Effects of Defects and Presence of Open-Metal Sites on the Structure and Dynamics of Water in Hydrophobic Zeolitic Imidazolate Frameworks.

Most of the chemistry in nanoporous materials with small pore sizes and windows takes place on the outer surface, which is in direct contact with the substrate/solvent, rather than within the pores and channels. Here, we report the results of our comprehensive atomistic molecular dynamics (MD) simulations to decipher the interaction of water with a realistic finite ∼5.1 nm nanoparticle (NP) model of ZIF-8, with edges containing undercoordinated Zn metal sites, vs a conventionally employed pristine crystalline bulk (CB) model. The hydrophobic interior surface of the CB model imparts significant dynamical behavior on water molecules with (i) increasing diffusivity from the surface toward the center of the pores and (ii) confined water, at low concentration, showing similar diffusivity to that of the bulk water. On the other hand, water molecules adsorbed on the surface of the NP model exhibit a range of characteristics, including "coordinated", "confined", and "bulk-like" behavior. Some of the water molecules form coordinative bonds with the undercoordinated Zn metal centers and act as nucleation sites for the water droplets to form, facilitating diffusion into the pores. However, diffusion of water molecules is limited to the areas near the surface and not all the way to the core of the NP model. Our atomistic MD simulations provide insights into the stability of ZIFs in aqueous solutions despite hydrolysis of their outer surface. Such insights are helpful in designing more robust nanoporous materials for applications in humid environments.

Time interval uncertainty-aware and text-enhanced based disease prediction.

Deep learning methods have achieved success in disease prediction using electronic health records (EHR) data. Most of the existing methods have some limitations. First, most of the methods adopt a homogeneous decay way to deal with the effect of time interval on patient's previous visits information. However, the effect of the time interval between patient's visits is not always negative. For example, although the time interval between visits for patients with chronic diseases is relatively long, the importance of the previous visit to the next visit is high, and we may not be able to consider the effect of the time interval as negative at this point. That is, the effect of the time interval on previous visits is exerted in a nonmonotonic manner, and it is either positive, negative, or neutral. In addition, the effect of text information on prediction results is not taken into account in most of methods. The text in EHR contains a description of the patient's past medical history and current symptoms of the disease, which is important for prediction results. In order to solve these issues, we propose a Time Interval Uncertainty-Aware and Text-Enhanced Based Disease Prediction Model, which utilizes the uncertain effects of time intervals and patient's text information for disease prediction. Firstly, we apply a cross-attention mechanism to generate a global representation of the patient using the patient's disease and text information from the EHR. Then, we use the key-query attention mechanism to obtain the two importance weights of the two visit sequences with and without time intervals, respectively. Furthermore, we achieve disease prediction by making slight adjustments to the encode part of the Transformer, a deep learning model based on a self-attention mechanism. We compare with various state-of-the-art models on two publicly available datasets, MIMIC-III and MIMIC-IV, and select the top 10 diseases with the highest frequency in the dataset as the target diseases. On the MIMIC-III dataset, our model is up to three percent higher than the optimal baseline in terms of evaluation metrics.

A drug molecular classification model based on graph structure generation.

Molecular property prediction based on artificial intelligence technology has significant prospects in speeding up drug discovery and reducing drug discovery costs. Among them, molecular property prediction based on graph neural networks (GNNs) has received extensive attention in recent years. However, the existing graph neural networks still face the following challenges in node representation learning. First, the number of nodes increases exponentially with the expansion of the perception field, which limits the exploration ability of the model in the depth direction. Secondly, the large number of nodes in the perception field brings noise, which is not conducive to the model's representation learning of the key structures. Therefore, a graph neural network model based on structure generation is proposed in this paper. The model adopts the depth-first strategy to generate the key structures of the graph, to solve the problem of insufficient exploration ability of the graph neural network in the depth direction. A tendentious node selection method is designed to gradually select nodes and edges to generate the key structures of the graph, to solve the noise problem caused by the excessive number of nodes. In addition, the model skillfully realizes forward propagation and iterative optimization of structure generation by using an attention mechanism and random bias. Experimental results on public data sets show that the proposed model achieves better classification results than the existing best models.

TAHDNet: Time-aware hierarchical dependency network for medication recommendation.

Medication recommendation is a hot topic in the research of applying neural networks to the healthcare area. Although extensive progressions have been made, current researches still face the following challenges: (i). Existing methods are poor at efficiently capturing and leveraging local and global dependency information from patient visit records. (ii). Current time-aware models based on irregularly interval medical records tend to ignore periodic variability in patient conditions, which limits the representational learning capability of these models. Therefore, we propose a Dynamic Time-aware Hierarchical Dependency Network (TAHDNet) for the medication recommendation task to address these challenges. Firstly, we use a Transformer-based model to learn the global information of the whole patient record through a self-supervised pre-training process. Secondly, a 1D-CNN model is used to learn the local dependencies on visitation level. Thirdly, we propose a dynamic time-aware module with a fused temporal decay function to assign different weights among different time intervals dynamically through a key-value attention mechanism. Experimental results on real-world datasets demonstrate the effectiveness of the model proposed in this paper.

Evaluation of serum exosomal LncRNA-based biomarker panel for diagnosis and recurrence prediction of bladder cancer.

Exosomes are small membrane vesicles released by many cells. These vesicles can mediate cellular communications by transmitting active molecules including long non-coding RNAs (lncRNAs). In this study, our aim was to identify a panel of lncRNAs in serum exosomes for the diagnosis and recurrence prediction of bladder cancer (BC). The expressions of 11 candidate lncRNAs in exosome were investigated in training set (n = 200) and an independent validation set (n = 320) via quantitative real-time PCR. A three-lncRNA panel (PCAT-1, UBC1 and SNHG16) was finally identified by multivariate logistic regression model to provide high diagnostic accuracy for BC with an area under the receiver-operating characteristic curve (AUC) of 0.857 and 0.826 in training set and validation set, respectively, which was significantly higher than that of urine cytology. The corresponding AUCs of this panel for patients with Ta, T1 and T2-T4 were 0.760, 0.827 and 0.878, respectively. In addition, Kaplan-Meier analysis showed that non-muscle-invasive BC (NMIBC) patients with high UBC1 expression had significantly lower recurrence-free survival (P = 0.01). Multivariate Cox analysis demonstrated that UBC1 was independently associated with tumour recurrence of NMIBC (P = 0.018). Our study suggested that lncRNAs in serum exosomes may serve as considerable diagnostic and prognostic biomarkers of BC.

Length changes in the distal oblique band during forearm rotation measured using four-dimensional CT: an in vivo study.

Length change in the distal oblique band during forearm rotation was measured using four-dimensional CT in seven volunteers. There was no significant change in length, which provides more theoretical support for distal oblique band reinforcement for treatment of instability of the distal radioulnar joint.

Upfront allogeneic transplantation versus JAK inhibitor therapy for patients with myelofibrosis: a North American collaborative study.

Allogeneic hematopoietic cell transplantation (HCT) is the only curative therapy for myelofibrosis (MF) and is recommended for patients with higher risk disease. However, there is a risk of early mortality, and optimal timing is unknown. JAK inhibitor (JAKi) therapy may offer durable improvement in symptoms, splenomegaly and quality of life. The aim of this multicentre, retrospective observational study was to compare outcomes of patients aged 70 years or below with MF in chronic phase who received upfront JAKi therapy vs. upfront HCT in dynamic international prognostic scoring system (DIPSS)-stratified categories. For the whole study cohort, median overall survival (OS) was longer for patients who received a JAKi vs. upfront HCT, 69 (95% CI 57-89) vs. 42 (95% CI 20-not reached, NR) months, respectively (p = 0.01). In patients with intermediate-2 and high-risk disease, median OS was 55 (95% CI 36-73) months with JAKi vs. 36 (95% CI 20-NR) months for HCT (p = 0.27). An upfront HCT strategy was associated with early mortality and difference in median OS was not observed in any risk group by 5 years of follow-up. Within the limitations of a retrospective observational study, we did not observe any benefit of a universal upfront HCT approach for higher-risk MF.

Outcomes of intensive and nonintensive blast-reduction strategies in accelerated and blast-phase MPN.

ABSTRACT: Transformation of BCR::ABL1-negative myeloproliferative neoplasms (MPN) to an accelerated or blast phase is associated with poor outcomes. The efficacy of acute myeloid leukemia (AML)-type intensive and nonintensive hypomethylating agent-based regimens is not well studied. We therefore performed a retrospective analysis of patients with MPN-AP/BP (N = 138) treated with intensive (N = 81) and nonintensive (N = 57) blast-reduction strategies. We used clinically relatable response criteria developed at the Princess Margaret Cancer Centre. The overall best response, comprising complete remission (CR), complete remission with incomplete hematologic recovery (CRi), and reversion to chronic phase MPN (cMPN), in the intensive and nonintensive groups was 77% (62 of 81) and 39% (21 of 54), respectively. Similar overall best response rates were observed in patients receiving induction with daunorubicin combined with cytarabine arabinoside (daunorubicin + ara-C) (74% [23 of 31]) or FLAG-IDA/NOVE-HiDAC (78% [39 of 50], P = .78). However, patients receiving daunorubicin + ara-C more often required second inductions (29% [9 of 31] vs 4% [2 of 50], P = .002). Most responses in the entire cohort were reversions to cMPN (55 of 83 [66%]). CR and CRi comprised 30% (25 of 83) and 4% (3 of 83) of responses, respectively. Mutations in TP53 (overall response [OR] 8.2 [95% confidence interval [CI] 2.01, 37.1], P = .004) and RAS pathway (OR 5.1 [95%CI 1.2, 23.7], P = .03) were associated with inferior treatment response for intensively treated patients, and poorer performance status (Eastern Cooperative Oncology Group) was associated with inferior treatment response in both intensively (OR 10.4 [95% CI 2.0, 78.5], P = .009) and nonintensively treated groups (OR 12 [95% CI 2.04, 230.3], P = .02). In patients with paired samples before and after therapy (N = 26), there was a significant residual mutation burden remaining irrespective of response to blast-reduction therapy.

In Silico High-Throughput Design and Prediction of Structural and Electronic Properties of Low-Dimensional Metal-Organic Frameworks.

The advent of π-stacked layered metal-organic frameworks (MOFs), which offer electrical conductivity on top of permanent porosity and high surface area, opened up new horizons for designing compact MOF-based devices such as battery electrodes, supercapacitors, and spintronics. Permutation of structural building blocks, including metal nodes and organic linkers, in these electrically conductive (EC) materials, results in new systems with unprecedented and unexplored physical and chemical properties. With the ultimate goal of providing a platform for accelerated material design and discovery, here we lay the foundations for the creation of the first comprehensive database of EC-MOFs with an experimentally guided approach. The first phase of this database, coined EC-MOF/Phase-I, is composed of 1,057 bulk and monolayer structures built by all possible combinations of experimentally reported organic linkers, functional groups, and metal nodes. A high-throughput screening (HTS) workflow is constructed to implement density functional theory calculations with periodic boundary conditions to optimize the structures and calculate some of their most relevant properties. Because research and development in the area of EC-MOFs has long been suffering from the lack of appropriate initial crystal structures, all of the geometries and property data have been made available for the use of the community through an online platform that was developed during the course of this work. This database provides comprehensive physical and chemical data of EC-MOFs as well as the convenience of selecting appropriate materials for specific applications, thus accelerating the design and discovery of EC-MOF-based compact devices.

Differences in the Proportion of CYP2C19 Loss-of-Function Between Cerebral Infarction and Coronary Artery Disease Patients.

Background: Cytochrome P450 2C19 (CYP2C19) genotypes and metabolic phenotypes (extensive metabolizer (EM), intermediate metabolizer (IM), and poor metabolizer (PM)) are related to the metabolism of therapeutic drugs for cardiovascular and cerebrovascular diseases. This study aimed to investigate the differences of CYP2C19 gene polymorphism distribution between coronary artery disease (CAD) patients and cerebral infarction (CI) patients. Methods: We identified 413 CI patients, 509 CAD patients, and 241 CI+CAD patients from 2016 to 2020 and studied genotypes of CYP2C19 rs4986893 (636G>A) and rs4244285 (681G>A) polymorphisms using PCR-gene chip detection method. Differences in CYP2C19 genotypes and metabolic phenotypes between the groups were compared. To analyze the efficacy of CYP2C19 metabolic phenotypes in discriminating between cerebral infarction and coronary artery disease, multiple logistic regression analysis was conducted after adjusting for gender, age, smoking history, drinking history, hypertension, and diabetes. Results: There were significant differences in the distribution of CYP2C19 genotypes and metabolic phenotypes between CI and CAD patients. The results of multivariate logistic regression (adjusted for sex, age, smoking, drinking, hypertension, and diabetes) indicated that CYP2C19 IM phenotype (IM vs EM: OR 1.443, 95% CI: 1.086-1.918, P=0.011) and CYP2C19 IM+PM phenotype (IM or PM vs EM: OR 1.440, 95% CI: 1.100-1.885, P=0.008) may be indicators of CI from CAD. Conclusion: CYP2C19 EM metabolic phenotype was dominant in CAD patients, and CYP2C19 IM metabolic phenotype was dominant in CI patients. After adjusting for other confounding factors, patients with the CYP2C19 IM metabolic phenotype were more likely to develop CI than CAD.

MVIRA: A model based on Missing Value Imputation and Reliability Assessment for mortality risk prediction.

BACKGROUND: Mortality risk prediction is to predict whether a patient has the risk of death based on relevant diagnosis and treatment data. How to accurately predict patient mortality risk based on electronic health records (EHR) is currently a hot research topic in the healthcare field. In actual medical datasets, there are often many missing values, which can seriously interfere with the effect of model prediction. However, when missing values are interpolated, most existing methods do not take into account the fidelity or confidence of the interpolated values. Misestimation of missing variables can lead to modeling difficulties and performance degradation, while the reliability of the model may be compromised in clinical environments. MATERIALS AND METHODS: We propose a model based on Missing Value Imputation and Reliability Assessment for mortality risk prediction (MVIRA). The model uses a combination of variational autoencoder and recurrent neural networks to complete the interpolation of missing values and enhance the characterization ability of EHR data, thus improving the performance of mortality risk prediction. In addition, we also introduce the Monte Carlo Dropout method to calculate the uncertainty of the model prediction results and thus achieve the reliability assessment of the model. RESULTS: We perform performance validation of the model on the public datasets MIMIC-III and MIMIC-IV. The proposed model showed improved performance compared with competitive models in terms of overall specialties. CONCLUSION: The proposed model can effectively improve the accuracy of mortality risk prediction, and can help medical institutions assess the condition of patients.

Lattice Compressive Strain of Co3O4 Induced by Synthetic Solvents Promotes Efficient Oxidation of Benzene at Low Temperature.

A series of Co3O4 with different surface defective structures were prepared by the solvothermal method and tested for the activity of benzene oxidation. The characterizations revealed that the synthetic solvent had a dramatic effect on the composition of Co3O4 precursors as well as the physicochemical properties of Co3O4. Although all Co3O4 exhibited a cubic spinel structure, Co3O4 prepared with triethylene glycol (Co-TEG) had the highest compressive strain due to the nature of high viscosity of triethylene glycol. These in turn affected the surface chemical structure and the low-temperature redox properties. Co-TEG exhibited the best benzene oxidation activity and showed excellent stability and good water resistance. In situ diffuse reflectance infrared Fourier transform spectroscopy was used to study the oxidation process of benzene. It was found that Co-TEG with more defective structures had abundant surface adsorbed oxygen and active lattice oxygen, which promoted the conversion of benzene and the corresponding intermediates at low temperature.

A Drug Recommendation Model Based on Message Propagation and DDI Gating Mechanism.

Drug recommendation task based on the deep learning model has been widely studied and applied in the health care field in recent years. However, the accuracy of drug recommendation models still needs to be improved. In addition, the existing recommendation models either give only one recommendation (however, there may be a variety of drug combination options in practice) or can not provide the confidence level of the recommended result. To fill these gaps, a Drug Recommendation model based on Message Propagation neural network (denoted as DRMP) is proposed in this paper. Then, the Drug-Drug Interaction (DDI) knowledge is introduced into the proposed model to reduce the DDI rate in recommended drugs. Finally, the proposed model is extended to Bayesian Neural Network (BNN) to realize multiple recommendations and give the confidence of each recommendation result, so as to provide richer information to help doctors make decisions. Experimental results on public data sets show that the proposed model is superior to the best existing models.

Right-to-left ventricle ratio determined by machine learning algorithms on CT pulmonary angiography images predicts prolonged ICU length of stay in operated chronic thromboembolic pulmonary hypertension.

OBJECTIVE: Right-to-left ventricle diameter ratio (dRV/dLV) on CT pulmonary angiography (CTPA) is a predictor of outcomes in non-operated chronic thromboembolic pulmonary hypertension (CTEPH) patients. The purpose of this study is to evaluate the performance of a novel machine learning (ML) algorithm for dRV/dLV measurement in operated CTEPH patients and its association with post-operative outcomes. METHODS: This retrospective study reviewed consecutive CTEPH patients who underwent pulmonary endarterectomy between 2013 and 2017. ML calculated dRV/dLV on pre-operative CTPA and compared with manual measures. Associations of dRV/dLV with patient characteristics and post-operative outcomes were evaluated including intensive care (ICU) and hospital length of stay (LOS) using multivariable linear regression analysis. Prolonged LOS was defined as greater than median. RESULTS: ML segmented the ventricles in 99/125 (79%) patients. The most common cause of failure was misidentification of the moderator band as the interventricular septum (7.9%). Mean dRV/dLV by ML was 1.4 ± 0.4 and strongly correlated with manual measures (r = 0.9-0.96 p < 0.0001). dRV/dLV was moderately correlated with measures of pulmonary hypertension on right heart catheterization and RV dilatation on echocardiogram (r = 0.5-0.6, p < 0.0001). dRV/dLV ≥ 1.2 was associated with proximal Jamieson type disease (p = 0.032), longer cardiopulmonary bypass (p = 0.037), aortic cross-clamp (p = 0.022) and circulatory arrest (p < 0.001) at surgery and dRV/dLV ≥ 1.6 with post-operative ECMO (p = 0.006). dRV/dLV was independently associated with prolonged ICU LOS (OR = 3.79, 95% CI 1.1-13.06, p = 0.035). CONCLUSION: dRV/dLV was associated with CTEPH severity and independently associated with prolonged ICU LOS. This CT parameter may therefore assist in perioperative planning. Further refinement of the ML algorithm or CTPA technique is required to avoid errors in ventricular segmentation. ADVANCES IN KNOWLEDGE: Automated right-to-left ventricle ratio measurement by machine learning is feasible and is independently associated with outcome after pulmonary endarterectomy.

A novel methylated cell-free DNA marker panel to monitor treatment response in metastatic prostate cancer.

Aim: This study examined circulating cell-free DNA (cfDNA) biomarkers associated with androgen treatment resistance in metastatic castration resistance prostate cancer (mCRPC). Materials & methods: We designed a panel of nine candidate cfDNA methylation markers using droplet digital PCR (Methyl-ddPCR) and assessed methylation levels in sequentially collected cfDNA samples from patients with mCRPC. Results: Increased cfDNA methylation in eight out of nine markers during androgen-targeted treatment correlated with a faster time to clinical progression. Cox proportional hazards modeling and logistic regression analysis further confirmed that higher cfDNA methylation during treatment was significantly associated with clinical progression. Conclusion: Overall, our findings have revealed a novel methylated cfDNA marker panel that could aid in the clinical management of metastatic prostate cancer.

Efficacy comparison of oxcarbazepine and levetiracetam monotherapy among patients with newly diagnosed focal epilepsy in China: A multicenter, open-label, randomized study.

AIMS: This multicenter, open-label, randomized study (Registration No. ChiCTR-OCH-14004528) aimed to compare the efficacy and effects of oxcarbazepine (OXC) with levetiracetam (LEV) as monotherapies on patient quality of life and mental health for patients with newly diagnosed focal epilepsy from China. METHODS: Patients with newly diagnosed focal epilepsy who had experienced 2 or more unprovoked seizures at greater than a 24-h interval during the previous year were recruited. Participants were randomly assigned to the OXC group or LEV group. Efficacy, safety, quality of life, and mental health were evaluated over 12-week and 24-week periods. RESULTS: In total, we recruited 271 newly diagnosed patients from 23 centers. Forty-four patients were excluded before treatment for reasons. The rate of seizure freedom of OXC was significantly superior to that of LEV at 12 weeks and 24 weeks (p < 0.05). The quality of life (except for the seizure worry subsection) and anxiety scale scores also showed significant differences from before to after treatment in the OXC and LEV groups. CONCLUSIONS: OXC monotherapy may be more effective than LEV monotherapy in patients with newly diagnosed focal epilepsy. Both OXC and LEV could improve the quality of life and anxiety state in adult patients with focal epilepsy.

Metal-to-Semiconductor Transition in Two-Dimensional Metal-Organic Frameworks: An Ab Initio Dynamics Perspective.

Two-dimensional (2D) π-stacked layered metal-organic frameworks (MOFs) are permanently porous and electrically conductive materials with easily tunable crystal structures. Here, we provide an accurate examination of the correlation between structural features and electronic properties of Ni3(HITP)2, HITP = 2,3,6,7,10,11-hexaiminotriphenylene, as an archetypical 2D MOF. The main objective of this work is to unravel the responsive nature of the layered architecture to external stimuli such as temperature and show how the layer flexibility translates to different conductive behaviors. To this end, we employ a combination of quantum mechanical tools, ab initio molecular dynamics (AIMD) simulations, and electronic band structure calculations. We compare the band structure and projected density of states of equilibrated system at 293 K to that of the 0 K optimized structure. Effect of interlayer π-π and intralayer d-π interactions on charge mobility is disentangled and studied by increasing the distance between layers of Ni3(HITP)2 and comparison to an exemplary case of Zn3(HITP)2 2D MOF. Our findings show how a structural change, which can be deformations along the layers, slipping of layers, or change of the interlayer distance, can induce metal-to-semiconductor or indirect-to-direct semiconductor transition, suggesting a way to adjust or even switch between the intralayer vs interlayer conductive anisotropy in Ni3(HITP)2, in particular, and 2D MOFs in general.

Identification of specific clinical risk factors associated with the malignant transformation of oral epithelial dysplasia.

BACKGROUND: Factors that increase the risk of malignant transformation of oral epithelial dysplasia (OED) are not completely elucidated. METHODS: A retrospective chart review was performed assessing risk factors for transformation of OED, and cancer staging for transformed cases at Sunnybrook Health Sciences Centre. RESULTS: Two-hundred four patients were diagnosed with OED, and 16.7% (34) underwent malignant transformation. Risk factors associated with transformation included: heavy tobacco smoking, excessive EtOH consumption, non-homogenous leukoplakia, size >200 mm2 , moderate dysplasia or greater than moderate, progression of dysplasia grades, and immunosuppression. Transformed cases followed for a dysplastic lesion were associated with a stage-I cancer diagnosis, and cancer cases with no prior biopsy were associated with a stage-IV diagnosis. CONCLUSIONS: In addition to commonly cited risk factors, immunosuppression was associated with malignant transformation, including the use of topical steroids. Analyzing risk factors can help clinicians define risk of progression in patients with OED.

Medical Treatment Migration Prediction Based on GCN via Medical Insurance Data.

Nowadays, prediction for medical treatment migration has become one of the interesting issues in the field of health informatics. This is because the medical treatment migration behavior is closely related to the evaluation of regional medical level, the rational use of medical resources, and the distribution of medical insurance. Therefore, a prediction model for medical treatment migration based on medical insurance data is introduced in this paper. First, a medical treatment graph is constructed based on medical insurance data. The medical treatment graph is a heterogeneous graph, which contains entities such as patients, diseases, hospitals, medicines, hospitalization events, and the relations between these entities. However, existing graph neural networks are unable to capture the time-series relationships between event-type entities. To this end, a prediction model based on Graph Convolutional Network (GCN) is proposed in this paper, namely, Event-involved GCN (EGCN). The proposed model aggregates conventional entities based on attention mechanism, and aggregates event-type entities based on a gating mechanism similar to LSTM. In addition, jumping connection is deployed to obtain the final node representation. In order to obtain embedded representations of medicines based on external information (medicine descriptions), an automatic encoder capable of embedding medicine descriptions is deployed in the proposed model. Finally, extensive experiments are conducted on a real medical insurance data set. Experimental results show that our model's predictive ability is better than the best models available.

Biological effects of toosendanin, a triterpenoid extracted from Chinese traditional medicine.

Toosendanin (TSN) is a triterpenoid extracted from Melia toosendan Sieb et Zucc, which was used as a digestive tract-parasiticide and agricultural insecticide in ancient China. TSN was demonstrated to be a selective presynaptic blocker and an effective antibotulismic agent. By interfering with neurotransmitter release through an initial facilitation followed by a subsequent depression, TSN eventually blocks synaptic transmission at both the neuro-muscular junction and central synapses. Despite sharing some similar actions with botulinum neurotoxin (BoNT), TSN has a marked antibotulismic effect in vivo and in vitro. Studies suggest that the antibotulismic effect of TSN is achieved by preventing BoNT from approaching its enzymatic substrate, the SNARE protein. It is also found that TSN can induce differentiation and apoptosis in several cell lines, and suppress proliferation of various human cancer cells. TSN inhibits various K(+)-channels, selectively facilitates Ca(2+)-influx via L-type Ca(2+) channels and increases intracellular Ca(2+) concentration ([Ca(2+)](i)). The TSN-induced [Ca(2+)](i) increase and overload could be responsible for the TSN-induced biphasic effect on transmitter release, cell differentiation, apoptosis as well as the cytoxicity of TSN.