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This research investigates the fire suppression effectiveness of sprinklers in a large atrium of a public transport hub in Taipei, Taiwan, during a fire using FDS. The thermally-activated sprinkler had the shortcoming of delayed activation, with a thick smoke layer accumulating at the ceiling. Thereafter, the interaction of sprinkler spray with the thick smoke layer would cause a large amount of smoke falling to the floor level, significantly impacting safe egress. The novelty of this study is to couple a sprinkler system with a vision-based fire flame detection system (VFDS) having the advantage of locating the fire instantly to suppress fire. A combination of different atrium heights, fire sizes, and scenarios for a total of 27 cases were investigated. The research results have indicated that VFDS-activated sprinklers can suppress an atrium fire at the early stage, greatly reduce the amount of smoke generation and smoke accumulation. In consequence, the interaction of water spray and smoke layer is largely mitigated to prevent early fall of smoke. When the fire is suppressed, the atrium can maintain a clear height that is crucial to safe egress. The research finding of VFDS-activated sprinklers system is significant for fire safety of buildings with large atria.

Background: Diabetic neuropathy (DN) is one of the common chronic complications, leading to limb disability and increased risks of hospitalization and mortality. Traditional Chinese medicine (TCM) has been commonly applied in Taiwan as an adjunctive treatment to ameliorate diabetes-associated chronic complications, including neuropathy. Aim: We aimed to investigate whether the clinical treatment of DN combined with TCM can reduce the associated hospitalization and mortality using the National Health Insurance Research Database (NHIRD) of Taiwan. Methods: We selected 1,152 patients with DN who received TCM treatment as the study cohort group, and 4,940 patients with DN who did not receive TCM treatment from Taiwan NHIRD were further matched 1:1 for sex, age, and index year as the comparison cohort group. Cox proportional hazards analysis was performed to compare hospitalization and mortality during a mean follow-up period of 15 years. Results: A total of 687/225 enrolled patients (29.82%/9.77%) had hospitalization/mortality, including 298/97 in the TCM group (25.87%/8.42%) and 389/128 in the comparison group (33.77%/11.11%). Cox proportional hazard regression analysis showed a lower rate of hospitalization and mortality for patients in the TCM group (adjusted hazard ratio [HR] of 0.434, 95 confidence interval [CI] =0.172–0.798, P < 0.001; adjusted HR of 0.689, 95 CI = 0.372–0.981, P = 0.039). The Kaplan–Meier analysis showed that the cumulative risk of hospitalization and mortality in the study and comparison cohort groups was significantly different (log-rank P < 0.001 and P = 0.007, respectively). Conclusion: Our results suggest that the application of TCM might be beneficial for patients with DN to lower the risks of hospitalization and mortality; however, further prospective cohort studies are still required to confirm our observations.

This study proposes the construction of indicators as a tool to evaluate how the services of an Emergency Care Unit (UPA) can be classified about the efficiency and quality of the services provided to its users. Based on metrics such as Length of Stay (LOS), Triage Cycle Time, Reception Service Time, among others, an unsupervised machine learning technique known as Exploratory Factor Analysis was used to obtain indicators. The results were obtained using the free software R. From the proposed indicators, it can be concluded that the higher the value found, generally, the worse the quality of the service provided. This indicates that the users remain in the Emergency Care Unit (UPA) for a long time.

Abstract In low- and middle-income countries, the substantial costs associated with traditional data collection pose an obstacle to facilitating decision-making in the field of public health. Satellite imagery offers a potential solution, but the image extraction and analysis can be costly and requires specialized expertise. We introduce SatelliteBench, a scalable framework for satellite image extraction and vector embeddings generation. We also propose a novel multimodal fusion pipeline that utilizes a series of satellite imagery and metadata. The framework was evaluated generating a dataset with a collection of 12,636 images and embeddings accompanied by comprehensive metadata, from 81 municipalities in Colombia between 2016 and 2018. The dataset was then evaluated in 3 tasks: including dengue case prediction, poverty assessment, and access to education. The performance showcases the versatility and practicality of SatelliteBench, offering a reproducible, accessible and open tool to enhance decision-making in public health.

Abstract Background This study investigates the potential of diffusion tensor imaging (DTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion–diffusion mismatch (PDM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability. Methods Sixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PDM at 30 and 90 min after occlusion. We used 11 DTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PDM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland–Altman analysis. Feature importance was determined for explainability. Results In the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6–157.3 mL), whereas the PDM-defined median PV was 102.0 mL (52.1–144.9 mL). These PVs had a volume similarity of 0.88 (0.79–0.96), a Pearson correlation coefficient of 0.93 (p

Background: Quantitative susceptibility mapping (QSM) is a post-processing technique that creates brain susceptibility maps reflecting metal burden through tissue magnetic susceptibility. We assessed topographic differences in magnetic susceptibility between participants with and without Wilson's disease (WD), correlating these findings with clinical severity, brain volume, and biofluid copper and iron indices. Methods: A total of 43 patients with WD and 20 unaffected controls, were recruited. QSM images were derived from a 3T MRI scanner. Clinical severity was defined using the minimal Unified Wilson's Disease Rating Scale (M-UWDRS) and Montreal Cognitive Assessment scoring. Differences in magnetic susceptibilities between groups were evaluated using general linear regression models, adjusting for age and sex. Correlations between the susceptibilities and clinical scores were analyzed using Spearman's method. Results: In age- and sex-adjusted analyses, magnetic susceptibility values were increased in WD patients compared with controls, including caudate nucleus, putamen, globus pallidus, and substantia nigra (all p < 0.01). Putaminal susceptibility was greater with an initial neuropsychiatric presentation (n = 25) than with initial hepatic dysfunction (n = 18; p = 0.04). Susceptibility changes correlated negatively with regional brain volume in almost all topographic regions. Serum ferritin, but not serum copper or ceruloplasmin, correlated positively with magnetic susceptibility level in the caudate nucleus (p = 0.04), putamen (p = 0.04) and the hippocampus (p = 0.03). The dominance of magnetic susceptibility in cortical over subcortical regions correlated with M-UWDRS scores (p < 0.01). Conclusion: The magnetic susceptibility changes could serve as a surrogate marker for patients with WD.

Abstract Background Hyperthyroidism is frequently under-recognized and leads to heart failure and mortality. Timely identification of high-risk patients is a prerequisite to effective antithyroid therapy. Since the heart is very sensitive to hyperthyroidism and its electrical signature can be demonstrated by electrocardiography, we developed an artificial intelligence model to detect hyperthyroidism by electrocardiography and examined its potential for outcome prediction. Methods The deep learning model was trained using a large dataset of 47,245 electrocardiograms from 33,246 patients at an academic medical center. Patients were included if electrocardiograms and measurements of serum thyroid-stimulating hormone were available that had been obtained within a three day period. Serum thyroid-stimulating hormone and free thyroxine were used to define overt and subclinical hyperthyroidism. We tested the model internally using 14,420 patients and externally using two additional test sets comprising 11,498 and 596 patients, respectively. Results The performance of the deep learning model achieves areas under the receiver operating characteristic curves (AUCs) of 0.725–0.761 for hyperthyroidism detection, AUCs of 0.867–0.876 for overt hyperthyroidism, and AUC of 0.631–0.701 for subclinical hyperthyroidism, superior to a traditional features-based machine learning model. Patients identified as hyperthyroidism-positive by the deep learning model have a significantly higher risk (1.97–2.94 fold) of all-cause mortality and new-onset heart failure compared to hyperthyroidism-negative patients. This cardiovascular disease stratification is particularly pronounced in subclinical hyperthyroidism, surpassing that observed in overt hyperthyroidism. Conclusions An innovative algorithm effectively identifies overt and subclinical hyperthyroidism and contributes to cardiovascular risk assessment.

Cancer immunotherapy has been regarded as a promising strategy for cancer therapy by blocking immune checkpoints and evoking immunity to fight cancer, but its efficacy seems to be heterogeneous among patients. Manipulating the gut microbiota is a potential strategy for enhancing the efficacy of immunotherapy. Here, we report that MS-20, also known as “Symbiota®”, a postbiotic that comprises abundant microbial metabolites generated from a soybean-based medium fermented with multiple strains of probiotics and yeast, inhibited colon and lung cancer growth in combination with an anti-programmed cell death 1 (PD1) antibody in xenograft mouse models. Mechanistically, MS-20 remodeled the immunological tumor microenvironment by increasing effector CD8+ T cells and downregulating PD1 expression, which were mediated by the gut microbiota. Fecal microbiota transplantation (FMT) from mice receiving MS-20 treatment to recipient mice increased CD8+ T-cell infiltration into the tumor microenvironment and significantly improved antitumor activity when combined with anti-PD1 therapy. Notably, the abundance of Ruminococcus bromii, which increased following MS-20 treatment, was positively associated with a reduced tumor burden and CD8+ T-cell infiltration in vivo. Furthermore, an ex vivo study revealed that MS-20 could alter the composition of the microbiota in cancer patients, resulting in distinct metabolic pathways associated with favorable responses to immunotherapy. Overall, MS-20 could act as a promising adjuvant agent for enhancing the efficacy of immune checkpoint-mediated antitumor therapy.

Abstract BackgroundEarly identification of impending in-hospital cardiac arrest (IHCA) improves clinical outcomes but remains elusive for practicing clinicians. ObjectiveWe aimed to develop a multimodal machine learning algorithm based on ensemble techniques to predict the occurrence of IHCA. MethodsOur model was developed by the Multiparameter Intelligent Monitoring of Intensive Care (MIMIC)–IV database and validated in the Electronic Intensive Care Unit Collaborative Research Database (eICU-CRD). Baseline features consisting of patient demographics, presenting illness, and comorbidities were collected to train a random forest model. Next, vital signs were extracted to train a long short-term memory model. A support vector machine algorithm then stacked the results to form the final prediction model. ResultsOf 23,909 patients in the MIMIC-IV database and 10,049 patients in the eICU-CRD database, 452 and 85 patients, respectively, had IHCA. At 13 hours in advance of an IHCA event, our algorithm had already demonstrated an area under the receiver operating characteristic curve of 0.85 (95% CI 0.815‐0.885) in the MIMIC-IV database. External validation with the eICU-CRD and National Taiwan University Hospital databases also presented satisfactory results, showing area under the receiver operating characteristic curve values of 0.81 (95% CI 0.763-0.851) and 0.945 (95% CI 0.934-0.956), respectively. ConclusionsUsing only vital signs and information available in the electronic medical record, our model demonstrates it is possible to detect a trajectory of clinical deterioration up to 13 hours in advance. This predictive tool, which has undergone external validation, could forewarn and help clinicians identify patients in need of assessment to improve their overall prognosis.

The cool temperate origin of gymnosperm Taxus species in East Asia is specifically diverse and widespread. Certain lineages have managed to extend their distribution further south to subtropical and tropical islands such as Taiwan and the Philippines. To address questions including whether these insular lineages, recently identified as T. phytonii, have become genetically distinct from each other and from their continental relatives, and when and how they colonized their residing islands, we sampled over 11 populations, covering 179 Taxus individuals from Taiwan and the Philippines. Using four cpDNA and one nuclear marker, we showed in population genetic and genealogical analyses that the two insular lineages were genetically distinct from each other and also from other continental Taxus and that they represented each other’s closest relative. Estimated with the coalescent-based multi-type tree (MTT) analyses, we inferred an origin of Taiwanese T. phytonii more ancient than 2.49 Mya and that of Philippine T. phytonii more ancient than 1.08 Mya. In addition, the divergence demographic history revealed by both MTT and isolation with migration (IM) analyses indicated the presence of recent post-split migrations from a continental taxon, T. mairei, to Taiwanese T. phytonii, as well as from Taiwanese T. phytonii to Philippine T. phytonii. Overall, this study suggests Taiwan as a stepping stone through which the temperate-origin yew trees can extend their distributions to tropical regions such as the Philippines.

Abstract Background The optimal timing for reimplantation for periprosthetic joint infection (PJI) has not been established and varies from a few weeks to several months. The aim of this study was to assess the commendable time between implant removal and reimplantation in patients who underwent two-stage exchange arthroplasty for PJI. Methods We retrospectively reviewed 361 patients who were treated with two-stage exchange arthroplasty for hip and knee chronic PJI at our institution between January 2000 and December 2018. Patient characteristics, comorbidities, surgical variables, microbiology data, and time to reimplantation were recorded. All patients were followed for a minimum of one year. Treatment failure was defined by Delphi criteria. Logistic regression analyses were used to calculate survival rates and adjusted odds ratios (ORs) of treatment failure. Results In final analysis, 27 (7.5%) had treatment failure. Factors related to treatment failure including interim spacer exchange (OR, 3.13; confidence interval (CI), 1.04–9.09, p = 0.036), higher ESR level at reimplantation (OR, 1.85; CI, 1.05–3.57; p = 0.04), and time to reimplantation (OR, 1.00; CI, 1.003–1.005, p = 0.04). Performing revision arthroplasty surgery from 16 to 20 weeks had highest successful rate. The reimplantation over 24 weeks had a lower successful rate. However, no statistical significance in comparing each interval group. Conclusion Our study emphasized the importance of timely reimplantation in achieving successful outcomes. Factors such as ESR levels, spacer exchange, and the duration of time to reimplantation influenced the likelihood of treatment failure in two-stage exchange arthroplasty for hip and knee PJI.

Summary: Background: It has been shown that AI models can learn race on medical images, leading to algorithmic bias. Our aim in this study was to enhance the fairness of medical image models by eliminating bias related to race, age, and sex. We hypothesise models may be learning demographics via shortcut learning and combat this using image augmentation. Methods: This study included 44,953 patients who identified as Asian, Black, or White (mean age, 60.68 years ±18.21; 23,499 women) for a total of 194,359 chest X-rays (CXRs) from MIMIC-CXR database. The included CheXpert images comprised 45,095 patients (mean age 63.10 years ±18.14; 20,437 women) for a total of 134,300 CXRs were used for external validation. We also collected 1195 3D brain magnetic resonance imaging (MRI) data from the ADNI database, which included 273 participants with an average age of 76.97 years ±14.22, and 142 females. DL models were trained on either non-augmented or augmented images and assessed using disparity metrics. The features learned by the models were analysed using task transfer experiments and model visualisation techniques. Findings: In the detection of radiological findings, training a model using augmented CXR images was shown to reduce disparities in error rate among racial groups (−5.45%), age groups (−13.94%), and sex (−22.22%). For AD detection, the model trained with augmented MRI images was shown 53.11% and 31.01% reduction of disparities in error rate among age and sex groups, respectively. Image augmentation led to a reduction in the model's ability to identify demographic attributes and resulted in the model trained for clinical purposes incorporating fewer demographic features. Interpretation: The model trained using the augmented images was less likely to be influenced by demographic information in detecting image labels. These results demonstrate that the proposed augmentation scheme could enhance the fairness of interpretations by DL models when dealing with data from patients with different demographic backgrounds. Funding: National Science and Technology Council (Taiwan), National Institutes of Health.

To better respond to the strategic goals of ''carbon peaking'' and ''carbon neutrality,” businesses are collaborating with suppliers and customers to jointly promote green and environmentally friendly practices to increase economic benefits, enhance social reputation, and improve environmental competitiveness while achieving environmental performance. Currently, some theoretical achievements in the field of green supply chain integration have been made. Nevertheless, systematic retrospective research needs to be improved to help us fully understand the status of research in China and elsewhere. Therefore, this article analyzed the concept of green supply chain integration using eight years of data (from 2014 to 2021) from a small and medium-sized environmental company1 and divided the concept into four systems: the water cycle system (H2O), renewable energy system (CaCl2), carbon peak system (CO2), and carbon-neutral system (CO2). These four systems were analyzed with regard to internal green integration, green supplier integration, and green customer integration, and the Scheffé method was used to analyze the impact of these four systems on corporate ecological impact and financial and social sustainability. The study also verified that the intensity of sunlight on solar panels is significantly affected by environmental temperature (ET), carbon neutrality, and the electricity generated by solar panels. The impact of carbon pricing was also explored using the probability density function (PDF) method. The contribution of this research lies in the fact that it provides evidence that solar energy can help reduce the use of fossil fuels, reducing demand for the carbon quota market. This can help lower carbon prices, thereby encouraging more businesses and countries to participate in carbon neutrality and emission reduction actions. In addition, solar power systems can replace renewable energy sources, making the carbon quota market more diverse and stable. Carbon pricing options can be used to manage the risks and uncertainties related to carbon pricing policies, and policy-makers can use them to develop appropriate risk management strategies.

BackgroundStudies have confirmed that osteoporosis has been considered as one of the complications of diabetes, and the health hazards to patients are more obvious. This study is mainly based on the Taiwan National Health Insurance Database (TNHID). Through the analysis of TNHID, it is shown that the combined treatment of traditional Chinese medicine (TCM) medicine in patients of diabetes with osteoporosis (T2DOP) with lower related risks.MethodsAccording to the study design, 3131 patients selected from TNHID who received TCM treatment were matched by 1-fold propensity score according to gender, age, and inclusion date as the control group. Cox proportional hazards analyzes were performed to compare fracture surgery, hospitalization, and all-cause mortality during a mean follow-up from 2000 to 2015.ResultsA total of 1055/1469/715 subjects (16.85%/23.46%/11.42%) had fracture surgery/inpatient/all-cause mortality of which 433/624/318 (13.83%/19.93%/10.16%) were in the TCM group) and 622/845/397 (19.87%/26.99%/12.68%) in the control group. Cox proportional hazards regression analysis showed that subjects in the TCM group had lower rates of fracture surgery, inpatient and all-cause mortality (adjusted HR = 0.467; 95% CI = 0.225-0.680, PConclusionThis study provides longitudinal evidence through a cohort study of the value of integrated TCM for T2DOP. More research is needed to fully understand the clinical significance of these results.

Abstract A rapidly aging world population is fueling a concomitant increase in Alzheimer's disease (AD) and related dementias (ADRD). Scientific inquiry, however, has largely focused on White populations in Australia, the European Union, and North America. As such, there is an incomplete understanding of AD in other populations. In this perspective, we describe research efforts and challenges of cohort studies from three regions of the world: Central America, East Africa, and East Asia. These cohorts are engaging with the Davos Alzheimer's Collaborative (DAC), a global partnership that brings together cohorts from around the world to advance understanding of AD. Each cohort is poised to leverage the widespread use of mobile devices to integrate digital phenotyping into current methodologies and mitigate the lack of representativeness in AD research of racial and ethnic minorities across the globe. In addition to methods that these three cohorts are already using, DAC has developed a digital phenotyping protocol that can collect ADRD‐related data remotely via smartphone and/or in clinic via a tablet to generate a common data elements digital dataset that can be harmonized with additional clinical and molecular data being collected at each cohort site and when combined across cohorts and made accessible can provide a global data resource that is more racially/ethnically represented of the world population.

Abstract Background The successful management of patients infected with coronavirus disease 2019 (COVID-19) with inhaled ciclesonide has been reported, however few studies have investigated its application among hospitalized patients. Methods This retrospective cohort study enrolled all adult patients admitted to our hospital with confirmed COVID-19 infection from May to June 2021. Critical patients who received mechanical ventilation within 24 h after admission and those who started ciclesonide more than 14 days after symptom onset were excluded. The in-hospital mortality rate was compared between those who did and did not receive inhaled ciclesonide. Results A total of 269 patients were enrolled, of whom 184 received inhaled ciclesonide and 85 did not. The use of ciclesonide was associated with lower in-hospital mortality (7.6% vs. 23.5%, p = 0.0003) and a trend of shorter hospital stay (12.0 (10.0–18.0) days vs. 13.0 (10.0–25.3) days, p = 0.0577). In subgroup analysis, the use of inhaled ciclesonide significantly reduced mortality in the patients with severe COVID-19 infection (6.8% vs. 50.0%, p

Abstract Data on the prevalence of conus branch artery (CBA) is scarce, and its utilization in percutaneous coronary intervention (PCI) for chronic total occlusion (CTO) is non-existing. The present study examined carefully in a large cohort the angiographic prevalence of CBA, its role as a collateral channel for the occlusion, and the potential usage of CBA in contemporary CTO PCI. We retrospectively examined consecutive CTO PCIs from our database between 2016 and 2019. All CTO PCIs were evaluated and the results with complications were recorded to determine the prevalence and utilization of CBA. From January 2016 to December 2019, a total of 556 CTO PCI attempts in 546 patients by high-volume operators were enrolled. The clinical, angiographic, and procedural details were collected. CBA was identifiable in 85.3% of these patients, and CBA providing visible collaterals connected to CTO distal lumen was found in 27.8% of patients. 84 CBA were used for balloon anchoring, 17 for selective distal true lumen visualization, and 9 as actual retrograde interventional collateral channel during CTO PCI. Only 1 patient suffered from chest pain during CBA balloon anchoring, and no other procedural complication such as arrhythmia or perforation occurred.CBA is frequently seen in coronary CTO. Its existence provided potential for various CTO PCI technique applications, without increase in risk.