MedComm - Future medicine最新文献

In November 2022, a large number of Omicron infections suddenly appeared in Beijing, but the epidemiological and clinical characteristics of the epidemic cases were unknown. We collected the data on COVID-19 cases in Fangcang Hospital in Beijing from November 20, 2022, to December 8, 2022, and analyzed the epidemiological and clinical characteristics. Of the enrolled study, 85.9% were asymptomatic and 14.1% were mild. Epidemiological data showed that the transmission speed of the Omicron variant was fast and the transmission range was wide, large-scale infections occurred in both rural and urban areas, and all age groups were susceptible to the Omicron variant. In addition, antipyretics and cough drugs were the two most used drugs, because 51.3% and 22.7% of patients had fever and cough, respectively, and 10.3% of patients took hypnotics. Furthermore, the proportion of patients with chronic diseases was low (13.9%), while the vaccination rate (71.2%) was relatively high. Based on the results, we found that most mild and asymptomatic cases did not need treatment, indicating that home isolation is correct and feasible. Although SARS-CoV-2 variants have characteristics such as high infectivity and immune-escape ability, the public should not be too afraid of COVID-19 infection; appropriate measures such as wearing masks and maintaining social distancing are sufficient to prevent reinfection.

Recently, researchers from Cancer Research UK and The Francis Crick Institute published a paper entitled “Lung adenocarcinoma promotion by air pollutants” in Nature.¹ The study focused on the impact of air pollutants, specifically PM2.5, on lung adenocarcinoma development. By analyzing human data and conducting subsequent animal experiments, the researchers found that air pollutants PM2.5 leads to an influx of macrophages into the lung and triggers the release of interleukin-1β. This, in turn, induces a progenitor-like cell state within estimated glomerular filtration rate (EGFR) mutant lung alveolar type II epithelial cells, fueling tumorigenesis, and potentially exacerbating pre-existing cancerous mutations in normal tissues.

While the association between smoking and lung cancer risk is well-established, attention has increasingly turned towards understanding the carcinogenic factors in never-smokers. As the eighth leading cause of cancer-related deaths in the United Kingdom, lung cancer in never-smokers (LCINS) is often an adenocarcinoma carrying the EGFR mutation.² In an effort to identify significant factors influencing the development of lung cancer LCINS, the researchers analyzed environmental and epidemiological data from 32,957 cases of EGFR-driven lung cancer in the United Kingdom, Canada, South Korea, Taiwan, and China. The findings revealed a correlation between increased levels of PM2.5 and a higher incidence of lung cancer among the study participants. Later analysis of 407,509 individuals from the UK Biobank support these results, demonstrating significant increase in the projected incidence of lung cancer among those exposed to high levels of PM2.5. The researchers also conducted a 3-year follow-up study involving 228 Canadian lung cancer patients. The incidence of lung cancer was found to be significantly higher (73%) in those exposed to high levels of PM2.5 compared to those exposed to low levels (40%). Notably, this association was not observed in the Canadian cohort over a 20-year period, suggesting that 3 years of exposure to high levels of pollution may be sufficient to produce cancer.

Hill et al. further employed genetically engineered mice carrying EGFR mutations (EGFR^L858R) associated with human cancer to functionally investigate whether PM2.5 exposure promoted the development of lung adenocarcinoma. The study revealed that mice were exposed to similar air pollution particles, resulting in a higher likelihood of developing lung tumors compared to control mice not exposed to pollution particles. The same experiments were performed on genetically engineered mice with Kras mutations, a common mutation in various lung tumors, yielding similar results. Through spatial analysis of clonal dynamics, the researchers discovered that PM2.5 promotes early tumorigenesis through two mechanisms: increasing the number of EGFR-mutated cells capable of forming tumors

Coronavirus disease-19 (COVID-19) is the global health emergency caused by SARS-CoV-2. Upon infection, antigenic determinants of the virus trigger massive production of proinflammatory/pyroptosis-associated proteins, resulting in cytokine storm, tissue damage, and multiorgan failure. Therefore, these proinflammatory/pyroptosis-associated mediators are promising therapeutic targets to combat COVID-19. Epicatechin gallate (ECG) is a polyphenol found in green tea. It has antioxidative and anti-inflammatory properties. Hence, in the present study, ECG was selected to explore its binding potential for inflammatory mediators such as interleukins, interferon-γ (IFNγ), and tumor necrosis factor-α (TNF-α), along with their native receptors. In addition, the interacting potential of ECG with pyroptosis-associated proteins, viz. caspases and BAX has also been investigated. Molecular docking analysis has revealed that ECG interacts with interleukins, IFNγ, TNF-α, cytokine receptors, caspase-1/4/11, and BAX with significant binding affinity. Several amino acid residues of these mediators were blocked by ECG through stable hydrogen bonds and hydrophobic contacts. ECG interacted with caspase-11, BAX, and TNF-R1 with better binding affinities. Therefore, the present in silico study indicates that ECG could be a potential drug to subvert cytokine storm and pyroptosis during COVID-19.

Large language models (LLMs) are rapidly becoming an important foundation model that has infiltrated our daily lives in many ways. The release of GPT-3 and GPT-4, a LLM that is capable of natural language processing (NLP) that has been trained on terabytes of text data through transfer learning to apply knowledge gained from a previous task to solve a different but related problem, immediately captured the attention of the medical field to investigate how LLMs can be used to process and interpret electronic health records and to streamline clinical writing.¹ NLP models have traditionally been used mainly as diagnostic aids in healthcare. Its use generally requires supervised learning on manually labeled and training datasets with a huge involvement of time from healthcare professionals.² NLP models often lack precision, accuracy and mostly only accessible by the developers. Recent LLMs with their transformer and reinforcement learning with human feedback, have enabled better precision in text generation. The advancement of GPT-3 (Generative Pre-Trained Transformer, commonly known as ChatGPT) demonstrated that LLMs can rapidly adapt to new tasks resulting in better generalization. Also, ChatGPT has a simple interface, which has enabled broad adoption and use. Having such a versatile and user-friendly tool at our fingertips means that we can adapt to use LLMs for basic tasks such as generating clinical reports, providing clinical support, or to synthesize patient data from multiple sources.

We have used this case report as an opportunity to demonstrate the practicality of ChatGPT in basic writing tasks in a clinical context. This case report is obtained from two teaching videos uploaded by TTMedcastTraining Texas Tech University on YouTube. The two videos are of a patient called Jonathan who presented with bilateral knee pain with a history of sickle cell disease. One video is the bedside presentation of the patient by a medical intern, another is a group discussion of treatment plans for this patient. Since GPT-3 can only deal with text input, we have downloaded the transcript from each video. The transcripts sometimes contain people talking at the same time, filler words, mispronounced words, or incomplete sentences. Unaltered transcripts were submitted to ChatGPT separately for interpretation.

The workflow of using ChatGPT to generate the case report is summarized in Figure 1. We fed the transcript of Video 1 into ChatGPT and asked it to write a case report from it (Case Report 1). Then, we used the transcript of Video 2 to create Case Report 2. ChatGPT was asked to combine the two reports without summarizing and offer a diagnosis and a treatment plan. We also asked ChatGPT to write the final case report in the style for the New England Journal of Medicine. This process took around 1.5 h, including time the authors spent watching the videos. The full case report is found in Supportin

Given the unprecedented phenomenon of population ageing, studies have increasing captured the heterogeneity within the ageing process. In this context, the concept of “biological age” has been introduced as an integrated measure reflecting the individualized ageing pace. Identifying reliable and robust biomarkers of age is critical for the accurate risk stratification of individuals and exploration into antiageing interventions. Numerous potential biomarkers of ageing have been proposed, spanning from molecular changes and imaging characteristics to clinical phenotypes. In this review, we will start off with a discussion of the development of ageing biomarkers, then we will provide a comprehensive summary of currently identified ageing biomarkers in humans, discuss the rationale behind each biomarker and highlight their accuracy and clinical value with a contemporary perspective. Additionally, we will discuss the challenges, potential applications, and future opportunities in this field. While research on ageing biomarkers has led to significant progress and applications, further investigations are still necessary. We anticipate that future breakthroughs in this field will involve exploring potential mechanisms, developing biomarkers by combining various data sources or employing new technologies, and validating the clinical value of existing and emerging biomarkers through comprehensive collaboration and longitudinal studies.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been suggested to purpose threats to health of mankind. Alcoholic hepatitis (AH) is a life-threatening acute and chronic liver failure that takes place in sufferers who drink excessively. During the epidemic, AH has an increasing incidence of severe illness and mortality. The intrinsic relationship of molecular pathogenesis, as well as common therapeutic strategies for two diseases are still poorly understood. The transcriptome of the COVID-19 and AH has been compared to obtain the altered genes and hub genes were screened out through protein–protein interaction (PPI) network analysis. Via gene ontology (GO), pathway enrichment, and transcription regulator analysis, a deeper appreciation of the interplay mechanism between hub genes were established. Finally, gene-disease and gene–drug analysis were displayed to instruct the clinical treatments. With 181 common differentially expressed genes (DEGs) of AH and COVID-19 were obtained, 10 hub genes were captured. Follow-up studies located that these 10 genes typically mediated the diseases occurrence by regulating the activities of the immune system. Other results suggest that the common pathways of the two ailments are enriched in regulating the function of immune cells and release of immune molecules. The top 10 drug candidates have been chosen primarily, some of which have been proved effective in treating AH sufferers infected with COVID-19. This study reveals the common pathogenesis of COVID-19 and AH and assist to discover necessary therapeutic targets to combat the ongoing pandemic induced via SARS-CoV-2 infection and acquire promising remedy strategies for the two diseases.

Large language models (LLMs) often refer to artificial intelligence models that consist of extensive parameters and have the ability to understand and generate human-like language. They are typically developed in a self-supervised learning manner and are trained on large quantities of unlabeled text to learn patterns in language. LLMs were initially used in natural language processing (NLP), but they have since been extended to a variety of tasks like processing biological sequences and combining text with other modalities of data. LLMs have the potential to revolutionize the way we approach scientific research and medicine. For example, by leveraging their ability to understand and interpret vast quantities of text data, LLMs can provide insights and make predictions that would otherwise be impossible.

In the medical domain, LLMs can be used to analyze immense electronic health records and improve communication between healthcare professionals and patients. For example, LLMs can be used to automate triage, medical coding, and clinical documentation, which can help to improve the accuracy and efficiency of these processes. They can also be used to improve NLP in medical chatbots and virtual assistants, allowing patients to interact with healthcare services more efficiently and effectively. They can also be used to process medical records and patient data, enabling better diagnoses and more personalized treatments. They can also be used to analyze clinical trial data and identify trends that could lead to better outcomes. Finally, LLMs can also be used to answer medical questions and provide guidance to healthcare professionals, which can help to improve the quality of care. In the accompanying Review, Zheng et al.¹ undertake a major effort to write a comprehensive review of this exciting and highly evolving field.

In research, LLMs can be used to search through diverse large datasets and identify patterns that would otherwise be difficult to detect. They can also be used to generate and test hypotheses and to summarize and analyze research papers. It is clear that LLMs will be transforming the way we communicate about medicine and research, and have the potential to revolutionize the field of healthcare.

The current state-of-the-art LLM is Generative Pre-trained Transformer 4 (GPT-4), developed by OpenAI, about which Technical details have not been made public yet.² Based on publicly available information, the number of parameters is comparable to its previous generation, GPT-3, which consists of 175 billion parameters. GPT-4 is a generative model, meaning it can generate human-like language and even create original content. Other notable LLMs include GPT-3, Bidirectional Encoder Representations from Transformers, and Text-to-Text Transfer Transformers, each with its unique strengths and capabilities. However, one example of an LLM developed specifically for the medical domain i

Large-scale artificial intelligence (AI) models such as ChatGPT have the potential to improve performance on many benchmarks and real-world tasks. However, it is difficult to develop and maintain these models because of their complexity and resource requirements. As a result, they are still inaccessible to healthcare industries and clinicians. This situation might soon be changed because of advancements in graphics processing unit (GPU) programming and parallel computing. More importantly, leveraging existing large-scale AIs such as GPT-4 and Med-PaLM and integrating them into multiagent models (e.g., Visual-ChatGPT) will facilitate real-world implementations. This review aims to raise awareness of the potential applications of these models in healthcare. We provide a general overview of several advanced large-scale AI models, including language models, vision-language models, graph learning models, language-conditioned multiagent models, and multimodal embodied models. We discuss their potential medical applications in addition to the challenges and future directions. Importantly, we stress the need to align these models with human values and goals, such as using reinforcement learning from human feedback, to ensure that they provide accurate and personalized insights that support human decision-making and improve healthcare outcomes.

Corneal volume (CV) is a useful index for detecting forme fruste keratoconus from normal corneas. It can be used to evaluate the whole cornea, since it can measure corneal areas up to 10 mm in diameter. Thus, CV has become the clinicians' interest as a diagnostic tool of corneal ectatic disease and a measure of corneal integrity to determine suitability for refractive surgery. We conducted a cross-sectional study including 7893 myopic patients from five ophthalmic centers to investigate the distribution pattern of CV. Our study showed that distribution of CV-3, CV-5, and CV-7 mm were slightly positively skewed and the 2.5th to 97.5th percentiles were 3.6–4.4, 10.4–12.8, 22.5–27.5 mm³, respectively. Central corneal thickness (CCT) was significantly correlated with CV in all measurement regions. The correlation between CV and CCT showed an inconsistent trend with the increase of age. The correlation coefficient between CV and CCT did not change significantly with the increase of myopia degree in low to moderate myopia, but fluctuated significantly in high myopia (less than −6.0 diopters). According to our results, corneal volume follows a slightly positively skewed distribution pattern in myopic Chinese patients. The information is useful for screening refractive surgery candidates and assessing the risk of corneal refractive surgery.

An accurate prediction of prognosis is important for clinical treatments of glioma. In this study, a multiparameter radiomic model is proposed for accurate prognostic prediction of glioma. Three kinds of region of interest were extracted from preoperative postcontrast T1-weighted images and T2 fluid-attenuated inversion recovery images acquired from 140 glioma patients. Radiomics score (Radscore) was calculated and the conventional image features and clinical molecular characteristics that may be related to progression-free survival (PFS) were evaluated. Five uniparameter and various combinations of biparameter and multiparameter models based on above characteristics were built. The performance of these models was evaluated by concordance index (C index), and the nomogram of the multiparameter radiomic model was constructed. The results show that the proposed multiparameter radiomic model has a better prediction performance than other models. In the training and validation sets, the calibration curves of the multiparameter radiomic model for the 1-, 2-, and 3-year PFS probability demonstrate a high consistence between predictions and observations. In conclusion, this study demonstrates that the multiparameter radiomic model based on Radscore, conventional image features and clinical molecular characteristics can improve the prediction accuracy of glioma prognosis, which could be informative for individualized treatments.