Tag: pubmed

J Biophotonics. 2023 Oct 3:e202300236. doi: 10.1002/jbio.202300236. Online ahead of print.

ABSTRACT

Many optical techniques have been used in various diagnostics and biomedical applications since a decade and polarization imaging is one of the non-invasive and label free optical technique to investigate biological samples making it an important tool in diagnostics, biomedical applications. We report a multispectral polarization-based imaging of oral tissue by utilizing a polarization microscope system with a broadband-light source. Experiments were performed on oral tissue samples and multispectral Stokes mapping was done by recording a set of intensity images. Polarization-based parameters like degree of polarization, angle of fast axis, retardation and linear birefringence have been retrieved. The statistical moments of these polarization components have also been reported at multiples wavelengths. The polarimetric properties of oral tissue at different stages of cancer have been analyzed and significant changes from normal to pre-cancerous lesions to the cancerous are observed in linear birefringence quantification as (1.7± 0.1) ×10^-3 , (2.5± 0.2) ×10^-3 and (3.3±0.2) ×10^-3 respectively. This article is protected by copyright. All rights reserved.

PMID:37789505 | DOI:10.1002/jbio.202300236

Fungal Biol Biotechnol. 2023 Oct 3;10(1):20. doi: 10.1186/s40694-023-00167-w.

ABSTRACT

BACKGROUND: Trichoderma atroviride is a competitive soil-borne mycoparasitic fungus with extensive applications as a biocontrol agent in plant protection. Despite its importance and application potential, reference genes for RT-qPCR analysis in T. atroviride have not been evaluated. Light exerts profound effects on physiology, such as growth, conidiation, secondary metabolism, and stress response in T. atroviride, as well as in other fungi. In this study, we aimed to address this gap by identifying stable reference genes for RT-qPCR experiments in T. atroviride under different light conditions, thereby enhancing accurate and reliable gene expression analysis in this model mycoparasite. We measured and compared candidate reference genes using commonly applied statistical algorithms.

RESULTS: Under cyclic light-dark cultivation conditions, tbp and rho were identified as the most stably expressed genes, while act1, fis1, btl, and sar1 were found to be the least stable. Similar stability rankings were obtained for cultures grown under complete darkness, with tef1 and vma1 emerging as the most stable genes and act1, rho, fis1, and btl as the least stable genes. Combining the data from both cultivation conditions, gapdh and vma1 were identified as the most stable reference genes, while sar1 and fis1 were the least stable. The selection of different reference genes had a significant impact on the calculation of relative gene expression, as demonstrated by the expression patterns of target genes pks4 and lox1.

CONCLUSION: The data emphasize the importance of validating reference genes for different cultivation conditions in fungi to ensure accurate interpretation of gene expression data.

PMID:37789459 | DOI:10.1186/s40694-023-00167-w

Artif Intell Med. 2023 Oct;144:102645. doi: 10.1016/j.artmed.2023.102645. Epub 2023 Aug 29.

ABSTRACT

The widespread use of information technology in healthcare leads to extensive data collection, which can be utilised to enhance patient care and manage chronic illnesses. Our objective is to summarise previous studies that have used data mining or process mining methods in the context of chronic diseases in order to identify research trends and future opportunities. The review covers articles that pertain to the application of data mining or process mining methods on chronic diseases that were published between 2000 and 2022. Articles were sourced from PubMed, Web of Science, EMBASE, and Google Scholar based on predetermined inclusion and exclusion criteria. A total of 71 articles met the inclusion criteria and were included in the review. Based on the literature review results, we detected a growing trend in the application of data mining methods in diabetes research. Additionally, a distinct increase in the use of process mining methods to model clinical pathways in cancer research was observed. Frequently, this takes the form of a collaborative integration of process mining, data mining, and traditional statistical methods. In light of this collaborative approach, the meticulous selection of statistical methods based on their underlying assumptions is essential when integrating these traditional methods with process mining and data mining methods. Another notable challenge is the lack of standardised guidelines for reporting process mining studies in the medical field. Furthermore, there is a pressing need to enhance the clinical interpretation of data mining and process mining results.

PMID:37783545 | DOI:10.1016/j.artmed.2023.102645

Artif Intell Med. 2023 Oct;144:102658. doi: 10.1016/j.artmed.2023.102658. Epub 2023 Sep 4.

ABSTRACT

Artificial intelligence (AI) offers opportunities but also challenges for biomedical research and healthcare. This position paper shares the results of the international conference “Fair medicine and AI” (online 3-5 March 2021). Scholars from science and technology studies (STS), gender studies, and ethics of science and technology formulated opportunities, challenges, and research and development desiderata for AI in healthcare. AI systems and solutions, which are being rapidly developed and applied, may have undesirable and unintended consequences including the risk of perpetuating health inequalities for marginalized groups. Socially robust development and implications of AI in healthcare require urgent investigation. There is a particular dearth of studies in human-AI interaction and how this may best be configured to dependably deliver safe, effective and equitable healthcare. To address these challenges, we need to establish diverse and interdisciplinary teams equipped to develop and apply medical AI in a fair, accountable and transparent manner. We formulate the importance of including social science perspectives in the development of intersectionally beneficent and equitable AI for biomedical research and healthcare, in part by strengthening AI health evaluation.

PMID:37783540 | DOI:10.1016/j.artmed.2023.102658

Artif Intell Med. 2023 Oct;144:102644. doi: 10.1016/j.artmed.2023.102644. Epub 2023 Sep 1.

ABSTRACT

The proliferation of wearable devices has allowed the collection of electrocardiogram (ECG) recordings daily to monitor heart rhythm and rate. For example, 24-hour Holter monitors, cardiac patches, and smartwatches are widely used for ECG gathering and application. An automatic atrial fibrillation (AF) detector is required for timely ECG interpretation. Deep learning models can accurately identify AFs if large amounts of annotated data are available for model training. However, it is impractical to request sufficient labels for ECG recordings for an individual patient to train a personalized model. We propose a Siamese-network-based approach for transfer learning to address this issue. A pre-trained Siamese convolutional neural network is created by comparing two labeled ECG segments from the same patient. We sampled 30-second ECG segments with a 50% overlapping window from the ECG recordings of patients in the MIT-BIH Atrial Fibrillation Database. Subsequently, we independently detected the occurrence of AF in each patient in the Long-Term AF Database. By fine-tuning the model with the 1, 3, 5, 7, 9, or 11 ECG segments ranging from 30 to 180 s, our method achieved macro-F1 scores of 96.84%, 96.91%, 96.97%, 97.02%, 97.05%, and 97.07%, respectively.

PMID:37783539 | DOI:10.1016/j.artmed.2023.102644

Artif Intell Med. 2023 Oct;144:102666. doi: 10.1016/j.artmed.2023.102666. Epub 2023 Sep 20.

ABSTRACT

The COVID-19 pandemic highlights the need for effective and non-intrusive methods to monitor the well-being of elderly individuals in their homes, especially for early detection of potential viral infections. Conspicuously, the present paper develops a Multi-scaled Long Short Term Memory (Ms-LSTM) model for the routine health monitoring of elderly patients to detect COVID-19. The proposed method offers home-based health diagnostics through urine analysis by leveraging the IoT-Fog-Cloud paradigm. Mainly, the proposed model constitutes a four-layered architecture: data acquisition, fog layer, cloud layer, and interface layer. Each layer serves distinct functionalities and provides specific services, thereby collectively enhancing the overall effectiveness of the model. The statistical results of the study demonstrate the superior performance of the proposed Ms-LSTM model in comparison to state-of-the-art methods, including Artificial Neural Networks (ANN), K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Random Forest, and LSTM. Further, the proposed model attains a mean temporal efficiency of 39.23 seconds. It exhibits high reliability (92.97%), stability (70.06%), and predictive accuracy (93.25%).

PMID:37783534 | DOI:10.1016/j.artmed.2023.102666

BMJ Open Qual. 2023 Sep;12(Suppl 2):e002242. doi: 10.1136/bmjoq-2022-002242.

ABSTRACT

BACKGROUND: Neck of femur fractures are common with associated high morbidity and mortality rates. National standards include provision of orthogeriatric care to any patient with a hip fracture. This study assessed the outcomes at 5 years following implementation of a collaborative orthogeriatric service at Southland Hospital in 2012.

METHODS: Retrospective data were collected for patients aged 65 years and older admitted with a fragility hip fracture. Data were collated for 2011 (preimplementation) and 2017 (postimplementation). Demographic data and American Society of Anesthesiologists (ASA) scores were recorded to ensure comparability of the patient groups. Length of stay, postoperative complications and 30-day and 1-year mortality were assessed.

RESULTS: 74 admissions with mean age at surgery of 84.2 years in 2011 and 107 admissions with mean age of 82.6 years in 2017. There was a higher proportion of ASA 2 and ASA 3 patients in 2017 compared with 2011 (p=0.036). The median length of stay in the orthopaedic ward was unchanged in the two cohorts but there was a shorter median length of stay by 6.5 days and mean length of stay by 11 days in 2017 in the rehabilitation ward (p<0.001 for both median and mean). Through logistic regression controlling for age, sex and ASA score, there was a reduction in the odds of having a complication by 12% (p<0.001). The study was too small to undertake statistical testing to calculate significant difference in overall 30-day and 1-year mortality between the groups.

CONCLUSION: The orthogeriatric service has reduced the frequency of complications and length of stay on the rehabilitation ward 5 years following implementation.

PMID:37783522 | DOI:10.1136/bmjoq-2022-002242

BMJ Open Qual. 2023 Sep;12(Suppl 2):e002303. doi: 10.1136/bmjoq-2023-002303.

ABSTRACT

Osteoporosis is a global health concern and a major contributor to worldwide mortality rates. Vertebral fractures due to osteoporosis are common and often undetected. Since vertebral fractures are often missed, evidence and guidelines suggest that vertebral fracture assessment (VFA) may optimise current approaches to bone density tests. We aimed to integrate VFA into an Australian Fracture Liaison Service (FLS) and measure the impact it had on osteoporosis treatment initiation.A retrospective case note review was undertaken to determine the number of clinic patients receiving VFA before the change in practice. Proctor’s seven domains of implementation strategy were used to facilitate quality improvement outcomes.The percentage of eligible patients receiving a routine VFA at the FLS imaging centre increased from 0% to 90%. The remaining 10% of patients did not receive a scan due to the patient not being able to assume the correct position, skilled staff being unavailable to perform the scan, or the patient declining. Post implementation, almost half (41%) of patients who underwent a VFA displayed abnormalities and 16 (4%) of these recorded a normal bone measure density score but abnormal VFA. Despite the successful adoption of the new screening protocol, there was no statistically significant increase in treatment initiation rates for patients with normal bone mass density scores.The FLS successfully integrated routine VFA into the osteoporosis care pathway. However, the introduction of VFA did not significantly increase treatment initiation. It may be more effective to offer VFAs to a proportion of patients based on a tailored approach rather than offering them routinely to all patients who access the FLS.

PMID:37783515 | DOI:10.1136/bmjoq-2023-002303

Br J Gen Pract. 2023 Jun 6:BJGP.2022.0150. doi: 10.3399/BJGP.2022.0150. Online ahead of print.

ABSTRACT

BACKGROUND: Technological advances have led to the use of patient portals that give people digital access to their personal health information. The NHS App was launched in January 2019 as a ‘front door’ to digitally enabled health services.

AIM: To evaluate patterns of uptake of the NHS App, subgroup differences in registration, and the impact of COVID-19.

DESIGN AND SETTING: An observational study using monthly NHS App user data at general-practice level in England was conducted.

METHOD: Descriptive statistics and time-series analysis explored monthly NHS App use from January 2019-May 2021. Interrupted time-series models were used to identify changes in the level and trend of use of different functionalities, before and after the first COVID-19 lockdown. Negative binomial regression assessed differences in app registration by markers of general-practice level sociodemographic variables.

RESULT: Between January 2019 and May 2021, there were 8 524 882 NHS App downloads and 4 449 869 registrations, with a 4-fold increase in App downloads when the COVID Pass feature was introduced. Analyses by sociodemographic data found 25% lower registrations in the most deprived practices (P<0.001), and 44% more registrations in the largest sized practices (P<0.001). Registration rates were 36% higher in practices with the highest proportion of registered White patients (P<0.001), 23% higher in practices with the largest proportion of 15-34-year-olds (P<0.001) and 2% lower in practices with highest proportion of people with long-term care needs (P<0.001).

CONCLUSION: The uptake of the NHS App substantially increased post-lockdown, most significantly after the NHS COVID Pass feature was introduced. An unequal pattern of app registration was identified, and the use of different functions varied. Further research is needed to understand these patterns of inequalities and their impact on patient experience.

PMID:37783512 | DOI:10.3399/BJGP.2022.0150

Emerg Med Australas. 2023 Oct 2. doi: 10.1111/1742-6723.14320. Online ahead of print.

ABSTRACT

OBJECTIVES: Despite significant treatment advances in paediatric diabetes management, ED presentations for potentially preventable (PP) complications such as diabetic ketoacidosis (DKA) remains a major issue. We aimed to examine the characteristics, rates and trends of diabetes-related ED presentations and subsequent admissions in youth aged 0-19 years from 2008 to 2018.

METHODS: Data were obtained from the Victorian Emergency Minimum Dataset and the National Diabetes Register. A diabetes-related ED presentation is defined using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification diagnosis codes. ‘Non-preventable’ presentations were the number of youths with newly diagnosed diabetes, and the remaining are classified as PP diabetes-related presentations. Poisson regression model was used to examine the trends in incidence rate and prevalence.

RESULTS: Four thousand eight hundred and seventy-two (59%) of 8220 presentations were PP, 4683 (57%) were for DKA whereas 6200 (82%) required hospital admission. Diabetes-related ED presentations decreased from 38.4 to 27.5 per 100 youth with diabetes per year between 2008 and 2018 (β = -0.04; confidence interval [CI] -0.04 to -0.03; P < 0.001). Females, those aged 0-4 years and rural youth had higher rates of ED presentations than males, older age groups and metropolitan youth. DKA presentations decreased from 20.1 presentations per 100 youth with diabetes in 2008-2009 to 14.9 presentations per 100 youth with diabetes in 2017-2018. The rate of DKA presentations was 68% higher in rural areas compared to metropolitan areas (incidence rate ratio 1.68; CI 1.59-1.78; P < 0.001).

CONCLUSIONS: Although the rates of diabetes-related ED presentations declined, PP diabetes-related presentations and subsequent hospitalisation remain high. Patient level research is required to understand the increased DKA presentations in rural youth.

PMID:37783473 | DOI:10.1111/1742-6723.14320