Efficacy of rituximab in treating steroid-resistant Graves' orbitopathy in active moderate-to-severe and sight-threatening forms: A retrospective observation from China.

Background and objectives: The efficacy of rituximab (RTX) in treating steroid-resistant Graves' orbitopathy (GO) has been limitedly studied in Asians. Moreover, RTX has been considered even less for patients with steroid-resistant dysthyroid optic neuropathy (DON) who failed to undergo orbital decompression surgery for physical or financial reasons, or who responded poorly to the procedure. This study aimed to investigate the efficacy of RTX in treating steroid-resistant active moderate-to-severe and sight-threatening GO in a Chinese population. Methods: Data from 28 patients with steroid-resistant GO prescribed a single dose of 500 mg RTX were retrospectively retrieved. Treatment responses and contributing factors were analyzed. Results: The median follow-up time was 22 (8-34) weeks. 23 (82.1 %) patients had a positive objective outcome recommended by the European Group on Graves' Orbitopathy (EUGOGO), while 25 (92.6 %) had a decrease in 7-item clinical activity score (CAS) by at least 2. Diplopia, visual dysfunction, and MRI-detected T2 relaxation time of the involved extraocular muscles improved significantly at the last follow-up compared to baseline (81.0 % vs. 47.6 %, 38.9 % vs. 16.7 %, and 87.8 (8.64) vs. 75.8 (10.9) ms, respectively; all p values < 0.05). No significant improvement was seen in terms of proptosis and eye muscle duction. Notably, a higher baseline IgG4 to IgG ratio was a predictor for RTX-induced positive EUGOGO outcomes. After RTX treatment, all 8 patients with DON demonstrated inactivation, and 4 improved in visual acuity by ≥ 1 line. No patient with DON experienced obvious deterioration. Conclusion: A single dose of 500 mg RTX seemed to be an effective and tolerable treatment for steroid-resistant GO. However, larger-scale studies with a control group are required for a more solid conclusion. The role of RTX in steroid-resistant DON management where surgery is unavailable or ineffective should be further explored.

Neural networks based on attention architecture are robust to data missingness for early predicting hospital mortality in intensive care unit patients.

Background: Although the machine learning model developed on electronic health records has become a promising method for early predicting hospital mortality, few studies focus on the approaches for handling missing data in electronic health records and evaluate model robustness to data missingness. This study proposes an attention architecture that shows excellent predictive performance and is robust to data missingness. Methods: Two public intensive care unit databases were used for model training and external validation, respectively. Three neural networks (masked attention model, attention model with imputation, attention model with missing indicator) based on the attention architecture were developed, using masked attention mechanism, multiple imputation, and missing indicator to handle missing data, respectively. Model interpretability was analyzed by attention allocations. Extreme gradient boosting, logistic regression with multiple imputation and missing indicator (logistic regression with imputation, logistic regression with missing indicator) were used as baseline models. Model discrimination and calibration were evaluated by area under the receiver operating characteristic curve, area under precision-recall curve, and calibration curve. In addition, model robustness to data missingness in both model training and validation was evaluated by three analyses. Results: In total, 65,623 and 150,753 intensive care unit stays were respectively included in the training set and the test set, with mortality of 10.1% and 8.5%, and overall missing rate of 10.3% and 19.7%. attention model with missing indicator had the highest area under the receiver operating characteristic curve (0.869; 95% CI: 0.865 to 0.873) in external validation; attention model with imputation had the highest area under precision-recall curve (0.497; 95% CI: 0.480-0.513). Masked attention model and attention model with imputation showed better calibration than other models. The three neural networks showed different patterns of attention allocation. In terms of robustness to data missingness, masked attention model and attention model with missing indicator are more robust to missing data in model training; while attention model with imputation is more robust to missing data in model validation. Conclusions: The attention architecture has the potential to become an excellent model architecture for clinical prediction task with data missingness.

Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit.

BACKGROUND: Clinical decision of extubation is a challenge in the treatment of patient with invasive mechanical ventilation (IMV), since existing extubation protocols are not capable of precisely predicting extubation failure (EF). This study aims to develop and validate interpretable recurrent neural network (RNN) models for dynamically predicting EF risk. METHODS: A retrospective cohort study was conducted on IMV patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Time series with a 4-h resolution were built for all included patients. Two types of RNN models, the long short-term memory (LSTM) and the gated recurrent unit (GRU), were developed. A stepwise logistic regression model was used to select key features for developing light-version RNN models. The RNN models were compared to other five non-temporal machine learning models. The Shapley additive explanations (SHAP) value was applied to explain the influence of the features on model prediction. RESULTS: Of 8,599 included patients, 2,609 had EF (30.3%). The area under receiver operating characteristic curve (AUROC) of LSTM and GRU showed no statistical difference on the test set (0.828 vs. 0.829). The light-version RNN models based on the 26 features selected out of a total of 89 features showed comparable performance as their corresponding full-version models. Among the non-temporal models, only the random forest (RF) (AUROC: 0.820) and the extreme gradient boosting (XGB) model (AUROC: 0.823) were comparable to the RNN models, but their calibration was deviated. CONCLUSIONS: The RNN models have excellent predictive performance for predicting EF risk and have potential to become real-time assistant decision-making systems for extubation.

Discovery of TIGIT inhibitors based on DEL and machine learning.

Drug discovery has entered a new period of vigorous development with advanced technologies such as DNA-encoded library (DEL) and artificial intelligence (AI). The previous DEL-AI combination has been successfully applied in the drug discovery of classical kinase and receptor targets mainly based on the known scaffold. So far, there is no report of the DEL-AI combination on inhibitors targeting protein-protein interaction, including those undruggable targets with few or unknown active scaffolds. Here, we applied DEL technology on the T cell immunoglobulin and ITIM domain (TIGIT) target, resulting in the unique hit compound 1 (IC50 = 20.7 µM). Based on the screening data from DEL and hit derivatives a1-a34, a machine learning (ML) modeling process was established to address the challenge of poor sample distribution uniformity, which is also frequently encountered in DEL screening on new targets. In the end, the established ML model achieved a satisfactory hit rate of about 75% for derivatives in a high-scored area.

Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis.

BACKGROUND: Early prediction of hospital mortality is crucial for ICU patients with sepsis. This study aimed to develop a novel blending machine learning (ML) model for hospital mortality prediction in ICU patients with sepsis. METHODS: Two ICU databases were employed: eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III (MIMIC-III). All adult patients who fulfilled Sepsis-3 criteria were identified. Samples from eICU-CRD constituted training set and samples from MIMIC-III constituted test set. Stepwise logistic regression model was used for predictor selection. Blending ML model which integrated nine sorts of basic ML models was developed for hospital mortality prediction in ICU patients with sepsis. Model performance was evaluated by various measures related to discrimination or calibration. RESULTS: Twelve thousand five hundred fifty-eight patients from eICU-CRD were included as the training set, and 12,095 patients from MIMIC-III were included as the test set. Both the training set and the test set showed a hospital mortality of 17.9%. Maximum and minimum lactate, maximum and minimum albumin, minimum PaO2/FiO2 and age were important predictors identified by both random forest and extreme gradient boosting algorithm. Blending ML models based on corresponding set of predictors presented better discrimination than SAPS II (AUROC, 0.806 vs. 0.771; AUPRC 0.515 vs. 0.429) and SOFA (AUROC, 0.742 vs. 0.706; AUPRC 0.428 vs. 0.381) on the test set. In addition, calibration curves showed that blending ML models had better calibration than SAPS II. CONCLUSIONS: The blending ML model is capable of integrating different sorts of basic ML models efficiently, and outperforms conventional severity scores in predicting hospital mortality among septic patients in ICU.

An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data.

With the rapid development of LBSs (location-based services) in recent years, researchers have increasingly taken an interest in trying to make travel routes more practicable and individualized. Despite the fact that many studies have been conducted on routes using LBS data, the specific routes are deficient in dynamic scalability and the correlations between environmental constraints and personal choices have not been investigated. This paper proposes an improved HMM-based (hidden Markov model) method for planning personalized routes with crowd sourcing spatiotemporal data. It tries to integrate the dynamic public preferences, the individual interests and the physical road network space in the same spatiotemporal framework, ensuring that reasonable routes will be generated. A novel dual-layer mapping structure has been proposed to bridge the gap from brief individual preferences to specific entries of POIs (points-of-interest) inside realistic road networks. A case study on Changsha city has proven that the proposed method can not only flexibly plan people's travel routes under different spatiotemporal backgrounds but also is close to people's natural selection by the perception of the group.

Th17 and Treg lymphocytes in obesity and Type 2 diabetic patients.

Assumption that the pathogenesis of obesity-associated type 2 diabetes (T2DM) encompasses inflammation and autoimmune aspects is increasingly recognized. In the state of obesity and T2DM, the imbalance of T helper 17 (Th17) cells and regulatory T (Treg) cells are observed. These alterations reflect a loss of T cell homeostasis, which may contribute to tissue and systemic inflammation and immunity in T2DM. In this review we will discuss the accumulating data supporting the concept that Th17/Treg mediated immune responses are present in obesity-related T2DM pathogenesis, and provide evidences that restoration of Th17/Treg imbalance may be a possible therapeutic avenue for the prevention and treatment of T2DM and its complications.