Hasil untuk "Specialties of internal medicine"

Woon Hean Keenan Chong, Wei Jun Dan Ong, Ronnie Voon Shiong Tan et al.

Introduction Chronic obstructive pulmonary disease (COPD) exacerbations are a major cause of hospitalization and healthcare utilization. The impact of inpatient specialty allocation on management practices remains unclear. This study examined variation in investigations and treatment practices among COPD patients admitted under respiratory medicine (RM) compared with internal medicine (IM), and evaluated clinical characteristics associated with RM admission. Methods We conducted a retrospective cohort study of adults aged ≥40 years admitted for COPD exacerbations between January 2017 and March 2025. Patients were identified using ICD-10 COPD codes, excluding those admitted directly to ICUs or with asthma or bronchiectasis. Demographics, comorbidities, investigations, treatments, and hospitalization outcomes were analyzed. The primary outcome was variation in investigations and treatment practices between RM and IM admissions. The secondary outcome was identification of clinical characteristics associated with RM admission using multivariable logistic regression. Results Among 6,277 COPD admissions, 3,211 (51.2%) were under RM and 3,066 (48.8%) under IM. Patients admitted under RM were younger (73.2 ± 10.7 vs. 75.6 ± 10.8 years, p  < 0.001) and had fewer comorbidities, including ischemic heart disease (IHD), diabetes (DM), stroke, and chronic kidney disease (CKD). RM admissions underwent more respiratory-focused investigations, including chest radiography (54.2% vs. 43.5%), chest CT (8.4% vs. 5.6%), and ABG testing (23.1% vs. 8.2%). Non-invasive ventilation (17.7% vs. 4.2%) and invasive mechanical ventilation (18.2% vs. 4.3%) were more frequently used in RM admissions. Use of intravenous antibiotics, routine laboratory testing, allied health involvement, and palliative care services were more frequent in IM admissions. Descriptive differences in hospital outcomes were also observed, with RM admissions demonstrating lower in-hospital mortality (3.9% vs. 6.5%, p  < 0.001) and shorter hospitalization duration (5.85 vs. 6.93 days, p  < 0.001), while 30-day readmissions were similar. In multivariable analysis, older age (OR 0.97, 95% CI 0.96–0.99), male sex (OR 0.49), IHD (OR 1.60), stroke (OR 1.70), and CKD (OR 1.94 were associated with RM admission). Conclusion Variation in investigations and treatment practices was observed between RM and IM admissions for COPD exacerbations. These differences likely reflect variation in patient characteristics, illness severity, and clinical workflows rather than differences in quality of care attributable to specialty alone.

Safwaan H. Khan, Safwaan H. Khan, Yeonjoo Choi et al.

Chimeric antigen receptor (CAR) T cell therapy has revolutionized the treatment of hematologic malignancies, achieving remarkable clinical success with FDA-approved therapies targeting CD19 and BCMA. However, the extension of these successes to solid tumors remains limited due to several intrinsic challenges, including antigen heterogeneity and immunosuppressive tumor microenvironments. In this review, we provide a comprehensive overview of recent advances in CAR T cell therapy aimed at overcoming these obstacles. We discuss the importance of antigen identification by emphasizing the identification of tumor-specific and tumor-associated antigens and the development of CAR T therapies targeting these antigens. Furthermore, we highlight key structural innovations, including cytokine-armored CARs, protease-regulated CARs, and CARs engineered with chemokine receptors, to enhance tumor infiltration and activity within the immunosuppressive microenvironment. Additionally, novel manufacturing approaches, such as the Sleeping Beauty transposon system, mRNA-based CAR transfection, and in vivo CAR T cell production, are discussed as scalable solution to improve the accessibility of CAR T cell therapies. Finally, we address critical therapeutic limitations, including cytokine release syndrome (CRS), immune effector cell-associated neurotoxicity syndrome (ICANS), and suboptimal persistence of CAR T cells. An examination of emerging strategies for countering these limitations reveals that CRISPR-Cas9-mediated genetic modifications and combination therapies utilizing checkpoint inhibitors can improve CAR T cell functionality and durability. By integrating insights from preclinical models, clinical trials, and innovative engineering approaches, this review addresses advances in CAR T cell therapies and their performance in solid tumors.

Rebecca Black, BApSc, Speech Pathologist(SP), Duy Duong Nguyen, MD PhD, Anna Miles, PhD et al.

Background: Oropharyngeal dysphagia and laryngeal dysfunction are complications of lung and heart transplantation. However, there is a lack of understanding around pre-operative function and an absence of standardized assessment protocols. We aimed to trial a pre- and post-operative protocol for assessing voice and swallowing function. Method: A prospective, longitudinal study of 14 adults undergoing investigation for lung or heart transplantation was conducted at a tertiary referral hospital. Patients were assessed pre-surgery and up to 6 months afterwards. The protocol involved phonation tasks with auditory-perceptual and acoustic analysis, videolaryngostroboscopy, a flexible endoscopic examination of swallowing and patient reported quality of life measures. Risk factors and clinical outcomes were extracted from patient records. Results: Patient self-reports of swallowing and voice difficulties were elevated pre-operatively. No evidence of swallowing difficulty was observed under endoscopic examination pre-transplant (Penetration-Aspiration Scale score <2; no accumulated secretions) and only one patient presented with incomplete glottic closure. Auditory perceptual ratings revealed voices were largely within the healthy range at baseline. One out of five patients presented with severe dysphonia post-operatively. Completion of evaluation measures prior to transplantation was 79% but post- operative rates were low due to feasibility challenges with follow up in this complex population. Conclusion: Novel evidence of self-reported pre-transplant voice and swallowing changes indicate value in baseline screening. Discrepancies between patient-report and instrumental assessment results highlight the need for multi-faceted evaluation. Large cohort studies are needed to determine the salient evaluation measures and time points for voice and swallowing assessment in this population.

S. Naderi, K. Samadi, A. A. Zeraati et al.

Introduction. COVID-19 has been associated with both acute and chronic extrapulmonary complications, including renal dysfunction. Understanding the long-term effects of COVID-19 on renal function is essential for managing recovery in affected individuals.Objective. This study aimed to evaluate the long-term renal outcomes in patients who recovered from COVID-19, focusing on changes in glomerular filtration rate (GFR), blood urea nitrogen (BUN), and serum creatinine levels in Iran.Materials &amp; methods. A retrospective cohort study was conducted using data from the Mashhad University of Medical Sciences cohort. The study included patients who had confirmed COVID-19 and a minimum follow-up period of six months post-recovery. Renal function was assessed by measuring the Glomerular Filtration Rate (GFR), Blood Urea Nitrogen (BUN), and serum creatinine levels both at baseline (when COVID-19 was initially diagnosed) and at follow-up. Statistical analysis was performed to explore the associations between renal outcomes and various factors, including gender, the severity of COVID-19, and blood pressure status.Results. In the study, 55.3% were male, and the mean age of 51.38 ± 13.41. Among the patients, 55.3% were male and 44.7% were female. The difference in mean creatinine level between baseline and follow-up was significant (p &lt; 0.001). The difference in mean GFR between baseline and follow-up was significant (p &lt; 0.001). In men, the mean blood urea nitrogen at the first visit and at the follow-up difference was not statistically significant (p = 0.241). In women, the mean blood urea nitrogen was a statistically significant decrease (p = 0.003). Other parameters, including creatinine and GFR, did not differ significantly in both male and female groups at the time of hospitalization and follow-up.Conclusion. Overall, the results of this study suggest that COVID-19 can affect kidney function, especially in association with underlying factors such as hypertension and diabetes, and female gender, which may be risk factors for more severe renal complications in patients with COVID-19. The decrease in GFR in patients with hypertension and diabetes highlights the importance of controlling these diseases in patients with COVID-19. Overall, this study showed that COVID-19 can have lasting effects on patients' kidney function.

Ashot A. Avagimyan, Mohammad Sheibani, Artem I. Trofimenko et al.

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are now uncovering new possibilities in the field of internal medicine owing to their diverse protective effects. In the second part of the literature review, we explore potential applications of SGLT2i in hepatology, neurology, ophthalmology, and oncology, mechanisms of action of such drugs as dapagliflozin, empagliflozin, canagliflozin, etc, and their effect on different organs and systems.

Ku Bean Jeong, Hee Seok Moon, Kyung Ryun In et al.

Abstract Background The incidence of lower gastrointestinal bleeding is on the rise, prompting the creation of various scoring systems to forecast patient’s outcomes. But there is no single unified scoring system and these scoring systems clinical data are small and not worldwide. Aims To evaluate how different scoring systems predict mortality and prolonged hospital stay (≥ 10 days). Methods A retrospective review was conducted on the medical records of 4417 patients who presented with hematochezia at the emergency department from January 2016 to December 2022. We evaluated the predictive accuracy of various scoring systems for 30-day mortality and prolonged hospital stay (≥ 10 days) by analyzing the areas under the receiver-operating characteristic curves, taking into account factors such as patient age, laboratory findings, and comorbidities (ABC); AIMS 65; Glasgow-Blatchford; Oakland; Rockall(pre-endoscopy); SHA2PE; and CHAMPS scores. Results We analyzed data from 1000 patients (mean age 66 years, 56.1% men, mean hospital stay 9.4 days) with lower gastrointestinal bleeding confirmed by any other means including DRE, colonoscopy and CT. The 30-day mortality rate was 3.7%. The primary etiologies of lower gastrointestinal bleeding were identified as ischemic colitis and diverticular bleeding, accounting for 18.8% and 18.5% of cases, respectively. In terms of forecasting 30-day mortality, the AIMS 65, CHAMPS, and ABC scoring systems demonstrated superior performance (p < 0.001). For predicting prolonged hospital stay, the SHA2PE score exhibited the highest accuracy among all evaluated systems (p < 0.001). Conclusions The newly developed scoring systems demonstrated superior accuracy in forecasting outcomes for patients with lower gastrointestinal bleeding, and the results of this study demonstrate that these scoring systems can be applied in clinical practice.

Tyler J Poore, Christopher J Pinard, Aleena Shabbir et al.

Large language models (LLMs) are increasingly used in clinical settings, yet their performance in veterinary medicine remains underexplored. We evaluated three commercially available veterinary-focused LLM summarization tools (Product 1 [Hachiko] and Products 2 and 3) on a standardized dataset of veterinary oncology records. Using a rubric-guided LLM-as-a-judge framework, summaries were scored across five domains: Factual Accuracy, Completeness, Chronological Order, Clinical Relevance, and Organization. Product 1 achieved the highest overall performance, with a median average score of 4.61 (IQR: 0.73), compared to 2.55 (IQR: 0.78) for Product 2 and 2.45 (IQR: 0.92) for Product 3. It also received perfect median scores in Factual Accuracy and Chronological Order. To assess the internal consistency of the grading framework itself, we repeated the evaluation across three independent runs. The LLM grader demonstrated high reproducibility, with Average Score standard deviations of 0.015 (Product 1), 0.088 (Product 2), and 0.034 (Product 3). These findings highlight the importance of veterinary-specific commercial LLM tools and demonstrate that LLM-as-a-judge evaluation is a scalable and reproducible method for assessing clinical NLP summarization in veterinary medicine.

Joshua Hatherley

Recently, a growing number of experts in artificial intelligence (AI) and medicine have be-gun to suggest that the use of AI systems, particularly machine learning (ML) systems, is likely to humanise the practice of medicine by substantially improving the quality of clinician-patient relationships. In this thesis, however, I argue that medical ML systems are more likely to negatively impact these relationships than to improve them. In particular, I argue that the use of medical ML systems is likely to comprise the quality of trust, care, empathy, understanding, and communication between clinicians and patients.

Robert Sparrow, Joshua Hatherley

In the much-celebrated book Deep Medicine, Eric Topol argues that the development of artificial intelligence for health care will lead to a dramatic shift in the culture and practice of medicine. In the next several decades, he suggests, AI will become sophisticated enough that many of the everyday tasks of physicians could be delegated to it. Topol is perhaps the most articulate advocate of the benefits of AI in medicine, but he is hardly alone in spruiking its potential to allow physicians to dedicate more of their time and attention to providing empathetic care for their patients in the future. Unfortunately, several factors suggest a radically different picture for the future of health care. Far from facilitating a return to a time of closer doctor-patient relationships, the use of medical AI seems likely to further erode therapeutic relationships and threaten professional and patient satisfaction.

Mariam Shamal, Hossein Hassani

Access to Kurdish medicine brochures is limited, depriving Kurdish-speaking communities of critical health information. To address this problem, we developed a specialized Machine Translation (MT) model to translate English medicine brochures into Sorani Kurdish using a parallel corpus of 22,940 aligned sentence pairs from 319 brochures, sourced from two pharmaceutical companies in the Kurdistan Region of Iraq (KRI). We trained a Statistical Machine Translation (SMT) model using the Moses toolkit, conducting seven experiments that resulted in BLEU scores ranging from 22.65 to 48.93. We translated three new brochures to improve the evaluation process and encountered unknown words. We addressed unknown words through post-processing with a medical dictionary, resulting in BLEU scores of 56.87, 31.05, and 40.01. Human evaluation by native Kurdish-speaking pharmacists, physicians, and medicine users showed that 50% of professionals found the translations consistent, while 83.3% rated them accurate. Among users, 66.7% considered the translations clear and felt confident using the medications.

Rachel H. Kon, Soham Ali, James R. Martindale et al.

Uppala Radhakrishna, Uppala Ratnamala, Devendrasinh D. Jhala et al.

BackgroundHidradenitis suppurativa (HS) is a chronic inflammatory skin condition that affects hair follicles in areas with apocrine sweat glands, such as the underarms, groin, and buttocks. The pathogenesis of HS is not fully understood, but considering the key role played by the biological clock in the control of immune/inflammatory processes the derangement of circadian and ultradian pathways could be hypothesized.MethodsWe analyzed genome-wide DNA methylation patterns in peripheral blood from 24 HS cases and 24 controls using the Infinium HumanMethylation450 BeadChip array (Illumina), followed by bioinformatics and statistical analyses.ResultsWe found that several circadian and ultradian genes were differentially methylated in HS patients, predominantly exhibiting hypomethylation. These genes were enriched in pathways such as MAPK and WNT cascades, acute phase response, cytokine release, inflammation, innate immune response, xenobiotic detoxification, and oxidative stress response.ConclusionAltered DNA methylation patterns of genes related to circadian and ultradian pathways could contribute to immune system derangement and inflammatory processes chronicization in addition to other comorbidities hallmarking HS onset and progression, at the same time representing possible druggable targets.

Heyi Zhang, Xin Wang, Zhaopeng Meng et al.

Large Language Models (LLMs) has made significant progress in a number of professional fields, including medicine, law, and finance. However, in traditional Chinese medicine (TCM), there are challenges such as the essential differences between theory and modern medicine, the lack of specialized corpus resources, and the fact that relying only on supervised fine-tuning may lead to overconfident predictions. To address these challenges, we propose a two-stage training approach that combines continuous pre-training and supervised fine-tuning. A notable contribution of our study is the processing of a 2GB corpus dedicated to TCM, constructing pre-training and instruction fine-tuning datasets for TCM, respectively. In addition, we have developed Qibo-Benchmark, a tool that evaluates the performance of LLM in the TCM on multiple dimensions, including subjective, objective, and three TCM NLP tasks. The medical LLM trained with our pipeline, named $\textbf{Qibo}$, exhibits significant performance boosts. Compared to the baselines, the average subjective win rate is 63%, the average objective accuracy improved by 23% to 58%, and the Rouge-L scores for the three TCM NLP tasks are 0.72, 0.61, and 0.55. Finally, we propose a pipline to apply Qibo to TCM consultation and demonstrate the model performance through the case study.

W. Benjamin, N. Lenze, Lauren A. Bohm et al.

P. Han, T. Strout, C. Gutheil et al.

Background Medical uncertainty is a pervasive and important problem, but the strategies physicians use to manage it have not been systematically described. Objectives To explore the uncertainty management strategies employed by physicians practicing in acute-care hospital settings and to organize these strategies within a conceptual taxonomy that can guide further efforts to understand and improve physicians’ tolerance of medical uncertainty. Design Qualitative study using individual in-depth interviews. Participants Convenience sample of 22 physicians and trainees (11 attending physicians, 7 residents [postgraduate years 1–3), 4 fourth-year medical students), working within 3 medical specialties (emergency medicine, internal medicine, internal medicine–pediatrics), at a single large US teaching hospital. Measurements Semistructured interviews explored participants’ strategies for managing medical uncertainty and temporal changes in their uncertainty tolerance. Inductive qualitative analysis of audio-recorded interview transcripts was conducted to identify and categorize key themes and to develop a coherent conceptual taxonomy of uncertainty management strategies. Results Participants identified various uncertainty management strategies that differed in their primary focus: 1) ignorance-focused, 2) uncertainty-focused, 3) response-focused, and 4) relationship-focused. Ignorance- and uncertainty-focused strategies were primarily curative (aimed at reducing uncertainty), while response- and relationship-focused strategies were primarily palliative (aimed at ameliorating aversive effects of uncertainty). Several participants described a temporal evolution in their tolerance of uncertainty, which coincided with the development of greater epistemic maturity, humility, flexibility, and openness. Conclusions Physicians and physician-trainees employ a variety of uncertainty management strategies focused on different goals, and their tolerance of uncertainty evolves with the development of several key capacities. More work is needed to understand and improve the management of medical uncertainty by physicians, and a conceptual taxonomy can provide a useful organizing framework for this work.

Hewei Peng, Junchao Zhang, Xianhua Huang et al.

Abstract Background Nonalcoholic fatty liver disease (NAFLD), a common liver disease worldwide, can be reversed early in life with lifestyle and medical interventions. This study aimed to develop a noninvasive tool to screen NAFLD accurately. Methods Risk factors for NAFLD were identified using multivariate logistic regression analysis, and an online NAFLD screening nomogram was developed. The nomogram was compared with reported models (fatty liver index (FLI), atherogenic index of plasma (AIP), and hepatic steatosis index (HSI)). Nomogram performance was evaluated through internal and external validation (National Health and Nutrition Examination Survey (NHANES) database). Results The nomogram was developed based on six variables. The diagnostic performance of the present nomogram for NAFLD (area under the receiver operator characteristic curve (AUROC): 0.863, 0.864, and 0.833, respectively) was superior to that of the HSI (AUROC: 0.835, 0.833, and 0.810, respectively) and AIP (AUROC: 0.782, 0.773, and 0.728, respectively) in the training, validation, and NHANES sets. Decision curve analysis and clinical impact curve analysis presented good clinical utility. Conclusion This study establishes a new online dynamic nomogram with excellent diagnostic and clinical performance. It has the potential to be a noninvasive and convenient method for screening individuals at high risk for NAFLD.

Bo Cai, Shixue Chang, Yuhan Tian et al.

Abstract Hepatitis B virus (HBV) infection remains a global health challenge. Despite the availability of effective preventive vaccines, millions of people are at risk of cirrhosis and hepatocellular carcinoma. Current drug therapies inhibit viral replication, slow the progression of liver fibrosis and reduce infectivity, but they rarely remove the covalently sealed circular DNA (cccDNA) of the virus that causes HBV persistence. Alternative treatment strategies, including those based on CRISPR/cas9 knockout virus gene, can effectively inhibit HBV replication, so it has a good prospect. During chronic infection, some virus gene knockouts based on CRISPR/cas9 may even lead to cccDNA inactivation. This paper reviews the progress of different HBV CRISPR/cas9, vectors for delivering to the liver, and the current situation of preclinical and clinical research.

Ghizal fatima, Risala H. Allami, Maitham G. Yousif

Precision medicine, tailored to individual patients based on their genetics, environment, and lifestyle, shows promise in managing complex diseases like infections. Integrating artificial intelligence (AI) into precision medicine can revolutionize disease management. This paper introduces a novel approach using AI to advance precision medicine in infectious diseases and beyond. It integrates diverse fields, analyzing patients' profiles using genomics, proteomics, microbiomics, and clinical data. AI algorithms process vast data, providing insights for precise diagnosis, treatment, and prognosis. AI-driven predictive modeling empowers healthcare providers to make personalized and effective interventions. Collaboration among experts from different domains refines AI models and ensures ethical and robust applications. Beyond infections, this AI-driven approach can benefit other complex diseases. Precision medicine powered by AI has the potential to transform healthcare into a proactive, patient-centric model. Research is needed to address privacy, regulations, and AI integration into clinical workflows. Collaboration among researchers, healthcare institutions, and policymakers is crucial in harnessing AI-driven strategies for advancing precision medicine and improving patient outcomes.

Aaron L. Sarvet, Mats J. Stensrud

Avoiding harm is an uncontroversial aim of personalized medicine and other epidemiologic initiatives. However, the precise mathematical translation of "harm" is disputable. Here we use a formal causal language to study common, but distinct, definitions of "harm". We clarify that commitment to a definition of harm has important practical and philosophical implications for decision making. We relate our practical and philosophical considerations to ideas from medical ethics and legal practice.

Jesutofunmi A. Omiye, Haiwen Gui, Shawheen J. Rezaei et al.

Large language models (LLMs) have been applied to tasks in healthcare, ranging from medical exam questions to responding to patient questions. With increasing institutional partnerships between companies producing LLMs and healthcare systems, real world clinical application is coming closer to reality. As these models gain traction, it is essential for healthcare practitioners to understand what LLMs are, their development, their current and potential applications, and the associated pitfalls when utilized in medicine. This review and accompanying tutorial aim to give an overview of these topics to aid healthcare practitioners in understanding the rapidly changing landscape of LLMs as applied to medicine.