Access to cyclohexadiene and benzofuran derivatives via catalytic arene cyclopropanation of α-cyanodiazocarbonyl compounds.

The arene cyclopropanation between diazo compounds and benzene is well known to produce a tautomeric mixture of norcaradiene and cycloheptatriene in favour of the latter species. Nevertheless, previous studies have suggested that the initially formed norcaradiene can be stabilized by a C-7 cyano group with prevention of its 6π-electrocyclic ring opening. According to this feature, a synthetic route to functionalized cyclohexadienes has been designed using α-cyanodiazoacetates and α-diazo-ß-ketonitriles as the starting materials, respectively. The Rh2(esp)2-catalyzed arene cyclopropanation of α-cyanodiazoacetates in benzene afforded the expected 7-alkoxycarbonyl-7-cyanonorcaradienes as isolable compounds, which then served as templates for the second cyclopropanation with ethyl diazoacetate or α-cyanodiazocarbonyls to enable the formation of bis(cyclopropanated) adducts. Their subsequent treatment with SmI2 triggered a double ring-opening process, allowing for the generation of 1,4- and/or 1,3-cyclohexadienes as either regio- or diastereomeric mixtures. On the other hand, the norcaradienes generated from phenyl- or methyl-substituted α-diazo-ß-ketonitriles were found to undergo an in situ rearrangement to yield dihydrobenzofurans that could be converted to benzofuran derivatives by DDQ oxidation.

Relationship between the magnitude of haemoglobin changes and long-term mortality in patients with sepsis: a retrospective cohort study.

BACKGROUND: Sepsis is a common and severe disease with a high mortality rate in intensive care unit (ICU). The hemoglobin (HGB) level is a key parameter for oxygen supply in sepsis. Although HGB is associated with the progression of inflammation in sepsis patients, its role as a marker following sepsis treatment remains unclear. Here, we studied the correlation between early temporal changes in HGB levels and long-term mortality rates in septic patients. METHOD: In this retrospective study of data on patients with sepsis from the Medical Information Mart for Intensive Care (MIMIC) IV database, the outcome was long-term mortality. Patients were divided based on the cut-off of the HGB percentage for receiver operating characteristic (ROC) curve calculation. Kaplan-Meier (KM) survival curves and Cox proportional hazards regression models were used to analyse the associations between groups and outcomes. Propensity score matching (PSM) was used to verify the results. RESULTS: In this study, 2042 patients with sepsis and changes in HGB levels at day 4 after admission compared to day 1 were enrolled and divided into two groups: group 1 (n = 1147) for those with reduction of HGB < 7% and group 2 (n = 895) for those with dropping ≥ 7%. The long-term survival chances of sepsis with less than a 7% reduction in the proportion of HGB at day four were significantly higher than those of patients in the group with a reduction of 7% or more. After adjusting for covariates in the Cox model, the hazard ratios (HRs) with 95% confidence intervals (CIs) for long-term all-cause mortality in the group with a reduction of 7% or more were as follows: 180 days [HR = 1.41, 95% CI (1.22 to 1.63), P < 0.001]; 360 days [HR = 1.37, 95% CI (1.21 to 1.56), P < 0.001]; 540 days [HR = 1.35, 95% CI (1.20 to 1.53), P < 0.001]; 720 days [HR = 1.45, 95% CI (1.29 to 1.64), P < 0.001]. Additionally, the long-term survival rates, using Kaplan-Meier analysis, for the group with a reduction of 7% or more were lower compared to the group with less than 7% reduction at 180 days (54.3% vs. 65.3%, P < 0.001), 360 days (42.3% vs. 50.9%, P < 0.001), 540 days (40.2% vs. 48.6%, P < 0.001), and 720 days (35.5% vs. 46.1%, P < 0.001). The same trend was obtained after using PSM. CONCLUSION: A ≥ 7% decrease in HGB levels on Day 4 after admission was associated with worse long-term prognosis in sepsis patients admitted to the ICU.

Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning.

BACKGROUND: Sepsis is a severe form of systemic inflammatory response syndrome that is caused by infection. Sepsis is characterized by a marked state of stress, which manifests as nonspecific physiological and metabolic changes in response to the disease. Previous studies have indicated that the stress hyperglycemia ratio (SHR) can serve as a reliable predictor of adverse outcomes in various cardiovascular and cerebrovascular diseases. However, there is limited research on the relationship between the SHR and adverse outcomes in patients with infectious diseases, particularly in critically ill patients with sepsis. Therefore, this study aimed to explore the association between the SHR and adverse outcomes in critically ill patients with sepsis. METHODS: Clinical data from 2312 critically ill patients with sepsis were extracted from the MIMIC-IV (2.2) database. Based on the quartiles of the SHR, the study population was divided into four groups. The primary outcome was 28-day all-cause mortality, and the secondary outcome was in-hospital mortality. The relationship between the SHR and adverse outcomes was explored using restricted cubic splines, Cox proportional hazard regression, and Kaplan‒Meier curves. The predictive ability of the SHR was assessed using the Boruta algorithm, and a prediction model was established using machine learning algorithms. RESULTS: Data from 2312 patients who were diagnosed with sepsis were analyzed. Restricted cubic splines demonstrated a "U-shaped" association between the SHR and survival rate, indicating that an increase in the SHR is related to an increased risk of adverse events. A higher SHR was significantly associated with an increased risk of 28-day mortality and in-hospital mortality in patients with sepsis (HR > 1, P < 0.05) compared to a lower SHR. Boruta feature selection showed that SHR had a higher Z score, and the model built using the rsf algorithm showed the best performance (AUC = 0.8322). CONCLUSION: The SHR exhibited a U-shaped relationship with 28-day all-cause mortality and in-hospital mortality in critically ill patients with sepsis. A high SHR is significantly correlated with an increased risk of adverse events, thus indicating that is a potential predictor of adverse outcomes in patients with sepsis.

Integrating Reinforcement Learning and Monte Carlo Tree Search for enhanced neoantigen vaccine design.

Recent advances in cancer immunotherapy have highlighted the potential of neoantigen-based vaccines. However, the design of such vaccines is hindered by the possibility of weak binding affinity between the peptides and the patient's specific human leukocyte antigen (HLA) alleles, which may not elicit a robust adaptive immune response. Triggering cross-immunity by utilizing peptide mutations that have enhanced binding affinity to target HLA molecules, while preserving their homology with the original one, can be a promising avenue for neoantigen vaccine design. In this study, we introduced UltraMutate, a novel algorithm that combines Reinforcement Learning and Monte Carlo Tree Search, which identifies peptide mutations that not only exhibit enhanced binding affinities to target HLA molecules but also retains a high degree of homology with the original neoantigen. UltraMutate outperformed existing state-of-the-art methods in identifying affinity-enhancing mutations in an independent test set consisting of 3660 peptide-HLA pairs. UltraMutate further showed its applicability in the design of peptide vaccines for Human Papillomavirus and Human Cytomegalovirus, demonstrating its potential as a promising tool in the advancement of personalized immunotherapy.

Association between stress hyperglycemia ratio and diabetes mellitus mortality in American adults: a retrospective cohort study and predictive model establishment based on machine learning algorithms (NHANES 2009-2018).

BACKGROUND: Stress hyperglycemia is a physiological response of the body under stress to make adaptive adjustments in response to changes in the internal environment. The stress hyperglycemia ratio (SHR) is a new indicator after adjusting the basal blood glucose level of the population. Previous studies have shown that SHR is associated with poor prognosis in many diseases, such as cardiovascular and cerebrovascular diseases and delirium in elderly patients. However, there are currently no studies on the correlation between SHR and the general U.S. POPULATION: The purpose of this study was to examine the association between SHR and adverse outcomes among adults in the United States in general. METHODS: Data on 13,315 follow-up cohorts were extracted from NHANES. The study population was divided into four groups according to quartiles of SHR. The primary outcomes were all-cause mortality and diabetes mellitus mortality. The relationship between SHR and outcomes was explored using restricted cubic splines, COX proportional hazards regression, Kaplan-Meier curves, and mediation effects. SHR is incorporated into eight machine learning algorithms to establish a prediction model and verify the prediction performance. RESULTS: A total of 13,315 individual data were included in this study. Restricted cubic splines demonstrated a "U-shaped" association between SHR and all-cause mortality and diabetes mellitus mortality, indicating that increasing SHR is associated with an increased risk of adverse events. Compared with lower SHR, higher SHR was significantly associated with an increased risk of all cause mortality and diabetes mellitus mortality (HR > 1, P < 0.05). The mediating effect results showed that the positively mediated variables were segmented neutrophils and aspartate aminotransferase, and the negatively mediated variables were hemoglobin, red blood cell count, albumin, and alanine aminotransferase. The ROC of the eight machine learning algorithm models are XGBoost (0.8688), DT (0.8512), KNN (0.7966), RF (0.8417), Logistic regression (0.8633), ENET (0.8626), SVM (0.8327) and MLP (0.8662). CONCLUSION: SHR can be used as a predictor of all cause mortality and diabetes mellitus mortality in the general adult population in the United States. Higher SHR is significantly associated with an increased risk of poor prognosis, especially in those aged < 65 years and in women.

Development and validation of a nomogram for predicting in-hospital mortality in ICU patients with infective endocarditis.

BACKGROUND: Infective endocarditis (IE) is a disease with high in-hospital mortality. The objective of the present investigation was to develop and validate a nomogram that precisely anticipates in-hospital mortality in ICU individuals diagnosed with infective endocarditis. METHODS: Retrospectively collected clinical data of patients with IE admitted to the ICU in the MIMIC IV database were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify potential hazards. A logistic regression model incorporating multiple factors was established, and a dynamic nomogram was generated to facilitate predictions. To assess the classification performance of the model, an ROC curve was generated, and the AUC value was computed as an indicator of its diagnostic accuracy. The model was subjected to calibration curve analysis and the Hosmer-Lemeshow (HL) test to assess its goodness of fit. To evaluate the clinical relevance of the model, decision-curve analysis (DCA) was conducted. RESULTS: The research involved a total of 676 patients, who were divided into two cohorts: a training cohort comprising 473 patients and a validation cohort comprising 203 patients. The allocation ratio between the two cohorts was 7:3. Based on the independent predictors identified through LASSO regression, the final selection for constructing the prediction model included five variables: lactate, bicarbonate, white blood cell count (WBC), platelet count, and prothrombin time (PT). The nomogram model demonstrated a robust diagnostic ability in both the cohorts used for training and validation. This is supported by the respective area under the curve (AUC) values of 0.843 and 0.891. The results of the calibration curves and HL tests exhibited acceptable conformity between observed and predicted outcomes. According to the DCA analysis, the nomogram model demonstrated a notable overall clinical advantage compared to the APSIII and SAPSII scoring systems. CONCLUSIONS: The nomogram developed during the study proved to be highly accurate in forecasting the mortality of patients with IE during hospitalization in the ICU. As a result, it may be useful for clinicians in decision-making and treatment.

Measured Regional Division Optimization for Acoustic Tomography Velocity Field Reconstruction in a Circular Area.

The acoustic tomography (AT) velocity field reconstruction technique has become a research hotspot in recent years due to its noninvasive nature, high accuracy, and real-time measurement advantages. However, most of the existing studies are limited to the reconstruction of the velocity field in a rectangular area, and there are very few studies on a circular area, mainly because the layout of acoustic transducers, selection of acoustic paths, and division of measured regions are more difficult in a circular area than in a rectangular area. Therefore, based on AT and using the reconstruction algorithm of the Markov function and singular value decomposition (MK-SVD), this paper proposes a measured regional division optimization algorithm for velocity field reconstruction in a circular area. First, an acoustic path distribution based on the multipath effect is designed to solve the problem of the limited emission angle of the acoustic transducer. On this basis, this paper proposes an adaptive optimization algorithm for measurement area division based on multiple sub-objectives. The steps are as follows: first, two optimization objectives, the condition number of coefficient matrix and the uniformity of acoustic path distribution, were designed. Then, the weights of each sub-objective are calculated using the coefficient of variation (CV). Finally, the measured regional division is optimized based on particle swarm optimization (PSO). The reconstruction effect of the algorithm and the anti-interference ability are verified through the reconstruction experiments of the model velocity field and the simulated velocity field.

Impact of enhanced recovery after surgery protocols on surgical site wound infection rates in urological procedures.

This meta-analysis assesses the impact of Enhanced Recovery After Surgery (ERAS) protocols on surgical site wound infections (SSWIs) in urological procedures. Analysing data from 10 studies, our focus was on SSWI rates on the third and seventh postoperative days. The results reveal a significant reduction in SSWI rates for patients managed under ERAS protocols compared with traditional care. Notably, Figure 4 demonstrates a substantial decrease in SSWI on the third day (I2 = 93%; random: standardized mean difference [SMD]: -6.25, 95% confidence interval [CI]: -7.42 to -5.05, p < 0.01), and Figure 5 mirrors this trend on the seventh day (I2 = 95%; random: SMD: -4.72, 95% CI: -6.28 to -3.16, p < 0.01). These findings underscore the effectiveness of ERAS protocols in minimizing early postoperative wound infections, emphasizing their importance for broader implementation in urological surgeries.

Impact of open and minimally invasive surgery on postoperative wound complications in patients undergoing prostate surgery: A meta-analysis.

In this article, we analysed the therapeutic efficacy of open radical prostatectomy (ORP) and minimally invasive surgery (MIS) after operation for the treatment of post-operation complications. In summary, because of the broad methodology of the available trials and the low number of trials, the data were limited. The investigators combined the results of six of the 211 original studies. We looked up 4 databases: PubMed, EMBASE, Web of Science and the Cochrane Library. A total of six publications were selected. The main result was the rate of post-operation wound complications. Secondary results were the time of operation and the duration of hospitalization. Our findings indicate that the minimal invasive operation can decrease the incidence of wound infections (OR, 0.61; 95% CI: 0.42,0.90, p = 0.01), bleeding (MD, -293.09; 95% CI: -431.48, -154.71, p < 0.0001), and length of stay in the hospital compared with open surgery (MD, -1.85; 95% CI: -3.52, -0.17, p = 0.03), but minimally invasive surgery increased patient operative time (MD, 51.45; 95% CI: 40.99, 61.92, p < 0.0001). Compared with the open operation, the microinvasive operation has the superiority in the treatment of the wound complications following the operation of radical prostatic carcinoma. But the operation time of the microinvasive operation is much longer. Furthermore, there is a certain amount of bias among the various studies, so it is important to be cautious in interpretation of the findings.

Negative emotions increase unhealthy eating: Evidence from the Wuhan lockdown during COVID-19.

This paper studies how negative emotions like stress, anxiety, and boredom can affect unhealthy food consumption. Using the Wuhan lockdown as an external shock, we examine the changes in food consumption in a city that was not in lockdown. We applied the difference-in-differences method to a large scanner dataset from a retail monopoly in China. Our findings reveal that negative emotions induced by the pandemic lockdown significantly elevated consumer spending on unhealthy food items such as crisps, sugary beverages, regular soda, and low-alcohol beverages. Notably, the effect of unhealthy food consumption was more pronounced among younger and wealthier demographics. Triggering factors, like information about confirmed new deaths and infections as well as proximity to local hospitals, were found to strongly influence the consumption of unhealthy foods. Overall, the lockdown's impact extended beyond short-term increases in snack consumption to substantial increases in overall dietary and nutritional intake.

Development and validation of an interpretable machine learning model-Predicting mild cognitive impairment in a high-risk stroke population.

Background: Mild cognitive impairment (MCI) is considered a preclinical stage of Alzheimer's disease (AD). People with MCI have a higher risk of developing dementia than healthy people. As one of the risk factors for MCI, stroke has been actively treated and intervened. Therefore, selecting the high-risk population of stroke as the research object and discovering the risk factors of MCI as early as possible can prevent the occurrence of MCI more effectively. Methods: The Boruta algorithm was used to screen variables, and eight machine learning models were established and evaluated. The best performing models were used to assess variable importance and build an online risk calculator. Shapley additive explanation is used to explain the model. Results: A total of 199 patients were included in the study, 99 of whom were male. Transient ischemic attack (TIA), homocysteine, education, hematocrit (HCT), diabetes, hemoglobin, red blood cells (RBC), hypertension, prothrombin time (PT) were selected by Boruta algorithm. Logistic regression (AUC = 0.8595) was the best model for predicting MCI in high-risk groups of stroke, followed by elastic network (ENET) (AUC = 0.8312), multilayer perceptron (MLP) (AUC = 0.7908), extreme gradient boosting (XGBoost) (AUC = 0.7691), and support vector machine (SVM) (AUC = 0.7527), random forest (RF) (AUC = 0.7451), K-nearest neighbors (KNN) (AUC = 0.7380), decision tree (DT) (AUC = 0.6972). The importance of variables suggests that TIA, diabetes, education, and hypertension are the top four variables of importance. Conclusion: Transient ischemic attack (TIA), diabetes, education, and hypertension are the most important risk factors for MCI in high-risk groups of stroke, and early intervention should be performed to reduce the occurrence of MCI.

Development and validation of a nomogram for predicting hospitalization longer than 14 days in pediatric patients with ventricular septal defect-a study based on the PIC database.

Background: Ventricular septal defect is a common congenital heart disease. As the disease progresses, the likelihood of lung infection and heart failure increases, leading to prolonged hospital stays and an increased likelihood of complications such as nosocomial infections. We aimed to develop a nomogram for predicting hospital stays over 14 days in pediatric patients with ventricular septal defect and to evaluate the predictive power of the nomogram. We hope that nomogram can provide clinicians with more information to identify high-risk groups as soon as possible and give early treatment to reduce hospital stay and complications. Methods: The population of this study was pediatric patients with ventricular septal defect, and data were obtained from the Pediatric Intensive Care Database. The resulting event was a hospital stay longer than 14 days. Variables with a variance inflation factor (VIF) greater than 5 were excluded. Variables were selected using the least absolute shrinkage and selection operator (Lasso), and the selected variables were incorporated into logistic regression to construct a nomogram. The performance of the nomogram was assessed by using the area under the receiver operating characteristic curve (AUC), Decision Curve Analysis (DCA) and calibration curve. Finally, the importance of variables in the model is calculated based on the XGboost method. Results: A total of 705 patients with ventricular septal defect were included in the study. After screening with VIF and Lasso, the variables finally included in the statistical analysis include: Brain Natriuretic Peptide, bicarbonate, fibrinogen, urea, alanine aminotransferase, blood oxygen saturation, systolic blood pressure, respiratory rate, heart rate. The AUC values of nomogram in the training cohort and validation cohort were 0.812 and 0.736, respectively. The results of the calibration curve and DCA also indicated that the nomogram had good performance and good clinical application value. Conclusion: The nomogram established by BNP, bicarbonate, fibrinogen, urea, alanine aminotransferase, blood oxygen saturation, systolic blood pressure, respiratory rate, heart rate has good predictive performance and clinical applicability. The nomogram can effectively identify specific populations at risk for adverse outcomes.

Deciphering a cell death-associated signature for predicting prognosis and response to immunotherapy in lung squamous cell carcinoma.

BACKGROUND: Lung squamous cell carcinoma (LUSC) is a subtype of non-small cell carcinoma, accounting for about 30% of all lung cancers. Yet, the evaluation of prognostic outcome and therapy response of patients with LUSC remains to be resolved. This study aimed to explore the prognostic value of cell death pathways and develop a cell death-associated signature for predicting prognosis and guiding treatment in LUSC. METHODS: Transcriptome profiles and corresponding clinical information of LUSC patients were gathered from The Cancer Genome Atlas (TCGA-LUSC, n = 493) and Gene Expression Omnibus database (GSE74777, n = 107). The cell death-related genes including autophagy (n = 348), apoptosis (n = 163), and necrosis (n = 166) were retrieved from the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology databases. In the training cohort (TCGA-LUSC), LASSO Cox regression was used to construct four prognostic signatures of respective autophagy, apoptosis, and necrosis pathway and genes of three pathways. After comparing the four signatures, the cell death index (CDI), the signature of combined genes, was further validated in the GSE74777 dataset. We also investigated the clinical significance of the CDI signature in predicting the immunotherapeutic response of LUSC patients. RESULTS: The CDI signature was significantly associated with the overall survival of LUSC patients in the training cohort (HR, 2.13; 95% CI, 1.62‒2.82; P < 0.001) and in the validation cohort (HR, 1.94; 95% CI, 1.01‒3.72; P = 0.04). The differentially expressed genes between the high- and low-risk groups contained cell death-associated cytokines and were enriched in immune-associated pathways. We also found a higher infiltration of naive CD4+ T cells, monocytes, activated dendritic cells, neutrophils, and lower infiltration of plasma cells and resting memory CD4+ T cells in the high-risk group. Tumor stemness indices, mRNAsi and mDNAsi, were both negatively correlated with the risk score of the CDI. Moreover, LUSC patients in the low-risk group are more likely to respond to immunotherapy than those in the high-risk group (P = 0.002). CONCLUSIONS: This study revealed a reliable cell death-associated signature (CDI) that closely correlated with prognosis and the tumor microenvironment in LUSC, which may assist in predicting the prognosis and response to immunotherapy for patients with LUSC.

Bioinspired hierarchical colloidal crystal paper with Janus wettability for oil/water separation and heavy metal ion removal.

Increasing attention has been paid recently to superwettability and its prospective potential applications in various fields. A new approach towards the establishment of flexible, self-assembled superhydrophobic surfaces with self-reported wettability on a variety of substrates has been advanced. The approach involves the fabrication of a dense monolayer of photonic crystal films that possess a layered structure with superior adhesion at the liquid-gas-solid interface. Thus, the resulting hierarchical photonic crystal film with a structurally hydrophobic surface offers a promising addition to the creation of durable and flexible superhydrophobic surfaces across a variety of substrates that exhibit the self-reported wettability. Furthermore, a bifunctional membrane that can effectively remove oil and adsorb heavy metal ions contained in wastewater has been developed for potential use in large-scale industrial wastewater treatment. This research sheds fresh light on the application of bionics and the lotus and mussel functions in oil/water separation.

Technical Notes and Clinical Outcomes of Full-Endoscopic Interbody Fusion Via Transforaminal Approach for Hard Disc Herniations in Thoracolumbar Junction.

OBJECTIVE: Obtaining sufficient decompression and solid fusion and avoiding approach-related injuries simultaneously are still challenging for the treatment of hard disc herniation in thoracolumbar junction. A combined full-endoscopic decompression and interbody fusion via a transforaminal approach was used to achieve this goal. The purpose of this study was to introduce the technical notes and clinical outcomes of this novel technique. METHODS: Twenty segments of hard disc herniations in the thoracolumbar junction of 14 patients treated with full-endoscopic interbody fusion via the transforaminal approach between January 2018 and September 2021 were analyzed. The patients were an average age of 43.3 years. Full-endoscopic interbody fusion and discectomy via the transforaminal approach were performed under local anesthesia, followed by percutaneous pedicle screw system fixation under general anesthesia. Imaging, including magnetic resonance imaging (MRI), computed tomography (CT), and X-ray, was carried out. MRI was performed on the second day and 3 months postoperatively. CT was performed on the second day, 6 months, and 1 year (as needed) postoperatively. Back and radicular pain, neurological function, and thoracic spine function were scored using a visual analog scale, the Nurick scale, and modified Japanese Orthopaedic Association (mJOA) scale, and the Oswestry disability index at 1 week, 3 months, 6 months, and 1 year postoperatively. RESULTS: All the operations were successfully completed, and no intraoperative conversion of the surgical methods occurred. Postoperative thoracolumbar junction MRI and CT examinations of all the patients revealed a sufficiently decompressed spinal cord or cauda equina, without any residual compression. At the 1-year follow-up, all the surgical segments were fused. Back and radicular pain was relieved in all the patients, and neurological function was restored. The average recovery rate of the mJOA was 72.5%, including seven excellent, five good, and two fair cases. Although dural tears occurred in two cases during the operation, no cerebrospinal fluid leakage or pseudomeningocele occurred during follow-up. No other surgical complications were noted. CONCLUSIONS: A combined full-endoscopic decompression and interbody fusion via a transforaminal approach can achieve complete spinal canal decompression and solid interbody fusion with fewer approach-related injuries. It is a safe and effective minimally invasive spine surgery for treating hard disc herniation in the thoracolumbar junction.

Adversarial Transferred Data-Assisted Soft Sensor for Enhanced Multigrade Quality Prediction.

Although recent transfer learning soft sensors show promising applications in multigrade chemical processes, good prediction performance mainly relies on available target domain data, which is difficult to achieve for a start-up grade. Additionally, only employing a single global model is inadequate to characterize the inner relationship of process variables. A just-in-time adversarial transfer learning (JATL) soft sensing method is developed to enhance multigrade process prediction performance. The distribution discrepancies of process variables between two different operating grades are first reduced by the ATL strategy. Subsequently, by applying the just-in-time learning approach, a similar data set is selected from the transferred source data for reliable model construction. Consequently, with the JATL-based soft sensor, quality prediction of a new target grade is implemented without its own labeled data. Experimental results on two multigrade chemical processes validate that the JATL method can give rise to the improvement of model performance.

Chemical-induced epigenome resetting for regeneration program activation in human cells.

Human somatic cells can be reprogrammed to pluripotent stem cells by small molecules through an intermediate stage with a regeneration signature, but how this regeneration state is induced remains largely unknown. Here, through integrated single-cell analysis of transcriptome, we demonstrate that the pathway of human chemical reprogramming with regeneration state is distinct from that of transcription-factor-mediated reprogramming. Time-course construction of chromatin landscapes unveils hierarchical histone modification remodeling underlying the regeneration program, which involved sequential enhancer recommissioning and mirrored the reversal process of regeneration potential lost in organisms as they mature. In addition, LEF1 is identified as a key upstream regulator for regeneration gene program activation. Furthermore, we reveal that regeneration program activation requires sequential enhancer silencing of somatic and proinflammatory programs. Altogether, chemical reprogramming resets the epigenome through reversal of the loss of natural regeneration, representing a distinct concept for cellular reprogramming and advancing the development of regenerative therapeutic strategies.

Highly efficient and rapid generation of human pluripotent stem cells by chemical reprogramming.

We recently demonstrated the chemical reprogramming of human somatic cells to pluripotent stem cells (hCiPSCs), which provides a robust approach for cell fate manipulation. However, the utility of this chemical approach is currently hampered by slow kinetics. Here, by screening for small molecule boosters and systematically optimizing the original condition, we have established a robust, chemically defined reprogramming protocol, which greatly shortens the induction time from ∼50 days to a minimum of 16 days and enables highly reproducible and efficient generation of hCiPSCs from all 17 tested donors. We found that this optimized protocol enabled a more direct reprogramming process by promoting cell proliferation and oxidative phosphorylation metabolic activities at the early stage. Our results highlight a distinct chemical reprogramming pathway that leads to a shortcut for the generation of human pluripotent stem cells, which represents a powerful strategy for human cell fate manipulation.

A blessing in disguise-The effect of China's Covid-19 health code system on older people's mobile payment usage.

In early 2020, China launched a health code system to combat the spread of Covid-19. The required health code led to a drastic uptake of smartphone usage among older residents. This paper uses rich commercial data from the Zhejiang Province of China to track the change in consumption among the older population. The paper finds that older people significantly increased spending after switching to mobile payment. The paper contributes to the literature by identifying the unexpected windfall from the effect of the health code mandate on the older population and demonstrating that digital payment works well in the older population.

Influence of systolic blood pressure trajectory on in-hospital mortality in patients with sepsis.

BACKGROUND: Numerous studies have investigated the mean arterial pressure in patients with sepsis, and many meaningful results have been obtained. However, few studies have measured the systolic blood pressure (SBP) multiple times and established trajectory models for patients with sepsis with different SBP trajectories. METHODS: Data from patients with sepsis were extracted from the Medical Information Mart for Intensive Care-III database for inclusion in a retrospective cohort study. Ten SBP values within 10 h after hospitalization were extracted, and the interval between each SBP value was 1 h. The SBP measured ten times after admission was analyzed using latent growth mixture modeling to construct a trajectory model. The outcome was in-hospital mortality. The survival probability of different trajectory groups was investigated using Kaplan-Meier (K-M) analysis, and the relationship between different SBP trajectories and in-hospital mortality risk was investigated using Cox proportional-hazards regression model. RESULTS: This study included 3034 patients with sepsis. The median survival time was 67 years (interquartile range: 56-77 years). Seven different SBP trajectories were identified based on model-fit criteria. The in-hospital mortality rates of the patients in trajectory classes 1-7 were 25.5%, 40.5%, 11.8%, 18.3%, 23.5%, 13.8%, and 10.5%, respectively. The K-M analysis indicated that patients in class 2 had the lowest probability of survival. Univariate and multivariate Cox regression analysis indicated that, with class 1 as a reference, patients in class 2 had the highest in-hospital mortality risk (P < 0.001). Subgroup analysis indicated that a nominal interaction occurred between age group and blood pressure trajectory in the in-hospital mortality (P < 0.05). CONCLUSION: Maintaining a systolic blood pressure of approximately 140 mmHg in patients with sepsis within 10 h of admission was associated with a lower risk of in-hospital mortality. Analyzing data from multiple measurements and identifying different categories of patient populations with sepsis will help identify the risks among these categories.