Evaluating the efficacy and safety of perianal injection of liposomal ropivacaine HR18034 for postoperative analgesia following hemorrhoidectomy: A multicenter, randomized, double-blind, controlled phase II clinical trial.

STUDY OBJECTIVE: HR18034, composed of the ropivacaine encapsulated in multi-lamellar, concentric circular structure liposomes as the major component and a small amount of free ropivacaine, has performed well in animal experiments and phase I clinical trials. This trial was to investigate the efficacy, safety, pharmacokinetic profile and the minimum effective dose of HR18034 for postoperative analgesia after hemorrhoidectomy compared with ropivacaine. DESIGN: A multicenter, randomized, double-blind trial. SETTING: 19 medical centers in China. PATIENTS: 85 patients undergoing hemorrhoidectomy between October 2022 to November 2022. INTERVENTIONS: Patients were randomly divided into HR 18034 190 mg group, 285 mg group, 380 mg group and ropivacaine 75 mg group, receiving single local anesthetic perianal injection for postoperative analgesia. MEASUREMENTS: The primary outcome was the area under the resting state NRS score -time curve within 72 h after injection. The second outcomes included the proportion of patients without pain, the proportion of patients not requiring rescue analgesia, cumulative morphine consumption for rescue analgesia, etc. Safety was evaluated by adverse events incidence and plasma ropivacaine concentrations were measured to explore the pharmacokinetic characteristics of HR18034. MAIN RESULTS: The areas under the NRS score (at rest and moving states)-time curve were significantly lower in HR 18034 380 mg group than ropivacaine 75 mg at 24 h, 48 h, and 72 h after administration. However, this superiority was not observed in HR18034 190 mg group and 285 mg group. There was no difference in cumulative morphine consumption for rescue analgesia between HR 18034 groups and ropivacaine group. CONCLUSIONS: HR 18034 380 mg showed superior analgesic efficacy and equivalent safety compared to ropivacaine 75 mg after hemorrhoidectomy, thus preliminarily determined as minimum effective dose.

A novel dilated contextual attention module for breast cancer mitosis cell detection.

Background and object: Mitotic count (MC) is a critical histological parameter for accurately assessing the degree of invasiveness in breast cancer, holding significant clinical value for cancer treatment and prognosis. However, accurately identifying mitotic cells poses a challenge due to their morphological and size diversity. Objective: We propose a novel end-to-end deep-learning method for identifying mitotic cells in breast cancer pathological images, with the aim of enhancing the performance of recognizing mitotic cells. Methods: We introduced the Dilated Cascading Network (DilCasNet) composed of detection and classification stages. To enhance the model's ability to capture distant feature dependencies in mitotic cells, we devised a novel Dilated Contextual Attention Module (DiCoA) that utilizes sparse global attention during the detection. For reclassifying mitotic cell areas localized in the detection stage, we integrate the EfficientNet-B7 and VGG16 pre-trained models (InPreMo) in the classification step. Results: Based on the canine mammary carcinoma (CMC) mitosis dataset, DilCasNet demonstrates superior overall performance compared to the benchmark model. The specific metrics of the model's performance are as follows: F1 score of 82.9%, Precision of 82.6%, and Recall of 83.2%. With the incorporation of the DiCoA attention module, the model exhibited an improvement of over 3.5% in the F1 during the detection stage. Conclusion: The DilCasNet achieved a favorable detection performance of mitotic cells in breast cancer and provides a solution for detecting mitotic cells in pathological images of other cancers.

Influence of Body Mass Index on the Prognostic Value of N-Terminal Pro-B-Type Natriuretic Peptide Level in Chinese Patients with Heart Failure.

N-terminal pro-B-type natriuretic peptide (NT-proBNP) is an essential biomarker for the prediction of heart failure (HF), but its prognostic ability across body mass index (BMI) categories needs to be clarified. Our study aimed to explore the association between BMI and NT-proBNP and assess the effect of BMI on the prognostic ability of NT-proBNP in Chinese patients with HF. We retrospectively analyzed clinical data from the FuWai Hospital HF Center in Beijing, China. According to the Chinese adult BMI standard, 1,508 patients with HF were classified into four groups: underweight (BMI < 18.5 kg/m2), normal weight (BMI 18.5-23.9 kg/m2, as a reference category), overweight (BMI 24-27.9 kg/m2), and obesity (BMI ≥ 28 kg/m2). NT-proBNP was examined for its prognostic role in adverse events as an endpoint. BMI was independently and negatively associated with NT-proBNP (ß = -0.074; P < 0.001), and NT-proBNP levels tended to decrease as BMI increased across the different BMI categories. The results of our study differ from those of other studies of European-American populations. In this study, NT-proBNP was a weak predictor of a 4-year adverse prognosis in underweight patients (BMI < 18.5 kg/m2). In other BMI categories, NT-proBNP was an independent predictor of adverse events in HF. BMI and sex significantly affected the optimal threshold for NT-proBNP to predict the risk of adverse events. There is a negative correlation between BMI and NT-proBNP, and NT-proBNP independently predicts adverse HF events in patients with a BMI of ≥ 18.5 kg/m2. The optimal risk prediction cutoffs are lower in patients who are overweight and obese.

Inter-patient ECG heartbeat classification for arrhythmia classification: a new approach of multi-layer perceptron with weight capsule and sequence-to-sequence combination.

Objective: The objective of this research is to construct a method to alleviate the problem of sample imbalance in classification, especially for arrhythmia classification. This approach can improve the performance of the model without using data enhancement. Methods: In this study, we have developed a new Multi-layer Perceptron (MLP) block and have used a Weight Capsule (WCapsule) network with MLP combined with sequence-to-sequence (Seq2Seq) network to classify arrhythmias. Our work is based on the MIT-BIH arrhythmia database, the original electrocardiogram (ECG) data is classified according to the criteria recommended by the American Association for Medical Instrumentation (AAMI). Also, our method's performance is further evaluated. Results: The proposed model is evaluated using the inter-patient paradigm. Our proposed method shows an accuracy (ACC) of 99.88% under sample imbalance. For Class N, sensitivity (SEN) is 99.79%, positive predictive value (PPV) is 99.90%, and specificity (SPEC) is 99.19%. For Class S, SEN is 97.66%, PPV is 96.14%, and SPEC is 99.85%. For Class V, SEN is 99.97%, PPV is 99.07%, and SPEC is 99.94%. For Class F, SEN is 97.94%, PPV is 98.70%, and SPEC is 99.99%. When using only half of the training sample, our method shows that the SEN of Class N and V is 0.97% and 5.27% higher than the traditional machine learning algorithm. Conclusion: The proposed method combines MLP, weight capsule network with Seq2seq network, effectively addresses the problem of sample imbalance in arrhythmia classification, and produces good performance. Our method also shows promising potential in less samples.

Automated inter-patient arrhythmia classification with dual attention neural network.

BACKGROUND AND OBJECTIVES: Arrhythmia classification based on electrocardiograms (ECG) can enhance clinical diagnostic efficiency. However, due to the significant differences in the number of different categories of heartbeats, the performance of classes with fewer samples in arrhythmia classification have not met expectations under the inter-patient paradigm. This paper aims to mitigate the adverse effects of category imbalance and improve arrhythmia classification performance. METHODS: We constructed a novel dual attention hybrid network (DA-Net) for arrhythmia classification under sample imbalance, based on modified convolutional networks with channel attention (MCC-Net) and sequence-to-sequence network with global attention (Seq2Seq). The refined local features of the input heartbeat are first extracted by MCC-Net and then sent to Seq2Seq for further feature fusion. By applying local and global attention in the feature extraction and fusion parts, respectively, the method fully fuses low-level feature details and high-level context information and enhances the ability to extract discriminative features. RESULTS: Based on the MIT-BIH arrhythmia database, under the inter-patient paradigm without any data augmentation methods, the proposed method achieved 99.98% accuracy (ACC) for five categories. The various performance indicators are as follows: Class N: sensitivity (SEN) = 99.96%, specificity (SPEC) = 99.93%, positive predictive value (PPV) = 99.99%; Class S: SEN = 99.67%, SPEC = 99.98%, PPV = 99.56%; Class V: SEN = 100%, SPEC = 99.99%, PPV = 99.91%; Class F: SEN = 100%, PPV = 99.98%, SPEC = 97.17%. In further experiments simulating extreme cases, the model still achieved ACC of 99.54% and 98.91% in the three-category and five-category categories when the training sample size was much smaller than the test sample. CONCLUSIONS: Without any data augmentation methods, the proposed model not only alleviates the negative impact of class imbalance and achieves excellent performance in all categories but also provides a new approach for dealing with class imbalance in arrhythmia classification. Additionally, our method demonstrates potential in conditions with fewer samples.

Applying negative sample denoising and multi-view feature for lncRNA-disease association prediction.

Increasing evidence indicates that mutations and dysregulation of long non-coding RNA (lncRNA) play a crucial role in the pathogenesis and prognosis of complex human diseases. Computational methods for predicting the association between lncRNAs and diseases have gained increasing attention. However, these methods face two key challenges: obtaining reliable negative samples and incorporating lncRNA-disease association (LDA) information from multiple perspectives. This paper proposes a method called NDMLDA, which combines multi-view feature extraction, unsupervised negative sample denoising, and stacking ensemble classifier. Firstly, an unsupervised method (K-means) is used to design a negative sample denoising module to alleviate the imbalance of samples and the impact of potential noise in the negative samples on model performance. Secondly, graph attention networks are employed to extract multi-view features of both lncRNAs and diseases, thereby enhancing the learning of association information between them. Finally, lncRNA-disease association prediction is implemented through a stacking ensemble classifier. Existing research datasets are integrated to evaluate performance, and 5-fold cross-validation is conducted on this dataset. Experimental results demonstrate that NDMLDA achieves an AUC of 0.9907and an AUPR of 0.9927, with a 5-fold cross-validation variance of less than 0.1%. These results outperform the baseline methods. Additionally, case studies further illustrate the model's potential in cancer diagnosis and precision medicine implementation.

Comparison of ciprofol (HSK3486) versus propofol for the induction of deep sedation during gastroscopy and colonoscopy procedures: A multi-centre, non-inferiority, randomized, controlled phase 3 clinical trial.

Ciprofol is a propofol analogue with improved pharmacokinetic properties. A multi-centre, non-inferiority trial was conducted to compare the deep sedation properties of ciprofol and propofol with a non-inferiority margin of 8% in patients undergoing gastroscopy and colonoscopy. In total, 289 patients were randomly allocated for surgery (259 colonoscopy and 30 gastroscopy) at a 1:1 ratio to be given intravenous injections of ciprofol (0.4 mg/kg) or propofol (1.5 mg/kg). The primary outcome was the success rate of colonoscopy defined as colonoscopy completion with no need for an alternative sedative or >5 ciprofol or propofol top up doses within any 15-min time period. The success rate of colonoscopy was 100% in the ciprofol group vs. 99.2% in the propofol group (mean difference 0.8%, 95% CI: -2.2% to 4.2%). Except for the gastrointestinal lesions found during the gastroscopy and colonoscopy procedures, the occurrence rates of adverse drug reactions in the ciprofol and propofol groups were 31.3% and 62.8%, respectively (P < 0.001). Pain on injection was less common in the ciprofol group (4.9% vs. 52.4%, P < 0.001). The outcomes demonstrated that ciprofol was non-inferior to propofol with regard to successful sedation for gastroscopy or colonoscopy procedures and no obvious important adverse events occurred.

Inter-patient automated arrhythmia classification: A new approach of weight capsule and sequence to sequence combination.

OBJECTIVE: We propose a new capsule network to compensate for the information loss in the deep convolutional networks in previous studies, and to improve the performance of arrhythmia classification. METHODS: We proposed the innovative weight capsule model which uses a weight capsule network combined with sequence-to-sequence (Seq2Seq) modeling to classify arrhythmia, and explored the performance of this approach. RESULTS: Based on the MIT-BIH arrhythmia database, we obtained better results compared with previous studies without data enhancement and balance for the samples. The specific performance was as follows: accuracy (ACC) = 99.85%; Class N: sensitivity (SEN) = 99.66%, positive predictive value (PPV) = 99.97%, specificity (SPEC) = 99.72%; Class S: SEN = 99.56%, PPV = 92.23%, SPEC = 99.68%; Class V: SEN = 99.97%, PPV = 99.38%, PPV = 99.96%; Class F: SEN = 93.81%, PPV = 100.00%, SPEC = 100.00%. When only half of the training sample was used, the method showed that the average accuracy and sensitivity of Class V and F were 1.57%, 2.01%, and 1.55% higher, respectively, than the traditional machine learning algorithm using the whole training sample. CONCLUSION: Applying a weight capsule network combined with a Seq2Seq model in the field of arrhythmia not only alleviates the problem of inter-category sample imbalance effectively, but also improves the arrhythmia classification. SIGNIFICANCE: Our study suggests a new idea for solving the problem of small sample sizes and inter-category sample imbalance in the medical field.

A systematic review and meta-analysis of different mask ventilation schemes on management of general anesthesia in patients with respiratory failure.

BACKGROUND: Difficulty in mask ventilation is one of the more dangerous factors in general anesthesia. The traditional mask has some problems, such as air leakage and facial skin compression injury. The head cover is a new interactive non-invasive ventilation (NIV) model. NIV studies comparing hoods and masks have all been single-center and small-sample randomized trials, and extensive clinical studies are lacking. METHODS: We conducted a computerized search in the databases of PubMed, Embase, Medline, Chinese Biomedical Literature (CBM), and others for randomized controlled trials (RCTs) on the effect of hoods and masks on patients with respiratory failure published since their establishment to March 2021. The quality of the included literature was assessed using the Cochrane Systematic Review Manual, and the data was analyzed using Review Manager 5.3 to assess the risk of bias. RESULTS: A total of 9 articles were included, involving 462 patients, with 233 patients in the hood group and 229 patients in the face mask group. The results of meta-analysis showed the comparative endotracheal intubation rate of the hood group and the mask group [odds ratio (OR) =0.26; 95% confidence interval (CI): 0.14 to 0.47; Z=4.48; P<0.00001], the complications rate (OR =0.54; 95% CI: 0.31 to 0.97; Z=2.08; P=0.04) was statistically considerable, although there was no considerable difference in in-hospital mortality (OR =0.56; 95% CI: 0.28 to 1.14; Z=1.59; P=0.11). DISCUSSION: NIV with a hood can reduce the rate of endotracheal intubation and the incidence of related complications in patients with acute respiratory failure (ARF), which has considerable advantages in contrast to the traditional mask.

Spatial and temporal variation of dissolved heavy metals in the Lijiang River, China: implication of rainstorm on drinking water quality.

Lijiang River is an essential drinking water source and natural scenery in the Guilin City. For the first time, implications of rainstorm were taken into consideration by investigating spatial and temporal variation of dissolved heavy metals (HMs) in the Lijiang River water. A total of 68 water samples were collected during low flow (normal) season and high flow (rainstorm) season from 34 sampling sites. Dissolved HMs including Cr, Mn, Co, Cu, Zn, As, Cd, Sb, and Pb were found to meet the respective drinking water standards, while comparatively higher concentration was observed after the rainstorm season, except for Cr. Multivariate statistical analysis showed that Co, Cu, Cr, Zn, Sb, and Pb in normal season were mainly controlled by anthropogenic sources. Furthermore, higher concentrations of Mn, Cu, Cd, Pb, Co, and Zn during the high flow season were attributed to rainstorm. The water quality index (WQI) showed good grades and comparatively lower in rainstorm season. The results of health risk assessment revealed that HMs in Lijiang River posed limited health risk; however, As posed potential health risk specially in rainstorm season. It is suggested to adopt preventive measures for mining activities and industrial waste-water discharge at the river's upstream and downstream.

Function-structure coupling: White matter functional magnetic resonance imaging hyper-activation associates with structural integrity reductions in schizophrenia.

White matter (WM) microstructure deficit may be an underlying factor in the brain dysconnectivity hypothesis of schizophrenia using diffusion tensor imaging (DTI). However, WM dysfunction is unclear in schizophrenia. This study aimed to investigate the association between structural deficits and functional disturbances in major WM tracts in schizophrenia. Using functional magnetic resonance imaging (fMRI) and DTI, we developed the skeleton-based WM functional analysis, which could achieve voxel-wise function-structure coupling by projecting the fMRI signals onto a skeleton in WM. We measured the fractional anisotropy (FA) and WM low-frequency oscillation (LFO) and their couplings in 93 schizophrenia patients and 122 healthy controls (HCs). An independent open database (62 schizophrenia patients and 71 HCs) was used to test the reproducibility. Finally, associations between WM LFO and five behaviour assessment categories (cognition, emotion, motor, personality and sensory) were examined. This study revealed a reversed pattern of structure and function in frontotemporal tracts, as follows. (a) WM hyper-LFO was associated with reduced FA in schizophrenia. (b) The function-structure association was positive in HCs but negative in schizophrenia patients. Furthermore, function-structure dissociation was exacerbated by long illness duration and severe negative symptoms. (c) WM activations were significantly related to cognition and emotion. This study indicated function-structure dys-coupling, with higher LFO and reduced structural integration in frontotemporal WM, which may reflect a potential mechanism in WM neuropathologic processing of schizophrenia.

Using Electronic Medical Records of Nursing Care to Characterize Constipation in Patients with Intracerebral Hemorrhage.

INTRODUCTION: Constipation is one of the common poststroke complications that directly affect the patients' quality of life in patients with intracerebral hemorrhage (ICH), which has not been paid enough attention. OBJECTIVE: This study investigates constipation's clinical characteristics and its risk factors in ICH patients driven by the electronic medical records of nursing care. METHODS: This retrospective chart review investigated patients with acute spontaneous ICH admitted at a tertiary care center from October 2010 to December 2018. Poststroke constipation was defined as a first stool passage occurring after 3 days postadmission and the use of enemas or laxatives after ICH. The associations between constipation present and potential factors were evaluated. RESULTS: Of 1,748 patients, 408 (70.3% men, mean age 58 ± 14 years) patients with poststroke constipation were identified. After adjusting for potential confounding variables, the risk factors independently associated with poststroke constipation are admission Glasgow Coma Scale score (odds ratio [OR] 0.62, 95% confidence interval [CI] 0.44-0.88; p = 0.007), use of mechanical ventilation (OR 3.74, 95% CI 2.37-5.89, p < 0.001), enteral nutrition (OR 2.82, 95% CI 1.85-4.30, p < 0.001), hematoma evacuation (OR 2.10, 95% CI 1.40-3.16; p < 0.001), opioid analgesics (OR 1.86, 95% CI 1.32-2.62; p < 0.001), sedation (OR 1.83, 95% CI 1.20-2.77; p = 0.005), and vasopressors (OR 1.81, 95% CI 1.26-2.61; p = 0.001) in order. Similar associations were observed in the prespecified length of the stay subgroup. Patients with constipation were associated with a longer hospital stay length (2.24 days, 95% CI 1.43-3.05, p < 0.001) but not with in-hospital mortality (OR 1.05, 95% CI 0.58-1.90, p = 0.871). CONCLUSIONS: Our findings suggested that risk factors influence the absence of constipation after ICH with the synergy of different weights. The occurrence of constipation likely affects a longer length of stay, but not in-hospital mortality. Future prospective investigations are warranted to validate our findings and identify the optimal management of constipation that may improve the quality of life in patients with ICH.

Connective profiles and antagonism between dynamic and static connectivity underlying generalized epilepsy.

This study aims to characterize the connective profiles and the coupling relationship between dynamic and static functional connectivity (dFC and sFC) in large-scale brain networks in patients with generalized epilepsy (GE). Functional, structural and diffuse MRI data were collected from 83 patients with GE and 106 matched healthy controls (HC). Resting-state BOLD time course was deconvolved to neural time course using a blind hemodynamic deconvolution method. Then, five connective profiles, including the structural connectivity (SC) and BOLD/neural time course-derived sFC/dFC networks, were constructed based on the proposed whole brain atlas. Network-level weighted correlation probability (NWCP) were proposed to evaluate the association between dFC and sFC. Both the BOLD signal and neural time course showed highly concordant findings and the present study emphasized the consistent findings between two functional approaches. The patients with GE showed hypervariability and enhancement of FC, and notably decreased SC in the subcortical network. Besides, increased dFC, weaker anatomic links, and complex alterations of sFC were observed in the default mode network of GE. Moreover, significantly increased SC and predominantly increased sFC were found in the frontoparietal network. Remarkably, antagonism between dFC and sFC was observed in large-scale networks in HC, while patients with GE showed significantly decreased antagonism in core epileptic networks. In sum, our study revealed distinct connective profiles in different epileptic networks and provided new clues to the brain network mechanism of epilepsy from the perspective of antagonism between dynamic and static functional connectivity.

[Pollution Characteristics and Health Risk Assessment of Heavy Metals in the Water of Lijiang River Basin].

In order to clarify the pollution levels of heavy metals in the drinking water sources of the Lijiang River Basin, surface water samples were collected from 62 sites throughout the Lijiang River during May 2019. Heavy metals, including As, Cd, Cr, Mn, Cu, Zn, Hg, Co, and Sb, in the water samples were analysed. Health risk assessments associated with these nine heavy metals were conducted using the health risk assessment model from the US EPA. The results indicated that the order of the average concentrations of heavy metals in the water samples were Mn > Zn > As > Cr > Cu > Sb > Co > Cd > Hg. No heavy metals exceeded the limit values of the drinking water health standards in China (GB 5749-2006), and the concentrations were lower than the limitations of Grade Ⅰ level in the environmental quality standards for surface water (GB 3838-2002). According to the spatial distribution, the high contents areas of As, Cr, Zn, and Sb were predominantly distributed downstream of the Lijiang River, while the high contents areas of Cd, Cu, Hg, Co, and Mn were mostly distributed in the upper reaches. Multivariate analysis indicated that Cd, Mn, Cu, and Co were primarily from agricultural production; Cr, Zn, and Sb were mainly from tourism transportation; As was predominantly from the weathering of rock parent material and soil erosion; Hg was mainly from the improper disposal of domestic garbage and atmospheric deposition. The results of the health risk assessment indicated that children were more susceptible to the threat of heavy metal pollution than adults, and the average annual risk of carcinogenic heavy metals to human health through drinking water ingestion were higher than those of non-carcinogenic metals. The maximum personal average annual health risk of Cr was higher than the maximum allowance levels recommended by the International Commission on Radiological Protection (5×10-5 a-1). The average annual risk of non-carcinogenic heavy metals (10-14-10-9 a-1) decreased in the order of Co > Cu > Hg > Zn > Sb > Mn, which were far below the maximum allowance levels recommended by the ICRP.

Enhancing Robustness of Machine Learning Integration With Routine Laboratory Blood Tests to Predict Inpatient Mortality After Intracerebral Hemorrhage.

Objective: The accurate evaluation of outcomes at a personalized level in patients with intracerebral hemorrhage (ICH) is critical clinical implications. This study aims to evaluate how machine learning integrates with routine laboratory tests and electronic health records (EHRs) data to predict inpatient mortality after ICH. Methods: In this machine learning-based prognostic study, we included 1,835 consecutive patients with acute ICH between October 2010 and December 2018. The model building process incorporated five pre-implant ICH score variables (clinical features) and 13 out of 59 available routine laboratory parameters. We assessed model performance according to a range of learning metrics, such as the mean area under the receiver operating characteristic curve [AUROC]. We also used the Shapley additive explanation algorithm to explain the prediction model. Results: Machine learning models using laboratory data achieved AUROCs of 0.71-0.82 in a split-by-year development/testing scheme. The non-linear eXtreme Gradient Boosting model yielded the highest prediction accuracy. In the held-out validation set of development cohort, the predictive model using comprehensive clinical and laboratory parameters outperformed those using clinical alone in predicting in-hospital mortality (AUROC [95% bootstrap confidence interval], 0.899 [0.897-0.901] vs. 0.875 [0.872-0.877]; P <0.001), with over 81% accuracy, sensitivity, and specificity. We observed similar performance in the testing set. Conclusions: Machine learning integrated with routine laboratory tests and EHRs could significantly promote the accuracy of inpatient ICH mortality prediction. This multidimensional composite prediction strategy might become an intelligent assistive prediction for ICH risk reclassification and offer an example for precision medicine.

Heavy metal pollution, ecological risk, spatial distribution, and source identification in sediments of the Lijiang River, China.

The Lijiang River is of great ecological and environmental importance for Guilin City, which is located in the karst area of southeast China. Given its importance, a detailed evaluation of the heavy metals (HMs) in the river sediment is required. For the first time, 61 sediment samples were collected along the entire Lijiang River to determine pollution level and ecological risk posed by 10 HMs (Co, Cr, Cu, Mn, Ni, Pb, Zn, As, Hg, and Cd). These were assessed using the geo-accumulation index, potential ecological risk index, and modified degree of contamination. The results showed that the mean concentrations of the majority of HMs exceeded their corresponding background values and followed the trend: midstream > downstream > upstream. Based on the spatial distributions and pollution indices of the 10 HMs, the Lijiang River was found to have a high accumulation of Cd, Hg, Zn, and Pb in the sediments. The midstream area was the most polluted with respect to Cd and Hg, and also posed a relatively higher potential ecological risk than the downstream and upstream areas. The sources of the assessed HMs were inferred based on a correlation analysis and principal component analysis, which identified both natural and anthropogenic sources. A higher pollution potential was associated with Cd, Hg, Pb, and Zn in the midstream and downstream areas due to higher organic and carbonate content, urbanization, agricultural activities, and leisure activities (e.g., boating and cruises). In contrast, natural erosion and weathering processes were responsible for the HM concentrations in the upstream area. The findings of this study will help the local authorities to protect the important water resource of the Lijiang River.

Remimazolam tosilate in upper gastrointestinal endoscopy: A multicenter, randomized, non-inferiority, phase III trial.

BACKGROUND AND AIM: Remimazolam tosilate (RT) is a new short-acting GABA(A) receptor agonist, having potential to be an effective option for procedural sedation. Here, we aimed to compare the efficacy and safety of RT with propofol in patients undergoing upper gastrointestinal endoscopy. METHODS: This positive-controlled, non-inferiority, phase III trial recruited patients at 17 centers, between September 2017 and November 2017. A total of 384 patients scheduled to undergo upper gastrointestinal endoscopy were randomly assigned to receive RT or propofol. Primary endpoint was the success rate of sedation. Adverse events (AEs) were recorded to evaluate safety. RESULTS: The success rate of sedation in the RT group was non-inferior to that in the propofol group (97.34% vs 100.00%; difference in rate -2.66%, 95% CI -4.96 to -0.36, meeting criteria for non-inferiority). Patients in the RT group had longer time to adequate sedation (P < 0.0001) but shorter time to fully alert (P < 0.0001) than that in the propofol group. The incidences of hypotension (13.04% vs 42.86%, P < 0.0001), treatment-related hypotension (0.54% vs 5.82%, P < 0.0001), and respiratory depression (1.09% vs 6.88%, P = 0.0064) were significantly lower in the RT group. AEs were reported in 74 (39.15%) patients in the RT group and 114 (60.32%) patients in the propofol group, with significant difference (P < 0.0001). CONCLUSION: This trial established non-inferior sedation success rate of RT compared with propofol. RT allows faster recovery from sedation compared with propofol. The safety profile is favorable and appears to be superior to propofol, indicating that it was feasible and well tolerated for patients.

1H NMR-based dynamic metabolomics delineates the therapeutic effects of Shenfu injection on laparoscopic hysterectomy.

ABSTRACT: To explore the effect and mechanism of Shenfu Injection on serum metabolomics in laparoscopic hysterectomy.1.5 mL/kg Shenfu injection was added to inject 200 mL of normal saline after the patients who entered the standard were admitted to the operating room. NMR metabolomics were performed at each time point before anesthesia (T0), immediately after pneumoperitoneum (T1), and at the end of surgery (T2).Multivariate trajectory analysis showed that SFI treatment could make laparoscopic hysterectomy interfere with the recovery of plasma metabolites to normal metabolic state, with a time-dependent trend. In addition, the key metabolic changes of laparoscopic hysterectomy at different stages of SFI treatment involve energy metabolism, oxidative stress response, amino acid metabolism, and pyruvate metabolism. Especially, the important role of SFI in the treatment of laparoscopic hysterectomy is antioxidant capacity. The results show that SFI can be used as a potential drug for laparoscopic hysterectomy.The current findings provided, for the first time, sound evidence of the protective effects of SFI on laparoscopic hysterectomy from both biochemical and metabolomics perspectives. The mechanisms of SFI could be related to regulating amino acid metabolism, pyruvate metabolism, and energy metabolism. The present study lays an important foundation for further research and for the broad clinical application of SFI.

Altered Static and Dynamic Spontaneous Neural Activity in Drug-Naïve and Drug-Receiving Benign Childhood Epilepsy With Centrotemporal Spikes.

The present study aims to investigate intrinsic abnormalities of brain and the effect of antiepileptic treatment on brain activity in Benign childhood epilepsy with centrotemporal spikes (BECTS). Twenty-six drug-naïve patients (DNP) and 22 drug-receiving patients (DRP) with BECTS were collected in this study. Static amplitude of low frequency fluctuation (sALFF) and dynamic ALFF (dALFF) were applied to resting-state fMRI data. Functional connectivity (FC) analysis was further performed for affected regions identified by static and dynamic analysis. One-way analysis of variance and post hoc statistical analyses were performed for between-group differences. Abnormal sALFF and dALFF values were correlated with clinical features of patients. Compared with healthy controls (HC), DNP group demonstrated alterations of sALFF and/or dALFF in medial prefrontal cortex (MPFC), supplementary motor areas (SMA), cerebellum, hippocampus, pallidum and cingulate cortex, in which the values were close to normal in DRP. Notably, sALFF and dALFF showed specific sensitivity in detecting abnormalities in basal ganglia and cerebellum. Additionally, DRP showed additional changes in precuneus, inferior temporal gyrus, superior frontal gyrus and occipital visual cortex. Compared with HC, the DNP showed increased FC in default network and motion-related networks, and the DRP showed decreased FC in default network. The MPFC, hippocampus, SMA, basal ganglia and cerebellum are indicated to be intrinsically affected regions and effective therapeutic targets. And the FC profiles of default and motion-related networks might be potential core indicators for clinical treatment. This study revealed potential neuromodulatory targets and helped understand pathomechanism of BECTS. Static and dynamic analyses should be combined to investigate neuropsychiatric disorders.

Clinical outcomes and anesthetic management of pregnancies with placenta previa and suspicion for placenta accreta undergoing intraoperative abdominal aortic balloon occlusion during cesarean section.

BACKGROUND: This retrospective study aimed to compare the clinical outcomes of parturients with placenta previa (PP) and placenta accreta (PA) according to their severity, when they were managed with intraoperative abdominal aortic balloon occlusion (IAABO) during cesarean section. METHODS: We retrospectively examined 57 cases of PP and suspicion for PA in which IAABO was performed during cesarean section between April 2014 and June 2016. Based on preoperative examination and clinical risk factors, patients were divided into the low suspicion PA group and the high suspicion PA group. We compared the demographic characteristics, methods of anesthesia, intra- and postoperative parameters, and maternal and neonatal outcomes. RESULTS: The two groups showed similar demographic characteristics and intraoperative outcomes. Four women underwent cesarean hysterectomy. Eight neonates were admitted to the neonatal intensive care unit and three did not survive. Neonatal Apgar scores were significantly higher in the low suspicion PA group. Eight patients experienced postoperative femoral artery thrombosis and one patient complicated hematoma in the front wall of the common femoral artery. Patients who received neuraxial anesthesia showed significantly lower intraoperative blood loss, lower intraoperative, postoperative and total blood transfusion and shorter surgery than patients who received general anesthesia. CONCLUSIONS: Our data suggested that the severity of aberrant placental position does not affect intraoperative blood loss during a cesarean section while the IAABO is performed. We propose that neuraxial anesthesia is preferred for conducting these surgeries without contraindications.