Medical Context Research Articles

Intramolecular Hydrogen Bonding Based CEST MRI Contrast Agents As an Emerging Design Strategy: A Mini-Review.

Intramolecular hydrogen bonding-based chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) contrast agents represent an innovative design strategy aiming to overcome limitations in diamagnetic CEST (diaCEST) MRI contrast agent specificity and also those associated with traditional metal-based MRI contrast agents. Ward and Balaban's proposal of small diamagnetic compounds marked a paradigm shift in contrast-based radiologic research, inspiring extensive investigations since 2000. These contrast agents leverage labile hydrogen bonds, serving as chemical exchange sites to induce saturation of water. The selective manipulation of radiofrequency (RF) allows for optimized signal contrast in soft tissue, with a significant signal amplification even at low probe concentrations, mitigating concerns about dose-dependent toxicities. This mini-review delves into the evolution of CEST MRI, its classification, and the strategic design principles of synthetic small molecules containing intramolecular hydrogen bonds. With a focus on applications and potential clinical relevance, the authors highlight the promising role of intramolecular hydrogen bonding-based CEST MRI in diverse medical contexts, especially renal imaging and pH mapping, paving the way for enhanced molecular imaging capabilities. Ongoing research endeavors aim to further optimize and expand the utility of these contrast agents, underscoring their transformative potential in clinical diagnostics and imaging.

Supercritical CO2 directional-assisted green synthesis of ZnO nanoparticles obtained from Euphorbia stricta L. for functional applications

The green synthesis method is increasingly favored due to its economic and environmental benefits. Metal oxides, such as zinc oxide (ZnO) nanoparticles produced via green synthesis, exhibit biocompatible and stable properties. In this study, the Euphorbia stricta plant was extracted using the supercritical CO2 extraction method. Subsequently, ZnO nanoparticles were synthesized using the Euphorbia stricta extract through a microwave-assisted hydrothermal method. The synthesized ZnO was characterized using X-ray diffraction (XRD), Ultraviolet–visible spectroscopy (UV–Vis), and Fourier-transform infrared spectroscopy (FT-IR). Euphorbia stricta L. extract was also characterized by FT-IR. To evaluate the photocatalytic activities against methylene blue, ZnO/polyacrylonitrile (PAN) membranes were fabricated via electrospinning for comparative analysis. The degradation efficiencies of ZnO and ZnO/PAN nanofibers were 76.37 % and 66.14 % over 180 min, respectively. Furthermore, the green synthesized ZnO nanoparticles demonstrated significant antibacterial properties against Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. These bacteria are of critical importance in medical and environmental contexts: cause of skin infections, pneumonia and bloodstream infections; foodborne illnesses and urinary tract infection and hospital-acquired infections. The findings suggest that ZnO nanoparticles synthesized from Euphorbia stricta extract have substantial potential for application in antibacterial treatments and photocatalysis, offering a promising approach to addressing challenges posed by these pathogenic bacteria.

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

Ambient Intelligence (AMI) represents a significant advancement in information technology that is perceptive, adaptable, and finely attuned to human needs. It holds immense promise across diverse domains, with particular relevance to healthcare. The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) to create an AMI environment in medical contexts further enriches this concept within healthcare. This survey provides invaluable insights for both researchers and practitioners in the healthcare sector by reviewing the incorporation of AMI techniques in the IoMT. This analysis encompasses essential infrastructure, including smart environments and spectrum for both wearable and non-wearable medical devices to realize the AMI vision in healthcare settings. Furthermore, this survey provides a comprehensive overview of cutting-edge AI methodologies employed in crafting IoMT systems tailored for healthcare applications and sheds light on existing research issues, with the aim of guiding and inspiring further advancements in this dynamic field.

Assessing AI Awareness and Identifying Essential Competencies: Insights From Key Stakeholders in Integrating AI Into Medical Education.

The increasing importance of artificial intelligence (AI) in health care has generated a growing need for health care professionals to possess a comprehensive understanding of AI technologies, requiring an adaptation in medical education. This paper explores stakeholder perceptions and expectations regarding AI in medicine and examines their potential impact on the medical curriculum. This study project aims to assess the AI experiences and awareness of different stakeholders and identify essential AI-related topics in medical education to define necessary competencies for students. The empirical data were collected as part of the TüKITZMed project between August 2022 and March 2023, using a semistructured qualitative interview. These interviews were administered to a diverse group of stakeholders to explore their experiences and perspectives of AI in medicine. A qualitative content analysis of the collected data was conducted using MAXQDA software. Semistructured interviews were conducted with 38 participants (6 lecturers, 9 clinicians, 10 students, 6 AI experts, and 7 institutional stakeholders). The qualitative content analysis revealed 6 primary categories with a total of 24 subcategories to answer the research questions. The evaluation of the stakeholders' statements revealed several commonalities and differences regarding their understanding of AI. Crucial identified AI themes based on the main categories were as follows: possible curriculum contents, skills, and competencies; programming skills; curriculum scope; and curriculum structure. The analysis emphasizes integrating AI into medical curricula to ensure students' proficiency in clinical applications. Standardized AI comprehension is crucial for defining and teaching relevant content. Considering diverse perspectives in implementation is essential to comprehensively define AI in the medical context, addressing gaps and facilitating effective solutions for future AI use in medical studies. The results provide insights into potential curriculum content and structure, including aspects of AI in medicine.

A contextual integrity approach to genomic information: what bioethics can learn from big data ethics

Genomic data is generated, processed and analysed at an increasingly rapid pace. This data is not limited to the medical context, but plays an important role in other contexts in society, such as commercial DNA testing, the forensic setting, archaeological research, and genetic surveillance. Genomic information also crosses the borders of these domains, e.g. forensic use of medical genetic information, insurance use of medical genomic information, or research use of commercial genomic data. This paper (1) argues that an informed consent approach for genomic information has limitations in many societal contexts, and (2) seeks to broaden the bioethical debate on genomic information by suggesting an approach that is applicable across multiple societal contexts. I argue that the contextual integrity framework, a theory rooted in information technology and big data ethics, is an effective tool to explore ethical challenges that arise from genomic information within a variety of different contexts. Rather than focusing on individual control over information, the contextual integrity approach holds that information should be shared and protected according to the norms that govern certain distinct social contexts. Several advantages of this contextual integrity approach will be discussed. The paper concludes that the contextual integrity framework helps to articulate and address a broad spectrum of ethical, social, and political factors in a variety of different societal contexts, while giving consideration to the interests of individuals, groups, and society at large.

Data set terminology of deep learning in medicine: a historical review and recommendation.

Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. The current rapid convergence of deep learning and medicine has led to significant advancements, yet it has also introduced ambiguity regarding data set terms common to both fields, potentially leading to miscommunication and methodological discrepancies. This narrative review aims to give historical context for these terms, accentuate the importance of clarity when these terms are used in medical deep learning contexts, and offer solutions to mitigate misunderstandings by readers from either field. Through an examination of historical documents, including articles, writing guidelines, and textbooks, this review traces the divergent evolution of terms for data sets and their impact. Initially, the discordant interpretations of the word 'validation' in medical and AI contexts are explored. We then show that in the medical field as well, terms traditionally used in the deep learning domain are becoming more common, with the data for creating models referred to as the 'training set', the data for tuning of parameters referred to as the 'validation (or tuning) set', and the data for the evaluation of models as the 'test set'. Additionally, the test sets used for model evaluation are classified into internal (random splitting, cross-validation, and leave-one-out) sets and external (temporal and geographic) sets. This review then identifies often misunderstood terms and proposes pragmatic solutions to mitigate terminological confusion in the field of deep learning in medicine. We support the accurate and standardized description of these data sets and the explicit definition of data set splitting terminologies in each publication. These are crucial methods for demonstrating the robustness and generalizability of deep learning applications in medicine. This review aspires to enhance the precision of communication, thereby fostering more effective and transparent research methodologies in this interdisciplinary field.

Prediction of cervix cancer stage and grade from diffusion weighted imaging using EfficientNet

Purpose. This study aims to introduce an innovative noninvasive method that leverages a single image for both grading and staging prediction. The grade and the stage of cervix cancer (CC) are determined from diffusion-weighted imaging (DWI) in particular apparent diffusion coefficient (ADC) maps using deep convolutional neural networks (DCNN). Methods. datasets composed of 85 patients having annotated tumor stage (I, II, III, and IV), out of this, 66 were with grade (II and III) and the remaining patients with no reported grade were retrospectively collected. The study was IRB approved. For each patient, sagittal and axial slices containing the gross tumor volume (GTV) were extracted from ADC maps. These were computed using the mono exponential model from diffusion weighted images (b-values = 0, 100, 1000) that were acquired prior to radiotherapy treatment. Balanced training sets were created using the Synthetic Minority Oversampling Technique (SMOTE) and fed to the DCNN. EfficientNetB0 and EfficientNetB3 were transferred from the ImageNet application to binary and four-class classification tasks. Five-fold stratified cross validation was performed for the assessment of the networks. Multiple evaluation metrics were computed including the area under the receiver operating characteristic curve (AUC). Comparisons with Resnet50, Xception, and radiomic analysis were performed. Results. for grade prediction, EfficientNetB3 gave the best performance with AUC = 0.924. For stage prediction, EfficientNetB0 was the best with AUC = 0.931. The difference between both models was, however, small and not statistically significant EfficientNetB0-B3 outperformed ResNet50 (AUC = 0.71) and Xception (AUC = 0.89) in stage prediction, and demonstrated comparable results in grade classification, where AUCs of 0.89 and 0.90 were achieved by ResNet50 and Xception, respectively. DCNN outperformed radiomic analysis that gave AUC = 0.67 (grade) and AUC = 0.66 (stage). Conclusion. the prediction of CC grade and stage from ADC maps is feasible by adapting EfficientNet approaches to the medical context.

Efektivitas Pemberian Murottal Al-Qur’an Surat Ar-Rahman dan Relaksasi Nafas Dalam Terhadap Kecemasan Pasien Sectio Caesarea G1P0AO

Sectio caesarea (SC) is an important action to prevent the risk of death of the mother or baby during labor. However, the increasing number of operations raises concerns about the health and psychological impacts on mothers. Pre-operative anxiety is often experienced by CS patients and can have a negative impact on the labor process. This study used a case study with 1 intervention patient and 1 control patient. Nursing care is provided to G1P0AO pregnant women who experience anxiety before undergoing SC surgery. Intervention in the form of murottal of Al-Qur'an Surah Ar-Rahman and deep breathing relaxation was given for 45 minutes before the operation process. Scoring of reduced anxiety levels in intervention and control patients before and after therapy showed a significant effect. Patients who received the intervention experienced significant reductions in blood pressure and pulse, indicating decreased anxiety. In contrast, control patients had increased blood pressure and pulse, indicating increased anxiety. Conclusion: Murottal therapy of Al-Qur'an Surah Ar-Rahman and deep breathing relaxation significantly reduce anxiety levels in SC patients. Integration of these nonpharmacological therapies in clinical protocols may aid in the preoperative preparation of SC patients. Further research is needed to confirm these findings and explore their potential applications in other medical contexts.

Correlation between serum uric acid level and atrial fibrillation in patients with hyperthyroidism on medical data analysis context of IoT.

Dysfunctions in metabolism and endocrine systems are outcomes of disruptions in human physiological processes, often leading to disease onset. External factors can hinder the human body's innate capacity for self-regulation and healing, particularly when immune responses are compromised, allowing these factors to interfere with normal bodily functions directly. To explore the effect of uric acid expression water in blood on the occurrence of atrial fibrillation in patients with hyperthyroidism, the expression level of uric acid in the blood and other physiological indexes were compared between patients with no symptoms of atrial fibrillation and patients with hyperthyroidism with symptoms of atrial fibrillation, to find the correlation between them. A group of 112 hyperthyroidism patients who were admitted to our hospital from September 2019 to March 2020 were chosen and split into two groups. The control group consisted of 56 individuals (21 men and 35 women) aged between 16 and 86 years old, with an average age of 46.23 years (± 7.63). The observation group consisted of 56 individuals (24 males and 32 females) between 15 and 79 years, with an average age of 53.44 years (± 8.91). In the patients who were not treated with drugs before hospitalization the disease course and symptoms varied. The patients' clinical medical and demographic data were recorded and the patients' physiological indexes were obtained through blood tests and analysis. The differences between the two groups were analyzed by renal function, blood lipid index, thyroid function, and cardiac ultrasound, and these influencing factors were analyzed by regression analysis. The research adhered to ethical norms and ensured clear data presentation by using a rigorous technique to compare uric acid levels and physiological indicators among various patient groups. The study concentrated on the validation, repeatability, and contextual interpretation of data to provide a robust and rigorously scientific comparison. The most common is the increase of uric acid in the blood, which can induce other diseases, and atrial fibrillation is one of the most common diseases of cardiovascular diseases.

Jacques Cartier, les merveilles du Nouveau Monde et l’annedda

Jacques Cartier (1491-1557) reports, in the account of his second voyage to Canada in 1535-1536, that his crew was afflicted with a “grosse maladie” (scurvy) as well as other diseases, including a case of “grosse verolle” (syphilis), but was cured thanks to herbal teas made with the leaves of a tree called annedda, and in only six days. Until now, research on the annedda episode in the récit du deuxième voyage de Cartier has focused essentially on the nature of the “grosse maladie”, on the identification of the plant called annedda, as well as its potential therapeutic virtues. This study aims to show that this episode must be understood in the context of Europe at the turn of the sixteenth century. It will see how the European imagination of the New World, as well as the medical context, notably the severe epidemic of syphilis prevalent at the time in Europe, may have influenced Cartier in the account he made to the king of his voyage.

Pharmacogénétique de l’ototoxicité des aminosides : état des connaissances et des pratiques – recommandations du Réseau francophone de pharmacogénétique (RNPGx)

The administration of aminoglycosides can induce nephrotoxicity or ototoxicity, which can be monitored through pharmacological therapeutic drug monitoring. However, there are cases of genetic predisposition to ototoxicity related to the MT-RNR1 gene, which may occur from the first administrations. Pharmacogenetic analysis recommendations have recently been proposed by the Clinical Pharmacogenetics Implementation Consortium (CPIC). The Francophone Pharmacogenetics Network (RNPGx) provides a bibliographic synthesis of this genetic predisposition, as well as professional recommendations. The MT-RNR1 gene codes for mitochondrial 12S rRNA, which constitutes the small subunit of the mitochondrial ribosome. Three variants can be identified: the variants m.1555A>G and m.1494C>T of the MT-RNR1 gene have a 'high' level of evidence regarding the risk of ototoxicity. The variant m.1095T>C has a 'moderate' level of evidence. The search for these variants can be performed in the laboratory if the administration of aminoglycosides can be delayed after obtaining the result. However, if the treatment is urgent, there is currently no rapid test available in France (a 'point-of-care' test is authorized in Great Britain). RNPGx considers: (1) the search for the m.1555A>G, m.1494C>T variants as 'highly recommended' and the m.1095T>C variant as 'moderately recommended' before the administration of an aminoglycoside (if compatible with the medical context). It should be noted that the level of heteroplasmy detected does not modify the recommendation; (2) pharmacogenetic analysis is currently not feasible in situations of short-term aminoglycoside administration, in the absence of an available analytical solution (rapid test to be evaluated in France); (3) the retrospective analysis in case of aminoglycoside-induced ototoxicity is 'recommended'; (4) analysis of relatives is 'recommended'. Through this summary, RNPGx proposes an updated review of the MT-RNR1-aminoglycoside gene-drug pair to serve as a basis for adapting practices regarding pharmacogenetic analysis related to aminoglycoside treatment.

Large language models to evaluate racial discrepancies in performance status assignment.

1561 Background: Oncologic treatment eligibility relies heavily on “Performance Status” (PS), a subjective gauge of a patient's overall health. Despite frameworks aimed at quantifying PS, these assessments remain prone to potential bias. A systematic evaluation of discrepancies in language between ECOG PS and race has yet to be conducted. Large language models (LLMs) have the ability to synthesize text and may enable an assessment of the relationship between race, note text, and physician-documented PS. We hypothesize that LLMs can quantify these relationships to understand potential inconsistencies in ECOG PS. Methods: In our single-institution cohort study, we examined patients from medical or radiation oncology clinics between January 2012 to December 2023 with documented ECOG PS. PS was extracted from clinical notes and redacted from downstream analyses. Notes were categorized by patient-reported race (Asian, Black, White, Other). 1,500 matched subjective assessments (from oncologist clinical notes) and PS were randomly sampled for each race, except for Black patients, where only 640 entries were available. We divided training and test sets using an 80-20 ratio across each race. Using the training cohort, we trained race-specific models to assign ECOG PS based on subjective assessment, using two LLMs: UCSFBERT (BERT pretrained on UCSF data) and RoBERTa. In the hold-out test sets, we applied each model across all races to assign ECOG PS based on subjective assessments. These were evaluated by micro-F1 (model accuracy) and Multiway ANOVA to compute model-specific p-values. Results: 13.6% of patients had a documented ECOG 2+, with variation across races (Asian: 16.1%, Black: 17.7%, White: 7.7%, Other: 15.3%). Models tended to assign Black patients with ECOG 0-1 with worse performance status (ECOG 2+). Other than RoBERTa models trained on Asian and White cohorts, all models trained on a specific race showed disparate classification results when applied to other races (out-of-domain), highlighting potential biases in LLM models trained where a specific race predominates (Table; ANOVA). Conclusions: This study demonstrates a novel way to use LLM to assess discordance within physician documentation and performance status assignment. It underscores the need for incorporating diverse demographic data when using LLM in medical contexts. This research has potential to dissect how bias may be propagated within physician practice and provide insight into known disparities in clinical trial enrollment and standard of care implementation.[Table: see text]

The misuse of cognitive enhancers by medical students: a cross-sectional study using questionnaires.

The misuse of prescription psychostimulants (PPs) is growing among medical students, who are subject to high levels of stress, not least because of a demanding and competitive educational environment. To investigate this phenomenon among medical students in Belgium to identify misuse risk factors. We conducted a cross-sectional study using an online questionnaire from 7 march 2023 to 3 April 2023. A semi-quantitative questionnaire based on a literature review and consultation with experts was drawn up, including the Perceived Stress Scale (PSS-10) and the Big Five Inventory (BFI-10). The questionnaire was administered to 323 final-year medical students (61.9% of whom responded) and comparatively to 325 second-to-last-year students and 205 first-year students (response rate was 36.9% and 18.5%, respectively). Descriptive and bivariate analyses were performed with Jamovi (version 2.3.21). Use of PPs increased over the years. In the final year, 12.6% reported that they had already used PPs to improve their cognitive performance, and 3.6% were currently using them. Various risk factors were associated with the misuse of PPs, including lack of consciousness, sensation-seeking, other substances use, high stress levels, social influence, curriculum high standards perception, and lack of ethical concern regarding the substance use. This study showed that more than one in ten final-year students have used PPs at least once during their studies, which can be explained mainly by challenging medical education context and personality traits. Tailored preventive strategies should be implemented.

Harnessing ChatGPT for Thematic Analysis: Are We Ready?

ChatGPT (OpenAI) is an advanced natural language processing tool with growing applications across various disciplines in medical research. Thematic analysis, a qualitative research method to identify and interpret patterns in data, is one application that stands to benefit from this technology. This viewpoint explores the use of ChatGPT in three core phases of thematic analysis within a medical context: (1) direct coding of transcripts, (2) generating themes from a predefined list of codes, and (3) preprocessing quotes for manuscript inclusion. Additionally, we explore the potential of ChatGPT to generate interview transcripts, which may be used for training purposes. We assess the strengths and limitations of using ChatGPT in these roles, highlighting areas where human intervention remains necessary. Overall, we argue that ChatGPT can function as a valuable tool during analysis, enhancing the efficiency of the thematic analysis and offering additional insights into the qualitative data. While ChatGPT may not adequately capture the full context of each participant, it can serve as an additional member of the analysis team, contributing to researcher triangulation through knowledge building and sensemaking.

Understanding Fentanyl A Synthetic Piperidine Opioid Drug: Deciphering the Pain-Relieving Double-Edged Sword

Fentanyl is a synthetic opioid analgesic that has revolutionized pain management in various medical contexts. It has remarkable potency and effectiveness in managing severe pain, owing to its unique mechanisms of action on opioid receptors and modulation of pain perception. Fentanyl can be administered through various routes, such as transdermal patches, intravenous injections, lozenges, and nasal sprays. It is commonly utilized for post-operative pain management, cancer-related pain, and palliative care. However, fentanyl also poses significant risks of addiction, abuse, and overdose. The alarming increase in fentanyl-related fatalities underscores the importance of understanding proper dosage, monitoring patients closely, and implementing harm reduction strategies. Furthermore, the responsible use of fentanyl requires comprehensive patient assessment, individualized treatment plans, and the active involvement of healthcare providers in educating patients and obtaining informed consent. The article also discusses ongoing research efforts to improve fentanyl’s safety profile while maintaining its efficacy. These include emerging technologies, potential formulations, and alternative pain management strategies that could help reduce the dependence on fentanyl and prevent its adverse effects. The article provides a balanced perspective on fentanyl, emphasizing the balance between its pain-relieving benefits and the potential risks it poses. It also reinforces the significance of responsible prescribing, vigilant monitoring, and patient education to ensure optimal pain relief and patient safety.

Exploring the Potential and Challenges of CRISPR Delivery and Therapeutics for Genetic Disease Treatment

AbstractHuman genetic disorders, arising from a range of genetic irregularities, can significantly affect human physiology, often with limited available treatment options. The development of the CRISPR system, facilitating precise editing of the genome, has opened new avenues for addressing a range of mutations found in various genetic disorders. However, there is currently a lack of comprehensive reviews that specifically address the application of CRISPR in genetic diseases. To bridge this gap, this review focuses on exploring the advancements in CRISPR technology and their utility in therapeutic approaches for various genetic disorders. This review introduces human genetic disorders, explains the fundamental mechanisms of CRISPR editing, and highlights the latest advancements in CRISPR technology. Additionally, it examines three CRISPR delivery techniques, including physical delivery, viral vectors, and nanocarriers. It further reviews CRISPR's applications in therapeutic approaches for genetic disorders. Finally, it identifies the primary hurdles associated with industrial development and ethics considerations that should be addressed before the application of CRISPR in a medical context.

The Critical Social Voice of Midwifery: Midwives in Ontario

Many researchers have called community midwifery of the 1970s and 1980s a social movement. At the heart of the many goals of that movement was the desire to bring about significant social change regarding the medical and social contexts of birth. This article is an initial exploration into the critical social voice of midwifery and its current expression in Ontario, through interviewing midwives about their thoughts on the connections between midwifery, social activism and social change. The regulation and funding of midwifery in Ontario has created a new structural environment for midwifery practice and it is the phase of socio-cultural change following legislative change that is the focus of this paper. Speaking with midwives about midwifery practice and social change will help make visible the oppositional, critical work that midwives continue to do. This paper focuses on four themes identified as particular sites of the social change work of midwives in Ontario.

Comparative analysis of essential oil efficacy against the Asian longhorned tick Haemaphysalis longicornis (Acari: Ixodidae).

This study evaluated the potential repellent and acaricidal effects of 4 essential oils (clove, eucalyptus, lavender, and mint) against the Asian longhorned tick Haemaphysalis longicornis, a vector of various tick-borne diseases in medical and veterinary contexts. Selected for their potential repellent and acaricidal properties, the 4 essential oils were tested on adult and nymph H. longicornis ticks at different concentrations. The experiment assessed mortality rates and repellency, particularly during tick attachment to host skin. There was a significant increase (p<0.05) in tick mortality and repellency scores across all groups. At a 1% concentration, adult tick mortality ranged from 36% to 86%, while nymph mortality ranged from 6% to 97%. Clove oil exhibited notable efficacy, demonstrating high mortality rates of nymphs and adults. Clove oil also displayed strong repellency properties, with a repellency index of 0.05, surpassing those of mint, eucalyptus, and lavender oils. Clove oil showed the highest effectiveness in deterring nonattached adult ticks (90%) and nymphs (95%) when applied to skin. Clove oil was the most effective against adult and nymph ticks, achieving mortality rates of 86% and 97%, respectively, and led to the highest nonattachment rates when applied to skin. In conclusion, essential oils such as clove, eucalyptus, lavender, and mint oils present promising results for tick population control.

Molecular cloning, identification, transcriptional analysis, and silencing of enolase on the life cycle of Haemaphysalis longicornis (Acari, Ixodidae) tick.

Ticks, blood-sucking ectoparasites, spread diseases to humans and animals. Haemaphysalis longicornis is a significant vector for tick-borne diseases in medical and veterinary contexts. Identifying protective antigens in H. longicornis for an anti-tick vaccine is a key tick control strategy. Enolase, a multifunctional protein, significantly converts D-2-phosphoglycerate and phosphoenolpyruvate in glycolysis and gluconeogenesis in cell cytoplasm. This study cloned a complete open reading frame (ORF) of enolase from the H. longicornis tick and characterized its transcriptional and silencing effect. We amplified the full-length cDNA of the enolase gene using rapid amplification of cDNA ends. The complete cDNA, with an ORF of 1,297 nucleotides, encoded a 432-amino acid polypeptide. Enolase of the Jeju strain H. longicornis exhibited the highest sequence similarity with H. flava (98%), followed by Dermacentor silvarum (82%). The enolase motifs identified included N-terminal and C-terminal regions, magnesium binding sites, and several phosphorylation sites. Reverse transcription-polymerase chain reaction (RT-PCR) analysis indicated that enolase mRNA transcripts were expressed across all developmental stages of ticks and organs such as salivary gland and midgut. RT-PCR showed higher transcript levels in syn-ganglia, suggesting that synganglion nerves influence enolase,s role in tick salivary glands. We injected enolase double-stranded RNA into adult unfed female ticks, after which they were subsequently fed with normal unfed males until they spontaneously dropped off. RNA interference significantly (P<0.05) reduced feeding and reproduction, along with abnormalities in eggs (no embryos) and hatching. These findings suggest enolase is a promising target for future tick control strategies.

A Qualitative Study of the Psychological Effects of Motivational Quotes

Motivational quotes wield and obtain a sense of power towards inspiring individuals to strive towards their goals, harnessing both internal and external motivations. The absence of motivational quotes tends to serve as catalysts, igniting the drive to propel one through challenges and pursue excellence. Externally, motivational quotes often encapsulate universal truths or wisdom, offering perspective and guidance from varied sources and figures. This paper explores the effectiveness of motivational quotes in medical and educational contexts, with a focus on their impact on individuals' behavior. While numerous studies have reported a generally positive attitude toward the use of motivational quotes, specific concerns have emerged. This paper has observed that motivational quotes, delivered through various media, have successfully motivated individuals to take positive actions, such as adhering to medication regimens and quitting smoking. However, some studies have highlighted potential limitations, including overexposure leading to reduced effectiveness, the contextual relevance of motivational quotes, and the distinction between external and internal motivation. This paper presents an overview of the existing research, offering insights into the mixed opinions on the use of motivational quotes for motivation. It calls for future studies to consider contextual factors, audience mindset, media selection, and quote suitability in order to enhance their efficacy.