This study explores the public library service evaluation domain through user-generated content on Google Maps, highlighting digital feedback's significant yet underexplored potential in understanding public library patronage across Taiwan's six major cities. Utilizing a mixed-methods research design, this study integrates Google Maps review content analysis with Social Network Analysis to delineate public perceptions and identify areas for service enhancement in public libraries. It innovatively leverages personal experiences extracted from over 60,000 Google Maps reviews to evaluate public library services in cities such as Taipei, New Taipei, Taoyuan, Taichung, Tainan, and Kaohsiung. The research taps into the National Library of Taiwan's National Library Statistics System to provide a robust analysis of library performance and user satisfaction, offering a novel perspective by emphasizing user-centric feedback from Google Maps as a primary data source. This approach provides quantitative data on library usage and geographic distribution and enriches our understanding of the qualitative experiences of library users. In analyzing the keywords from Google Maps reviews of public libraries, we categorize and interpret these under the three core LibQUAL+ dimensions—Affect of Service, Information Control, and Library as Place. The findings expose variances in perceived service quality among the cities, with Kaohsiung and Taichung receiving the highest accolades for service satisfaction. Simultaneously, the study identifies potential areas for improvement, particularly in cities with lower satisfaction ratings like Taipei. This personalized feedback illustrates the intimate relationship between public libraries and their communities, offering invaluable insights for policymakers and library management to enhance service delivery and user experience.

In Taiwan, colorectal cancer is ranked second and third in terms of mortality and cancer incidence, respectively. In addition, medical expenditures related to colorectal cancer are considered to be the third highest. While advances in treatment strategies have provided cancer patients with longer survival, potentially harmful second primary cancers can occur. Therefore, second primary colorectal cancer analysis is an important issue with regard to clinical management. In this study, a novel predictive scheme was developed for predicting the risk factors associated with second colorectal cancer in patients with colorectal cancer by integrating five data mining classification techniques, including support vector machine, random forest, multivariate adaptive regression splines, extreme learning machine, and extreme gradient boosting. In total, 4,287 patients in the datasets provided by three hospital tumor registries were used. Our empirical results revealed that this proposed predictive scheme provided promising classification results and the identification of important risk factors for predicting second colorectal cancer based on accuracy, sensitivity, specificity, and area under the curve metrics. Collectively, our clinical findings suggested that the most important risk factors were the combined stage, age at diagnosis, BMI, surgical margins of the primary site, tumor size, sex, regional lymph nodes positive, grade/differentiation, primary site, and drinking behavior. Accordingly, these risk factors should be monitored for the early detection of second primary tumors in order to improve treatment and intervention strategies.

Due to the high effectiveness of cancer screening and therapies, the diagnosis of second primary cancers (SPCs) has increased in women with endometrial cancer (EC). However, there’s no previous literature mentioned about adequate evidence to support screening for SPCs in endometrial cancer. This study was aimed to develop effective risk prediction models of second primary endometrial cancer in women with obesity (Body-mass index; BMI > 25) and this study includes datasets of the incidence of SPCs and the other risks of SPCs in 4480 primary cancer survivors by a hospital-based cancer registry database. In our study, we found the obesity played a key role in SPCs. There’re 10 independent variables used as predicting variables, which corelated to obesity should be monitored for the early detection of SPCs in endometrial cancer. In conclusion, it is a promising SPCs prediction. The proposed scheme can support the important influence of obesity and clinical data representations in all cases after primary treatments. Our results suggested that obesity is still a crucial risk factor to SPCs in endometrial cancer.

Background and Objectives: Although researchers suggested that non-pharmaceutical intervention (NPI) and vaccination together can provide the best protection from the COVID-19 [1], people’s willingness to adopt personal NPIs seems to decrease through the COVID-19 waves. This study seeks for an explanation for this observation based on the framework of risk perception. Materials and Methods: Two cross-sectional surveys (N = 407 in Survey 1 and N = 505 in Survey 2) were administered before and in the first outbreak of COVID-19 in Taiwan. The survey items were designed to measure people’s recognized severity of COVID-19, worry of COVID-19, intention to adopt personal NPIs, and attitude toward COVID-19 vaccines. We established a structural model according to the framework of risk perception, which was applied to fit the data of the two surveys. Results: The multigroup SEM (Structural Equation Modeling) results showed that worry (i.e., emotional component of risk perception) fully mediates the influence of recognized severity of COVID-19 (cognitive component of risk perception) on the intention to adopt NPIs in each survey. Before the outbreak (i.e., Survey 1), people’s attitude toward COVID-19 vaccines has nothing to do with their worry of COVID-19. However, when people have experienced the real outbreak of COVID-d19 (i.e., Survey 2), their attitude toward COVID-19 vaccines negatively predicts their worry of COVID-19, forming an indirectly negative effect on their intention to adopt personal NPIs, suggesting the occurrence of the Peltzman effect. That is, receiving vaccination makes people feel safer, that the other way around relaxes their alert for COVID-19, thus, declining the intention to adopt personal NPIs.

Field-effect transistor (FET)-based biosensors stand as powerful analytical tools for detecting trace-specific biomolecules in diverse sample matrices, especially in the realms of pandemics and infectious diseases. The primary concern in applying these biosensors is their stability, a factor directly impacting the accuracy and reliability of sensing over extended durations. The risk of biosensor degradation is substantial, potentially jeopardizing sensitivity and selectivity and leading to inaccurate readings, including the possibility of false positives or negatives. This paper delves into the documented degradation of silicon nanobelt FET (NBFET) biosensors induced by buffer solutions. The results highlight a positive correlation between immersion time and the threshold voltage of NBFET devices. Secondary ion mass spectrometry analysis demonstrates a gradual increase in sodium and potassium ion concentrations within the silicon as immersion days progress. This outcome is ascribed to the nanobelt's exposure to the buffer solution during the biosensing period, enabling ion penetration from the buffer into the silicon. The study emphasizes the critical need to address buffer solution-induced degradation to ensure the long-term stability and performance of FET-based biosensors in practical applications.

Lung cancer has been the leading cause of cancer death in the world. In addition to smoking, estrogen is supposed to play an important role in the lung cancer development because women have a higher proportion of adenocarcinoma than men. In the environment, there are many metabolites and wastes that mimic human estrogen structurally and functionally. As an oral contraceptive, 17α-ethynylestradiol (EE2) is released to wastewater after being utilized. Moreover, 4-nonylphenol (NP) exiting in the petrochemical products and air pollutants has estrogenic activity. In our study, 17β-estradiol (E2), EE2, and NP are administered to stimulate A549 male lung adenocarcinoma cells and H1435 female lung adenocarcinoma cells. The results demonstrate that EE2 and NP stimulate A549 and H1435 cells proliferation in a dose- and time-dependent trend. Both estrogen receptor α and β are activated simultaneously during these processes. Up-regulation of epidermal growth factor receptor (EGFR) and ERK expression is involved in response to estrogens. In conclusion, we first time report that EE2 and NP exert biotoxic effect to stimulate the proliferation of both male and female lung cancer cells in a dose- and time- response manner. New challenges from environmental hormones to lung cancer deserved further investigation.

Butyric acid as a histone deacetylase (HDAC) inhibitor was produced by a number of periodontal and root canal microorganisms (such as Porphyromonas, Fusobacterium etc.). Butyric acid may affect the biological activities of periodontal/periapical cells such as osteoblasts, periodontal ligament cells etc., and thus affect periodontal/periapical tissue destruction and healing. The purposes of this study were to study the toxic effects of butyrate on matrix and mineralization markers’ expression of MG-63 osteoblasts. Cell viability and proliferation were determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay. Cellular apoptosis and necrosis were analyzed by propidium iodide/Annexin V flow cytometry. Protein and mRNA expression of OPG, and RANKL were analyzed by western blotting and RT-PCR. OPG, soluble RANKL (sRANKL), 8-isoprostane, pro-collagen I, MMP-2, osteonectin (SPARC), osteocalcin and osteopontin secretion into culture medium were measured by enzyme-linked immunosorbant assay. Histone H3 acetylation levels were evaluated by immunofluorescent staining (IF) and western blot. We found that butyrate induced morphologic changes of growing MG-63 cells, with bigger and flattened in appearance. Butyrate activated histone H3 acetylation of MG-63 cells. Exposure of MG-63 cells to butyrate partly decreased cell number with no marked increase in apoptosis and necrosis. Butyrate stimulated RANKL protein expression, whereas it inhibited OPG protein expression. Butyrate also inhibited the secretion of OPG in MG-63 cells, whereas sRANKL level was below detection limit. Butyrate stimulated 8-isoprostane, MMP-2 and osteopontin secretion, but not procollagen I, osteonectin, osteocalcin in MG-63 cells. In conclusion, butyric acid generated by periodontal and root canal microorganisms may potentially induce bony destruction and impair bone repair by alteration of OPG/RANKL expression/secretion, 8-isoprostane, MMP-2, and osteopontin secretion, and affect cell proliferation. These effects are possibly related to increased histone acetylation. These events are important in the pathogenesis of periodontal and periapical destruction.

Taiwan developing offshore wind power to promote green energy and self-electricity production. In this study, a Light Detection and Ranging (Lidar) was set up at Chang-Hua development zone one on the sea and 10km away from the seashore. At Lidar location, WRF (3.33km & 2km grid lengths) model and WAsP were used to simulate the wind speed at various elevations. Three days mean wind speed of simulated results were compared with Lidar data. From the four wind data sets, developed five different comparisons to find an error% and R-Squared values. Comparison between WAsP and Floating Lidar was shown good consistency. Lukang meteorological station 10 years wind observations at 5m height were used for wind farm energy predictions. The yearly variation of energy predictions of traditional and TGC wind farm layouts are compared under purely neutral and stable condition. The one-year cycle average surface heat flux over the Taiwan Strait is negative (-72.5 (W/m2) and 157.13 STD), which represents stable condition. At stable condition TGC (92.39%) and 600(92.44%), wind farms were shown higher efficiency. The Fuhai met mast wind data was used to estimate roughness length and power law exponent. The average roughness lengths are very small and unstable atmosphere.

Huntington's disease (HD) is a progressive and fatal neurodegenerative disease caused by CAG repeat expansion in the coding region of huntingtin (HTT) protein. The accumulation of mutant HTT (mHTT) contributes to neurotoxicity by causing autophagy defects and oxidative stress that ultimately lead to neuronal death. Interestingly, epidemiologic studies have demonstrated that the prevalence of type-2 diabetes, a metabolic disease mainly caused by defective insulin signaling, is higher in patients with HD than in healthy controls. Although the precise mechanisms of mHTT-mediated toxicity remain unclear, the blockade of brain insulin signaling may initiate or exacerbate mHTT-induced neurodegeneration. In this study, we used an in vitro HD model to investigate whether neuronal insulin signaling is involved in mHTT-mediated neurotoxicity. Our results demonstrated that mHTT overexpression significantly impairs insulin signaling and causes apoptosis in neuronal cells. However, treatment with liraglutide, a GLP-1 analogue, markedly restores insulin sensitivity and enhances cell viability. This neuroprotective effect may be attributed to the contribution of the upregulated expression of genes associated with endogenous antioxidant pathways to oxidative stress reduction. In addition, liraglutide stimulates autophagy through AMPK activation, which attenuates the accumulation of HTT aggregates within neuronal cells. Our findings collectively suggest that liraglutide can rescue impaired insulin signaling caused by mHTT and that GLP-1 may potentially reduce mHTT-induced neurotoxicity in the pathogenesis of HD.

Dust storms originating from Central Australia and western New South Wales frequently cause high particles concentration at many sites across New South Wales, both inland and along the coast. This study focussed on a dust storm event in February 2019 which affect air quality across the state as detected at many ambient monitoring stations in the Department of Planning, Industry and Environment (DPIE) air quality monitoring network. The WRF-Chem (Weather Research and Forecast Model – Chemistry) model is used to study the formation, dispersion and transport of dust across the state of New South Wales (NSW, Australia). Wildfires also happened in northern NSW at the same time of the dust storm in February 2019, and their emissions are taken into account in WRF-Chem model by using Fire Inventory from NCAR (FINN) as emission input. The model performance is evaluated and is shown to predict fairly accurate the PM2.5 and PM10 concentration as compared to observation. The predicted PM2.5 concentration over New South Wales during 5 days from 11 to 15 February 2019 is then used to estimate the impact of the February 2019 dust storm event on three health endpoints namely mortality, respiratory and cardiac diseases hospitalisation rates. The results show that even though as the daily average of PM2.5 over some parts of the state, especially in western and north western NSW near the centre of the dust storm and wild fires, are very high (over 900 µg/m3), the population exposure is low due to the sparse population. The top five Statistical Area Level 4 regions with the most impact in term of mortality, respiratory diseases hospitalisation and cardiac disease hospitalisation are Far West and Orana, Newcastle and Lake Macquarie, New England and North West, Sydney – Inner South West and either Central Coast (mortality) or Sydney – Parramatta (respiratory diseases hospitalisation) or Sydney – Inner West (cardiac diseases hospitalisation). Generally, the health impact is similar in order of magnitude to that caused by biomass burnings events from wildfires or from hazardous reduction burnings (HRBs) near populous centres such as in Sydney in May 2016. One notable difference is the higher respiratory diseases hospitalisation for this dust event (161) compared to fire event (24).

In early 2020 from April to early June, the metropolitan area of Sydney as well as the rest of New South Wales (NSW, Australia) experienced a period of lockdown to prevent the spread of Covid-19 virus in the community. The effect of reducing anthropogenic activities including transportation had an impact on the urban environment in term of air quality which is shown to have improved for a number of pollutants, such as nitrogen dioxides (NO2) and carbon monoxide (CO), based on monitoring data on ground and from satellite. Besides primary pollutants CO and NOx emitted from mobile sources, PM2.5 (primary and secondary) and secondary ozone (O3) during the lockdown period will also be analysed using both air quality data and modelling method. The results show that NO2, CO and PM2.5 levels decreased during the lockdown, but O3 instead increased. The change in the concentration levels however are small considering the large reduction in traffic volume of ~30%. By estimate the decrease in traffic volume in Sydney region, the corresponding decrease in emission input to the WRF-CMAQ (Weather Research and Forecasting - Community Multiscale Air Quality Modeling System) air quality model is then used to estimate the effect of lockdown on the air quality especially CO, NO2, O3 and PM2.5 in the Greater Metropolitan Region (GMR) of Sydney. COVID-19 lockdown period is an ideal case to study the effect of motor vehicle and mobile source contribution to air pollutants such as those listed above in the GMR.

Background Successful treatment for patients with metastatic colorectal cancer who failed to response to first-line treatment has been a challenge due to their low response rate for later-line treatment and poor progression free survival and overall survival. The application of dendritic cell-cytokine-induced killer cells (DC-CIK) immunotherapy combined with conventional treat-ment, either surgical resection or chemotherapy, showed improvement in survivals in first-line treatment for metastatic colorectal cancer. In this retrospective study, we aimed to evaluate the benefit of dendritic cell-cytokine-induced killer cells (DC-CIK) immunotherapy for patient with refractory metastatic colorectal cancer. Methods A total of 20 patients with refractory metastatic colorectal cancer receiving cell-cytokine-induced killer cells (DC-CIK) immunotherapy were enrolled to this study. Among these patients, 11 patients responded to the treatment and the remaining 9 patients did not. All patients were followed for at least one years and the determination of treatment response was mainly based on image study at 6 months after the completion of treatment. Data were analyzed with Kaplan-Meier method and log-rank test. Results The treatment response rate of the study group is 55% (11/20). The median progression free sur-vival (PFS) and median overall survival (OS) of responsive patients was 7 months and 12 months, respectively. The median overall survival of irresponsive patients was 9.5 months. Four responsive patients received subsequent metastectomy or cytoreduction plus hyperthermic in-traperitoneal chemotherapy surgery (CRS + HIPEC). Conclusion DC-CIK cell-based immunotherapy may provide benefits for patients with refractory mCRC with improved response rate and progression free survival.

This study was to identify clinical cancer biomarkers for recurrent gastric cancer survivors using artificial intel-ligence. From a hospital-based cancer registry database in Taiwan, the datasets of the incidence of recurrence and clinical risk features were included in 2476 gastric cancer survivors. We benchmarked Random forest with MLP, C4.5, AdaBoost, and Bagging algorithms on metrics and leveraged synthetic minority oversampling technique (SMOTE) for imbalanced dataset issues, cost-sensitive learning for risk assessment, and SHapley Additive ex-Planations (SHAP) for feature importance analysis in this study. Our proposed Random forest outperformed the other models with an accuracy of 87.9%, a recall rate of 90.5%, an accuracy rate of 86% and F1 of 88.2% on re-current category by a 10-fold cross-validation in a balanced dataset. We identified clinical features of recurrent gastric cancer, which are the top five features, stage, number of regional lymph node involvement, Helicobacter pylori, BMI(body mass index), and gender, which significantly affect the prediction model's output, are worth paying attention to in the following causal effect analysis. Using an artificial intelligence model, the risk factors for recurrent gastric cancer could be identified and cost-effectively ranked according to their feature importance. In addition, they should be crucial clinical features to provide physicians with the knowledge to screen high-risk patients in gastric cancer survivors as well.

This study investigated the inhibitory effect of Siegesbeckia orientalis ethanolic extract (SOE) on growth and metastasis of hepatic tumors in C57BL/6JNarl mice, which is the model of tumor-igenesis induced by subcutaneous injection of Hepa1-6 murine hepatoma cells on the back of mice. Feeding 25 mg SOE/kg BW daily for 14 days effectively reduced the hepatoma weight in tu-mor-bearing mice by 59.7%. SOE intervention reduced the expression of anti-apoptotic Bcl-2 gene in tumor tissue by 48.5% and down-regulated the antioxidant system, by reducing the gene ex-pression of catalase (48.0%), glutathione peroxidase (46.3%), and superoxide dismutase (32.0%). In addition, SOE intervention increased the production of ROS by 21.4%, and the gene expression of pro-inflammatory cytokines TNF-α, IL-1β, and IL-6 by 135.3%, 179.3% and 91.7%, respectively, indicating that SOE intervention promoted the acute inflammatory response. Results of H&E staining analysis showed that SOE had the ability to inhibit angiogenesis in tumor tissue. Fur-thermore, SOE treatment inhibited the expression of migration-related genes, such as MMP-2, MMP-7, MMP-9 and β-catenin, and the inhibition rates were 24.8%, 27.4%, 10.4% and 21.9%, respectively. These results imply that SOE had the ability to suppress tumor metastasis. On the other hand, SOE intervention decreased the serum contents of inflammatory cytokines, such as TNF-α, IL-1β and IL-6 by 27.4%, 30.4%, and 30.7%, respectively, indicating that SOE intervention has an anti-inflammatory effect on normal tissues of mice. Since SOE intervention had no signif-icant effect on liver and kidney weights, and no mice died throughout the experiment, it shows that it is safe to feed SOE under this experimental condition. Based on the above results, SOE has a great potential to be used in the treatment of hepatoma.