SNHG4-mediated PTEN destabilization confers oxaliplatin resistance in colorectal cancer cells by inhibiting ferroptosis.

Oxaliplatin resistance poses a significant challenge in colorectal cancer (CRC) therapy, necessitating further investigation into the underlying molecular mechanisms. This study aimed to elucidate the regulatory role of SNHG4 in oxaliplatin resistance and ferroptosis in CRC. Our findings revealed that treatment with oxaliplatin led to downregulation of SNHG4 expression in CRC cells, while resistant CRC cells exhibited higher levels of SNHG4 compared to parental cells. Silencing SNHG4 attenuated oxaliplatin resistance and reduced the expression of resistance-related proteins MRD1 and MPR1. Furthermore, induction of ferroptosis effectively diminished oxaliplatin resistance in both parental and resistant CRC cells. Notably, ferroptosis induction resulted in decreased SNHG4 expression, whereas SNHG4 overexpression suppressed ferroptosis. Through FISH, RIP, and RNA pull-down assays, we identified the cytoplasmic localization of both SNHG4 and PTEN, establishing that SNHG4 directly targets PTEN, thereby reducing mRNA stability in CRC cells. Silencing PTEN abrogated the impact of SNHG4 on oxaliplatin resistance and ferroptosis in CRC cells. In vivo experiments further validated the influence of SNHG4 on oxaliplatin resistance and ferroptosis in CRC cells through PTEN regulation. In conclusion, SNHG4 promotes resistance to oxaliplatin in CRC cells by suppressing ferroptosis through instability of PTEN, thus serves as a target for patients with oxaliplatin-base chemoresistance.

The dynamic variation position and predominant quasispecies of hepatitis B virus: Novel predictors of early hepatocarcinoma.

To find the predictors of early HCC based on the dynamic changes of HBV quasispecies, this study utilizing the second-generation sequencing (NGS) and high-order multiplex droplet digital PCR (ddPCR) technology to examine the HBV quasispecies in serum of total 247 subjects recruited from high-incidence area of HCC. In the discovery stage, 15 non-synonymous Single Nucleotide Polymorphisms (SNPs) with higher variant proportion in HCC case group were founded (all P<0.05). Furthermore, the variant proportions in some of these SNPs were observed changing regularly within 5 years before the onset of HCC, and 5 of them located in HBX, 2 in HBS and 2 in HBC. The HBV predominant quasispecies and their consensus sequences were identified by genetic evolution analysis, in which the high HBS and HBC quasispecies heterogeneity were found associated with the forming of multifarious quasispecies clones, and the HBX gene had the highest proportion of predominant quasispecies (46.7 % in HBX vs 12.7 % and 13.8 % in HBS and HBC respectively) with the key variations (G1512A, A1630G, T1753C/G/A, A1762T and G1764A) determined. In the validation stage, we confirmed that the combined double mutations of G1512A+A1630G, A1762T+G1764A, and the combined triple mutations of T1753C/G/A + A1762T+G1764A, all expressed higher in early HCC cases when comparing with control group (all P<0.05). We also demonstrated the advantages of ddPCR using in multi-variations detection in large-sample for early HCC surveillance and screening. So we think that the dynamic of key HBV variation positions and their different combinations determined by quasispecies anlysis in this study can act as the novel predictors of early hepatocarcinoma and suitable to popularize and apply in HCC screening.

Population-Specific Glucose Prediction in Diabetes Care With Transformer-Based Deep Learning on the Edge.

Leveraging continuous glucose monitoring (CGM) systems, real-time blood glucose (BG) forecasting is essential for proactive interventions, playing a crucial role in enhancing the management of type 1 diabetes (T1D) and type 2 diabetes (T2D). However, developing a model generalized to a population and subsequently embedding it within a microchip of a wearable device presents significant technical challenges. Furthermore, the domain of BG prediction in T2D remains under-explored in the literature. In light of this, we propose a population-specific BG prediction model, leveraging the capabilities of the temporal fusion Transformer (TFT) to adjust predictions based on personal demographic data. Then the trained model is embedded within a system-on-chip, integral to our low-power and low-cost customized wearable device. This device seamlessly communicates with CGM systems through Bluetooth and provides timely BG predictions using edge computing. When evaluated on two publicly available clinical datasets with a total of 124 participants with T1D or T2D, the embedded TFT model consistently demonstrated superior performance, achieving the lowest prediction errors when compared with a range of machine learning baseline methods. Executing the TFT model on our wearable device requires minimal memory and power consumption, enabling continuous decision support for more than 51 days on a single Li-Poly battery charge. These findings demonstrate the significant potential of the proposed TFT model and wearable device in enhancing the quality of life for people with diabetes and effectively addressing real-world challenges.

Effectiveness and safety of camrelizumab-containing neoadjuvant therapy in patients with esophageal squamous cell carcinoma: a prospective multicenter observational cohort study.

Background: Camrelizumab has been demonstrated to be a feasible treatment option for locally advanced esophageal squamous cell carcinoma (ESCC) when combined with neoadjuvant chemotherapy. This trial was conducted to investigate the effectiveness and safety of camrelizumab-containing neoadjuvant therapy in patients with ESCC in daily practice. Methods: This prospective multicenter observational cohort study was conducted at 13 tertiary hospitals in Southeast China. Patients with histologically or cytologically confirmed ESCC [clinical tumor-node-metastasis (cTNM) stage I-IVA] who had received at least one dose of camrelizumab-containing neoadjuvant therapy were eligible for inclusion. Results: Between June 1, 2020 and July 13, 2022, 255 patients were enrolled and included. The median age was 64 (range, 27 to 82) years. Most participants were male (82.0%) and had clinical stage III-IVA diseases (82.4%). A total of 169 (66.3%) participants underwent surgical resection; 146 (86.4%) achieved R0 resection, and 36 (21.3%) achieved pathological complete response (pCR). Grades 3-5 adverse events (AEs) were experienced by 14.5% of participants. Reactive cutaneous capillary endothelial proliferation occurred in 100 (39.2%) of participants and all were grade 1 or 2. Conclusions: Camrelizumab-containing neoadjuvant therapy has acceptable effectiveness and safety profiles in real-life ESCC patients.

Offline Deep Reinforcement Learning and Off-Policy Evaluation for Personalized Basal Insulin Control in Type 1 Diabetes.

Recent advancements in hybrid closed-loop systems, also known as the artificial pancreas (AP), have been shown to optimize glucose control and reduce the self-management burdens for people living with type 1 diabetes (T1D). AP systems can adjust the basal infusion rates of insulin pumps, facilitated by real-time communication with continuous glucose monitoring. Deep reinforcement learning (DRL) has introduced new paradigms of basal insulin control algorithms. However, all the existing DRL-based AP controllers require extensive random online interactions between the agent and environment. While this can be validated in T1D simulators, it becomes impractical in real-world clinical settings. To this end, we propose an offline DRL framework that can develop and validate models for basal insulin control entirely offline. It comprises a DRL model based on the twin delayed deep deterministic policy gradient and behavior cloning, as well as off-policy evaluation (OPE) using fitted Q evaluation. We evaluated the proposed framework on an in silico dataset generated by the UVA/Padova T1D simulator, and the OhioT1DM dataset, a real clinical dataset. The performance on the in silico dataset shows that the offline DRL algorithm significantly increased time in range while reducing time below range and time above range for both adult and adolescent groups. Then, we used the OPE to estimate model performance on the clinical dataset, where a notable increase in policy values was observed for each subject. The results demonstrate that the proposed framework is a viable and safe method for improving personalized basal insulin control in T1D.

GluGAN: Generating Personalized Glucose Time Series Using Generative Adversarial Networks.

Time series data generated by continuous glucose monitoring sensors offer unparalleled opportunities for developing data-driven approaches, especially deep learning-based models, in diabetes management. Although these approaches have achieved state-of-the-art performance in various fields such as glucose prediction in type 1 diabetes (T1D), challenges remain in the acquisition of large-scale individual data for personalized modeling due to the elevated cost of clinical trials and data privacy regulations. In this work, we introduce GluGAN, a framework specifically designed for generating personalized glucose time series based on generative adversarial networks (GANs). Employing recurrent neural network (RNN) modules, the proposed framework uses a combination of unsupervised and supervised training to learn temporal dynamics in latent spaces. Aiming to assess the quality of synthetic data, we apply clinical metrics, distance scores, and discriminative and predictive scores computed by post-hoc RNNs in evaluation. Across three clinical datasets with 47 T1D subjects (including one publicly available and two proprietary datasets), GluGAN achieved better performance for all the considered metrics when compared with four baseline GAN models. The performance of data augmentation is evaluated by three machine learning-based glucose predictors. Using the training sets augmented by GluGAN significantly reduced the root mean square error for the predictors over 30 and 60-minute horizons. The results suggest that GluGAN is an effective method in generating high-quality synthetic glucose time series and has the potential to be used for evaluating the effectiveness of automated insulin delivery algorithms and as a digital twin to substitute for pre-clinical trials.

SPINK4 promotes colorectal cancer cell proliferation and inhibits ferroptosis.

BACKGROUND: Little is known about the role of serine peptidase inhibitor Kazal type 4 (SPINK4) in colorectal cancer (CRC) and ferroptosis. Therefore, this study aimed to determine the effect of SPINK4 on CRC pathogenesis and ferroptosis. METHODS: SPINK4 expression was analyzed in public datasets and examined using immunohistochemistry. The biological function of SPINK4 in CRC cell lines and its effect on ferroptosis were tested. An immunofluorescence assay was performed to determine the location of SPINK4 in cells, and mouse models were established to determine the effects of SPINK4 in vivo. RESULTS: CRC datasets and clinical samples analysis revealed that SPINK4 mRNA and protein levels were significantly reduced in CRC tissues compared to control tissues (P < 0.05). Two CRC cell lines (HCT116 and LoVo) were selected, and the in vitro and in vivo experiments showed that overexpression of SPINK4 greatly promotes the proliferation and metastasis of CRC cells and tumor growth (P < 0.05). The immunofluorescence assay indicated that SPINK4 is mainly located in the nucleoplasm and nucleus of CRC cells. Furthermore, SPINK4 expression was reduced after cell ferroptosis induced by Erastin, and overexpression of SPINK4 greatly inhibited ferroptosis in CRC cells. The results of mouse model further demonstrated that SPINK4 overexpression inhibited CRC cell ferroptosis and facilitated tumor growth. CONCLUSIONS: SPINK4 was decreased in CRC tissues and promoted cell proliferation and metastasis; overexpression of SPINK4 inhibited CRC cell ferroptosis.

Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App.

INTRODUCTION: Smoking lapses after the quit date often lead to full relapse. To inform the development of real time, tailored lapse prevention support, we used observational data from a popular smoking cessation app to develop supervised machine learning algorithms to distinguish lapse from non-lapse reports. AIMS AND METHODS: We used data from app users with ≥20 unprompted data entries, which included information about craving severity, mood, activity, social context, and lapse incidence. A series of group-level supervised machine learning algorithms (eg, Random Forest, XGBoost) were trained and tested. Their ability to classify lapses for out-of-sample (1) observations and (2) individuals were evaluated. Next, a series of individual-level and hybrid algorithms were trained and tested. RESULTS: Participants (N = 791) provided 37 002 data entries (7.6% lapses). The best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.969 (95% confidence interval [CI] = 0.961 to 0.978). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUC = 0.482-1.000). Individual-level algorithms could be constructed for 39/791 participants with sufficient data, with a median AUC of 0.938 (range: 0.518-1.000). Hybrid algorithms could be constructed for 184/791 participants and had a median AUC of 0.825 (range: 0.375-1.000). CONCLUSIONS: Using unprompted app data appeared feasible for constructing a high-performing group-level lapse classification algorithm but its performance was variable when applied to unseen individuals. Algorithms trained on each individual's dataset, in addition to hybrid algorithms trained on the group plus a proportion of each individual's data, had improved performance but could only be constructed for a minority of participants. IMPLICATIONS: This study used routinely collected data from a popular smartphone app to train and test a series of supervised machine learning algorithms to distinguish lapse from non-lapse events. Although a high-performing group-level algorithm was developed, it had variable performance when applied to new, unseen individuals. Individual-level and hybrid algorithms had somewhat greater performance but could not be constructed for all participants because of the lack of variability in the outcome measure. Triangulation of results with those from a prompted study design is recommended prior to intervention development, with real-world lapse prediction likely requiring a balance between unprompted and prompted app data.

Personalized Blood Glucose Prediction for Type 1 Diabetes Using Evidential Deep Learning and Meta-Learning.

The availability of large amounts of data from continuous glucose monitoring (CGM), together with the latest advances in deep learning techniques, have opened the door to a new paradigm of algorithm design for personalized blood glucose (BG) prediction in type 1 diabetes (T1D) with superior performance. However, there are several challenges that prevent the widespread implementation of deep learning algorithms in actual clinical settings, including unclear prediction confidence and limited training data for new T1D subjects. To this end, we propose a novel deep learning framework, Fast-adaptive and Confident Neural Network (FCNN), to meet these clinical challenges. In particular, an attention-based recurrent neural network is used to learn representations from CGM input and forward a weighted sum of hidden states to an evidential output layer, aiming to compute personalized BG predictions with theoretically supported model confidence. The model-agnostic meta-learning is employed to enable fast adaptation for a new T1D subject with limited training data. The proposed framework has been validated on three clinical datasets. In particular, for a dataset including 12 subjects with T1D, FCNN achieved a root mean square error of 18.64±2.60 mg/dL and 31.07±3.62 mg/dL for 30 and 60-minute prediction horizons, respectively, which outperformed all the considered baseline methods with significant improvements. These results indicate that FCNN is a viable and effective approach for predicting BG levels in T1D. The well-trained models can be implemented in smartphone apps to improve glycemic control by enabling proactive actions through real-time glucose alerts.

Monolith or Powder: Improper Sample Pretreatment May Mislead the Understanding of Industrial V2O5-WO3/TiO2 Catalysts Operated in Stationary Resources.

Although various characterizations are widely applied to commercial V2O5-WO3/TiO2 catalysts, the influence of the catalyst physical structure, i.e., monolith or powder, on the characterization results has not been investigated. Several important catalytic behaviors and phenomena were observed in this study using V2O5-WO3/TiO2 monolithic catalysts employed for over 5000 h in various stationary flue gases, and many of the results were only observable on monolithic catalysts, such as depth-dependent distribution of external elements, penetration of As2O3, and the formation of Tl2O-TiO2 p-n junctions. If the monolith is ground into powder states, it will alter or destroy the catalyst surface and remove important clues closely related to catalytic performance under working conditions. The redox and acidity properties of V2O5-WO3/TiO2 obtained from powder samples may be significantly different from their true state under working conditions, resulting in a misperception of catalyst performance. Therefore, a cautious pretreatment should be taken into careful consideration when analyzing commercial honeycomb V2O5-WO3/TiO2 catalysts.

Construction and Validation of an Oxaliplatin-Resistant Gene Signature in Colorectal Cancer Patients Who Underwent Chemotherapy.

Aberrant expression of genes contributes to the chemoresistance of colorectal cancer (CRC) treatment. This study aimed to identify genes associated with the chemoresistance of oxaliplatin-based chemotherapy in CRC patients and to construct a signature. Oxaliplatin resistance-related genes were screened by analyzing the gene profiles of cell lines and tissue samples that underwent oxaliplatin-based treatment. Oxaliplatin resistance-related genes were used to establish a signature. The association of the signature had clinical significance, so the prognostic value of the signature was analyzed. Independent cohorts and CRC cell lines were used to validate the value of the gene signature and the oxaliplatin-resistant genes. There were 64 oxaliplatin resistance-related genes identified after overlapping the genes from the dataset of oxaliplatin-treated CRC cells and the dataset of patients treated with oxaliplatin-based chemotherapy. A gene signature based on five oxaliplatin resistance-related genes was established. This gene signature effectively predicted the prognosis of CRC patients who underwent chemotherapy. No significant associations were found between the gene mutations and survival of the patients; however, two genes were associated with microsatellite instability status. Two external independent cohorts and CRC cell line experiments validated the prognostic values of the signature and expression of the genes after oxaliplatin treatment. In conclusion, the oxaliplatin resistance-related gene signature involving five genes was a novel biomarker for the prediction of the chemotherapy response and prognosis of CRC patients who underwent oxaliplatin-based chemotherapy.

Exosomal lncRNA PCAT1 Promotes Tumor Circulating Cell-Mediated Colorectal Cancer Liver Metastasis by Regulating the Activity of the miR-329-3p/Netrin-1-CD146 Complex.

Objective: This study explored the colorectal cancer exosome lncRNA prostate cancer associated transcript 1- (PCAT1) mediated circulating tumors and the mechanism of cell colorectal cancer liver metastasis. Methods: Exosomes were extracted from the primary colorectal cancer (CRC) cell lines HCT116 and SW480 and cultured with T84 and human umbilical vein endothelial (HUVE) cells. The expression of PCAT1 and miR-329-3p was detected by real-time quantitative polymerase chain reaction (RT-qPCR), the expression of Netrin-1, CD146, and epithelial mesenchymal transition (EMT) related proteins was detected by Western blot, the proliferation activity of T84 cells was detected by cell counting kit 8 (CCK-8), and cell migration was detected by Transwell. The expression of the F-actin signal was detected by immunofluorescence after coculture of exosomes with human umbilical vein endothelial cells (HUVECs). Changes in subcutaneous tumor and liver nodule size after PCAT1 deletion were observed in a mouse model of liver metastasis from rectal cancer. Results: PCAT1 expression was upregulated in primary cell lines and their exosomes. After exosomes were cocultured with colorectal cancer tumor circulating T84 cells, the expression of Netrin-1 and CD146 was upregulated, the expression of miR-329-3p was downregulated, the proliferation and migration ability of T84 cells were enhanced, and EMT occurred. After knocking down PCAT1, the above phenomenon was reversed. Similarly, after exosomes were cocultured with HUVECs, the expression of the F-actin signal increased, and after PCAT1 was knocked down, the F-actin signal also decreased. PCAT1 regulates miR-329-3p/Netrin-1 and affects the biological behavior of T84 and F-actin signal expression in HUVECs. In a mouse model of colorectal cancer liver metastasis, knocking down PCAT1 significantly reduced the nodules formed by liver metastasis in mice. Conclusions: LncRNA PCAT1 derived from colorectal cancer exosomes regulates the activity of the Netrin-1-CD146 complex in circulating tumor cells (CTCs) to promote the occurrence of colorectal cancer EMT and liver metastasis and provides new molecular targets for the treatment of colorectal cancer liver metastasis.

Formic Acid-Mediated Regeneration Strategy for As-Poisoned V2O5-WO3/TiO2 Catalysts with Lossless Catalytic Activity and Simultaneous As Recycling.

Regeneration of spent V2O5-WO3/TiO2 catalysts is highly desirable, especially for those containing hypertoxic As, which is categorized as hazardous waste. However, common solution-leaching methods suffer from the trade-off between As removal and V2O5 retention, and it would be necessary to introduce extra proceedings like ingredients reimplantation and As-bearing waste treatment after regeneration. Herein, a formic acid-mediated regeneration strategy has been developed to achieve superior catalytic activity, short timescale regeneration, and nontoxic metallic As recycling with controllable and safe conduction. The specific activity of the optimal regenerated catalyst reaches 98.3% of the fresh catalyst with 99.1% As removal and less than 1.8% V loss within 15 min. Structure characterizations reveal that the distorted VOx molecular structure, surface acidity, and redox property recover to the fresh level after regeneration. In situ investigation of the regeneration process indicates that As-OH removal together with V-OH generation occurs at the first regeneration stage, followed by the active center V═O sites over-reduction at the second stage. The retained V═O species by suitable regeneration temperature and time are essential for NH3-selective catalytic reduction (SCR) since As existence and VOx over-reduction will separately cause unstable and excessive NH3 adsorption to further suppress the reaction cycle. The developed strategy and improved understanding of active site protection would exert benefits on the development of efficient and time-saving regeneration methods for spent catalysts.

Fatty acid binding protein 5 promotes the proliferation, migration, and invasion of hepatocellular carcinoma cells by degradation of Krüppel-like factor 9 mediated by miR-889-5p via cAMP-response element binding protein.

Mounting evidence has demonstrated that fatty acid binding protein 5 (FABP5) is commonly upregulated in many human malignancies. However, the mechanisms explaining the involvement of FABP5 in hepatocellular carcinoma (HCC) remain unclear. In this study, we demonstrated the involvement of FABP5 and its downstream signaling molecules in HCC progression. We first confirmed that FABP5 expression was upregulated in HCC. Additionally, FABP5 promoted HCC cells proliferation, migration, and invasion. Mechanistic investigation showed that FABP5 could improve cAMP-response element binding protein (CREB) phosphorylation. Meanwhile, CREB, as a transcription factor, upregulated the miR-889-5p expression by binding to the miR-889-5p promoter region. Consequently, miR-889-5p led to downregulation of Krüppel-like factor 9 (KLF9) by binding to the 3'-UTR of the KLF9 mRNA, potentiating the PI3K/AKT signaling pathway and promoting the proliferation, migration, and invasion of HCC cells. Our findings have identified a FABP5/CREB/miR-889-5p/KLF9 axis for HCC progression, and we postulate that blocking this key signaling pathway may represent a promising strategy for HCC treatment.

Machine learning for real-time aggregated prediction of hospital admission for emergency patients.

Machine learning for hospital operations is under-studied. We present a prediction pipeline that uses live electronic health-records for patients in a UK teaching hospital's emergency department (ED) to generate short-term, probabilistic forecasts of emergency admissions. A set of XGBoost classifiers applied to 109,465 ED visits yielded AUROCs from 0.82 to 0.90 depending on elapsed visit-time at the point of prediction. Patient-level probabilities of admission were aggregated to forecast the number of admissions among current ED patients and, incorporating patients yet to arrive, total emergency admissions within specified time-windows. The pipeline gave a mean absolute error (MAE) of 4.0 admissions (mean percentage error of 17%) versus 6.5 (32%) for a benchmark metric. Models developed with 104,504 later visits during the Covid-19 pandemic gave AUROCs of 0.68-0.90 and MAE of 4.2 (30%) versus a 4.9 (33%) benchmark. We discuss how we surmounted challenges of designing and implementing models for real-time use, including temporal framing, data preparation, and changing operational conditions.

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records.

BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK.

Enhancing self-management in type 1 diabetes with wearables and deep learning.

People living with type 1 diabetes (T1D) require lifelong self-management to maintain glucose levels in a safe range. Failure to do so can lead to adverse glycemic events with short and long-term complications. Continuous glucose monitoring (CGM) is widely used in T1D self-management for real-time glucose measurements, while smartphone apps are adopted as basic electronic diaries, data visualization tools, and simple decision support tools for insulin dosing. Applying a mixed effects logistic regression analysis to the outcomes of a six-week longitudinal study in 12 T1D adults using CGM and a clinically validated wearable sensor wristband (NCT ID: NCT03643692), we identified several significant associations between physiological measurements and hypo- and hyperglycemic events measured an hour later. We proceeded to develop a new smartphone-based platform, ARISES (Adaptive, Real-time, and Intelligent System to Enhance Self-care), with an embedded deep learning algorithm utilizing multi-modal data from CGM, daily entries of meal and bolus insulin, and the sensor wristband to predict glucose levels and hypo- and hyperglycemia. For a 60-minute prediction horizon, the proposed algorithm achieved the average root mean square error (RMSE) of 35.28 ± 5.77 mg/dL with the Matthews correlation coefficients for detecting hypoglycemia and hyperglycemia of 0.56 ± 0.07 and 0.70 ± 0.05, respectively. The use of wristband data significantly reduced the RMSE by 2.25 mg/dL (p < 0.01). The well-trained model is implemented on the ARISES app to provide real-time decision support. These results indicate that the ARISES has great potential to mitigate the risk of severe complications and enhance self-management for people with T1D.

Integrative Analysis Revealed Stemness Features and a Novel Stemness-Related Classification in Colorectal Cancer Patients.

Cancer stem cells play crucial roles in colorectal cancer (CRC) tumorigenesis and treatment response. This study aimed to determine the value of the mRNA stemness index (mRNAsi) in CRC and introduce a stemness-related classification to predict the outcome of patients. mRNAsi scores and RNA sequence data of CRC patients were analyzed. We found that high mRNAsi scores were related to early-stage CRC and a better patient prognosis. Two stemness-based subtypes (subtype I and II) were identified. Patients in subtype I presented a significantly better prognosis than those in subtype II. Patients in these two subtype groups presented significantly different tumor immunity scores and immune cell infiltration patterns. Genomic variations revealed that patients in subtype I had a lower tumor mutation burden than those in subtype II. A three-gene stemness subtype predictor was established, showing good diagnostic value in discriminating patients in different subtypes. A prognostic signature based on five stemness-related genes was established and validated in two independent cohorts and clinical samples, showing a better predictive performance than other clinical parameters. We concluded that mRNAsi scores were associated with the clinical outcome in CRC patients. The stemness-related classification was a promising prognostic predictor for CRC patients.

Crystalline structures of L-cysteine and L-cystine: a combined theoretical and experimental characterization.

It is assumed that genetic diseases affecting the metabolism of cysteine and the kidney function lead to two different kinds of pathologies, namely cystinuria and cystinosis whereby generate L-cystine crystals. Recently, the presence of L-cysteine crystal has been underlined in the case of cystinosis. Interestingly, it can be strikingly seen that cystine ([-S-CH2-CH-(NH2)-COOH]2) consists of two cysteine (C3H7NO2S) molecules connected by a disulfide (S-S) bond. Therefore, the study of cystine and cysteine is important for providing a better understanding of cystinuria and cystinosis. In this paper, we elucidate the discrepancy between L-cystine and L-cysteine by investigating the theoretical and experimental infrared spectra (IR), X-ray diffraction (XRD) as well as Raman spectra aiming to obtain a better characterization of abnormal deposits related to these two genetic pathologies.