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Background:</b> Within contemporary medical education systems dominated by the biomedical paradigm, medical students commonly experience a decline in empathy alongside uncertainty in professional identity formation. Narrative medicine, as an educational approach designed to bridge objective technical reasoning and subjective human experience, has been proposed as a response to this humanistic crisis. However, the psychological mechanisms underlying its effects, the boundaries of existing empirical evidence, and strategies for systematic implementation remain insufficiently clarified. <b>Content:</b> Drawing on a comprehensive review of the literature, this article elucidates two core theoretical mechanisms through which narrative medicine operates. First, through the cycle of attention, representation, and affiliation combined with reflective practice, narrative medicine facilitates the coordinated development of cognitive empathy and emotional resonance. Second, through narrative integration, it supports learners in transforming fragmented clinical experiences into coherent professional identities. A synthesis of empirical studies published over the past decade indicates that well designed narrative medicine curricula can enhance reflective capacity, communication skills, and a sense of professional meaning, although their effectiveness is substantially moderated by curricular intensity, pedagogical modality including digital and multimodal approaches, and cultural context. <b>Conclusion:</b> The integration of narrative medicine currently faces structural barriers, including limited curricular space, the scarcity of standardized assessment tools, and insufficient faculty development. Future efforts should adopt implementation science frameworks to shift from isolated elective offerings toward longitudinal spiral curricula, develop multidimensional evaluation strategies, and explore the use of emerging technologies such as artificial intelligence and virtual reality in narrative pedagogy. These efforts may establish narrative medicine as a foundational pathway for cultivating physicians with core humanistic competencies.

<b>Objective:</b> To evaluate maternal and neonatal outcomes of women with placenta accreta spectrum (PAS) disorders managed by a dedicated multidisciplinary team at a tertiary referral centre in Riyadh, Saudi Arabia. <b>Methods:</b> We conducted a prospective case series of all women with antenatally suspected and intraoperatively or histopathologically confirmed PAS managed at King Fahad Medical City between April 2018 and December 2024. Women with high suspicion of PAS were electively admitted at 31+6 weeks’ gestation for optimisation when feasible and delivered by midline laparotomy and fundal or classical caesarean incision with the placenta left in situ. Definitive management consisted of hand-assisted retrograde caesarean hysterectomy or segmental uterine resection with reconstruction. Outcomes included operative time, quantified blood loss, transfusion requirements, intra- and postoperative complications, intensive care unit (ICU) admission, hospital stay, and neonatal morbidity. <b>Results:</b> A total of 236 women with confirmed PAS were managed. Median maternal age was 36 years and placenta previa coexisted in 86.9%. Elective caesarean delivery at 34+0–35+6 weeks occurred in 72.0%, whereas 28.0% required emergency delivery for haemorrhage or labour. Caesarean hysterectomy was performed in 85.2% and conservative segmental uterine resection in 14.8%. Median operative time was 135 minutes and median blood loss 4.3 L; the median transfusion requirement was six units of packed red blood cells. Intraoperative complications occurred in 27.1%, most commonly bladder injury (14.8%). One woman (0.4%) died intraoperatively from disseminated intravascular coagulation with intracardiac thrombosis. ICU admission was required in 66.0%. Neonatal intensive care unit admission occurred in 53.0%, mainly because of prematurity; there were no neonatal deaths. <b>Conclusion:</b> Centralised multidisciplinary management of PAS with planned delivery at 34–35 weeks, avoidance of placental removal, and use of retrograde hysterectomy or segmental resection can minimise haemorrhage and maternal mortality while maintaining acceptable neonatal outcomes in a high-volume referral centre.

This research is a collaboration involving a university team, a partnering corporation, and a hemodialysis clinic, which is a cross-disciplinary research initiative in the field of Artificial Intelligence of Things (AIoT) within the medical informatics domain. The research has two objectives: (1) The development of an <i>Internet of Things (IoT)</i><i>-based</i><i> Information System</i> customized for the hemodialysis machines at the clinic, including transmission bridges, clinical personnel dedicated web/app, and a backend server. The system has been deployed at the clinic and is now officially operational; (2) The research also utilized de-identified, anonymous data (collected by the officially operational system) to train, evaluate, and compare <i>Deep Learning-based Intradialytic Blood Pressure</i><i> (BP)</i><i>/Pulse </i><i>Rate (PR) </i><i>Predictive Models</i>, with subsequent suggestions provided. Both objectives were executed under the supervision of the Institutional Review Board (IRB) at Mackay Memorial Hospital in Taiwan. The system completed for objective one has introduced three significant services to the clinic, including automated hemodialysis data collection, digitized data storage, and an information-rich human-machine interface as well as graphical data displays, which replaces traditional paper-based clinical administrative operations, thereby enhancing healthcare efficiency. The graphical data presented through web and app interfaces aids in real-time, intuitive comprehension of the patients’ conditions during hemodialysis. Moreover, the data stored in the backend database is available for physicians to conduct relevant analyses, unearth insights into medical practices, and provide precise medical care for individual patients. The training and evaluation of the predictive models for objective two, along with related comparisons, analyses, and recommendations, suggest that in situations with limited computational resources and data, an Artificial Neural Network (ANN) model with six hidden layers, SELU activation function, and a focus on artery-related features can be employed for hourly intradialytic BP/PR prediction tasks. It is believed that this contributes to the collaborating clinic and relevant research communities.

<b>Background:</b> Scapulohumeral periarthritis commonly afflicts individuals in their middle age. Its etiology is multifaceted, and treatment presents a challenge with a high risk of recurrence. The main symptoms include shoulder pain and limited joint mobility, seriously affect the quality of life of the patients. Recent research indicate that acupuncture combined with bloodletting cupping can significantly improve the function of activity of shoulder joint and the pain in individuals with scapulohumeral periarthritis. However, these studies have typically been limited in scope, therefore additional research to substantiate the efficacy and safety of these intervention. <b>Methods:</b> To evaluate the efficacy of acupuncture combined with bloodletting cupping for treating patients with scapulohumeral periarthritis. We conducted an online search of databases in both Chinese and English, including PubMed, the Cochrane Library, Embase, Web of Science, CNKI, Wangfang Data, China Science and Technology Journal Database (VIP) and Chinese BioMedical Literature Database (CBM), to collect randomized controlled trials (RCTs) concerning the use of acupuncture combined with bloodletting cupping in scapulohumeral periarthritis patients. We also examined the references within the identified literature. Search utilised subject headings and free-text terms in both languages, without racial restrictions, for records up to April 3, 2024. Two researchers independently screened the literature, extracted data, and evaluated their qualities. RevMan 5.3 software was used for meta-analysis of the included studies. The protocol of this review was recorded in the International Platform of Registered Systematic Review and Meta-analysis Protocols (PROSPERO). Its registration number is CRD42023454614. <b>Results:</b> This review incorporated 22 RCTs involving a total of 1,774 patients. The results of meta-analysis showed that the clinical effective rate (RR=1.25, 95%CI [1.20, 1.30], P＜0.00001) of treating scapulohumeral periarthritis with acupuncture combined with bloodletting cupping was higher in the experimental group than in the control group. The all of Visual Analogue Scale (VAS) score (MD=-1.70, 95% CI [-2.17, -1.22], P＜0.00001). Melle score (SMD=-2.45, 95% CI [-2.55, -2.34], P=0.007]) and recurrence rate (RR=0.23, 95% CI [0.07, 0.77], P=0.02) were lower in the experimental group than in the control group with statistical significance (P<0.05). <b>Conclusion:</b> The acupuncture combined with bloodletting cupping for the treatment of shoulder impingement syndrome demonstrates definite efficacy and safety, with superior clinical effectiveness, pain relief, improvement in shoulder joint mobility, and reduction in recurrence compared to acupuncture alone. Therefore, it is worthy of being promoted and applied clinically.

Rabies is a virus present in more than 150 nations and territories worldwide, but it can be prevented through vaccination. Each year, tens of thousands of people die from rabies, primarily in Asia and Africa, with children under the age of 15 representing 40% of these fatalities. Up to 99% of rabies transmissions to humans occur through dog bites, making dogs the leading cause of rabies-related deaths in humans. We present a fatal case of rabies after a 5-month exposure incident. The patient, a 55-year-old Nigerian missionary from Enugu state, Nigeria presented with restlessness, hydrophobia, occasional barking gestures, abdominal discomfort and agitation. His condition was said to have progressively worsened as patients was unable to eat and drink fluids for days. His wife reported that the patient had experienced a bite from a stray dog on his right arm 5 months before presentation, for which no antirabies prophylaxis was given. The patient died on the second day of admission to the hospital (within 7 days of symptom onset).

<b>Background: </b>Chronic medical conditions are major drivers of healthcare spending, morbidity, and mortality in the United States, as well as critical indicators of health disparities. The disproportionately high rates of chronic medical conditions among Black, Latino, and American Indian and Alaska Native adults compared to non-Latino Whites highlight the urgent need to examine the factors contributing to these disparities. While higher socioeconomic status is generally associated with better health outcomes, this benefit may be diminished for racialized and minoritized populations. <b>Objective:</b> This study investigates the protective effects of educational attainment and income-to-poverty ratio on the prevalence of chronic medical conditions and examines whether these effects vary across racial and ethnic groups, specifically among Black, Latino, and American Indian and Alaska Native adults compared to non-Latino White adults. <b>Methods:</b> Using data from the 2023 National Health Interview Survey (NHIS), this cross-sectional study analyzed the association between educational attainment and chronic medical conditions across racial and ethnic groups. Logistic regression models were employed to assess whether the strength of the relationship between education and chronic medical conditions differed by racial/ethnic group, controlling for key demographic and socioeconomic covariates. Sample size was 29,373 which was reflective of 256,566,689 US population. <b>Results:</b> Consistent with the theory of Minorities' Diminished Returns, findings showed that the protective effects of higher educational attainment on chronic medical conditions were significantly weaker for Black, Latino, and American Indian and Alaska Native adults than for their non-Latino White counterparts. Even among individuals with higher education, Black, Latino, and American Indian and Alaska Native adults faced elevated risks of chronic medical conditions. <b>Conclusion:</b> While educational attainment generally reduces the prevalence of chronic medical conditions, this protective effect is moderated by racial and ethnic background. Structural barriers limit the health benefits of educational attainment. This underscores the need for policies that address structural inequities—such as low-quality education and occupational segregation—that constrain the protective health effects of educational attainment for minoritized groups.

Digital therapeutics (DTx) play a transformative role in diabetes management by leveraging technology to provide personalized, data-driven medical interventions. These tools enhance self-management by offering continuous monitoring and real-time feedback on glucose levels, diet, and physical activity. This personalized approach helps patients adhere to treatment plans and make informed lifestyle changes, leading to improved clinical outcomes such as reduced HbA1c levels and better overall diabetes control. The importance of DTx lies in their ability to make diabetes care more accessible and convenient. Mobile apps and telemedicine platforms enable patients to receive support and guidance from anywhere, reducing the need for frequent in-person visits. Additionally, DTx often include behavioral support features like reminders, educational content, and motivational tools, which are crucial for maintaining healthy habits and managing stress. Currently, the dynamics of DTx in diabetes are rapidly evolving, with increasing integration of artificial intelligence and machine learning to further personalize and optimize care. As the adoption of these technologies grows, they hold the potential to significantly improve patient outcomes and revolutionize diabetes management on a global scale. This article will focus on the benefits of novel digital therapeutics for prevention and management of type II diabetes that are currently available in the market.

A Medical devices and pharmaceutical drugs are packaged to maintain their stability and integrity during post-production shipping and storage prior to clinical usage. During delivery and storage, the packaging may come into direct or indirect contact with the drug product or medical device, which may result in chemical interactions between the two. Packaging can be crucial for success, protection, and sale. Like other supermarket items, prescription pharmaceuticals must be packaged in a way that will meet the needs of security and provide speedy packaging, safety, identity, superiority of products, patient safety, and goods superiority. Packaging is a science and an art where many factors are taken into account, starting with the fundamental design and technology used to pack the product without any instability and providing protection, presentation and observance of manufactured goods during transportation, storage, and consumption. In order to keep the drug physiochemical, biological, and chemical stability, packaging professionals create containers that can withstand the pressures that are applied during the supply and shipping processes. Improvements in the analysis of prescription drug development had long been fixated on packaging expertise.

Women must be aware of the risk factors of pregnancy complications and the negative maternal and fetal health consequences to take preventative measures and management strategies that will result in a successful pregnancy. This study aimed to assess the Level of Knowledge about obstetric warning signs and the associated factors among pregnant Saudi women attending antenatal care in a tertiary care maternity set-up in Riyadh City. Data for this cross-sectional study were obtained using a structured questionnaire from eligible pregnant women admitted to King Saud Medical City (KSMC) between August 2020 and March 2021. Data were analyzed using descriptive and inferential statistics. Out of a minimum estimated sample size of 170, researchers recruited 362 participants. As observed, only 92 people, or 25.4%, had enough knowledge about various educational levels. The majority (91.2%) lived in villages, had no history of chronic disease (74.0%), and said that it took them at least 30 minutes to commute from their home to the hospital (69.3%). Two hundred two (55.7%) lacked appropriate knowledge, although most women (76.2%) had one to four pregnancies. Poor psychological health resulted from 37 people's (10.2%) inadequate Awareness. Even though 139 (38.4%) had spent more than 15 minutes receiving education from medical staff and 200 (61.5%) had attended the prenatal care clinic more than four times, most lacked adequate knowledge. The variables education level (P=0.000), working status (P=0.022), and place of residence (P=0.044) showed a statistically significant association with the knowledge level, also only education level statistically significantly affected the likelihood of knowledge gaps. In conclusion, early identification of obstetric warning signs and associated risk factors of pregnancy complications is integral to prevention.

The initial omicron SARS-CoV-2 subvariants, BA.1 and BA.2 (BA.1.2), were progressively displaced by BA.5in Japan in 2022. In the BA.5-dominant period, there were significantly more healthcare staff infected by nosocomial contact with persons with confirmed SARS-CoV-2 infection than those infected by household contact, compared with the BA.1.2-dominant period. The staff infected via nosocomial contact included non-patient-facing staff, in the BA.5-dominant period, although they did not become infected by SARS-CoV-2 through nosocomial contact in the BA.1.2-dominant period. These data suggest the importance of infection control and care for non-patient-facing staff, in the same way as for patient-facing staff.

This review aimed to analyze some problems and challenges that emerged from the COVID-19 pandemic since it has affected all global business sectors. During the pandemic, remote work was increased in response to social distance requirements to decrease the transmission of disease. In addition, increased delegation, teamwork, supply chains, sales activities, and business operations have all been disrupted. Many challenges have emerged in the management of organizations due to the pandemic including the lack of direct contact as the lockdown made many people work from home and placed restrictions on movement and travel and uncertainty about the future. The healthcare system was also affected by the COVID-19 pandemic and faced several difficulties including increased demand for medical supplies and personal protective equipment, a greater need for nurses and other skilled healthcare professionals, and increased pressure on healthcare facilities, emergency services, and critical care departments. These challenges have created some opportunities in the management systems of healthcare organizations and other sectors. It is recommended for leaders prepare and continuously work proactively to be ready for unexpected future crises. Risk management and planning for any unexpected situation are among the very important aspects of organizational management.

The effects on the elderly are disproportionately Alzheimer’s disease (AD) is one of the most prevalent and chronic types of dementia. Alzheimer's disease (AD), a fatal illness that can harm brain structures and cells long before symptoms appear, is currently incurable and incurable. Using brain MRI pictures from a publicly accessible Kaggle dataset, this study suggests a prediction model based on Convolutional Neural Networks (CNNs) to help with the early detection of Alzheimer's disease. Four levels of dementia have been applied to the 6,400 photos in the collection: not demented, slightly demented, moderately demented, and considerably mildly demented. Pixel normalization, class balancing utilizing data augmentation techniques, and picture scaling to 128×128 pixels were all part of a thorough workflow for data preparation. To improve the gathering of spatial dependence in volumetric MRI data, a 3D convolutional neural network (CNN) architecture was used. We used important performance measures including F1-score, recall, accuracy, precision, and log loss to gauge the model's effectiveness. A review of the available data indicates that the total F1-score, accuracy, recall, and precision were 99.0%, 99.0%, and 99.38%, respectively. The findings demonstrate the model's potential for practical use in early AD diagnosis and establish its robustness with the help of confusion matrix analysis and performance curves.

This essay explores how AI can enhance clinical research and, particularly, its pivotal role in the development of medical devices. A data-driven approach to medical device development that can streamline regulatory compliance and quality assurance is discussed. Methods that generate insights from pre-stage data and utilize it during development are detailed. The effectiveness of this approach in compliance audits, 510(k) submissions, and quality system audits - reducing time, effort, and risks is analyzed. The findings are illustrated with practical examples and takeaway recommendations. When reading a scientific article, how many times have you judged the quality of the research by looking at the methodology section? Artificial intelligence algorithms can be developed with the most robust and innovative technology, but if they are not properly validated, they will be worthless in the eyes of regulatory authorities. Conversely, outdated and simplistic models can still gain regulatory clearance if robustness is effectively demonstrated. For better or worse, ethics, economics, and robustness are often sacrificed in the constant government struggle to keep up with the technological edge of AI development. The slow crawl of lawmakers is constant in every field. Automating small tasks can save time and reduce risks when playing catch-up with a changing regulatory framework so the rest of the AI development can continue uninhibitedly. This dives into using FDA open data to collaborate with a food and drug law company and develop several bottom-up initiatives that supply knowledge needed for regulatory compliance and quality systems development. Methods that input pre-stage data and output actionable insights as models are provided. By sharing these resources and advice as academic researchers, efficiency in streamlining processes is maximized, thereby letting more time and resources be allocated to the actual development [1].

Artificial intelligence systems have been previously used to predict post-operative complications in small studies and single institutions. Here we developed a robust artificial intelligence model that predicts the risk of having cardiac, pulmonary, thromboembolic, or septic complications after elective, non-cardiac, non-ambulatory surgery. We combined structured and unstructured electronic health record data from 3.5 million surgical encounters from 25 medical centers between 2009 and 2017. Our neural network model predicted postoperative comorbidities 15 to 80 times faster than classical models. As such, our model can be used to assess the risk of having a specific complication postoperatively in a fraction of a second. With our model, we believe clinicians will be able to identify high-risk surgical patients and use their good judgment to mitigate upcoming risks, ultimately improving patient outcomes [1].

<b>Introduction:</b> Stroke is associated with high mortality. It is the main neurological cause of mortality and the most important cause of disability worldwide. In the year 2007, Stroke was the third cause of admission at the Korle-Bu Teaching Hospital, and the number one cause of death. <b>Objective:</b> To determine the probability of survival (case-fatality rates) of stroke patients admitted at the Korle- Bu Teaching Hospital during the period 2007. <b>Method:</b> A Retrospective descriptive study of Surgical/Medical Emergency, SME and the Medical wards admissions and discharges from 1st January 2007 to 31st December, 2007. <b>Results:</b> A total of 250 hospitalized stroke patients were identified, of which 68(27%) were from the SME and 182(72%) from the medical wards. The mean age (SD) was 57.6(14.7) and 52% were males. Case fatality rate was 52% at the SME versus 35% at the Medical wards) whilst the risk of death in males expressed as risk ratio (RR) was 2.1, (95% CI 0.70-5.6) vs. RR=1.3, (95% CI 0.73-2.5) in females and the median survival time was 2days (95% CI 1.5-2.4) versus 7 days (95% CI 6.3-7.6) at the SME and Medical respectively. The type of admission and stroke outcome was significant P=0.01 (95%CI 0.02-0.14). <b>Conclusion:</b> Stroke was associated with high mortality. The risk of dying from stroke was higher at the SME Findings were independent of stroke subtype, stroke onset and any associated co-morbidities.

The deleterious health effects of ethanol abuse are widely known and a diversity of medical conditions appear with excessive consumption. Acute and chronic abuse have different clinical characteristics, although severe intoxications are rare among alcoholics of a long career, where the systemic complications will dominate the symptomatology. While the medical focus acutely will mostly stay on the nervous and circulatory effects, gastrointestinal complications that will come up chronically (liver, pancreas, bowel dysfunction) need proper consideration. Ethanol will affect gastrointestinal functionality through a triple action, directly on the epithelial cell and the secretory activity, by altering the motility and in the long-term scope, through promoting carcinogenesis. Medical teams dealing with acute and chronic ethanol intoxication or abuse must be aware of the multiple effects it shows, and perform a prompt diagnosis as well as appropriate intervention.

Digital therapeutics (DTx) is an evidence-based treatment that makes use of high-quality software. As many healthcare systems confront increasing expectations for quality results, the need for digital medications is steadily growing in the clinical arena. To ensure that patients are supported during chemotherapy and that needless hospital visits are avoided, digital therapeutics must be integrated into the cancer care pathway. Oncology patients are usually immunocompromised die to their disease and treatment, rendering them more susceptible to infection than the general population. As a result, visiting to a hospital might endanger their health. In addition, when cancer patients and survivors return home after treatment, digital health interventions provide them with the tools they need to manage their illness and its side effects in the privacy of their own homes. Considering the increasing prevalence of cancer patients and the solution that digital therapeutics has to offer in oncology, its future looks promising. This review article aims to summarize the existing companies in this domain, while evaluating the prospects as well.

The International Association for the Study of Pain has classified pain into nociceptive pain, neuropathic pain, and nociplastic pain based on the cause of the pain. At present, nociplastic pain is pain that is not nociceptive pain and has the following characteristics: no clear evidence of actual or threatened tissue damage causing the activation of peripheral nociceptors or evidence for disease or lesion of the somatosensory system causing the pain. If there is tissue damage, disease or lesion, it is neuropathic pain; if there is none, it is nociplastic pain. In other words, the difference in diagnosis is whether or not tissue damage, disease, or lesion can be found at the current medical level (testing equipment). The treatment of nociplastic pain is almost the same as the treatment of neuropathic pain. Fibromyalgia is included in nociplastic pain. To my knowledge, of the nociplastic pain and neuropathic pain, fibromyalgia is the disease with the highest number of pharmacological and non-pharmacological treatments with evidence of efficacy. Effective treatments for fibromyalgia are often given to neuropathic pain. This expands treatment options. I presume that tissue damage, disease or lesion will be found in fibromyalgia through advances in the medical science by the time humans emigrate to Mars. The distinction between nociplastic pain and neuropathic pain complicates the diagnosis of chronic pain. However, the distinction does not improve the treatment outcomes. Medical science is the discipline to find a treatment method that can produce better outcomes. In the event of a medical controversy, the medical theory with better treatment outcomes should be adopted. It is desirable to combine nociplastic pain and neuropathic pain into one pain. This will simplify diagnosis and increase treatment options (improve treatment outcomes) in nociplastic pain and neuropathic pain.

Emerging smart medical instruments combined with advanced smart industrial equipment facilitate the collection of vast volumes of critical data. This data not only enables significantly more accurate and cost-effective diagnosis and maintenance but also enriches the datasets available for AI algorithms, leading to improved insights and outcomes. The integration of high-speed and ultra-reliable telecommunications infrastructure is crucial, as it supports the cloud model. This model allows for off-device aggregation in the cloud, which effectively offloads infrastructure demands and provides an extended runway for future technological improvements before the deployment of the next generation of devices. However, in certain scenarios, latency and bandwidth limitations present significant challenges. These limitations require that a substantial amount of AI and machine learning processing is conducted directly on the transmitted data, which places rigorous demands on both the processing subsystems and the communications links themselves. The current project directly addresses the accelerator side of this multifaceted issue. It will carry out comprehensive end-to-end demonstrations leveraging pilot 5G networks and telemedicine facilities, collaborating closely with major industry participants to showcase the capabilities and potential of this innovative technology. This collaborative effort is essential to pushing the boundaries of what is possible in smart medical instruments and industrial applications [1].