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The Science of the Total Environment Jun 2024Since the discovery of antibiotics, penicillin has remained the top choice in clinical medicine. With continuous advancements in biotechnology, penicillin production has... (Review)
Review
Since the discovery of antibiotics, penicillin has remained the top choice in clinical medicine. With continuous advancements in biotechnology, penicillin production has become cost-effective and efficient. Genetic engineering techniques have been employed to enhance biosynthetic pathways, leading to the production of new penicillin derivatives with improved properties and increased efficacy against antibiotic-resistant pathogens. Advances in bioreactor design, media formulation, and process optimization have contributed to higher yields, reduced production costs, and increased penicillin accessibility. While biotechnological advances have clearly benefited the global production of this life-saving drug, they have also created challenges in terms of waste management. Production fermentation broths from industries contain residual antibiotics, by-products, and other contaminants that pose direct environmental threats, while increased global consumption intensifies the risk of antimicrobial resistance in both the environment and living organisms. The current geographical and spatial distribution of antibiotic and penicillin consumption dramatically reveals a worldwide threat. These challenges are being addressed through the development of novel waste management techniques. Efforts are aimed at both upstream and downstream processing of antibiotic and penicillin production to minimize costs and improve yield efficiency while lowering the overall environmental impact. Yield optimization using artificial intelligence (AI), along with biological and chemical treatment of waste, is also being explored to reduce adverse impacts. The implementation of strict regulatory frameworks and guidelines is also essential to ensure proper management and disposal of penicillin production waste. This review is novel because it explores the key remaining challenges in antibiotic development, the scope of machine learning tools such as Quantitative Structure-Activity Relationship (QSAR) in modern biotechnology-driven production, improved waste management for antibiotics, discovering alternative path to reducing antibiotic use in agriculture through alternative meat production, addressing current practices, and offering effective recommendations.
PubMed: 38942308
DOI: 10.1016/j.scitotenv.2024.174236 -
Clinics in Dermatology Jun 2024Melanoma is the deadliest skin cancer, presenting typically with changing pigmented areas and usually treated with surgical removal. As benign cutaneous pigmented...
Melanoma is the deadliest skin cancer, presenting typically with changing pigmented areas and usually treated with surgical removal. As benign cutaneous pigmented lesions are very common in all populations, it can be challenging to identify which areas should be cut out or left untreated. Delayed treatment in melanoma increases the risk of death, but it is not possible to remove all lesions. Dermatoscopy uses polarised light and can be used to help distinguish melanomas from benign lesions. Dermatoscopy images with a confirmed diagnosis can be utilized to develop artificial intelligence as a medical device (AIaMD) tool. This contribution discusses the utilization of artificial intelligence (AI) in melanoma management and describes an AIaMD tool that has been used in current UK clinical practice on over 80,000 patients. This is a springboard for discussing the scope, risks, and mitigations for future AI use by all clinicians involved in managing people with melanoma.
PubMed: 38942155
DOI: 10.1016/j.clindermatol.2024.06.015 -
Clinics in Dermatology Jun 2024The integration of teledermatology and artificial intelligence (AI) marks a significant advancement in dermatologic care. This study examines the synergistic interplay...
The integration of teledermatology and artificial intelligence (AI) marks a significant advancement in dermatologic care. This study examines the synergistic interplay between these two domains, highlighting their collective impact on enhancing the accuracy, accessibility, and efficiency of teledermatological services. Teledermatology expands dermatologic care to remote and underserved areas, while AI technologies show considerable potential in analyzing dermatologic images and performing various tasks involved in teledermatology consultations. This integration facilitates rapid, precise diagnoses, personalized treatment plans, and data-driven insights. Our explorative study involved designing a GPT-based chatbot named 'Dr. Dermbot' and exploring its performance in a teledermatologic consultation process. The design phase focused on the chatbot's ability to conduct consultations autonomously. The subsequent testing phase assessed its performance against the backdrop of current teledermatologic practices, exploring the potential of AI and chatbots to simulate and potentially enhance teledermatologic healthcare. This study demonstrates the promising future of combining teledermatology with AI. It also brings to light ethical and legal concerns, including the protection of patient data privacy and adherence to regulatory standards. The union of teledermatology and AI not only aims to enhance the precision of tele-dermatologic diagnoses but also broadens the accessibility of dermatologic services to previously underserved populations, benefiting patients, healthcare providers, and the overall healthcare system.
PubMed: 38942153
DOI: 10.1016/j.clindermatol.2024.06.020 -
Survey of Ophthalmology Jun 2024Cystoid macular edema (CME) is a sight-threatening condition often associated with inflammatory and diabetic diseases. Early detection is crucial to prevent irreversible... (Review)
Review
Cystoid macular edema (CME) is a sight-threatening condition often associated with inflammatory and diabetic diseases. Early detection is crucial to prevent irreversible vision loss. Artificial intelligence (AI) has shown promise in automating CME diagnosis through optical coherence tomography (OCT) imaging, but its utility needs critical evaluation. This systematic review assesses the application of AI to diagnosis CME, specifically focusing on disorders like postoperative CME (Irvine Gass syndrome) and retinitis pigmentosa without obvious vasculopathy, using OCT imaging. A comprehensive search was conducted across 6 databases (PubMed, Scopus, Web of Science, Wiley, ScienceDirect, and IEEE) from 2018 to November, 2023. Twenty-three articles met the inclusion criteria and were selected for in-depth analysis. We evaluate AI's role in CME diagnosis and its performance in "detection", "classification" and "segmentation" of OCT retinal images. We found that convolutional neural network (CNN)-based methods consistently outperformed other machine learning techniques, achieving an average accuracy of over 96% in detecting and identifying CME from OCT images. Despite certain limitations such as dataset size and ethical concerns, the synergy between AI and OCT, particularly through CNNs, holds promise for significantly advancing CME diagnostics.
PubMed: 38942125
DOI: 10.1016/j.survophthal.2024.06.005 -
Survey of Ophthalmology Jun 2024Diabetic macular edema (DME), defined as retinal thickening near, or involving the fovea caused by fluid accumulation in the retina, can lead to vision impairment and... (Review)
Review
Diabetic macular edema (DME), defined as retinal thickening near, or involving the fovea caused by fluid accumulation in the retina, can lead to vision impairment and blindness in patients with diabetes. Current knowledge of retina anatomy and function and DME pathophysiology has taken great advantage of the availability of several techniques for visualizing the retina. Combining these techniques in a multimodal imaging approach to DME is recommended to improve diagnosis and to guide treatment decisions. We review the recent literature about the following retinal imaging technologies: optical coherence tomography (OCT), OCT angiography (OCTA), wide-field and ultrawide-field techniques applied to fundus photography, fluorescein angiography, and OCTA. The emphasis will be on characteristic DME features identified by these imaging technologies and their potential or established role as diagnostic, prognostic, or predictive biomarkers. The role of artificial intelligence in the assessment and interpretation of retina images is also discussed.
PubMed: 38942124
DOI: 10.1016/j.survophthal.2024.06.006 -
NeuroImage Jun 2024The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificial Intelligence (AI) in AD has...
BACKGROUND
The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificial Intelligence (AI) in AD has experienced a notable surge in recent years. However, existing investigations predominantly concentrate on distinguishing clinical phenotypes through cross-sectional approaches. This study aims to investigate the potential of modeling additional dimensions of the disease, such as variations in brain metabolism assessed via [F]-fluorodeoxyglucose positron emission tomography (FDG-PET), and utilize this information to identify patients with mild cognitive impairment (MCI) who will progress to dementia (pMCI).
METHODS
We analyzed data from 1,617 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had undergone at least one FDG-PET scan. We identified the brain regions with the most significant hypometabolism in AD and used Deep Learning (DL) models to predict future changes in brain metabolism. The best-performing model was then adapted under a multi-task learning framework to identify pMCI individuals. Finally, this model underwent further analysis using eXplainable AI (XAI) techniques.
RESULTS
Our results confirm a strong association between hypometabolism, disease progression, and cognitive decline. Furthermore, we demonstrated that integrating data on changes in brain metabolism during training enhanced the models' ability to detect pMCI individuals (sensitivity=88.4%, specificity=86.9%). Lastly, the application of XAI techniques enabled us to delve into the brain regions with the most significant impact on model predictions, highlighting the importance of the hippocampus, cingulate cortex, and some subcortical structures.
CONCLUSION
This study introduces a novel dimension to predictive modeling in AD, emphasizing the importance of projecting variations in brain metabolism under a multi-task learning paradigm.
PubMed: 38942101
DOI: 10.1016/j.neuroimage.2024.120695 -
Journal of Pain and Symptom Management Jun 2024Artificial intelligence-driven tools, like ChatGPT, are prevalent sources for online health information. Limited research has explored the congruity between AI-generated...
CONTEXT
Artificial intelligence-driven tools, like ChatGPT, are prevalent sources for online health information. Limited research has explored the congruity between AI-generated content and professional treatment guidelines.
OBJECTIVES
This study seeks to compare recommendations for cancer-related symptoms generated from ChatGPT with guidelines from the National Comprehensive Cancer Network (NCCN).
METHODS
We extracted treatment recommendations for nine symptoms from NCCN, separated into four full Supportive Care sections and five subsections of the Palliative Care webpage. We entered "How can I reduce my cancer-related [symptom]" into ChatGPT- 3.5 for these same symptoms and extracted its recommendations. A comparative content analysis focused on recommendations for medications, consultations, and non-pharmacological strategies. We compared word count and Flesch-Kincaid Grade Level (FKGL) readability for each NCCN and ChatGPT section.
RESULTS
The mean percent agreement between NCCN and ChatGPT recommendations was 37.3% (range 16.7%- 81.8%). NCCN offered more specific medication recommendations. ChatGPT did recommend medications in the constipation and diarrhea sections that were not recommended by NCCN. Significant differences in word count (p=0.03) and FKGL (p<0.01) were found for NCCN Supportive Care webpages, with ChatGPT having lower word count and reading level. In the NCCN Palliative Care webpage subsections, there was no significant difference in word count (p=0.076), but FKGL was significantly lower with ChatGPT (p<0.01).
CONCLUSIONS
While ChatGPT provides concise, accessible supportive care advice, discrepancies with guidelines raise concerns for patient-facing symptom management recommendations. Future research should consider how AI can be used in conjunction with evidence-based guidelines to support cancer patients' supportive care needs.
KEY MESSAGE
This study compares cancer-related symptom recommendations from ChatGPT with NCCN guidelines, revealing significant differences and highlighting the need for cautious integration of AI-generated content in patient care. It emphasizes the importance of professional guidance and collaboration to ensure data accuracy and quality.
PubMed: 38942093
DOI: 10.1016/j.jpainsymman.2024.06.019 -
The Lancet. Diabetes & Endocrinology Jun 2024Osteoporotic fractures are a major health challenge in older adults. Despite the availability of safe and effective therapies for osteoporosis, these therapies are... (Review)
Review
Osteoporotic fractures are a major health challenge in older adults. Despite the availability of safe and effective therapies for osteoporosis, these therapies are underused in individuals at high risk for fracture, calling for better case-finding and fracture risk assessment strategies. Artificial intelligence (AI) and machine learning (ML) hold promise for enhancing identification of individuals at high risk for fracture by distilling useful features from high-dimensional data derived from medical records, imaging, and wearable devices. AI-ML could enable automated opportunistic screening for vertebral fractures and osteoporosis, home-based monitoring and intervention targeting lifestyle factors, and integration of multimodal features to leverage fracture prediction, ultimately aiding improved fracture risk assessment and individualised treatment. Optimism must be balanced with consideration for the explainability of AI-ML models, biases (including information inequity in numerically under-represented populations), model limitations, and net clinical benefit and workload impact. Clinical integration of AI-ML algorithms has the potential to transform osteoporosis management, offering a more personalised approach to reduce the burden of osteoporotic fractures.
PubMed: 38942044
DOI: 10.1016/S2213-8587(24)00153-0 -
Cancer Cell Jun 2024Global investigation of medulloblastoma has been hindered by the widespread inaccessibility of molecular subgroup testing and paucity of data. To bridge this gap, we...
Global investigation of medulloblastoma has been hindered by the widespread inaccessibility of molecular subgroup testing and paucity of data. To bridge this gap, we established an international molecularly characterized database encompassing 934 medulloblastoma patients from thirteen centers across China and the United States. We demonstrate how image-based machine learning strategies have the potential to create an alternative pathway for non-invasive, presurgical, and low-cost molecular subgroup prediction in the clinical management of medulloblastoma. Our robust validation strategies-including cross-validation, external validation, and consecutive validation-demonstrate the model's efficacy as a generalizable molecular diagnosis classifier. The detailed analysis of MRI characteristics replenishes the understanding of medulloblastoma through a nuanced radiographic lens. Additionally, comparisons between East Asia and North America subsets highlight critical management implications. We made this comprehensive dataset, which includes MRI signatures, clinicopathological features, treatment variables, and survival data, publicly available to advance global medulloblastoma research.
PubMed: 38942025
DOI: 10.1016/j.ccell.2024.06.002 -
Klinische Monatsblatter Fur... Jun 2024Corneal nerves and dendritic cells are increasingly being visualised to serve as clinical parameters in the diagnosis of ocular surface diseases using intravital... (Review)
Review
Corneal nerves and dendritic cells are increasingly being visualised to serve as clinical parameters in the diagnosis of ocular surface diseases using intravital confocal microscopy. In this review, different methods of image analysis are presented. The use of deep learning algorithms, which enable automated pattern recognition, is explained in detail using our own developments and compared with other established methods.
Topics: Cornea; Dendritic Cells; Humans; Microscopy, Confocal; Ophthalmic Nerve; Deep Learning; Corneal Diseases; Pattern Recognition, Automated; Image Processing, Computer-Assisted; Intravital Microscopy; Algorithms
PubMed: 38941998
DOI: 10.1055/a-2307-0313