Clinical Care Of Patients Research Articles

Enteric Tube Placement and Management: Tips for Interventional Radiologists

AbstractThere is a significant risk of malnutrition and aspiration among hospitalized patients, particularly the elderly, patients with head and neck malignancy, and those with a chronic debilitating disease. Enteral feeding is preferred over parenteral feeding for patients with functional gastrointestinal tracts but who have impaired swallowing due to its cost-effectiveness, lesser complications, and theoretical benefits such as preserving mucosal architecture and gut-associated lymphoid tissue. While nasogastric and nasojejunal tubes address short-term feeding needs, long-term enteral feeding tubes are crucial for patients requiring nutritional support beyond 4 weeks. Enteric tube placement can be performed surgically, endoscopically, or radiologically, with radiologically inserted gastrostomy emerging as a safe and effective method. This review article discusses various interventional radiological techniques for enteral feeding, including percutaneous gastrostomy, gastrojejunostomy, jejunostomy tube placement, and gastrointestinal tract decompression procedures like cecostomy. It covers procedural considerations, patient selection criteria, technical aspects, and outcomes, drawing on current evidence and clinical experience. The review also addresses emerging trends, advancements, and future directions in interventional radiology for enteral feeding and gastrointestinal tract decompression, aiming to contribute to the ongoing evolution of clinical practice and patient care. Key recommendations from international guidelines are summarized, emphasizing individualized risk assessment for optimal enteral feeding strategies.

Unraveling Antiseizure Medication Adherence in Paediatric Epilepsy: Implications for Clinical Practice and Patient Care

ObjectiveNon adherence to antiseizure medications (ASM) is a common and modifiable risk factor for poor control of epilepsy and contributes to increased hospitalizations, bias in assessing the effectiveness of therapy and inaccurate clinical decision making.The aim of the study was to estimate proportion of nonadherence to antiseizure medications among children with epilepsy and to identify clinical and demographic factors that contribute to antiseizure medication non adherence. MethodsConsecutive subjects ≤ 18 years with epilepsy, on antiseizure medications for atleast three months, attending Pediatric Neurology OPD, Government Medical College Thiruvananthapuram, Kerala, India were included in the study. Self reported adherence to antiseizure medications was measured using Medication Adherence Report Scale questionnaire. Perceptions towards medications were assessed using Beliefs about Medicines Questionnaire Clinicodemographic factors and medication beliefs were analysed to examine their association with non-adherence in the participating children. ResultsThe rate of non-adherence in children with epilepsy was 32%. 68% of the study population had high necessity beliefs and 60% showed low concern beliefs, which indicated overall positive perception towards medications. Univariate analysis showed that the following were significantly associated with ASM non adherence: ASM side effects (OR:3.01; 95% CI:1.52–5.92; p < 0.001) and ASM Concerns (OR: 0.84; 95% CI: 0.77–0.92; p 0.0003) and Necessity-Concern Differential Score (OR: 1.19; 95% CI: 1.10–1.29; p 0.0001). Other clinic-demographic variables did not exhibit a significant association with adherence. Multivariate analysis showed that both ASM side effects and ASM beliefs, represented by NCD score remained significant. ConclusionPerceptions towards medications have an important and potentially modifiable association with medication non adherence. Interventions to improve adherence should be tailored to each patient, including measures to address the specific beliefs of the patient and the family, along with practical barriers such as side effects of medications.

Gout diagnoses uncertain when made by general practitioners without serum uric acid testing: an observational study.

General practitioners (GPs) diagnose the majority of all gout patients. They make their diagnosis clinically. Serum uric acid (SUA) level >0.35mmol/l is largely determinative for the clinical diagnosis of gout. We aimed to assess to what extent GPs test SUA when making a first gout diagnosis, and to consider consequences regarding diagnostic certainty of not testing it. We calculated proportions of patients from 87 Dutch general practices (1-1-2013 to 1-1-2022) with a first gout diagnosis and a recorded SUA test, evaluated if SUA testing was punctually timed with respect to the diagnosis date, whether SUA levels were >0.35mmol/L, and whether diagnoses corresponded with diagnoses according to the 'Acute Gout Diagnosis Rule' (AGD-rule). In 43.0% of 7782 patients (mean age 64.4 years; 68.8% male) no SUA test result was recorded, with substantial variation among practices: median 41.9% (IQR 20.2%). 'Gout very likely' according to the AGD-rule was seen in all males and in 83.1% of females when a punctually timed SUA >0.35mmol/L was present. When such SUA level was lacking, 'Questionable or indeterminate gout diagnosis' was seen in all males and in 67.1% of females, and 'Arthritis diagnosis other than gout' in 32.9% of females. GPs diagnosed gout without testing SUA in ~40% of cases. This implies avoidable diagnostic uncertainty with impact for the clinical care of many patients as well as for studies that include primary care patients with gout. Key Points • Serum uric acid (SUA) level > 0.35mmol is determinative when diagnosing gout: however, it is unknown how often SUA testing is applied as such. • More than 40% of patients with a first gout diagnosis according to general practitioners (GPs) had no SUA test result registered in their medical record. • Gout diagnosing by GPs without SUA testing appeared to lead to avoidable diagnostic uncertainty, as diagnoses are often questionable or sometimes even wrong. • Researchers investigating gout need to take into account diagnostic uncertainty if they include gout patients who are diagnosed in primary care in their studies without SUA information.

Automated variant re-evaluation is labor-balanced and gives clinically relevant results: Hereditary cardiac disease as a use case

BackgroundGenetic findings influence clinical care of patients suspected of hereditary cardiac diseases. As additional knowledge arises over time, the classification of genetic variants may change. The labor cost associated with systematic manual reevaluation for reported variants is substantial. We applied an automated variant classifier for reevaluation of previous reported variants to assess how such tools may assist in manual reevaluation. MethodsHistorically (2010–2022), patients (N = 2987) suspected of inherited cardiomyopathies or ion-channel disorders were screened for genetic variants in at least one of up to 114 genes. We had reported 1455 unique variants, of which 742 were among the 14 most relevant genes. In the 14-gene-group, we compared our reported classification to that of an autoclassifier and manually reevaluated variant classification of all variants. Among the remaining genes (N = 100), only variants where the autoclassifier predicted change of clinical impact, such as variant of uncertain significance to likely pathogenic or oppositely, were manually reevaluated. ResultsWe identified 9% (66/742) of variants with clinical impact in the 14-gene-group. Of these, 91% could have been identified solely evaluating the 120 variants where the autoclassifier had predicted a change of clinical impact. In the 100 remaining genes, a change of clinical impact was identified in 3% (22/713) after manual reevaluation. ConclusionUsing an autoclassifier reduces the workload to identify variants likely to have a change in variant class with clinical impact. Hence, we recommend using such tools to identify the variants most relevant to manually reevaluate to improve patient care.

Development and implementation of a guideline-based decision support system for etiological diagnosis of Sustained Monomorphic Ventricular Tachycardia (SMVT)

Abstract Background Guideline (GL)-based decision support systems (DSSs) play a significant role in supporting physicians in their decision-making processes by providing structured clinical guidance. While these systems have shown significant success in enhancing practitioner performance and patient outcomes in general practice, their application in the context of ventricular arrhythmias remains limited. Moreover, the clinical etiological diagnosis of ventricular arrhythmias presents various challenges for healthcare professionals, such as adherence to updated diagnostic criteria and understanding of complex arrhythmia mechanisms. Purpose In this study, we aim to design, implement, and evaluate a guideline-based decision support systems for etiological diagnosis of sustained monomorphic ventricular tachycardia (SMVT), which could potentially address critical challenges associated with the disease diagnosis and management. Methods To comprehensively capture the semantics embedded in clinical guidelines, we first developed a dedicated knowledge graph tailored to the clinical decision-making. This involved extracting entities and relationships pertinent to key decision procedures from ESC guidelines for ventricular arrhythmias, clinical textbooks or literatures. Subsequently, we translated instances within the knowledge graph into archetypes within openEHR, amplifying the interoperability of clinical information. To enhance the interpretability of the DSS, we established executable diagnostic and treatment processes to offer visual representation of clinical steps in diagnosis and treatment. Ultimately, we constructed a decision support engine to implement the GL-DSS, and its effectiveness was evaluated with anonymous real-world data. Results The proposed GL-DSS was found to be quite feasible and could provide etiological diagnosis for SMVT. The knowledge base for the constructed GL-DDS included more than 2000 entities and relations, which played a vital role in representing disease pathogenesis. The proposed system can map the clinical entities to a set of openEHR archetypes, thus mitigate the interoperability issues of the hospital information systems and CDSS data integration. Also, this module improved system usability and ensured that guidelines could be easily translated and updated into decision support tools. Real-word medical records from 20 patient were collected for model evaluation, the results showed that the GL-DSS covered most of the entities and relations between diseases and diagnostic evidence recorded in clinical texts and achieved a diagnosis accuracy of 80%. Conclusions The results demonstrated that our proposed GL-DSS could completely and accurately capture the diagnostic information under the concrete EHR data. Thus, the implement decision support system could have the potential to improve clinical decision-making, patient care, and outcomes in this specialized medical domain.

Artificial intelligence in rheumatology: perspectives and insights from a nationwide survey of U.S. rheumatology fellows.

Artificial Intelligence (AI) is poised to revolutionize healthcare by enhancing clinical practice, diagnostics, and patient care. Although AI offers potential benefits through data-driven insights and personalized treatments, challenges related to implementation, barriers, and ethical considerations necessitate further investigation. We conducted a cross-sectional survey using Qualtrics from October to December 2023 to evaluate U.S. rheumatology fellows' perspectives on AI in healthcare. The survey was disseminated via email to program directors, who forwarded it to their fellows. It included multiple-choice, Likert scale, and open-ended questions covering demographics, AI awareness, usage, and perceptions. Statistical analyses were performed using Spearman correlation and Chi-Square tests. The study received IRB approval and adhered to STROBE guidelines. The survey aimed to reach 528 U.S. rheumatology fellows. 95 fellows accessed the survey with response rate to each question varying between 85 and 95 participants. 57.6% were females, 66.3% aged 30-35, and 60.2% in their first fellowship year. There was a positive correlation between AI familiarity and confidence (Spearman's rho = 0.216, p = 0.044). Furthermore, 67.9% supported incorporating AI education into fellowship programs, with a significant relationship (p < 0.005) between AI confidence and support for AI education. Fellows recognized AI's benefits in reducing chart time (86.05%) and automating tasks (73.26%), but expressed concerns about charting errors (67.86%) and over-reliance (61.90%). Most (84.52%) disagreed with the notion of AI replacing them. Rheumatology fellows exhibit enthusiasm for AI integration yet have reservations about its implementation and ethical implications. Addressing these challenges through collaborative efforts can ensure responsible AI integration, prioritizing patient safety and ethical standards in rheumatology and beyond.

Students and Patients’ Compliance towards Dental Software Used In Private Dental College in Chennai - A Prospective Trial

Objective: This study aimed to assess the compliance and satisfaction of third-year undergraduate dental students and patients using the Dental Information Archiving System (DIAS) in a private dental college in Chennai, focusing on ease of use, time efficiency, and overall experience. Methods: A prospective trial was conducted at Saveetha Dental College, involving 100 dental students and 100 patients. Compliance and user satisfaction were measured using a questionnaire administered at two-time points: at the initial clinical appointment and after four weeks of using DIAS. The questionnaire included multiple-choice and Likert-scale questions addressing key aspects such as ease of use, perceived value, and efficiency of the system. Results: Patients: Awareness of DIAS increased significantly from 78% to 96% between the first and second visits. Patient satisfaction with the software’s time efficiency and perceived security improved drastically, with 93% finding the time spent reasonable by the second visit (p = 0.000). Confidence in data security rose from 4% to 85% (p = 0.000). Students: Familiarity with DIAS improved from 7% to 61% (p = 0.000), and preference for DIAS over traditional methods increased from 28% to 67% (p = 0.000). The time required for DIAS usage decreased, while the application of clinical knowledge increased from 67% to 74% (p = 0.008). Conclusion: The study demonstrated that both students and patients showed significant improvements in compliance and satisfaction with the dental software over time. Increased exposure led to better understanding and trust in the system, highlighting the potential of DIAS to enhance clinical education and patient care. However, challenges related to data security, interoperability, and the need for ongoing training remain critical for wider implementation.

OS02.4.A CAREGIVER PROGRAM IN NEURO-ONCOLOGY: UCSF EXPERIENCE CARING FOR MORE THAN 4,000 CAREGIVERS OVER THE LAST 10 YEARS

Abstract BACKGROUND Caregivers of patients with primary brain tumors are at high risk for distress and as they navigate caring for their loved one with both a cancer diagnosis and neurologic disorder. The last decade has seen growth of caregiver research in the brain tumor community, with evidence that caregiver mastery may correlate with survival in patients with glioblastoma (GBM). The UCSF Gordon Murray Neuro-Oncology Program, the first of its kind, launched in 2014 with a mission to address their unique needs. Caregiver care is now fully integrated into the clinical care of patients with brain tumors at UCSF, providing personalized care across the disease trajectory and programming to address common challenges and foster resilience. The Program includes a nurse, administrator, and social worker. Referrals occur 1) at first clinic appointment; 2) from clinical staff; and 3) caregivers of patients with GBM receive proactive outreach over the first 6 months after diagnosis. Caregivers are also contacted for bereavement support. Dedicated programming provides educational materials focusing on common concerns and building community through monthly support groups, educational webinars, annual retreats and family camp. We report our practical experience of the Program serving over 4,000 caregivers since its inception. MATERIAL AND METHODS Total number of individual caregivers were included from 01/2014 - 12/2023. Detailed demographic information was extrapolated during clinical interviews from a subset. Information regarding caregiver concerns and interventions was extracted from the medical records between 01/2017 - 12/2023. RESULTS The UCSF Gordon Murray Caregiver serves an annual average of 400 caregivers and over 800 encounters. Demographic information obtained from a subset revealed 66% females; 77% between 41-64 years of age; 60% a spouse; 89% working full time; and 30% had children in the home. The most common concerns for caregivers were informational (42%), followed by emotional (25%), and practical (23%). Examples of informational concerns included desire for informational resources, understanding the treatment plan/side effects, and understanding patient behavioral changes. Emotionally, they feel overwhelmed, uncertain, and sad. Practical concerns included physically caring for patients and issues around medical insurance, disability, and finances. Caregivers also expressed frequent concerns around talking with children/family members and feelings of isolation. CONCLUSION Caregivers of patients with primary brain tumors are at high risk of distress and have unique needs based on the tumor location and symptoms from treatment. Our practical 10 year experience of a dedicated, fully integrated, caregiver program highlights real world concerns of caregivers and identifies opportunities to develop tailored interventions to address these needs.

Malnutrition risk screening in adult oncology outpatients: An ASPEN systematic review and clinical recommendations.

Malnutrition screening is not widely practiced in outpatient cancer centers. This review aims to determine the validity of malnutrition screening tools and provide recommendations for clinical use. Studies identified by a systematic review assessed the general validity of screening tools in adult oncology outpatients from five databases through 2022. The American Society for Parenteral and Enteral Nutrition (ASPEN) convened a working group of members from the Academy of Nutrition and Dietetics, Academy of Oncology Nurse and Patient Navigators, American Cancer Society, American Society for Clinical Oncology, American Society for Nutrition, American Society for Radiation Oncology, Association of Cancer Care Centers, and Oncology Nursing Society to answer the following questions: (1) should clinicians screen for malnutrition, (2) which malnutrition screening tools are recommended, and (3) what are the clinical applications for malnutrition risk screening in adult oncology outpatients? Twenty of 738 studies met the criteria and were reviewed. Six screening tools with specific cut-points demonstrated validity and are recommended, including the Mini Nutritional Assessment (≤23.5), Malnutrition Screening Tool (MST; MST ≥ 2 and patient-led MST ≥ 2), Malnutrition Universal Screening Tool (MUST; MUST ≥ 1 and MUST ≥ 2), Nutrition Risk Screening-2002 (NRS-2002; NRS-2002 ≥ 2 and NRS-2002 ≥ 3), NUTRISCORE ≥ 5, and Patient-Generated Subjective Global Assessment Short Form (PG-SGA SF; PG-SGA SF ≥ 7 and PG-SGA SF ≥ 8). Six screening tools are valid for malnutrition risk identification in oncology ambulatory settings and recommended before treatment initiation and regularly thereafter, depending on treatment course. Research is needed to understand to what extent early diagnosis and management of malnutrition improves the clinical care of oncology patients.

Enhancing fieldwork readiness in occupational therapy students with generative AI.

The rapid integration of artificial intelligence (AI) into health professions education is revolutionizing traditional teaching methodologies and enhancing learning experiences. This study explores the use of generative AI to aid occupational therapy (OT) students in intervention planning. OT students often lack the background knowledge to generate a wide variety of interventions, spending excessive time on idea generation rather than clinical reasoning, practice skills, and patient care. AI can enhance creative ideation but students must still adhere to evidence-based practice, patient safety, and privacy standards. Students used ChatGPT v. 3.5 in a lecture and assignment to integrate generative AI into intervention planning. Students analyzed a case study, generated ideas with ChatGPT, selected interventions that aligned with the client's needs, and provided a rationale. They conducted evidence-based searches and wrote an analysis on how the research influenced their decisions. The results demonstrate generative AI's potential as a valuable tool for OT students, enhancing their comfort with AI and understanding of ethical and safety considerations. Qualitative feedback highlighted AI's role in boosting efficiency and creativity in intervention planning, with most students expressing strong intent to use ChatGPT in clinical practice due to its ability to reduce cognitive load and generate innovative ideas. These findings suggest that integrating generative AI into the OT curriculum could enhance intervention planning and improve clinical readiness.

OPTIC PATHWAY GLIOMAS AFTER TRANSITIONING INTO ADULTHOOD – AN INSTITUTIONAL EXPERIENCE

Abstract AIMS Optic pathway gliomas (OPG) are rare tumors affecting the optic nerve and the chiasm that occur predominantly during childhood and are generally managed by pediatric neurosurgeons/ pediatric oncologists. Prognosis is quite good for these benign tumors, and many patients survive into adulthood. While clinical care for these patients in childhood is well-standardized, the clinical course of patients with OPG after transitioning into adulthood is not well studied.We herein aimed to characterize patients with OPG diagnosed in childhood after transitioning into adult neurosurgical care in order in order to ellucidate the clinical course of OPG in adulthood and to identify potential problems with clinical care of these special patients in adult neurosurgery or neuro-oncology services. METHOD Clinical and radiographical data from adult patients (&amp;gt; 16 years of age) with OPG diagnosed in childhood and treated in our institution were collected from patient charts. Descriptive statistics and outcome analysis were performed using commercial statistical software. RESULTS Of 124 patients with OPG diagnosed in childhood, 35 patients transitioned into adulthood. The population included equal distribution of male (18, 51.4%) vs female (17, 48.6%) patients. Median age at diagnosis was 8.0 years (range 0.5 – 15.0) and median age at last follow up was 20.8 years (range 17.1 – 38.6). 12 patients were positive for NF (34.3%). 9 patients had undergone surgery for the OPG (25.7%), 11 had chemotherapy (31.4%) and 15 (42.8%) had received radiotherapy. 8 patients showed an endocrine deficiency (22.9%), 4 hypothalamic dysfunction (11.4%) and 8 cognitive/behavioral difficulties (22.9%). Visual acuity impairment was detected in 12 patients (34.3%). 9 patients experienced recurrence (25.7%). CONCLUSION Patients with OPG transition into adulthood frequently and experience a wide range of clinical problems. Close neurosurgical/neuro-oncology specialist follow-up is recommendable.

Elucidating Clinical Queries for Tailored Therapy in Patients with Prolactinoma.

Prolactinomas are the most prevalent type of pituitary neuroendocrine adenomas, primarily affecting women of reproductive age. Unlike other pituitary tumors, the first-line management has traditionally been pharmacological rather than surgical. This preference is due to the effectiveness of dopamine agonists (DAs), which typically reduce tumor size and normalize prolactin levels in most patients. However, this does not imply that there is no room for improvement; the duration of treatment and medication side effects often lead to compliance issues among patients. Recent advances in surgical techniques and molecular biology have paved the way for the development of precision medicine, allowing for more flexible and personalized treatment strategies for prolactinomas. This review aims to enhance clinical decision-making and patient care for endocrinologists by focusing on several key factors: predictive markers of DA sensitivity, clinical characteristics and suitability for transsphenoidal adenomectomy as a potential first-line treatment, factors determining the successful withdrawal of DAs after prolonged use, safety concerns during pre/post-pregnancy and breastfeeding, and determinants of tumor aggressiveness. Through tailored therapy-a patient-focused, multidisciplinary approach- we aim to improve the management of prolactinoma patients.

Initial diagnosis and treatment of shock

Structured procedures have been established internationally for the initial clinical care of patients with traumatic injuries. Comparable concepts have not yet been applied to the initial clinical care of life-threatening nontraumatic emergencies. In 2022, aworking group of the German Society for Acute and Emergency Medicine (DGINA) presented the Advanced Critical Illness Life Support (ACiLS) concept for the care of nontraumatic emergencies and offers corresponding training courses. To present systematic clinical first aid for patients with the leading symptom of shock according to the ACiLS concept. The (PR_E-)AUD2IT basic algorithm used in the ACiLS concept divides the initial care of acritically ill patient into the elements of preparation, resources, initial care, medical history, examination, differential diagnosis, diagnostics, interpretation and to do, interrupted by three team time-out elements for structured communication. The use of this concept is demonstrated here using the example of shock. The ACiLS concept has the potential to improve the quality of initial care of nontraumatic emergencies in emergency department shock rooms and intensive care units. Further evaluations in practice and training capacities are essential.

Artificial intelligence and machine learning in disorders of consciousness.

As artificial intelligence and machine learning technologies continue to develop, they are being increasingly used to improve the scientific understanding and clinical care of patients with severe disorders of consciousness following acquired brain damage. We here review recent studies that utilized these techniques to reduce the diagnostic and prognostic uncertainty in disorders of consciousness, and to better characterize patients' response to novel therapeutic interventions. Most papers have focused on differentiating between unresponsive wakefulness syndrome and minimally conscious state, utilizing artificial intelligence to better analyze functional neuroimaging and electroencephalography data. They often proposed new features using conventional machine learning rather than deep learning algorithms. To better predict the outcome of patients with disorders of consciousness, recovery was most often based on the Glasgow Outcome Scale, and traditional machine learning techniques were used in most cases. Machine learning has also been employed to predict the effects of novel therapeutic interventions (e.g., zolpidem and transcranial direct current stimulation). Artificial intelligence and machine learning can assist in clinical decision-making, including the diagnosis, prognosis, and therapy for patients with disorders of consciousness. The performance of these models can be expected to be significantly improved by the use of deep learning techniques.

Systematic Review of the Application of Artificial Intelligence in Healthcare and Nursing Care.

This systematic review explores the complex relationship between artificial intelligence (AI) and healthcare, with an explicit focus on nursing care. Examining a range of studies from 2020, the research investigates the impact of AI on clinical decision-making, patient care and healthcare administration. Through a comprehensive literature review, the study highlights the potential benefits of AI integration in improving the efficiency and efficacy of healthcare. AI technologies offer opportunities for personalised patient care, predictive analytics and enhanced clinical processes, with the ultimate aim of transforming the healthcare system. However, ethical considerations and regulatory frameworks are crucial, emphasising patient privacy, autonomy and data security. The findings underscore the need for transparency, accountability and fairness in the application of AI in healthcare. While AI promises to improve patient outcomes and streamline healthcare delivery, careful consideration of ethical implications and regulatory compliance are essential for responsible implementation.

Granular Porous Nanofibrous Microspheres Enhance Cellular Infiltration for Diabetic Wound Healing.

Diabetic foot ulcers (DFUs) are a significant challenge in the clinical care of diabetic patients, often necessitating limb amputation and compromising the quality of life and life expectancy of this cohort. Minimally invasive therapies, such as modular scaffolds, are at the forefront of current DFU treatment, offering an efficient approach for administering therapeutics that accelerate tissue repair and regeneration. In this study, we report a facile method for fabricating granular nanofibrous microspheres (NMs) with predesigned structures and porosities. The proposed technology combines electrospinning and electrospraying to develop a therapeutic option for DFUs. Specifically, porous NMs were constructed using electrospun poly(lactic-co-glycolic acid) (PLGA):gelatin short nanofibers, followed by gelatin cross-linking. These NMs demonstrated enhanced cell adhesion to human dermal fibroblasts (HDF) during an in vitro cytocompatibility assessment. Notably, porous NMs displayed superior performance owing to their interconnected pores compared to nonporous NMs. Cell-laden NMs demonstrated higher Young's modulus values than NMs without loaded cells, suggesting improved material resiliency attributed to the reinforcement of cells and their secreted extracellular matrix. Dynamic injection studies on cell-laden NMs further elucidated their capacity to safeguard loaded cells under pressure. In addition, porous NMs promoted host cell infiltration, neovascularization, and re-epithelialization in a diabetic mouse wound model, signifying their effectiveness in healing diabetic wounds. Taken together, porous NMs hold significant potential as minimally invasive, injectable treatments that effectively promote tissue integration and regeneration.

HNF1B Transcription Factor: Key Regulator in Renal Physiology and Pathogenesis.

The HNF1B gene, located on chromosome 17q12, encodes a transcription factor essential for the development of several organs. It regulates the expression of multiple genes in renal, pancreatic, hepatic, neurological, and genitourinary tissues during prenatal and postnatal development, influencing processes such as nephrogenesis, cellular polarity, tight junction formation, cilia development, ion transport in the renal tubule, and renal metabolism. Mutations that alter the function of Hnf1b deregulate those processes, leading to various pathologies characterized by both renal and extrarenal manifestations. The main renal diseases that develop are polycystic kidney disease, hypoplastic or dysplastic kidneys, structural abnormalities, Congenital Anomalies of the Kidney and Urinary Tract (CAKUT), and electrolyte imbalances such as hyperuricemia and hypomagnesemia. Extrarenal manifestations include Maturity-Onset Diabetes of the Young (MODY), hypertransaminasemia, genital and urinary tract malformations, Autism Spectrum Disorder (ASD), and other neurodevelopmental disorders. Patients with HNF1B alterations typically carry either punctual mutations or a monoallelic microdeletion in the 17q12 region. Future research on the molecular mechanisms and genotype-phenotype correlations in HNF1B-related conditions will enhance our understanding, leading to improved clinical management, genetic counseling, monitoring, and patient care.

Bullous Pemphigoid Severity and Levels of Antibodies to BP180 and BP230

The correlation between serum levels of autoantibodies against bullous pemphigoid (BP) antigens 180 (BP180) and 230 (BP230) with BP disease severity is unclear. To investigate the correlation of anti-BP180 and anti-BP230 immunoglobulin G (IgG) antibody levels with BP disease severity. A search was performed of the Cochrane Central Register of Controlled Trials, Embase, and PubMed databases from their respective inception to April 11, 2024. Studies evaluating the correlation between serum levels of anti-BP180 or anti-BP230 IgG measured using enzyme-linked immunosorbent assay (ELISA) and disease severity assessed per the Autoimmune Bullous Skin Disorder Intensity Score (ABSIS) or BP Disease Area Index (BPDAI) were included. No language or geographic restrictions were imposed. Nearly 0.4% of initially identified studies met the selection criteria. One researcher extracted data and another researcher confirmed data. The risk of bias was independently assessed by these researchers using the Quality Assessment of Diagnostic Accuracy Studies 2 tool, with discrepancies resolved by discussion with a third researcher. A random-effects model meta-analysis and a subgroup analysis were conducted based on the ELISA kit manufacturers. Pooled correlation coefficients of antibody levels with ABSIS and BPDAI. In all, 14 studies with 1226 participants were analyzed. The risk of bias of included studies was generally low. The meta-analysis found anti-BP180 autoantibody levels showed moderate correlation with objective BPDAI (r = 0.56; 95% CI, 0.46-0.64) at baseline, strong correlation (r = 0.63; 95% CI, 0.39-0.79) at 3-month follow-up, and moderate correlation (r = 0.53; 95% CI, 0.25-0.72) at 6-month follow-up. Anti-BP180 autoantibody levels also showed moderate correlation (r = 0.52; 95% CI, 0.39-0.62) with ABSIS at baseline, strong correlation (r = 0.62; 95% CI, 0.39-0.79) at 3-month follow-up, and moderate correlation (r = 0.53; 95% CI, 0.25-0.72) at 6-month follow-up. By contrast, anti-BP230 autoantibody levels showed no association with objective BPDAI and ABSIS at diagnosis and follow-up. The subgroup analysis found similar results when using different ELISA kits. The findings of this systematic review and meta-analysis indicated that anti-BP180 autoantibody levels may serve as an adjunctive tool for monitoring BP disease severity and guiding clinical care for patients with BP.

Experiment on Lung Disease Classification using YOLOv8

Lung diseases are a leading cause of illness and mortality worldwide. Accurate and timely diagnosis is crucial for improving patient outcomes, but manual interpretation of chest X-rays by radiologists may consume a significant amount of time and is susceptible to errors. This study leverages the state-of-the-art YOLOv8 deep learning model to develop an automated system for classifying lung diseases from chest X-ray images. The proposed approach utilizes a large, diverse dataset of 21,165 chest X-ray images categorized into four classes: COVID-19, viral pneumonia, lung opacity, and normal. The YOLOv8-cls model is fine-tuned using transfer learning and advanced data augmentation techniques. The system achieves a high accuracy of approximately 95% across all disease classes while maintaining real-time performance. Confusion matrix analysis demonstrates strong performance in correctly identifying each condition, with COVID-19, normal, viral pneumonia and lung opacity cases correctly identified 97%, 97%, 99% and 93% of the time respectively. The outcomes validate the adaptability and reliability of deep learning, specifically YOLOv8, for automated lung disease detection, offering the potential to improve clinical workflows and patient care by providing efficient screening tools for practitioners. The study addresses limitations of previous works by utilizing a large consolidated dataset, reporting comprehensive computational efficiency metrics, and extending classification complexity without substantially impacting accuracy or speed. Future research should focus on further optimizations and extending the curated image repositories to include under-represented patient groups to enhance the model's inclusiveness and robustness.

WhatsApp: A Blessing or Bane for Communication between Emergency Physicians and Vascular Surgeons?

Abstract The integration of WhatsApp (WhatsApp Inc., Mountain View, CA, USA) into the health-care sector is transforming the dynamics of medical communication. Launched in 2009 and incorporated into the Facebook suite of companies in 2014, WhatsApp has become a pivotal platform for health-care professionals. It allows for quick, personal, and group messaging, which is increasingly used to exchange patient information, discuss cases, and make timely decisions. The app’s end-to-end encryption is critical for maintaining patient confidentiality, ensuring that sensitive data are only accessible to the intended recipients. This security feature and the group sharing format enable health-care teams to communicate confidentially and efficiently, bolstering the oversight capabilities of attending senior consultants or physicians over their junior peers. The real-time exchange of information is crucial in providing continuous supervision and facilitating immediate feedback, which is indispensable in clinical training and patient care. However, the widespread adoption of WhatsApp also comes with challenges and potential drawbacks, which have been the focus of past research. These studies assessed the impact of WhatsApp on health-care delivery, considering both the enhancement of communication it offers and the issues it may present, such as data security beyond encryption and the blurring of professional and personal boundaries. In this “medical opinion” from an academic tertiary care center in Muscat, we explored how emergency medicine physicians can effectively use WhatsApp to expedite or bypass referral processes for emergency cases, particularly those involving vascular and other surgical emergencies.