Multimodal Approach Research Articles

Current concepts and targets for preventing the transition of acute to chronic postsurgical pain.

It is estimated that approximately a third of patients undergoing certain surgeries may report some degree of persistent pain postoperatively. Chronic postsurgical pain (CPSP) reduces quality of life, is challenging to treat, and has significant socio-economic impact. From an epidemiological perspective, factors that predispose patients to the development of CPSP may be considered in relation to the patient, the procedure or, the care environment. Prevention or management of transition from acute to chronic pain often need a multidisciplinary approach beginning early in the preoperative period and continuing beyond surgical admission. The current concepts regarding the role of central and peripheral nervous systems in chronification of pain may provide targets for future therapies but, the current evidence seems to suggest that a multimodal analgesic approach of preventive analgesia along with a continued follow-up and treatment after hospital discharge may hold the key to identify and manage the transitioning of acute to chronic pain. A comprehensive multidisciplinary approach with prior identification of risk factors, minimizing the surgical insult and a culture of utilizing multimodal analgesia and continued surveillance beyond the period of hospitalization is an important step towards reducing the development of chronic pain. A transitional pain service model may accomplish many of these goals.

Quelles avancées dans la prise en charge du carcinome anaplasique de la thyroïde en 2024 ?

LATEST DEVELOPMENTS IN ANAPLASTIC THYROID CARCINOMA MANAGEMENT IN 2024: Anaplastic thyroid carcinomas (ATCs) represent a rare and undifferentiated form of thyroid cancer with a poor prognosis, typically marked by a median overall survival of four to ten months. However, recent advances have shown improvements due to the more systematic application of molecular testing, targeted therapies, and immunotherapy, alongside the establishment of rapid specialized care protocols in expert centers. Clinically, ATCs often present as a rapidly enlarging cervical mass originating from the thyroid, causing neck, pain and tenderness, dyspnea and dys-phagia, and associated lymphadenopathy, typically in elderly patients. Diagnostic confirmation requires an urgent biopsy, reviewed by a pathologist within the TUTHYREF-path network, with a mandatory search for BRAFV600E molecular alterations. Following diagnosis, care coordination is expedited within an expert center of the ENDOCAN-TUTHYREF network whenever feasible. Initial surgery is rarely an option due to frequent loco-regional cervical invasion. A multimodal treatment approach is essential: BRAF/MEK inhibitors (dabrafenib/trametinib) in cases of BRAFV600E mutation, or chemotherapy in the absence of a molecular target, combined with radiotherapy and, potentially, surgery if the disease becomes resectable following induction therapy. PD-1/PD-L1 targeted immunotherapy, either alone or in combination with targeted therapies, has shown potential to extend survival in some patients; however, predictive biomarkers and the optimal sequencing of immunotherapy (whether as induction and/or maintenance) require further clarification and may vary depending on the clinical context.

Medicinal Plants Derived Green Carbon Dots: Synthesis, Characterization and Their Potential Applications in Cancer Therapy.

This review aims to explore the synthesis, characterization, and potential applications of carbon dots (CDs) derived from medicinal plants for cancer prevention, highlighting their role as a promising alternative in nanotechnological approaches. A comprehensive literature search was conducted to gather information on the synthesis methods, complex matrices, characterization techniques, and potential applications of CDs derived from medicinal plants in cancer therapy. Carbon dots (CDs) have emerged as a subject of significant interest due to their favorable chemical and biological properties. Various precursors, including graphite, carbon black, and organic molecules, are utilized in the synthesis of CDs through chemical or physical methods. Notably, CDs derived from medicinal plants offer environmentally friendly alternatives, leveraging complex matrices such as aqueous, alcoholic, and hydroalcoholic extracts. This review emphasizes the green synthesis approaches, characterization techniques, and diverse applications of CDs, including drug transport, bioimaging, biosensing, and anti-cancer therapies. Furthermore, it highlights the advantages and disadvantages of different synthesis methods, aiding researchers in selecting appropriate techniques for continuous production. Carbon dots (CDs) represent a transformative advancement in nanotheranostics, offering a versatile platform for precise cancer diagnosis and therapy. With inherent anticancer properties, CDs hold promise in photodynamic therapy (PDT) and photothermal therapy (PTT), enabling precise tumor targeting while minimizing systemic toxicity. To address the limitations of standalone PDT and PTT, researchers are exploring multimodal treatment approaches integrating CDs. By leveraging the unique properties of CDs derived from medicinal plants, a new era of precision cancer therapy may be realized, emphasizing enhanced therapeutic outcomes and reduced adverse effects.

Application of multimodal deep learning and multi-instance learning fusion techniques in predicting STN-DBS outcomes for Parkinson's disease patients

Parkinson's Disease (PD) is a progressive neurodegenerative disorder with substantial impact on patients' quality of life. Subthalamic nucleus deep brain stimulation (STN-DBS) is an effective treatment for advanced PD, but patient responses vary, necessitating predictive models for personalized care. Recent advancements in medical imaging and machine learning offer opportunities to enhance predictive accuracy, particularly through deep learning and multi-instance learning (MIL) techniques. This retrospective study included 127 PD patients undergoing STN-DBS. Medical records and imaging data were collected, and patients were categorized based on treatment outcomes. Advanced segmentation models were trained for automated region of interest (ROI) delineation. A novel 2.5D deep learning approach incorporating multi-slice representation was developed to extract detailed ROI features. Multi-instance learning fusion techniques integrated predictions across multiple slices, combining radiomics and deep learning features to enhance model performance. Various machine learning algorithms were evaluated, and model robustness was assessed using cross-validation and hyperparameter optimization. The MIL model achieved an area under the curve (AUC) of 0.846 for predicting STN-DBS outcomes, surpassing the radiomics model's AUC of 0.825. Integration of MIL and radiomics features in the DLRad model further improved discriminative ability to an AUC of 0.871. Calibration tests showed good model reliability, and decision curve analysis demonstrated clinical utility, affirming the model's predictive advantage. This study demonstrates the efficacy of integrating MIL, radiomics, and deep learning techniques to predict STN-DBS outcomes in PD patients. The multimodal fusion approach enhances predictive accuracy, supporting personalized treatment planning and advancing patient care.

Artificial intelligence (AI) applications in healthcare and considerations for nursing education

Aim/objectiveTo review the current AI applications in healthcare and explore the implications for nurse educators in innovative integration of this technology in nursing education and training programs. BackgroundThere are a variety of Artificial Intelligence (AI) applications currently supporting patient care in many healthcare settings. A nursing workforce that leverages healthcare technology to enhance efficiency and accuracy of patient health outcomes is necessary. Nurse educators must understand the various uses of AI applications in healthcare to equip themselves to effectively prepare students to use the applications. DesignQualitative synthesis and analysis of existing literature. MethodsGenerative AI (ChatGPT) was used to support the development of a list of the current AI applications in healthcare. Each application was evaluated for relevance and accuracy. A literature review to define and understand the use of each application in clinical practice was completed. The search terms “AI” and “Health Education” were used to review the literature for evidence on educational programs used for training learners. ResultsTen current applications of AI in healthcare were identified and explored. There is little evidence that outlines how to integrate AI education into educational training for nurses. ConclusionA comprehensive multimodal educational approach that uses innovative learning strategies has potential to support the integration of AI concepts into nursing curriculum. The use of simulation and clinical practicum experiences to support experiential learning and to offer opportunities for practical application and training. Considerations for ethical use and appropriate critical evaluation of AI applications are necessary.

S2677 Managing Disrupted Pancreatic Duct Syndrome With a Multimodal Endoscopic Approach

S2677 Managing Disrupted Pancreatic Duct Syndrome With a Multimodal Endoscopic Approach

Comparison of the Analgesic Effect of Erector Spinae Plane Block Versus Caudal Epidural Block Following Unilateral Pediatric Inguinal Hernia Surgery: A Prospective, Randomized Study

Background: Caudal epidural block (CEB) has long been used as a standard method for pain control in pediatric surgery. Erector spinae plane block (ESPB) is a newer technique that has attracted attention for its potential to provide analgesia in children. Aim: The aim of this study was to compare and evaluate the effectiveness of ESPB and caudal block on postoperative pain in pediatric patients who underwent unilateral inguinal hernia surgery. Methods: This prospective, randomized trial was conducted with a total of 60 pediatric patients, aged 1–8 years, in the ASA I–II group. The patients were randomly divided into two groups as Group CEB (n = 30), and Group ESPB (n = 30). In the caudal block group, 0.25% bupivacaine 0.5 ml/kg (max 20 ml) was administered in the caudal space, while in the ESPB block group, the block was performed with 0.5 ml/kg 0.25% (max 20 ml) bupivacaine at the L1 vertebral level. Face, legs, activity, cry, and consolability (FLACC) scores were recorded postoperatively at 1, 2, 4, 6, 12, and 24th hours. The first analgesic requirements and time for rescue analgesia were also recorded. Results: While no significant difference was noticed between the two groups’ FLACC scores at the zeroth hour, first hour, second hour, fourth hour, and sixth hour (i.e. postoperatively [P > 0.05]), the FLACC scores of 12th hour in the ESPB group were significantly lower than the caudal block group (P < 0.05). There was also no significant difference in intraoperative additional analgesia requirements between the groups (P > 0.05). The time to first postoperative analgesia in the ESPB group was higher than that in the caudal block group (P < 0.05). Conclusion: The ESPB provided superior postoperative analgesia to the CEB in pediatric patients undergoing inguinal hernia surgery, and ESPB can be used as a relatively safer postoperative multimodal analgesia approach as an alternative to CEB.

Effective integration of COVID-19 vaccination with routine immunization: A case study from Kinshasa, DRC

BackgroundIn response to the challenge of maintaining COVID-19 vaccination coverage amidst the pandemic, VillageReach, in collaboration with the Ministry of Public Health Prevention and Hygiene in Kinshasa, DRC, integrated COVID-19 vaccination with routine immunization services at two primary healthcare facilities. This initiative, launched in July 2022, represented the first of its kind in the DRC, aiming to assess the effectiveness and scalability of a multimodal vaccination approach. MethodsThrough a rapid appraisal involving key informant interviews and analysis of pre- and post-integration service delivery data, this case study explores the operational dynamics and outcomes of integrating COVID-19 and routine immunizations. ResultsResults demonstrated that the integrated approach not only maintained COVID-19 vaccine coverage but also significantly enhanced routine immunization uptake, particularly among under-immunized and zero-dose children. Overall, the vaccination sites, outreach, and integrated health facilities administered 229,983 (33 %) of COVID-19 vaccines in Kinshasa, of which 53 % were referred by community health workers. Additionally, 998 under-immunized children received routine immunizations, of whom 126 were zero-dose children. Key success factors included sustained community health worker engagement, neighborhood-specific strategies, accessible vaccination points, and robust data management. The findings suggest that such integrative strategies can effectively bolster immunization coverage in urban poor communities, offering valuable insights for similar initiatives in the DRC and beyond. ConclusionThis study advocates for sustained investment in innovative immunization models to strengthen primary healthcare systems post-pandemic.

ITIMCA: Image-Text Information and Cross-Attention for Multi-Modal Cassava Leaf Disease Classification Based on a Novel Multi-Modal Dataset in Natural Environments

With artificial intelligence and deep learning development, crop disease recognition methods leverage deep networks for automatic feature learning but rely on the volume of training data. Addressing the scarcity of data in agriculture, few-shot learning (FSL) and multi-modal learning have become focal points. However, existing methods are confined to a single modality or insufficiently exploit cross-modal features. To address this, we propose a multi-modal contrastive learning approach integrating images and text to tackle the problem of small-sample recognition. This method combines CLIP multi-modal pre-training with cross-attention, termed ITIMCA. Experimental validation demonstrates the effectiveness of our approach in cassava leaf disease recognition tasks under natural conditions. Experimental results show that the proposed model achieved an accuracy of 78.00%, a precision of 88.48%, a recall rate of 80.00%, and an F1-Score of 79.00% on the cassava leaf disease identification and classification dataset. These results suggest that the proposed network effectively identifies cassava leaf diseases.

Beyond Guideline-Directed MedicalTherapy: Nonpharmacologic Management for Patients With HeartFailure.

Heart failure (HF) is a leading cause of cardiovascular morbidity, mortality, and health care expenditure. Guideline-directed medical therapy and device-based therapy in HF are well established. However, the role of nonpharmacologic modalities to improve HF care remains underappreciated, is underused, and requires multimodal approaches to care. Diet,exercise and cardiac rehabilitation, sleep-disordered breathing, mood disorders, and substance use disorders are potential targets to reduce morbidity and improve function of patients with HF. Addressing these factors may improve symptoms and quality of life, reduce hospitalizations, and improve mortality in heart failure. This state-of-the-art reviewdiscusses dietary interventions, exercise programs, and the management of sleep-disordered breathing, mood disorders, and substance use in individuals with heart failure. The authors review the latest data and provide optimal lifestyle recommendations and recommended prescriptions for nonpharmacologic therapies.

Single-cell multimodal chromatin profiles revealing epigenetic regulations of cells in hepatocellular carcinoma.

AbstractHepatocellular carcinoma (HCC), with its increasing prevalence globally, is emerging as a major health challenge. Hepatocellular carcinoma cells exhibit both significant homogeneity and heterogeneity, governed by complex epigenomic regulations. Recently, numerous single‐cell unimodal techniques have been used to study HCC at various levelswhich have already revealed a complex interplay at genomic, transcriptomic, spatial and epigenomic levels. However, the use of single‐cell multimodal techniques combining different unimodal layers in HCC remains quite limited, necessitating further studies focusing on these methods to uncover novel markers, and mechanisms at epigenetic levels. In this commentary, we highlight how integrating multimodal approaches with epigenetic modifications can provide new insights into HCC and foster future therapeutic advancements.

Assessment of the Right Ventricle Function in Patients With Significant Tricuspid Regurgitation: A Review.

Tricuspid regurgitation (TR) is an increasingly prevalent condition, especially in older populations, and presents significant challenges due to its association with right heart failure, hospital admissions, and high mortality rates. The management of TR has evolved, with new percutaneous valve repair and replacement techniques emerging alongside traditional surgical approaches. However, accurately assessing right ventricular (RV) function-a key prognostic factor in TR-remains difficult due to the RV's unique anatomy and sensitivity to loading conditions. Current echocardiographic methods, such as Tricuspid Annular Plane Systolic Excursion (TAPSE), S' wave analysis, and RV fractional area change (FAC), offer valuable insights but have limitations, particularly regarding load dependence and incomplete assessment of RV function. Advances in 3D echocardiography and myocardial strain imaging provide more comprehensive evaluations, yet challenges persist in integrating these measures in routine clinical practice. The review highlights the importance of a multimodal approach to RV assessment in TR patients, considering both the right atrium and pulmonary artery interactions, and explores potential future tools such as myocardial work and dynamic testing to improve prognostic accuracy and patient outcomes.

Gabapentin Dosing Guidance: a Multimodal Approach to Topical and Oral Concomitant Therapy

As researchers in pain management search for safer alternatives to oral analgesics, transdermal or externally applied analgesic options are becoming increasingly available. The search for therapeutic alternatives is fueled by not only the risk of misuse of opioids and similar CNS depressant drugs, but also the impact that chronic pain has on a patient’s quality of life. In this review, we determine an approach by which topical and oral gabapentin can be used safely and effectively, either as monotherapy, or as synergistic concomitant therapy.

Enhancing Preoperative Diagnosis of Subscapular Muscle Injuries with Shoulder MRI-based Multimodal Radiomics

PurposeRotator cuff injury is a common ailment in the musculoskeletal system, with the subscapularis muscle being the largest and most robust muscle of the rotator cuff. The occurrence of subscapularis muscle tears is more frequent than previously reported. The main objective of this research is to harness the power of artificial intelligence to enhance the precision in diagnosing subscapularis muscle injuries via magnetic resonance imaging of the shoulder joint, prior to surgical intervention. This study seeks to integrate advanced artificial intelligence algorithms to analyze magnetic resonance imaging data, aiming to provide more accurate preoperative assessments, which can potentially lead to better surgical outcomes and patient care and promote technological progress in the field of medical imaging analysis. MethodThis is a multicenter study that involves 324 patients from a major medical center serving as both the training and testing groups, with an additional 60 patients from two other medical centers comprising the verifying group. The imaging protocol for all these subjects included a series of shoulder magnetic resonance imaging scans: T1-weighted coronal sequences, T2-weighted coronal, axial, and sagittal images. These comprehensive imaging modalities were utilized to thoroughly examine the shoulder joint's anatomical details and to detect any signs of subscapularis muscle damage. To enhance the diagnostic accuracy before surgical procedures, radiomic analysis was employed. This technique involves the extraction of a multitude of quantitative features from the magnetic resonance imaging, which can provide a more nuanced and data-driven approach to identifying subscapularis muscle injuries. The integration of radiomics in this study aims to offer a more precise preoperative assessment, potentially leading to improved surgical planning and patient outcomes. ResultIn the course of this study, a comprehensive extraction of 1197 radiomic features was performed for each imaging modality of every patient. The coronal T1-weighted modality, when assessed within the internal verifying cohort, delivered a diagnostic accuracy of 0.766, coupled with an AUC of 0.803. In the case of the T2-weighted modality, the coronal planes exhibited a diagnostic accuracy of 0.781 and an AUC of 0.844. The axial T2-weighted images recorded an accuracy of 0.719 and an AUC of 0.761, while the sagittal T2-weighted images scored an accuracy of 0.766 and an AUC of 0.821. The amalgamation of these imaging techniques through a multimodal strategy markedly enhanced the accuracy to 0.828, with an AUC of 0.916 for the internal verifying group. The diagnostic performance of the coronal T1-weighted modality in the external verifying cohort yielded an accuracy of 0.833, with an area under the curve (AUC) of 0.819. For the T2-weighted modality, the coronal imaging demonstrated an accuracy of 0.767 and an AUC of 0.794. The axial T2-weighted images had an accuracy of 0.783 and an AUC of 0.797, while the sagittal T2-weighted images achieved an accuracy of 0.833 and an AUC of 0.800. When combining the modalities, the multimodal approach significantly improved the accuracy to 0.867, with an AUC of 0.803 in the external verifying group, indicating a robust diagnostic capability. ConclusionOur study demonstrates that the application of multimodal radiomic techniques to shoulder magnetic resonance imaging significantly enhances the precision of preoperative diagnosis for subscapularis muscle injuries. This approach leverages the comprehensive data provided by various magnetic resonance imaging modalities to offer a more detailed and accurate assessment, which is crucial for surgical planning and patient care.

Serial Multimodal Biometrics Authentication and Liveness Detection Using Speech Recognition with Normalized Longest Word Subsequence Method

Biometric authentication aims to verify whether an entity matches the claimed identity based on biometric data. Despite its advantages, vulnerabilities, particularly those related to spoofing, still exist. Efforts to mitigate these vulnerabilities include multimodal approaches and liveness detection. However, these strategies may potentially increase resource requirements in the authentication process. This paper proposes a multimodal authentication process incorporating voice and facial recognition, with liveness detection applied to voice data using speech recognition. This paper introduces Normalized Longest Word Subsequence (NLWS), a combination of Intersection Over Union (IOU) and the longest common subsequence, to compare the prompted system sentence with the user's spoken sentence at speech recognition. Unlike the Word Error Rate (WER), NLWS has a measurable range between 1 and 0. Furthermore, the paper introduces decision-level fusion in the multimodal approach, employing two threshold levels in voice authentication. This approach aims to reduce resource requirements while enhancing the overall security of the authentication process. This paper uses cosine similarity, Euclidean distance, random forest, and extreme gradient boosting (XGBoost) to measure distance or similarity. The results show that the proposed method has better accuracy compared to unimodal approaches, achieving accuracies of 98.44%, 98.83%, 97.46%, and 99.22% using cosine similarity, Euclidean distance, random forest, and XGBoost calculations. The proposed method also demonstrates resource savings, reducing from 5.19 MB to 0.792 MB, from 7.3294 MB to 1.9437 MB, from 6.6512 MB to 1.3284 MB, and from 7.8632 MB to 2.1517 MB in different distance or similarity measurements

ChatGPT의 수학적 성능과 오류 분석: 다중양식적 접근을 통한 수학교육 활용 가능성 탐색

This study explores the potential of utilizing Large Language Models (LLMs) like OpenAI’s ChatGPT in mathematics education by analyzing its mathematical performance using a multimodal approach that includes language and image processing evaluations. A total of 1,836 middle school-level mathematical problem images were input to assess accuracy rates, error rates, and characteristics, patterns, and contexts of errors. The integrity of the solution processes was evaluated in terms of accuracy, relevance, transparency, fluency, and safety. The analysis revealed that ChatGPT generally maintained high accuracy rates in the algebra domain but showed relatively lower accuracy in the geometry domain. Notably, recognition errors were found to be frequent and often triggered a chain of subsequent errors, making them a key area of concern. Strategies to mitigate these errors included enhancing the linguistic, symbolic, and numerical explanations provided with the problems. Furthermore, through an analysis of ChatGPT’s problem-solving processes, the study identified its potential for educational use in areas such as problem- solving, personalized tutoring, problem posing, and grading and error analysis. This study provides a comprehensive multimodal analysis and confirms the potential of ChatGPT in mathematics education, and offers insights and recommendations for future research to ensure its reliable application in educational settings.

Could ASA and Charlson Comorbidity Index Scores Help the Current Comorbidities Stratification in Non-surgical Knee Osteoarthritis Multimodal Pain Management?

Non-surgical management of knee osteoarthritis emphasizes on multimodal approach according to patients subphenotypes (with or without comorbidities). Surgical treatment outcomes data suggest the role of ASA and Charlson Comorbidity Index (CCI) scores in the field. There are no comparative data on ASA and CCI stratification in non-surgical knee osteoarthritis pain management, which is the aim of our study. Eighty ASA 1–3 in-hospital patients, aged ≥ 36, treated for symptomatic chronic knee osteoarthritis with implemented intra-articular Betamethasone (7 mg/ml) or Hyaluronic acid (30 mg/2 ml), followed by as needed Dexketoprofen 50 mg i.v. or Paracetamol 1.0 g i.v., along with recommended 2 weeks (10 working days) exercise-based physiotherapy programme were examined retrospectively. Data regarding demographics, comorbidities, ASA low-risk(1–2)/high-risk(3), CCI low-risk(0–1/0–2)/high-risk(≥ 2/≥ 3) scoring subgroups, effusions, WBC, ESR, vital signs, clinical laboratory parameters, adverse events, and analgesic consumptions were collected as well. We tested the effect of different ASA and CCI scores on analgesic consumption (primary outcomes), and the effect of implemented risk-adjusted multimodal analgesia on subsequent participation or non-participation in the physiotherapy programme (secondary outcomes). Among all outcomes variables, ASA and CCI stratification confirmed only the higher age and ESR determinants in both the high-risk ASA 3 and high-risk CCI $ ≥ 2 subgroups, as well as more Hyaluronic acid applications in the elderly. The participants in the physiotherapy programme were mainly low-risk patients who received significantly more intra-articular Hyaluronic acid than Betamethasone. The ASA and CCI scores could help current (yes/no comorbidities) decision-making by implementing risk-adjusted pain management, emphasizing on severity rather than the type of comorbid conditions in non-surgical knee osteoarthritis population.

Exploring the competitiveness of tourism in Hungary: Recent findings and results

This research evaluates the regionalization of tourism in Hungary, revealing the breakdown of the national gross domestic product (GDP) of tourism. It also explores the density, spatial variations, and features of these indicators. A multimodal approach is used to evaluate the competitiveness of Hungarian counties, and the distribution of these tourism regions is analyzed using the tourism penetration index. Furthermore, regional GDP is calculated for the whole territory of Hungary. The study identifies significant regional disparities in tourism competitiveness, highlighting Budapest-Central Danube as the most competitive region and Lake Balaton as underperforming despite its potential. The research contributes by providing a detailed regional GDP analysis and emphasizing the need for targeted policy interventions to enhance tourism development across all regions.

Joint self-supervised and supervised contrastive learning for multimodal MRI data: Towards predicting abnormal neurodevelopment

The integration of different imaging modalities, such as structural, diffusion tensor, and functional magnetic resonance imaging, with deep learning models has yielded promising outcomes in discerning phenotypic characteristics and enhancing disease diagnosis. The development of such a technique hinges on the efficient fusion of heterogeneous multimodal features, which initially reside within distinct representation spaces. Naively fusing the multimodal features does not adequately capture the complementary information and could even produce redundancy. In this work, we present a novel joint self-supervised and supervised contrastive learning method to learn the robust latent feature representation from multimodal MRI data, allowing the projection of heterogeneous features into a shared common space, and thereby amalgamating both complementary and analogous information across various modalities and among similar subjects. We performed a comparative analysis between our proposed method and alternative deep multimodal learning approaches. Through extensive experiments on two independent datasets, the results demonstrated that our method is significantly superior to several other deep multimodal learning methods in predicting abnormal neurodevelopment. Our method has the capability to facilitate computer-aided diagnosis within clinical practice, harnessing the power of multimodal data. The source code of the proposed model is publicly accessible on GitHub: https://github.com/leonzyzy/Contrastive-Network.

A Multi-Modal Study of Persuasive Strategies in Native and Non-Native School Advertisements

The current study investigates how private schools target native and non-native English speakers through YouTube advertisements using a multi-modal discourse analysis approach. The study examines persuasive strategies, how ads appeal to viewers, and influence decision making in native and non-native private school ads. Native school ads may emphasize communicating in English, prestige of private school, and high academic standards. Non-native ads may emphasize diversity of students and resources for learners.Native school ads may use formal, dramatic language while non-native ads use inclusive, accessible language. Ads reflect assumptions about language, education, values and status - native schools emphasize fluency, non-native emphasize inclusive learning. Ads use humor and emotion differently - native ads use culture-specific humor while non-native appeals to shared values like diversity. The study analyzed ads using models from Kress and van Leeuwen(2006), and Halliday(1979) then draw conclusions that verify the hypothesis. Both ad types aim to present a nurturing school but native ads use clearer language while non-native use aspirational, indirect language. Native ads value honesty, respect, collaboration while non-native value inclusiveness, diversity, cultural diversity. The study concludes that ads have similar visual, textual strategies but values and language use varies based on native or non-native audience.