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PURPOSEBeliefs on causes of cancer, although sometimes aligned with known risk factors, may be influenced by personal experiences, cultural narratives, and misinformation. We investigated the prevalence of beliefs on causes of cancer and their association with cancer risk perception and lifestyle in a multiethnic Asian population.METHODSIn total, 2,008 Malaysian adults with no previous cancer were surveyed using a 42-item questionnaire adapted from the Awareness Measure and the Cancer Awareness Measure–Mythical Causes Scale. Partial least squares structural equation modeling was used to evaluate measurement models.RESULTSDespite high educational attainment, only about half of the respondents believed that 7 of the 21 listed established risk factors caused cancer. Factors associated with accurate beliefs included higher socioeconomic status (SES) and having family or friends with cancer. However, 14 of the 21 listed mythical/unproven factors were correctly believed as not cancer-causing by the majority. Women and those with lower SES were more likely to hold misconceptions. Beliefs on established risk factors were significantly associated with perceived risk of cancer. Individuals with stronger beliefs in established risk factors were less likely to be associated with healthy behaviors. Conversely, stronger beliefs in mythical or unproven factors were more likely to be associated with healthy lifestyles.CONCLUSIONFindings highlight the importance of prioritizing cancer literacy as a key action area in national cancer control plans. The counterintuitive associations between cancer beliefs and lifestyle emphasize the complexity of this relationship, necessitating nuanced approaches to promote cancer literacy and preventive behaviors.

This manuscript describes the Advanced Breast Cancer (ABC) international consensus guidelines updated at the last two ABC international consensus conferences (ABC 6 in 2021, virtual, and ABC 7 in 2023, in Lisbon, Portugal), organized by the ABC Global Alliance. It provides the main recommendations on how to best manage patients with advanced breast cancer (inoperable locally advanced or metastatic), of all breast cancer subtypes, as well as palliative and supportive care. These guidelines are based on available evidence or on expert opinion when a higher level of evidence is lacking. Each guideline is accompanied by the level of evidence (LoE), grade of recommendation (GoR) and percentage of consensus reached at the consensus conferences. Updated diagnostic and treatment algorithms are also provided. The guidelines represent the best management options for patients living with ABC globally, assuming accessibility to all available therapies. Their adaptation (i.e. resource-stratified guidelines) is often needed in settings where access to care is limited.

ObjectiveGenetic testing and counselling are critical in assessing breast cancer risk and tailoring treatment strategies. However, several barriers hinder patients from opting for genetic testing/counselling, leading to fewer than one-third of patients undergoing testing and even fewer being offered counselling. A granular understanding of these barriers is essential in overcoming them.MethodsA multinational survey developed by patient authors was conducted in 9 countries, to identify the specific local/regional barriers. The survey question pathway was individualized, based on responses to prior questions. Percentage responses to a response option were calculated based on the total number of respondents to that question. Chi-square tests were used to assess the significance of the results, if applicable.ResultsThe final analysis set (FAS) included 1,176 respondents, with a subset of this responding to all questions. In the FAS, 63% of respondents had undergone testing. Among those who got tested, 70% were offered testing. Among untested respondents, only 40% were offered the test but eventually did not get tested. In the tested population, 44% received counselling, which was significantly higher than 7% (p

Cardiovascular disease is a major cause of death worldwide, and the COVID-19 pandemic has only made the situation worse. The purpose of this work is to explore various time-frequency analysis methods that can be used to classify heart sound signals and identify multiple abnormalities in the heart, such as aortic stenosis, mitral stenosis, and mitral valve prolapse. The signal has been modified using three techniques—tunable quality wavelet transform (TQWT), discrete wavelet transform (DWT), and empirical mode decomposition—to detect heart signal abnormality. The proposed model detects heart signal abnormality at two stages, the user end and the clinical end. At the user end, binary classification of signals is performed, and if signals are abnormal then further classification is done at the clinic. The approach starts with signal preprocessing and uses the discrete wavelet transform (DWT) coefficients to train the hybrid model, which consists of one long short-term memory (LSTM) network layer and three convolutional neural network (CNN) layers. This method produced comparable results, with a 98.9% classification accuracy for signals, through the utilization of the CNN and LSTM model. Combining the CNN’s skill in feature extraction with the LSTM’s capacity to record time-dependent features improves the efficacy of the model. Identifying issues early and initiating appropriate medication can alleviate the burden associated with heart valve diseases.

The High-level synthesis (HLS) is a key step in the digital VLSI circuit design. HLS can be seen in the present study as an important matter of intelligently map a Data Flow Graph (DFG) specification of digital filters to FPGA. In this article, the powerful features of the Harris hawks optimization (HHO) and Cuckoo search (CS) algorithm have improved the performance of the proposed method. This proposed strategy is also known as the GChO algorithm. In GChO, the phase of CS helps to force the HHO to escape from stagnation, to ignore local optima and premature convergence. In addition for HLS, the three retiming-based models were also proposed in the work. The standard pipelining was employed in Model 1. The modified Min-Period Retiming on the multiplier less digital filters was incorporated by Model 2 and led to a significant improvement in clock frequency. Model 3 adopted modified Min-Area Retiming on digital filters and reduced the complexity of the circuit; however these designs therefore offer single parameter-based heuristic solution. This can be resolved by using meta-heuristic approach focused on retiming. The use of meta-heuristic methods and swarm intelligence has been considered as a highly desirable choice when searching the digital block solution space with higher frequency and reduced complexity in HLS problems. The robustness of the GChO method has been verified on IEEE CEC’ 2020 standard benchmark test suites. The paper analyzes that the performance of the proposed GChO algorithm outperforms the Harris hawks optimization (HHO), Moth Flame optimization algorithm, Particle Swarm optimization algorithm, Chimp optimization algorithm for the HLS digital filters. The experimental results show that the proposed Model 2 and 3 for retimed PDR-FIR filter is improved in term of MUF by 7.38% and utilized slices by 37.77%, PRO-DFII IIR retimed filter by 10.54%, and complexity reduced by 29.69%, PRO-LAT-ARF by 41.61% and slice by 11.17% and PRO-LATLAD-IIR improved in term of MUF, slices by 35.69% and 38.095% in relative to the standard retimed filters of various architecture. For the evolutionary GChO-retimed filter, an improvement in MUF by 58.52%, and utilized slices by 49.61%, for PDR-FIR, 48.09%, 80.76% for PRO-DFII-IIR , 52.68%, 54.33% for PRO-LAT-ARF, 27.85% and 18.46% for PRO-LATLAD-IIR are achieved in respect of Model 2 and 3 whereas an improvement in term of MUF by 34.60%, and utilized slices by 37.61%, for PDR-FIR, 40.55%, 39.66% for PRO-DFII-IIR, 40.08%, 31.33% for PRO-LAT-ARF, 26.95%, 24.41% for PRO-LATLAD-IIR are achieved with respect of other meta-heuristic methods.

Abstract The HLS of digital filters is a complex optimization task in electronic design automation that increases the level of abstraction for designing and scheming digital circuits. The complexity of this issue attracting the interest of the researcher and solution of this issue is a big challenge for the researcher. The scientists are trying to present the various most powerful methods for this issue, but keep in mind these methods could be trapped in the complex space of this problem due to own weaknesses. Due to shortcomings of these methods, we are trying to design a new framework with the mixture of the phases of the powerful approaches for high level synthesis of digital filters in this work. This modification has been done by merging the chimp optimizer with sine cosine functions. The sine cosine phases helped in enhancing the exploitation phase of the chimp optimizer and also ignored the local optima in the search area during the searching of new shortest paths. The algorithms have been applied on 23-standard test suites and 14-digital filters for verifying the performance of the algorithms. Experimental results of single and multi-objective functions have been compared in terms of best score, best maxima, average, standard deviation, execution time, occupied area and speed respectively. Furthermore, by analyzing the effectiveness of the proposed algorithm with the recent algorithms for the HLS digital filters design, this can be concluded that the proposed method dominates the other two methods in HLS digital filters design. Another prominent feature of the proposed system in addition to the stated enhancement, is its rapid runtime, lowest delay, occupied area and lowest power in achieving an appropriate response. This could greatly reduce the cost of systems with broad dimensions while increasing the design speed.

Abstract Wireless sensors are the basic requisite of today’s smart infrastructure based on internet of things (IoTs), 5G and wireless sensor networks (WSNs). WSNs are widely used in industrial applications, precision agriculture and animal tracking systems, environment monitoring, smart grids, energy control systems, smart buildings and entertainment industry etc. The distributed and dynamic scheme of WSNs establishes very unique demands in developing clustering and routing protocols. In order to meet the demand of efficient WSNs, most important requirement is energy management and extension of network lifetime. So energy constraints issue is one of the most emerging area for research to reduce the complexity of network functioning. Due to the complexity of this task we need more robustness optimizer algorithms which can tackle these types of tasks. In this article we are trying to develop one improved version of chimp optimizer for energy constraint issues. In this modification have been integrated the chimp optimizer with dimension learning based hunting (DLH) search technique, known as Improved Chimp Optimizer Algorithm (IChoA). Here the DLH search strategy helps in maintaining diversity and improves the balance between exploitation and exploration. To compute the robustness in solving the optimizer issues, IChoA has been tested on 29-CEC-2017 test suites and energy constraint issues. Experimental solutions obtained by proposed methods are verified with recent methods. All simulation shows that the IChoA method can be most effective in solving the standard complex suites and energy constraint issues.

PURPOSEThe post–COVID-19 funding landscape for cancer research globally has become increasingly challenging, particularly in resource-challenged regions (RCRs) lacking strong research ecosystems. We aimed to produce a list of priority areas for cancer research in countries with limited resources, informed by researchers and patients.METHODSCancer experts in lower-resource health care systems (as defined by the World Bank as low- and middle-income countries; N = 151) were contacted to participate in a modified consensus-seeking Delphi survey, comprising two rounds. In round 1, participants (n = 69) rated predetermined areas of potential research priority (ARPs) for importance and suggested missing ARPs. In round 2, the same participants (n = 49) rated an integrated list of predetermined and suggested ARPs from round 1, then undertook a forced choice priority ranking exercise. Composite voting scores (T-scores) were used to rank the ARPs. Importance ratings were summarized descriptively. Findings were discussed with international patient advocacy organization representatives.RESULTSThe top ARP was research into strategies adapting guidelines or treatment strategies in line with available resources (particularly systemic therapy) (T = 83). Others included cancer registries (T = 62); prevention (T = 52); end-of-life care (T = 53); and value-based and affordable care (T = 51). The top COVID-19/cancer ARP was strategies to incorporate what has been learned during the pandemic that can be maintained posteriorly (T = 36). Others included treatment schedule interruption (T = 24); cost-effective reduction of COVID-19 morbidity/mortality (T = 19); and pandemic preparedness (T = 18).CONCLUSIONAreas of strategic priority favored by cancer researchers in RCRs are related to adaptive treatment guidelines; sustainable implementation of cancer registries; prevention strategies; value-based and affordable cancer care; investments in research capacity building; epidemiologic work on local risk factors for cancer; and combatting inequities of prevention and care access.

Firefly algorithm is a powerfulalgorithm, however, it may show inferior convergence rate towards global optimum because the optimization process of the firefly algorithm depends upon on a random quantity that facilitates the fireflies to explore the search space at the cost of the exploitation. As a result, to improve the performance of firefly algorithm in terms of convergence rate even as its ability to exploit the solutions is preserved, firefly algorithm is altered using two different approaches in this research work. In the first approach, a local search strategy, i.e., classical unidimensional local search is employed in the firefly algorithm in order to improve its exploitation capability. In the second approach, a new solution search strategy, i.e., stochastic diffusion scout search is integrated with the searching phase of the firefly algorithm in order to increase the probability of inferior solutions to improve themselves. Both the approaches are evaluated on various benchmark problems having different complexities and their performances are compared with artificial bee colony and classical firefly algorithm. Results prove the capability of the proposed approaches in finding the global optimum in the search space and avoid the local optima at the same time.

Blackgram (Vigna mungo L. Hepper) is an important tropical and sub-tropical short-duration legume that is rich in dietary protein and micronutrients. Producing high-yielding blackgram varieties is hampered by insufficient genetic variability, absence of suitable ideotypes, low harvest index and susceptibility to biotic-abiotic stresses. Seed yield, a complex trait resulting from the expression and interaction of multiple genes, necessitates the evaluation of diverse germplasm for the identification of novel yield contributing traits. Henceforth, a panel of 100 blackgram genotypes was evaluated at two locations (Ludhiana and Gurdaspur) across two seasons (Spring 2019 and Spring 2020) for 14 different yield related traits. A wide range of variability, high broad-sense heritability and a high correlation of grain yield were observed for 12 out of 14 traits studied among all environments. Investigation of population structure in the panel using a set of 4,623 filtered SNPs led to identification of four sub-populations based on ad-hoc delta K and Cross entropy value. Using Farm CPU model and Mixed Linear Model algorithms, a total of 49 significant SNP associations representing 42 QTLs were identified. Allelic effects were found to be statistically significant at 37 out of 42 QTLs and 50 known candidate genes were identified in 24 of QTLs.

Background: Needs assessment tools may be used to guide the optimisation of cancer survivorship services. We sought to develop and validate a dual-language needs assessment tool for women with breast cancer. Methods: The study comprised two phases; (I) co-design, and (II) psychometric testing. In Phase I, items were generated based on focus group discussions with patients and a literature review. These items were then translated into the Malay language. Content and face validation were undertaken with an expert panel. In phase II, 315 Malaysian women living with breast cancer were recruited. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to determine construct validity and reliability. Criterion validity was assessed using the EORTC QLQ-C30 questionnaire. Results: We co-designed and validated a 48-item dual-language needs assessment tool for breast cancer (NeAT-BC). Five underlying constructs were revealed in the EFA: (1) emotional support, (2) social and intimate relationships, (3) hospital appointments, (4) personal care and health, and (5) information and services. The NeAT-BC demonstrated good reliability across all constructs (Cronbach’s alpha: 0.90 to 0.96). CFA also demonstrated acceptable convergent and divergent validity, composite reliability ≥ 0.87, and Heterotrait–Monotrait index < 0.85 for all constructs. Criterion validity was established given the significant negative correlation between overall needs and quality of life (r = −0.14; p = 0.02). The NeAT-BC took approximately 25 min to be completed and could be interviewer-administered or self-administered. Conclusion: The utilization of the NeAT-BC is expected to guide establishment of evidence-based cancer survivorship services in Malaysia, with wider potentials for adoption in other multi-ethnic and/or low-and-middle income settings.

Abstract Background To evaluate the effectiveness of a structured prescription review and prescriber feedback program in reducing prescribing errors in government primary care clinics within an administrative region in Malaysia. Methods This was a three group, pragmatic, cluster randomised trial. In phase 1, we randomised 51 clinics to a full intervention group (prescription review and league tables plus authorised feedback letter), a partial intervention group (prescription review and league tables), and a control group (prescription review only). Prescribers in these clinics were the target of our intervention. Prescription reviews were performed by pharmacists; 20 handwritten prescriptions per prescriber were consecutively screened on a random day each month, and errors identified were recorded in a standardised data collection form. Prescribing performance feedback was conducted at the completion of each prescription review cycle. League tables benchmark prescribing errors across clinics and individual prescribers, while the authorised feedback letter detailed prescribing performance based on a rating scale. In phase 2, all clinics received the full intervention. Pharmacists were trained on data collection, and all data were audited by researchers as an implementation fidelity strategy. The primary outcome, percentage of prescriptions with at least one error, was displayed in p-charts to enable group comparison. Results A total of 32,200 prescriptions were reviewed. In the full intervention group, error reduction occurred gradually and was sustained throughout the 8-month study period. The process mean error rate of 40.7% (95% CI 27.4, 29.5%) in phase 1 reduced to 28.4% (95% CI 27.4, 29.5%) in phase 2. In the partial intervention group, error reduction was not well sustained and showed a seasonal pattern with larger process variability. The phase 1 error rate averaging 57.9% (95% CI 56.5, 59.3%) reduced to 44.8% (95% CI 43.3, 46.4%) in phase 2. There was no evidence of improvement in the control group, with phase 1 and phase 2 error rates averaging 41.1% (95% CI 39.6, 42.6%) and 39.3% (95% CI 37.8, 40.9%) respectively. Conclusions The rate of prescribing errors in primary care settings is high, and routine prescriber feedback comprising league tables and a feedback letter can effectively reduce prescribing errors. Trial registration National Medical Research Register: NMRR-12-108-11,289 (5th March 2012).

Background and Objective: Despite the increasing treatment options for patients with metastatic breast cancer (MBC), unmet needs remain common, especially in low and middle-income countries where resources are limited and MBC patients face many challenges. They often join support groups to cope with their unmet needs. Currently, many MBC patients connect with each other via online support group in view of the constant availability of support and rapid information exchange. The objective of this study is to determine the unmet needs of women with MBC from an online support group. Material and Methods: Messages in an online support group of twenty-two MBC patients over a period of three years from August 2016 till August 2019 were thematically analyzed. Results: Three themes were generated, (1) unmet information needs (2) unmet financial needs (3) unmet support needs. Women needed information on side effects of treatment, new treatment options and availability of clinical trials. Although Malaysia has universal health care coverage, access to treatment remains a major challenge. When treatment was not available in the public hospitals, or waiting lists were too long, women were forced to seek treatment in private hospitals, incurring financial catastrophe. Insufficient private insurance and inadequate social security payments force many women to consider stopping treatment. Women felt that they were not getting support from their clinicians in the public sector, who were quick to stop active treatment and advise palliation. On the other hand, clinicians in the private sector advise expensive treatment beyond the financial capability of the patients. Women with families also face the challenge of managing their family and household in addition to coping with their illness. Conclusions: There is a need for healthcare professionals, policy makers, and civil society to better address the needs of MBC patients through patient-centered, multidisciplinary and multi-organizational collaboration.

Primary ciliary dyskinesia (PCD) is a disorder affecting motile cilia. An early accurate diagnosis helps prevent lung damage and preserve lung function. To make a diagnostic assessment, one of the commonly used methods that allows for the examination of ciliary ultrastructure is transmission electron microscopy (TEM). This allows for a quantitative assessment of ciliary components to identify defects associated with PCD. Heavy metal staining is required to provide a contrast when imaging cilia in the TEM. One of the most commonly used stains is uranyl acetate (UA). UA can be applied to cellular material before embedding (en bloc), or to ultrathin sections of embedded samples (grid staining). UA is radioactive and, due to growing safety concerns and restrictions by government bodies, universities and hospitals, it is essential to find a suitable alternative. We show UA-zero (UAZ), when used en bloc, provides a high contrast and is a suitable replacement for UA. PCD diagnostic experts, having reviewed ciliary cross-sections stained with UAZ en bloc, are confident that the staining and PCD defects are readily detectable similar to samples that have been stained with UA.

A new greedy search method is applied in this paper to design the optimal digital infinite impulse response (IIR) filter. The greedy search method is based on binary successive approximation (BSA) and evolutionary search (ES). The suggested greedy search method optimizes the magnitude response and the phase response simultaneously and also finds the lowest order of the filter. The order of the filter is controlled by a control gene whose value is also optimized along with the filter coefficients to obtain optimum order of designed IIR filter. The stability constraints of IIR filter are taken care of during the design procedure. To determine the trade-off relationship between conflicting objectives in the non-inferior domain, the weighting method is exploited. The proposed approach is effectively applied to solve the multiobjective optimization problems of designing the digital low-pass (LP), high-pass (HP), bandpass (BP), and bandstop (BS) filters. It has been demonstrated that this technique not only fulfills all types of filter performance requirements, but also the lowest order of the filter can be found. The computational experiments show that the proposed approach gives better digital IIR filters than the existing evolutionary algorithm (EA) based methods.

Localization is one of the most important factors highly desirable for the performance of Wireless Sensor Network (WSN). Localization can be stated as the estimation of the location of the sensor nodes in sensor network. In the applications of WSN, the data gathered at sink node will be meaningless without localization information of the nodes. Due to size and complexity factors of the localization problem, it can be formulated as an optimization problem and thus can be approached with optimization algorithms. In this paper, the nature inspired algorithms are used and analyzed for an optimal estimation of the location of sensor nodes. The performance of the nature inspired algorithms viz. Flower pollination algorithm (FPA), Firefly algorithm (FA), Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) for localization in WSN is analyzed in terms of localization accuracy, number of localized nodes and computing time. The comparative analysis has shown that FPA is more proficient in determining the coordinates of nodes by minimizing the localization error as compared to FA, PSO and GWO.