Volume 26

Lei Zhang 1,a, Yanjun Ren1,b,Jiaxu Li1,c，Huihang Zhang1,d,Dongxin Wang1,e，Jiantong Wang1,f，*   Department of Stomatology, The Third Medical Center, Chinese PLA General Hospital, No. 69, Yongding Road, 100039 Beijing, China. *Corresponding Author: Jiantong Wang ，wj18501307059@sina.com     Abstract：Objective To investigate the multi-level mechanisms and synergistic enhancement patterns of functional orthodontic treatment combined with orofacial myofunctional training for early intervention of maxillary protrusion in children.Methods A prospective randomized controlled trial was conducted, enrolling 90 children aged 8-11 years with maxillary protrusion. They were randomly divided into a functional orthodontic treatment group (n=30), combined treatment group (n=30), and conventional observation group (n=30). A 12-month follow-up was conducted using a multi-modal assessment system including cone-beam CT, high-density electromyography, and biomechanical modeling. Mediation analysis and machine learning were employed to construct treatment efficacy prediction models.Results The combined treatment group showed a reduction in ANB angle of 3.4±0.5° (P<0.01), with an improvement rate of perioral muscle coordination reaching 81.2%, significantly superior to the functional orthodontic treatment group alone (P<0.05). Bone-muscle interface analysis revealed a significant positive correlation between muscle force increment and condylar anterior displacement rate (r=0.76). The indirect effect of orofacial muscle function improvement through optimized occlusal force distribution promoting bone remodeling accounted for 42.3% of the total effect. The prediction model identified key predictive factors including baseline electromyographic activity and initial ANB angle (AUC=0.89).Conclusion Combined treatment achieves dual optimization of structure and function through a synergistic loop of “mechanical regulation-neural remodeling-bone adaptation,” providing theoretical basis for individualized precision intervention. Keywords: Maxillary protrusion; Bone-muscle interface; Neural plasticity; Synergistic mechanism; Prediction model Introduction Maxillary protrusion, as a challenging type of craniofacial developmental abnormality in children, has a critical window for early intervention that is often fleeting—once the golden period of growth and development is missed, treatment difficulty and costs escalate significantly. An expert consensus published by Li Xiaobing et al. (2021)[1] indicated that the prevalence of malocclusion in Chinese children is rising annually, affecting the dental-maxillofacial function and facial aesthetics of nearly 260 million children. While traditional functional appliances can improve jaw relationships to some extent, single mechanical intervention is often insufficient for functional disorders caused by perioral muscle imbalance. A meta-analysis by Li Yulin et al. (2023)[2] confirmed that Twin-block appliances can promote condylar growth in the posterosuperior direction and anterior displacement, providing skeletal evidence for correcting Class II malocclusion. However, as myofunctional abnormalities are the “behind-the-scenes driver” that induces and aggravates deformities, pure mechanical correction cannot fundamentally interrupt the pathogenic chain. A systematic review by Mohammed et al. (2020)[3] showed that prefabricated myofunctional appliances can effectively improve Class II division 1 malocclusion, but their efficacy is closely related to patient compliance. An international expert consensus published by Zhou et al. (2024)[4] further emphasized that orofacial myofunctional training has unique advantages in optimizing occlusal force distribution and promoting neuromuscular remodeling. Based on this research background, this study proposes a synergistic strategy of “functional orthodontic treatment-orofacial myofunctional training” combined intervention, aiming to explore the dual goals of achieving skeletal remodeling and myofunctional optimization through constructing a closed-loop mechanism of “mechanical regulation-neural remodeling-bone adaptation,” providing evidence-based support and theoretical foundation for individualized precision treatment of maxillary protrusion in children. 1. Materials and Methods 1.1 Study Design and Ethics Review This study adopted a prospective three-arm parallel randomized controlled trial design, conducted in the Department of Orthodontics from June 2022 to June 2024. The study protocol was approved by the hospital ethics committee and registered with the Chinese Clinical Trial Registry. All guardians of subjects signed written informed consent after being fully informed of the study purpose, intervention measures, potential risks, and right to withdraw, while obtaining verbal consent from the children themselves. The entire study followed the Declaration of Helsinki and CONSORT statement specifications, establishing an independent data safety monitoring committee to assess adverse event incidence quarterly, ensuring subject rights and research quality. 1.2 Subject Inclusion and Exclusion Criteria Inclusion criteria: (1) Age 8-11 years, in mixed or early permanent dentition stage; (2) Cephalometric diagnosis of skeletal Class II malocclusion (ANB angle ≥5°) with maxillary protrusion (SNA angle ≥84°); (3) Perioral muscle function assessment showing insufficient labial muscle tension or abnormal swallowing patterns; (4) Cervical vertebral bone age at CS2-CS4 growth stage; (5) Good compliance of children and parents. Exclusion criteria: (1) Previous orthodontic or orthognathic surgical treatment; (2) Concurrent cleft lip and palate, craniofacial syndrome, or organic temporomandibular joint disease; (3) Systemic diseases affecting bone metabolism; (4) Severe dental caries or periodontal disease requiring priority treatment; (5) Unable to complete 12-month follow-up period. 1.3 Randomization and Blinding Implementation Computer-generated random number tables were used for central randomization, allocating 90 subjects into functional orthodontic treatment group, combined treatment group, and conventional observation group at a 1:1:1 ratio. Random sequences were generated by an independent statistician and placed in opaque sealed envelopes, opened sequentially according to enrollment order. Due to the nature of intervention measures, operators and subjects could not be blinded, so an assessor-blinded design was adopted—cone-beam CT measurements, electromyographic analysis, and biomechanical modeling were all completed by independent researchers unaware of group allocation. Data entry and statistical analysis stages also maintained blinding until database lock and unblinding. 1.4 Sample Size Calculation Based on pilot study data, the expected ANB angle improvement in the combined treatment group was 3.4°±0.8°, compared to 2.1°±0.9° in the functional orthodontic treatment group, with clinically meaningful differences. With two-sided test α=0.05 and test power 1-β=0.90, PASS 15.0 software was used for sample size estimation comparing means of two independent samples, calculating a minimum of 24 cases per group. Considering approximately 20% dropout rate during the 12-month follow-up, 30 cases per group were finally determined, totaling 90 subjects, to ensure sufficient statistical power and meet subgroup analysis needs, while reserving adequate sample size for mediation analysis and machine learning model construction. 1.5 Intervention Protocol 1.5.1 Design Parameters of Functional Appliances and Orofacial Myofunctional Training Functional appliances used a modified Twin-block design with upper baseplate occlusal plane inclined at 70°. Lower jaw advancement was individually adjusted according to initial overjet to 3-5 mm anterior

DOI:https://doi.org/10.65281/738001   Jingchao Wang1, Wenbo Ji2, Liang Yao3, Zhixian Qiu4, Dianhao Wu5, Hanlin Wang6, Shan Zhou7, Xiaofeng Wang8 1 The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China 2 The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China 3 Linyi University, Linyi, Shandong, China 4 Harbin University of Science and Technology, Harbin, Heilongjiang, China 5 University of Science and Technology Liaoning, Anshan, Liaoning, China 6 Jinzhou Medical University, Jinzhou, Liaoning, China 7 The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China 8 Harbin Medical University, Harbin, Heilongjiang, China Corresponding authors: Shan Zhou                     (h04380@hrbmu.edu.cn) Xiaofeng Wang             (13633617656@163.com) KEYWORDS:Intrusion arch; prediction of orthodontic force; artificial neural networks; multivariate regression analysis; correlation analysis   SUMMARY: This protocol aims to develop and validate predictive models for orthodontic force generated by an intrusion arch using multiple nonlinear regression analysis (MNLA) and artificial neural networks. The MNLA model demonstrated superior accuracy, with a clinically applicable mean absolute percentage error of 3.66%, identifying arch height as the most influential parameter.   ABSTRACT: This study aims to develop predictive models for orthodontic force using multiple regression analysis (MRA) and artificial neural networks (ANNs), enabling parametric representation and accurate prediction of applied force. A total of 300 samples were collected to measure the characteristic parameters of intrusion arches—arch length, height, and bending angle—and their corresponding orthodontic force. A comprehensive database was constructed to analyze the correlation between each parameter and the force. Four predictive models were established using MRA and ANN methodologies. Their accuracy was evaluated using root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R²). A statistically significant correlation (p < 0.001) was found between orthodontic force and the three parameters, with influence ranked as: arch height > bending angle > arch length. Comparative analysis revealed that the multiple nonlinear regression analysis (MNLA) model demonstrated the best predictive performance, achieving superior fitting and the lowest prediction error in both the training and testing datasets, with R² values of 0.934 and 0.900, RMSE values of 1.050 and 0.988, MAE values of 0.834 and 0.772, and MAPE values of 4.09% and 3.66%, respectively. The ANN model using Rectified Linear Unit activation also showed strong performance, with a slightly higher testing set MAPE of 4.17%, indicating its potential applicability where nonlinear modeling is preferred. These findings suggest that the orthodontic force generated by an intrusion arch can be effectively modulated by adjusting its characteristic parameters and that the MNLA model provides clinicians with a precise and efficient method for predicting orthodontic force in clinical practice.   INTRODUCTION: Malocclusion is a common oral condition caused by abnormal spatial relationships among the teeth,  jaws, and  facial  structures1, and fixed orthodontic treatment remains one of the primary therapeutic  approaches for such conditions. During the course of treatment, positional deviations of individual teeth  may occur in the fine-tuning phase due to bracket structure design and bonding inaccuracies. However,  rebonding the brackets  can  compromise  their  structural  integrity  and  significantly  prolong treatment  duration2,3,4. To  address  this,   clinicians  commonly  bend   stainless   steel  archwires  into  specific  configurations that generate orthodontic forces in desired directions to reposition the teeth accurately.  The intrusion arch is one of the most widely used archwire configurations in clinical practice. It delivers  intrusive orthodontic force, which, if excessive, may lead to tooth mobility, root resorption, and alveolar  bone  loss5,6,  with  the  maxillary  and  mandibular  anterior  teeth  being  particularly  susceptible7.Currently, there exists no established method to predict the orthodontic force generated by an intrusion  arch. In most cases, clinicians rely solely on their empirical experience to guide its application. Therefore, elucidating the  quantitative relationship between the design parameters of the  intrusion  arch and the  resulting  orthodontic  force  is  essential  for  enabling  its parameterized representation  and providing  a  theoretical basis for the rational application of force in clinical settings.   In  addition,  to  visualize  orthodontic  force  values,  researchers  have  employed  various  approaches, including in vitro measurements, theoretical modeling, and finite element analysis. In 2021, Fumi et al.8conducted   dynamic  measurements   of  orthodontic   force   generated  by   archwires   during  tooth alignment using a custom-fabricated force-measuring apparatus. Their findings indicated a correlation between  the  magnitude  of  orthodontic  force  and  the  degree  of  dental  crowding.  Mayer  et  al.9constructed an in vitro measurement platform to quantify the range of orthodontic forces released by various nickel-titanium wires under specific tooth alignment conditions. However, the generalizability of their results was limited due to the experimental setup configurations. Zamani et al.10employed thin-film pressure sensors combined with image processing techniques to visualize both the magnitude and  distribution  of  orthodontic  force.  Jiang11,  Ahmad12,  and  Ma13 independently  utilized theoretical modeling and finite element models to predict orthodontic forces, reporting prediction errors of 3.24%, 9.55%, and 3.61%, respectively. Although these studies successfully captured the variation trends of orthodontic force during treatment and demonstrated the high predictive capability of finite element simulation, orthodontists are still unable to directly derive accurate force values from clinically available measurement data. As a result, the precise prediction of orthodontic force in clinical practice remains a significant challenge.   Multiple regression analysis (MRA) and artificial neural networks (ANN) are widely used tools for  modeling  linear  and  nonlinear  relationships   among  variables,   and  have   demonstrated  broad applicability  in  force  prediction  tasks14,15,16 .  Accordingly,  this  study  leverages  a  large  dataset  of experimental measurements to explore the quantitative relationship between orthodontic force and the structural  parameters  of  the  intrusion  arch,  utilizing  both  MRA  and  ANN.  Despite  the  potential significance of this approach, no relevant studies have been identified to date.   The primary objectives of this study are: first, to identify the structural factors influencing the orthodontic force generated by the intrusion arch based on beam bending deformation theory; second, to construct an orthodontic force measurement platform and establish a comprehensive database; and third,  to  analyze  the  correlation  between  various  structural  features  of the  intrusion  arch  and  the resulting orthodontic force, thereby providing a theoretical foundation for clinical modulation of force application.  Furthermore,  this  study  aims  to  establish  an  MRA-based  orthodontic  force  prediction

Xiaohong Zhu1, Lu Lu1, Mei Huang1, Guohui Xiong1, Bingqing Tian1, Xiaohui Fu1, Lijun Song2,*. 1, Department of Ophthalmology, Minda Hospital Affiliated to Hubei University for Nationalities, Enshi, Hubei, 445000, China. 2, Department of Nursing, Minda Hospital Affiliated to Hubei University for Nationalities, Enshi, Hubei, 445000, China. 13607246512@163.com The First Author：Xiaohong Zhu; zxh89892002@163.com(ORCID: 0009-0002-0487-6424) The Second Author：Lu Lu; 13402726523@163.com(ORCID: 0009-0005-7521-9837) The Third Author: Mei Huang; 15826686007@163.com(ORCID: 0009-0003-7604-6304) The Fourth Author: Guohui Xiong; 13257181650@163.com(ORCID: 0009-0004-4513-3779) The Fifth Author: Bingqing Tian; 13477236926@163.com(ORCID: 0009-0002-5811-4089) The Sixth Author: Xiaohui Fu; 15027221260@163.com(ORCID: 0009-0000-8418-8132) Corresponding Author: Lijun Song;  13607246512@163.com(ORCID: 0009-0007-9325-9634) Abstract Introduction: One sort of visual issue that impairs vision clarity is refractive errors. It occurs when an eye’s morphology prevents light from properly concentrating on the retina. Predicting a child’s refractive error with accuracy is essential for identifying amblyopia, a condition that can cause irreversible vision loss but may be treated if caught early. Objective: The most crucial elements for effective screening are a precise prediction algorithm and simple access to photo screening applications. Method: In this study, we proposed a novel Kookaburra-optimized lightweight dense convolutional neural network (KO-LDCNN) to forecast children’s refractive error range. We are utilizing data from eccentric photorefraction images that were taken using a smartphone. Using cycloplegic refraction to quantify spherical values, photorefraction images were classified into several groups. The collected data noise reduction using min-max normalization. It improves the quality and clarity of the images. Data segmentation using Regions of Interest (ROI) involves identifying specific image areas. Subsequent to the segmentation process, the data was extract from the features using linear discriminate analysis (LDA). Result: The proposed method is compared to the other traditional algorithms. According to these result, our suggested approach work efficiently sufficient to be correct. Conclusion: The importance of early photo screening intervention in controlling refractive errors and fostering the best possible visual outcomes in children. Keywords:  Photo Screening, Refractive Errors, Photorefraction Images, Regions of Interest (ROI), Smartphone, Amblyopia, Kookaburra optimized lightweight dense convolutional neural network (KO-LDCNN) 1. Introduction Clinical evaluation, which provides necessary information in sequence about the circumstances of a patient, their reaction to therapy, and overall well-being, is the basis of useful healthcare. A clinical evaluation is mostly crucial for early screening and indicative method for child myopic disorder [1]. Particular techniques counting autorefraction, visual acuity testing, retinoscopy, and ocular health assessment are part of the clinical assessment practice for pediatric eye care. These approaches are tailored to the stage of development and level of support of young patients to detect refractive issue early on, supply suitable treatment, and ensure optimal visual outcome [2]. Clinical assessment must be precise and complete for the regimen to be effective. For this reason, clinical consideration is a crucial tool for pediatric ophthalmologists and healthcare administrators in general. Since myopia maculopathy, a disorder in which detached retinas cause and persistent vision loss carries such a high risk of everlasting loss of vision, it has been labeled a serious worldwide health concern. It is well known that the percentage of people with severe myopia increases with increasing onset age [3]. Consequently, using a myopia preventive approach is essential to lessen or postpone the earliest signs of myopia. In addition to refractive abnormalities, strabismus, and anisometropia are other visual issues that can hinder a child’s normal growth of vision. These changes are a frequent reason for either unilateral or bilateral loss of vision associated with an eye disease [4]. Amblyopia caused by refractive problems can occasionally be treated with glasses. To prevent vision problems in children and enhance the quality of life for adults, early detection and management of refractive imperfections and the condition were essential. Since childhood eye issues usually don’t have any symptoms, they often go organic and receive incorrect conduct. Schools must regulate vision screening programs during a child’s growing period to guard against potential harms at school brought by untreated refractive defects [5]. Somewhat more than identifying or treating degrees, such eye tests aim to promptly identify refractive defects or vision issues [6]. By using these diagnostic measures, healthcare providers could more effectively diagnose, treat, and monitor children’s refractive issues, ultimately contributing to the greatest possible visual health and wellness [7]. To accurately assess a child’s visual acuity and identify refractive irregularities in situations of refractive errors, a range of specialized methods are employed in the diagnostic process. The primary screening technique for astigmatism, hyperopia, and myopia is visual acuity screening, which uses standardized eye charts. Autorefraction offers computerized estimates of refractive errors, while retinoscopy provides a human evaluation that is particularly useful for younger children [8]. Cycloplegic refraction gives events that are more precise since the edition is uninvolved by completely freezing the ocular muscle. Ocular health examination, in addition to corrective exams, detects the original medical environment that may affect vision [9]. These screening techniques, which are tailored to the unique needs and stage of growth conversant by every child, are central for the early detection, accurate diagnosis, and competent dealing that ultimately support the optimal state of visual health and console in children and adolescents [10]. The aim of this study is to expand and evaluate a precise prediction approach for the spectrum of refractive errors among kids whose smartphone-captured eccentric photorefraction pictures are used. The method employs a novel  Kookaburra-optimized lightweight dense convolutional neural network (KO-LDCNN).  Contributions of the study The study is organized as follows: section II lists relevant works; section III outlines the recommended methodology; section IV addresses the findings and section V wraps up the conclusion. 2. Related Works When doing close tasks, convergent insufficiency (CI) [11] common monocular vision impairment, frequently produces symptoms. It was uncertain, yet, which screening test was optimal for CI. This study set out to determine if standard measures of compensatory and binocular function might reliably identify kids who have CI in a school screen context [12]. These digital technologies, which tackle COVID-19 and other difficulties, comprise artificial intelligence (AI), the Internet of Things (IoT), and fifth-generation (5G) telecommunication networks [13]. Together, they form an

The Efficacy of Interventional Therapy and Collaborative Nursing in Postoperative Pain Management for Orthopedic Surgery Patients Ming Zhang, Lining Xue* Tongchuan People’s Hospital, Shaanxi Province 727000 China. 343794741@qq.com Author’s details The First Author: Ming Zhang, Female, Bachelor’s degree, Nurse in Charge, 1966778783@qq.com Corresponding author: Lining Xue, Female, Bachelor’s degree, Nurse in Charge, 343794741@qq.com Abstract Research motivation: Orthopedic surgery (OS) results in significant postoperative pain, which could recovery and quality of life (QoL). Effective pain management is crucial for optimal patient outcomes. This investigation seeks to determine whether combining interventional therapy with collaborative nursing (CN) can improve postoperative pain management compared to interventional therapy alone. Introduction:OS is a medical field that diagnoses, treats, prevents, and rehabilitates musculoskeletal disorders and injuries, affecting mobility and functionality. It uses surgical and non-surgical techniques to address fractures, arthritis, sports injuries, congenital deformities, and spinal disorders. Through advanced technology and comprehensive care, it improves patients’ QoL. Objective: The purpose of investigations to assess and compare the efficacy of individual physical therapy (PT) and PT combined with CN (PT+CN) for the management of postoperative pain in OS patients. Methods: We investigated 169 OS patients. For conducting a randomized controlled trial nurses were divided into two teams including PT and PT+CN.The intervention involved introducing a pain management procedure and conducting training sessions for nurses. The PT Team received standard pain management, while the CN Team received additional CN support. Pre- and post-intervention, nurses’ knowledge, attitudes, and pain management practices were assessed using questionnaires. Patient-reported outcomes on pain intensity and interference with activities were collected through surveys. Results and Conclusion: The obtained questionnaires were assessed using the SPSS analytical tool. Data analysis was performed using chi-square and t-tests to compare variations within and between Teams.The PT+CNTeam demonstrated superior outcomes compared to the PT team. Nurses in the PT+CNTeam showedconsiderably improved knowledge and attitudes about pain management. Additionally, patients in this Team reported lower pain intensity, less interference with activities, and better overall pain management. Keywords: Orthopaedic Surgery (OS), Physical Therapy (PT), Collaborative Nursing (CN), Statistical Analysis, Pain Management Orthopedic procedures are widely known to cause significant discomfort for patients. According to various studies, trauma or orthopedic procedures affecting the extremities rank among the highest in terms of postoperative pain [1]. Effective pain management is critical in the postoperative treatment of these patients as severe pain can delay recovery, increase the risk of complications, and lead to longer hospital stays and higher re-admission rates. Consequently, the management of analgesic needs following OS involves a variety of pain control techniques [2]. Orthopedic physicians are noted for prescribing more opioids than any other medical specialty, while orthopedic nurses play a vital role in providing post-operative care and pain management. Despite the known efficacy of alternative therapies in reducing procedural or post-operative pain, their application specifically for orthopedic patients remains under-researched [3]. Figure 1 depicts various types of orthopedic aspects occurred. Figure 1:  overview of orthopedic occurred parts Post-surgical pain can be complex and multifaceted, stemming from both neuropathic and nociceptive sources. Nociceptive pain arises from tissue damage, which activates nociceptive receptors present in terminal nerve fibers. Higher neuronal regions in the brain receive these impulses via ascending pathways, which affect pain perception [4]. An individual’s experience of pain is influenced by several elements, such as feelings, sex, personality, and cultural origin. Conversely, neuropathic pain is caused by actual harm to the neurological system and is characterized by the activation of bigger nerve fibers [5]. When tissue is injured, chemicals like bradykinin, prostaglandins, and serotonin are released, which increases neural activation and causes pain [6]. Central sensitization, a condition that makes pain management more difficult, can be brought on by persistent simulation.Medication and non-pharmacological techniques are employed to effectively control pain after surgery in patients with OS. Nonsteroidal Anti-Inflammatory Drugs (NSAIDs,) opioids, nerve blocks, and local anesthetic infiltration are a few of the often used medicinal methods [7]. Orthopedic nurses are crucial when it comes to administering these medications and providing care. In addition, it employs complementary therapies such as guided imagery, music therapy, media diversion, and relaxation training to address pain and anxiety [8]. In a number of medical situations, supplemental treatments have demonstrated potential in lowering pain. Opioids have been the go-to medication for treating postoperative pain for a long time. In addition to having a high risk of addiction, its usage is restricted by adverse effects such as ileus, pruritus, nausea and vomiting, respiratory depression, and urine retention [9-11]. Drugs such as these are mostly used as life-saving drugs when other methods of pain management are ineffective when combined with effective no-opioid usage. Table 1 presents the maximum frequently prescribed treatments in OSs. Table 1: Types of opioid analgesics and their classification   Types of Drug Dosage Duration of Analgesia (hours)   process of metabolism     Remarks     Immediate-release opioid medication administered orally   Codeine 15to 60 mg 4to 6 CYP2D6 Protect in young children as it raises intracranial pressure Hydrocodone 2.5to 10 mg 4to 6 CYP3A4 and CYP2D6 Steer clear of adolescents.An increase in brain pressure. Oxycodone 5to 10 mg 3to 6 CYP2D6 and CYP3A Avoid CYP3A4 inhibitors.This leads to a rise in cerebral pressure Tramadol 50to 100 mg 3to 6 CYP2D6 and CYP3A4 Avoid CYP3A4 inhibitors.   Oxymorphone 5to 10 mg 3to 6   Glucuronidation –   Intrauterine Opioids   Hydromorphone 0.5 to 1 mg 2 to 4 Glucuronidation, Renal clearance Useful for people with reduced kidney functionAvoid individuals with hypovolemic shock. Genitourinary obstructions should not be treated with this medication. Meperidine 50to 150 mg 2to 3 CYP3A4 and CYP3A5 Seizures can be brought on by normeperidine, an active metabolite.Not recommended when using monoamine oxidase inhibitorsThe elimination rate is slower in the elderly. Fentanyl 1to 2 mcg 30 min–1 CYP3A4 andCYP3A5 A hundred times as potent than morphineSuitable for people with compromised kidney function.Suitable for individuals with bronchospasm or hemodynamic instability Morphine 2to 4 mg 3to 4 Glucuronidation, Renal clearance. Histamine release can cause Bradycardia, hypotension, and vagally mediated vasodilation. Research objective: This study evaluates and

Feiling Lin1, Yuan Fui2, Lei Huo3, fang Yuhn3,*. 1, Department of Interventional Medicine, Zhangye People’s Hospital Affiliated to Hexi University, 734000, China. 2, Department of Orthopedics, Zhangye People’s Hospital Affiliated to Hexi University, 734000, China. 3, Department of Gastroenterology, Zhangye People’s Hospital Affiliated to Hexi University, 734000, China. 13993686972@163.com Abstract Introduction: Acute Pancreatitis (AP) is an acute clinical concern, which affects the patients’ condition in a severe manner due to inflammation. Some of the many problems associated with acute or chronic pancreatitis include gastrointestinal, metabolic, and psychological issues. Objective: To investigate the effects on the psychological state, quality of life (QoL), and medical results of patients with AP of comprehensive nursing treatments in conjunction with stress awareness programs Methods: Overall, 153 patients with AP were randomly separated into two groups over two years: Group A acknowledged systematic nursing interventions and mindfulness-based stress reduction training, while Group B acknowledged routine care. Stress, depression, and four domains (environmental, social connections, and wellness of psychological and physical) of QoL were considered as key outcome indicators. Data were examined with SPSS software. Results: Results showed that compared with Group B, Group A experienced significantly shorter recovery times for bowel sounds, abdominal pain, and complications. Psychological assessments revealed that Group A had lower anxiety and depression scores. In addition, the complication rate in Group A was lower. QoL scores in Group A showed significant improvement across all domains, with notable differences including their physical, psychological, and social interactive capabilities. Conclusion: The integration of systematic nursing interventions with stress awareness training advanced the QoL, psychological state, and medical results of patients with AP. Keywords: Systematic nursing interventions, stress awareness training, acute pancreatitis, quality of life (QoL), psychological status With an increasing speed of lifestyle and an extensive food supply, the overall incidence of acute pancreatitis (AP) has increased annually, varying from 4.8 to 74.1 per 10, 000 individuals [1]. AP is a frequent but multifactorial disorder, associated with inflammation of the pancreas, and symptoms and complications can vary. This condition presents clinical symptoms like abdominal pain, bloating, and lower indigestion, lack of appetite, and rapid pulse, which leads to a poor quality of life (QoL) among the patients [2]. Various factors contribute to the development of acute pancreatitis, including gallstones, alcohol consumption, and other causes such as trauma, medication use, and hyperlipidemia (Figure 1). The condition has a fast beginning and speedy development. It is commonly followed by repercussions such as peritonitis, infections, and unbelief, any of which can endanger patients’ lives if not treated immediately [3]. Figure 1: Symptoms and Causes of Acute Pancreatitis (AP)  The therapeutic procedure can result in several dangerous consequences, and patients are vulnerable to stress, depression, and other feelings, so treatments must involve treatments using a methodological and suitable nursing approach [4]. Through the continual growth of the concept of biopsychosocial medicine, scientists have shown a strong interest in the use of psychological interventions for mindfulness stress management training [5]. Self-control is a function of awareness in which people are aware of thoughts, behaviors, and actions beyond criticism. Mindfulness stress management guidance is a training method based on mindfulness theory that uses extensive meditation events to assist the trainees to manage their feelings and deal with difficulties with the ultimate objective of alleviating mental and physical issues [6]. At present, there is limited research that emphasizes training for awareness managing stress in AP treatment. As a result, it is critical to investigate the efficacy of standard nursing assistance along with the awareness of specialization in decreasing stress for the management of AP [7]. The patient can be aware of the negative effects, but due to reduced ventilator function or apparent disease of the lung, has reduced capacity for down regulation, leading to mood swings and in far advanced stages, normal life impartment in the patients’ and family lives [8]. Focusing on the patient’s emotions, narrative nursing is a micro-level theory linked to the stories that are told within the healthcare process when patients are presented with issues [9]. In the procedure, they help them to address the issues that are concerned by knowing how others feel and evaluating their opinions and paying appropriate, active, and tender concern to patients, altering the perceptions of life and disease narratives [10]. Consequently, nursing is very vital in an improved QoL along with the outcomes and prognosis of diseases in AP patients [11]. Incorporating the content of stress awareness and systematic nursing therapy has some benefits in the treatment of physiological and psychological disorders of patients [12]. Such programs, which rely on mindfulness-based approaches, aim to reduce emotional pain, enhance methods of managing stress, and increase overall mental health [13]. These therapies will be most beneficial to patients with AP who often experience severe psychological stress due to the severity and uncontrolled nature of the situation. Such an extensive treatment plan corresponds with recent tendencies in modern medicine based on patient-centered approaches [14]. This study aims to evaluate the effectiveness of systematic nursing interventions and stress awareness training in patients with AP. The objective of the study is to assess the impact of these interventions on systemic inflammation, nutritional status, recovery time, and psychological well-being, compared to routine nursing care. The study further investigated inflammatory markers, clinical complications, and QoL outcomes. Research Aim · This study demonstrated the effectiveness of systematic nursing interventions and stress training in reducing systemic inflammation. · Highlighted the positive impact of these interventions on nutritional status, including improved food intake and protein consumption. · Provided evidence that nursing interventions contributed to quicker recovery times and better psychological health. · Exhibited improvement in psychological well-being positively influences physical health outcomes, reducing complications and hospital stays. 2. Literature Reviews The author of [15] assessed the mental and physical wellness of patients with recently discovered pancreatic malignancy and found high symptom burden, low well-being, and high distress levels. The study suggested that to enhance patient results and general health, comprehensive care techniques that prioritize symptom treatment and mental wellness could be

        Running title: Baduanjin on Cardiac Rehabilitation After PCI 22      Abstract 23          Background 27          Objective 30          Methods 41          Result 50          Conclusion 53 56 57      Introduction 107 108      Methods 113 114          Inclusion and Exclusion Criteria 129 130          Literature Search Strategy 145 146          Study Selection and Data Extraction 155 156          Quality Assessment 165 166          Certainty of evidence 175 176          Statistical Analysis 194 195      Results 196          Literature Search 203 204      Fig 1. PRISMA flow diagram of the literature selection process. 205 206          Basic Characteristics of the Included Studies 217 218          Quality Assessment 228 229      Fig 2. Risk-of-bias assessment of the included randomized controlled trials. 230 231          Results of Meta-Analysis 232          N-terminal pro-B-type Natriuretic Peptide (NT-proBNP) 243 244      Fig 3. Forest plots for (A) NT-proBNP, (B) LVEF, (C) 6MWT, and (D) LVEDD. 245 246          Left Ventricular Ejection Fraction (LVEF) 261 262          Six-Minute Walk Test (6MWT) 277 278          Left Ventricular End-Diastolic Diameter (LVEDD) 289 290          Quality of Life (QoL) 302 303      Fig 4. Forest plots for (A) QoL, (B) anxiety, (C) depression, and (D) mental health. 304 305          Anxiety 315 316          Depression 326 327          Mental Health 336 337          Physical Functioning 346 349 350          Social Functioning 360 361          Incidence of Adverse Events 371      = 0.16, 95% CI: 0.02 to 1.24). (Fig 5C) 372 373          Meta-Regression 379          (Supplementary File 3) 380 381          GRADE Assessment 389          (Supplementary File 4). 390 391          Sensitivity Analysis and Publication Bias 397 398      Discussion 482 483          Conclusion 488 489      Acknowledgments 490      None. 491 492      Conflict of Interest 493      The authors declare that the research was conducted in the absence of any commercial 494      or financial relationships that could be construed as a potential conflict of interest. 495 496          Author Contributions 502 503      Data Availability Statement 504      Data sharing is not applicable to this article as no datasets were generated or analysed 505      during the current study. 506 507          Reference 687 688      Table 1. Basic Characteristics of the Included Studies.   Study   Year Age   Intervention       Control   N1   Intervention   N2   Control Rehabilitation   Phase Population   category Xuejuan   2021   59.54±14.78   58.65±17.38   40   Baduanjin   40   Standard care   I   AMI after PCI Wang                   Juan Wang 2024 59.96±6.39 60.02±6.38 46 Baduanjin 46 Standard care Ⅰ AMI after PCI Yu Cai 2022 49.58±9.41 49.47±9.32 45 Baduanjin 45 Standard care Ⅰ AMI after PCI Hongmei Hu 2025 47.02±7.34 46.95±7.29 40 Baduanjin 40 Standard care Ⅰ CHD after PCI Liang Kang 2024 52.2±10.9 53.0±10.7 60 Baduanjin 60 Standard care Ⅱ AMI after PCI Xiaoyu   2023   53.8±14.2   52.4±13.6   60   Baduanjin   60   Standard care   Ⅱ   AMI after PCI Zhang                   Guoguo Liu 2022 56.21±10.44 57.32±11.36 30 Baduanjin 30 Standard care Ⅱ AMI after PCI Hongyun 2023 57.44±7.32 56.38±7.31 45 Baduanjin 45 Standard care Ⅱ AMI after PCI Zheng   Xuefei Liang 2022 59.9±11.9 61.2±10.2 24 Baduanjin 24 Standard care II AMI after PCI Panpan Liu 2024 59.37±4.94 58.72±5.51 61 Baduanjin 61 Standard care Ⅱ AMI after PCI Xing Wang 2023 52.0±16.7 52.5±15.5 40 Baduanjin 40 Standard care Ⅱ CHD after PCI Ting Tang 2019 60.02±8.66 61.38±9.21 50 Baduanjin 50 Standard care Ⅱ CHD after PCI Yingchun Du 2023 60.98±6.04 60.39±6.12 53 Baduanjin 53 Standard care Ⅱ CHD after PCI Manzhen Wu 2024 64.27±4.61 64.74±4.57 51 Baduanjin 50 Standard care Ⅱ CHD after PCI Miaomiao   2022   69.88±2.47   69.48±2.47   39   Baduanjin   39   Standard care   II   CHD after PCI Gao                   Yunxiao Cai 2022 58.42±6.61 59.02±6.78 37 Baduanjin 37 Standard care Ⅱ CHD after PCI Tong Sun 2025 53.07±9.81 52.80±11.26 59 Baduanjin 60 Standard care Ⅱ CHD after PCI Kai Zhao 2022 61.63±5.91 61.89±6.02 41 Baduanjin 41 Standard care Ⅱ CHD after PCI Jinxin Zhao 2023 51.37±7.89 52.27±8.95 46 Baduanjin 47 Standard care Ⅱ ACS after PCI Li Hua 2018 NA NA 60 Sitting Baduanjin 60 Standard care I CHD after PCI Jiamei Wang 2020 59.32±15.43 58.82±12.56 171 Sitting Baduanjin 171 Standard care II CHD after PCI Yan Zhang 2021 60.48±13.28 65.53±13.23 40 Sitting Baduanjin 40 Standard care II CHD after PCI Yueyan Yu 2022 61.13±11.06 60.4±11.37 53 Sitting Baduanjin 53 Standard care II AMI after PCI Dan Zhang 2024 61.4±8.5 62.8±7.5 55 Sitting Baduanjin 55 Standard care II AMI after PCI Zeyun Xu 2025 70.68±9.52 70.94±9.14 43 Sitting Baduanjin 43 Standard care II CHD after PCI Yuying   2024   64.25±1.65   64.31±1.71   38   Sitting Baduanjin   38   Standard care   II   AMI after PCI Huang                   Rui Si 2025 59.68±5.93 60.26±6.14 73 Sitting Baduanjin 73 Standard care II AMI after PCI Chenchen Hu 2023 57.16±5.28 56.84±6.27 60 Sitting Baduanjin 60 Standard care II ACS after PCI Jingjing   2019   58.32±9.74   60.32±7.23   55   Sitting Baduanjin   55   Standard care   II   CHD after PCI Wang                   689 690          Supplementary Materials