Development and validation of machine learning models for postoperative venous thromboembolism prediction in colorectal cancer inpatients: a retrospective study.

Background: Colorectal cancer (CRC) is a heterogeneous group of malignancies distinguished by distinct clinical features. The association of these features with venous thromboembolism (VTE) is yet to be clarified. Machine learning (ML) models are well suited to improve VTE prediction in CRC due to their ability to receive the characteristics of a large number of features and understand the dataset to obtain implicit correlations. Methods: Data were extracted from 4,914 patients with colorectal cancer between August 2019 and August 2022, and 1,191 patients who underwent surgery on the primary tumor site with curative intent were included. The variables analyzed included patient-level factors, cancer-level factors, and laboratory test results. Model training was conducted on 30% of the dataset using a ten-fold cross-validation method and model validation was performed using the total dataset. The primary outcome was VTE occurrence in postoperative 30 days. Six ML algorithms, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), weighted support vector machine (SVM), a multilayer perception (MLP) network, and a long short-term memory (LSTM) network, were applied for model fitting. The model evaluation was based on six indicators, including receiver operating characteristic curve-area under the curve (ROC-AUC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), and Brier score. Two previous VTE models (Caprini and Khorana) were used as the benchmarks. Results: The incidence of postoperative VTE was 10.8%. The top ten significant predictors included lymph node metastasis, C-reactive protein, tumor grade, anemia, primary tumor location, sex, age, D-dimer level, thrombin time, and tumor stage. In our results, the XGBoost model showed the best performance, with a ROC-AUC of 0.990, a SEN of 96.9%, a SPE of 96.1% in training dataset and a ROC-AUC of 0.908, a SEN of 77.5%, a SPE of 93.7% in validation dataset. All ML models outperformed the previously developed models (Caprini and Khorana). Conclusions: This study developed postoperative VTE predictive models using six ML algorithms. The XGBoost VTE model might supply a complementary tool for clinical VTE prophylaxis decision-making and the proposed risk factors could shed some light on VTE risk stratification in CRC patients.

Ultrasensitive detection of SARS-CoV-2 S protein with aptamers biosensor based on surface-enhanced Raman scattering.

A rapid and accurate diagnostic modality is essential to prevent the spread of SARS-CoV-2. In this study, we proposed a SARS-CoV-2 detection sensor based on surface-enhanced Raman scattering (SERS) to achieve rapid and ultrasensitive detection. The sensor utilized spike protein deoxyribonucleic acid aptamers with strong affinity as the recognition entity to achieve high specificity. The spherical cocktail aptamers-gold nanoparticles (SCAP) SERS substrate was used as the base and Au nanoparticles modified with the Raman reporter molecule that resonates with the excitation light and spike protein aptamers were used as the SERS nanoprobe. The SCAP substrate and SERS nanoprobes were used to target and capture the SARS-CoV-2 S protein to form a sandwich structure on the Au film substrate, which can generate ultra-strong "hot spots" to achieve ultrasensitive detection. Analysis of SARS-CoV-2 S protein was performed by monitoring changes in SERS peak intensity on a SCAP SERS substrate-based detection platform. This assay detects S protein with a LOD of less than 0.7 fg mL-1 and pseudovirus as low as 0.8 TU mL-1 in about 12 min. The results of the simulated oropharyngeal swab system in this study indicated the possibility of it being used for clinical detection, providing a potential option for rapid and accurate diagnosis and more effective control of SARS-CoV-2 transmission.

AlCl3-catalyzed chemoselective cascade reactions of 4-anilinocoumarins with 2-furylcarbinols: access to densely functionalized chromeno[4,3-b]pyrrol-4(1H)-one derivatives.

An unprecedented protocol has been developed for the preparation of highly functionalized chromeno[4,3-b]pyrrol-4(1H)-ones, which are not only valuable architectures of many biologically active molecules but also key building blocks for rich photophysical properties. The transformation proceeded through chemoselective intermolecular α-carbon nucleophilic attacking/ring-opening/Michael addition/deprotonation aromatization processes from 4-aminocoumarins and 2-furylcarbinols.

Rapid Point-of-Care Assay by SERS Detection of SARS-CoV-2 Virus and Its Variants.

Addressing the spread of coronavirus disease 2019 (COVID-19) has highlighted the need for rapid, accurate, and low-cost diagnostic methods that detect specific antigens for SARS-CoV-2 infection. Tests for COVID-19 are based on reverse transcription PCR (RT-PCR), which requires laboratory services and is time-consuming. Here, by targeting the SARS-CoV-2 spike protein, we present a point-of-care SERS detection platform that specifically detects SARS-CoV-2 antigen in one step by captureing substrates and detection probes based on aptamer-specific recognition. Using the pseudovirus, without any pretreatment, the SARS-CoV-2 virus and its variants were detected by a handheld Raman spectrometer within 5 min. The limit of detection (LoD) for the pseudovirus was 124 TU µL-1 (18 fM spike protein), with a linear range of 250-10,000 TU µL-1. Moreover, this assay can specifically recognize the SARS-CoV-2 antigen without cross reacting with specific antigens of other coronaviruses or influenza A. Therefore, the platform has great potential for application in rapid point-of-care diagnostic assays for SARS-CoV-2.

Manipulating the light-matter interactions in plasmonic nanocavities at 1 nm spatial resolution.

The light-matter interaction between plasmonic nanocavity and exciton at the sub-diffraction limit is a central research field in nanophotonics. Here, we demonstrated the vertical distribution of the light-matter interactions at ~1 nm spatial resolution by coupling A excitons of MoS2 and gap-mode plasmonic nanocavities. Moreover, we observed the significant photoluminescence (PL) enhancement factor reaching up to 2800 times, which is attributed to the Purcell effect and large local density of states in gap-mode plasmonic nanocavities. Meanwhile, the theoretical calculations are well reproduced and support the experimental results.

A New Approach for Quantitative Surface-Enhanced Raman Spectroscopy through the Kinetics of Chemisorption.

Surface enhanced Raman spectroscopy (SERS) is a non-destructive, highly sensitive, and rapid analytical tool, which has been widely used in different fields, especially for trace quantities of analyte. However, using SERS for reliable quantitative sample analysis is still a great challenge. Herein, a new approach to quantitative SERS analysis at nanostructured substrates that does not require an internal standard or well-ordered nanostructured SERS substrates is developed. This method is based on the kinetics of chemisorption, that is, on a homogeneous surface, the time taken for adsorption of an adsorbate (adenine or melamine) to reach equilibrium negatively correlates with the concentration of the adsorbate. Quantitative analysis is achieved by using in situ SERS to acquire the adsorption profile of the adsorbate and enabling the adsorption equilibrium time to be calculated. There is excellent correlation between the adenine and melamine SERS response over adsorption equilibrium time with concentration, and the correlation coefficients are 0.9906 and 0.9682, respectively. Moreover, milk sample spiked with the melamine is also studied, and the standard recovery rate is 106%. This work demonstrates a novel, non-destructive, and cost-effective quantitative SERS detection technique, which can broaden applications across multiple fields.

Physicians' perceptions of physician-nurse interactions and information needs in China.

BACKGROUND: Good communication between physicians and nurses is important for the understanding of disease status and treatment feedback; however, certain issues in Chinese hospitals could lead to suboptimal physician-nurse communication in clinical work. METHODS: Convenience sampling was used to recruit participants. Questionnaires were sent to clinical physicians in three top tertiary Grade-A teaching hospitals in China and six hundred and seventeen physicians participated in the survey. RESULTS: (1) Common physician-nurse interactions were shift-change reports and provisional reports when needed, and interactions expected by physicians included face-to-face reports and communication via a phone or mobile device. (2) Most respondents believed that the need for information in physician-nurse interactions was high, information was moderately accurate and timely, and feedback regarding interaction time and satisfaction indicated that they were only average and required improvement. (3) Information needs in physician-nurse interactions differed significantly according to hospital category, role, workplace, and educational background (p < .05). CONCLUSIONS: There was a considerable need for information within physician-nurse interactions, and the level of satisfaction with the information obtained was average; requirements for the improvement of communication differed between physicians and nurses because of differences in their characteristics. Currently, the use of information technology in physician-nurse communication was less common but was highly expected by physicians.

Characteristics of health IT outage and suggested risk management strategies: an analysis of historical incident reports in China.

BACKGROUND: The healthcare industry has become increasingly dependent on using information technology (IT) to manage its daily operations. Unexpected downtime of health IT systems could therefore wreak havoc and result in catastrophic consequences. Little is known, however, regarding the nature of failures of health IT. OBJECTIVE: To analyze historical health IT outage incidents as a means to better understand health IT vulnerabilities and inform more effective prevention and emergency response strategies. METHODS: We studied news articles and incident reports publicly available on the internet describing health IT outage events that occurred in China. The data were qualitatively analyzed using a deductive grounded theory approach based on a synthesized IT risk model developed in the domain of information systems. RESULTS: A total of 116 distinct health IT incidents were identified. A majority of them (69.8%) occurred in the morning; over 50% caused disruptions to the patient registration and payment collection functions of the affected healthcare facilities. The outpatient practices in tertiary hospitals seem to be particularly vulnerable to IT failures. Software defects and overcapacity issues, followed by malfunctioning hardware, were among the principal causes. CONCLUSIONS: Unexpected health IT downtime occurs more and more often with the widespread adoption of electronic systems in healthcare. Risk identification and risk assessments are essential steps to developing preventive measures. Equally important is institutionalization of contingency plans as our data show that not all failures of health IT can be predicted and thus effectively prevented. The results of this study also suggest significant future work is needed to systematize the reporting of health IT outage incidents in order to promote transparency and accountability.

Investigating incidents of EHR failures in China: analysis of search engine reports.

As the healthcare industry becomes increasingly dependent on information technology (IT), the failure of computerized systems could cause catastrophic effects on patient safety. We conducted an empirical study to analyze news articles available on the internet using Baidu and Google. 116 distinct EHR outage news reports were identified. We examined characteristics, potential causes, and possible preventive strategies. Risk management strategies based are discussed.

A comparison of electronic health records at two major Peking University Hospitals in China to United States meaningful use objectives.

BACKGROUND: In accordance with the People's Republic of China's (China) National Health Reform Plan of 2009, two of the nation's leading hospitals, located in Beijing, have implemented electronic medical record (EMR) systems from different vendors.To inform future EMR adoption and policy in China, as well as informatics research in the US, this study compared the United State's Hospital Meaningful Use (MU) Objectives (phase 1) objectives to the EMR functionality of two early hospital EMR adopters in China. METHODS: At both hospitals, the researchers observed a physician using the EMR and noted MU functionality that was seen and functionality that was not seen yet was available in the EMR. The information technology department was asked about the availability of functionality neither observed nor known to the physician. RESULTS AND CONCLUSIONS: Approximately half the MU objectives were available in each EMR. Some differences between the EMRs in the study and MU objectives were attributed to operational differences between the health systems and the cultures in the two countries.