Hasil untuk "Internal medicine"

Kurt J.isselbacher

M. Graber, N. Franklin, Ruthanna Gordon

Fuzhuo Wang, Jiashuang Xu, Hong Sun et al.

BackgroundNumerous studies have demonstrated a close association between perceived stress and depression in colorectal cancer patients; however, the underlying mechanisms remain incompletely understood. This study aims to investigate the impact of perceived stress on depression in this population, as well as the mediating role of illness perception and the moderating role of self-efficacy.MethodA cross-sectional design was employed. From May to November 2024, a questionnaire survey was conducted among 290 colorectal cancer patients at two Grade A tertiary hospitals in Shenyang and Jinzhou, Liaoning Province, China. The questionnaire comprised sections on general demographics, perceived stress, illness perception, self-efficacy, and depression. Descriptive statistics and correlation analyses were performed using SPSS 25.0 and the PROCESS 3.5 macro. Mediation and moderation effects were tested using bootstrap resampling.ResultsA significant positive correlation was found between perceived stress and depression (β = 0.483, P < 0.001) and this relationship was partially mediated by illness perception (β = 0.083). Self-efficacy moderated the association between perceived stress and illness perception (β = 0.024, P < 0.001), with higher levels of self-efficacy strengthening the relationship between perceived stress and illness perception.ConclusionThis study identifies illness perception as a mediating pathway in the association between perceived stress and depression, while self-efficacy moderates the relationship between perceived stress and illness perception. Accordingly, a multidimensional clinical approach may be considered for addressing depressive symptoms in colorectal cancer patients. Such an approach could concurrently target perceived stress reduction, modification of illness perception, and enhancing self-efficacy.

Minghui Lu, Jiajun Wei, Qiang Cai

BackgroundThe optimal timing for tracheostomy in patients with intracerebral hemorrhage extending into the ventricles who require mechanical ventilation remains controversial, and there is a paucity of evidence to guide clinical practice. This study aimed to elucidate the impact of early vs. late tracheostomy on clinical outcomes and complications in this population, utilizing multivariable models to identify risk factors and define the potential beneficiary population.MethodsThis single-center retrospective cohort study consecutively enrolled 157 patients with severe spontaneous intracerebral hemorrhage extending into the ventricles requiring mechanical ventilation (GCS score ≤8) between January 2020 and December 2023. Based on the timing of tracheostomy, patients were classified into an early group (ET, ≤7 days after mechanical ventilation, n = 81) and a late group (LT, >7 days after mechanical ventilation, n = 76). Baseline characteristics, treatment measures, and outcome data were collected. Hematoma volumes in both the brain parenchyma and ventricles on admission CT scans were precisely quantified using 3D Slicer software. The primary outcome was the 6-month modified Rankin Scale (mRS) score. Secondary outcomes included the duration of mechanical ventilation, ICU length of stay (LOS), and the incidence of short-term complications [ventilator-associated pneumonia (VAP), new-onset arrhythmia, shock, and acute kidney injury (AKI)]. Multivariable logistic regression analysis was employed to identify independent risk factors for complications and to assess the protective effect of early tracheostomy.ResultsIn this cohort of 157 mechanically ventilated patients with severe intraventricular hemorrhage, baseline characteristics were well-balanced between Early (ET, n = 81) and Late Tracheostomy (LT, n = 76) groups. While 6-month functional outcomes (mRS) showed no significant difference (P = 0.360), the ET group demonstrated substantially shorter duration of mechanical ventilation (13 vs. 19 days, P < 0.001) and ICU stay (17 vs. 25 days, P < 0.001). ET was associated with significantly lower incidence of ventilator-associated pneumonia (28.40 vs. 48.68%, P = 0.009), new-onset arrhythmia (18.52 vs. 32.89%, P = 0.039), and shock requiring vasopressors (24.7 vs. 40.79%, P = 0.031). Multivariable analysis identified GCS score <6 (OR 3.588, P = 0.008) and Graeb score ≥8 (OR 8.735, P = 0.037) as independent risk factors for complications, while confirming early tracheostomy as an independent protective factor (aOR 0.306, P = 0.019) after adjustment for confounders.ConclusionIn this single-center retrospective cohort study, early tracheostomy was associated with shorter durations of mechanical ventilation and ICU stay, as well as a lower incidence of major complications, and demonstrates a favorable safety profile. Although it does not improve long-term neurological function, early tracheostomy serves as an independent protective factor. When combined with the identification of risk factors such as GCS <6 and Graeb score ≥8, it provides a basis for individualized treatment. These findings suggest an association that warrants further investigation in prospective studies.

Pinar Bisgin, Tom Strube, Niklas Tschorn et al.

Noisy labels pose significant challenges for AI model training in veterinary medicine. This study examines expert assessment ambiguity in canine auscultation data, highlights the negative impact of label noise on classification performance, and introduces methods for label noise reduction. To evaluate whether label noise can be minimized by incorporating multiple expert opinions, a dataset of 140 heart sound recordings (HSR) was annotated regarding the intensity of holosystolic heart murmurs caused by Myxomatous Mitral Valve Disease (MMVD). The expert opinions facilitated the selection of 70 high-quality HSR, resulting in a noise-reduced dataset. By leveraging individual heart cycles, the training data was expanded and classification robustness was enhanced. The investigation encompassed training and evaluating three classification algorithms: AdaBoost, XGBoost, and Random Forest. While AdaBoost and Random Forest exhibited reasonable performances, XGBoost demonstrated notable improvements in classification accuracy. All algorithms showed significant improvements in classification accuracy due to the applied label noise reduction, most notably XGBoost. Specifically, for the detection of mild heart murmurs, sensitivity increased from 37.71% to 90.98% and specificity from 76.70% to 93.69%. For the moderate category, sensitivity rose from 30.23% to 55.81% and specificity from 64.56% to 97.19%. In the loud/thrilling category, sensitivity and specificity increased from 58.28% to 95.09% and from 84.84% to 89.69%, respectively. These results highlight the importance of minimizing label noise to improve classification algorithms for the detection of canine heart murmurs. Index Terms: AI diagnosis, canine heart disease, heart sound classification, label noise reduction, machine learning, XGBoost, veterinary cardiology, MMVD.

Yuhan Tang

Type 2 diabetes prevention and treatment can benefit from personalized lifestyle prescriptions. However, the delivery of personalized lifestyle medicine prescriptions is limited by the shortage of trained professionals and the variability in physicians' expertise. We propose an offline contextual bandit approach that learns individualized lifestyle prescriptions from the aggregated NHANES profiles of 119,555 participants by minimizing the Magni glucose risk-reward function. The model encodes patient status and generates lifestyle medicine prescriptions, which are trained using a mixed-action Soft Actor-Critic algorithm. The task is treated as a single-step contextual bandit. The model is validated against lifestyle medicine prescriptions issued by three certified physicians from Xiangya Hospital. These results demonstrate that offline mixed-action SAC can generate risk-aware lifestyle medicine prescriptions from cross-sectional NHANES data, warranting prospective clinical validation.

Omar Alhaj Omar, Stefan T. Gerner, Slava Alikevitch et al.

<b>Background/Objectives:</b> Acute ischemic stroke (AIS) remains a major cause of morbidity and mortality worldwide. Although advanced imaging modalities, such as CT perfusion (CTP), are increasingly being used in clinical decision-making, the necessity and added value of perfusion imaging prior to intravenous thrombolysis (IVT) within early time windows remains uncertain. We aim to evaluate the safety and functional outcomes of IVT in AIS patients without perfusion deficits on CTP. We question the requirement of perfusion mismatch for IVT eligibility and hypothesize that IVT is safe and beneficial even in the absence of a perfusion deficit. <b>Methods:</b> A retrospective analysis was conducted using data from the Giessen Stroke Registry, focusing on AIS patients who underwent CTP imaging and received IVT between 01/2018 and 12/2020. Patients who underwent endovascular therapy were excluded. Clinical data, including demographics, National Institutes of Health Stroke Scale (NIHSS) scores, modified Rankin Scale (mRS) scores, and complications, were collected. Patients were dichotomized based on the presence of perfusion lesions and compared in terms of efficacy outcomes (i.e., NIHSS or mRS improvement during the hospital stay) and safety outcomes (i.e., post-thrombolytic hemorrhagic complications). <b>Results:</b> Of the 89 AIS patients with available CTP data who received IVT, 34 (38%) had a perfusion deficit and 55 (62%) did not. There were no significant differences between the groups in terms of hemorrhagic complications or functional outcomes at discharge (NIHSS and mRS). Clinical improvement from admission to discharge was similar in both groups. <b>Conclusions:</b> Our findings suggest that IVT is safe and clinically effective even in AIS patients without detectable perfusion deficits on CTP within the standard therapeutic window. These results support current guideline recommendations that do not mandate perfusion imaging for early presenters. Routine use of CTP in this context may be of limited clinical utility and could unnecessarily delay treatment or introduce additional risks in the first 4.5 h.

Kayla H Szymanik, Emily A Rex, Vamshikrishna R Pothireddy et al.

Proper recognition of viral pathogens is an essential part of the innate immune response. A common viral replicative intermediate and chemical signal that cells use to identify pathogens is the presence of a triphosphorylated 5' end (5'ppp) RNA, which activates the cytosolic RNA sensor RIG-I and initiates downstream antiviral signaling. While 5'pppRNA generated by viral RNA-dependent RNA polymerases (RdRps) can be a potent activator of the immune response, endogenous RNA polymerase III (RNAPIII) transcripts can retain the 5'ppp generated during transcription and induce a RIG-I-mediated immune response. We have previously shown that host RNA triphosphatase dual-specificity phosphatase 11 (DUSP11) can act on both host and viral RNAs, altering their levels and reducing their ability to induce RIG-I activation. Our previous work explored how experimentally altered DUSP11 activity can impact immune activation, prompting further exploration into natural contexts of altered DUSP11 activity. Here, we have identified viral DUSP11 homologs (vDUSP11s) present in some avipoxviruses. Consistent with the known functions of host DUSP11, we have shown that expression of vDUSP11s: 1) reduces levels of endogenous RNAPIII transcripts, 2) reduces a cell's sensitivity to 5'pppRNA-mediated immune activation, and 3) restores virus infection defects seen in the absence of DUSP11. Our results identify a context where DUSP11 activity has been co-opted by viruses to alter RNA metabolism and influence the outcome of infection.

M. Rethlefsen, Ann M. Farrell, Leah C Osterhaus Trzasko et al.

Shogo Watanabe

This report presents a small language model (SLM) for Japanese clinical and medicine, named NCVC-slm-1. This 1B parameters model was trained using Japanese text classified to be of high-quality. Moreover, NCVC-slm-1 was augmented with respect to clinical and medicine content that includes the variety of diseases, drugs, and examinations. Using a carefully designed pre-processing, a specialized morphological analyzer and tokenizer, this small and light-weight model performed not only to generate text but also indicated the feasibility of understanding clinical and medicine text. In comparison to other large language models, a fine-tuning NCVC-slm-1 demonstrated the highest scores on 6 tasks of total 8 on JMED-LLM. According to this result, SLM indicated the feasibility of performing several downstream tasks in the field of clinical and medicine. Hopefully, NCVC-slm-1 will be contributed to develop and accelerate the field of clinical and medicine for a bright future.

Xiaofei Chen

This article explores the critical role of statistical analysis in precision medicine. It discusses how personalized healthcare is enhanced by statistical methods that interpret complex, multidimensional datasets, focusing on predictive modeling, machine learning algorithms, and data visualization techniques. The paper addresses challenges in data integration and interpretation, particularly with diverse data sources like electronic health records (EHRs) and genomic data. It also delves into ethical considerations such as patient privacy and data security. In addition, the paper highlights the evolution of statistical analysis in medicine, core statistical methodologies in precision medicine, and future directions in the field, emphasizing the integration of artificial intelligence (AI) and machine learning (ML).

Magdalena Sevilla-González, Maria Fernanda Garibay-Gutiérrez, Arsenio Vargas-Vázquez et al.

Background: A risk haplotype in SLC16A11 characterized by alterations in fatty acid metabolism emerged as a genetic risk factor associated with increased susceptibility to type 2 diabetes (T2D) in Mexican population. Its role on treatment responses is not well understood. Objectives: We aimed to determine the impact of the risk haplotype on the metabolomic profile during a lifestyle intervention (LSI). Methods: We recruited Mexican-mestizo individuals with ≥1 prediabetes criteria according to the American Diabetes Association with a body mass index between 25 and 45 kg/m2. We conducted a 24-wk quasiexperimental LSI study for diabetes prevention. Here, we compared longitudinal plasma liquid chromatography/mass spectrometry metabolomic changes between carriers and noncarriers. We analyzed the association of risk haplotype with metabolites leveraging repeated assessments using multivariable-adjusted linear mixed models. Results: Before the intervention, carriers (N = 21) showed higher concentrations of hippurate, C16 carnitine, glycine, and cinnamoylglycine. After 24 wk of LSI, carriers exhibited a deleterious metabolomic profile. This profile was characterized by increased concentrations of hippurate, cinnamoglycine, xanthosine, N-acetylputrescine, L-acetylcarnitine, ceramide (d18:1/24:1), and decreased concentrations of citrulline and phosphatidylethanolamine. These metabolites were associated with higher concentrations of total cholesterol, triglycerides, and low density lipoprotein cholesterol. The effect of LSI on the risk haplotype was notably more pronounced in its impact on 2 metabolites: methylmalonylcarnitine (β: −0.56; P-interaction = 0.014) and betaine (β: −0.64; P-interaction = 0.017). Interestingly, lower consumption across visits of polyunsaturated (β: −0.038; P = 0.017) fatty acids were associated with higher concentrations of methylmalonylcarnitine. Covariates for adjustment across models included age, sex, genetic ancestry principal components, and body mass index. Conclusions: Our study highlights the persistence of deleterious metabolomic patterns associated with the risk haplotype before and during a 24-wk LSI. We also emphasize the potential regulatory role of polyunsaturated fatty acids on methylmalonylcarnitine concentrations suggesting a route for improving interventions for individuals with high-genetic risk.

B. Sykes, M. Hewetson, R. Hepburn et al.

The term Equine Gastric Ulcer Syndrome (EGUS) was first used in 1999 to describe gastric ulceration in the horse. However, as discussed by Merritt, the terminology is commonly misused. The committee reinforces the importance of distinguishing between diseases of the squamous and glandular mucosa because, as discussed in this statement, important differences exist between the two. In human medicine, the term peptic ulcer disease (PUD) is used as an umbrella term to describe erosive and ulcerative diseases of the stomach and it is recognized that a large number of individual diseases are present under the term. Furthermore, while some different diseases might share similarities in pathophysiology and treatment regimens, it is recognized in human medicine that the direct extrapolation of either from one specific disease (such as NSAID-associated ulceration) to another (such as Helicobacter pylori associated ulceration) is inappropriate. The committee recognizes that the terminology for EGUS requires clarification and proposes that the nomenclature be: Equine Gastric Ulcer Syndrome (EGUS) as a general all encompassing term to describe erosive and ulcerative diseases of the stomach consistent with the use of the term PUD in man; Equine Squamous Gastric Disease (ESGD) and Equine Glandular Gastric Disease (EGGD) as terms that more specifically describe the affected region anatomically.Within ESGD, both primary and secondary disease is recognized. Primary ESGD, the more common of the 2 forms, occurs in animals with an otherwise normal gastrointestinal tract. In contrast, secondary ESGD occurs in animals with delayed gastric outflow secondary to an underlying abnormality such as pyloric stenosis. The pathophysiology of EGGD remains to be elucidated and as such further subclassification of lesion type is not possible at this time. Instead, the committee recommends the use of descriptive terminology with a clear distinction of the anatomical region affected (cardia, fundus, antrum, or pylorus as shown in Figure 2) and the gross appearance of the lesion. The committee emphasizes that the affected region of the stomach should be clearly identified when communicating research and clinical findings. A summary of the proposed terminology is depicted in Figure 1. Recommendation: Expansion of the existing EGUS terminology to specifically identify squamous and glandular disease as ESGD and EGGD, respectively, as shown in Figure 1.

Dongyeop Jang, Tae-Rim Yun, Choong-Yeol Lee et al.

Traditional Korean medicine (TKM) emphasizes individualized diagnosis and treatment. This uniqueness makes AI modeling difficult due to limited data and implicit processes. Large language models (LLMs) have demonstrated impressive medical inference, even without advanced training in medical texts. This study assessed the capabilities of GPT-4 in TKM, using the Korean National Licensing Examination for Korean Medicine Doctors (K-NLEKMD) as a benchmark. The K-NLEKMD, administered by a national organization, encompasses 12 major subjects in TKM. We optimized prompts with Chinese-term annotation, English translation for questions and instruction, exam-optimized instruction, and self-consistency. GPT-4 with optimized prompts achieved 66.18% accuracy, surpassing both the examination's average pass mark of 60% and the 40% minimum for each subject. The gradual introduction of language-related prompts and prompting techniques enhanced the accuracy from 51.82% to its maximum accuracy. GPT-4 showed low accuracy in subjects including public health & medicine-related law, internal medicine (2) which are localized in Korea and TKM. The model's accuracy was lower for questions requiring TKM-specialized knowledge. It exhibited higher accuracy in diagnosis-based and recall-based questions than in intervention-based questions. A positive correlation was observed between the consistency and accuracy of GPT-4's responses. This study unveils both the potential and challenges of applying LLMs to TKM. These findings underline the potential of LLMs like GPT-4 in culturally adapted medicine, especially TKM, for tasks such as clinical assistance, medical education, and research. But they also point towards the necessity for the development of methods to mitigate cultural bias inherent in large language models and validate their efficacy in real-world clinical settings.

Zachary Huemann, Changhee Lee, Junjie Hu et al.

With the growing use of transformer-based language models in medicine, it is unclear how well these models generalize to nuclear medicine which has domain-specific vocabulary and unique reporting styles. In this study, we evaluated the value of domain adaptation in nuclear medicine by adapting language models for the purpose of 5-point Deauville score prediction based on clinical 18F-fluorodeoxyglucose (FDG) PET/CT reports. We retrospectively retrieved 4542 text reports and 1664 images for FDG PET/CT lymphoma exams from 2008-2018 in our clinical imaging database. Deauville scores were removed from the reports and then the remaining text in the reports was used as the model input. Multiple general-purpose transformer language models were used to classify the reports into Deauville scores 1-5. We then adapted the models to the nuclear medicine domain using masked language modeling and assessed its impact on classification performance. The language models were compared against vision models, a multimodal vision language model, and a nuclear medicine physician with seven-fold Monte Carlo cross validation, reported are the mean and standard deviations. Domain adaption improved all language models. For example, BERT improved from 61.3% five-class accuracy to 65.7% following domain adaptation. The best performing model (domain-adapted RoBERTa) achieved a five-class accuracy of 77.4%, which was better than the physician's performance (66%), the best vision model's performance (48.1), and was similar to the multimodal model's performance (77.2). Domain adaptation improved the performance of large language models in interpreting nuclear medicine text reports.

Mark Connor, Michael O'Neill

This paper explores the potential opportunities, risks, and challenges associated with the use of large language models (LLMs) in sports science and medicine. LLMs are large neural networks with transformer style architectures trained on vast amounts of textual data, and typically refined with human feedback. LLMs can perform a large range of natural language processing tasks. In sports science and medicine, LLMs have the potential to support and augment the knowledge of sports medicine practitioners, make recommendations for personalised training programs, and potentially distribute high-quality information to practitioners in developing countries. However, there are also potential risks associated with the use and development of LLMs, including biases in the dataset used to create the model, the risk of exposing confidential data, the risk of generating harmful output, and the need to align these models with human preferences through feedback. Further research is needed to fully understand the potential applications of LLMs in sports science and medicine and to ensure that their use is ethical and beneficial to athletes, clients, patients, practitioners, and the general public.

Hassan Sartaj, Shaukat Ali, Tao Yue et al.

Healthcare applications with the Internet of Things (IoT) are often safety-critical, thus, require extensive testing. Such applications are often connected to smart medical devices from various vendors. System-level testing of such applications requires test infrastructures physically integrating medical devices, which is time and monetary-wise expensive. Moreover, applications continuously evolve, e.g., introducing new devices and users and updating software. Nevertheless, a test infrastructure enabling testing with a few devices is insufficient for testing healthcare IoT systems, hence compromising their dependability. In this paper, we propose a model-based approach for the creation and operation of digital twins (DTs) of medicine dispensers as a replacement for physical devices to support the automated testing of IoT applications at scale. We evaluate our approach with an industrial IoT system with medicine dispensers in the context of Oslo City and its industrial partners, providing healthcare services to its residents. We study the fidelity of DTs in terms of their functional similarities with their physical counterparts: medicine dispensers. Results show that the DTs behave more than 92% similar to the physical medicine dispensers, providing a faithful replacement for the dispenser.

Akhil P Santhosh, Ajay Gogia

Sudeep Pasricha

The dawn of the digital medicine era, ushered in by increasingly powerful embedded systems and Internet of Things (IoT) computing devices, is creating new therapies and biomedical solutions that promise to positively transform our quality of life. However, the digital medicine revolution also creates unforeseen and complex ethical, regulatory, and societal issues. In this article, we reflect on the ethical challenges facing digital medicine. We discuss the perils of ethical oversights in medical devices, and the role of professional codes and regulatory oversight towards the ethical design, deployment, and operation of digital medicine devices that safely and effectively meet the needs of patients. We advocate for an ensemble approach of intensive education, programmable ethical behaviors, and ethical analysis frameworks, to prevent mishaps and sustain ethical innovation, design, and lifecycle management of emerging digital medicine devices.

Xiao Gan, Zixin Shu, Xinyan Wang et al.

Traditional Chinese medicine (TCM) relies on natural medical products to treat symptoms and diseases. While clinical data have demonstrated the effectiveness of selected TCM-based treatments, the mechanistic root of how TCM herbs treat diseases remains largely unknown. More importantly, current approaches focus on single herbs or prescriptions, missing the high-level general principles of TCM. To uncover the mechanistic nature of TCM on a system level, in this work we establish a generic network medicine framework for TCM from the human protein interactome. Applying our framework reveals a network pattern between symptoms (diseases) and herbs in TCM. We first observe that genes associated with a symptom are not distributed randomly in the interactome, but cluster into localized modules; furthermore, a short network distance between two symptom modules is indicative of the symptoms' co-occurrence and similarity. Next, we show that the network proximity of a herb's targets to a symptom module is predictive of the herb's effectiveness in treating the symptom. We validate our framework with real-world hospital patient data by showing that (1) shorter network distance between symptoms of inpatients correlates with higher relative risk (co-occurrence), and (2) herb-symptom network proximity is indicative of patients' symptom recovery rate after herbal treatment. Finally, we identified novel herb-symptom pairs in which the herb's effectiveness in treating the symptom is predicted by network and confirmed in hospital data, but previously unknown to the TCM community. These predictions highlight our framework's potential in creating herb discovery or repurposing opportunities. In conclusion, network medicine offers a powerful novel platform to understand the mechanism of traditional medicine and to predict novel herbal treatment against diseases.