An inclusive study to elucidation the heavy metals-derived ecological risk nexus with antibiotic resistome functional shape of niche microbial community and their carbon substrate utilization ability in serpentine soil.

Heavy metals (HMs) contained terrestrial ecosystems are often significantly display the antibiotic resistome in the pristine area due to increasing pressure from anthropogenic activity, is complex and emerging research interest. This study investigated that impact of chromium (Cr), nickel (Ni), cobalt (Co) concentrations in serpentine soil on the induction of antibiotic resistance genes and antimicrobial resistance within the native bacterial community as well as demonstrated their metabolic fingerprint. The full-length 16S-rRNA amplicon sequencing observed an increased abundance of Firmicutes, Actinobacteriota, and Acidobacteriota in serpentine soil. The microbial community in serpentine soil displayed varying preferences for different carbon sources, with some, such as carbohydrates and carboxylic acids, being consistently favored. Notably, 27 potential antibiotic resistance opportunistic bacterial genera have been identified in different serpentine soils. Among these, Lapillicoccus, Rubrobacter, Lacibacter, Chloroplast, Nitrospira, Rokubacteriales, Acinetobacter, Pseudomonas were significantly enriched in high and medium HMs concentrated serpentine soil samples. Functional profiling results illustrated that vancomycin resistance pathways were prevalent across all groups. Additionally, beta-lactamase, aminoglycoside, tetracycline, and vancomycin resistance involving specific bio-maker genes (ampC, penP, OXA, aacA, strB, hyg, aph, tet(A/B), otr(C), tet(M/O/Q), van(A/B/D), and vanJ) were the most abundant and enriched in the HMs-contaminated serpentine soil. Overall, this study highlighted that heavy-metal enriched serpentine soil is potential to support the proliferation of bacterial antibiotic resistance in native microbiome, and might able to spread antibiotic resistance to surrounding environment.

The effect of social support and resource support on emotional exhaustion, insomnia, and suicidal ideation among allied health trainees and post-graduate year doctors in Taiwan.

BACKGROUND: COVID-19-related stigmatization refers to COVID-19-related judgements by others that devalue the individual. Such stigmatization towards healthcare workers may cause psychological burden and negative consequences. Such stigmatization may have particularly overwhelmed allied health trainees (AHTs) and post-graduate year doctors (PGYDs) because they just started their medical career. Social support and resource support have been reported to benefit psychological health and reduce stigmatization. Therefore, the present study used a cross-sectional study design to investigate the association between perceived stigma, self-stigma, psychological distress, and negative outcomes (including emotional exhaustion, insomnia and suicidal ideation) among AHTs and PGYDs in Taiwan. METHODS: An online survey distributed between July and December, 2022 received 522 responses. Variables were assessed using the 21-item Depression, Anxiety and Stress Scale, Insomnia Severity Index and a series of self-designed questions to assess social support, resource support, perceived stigma, self-stigma, emotional exhaustion, and suicidal ideation. RESULTS: Structural equation modeling showed that perceived stigma was associated with self-stigma (standardized coefficient [ß] = 0.428, p < 0.001), and self-stigma was associated with psychological distress (ß = 0.197, p < 0.001), as well as being associated with emotional exhaustion, insomnia, and suicidal ideation (ß = 0.349, 0.556 and 0.212, all p-values < 0.001). While social support and resource support were negatively associated with perceived stigma (ß= - 0.175 and - 0.152, p < 0.01), additional associations were found between social support and emotional exhaustion (ß= - 0.093, p < 0.001), as well as between resource support and insomnia (ß= - 0.120, p < 0.001). CONCLUSIONS: The results showed that COVID-19 related stigmatization was correlated to the detrimental consequences of emotional exhaustion, insomnia and suicidal ideation. Clear paths regarding the associations of social support and resource support with the three negative associations were found as the possible solutions. Strategies to reduce the stigmatization and these negative outcomes, or improve the psychological health will benefit AHTs and PGYDs in maintaining a healthy mental status.

The escalating threat of human-associated infectious bacteria in surface aquatic resources: Insights into prevalence, antibiotic resistance, survival mechanisms, detection, and prevention strategies.

Anthropogenic activities and climate change profoundly impact water quality, leading to a concerning increase in the prevalence and abundance of bacterial pathogens across diverse aquatic environments. This rise has resulted in a growing challenge concerning the safety of water sources, particularly surface waters and marine environments. This comprehensive review delves into the multifaceted challenges presented by bacterial pathogens, emphasizing threads to human health within ground and surface waters, including marine ecosystems. The exploration encompasses the intricate survival mechanisms employed by bacterial pathogens and the proliferation of antimicrobial resistance, largely driven by human-generated antibiotic contamination in aquatic systems. The review further addresses prevalent pathogenic bacteria, elucidating associated risk factors, exploring their eco-physiology, and discussing the production of potent toxins. The spectrum of detection techniques, ranging from conventional to cutting-edge molecular approaches, is thoroughly examined to underscore their significance in identifying and understanding waterborne bacterial pathogens. A critical aspect highlighted in this review is the imperative for real-time monitoring of biomarkers associated with waterborne bacterial pathogens. This monitoring serves as an early warning system, facilitating the swift implementation of action plans to preserve and protect global water resources. In conclusion, this comprehensive review provides fresh insights and perspectives, emphasizing the paramount importance of preserving the quality of aquatic resources to safeguard human health on a global scale.

Impact of COVID-19-Induced Academic Stress on Insomnia and Suicidal Ideation among Taiwanese Health Trainees and Junior Doctors.

BACKGROUND Clinical training for allied health trainees (AHTs) and postgraduate-year (PGY) doctors needed to go online during the outbreak of coronavirus disease 2019 (COVID-19), which may have caused academic stress and consequent outcomes among this cohort. MATERIAL AND METHODS To evaluate academic-related stress, clinical confidence, psychological distress, and insomnia, an online survey-based study was conducted among Taiwanese AHTs and PGY doctors between July and December, 2022, during the COVID-19 pandemic. The survey included the 21-item Depression, Anxiety, and Stress Scale (DASS-21), the Insomnia Severity Index (ISI), and self-designed questions. It was distributed using convenience sampling and snowball sampling and was completed by 522 participants. RESULTS Structural equational modelling showed that academic stress was negatively associated with clinical confidence (standardized coefficient [ß]=-0.382, p<0.001). Clinical confidence was negatively associated with psychological distress (ß=-0.397, p<0.001), which was associated with insomnia (ß=0.648, p<0.001). Additionally, clinical confidence and psychological distress were the significant mediators. Results indicated that higher academic stress was associated with higher level of insomnia via the mediation of clinical confidence and psychological distress. CONCLUSIONS Academic stress related to changes in clinical training may have led to insomnia among AHTs and PGY doctors during the pandemic. Factors to reduce academic stress should be investigated to promote good mental health while providing sufficient clinical training, especially during events that can cause increased stress (eg, epidemics, pandemics).

Influence of Geothermal Fumaroles in Driving the Microbial Community Dynamics and Functions of Adjacent Ecosystems.

Growing evidence suggests that the hydrochemical properties of geothermal fumaroles may play a crucial role in shaping the diversity and functions of microbial communities in various environments. In the present study, the impact of geothermal furaneols on the microbial communities and their metabolic functions across the rock-soil-plant continuum was explored considering varying distances from the fumarole source. The results revealed that bacterial phylum Proteobacteria was predominant in all sample types, except in the 10 m rock sample, irrespective of the sampling distance. Archaeal phyla, such as Euryarchaeota and Crenarchaeota, were more prevalent in rock and soil samples, whereas bacterial phyla were more prevalent in plant samples. Thermoacidophilic archaeons, including Picrophilus, Ferroplasma, and Thermogymnomonas were dominant in rocks and soil samples of 1 and 5 m distances; acidophilic mesophiles, including Ferrimicrobium and Granulicella were abundant in the rhizoplane samples, whereas rhizosphere-associated microbes including Pseudomonas, Pedobacter, Rhizobium, and Novosphingobium were found dominant in the rhizosphere samples. The functional analysis highlighted the higher expression of sulfur oxidative pathways in the rock and soil samples; dark iron oxidation and nitrate/nitrogen respiratory functions in the rhizosphere samples. The findings underscore microbial adaptations across the rock-soil-plant continuum, emphasizing the intricate relationship between geothermal fumaroles and microbial communities in adjacent ecosystems. These insights offer a crucial understanding of the evolution of microbial life and highlight their pivotal roles in shaping ecosystem dynamics and functions.

Uncovering the microbial community structure and physiological profiles of terrestrial mud volcanoes: A comprehensive metagenomic insight towards their trichloroethylene biodegradation potentiality.

Mud volcanoes are dynamic geological features releasing methane (CH4), carbon dioxide (CO2), and hydrocarbons, harboring diverse methane and hydrocarbon-degrading microbes. However, the potential application of these microbial communities in chlorinated hydrocarbons bioremediation purposes such as trichloroethylene (TCE) has not yet been explored. Hence, this study investigated the mud volcano's microbial diversity functional potentiality in TCE degradation as well as their eco-physiological profiling using metabolic activity. Geochemical analysis of the mud volcano samples revealed variations in pH, temperature, and oxidation-reduction potential, indicating diverse environmental conditions. The Biolog Ecoplate™ carbon substrates utilization pattern showed that the Tween 80 was highly consumed by mud volcanic microbial community. Similarly, MicroResp® analysis results demonstrated that presence of additive C-substrates condition might enhanced the cellular respiration process within mud-volcanic microbial community. Full-length 16 S rRNA sequencing identified Proteobacteria as the dominant phylum, with genera like Pseudomonas and Hydrogenophaga associated with chloroalkane degradation, and methanotrophic bacteria such as Methylomicrobium and Methylophaga linked to methane oxidation. Functional analysis uncovered diverse metabolic functions, including sulfur and methane metabolism and hydrocarbon degradation, with specific genes involved in methane oxidation and sulfur metabolism. These findings provide insights into the microbial diversity and metabolic capabilities of mud volcano ecosystems, which could facilitate their effective application in the bioremediation of chlorinated compounds.

Molecular surveillance of Helicobacter species with high prevalence from two streams with various wastewater pollution in Taiwan.

Helicobacter species are potential zoonotic pathogens classified as either enterohepatic or gastric. Helicobacter infection can be transmitted through wastewater from households and livestock and through water from irrigation and streams. In this study, the distribution and source of Helicobacter species in the Donggang and Yenshui rivers, two natural water bodies with different characteristics, were analyzed. A total of 44 water samples were collected over the four seasons. The samples were subjected to Helicobacter 16 s rRNA gene PCR, followed by sequencing and comparison for identification and analysis. The detection rate of Helicobacter species in both rivers was 79.55%, with H. kayseriensis (10/35, 28.57%) being the most common species. Analysis of the environment around the sampling sites showed a high detection rate in the livestock-rich area, and the results of BLAST for species identification and comparison indicated feces as the contamination source. The area around the Donggang River was developed for animal husbandry, led to a high detection rate of Helicobacter species. Many Helicobacter species were identified to have a risk of zoonotic transmission, especially if the stream is used as a source of drinking, agricultural, or even aquacultural water. The high presence of Helicobacter species in natural water bodies suggests that wastewater treatment is an effective strategy to control pathogen spread. Therefore, investigation and monitoring of pathogens in wastewater are highly important. However, methods for the isolation and culture of Helicobacter species in natural waters have yet to be developed. Hence, future research should focus on developing such methods.

ECG-surv: A deep learning-based model to predict time to 1-year mortality from 12-lead electrocardiogram.

BACKGROUND: Electrocardiogram (ECG) abnormalities have demonstrated potential as prognostic indicators of patient survival. However, the traditional statistical approach is constrained by structured data input, limiting its ability to fully leverage the predictive value of ECG data in prognostic modeling. METHODS: This study aims to introduce and evaluate a deep-learning model to simultaneously handle censored data and unstructured ECG data for survival analysis. We herein introduce a novel deep neural network called ECG-surv, which includes a feature extraction neural network and a time-to-event analysis neural network. The proposed model is specifically designed to predict the time to 1-year mortality by extracting and analyzing unique features from 12-lead ECG data. ECG-surv was evaluated using both an independent test set and an external set, which were collected using different ECG devices. RESULTS: The performance of ECG-surv surpassed that of the Cox proportional model, which included demographics and ECG waveform parameters, in predicting 1-year all-cause mortality, with a significantly higher concordance index (C-index) in ECG-surv than in the Cox model using both the independent test set (0.860 [95% CI: 0.859- 0.861] vs. 0.796 [95% CI: 0.791- 0.800]) and the external test set (0.813 [95% CI: 0.807- 0.814] vs. 0.764 [95% CI: 0.755- 0.770]). ECG-surv also demonstrated exceptional predictive ability for cardiovascular death (C-index of 0.891 [95% CI: 0.890- 0.893]), outperforming the Framingham risk Cox model (C-index of 0.734 [95% CI: 0.715-0.752]). CONCLUSION: ECG-surv effectively utilized unstructured ECG data in a survival analysis. It outperformed traditional statistical approaches in predicting 1-year all-cause mortality and cardiovascular death, which makes it a valuable tool for predicting patient survival.

Transformative and sustainable insights of agricultural waste-based adsorbents for water defluoridation: Biosorption dynamics, economic viability, and spent adsorbent management.

With the progression of civilization, the harmony within nature has been disrupted, giving rise to various ecocidal activities that are evident in every spheres of the earth. These activities have had a profound and far-reaching impact on global health. One significant example of this is the presence of fluoride in groundwater exceeding acceptable limits, resulting in the widespread occurrence of "Fluorosis" worldwide. It is imperative to mitigate the concentration of fluoride in drinking water to meet safety standards. While various defluoridation techniques exist, they often have drawbacks. Biosorption, being a simple, affordable and eco-friendly method, has gained preference for defluoridation. However, its limited commercialization underscores the pressing need for further research in this domain. This comprehensive review article offers a thorough examination of the defluoridation potential of agro-based adsorbents, encompassing their specific chemical compositions and preparation methods. The review presents an in-depth discussion of the factors influencing fluoride biosorption and conducts a detailed exploration of adsorption isotherm and adsorption kinetic models to gain a comprehensive understanding of the nature of the adsorption process. Furthermore, it evaluates the commercial viability through an assessment of regeneration potential and a cost analysis of these agro-adsorbents, with the aim of facilitating the scalability of the defluoridation process. The elucidation of the adsorption mechanism and recommendations for overcoming challenges in large-scale implementation offer a comprehensive outlook on this eco-friendly and sustainable approach to fluoride removal. In summary, this review article equips readers with a lucid understanding of agro-adsorbents, elucidates their ideal conditions for improved performance, offers a more profound insight into the fluoride biosorption mechanism, and introduces the concept of effective spent adsorbent management.

Association Between Serratia marcescens Contamination and Hygiene Compliance in Orthokeratology.

BACKGROUND/AIM: Given the characteristics of Serratia marcescens (S. marcescens), this study aimed at investigating its presence in the hands and contact lens cases of orthokeratology wearers, along with the status of bacterial contamination. PATIENTS AND METHODS: The 39 patients received the questionnaires about the background of orthokeratology and hygiene habits. A total of 39 contact lens cases and 39 hand samples from the patients were collected at Show Chwan Memorial Hospital from June to August in 2020 and sent to National Chung Cheng University for DNA extraction and PCR identification. RESULTS: The results indicated a detection rate of 5.13% for S. marcescens in the contact lens cases and 12.82% in the hand samples. Additionally, 66.67% of contact lens case samples and 30.77% of hand samples found positive for 16s bacterial amplicons. The relationship between hand contamination and the duration of contact lens usage were revealed for both S. marcescens (p=0.021) and 16s bacterial amplicons (p=0.048). CONCLUSION: The results indicated that hand hygiene is more critical than focusing on contact lens hygiene when it comes to preventing S. marcescens infections. Nevertheless, both proper hand and contact lens hygiene practices can reduce the detection of bacterial eye pathogens, especially a common intestinal bacterium.

Fear, Stress, Susceptibility, and Problematic Social Media Use Explain Motivation for COVID-19 Preventive Behaviors Among Patients With Stroke and Their Caregivers.

The COVID-19 pandemic presented significant challenges for individuals who experienced stroke and their caregivers. It is essential to understand the factors affecting preventive behavior in these populations. Therefore, the present study examined the factors that influenced COVID-19 preventive behavior and motivation for COVID-19 vaccine uptake among patients with stroke and their caregivers. A cross-sectional study comprising 191 participants (81 patients with stroke and 110 caregivers) was carried out. Participants completed a survey assessing fear of COVID-19, stress, perceived susceptibility, problematic social media use, preventive behaviors, and motivation for vaccine uptake. Statistical analyses included descriptive statistics, Pearson correlations, and multiple linear regressions. Motivation for COVID-19 vaccine uptake was significantly positively correlated with problematic social media use (r = 0.225, P = .002), perceived susceptibility (r = 0.197, P = .008), and fear of COVID-19 (r = 0.179, P = .015), but negatively correlated with stress (r = -0.189, P = .010). Caregivers, compared to patients, showed a lower level of preventive behavior (standardized coefficient = -0.23, P = .017). Furthermore, higher levels of fear were associated with increased preventive behavior (standardized coefficient = 0.22, P = .006), while greater stress correlated with lower preventive behavior (standardized coefficient = -0.38, P < .001). Among patients with stroke and their caregivers, motivation of COVID-19 vaccine uptake and preventive behaviors were influenced by factors such as fear, perceived susceptibility, social media use, and stress. By using strategies such as targeted education, support, and communication campaigns, healthcare providers and policymakers may be able to enhance the well-being of patients with stroke and their caregivers during future pandemics.

Evaluating groundwater ecosystem dynamics in response to post in-situ remediation of mixed chlorinated volatile organic compounds (CVOCs): An insight into microbial community resilience, adaptability, and metabolic functionality for sustainable remediation and ecosystem restoration.

The in-situ remediation of groundwater contaminated with mixed chlorinated volatile organic compounds (CVOCs) has become a significant global research interest. However, limited attention has been given in understanding the effects of these remediation efforts on the groundwater microbial communities, which are vital for maintaining ecosystem health through their involvement in biogeochemical cycles. Hence, this study aimed to provide valuable insights into the impacts of in-situ remediation methods on groundwater microbial communities and ecosystem functionality, employing high-throughput sequencing coupled with functional and physiological assays. The results showed that both bioremediation and chemical remediation methods adversely affected microbial diversity and abundance compared to non-polluted sites. Certain taxa such as Pseudomonas, Acinetobacter, and Vogesella were sensitive to these remediation methods, while Aquabacterium exhibited greater adaptability. Functional annotation unveiled the beneficial impact of bioremediation on the sulfur cycle and specific taxa such as Cellvibrio, Massilia, Algoriphagus, and Flavobacterium which showed a significant positive relationship with dark oxidation of sulfur compounds. In contrast, chemical remediation showed adverse impacts on the nitrogen cycle with a reduced abundance of nitrogen and nitrate respiration along with a reduced utilization of amines (nitrogen rich substrate). The findings of this study offer valuable insights into the potential impacts of in-situ remediation methods on groundwater microbial communities and ecosystem functionality, emphasizing the need for meticulous consideration to ensure the implementation of effective and sustainable remediation strategies that safeguard ecosystem health and function.

Comparing Artificial Intelligence-Enabled Electrocardiogram Models in Identifying Left Atrium Enlargement and Long-term Cardiovascular Risk.

BACKGROUND: The role of P-wave in identifying left atrial enlargement (LAE) with the use of artificial intelligence (AI)-enabled electrocardiography (ECG) models is unclear. It is also unknown if AI-enabled single-lead ECG could be used as a diagnostic tool for LAE surveillance. We aimed to build AI-enabled P-wave and single-lead ECG models to identify LAE using sinus rhythm (SR) and non-SR ECGs, and compare the prognostic ability of severe LAE, defined as left atrial diameter ≥ 50 mm, assessed by AI-enabled ECG models vs echocardiography. METHODS: This retrospective study used data from 382,594 consecutive adults with paired 12-lead ECG and echocardiography performed within 2 weeks of each other at Chang Gung Memorial Hospital. UNet++ was used for P-wave segmentation. ResNet-18 was used to develop deep convolutional neural network-enabled ECG models for discriminating LAE. External validation was performed with the use of data from 11,753 patients from another hospital. RESULTS: The AI-enabled 12-lead ECG model outperformed other ECG models for classifying LAE, but the single-lead ECG models also showed excellent performance at a left atrial diameter cutoff of 50 mm. AI-enabled ECG models had excellent and fair discrimination on LAE using the SR and the non-SR data set, respectively. Severe LAE identified by AI-enabled ECG models was more predictive of future cardiovascular disease than echocardiography; however, the cumulative incidence of new-onset atrial fibrillation and heart failure was higher in patients with echocardiography-severe LAE than with AI-enabled ECG-severe LAE. CONCLUSIONS: P-Wave plays a crucial role in discriminating LAE in AI-enabled ECG models. AI-enabled ECG models outperform echocardiography in predicting new-onset cardiovascular diseases associated with severe LAE.

Microbial induced carbonate precipitation for remediation of heavy metals, ions and radioactive elements: A comprehensive exploration of prospective applications in water and soil treatment.

Improper disposal practices have caused environmental disruptions, possessing by heavy metal ions and radioactive elements in water and soil, where the innovative and sustainable remediation strategies are significantly imperative in last few decades. Microbially induced carbonate precipitation (MICP) has emerged as a pioneering technology for remediating contaminated soil and water. Generally, MICP employs urease-producing microorganisms to decompose urea (NH2CONH2) into ammonium (NH4+and carbon dioxide (CO2), thereby increasing pH levels and inducing carbonate precipitation (CO32-), and effectively removing remove contaminants. Nonetheless, the intricate mechanism underlying heavy metal mineralization poses a significant challenge, constraining its application in contaminants engineering, particularly in the context of prolonged heavy metal leaching over time and its efficacy in adverse environmental conditions. This review provides a comprehensive idea of recent development of MICP and its application in environmental engineering, examining metabolic pathways, mineral precipitation mechanisms, and environmental factors as well as providing future perspectives for commercial utilization. The use of ureolytic bacteria in MICP demonstrates cost-efficiency, environmental compatibility, and successful pollutant abatement over tradition bioremediation techniques, and bio-synthesis of nanoparticles. limitations such as large-scale application, elevated Ca2+levels in groundwater, and gradual contaminant release need to be overcome. The possible future research directions for MICP technology, emphasizing its potential in conventional remediation, CO2 sequestration, bio-material synthesis, and its role in reducing environmental impact for long-term economic benefits.

Problematic Gaming in Malaysian University Students: Translation and Psychometric Evaluation of the Malay Language Versions of Gaming Disorder Test and Gaming Disorder Scale for Young Adults.

As research on gaming disorder (GD) is growing globally, the need for a valid and reliable instrument to assess GD has become crucial. Therefore, the present cross-sectional study translated and evaluated the psychometric properties of Gaming Disorder Test (GDT) and Gaming Disorder Scale for Young Adults (GADIS-YA) into Malay language versions. The sample comprised 624 university students (females = 75.6%; mean age = 22.27 years) recruited via an online survey from May to August 2022, using a convenience sampling method. Participants completed both GDT and GADIS-YA scales and other relevant measures including Bergen Social Media Addiction Scale (BSMAS), Internet Gaming Disorder Scale-Short Form (IGDS9-SF), and time spent on social media and gaming. Results showed that both instruments reported satisfactory internal consistency, and confirmatory factor analysis supported the one-factor structure for GDT and two-factor structure for GADIS-YA. Both scales were strongly correlated with each other and with the IGDS9-SF, BSMAS, and time spent on social media and gaming, supporting concurrent validity. Measurement invariance of both scales was confirmed across gender and gaming time. These findings suggest that the Malay versions of GDT and GADIS-YA are reliable and valid measures of problematic gaming among Malaysian university students.

Deciphering microbial communities and their unique metabolic repertoire across rock-soil-plant continuum in the Dayoukeng fumarolic geothermal field of the Tatun Volcano Group.

High temperature and sulfur concentrations in geothermal sulfur fumaroles host unique microbial ecosystems with niche-specific metabolic diversity and physiological functions. In this study, the microbial communities and their functionalities associated with the Dayoukeng geothermal field and the rock-soil-plant continuum were investigated to underpin the microbial modulation at different distances from the fumaroles source. At the phylum level, Bacteroidota, Planctomycetota, Armatimonadota, and Patescibacteria were abundant in plant samples; Elusimicrobiota and Desulfobacterota were in the rock samples while Nitrospirota, Micrarchaeota, and Deinococcota were dominant in the soil samples. Acidophilic thermophiles were enriched in samples within close proximity to the fumaroles, primarily at a distance of 1 m. The sulfur and iron-oxidizing acidophilic bacterial genera such as Acidothiobacillus and Sulfobacillus were abundant in the rock samples. The thermoacidophilic archaeon Acidianus and acidophilic bacteria Acidiphilium were abundant in the soil samples. Additionally, Thermosporothrix and Acidothermus were found abundant in the plant samples. The results of the functional annotation indicated that dark sulfur oxidation, iron oxidation, and hydrogen oxidation pathways were abundant in the soil samples up to 1 m from the fumaroles, while methanogenic and fermentation pathways were more prevalent in the soil samples located 10 m from the fumaroles. Interestingly, the results of this study indicated a higher microbial richness and abundance of acidophilic communities in the soils and plants compared to the rocks of the DYK fumarolic geothermal field.

Development of a Machine Learning Model to Predict the Use of Surgery in Patients With Rheumatoid Arthritis.

OBJECTIVE: One in five patients with rheumatoid arthritis (RA) rely on surgery to restore joint function. However, variable response to disease-modifying antirheumatic drugs (DMARDs) complicates surgical planning, and it is difficult to predict which patients may ultimately require surgery. We used machine learning to develop predictive models for the likelihood of undergoing an operation related to RA and which type of operation patients who require surgery undergo. METHODS: We used electronic health record data to train two extreme gradient boosting machine learning models. The first model predicted patients' probabilities of undergoing surgery ≥5 years after their initial clinic visit. The second model predicted whether patients who underwent surgery would undergo a major joint replacement versus a less intensive procedure. Predictors included demographics, comorbidities, and medication data. The primary outcome was model discrimination, measured by area under the receiver operating characteristic curve (AUC). RESULTS: We identified 5,481 patients, of whom 278 (5.1%) underwent surgery. There was no significant difference in the frequency of DMARD or steroid prescriptions between patients who did and did not have surgery, though nonsteroidal anti-inflammatory drug prescriptions were more common among patients who did have surgery (P = 0.03). The model predicting use of surgery had an AUC of 0.90 ± 0.02. The model predicting type of surgery had an AUC of 0.58 ± 0.10. CONCLUSIONS: Predictive models using clinical data have the potential to facilitate identification of patients who may undergo rheumatoid-related surgery, but not what type of procedure they will need. Integrating similar models into practice has the potential to improve surgical planning.

Differential item functioning for the Tendency of Avoiding Physical Activity and Sport Scale across two subculture samples: Taiwanese and mainland Chinese university students.

Aims: The aims of the study were to examine the differential item functioning (DIF) of the Tendency of Avoiding Physical Activity and Sport Scale (TAPAS) among three subgroups (gender, weight status, and region) and to test the construct and concurrent validities of the scale. Methods: Using an online survey, university students (608 Taiwanese and 2319 mainland Chinese) completed the TAPAS. Rasch analysis examined if all the 10 TAPAS items fitted the same construct and displayed no substantial DIF across three subgroups: gender (male vs. female), weight status (overweight vs. non-overweight), and region (Taiwan vs. China). Concurrent validity was examined using the scores on the Weight Self-Stigma Questionnaire (WSSQ) and Weight Bias Internalization Scale (WBIS). Results: All TAPAS items, except for Item 10 ("Prefer to participate in physical activity in a more private setting"), fitted the same construct. None of the TAPAS items displayed DIF in any of the subgroups except for Item 10 across participants from Taiwan and China (DIF contrast = -1.41). Conclusion: The TAPAS can appropriately assess the tendency to avoid physical activity and sport among both Taiwanese and mainland Chinese university students. However, Item 10 may need to be further examined.

Machine learning-based prediction of acute mortality in emergency department patients using twelve-lead electrocardiogram.

Background: The risk of mortality is relatively high among patients who visit the emergency department (ED), and stratifying patients at high risk can help improve medical care. This study aimed to create a machine-learning model that utilizes the standard 12-lead ECG to forecast acute mortality risk in ED patients. Methods: The database included patients who visited the EDs and underwent standard 12-lead ECG between October 2007 and December 2017. A convolutional neural network (CNN) ECG model was developed to classify survival and mortality using 12-lead ECG tracings acquired from 345,593 ED patients. For machine learning model development, the patients were randomly divided into training, validation and testing datasets. The performance of the mortality risk prediction in this model was evaluated for various causes of death. Results: Patients who visited the ED and underwent one or more ECG examinations experienced a high incidence of 30-day mortality [18,734 (5.42%)]. The developed CNN model demonstrated high accuracy in predicting acute mortality (hazard ratio 8.50, 95% confidence interval 8.20-8.80) with areas under the receiver operating characteristic (ROC) curve of 0.84 for the 30-day mortality risk prediction models. This CNN model also demonstrated good performance in predicting one-year mortality (hazard ratio 3.34, 95% confidence interval 3.30-3.39). This model exhibited good predictive performance for 30-day mortality not only for cardiovascular diseases but also across various diseases. Conclusions: The machine learning-based ECG model utilizing CNN screens the risks for 30-day mortality. This model can complement traditional early warning scoring indexes as a useful screening tool for mortality prediction.