Hasil untuk "Internal medicine"

W. P. Thomas, C. Gaber, G. Jacobs et al.

Tanvi Kale, Vijay Hegde, Nikhil V Dhurandhar

Tingting Zhu, Alexander Sack, Inge Leunissen

Markus Bertl, Alan Mott, Salvatore Sinno et al.

The digitization of healthcare presents numerous challenges, including the complexity of biological systems, vast data generation, and the need for personalized treatment plans. Traditional computational methods often fall short, leading to delayed and sometimes ineffective diagnoses and treatments. Quantum Computing (QC) and Quantum Machine Learning (QML) offer transformative advancements with the potential to revolutionize medicine. This paper summarizes areas where QC promises unprecedented computational power, enabling faster, more accurate diagnostics, personalized treatments, and enhanced drug discovery processes. However, integrating quantum technologies into precision medicine also presents challenges, including errors in algorithms and high costs. We show that mathematically-based techniques for specifying, developing, and verifying software (formal methods) can enhance the reliability and correctness of QC. By providing a rigorous mathematical framework, formal methods help to specify, develop, and verify systems with high precision. In genomic data analysis, formal specification languages can precisely (1) define the behavior and properties of quantum algorithms designed to identify genetic markers associated with diseases. Model checking tools can systematically explore all possible states of the algorithm to (2) ensure it behaves correctly under all conditions, while theorem proving techniques provide mathematical (3) proof that the algorithm meets its specified properties, ensuring accuracy and reliability. Additionally, formal optimization techniques can (4) enhance the efficiency and performance of quantum algorithms by reducing resource usage, such as the number of qubits and gate operations. Therefore, we posit that formal methods can significantly contribute to enabling QC to realize its full potential as a game changer in precision medicine.

Pedram Fard, Alaleh Azhir, Neguine Rezaii et al.

Artificial intelligence in medicine is built to serve the average patient. By minimizing error across large datasets, most systems deliver strong aggregate accuracy yet falter at the margins: patients with rare variants, multimorbidity, or underrepresented demographics. This average patient fallacy erodes both equity and trust. We propose a different design: a multi-agent ecosystem for N-of-1 decision support. In this environment, agents clustered by organ systems, patient populations, and analytic modalities draw on a shared library of models and evidence synthesis tools. Their results converge in a coordination layer that weighs reliability, uncertainty, and data density before presenting the clinician with a decision-support packet: risk estimates bounded by confidence ranges, outlier flags, and linked evidence. Validation shifts from population averages to individual reliability, measured by error in low-density regions, calibration in the small, and risk--coverage trade-offs. Anticipated challenges include computational demands, automation bias, and regulatory fit, addressed through caching strategies, consensus checks, and adaptive trial frameworks. By moving from monolithic models to orchestrated intelligence, this approach seeks to align medical AI with the first principle of medicine: care that is transparent, equitable, and centered on the individual.

Zihao Cheng, Yuheng Lu, Huaiqian Ye et al.

Large Language Models (LLMs) have demonstrated remarkable capabilities in modern medicine, yet their application in Traditional Chinese Medicine (TCM) remains severely limited by the absence of standardized benchmarks and the scarcity of high-quality training data. To address these challenges, we introduce TCM-Eval, the first dynamic and extensible benchmark for TCM, meticulously curated from national medical licensing examinations and validated by TCM experts. Furthermore, we construct a large-scale training corpus and propose Self-Iterative Chain-of-Thought Enhancement (SI-CoTE) to autonomously enrich question-answer pairs with validated reasoning chains through rejection sampling, establishing a virtuous cycle of data and model co-evolution. Using this enriched training data, we develop ZhiMingTang (ZMT), a state-of-the-art LLM specifically designed for TCM, which significantly exceeds the passing threshold for human practitioners. To encourage future research and development, we release a public leaderboard, fostering community engagement and continuous improvement.

Aahsan Iqbal, Sohail Khalid, Mujeeb ur Rehman

Hematology analyzers are essential diagnostic and monitoring tools for detecting blood diseases. Although contemporary analyzers produce only basic insights, they are often not as detailed as required under the personalized medicine paradigm. Next-Generation Hematology Analyzers (NGHAs) are revolutionary newcomers in the field, with significant advantages over regular hematology analyzers. They provide deeper insights into cellular morphology, function, and genetic profiles. This detailed information opens up possibilities for tailor-made diagnostic and therapeutic approaches in precision medicine. This review presents some revolutionary technologies that have changed hematology analyzers and provides an overview of their limitations, basic functions, and influence on clinical practice. It focuses on the integration of state-of-the-art technologies, such as microfluidics, advanced optics, artificial intelligence, flow cytometry, and digital imaging, empowering NGHAs to improve diagnostic accuracy, rapidly detect diseases, and support flexible, targeted therapy. Hints regarding point-of-care hematology testing are also provided to discuss its implications for transforming healthcare patterns. This review highlights the data management, standardization, regulatory, and ethical challenges associated with these technologies. A review tracking the current state-of-the-art and trends for the future is provided to show how these advancements may reconfigure hematology analyzer design and act as a stepping stone for future therapeutic reforms.

Lauren D. Branditz, Andrew P. Kendle, Cynthia G. Leung et al.

Background The transition from medical student to intern is a recognized educational gap. To help address this, the Association of American Medical Colleges developed the Core Entrustable Professional Activities for entering residency. As these metrics outline expectations for all graduating students regardless of specialty, the described procedural expectations are appropriately basic. However, in procedure-heavy specialties such as emergency medicine, the ability to perform advanced procedures continues to contribute to the disconnect between undergraduate and graduate medical education. To prepare our graduating students for their internship in emergency medicine, we developed a simulation-based mastery learning curriculum housed within a specialty-specific program. Our overall goal was to develop the students’ procedural competency for central venous catheter placement and endotracheal intubation before graduation from medical school.Methods Twenty-five students participated in a simulation-based mastery learning procedures curriculum for ultrasound-guided internal jugular central venous catheter placement and endotracheal intubation. Students underwent baseline assessment, deliberate practice, and post-test assessments. Both the baseline and post-test assessments used the same internally developed checklists with pre-established minimum passing scores.Results Despite completing an emergency medicine rotation and a critical care rotation, none of the students met the competency standard during their baseline assessments. All twenty-five students demonstrated competency on both procedures by the end of the curriculum. A second post-test was required to demonstrate achievement of the central venous catheter and endotracheal intubation minimum passing scores by 16% and 28% of students, respectively.Conclusions Students demonstrated procedural competency for central venous catheter placement and endotracheal intubation by engaging in simulation-based mastery learning procedures curriculum as they completed their medical school training. With three instructional hours, students were able to achieve basic procedural competence for two common, high-risk procedures they will need to perform during emergency medicine residency training.

Shalindu Malshan Jayawickrama, Piyumi Madhushani Ranaweera, Ratupaskatiye Gedara Gunaratnege Roshan Pradeep et al.

Abstract Background Precision healthcare has entered a new era because of the developments in personalized medicine, especially in the diagnosis and treatment of head and neck squamous cell carcinoma (HNSCC). This paper explores the dynamic landscape of personalized medicine as applied to HNSCC, encompassing both current developments and future prospects. Recent Findings The integration of personalized medicine strategies into HNSCC diagnosis is driven by the utilization of genetic data and biomarkers. Epigenetic biomarkers, which reflect modifications to DNA that can influence gene expression, have emerged as valuable indicators for early detection and risk assessment. Treatment approaches within the personalized medicine framework are equally promising. Immunotherapy, gene silencing, and editing techniques, including RNA interference and CRISPR/Cas9, offer innovative means to modulate gene expression and correct genetic aberrations driving HNSCC. The integration of stem cell research with personalized medicine presents opportunities for tailored regenerative approaches. The synergy between personalized medicine and technological advancements is exemplified by artificial intelligence (AI) and machine learning (ML) applications. These tools empower clinicians to analyze vast datasets, predict patient responses, and optimize treatment strategies with unprecedented accuracy. Conclusion The developments and prospects of personalized medicine in HNSCC diagnosis and treatment offer a transformative approach to managing this complex malignancy. By harnessing genetic insights, biomarkers, immunotherapy, gene editing, stem cell therapies, and advanced technologies like AI and ML, personalized medicine holds the key to enhancing patient outcomes and ushering in a new era of precision oncology.

Yasin Cetin, Asli Tok Ozen, Mumin Savas

The aim of this study is to identify the willingness of generation Z nursing students to work in the specialized units, as well as their views about these units, and to find answers to issues such as which units they want to work in and why. In this study, the phenomenological design of the qualitative research method was used. The purposive sampling technique was preferred in the sample selection of the study. The sample consisted of 16 students who completed their internal medicine nursing course and continued their education in the second year at Adıyaman University Faculty of Health Sciences. A semi-structured 13-item interview form was used as a data collection tool. The content analysis technique was used to analyze the data. Following the analysis, seven sub-themes were obtained. In the study, seven sub-themes were determined including explaining the reasons for specialized units, willingness to work in specialized units, choosing a particular specialized unit, getting support in deciding on the specialized units, the contribution of nursing education to deciding on the specialized units, research for specialized units, and physical opportunities in the specialized units. In accordance with the determined sub-themes, it was concluded that Z-generation nursing students desired to work in specialized units due to their professional development, improved patient care, more economic income, less violence towards healthcare workers in these units, as well as being informed by the lecturers, and sufficient physical opportunities. [Med-Science 2024; 13(1.000): 108-15]

Williams Monier Texidor, Matthew A. Miller, Kyle C. Molina et al.

Abstract Background Oritavancin, a long-acting lipoglycopeptide approved for use in acute bacterial skin and skin structure infections, has limited data evaluating use in serious infections due to Gram-positive organisms. We aimed to assess the effectiveness and safety of oritavancin for consolidative treatment of Gram-positive bloodstream infections (BSI), including infective endocarditis (IE). Methods We conducted a retrospective cohort study evaluating adult patients admitted to University of Colorado Hospital from March 2016 to January 2022 who received ≥ 1 oritavancin dose for treatment of Gram-positive BSI. Patients were excluded if the index culture was drawn at an outside facility or were > 89 years of age. The primary outcome was a 90-day composite failure (clinical or microbiological failure) in those with 90-day follow-up. Secondary outcomes included individual components of the primary outcome, acute kidney injury (AKI), infusion-related reactions (IRR), and institutional cost avoidance. Results Overall, 72 patients were included. Mean ± SD age was 54 ± 16 years, 61% were male, and 10% had IE. Organisms most commonly causing BSI were Staphylococcus aureus (68%, 17% methicillin-resistant), followed by Streptococcus spp. (26%), and Enterococcus spp. (10%). Patients received standard-of-care antibiotics before oritavancin for a median (IQR) of 11 (5–17) days. Composite failure in the clinically evaluable population (n = 64) at 90-days occurred in 14% and was composed of clinical and microbiological failure, which occurred in 14% and 5% of patients, respectively. Three patients (4%) experienced AKI after oritavancin, and two (3%) experienced an IRR. Oritavancin utilization resulted in earlier discharge for 94% of patients corresponding to an institutional cost-avoidance of $3,055,804 (mean $44,938/patient) from 1,102 hospital days saved (mean 16 days/patient). Conclusions The use of oritavancin may be an effective sequential therapy for Gram-positive BSI to facilitate early discharge resulting in institutional cost avoidance.

Abbi Abdel-Rehim, Oghenejokpeme Orhobor, Gareth Griffiths et al.

The concept of personalised medicine in cancer therapy is becoming increasingly important. There already exist drugs administered specifically for patients with tumours presenting well-defined mutations. However, the field is still in its infancy, and personalised treatments are far from being standard of care. Personalised medicine is often associated with the utilisation of omics data. Yet, implementation of multi-omics data has proven difficult, due to the variety and scale of the information within the data, as well as the complexity behind the myriad of interactions taking place within the cell. An alternative approach to precision medicine is to employ a function-based profile of the cell. This involves screening a range of drugs against patient derived cells. Here we demonstrate a proof-of-concept, where a collection of drug screens against a highly diverse set of patient-derived cell lines, are leveraged to identify putative treatment options for a 'new patient'. We show that this methodology is highly efficient in ranking the drugs according to their activity towards the target cells. We argue that this approach offers great potential, as activities can be efficiently imputed from various subsets of the drug treated cell lines that do not necessarily originate from the same tissue type.

Yunfei Xie, Juncheng Wu, Haoqin Tu et al.

Large language models (LLMs) have exhibited remarkable capabilities across various domains and tasks, pushing the boundaries of our knowledge in learning and cognition. The latest model, OpenAI's o1, stands out as the first LLM with an internalized chain-of-thought technique using reinforcement learning strategies. While it has demonstrated surprisingly strong capabilities on various general language tasks, its performance in specialized fields such as medicine remains unknown. To this end, this report provides a comprehensive exploration of o1 on different medical scenarios, examining 3 key aspects: understanding, reasoning, and multilinguality. Specifically, our evaluation encompasses 6 tasks using data from 37 medical datasets, including two newly constructed and more challenging question-answering (QA) tasks based on professional medical quizzes from the New England Journal of Medicine (NEJM) and The Lancet. These datasets offer greater clinical relevance compared to standard medical QA benchmarks such as MedQA, translating more effectively into real-world clinical utility. Our analysis of o1 suggests that the enhanced reasoning ability of LLMs may (significantly) benefit their capability to understand various medical instructions and reason through complex clinical scenarios. Notably, o1 surpasses the previous GPT-4 in accuracy by an average of 6.2% and 6.6% across 19 datasets and two newly created complex QA scenarios. But meanwhile, we identify several weaknesses in both the model capability and the existing evaluation protocols, including hallucination, inconsistent multilingual ability, and discrepant metrics for evaluation. We release our raw data and model outputs at https://ucsc-vlaa.github.io/o1_medicine/ for future research.

D. Gunn-Moore

Mark W. D. Sweep, Mark W. D. Sweep, Martijn J. H. Tjan et al.

Immune checkpoint inhibitor therapy for cancer treatment can give rise to a variety of adverse events. Here we report a male patient with metastatic melanoma who experienced life-threatening colitis and duodenitis following treatment with ipilimumab and nivolumab. The patient did not respond to the first three lines of immunosuppressive therapy (corticosteroids, infliximab, and vedolizumab), but recovered well after administration of tofacitinib, a JAK inhibitor. Cellular and transcriptional data on colon and duodenum biopsies shows significant inflammation in the tissue, characterized by a large number of CD8 T cells and high expression of PD-L1. While cellular numbers do decrease during three lines of immunosuppressive therapy, CD8 T cells remain relatively high in the epithelium, along with PD-L1 expression in the involved tissue and expression of colitis-associated genes, indicating an ongoing colitis at that moment. Despite all immunosuppressive treatments, the patient has an ongoing tumor response with no evidence of disease. Tofacitinib might be a good candidate to consider more often for ipilimumab/nivolumab-induced colitis.

Kewei Wang, Kewei Wang, Rong Zhang et al.

Background and aimsWnt/β-catenin signaling plays an important role in regulating hepatic metabolism. This study is to explore the molecular mechanisms underlying the potential crosstalk between Wnt/β-catenin and mTOR signaling in hepatic steatosis.MethodsTransgenic mice (overexpress Wnt1 in hepatocytes, Wnt+) mice and wild-type littermates were given high fat diet (HFD) for 12 weeks to induce hepatic steatosis. Mouse hepatocytes cells (AML12) and those transfected to cause constitutive β-catenin stabilization (S33Y) were treated with oleic acid for lipid accumulation.ResultsWnt+ mice developed more hepatic steatosis in response to HFD. Immunoblot shows a significant increase in the expression of fatty acid synthesis-related genes (SREBP-1 and its downstream targets ACC, AceCS1, and FASN) and a decrease in fatty acid oxidation gene (MCAD) in Wnt+ mice livers under HFD. Wnt+ mice also revealed increased Akt signaling and its downstream target gene mTOR in response to HFD. In vitro, increased lipid accumulation was detected in S33Y cells in response to oleic acid compared to AML12 cells reinforcing the in vivo findings. mTOR inhibition by rapamycin led to a down-regulation of fatty acid synthesis in S33Y cells. In addition, β-catenin has a physical interaction with mTOR as verified by co-immunoprecipitation in hepatocytes.ConclusionsTaken together, our results demonstrate that β-catenin stabilization through Wnt signaling serves a central role in lipid metabolism in the steatotic liver through up-regulation of fatty acid synthesis via Akt/mTOR signaling. These findings suggest hepatic Wnt signaling may represent a therapeutic strategy in hepatic steatosis.

Walla Malkawi, Areeb Lutfi, Maaz Khan Afghan et al.

ObjectiveMost of the work in terms of liquid biopsies in patients with solid tumors is focused on circulating tumor DNA (ctDNA). Our aim was to evaluate the feasibility of using circulating tumor cells (CTCs) in peripheral blood samples from patients with advanced or metastatic gastrointestinal (GI) cancers.MethodsIn this prospective study, blood samples were collected from each patient in 2 AccuCyte® blood collection tubes and each tube underwent CTC analysis performed utilizing the RareCyte® platform. The results from both tubes were averaged and a total of 150 draws were done, with 281 unique reported results. The cadence of sampling was based on convenience sampling and piggybacked onto days of actual clinical follow-ups and treatment visits. The CTC results were correlated with patient- and tumor-related variables.ResultsData from a total of 59 unique patients were included in this study. Patients had a median age of 58 years, with males representing 69% of the study population. More than 57% had received treatment prior to taking blood samples. The type of GI malignancy varied, with more than half the patients having colorectal cancer (CRC, 54%) followed by esophageal/gastric cancer (17%). The least common cancer was cholangiocarcinoma (9%). The greatest number of CTCs were found in patients with colorectal cancer (Mean: 15.8 per 7.5 ml; Median: 7.5 per 7.5 ml). In comparison, patients with pancreatic cancer (PC) had considerably fewer CTCs (Mean: 4.2 per 7.5 ml; Median: 3 per 7.5 ml). Additionally, we found that patients receiving treatment had significantly fewer CTCs than patients who were not receiving treatment (Median 2.7 versus 0.7). CTC numbers showed noteworthy disparities between patients with responding/stable disease in comparison to those with untreated/progressive disease (Median of 2.7 versus 0). When CTCs were present, biomarker analyses of the four markers human epidermal growth factor receptor 2 (HER2)/programmed death-ligand 1 (PD-L1)/Kiel 67 (Ki-67)/epidermal growth factor receptor (EGFR) was feasible. Single cell sequencing confirmed the tumor of origin.ConclusionOur study is one of the first prospective real-time studies evaluating CTCs in patients with GI malignancies. While ctDNA-based analyses are more common in clinical trials and practice, CTC analysis provides complementary information from a liquid biopsy perspective that is of value and worthy of continued research.

Alkhateeb A, Mahmoud HEM, AK M et al.

Areej Alkhateeb,1 Hossam Eldin M Mahmoud,1 Mohammed AK,1 Mohammed H Hassan,2 Abdel Rahim Mahmoud Muddathir,3 Ahmed G Bakry1 1Cardiology Division of Internal Medicine Department, South Valley University Hospital, Faculty of Medicine, South Valley University, Qena, 83523, Egypt; 2Department of Medical Biochemistry, Faculty of Medicine, South Valley University, Qena, 83523, Egypt; 3Department of Hematology and Blood Transfusion, Faculty of Medical Laboratory Science, Alzaeim Alazhari University, Khartoum, SudanCorrespondence: Abdel Rahim Mahmoud Muddathir, Department of Hematology and Blood Transfusion, Faculty of Medical Laboratory Science, Alzaeim Alazhari University, Khartoum, Sudan, Email abdelrahimm@gmail.comBackground: The prognostic role of the soluble circulating suppression of tumorigenicity 2 marker (sST2) in different cardiovascular diseases (CVD) is still under investigation. This research aimed to assess the serum levels of sST2 in the blood of individuals with ischemic heart disease and its relation to disease severity, also to examine any changes in sST2 levels following a successful percutaneous coronary intervention (PCI) in those patients.Methods: A total of 33 ischemic patients and 30 non-ischemic controls were included. The plasma level of sST2 was measured using commercially available ELISA assay kit, at baseline and 24– 48 h after the intervention in the ischemic group.Results: On admission, there was a significant difference between the group of acute/chronic coronary syndrome cases and controls regarding the sST2 plasma level (p < 0.001). There was an insignificant difference between the three ischemic subgroups at the baseline sST2 level (p = 0.38). The plasma sST2 level decreased significantly after PCI (from 20.70 ± 1.71 to 16.51 ± 2.43, p = 0.006). There was a modestly just significant positive correlation between the acute change in post-PCI sST2 level and the severity of ischemia as measured by the Modified Gensini Score (MGS) (r = 0.45, p = 0.05). In spite of the highly significant improvement in the coronary TIMI flow of ischemic group after PCI, there was insignificant negative correlation between the post- PCI delta change in the sST2 level and the post-PCI TIMI coronary flow grade.Conclusion: A significantly high plasma level of sST2 in patients with myocardial ischemia and controlled cardiovascular risk factors showed an immediate reduction after successful revascularization. The high baseline level of the sST2 marker and the acute post-PCI reduction was mainly related to the severity of ischemia rather than left ventricular function.Keywords: soluble circulating suppression of tumorigenicity 2 marker, percutaneous coronary intervention, acute/chronic coronary syndrome, severity of ischemia, modified gensini score, revascularization

Zhongru Fan, Dongyu Han, Xin Fan et al.

BackgroundOvarian cancer (OC) is one of the malignant tumors that poses a serious threat to women’s health. Natural killer (NK) cells are an integral part of the immune system and have the ability to kill tumor cells directly or participate indirectly in the anti-tumor immune response. In recent years, NK cell-based immunotherapy for OC has shown remarkable potential. However, its mechanisms and effects remain unclear when compared to standard treatment.MethodsTo explore the value of NK cell-based immunotherapy in the treatment of OC, we conducted a literature review. In comparison to standard treatment, our focus was primarily on the current anti-tumor mechanisms, the clinical effect of NK cells against OC, factors affecting the structure and function of NK cells, and strategies to enhance the effectiveness of NK cells.ResultsWe found that NK cells exert their therapeutic effects in OC through mechanisms such as antibody-dependent cell cytotoxicity, perforin release, and granule enzyme secretion. They also secrete IFN-γ and TNF-α or engage in Fas/FasL and TRAIL/TRAILR pathways, mediating the death of OC cells. In clinical trials, the majority of patients experienced disease stability with mild side effects after receiving NK cell-based immunotherapy, but there is still a lack of high-quality research evidence regarding its clinical effectiveness. OC and prior experience with standard treatments have an effect on NK cells, and it may be considered to maximize NK cell effects through the modulation of the tumor microenvironment or combination with other therapies.ConclusionsIn this review, we have summarized the current evidence of NK cell applications in the treatment of OC. Furthermore, factors and strategies that influence and enhance the role of NK cell immunotherapy are discussed.

Joshua Durso-Finley, Jean-Pierre Falet, Raghav Mehta et al.

Image-based precision medicine aims to personalize treatment decisions based on an individual's unique imaging features so as to improve their clinical outcome. Machine learning frameworks that integrate uncertainty estimation as part of their treatment recommendations would be safer and more reliable. However, little work has been done in adapting uncertainty estimation techniques and validation metrics for precision medicine. In this paper, we use Bayesian deep learning for estimating the posterior distribution over factual and counterfactual outcomes on several treatments. This allows for estimating the uncertainty for each treatment option and for the individual treatment effects (ITE) between any two treatments. We train and evaluate this model to predict future new and enlarging T2 lesion counts on a large, multi-center dataset of MR brain images of patients with multiple sclerosis, exposed to several treatments during randomized controlled trials. We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error. Lastly, we demonstrate how knowledge of uncertainty could modify clinical decision-making to improve individual patient and clinical trial outcomes.