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BackgroundClinical decision support systems (CDSS) are an integral component of health information technologies and can assist disease interpretation, diagnosis, treatment, and prognosis. However, the utility of CDSS in the clinic remains controversial. ObjectiveThe aim is to assess the effects of CDSS integrated with British Medical Journal (BMJ) Best Practice–aided diagnosis in real-world research. MethodsThis was a retrospective, longitudinal observational study using routinely collected clinical diagnosis data from electronic medical records. A total of 34,113 hospitalized patient records were successively selected from December 2016 to February 2019 in six clinical departments. The diagnostic accuracy of the CDSS was verified before its implementation. A self-controlled comparison was then applied to detect the effects of CDSS implementation. Multivariable logistic regression and single-group interrupted time series analysis were used to explore the effects of CDSS. The sensitivity analysis was conducted using the subgroup data from January 2018 to February 2019. ResultsThe total accuracy rates of the recommended diagnosis from CDSS were 75.46% in the first-rank diagnosis, 83.94% in the top-2 diagnosis, and 87.53% in the top-3 diagnosis in the data before CDSS implementation. Higher consistency was observed between admission and discharge diagnoses, shorter confirmed diagnosis times, and shorter hospitalization days after the CDSS implementation (all P

The quantum metric of single-particle wave functions in topological flat bands plays a crucial role in determining the stability of fractional Chern insulating (FCI) states. Here, we unravel that the quantum metric causes the many-body Chern number of the FCI states to deviate sharply from the expected value associated with partial filling of the single-particle topological flat band. Furthermore, the variation of the quantum metric in momentum space induces band dispersion through interactions, affecting the stability of the FCI states. This causes a reentrant transition into the Fermi liquid from the FCI phase as the interaction strength increases.

Abstract Background Type 1 diabetes is believed to be an autoimmune condition, characterized by destruction of insulin-producing cells, due to the detrimental inflammation in pancreas. Growing evidences have indicated the important role of type I interferon in the development of type 1 diabetes. Methods Trex1-deficient rats were generated by using CRISPR-Cas9. The fasting blood glucose level of rat was measured by a Roche Accuchek blood glucose monitor. The levels of insulin, islet autoantibodies, and interferon-β were measured using enzyme-linked immunosorbent assay. The inflammatory genes were detected by quantitative PCR and RNA-seq. Hematein-eosin staining was used to detect the pathological changes in pancreas, eye and kidney. The pathological features of kidney were also detected by Masson trichrome and periodic acid-Schiff staining. The distribution of islet cells, immune cells or ssDNA in pancreas was analyzed by immunofluorescent staining. Results In this study, we established a Trex1-deletion Sprague Dawley rat model, and unexpectedly, we found that the Trex1 −/− rats spontaneously develop type 1 diabetes. Similar to human diabetes, the hyperglycemia in rats is accompanied by diabetic complications such as diabetic nephropathy and cataract. Mechanistical investigation revealed the accumulation of ssDNA and the excessive production of proinflammatory cytokines, including IFN-β, in Trex1 null pancreas. These are likely contributing to the inflammation in pancreas and eventually leading to the decline of pancreatic β cells. Conclusions Our study links the DNA-induced chronic inflammation to the pathogenesis of type 1 diabetes, and also provides an animal model for type 1 diabetes studies.

miRNAs are small non-coding RNA molecules which serve as promising biomarkers due to their important roles in the development and progression of various cancer types. The detection of miRNAs is of vital importance to the early-stage diagnostics and prognostics of multiple diseases. However, traditional detection strategies have faced some challenges owing to the intrinsic characteristics of miRNAs including small size, short sequence length, low concentration level and high sequence homology in complex real samples. To overcome these challenges, we proposed a MXene-enhanced plasmonic biosensor for real-time and label-free detection of miRNA. By utilizing MXene nanomaterial which possesses unique characteristics including large surface area and strong carrier confinement abilities, we tuned the absorption of our plasmonic sensing substrate to reach a “zero-reflection” state and induced an extremely sharp phase change at the resonance angle. Combined with the sensing mechanism based on phase-induced lateral displacement measurement, this MXene-enhanced plasmonic biosensor can achieve a much superior sensing performance compared to traditional SPR devices. Based on this biosensing scheme, the ultrasensitive detection of target miRNA with a detection limit down to 10 fM has been successfully demonstrated. More importantly, single-base mismatched miRNA can be easily distinguished from the target miRNA according to the sensing signal. Furthermore, our plasmonic biosensor is capable of detecting miRNA in complex media such as 100 % human serum samples without compromising the detection sensitivity. This MXene-enhanced plasmonic sensing scheme has the ability of detecting miRNAs with extremely low concentration levels in complex surrounding media without the need of introducing extra labels or amplification tags, which holds great potential in various biological applications and clinical diagnostics.

Abstract Quantum spin liquids (QSL) have emerged as a captivating subject within interacting spin systems that exhibit no magnetic ordering even at the lowest temperature accessible experimentally. However, definitive experimental evidence remains elusive. In light of the recent surge in theoretical and experimental interest in the half-filled Hubbard model on a triangular lattice, which offers the potential for stabilizing a chiral QSL, we investigate the electromagnetic signatures of this phase to facilitate experimental detection. Utilizing a combination of parton mean-field theory and unbiased density-matrix renormalization group calculations, we systematically examine the electrical charge and orbital electrical current associated with a spinon excitation in the chiral QSL. Additionally, we calculate the longitudinal and transverse optical conductivities below the Mott gap. Furthermore, employing quantum field theory analysis, we unravel the connection between spinon excitations and emergent as well as physical gauge fields. Our results demonstrate that the chiral QSL phase exhibits a distinct electromagnetic response, even within a Mott insulator regime. This finding holds great potential for enabling the experimental detection of this long-sought-after phase.

New applications such as augmented reality/virtual reality (AR/VR), Internet-of-Things (IOT), autonomous mobile robot (AMR) services, etc., require high reliability and high accuracy real-time positioning and tracking of persons and devices in indoor areas. Among the different visible-light-positioning (VLP) schemes, such as proximity, time-of-arrival (TOA), time-difference-of-arrival (TDOA), angle-of-arrival (AOA), and received-signal-strength (RSS), the RSS scheme is relatively easy to implement. Among these VLP methods, the RSS method is simple and efficient. As the received optical power has an inverse relationship with the distance between the LED transmitter (Tx) and the photodiode (PD) receiver (Rx), position information can be estimated by studying the received optical power from different Txs. In this work, we propose and experimentally demonstrate a real-time VLP system utilizing long short-term memory neural network (LSTM-NN) with principal component analysis (PCA) to mitigate high positioning error, particularly at the positioning unit cell boundaries. Experimental results show that in a positioning unit cell of 100 × 100 × 250 cm3, the average positioning error is 5.912 cm when using LSTM-NN only. By utilizing the PCA, we can observe that the positioning accuracy can be significantly enhanced to 1.806 cm, particularly at the unit cell boundaries and cell corners, showing a positioning error reduction of 69.45%. In the cumulative distribution function (CDF) measurements, when using only the LSTM-NN model, the positioning error of 95% of the experimental data is >15 cm; while using the LSTM-NN with PCA model, the error is reduced to