Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that results in increased morbidity and mortality after cardiac surgery. Most established prediction models are ...limited to the analysis of nonlinear relationships and fail to fully consider intraoperative variables, which represent the acute response to surgery. Therefore, this study utilized an artificial intelligence-based machine learning approach thorough perioperative data-driven learning to predict CSA-AKI.
A total of 671 patients undergoing cardiac surgery from August 2016 to August 2018 were enrolled. AKI following cardiac surgery was defined according to criteria from Kidney Disease: Improving Global Outcomes (KDIGO). The variables used for analysis included demographic characteristics, clinical condition, preoperative biochemistry data, preoperative medication, and intraoperative variables such as time-series hemodynamic changes. The machine learning methods used included logistic regression, support vector machine (SVM), random forest (RF), extreme gradient boosting (XGboost), and ensemble (RF + XGboost). The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). We also utilized SHapley Additive exPlanation (SHAP) values to explain the prediction model.
Development of CSA-AKI was noted in 163 patients (24.3%) during the first postoperative week. Regarding the efficacy of the single model that most accurately predicted the outcome, RF exhibited the greatest AUC (0.839, 95% confidence interval CI 0.772-0.898), whereas the AUC (0.843, 95% CI 0.778-0.899) of ensemble model (RF + XGboost) was even greater than that of the RF model alone. The top 3 most influential features in the RF importance matrix plot were intraoperative urine output, units of packed red blood cells (pRBCs) transfused during surgery, and preoperative hemoglobin level. The SHAP summary plot was used to illustrate the positive or negative effects of the top 20 features attributed to the RF. We also used the SHAP dependence plot to explain how a single feature affects the output of the RF prediction model.
In this study, machine learning methods were successfully established to predict CSA-AKI, which determines risks following cardiac surgery, enabling the optimization of postoperative treatment strategies to minimize the postoperative complications following cardiac surgeries.
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CO-V-2), was first reported in Wuhan, Hubei province, China has now rapidly spread over 50 ...countries. For the prevention and control of infection, Taiwan Centers for Disease Control initiated testing of SARS-CoV-2 on January 24th 2020 for persons suspected with this disease. Until February 28th, 43 flu-like symptomatic patients were screened in China Medical University Hospital.
Two patients were confirmed positive for SARS-CoV-2 infection by rRT-PCR as COVID-19 patients A and B. Causative pathogens for included patients were detected using FilmArray™ Respiratory Panel. We retrospectively analyzed the clinical presentations, laboratory data, radiologic findings, and travel and exposure contact histories, of the COVID-19 patients in comparison to those with other respiratory infections.
Through contact with Taiwan No. 19 case patient on 27th January, COVID-19 patients A and B were infected. Both patients had no identified comorbidities and developed mild illness with temporal fever, persistent cough, and lung interstitial infiltrates. Owing to the persistence of positive SARS-CoV-2 in respiratory specimen, the two COVID-19 patients are still in the isolation rooms despite recovery until 10th of March. The results of FilmArrayTM Respiratory Panel revealed 22 of the 41 non-COVID-19 patients were infected by particular pathogens. In general, seasonal respiratory pathogens are more prevalent than SARS-CoV-2 in symptomatic patients in non- COVID-19 endemic area during the flu season. Since all patients shared similar clinical and laboratory findings, expanded surveillance of detailed exposure history for suspected patients and application of rapid detection tools are highly recommended.
Purpose Clinically, we have observed the phenomenon of postoperatively accelerated orthodontic tooth movement in patients who had orthognathic surgery. This phenomenon lasts for a period of 3 to 4 ...months. However, the underlying mechanisms of this phenomenon have not been well studied yet. The purpose of this prospective clinical pilot study was to study the postoperative changes in bone physiology and metabolism and the corresponding responses in the dentoalveolus, such as the changes in tooth mobility. Materials and Methods Twenty-two consecutive adult patients who had 2-jaw orthognathic surgery were included in this study. The levels of serum alkaline phosphatase and C-terminal telopeptide of type I collagen (ICTP), as well as the tooth mobility of the maxillary and mandibular incisors based on the Periotest method (Siemens AG, Bensheim, Germany), were examined preoperatively and 1 week, 1 month, 2 months, 3 months, and 4 months postoperatively. The data were analyzed statistically. Results Both tooth mobility of the maxillary and mandibular incisors and ICTP significantly increased from 1 week to 3 months postoperatively and then decreased to their preoperative levels in the fourth month postoperatively. The changes in tooth mobility were significantly in correspondence with the changes in ICTP. The alkaline phosphatase level significantly increased from the first to fourth month postoperatively, but it was not significantly correlated to the changes in tooth mobility. Conclusion The orthognathic surgery triggers a 3- to 4-month period of higher osteoclastic activities and metabolic changes in the dentoalveolus postoperatively, which possibly accelerates postoperative orthodontic tooth movement.
Abstract Dental caries, or tooth decay, is a widespread problem and is generally considered irreversible, yet a regeneration solution exists to cure them. In this study, a multifunctional and ...biocompatible dental scaffold is fabricated by a unique vapor sublimation and deposition polymerization process with the well‐accepted material Parylene, resulting in the construction of a 3D and porous polymer scaffold that accommodates living cells and a combination of growth factor molecules in a single fabrication process, which differentiates the coating formation from a conventional vapor process. Physically, a directional interior structure is constructed to guide dental pulp stem cells (DPSCs) attachment and alignment. Biochemically, necessary growth factors, including Wnt‐3a and FGF‐2, are incorporated within the scaffold during fabrication to guide the cell differentiation of odontogenesis. The synergistic effects of the attachment and alignment of DPSCs, as well as the biocompatibility and odontogenic activities of the components accommodated in the scaffold, result in the upregulation of the cell differentiation into odontoblasts, as shown by the morphology of odontoblasts and the expressions of odontogenesis markers. Thus, the reported fabrication technique and its products represent an alternative approach for dentin regeneration in dental caries and tooth decay.
Coronavirus Disease 2019 (COVID-19) is rapidly transmitted from person to person, causing global pandemic since December 2019. Instantly detecting COVID-19 is crucial for epidemic prevention. In this ...study, olfactory dysfunction is a significant symptom in mild to moderate COVID-19 patients but relatively rare in other respiratory viral infections. The Taiwan smell identification test (TWSIT) is a speedy and inexpensive option for accurately distinguishing anosmia that also quantifies the degree of anosmia. Using TWSIT in the outpatient clinic for early identifying the patients with mild to moderate COVID-19 can be promising.
Nineteen patients confirmed COVID-19 in central Taiwan were collected and divided into two groups: olfactory dysfunction and non-olfactory dysfunction. Demographic characteristics, laboratory findings, and the results of the olfactory test were compared between these two groups.
Thirteen (68.4%) of the 19 patients had olfactory dysfunction. The patients with olfactory dysfunction were younger than those without this symptom. The statistical difference in age distribution was significant between these two groups (IQR: 25.5–35.5 vs. IQR: 32.5–60.3; p-value: 0.012). There was no significant difference in gender, smoking history, comorbidities, travel history, respiratory tract infection symptoms, and laboratory findings between these two groups.
This study demonstrated that young adults were prone to develop olfactory dysfunctions. In the flu season, olfactory dysfunction is considered a specific screening criterion for early detecting COVID-19 in the community. TWSIT can serve as a decent test for quantifying and qualifying olfactory dysfunction.
Molecular analysis of circulating tumor cells (CTCs) at single-cell resolution offers great promise for cancer diagnostics and therapeutics from simple liquid biopsy. Recent development of massively ...parallel single-cell RNA-sequencing (scRNA-seq) provides a powerful method to resolve the cellular heterogeneity from gene expression and pathway regulation analysis. However, the scarcity of CTCs and the massive contamination of blood cells limit the utility of currently available technologies. Here, we present Hydro-Seq, a scalable hydrodynamic scRNA-seq barcoding technique, for high-throughput CTC analysis. High cell-capture efficiency and contamination removal capability of Hydro-Seq enables successful scRNA-seq of 666 CTCs from 21 breast cancer patient samples at high throughput. We identify breast cancer drug targets for hormone and targeted therapies and tracked individual cells that express markers of cancer stem cells (CSCs) as well as of epithelial/mesenchymal cell state transitions. Transcriptome analysis of these cells provides insights into monitoring target therapeutics and processes underlying tumor metastasis.
Anti-cytokine autoantibodies may cause immunodeficiency and have been recently recognized as ‘autoimmune phenocopies of primary immunodeficiencies’ and are found in particular, but not exclusively in ...adult patients. By blocking the cytokine’s biological function, patients with anti-cytokine autoantibodies may present with a similar clinical phenotype as the related inborn genetic disorders. So far, autoantibodies to interferon (IFN)-γ, GM-CSF, to a group of TH-17 cytokines and to IL-6 have been found to be causative or closely associated with susceptibility to infection. This review compares infectious diseases associated with anti-cytokine autoantibodies with primary immunodeficiencies affecting similar cytokines or related pathways.
Triboelectric nanogenerators (TENGs) open up a new field of sustainable energy harvesting and can specifically satisfy the growing demand for self-powered electronics. For practical applications of ...TENGs in self-powered wearable electronics, apart from their output performance and stability, other aspects such as mechanical robustness and scalability to large areas are crucial. In this regard, here we present a promising strategy to enhance the performance and stability of flexible all-polymer TENGs
via
rational surface engineering, which involves using cationic poly-
l
-lysine (cPLL) and 1
H
,1
H
,2
H
,2
H
-perfluorodecyl (F
17
) fluorosilane polymer as a surface modification layer for a highly-conductive poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) electrode and polydimethylsiloxane (PDMS) dielectric layers, respectively. Our results indicate that both PLL and F
17
polymer films can effectively modulate the work-function (WF) values of triboelectric layers as a result of the formation of surface dipoles, thereby facilitating the generation of triboelectric charges. Additionally, the presence of interaction at the PDMS/F
17
polymer and PEDOT:PSS/cPLL interfaces can alleviate the adhesion issue between layers. With these appealing advantages, the resulting TENG delivers an open circuit voltage (
V
oc
) of 688 V and short circuit current (
I
sc
) of 33.0 μA, which are much superior to those of the TENG without modification layers. In particular, a remarkable power output with a power density of 13.4 W m
−2
and specific power of 95.3 mW g
−1
are attained for our flexible TENG, enabling it to light up 122 light emitting diodes and charge commercial capacitors quickly. To the best of our knowledge, the power output achieved in this study sets a new record for TENG technology. More encouragingly, such a TENG also possess excellent durability, revealing almost negligible degradation in
I
sc
and
V
oc
after 200 000 cycles of continuous operation under ambient conditions. This work provides a promising strategy for enhancing the performance and durability of flexible TENGs based on scalable cost-effective manufacturing, which is expected to inspire next-generation wearable energy harvesting technology.
We present a promising strategy to enhance the performance and stability of flexible all-polymer TENGs
via
rational surface engineering, yielding stable output characteristics together with record high specific power for a TENG.
Abstract
Purpose
Currently, there are no accurate markers for predicting potentially lethal prostate cancer (PC) before biopsy. This study aimed to develop urine tests to predict clinically ...significant PC (sPC) in men at risk.
Methods
Urine samples from 928 men, namely, 660 PC patients and 268 benign subjects, were analyzed by gas chromatography/quadrupole time-of-flight mass spectrophotometry (GC/Q-TOF MS) metabolomic profiling to construct four predictive models. Model I discriminated between PC and benign cases. Models II, III, and GS, respectively, predicted sPC in those classified as having favorable intermediate risk or higher, unfavorable intermediate risk or higher (according to the National Comprehensive Cancer Network risk groupings), and a Gleason sum (GS) of ≥ 7. Multivariable logistic regression was used to evaluate the area under the receiver operating characteristic curves (AUC).
Results
In Models I, II, III, and GS, the best AUCs (0.94, 0.85, 0.82, and 0.80, respectively; training cohort, N = 603) involved 26, 24, 26, and 22 metabolites, respectively. The addition of five clinical risk factors (serum prostate-specific antigen, patient age, previous negative biopsy, digital rectal examination, and family history) significantly improved the AUCs of the models (0.95, 0.92, 0.92, and 0.87, respectively). At 90% sensitivity, 48%, 47%, 50%, and 36% of unnecessary biopsies could be avoided. These models were successfully validated against an independent validation cohort (N = 325). Decision curve analysis showed a significant clinical net benefit with each combined model at low threshold probabilities. Models II and III were more robust and clinically relevant than Model GS.
Conclusion
This urine test, which combines urine metabolic markers and clinical factors, may be used to predict sPC and thereby inform the necessity of biopsy in men with an elevated PC risk.
Rapid progress in information and communication technologies (ICTs) has fueled the popularity of e‐learning. However, an e‐learning environment is limited in that online instructors cannot monitor ...immediately whether students remain focus during online autonomous learning. Therefore, this study tries to develop a novel attention aware system (AAS) capable of recognizing students' attention levels accurately based on electroencephalography (EEG) signals, thus having high potential to be applied in providing timely alert for conveying low‐attention level feedback to online instructors in an e‐learning environment. To construct AAS, attention responses of students and their corresponding EEG signals are gathered based on a continuous performance test (CPT), ie, an attention assessment test. Next, the AAS is constructed by using training and testing data by the NeuroSky brainwave detector and the support vector machine (SVM), a well‐known machine learning model. Additionally, based on the discrete wavelet transform (DWT), the collected EEG signals are decomposed into five primary bands (ie, alpha, beta, gamma, theta, and delta). Each primary band contains five statistical parameters (including approximate entropy, total variation, energy, skewness, and standard deviation), thus generating 25 potential brainwave features associated with students' attention level for constructing the AAS. An attempt based on genetic algorithm (GA) is also made to enhance the prediction performance of the proposed AAS in terms of identifying students' attention levels. According to GA, the seven most influential features are selected from 25 considered features; parameters of the proposed AAS are also optimized. Analytical results indicate that the proposed AAS can accurately recognize individual student's attention state as either a high or low level, and the average accuracy rate reaches as high as 89.52%. Moreover, the proposed AAS is integrated with a video lecture tagging system to examine whether the proposed AAS can accurately detect students' low‐attention periods while learning about electrical safety in the workplace via a video lecture. Four experiments are designed to assess the prediction performance of the proposed AAS in terms of identifying the periods of video lecture with high‐ or low‐attention levels during learning processes. Analytical results indicate that the proposed AAS can accurately identify the low‐attention periods of video lecture generated by students when engaging in a learning activity with video lecture. Meanwhile, the proposed AAS can also accurately identify the low‐attention periods of video lecture generated by students to some degree even when students engage in a learning activity by a video lecture with random disturbances. Furthermore, strong negative correlations are found between the students' learning performance (ie, posttest score and progressive score) and the low‐attention periods of video lecture identified by the proposed AAS. Results of this study demonstrate that the proposed AAS is effective, capable of assisting online instructors in evaluating students' attention levels to enhance their online learning performance.
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