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Abstract The current throughput of conventional organic chemical synthesis is usually a few experiments for each operator per day. We develop a robotic system for ultra-high-throughput chemical synthesis, online characterization, and large-scale condition screening of photocatalytic reactions, based on the liquid-core waveguide, microfluidic liquid-handling, and artificial intelligence techniques. The system is capable of performing automated reactant mixture preparation, changing, introduction, ultra-fast photocatalytic reactions in seconds, online spectroscopic detection of the reaction product, and screening of different reaction conditions. We apply the system in large-scale screening of 12,000 reaction conditions of a photocatalytic [2 + 2] cycloaddition reaction including multiple continuous and discrete variables, reaching an ultra-high throughput up to 10,000 reaction conditions per day. Based on the data, AI-assisted cross-substrate/photocatalyst prediction is conducted.

Abstract Alkenyl sulfides have gained increasing prominence in medicinal chemistry and materials. Hydrothiolation of alkynes for the diverse synthesis of alkenyl sulfides is an appealing method. Herein, we report a cobalt-catalyzed Markovnikov hydromethylthiolation of alkynes to afford branched alkenyl methylsulfanes with good yields and high regioselectivity. This method also enables the diverse synthesis of branched alkenyl sulfides. The reaction shows good functional group tolerance and could be scaled up. The mechanistic studies including control experiments, deuterium-labeling experiments, and Hammett plot indicated alkynes insertion followed by electrophilic thiolation pathway.

Abstract C − N axially chiral compounds have recently attracted significant interest among synthetic chemistry community due to their widespread application in pharmaceuticals, advanced materials and organic synthesis. Although the emerging asymmetric Catellani reaction offers great opportunity for their modular and efficient preparation, the only operative chiral NBE strategy to date requires using half stoichiometric amount of chiral NBE and 2,6-disubstituted bromoarenes as electrophiles. We herein report an efficient assembly of C–N axially chiral scaffolds through a distinct chiral ligand strategy. The crucial chiral source, a biimidazoline (BiIM) chiral dinitrogen ligand, is used in relatively low loading and permits the use of less bulky bromoarenes. The method also features the use of feedstock plain NBE, high reactivity, good enantioselectivity, ease of operation and scale-up. Applications in the preparation of chiral optoelectronic material candidates featuring two C–N chiral axes and a chiral ligand for asymmetric C–H activation have also been demonstrated.

Abstract Background The role of circulating tumor cells (CTCs) in prognosis prediction has been actively studied in hepatocellular carcinoma (HCC) patients. However, their efficiency in accurately predicting early progression recurrence (EPR) is unclear. This study aimed to investigate the clinical potential of preoperative CTCs to predict EPR in HCC patients after hepatectomy. Methods One hundred forty-five HCC patients, whose preoperative CTCs were detected, were enrolled. Based on the recurrence times and types, the patients were divided into four groups, including early oligo-recurrence (EOR), EPR, late oligo-recurrence (LOR), and late progression recurrence (LPR). Results Among the 145 patients, 133 (91.7%) patients had a postoperative recurrence, including 51 EOR, 42 EPR, 39 LOR, and 1 LPR patient. Kaplan–Meier survival curve analysis indicated that the HCC patients with EPR had the worst OS. There were significant differences in the total-CTCs (T-CTCs) and CTCs subtypes count between the EPR group with EOR and LOR groups. Cox regression analysis indicated that the T-CTC count of > 5/5 mL, the presence of microvascular invasion (MVI) and satellite nodules were the independent risk factors for EPR. The efficiency of T-CTCs was superior as compared to those of the other indicators in predicting EPR. Moreover, the combined model demonstrated a markedly superior area under the curve (AUC). Conclusions The HCC patients with EPR had the worst OS. The preoperative CTCs was served as a prognostic indicator of EPR for HCC patients. The combined models, including T-CTCs, MVI, and satellite nodules, had the best performance to predict EPR after hepatectomy.

BackgroundWe examined the impact of the Clonorchis sinensis (C. sinensis) infection on the survival outcomes of spontaneous rupture Hepatocellular Carcinoma (srHCC) patients undergoing hepatectomy.MethodsBetween May 2013 and December 2021, 157 consecutive srHCC patients who underwent hepatectomy were divided into an no C. sinensis group (n = 126) and C. sinensis group (n = 31). To adjust for differences in preoperative characteristics an inverse probability of treatment weighting (IPTW) analysis was done, using propensity scores. Overall survival (OS) and recurrence-free survival (RFS) were compared before and after IPTW. Multivariate Cox regression analysis was performed to determine whether the C. sinensis infection was an independent prognostic factor after IPTW.ResultsIn original cohort, the no C. sinensis group did not show a survival advantage over the C. sinensis group. After IPTW adjustment, the median OS for the C. sinensis group was 9 months, compared to 29 months for the no C. sinensis group. C. sinensis group have worse OS than no C. sinensis group (p = 0.024), while it did not differ in RFS(p = 0.065). The multivariate Cox regression analysis showed that C. sinensis infection and lower age were associated with worse OS.ConclusionsThe C. sinensis infection has an adverse impact on os in srHCC patients who underwent hepatectomy.

Abstract Background The assumptions of conventional spatial models cannot estimate the responses across space and over time. Here we propose new spatial panel data models to investigate the association between the risk factors and incidence of end-stage renal disease (ESRD). Methods A longitudinal (panel data) study was conducted using data from the National Health Insurance Database in Taiwan. We developed an algorithm to identify the patient’s residence and estimate the ESRD rate in each township. Corresponding covariates, including patient comorbidities, history of medication use, and socio-environmental factors, were collected. Local Indicators of Spatial Association were used to describe local spatial clustering around an individual location. Moreover, a spatial panel data model was proposed to investigate the association between ESRD incidence and risk factors. Results In total, 73,995 patients with ESRD were included in this study. The western region had a higher proportion of high incidence rates than the eastern region. The proportion of high incidence rates in the eastern areas increased over the years. We found that most “social environmental factors,” except average income and air pollution (PM 2.5 and PM10), had a significant influence on the incidence rate of ESRD when considering spatial dependences of response and explanatory variables. Receiving non-steroidal anti-inflammatory drugs and aminoglycosides within 90 days prior to ESRD had a significant positive effect on the ESRD incidence rate. Conclusion Future comprehensive studies on townships located in higher-risk clusters of ESRD will help in designing healthcare policies for suitable action.

Abstract Background Mesenchymal circulating tumor cells (M-CTCs) may be related to tumor progression, and Ki67 expression is known to be involved in tumor proliferation. The aim of the present study was to explore the relationship between M-CTCs and Ki67 in hepatocellular carcinoma (HCC) and their ability to predict prognosis. Methods Peripheral blood samples were obtained from 105 HCC patients before radical surgery. CTCs were isolated using CanPatrol enrichment and classified via in situ hybridization. Ki67 expression in HCC tissue was assessed through immunohistochemistry. Potential relationships of M-CTC, Ki67 with clinicopathological factors and prognosis were evaluated. Overall survival (OS) was analyzed using the Kaplan–Meier method and Cox regression. The prognostic efficacy of M-CTC, Ki67 and both together (M-CTC + Ki67) was assessed in terms of time-dependent receiver operating characteristic (ROC) curves and Harrell's concordance index. Results Of the 105 patients, 50 were positive for M-CTCs (count ≥ 1 per 5 mL) and 39 showed high Ki67 expression (≥ 50% tumor cells were Ki67-positive). The presence of M-CTC was significantly associated with alpha-fetoprotein (AFP) ≥ 400 ng/mL (P = 0.007), tumor size ≥ 5 cm (P = 0.023), multiple tumors (P

Microbiota tryptophan metabolism and the biosynthesis of indole derivatives play an important role in homeostasis and pathogenesis in the human body and can be affected by the gut microbiota. However, studies on the interplay between gut microbiota and tryptophan metabolites in patients undergoing dialysis are lacking. This study aimed to identify the gut microbiota, the indole pathway in tryptophan metabolism, and significant functional differences in ESRD patients with regular hemodialysis. We performed the shotgun metagenome sequencing of stool samples from 85 hemodialysis patients. Using the linear discriminant analysis effect size (LEfSe), we examined the composition of the gut microbiota and metabolic features across varying concentrations of tryptophan and indole metabolites. Higher tryptophan levels promoted tyrosine degradation I and pectin degradation I metabolic modules; lower tryptophan levels were associated with glutamate degradation I, fructose degradation, and valine degradation modules. Higher 3-indoxyl sulfate concentrations were characterized by alanine degradation I, anaerobic fatty acid beta-oxidation, sulfate reduction, and acetyl-CoA to crotonyl-CoA. Contrarily, lower 3-indoxyl sulfate levels were related to propionate production III, arabinoxylan degradation, the Entner–Doudoroff pathway, and glutamate degradation II. The present study provides a better understanding of the interaction between tryptophan, indole metabolites, and the gut microbiota as well as their gut metabolic modules in ESRD patients with regular hemodialysis.

Abstract An online sequence extreme learning machine neural network prediction model based on sample aggregation method (AOSELM) is proposed, which is used to solve the prediction of the charging process of lead‐acid batteries. The model is based on the online sequence extreme learning machine (OSELM) neural network and finds the similar samples of the test samples based on the mean influence value (MIV) algorithm. Similar samples are used for temporary incremental learning of the neural network, then the updated neural network makes predictions on the test samples. Taking the terminal voltage prediction of the battery charging process as an example, the BP neural network, the OSELM neural network and the AOSELM neural network are used to compare the predictions, and the charging process of the battery is predicted based on the AOSELM neural network. The comparison results show that the AOSELM neural network has higher prediction accuracy and adaptive ability than the BP neural network, and reduces the maximum absolute percentage error and the maximum absolute error of the prediction result when compared with the OSELM neural network. The prediction shows that the charging process of lead‐acid batteries can be accurately predicted by the AOSELM neural network.

Selectively generating “skipped” dienes, where two carbon–carbon double bonds are separated by a saturated carbon center, is an interesting problem in organic chemistry, with few reliable, catalytic methods currently available. Here, the authors report branched selective hydroallylation of terminal alkynes with allylic bromides to form skipped dienes, via cobalt catalysis.