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Background and Purpose: Size-specific dose estimates (SSDE) have been introduced into computed tomography (CT) dosimetry to tailor patients' unique sizes to facilitate accurate CT radiation dose quantification and optimization. The purpose of this study was to develop and validate an automated algorithm for the determination of patient size (effective diameter) and SSDE. Materials and Methods: A MATLAB platform was used to develop software of algorithms based on image segmentation techniques to automate the calculation of patient size and SSDE. The algorithm was used to automatically estimate the individual size and SSDE of four CT dose index phantoms and 80 CT images of pediatric patients comprising head, thorax, and abdomen scans. For validation, the American Association of Physicists in Medicine (AAPM) manual methods were used to determine the patient's size and SSDE for the same subjects. The accuracy of the proposed algorithm in size and SSDE calculation was evaluated for agreement with the AAPM's estimations (manual) using Bland-Altman's agreement and Pearson's correlation coefficient. The normalized error, system bias, and limits of agreement (LOA) between methods were derived. Results: The results demonstrated good agreement and accuracy between the automated and AAPM's patient size estimations with an error rate of 1.9% and 0.27% on the patient and phantoms study, respectively. A 1% percentage difference was found between the automated and manual (AAPM) SSDE estimates. A strong degree of correlation was seen with a narrow LOA between methods for clinical study (r > 0.9771) and phantom study (r > 0.9999). Conclusion: The proposed automated algorithm provides an accurate estimation of patient size and SSDE with negligible error after validation. Keywords: Algorithm, automation, body size, computed tomography scan, image segmentation, pediatric, radiation dosage, size-specific dose estimate, Author(s): Muhammad Kabir Abdulkadir (corresponding author) [1,2]; Noor Diyana Osman [1]; Anusha Achuthan [3]; Radin A. Nasirudin [1]; Muhammad Zabidi Ahmad [1]; Noor Hasyima Mat Zain [1]; Ibrahim Lutfi Shuaib [...]

Abstract It has been established that the Hydrazone derivatives have important pharmacological effects. In the first step, hydrazine (NH2NH2) reacts with a compound containing a carbonyl group (C = O) in the presence of ethanol and heat, leading to the formation of hydrazone compound (H1). The second step is the formation of the Schiff base (H2) by the reaction of compound (H1) with indole, ethanol, and acetic acid, which contain a double bond (C = N). In the third step, the (H1) reacts with thiophene, ethanol, and acetic acid to form a compound (H3) containing multiple bonds between the indole and thiophene rings. The synthetic test compounds underwent characterization using TLC, IR, 1 H - NMR, and ¹3C NMR spectral examinations. Both compounds, H2 and H3, exhibit antioxidant activity at different concentrations (from 12.5 to 100 µgmL− 1), where the effect increases gradually with the increase in concentration. The compounds (H2 and H3) exhibited an apparent inhibitory effect on the growth of Staphylococcus aureus, Escherichia coli, and Candida albicans. Calculations have been performed for DFT, molecular docking, molecular dynamics simulations, and the ADMET protocol, and they are essential for describing the interaction and stability. In hydrazone derivatives, groups like amine, hydroxy, thiophene, and indole form hydrogen bonds and electrostatic interactions with amino acids such as arginine, lysine, glutamic acid, and aspartic acid. These interactions are crucial to evaluating the compound’s stability and its potential to inhibit enzyme activity. The results indicate that the compound shows strong binding and stability at the active site, making it a promising candidate for further studies as an anti-colon cancer agent.

Zinc (Zn) and iron (Fe) are necessary for structural and metabolic functions of the body like, immune competence, physical growth, reproductive function, and neurobehavioral development. The deficiency of micronutrients is a severe public health distress in nearly all developing countries which leads to the syndrome of malnutrition mostly in children and women. The present study was conducted to compare the effect of different levels of Fe and Zn on feed intake (FI), feed conversion ratio (FCR), daily egg production, and Fe and Zn depositions in the egg yolk of laying hens. For this purpose, 80 commercial laying birds were allocated into four groups consisting of four replicates each (n = 5). The basal ration was provided to the control group (C) while three different levels (100, 200, and 300 mg/kg diet/element) of Fe and Zn (1:1) were provided to the FZ100, FZ200, and FZ300 groups, respectively. Overall FI, hen day egg production, and FCR were not affected (p > 0.05). The Fe and Zn content in egg yolk were significantly increased in the FZ300 group after 4 weeks of supplementation. The cost per egg produced in response to different levels of Fe and Zn supplementation was not affected however a numerical increase in cost/egg was found in the FZ300 group. In conclusion, Fe and Zn supplementation (300 mg/kg diet/element) significantly enhanced the egg Fe and Zn contents without adversely affecting production parameters including FI, egg production, and FCR of laying hens.

This study assessed the potential of a commercial probiotic mixture on performance, gut health and nutrient utilisation of broilers-fed poultry by-product meal (PBM)-based diet. Three-hundred-and-twenty Ross-308-day-old broiler chicks were obtained from a commercial poultry hatchery. The chicks were randomly assigned to four different treatment groups, each consisting of four replicates (n = 10) in a randomised complete design. The positive control group (CON) was fed commercial poultry ration, and the negative control group (N-CON) was fed PBM partially replacing soybean meal at a 65% level. The MSP1 and MSP2 groups contained the same diet as the N-CON group but supplemented with multi-strain probiotics (MSP) at 50 mg/kg and 100 mg/kg. Overall N-CON group performed poorly, but the addition of probiotics in group MSP1 and MSP2 diets significantly improved performance. Feed efficiency and bird weight gain were improved (p

Abstract Worldwide, stroke is a leading cause of long-term disability in adults. Alteplase is the only approved treatment for acute ischemic stroke (AIS) and results in an improvement in a third of treated patients. Most studies evaluated the post-alteplase haemorrhagic transformation of brain infarction as a homogeneous entity but we evaluated the predictors of each subtype of haemorrhagic transformation of brain infarction. Our trial included 616 AIS alteplase-treated patients. We evaluated the ability of different risk factors, clinical presentation, and imaging features to predict different haemorrhagic transformation (HT) subtypes. HT was seen in 152 patients (24.7%), higher NIHSS, cardioembolic stroke and atrial fibrillation were independent predictors of all ECASS-based subtypes of hemorrhagic infarction, in addition, anterior-circulation stroke was an independent predictor of hemorrhagic infarction type 1 (odds ratio [OR], 11.04; 95% CI, 9.81 to 12.70; P-value > 0.001) and type2 (OR, 11.89; 95% CI, 9.79 to 14.44; P-value > 0.001), while older age was also an independent predictor of parenchymal hematoma type1 (OR, 1.312; 95% CI, 1.245 to 1.912; P-value 0.02). In AIS patients treated with alteplase in Egypt and Saudi Arabia, higher NIHSS, cardioembolic stroke and atrial fibrillation were independent predictors of all ECASS-based subtypes of hemorrhagic infarction; in addition, anterior-circulation stroke was an independent predictor of hemorrhagic infarction type 1 and 2, while older age was also an independent predictor of parenchymal hematoma type1. Trial registration: (clinicaltrials.gov NCT06337175), retrospectively registered on 29/03/2024.

Abstract Background Early diagnosis of large vessel occlusion (LVO) in acute stroke often requires CT angiography (CTA). Automated CT perfusion (CTP) software, which identifies blood flow abnormalities, enhances LVO diagnosis and patient selection for endovascular thrombectomy (EVT). This study evaluates the sensitivity of automated CTP images in detecting perfusion abnormalities in patients with acute ischemic stroke (AIS) and LVO or medium vessel occlusion (MeVO), compared to CTA. Methods We screened acute ischemic stroke patients presenting within 24 h who underwent CT, CTA, and CTP as per institutional protocol. RAPID AI software processed CTP images, while neuroradiologists reviewed CTA for intracranial arterial occlusions. Sensitivity, specificity, and accuracy of automated CTP maps in detecting occlusions were assessed. Results Of 790 screened patients, 31 were excluded due to lack of RAPID CTP data or poor‐quality scans, leaving 759 for analysis. The median age was 71 years (IQR: 61–81), with 47% female. Among them, 678 had AIS, and 81 had AIS ruled out. CTA identified arterial occlusion in 562 patients (74%), with corresponding CTP abnormalities in 537 patients (Tmax > 6 sec). In the 197 without occlusion, CTP was negative in 161. Automated CTP maps had a sensitivity of 95.55% (CI 95: 93.50–97.10%), specificity of 81.73% (CI 95: 75.61–86.86%), negative predictive value of 98.22% (CI 95: 97.39–98.79%), positive predictive value of 63.54% (CI 95: 56.46–70.09%), and overall accuracy of 85.18% (CI 95: 82.45–87.64%). Conclusions Automated CTP maps demonstrated high sensitivity and negative predictive value for LVOs and MeVOs, suggesting their usefulness as a rapid diagnostic tool, especially in settings without expert neuroradiologists.

The prevalence of cardiovascular disease (CVD) is a major issue in world health. There is a compelling desire for precise and effective methods for making decisions to determine the most effective technique for treating CVD. Here, we focused on the urgent matter at hand. Pythagorean fuzzy dynamic settings are exceptionally proficient at capturing ambiguity because they can handle complex problem specifications that involve both Pythagorean uncertainty and periodicity. In this article, we introduced a pair of novel aggregation operators: The Pythagorean fuzzy dynamic ordered weighted averaging (PFDOWA) operator and the Pythagorean fuzzy dynamic ordered weighted geometric (PFDOWG) operator, and we proved various structural properties of these concepts. Using these operators, we devised a systematic methodology to handle multiple attribute decision-making (MADM) scenarios incorporating Pythagorean fuzzy data. Moreover, we endeavored to address a MADM problem, where we discerned the most efficacious strategy for the management of CVD through the application of the proposed operators. Finally, we undertook an exhaustive comparative analysis to evaluate the ability of the suggested methods in connection with several developed procedures, therefore demonstrating the reliability of the generated methodologies.

The spectacular concept of integrating OCR with the Internet of Things (IoT) and Wireless Sensor Networks (WSN), presented in this study, intends to increase the autonomy and social inclusion of people with visual impairments and deaf-blindness. This application converts text from photographs into real-time Braille representations by utilizing the capabilities of Optical Character Recognition (OCR) technology, Braille displays, and IoT. Braille is a tactile form of writing which assists the visually impaired by representing letters and numbers with raised dots. Text recognition and conversion through OCR helps people with vision issues by converting printed text into accessible formats like audio or Braille, assisting people in with recognizing things around them. Individuals with visual impairments are given autonomous access to critical information by smoothly integrating modern technologies, boosting their self-confidence and promoting active social involvement. This OCR-Braille approach's primary objective is to minimize the knowledge gap and promote social equality. Users' ease of access to labels, educational resources, and digital information about their environment fosters a sense of empowerment and independence. This ground-breaking study not only eliminates the accessibility gap but also provides the groundwork for a society that is better linked and accessible. Individuals have access to the tools they need for independently navigating their surroundings through simple IoT connectivity and WSN. This research lays the path for a future for assistive technology to catalyze equality and independence, independent of one's visual ability, by fusing assistive technology and IoT connectivity.

Abstract Vaccine acceptance is a crucial component of a viable immunization program in healthcare system, yet the disparities in new and existing vaccination adoption rates prevail across regions. Disparities in the rate of vaccine acceptance result in low immunization coverage and slow uptake of newly introduced vaccines. This research presents an innovative AI-driven predictive model, designed to accurately forecast vaccine acceptance within immunization programs, while providing high interpretability. Primarily, the contribution of this study is to classify vaccine acceptability into Low, Medium, Partial High, and High categories. Secondly, this study implements the Feature Importance method to make the model highly interpretable for healthcare providers. Thirdly, our findings highlight the impact of demographic and socio-demographic factors on vaccine acceptance, providing valuable insights for policymakers to improve immunization rates. A sample dataset containing 7150 data records with 31 demographic and socioeconomic attributes from PDHS (2017–2018) is used in this paper. Using the LightGBM algorithm, the proposed model constructed on the basis of different machine-learning procedures achieved 98% accuracy to accurately predict the acceptability of vaccines included in the immunization program. The association rules suggest that higher SES, region, parents’ occupation, and mother’s education have an association with vaccine acceptability.

Abstract The resilient framework of Linguistic Intuitionistic Fuzzy Sets (LIFSs) allows for the representation and management of uncertainties related to intuitionistic judgments and linguistic expressions. Recent advances in passive and active safety systems have reduced highway fatalities. Autonomous vehicles can improve safety, efficiency, and mobility by navigating traffic without a driver. One of the primary benefits associated with this technology is that it reduces the number of traffic collisions that result in millions of fatalities and numerous injuries. In this research work, we devise two novel aggregation operators: the linguistic intuitionistic fuzzy Dombi ordered weighted averaging (LIFDOWA) operator and the linguistic intuitionistic fuzzy Dombi ordered weighted geometric (LIFDOWG) operator, and explore their fundamental structural properties. We provide innovative score and accuracy functions for multiple attribute decision-making (MADM) problems within the framework of LIF knowledge. Moreover, we use these techniques to develop a specialized algorithm for MADM issues that addresses the complexities arising from ambiguous data during the selection process. We also demonstrate the effectiveness of our proposed methods by applying them to solve the MADM scenario of selecting an optimal approach to improve the credibility of autonomous vehicle control systems on a commercial scale. In addition, we also compare and evaluate the authenticity and practicability of the newly designed techniques in comparison to existing methodologies.

Introduction: Primary extrapulmonary small cell carcinoma (EPSCC) is a rare and aggressive tumor with diverse clinical manifestations. In the realm of head and neck pathology, EPSCC most commonly presents in the larynx or oral cavity. Sinonasal small cell neuroendocrine carcinoma represents a particularly infrequent and aggressive neoplasm affecting the nasal cavity and paranasal sinuses. Case Presentation: Here in, we are presenting a case of a 40-year-old nonsmoking male with left maxillary sinus EPSCC, demonstrating extensive local invasion and regional lymphadenopathy. The patient presented with nasal obstruction, ocular swelling, visual loss, and multiple neck swellings. Diagnosis was challenging, requiring a multidisciplinary approach and histopathological confirmation. Immunohistochemistry revealed positive cytokeratin AE1/AE3 and synaptophysin markers validating the diagnosis of small cell carcinoma. Conclusion: This case underscores the diagnostic complexities and highlights the importance of swift intervention and a multidisciplinary treatment approach for optimizing patient outcomes in rare and aggressive tumors like EPSCC. Early detection and prompt management are crucial in resource-limited settings to mitigate morbidity and mortality.

Abstract The Web 3.0 network system, the next generation of the world wide web, incorporates new technologies and algorithms to enhance accessibility, decentralization, and security, mimicking human comprehension and enabling more personalized user interactions. The key component of this environment is decentralized identity management (DIM), embracing an identity and access management strategy that empowers computing devices and individuals to manage their digital personas. Aggregation operators (AOs) are valuable techniques that facilitate combining and summarizing a finite set of imprecise data. It is imperative to employ such operators to effectively address multicriteria decision-making (MCDM) issues. Yager operators have a significant extent of adaptability in managing operational environments and exhibit excellent effectiveness in addressing decision-making (DM) uncertainties. The complex spherical fuzzy (CSF) model is more effective in capturing and reflecting the known unpredictability in a DM application. This research endeavors to enhance the DM scenario of the Web 3.0 environment using Yager aggregation operators within the CSF environment. We present two innovative aggregation operators, namely complex spherical fuzzy Yager-ordered weighted averaging (CSFYOWA) and complex spherical fuzzy Yager-ordered weighted geometric (CSFYOWG) operators. We elucidate some structural characteristics of these operators and come up with an updated score function to rectify the drawbacks of the existing score function in the CSF framework. By utilizing newly proposed operators under CSF knowledge, we develop an algorithm for MCDM problems. In addition, we adeptly employ these strategies to handle the MCDM scenario, aiming to identify the optimal approach for ensuring the privacy of digital identity or data in the evolving landscape of the Web 3.0 era. Moreover, we undertake a comparative study to highlight the veracity and proficiency of the proposed techniques compared to the previously designed approaches.

Students' living conditions may suffer as a result of climate change. This research examined the impact of climate change awareness on undergraduates' beliefs about socio-emotional well-being in Nigeria. The total number of undergraduate students in Kwara state made up the study's population, with the sample size being 589. The Climate Change and Mental Wellbeing Questionnaire (CCMWQ) was used to collect data from randomly selected undergraduate participants for the study. The acquired data was evaluated using descriptive statistics, t-test, ANOVA and PPMC at a significance level of 0.05. The results showed that most undergraduates are aware of climate change but have little understanding of its origins, effects, and preventive strategies. The results further revealed that the undergraduates' level of climate change awareness influences their beliefs about their socio-emotional wellbeing. At different ages, participants' opinions on how climate change would affect their socio-emotional health varied dramatically. Similarly, a correlation exists between climate change awareness and beliefs about socio-emotional well-being and awareness and attitude towards protecting the environment.

This study analyzed how people with disabilities are portrayed in picture books with the Coretta Scott King Award (CSKA) to address the intersectionality of African/African American racial identity and disabilities. Disability critical race theory was foundational for this study. The pool of 134 picture books that received the CSKA from 1971 to 2020 was used as the data for the systematic content analysis. For analysis, the researchers utilized a qualitative approach that guided axial coding and selective coding in looking for emerging themes. They found that 13 picture books portrayed African/African American characters with disabilities. The majority of these books did not necessarily emphasize the disabilities of these characters. When focusing on other dimensions of identity such as gender, age, disability type, and so on, the researchers found underrepresentation, invisibility, and marginalization issues, which led them to discussions of power relations. This study calls for future studies that can discover more portrayals of African/African American people with disabilities and that can deepen scholarly discussions about their representation in children's literature.

Malaria-associated pathogenesis such as parasite invasion, egress, host cell remodelling and antigenic variation requires concerted action by many proteins, but the molecular regulation is poorly understood. Here we have characterized an essential Plasmodium-specific Apicomplexan AP2 transcription factor in Plasmodium falciparum (PfAP2-P; pathogenesis) during the blood-stage development with two peaks of expression. An inducible knockout of gene function showed that PfAP2-P is essential for trophozoite development, and critical for var gene regulation, merozoite development and parasite egress. Chromatin immunoprecipitation sequencing data collected at timepoints matching the two peaks of pfap2-p expression demonstrate PfAP2-P binding to promoters of genes controlling trophozoite development, host cell remodelling, antigenic variation and pathogenicity. Single-cell RNA sequencing and fluorescence-activated cell sorting revealed de-repression of most var genes in Δpfap2-p parasites. Δpfap2-p parasites also overexpress early gametocyte marker genes, indicating a regulatory role in sexual stage conversion. We conclude that PfAP2-P is an essential upstream transcriptional regulator at two distinct stages of the intra-erythrocytic development cycle.