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Blood pressure targets, medication considerations and special concerns in elderly hypertension: Focus on atherosclerotic cardiovascular diseases, atrial fibrillation, heart failure, and aortic stenosis
No full text[[abstract]]The management of hypertension in the elderly, particularly concerning atherosclerotic cardiovascular disease (ASCVD) and comorbidities, is crucial given its prevalence and impact on morbidity and mortality. It is important to individualize pharmacotherapy in elderly patients, including basing the dose on age-related changes in organ function and drug pharmacokinetics. In elderly patients with hypertension and ASCVD, despite slight differences among guideline recommendations, the emphasis is on achieving a systolic blood pressure (BP) < 130 mmHg. The optimal target for patients with atrial fibrillation remains under debate, with suggestions ranging from <130/80 mmHg to <140/90 mmHg. The role of anticoagulation treatment further complicates BP management in these patients. BP targets also differ among guidelines regarding heart failure in the elderly, although maintaining systolic BP < 140/90 mmHg is generally advocated. In hypertensive elderly patients with aortic stenosis, the optimal BP targets are even less clear, and should consider the risks of adverse outcomes. Overall, individualized treatment plans considering age, comorbidities, and tolerability are paramount. Further research is warranted to elucidate optimal BP targets and pharmacotherapeutic strategies tailored to the elderly
Application of machine learning algorithms to identify risk factors for depression in type 2 diabetes mellitus patients: A Taiwan diabetes registry study
No full text[[abstract]]BACKGROUND: We analyzed variables reported during routine clinical practice using a registrational database to estimate risk factors for depression in people with type 2 diabetes mellitus. METHODS: A Patient Health Questionnaire (PHQ-9) score of 15 was selected as the cut-off for clinically meaningful depression. Missing data was either filled in with a median value, the k-nearest neighbors' method, or the entire variable was removed. Logistic regression, random forest, and decision tree machine learning models were used to decide which factors were most relevant to depression. The accuracy of each algorithm was evaluated with a testing set. RESULTS: When all variables were included in the logistic regression model, the area under the receiver operating characteristic curve was 0.81. In the random forest model, the most important factor was quality of life (QoL). Upon removing QoL-related variables, bloating, and autoimmune disease became the greatest contributing factors. Model accuracy was 83.1%. In the decision tree model, QoL was also observed as the most decisive factor. Upon removing QoL variables, bloating was the first node. Model accuracy was 82.5%. CONCLUSION: Quality of life, bloating, and autoimmune disease were the most important factors associated with depression in type 2 diabetes mellitus patients
Development of a novel EV-A71 monoclonal antibody for monitoring vaccine potency
No full text[[abstract]]Enterovirus A71 (EV-A71) is one of the major causes of hand, foot, and mouth disease (HFMD), primarily affecting children under five. It can lead to neurological and cardiac complications, or even death, in some cases. Inactivated monovalent vaccines have been licensed in China and Taiwan; however, the stability of EV-A71 vaccines is often compromised by factors such as extreme temperatures or ultraviolet (UV) irradiation. Currently, no commercially available tools can assess the stability of EV-A71 throughout vaccine development. In this study, we report the development of a monoclonal antibody (mAb), NHRI2016-1, which can be used in in vitro immunoassays to evaluate EV-A71 vaccine potency and effectiveness. NHRI2016-1 exclusively recognizes effective EV-A71 antigens in in vitro potency assays. Similarly, rat experiment confirmed that effective vaccine antigens could induce neutralizing antibodies, while ineffective antigens could not. Thus, NHRI2016-1 shows potential for correlating in vitro potency with in vivo immunogenicity of EV-A71 vaccine antigens. These data suggest that NHRI2016-1 could be a promising tool for characterizing EV-A71 vaccines and monitoring vaccine potency
Changes in spontaneous alpha - gamma coupling in primary dysmenorrhea
No full text[[abstract]]Women of reproductive age who experience cyclic menstrual pain without pelvic abnormalities are considered primary dysmenorrhea (PDM). While our earlier research discovered the associations between BDNF Val66Met polymorphism and functional changes in the descending pain modulatory systems in PDM, as well as weakened slow/fast interconnections across the menstrual cycle, there is a lack of understanding of how BDNF Val66Met polymorphism influences neural oscillations across the menstrual cycle. This study aimed to address this gap by investigating the effects of gene polymorphism on resting-state oscillations during and after long-term menstrual discomfort. Resting-state magnetoencephalography (MEG) recordings were evaluated for n:m cross-frequency coupling (CFC) in 50 individuals with PDM and 49 healthy women (CON). Psychological assessments and cross-frequency couplings at each menstruation (MENS) and periovulatory (POV) period were compared between BDNF Val66Met gene polymorphisms. The state-anxiety scale for CON with Met/Met was inversely correlated with alpha/gamma phase-phase coupling in the left superior parietal regions during menstruation. In contrast, PDM with Met/Met showed an inverse pattern of n:m CFC couplings. Altered functional coupling in pain-related regions may arise from the interaction between the BDNF Val66Met genotyping and long-term pain experience, ultimately affecting the perception, emotion, and attention to pain in those experiencing cyclic pain
TNFR1 and TNFR2 levels in patients with severe alcohol use disorder undergoing withdrawal and their relationship with delirium tremens
No full text[[abstract]]BACKGROUND: Chronic alcohol consumption has been associated with cytokine dysregulation. Tumor necrosis factor-alpha (TNF-α), mediated through two distinct receptors-TNF receptor type 1 (TNFR1) and TNF receptor type 2 (TNFR2)-plays a role in alcohol use disorder (AUD). Evidence also suggests a potential role of TNFR1 and TNFR2 in delirium development. We aimed to investigate the role of TNFR1 and TNFR2 in patients with AUD undergoing withdrawal and the differences in these levels between those with and without delirium tremens (DT). METHODS: Ninety treatment-seeking patients with severe AUD and 117 healthy controls (HC) were enrolled and measured for blood levels of TNF-α, TNFR1 and TNFR2 using enzyme-linked immunosorbent assays. We followed the levels in AUD group after 2 weeks of withdrawal and categorized them based on the occurrence of DT (DT group, n = 19) and non-DT group (n = 71) during this period. RESULTS: At both week 0 and week 2, patients with AUD had higher plasma TNFR1, TNFR2, and TNF-α levels than healthy controls, with the DT subgroup showing greater elevations than the non-DT subgroup. Although levels declined after two weeks of alcohol withdrawal, they remained elevated compared to controls. Regression analysis indicated that age, sex, and TNF-α levels were significant contributors to TNFR1 and TNFR2 levels. CONCLUSIONS: This study is the first to indicate that TNFR1 and TNFR2 levels were increased in patients with AUD but decreased, though not normalized, after early abstinence. DT subgroup is associated with more severe TNFR1 and TNFR2 dysregulation than non-DT subgroup
The increased risk of exposure to fine particulate matter for depression incidence is mediated by elevated TNF-R1: The healthy aging longitudinal study
No full text[[abstract]]BACKGROUND: Depression among older adults is an important public health issue, and air and noise pollution have been found to contribute to exacerbation of depressive symptoms. This study examined the association of exposure to air and noise pollutants with clinically-newly-diagnosed depressive disorder. The mediating role of individual pro-inflammatory markers was explored. METHODS: We linked National Health Insurance claim data with 2998 healthy community-dwellers aged 55 and above who participated in the Healthy Aging Longitudinal Study between 2009 and 2013. Newly diagnosed depressive disorder was identified using diagnostic codes from the medical claim data. Pollutants were estimated using nationwide land use regression, including PM(2.5) and PM(10), carbon monoxide, ozone, nitrogen dioxide, sulfur dioxide, and road traffic noise. Cox proportional hazard models were employed to examine the association between pollutants and newly developed depressive disorders. The mediating effect of serum pro-inflammatory biomarkers on the relationship was examined. RESULTS: Among the 2998 participants, 209 had newly diagnosed depressive disorders. In adjusted Cox proportional hazard models, one interquartile range increase in PM(2.5) (8.53 µg/m(3)) was associated with a 17.5% increased hazard of developing depressive disorders. Other air pollutants and road traffic noise were not linearly associated with depressive disorder incidence. Levels of serum tumor necrosis factor receptor 1 mediated the relationship between PM(2.5) and survival time to newly onset depressive disorder. CONCLUSION: PM(2.5) is related to an increased risk of newly developed depressive disorder among middle-aged and older adults, and the association is partially mediated by the pro-inflammatory marker TNF-R1
Multi-label advertising image classification using traditional deep neural networks and vision language models: dataset and annotation agreement method
No full text[[abstract]]Effectively classifying advertising images is crucial in targeting the right audience and maximizing marketing performance. To address this problem, this paper presents a multi-label advertising image classification study using popular deep-learning architectures. First, we compile a dedicated dataset for this task and evaluate the performance of traditional deep learning-based models based on the convolutional neural network (CNN) and vision transformer architectures. To ensure the quality of dataset annotations, we introduce an extended Krippendorf’s Alpha (α) method based on the Jaccard index to provide a reliable measure of inter-annotation agreement which can address the missing annotations and multiple labels to establish the dataset’s annotation consistency. Our results demonstrate that transformer-based architectures like ViT and Swin outperform the CNN-based model’s baseline and differential learning rate settings. Through the visualization analysis of saliency maps, we gain insights into the model’s decision-making processes and identify the factors influencing their predictions. Furthermore, we assess the impact of annotation quality on model performance, comparing models trained on different annotation reliability levels. Our results indicate that higher annotation consistency, as quantified by α-Jaccard, leads to improved model performance, emphasizing the importance of high-quality datasets in advertising image classification. Beyond traditional deep learning models, we explore the effectiveness of vision language models (VLMs) in this task by employing prompt engineering and comparing their performance with fine-tuned deep learning models. Our findings indicate that while VLMs provide richer contextual annotations, they suffer from over-classification tendencies, subjective biases, and significantly higher computational costs. In contrast, deep learning models remain a more efficient and scalable solution for structured, large-scale advertising classification tasks. Our study gives practical insights for designers and advertisers, demonstrating how deep learning architectures and VLMs can be applied to digital marketing to enhance advertising image classification, reduce testing costs and improve marketing efficiency. Furthermore, our dataset and findings serve as a benchmark for future research in advertising image classification and multimodal AI applications
Innovative synthesis of single-crystal silver metal-organic framework in water at room temperature for antimicrobial applications
No full text[[abstract]]This work presents a sustainable and efficient method for preparing a single crystal Ag-based MOF, namely VNU-30. VNU-30 was synthesized from AgNO3 and 4,4′-bipyridine, with water as a solvent at room temperature. The method features important improvements compare to previous methods, including the absence of additives of bases or organic solvents, a low metal-to-ligand ratio, reduced energy consumption, and ability to achieve single crystals at room temperature. The as-synthesized VNU-30 demonstrated broad-spectrum antimicrobial activities against both Gram-negative bacteria (E. coli and P. aeruginosa) and Gram-positive bacteria (S. aureus and B. subtilis), as well as yeast (C. albicans). The inhibition zone diameters ranged from 12 to 14 mm, making it comparable or even superior to previously reported MOF materials
[[alternative]]Nucleic acid-lipid nanoparticle and method using the same
No full text[[abstract]]本發明提供一種核酸-脂質奈米粒子，包含：一核酸分子和一脂質混合物。脂質混合物包含：一可離子化胺基脂質，以20mol%至60mol%的量存在；一磷脂，以5mol%至20mol%的量存在；一膽固醇，以25mol%至60mol%的量存在；以及一聚乙二醇化脂質，以0.2mol%至6mol%的量存在。此外，本發明還提供了使用上述核酸-脂質奈米粒子的方法
[[alternative]]IL12p40 METHODS TO ENHANCE NERVE REGENERATION UTILIZING NEURAL STEM CELLS AND IL12P40
No full text[[abstract]]본 출원은 신경 줄기 세포 또는 IL12p40 중에서 최소한 하나의 성분을 활용하여 신경 재생을 증강하는 조성물 및 방법을 제공한다. 상기 조성물은 신경 줄기 세포, 그리고 IL12p40에 의해 최소한 하나의 아단위로서 구축되는 신경영양 인자를 포함한다. 신경 재생을 증강하는 방법은 IL12p40을 최소한 하나의 아단위로서 내포하는 신경영양 인자를 포함하는 신경 재생 조성물을 개체에 제공하는 것을 포함한다. 이들 방법의 조성물은 신경 줄기 세포를 더욱 포함할 수 있다