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Dear Editor We have read with great interest the study of Furukawa, 1 which was conducted at the Minami Seikyo hospital and assessed whether the use of Tasuki‐style posture supporter improves non‐specific chronic neck pain in adults. [...]convenient sampling of study subjects in combination with individual randomization and lack of allocation concealment within a study of such a small sample size, raises serious concerns regarding selection bias. A Cochrane review of clinical trials showed that lack of allocation concealment results in larger effect estimates. 3 A more rigorous randomization method should be employed in future studies, as poor choice of randomization could adversely affect the validity and interpretation of research findings. 4 In addition, the author refers to a possible placebo effect as a study limitation, whereas, the results may also be attributed to a Hawthorne effect and more recent forms of experimental bias such as demand characteristics and socially desirable responding, given that the participants were provided with an explanation why the Tasuki may help improve neck pain at the beginning of the study.

Automatic Text Summarization (ATS) is gaining popularity as there is a growing demand for a system capable of processing extensive textual content and delivering a concise, yet meaningful, relevant, and useful summary. Manual summarization is both expensive and time-consuming, making it impractical for humans to handle vast amounts of data. Consequently, the need for ATS systems has become evident. These systems encounter challenges such as ensuring comprehensive content coverage, determining the appropriate length of the summary, addressing redundancy, and maintaining coherence in the generated summary. Researchers are actively addressing these challenges by employing Natural Language Processing (NLP) techniques. While traditional methods exist for generating summaries, they often fall short of addressing multiple aspects simultaneously. To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. The performance of this enhanced algorithm is assessed by comparing it with state-of-the-art algorithms using the DUC2002 dataset. Experimental results demonstrate that the proposed algorithm significantly outperforms the compared approaches.

Background Studies on genome-wide associations help to determine the cause of many genetic diseases. Genome-wide associations typically focus on associations between single-nucleotide polymorphisms (SNPs). Genotyping every SNP in a chromosomal region for identifying genetic variation is computationally very expensive. A representative subset of SNPs, called tag SNPs, can be used to identify genetic variation. Small tag SNPs save the computation time of genotyping platform, however, there could be missing data or genotyping errors in small tag SNPs. This study aims to solve Tag SNPs selection problem using many-objective evolutionary algorithms. Methods Tag SNPs selection can be viewed as an optimization problem with some trade-offs between objectives, e.g. minimizing the number of tag SNPs and maximizing tolerance for missing data. In this study, the tag SNPs selection problem is formulated as a many-objective problem. Nondominated Sorting based Genetic Algorithm (NSGA-III), and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), which are Many-Objective evolutionary algorithms, have been applied and investigated for optimal tag SNPs selection. This study also investigates different initialization methods like greedy and random initialization. optimization. Results The evaluation measures used for comparing results for different algorithms are Hypervolume, Range, SumMin, MinSum, Tolerance rate, and Average Hamming distance. Overall MOEA/D algorithm gives superior results as compared to other algorithms in most cases. NSGA-III outperforms NSGA-II and other compared algorithms on maximum tolerance rate, and SPEA2 outperforms all algorithms on average hamming distance. Conclusion Experimental results show that the performance of our proposed many-objective algorithms is much superior as compared to the results of existing methods. The outcomes show the advantages of greedy initialization over random initialization using NSGA-III, SPEA2, and MOEA/D to solve the tag SNPs selection as many-objective optimization problem.

The important function of a sensor network is to collect and forward data to destination. It is very important to know about the location of collected data. This kind of information can be obtained using localization technique in wireless sensor networks (WSNs). Localization is a way to determine the location of sensor nodes. Localization of sensor nodes is an interesting research area, and many works have been done so far. It is highly desirable to design low-cost, scalable, and efficient localization mechanisms for WSNs. In this paper, we discuss sensor node architecture and its applications, different localization techniques, and few possible future research directions.

Sumyia Mehrin Omar, Aboma Zewude Abdissa, Maryam Mohammed BashirInstitute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab EmiratesCorrespondence: Maryam Mohammed Bashir, Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates, Email [email protected]View the original paper by Dr Hussein and colleaguesA Response to Letter has been published for this article.

Gestational diabetes mellitus (GDM) burden is burgeoning globally. Correct knowledge about GDM among young people is paramount for timely prevention. This study assesses GDM knowledge and identifies factors associated with it among United Arab Emirates (UAE) University students. A validated self-administered questionnaire collected data from the university students. We analyzed the data for GDM knowledge status (ever heard of GDM) and GDM knowledge levels (poor, fair, and good) and conducted ordinal logistic regressions to assess for associated factors. A total of 735 students were surveyed with a mean age of 21.0 years. Of these, 72.8% had heard of GDM, and 52.9% of males versus 20.3% of female students had never heard of the condition before. Higher age (p = 0.019) and being a postgraduate student (p = 0.026) were associated with higher GDM knowledge status in males. GDM knowledge level analysis showed that 24.0%, 58.5%, and 17.5% had poor, fair, and good knowledge. The mean GDM-knowledge score was 6.3 ± 2.4 (out of 12). Being married [aOR-1.82 (95%CI 1.10–3.03)] and knowing someone who had GDM [aOR-1.78 (95%CI 1.23–2.60)] were independently associated with higher GDM knowledge levels among students. Students’ primary source of GDM knowledge was family/friends. There is an observed knowledge gap related to GDM among the students, especially males. This study urges the need to accelerate targeted GDM awareness campaigns among university students and the general population in the UAE.

The December of 2019, marked the start of one of the biggest pandemics that the human race had seen for some centuries. COVID-19 after  originating from China was in full force and was spreading quickly. This, however, was different from the previous pandemics as this is the age of technology and social circles on the internet. Thus, a sinister form of situation arose where fake news and misinformation flooded social media. The situation got to the point that WHO termed it as an “infodemic”. Thus, NLP was again implored to find a solution and massive research was conducted for the detection of fake news on these platforms. The success of fake news detection improved and by today i.e. in 2023 the techniques have matured quite a bit. Keeping both of these aspects in mind, we have conducted a detailed review on fake news detection techniques for COVID-19. We have discussed the collection of data by providing a deep analysis of 7 COVID-19 Fake News datasets. Moreover, during the analysis of different methodologies, domination of deep learning and hybrid models was observed - specifically ensemble of transformer based models. Additionally, we explored the practical implications of COVID-19 Fake News detectors as components in generative AI models and as browser extensions to keep the common people safe. Finally, we discussed the limitations in existing research and how it can be improved in the future by exploring multi-modal, feature rich and cross-lingual approaches.

Background: Antibiotic resistance is a significant health problem in healthcare settings, especially intensive care units (ICUs), where patients are critically ill. This study aims to identify the bacterial profile and antibiotic resistance patterns of Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter, and Escherichia coli (ESKAPEE) in blood specimens collected from adult patients admitted to the ICUs of public hospitals in Abu Dhabi, United Arab Emirates. The World Health Organization lists these pathogens as priority pathogens that greatly threaten humans. Methods: This cross-sectional study used routinely collected data through the AMR surveillance system between 2018 and 2022. Results: A total of 838 culture-positive blood specimens were reported during the study period, and 965 ESKAPEE pathogens were isolated. The most frequently isolated bacteria were Klebsiella pneumoniae (31%), Escherichia coli (22%), and Staphylococcus aureus (20%). Acinetobacter baumannii exhibited high resistance to Amikacin (81%), Meropenem (72%), and Imipenem (87%). Escherichia coli demonstrated resistance to Imipenem (42%) and Cefotaxime (54%). Klebsiella pneumoniae showed resistance to Imipenem (37%) and Cefotaxime (39%). Staphylococcus aureus showed resistance to Penicillin G (80%), Oxacillin (4%), and Ciprofloxacin (54%). Conclusions: The study showed a high prevalence of resistance in the most frequently isolated ESKAPEE pathogens in adult ICU patients. This brings into focus the need for appropriate infection control measures and strong antibiotic stewardship programs. The findings of the study support the ongoing efforts to deploy a better diagnostic tool for rapid pathogen identification, which is key in the targeted management of patients with bloodstream infection, especially in ICUs.

Alzheimer’s disease (AD) causes dementia and continuous damage to brain cells. Cholinesterase inhibitors can alleviate the condition by increasing communication between the nerve cells and reducing the risk of dementia. In an effort to treat Alzheimer’s disease, we synthesized flurbiprofen-based diamines (1,2 diaminoethane and 1,3 diaminopropane) Zn(II), Cu(II) metal complexes and characterized them by single-crystal X-ray analysis, NMR, (FT)-IR, UV-Vis, magnetic susceptibility, elemental analysis and conductivities measurements. Synthesized diamine metal complexes appeared in ionic forms and have distorted octahedral geometry based on conductivity studies, magnetic susceptibility and electronic studies. Single crystal X-ray diffraction analysis confirmed (2b) Cu(H2O)2(L1)2(L2)2 complex formation. Moreover, we tested all synthesized metal complexes against the cholinesterase enzyme that showed higher inhibition potential. In general, copper metal complexes showed higher inhibitory activities than simple metal complexes with flurbiprofen. These synthesized metal complexes may derive more effective and safe inhibitors for cholinesterases.