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Background: Observational Alzheimer's disease (AD) cohorts including the Australian, Biomarkers, Imaging and Lifestyle (AIBL) Study have enhanced our understanding of AD. The generalizability of findings from AIBL to the general population has yet to be studied. Objective: We aimed to compare characteristics of people with AD dementia in AIBL to 1) the general population of older Australians using pharmacological treatment for AD dementia, and to 2) the general population of older Australians who self-reported a diagnosis of dementia. Methods: Descriptive study comparing people aged 65 years of over (1) in AIBL that had a diagnosis of AD dementia, (2) dispensed with pharmacological treatment for AD in Australia in 2021 linked to the Australian census in 2021 (refer to as PBS/census), (3) self-reported a diagnosis of dementia in the 2021 Australian census (refer to as dementia/census). Baseline characteristics included age, sex, highest education attainment, primary language, and medical co-morbidities. Results: Participants in AIBL were younger, had more years of education, and had a lower culturally and linguistically diverse (CALD) population compared to the PBS/census cohort and dementia/census cohort (mean age±standard deviation – AIBL 79±7 years, PBS/census 81±7, p < 0.001, dementia/census 83±8, p < 0.001; greater than 12 years of education AIBL 40%, PBS/census 35%, p = 0.020, dementia/census 29%, p < 0.001; CALD – AIBL 3%, PBS/census 20%, p < 0.001, dementia/census 22%, p < 0.001). Conclusions: Our findings suggest that care should be taken regarding the generalizability of AIBL in CALD populations and the interpretation of results on the natural history of AD. [ABSTRACT FROM AUTHOR]

Glaucoma infection is rapidly spreading globally and the number of glaucoma patients is expected to exceed 110 million by 2040. Early identification and detection of glaucoma is particularly important as it can easily lead to irreversible vision damage or even blindness if not treated with intervention in the early stages. Deep learning has attracted much attention in the field of computer vision and has been widely studied especially in the recognition and diagnosis of ophthalmic diseases. It is challenging to efficiently extract effective features for accurate grading of glaucoma in a limited dataset. Currently, in glaucoma recognition algorithms, 2D fundus images are mainly used to automatically identify the disease or not, but do not distinguish between early or late stages; however, in clinical practice, the treatment of early and late glaucoma is not the same, so it is more important to proceed to achieve accurate grading of glaucoma. This study uses a private dataset containing modal data, 2D fundus images, and 3D-OCT scanner images, to extract the effective features therein to achieve an accurate triple classification (normal, early, and moderately advanced) for optimal performance on various measures. In view of this, this paper proposes an automatic glaucoma classification method based on the attention mechanism and EfficientNetB3 network. The EfficientNetB3 network and ResNet34 network are built to extract and fuse 2D fundus images and 3D-OCT scanner images, respectively, to achieve accurate classification. The proposed auto-classification method minimizes feature redundancy while improving classification accuracy, and incorporates an attention mechanism in the two-branch model, which enables the convolutional neural network to focus its attention on the main features of the eye and discard the meaningless black background region in the image to improve the performance of the model. The auto-classification method combined with the cross-entropy function achieves the highest accuracy up to 97.83%. Since the proposed automatic grading method is effective and ensures reliable decision-making for glaucoma screening, it can be used as a second opinion tool by doctors, which can greatly reduce missed diagnosis and misdiagnosis by doctors, and buy more time for patient's treatment. [ABSTRACT FROM AUTHOR]

Named-entity recognition (NER) is a crucial task in natural language processing, especially for extracting meaningful information from unstructured text data. In the healthcare domain, accurate NER can significantly enhance patient care by enabling efficient extraction and analysis of clinical information. This paper presents MedNER, a novel service-oriented framework designed specifically for medical NER in Chinese medical texts. MedNER leverages advanced deep learning techniques and domain-specific linguistic resources to achieve good performance in identifying diabetes-related entities such as symptoms, tests, and drugs. The framework integrates seamlessly with real-world healthcare systems, offering scalable and efficient solutions for processing large volumes of clinical data. This paper provides an in-depth discussion on the architecture and implementation of MedNER, featuring the concept of Deep Learning as a Service (DLaaS). A prototype has encapsulated BiLSTM-CRF and BERT-BiLSTM-CRF models into the core service, demonstrating its flexibility, usability, and effectiveness in addressing the unique challenges of Chinese medical text processing. [ABSTRACT FROM AUTHOR]

Objective To describe changes in atherosclerotic cardiovascular disease (ASCVD) risk over time among people living with HIV (PLHIV). Methods We used data from the TREAT Asia HIV Observational Database (TAHOD) and the Australian HIV Observational Database (AHOD). Five-year ASCVD risk was calculated using the D:A:D equation. Individuals were eligible for inclusion if they were aged ≥ 18 years, had started ART, had no previous history of ASCVD and had complete ASCVD risk factor data available within the first 5 years of ART initiation. Results A total of 3368 adults contributed data, 3221 were from TAHOD and 147 were from AHOD. The median age at ART initiation was 36 [IQR 31–43] years for TAHOD participants, and 42 [IQR 35–50] years for AHOD participants. Most TAHOD (70.4%) and AHOD (91.8%) participants were male. Overall, ASCVD risk increased from 0.84% (95% CI 0.81%–0.87%) at ART initiation to 1.34% (95% CI 1.29%–1.39%) after 5 years on ART. After adjusting for traditional and HIV-associated ASCVD risk factors, ASCVD risk increased at a similar rate among sub-populations defined by HIV exposure (heterosexuals, men who have sex with men, people who inject drugs), race/ethnicity (Caucasian and Asian) and nadir CD4 at ART initiation (<200 and ≥200 cells/mm3). Conclusions These findings emphasize the growing burden of ASCVD risk among PLHIV and the need to develop interventions that are effective across a broad range of HIV sub-populations. [ABSTRACT FROM AUTHOR]

News impact analysis has become a common task conducted by finance researchers, which involves reading and selecting news articles based on themes and sentiments, pairing news events and relevant stocks, and measuring the impact of selected news on stock prices. To facilitate more efficient news selection, topic modeling can be applied to generate topics out of a large number of news documents. However, there is very limited existing literature comparing topic models in the context of finance-related news impact analysis. In this paper, we compare three state-of-the-art topic models, namely Latent Dirichlet allocation (LDA), Top2Vec, and BERTopic, in a defined scenario of news impact analysis on financial markets, where 38,240 news articles with an average length of 590 words are analyzed. A service-oriented framework for news impact analysis called "News Impact Analysis" (NIA) is advocated to leverage multiple topic models and provide an automated and seamless news impact analysis process for finance researchers. Experimental results have shown that BERTopic performed best in this scenario, with minimal data preprocessing, the highest coherence score, the best interpretability, and reasonable computing time. In addition, a finance researcher was able to conduct the entire news impact analysis process, which validated the feasibility and usability of the NIA framework. [ABSTRACT FROM AUTHOR]

Rice allelopathy is a natural method of weed control that is regarded as an eco-friendly practice in agroecology. The allelopathic potential of rice is regulated by various genes, including those that encode transcription factors. Our study characterized a MYB transcription factor, OsMYB57, to explore its role in the regulation of rice allelopathy. Increasing the expression of OsMYB57 in rice using the transcription activator VP64 resulted in increased inhibitory ratios against barnyardgrass. The gene expression levels of OsPAL , OsC4H , OsOMT , and OsCAD from the phenylpropanoid pathway were also up-regulated, and the content of l -phenylalanine increased. Chromatin immunoprecipitation incorporated with HiSeq demonstrated that OsMYB57 transcriptionally regulated a mitogen-activated protein kinase (OsMAPK11); in addition, OsMAPK11 interacted with OsPAL2;3. The expression of OsPAL2;3 was higher in the allelopathic rice PI312777 than in the non-allelopathic rice Lemont, and OsPAL2;3 was negatively regulated by Whirly transcription factors. Moreover, microbes with weed-suppression potential, including Penicillium spp. and Bacillus spp., were assembled in the rhizosphere of the rice accession Kitaake with increased expression of OsMYB57, and were responsible for phenolic acid induction. Our findings suggest that OsMYB57 positively regulates rice allelopathy, providing an option for the improvement of rice allelopathic traits through genetic modification. [ABSTRACT FROM AUTHOR]

This study aimed to determine the healing effect of hydrogen-rich water (HRW) on radiotherapy-induced skin injury. Rats were irradiated with a 6 MeV electron beam from a Varian linear accelerator. After skin wound formation, rats were individually administrated with distilled water, HRW (1.0 ppm) or HRW (2.0 ppm). We measured the healing time and observed the healing rate of the wounded surface. After irradiation, the malondialdehyde (MDA) content and the superoxide dismutase (SOD) activity in the wounded tissues were evaluated, as determined using an MDA and SOD assay kit. Interleukin-6 (IL-6) and epidermal growth factor (EGF) levels were assessed by enzyme-linked immunosorbent assay (ELISA). Models of skin damage were successfully established using a 44 Gy electronic beam. The healing time was shortened in the two HRW-treated groups (P < 0.05). Furthermore, interventions of HRW resulted in a marked reduction in the MDA (P < 0.05) and IL-6 levels (P < 0.01). Additionally, the SOD activity in the two HRW-treated groups was higher than that in the distilled water group at the end of the 1st, 2nd and 3rd weeks (P < 0.001). The EGF level was also significantly increased at the end of the 1st and 2nd weeks (P < 0.05). Compared with the HRW (1.0 ppm) group, the healing rate was higher and the healing time was reduced in the HRW (2.0 ppm) group. A significant decrease was observed in the IL-6 level at the end of the 1st, 3rd and 4th weeks (P < 0.05) and in the EGF content at the end of the 1 week after the HRW administration (P < 0.01). Collectively, our data indicate that HRW accelerates wound healing of radiation-induced skin lesions through anti-oxidative and anti-inflammatory effects, suggesting that HRW has a healing effect on acute radiation-mediated skin injury, and that this is dependent on the concentration of the hydrogen. [ABSTRACT FROM AUTHOR]