breast (24%) and head/neck (22%) cancers. After adjusting for sociodemographic variables (age, gender, race, marital status, education years), cancer participants had lower BDNF levels (ng/mL) at baseline (median: 10.7 vs 21.6, p

Abstract Background There is little information about cancer‐related cognitive impairment (CRCI) in adolescent and young adults (AYA, 15–39 years old) due to its rare incidence. Here, we present the pre‐treatment (before chemotherapy or radiotherapy) evaluation of cognitive function and ability of AYA with cancer (AYAC) in a multicentered cohort study. Methods Newly diagnosed AYAC and age‐matched healthy controls (HC) were recruited between 2018 and 2021. The primary outcome was the comparison of pre‐treatment cognitive impairment defined as 2 standard deviations (SDs) below the HC on ≥1 cognitive test, or >1.5 SDs below on ≥2 tests using CANTAB® between AYAC and HC. Secondary outcomes included self‐perceived cognitive ability assessed by FACT‐Cog v3 and biomarkers (inflammatory cytokines and brain‐derived neurotrophic factor [BDNF]). Results We recruited 74 AYAC (median age = 34) and 118 HC (median age = 32). On objective cognitive testing, we observed three times more AYAC patients performed poorly on at least 2 cognitive tests compared to HC (40.5% vs. 13.6%, p

Differential evolution (DE) is a popular optimization algorithm with easy implementation and fast convergence rate. For evolutionary algorithms such as DE, the initialization process of solution members is crucial because the distribution of initial population can govern the overall quality of final solution obtained in terms of accuracy and convergence speed. This study leverages the strengths of both chaotic maps and oppositional-based learning strategy to design a new DE variant with modified initialization scheme, namely chaotic oppositional DE (CODE) in order to generate the initial population with good quality of mean fitness and diversity of the solutions. The effectiveness of CODE variants incorporated with seven different chaotic maps are investigated using CEC 2014 benchmark functions and the chaotic circle oppositional DE (CCODE) is revealed as the best performing CODE variants. The optimization performance of CCODE is further compared with other existing optimization algorithms in terms of solution accuracy and convergence speed. Extensive simulation studies prove that the proposed algorithm is able to outperform its peers by achieving better trade-off between two contradicting requirements of fast convergence speed and population diversity preservation.

This study introduces ETLBOCBL-CNN, an automated approach for optimizing convolutional neural network (CNN) architectures to address classification tasks of varying complexities. ETLBOCBL-CNN employs an effective encoding scheme to optimize network and learning hyperparameters, enabling the discovery of innovative CNN structures. To enhance the search process, it incorporates a competency-based learning concept inspired by mixed-ability classrooms during the teacher phase. This categorizes learners into competency-based groups, guiding each learner’s search process by utilizing the knowledge of the predominant peers, the teacher solution, and the population mean. This approach fosters diversity within the population and promotes the discovery of innovative network architectures. During the learner phase, ETLBOCBL-CNN integrates a stochastic peer interaction scheme that encourages collaborative learning among learners, enhancing the optimization of CNN architectures. To preserve valuable network information and promote long-term population quality improvement, ETLBOCBL-CNN introduces a tri-criterion selection scheme that considers fitness, diversity, and learners’ improvement rates. The performance of ETLBOCBL-CNN is evaluated on nine different image datasets and compared to state-of-the-art methods. Notably, ELTLBOCBL-CNN achieves outstanding accuracies on various datasets, including MNIST (99.72%), MNIST-RD (96.67%), MNIST-RB (98.28%), MNIST-BI (97.22%), MNST-RD + BI (83.45%), Rectangles (99.99%), Rectangles-I (97.41%), Convex (98.35%), and MNIST-Fashion (93.70%). These results highlight the remarkable classification accuracy of ETLBOCBL-CNN, underscoring its potential for advancing smart device infrastructure development.

Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin’s theory of evolution and has been studied extensively to solve different areas of optimisation and engineering applications since its introduction by Storn in 1997. This study aims to review the massive progress of DE in the research community by analysing the 192 articles published on this subject from 1997 to 2021, particularly studies in the past five years. The methodology used to search for relevant DE papers and an overview of the original DE are firstly explained. Recent advances in the modifications proposed to enhance the effectiveness and efficiency of the original DE are reviewed by analysing the strengths and weaknesses of each published work, followed by the potential applications of these DE variants in solving different real-world engineering problems. In contrast to most existing DE review papers, additional analyses are performed in this survey by investigating the impacts of various parameter settings on given DE variants to identify their optimal values required for solving certain problem classes. The qualities of modifications incorporated into selected DE variants are also evaluated by measuring the performance gains achieved in terms of search accuracy and/or efficiency against the original DE. The additional surveys conducted in this study are anticipated to provide more insightful perspectives for both beginners and experts of DE research, enabling their better understanding about current research trends and new motivations to outline appropriate strategic planning for future development works.

Convolutional neural networks (CNNs) have excelled in artificial intelligence, particularly in image-related tasks such as classification and object recognition. However, manually designing CNN architectures demands significant domain expertise and involves time-consuming trial-and-error processes, along with substantial computational resources. To overcome this challenge, an automated network design method known as Modified Teaching-Learning-Based Optimization with Refined Knowledge Sharing (MTLBORKS-CNN) is introduced. It autonomously searches for optimal CNN architectures, achieving high classification performance on specific datasets without human intervention. MTLBORKS-CNN incorporates four key features. It employs an effective encoding scheme for various network hyperparameters, facilitating the search for innovative and valid network architectures. During the modified teacher phase, it leverages a social learning concept to calculate unique exemplars that effectively guide learners while preserving diversity. In the modified learner phase, self-learning and adaptive peer learning are incorporated to enhance knowledge acquisition of learners during CNN architecture optimization. Finally, MTLBORKS-CNN employs a dual-criterion selection scheme, considering both fitness and diversity, to determine the survival of learners in subsequent generations. MTLBORKS-CNN is rigorously evaluated across nine image datasets and compared with state-of-the-art methods. The results consistently demonstrate MTLBORKS-CNN’s superiority in terms of classification accuracy and network complexity, suggesting its potential for infrastructural development of smart devices.

Teaching learning-based optimization (TLBO) is a popular algorithm used to solve various optimization problems. Nevertheless, conventional TLBO and some improved variants tends to suffer with premature convergence due to rapid loss of population diversity, especially when handling the challenging optimization problems. Furthermore, it is not practical to tackle real-world multiobjective problems using prior approach given the frequent changes of customers’ requirements. Motivated by these challenges, an improved variant known as Modified Multi-objective Teaching Learning Based Optimization-Refined Learning Scheme (MMTLBO-RLS) was proposed as a posterior approach to solve challenging multiobjective optimization problems, including the prediction of optimum turning parameters to machine Polyether ether ketone material (PEEK). Substantial modifications were introduced for teacher and learner phases of MMTLBO-RLS to achieve better balancing of exploration and exploitation searches without incurring excessive computational cost. For modified teacher phase of MMTLBO-RLS, each learner was guided by a unique teacher solution and unique mean position to perform searching with better diversity. Meanwhile, two new learning strategies are incorporated into the modified learner phase of MMTLBO-RLS, enabling all learners to enhance their knowledge more efficiently based on their learning preferences. A systematic approach was followed to develop modelling equations required for optimization. The developed algorithm was then employed in single objective optimization as well as multiobjective optimization to cater its performances in any real-world environment. The prediction model reports that surface roughness of $1.1042~\mu m$ and material removal rate of 22.8991 cm3/minute can be achieved. The predicted results differ from validation results by less than 2.69% in any case of optimization. A benchmarking on the performance of MMTLBO-RLS in solving CEC 2009 multiobjective benchmark functions was further carried out with other seven meta-heuristic algorithms. The superior performance of MMTLBO-RLS proves that it is not only suitable to be used in industries to produce the parts of PEEK with supportive quality and quantity, but it is also able to solve other multiobjective optimization problems with competitive performances.

Convolutional neural networks (CNNs) have exhibited significant performance gains over conventional machine learning techniques in solving various real-life problems in computational intelligence fields, such as image classification. However, most existing CNN architectures were handcrafted from scratch and required significant amounts of problem domain knowledge from designers. A novel deep learning method abbreviated as TLBOCNN is proposed in this paper by leveraging the excellent global search ability of teaching–learning-based optimization (TLBO) to obtain an optimal design of network architecture for a CNN based on the given dataset with symmetrical distribution of each class of data samples. A variable-length encoding scheme is first introduced in TLBOCNN to represent each learner as a potential CNN architecture with different layer parameters. During the teacher phase, a new mainstream architecture computation scheme is designed to compute the mean parameter values of CNN architectures by considering the information encoded into the existing population members with variable lengths. The new mechanisms of determining the differences between two learners with variable lengths and updating their positions are also devised in both the teacher and learner phases to obtain new learners. Extensive simulation studies report that the proposed TLBOCNN achieves symmetrical performance in classifying the majority of MNIST-variant datasets, displays the highest accuracy, and produces CNN models with the lowest complexity levels compared to other state-of-the-art methods due to its promising search ability.

Neuropathic pain is a chronic and sometimes intractable condition caused by lesions or diseases of the somatosensory nervous system. Many drugs are available but unfortunately do not provide satisfactory effects in patients, producing limited analgesia and undesirable side effects. Thus, there is an urgent need to develop new pharmaceutical agents to treat neuropathic pain. To date, highly specific agents that modulate a single target, such as receptors or ion channels, never progress to the clinic, which may reflect the diverse etiologies of neuropathic pain seen in the human patient population. Therefore, the development of multifunctional compounds exhibiting two or more pharmacological activities is an attractive strategy for addressing unmet medical needs for the treatment of neuropathic pain. To develop novel multifunctional compounds, key pharmacophores of currently used clinical pain drugs, including pregabalin, fluoxetine and serotonin analogs, were hybridized to the side chain of tianeptine, which has been used as an antidepressant. The biological activities of the hybrid analogs were evaluated at the human transporters of neurotransmitters, including serotonin (hSERT), norepinephrine (hNET) and dopamine (hDAT), as well as mu (μ) and kappa (κ) opioid receptors. The most advanced hybrid of these multifunctional compounds, 17, exhibited multiple transporter inhibitory activities for the uptake of neurotransmitters with IC50 values of 70 nM, 154 nM and 2.01 μM at hSERT, hNET and hDAT, respectively. Additionally, compound 17 showed partial agonism (EC50 = 384 nM) at the μ-opioid receptor with no influence at the κ-opioid receptor. In in vivo pain animal experiments, the multifunctional compound 17 showed significantly reduced allodynia in a spinal nerve ligation (SNL) model by intrathecal administration, indicating that multitargeted strategies in single therapy could considerably benefit patients with multifactorial diseases, such as pain.

Background: The Cancer Survival in Africa, Asia, and South America project (SURVCAN-3) of the International Agency for Research on Cancer aims to fill gaps in the availability of population-level cancer survival estimates from countries in these regions. Here, we analysed survival for 18 cancers using data from member registries of the African Cancer Registry Network across 11 countries in sub-Saharan Africa., Methods: We included data on patients diagnosed with 18 cancer types between Jan 1, 2005, and Dec 31, 2014, from 13 population-based cancer registries in Cotonou (Benin), Abidjan (CÔte d'Ivoire), Addis Ababa (Ethiopia), Eldoret and Nairobi (Kenya), Bamako (Mali), Mauritius, Namibia, Seychelles, Eastern Cape (South Africa), Kampala (Uganda), and Bulawayo and Harare (Zimbabwe). Patients were followed up until Dec 31, 2018. Patient-level data including cancer topography and morphology, age and date at diagnosis, vital status, and date of death (if applicable) were collected. The follow-up (survival) time was measured from the date of incidence until the date of last contact, the date of death, or until the end of the study, whichever occurred first. We estimated the 1-year, 3-year, and 5-year survival (observed, net, and age-standardised net survival) by sex, cancer type, registry, country, and human development index (HDI). 1-year and 3-year survival data were available for all registries and all cancer sites, whereas availability of 5-year survival data was slightly more variable; thus to provide medium-term survival prospects, we have focused on 3-year survival in the Results section., Findings: 10 500 individuals from 13 population-based cancer registries in 11 countries were included in the survival analyses. 9177 (87·4%) of 10 500 cases were morphologically verified. Survival from cancers with a high burden and amenable to prevention was poor: the 3-year age-standardised net survival was 52·3% (95% CI 49·4-55·0) for cervical cancer, 18·1% (11·5-25·9) for liver cancer, and 32·4% (27·5-37·3) for lung cancer. Less than half of the included patients were alive 3 years after a cancer diagnosis for eight cancer types (oral cavity, oesophagus, stomach, larynx, lung, liver, non-Hodgkin lymphoma, and leukaemia). There were differences in survival for some cancers by sex: survival was longer for females with stomach or lung cancer than males with stomach or lung cancer, and longer for males with non-Hodgkin lymphomas than females with non-Hodgkin lymphomas. Survival did not differ by country-level HDI for cancers of the oral cavity, oesophagus, liver, thyroid, and for Hodgkin lymphoma., Interpretation: For cancers for which population-level prevention strategies exist, and with relatively poor prognosis, these estimates highlight the urgent need to upscale population-level prevention activities in sub-Saharan Africa. These data are vital for providing the knowledge base for advocacy to improve access to prevention, diagnosis, and care for patients with cancers in sub-Saharan Africa., Funding: Vital Strategies, the Martin-Luther-University Halle-Wittenberg, and the International Agency for Research on Cancer., Translations: For the French and Portuguese translations of the abstract see Supplementary Materials section., Competing Interests: Declaration of interests We declare no competing interests. Where authors are identified as personnel of the International Agency for Research on Cancer and WHO, the authors alone are responsible for the views expressed in this Article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer and WHO., (This is an Open Access article published under the CC BY-NC-ND 3.0 IGO license which permits users to download and share the article for non-commercial purposes, so long as the article is reproduced in the whole without changes, and provided the original source is properly cited. This article shall not be used or reproduced in association with the promotion of commercial products, services or any entity. There should be no suggestion that WHO endorses any specific organisation, products or services. The use of the WHO logo is not permitted. This notice should be preserved along with the article's original URL.)

Background: The increased use of telehealth to visit patients in their home permits greater access to care, and also increases the opportunity for whole-person assessments that improve individualized care. The videoconferencing camera is a proxy for home visit provider's eyes. However, cameras limit views, thereby reducing environmental cues. The Novice to Expert Theory of skill acquisition supports the use of an intentional viewing guide to assure a comprehensive patient assessment using telehealth in the home (CPATH). This study advances the development of a CPATH framework to guide providers to be intentional when using televideo technology., Methods: A quantitative content validity approach was used to determine the validity of a priori items within domains that were in the original protocol framework. A content validity determination requires 5-10 experts to rate agreement (range 1-5) on items within domains. Our sample was composed of seven expert home health providers. More than five experts had to agree to achieve statistical significance ( p  < 0.05) for validity., Results: Of the 15 items in the protocol, only 8 items had significant agreement for the sample size. These items were breathing, nonverbal gesturing, positioning, oxygen, safety, and types, dosages, and administration guidance of medication. Other items were added within the existing domains of Patient Characteristics, Treatment and Equipment Functioning, Medications and Environmental Quality, with the exception of Caregivers., Conclusion: The domains triggered considerations for existing or additional items that require assessment, thereby developing the intentional guide framework that permits individualization of a telehealth home-based visit., Competing Interests: No competing financial interests exist., (© Kimberly D. Shea et al., 2021; Published by Mary Ann Liebert, Inc.)